SacHacks2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import important libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import requests as req\n",
    "import sklearn\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('H:\\Sacahcks\\Medicare_Provider_Utilization_and_Payment_Data__2017_Part_D_Prescriber.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_data = pd.read_csv('H:\\Sacahcks\\sub_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25209130, 21)"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "opioid_data  = pd.read_excel('H:\\Sacahcks\\OpioidMedicationsIntheDataSet.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pay_zip['Recipient_Zip_Code'] = pay_zip['Recipient_Zip_Code'][0:5].astype(int)\n",
    "#pay_zip['Total_Amount_of_Payment_USDollars'] = pay_zip['Total_Amount_of_Payment_USDollars'].astype(int)\n",
    "#pay_zip.to_csv('pay_zip_state.csv')\n",
    "#pay_zip.to_csv('pay_zip_conv.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take the part of the data from main data using given opioid medications\n",
    "sub_data = data.loc[data['generic_name'].isin(list(opioid_data['Opioid Medications'][:]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#count of generic_name/drug_name \n",
    "y = sub_data['generic_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_last_org_name</th>\n",
       "      <th>nppes_provider_first_name</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>...</th>\n",
       "      <th>total_30_day_fill_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>bene_count_ge65_suppress_flag</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>ge65_suppress_flag</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>1477804466</td>\n",
       "      <td>ROBINSON</td>\n",
       "      <td>LAKEESHA</td>\n",
       "      <td>MILWAUKEE</td>\n",
       "      <td>WI</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>MORPHINE SULFATE ER</td>\n",
       "      <td>MORPHINE SULFATE</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>570</td>\n",
       "      <td>874.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>1740288091</td>\n",
       "      <td>GEHRING</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>WOODBURY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCODONE-ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE/ACETAMINOPHEN</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>745</td>\n",
       "      <td>555.88</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1801810007</td>\n",
       "      <td>ADLER</td>\n",
       "      <td>BRIAN</td>\n",
       "      <td>SURFSIDE BEACH</td>\n",
       "      <td>SC</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>S</td>\n",
       "      <td>ACETAMINOPHEN-CODEINE</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>161</td>\n",
       "      <td>103.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>*</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>103.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>1437482346</td>\n",
       "      <td>GRAY</td>\n",
       "      <td>DONNA</td>\n",
       "      <td>SALISBURY</td>\n",
       "      <td>MD</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>675</td>\n",
       "      <td>156.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1568402501</td>\n",
       "      <td>BUSCH</td>\n",
       "      <td>JULIE</td>\n",
       "      <td>SAINT LOUIS</td>\n",
       "      <td>MO</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCODONE-ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE/ACETAMINOPHEN</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1174</td>\n",
       "      <td>1284.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>1089.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         npi nppes_provider_last_org_name  \\\n",
       "0          13  1477804466                     ROBINSON   \n",
       "1          52  1740288091                      GEHRING   \n",
       "2          70  1801810007                        ADLER   \n",
       "3          91  1437482346                         GRAY   \n",
       "4         100  1568402501                        BUSCH   \n",
       "\n",
       "  nppes_provider_first_name nppes_provider_city nppes_provider_state  \\\n",
       "0                  LAKEESHA           MILWAUKEE                   WI   \n",
       "1                     DAVID            WOODBURY                   NJ   \n",
       "2                     BRIAN      SURFSIDE BEACH                   SC   \n",
       "3                     DONNA           SALISBURY                   MD   \n",
       "4                     JULIE         SAINT LOUIS                   MO   \n",
       "\n",
       "  specialty_description description_flag                  drug_name  \\\n",
       "0    Nurse Practitioner                S        MORPHINE SULFATE ER   \n",
       "1       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "2     Internal Medicine                S      ACETAMINOPHEN-CODEINE   \n",
       "3    Nurse Practitioner                S               TRAMADOL HCL   \n",
       "4       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "\n",
       "                 generic_name  ...  total_30_day_fill_count  total_day_supply  \\\n",
       "0            MORPHINE SULFATE  ...                     19.0               570   \n",
       "1   HYDROCODONE/ACETAMINOPHEN  ...                     30.0               745   \n",
       "2  ACETAMINOPHEN WITH CODEINE  ...                     12.0               161   \n",
       "3                TRAMADOL HCL  ...                     23.0               675   \n",
       "4   HYDROCODONE/ACETAMINOPHEN  ...                     52.0              1174   \n",
       "\n",
       "   total_drug_cost  bene_count_ge65  bene_count_ge65_suppress_flag  \\\n",
       "0           874.27              0.0                            NaN   \n",
       "1           555.88              NaN                              *   \n",
       "2           103.83              NaN                              *   \n",
       "3           156.69              NaN                              #   \n",
       "4          1284.58              NaN                              #   \n",
       "\n",
       "   total_claim_count_ge65 ge65_suppress_flag  total_30_day_fill_count_ge65  \\\n",
       "0                     0.0                NaN                           0.0   \n",
       "1                     NaN                  *                           NaN   \n",
       "2                    12.0                NaN                          12.0   \n",
       "3                     NaN                  #                           NaN   \n",
       "4                    34.0                NaN                          40.0   \n",
       "\n",
       "  total_day_supply_ge65  total_drug_cost_ge65  \n",
       "0                   0.0                  0.00  \n",
       "1                   NaN                   NaN  \n",
       "2                 161.0                103.83  \n",
       "3                   NaN                   NaN  \n",
       "4                 854.0               1089.58  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing the first five raws of data\n",
    "sub_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUKDua1u4bNmyjBs3Li+88EKWLl2aU045Jf/7f//vnHzyyfnoRz+aJBk6dGgOPvjgXHbZZbn//vvTvXv3jBs3LgMHDsyzzz6bM888Mw0NDdlhhx0yadKkdOvWrUNjAQAA6Li1hr0777wzffv2zQUXXJA//vGP+eIXv5hTTz01xx13XI4//vjquIULF2bevHm55ZZb8tJLL2XEiBGZNWtWpk2blpEjR+ZTn/pUJk6cmNmzZ2ebbbZp99j999+/7ncAAABAidYa9g466KAceOCB1cuNjY1ZsGBBfvvb32b27NnZbrvtMm7cuMyfPz+DBw9OQ0NDttlmm6xYsSKvvvpqFi5cmD322CNJsvfee+eBBx5Iv3792j1W2AMAAOichpaWlpZ1DVq8eHFOOeWUfPnLX87SpUuz0047ZcCAAbniiivyxhtvpHfv3unbt2+OOeaYJMmwYcMyderUDBs2LHPnzk2SPPTQQ5k1a1a23377do+98MIL19jTkiVLsmDBgjaX7THziXat/Lxj+rdrHAAAwPvVgAED0rNnz9Xmr3XLXpK89NJLOfXUU3PMMcfk0EMPzRtvvJE+ffokSfbff/9MmTIln/vc59Lc3Fy9TnNzc3r37t3qmLvm5ub06dMnTU1N7R7b6RVrZ9jbbbfd2jVubebPn98ldTbEmvWqq1e96nXD6XVjX/961dWrXjf2Xjf29a9XXb2W1+vaNoAl6zgb5yuvvJLjjz8+o0ePzpFHHpkkOeGEE/L4448neWcL3M4775xdd901c+fOTaVSyYsvvphKpZItt9wy/fv3z8MPP5wkmTNnTgYNGtShsQAAAHTOWrfsXXnllXnjjTdy+eWX5/LLL0+SnHnmmZk6dWp69OiRrbbaKlOmTElTU1MGDRqUo48+OpVKJRMnTkySjBkzJhMmTMiMGTOy/fbb58ADD0xjY2O7xwIAANA5aw1748ePz/jx41ebf+ONN642b8SIERkxYkSref369csNN9xQ01gAAAA6zg/ZAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAArUfX03sKFoHHX9mhfOfKLVxRUXDa9zNwAAAGtnyx4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBuq9t4bJlyzJu3Li88MILWbp0aU455ZR8/OMfz5lnnpmGhobssMMOmTRpUrp165bLLrss999/f7p3755x48Zl4MCBefbZZ2seCwAAQMetNU3deeed6du3b2bOnJmrr746U6ZMybRp0zJy5MjMnDkzLS0tmT17dhYuXJh58+bllltuyYwZMzJ58uQkqXksAAAAndPQ0tLSsqaFzc3NaWlpSVNTU/74xz/myCOPzNKlSzNnzpw0NDTk3nvvzQMPPJB+/frl7bffzkknnZQkOfzww3PNNdfksMMOq2nspEmT1tj4kiVLsmDBgjaX7THziXat/Lxj+rdrXEdqdrQuAABALQYMGJCePXuuNn+tu3H26tUrSbJ48eJ84xvfyMiRI3PeeeeloaGhuvzNN9/M4sWL07dv31bXe/PNN9PS0lLT2E6vWDuD2W677daucR2p2eG6bZg/f37NNd6LmvWqq1e96nXD6XVjX/961dWrXjf2Xjf29a9XXb2W1+vaNoAl7ThBy0svvZSvfOUrOeyww3LooYe2Oo6uubk5ffr0SVNTU5qbm1vN7927d81jAQAA6Jy1hr1XXnklxx9/fEaPHp0jjzwySdK/f/88/PDDSZI5c+Zk0KBB2XXXXTN37txUKpW8+OKLqVQq2XLLLWseCwAAQOesdTfOK6+8Mm+88UYuv/zyXH755UmSs846K+eee25mzJiR7bffPgceeGAaGxszaNCgHH300alUKpk4cWKSZMyYMZkwYUKnxwIAANA5aw1748ePz/jx41ebf8MNN6w2b8SIERkxYkSref369at5LAAAAB3nh+wAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAJ1X98NbOwaR13f9oKZT7S6uOKi4e9BNwAAQCls2QMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKFC7wt5jjz2W4cOHJ0kWLlyYvfbaK8OHD8/w4cNzzz33JEkuu+yyHHnkkRkyZEgef/zxJMmzzz6boUOH5phjjsmkSZNSqVQ6PBYAAICO676uAVdffXXuvPPObLbZZkmSJ554Iscdd1yOP/746piFCxdm3rx5ueWWW/LSSy9lxIgRmTVrVqZNm5aRI0fmU5/6VCZOnJjZs2dnm222affY/fffv35rDgAAULB1btn7yEc+kksvvbR6ecGCBbn//vszbNiwjBs3LosXL878+fMzePDgNDQ0ZJtttsmKFSvy6quvZuHChdljjz2SJHvvvXcefPDBDo0FAACgcxpaWlpa1jXo+eefz2mnnZabb745s2bNyk477ZQBAwbkiiuuyBtvvJHevXunb9++OeaYY5Ikw4YNy9SpUzNs2LDMnTs3SfLQQw9l1qxZ2X777ds99sILL1xjT0uWLMmCBQvaXLbHzCfatfLzjunfrnEdqVmvuh2pCQAAbDwGDBiQnj17rjZ/nbtxvtv++++fPn36VKenTJmSz33uc2lubq6OaW5uTu/evdOtW7dW8/r06ZOmpqZ2j+30irUzQO22227tGteRmvWq26GabZg/f37NNd6runrVq143nF439vWvV1296nVj73VjX/961dVreb2ubQNY0omzcZ5wwgnVk6o89NBD2XnnnbPrrrtm7ty5qVQqefHFF1OpVLLlllumf//+efjhh5Mkc+bMyaBBgzo0FgAAgM7p8Ja9s88+O1OmTEmPHj2y1VZbZcqUKWlqasqgQYNy9NFHp1KpZOLEiUmSMWPGZMKECZkxY0a23377HHjggWlsbGz3WAAAADqnXWHvwx/+cG6++eYkyc4775wbb7xxtTEjRozIiBEjWs3r169fbrjhhprGAgAA0HF+VB0AAKBAHd6Nk/e/xlHXt72gjZPBrLhoeJ27AQAA1gdb9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACtSvsPfbYYxk+fHiS5Nlnn83QoUNzzDHHZNKkSalUKkmSyy67LEceeWSGDBmSxx9/vMvGAgAA0HHrDHtXX311xo8fnyVLliRJpk2blpEjR2bmzJlpaWnJ7Nmzs3DhwsybNy+33HJLZsyYkcmTJ3fJWAAAADpnnWHvIx/5SC699NLq5YULF2aPPfZIkuy999558MEHM3/+/AwePDgNDQ3ZZpttsmLFirz66qs1jwUAAKBzuq9rwIEHHpjnn3++ermlpSUNDQ1Jkl69euXNN9/M4sWL07dv3+qYlfNrHdseCxYsaNe4tsyfP7/T132v676fe93Y1ve9qFmvunrV64ZSs1519apXvW44vW7s61+vunrduHpdZ9h7t27d/rwxsLm5OX369ElTU1Oam5tbze/du3fNY9tjwIAB6dmzZ+uZM59o13V32223do3rSM161X1f9NqG+fPn11zjvahZr7p61evG3uvGvv71qqtXvW7svW7s61+vunotr9clS5asdeNXh8/G2b9//zz88MNJkjlz5mTQoEHZddddM3fu3FQqlbz44oupVCrZcsstax4LAABA53R4y96YMWMyYcKEzJgxI9tvv30OPPDANDY2ZtCgQTn66KNTqVQyceLELhkLAABA57Qr7H34wx/OzTffnCTp169fbrjhhtXGjBgxIiNGjGg1ryvGAgAA0HF+VB0AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBuq/vBthwNI66vu0FM59odXHFRcPfg24AAIC1sWUPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAXqvr52R/CkAAAgAElEQVQbYOPWOOr6thfMfGK1WSsuGl7nbgAAoBy27AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQoO7ruwHoao2jrl/zwplPtLq44qLhde4GAADWD1v2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIE6/dMLhx9+eHr37p0k+fCHP5yjjz463/72t9PY2JjBgwfn61//eiqVSs4+++w8+eST2WSTTXLuuedmu+22yy9/+ct2jwUAAKDjOhX2lixZkiS5/vo//57ZYYcdlksvvTTbbrttTjrppCxcuDAvvPBCli5dmptuuim//OUvM3369FxxxRWZNGlSu8cCAADQcQ0tLS0tHb3SY489ljPOOCMf+tCHsnz58owYMSITJ07Mj3/84yTJddddl2XLluXll1/OwIED8/nPfz5Jstdee+XHP/5xjjrqqHaN/fd///c19rBkyZIsWLCgzWV7vOuHs9dk3jH9273O7a1Zr7p6bX/deq0/AAC8Hw0YMCA9e/ZcbX6ntuxtuummOeGEE3LUUUfld7/7Xf7hH/4hffr0qS7v1atXnnvuuSxevDhNTU3V+Y2NjavNW9vY5cuXp3v3tbfY5oq188P+brvt1q5xHalZr7p67UDdeq1/G+bPn19zjfeqrl71uqHUrFddvepVrxtOrxv7+terrl7L63VtG8CSToa9fv36ZbvttktDQ0P69euX3r1757XXXqsub25uTp8+ffL222+nubm5Or9SqaSpqanVvLWNXVfQAwAAoG2dOhvnrbfemunTpydJFi1alLfeeiubb755fv/736elpSVz587NoEGDsuuuu2bOnDlJkl/+8pfZcccd09TUlB49erRrLAAAAJ3TqU1nRx55ZMaOHZuhQ4emoaEhU6dOTbdu3XL66adnxYoVGTx4cD75yU/mE5/4RB544IEMGTIkLS0tmTp1apJk8uTJ7R4LAABAx3Uq7G2yySa56KKLVpt/8803t7rcrVu3nHPOOauN22WXXdo9FgAAgI7zo+oAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBhD0AAIACCXsAAAAFEvYAAAAKJOwBAAAUSNgDAAAokLAHAABQIGEPAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIG6r+8GYEPROOr6thfMfGK1WSsuGl7nbgAAYO2EPViP1hggk9VCpAAJAEBH2I0TAACgQMIeAABAgYQ9AACAAgl7AAAABRL2AAAACiTsAQAAFEjYAwAAKJCwBwAAUCBhDwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQsAcAAFAgYQ8AAKBAwh4AAECBuq/vBoCu1zjq+rYXzHxitVkrLhpe524AAFgfhD2gXdYYIJPVQqQACQCw/tmNEwAAoEDCHgAAQIGEPQAAgAIJewAAAAUS9gAAAAok7AEAABRI2AMAACiQ39kD1qv2/gC83+4DAOgYW/YAAAAKJOwBAAAUSNgDAAAokLAHAABQICdoAYqzxpO+JE78AgBsNGzZAwAAKJAtewDt5GciAIANiS17AAAABbJlD2A9qtfxhbZCAgC27AEAABRI2AMAACiQ3TgBaJf27hqa2D0UAN4P3ldhr1Kp5Oyzz86TTz6ZTTbZJOeee26222679d0WAHVUj+ML6xFM1/fxlR2tCwDvq90477333ixdujQ33XRTRo0alenTp6/vlgAAADZI76ste/Pnz89ee+2VJNlll12yYMGCNY5taWlJkixdunS1ZVv36tGu21uyZEm7e2tvzXrV1Wv7667v9e9IXb1uOM+rjtTVa5m9ru/170jdj337h2teeMfTrS4+c9YX2337a6z7rpodqVuvXtekI4/N+q67sfe6sa9/verqtaxeV2ahldno3Rpa1rRkPTjrrLNywAEHZJ999kmSfPazn829996b7t1Xz6Rvvvlmnnrqqfe6RQAAgPeVHXfcMb17915t/vtqy15TU1Oam5urlyuVSptBL0l69eqVHXfcMT169EhDQ8N71SIAAMD7QktLS5YtW5ZevXq1ufx9FfZ23XXX3HfffTn44IPzy1/+MjvuuOMax3br1q3N9AoAALCx2HTTTde47H21G+fKs3E+9dRTaWlpydSpU/Oxj31sfbcFAACwwXlfhT0AAAC6xvvqpxcAAADoGsIeAABAgYQ9AACAAgl7sBH5xS9+sb5bAAq08kd9S/ff//3f67sFNiJPPvnk+m7hPbN8+fL13UKxhL0a3XPPPZ2+7vDhw/OVr3ylzb96GD16dKevu6bz+LzwwgudrpkkTzzxRJvz77333prqvpf+5V/+pctr1vJYrc306dM7fd3bb799jX/1UK/7oB7q8Rw4//zzu7xmrf7whz+0Of+xxx57jztZt3r0OmvWrOr0008/XZ2+7LLLOl2zXurV68iRI6vT11xzTXX6xBNPrKluW2p9DbjsssvW+NdZZ5xxRnX6qquuqqm/Vb2Xz61aXq++8Y1vVKd//vOfd0E371j1vnzooYeq05MmTaq59nPPPZfHH388ixYtqrlWkvzqV7/qkjqr+vd///cceuih+fu///s899xzWb58ec4///x87Wtf6/LbqtWqj/sf//jH6vSNN95YU93jjz++Or3q/0M91PLZvV7mzp3b5vzLL7+85trCXo1WfbPrqMmTJ+fss8+u/g0ZMiS/+c1v0r9//y7s8M9++9vfdvq6xx57bHX6vPPOq06PHTu2pp5WDR/HHXdcdfpf//Vfa6q7JvUIEHfffXeX16zlsVqbWk6++8wzz7T6+6//+q+cf/75ueSSS7qwwz+r9T4499xzq9N33nlndfrUU0+tqW5b6vEcmDdvXk3Xf/HFF9f411mnn356dXrV/6WLLrqopl7r8eGhHr3ecccd1ekpU6ZUp2t9rOrxRUq9ev2f//mf6vT9999fna7Hib1rfQ3YaqutWv316tUrM2fOrCmkrLqeDzzwQE39rapej1dbanm9WvX/83vf+15XtJOk9X15xRVXVKdreQ48//zzOfLII3PGGWfku9/9bk455ZQMGzZsjV8EtdcFF1xQnV71fabWmpdccklOPvnkXHTRRTnuuOPyP//zP62eF51Rjy+9Vn3cv/nNb1anaw1Qq/5v1bre61LLZ/ekPhtArr766lx66aXVyy+//HK+8pWvdMnW3ffVj6p3lcGDB682r7m5OW+//Xb+8z//s0tvq5Y3uO23375a47vf/W5uv/32zJgxI3vssUdXtddlVl3PhQsXtjm/1rqrbsKv1y+C1CNEbUi/XtLQ0NDp644aNao6/eyzz+bMM8/MZz/72YwbN64rWutyTz31VHX61ltvzRe+8IUkyRtvvNHlt/V+fA5861vfSkNDQ7W3hoaGPPvss3nzzTezYMGCTtVcdT1X3Z2t1vX/3ve+l3322SfJOx8eVn7Zc88992TIkCGdqlmPXle97pqmO+OZZ55Z7XZuu+22bLrppjn88MM7VbNeva7pNmp5bamXVZ87v/jFLzJhwoQMGzYsJ598cqdr1ms934vHq6tr1uu51FV1p0+fnjPPPDODBg2qznvggQdyzjnn1LTFdNX+Vn2fqUWfPn3Sr1+/9OvXL2eddVZOPfXUfPnLX6657umnn159PR09enQ1qF500UWd/lK9Xs/V9/I1pNZejz322Or9d95552XMmDFJ3tkA0tn79dprr80FF1yQE044IV/60pcyY8aMnHzyyTnqqKNq6jUpNOy9e1PoD37wg1xzzTU588wzu/y2an1y/u53v8uYMWOy00475dZbb02vXr1q7qmtTcEtLS1ZvHhxzbVX1lqp1vVf9fprmn6/qMcxKfV6rI4++ujV7sOWlpb85je/qaluknz/+9/Pddddl7Fjx2bfffetuV697oM1vQnV8tyqx3OgrS8gWlpasmTJkprq3nTTTdXppUuX5pJLLklzc3Ouvvrqmuq2pdb/1/fyg24tvdbr9aoeX6TUq9d6vE7X8z1r2bJlmTFjRh566KFcdNFFNe8589prr+WBBx5IpVLJ66+/3qr3tr5obq96PF71Oo5y2bJl1f/NVac32WSTTtesx/Pq1VdfbRX0kmTPPfes+TWwHp9PunX78452W2+9dZcEvaQ+X3rV67Xlrbfeyu9+97tUKpW8/fbb+d3vflfts1+/fjXVfreufM/qqg0g3bp1y2mnnZZTTz01o0ePzsSJE7sk6CWFhr2VFi1alLPOOiu9evXKzTffnP/1v/5Xp2ut6UX8tdde63TNG264Iddee23Gjh2bvffeO8mfX5xredH80Y9+1Ob8//N//k+na9YriLW0tFTfLN49XYt6fHg46KCDqltK3r3FpLPq8VglyYwZM2q6flsWLVqUsWPHZosttsgtt9ySLbbYokvq1us+qMdzth7PgYkTJ7Y5v2/fvp2uuapf//rXOfPMM/PpT386s2bNet99IFtb3XoEs1q09UG/paUlr7/+epfU78ovUurV63/9139l1KhRaWlpaTX97q2THVGv14AnnngiY8eOzV577ZVbbrklPXr0qKlekuy8887V3SD79+/fqvdawl49Hq96vF698MILOeigg6q1Vk43NDRk9uzZna67aNGi3HTTTWlpaWk1Xcsul927t/0Rt1KpdLpm0navKx199NGdqrlq0KlUKnUNOkltz4G2QtnK6VpsuummmTBhQpKkZ8+e1emGhoZOby2rx2f3d+uqL5N/+9vfZtSoUdl9991z1llnZcyYMXnxxRfzzW9+M42NjTX12NDyftz/qAvccccdueyyy/LNb34zhxxyyPpup0377bdfdXrlE6QrXjSTZMWKFdUnx+LFi7Ppppuu8YWvPXbbbbfssMMO1Tf4ldPPPPNMTWd43G+//VZb9664D9Z2LOG0adM6XbceuvqxWummm27KEUccke7du+cXv/hFnn766QwdOrTT9Xbffff06NEjf/M3f7PaC1qtx2zV4z7Yd999c+ihh6alpSV33313dfpHP/pR/t//+3811a6n5cuXd8njX6lUcuWVV+buu+/OlClTsttuu9Vcc8CAAdUQ+tprr1WnX3/99ZpOWnDUUUflggsuSKVSyZlnnpnzzz+/On3zzTe/b3qt1+vKql+knH322V3yRUq9el3bMWS1HIJQj9eAAQMGpFevXvnoRz+62vtMrSeT6Gob0ntWPaxtt8qvf/3rnar5hS98odUJdZJ3Hv8LL7ywpmPC6tHr8OHD25xfS9BJkq985SvV669puqPW1GuSXH/99Z2quaGpx/36uc99LhMnTqwe0rB8+fJMnz49CxYsqPn1qsiwN2LEiDz66KMZNWpU/vIv/7LVss5+87Z8+fJccsklOfXUU9OzZ8/cf//9+cUvfpGRI0d2yQezrvTUU0/l1FNPza233potttgi9957b6ZPn54rr7wyH//4xztVc20HnX7oQx/qbKtZtGhRPvjBD3b6+mvy5JNPZqeddurSmosXL86kSZMyefLkNDU15e67787s2bMzZcqUNDU1dapmPR6rJLn00kvz9NNP57zzzstmm22W559/PtOnT89f//Vfd/oEJfX6kFev++CHP/zhGpd98Ytf7FTNejwH/vu//zsjR47MVVddlS222CJ33313/vVf/zWXXnppTf8bRx11VF588cWceOKJ2XzzzVst6+y3z5VKpdXuRl2lHh8e6tVrPdTji5Sf//zn1Q8NXencc8/N+PHju7RmvV4D6vG+9ZOf/CTTpk3LpptumgsuuCADBw7sbHut1OPxqsfr1TPPPJPvfOc72XzzzXP66adnq6226tKeu1I9A/TixYvT2NiYzTbbrKY69VavL+jq4ZFHHsm0adPSq1evTJkyJR/96Edrrlmvz+712ADy8ssv5wMf+MBq83/2s59l//3373SvSaFhrx7/4Oecc04aGxszevTobLLJJnnttdeq3+509o3vqquuqp5W96GHHsqnP/3pJKm+OHfWsccem9NPPz2f+MQnqvMeffTRXHLJJZ0+5fIjjzyyxmW77757p2omtX0LsjYHHXRQhgwZkq9+9atdVnPUqFH5xCc+kWOPPTYNDQ1Zvnx5rrvuuvznf/5nLrzwwk7VrMdjlbzzQf/mm29u9cFx2bJlGTJkSKdPafzEE0+0ebzLvffem7/927/tdK/1ug/qoR7Pga997Ws56qijWt2HP/nJT3L77bfnyiuv7HSvl1566Rp3Kenst8/1+n+th3r0Onz48Dbv04aGhlx33XWdrluPL1Lq9VjVo269XgPWdCrzpPNf/A4ZMiRXXXVV3njjjXz729+u6X90VfW4X+vxevX3f//3Oemkk6q7mq56du5arLqXz7vVuqfT0qVL88orr2SrrbaqaTf2lb7//e/ne9/7Xrp3754JEyZkr732qrnmK6+8kiuvvDIf+tCHsvfee+eUU07J8uXLM3ny5C6p35XW9Bm7oaEhU6dO7XTdoUOH5txzz81rr72W6667rkvO9F2Pz+5Jfb5ImjVrVo444ogk7/z8yg477JDknS3JnX3PXun9tUmqi6wp0NVysPLChQtb7Zfdt2/fnHXWWTUdPPnAAw9Uw94VV1xRDXu1nkSjUqm0etNMkl133TXLli3rdM0f/OAHrS43NDTkP/7jP7J06dK1BsH1ZdasWTn//PNzwgknZPr06W1+W9JRL730Uqtv2bt3754TTjih01tJkvo8Vkmy+eabr/bG2aNHj5pOADR9+vTqh5Hjjjsu1157bZJ3fiajlrBXr/ugHmflrcdzoLm5ebX776CDDqopPCTvBLq2PjzVcmroen03WI8PD/Xo9d1fwv3617/O1KlTaz5UoCten95t1WOg362WD7zvPkZpVbVsMa7Ha8CajgVMOh/2Ntlkk2yxxRbZYost8tZbb3W2tdXU4/Gqx+tVt27dqucY6MrfQvvJT37S6vKcOXMyderUmr6wXb58eaZOnZo5c+Zkq622yh/+8Id89rOfzdixY2s6fvOuu+7KT37ykyxevDhnnHFGl4SxM844IwcddFBef/31DBs2LBdffHG23nrrjBkzpqb6a/v5ls6e6ffggw9udfkPf/hDLrroopoPFejRo0c+9rGPJUmrnyCoRT0+uydZ608YdTbs3XHHHdWwN2XKlOrnra74+ZUiw97IkSPzT//0T0ne+S2NlT/UeOKJJ3b6m7OePXuuNq+hoaGmTfj1OFtgsuaDj1f9aYOOWvWEH6+99lomT56cHXfcsaZvcZJ3/hHffWr1rjimolevXpk8eXIeeeSRDB06NJ/85Ceryzq7W9Sadgmr5U2jHo9V8s6Bzs8991y23Xbb6rznnnuupudWvX4mo173QT3OyluP58Ca7r/346mhn3nmmVZnjlxVLcdt1uPDQz16rdfP5UycOLHVCTRWquV4nccee6zVyTNW9l3r8dDLli3Lyy+/3Onrt6VerwFr+uK3q/rvyi8U6vF41eP1alW1nuhkVSsD7VtvvZVp06blqaeeyvf+P3vfHlZT2r9/J6VSMmkolYYxpnmZQe8wDjOVnHKWKRqpmBRi0IEyySlpk7NyLuZ92xVFopAxKDJyyLxGhmTUpIOkg3YHUev3x/6t9V17t7d3rGc98zX9fvd1ua7V7tofT+vwrM/xvqOjichJIiMj0aVLF5w/fx6AfC40KioKO3bsUNDhFLJWbW1tGBkZESckWLx8+ZJj4Dx79iyX/FduwX9bKBMntbS0IDk5mUjWhR98pqamYs+ePQgMDMSUKVOI1sqHWPcWDd8doFMAoclK3SaDPWXRVzbYIzlhRkZG+PXXXxWyj7/++ivRDUOL2c7GxgYbN26Ej48PDAwMUFdXh8jISAwZMoTYdkZGBjZs2AB3d3e4uroS2+vduzcxuYc6PHr0CFu2bMHgwYMFb2p8WFpatmpZ/Omnn4iy8rSuVUBAAHx8fDB06FBYWFigpKSEuOWGFt0yzfsVEJeVl8Y98Nlnn+Ff//oX3N3duc/+/e9/E8+c0qCG7tq1K1FVQB1oOA+01kpDLocGqUH//v2p2DUzMyNuKVIGrT0gNzcXu3btgqGhIQIDA2FkZASpVIq9e/fi8uXLgmwWFRVh69atYBiGO2bh5+cneK00rheN/aq6ulqBKVQs6QlAPi4SEhICR0dHrFmzhnjmNjs7W8Ep19TUxOLFi0WTNQDEC/j5bIv8PaW5uZnILi193OrqaqxatQp1dXWQSqWicC/QYDml4bsDdAogNKXI2mSwx4dYFbOgoCD4+PjA1NSUc56Li4uxY8cOwTZpUA0DgLe3Nw4cOABHR0c0NjbC0NAQU6ZMwdy5cwXbrKurQ3h4OB49eoQDBw6gR48eRGtkoa2tTUTwog779+9HQkICQkJCRNGCA4DAwED4+fkhKioK5ubmKC0thZGREVEAReNaAcBHH32EuLg4/PTTTygvL0ffvn2xcOFCwUP5AD2ZDFrnABCflZfGPeDr64uwsDB8+eWX6Nq1K2pqavDVV1+9cfb4bSHWPmhgYEBcxVIHsZ0HGmulJZdDa36bBmgQatHaA0JCQuDn54eSkhJs27YN9fX1KC8vh1QqFWxz8eLFKo/fRdDYr/r27cu1x4opPSGRSJCamorg4GBYWVmhsLCQ+53Q6p66CiZpZVOV/AgLoclrVUkEhmHw5MkTorWyEFPW5cKFC5BIJJgzZw4Rw7cyJk2axFXd+cckoOG78yFmAYSmtE+bJGihQYkKyMvKt27dQnl5Obp3744BAwYQOU406Htpwd7eHi9fvsSUKVNaMRiRZDNTUlJUZu9JWToXL16MtWvXElVx1KGkpATl5eUwNTWl4viIARqEOuwAvapWM9IBehqgwcrLgsY98OrVK1RXV+O9994TheGXxj4YHR0NT0/PVp+/fPlSZbvMnwUN54HGWpXlcvi6ZSTPgLrr4+bmJrjak5eXhz59+ih89vr1a6Snp2PChAmC16oKFy5c4Egr3iW4urpygZ2trS2mTp2KpUuXEr23Fy1aBGtra9jZ2XFtvWKA5vUSc7/atWsXRowYgX79+hHZUQYN6QEvLy+EhIQoJKf/+OMPrFmzBjExMYJsAnQIlWiwRwN0ZF2srKygq6ursqvhTaRI/w3ff/89bG1t8eWXX4rSMcFCbN8dUCyAbNy4UZQCCE322DYZ7A0bNgxDhw4FwzC4du0ad5ydnY2srCxBNmkMudKCOsY4AII3zePHj6u1SbIRKePatWuQSqXIyckRfK2ANw/Pdu/eXZBNWto6Yl8rQDEAz87OxpAhQ7j5D1pts0JB6xwEBQWptSt046RxD9Da4GlpY/JRVFQEqVSKkydP4urVq4Lt0HIe+BBrrTTAD+r4x2IxNJaXlyMhIQFJSUn45JNPsG/fPmKb1dXVSExMxNGjR9GjRw84OTlh3LhxgmzR2gP458/Z2RmJiYmCbbFgHceLFy/i999/h6WlJezs7DBo0CDRZJjEul409qvc3FxcvHgR9+7dg7GxMWxsbDB8+PB3UoLg/v378Pf3h5OTEywsLPDkyRMkJiZi06ZN6Nu3r2C78+fPh7m5OWxtbfHFF1+IwvCpDD4zIwlo6uOKjaqqKmRkZODKlStoamrCP//5T4wYMYIokKLlu9MqgKiCGFJibbKNkyVnAaBA/qFMBPI24A+5pqWlYeLEiQqD1ELAry7U1NQoZFxIHBy27YdhGAQFBYlCjUxTmL6+vh7JycmIj4/Hs2fPEBISQrwJ+fr6tsq8FxYWora2Fnfv3hVkk68ndPjwYVFkHWhcK0Cxn9zNzU2UTZ1WUELrHEgkElHs8EHjHuCTk0RERGDZsmXENgHg5MmTothRhYyMDMTGxiInJwfe3t5vfKH+Gdy/f1+klbWG2GtNT09HbGwsSkpK0L17d7i6usLBwYHIJq357evXryM2Nha//fYb2rVrhyNHjsDU1JTI5t27d7mE3Lhx42BiYkJc0aO1B/DbzXV0dBTYLoU66O3atcOgQYO4DomioiJcvHgR8fHxxFTxYl8vGvtV3759uUCpoqICly5dQnBwMJqbm4la4/z8/NTe+0LfX1ZWVvjXv/6FlJQUZGVlwcTEBDExMcTVzb1796K0tBQXLlxAYmIiF0zZ2dmJxqzLZ2YkQVRUlAirUYQ6Nl5A+GwdALz33nuYOnUqpk6ditevX+PGjRuQSqUoLCwULHFCw3cH1LNdk+Dy5cvYtGkTDA0NER4eDlNTU2zduhWnT5/GpUuXiGy3yWBP7AsAKA65/vLLL6JE7vyAjqRdRxn81hJdXV1RWk0cHBxanVcxmN1CQ0Nx7do1jBo1ClFRUQgNDRUlsORvRk1NTdi5cyfq6upw4MABwTb5yYK0tDSi5AELGtdKGWI9D7SCElrnYMaMGWr/dqFMrzTuAT45yf79+0XTVEpJSYGPjw8AeaVAuZVVCGJiYpCcnIyPP/4Y3377LVpaWrhZMxLQcB5orPXEiRM4c+YM1qxZAwsLCxQUFCAiIgJ1dZaNybQAACAASURBVHVEjhmN+e1p06ahV69ecHFxwZAhQ+Dt7U0c6AHyZ8DT0xOnTp2CtrY2vLy8iG3S2gOKi4s5hksAGDt2LABxW88tLCzg7u6OO3fuENmhcb1o7Fd8GBsbw8nJCU5OTjh48CCRLbHXxqK6upoj6fvjjz9QU1MjSuu9qakpXF1d4erqisbGRmRlZSEyMlK0GVuxmu5oyLqIzcbLgq/l2759ewwdOhRDhw7l2FSFgIbvDtApgERERGDnzp0oLi7Gli1b8Pz5c5iamiIlJYXYdpsM9vjsS8otbCQC4CxoBJM0bIqJCxcuULF769Yt9O3bF/3794eFhYXo5+H+/fsICgrC0KFDcezYMdHaLd7160UDtIISWuBXN2ngXd8Hrl27xgV7AQEBorQDxsTEYMKECZg2bRo+/vhjorkXPmg4DzTWmpiYiEOHDnH7SJ8+fbB9+3Z4enoSBXvqiAlIHIpPP/0Ut2/fRmZmJrp16ybavSWVSpGUlISJEydi9OjRqK+vF8UuDdB6b6lCQUEB0fdpXS8WtN9ZZ8+eJSLU+eCDD1QmpP7zn/8Itpmeno6tW7ciKSkJBgYGqKiowIoVK7Bs2TIibVhV4tcjR44UrN+qCmFhYaLYoSHroq4FmETLGqCn5ctCzGeARgGkU6dO6NmzJ3r27Ing4GAsXLhQNObYNhns0Whh+zuB/8ApC7UKDXZUbW6AfCaAhEzmxIkTyMnJQWJiIiQSCTdTxAprCkVLSwv27t2L1NRUhIaGEot90gKNawXI217YjUhMenAWYm6atM4BDdHTvxPE1ukB5M5zeno6wsLC0NjYiIaGBtTW1sLAwIDILg3ngcZaNTU1W92THTt2VKBNFwJbW9tWouKkWLt2LRobG3HmzBmEhITg4cOHiIuLw/jx49G5c2fBdvv374/+/fujvr4eaWlpuHnzJpydnTFlyhTMmjVLkE1ae8CbEj5iz9WQgtb1+qtAusfwE1LLli1DREQEAPm7TGhQEhMTgyNHjnDPvLW1NeLi4rBgwQKi4IGG+PX69euxcuVKAPIW/MmTJwMAFi5cSNSKSUN+hYaWNUBPy5cGaCSS+FIjpqamokqEtMlgjw+xnFK2n1xMql1+G6eYejX8jAPDMApCrUIzDjQ2NxbW1tawtraGTCbDyZMnufbA48ePC7Y5Y8YMlJSUYO7cucjPz0d+fr7C74Ta5N8DLi4uxALwNK4VoNgW9a7Tg9M6BzQq/DTuAf6zXl1drfAzyewujTkwbW1tTJo0CZMmTUJhYSESExMxZcoU9OvXj2heiYbzQGOtLS0tqKurUyCSkclkxFpYERER3N/Jd/pIoaOjA0dHRzg6OuLRo0dISkrClClTkJGRQWxbT08Pzs7OcHZ2xoMHD4jIT2jtASSC3Oqg6plkGAYymYzYtvL1Yu9ZodeLxn5FWr1RB75DX1ZWpvLzt4W2tnarQLlLly5EzMHKaxIrqZaXl8cdJyUlccHeixcvBNsE6Mi60NCyBujozNHw3QE6BZCGhgYUFBSgpaUFLS0tKCgo4M4p6V7W5oM9sSAW0QsffH0aMfVqaGQcaGxugHxTNzExAQDo6+tj5syZmDlzJnE7hK2tLQA5PW5dXR2RLRY02gJptRmJyZDKglZQQusc0Kjw07gHxGKbVEZubi7n3Inl6PFhaWmJgIAA+Pr64uzZs0S2aDkPLMRaq6urKxYtWoSAgAD06NEDxcXF2LRpk+CKFgv+38l3+kjAd/IA4MMPP+R010iwbt06rFq1CsD/zNh8/PHHRAEqzX0wLy8Purq6sLCwEMUm/z3Nx8CBA4ltV1ZWQkdHB3p6etz1EsoeDdDZr9jAnN1L+CRoNEBiV0NDA42NjdDR0eE+a2howKtXr0Rbk1hBiTq/ivS8ZmVlcfvAnj17uGDv999/J7LLQsy10tDypeG7A3QKIDo6OggJCQEAdOjQgTsmabll0SaDPbaFjS9OyULoi46GkHB4eDhkMhk0NTXfSdpiPmhsbgCwfPly7ibmOyeffPIJkV1VmZUHDx4Qien+v9D69ybQCkr+CojliPyd7gEabJy5ubnYtWsXDA0NERgYCCMjIxw5cgR79uwRTbtNLOeBxlrHjRsHAwMD7Nq1C0VFRTAxMYGbmxuxSDENR5nv5PEhhqA0C/6MzbuIbdu2ITs7G01NTfDw8FCp6fq2CA8PR3NzM9e6K5PJoKOjQyy7sG/fPiQlJaG5uRlhYWGwtLSEr68v9PX14e7uLsgmjf2KVmBOoxPB3d0dXl5e8PDwgIWFBUpLSxEdHU2cnKEhfk2LkZdGEElrrSyhEtC6wi8UNHx3gE4BhEbLLYs2Gez9XVrYWBHa9u3bIyQk5J0mvOBvbtXV1aJsboDig6HOOSFBc3Mzzp07B6lUioqKCjg7O4tq//8lPHr0CDt27ICenh4CAgIUaL3/P949mJmZQSaT4ccff0RxcTG6d++OMWPGQF9fX7DNkJAQ+Pn5oaSkBNu2bUN9fT3Ky8uJkigAHeeBxlrLy8vx5Zdftuq8uHHjBhH5lyo2ThZC287ZfVoVSDpHaMyC0kJ2djYSEhJQX1+PRYsWiRLs5eXlYeHChUhKSoKhoSGuXbsGiUSCvXv3onfv3oLtpqWlIS0tDVVVVfDz80NFRQW8vLzg5OREvGYxIZPJuBZAfX19pKam4qeffkJoaCjR3pKTk8Pdl/zOERIfY9SoUTAyMkJiYiLKy8thZmYGf39/DBgwQLBNQC4/kZqaCkCxK4tlkhQCtjDBL1IwDIMnT54QrZXG3sq2Q/JbI1m+BRLEx8eLwpT6V4Bmy6kqkHYmtclgb/LkycjMzISenh6++OKL/+3lqMWpU6dw9uxZyGQyLF++nHqw19TUJHjYnb+59e3bV5TNDaDX+vHs2TMcOXIEKSkpGDBgAJqamohbzf5KkFwrQK4txrayVlVV4b333gMglxwQ2sqwevVqeHt7o6amBhEREaJpYakD6TmgUeFXh4aGhneuOl9QUICFCxfC3t4e5ubmePjwIQ4cOICoqCjB1Pa6urqcExYVFYWpU6di8+bNxM8xDeeBxlo9PT3x/fffc61QDMNg165dSEtLQ3p6umC76tg4SVBZWam25ZAk2KOV1VcF0j2A/a6enp4C4QMJwsLCsHXrVk4Xlw0o1q9fj8OHDwu2a2hoCG1tbXTr1g1Pnz7Fjh07iIS/aWH16tX49NNPublVBwcHPH36FGvWrMHmzZsF2xWqf/vfYG1tjffffx+lpaXo3r07zM3NiW06ODhw71exwC9M8I+/++47Irs0ZF1oaFkDcmKed7lTgA8aBRBa8iNAGw321q5di9raWtTX1yM3N5eb/6ABDw8P/PDDD4K+q62tDW1tbRgZGRH3kPNBg+yAFc1uampCbW0tunTpIspaVbVDsCBxSMaMGQM3NzckJydDX1+fiBKaBY1BZ1qsVtHR0dzLaMmSJZyt06dPC95Q2rVrBxsbGwDy4WSxQOsc/JUVfjc3NyQlJQn6Li0Wto0bN2LLli2wsrLiPps4cSI2bdokWKCWzzrZtWtX+Pr6Cl4fHzScBxprPXjwIPz8/HD79m1MmzYNAQEB6N69O/Hz4OTkxM0ui4WePXty+7aYUFeBAYS3etPaA2igpaWlFXOqtbW1qHNgpqamogR6NN5ZpaWlClWG9u3bw9PTk0hMm8WlS5dw+vRpVFVVwcTEBBMmTMCQIUME26urq4O/vz+qq6thZmaGgoICdOnSBVu3biWqQvLfr2KBVmGChqzL4MGD8fz5c1y8eJG7Vra2tujUqZMoaxYTtJjkaRRAbt++LXp3G4s2Gezl5+cjLi4Or169gpeXF9VgTwwGLkDcdhgaZAc1NTUICQlBbm4uDA0N8ezZMwwdOhSrVq0i2jTVtUMAZMFeWFgYkpKS4OHhga+//lqUYFrdoPPjx48F26RFTEGLUIdFS0uLKHYAeufgr6zwv4ssbDKZTCHQA+TPG0nmkT80r6OjIxpFPg3ngcZau3Xrhh9++AE+Pj7YvXs3li1bBg8PD8FrZKFudpkEpHIQ6kCjAkNrD6BBUqRu7yOtHCpXXcRo5aXxzuLTw/NBOgsqlUqRmZkJd3d3dOnSBSUlJdi7dy8KCwsF//1btmyBg4MDpk6dyn2WmJiITZs2Yd26dYLXqiwPwofQvcXe3h5mZmacCDqf+IaEUIsNaIqKilBVVYVu3boRt0peu3YNa9aswdixY9GlSxduPjo8PJxI5op9XvkgfV5pMcnTKIDQGGVi0SaDPXZQWktLS1SnVBVI2lhUtS6xEEsbUKyB3LCwMIwePVqBsjwxMRHr1q3Dpk2bBNtVl3kuLCwUbBMAxo8fj/Hjx+PJkydISkpCUVERli5diilTpggmU6A9qyImqxWNfnLlVgWxqrB8iHkO/soK/7vIwqbuHiWRCXjTAD0JRT4N54HGWpuamiCRSFBdXY3Vq1cjOjoavXr1Im7BpzG7rNxS+PDhQ2hpaeGDDz4gtq0KfG00Eoj5DNAgKbKxscHGjRvh4+MDAwMD1NXVITIykqgCBdBp5aXxzrK0tMT58+cVdOp++uknLkgRilOnTkEqlXJJCisrK3z55Zf49ttvBQd79+/f55hjWTg7OwvuwmDxn//8pxV5COnesnPnTpw+fRovX76Eg4MDxowZI8powJMnT7B06VJoaWlxQbSuri62bdumUsT+zyAqKgpSqVQhwPn222+xfPlyolbm3r17i66LTSvxTaMAQmvOGmijwR4N8LNsLBiGQWVlpWCb6lqXSEFjpqKoqAiTJk1S+MzZ2RmnTp0Sxb4y/P39iTdkADA3N8fSpUuxePFiXLp0CYmJiYKDPRrnldb8C1+vpbGxUeFYKJRbFcSqwtI6BzQq/HyxehbsLIRQ0Pr7P/nkE0ilUri6unKfxcXFEbWH0Rqgp+E80Firs7MzvvrqK8TFxaF9+/YYNmwYfH19cfXqVQQGBgq2S4uNMzg4GD/++COSkpIQHR0NIyMjThtPbJBQudN6BszMzLjARCaTISoqCtra2kTBtLe3Nw4cOABHR0c0NjbC0NAQU6ZMIR4VMDU15SoQYoHGeWXlO6KiomBubo7S0lIYGRkRz3BraWm1qkZra2sTVajVMaSSVr379+8vOnPimDFjMGbMGNTW1uLs2bPw9fWFoaEhJk6cSJRMkkgkCAoKwueff859lpWVhXXr1iEyMlKQTYZhWlWyhAaOfGhra4vOIEuLSZ5GAYTWnDXQRoM9GjMF6rJs06ZNE2QPkLcuic2WB9AhO1DXokFrQJ8068KfAQHkrSf29vawt7cXbJPGoDMtVit1ei0kYrL8toWKigoYGxsTte6xoHUOaFT41RGbkBC+0GJh8/X1RUhICBISEjhNuB49ehA5ZbQG6Gk4DzTWGhgYiGHDhnE/m5mZITY2lqi7AaAzuxwdHY2jR49CS0sLBw4cwKFDh2Bqago3NzcqwZ7YXS5i7AGbN29GYWEhRowYgXXr1kFXVxfdunXDmjVrBF8zDQ0NeHt7w9vbm2htyuC3m4kFGu+sTp064eDBgygpKUF5eTlMTU1FSaqou39IfIHOnTvj119/VZix/PXXXzlynXcRBgYGcHZ2Ru/evXHo0CGsWLGCSPaosrJSIdADgOHDh+PAgQOCbapr5SV9z6pjnn369Knge4yGTAZApwBCa84aaKPBHo2ZArbv+cmTJygtLYWpqSkxq5M6trzdu3ejZ8+egu3SIDtgq0PKG29DQ4Ngm28CaRBJUnFVBxqDzrRYrWjotbx+/RobNmxAZmYmjI2NUV5eDjs7O6xYsYJoXoPWOaABR0dHMAyDGzduoKSkBKamphg8eDDR/UqLhU1PTw9btmxBVVUVioqKRJnVoAVazoPY6Ny5c6vPtLW1iVv4aMwua2hooGvXrigqKoKWlhYsLS0BkFc1VDmeDMMQza/T2gNyc3Nx6NAhvH79GhkZGbh06RJ0dXXxzTffCLbp5uam9nknSS6w3Riqghuh/gCNd9abqkEkhBfq5rVIAv7ly5djwYIF+OKLL2BhYYEnT57g559/xp49ewTbBMAlT/l4/fo10tPTifRG79+/j9TUVGRmZuIf//gHnJ2dFZ4NIVBX3STZW5XZrQGIkqBUlka5du0apFIpcnJykJWVJcgmDZkMgE4BhNacNdBGgz0AuHPnDuLj47mK2TfffIP+/fsLtkeD1UkdW97GjRsFs+UBEIVaWBn86pDy5yRQpSvCVjhIoGoz4v+fQkDyIlMHGtcKAFasWKHycw0NDWzYsEGQzcjISHTp0gXnz58HIJ/9ioqKwo4dOxAQECB4rbTOAY0Kf0VFBebNmwdLS0uYm5vjwoULkEgk2Ldvn+BKlKOjo6Dv/TfU1tYiKSkJnTp1gqOjI9q1a4cHDx5g9erVgofdaQzQA3ScBxpr5YuIz5kzB4cOHQIgd/L5M0xvC1XZ3FevXuHcuXOCbb5+/RqvX7/GpUuXuHv/xYsXxAk6dW1GAwcOFGyT1h7AOk937tzBRx99xM1AkRB2sSyWDMMgKChINAmagoICrFq1qlWwp6GhITiIpPHO4uurHj58GLNnzxbFLo35SnNzcyQlJeHSpUsoKirCZ599Bl9fX+jp6RHZ7dOnD3dcXl6OhIQEJCUl4ZNPPhEc7LHfmzBhAjZt2sT5VoWFhUTJf1VzYKSVLXXs1qQJSgCor69HcnIy4uPj8ezZM4SEhBDN8dGqlNEogNCcs26TwV5mZiZHq2phYYGCggKEhoZi4cKFgue1aLA60WDLA+TtW6y+GCB/WRQWFqK2tlZw1ZNGpQhQn7klzejq6OgQbZCqYG9vrzZrI3Qom8a1AuQENXyUl5djy5YtRExZ2dnZiI+P537W1NTE4sWLMX36dME2AXrnQN13SdqCJBIJAgICOFY7QL7fhIeHY9u2bYJsqqrc1NXVobGxEb/99pvgtS5ZsgT9+vXDvXv3UFpaCmNjY0RGRhLNltEYoAfoOA+0h/357ItiEjaxzuOxY8dgZWUl2HmcOnUqxo8fj+bmZhw+fBh5eXkICAiAu7s70fpoOE+09gBNTU1cuXIFycnJGDNmDADg6tWrRCyv/FZuXV1dwZqVyrCyshK97ZjGO4v/bk5LSxOtA8PMzAzFxcVITk7mkvSOjo7EiYD8/Hx88sknGDt2LADg5cuXiIiIwLJly4jsXr9+HbGxsfjtt9/Qrl07HDlyBKampoLtGRkZAZBLZFy7dg3A/ySnSO4L/qw9HySVLRodLoCcLfPatWsYNWoUoqKiEBoaSlSFBtRX4jU0NATLpgF0CiA056zbZLB38OBB7N+/n2u56dmzJz777DMsWbJEcLBHg9WJBlseoEgm09TUhJ07d6Kuro6oRxuQ6x8dOXIEDQ0N0NLSwsyZM+Hp6UlkU3lgtkOHDujduzcxC5WxsbHoFRNlUfbMzExs2LCBKLNJ61rxB7pTU1OxZ88eBAYGtmqTeBuoa1sgpdymdQ5oVLbKysoUAj1Azs63e/duwetUzrrGx8cjJiYGQUFBgm0C8oDRz8+PY6I0MzNDSkoKEUU0jQF6gI7z8Hca9gdaO48JCQlEzqOjoyNGjx4NPT09tGvXDuXl5ZBIJMTtS/wAQkNDAzo6Ovj0008REBAg+N6itQcEBwdj69atMDMzwzfffIPLly8jIiKCuDXu7wIa7yw+xJzZv3PnDoKDg+Hq6ooBAwagsLAQ8+fPR1hYmOCurNDQUNy7dw8ymQwLFiyApaUlFi9ejOHDhxOtddq0aejVqxdcXFwwZMgQeHt7Ez2rAL2EOo1ZexodLgBw69Yt9O3bF/3794eFhYUo95eynuT9+/exYcMG4iCSxvWiOWfdJoM9oPVsRZcuXYiyrzRYnWiw5fFx//59BAUFYejQoTh27BjRA3748GE8fvwYx44dg76+PmQyGTZs2ICDBw8SsZDxK0WAvIT/8OFDhISEwM7OTrDdfv36Cf6uOrDnr6GhAeHh4cjLy0N0dLQoFUQxrxWL6upqrFq1CnV1dZBKpcTzWh06dMAff/yBHj16cJ/98ccfxK28LMQ+BzQqW+pmy8TA06dPERwcjI4dO+Lo0aN47733iOyx549NouzZs4f4WqkboCcFDeeBxlr5+lrKxySg4Tyqa1W+cuUKEbObcgBRV1eHjIwMrFy5kngWSuw9oEePHgqB3VdffYUhQ4bg3LlzgityTU1N3LGy3hrJevmsfmKB5jtLbOzYsQP79u1D9+7dAcg7HmxsbLBq1SquXfptkZOTg+TkZNTW1sLDwwMNDQ1Yv359q4Td2+LTTz/F7du3kZmZiW7dulEjqhMDNGbtaXS4AMCJEyeQk5ODxMRESCQSbmbzww8/FGyTfc4ZhsH+/ftx4sQJbN26FYMHDxZsk4XYBRBac9ZAGw32Xr9+jaamJoWNt6mpiahPnwarkzq2PFJmt5aWFuzduxepqakIDQ0lat1jkZ6eDqlUyjm7+vr6WLt2LWbNmkUU7Kmaq6uursb8+fOJgj03NzeUlJSo/B37MhGCGzduICQkBI6OjlizZg2x80/jWgHgnOU5c+YQkRHw4e/vjwULFsDJyYkbdmdbmUlA6xzQqGx1794dFy9eVOgQuHTpEnEFKSUlBZGRkViyZAlxxpEF3wHp3LmzKEH5iBEjcOjQIVGrpQAd54HGWlntPta5V9baEgoazqO62TqAjMZbOaDR1tbG1KlTcfToUcE2ae0BfIjVHuvg4KCgrSaW1uT8+fNVzq+TzsOK/c6aMWMG13IrllA9IPfRlN/NFhYWCsH124LlU2D1EKOjo0WZD127di0aGxtx5swZhISE4OHDh4iLi8P48eNVkjj9b4LGrD2NDhcW1tbWsLa2hkwmw8mTJ7l22+PHjwu2WVBQgMDAQHz88cdISkpCx44diddJowBCa84aaKPB3sSJExEcHIzg4GB07twZL168wPr164lYkmiwOtFiy5sxYwZKSkowd+5c5OfnIz8/X+F3QqClpdXqJaGlpaW24kmCzp07E9ulMQMikUiQmpqK4OBgWFlZKQi/C82U0rhWAODj4wNdXV1ERUUhKipK4XdCyUmsrKzwww8/4OTJk8jKykK3bt0QExNDfM/SOgc0KlvLly/Hd999hyNHjqBHjx548uQJnj9/TrQPfPfdd8jJyYG/vz86d+4smlg9S1AiplO2dOlSldVS0pZTGs4DjbVeuHBB5eck+pUAHedR3WydGGLdqvDy5UvB36W1BwDit8equwdIoY5QjAQ03lk01gmoZodkGIYo2OMHz127dhWVCEhHRweOjo5wdHTEo0ePkJiYiClTpiAjI0Owzby8POjq6sLCwkK0ddKYtafV4VJWVgYTExMA8kB95syZmDlzJtHsemxsLCdhYWNjA+B/qvMklXgaBRBHR0cqc9ZAGw32Zs6ciXbt2sHNzQ1VVVXQ19fHrFmzMGvWLME2lVmd+vfvT8zqdOLECYWf+aK0fCKYt4WtrS0AeWWjrq5OsB0+NDQ08Pz5c4WqSEVFBZWHvr6+nojGG6AzA5Kbm4uePXsiLi5O4XOSAWoa1wqQt0PRgLGxsSji5HzQOgc0KltGRkaQSqW4e/cuioqK4ODgAGtrayKbHTt2hI2NDW7cuNHqdyTBHg12OxrVUoCO80BjrcXFxVy10MvLC7q6usjIyMD69evx448/Eq1X2XlMSkoich5zc3Oxa9cuGBoaIjAwkLt39+7di8uXLwte5+PHjxV+bmpqQnp6OhFjnI2NDTQ0NETfA2i0x9KCmZkZrly5guHDh0NDQwP3799HeXk556AKAY13Fo2ZXQAYNmwYNm/eDD8/P7Rr1w4tLS3Ytm0b0Xydsm4pP1Al0UYF5PJOOjo60NPTw4cffojAwECirqFt27YhOzsbTU1NcHd3J/IB+aAxa0+rw2X58uXcfblv3z7MmzcPgHzkSShiYmIAABs2bEB4eLhCAYCkEk+jADJ16lSMGjWKm7N+9uyZKHPWQBsN9gA5Y5TYOl0ymQy1tbXcjAafjU0IlPVjGIbB8ePHoaOjQ/Sg09AEXLBgAby8vDB//nyuorFnzx7iDXPLli0KTvnLly9x9epVhTlGEog5A0JjIJfGtWIhtvwILdZIWueARmULkN9TP/30E6qqqmBiYoIuXbpwvfVCIJFIBH/3TdDW1kZMTAz09PTg6elJTDfO2gTErZYCdJwHGmv19/eHo6MjSkpKsHPnTmhpaeHcuXOiMFTyZ1O0tbXh6OhItL+GhITAz88PJSUl2LZtG+rr61FeXg6pVEq0TmWiMh0dHfzjH/9oRYTwNhCDsl0V/k6zVXFxcTh58iQGDBjAtR9GRUWhtLRUcHWTFukHDcybNw87duyAvb09OnfujJqaGjg4OMDX11ewTXUapqTYt28fkpKS0NzcjLCwMFhaWsLX1xf6+vqCqzDZ2dlISEhAfX09Fi1aJFqwR2PWnkaHC6BIWpiVlcUFeySgVYmnVQDhS7nt27cPK1euJLLHok0Ge+o0xgDhtNHXrl3DmjVrMHbsWHTp0oXLmoaHhwueL/D39+eOCwsLERQUBDs7O3z//feC7LGgoQk4ZMgQbNy4kdOTMTExQWhoKHHGQXlIvkOHDnBxcSEeIKc1AyL2QC6NawXQkR+hyRpJ4xzQqGydOXMGBw4cgIuLC/r164eSkhIsWrQIS5YsEayzxs7AqAJJUBoYGIjRo0ejpqYGERERWL16tWBbLGhUSwE6zgONtWpoaHCOt729PQYNGoSUlBRi2+np6di6dSuSkpJgYGCAZ8+eYcWKFVi2bJng+0pXV5dL0ERFRWHq1KnYvHkzccBDI4B4UwVbaNs58NfOVinzBLwtkpOTERsby91LVlZWiImJgbu7u+BgT5WOLQsaEiokaN++Pfz9/eHv74/KykoYGBgQMz07OjqiqakJt27d4pJzAwYMIHbI09LSkJaWVJXTlQAAIABJREFUhqqqKvj5+aGiogJeXl5EpFDsvaOnp0dcSOCDxqw9jQ4XQFx2Vz7S09MRGxuLkpISdO/eHa6urnBwcCCySasAwkdeXp5otsC0QWRmZir8S0pKYoYOHcosWrRIsM1Zs2YxFRUVCp89ffqU8fDwIFwtw8TGxjKjR49mLly4QGyLYRhm7dq1THJyssJnR48eZUJCQojsVlVVccfPnj1jnj9/TmSPBd/OxYsXmaysLGKbTk5OzLBhw5iYmBgmISFB4Z9QHDp0iFm5ciVTW1vLMAzD1NbWMitWrGAOHDgg2Cata+Xm5qZwvRiGYSoqKhhXV1ciuwzDMGVlZYynpyezePFiprKyktgerXPAMAzz5MkTZteuXUxQUBCzc+dOpqioiMiei4sLU1dXp/BZbW0tM2vWLKI1qvtHAjc3N+5YjH2KYRjG2tqamTFjBjN9+nSF4xkzZohi/9dff2VOnz7N3Lp1i9gWjbXyr7OjoyPT0tJCvE6GYZjp06erfF6dnZ0F2+RffycnJ8F2lFFUVMT4+Pgwr169Yq5fv84MGzaMGTVqFJOTkyPa/0EL+fn5jEQiYWxsbATbWLJkCXccHR3NHfPPtxCo20NI7GZnZ6v9JxR79+7ljq9evcodr1q1SrBNhpHvo35+ftz79dSpU8zSpUu5n4Xg3r17jIODA+Pv789s2LCBWbRoETNu3DgmPz+faK38azVy5Ejm7t27RPYYRvE6k95LyqioqGCio6OZNWvWMHv37mXKysqIbSYkJDCvXr1iGIZhbty4wcTFxRHbnDRpEnPlyhUmMzOTmTx5MnP58mXun1AkJycz3t7eTH5+PvPy5UvmwYMHzNy5c5mkpCTi9ebl5THr1q1jvLy8mJCQECY3N5fYJh/z5s0TzVabrOzR0BhjGKbVrAeJngggp1pfsWIFDA0NkZiYSMTsyQcNTcDr168jMDAQJ06cgKGhIR48eICVK1ciIiICn3/+uWC7p06dws6dO3H69Gns27cPly9fhrGxMX755Rf4+PgItktjBoTGQC6Na8VCbPkRgA5rJK1zQEO3qX379q3aIfX19YmokdWxxgJk8zH8LKkq8gMhUFctJSUoAeRztl9//TX69euHmzdvIj4+nohJlsZa+edUX19ftEy0tra2yueVpGLI8GQBdHR0RJMICAsLg5OTE9q3bw+JRIJNmzahd+/eCAgIIKr63b9/H+np6VwFZty4cUTt0YC8Rdza2hp2dnbo1asXN1tFkn1//vw5d3zp0iVuhpl0b9XS0kJlZSUnrg3I58JIdHfFJCRhwW+v27NnD0espDzL+bZYvXo1Pv30U44p0cHBAU+fPsWaNWuwefNmQTY3b96MqKgohQ6ivLw8SCQSovl9/nNvamoqilwWrbEDQM5wzt6nf/zxB2pqaoiI1Xbt2oWHDx9i8uTJaN++PUxMTHD48GFUVlZi4cKFgu327dsXqampAOSi73xGYaHz64mJiTh06BC35/Xp0wfbt2+Hp6cnvv76a8FrBYD333+fE1YXi8OCT9Kzd+9eYnss2mSwB4ivMabuIpI4URMnToSWlhaGDBmCdevWKfyOpMWChibg9u3b8e9//5sLSIcPH46YmBgEBwe3Gv5+Gxw7dgwpKSnQ0tJCQkICjh8/DmNjY7i4uBAFe4sWLVLpiBUXFwu2SWMgl8a1AujIj9BijaR1DmjoNqlz7kn2AT5TWnZ2NoYMGcK95AcNGiTYbkNDAwoKCtDS0oLGxkYUFBRwDqnQNmnl4LOoqAhSqRQnT57E1atXBa+VhvNAY605OTncvV5dXa1w35O0G2poaKCxsRE6OjrcZw0NDUTPKysTAYgrEdDU1ISRI0eiqqoKZWVlHIEGyTNAoz0akGvX3bp1C0lJSfj9999haWkJOzs7oueKD36ARxr4+/j4wNPTE1OnToWFhQVKS0uRlJTEUc8LAZ+V+tGjR+jduzdxAMH/m0kDXD5KS0sV/J727dvD09OTiI21sbGx1ahInz59iJ4rQJ6oP3LkCBiGQXl5uQIhnND10hg7AFq3iFdUVBC3iGdmZuLo0aPcPW9ubo5t27bBxcWFKNhTN2bFZ5F9W2hqarZKbnXs2JHYv6BRAKFF0gO00WCPhsYYy+rEB/N/GZ6EQpkSXyzQ0ATU1NRslSXs2bMncSZDU1MTenp6yM/Ph5GREVctJbXr4eHBsTpt3LiRE9JesWKFYBYyGgO5NK4VQEd+hBZrJK1zQEO3KT8/X2HWFgDnSAkFf19xc3MTbZamQ4cOXNaRf0zCxMciIyMDsbGxyMnJgbe3dytm4bcFLedB7LXeuXOHCnOou7s7vLy84OHhAQsLC5SVleHgwYNEDNJpaWnQ1dUVcZWK+PnnnzFkyBAA8kCvtrZWsK1//etfiI2NVaiaOzo6YsGCBUTBXrt27TBo0CAuuCsqKsLFixcRHx8vWMScH9SJOWP0+eefY+fOnUhJSeHIiSIjI4mq+/wgxM3NTZR5S1p/v7rnimRuT51DT9rpMGnSJE7ChH9MAjMzMxQXFyM5OZkjVXN0dCSuzrI8AwYGBgDkOnZxcXFEz5aenl6ra6+lpSWKfp0q+Pv7C+70aWlpQV1dncLaZDIZUcUcoFMAoUXSA7TRYI+Gxpg6JicShqf3339f8HffBBqagAzDoKWlRWFDbm5uJs6QNTc3QyaT4ezZsxzFdFlZGfGAMj/jmJubq/LztwWNgVwa1wqgIz9CizVy2bJl8PHxEf0c0NBt2r59u8rPxWL+FdN5okGkERMTg+TkZHz88cf49ttv0dLSIgpjGg3ngcZaZ8+eTRwoq8KoUaPQpUsXHD16FOXl5TAzM4O/vz8GDBgg2KaTkxMkEolCEkUMfPTRR/D398fdu3cRGhqK8vJybN26lQv8hIBGe7QqWFhYwN3dHXfu3BFsg034sO127DFJwoe/PpadWGyItbfwq1rKFS4SWFpa4vz58woByE8//UTkJ7Hr40OMtZqamhK3ACqDxtgBQKdFXEdHB0VFRQp6gEVFRdQIVkj8NldXVyxatAgBAQHo0aMHiouLsWnTJiJfCKBTAKFF0gO00WCPhsaYo6Njq88uXLgAqVQqOPpWnlNiQZp5p6EJOHnyZPj5+WH+/PkwNzdHWVkZdu/ejXHjxgm2CQBz5szB5MmTYWxsjD179uDOnTtYunQpV4UQA2K12igzknbr1o2YkZTGtQLkWoWq5EceP35MJABPgzXSwsJC4Rx89tlnopwDGrpNgwcPVvhZJpMhOTkZ8fHxOH36NNF6xYabmxt3vTQ0NKCjo4NPP/0Unp6egis+MTExmDBhAqZNm4aPP/6Y0zAiBQ3ngcZaxWxb4+P777+Hra0tVq5cKVp2PCIiAitXrsTo0aMxf/580RyxwMBAZGZmYt68eejTpw8ePHgAKysruLm5CbZJoz36TSgoKBD8XX7Ch7+/ii319K5CXVWLdIabnaWMioqCubk5SktLYWRkRMQaqa7qRrrWlJQU0YM9GmMHAJ0W8YCAAPj4+GDo0KGwsLBASUkJrly5go0bNwq2+SaQ7F3jxo2DgYEBdu3ahaKiIpiYmMDNzU0wKzkLWgUQWtBgaL29/hehnMnhg6T/G5DPaSQmJuLo0aPo0aMHnJyciAMeGnj+/DkuXrzIDbvb2tqiU6dORDZPnz6NhIQELvP89ddfY/z48SKtWI4XL16gqakJxsbGRHbc3d25gFnd8bsCGtdq2rRpiIiI4HS7APl8ZGRkJC5evCjI5pvmHUlajVS1hbIgma15/fo1duzYgVOnTinoNgUEBBBXDPLz8xEbG4szZ85g7NixcHJywmeffSbIFqs1yTAMUlNTMWnSJO53JFXj33//XeHn+vp6ZGZmoqCgQLADxQpoJyYmorGxEQ0NDYiLi+NahITi4cOH8PPzU+k8CE2m0FjrsGHDOEIKZZC031ZVVSEjIwNXrlxBU1MT/vnPf2LEiBEK2lhC8OrVKxw4cAA3b97E2LFjuc9J34NiQ9V5ZRgG2dnZyMrKEv3/c3JyEtwW9iZCJRJRbRrg+0IxMTEcSQfw7t0DxcXFMDMzQ0lJCcrLy2FqakrMtXD58mUFwj6x4OzsjIiICJXJH6HJVHVttq6urkTamOfPn8cPP/zQqkV8xowZREFvbW0tfvrpJ84ftLW1JZJKAlRLhTAMg6ysLGRnZxPZbmpqwosXL4h9SxZHjhzBzz//3KoA8tlnn2H27NmCbP7zn//ERx99xHUNsMdikPS0yWAvMjJS7e+EtkjcvXsXUqkUOTk5GDduHG7dukXcJkVDDxBorQlYUlKCCxcuEGkC/t2g7qF59OgRbt68Kcgmfy6tpqZGYaZMaHswrWv14MEDLF++HN9++y1Gjx6NVatW4enTp4iIiICJiYkgm7SCMn5Ao0xQItb8mli6TSwj66tXrzBt2jSkpqbihx9+ILKZnJys9neqOgpI4eLiQvziAORD84mJiTh9+jT69esneAaKBd956N69O+zs7IidB7HXOnXqVLU6qMpVX6F4/fo1bty4gUuXLqGwsJCIka2pqQmRkZG4fPkyRo4cyX1Oq1VQKK5fv672dyTnVdW+zDAMwsLCcPbsWUE22Q4H1nXS0NBAYWEhamtrcffuXcFrVYdly5YhIiJC0Hdp+EL29vZqKy0kxD/r169HcXExevfuDTs7O1hbWxNXo6dPn47+/ftj2bJlRAy0yhg0aBA++eSTVsEeSVeWqqCOYRhMnz4diYmJgtcKALdv31ZoEZ82bRpRizgg72o5d+4cp103ZswY4v2axj7AkjXm5ubC0NAQz549w9ChQ7Fq1Sri9YpdAKGVUAfaaLBHA/369YOnpycWLlwIbW1teHl5EVH3AvIWg8bGRkyePBkDBw5U2DhIslFubm7Yvn27ApFIeXk5li9fjsOHD5Ms+W8DVkQYkPft8zOEpA8NIN6wO81rJZPJsHjxYty7dw/ffPMNFi9eTPTytLKyQo8ePbgZIL6zI1ZQJtZ5Beg4OgMGDICHhwdmz56N9957T5R9oLm5GZmZmdDT08MXX3xBZOvPwNnZWbDzcO/evVaVtubmZly4cAGjR48WvCY2O2pra4svvvhCFMeMxlrFvD/fBLY9OC4uDmfOnBFk4/bt2wgODoatrS18fX1FdXbFxpto+4VWSgB6CVUWTU1N2LlzJzIyMhAeHo5+/foR21QGSRUSUKRyFwPKM8+ZmZnYsGEDZs+eDXd3d2L7+fn5uHjxIn755Rd06tQJX331lWAHuqWlBQcPHkR6ejrCw8PRp08f4vUBdPaBqKgoNDQ0tBo70NTUxNKlSwXbZVvEv/zyS9FaxAsKCrBw4ULY29vD3NwcBQUFuHTpUiuZi7cFP6GsoaGBDh06oHfv3kREU8uXL8dXX32l0DGTmJiIGzduELUIR0ZGws7OjsozTwNtcmaPn8FkQUo5LZVKkZSUhIkTJ2L06NGor68nXSZOnTqFvLw8nDx5Evv378egQYMwefJkYm0hGpqAfzdkZ2dzLH4kDJzqINb8C81rdeTIEZSUlMDFxQUXL16Eg4MDPv74Y8H2jh07htTUVOTm5mLIkCGYNGmSaA4ECzEHvJXbNRoaGnDgwAGYmZkJDvbOnTuH48ePw9XVFX369EFVVRXxOteuXYva2lrU19cjNzdXodWKBMpOGdvWyNfxeltIJJJWz5KmpiZRoAcAe/fuRWlpKS5cuIDExEROksbOzk4wQQONtdrZ2Qn+7p+BcnswyQxMYGAgwsLCRJMZYJGRkQFbW1sA8vbT9957D4B8blfo3Bqt+fXw8HA0NzdzbdsymQw6OjpEcjks7t+/j6CgIAwdOhTHjh17J4NpPpW7h4cHkdYwC/bvbGhoQHh4OPLy8hAdHU0UlPPRu3dv9O7dG4C8g4akhbFdu3bw9vaGra0tPDw8YGlpKVpbnNiYN28eduzYAXt7e4WxA5JAD5BXhjMyMrB69WrRWsQ3btyILVu2wMrKivts4sSJ2LRpE1EnAl+GCJCPHjx8+BAhISGC996ioiKFQA+QJzxPnToldJkAgBEjRuDixYvYvXs3jI2NYWNjg+HDh1NlQCaCWOrs7xJCQ0OZGTNmMDt27GAeP37MvHz5kvtHirq6Oubo0aPM9OnTGScnJ+bf//63CCuW4/r168x3333HODs7E9lxc3N7q8//DIqLi9X+I0FoaCh3nJKSwh37+PgQ2Z01a5bKY7FAci7/jB1S+99++y3j6+vL1NbWMgzDMHfu3GEcHByY2NhYIrsMwzAtLS3M1atXmRUrVjBeXl5MfHw8sU0WYp1XZdy4cYNxcHBgIiMjmdevX4ti8+rVq8zSpUuZESNGMBKJRLCdb775hmEYhmlqamI8PDxEWRvDMMyIESMYe3t7ZsSIEcyIESOYcePGMQEBAUxZWZlgm7SujzIaGhqY8+fPM6tWrRJs469aK8MwTEBAANH3z549y7i5uTEuLi7M0aNHGXd3d+I11dXVEdtQBf55VXcsFhoaGoi+/+DBA2bUqFFMdXU1wzAM8+OPPzIjR45kHj58KNhmc3MzExUVxYwbN465efMm0fr4uHz5cqt/mZmZzNixYwXbnDFjBsMw8nthzpw5Yi2VuX79OjN27Fhm7969THNzs2h2VeHrr78m+v7Vq1e5vf/JkyfcPxJUVlYSff+/4fnz50xTUxPDMOTPAB+vXr1irl69ymzYsIGZN2+eYDvqfCoXFxfBNtWhqqqKu4+FQN2+JMYey+LZs2dMYmIi4+vryyxevFg0u2KiTVb2Vq5ciZaWFly5cgW7d+9GTU0NRo0ahXHjxhFn3/T09ODs7AxnZ2fk5eUR91ID8mzjjz/+iNTUVDQ0NGDy5MlE9mhoAtIQZwXkLSYskpKSuL/9xYsXgm0CdLSA+PMfNTU1ooiK07hWAODg4ABnZ2fu508//RQJCQlYsWIFXF1diWxraGhg4MCBeP78OU6cOIHExEQiJjo+QYny+SAhKAHkBBVbt27Fzz//jC1bthAxpwLyFlu28jp06FAMHToUlZWVRIK4bJVBS0tLVPbBCxcuiGaLBV9UXBkkouLr1q3jqjts++XIkSNVdmn8WdBaqyq8qQ3xzyAwMFChPfjcuXPEa7K2toahoaHKOVWSv59RI6rNEEyEFBcX49ChQ+jUqRO8vLygq6uLjIwMrF+/Hj/++KNgu2FhYdi6dSs3Xz1q1CgYGRlh/fr1gtvkZ8yYgZKSEsydOxf5+fnIz89X+J1QpKWlqfx84MCBgm3SoHKXSCRITU1FcHAwrKysFASvxaru8UFyX61evRo5OTmi7P18qGK3JfWHlJ8BLS0tUZ4BZUIhS0tLeHh4CLYHqL8mpNp1qtC5c2eiSnxjYyMKCgparbmhoYFoXcrnddiwYRg2bBgRGyeN0RMWbTLYA+TlexsbG9jY2KC6uhpr1qxBaGioYH0ddReBRPj5zJkzSEtLQ0lJCcaMGYO1a9cSC2gC6rX/vvvuO8E2aYizAuqdBdIALTc3Fy4uLhxBC3tMshnzX8b/+Mc/FH4WGuzR0G8E5G0KDMPgxo0bKCkpgampKQYPHozdu3cLtvnq1StkZmYiNTUVBQUFsLe3R3BwMPELnt/jT/p383Hv3j2sWLECX331FdcaSIqAgIBWbWVGRkaC2bdoor6+HkeOHIGZmRkGDBiAwMBAvH79GoGBgYLnDAYOHEhlZo3vMKtqvxQCWmulARrtwaxEgoWFBSZPnozPP/9chJWqT6SR7Nn+/v5wdHRESUkJdu7cCS0tLZw7d454rq6lpaWVzqC1tTWRQ2ZjYwMNDQ3U1dWhrq6OaH18zJ49m6jN/q9Cbm4uevbs2Uo4mrTlVh1I7is9PT0qLbbKCVoxQOsZ4CfqAXEIhT755BNIpVKFxHFcXBz69u1LtFZVqK+vh0wmE/z9Dh06qJTyItEZBOgUQGiMnrBos8FeS0sLsrKykJaWht9++w02NjZEQ86xsbHo1KkTJkyYABMTE1H0lnx9fdGrVy9YWVkhLy8P27Zt435HQnjh6Oio0tEXq8Il5lwVjQocAKJKizqEh4dDJpNBU1NTtL5sGvqNAFBRUYF58+bB0tIS5ubmuHDhAiQSCfbv3y94BmrYsGHo2rUrp12moaGB4uJiFBcXCw52AbmGIw2CkunTp6Njx464ceMGpwEmRjVabPArUNXV1QrnkqQCExQUhJ49e6KwsBARERGYN28eTE1NERYW1mo24n8bYlWIaEMduyOJMwLI53Tnz5+P+fPn4+eff8bRo0dhb2+PsWPHIjAwUJDNOXPmYM6cOfj9999x8uRJ7Nq1CwMHDsTkyZOJSBQaGhpQUFCAlpYWLmvOHguFhoYGVxWzt7fHoEGDkJKSQuyQqauUk1S5SJKmb8KSJUvg4uIiauKIRtKTVgJFHe1+UVGRYJt9+vRRq39K8n41MzPDlStXMHz4cGhoaOD+/fsoLy+HjY2NYJu0ngF+op4lFKqrqyMiFvP19UVISAgSEhI4ofIePXoQ6+yxXT4sXr58iatXrxJ1I9G6X2kUQPgdUjdv3kRISAhcXV0xf/58YtttMthbu3Ytbty4gcGDB2P69OmwtrYmtnnlyhVcvnwZqamp+O233zBmzBiMHTuWiN2Ilt6bOkd/37597xxRC9u2x2/hE6ONUQzGTWVIpVJER0ejffv2CAkJEVW/R5V+IwkkEgkCAgIUtKtY1jR+UuFtMHLkSGhoaKCoqKjVC5gk2KNFUJKeni6KHT5Y54kPUudJXXaVNOipqKjgJAYmT57M3VMHDx4UbHPBggVEa1IHGkkfGmul0WqnDLY9uKqqCikpKcT2evXqhaVLl6KsrAwSiQRTpkzBr7/+Ktiejo4OlynnZ81JnFJ+m1bnzp0hkUhEuQ9sbGywceNG+Pj4wMDAAHV1dYiMjMSQIUME23zTXkeSnDl27Bg2bdoET09PSCQSwUk5PmgkPQG5Zt+RI0fQ0NAALS0tzJw5E56enkQ21Y0CkIwI8LVG09LSMGHCBADke0xcXBxOnjyJAQMGcPT9UVFRKC0tFdzKS+sZYCEmoZCenh62bNmCqqoqFBUVoVu3bsSaiABaJaE6dOgAFxcX4u4hGvcrH2JeJ7FHT1i0yWAvPj4enTt3xrlz51rNPgjdjNu3b48RI0ZgxIgRqKurw48//gh/f3/o6uoKdp6VdUNYuu34+Hi12ag/A3WOfnh4uOC18rMYT58+VfiZZE6B37bHP6aVPSXBqVOncPbsWchkMo7OlxTK+o0mJiaIjo4mtltWVtZKpNjGxoaojVMikbT6rLKykqhiDshb+OLi4vDq1St4eXmJFuzl5+eLzhrYu3dv0WQmWNTW1iIpKQmdOnWCo6Mj2rVrhwcPHmD16tVEFUhl54EFyVzFzz//jGvXrqn8Hcl8JY3q5rBhwwSvRx1UtVSJ8QxUV1dj9+7dCAoKQn5+PoKCgtChQweEhYUR2z1z5gwn3zB+/HisWbOGyCaNTDnfWdLX1xfNefL29saBAwfg6OiIxsZGGBoaYsqUKZg7d65gm2LPe7Lo2LEjl6j+5ptv0L9/f+53QvccZW1UHR0d9O3bl4hF+fDhw3j8+DGOHTsGfX19yGQybNiwAQcPHiQ6r4MHDxa9Wubv788d//LLLwo/kyA5ORmxsbFcgsPKygoxMTFwd3cX7A/RegZaWlqwd+9epKamIjQ0VBStZVrvLEdHR1RWVnKM0ZcuXUJpaSlRsEfrfqUBGqMnLNpksHf//n2Vn5eVlYliPzc3Fzk5OSgpKcHw4cOJ7SnTbatyqt8GNBz9Z8+ecceTJk1S+JkENESjaUFbWxva2towMjIimvngw8XFBZ6enjh16hSn3ygG2rVrJ4oddbhz5w6kUimysrIwZswYIlu0CEqio6O5YG/JkiVcJf306dOCgz1tbW3Rq8ZLlixBv379cO/ePZSWlsLY2BiRkZGC2/dYsEkZhmEUjsvLywXbJGn/exNoiFHThpjPwNq1a7nq4Pr16+Hm5oY+ffogLCxMcPLH29sbZWVlcHBwwPr162FiYkK0RhbqtOs0NDSwYcMGQTZptTJraGjA29sb3t7egm2owv3795Geno6qqiqYmJhg3LhxxJJJAPDo0SNs2bIFgwcPJmoz5Nvjo76+Hrt374a7u7vg7pH09HRIpVLuHaOvr4+1a9di1qxZRM4zjWoZH2JWX3R0dFpVsjt27EjU6UXrGaBBKETrnXXq1Cns3LkTp0+fxr59+3D58mUYGxvjl19+gY+PjyCbtO5XGgUQmqMnbTLYU8a1a9e46klWVpYgG3fu3EFaWhquXr2KAQMGYOLEiVi7di3RBsLehK9evcK0adPw+PFjrFu3TrA9FjQcfVNTU3z99dei21XVElNXV4fGxkb89ttvov9/YkGsuSIa+o0A0L17d1y8eBEjRozgPrt06RJRoNLU1IS0tDRIpVJoa2tDJpPh/Pnz0NHREWPJooMGayBpe60q1NXVwc/PDwzDwMHBAWZmZkhJSWmlv/i24Cdl+McTJ04UbFM5OVNaWorXr18T6y3u3r2be5nzGU9p4ObNm4LJSmg9Ay9evIC7uztkMhkePHiAqVOnQkNDg2g/YJ26pKQkHDt2DAC53iyAVgLX5eXl2LJlC1HFgFaw7+bmpvYdLXSM4syZMzhw4ABcXFzQr18/lJSUYNGiRViyZAlGjRoleK379+9HQkICQkJCFPZtEqiqZL18+RJubm6C9zItLa1WPoaWlhaxdiGNahktaGlpKVSgAHmFn6Rr4s6dO1R8NxqEQrTeWceOHUNKSgq0tLSQkJCA48ePw9jYGC4uLoKDPVr3K40CCI3RExZtNtirr6/nWiKfPXuGlStXErVfTZ8+HR9++CG++uoraGlpISsriwschbYv0aDbBug4+ikpKVSCPeWMVXx8PGJiYhAUFCT6/0WK/Px8+Pv7c8Pu/Bc45mGOAAAgAElEQVSp0Hurf//+6N+/P+rr65GWloabN2/C2dkZU6ZMwaxZswSvdfny5fjuu+9w5MgR9OjRA0+ePMHz58+xZ88ewTbt7e0xceJEbN68GR988AHmzp0rSqBHM6v/347fFgzD4MSJEyp/JzQTz85OaGhooEOHDtizZw/xUD5ATtWsCrdv38aqVatgZmaGiRMnQiKRQFdXF9OnTyeqSl+7do17matiPBUTEolEcNslrWeAxY0bN/D5559z9ygJ6QkN6Q0ACu3rqamp2LNnDwIDA4kEu48dO8a9Xx4+fIiPPvoIgJwFm+Q+Xrt2LQD5cxsUFERMIAHIg8TY2Fjo6elxnzk6OmLBggVEwd7du3dx7Ngxrt2cFjp06EDUHqahoYHnz58rOPYVFRXEgQqNahlL+iLmOxsAfHx84OnpialTp8LCwgKlpaVISkrCsmXLBNucPXs2lX2PxkgMrXeWpqYm9PT0kJ+fDyMjIy7pR3Jv0bpfFy1ahLy8POjq6hInO1nQGD1h0SaDvdDQUFy7dg2jRo1CVFQUQkNDMWnSJCKb6uhvSRxHGnTbAB1Hn2VgU1UVEUNb5+nTpwgODkbHjh1x9OhR6i88Idi+fTt3TPrgKUNs/UYjIyNIpVLcvXsXRUVFcHBwICYqcnd3R2pqKoqLi+Hk5CRadZMWQQkN1kDltiiGYXD8+HHo6OgIDvb4e0jnzp1FeWkCdIgkNmzYgF27dqGmpgazZ8/G+fPnYWBgADc3N6Jg769k4ySxT+sZ6Nq1K7Zu3YorV67Ax8cHMpkMBw8efGep+Kurq7Fq1SrU1dVBKpUSkzPwk4mhoaGc03v9+nUiu/y2Y11dXVHakNu3b68Q6AHy1jBNTU0iu0FBQWhoaFCp/9W9e3ci23w8e/aMSGNswYIF8PLywvz58zn/Ys+ePcSaqDSqZfz3tJjv7M8//xw7d+5ESkoKl0iPjIwkSqjT2vdovAdovbOam5shk8lw9uxZblazrKyMiD2X1v26bds2ZGdno6mpCR4eHkTJLhY0Rk9YtMlg79atW+jbty/69+8PCwsLUXq1aVDk06DbBug4+gUFBVi1alWrDUkMbZ2UlBRERkZiyZIlRC1mtDF48GDk5ubCwMAAPXr0ACBvidm5c2crsp0/Cxr6jXz069dPQVNt2bJliIiIEGSLnX25fv06EhMTcffuXURERGDKlCno06eP4DXSGvamwRrIzwwXFhYiKCgIdnZ2+P777wXbpEGPDoBKi62Ojg4++OADAHKtJTZbSvr/0JJg+W//19uC1jOwZs0aHDt2DEuWLIGtrS1++eUXyGQyTmj+XQLL7jxnzhx88803otik0XJNC+ruH9J5Yxp6aMpyBi9fvsRvv/2mdu7yz2DIkCHYuHEjEhISkJSUhG7duiE0NJSYNZBGtUwMRlN1sLCwELV74tGjR2oJZEiqkDQIhWi9s+bMmYPJkyfD2NgYe/bswZ07d7B06VKVOnl/Fsr3q4mJiSj3a3Z2NhISElBfX49FixaJEuzR3AfbZLB34sQJ5OTkIDExERKJhBM9/PDDD4lti0mRz59NYem2KysrRaFLPnLkCL7++mv069cPN2/eRHx8PNGL2crKilqLQU5ODvz9/dG5c2eFjYmEzp8GQkNDce/ePchkMixYsACWlpZYvHgxEUkPX0Tz8OHD1MW5Hz9+TGxj8ODBGDx4MF68eIGUlBQsX75cbWvjnwGtYW91rIFi9NZLpVL88MMPWLFiBfF8DS169KCgIGhoaOCLL76AnZ2dKAQdfMeRP/NA+jKi4TzMmDFDpW4Xn45dKMR+Bjp06ICZM2dyPw8YMAC6urqQSCSizHHz0dTURES77uPjA11dXURFRSEqKkrhd2JUCsQM/JuamrhjhmHw6tUr7l4Veg6U2wFZ28pV/7cFDT005WqAjo4OevXqxRGgCMVHH31E5ICrAo1qmbpkCS0BeBJ07dqVymzi69evceHCBXTq1ImTHKmoqMD69esVupXeBrTeWba2tgrt51paWjh69GgrsfG3xfvvv8/dr2K0cAL/s3/o6ekRVR75oLUPAm002AMAa2trWFtbQyaT4eTJk1x26Pjx44Ls0aDIVzWbYmRkROzw79q1Cw8fPsTkyZPRvn17mJiY4PDhw6isrMTChQuJbIuNjh07wsbGphVFNPDuBXs5OTlITk5GbW0tPDw80NDQgPXr17diPn0b8F/GaWlporeHion169dj5cqV3M+dOnWCm5sbxxolFLSGvXNzc7Fr1y4YGhoiMDCQq3jv3bsXly9fFmTz6dOnWLFiBQwNDZGYmChKBdbMzAzFxcVITk5GcXExunfvDkdHR5ibmxPZ3b59O5qamvDzzz9j3759ePr0Kfr06YMRI0Yo0Lq/DdQFZaSOLg3nYevWraLbLCkpafXZyJEjMXLkSFHsNzc349y5c5BKpaioqICzs7NgW0uXLuWcuZiYGE7SZO7cuUSOrjq2axJUV1cjKysLLS0tqKmpwZUrV8AwDGpqaojsOjg4cI4Su7+QktSoc5DF2rvF1EMbPHgwKisr0alTJ7Rv3x6nTp1Cfn4+Jk2aJHhuj/9erqmpUdgDSapIJSUl0NTUxLRp01p9LrSNlZagNg0YGBgI7hB6EwICAqCpqYlnz54hPz8f5ubmCA4Ohru7u2CbZmZmrRhpHRwcuK4PsdCpUydiG9evX0dgYCBOnDgBQ0NDPHjwACtXrkRERIRgoi5aoDF6wqJNBnsZGRlc36u+vj5mzpyJmTNnErE70qLIp4HMzEwcPXqUe8mZm5tj27ZtcPk/7L17WE15/z5+R0eVmuoxiRgzxmEypDEOM3QaDzlFDCLJOEQSHY2+CQnlUKmEUBoz26G2acrZGNJoYqJnHiMzoqFSkqRUqq1avz/6rfVZu/b2jPVe75lq3Nflularq5d37XV4v16v+3Xfjo6Ckz3WnFlskNpM/JVgq6GsOW9cXBzxhpwPMelril66DMOgurpacMzc3FySJSkFrWHvwMBAeHt7o7i4GBEREXjx4gVKS0shkUgEx5w8eTLU1NQwcuTIVh0XoVSbmzdvIiAgAE5OTjA3N0d+fj6WLVuGzZs3C07KWKirq8PKyop7Hv7+++9IT08XHJdWRbdl9V4Mz1EaSTQNqh3Q3G0+duwYUlJSYG5uDplMhrNnzwqOBwBPnz7ljtPS0rhkj7QLy+9AtYTQzoSZmRlOnjwJAPjggw8483pSqhUNkRpa1EAafmgSiQQJCQnQ1NTERx99hPz8fBgaGuKnn37Cjh07BMXkv1ucnZ1FS6j491ZeXh769u0rCjVQIpHg9OnTXFIyadIkKmJzANmYhLW1tcLz9fX1RO/DgoICfPvtt5DJZJgxYwbU1NRw6NAhIqabIkVaDw8PYkVaGti5cye+/vprrijx6aefIj4+HgEBATh8+LDguDTYKDRGT1h0yGSPP+TIx8CBAwXHpCGRz14sfIjxcOvSpUurxEFNTY1I1WrZsmUKKVGka1VEtWJB6isiNvjr7Natm6iJnthgN0stwXp5CUFLLxk+SOgntIa9tbS0uCp0TEwMpk2bhh07dhAl1S1pa2IgMjISsbGxXPV69OjRsLS0xLp163Dw4EHBcWNjY7F06VIAzWboo0aNwoABA3DkyBHBMcX2GGwJMT1HaSTRNKh2ADBu3Dg4OzsjOTkZOjo6opv98hM80qKSWB6rfISEhKC6uhqdO3eGlpaW6PHFBC1qIA0/tOTkZJw5cwYvXrzAxIkTkZaWBlVVVSKlZz7ELFDy7y2xksjo6Gg8efIEmzdvhpGREYqKihAfH4/Hjx8LlvJ/FUjGJBYtWiT3dWFhISQSCVJTU/HTTz8JjssWqdXV1dHU1IT4+Hjo6+sLjgfQU6Slgc6dO7faq/Xp04eYykmj8EmzE90hk72W3Hw+hFIiaEjk9+3bl2jwVhk0NTVRWFgoJwdbWFhI9GCmQYmiGZcGCgsLER4eDoZh8PDhQ7m1C1V2YpNdsQedFanHlpeXC5acB4CXL19S2ejRGvbmq+N169YNXl5exGvV0dFR2G24cOGC4JgymawVTcnU1FRu3kgIMjIyuGRvz549HN1YjLlNsUHDc5RWEg2IS7UDgM2bN0MqlcLFxQUzZszAy5cvieIB9ERvlIlSkFyvEokEcXFxUFVVRWBgoJy9Q1uDsg0ZKdWKLVCL6YempaUFVVVVdO3aFX369OHmbEmVQ2lDrOv1ypUrcklk//79ERISgvnz51NJ9sTA5cuX8c033yA7Oxuurq5Es8AtYWhoSJzoAfQUafmjIqmpqbC3twcAuLu7Cy60MgyDpqYmueSusbGR+BlLwyaBhi8oiw6Z7P33v/+V4+YD4pjJAvIS+Xfu3CHaPKurq1OplPv6+mL58uUYNWoUTE1NUVxcjCtXrhB5DIWEhMDS0hJjxoxB9+7dRVurohkYFrS7CK+LlStXKjwmwV+R7N68eRMSiQQZGRkYN26c4Dg9evSg4t1GixrIL/poamqKIs4QGhrKPXS/+OILLmk4dOiQ4IqmIhU/hmGIk732pHBIw3OURhJNg2oHNBuVT5w4EQ8fPoRUKkVhYSE8PT0xdepUwQJAinxBxZivpDELeOLECZw9exbV1dVYvXo19WSPRKSmqKgIBw8eRNeuXbFkyRJoaWnh8uXL2LRpE77//nvBa1L0bL1z5w4R7RyA3HOPPSZRDuXTONn5ShZtbc5e0WfcqVMn4qSExphEfHw8kpOT0b9/fyxcuBBNTU1csY4ENPyBaSnS8kdFpFIpl+w9f/5ccEx7e3t4e3tj2bJl6NmzJ0pKSrB7925MmDCBaK00bBJo+IKy6JDJ3pAhQ0RvhyqShweAX3/9VXBMEiXPV+H999/H4cOH8cMPP6C0tBRmZmZwd3cnUuBydnbGzz//jNWrV6OmpgbDhw/HmDFj8PHHHxNVtfmUsmvXrmHkyJFcYv7xxx8LjksDDg4OYBgGWVlZKC4uRvfu3TF8+HCiKuT27dsFK2K9CjKZDKdOnYJEIoG6ujqqq6uJpfhJvbSUgZZASVFREVf0ASCKOAM/UeIrcJEkUJ988gl27NgBb29vdOrUCU1NTYiIiCBSeQXoKHspooYCwPr167kXlRDQ8BylkUTToNoB/zeX07NnT3h6emLlypVIS0tDYmKi4GRPmS8oqZAIjVlAdXV1qKurw8DAQJSuJgsaiamPjw8cHBxQXFyMqKgoqKmp4fz580q9eF8XYor0vOoZKBT8EQH+fCVAluzxO3AtRwaE3lvKfk/SgheNMYn4+HhMmjQJ06dPR//+/REfHy84Fh80/IFpKdIqK0qSXK+zZs2CtrY2tmzZgtLSUvTo0QMzZszAxIkTqayV5Nqi4QvKokMmezSgTB5+zZo1gmPyN4t3797F+++/D6DZe42kgxIeHg5PT0/B/n+KMGLECIwYMQJAcyKRnp6OmJgY3L59G7/88ovguPzOlrOzMxVaq1goKyvD0qVL0bt3b/Ts2ZPzm4qNjeUsNF4X5eXlIq+yGba2tpg8eTJ27NiBd955B4sXLyb2Qlu9erXC8//973+JhERoCZTQEGegkUAtXboUkZGRsLW1hb6+PiorK2FnZwdPT0+itbIbJoZh5I5LS0sFx6RFDaXhOUojiaZBtQOaO5stbTJsbW1ha2srOOZfMVMs5iygopikoJGYqqiocMmHra0tPv74Y6SkpBDPGtMQ6VH2DCTRHKA1X8kfEZgyZYooIwPZ2dkKE1BSldcFCxagf//+RDFa4uLFizh37hw2b96Muro61NbWoqqqCrq6ukRxaQgK0VKkpUE9v3PnDiZNmoRJkyaJEo8FTZsEGuiQyZ7Y/i8AHXn4lJQUThUqODiYqzT+/PPPRGutr6+Ho6Mjtm3bJpoUblNTE7Kzs3Hp0iVkZmZCR0cH1tbWopr+ttWbhEVoaCh8fX3lrBbS09MREhKCiIgIQTHZOUBFEDoHCADz58/HyZMnUVRUhM8//1yUDRTfKoSvOhYWFkbEJ6c1W5WVlYWQkBBoa2tj06ZN6N27t+BYLPjU0JbHQqGqqgofHx/4+PigvLwcurq6gmXR+eBvmPjHkydPFhzzr6CDsp6jz549Q0pKiuA4NJLo4uJiWFlZYfTo0USCVy1BwyaDlnIojY0NDapZS4iVmPL9JfX19REaGirK34G2SA8f8+fPFzyCQmu+ksaIwNmzZ6kUPVatWgVHR0dRfXHV1dUxZcoUTJkyBQ8ePEBSUhKmTp2KQYMGEamh0xAUunLlClatWiX67CdfF6GlRoJQ0PisADo2CTR8QVl0yGQvMTFR9CFPGvLwtGZq/P39OaPy2bNny3X4hF4wo0aNwsiRIzFp0iS4ubkRm7K2R5SUlLTy1LO0tMTu3bsFx9TU1ESfPn1Il9YKrq6ucHV1xc8//4ykpCTcunUL27dvx9SpU9GvXz9BMfnXZUlJicLzQkBLoCQ8PBzbt29HRUUFwsLCRLEPoUGL2rVrl9LvkWyAaGyeaFUwq6urOSqojo4OTp48iR9++AHBwcGCY9JIov38/HD58mWsX78eMpkMH330EWxsbNCrVy+iuIBim4yLFy+2OeVQGrOANKhmAJ3rlR9HR0dHtLg0RHqUgeSZ/VfPV5IgISEBRUVF6Nu3L6ytrWFhYSHK53X8+HFs27YNixYtQmhoqOjds3feeQd+fn7w9vbGpUuXiGLRUHiUyWSYM2eOqA0FQLkugoeHh+CY3377LbZu3Sr6Z6XMJoGEQcX6goo5esKiQyZ7NIY8acjD02wDW1hYIDg4GC4uLoiNjSW+YBYuXIgrV67g66+/Rl5eHqysrIg9kIDmqi17cbfscpF0tmiAVKpXEYyMjODg4CB6XBbDhw/H8OHD8fz5c6SkpGD16tWiqnsB5NcrLYESNTU1zksoOjqaKBYLGtRQIyMjua9ra2uxf/9+YkEcW1tbpZ+N0OcADWoo0Dzz9+GHH3LdMjs7Ozx+/BgbNmwQ7AdGI4l+6623MG3aNEybNg0NDQ3IysqCRCJBfn4+9u7dKyimMgwYMABr164lVg0UWzmUxiwgDZVbgE5iyqcGVlRUyNEESUzFaYj0KAPJM5vWfCUNsEX/e/fu4dKlS4iPj0fXrl0xZswYopktbW1tBAUFISsrC3PmzJEryAjtRL/K5JzEzoAGw2XNmjWiNxQAUNkLdenSRfTPClCeROfn5wuOSWN/waJDJns0hjxpyMNXVFQgIyMDTU1NnKoVwzDEfPKGhgbExMTg9OnT2L17tyhCJ0uXLsXSpUtRVVWFK1eu4JtvvsHdu3fRt29fosF0/gCqWAqXtGBiYoJLly7JvXjT0tKIVEMHDRokxtJaQZHK6WeffYbPPvtMcExaXR1aAiV8kKqEsaDx4uRvkq9fv47AwEA4OTlh2bJlRHFbzvukp6djy5YtRHQWGtRQAHj06JHci1dVVRWLFi3CrFmzBMekkUTfvn2bS0pUVVU5yilpUqIMJB0YWsqhw4cPx9OnT3Hp0iXOqNrKygpdu3YVHJOGyi1AJzElocD+GbQU6UlKShKc7LHFVD7YQo0YEJPKzb+3+Lhw4YLga6ChoQGqqqro27cv+vbtC6B5Xu/HH38kWisA5OXlISwsDMOHDxdFH6FLly4oKCjAhAkTMHbsWNH8ZmkwXADxGwqAYoGfmpoa1NXV4bfffhMcV+zP6lXw8fERTJGmsb9g0SGTPRqbUhry8GZmZjh58iQAeVUr0o7Z9OnTMXjwYHz77beizpUAzTS2p0+f4sWLF1BTUyPudtnb2yM9PR1dunThBGDaKlavXg0PDw8cO3YMvXr1wsOHD/H06VPs2bNHcMwpU6YoPE/yggPozOsoq2iTFif+SoESFkLV3Wi9OF++fInw8HBkZmYiLCxMlK45W2Gtra1FSEgIcnNzERcXR0QbpkENBZR3zUmqxDSSaFpJiTKQvL9oKYdevXoVGzZswPjx42FoaIicnBxER0cjJCREcEJJQ+UWoCNSc/z4cW7WXkxhNb5yKNB8T5CK9ChT82uppPg6oDVfSePeWrhwIReT/dz09PSIi1P79u3D0aNHERgYKFrXde/evaisrMTp06cRFhaGf/3rX5gyZUqr0ZHXBQ2GC42GAtC6M37kyBHEx8cTCSGyn9W6detgbW1NuML/DZJnFq39BdBBkz0aQ540JPJDQkLQ2NjIDblWV1dDU1NTbgBcCLy8vBQ+gMrLy2FgYCAopqurK3JzczFw4EB8+umn8PDw4B4gJAgKCkJVVRVevHiBnJwcTi2tLcLAwAASiQS3bt1CYWEh7OzsYGFhQRST1uaRxrwOrYr2Xy1QQgIaL87bt2/D398fY8aMQVJSkii/O4usrCwEBgbCwcEBGzZsIC7O0KCGAkDv3r1bFTh++OEH4vkKsZNoWkkJjQ4MLeXQmJgYSCQSOXGyhQsXYvXq1UhISBAUk9ZIA42iFy1hNRrKzIpocRcvXoREIhHc4aA1X0nj3uL/LP9zI8WtW7dw/PhxzkhbLOjp6WHOnDmYM2cOioqKsH37dnz55ZeidCIB8RguNBsKQHOhNiAgANra2khMTCT6O9P6rJSB5JlFY3/BokMmezSGPGk8iHNzc+Hu7g6pVAo9PT1cvXoVoaGh2Lt3L0c5EIKEhAQMHjxY7mWcmZmJgIAAwZxgV1dXmJubEyeiLXHv3j0cPnwYL1++xJIlS9p0stdy1q2goAAFBQUAIPjFSWvzyELMeZ2GhgZERUXB3d0dGhoaSEtLw/Xr1+Hp6Ul0Xfj7+yv9HglFmFYXioVYL07WBygrKwvOzs4AQEwRB5oLCSdPnkRAQAAGDBggN0sgtLtHgxoKNFsPeHt7IyYmBj179sSjR49gYGCAbdu2CY5JI4mmlZTQ6MCw1//Dhw/x6NEjdO/eXZROF8MwrVSohVrP8GOKrXIL0Cl60RJWo6XMDDQzMZKSkpCYmIhevXoRefwOHz4cOTk50NXV5YSJ6uvrERUVheHDhwuOS+PeoqXwvWbNGtTW1qK2trbV91qKjb0u/vjjD5w6dQoXL15Enz59sHHjRqJ4NBguyhoKYiAlJQW7du3CqlWriDuwADBmzJhWiV59fT02btyIzZs3C47r7e2tsEBXWFgoOCYfYu0vWHTIZM/BwQF5eXlchlxQUIC6ujrBKoQAnQfx5s2bER4eDj09PQDNQ7gGBgbYtGmT4AopACxZsgQuLi5Yt24dhg0bhsjISFy4cOGVggX/C8OGDRP8s68CmySoqamJfnGLjbVr18LExAQ2NjbQ0NAQJSGjtXmkMa+zZcsWdO7cmVububk5rly5gtDQUG4QXghu3bqFuro62NvbY+jQoaIlunz+v4qKChobG/Hee+8hODhYsIIYjRfnuXPnBP3c/0JOTg769OmDw4cPy50nkdymQQ0FgKqqKhw4cADFxcUoLS1F9+7d8fbbbxPFpJFE00pKaHRgampq4OPjg4qKCvTo0QMPHjyAoaEhwsPDidSUlXWHSZ7fLVVux48fD0C8zbqYRS9az2waysy3bt2CRCJBdnY2JkyYAGNjY8TFxRHFDA4Oxu3bt1FdXQ03Nzf07t0bK1euJJ6xpnFvKZLHZ+OR/K1pdIwPHDiAc+fOwdDQEJMmTcLhw4dF8TGkwXA5f/48zp8/r/B7JAVaDw8PTvhFX19fjtapaJ7vzyArKwvXr1/Hxo0boaGhgdzcXHh7e8PS0lLwOgHlXW2SbjeN/QWLDpnsnTt3DuHh4ZBKpdDV1UVZWRn8/f3h5+cnmBpH40Hc1NSEDz/8UO6chYUFscLV6NGjsX//fvj4+OD58+cYOnQopFKpqAao/0Skp6fj1KlTSEtLQ/fu3TFlyhTiOUNam0ca8zo5OTlyDx99fX0EBARg5syZRGs9ceIEcnNzkZqain379uHjjz+Gvb098XCyImW869evIygoSLB/H40X57179zjK3bNnz7gq5NGjR4leHDQktwHxqaEAcPDgQTmJdNJOEaA8iSbxQWKTEgCc5yqp9QYfYnZgwsLCYGdnJ5csJiUlYdu2bUTdAkWFT9IxCVoqdDSKXrSE1WgoMzs6OmLRokU4ceIE1NXVsWTJEuKY2dnZSE5ORlVVFVxcXFBbW4tNmzYRz5bRSPiVyeOTFLwAOh3jHTt2oFevXujUqRO++eYbSCQS7nskDI+WDJe7d+9CTU2NyDKBr2S6fft2+Pn5CY7Fh7a2NiwtLZGVldXqe0KTvW3btkEikcDJyQnjxo1DUlISNmzYQFycoOE1SGN/waJDJnvx8fE4duwYdHV1ATQnUIcPH4abm5vgZI/Gg1hZJZRP5xOKX375BU+ePIG5uTkKCgpQVVXVJpM9WjLWNGBgYABnZ2c4OzujoKAAqampiI2NhZmZmWC6Fa2KNo15HUXqYCoqKqJcV/369YOvry+A5mQiLCwMJSUlSExMJI7Nx7Bhw4iKKYqooXfu3JF7Mb8u4uLiuM9r1apV3Cbk9OnTxDMx7LOwtrYWampqmDt3LhYtWiQ4Hg1qKEBHIr2lSm5hYSEkEglSU1Px008/CYp55MgR4o6jItDowPz++++tDJVnzpwpWCmOhTLVZJIxCVpUbhpFL1rCajSUmSUSCaRSKSZPnox///vfePHiBXFMtiusq6uLmpoaxMXFiUIPppHw0yp4sRCzY0zqo6YMGRkZCAgIwPfffw+pVIq4uDgYGBhg5syZggu1fG/Fffv2iea1GBoaKkqclpg8eTIuXLiAvXv3YsWKFaIofdPwGlQ2enL58mXi2B0y2VNXV4e+vr7cOUNDQyIpWxoPYktLS2zduhXLly/nHpy7du3CyJEjieKuXbsWf/zxB7766iuYmJjgwoULcHZ2hr+/v2A1Ir4wQ8tKNslDShnlQUxJZxro1KkT1NTUUF1d3SZ9VYqLi2FlZYXRo0eLNkBtYGCAX3/9Va4b/euvv4pWRKiurlf0FGUAACAASURBVMb333+PkydPora2lvPHFBtiJL+NjY04f/48JBIJysrKiLqbtGaAEhIScP/+fRw/fhw6Ojqorq7Gli1bcODAASxevFhQTBrUUD5aSqSLUfC5fPkyvvnmG2RnZ8PV1ZXIZ9LPz0+U37MlaHRglM3RklaiHRwcwDAMsrKyUFxcjO7du2P48OFEBSpaVG4aRa+QkBBUV1ejc+fOohZQnZ2dFVrmAMLnwIYMGYIhQ4bgxYsXOHXqFK5fv46ZM2di6tSpmDdvnqCY/M+5W7duoimeKhszUVFRgbu7u6CYK1eu5BQNL1++zF0PpKDRMe7RoweKioqQnJyMoqIimJiYwMHBgfjvGxcXh8TERKipqWH//v04ePAgunfvDmdnZ2JWDiDuXOTs2bOVxhPa3czKykJAQADmzZuHqKgoBAQEwNvbGxs3biSis9PwGkxOTkZYWBg0NTURFRUFU1NTbj9Peu12yGRPRUUFdXV1ck72tbW1RBX9L7/8UoylycHV1RX79++Hg4MD6urqoKenh6lTpxK/6I2MjBAUFMS91MeOHYv+/fvDy8tLcLJHS5ihqqoKUqkUXbt2hYODAzp16oQ7d+5g/fr1RNQFGnjy5AnOnDmDs2fPQktLC5MmTUJ8fDzRA4NWRdvPzw+XL1/G+vXrIZPJ8NFHH8HGxoYbqBeCNWvWYPny5ejevTtMTU1RXFyMoqIiYpXaM2fO4NSpUyguLsa4ceMQFBQkygaiZaIgk8lw/vx5DB06VHDMJ0+e4NixY0hJSYG5uTlkMlmre+N1QWsG6Ny5c5BIJBzNUkdHB0FBQZg3b57gZI9WpfxV98GkSZMExYyPj0dycjL69++PhQsXoqmpCUuXLhW6RKqg0YHR19dXWJxhZ8SFoqysDEuXLkXv3r3Rs2dPXLx4EaGhoYiNjRVMv6VF5aYhUiORSBAXFwdVVVUEBgaK1tWgMQfGokuXLlwnJzc3l4gx0VLhnE/pJRGSeZUvptBk79mzZ9wxn0FBChod45s3byIgIABOTk4wNzdHfn4+li1bhs2bN8sZgb8uVFRU0K1bNxQWFkJNTY27p8SkH4oFZboYJAgKCkJkZCQGDhwIAIiKikJCQgJmzpyJM2fOEMUW22swPj4ep06dwpMnTxAaGorS0lJ89tln2LFjB9E6gQ6a7M2fP58TKTE1NUVJSQkOHDgAJyenv3tpclBRUYGrqytcXV25cw0NDTh9+rTgDQ4Azp+M/4IzNTXFkSNHBMekJcywatUqDBo0CLdv38ajR49gZGSEXbt2UUmuSWFlZYU+ffpgwoQJMDIywsuXLzkKj9AHPK2K9ltvvYVp06Zh2rRpaGhoQFZWFiQSCfLz87F3715BMY2NjSGVSnHjxg2UlpZi/PjxXMJDAi8vL7z77rsYMGAAcnNzERERwX2PxLeJ/WxYqKio4MMPPySiRo4bNw7Ozs5ITk6Gjo6O4KSJD0UiAuwxCRT5YKqpqREr6opNDQXQiqpZWlqKsLAwoop5fHw8Jk2ahOnTp6N///6Ij48nWiPQ3Nlsef2IoZxKowOzevVquLm5YcSIETA1NcXDhw+RmZlJ5AsKNFOtfH195Wa00tPTERISInfvvi5oULlpiNScOHECZ8+eRXV1NVavXi1askdjDkxZF5uEqaRM7ZwUNHwx+RCTLUSjYxwZGYnY2Fiuizt69GhYWlpi3bp1gmfMgeY9ZUNDA9LS0rgxmefPnytUEv2z4I/biDl+o6yzDbSm5f9ZJCUlterAL1iwgKjoC9DxGtTX14eenh709PSQl5eHDRs2iFag6JDJ3tixY2FoaIjExESUlpaiR48e8PHxgbm5+d+9NKUoLS3F0aNHIZVKMXDgQKJk71UvOBL5cRrCDDU1NfD29ubEDnr06IGUlJRW0t5tAW5ubly3paysTJSYtCrafKiqqmLUqFEYNWoUtm7dShSrU6dO3EOtsLAQW7duJZqBAkCFFgc0q9Ju27YNPXv2xLhx4+Dh4YEffvgB2traghUON2/eDKlUChcXF8yYMYNYTAlQLiJAQjsHmpPbp0+fyt1LZWVlRPctDWooID8DcvLkSezZswerV68W/DkBzRTpc+fOYfPmzairq0NtbS2qqqq4WW4h6Nu3L1EBQhnYhJHfgblz5w6SkpIEx+zZsyekUinS0tJQWFiIIUOGwMvLC126dCFaa0lJSSsxDktLS+zevZsoLiA+lZuGSI26ujrU1dVhYGAgyv3fEmLOgeXl5cl93dTUhOTkZGhqagq+t2jQeFmI7YvJxmQTPf4xyd+Vxuy2TCZrRdc1NTUlLqY6ODhg4sSJaGxsREJCAnJzc+Hr68spFAsBLT0FfkPi2rVrGDlyJPdsFJpMaWlp4dy5c/jmm29QXFwMExMTODk5cUJbQkHDa5B/D5mYmIiW6AEAmH8I7ty5wwQGBv7dy2iFa9euMR4eHszYsWOZcePGMcXFxcQxg4KCmOTkZLlziYmJRL9/SEgI8+mnnzKnT59m/vjjD7l/JJg3bx53PGXKFKauro4o3l+BwsJC5ueff2YKCwtFj/3zzz8zHh4ezMyZM0WPzTAMM2PGDOIYaWlpzOLFixkLCwtm7969zOPHj4nihYWFMQ0NDcTraglHR0cmIyODOXXqFGNubs7cv3+fqaysFOVvW1hYyERERDA2NjbMqlWrmIsXL4qwYnmUlpYS/XxmZibj4ODAnDt3jvntt9+Y77//npk+fTpz5coVwTEdHR2ZxsZGuXMymYyZNWsW0VoZhmGePXvGeHh4MAsXLmRKSkqI4/Fx//59Ztu2bYyNjQ3j4eEhOA7/eSUmnJ2dqcQtKytjkpKSmH379jGpqalMZWUlcUxla509e7bgmKdPn2bc3d0ZBwcHZs+ePaI9W+fMmaPwPMn1yv/9xfzcGhsbmZiYGGbChAnM9evXRYvL4sGDB4yjoyPj7+/PVFVVCY7z5MkTZvr06YyXlxcTFhbGeHh4MNOmTSN+D+Tk5DD29vbM9u3bGZlMRhSLhY2NDWNra8vY2toyNjY23D9bW1tR4jc0NDCnT59mnJycmPHjxzMHDhwQHGvu3LmtzjU1NTGff/45yRIZhmGYqqoq7rn9+PFjJicnh2ivdfToUaX/xIJYz9rk5GTG1dWVuXfvHlNfX8/cuXOHWbx4MXP8+HGiuDTe+VOmTGGuXLnCpKenM/b29syPP/7I/SNFh+zssRBTRIEGpk+fjnfffReOjo4YOXIkXF1d0b17d+K4NFTYaAkz8CsZ+vr6xN0MmqDlWwX8deIkJKA1A0VD1Qpo7mh+8sknAJq7h2xs0s4G0Nw18fT0xMqVK5GWloakpCTBJrM5OTmIjo6Gnp4evvzySxgYGEAikWDv3r348ccfBa9x5MiR2Lp1K8cYMDY2RnBwMFG1nBY1lJ37+uKLLzBnzhyiWIrwzjvvwM/PD97e3rh06ZLgOMqsEB4/fkxFpZMEV69exYYNGzB+/HgYGhpy11lISAgRPdbExASXLl2Su97T0tIE06wAelRuGiI19+7dg4+PDxiG4Y5ZkKyVxhwYC4lEgq+++gr+/v7EZti0aLw0fDGVCaCVlJQIXidAZ3b7k08+wY4dO+Dt7Y1OnTqhqakJERERoihH8vcn9fX1SE1NJWLkiGkJoAxiCb8kJSXh4MGDXCe3X79+2LlzJxYtWoTp06cLjpuTk4OcnByF31Omqvm/oEzpFxBuPcGiQyZ7NG5EGvjwww/xn//8B+np6Xj77bdFu7hpvOBoCTOwMzDsi5M9Jp2BoQEalCBa4iT3799vdY5hGNTX1wuOSWMGCqCjagXIvyz4cUjMnz09PeUEaTp16gRbW1vY2toKjhkYGAhvb28UFxcjIiICL168QGlpKREliMX777+PBQsWoLy8HMbGxsQJCQ1qKAAsX74cWlpaiImJQUxMjNz3hFKGnJ2dlT5ThVrwTJ06Ve7rq1evcpYJGRkZgmICaJU08CE0gYiJiYFEIpH7rBYuXIjVq1cjISFBUEygeRbQw8MDx44dQ69evfDw4UM8ffqUaBaQFpWbhkgN//4ntUbhw9LSEioqKqLOgT1+/Bj+/v7Q09NDUlISsTgPQI/Gq8wXU0yIdb/SmN1eunQpIiMjYWtrC319fVRWVsLOzo7TYCCFmKrEbDIjk8lQVVXVJsduWHTu3LnVPkJbW5tYoOZVgkJCkz1WlE8mk6GsrAxGRkZEeyA+OmSyR+NGpIGgoCDU1dXhzJkzCAwMxN27d3H48GFMnDixlXXE64DGC27Tpk2cF1ZqairXeXJ3d2+1OXsdpKamCv7Zvxo0Oqa0Ktot18mC5LqiMQPFQmxVK0BxBZ5hmFZzLK+D8vJywT+rDFpaWlzVLiYmBtOmTcOOHTuIiz8PHz6Ep6cn1NTUYGhoiOLiYmhpaSEiIkKwaqKbmxuWLFmCZcuWcRv9PXv2EKnwAc33ltgICgoC0FzkWLNmDfG8KosXL14gOTkZR44cwZMnTxAYGEg8x9etWzfi7k1LMAzTahMmhlk923m+desWCgsLYWdnBwsLC6KYw4cPJ16XItAQqdHR0VHYHb9w4QLJUrFixQqF93xRUZHgmJMnT4aamhpGjhzZqiAp9JoVY1ZfERQVKFmQdI1b3q9r164lvl9pzG6rqqrCx8cHPj4+KC8vh66uLpHGAgsajJzKykoEBgYiJycHenp6ePLkCUaNGoV169YRsZzCwsI4RVpW9ZWF0HdMU1MTampq5Obqqqur0djYKHidAB1BoYaGBmzZsgXp6ekwMjJCaWkprK2t4e/vT3wtdMhkj8aNSAuamppwcHCAg4MD8vLyIJVKMXXqVCITRRovuDt37nDHUqmUS/aeP38uOCZAz1uGBmh0TGlVtFt2YhsaGoipdurq6pgyZQqmTJmC/Px8JCYmYurUqRg0aBDnZSQENFStAOUVeJJqfMsXEB9CX0b866dbt27w8vISFKclQkNDsWbNGgwbNow7l5GRgY0bNyr1tPpfoEENBeSVCFtCaBL07rvvcsdaWlpyXwtFcHAwrl69irFjxyImJgbBwcGYPHkycVxdXV3REx5lm3KSzjaLY8eOYcaMGRg0aBCuX7+OI0eOUKHfkoKGSE1oaCj33P7iiy84pcRDhw4J7hgDgIuLCxd369atnCK1v7+/4PcESSFWGWjQeIHW6sl8CKWwKbpfp0yZInSJHCZOnIiJEyfi4cOHkEqlKCwshKenJ6ZOnSqYJkvLhokGI2fz5s3497//LffeT0pKwsaNG7Ft2zbBcfnPaLGUXp2cnLBixQr4+vqiV69eKCoqwrZt2wSrHPMhtqDQrl27YGhoyBWOGhsbERMTg8jISE6pWCg6ZLJH40akgRUrVsDCwgLW1tZ499138d577+HLL78krpK3fMENHjxYFBU2FgxPwpi0+0DLW4YGaHRMaVW0S0pK4OnpidjYWOjp6eHs2bM4dOgQoqOjBVP5kpOTYWVlBQMDA/Tu3Rt+fn7w8vIimoEC6KhaAXT+tpqamsR2Iy3BMAynEqepqSmaYlx5eblcogcAn376KZGUOyA+NRT4a2ZAxMCNGzdgZmaGIUOGwNTUVDTqvVD/01dBUWGC+f+90UgQHR2Nu3fvwt7eHqqqqjA2NkZCQgLKy8sF+6HRRHV1NaqqqsAwDBiGQUNDA1E8/vuPH4shlPbn/zx/FogkLo1nIA0aL9CsnqyoIJOdnS04Jq37lYWYs9t8+5nt27fDz89PlDXSYOQUFha2SppnzpyJEydOEK3V3t4e6enp6NKlC0aMGEEUi8WECRPQtWtXREdHo7CwEMbGxnB2dibOBW7fvg1/f3+MGTMGSUlJonRhr127JqdI2rlzZ6xcuRKzZs0ijt0hk736+npoaGi0uhETExPbVLIXFRWFGzduQCqV4o8//kDv3r1hbW0tSmdDQ0MD48ePF2GVzRDT7JkPWt4yNEDLt4oG1q9fj8WLF3OJ6OTJk6Gqqor169cL9tnT1tZGWFgYKioqMGjQINjY2GDAgAH497//TbRWLy8vhfdleXk5DAwMiGKLDSMjIzg4OIgas6ioCHZ2dtymjj0mpbEq6+SSdHZoUEMB4QPtrwJfspyfUAPCk+jvvvsO2dnZSEpKQmhoKEcLfu+994jW2r9/f6WziUK7Gsoq4x4eHoLisUhPT0diYiL3HujZsyciIiLg6OjY5pI9GiI1yt6FYr4XxSyoig0aNF4AmDRpEpycnODv7y/Hdti5c6fgziat+5XG7Dbffmbfvn2i+TfSYOQoS2xIr9WgoCBUVVXhxYsXyMnJwcKFC4niAc3dMmtra8H7HmWgISik7O8qRiLZIZO9L7/8EioqKhgxYgSsra1hbGxMfCPSAOtZxvctu3TpEo4cOUJEi6MBtkrM51KLUSWm5S1DA7Q7pmKipqamFaXIzs4OX331leCY48aNw7hx4wA0d2TPnTuHnTt3wtjYGBs2bBAcNyEhAYMHD5abL8rMzERAQIBSNbW/CyRmxMpA63esqKholUAwDIPKykrBMWlQQwHgs88+a3WONOG1s7Pj5j/Yr8VIoi0sLGBhYYHq6mqkpqZyFfhvv/1WcEwaFDZafmhdunRpFUNNTU3UrrxYoCFSwy8ctDwmAa3EUWy0FPYoKChAQUEBABD5Yg4dOhQymQwLFixAVFQU3nrrLQDkHVMa9yuN2W0+xPz8aTByamtr8eDBg1afDYlRO9A8Z3/48GG8fPkSS5YsESXZs7GxwaVLl7B7924YGRnB0tISn376aSuj9dcFDUEhDQ0NFBQUoFevXty5goICUVTqO2Syt3PnTshkMmRmZiI2NhaPHz9Gv379YGNj0+aogXyYmppi/vz5uHnz5t+9lFbgV4n5x6RVYkVdBoZh2mSyBzQ/jAYOHMh1Tevr60WlXIgFZS9I0hcni8GDB2Pw4MEAwEkFC8WSJUvg4uKCdevWYdiwYYiMjMSFCxeIkgdaYOdoxERWVhZCQkKgra2NTZs2oXfv3qLENTMzU5hEkMwV0KKG2tjY4NatW/jkk09gb2/fqgAkBDSS6JKSEhgbGwNoFuqYO3cu5s6di99++40oLslMjjKUlZVh6dKl6N27N3r27MnZW8TGxhJ1YTU1NVFYWAhTU1PuXGFhYZtMUGiI1LTsxLPvAtLfX5kyNYmgFA2sXbsWJiYmsLGxgYaGhmjvFFVVVWzcuBGHDx+Go6Mjdu7ciYEDBxLHbWxsROfOnaGjo4OpU6di5syZcrYWQkBjdpsWaDByNDU1ERgY2Oo8aVLCslHU1NREmS0Gmt+DZmZmAJqfiWlpaQgICEBjYyMiIyMFx7137x5nev7s2TOuOHH06FHBugA+Pj5wc3PD559/zrHHWMV3UnTIZA9obl1bWVlxH8bvv/+O9PT0Np3ssXjw4MHfvYRWEIs/3RI0vWXERnBwMG7fvo3q6mq4ubmhd+/eWLlyZZtc6+DBg3Ho0CHMnz+fO/f111+jf//+ov9fCQkJRCIVo0ePxv79++Hj44Pnz59j6NChkEqlxJW39oLw8HBs374dFRUVCAsLE62rT0PGmQY1FGjeQDY1NeHKlSvYvXs3KisrMXbsWEyYMEHwmmkk0atXr+YoZbGxsZyqHemmtKysDHv37kWPHj1gaWkJNzc3NDQ0ICgoSDCdi5Yfmq+vL5YvX45Ro0bB1NQUxcXFuHLlimhqp2KChkgNrU58e1GmTk9Px6lTp5CWlobu3btjypQpou4P5s6di/79+8PDwwOenp5ESXRubi7c3d0hlUqhp6eHzMxMhIaGElP6aMxu8zv4FRUVcl8LtZ8B6DByHBwcYG1t3ebGLP4XjIyM8Pnnn+Pzzz8nnt2Ni4vj8otVq1Zx74XTp08LTvYGDBiAQ4cOISUlBRkZGXj77bcRHx8vylx8h0z2+C/hzMxMjBo1CgMGDJAbfHyD14OXlxdHicrLy0Pfvn1F8cOj7S0jJrKzs5GcnIyqqiq4uLigtrYWmzZtauU51Bbg5eWFzZs3Y/To0ejWrRsqKysxZsyYVyp+CYUYld1ffvkFT548gbm5OQoKClBVVfWPSfbU1NS4GZLo6GjR4tKQcaZBDWXRqVMnWFpawtLSEhUVFdiwYQOCg4MFMx1oJNH8az0jI4NYwpzF6tWrYWdnh8rKSjg5OSEiIgLdu3fHl19+KTjZo+WH9v777+Pw4cP44YcfUFpaCjMzM7i7uxNJrtMCDZEaWqqJKSkpWL58OQCgtLRUFJsMGjAwMICzszOcnZ1RUFCA1NRUxMbGwszMTKlX5J8Bvyv00UcfQSKRYNWqVcjNzRUcc/PmzQgPD+dm18eOHQsDAwNs2rSJyGuSxuz20aNHqauQi8XI0dHRUdgtJEV2djaX5IqV8CqiwdfU1KCuro6IkcF/Fyg7FgJDQ8NW9NWEhAQsWLCAKG6HTPb4L+E9e/ZwL7xX+bj8HVB08TIMg+rq6r9hNa8GXxrd2dlZNJN1Wt4yNMBuZnR1dVFTU4O4uLg2aREBNHe2g4KCsHbtWlRUVOCtt94itl5QBlL60tq1a/HHH3/gq6++gomJCS5cuABnZ2f4+/tTUSlsyxCLugLQkXGmQQ1l0dTUhIyMDJw6dQq//fYbLC0tiTwsaSTRtKiK9fX1nOLa2bNnuXcWyTwwLT80oPkZSDKf9VeBhkjNrVu3UFdXB3t7ewwdOlQ0GuPVq1e5ZM/X15eaLY+Y6NSpE9TU1FBdXY38/HyiWC2p4G+//Ta+/vprIquApqYmOfVsoHmGj9SOi8bsdkJCAoqKitC3b19YW1vDwsKCKjWahJFDa37/1q1bCs+T3GMt99lHjhxBfHw81qxZIzgm8NcINbE4efLkm2RPEWhl3GJD2VD+0KFD/+KVvB7EvJhpVUlpgP97d+vWrc0megCdv6syoYiKigpB8VgYGRkhKCiIU2AbO3Ys+vfvDy8vr39Esvf48WMcO3YMDMNwxyxIjLZpyDjToIYCzSpsWVlZGD58OGbNmiWKuh8fYiXRFRUVyMjIQFNTEyorK+U2EkKFVAB5r0W+0AmJ8S8tP7T2BBoiNSdOnEBubi5SU1Oxb98+fPzxx7C3tyemCbflvQofT548wZkzZ3D27FloaWlh0qRJiI+PF7WzK5PJcOLECUgkEshkMsEddGX3PSmFz9nZGcXFxQq/J3TeeO3atQCaZ8EuXbqE+Ph4dO3aFWPGjJGzZRALNOb3SS10qqqqIJVK0bVrVzg4OKBTp064c+cO1q9fT8QgA5rfswEBAdDW1kZiYiI3YycUrEhNU1MT6urq5I7FhhifVYdM9v7KjJsEISEh3PAw0OwHpKmpSa0D0xZBq0pKAy1VSPn0oLY2lE3Ds4dkbuBVYCm7Dx8+xKNHj9C9e3eYmpr+Y2jXU6ZM4V6S/GNS0JBxpkENBZqrrfr6+jh//jzOnz8v9z2h1x2NJNrMzIyjP33wwQdyBTuSZI+G2jEtP7T2BFoiNf369eO641lZWQgLC0NJSQkSExMFx2zLexU+rKys0KdPH0yYMAFGRkZ4+fIldx+QFKeA5neARCLBmTNnwDAMIiIiiAo/lpaW2Lp1K5YvX84xcnbt2oWRI0cSrZM/1gI0f175+fmoqqpS2p36X7h9+zY++OAD9O3bF3379gUAVFZW4scffyRaqzKQXGO2trZKf55E6XjVqlUYNGgQbt++jUePHsHIyAi7du0iFkZLSUnBrl27sGrVKiJ9AT74IjUaGhpyx0JBU5hQhWnLu2uBGD9+PBYuXAiGYXDw4EHuOCEhAWfPnv27l8eh5fDwhQsXuOFh9mZvK+BvlOLj4+U4xaQPeLZKevPmTdGqpDSQnJys9Hti8/fFhFi024aGBkRFRcHd3R0aGhpIS0vD9evX4enpSVSgqKmpgY+PDyoqKtCjRw88ePAAhoaGCA8Pb5NzQO0FS5YsQWBgYCsZ5w0bNgimRu3cuRNqamqcnxpLDZXJZIKpoa8CX/3ydfEqNVexff3y8/OJnlk0ny2sH9rbb78tese0rcPX1xczZsxoJVKTnJxMJFIDNBdnv//+e5w8eRK1tbWYOHEi5s2bJzjeRx99hPfff59T42SPSefixUZ0dLTSjT7JfeXm5obnz59j2rRpmDBhAjw9PXHgwAHB8YDmjsj+/fuRmJiIuro66OnpYdq0aVi0aJFoNGeZTIaoqChcvnwZISEhgime8+fPp0LdfRUjR2hi2jIpSU9Px5YtW7BgwQI5UbjXxezZs7kCnZ2dHXr06IHt27e3UtR9HXh4eCA7Oxs+Pj6tCjwkBToaYJNoRWkZqTBUh2whKauUi5XRiwVaw8M0wO82iNl9AOhUSWnAwcEBMpkMN27cwLNnz2BsbAxzc3OqszFiQKwq8ZYtW9C5c2cunrm5Oa5cuYLQ0FCOgiIEYWFhsLOzk5sBYuWGN27cSLzutg7+C0dFRQWNjY147733EBwcjHfeeUdwXBoyzjSooYpw9epVSCQSZGdnIyMjQ1AMZRvPy5cvkyxNIXx8fIjmC5UldKTvgWPHjmHGjBkYNGgQrl+/jiNHjmDOnDlEMdsTaIjUnDlzBqdOnUJxcTHGjRuHoKAgUSj97UWNk5135DMxxPj9GYaBqqoq6urq0NTUJMp767vvvsPnn38OV1dX4liK8Pvvv2PNmjUYNWoUjh8/LgqlXWzQYOSwv2dtbS1CQkKQm5uLuLg4YoVSNq6Kigo0NDSwZ88eYjsHHR0dWFpaIisrq9X3SJI9Z2dnpdeo0KSdpq9wh0z2xK7a0gKt4WEaWLFiBXJzc6GlpSXnryQWWlZJ7e3tRf8/SPHbb7/B29sbZmZmMDQ0xJkzZ5CXl4fo6GhOCKIjIycnR67Dq6+vj4CAAMycOZMo7u+//45169bJnZs5cybR5rk9QdHL+Pr16wgKCsLBgwcFx20p42xsbEws40yDGsrixYsXSE5OxpEjR/DkyROsXbsWYWFhXREbPQAAIABJREFUguMlJycjLCwMmpqaiIqKgqmpKScGxEpmiwVaBBmSwfzo6GjcvXsX9vb2UFVVhbGxMRISElBeXs51Zjs6aBTivLy88O6772LAgAHIzc2V6xCSXK/tZZaSFhNj7969KCkpgVQqxcyZM/HixQukp6dj9OjRgj9HZR5zpGhqasLevXtx8uRJBAcH46OPPiKOyVeibAnShO3mzZs4cuQIioqKYGJigjlz5hDbkGVlZSEwMBAODg7YsGGDKPcaP3nS19cXxUxcmUYBKWUyKCgIQPOzf82aNaJYz9DUsOiQyR4tPrHYoDU8TAMRERG4du0aZDIZXFxcMHXqVFHi0qqS0sCOHTsQExODd999lzuXm5uL0NBQYlNpsUHDs0fRg1dFRYXYIkEZBZQvWvFPw7Bhw0Qp+lRUVHCU64KCAlRWVhIlexoaGigoKGhFDSV9KQcHB+Pq1asYO3YsYmJiEBwcjClTphDFjI+Px6lTp/DkyROEhoaitLQUn332GXbs2EEUVxFozViRJJHp6elITEzk1tazZ09ERETA0dHxH5Ps0RCpaQ8qmTRBk4lhbGyMFStWwN3dHenp6ZBKpVi3bh3S0tIExaOlGjl79mwUFxdj8eLFuHfvnpxJu9CxlqFDh4qmcs5Heno6du3ahRUrVsDU1BQPHjxAcHAw3N3d5e6L10FoaChOnjyJgIAADBgwQE6JlaS7l5OTA0dHR47KzB6TUJk9PT2xc+dOAPIjSIsXLya6l/n7QC0tLbmvhYKG1gKLDpnstZzL4/OJ2xJoDQ/TwLVr13D06FG8ePECK1asEC3Zo1UlpYG6urpWN3S/fv3aZCeWBnXDwMAAv/76q1w3+tdffyVO9vT19RXGZenN/1TU1NQQ/fy5c+cQHh4OqVQKXV1dlJWVwd/fH35+fhg7dqygmDSooQBw48YNmJmZYciQITA1NRUledLX14eenh709PSQl5eHDRs2EHf0vL29W62NFVUhAY3B/C5durRaq5qampzaZ0cHDZGa4cOHi7jC9oe/gomhoqICKysrWFlZoby8XJSYYqpGWlpaQkVFBTU1NcTPado4cOAA9u3bB319fQDNydjgwYOxatUqwcleTk4O+vTpg8OHD8udV1FRIUqgaFCZnz59yh2npaVxyV5blCvhe6ru27dPsMeqInTIZI8Wn1hsuLq6Yv/+/XBwcOCGh6dOnYrFixf/3UtrBfZv2qVLF1E7j+2pSqqs0ySmN1pbxpo1a7B8+XJOLbO4uBhFRUWIjIwkirt69Wq4ublhxIgRXAKRmZn5j1ENbJmYy2QyfP/998QWLPHx8Th27Bh0dXUBNFPEDx8+DDc3N8HJHg1qKNA8W5OdnY2kpCSEhoaCYRjk5eUR0aP5iY6JiYko1E1HR8fXOv9nYWdnx62XvwkhSXo1NTVRWFgoR7svLCxs00qPYsPAwAASiYQTqbGzs/vHidSIDVpMDD77pLKyUq7YJ7R4SYvltWLFCoVxi4qKBMd0c3MT/LP/C2yix8LQ0JAo2aHRgQSaqcxFRUVITk7mKKcODg6isb3EerYC8gU6hmHw8uVLLr4Ys5tiP6c7ZLIH0OETiw0VFRW4urpSGx5uD2hPVdKW8u1A801eWlr6N63or4WxsTGkUilu3LiB0tJSjB8/Hubm5sQPpZ49e0IqlSItLQ2FhYUYPHgwvLy8iAyl2xNa+m1qaGjAwsKCWIVRXV1d4UuelHIpNjWUhYWFBSwsLFBdXY3U1FSOwvLtt98KXifriVddXS2KJ15LeXwNDQ307duXuLutbDD/l19+ERzT19cXy5cvx6hRo7jizJUrV0SZLWlP+KeL1IgNWkwM/v0ploI0LZaXi4sLV6jeunUrZw3g7+8vuICdmZmJq1evKvweibVTQ0MDZDKZXAIik8mIGEmKGA4sSBhZN2/eREBAAJycnGBubo78/HwsW7YMmzdvFjxjSMvShC3QsQmenZ0dRzltS+NiLDpkskeLTyw2aKj50AINLnV7gzIV0ram8koL3333ndzX+fn53L3Fn98QAg0NDYwfP54oRntFy8FrtmJI6repoqKCuro6aGpqcudqa2uJXvI0qKFAs0Im23nT0dHB3LlzMXfuXPz222+CY9LwxGvp/fjixQvcvXsXgYGBsLa2FrxWPloaSrO/w+vi/fffx+HDh/HDDz+gtLQUZmZmcHd3/0fZmbwRqREffwUTQ6xNOS2WF79DlJOTo/D860KMmS9FmDx5MgICAhAQEAB9fX08f/4cmzZtwqRJkwTHJGUyKENkZCRiY2M5Y/rRo0fD0tIS69atEyxWdu/ePfj4+HB7V/Y4Ly+PaK00lDNpaC2w6JDJHi0+sdigoeZDC+1FFpomhgwZIiqHmiY2bdrE2SGkpqZy6qbu7u6IiYkRFJP/cDx16hQmT57MJfxvIBz379/H1q1b0bNnT4wfP55TE/b39ydKoufPn48lS5bAxcUFpqamePToEeLi4oi8wGhQQwEgLi5OIc1y4MCBgmOySbRMJkNZWRmMjIyI6TXh4eGtzlVUVGDZsmXEyZ7YhtLh4eHw9PQkLsS0Z7wRqREf7Y2JQZvlJRY1sCWT49GjR2hoaCBWP587dy46deoEZ2dnPHv2DDo6Opg3bx7Re+Cdd95p5VkHAP/9739JlgqZTMYleixMTU2JZppZcRZAPkklTVizsrIQEhICbW1tbNq0SRRvaBpaCyw6ZLJHi08sNmio+dACbS51e0B0dDTS09Ph5+fXJv10+MjNzeWOpVIpl+w9f/5ccEwfHx/u+JdffiGilrzB/+H//b//hxUrVqCyshKurq5ITk6GgYEBFi9eTLRRZ307k5KSUFpaih49esDHxwfm5uaCY9KihraceWj5fwpBQ0MDtmzZgvT0dBgZGaG0tBTW1tbw9/cXxSqChb6+PnEXlm8offLkSXh6ehLPltXX18PR0RHbtm0j8mtsz3gjUkMH9+7dw8CBAzk2Rn19PbF6IH+jW1lZKQrtmhbLiwY18D//+Q/WrVuHHj16YPLkyQgNDYWWlhZmzZqFJUuWEMV2dHQUtRvn6+vLNU78/Pywfft2AM0UTpKGiiL9A4ZhiJK9/Px8zh5KzOJ0eHg4tm/fjoqKCoSFhSEqKoo4ZkNDA6KiouDu7g4NDQ2kpaXh+vXr8PT0JH7HdMhkD/i/CnRtbS3U1NQwd+5cLFq06O9eVrsFDS51e8PRo0dx4MABzJkzByEhIejXr9/fvSSl4G+axRxKFjvOGzTTNT/99FMAzfRtdmMuRqXcwsIC//rXv/Do0SOYmJgQF2doUEOB5oowf+YBAPH8w65du2BoaIgLFy4AABobGxETE4PIyEj4+voSrZePFy9eoLq6migGDUNpf39/ZGdnw8fHB7Nnz5YrHLT1YpVYeCNSIz6Cg4Nx+/ZtVFdXw83NDb1798bKlSu5Z5hQ8GnWYtGuabG8lI21kFADt2zZgujoaFRWVmLBggW4cOECdHV14ezsTJTsyWQyRERE4Pz586ivr4e2tjYmTpwId3d3wQkEf09RUlKi8LwQfPLJJ9ixYwe8vb3RqVMnNDU1ISIigujaOnHiBJfs8WctSaGmpsYJiEVHR4sSc8uWLejcuTP3fDI3N8eVK1cQGhrKMbWEokMmewkJCbh//z6OHz8OHR0dVFdXY8uWLThw4ECbUrqkreYjJmhwqdsbOnXqBFdXV1hZWcHFxQW9e/dus3OLtIaS30B88D8f/n1PqvJKw/yYBjUUaKZIi83IuHbtmtyMXefOnbFy5UrMmjVLcMywsDC5z6u+vh4//fQTnJyciNZKw1AaaE72g4OD4eLigtjY2DYtIEADb0RqxEd2djaSk5NRVVUFFxcX1NbWYtOmTRg1ahRRXDs7O1EUc/mgxfKiMdaiqanJFfoGDhwIQ0ND7jwJtm7din/96184ffo0NDQ0UF1djQMHDmDr1q0ICAggXbYcSPcaS5cuRWRkJGxtbaGvr4/KykrY2dnB09NTcExlhW8xIZYie05OjpwIoL6+PgICArhklQQdMtk7d+4cJBIJ95LU0dFBUFAQ5s2b16aSvZZy221ZzYcGl7o9IjMzExs3bsS8efPa9CxMYWEhwsPDOQ8w9vjhw4eCY7IKXPxBZxZtzROxPYHWADkN82Ma1FBaUEbVJKFwtqTaa2howNHRURThL76h9I8//khsKN3Q0ICYmBicPn0au3fvxscff0y8xvaGNyI14oP927HewHFxcaKMcyib2yUBjdl1AEhJScHy5csBAKWlpQrn114X/ESJ33EjTVBycnLkitE6Ojrw9PSEs7Oz4Ji0ismqqqrw8fGBj48PysvLoaurS0y5p7VWVp2dYZhWSu2zZ88WFFPROISKigqx2jPQQZM9NTW1VtVQNTU1Ys6r2KCh5kMLNLjU7Q3r169HdnY2wsLC8MEHH/zdy3klVq5cqfDYw8NDcEwxh5vf4P9Aa4Cclvmx2NRQAAgMDCSO0RIaGhooKChAr169uHMFBQVE84UODg4oLy+HgYEBgGaT3kePHhEne8ePH8eMGTMANL/cLS0tYWlpSWQoPX36dAwePBjffvvtP3ZG7Y1Ijfjgb5i7desmqgea2HO7d+7c4Y7Fml0HgKtXr3LJHn9+jQQ0qKGA8uIWSeKTnZ3NUWv5qpGVlZWCYwLN1HNlaKla/WehqJjKgqRIzVdnV6bU/rowMDBQaGvyJtlTAhUVFTx9+pRrgwNAWVlZm/Taay+gwaVub+jSpQuOHz/e5ii2ikDq0aYI7ckTsT2B1t+VhvkxDWooACQmJopegffx8YGbmxs+//xzTiKe7WwKxYkTJxAVFYXTp08jNjYWP/74I4yMjPDLL79wmz8hSElJ4ZI9PtikUgi8vLxgY2PT6jw/We3oeCNSIz5aMkX4CrUkol005nb5EHN2nQY1kKbiuaIkmmTdt27dIl2S0rh1dXWwt7fH0KFDRfnbKiumkqK4uBhWVlYYPXq0aMW0NWvWYPny5ejevTtHOy8qKkJkZCRx7A6Z7Lm5uWHJkiVYtmwZevXqhYcPH2LPnj1v1AMJQINL3d7Qr18/nD59WuH32lrlWNFAe01NDerq6oi8y96g/YCG+TENaihARz12wIABOHToEFJSUpCRkYG3334b8fHxRAbwx48fR0pKCtTU1HD06FF8++23MDIygqOjI1GyV1dXhwcPHijc3AjtGiYkJGDw4MFyRc/MzEwEBAS0K1YJCd6I1IgPZawRUtCY26VF4aMRt0ePHqLEaYmioiIuieaDZN0bN27kWCO3b98Wjel04sQJ5ObmIjU1Ffv27cPHH38Me3t7IluD4cOHg2EYZGVlobi4GN27d8fw4cOJPzc/Pz9cvnwZ69evh0wmw0cffQQbGxs5Jsnr4q233oJUKsWNGzdQWlqK8ePHw9zcXJRrrEMmeyNHjsTWrVtx9OhRSKVSGBsbIzg4uM1T71jIZLI29yKiwaVub/jjjz+441OnTnGmpG1RAKWlX8uRI0cQHx+PNWvW/E0reoO/GjTMj2lRQ2mox8bGxmLp0qVYuHAh0dr46Ny5M7p06YJ79+7BwMCAm9UhZY3cv38f69atU7ghE0oRY4V01q1bh2HDhiEyMhIXLlzArl27iNba3vBPF6kRGw4ODpDJZLhx4waePXsGY2NjmJubt0nmFI3ZdUA55bItirXRKOzcvXuXOw4NDRXVv7pfv36cWnJWVhbCwsJQUlKCxMREQfHKysqwdOlS9O7dGz179sTFixcRGhqK2NhYolnLt956C9OmTcO0adPQ0NCArKwsSCQS5OfnY+/evYJifvnll1BRUcGIESNgbW0NY2NjwetriQ6Z7AHNg9kLFixAeXk5jI2Niaq5tODp6cm1mOPj47lNyeLFi9uU+TuAV24QWBPojo6WPnP8r9sqHj9+jICAAGhrayMxMRFvvfWW6P+Hi4sLvvrqK9HjvgEZaJgf06CGAnQq5RkZGVi6dKkosVg0NjaiuroaZ8+ehaWlJYBm6fGGhgaiuGwXUkyMHj0a+/fvh4+PD54/f46hQ4dCKpWKMv/RXvBGpEZ8/Pbbb/D29oaZmRkMDQ1x5swZ5OXlITo6mpOiFwIac7s0ZtcBupRLGjh37hy++eYbFBcXw8TEBE5OTrCzsxMlNg2Fy+rqanz//fc4efIkamtrOaaHEISGhsLX11dOLTY9PR0hISGIiIgQHJff0VRVVcWoUaMwatQozupHCHbu3AmZTIbMzEzExsbi8ePH6NevH2xsbIgtzjpksvfw4UN4enpCTU0NhoaGKC4uhpaWFiIiIkRRTRILT58+5Y7T0tK4ZI+WPCwJjIyM5L6ura3F/v370aNHj39MssdHW+zmtURKSgp27dqFVatWYfLkydT+H1KPsTegB7HNj2lQQwE6FfiKiopWHW4WQn27vvjiC9jb28PIyAh79uzBzZs34enpSWWjKgZ++eUXPHnyBObm5igoKEBVVdU/Ktl7I1IjPnbs2IGYmBg5Zdrc3FyEhoZi//79guPSmNsdMWKE4PW8CjQolywTAWimW7PJyfr16xEUFCQ47nfffYczZ85gw4YNMDU1xYMHD7B9+3bU1NQonBP+M6BFjz1z5gxOnTqF4uJijBs3DkFBQcQCQCUlJa1sQSwtLbF7926iuPyO5hdffMFZkB06dAhjx44VHFddXR1WVlacMu3vv/+O9PT0N8meIoSGhmLNmjUYNmwYdy4jIwMbN25ssxQWGsbXYoI/2Hr9+nUEBgbCyckJy5Yt+xtX9QbK4OHhwc2q6Ovry216hW50laEtXq9vQMf8mAY1FKBTgS8vL5czZuZD6D1gZWUlR4tSU1NDYmJiq2LY6yIqKoro5xVh7dq1+OOPP/DVV1/BxMQEFy5cgLOzM/z9/WFtbS36/9cW8UakRnzU1dW1siDp168fXr58SRSXxtyul5cXZxeUl5eHvn37tlm6JZ+JsGfPHi5BuX//PlHcpKQkHDx4kBsN6tevH3bu3IlFixYJTvaUqXECrUdIXgdeXl549913MWDAAOTm5sp13oQqZ9KiF/P37HxmB0mzRlHCP2DAADm/WKHokMleeXm5XKIHAJ9++ilR1YkG2pvx9cuXLxEeHo7MzMx2YT8gNtqTz5y2tjYsLS2RlZXV6ntCN7p8HxkWDMMQycO/AT3QMD+mQQ0FmueA8vLyOBpYQUEB6urq0K9fP8Ex+/TpI1iu+8+ia9euosSZMWMG9w5QUVGBpqYmPvzwQ/j6+soJrLwOjIyMEBQUxFFsx44di/79+8PLy+sfk+y9EakRH8oo26TG0jTmdvnvLGdnZ2om62KAlvl3586dW2lAaGtrE1Hvaalx0hhfMjExwaVLl+SKPmlpacTdWWX7d5LrlVbCD3TQZE/ZXIlYLvdigZaZMg3cvn0b/v7+GDNmDJKSkv5x4ixA+/KZCw0NFT2mMh+Z6dOni/5/vQE5aJkfi00NBZpnSsLDwyGVSqGrq4uysjL4+/vDz89PMCWGdI7wr8TZs2flvq6pqcHly5exdu1awV1TVin54cOHePToESfnLUaVuL3gjUiN+GhpIA00JyelpaVEcWkXv9t6QZ3W79/U1ISamho5GnN1dTUaGxsFx9y9e7fopvIAHRui1atXw8PDA8eOHePU+Z8+fUrMRuH7QrY8Jon5v46FokMme4pmNRiGITZ8FBu0zJRpYNasWdDW1kZWVhacnZ0BoM3SIWihPfnMzZ49W+kLQ+jnxc5m8jePYhnqvoH4oGF+TIMaCjQLVB07dgy6uroAmhUUDx8+DDc3N8HJXkJCAtGa/kq0rLyrq6tj2rRpghXogFd7Iv5TinVvRGrEhzIDadK5cFrKme0FbBLNMIzcMWkS7eTkhBUrVsDX1xe9evVCUVERtm3bhnnz5gmOScNUnhYMDAwgkUhw69YtFBYWws7ODhYWFsRxW1pasMVPkkSdRreQRYdM9szMzBTOarQ12mF72iifO3fu717CG7wG+Ea3YoGWofYb0AEN82Ma1FCgObnR19eXO2doaAgNDQ3BMfl0ZRUVFTQ2NuK9995DcHCwYIPtTZs2iS4i8SrU19cL/llanojtDf90kRqxMWTIEIwZM0b0uDTmdvkdyJYdydmzZwuOSwP8JJp/TJpET5gwAbq6uoiOjkZhYSGMjY3h7OyscJb1z4IW5ZQGGhoacPHiRXTt2hUTJkwA0GzHsGnTJrmGy+uCBg2cVsIPdNBkj53TkMlkKCsrg5GRUZvzrQPkh4eB5g1Jfn4+qqqqqHGiheJVnGFaZqBvIBzFxcVKvyf083qzeWxfoGF+TIsaqqKigrq6OmhqanLnamtriUQfFAkFXL9+HUFBQZxy2uuChogE0Pr5KpPJcO7cOcFJKUDPE7E94Y1IjfiIjo5Geno6/Pz8RN1X0VDO5HcglXUk2wpoqpqPHj261az+nTt30L9/f0Hx2pPehK+vLzp37ownT57g3r176NmzJwICAjB//nyiuMqo4CoqKnB3dxcUk1bCD3TQZK+hoQFbtmxBeno6jIyMUFpaCmtra/j7+7cp+gq/yiSTyRAVFYWampo2JyQDQKmqHSC+uuMbkIM/l3Pt2jWMHDmSo90K9Zp6s3lsX6BhfkyDGgoA8+fP5+arTE1NUVJSggMHDsDJyUmU+CyGDRtGlEDSEJEA0Oq+0tTUxAcffEAkuU7LE7E94Y1Ijfg4evQoDhw4gDlz5iAkJIRIRIkPW1tb9Ojx/7V353FRlW//wD+DbKIIgSarSK7lApphuGcmJCCSopgiPioouSSgEF9cQFJ2UJTURwUzBxFGJxRQLEhRo1z49XVL3JVFRUSQ1SGY3x+85jwzwlTOOcc5M9zvfzrMeXV7icxwrnu5LnP06tULAGQmwekcPbh16xa6du0KS0tLRuJky+TJk+V+juTl5Sk8bkFBAWJiYmBgYICIiAiYmpoiPj4eOTk5OH36tEJjqlJT+UePHuHo0aMQiUSYOXMmtLS0cODAAVo9IYG/b0emaLLHZsKvlsnejh07YGxsTDU3bGlpQVJSErZt24Y1a9YoObr2bt68iW+++Qb29vY4cuQIJ1ch2a5qRzBLesuep6cnI9VCycOjamGj+TEbW0OBtodwY2NjpKeno6KiAubm5ggICICtra3CY8pTX1+v8P/L1ow2G1UC2eqJqEpIkRrmaWhowMfHBxMnToSXlxesrKwYedBPTExETk4OXr16BUdHR0ydOpX2dtuEhAT8/vvvEIlE8PLygqurK63x2PR6kaaCggJs2bIFCxcupDVubGwsEhMTUVZWhri4ODx//hxmZmbIzMxUeExVaiov2Y2ira2N1tZWJCcntzsyoAg22pGxlfADaprs/f777zIf5l26dMGqVaswe/ZsJUbVXmtrK3bt2oWsrCyEh4fjww8/VHZIcv3dkjeXD+cSzD2UkodH1cJG82M2toZKjBgxAiNGjKC+vnXrFjZs2KDwFuHXt3GKRCL89NNPMn/Gm2KriERlZSV27doFc3NzTJgwAb6+vvjrr78QFham8PkotnoiqhJSpIYdhYWF2LRpE+bPny+zrZ+OqVOnYurUqaitrcXJkyfh5+cHAwMDODs7K/we+P3335GWloaGhgasWLGC08meZJK/sbERERERuHXrFvbt2wdra2ta4/bo0QPW1tawtrZGSEgIli9fTvtZ+PWjIHV1dRAKhTh06BBycnJojc0mY2NjRhI9CabbkbGV8ANqmuzJ+xDn2of7nDlzUF5ejiVLluDOnTu4c+eOzD0u0dPTw6NHj/D5559jypQptAonEKqJPDyqFjaaH7OxNVRaS0sLTp06BT6fj8rKSri7uys81utbz3V0dDBy5Ei4ubkpPCYbRSSAtveWo6MjampqMG/ePCQkJMDU1BRBQUEKP+iy1RNRlZBzxszbuHEjioqKWOu1q6+vD3d3d/Tv3x8pKSkIDg5WuFG3JIHS09OTaXzNVRcvXsT69evh5uaG0NBQRj5XpccwNTVldNHjzp07OHjwIE6cOAEHBwdWWj7R1VGLMwk6O57YaEfGVsIPqGmyp6Ojg0ePHqFPnz7Ua48ePeJcgjJx4kQAbbOPdLYWvQ27du1CTU0NcnJyEBcXh169esHFxYV2FT6CHXFxcVTxH8kKhISi2+1ef3i0sbHpdA+PqoSN5sdsbA0F2gopHD58GJmZmbC1tYVIJGo3y/mmXt96LhKJAMjfjvxv0EkU/86rV6+oh7CTJ09Sn6t031s6OjpUSfDOiJwzZp6enh5rx01u3ryJrKwsFBQU4IMPPoC7uzutiomqJDIyEllZWQgJCcHgwYPx8OFD6h6dh/3GxkY8ePAAra2taG1txYMHD6jzkIqOm5ubCz6fj+bmZnzxxRe4f/8+ZydP5LU4o4utdmRsJPyAmiZ7AQEB8PX1xaxZs6gVCMlsHpeoWt8yAwMDzJ07F3PnzkVZWRliYmIQFBSEs2fPKjs04jXSKzpMbrerq6tDbW0t1TxUFWZLOys2mh+zsTUUaNvC5enpCaFQiO7du2PJkiUKjyVx//59REVFwcLCAg4ODtTnbXBwsMJbzzoqRlVfX4+mpib8+eefCscqnZhLNz+m0/iYIOeM2TBw4EC5W/XobOl0cnKi/hsdHU1Nzj98+FDhpESVColcv34d1tbWSE1NlXmdx+PROiqjq6uL9evXA2ib/JFc0xk3KCgIXl5eWLhwId555x2cOnVK4fjYNnToUBw9ehR6enqYMWMGY8kTG+3I2Er4ATVN9gYPHowDBw4gMzMT58+fh4mJCZKTk9G7d29lhyZDFfuW3bt3D9nZ2cjPz4e1tTVnZ3M6u+nTp6OgoAB6enqMlbT+7bffEBoaCgcHBxgbG+P69evYvn07IiIiOH3etLNio/kxG1tDAWDz5s0QCATw8vLCzJkzaY8HAP/5z3+wYsUK1NTUwMfHB0KhEEZGRlhnP/opAAAgAElEQVSyZInCD6Wvbyc7dOgQkpOT8c0339CKtbM3lGYLOWfMvHv37lHX2dnZVJJG92y4kZERgLbzgIWFhdTrdJISVSokwkaRJrbGPXXqFI4ePYp58+Zh4MCBePHiBeN/BlO++eYb9OnTBy9fvsSDBw9oFRKTxkY7MrYSfgDgibneEVFBd+/epbYVPXr0CE1NTYyVCGbKpk2bMHz48HbnCa5evcq5JGrv3r3Izc2FsbExnJycMGXKFNKYlsM2bNiA2tpaNDQ0YPTo0Vi0aBHtMT09PbF161YYGxtTr1VUVCAwMBD79++nPT7BrLNnzzLe/HjBggUd/tKR9/qbKi0thUAgwLFjxzB8+HC4uroq3PzX09OTetDx8PCgZvIXLlxI++f16dOnCAkJQbdu3RAaGop33nmH1nhCoVDuPba2jnYGpaWlcs8Zc70UvyqQfo9xVVlZGYRCIcrKymBmZgY3NzfO7qBKTk7G4cOH0djYCC0tLXz55ZdYvHgxrTFPnjyJiIgI6OrqIiYmBsOHD2co2jaFhYVIT0/Hf//7Xzg4OCAoKIjR8emS/Iy2trZi0aJFjD2rBAcHy73Hxer1armyl5ubi/j4eAgEAujr66OyshLBwcFYu3YtpkyZouzwKKp0niA2NhZ9+vSBhoYGDh48CD6fT93j2nYIou1QcmpqKpqbm+Ht7c1IsicWi2USPaCt1xrBTWw0P2ZjayjQdmZNR0cHFhYWWL16NVatWoXTp08jPT1d4WRPeqVB+u9P58wiAGRmZmLHjh34+uuvGWl2C7SV1R45ciQmTZrUbuWUUBwpUsMuphtqJycnIy0tDU1NTYwkO1euXEFISAjmzZsHW1tbPHz4EMuWLcPmzZthY2PDYOT07d+/H/fv38eRI0fQvXt31NXVYcuWLdi7dy+tbe379+/HsWPH8PLlS2zevBm7du1iMGrA3t4e9vb2ePHiBa12DmyR/IxqaGjQ/uyX5u3t3eFndVFREa1x2Uj4ATVN9iTfLH19fQDAyJEjkZqaCl9fX04le6p0nkBej4+mpqa3HAnxb0h+trS0tBj7gJO3153JD1CCOWw0P2ZjayjQdgaEx+Nh9OjRmDRpEkxMTDB58mRMnjxZ4TE7qsImFotx9+5dhcdcuXIlioqKEBAQAENDQ5ltnR2d5/u3EhMTcfnyZQgEAty7dw9WVlaYNGkSPvroI1oFZQhSpEZVSJKdo0ePMpbsbNu2Dbt374aZmRmAtvfohAkTsGHDBqSkpDAZPm2SoieS37Pdu3dHWFgY5s+fTyvZ09bWhoGBAQwMDNDY2MhIrHV1ddi4cSPCwsLQvXt3ZGVlIS8vD+Hh4YyMzySxWIzm5maqzoDkGgCtSVAnJyfMmzcPwcHBMs/sW7duVXiXC1sJP6CmyZ62tna7XhrGxsacq8apSucJXt+DXFJSAj6fj2PHjuHXX39VUlTE2/R6VU8A5FwRh7HR/NjGxobxraFA2y9IkUiEwsJC7N69G0+fPsXAgQPxySefKDwDL68KG52KbN26dcOECRNw8eLFdvfoJHsaGhr46KOP8NFHHwFoe6/98ssvOHToEBITExUelyCY5u/vT1V6ZrKUPRvJjkgkohI9CUtLS6oyL5doaWm1m1DV0tJidLKHqVNbGzduxLBhw6hiUo6Ojnj69ClCQ0MRGxvLyJ/BlLKyMjg6OlJ/d8nED4/Ho9WofMSIERCJRFi4cCESExOprfx0vsdsJfyAmiZ7PB4PTU1N0NXVpV5rbGxk5NA/k1Sxb9mZM2dw8OBBFBUVwcfHBz/++KOyQyI6UFRURD18VldXyzyIKtqzSF5VT6abaxPMKSwspH5ZMNH8mI2toUDb2c93330XEydOpFrS3Lx5E2fOnFE42bOzs2MsPgm2+kidOXOG+nu/ePEClpaWWLBgAed6wxIEUxMnr2Mj2elo14lYLOZkssfj8fD8+XOZoxKVlZW0q0d2VPxJQtFiJY8fP5ZJ7DU1NbF48WJGe/gxJT8/n5VxNTU1sWnTJqSmpsLDwwNbt27F+++/T2tMNhN+tUz2FixYAG9vb3h5ecHS0hKPHz/Gvn37MH/+fGWHJkOV+pYlJydDKBRi0KBBWLRoEVpbW7F06VJlh0XIce3atQ5fpzPr1FGhiPz8fPD5fEYSCYJZoaGhuHz5MhISEmj/EpJgY2soAKxZs6bd1pfBgwdj8ODBjIzPlDlz5sg9p0Tn7PK+ffuoZO/rr7+mvhcnTpzA3LlzFR6XIJjGxiQKwE6yM2bMGMTGxsLf3586s5WQkICxY8cyETKjfH194e3tjWXLlqFPnz4oLS3Fzp07aVePlJ6MZWpiVt6/CRu9F+mSLqTC4/Ggq6uLYcOGwdXVlZE2DF9++SUGDRqElStXYvXq1bTOsbKV8ANqmuxNmTIFRkZGyMjIQEVFBczNzREQEABbW1tlh9aOqvQtS05OhpOTE7744gsMGjQIycnJyg6J+Bu1tbUQCATo0aMH3NzcoKGhgeLiYmzcuJF2QZ3q6mpkZGQgPT0dffr0waxZsxiKmmDSiRMnEBAQgOLiYhQXF8vcUzQ5Z2NrqCp5fRszU6QnYeRdE4Q6YyPZWbp0KbZt24bJkyfD0NAQNTU1cHR0xOrVqxmMnBkff/wxoqKikJaWBoFAABMTE4SHh+ODDz6gNa6bmxvEYjEuXryI8vJymJqaws7OjlZSYmVlhZ9//lmmBkZeXh569epFK1Y2TJs2TebrhoYGnD17Fjdu3EBISIjC40ofC/vwww/B5/Px9ddf49atWwqPyVbCD6hx6wWgbfn68ePHMDMz42Sp3df7lpWXlyM/P5+TfctEIhFyc3ORkZGBpqYmNDY2IjU1lSqCQ3DLokWLMHToUDx+/BhWVlbo2bMnduzYgaCgILi4uCg05rVr18Dn81FUVITPP/8cly9f5nzZ7c5MepvN6/2w6PzyKCwsxKZNm+Ds7CyTNCraWwho+2U5YMAAmde4mER2dFZPQnLeThHSrSvkXROEurt9+zbS0tJQUlICExMTeHh40E52JKqqqqCvr8/5rdElJSWoqqqCiYkJI72hKysrsXTpUlhZWcHCwgIPHjxASUkJdu/erXA17ZcvX8Lf3x/Pnz+HhYUFHj9+DCMjI0RHR7erl8FVs2fPRnp6OqNjNjc3Izk5mdauN7beA2q5sqcqzcqTkpLA5/NllmwXLVrEyb5l2tracHFxgYuLCx4+fIiMjAy4urpi6NChpIAAB9XX18Pf3x9isRiOjo4wNzdHZmZmu9YJb8LDwwOLFy/G8ePHoa2tDW9vbwYjJpgmXTjhjz/+kPlaURs3bkRRURHi4uIYewgDgP79+9Mq7vC2HDp0iLr+/fff8fHHH1NJKZ1kr7GxEQ8ePEBrayuamppkrgmisxgwYADWr18v85r0edY3tWPHDrn3VqxYodCYbCktLcXq1auhpaVFTf537doVCQkJtFocRUZGYs2aNbC3t6deKygoQEREBBISEhQas7a2Fnv37kV5eTkqKipgamrKSGL6NjGxNVJCJBLh+PHj4PP5EIlEtJK9AQMGYOHChYwm/ICaJntxcXFwdHRs16w8OjqaU83KValv2Y0bN6iHOysrK6xZswZ+fn6sHX4l6JHsnefxeNDR0cHOnTtpV6Pl8/kQCARwdnbGZ599hoaGBiZCJd4Cpvph6enp4ciRI4yfzdDW1qa1Mvi2SG/j9PT0ZCxB1dXVpR5ydXR0ZK4JojM4evQo4uPjoauri8TERFhaWmLdunW4d++ewslez549Zb5ubGzEnj17YG5uzrlkLzIyEt988w1GjRpFvXb+/Hls2rTpb5PWf/LkyROZRA8AJkyYgO+++07hMVNSUlBWVob+/ftj0qRJnH1ulee3335jZIW3tLQUfD4fJ06cgFgsRkJCAkaOHElrPDYSfkBNkz1VaVauSn3LIiMj220n6tKlCz777DMlRUT8HemHe0NDQ0YeGm1sbGBjY4OGhgZkZ2fj0qVLcHd3h6urK+eKHxHsGDhwIHJycjq8R6dIjyqe+2SyoTTZDk10dikpKcjOzsazZ88QGRmJiooKfPrpp7RK+UtXC7106RLWr1+PefPmYdmyZUyEzKiqqiqZRA8Axo4diz179tAal8kVLIl169YBaOtl+ssvvyA5ORk9evTA+PHj252RU7bXW+LweDxYWlrS7gno6+uLly9fYsaMGcjKysLq1atpJXoAewk/oKbJnqo0Kyd9ywi2XL9+HR4eHlQvJMk1E2eg9PT04O7uDnd3d9y6dQsZGRkMRU0wiY1+WPfu3aOuXz8HSEdjYyMOHz7c4b05c+bQGlsVSFeMk8bj8bBly5a3HA1BvH2GhoZU8++7d+8iNDRU4RU9ac3NzYiPj0dhYSHj28+ZJO+5le7kv5mZGX755Rd88skn1GunT59mZCdF//790b9/fwBATU2Nwm2d2MRWTGKxGJqammhqakJraysjk39sJfyAmiZ7qtKsXF4Z3JUrV77lSP6ZdN+213HxDd7ZHTt2jPEx5c0sce19RbRhox8WG+cAgbYiAqogLi6OSqCZ6lkFtK8YV1FRgbi4OM4V6iIItkg/LJuZmTGS6N24cQPBwcEYP348MjIyOF2cpbq6ut2zlFgsRk1NDa1xAwMDsXLlShw+fJiq8Pj8+XNa/ZzlTU4BoCYAuaKlpQV5eXkwMzND3759ERUVhebmZqxYsYJW4cZdu3bhyZMnEAgEcHd3R0NDAwoKCjBu3DiFV1PZSvgBNa3GWVpaKrdZuaWlpbLDk8F0SVy2eHp6kq1GKqasrAxCoRBlZWUwMzODm5sbrQ+3jz/+GD169ICTkxNMTExkysIz2VyXUA1sVoqUND3mWt8moVAo915HfSgVkZWVha1bt2LlypVwdXVlZEyC4Lrp06cjMDAQYrEYsbGxWLt2LXVP3kTzPxk6dCi6deuGvn37Us9VXKzyC/x9AhUREUF7/GvXrlEVHkeMGEFrrLNnz8p8LT05tX37dlpjM23Dhg2or69HfX09qqqqMG7cOJiamuL48eOM/f4Si8UoKCjAkSNHcOXKFZw+fVqhcSTvgdfHjo2NRWZmJq0Y1TLZA4BXr15Rzcp79+6NTz/9lHPNytkoicsWkuyplitXriAkJATz5s2DhYUFHj58iEOHDmHz5s2wsbFRaMy//voLZ8+eRVZWFurr6zF16lQ4ODigW7duDEdPqAImk7379+8jKioKFhYWcHBwoIonBAcH0zoLyLSWlhYUFBRAT08Po0ePZnTs6upq6sFky5YtKlfdjiDoYCPZKSsrk3uPqwWhRCIRKisr0bNnT8YmuyQ98Wpra/Hdd99BW1sbS5cuZeSZWDI5tWLFCk59Vkt4eHggLS0NLS0tmDZtGnJzcwEACxcuZKXqfVVVFYyMjBT6f9lM+NU22bt+/Tr09fXRp08fAG3JX2JiosxskbKtWbMGM2fObFcSVygUKlwSly2//vorxowZo+wwiH9p8eLFCA8Ph5mZGfVaSUkJNmzYgJSUFNrj19fX46effsLJkyepalGE+pM+B/jbb7/JfHbRqUw5d+5crFixAjU1NQgJCYFQKISRkRGWLFnCeC8kOjZs2IDa2lo0NDRg9OjRWLRoESPj5ufnIzIyEv/zP/+DuXPnMjImQaiauro6dOnSBV27dmVkvL87YqLoaiFb/vrrL2zZsgUFBQXo2bMnKioqMGnSJAQHB9PafhobG4uHDx9i69atCA4ORteuXWFtbY0bN24gOjpa4XFVZXKKrR6m0j8/NTU1MsdZ6B5tYiPhV8sze+Hh4bhx4wbq6urg6+sLKysrrFq1CmPHjlV2aDLYKInLlqtXr1LJXmFhIRX3xo0bERYWpszQiA6IRCKZRA8ALC0tqe1xdF2/fh1FRUUoLy/n3PuKYA8b5wCBtrMKkp+jAwcOoG/fvgDAud0Yd+7cQWpqKpqbm+Ht7c1YsvfVV1+ha9euSEpKQlJSksw9ciaa6Az4fD727dsHTU1NrF+/HuPHj6c9ZnZ2ttx7XEv2duzYAWNjY/z8888A2nYRJCUlYdu2bVizZo3C416/fh0pKSn466+/cPr0aZw5cwZdu3alNamkSpNTkrOQkvOP0td0SH8uM7Xzja2EH1DTZK+oqAhCoRC1tbXw8vJCY2Mjvv3223aJlbKxURKXLefPn6caRe7cuZP6Xt6/f1+ZYRFydHSgVywW00r2rly5guzsbPz666+wtbWFs7MzwsLCOHnGlGCHnZ0dK+NK/wxJz2RyrQ2N5AC9lpYWo7HdvHmTsbEIQhUdP34cJ0+eRF1dHQIDAxlJ9pg46/a2/P777zh06BD1dZcuXbBq1SrMnj2b1riSKvRXrlzBwIEDqVXT5uZmhcdUpcmpIUOGUEn/Bx98IHPNFKaegdhK+AE1Tfa6d+8OANDX10d9fT327dtHqzAFW9gsics06d2+arrzV62MGTMGsbGx8Pf3h4aGBlpbW5GQkEBrFW727Nno168fxo8fDy0tLZw/fx7nz58HQK8SIUFIWkNIt4kQi8W4e/euskN7K+S1nQA6R+sJgtDW1oa2tjaMjIxoJSLSFixYIPceW8WlFCVv5Ybuik6XLl1w7tw5CIVCTJ06FUDbsZwePXooPKYqTU6RhL+NWiZ70ln2u+++y8lED2CnJC5bpL+nZCWH+5YuXYpt27Zh8uTJMDQ0RE1NDRwdHbF69WqFx5T3oUl+Hgi6tm7dSl2ztVWUCdItaKqrq2W2gtGZ0X727Bnt2AhCXTA1oaynp4dHjx7h888/x5QpU6Cjo8PIuGzQ0dHBo0ePqDoTAPDo0SPaMYeEhCA+Ph5mZmbw8PDA2bNnERMTI/OZ+6ZUaXJqzpw5cp9R6FRklf68f73HoKJbhNlK+AE1LdDyySefwMXFBWKxGFlZWXBxcaHucXEFQlISt3fv3hg5cqSyw+mQg4MDFi1aBLFYjJSUFOp6//79OHnypLLDI/5GVVUV9PX1WekxlJ+fT521IAimRUdHtytFzUWScu5ME4lEnGs/QRBsGDNmDOzt7Rkv/lRTU4OcnBzk5eWhV69ecHFxgb29PecmKW/evImAgADMmjWLahmWkZGB6OhoDBkyRNnhyZDXcxcAVUmZK9iqyMpG5Uxvb2+sX7++XcIfGhqK5ORkhcaUUMtk7230QmLK4cOHMXPmTGhqauLSpUu4ffs2Jw+8qtKbm2D336u6uhoZGRlIT09Hnz59MGvWLHz++ee0xiSIjsyaNQsCgUDZYVBqa2shEAjQo0cPuLm5QUNDA8XFxdi4cSOtWeLVq1dTM+3JyclU4Rc2exkSBJdcuHBB7j2mzgqXlZUhJiYGly9fbtcrjgueP3+OzMxMqh/ejBkzaFe5lFRQ7gidJLojxcXFGDRoEKNjctWZM2cwceJERsdkM+FXy22cbm5uEIlEuHz5Ml68eAETExPY2tpyriDK9u3bcfv2bUyfPh2ampowMTHB/v37UVVVheXLlys7PBkrVqxAdXU1DA0NAbT1CNTQ0FC4nwjBrp49e8p83djYiD179sDc3FzhZO/atWvg8/koKirC559/DhMTE7KiR3QqX3/9NYYOHYobN27g8ePH6NmzJ3bs2IGgoCBa4z5//py6Pn36NJXsqeFcLEF0iK3iTwBw7949ZGdnIz8/H9bW1ti0aRNrfxYd1dXV1Hv/0aNHqKmpoZ3ssbEV/uzZs4iOjoaBgQEiIiJgamqK+Ph45OTkKNxQXNXs27eP8WRv8ODBOHDgADIzM3H+/HmYmJggOTmZkbYWapns/fnnn/D398eQIUNgbGyMEydO4O7du9i+fTv69eun7PAoBQUFSE9Pp2ZdLCwskJCQAA8PD84lexcuXEBQUBB+/PFHGBgYoLi4GOvWrUNMTAxGjRql7PCI10h/wF+6dAnr16/HvHnzsGzZMlpjLl68GMePH4e2tja8vb2ZCJUgOqzqKxaL8erVKyVEI199fT38/f0hFovh6OgIc3NzZGZmwtjYmLE/QzrB49pWM4JQJXv37kVubi6MjY3h5OSE1NRUxnr4MS03Nxfx8fEQCATQ19dHZWUlgoODsXbtWkyZMkXhce3s7FBVVQVdXV2ZVjapqakKJ9gxMTFITExEWVkZ4uLi8Pz5c5iamiIzM1PhOFWNWCxGc3NzhxNydLbes5HwA2qa7MXGxiIpKQnvvfce9dqtW7cQGRmJPXv2KDEyWXp6eu1+mWtpaaFbt25Kiki+rVu34ocffqAaR44dOxbJyckICQlBamqqkqMjOtLc3Iz4+HgUFhYiLi6OdqlhPp8PgUAAZ2dnfPbZZ2hoaGAoUqKz27BhQ4evv/POO285kr8n+SXO4/Ggo6ODnTt3MlL0gRTAIgjmxcbGok+fPtDQ0MDBgwfB5/Ope3S2XbMhOTkZhw8fhr6+PgBg5MiRSE1Nha+vL61kb/fu3RAIBGhpacHmzZthZWUFPz8/dO/eHV9++aVCY/bo0QPW1tawtrZGSEgIli9fzkjFSFXy3//+F46OjjLntSXXeXl5Co3JVsIPqGmy19TUJJPoAcDAgQMZK+fLFF1dXZSUlMDS0pJ6raSkhJO/7Lt06dKuqqm1tTXntsYSbW7cuIHg4GCMHz8eGRkZjBRnsbGxgY2NDRoaGpCdnY1Lly7B3d0drq6umD9/PgNRE51VRw1pm5ubcerUKSVEI5/0Z7OhoSFj1f06e+sJgmCDvIfupqamtxzJP9PW1qaOyUgYGxvT/ozJzs5GdnY2Xrx4AX9/f1RWVsLb2xuzZs1SeEzp5z5TU9NOl+gBbc9DTDRSl8ZWwg+oabInaSL5Oq416F2zZg2++uor2Nvbw9LSEuXl5Th37hyioqKUHVo7YrEYra2tMm/ylpYWziXQRJvZs2ejW7duuHjxIjw9PQH836wT3RlNPT09uLu7w93dHcXFxZwqoEGovoqKCqSlpeHIkSMYPHgwnJyclB0S5fr16/Dw8KCSMsk13feVqrSeIAhV8nq1xZKSEvD5fBw7dgy//vqrkqLqGI/HQ1NTE3R1danXGhsbaT9jGRgYQFtbG71798bTp0+xbds22sU+Ghsb8eDBA7S2tqK1tRUPHjygtjNaW1vTGrszYyvhB9Q02Xv69Gm7PiBisRgVFRVKiqhjAwYMQGpqKvLy8lBRUYEhQ4Zg+fLlVFN4Lpk+fTr8/f2xbNkyWFhY4MmTJ0hKSsK0adOUHRrRgdzcXMbH7KgSIQBcvXqV8T+L6HwuXLiAgwcP4s8//4SGhgbS0tJgamqq7LBkHDt2jJVx7ezs8Pz5c/zyyy9UUbGJEyfSanxMEESbM2fO4ODBgygqKoKPjw9+/PFHZYfUzoIFC+Dt7Q0vLy9YWlri8ePH2LdvH+1dM9K7EUxNTRlp46Crq4v169cDaOsPKLnm8Xidpnqw5O/MJLYSfkBNkz0XF5cOm9Q6OzsrIRr54uPjsXr1asyYMUPZofwjyUrRli1b8OzZM5iZmcHV1RVPnjxRdmhEB+7cuUNVinrx4gV19iktLU3h1QJ5lQi/+eYbxuImOqcvvvgC7733Hjw8PPDxxx/Dx8eHc4ke0LZSUFZWBqFQiLKyMpiZmcHNza3dFvc39dtvvyE0NBQODg4wNjbG9evXsX37dkRERODDDz9kKHqC6FySk5MhFAoxaNAgLFq0CK2trVi6dKmyw+rQlClTYGRkhIyMDFRUVMDc3BwBAQGwtbWlNa5k8UOy4CG9EKJoA3Smty+qovT0dKxbtw5A2yTg9OnTAQDLly9HUlKSQmOylfADaprs2djYYPz48coO4x+9evUKHh4eiI6ORt++fZUdzj9ycnKCk5MTrly5goMHDyIqKgoODg7KDovogHRZ4K+//pqabcvJyVE42XsblQiJzmnYsGH4f//v/6GgoAC9e/fm5LllALhy5QpCQkIwb9482Nra4uHDh1i2bBk2b94MGxsbhcdNSkoCn8+XeS8tWrQIgYGB2L9/PwORE0Tnk5ycDCcnJ3zxxRcYNGgQ7cbUbBs5ciR69eqFx48fw8zMjPYkEiC7+CFvIeRNvc3efVx169Yt6logEFDJ3suXLxUek62EH1DTZG/79u0oKCjA2rVraZVAZVtwcDCKiooQEBCAOXPmyKzwcS1ukUiE7OxspKamQktLC3V1dcjLy5NZbia4Q7ocsLzrN8VWJUKCCAsLQ1NTE06cOIH169fj9u3bSE1NxbRp09qdYVCmbdu2Yffu3TAzMwMAjBs3DhMmTMCGDRuQkpKi8LhisbjdpMm7775LK1aC6Ozy8/ORm5uLzZs3o6mpCY2NjaitraUKYHBJfX09AgICUF1dDXNzczx48ADGxsaIj4+ndbSno766xcXFMpVJ3xQ5Syz/uYruRCUbCT+gpsleWloa9u7di7lz5yIiIgIDBw5UdkhyjRw5EuHh4fDy8sLu3btpl25ly+TJk+Hs7IyYmBj07dsXS5YsIYkeh8kr5U7ng4itSoQEAbSdA3Fzc4Obmxvu3r0LgUAAV1dXnDlzRtmhUUQiEZXoSVhaWkIkEtEaV15VY64VFSMIVaKtrQ0XFxe4uLjg4cOHyMjIgKurK4YOHYrExERlhycjLi4Ojo6OMpP+GRkZiI6OZqQJfEtLC06dOgU+n4/Kykq4u7srPNaoUaNw5swZ6OnpYfTo0bRjU0VstMthK+EH1DTZ09DQgI+PDyZOnAgvLy9YWVkxVomQSX/99ReSkpKQk5OD7777Dh999JGyQ5JrwYIFyMrKQllZGWbNmkVrhYhgn3S1rKamJplrRbFViZAgdu/eLXOWpl+/fggKCoK/v78So2qvo+RLLBbTTvZKSkoQHx/fbtzS0lJa4xJEZ3bjxg2qv6yVlRXWrFkDPz8/5OfnKzmy9m7evNmu36i7uzvtatfPnj3D4cOHkZmZCVtbW4hEIpw8eZLWmKGhoaitrUVDQwOuX79ONQHvTCSf2a5srYIAABFqSURBVGKxWOaazmc2mwk/T6ymT+2FhYUICwuDi4uLzDfu9VK8yjR9+nQMHz4cwcHBnGyk3pELFy4gIyMDBQUFmDVrFlxdXTm9ctpZSdotdETRw9VlZWVy73HpfUWongULFqhEFbekpCQ0NjbC398fGhoaaG1tRUJCArp06YLVq1crPK5QKJR7z83NTeFxCaIzU5XPFUB+rF9++SVSU1MVHnfEiBHw9PSEj48PunfvjiVLlmDv3r10QqViam5uhre3d6c8V8zGZ7a8f+s5c+a06zDwptRyZS80NBSXL19GQkIC3n//fWWHI5efnx8++eSTdq9XVVXByMhICRH9Mzs7O9jZ2eHly5fIzMxEYGAgJ8sYd3byEjo6h7NjYmJk+oERBFOqq6tx7ty5Du+NGzfuLUcj39KlS7Ft2zZMnjwZhoaGqKmpgaOjI61ED2h7OBCLxbh48SLKy8thamoKOzs7zhaqIQiCWYaGhrh69SqGDRtGvXb16lUYGBjQGnfz5s0QCATw8vLCzJkzmSnjr9mWOmhpaXXareZsbF+VfF9fJ693+BuNTXsEDjpx4gQCAgJQXFyM4uJimXtcanOwf/9+DB8+XOZgfmFhIUJCQji5zUBajx494Onp+bcrSITySEq3GxgYICgoCEZGRuDz+di1axfOnj2r0JhVVVUMR0kQbaqqqpCdnd3hPS4le5qamggICEBAQACqqqqgr68PLS0t2uNWVlZi6dKlsLKygoWFBfLz8xEZGYndu3eTQi0EoaCioiK5nx/yJpeUJTAwEL6+vhg9ejQsLS1RWlqKwsJC7Ny5k9a406ZNw7Rp01BaWgqBQICSkhKsXr0arq6uHS42EP/O5MmTYW5ujl69egH4vyItdI61sJXwA2q6jVO69Gt2djacnJwAtP0jcOkMyLlz5xAZGYkNGzZg1KhR2LZtG37++WfExMRQ+8wJQhFffPEF/P39UV5ejqtXr6KhoQEVFRXYvHkz+vTpo9CYn3zyCVxcXDq8x6X3FaF6PD09VaJ3U3BwsNx7ERERCo+7Zs0azJw5E/b29tRrBQUFEAqFSEhIUHhcgujMVOVzReLVq1c4ffo0SkpK0Lt3b3z66afQ09OjPe7hw4cxc+ZMaGpq4uLFi/jzzz9pJZJDhw6lqiRXV1fLVEzmWhLNllOnTiEnJwevXr2Co6Mjpk6diq5du9Ias7S0VG7Cb2lpSWtstVzZCwgIoK7/+OMPma+5ZNy4cdizZw8CAgLw8uVLjBgxAgKBgPYPDEF07dqVmtFMSkrCjBkzEBsbS2tbmK6uLqytrZkKkSAoTGxTeRuuXbuGpqYmTJ8+HSNGjGCsUNWTJ09kEj0AmDBhAr777jtGxicIgvvu3LmD999/n+pf/OrVK8TExGDt2rUKj7l9+3bcvn0b06dPh6amJkxNTfH9999jyJAhCo957do1hf9fdTF16lRMnToVtbW1OHnyJPz8/GBgYABnZ2eF+3xbWFhAIBBQCf/w4cPh5+fHSMKvlsmeNK6fefjjjz/w7Nkz2Nra4tGjR6itrSXJHkGb9MPzu+++Cz8/P9pj9uzZkxSLIFihKgf8jx8/jlu3buHYsWP43//9X3z00UeYPn06rKysaI0rr/UCQRCK8/X1VXYI/1p4eDhu3LiBuro6+Pr6wsrKCqtWrcLYsWNpjVtQUID09HTqWdjCwgIJCQnw8PDosAffvyESiXDo0CEsWLCA2jGkra2NoKAgaltjZ6Gvrw93d3f0798fKSkpCA4OprW6yUbCD3SCZI/L1q1bh3v37uH777+HmZkZfv75Z3h6eiI4OBiTJk1SdniEChOLxWhuboZYLIauri51Dfxfc/Q3NXToUCZDJAiK9LkaHo+HlpYW9OvXD+Hh4ejbt6/yAuvAwIEDsWbNGgDAxYsXERcXhydPniA9PV3hMc3MzJCfn4/JkydTr50+fZpUuSUIGq5evYoxY8YAaKuHIFk937hxI8LCwpQZWjtFRUUQCoWora2Fl5cXGhsb8e2337Zb8X9Tenp67RY9tLS0aK0WhYeHQ09PD62trQgNDcWwYcMwYMAAhIaGIikpiVa8quTmzZvIyspCQUEBPvjgA7i7u9MqYsdWwg+oabLn7+8PHo9H9QOT3sYpfZ5P2Xr27ImwsDBqFWbKlCkYNGgQ/Pz8SLJH0FJWVgZHR0cqwZNc83g85OXlKTRmUFAQkyESBKWjmdBLly4hLCwMKSkpSojo79XV1eGnn35CVlYWGhsbMX36dFrjBQYGYuXKlUhPT0efPn1QWlqK58+f0y7OQBCd2fnz56n+nTt37qQSp/v37yszrA5Jmmbr6+ujvr4e+/btg4WFBe1xdXV1UVJSInPmq6SkhNZugvLycuzbtw+vXr3C5cuXkZiYCC0tLSQnJ9OOV1VIaoE4OTkhOjoaOjo6AICHDx8qfNyFrYQfUNNkz8PDo8NrrpGU6y4tLcXjx49hamoKS0tLHDp0SMmREaqO69VcCeKfjBo1ipEy4Uw6ceIEsrOzUV5ejqlTpyIsLIyRBzIjIyM4OztjyJAhKCsrw2effYbbt29ztgUPQagC6TO1XK9FKL369u677zLyuQK0FX/66quvYG9vD0tLS5SXl+PcuXOIiopSeExJrEVFRRg2bBhVkfjVq1eMxKwKJJ/NhYWFKCwspF7n8XgK93ZkK+EH1DTZs7OzU3YI/0p9fT0CAgJQXV0Nc3NzPHjwAMbGxoiPj2eknDfReV28eBERERHo1q0bvv32W9pnighCGerr65Udggw/Pz+89957GDx4MG7duiVTKZPOrhFJEYUZM2Zg+PDhKC0txffff48XL15g+fLlTIROEJ2OdALF9foNJSUliI+Ph1gsRmlpKeLj46l7dKpdDxgwAKmpqcjLy0NFRQWGDBmC5cuXU4mFIvT09HD48GHk5ubC2dkZra2tOHLkCMzMzBQeU9WwUeWVrYQfUNPWC6pi06ZNGD58uEzvv4yMDFy9ehWbNm1SYmSEqps7dy6+/fZbVFdX4/vvv0diYqKyQyIIuV7fxikSiXDq1Cno6elhw4YNSoqqvQsXLsi9R2eS0d3dXaaIAgA0NzfDw8MDR44cUXhcgujMHBwcsGjRIojFYqSkpFDX+/fvx8mTJ5UdngyhUCj3HtcKo1VVVWHfvn0wMzPDvHnzUFhYiJSUFIwaNQo+Pj7KDu+tSU5ORlpaGpqamqClpYUvv/wSixcvVng8SXsrsViMrKwsmVZXdNtbqeXKnqq4efNmuwcZd3d3CAQCJUVEqAstLS3069cPQNuqAUFw2esN1Xk8HoYNG8a5bfhs7RqRV0ShW7durPx5BNEZuLi44NmzZ+2unZ2dlRlWh9zc3CASiXD58mW8ePECJiYmsLW15WSlXiMjI6o65JUrVyAUCnH9+nVGV6K4bv/+/bh//z6OHj2K7t27o66uDlu2bMHevXuxZMkShcZctWpVh9dMIMmeEmlqdvztV5WeU4RqaG1tVXYIBPG3vL29ER0dDQsLC0ydOhUrV65EXl4eunXrJrPzQV3JK6LA9a1nBMFlK1askGn6XVlZCQ0NDU6ehf3zzz/h7++PIUOGwNjYGCdOnMDdu3exfft2auKWK0QiEbKzs8Hn86GtrY26ujrk5eVBV1dX2aG9Nbm5ueDz+VQy3r17d4SFhWH+/PkKJ3tsJvwk2VMiQ0NDXL16FcOGDaNeu3r1KgwMDJQYFaEOnj59isOHD0MsFlPXEnPmzFFiZATRXkhICFauXInq6mosXboUQqEQRkZGWLJkSadI9tgookAQnd2FCxcQFBSEH3/8EQYGBiguLsa6desQExODUaNGKTs8GbGxsUhKSsJ7771HvXbr1i1ERkZiz549SoysvcmTJ8PZ2RmxsbHo27cvlixZ0qkSPaBt58XrSZiWlpbcRZx/g82EnyR7ShQYGAhfX1+MHj0alpaWKC0tRWFhISm3TdAmb/sKQXCRpqYm1Q/rwIEDVG89Or2gVAkbRRQIorPbunUrfvjhB2oCfezYsUhOTkZISAhSU1OVHJ2spqYmmUQPaOvpybWKxACwYMECZGVloaysDLNmzeJ8pVM28Hg8PH/+HMbGxtRrkpVjRbGZ8JNkT4ksLCwgEAhw+vRplJSUYPjw4fDz8+s0DzgEe1asWKHsEAjiX5PerqitrU1dd6YtyPr6+p1iFZMg3pYuXbq0O0dmbW3NyXNw8o7vcPEz0MfHBz4+Prhw4QIyMjJw7do1xMTEwNXVFQMHDlR2eG+Fr68vvL29sWzZMqo36s6dO2kVUmEz4SfJnpLp6OjAwcFB2WEQambcuHHUNY/HQ0tLC/r164fw8HBq1YQguOLOnTsICAiAWCyWub57966yQyMIQkWJxWK0trbKJHctLS2cXC17/bgF0BZ/RUWFkiL6Z3Z2drCzs8PLly+RmZmJwMBA/Pjjj8oO6634+OOPERUVhbS0NAgEApiYmCA8PBwffPCBwmOymfCT1gsE0UlcunQJSUlJSElJUXYoBCGDrZYGBEF0Xunp6fj111+xbNkyWFhY4MmTJ0hKSoKtrS28vLyUHZ6MHTt2yL1HduqojjNnzmDixIkK/b+SViHSmGoVQlb2CKKTGDVqFCdnNAmCJHQEQTBt9uzZ6NatG7Zs2YJnz57BzMwMrq6uePLkibJDa8fGxgbjx49XdhjEv3T06FHEx8dDV1cXiYmJsLS0xLp163Dv3j2Fkz159RWYaBVCkj2C6ETq6+uVHQJBEARBvBVOTk5wcnLClStXcPDgQURFRXHy6Mz27dtRUFCAtWvXypxbJrgpJSUF2dnZePbsGSIjI1FRUYFPP/0UsbGxCo/JZsJPkj2CUEPnzp2T+VokEuGnn37CiBEjlBQRQRAEQbw9kn5wqamp0NLS4nQ/uLS0NOzduxdz585FREREpyl0oqoMDQ1hYGAAAwMD3L17F6GhoQqv6EmwmfCTZI8g1FB2drbM1zo6Ohg5ciTc3NyUFBFBEARBvD2SfnAxMTGc7wenoaEBHx8fTJw4EV5eXrCysoJYLAaPx0NaWpqywyNeI11B2szMjHaiB7Cb8JNkjyDUUEREhMzXIpEIAGg1/CQIgiAIVaFq/eAKCwuxadMmzJ8/n7Rh4bjq6mqcO3cOYrEYdXV1MruppKuhvwk2E35SjZMg1ND9+/cRFRUFCwsLODg4UNW8goODyS8RgiAIotOQ9IMrKCjArFmzONkPbuPGjSgqKkJUVBSt8v3E2xEcHIza2lp06dKlXW/s1yfb34Qk4Xd2dpZ5VjM3N1d4TICs7BGEWvrPf/6DFStWoKamBj4+PhAKhTAyMsKSJUtIskcQBEF0GqrQD05PTw9HjhwhxVlUxJAhQ5CcnIwuXbpg/fr1mDBhAu0xJQl/XFwc4wk/SfYIQg1pampi7NixAIADBw5QjdRfn4EiCIIgiM6gR48e8PT0hKenp7JDaWfgwIHIycnp8B6ZoOWerKws5Obmora2FoGBgYwke2wm/CTZIwg1JH14WPqDo7W1VRnhEARBEAQhx71796jr7OxsODk5AZD9XU5wh7a2NrS0tGBkZMRY/2I2E36S7BGEGrpz5w4CAgIgFotlru/evavs0AiCIAiCkBIQEEBd//HHHzJfE9zGVOkTNhN+UqCFINTQhQsX5N6zs7N7i5EQBEEQBPFvLViwAAcOHFB2GMTfGDNmDOzt7SEWi/Hbb7/B3t6euhcXF0d7fE9PT/zwww+0x5EgK3sEoYZIQkcQBEEQBMG8rVu3UtceHh6Mj8/09l2S7BEEQRAEQRCEkvj7+4PH48kcvZBgYqWIYJaqTaiTbZwEQRAEQRAEoSTk6AUhnfAzvTWUJHsEQRAEQRAEQRBKwmbCT5I9giAIgiAIgiAINaSh7AAIgiAIgiAIgiAI5pFkjyAIgiAIgiAIQg2RZI8gCIIgCIIgCEINkWSPIAiCIAiCIAhCDf1/v+K9Z1b3pp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph for the count of generic_name\n",
    "fig, ax1 = plt.subplots(1,1,figsize=(15,10))\n",
    "y.plot(kind=\"bar\", ax=ax1, title=\"Counts for generic names\")\n",
    "plt.rc('xtick',labelsize=15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the plot as a figure\n",
    "fig.savefig('generic_names_count.png',dpi=450)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1134167455    31\n",
       "1215924154    31\n",
       "1497953749    31\n",
       "1861572653    30\n",
       "1952383887    30\n",
       "1801819206    29\n",
       "1144227596    29\n",
       "1780672543    29\n",
       "1366485229    29\n",
       "1558460261    29\n",
       "1447238415    29\n",
       "1538117049    29\n",
       "1114938586    28\n",
       "1538130018    28\n",
       "1356410443    28\n",
       "1760418131    28\n",
       "1811931546    28\n",
       "1710085196    28\n",
       "1669437869    28\n",
       "1558353078    28\n",
       "1689785073    28\n",
       "1356427850    28\n",
       "1689865891    28\n",
       "1497778005    27\n",
       "1588812762    27\n",
       "1912993478    27\n",
       "1841322104    27\n",
       "1609912351    27\n",
       "1043245749    27\n",
       "1619978236    27\n",
       "              ..\n",
       "1952371999     1\n",
       "1730116906     1\n",
       "1750515615     1\n",
       "1366722456     1\n",
       "1851766067     1\n",
       "1043734411     1\n",
       "1033244553     1\n",
       "1649665209     1\n",
       "1861457079     1\n",
       "1114136645     1\n",
       "1720252661     1\n",
       "1003975723     1\n",
       "1750549549     1\n",
       "1265594535     1\n",
       "1700250685     1\n",
       "1265582241     1\n",
       "1255423042     1\n",
       "1871620252     1\n",
       "1508806811     1\n",
       "1114993771     1\n",
       "1104100197     1\n",
       "1053689992     1\n",
       "1689623670     1\n",
       "1396032637     1\n",
       "1548322936     1\n",
       "1588735104     1\n",
       "1184005258     1\n",
       "1407830128     1\n",
       "1588769939     1\n",
       "1275759516     1\n",
       "Name: npi, Length: 413565, dtype: int64"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the number of NPI\n",
    "y = sub_data['npi'].value_counts()\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the graph for statewise count of npi\n",
    "count=sub_data.groupby(['nppes_provider_state']).agg({'npi':'count'})\n",
    "fig, ax1 = plt.subplots(1,1,figsize=(15,10))\n",
    "count.plot(kind=\"bar\", ax=ax1, title=\"Statewise Count\")\n",
    "plt.rc('xtick',labelsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save the plot as a figure\n",
    "fig.savefig('statwise_count.png',dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting dataframe to csv for tableau\n",
    "sub_data.to_csv('sub_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checking the response/json data using an API given by CMS using NPI number\n",
    "response = req.get(\"https://npiregistry.cms.hhs.gov/api/?version=2.1&number=1245201227\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'result_count': 1,\n",
       " 'results': [{'enumeration_type': 'NPI-1',\n",
       "   'number': 1245201227,\n",
       "   'last_updated_epoch': 1479375798,\n",
       "   'created_epoch': 1138579200,\n",
       "   'basic': {'first_name': 'JOHN',\n",
       "    'last_name': 'GRUENDEL',\n",
       "    'middle_name': 'CHARLES',\n",
       "    'credential': 'M.D.',\n",
       "    'sole_proprietor': 'NO',\n",
       "    'gender': 'M',\n",
       "    'enumeration_date': '2006-01-30',\n",
       "    'last_updated': '2016-11-17',\n",
       "    'status': 'A',\n",
       "    'name': 'GRUENDEL JOHN'},\n",
       "   'other_names': [],\n",
       "   'addresses': [{'country_code': 'US',\n",
       "     'country_name': 'United States',\n",
       "     'address_purpose': 'LOCATION',\n",
       "     'address_type': 'DOM',\n",
       "     'address_1': '672 STONELEIGH AVE',\n",
       "     'address_2': '',\n",
       "     'city': 'CARMEL',\n",
       "     'state': 'NY',\n",
       "     'postal_code': '105124634',\n",
       "     'telephone_number': '945-279-2000',\n",
       "     'fax_number': '845-279-7730'},\n",
       "    {'country_code': 'US',\n",
       "     'country_name': 'United States',\n",
       "     'address_purpose': 'MAILING',\n",
       "     'address_type': 'DOM',\n",
       "     'address_1': '110 S BEDFORD RD',\n",
       "     'address_2': 'CAREMOUNT MEDICAL PC',\n",
       "     'city': 'MOUNT KISCO',\n",
       "     'state': 'NY',\n",
       "     'postal_code': '105493446',\n",
       "     'telephone_number': '914-241-1050',\n",
       "     'fax_number': '914-242-1516'}],\n",
       "   'taxonomies': [{'code': '207RC0000X',\n",
       "     'desc': 'Internal Medicine Cardiovascular Disease',\n",
       "     'primary': True,\n",
       "     'state': 'NY',\n",
       "     'license': '175379'}],\n",
       "   'identifiers': [{'identifier': '01358130',\n",
       "     'code': '05',\n",
       "     'desc': 'MEDICAID',\n",
       "     'state': 'NY',\n",
       "     'issuer': ''}]}]}"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#priting the json data for clarification\n",
    "response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25209130, 21)"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                            0\n",
       "npi                                   0\n",
       "nppes_provider_last_org_name         29\n",
       "nppes_provider_first_name            12\n",
       "nppes_provider_city                   0\n",
       "nppes_provider_state                  0\n",
       "specialty_description                 0\n",
       "description_flag                      0\n",
       "drug_name                             0\n",
       "generic_name                          0\n",
       "bene_count                       517561\n",
       "total_claim_count                     0\n",
       "total_30_day_fill_count               0\n",
       "total_day_supply                      0\n",
       "total_drug_cost                       0\n",
       "bene_count_ge65                  933434\n",
       "bene_count_ge65_suppress_flag    221659\n",
       "total_claim_count_ge65           558778\n",
       "ge65_suppress_flag               596315\n",
       "total_30_day_fill_count_ge65     558778\n",
       "total_day_supply_ge65            558778\n",
       "total_drug_cost_ge65             558778\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_columns = ['bene_count','bene_count_ge65','','','','','','']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting the postal code (first five digits) from json for the data visualisation on map\n",
    "p_code = response.json()['results'][0]['addresses'][0]['postal_code'][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing the following areas from the data which are not the part of USA states \n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'XX']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'AA']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'AE']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'AP']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'AS']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'GU']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'MP']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'PR']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'VI']\n",
    "sub_data = sub_data[sub_data.nppes_provider_state != 'ZZ']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1148099, 21)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the dimensions of the data\n",
    "sub_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "npi                                   0\n",
       "nppes_provider_last_org_name         29\n",
       "nppes_provider_first_name            12\n",
       "nppes_provider_city                   0\n",
       "nppes_provider_state                  0\n",
       "specialty_description                 0\n",
       "description_flag                      0\n",
       "drug_name                             0\n",
       "generic_name                          0\n",
       "bene_count                       515008\n",
       "total_claim_count                     0\n",
       "total_30_day_fill_count               0\n",
       "total_day_supply                      0\n",
       "total_drug_cost                       0\n",
       "bene_count_ge65                  928067\n",
       "bene_count_ge65_suppress_flag    220032\n",
       "total_claim_count_ge65           555124\n",
       "ge65_suppress_flag               592975\n",
       "total_30_day_fill_count_ge65     555124\n",
       "total_day_supply_ge65            555124\n",
       "total_drug_cost_ge65             555124\n",
       "dtype: int64"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values in dataset\n",
    "sub_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "na_cols = ['bene_count','bene_count_ge65','total_claim_count_ge65','total_30_day_fill_count_ge65','total_day_supply_ge65','total_drug_cost_ge65']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in na_cols:\n",
    "    mean = sub_data_copy[i].mean()\n",
    "    sub_data_copy[i] = sub_data_copy[i].fillna(value=mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_last_org_name</th>\n",
       "      <th>nppes_provider_first_name</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>description_flag</th>\n",
       "      <th>drug_name</th>\n",
       "      <th>generic_name</th>\n",
       "      <th>...</th>\n",
       "      <th>total_30_day_fill_count</th>\n",
       "      <th>total_day_supply</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>bene_count_ge65_suppress_flag</th>\n",
       "      <th>total_claim_count_ge65</th>\n",
       "      <th>ge65_suppress_flag</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
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       "      <td>1477804466</td>\n",
       "      <td>ROBINSON</td>\n",
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       "      <td>MILWAUKEE</td>\n",
       "      <td>WI</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>MORPHINE SULFATE ER</td>\n",
       "      <td>MORPHINE SULFATE</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>570</td>\n",
       "      <td>874.27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>1740288091</td>\n",
       "      <td>GEHRING</td>\n",
       "      <td>DAVID</td>\n",
       "      <td>WOODBURY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCODONE-ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE/ACETAMINOPHEN</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>745</td>\n",
       "      <td>555.88</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>*</td>\n",
       "      <td>53.970408</td>\n",
       "      <td>*</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1801810007</td>\n",
       "      <td>ADLER</td>\n",
       "      <td>BRIAN</td>\n",
       "      <td>SURFSIDE BEACH</td>\n",
       "      <td>SC</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>S</td>\n",
       "      <td>ACETAMINOPHEN-CODEINE</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>161</td>\n",
       "      <td>103.83</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>*</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>103.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>1437482346</td>\n",
       "      <td>GRAY</td>\n",
       "      <td>DONNA</td>\n",
       "      <td>SALISBURY</td>\n",
       "      <td>MD</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>675</td>\n",
       "      <td>156.69</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>#</td>\n",
       "      <td>53.970408</td>\n",
       "      <td>#</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "      <td>1904.225175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1568402501</td>\n",
       "      <td>BUSCH</td>\n",
       "      <td>JULIE</td>\n",
       "      <td>SAINT LOUIS</td>\n",
       "      <td>MO</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCODONE-ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE/ACETAMINOPHEN</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1174</td>\n",
       "      <td>1284.58</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>#</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>1089.580000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         npi nppes_provider_last_org_name  \\\n",
       "0          13  1477804466                     ROBINSON   \n",
       "1          52  1740288091                      GEHRING   \n",
       "2          70  1801810007                        ADLER   \n",
       "3          91  1437482346                         GRAY   \n",
       "4         100  1568402501                        BUSCH   \n",
       "\n",
       "  nppes_provider_first_name nppes_provider_city nppes_provider_state  \\\n",
       "0                  LAKEESHA           MILWAUKEE                   WI   \n",
       "1                     DAVID            WOODBURY                   NJ   \n",
       "2                     BRIAN      SURFSIDE BEACH                   SC   \n",
       "3                     DONNA           SALISBURY                   MD   \n",
       "4                     JULIE         SAINT LOUIS                   MO   \n",
       "\n",
       "  specialty_description description_flag                  drug_name  \\\n",
       "0    Nurse Practitioner                S        MORPHINE SULFATE ER   \n",
       "1       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "2     Internal Medicine                S      ACETAMINOPHEN-CODEINE   \n",
       "3    Nurse Practitioner                S               TRAMADOL HCL   \n",
       "4       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "\n",
       "                 generic_name  ...  total_30_day_fill_count  total_day_supply  \\\n",
       "0            MORPHINE SULFATE  ...                     19.0               570   \n",
       "1   HYDROCODONE/ACETAMINOPHEN  ...                     30.0               745   \n",
       "2  ACETAMINOPHEN WITH CODEINE  ...                     12.0               161   \n",
       "3                TRAMADOL HCL  ...                     23.0               675   \n",
       "4   HYDROCODONE/ACETAMINOPHEN  ...                     52.0              1174   \n",
       "\n",
       "   total_drug_cost  bene_count_ge65  bene_count_ge65_suppress_flag  \\\n",
       "0           874.27         0.000000                            NaN   \n",
       "1           555.88        26.058419                              *   \n",
       "2           103.83        26.058419                              *   \n",
       "3           156.69        26.058419                              #   \n",
       "4          1284.58        26.058419                              #   \n",
       "\n",
       "   total_claim_count_ge65 ge65_suppress_flag  total_30_day_fill_count_ge65  \\\n",
       "0                0.000000                NaN                      0.000000   \n",
       "1               53.970408                  *                     55.420113   \n",
       "2               12.000000                NaN                     12.000000   \n",
       "3               53.970408                  #                     55.420113   \n",
       "4               34.000000                NaN                     40.000000   \n",
       "\n",
       "  total_day_supply_ge65  total_drug_cost_ge65  \n",
       "0              0.000000              0.000000  \n",
       "1           1276.364093           1904.225175  \n",
       "2            161.000000            103.830000  \n",
       "3           1276.364093           1904.225175  \n",
       "4            854.000000           1089.580000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy the data in new variable for backup\n",
    "sub_data_copy = sub_data.copy()\n",
    "#sub_data.groupby('npi').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1381.814719810748"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take the mean of total_day_supply to decide the factor\n",
    "sub_data_copy['total_day_supply'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding one column using total_day_supply if total_day_supply > 1000 then 1 else 0\n",
    "sub_data_copy['Opoid_prescriber'] = sub_data_copy['total_day_supply'].apply(lambda x: 1 if x>1000 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    813198\n",
       "1    341895\n",
       "Name: Opoid_prescriber, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count the number of Opoid_prescriber\n",
    "sub_data_copy['Opoid_prescriber'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>npi</th>\n",
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       "      <th>bene_count_ge65_suppress_flag</th>\n",
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       "      <th>ge65_suppress_flag</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
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       "      <th>2</th>\n",
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       "      <td>ADLER</td>\n",
       "      <td>BRIAN</td>\n",
       "      <td>SURFSIDE BEACH</td>\n",
       "      <td>SC</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>S</td>\n",
       "      <td>ACETAMINOPHEN-CODEINE</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
       "      <td>...</td>\n",
       "      <td>161</td>\n",
       "      <td>103.83</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>*</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>103.830000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
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       "      <td>GRAY</td>\n",
       "      <td>DONNA</td>\n",
       "      <td>SALISBURY</td>\n",
       "      <td>MD</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>S</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
       "      <td>TRAMADOL HCL</td>\n",
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       "      <td>675</td>\n",
       "      <td>156.69</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>#</td>\n",
       "      <td>53.970408</td>\n",
       "      <td>#</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "      <td>1904.225175</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1568402501</td>\n",
       "      <td>BUSCH</td>\n",
       "      <td>JULIE</td>\n",
       "      <td>SAINT LOUIS</td>\n",
       "      <td>MO</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>S</td>\n",
       "      <td>HYDROCODONE-ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE/ACETAMINOPHEN</td>\n",
       "      <td>...</td>\n",
       "      <td>1174</td>\n",
       "      <td>1284.58</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>#</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>1089.580000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         npi nppes_provider_last_org_name  \\\n",
       "0          13  1477804466                     ROBINSON   \n",
       "1          52  1740288091                      GEHRING   \n",
       "2          70  1801810007                        ADLER   \n",
       "3          91  1437482346                         GRAY   \n",
       "4         100  1568402501                        BUSCH   \n",
       "\n",
       "  nppes_provider_first_name nppes_provider_city nppes_provider_state  \\\n",
       "0                  LAKEESHA           MILWAUKEE                   WI   \n",
       "1                     DAVID            WOODBURY                   NJ   \n",
       "2                     BRIAN      SURFSIDE BEACH                   SC   \n",
       "3                     DONNA           SALISBURY                   MD   \n",
       "4                     JULIE         SAINT LOUIS                   MO   \n",
       "\n",
       "  specialty_description description_flag                  drug_name  \\\n",
       "0    Nurse Practitioner                S        MORPHINE SULFATE ER   \n",
       "1       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "2     Internal Medicine                S      ACETAMINOPHEN-CODEINE   \n",
       "3    Nurse Practitioner                S               TRAMADOL HCL   \n",
       "4       Family Practice                S  HYDROCODONE-ACETAMINOPHEN   \n",
       "\n",
       "                 generic_name  ...  total_day_supply  total_drug_cost  \\\n",
       "0            MORPHINE SULFATE  ...               570           874.27   \n",
       "1   HYDROCODONE/ACETAMINOPHEN  ...               745           555.88   \n",
       "2  ACETAMINOPHEN WITH CODEINE  ...               161           103.83   \n",
       "3                TRAMADOL HCL  ...               675           156.69   \n",
       "4   HYDROCODONE/ACETAMINOPHEN  ...              1174          1284.58   \n",
       "\n",
       "   bene_count_ge65  bene_count_ge65_suppress_flag  total_claim_count_ge65  \\\n",
       "0         0.000000                            NaN                0.000000   \n",
       "1        26.058419                              *               53.970408   \n",
       "2        26.058419                              *               12.000000   \n",
       "3        26.058419                              #               53.970408   \n",
       "4        26.058419                              #               34.000000   \n",
       "\n",
       "   ge65_suppress_flag total_30_day_fill_count_ge65  total_day_supply_ge65  \\\n",
       "0                 NaN                     0.000000               0.000000   \n",
       "1                   *                    55.420113            1276.364093   \n",
       "2                 NaN                    12.000000             161.000000   \n",
       "3                   #                    55.420113            1276.364093   \n",
       "4                 NaN                    40.000000             854.000000   \n",
       "\n",
       "  total_drug_cost_ge65  Opoid_prescriber  \n",
       "0             0.000000                 0  \n",
       "1          1904.225175                 0  \n",
       "2           103.830000                 0  \n",
       "3          1904.225175                 0  \n",
       "4          1089.580000                 1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the data \n",
    "sub_data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#divide the variables into categorical, dependanat, discrete adn continous variable\n",
    "categorical_vars = ['nppes_provider_state','specialty_description','nppes_provider_city']\n",
    "dep_var = ['Opoid_prescriber']\n",
    "discrete_vars = ['total_day_supply']\n",
    "continuous_vars = ['bene_count','total_day_supply_ge65','total_drug_cost','bene_count_ge65','total_30_day_fill_count_ge65','total_drug_cost_ge65']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop teh unnecessary columns that are not required to predict the test \n",
    "\n",
    "#sub_data_copy = sub_data_copy.drop('nppes_provider_state', axis='columns')\n",
    "#sub_data_copy = sub_data_copy.drop('specialty_description', axis='columns')\n",
    "#sub_data_copy = sub_data_copy.drop('nppes_provider_city', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('generic_name', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('nppes_provider_last_org_name', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('nppes_provider_first_name', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('description_flag', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('drug_name', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('bene_count_ge65_suppress_flag', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('ge65_suppress_flag', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('total_day_supply', axis='columns')\n",
    "sub_data_copy = sub_data_copy.drop('total_claim_count_ge65', axis='columns')\n",
    "#sub_data_copy = sub_data_copy.drop('Opoid_prescriber', axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_30_day_fill_count</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "      <th>total_drug_cost_ge65</th>\n",
       "      <th>Opoid_prescriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>1477804466</td>\n",
       "      <td>MILWAUKEE</td>\n",
       "      <td>WI</td>\n",
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       "      <td>31.571041</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>1740288091</td>\n",
       "      <td>WOODBURY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>30</td>\n",
       "      <td>30.0</td>\n",
       "      <td>555.88</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "      <td>1904.225175</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>1801810007</td>\n",
       "      <td>SURFSIDE BEACH</td>\n",
       "      <td>SC</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>103.83</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>103.830000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91</td>\n",
       "      <td>1437482346</td>\n",
       "      <td>SALISBURY</td>\n",
       "      <td>MD</td>\n",
       "      <td>Nurse Practitioner</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>156.69</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "      <td>1904.225175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>1568402501</td>\n",
       "      <td>SAINT LOUIS</td>\n",
       "      <td>MO</td>\n",
       "      <td>Family Practice</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>46</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1284.58</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>1089.580000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         npi nppes_provider_city nppes_provider_state  \\\n",
       "0          13  1477804466           MILWAUKEE                   WI   \n",
       "1          52  1740288091            WOODBURY                   NJ   \n",
       "2          70  1801810007      SURFSIDE BEACH                   SC   \n",
       "3          91  1437482346           SALISBURY                   MD   \n",
       "4         100  1568402501         SAINT LOUIS                   MO   \n",
       "\n",
       "  specialty_description  bene_count  total_claim_count  \\\n",
       "0    Nurse Practitioner   31.571041                 19   \n",
       "1       Family Practice   31.571041                 30   \n",
       "2     Internal Medicine   31.571041                 12   \n",
       "3    Nurse Practitioner   31.571041                 23   \n",
       "4       Family Practice   13.000000                 46   \n",
       "\n",
       "   total_30_day_fill_count  total_drug_cost  bene_count_ge65  \\\n",
       "0                     19.0           874.27         0.000000   \n",
       "1                     30.0           555.88        26.058419   \n",
       "2                     12.0           103.83        26.058419   \n",
       "3                     23.0           156.69        26.058419   \n",
       "4                     52.0          1284.58        26.058419   \n",
       "\n",
       "   total_30_day_fill_count_ge65  total_day_supply_ge65  total_drug_cost_ge65  \\\n",
       "0                      0.000000               0.000000              0.000000   \n",
       "1                     55.420113            1276.364093           1904.225175   \n",
       "2                     12.000000             161.000000            103.830000   \n",
       "3                     55.420113            1276.364093           1904.225175   \n",
       "4                     40.000000             854.000000           1089.580000   \n",
       "\n",
       "   Opoid_prescriber  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#print the data\n",
    "sub_data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(750810, 14)\n",
      "(404283, 14)\n"
     ]
    }
   ],
   "source": [
    "#split data into train and test\n",
    "train,test = train_test_split(sub_data_copy,test_size=0.35,random_state=40)\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\harsh\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\harsh\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "#factorize categorical data\n",
    "for col in categorical_vars:\n",
    "    train[col]=pd.factorize(train[col], sort=True)[0]\n",
    "    test[col] =pd.factorize(test[col],sort=True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750810, 11)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#take features from train\n",
    "features=train.iloc[:,1:12] #make sure we only use the columns that we want as our features\n",
    "features.head()\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>npi</th>\n",
       "      <th>nppes_provider_city</th>\n",
       "      <th>nppes_provider_state</th>\n",
       "      <th>specialty_description</th>\n",
       "      <th>bene_count</th>\n",
       "      <th>total_claim_count</th>\n",
       "      <th>total_30_day_fill_count</th>\n",
       "      <th>total_drug_cost</th>\n",
       "      <th>bene_count_ge65</th>\n",
       "      <th>total_30_day_fill_count_ge65</th>\n",
       "      <th>total_day_supply_ge65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>806720</th>\n",
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       "    <tr>\n",
       "      <th>59483</th>\n",
       "      <td>1295788529</td>\n",
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       "      <td>4</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>18</td>\n",
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       "      <td>754.81</td>\n",
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       "      <td>85</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>735990</th>\n",
       "      <td>1821078411</td>\n",
       "      <td>8476</td>\n",
       "      <td>27</td>\n",
       "      <td>94</td>\n",
       "      <td>31.571041</td>\n",
       "      <td>27</td>\n",
       "      <td>27.0</td>\n",
       "      <td>362.74</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911340</th>\n",
       "      <td>1972555191</td>\n",
       "      <td>3501</td>\n",
       "      <td>32</td>\n",
       "      <td>72</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>18.7</td>\n",
       "      <td>355.95</td>\n",
       "      <td>26.058419</td>\n",
       "      <td>55.420113</td>\n",
       "      <td>1276.364093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               npi  nppes_provider_city  nppes_provider_state  \\\n",
       "806720  1134549082                 2870                    13   \n",
       "59483   1295788529                 2723                    13   \n",
       "906598  1467458034                 5048                    22   \n",
       "735990  1821078411                 8476                    27   \n",
       "911340  1972555191                 3501                    32   \n",
       "\n",
       "        specialty_description  bene_count  total_claim_count  \\\n",
       "806720                     51   14.000000                 49   \n",
       "59483                       4   31.571041                 18   \n",
       "906598                     85   31.571041                 12   \n",
       "735990                     94   31.571041                 27   \n",
       "911340                     72   11.000000                 18   \n",
       "\n",
       "        total_30_day_fill_count  total_drug_cost  bene_count_ge65  \\\n",
       "806720                     49.0           612.27        26.058419   \n",
       "59483                      18.0           754.81        26.058419   \n",
       "906598                     12.0            23.53         0.000000   \n",
       "735990                     27.0           362.74        26.058419   \n",
       "911340                     18.7           355.95        26.058419   \n",
       "\n",
       "        total_30_day_fill_count_ge65  total_day_supply_ge65  \n",
       "806720                     26.000000             765.000000  \n",
       "59483                      55.420113            1276.364093  \n",
       "906598                      0.000000               0.000000  \n",
       "735990                     55.420113            1276.364093  \n",
       "911340                     55.420113            1276.364093  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404283, 11)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train and test data\n",
    "train=train.iloc[:,2:]\n",
    "test=test.iloc[:,2:]\n",
    "train.shape\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.703870 of model Logistic Regression\n",
      "Accuracy: 0.718196 of model Naive Bayes\n",
      "Accuracy: 0.973262 of model Random Forest\n"
     ]
    }
   ],
   "source": [
    "#model for predict \n",
    "target = train['Opoid_prescriber']\n",
    "Name = []\n",
    "Accuracy = []\n",
    "model1=LogisticRegression(random_state=22,C=0.00000001,solver='liblinear',max_iter=200)\n",
    "model2=GaussianNB()\n",
    "model3=RandomForestClassifier(n_estimators=200,random_state=20)\n",
    "model4=GradientBoostingClassifier(n_estimators=200)\n",
    "model5=KNeighborsClassifier()\n",
    "model6=DecisionTreeClassifier()\n",
    "model7=LinearDiscriminantAnalysis()\n",
    "model8=BaggingClassifier()\n",
    "Ensembled_model=VotingClassifier(estimators=[('lr', model1), ('gn', model2), ('rf', model3),('gb',model4),('kn',model5),('dt',model6),('lda',model7), ('bc',model8)], voting='hard')\n",
    "for model, label in zip([model1, model2, model3, model4,model5,model6,model7,model8,Ensembled_model], ['Logistic Regression','Naive Bayes','Random Forest', 'Gradient Boosting','KNN','Decision Tree','LDA', 'Bagging Classifier', 'Ensemble']):\n",
    "    scores = cross_val_score(model, features, target, cv=5, scoring='accuracy')\n",
    "    Accuracy.append(scores.mean())\n",
    "    Name.append(model.__class__.__name__)\n",
    "    print(\"Accuracy: %f of model %s\" % (scores.mean(),label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.pairplot(sub_data_copy, kind = \"reg\")\n",
    "#plt.show()"
   ]
  }
 ],
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