2 - City Center Diversity Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Street Trees: Diversity Indices and the 10/20/30 Rule\n",
    "\n",
    "This notebook analyzes the diversity indices of the street trees inside and outside the city center you've selected, and then check the tree inventory according to the 10/20/30 rule, discussed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library import\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import descartes\n",
    "import treeParsing as tP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Tree Inventory and City Center Boundary\n",
    "\n",
    "Import your tree data and city center boundary data below. These data may use any geospatial data format (SHP, Geojson, Geopackage) and should be in the same coordinate projection.\n",
    "\n",
    "Your tree data will need the following columns:\n",
    "* Point geographic location\n",
    "* Diameter at breast height (DBH)\n",
    "* Tree Scientific Name\n",
    "* Tree Genus Name\n",
    "* Tree Family Name\n",
    "\n",
    "Your city center geography simply needs to be a single, dissolved geometry representing your city center area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter the path to your data below ###\n",
    "tree_data_path = 'example_data/trees_paris.gpkg'\n",
    "tree_data = gpd.read_file(tree_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14e97c86188>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter the path to your data below ###\n",
    "city_center_boundary_path = 'example_data/paris.gpkg'\n",
    "city_center = gpd.read_file(city_center_boundary_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14e97d1b2c8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_center.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean Data and Calculate Basal Area\n",
    "\n",
    "To start, we need to remove features missing data and remove the top quantile of data. Removing any missing data and the top quantile helps remove erroneous entries that are too large or too small than what we would expect. If your data has already been cleaned, feel free to skip the second cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Enter your column names here ###\n",
    "scientific_name_column = 'Scientific'\n",
    "genus_name_column = 'genus'\n",
    "family_name_column = 'family'\n",
    "diameter_breast_height_column = 'DBH'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Ignore if data is already cleaned ###\n",
    "# Exclude Data Missing DBH\n",
    "tree_data = tree_data[tree_data[diameter_breast_height_column]>0]\n",
    "\n",
    "# Exclude data larger than the 99th quantile (often erroneously large)\n",
    "tree_data = tree_data[tree_data[diameter_breast_height_column]<=tree_data.quantile(0.99).DBH]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate Basal Area\n",
    "basal_area_column =  'BA'\n",
    "tree_data[basal_area_column] = tree_data[diameter_breast_height_column]**2 * 0.00007854"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating Simpson and Shannon Diversity Indices\n",
    "\n",
    "The following cells spatially join your city center geometry to your tree inventory data, and then calculates the simpson and shannon diversity indices for the city center, area outside the city center -- based on area and tree count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add dummy column to city center geometry\n",
    "city_center['inside'] = True\n",
    "city_center = city_center[['geometry','inside']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spatial Join -- this may take a while\n",
    "sjoin_tree_data = gpd.sjoin(tree_data, city_center, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GenerateIndices(label, df, scientific_name_column, genus_name_column, family_name_column, basal_area_column):\n",
    "    # Derive counts, areas, for species, genus, and family\n",
    "    species_count = df[[scientific_name_column, basal_area_column]].groupby(scientific_name_column).count().reset_index()\n",
    "    species_area = df[[scientific_name_column, basal_area_column]].groupby(scientific_name_column).sum().reset_index()\n",
    "\n",
    "    genus_count = df[[genus_name_column, basal_area_column]].groupby(genus_name_column).count().reset_index()\n",
    "    genus_area = df[[genus_name_column, basal_area_column]].groupby(genus_name_column).sum().reset_index()\n",
    "\n",
    "    family_count = df[[family_name_column, basal_area_column]].groupby(family_name_column).count().reset_index()\n",
    "    family_area = df[[family_name_column, basal_area_column]].groupby(family_name_column).sum().reset_index()\n",
    "\n",
    "\n",
    "    # Calculate Percentages by count and area\n",
    "    species_count[\"Pct\"] = species_count[basal_area_column]/sum(species_count[basal_area_column])\n",
    "    species_area[\"Pct\"]  = species_area[basal_area_column]/sum(species_area[basal_area_column])\n",
    "\n",
    "    genus_count[\"Pct\"] = genus_count[basal_area_column]/sum(genus_count[basal_area_column])\n",
    "    genus_area[\"Pct\"] = genus_area[basal_area_column]/sum(genus_area[basal_area_column])\n",
    "\n",
    "    family_count[\"Pct\"] = family_count[basal_area_column]/sum(family_count[basal_area_column])\n",
    "    family_area[\"Pct\"] = family_area[basal_area_column]/sum(family_area[basal_area_column])\n",
    "\n",
    "    # Calculate Shannon Indices\n",
    "    species_shannon_count = tP.ShannonEntropy(list(species_count[\"Pct\"]))\n",
    "    species_shannon_area = tP.ShannonEntropy(list(species_area[\"Pct\"]))\n",
    "\n",
    "    genus_shannon_count = tP.ShannonEntropy(list(genus_count[\"Pct\"]))\n",
    "    genus_shannon_area = tP.ShannonEntropy(list(genus_area[\"Pct\"]))\n",
    "\n",
    "    family_shannon_count = tP.ShannonEntropy(list(family_count[\"Pct\"]))\n",
    "    family_shannon_area = tP.ShannonEntropy(list(family_area[\"Pct\"]))\n",
    "\n",
    "    # Calculate Simpson Indices\n",
    "    species_simpson_count = tP.simpson_di(list(species_count[scientific_name_column]), list(species_count[basal_area_column]))\n",
    "    species_simpson_area = tP.simpson_di(list(species_area[scientific_name_column]),list(species_area[basal_area_column]))\n",
    "\n",
    "    genus_simpson_count = tP.simpson_di(list(genus_count[genus_name_column]), list(genus_count[basal_area_column]))\n",
    "    genus_simpson_area = tP.simpson_di(list(genus_area[genus_name_column]), list(genus_area[basal_area_column]))\n",
    "\n",
    "    family_simpson_count = tP.simpson_di(list(family_count[family_name_column]), list(family_count[basal_area_column]))\n",
    "    family_simpson_area = tP.simpson_di(list(family_area[family_name_column]), list(family_area[basal_area_column]))\n",
    "\n",
    "    return {\n",
    "            'Geography':label,\n",
    "            'species_simpson_count': species_simpson_count,\n",
    "            'species_simpson_area': species_simpson_area,\n",
    "            'genus_simpson_count': genus_simpson_count,\n",
    "            'genus_simpson_area': genus_simpson_area,\n",
    "            'family_simpson_count': family_simpson_count,\n",
    "            'family_simpson_area': family_simpson_area,\n",
    "            'species_shannon_count': species_shannon_count,\n",
    "            'species_shannon_area': species_shannon_area,\n",
    "            'genus_shannon_count': genus_shannon_count,\n",
    "            'genus_shannon_area': genus_shannon_area,\n",
    "            'family_shannon_count': family_shannon_count,\n",
    "            'family_shannon_area': family_shannon_area\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>species_simpson_count</th>\n",
       "      <th>species_simpson_area</th>\n",
       "      <th>genus_simpson_count</th>\n",
       "      <th>genus_simpson_area</th>\n",
       "      <th>family_simpson_count</th>\n",
       "      <th>family_simpson_area</th>\n",
       "      <th>species_shannon_count</th>\n",
       "      <th>species_shannon_area</th>\n",
       "      <th>genus_shannon_count</th>\n",
       "      <th>genus_shannon_area</th>\n",
       "      <th>family_shannon_count</th>\n",
       "      <th>family_shannon_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inside City Center</td>\n",
       "      <td>0.830921</td>\n",
       "      <td>0.789103</td>\n",
       "      <td>0.695626</td>\n",
       "      <td>0.605508</td>\n",
       "      <td>0.689964</td>\n",
       "      <td>0.603462</td>\n",
       "      <td>2.625553</td>\n",
       "      <td>2.267669</td>\n",
       "      <td>1.939313</td>\n",
       "      <td>1.570327</td>\n",
       "      <td>1.788987</td>\n",
       "      <td>1.504692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Outside City Center</td>\n",
       "      <td>0.887075</td>\n",
       "      <td>0.795369</td>\n",
       "      <td>0.850146</td>\n",
       "      <td>0.749201</td>\n",
       "      <td>0.821799</td>\n",
       "      <td>0.730654</td>\n",
       "      <td>3.135827</td>\n",
       "      <td>2.459517</td>\n",
       "      <td>2.490724</td>\n",
       "      <td>1.947589</td>\n",
       "      <td>2.115954</td>\n",
       "      <td>1.738513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Geography  species_simpson_count  species_simpson_area  \\\n",
       "0   Inside City Center               0.830921              0.789103   \n",
       "1  Outside City Center               0.887075              0.795369   \n",
       "\n",
       "   genus_simpson_count  genus_simpson_area  family_simpson_count  \\\n",
       "0             0.695626            0.605508              0.689964   \n",
       "1             0.850146            0.749201              0.821799   \n",
       "\n",
       "   family_simpson_area  species_shannon_count  species_shannon_area  \\\n",
       "0             0.603462               2.625553              2.267669   \n",
       "1             0.730654               3.135827              2.459517   \n",
       "\n",
       "   genus_shannon_count  genus_shannon_area  family_shannon_count  \\\n",
       "0             1.939313            1.570327              1.788987   \n",
       "1             2.490724            1.947589              2.115954   \n",
       "\n",
       "   family_shannon_area  \n",
       "0             1.504692  \n",
       "1             1.738513  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate results and load into dataframe\n",
    "temp_results = []\n",
    "\n",
    "city_center_data = sjoin_tree_data[sjoin_tree_data.inside == True]\n",
    "outside_center_data = sjoin_tree_data[sjoin_tree_data.inside != True]\n",
    "\n",
    "temp_results.append(\n",
    "    GenerateIndices(\n",
    "    'Inside City Center', \n",
    "    city_center_data,\n",
    "    scientific_name_column, \n",
    "    genus_name_column, \n",
    "    family_name_column, \n",
    "    basal_area_column\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "temp_results.append(\n",
    "    GenerateIndices(\n",
    "    'Outside City Center', \n",
    "    outside_center_data,\n",
    "    scientific_name_column, \n",
    "    genus_name_column, \n",
    "    family_name_column, \n",
    "    basal_area_column\n",
    "    )\n",
    ")\n",
    "\n",
    "results = pd.DataFrame(temp_results)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split up results for plotting\n",
    "shannon_area = results.round(4)[['species_shannon_area','genus_shannon_area','family_shannon_area']].values\n",
    "shannon_count = results.round(4)[['species_shannon_count','genus_shannon_count','family_shannon_count']].values\n",
    "simpson_area = results.round(4)[['species_simpson_area','genus_simpson_area','family_simpson_area']].values\n",
    "simpson_count = results.round(4)[['species_simpson_count','genus_simpson_count','family_simpson_count']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3LrfeeivnnylOTk7mlltu4ciRI6nKfvnll3nmmWe47bbbAAgKCvJfNT98+DCdOnWiQYMGNGjQgFWrVgHwwgsv0LdvXyIjI6lRowYTJ04EYMSIEezatQuPx8PTTz8NwPjx42nQoAHh4eE8/7x3FbA9e/YQEhLCgAEDiIiIYP/+/QF89wosxXIRkXxEsTz/C9gQd+fcJqBuOtufS/F6JDAyUHUQEZGs27x5M/Xq1cs0TdGiRRk9ejTr1q1j8uTJAHz//ffMnj2bwYMHs2zZMurUqUOFChUuKHvo0KHpljlo0CCGDBlCkyZN2LdvH61atWLr1q3+sj///HPi4+OpVasW/fv3Z+zYsWzevJnY2FgAli5dyo4dO4iJicE5R9u2bfnyyy+pVq0a27Zt45133sn2VXrxUiwXEclfFMvzvxx5Bl1ERAquvn370q5dOwYPHsy0adPo06dPtvIvW7aMLVu2+H8/ceIE8fHxADzwwAMUK1aMYsWKUalSJX7++ecL8i9dupSlS5dSt663H3ny5El27NhBtWrVuPHGG7njjjsuo3UiIiIFn2J53qEOuoiIABAaGup/Ni07brjhBipXrkx0dDRr1qxh9uzZ6Za9fv166tSpc8G+5ORkVq9eTYkSJS7YV6xYMf/rwoULk5iYeEEa5xwjR47kD3/4Q6rte/bsoWTJktluj4iISH6lWJ7/BXSZNRERyT+aN2/O2bNneeutt/zb1q5dyxdffJEqXenSpf1Xxc979NFHefjhh+nSpQuFCxe+oOynn36aMWPGsH37dsAbyP/2t78B0LJlS/8QO8A/3C0jaY/fqlUrpk2bxsmTJwH48ccfOXToUFaaLJLvnTlzhoYNG1KnTh1CQ0P9z22m9OWXXxIREUFQUFCqE/e9e/dSr149PB4PoaGhTJkyxb8vMjKSWrVq4fF48Hg8/v9T+/bt4+6776Zu3bqEh4dne5IpEQksxfL8T3fQRUTyqKwspXIlmRkLFixg8ODBjB07luLFi/uXZknp7rvvZuzYsXg8HkaOHEnXrl1p27Ytffr0yXBIXHh4OBMmTKB79+6cPn0aM+OBBx4AYOLEiTzxxBOEh4eTmJhI06ZNU3UU0ipfvjy//e1vCQsL4/7772f8+PFs3bqVxo0bA1CqVClmzZqV7smFSEFTrFgxoqOjKVWqFAkJCTRp0oT7778/1XDQatWqMX369AuWQqpSpQpff/01xYoV4+TJk4SFhdG2bVuqVq0KwOzZs6lfv36qPH/5y1/o0qUL/fv3Z8uWLbRu3Zo9e/YEvJ0i+ZViefoUyzNmzrncrkO21K9f361bty63qyEicsVt3bqVkJCQ3K7GJVm3bh1Dhgxh5cqVuV2VAie974WZrXfO1c8gS56nWB4Yp0+fpkmTJrz55ps0atTogv29e/emTZs2dO7c+YJ9R48epW7dunzzzTdUrVqVyMhIXnnllQs66H/4wx+oUaMGw4cPZ/Xq1QwdOpSvv/46YG0SyW8UyyU92YnlGuIuIiKXZezYsXTq1ImXXnopt6siclVKSkrC4/FQqVIlWrRokW7nPCP79+8nPDycG264geHDh/vvngP06dMHj8fDiy++yPkbOi+88AKzZs0iODiY1q1bM2nSpCveHhHJeYrleYc66CIicllGjBjB3r17adKkSW5XReSqVLhwYWJjY4mLiyMmJobNmzdnOe8NN9zApk2b2LlzJzNmzPDPrjx79mz+85//sHLlSlauXMnMmTMBmDNnDr179yYuLo4lS5bw+9//nuTk5IC0S0RyjmJ53qEOuoiIiEgBULZsWSIjI/n000+znbdq1aqEhob6h7Zef/31gHcip9/97nfExMQAMHXqVLp06QJA48aNOXPmDEeOHLlCLRAREXXQRURERPKpw4cPc/z4cQB+/fVXli1bxm233ZalvHFxcfz6668AHDt2jFWrVlGrVi0SExP9ne6EhAQWL15MWFgY4J1wbvny5YD3mcozZ85QsWLFK90sEZGrlmZxFxEREcmnDhw4QK9evUhKSiI5OZkuXbrQpk0bnnvuOerXr0/btm1Zu3YtHTp04NixY3z00Uc8//zzfPfdd2zdupWhQ4diZjjnGDZsGLVr1+bUqVO0atWKhIQEkpKSuPfee3nssccAePXVV3nsscd47bXXMDOmT5+OmeXyuyAiUnCogy4iIiKST4WHh7Nhw4YLto8ePdr/ukGDBsTFxV2QpkWLFmzatOmC7SVLlmT9+vXpHu/2229n1apVl1FjERHJjDroIiJ5lD12Ze9KubcuvqxmXFwcTzzxBFu2bCE5OZk2bdowfvx4ihYtmmm+MWPG8Oc//znTNI8++ihPPfUUt99+e6rt06dPZ926dUyePPnijfCJiYlh2LBh/Pzzz5gZTZo0YeLEiSxbtowtW7YwYsQIPvzwQ2rWrHnB8S7mlVde4e233yYoKIjChQszdOhQevbsma0yAGJjY/npp59o3bp1tvOKiEjBoFieMcXy9OkZdBERAcA5R8eOHWnfvj07duxg+/btnDx5kmeeeeaieceMGXPRNG+//Xa2A2x6fv75Zx566CHGjRvHtm3b2Lp1K/fddx/x8fG0bduWESNGAPDhhx+yZcuWbJU9ZcoUPvvsM/9M2F9++aV/eansio2NZcmSJdnKk5iYeEnHEhERAcVyyP+xXB10EREBIDo6muLFi9OnTx/Au3TTa6+9xrRp0zh9+jTTp09n4MCB/vRt2rRhxYoVjBgxgl9//RWPx0OPHj04deoUDzzwAHXq1CEsLIy5c+cCEBkZybp16wB45513qFmzJs2aNUs1XPbw4cN06tSJBg0a0KBBg3SH0r7xxhv06tWLxo0bA2BmdO7cmcqVK/vr+PXXX7No0SKefvppPB4Pu3btIiIiwl/Gjh07qFev3gVljxkzhr///e+UKVMGgGuvvZZevXoBsH79epo1a0a9evVo1aoVBw4c8Ldr+PDhNGzYkJo1a7Jy5UrOnTvHc889x9y5c/F4PMydO5dTp07Rt29fGjRoQN26dVm4cCHgvevw0EMP8eCDD9KyZctL/PREREQUyyH/x3INcRcREQC+++67CwJdmTJlqFatGjt37sww39ixY5k8eTKxsbEAzJ8/n6pVq/Lxxx8D8Msvv6RKf+DAAZ5//nnWr1/Ptddey913303dunUBGDRoEEOGDKFJkybs27ePVq1asXXr1lT5N2/e7A+0Gbnzzjtp27Ytbdq0oXPnzoA3QMfGxuLxeHjnnXfo3bt3qjzx8fHEx8dz8803X1BeQkICf/zjH1m4cCEVK1Zk7ty5PPPMM0ybNg3wXi2PiYlhyZIljBo1imXLljF69OhUw/3+/Oc/07x5c6ZNm8bx48dp2LAh9957LwCrV69m06ZNlCtXLtN2ydXjSg+LzSuyMjxXRC6dYnn+j+XqoIuICOAdFpfebMwZbc9I7dq1GTZsGMOHD6dNmzbcddddqfavWbOGyMhI/9JMXbt2Zfv27QD+587OO3HiBPHx8ZQuXfpSmpTKo48+yjvvvMPf/vY35s6d61/X+bzM2rlt2zY2b95MixYtAEhKSqJKlSr+/R07dgSgXr167NmzJ90yli5dyqJFi3jllVcAOHPmDPv27QO8k3Wpcy4iIpdLsTz/x3J10EVEBIDQ0FDmz5+fatuJEyfYv38/N998Mxs3biQ5Odm/78yZM+mWU7NmTdavX8+SJUsYOXIkLVu25LnnnkuVJqPgmZyczOrVqylRokSm9Vy/fj3t2rXLatMA6NSpE6NGjaJ58+bUq1eP8uXLp9pfpkwZSpYsyQ8//ECNGjVS7XPOERoayurVq9Mtu1ixYoB3KGFGz54555g/fz61atVKtX3NmjWULFkyW20RERFJj2J5/o/legZdREQAuOeeezh9+jTvvvsu4L2yPHToUHr37s0111xD9erViY2NJTk5mf3796e6al2kSBESEhIA+Omnn7jmmmt4+OGHGTZsGN9++22q4zRq1IgVK1Zw9OhREhISeP/99/37WrZsmWoG2PND7VIaOHAgM2bMYM2aNf5ts2bN4uDBg6nSlS5dmvj4eP/vxYsXp1WrVvTv39//bF5aI0eO5IknnuDEiROA96Tmn//8J7Vq1eLw4cP+oJ6QkMB3332Xybt54fFbtWrFpEmT/BPVpLc0loiIyOVQLM//sVx30EVE8qicflbTzFiwYAEDBgzgxRdfJDk5mdatW/tndf3tb3/LTTfdRO3atQkLC0s1Ucvjjz9OeHg4ERER9OzZk6effppChQpRpEgR3nzzzVTHqVKlCi+88AKNGzemSpUqREREkJSUBMDEiRN54oknCA8PJzExkaZNmzJlypRU+StXrkxUVBTDhg3j0KFDFCpUiKZNm/qHpp3XrVs3HnvsMSZOnMi8efO4+eab6dGjBx988EGGE7j079+fkydP0qBBA4oUKUKRIkUYOnQoRYsWZd68eTz55JP88ssvJCYmMnjwYEJDQzN8P++++27Gjh2Lx+Nh5MiRPPvsswwePJjw8HCcc1SvXp3Fixdn/QMSEZF8R7FcsTy77FKnnL9owWbFgS+BYngvBMxzzj2fJk0x4F2gHnAU6Oqc25NZufXr13fnZw4UESlItm7dSkhISG5Xo0B75ZVX+OWXX3jxxRdzuypZlt73wszWO+fqB/rYiuW5R5PEieRPiuWBV9BjeSDvoJ8FmjvnTppZEeArM/vEOfdNijSPAMecc7eYWTdgHNA1gHUSEZGrVIcOHdi1axfR0dG5XZX8RLFcRETyjKshlgesg+68t+ZP+n4t4vtJe9m0HfCC7/U8YLKZmQvUbX0REblqLViwILerkO8olouISF5yNcTygE4SZ2aFzSwWOAR85pxbkybJ9cB+AOdcIvALUD5NGszscTNbZ2brDh8+HMgqi4jkKvVpJKW88H1QLBcRyZ688Ldb8o7sfh8C2kF3ziU55zxAMNDQzMLSJEnvAasLWuCc+6dzrr5zrv75tfZERAqa4sWLc/ToUQV2AbwB/ejRoxQvXjy366FYLiKSRYrlktKlxPIcmcXdOXfczFYA9wGbU+yKA24A4swsCLgW+G9O1ElEJK8JDg4mLi4O3V2U84oXL05wcHBuVwNQLBcRyQrFckkru7E8YB10M6sIJPgCegngXrwTx6S0COgFrAY6A9F6Zk1ErlZFihThpptuyu1qiPgplouIZI9iuVyuQN5BrwLMMLPCeIfS/8s5t9jMRgPrnHOLgKnATDPbifdqe7cA1kdERESyR7FcREQkBwVyFvdNQN10tj+X4vUZ4KFA1UFEREQunWK5iIhIzgroJHEiIiIiIiIikjXqoIuIiIiIiIjkAeqgi4iIiIiIiOQB6qCLiIjIVWH//v3cfffdhISEEBoayuuvv55uuhUrVuDxeAgNDaVZs2YXzfvCCy9w/fXX4/F48Hg8LFmyxL9v06ZNNG7cmNDQUGrXrs2ZM2cC20gREcnXcmQddBEREZHcFhQUxKuvvkpERATx8fHUq1ePFi1acPvtt/vTHD9+nAEDBvDpp59SrVo1Dh06lKW8Q4YMYdiwYamOl5iYyMMPP8zMmTOpU6cOR48epUiRIjnXYBERyXd0B11ERESuClWqVCEiIgKA0qVLExISwo8//pgqzXvvvUfHjh2pVq0aAJUqVcpy3rSWLl1KeHg4derUAaB8+fIULlz4irZJREQKFnXQRURE5KqzZ88eNmzYQKNGjVJt3759O8eOHSMyMpJ69erx7rvvZinv5MmTCQ8Pp2/fvhw7dsxflpnRqlUrIiIiePnllwPbKBERyffUQRcREZGrysmTJ+nUqRMTJkygTJkyqfYlJiayfv16Pv74Y/7973/z4osvsn379kzz9u/fn127dhEbG0uVKlUYOnSov6yvvvqK2bNn89VXX7FgwQKWL1+ecw0VEZF8Rx10ERERuWokJCTQqVMnevToQceOHS/YHxwczH333UfJkiWpUKECTZs2ZePGjZnmrVy5MoULF6ZQoUI89thjxMTE+Mtq1qwZFSpU4JprrqF169Z8++23OdNQERHJl9RBFxERkauCc45HHnmEkJAQnnrqqXTTtGvXjpUrV5KYmMjp06dZs2YNISEhmeY9cOCA//l2xVsAACAASURBVPWCBQsICwsDoFWrVmzatInTp0+TmJjIF198kWpCOhERkbTUQRcREZGrwqpVq5g5cybR0dGplkSbMmUKU6ZMASAkJIT77ruP8PBwGjZsyKOPPkpYWFiGeQH+9Kc/Ubt2bcLDw/n888957bXXALjuuut46qmnaNCgAR6Ph4iICB544IFca79IdmV1aUKAtWvXUrhwYebNm+ffNnz4cMLCwggLC2Pu3Ln+7bt376ZRo0bceuutdO3alXPnzgHe1RDO//+qWbMmZcuWDVzjRPIoc87ldh2ypX79+m7dunW5XQ0REZFcY2brnXP1c7sel0qx/OLsMcvtKgSEeyt/nXde7Q4cOMCBAwdSLS/44YcfXjASJCkpiRYtWlC8eHH69u1L586d+fjjj5kwYQKffPIJZ8+epVmzZkRHR1OmTBm6dOlCx44d6datG/369aNOnTr0798/VZmTJk1iw4YNTJs2LSebLJJjMorluoN+lbvcK6OFCxf2X+ls27atf3t0dDQRERGEhYXRq1cvEhMTAVixYgXXXnutP8/o0aMD1zgRERERuWRZXV5w0qRJdOrUyb8sIcCWLVto1qwZQUFBlCxZkjp16vDpp5/inCM6OprOnTsD0KtXLz788MMLypwzZw7du3cPUMtE8i510K9yQUFBvPrqq2zdupVvvvmGN954gy1btlyQLikpieHDh9OqVatU20uUKEFsbCyxsbEsWrQIgOTkZHr16kVUVBSbN2/mxhtvZMaMGf48d911lz/Pc889F9gGioiIiMhly2hpwh9//JEFCxbQr1+/VNvr1KnDJ598wunTpzly5Aiff/45+/fv5+jRo5QtW5agoCDAO5li2k7/3r172b17N82bNw9so0TyIHXQr3KXc2U0I0ePHqVYsWLUrFkTgBYtWjB//vwrW3ERERERyRGZLU04ePBgxo0bR+HChVNtb9myJa1bt+bOO++ke/fuNG7cmKCgINJ7vNYs9SMdUVFRdO7c+YIyRa4GQbldAck7LnZlNDo6mrVr16bad+bMGerXr09QUBAjRoygffv2VKhQgYSEBNatW0f9+vWZN28e+/fv9+dZvXo1derUoWrVqrzyyiuEhobmSPtERKRgWrhqd25XQaTAutjShOvWraNbt24AHDlyhCVLlhAUFET79u155plneOaZZwD43e9+x6233kqFChU4fvw4iYmJBAUFERcXR9WqVVOVGRUVxRtvvBH4xonkQeqgC3BpV0YB9u3bR9WqVfnhhx9o3rw5tWvX5uabbyYqKoohQ4Zw9uxZWrZs6R/GFBERwd69eylVqhRLliyhffv27NixI0faKCIiIiJZl5WlCXfv/t8Fst69e9OmTRvat29PUlISx48fp3z58mzatIlNmzbRsmVLzIy7776befPm0a1bN2bMmEG7du38ZWzbto1jx47RuHHjgLdPJC9SB10u68ro+SueNWrUIDIykg0bNnDzzTfTuHFjVq5cCcDSpUvZvn07QKrOf+vWrRkwYABHjhyhQoUKgW6miIiIiGTD+eUFa9eujcfjAWDMmDHs27cP4ILnzlNKSEjgrrvuArznf7NmzfLfsBk3bhzdunXj//7v/6hbty6PPPKIP9+cOXPo1q3bBcPeRa4W6qBf5S7nyuixY8e45pprKFasGEeOHGHVqlX86U9/AuDQoUNUqlSJs2fPMm7cOP/wpoMHD1K5cmXMjJiYGJKTkylfvnzgGyoiIiIi2dKkSZN0nxnPyPTp0/2vixcvnu7Ew+C9sRMTE5PuvhdeeCE7VRQpcALWQTezG4B3gd8AycA/nXOvp0kTCSwEzvcAP3DOad2tHHQ5V0a3bt3KH/7wBwoVKkRycjIjRozwr4s5fvx4Fi9eTHJyMv379/fPwjlv3jzefPNNgoKCKFGiBFFRUbpCKiKSRymWi4iI5CzLzlWxbBVsVgWo4pz71sxKA+uB9s65LSnSRALDnHNtslpu/fr13bp16654fUVERPILM1vvnKufA8fJF7G8IE4S1356jdyuQkC4twJz3ikikt9kFMsDtsyac+6Ac+5b3+t4YCtwfaCOJyIiIleWYrmIiEjOypFn0M2sOlAXWJPO7sZmthH4Ce8V+O/Syf848DhAtWrVAldRERERSZdiuUjBYY8VvMcLNTpDCoqA3UE/z8xKAfOBwc65E2l2fwvc6JyrA0wCPkyvDOfcP51z9Z1z9StWrBjYCmdg//793H333YSEhBAaGsrrr79+QZrZs2cTHh5OeHg4d955Jxs3bvTvO378OJ07d+a2224jJCSE1atXA9C1a1c8Hg8ej4fq1av7nwOfPXu2f7vH46FQoULExsbmTGNFRERSKCixXEREJK+76B10M5sJDHTO/eL7/UZgmnPunizkLYI3oM92zn2Qdn/KIO+cW2JmfzezCs65I9lpRE4ICgri1VdfJSIigvj4eOrVq0eLFi38k6IB3HTTTXzxxRdcd911fPLJJzz++OOsWeO90TBo0CDuu+8+5s2bx7lz5zh9+jQAc+fO9ecfOnQo1157LQA9evSgR48eAPznP/+hXbt2/s57VujKqIiInKdYLiIikj9kZYj7V8AaM3sK73NnTwNDL5bJvFNzTwW2Ouf+lkGa3wA/O+ecmTXEe0f/aFYrn5OqVKlClSpVAChdujQhISH8+OOPqTrod955p//1HXfcQVxcHAAnTpzgyy+/9C89UbRoUYoWLZqqfOcc//rXv4iOjr7g2HPmzKF79+5XukkiInL1UCwXERHJBy7aQXfO/cPMvgM+B44AdZ1zB7NQ9m+B3wP/MbPzY7P/DFTzlTsF6Az0N7NE4FegmwvUtPJX0J49e9iwYQONGjXKMM3UqVO5//77Afjhhx+oWLEiffr0YePGjdSrV4/XX3+dkiVL+tOvXLmSypUrc+utt15Q1ty5c1m4cOGVb4iIiFwVFMtFRETyh6wMcf898CzQEwgHlphZH+fcxszyOee+AjIdZ+2cmwxMznp1c9/Jkyfp1KkTEyZMoEyZMumm+fzzz5k6dSpfffUVAImJiXz77bdMmjSJRo0aMWjQIMaOHcuLL77oz5PRXfI1a9ZwzTXXEBYWFpgGiYhIgadYLiIikj9kZYh7J6CJc+4QMMfMFgAzgKw/EF1AJCQk0KlTJ3r06EHHjh3TTbNp0yYeffRRPvnkE8qXLw9AcHAwwcHB/jvunTt3ZuzYsf48iYmJfPDBB6xfv/6C8qKiojS8XURELpdiuYiISD6QlSHu7dP8HuN7xuyq4pzjkUceISQkhKeeeirdNPv27aNjx47MnDmTmjVr+rf/5je/4YYbbmDbtm3UqlWL5cuXp3p2fdmyZdx2220EBwenKi85OZn333+fL7/8MjCNEhGRq4JiuYiISP6QlSHuNYE3gcrOuTAzCwfaAn8JdOXyklWrVjFz5kxq167tn019zJgx7Nu3D4B+/foxevRojh49yoABAwDvzO/r1q0DYNKkSfTo0YNz585Ro0YN3nnnHX/ZGd0l//LLLwkODqZGjRqBbp6IiBRgiuUiIiL5g11sHhcz+wLvbK//cM7V9W3b7JzLlYei69ev7853eiVjWmZNRKTgMrP1zrn62UhfoGP5wlW7r1hZeUX76QXz4rxied6g80TJa/bv30/Pnj05ePAghQoV4vHHH2fQoEGp0nz//ff06dOHb7/9lr/+9a8MGzbMv69v374sXryYSpUqsXnz5lT5Jk2axOTJkwkKCuKBBx7g5ZdfzpE2XUxGsTwrz6Bf4xsKl3Jb4hWrmYiIiASaYrmIiORZQUFBvPrqq0RERBAfH0+9evVo0aJFqseCy5Urx8SJE/nwww8vyN+7d28GDhxIz549U23//PPPWbhwIZs2baJYsWIcOnQo4G25XIWykOaImd0MOAAz6wwcCGitRERE5EpSLBcRkTyrSpUqREREAFC6dGlCQkL48ccfU6WpVKkSDRo0oEiRIhfkb9q0KeXKlbtg+5tvvsmIESMoVqyYv4y8Lisd9CeAfwC3mdmPwGCgf0BrJSIiIleSYrmIiOQLe/bsYcOGDf4VsC7H9u3bWblyJY0aNaJZs2asXbv2CtQwsLIyi/sPwL1mVhIo5JyLD3y1clZBfHZNRETkvKshlouISP538uRJOnXqxIQJEyhTpsxll5eYmMixY8f45ptvWLt2LV26dOGHH34gzSNfeUqGHXQzS3ctsfONcc79LUB1EhERkStAsVxERPKLhIQEOnXqRI8ePejYseMVKTM4OJiOHTtiZjRs2JBChQpx5MgRKlaseEXKD4TMhriX9v3UxzsM7nrfTz/g9kzyiYiISN6gWC4iInmec45HHnmEkJAQnnoq3WvLl6R9+/ZER0cD3uHu586do0KFCles/EDI8A66c24UgJktBSLOD4czsxeA93OkdiIiInLJFMtFRCQ/WLVqFTNnzqR27dp4PB4AxowZw759+wDo168fBw8epH79+pw4cYJChQoxYcIEtmzZQpkyZejevTsrVqzgyJEjBAcHM2rUKB555BH69u1L3759CQsLo2jRosyYMSNPD2+HrC2zVg04l+L3c0D1gNRGREREAkGxXERE8qwmTZrgXOZr2f/mN78hLi4u3X1z5sxJd3vRokWZNWvWZdcvJ2VlFveZQIyZvWBmzwNrgHcDWy0RKQj69u1LpUqVCAsLS3f/sWPH6NChA+Hh4TRs2JDNmzen2p+UlETdunVp06aNf9tdd92Fx+PB4/FQtWpV2rdv79+3YsUKPB4PoaGhNGvWLDCNEsmfFMtFRETygYt20J1zfwX6AseA40Af59yYQFdMRPK/3r178+mnn2a4f8yYMXg8HjZt2sS7777LoEGDUu1//fXXCQkJSbVt5cqVxMbGEhsbS+PGjf2TiBw/fpwBAwawaNEivvvuO95/X6N3Rc5TLBcJvEu9KH3mzBkaNmxInTp1CA0N5fnnn/fniY6OJiIigrCwMHr16kViYiIACxcuJDw8HI/HQ/369fnqq68C30ARyRFZuYMOEIv3WbUFwFEzqxa4KolIQdG0aVPKlSuX4f4tW7Zwzz33AHDbbbexZ88efv75ZwDi4uL4+OOPefTRR9PNGx8fT3R0tP8O+nvvvUfHjh2pVs3756lSpUpXsikiBYFiuUgAXepF6WLFihEdHc3GjRuJjY3l008/5ZtvviE5OZlevXoRFRXF5s2bufHGG5kxYwYA99xzjz/9tGnTMoyVIpL/XPQZdDP7I/A88DOQBBjggPDAVk1ECro6derwwQcf0KRJE2JiYti7dy9xcXFUrlyZwYMH8/LLLxMfn/5yzQsWLOCee+7xr5G5fft2EhISiIyMJD4+nkGDBtGzZ8+cbI5InqVYLhJ4TZs2Zc+ePRnu37JlCyNHjgRSX5SuXLkypUqVArzLTCUkJGBmHD16lGLFilGzZk0AWrRowUsvvcQjjzziTw9w6tSpPD/plVxd7LGC9310b2X+fPyVlJU76IOAWs65UOdcuHOutnNOAV1ELtuIESM4duwYHo+HSZMmUbduXYKCgli8eDGVKlWiXr16GeadM2cO3bt39/+emJjI+vXr+fjjj/n3v//Niy++yPbt23OiGSL5gWK5SC47f1EaSHVRGrxzrng8HipVqkSLFi1o1KgRFSpUICEhgXXr1gEwb9489u/f7y9vwYIF3HbbbTzwwANMmzYt5xskIgGRlQ76fuCXQFdERK4+ZcqU4Z133iE2NpZ3332Xw4cPc9NNN7Fq1SoWLVpE9erV6datG9HR0Tz88MP+fEePHiUmJoYHHnjAvy04OJj77ruPkiVLUqFCBZo2bcrGjRtzo1kieZFiuUguy+iiNEDhwoWJjY0lLi6OmJgYNm/ejJkRFRXFkCFDaNiwIaVLl/anB+jQoQPff/89H374Ic8++2xuNUtErrCsdNB/AFaY2Ugze+r8z8UymdkNZva5mW01s+/MbFA6aczMJprZTjPbZGYRl9IIEcmfjh8/zrlz3pWf3n77bZo2bUqZMmV46aWXiIuLY8+ePURFRdG8efNUS2S8//77tGnThuLFi/u3tWvXjpUrV5KYmMjp06dZs2bNBRPMiVzFFMtFcllGF6VTKlu2LJGRkf5n2Rs3bszKlSuJiYmhadOm3HrrrReU27RpU3bt2sWRI0dypB0iElhZWQd9n++nqO8nqxKBoc65b82sNLDezD5zzm1JkeZ+4FbfTyPgTd+/IlIAdO/enRUrVnDkyBGCg4MZNWoUCQkJAPTr14+tW7fSs2dPChcuzO23387UqVOzVG5UVBQjRoxItS0kJIT77ruP8PBwChUqxKOPPprhTLoiVyHFcpFcdvz4ca655hqKFi2a6qL04cOHKVKkCGXLluXXX39l2bJlDB8+HIBDhw5RqVIlzp49y7hx43jmmWcA2LlzJzfffDNmxrfffsu5c+coX758bjZPRK6Qi3bQnXOjLqVg59wB4IDvdbyZbQWuB1IG9XbAu867Kv03ZlbWzKr48opIPjdnzpxM9zdu3JgdO3ZkmiYyMpLIyMhU21asWJFu2qeffpqnn346O1UUuSoolosE3qVelD5w4AC9evUiKSmJ5ORkunTpQps2bQAYP348ixcvJjk5mf79+9O8eXMA5s+fz7vvvkuRIkUoUaIEc+fO1URxIgVEhh10M/sI7wyv6XLOtc3qQcysOlAXWJNm1/V4n4s7L863TUFdRETkMimWi+ScS70oHR4ezoYNG9LNM378eMaPH3/B9uHDh/vvssvVp2/fvv4JdTdv3nzB/vHjxzN79mzAO4nu1q1bOXz4MOXKleO1117j7bffxsyoXbs277zzDsWLFyc6Opphw4Zx7tw56tWrx9SpU1PNeSA5J7N3/ZUrcQAzKwXMBwY7506k3Z1OlgtOJMzsceBxwL/GsYiIiFyUYrmISAHTu3dvBg4cmOFysilHFH700Ue89tprlCtXjh9//JGJEyeyZcsWSpQoQZcuXYiKiqJnz5706tWL5cuXU7NmTZ577jlmzJjBI488kpPNEp8MO+jOuS8ut3AzK4I3oM92zn2QTpI44IYUvwcDP6VTl38C/wSoX79+zi1CJyIiko8plouIFDxNmzZlz549WUqb3rK0v/76K0WKFOH06dNUrVqVo0ePUqxYMWrWrAlAixYteOmll9RBzyVZmcX9kpj3QZipwFbn3N8ySLYI6OmbAfYO4Bc9syYiIlnRt29fKlWqlOFkgOPHj8fj8eDxeAgLC6Nw4cL897//5cyZMzRs2JA6deoQGhrK888/78+zfPlyIiIi8Hg8NGnShJ07dwIwffp0Klas6C/v7bffzpE25jbFchGR/Ov06dN8+umndOrUCYDrr7+eYcOGUa1aNapUqcK1115Ly5YtqVChAgkJCaxbtw6AefPmsX///syKlgAK5IMFvwV+D/zHzGJ92/4MVANwzk0BlgCtgZ3AaaBPAOsjIleIPVbwJqJxb+mGXn5zqUP8nHNER0dTqlQpEhISaNKkCffffz933HEH/fv3Z+HChYSEhPD3v/+dv/zlL0yfPh2Arl27Mnny5JxqXl6hWC5XvYWrdud2FUQuyUcffcRvf/tbypUrB8CxY8dYuHAhu3fvpmzZsjz00EPMmjWLhx9+mKioKIYMGcLZs2dp2bKlnj/PRRd9580szDl34ewDF+Gc+4r0n0tLmcYBT2S3bBERkUsd4mdmlCpVCoCEhAQSEhL8sx+bGSdOeB+x/uWXX6hateqVr3guUCwXEbn6REVFpRrevmzZMm666SYqVqwIQMeOHfn66695+OGHady4MStXrgRg6dKlbN++PVfqLFkb4j7FzGLMbICZlQ14jURERK6gtEP8AJKSkvB4PFSqVIkWLVrQqJF32e63336b1q1bExwczMyZMxkxYoQ/z/z58wkPD6dz5875ceifYrmIyFXkl19+4YsvvqBdu3b+bdWqVeObb77h9OnTOOdYvnw5ISEhABw6dAiAs2fPMm7cOPr165cr9ZYsdNCdc02AHngngFlnZu+ZWYuA10xEROQKSDvED6Bw4cLExsYSFxdHTEyMf5ma1157jSVLlhAXF0efPn146qmnAHjwwQfZs2cPmzZt4t5776VXr1650pZLpVguIlJwdO/encaNG7Nt2zaCg4OZOnUqU6ZMYcqUKf40CxYsoGXLlpQsWdK/rVGjRnTu3JmIiAhq165NcnIyjz/+OOCdtyUkJITw8HAefPBBmjdvnuPtEq8sPVzgnNthZv8HrAMmAnV9E8f8OYMZXUVERPKEtEP8UipbtiyRkZF8+umnVK5cmY0bN/rvpnft2pX77rsPgPLly/vzPPbYY/ly/WHFchGRgmHOnDkXTdO7d2969+59wfZRo0YxatSoC7aPHz+e8ePHX4nqyWW66B10Mws3s9eArUBz4EHnXIjv9WsBrp+IiMglS2+I3+HDhzl+/DgAv/76K8uWLeO2227juuuu45dffvE/d/fZZ5/5h/4dOPC/SckXLVrk355fKJaLiIjkD1m5gz4ZeAvvFfZfz290zv3kuxIvIiKS47p3786KFSs4cuQIwcHBjBo1ioSEBAD/s3PpDfE7cOAAvXr1IikpieTkZLp06UKbNm0AeOutt+jUqROFChXiuuuuY9q0aQBMnDiRRYsWERQURLly5fwzu+cjiuUiIiL5QFY66B8452am3GBmg5xzr6fdLiIiklMudYhfeHg4GzZsSDd9hw4d6NChwwXbX3rpJV566aVLqmceoVguIiKSD2Slg94TmJBmW2/g9SteG5GrWN++fVm8eDGVKlXyT1iV0ooVK2jXrh033XQT4F0a47nnnmPbtm107drVn+6HH35g9OjRDB48mP/+97907dqVPXv2UL16df71r39x3XXXsXDhQp599lkKFSpEUFAQEyZMoEmTJjnWVhHJcYrlIiJ50MJVu3O7CpLHZNhBN7PuwO+Am8xsUYpdpYGjga6YyNWmd+/eDBw4kJ49e2aY5q677mLx4sWpttWqVYvY2FjAu3TU9ddf778DOHbsWO655x5GjBjB2LFjGTt2LOPGjeOee+6hbdu2mBmbNm2iS5cufP/994FrnIjkCsVyERGR/CWzO+hfAweACsCrKbbHA5sCWSmRq1HTpk3Zs2fPZZWxfPlybr75Zm688UYAFi5cyIoVKwDo1asXkZGRjBs3jlKlSvnznDp1Cu9EziJSACmWi4iI5CMZdtCdc3uBvUDjnKuOiGRm9erV1KlTh6pVq/LKK68QGhqaan/a5aR+/vlnqlSpAkCVKlU4dOiQf9+CBQsYOXIkhw4d4uOPP86ZBohchD1WMC8Wubdc7hxXsVxERCRfyXCZNTP7yvdvvJmdSPETb2Yncq6KIgIQERHB3r172bhxI3/84x9p3759qv3nzp1j0aJFPPTQQ1kqr0OHDnz//fd8+OGHPPvss4GosuQhffv2pVKlSoSFhaW7f8WKFVx77bV4PB48Hg+jR4/27/v000+pVasWt9xyC2PHjr0g7x//+MdUozKGDBniL6dmzZqULVv2yjdIskSxXEREJH/J7A56E9+/pXOuOiKSkTJlyvhft27dmgEDBnDkyBEqVKgAwCeffEJERASVK1f2p6tcuTIHDhygSpUqHDhwgEqVKl1QbtOmTdm1a1eqsqTgudQ5DpKSknjiiSf47LPPCA4OpkGDBrRt25bbb78dgHXr1vnXFD/vtdf+t6z2pEmTMpwxXQJPsVxERCR/yfAO+nlmdrOZFfO9jjSzJ81Mt0NEctjBgwdxzjtMNiYmhuTkZMqXL+/fP2fOnFTD2wHatm3LjBkzAJgxYwbt2rUDYOfOnf6yvv32W86dO5eqLCl4mjZtSrly5bKdLyYmhltuuYUaNWpQtGhRunXrxsKFCwFv5/3pp5/m5ZdfzjB/et9LyXmK5SIiIvlDVpZZmw/UN7NbgKnAIuA9oHUgKyZytenevTsrVqzgyJEjBAcHM2rUKBISEgDo168f8+bN48033yQoKIgSJUoQFRXln9zt9OnTfPbZZ/zjH/9IVeaIESPo0qULU6dOpVq1arz//vsAzJ8/n3fffZciRYpQokQJ5s6dq4niJN05Dn788UduuOEGf5rg4GDWrFkDwOTJk2nbtq1/noO09u7dy+7du2nevHmO1F8ypVguIiKSD2Slg57snEs0sw7ABOfcJDPTeEWRK2zOnDmZ7h84cCADBw5Md98111zD0aMXrphUvnx5li9ffsH24cOHM3z48EurqBRI5+c4KFWqFEuWLKF9+/bs2LHDP9IiJTPjp59+4v333/evEpCeqKgoOnfuTOHChQNYc8kixXIREZF84KJD3IEE3zqqvYDzDycWCVyVREQkp5UpU8Y/0Vvr1q1JSEjwj+bYv3+/P11cXBxVq1Zlw4YN7Ny5k1tuuYXq1atz+vRpbrnlllRlpl1VQHKVYrmIiEg+kJU76H2AfsBfnXO7zewmYFZgqyUiIjnp4MGDVK5cGTNLNcdB2bJl2bFjB7t37+b6668nKiqK9957j9DQUA4ePOjPX6pUKXbu3On/fdu2bRw7dozGjbW6Vx6hWC4iIpIPZNpBN7PCwJ+dcw+f3+ac2w1cuM6OiIjkWZc6x0FQUBCTJ0+mVatWJCUl0bdvX0JDQy96vDlz5tCtWzfNbZAHKJaLiIjkH5l20J1zSWZW0cyKOufO5VSlRETkyrqcOQ5at25N69aZzyV28uTJVL+/8MIL2aqfBI5iuYiISP7x/+zde5yN5f7/8deHcahEJ9qYpIjkMBOm6IBqy6FoK4VOZHdW0ZZi90uyO2hrp51SW+2ixIgK+xuSdLAljBq2QyiHDEKS89nn98e9ZrXGjDFjZs2s4f18POZhrfu+7uu+7jXLfO7rvk456eK+EphhZhOAHekb3f3F7A4ys7eAa4EN7l4ni/3NgPHAitCmD929f86KLVI0jJ+x4siJCX5YDgAAIABJREFURESibyWK5SIiIjEvJxX0taGfYsDJuch7GPAK8E42aaa7+7W5yFNERERyT7FcRESkCDhiBd3dnwIws5PcfceR0kcc95WZVT36oomIiEh+UCwXEREpGo5YQTezxsC/gTJAFTNLAO5x9/vz4fyNzWwewVP9R9x9YT7kKSJyXNAQCskpxXIREZGiISfroL8EtAA2Abj7PKBJPpz7W+Bsd08ABgPjDpfQzO42sxQzS9m4cWM+nFpEROS4olguIiJSBOSkgo67rz5k04G8ntjdt7r79tDriUAJMzvjMGmHuntDd29Yvnz5vJ5aRETkuKNYLiIiEvtyUkFfbWaXAG5mJc3sEWBxXk9sZn+w0AK5ZnZRqCyb8pqviIiIZKJYLiIiUgTkZBb3e4F/ApWBNGAK0O1IB5nZKKAZcIaZpQFPAiUA3P11oD1wn5ntB3YBHd3dj+IaREREJHuK5SIiIkVATiro5u635DZjd+90hP2vECzdIiIiItGlWC4iIlIE5KSL+9dmNsXM/mxmp0S9RCIiIpLfFMtFRESKgCNW0N39POD/AbWBb83s/8zs1qiXTERERPKFYrmIiEjRkNNZ3Ge7+1+Ai4BfgeFRLZWIiIjkK8VyERGR2HfECrqZlTWzzmY2CfgaWEcQ3EVERKQIUCwXEREpGnIySdw8YBzQ391nRrk8IiIikv8Uy0VERIqAnFTQz9WSKSIiIkWaYrmIiEgRcNgKupm95O49gAlmlimou3vbqJZMRERE8kSxXEREpGjJrgX93dC/LxREQURERCTfKZaLiIgUIYetoLv73NC/X5pZ+dDrjQVVMBEREckbxXIREZGi5bCzuFugn5n9AnwPLDWzjWbWt+CKJyIiIkdLsVxERKRoyW6ZtR7ApUCSu5/u7qcCFwOXmtnDBVI6ERERyQvFchERkSIkuwr67UAnd1+RvsHdlwO3hvaJiIhIbFMsFxERKUKyq6CXcPdfDt0YGrtWInpFEhERkXyiWC4iIlKEZFdB33uU+0RERCQ2KJaLiIgUIdkts5ZgZluz2G5A6SiVR0RERPKPYrmIiEgRkt0ya8ULsiAiIiKSvxTLRUREipbsuriLiIiIiIiISAFRBV1EREREREQkBqiCLiIiIiIiIhIDolZBN7O3zGyDmS04zH4zs5fN7Aczm29m9aNVFhEREck9xXIREZGCFc0W9GFAy2z2twLOC/3cDbwWxbKIiIhI7g1DsVxERKTARK2C7u5fAb9mk+Q64B0PfAOcYmYVo1UeERERyR3FchERkYJVmGPQKwOrI96nhbZlYmZ3m1mKmaVs3LixQAonIiIiR6RYLiIiko8Ks4JuWWzzrBK6+1B3b+juDcuXLx/lYomIiEgOKZaLiIjko8KsoKcBZ0W8jwfWFlJZREREJPcUy0VERPJRYVbQJwC3h2aAbQRscfd1hVgeERERyR3FchERkXwUF62MzWwU0Aw4w8zSgCeBEgDu/jowEWgN/ADsBO6IVllEREQk9xTLRUREClbUKuju3ukI+x3oFq3zi4iISN4olouIiBSswuziLiIiIiIiIiIhqqCLiIiIiIiIxABV0EVERERERERigCroIiIiIiIiIjFAFXQRERERERGRGKAKuoiIiIiIiEgMUAVdREREREREJAaogi4iIiIiIiISA1RBFxEREREREYkBqqCLiIiIiIiIxABV0EVERERERERigCroIiIiIiIiIjFAFXQRERERERGRGKAKuoiIiIiIiEgMUAVdREREREREJAaogi4iIiIiIiISA1RBFxEREREREYkBqqCLiIiIiIiIxABV0EVERERERERiQFQr6GbW0syWmNkPZtY7i/1dzGyjmaWGfu6MZnlEREQkdxTLRURECk5ctDI2s+LAq0BzIA2YY2YT3H3RIUlHu/sD0SqHiIiIHB3FchERkYIVzRb0i4Af3H25u+8FkoHrong+ERERyV+K5SIiIgUomhX0ysDqiPdpoW2HusHM5pvZWDM7K6uMzOxuM0sxs5SNGzdGo6wiIiKSmWK5iIhIAYpmBd2y2OaHvP8PUNXd6wFTgeFZZeTuQ929obs3LF++fD4XU0RERA5DsVxERKQARbOCngZEPkWPB9ZGJnD3Te6+J/T2DaBBFMsjIiIiuaNYLiIiUoCiWUGfA5xnZueYWUmgIzAhMoGZVYx42xZYHMXyiIiISO4olouIiBSgqM3i7u77zewB4BOgOPCWuy80s/5AirtPAB4ys7bAfuBXoEu0yiMiIiK5o1guIiJSsKJWQQdw94nAxEO29Y143QfoE80yiIiIyNFTLBcRESk40eziLiIiIiIiIiI5pAq6iIiIiIiISAxQBV1EREREREQkBqiCLiIiIiIiIhIDVEEXERERERERiQGqoIuIiIiIiIjEAFXQRURERERERGKAKugiIiIiIiIiMUAVdBEREREREZEYoAq6iIiIiIiISAxQBV1EREREREQkBqiCLiIiIiIiIhIDVEEXERERERERiQGqoIuIiIiIiIjEAFXQRURERERERGKAKugiIiIiIiIiMUAVdBEREREREZEYoAq6iIiIiIiISAxQBV1EREREREQkBkS1gm5mLc1siZn9YGa9s9hfysxGh/bPMrOq0SyPiIiI5I5iuYiISMGJWgXdzIoDrwKtgAuATmZ2wSHJ/gxsdvfqwCDg+WiVR0RERHJHsVxERKRgRbMF/SLgB3df7u57gWTgukPSXAcMD70eC1xlZhbFMomIiEjOKZaLiIgUoLgo5l0ZWB3xPg24+HBp3H2/mW0BTgd+iUxkZncDd4febjezJVEp8bHlDA75HIs6e1P3ezFE3y+JlmPuuwVR+X6dnd8ZHoZieeHR/wWJpmPu+6XvVkzR9ytnsozl0aygZ3UVfhRpcPehwND8KNTxwsxS3L1hYZdDjk36fkm06LsVcxTLC4n+L0g06fsl0aTvV95Es4t7GnBWxPt4YO3h0phZHFAO+DWKZRIREZGcUywXEREpQNGsoM8BzjOzc8ysJNARmHBImglA59Dr9sA0d8/01F1EREQKhWK5iIhIAYpaF/fQOLQHgE+A4sBb7r7QzPoDKe4+Afg38K6Z/UDwtL1jtMpzHFI3Qokmfb8kWvTdiiGK5YVK/xckmvT9kmjS9ysPTA+5RURERERERApfNLu4i4iIiIiIiEgOqYIuIiIiIiIiEgNUQY8xZva4mS00s/lmlmpmh643m5e8J5rZKfmVnxRtZnammY00s+VmNtfMZppZu8IulxR9ZnYg9Pcr/adqPuR5r5ndHno9zMza5zVPkWhRLJeColgu0aA4XriiuQ665JKZNQauBeq7+x4zOwMomV/5u3vr/MpLijYzM2AcMNzdbw5tOxtoW6gFk2PFLndPzM8M3f31/MxPJFoUy6WgKJZLFCmOFyK1oMeWisAv7r4HwN1/cfe1ZrbSzJ43s9mhn+oAZlbezD4wszmhn0tD28uY2dtm9r/Q0/sbQttXhm4UMLNbQ3mlmtm/zKx46GeYmS0IHftwIX0OEn1XAnsj/1i6+yp3Hxz6HgwMfafmm9k9AGbWzMy+MLOxZva9mb0Xujk49LvV0My+CL1uGvH09TszO7ngL1VigZlVNbPpZvZt6OeS0PZmZvalmb1vZkvNbICZ3RL6+/Q/M6sWStfPzB45JM+rzOyjiPfNzezDgr0ykUwUy6WgKJZLgVEcLzhqQY8tU4C+ZrYUmAqMdvcvQ/u2uvtFFnQNeYng6fw/gUHu/l8zq0KwDE4t4Algi7vXBTCzUyNPYma1gA7Ape6+z8yGALcAC4HK7l4nlE5d6I5dtYFvD7PvzwTfnyQzKwXMMLMpoX0Xho5dC8wALgX+m815HgG6ufsMMysD7M6X0kusO8HMUkOvV7h7O2AD0Nzdd5vZecAooGEoTQLB365fgeXAm6G/d92BB4EehznPNOBVMyvv7huBO4C3o3NJIjmmWC4FRbFcokVxvBCpgh5D3H27mTUALgeuAEabWe/Q7lER/w4Kvf4jcEHowSdA2dBTzT8SsQ6tu28+5FRXAQ2AOaFjTyD4T/cf4FwzGwx8THCTIccBM3sVuAzYC6wC6tnvY4PKAeeF9s1297TQMalAVbIP6jOAF83sPeDD9GPlmJdV17gSwCtmlggcAGpE7Jvj7usAzOxHfv/b8z+Cv4VZcnc3s3eBW83sbaAxcHs+XYPIUVEsl8KiWC75SHG8EKmCHmPc/QDwBfCFmf0P6Jy+KzJZ6N9iQGN33xWZR6irUnYL3BvBeKU+mXaYJQAtgG7ATUDXo7gMiX0LgRvS37h7t1C3thTgJ+BBd/8k8gAzawbsidh0gN//huzn9yEzpSPyHWBmHwOtgW/M7I/u/n0+X4sUDQ8D6wmeshcjYwtM5PfqYMT7gxw5Tr1NUCHZDYxx9/35UlqRPFAslwKiWC4FSXG8gGgMegwxs5qhLiPpEgmegELQjS3935mh11OAByKOTzzM9gzd4oDPgPZmViG0/zQzOzv0R72Yu39A0LWuft6vSmLUNKC0md0Xse3E0L+fAPeZWQkAM6thZicdIb+VBC05EHGzYGbV3P1/7v48wQ3D+flReCmSygHr3P0gcBtQPD8ydfe1BN00/x8wLD/yFMkLxXIpQIrlUpAUxwuIKuixpQww3MwWmdl84AKgX2hfKTObBXQneIIF8BDQ0ILJPxYB94a2Pw2casEEMfM4pGuJuy8i+E8wJXSeTwkmtalM8LQ/leA/SKan8nJscHcH/gQ0NbMVZjYbGA48BrwJLAK+NbMFwL848tPPp4B/mtl0gqfx6XpEfA93AZPy+VKk6BgCdDazbwi6xe3Ix7zfA1aH/raJFDbFcikQiuVSwBTHC4gF/7cllpnZSqChu/9S2GUREYk1ZvYK8J27/7uwyyJyOIrlIiJZUxzPSGPQRUSkyDKzuQRP8XsWdllEREQkdxTHM1MLuoiIiIiIiEgM0Bh0ERERERERkRigCrqIiIiIiIhIDFAFXURERERERCQGqIIuIiIiIiIiEgNUQRcRERERERGJAaqgi4iIiIiIiMQAVdBFREREREREYoAq6CIiIiIiIiIxQBV0ERERERERkRigCrqIiIiIiIhIDFAFXY4ZZnaLmU0p7HJEk5ldbmZLCuG8283s3HzKa6WZ/TE/8ioMZjbMzJ4u7HKIiEhGug+I6nl1HyBSQFRBlyLFzC4zs6/NbIuZ/WpmM8wsCcDd33P3qwu7jEfLzPqZ2T4z2xb6WWpmr5hZxfQ07j7d3WsWdNncvYy7Lw+VMyYqqGbWzMwOhm4atpvZGjN7qrDLBeGyuZk9WthlERE5lug+QPcBkcysrJm9ZGY/he4Ffgi9PyPK5+1iZv+N5jnk+KUKuhQZZlYW+D9gMHAaUBl4CthTmOXKZ6Pd/WSC62sH/AGYGxmco8HM4qKZfxStDd00lAEuA/5sZn8q7EIBnYFfQ/8eVhH+3EVECpzuA6KnKMYjMysJfAbUBloCZYFLgE3ARYVYNJE8UQVdipIaAO4+yt0PuPsud5/i7vMh89PMUAvm/Wa2LPQk+m9mVs3MZprZVjN7P/THPb3FM83M/mpmv4S6X90SkVdrM1sUymeNmT0Sse+u0BPbX81sgplVOqQM94bKsNnMXjUzO9KFuvs+d18IdAA2Aj0jyxl63dvMxkYeZ2b/NLOXQ6/Lmdm/zWxdqMxPm1nxiM9qhpkNMrNfgX5mVt3Mvgy1SvxiZqMPuY7qZnY3cAvwaOhJ9X/MrJeZfXBIOQab2UvZXGJS6PPcbGZvm1np0HELzKxNRD4lQmVJzMFntgL4GrjgkM9jdej3PdfMLo/Yd5GZpYT2rTezFyP2jTGzn0OfxVdmVvtI54849kSgPdANOM/MGkbsqxr6LP9sZj8B00LbG1nQIvSbmc0zs2YRx9xhZotD373lZnZPTssiInKM0X2A7gMi3Q5UAdq5+yJ3P+juG9z9b+4+MXR8LTP7IhRfF5pZ24i8vzCzOyPeZ/X9yfS7M7NawOtA49Bn8Fs21ymSa6qgS1GyFDhgZsPNrJWZnZqDY1oCDYBGwKPAUILAchZQB+gUkfYPwBkET+Q7A0PNLL0b2b+Be0JPtevwe8XqSuA54CagIrAKSD6kDNcCSUBCKF2LnF6wux8AxgOXZ7F7FNDaghYFQkH3JmBkaP9wYD9QHbgQuBq4M+L4i4HlQAXgGeBvwBTgVCCeoIXi0PIMBd4D/h5quW4DjABamtkpoXLEEdxQvJvNpd1C8DlUI7jh+n+h7e8At0akaw2sc/fUbPIidN7zgEuBbyI2zwESCVoiRgJj0m8CgH8C/3T3sqFyvB9x3CTgPILP5tvQNefUDcB2YAzwCcENxKGaArWAFmZWGfgYeDpUzkeAD8ysfCjtBoLvUFngDmCQmdXPRXlERI4Vug/I6Hi/D/gjMNndt2d1AjMrAfwndE0VgAeB9yJ+pzmR6Xfn7ouBe4GZoc/glFzkJ3JEqqBLkeHuWwm6MTvwBrAx9KT6zGwOe97dt4aeQi8Aprj7cnffQlAJu/CQ9E+4+x53/5Kg0nRTaPs+4AIzK+vum93929D2W4C33P1bd98D9CF4olo1Is8B7v6bu/8EfE5QYcyNtQQVtwzcfRVB5TG9S/eVwE53/yb0mbQCerj7DnffAAwCOkbm6+6D3X2/u+8KXePZQCV33+3uORpb5e7rgK+AG0ObWgK/uPvcbA57xd1Xu/uvBDcF6TdII4i42QBuI/sAXyn0VHwrwY3bLCBcbncf4e6bQtf4D6AUkB6Y9wHVzewMd9/u7t9EHPeWu28L/U77AQlmVu5In0VIZ4IuigcIbpI6hW4SIvUL/V52EdyITHT3iaGn/58CKQQ3Jbj7x+7+owe+JLjRyOpGTUTkmKb7gIx0H8DpwLpsztEIKEPw+e9192kEQyQ6ZXPMofL6uxPJNVXQpUhx98Xu3sXd4wmeYFcCsutCtT7i9a4s3peJeL/Z3XdEvF8Vyh+CVtHWwKpQ96/Goe2VQunSy7edYOxT5Yh8fo54vfOQc+ZEZYLxzFkZye+B5mZ+f2p+NlACWBeqwP4G/IvgCXK61Yfk9ShgwOxQN7CuuSjjcH5/4n0r2VeqDz13+HN297XADOCG0JP4VmTfer3W3U8JtYKfQvA7HZ6+08x6WtA9fEvoMyhH0DoC8GeCp/bfm9kcM7s2dExxMxtgZj+GKv4rQ+mPOOGMmZ0FXBFR5vFAaeCabK7/bODG9N9TqJyXEbTEEGol+saCrpO/EXwPozr5jYhIrNJ9QCbH833AJkKx8jAqAavd/eAh56p8mPRZyevvTiTXVEGXIsvdvweGEQTo/HCqmZ0U8b4KwVNr3H2Ou19HENjG8Xt36LUEQRCA0PGnA2vyo0BmVgxoA0w/TJIxQDMziyeYTCY9MK8mmDTnjFAF9hR3L+vukWOpPTIjd//Z3e9y90rAPcAQM6uexTk9i23jgHpmVoegO9iRuoSfFfE6/DmHpAf5Gwm6j+Xoswy1howk+LywYLz5YwStH6eGuqBtIbj5wN2XuXsngt/p88DY0O/vZuA6gq5z5YCqoVMcccwgwZP+YsB/zOxngq6DpcnczT3yM1wNvBvxezrF3U9y9wFmVgr4AHgBODN0DRNzWBYRkWOa7gOA4/s+YCrBULGTDrN/LXBW6DOMPFd6fjuAEyP2/eEIZY6U1Wcgki9UQZciw8zOD7WIxofen0Xw1Pib7I/MlafMrGSocnctwZjlkhasrVrO3fcBW4EDofQjgTvMLDFUmXoWmOXuK/NSCAsmRalFML7sD8CLWaVz943AF8DbwAoPxkWldzebAvzDgiVIilkwMU7TbM55Y/pnC2wmCD4Hski6HsiwFqq77wbGEnwes0NdwbLTzczizew04K/A6Ih944D6QHeCsWg5YmZlCLruLQxtOplg7N1GIM7M+hKM405Pf6uZlQ89WU+f4OVA6Lg9BE/mTyT4nebU7QQzCidG/NwAXGNmpx/mmBFAGzNrEWq9L23BJEDxQEmCbvkbgf1m1opgDKGIyHFH9wGZHef3Ae8SPIj4IPTdKGZmp1sw0V9rgmFvOwgmtCthwQSsbfh9joBU4HozOzH0IOLPRyhzpPVAvIUmGRTJT6qgS1GyjWBCk1lmtoMgIC8gNLNpPviZICCtJXjye2/o6TwELaMrQ12e7yXUjcvdPwOeIGjlXEcw2UlHjl4HM9tOUGGcQFBJbBDq8nU4Iwlae0cesv12ggreotB1jSX7rmBJBJ/t9tC5u3swM/qh/k0wDu83MxsXsX04UJcjd2tLL/MUghbm5QQTpAEQGgf3AXAO8OER8qlkoXXQCbqtnUYwHhCCCdomEYxNXwXsJmOXupbAwtCx/wQ6hm4w3gmlX0Pw2eXoxs/MGhG0tr8aaoVI/5kA/MBhxry5+2qCFvu/ElTEVwO9gGLuvg14iKClZjNB6/6EnJRHROQYpPuArB2X9wGhMf9/BL4HPiV4cDKbYBjYLHffC7Ql6Cb/CzAEuD3idzoI2EtQ2R5O7iaEnUbQIPCzmf2Si+NEjsjc1UNDJPRUdURoTJscBTOrQhAk/xCayCcvefUFarj7rUdMLCIikke6D8g73QeI5I+4wi6AiBR9ofFdfwGS8yEon0bQzey2/CibiIiIRJfuA0TyT9S6uJvZW2a2wcwWHGa/mdnLZvaDmc03resrUiSFJmfZCjQHnsxjXncRdPGe5O5f5UPxRCQPFMtF5Eh0HyCSv6LWxd3MmgDbgXfcPdPsmqHJGx4kWLLiYuCf7n5xVAojIiIiuaZYLiIiUrCi1oIeeup1uDUbIZgU6R0PfAOcYmbZTVwhIiIiBUixXEREpGAV5hj0ymScUTkttG3doQnN7G7gboCTTjqpwfnnn18gBRQREYlFc+fO/cXdyxd2OVAsFxEROSqHi+WFWUG3LLZl2d/e3YcCQwEaNmzoKSkp0SyXiIhITDOzVYVdhhDFchERkaNwuFhemOugpwFnRbyPJ1h3UkRERIoGxXIREZF8VJgV9AnA7aEZYBsBW9w9U5c4ERERiVmK5SIiIvkoal3czWwU0Aw4w8zSCJZdKAHg7q8DEwlmff0B2AncEa2yiIiISO4plouIiBSsqFXQ3b3TEfY70C1a5xcRKWr27dtHWloau3fvLuyiSIwoXbo08fHxlChRolDOr1guIpI7iuVyqNzG8sKcJE5ERCKkpaVx8sknU7VqVcyymntLjifuzqZNm0hLS+Occ84p7OKIiEgOKJZLpKOJ5YU5Bl1ERCLs3r2b008/XQFdADAzTj/9dLXCiIgUIYrlEuloYrkq6CIiMUQBXSLp+yAiUvTob7dEyu33QRV0ERERERERkRigMegiIjFq/IwV+ZrfdZceeezTzz//TI8ePZgzZw6lSpWiatWqvPTSS5QpU4aHHnqIsWPHkpqaytq1a2ndunWuzj9p0iSeeOIJduzYgbtz7bXX8sILL+T6On777TdGjhzJ/fffn+tjRURECpJiedYUyw9PLegiIgIEE5m0a9eOZs2a8eOPP7Jo0SKeffZZ1q9fT6VKlRg7diwAqampTJw4MVd5L1iwgAceeIARI0awePFiFixYwLnnnntU5fztt98YMmRIro5xdw4ePHhU5xOJdZMnT6ZmzZpUr16dAQMGZNr/008/ccUVV3DhhRdSr1698P/fffv20blzZ+rWrUutWrV47rnnAFiyZAmJiYnhn7Jly/LSSy8BMGbMGGrXrk2xYsVISUkpuIsUkRxRLC/6VEEXEREAPv/8c0qUKMG9994b3paYmMjll1/OypUrqVOnDnv37qVv376MHj2axMRERo8ezXnnncfGjRsBOHjwINWrV+eXX37JkPff//53Hn/8cc4//3wA4uLiwk/NN27cyA033EBSUhJJSUnMmDEDgH79+tG1a1eaNWvGueeey8svvwxA7969+fHHH0lMTKRXr14ADBw4kKSkJOrVq8eTTz4JwMqVK6lVqxb3338/9evXZ/Xq1VH89EQKx4EDB+jWrRuTJk1i0aJFjBo1ikWLFmVI8/TTT3PTTTfx3XffkZycHP6/N2bMGPbs2cP//vc/5s6dy7/+9S9WrlxJzZo1SU1NJTU1lblz53LiiSfSrl07AOrUqcOHH35IkyZNCvxaReTIFMuLPnVxFxERIHgy3qBBg2zTlCxZkv79+5OSksIrr7wCwPfff897771Hjx49mDp1KgkJCZxxxhmZ8u7Zs2eWeXbv3p2HH36Yyy67jJ9++okWLVqwePHicN6ff/4527Zto2bNmtx3330MGDCABQsWkJqaCsCUKVNYtmwZs2fPxt1p27YtX331FVWqVGHJkiW8/fbbuX5KL1JUzJ49m+rVq4dbsTp27Mj48eO54IILwmnMjK1btwKwZcsWKlWqFN6+Y8cO9u/fz65duyhZsiRly5bNkP9nn31GtWrVOPvsswGoVatWQVyWiBwlxfKiTxV0ERHJk65du3LdddfRo0cP3nrrLe64445cHT916tQMLX5bt25l27ZtAFxzzTWUKlWKUqVKUaFCBdavX5/p+ClTpjBlyhQuvPBCALZv386yZcuoUqUKZ599No0aNcrD1YnEtjVr1nDWWWeF38fHxzNr1qwMafr168fVV1/N4MGD2bFjB1OnTgWgffv2jB8/nooVK7Jz504GDRrEaaedluHY5ORkOnXqFP0LEZFCpVgeO1RBFxERAGrXrh0em5YbZ511FmeeeSbTpk1j1qxZvPfee1nmPXfuXBISEjLtO3jwIDNnzuSEE07ItK9UqVLh18WLF2f//v2Z0rg7ffr04Z577smwfeXKlZx00km5vh6RosTdM207dEmfUaNG0aVLF3r27MnMmTO57bbbWLBgAbNnz6Z48eKsXbuWzZs3c/nll/PHP/4x3Bq/d+9eJkyYEB6bLiKxT7G86NMYdBERAeDKK69kz549vPHGG+HJv09qAAAgAElEQVRtc+bM4csvv8yQ7uSTTw4/FU935513cuutt3LTTTdRvHjxTHn36tWLZ599lqVLlwJBIH/xxRcBuPrqq8Nd7IBwd7fDOfT8LVq04K233mL79u1A0KK4YcOGnFyySJEXHx+fYUxmWlpauAt7un//+9/cdNNNADRu3Jjdu3fzyy+/MHLkSFq2bEmJEiWoUKECl156aYaJ3yZNmkT9+vU588wzC+ZiRCTPFMuLPrWgi4jEqJwspZKfzIyPPvqIHj16MGDAAEqXLh1emiXSFVdcwYABA0hMTKRPnz506NCBtm3bcscddxy2S1y9evV46aWX6NSpEzt37sTMuOaaawB4+eWX6datG/Xq1WP//v00adKE119//bDlPP3007n00kupU6cOrVq1YuDAgSxevJjGjRsDUKZMGUaMGJHlzYXIsSYpKYlly5axYsUKKleuTHJyMiNHjsyQpkqVKnz22Wd06dKFxYsXs3v3bsqXL0+VKlWYNm0at956Kzt37uSbb76hR48e4eNGjRql7u0ieaRYnjXF8my4e5H6adCggYuIHIsWLVpU2EU4anPmzPHLLrussItxTMrqewGkeAzE5KP9USzPXx9//LGfd955fu655/rTTz/t7u5PPPGEjx8/3t3dFy5c6JdcconXq1fPExIS/JNPPnF3923btnn79u39ggsu8Fq1avnf//73cJ47duzw0047zX/77bcM5/rwww+9cuXKXrJkSa9QoYJfffXVBXSVUlgmTZrkNWrU8GrVqvlzzz2Xaf+qVau8WbNmnpiY6HXr1vWPP/7Y3d1XrFjhpUuX9oSEBE9ISPB77rknfExKSorXqVPHq1Wr5g8++KAfPHjQ3d3ff/99v+CCC9zMfM6cOQVzgflMsVyykptYrhZ0ERHJkwEDBvDaa69lOV5NRKKvdevWtG7dOsO2/v37h19fcMEF4SWPIpUpU4YxY8ZkmeeJJ57Ipk2bMm1v165deMk1OfalL+P36aefEh8fT1JSEm3bts2wSkD6Mn733XcfixYtonXr1qxcuRKAatWqZdnV+b777mPo0KE0atSI1q1bM3nyZFq1ahVexu/QccgSfYrlsUNj0EVEJE969+7NqlWruOyyywq7KCIiko8il/ErWbJkeBm/SIdbxu9w1q1bx9atW2ncuDFmxu233864ceOAYBm/mjVrRudiJFuK5bFDFXQREREREckkq2X81qxZkyFNv379GDFiBPHx8bRu3ZrBgweH961YsYILL7yQpk2bMn369HCe8fHx2eYpcjxTBV1ERESOG5MnT6ZmzZpUr16dAQMGZNr/008/ccUVV3DhhRdSr149Jk6cCAQtiYmJiSQmJpKQkMBHH310xDynTZtG/fr1qVOnDp07d85yaSGRWOa5WMYvLS2NiRMnctttt3Hw4EEqVqzITz/9xHfffceLL77IzTffzNatW3OUp8jxTGPQRURE5LiQl/G0derUISUlhbi4ONatW0dCQgJt2rTBzLLM8/zzz6dz58589tln1KhRg759+zJ8+HD+/Oc/56isdtexWWHxNzJXziR25XQZv8mTJwMZl/GrUKFCeP3rBg0aUK1aNZYuXUp8fDxpaWnZ5ilyPFMLuoiIiBwX8jKe9sQTTyQuLmjX2L17d7jF73B5btq0iVKlSlGjRg0AmjdvzgcffFBQlyqSLyKX8du7dy/Jycm0bds2Q5r0ZfyADMv4bdy4kQMHDgCwfPlyli1bxrnnnkvFihU5+eST+eabb3B33nnnHa677roCvzaJDVu2bGHBggX873//Y926dZn279mzhyVLlrBo0SIWLlzIli1bANi6dWt426JFi8J/tyEYRjF//ny+/fbbHOUVa9SCLiISo/K7BS0nLVdpaWl069aNRYsWcfDgQa699loGDhxIyZIlsz3u2Wef5a9//Wu2ae68807+8pe/ZGitBBg2bBgpKSm88sorR76IkNmzZ/PII4+wfv16zIzLLruMl19+malTp7Jo0SJ69+7NuHHjqFGjRqbzHckLL7zAm2++SVxcHMWLF6dnz57cfvvtucoDIDU1lbVr12aaXVsKT1bjaWfNmpUhTb9+/bj66qsZPHgwO3bsYOrUqeF9s2bNomvXrqxatYp3332XuLi4w+Z5xhlnsG/fPlJSUmjYsCFjx47N0BIpUhTExcXxyiuv0KJFCw4cOEDXrl2pXbs2ffv2pWHDhrRt25Z//OMf3HXXXQwaNAgzY9iwYZgZX331FX379g3/LX399dc57bTTAHjttdfo0qULu3btolWrVrRq1QqAjz76iAcffJCNGzdyzTXXkJiYyCeffFKYH0GeKZYf3qxZs7j//vvZtm0bxYoVo3bt2gwdOpQZM2aEY/nw4cOpVasWl19+Obt27WLZsmXUq1ePuLg4qlevTsmSJdm1axdLly4lISEBgHLlyvHuu+/y+uuvU6ZMmXAsb9KkCaeeeioVKlTIkFd2CiOWqwVdRESAYKzh9ddfz5/+9CeWLVvG0qVL2b59O48//vgRj3322WePmObNN9/MdWU5K+vXr+fGG2/k+eefZ8mSJSxevJiWLVuybds22rZtS+/evQEYN24cixYtylXer7/+Op9++imzZ89mwYIFfPXVV1mOl8yJ1NTU8PjlnNIY5ejKy3hagIsvvpiFCxcyZ84cnnvuOXbv3n3YPM2M5ORkHn74YS666CJOPvnkcAu8SFHSunVrli5dyo8//hiOB/379w+3pKcv4zdv3jxSU1O5+uqrAbjhhhtYuHAh8+bN49tvv6VNmzbhPBs2bMiCBQv48ccfeeWVV8L/D9u1a0daWhp79uxh/fr1Rb5yXhiKWix/9NFHWbp0aTiWr1mzJkMsnzJlCt9//z0QDFMqUaIEEPRqSn/gULp0adw9/Ld6xIgRfP7557zzzjsZYrmZhXt1ROaVncKI5aqgi4gIEExoVbp0ae644w4AihcvzqBBg3jrrbfYuXMnw4YN44EHHginv/baa/niiy/o3bs3u3btIjExkVtuuYUdO3ZwzTXXkJCQQJ06dRg9ejQAzZo1IyUlBYC3336bGjVq0LRp0wzrM2/cuJEbbriBpKQkkpKSsly7+dVXX6Vz5840btwYCCpD7du358wzzwyX8euvv2bChAn06tWLxMREfvzxR+rXrx/OY9myZTRo0CBT3s8++yxDhgyhbNmyQPAUvnPnzgDMnTuXpk2b0qBBA1q0aBHuitesWTMee+wxLrroImrUqMH06dPZu3cvffv2ZfTo0SQmJjJ69Gh27NhB165dSUpK4sILLwx3rR42bBg33ngjbdq0Cd/YSnTkdDztTTfdBGQcTxupVq1anHTSSSxYsCDbPBs3bsz06dOZPXs2TZo04bzzzovWpYmIAEUrlnfo0IGLL74YCGL59ddfzymnnJIhlk+bNo2nnnqKmjVrMm3aNDp16hTOIz2Wb968mRNOOIFixYKqbXosL1OmDPB7LK9YsSLTp0+nYcOGJCUl8eCDD8ZkLFcFXUREAFi4cGGmSmvZsmWpUqUKP/zww2GPGzBgACeccAKpqam89957TJ48mUqVKjFv3jwWLFhAy5YtM6Rft24dTz75JDNmzODTTz/N0MrdvXt3Hn74YebMmcMHH3zAnXfemel8CxYsyLJyHemSSy6hbdu2DBw4kNTUVKpVq0a5cuVITU0FgpuKLl26ZDhm27ZtbNu2jWrVqmXKb9++fTz44IOMHTuWuXPn0rVr1wytEfv372f27Nm89NJLPPXUU5QsWZL+/fvToUMHUlNT6dChA8888wxXXnklc+bM4fPPP6dXr17s2LEDgJkzZzJ8+HCmTZuW7XVJ3uRlPO2KFSvCrSKrVq1iyZIlVK1aNds8N2zYAATjHp9//nnuvffeArxaETkeFaVYnt4l/XAuueQSmjdvTr9+/ViyZAlXXnklJUuW5LvvvgOCWH7LLbewZs0azj77bCD7WL5+/XoGDhzIpEmTmDNnDi1btszQpT9WYrkq6HLEJWcefvjh8NIyNWrU4JRTTgnve+yxx6hTp06GJ2sAXbp04Zxzzgkfl35TnG7OnDkUL16csWPHRu/CRCRX0rt/5XT74dStW5epU6fy2GOPMX36dMqVK5dh/6xZs2jWrBnly5enZMmSdOjQIbxv6tSpPPDAAyQmJtK2bVu2bt3Ktm3bjv6iItx55528/fbbHDhwgNGjR3PzzTdn2J/ddS5ZsoQFCxbQvHlzEhMTefrppzPMQnz99dcDwUzFK1euzDKPKVOmMGDAABITE2nWrBm7d+/mp59+AoIJxNLHZkr0RI6nrVWrFjfddFN4PO2ECRMA+Mc//sEbb7xBQkICnTp1Co+n/e9//0tCQgKJiYm0a9eOIUOGcMYZZxw2T4CBAwdSq1Yt6tWrR5s2bbjyyisL8/JF5DhQlGJ5XFwce/fuDb/fu3dvpm7nu3fvDreElylThuuvv5633nqLAwcOkJycTFJSEueccw6lS5c+4nWmpKSwZMkSmjdvzmWXXca//vWvmIzlGgx1nMvJkjODBg0Kvx48eHD4qdXHH3/Mt99+S2pqKnv27KFp06a0atUq3DV04MCBtG/fPstzPvbYY7Ro0SLKVyciuVG7du1Ms0xv3bqV1atXU61aNebNmxce3wVB0MxKjRo1mDt3LhMnTqRPnz5cffXV9O3bN0OawwXPgwcPMnPmTE444YRsyzl37txcz/p7ww038NRTT3HllVfSoEEDTj/99Az7y5Yty0knncTy5cs599xzM+xzd2rXrs3MmTOzzDt9KaHixYsfduyZu/PBBx9Qs2bNDNtnzZrFSSedlKtrkaPXunXrTJP99O/fP/w6fTztoW677TZuu+22HOcJQRwcOHBgHksskv+OxWX8tIRfoCjF8oULF1K7dm327NlDiRIl+PXXXzPF3+LFi7Nz504Adu3axZVXXsmtt97K+PHjqVGjBnXq1AlX4CFjLD9UXFwcNWvWZM6cOeGJ5SIniYuVWK4W9FzIS0vzo48+Su3atalVqxYPPfRQeFKZ0aNHU69ePWrXrs2jjz4aTv/VV19Rv3594uLiotrKnJMlZyKNGjUqPPZj0aJFNG3alLi4OE466SQSEhLC62BmZ/Dgwdxwww1UqFAh365DRPLuqquuYufOnbzzzjtA8DCtZ8+edOnShRNPPJGqVauSmprKwYMHWb16NbNnzw4fW6JECfbt2wfA2rVrOfHEE7n11lt55JFHMi1zcvHFF/PFF1+wadMm9u3bx5gxY8L7rr766gwzwB7a+wbggQceYPjw4Rlm3x4xYgQ///xzhnQnn3xyhif2pUuXpkWLFtx3333hsXmH6tOnD926dQsv17J161aGDh1KzZo12bhxY7iCvm/fPhYuXJjNp5n5/C1atGDw4MHhv//pDztFRETyS1GK5e+88w4bNmxg6dKlLFy4kM8//5wtW7awefNm9uzZA8CZZ57Jzz//zMKFC1m+fDk1a9akRYsW3H///VxzzTWsXbuWhQsXsnDhwnDZ7733Xm6//Xa2bt3KvHnzWLJkCUOHDqVJkyZs2LCB9957j+XLl1O5cuUjTiZbGLFcLeg5lJeW5q+//poZM2Ywf/58AC677DK+/PJL6tatS69evZg7dy7ly5enc+fOfPbZZ1x11VVUqVKFYcOG8cILL0T1unKy5Ey6VatWsWLFinAXvYSEBJ566in+8pe/sHPnTj7//PMMn8fjjz9O//79ueqqqxgwYAClSpVizZo1fPTRR0ybNo05c+ZE9dpEirqCbg0wMz766CPuv/9+/va3v3Hw4EFat24dntX10ksv5ZxzzqFu3brUqVMnw6Rrd999N/Xq1aN+/frcfvvt9OrVi2LFilGiRAlee+21DOepWLEi/fr1o3HjxlSsWJH69euHZ1V9+eWX6datG/Xq1WP//v00adKE119/PcPxZ555JsnJyTzyyCNs2LCBYsWK0aRJk3DXtHQdO3bkrrvu4uWXX2bs2LFUq1aNW265hQ8//PCwE7jcd999bN++naSkJEqUKEGJEiXo2bMnJUuWZOzYsTz00ENs2bKF/fv306NHj3BX5qxcccUV4W5wffr04YknnqBHjx7Uq1cPd6dq1ar83//9X85/QSIiUuQolmcfyx999NEMsbxr166ceuqp4dbsW2+9lbvuuosxY8YwduxYypUrF47l999/P8WLF8/0Gfz1r3+lRIkS3HPPPRliebly5Rg/fnzMx3I72uVjCkvDhg09febAgjRz5kz69esXXu7hueeeA4LWlqxccsklPPXUUzRv3pyZM2fywAMP8N///hd3p0mTJrz77rts376dPn36hNdYfffdd5k5cyZDhgwJ59OlSxeuvfbaLLuK54cxY8bwySef8Oabb4bLMHv2bAYPHpwp7fPPP09aWlqGfc888wxjxoyhfPnyVKhQgYsuuoju3buzbt06/vCHP7B3717uvvtuqlWrRt++fbnxxhvp2bMnjRo1ivq1iRQ1ixcvplatWoVdjGPaCy+8wJYtW/jb3/5W2EXJsay+F2Y2190bFlKR8qywYnlRcix2QQZ1Q44Vx+L3K1a+W4rl0Xesx3K1oOdQXlqaGzduzBVXXEHFihVxdx544AFq1arF5s2b+f7771m5ciXx8fGMGzcuw0QJBSEnS86kS05O5tVXX82w7fHHHw/PZHzzzTeHl5CpWLEiEIzluOOOO8I9AVJSUujYsSMAv/zyCxMnTiQuLo4//elP+XthIiKHaNeuHT/++KNmSj8GjZ+xorCLICIiBeB4iOWqoOdQVj0NDjcxQnJyMu3btw93ufjhhx9YvHhxeJbA5s2b89VXX9GkSRNee+01OnToQLFixbjkkkuynNAgmiKXh6lcuTLJycmMHDkyU7olS5awefPm8LrDEHT7/+233zj99NOZP38+8+fPD3cbXbduXfiBxLhx46hTpw4AK1b8fhOV3oKuyrmIFISPPvqosIsgIiIieXA8xHJV0HMoLy3NH330EY0aNQrPMNiqVSu++eYbmjRpQps2bWjTpg0AQ4cOzXIcRTRFLg9z4MABunbtGl5ypmHDhuG1XEeNGkXHjh0zPJTYt28fl19+ORDMmDhixAji4oKv1C233MLGjRtxdxITEzONOxGRrOV2GRQ5thW1YWgiIqJYnrLy2BvC1LDq0Y8qy20sj2oF3cxaAv8EigNvuvuAQ/ZXAYYDp4TS9Hb3idEs09HKS0tzlSpVeOONN+jTpw/uzpdffkmPHj0A2LBhAxUqVGDz5s0MGTKE999/v8CuKd2RlpwB6NevX6bjSpcufdiZD3PS7WTYsGE5LqPI8aB06dJs2rSJ008//bgO7BJwdzZt2hRe27WwHEuxXEQk2hTLJdLRxPKoVdDNrDjwKtAcSAPmmNkEd4+s0f0/4H13f83MLgAmAlWjVaa8yEtLc/v27Zk2bRp169bFzGjZsmW41bx79+7MmzcPgL59+1KjRg0A5syZQ7t27di8eTP/+c9/ePLJJ4+4pI+IFG3x8fGkpaWxcePGwi6KxIjSpUsTHx9faOc/1mK5iEi0KZbDL5t+Kewi5LvFuxYf9bG5jeXRbEG/CPjB3ZcDmFkycB0QGdQdKBt6XQ5YG8Xy5NnRtjQXL16cf/3rX1nmOWrUqCy3JyUlhcesixRVkydPpnv37hw4cIA777yT3r17Z9j/8MMP8/nnnwOwc+dONmzYwG+//Rbev3XrVmrVqkW7du3C62mOHj2aZ555hgMHDnDNNdfw97//HYAXX3yRN998k7i4OMqXL89bb73F2WefXUBXmj9KlCjBOeecU9jFEIl0zMVyEZFoUiyHC+664MiJipiCXCWgWBTzrgysjnifFtoWqR9wq5mlETxxfzCK5RGRAnTgwAG6devGpEmTWLRoEaNGjco0JGLQoEGkpqaSmprKgw8+mGkd6yeeeIKmTZuG32/atIlevXrx2WefsXDhQtavX89nn30GwIUXXkhKSgrz58+nffv2PProo9G/SJFjn2K5iIhIAYpmC3pWgy4OffTQCRjm7v8ws8bAu2ZWx90PZsjI7G7gbgjGc8uRaX1LKWyzZ8+mevXqnHvuuQB07NiR8ePHc8EFWT9VHTVqFE899VT4/dy5c1m/fj0tW7Ykfb3k5cuXU6NGDcqXLw/AH//4Rz744AOuuuoqrrjiivCxjRo1YsSIEdG6NJHjiWK5iIhIAYpmC3oacFbE+3gyd3v7M/A+gLvPBEoDZxyakbsPdfeG7t4w/cZcRGLbmjVrOOus3/8ExMfHs2bNmizTrlq1ihUrVnDllVcCcPDgQXr27MnAgQMzpKtevTrff/89K1euZP/+/YwbNy7D6grp/v3vf9OqVat8vBqJRZMnT6ZmzZpUr16dAQMGZNr/8MMPk5iYSGJiIjVq1OCUU04Bgu9bgwYNSExMpHbt2hlWmZg7dy5169alevXqPPTQQ+GZVzt06BDOq2rVqiQmJhbMRRY+xXIREZECFM0W9DnAeWZ2DrAG6AjcfEian4CrgGFmVosgqBf4jArjZ6w4ciIRyZWslpQ43GymycnJtG/fPrzM4JAhQ2jdunWGCj7AqaeeymuvvUaHDh0oVqwYl1xyCcuXL8+QZsSIEaSkpPDll1/m05VILEofQvHpp58SHx9PUlISbdu2zdBDY9CgQeHXgwcP5rvvvgOgYsWKfP3115QqVYrt27dTp04d2rZtS6VKlbjvvvsYOnQojRo1onXr1kyePJlWrVoxevTocF49e/akXLlyBXexhavIxHIREZFjQdQq6O6+38weAD4hWHblLXdfaGb9gRR3nwD0BN4ws4cJusx1cS36KnJMiI+Pz9C6nZaWRqVKlbJMm5yczKuvvhp+P3PmTKZPn86QIUPYvn07e/fupUyZMgwYMIA2bdqEV0EYOnRouFIPMHXqVJ555hm+/PJLSpUqFaUrk1iQlyEUJUuWDG/fs2cPBw8GPbHXrVvH1q1bw8tk3n777YwbNy5Dbwx35/3338/RUpLHAsVyERGRghXVddBD66BOPGRb34jXi4BLo1kGESkcSUlJLFu2jBUrVlC5cmWSk5MZOXJkpnRLlixh8+bN4UoRwHvvvRd+PWzYMFJSUsJdmDds2ECFChXYvHkzQ4YM4f333wfgu+++45577mHy5MlUqFAhylcnhS2rIRSzZs3KMu2hQygAVq9ezTXXXMMPP/zAwIEDqVSpEikpKRmWQclqWMb06dM588wzOe+88/L5imKXYrmIiEjBiWoFXUSOX3Fxcbzyyiu0aNGCAwcO0LVrV2rXrk3fvn1p2LAhbdu2BYKWzY4dOx62+/uhunfvzrx58wDo27cvNWrUAKBXr15s376dG2+8EQgmoZowYUIUrkxiQV6GUACcddZZzJ8/n7Vr1/KnP/2J9u3b5yjPUaNG0alTpzyWXkRERCRrqqCLSNS0bt2a1q1bZ9jWv3//DO/79euXbR5dunShS5cu4fejRo3KMt3UqVOPqoxSNOVlCEWkSpUqUbt2baZPn86ll15KWlraYfPcv38/H374IXPnzs2nqxARERHJKJqzuIuIiERF5BCKvXv3kpycHO6VESmrIRRpaWns2rULgM2bNzNjxgxq1qxJxYoVOfnkk/nmm29wd9555x2uu+668HFTp07l/PPPz9ANXkRERCQ/qQVdRESKnLwMoVi8eDE9e/bEzHB3HnnkEerWrQvAa6+9RpcuXdi1axetWrXKMEFccnKyureLiIhIVKmCLiIiRdLRDqFo3rw58+fPzzLPhg0bsmDBgiz3DRs27KjKKSLHj8mTJ9O9e3cOHDjAnXfeSe/evTOlef/99+nXrx9mRkJCQngC1ccee4yPP/4YgCeeeIIOHToAwVCvL7/8Mry847Bhw0hMTATgiy++oEePHuzbt48zzjhDS4yKHANUQReRXLO7cjahW1Hib2hVKBEROXoHDhygW7dufPrpp8THx5OUlETbtm0zLP+4bNkynnvuOWbMmMGpp57Khg0bAPj444/59ttvSU1NZc+ePTRt2pRWrVpRtmxZAAYOHEj79u0znO+3337j/vvvZ/LkyVSpUiWclxx/ovFgSAqPKugiIiIiInk0e/ZsqlevzrnnngtAx44dGT9+fIYK+htvvEG3bt049dRTAcLLgi5atIimTZsSFxdHXFwcCQkJTJ48mZtuuumw5xs5ciTXX389VapUyZCXHF+i+WBICocmiRMRERERyaM1a9Zw1llnhd/Hx8ezZs2aDGmWLl3K0qVLufTSS2nUqBGTJ08GICEhgUmTJrFz587/397dR1tVl4se/z6C6DkEhYgNBUtIJMCLlBuPr+UpFXPcNnRjnBA9mSZeu/ZmaaeOxfXlnKEeu1odbORLJ8lM0kylJMGb0iUrAQtRycDEjigjRbFTliC75/6xFrsF7JeFrLn2XPj9jLHGXvPtt54Jv72e/cz5m3Oyfv167rvvvq2eVHHBBRcwYcIEzj33XDZu3NjZ1oYNGzj22GM59NBD+eY3v9mEvVTZ1B4YGjBgQOeBoVr1HBgaOHBg54Eh9S0LdEmSJGknZW5/qVTtDSqh8rjG1atXs2jRIm6++WbOPPNMXnzxRU444QROOukkjjzySE4++WSOOOII+vevDHS99NJLeeyxx1i6dCkvvPACl19+eWdbDz74IHfddRcLFizgkksuYdWqVcXvqEqlyAND6hsOcZcklcaueH8D8B4H0mvBiBEjtipu1q5dy3777bfdOocffji77747I0eOZMyYMaxevZpJkyZxwQUXcMEFFwAwY8YMRo8eDcC+++4LwB577MHpp5/OF7/4xc629t57bwYOHMjAgQN5xzvewUMPPcRBBx3UjN1VSezogaG1a9dyzDHH8Mgjj3DCCSewdOlSjjzySIYNG7bVgSH1Hc+gS5IkSTtp0qRJrF69mjVr1rBp0ybmzp3b+cjHLaZOnWdevpkAABl4SURBVMp9990HwPr161m1ahWjRo2io6OD559/HoAVK1awYsUKTjjhBADWrVsHVAqxO+64g4MPPhiAKVOmsHjxYjZv3syf/vQnHnjgAcaOHdus3VVJ1HtgaMqUKdsdGILK5RPLly/nnnvuITM7Dwyp73iIRJIkSdpJ/fv3Z/bs2UyePJmOjg7OOOMMxo8fz6xZs2hra6O9vZ3JkyezcOFCxo0bR79+/bjiiisYOnQoL7/8MscccwwAgwcP5lvf+lbnmcxTTjmF5557jsxk4sSJfO1rXwNg7NixnHjiiUyYMIHddtuNM888s7N412tH7YGh4cOHM3fu3M47tG8xdepUbr75Zj70oQ9td2DoxRdfZOjQodsdGFLfsUCXJEmSGuCkk07ipJNO2mrexRdf3Pk+Irjyyiu58sort1pnzz33ZOXKlV22ee+993b7eeeffz7nn3/+TkSsVlfUgSH1Hf8HJEmSJKlFFXFgSH3Ha9AlSZIkSSoBC3RJkiRJkkrAIe6SJElSN+68f01fhyDpNcQCXZIkSZL6gAeAtC2HuEuSJEmSVAIW6JIkSZIklUCvBXpE3BgRr6+ZfnNE/KjYsCRJUqOYyyVJag31nEH/CfBARJwUETOBe4AvFRuWJElqIHO5JEktoNcCPTOvAc4E7gQuBt6Rmd8vOjDptebuu+9mzJgxHHjggVx22WVdrnPLLbcwbtw4xo8fz4wZMzrnz5kzh9GjRzN69GjmzJnTOf/YY49lzJgxTJw4kYkTJ/Lss88CcMMNNzBs2LDO+ddff32xO6c+V0T/OvHEEznkkEMYP348Z599Nh0dHQBceOGFDB8+vLN/zZ8/v9idU6/M5ZIktYZe7+IeEf8IfAH4IDABmB8Rp2fmQ0UHJ71WdHR0cM4553DPPfcwYsQIJk2aRHt7O+PGjetcZ/Xq1Vx66aXcf//9DBkypLPYfuGFF7joootYtmwZEcGhhx5Ke3s7Q4YMAeCmm26ira1tu8/8wAc+wOzZs5uzg+pTRfWvW265hcGDB5OZTJs2jVtvvZXp06cDcO6553Leeef1yf5qe+ZySZJaQz1D3N8PHJ2ZN2fm54CzgTm9bCNpByxZsoQDDzyQUaNGMWDAAKZPn86dd9651TrXXXcd55xzTmfhvc8++wCwYMECjj/+ePbaay+GDBnC8ccfz9133930fVB5FdW/Bg8eDMDmzZvZtGkTEdHEvdIOMpdLktQC6hniPjUzn62ZXgIcVmhU0mvM008/zf777985PWLECJ5++umt1lm1ahWrVq3iqKOO4vDDD+8sknrb9vTTT2fixIlccsklZGbn/Ntuu40JEyYwbdo0nnrqqaJ2TSVQZP+aPHky++yzD4MGDWLatGmd82fPns2ECRM444wz2LBhQ1G7pjqZyyVJag313MX9oIj4UUQ8Up2eAHym8Mik15DawnmLbc9Gbt68mdWrV7No0SJuvvlmzjzzTF588cUet73pppt4+OGHWbx4MYsXL+bGG28E4L3vfS9PPvkkK1as4LjjjuO0004rYK9UFkX1L6icYV+3bh0bN27k3nvvBeAjH/kIv/nNb1i+fDn77rsvn/70pxu8R9pR5nJJklpDPUPcrwM+B7wCkJkrgOlFBiW91owYMWKrs9hr165lv/32226dKVOmsPvuuzNy5EjGjBnD6tWre9x2+PDhAAwaNIgZM2awZMkSAIYOHcoee+wBwMyZM3nwwQcL3T/1raL61xZ77rkn7e3tncPm3/jGN9KvXz922203Zs6c2dnv1KfM5ZIktYB6CvS/rQ6Fq7W5iGCk16pJkyaxevVq1qxZw6ZNm5g7dy7t7e1brTN16lTuu+8+ANavX8+qVasYNWoUkydPZuHChWzYsIENGzawcOFCJk+ezObNm1m/fj0Ar7zyCj/4wQ84+OCDAVi3bl1nu/PmzWPs2LFN2lP1hSL61x//+MfOfrR582bmz5/PW9/6VmDr/nX77bd39jv1KXO5JEktoNe7uAPrI+ItQAJExDRgXc+bSNoR/fv3Z/bs2UyePJmOjg7OOOMMxo8fz6xZs2hra6O9vb2zUBo3bhz9+vXjiiuuYOjQoQB84QtfYNKkSQDMmjWLvfbai5deeonJkyfzyiuv0NHRwXHHHcfMmTMB+MpXvsK8efPo378/e+21FzfccENf7bqaoIj+9bvf/Y729nY2btxIR0cH73rXuzj77LMB+MxnPsPy5cuJCA444ACuueaaPtt3dTKXS5LUAqKr6wu3WiFiFHAtcCSwAVgDnJqZTxYeXRfa2tpy2bJlDW3zzvvXNLS9Mph6w6i+DqHh8rqe+6qaJ2buenfrtn+Vw67Yt6Dx/SsiHszM7Z+f2P36u3QuN4+3jlb8rrV/tYZW7Ftg/2oVRfSv7nJ5r2fQM/MJ4LiIGAjslpl/aHh0kiSpMOZySZJaQ7cFekR8qpv5AGTmlb01HhEnAl8G+gHXZ+ZlXazzD8CFVIbdPZSZM+oJXJIk9cxcLklSa+npDPqg6s8xwCRgXnX6vcD/663hiOgHXA0cD6wFlkbEvMxcWbPOaCp3lT0qMzdExD47vguSJKkb5nJJklpItwV6Zl4EEBELgbdvGQ4XERcCt9bR9mHA49VhdUTEXGAKsLJmnZnA1Zm5ofqZz76KfZBKa1e8rkjlYf9Sb8zlkiS1lnoes/YmYFPN9CbggDq2Gw48VTO9tjqv1kHAQRFxf0T8vDqMbjsRcVZELIuIZc8991wdHy1JkmqYyyVJagH1PGbtRmBJRNxO5dqy9wHfrGO7rm7Fu+3t7/oDo4FjgRHA4og4ODNf3GqjzGup3H2Wtra21rxFoyRJfcdcLklSC6jnLu7/GhF3A0dXZ52emb+so+21wP410yOAZ7pY5+eZ+QqwJiJ+TSXJL62jfUmSVAdzuSRJraGeIe4Ay6lcq3Y78HxEvKmObZYCoyNiZEQMAKbz15vTbHEH8PcAEbE3lWFyT9QZkyRJqp+5XJKkkuv1DHpEfAz438DvgA4qw90SmNDTdpm5OSI+Ciyg8miW/8jMRyPiYmBZZs6rLjshIlZW2z4/M5/fmR2SJElbM5dLktQa6rkG/RPAmFeTbDNzPjB/m3mzat4n8KnqS5IkFcNcLklSC6hniPtTwO+LDkSSJBXGXC5JUguo5wz6E8CiiLgL2LhlZmZeWVhUkiSpkczlkiS1gHoK9P+svgZUX5IkqbWYyyVJagH1PGbtomYEIkmSimEulySpNXRboEfE96nc4bVLmdleSESSJKkhzOWSJLWWns6gf7FpUUiSpCKYyyVJaiHdFuiZ+eNmBiJJkhrLXC5JUmup5zFrkiRJkiSpYBbokiRJkiSVQK8FekQc3IxAJElSMczlkiS1hnrOoH8tIpZExP+KiDcUHpEkSWo0c7kkSS2g1wI9M48GTgH2B5ZFxLcj4vjCI5MkSQ1hLpckqTXUdQ16Zq4GPg/8E/BO4CsR8VhE/I8ig5MkSY1hLpckqfzquQZ9QkRcBfwKeBfw3swcW31/VcHxSZKknWQulySpNXT7HPQas4HrgH/OzD9vmZmZz0TE5wuLTJIkNYq5XJKkFlDPEPfvZeaNtQk9Ij4BkJk3FhaZJElqFHO5JEktoJ4C/YNdzPtQg+OQJEnFMZdLktQCuh3iHhEnAzOAkRExr2bRIOD5ogOTJEk7x1wuSVJr6eka9J8C64C9gf9TM/8PwIoig5IkSQ1hLpckqYV0W6Bn5m+B3wJHNC8cSZLUKOZySZJaS09D3H+SmUdHxB+ArF0EZGYOLjw6SZL0qpnLJUlqLT2dQT+6+nNQ88KRJEmNYi6XJKm19HoX94h4S0TsUX1/bER8PCLeUHxokiSpEczlkiS1hnoes3Yb0BERBwJfB0YC3y40KkmS1EjmckmSWkA9BfpfMnMz8D7gS5l5LrBvsWFJkqQGMpdLktQC6inQX6k+R/U04AfVebsXF5IkSWowc7kkSS2gngL9dCqPZ/nXzFwTESOBbxUbliRJaiBzuSRJLaDbu7gDREQ/4J8z89Qt8zJzDXBZ0YFJkqSdZy6XJKl19HgGPTM7gGERMaBJ8UiSpAYyl0uS1Dp6PINe9SRwf0TMA17aMjMzrywqKEmS1FBPYi6XJKn06inQn6m+dgMGFRuOJEkqgLlckqQW0GuBnpkXAUTEwMx8qbf1a0XEicCXgX7A9ZnZ5fVuETENuBWYlJnLduQzJElSz8zlkiS1hl7v4h4RR0TESuBX1elDIuKrdWzXD7gaeA8wDjg5IsZ1sd4g4OPAAzsYuyRJqoO5XJKk1lDPY9a+BEwGngfIzIeAd9Sx3WHA45n5RGZuAuYCU7pY7xLg34CX64pYkiTtKHO5JEktoJ4Cncx8aptZHXVsNhyo3W5tdV6niHgbsH9m/qCeOCRJ0qtjLpckqfzquUncUxFxJJDVR7R8nOoQuV5EF/Oyc2HEbsBVwId6bSjiLOAsgDe96U11fLQkSaphLpckqQXUcwb9bOAcKkfM1wITq9O9WQvsXzM9gsodZLcYBBwMLIqIJ4HDgXkR0bZtQ5l5bWa2ZWbbsGHD6vhoSZJUw1wuSVILqOcMemTmKa+i7aXA6IgYCTwNTAdmbFmYmb8H9u78kIhFwHne+VWSpIYzl0uS1ALqOYP+04hYGBEfjog31NtwZm4GPgosoDKM7pbMfDQiLo6I9lcZryRJ2nHmckmSWkA9z0EfHRGHUTlqfkH1MS1zM/NbdWw7H5i/zbxZ3ax7bF0RS5KkHWIulySpNdR7F/clmfkpKo9beQGYU2hUkiSpoczlkiSVX68FekQMjojTIuKHwE+BdVSSuyRJagHmckmSWkM9N4l7CLgDuDgzf1ZwPJIkqfHM5ZIktYB6CvRRmZm9ryZJkkrKXC5JUgvotkCPiC9l5iepPM90u6Semd69VZKkEjOXS5LUWno6g35j9ecXmxGIJElqOHO5JEktpNsCPTMfrP78cUQMq75/rlmBSZKknWMulySptXR7F/eouDAi1gOPAasi4rmI6PLZp5IkqVzM5ZIktZaeHrP2SeAoYFJmDs3MIcDfAUdFxLlNiU6SJO0Mc7kkSS2kpwL9g8DJmblmy4zMfAI4tbpMkiSVm7lckqQW0lOBvntmrt92ZvXatd2LC0mSJDWIuVySpBbSU4G+6VUukyRJ5WAulySphfT0mLVDIuK/upgfwJ4FxSNJkhrHXC5JUgvp6TFr/ZoZiCRJaixzuSRJraWnIe6SJEmSJKlJLNAlSZIkSSoBC3RJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKgELdEmSJEmSSsACXZIkSZKkErBAlyRJkiSpBCzQJUmSJEkqAQt0SZIkSZJKwAJdkiRJkqQSsECXJEmSJKkELNAlSZIkSSoBC3RJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKoFCC/SIODEifh0Rj0fEZ7tY/qmIWBkRKyLiRxHx5iLjkSRJO8ZcLklS8xRWoEdEP+Bq4D3AOODkiBi3zWq/BNoycwLwXeDfiopHkiTtGHO5JEnNVeQZ9MOAxzPziczcBMwFptSukJn3ZeafqpM/B0YUGI8kSdox5nJJkpqoyAJ9OPBUzfTa6rzufBj4YVcLIuKsiFgWEcuee+65BoYoSZJ6YC6XJKmJiizQo4t52eWKEacCbcAVXS3PzGszsy0z24YNG9bAECVJUg/M5ZIkNVH/AtteC+xfMz0CeGbblSLiOOAC4J2ZubHAeCRJ0o4xl0uS1ERFnkFfCoyOiJERMQCYDsyrXSEi3gZcA7Rn5rMFxiJJknacuVySpCYqrEDPzM3AR4EFwK+AWzLz0Yi4OCLaq6tdAbwOuDUilkfEvG6akyRJTWYulySpuYoc4k5mzgfmbzNvVs3744r8fEmStHPM5ZIkNU+RQ9wlSZIkSVKdLNAlSZIkSSoBC3RJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKgELdEmSJEmSSsACXZIkSZKkErBAlyRJkiSpBCzQJUmSJEkqAQt0SZIkSZJKwAJdkiRJkqQSsECXJEmSJKkELNAlSZIkSSoBC3RJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKgELdEmSJEmSSsACXZIkSZKkErBAlyRJkiSpBCzQJUmSJEkqAQt0SZIkSZJKwAJdkiRJkqQSsECXJEmSJKkELNAlSZIkSSoBC3RJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKgELdEmSJEmSSsACXZIkSZKkEii0QI+IEyPi1xHxeER8tovle0TEd6rLH4iIA4qMR5Ik7RhzuSRJzVNYgR4R/YCrgfcA44CTI2LcNqt9GNiQmQcCVwGXFxWPJEnaMeZySZKaq8gz6IcBj2fmE5m5CZgLTNlmnSnAnOr77wLvjogoMCZJklQ/c7kkSU3Uv8C2hwNP1UyvBf6uu3Uyc3NE/B4YCqyvXSkizgLOqk7+MSJ+XUjEu5a92ebfsdXF9f69VyL2LxVll+tbUEj/enOjG+yGubzv+LugIu1y/cu+VSr2r/p0mcuLLNC72ot8FeuQmdcC1zYiqNeKiFiWmW19HYd2TfYvFcW+VTrm8j7i74KKZP9SkexfO6fIIe5rgf1rpkcAz3S3TkT0B14PvFBgTJIkqX7mckmSmqjIAn0pMDoiRkbEAGA6MG+bdeYBp1XfTwPuzcztjrpLkqQ+YS6XJKmJChviXr0O7aPAAqAf8B+Z+WhEXAwsy8x5wNeBGyPicSpH26cXFc9rkMMIVST7l4pi3yoRc3mf8ndBRbJ/qUj2r50QHuSWJEmSJKnvFTnEXZIkSZIk1ckCXZIkSZKkErBAL5mIuCAiHo2IFRGxPCK2fd7szrQ9PyLe0Kj21Noi4o0R8e2IeCIiHoyIn0XE+/o6LrW+iOiofn9teR3QgDbPjogPVt/fEBHTdrZNqSjmcjWLuVxFMI/3rSKfg64dFBFHAP8deHtmboyIvYEBjWo/M09qVFtqbRERwB3AnMycUZ33ZqC9TwPTruLPmTmxkQ1m5tca2Z5UFHO5msVcrgKZx/uQZ9DLZV9gfWZuBMjM9Zn5TEQ8GRGXR8SS6utAgIgYFhG3RcTS6uuo6vzXRcQ3IuLh6tH791fnP1n9Q4GIOLXa1vKIuCYi+lVfN0TEI9Vtz+2jfwcV713Aptovy8z8bWb+e7UfXFHtUysi4n8CRMSxEbEoIr4bEY9FxE3VPw627VttEbGo+v6dNUdffxkRg5q/qyqDiDggIhZHxC+qryOr84+NiB9HxC0RsSoiLouIU6rfTw9HxFuq610YEedt0+a7I+L2munjI+J7zd0zaTvmcjWLuVxNYx5vHs+gl8tCYFZErAL+L/CdzPxxddl/ZeZhURka8iUqR+e/DFyVmT+JiDdReQzOWOALwO8z878BRMSQ2g+JiLHAB4CjMvOViPgqcArwKDA8Mw+urucQul3XeOAX3Sz7MJX+Myki9gDuj4iF1WVvq277DHA/cBTwkx4+5zzgnMy8PyJeB7zckOhVdn8TEcur79dk5vuAZ4HjM/PliBgN3Ay0Vdc5hMp31wvAE8D11e+7TwAfAz7ZzefcC1wdEcMy8zngdOAbxeySVDdzuZrFXK6imMf7kAV6iWTmHyPiUOAY4O+B70TEZ6uLb675eVX1/XHAuOqBT4DB1aOax1HzHNrM3LDNR70bOBRYWt32b6j80n0fGBUR/w7cReWPDL0GRMTVwNHAJuC3wIT467VBrwdGV5ctycy11W2WAwfQc1K/H7gyIm4CvrdlW+3yuhoatzswOyImAh3AQTXLlmbmOoCI+A1//e55mMp3YZcyMyPiRuDUiPgGcATwwQbtg/SqmMvVV8zlaiDzeB+yQC+ZzOwAFgGLIuJh4LQti2pXq/7cDTgiM/9c20Z1qFJPD7gPKtcrfW67BRGHAJOBc4B/AM54Fbuh8nsUeP+Wicw8pzqsbRnwn8DHMnNB7QYRcSywsWZWB3/9DtnMXy+Z2bOm3csi4i7gJODnEXFcZj7W4H1RazgX+B2Vo+y7sfUZmNp+9Zea6b/Qe576BpWC5GXg1szc3JBopZ1gLleTmMvVTObxJvEa9BKJiDHVISNbTKRyBBQqw9i2/PxZ9f1C4KM120/sZv5Ww+KAHwHTImKf6vK9IuLN1S/13TLzNipD696+83ulkroX2DMiPlIz72+rPxcAH4mI3QEi4qCIGNhLe09SOZMDNX8sRMRbMvPhzLycyh8Mb21E8GpJrwfWZeZfgH8E+jWi0cx8hsowzc8DNzSiTWlnmMvVROZyNZN5vEks0MvldcCciFgZESuAccCF1WV7RMQDwCeoHMEC+DjQFpWbf6wEzq7O/xdgSFRuEPMQ2wwtycyVVH4JFlY/5x4qN7UZTuVo/3IqvyDbHZXXriEzE5gKvDMi1kTEEmAO8E/A9cBK4BcR8QhwDb0f/bwI+HJELKZyNH6LT9b0wz8DP2zwrqh1fBU4LSJ+TmVY3EsNbPsm4Knqd5vU18zlagpzuZrMPN4kUfndVplFxJNAW2au7+tYJKlsImI28MvM/HpfxyJ1x1wuSV0zj2/Na9AlSS0rIh6kchT/030diyRJ2jHm8e15Bl2SJEmSpBLwGnRJkiRJkkrAAl2SJEmSpBKwQJckSZIkqQQs0CVJkiRJKgELdEmSJEmSSuD/A7ZtZWUXB2i/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def autolabel(rects, axis):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        axis.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "        \n",
    "labels = ['Species', 'Genus', 'Family']\n",
    "plt.rcParams[\"figure.figsize\"] = [14, 7]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "\n",
    "rects1 = [axs[0,0].bar(x - width/2, shannon_area[0], width, color=\"lightsteelblue\", label='City Center'), axs[0,0].bar(x + width/2, shannon_area[1], width, color=\"darkgreen\", label='Outside City Center')]\n",
    "rects2 = [axs[0,1].bar(x - width/2, shannon_count[0], width, color=\"lightsteelblue\", label='City Center'), axs[0,1].bar(x + width/2, shannon_count[1], width, color=\"darkgreen\", label='Outside City Center')]\n",
    "rects3 = [axs[1,0].bar(x - width/2, simpson_area[0], width, color=\"lightsteelblue\", label='City Center'), axs[1,0].bar(x + width/2, simpson_area[1], width, color=\"darkgreen\", label='Outside City Center')]\n",
    "rects4 = [axs[1,1].bar(x - width/2, simpson_count[0], width, color=\"lightsteelblue\", label='City Center'), axs[1,1].bar(x + width/2, simpson_count[1], width, color=\"darkgreen\", label='Outside City Center')]\n",
    "\n",
    "axs[0,0].set_ylabel('Diversity Index')\n",
    "axs[0,0].set_title('Shannon Diversity by Basal Area')\n",
    "axs[0,0].set_xticks(x)\n",
    "axs[0,0].set_xticklabels(labels)\n",
    "axs[0,0].legend()\n",
    "\n",
    "axs[0,1].set_ylabel('Diversity Index')\n",
    "axs[0,1].set_title('Shannon Diversity by Count')\n",
    "axs[0,1].set_xticks(x)\n",
    "axs[0,1].set_xticklabels(labels)\n",
    "axs[0,1].legend()\n",
    "\n",
    "axs[1,0].set_ylabel('Diversity Index')\n",
    "axs[1,0].set_title('Simpson Diversity by Basal Area')\n",
    "axs[1,0].set_xticks(x)\n",
    "axs[1,0].set_xticklabels(labels)\n",
    "axs[1,0].legend()\n",
    "\n",
    "axs[1,1].set_ylabel('Diversity Index')\n",
    "axs[1,1].set_title('Simpson Diversity by Count')\n",
    "axs[1,1].set_xticks(x)\n",
    "axs[1,1].set_xticklabels(labels)\n",
    "axs[1,1].legend()\n",
    "\n",
    "autolabel(rects1[0], axs[0,0])\n",
    "autolabel(rects1[1], axs[0,0])\n",
    "autolabel(rects2[0], axs[0,1])\n",
    "autolabel(rects2[1], axs[0,1])\n",
    "autolabel(rects3[0], axs[1,0])\n",
    "autolabel(rects3[1], axs[1,0])\n",
    "autolabel(rects4[0], axs[1,1])\n",
    "autolabel(rects4[1], axs[1,1])\n",
    "\n",
    "\n",
    "axs[0,0].set_ylim([0,max(shannon_count.max(), shannon_area.max())+0.5])\n",
    "axs[0,1].set_ylim([0,max(shannon_count.max(), shannon_area.max())+0.5])\n",
    "axs[1,0].set_ylim([0,1])\n",
    "axs[1,1].set_ylim([0,1])\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting these Results\n",
    "\n",
    "For both indices, a higher score represents a more diverse body of street trees. If your city follows our general findings, the city center tends to be less diverse. The results provide some context on the evenness of the diversity in the city center and outside areas. The cells below calculate how well your street trees adhere to the 10/20/30 standard."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____\n",
    "# 10/20/30 Standard\n",
    "\n",
    "The 10/20/30 rule suggests that urban forests should be made up of no more than 10% from one species, 20% from one genus, or 30% from one family. A more optimistic version, the 5/10/15 rule, argues that those values should be halved. \n",
    "\n",
    "Below, we'll calculate how well your tree inventory data adheres to these rules and then chart the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GetPctRule(df, column, basal_area_column, predicate, location):\n",
    "    if predicate == 'area':\n",
    "        tempData = df[[basal_area_column,column]].groupby(column).sum().sort_values(basal_area_column, ascending=False).reset_index()\n",
    "    else:\n",
    "        tempData = df[[basal_area_column,column]].groupby(column).count().sort_values(basal_area_column, ascending=False).reset_index()\n",
    "    \n",
    "    total = tempData[basal_area_column].sum()\n",
    "    \n",
    "    return {\n",
    "        'name':column,\n",
    "        'location': location,\n",
    "        'predicate':predicate,\n",
    "        'most common': tempData.iloc[0][column],\n",
    "        'amount': tempData.iloc[0][basal_area_column],\n",
    "        'percent': round(tempData.iloc[0][basal_area_column]/total*100,2),\n",
    "        'total': total,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>predicate</th>\n",
       "      <th>most common</th>\n",
       "      <th>amount</th>\n",
       "      <th>percent</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>City Center</td>\n",
       "      <td>area</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>405.118253</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1017.000658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>City Center</td>\n",
       "      <td>count</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>4699.000000</td>\n",
       "      <td>35.94</td>\n",
       "      <td>13074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>genus</td>\n",
       "      <td>City Center</td>\n",
       "      <td>area</td>\n",
       "      <td>Platanus</td>\n",
       "      <td>618.680725</td>\n",
       "      <td>60.86</td>\n",
       "      <td>1016.540898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>genus</td>\n",
       "      <td>City Center</td>\n",
       "      <td>count</td>\n",
       "      <td>Platanus</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>52.45</td>\n",
       "      <td>13065.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>family</td>\n",
       "      <td>City Center</td>\n",
       "      <td>area</td>\n",
       "      <td>Platanaceae</td>\n",
       "      <td>618.680725</td>\n",
       "      <td>60.86</td>\n",
       "      <td>1016.540898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name     location predicate          most common       amount  \\\n",
       "0  Scientific  City Center      area  Platanus  Hispanica   405.118253   \n",
       "1  Scientific  City Center     count  Platanus  Hispanica  4699.000000   \n",
       "2       genus  City Center      area             Platanus   618.680725   \n",
       "3       genus  City Center     count             Platanus  6853.000000   \n",
       "4      family  City Center      area          Platanaceae   618.680725   \n",
       "\n",
       "   percent         total  \n",
       "0    39.83   1017.000658  \n",
       "1    35.94  13074.000000  \n",
       "2    60.86   1016.540898  \n",
       "3    52.45  13065.000000  \n",
       "4    60.86   1016.540898  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_results = []\n",
    "\n",
    "for location in ['City Center', 'Outside City Center']:\n",
    "    for column in [scientific_name_column, genus_name_column, family_name_column]:\n",
    "        for predicate in ['area', 'count']:\n",
    "            if location == 'City Center':\n",
    "                df = city_center_data\n",
    "            else:\n",
    "                df = outside_center_data\n",
    "            temp_results.append(GetPctRule(df, column, basal_area_column, predicate, location))\n",
    "            \n",
    "results = pd.DataFrame(temp_results)\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>location</th>\n",
       "      <th>predicate</th>\n",
       "      <th>most common</th>\n",
       "      <th>amount</th>\n",
       "      <th>percent</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>City Center</td>\n",
       "      <td>area</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>405.118253</td>\n",
       "      <td>39.83</td>\n",
       "      <td>1017.000658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>City Center</td>\n",
       "      <td>count</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>4699.000000</td>\n",
       "      <td>35.94</td>\n",
       "      <td>13074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>Outside City Center</td>\n",
       "      <td>area</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>2716.026453</td>\n",
       "      <td>40.07</td>\n",
       "      <td>6778.510383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Scientific</td>\n",
       "      <td>Outside City Center</td>\n",
       "      <td>count</td>\n",
       "      <td>Platanus  Hispanica</td>\n",
       "      <td>25356.000000</td>\n",
       "      <td>28.25</td>\n",
       "      <td>89755.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name             location predicate          most common  \\\n",
       "0  Scientific          City Center      area  Platanus  Hispanica   \n",
       "1  Scientific          City Center     count  Platanus  Hispanica   \n",
       "6  Scientific  Outside City Center      area  Platanus  Hispanica   \n",
       "7  Scientific  Outside City Center     count  Platanus  Hispanica   \n",
       "\n",
       "         amount  percent         total  \n",
       "0    405.118253    39.83   1017.000658  \n",
       "1   4699.000000    35.94  13074.000000  \n",
       "6   2716.026453    40.07   6778.510383  \n",
       "7  25356.000000    28.25  89755.000000  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[results.name=='Scientific']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,2)\n",
    "\n",
    "columns = [scientific_name_column, genus_name_column, family_name_column]\n",
    "predicates=['area', 'count']\n",
    "max_value = results.percent.max() * 1.2\n",
    "\n",
    "for row in [0,1,2]:\n",
    "    temp_data = results[results.name==columns[row]]\n",
    "    for col in [0,1]:\n",
    "        temp_col_data = temp_data[temp_data.predicate==predicates[col]]\n",
    "        if row == 0:\n",
    "            x_value = 10\n",
    "            text=\"Species Benchmark\"\n",
    "        elif row == 1:\n",
    "            x_value = 20\n",
    "            text=\"Genus Benchmark\"\n",
    "        else:\n",
    "            x_value = 30\n",
    "            text=\"Family Benchmark\"\n",
    "         \n",
    "        if col == 0:\n",
    "            title = text + ' (Area)'\n",
    "        else:\n",
    "            title = text + ' (Count)'\n",
    "            \n",
    "        axs[row,col].set_xlabel('Percent of Tree Inventory')\n",
    "        axs[row,col].set_xlim([0,max_value])\n",
    "        axs[row,col].set_ylim([-0.1,0.1])\n",
    "        axs[row,col].get_yaxis().set_visible(False)\n",
    "        axs[row,col].plot([0,max_value], [0,0], c='darkgray')\n",
    "        axs[row,col].scatter(x=x_value, y=0, marker='|', s=1000, c='darkgray')\n",
    "        axs[row,col].text(x=x_value+1, y=0.02, linespacing=2, s=text, c='black')\n",
    "        axs[row,col].set_title(title)\n",
    "        axs[row,col].scatter(x=float(temp_col_data[temp_col_data.location=='City Center'].percent), y=0, s=100, c='lightsteelblue', label='City Center')\n",
    "        axs[row,col].scatter(x=float(temp_col_data[temp_col_data.location=='Outside City Center'].percent), y=0, s=100, c='darkgreen', label='Outside City Center')\n",
    "        axs[row,col].legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting these Results\n",
    "\n",
    "The closer the green dots are to the vertical benchmark line, the closer the tree inventory is to meeting the benchmark. Ideally, each dot should be at or to the left of that taxonomy level's benchmark (10, 20, or 30%). The charts on the left reflect tree species diversity by area, which may better reflect the street trees scaled to their mass, and the right column tracks the count of trees, which is more widely used in urban forestry. \n",
    "\n",
    "These charts do not define the success of the street tree inventory you are exploring, but they do highlight whether or not the data adheres to suggested urban forestry standards.\n",
    "___\n",
    "\n",
    "Want to share you results? Contact us at ***senseable-trees@mit.edu***, we'd love to hear how you used this notebook!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}