{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.010856,
     "end_time": "2020-05-12T09:31:02.590456",
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     "start_time": "2020-05-12T09:31:02.579600",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Correlational analysis\n",
    "\n",
    "This notebook contains correlational analyses of the recommendation list inputs and outputs, for comparison purposes and also to analyze the control bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.010043,
     "end_time": "2020-05-12T09:31:02.610497",
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     "start_time": "2020-05-12T09:31:02.600454",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "papermill": {
     "duration": 0.017,
     "end_time": "2020-05-12T09:31:02.656836",
     "exception": false,
     "start_time": "2020-05-12T09:31:02.639836",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "papermill": {
     "duration": 0.874543,
     "end_time": "2020-05-12T09:31:03.542410",
     "exception": false,
     "start_time": "2020-05-12T09:31:02.667867",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import logit, expit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "papermill": {
     "duration": 0.030585,
     "end_time": "2020-05-12T09:31:03.582992",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.552407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 25000 entries, ('AZ', 529) to ('GR-I', 875516)\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   count       25000 non-null  int32  \n",
      " 1   linked      25000 non-null  int32  \n",
      " 2   ambiguous   25000 non-null  int32  \n",
      " 3   male        25000 non-null  int32  \n",
      " 4   female      25000 non-null  int32  \n",
      " 5   dcknown     25000 non-null  int32  \n",
      " 6   dcyes       25000 non-null  int32  \n",
      " 7   PropDC      25000 non-null  float64\n",
      " 8   Known       25000 non-null  int32  \n",
      " 9   PropFemale  25000 non-null  float64\n",
      " 10  PropKnown   25000 non-null  float64\n",
      "dtypes: float64(3), int32(8)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "with open('data/profile-data.pkl', 'rb') as f:\n",
    "    profiles = pickle.load(f)\n",
    "profiles.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "papermill": {
     "duration": 0.030981,
     "end_time": "2020-05-12T09:31:03.624960",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.593979",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 94146 entries, ('AZ', 'als', 529) to ('GR-I', 'wrls', 875516)\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   ambiguous   94146 non-null  int64  \n",
      " 1   female      94146 non-null  int64  \n",
      " 2   male        94146 non-null  int64  \n",
      " 3   unknown     94146 non-null  int64  \n",
      " 4   Total       94146 non-null  int64  \n",
      " 5   Known       94146 non-null  int64  \n",
      " 6   PropKnown   94146 non-null  float64\n",
      " 7   PropFemale  93926 non-null  float64\n",
      " 8   dcknown     94146 non-null  int64  \n",
      " 9   dcyes       94146 non-null  int32  \n",
      " 10  PropDC      94115 non-null  float64\n",
      "dtypes: float64(3), int32(1), int64(7)\n",
      "memory usage: 8.7+ MB\n"
     ]
    }
   ],
   "source": [
    "with open('data/rec-data.pkl', 'rb') as f:\n",
    "    recs = pickle.load(f)\n",
    "recs.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012992,
     "end_time": "2020-05-12T09:31:03.649959",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.636967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Helpers from RecModel Analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "papermill": {
     "duration": 0.021514,
     "end_time": "2020-05-12T09:31:03.683473",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.661959",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def select_implicit(data, reset=True):\n",
    "    if reset:\n",
    "        data = data.reset_index()\n",
    "    implicit = data['Set'].str.endswith('-I')\n",
    "    if 'Algorithm' in data.columns:\n",
    "        implicit |= data['Algorithm'].str.endswith('-imp')\n",
    "    else:\n",
    "        implicit |= data['Set'] == 'AZ'\n",
    "    data = data.loc[implicit].assign(Set=data['Set'].str.replace('-I', ''))\n",
    "    if 'Algorithm' in data.columns:\n",
    "        data['Algorithm'] = data['Algorithm'].str.replace('-imp', '').str.replace('wrls', 'als')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "papermill": {
     "duration": 0.016958,
     "end_time": "2020-05-12T09:31:03.712473",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.695515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def select_explicit(data, reset=True):\n",
    "    if reset:\n",
    "        data = data.reset_index()\n",
    "    implicit = data['Set'].str.endswith('-I') \n",
    "    if 'Algorithm' in data.columns:\n",
    "        implicit |= data['Algorithm'].str.endswith('-imp')\n",
    "    data = data[~implicit].assign(Set=data['Set'].str.replace('-E', ''))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011,
     "end_time": "2020-05-12T09:31:03.734473",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.723473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Observed Profile Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012019,
     "end_time": "2020-05-12T09:31:03.757492",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.745473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What do proportions look like, for both female authors and dummy codes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "papermill": {
     "duration": 0.031017,
     "end_time": "2020-05-12T09:31:03.799501",
     "exception": false,
     "start_time": "2020-05-12T09:31:03.768484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Set</th>\n",
       "      <th>AZ</th>\n",
       "      <th>BX-E</th>\n",
       "      <th>BX-I</th>\n",
       "      <th>GR-E</th>\n",
       "      <th>GR-I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PropFemale</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.414445</td>\n",
       "      <td>0.418886</td>\n",
       "      <td>0.407030</td>\n",
       "      <td>0.446998</td>\n",
       "      <td>0.450201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.329436</td>\n",
       "      <td>0.267354</td>\n",
       "      <td>0.254304</td>\n",
       "      <td>0.276480</td>\n",
       "      <td>0.269127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PropDC</th>\n",
       "      <th>mean</th>\n",
       "      <td>0.498460</td>\n",
       "      <td>0.494217</td>\n",
       "      <td>0.499506</td>\n",
       "      <td>0.494855</td>\n",
       "      <td>0.497091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.160820</td>\n",
       "      <td>0.151552</td>\n",
       "      <td>0.144868</td>\n",
       "      <td>0.082914</td>\n",
       "      <td>0.069235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Set                    AZ      BX-E      BX-I      GR-E      GR-I\n",
       "PropFemale mean  0.414445  0.418886  0.407030  0.446998  0.450201\n",
       "           std   0.329436  0.267354  0.254304  0.276480  0.269127\n",
       "PropDC     mean  0.498460  0.494217  0.499506  0.494855  0.497091\n",
       "           std   0.160820  0.151552  0.144868  0.082914  0.069235"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profiles.groupby('Set')[['PropFemale', 'PropDC']].agg(['mean', 'std']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.010968,
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     "start_time": "2020-05-12T09:31:03.810533",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What does the distribution look like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "papermill": {
     "duration": 0.028999,
     "end_time": "2020-05-12T09:31:03.861501",
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     "start_time": "2020-05-12T09:31:03.832502",
     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>529</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1723</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>1810</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>2781</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>2863</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set  user    variable     value\n",
       "0  AZ   529  PropFemale  0.800000\n",
       "1  AZ  1723  PropFemale  0.285714\n",
       "2  AZ  1810  PropFemale  0.000000\n",
       "3  AZ  2781  PropFemale  0.166667\n",
       "4  AZ  2863  PropFemale  0.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props = pd.melt(profiles.reset_index(), ['Set', 'user'], ['PropFemale', 'PropDC'])\n",
    "props.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "papermill": {
     "duration": 1.997559,
     "end_time": "2020-05-12T09:31:05.870059",
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     "start_time": "2020-05-12T09:31:03.872500",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bed0aa62c8>"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
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kaVA0S7dV8MRHe/nu+RPcDi1s2D0iY0xYe2ZFMaMy4hmeNpiUwTGMyUzgtXX73Q4rrFgiMsaErV0VdSzffYjPzB6OZzBqGJeVyK6KOooOHunye48v29vtGbvpHUtExpiw9eyKYiIErpiVc3Ta2MwEAJZss2e1AsUSkTEmZLWfuXR29tKmynMrizltXAZZSXFHp2ckxjIsOY6lWysCGWpYs0RkjAlLm0qrKalq4KqC4Z+YLiKcOjaD93ZU0NJqzxQFQo+JSEQeEpEyEVnfxfwzRKRKRFY7rx/7P0xjjPGv97ZXkpMyiHMnZR0z79RxQ6hpaGFNcZULkYUfX5pvPwzcAzzaTZmlqnqxXyIyxph+tr2slt2VdfzwwolERR57PH7y6CGIwF8XbefsiZ5EZY8M9J8ez4hUdQlwMACxGGNMv2tsaeXlNSWkxcdww7wRnZZJjY9hWk4yWw/UBDi68OSvB1rnicgaoAT4jqpu6KyQiCwAFgDk5Q2cowt7sNKYga+9Hr+ytoTy2kZuPCmfuOjILsufNSGLu/67ler6ZpIGRQcqzLDkj8YKK4ERqjod+DPwQlcFrfdgY4ybtpfV8v6OSuaNTmdcVmK3ZS+aNhQFNpTYfaL+1udEpKrVqlrrvH8NiBaRIX2OzBhj/Ki5tY0XV+8jPT6G+ZOH9lh+TGYimYmxrNtniai/9TkRichQcR5JFpE5zjIr+7pcY4zxp5V7D1FZ18Ql04cR3UkDhc7Myktld+URig913cuC6Ttfmm8/AXwAjBeRYhG5SURuEZFbnCJXAuude0R3A1erqvZfyMYY0zuqygc7KhmWEne05wRfzBmZRlx0xNEhIkz/6LGxgqpe08P8e/A07zbGmKC0s6KOsppGrpyde7RPOV/ERUdyypgM/rvpAK+sLeHiacP6McrwZcNA9JG1qDMm+K3fV0V0pDA1J7nX3z19XAZb9ldzx7PrmJaTQl66jVPkb9bFjzEmpLWpsqm0mnFZiT7fG/IWGSFcfUIeCHzjyVU2lHg/sERkjAlp+w7VU93QwqTspONeRmp8DBdPG8aaosPc9MhyP0ZnIAwvzakqy3YdZFdFHamDYzh17BDiY8NuNxgTNnaU1wIwvofnhnoyNSeZHSPTWLqtghnDU8hOHuSP8AxheEb0+ob9vLSmhKJDR1i6rZy7/ruV/VUNbodljOknuyvryEiMZbAfDjjPm5RFTGQE727rfogIGzivd8IqES3ceICl2yo4cVQa3z1vPF8/awyREcID7+6korbR7fCMMX7Wpsreg0cYkeafBgaDY6KYnZ/K2uIqahqa/bJME0aJSFW55+1tpMXHcNHUYYgI2cmD+NIpowB49IM9VNXbD8uYUFJW00hDcxsj0uP9tsy5+Wm0qrLeelzwm7BJRO9tr2RNcRWnj80gMuLj5wiGJMZy3dwRHKpr4uuPr/RpIKyWtjbqGltotkGzjHFVdyOwAuyprANghB+bXGcmxTld/1T7bZnhLmzu0j+5fC/p8THMzEs5Zt7IIfFcNmMYz63axw+eX8evr5hGRMSxD73tr2pg8dYyNpfW0NTaRkxUBNNzU5g/eSiDYrruxdcY4469lUeIj4kkPT7Gr8udmpPM25vLKKtuINNrmHFzfMIiETU0t7JocxmXzsjpdBAsgIL8NHJTB3H329upa2rll5dNIdX58ZbVNLB4Szlrig4TGx3B9OEpZCXFUlrVwIo9B9lZXssXTh5Jmp9/7CY8iMhwPANPDgXagPtV9U/uRhUa9hw8woj0+F71puCLidlJvLW5jCXbKrhydq5flx2OwiIRvbe9grqmVuZPGcq+Q/VdlvvWeeMZFBPF797cwpIt5czIS+FgXRMbSqqJjhROG5fBqWOHMDjm4902Ky+Vf364m39+uJtbThtNbDfjmxjThRbg26q6UkQSgRUislBVN7od2EBWVtPAwbom5o5M8/uyhybHMTgmkvd3WCLyh7C4R/T6+v0kxkUxb1R6j2W/csZoXr31FC6cms2hI02kxcdw3qQsvnv+BM6fPPQTSQg8l/WumZNHeU0jL64p6a9NMCFMVUtVdaXzvgbYBOS4G9XAt2L3IQC/NlRoFyHCqIwEPthRifXx3Hchf0akqizZVs5p4zKIifIt704YmsRvrpx29HNPzwOMzUzkjPGZvL25jOm5KYwf2rcH50z4EpF8YCawrJN5A3KEY7cU7jlEVIQwLKV/7uGMzohn/b4qdlceYeQQ/ye7cBLyiWh35REOVDf6dDbUF2eMy2DdvipeWrOPb2aM6/JeVE+sE9XwJSIJwLPA7ap6TJMsVb0fuB+goKDADsN7ULjnELmpg4iK6J8LP6OHeIaTeH9HhSWiPgr5S3PLdnrG6DuxnxNRVGQEF03N5tCRZj7afbBf12VCj4hE40lCj6nqc27HM9DVN7WyYV9Vv1yWa5eeEEN2chzv77BxQPsq5BPRhzsrGZIQy+iM/j9iGZuZwKiMeN7eXEZDc2u/r8+EBmeE4weBTar6B7fjCQVrig/T0qZ+61GhMyLCvNHpfLijkrY2O0Hti5BORO0dnM4dleb35pudERHmTx7KkaZWlvbQF5UxXk4GbgDOEpHVzutCt4MayFbs8TRU6O+xg04aPYTKuia2HKjp1/WEupC+R1Ra1UBpVQMnjEgN2DpzUwczNSeZ97ZXUFbTQGaiPexmuqeq7wL9f6QURgp3H2RMZsIxrVz97eQxnkv+722vYGIfhpkIdz2eEYnIQyJSJiLru5gvInK3iGwXkbUiMsv/YR6fNUWHAZiRF7hEBJ4eelva2rj7rW0BXa8xBtralBV7DlEQgAPQ7ORBjBoSb/eJ+siXS3MPA/O7mX8BMNZ5LQDu7XtY/rG6+DDRkcLE7MA2p05PiGXOyHSe+Kjo6FgoxpjA2F5eS3VDC7MDdCXkpDHpLNtZaX1P9kGPiUhVlwDdNQO7DHhUPT4EUkQk218B9sXaoiomZicRGxX43g7OmpDJoOhIfvv65oCv25hwVug8yFqQ7/8eFTpz8ugh1DW1Hr0CY3rPH40VcoAir8/FdPFUuIgsEJFCESksLy/3w6q71tqmrNtXxfTcYzs5DYSE2ChuOX0Ub2w4QKE15zYmYD7aVcmQhBjy+7mhQrt5o9MR8fTwb46PPxJRZzdZO23LqKr3q2qBqhZkZGT4YdVd21leS21jC9OHu5OIAL54ykgyE2P5xSsbabXmncb0O1Xl3e2VnDR6SEBaygKkDI5hyrBk3tthLWWPlz+alBQDw70+5wKud7q2vsQzaNXUnGTXYhgcE8UPL5rIbU+u5qF3d3HzaaNci6Uz1ouDCTVbD9RSUdvIKWOGBHS9J41J56F3d3GkqaXfW+qFIn+cEb0EfM5pPXciUKWqpX5Ybp9sKq0hJioiIA+ydufS6cM4Z2IWv3tzC2uL7RqyMf3p3e2es5KTxwY2EZ08egjNrcpHu+wy/PHoMXWLyBPAGcAQESkGfgJEA6jqfcBrwIXAduAI8IX+CrY3NpZUMz4r8bj7fPMXEeHXn57KZfe8x82PFvLvL5/U7UN2+6sa2Fhaxf6qBppa20iIjWJEWjxTc907szNmoFi0uYxRGfG8s8W3e9A9dWjs6/ebWtqIi47grU1lnDE+s0/LDEc9JiJVvaaH+Qp8zW8R+YGqsrG0mnMnZrkdCgBDEmJ58MYCrvrbh1z+1/f49RVTOXdS1tFr2EUHj/Dy2hJeXFXClgM1CJAWH8OgmEhKDjewcu9hXl1fSn1zKzefOsrnXsSNCScH65r4YGclt5we+EvgMVERnD0hi/+sL+Unl0wK+PoHupC8mHmgupGDdU1MGhY8TzpPGJrE8189iZsfLWTBP1cwLDmO3NTB7K9uYO/BIwDMHpHKJdOHMTUnmYRYz3+NqrLvcD2Lt5Rz5xtbWLjxAPdeP4vs5EFubo4xQeeNDftpbVMunJrNmqKqgK//omnZvLqu1C7PHYeQPLTeWOr5EQZTIgIYlZHA67efxpWzchmSGEtZTSNJcVFcODWb75w3nk/PymXeqPSjSQg8l/ZyUwdz/YkjuPe6WWw7UMPFd7/L+9ZCx5hPeH7VPvLTBzPJpa52zhyfSUJsFE8XFvVc2HxCSJ4RbSzxDOUyIQgHqIuOjGDWiFRmHcdT3xdMzWZsVgJf/ucKbnjwI355+RSumWOt24zZX93AR7sO8v0LJgSs2XZHg2IiuXJ2Lo8t28OE7CSS4qJdiWMgCskzok2lNeSlDSYxBH8IYzITeeFrJ3PKmCHc8dw6fvXaJuuC3oS9D3dWEhMVwWcLhvdc2AePL9t7XA0Zbjwpn5Y2az3XWyGZiDaWVrt2eh4IiXHRPPj5Aq4/MY+/LdnJFx9ZzqG6JrfDMsYVNQ3NrNxziE/NyCEtPsbVWPKHxHPm+EyW7TpIi/U957OQS0S1jS3srqwLuvtD/hYVGcEvLpvCLy6fwvvbKzn3j0t4cfU+OzsyYefd7RW0tilfOWO026EA8IWT86lrbGHtvsA3mBioQu4e0Zb91agS0mdE7USEG04cway8FP7n2bXc9uRq7n5rG5fPyGFmXiqZSbE0t7ZRXtPoedV6/m1obiNpUBSH6poYl5XotyfBracG05+8f1/tv6f6plaW7TrI1Nxk8oe4+/B6u1PGDCEzMZb3d1Sgqq7dsxpIQi4RtTdUOJ4zor4+3OaWycOSufqEPNYNq+KDHZX8fuHWLssmxkYRGx1JdX0zTa1tRIowY3gK50zKInlQ6N1TM6Ft+e6DNLW0cdrY/u27sjfahxB/cXUJhXsOcUKAegEfyEIvEZVWkzwomuzk8BoZNUKE6bkpTM9NobaxhQPVDdQ1tnD6uAwyk2LJSIgjIzGWQTGeITFaWtv4w8KtrNx7mOW7D7K+pIrLZ+b0W2/ldrZk/K2ppY33d1QwOiOeYSnB9VzdzOGpvLnhAI+8v/toIursjM54hF4iKvE0VAjn0+GE2CgSMhIAT5PvzkRFRpCbOpjc1MGcMmYITxcW8dTyIipqGjlrgnVRYoLfy2tKqG5o4YpZuW6HcoyYqAhm5KXw5oYDHD7SRMpgdxtRBLuQaqzQ0trG5v01TA7xhgr+lhYfw82njmJWXipvbS7jxdUlQTdsRUtrG/urGzhQ3YCnVykTzlSVvy/dSVZSLGMzE9wOp1Oz81Jpam3jpTWuD0YQ9ELqjGhHeR2NLW1MzrFE1FuREcKnZ+WQGBfFO1vLufXJVdx11QyiXe40tk2VD3ZU8s7WcmobWwDISRlEQX4a44PwgWUTGBtKqtm8v4ZLpw8L2qsfw1IGMTE7iedW7uNz8/LdDieohVQi2uCMQTQp23qqPh4iwvmThzI4JpJX15bS1NLGPdfO7PVQ6w3NrawpPsyW/TVU1DYiImQlxjIhO4mpOck+J7f6plaeLixiy4EaxmQkMGtECvXNbSzeUsY1f/+Qx2+ey4ShdtARjp5buY+YyAimBXmv9BdPy+bON7ZQcrje7VCCWkgloo0l1cQGwRhE7QZqK7xTx2Ywb3Q6P35xA1/+5wr+et0sn5p4t7S1sWznQd7eXEZ9cyvp8TFkpwxCVSk6VM/6kmpeX7+fsydmUjAijciIro9k1++r4p5F26iub+HS6cOYOzLt6JHvuMwE/vnhHr7x+CpevfVU6408zLS2KS+tKeHMCRlBPwjdBVOGcucbW3h9/X7iont3QBdOgvt/sZc2lFQzYaj7YxCFgs/NyycmMoIfPL+Oq/72IfdeP4vc1M7HUVJVFm48wN1vbaOitokxGQmcOymL3NRBR5OHqrKjvI63Nh/gxdUlvLe9kvmTs5jY4Xmv1jblsWV7+OWrmxgUHcnNp40iL+2T601PiOXXn57KFx8u5N7FO7jtnLH9sxMCREQeAi4GylR1itvxBLvtZZ5RWD81M5eDQd6jyKiMBCYMTeQ/60v51Mzga1QRLEImEakqG0qquGjaMLdD6TeBPsO6ek4eGYmx3PrEKi7401K+d/54PnvC8KOX6lSVlXsP87s3tvDBzkoyEmL5/LwRjMtKPOa6vYgwJjOB0RnxbN5fw+vr9/OvZXvJTo7jcH0zeWmDKTlcz/Or9rF5fw2njh3CqWMzPtETubezJmRx0bRs/rZkBzfMG+F612yt+DIAACAASURBVC599DBwD/Coy3EMCKuKDpE8KJozJ2Tw7Ip9bofTowunZvPH/27l7AlZJNmzep0KmURUfKie6oYWazHnZ2dPzOK1207lu8+s5X9f3MCdb2xh1ohUYqMi2FRaw96DR0gdHM0vLpsMSLeX28CTkCZmJzEuK5FVew+xbNdB7nxjy9H5U3KS+NPVM7h0+jCe+Kj77vRvP3ssr60r5R/v7eLb5433x+a6QlWXiEi+23EMBI3NrWwqreazBcN7fe/SLRdOHcofFm5lQ2k180alux1OUAqZRLTB6VHBEtEn+eMsakR6PE8tOJGl2yp4eU0J60uqaW5tY/zQRL5yxmgunpZNYlx0r9YVGSEU5KdRkJ/GRVOzKa2uJz0+lozEWJ+XMTYrkfmTh/Lw+7u55fTRxHdx9hQqRGQBsAAgLy88H4j0/PaUK2bluB2Kz8ZkJjI2M4H1+6osEXUhZGruxpIqIgRrRdVPRITTxmVw2jj/d6WSPDia5MHHd8niS6eO4j/r9/PSmpKQH5tJVe8H7gcoKCgIy4epVhUdIi0+hll5vR/Py00XTM3mz29to6ahOSSHp+krnxKRiMwH/gREAg+o6q87zL8RuBNov2B7j6o+4Mc4e7SxtJrRGQlHu7AxfTNQuuSZlZfChKGJPL5sb8gnonB3+EgTu8rrOGtCZtA+O9SVC6cO5e63trGxtJq5I+2sqKMem5eJSCTwF+ACYBJwjYhM6qToU6o6w3kFNAmB59KcXZYLPyLCtXPzWLevinXF1u1+KFtTdBgFZgzvn/4Q+9P4rETS42PYsK/a7VCCki/tnOcA21V1p6o2AU8Cl/VvWL1zsK6J0qqGkB+DyHTushk5xERF8OzKYrdDOS4i8gTwATBeRIpF5Ca3Ywo2qsqqosPkpQ0mPcH3+4jBQkSYkpPMzopajjg9hJiP+ZKIcgDv5kvFzrSOPi0ia0XkGRHpdLxeEVkgIoUiUlheXn4c4XZunTMA1ZRhwf2UtekfyYOiOXdiFi+vKaF5AI6KqarXqGq2qkaraq6qPuh2TMFm/b5qymoamZk38M6G2k3JSaZNPbcRzCf5kog6uxjb8Ubpy0C+qk4D/gs80tmCVPV+VS1Q1YKMDP/d9F699zAiMDXIu/sw/edTM3OorGtiyVb/HeCY4PFU4V6iIoRpOQM3EQ1LjiN1cDTrS+wScke+NFYoBrzPcHKBT3Qnq6qVXh//Dvym76H5blXRIcZlJlprlAAJxq6LTh+fQVp8DM+t2sfZE7PcDsf4UUNzKy+uLmFKTnK3jZHc/l32tH4RYWpOMu9ur6CytnFAXmLsL76cES0HxorISBGJAa4GXvIuICLeg95cCmzyX4jdU1VWFx0e0Kfspu+iIyO4ZFo2CzceoLqh2e1wjB+9sWE/NQ0tzB4xsJpsd2bG8FTaFF5ZW9ptuceX7XU9sQZSj4lIVVuArwNv4EkwT6vqBhH5uYhc6hS7VUQ2iMga4Fbgxv4KuKPdlUc4fKTZEpHhilm5NLW08Z913VdyM7A8tbyI4WmDGDkkODoz7ouhyXFkJ8cN2IY1/cWn3kFV9TVVHaeqo1X1/5xpP1bVl5z3d6jqZFWdrqpnqurm/gza26q9hwDPkYYJb9NykxmVEc+zK4O//zHjm72VR3h/RyWfmT2ciAH27FBXZuWlsra4ivX77F5RuwHfTfXy3YdIjItiTJCO0mgCR0S4YmYOH+06SNHBI26HY3zU3WWoB97dSXSk8NmCThviDkiz8lIZFB3JPz/Y43YoQWPAJ6JlOyuZO7L7sW1M+LhshufJghdX21lRMGtPPt3dB6mobeTpwiIun5HD0OS4AEbXvwbFRHL5zGG8sHofZTUNbocTFAZ0X3MHqhvYWVEXVF3OmN7z503Z4WmDmTMyjedW7uNrZ44ZcF3BGI/Hl+3l+VX7aG5Vvnz6aLfD8bsFp43m6cJi/rpoBz+9dLLb4bhuQJ8RfbjT02r8ROvR1nj59KwcdlbUsca6/BmwdlbUUrj7IDeelB+Sl91HDonnswW5PL5sL5v32wOuAz4RJcZFHTPKpwlvF0zNJjYqguetZdKA9OHOSh77cC/pCTHcPsBH3+3Ot88bT/LgaL722Mqwv0Q3YC/NqSrvbCnnxFHpdn/IfEJSXDTnTMri5bWl/PCiScREDejjrZDV1NLG2uLD7Kyoo7qhmRdW7+NgXRPby2pJHRzNF04aGdIPqQ9JiOWy6cN45IPdnHHnYuZPHkp9cytlNY3srqgjIzGWLftrGD80EQiuXu/9bcAmok2lNZRUNXBbCB8xmeN3xcwcXl1byjtbyzl3kvW0EGx2VdTx78IiDtc3kxQXRcrgGATIT/dcsoqJjAyLA4hRGQl85YwxLNywn3e3V5A0KJrMxFiGJsdRfKieRz7Yzay8VC6fOcztUPvVgE1Eb28+AMCZEzJdjsQEo9PGZZAeH8Pzq4otEQWZNzfs56F3d5EyOJqbThnJqCHxR4fzaBdOvQoMTYrjhnn5x2x/S1sbizaXsWhLOfVNLVw7J4+oyNBMzgN2q/67qYzpuclkJoZOs07jP9GREVwyfRj/3VRGVb11+RMsVu09xNefWMWwlDi+esYYRmckWMvGLkRFRHDupKFcMi2bTftr+MlLG1ANzYF5B2QiKjp4hNVFh+1I13Trilk5NLW08crakp4Lm35XVd/MV/61kqykWD43L99GU/bRvNFDOG1sBo8t28vflux0O5x+MSAT0fOrPA8rXj6zs2GRjPGYmpPMpOwkHnx3F21toXkkOZD88pWNlNc2cs81s4iP7dtdgXDrFPS8yVlcNC2b376+mQ92VPb8hQFmwCUiVeW5lcXMG5VObupgt8MxQUxEuOWM0ewsr2PhpgNuhxPWFm8p498rivnyaaOY3sVQ3770thDKutv+CBEK8lJJi49hwaOFlNc0uhBh/xlwieiDnZXsrjzCFbPsbMj07MIpQ8lLG8yf395mZ0V9dLyJorqhmTueW8fYzISwa+Xqz+QaGx3JNXPyqG9u5bYnV9EaQr/nAddq7q+LdjAkIZZLpod2c0bjH1GREdx+zli+9fQaXlpTYpdzXfD/Xt3EgeoGLj9tNM+u6F0fgOF6dtSV7ORBXDp9GM+t2sfdb23jm+eO8/m73vsy2J5JGlBnRKuLDvPu9gpuPnUkcdF2o9P45vIZOUzJSeK3r2+mxgbNC6i3Nh3gyeVF3HzaKIan2aX0dn05S5o9IpUrZuVw99vbWLylzM+RuWPAJKLWNuUnL20gPT6G604c4XY4ZgCJiBB+dukU9lc38MtXAjZ4cNgrr2nke8+sZWJ2Et/qxZG76Z6I8H+XT2V8ViK3P7U6JIY8GTCJ6B/v7WJN0WF+fMkkEvrY4saEn9kjUvny6aN5qrCIZ1ZYH3T9TVX5n2fXUtvYwnmTsnp9Sc50b1BMJPdeP5vWVuUrj62grrHF7ZD6ZEAkosVbyvjVfzZz9oRMLrV7Q+Y4ffOccZw8Jp3vP7uWNzbsdzucAaOtTdl2oIaig0d8/oN35xtbeHtzGXdcMIGsJHvovD+MHBLPXVfPYGNJNV95bCUNza09fkdVaWxpDbqGOz6dWojIfOBPQCTwgKr+usP8WOBRYDZQCVylqrv7Gpyq8u/CYn704nomDE3kT9fMtKewzXGLiYrgvutnc/0Dy7jlXyu49ayxfPXM0cRGuX+/sac65ob6plae+Ggvf1+6k9IqT+/QAozOTGB42iBOGTPkmPrY2qb89o3N/O2dnVwzJ4/Pn5TPEx8VuRB9aPO+v3T5jByeX7WPzz30EfdcO/OY3mZUlRV7DvHU8r1sOVBDQ3Mbv39zK6ePz+BLp4xkZl5qoMM/Ro+JSEQigb8A5wLFwHIReUlVN3oVuwk4pKpjRORq4DfAVccb1L7D9Z7nDgqLWV10mJNGp/Pna2baJTnTZ4lx0Ty5YB53PLeWP721jWdWFHPl7FxOHJXO0OQ4UgZFkxofE9CYfKxjAVN1pJl/fribf7y3m8q6JuaMTONb545j3b4qig7WU7jnIDc8+BGThyXxhZNHMndkGlGRwqq9h7nvnR2sLa7i+hPz+Oklk+3AMQAK8tOIjozghdX7OP+PS7jplJGcPi6TqEhh5d5DPPbhXjaWVhMXHcHkYclkJMSSlhDDy2tKeHVtKQUjUrnplJGcN3moayMZSE99F4nIPOCnqnq+8/kOAFX9lVeZN5wyH4hIFLAfyNBuFl5QUKCFhYWdzrv50UIWbjzAiPTB3HL6aD4zO9dvnf1Zc9DQ4I/mp0u3lXPfOzt4b/vHT6qfNSGTh248obuv+b2m+lLHOuqu/vRGaVU972+vpKq+mYraRjaWVvPBjkoaW9o4c3wGXz1zDCfkpwEf152W1jbioiO5b8kOdpbXfWJ52clxfP+CCdQ2tFgSCrAT8lP5xaubWLK1/BPTJwxN5IZ5I2hu0aM9ml87N4/axhaeXl7EP97fRdHBetLjY5g9IpXJw5JJjY8meVA050zM6nMvGF66/EH4koiuBOar6peczzcAc1X1615l1jtlip3PO5wyFR2WtQBY4HwcD2zp/bb02RCgosdSA5ttY/+pUNX5/lygL3XMme52/bHfVehwYzu7rDu+pLrOsljH7OVLGVT1fuB+H9bZb0SkUFUL3Iyhv9k2DjgDov6E2D7vVDhsIwTfdvpyvasYGO71ORfo2J3x0TLOpblk4KA/AjQmDPhSx4wJWb4kouXAWBEZKSIxwNXASx3KvAR83nl/JfB2d/eHjDGf4EsdMyZk9XhpTlVbROTrwBt4mpY+pKobROTnQKGqvgQ8CPxTRLbjORO6uj+D7iNXLw0GiG3jANJVHXM5rM6EzD7vRjhsIwTZdvbYWMEYY4zpTwOiZwVjjDGhyxKRMcYYV1kiMsYY4ypLRMYYY1xlicgYY4yrLBEZY4xxlSUiY4wxrrJEZIwxxlWWiIwxxrjKEpExxhhXWSIyxhjjKktExhhjXGWJyBhjjKtcSUQi8kMR2SAia0VktYjM7aH8jSIyzA/rzReRemeda0TkfREZ78y7QkTe8ip7ilPumKEyRGSxiGxx5q8WkWe6WNfivsbcxXY8ICKTeiizW0SGdDL9pyLynf6Iy2u965z9+6aIDPXTcn8qIvu89vmv/bHcbtbX6f4bSKye9Xk7wr2ebROR57z3gYhEi8ivnXnrReQjEbmgr+sNeCISkXnAxcAsVZ0GnAMU9fC1G4E+VxDHDlWdoarTgUeAHwCo6nNAg4hc61SKvwJfVdWWLpZznbOcGap6pZ9i65GIRKrql1R1Y6DWeRzOdPZvIc7+9SYikce53D967fPv9ynCEGf1rG+snukMVR0LPAW8LSIZzrxfANnAFFWdAlwCJB7neo5y44woG6hQ1UYAVa1Q1RIAEZktIu+IyAoReUNEskXkSqAAeMzJ0oP8GEsScMjr8zeAXwI/A5ar6vt+XBcAIvIVEfmt1+cbReTPzvsXnG3fICILvMrUisjPRWQZMM85Uixw5t0rIoXOd37WYXXfdY5YPhKRMZ3EMlpEXnfWuVREJvh5c5cAY7rYhrNFZJVzVPeQiMQ65XaLyG+6i7vDNhzzm3GmLxaRP4rIEhHZJCInOEd320Tkl17f73Sfd1jH9U4sq0Xkb32o4IFk9czqWZ/rmao+BbwJXCsig4GbgW94/a4OqOrTfd4CVQ3oC0gAVgNb8RwNne5MjwbeBzKcz1fhGakSYDFQ0MXyvussr+Pr7k7K5gP1zvwdQCmQ16HMr4AqYEg327AY2OK1rju7WNfiTqZnANu9Pv8HOMV5n+b8OwhYD6Q7nxX4bIf1F3T4TqQzfZrzeTfwQ+f954BXnPc/Bb7jvH8LGOu8n4tniPeO8Z7Zxf59v4t9s7t93wH3AL/puA1AHJ6j83HO50eB232Ie5/X+s/34TfTvu7bgBI8f5xjgWKvfdvVPt8NDAEmAi8D0c70vwKfC3S9sXpm9SyA9ew7HdZ1O3AvMA1Y1R+/1x6HCvc3Va0VkdnAqXh2/lMi8n08p5dTgIUiAp7/8FIflncncGcvQtihqjMAROQqPEPmznc+R+C5hFELjAAqulnOdapa2Iv1tsdbLiI7ReREYBswHnjPmX2riHzKeT8cGAtUAq3As10s8rPOUV0Unj+0k4C1zrwnvP79o/eXRCQBOAn4t7O/wfNHumO8i4AZvdlGYJGItDpx/MiZ5r0N44FdqrrV+fwI8DXgrh7i/qOq/s5rG6bQ/W/mJeffdcAGVS11vrcTz/6tpOt93u5sYDaw3FnHIKDMp73gIqtnVs84/nrWkXQzzy8CnogAVLUVz1HFYhFZB3weWIHnj8W83ixLRL4LXNfJrCWqemsPX38J+IfX56/hOUL6X+AvIjJPVVVE3gCygEJV/VJv4uvCU8Bngc3A8846zsBTOeep6hHx3ICNc8o3OPvsE0RkJPAd4ARVPSQiD3t9BzxHR529B89l2cPtfyy6IiJn0vmP9IiqntTF185U1Y5/XLy3oacfdndxfyI8uv/NNDr/tnm9b/8c1cM+917HI6p6Rw8xBx2rZ1bPulsnvtezmXgOYLYDeSKSqKo1PSy7V9xorDBeRMZ6TZoB7MFzCp4hnpus7a0zJjtlaujihpiq3qkf38z0fvVUOQBOwXPpAPG0OvkW8D1VfR3PZaAvOes431mmPyoHwHPA5cA1eCoLQDJwyKkcE4ATfVhOElAHVIlIFtCx9cpVXv9+4D1DVauBXSLyGQDxmN5xBaq6qIv921Xl8MVmIN/ruvQNwDu+xN1Bd78ZX/iyz98CrhSRTGcdaSIyohfrcIXVM8DqWZ/rmYh8GjgPeEJVjwAPAneLSIwzP1tEru9DjIA7Z0QJwJ9FJAVowZNlF6hqk3humN4tIslObHcBG4CHgftEpB7PkUx9H9Y/WkRW4zlaaMKpBMAfgN+qarnz+XZgqYg8q6oHO1nOY0484LkpfI6vAThHVRuBSar6kTP5deAWEVmL54/Fhz4sZ42IrMKzj3by8aWHdrHOTcsIPJWxo+uAe0XkR3juHTwJrPF1O46XqjaIyBfwXK6IApYD93kV6Snu9uV095vxRY/7XFU3OvvnTeeSUjOeI/o9Pq7DLVbPrJ4dbz37ppNc4vGcuZ7l9f/1IzwNTTaKSAOeBP3jvsYqqt2dkZnjJSL5wMOqeoa7kQwsIrIbzw3i7u4bGANYPTtewVbPrGcFY4wxrnKlsUKYOIznUofpBVXNdzsGM6BYPTsOwVbP7NKcMcYYV7l2aW7+/PmKp8mgvew1kF5BweqPvQbgq0uuJaKKiqC4R2bMgGT1x4QSa6xgjDHGVZaIjDHGuMoSkTHGGFdZIjLGGOMqS0TGGGNcZYnIGGOMqywRGWOMcVXYdfHz+LK9x0y7dm6eC5EYY4KN998H+7sQOHZGZEwQEJEUEXlGRDaLyKb28YKMCQdhd0ZkTJD6E/C6ql7pDDo22O2AjAkUS0TGuExEkoDTgBsBVLUJz2ByxoQFuzRnjPtGAeXAP0RklYg8ICLxHQuJyAIRKRSRwvLy8mOXYswAZYnIGPdFAbOAe1V1Jp7hl7/fsZCq3q+qBapakJGREegYjek3loiMcV8xUKyqy5zPz+BJTMaEBUtExrhMVfcDRSIy3pl0NrDRxZCMCageE5GIDBeRRU6T0g0iclsnZURE7haR7SKyVkTsaM6Y3vkG8JiIrAVmAP/P5XiMCRhfWs21AN9W1ZUikgisEJGFqup9xHYBMNZ5zQXudf41xvhAVVcDBW7HYYwbejwjUtVSVV3pvK8BNgE5HYpdBjyqHh8CKSKS7fdojTHGhJxe3SMSkXxgJrCsw6wcoMjrczHHJitrfmqMMeYYPiciEUkAngVuV9XqjrM7+YoeM8GanxpjjOnAp0QkItF4ktBjqvpcJ0WKgeFen3OBkr6HZ4wxJtT50mpOgAeBTar6hy6KvQR8zmk9dyJQpaqlfozTGGNMiPKl1dzJwA3AOhFZ7Uz7AZAHoKr3Aa8BFwLbgSPAF/wfqjHGmFDUYyJS1Xfp/B6QdxkFvuavoIwxxoQP61nBGGOMqywRGWOMcZUlImOMMa6yRGSMMcZVloiMMca4yhKRMcYYV1kiMsYY4ypLRMYYY1xlicgYY4yrLBEZY4xxlSUiY4wxrvKl01MTYI8v29vp9Gvn5gU4EmOM6X+WiIwJAiKyG6gBWoEWVS1wNyJjAscSkTHB40xVrXA7CGMCze4RGWOMcZUlImOCgwJvisgKEVnQWQERWSAihSJSWF5eHuDwjOk/dmkuBHXW2MEaOgS9k1W1REQygYUisllVl3gXUNX7gfsBCgoK1I0gjekPdkZkTBBQ1RLn3zLgeWCOuxEZEziWiIxxmYjEi0hi+3vgPGC9u1EZEzh2ac4Y92UBz4sIeOrk46r6urshGRM4loiMcZmq7gSmux2HMW6xS3PGGGNcZYnIGGOMq+zSnB9Yc2ljjDl+dkZkjDHGVZaIjDHGuMoSkTHGGFdZIjLGGOMqS0TGGGNcZYnIGGOMq3pMRCLykIiUiUinfV+JyBkiUiUiq53Xj/0fpjHGmFDly3NEDwP3AI92U2apql7sl4iMMcaElR7PiJwxUQ4GIBZjjDFhyF/3iOaJyBoR+Y+ITPbTMo0xxoQBf3TxsxIYoaq1InIh8AIwtrOCzhDICwDy8qwLHGOMMX44I1LValWtdd6/BkSLyJAuyt6vqgWqWpCRkdHXVRtjjAkBfU5EIjJUnBG9RGSOs8zKvi7XGGNMeOjx0pyIPAGcAQwRkWLgJ0A0gKreB1wJfEVEWoB64GpV1X6L2BhjTEjpMRGp6jU9zL8HT/NuY4wxptesZwVjgoSIRIrIKhF5xe1YjAkkGxgvjHU2oB/YoH4uug3YBCS5HYgxgWRnRMYEARHJBS4CHnA7FmMCzc6IgpSqsm5fFYu2lHH4SDNDk+PITonjzPGZbodm+sddwPeAxK4K2HN4JlTZGVEQUlVe37CfJ5cXIQgz81KoaWjhC/9Yzj1vb3M7PONnInIxUKaqK7orZ8/hmVBlZ0RB6P0dlSzdVsHckWlcMn0YESI0t7axuugwv3tzK4NiorjplJFuh2n852TgUqdnkjggSUT+parXuxyXMQFhZ0RBZmNJNa9v2M/E7KSjSQggOjKC331mOvMnD+WXr25k0ZYylyM1/qKqd6hqrqrmA1cDb1sSMuHEElEQaW1T/ufZtQyOjuSKmTlHk1C7yAjhrqtnMGFoEt98ajVFB4+4FKkxxviPJaIg8nRhEev2VXHh1GziYzu/ahoXHcm9182itU356mMraWhuDXCUpj+p6mIb28uEG0tEQaKusYXfv7mFOflpTMtN7rZs/pB4fv+Z6azbV8XPXt4YoAiNMaZ/WGMFl7U/VLp4SxkVtU1cOTsV6XBJrjPnTR7KV84Yzb2LdzArL4XPFAzv71CNMaZf2BlREKhvamXJtnImDk0kL22wz9/79rnjmDcqnR+9sJ6NJdX9GKExxvQfOyMKAku2ldPY3MY5k7K6LddZlzx3XzOTi/+8lJsfLeTpW+aRkzKov8I0xph+YWdELqtpaOb9HRVMy00mO7n3SSQjMZYHPncC1fXNXPv3D9lbaS3pjDEDiyUil721uYzWNuWcid2fDXVnam4yj9w0h6r6Zj711/fYXlbrxwiNMaZ/WSJy0Y7yWgp3H2TOyHTSE2L7tKxZeak8c8tJpMbH8I/3dvHcymKq65v9FKkxxvQfS0QuuvP1LURFRnDWBP90ZDomM4GXvn4yJ41OZ9Xew/x+4RZeX1/K4SNNflm+Mcb0B2us4AetbcrBuiZioyJIGhTt03dW7DnE6xv2c87ETBK6eHj1eAyOieKiacOYN3oIb27cz9JtFSzdVsGa4sPceNJIThyV5lPzcGOMCRRLRH20rriKvyzazv7qBgBPH3HTsrv9TnNrGz96YT1ZSbGcMqZ/elFOi4/h6hPymD+5iWW7DvLRroO8seEAp43L4P99agq5qb43EzfGmP5kl+b6oORwPZ//x0ccaWrh0unDOGtCJjvKa/nr4h2s3Huoy+/9ZdF2NpVW84vLphAT1b//BSmDYzh/8lA+uONs/vfiSazcc4jL7nmPVd3EZ4wxgWSJ6DipKrc/tZrG5lZuOmUUJ45K55yJWXz19NHEREVw9f0f8uLqfZ/4zuPL9vK/L6znT//dxozhKVTUBu7eTVx0JDedMpIXvnYy8bFRfPHh5VTWNgZs/cYY0xVLRMfpna3lfLTrIHdcOJGMxI9bvGUmxfHV00czc3gKtz25ml++spGq+maaW9tYvusgT3y0l2Epg7h8Ro4rcY/JTODRL85BgceW7aW1TV2Jw5hg09DcSptafXCDJaLjoKr8ceFWclIG8dlO+ngbHBvFP2+ay3Vz83jg3V3M+sVCpv/sTZ5fvY+89MHceFJ+v1+S607+kHjuvHI6+6sbWLqt3LU4jAkWuyvqOO23i3j4/d2WjFxgjRWOw6qiw6wpruKXl3d9jycmKoL/+9RUrp2bx2vrSqlpaKGtDSZkJx4zzpAbzp2UxeRhSSzaUsbsEakkxvnW2s+YUNPWptz0yHJqGlooq2nkna3lnDneP49UGN9YIuqF9r7enltZTExkBM0tbZ32/+Zt8rBkJg9L/sT3g8X5k4eysaSapdsquHBq9y39jAlVH+6sZEd5HX+8ajp/eXsHK/ccskQUYHZprpcam1tZW1zFtNxkYqMj3Q6nT4YkxDJjeArLdlVS19jidjjGuOLfK4pJjI1i/uRsJmQnUlnXZA15AswSUS9t2l9NU2sbs/JS3Q7FL04bl0Fzq7JijzXnNuHnSFMLr6/fz8XThzEoJpJxWYkAbLP+GgPKElEvbSipJikuirz00HggNCspjpFD4lm2q9Ju0pqw886WcuqbW48+hJ4eH0Pq4GhLRAFm94h6oamlja0Hapg9IjUoGhz08DJPcgAAIABJREFURnf3p+aOTOPJ5UVsL6s9ekRoAkdE4oAlQCyeOvmMqv7E3ahCl3dd+GBnJWnxMcwZmQaAiJCfHs+2slpU1brDChA7I+qFrQdqaG7Vo40PQsWk7CTioiNYU3TY7VDCVSNwlqpOB2YA80XkRJdjCnlNLW28vekA503KIiry4z+FuamDqG1sobSqwcXowoslol7YUFLF4JhI8tPj3Q7Fr6IiI5gyLJkNpdU0t7a5HU7YUY/2a0HRzsuuk/azDSVV1DW1cvnMTz5c3t4P49piOzALlB4TkYg8JCJlIrK+i/kiIneLyHYRWSsis/wfpvsaW1r/P3v3HSdZWSZ6/PdUdVfnHKZzmhyZPAxDmAGEAVlYlVVAVFwUWUVx1d277l7vmu7uuu51g4iKiAoKqAQl54EhTM55psNMT+ecc9d7/6jqoZnpUN1dVafC8/18+jMVTp96qqZPPecN53k5XtfJwuxE7LbQa65flJ/MwJCT43WdVocSlkTELiL7gQbgVWPMjjG2uUtEdovI7sZGvRDZE4/uqDz3c769la3kp8awtij1A49nJ0VjF2H/2XZ/hRn2PGkR/RrYPMHz1wFz3T93AT+deViB573SZvqHnCzJSbQ6FJ8oTo8jITpCu+csYowZNsYsB/KAtSKyZIxtHjDGrDbGrM7I8E3V9nBR195HeWM3N6/Mx3beiWWE3UZWUrQeC340aSIyxmwFWibY5CbgYXf3wnYgWURC7urIFw/XEhVhY3ZGvNWh+IRNhGW5SZyo76RdV3a1jDGmDXiTiU/+1Ay9dqweR4SNz1xSOObzeSkxHKpux6m1GP3CG2NEucDZUfer3I9dIFi7FoaGnbx6tJ4FWQkfGNQMNcvykhl2Gl4+Umd1KGFFRDJEJNl9Owa4GjhubVShq6q1h6O1HVw6N53kWMeY2+SlxNLVP0R5k07j9gdvfKuONWAy5mlEsHYt7KhoobVnMORmy50vLyWG1DgHzx6osTqUcJMNbBGRg8AuXGNEz1kcU8gZGSt69Wg9sQ47G2anj7ttXkoMgI4T+Yk3riOqAkaXoM4DQuqb7MXDtcRE2kP+GhsRYWluEu+UNtHSPUBq3Nhni8q7jDEHgRVWxxEOKpq6OdXQxXVLsoieoERXRkIUcQ47B6vauHlV3gXPj0x+uG1dgc9iDSfeaBE9A3zaPXvuYqDdGFPrhf0GBKfT8PKRejbOz7B06QZ/WZqbxLDT8Ip2z6kQY4zh1aN1JERHcHFJ2oTb2kRYmpc04UrLyns8mb79GLANmC8iVSJyp4jcLSJ3uzd5ASgHSoFfAF/0WbQW2FPZSmNnP5uXZFkdil9kJ0VTlBbL84dC5lxCKQDONPdwurmHjfMzifRgrHd9STpHajpo7fbfSsrhypNZc7caY7KNMZHGmDxjzC+NMT8zxvzM/bwxxnzJGDPbGLPUGLPb92H7z4uH6nDYbVy5IDzKwosI1y/N5r2yZlr0AFQhZHtFM9GRNlZ5WLD40rnpGAPvlTX7ODIV+n1NMzDsNLx0uJbL5qaH1cJxH16WrbPnVEjp6h/iSHUHqwpSPO5ivygviYToCN4pDZ4ZvsEqLBNRZUsP28qa6Oyb+HqZracaqWnv46MrLxysDGWLshMpSovlBe2eUyHiSE07w8awqvCDVRTGq7oArgtbN8xO5/VjDQxp6SufCrtE9OaJBn72VhnPHqzlhy+foHSCcu+P7qgkPd7BhxbN8mOE1hMRPrxMu+dU6Dhc3U56vINZiVFT+r2PrcqjobOfN443+CgyBWGWiKrbenn9eAOLshO5Z9Mc0uOj+O32MxyquvBagR+/forXj9WzKDuJJ/ZUBdwy3752/VLtnlOhoad/iIqmbpbkJE15WYdN8zOYlRjF79zH/0QtKDV9YZWI7nvjFAA3LMsmJzmGz24oItZh5wuP7L5gaeBXjtZjtwmXzJ54mmeoWpSdSHF6HM8f1O45FdxONXThNLAwe+p1IiPsNj69voi3TjayvVwnLfhK2CSi3oFhntpbzcqClHNlPRKiI/nkukKauge48ze76eofAmDLiQYOVbdz6Zx0EmPCZ5LCiEd3VPLYzrMUpsbyXlkTD2wttzokpaatrLGL6Egbue5qCVMV54ggKSaSr/1+v65i7CNhk4jeLW1yVc/O/eBZUW5KDD++dQWHqtu56b53+P5zR7n7kT1kJ0Vz+dzgKUPkC8vyk3EatAqxCmpljV2UpMdPe1VlR4SNzUuyqGnvY+8ZvcDVF8ImEb1+vIE4h53iMRa1u3ZxFg9+ZjUAv37vNKuLUvjshmKiJigBEg6yEqPJS4lh95kWjJ4JqiDU0j1Aa88gJRkzW8xyWW4SBamxvHq0XheP9IGwSETGGN44Xs/l8zLGrZ69aX4mr33tCo59bzO/+9zFxEd5owxf8FtdmEp9Rz8HxpjQoVSgO9PcDbjW25oJEeFDi2bR2T+kPQQ+EBbftuVN3dR39HP5vAwmOrEXESLtobf66kwsy0vi+UM1/H5XJcvzky2JYaxZSlpsUnmiqq2XSLuQmRA9432VpMeRnRTNO6VNrCpMmfIMPDW+sGgRjZzBrCiw5os0mEVH2lmam8Qz+2vodk/mUCpYVLf2kpMcg90286QhIlxcnEZDZz+17X1eiE6NCItEtP9sG3EOO3MzQ3sZB19ZXZhK98CwFkJVQWVw2ElNWy95ydObLTeWRTmJ2AQOVWtXtTeFRdfc/rNtLM1L8spZUTgqTItlbmY8v3r3NH+1Km/CLgntRlOB4mR9J0NOQ15KrNf2GRcVweyMeA5Vt3NNmFVc8aWQbxH1DQ5zrLaD5fmeVdxVFxIRPn9ZCcdqO3intMnqcJTyyEH3BJu8aV4/NJ5FOYm0dA/Q1DVx+auRKgxaiWFyIZ+IjtV2MDhsWJ4f2st8+9pNK3LITIji/i1lVoeilEcOnG0jJtLu9ZWG52TEA1DaOH6dSjU1IZ+ITtW7/lgWZE29vId6X1SEnS9cMZtt5c28q60irxKRfBHZIiLHROSIiNxrdUyh4EBVO7kpMV6f3ZYa5yAlNpKyCQomq6kJ+TGi0sYuHHYb+ane6yf2RCg2xz+5roBfvl3Ov790nKe/uAGbjrl5yxDwdWPMXhFJAPaIyKvGmKNWBxYMRh9rI+ORvQPDnKzv5LI56V7Z72giwuyMeA7XtDM07Bz32kTluZD/BEsbuihOj9OJCl4QHWnn69fM50BVO7/ffdbqcEKGMabWGLPXfbsTOAbkWhtVcDta286w03h9fGhESUY8fYNOjtd1+mT/4SbkE1FZYxdzMuOtDiNkfHRlLuuKU/m3F49T36HXUnibiBQBK4AdYzx3l4jsFpHdjY26auhERpZ2yfXijLnRCtNc+91bqbXnvCGkE1Hf4DBnW3qYrYloxkZm/zy28yyXzE6nZ2CIex/fx7BTa9B5i4jEA08CXzXGdJz/vDHmAWPMamPM6oyM8C7IO5lD1R2kxztIjPbN6ENyTCSJ0RHs0SKoXhHSiaiiqRunQVtEXpaREMWNF+WyvbyF7zx7RAuieoGIROJKQr8zxjxldTzB7khNO0typ74QnqdEhILUWHaf1kTkDSGdiEaWAR+Zbqm8Z1VhCl+4vISHt53hX144hlNbRtMmrm/LXwLHjDE/sjqeYNc3OMyphi6W5vr2ko3CtDiq23qp03I/MxbyiUiEGZeAV2P7X5sX8On1hfzi7Qq+8Ns9F6xyqzy2AfgUcKWI7Hf/XG91UMHqaG0Hw05DS/fEF5zOVEGqjhN5S0hP3y5t7CI/JZboMF9XyFdsNuE7Ny6mMC2OH7x4nGv/622uX5ql12xNkTHmHUCndXrJEXcduFwv1pgbS3ZyNFERNvacaeX6pdk+fa1QF9ItorIGnTHnayLCnZcW8+d7NpAe7+DhbWf40/5qBoZ08TBljUPV7cQ67CTFRPr0dSJsNi7KT2a3TliYsZBNRMNOQ3lTtyYiP1mYncif79nAZXPS2VXRwo/fOEWdTu9WFjhc3UFusvcrKoxlVWEKR6rb6Rsc9vlrhbKQTURnW3oYGHLqRAU/ioqwc93SbO68tJiBYScPbC07t0KmUv7QN+iqqJDj4265EasKUhhymnMFVtX0hGwiKnMXJJydqRMV/K0kI567r5hNnCOC32w7zQm9+lz5ycjSD/5KRCsLXVX99XqimQnZRPT+1G1dDM8KKbEO/vrSYiLtNj7/8G46+gatDkmFgZGWia8nKoxIjXNQkhHHnjMtfnm9UBXSiSg9PoqkWN8OWKrxpcQ6uG1tAdVtvXzzqUNWh6PCwO7TLWQkRJHix+N+VUEKe8606oXdMxC6iaixiznaLWe5wrQ4vvaheTx/sJaXDutS48p3jDHsqGhhbXGqXyYqjFhdlEJrzyBljToeOl0eJSIR2SwiJ0SkVET+YYzn7xCRxlEX433O+6F6zhhDqU7dDhhfuLyExTmJfOvPR+jULjrlI209g9S297GuONWvr7u+xLXUxDuntBDtdE2aiETEDvwEuA5YBNwqIovG2PT3xpjl7p8HvRznlDR29tPZN6Qz5gJEhN3Gv3xkKY2d/fz8rXKrw1EhqqLJ1SJZ6+dEVJAWS3F6HI/tPBuS65D5gyeVFdYCpcaYcgAReRy4CQjYRbvOTVTI9GyiQjD/8QRD7CMxXpSXxM/eKiMuKoKkmMhzi5gp5Q0n6jtJj3cwLzOBvWfa/Pral89N59GdlQwN64Xc0+FJ11wuMHoVtCrGXrTrYyJyUESeEJH8sXbkr/VURtaS1665wHLNoiwAXj1aZ3EkKtQMOZ2crO/kqgWzLFk5eOP8TAaHzbmTYDU1nrSIxvpfPX96yLPAY8aYfhG5G/gNcOUFv2TMA8ADAKtXr/bZFJPShi7ioyKYlRjlq5dQTL01lhLn4JLZabx9qolLZk9/CWelzne6qYf+ISdXL5plyetvmJNOrMPOHi2AOi2etIiqgNEtnDygZvQGxphmY8xI6eVfAKu8E970lDV2MTsjzq8zZ5RnrpiXSXSknZePaKtIec/h6nYi7cKlc6w5wXFE2FiRn8zx2k6fV/0ORZ4kol3AXBEpFhEHcAvwzOgNRGR06dkbgWPeC3HqShu6dFXWABXjsLNxfganGrp4r6zJ6nBUCBgcdnKwuo0lOUnEOKyrtL+6KJVhY3hgq07ImapJE5ExZgi4B3gZV4L5gzHmiIh8V0RudG/2FRE5IiIHgK8Ad/gq4Ml09A1S39Gv40MB7OKSNJJiIvnBSyf0IkA1Y0drOugbdJ4rtzMdj+6onPHEn1mJ0SzPT+ZX71ZQ09Y7o32FG4+uIzLGvGCMmWeMmW2M+b/ux/6PMeYZ9+1vGmMWG2MuMsZsMsYc92XQEynTVVkDXqTdxlULMjlwto2Xj9RbHY4KcjsqmkmJjaQ43foL2D/kHqP67rMBO6k4IIVcZYX3p25rIgpkKwpSmJMZz7+/dFzXLlLTdri6ndPNPawvScMWAGPCKbEOvnLVXF46Usfx2g6rwwkaoZeIGrtw2G3nlvFVgcluE/7p+oWUN3Xzm/dOWx2OClK/ee80kXZhVaF/L2KdyOcvK2FuZjzPHKzRkywPhVwiKmvooig9lgh7yL21kLNpQSab5mfwX6+dpKq1x+pwVJBp7urnzwdqWFmQYukkhfM5Imz8y0eX0tYzqBNyPBRy39ZaYy64fPemJQD8rycP4nSG58QFEXlIRBpE5LDVsQSTx3ZWMjDkZH1JmtWhXGBNUSrzZsXzbmkTg1ptYVIhlYj6BoepbOnRiQpB4tEdlbx9qolrFmfxbmkzn/31LqtDssqvgc1WBxFMBoedPLL9DJfNTSczMdrqcMZ0xbxMugeG2TvqIteR2XnBUJrLn0IqEZU2dOE0MD8r0epQ1BSsLkxhTVEqb51s5L9eOznmlO6hYScDQ86QnO5tjNkK6MpqU/DS4TrqO/r57IYiq0MZV1FaLDnJ0Wwraw7Jv1tv8qTET9AYWZJ6fpauyhpMRIQbL8ph2Onkv147xWvH6vnw0hwcETbKGrt441gD9R19GCA9PorL5qZz69r8sKucISJ3AXcBFBSEb8FYYwy/eLucorRYNs7L5PFdZyf/JQuICOtL0nhybzXby1tYPzvwuhADRUglopP1nTjsNorSdMZcsLHbhI+tzOO2dYX89M1SfvCS61K0hKgIspKiWZidQaTdxrHaDp7eV01CdATfuXFxWCUjf9VqDESju7LyUmI4WNXOv350qSUFTqdiWV4yLxyq45HtpzURTSCkEtGJ+k5mZ8brjLkgJSLcvCqPm1fl0dk3yOCwISU2ksd2vn/Ge/m8DF48VMvD284wd1YCn7q40MKIlRXu21JKVmI0H1051iIAgSXSbmN1UQovH6mntl2rLYwnpL6xT9R1skC75UJCQnQkqXGOC1o8NhGuW5rNxvkZfO/Zo5xu0uWZg91UBu9PN3Wzs6KFuy4vISriwinbgTgRYF1xGk5jeHjbGatDCVghk4jae13LBM+bpYko1NlE+PeblxFpF/7tRcuqSXmNiDwGbAPmi0iViNxpdUyB6o0TDaTFObh1bfCMkaXGObh+aTYPv3eanoEhq8MJSCGTiI7WuMppLMzWRBQOMhOiufuK2bx0pO4D02ODkTHmVmNMtjEm0hiTZ4z5pdUxBaKT9Z2UNnTxxU1zAuoCVk985cq5dA8M89ZJ3y0IGsxCJhEdqnYtDbwsL9niSJS/3HlZMUkxkfxCy+6HvGGn4YVDtaTFOYJyXHB+VgIfX53Hu6VNOlY0hpCZrHCouoPc5BhS4xxWh6L8JNYRwW3rCvj5W2WcbekhX+sLhpyR8Z7dZ1po6OzntrUFOCIC5/x5KuNR/3j9Qp4/WMvT+6q5+4rZPowq+ATO/+gMHapqY2luktVhKD/7zPoibCL8Wgunhqzu/iFePVpPUVosi3OC92L15FgHNyzLoaq1l21lzVaHE1BCIhG19w5yurmHpXmaiMJNVlI0H16Wze93naWzb9DqcJQPPH+olv5BJzctz0VEAnJmnKeW5SWxICuBl4/UnRvXDub34y0hkYgOV7cDaIsoTN15aTFd/UP8YXeV1aEoLztZ38n+s21cPi+DWQFaU24qRISPrswjxmHnnsf2jjuLLtySU0gkop0VLdgElhfoRIVwtCwvmTVFKTy87XTYVvAORU1d/Ty9r5qM+Cg2zc+wOhyviY+K4OOr86lo6uaf/3zE6nACQkgkoh0VzSzKSSQxOtLqUJRFPrW+iDPNPbx1SqfHhoK2ngE+//BuegaG+Pjq/JCrljI7I557Ns3hj3uq2Bfklx94Q9DPmusbHGZfZRu3B+GUTnWh6XZHbF6cRUZCFI9sO8Om+Zlejkr5iuv4baW6rZeT9Z1ERboSzjP7a2juGuDjq/PJTYmxOErfuPequewob+GpfdXERwf9V/GMBP27P3C2jf4hJxcH4OJYyn8cETZuXVvAj984RWVzDwVa+DagjJxg3Lau4Nz9iqZu/rj7LG29gzjsNg5Xt9M35FruY21RKj/55EqO13ZaGbbXjHWCFWG38cCnV7H5v97mN++dJiXWwV9fWkx8VNB/LU9Z0L/jd8uaEYG1RYGzZr2yxifXFXD/llJ+u+MM/3j9QqvDURPYV9nKk3urXF++G4opyYg716thjDlXYzBUEtF4kmMdfP6yEp7aV8WPXj3JfVtKWV+SRkJ0BAvDaF21oE9ErxypY3VhCkmxOj4U7mYlRnPt4ix+v+ss9141l7gwPLMMBr/bcYYn9lRRnO5KPtGRHyzXE05LewDEOOx8cl0hi3MSeeZADVtONFDe2M3zB2upbOnhf9+wkIQQH/8O6hHAyuYejtd1cu3iLKtDUQHizsuKae8d5LGd4TP1NZg8+HY5//T0YebNSuAzlxRdkITC2UX5yXzrhkW88fWNfP1D87hkdhpP7K3ihh+/w6Gq9pCe0h3UiejlI3UAmojUOSsLUlhfksYv3i6nf2jY6nDUKG+fauT7zx/jw0uz+eTFBUSG2Ew4b0qLj+LDy3L4/V0XMzjk5GM/fe/c9ZKhKGj/EowxPLm3iqW5SVpjTH3APVfOob6jn0d0/ZeAsb28mRcP13HDsmz++5blRNiC9qvHZ0ZaPKNbPauLUnn+K5exNC+Jx3ZWsrOixav7DxRB+9ewrbyZ43WdQVmJV/nWhjnpXDY3nR+/UUpbz4DV4YS9J/ZU8cyBGhZkJfCfn1g+7jVBgfolabWUOAe/vXMd82Yl8Kf91dz3ximMCa0Lt4M2ET30TgWpcQ5uXJ5jdSgqAP3j9Qvp6h/i+88fszqUkDGdM+oXDtXy908cYE5GPLeu9W53XKAmLl/EFeOwc/vFhSzPT+Y/XjnJ7Q/uCKkqIkGZiN4+1chrxxr4rA52qnEszE7kixtn88SeKl48VGt1OGHptaP1fOWxfawsSOH2iwt1TGiG7Dbh5lV5rJ+dxrtlzfzdEwcZGnZaHZZXBN381vaeQb71p8MUpcXy+ctLrA5HBbAvXzmXrScb+dofDpCVFM2KghSrQwobzx+s5d7H97E4J5GHPruG5w7oycD5ptNqsolww9Js4hx2ntxbxdHaDm5Zk89nLiny6PeNMQw5DU6nwWYLnGnyHp2iiMhmETkhIqUi8g9jPB8lIr93P79DRIq8HSi4ktDnHt5FdVsvP/jYMm0NqQk5Imw8+Jk1ZCRE8ckHdwR0y2iyY8xKg8NOdp9u4URdB/UdfTgnGJ8wxvDIttN8+bG9LM9P5pHPrdMakF4mIly5YBZ/cVEOx2s7+OmbZZyom/jC39buAbaebOQ/XjnBPz9zhOXffYWv/WF/wMzEm7RFJCJ24CfAh4AqYJeIPGOMOTpqszuBVmPMHBG5BfgB8AlvBdnSPcCf9lXz861lNHcN8N+3rGCdlvRRHshIiOKPd6/nrod38ze/28ulc9L5xJp8lucnEx8VQXSknRiHtSc0Hh5jftXZN8hbJxt59Wg9W4430NH3/nIFidERnKrvZPOSbNYUpRBht+F0GnadbuG+LaW8faqJjfMzuP+TK4l1TL3TJRDHfawy0WexviSN1NhI/rinig//z9vcvCqPj6/JZ1luEhF2G8NOw77KVh7fdZZnD9TQP+SkKC2WNUWpJMVE8uLhOp7aW8264lQ+d1kJVy3ItKyVJJPNvhCR9cC3jTHXuu9/E8AY86+jtnnZvc02EYkA6oAMM8HOV69ebXbv3u1RkN986iCP7TzLioJkvnfTEpbMYN0h/SMPDSM1yzw1MOTk4W2neWBrOQ2d/R/Yz798ZOlUduX1I9WTY+x8Uzl+JnOstoPnD9bS1T9Ea88Ax2s7KW3sYthpSI1zcOWCTK5eOIv9Z9to7OzneF0HpQ1d9A85cdhtpMY5aOsdoG/QSVJMJJfOSWf97DRsY1RIGF1rTnlHd/8QZ1t7+P2us67/kwgbyTGRtPUOMjDkJNZh5y9X5JIeF0VWkmtNp9vWFdDRN8gfdp3lV++eprqtl6zEaBZmJ1CYFkdCdASfXl9ERkKUN0Md99jxJBHdDGw2xnzOff9TwDpjzD2jtjns3qbKfb/MvU3Tefu6C7jLfXc+cGLq72XG0oGmSbcKbvoefafJGLPZmzv05BhzP2718aN/V6HDivc57rHjSbt5rCx2fvbyZBuMMQ8AD3jwmj4jIruNMautjMHX9D0GnaA4fkLsMx9TOLxHCLz36clkhSogf9T9PKBmvG3cXXNJwPQvAVYqvHhyjCkVsjxJRLuAuSJSLCIO4BbgmfO2eQb4jPv2zcAbE40PKaU+wJNjTKmQNWnXnDFmSETuAV4G7MBDxpgjIvJdYLcx5hngl8AjIlKKqyV0iy+DniFLuwb9RN9jEBnvGLM4rLGEzGc+gXB4jxBg73PSyQpKKaWUL2nNDaWUUpbSRKSUUspSmoiUUkpZShORUkopS2kiUkopZSlNREoppSyliUgppZSlNBEppZSylCYipZRSltJEpJRSylKaiJRSSllKE5FSSilLWZqIROSfROSIiBwUkf0ism6S7e8QkRwvvG6RiPS6X/OAiLwnIvPdz31URF4fte2l7u0uqFQuIm+KyAWLS4nIt0XkjpnGOcZ+c0TkiUm22Sgiz43z3GkRSfd2XKNet11E9onIMRH5Zy/u+7SIHHL/P+wXkUu8te8xXmvczy9Y6XE25bjD/Tg7JCJHReT7IhI16vl5IvKCiJS6X/sPIjLLG69tWSISkfXADcBKY8wy4Grg7CS/dgcw4wPErcwYs9wYcxHwG+AfAYwxTwF9InKb+6C4H/iiMWbIS687LSISYYypMcbcbGUck3jbGLMCWA3cLiKrRj851pfMFGxy/38tN8a8N6Mow4geZ1OjxxmbjDFLgbVACe7lIkQkGnge+KkxZo4xZiHwUyBjBq91jpUtomxca5j3AxhjmowxNQAiskpE3hKRPSLysohki8jNuD7437nPnGK8GEsi0Drq/peB7wPfAXZ564tPRHaIyOJR9990v9e17rPFfeedNd4hIn8UkWeBV9xnmIfdzxWJyNsistf9M7qVkCgiT7vPan4mIhf8P4vI7SKy0/1Z/lxE7N54jwDGmG5gDzB7jPcgIvJDETnsPvP6hDuejSKydbK4z3sPfyciu9xn+t8Z9bkcF5EH3a/xOxG5WkTeFZFTIrLWvd2Yn/l5+48TkYfcr7FPRG7y1mfkR3qc6XE25ePMGNMF3A38pYikArcB24wxz47aZosx5rC33owlP0A8sB84iets6Ar345HAe0CG+/4ncC0UBvAmsHqc/f2de3/n//zPGNsWAb3u58uAWqDgvG3+FWgH0id4D2PGA3wbuGOMx/8W+I77djZw0n07EYhw374aeNJ9+w5cy0injor7sPt2LBDtvj0X1yKFABuBPlxnM3bgVeBm93OngXRgIfAsEOl+/H7g02PE+5/jfKb/MMa2G4Hn3LfT3K+1eIz38DHPst+xAAAgAElEQVR3THZgFlDp/iwmi/uQ+7V3uB+7BtfZmuA6oXoOuNz9GQ0BS92P7wEecm93E/CnST7z0e/jX4Db3beTcf2txll1zOhxpscZvj/O0s97vf3AOuBHwL2++judSRNuRowxXeJqUl4GbAJ+LyL/AOwGlgCvigi4PqxaD/b3Q+CHUwihzBizHMB9tvAAsNl934brD7ULKASaprDfifwB13/8PwMfB/7ofjwJ+I2IzAUMri+JEa8aY1rG2FckcJ+ILAeGgXmjnttpjCl3v5fHgEuB0X3eVwGrgF3uzzgGaDj/BYwxfzvF93eZiOwDnMC/GddKvmvOew+XAo8ZY4aBehF5C1gDdEwS9yZjzOj/h2vcP/vc9+NxfVFUAhXGmEPu/RwBXjfGGBE5hOtLBib+zEe/xo0i8g33/WigADg2xc/FMnqc6XHG1I6z88kUY5sWyxIRgPtDehN40/0l8RlcZ7BHjDHrp7IvEfk74JNjPLXVGPOVSX79GeBXo+5/CTgMfAv4iYisd3+RvYzr7GK3MeZzU4kPwBhTLSLNIrIM1xnoF9xPfQ/YYoz5iIgU4fpMRnSPs7u/BeqBi3Cd+feNfqnzX/q8+wL8xhjzzYniFZH/xPXldb7HjTH/Nsbjbxtjbhjj8dHvYaI/7Mni/kB4wL8aY37+gQddn1//qIeco+47ef9vfqLPfPRrfMwYc2KCOAKeHmd6nE0S55jHmYgk4DpxOwkcAa6YYJ8zYuVkhfnuM5MRy4EzwAkgQ1yDrIhI5Kj+3k4gYaz9GWN+aN4fzB79M9nBAa4zgjL362UBXwP+3hjzElANfM79Gte69znlg2OUx4G/B5JGztpxnalVu2/f4eF+koBaY4wT+BSuM9oRa0Wk2H3G+QngnfN+93XgZhHJBBCRVBEpPP8FjDF/O85nOtbB4amtwCdExC4iGbi603Z6GPdoLwN/LSLx7veQO/J+POTJZ/4y8GVxn86KyIop7D8g6HGmx9l0jjP3cXU/rq7sVuBR4BIR+fCobTaLyNIZxHiOlZMV4nE1k4+KyEFgEfBtY8wAcDPwAxE5gKuPcmSA8NfAz8Q7g6iz3fs5gGssYOSP/kfAvxtjGt33vwr8k3vAzhueAG7B1X0w4t+BfxWRd/ngH/pE7gc+IyLbcXUXjD4b2gb8G66zzQrg6dG/aIw5CvxvXIOaB3F1Y2RP/a1My9PAQeAA8AauL6I6T+IezRjzCq6DY5v7LP8JxvnyHIcnn/n3cHXNHBTX4PX3prD/QKHH2fv0OJs87i3uv/WduLq5vwBgjOnFNfvyy+Ka9HMUVzK/oKtxOsQ9IKW8SES+DZw2xvza4lCChohsBL4xTpeDUhfQ42zqAvU408oKSimlLKUtIh9wn3W0GWP2Wx2LUqFKj7PQoYlIKaWUpbRrTimllKUsS0SbN282uOav64/+BNNPQNDjR3+C8GdcliWipiZvXUStVPjR40eFEu2aU0opZalJE5GI5IvIFnGtP3FERO4dYxsRkf8R1zoVB0VkpW/CVUopFWo8qTU3BHzdGLPXXXtoj4i86r5qeMR1uApOzsVVqfWn7n+VUkqpCU3aIjLG1Bpj9rpvd+KqPJx73mY3AQ8bl+1Asoj4q5SFUkHNXQ9u/6ifDhH5qtVxKeUvU6q+7a5YuwLYcd5TuXxw1ccq92MfKCsvIncBdwEUFBRMLVKlQpS7uvfIUgl2XIU5x62zp1So8Xiygrsa65PAV40xHec/PcavXDBdzxjzgDFmtTFmdUaGV1aYVSrUXIVrDZ8zVgeilL94lIhEJBJXEvqdca01f74qIH/U/TygZubhKRV2bgEeG+sJEblLRHaLyO7GxsaxNlEqKE3aNedei+WXwDFjzI/G2ewZ4B4ReRzXJIV2Y8ykqz1a4dEdlRc8dts67SZU1hMRB3AjMOZCasaYB3CtcMrq1asnvEBQTc/o7wf9XvAfT8aINuBaEOqQiIwUF/xHXEsmY4z5GfACcD1QCvQAn/V+qEqFvOuAvcaYeqsDUcqfJk1Exph3mGTdcuOqnPolbwWlVJi6lXG65ZQKZVpZQakAICKxwIeAscZglQppU5q+rayh41qhzxjTA6RZHYdSVtAWkVJKKUtpIlJKKWUpTURKKaUspYlIKaWUpTQRKaWUspQmIqWUUpbSRKSUUspSmoiUUkpZShORUkopS2kiUkopZSlNREoppSyliUgppZSlNBEppZSylCYipZRSltJEpJRSylKaiJQKACKSLCJPiMhxETkmIuutjkkpf9GF8ZQKDP8NvGSMuVlEHECs1QEp5S+aiJSymIgkApcDdwAYYwaAAStjUsqftGtOKeuVAI3Ar0Rkn4g8KCJxVgellL9oIlLKehHASuCnxpgVQDfwD+dvJCJ3ichuEdnd2Njo7xiV8hlNREpZrwqoMsbscN9/Aldi+gBjzAPGmNXGmNUZGRl+DVApX9JEpJTFjDF1wFkRme9+6CrgqIUhKeVXOllBqcDwZeB37hlz5cBnLY5HKb/RRKRUADDG7AdWWx2HUlbQrjmllFKW0kSklFLKUpqIlFJKWUoTkVJKKUtpIlJKKWUpTURKKaUspYlIKaWUpfQ6Igs9uqPygsduW1dgQSRKKWWdSRORiDwE3AA0GGOWjPH8RuDPQIX7oaeMMd/1ZpDKM5rYlFLByJMW0a+B+4CHJ9jmbWPMDV6JSAHQ2j3AT7aUcqK+E4BLZqeTFBNpcVRKKeV9kyYiY8xWESnyfShqRGlDF7c/uIPm7n7mZiZwvK6D/ZVt/PWlxcxKjLY6PKWU8ipvTVZYLyIHRORFEVk83ka6nsrkOvsGueuR3Qw5nTz9xQ28cO9lfPnKuSDw0DsVdPUPWR2iUkp5lTcS0V6g0BhzEfBj4E/jbajrqUzuBy8d50xzD/fdtpIluUkAzEqM5o5LiugZHObpvVUYYyyOUimlvGfGicgY02GM6XLffgGIFJH0GUcWhmrbe3l0RyWfuriQi0vSPvBcdlIM1yyaxbG6To7VdloUoVJKed+ME5GIZImIuG+vde+zeab7DUevHKknMSaSr149d8znL5mdTmZCFC8crqV/aNjP0SmllG9MmohE5DFgGzBfRKpE5E4RuVtE7nZvcjNwWEQOAP8D3GK072jKatt7OVHfyZ0bikmOdYy5jd0mXL80m5buAf6w66yfI1RKKd/wZNbcrZM8fx+u6d1hyVvX7mw92Ygjwsan1xdNuN3czHgKU2O5/80yPr4mn6gI+5ReR681CkwichroBIaBIWOMLpKnwoaW+AkAXf1DHK7uYFVhCkmxE18rJCJctXAWte19/GF3lZ8iVH6yyRizXJOQCjeaiALAnjOtDBvDuqJUj7afnRHH6sIU7t9SqmNFSqmgp4nIYk5j2HW6haK0ODI9vFhVRLj36rnaKgotBnhFRPaIyF1jbaDX4alQpYnIYmWNXbR0D7C22LPW0IhL56SzSltFoWSDMWYlcB3wJRG5/PwN9Do8Fao0EVlsZ0ULsQ47S3ISp/R7IsK9V2mrKFQYY2rc/zYATwNrrY1IKf/RRGShzr5BjtV2sLIghQj71P8rLpubzsqCZO7fUkrfoLaKgpWIxIlIwsht4BrgsLVRKeU/mogstPdMK04Daz2cpHA+EeEb18yntr1vzGnZKmjMAt5xX4u3E3jeGPOSxTEp5Te6MJ5FnE7DrjOtFKfHkZ4QNe39XDInnfUladz/Zin3bJqLI0LPLYKNMaYcuMjqOJSyin5rWeTdsqZpTVIYyzeunUdT1wDbyrWyklIq+GgisshjOyuJddhZnD21SQpjWVWYyqb5GWw92ahjRUqpoKNdc1PgrXGYxs5+XjlSz8UladOapDCWr31oPn9x3zu8W9rEVQtneWWfSinlD9oissDjOysZchrWTHOSwliW5iWxOCeRd0qb6NbF85RSQUQTkZcMOz0rON47MMyv3jvNpvkZZMxgksJYrl44i8FhJ28cb/DqfpVSype0a26G2noG+NP+akobukiNi+LyuencujYf9xJNF3h8VyUt3QN8cdMcTtV3eTWWWYnRrClKZUdFM+tKUslM8KxkkFJKWUlbRDPQ1T/EQ+9WUNnSw7qSNGIibTy1r5qv/eEAA0POC7Zv6R7gv18/xfqSNK92y4121cJZRNptvHiozif7V0opb9MW0Qy8dLiO1p5BPndpMYVpcTiN4c0TDTy9r5qmrn5+evsq4qNcH7Exhu8/d5TOviG+c9Nin8UUHxXBpvmZvHSkjlMNuqS4UirwaYtomuo6+thX2colJWkUpsUBYBPhygWz+OHNy3ivrJlP/Hwbh6vb6Rsc5l9fPM5T+6q5Z9Mc5s1K8Glsl8xOIzXOwQuHaj0eu1JKKatoi2ia3jrRgCPCxhXzL6yC/Fer80mLd/CNPx7khh+/g03AaeDWtfl89eq5Po8twm5j8+IsHt1ZyeO7KvnkukKfv6ZSSk2XJqJp6B0Y5khNB6uLUol1jP0RXrlgFlu+vpGn91VR29HHpvmZrCtOHXcSg7ctzkmkKC2Of3/pBNcuziI93rsz9JRSylu0a24aDlS1MeQ0rC5MmXC7pNhI7thQzDevW8jFJWl+S0LgKoj6l8tz6BkY4nvPHfXb6yql1FRpi2ga9lW2kp0UTU5yjNf37c0q2pmJ0Xxx4xz++/VTfGRFLhvnZ3pt30op5S3aIpqizr5Bzrb2sjgnyepQPPLFTbMpyYjjn54+rBUXlFIBSRPRFB2vdU2JXuSFYqX+EBVh5wcfW0ZNey//8sIxq8NRSqkLaCKaomN1HaTERjIrMXgG/9cUpXLnhmJ+t6NSry0KYCJiF5F9IvKc1bEo5U+aiKZgaNhJWWMXC7IS/TrxwBu+ce18ZmfE8dTeal0qInDdC2izVYUdTURTcLa1l8Fhw+yMOKtDmbLoSDv/8VcX0dE7yPOHaq0OR51HRPKADwMPWh2LUv6ms+amoKKpCwGK0q1PRNOZXbeiIIXL52Xw1slGluQkMj8rOMa5wsR/AX8PjFt2Q0TuAu4CKCgo8FNY4WPryUaO1nSwIDsBW5D1eAQ7TURTUN7YTVZS9LgXsU7Em9OyZ+KqBZkcq+3gmQM13JsejyNCG8VWE5EbgAZjzB4R2TjedsaYB4AHAFavXq21m7xo2Gn49EM7AdgwO40PL8uxOKLwot9CHuobHKaypYeSAGgNzUSE3caNF+XQ2jPIWycbrQ5HuWwAbhSR08DjwJUi8ltrQwovx2o7zt0+Ua8TevxNE5GH9p91VVMoyYi3OpQZK8mIZ1leEu+UNtLRO2h1OGHPGPNNY0yeMaYIuAV4wxhzu8VhhZXt5c0AXFySSlPXAL0DOqHHnzQReWh7ebNrfCgtuFtEI65ZlIXTCa8fr7c6FKUst728meL0OBZluy5Ur2rrsTii8KJjRB7aVtZMdnI0MQ77pNsGynjQRFLjHKwpTmVnRTNVrT3kpcRaHZICjDFvAm9aHEbY2X+2nY3zM8h1l+2qau21OKLwoi0iD/QNDrPvbBsl6cHfLTfa5XPTEYRfbC23OhSlLNPZN0hTVz9zMuOJcdhJj3dQrYnIrzQReWBfZRsDQ86gn6hwvuRYBysKknl811lauwesDkcpS5xucnXDjXS7Z8RH0dTVb2VIYWfSRCQiD4lIg4gcHud5EZH/EZFSETkoIiu9H6a1tpU3Y5PAuH7I2y6Zk07/kJMn9lRZHYpSlqho7gag2H18p8VH0dI9gFNXN/YbT1pEvwY2T/D8dcBc989dwE9nHlZg2V7ezJLcJKIjJx8fCjZZidGsLUrltzvO6IGnwtLpJlciKkh1jZOmxTsYchpqO/qsDCusTJqIjDFbgZYJNrkJeNi4bAeSRSTbWwFarW9wmP2VbVxckmZ1KD5z+/pCzjT38HZpk9WhKOV3p5u6yU56fyJSWpyroPEZd4JSvueNWXO5wNlR96vcj11Q0CyYSpSMzHwra+xiYNhJfwgXCt28OIv0eAePbDvDFfMyrA5HKb+qaO7+wGUZ6fGOc49fMifdqrDCijcmK4xVlGnMPh5jzAPGmNXGmNUZGcHxhVfe2IVNoDBErh8aiyPCxi1rCnjjeD1VrXr9hAovZ5p7PjD+mxgTSYRNznXZKd/zRiKqAvJH3c8Daryw34BQ3tRNTnJMSI4PjXbL2nwM8OSeaqtDUcpv2nsHaekeoDj9/evobCKkxjk43awnZf7ijUT0DPBp9+y5i4F2Y0xIrDMwMOSkqqU35KZtjyUvJZaLi9N4el8VxuikBRUeRlo951dMSYuP0haRH3kyffsxYBswX0SqROROEblbRO52b/ICUA6UAr8AvuizaP2ssqWHYRMa9eU88dGVuZxu7mFvZZvVoSjlF6fPm7o9Ij3OwZmWHp1J6ieTTlYwxtw6yfMG+JLXIgog58aHUsOj/M11S7P51p8P8/S+KlYVplgdjlI+V9HUjQjkn3eMp8VHMTDkpLaj71zZH+U7WllhAicbOslPiSUqxMeHRsRHRXDt4iyePVBL/1DozhJUasTppm5yki4cA05zz5zT7jn/0KKn4+jsG6SmrY9rFs2yOpQZmWoB1o+uzOPP+2vYcryRzUuyfBSVUoGhornngm45gLQ4dyJq7maDTuH2OW0RjeNUfRcA82aNu3JzSNowO42MhCie2qslf1RoM8ZQ0dhFUfqFXe+JMZFERdi0ReQn2iIax4n6ThKiI8hOirY6FL+KsNv4y+U5PPTOaR7cWk5s1Pt/IretC+yLkJWaiobOfjr6hsY82bSJUJQWR4UmIr/QFtEYuvuHOF7XwcLsRETGul43tH1kRR7DxnCwut3qUMKCiESLyE4ROSAiR0TkO1bHFEoe3VE5Zhf1iTrXkuBzM8fu9ZiXlcCxWl023B80EY3hlaN1DA4bluclWx2KJRblJJKVGM2+ylarQwkX/cCVxpiLgOXAZvc1ecqHTta7ksy8WWNfnrE4J5Hqtl5dIsUPNBGN4el9NSTHRlKQFh7TtseyoiCZs629NHbquiy+5i4Y3OW+G+n+0QtYfOxkfSfp8Q7S4qPGfH5JjmvZ8KO1Hf4MKyyFZSIaGHJS2947ZiHTozUdbD3ZyMqCFGxh2C034qL8ZATYd1ZbRf4gInYR2Q80AK8aY3aMsc1dIrJbRHY3Njb6P8gQc7K+a8LJSItzEgE4UqNd1L4WdomovqOPH7x0nB+/UcoPXznB3vO6n3706kkSoyPYMDu8p2wmRkcyd1Y8+yvbcGrJH58zxgwbY5bjqtW4VkSWjLFN0BUNDlRDw05O1ndOmIhS4hzkJEXz3MHaKV8GoaYmrBJRz8AQj+6oxGYTbl6VR0Z8FE/sqeIbfzzAmeZu7nvjFK8dq+cLV8w+tzZJOFtRkEJb76BOYfUjY0wb8CYTL0apZuhEfSc9A8OsKJh4HHhFQQqnm7q1/qKPhVUievZADY1d/fzVqjxWFqTw+ctL2Dg/g6f3VXPFD9/kP145yU3Lc/jC5SVWhxoQFmUnEhVh09pzPiYiGSKS7L4dA1wNHLc2qtC254yrJ2SyUlYb52fQ0TdEbbuu1upLYXUd0ZN7q0mPj2JupmuWjE2EaxZl8e2/WMyWEw3kJsdw1cJZ2G3hOzY0WqTdxtLcJA5Wt3PjRTlWhxPKsoHfiIgd18nhH4wxz1kcU0h7ck8VidERk9aR2zg/E3C1oJTvhE0iOtvSw86KFq5ZNOuCa4OK0uP4bHqxRZEFthUFKew+08rRWh2w9RVjzEFghdVxhAtjDGdaeihIi+Oxna7Fpce7WDsjIYrc5BiO1ujMOV8Km665l4/UAa7ZYMpzhWmxpMRGavecChlHazto6xlkdoZn64ytKEimuq2Xw2Nc4D3exbJqasImEe2saHF/qTqsDiWo2ERYUZBCWUMXte29Voej1Iw9d7AWm7x/ndBkVuSnEGETHt2pCcdXwiIROZ2GXadbWFOUanUoQWlFfjIGeGK3FkJVwc0Yw7MHapidEU9clGcjEzEOO8vykvjzvmq6+od8HGF4CotEVNbYRWvPIGuLNRFNR5p7gsejOysZGnZaHY5S03agqp2q1l6WeVC+a3S329qiVLoHhnlmf42vQwxLYZGIdp5uAVx/TGp61hWnUdvex2vHGqwORalpe/ZADQ67jUXZiVP6vfzUWLISo7lvyykdE/KBsEhE+yvbSItzUBjGteNman5WAjlJ0fx2+xmrQ1FqWpxOw/MHa7l8XsaUL1gXEdYUp1LT1kdVa4+PIgxfYZGIjtV1sCgnPJd08Ba7TbhtXQHvlDZR1tg1+S8oFWD2VLZS19HHX1yUPa3fX5GfTKRd2FnR4uXIVMgnIldNqS4WTrEpri708TX5RNqFR7ZN3Coa6Vsf/aOU1f7fKyeItAst01zWITrSztLcJA7XtDOoY6VeFfKJqKKpm4EhJwuywmvJb1/ITIjmLy7K4fFdlTR36fIQKngMOw2HqjuYPyuBqIjp15FclJ1E36BTW0VeFvKVFUbWEtEW0fim0mL54sY5PL2vmofereDvrl3gw6iU8p6Kpm66+4c8mi03kTmZ8UTYhFeP1rNhTnhX6PemkG8RHavtJNIuzM4YexVG5blHd1Sys6KFJTlJ/GJrBT97q8zqkJTyyKHqNhwRNubPsGfEEWFjTmY8rx+v91JkCsIgEZ2o62B2RjyOiJB/q35zzaJZDDsNrx3Vg1EFvmGn4XB1B4uyE4m0z/x7YE5mPGdbenX2nBeF/LdzWWM3czK1NeRNafFRrJ+dxp4zrewob7Y6HKUmVNrQRe/gMEtzPSvpM5mSdNf3ybYy/dv3lpBORP1Dw1S19lCS7llxQ+W5qxZmkhLn4BtPHKC9d9DqcJQa18GqNqIjbeeWf5mpzMQoUuMcbNOTMK8J6ckKlc09OA2U6PiQ10VF2PmrVXk89G4Fdz+yh1//9ZoZzUaayFiTKcYr26/UaP1Dwxyt7WBJThIRXuiWA1ch4ItLUtlR3sLqQq3W4g0h3SIqa3QtcV2sLSKfKEyL4wcfW8a28mbueGgX7T3aMpoOEckXkS0ickxEjojIvVbHFCr2nGmlf8jJohzvzppdnu9aGkKLoHpHSCeiiiZXIirxcN0RNXUfXZnHf37iInafaeH6/3lbx4ymZwj4ujFmIXAx8CURWWRxTCHh7VNN2ASvd88vzXVNA69u1aVRvCGkE1F5YxcZCVEkREdaHUpI+8iKPP549yVE2oVbfrGdV47UMew0VocVNIwxtcaYve7bncAxINfaqILDZJU73jnVREFqLFGR3u02XpKbiAhUt+nMOW8I7UTU1K3dcn6yPD+Z579yGR9flc+bJxv52VtlNHVq9YWpEpEiXMuG77A2kuDX3NXP4Zp25mR6v6pKQnQkJelx2iLykpBORBVN3R4vB6xmLi4qgh/cvIzb1hbQ0j3AfVtKOduiZ4yeEpF44Engq8aYjjGev0tEdovI7sbGRv8HGGTeLWvGGLw2W+58y/Jc40Rq5kI2EbX1DNDSPXBuzr/ynyW5SXzlqrnER0fw8LbTtPVMr8hkOBGRSFxJ6HfGmKfG2sYY84AxZrUxZnVGRoZ/AwxCb59sJCkmktyUmEm3nU5x3qW5SXT0DdGhly/MmEeJSEQ2i8gJESkVkX8Y4/k7RKRRRPa7fz7n/VCnprxJZ8xZKSkmks+sL2LQaXhybxXG6JjReMS1PskvgWPGmB9ZHU8oMMbwTmkTG+akYfPR8i/L8lwXyI7XKtLK856bNBGJiB34CXAdsAi4dZwZPb83xix3/zzo5TinrLxRZ8xZLSMhiuuWZFHW2M0fd1dZHU4g2wB8Crhy1Mnc9VYHFczKGruobe/jsrm+azkuyklEGD8RKc950iJaC5QaY8qNMQPA48BNvg1r5sobu4iwCfmpuiqrldYWpVKQGsv/e/UEvQPDVocTkIwx7xhjxBizbNTJ3AtWxxXMtp5sAuBSH1bIjnVEMCsxWicseIEnlRVygbOj7lcB68bY7mMicjlwEvhbY8zZ8zcQkbuAuwAKCnx7ZXxFUzcFqbEeFTnU5rPviAibF2fxwNvl/Oq9Cr64cY7VIakQNnIs/37XWYrT43x+IpqbHMPxug6MMboC9Ax40iIa69M9v8P/WaDIGLMMeA34zVg78udga3ljt3bLBYii9DjmzYrn/i1lPPzeae07Vz41NOykvKmLy+b6fr2gnORougeGqevo8/lrhTJPElEVkD/qfh5QM3oDY0yzMWbkopFfAKu8E970OJ2Gima9hiiQbJiTTlf/EAer2q0ORYW4ypYeBoeNT8eHRuQku2bkHa6+YLa9mgJPEtEuYK6IFIuIA7gFeGb0BiKSPerujbiuDLdMdVsvA0NOLXYaQOZkxJOZEMW7ZU06g0751KmGLmwCF5f4viBpdlIMAhyu1hOsmZg0ERljhoB7gJdxJZg/GGOOiMh3ReRG92ZfcRdrPAB8BbjDVwF7oqyxC/B+fSk1fSLChjnp1Lb3nZtar5QvlDZ0kZ8a65fSXo4IG+kJURyp0UQ0Ex4tA+GewfPCeY/9n1G3vwl807uhTd/7U7e1RRRIlucn8/KROt4tbdKl25VPdPcPUdPWy1ULM/32mrnJMRysatcJCzMQkusRlTd1kRAdQXq8w+pQQt5UJh1E2m2sK07lzRONtHRrtQXlfUdrOzDA/CzvLvswkfyUGPafbaO6rZe8FL1cZDpCssSPa8ZcvJ6dBKA1Ra5++50VLRZHokLR4ep2UuMc5CRF++01C9NcQwB7zrT67TVDTUgmooqmbmbr+FBASo51sCA7kd1nWugf0gtclfc0dvZT1tjF0twkv56EzkqMJj4qgl2n9eRqukIuEfUMDFHb3qfXEAWwdcWp9AwM89LhOqtDUSHkp2+WYQysLEjx6+vabcKKgmR2n9YW0XSFXCLSiQqBb05mPKlxDn67/YzVoagQUd3Wy2+3n2FlYQoZCVF+f/31swN5mEsAACAASURBVNM4XtepdeemKfQSkS4PHvBsIqwrTmXX6VaO1+mFgGrm/vu1kwBctcB/s+VG+/BS16WUzx+smWRLNZbQS0SNXYhAUZomokC2qiAFR4SN323XUj9qZho6+3hiTxW3X1xIcqw1M2UL0+JYlpfEMwdq9ILtaQjBRNRNbnIM0V5eo155V2xUBDcszebpfdV09w9ZHY4KYq8drScm0s6XNs22LIZHd1RSlBbH4eoOnj1Ya1kcwSr0ElFTl44PBYlPXlxIV/8QT+3VtYrU9NS193G4poPPXVZCWrz/xobGKty7piiVi/KS+NafDrOtrNlvsYSCkEpExhgqGru1tE+QWFmQzPL8ZH72VjmDw06rw1FBaG9lK3YR7rikyOpQsNuEH9+6kvR4B7f/cgfvlTVZHVLQCKlEVN/RT/fAMLN1okJQEBG+fOUcqtt6+dO+aqvDsYyIPCQiDSJy2OpYgsmw03Cgqo15WQmkxAVGFZWCtFj+9KUNbJqfyXMHazlao5NxPBFSiah8pNipds0FjSsXZLIkN5EfvXqSnoGwHSv6NbDZ6iCCzbayZjr7hlien2x1KB+QEB3JTz65guykaJ7aV0V7z6DVIQW8kEpEpe5EpAU1g4eI8H9uWExtex/3bymzOhxLGGO2AnpZ/hQ9va+aqAgbC7ISrA7lAlERdm5elUfvwDD3v1kKvD+upItCXiikEtGx2g6SYyOZlej/C9rU9K0tTuUjK3K5/81StpfrIO94ROQuEdktIrsbGxutDsdSvQPDvHS4lqW5SUTaA/NrLDsphhUFyfzqvdNUtfZYHU5AC8z/wWk6VtvJgqwELXYahL5702KK0uP4m9/uuaAganvvIDsqmvnz/mpeOFRLZUt4HtTGmAeMMauNMaszMny/+mgge/VYPd0DwwHXLXe+qxfOAuBHr5y0OJLAFjLLQDidhhN1nXxiTf7kG6uAkxAdya/uWMNnf7WLWx7YxoY56WTER3GkpoMT9Z0AxETaGRx28k5pEz0DQ3zrhkUBezasfOvpvVVkJ0VTFOAzZJNjHfz1hmJ+vrWMnOSYc0uLqw8KmUR0pqWH3sFhFmX7bx0S5V2FaXE8/aUN/GJrOS8dqaOsoYvZmfEUp8exICuBjIQoBoacvH68gYe3nWHYafi/H1lqddjKz5q6+tl6qonPX1aCLQh6P/5m42we31XJy0fq+OyGYqvDCUghk4iO17qmSS7IDryBSzW2sQZtb1tXwDeunc83rp0/5nZRkXauX5rN4pxEfr61nLXFqdy0PNcv8fqKiDwGbATSRaQK+GdjzC+tjSpwPb6zkmGn4aMrc4Oi4nVSTCRfvnIu33vuKEdr2lmUk2R1SAEnZPo1jtV2YBOYm6mJKBz83bXzWZ6fzHefPUp7b3BPjzXG3GqMyTbGRBpj8jQJja9vcJhfv3eGy+dlMG9W8Bzrn15fSFZiNM8cqKFvUNfhOl/IJKL9Ve3Mm5VAjENrzIWDCLuN7//lElp6Bvjx66esDkf5yUPvVtDU1c/dl5dYHcqURNptfGRFLp19Q7xytN7qcAJOSCQip9Owv7KVFX5eEEtZa0luEjevzOPh7Wdo6OyzOhzlY//56kl+9MpJFuckcsmcdKvDmbL81FguLkljR3kzR2rarQ4noIREIipv6qajb4gVBYE9lVN535c2zWFo2MmDb1dYHYryodbuAR7ZfoaoSDt/cVGOR78TKBePjo7j6oWziI6088OXT0zp90JdSCSivZWuAcuVmojCTlF6HDdelMNvt5+h5f+zd9/xcVVnwsd/j0a9V8u2ijtgx2AbCzuUEEggr0lYSkJCSzZOAqSxqcsbtrxJdpPdJJsNm03bhEAgyUKAkOYQCBCaKcaxDO7GTS6Sm3pvI83z/nGvjBAjaSTNzNXMPN/PRx9Nubr3uVdz5rnn3HPP6er3OhwTAf7BAJ+8bzNtPX4+tLqS3PQUr0OatIxUH28/rYRn9zTYzdvDxEUievVIKznpycwvtqF9EtGnL15Ij3+Qe160WlGsGu3sX1X5yrqdvFzTzHtXlFEZBxNenrugiJm56Xzrz6/ZJHquuOi+/XJNE1VzCkhKmv73FJixTaYpYlFpDpctncm9Lx7iprfNJy8jds+YE8XQ//mG1ZVjLnfPi4e4f+MRPnnRAioKMt/099NNKHGl+JL43CWLuP232/l/v9/Jktm54x6HeBfzNaLDTV0cbOzi7acl9pAnie7TFy+ko2+Ae1885HUoJkz+6Xfb+doju1jzlpnc9q7Tx1w21q6nXLOynPnFWTyx6wQBqxXFfiJav9cZ/PHtp8/wOBLjpbfMzuPSJaXc/UIN7b2xfV+RgZf2N/JQdS3lBRl897rlcdfakexL4rb/czr1HX08v2/sCfRiLclORsw3zT23t4HKwkzmFmWOv7CJa595xyL+ZtdJfvHSIW59xyKvwzGjCASU3cfb2XmsnV/99Qh9A4MMBpTS3HR6/IPsOdHOb145SmFWKh86dy6/fSU+J01cs3QmS8vyeHLXCZ7YeYJ3vWUmAMfbeth8uIVdx9qYW5RFZlrMf02PK6b3sK3Hz/P7Grl+VaWNuG04szyPd54xg7teOMja8+eRnQAFONbUNHTyhYe2sqW2lcxUH1VzC8lISWLnsXY2HWrmpQNNZKT4uO6cChaWZJOWEr83qIsIVy8vo7W7n4//72aWlefT3uunpqHr1DJJAisqCrj4jPhu8YnpkvrHrcfoGwjwvrPLvQ7FTBOfeecirvzhi/x0fQ2fv/Q0r8Mxw1QfauZjP69GBK45u5xlFfl86Nw5gNP8FFDlPWfOIjs9mRRfUtw3R4HTnfumC+bT2tPPC/samV+czXXnVHDu/GIe2XaMbXVtbDrUzKu1LbR29/O5S0+LyxOsmN6jX2+u44yZOSwtsxG3jWNZRT7vOWsWP37uANesLKei0Jpsp4PqQ83c8NON5GYks/a8eRRmpb5pmSQRHttxwoPovJWanMTnLjmNz13yxhOn7UfbmFOUxYWnlfDU7pPc9cJBHtl2nG+870wujrNr4jHbWWHDgSa21rZy7TkV1ixn3uCf3r2YJBH+8XfbCQSsR5LXXj3Swtp7NpGbkcxNb5sfNAmZ0eVlpPDes8v57afOIy8jhY/cs4lvPLYb/2BgQuuZzlOVx2QiUlW++efXmJWXzvWrErv/vXmz2fkZ/NN7FvP8vkbueqHG63AS2ksHGvngXRspyk7lYxfMj+lREcJpZEIIJUG8dryDG1ZXsmpuIT95roYbfvoy9e3xMcZiTCainz5fw9baVj5/6Wmkx/HFTDN5N66u5LKlM/nGY6+xbusxr8OJCxM9o35s+3HW3rOJsoIMHvr4uXajcRik+JK4akUZ11ZVsKW2lcu//wLVh5q9DmvKQkpEIrJGRPaIyH4RuT3I+2ki8qD7/kYRmRvuQIf8urqWbz72Gu8+cybvX2mdFExwIsJ/Xbucc+YU8tkHXuX7T+2bcFNGNI1XxrzWNzBIr38wpCFpevoH+effb+eT973Cklm5fGBlBU/tro9ClIljWUU+n3z7QjJSfVx358v8z7MHGBjj8+0fDLDpUDN3rj/ANx/bzZU/eIG7nq+hq28gilGPbtzOCiLiA34IXArUAZtEZJ2q7hq22MeAFlVdKCLXAd8Crg1noC/sa+Snz9fw3N4Gzp1fxLevWWbXhsyY0lN83PvRc7j9N9v5zpN7efiVOt6/spwVlQVkpSWTneZj4TSYSDHEMhZVgwHltRPtPLnrJE/uOsnu4+0EFDJTfcwrzsKXBBefPoMZuemn/qaho48/7zjOnc/XUNvcwwULi3nXklKSfaOf707H6xWxYmZeOutuvYAvPbyNb/35NX69uZaPXTCPS5eUMiPH+b+0dvfzyLbj/Pi5A9S19FCam8ai0hwGAsrX/7Sb7z+9n4+cP5cPnzuXAg+v3cl4Zzgici7wVVX9P+7zfwBQ1W8MW+Zxd5kNIpIMnABKdIyVV1VVaXV1dciBfuKXm6k+3MJHL5jLLW+bP+aHeyz2wY8PExmbS1V5dk8DP3hmP5sPvz619AULi/nfm1ZPdNNhP/sJpYyNNNHyM54Tbb3826O76ej109rtZ+/JDrr7BxGBqjkFvHV+EftOdtLQ2cf++s5Ts+LOyksnOy2Zth4/9R19AJxVnsfta87gUFN32OIzo1NVdh/v4C+7T3LCvWaUn5lCcpLQ2OmMSL+sPI/lFQWcVpqNiHDD6kpePdLCj549wJO7TpIkML8km9LcNGbkpHPHByJyoj/qCkNJRNcAa1T1Jvf5h4DVqnrrsGV2uMvUuc8PuMs0jljXLcAt7tPTgfEn5Qi/YmDsMTVin+1j5DSq6ppwrjCUMua+7nX5sc9V/PBiP0ctO6HcRxQsi43MXqEsg6reCdwZwjYjRkSqVbXKyxgizfYx5sRE+YmzYx5UIuwjTL/9DKV9qw6oGPa8HBjZDenUMm7TXB4Q+105jImOUMqYMXErlES0CVgkIvNEJBW4Dlg3Ypl1wIfdx9cAT491fcgY8wahlDFj4ta4TXOqOiAitwKPAz7gZ6q6U0T+FahW1XXA3cAvRWQ/Tk3oukgGPUWeNg1Gie1jDBmtjHkcVjBxc8zHkAj7CNNsP8ftrGCMMcZEUkyOrGCMMSZ+WCIyxhjjKUtExhhjPGWJyBhjjKcsERljjPGUJSJjjDGeskRkjDHGU5aIjDHGeMoSkTHGGE9ZIjLGGOMpS0TGGGM8ZYnIGGOMpzxJRCLyTyKyU0S2icgWERlzvmYRWSsis8O07UUi8oiIHBCRzSLyjIhcOGw7DW5Mr4nI58dYzyER2e4uu0VEvhdkmYtE5N5wxB1k3Y+KSP44y3SO8vq97qygESEig+4x2SEivxaRzDCt914ROTjsmH8mHOsdY3tBj1+ssHIWlv1I5HK2VUT2isgvRKRs2PvZIvIT93+7U0TWj/fZGk8oM7SGlYicC1wOnK2qfSJSDKSO82drgR1McbIwEUkH/gT8vTt9BSKyFKgC1ruLPaiqt4pIEbBHRB5W1dpRVnnxyOnQI02cieRFVd8dze1OUI+qLgcQkfuATwB3DL05bB8Ck1j3bar6cHjCjF9WzqbGyplTztx1fA54RkSWqmo/cBdwEFikqgERmQ8snsqOeFEjmoUzd3kfgKo2quoxABFZKSLPuWdQj4vILPeMogq4z83+GVPY9o3AhqHC4W5/h6reO3JBVW0C9rvxhpWIfEtEPjXs+VdF5IvumcZTIvKKexZ4pfv+XBHZLSI/Al4BKtwzxWL3/d+7x2yniNwyYlvfcdf3lIiUBInlTcc8zLv7PLBwlH243t3PHSLyrWExdY4X94h9eJeIbHCX/7WIZLuvHxKRf3ffqxaRs919PCAin3CXCXrMg2zjNhHZJE7t4l/CdnQix8qZlbMplzN1/BdwArhMRBYAq4F/Hkpwqlqjqn+aUvSqGtUfIBvYAuwFfgS83X09BXgJKHGfX4szQRjAs0DVKOu7zV3fyJ/vBVn2DuCzY8S2FviB+7jSXU/6KMseArYP297ngyxzEXBvkNdXAM8Ne77L3V4ykOu+VoxTQAWYCwSAt47YfrH7uND9nYFzRlvkPlfgRvfxl4ft2704M+mOesxHxHvjKMf44VGOTaf7Oxn4A/DJkfsAzAaOACXuck8DV4UQ98Fh2z/TPU7rgSx3mS8BXx52jD7pPv4vYBuQ426zfliMbzrmI/bjXTgTiQnOydsjwIXRLjtWzqycRbGcXTNiW9/FKVtXAL8L9+c16k1zqtopIiuBtwEXAw+KyO1ANbAUeNKpDeIDjoewvm8D355MLCLyO2ARsFdV3+u+fK2IXAycDtysqr1jrGJSTQaq+qqIzBCnPb4EaFHVIyKSAvy7OG3pAaAMKHX/7LCqvjzKKj8jIle7jyvcfWpy1/Gg+/r/Ar8d8XenE8IxV9X7gPsmsIsZIrLFffw8zgy+s0fswznAs6raAKeaFi4Efj9O3G9omhORy4ElwIvuPqQCG4YtP3RWvh3IVtUOoENEesVp++8i+DE/MWwd73J/XnWfZ+Mc4/VMU1bOrJy5r0+2nI0kE4hrwqKeiABUdRDn7OtZEdkOfBjYDOxU1XMnsi4RuQ3nTGKk9ao68mL2Tpx/wlAcV4tIFfCfw5YZars+F/iTiDwGNLjxAaxT1S9PJMZRPIxztjQTeMB97UacArNSVf0icghId9/rCrYSEbkIuAQ4V1W7ReTZYX8z0sjpeIUQjrmI3IhzRjzSflUNdjH2VNv1sHXAG/dhIh/ssaYRFuBJVb1+lPf73N+BYY+Hnicz9jEfvo1vqOpPJhCz56ycAVbOwlXOVgBP4fxvl4lIkk7u2lNQUb9GJCKni8iiYS8tBw4De4AS94OJiKSIyFvcZTpwmlTeRFW/rarLg/wE61F1P3C+iFwx7LWgPU1UdQPwS5wmhsFh6w1H4QCnUFyHU0iGzvDzcJqM/O7Z4pwQ1pOHc6bXLSJnAG8d9l6Su36AG4AXRvztWMf8FFW9b5RjPJUeQRuBt4tIsYj4gOuB50KMe7iXcf6nC919yBSR0yYQRyjH/HHgo/L6tacyEZkxgW1EnZWzU6ycTaGcieMzONfw/qyqB3Bq1f8ibtYTp4dk0GurofKiRpQNfN9tFhnAaZ+9RVX7xblg+j0RyXNj+y5OBr4X+LGI9OCckfRMZsOq2uM25dwhIt8FTuIUvq+P8iffAl4RkX93m3RGekZEBt3H21T1bycQy04RyQGOqupQNf0+4I8iUo3TNvxaCKv6M/AJEdmG84Ef3qzQBbxFRDYDbTht08NjGOuYR5SqHheRfwCewTlre1RV/xBK3CPW0yAia4FfiUia+/I/41wbCcW4x1xVnxCRxcAGt+x1Ah8E6kPchhesnGHlbArl7Nsi8v9wTiBexmke7Xffuwn4DrBfRLpxmieD1eRCNnRR1kSAW51fq6prPQ4lpohIp6pmex2HiQ1WziZnOpUzG1nBGGOMpywRRdYhnN4pZgKmy1maiRmHsHI2YdOpnFnTnDHTgDjD3NyE03NpO/CRcbo0GxM3rEZkjMfEGcfrMzg3ky7Fuc/kOm+jMiZ6PEtEa9asUZyzP/uxn1j6iZRknBsUk3F6Ko053puVH/uJwZ9ReZaIGhujOoahMdOWqh7FudnzCM4d922q+sTI5UTkFnHGzKvevXt3tMM0JmKsac4Yj4lIAXAlMA9niJYsEfngyOVU9U5VrVLVqpKSMceBNSamWCIyxnuXAAdVtUFV/Thjfp3ncUzGRI0lImO8dwR4qzs8kQDvBKztzSQMS0TGeExVN+KMg/YKTtftJJxpJ4xJCJ6Mvm2MeSNV/QrwFa/jMMYL49aIRORnIlIvIjvGWe4cceZQj9gc7cYYY+JPKDWie4EfAL8YbQF3ePFv4QyXbzx2/8YjQV+/YXVllCMxJrYMlR0rK9E1bo1IVdcDzeMs9nfAb5jew+IbY4yZhqbcWcEdnuRq4MchLHvqhryGhoapbtoYY0wcCEevue8CX3KnJR6T3ZBnjDFmpHD0mqsCHnBnriwG3i0iA6pqw7IbY4wZ15QTkarOG3osIvcCj1gSMsYYE6pxE5GI/Aq4CCgWkTqcex1SAFR13OtCxhhjzFjGTUSqen2oK7M5440xxkyUDfFjjDHGU5aIjDHGeMoSkTHGGE9ZIjLGGGBgMMBrx9sJ6JizWpsIsERkjDHAl36znV+8fJh9Jzu8DiXhWCIyxiS8+o5efvNKHQCtPX6Po0k8loiMMQlv/8nOU4/bLBFFnSUiY0zCO9TUfepxW7clomizGVpjRLA5hmzOFGPC43BTF6m+JEpz06xG5AFLRGOwCeaMSQyHmrqoKMwgMzWZY609XoeTcCwRJRCrVRkT3OGmbuYWZdE/EGD38XZUFXdGARMFdo3IGJPQVJXDTd1UFmWSl5nCQEBpsetEUWWJyBiT0Fq7/fT4BykvyCQnPQVwunOb6Em4prl4vO6zpbaV9XsbaOvxs3BGNr3+QdJTfF6HZUxMOOkmndLcNI62ONeHOnoHvAwp4SRcIoonDR19/N+Ht/LMngYA0lOS6PUHSE9J4t1LZ7FyToG1cxszjvr2PgBKc9NJT3Eaidqt51xUhTIx3s+Ay4F6VV0a5P0bgS+5TzuBT6rq1rBGad6kvr2Xy7//PK3dfv7hsjO4fnUlOWnJbKtr4/MPbuG3rx7lWFsvf3PWLEtGxozhZLtbI8pJJ8NtSWjvtUQUTaFcI7oXWDPG+weBt6vqWcDXgDvDEJcZQ2NnHz994SABhd9/+nw+/vYF5KanICIsq8jnoxfM44KFxbxc08Qj246jNoijMaOq73BqRDNy0041abf3WNNcNIUyQ+t6EZk7xvsvDXv6MlA+9bDMaLr7Bvj5S4dQVR645a0sKMl+0zJJIly2dCaqyosHmijKTuW8BcUeRGvM9HeyvZe8jBTSU3zDEpHViKIp3L3mPgY8NtqbInKLiFSLSHVDQ0OYNx3/Aqo8WF1LW4+fD711TtAkNEREuOzMWSyelcuj249T29w96rLGJLL69j5Kc9MA8CUJqb4ka5qLsrAlIhG5GCcRfWm0ZVT1TlWtUtWqkpKScG06Yazf28C++k7ec9Ys5hRljbt8kgjXnF1ObkYKD1bX0usfjEKUxsSWkx29BPT1HrXpKUnWNBdlYUlEInIWcBdwpao2hWOd5o1qGjt5ctdJzirPY9XcwpD/LiPVx7VVFbR09fPHrcciGKExsam+vY+ctNevUqSn+KxGFGVTTkQiUgn8FviQqu6dekhmpMbOPh7cVEtRdipXLy+bcC+4OUVZvGPxDF6tbWX70bYIRWlMbGrt7icz9fX77jIsEUVdKN23fwVcBBSLSB3wFSAFQFV/DHwZKAJ+5H5BDqhqVaQC9kKff5DHd51k38kOkpKExs4+PvH2BaQmj5/Hp3oD7WBA+dwDW+jpH2TteXNJm+SNqhedNoPdx9t5ZOsxFs3IthtejQH8gwG6+gfJGJaI0lN81jQXZaH0mrt+nPdvAm4KW0TTTFffAD9ZX0NTZx+LZ+XSNzDIHU/u5Zk99dyz9hzyM1Mjuv0fPbOfF/Y3cvWKMmblZUx6Pb4k4erl5fzo2f08vvMEVy4vC2OUxsSmoSkfMoadmGWk+mjp7vcqpIRkIyuMIaDKQ9W1tHb389EL5p3qpZafmcLnHtjCLb/czC8/toq05MjULjYcaOK//rKXq5bPpmpOwZTXV1aQwbkLithwoIkVlQVUFmaGIUpjYtepRJQ6/BpRknXfjjIb9HQMmw+3sK++k8vPmv2GrtLvPnMW//mBZfz1YDPfeSIyl8UaOvr4zAOvMrc4i3+7+sywjY5w6eJScjNS+OPWYwTsRleT4ILViJzOCgN2I3gUWSIaRa9/kKd2n6SyMJNz5r65NnLFstncsLqSnz5fw+bDLWHf9i2/rKaj188PbzibrLTwVVzTUnxcuqSUo6091nFhGhGRfBF5WEReE5HdInKu1zElgtdrRG/srDAYULr77XaHaLGmOdfITgUv7m+kvXeAa8+pHLU28o/vXsxzexq47ddbefSzbwtLB4BAQPnir7eypbaV/7nxbBbPyp3yOkdaXpHPC/saeXLXSb52ZSCkThcm4v4b+LOqXiMiqYC1m0ZB+yg1InDGmwvnSaAZnR3lIFSVjQebqCzMZF7x6DeOZqcl8633ncUH797IHU/u5R/fvXjK2/2Px/fwp23H+YfLzmDN0lljLj9aj7zxJImwZulM7n3pEPdvPMza8+dNaj0mPEQkF7gQWAugqv2AXS2PgmA1ouHjzc3K8ySshGOnwkHUNHbR2NnP6nnj3zh6waJirl9VyV3P17Bjik1d//3UPn783AFuXF3JLRfOn9K6xrNoRjbzi7P43tP76eqzrqoemw80APeIyKsicpeIvOkMyIbICr+27iC95mwE7qizRBRE9aFmMlJ8LC0L7XTo9svOoCg7jS/9ZhsDg4FJbfMHT+/ju3/ZxzUry/nalUsjPnWDiHDpklKau/p5qLo2otsy40oGzgb+R1VXAF3A7SMXsiGywq+tx09mqg9f0uvlzeYkij5rmhvBPxhg94kOlpXnk+ILnqeDNYldsriUX/31CPe8eIibQ6jNDK0joMoTO0+wfl8jKyry+db7ziIpKTrzB80pymLlnALufuEgH3rrHJJH2V8TcXVAnapudJ8/TJBEZMKvrcdPXkbKG15LtxpR1Nk3zwj7TnbSPxBgadnEOgksnZ3LJYtL+c6Te9hf3xnS3wwGlN9srmP9vkZWzyvkfSvL33BmFg0fv3A+dS09PLrjRFS3a16nqieAWhE53X3pncAuD0NKGK1jJKKhZjsTeZaIRthxrI2MFB/zi0efYiEYEeHrVy0lMzWZT9/3Cj3jdP3s6PVz9wsHebW2lUuXlHLFstkkeTCT6iWLS5lfksWd6w/YfRPe+jvgPhHZBiwH/t3jeBJCW4+f3DclIrdprteunUaLJaJhAqrsOdHB4lk5k6qZzMxL544PLGNvfQefum8z/QPBrxcdbOziB8/s52hrNx+oKufi02d4Np13UpJw89vms+NoOxsO2MDpXlHVLe71n7NU9SpVDe/NaSaoI03ddI5IOMlJSaT4hL8ebPYoqsRjiWiYoy099PgHWVSaM+l1XHT6DP7tqjN5Zk8DN971Mk2dfafea+3u5/dbjnLX8zWk+pL45NsXsrxi6kP3TNXVK8ooyEzhfzce9joUY6Kqxz/4hh5zQzJSfPTY/F1RY50VhtlX34EAC8eY+TQUN6yuJCvNxz/+djvVh1oozU0noEpDRx8i8NYFRVy6uHTajICdnuLjvWeX84sNh2js7KM4O83rkIyJip4RI28PSU/x2USSUWQ1omH21XcyOz8jLHdTX7m8jKe+eBHvWDyD/MwUCjJTeefiGfz9u07nb86aPW2S0JDrzqnADjTFawAAIABJREFUP6j87pWjXodiTFT4BwP0DwaClsV0qxFFldWIXH0Dg9Q2d/O2ReG7P2NmXjrvPKM0bOuLpEWlOaycU8ADm45w09vmeXbNyphoCTaqwpCMFB8dfdZrLlrGrRGJyM9EpF5EdozyvojI90Rkv4hsE5Gzwx9m5NU29xBQmD/GkD7x7tpzKjjQ0BX2QVyNmY6Cjbw9JD0liV7/5G5ONxMXStPcvcCaMd6/DFjk/twC/M/Uw4q+Q01dCFCRwHP0vOfMWWSnJfOrv9pICyb+tQYZ3mdIeopv3FswTPiMm4hUdT0wVj/GK4FfqONlIF9Exh6tcxo63NTFrLz0aXftJpqy0pK5/KxZPLbjuBVCE/fax2ma6/UP2r11URKOzgplwPBT6Dr3tTeZroM2DgaUI83dzClK3Ga5IVcuL6O7f5C/7D7pdSjGRNTYTXM+FOiyE7KoCEciCnZVO+hpxHQdtPF4Ww/+QWVOUeI2yw1ZNa+Q0tw01m095nUoxkTUeJ0VwAY+jZZw9JqrAyqGPS8HYupbrK6lB0jM60PBBnBdNCOHZ/fU09btJy8zJchfGRP7xqwRpb4+8OlsMqIaVyIKRyJaB9wqIg8Aq4E2VT0ehvVGzdHWHjJTfeRnTO1Ld7IT1U03Z5Xn8cL+Rh7bcZzrVlV6HY4xEdHW4yc1OSnocF6vTwVh481FQyjdt38FbABOF5E6EfmYiHxCRD7hLvIoUAPsB34KfCpi0UbI0ZYeygsy7N4ZV1l+BvOKs6x5zsS1th5/0NoQWNNctI1bI1LV68d5X4FPhy2iKOsfCFDf0cviWdPnmpXXRIQrls3me0/v42R7L6W56V6HZEzYjZWIbE6i6Er4IX6Otzk3spYXJN71obFcsXw2qvBHqxWZONXW7Q/aUQGGJSKrEUVFwg/xc7TV6agwO396XJCcLteZFpRks7Qsl3Vbj3HT28afcdaYWBNS05zNSRQVCV8jOtHWS2aqj9z0hM/Jb3LlsjK21bVxsLHL61CMCbuxEpEvSUj1JVmNKEosEbX3MjMv3ToqBHH5slmIwB+22IjcJv609YzeNAdOzzm7RhQdCZ2IAqqcbO9lll2MD2pWXgar5haybusxG+rExJX+gYAzKd6Yichn3bejJKHbo5q7+vEPKjPzLBGNNHStalZeBhsPNvOfT+ylLD+DG1bbfUUm9o11M+uQjBSf1YiiJKFrRCfaegGYmTs9OipMR0tn5+ITYWttq9ehGBM2oSSidEtEUZPQieh4Wy8CzMi1qbFHk5mWzKLSbLbVtRKw5jkTJ8YaZ25IRqo1zUVLQiei+o5eirJTSfEl9GEY17KKfNp7BzjUZL3nTHxo6+kHxqsRWWeFaEnob+D6jj5m5Nj1ofEsnplLqi/JmudM3Ai5aa7Hbx11oiBhE9FgQGnq7KMkx5rlxpOanMRbZueyra7NJswzcaHNnZ01faymuRQfAbU5iaIhYRNRU2cfAYUZlohCsnJuAX0DAR7bEVMDqxsTVJt77We8GhHYMD/RkLCJqL6jD8Ca5kI0ryiLoqxUHtxUO/7CxkxzbT1+slJ9QaeAGGIDn0ZPwiaihk4nEVnTXGhEhJVzCth4sJlDNuSPiXGt3f3kZ6aOuczrU0FYz7lIS9hEVN/eS35mCqnJCXsIJuzsygKSBB6qtlqRiW0t3f0UZI09Eebrk+NZjSjSQvoWFpE1IrJHRPaLyO1B3q8UkWdE5FUR2SYi7w5/qOHV0NlHSbbVhiYiNyOFi0+fwcOb6xgYDHgdjjGT1trjpyDUGpE1zUVcKDO0+oAfApcBS4DrRWTJiMX+GXhIVVcA1wE/Cneg4aSqNHX2U2SJaMKuW1VJfUcfj+044XUoxkxaa7d/3KY566wQPaHUiFYB+1W1RlX7gQeAK0cso0Cu+zgPmNazqXX1D9I3EKA4e+wPonmzd54xg/nFWfxk/QG7v8LErJbufvIzxmuaszmJoiWURFQGDL8oUOe+NtxXgQ+KSB3wKPB3wVYkIreISLWIVDc0NEwi3PBodjsqFGVZIpqopCTh5gvns+NoOxsONHkdjjETNhhQ2nr8FGSOnYh8SUJmqs9qRFEQyujbwfo3jjwVvh64V1W/IyLnAr8UkaWq+oYLCap6J3AnQFVVlWen041dzvAeRVnWNDcZV68o4ztP7OUn62s4b2FxRLc12oy1Ngq4mSxntATGbZoDyE1PsWtEURBKjagOqBj2vJw3N719DHgIQFU3AOlAZL+hpqCpsx8B8sfpNWOCS0/xsfa8OTy3t4Hdx9u9DseYCWl1azjj9ZoDyM1IPjUckImcUBLRJmCRiMwTkVSczgjrRixzBHgngIgsxklE3rW9jaOpq4/8zBSSk6zr9mR98K1zyEr18b2n9nkdijET0tLttIhsOdI27rL5Gam0dFsiirRxm+ZUdUBEbgUeB3zAz1R1p4j8K1CtquuALwI/FZHP4zTbrdVpfCW7uaufYusxNyX5mancfOF8vvuXfbxypIWzKwvetIw1q02M20O1Gjiqqpd7HU+8anUTUeYY48wNKcxKZX9DZ6RDSnghVQlU9VFVPU1VF6jqv7mvfdlNQqjqLlU9X1WXqepyVX0ikkFPharS2NlHoXVUmLKb3zaf4uw0vv7ILgKBaXveEUs+C+z2Ooh419Ll1HBCSURF2ak0u9eUTeQkXNtUT/8gvf6A3UMUBllpydx+2Rm8cqSVhzfXeR1OTBORcuA9wF1exxLvhq4RZaaO31erKCuVlu5+Bu1EK6ISLhE1neoxZzWicHjf2WWsmlvI1/+0i6OtPV6HE8u+C/xfYNQhK6bL7Q+xrrXb6ayUljL+119hViqqrzfnmchIwERk9xCFk4jwH9ecxWBA+dwDr9rQP5MgIpcD9aq6eazlVPVOVa1S1aqSkpIoRRd/Wrr7yUj1kSSjj7w9pNBtObHmuchKuETU6HbdLrBEFDZzi7P4t6vPZNOhFr7/9H6vw4lF5wNXiMghnJFL3iEi/+ttSPGrpdsf0vUheP2EtckSUUQlXCJq7uonLzOFFF/C7XpEXbWijPeeXcb3n97Hs3vqvQ4npqjqP6hquarOxbk94mlV/aDHYcWttm5/SNeHgFOdmqxGFFkJ923c1NlnzXIR8rUrl3LGzFw+fd8r7Dg6/j0axnihpbvfakTTTOIloq5+G9onQrLSkrnnI+eQn5nKR+7dRIsV3glT1WftHqLIap1A09zjO08C0Nxpn+VISqhE1Nbtp7t/kCIbdTtiSnPTuecj59DnH+TuFw/a8Chm2nFqRKE1zfmShPSUJJrdTk4mMkL7b8SJI83dAHYz6xSEMlrCaaU53PvRVVx/58vc9XwNN184n9x0G9fPeK9vYJDu/kEyQqwRAWSlJlvTXIQlVCKqbXES0XgzM5qJC5ag1p43l3teOsTdzx/kprfNI8eSkfFYW3fooyoMyUpLts4KEZZQTXN1loiiak5RFh8+dy6tPf3c/cJBOvtsgjHjrZbu0EdVGJJtiSjiEioR1Tb3kJ6SNKFquZmaecVOMmrp7ufuF2osGRlPtUxgwNMhWWk+a5qLsMRKRC3dFFptKOrml2Tzt+fOpamzn7uer6G+vdfrkEyCmsjI20OyUp0akQ3sGzkJlYjqWnpCmpXRhN+CkmzWnjeX1m4/H/jJBhuXzniiwe2GnZUWetNcVloygwG1mVojKGESkapS19JtPeY8NL8km4+eP5emrn4+8OMNHG7q8jokk2AaO/oQcWo5ocpKc2pP1jwXOSElIhFZIyJ7RGS/iNw+yjIfEJFdIrJTRO4Pb5hT19jZT68/QH6m9dzyUmVRFr+6+a109w/w/h9vYM+JDq9DMgmksbOPgsxUfEnjD3g6ZChpWYeFyBn3tMCdNfKHwKVAHbBJRNap6q5hyywC/gE4X1VbRGRGpAKerKGu23aNyHtLy/J44JZz+dufbeT9P36Jez5yDivnFI56j5Ix4dLY2UfJBOciG2rGa7LRFSImlBrRKmC/qtaoaj/O6MBXjljmZuCHqtoCoKrTbtTLuhbnmoSNuj09nD4zh4c/cR5F2WnceNdGnnlt2n1kTBxq6OijOGdi3wFDichqRJETSiIqA2qHPa9zXxvuNOA0EXlRRF4WkTXBVuTlxF617qgK1jQ3fVQUZvLrT5zLwhnZ3PyLarbUtngdkolzjZ39FE+0RuT2sGvqtGF+IiWURBSsMXVkP8ZkYBFwEXA9cJeI5L/pjzyc2KuupYeirFTSku0eoumkODuNX938Vs6ZW8hD1XVsqGnyOiQTxxo7+yaciJJ9SeSmJ9NoiShiQklEdUDFsOflwLEgy/xBVf2qehDYg5OYpo26lm7KCzK8DsMEkZOewj0fOYfFs3L549ZjvHrEakYm/Lr6BujuH6QkZ+Kj76cl+9h8pDUCURkILRFtAhaJyDwRScWZuGvdiGV+D1wMICLFOE11NeEMdKpqm7spL8z0OgwzivQUH9efU8GCkix++8rRUwPUGhMuQzWaidaIALLTk+m0+4giZtxEpKoDwK3A48Bu4CFV3Ski/yoiV7iLPQ40icgu4BngNlWdNm0sgYBytLWHigJLRNNZsi+JG1bNIS8zhfs3Hqanf9DrkEwcaegYSkQT77CUnZZMR68NTxUpId1HpKqPquppqrpAVf/Nfe3LqrrOfayq+gVVXaKqZ6rqA5EMeqJOdvTiH1RrmosBGak+rj+nks6+AR7bcdzrcEwcOeEOLTUzL33Cf5uTnmzjJEZQQoysMNR1u8Ka5mJCWUEG5y8opvpwizXRmbA50eYmotxJJKK0ZPoGAlZLj5CEmI9oqOt2eUEGR1tsjDOvhXLj6jsXl/JKbStP7jrBxy6YH4WoTLw72d5LWnISeRkTv4Uj251Lq7Gzz05oIyChakRl+dY0FytSk5N4+2klHGjooqax0+twTBzYeLCZrLRkfvXX2vEXHiHbvam1vsO6cEdCQiSi2uZuSnPTSE+xe4hiyep5hWSl+nhx/7Tp92JiWFuPf9JT1uekO4mowRJRRCREIqpr6aHceszFnBRfElVzC3ntePupeWSMmaz2Hj95GZO7GpE9lIjsptaISIhEVNvSTYX1mItJq+YVAvDXQ80eR2JimarS0TtA7iSuD4EzArfgTCNhwi/uE9HAYIDjbb1WI4pRBZmpLCrNZmttK6o2Q6aZnJZuPwMBnXTTnC9JyEz1WY0oQuI+ER1v62UwoFQUWo0oVp1Vnk9Lt59a6/FoJmmo6/Zka0TgDEVl14giI+4T0dA8RDaqQuxaMiuX5CRhW52N9WUm56R7M2te+uTvWMlOT7ZEFCFxn4iGum5b01zsSk/xsag0h53H2q15zkzK0KgKU6oRpdkI3JES/4mouZskgVn5E7+b2kwfi2fm0NbjP/WFYsxEnGjrRXCa1yYrO82pEdnJUPjFfyJq6WFWXgYpvrjf1bh22swcAPac6PA4EhOLTrb3kpWWjC8p2PRqoclOd4b56bAx58Iu7r+djzTbPETxIDc9hdn56ew5aYnITNzxtl5yJ3kP0RC7qTVyEiIRzSmy60Px4PTSHI40ddPrt4EnzcScbO+ddNftIdlpzt/Xt1siCre4TkQ9/YPUd/RRaYMUxoUFJdkocLCxy+tQTIw50d47pY4KwKka1Um7Thl2ISUiEVkjIntEZL+I3D7GcteIiIpIVfhCnLyhrtuVRVkeR2LCoaIwk+QkoabBBkE1oevuH6C12z+pUbeHG/r7Y212P1u4jZuIRMQH/BC4DFgCXC8iS4IslwN8BtgY7iAn63CTm4isRhQXUnxJVBZlUmM1IjMBQ1O/FGROfGbW4dKSfeSmJ5+6OdaETyg1olXAflWtUdV+4AHgyiDLfQ34D2Da/JeGJlWbY4kobiwoyeZ4Wy/NXTYIqgnNUMtIYebUakQAs/MzONY6bb7i4kYoiagMGD6BR5372ikisgKoUNVHxlqRiNwiItUiUt3Q0DDhYCfqSFMXOWnJ5IfhA2imh3luM2t1HA2CKiIVIvKMiOwWkZ0i8lmvY4onQze152dNrUYEzjTjJ9qtaS7cQklEwTren7qjS0SSgP8CvjjeilT1TlWtUtWqkpKS0KOcpMPN3VQWZSIy+XsHzPRSVpCBT4TNR1q8DiWcBoAvqupi4K3Ap4M1f5vJqW3uJi05iZy0qU9IPSsvg+NWIwq7UBJRHVAx7Hk5cGzY8xxgKfCsiBzCKUjrpkOHhSPN3XZ9KM6k+JKYnZ/OK4fjJxGp6nFVfcV93AHsZkSrg5m8lw40kZOeEpYT0ll56TR19dstBGEWSiLaBCwSkXkikgpcB6wbelNV21S1WFXnqupc4GXgClWtjkjEIRoMKHXNPVTaPURxp7Iwk611bfQPBLwOJexEZC6wgmnU6SfWtXb7KcwKT/P8UAeo49ZhIazGTUSqOgDcCjyOc6b2kKruFJF/FZErIh3gZJ1s76V/MMCcQuu6HW8qi7LoHwiw63i716GElYhkA78BPqeqb9q5aF9jjRfNXf3kT7HH3JAi9zrToSbruRlOITWaquqjwKMjXvvyKMteNPWwps66bsevoV6Qmw+3sLwi3+NowkNEUnCS0H2q+ttgy6jqncCdAFVVVTbyZgg6ev30+Aen3HV7SFG2m4gau+D0sKzSEMcjK9QOdd22prm4k5uRQll+RtxcJxLn4sXdwG5VvcPreOLJ0dahe4jC0zSXnZZMWnKSk4hM2MRtIjrc3EVykjArz6Z/iEcr5xRQfbg5XobkPx/4EPAOEdni/rzb66DiQW1zeG5mHSIiFGen2U3VYTb1/ozT1JHmHsoKMki26R/i0so5Bazbeoxjbb2U5cf26Oqq+gLBb5MwU1Tn3sxaEIZ7iIYUZafaNaIwi9tv6SNNXXZ9KI6tnFMAONeJjBlNbXMPKT4hK9UXtnUWZ6dxtKXHunCHUfwmIruHKK6dMTOHjBRf3FwnMpFR19JNQWZqWG9qn52XTkBh57H46rXppbhMRO29flq6/ZaI4liyL4mzyvN4tbbV61DMNFbX0hO260NDygqc75VtdfbZC5e4TEQ1DU777bxiu4conq2oLGDXsTZrIjGjqm3ppiBMN7MOyctIYUZOGtvq2sK63kQWp4nIma9mfkm2x5GYSFpRmY9/UK2JxATV1uOno3cg7DUicG5sfX5fY9jXm6jiMhEdaOgkOUnsHqI4t8K9mfXV+BoA1YTJ0L2EkUhE5YWZNHb20d7rD/u6E1FcJqKaBqfHXIp13Y5rM3LTKcvPYItdJzJBDE3/EM6u20PK3VsGtlvzXFjE5Tf1gYZOa5ZLEMsr83n1iCUi82brtjqTBIRrVIXhygqcRLTVOiyERdwlosGAcqixmwUzrKNCIlhRkc/R1h7q2200ZPNGLV39pCUnkZESvnuIhmSmJlOYlcq2WqsRhUPcJaK6lm76BwMsKLYaUSJYUeleJ7LmOTNCU1cfhVnhvYdouPKCDOvCHSZxl4j21zs95qxGlBjeMjuPFJ/YdSLzJg0dfZTkpEVs/eX5GRxr66W+w2rjUxV3iei1Ex0ALCrN8TgSEw3pKT6WzMq1nnPmDXr6B2nt9kc2EQ3d2GrNc1MWUiISkTUiskdE9ovI7UHe/4KI7BKRbSLylIjMCX+ooXntRAdl+Rnkpof/AqWZnlZUFrC1tg3/YPzN2Gom50BDJwrMyInc6Puz8zNIEhthIRzGTUQi4gN+CFwGLAGuF5ElIxZ7FahS1bOAh4H/CHegodpzop0zZlptKJGcM7eQHv8gO47amalxHHBvao9kjSg1OYnTSnPs+mQYhFIjWgXsV9UaVe0HHgCuHL6Aqj6jqt3u05eB8vCGGZq+gUFqGro4Y5YlokSyal4hABsPNnsciZku9p3sRHh9au9IWTmngFePtDIYiIt5sTwTSiIqA2qHPa9zXxvNx4DHgr0hIreISLWIVDc0NIQeZYgO1HcxEFBOn5kb9nWb6askJ40FJVlsrGnyOhQzTWw61Mys/PSI39R+ztxCOvsGeO2EDTM1FaH8l4L1fQya/kXkg0AV8O1g76vqnapapapVJSUloUcZoj0nnQ/DYmuaSzir5xdRfajFzkwNvf5BXq1tZX4UbuGomuvMi1V9yDrLTEUoiagOqBj2vBw4NnIhEbkE+CfgClXtC094E7PzaDtpyUnMtVG3E87qeYV09A2wywZATXivHGmhfyDA/Ch8D5TlZ1BRmMEze+ojvq14Fkoi2gQsEpF5IpIKXAesG76AiKwAfoKThDz7j2yta2VpWZ6NMZeAVs8rAmDjQWueS3RP7a4nxSdROSEVEd5z5mxe2NdIc1d/xLcXr8b9xlbVAeBW4HFgN/CQqu4UkX8VkSvcxb4NZAO/FpEtIrJulNVFzMBggO1H21hWnh/tTZtpYGZeOnOKMq3DQoIbGAzw4KZaFs3IIT0CQ/uMdP/GIyQnCQMBZd2WoxHfXrxKDmUhVX0UeHTEa18e9viSMMc1YXtPdtLrD7CsIs/rUIxHVs8r5IldJwkElKSkyAzrYqa35/Y20Nk3cGrop2iYlZdOZWEmP1lfw/WrK0lLjnwCjDdx04Y1NMTL8gqrESWq1fOKaO32s+dkh9ehmCi6f+MR7t94BIA719eQl5HCGVHsOSsivHPxDI639XLbr7dFbbvxJI4SUQv5mSlUFtpkeInq3AXOdaLn94X/1gAz/T392kk2Hmzm/IXF+KJcI15Yks2cwkye3VNvU9dPQtwkopdrmqmaUxixkXbN9Dc7P4MzZubw1G7rwZRodh9v5/MPbuWMmTmsdm9wjiYR4ZIlpbT3DvDAX49EffuxLi4SUW1zN0eauzl/YZHXoRiPvXPxDKoPt9DWbVM4J4o/bjvGL18+TEaKj/ecOcuzXrPzi7OYW5TFj549YLWiCYqLRPTi/kYAzl9Y7HEkxmvvOKOUwYDy7F6rFSWCl2ua2HCgiVXzCvnUxQsoyo7c2HLjEREuXVJKfUcfd79w0LM4YlF8JKIDTZTkpLFohk2Gl+iWV+RTnJ3Gn3ec8DoUEwXf/cte8jJSePfSWSQnef91Nq84i0uXlPLDZ/ZzpKl7/D8wQBwkIv9ggOf3NXDBwmK7PmTwJQmXnzWLp16rp73XmufiWW1zNy/XNLNqXiGpydPnq2x5RT4BVT5490bue/mw1+HEhOnz35uklw400drt57KlM70OxUwTV60oo38gwJ+3W60onv3BvYF0+TS7ib0gM5V3L53FkeZudtqQUyGJ+UT0p23HyElL5sLTwj+IqolNy8rzmFecxUPVteMvbGJSr3+QX2w4zPkLiyiI8FQPk7GisoCSnDSefq0eVRuIdzwxnYh6/YM8vvMkly4pjcpwHiY2iAg3rq6k+nCLzZ4ZhwYDyn8+vof6jj4+fdFCr8MJypckXLCgmBPtvWw+bCNzjyemE9EfthylrcfPNSs9mYfPTGPXnlNBdloydz1vvZfiyf76Ts775lPc9cJBrq2qOHUT83S0rCKftOQkfvaifQbHE7OJKBBQfvr8QZbMyp3WH0bjjZz0FG5YXckj247Z1BBx4nBTFzfe9TKdfYPcsKqSb77vzGndQSk1OYnzFhTz6PYTVB9qfsNQROaNYjYR/XHbMfbXd3LLhfOn9YfReOfTFy0kNyOFrz2yy9rpY1xdSzc3/HQj/QMBPnbBPJaW5cVEub9wUTEzc9O58a6N/NeTe/neU/v4+iO76Owb8Dq0aSUmE1Fbt5+vPbKLZRX5/M2y2V6HY6apvMwU/v5dp7OhpomfvXjI63DMJB1r7eH6n75MU1cfN6yew8zcdICYqGGkpfj4w63n8/6qckrz0snNSOaelw5x1Q9f5GR7r9fhTRshTQMxnfQNDPKp+zfT2u3n5x9dGvXBDU1suXF1Jc/va+Abj+6msjCTS5eUeh2SCUEgoBxp7ub5/Y18/6l99PQP8tHz51GWn+F1aBP21O56lszKY8ksZ4qaucWZ3Pzzat77o5f44rtOo2pOIRWFGTFRw4uUkBKRiKwB/hvwAXep6jdHvJ8G/AJYCTQB16rqofCGCifaevnCQ1t46UAT33n/Mt4y2+YeMmMTEb79/mV86O6/8on/3cwXLj2NWy6cP+1m8R2vjMUT/2CAth4/RVmpdPYN8Oj24+w72YnPJ/y1ppmGzj7auv10uM1XZ5bl8c33ncnW2jaPIw+PQ43dfPi8uTy4qZYvPLQVgILMFJZX5HN2ZQErKgtYXplPdlrM1RMmTcZrOxcRH7AXuBSow5k6/HpV3TVsmU8BZ6nqJ0TkOuBqVb12rPVWVVVpdXX1uAG2dft5fNcJNhxo4k/bj+MT4WtXLZ10T7npXpU3oblhdeWElu/qG+C2h7fy6PYTlOam8Z4zZ/OW2bkUZKWwck4heRkpoa4q7KetoZSxkUItP+HS3e8khYwU35hn7qpKTWMXe0500NM/SN9AgF7/ICfae6lp6KSmsYsjTd0MBJS05CQCqvgHlfSUJAYGlcxUH8U5aZRkpzE7L4OKokxKc9LisrYwGFBOtvdS19JDbUs3tc3dNHT0oUBykrByTgEXnzGDoqxU2nr8ZKT6yM9IJSc9mWSfkOJLIj3ZR1aaj+y0ZNKSfaSnJk3niflG/SeGknJXAftVtQZARB4ArgSGF5Irga+6jx8GfiAiomG4QtzU1cf/fXgb+ZkpXLOynI9fOJ85RZGfi97El6y0ZH5040qe3VPPz186xH0bD9M3EADgkb+7gLwyT2vXoZSxqPrj1mP8z7MH6PEP0tLdT6s7mrlPhOKcVAoyU0kSYSg/iEBX3yANHX1BL8QnJwmFWamcXVnA/3nLTI40ddPU1Q/Aiop8ygsSr2nKlyTMzs9gdn4Gq9ypK3r6B6lr6aamsYv6jj6++dhrE15vdloyRdmpZKcls2peIV/5m7eEO/SwC6VGdA2wRlVvcp9/CFitqrcOW2aHu0yd+/yAu0zjiHXdAtziPj3gxBXEAAAgAElEQVQd2BOuHZmAYqBx3KVim+1j5DSq6ppwrjCUMua+7nX5sc9V/PBiP0ctO6HUiIKdpozMXqEsg6reCdwZwjYjRkSqVbXKyxgizfYx5sRE+YmzYx5UIuwjTL/9DOWKbR1QMex5OXBstGVEJBnIA5rDEaAxCSCUMmZM3AolEW0CFonIPBFJBa4D1o1YZh3wYffxNcDT4bg+ZEyCCKWMGRO3xm2aU9UBEbkVeByna+nPVHWniPwrUK2q64C7gV+KyH6cmtB1kQx6ijxtGowS28cYMloZ8zisYOLmmI8hEfYRptl+jttZwRhjjImk6XVXnzHGmIRjicgYY4ynLBEZY4zxlCUiY4wxnrJEZIwxxlOWiIwxxnjKEpExxhhPWSIyxhjjKUtExhhjPGWJyBhjjKcsERljjPGUJSJjjDGe8iwRicg/ichOEdkmIltEZPU4y68Vkdlh2vYiEXlERA6IyGYReUZELhy2nQY3ptdE5PNjrOeQiBQHef1eEbkoHLGOWG+ViHxvnGXWisgPRnmvM9wxjdju0HHbJSI3h3Hdg+56h37mhmvdQbY16vGLVVbWJhV3ope1nSKyVUS+ICJJw95fJSLrRWSP+z+7S0Qyp7rdUGZoDTsRORe4HDhbVfvcD1jqOH+2FtjBFCcME5F04E/A37tTWCAiS4EqYL272IOqequIFAF7RORhVa2dynanSkSSVbUaqPYyjnEMHbcZwE4RWaeqJ4fedPdhYBLr7VHV5eELM3FYWZs4K2tOWXPXfT/ORKdfEZFS4NfAdaq6QUQEeB+QA3RPZWe8qhHNwpm/vA9AVRtV9RiAiKwUkefcs6fHRWSWiFyD8+G9z83WGVPY9o3AhqGC4W5/h6reO3JBVW0C9rvxTomIJLlndfnDXtsvIqUi8jcislFEXhWRv7j/cETkqyJyp4g8AfxCRC4SkUfc91aJyEvu37wkIqcP21yFiPzZPWv5yijx3CYim9yz5H+Z6v4Np6r1wAFgTpB9SBeRe0Rkuxv7xW48a0XkD+PFPSx+n4h8e9g+fNx9/SL38/OQiOwVkW+KyI0i8ld3mwvc5YIe8xHbKBGR37jb2CQi54fzOEWJlTWsrE2mrLnrvgW41U06nwZ+rqob3PdVVR8engCnsiNR/wGygS3AXuBHwNvd11OAl4AS9/m1OJOEATwLVI2yvtvc9Y38+V6QZe8APjtGbGuBH7iPK931pI+y7CGgOMjr9wIXBXn9v4GPuI9XA39xHxfw+txQNwHfcR9/FdgMZLjPLwIecR/nAsnu40uA3wyL/zhQBGTgnNlWue91ur/fhTMxluCcjDwCXBgk3gdHOa5/O85xmw/UA4VB9uGLwD3u4zOAI0D6OHEPDtv279zXbgH+2X2chnP2Os89Rq04X2hpwFHgX9zlPgt8d5xjPnw/7gcuGPZZ2O1FebGyZmUtimWtM8j2WoBS4LfAlZH4nHrSNKeqnSKyEngbcDHwoIjcjvNlshR40knA+HAO2Hjr+zbw7cnEIiK/AxYBe1X1ve7L17pnD6cDN6tq72TWHcSDwJeBe3BmsX3Qfb0c5xjMwmk2OTjsb9apak+QdeUBPxeRRYDifLEMeVKdM0xE5LfABbyxmeFd7s+r7vNsnGOwftgyqOq1E9y/a0XkAqAP+LiqNrv/x+H7cAHwfXf9r4nIYeC0ceIO1jT3LuAs9wwenOOxCOgHNqnqcXc9B4An3GW243zeYOxjPuQSYIm7DwC5IpKjqh0TOShesrJmZc1df6hlLRgZ5fWw8SQRAajqIM6Z17Mish34ME4236mq505kXSJyG04zwEjrVfUzI17bCVw4LI6rRaQK+M9hywy1v54L/ElEHgMa3PjA+Wd/eSIxujYAC0WkBLgK+Lr7+veBO1R1nTgXXr867G+6RlnX14Bn3Pjn4hzLU7s1YtmRzwX4hqr+ZKxgReRBnC+Ike5Q1V8Eef1BVb01yOvD92GsD/V4cb8hPODvVPXxN7zoHL++YS8Fhj0P8PpnfqxjPiQJOHeUL6eYYWXNyloQIZU1EZmP0yJRj/P/XAn8YYz1Toon14hE5HT37GLIcuAwsAcocT+UiEiKiLzFXaYD56LYm6jqt1V1eZCfkQUDnOaW80XkimGvBe31oU5b6C9xmhcGh613MgUDdeq5v8Npstg9dEaCc8Z11H384RBXN/xv1o5471IRKXTb968CXhzx/uPAR0UkG0BEysS5MDky3mtHOa7BCkao1uN+kYnIaThNMntCjHvkPnxSRFKG1iUiWROII5Rj/gRwqrCLSMx1mLCyZmXN3e6Ey5qbxH+M0wyowA+AD8uwXpci8kERmTmFGAHvakTZwPfdi4kDOBcpb1HVfrep5XsikufG912cTHwv8GMR6WEKZ6mq2iMilwN3iMh3gZM4Be/ro/zJt4BXROTfw9Qk8yCwiTd+oL8K/FpEjgIv41zrGM9/4DQXfAF4esR7L+AU6oXA/er0ADpFVZ8QkcXABrc63wl8EOesJ9J+hPN/3I7zv1+rTm+uceMe4S5gLs7/RnDOoq+aQBxfZfxj/hnghyKyDeezuB74xAS2MR1YWbOyNpGyliEiW3CaHwfcZe5w9+WkiFwH/KebTAM4ZeK3Uw106KKdCSMRuRe4V1Wf9TiUmCEia3EumAZrbjAmKCtrEzcdy5qNrGCMMcZTViOKABG5Ctiiqoe8jsWYeGZlLT5YIjLGGOMpa5ozxhjjKc8S0Zo1axSn77r92E8s/UwLVn7sJwZ/RuVZImpsbPRq08Z4QkR+JiL1IrJj2GuFIvKkiOxzfxeEsi4rPyaeWNOcMdFzL7BmxGu3A0+p6iLgKfe5MQnFEpExUaKq64HmES9fCfzcffxzJnZTrjFxwRKRMd4qHRqg1f39puFfhojILSJSLSLVDQ0NUQvQmEizRGRMjFDVO1W1SlWrSkpKvA7HmLCxRGSMt066UxLg/o7GGGTGTCuWiIzx1jpeHwX6w0RgiH0Tmq6+AR7aVIvd5B99ns1HZKLv/o1H3vTaDasrPYgkMYnIr3Bm/iwWkTrgK8A3gYdE5GM4M2i+37sIE9ttD2/l0e0nWDI7l6VleV6Hk1AsERkTJap6/ShvvTOqgZignn7NaRX1DwY8jiTxWNOcMSbhqSq9ficBdfUNehxN4rFEZIxJeIeauk897uwb8DCSxGRNczHCru8YEzkn23tPPf7LrpOsWTrl2a/NBFiNyBiT8Np7/Kce9w1Y01y0WY0oDgWrPRljRtfe+3pzXN+AdVaINqsRGWMS3htrRJaIos0SkTEm4bW5iSgtOckSkQesac4Yk/Dae/3kpCXjSxL67RpR1FkiMsYkvPaeAXIz/n97dx4md1Umevz71tr73tm6O+nskIQIJCwCKogoIBdcUEFAcXBwRh316nh15t5RH5nlzjh3nHHGURFFRREVHY0MCggIyhISdpLQZO/ubN3pvbuqu7b3/lFVSSfppbq7qn61vJ/nyfN0df2q+q10n3rrnPOec7yEo7Fj64lM9tjQnDGm6A2Ohqks8eD3uAjZ0FzWTdsjEpHvAlcBXaq6boL7bwA+l7g5DPy5qr6Y1igdMln1ma3fMaaw7OoaBsDvdTMWtqG5bEulR/Q9Tj3eeLy9wJtUdT1wG3B7GuIyxpisGQ1HKfG6rVjBIdMmokmONx5//5Oq2pe4+TTQnKbYjDEmK4LhKKVeF36P2xKRA9JdrHAL8JvJ7hSRW4FbARYvzq3hLVsEakzxCobiPSJV21nBCWkrVhCRS4gnos9Ndo0ddTw3qmqHdhmTZtGYMhaJHR+aC8esnWVZWnpEIrIeuAO4QlV70vGcJu7Fjn7ufGIvD+/oYmgsgtctzK8q4bQFlWxsrWXlvApExOkwjclbw4ntfUq9bsLRGAqEojH8HrezgRWROSciEVkM/AK4SVVfm3tIuSsaU149PMjnf/4SqrCxtZYrz1hIuT/9y7GCoSi3/fd27t7cTmWJh+XzKqgr9zEWjtLRF+ThHV38bkcXK+dV8I6zmrhi3QKWNpSfkJRUld6REAf6gxzoD9I1OEZMlXmVftY319BSV5b2uI3JN4Oj8V0VSrzHE89oyBJRNqVSvj3R8cZeAFX9JvAFoB74z8SbYERVN2YqYKeMjEW4+5l29h4doaokvgL7J1s7+OcH2/jyNet429qJt42fTQl499AYt3x/Cy8fGOBP37CUT75lFZteOHjCNYOjYcp9bja9eJCvPNDGVx5oo7HSz9KGcgYCYUbDUY4MjR5bnOd2CY0VftwuYfPeXp7Y3cPZi2t594Yma3CmqCW39yn1uonG4kNywXCU6vjbnMmCaRPRFMcbJ+//MPDhtEWUgyLRGN9/ah+HB0Z511lN/NO164+9od9233Y+ctez/MWbV/Dpy1bNeZisPxDixjs2094b4Ns3beQta+ZPeF1ViZf3n7eYm17fSmdfgN+3dfNcex8dvQGGRsP4vW7WN9XQVFtKU00p86r8eFzxKcGxSJTft3Xz2Gvd3PK9rXz35nPweWxtsylOx3pEPhdjkXj7DdpaoqyyLX5S8MC2w3T2BXn/uYtZ11SNxx1/0z5/WT2/+OgF/M0vX+HfH9nF8FiEL1y1ZtbJaHgswgfv3MLenhG+d/M5XLCiIaXHNdeWceP5S7jx/CXA9BWAfo+bt61dQEOFj58/d4Db7tvObe84Za2yMUVhMHh8jijgjiegYMgSUTZZIppG19AoT+7u4byldaxrqgZOfaN/XXMNB/qC3PnEPmIx5UtXr51xMhoNR/nw97fwcmc/N5y3hH09Afb1ZLakfMOSOuor/Nz++B7etKpx0t6XMYVs/BxRcmTAekTZZeMx03ho+xG8HheXnj75m7SIcOUZC7n1jcv4/lP7+ZtfvUIslnr5ZygS42M/eo7Ne3u5dkMLpy+sSkfoKfnLt65m1fwKvrhpGyNjkekfYEyBGRw3R+RNjHaMWiLKKktEU+gZHmPbwUEuXF5PxTSVcSLCX11xGh950zJ++HR7ysloLBLlz3/4LA+/2sVt16zjzJaadIWfEp/Hxd+/8wwO9Ae5/fE9Wf3ZxuSCwWAYId4WfIlEZENz2WWJaApb9/chwLlL61O6XkT4/OWn8dGLl/Ojze189t6XiEQn3y5kNBzlz+6KJ6G/fce6Y3M82baxtY4rz1jAHX/YQ8/wmCMxGOOUwdEIfq8LlwhetxUrOMES0SQisRhb9/dx2oJKqktTL+MUET77ttV8+rJV/Py5Tr79hz10D5365r6ra5hrv/kkj7Z18/fvPMOxJJT06ctWEwxH+Zb1ikyRGQyGKU2sIfLaHJEjrFhhEnu6RxgZi7BhSd2MHysifOLSlSxvrOAvf/Yi//bwa6xrqmZZQ0X8uY8O8ze/eoVyn5tvf2Ajl+VAkcCKeRVctX4Rd29u52OXrJhR8jUmnw2OjktENkfkCEtEk9h+cBCf28XK+RWzfo63r19IZ1+Ax1/r5rn2fl7qHACgzOfmpvOX8LFLVtBY6U9XyHN26xuXsenFg9y9uZ0/v3i50+EUFRH5n8TX4ynwMvAhVR11NqriMBiMHNtVweaInGGJaAIxVXYcHmTV/Ipjn5Bmq7LEy9vXL+KKMxYyEAgjAlWlXseH4iayrqmai1Y0cOcTe/mTi1ptx4UsEZEm4BPAGlUNishPgeuInwVmMmxwNHwsEXlsjsgRNkc0gQN9QYZGI6xZlL4yapcIteU+asp8uHJ4k9KPvGkZXUNj/Or5g9NfbNLJA5SKiAcoA+wXkCWDweOJyCWCxyXWI8oy6xFNYGfXEAKsml+ZsZ+Rq+cfXbSigTULq/jW47t5z8Zm29k7C1T1gIj8M9AOBIEHVfXBk6/L5fO88lkgHD1hiyuv22U9oiyzHtEE9nSPsLC6hDJf8eVpEeGWi5ayu3uEJ3bZiR7ZICK1wDXAUmARUC4iN558nZ3nlRmBUPTY3BDE1xNZjyi7LBGdJByN0d4bYFnj7IsU8t3b1y+krtzH95/a53QoxeItwF5V7VbVMPFjVS5wOKaiEI0poUgMr+d4z996RNlXfB/5p9HeGyASU5Y1lM/4sbk63DaVyWK+/twWvvH73XT0BuzcosxrB84XkTLiQ3OXAludDak4JBPOCT0it1j5dpZZj+gke4+OIEDrLBJRIbnhvCWICD/cvN/pUAqeqm4G7gWeI1667QJudzSoIpEcghtfHWs9ouyzRHSSjt4AC6pLTjitsRgtqinlrWvm85MtHfbpMAtU9YuqepqqrlPVm1TV9lrKgtEJekRemyPKumkTkYh8V0S6ROSVSe4XEfmaiOwSkZdE5Oz0h5kdMVU6+gI015Y6HUpO+MDrW+kPhE85HdaYQhFI9ohOqZqbfI9Ik36p9Ii+B1w+xf1XACsT/24FvjH3sJzROxxiNByjpdbmRADOX1bH6vmVfO/JfaimfqyFMfni+BzR8WIFn1sIhOxIlGyaNhGp6uNA7xSXXAP8QOOeBmpEZGG6Asymjr4AAM02OQ/ES7k/cMESth8a5Nn9fU6HY0zaJRPO+B6R3+O2s7myLB1zRE1Ax7jbnYnv5Z2OvgA+j4t5ObT/m9PeeVYTlSUevv+UFS2YwjPRHJHf62LYElFWpSMRTbT0fsJxHBG5VUS2isjW7u7uNPzo9DrQF2RRdWlOb8GTbWU+D+/d2MJvXj7EkUHbg9MUlsAEVXN+j4vRcGzKs8RMeqUjEXUCLeNuNzPJPlm5vDI8psqRwTEW1ZQ4HUrOuen8JURV83KdlDFTSVbHndAjSmz2OzJmlXPZko5EtAn4QKJ67nxgQFUPpeF5s6p3OEQoGmNhtSWik7U2lHPxqkbufqadUMQ+JZrCkRyaO3GOKP71sBUsZM20OyuIyI+Bi4EGEekEvgh4AVT1m8D9wJXALiAAfChTwWbSocSw04JqK92GU3dcWFxXzqNt3fzmlUNcc2ZeTgEac4rARD2ixBrC4VFLRNkybSJS1eunuV+Bj6UtogybbHjp0EAQl5BXhQrZHCpbOb+C+nIfdz213xKRKRjJ8m3PuPLtYz0iK1jIGttrLuHwwCiNlf45H4RXqFwinLu0jt+8cpivPvQa86tOHMJ8/3l2LIHJP8FQFL/HdUKBkiWi7LN33YRDA6MsqLL5oamctbgWtwhb9021rMyY/BEMRynznbidV3JoztYSZY8lIuITlgPB8Cmf8s2JKvweTl9UxXPt/YSttNUUgGAoSulJ+0oe6xHZHFHWWCICuofi+0vOq7RENJ1zWmsJhqNsPzjodCjGzFkgHKX05B6RDc1lnSUioCuZiKryp1DBKcsbK6gt87LFhudMAdjdNXzKkQ/H1xFZIsoWS0RA19AobpdQW+ZzOpSc5xJhY2sde46O0DNsJxWY/BaKxE4o3QZwuwS/x7b5ySZLREDX4BiNFX7cLtvaJxUbFtfiEthqG6GaPBeOxiaslK0s8VgiyiJLRED38BiNebR+yGlVpV5Wz6/k2f19RGN2PITJX+GoTpiIyv2WiLKp6BNRKBKjbySUVwtZc8E5rXUMj0V49bAVLZj8FYrG8HkmSEQ+j80RZVHRJ6KekTEUrEc0QyvnV1JZ4uE5G54zeSwcmXhorsKG5rKq6BPR0eEQAA0Vlohmwu0Szmqpoe3IEEOjYafDMWZWQtHYCaezJlXY0FxWFX0iSlZ+1VdYxdxMnb24lpjCix39TodizIyparxYYYKhuQq/x46ByKKiT0RHh0NUlXiOrR0wqZtXVUJLbSnPtvcR3/vWmPwRisaIKaeUb0O8WGHIdlbIGktEw2PU27DcrJ29pJYjg2O8fGDA6VCMmZHRUHybqsnKt61YIXssEQ2P2fzQHKxvqsHjEu59ttPpUIyZkeSOChP2iHweguGoHReeJUWdiIKhKIFQlAabH5q1Up+bNYuq+NULB4+ddmlMPggkTmCdaI6o7cgQACMh+5vOhqJOREeThQrl1iOaiw1LahkIhnl4R5fToeQtEakRkXtF5FUR2SEir3c6pkJ3vEd0atVcSSI52fBcdqSUiETkchFpE5FdIvL5Ce5fLCKPisjzIvKSiFyZ/lDTry8QL922irm5Wd5YwcLqEn72bIfToeSzfwN+q6qnAa8DdjgcT8ELJno7E80R+WwH7qyaNhGJiBv4OnAFsAa4XkTWnHTZ/wF+qqpnAdcB/5nuQDOhLxBf/1JT5nU4kvzmEuHdZzfz+GvdHBkcdTqcvCMiVcAbge8AqGpIVa0mPsOO9YgmGJorSZxRZIkoO1LpEZ0L7FLVPaoaAu4BrjnpGgWqEl9XAwfTF2Lm9I2EKPO5rXQ7Dd69oZmYwi+eO+B0KPloGdAN3JkYVbhDRMqdDqrQTdUjssPxsiuVRNQEjB9z6Ux8b7wvATeKSCdwP/AXEz2RiNwqIltFZGt3d/cswk2vvkDIjn5Ik6UN5ZzTWsvPnu2wNUUz5wHOBr6RGFUYASYaAs+p9pPvpqqa89kcUValkogmOhvh5Hea64HvqWozcCVwl4ic8tyqeruqblTVjY2NjTOPNs36AmFqbVguba7d0Mye7hGet50WZqoT6FTVzYnb9xJPTCfItfaT7471iCYamvPY0Fw2pZKIOoGWcbebOXXo7RbgpwCq+hRQAjSkI8BMUVX6rUeUVm9fv4hSr5ufbbU1RTOhqoeBDhFZnfjWpcB2B0MqCoHQ5D0iOy48u1JJRFuAlSKyVER8xIsRNp10TTvxxoOInE48EeX02MHwWIRITKkpt0SULhV+D1esW8B9L9qaoln4C+BHIvIScCbw9w7HU/CSQ3PeCcq3fV4bmsumaRORqkaAjwMPEC8p/amqbhORL4vI1YnLPgP8qYi8CPwYuFlzfKIgWTFXZ0NzafWejS0MjUXY9GJe1KvkDFV9ITHstl5V36Gqdr5Gho2GowhMeDKzx+XC4xKGLBFlhSeVi1T1fuJFCOO/94VxX28HLkxvaJmVXENUY0NzaXX+sjpWz6/kzif28Z4NzYjY8esmNwVCUXwe16R/o36Py3pEWVK0Oyv0j8QTkc0RpZeIcPOFrew4NMjmvb1Oh2PMpAKh6ISl20l+r9vKt7OkaBNRXyBMuc894WI2MzfvOLOJmjIvdz6x1+lQjJlUMBSZsv37PS4rVsiSlIbmClFfIEStFSqkzd2b20+4/brmGh7afoSO3gAtdWUORWXM5EZC0Qkr5pL8Hrcloiwp2u5AXyBk80MZdN7SOkSEO5/Y53QoxkwomJgjmkyJ13pE2VKUPaKYKv2BMKcvrJr+YjMrNWU+1jdVc9fT+5hf5aey5Hh14vvPW+xgZMbEjaQwNDcQDGcxouJVlD2i5BoiK1TIrDefNo9oTHn8tZxeUmaKVNCG5nJGUSai4xVztoYok+or/JzZUsvmvb0MjtonS5Nbpu0R2dBc1hRlIkouZrUeUeZdsrqRmCqPWa/I5JjA2NRzRH6Pm9FwjLAdF55xRZqIbDFrttRX+Dl7cS3P7O2lN9ETNSYXBKYdmrNtfrKlSBNRmHK/x9YQZcmlp8/HLcL9Lx9yOhRjAIjFlGB4+qo5gCFb1JpxRflOHN912+aHsqW61MvFqxvZfmiQ3d3DTodjzJRnESUlD8wcCVkiyrSiTES9I3b8Q7ZduKKB2jIv//3SISI25m4clkwu0xUrgJ3Smg1Fl4hiqvQH7UC8bPO6XVyxbiGHB0f58ZaO6R9gTAYlD8WbrlgBsB24s6DoEtHwaIRoTK1QwQFrF1WxtKGcf3mwjf6AFS4Y54yMpTI0Z8UK2VJ0iShZMWdDc9knIly1fiEDwTD/+rudTodjilgwPP3QXIk3cVy4Dc1lXNFt8XN8DZENzTlhYXUpG5fU8YOn9lFT6mVeVcmx+2zrH5MtyR6Rf5otfsCOC8+GousRJYeEbOdt57xlzXx8Hhf3v2Ll3MYZgVDymPDJ3wKTvSUr3868lBKRiFwuIm0isktEPj/JNe8Vke0isk1E7k5vmOnTFwhR4fdM+QdoMqvC7+HNq+fx2pFh2g4POh2OKUKBRNXcVD0ilwjlPrfNEWXBtO/GIuIGvg5cAawBrheRNSddsxL4K+BCVV0LfCoDsaZFX8Aq5nLB+cvraajwcf/Lh4nG1OlwTJEZSfaIplnUXlHisaG5LEilW3AusEtV96hqCLgHuOaka/4U+Lqq9gGoald6w0yfvhE7hygXeFwu3rpmAd3DY7zY0e90OKbIJHs5U/WIAMr9HivfzoJUihWagPELPzqB8066ZhWAiDwBuIEvqepvT34iEbkVuBVg8eLsT0zHYvE1RGsXVWf9Z5tTrV1URVNNKQ+/eoT1LRP/Tk4++TXJChvMXDy9pwdh6vJtgEq/x6rmsiCVHpFM8L2Tx1I8wErgYuB64A4RqTnlQaq3q+pGVd3Y2Ng401jnrGtojGhMqS23oblcICJctmY+fYEwW/f1OR2OKSJj4Rh+rwuRid7ejqso8dgcURakkog6gZZxt5uBgxNc8ytVDavqXqCNeGLKKZ19AcDWEOWSlfMqWFJfxqNtXcdWuxuTaaPhKCWJnROmUu6zOaJsSCURbQFWishSEfEB1wGbTrrml8AlACLSQHyobk86A02Hzr4gADVWrJAzRITLTp/P0GiEe5+1rX9MdoxFYscWrE6losRj5dtZMG0iUtUI8HHgAWAH8FNV3SYiXxaRqxOXPQD0iMh24FHgs6rak6mgZ8t6RLlpaUM5zbWl3PHHvVZBZ7JiNBydtlAB4nNEtvt25qW0mEZV71fVVaq6XFX/LvG9L6jqpsTXqqqfVtU1qnqGqt6TyaBnq7MvSKWtIco5IsIbVjayvyfAg9sOOx2OY0TELSLPi8h9TsdS6FLtEe3vCTAYDKNqH5AyqajekTv7gjYsl6PWLqpicV0Z33p8TzE3+k8SH3UwGTYajh475mEqfo+LmMYTl8mcIktEAdvaJ0e5RPjwG5byQkc/W/cXXwWdiDQDbwfucJy/150AABbgSURBVDqWYjAaiaVUrOBP9JpsniiziiYRxWLKgf6gzQ/lsPdsaKGmzMudT+x1OhQn/Cvwv4BJP3qLyK0islVEtnZ3d2cvsgI0NoMeEdhREJlWNInoyNAo4aja0FwOK/W5eddZzTy0/Qg9w2NOh5M1InIV0KWqz051ndPr8ApFKBIjEtNjB99N5djheNYjyqiiSUTJ0m3rEeW2685tIRxVfvHcAadDyaYLgatFZB/xLbTeLCI/dDakwpVcF1SSQo+oxBe/ZnA0nNGYil3RnEdkpdu5L7mdz+K6Mm5/fA9lPve0K98Lgar+FfFNgxGRi4G/VNUbHQ2qgCW37ElljqjMF3+L7A9YIsqk4ukR9dpi1nxxTmst3cNjtPcGnA7FFKBk7yaVHlFZolihz462z6jiSUR9QeZV+m0NUR44o6kGv8fFln29ToeSdar6e1W9yuk4CllyaM6fwjqiMl/8mn5LRBlVNO/Knf0BmmtLnQ7DpMDncbG+uYaXDwwQsvUbJs0Ggske0fSJyON24XO7bGguw4onEfUFaa4tczoMk6L1zdWEo0rbkSGnQzEFZiCRVJK9nemU+tz0WSLKqKJIRNGYcrA/aD2iPLK0oZxyn5tXDgw4HYopMMn5nlQTUZnPbUNzGVYUiagrsYbIekT5wyXC2kXVtB0eIhy14TmTPn2BMG6RaQ/FSyrzuekPWo8ok4oiESXXEFmPKL+sa6omFI3xmg3PmTTqD4RmtDSg1OexqrkMK4pE1JEoA7ZElF+WNpRT5nPzsg3PmTTqD4QpTXFYDpJDc9YjyqSiSETJHtGiGktE+cTtEtYuquJVG54zadQXCB1bqJqK5BxRzM7KypgiSUQB5lX6UyrXNLll3aJqQpEYu7qGnQ7FFIj+QDjlQgWI764QU9tvLpOKJBEFaamzQoV8tLShHK9b2Nll80QmPfoSc0SpqvDHe0/dRbQRb7allIhE5HIRaRORXSLy+Smuu1ZEVEQ2pi/EuYuvIbJhuXzkcbtY1lDBziPWIzJzp6r0B2fWI6osSSSiIUtEmTJtIhIRN/B14ApgDXC9iKyZ4LpK4BPA5nQHORe2hij/rZxfQc9IqKiOhjCZEQxHCUViM5ojSvaIuoZGMxVW0UulR3QusEtV96hqiPg29ddMcN1twD8BOfXbOjI4SiRma4jy2ap5lQDstHkiM0e9IzNbzApQVRLfKNl6RJmTSiJqAjrG3e5MfO8YETkLaFHV+6Z6IidOmLQ1RPmvvsJHbZmXnbaeyMzRkcF4MqkqTX0X/hJvfL85myPKnFQS0USrvo7VMYqIC/gq8JnpnsiJEybbj60hsh5RvhIRVs6rZPfRESIxK+M2s3d4ID5gk+zlpEJEaKz00z1oiShTUklEnUDLuNvNwMFxtyuBdcDvEydMng9sypWChfaeEVwCTbaGKK+tml9BKBKzM4rMnBwaiI+QVM+gRwTEE5H1iDImlUS0BVgpIktFxAdcB2xK3qmqA6raoKqtqtoKPA1crapbMxLxDO3vDbCophSfpygq1QvWssYKXIJVz5k5OTwwSqnXndKheOM1VvptjiiDpv1tqGoE+DjwALAD+KmqbhORL4vI1ZkOcK729wRYUm/DcvmuxOumubaMPd2WiMzsHRocZWF1yYyPoG+s9NNliShjUqphVNX7gftP+t4XJrn24rmHlT7tvQHetnaB02GYNFjaUM4fdnbbYXlm1g4PjLKgumTGj5tX6ad3JEQ4GrNTnjOgoP9HB0fD9I6ErEdUIJY2lBNTbJ7IzNpsE1FjpR+AnmHbhTsTCjoRtffE37CW2PY+BWFxXRkC7OsZcToUk4ci0RiHE0NzM7XjYHzpgC1qzYyCTkT7k4movtzhSEw6lHjdLKopZe9RS0Rm5g4NjBKNKYtn8cHUtvnJrMJORL3xN6zFNjRXMFrry+joDTAWiTodiskzyXPJWmaxptASUWYVdCJq7wnQUOE7tleUyX9LG8qJxJSXO+2wPDMzHX3xRPR8e/+MH3t8vzlLRJlQ0Ilof09gVt1wk7uSw6yb9/Y6HInJN+29AVwys+19kjxuF6Vet/WIMqSgE1F7b8DmhwpMud/DvEo/z1giMjPU0RukpsyH2zWzNURJlSUeS0QZUrCJaCwS5eBA0HpEBWhpQzlb9/USsePDzQy09waoLZt5byipssTDEauay4iCTUSdfUFUsTVEBai1vpyRUJRXDxfGbtwi0iIij4rIDhHZJiKfdDqmQtTZF6Cu3Dfrx1eXejnUb4koEwo2Ee1PrDWxRFR4kr/TLfsKZnguAnxGVU8nvmnwxyY6fNLM3shYhKPDIWrL5paIuoZGrSeeAQWciOIVMovrbI6o0NSU+WiqKWXrvj6nQ0kLVT2kqs8lvh4ivqdj09SPMjORPJdsbj0iHzGFIzZPlHYFnYjKfG4aKmb/h2dy18bWWrbs60VVp784j4hIK3AWsHmC+7J+sGShSG4LNZceUU1ifulQfzAtMZnjCjgRjcS3hJnhLrsmP2xsraNraIyO3sJ5UxCRCuDnwKdUdfDk+504WLJQJBezznWOCODggM0TpVvBJqJd3cOsmFfhdBgmQ85prQUKZ55IRLzEk9CPVPUXTsdTaNp7A5T73JT53LN+jmQish5R+hVkIgqGonT2BS0RFbBV8yqpLPGwdX/+JyKJd9u/A+xQ1X9xOp5C1NkXoGWOIyQlXjeVfg8HLRGlXUEmot3dw6jCynmVTodiMsTlEjYuqWVLYRQsXAjcBLxZRF5I/LvS6aAKSXtvPBHNVXNdGR19lojSrSA3YdudOMXTekSFbWNrHY+2tdE7EprT2L/TVPWPgE1mZoiq0tEb5KIVc59Xa60vo+1IYaxfyyUp9YhE5HIRaRORXSLy+Qnu/7SIbBeRl0TkYRFZkv5QU7eraxiXQGuDrSEqZOcurQPg2f0F0SsyGXJ0OEQwHE3LWUJL6svp6A0QjRVWtabTpk1EIuIGvg5cAawBrp9gsd3zwEZVXQ/cC/xTugOdiZ1HhllSX47fM/uJSZP7zmiqxud2sbVAChZMZiR33a6bQ+l2Umt9GeGo2jxRmqXSIzoX2KWqe1Q1BNwDXDP+AlV9VFWT5zc/DTSnN8yZ2dU9zPJGG5YrdCVeN+ubqwumcs5kRrJ0uzYNw7c7u+LD/skF8yY9UklETUDHuNudTL3q+xbgNxPdkY0FeaPhKHu6hzl9oRUqFIONrXW8fGCA0bAdlGcmlkwac1nMmlSfSGZ77bj6tEolEU00iTrhAKmI3AhsBL4y0f3ZWJDXdniImMKahVUZeX6TW85prSUcVV7omPlhZ6Y4bDs4QH25D59n7kXCVaVeSrwudhw6Zb2xmYNUfjOdQMu4283AwZMvEpG3AP8buFpVHduMaXviD2TNIktExWDDksTCVjufyEzipc4BmmpL0/JcLhGaa8p40T74pFUqiWgLsFJEloqID7gO2DT+AhE5C/gW8STUlf4wU7f94CAVfs+szqU3+aemzMdpCyp5em+P06GYHNQ1NMqhgVGa0/h+0FRbStvhIRsOTqNpE5GqRoCPAw8Q3xX4p6q6TUS+LCJXJy77ClAB/CyxGG/TJE+XcdsPDXL6wkpcszyF0eSfC5Y3sHVfn70xmFO81DEAQHNNenpEAM21pURiyraDNjyXLikNmqrq/aq6SlWXq+rfJb73BVXdlPj6Lao6X1XPTPy7eupnzIxYTNlxaNDmh4rMBcvrGYvEeK7d1hOZE23e24PP40rb0BzED2Z0CTzW5ujgT0EpqC1+dncPEwhFWdtU7XQoJovOW1aH2yU8tduG58yJntzdw4bFtXjd6XurK/d7WFJfzj1bOqa/2KSkoBJRcoX9xsQEtikOlSVezmiq5vGdR50OxeSQvpEQ2w8OUu5P/05maxdV0TU0xr6jVsadDgWXiGrLvCxtsFNZi83Fqxt5qbOfnmE7PdPEPb2nBwWWN6b//WD1/Pg6xT/stAMK06GwElF7H2cvrrXD8IrQm0+bhyo8bm8MJuHJ3T343K60Vswl1ZX7qC3z8thr1gtPh4JJRH0jIfZ0j3C2DcsVpXWLqmmo8PPIq5aITNyTu4/S2lCGOwMVtCLCyvmVPLX7KOFoLO3PX2wKJhFtTixotPmh4uRyCZesbuSxti5CEXtjKHYH+oPs7h7J6J6TK+dVMBKK8ny7LW6dq4JJRI+91k2F32M9oiJ2xRkLGByN8MQuGy4pdr/bfgSA0xZkbinHsoYK3C6xeaI0KIhEpKo81tbFhSvq01qmafLLRSsaqSzxcN9Lh5wOxTjsoe1HWNZYTmOlP2M/o9Tn5syWGn71wkE+ctezfPWh1zL2swpdQbxr7+oa5uDAKBevnud0KMZBPo+Lt61dwIPbDtsuC0Vs39ERnth1lCVpOBp8Op956yp6hsd4YNthfrS53f7uZqkgEtGDiW74m1ZlZkdvkz/ecWYTQ2MRfvvKYadDMQ75zh/34nIJ5y2rz/jPumB5Aw9++k2866wmjg6P8eNn2jP+MwtR3iciVeW/nj/AOa21LErjflImP12wvJ7W+jJ+tHm/06EYB7T3BLhnSztnL66hqsSblZ/ZVFPKxtY6mmtL+fEz7ajaMeIzlfeJ6OUDA+zqGuZdZzt6KKzJES6X8P7zFrNlXx/bbVPKovOVB9twu4RLT5uflZ939+Z27t4c7wWd21rHa0eG+aMVy8xY3ieiHz/Tgc/j4sozFjodiskR793YQoXfw9ce3ul0KCbDvvrQa7zhHx/hb375Cr995TC/fvEgH75oGVWl2ekNjfe6lhpa6kr52/t22BKCGcrrRHSwP8i9z3bwng3NVDvwh2dyU02Zjz+5aCm/3XaYVw4MOB2OyZBoTLnr6f10D4/xw837+bMfPkt9uY+68rkfCT4bXreLL1y1lrYjQ9x233aiMRuiS1VeJ6J/f2QXAB+9ZIXDkZhcc8tFS6kr9/HX//UyEVv5XpAef62b3pEQ7ziziQc+9Ub+4V1n8LFLVlDidTsWU/fQGBcur+eup/dz7TefZK9tipqSvE1ET+46yo+faeeG85bQZEUK5iTVpV5uu2YdL3UO8LXEBxZTOFSVbzy2mwq/hzWLqti6rw9VHE1CSVeesZD3bmzh1UNDXHf7U+zqGkZVrYc0hfTvj54Fe4+O8KmfvMCyxnI+d/lpTodjctTb1y/kkVeb+drDO5lX6efG85c4HZKZg67BUbbu7+OPu45yoC/IM3t7eceZTXhcufV5WkQ4s6WGBdUl3PGHPVz2L48B8SR5wfJ6PnhBK2+0pSYnSCkRicjlwL8BbuAOVf2/J93vB34AbAB6gPep6r70hhr3yKtH+NzPXyYaU75xwwZKfc5/AjK56x/edQY9I2P8n1++wraDg3z2basdm0OYynRtrJB19AZ4oaOfunIf65urqSzxMhaJ8vSeXrwuIRJTfvDUfh5+9Uii1+OipbaMD75+CSsTxzHkogVVJXzy0pU8s7cXBUbGImzd38cjr3bxP163iK9df9YJ1/cMj9E9PMb8yhJqyrxFdYrAtIlIRNzA14HLgE5gi4hsUtXt4y67BehT1RUich3wj8D70hXkH3ce5ak9R3n01W62Hxpk5bwK/vOGs3P6j9DkBp/Hxbc/sJGvPNDGHX/Ywy+fP8DFqxs5s6WGxko/LXVlnNNa52iMKbaxvKCqDI5GaO8JsKt7iJ1HhukPhjmjqZo3nzaPsXCMXzzfyfPt/ayYV0HP8BibXjxIctTKJbBmURUH+0fpHQkde94Sr4uLVzWyekEVTTWlGdlROxMqS7xcevrxUvIrIjHu2dLOphcP0jU0SjiqHB4YJRKLcWRwbNzjPCxrrOD0BZVUl3rxeVysXVTFssYKDvYH2XFoiPbeEVbMq+Tc1jpWL6jE58mtnuFMpNIjOhfYpap7AETkHuAaYHwjuQb4UuLre4H/EBHRNK3s+snWDn7z8iHWNlVz2zVred85i/P6P91kl9ft4q+vPJ33bmzmzif28cirXfwmsfPCJasbufND5zocYUptLOvu3tzO3/73dlRBUSZrzeM/uIejJ86FeFyC1+06ttYmaV6lnyd2HaXU5+aDF7RS7vMwMhZhX0+AfT0jLKou4ar1C/G6XURjSmt9eUG0eZ/HxQ3nLeGPO7vZc3SEmjIvjZV+XCKc1VJLbbmPwWCYo4ne0a9fPEgoGiMSVU7+7y/1ugmO21LI53HxZ29azqcvW5XdF5UGMl2uEJFrgctV9cOJ2zcB56nqx8dd80rims7E7d2Ja46e9Fy3Arcmbq4G2tL1QmagASj0FWf2GjPnqKpens4nTKWNJb7vdPuxv6vC4cTrnLTtpNIjmqgPfHL2SuUaVPV24PYUfmbGiMhWVd3oZAyZZq8x7+RF+ymw//MJFcNrhNx7nan0dTuBlnG3m4GDk10jIh6gGuhNR4DGFIFU2pgxBSuVRLQFWCkiS0XEB1wHbDrpmk3ABxNfXws8kq75IWOKQCptzJiCNe3QnKpGROTjwAPES0u/q6rbROTLwFZV3QR8B7hLRHYR7wldl8mg58jRocEssdeYRyZrYw6HNZGC+T+fQjG8Rsix1zltsYIxxhiTSflfD2mMMSavWSIyxhjjqIJNRCJyuYi0icguEfn8BPf7ReQnifs3i0hr9qOcmxRe480i0i0iLyT+fdiJOGdLRL4rIl2JdWoT3S8i8rXE639JRM7OdoyFyNpO/rcdyLP2o6oF94/4hO9uYBngA14E1px0zUeBbya+vg74idNxZ+A13gz8h9OxzuE1vhE4G3hlkvuvBH5DfB3O+cBmp2PO93/Wdo5dk9dtJ/Ea8qb9FGqP6NiWKaoaApJbpox3DfD9xNf3ApdKfu0ymMprzGuq+jhTr0e7BviBxj0N1IiIHdU7N9Z2CkQ+tZ9CTURNQMe4252J7014japGgAGgPivRpUcqrxHg3Ylu970i0jLB/fks1f8DkzprO8cVctuBHGo/hZqI0rYtUQ5LJf5fA62quh74Hcc/xRaKfP8d5iJrO3GF3nYgh36PhZqIimFbomlfo6r2qGpyb/lvEz8vqpDY1jjpZ22Homg7kEPtp1ATUTFsSzTtazxpvPdqYEcW48uGTcAHEtU/5wMDqnrI6aDynLUdiqLtQA61n7w8Knw6WnjbEp0ixdf4CRG5GogQf403OxbwLIjIj4GLgQYR6QS+CHgBVPWbwP3EK392AQHgQ85EWjis7RRG24H8aj+2xY8xxhhHFerQnDHGmDxhicgYY4yjLBEZY4xxlCUiY4wxjrJEZIwxxlGWiAqMiAw7HYMx+cjajnMsERljjHGUJaIcJyL/KCIfHXf7SyLyRRF5WESeE5GXReSUnYNF5GIRuW/c7f8QkZsTX28QkcdE5FkRecB2rDaFyNpO/rBElPvuAd437vZ7gTuBd6rq2cAlwP9LdRt+EfEC/w5cq6obgO8Cf5fekI3JCdZ28kRBbvFTSFT1eRGZJyKLgEagDzgEfFVE3gjEiG/dPh84nMJTrgbWAQ8l2p878XzGFBRrO/nDElF+uJf45pILiH/Ku4F4w9qgqmER2QeUnPSYCCf2eJP3C7BNVV+f0YiNyQ3WdvKADc3lh3uIbyx5LfGGVQ10JRrSJcCSCR6zH1gjIn4RqQYuTXy/DWgUkddDfLhBRNZm/BUY4wxrO3nAekR5ILEzcCVwQFUPiciPgF+LyFbgBeDVCR7TISI/BV4CdgLPJ74fEpFrga8lGpkH+FdgW5ZejjFZY20nP9ju28YYYxxlQ3PGGGMcZYnIGGOMoywRGWOMcZQlImOMMY6yRGSMMcZRloiMMcY4yhKRMcYYR/1/0PaBoeVjXiMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x1080 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(props, col='variable', row='Set', sharey=False)\n",
    "grid.map(sns.distplot, 'value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.013,
     "end_time": "2020-05-12T09:31:05.896059",
     "exception": false,
     "start_time": "2020-05-12T09:31:05.883059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Rec list distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "papermill": {
     "duration": 0.056029,
     "end_time": "2020-05-12T09:31:05.965087",
     "exception": false,
     "start_time": "2020-05-12T09:31:05.909058",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>user</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>als</td>\n",
       "      <td>529</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>als</td>\n",
       "      <td>1723</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>als</td>\n",
       "      <td>1810</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>als</td>\n",
       "      <td>2781</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>als</td>\n",
       "      <td>2863</td>\n",
       "      <td>PropFemale</td>\n",
       "      <td>0.137931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set Algorithm  user    variable     value\n",
       "0  AZ       als   529  PropFemale  0.296296\n",
       "1  AZ       als  1723  PropFemale  0.571429\n",
       "2  AZ       als  1810  PropFemale  0.400000\n",
       "3  AZ       als  2781  PropFemale  0.320000\n",
       "4  AZ       als  2863  PropFemale  0.137931"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rec_props = pd.melt(recs.reset_index(), ['Set', 'Algorithm', 'user'], ['PropFemale', 'PropDC'])\n",
    "rec_props.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.011988,
     "end_time": "2020-05-12T09:31:05.990084",
     "exception": false,
     "start_time": "2020-05-12T09:31:05.978096",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Implicit algorithm distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "papermill": {
     "duration": 2.717339,
     "end_time": "2020-05-12T09:31:08.720427",
     "exception": false,
     "start_time": "2020-05-12T09:31:06.003088",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bed2231cc8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 959x648 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(select_implicit(rec_props), row='Set', col='Algorithm', hue='variable')\n",
    "grid.map(sns.kdeplot, 'value')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015978,
     "end_time": "2020-05-12T09:31:08.753397",
     "exception": false,
     "start_time": "2020-05-12T09:31:08.737419",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Explicit algorithm distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "papermill": {
     "duration": 1.981721,
     "end_time": "2020-05-12T09:31:10.754133",
     "exception": false,
     "start_time": "2020-05-12T09:31:08.772412",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1bed2ba9508>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 743x648 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(select_explicit(rec_props), row='Set', col='Algorithm', hue='variable')\n",
    "grid.map(sns.kdeplot, 'value')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01712,
     "end_time": "2020-05-12T09:31:10.788225",
     "exception": false,
     "start_time": "2020-05-12T09:31:10.771105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Let's look at correlations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019118,
     "end_time": "2020-05-12T09:31:10.823326",
     "exception": false,
     "start_time": "2020-05-12T09:31:10.804208",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Implicit feedback mode\n",
    "\n",
    "First, extract the recommendation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "papermill": {
     "duration": 0.244038,
     "end_time": "2020-05-12T09:31:11.083363",
     "exception": false,
     "start_time": "2020-05-12T09:31:10.839325",
     "status": "completed"
    },
    "tags": []
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>recs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">529</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">als</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bpr</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">875516</th>\n",
       "      <th>bpr</th>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.093023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">item-item</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.361111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">user-user</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119950 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     recs\n",
       "Set user   Algorithm variable            \n",
       "AZ  529    als       PropDC      0.489796\n",
       "                     PropFemale  0.800000\n",
       "           bpr       PropDC      0.466667\n",
       "                     PropFemale  0.464286\n",
       "           item-item PropDC      0.600000\n",
       "...                                   ...\n",
       "GR  875516 bpr       PropFemale  0.093023\n",
       "           item-item PropDC      0.400000\n",
       "                     PropFemale  0.361111\n",
       "           user-user PropDC      0.520000\n",
       "                     PropFemale  0.083333\n",
       "\n",
       "[119950 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_rd = select_implicit(recs)\n",
    "imp_rd = imp_rd.melt(['Set', 'user', 'Algorithm'], ['PropFemale', 'PropDC'], value_name='recs')\n",
    "imp_rd.set_index(['Set', 'user', 'Algorithm', 'variable'], inplace=True)\n",
    "imp_rd.sort_index(inplace=True)\n",
    "imp_rd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.021745,
     "end_time": "2020-05-12T09:31:11.125078",
     "exception": false,
     "start_time": "2020-05-12T09:31:11.103333",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now the profile data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "papermill": {
     "duration": 0.072089,
     "end_time": "2020-05-12T09:31:11.215282",
     "exception": false,
     "start_time": "2020-05-12T09:31:11.143193",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>profile</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">529</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1723</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th>875408</th>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.284211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">875441</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.485714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.821990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">875516</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.523148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.115044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        profile\n",
       "Set user   variable            \n",
       "AZ  529    PropDC      0.375000\n",
       "           PropFemale  0.800000\n",
       "    1723   PropDC      0.560000\n",
       "           PropFemale  0.285714\n",
       "    1810   PropDC      0.125000\n",
       "...                         ...\n",
       "GR  875408 PropFemale  0.284211\n",
       "    875441 PropDC      0.485714\n",
       "           PropFemale  0.821990\n",
       "    875516 PropDC      0.523148\n",
       "           PropFemale  0.115044\n",
       "\n",
       "[30000 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_prof = profiles.reset_index()\n",
    "imp_prof = imp_prof[~imp_prof['Set'].str.endswith('-E')]\n",
    "imp_prof['Set'] = imp_prof['Set'].str.replace('-I$', '')\n",
    "imp_prof = imp_prof.melt(['Set', 'user'], ['PropFemale', 'PropDC'], value_name='profile')\n",
    "imp_prof.set_index(['Set', 'user', 'variable'], inplace=True)\n",
    "imp_prof.sort_index(inplace=True)\n",
    "imp_prof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017023,
     "end_time": "2020-05-12T09:31:11.249282",
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     "start_time": "2020-05-12T09:31:11.232259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And now merge it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "papermill": {
     "duration": 0.20807,
     "end_time": "2020-05-12T09:31:11.474388",
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     "start_time": "2020-05-12T09:31:11.266318",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>profile</th>\n",
       "      <th>recs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>variable</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">529</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">PropDC</th>\n",
       "      <th>als</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bpr</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.489796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <th>als</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">875516</th>\n",
       "      <th>PropDC</th>\n",
       "      <th>user-user</th>\n",
       "      <td>0.523148</td>\n",
       "      <td>0.520000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">PropFemale</th>\n",
       "      <th>als</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bpr</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.093023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.361111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.115044</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119950 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  profile      recs\n",
       "Set user   variable   Algorithm                    \n",
       "AZ  529    PropDC     als        0.375000  0.489796\n",
       "                      bpr        0.375000  0.466667\n",
       "                      item-item  0.375000  0.600000\n",
       "                      user-user  0.375000  0.489796\n",
       "           PropFemale als        0.800000  0.800000\n",
       "...                                   ...       ...\n",
       "GR  875516 PropDC     user-user  0.523148  0.520000\n",
       "           PropFemale als        0.115044  0.040000\n",
       "                      bpr        0.115044  0.093023\n",
       "                      item-item  0.115044  0.361111\n",
       "                      user-user  0.115044  0.083333\n",
       "\n",
       "[119950 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint = imp_prof.join(imp_rd)\n",
    "joint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.016995,
     "end_time": "2020-05-12T09:31:11.508389",
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     "start_time": "2020-05-12T09:31:11.491394",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And compute profile-rec correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "papermill": {
     "duration": 0.064964,
     "end_time": "2020-05-12T09:31:11.591355",
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     "start_time": "2020-05-12T09:31:11.526391",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>PropDC</th>\n",
       "      <th>PropFemale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">AZ</th>\n",
       "      <th>als</th>\n",
       "      <td>-0.024951</td>\n",
       "      <td>0.822392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bpr</th>\n",
       "      <td>-0.021407</td>\n",
       "      <td>0.630675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.012452</td>\n",
       "      <td>0.705908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.005395</td>\n",
       "      <td>0.803753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">BX</th>\n",
       "      <th>als</th>\n",
       "      <td>-0.002632</td>\n",
       "      <td>0.735219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bpr</th>\n",
       "      <td>-0.001114</td>\n",
       "      <td>0.774075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>-0.023173</td>\n",
       "      <td>0.560200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>-0.045251</td>\n",
       "      <td>0.595326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">GR</th>\n",
       "      <th>als</th>\n",
       "      <td>-0.008759</td>\n",
       "      <td>0.902928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bpr</th>\n",
       "      <td>0.001138</td>\n",
       "      <td>0.892903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>-0.064934</td>\n",
       "      <td>0.773523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>-0.062454</td>\n",
       "      <td>0.813079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable         PropDC  PropFemale\n",
       "Set Algorithm                      \n",
       "AZ  als       -0.024951    0.822392\n",
       "    bpr       -0.021407    0.630675\n",
       "    item-item  0.012452    0.705908\n",
       "    user-user  0.005395    0.803753\n",
       "BX  als       -0.002632    0.735219\n",
       "    bpr       -0.001114    0.774075\n",
       "    item-item -0.023173    0.560200\n",
       "    user-user -0.045251    0.595326\n",
       "GR  als       -0.008759    0.902928\n",
       "    bpr        0.001138    0.892903\n",
       "    item-item -0.064934    0.773523\n",
       "    user-user -0.062454    0.813079"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint.groupby(['Set', 'Algorithm', 'variable']).apply(lambda df: df['profile'].corr(df['recs'])).unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.020982,
     "end_time": "2020-05-12T09:31:11.630355",
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     "start_time": "2020-05-12T09:31:11.609373",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Explicit-feedback mode\n",
    "\n",
    "First, extract the recommendation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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     "start_time": "2020-05-12T09:31:11.655355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>recs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">529</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">als</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.522727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">item-item</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.512195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.613636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th>875920</th>\n",
       "      <th>user-user</th>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">875927</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">item-item</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">user-user</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.304348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68342 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     recs\n",
       "Set user   Algorithm variable            \n",
       "AZ  529    als       PropDC      0.522727\n",
       "                     PropFemale  0.296296\n",
       "           item-item PropDC      0.512195\n",
       "                     PropFemale  0.684211\n",
       "           user-user PropDC      0.613636\n",
       "...                                   ...\n",
       "GR  875920 user-user PropFemale  0.192308\n",
       "    875927 item-item PropDC      0.560000\n",
       "                     PropFemale  0.500000\n",
       "           user-user PropDC      0.540000\n",
       "                     PropFemale  0.304348\n",
       "\n",
       "[68342 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_rd = select_explicit(recs)\n",
    "exp_rd = exp_rd.melt(['Set', 'user', 'Algorithm'], ['PropFemale', 'PropDC'], value_name='recs')\n",
    "exp_rd.set_index(['Set', 'user', 'Algorithm', 'variable'], inplace=True)\n",
    "exp_rd.sort_index(inplace=True)\n",
    "exp_rd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "duration": 0.020987,
     "end_time": "2020-05-12T09:31:11.894589",
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     "start_time": "2020-05-12T09:31:11.873602",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now the profile data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "papermill": {
     "duration": 0.083739,
     "end_time": "2020-05-12T09:31:11.998463",
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     "start_time": "2020-05-12T09:31:11.914724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">529</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1723</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th>875863</th>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.964602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">875920</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">875927</th>\n",
       "      <th>PropDC</th>\n",
       "      <td>0.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PropFemale</th>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        profile\n",
       "Set user   variable            \n",
       "AZ  529    PropDC      0.375000\n",
       "           PropFemale  0.800000\n",
       "    1723   PropDC      0.560000\n",
       "           PropFemale  0.285714\n",
       "    1810   PropDC      0.125000\n",
       "...                         ...\n",
       "GR  875863 PropFemale  0.964602\n",
       "    875920 PropDC      0.451613\n",
       "           PropFemale  0.133333\n",
       "    875927 PropDC      0.608696\n",
       "           PropFemale  0.588235\n",
       "\n",
       "[30000 rows x 1 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_prof = profiles.reset_index()\n",
    "exp_prof = exp_prof[~exp_prof['Set'].str.endswith('-I')]\n",
    "exp_prof['Set'] = exp_prof['Set'].str.replace('-E$', '')\n",
    "exp_prof = exp_prof.melt(['Set', 'user'], ['PropFemale', 'PropDC'], value_name='profile')\n",
    "exp_prof.set_index(['Set', 'user', 'variable'], inplace=True)\n",
    "exp_prof.sort_index(inplace=True)\n",
    "exp_prof"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01904,
     "end_time": "2020-05-12T09:31:12.036155",
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     "start_time": "2020-05-12T09:31:12.017115",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And now merge it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "papermill": {
     "duration": 0.152376,
     "end_time": "2020-05-12T09:31:12.208097",
     "exception": false,
     "start_time": "2020-05-12T09:31:12.055721",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>profile</th>\n",
       "      <th>recs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>user</th>\n",
       "      <th>variable</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">529</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">PropDC</th>\n",
       "      <th>als</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.522727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.512195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.613636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PropFemale</th>\n",
       "      <th>als</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">GR</th>\n",
       "      <th>875920</th>\n",
       "      <th>PropFemale</th>\n",
       "      <th>user-user</th>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.192308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">875927</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">PropDC</th>\n",
       "      <th>item-item</th>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">PropFemale</th>\n",
       "      <th>item-item</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.304348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68364 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  profile      recs\n",
       "Set user   variable   Algorithm                    \n",
       "AZ  529    PropDC     als        0.375000  0.522727\n",
       "                      item-item  0.375000  0.512195\n",
       "                      user-user  0.375000  0.613636\n",
       "           PropFemale als        0.800000  0.296296\n",
       "                      item-item  0.800000  0.684211\n",
       "...                                   ...       ...\n",
       "GR  875920 PropFemale user-user  0.133333  0.192308\n",
       "    875927 PropDC     item-item  0.608696  0.560000\n",
       "                      user-user  0.608696  0.540000\n",
       "           PropFemale item-item  0.588235  0.500000\n",
       "                      user-user  0.588235  0.304348\n",
       "\n",
       "[68364 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint = exp_prof.join(exp_rd)\n",
    "joint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.019002,
     "end_time": "2020-05-12T09:31:12.245096",
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     "start_time": "2020-05-12T09:31:12.226094",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And compute profile-rec correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "papermill": {
     "duration": 0.049969,
     "end_time": "2020-05-12T09:31:12.314104",
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     "start_time": "2020-05-12T09:31:12.264135",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>variable</th>\n",
       "      <th>PropDC</th>\n",
       "      <th>PropFemale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AZ</th>\n",
       "      <th>als</th>\n",
       "      <td>-0.030883</td>\n",
       "      <td>0.220817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item-item</th>\n",
       "      <td>0.011453</td>\n",
       "      <td>0.619091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>-0.026146</td>\n",
       "      <td>0.535963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BX</th>\n",
       "      <th>item-item</th>\n",
       "      <td>-0.034358</td>\n",
       "      <td>0.296007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.001537</td>\n",
       "      <td>0.379183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">GR</th>\n",
       "      <th>item-item</th>\n",
       "      <td>-0.035269</td>\n",
       "      <td>0.622149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user-user</th>\n",
       "      <td>0.006073</td>\n",
       "      <td>0.487080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "variable         PropDC  PropFemale\n",
       "Set Algorithm                      \n",
       "AZ  als       -0.030883    0.220817\n",
       "    item-item  0.011453    0.619091\n",
       "    user-user -0.026146    0.535963\n",
       "BX  item-item -0.034358    0.296007\n",
       "    user-user  0.001537    0.379183\n",
       "GR  item-item -0.035269    0.622149\n",
       "    user-user  0.006073    0.487080"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joint.groupby(['Set', 'Algorithm', 'variable']).apply(lambda df: df['profile'].corr(df['recs'])).unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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