[IL181] Application of Causal Inference Tools.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $$\\text{Application of Causal Inference Tools}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some imports to get us started\n",
    "# Utilities\n",
    "import os\n",
    "import urllib.request\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from networkx.drawing.nx_pydot import to_pydot\n",
    "from IPython.display import Image, display\n",
    "\n",
    "# Generic ML imports\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "# DoWhy imports \n",
    "import dowhy\n",
    "from dowhy import CausalModel\n",
    "\n",
    "# EconML imports\n",
    "from econml.dml import LinearDMLCateEstimator, ForestDMLCateEstimator\n",
    "from econml.cate_interpreter import SingleTreeCateInterpreter, SingleTreePolicyInterpreter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the sample pricing data\n",
    "file_url = \"https://msalicedatapublic.blob.core.windows.net/datasets/Pricing/pricing_sample.csv\"\n",
    "train_data = pd.read_csv(file_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_age</th>\n",
       "      <th>age</th>\n",
       "      <th>avg_hours</th>\n",
       "      <th>days_visited</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>has_membership</th>\n",
       "      <th>is_US</th>\n",
       "      <th>songs_purchased</th>\n",
       "      <th>income</th>\n",
       "      <th>price</th>\n",
       "      <th>demand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>1.834234</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.903237</td>\n",
       "      <td>0.960863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.917117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>54</td>\n",
       "      <td>7.171411</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.330161</td>\n",
       "      <td>0.732487</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.585706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>5.351920</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.036203</td>\n",
       "      <td>1.130937</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.675960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>6.723551</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.911926</td>\n",
       "      <td>0.929197</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.361776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>2.448247</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.148967</td>\n",
       "      <td>0.533527</td>\n",
       "      <td>0.8</td>\n",
       "      <td>12.624123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account_age  age  avg_hours  days_visited  friends_count  has_membership  \\\n",
       "0            3   53   1.834234             2              8               1   \n",
       "1            5   54   7.171411             7              9               0   \n",
       "2            3   33   5.351920             6              9               0   \n",
       "3            2   34   6.723551             0              8               0   \n",
       "4            4   30   2.448247             5              8               1   \n",
       "\n",
       "   is_US  songs_purchased    income  price     demand  \n",
       "0      1         4.903237  0.960863    1.0   3.917117  \n",
       "1      1         3.330161  0.732487    1.0  11.585706  \n",
       "2      1         3.036203  1.130937    1.0  24.675960  \n",
       "3      1         7.911926  0.929197    1.0   6.361776  \n",
       "4      0         7.148967  0.533527    0.8  12.624123  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data sample\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define estimator inputs\n",
    "train_data[\"log_demand\"]=np.log(train_data[\"demand\"])\n",
    "train_data[\"log_price\"]=np.log(train_data[\"price\"])\n",
    "\n",
    "X = train_data[[\"income\"]]  # features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get test data\n",
    "X_test = np.linspace(0, 5, 100).reshape(-1, 1)\n",
    "X_test_data = pd.DataFrame(X_test, columns=[\"income\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_causes=[\"account_age\",\"age\",\"avg_hours\",\"days_visited\",\"friends_count\",\n",
    "               \"has_membership\",\"is_US\",\"songs_purchased\",\"income\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.\n",
      "INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named \"Unobserved Confounders\" to reflect this.\n",
      "INFO:dowhy.causal_model:Model to find the causal effect of treatment ['log_price'] on outcome ['log_demand']\n"
     ]
    }
   ],
   "source": [
    "model = CausalModel(\n",
    "    data=train_data, \n",
    "    treatment='log_price', \n",
    "    outcome='log_demand', \n",
    "    common_causes=common_causes,\n",
    "    effect_modifiers='income'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:dowhy.causal_graph:Warning: Pygraphviz cannot be loaded. Check that graphviz and pygraphviz are installed.\n",
      "INFO:dowhy.causal_graph:Using Matplotlib for plotting\n"
     ]
    },
    {
     "data": {
      "image/png": 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zEZH54rYYX+w33n2fKcGWKYjIMExgLQbOVtUlcTZJtn9v6kkIp570VIpL/F5xAv114F1VvcSzn1LgHGA85u1yo0YO4AiQAQRCN0ANOOPZ4diDvjth74dpudBAXZKXAdT0ohiE+cT6teIZqro622OKBRdEMQ5z77oSuCfT5ylz3g+WtAZzbfuXqt7qW9cTyz3cFhirqp+nOfQAERAI3QBRkQv6IcFxhPhiv1bcHwtO8GvFc1Q1ifCBjIxxAKb1bsTcy+ZmaT+RvB8mUot+iNlHT2AaltT9Sd86wXIYX49FX/6fqm7MzOgDQCB0AySAfNAPCY6rFOOG/VpxT+AbIvsXZ01bd+MJabzXAf/I5jQ9Mfoh6rbbA68CR6nqOxHWd8K8XQZgWu+7/jYBUkMgdAMkhXzTD4nA8cV9qW28a0Nk/+KM8sUi0hubprfEBNYXmeo7yv5Soh9E5AAsZHkftQi7SG2OxqIpnwQuV9W1GR18PUQgdAOkDEc/nIBZ2vNGPyQKEWlFmC/2Rt9VUVsrnqHhhEWp7EuwdJl/A+7Aci1nnfJIln4QkZOwFI27aZTacCLSBvg75i53hqq+mo2x1xcEQjdA2nACZij2oB9HgdAPicCNvRO1Q6D7YXyxXyueraoJ58sQkc5YNNs2mNbrz2eQNSRKP4jIOOy67akxou1E5CDMl3gqcJGq/pKFYdd5BEI3QEZRDPRDInD8bG9qG+96Ynzxl9QUyAujHZ8T7COBW7AMb1fm8mUUj35w62/DXjSHxHqpiEhzLBnQUcDvVfXf2R193UMgdANkDcVGPyQCxxf3ozZf3BrLzFaDplDV7z3btsWyuu2OTdNfJ8eIQT985L5vAE6K94IUkd2Be7Fj/p2qLs/eqOsWAqEbIOuIQD98SZh+qBOGGccXD6S2MK6kNl/cGRO+r2PT9JV5GrOffngES2s51Rs8EWP7RsCfgNOBS4AHCy2ApRARCN0AOYWPftgDox8eoMjoh0Tg4Yv9Lm39gB+wgI92mKfDRMy/OKX8yhkYZ4h+OAaLansSi7KLG3zh3M/ux/I4nFHMM5lcIBC6AfKGukg/JALHF2+NCeHDMZ5VMWE3n9rGu6h8cRbG1hhLtH4DFk48iQSCL8Tq6V2EVc6YgEW81fmcy6kgELoB8o76QD/EghN047GXz21YLgcvTdGKsH+xly/+IYtj2h6jP/6NuYpBAsEXIrIdxvWWYAl0vvKs66uqs7M15mJBHRO6Mgg4C0v+sTWWfWkzpj1MBe7MZKKYAJlHfaIf/BCRHbFQ4uXAmaq62P3fmsj+xRXU1opnpuNf7BvPluAJLNBjDAkEX7jMcmdjGu8/sJDig7B0ovuo6n8j7K3ePLt1ROhKb6xm0/ZYvthI5Uy8KRFPBv0mQpsABYT6SD+4afrFWMrI8cAdkV42bnbQmcj+xd9R23j3dSp8sT94IpngC7FinHe5cXbHBPcCYNtweHT9e3brgNCVkdhFj3bB/KjCiumNAZ2SvXEFyBTqI/2Qap5bD1/s9y/ugaV79AvjRQm4h12KlWevETyRSPCFu3ZLgC7ur3VYOPGt9fXZLXKhKyMx7adxChtvAEYX88Wrj4gQfPEM4cKbdYp+yGSeW6eh9qU2RdES87X1h0D/4Nk2ZvBErOALLEvd3b7hbJg3j99vvTW3UQ+f3awKXRGZCHyrqldmofeki/QtXgz9+8OqVVBq79VoBR1PAEar6oEZGWlWz0P9hY9+aEKYfki5mkYhIpt5bh1fHPIvvg6rfdYNm86HhPCXmCFvHFZPLWrwRAT6YROWljOEjb160WjcOPShh5Bp01IeesRnN9MQkauAPqp6Yqb6LMlUR3nAw9i0JGF07w5r124RuLjtH/a3U9VHvQJXRFRE+qQx1gBZgKp+p6o3YRrbSMzndbqITBWR0SLSLL8jzAwch30Qpm2+KiLXuMCETPS9QlXfUdU7sPpsF2LZ2IZibmPfYjX0/onVvzsKmOPGcKyIDHQ8dKi/Dao6WVUPAg6kpsCtAr59/33mlJWRrrYX8dktBhSp0JXBGPGeCA8UC6XADs5yGqBIoYZPVPX3GHd4O3A0sEREHhCRvZxFvWjhjnEiVtSzL/C5iIzI4r6WqurLqnqjqo5W1aFAc2AvjBIYip3jfwOrReQLEZkkIpeJyBFOOz/c3/egQfRp0YJtSkrSlj1F++xm9EYUkR1E5FMRWSMijwON3P+tReR5EflRRFa4713dupEi8omvn4tE5Bn3/VARmeX6XCoif8RqQkXUcvv1g+efD/+urIR27eDTT2HhQhCx/wAmToTevWnSqBGfiMgCRysgImNEZJr7/rbr6n8islZERrn/DxeRz0VkpYi8JyKD452HANmHqm5S1X+r6hEYBzkD09Lmich4EemV3xGmB1VdrqpHYZV8p4jIrZnW6EWkoYjcLCLL3HKziDRU1SpV/RDTMA8E9sW04UZY4vaXMS35XKwyhZdOU6Bk7lyq99yTkvnza+5z9mw44ABo0wa22w6eeCK8bswYOOccOOQQaNYMRoyA776D88+nUdOmTBeR2SKyg2f840Rkvnv+ZonIbzzrxojINBG50cmiBSJyiGd9LxF5y237GjZ7yiw0Q2WFMb+6RVj57XLsLViBuZu0xaYlTbC35RTMxw9MeP4C9PP09RmW0R7MZ3EP9701MFSVWdGGMmECevzx4d/PP49ut519X7AABbSiAl27Fm3eHJ09G1VlJhauOcDtZwzmFxoaj2K8Tuj3UCyMczj2xh2NcWENY52HTJ3rYEn63gxVvrgNC1Wd6q5Zs3yPLc3jaoP5MC8EDsxAfwuB/YG/AB8A7YGtgPewsj0AB2Muab91z8Ar/ufD0993bt2W5eOP0S+/RDt3RkeMsKZr16Jdu6L332/P5iefoG3bojNm2PrRo+33xx+jGzag++yD9uyJPvggWlHBTCdjpnr2OxJzUysBRmEeE53cujHueTzdPbtnA8sI27fex3IHN8RolTXAI5m8bpHKNqeKXTAhc7Pa6J8UkQsBVPVnbBoCgIhcg934qOompw2eCFwhVmuqJxDSVyuA/iLyP1VdAawA2TraII4/HnbYAdavhyZNYNIk+y8SSkpgxgzo3JltsWkTIjIIE6btRORYT/PDRCSUMWos8A5WKqYXZixoAFyO3VzNsZfFUa79N+4YvP0FyD3eBT7EXprnAf8UkY+Bt4DZkDbPmA+8gt1rj4rITEwLTTVtZFMs+ux0TJjv6/7/L3CW6/9MTCA3wIx757k23ucjhFa+3xWDB1NaXk7J6NHwtptDPv889OwJp5xiv4cOhaOOgiefhAED7L/f/AZ23DH8/V//gpNPBqwC8uPA70I7Ua3h1fC4iFwG7Aw86/5bpKr3AIjIg1i+4w5iRUaHAfurJZx/W0Sei3POkkYmhW5nYKkTuCEsAhCRJlhkysGYtgrQXERK1eKzHwQmi8iVmN/fExrOsn8UNk35m4h8AYxTpUG0QfTpYxTDc8/BEUfAf/4Dn31Wu13TpvD443DjjTB2LGXY2+0z7M3WC9POj/RscgBmuQWLDmoPHOFZX+LagD283m1D5ca9/wXIL+Zi/qM9gPOxZ2EBpu0VdOL1KHgLGIzx2Z9gBrBk0QQYgd37gzHlB0yJaIPdv32BpYTv5VmYknIENovwotz3u6S83CjNHj3Cfy5aBB9+CK08IrqyEk46Kfy7Q4fw98aNa/wux1zItlAsInIyZhAMjb8ZNWmC70JfVHW9ebxtabNCa+Y6XoR5c2QMmRS6y4EuIiIewdsdC+O7CBM6w1X1O7G47s+waR+q+oGIbMbCPo93C27ddODXzkL6O+AJzJ0lquA97jiYPBmqq81FrE8Uv4ODDrJlzRqqWrRgOvbi+BUm+E9T1WMBHI97garOc7/vAhar6jX+PkVkL2AycFzoPIjIu9j0J3AZK0A4P9M6EXzhjGv3YYLld6r6XZxNvNsuBK7BoshuV9UX3f8HYtTbsSLyALBcVS936/pgL7AuGCXoDZ7Yj5rCTjZtgoYNzX0zhG7dYK+94LXXUjliKnzH0AOrYrwf8L6qVonI5zhZEwfLgdYi0tQjeLuT4RlQJg1p72PheueJSJmI/BZT6cHelBuAlWL1lsZH2P4h7C1dqaohI1YDETlBRFqqagWwGnM7mR9h+y049lh49VW4447o1ML335sWvG4drFnDCsKO46vd+LZxBr2DsLItvT2b34NNt4aLoamIHCaWVT/WeQhQgFBDnfB+UKvauz0wB/jCGY4SETheTAauFJGtRKQd8GcsBwOY0nOKiPRzM9g/u/8/Bv4jIvuKyDkicgdGV3ghc+eyadYsePDB8J+HHw5ffw0PPwwVFbZMnw5ffUUimOf73RQTkj8CiMgpmA9yXKjqInccE5zs2Z2as9mMIGM3klqUym8xbWEFRmA/5VbfjLmZ/ITxQS9H6OJh7OT4fe9OAhaKyGosIcaJGB8cNTKnUyfYdVd47z0YNSpym+pquOkm6NwZ+vShBfaWG4oZ1B7BhG8PzCG8CfCyiFSKyMvAThhdcoc71nnuuOOdhwAFDq0D3g+qulFVr8A8DM4DXhFz4UoUV2PC5wtM6//U/YeqvoRxoO9gNEPID/cAYDeMX90B+Ap409/xsGE0HD06zN8CNG9uStJjj9nz2LEjXHopbIpfxrMSZxsKQa2q8U2Y8vM9pkglUz7+eIwu+QVTvh5KYtuEUDBhwGKRLD8AQ1V1bpzWg7GTmnA0WgysB3aJlcHIaQodqF0VoD92cbyROzOArzQHlV8D5AbFTD9I7Ty3/9QEw6VFpIzIeRy6Y4rGl5hgOw/YFlNcXgXeU9VLRGQYVgZoC4YMYdMnn6ClpRlxo4z77BYiCknoXggcrqr7xm1sW7xL2GUrVVQBH4DunsrGbsrZi9o35daYUcafdu8bDRI7FzUkcurJiRR47geJnedWMFrFr1T0xbhh/33cD/MEaIoZwatV9UjXV1vMxewLzJC1PWHbUTXwy7vv0m7nndGysoR41mhI69nNJwpC6DoCX4AjVTWCr0Gt9peLcEWTJjU13T32gJdeSmrXWYnfdg/mdtQu09IOm3b5K8l+p4VwIQIkBSmy3A9OSbgQ8wZ6C9NMB2D3pzfXQuhzlkbIzesotl0xwfcWlpTnR8zW0oWwIlSBPdc1DPa9e1P19ddUpKnt5iT3QjZQEEI3dRRXljERaUn4JvcKY6G2NjHDawkOULgoRPrB0XWRqha3xLwNtsLuu/8DntYUq1A4v/oxmK1lIXbcj2Na7qvY+XiJsPtYBfDcZZdRfuWVHNEkNYIwyDKWXxR3Tk4PX+zXivtjhke/9jE74IsLF7mmHyRy/txBhHnXiPlz3X13IpYy8n7gL5pAEUq3zzaYMB2DuVk+hFUCnu1rdwAWFKXYTKAMe/4qgfKRI5HHHmNTSQnlFOGzmyrqgNAFl+bxIcxq2oDI/sfe7PMnFfq0xD1MvaitqfTGItz8xrtvCplTrI/IJP3ghGRnatsP+mHGLD9llVClCBHpgIVHD8G43neitCvDMp2NwbwiXsReJq9HslOIyLYYh9wJ436/xkV9erBi40Z2WraMNzt0oEejRlSVlNQWvlVVVG/aRMnatXzVvj2HFfqzGw91ROiGIIOwMMV9sPDAcmw6Mw9zLbmr2Cydfoil9Avxxd6Hrx3h4oVe7Sbgi/OMZOkHEWlF7ZftQExxyEpNNLGkMLdjicfHhfqMRh+o6soo/ZRhvPElGHVxO+Y5cQJmxNuGcGDT45hr5uhBgyidPJkXBwxg68pKtnPCt6KkhLlTp7LqvPPYdcYMNgGXqOqt6R5vPlHHhG79hYcv9tMUSm2teIaqrs7TUOs1ItAPb2HXpJya1X9n4qMGUuVdkxhba4xuOBD4DxbUE5U+iLD9EIyq+AU4I6TRu5fObZgy1JUwvzsfm7k1wQxjp2N0zHKMe/5EVXcSkRswwY1r9wBwXrHO7DIZBhwgj3BGt/fcAmy52TsSfph3xW7s/iLyE7W1poAvzgI8vKtXa90Gs0PsjE27q7GgoRPIQ+Vjp6HuBrTA8qOMxgKZDlXVH+Ns2xDziDgTuBSY6J1dqaqKyLPAacAbGDVxK2a3CAVolWA5H84kLFxa+gAAACAASURBVJT7isjemOAPoQmWGWw1lmCq6BBouvUQHr7YrxX3wrQPv/FlQbFqFbmEe8l1orZRqy8W+OOfccxR1c359H6I4X1QiUWhjcKCH56MRFOJyK5YrofZwLmqujxCmyHAa9gLZTymGDwJXAAcg71wyrG8D+dSMzvZJ1hk5/5uTFVYJOhN6imAWUwIhG6ALXB8cV9q84ltML7YLzS+r698sYd39Z+rKmobtWYlSufkwvshUe8D1zYkVOcA54SEqlji9KsxoXke9oKIJJR7YAnNL1LVJ9y+38UE52TMfe0GzNd3OpbrYC2Wr2UNpnlPw7TvKZgG3qeYlYBA6AaICydgQnyxV4OrorZWPLMu8cWeF5HfqNWGyFV0/Tll09l3yPthDOHor1S9H5LyPvBt2xCrVHEWlotkCZZL923gQrV82ZG2a4MJzLtV9WbP/z0wwTsVS3B1itP2m2IRbK3dsf4K04I/UtVK12Y6cKWqRsrfUhQIhG6AlOCZSnuFcMiF6Udqa8WzE3Fhyhcc5dKb2sK1J5Epl4W50rbcud6BMP0wgwTph2S9D+L0FSp53xQ4W61mW7S2jTBK4UNV/WOE9UMxAXqWuoTinnUdgC9UtUOE7cYCv1bVXyU7/kJBIHQDZBRxhNc31Dbe5ZQvjvCyCH2GXhaR/F0LxrjotM7DCNMPz+CjH5KhD5LYb8il7GksH8MFGL1wm19TdvfA45i75gmRrq+IHIpl6msF7OOyg4XWxRK6TYDFwI4uFWPRIRC6AXKCKNP0QdhUspZ7FPBDunyxc6Pz8q6hfVdTW/gXHS3ihFMo+KIpZqBqDexOEvRBnH10xNy9BmHBE6Fc19tieaUbAGNDQtO91G5x7Q+O9sISK4PzNBawdA2wm6ou9RxXRKHr1t8MrHPpK4sOgdANkFckYJDy0hQRAwGc9hcpz0CId824QC8UeOiDMZh1vxnwORYNlrL3gxOeJ2FGrvuwMOGNvjYlwBlYEMStwHVY6aOTsWKy0QIoemL5erurlcu5FHt57KGqqxIQutth/s3dC5myioZA6AYoOPhcr/wUwM/Y9HY95ue6FTaFzjt1kSv46INOhOmDOYnQDwn03wMr2dMB02I/jdO+G3Andp0aAMNiuXOJyLVAY1X9g/stmNAeAByCUQ5Rha7b5nXgXlV9LJFjKiQURQmSAPUSobLd1b5PcYvXqT7UPtJnnYBY6afDRGQKlqt5d8yjoIeqXq6qc2BL5YunnKGpL/YCup0EKl+ISImInItpoW8BO8cTuG6fS7DCrq0wf9vzHPcaaR8NsWrad3q2V4wjXolFmyWSZ/cOzM2s6BBougHyChFpQeQ8A1DbqBWRd3V8cb8IfYT44qy5dWUbmfA+SMT7wU3Z73ObnJaM0c0T/DASyxd9C1bS6jRVfcvX9njgFFU9IEI/jV0/nwMj42i65dj5OEhVZyQ61kJAIHQD5AROw/EGXoQEY1uyFHjhcglE8i+uiLC/jCSOyQRi0QcZ6NtPPzyLGbN+C1wF/CsZSsYf/OD5/1dYLbXngEtDL0sReQf4h6pGrBvojv0DoKOqtoiz7/FAe1U9N9HxFgICoRsgo3DGld7U1l57UTMlZV5CjJ3WFy9FoneMc3JhrIkSvPAA8EY63gdx9rkfFoTQEgu1vY8kgi+iBT941rcCrgcOxvIlLMGOq6eqRi0sKyI7YrXVRqnqkzHadcGuU49CeWEmgkDoBkgJTniFkq97hVd/zNjl9zyYU0j+rn5IzWTg3mPqQbgIY61k4BnY70AstDXt4IUk9tkIK51+GnAx4VzUY0gw+CJe8IOv7b6Ye1kVVqXi0jjtO2CznyrgaFV9O0bbJzG3uDujtSk0BEI3QFx4eNdoZYb80/Q6U2ZIYpe9SSn9YjbpgwT2PQLTaL8Efq+q3/nWJxJ8ETf4IcJ+O2IGwDVYrobHo9FHIZcxzI3sUWBfVZ0Zpe1+WJDFkGJxAwyEboAtkJoFNb1CZiuCBOk14PhivyAehJWTqZWPAthImD44gHDwQtboA994mwPXAkcBv4vGqfq28QdfPIi9IP5AnOCHCH2dg+XTvRET+vOxBDpLo+z3C1XtICIn4gue8LUVzHi3JXCj0BEI3XoIiV46vjemjUTiXYPS8XHgBIC/lPkwLHeuYDlgP8DSGn6EUS4VORjXQZjf7ZuYwWtFktt7vR9OdX9fDDycSPCF2/4L4HxVfVNEGmC5cM91n/d6X97+4Ah/8ESE/s/H3NtOSOa48oVA6NZheHhXv0YW4l39wnWOP+ooQPKIQB88jPm9NqPmtQgVj/TPIDLFF7fBpt57YpUcXkuzv+OBv2I5F44gweALEdkD43T7+YTrIEzrXQucrqrz3f9+oVsjeMKvXTuD3QJgu0TonXwjELp1BG76GMnftYSgvHvW4fM+SIg+8PDFfuNdC8L+xV6+OGYFB1/fR2OCagpwRarhwJ7+9gMm4eFXo9EPfu8HEZkMvK8Raps5fvgC4DKM/rgFq/dXIyLNtZuC0TQn+gW8iNwLzFPVv6VznLlAIHSLDG5qFinR+FYYt+W3stdb3jUX8HkfLMAE7RPpeB84DTWS4XITkRP1rPVs2wmLQOuPhfC+R5rwBj/4gx3c+qjBF5gwng30inVORKQPpg03wYpaPuEPjvAET7yvqhf71u3o9rd1oVNhgdAtUDjetSe1DTW9Mdci/8P3TaHfbHUF+fA+8PDFfq14O6yQ4wyMN94Lq8jwx3S1W7ffiMEPMdr7vR++AZZhOXBjUibunh+LURiNgDZ+H2kJV564E3u53A58q6rXiMiHwP+p6vNJHWSOEQjdAoCbpvlpgQFYVVU/7zo74F1zj1TogxyOay/gJqA95mXSHeiGlcLxR94tSnTmEy/4IYHtO2E0yc9AKQlWvnCa9Ydu/GNV9SPf+lDliZ8wemaZqvYSkdHAMap6WLJjzSUCoZtDON7VH5Y6EKvKHCmNYdYc5AMkhmzQB5mC4znPxQIdrgf+Hor0cglnIuWjaE7kfBQ/+vpOOPghxviOwJLy7EoSwRceP93zgZuBR4A/q+p6t34AViK+F6bdb8KCWFZjCc6Hq+o3qYw5FygAoSuDsNpL+2ARQQ2wWPD5WA2lO0G/zN/4kofjXSP5u7bHeFe/9ro84F0LByLSFjiWPAQvJAoR6YdZ/isxH9WvE9yuLeEXf+i+HIgZqLz35fGYhnpsqp4UIvISMFlVH/L8l0jwhddPtx1mXBsOnA68g6X1LA/viXXAmar6qIjcePDBtH7pJTZSoDIlj0JXemOuNNtjeVFLIzSqxE7WZ8DJUFhvLw/v6jd4bA0sojbvOj/gXQsThUof+OGya12CWfz/DNyVrnuZ44u7Er6HT8X44zKMj/Xfx1/H8y8Wka0xn+Ru0eiwGMEX66ntvXA4ls7xOzfOcixMWNw4n1fl/HXrmCLC0MaNqRIpTJmSJ6ErI7EbOpqw9aMKm0KMAZ2SvXFFh7tBIvm7rqQ2b/ZVwLsWBwqZPvDDWejvB5ZiBR0XZ2EfF2MVI/bE/Gf7UNuY2xX4mtoztsWhGZuIXI/Jl4v9+4iwT7/3wxy3ny4+z4yxmIdDKN/ueux6jT3mGEoff5zNFIFMyYPQlZHYG61xChtvAEZn8yR5eFe/gC0ncn7XpKJ7AuQfxUAfeOFcpcZjGuFFwKPZoKM8wQ8jNHblhyaYwuF/RprhlA7gGIw2fEVVf0piDA0xauMO7HnfQj9gCk5zT/Nq4PF163iuYUMeKC2lYaL78SDrMsWPHAtd6Y0Jq4hZ5RPEemAgJJZ+LupIjHfdltouOB2oybuGhOyygHctXhQLfeCHiOyJ1Tv7FDgvWxFXkYIfUuijLWF6Yh8sleNA7Jn1a8WzMIPeaar6uq+fkCFtMCaAT8ES0nchrOUqUN2rF6Vz57KxtJRGqYzZYT0wUIQvgcHZNsLlWui+K8Juc+dCnz4pd1IFfAC6e0J7NN61B7WNWn0wS6dfew141zqEYqIPvHCZ3f4G/Ao4V1WfzeK+YgY/pNDf+8BfVfU/jjroRm2teDuMi/0IywkRev7mYgVF/WHAn2L2nxA2A9f98ANHt2vHtlH420SRlExJFzkUujIYeF+EJmkKXbA30y5+C6SItKf2xR0ArKK2MeArVd0Qd9QiewOPqGrXtEYcIGcoNvrADxE5FJtevwpcnM0XhMfn9cJEgh8S6G8HrBpFr1jKi5t5LMI4aiX8zHbFAip6AxPwGO8wLwQwIcmgQZR+9hmbUqQVtqCyEsrKIsuUrEBVc7TwT1UqAZ07N72uqqupmD+fKVgS5puBN7Cs/ysw7udfWNG6PYDW6ewM2BuLeMnhuQqWFK5TGeaKNAXj/iZhFRhK8z22JI6hHebRMx/YLwf7a4NN88/PYJ93Y7keEmm7ENgfM37djHlKLAeewhSlm7AXzxrChUpDy8/Tp/NadTWVkbofPRo980x0//3RZs3QPfdEFy4Mrwf09tvRPn3Qnj1RVSpcv33c2Bq7/S9yY5mGVTAG2AV4z91n/wP2TuocJXhyDseKxa10OxvsO3F/xDiYVVhy40ae9RcDy9u3p+K+++xgQ0L3hRfQfv3spHTujN5wQ/zhPPMMOmQI2rQp6i7GvVg5kB0xh+lfsMxNp3vGMBG42vO7hiCNdgyYG8sGjLBf65bO+XgggyXqvTkQuME9rO8BZwCt8j2uJI9BMMPTcqyqbtMc7LORU1BuzGCfrTDFp2OC7UNC9y+Ye1l7LIfIdGCta3MwFnnmF7o6dy6bonU/erTJlbfeQjduRM87Dx0xIrweTCD//DO6fn34P4/Q/SfwX4xHLgV2cy+HLpj/8qFYMqkD3O+tEj1PcekFERkKvIylcvsY48YmYGnUNonIQuAH4EjMwfpd4BZVvVNEDsY8FfZbs4ZPzjiDBpMnQ4he6NQJnngC9tgDVqyABQtg6NDoY/noIzjgAHjySdh7b6oaNGC8u3m+x6JXQm/JDsDvsBpTX7sxrwRCMdnbACcDf3K/J2AC/B4sG/6F7oRPi9A2QP7RBHvJ7oJl5PoICxst+LR+EdASE7jtsSoJC3OwT8GMXVXY85kpjnEvjBZ4IMH2E7AZybHYDGWW+38oRg2dh/nxhlJierGqspKmpaWURep4zBjYuBEee8x+r10LLVvCwoXQrRuIwBtvwL77hrcRM9Ftg9Eb64BdVPV/3n5dbt+BqnqS579XgEmq+mAiBx1xwD6cjjlgf+h+Pygil2M3fIh0v1VVl7kBPEeY8D4GeEBVZ4A0uOoqmDw53HF5OcyaBUOGQOvWtsTCfffBqaea4FWlFOPrwIj6rTFCPsQWz8HeQiXYjV2KZecCe1uVe36XY8I51N8y7CL/FKFtgPxAsJwC/bDrvQibfX2LCY02bikm9MNCZGcAT2MKRC7usz2wKsz/wQxamcJ+mLKS6DGUY9eyNaYlh7ZrgV3vvlgR0Ug+71EFbgjduoW/N2sGbdrAsmXh/73rfWiHXYv5Edb1AEa6EGfvcUyNNZYaSGAK8CJmuFrpWdYDx3mnCJ72V2GGJzAN+Vxbx6aNG2vSCx99hP7qV2irVsa5vPde7OEccgh6221bfm/y7HM48KNv3GcBr7nvE4lPL0Q7hhptgyW3C3WAPohwTL0xO8R0YFCO930xRqNl9BxiLmIzcMb5BLdZiNEL84FDPf8fA1S57w8Q1sa9S9WmTVRH6370aHTUqPDvNWvQkhJ08WL7TQTbkuu3D6aobcDqrvnHfBlwTzrnqiQBubwEuEZVW3mWJqo6Oe6W9qCE3ifzF/viZ4YNg2efhR9+gCOPhGOOid1Zt24wP/zumedZtQxo4wIbQuiORe6ATRW8vsEdExh7CIFvbo4hIm1F5FwRmY69uCswY8Vuqnq3Fri7VzSISKmI/AGjQ14EdlXNXQ4AETkBo90OzcI5PBu4Q51kShKTgStFZCuXa+FiYLOInI7NXo6LsM3mdetYHqvTF1+EadNg82b4059g+PCY2u0WqIVV3w/8XUQ6u+u2qwvceAQ4QkQOcv83EpG9RSRh76ZEhO49wFkiMlwMTUXkMJ+Ai4YngDEi0n/FCt656qqwANu8GR59FFatMpqhRQsojeNpN3YsPPAAvP46VZWVTBWRLiLSV1WXYFrQX91JGIzl5XzUbfo5cKiItBGrSnpBAmMP4XugrYi0TGKbAElCRMrcfTUF03xGYBmqeqjq5Vok7l7R4DJjvQv8GuMKb1KXESxH+98PM9IdqjGizVLsuzNG5T2cwua9MN/cBpiC9x1GT5Zh98DbmKDzo3zSJDpXVxPVLe3442HCBKMVPvnE5E0M+K/FHzF3temYcf46oMTJml9jtd1+dGO+mMRkqSHBacDBbucrMe11CtDcO0XwtL0KNzV3v8cB35WX88Pdd7MJp9Zv2oQedJBRC82bozvthL7zTvzhPPUUOnAg1SUlrMO03YPcfrpihrJfsIf2LM8YGmEeCauxqdUfSJBecL/vxyyUKwm8FzK6UAfpA9/xNcAS0/zojq0kD2MYghkZ98xS/38G7oyxvgSjVH6NvUgfw6iIDVhViSmuj99ihqxOwPe+Pr6kJr2wee+9eaG6mvWRdjt6NHrFFUkdxjrV3FA9OY9Iw/jXookeCZB5FHvwQqIQkWFY+sVFwNmaYQ0zwTGEgh/+oJr5/AIuyGEhcJiq/s+XGMqbkH8FkQOUahnJPGHAXTBf6yuwkOCmhMOANwLl06ZRuuuuVJWU1JQpY8ZA165w9dUJHUZOZUoi3guZxEmkn3thk+snQBEhSu6DKyjw3AepwCWEmYDdp38AHtPcajehcbQBXgJuyLTA9SSGOhnTZP/hQq69CfmnY4awGZoch7wt5ia2GHthNcd85cXTphHAHntQ3aRJTYF7111JH05uZUqupzqqjFSNPCVQRa+5xgIf/MvBB6Nuu5G5H3OwpLpQx+mDCMe7N0Z7PUoSDvNZGEdGgh8wemQQZsy6FnMzW4B5MH2CGbEfxV6oXUjCe8G3nzZYFOlH7l5ZB/T1tbkCE74himE98McTT+THdetSPsScy5Qgn26AjKO+0AdeOEPr9Vik0jmq+lwex1KK2TAqgBM0gSTnvsRQkRLy+7PuzXfr3gG6q+qmFMZZhtEHY9znS5hc+AL4XGsmMe+PUTU9MB/jEkzwVgKNTzuN2ffcQ3dVGiaY/KY+5dPdsuve2MO4A/Y2jUR1eLO8n5RuOscA2UOxpk7MBDxVDV4ALlXVVXkciwC3YlP/QyIJQ5cYKlIhVG9Cfm8h1IiJoUTk78BmVR2X5Bj7Y/fJiZhAnwg8ro6C8JXraQBcikWn/QnTqldS01tgI3D+tdfy9d578/qQIZQ2aUIlhSpT8jX9CS8MUuV2VWaqskmVavc50/2fU+fxYEluoZ7RB75j3woLY50H7JPv8bgxXYILfsB40eFYYqhbsICMHzCj1ttYfoGzgd1JMjEUZpf5Ccsmlkh7L32wFEuW3jdK2w6Yq+ZOWEKZF7CyP6H1v8EEb4hqWI95PqwCdPvt+UELWKYUQGHKAMWG+kgfeOG0yWOBf2C+qePVVarN03hCCfnPwgxCH2KRVR2JXAg17YT8InIKcLTGKHcegz54XWOnfeyB5WFYi+VBmeQdr6Ny3sdeei0JF6oMGehXqGrBhoQHQjdAQqjP9IEXLvLoDoxbHKuq03O47xDv6s8Z3QfTOrfCeM83yXJCfhcteJWqvhBhXUz6IE6/e2EeD50xrvgH3/qGmPCeiRnW/otRlF5Uqmo5BYpA6AaICecGNAZ7gL6hSCovZBpO4J0GXAPcBvxNVTdncX9bUduoFS0hfxkWGHS0qr6drTF5xjYMizbtExLqzj0tNPvpQnj2MzvBPltgUV9HYPkNblSPIc21KcE43QbAMapa5f47EuN7t8eMh2VYesy4RQrygVz76QYoAjj64DjsAeqIPUB71Rf6wA8R6YOFwzfGckCkVEMsSt/NiFwItQFhwfopdg1mqK8Qqif44dxcCFyHs4G7bPdyKDXpgz8Rhz7wQ0QOw2YPL2PHH60SxHVY5OmBof7VPDOeEpFdMb/gzZgQLljZFmi6AYCAPogEd04uwELZr8FSmKZ0LkSknMiFUDtiobB+7XVpPN7VaZfTsNSrt6QyrmTh9rkQewn8liTpA19f7bCKEbtiRQfedP9v8V7wtD0fOBPYXVV/8fXTGAukGK5ZLiqZCRTs2yBAbhCFPji9vtEHfojIIIwfXQPsnOjD7Ka73antkrUNJhhCgvUh932+ppD4xgma/wAv5kLgeuiDSzG5sQarHJwQfeDrS4BRmMB9FKtEsy5G+5FYUpkRfoHrcAwwvRgELgRCt14ioA+iwxlqLsem0JcB90fTOB3vGqkQ6mrCwvUV4EYSLISa4BhLscxbSzAXsawgivdBKZZk6p0U++yC1TDcGvi1hosjRGu/J+badoCqLorS7GwgsSwLBYBA6NYTRKEPLqce0wd+iMhwTLudB2yv4WooTTFh6tdeGxGO0voccx+bGUUby9QYBdMQW2PBD3GjzVLYxwDCZetD9MEZwDCgP0ZpJNunYIbIazEhOjKeIdKNYwpWMOF/UdoMxRSHl5IdU74QCN06Dg99cAJh+uA0zWPUVKHBCdX/w7T/G7ESQOe4czcI80WeTVh7fdV9xuVds4CLsVpke2gKobfREMX7oAZ9ICIpJSoXka0xQ2Qz12ciidtLMMXgQlV9I0a7szFOu2gUh8CQVgcRhT6oN8EL8eC0rhDveiTGL1ZimusSahq1Qv6uOUs4Hg1ilR+uBXZT1aXx2ifQX8LBC84/+QssqfyaBPsvxQrGXo5FoN2ciHB03iJfYaXcr4/RrhWWfGc7vz9vISPQdOsIAvogMpyF3E8LDMSinaqwiKa7sXDer/IZWRYLnsoP+6YrcKPRB3GMp6djkWGJCtyBGFWzHquUMS/OJqHtGmLBERVYeHksjAZeKiaBC4GmW/SIQh88Ud/oA0cR9KemgB2Eaa/+DFndMJ/PZ4HLVHV1PsacKERkCPAaaQQ/pBO84NzdFmHGrJg+yi4k+TKsFtvlwH2J8s6e4IfmwDB/cISvrWDa8GmqmjTHnE8Emm4RwkcfdMAeoL3rA33gBMA21NZeOwNzCAvW19zntyEO0mXXuhUYChyfw2CClOGCH14gheCHKPRB0sELGAUzNwGBuzOm3S7ADJHJauSh4IcTscQ4sbAPpg2/m+Q+8o5A6BYJ3AN0MPYA7Y89iJcBb9ZF+sDDu/pdsrbFeNeQ9vqo+5wXjXd1fZ0A3IRNXU8p1BBRL5x2+jJJVn7w0AcnYYEME4lPH8TC2VjEWLT9NcEMkSdgwSSPp2BsOx84DMt4lkjehHOAf+XBkJk2AnqhwFEf6AOnuUfKM7CW2hmykuJdRaQ7cCemCY9V1U8yO/rswAU/vAp8oKoXJ9A+rdwHMfrtB0zFks/UcvESkX2Ae4EPgPNV9acU9jESy9g2QlUXRYpI87XvjN0PCRv1CgmBpluAqKv0gYd39WuvjbGHaAZmIZ+E5Rn4OY19lWBho3/B/FqvV9WKtA4gR/AEP3yLRYBFa5cp+iAWzgLu9Qtcl17xBuAQrOjm86l07rKKxQt+8ON0rO5c0QlcCIRuwaAu0QfuWEK8q1e4dqEm7/q6+/5tJqeJIrItpn2VYWXHv8pU39lGIsEPWaAPoo2lKcav7uD7/wgsquwFYGCqsy53HE8QI/ghwjblmNA9JJV9FgICoZtnRKEPxhYDfeAERDdqG7W2xaoDhCiBSe773Gz6uzphfyEWGvsX4J/F9sIiSvCDow9Cs5/O2Oxnn3Tpgzg4HpimqovdGLbCDJE7ASep6n9T7dj5/SYS/ODHEcCCBAMsChKB0M0DipE+cGP2umKF/F3XERaub2Ba2lexEphkaXxDgPuBnzF3o6Krp+eCH87Fgh9WeeiDU9zni8CVZJY+iDYWwYxV49z34zA/4YcwpSBlf2ZHTbyIvRQfTXLzczAtu2gRCN0coVjoA2eJ9vu7DgSaEuZdQ9rrzFQMJ5mEiDTCBNEZmIb7YFFatEX2xwU/AK2cNd9LH+Q689twzF/2K+A5zJPkcFX9OJ1OXfDD01jFh3jBD/5tt8PuxafSGUO+EQjdLKNQ6QMP7+o3anXFeNeQcL3FfS4pNGEmIrthfqFfAUNUdXmeh5QSRGR7YDJmPJtI7uiDWDgbS+LzCUYp/DZegpp4cMbNiVhhzD+kcD+dhWV9y1jOiXwgELpZQCHRB25q2JXaRq3tMN41ZNR6DNMY5xa6lV+s2sI1wEjg96r67zwPKSW4F9+J2HRZsTwZOaEP4oxrZ0xJ+JzMVsqoVfkhiTE1wTT/HTM0lrwhELoZQiHQB87YEinPwAbCmuubmOYyK9e8ayYgIgdguRLewiznWUujmC04q/0YTIi0wKbvZ+aYPog0rjLgD8BVWImgXTN174rIBbjghxQDU44F3k/CraxgEQjdNCE1Ky/MJwf0gYd39VMDzagZTPAY5u+aV941ExCR1lhE2b6YgHolz0NKChG8DyZhM42HEwl+yDZEZDBG1azCKgv/PoMCdyTwR8xAmOpL8hzMB7noUceErgzCeJ99sMz0DbBCdfOxqJo7IX1Xkyj0wZ6q+nW6ffv2U4aV1/Zrr92ArwkL2Nvc5+JC410zARH5LXaMTwGDisUp3pf5zet98Cb2QvyaGMEPuYAzbF2BPTfjgGVY+sh4uQ8S7d8b/LA4Rsstz251NVuL0ABkEzB/wQJmDh1Kh08/pahetNFQR8KApTeWtX97rJJoaYRGlZgA/gw4GZKrpxSFPphIBugDH+/qFa7bAcupnd+14HnXTEBEOgK3Y+eiaLJJeeiDEwl7Hzyuqivdtb4VC3M+JJ9GIbEKuvdhwv8cVV0mIs8Cz6nqvRnofwD2gjk+ui9u/Ge3qgqtrqayvJyPSOHZLTioapEvjFRlnSqVAvvK/AAAG31JREFUCW5S6dqPTKQx9sDfCHyHZTQ6HWiZ6niBNsCemD/mHVjpk5Wu/9ewGPRTgZ2BZvk/v7lfAMEirr7HtK5G+R5Tgtf1XKwM+FIsaXffCO0uwUKdU76HMjDWppg/9XLMGBlSvnpgfs5NM7CPrlg6yBOit8vus1uoS5HTCzISeBCL3U8UpUAT206A2tmbMkEfuIQlXt41pL22oCbv+gTGu/6YxDHUWbhUhndh5/1gVf0sz0OKihj0QUTvA3/wQw6H6h3D/pghchpmiPTmtzgDeETTNLCKVXR4iZjBD9l5dosC+Zb6qS/0dm+9pDcdPx494QTUbd9LVcH47cOBJzHN81GsAkNprM7cdn2Bo4EJwL+x6doGzH9UMc7scKAnTqtw216OJRMpgPOZ3wWrifU7zIhzGVCe7zHFGOsAzLF/OfA+llinVZxt9sc09wF5GnMrjEpYhNEa/vUNsNlWLe08yf00xOwnt3rv9ZpL6s+uZ9ny7BbbUsya7sPYBU4HDdes4d8tWsibxPE+cFxcF2rzrn2xhy+kvU4B/owJ3i5YQufrNELOAVW9Ns3x1wmISF8sQQ2YS1G+AgKiIp3cBy74YRJW+SFTPq8JQ0SOxIxZT2PabSRD5G8xN8KUz70n+OFnYgc/RHx2RWDuXOjTJ/zfVVfBvHnwyCO1+mjo+tk91fHmC0UqdGUwRrxHMpglg9KyMrY/9FA+fvHFMH0gIq1FZE9q+7tuJkwLvIUZeeb4BfSWUYqkOby6DZcx6mIsSc14rNJsxkuKp4pk6YMoffQAnieFyg/pwuWlvQ0YAhyrqu/EaH62a5sOrse43AOin5/MPbvADub1UFzJb0pirRSRVSLyuIg0coLoeRH5UURWuO9dPW3HiMg3IrJGRBY4/ipW32NE5F0R+YeIrHTb7ub+XyIiP4jIaE/7hiJyo4gsbtGCD848k8YbnIv1f/8LXbvC9ddD+/bQqRM88wy8+CJsuy20aQPX+nTKjRth1Cho3x6ZMYNjgctF5GUR+RarTPA05hd6DBaZs51aUuVp2I21D5aacKSI7CwiH4vIahH5XkT+7jvcE0RksYj8JCJXeI7pKhF5xH3vKSIqImeIyDIRWS4iF8U6h8UMEdkBc0vaE9hRVf9ZKAJXRAaIyA3YfXAldp17qOpxqvpKEgI3VPnhek2i8kO6EMPJmMFuPlY6J6rAdb7mfbCacanu83zgUODXqroxRtMzSX+GGkID119xIQ4/0wbjJc8C2gJHYUR2c2wa/Yxr1xRYjQkmgE7E4a4w7aESy6BUClwNLMamQQ0xzWINzoKPWVv/A7RZsYLZhx+Ojhtn3U2dipaWohMmoJs3o3ffjbZrhx53HLp6NTpjBtqwITp/vrUfPx4tK0OnTLH248axAfgFqwXVG4s3/zN2UXtjORMOcuO4CqvNdCT20mqM8XonufXNsOqnYByuAve4dkOATUA/T1+P+NpOdudzEPAjsH++OahMLlihyL9i/ObJROX9cj6uhLwPkuivMfaCviHHx9EdM2J9jr3MEtnmn8CENPY5Eku43j1+e2ZFWwXo3Lk1//PYX6ItM/N97yR9vhI4odcDd0b4f3tghfveFDM+HQU0TvBCjcH8TUO/Bzmh08Hz389uP4KlENzaXbhN772H9uxp3U2dijZqhFZW2u/Vq+0CfvBBeJdDh6JPPx2+kMOHh9dVVrIJ42X3wLIrLfaN9TLgAff9KuBt3/q3MSNaO9//IUHa1fPfR9hUL5rQ7etpez1WTTXvN0pGbjbj3+ZgL+wOBTCeMiw0dQoWiTUZoxNiGk8T6LcUM6hOAkpydCwl7qXxE2agTcgQiSlQv3jv0ST3uxfwA5ZwKIFt2BRtVYpCd1O+76Nkl0Q43fVAZxd6+g8sQKC1W9dcREpVdZ2IjMJC/e4TkXeBizQ+Kf+95/sGAFX1/9cM2ArTsD8REVq2pIEqVHkmeW3bQqljiRo7J5QOngpLjRvD2rXh3926hb+XllKOvak7Y4Kvs4h44+BLAe/0bInvOMZiSbNni8gCTGvwli/5zvN9vTumaPD2vQh7GRU1RKQ5pjn+BgsvzWtqvijBCxlJnegMrjErP2QaYikP78UE7x6aXKWME4CpqvptCvsdSJKVH7DZY0SUlkKFL+SnogLKY5epTKSIZUEhJqfrw0VYhNRwVW2BcXFgWihqXNcBGLUwG5tSZwo/YQJ4gKq2WrmSzatW1RSiyWKJR7RVVVGB8bTLMKG3QFVbeZbmqnqoZ/MaVllVnauqxwHtsUxKT4qVOkkFntcB3d2YihYicjBmeGyCWc7zInBFpI2InCsi07GCj5WY98GuqnpXJgSuQ6jyw280y9FmIlImIuOwoJ0nSFLguhfEOcSo9Btj21QrP0RND9m9OyxcWPO/BQugR4+Y/RVdZGYyQrc5JvhWOgPB+NAKEekgIr9ygmYTVsU1Y5m1nLZwD/APEWkPzF+6FF5JIxL7k0/gqaegshKuuYYV2Lg/wKb/q0XkUhFpLCKlIjJQRIZF60tEThSRrdw4Qw9vqsf/JxFp4rSxU4DHU+wnrxCRtiLyIPZAn6aqp6rqihyPoUxEDhORKZjr3u6YYay7ql6WwEws2f2diE3xD9EsBz84N7QPsQRAO6nqbSlo1bth9pM3k9x3KPjhdk2+8sP8aCtGjYKrr4Zvv4Xqanj9dXjuOTj66Jj9zUty/3lHMkL3Zsw48BMmnF729XMRppX9gr3pz8nQGEO4FDvBHzRuTJ/994c5aWSn/fWv4fHHoXVruO8+SrEkzRVqlukjMC55AXa89wItY3R3MDBTRNZiSb+P1dgW3Fh4CzvON4AbVfXVFPvJC5zl/GjMtW4FlqDmtRyPYWAmvA+S3Of+mLfLoaq6NNP9e/bTSESuwbT12zAD78IUuzsHs9ckLKwlXPlhKklWfnCYis0yauHPf4bddoPdd7fn8pJL4NFHYeDAqH1Vuv6KCkWa8EYGYx4DTTLQ2Xpgl3z7+olIT0zIl2sWizdmEyLSCbOE98UCTN7P4b4jBS88mGltNsq+t8eE4FEa2xc23f2MwKLKZmJ+v9/F2SRWX+0xo2bvRGcgLvjhUYxHHZXay6vuPbvJIhlNt4CgX2AuMelqLFXAZ8V20QoNTrs9FfgfJhB2yIXAzTV9EGUMoeCHc7IlcEWkmYjcinlZXKGqR6UjcB1OBZ5KkvIJBT+cmPpsIXh2s+3GcifG7/qXWi5oyS91K36bsMtYWb7HkuS4e2HZ0T4mYbehtPc5kCRzH2RpHCE/9vOyuI8DMQ+LB4A2Geqz1PW5UxLbXADMyswY6tazm/T5z/cA0rx4I1VZn+Lm67XIU8Tlc3EP7vkY531Jtl8WWHBOxoIXMjCerAY/OIH+gBOOB2a478OAj5JofwwJBz8kutTfZ7dIcy+EoFOcx9pEoicv96MK81QYU7Sp4fIMEemPcYsVWJrCjFbM8Own7dwHWRpXKVa5dzFZqPwgIkdhRrInMTe7NJwjIyJhNzGxyg+3E7fyQ7Kox89uvqV+ht6avVWZpjblqIjSrMKtn6ZFOi3J94I5tv8JC08+myxFW1Eg9EGUsQkmhN4AGma4705YJNtsYESWxt8Lm500SfA6fA/sl71zWv+e3SLXdEPQb4DdXZ2lM7FkNH0wK2sF5oI1FbirKIn3AoCI7IRpt0uBoarqj8pLt/+2WMXXMZjwyVvZ+ji4BAsX31MzFPzgghRGY4aqe7BqC6m6HMbDmZhXx/o4Y0o1+CFJ1L9nt0hdxgLkCmIVMCZgQuFCYJJm6KaJQh88ALyheaQPosEFP1yDUSoZ8cV1roJ3A+2AU1X180z0G2VfDTHf5RGqOjdGu1ZY2PvDqnp9tsZTX1GkLmMBcgHH532BhSMPUtVHMyFw4wQvvFqgAjcU/HBIJgSuiJSIyO8xw+AbWHh91gSuw9HA53EEbrrBDwHioI7QCwEyCRFpgeWQOAJzwk85z6qnz2KhD2pBwpUfjlLVWRnorx8W5ViNVcrI1Tk4hxiC1AU/PEj8yg8B0kCg6QaoARE5DEtQU4ZZztNJbO0NXpgPjMDqxfVQ1cuLROD2JEPBDyJSLpbE/h0ssmuvXJ0DERmCzViej9HseqzEVBrBDwHiIdB0AwAgIu2w/Bq7AqekYzxxKf9GY6kTF5DB1Im5hAstfgmr/PBkmn0NBe7HPDJ2VNVFGRhiMjgbuFujhJiLyAVY5Yfds2jEC0AgdOs9nOV8FCZwHwUGawoluIuZPogEZ0D8D/Ccqt6aZj/jsYxxf8SS1ud02u7oolH8f3vnHiRVdSbw3zczvMZBEBxxwRkQWBQJWNkUjkqIBI2GJJtsuUB0WQQh6wMognEtdiHZpYwmGGPhAzesEGFVFKRqya6m4lYo0SqMGEF2rQLd5SnI8HDig8cwzOvbP77TzGXo7umZnulupr9f1am5fc+9t8+5fe43537ne8BVCeonh7Zdr6qfZrJt+YgL3TxGRAYA/4KZ6HxPVd9p5flR64NvYNYHC8lR64NUCc4PqzHnh39I4zpjMd3t/2D/zI60cEpHMRX4vaoeal7Rcc4PTiJc6OYhYXb7A+BnWFSwya2xOe0s6oN4hHvzBBbK83ZtQ+aHkCljMZZHb46qrm/fVraqLbFA5bPj1LUl84OTJi508wwRGYIZ4PcExqumZnDe2dQHSUjL+UFEJmCBnn6PLURmNHB7HMZiXnRvRndmzvnBaY4L3TwhvDL/EEta+HPgiUSLKpFzOqX6IBHB+WEWpttsVeaH8E9pCRZecqaqbuiAJraFWcCvonrkNDM/OGniHml5QHiN/DUW9PnvVDVpipNw/HRMfbAHUx+83BnUB4kQkW9gQWy+3hpb3PD6PglTSawBftyWhciOQEQuxUJPDor9EwnOD69hmT1+6La4mcdnup0YEemKpY+fg81QVyTSUYaZWizzwqWY+iBjdqTZJDg/rKaVzg8i0h/TiV+BpXvKWKaMFJkJrIsI3Jjzw6e480PWcKGbk8hI4B4s+McQLLpXLeZgsBFY1lLwDxG5Bpvd7sMyOZyTYjuB+mABnVR9EI+2OD+E2e0MTE2zDMuJ16GZf5O0Ju5YUWXPihVc9skn3Bk5OOb8cHO+/L65iKsXcgoZDDyPJcVMFGO0HhPA24A7QpSmpiuIFAM/BaYA9wFrms9o8lF9EI/g/PAWpvNMyRZXRAZjAWp6YwFq3u/AJiZrSYtjpb4eLSriFLDtxhvZ+Prr/DXm/OC2uNkk07EkvSQqTFKLGVqf4in14fgzEfSx2c5u7FW5NHowlnlhDpZW52PMXOyK7Pc7O4WmzA+/SPH4QixlTRXwAFlNq9S6sdLQQMPJkzRu28a92b7vXtRnurmBTMJ0bT3acPKpykruHTCAMcAE7DX5FUioPlhFHqkP4hEsOdYBNVicgaS2uCIyAlPVnAZ+oEmidHU86Y0VYNp5nXWhE+BCN+vIYGwluc0pqaur0bFjeem995ilql+4+iAxQR/7FDAc+JYm0cWGhcj5wFwsY8YzLQnoVrYllkr9jRTPSHusYBYsXwLdm8Y1nDRwoZtB4j9k8hZQQWo5ouLS2EhDfT1bunXjBc62Pvg3zQPrg9YgIvOBv8GcHxLa4orIaGx2ewC4R9s5U0bbSH+sYHnGNoN+tX3a5LQWD+2YQVR1RDOBOwpbCEn6EL3xBlx22bn7x42DFSugoIDCqioq+vZlPjAYc2GdiLmhOgERmYpF25qQSOCKSLGI/AKzaHgE+E6OCNyUxkoKFAJfDlYPThZwoZtd7sZWntNmwQJ02DBqMLOhXsB3sUU1hzPOD7/EVAqVCY4ZhwWnKaMdM2UkadM+EblJRBaJyMsi8pyIHBeR7SEnXey4sooK1peWUty3L8yZY/sbG+Ghh2DgQLjkErjjDvgi/CvZtw9EYOVKKCuDiy6CZcvg3Xdh1CiKi4vZIiJLm7Vnhoh8ICKfich/icjAjup7PuNCN4NEHrJrRGRLSQn39OtH4Y9+lP61t2xBZs+mm6p+pqqNqvqhphkDtrMQcX6YqHGcH0Skl4gswzzS7ldLG3Q0w838LubR1hsLKbk0tK0QePXqq+m9bx8cPAi33WYnrFplZeNG2LMHTpxoEsgx3nkHdu6EtWth3jx4+GHYsAF27GAfMDlEGUNE/gqz0b4VKMUCrb/UwX3OT7JtPpFPBXNUuAlLKz5VldPHj6Nvv5381I0b0QEDzt1/ww3o8uW2PXMmOnw4jVjc1j/Pdl9zpQCDMBO5iQnqv43pbZ8BemVpPCwCNkT2XwWcCtvXAZ/U1nK6+SXGj0effrrp84cfokVFaF0duncvCujHHzfV9+mDrllz5vNpLN37vPA9v8NiRsTaUIAtug3M9m/Y2YrPdLNDHTC0qoquJSVw7bXpX/Cpp2DKFMBscXeIyK4Q8SpvCa7NrwGPaLNZv4iUishq4Elgmqrepa0MctPOHI5sVwPdg8lfGfBRly50bX5CZaWpFmIMHAj19XAkErW3X7+m7R49zvrcBTMhK4mdDjwhIp+LyOeYq7BgHmxOO+JCNzvMBIZdeSWMHg2vJstaBRQVQV3dufvr6qBLF9vu0QMWLqROVb+COUK8DKwLXld5RyTzw3+q6lOR/SIit2GmV4cx3e3rWWpmKhwAyuvqqG1e0b8/fBRJ+rN/v42VqKBNQvMRdQC4W1V7R0oPVf1D25vuxMOFbhZQ1Z2qevuRI3wwfz5MnAgnk8SlKi+HqirT2TVdwx64gWcvdewK1z+GeZxdAFze/j3IFjIS5GmQHSCnQTT83RH2j4SzMj/sI5L5IWTK+A8s7fv3VPV+Va3OQkdawx+BQ3PncvzkSaipgbfesorbb4clS2DvXhsbCxbA979vgjcFmkeaWwb8Y3AEiem5J7VfN5wYLnSzgIj8rYiUFhaysVcvGgAKkxgClZdDRQXMn28P1+nT8Oij9nDFVBMPPkjjb37DdhHpKiLdsdi5nwOdwE5XBgcb1c2YxcdwOPO63TV8vgvYrCqbRo9mJWbBcaeqNopIgYjcBfw3sBX4C21laqJsoeY5+Jdbt3KsvNxMB9eutboZM2DqVPja1+Dyy6F7d1MzpUA9Fjgp+j3rMRO5NSJyDMsIndfqqQ4j20rlfCo0LZy8ABwtKKB6+HAa169v+fT9+9GJE9F+/dC+fdGbb0a3b2+qX7SI2u7d2Qkcw/Rxb2DBuLPe7/RK6+IM1NfTUF1N48GDTAv3fCgmYN7BMjnkQJ/adB9GhfvQHpc7qcrI7PcpP4t7pGUd9zJKTNvjDKhyavFi1i1YwLcxVcsTet7Hm/Cx0hlwoZt13J8+Punfl5oaGh97jPELF+qbzetCHN29QBdtIW1RRyIiipn4Jc3mEY72sdIJcJ1u1tE9wPTnn6e2pASalxEjWrzAKWB6J3yInidNb71u3dCFC3m4ndqTA9hYwX7zttBZx8p5hc90cwaZhEUDSxS8vDkNWKjB6Z0vVJ+MwhxI0pnRUV8PRUVUA9c2z7Rxfs50z5zlY+U8xme6OYOuA0ZiK/TV2ApzPOpD/WbsNbFVD5GIzBeRg8HH/39F5EYR6SYij4tIZSiPhwSGiMg4EflYRO4XkaMickhE7oxcr6+IvCIix0TkXRF5SEQ2hToRkSXhvC9E5P0QdjJZ+1ZNmMCLN91Ej5494YYbmmxRY/EE6iN3Jhb0B8wldswYuO8+6NMHFi2C6mq63norz4nIR6ENm4INb4wpIrJfRKpEZGGkHdeIyNvBWeCQiCwNoR6T9ivcy1+Gax4RkWXR7xORB8L1KkVkRmt+uyYyM1acDiLbK3le4hVGqrJUle2qnFalMfzdHva3aeUZS6B4AOgfPg/CAuQ8iD2Yl2B+938AfhqOGYc9vA9iXkzfwh7ki0L9mlCKMffVA8CmUHcLZqLVG/NuGg78WQttXFVcTMObb6I1NejcueiYMVYdc22tq2s6JeoKvXIlWliIPvmkHVNdjc6ahVZUcBLzrCoErsdmiIMABZZjC3VXY7PB4aEdXwGuxfIIDsKy6s5rqV/A45hTRh+gJ/AK8PNQ903gCPAlzIb6xdCGobk2Vrx0XMl6A7xk8Mc286mjmNlal8j+3Vj0rdjnW4B9YXscpgssitQfDQKpEPNsuiJS91BE6I4H/i8cW5BiG1dNnkxDbNfx42hBgZnMpSJ0y8qa6hoa0O7d0W3bqI3zPTGhe1lk3x+xJJPx2jUPWJ+sX0EAnwSGRPZdB+wN288CiyN1w9IXul7Ot+LqhTxCTW84DwuwclRE1oilEe8PRBxK+Sjsi/EnPVvvWY357JdiM8FovNkz22rutUuxNOVHROQZEbmwpXaWlzeNy5ISUxVUxg3GeC5lZU3bVVXmwTVkSNKs181jHpQAiMgwEXlVRA4HZ4GfARe30K9SbMa/NRLD4LWwH+yeRu9V9J47eYIL3TxDVV9U1a9iAU4U80KqDJ9jlId9LfEJpnqIhlgvix6gqk+qxYMYgc3sHmjpovv3cyYlzokT8OmnFmfgggtsX3XEcffw4bPPFWnavvhi89LavTuhzjMZvwI+xBa5LsTCHp65eoJ+VWFvBSO0KX5BL1WNBZU5xNn3p7wN7XLOc1zo5hEicoWIjA+LZDWYgGjA4qb+OETeuhj4J8xrLilqzgb/DiwSy7hwJXBH5PtGi0iFiHTBXrtrwvcl5be/hU2boLYWfvITc4EuK4PSUhgwAF54ARoa4NlnYXeSMO0FBeYqO3s2tSLSX0QKReS62CJhC/TEvPtOhH7d21K/1PKnLQeWiMgl4dgBInJLOPVlYLqIXCUixcA/p9AOp5PhQje/6AYsxmZkh7GFswWYHnYL8D5mfP9e2JcKc7A4B4cx29qXsAUpgAsxIfQZ9ir9Jyx7Q1LGjuWDRYvQPn1g61ZYvbqpbvlyizvRty9s3w7XX5/8Wo8+Sn1pKbuAdzH36EdIbdz/PZZL7Xjow9pIXbJ+zceCyWwOaokN2AImqvo7bKHt9XBMLkc3czoIt9N12hUReQS4VFWntfH8VUOHUrtzJ1NI0043ENdO13Gyhc90nbQQkStFZFSwXb0GixW8Pp1r7trFUSwiWLqxEhqAbS5wnVwitcibjpOYnphKoT9mSvYYFrM2IWKp6OMlPbw7sj2V9OMMnA7XcZycwdULTg7T9ihj2CLhNPfCcnINn+k6OYyuC1Zaq/A4A04nwWe6znmADAaeA76MZYqIN1moB2qBbcBUj6Tl5CoudJ3zCBmJ6X2/jrk0d8HckHdh2SH+1RfNnFzHha7jOE4GcZMxx3GcDOJC13EcJ4O40HUcx8kgLnQdx3EyiAtdx3GcDOJC13EcJ4O40HUcx8kgLnQdx3EyiAtdx3GcDOJC13EcJ4O40HUcx8kgLnQdx3EyiAtdx3GcDOJC13EcJ4P8Pxxsq2Vkj3W3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize causal graph\n",
    "\n",
    "try:\n",
    "    # Try pretty printing the graph. Requires pydot and pygraphviz\n",
    "    display(Image(to_pydot(model._graph._graph).create_png()))\n",
    "    \n",
    "except:\n",
    "    # Fall back on default graph view\n",
    "    model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['account_age', 'songs_purchased', 'income', 'has_membership', 'is_US', 'age', 'friends_count', 'days_visited', 'avg_hours', 'U']\n",
      "WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.\n",
      "INFO:dowhy.causal_identifier:Continuing by ignoring these unobserved confounders because proceed_when_unidentifiable flag is True.\n",
      "INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimand type: nonparametric-ate\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "     d                                                                        \n",
      "────────────(Expectation(log_demand|account_age,songs_purchased,income,has_mem\n",
      "d[log_price]                                                                  \n",
      "\n",
      "                                                        \n",
      "bership,is_US,age,friends_count,days_visited,avg_hours))\n",
      "                                                        \n",
      "Estimand assumption 1, Unconfoundedness: If U→{log_price} and U→log_demand then P(log_demand|log_price,account_age,songs_purchased,income,has_membership,is_US,age,friends_count,days_visited,avg_hours,U) = P(log_demand|log_price,account_age,songs_purchased,income,has_membership,is_US,age,friends_count,days_visited,avg_hours)\n",
      "### Estimand : 2\n",
      "Estimand name: iv\n",
      "No such variable found!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)\n",
    "print(identified_estimand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define underlying treatment effect function given DGP\n",
    "def gamma_fn(X):\n",
    "    return -3 - 14 * (X[\"income\"] < 1)\n",
    "\n",
    "def beta_fn(X):\n",
    "    return 20 + 0.5 * (X[\"avg_hours\"]) + 5 * (X[\"days_visited\"] > 4)\n",
    "\n",
    "def demand_fn(data, T):\n",
    "    Y = gamma_fn(data) * T + beta_fn(data)\n",
    "    return Y\n",
    "\n",
    "def true_te(x, n, stats):\n",
    "    if x < 1:\n",
    "        subdata = train_data[train_data[\"income\"] < 1].sample(n=n, replace=True)\n",
    "    else:\n",
    "        subdata = train_data[train_data[\"income\"] >= 1].sample(n=n, replace=True)\n",
    "    te_array = subdata[\"price\"] * gamma_fn(subdata) / (subdata[\"demand\"])\n",
    "    if stats == \"mean\":\n",
    "        return np.mean(te_array)\n",
    "    elif stats == \"median\":\n",
    "        return np.median(te_array)\n",
    "    elif isinstance(stats, int):\n",
    "        return np.percentile(te_array, stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:NumExpr defaulting to 4 threads.\n"
     ]
    }
   ],
   "source": [
    "# Get the estimate and range of true treatment effect\n",
    "truth_te_estimate = np.apply_along_axis(true_te, 1, X_test, 1000, \"mean\")  # estimate\n",
    "truth_te_upper = np.apply_along_axis(true_te, 1, X_test, 1000, 95)  # upper level\n",
    "truth_te_lower = np.apply_along_axis(true_te, 1, X_test, 1000, 5)  # lower level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "lineardml_estimate = model.estimate_effect(\n",
    "    identified_estimand,\n",
    "    method_name=\"backdoor.econml.dml.LinearDMLCateEstimator\",\n",
    "    control_value=0,\n",
    "    treatment_value=1,\n",
    "    target_units=X_test_data,\n",
    "    method_params={\n",
    "        \"init_params\": {\"model_y\": GradientBoostingRegressor(), \n",
    "                        \"model_t\": GradientBoostingRegressor(),\n",
    "                        \"featurizer\":PolynomialFeatures(degree=2, include_bias=False)},\n",
    "        \"fit_params\": {\"inference\": \"statsmodels\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = lineardml_estimate._estimator_object\n",
    "# Get treatment effect and its confidence interval\n",
    "te_pred = est.effect(X_test).flatten()\n",
    "te_pred_interval = est.effect_interval(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2NodZv6uTDQ2dbGzoYmtLeE81ZCjgZWpZHsfPLGdqmRPYVBflUJYXHBFBgoiQH/SRH/RRUxrqcx3LNjR3xahrjVDXEmZba5i6ljDPrGvgMbcE0OcRasvymFGRx9wJhcyrLmRicc6IKrkyhj3vpUiEghwfxbkBCnJ8OkabGtV0MFul1LiRdEvMWsP7BmYJy2ZjQxdrdnSwZkc7a3d10B1z1skP+phVmc/MynxmVRUwoyKPivzgiApUhpJtDLvao7zT2MU7jV1sbOhiY2PXnuNRlOtn7oQC5lcXctikIqZX5I+IwLQ3ESevRSGtElUjmw5mq5Qal2zb0BFN0BZO0BVL7qnKNMZQ3xrhzbpW3tzWxqrt7cSSTgO0ScW5nDCjnPkTi5hXXcCEwpFVcpRpHhEmFucysTiXE2dVAE7gVt8aYe3ODt7a2cHanR28urkFcILYwyYVsaSmmCU1xVQXjYzjZQy0hZ333ucVikN+SkIBcvza6UCNDlqSppQak8LxJC3dcdojPY3/owmLFfVtvLq5hTe3tdLU5Qz1MLEoh8OnlLBochHzqwspDgUOKC2vR/B7BY9H8Iozx2XqfqrBf3oHAGfu81S06EyCvmfIDQOWMXsej+TZCVq746zc3s6KujaW17fR2BkDoKowyFG1pRxdW8rCSUUjrk1bbsBDSShAcSgwIksA1fjTX0maBmlKqTEjadm0hhO0huN7xjBrC8d5bUuLE5jVtRFP2oQCXpbUFHN4TQlLphQzoTCn332KONMaBbxOj8vUsBd+d5JxvzfzY5algrXU/3hqrk/LmYQ9kcz+ddwYw872KMvr2li2tYUVde3ELZtcv3Osj5leytHTysgPjpwKHBEoDvkpHWVDk6ixR4M0pdSY1RVL0tIVpyOa2NOI/KV3mvjXhiZW72jHNlBREGTptFKWTitjwcTCPkt3gn4POT6vMzm437tnkvCRzradCdujCYto0hlkN5KwSPYxBMdwiSYsVta389qWFl7b0kJzdxyfR1hSU8wJM8tZOq2M/JyRExjlBjyU5gUpzvVrZwM17DRIU0qNKZZtaA3Hael2Ss3C8SQvbWzmhY2NLK9rwzZO27ITZ5Vz3Iwyasvy9irx8nllr3kjQ37vmPtyTlg24bhFNGG5c4kmBx33LROMMby9u4t/bWzixXeaaOyM7QnYTp1bydHTSkdMMOz1CKV5AUrzAjoJvBo2GqQppcaEaMKiudsZ9ytpGVZtb+efa3fz0qZm4kmbqsIgJ86s4MRZ5Uwr7wnMfF5nSIrUiPwjJSgYbtGERVcsuWfw2+GeAN4Yw4YGJ2B7/u1GmrvjhAJejp9ZzqlzKpk/sRDPCOh0IOIM/FueHyRvBFXRqrFJgzSl1KjWGU3Q1BWnK5pkV3uUJ9/axTPrG2jqipMX9HLSrApOnVvJnKoCxG2wn++O6J8X9GmPvn5EE5Y70fnAA/lmgmUbVm9v5+n1Dbz0ThPRhE1lQZAzF0zgjHlVlOYdWAeOTAkFvZTnBykawTMyqNFNgzSl1KiTmnKpqStGOGbx2pYW/r56J29sa8MjcPiUEk6bW8nSaWUEfJ49UwWlplwaa9WXmWbbhq540pkSK5IY1jZt0YTFy5uaeWrtblbWt+MRWDqtjLMWTODwKcUjonQt6PdQnh+kJOQfEUOMqLFDgzSl1Khh2YaW7jhNXTEaOmI8sWYXT761i6auOGV5Ac5aMIEz5ldRnh/E5xVnwNJcv1ZLDbGwOwdoRyRJPDl8jdl2tEV48q1dPLW2gfZIgsqCIOcdVs2Z8yeMiM4GPq9QURCkNBTQHwJqSGiQppQa8RKWTXNXnObuGBt2d/HQ8u28sKGJpG04Ykox5yys5qjaUvw+oTgUoCjXP6KGdBjLInGLtogz7txwDfmRsGxe3dzCY6t2smp7O0Gfh1PnVnLBoon9TnU1nHxeoTw/SFmeBmvq0GiQppQasRKWTWNnjMbOGP/e3MJDy7ezekcHOX4Pp8+r4oJFE5lYnEt+jo/SUIDCXJ3iJ5u6YknawnsPFJxpm5u6+NuKnTz7dgMJy7CkpphLDp/EkprirJ8LXo9QXhCgPC+owZo6KBqkKaVGnHjSprErRkNHlGfWNXDfG9vZ3hahoiDIBYuqOWP+BErynMFGS0KBETdy/XiXmnKrpTu+Z17PTGuPJHh8zS4eXbmD1nCCGRV5vPeIyRw3ozzrswekqkHL8gJZDxzV6KJBmlJqxEhYNg2dMXa2RXhizS4eeHM7TV1xplfkcan7hVsU8lOWH6AwR3vUjQbRhEVrOE5rd2JYhvVIWDZPr2vggTedwL66KIf3HD6J0+ZWZX18M79PqCzI0Q4Gar9pkKaUyrqkG5zVt4b524qdPLR8Ox3RJAsmFnL5u2p4V20xpXlByvID43Ycs9HOGEN7xBkuJRLPfOmaZRte2dTMfW/Us6Ghi7K8AJe9azJnzJ+Q9WAt6PdQVZijQ3eoQWmQppTKGss2NLrB2SMrd3LfG/V0RpMcObWEy46sYdHkIsryA5TlBbNeZaWGTjiepKmzZ7quTDLGsKK+nT/9extv7ewYUcFaKOiluihH5wdV/dIgTSk17IwxNHXF2dbSzWMrd3HfG/W0RRIcMaWE9y+dwmGTi3TcqXEgYdk0dcVo6Y5nvKOBMYaV9e38MS1Yu+KoGs6YV4Uvy20ai3L9VBUFtZRY7UODNKXUsGrtjrOjPcLfV+3ij69uoyUcZ0lNMe87egqHTymmsiCHopBWA40nlm1o7orR1BXPeLs1Ywwrt7fzh1e3sXZnB9VFOXzwmKkcP7M8qwPjikBZfoDKghwtNVZ7aJCmlBoWXbEkO9vCPLO+id+9vIX61ghzJxTwoWNrOaq2RIMzhW0bWsLOYMWZHnPNGMNrW1r53ctb2NoSZkZFHh86tpbDszx0h88rVBXmjJipr1R2aZCmlMqoWNJiV3uUVze18JuXtrB2ZweTinO5+tipnDKngqrCXA3O1F6McWaWaOiMZXwKKss2PPd2I394dSsNnTGW1BTzseOnUVuel9F0B5Mb8FBdlKuzZYxzGqQppTLCsg0NnVHW7uzgNy9u4YUNTZSE/Lzv6Kmcu2gCE4tyKdHSAjUAYwzN3XEahyFYS1g2f1+9kz/9u45wPMkZ8yfwgaVTKA5l9xwtDvmpLsrJers5lR0apCmlhlxLd5wtTd3cs6yO+9/YDsAlR0zi8iNrqCkN6XQ56oDYthOsNXRGM97BoDOa4M+v1fHoqp0EvB6uOKqGCxdPzOqAyR4PTCjMoSw/mLU8qOzQIE0pNWQicYv61jCPr97FXS9tobk7zkmzyvnw8bXMqy7URtHqkKRKZ5u74hkfuqO+NcyvX9zMa1tamVCYwzUnTeeo2tLMJjqI3ICXScW55Aa0F+h4MSKDNBE5G/gh4AV+aYz5Tq/l4i4/FwgDHzbGvDHYfjVIUyozLNuwuyPK8ro2fvbMRlbv6GBmRT4fP3Eax84oY0JRjg4voIZMPGmzuyNKWziR8bTe3NbKL57fxPa2CEunlfLxE6czoTAn4+n2RwRK8wJMKMzR0uhxYMQFaSLiBd4GzgDqgdeAq4wxb6Wtcy7wGZwgbSnwQ2PM0sH2rUGaUkOvLRxnc1M3d7+yjQeXbyfX7+XDx9VyweJqJhbnUqDTN6kMicQtdrRHCGd4ftCEZfPwih38+bVt2DZcduRkLjl8clYHw/X7hEn6+Rrz+gvSstmd5GhgozFmE4CI/Bm4CHgrbZ2LgN8ZJ5J8RUSKRaTaGLNz+LOr1PgUS1psb43w1NoG7nx+E01dMc6YV8VHjq9l9oQCnUxaZVxuwMuMinzawnF2tkcz1rnA7/Xw3iMmc9KsCn714mb+8Oo2nl7XwKfePZPFk4szkuZgEknDlqawdiwYp7IZpE0C6tIe1+OUlg22ziRgnyBNRK4BrgGYMmXKkGZUqfEoNVvAmh3t/O+z7/Dq5hZqy0J88axFHOdWbWazkbUaf4pDAQpz/DR0xmjqimWsvVpFQZAbzp7Lm9ta+d/n3uGWB1dz+rxKPnLcNAqzNA9nWzhBZzTJxOKcrPdEVcMnm0FaXz+9e3/k9mcd50lj7gTuBKe689CyptT4Folb1LV28+CbO7jrpS0kLcOHj6t1e21q1YvKHo9HmFCUQ0menx1tUbqiyYyldfiUEn581eH85bU67n9zO69taeXjJ0zj5NkVWSk9tmxDXUuE9kiCScW5Wqo2DmQzSKsHatIeTwZ2HMQ6SqkhYoyhoTPGm9ta+fHTG1mzo4NFk4v49LtnsrimmMqCoFZtqhEh6PMyrTwv41WgQZ+XDx1by4mzKvjJMxv43j/e5tm3G/nUKTOpKMjOUBkdkSTdsS4tVRsHstlxwIfTceA0YDtOx4H3GWPWpK1zHvBpejoO/MgYc/Rg+9aOA0oduEjcYmtzN395rY67X91KwOfhY8dP44LFE6kpDZHj116bamSybMOujigtXfGMp/PYqp387pUteET42AnTOGNeVVZ/uBTl+plYrG3VRrsR13HAGJMUkU8DT+AMwfFrY8waEfmku/znwGM4AdpGnCE4PpKt/Co1VqVKz5bXtfGDf7zN2l2dHDu9jP88ZQZzqwuzVlqg1P7yepwekCUhP/WtEWKJzIyE6/UIFyyeyFG1pfzo6Q38+OmNvLixmc+cOpPyLA1A2x5J0B1PMqkkl0JthjDm6GC2So1j0YRTevbX17fz25e34PcKnzxpBuceNoHJpSEd80yNOsYYdndktmMBgG2cUrW7XtqC1yN8/IRpnJ7lUrWSPD8Ti3J1XLVRaMSNk5ZJGqQpNbjGzhir6tv4n6c2sGp7O0dOLeEzp85k4aQinZZGjXqpWTGiGSpVS9nZHuFH/9zA6h0dLJ1WyqffPTOr7cQCPg81pbmEAjph+2iiQZpSCnAG7KxvjfDIih3873PvYAx8/MRpXLTEaXumpWdqrDDG0NgZo6Ez86VqD6/Ywe9e3kJewMdnT5uV1amlRKCyIEhlFmdMUAdGgzQ1PoRbIN7NnpFajOm5nxrRJb06IrV8z+cg/X7v7dl7vfR9pojH2b94eu4bu4902Hsd8QyQl7Tn+vq8pr8eESdPqef2pO1s2xlNsLE5yo9eaeOZzWHmVwa5/qQqFlaHKAv53FdjerYztnOzLfc1p7+2VN57pdnn/d7HPi1/drLnZiWcdDw+EC94vM59j7fXcXTv75W27H0seqez5+Zu31d+Uq8n/TWC8/rTt99zLNLWp/f7wL773rNOr2PMYO9vX8ex1+vd61zp9b/3ebxPvtL3mTpv+zgfU9v1Pm57Ld/nBey9Tu/99v7MwN7nXZ/vV3/5T0+qJz/ReJKd7RESSauPY9GzvXHzYMQL4nUf7/2jxYgg6echBnHzu7k1xm3PN7OlLcF5s/O45ogCp8ONCCb9s5P6pPU6J8S2AHvP/lLH2smPx8kPOOsZCzE2YpzX1JP3njRCAQ8TCnNwpgA1aZ+Z3u/Nfl73Uu+Vx+ve97L3e2r3cXzT9XUd6Ouc6P0ZTF1H+/gcp98w7nXDvWZ4/c596PUZtnul4+059+i138JJ4Mt8zcKI6zigVEZ0N0GsveeDOZqlX7AOdns7CSLYxkNjV4wX62J855UITRHD1QuDXL0oh0nFcYKShMgB5sm2wE4A6RftIephZg/B2Fd20r0wW85F2OsfuvyNR+nvferc2vOlOMylr3YSktGeL1iPryeA6EMOMDUfmrsTtEUSToBjJ/f5QWR8wX2Csj7tE+w4Zobgp2fkcNcq4a/ruli1o5sbjwkys9Q97zxuw35jsScgHiJ9vfJYDOq7hIrCIAXBA7gmxsOADXicHaeOs9fNv5XWi7b3D5re0oPsYTlPkmDFetJOBV2pPNoWJCP0/Mhyr10eb9+vIb8KyF7zjzHwTaaUq+41+M3ZkF8JV/7Ree7Ra2HHm2kXcA+Uz4SLfuosf/gz0LDOuWimPsjVS+D87zvL//Z5aK/vWW5smPQuOP2rzvI/vx+6G/f+9TjtZDjty879317olOztKaUAZp8DJ37BWf7rs3v9CrVh4Xvh2E85F5pfn+3+KvT1/CpcdAUsvhIirXDP1c5+bKunNGrpNc46HdvhLx8A51VTBVwCbPB8jHmnXzCPtDUAACAASURBVMCJoe0UP/g5pHdJ1MnXw4xTYedKeOy6fX99nvktqD0e6l6FJ27q9SaIc+wmHg4b/wnPfGvvL3ePFy78MVTMhQ1Pwss/6/kFbGznEL7n51BcA6vvh9d+6e437Zf55b+HvHJ482544/dpy43z70MPQSAEr94Jq+7plT0vfOxJJx8v/wzefnzv5b4ceL+7zTPfhk1Pu4Vm7vEJlcFV7rn11Neg/t89rxugaLKTf4Anb4Hda/YuXSidDhf80Fn+8GegeaObfff1VS+Cc77r3L/vY9CxoycIEi9MPgrefaOz/C8fcH6U2MmeUpcZp8KpNzvLf/8eSETSzn2B2WfDcZ920vvjZc6ZkV7qOvd8eNfVzjn7p6v2LuU0NhxxNRz5EQg3wR8u2/u4ev1w9DWw8BLnM/O3z/UqzbPh2E/D7DOh6W3ns5V+3ghw0vUw/WTYuQIevyntfXWDwjO/CVOOgW0vw5NfZh/n/wAmLnHOveduY885bWw8tkXFe35OqHgakeX3U778Z/tsvuX0X5DIn0TxxocoW3c3tscJ7MVOILbFljN/hRUsonTdHyhdfw/G43FL3JzSss1n/56AN8BN3t9ze85DkAT+5R5ePGy8+GEAKpf/lKKtT2Lw7NmH5c9ny9m/dZa/+WPydv2bnlI6CytYwtbTnXOr+uWvkdfwBumfi3hBDdtOda5rk164gZzWdW7efBiPl1jZHPzn3Y7HAzzyBWjZ5J6bFlhJmLg47dz7KHTu2vvg1J7gHH9w3vto+97LZ57Rc+795lwniE5dMwDmXwQn/JfzPv767J7rcer9XXQFHP1xiHXC7y7a97098qNw+Aegazf88Yp9lx/3Gefa2bIZ/trHQBAnfwnmnAON6+ChT+27/PSvwvRToH6Zc91LufoRmHbivusPEw3S1NjRvNG5AMw+p+e5madD5by9i8ZDZT3Lp78bqha6X4QesG3Ir+hZPmEhFE0CPM5y8UBJbc/y2Wc5X4TQ80VYOr1n+cL3ur8604ryK+b0LF/wnp7nU/uvOszdn8f5UrSTTomV5f4vcsd39gacL2VIqxb0QcU857lgAdElH6GxK87z2+I0hi1mFAmXHr6Aqhn55CcqYdHlPemngqWiyc7jvHIn/72rHord5SVTnS9lY/YOYvMnuMtrYcn7SP+ixLYg122rkz8Bpp2Utm/3+AXynOWl02DueWlvsBtM+N12NhVzYcHFPc+ngo3Ur/Upx0BusbNvYzlVqVayZ3nFnL1LBAB8aQ2+a46GUFq7ImODP9TzeNK7ei03TnopExZBbklPdY14nB8QKbUn9rxXqZdXMLHn8YzTnUDcWG4QbjnHZM/y05wSAY+PPV926efenPPSzj03UKxMS69mqXO+G7vnPS5OnVt+59xKnVOp6qOJS5zl/jw48mM9JapWAux4T/r+XOf4p0piUq+/sNpZnlMMc87tKZVLBRsF7rkTKoM5Z9NT2iFOKVSBu33pDDjmUz3HJvU/tX1xDSy4hD1Bnlt9SW4xeQEfwdoldNgfIZ40Tk2aU1yEFXTev1jRNNqnnuUEZ8bCePwYjw/jloRFyhfSYgziVjmmqh5T52K4Ygm2L0TMEl7YbrGtwzC50MvsmE1h0EP3hKUkc8vBtvbsw6SV/kdLZrnnhFPVacSDHSjYs7xr4vHEi2rd9Jw0rWDhnuWdNacQLZ3rVIXaScROkgxVEG4LM6Eoh8DEJc7nPFWC5PH3fK4BDv8QxLvoCbCNex1MLf8AJNPPLdt5T1IOu3Tv0ivxpF33xAnIUtulSq8muNc9b8C9bvRStcD5H8hzrot7Mc71AJzP4BEfYq/rgnigbKbzuGACHPOf7mZukGrbUOJ+tgqr995/6pzKEm2TpsaO137plJx98IGeQKzPKsM+2tHs9Tno6zPRT7unfdrr9GGwNhp7tUvZZ+N+9tFX244eljE0dMT55+YIt7/SRcwyfPaofC6ck0dVYQC/uPserM3ePvvur31T79fTn76WDdBmqs92bv3liQHy1Y8BX0tf6fa1ffrq/bWZ6nX+9Nd+rM889dqPs9L+HbO+zu++2rj13ueAr6vXa9/v6vhe6+0pwevjGKRvM9j++3zvex8H+kyjLZxwhuqgv3OrV14HzUMfi4AH3o7xf8sjlOR4uOG4AhZV+IBUCWtfvU/7uib0c13aZ/nAnwGPQGVBjjO1W+9zas+uelfnDvYe9/cZTNuur/afqbT22Ucf6++1Th/nyV7b7IcBrxlpn4+ymU7JfIZpmzQ19qVKtCYdCTmFA6+7v/ppe3JI+0vtM0PjKUUTFpsau/jlis3c/2YDtWUhvnT2XN5VW0JlwRD39jrUdnP97XO4x5oyZu/XolNfZc9wvP/ue10sQk7CYltLOGMD4AKcWw6zZ3bx3SfW8cV/tvP+pVO59F2T8fQOVIb6dfcTPG4RoSwUoLooJ6vjuo0YfQaHI4MGaWrsCOQ5v3r8Q/irZ6g/sBkOANrCcZbXtXHb39exdlcnZy+YwH+8ezozKwrIO5CGw/srE68lGxdJDcxGjuF4H9LSyPF7mVmRz472CK3diYwlObMynx9csYSfPrOR37+ylXW7OvjC6XPIz/Fl7jUPsN/mrjjhuMWU0hAB3zjvUDOCP/ta3anGjmTMafAcyt74RNlijGFHe5Rn1zVw+xPriSYtPvPuWZy3qJrJJbk6r59S+6G1O872tkhGx1QzxvDoqp386l+bKc0LcOM585hZmZ+5BAfh9Qg1pblO9afKmv6qO/XKrcYOd7iJ8SaetNnY0MX/Pb+JLz+0mvwcH9+/fAmXHTWZ2vI8DdCU2k8leQFmVuZntGRJRDh/0US+c8kibANf/OsKHl+9i2wVmFi2YUtTmIaOaFbSVwPTq7caO56/Ax74RLZzMay6Y0lW1LXx5YdWc9dLWzh2ehk/vHIJJ8+uGPr2Z0qNAzl+LzMr8ynMzWxroDkTCvjBFUtYNLmInz67kR/+cwPxZGansBrI7o4YW5u7seyxV7s2mmmbNDV2NG2AxreznYth09wV49+bW/jmo2vZ2R7hYydM46qja5halodfS8+UOmhejzC1LI/Gzhi7O6IZq/4syvXzlfMX8OfXtvHn1+rY1hLmpnPnUZ6luXM7IkneSXYxpTTkzJSgsk6v5GrsSHQPbaeBEcoYQ31rmAff3MG1966gK5bkWxcfxsdOmMaMinwN0JQaIhUFQWrL8/B6MteMwusR3r90KjedO4/61gj/9ZflrNnRPviGGRJLOM0n2iOZ60Sh9p9ezdXYkQg7g2iOYQnLuYD+4rlNfPPRt6guyuF/rljMOYdNYGJxrnanV2qI5Qd9zKzMJ8ef2a/LY6eXccdliwkFvNz84GoeXbUza+3UjIFtzdpObSTQIE2NHfHwmC5Ji8Qt1uxo56t/e4vfv7KVk2ZX8L3LF3Ps9HKKQ4HBd6CUOigBn4cZFfkU5Wa2B+SU0hDfu3wJh9cU8/Pn3uEnz2wkYWW3ndq25jC2tlPLGm2TpsaO0mlZn8IjU9ojCd7Y2srXH3mLrc3dfOS4Wt5/zBRqy7T3plLDweMRppSFaOiMsrs9lrF08oM+vnz+fO5+ZSv3vl7PjrYIN54zj8IMB4j9aY8kiFsWU0rzdDy1LNBx0tTY0bTBmY9ujFV5NnRGeeHtJr756FvEkjbXnzWXMxZUMVFHC1cqKzqiCbY1hzM6nhrAs+sb+NHTGyjPD/Ll8+ZTU5q9mgKvR6gtDxEKaNlOJug4aWrss5MMPsfc6GGMoa4lzF+X1XPj/asI+DzccdliLlhczSRtf6ZU1hTm+JlZmY/fl9nP4ClzKrn14sOIxC2u++sK3tjamtH0BmLZhk2N3bSF41nLw3ikQZoaO+69Gl75abZzMSScC2IXv3juHb77xHpmVObzg8uXcNLsCsqy1D1fKdUjx+9lRkU+uYHMDlUxt7qQ712+mKrCHL72yBoeWbkjo+kNxBioa4loh4JhpEGaGhuMgaaNEMle1/WhEk/arNvZwTceXcvdr27jlDkVfPe9izh8agn5mZh/Uyl1UPxeD9PL8zLeoaCyIIfbLlnEUbWl/OL5TfzyhU3YWWyqtLsjRl1LOGu9T8cTDdLU2JCIgrEgMLp7d0biFsvrWvnSfSt5dn0jH1g6hS+fN4951QU6uKRSI1CqQ0FFQWZLuHMDXm48Zx4XLKrmoRU7+M7f1xFNWBlNcyBt4QSbmrpJZrH36XigQZoaG2Kdzn9/XnbzcQg6owle2dTEtfeuZN2uTq49YzafPGUG0yvytQenUiPchKIcJhbnZHT6YK9HuOakGfy/E6fxyqZmbnlwdVbbiIVjFu80dhNLZi9YHOv0yq/GhrgbpI3SkrTW7jhPr2vgC/esoKUrxtcuXMAVR9cwuSSkHQSUGiXK8oNMKQtlNFADuHDxJG48Zy6bm7v54l9XUt8azmyCA4gnbd5p6CYcT2YtD2OZBmlqbDA21BwDxbXZzskBa+iM8tDyHdxw3yq8Hg/fvXQR5y2q1gnSlRqFCnP8TK/I7FRSAMfOKHd6fiYsrv/rStbt6shoegNJ9fzUqaSGngZpamzILYVzvgMzT812Tg7IzvYIf3xlG19/ZA3VRTl8//JFnDynUmcQUGoUCwV8zKjM/OCvcyYUcPuli8jP8XHzg6t5bUtLRtMbSGoqqaauzA30Ox5pkKbGBtstah8lVYOpMdDufG4TP/jnBg6bVMTtly3i6Gll2oNTqTEg6PMyoyKP3EBmv2ari3L57nsXMaUkxDcffYun3tqd0fQGs7Mtyq52HaJjqGiQpsaGba/AHy6D7a9nOyeDsm3D5qZuvvfk2/zmpS2cOKucb19yGIsmF2sPTqXGEJ/Xw7TyfPKCmf1cF4cCfOs9C1k0uZgfPr2Be5fVZXV4jMZOHaJjqGiQpsaGaCt0N4KM7FPasg0bGjr5xiNv8eDy7Zx/WDX/fcF85kwoxK89OJUac7weYVp5HoW5mS0hDwV8fOX8+Zw0q4LfvbKV/8vyWGpt4QRbdXL2Q6b1KmpsiLu9m0bwEBxJy2bdrg6++vBbLNvayvuXTuGaE6e7vcFGRzWtUurAiQhTSkNsb4vQ2p25xvV+r4drz5xNScjPQyt20BlL8vnTZme8E0N/OqNJNjV1U1sW0mGEDlJWgjQRKQX+AtQCW4DLjTH7TEomIluATsACkn1NPqoUkBakjczJ1ROWzert7dz8wGrW7ergP0+ZwfuPmcqk4pGZX6XU0BIRJpeE8HmiNHZmrnG9R4SPnTCNglw/d7+ylUjc4vqz5ma8E0N/InHLDdQy35FiLMrWEbsB+KcxZhbwT/dxf95tjFmiAZoaUKLb+R8YeSVp8aTNm9vauO7eFazf3cl1Z87hw8fVaoCm1Dg0oSiHqsLMzk4gIlxxZA2fOGk6r25u4WuPrMnqOGaxhM2mpi4d9PYgZCtIuwj4rXv/t8DFWcqHGivyq2DGqeAfWYPZRhMW/97czLX3rqCuJcLN587jiqNqqCzUMdCUGq8qC3OYUJT5a8D5iybyX6fPZvX2dr780Go6o9kbxyyRdMZSy+ZUVqNRtoK0KmPMTgD3f2U/6xngSRF5XUSuGWiHInKNiCwTkWWNjY1DnF01ohkDU5bCWbeCf+QEP9GExUsbm7nu3pU0dcb46oXzueSISZTlZ/ZXtFJq5KsoCDKxOPPXq1PnVnLjOfPY1NjNjfevorU7e9NIJS3DO41dOjvBAchYkCYiT4nI6j5uFx3Abo43xhwBnAN8SkRO6m9FY8ydxpgjjTFHVlRUHHL+1SiSGiONkdP4PpqweG59I1/86wq6Ykm+efFCzl88UQepVUrtUZYfZHJJbsaHdzxmehlfvXABuzuj3PjAKpqzOOCsbcOmxu6sluqNJhkL0owxpxtjFvZxewjYLSLVAO7/hn72scP93wA8ABydqfyqUcxOwos/gj9cmu2cAE5D2afXNfCl+1ZiGcNt7z2MsxZOoDDHn+2sKaVGmJK8ADUlmZ/vc/HkYr56wQJauuPccP8qGjqyN+CsMbC1OazTSO2HbFV3Pgxc7d6/Gnio9woikiciBan7wJnA6mHLoRo97CTEu8HOfluHSNziqbW7uOG+lXhEuO2SRbx7bqXOIqCU6ldRyD8sgdqCiUV88+KFdMYS3PDAKna2RzKb4ACMgbqWMO1hDdQGkq0g7TvAGSKyATjDfYyITBSRx9x1qoB/icgK4N/Ao8aYx7OSWzWy2UlIRrLeHi0cT/LkW7u46f7VBHzOROknz6kgFNAATSk1sKKQn5rSzAdqs6sK+OZFhxFNWNx4/yrqW8OZTXAAxkBdazir7eRGuqwEacaYZmPMacaYWe7/Fvf5HcaYc937m4wxi93bAmPMt7KRVzUK2BYkY+DLXpAWjid5fPUubnpgFbkBL7dfupgTZpXrNE9Kqf1WlDs8gdrMynxuvfgwLNtw4wOr2NaS3UCtvjVCiwZqfdKR5dToZychGc3aQLaRuMVjq3Zyy4OrKQj6uf2yRRw3s0wDNKXUARuuQK22PI9bLzkMAW5+cBV1WQzUALa3RmjKYoeGkUqDNDX6WQmoOQamnTzsSUcTFn9ftZMvP7iGolw/d1y2mGOnlxP0aYCmlDo4wxWo1ZSE+NZ7nEDtphEQqO1sy+xsDKORBmlq9LOTsOQqOOJDw5psNGHx+Opd3PLQaopDToB29LRSnfpEKXXIinKHpzNBKlADN1DLYhs1gF3tURo6s9fzdKTRbxM1+tmWE6gN4yTl0YTFk2t2cfODqyjMcao4NUBTSg2lopCfySWZb8ZRUxLi1osPAwM3P5D9QG13e0wDNZd+o6jRz07C3ZfCM7cOS3KxpMU/3trNTQ+sJi/g447LFrF0Whl+r36clFJDqzgUGJ5ArdQpUTNuoJbNXp+ggVqKfquo0S/VcWAYenfGkzb/XLubm+53enHecdlilk7XAE0plTkleYFhmUJqSmmIb168ENvALQ+uZlcWB7wFDdRAgzQ1Fuzp3ZnZydUTls1zbzdww32rCPq93HHZIo6doQGaUirzyvKDVA9DoDa1LI9vXLSAWNLm5gdWZb0h/3gP1PTbRY1utu0MZAsZHYLDsg0vbmziS39dhdcjfPfSRRw3o1wDNKXUsCnPD1JVGMx4OtPK8/n6hQvoiiW55cFVWR/DbHd7LOvBYrboN4wa3VKlaJCxIM22Da9sauaLf11Jwrb5znsXceIsDdCUUsOvsjCH8oJAxtOZVVXgzPUZjnPLQ6uzPs/mrvbouBxHTb9l1OhmJ8Djg8VXQdXCod+9bXh9awvX3buCcCzJty85jHfPqdQATSmVNdVFuZTk+TOezrzqQr5y3nx2t0f5ykOr6YomM57mQHa2RbNeqjfc9JtGjW52EgL5sPQTMOmIId21MYZV29u49t6VtIbjfOPihZwxv0qH2VBKZd2k4lyKcjMfqB02uZibz53HtpYwX/3bGiJxK+NpDmR7a2RczfWp3zZqdEuNkRbrdNqnDaH1uzr5wj0r2NUe5asXLODcw6p1JgGl1IggItSU5pKf48t4WkdMLeH6s+awoaGTW/++loQ1tNfaA1XfGqEtPD4CNQ3S1OhmJ2H3GvjtBVD/7yHb7eamLq67dwVbmsPcdO48Lj58ks7FqZQaUUSEqaUhcgOZvzYdO6Ocz546i+V1bdz+xHos22Q8zYHUt0ay3k5uOGiQpka3DHQc2Nke4Yb7VrFmRwdfOGM2VxxVowGaUmpE8niE2rIQQX/mv85Pm1fF/ztxGi9vauYnz2zANtkL1IyBupYwndGxHagN+q6KSOlwZESpg2InIZEK0vIOeXfNXTFuvn81r25u4ZMnz+DDx9UOy69UpZQ6WD6vh9qyPHzezE+Nd+HiSVx1VA1PrW3g1//ajMlyoLa1OUx3LLsdGjJpf0LvV0XkXhE5V2QYJ0dUan/YSUi63bIDhzaYbXskztf+9hZPr2/g/Uun8J/vnkFeMPPtPZRS6lAFfB6mlefhGYb6sauOnsIFi6p5aMUO7nm9PvMJDsAY2NLcnfUODZmyP2/nbOBO4IPARhG5VURmZzZbSu0n20obzPbgg7RwPMntT6zn4RU7uGjJRL545hwKcjLfc0oppYZKjt9LbVkemS5OERE+fuJ03j2ngrtf2coTa3ZlNsFB2DZsbuommhh7gdqgQZpx/MMYcxXwceBq4N8i8pyIHJvxHCo1EDsJFXPgXR+GnMKD2kUsafHzZ9/h7le2ceqcSr5y/nyK8zI/WKRSSg21vKCPmtJQxgM1jwifPXUW75paws+e3cgrm5ozm+AgLNuwuambWHJsBWr70yatTEQ+JyLLgOuAzwDlwLXAHzOcP6UGZiehYq4TpB1ESVrSsvnTq3X85JmNHDm1hFsvWUhZfuanXVFKqUwpyvVTXZT5eT59Xg83nD2XmZX53P7EetbsaM94mgNJWoYtTeGsDxEylPanuvNloBC42BhznjHmfmNM0hizDPh5ZrOn1CBsC6Lt0N3Egf50tG3Do6t2cOtja5lZmc8dly1mQlHm5v9USqnhUpYfHJbpo3L8Xr5y/gIqCoJ849G32NrcnfE0BxJP2mxt7s76ECFDZX+CtFuMMd8wxuxpHSgilwEYY27LWM6UGoyVBAy88Tu492pg/4M0YwwvbGjk5gfWUFEQ5PuXL6a2/NB7hyql1EhRXTQ8sxIU5fr5+oULCPq8fOXhNTR0RDOe5kAicZstzd3YYyBQ258g7YY+nrtxqDOi1AGz3W7XiQj4cg+oJG15nTPdk98rfO+yRSyYWJShTCqlVPbUlOYSCmZ+GKHKwhy+dsECYgmL//7bmqyPXxaOWWxrCWd1iJCh0G+QJiLniMiPgUki8qO0213A2B2URI0eqSAtGQNfkP0tSXunsZPP/3k53bEk33nvIo6eVoaOLqOUGotSsxIMx2C3teV53HLefHa1R/nWY2uJJ7PbNqwzmqS+NZLVPByqgd61HcAyIAq8nnZ7GDgr81lTahB7grQo+HNABr8INXXG+K+/rGB7W4SvXjif0+dV4fFogKaUGrt8Xg9Ty0J4h+Fat3BSEf91+mzW7Ojgf556O6uzEgC0hRPsas9u9euh6HekTmPMCmCFiPzBGKMlZ2rksd2u1sko+HIGre7siia4/r6VrKxv54tnzeGSIyYPy0VLKaWyLejzUlseYlNjN5mOm06aXUFTV4zfvLSFioIgHz1+WmYTHERjZwyfVygfhT33+w3SROQeY8zlwJsiss9baoxZlNGcKTWYVEna/IvBWAxU3RlLWtz62DqeXtfAB46ZysdOmIbfq1PXKqXGj1DAx+SSXOpaMl8F+J7DJ7G7M8YDb26nsiDI+YsmZjzNgexsi+L3eCgKja5Bygea8+Zz7v/zhyMjSh2wVJA27UTnfz8laUnL5s7nN/HHf2/jjPlV3HD2HJ0wXSk1LhWHAsSTNrs7YhlNR0S45sTpNHfFuPP5TZTlBzl2ellG0xxMXWsYrzeP/FE03V+/RQnGmJ1p6+w2xmw1xmwFGjiQsQ5UdiQiEOt0/ifjzrwZh8JKQrwbrEF67BjjNOS3hqiGPBGBls2wew201TmvKVVWnwrSWrc446T1cVoaY7j/jXp+8NQGltQU8+1LDiNfp3tSSo1jlYU5FA9DiZLXI1x35hxmVxVwx5PreXt3Z8bTHIgzIfvomj5KBuue6s40cJwxJu4+DgAvGmOOGob8HZQjjzzSLFu2bHgSsy0ncIh3OQGMNwBev3sLgMfXE1SQdqzF6zR093j3LgGykk7wYSec/8Y4aRi755baty8I3iB4PG4+Opy8RDuc7XsTT9+N68WTlu+gm28PJKKQCDuBUvr+xOu0AfO569oJJxC0Ym4QZ3r26/H3HI/0tI1xXrc34MwUEMhzjkVKPAxdu5yBanvz+CBY6AagEbj7Uqg5Gi7/nZNOmuffbuATv3+DqsIgd330aGrLdCw0pZQyxrCpqZtwLPMBS2s4znX3riBh2dxx2WIqCzI/G8JAfF5hRkU+Ad/IafIiIq8bY47s/fz+lPn5UgEagDEm7gZq45cx0LnLLakKs1fwdTBSwZNtHdy+PH63VGmQbVNBXl+seN/P97kfCxLdzm2w9KyYc9sfvhwnWLMSTsDZHzsJkZaex8mIs22vkrR1Ozu47t6V5Aa8/PDKJRqgKaWUKzU0x8bGLhLJzPYkKAkF+Mr587n+vpV845G3uO29iwgFslflmLQMW5u7mV6RP+I7j+1PGNkoIhemHojIRUDToSQqIpeJyBoRsUVkn8gxbb2zRWS9iGwUkb4G1c0OYzulPIluDjlAS+1vf4Ks/thppVejWTIK4eaBA7Q+t4u5Q3D0fNgaOqN87s/LaY8kuO2SRSyuKRnizCql1Ojm83qoLcvL+GTsAFPL8rjh7Llsawnz3SfWZ33apmjCmT5qpA92uz9B2ieBm0Rkm4jUAV8CPnGI6a4GLgGe728FEfECPwXOAeYDV4nI/ENMV401tls9nFaSFo4lue6eFby9u5Obzp3HafMqs5tHpZQaoXL8XmpKQ8OS1uFTSviPk2fy+tZWfvnCpmFJcyDdMWvED3Y7aHmjMeYd4BgRycdpw3bILf+MMWuBwUZ5PxrYaIzZ5K77Z+Ai4K1DTV+NIUl3kEJfDng8JC2bbz22luc3NPGxE6bx/qVTdLBapZQaQFGun6rCYMZ7fAKcvXAC29siPLh8O9XFuVy4OLtDc7SFEwR8UaoKs9tOrj8DjZP2AWPM3SLyhV7PA2CM+X6G8zYJqEt7XA8szXCaarTx+OGUG6F8DsYYfvXiZv7wqjPUxnVnzsanY6EppdSgKgtziCZs2iOZn3Pzw8fVsqsjwq/+tYmJxTkcObU042kOpKEjRsDroSRv5DW3H+gbLNXKuqCPW/5gOxaRp0RkdR+3i/Yzb30Vf/RbeSwi14jIMhFZ1tjYuJ9JqFHPF4TZZ0HZJ8f47gAAIABJREFUdB5fvYvbH1/PoslFfPe9i8jNYsNUpZQabSaX5JIbyPwPW69HuPaMOUwty+P2J9ZT1xLOeJqD2d4Wyfqk8H0ZaFqoX7h3nzLGvJi+TESOH2zHxpjTDzFv9UBN2uPJOPOJ9pfencCd4AzBcYhpq9EiHoaWjayzJ3Hjo7upKAjyoysPH5G/iJRSaiTzeIQppXm809hF0srs12iO38st583j2ntX8I1H3+KOSxdTmJu9MSyNgW0tYWZU5I+owc73J2T+8X4+N9ReA2aJyDR3yI8rcSZ3V6pH62Z4+LPc9eQy4pbN/1yxhNpyHWpDKaUORsDnYUppaFh6fFYW5HDzOfNo7Ixx2+PrSFqHOOj6IbJt2Noczno+0vUbpInIsSJyLVAhIl9Iu30VOKQwU0TeIyL1wLHAoyLyhPv8RBF5DMCd1P3TwBPAWuAeY8yaQ0lXjT3xmFNMvrnLx39fMJ9jsjztiFJKjXZ5QR/VRcPTkH5udSGfOXUmK7e3c+cLm7I+JEY8abO1JZz1fKQM1GgngNP2zIfTDi2lA7j0UBI1xjwAPNDH8zuAc9MePwY8dihpqbHLtuG+lc1cBZw3r4jL3lUz6DZKKaUGV5YfJBy3aAtnvp3WqXOr2NYS5r43tjO1NMR5WZ6MPewOzTFcQ5MMZKA2ac8Bz4nIXe6cnYiIB8g3xhzgaKNKDb0/r+7g5S1dXBWAKw+foENtKKXUEJpckkssaROJZ37qqA8eU0tdS4Q7X9jE5NIQiycXZzzNgYyUoTn2p03at0WkUETycMYoWy8iX8xwvpQa0Mvbwnz9hS5m5Du/8gK52g5NKaWGkogwpTQ0LFMneT3CtWfOZlJJiNseX8eujmjG0xxMQ0cs65Ox70+QNt8tObsYp+pxCvDBjOZKqQHUtcf5/JPt5AeE9552PJz+NcjN7jg7Sik1FgV8HqaUDU9HglDAxy3nzsM2hm89+v/Zu/PwKKvz4ePfM/tk3wiEHZQ9JAHCIpStbBaXIqhYqQpal/IirVaEirUUpVLFuv9wF6moKFa0iCuCKOKSICDIriA7gRBISGYyy3n/eJIQIHsyk2S4P9c1F7M885wzQ+C5c59z7vNjvQdIAP56nptWlSDNqpSyYgRp72qtQ2SjSNEY5Xt8TP3wONkuzSPDo2jVqjW0HwzW+p87IIQQoSjCbqFZkBYSNI9xMm2kscfn4yt2NJgJ/PWlKkHas8BujOK2q5VSbTAWDwgRVH4/zFqZw/eHfUy/KJzB7cLh+B7Y9x1B+TVPCCHOUwkRdqKDVMesV5tYrr+oLV/uPMqSdfuC0mZDVWmQprV+QmvdQms9Whv2AEOD0DchzvDK+hO8udXN2E42JvWINp7c9j58/DfK3qBCCCFEXWkZ68RuDc5We2N7tGBQhwT+s3YPGXuyg9JmQ1SlfXOUUpcA3YDS+c7ZAemREGVY80s+D649RWqimdlDYinZktPrNjZXl0yaEEIElLEjQRg7j+QR6FFIpRS3/7oD+44XMO+jbfz76jSaxzgD22gDVGlIrJR6BhgP3I6RrrgKaBPgfglRYt8JD3d8fIJou+KJ38QSYS9VS9njMvbvFEIIEXAOq5mWscEJlhxWMzNHd8FkUvxz+ZYGsZAg2KqSt+yvtb4eOK61/gfGLgFSNVQERYHHz+0fZpPj1jw6Mpo20Wftyel1gdUpmTQhhAiSmDAb8RHB2R85McrB3aM6s/d4Pk9+dv4tJKhKkFZQ9Ge+Uqo54AHaBa5LQhi0hgdWH+f7wz7u7hfOr1qXsYLT6y7KpEmQJoQQwZIU7cBpC85G5GmtYvh9vzas3nGU9zYcCEqbDUVV5qQtU0rFAA8D6zDKb7wQ0F4JAby5+SSLNrv5bQcbNxYvFDhbn5vB55ZMmhBCBJFSijbxYew4nIfPH/js1pU9W7LjcB4vrfmZ9k0i6N6inGtCiKnK6s77tdY5Wuu3MeaiddZa/y3wXRPnsx8OufjH6jw6x5uYMywWU3k/qfEXQGJXUMFZcSSEEMJgNZtoFRec+WlKKf48vANJ0U4e+nArx/LcQWm3vpV7ZVNKjT37BlwCDCu6L0RAZBd4uf2jHKxmxZMXxxJRUUp9z1dw5EdkuFMIIYIv0mGlSWRwFm+F2SzcM7oLLq+PBz/YisfnD0q79ami4c7LKnhNA/+t474Igden+cvHx/nlpJ+nRkXRIb6Sf/xfPQHNUqHd4OB0UAghxBmaRtk5Vegl3x341Zet48L487COzP1wKy+t+ZlbB10Q8DbrU7lBmtZ6UjA7IgTAU9+eYOUeD7f1cDK6Y0Tlb5CFA0IIUa+KN2IP1vy0ARcmMCatOUvXH6BrUhQDOzQJeJv1paLhzsdK3f/TWa8tCGCfxHlq1c/5PJmRz4CWFv7Sv4qTQj0FYJVitkIIUZ+COT8N4IaL2tKlWSRPfraTvcfzg9ZusFU023pQqfs3nPVaSgD6Is5jB056mPbpCZqEKR4dGYfVXIWFAFqf3nFAMmlCCFGvgjk/zWI2Mf3iztgsJuZ+sDVkC91WdCVU5dwXok4V+vzc8fFxctyaf4+IJjGiSruVga8Q0LItlBBCNBBNo+yE2YNTPy0+ws5dIzuxNzufp1ftDMlCtxUFaSalVKxSKr7U/TilVBwQnL8BcV6Yt+YE3xzwckefMPqXVbC2PCYLjH0OOoxEfo8QQoj6p5SiVWxY+WWT6lhaqxiu7duaVduy+Gjz4eA0GkQVpSyigUxOX/3WlXot9MJVUS8+2pHHCxsKGN7Wyq29qlmc0GSGhI7GfcmkCSFEg2CzmGgZG8Yvx4IzV+zq9FZsOXiSZ1fv4sLECC5MrMKis0ai3FhXa91Wa91ea92ujFv7YHZShKY9xwuZsTKXlpEmHhoRi9lczUDLdQJ+fBdyD0oxWyGEaECinVbigrS/p0kp7hzRiZgwKw99tJVTbm9Q2g0GubKJeuHy+vnTR8fJ92geGxFDnLOK89BKyzsMXz4Kx35ChjuFEKJhSYpy4LAGJ8yIdlqZNqozh0+6eHJl6MxPkyBN1IuHvsxh/REf0y8Kp2cLR81O4nEZf0oJDiGEaHBMJkWruLCg/ffcNSmK6/q1Zc3Oo3yw6VBwGg0wCdJE0H24I4+XN7q4uL2ViWm12CTXWxSkWWoY5AkhhAgoh9VM85jg1U8b27MFvdrE8sKXP/FTVl7Q2g2USoM0pdQFSil70f0hSqmpSqmYwHdNhKK9JzzcszKXVlEm/lnRxulVUTpIk0yaEEI0SHHhNqKd1qC0ZVKKO4Z3JNJh5V8fbiW/sHHPT6vKJfJtwKeUuhB4EWgHvBbQXomQVOjzc8dHx8nzaP49vIbz0EorCdJkWyghhGjImsc4sFR3cVgNRTutTBvZiUMnXTy9clejnp9WlSDNr7X2AlcAj2mt7wCSAtstEYoe+eoEGYe83NknjPSWdTBE2eZXcPVCiGgqmTQhhGjALGYTreKqUQezlpJbRHNt3zas3pHFxz823vppVQnSPEqp32FsDbWs6Lng5C1FyFjx0ymeX1/A0DZWbu5Zi3lopdnCIKY1mK1IJk0IIRq2CLuFhMjglOUAuKpXS9JaxfDcFz/xS3bj3N+zKkHaJOAiYI7W+melVDvg1cB2S4SSg7keZnx2kmbhioeH16AeWrkn3ggbFxt7eEomTQghGrxmUQ6ctuCsWSyen+a0mnn4o624vY1vf89Kvymt9Y/AdIp2HNBa/6y1nhvojonQ4PVp7v70ONkFmoeGRZMQXst5aKXt/Qa+fd4I0KSYrRBCNHhKKVrGBq8sR1y4jT8P78DuY/m8vGZ3cBqtQ1VZ3XkZsB74sOhxmlLqvdo0qpS6Sim1WSnlV0qlV3DcbqXUD0qp9UqpjNq0KerHc5kn+GKvl9t6OPlVmzqej+B1FS0aABnuFEKIxsFhNZMUHbzSSelt4hiT1pz3fzjI1z8dC1q7daEq6YdZQB8gB0BrvR5jhWdtbALGAqurcOxQrXWa1rrcYE40TJn7C3j8u3zSm1n400V1NA+tNK/rdI00Ge4UQohGIz7CTqSjDkdWKnH9RW25sEkET6zYwdE8d9Dara2qBGlerfWJs56r1XpWrfUWrfW22pxDNGwnXT7u+vQEToti3shobOYADEd63aUK2UqQJoQQjUmLWCdmU3D+77aaTUwb1QmvXzPv4234/I2jLEdVrpyblFLXAmalVAel1JPAVwHuVzENfKyUylRK3RKkNkUt+f3w98+P8/MJP7MGRdA2xl75m2rCUyCZNCGEaKSsZhMtYoO3G0HzGCe3Db6AzQdO8mbG3qC1WxtVyTXeDswE3MDrwEfA/ZW9SSn1KdCsjJdmaq3frWL/BmitDyilEoFPlFJbtdZlDpEWBXG3ALRu3bqKpxeB8N8tJ3lnWyFXdrbx286RgWto6D3gKyx6IEGaEEI0NtFOKzFhVnLyPUFp79edE/n+l+O88d0v9GgVQ+ekqKC0W1OqPivxKqVWAXdprStdFKCUmgXkaa3nVXZsenq6zsgI4DoDvw8ObQzc+Ruxn7LdXPFWNglOxdLxTYi0m4PTcNPkonppQgghGhOfX7PjSC4eb3DikVNuL1Pf+B6l4IlrehBmKz9fdUFieIWv1xWlVGZZc+/LHe5USv1PKfVeebfAdheUUuFKqcji+8BIjAUHooFye/3c/ekJCryah4dFBz5A+2EJ7Pqs6IFk0oQQojEymxStYoO3G0G43cJfRnYiK9fNs6t/Clq7NVFReFhpxqqmlFJXAE8CTYD3lVLrtdajlFLNgRe01qOBpsA7yphrZAFe01p/GKg+idp7+ltj26e/9AmjZ4sgzDP4cSkkdIQLfi110oQQohELL9qN4GhuYeUH14GuSVFcnd6KN77bS3qbWAZ2aBKUdqur3CBNa/15oBrVWr8DvFPG8weA0UX3fwJSA9UHUbe+3ZvP/HUF9Gtu4bbeQRrjlxIcQggRMppFOchzeXF5/EFp75rerfn+lxyeXrWTTs0iSYwMXu22qqpKMdsOSqklSqkflVI/Fd+C0TnROJxweZmx8iThVsWDw2KwBqLcRlk8pYrZSpAmhBCNWrB3IzCbFH8Z2RG/Hx79ZHuDLMtRlavpy8B8wAsMBRYC/wlkp0Tj4fPDnNUn+CnHz30Dw2kXG7zNc0/XSZMATQghQoHTZiYxMkBlm8qQFO3klkHt2XTgJP/9fl/Q2q2qqgRpTq31CoyVoHu01rOAXwe2W6KxeH97Lm9udTOmg43fdg7iUma/F/weI0iTLJoQQoSMJpH2oG3CDjCscyIDLkxg0Te/sCsrL2jtVkVVvgWXUsoE7FBKTSma9J8Y4H6JRmBfTiH/+CKPVlEm7hsUQ7BGOQFQZrjpY0j7HZJJE0KI0BHsYU+lFP9vyAVEO6w88sl2Cr3BmRNXFVW5rP4ZCAOmAr2A64AbAtkp0fB5fJp7V+WQ49I8OCSKuPDg7cEGGNkzs824SSZNCCFCisNqJjEqeMOekQ4rfxrWgb3Z+Sxcuzto7Vam0iBNa/2d1jpPa70PuBMYp7X+OvBdEw3ZqxtOsuoXL5NSHAxoE7z6NiUKjsOax+HodiSTJoQQoadJhB2nLUgF0YGebWK5pHsS7244wIa9OUFrtyIVFbO9TynVuei+XSm1EtgFHFZKDQ9WB0XDszWrgEe+OUWXeDN3XBRdP4msguOw+R04eUAyaUIIEYKMYU9nUP+Ln9i/LS1inDy2Yjt5bm/wGi5HRZm08cC2ovvFw5tNgMHAPwPZKVEOVw6s/T/Irr8KKAWFPmauPInXDw8OjSI8iL/lnMFTYPxpcSKZNCGECE0Oq5mmUcGrX+awmrlzREeyTxXy7OpdQWu3PBUFaYX69Maeo4A3tNY+rfUWqrYxu6hLR7bC2zfDD2/CJ/eBJz/oXfD74dnMk2Qe8jG5l5PUpDJ2FfAUwFdPwo/vgjs3cJ3xuo0/LXbZbUAIIUKYsdozeAmBjk0juaZ3a1Zty+KDTYeC1m5ZKrq6uZVSyUqpJhj10T4u9Vo9TEI6z9nCwBEFA/4EJ/YbGbUgyzxg7CqQ3szMLb2izk1B+zzw6d9h09vw5aPw6jj4ZW1gOuN1GX9KCQ4hhAh5wR72vDq9FR2bRvCP/20mJz84W1WVpaKM2J+AJRhDnI9qrX8GUEqNBr4PQt8aL08BHN4MuYeMuVN+r3FrOxASOxvH+DxgtlZ8HncubP8Qkq+EmNYw9gUjIMk7Avszz9wWqU77nw/Hf4GEDmAyfnvJcXn42+e52MwwZ2gUDmsZv9VkLoC938KgaRB/AWz7ABI6Ga/tXgMH1kHTbtA0GSLOquJSeAqyd8GxXcZ9eyREt4QWvYr6VADWUpk7n8cow2GVYrZCCBHqild7Hj7hDkp7ZpPizuGdyHV7iAkLYpH2s1S0d+c3QOcynl8OLA9kpxo8dy6suN8Ikrxu8LmNP7tdAZ1+A6eyYPldZ75HmSCquRGk/bQKvnsRht1nBEJn09rIQK15HE4dNQKVuPanM0bpNxq3yoK8yuQegj1rigLKg9BhFHQbA66TsPQ2CIuH9kPwthvCk9uasfWYj1m/CqdjQjmbp6eMh9g20GGk8bhJqR+fw5tgyzIjywYQngDNUuDXfzM+13tTjSCttJa9Twdpb//BCNouHA5tfwXtB0P7FcZ3JZk0IYQIeU0i7Jws8FBQGJw6Zi1inVyQmBCUtsojc8tqQpng6DZjPpTFAWY7hEWAqejrjEyCSx+DyGYQFgcm65mBhCPGyAwtnQx9bzEyZcWvZ/9kDGXuz4DoVnD5E0aAVlpxcOY6CVv+ZxR0PXteVt4RKMg2JtZbHUV9dRp/+r1G20e3G8dGNIXoFsaQKkB4Exh6D/z8BWx5D8umt7lJx+Ntdhe/S+l7bkz00ypo098Yji0O0M7W91bofZORKTu82bh5Cs4MPE0miLsAHNFGIKxL/UO8cLiRVVw5x/jO2/4Ket4AMa2QTJoQQoS+4iK3O4/koRveNpsBoXQIftL09HSdkZERuAb8Pji0sXbncOXA5w8bmaxWfWHIDGOI740JxnBjr4nQ9benA7+ybPsAPv8XXPT/oPtVUJh/OtBaOhmO/Hjm8bFt4aoFxv01jxvBWZsBRYFO2bKP5/Ds0k/oV/g17X57D22bxRulL375Blr1NtrMeBH6TYaUq2vzjVRO++HQD7DjU9j7tTF83H+KEfTGXxDYtoUQQjQIh0+6OHIyOMOeFySGE2YLfD5LKZWptU4/+3nJpNUXRwyMfMBYBfn103Dge7jg18YQaHRLI5tUmY4Xw+4v4Zvn4JevjezUhLeMYK//VMg/dnpI1us6HcCBsQChEh6f5rHvvSzMHUCbwSMZ2qy4TwpO7oOvimoat/0VJI+t/ndQXcoESanGzecBzylZ2SmEEOeZxEhj2NPlaTjbNwVKpUGaUuoq4EOtda5S6l6gJ/CA1npdwHsX6pQy5oC16X96In3TbtV7/6Bp8N9bjKKu3a88PUSYeM50wmr7/Oc8Fm0uZHhbK+O6RZ5+odsY43byoDHs2/qiijN+gWC2gjnGuC9z0oQQ4rxRPOy5Kyv0hz2rcmX9m9b6LaXUrzDqpc0D5gN9A9qz88nZKx2rwxkDv3vdyCjVYbByKNfNA2tOEeNQ3PurSOyWMjJWUUnGrd5JkCaEEOcTp81MfISNo7n1Vx4jGKoyVuQr+vMSYL7W+l2g/tajinOZzHUaoLm9fh7/OpfdJ/zM6BdGm9jgVXuuERnyFEKI807TSAe2shIIIaQqn26/UupZ4GpguVLKXsX3iUZIa1j5Ux6Ltxbym/ZWLusc2fBHExt8B4UQQtQ1k0nRIracklAhoirB1tXAR8DFWuscIA6YFtBeiXpz8KSbf36VT7xTMX1AJA5rY4jHJUgTQojzUYTdQkxYLWuGNmBVuQI7gFXAMaVUHOAGVgayU6J+FHh8PPltLr+c9HN3vzDaxDTwYc5ikkkTQojzVvMYJxZzaF4HqhKkrQOygO3AjqL7Pyul1imlegWycyJ4/H74YvcpFm8tZFQ7K5d2imhEsU+j6agQQog6ZjYpmkeH5rBnVYK0D4HRWusErXU88BvgTWAyEPxdvkVAHDrp4sGv8ol1KKZfFIGzrL05G6rGE00KIYQIgOgwK1HO0Cv9WpUgLV1r/VHxA631x8AgrfXXgD1gPRNBk1/oY35mHj+f8POXPk7axjW230gkSBNCiPNd8xgnpsYwjboaqvJxspVS05VSbYpudwPHlVJmIPTL/YY4nx/W/HKK134sZFgbK5d3iWh8P+SSSRNCiPOe1WyiaVQjmUtdRVW5HF8LtASWAu8CrYueM2Os/BSN2KGTBfxrbT5RdsW0i8KJCMIeZXVPgjQhhBCQEGHHaWtE03UqUekVWWt9FLi9nJd31m13RDCdKvTywvf57Dzu5x+/cnJBQmMb5iwixWyFEEIUaRnrZOeR0Ngyqip7d3YE7gLalj5ea/3rwHVLBJrPDxn78nl1s5tBrSyM6RKJ1dRIM1Iy3CmEEKKIwxo6W0ZVZWzrLeAZ4AVObxElGrkjuQU89HUBDgvc1Tec6Ea9KkaCNCGEEKc1jXRwosCDx9u402lVuTJ7tdbzA94TETSnCr0s2lTA5qM+7u7roHPTRjrMWUwyaUIIIUoxmRTNY5zsOZpf312plapM5vmfUmqyUipJKRVXfAt4z0RA+Pzww8ECXtzgolczM+O7hWMzy5wuIYQQoSXKYSXa2bi3jKrK1fkGjL06vwIyi24ZtWlUKfWwUmqrUmqjUuodpVRMOcddrJTappTaqZSaUZs2heFIbgH//rYArWFavzDiwkOg1J1k0oQQQpQhKcbR+MpKlVJp17XW7cq4ta9lu58AyVrrFIztpv569gFFddiextjhoCvwO6VU11q2e17LK/Ty3jYX3x70MinFTkqzsBCJb0LiQwghhKhjjb12WqVBmlLKqpSaqpRaUnSbopSqVf5Qa/2x1tpb9PBrjDpsZ+sD7NRa/6S1LgTeAH5bm3bPZz4/7Mwq4P++d9E53szvu4cRFiq1ZEIj0hRCCBEARu20xplOq0qv5wO9MPbp/L+i+3W5kOBG4IMynm8B7C31eF/Rc2VSSt2ilMpQSmVkZWXVYfdCw9E8F09muDjl0Uzr62zUv1mcQ+qkCSGEqECLmLD67kKNVGV1Z2+tdWqpx58ppTZU9ial1KdAszJemqm1frfomJmAF1hU1inKeK7ctbRa6+eA5wDS09Mb95rbOnaq0MuKn12s2ONhQjcbvVuEYWmsNdHKFEqfRQghRF1z2szERdjIzmtctdOqEqT5lFIXaK13ASil2lOFemla6+EVva6UugG4FBimdZl1gfcBrUo9bgkcqEJ/RSl+P/xy3MUTGQW0ijRxY2oYUY26JloZZLhTCCFEJZpFOThZ4MHrazx5nKpcracBK5VSP2GkLNoAk2rTqFLqYmA6MFhrXV4Rk++ADkqpdsB+4BqMPUNFNRw95eblDS4OndI88msnLWNCaJizhARpQgghKmY2KZKiHezNLqjvrlRZVfbuXKGU6gB0wrgabtVau2vZ7lOAHfhEGVmQr7XWtymlmgMvaK1Ha629SqkpwEcYm7m/pLXeXMt2zysFhT4yD7hYsq2Qi9tbGdw2DLslBOdvSSZNCCFEFcSE2cg+Vcgpd+PYQKncIE0p1RvYq7U+pLV2K6XSgHHAHqXULK11dk0b1VpfWM7zB4DRpR4vB5bXtJ3zmd8PB064ePS7AqJsij/2dBAXZqvvbgWIBGlCCCGqpnlM49mAvaK0yrNAIYBSahAwF1gInKBogr5ouI7lu3l7m4vt2X4m93TQPt7ZqAv6VUgyaUIIIarIYTWTENE4CrlXdNk2l8qWjQee01q/rbX+G1BmJkw0DAUeH9uz3Ly00U3vJAuXXOgg0h5iiwXOIEGaEEKIqkuMtGO1NPxrR4VBmlKq+Mo+DPis1GuhfMVv1LSGIyddPJnpAg1/6uWgSVTj+I2hxiSTJoQQohpMJkVSlLO+u1GpioKt14HPlVJHgQLgCwCl1IUYQ56iATqeX8jKPYV8fcDLLWl2ujZ1hP4G6lLMVgghRDVFh1mJyLeQ5/JWfnA9KTdI01rPUUqtAJKAj0vVMjMBtwejc6J63F4/+3PcPJ3pon2Miau7OIgN2cUCpUkmTQghRPUlRTvY6W64iwgqHLbUWn9dxnPbA9cdURtHct0s2OQmq0Bz7wAnSdH282Mk8Lz4kEIIIepa8SKCrNzaVhYLDBknChHH8z1sOlLIO9sLueQCK31aOAi3nSdTByVIE0IIUUMNeRGBBGkhwOPTZOW6eDzDRZRN8YdUR6NZXlx7DfMflhBCiMahIS8ikCAtBGTlulm2y8PWYz5u7WGnTawdq/k8CV4kiyaEEKKWosOshNvN9d2Nc0iQ1sjlurzsO1nIixtcpCWaubi9nRjn+bBYoJgEaUIIIWqveYyzwf3eL0FaI+b1a7Jy3Tz7vZtCH0xNd5AQaQ/dnQXK0tD+RQkhhGiUHFYzceENK8lxPl3OQ87RPDcZhzys2OPh6i42OifYQ3xngbJIkCaEEKJuNI1yYDY1nOuKBGmNVH6hj+xTHp7MdNEsXHFtFzsJkQ3rN4CgkEyaEEKIOmI2KZpFO+q7GyUkSGuE/H6jJtrb2wvZe9LPlF4OmkbZQ39ngbLIbgNCCCHqUFy4DaetYVxbGkYvRLVk5xey/6SXRZvc9G9hYUBLG3Hnxc4CZZFMmhBCiLqVFN0wSnJIkNbIuL1+cvILmf+9Cw1M7mnURDuvFguUJsOdQggh6li43UJMmLW+uyFBWmNz5KSbbw96+XKflwnd7LSJsRLpON8WC5QmQZoQQoi61yzagameEwHn89W90cnJ93DS7eWpzAJaRZq4spONJufNzgLlkEyaEEKIALCaTVjrub6tZNIaCY9fc+yUm8VbCjmQp5nSy0F8uBWH9XwWUrbRAAAgAElEQVT/K5QgTQghRGg636/wjcaxPDf7c/28scXNkNYW0pMsxJ3vWTSQTJoQQoiQJUFaI3Cq0Euuy8v8712YFNya5iAu3I61ARXcE0IIIUTdkiCtgfP7ISu3kG8OeFi738vvu9lJirQQ46z/VScNgmTShBBChCgJ0hq47PxCThX6+L91blpGmhjb0UZCpE1ik2JSzFYIIUSIkitcA1ZcE+3tbYUcyPPz/3o6iHZaibDJotzTJFoVQggRmiRIa8CO5Lo5fMrPa5vdDGhpoXeShYSI83VngXJISlEIIUSIkiCtgTpZ4MXl8fHcehd+4LY0B1FOK3aL/JWdSYI0IYQQoUmu+A2Q1685mufm+8NePt/r5ZoudppHmogLlyzaOSSTJoQQIkRJkNYAHctz4/b5eTrTRbNwxdWdbcSF27FIyY0yyHcihBAiNEmQ1sAUeHycdHl5b0che076+WMPBxF2s5TcKI9k0oQQQoQoCdIaEK0hK9dNjsvPwk1uejUzc1ELC/ERUnKjfPLFCCGECE31UstBKfUwcBlQCOwCJmmtc8o4bjeQC/gAr9Y6PZj9DLacAg9ur5+XN7pxeeGPPRyE2SxE2qXkRrmkTpoQQogQVV9XuE+AZK11CrAd+GsFxw7VWqeFeoDm8WuO5bnZke3jg588/LaDjTbRZhJkf86KSYpRCCFEiKqXIE1r/bHW2lv08GugZX30oyE5muvGrzVPr3MRbVdcl2wn0mHBYZVMUcUkSBNCCBGaGkIEcCPwQTmvaeBjpVSmUuqWik6ilLpFKZWhlMrIysqq804G0qlCL3luLyt/8bL5qI9JKXYibYp4yaJVTjJpQgghQlTAJjsppT4FmpXx0kyt9btFx8wEvMCick4zQGt9QCmVCHyilNqqtV5d1oFa6+eA5wDS09N1rT9AkGgNR/MKKfBqXljv4sJYE6PaWYkNt2GVkhtVIN+REEKI0BSwIE1rPbyi15VSNwCXAsO01mUGVVrrA0V/HlFKvQP0AcoM0hqr4wWFFHr9LN7iJqtAc09/J3aLiVinFK6tEsmkCSGECFH1MtyplLoYmA5crrXOL+eYcKVUZPF9YCSwKXi9DDyPT5OdV8ihPD9vbS1kaGsLyU0sxIXbMTWEgWghhBBC1Jv6CgWeAiIxhjDXK6WeAVBKNVdKLS86pinwpVJqA/At8L7W+sP66W5gHM1zo4HnN7gwATenObBbTEQ5pORGlUkmTQghRIiql2hAa31hOc8fAEYX3f8JSA1mv4KpeLHAD0e8rN7r5fpkO03CTMRJ4dpqki9LCCFEaJJBtXpg7CxQiF9r5n/voolTcVVnG2E2CxE2yaJVixSzFUIIEaLkClcPjhcU4vH5+fhnDzuO+/lDqgOHRZEQIYsFqk3SjkIIIUKUBGlBVrxYIN+jeXmjmy7xZoa2sRDpsGC3yF9H9UmQJoQQIjRJVBBkxYsF3tjiJtul+WNPOyaliA+XwrU1Ipk0IYQQIUqCtCDKL/SR5/ZyMM/Pkq2FDGtjpUu8hZgwG1azBBs1I9+bEEKI0CSz1INEa8jKcwPwwgYXJgU3pdoxK0VsmMxFqzHJpAkhKuHxeNi3bx8ul6u+uyLOcw6Hg5YtW2K1Wqt0vARpQZJT4KHQ6z+n5EZsuA2z5DNrQYI0IUTF9u3bR2RkJG3btkXJL3ainmitOXbsGPv27aNdu3ZVeo+EB0Hg8WuyT7nxa80z610kOBVXdrZhMStinFWLpkU55D9cIUQlXC4X8fHxEqCJeqWUIj4+vloZXQnSguBYnhu/hs/2eNie7efGFDtOiyIh3C4xRm1JnTQhRBVIgCYagur+HMoVLsAKPD5yXV7cXs1LG910iDUxrK0Vh8VEpGz/VAfkP14hRMM3Z84cunXrRkpKCmlpaXzzzTcVHj9x4kSWLFlSqzZXrVpFdHQ0aWlpJbdPP/0UgIiIiBqd87HHHiM///SW26NHjyYnJ6fc45955hkWLlwIwIIFCzhw4EC12hsyZAidOnUiNTWVAQMGsG3bthr1u7j9KVOmnNOvsuzevZvXXnut5HFGRgZTp06tcds1JVFCgB3NNRYLvL2tkKx8zfR+TkxKESeFa+uG/HYshGjg1q5dy7Jly1i3bh12u52jR49SWFgYlLYHDhzIsmXL6ux8jz32GL///e8JCwsDYPny5RUef9ttt5XcX7BgAcnJyTRv3rxabS5atIj09HSee+45pk2bxnvvvXfG6z6fD7PZXK1zlu5XWYqDtGuvvRaA9PR00tPTq9VGXZBMWgCdKPDi8vrJLvDzxhY3/VtYSE20EGazEC7bP9URCdKEEA3bwYMHSUhIwG436mEmJCSUBCqzZ8+md+/eJCcnc8stt6C1Puf9mZmZDB48mF69ejFq1CgOHjwIwBNPPEHXrl1JSUnhmmuuqVHf8vLyGDZsGD179qR79+68++67AJw6dYpLLrmE1NRUkpOTWbx4MU888QQHDhxg6NChDB06FIC2bdty9OhRABYuXEhKSgqpqalcd911AMyaNYt58+axZMkSMjIymDBhAmlpabz//vtcccUVJf345JNPGDt2bIV9HTRoEDt37gSMTOB9991H3759Wbt2La+++ip9+vQhLS2NW2+9FZ/PB8DLL79Mx44dGTx4MGvWrCk5V3G/AHbu3Mnw4cNJTU2lZ8+e7Nq1ixkzZvDFF1+QlpbGo48+yqpVq7j00ksByM7OZsyYMaSkpNCvXz82btxYcs4bb7yRIUOG0L59e5544oka/Z2UJpFCgPj8xlw0gIWb3BT64OZU4x9ofLhk0eqMZNKEENXwj/9t5scDJ+v0nF2bR/H3y7qV+/rIkSOZPXs2HTt2ZPjw4YwfP57BgwcDMGXKFO677z4ArrvuOpYtW8Zll11W8l6Px8Ptt9/Ou+++S5MmTVi8eDEzZ87kpZdeYu7cufz888/Y7fZyhxyLA41ib7/9NhdccEHJY4fDwTvvvENUVBRHjx6lX79+XH755Xz44Yc0b96c999/H4ATJ04QHR3Nv//9b1auXElCQsIZ7WzevJk5c+awZs0aEhISyM7OPuP1K6+8kqeeeop58+aRnp6O1pq//OUvZGVl0aRJE15++WUmTZpU4ff8v//9j+7duwNGEJmcnMzs2bPZsmUL//rXv1izZg1Wq5XJkyezaNEiRowYwd///ncyMzOJjo5m6NCh9OjR45zzTpgwgRkzZnDFFVfgcrnw+/3MnTuXefPmlWQhV61aVXL83//+d3r06MHSpUv57LPPuP7661m/fj0AW7duZeXKleTm5tKpUyf++Mc/VrncRlkkSAuQ7Hw3Pq35OcfHBz95uLyDjZZRZiLsFhxWSWDWGQnShBANXEREBJmZmXzxxResXLmS8ePHM3fuXCZOnMjKlSt56KGHyM/PJzs7m27dup0RpG3bto1NmzYxYsQIwBjaS0pKAiAlJYUJEyYwZswYxowZU2bblQ13aq255557WL16NSaTif3793P48GG6d+/OXXfdxfTp07n00ksZOHBghZ/xs88+48orrywJ3uLi4io8XinFddddx6uvvsqkSZNYu3ZtuXPEJkyYgNPppG3btjz55JMAmM1mxo0bB8CKFSvIzMykd+/eABQUFJCYmMg333zDkCFDaNKkCQDjx49n+/btZ5w7NzeX/fv3l2T1HA5Hhf0G+PLLL3n77bcB+PWvf82xY8c4ceIEAJdccgl2ux273U5iYiKHDx+mZcuWlZ6zPBKkBYDb6+dEvgeA59a7CLPAdd1sKCBe5qLVIQnQhBDVU1HGK5DMZjNDhgxhyJAhdO/enVdeeYVrrrmGyZMnk5GRQatWrZg1a9Y55Rm01nTr1o21a9eec87333+f1atX895773H//fezefNmLJbqXdYXLVpEVlYWmZmZWK1W2rZti8vlomPHjmRmZrJ8+XL++te/MnLkyJKMX1m01tVeuThp0iQuu+wyHA4HV111Vbl9L56TVprD4SiZh6a15oYbbuDBBx8845ilS5dW2qeyhpcrU9Z7itspHtIG4+/c6/VW+/ylSUonAI7mFaKB7w56yTjkY0I3O1F2E1FOKzapXFt3JIsmhGgEtm3bxo4dO0oer1+/njZt2pQEZAkJCeTl5ZW5mrNTp05kZWWVBGkej4fNmzfj9/vZu3cvQ4cO5aGHHiInJ4e8vLxq9+3EiRMkJiZitVpZuXIle/bsAeDAgQOEhYXx+9//nrvuuot169YBEBkZSW5u7jnnGTZsGG+++SbHjh0DOGe4s6z3Nm/enObNm/PAAw8wceLEave9dNtLlizhyJEjJW3v2bOHvn37smrVKo4dO4bH4+Gtt946571RUVG0bNmSpUuXAuB2u8nPzy/3c4IxN27RokWAMQyakJBAVFRUjftfEcmk1bG8Qi/5hV58fs1z610khSsu72Bk0WJlLlodkyBNCNHw5eXlcfvtt5OTk4PFYuHCCy/kueeeIyYmhptvvpnu3bvTtm3bkuG60mw2G0uWLGHq1KmcOHECr9fLn//8Zzp27Mjvf/97Tpw4gdaaO+64g5iYmHPef/actHvvvZcrr7yy5PGECRO47LLLSE9PJy0tjc6dOwPwww8/MG3aNEwmE1arlfnz5wNwyy238Jvf/IakpCRWrlxZcp5u3boxc+ZMBg8ejNlspkePHixYsOCMvkycOJHbbrsNp9PJ2rVrcTqdTJgwgaysLLp27Vrj77dr16488MADjBw5Er/fj9Vq5emnn6Zfv37MmjWLiy66iKSkJHr27FmyoKC0//znP9x6663cd999WK1W3nrrLVJSUrBYLKSmpjJx4sQz5rLNmjWLSZMmkZKSQlhYGK+88kqN+14ZVZNUX0OXnp6uMzIyAteA3weHNp7ztNawJzsfj8/Phz8V8si3Lu7t72RwayuxYTYSZKizbpks0Kx7ffdCCNHAbdmyhS5dutR3N0QZpkyZQo8ePbjpppvquytBU9bPo1IqU2t9To0PyaTVoZwCDx6fnwKvZsEPbjrHmxnUyoJJIZuoB4Rk0oQQorHq1asX4eHhPPLII/XdlQZLgrQ6Urw/J8B/txVyrEBzb387SinZRD1QZEsoIYRotDIzM+u7Cw2eXOXqSHbR/pzHXX7e3OJmQAsLyU0sWEyKGIdk0QJCFg4IIYQIYRKk1QGXx89Jl7HM9j+b3Lh8cFNR4drYMBsm+ZYDRII0IYQQoUvChzpwtGhngb0nfby/y8MlF1hpFWXGajYR7ax5pWFRCcmkCSGECGESpNVSrstLgcdY0vviBjd2M1yXfHr7J4kjAkm+XCGEEKFLgrRa0BqOnSoE4IcjXtbs9zK+i51Yhwm7xUSkQ9ZlBJREwEKIBu7YsWOkpaWRlpZGs2bNaNGiRcnjwsLCOmljyJAhdOrUqeS8xXXQSm8iXh3r169n+fLlJY/fe+895s6dW+F7+vfvD8Du3bt57bXXqt2mKJtEEbVwvKAQj8+P1prnN7iJcyjGdjIWCUjh2mCQIE0I0bDFx8eXbL49a9YsIiIiuOuuu0pe93q91d7KqSxlbZ1UU+vXrycjI4PRo0cDcPnll3P55ZdX+J6vvvoKOB2kXXvttXXSl/OdZNJqyOPXHC/Kon25z8uWYz5u6G7HaVE4LGYi7RL/Bpxk0oQQjdDEiRO58847GTp0KNOnTz8n45WcnMzu3bsBePXVV+nTpw9paWnceuutZVbMr4rnn3+e3r17k5qayrhx48jPzwfgrbfeIjk5mdTUVAYNGkRhYSH33XcfixcvJi0tjcWLF7NgwQKmTJkCwOHDh7niiitITU0lNTW1JDiLiIgAYMaMGSW7HDz66KMMHDiwJEgFGDBgABs3nlsMXpRNIokaKi654fNrXtroplWUiVHtjEUCcRGyWCAoJEgTQtTEy5ec+1y3MdDnZijMh0VXnft62rXQYwKcOgZvXn/ma5Per3YXtm/fzqefforZbGbWrFllHrNlyxYWL17MmjVrsFqtTJ48mUWLFnH99defc+yECRNwOp0AjBgxgocffviM18eOHcvNN98MGFtDvfjii9x+++3Mnj2bjz76iBYtWpCTk4PNZmP27NlkZGTw1FNPAZyxvdPUqVMZPHgw77zzDj6f75z9QufOncu8efNYtmwZAHFxcSxYsIDHHnuM7du343a7SUlJqfb3db6SIK0G/H5dUnLjw5887Mv1849fOTGbFE6rmXCbfK3BIUGaEKJxuuqqqzCbzRUes2LFCjIzM0v29CwoKCAxMbHMYysb7ty0aRP33ntvyUbso0aNAozM1sSJE7n66qsZO3Zspf3+7LPPWLhwIQBms5no6OgKj7/qqqu4//77efjhh3nppZdqtZH6+UiiiRoo3u20wKtZuMlN1wQzF7Uwvsp42Z8zeCSTJoSoiYoyX7awil8Pj69R5uyc04SHl9y3WCz4/f6Sxy6XCwCtNTfccAMPPvhgrdubOHEiS5cuJTU1lQULFrBq1SoAnnnmGb755hvef/990tLSzhiarAthYWGMGDGCd999lzfffJOA7qsdguplTppS6n6l1Eal1Hql1MdKqeblHHexUmqbUmqnUmpGsPtZmXe2FZLt0tycamz/FG6z4LRW/JuRqEsSpAkhGr+2bduybt06ANatW8fPP/8MwLBhw1iyZAlHjhwBIDs7mz179tSojdzcXJKSkvB4PCxatKjk+V27dtG3b19mz55NQkICe/fuJTIyktzc3DLPM2zYMObPnw+Az+fj5MmTZ7xe1nv/8Ic/MHXqVHr37k1cXFyN+n++qq+FAw9rrVO01mnAMuC+sw9QSpmBp4HfAF2B3ymluga3m+U74fazeKubi4q2fwKIkxWdgWO2n/ucZNKEECFg3LhxZGdnk5aWxvz58+nYsSMAXbt25YEHHmDkyJGkpKQwYsQIDh48WOY5JkyYUFKCY/jw4ee8fv/999O3b19GjBhB586dS56fNm0a3bt3Jzk5mUGDBpGamsrQoUP58ccfSxYOlPb444+zcuVKunfvTq9evdi8efMZr6ekpGCxWEhNTeXRRx8FjI3Uo6KimDRpUq2+p/OR0lpXflQgO6DUX4HWWus/nvX8RcAsrfWoUsehta4075uenq4DmVL1eb1MW7iSpTsKee7icNpEm4mwW0iKdgSszfOaNRycsXBy35nPhyVATKv66ZMQotHYsmULXbp0qe9unLcOHDjAkCFD2Lp1KybZJ7HMn0elVKbW+pxJhfX2bSml5iil9gITKCOTBrQA9pZ6vK/oufLOd4tSKkMplZGVlVW3nT3L3ux83ttZyMh2VtpEG8ObkkULIGcsOGPOfV4yaUII0aAtXLiQvn37MmfOHAnQaiBg35hS6lOl1KYybr8F0FrP1Fq3AhYBU8o6RRnPlZv201o/p7VO11qnN2nSpG4+RDkeW7ETk4Lri7Z/inRYsFvkhy8wlBGgma1gizz3NSGEEA3W9ddfz969e7nqqjLKmohKBWx1p9b63EHxsr0GvA/8/azn9wGlx7JaAgfqoGu14vdrTCbF2I42moSZUEgWLaBsEUaABkZGrbDUhFTJpAkhhAhh9bW6s0Oph5cDW8s47Dugg1KqnVLKBlwDvBeM/lXEZFI8PK47N6YYWbQIhwWbWbJoAeOMLXU/hjOyZ0q+dyGEEKGrvq5yc4uGPjcCI4E/ASilmiullgNorb0Yw6AfAVuAN7XWm8s7YbAppVBAfHgZqw4bAlsEjX84UJ05F81kBkfUma8LIYQQIapeitlqrceV8/wBYHSpx8uB5cHqV3VFOa1YzQ0tUFAQ1RwiEiH7Z3Dl1HeHas4RZQRmpTljwXXCuC/DnUIIIUKYjBfVkAJiwxrYXDSLE5p0MgI0gPCE+u1PZSyOiocsHWWs6LRHgyoO3CRIE0I0Do8//jjJycl069aNxx57rOT57OxsRowYQYcOHRgxYgTHjx8HYM2aNaSkpNC7d2927twJQE5ODqNGjaK80lkej4cZM2bQoUMHkpOT6dOnDx988EGN+puVlUXfvn3p0aMHX3zxBaNHjyYn59xf+s/eHD5YSm/6Xp7du3fz2muvBaU/xRvM1zXZFqqGohtaFi080ciglc4u2SONIrA+d/31qywmK0Q2g7B4KDgOOWVU0FamsoM0kwkc0VCQLZk0IUSN/LDvRJ2er3vLivev3LRpE88//zzffvstNpuNiy++mEsuuYQOHTowd+5chg0bxowZM5g7dy5z587lX//6F4888ghvv/02u3fvZv78+TzyyCPcf//93HPPPahy/u/729/+xsGDB9m0aRN2u53Dhw/z+eef1+gzrVixgs6dO/PKK68AMHDgwBqdpz4VB2nXXnttld/j8/kq3VM1mCSTVgMKiGlIKzrDEyG6RdlBS1h88PtTHmWCiGaQ2NXI8ikFYXFGUdqzOaKNgKwsJYsJJEgTQjR8W7ZsoV+/foSFhWGxWBg8eDDvvPMOAO+++y433HADADfccANLly4FwGq1UlBQQH5+PlarlV27drF//34GDx5cZhv5+fk8//zzPPnkk9jtxlzppk2bcvXVVwPw+uuvl+wsMH369JL3RUREMHPmTFJTU+nXrx+HDx9m/fr13H333Sxfvpy0tDQKCgpo27YtR48eBWDOnDl06tSJ4cOHs23btpJz7dq1i4svvphevXoxcOBAtm411gROnDiRqVOn0r9/f9q3b8+SJUtK3vPQQw/RvXt3UlNTmTFjRoXnKU95558xYwZffPEFaWlpPProo/h8PqZNm0bv3r1JSUnh2WefBWDVqlUMHTqUa6+9lu7duzN9+nT+7//+r+T8s2bN4pFHHiEvL49hw4bRs2dPunfvzrvvvlthv+qCZNJqwGRSmEwNJEAwWYysVHnC4iH3IBWUmAsOazjEtTtdTqO06JbgKQDPqdPPlV7VeTZ7pPG5JZMmhGgEkpOTmTlzJseOHcPpdLJ8+XLS043i8ocPHyYpKQmApKSkkn06//rXv3LLLbfgdDr5z3/+w1133cX9999fbhs7d+6kdevWREVFnfPagQMHmD59OpmZmcTGxjJy5EiWLl3KmDFjOHXqFP369WPOnDncfffdPP/889x7773Mnj2bjIwMnnrqqTPOlZmZyRtvvMH333+P1+ulZ8+e9OrVC4BbbrmFZ555hg4dOvDNN98wefJkPvvsMwAOHjzIl19+ydatW7n88su58sor+eCDD1i6dCnffPMNYWFhZGdnV3qe8pR1/rlz5zJv3jyWLVsGwHPPPUd0dDTfffcdbrebAQMGMHLkSAC+/fZbNm3aRLt27fj+++/585//zOTJkwF48803+fDDD3E4HLzzzjtERUVx9OhR+vXrx+WXX15uZrMuSJDW2EU0O3dyfWlmi5GVqs8FBMoEsW3KDtDACLbi2kHWNvB7jDln9nP/oznj+IqCOCGEaEC6dOnC9OnTGTFiBBEREaSmpmKxVHz5TUtL4+uvvwZg9erVNG/eHK0148ePx2q18sgjj9C0adMqtf/dd98xZMgQigu9T5gwgdWrVzNmzBhsNhuXXnopYOyx+cknn1R4ri+++IIrrriCsLAwAC6//HIA8vLy+Oqrr84oWut2n55qM2bMGEwmE127duXw4cMAfPrpp0yaNKnkXHFxcZWepzxlnf9sH3/8MRs3bizJtJ04cYIdO3Zgs9no06cP7dq1A6BHjx4cOXKEAwcOkJWVRWxsLK1bt8bj8XDPPfewevVqTCYT+/fv5/DhwzRrVkGipJYkSGsolAm0v3rvMdurtjggPKF+g7TIJLBUUqrEbIXYtnBsp1F2o7LfTJyx4PPUWReFECKQbrrpJm666SYA7rnnHlq2bAkYQ5IHDx4kKSmJgwcPkpiYeMb7tNY88MADLF68mClTpvCPf/yD3bt388QTTzBnzpyS4y688EJ++eUXcnNziYyMPOcc5bFarSWZILPZjNfrrfSzlJU58vv9xMTEsH79+jLfUzwEW7o/WutzzlXZecpT1vnPprXmySefZNSoUWc8v2rVKsLDw8947sorr2TJkiUcOnSIa665BoBFixaRlZVFZmYmVquVtm3b4nK5qtXP6pI5aQ2BxQFNOoO5mvPczl4oUJ7iBQT1wRoO4VXcpsseAVEtqpYls4VXHvgJIUQDUTyM+csvv/Df//6X3/3ud4CRiSqenP/KK6/w29/+9oz3vfLKK1xyySXExsaSn5+PyWTCZDKRn59/xnFhYWHcdNNNTJ06lcLCQsAYAnz11Vfp27cvn3/+OUePHsXn8/H666+XO7etMoMGDeKdd96hoKCA3Nxc/ve//wEQFRVFu3bteOuttwAjINqwYUOF5xo5ciQvvfRSyWfJzs6u0XnKExkZSW7u6V1qRo0axfz58/F4jF/wt2/fzqlTp8p87zXXXMMbb7zBkiVLuPLKKwEj85aYmIjVamXlypXs2VPGorc6Jpm0+qZMRgbJYoe49nB0e9UyaraIsjcdL09YPOQGe1ctBTGtqzd3LKIa+65andXvkhBC1INx48Zx7NgxrFYrTz/9NLGxxi+jM2bM4Oqrr+bFF1+kdevWJcEJGIsBXnnlFT7++GMA7rzzTsaNG4fNZuP1118/p40HHniAe++9l65du+JwOAgPD2f27NkkJSXx4IMPMnToULTWjB49+pxgsKp69uzJ+PHjSUtLo02bNmes+ly0aBF//OMfeeCBB/B4PFxzzTWkpqaWe66LL76Y9evXk56ejs1mY/To0fzzn/+s9nnKk5KSgsViITU1lYkTJ/KnP/2J3bt307NnT7TWNGnSpGShxtm6detGbm4uLVq0KJkzOGHCBC677DLS09NJS0ujc+fO1e5TdamK0qCNVXp6us7IyAhcA34fHNpYN+eKaWOscCxWkAPHf678fQkdjWxSVfm8cHgTQV1AENkcIqs2Z0IIIQJly5YtdOnSpb67IQRQ9s+jUipTa51+9rEy3FmfwuLPDFnOCEwAAA4JSURBVNDAyI5FNq/4fc7Y6gVocHoBQbBYw04X1RVCCCFEtUmQVl+sYRDVsuzXIpuCM67s15Sp8iCuPIGomRaeeHoemcWBUbusBsOcQgghhDiDzEmrD8pszEMrr1grGEGO32vczDZjzprZbszDstSwkK4jCuIuKOpDUTClFBTmG/PVqru61OI8d/GC32+U0ZBJ/UIIIUStSJAWbBaHkXmqLIhRCuIvqPv2HWXUH7OFG0OhJ/aC+2TVzxXd8txsmckEJgnQhBBCiNqSIC0YivehDIs3ykw0RBabERSeOgYn94P2VXy8M7bhfhYhhBAiBEiQViPKyIh5KyliV7yRuDO24l0BGpLweCPblvNL+Vk1ZTKygUIIIYQIGFk4UBMmk1F8NqqlMb/sbGdvJN5YArRiZqtRs628hQYRzcrf4kkIIUSJY8eOkZaWRlpaGs2aNaNFixYlj4uLztbWkCFD6NSpU8l5i4uvzpo1i3nz5lX7fOvXr2f58uUlj9977z3mzp1b4Xv69+8PwO7du3nttdeq1d6qVauIjo6mR48edO7cmbvuuqvafQ5VkkmrKaWMwqvOWGMD8/xjgDaGNaNa1Hxyf0OhilZoWhzG8Gcxs11KawghGrcD39ft+Zr3KPel+Pj4ki2OZs2aRURExBlBiNfrrXQfz6pYtGhRyabttbV+/XoyMjIYPXo0YOyKULxHZ3m++uor4HSQdu2111arzYEDB7Js2TIKCgro0aMHV1xxBQMGDKjZBwghkkmrLbMFYlpBk04Q38HYKLyxB2ilRSQaK1FV0Y9KdAsprSGEELUwceJE7rzzToYOHcr06dPPyXglJyeze/duAF599VX69OlDWloat956Kz5fJfOFy/H888/Tu3dvUlNTGTduXMlWTG+99RbJycmkpqYyaNAgCgsLue+++1i8eDFpaWksXryYBQsWMGXKFAAOHz7MFVdcQWpqKqmpqSXBWUSEMUd5xowZfPHFF6SlpfHoo48ycODAM/bhHDBgABs3ll8M3ul0kpaWxv79+yvs98SJE5k6dSr9+/enffv2JZum+/1+Jk+eTLdu3bj00ksZPXp0yWuZmZkMHjyYXr16MWrUKA4ePFij7zKYJEirK1Zn6E6kd8ZC/IXG8GcwC+IKIUSI2r59O59++imPPPJIucds2bKFxYsXs2bNGtavX4/ZbGbRokVlHjthwoSS4c5p06ad8/rYsWP57rvv2LBhA126dOHFF18EYPbs2Xz00Uds2LCB9957D5vNxuzZsxk/fjzr169n/PjxZ5xn6tSpDB48mA0bNrBu3Tq6det2xutz584tCczuuOMO/vCHP7BgwYKSz+x2u0lJSSn3Mx8/fpwdO3YwaNCgCvsNxt6kX375JcuWLWPGjBkA/Pe//2X37t388MMPvPDCC6xduxYAj8fD7bffzpIlS8jMzOTGG29k5syZ5fajoZDhTlE1tvDq73IghBCiTFdddRVmc8XzlVesWEFmZia9e/cGoKCggMTEsqebVDbcuWnTJu69915ycnLIy8tj1KhRgJHZmjhxIldffTVjx46ttN+fffYZCxcuBMBsNhMdXfEv7lf9//buPjar8ozj+PdXi3by4mJwgtYJWSwIpX0cL00GIVQocY4ohhC3uNUajZLM6WLigBD3Ii7OsIDGRJzZmCFzcwwmTEmcLIPwsglIhfFaplZCgwFWgrTJFKXX/ngOpYwWqErPof19kibnnN7POdfD3dLruc917nv6dObOncu8efNYtGgRNTU17bZbt24dZWVl1NXVMWvWLAYMGHDWuAGmTp1KQUEBw4YN4+DBgwCsX7+e6dOnU1BQwIABA6isrASgrq6OHTt2UFVVBcCJEyda1+TMMidpZmZmXax371MfegsLC2lpOTWZ+Ecf5WcOiAjuvvtunnzyyc99vZqaGpYvX055eTkvvvgia9asAeD5559n48aNrFy5klwud9qtyS/C5ZdfTlVVFStWrGDJkiV0tK72yZq0vXv3Mm7cOO644w5yuVyHcQNcdtmpOTlPrkPe0XrkEcHw4cNbR9YuFr7daWZmlqJBgwZRW1sLQG1tLfX19QBMnDiRpUuXcujQIQCOHDnCvn37PtM1mpqaGDhwIJ988slpt0zfffddKioqePzxx+nfvz/79++nb9++NDU1tXueiRMnsnDhQiA/GnXs2OlTNbX32vvuu4+HHnqI0aNHc+WVHSx5mCgpKWH27Nk89dRTZ427I+PGjWPZsmW0tLRw8ODB1qRuyJAhHD58+LTbnzt37jzn+dLmJM3MzCxF06ZN48iRI+RyORYuXEhJSQkAw4YN44knnmDy5MmUlZVRVVXVYbF725q0SZMmnfH9uXPnUlFRQVVVFUOHDm09/uijjzJixAhKS0sZP3485eXlVFZWsmvXrtYHB9p65plnWL16NSNGjGDkyJFnJDplZWUUFhZSXl7OggULABg5ciT9+vXjnnvuOa9/jxkzZrB27Vrq6+s7jLsj06ZNo7i4mNLSUh544AEqKiq44ooruPTSS1m6dCkzZ86kvLycXC7X+tBDlqmjocGL2ahRo6KjIVUzM+tZdu/ezY033ph2GD3WgQMHmDBhAnv27KHgbGtWf0Gam5vp06cPjY2NjBkzhg0bNrTWuGVBez+PkrZExBlFha5JMzMzswti8eLFzJkzh/nz53dJggYwZcoUjh49yvHjx3nssccylaB1lpM0MzMzuyCqq6uprq7u0mu2fbjgYueaNDMzM7MMcpJmZmbdXnesv7aLT2d/Dp2kmZlZt1ZUVERjY6MTNUtVRNDY2EhRUdF5v8Y1aWZm1q0VFxfT0NDA4cOH0w7FeriioiKKi4vPu30qSZqkucDtQAtwCKiJiAPttHsfaAJOAJ+293iqmZnZ2fTq1YvBgwenHYZZp6V1u3NeRJRFRA54DfjxWdpWRkTOCZqZmZn1JKkkaRHRdh2J3oALBczMzMzaSK0mTdLPgWrgQ6Cyg2YBvCEpgF9FxAtdFZ+ZmZlZmi7YslCS/ga0N83vnIhY0abdbKAoIn7SzjmuiYgDkr4CrAJ+EBFrO7je/cD9ye4QoO7zvodz6A/85wJfwzrHfZJN7pfscZ9kk/sle7qqT66PiKv+/2Dqa3dKuh5YGRGl52j3U6A5In7ZJYGdg6S3XCeXLe6TbHK/ZI/7JJvcL9mTdp+kUpMm6YY2u7cBe9pp01tS35PbwGRgR9dEaGZmZpautGrSfiFpCPkpOPYBMyB/exP4dUTcClwNvCLpZJy/j4jXU4rXzMzMrEulkqRFxLQOjh8Abk223wPKuzKuTvJDDNnjPskm90v2uE+yyf2SPan2Seo1aWZmZmZ2Jq/daWZmZpZBTtI6SdItkuokvSNpVtrxGEhaJOmQJD9YkhGSrpO0WtJuSTslPZx2TAaSiiRtkrQt6ZefpR2T5Um6RNLbkl5LOxbLk/S+pO2Stkp6K5UYfLvz/Em6BNgLVAENwGbgOxGxK9XAejhJ44FmYPG5pnKxriFpIDAwImqTp7S3AFP9u5Iu5Z/E6h0RzZJ6AeuBhyPizZRD6/EkPQKMAvpFxJS047HW9cNHRURqc9d5JK1zxgDvRMR7EXEceJn8QvGWomSC4yNpx2GnRMQHEVGbbDcBu4Fr043KIq852e2VfPmTesokFQPfAn6ddiyWLU7SOudaYH+b/Qb8h8fsrCQNAm4CNqYbiUHrbbWtwCFgVUS4X9L3NPAj8tNSWXacXJpyS7KqUZdzktY5aueYP4WadUBSH2AZ8MOIOJZ2PAYRcSIickAxMEaSSwRSJGkKcCgitqQdi51hbER8Hfgm8P2ktKZLOUnrnAbgujb7xcCBlGIxy7Sk5mkZ8FJE/DnteOx0EXEUWAPcknIoPd1Y4Lak/ull4GZJv0s3JIPWuVuJiEPAK+RLnrqUk7TO2QzcIGmwpEuBbwN/STkms8xJCtR/A+yOiPlpx2N5kq6S9OVk+0vAJNpZls+6TkTMjojiiBhE/m/K3yPiuymH1eNlZWlKJ2mdEBGfAg8CfyVfCL0kInamG5VJ+gPwT2CIpAZJ96YdkzEW+B75UYGtydetaQdlDARWS/oX+Q+dqyLCUz6YnelqYL2kbcAmYGUaS1N6Cg4zMzOzDPJImpmZmVkGOUkzMzMzyyAnaWZmZmYZ5CTNzMzMLIOcpJmZmZllkJM0M+v2JDWfu5WZWbY4STMzMzPLICdpZtZjSJogaY2kpZL2SHopWR0BSaMl/UPSNkmbJPWVVCTpt5K2S3pbUmXStkbSckmvSqqX9KCkR5I2b0q6Mmn3NUmvJws0r5M0NM33b2YXl8K0AzAz62I3AcPJr7u7ARgraRPwR+DOiNgsqR/wX+BhgIgYkSRYb0gqSc5TmpyrCHgHmBkRN0laAFQDTwMvADMi4t+SKoDngJu76o2a2cXNSZqZ9TSbIqIBQNJWYBDwIfBBRGwGiIhjyffHAc8mx/ZI2gecTNJWR0QT0CTpQ+DV5Ph2oExSH+AbwJ+SwTqAyy7wezOzbsRJmpn1NB+32T5B/v9BAe2tkad2jrV3npY2+y3JOQuAoxGR++yhmllP5po0MzPYA1wjaTRAUo9WCKwF7kqOlQBfBerO54TJaFy9pOnJ6yWp/EIEb2bdk5M0M+vxIuI4cCfwrKRtwCrytWbPAZdI2k6+Zq0mIj7u+ExnuAu4NznnTuD2LzZyM+vOFNHeCL+ZmZmZpckjaWZmZmYZ5CTNzMzMLIOcpJmZmZllkJM0MzMzswxykmZmZmaWQU7SzMzMzDLISZqZmZlZBjlJMzMzM8ug/wHKqKzSgl1OeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the estimate and the truth\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n",
    "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n",
    "plt.fill_between(\n",
    "    X_test.flatten(),\n",
    "    te_pred_interval[0].flatten(),\n",
    "    te_pred_interval[1].flatten(),\n",
    "    alpha=0.2,\n",
    "    label=\"90% Confidence Interval\",\n",
    ")\n",
    "plt.fill_between(\n",
    "    X_test.flatten(),\n",
    "    truth_te_lower,\n",
    "    truth_te_upper,\n",
    "    alpha=0.2,\n",
    "    label=\"True Elasticity Range\",\n",
    ")\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Songs Sales Elasticity\")\n",
    "plt.title(\"Songs Sales Elasticity vs Income\")\n",
    "plt.legend(loc=\"lower right\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Coefficient Results</caption>\n",
       "<tr>\n",
       "      <td></td>     <th>point_estimate</th> <th>stderr</th>  <th>zstat</th>  <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>income</th>        <td>2.417</td>      <td>0.086</td> <td>28.172</td>    <td>0.0</td>    <td>2.276</td>    <td>2.558</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>income^2</th>      <td>-0.43</td>      <td>0.029</td> <td>-14.585</td>   <td>0.0</td>   <td>-0.479</td>   <td>-0.382</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Intercept Results</caption>\n",
       "<tr>\n",
       "      <td></td>      <th>point_estimate</th> <th>stderr</th>  <th>zstat</th>  <th>pvalue</th> <th>ci_lower</th> <th>ci_upper</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>     <td>-3.019</td>      <td>0.052</td> <td>-57.893</td>   <td>0.0</td>   <td>-3.105</td>   <td>-2.934</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'econml.utilities.Summary'>\n",
       "\"\"\"\n",
       "                      Coefficient Results                      \n",
       "===============================================================\n",
       "         point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
       "---------------------------------------------------------------\n",
       "income            2.417  0.086  28.172    0.0    2.276    2.558\n",
       "income^2          -0.43  0.029 -14.585    0.0   -0.479   -0.382\n",
       "                       Intercept Results                        \n",
       "================================================================\n",
       "          point_estimate stderr  zstat  pvalue ci_lower ci_upper\n",
       "----------------------------------------------------------------\n",
       "intercept         -3.019  0.052 -57.893    0.0   -3.105   -2.934\n",
       "----------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the final coefficient and intercept summary\n",
    "est.summary(feat_name=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "forestdml_estimate = model.estimate_effect(\n",
    "    identified_estimand,\n",
    "    method_name=\"backdoor.econml.dml.ForestDMLCateEstimator\",\n",
    "    control_value=0,\n",
    "    treatment_value=1,\n",
    "    target_units=X_test_data.values,\n",
    "    method_params={\n",
    "        \"init_params\": {\"model_y\": GradientBoostingRegressor(), \n",
    "                        \"model_t\": GradientBoostingRegressor()},\n",
    "        \"fit_params\": {\"inference\": \"blb\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "est = forestdml_estimate._estimator_object\n",
    "# Get treatment effect and its confidence interval\n",
    "te_pred = est.effect(X_test).flatten()\n",
    "te_pred_interval = est.effect_interval(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the estimate and the truth\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(X_test.flatten(), te_pred, label=\"Sales Elasticity Prediction\")\n",
    "plt.plot(X_test.flatten(), truth_te_estimate, \"--\", label=\"True Elasticity\")\n",
    "plt.fill_between(\n",
    "    X_test.flatten(),\n",
    "    te_pred_interval[0].flatten(),\n",
    "    te_pred_interval[1].flatten(),\n",
    "    alpha=0.2,\n",
    "    label=\"90% Confidence Interval\",\n",
    ")\n",
    "plt.fill_between(\n",
    "    X_test.flatten(),\n",
    "    truth_te_lower,\n",
    "    truth_te_upper,\n",
    "    alpha=0.2,\n",
    "    label=\"True Elasticity Range\",\n",
    ")\n",
    "plt.xlabel(\"Income\")\n",
    "plt.ylabel(\"Songs Sales Elasticity\")\n",
    "plt.title(\"Songs Sales Elasticity vs Income\")\n",
    "plt.legend(loc=\"lower right\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours+w_random | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add a Random Common Cause\n",
      "Estimated effect:-0.9634222159593084\n",
      "New effect:-0.9666807589581092\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_random = model.refute_estimate(identified_estimand, forestdml_estimate, method_name=\"random_common_cause\")\n",
    "print(res_random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Add an Unobserved Common Cause\n",
      "Estimated effect:-0.9634222159593084\n",
      "New effect:0.19825589289569848\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_unobserved = model.refute_estimate(\n",
    "    identified_estimand,\n",
    "    forestdml_estimate,\n",
    "    method_name=\"add_unobserved_common_cause\",\n",
    "    confounders_effect_on_treatment=\"linear\",\n",
    "    confounders_effect_on_outcome=\"linear\",\n",
    "    effect_strength_on_treatment=0.1,\n",
    "    effect_strength_on_outcome=0.1,\n",
    ")\n",
    "print(res_unobserved)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_refuters.placebo_treatment_refuter:Refutation over 3 simulated datasets of permute treatment\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~placebo+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~placebo+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~placebo+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "WARNING:dowhy.causal_refuters.placebo_treatment_refuter:We assume a Normal Distribution as the sample has less than 100 examples.\n",
      "                 Note: The underlying distribution may not be Normal. We assume that it approaches normal with the increase in sample size.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a Placebo Treatment\n",
      "Estimated effect:-0.9634222159593084\n",
      "New effect:-0.020743070949714924\n",
      "p value:0.21286271662643919\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_placebo = model.refute_estimate(\n",
    "    identified_estimand, forestdml_estimate,\n",
    "    method_name=\"placebo_treatment_refuter\", placebo_type=\"permute\", \n",
    "    num_simulations=3)\n",
    "\n",
    "print(res_placebo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:dowhy.causal_refuters.data_subset_refuter:Refutation over 0.8 simulated datasets of size 8000.0 each\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "INFO:dowhy.causal_estimator:INFO: Using EconML Estimator\n",
      "INFO:dowhy.causal_estimator:b: log_demand~log_price+account_age+songs_purchased+income+has_membership+is_US+age+friends_count+days_visited+avg_hours | income\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Taha\\Anaconda3\\Anaconda4\\lib\\site-packages\\sklearn\\ensemble\\gradient_boosting.py:1450: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "WARNING:dowhy.causal_refuters.data_subset_refuter:We assume a Normal Distribution as the sample has less than 100 examples.\n",
      "                 Note: The underlying distribution may not be Normal. We assume that it approaches normal with the increase in sample size.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refute: Use a subset of data\n",
      "Estimated effect:-0.9634222159593084\n",
      "New effect:-0.9593069772948165\n",
      "p value:0.22483334734579585\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res_subset = model.refute_estimate(\n",
    "    identified_estimand, forestdml_estimate,\n",
    "    method_name=\"data_subset_refuter\", subset_fraction=0.8, \n",
    "    num_simulations=3)\n",
    "\n",
    "print(res_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "intrp = SingleTreeCateInterpreter(include_model_uncertainty=True, max_depth=2, min_samples_leaf=10)\n",
    "intrp.interpret(est, X_test)\n",
    "plt.figure(figsize=(25, 5))\n",
    "intrp.plot(feature_names=X.columns, fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "intrp = SingleTreePolicyInterpreter(risk_level=0.05, max_depth=2, min_samples_leaf=1, min_impurity_decrease=0.001)\n",
    "intrp.interpret(est, X_test, sample_treatment_costs=-1, treatment_names=[\"Discount\", \"No-Discount\"])\n",
    "plt.figure(figsize=(25, 5))\n",
    "intrp.plot(feature_names=X.columns, fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define function to compute revenue\n",
    "def revenue_fn(data, discount_level1, discount_level2, baseline_T, policy):\n",
    "    policy_price = baseline_T * (1 - discount_level1) * policy + baseline_T * (1 - discount_level2) * (1 - policy)\n",
    "    demand = demand_fn(data, policy_price)\n",
    "    rev = demand * policy_price\n",
    "    return rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy_dic = {}\n",
    "# our policy above\n",
    "policy = intrp.treat(X)\n",
    "policy_dic[\"Our Policy\"] = np.mean(\n",
    "    revenue_fn(train_data, 0, 0.1, 1, policy))\n",
    "\n",
    "## previous strategy\n",
    "policy_dic[\"Previous Strategy\"] = np.mean(\n",
    "    train_data[\"price\"] * train_data[\"demand\"])\n",
    "\n",
    "## give everyone discount\n",
    "policy_dic[\"Give Everyone Discount\"] = np.mean(\n",
    "    revenue_fn(train_data, 0.1, 0, 1, np.ones(len(X))))\n",
    "\n",
    "## don't give discount\n",
    "policy_dic[\"Give No One Discount\"] = np.mean(\n",
    "    revenue_fn(train_data, 0, 0.1, 1, np.ones(len(X))))\n",
    "\n",
    "## follow our policy, but give -10% discount for the group doesn't recommend to give discount\n",
    "policy_dic[\"Our Policy + Give Negative Discount for No-Discount Group\"] = np.mean(\n",
    "    revenue_fn(train_data, -0.1, 0.1, 1, policy))\n",
    "\n",
    "## give everyone -10% discount\n",
    "policy_dic[\"Give Everyone Negative Discount\"] = np.mean(\n",
    "    revenue_fn(train_data, -0.1, 0, 1, np.ones(len(X))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Our Policy</th>\n",
       "      <td>14.686241</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Previous Strategy</th>\n",
       "      <td>14.349342</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Give Everyone Discount</th>\n",
       "      <td>13.774469</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Give No One Discount</th>\n",
       "      <td>14.294606</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Our Policy + Give Negative Discount for No-Discount Group</th>\n",
       "      <td>15.564411</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Give Everyone Negative Discount</th>\n",
       "      <td>14.612670</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      Revenue  Rank\n",
       "Our Policy                                          14.686241   2.0\n",
       "Previous Strategy                                   14.349342   4.0\n",
       "Give Everyone Discount                              13.774469   6.0\n",
       "Give No One Discount                                14.294606   5.0\n",
       "Our Policy + Give Negative Discount for No-Disc...  15.564411   1.0\n",
       "Give Everyone Negative Discount                     14.612670   3.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get policy summary table\n",
    "res = pd.DataFrame.from_dict(policy_dic, orient=\"index\", columns=[\"Revenue\"])\n",
    "res[\"Rank\"] = res[\"Revenue\"].rank(ascending=False)\n",
    "res"
   ]
  }
 ],
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