docs/source/tutorials/tutorials_assess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8cfd172",
   "metadata": {},
   "source": [
    "## 检查VIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4ba5b4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.97,\n",
       "     variables  VIF\n",
       " 0          ID 1.02\n",
       " 1   LIMIT_BAL 1.48\n",
       " 2         SEX 1.02\n",
       " 3   EDUCATION 1.12\n",
       " 4    MARRIAGE 1.06\n",
       " 5         AGE 1.03\n",
       " 6       PAY_0 1.95\n",
       " 7       PAY_2 2.91\n",
       " 8       PAY_3 2.73\n",
       " 9       PAY_4 2.78\n",
       " 10      PAY_5 2.97\n",
       " 11      PAY_6 2.17\n",
       " 12  BILL_AMT1 1.94\n",
       " 13  BILL_AMT2 1.90\n",
       " 14  BILL_AMT3 2.10\n",
       " 15  BILL_AMT4 1.56\n",
       " 16  BILL_AMT5 2.30\n",
       " 17  BILL_AMT6 1.93\n",
       " 18   PAY_AMT1 1.90\n",
       " 19   PAY_AMT2 1.89\n",
       " 20   PAY_AMT3 1.91\n",
       " 21   PAY_AMT4 2.07\n",
       " 22   PAY_AMT5 1.82\n",
       " 23   PAY_AMT6 1.68)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_vif(data_train_woe,data_train_woe.columns,return_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "18260f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: 508.4396430011438  b: 72.13475204444818\n"
     ]
    }
   ],
   "source": [
    "# 查看评分卡模型的参数\n",
    "print('a:',a,'','b:',b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924549d2",
   "metadata": {},
   "source": [
    "## 预测模型分数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "9c590891",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3135ee64b13e42e2bc752e76b2352ff2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>PAY_0</th>\n",
       "      <th>PAY_0_Score</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT1_Score</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>LIMIT_BAL_Score</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT2_Score</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT3_Score</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT4_Score</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT5_Score</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>PAY_AMT6_Score</th>\n",
       "      <th>y</th>\n",
       "      <th>Score</th>\n",
       "      <th>Proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>131062.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>642.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2321.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>163597.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>6680.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3963.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2514.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>665.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>4038.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>3199.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>914.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>850.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>2055.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>8318.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>640.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>1596.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-27.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>608.00</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>90000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1087.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>638.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22495</th>\n",
       "      <td>2</td>\n",
       "      <td>-137.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>2525.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>3900.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "      <td>452.00</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22496</th>\n",
       "      <td>-1</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>210000.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>358.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>12816.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>210.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>636.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22497</th>\n",
       "      <td>1</td>\n",
       "      <td>-38.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>390000.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1266.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>563.00</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22498</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30000.00</td>\n",
       "      <td>-27.00</td>\n",
       "      <td>1600.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>1287.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1296.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>500.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>1550.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>614.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22499</th>\n",
       "      <td>2</td>\n",
       "      <td>-137.00</td>\n",
       "      <td>6300.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>6100.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4900.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>10200.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5100.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>502.00</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22500 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PAY_0  PAY_0_Score  PAY_AMT1  PAY_AMT1_Score  LIMIT_BAL  \\\n",
       "0         -1        28.00   1671.00            0.00  120000.00   \n",
       "1         -2        28.00   1468.00            0.00  200000.00   \n",
       "2          0        43.00   4038.00            0.00   80000.00   \n",
       "3          0        43.00   1596.00            0.00   20000.00   \n",
       "4          0        43.00   3000.00            0.00   90000.00   \n",
       "...      ...          ...       ...             ...        ...   \n",
       "22495      2      -137.00   3000.00            0.00   50000.00   \n",
       "22496     -1        28.00      0.00           -7.00  210000.00   \n",
       "22497      1       -38.00      0.00           -7.00  390000.00   \n",
       "22498      0        43.00   1700.00            0.00   30000.00   \n",
       "22499      2      -137.00   6300.00            5.00  150000.00   \n",
       "\n",
       "       LIMIT_BAL_Score  PAY_AMT2  PAY_AMT2_Score  PAY_AMT3  PAY_AMT3_Score  \\\n",
       "0                -6.00    380.00           -0.00 131062.00           14.00   \n",
       "1                14.00   2321.00           -0.00 163597.00           14.00   \n",
       "2                -6.00   3199.00           -0.00    914.00            0.00   \n",
       "3               -27.00   2000.00           -0.00   3000.00            0.00   \n",
       "4                -6.00   2000.00           -0.00   2000.00            0.00   \n",
       "...                ...       ...             ...       ...             ...   \n",
       "22495            -6.00   2525.00           -0.00   3900.00            0.00   \n",
       "22496            14.00    358.00           -0.00  12816.00            7.00   \n",
       "22497            28.00   1266.00           -0.00      0.00           -8.00   \n",
       "22498           -27.00   1600.00           -0.00   1287.00            0.00   \n",
       "22499            14.00   6100.00            8.00   4900.00            7.00   \n",
       "\n",
       "       PAY_AMT4  PAY_AMT4_Score  PAY_AMT5  PAY_AMT5_Score  PAY_AMT6  \\\n",
       "0       2000.00            2.00   3000.00            3.00   3000.00   \n",
       "1       6680.00            5.00   3963.00            3.00   2514.00   \n",
       "2        850.00           -1.00   2055.00            3.00   8318.00   \n",
       "3          0.00           -5.00   1600.00           -1.00      0.00   \n",
       "4       2000.00            2.00   2000.00           -1.00   1087.00   \n",
       "...         ...             ...       ...             ...       ...   \n",
       "22495      0.00           -5.00   2000.00           -1.00   4500.00   \n",
       "22496      0.00           -5.00    102.00           -1.00    210.00   \n",
       "22497      0.00           -5.00      0.00           -5.00      0.00   \n",
       "22498   1296.00           -1.00    500.00           -1.00   1550.00   \n",
       "22499      0.00           -5.00  10200.00            9.00   5100.00   \n",
       "\n",
       "       PAY_AMT6_Score  y  Score  Proba  \n",
       "0                1.00  0 642.00   0.14  \n",
       "1                1.00  0 665.00   0.10  \n",
       "2                1.00  0 640.00   0.14  \n",
       "3               -2.00  0 608.00   0.20  \n",
       "4               -0.00  0 638.00   0.14  \n",
       "...               ... ..    ...    ...  \n",
       "22495            1.00  1 452.00   0.69  \n",
       "22496           -0.00  0 636.00   0.15  \n",
       "22497           -2.00  0 563.00   0.32  \n",
       "22498           -0.00  0 614.00   0.19  \n",
       "22499            1.00  0 502.00   0.52  \n",
       "\n",
       "[22500 rows x 19 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测训练集模型分数\n",
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "47603dec",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_0_Score</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT1_Score</th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>LIMIT_BAL_Score</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT2_Score</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT3_Score</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT4_Score</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT5_Score</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>PAY_AMT6_Score</th>\n",
       "      <th>y</th>\n",
       "      <th>Score</th>\n",
       "      <th>Proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>400000.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-11.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>621.00</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>2600.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>641.00</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-38.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>200000.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2317.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>7588.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7614.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>14053.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>591.00</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1087.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>20000.00</td>\n",
       "      <td>-27.00</td>\n",
       "      <td>1140.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>7014.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>800.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>598.00</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>-137.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>70000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>2000.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3000.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>0</td>\n",
       "      <td>468.00</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7495</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>1128.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>8479.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>236.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>2990.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>671.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7496</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>8080.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>110000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>14298.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2519.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6616.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>5300.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>658.00</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7497</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>4539.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4218.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>4204.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3296.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3408.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3416.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0</td>\n",
       "      <td>666.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7498</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>5059.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>140000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>14659.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8000.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0</td>\n",
       "      <td>671.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7499</th>\n",
       "      <td>0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>3500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>80000.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>2934.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>897.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>929.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>935.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>984.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>638.00</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7500 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PAY_0  PAY_0_Score  PAY_AMT1  PAY_AMT1_Score  LIMIT_BAL  \\\n",
       "0        -2        28.00      0.00           -7.00  400000.00   \n",
       "1         0        43.00   2600.00            0.00   80000.00   \n",
       "2         1       -38.00      0.00           -7.00  200000.00   \n",
       "3        -1        28.00   1087.00            0.00   20000.00   \n",
       "4         2      -137.00   5000.00            5.00   70000.00   \n",
       "...     ...          ...       ...             ...        ...   \n",
       "7495      0        43.00   1128.00            0.00  500000.00   \n",
       "7496      0        43.00   8080.00            5.00  110000.00   \n",
       "7497      0        43.00   4539.00            0.00  150000.00   \n",
       "7498      0        43.00   5059.00            5.00  140000.00   \n",
       "7499      0        43.00   3500.00            0.00   80000.00   \n",
       "\n",
       "      LIMIT_BAL_Score  PAY_AMT2  PAY_AMT2_Score  PAY_AMT3  PAY_AMT3_Score  \\\n",
       "0               28.00      0.00          -11.00      0.00           -8.00   \n",
       "1               -6.00   4300.00           -0.00   2000.00            0.00   \n",
       "2               14.00   2317.00           -0.00   7588.00            7.00   \n",
       "3              -27.00   1140.00           -0.00      0.00           -8.00   \n",
       "4               -6.00   3000.00           -0.00   2000.00            0.00   \n",
       "...               ...       ...             ...       ...             ...   \n",
       "7495            28.00   1000.00           -0.00      0.00           -8.00   \n",
       "7496            -6.00  14298.00            8.00   2519.00            0.00   \n",
       "7497            14.00   4218.00           -0.00   4204.00            0.00   \n",
       "7498            -6.00  14659.00            8.00   5000.00            7.00   \n",
       "7499            -6.00   2934.00           -0.00    897.00            0.00   \n",
       "\n",
       "      PAY_AMT4  PAY_AMT4_Score  PAY_AMT5  PAY_AMT5_Score  PAY_AMT6  \\\n",
       "0         0.00           -5.00      0.00           -5.00      0.00   \n",
       "1      2000.00            2.00   2000.00           -1.00   2000.00   \n",
       "2      7614.00            5.00  14053.00            9.00      0.00   \n",
       "3      7014.00            5.00    800.00           -1.00      0.00   \n",
       "4      3000.00            2.00   5000.00            3.00      0.00   \n",
       "...        ...             ...       ...             ...       ...   \n",
       "7495   8479.00            5.00    236.00           -1.00   2990.00   \n",
       "7496   6616.00            5.00   1953.00           -1.00   5300.00   \n",
       "7497   3296.00            2.00   3408.00            3.00   3416.00   \n",
       "7498   8000.00            5.00   5000.00            3.00  10000.00   \n",
       "7499    929.00           -1.00    935.00           -1.00    984.00   \n",
       "\n",
       "      PAY_AMT6_Score  y  Score  Proba  \n",
       "0              -2.00  0 621.00   0.18  \n",
       "1              -0.00  0 641.00   0.14  \n",
       "2              -2.00  0 591.00   0.25  \n",
       "3              -2.00  0 598.00   0.23  \n",
       "4              -2.00  0 468.00   0.65  \n",
       "...              ... ..    ...    ...  \n",
       "7495            1.00  0 671.00   0.10  \n",
       "7496            1.00  0 658.00   0.12  \n",
       "7497            1.00  0 666.00   0.10  \n",
       "7498            3.00  0 671.00   0.10  \n",
       "7499           -0.00  0 638.00   0.15  \n",
       "\n",
       "[7500 rows x 19 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测测试集模型分数\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "data_test_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10f8b357",
   "metadata": {},
   "source": [
    "## 计算AUC 和 KS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "57fb4065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3676bb4427f344c7bdef75adc8445884",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_ks(data_train,scorecard)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af7c0f0",
   "metadata": {},
   "source": [
    "## 根据 预测得分计算ks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ac7d80bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41ee6a6fbcbd49819775457c04a26a92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_proba = get_predict_score(data_train,\n",
    "                                       scorecard,\n",
    "                                       init_score=600,\n",
    "                                       pdo=50,\n",
    "                                       odds=0,\n",
    "                                       target='y',\n",
    "                                       precision=2)\n",
    "\n",
    "\n",
    "plot_ks(data_train,data_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e8b98548",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17626dfacb94402097f79826ca59d881",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EMmXMlmJJav16aNjQbEM2Ywb8Y70PvQfOmDRmEhEREREr2e0GATfCOfj3Wi4HLpjtxUKj4u4518nRgWI5vBIKLqXz+FAypzfuLrbHDxIbCbM6wclVYHOB9lOgeKO7TkmK98BpckbMf/H398ff39/qGCIiIiKSQcTEwMSJ8OWXcPHi3Y/lzQuNGplfdeok0QyXh7VnDzRvDtHR5qKTtiQYqEi6oDGTiIiIiKSUeLvB6ethf6/pEvJ38SWY8Jj4e851sTlSIpcXpXP7/L2mizfFc3rh6pQMY5noMPjtBTizCZzc4cUZULh20n8f0mkhRkREREQkJcTEmJNMPv/8zvou+fJBly5QsqTZZqxEicdY0+VxnDxpzoQJDTV7nP32mzklR0REREREJJnExts5ec0suhy6GMyBi8EcuRxKZOy9RRdXJ0dK5vJOKLg8kceHotm9cHFyTP6gUcEwvR2c3w4uXvDSbChQNdm+nUZiIiIiIiKJEB0Nly7BlCkwdixcuWIez5EDPvrILML8o/tXyrt8GRo0gGvXzLVhFi0CNzeLQ4mIiIiISHoSE2fn+NVQDv5dcDl4KYQjl0OIibPfc66Hi41Subz/XtPFnO1SOFsmnGwpUHT5p4ibMLUVXN4Lbj7QcQHkrZCs31KFmCRiGAaxsbHY7ff+IRORB7PZbDg7O1sdQ0RE5L7Cw2HHDnPNl82bYft2uHnz7nNy5YIePeDdd8HDw5KYdwsKMmfCnD4NhQvD8uXg42N1KhEAYmNjiY+/925IEXkwjZlERCQ1iIqN5+gVs+hy8KK5nsuxK6HExt+7BL2nqxOlc3snFFyeyOONv58nNkcrWgX8Q+hVmNoSrh0GDz94eSHkLJPs31aFmMcUExPDtWvXiIiI0IBC5BG5urri5+enBV9FRMRy0dFm3WLtWrPwsncv3O8tnqMjVK1qFl9at4ZU9fnYgQNw/DjkzAkrV5pTdUQsFhISQmBgINHR0VZHEUmTNGYSEZGUFBETx5HLIRy8GPL3ui7BnLgWRrz93qKLt5sTZfL68ERun4TZLgV8PXBMDUWXfwq+CFOaw42T4JULXl4E2YqnyLdWIeYxREREcP78eWw2G1myZMHd3R2bzYaDJU3ARdKe2zPJgoODufj3ysYaWIiISEozDNi5E379FWbOvHfGS5488OyzZuGlalUoUsScYOJowQz6h1K9OqxeDZ6eUKiQ1WlECAkJ4eLFi3h6euLn54ezs7PGTCIPSWMmERFJKXvPBzFt21n2nQ/i1PUw7lNzIYuH8//NcjH/mzeLe9p4b3czwCzCBJ0Dn/zwyiLwTbnxkgoxjyEwMBBnZ2cKFCiAzWazOo5ImuTu7o6XlxcXLlwgMDBQgwoREUkx58/DtGnmWi9Hj945njs3tGwJ1aqZBZh8+SDVjysMA65eNWfBgBlcJJUIDAzE09OTvHnzpo1BukgqozGTiIgkp0OXgvl21XFWH7l213E/T1fK5PGmTB4fSv9ddMnl45Y2389dPw5TWkDoJbP48vJiyJwvRSOoEPOI4uLiCA8PJ1euXCrCiDwmBwcHfHx8uHjxIrGxsep/LCIiySY8HObPN2e/rF1r1i8A3N2hVSt45RWoUwfS3Nu7/v3NitLy5fDkk1anEUkQGxtLdHQ0fn5+aXPQLpJKaMwkIiJJ7fjVUL5ddZw/Dl4BwNEBWpbPQ5MyuXgijw85vN0sTphErhw014QJvw7ZSpjtyLxypngMFWIeUVxcHGD2aRWRx3d7IBEfH69BhYiIJCm7HTZsMIsvc+eaxZjbatQwiy9t20KavcF4+HD45htze+9eFWIkVbm9jqbe34k8Po2ZREQkKZy6Hsbo1SdYsv8ShmHO/m9WNjfv1y1K4WyeVsdLWhd3w9RWEBUEOctCp4WQKaslUVSIeUy6q0skaeh3SUREktrx4+YkkalT4dy5O8cLF4aXX4ZOncDf37p8SeLXX6FvX3N76FCzqiSSCum9nsjj0++RiIg8jrM3wvluzUkW7LmQsP5Loydy0qNuMYrn9LI2XHI4tw2mt4PoEMhbEV6aC+6ZLYujQoyIiIiIpBu3bsGsWWZ9Ytu2O8d9fOD5580CTNWqaWDNl4exZAm8/rq53bv3nYKMiIiIiIjI3y7ciuCHtSeZ+9cF4v6uwNQtmZ0edYvxRB4fi9Mlk9Pr4bcXITYCClaHF38DV2uLTSrEiIiIiEiaFhtrLo0yZQosXgwxMeZxmw0aNDCLL82bm+vApBt//gnt20N8vPkCv/kmnVSXREREREQkKVwJjuLHdSeZufMcsfFmAaZGsWz0qleMcvkyWxsuOR1fAbM6QXw0FK4Dz08DFw+rU6kQIyIiIiJpj2GYy6FMmQIzZsC1a3ceK1vW7NDVoQPkTPk1GFPG559DVBQ0bQrjx4Ojo9WJREREREQkFbgeGs1P608xbftZYuLsAFQtnJVe9YrxdEFfi9Mls8OLYO7rYI+FEk2h7URwSh1rvKsQIyIiIiJpxuXLMH26WYA5cODO8ezZ4aWXzAJMhlirft48sxjz2WegBZtFRERERDK8m+Ex/LzxFFO2nCUyNh6ApwtkoVf9YlQt7GdxuhSwbxYs7AqGHZ5oA61+BlvqGSvp1jlJEf369cPBwYEvvvjivo8XLFgQBwcHzpw589CPHTt2jLZt2+Lr60umTJmoVasWGzduTIb0SePEiRO0a9eOHDly4O3tTYcOHbj2/7fvJtKZM2dwcHD4z6///7m1a9fugefNnDnzrutfvHiRDh06ULBgQdzd3fH39+ett97i8uXL/5nNMAzq1KlDwYIFH/n1iYiIREXBvn0wdy4MGQKNG0PevOZSKAcOgIsLtGsHv/8OFy7AyJHpvAgTHX1n28sLhg0DD+un2IvI47n9fvyfvvvuOxwcHChevDghISGPdO2IiAj69+9PwYIFcXFxIW/evHTt2pUbN248buxks3r1aqpXr463tze5c+fm448/Ji4u7rGvGxcXx+zZs+nevTtdunRhxIgRXLx48b7nzpw5k9q1a+Pt7Y23tzeNGzdm8+bN/5rXy8sLV1dXChYsyLBhwzAM477nL126lN69e9OlSxe+/PJLTpw48divTUREMrbgiFhGrDxG9aFr+XnDaSJj43kyX2amvPYMc7pWyRhFmF2TYMFbZhGmfEdoPS5VFWFAM2IkhSxduhSAZcuW8cknnzz29Xbv3k3NmjWJjo6mXbt2eHp6smjRIurUqcOCBQto2rTpY3+PpHT48GFq1KjBjRs3aNWqFXnz5mXGjBk899xz/PXXX7i5uSX6mpkzZ+ajjz564OPjxo0jJCQEH587i27t3LmTIkWK8Pzzz99zfunSpRO2b926RbVq1QgMDKRdu3ZkzpyZvXv3Mm7cOP744w927dpF9uzZH/i9f/zxR9auXUuBAgUS/bpERCTjiouDDRtg7VrYtAm2b7+z3sv/q1rVXBalfXvIkiXlc1oiJASeew7atIEBA7QejEg6t337dvr06UOmTJlYsGAB3t7eib5GVFQUzz33HDt27KB27dq0atWKTZs28fPPP7N37162bt163wKQlX777Tc6duyIu7s7HTp0IDo6mqFDh3L58mUmTJjwyNcNCQlJKKb4+fnh6urKuHHj+OKLL1i0aBE1a9ZMOPedd95hzJgxFCtWjC5dunDr1i1mzZrFypUrGT9+PK+++mrCuTNmzKBjx44AVKhQAR8fHzZu3MgHH3zA9evX+eabbxLOjYuLo3379ixYsAAfHx+yZMnCmTNn+PLLL/n111/vO0YTERH5N6FRsUzafIZxm04TGmXetFA6tze96hWjdonsqe7f+WSz7SdY3t/cfqYLNByaOls3G5IgODjYAIzg4OD/PDcyMtI4fPiwERkZmQLJ0razZ88agAEYjo6ORmBg4D3nFChQwACMgICA/3wsJibGKFasmOHg4GAsXbo04bzTp08bHh4eRq5cuYzo6OjkejmJZrfbjUqVKhmAMWbMmITjBw4cMJycnIwBAwYk+ffcunWrARh9+/ZNOHb16lUDMLp37/6fz//kk08MwNi+fftdx7/++msDMAYOHPjA5544ccLw8PAwAKNAgQIPnVm/UyIiGdfevYbRq5dh5MxpGObqL3e+smQxjEqVDKNjR8P46ivDOH7c6rQWiIw0jOeeM38g2bIZxtWrSXr5xLwHFknsnxe9x/tvt8dKtwUGBhr58uUzAGPmzJmPfN1vv/3WAIxu3bolHIuNjTXq1Knz2NdODleuXDG8vLwMNzc346+//ko4PnbsWAMwVqxY8cjXfuWVVwybzWbMmDEj4djKlSsNNzc3o0iRIgnHli1bZgBGnTp1jKioqITjR48eNXx9fQ0XFxfj3LlzhmEYxs2bN43MmTMb2bNnN7Zu3Zpw7p49e4xMmTIZNpvNuH79esLxgQMHGoAxbNgwIz4+3jAMw9i9e7fh5+dn+Pj4GOHh4f/5OvT7JCIihmEY4dGxxph1J40nP1thFOj3u1Gg3+9G/ZEbjD8OXDLi4+1Wx0tZG4YZxkBv82vlJ4ZhT57XnxRjJs2IkWS3bNkyAN566y1+/vlnli9fzksvvfTI11u4cCHHjx+ncePGNG7cOOG4v78/rVq1Yvr06axfv5769es/9DWDgoIYPnz4f57n7+/P66+/nqi8u3btYvv27ZQvX55u3bolHH/iiSdo164dkydPZvDgwUlape7Xrx++vr58+OGHCcd27twJwNNPP/2fz9+5cyd+fn4888wzdx1v0qQJ/fv35/jx4/d9nt1up3PnzsTExJA7d+7HeAUiIpLe3bwJEyfeu9aLr6+5/nzNmlC9OhQpksEnf8THm4vfrFtntiNbvtxcEEdE0iXDMOjYsSPnz5+nR48ejzVLYu3atYD5Hv42JycnOnfuzJo1a9iwYUOir79nzx7mzZv3n+fVrl2b2rVrJ+raU6ZMITQ0lJ49e/LUU08lHH/99dcZPHgwEydOTNQY77YTJ04wdepU3n33XV588cWE4/Xq1aNhw4YsXLiQCxcukDdvXsaNGwfA119/javrnYV9ixcvzssvv8yoUaNYvnw5b775JnPnziU4OJjff/+dypUrJ5xbrlw5mjRpwuzZs9mzZw/16tUjODiYESNG0Lx5c/r06ZNwbvny5enYsSOjRo1i9+7dVKtWLdGvT0REMo6o2HimbTvLT+tPcSPcbB1QKFsmetQtRtMyuXB0zEADJ8OAtV/Cpr8/z631IdT8IFUPHlWISSaGARERVqd4NB4eSftndtmyZdhsNnr37s3PP//MsmXLHqsQs2TJEuDuAcVtJUqUAODQoUOJLsQMHjz4P8+rWbNmogsxGzZsAKBNmzb3PFa/fn1+++03Dh48SJkyZRJ13QdZtmwZGzduZOTIkWTOnDnh+I4dOwCoWrXqf14jU6ZM3Lx5kytXrpAzZ86E4wf+/qTsQWu/jBw5kj///JPPP/+cNWvW3HfNHxERybhCQmDPHpg5E379FSIjzeMuLtCsGXTqBI0amfuC+YayWzeYP9/8oSxcCP/34aRIWmcYBpFxkVbHeGTuTu5J3vJj8ODBLF++nOrVqzNs2LDHutbtQsKePXvuGjs9//zzNGnSBJdH+Mt23759DzVucnJySnQh5kHjptvXuj0OTKw8efKwefNmChcufM9jt9fecXd3B+DgwYOA2Wbsn/z8zP76gYGBALz55pu8+OKLeHp63nPu7TVtbDZbwvXXr19PtmzZ/jODiIjIP0XHxTNzx3l+XHeSa6HmupH5fT14v05RWpTLjZMtFbbhSk6GASs+hG1jzP16X8Cz3a3N9BBUiEkmERFwn/djaUJYGGTKlDTXio6OZu3atZQpU4aiRYtSuHBhVqxYgd1ux/ERe/Xt27cPgJIlS97zWPv27SlYsCDlypVL1DULFiz4wMUUH9elS5cA7rqr67aiRYsCcOrUqSQrxHz55ZdkzZqVrl273nV8+/btuLi40K9fP/7880+Cg4MpXLgwHTt2pGfPnnfd8fXiiy8yb948OnTowE8//UTevHnZunUrffv2xd3dnVdeeeWe73vkyBE++eQTKlWqxIcffsiaNWuS5PWIiEjaduUKDB0Ky5bBPydUPvkkdO0Kzz+fgdZ6SYxPPoFx48z+xjNmQCI/1BRJ7SLjIqk0o5LVMR7Z9g7b8XD2SLLrrV27loEDB5I9e3Zmz56Nk9PjDdebN2/O3Llz+eKLLwDo0aMHnp6eODk53XXDVmK8+uqrd62RkpQuXbqEg4MD5cuXv+exokWLcvPmTYKDg+9aA/NheHh43DVj5bYtW7awceNGKleuTNasWYE7hZPAwMB7iia3izT/vw7m/YowkZGRrF69GldX14SCjouLy32LO6dOnWLOnDnky5ePsmXLJup1iYhI+hcbb2fOrgv8sPYEl4KjAMiT2Z33ahehTYW8OGe0AgyA3Q5Le8Ffk8z9xsPhmTetzfSQVIiRZLVhwwbCw8N59tlnAahWrRq//vor27dvp0qVKo90zfPnzwOQI0eOex4rVqwYxYoVe/TAyeD2HU73y5vl70+dLl68mCTfa+vWrWzdupWPPvrorjuqDMNg+/btxMTEsGfPHlq3bo2TkxNLly5lwIABrF69mpUrVyYUx9q0acP333/Pe++9lzDLCKBw4cKsWbOG4sWL3/V94+PjeeWVV3B0dGTq1KkJAxgREcm4rlyB0aPNr8j/u+E9Xz6oWtUswNSsmapnjltrxw64fdf5Tz/BfWbWikj68uKLL2K324mIiLjvTWtr165NaDf2b9q0aUP58uXp1KkTGzZsYMKECXzyyScMHz6crl270rdv34TCQ2oSEhKCp6cnHh73Frduj5suXLiQ6ELMP82ePZv169czYcIE8ubNy7Rp0xIeK1myJEePHmXEiBF8/fXXCcd37NjB3LlzcXFxoV69ev96/VGjRhESEsJbb731wKzLly9n7dq1jB07FhcXF+bOnYuzs/NjvS4REUk/4uLtLNhzke/WnuD8TXMwlcPblXefK0L7ivlwdcqgn7vFx8Gid2D/THBwhObfQ/mOVqd6aCrEJBMPD3NmSVp0n/e9j+z2+jC3e90+++yz/PrrryxbtuyRCzFhf/9g3dzckiZkMrtdlLjfgOL2LJSIJOpjN3LkSFxcXHj33XfvOn7t2jWefvppypYty7BhwxIGdsOHD6dZs2asXLmSefPm0a5dO8AsDP38888AODs74+vry/Xr1zl79ixjx46lXLlydxV6vv76a3bu3MmYMWMSZvmIiEjGEx5uds+aOhVWrTJvVgKoXBn69YNnn4X7dGWR+3nmGRg7Fm7cgC5drE4jkizcndzZ3mG71TEembtT0raSunbtGv7+/gQEBPDZZ5/x448/3vX4xo0bH6otWJEiRRJmlYwfP57WrVvzxRdfsG3bNoYOHcpPP/3E5MmTadWqVZLmf1w2m+2+YyZI2nHTa6+9Rnh4OAAvvfQS+fPnT3isT58+LFmyhKFDhxIQEECdOnU4efIkP//8M3FxcXTo0OG+7cVuO378OF9++SVZsmTh888/f+B5ffr04dChQwB07tyZ0qVLP/brEhGRtC/ebvD7/kuMXn2C04Hmv1V+ni50q1WElyrlx805gxZgAOJiYP4bcHgRONig9S9Qpq3VqRJFhZhk4uCQdO290rKlS5cCJMyIuf3fpUuXJkyRT6zbfZjj4+OTIKEpKCiI4cOH/+d5/v7+iV4j5vZ09ejo6Hsei4mJueu/j+PSpUssWLCAdu3a3bWuC5izcVatWnXPc1xcXBg6dCgrV65k7ty5CYWY1157jYMHD/Lll1/Su3dv3NzcOH/+PO+++y5TpkzBZrMxceJEAPbv38/nn39Ow4YN6dat22O/DhERSXvOnIGvv4Zp08xizG1VqsCAAdC0qWa+PDTDuPPDeusta7OIJDMHB4ckbe2V1rVt25Zhw4ZRvHhxfvnlF3r06HHXTU6DBg1i0KBBib5u48aNady4Mdu2baN///5s2LCBNm3aMGPGDF544YVEXWvPnj3MmzfvP8+rXbt2oteI8fT05Nq1a/d9LCnHTcHBwezatYsvv/ySwYMHc/bsWaZOnQqY62kuWrSIbt26MXv2bGbPnp3wPJvNxieffPLA60ZHR/PCCy8QERHB5MmTyZ49+wPPPXDgAAcPHmTEiBFMmjSJgIAAVq9erc4CIiIZlN1usPzQFb5ddZwT18wb0LN4ONO1ZmE6VSmAh0sG/wg/NgpmvwwnVoDNBdpNhhL3rh2e2mXw/4uSnE6cOMHJkycB7rrLCGDv3r1cvnyZXLlyJfq6Pj4+XL9+naCgoHseGzNmDL169eKLL76gb9++D33NoKCgh7q7rGbNmokuxOTNmxeA06dP8+STT9712NWrV4E7U+0fx/Tp04mPj0/0YOr2Wju3/19dvHiRlStX0qxZMz766KOE8/Lly8e8efPw9/dn+vTp/PLLLxiGwcsvv4yXl1dCYUZERDKO06fN7llTpsDf6xJTqBB06gQdO0KRItbmS3O2bYMPP4TZs+HvRaFFJOOYMGEC3t7edOvWjdGjR/Phhx8yZ86cJLt+5cqVWbt2Le+88w5jx46ld+/ePP/88wk3uj2Mffv2PdS4ycnJKdGFmLx587J7925u3bp1z/goKcdNNpuNSpUqsWjRIqpVq8a0adPo379/wqyUxo0bc/r0aQ4cOMC1a9f47rvvWLp0KW+88cZdbZv/6Z133mHPnj289957CTe4PYiDgwNlypRh8uTJREZGMnv2bJYvX06TJmnvQyUREXl0hmGw6vBVvl19giOXzaUNvN2c6FKjEK8+64+nqz66JyYcZnaA0+vByQ1emA5F6lqd6pFkwBV9JKXcbktWo0YNPvroo4SvypUrYxgGf/zxR8K5t+/8sd/uYfJ/bs98uX3O7cLBqVOn7jn3+vXrREdH4+3tnaisBQsWxDCM//xav359oq4LJLQF2LFjxz2Pbd26FeCRClL/NG3aNHx8fGjUqNF9H7/fzwvMnxlAVJS56NftNXjuNz3eycmJEiVKEBMTw/Xr1/nll1/Yt28fJUuW5Mcff+Tjjz9O+AoICCAoKIiPP/6YESNGPPbrExER69ntcOAA/PgjtG0LxYrBxIlmEaZePVi3Dk6ehEGDVIRJtMOHoUkT84f46adWpxERC9wew3z88cd4eXkxd+5ctm9/tNZtV65coXLlyrRs2fKu446OjowaNQp3d3cuXbrElStXEnXdV1999aHGTY8yc+dhxk3/nPn/OBwdHWncuDEAR44cuesxm81GuXLlKFOmDGvWrMHb25vPPvvsgdcaPXo0EyZMoEaNGoke+zRr1uy+GUREJP0yDIN1x67R4sfNdJn6F0cuh+Dp6kT3OkXZ1K8279YuqiIMQFQITGtjFmFcPKHjvDRbhAEVYiQZ3S7EfPDBB3z55ZcJX++///5djwMJfXZvFwFui4uL48qVKzg6OiYsKFmnTh0AVq5cec/33L17NwBly5ZN4lfz6J599lmyZ8/Or7/+mtCLGMwC09SpU3FycqJmzZqP9T0OHTrE/v37adGiBS4uLvc8PmjQIIoUKcKBAwfueWzx4sXAnZ9Zjhw5gDszZP5fbGwse/fuxWazkSVLloQizp9//sngwYPv+jp37hzBwcEMHjyY77///rFen4iIWOf8efjlF2jd2pykUbYsvPsuzJsH8fHQoAFs2QIrV0KtWmpB9kjOnYP69eHmTahUCYYNszqRiFjIz88vYXb/Bx988EjXcHd3Z/v27SxbtozY2Ni7HnN2dsbBwQEHB4dE38CWnG6vWfPPtXHOnj3L+vXreeqpp/D19U30dadNm4a3tzcbN26857EbN24Ad9pJ/9OQIUOIiori008/TRgn/dOcOXPo1asXBQsWZO7cuTg7O99zzvr168maNStTpkxJdAYREUk/DMNg88lA2o7dSudJO9l/IRh3ZxvdahVm0wfP0ateMXzc7/13JEOKuAlTmsO5reDmA50WQsFqVqd6PIYkCA4ONgAjODj4P8+NjIw0Dh8+bERGRqZAsrQnPDzccHV1NVxcXIywsLC7Hrt+/brh4OBgeHt7G7GxsYZhGEb//v0NwGjXrp1ht9sTzv3+++8NwKhSpUrCsQsXLhienp6Gq6ursWPHjoTjW7ZsMZycnIz8+fMb8fHxyfwKE+frr782AKNZs2ZGYGCgERkZaXTt2jXhNT+uoUOHGoDx66+/3vfx7du3G4Dx3HPP3fVndufOnYavr68BGKtWrTIMwzBiY2MNX19fw8XFxZg/f37C/4+rV68anTp1MgCjXr16/5mpZs2aRoECBR76Neh3SkQkdYiKMoxVqwyjd2/DKFXKMMxFS+58ZcpkGPXqGcbnnxvGrl1Wp00Hrl83jOLFzR9uyZKGERiY4hES8x5YJLF/XvQe778Bxj+H5mFhYUaOHDkMwFiyZMkjXbdKlSoGYHz11Vd3Hf/pp58MwKhevfojZ04uDRs2NABj4MCBRkxMjHHx4kWjatWqBmD8+OOPj3TNM2fOGI6OjkaJEiWMa9eu3XU8S5YsRubMmY3Q0NB7nnfixAnDxcXFKF68uBETE3Pfay9atMhwcnIyfHx8jIMHDz4wQ3h4uOHl5WXkyJHDOHnyZMLxGzduGEWKFDGcnJyMgICA/3wt+n0SEUm7tp++YbQfu8Uo0O93o0C/341iHy0zvlhyyLgeGmV1tNQn9JphjKlqGAO9DWOov2Fc2mt1oiQZMzkYhmGkcO0n1QoJCcHHx4fg4OD/vDMoKiqKgIAA/P39cXNzS6GEaceSJUto3rw5NWvWvG87rwoVKrB7927WrVtHrVq1uHbtGuXKlePy5cs8/fTTVKtWjVOnTrFkyRKcnJxYtWoVtWrVSnj+nDlz6NChAy4uLrzwwgvY7XZmzZpFXFwcixcvpmHDhin3Yh+C3W6nQ4cOzJo1C5vNhs1mIyYmhkKFCrF9+3b8/q8P/Nq1a1m7dm2iFrisW7cua9asISAggIIFC973nNdee41JkyaRO3dunnzySYKDg9m2bRt2u50BAwbw1VdfJZw7btw4unTpAph3Znl4eHD9+nUMwyBTpkxs3bqVMmXK/GumWrVqcebMGc6cOfNQr0G/UyIi1jEMc1bLuHEwdy783wROHB2hcmVo2NCctPHUU3Cfm33lUYSFQe3asHMn5MsHmzeb/01hiXkPLJLYPy96j/ffbq/R8s+h+ZgxY3jnnXcoXbo0+/btS/RC7n/99Rc1a9YkPDyc2rVr8+STT3Ls2DGWLVuGp6cnGzduTGgHllpcvXqVOnXqcOjQIVxdXYmLiyM+Pp4WLVqwYMGCu9azmTBhAgEBAbz++uv4+/v/63VHjBhBnz59yJo1Ky1btsTR0ZEFCxYQFBTEb7/9Rtu2be95TsuWLVm0aBGrVq2ibt1726CEh4eTN29egoKCeO6556hateo95/z/mG7u3Lm0b98ed3d32rRpg5eXF7///jvnzp3j22+/pUePHv/589Hvk4hI2rPn3C1GrjrOphOBALjYHHnxmXy8/VwRcnjr7/J7hFyCX5vDjRPgmQNeXgzZH7xGW4rFSooxUxIVhdIFzYhJOrdnewwePPi+j9+eAdO3b9+EY2fOnDFefvllI1euXIaTk5OROXNmo0GDBsamTZvue43t27cbjRs3Nry9vQ0PDw+jTp06xpYtW5Ll9SSVOXPmGM2aNTNq1KhhDBw48J7ZQoZhGAMHDky4C+xhREREGK6urka+fPn+9by4uDjjxx9/NEqVKmW4uroa2bNnN1q0aJEwE+afVqxYYTRo0MDw9fU1nJycjGzZshmtWrUy9u3b91C5NCNGRCT1Cww0jG+/vXfmS65chtG5s2HMmmUYN29anTId69DB/IFnzWoYR45YFkMzYiQxNCMm6XGfGTGGYRgxMTFGkSJFDMCYMGHCI1370KFDRvv27Y1s2bIZTk5ORs6cOY2XXnrJOHbs2OPGTjbR0dHGsGHDjNq1axsNGjQwJk6ceFfXhNtq1qxpAMa6dese6rqLFi0ynn32WcPb29vIlSuX0bp1a2P//v33PXfNmjUGYLRp0+aB19u8eXPC/7sHff1zTLdp0yajXr16RtasWQ0/P79/He/ej36fRETSjgMXgozOk3YkzIApPGCp0X/efuPirQiro6VeN88Yxqiy5kyYEaUMI/Dkfz8nhWhGTBLTjBgR6+h3SkQkZRgGbNxorvsybx5ER5vH3d3hhRfgjTegShWt9ZIiTp40F98ZPx6eecayGJoRI4mhGTEi1tHvk4hI6nf0SggjVx5n5eGrANgcHWhdPg/d6xQln6+HxelSscCT5powIRchiz+8shgy57c6VYKkGDM5JXEmEREREUmFrl+HX381248dP37neLly0KULdOgAPj6WxcuYihSBvXvN/m8iIiIiIpJmnbwWyrerT7B0/2XAvLGtxZO5eb9uMfz9MlmcLpW7ehimtIDwa+BXHF5eBN65rE6V5FSIEREREUmHDAOOHIGVK2HFClizBmJjzcc8PeHFF80CTIUKmv2SooYMgSefhMaNzX0VYURERERE0qwzgeGMXnOCRXsvYv+771STMrnoUbcoRXN4WRsuLbi0F6a2gsibkKMMvLwQMvn917PSJBViRERERNKRAwdgwgSz7diFC3c/VrGiWXx5/nnw0pgg5Y0ZAx9+CE5OcPgwFC1qdSIREREREXkE529G8P3aE8zbfZH4vysw9UrloGfdYpTKrXa/D+X8DpjWFqKDIU8F6DgP3LNYnSrZqBAjIiIiksaFhsLMmeZSIzt23Dnu5gY1akD9+tCwIZQubV3GDG/2bHj3XXP7o49UhBERERERSYMuB0fyw9qTzN51nth4swBTq3g2etUrRtm8ma0Nl5YEbIQZL0BsOOSvCh1mgVv6LmCpECMiIiKSBsXHw7Zt5uyX2bMhPNw87uQEzZtD585Qpw64u1ubU4DVq6FjR7Nf3Ntvw8CBVicSEREREZFEuBYaxZh1p5ix4xwxcXYAqhXxo2e9YlQokH5ncSSLE6th1ksQFwWFnoMXZoCLh9Wpkp0KMSIiIiJpQGSkud7Lli3mrJdduyAs7M7jxYvDG29Ap06QI4d1OeUfdu6Eli3NBXrat4fvvtOiPCIiIiIiacSNsGh+3niaKVvPEBVrFmCeKehLr/rFqFwoq8Xp0qAjS2BOZ7DHQrFG0G4yOLtZnSpFqBDzmAzDsDqCSLqg3yURkXvFxMCqVWbbsYUL7y68AHh6Qtu2ZgGmalV9vp/qnD0LjRub05Xq1oUpU8BmszqViCX0Xk/k8en3SEQk5QRFxPDLxtNM3nKGiJh4AMrnz0zvesV5tkhWHDT4Srz9c2DBW2DEQ+lW0Hoc2JytTpViVIh5RE5O5o8uOjoad/X8EHlssbGxANj0AZWICBER8MMP8M03cOPGneP585trvVSqBM88AyVL6nP9VC1PHmjRAvbtg/nzwdXV6kQiKe72e7vY2FiNm0Qek8ZMIiLJLyQqlgmbApj4ZwCh0XEAPJHHm971ilOreDYVYB7V7imwuDtgwJMdoMUP4Jix/j1TIeYROTk5kSlTJm7evImXl5feCIk8BsMwCA4OxtXVFWfnjFMJFxH5p9hYmDgRPvsMLl82j+XMaXa0euEFswDj6GhtRkkEJycYN86cyuTlZXUaEUs4Ozvj6upKcHAwXl5e+vBC5BFpzCQikrzCo+OYvOUMv2w8TXCkWfgukdOLnvWKUb9UDr2HeRzbf4Y/PjC3n34dGg/PkANbFWIeg5+fH+fPnycgIAAfHx/c3d2x2Wz6xRR5SIZhEBsbS3BwMGFhYeTJk8fqSCIilrDbYfZs+OQTOHnSPFagAHz+Obz0kma9pCnh4fD999Cnj1mIcXBQEUYyPD8/Py5evMiFCxfw8fHB2dlZYyaRh6Qxk4hI8oqMiWfqtjOM3XCam+ExABTOlome9YrR+IlcODrqPctj+fNbWD3I3K7yLtT/MsP21FYh5jF4eHjg7+/PtWvXuHXrFoGBgVZHEkmTXF1dyZMnD97e3lZHERFJUeHhMHcujB4Ne/aYx7JlMwsyXbqok1WaExsL7drBH3/A0aMwebLViURShdvv8QIDA7l48aLFaUTSJo2ZRESSVlRsPL/tOMeY9ae4HhoNQMGsHrxftyjNn8yDTQWYx2MYsH4IbBhq7tfsB7UGZNgiDKgQ89hcXFzImzdvwl0qdrvd6kgiaYrNZtPUehHJUAwDtmyBSZNg1iyzaxWYkyb69oUePTSBIk2y26FzZ7MI4+4Ob71ldSKRVMXb2xtvb29iY2OJj4+3Oo5ImqIxk4hI0omKjWfOrvOMWX+Ky8FRAOTJ7M77dYrS+qk8ONkyXsusJGcYsPJj2PqDuV93EFTraWmk1ECFmCTi4OCAi4uL1TFEREQklbp4EaZMMSdJHD9+53jhwvDqq9C1K/j5WZVOHothQK9eMH262Y5s3jyoUsXqVCKpkrOzsz5QFhERkRQXFBHD1K1nmbzlDDf+bkGW09uNd2sXof3T+XBxUgEmSdjtsKwP7Jpg7jf6BirpJjVQIUZEREQk2URHw+LF5uyXFSvM96QAmTKZHaw6d4bq1TP07Oz04euvzf5yYFbaGjWyNI6IiIiIiJguBUUy4c8AfttxjogYc1ZunszudKlRiOcr5sPNWQtyJhl7PCx+D/ZOBxyg2Wio8IrVqVINFWJEREREklhEBPzwA3zzDdy4ced49epm8aVdO/D0tC6fJKHx4+HDD83tUaPgpZcsjSMiIiIiInDiaihjN5xm0d6LxNkNAErk9KJrzcI0KZsLZ7UgS1rxsTC/CxyaDw42aDUWyra3OlWqokKMiIiISBKJjTVnv3z2GVy6ZB7LmxdeecVsP1akiKXxJDnkywceHubiPu+/b3UaEREREZEMbdeZm4zdcIrVR64lHKvk70vXWoWpVSwbDmpHkPRio2BuZzi2DBydoe1EKNXc6lSpjgoxIiIiIo8pLg7mzIGBA+HECfNYgQLwxRfQoQPYNNs9/WrQAPbvh0KFrE4iIiIiIpIh2e0Ga49eY+yGU+w6ewsw2z/XL5WDrjULUz5/FosTpmMxETCzA5xeB05u0H4qFKtvdapUSYUYERERkUcUEmJ2pho9Gs6dM49lywYffwxvvQWurtbmk2Syd685C6ZYMXO/cGFL44iIiIiIZESx8XYW773EzxtPcfxqGADONgdal89Ll5qFKJxN/aCTVXQozHgezm4G50zw4m9QqKbVqVItFWJEREREEiEqClatgvnzza+QEPO4nx907252qPLysjSiJKeTJ81ZMIYBa9fCE09YnUhEREREJEMJj45j5s7zTNh0mkvBUQB4ujrxUqX8vFbNnxzebhYnzAAib8G0NnDxL3D1hpfmQv5KVqdK1VSIEREREfkPYWHwxx9m4eX3383920qUgF69oGNHcHe3LqOkgMuXoX59uHYNypeH/PmtTiQiIiIikmHcCIvm1y1n+HXrWYIjYwHw83TltWoFealSAXzcnS1OmEGEB8LUlnDlALhngU4LIHd5q1OleirEiIiIiNxHUJBZdJk3D5YvN2fC3JYnD7RuDW3aQPXq4OhoWUxJKUFB0LAhBASYrcj++AO8va1OJSIiIiKS7p2/GcG4TaeZves8UbF2AApm9aBLjcK0fioPbs5alDPFhFyGKS0g8Bhkyg4vL4Qcpa1OlSaoECMiIiLyt+vXYdEis/iyZg3Ext55rFAhs/DSpg1UrKjiS4YSGQnNmsH+/ZAzJ6xcCTlyWJ1KRERERCRdO3wphLEbTrH0wGXi7QYAZfP60LVmYRqUzonN0cHihBlM0Dn4tTncCgCv3PDKYvAranWqNEOFGBEREcnQLl82Cy/z5sHGjWC333msVKk7xZeyZcFB7/Mznrg4eP55+PNP8PGBFSvMqpyIiIiIiCQ5wzDYevoGYzecZuPx6wnHqxf1o1vNwlQpnBUHDcxS3o1TZhEm5AJkLmAWYbIUtDpVmqJCjIiIiGQ4hmEWXX74ARYsgPj4O4899ZRZeGnd2lz/RTK48HBzqpSbGyxZYlbkREREREQkScXbDVYeusLYDafYdyEYAEcHaFI2N2/VKMQTeXwsTpiBXTtqtiMLuwJZi8DLi8Enj9Wp0hwVYkRERCTDCAuDadPgxx/h4ME7xytXhrZtzeKLv791+SQV8vGB1ath71549lmr04iIiIiIpCvRcfHM332RcRtPczowHABXJ0faPZ2XN6sXokDWTBYnzOAu74OprSDiBmQvba4J45nd6lRpkgoxIiIiku4dOwZjxsDkyRASYh7z8ICOHeGddzTJQe5j714oV87czpRJRRgRERERkSQUEhXL9G3nmLg5gOuh0QD4uDvzcpUCvFK1IH6erhYnFC7sgmmtISoYcpeHjvPBw9fqVGmWCjEiIiKSLkVFweLFMGGCubb6bUWLmsWXV16BzJktiyep2eTJ0LkzfPopDBqkxYFERERERJLItZAoJmwOYMa2c4RGxwGQ09uNN6r788Iz+fF01cfVqcKZP2HG8xATBvkqw0uzwU3t4R6H/mSLiIhIumG3w+bNMGUKzJkDwWZrYRwcoGlTePddqFsXHB2tzSmp2OLF8MYb5nZkpIowIiIiIiJJ4PT1MH7ZeJr5uy8SE28HoEh2T96qUYgW5fLg4qRBWqpxcjXM7AhxkeBfA16cCS5qEfe4VIgRERGRNC0wENauNZfxWLECzp2781j+/Gb7sTfe0Nov8hA2bYLnn4f4eHj1VRg61OpEIiIiIiJp2r7zQYzdcIrlh65gGOaxCgWy0LVmYeqUyI6jo258SlWOLoU5r0J8DBStD+2ngLO71anSBRViREREJE2JjoYNG8zCy+rVsGfP3Y97eUG7dtCpE9Soodkv8pD27YNmzcyeds2awbhxmg0jIiIiIvIIDMNg44lAxq4/xdbTNxKO1ymRna61ClOxoNYZSZVOrYXZL4M9Dko2hzYTwMnF6lTphgoxIiIikuoZhllwmTQJpk+HW7fufrxMGbPlWN26UKsWeHhYElPSqtOnoWFDs5ddtWowaxY46W2yiIiIiEhixMXbWXrgMj9vOM3hyyEAODk60Lxcbt6qUZjiOb0sTigPFB0Ki94zizBPtIVWP4NNY6KkpJ+miIiIpFrXr5uFl0mTYP/+O8dz5TI/N69bF2rXhpw5rcso6cDq1XDlilnRW7IE3DX1XkRERETkYUXGxDPnr/OM23Sa8zcjAfBwsfFCxfy8Xt2fPJn1/jrVW/slhFyAzAWg+XcqwiQD/URFREQkVYmLg+XLzeLLkiUQG2sed3WFli2hc2ezAGOzWRpT0pMuXcyedrVqQebMVqcREREREUkTgiJimLL1LJO3nOFmeAwAvplceLVqQV6uUoDMHmprlSZc2AXbfza3m40Cl0yWxkmvVIgRERGRVCE8HMaOhREj4PLlO8crVDCLLy++CL5qJSxJJSrKrPJ5/d0e4cUXrc0jIiIiIpJGXAqKZPymAGbuPEdETDwAebO406VGIdpVyIe7i+6aSzPiYmBxd8CAsi9A4dpWJ0q3VIgRERERS4WFwZgxMHy42YoMwM8POnUyCzBlylibT9KhuDjo0AHOnYNlyyB7dqsTiYiIiIikesevhjJ2wykW771EnN0AoGQub7rWLESTMrlwsjlanFASbctouHYIPLJCg6+sTpOuqRAjIiIilrhyBX75Bb7/HgIDzWOFCsFHH0HHjuCiWeySHAwDunWDBQvMP2THjqkQIyIiIiLyL3aeucnY9adYc/RawrEqhbLStVZhahT1w8HBwcJ08sgCT8CGYeZ2w68hU1Zr86RzKsSIiIhIijEM2LYNfvgB5sy5s/5LkSLw8cfmJAVnZ2szSjr38ccwfjw4OsJvv0H16lYnEhERERFJdex2gzVHrzF2wyn+OnsLAAcHaFg6J2/VLEy5fJmtDSiPx26HJe9DfDQUqQtl2lmdKN1TIUZERESSXWQkzJxpFmB2775zvGpVePddaNcOnPSuRJLbqFHw1d/T7ceOhdatLY0jIiIiIpLaxMTZWbzvEj9vOMWJa2EAuNgcaVMhD29WL0ShbJ4WJ5QksWcKnN0Mzh7QZKRZZZNkpY88REREJFnY7bB9O8ydC5Mnw82b5nFXV3Pmy7vvwlNPWRpRMpLp06FnT3N78GB4801r84iIiIiIpCLh0XH8tuMcE/4M4HJwFABerk50qJyf15/1J7u3m8UJJcmEXoGVn5rbtT+BLAWszZNBqBAjIiIiSSY6GtatM5ffWLzYXAfmtgIF4O234bXXwM/PuoySAUVFwYcfmtvvvw8DBlibR0REREQklQgMi+bXLWeYsvUswZFm7+hsXq689qw/L1XOj7ebekenO8v6QnQw5H4KKr1ldZoMQ4UYEREReSzBwfDHH7BwISxbBqGhdx7z9oYmTeD556FpU7DZLIspGZmbG6xfDz//bLYm07R7EREREcngzt2IYNym08zedZ7oODsA/n6Z6FKjEK3K58HNWYO3dOnI73BkMTg6QfPvwFH/n1OKCjEiIiKSaJcvmzNeFi6ENWsgNvbOY7lyQcuW5letWuDiYk1GEWJjwfnvO/j8/eHrr63NIyIiIiJisUOXghm74TRL91/CbpjHnszrQ9eahalfOic2R920lG5FBcOyPuZ21e6Qs4y1eTIYFWJERETkocTHw6JFMHo0bNx492MlStwpvlSsCI6OViQU+T9nz0KdOjBiBLRoYXUaERERERHLGIbB1lM3+GnDKTadCEw4XqNYNrrWLESVQllx0Kzx9G/1IAi9DL6FoeYHVqfJcFSIERERkX8VFgYTJ8KoURAQcOd4pUrQqpX5GXeJEpbFE7nX9etQvz6cOgWffmr2x3PS214RERERyVji7QYrDl1h7IZT7L8QDICjAzQtm5u3ahaidG4fixNKijm7FXZNNLebjQZnd2vzZEAakYqIiMgD/fqrubZ5sPmeHV9fePtteOstyJvX2mwi9xUaCo0bw/HjkD8/LF2qIoyIiIiIZDgbjl/ns8WHOB0YDoCrkyPPV8zHm9ULkc/Xw+J0kqLiomFJd3O7fCfwr25tngxKo1IRERG5r9GjoUcPc7toUejZE155BTz0nl1Sq+hoc5rWrl3g5wcrV6piKCIiIiIZyq3wGL74/TDz91wEwMfdmVeqFOCVqgXJ6ulqcTqxxKYREHgcMmWH+l9YnSbDUiFGRERE7vHVV/DRR+Z2797wzTda90VSufh46NQJ1qyBTJlg2TIoXtzqVCIiIiIiKcIwDH7ff5lBiw9xIzwGBwfoXNWfXvWL4emqj4AzrGtHYNNIc7vxN+Cexdo8GZh+C0VERCSBYZgFmCFDzP1Bg8wlNrRuo6R6v/4Kc+aAszMsWAAVK1qdSEREREQkRVwJjuLjhQdZfeQqAEWzezK0bVmeyq8P3TM0ux0Wdwd7LBRvDKVaWp0oQ1MhRkRERABzaY2334Zp08z9YcOgTx9rM4k8tFdegT17oHp1qFfP6jQiIiIiIsnObjeYufM8Q5YdITQ6DmebA2/XKsLbzxXG1clmdTyx2q4JcGEHuHhB4+G6w9JiKsSIiIgIO3fCiy/CqVNgs8EPP0DXrlanEkkEmw2+/97qFCIiIiIiKSIgMJz+8/azPeAmAE/my8w3bcpSPKeXxckkVQi+AKsHmdt1B4JPHkvjiAoxIiIiGVpwMIwZY7Yfi4uD/Plhxgx49lmrk4k8hFmzzLVgxo83W5KJiIiIiKRzcfF2xv8ZwLerjhMdZ8fd2UafBsV5tWpBbI6a8SCYPceX9oaYMMhXCZ5+3epEggoxIiIiGU58PKxebS6psWABREWZx9u2hV9+gSxqIyxpwapV0KkTxMZC5crQrZvViUREREREktWhS8H0m7efgxdDAKhWxI8hrcuQz9fD4mSSqhxaAMeXg6MzNPsOHB2tTiSoECMiIpJhHDliFl+mToVLl+4cL1kS+vaFV19Vy1hJI3buhFatzCJM+/bQpYvViUREREREkk1UbDzfrz3B2A2nibcbeLs58XHTUrSrkBcHDeLk/0XchD8+MLer94bsJazNIwlUiBEREUnHDAPWrYMhQ8xZMLf5+pprwrzyCjz9tAowkoYcPQqNGkF4ONStC1OmmOvDiIiIiIikQzvP3KTfvP2cvh4OQKMncvJZi9Jk93KzOJmkSqs+gfDr4FccqveyOo38HxViRERE0iG7HRYtgq+/hh07zGM2GzRpYhZfmjQBV1drM4ok2oULUL8+3LgBFSvC/Pn6gywiIiIi6VJoVCzfLD/G1G1nAcjm5coXLUrT8IlcFieTVCtgI+yZZm43/w6cNFZKTVSIERERSUdCQ2HaNPjuO3PiAICbG7zxBvTuDQULWhpP5NHZ7dC8OZw/D8WLw9Kl4OVldSoRERERkSS37ug1PlpwgEvB5oKezz+djw8bl8THw9niZJJqxUbCkvfN7adfh/yVrc0j91AhRkREJB04eBB++sns0hQWZh7LnBneeQe6d4fs2S2NJ/L4HB1h2DB47z1YvhyyZbM6kYiIiIhIkroZHsPnSw6xcK+5qGd+Xw+GtC7Ds0X8LE4mqd6GoXDzNHjlhroDrU4j96FCjIiISBplGGZnpu++g40b7xwvXhy6dYPOncHb27p8IkmuTh04cEBrwoiIiIhIumIYBov3XeKzJYe5GR6DowO89qw/veoXw8NFH9/Kf7hyADZ/Z243GQ5uPtbmkfvSb7KIiEgadOoUdO0Kq1eb+zYbtGwJb78Nzz0HDg6WxhNJGnY79O1r9tYrWdI8piKMiIiIiKQjl4Mj+XjBQdYcvQZA8RxeDG1blnL5MlsbTNIGezwsfg+MeCjVAko0sTqRPIAKMSIiImlIbCyMHAmDBkFUlLn+S+/e5gyYPHmsTieShAwDevY0p3zNmAEnT0KmTFanEhERERFJEna7wYwd5/j6j6OERcfhbHPgvdpF6VqzMC5OjlbHk7Ri+1i4tAdcfaDRN1ankX+hQoyIiEgasWMHvPkm7N9v7tepA2PHQpEi1uYSSRZDhphFGIARI1SEEREREZF04/T1MPrPP8COgJsAPJU/M0PblKVoDi+Lk0macusMrP3S3K7/BXjltDSO/DsVYkRERFK50FD4+GP4/ntzkoCvrzkr5uWX1YJM0qlffoGPPjK3R4+GDh2szSMiIiIikgRi4+2M23SaUatPEBNnx8PFRt8GxXm5SkFsjhrcSSIYBvzeC2IjoEA1eOplqxPJf1AhRkREJBVbssRc9+XCBXO/Y0ezCJMtm7W5RJLNvHlmrz0wizHdu1ubR0REREQkCRy8GEy/efs5dCkEgOpF/fiqVRny+XpYnEzSpP2z4dQasLlCs9G6SzMNUCFGREQkFbp1y/wsetYsc9/f32xDVr++tblEktXmzebsF7sdunSBL76wOpGIiIiIyGOJio1n9JoT/LLxNPF2Ax93Zz5tWorWT+XBQR+ey6MID4Tl/c3tmh+An/qVpwUqxIiIiKQytz+LPncObDbo3RsGDgQP3Sgl6V3p0lCpkjnla8wY3dUlIiIiImna9tM36D//AAGB4QA0KZuLQc1Kk83L1eJkkqat+BAib0L20vDs+1ankYekQoyIiEgqER8PX39tFl3i46FQIZg5EypWtDqZSArJnBlWrDALMDab1WlERERERB5JaFQsX/9xlOnbzwGQw9uVL1o8Qf3SWkxdHtPJ1bB/FuAAzb8Hm7PVieQhqRAjIiKSCuzbZy6FsXGjud+hA/z0E3h7W5tLJNldvgxLl8Ibb5j77u7W5hEREREReQxrjlzl44UHuRwcBcCLz+Sjf6OS+LjrA3N5TDHh8HtPc7tSV8hbwdo8kigqxIiIiFjo4kX45BOYPBkMw2w/9uOP8Mor6sokGcCtW9CgARw4AGFh0KOH1YlERERERB7JjbBoPltymMX7LgFQIKsHQ1qXoWphP4uTSbqx7isIOgc++aD2x1ankURSIUZERMQCYWEwbBgMHw4REeax55+HIUPA39/abCIpIiICmjUzizC5ckGLFlYnEhERERFJNMMwWLT3Ep8tOcStiFgcHeDN6oXoUbcY7i5qtytJ5OJu2DbG3G76Lbh6WptHEk2FGBERkRRkGPDrr9C/P1y9ah6rWhVGjIDKla3NJpJiYmPNyuPmzXfWhVEFUkRERETSmItBkXy04ADrj10HoEROL75pW5ayeTNbG0zSl/hYWNwdDDuUaQdF61mdSB6BCjEiIiIp5OpVePNNWLLE3C9cGIYOhdat1YZMMhC73VwP5vffwc3N/IUoU8bqVCIiIiIiD81uN5i2/SxD/zhKeEw8LjZHutcpwls1C+Nsc7Q6nqQ3W3+AqwfAPQs0GGJ1GnlEKsSIiIikgMWLzc+er18HFxf4/HPo2dPcFslQ+vWDKVPAZoM5c6BaNasTiYiIiIg8tJPXwhgwfz87z9wC4OkCWfi6TVmKZFerKEkGN07B+q/N7QZDwDObtXnkkakQIyIikoxCQ82Cy4QJ5n6ZMjBtGpQta20uEcvkyWNOAZs4EZo2tTqNiIiIiMhDiY2388vG04xefYKYeDuZXGz0a1SCjpUK4OioFgeSDAwDlrwPcVFQ6Dl48gWrE8ljUCFGREQkmWzZAp06wenT5ufOvXvDF1+Y3ZhEMqwePaBBAyhZ0uokIiIiIiIP5cCFYD6Yt58jl0MAqFU8G4NblSFPZneLk0m6tmcanNkETu7Q9Fv1NE/jVIgRERFJYjEx8Nln8PXX5nIY+fPDr79CrVpWJxOxyIYN8OSTkDmzua8ijIiIiIikAZEx8YxafZxxm05jNyCLhzOfNitFy3J5cNCH4pKcQq/Cyo/N7ec+BF9/a/PIY1MhRkREJImEhcH06TB6NBw5Yh7r1Am+/x58fKzNJmKZjRuhYUMoVgzWroWsWa1OJCIiIiLyn7aeusGA+fs5cyMCgGZP5mZgs1L4ebpanEwyhOX9ICoIcj0Jld+2Oo0kARViREREHtPx4zBmDEyeDMHB5jFfXxg7Ftq1szSaiLX27YPmzSEqCgoWVEVSRERERFK9kKhYhiw7ym87zgGQ09uNL1s+Qd1SOSxOJhnGseVwaAE42KD592DTR/jpgf4vioiIPKKDB6FvX1i+/M6xIkXg7bfh1VchSxbLoolY7/RpcyZMcDBUrw4zZ4KT3nqKiIiISOq16vBVPl54gKsh0QC8VCk//RqVwNvN2eJkkmFEh8LSXuZ2lXfMGTGSLmg0LCIikkihoeYaMKNGQXy8uV5ekybw7rtQrx44OlqdUMRiV69C/fpw5QqULQuLF4O7FjIVERERkdTpemg0g5YcYun+ywD4+2ViSOsyVC6ktrqSwtZ8DiEXIUtBqDXA6jSShFSIEREReUiGAXPmQM+ecOmSeaxVKxg2DAoXtjabSKoRHGzOhDl1Cvz9zSljmTNbnUpERERE5B6GYTB/90W+WHqYoIhYbI4OvFm9ED3qFsXN2WZ1PMlozu+AHePM7aajwMXD0jiStFSIEREReQjHj5szXlatMvcLF4bvv4dGjazNJZLqXL8OgYGQPTusXAm5clmdSERERETkHhduRfDhgoNsPH4dgFK5vPmmbVmeyKN1DcUCcTGwuDtgQLmXoPBzVieSJKZCjIiIyL+IiICvvjJnvcTEgKsrDBgA/fqBm5vV6URSoSJFYPNmuHXL3BYRERERSUXi7QZTt57hmxXHiIiJx8XJkR51i/Jm9UI429RnWiyyeRRcPwIeflD/S6vTSDJQIUZEROQBliyB7t3hzBlzv1EjcxaM2pCJ/INhwJEjUKqUuZ8/v/klIiIiIpKKnLgaSr95+9l9LgiAZwr6MqRNGQpn87Q2mGRs14/BxmHmdqOh4OFrbR5JFirzioiI/ENAADRvbn6dOQP58sH8+bB0qYowIvf14YdQvjzMnWt1EhERERGRe8TE2fluzQmafPcnu88F4enqxJctn2Bml8oqwoi17HZY8j7Ex0DR+vBEG6sTSTLRjBgREZG/RUebLcgGD4aoKHBygt694ZNPIFMmq9OJpFIjR8LXX5vbQUGWRhERERER+ad954PoN28/R6+EAlC7RHa+bPkEuTO7W5xMBPhrEpzbCs6ZoMlIcHCwOpEkExViREREMNcUf/ddOHHC3H/uOfjxRyhZ0tpcIqna1KlmtRJgyBB44w1r84iIiIiI/C0yJp6Rq44x4c8A7Ab4ZnJhYLNSNH8yNw76sFtSg5BLsHqQuV3nU8icz9I4krxUiBERkQztwgXo1QvmzDH3c+aEESPgxRd1I4rIv1q6FDp3Nrd79oR+/azNIyIiIiLyty0nA+k//wDnbkYA0LJcbj5tVhrfTC4WJxP5P8v6QnQI5HkannnT6jSSzFSIERGRDCk2FkaPhkGDIDwcHB3hvffgs8/Ax8fqdCKp3ObN0K4dxMdDx44wfLgqlyIiIiJiueDIWL5aeoRZu84DkNvHjcGtyvBciewWJxP5h8OL4ejv4OgEzb8DR5vViSSZqRAjIiIZzsaN8PbbcOiQuV+lCowZA+XKWRpLJO2YMQMiI6FRI5g40axkioiIiIhYaMWhK3yy8CDXQqMBeLlKAT5oWAJPV338KalMZJA5Gwbg2R6Qo7SVaSSF6G8iERHJMC5fhv79YcoUcz9rVvjmG3j1VX2OLJIo338PxYvD66+Ds7PVaUREREQkA7sWGsWgxYdYduAKAIWyZWJom7JULOhrcTKRB1g9EMKuQNYiUKOv1WkkhagQIyIi6d7BgzByJEyfDjExZgelLl1g8GCzGCMiDyEoCLy8wGYzK5fdu1udSEREREQysNh4O79uOcPo1ScIjY7D5uhA15qFeK92Udyc1eZJUqkzm+GvyeZ2s+/A2c3SOJJyVIgREZF0yTBg5UqzALNy5Z3jVavCt9/CM89Yl00kzQkNhbp1oXBhc0qZq6vViUREREQkA9tyMpCBiw9x4loYAGXz+jCkdRlK59aCn5KKxUbBkr9vaKvwKhR81tI4krJUiBERkXQlKsqc+TJyJBw+bB5zdIQ2baBnT3M9GBFJhOhoaNUK/voLzp6FixehUCGrU4mIiIhIBnQxKJLBSw8ntCHzzeTCBw2K0/7pfDg6OlicTuQ/bBoON06CZ06o+5nVaSSFqRAjIiLpxpw58O67cO2aue/lBW+8Ae+9B/7+1mYTSZPi46FjR1izBjw94Y8/VIQRERERkRQXFRvPuI2n+XH9SaJi7Tg6wMtVCtKzbjF8PLRmoaQBVw/Bn9+a242HgXtmS+NIylMhRkRE0rz4ePjwQ/jmG3M/Xz54/32zCOOjmekij8YwzMrm3Lng7AwLFsDTT1udSkREREQyEMMwWHPkGp//fphzNyMAeMbfl8+al6ZkLm+L04k8JHs8LO4O9jgo0RRKNbc6kVhAhRgREUnTbtyAF1+EVavM/b59YfBg83NjEXkMgwbB2LHg4GD2+6tb1+pEIiIiIpKBBASG89mSQ6w/dh2AHN6ufNi4JM2fzI2Dg9qQSRqyYxxc3AWu3uZsGMmQVIgREZE0a98+c+mKgADw8ICJE+H5561OJZIOnDkDQ4ea2z/+CO3aWRpHRERERDKO8Og4flh3kgmbAoiJt+Nsc+CN6oV497kiZHLVR5mSxgSdhzWfm9t1B4F3bkvjiHX0t5eIiKRJv/0Gr78OkZHmkhULFkDZslanEkknChaEFStg+3bo1s3qNCIiIiKSARiGwZL9l/lq6RGuhEQBUKt4Nj5tWopC2TwtTifyCAwDlvaC2HDIXwUqdLY6kVhIhRgREUlTYmKgXz8YNcrcb9AAZswAX19LY4mkD3Fx4PT328OaNc0vEREREZFkduRyCIMWH2J7wE0A8vt68GnTUtQpmV1tyCTtOjgPTqwEmws0+w4cHa1OJBZSIUZERNKMU6fM1mN//WXuDxgAX3wBNpu1uUTShe3boVMnmDcPypSxOo2IiIiIZADBEbF8u/o4U7aewW6Am7Mj79Qqwps1CuHmrIGepGERN+GPfuZ29T6QrZi1ecRyKsSIiEiaMHMmdOkCoaHm7JdJk6B5c6tTiaQTR45AkyZw44ZZ3Zw92+pEIiIiIpKO2e0Gs3ed55sVx7gZHgNA4zI5+ahJKfJkdrc4nUgSWPkxRARCtpJQrafVaSQVUCFGRERStYgI6N4dJkww96tVM1uR5ctnbS6RdOP8eahf3yzCPPMMTJxodSIRERERScf2ng9i4KKD7LsQDEDR7J4Mal6aZ4v4WZxMJImcWgd7pwMO0Pw7cHKxOpGkAirEiIhIqnXwoNmK7PBhcHCAjz6CgQPvLGEhIo/pxg2zCHPhAhQvDkuXgqcWQhURERGRpBcYFs03y48ye9cFALxcnXi/blFeqVoQZ5vWzpB0IiYCfu9hbj/zJuR7xtI4knrooywREUl1DAPGjzdnwkRFQc6cMH061K5tdTKRdCQszGxHdvQo5M0LK1eCn+5CFBEREZGkFRdvZ8rWs3y7+jihUXEAtK2Qlw8aFie7l5vF6USS2Iav4dYZ8M4DdT61Oo2kIirEiIhIqhIcDG+9BbNmmfsNGsCUKZA9u7W5RNKdTz6B7dvNRZdWrID8+a1OJCIiIiLpzNZTNxi0+BDHroYCUCaPD4Oal6ZCgSwWJxNJBpf3wZYfzO0mI8HVy9o8kqqoECMiIqnGzp3wwgtw+rTZfuyrr6B3b3DULHWRpPfZZ3DqFHz4IZQqZXUaEREREUlHLgVFMnjZEZbuvwxAFg9n+jYowfMV82FzdLA4nUgyiI+Dxe+BEQ+lW0HxhlYnklRGhRgREbGc3Q7ffgv9+0NcHBQsCL/9BpUrW51MJB3z9obFi61OISIiIiLpSHRcPOM3BfDD2pNExsbj6AAdKxegV71iZPbQguWSjm0bY86IccsMjb6xOo2kQirEiIiIpQID4ZVXYNkyc79NG3N9mMyZLY0lkj4NHgzOzvDBB1YnEREREZF0Zu3Rq3y25DBnb0QAULFgFj5r/gSlcntbnEwkmd0MgHVfmdv1vwRP9VaXe6kQIyIillm6FLp0gUuXwNUVRo0y14dx0Ex1kaT388/w8cfmdpUqUL26tXlEREREJF04ExjO578fZu3RawBk93LloyYlaf5kbhw0uJP0zjDg9x4QFwn+NaB8R6sTSSqlQoyIiKS406ehRw9YssTcL1ECZs2CsmUtjSWSfs2dC926mdsffaQijIiIiIg8toiYOH5cd5JxGwOIibfjbHPgtWr+vFe7KJ6u+shRMoh9M+H0enByg6ajdGepPJD+VhQRkRQTGQlDh8LXX0N0NDg5Qc+eMHAgZMpkdTqRdGrtWnjpJfNOrS5d4IsvrE4kIiIiImmYYRj8vv8yXy07wuXgKABqFMvGwGalKJzN0+J0Iiko7DqsGGBu1+wHWQtbm0dSNRViREQk2RmGOfulRw8ICDCP1akD338PJUtaGk0kffvrL2jRAmJizAWYxozRHVoiIiIi8siOXQll4OKDbDt9E4B8vu580qQU9UrlUBsyyXhWDIDIW5CjDFR9z+o0ksqpECMiIsnq1Cno3h2WLTP38+SBb7+Ftm31ebBIsrp5Exo1grAwqF0bpk8Hm83qVCIiIiKSBgVHxjJq9XGmbD1LvN3A1cmRt2sV4a2ahXBz1ntMyYBOrIIDc8DBEZp/BzZnqxNJKudodYB/c/DgQSpWrEiWLFno27cvhmH86/mGYdCtWzd8fX3JnDkzr776KpGRkSmUVkRE/p9hwPjx8OSTZhHG2Rn69YOjR6FdOxVhRJKdry98+ik8/TQsWACurlYnEpFkoDGTiIgkJ7vdYPbO89Qevp5Jm88Qbzdo9ERO1vSuyft1i6oIIxlTdBj83tPcrvw25HnK2jySJqTaQkx0dDTNmjWjQoUK7Nq1i8OHDzN58uR/fc7UqVM5duwYe/bsYdOmTRw6dIghQ4akTGAREUkQGAitW8Obb0J4ONSsCQcOmGvDeKplsEjKefdd2LIFvL2tTiIiyUBjJhERSU77zgfR6qctfDBvPzfCYyiS3ZNpr1fip44VyJvFw+p4ItZZNxiCz0Pm/PDch1ankTQi1RZi/vjjD4KDgxk5ciSFCxfmq6++YsKECf/6nB07dtC2bVsKFChAmTJlaNmyJSdPnkyhxCIiArBiBZQpAwsXmrNghg6FNWugeHGrk4lkABER8N57Zluy25w1RV4kvdKYSUREksONsGj6zd1PyzGb2Xc+CE9XJz5uUpI/3q9OtaJ+VscTsdaFv2DbT+Z202/BJZO1eSTNSLVrxOzbt4/KlSvj4WFW2MuWLcvhw4f/9TmlS5dm6tSptGnThqioKGbOnEmvXr0eeH50dDTR0dEJ+yEhIUkTXkQkA4qMhP794bvvzP2SJc0lKcqXtzaXSIYRGwvt28PSpbB3L2zcqB6AIumcxkwiIpKU4uLtTNt2lpGrjhMSFQdA66fy0L9RCbJ7uVmcTiQViI+Fxe8BBpR9HorUtTqRpCGpdkZMSEgI/v7+CfsODg7YbDZu3br1wOe88cYbhIWFkTNnTgoWLIi/vz+vvPLKA88fMmQIPj4+CV/58uVL0tcgIpJR7N8PFSveKcK88w7s2qUijEiKsdvh9dfNIoy7u9kHUEUYkXRPYyYREUkq207foOn3fzJoyWFCouJ4Io8387pVYWT7cirCiNy2eTRcOwQeWaGBWrtK4qTaQoyTkxOu/1hU1s3NjYiIiAc+Z/To0WTOnJmzZ89y7tw54uLi6Nu37wPPHzBgAMHBwQlf58+fT7L8IiIZgd0OI0eaRZhDhyBHDvNz4B9+AA+1DBZJGYYBffvC1Klgs8GcOfDss1anEpEUoDGTiIg8rsvBkbz32x5e+GUbR6+EktnDmcGtnmDRO9WoUMDX6ngiqUfgSdjwjbndYAhkymptHklzUm1rMl9fXw4ePHjXsdDQUFxcXB74nOnTp/P555+TP39+wLx7q2bNmowYMeK+57u6ut4zcBERkYdz8SK88oq5/gtAs2Ywfjxkz25tLpEMZ9gwsyIKMHEiNGlibR4RSTEaM4mIyKOKjotnwp8B/LD2JBEx8Tg6wEuVCtC7fjEyezz43xGRDMluhyXvQ3w0FK4DZdtbnUjSoFRbiKlYsSLjxo1L2A8ICCA6Ohpf3wdX4+12O9euXUvYv3LlCvHx8cmaU0QkI5o7F7p0gVu3zC5I335r7qsTkkgKmzoV+vUzt0eMgJdftjaPiKQojZlERORRrDt6jc9/P0xAYDgATxfIwmctSlM6t4/FyURSqT1T4eyf4OwBTUfqww95JKm2EFOjRg1CQkKYNGkSnTt35quvvqJu3brYbDaCgoLw8vLCZrPd9Zzq1avz9ddfY7PZiImJYejQoTRv3tyiVyAikv6EhkL37jB5srn/9NMwbRoUL25pLJGMq1IlKFAAXngB/mWxbRFJnzRmEhGRxDh7I5wvfj/M6iNmQT67lysfNi5Ji3K5cdAHyyL3F3oFVn1ibj/3EWQpaGkcSbscDMMwrA7xIIsXL+bFF1/E3d0dR0dH1q9fT6lSpXBwcGDPnj2UK1furvODgoLo3r07y5cvJzQ0lAYNGjB+/Hj8/Pwe6vuFhITg4+NDcHAw3t7eyfCKRETSrl274Pnn4fRp8+aPAQNg0CBwdrY6mUgGd+MG+Prqrix5ZHoPnLZpzCQiIv8lMiaeMetP8vPG08TE2XFydOD1av68V6conq6p9h5tkdRh9stweBHkLg+vrwabfmcyoqR4D5yqCzFgTpX/66+/qFy5MlmzJu8iSBpUiIjcyzBg7Fjo0QNiYsyb76dOherVrU4mkkHt2wdXr0L9+lYnkXRC74HTPo2ZRETkfgzDYNmBKwxeephLwVEAVC/qx8BmpSmS3dPidCJpwNGlMLMDONjgrQ2Qs4zVicQiSfEeONWX8HLmzEkTLTorImKJsDB46y2YMcPcb9kSJk2CzJmtTCWSgZ0+DQ0awM2b8PvvKsaICKAxk4iI3Ov41VAGLT7EllM3AMibxZ1PmpaifqkcakMm8jCiQmBpH3P72e4qwshjS/WFGBERscbhw9C2LRw5AjYbDB1qLkGh9+wiFrlyBerVM2fDPPmkuT6MiIiIiMj/CYmKZdSqE/y69QzxdgNXJ0e61SpM15qFcXO2/fcFRMS05jMIvQS+haBmP6vTSDqgQoyIiNwlMhK+/tosvERHQ+7cMGsWVKtmdTKRDCw4GBo2NGfEFCoEy5eDj4/VqUREREQklbDbDebtvsDQ5UcJDIsBoEHpHHzcpBT5fD0sTieSxpzbBjvHm9vNRoOzu7V5JF1QIUZERABzLZjFi821YM6cMY81bAi//grZs1uZTCSDi4qC5s3NtWFy5ICVKyFnTqtTiYiIiEgqsf9CEAMXH2LPuSAACmfLxKDmpaleNJu1wUTSorhoWNzd3C7fEfxrWJtH0g0VYkREhJMn4f33Ydkycz9fPvj2W2jdWq3IRCwVFwcvvAAbN4K3tzkTpnBhq1OJiIiISCpwIyya4SuPMXPneQwDMrnYeL9uUV6t6o+Lk6PV8UTSpk0jIfAYZMoO9b6wOo2kIyrEiIhkYBERd9qQxcSAszP07QsffgiZMlmdTkQA8PUFV1dzylq5clanERERERGLxcXbmb79HCNWHiMkKg6A1uXz0L9RCbJ7u1mcTiQNu3YUNo0wtxsNBQ9fa/NIuqJCjIhIBnS7Ddn778PZs+ax+vXh+++hWDFrs4nI/3FyggkToFcveOIJq9OIiIiIiMW2n77BwMWHOHolFIBSubz5vEVpni6oD4xFHovdDku6gz0WijWE0q2sTiTpjAoxIiIZzMmT0L07/PGHuZ8/v9mGrFUrtSETSTVWrYLnnjMLMQ4OKsKIiIiIZHChUbF8svAgC/deAiCzhzN96hfnxWfyY3PUQE7kse2aAOe3g4snNBmhD0gkyakQIyKSQUREwJAh8M03ZhsyFxfo00dtyERSnalT4eWXzero7NlmMUZEREREMqzLwZF0nrSTo1dCcXCADs/kp0/94mTJ5GJ1NJH0IfgirP7M3K4zEHzyWptH0iWN7EVE0jnDgEWLoEePO23IGjQw25AVLWppNBH5p6VLoXNnc9vfH2w2a/OIiIiIiKWOXA6h86SdXAmJIpuXK790qkD5/FmsjiWSfhgGLOsDMaGQ9xmo+LrViSSdUiFGRCQdO3HCbEO2fLm5nz8/jBoFLVtqlq1IqrN5M7RrB/Hx0KkTDBumX1QRERGRDGzTiet0m7absOg4imb3ZFLniuTN4mF1LJH05fAiOLYMHJ2h+XfgqJvhJHmoECMikg5FRMBXX5mf495uQ9a3r9mGzEPv20VSnwMHoGlTiIyExo1hwgRwdLQ6lYiIiIhYZPau83w4/wBxdoPKhXz5udPT+Lg7Wx1LJH2JvAXL+prb1XtB9pLW5pF0TYUYEZF0xDBgwQLo2RPOnTOPNWwI332nNmQiqdaZM2a/wKAgqFoV5swBZw2yRURERDIiwzAYtfoEo9ecAKBludwMbVsWVyfdpS+S5FZ9CuHXwK8YVO9tdRpJ51SIERFJJ44fN9uQrVhh7hcoYLYha9FC3Y1EUrWTJ80izBNPwO+/a9qaiIiISAYVE2dnwPwDzNt9AYB3nytC7/rFcNCATiTpBWyC3VPM7WbfgZOrtXkk3VMhRkQkjQsPN9uQDR9+pw1Zv37Qv78+zxVJE+rWhbVrIV8+yKKFV0VEREQyopCoWN6etps/TwZic3Tgy5ZP8OIz+a2OJZI+xUbCkvfN7adfgwJVrM0jGYIKMSIiadTtNmQ9esD58+axRo3MNmRFilgaTUT+S1QUXL1qTl0DqFzZ2jwiIiIiYpnLwZF0nrSTo1dCyeRi44eXnuK54tmtjiWSfm34Bm6eAq9cUHeQ1Wkkg9AqsCIiadDx4+baL23amEWYAgVg4UJYulRFGJFULz4eOnaESpVgzx6r04iIiIiIhQ5fCqHVj1s4eiWU7F6uzHqrioowIsnpzJ+weZS53Xg4uPlYGkcyDs2IERFJQ8LDYfBgsw1ZbCy4usIHH6gNmUiaYRjw9tswb57ZR/DmTasTiYiIiIhFNh6/ztvTdxMWHUexHJ5M6vwMeTK7Wx1LJP0KD4R5b4Bhhyc7QMmmVieSDESFGBGRNMAwYP586NnzThuyxo3NNmSFC1ubTUQS4dNP4ZdfwMEBpk+HOnWsTiQiIiIiFpi96zwfzj9AnN2gSqGsjO1UAR93Z6tjiaRfdjvM7wKhl8GvODQZbnUiyWBUiBERSeWOHYP33oNVq8z9ggVh9Gho1sz8LFdE0ojvvoMvvzS3f/oJ2ra1No+IiIiIpDjDMPh29Qm+W3MCgFbl8zC0TVlcnLR6gEiy2jwKTq0BJ3doNxlcMlmdSDIYFWJERFKp8HDzM9sRI+60IevXz2xD5q7Z6iJpy4wZ8P775vYXX8Bbb1mbR0RERERSXEycnf7z9zN/90UA3qtdhF71iuGgO+xEktfZrbD275viGn8DOUpZm0cyJBViRERSGcOAuXOhVy+4cME81rQpjBqlNmQiaZLdDmPGmNvvvQcffWRtHhERERFJcSFRsXSb9hebT97A5ujA4JZP8MIz+a2OJZL+RdyEea+DEQ9l2kP5TlYnkgxKhRgRkVTk6FHzc9rVq819f/87bchEJI1ydITly812ZL17q6egiIiISAZzKSiSzpN2cuxqKJlcbPz40lPUKp7d6lgi6Z/dDgu6QshFyFoEmo7UeEwso0KMiEgqEBZmtiEbOfJOG7IBA+CDD9SGTCTNCg4GHx9z29MT+va1No+IiIiIpLjDl0LoPHkHV0Oiye7lyqTOFSmd28fqWCIZw9Yf4MQKsLma68K4elmdSDIwrQQmImIhw4A5c6BkSRg61CzCNGsGhw/DwIEqwoikWefOQZkyMHiw+YsuIiIiIhnOxuPXaf/zVq6GRFMshycL3nlWRRiRlHJ+J6z5zNxu9DXkLGNtHsnwVIgREbHI0aNQrx60b2+uBVOoECxZAosXm9sikkYFBkKDBnD+PEybBuHhVicSERERkRQ2e+d5Ok/eSVh0HFUKZWVO16rkyaw77URSRMRNmNsZ7HFQujVU6Gx1IhG1JhMRSWlhYfDFF/Dtt+YMGDe3O23I3NysTicijyUsDJo0MSutefPCihVmWzIRERERyRAMw+DbVcf5bu1JAFqXz8PXbcri4qR7oUVShGHAoncg+Dz4FoJmo7UujKQKKsSIiKSQ223IevWCixfNY82bw6hR4O9vaTQRSQoxMdCmDezYAb6+sHIl5M9vdSoRERERSSExcXb6z9vP/D3mgK977SL0rFcMB30ILJJytv0Ex5aBzcVcF8bN2+pEIoAKMSIiKeLIEXj3XVi71twvVAi++868cV5E0gG7HV55xSy+eHjAsmXm4k8iIiIikiGERMXSdepfbDl1A5ujA1+1eoLnK+qmHJEUdfEvWPWpud3gK8j1pLV5RP6PCjEiIskoNPROG7K4OLUhE0m3Vq+GmTPByQnmz4dKlaxOJCIiIiIp5FJQJJ0n7eTY1VAyudgY07ECNYtlszqWSMYSGQRzXgV7LJRqARXfsDqRyF1UiBERSQaGAbNmQe/ecOmSeaxFC7MgozZkIulQ/frwyy/mejANGlidRkRERERSyKFLwbw2eSdXQ6LJ7uXKpM4VKZ3bx+pYIhmLYcDidyHoHGQuAM2/17owkuqoECMiksQOHzbbkK1bZ+4XLmy2IWvc2NpcIpIM4uPBZjO333zT2iwiIiIikqI2HL/O29P+IjwmnmI5PJnU+RnyZHa3OpZIxrNjHBxZAo7Of68Lo2KopD6OVgcQEUkvQkOhb1948kmzCOPmZrYlO3hQRRiRdGnOHHj2Wbh+3eokIiIiIpLCZu08x2uTdxIeE0/VwlmZ07WqijAiVri0F1Z+ZG7X/wLyPGVpHJEHUSFGROQxxcfDr79CiRIwfLi5FkzLlnDkCHz8sdaCEUmXVq+Gl16C7dvhp5+sTiMiIiIiKcQwDEasPEa/eQeItxu0fioPkzs/g4+7s9XRRDKeqBBzXZj4GCjRFCp1tTqRyAOpNZmIyCMyDFi2DPr3N2e9ABQpYrYha9TI2mwikox27YJWrSA2Ftq2hY8+sjqRiIiIiKSAmDg7/eftZ/6eiwB0r1OUnnWL4qC1KERSnmHAku5wKwB88kOLH7QujKRqKsSIiDyCAwfMdWA2bjT3s2SBAQPgvfc0A0YkXTt+3Ky0hoVB7dowbdqdNWJEREREJN0Kjoyl27S/2HLqBjZHB4a0KkP7ivmsjiWSce2aCIcWgKMTtJsE7lmsTiTyr1SIERFJpOnTzTW5IyPNosv770O/fmYxRkTSsYsXoV49CAyEChVg4UJwdbU6lYiIiIgks4tBkXSetIPjV8PI5GJjTMcK1CyWzepYIhnX5f2wfIC5XXcQ5H3a0jgiD0OFGBGRhxQbC336mK3HABo0gHHjIJ9ughLJGF59Fc6dg2LF4I8/wMvL6kQiIiIikswOXgzmtck7uRYaTQ5vVya+WpHSuX2sjiWScUWH/r0uTDQUawhV3rU6kchDcbQ6gIhIWnD5stmF6HYR5pNPYOlSFWFEMpSxY6FGDVi5ErLpDkgRERGR9G79sWs8//NWroVGUzyHFwveflZFGBErGQb83hNungLvvNDyJ60LI2mGZsSIiPyHzZuhXTuzGOPtDVOnQvPmVqcSkRRXuDCsX683+iIiIiIZwMwd5/ho4UHi7QbPFsnKTx0r4O3mbHUskYxt9xQ4MAccbNB2Inj4Wp1I5KFpRoyIyAMYBnz/PdSqZRZhnngCdu1SEUYkw7Db4a23zOlvt6kIIyIiIpKuGYbB8BXH6D//APF2gzZP5WXSq8+oCCNitauH4I8PzO06n0D+StbmEUkkFWJERO4jIgJefhm6d4e4OHjhBdi2DYoWtTqZiKQIwzAXhfrlF2jbFq5csTqRiIiIiCSzmDg7vWbv44d1JwHoXqcow9uVxcVJH5+JWCo6zFwXJi4KitSDqu9bnUgk0dSaTETkH06fhtatYd8+sNlg2DDo0UM3wotkKN98A99+a27/8gvkzGltHhERERFJVsGRsXSd+hdbT9/AydGBr1qVoX1FLQoqkios6wOBx8ErF7QaC44qjkrao0KMiMj/WbYMXnoJgoIge3aYPRtq1rQ6lYikqAkToH9/c3vkSOjUydo8IiIiIpKsLgZF0nnSDo5fDcPT1YkxLz1FjWLZrI4lIgB7psO+38DBEdpMgEx+VicSeSQqxIiIYC4F8eWXMGiQ2ZGocmWYOxfy5LE6mYikqIULoUsXc7t/f+jZ09I4IiIiIpK8Dl4M5rXJO7kWGk0Ob1cmvfoMpXJ7Wx1LRACuHTVnwwA89yEUfNbaPCKPQYUYEcnwgoLMG95//93c79bN7Ejk6mppLBFJafv3mwtC2e3w+uvw1VdWJxIRERGRZLTu2DXemb6biJh4SuT0YuKrFcmd2d3qWCICEBNhrgsTGwGFnoNqva1OJPJYVIgRkQxt/35zPZhTp8zCy9ix8OqrVqcSEUuULg0vvwzXr5t/GWhhKBEREZF067cd5/h44UHi7QbVivgxpuNTeLs5Wx1LRG77oy9cPwKeOaD1L1oXRtI8FWJEJEOKj4fx46FXL4iIgAIFYP58eOopq5OJiGVsNvj5Z4iNBSe9RRIRERFJjwzDYPjKY/y47hQAbZ7Ky5DWZXBx0oe8IqnGvlmwZ9rf68KMB8/sVicSeWz6V0ZEMpzVq6F8eeja1SzC1K8Pf/2lIoxIhnT5srkWTGysue/gAC4u1mYSERERkWQRE2en56y9CUWY9+sUZXi7sirCiKQm14/D73+v1VmzH/jXsDaPSBLR7Z4ikmEcOwZ9+txZCyZLFhg4EN5917wRXkQymKAgaNQI9u2DsDD44QerE4mIiIhIMgmOjKXr1L/YevoGTo4OfNW6DO2fzmd1LBH5f7GRf68LE24WYGr0tTqRSJJRIUZE0j3DgB9/hN69ISbG7Dj09ttmEcbX1+p0ImKJyEho3twswuTMafYpFBEREZF06cKtCDpP2smJa2F4ujrxU8enqF40m9WxROSflveHa4cgUzZoPR4cddespB8qxIhIunbrFrz+OixYYO43bAijRkHx4pbGEhErxcXBCy/Apk3g7Q3Ll0OhQlanEhEREZFkcPBiMJ0n7+R6aDQ5vd2Y1LkiJXN5Wx1LRP7pwFz4azLgAK3HgVcOqxOJJCkVYkQk3dq2zfys9exZcHaG4cPhvffMJSBEJIMyDOjSBRYvBjc3WLIEnnzS6lQiIiIikgzWHbvGO9N3ExETT4mcXkzqXJFcPu5WxxKRf7pxCpa8b27X6AOFn7M2j0gyUCFGRNIdw4ARI2DAAPPG90KFYNYsePppq5OJiOU+/hgmTTIXhpo1C2po4UcRERGR9GjG9nN8sugg8XaDakX8GNPxKbzdnK2OJSL/FBsFc16BmDAo8CzU7G91IpFk4Wh1ABGRpGQY5lowffuaRZj27WH3bhVhRORvNWuCpyeMG2euESMiIiIi6YphGAxbcZQPFxwg3m7QtkJeJnWuqCKMSGq18iO4cgA8skKb8WDTvAFJn/QnW0TSlaFD4dtvze3vv4d33lErMhH5P/Xrw8mTkEP9hkVERETSm+i4eD6Yu59Fey8B0KNuUd6vUxQHDQpFUqdDC2HneHO71S/gndvSOCLJSYUYEUk3xo8325EBjBwJ775rbR4RSSVWrICCBaF4cXNfRRgRERGRdCc4Ipa3pu1i2+mbODk6MKR1Gdo9nc/qWCLyIDdPw+L3zO1qPaFoXWvziCQzFWJEJF1YsADeesvcHjAAeva0No+IpBJ//gktW5rtyLZsgaJFrU4kIiIiIknswq0IXp20k5PXwvB0dWJsxwpUK+pndSwReZC4aJjTGaJDIF9leO5jqxOJ/I+9+w6PomrfOP7dFBIIJEDovRcBaVIEASmC9KbSu4jSEcsrimJDuiAgFqT3KtIRBQWld6T3LiWQBNKz8/tjXvHlB2ggm5xscn+uK1fO7s7u3NGwmZ1nznkSnAoxIuL21q2D1q3B6YRu3eDTT00nEpEk4cABaNwYIiKgdm17VoyIiIiIJCsHLgTTdfoOroVGks3fl6ldKlA8u7/pWCLyT358Hy7vhdQZ4IXv1BdGUgT9louIW/v1V/ti96goaNECvvpKPWFEBDh9GurVg1u3oGpVWLAAvNWgVURERCQ52XDkKr3m7CYsKpZi2dIxtUsFsgekNh1LRP7J4eWw7St73PxrCMhlNo9IIlEhRkTc1vbt0LAhhIdD/fowdy546V1NRK5ehbp14fJlKFkSli+HNGlMpxIRERERF5qz7RyDlx0k1mlRrXAmvmxXjnS+uvBGJEm7eRaW9bLHVfpCkXpm84gkIp2yFBG3tG+ffbH77dtQsyYsXgypUplOJSLGhYTYldkTJ+ylyNauhQwZTKcSERERERdxOi1GrTvKlxtPAvBi+VwMbVEKb08Pw8lE5B/FRMGiLhARDLkqQu33TScSSVQqxIiI2zl0CJ57zl5x6Omn4YcfILVmn4sIQGws+PpC5sx2A6kcOUwnEhEREREXiYyJ5a1F+1m29xIAA+oUoW/tQji0PrVI0vfTh3BxF/imhxemgKdmsEnKokKMiLgNy4LJk2HAALhzB8qVg1WrIG1a08lEJMnIkAF+/BHOnoXChU2nEREREREXCQ6L5pWZO9l2OggvDwfDWj7JC+XVW0LELRxdDVsm2ONmkyB9brN5RAzQvE0RcQtXrkCTJvDKK3YRpnp1e8Wh9OlNJxMR4ywLfv7579tp0kDx4ubyiIiIiIhLnQ8Ko+VXv7PtdBDpfLyY1qWiijAi7uLWeVj6qj2u3AuKNTCbR8QQFWJEJMlbtw5KlYIVK+w+MKNGwYYNkCmT6WQikiS8/z7Urg0ffmg6iYiIiIi42IELwTT/8ndOXL1NNn9fFr72NM8U1odBEbcQGw2LukLELchZHuoMMZ1IxBgtTSYiSZZlwbBh8O679rh0aZg50y7KiIgA8MUX8Mkn9jh7drNZRERERMSlfj7yJ71m7yE8OpZi2dIxtUsFsgeoQaiI2/j5Y7iwHXwC7L4wXqlMJxIxRoUYEUmSQkKgc2dYutS+/fLLMH683YNbRASAOXOgXz97/Mkn9tqFIiIiIpIszN52lsHfH8RpQbXCmfiyXTnS+aq5t4jbOLYOfhtnj5tNhAz5jMYRMU2FGBFJcg4fhubN4ehReymyCROge3fTqUQkSVmzBjp1ssd9+8KgQWbziIiIiIhLOJ0WI9cdZdLGkwC8WD4XQ1uUwttTq+uLuI3gi7C0hz2u2AOKNzabRyQJUCFGRJKUn3+GZs0gNBRy5oTFi6FSJdOpRCRJ2boVWraEmBho0wY+/xwcDtOpRERERCSeYmKdvLVoP0v2XATg9eeK0KdWIRw61hNxH7ExsLgbhAdB9jJQ92PTiUSSBBViRCTJWL0aWrSAiAioVg0WLoSsWU2nEpEkZ8cOCAuDevVg2jTw0NWRIiIiIu4uKsZJv3l7WH3wCl4eDoa1fJIXyucyHUtEHtXGoXBuC/j4w4tTwcvHdCKRJEGFGBFJEpYuhVatIDoaGjeGBQvUD0ZEHqJPH8iTB+rUsdcvFBERERG3FhEdy2uzdrHh6DVSeXrwZbty1HlCV+WJuJ0TP8GmMfa4yReQsYDZPCJJiC4hFRGjLAu++gpefNEuwrz4IixapCKMiPw/169DSMjft5s2BT8/c3lERERExCXuRMbQddoONhy9hq+3B991fkpFGBF3FHIZlrwCWPBUNyjR3HQikSRFM2JExJgzZ6B7d1i/3r7doQNMmQJeemcSkf8VGgr160NsrL2GodYsFBEREUkWQiKi6TJ1B7vO3sQvlSdTOlegUoFA07FE5FE5Y2FJdwi7DtlKQb2hphOJJDk63Skiic7phC+/hP/8B+7csWe/fPIJDBigVg8i8v9ERtrNo3buhMBAuHlThRgRSZI8PT3j/RoOh4OYmBgXpBERSfpu3omi45TtHLgYjL+vFzO6VaJM7vSmY4nI4/hlOJzZBKnSwovTwVvLnIj8fyrEiEiiOn4cunWDTZvs29Wrw+TJULiw2VwikgTFxkKnTva0OT8/WLUKihUznUpE5IEsy0oSryEi4g6uhUbSfvI2jv4ZSka/VMzsVpESOQJMxxKRx3FqI/wywh43HgeBBY3GEUmqdO25iCSK2FgYPRqefNIuwvj5wYQJsGGDijAi8gCWBf36wfz54O0NS5dCxYqmU4mIPNTp06cf+tW1a1cAypQpw8yZM9mxYwd79uxh3rx5VKpUCYDGjRtz6tQpkz+CiEiiuBwcTquvt3D0z1CypPNhQY/KKsKIuKvQP2Fxd8CCcp2g1AumE4kkWZoRIyIJ7tAh6NoVtm2zb9epA99+C/nyGY0lIknZRx/BxIngcMDMmfDcc6YTiYj8o7x58z7w/hkzZjB16lTatWvHzJkz73msdOnSvPTSS3Tt2pXp06dTu3Zt+vbtmxhxRUSMOB8URtvJWzkfFE7O9KmZ/XIl8mXyMx1LRB7HX31h7lyFLCWg/nDTiUSSNM2IEZEEY1kwfjyULWsXYfz97WXI1q1TEUZE/sHNm/DNN/Z4/Hho1cpsHhGReBgzZgwAo0aNeug2w4YNw7IsvvnrvU9EJBk6ee02L361hfNB4eQLTMOCV59WEUbEnW0aDad/AW8/eHEaeKc2nUgkSdOMGBFJEKGh8PLLsGCBfbthQ/jqK8iVy2wuEXEDGTLA77/D8uXQq5fpNCIi8XLs2DE8PDzImjXrQ7fJkiULHh4enDx5MhGTiYgkniNXQmg/eRvXb0dROEtaZr9ciSz+auYt4rbObIaNn9njRmMgcxGzeUTcgGbEiIjLHT4MFSrYRRgvLxg71j6fqiKMiPyj27f/HufNC717m8siIuIi6dKlw+l0smfPnodus3fvXpxOJ+nSpUvEZCIiiWP/hVu0/mYr129H8UR2f+a9UllFGBF3dvsaLOoGlhPKtIfSrU0nEnELKsSIiEvt2wfVqsHRo5AzJ/z6q91v2+EwnUxEkrQdOyB/fvj+e9NJRERcqlatWliWxSuvvML58+fve/zy5cu89tprOBwOnn322cQPKCKSgHaeCaLdt9u4FRZNmdzpmdu9MoFpfUzHEpHH5XTC0lfg9hXIXAwajDCdSMRtaGkyEXGZ3bvtftpBQfaMmJUrIXNm06lEJMk7ehQaNIDr1+01DJs2VfVWRJKNwYMHs2zZMnbv3k2hQoWoXbs2hQoVwtPTk9OnT7N27VqioqLw9vZm0KBBpuOKiLjM7yeu0236TsKjY6mUPyPfda5AWh+dhhJxa799Did/Bq/Udl+YVOrzJBJX+gsoIi6xYwfUrQu3bkHlyrBmDQQEmE4lIknexYv2m8f161C+PCxcqCKMiCQrTzzxBAsWLKB9+/aEhISwZs0aHP/zPmdZFr6+vkydOpUyZcqYCyoi4kIbjlylx6xdRMU4qVY4E990eIrUqTxNxxKR+Di7BX7+1B43HAVZipvNI+JmVIgRkXjbuhXq1YOQEKhaFVatAn9/06lEJMkLCrKLMOfOQZEisHo1qD+CiCRDjRo14ujRo3z77bf88ssvXLp0CafTSbZs2XjmmWfo3r07efLkMR1TRMQlVh+4TN95e4iOtXjuiaxMaFsWHy8VYUTc2p0bsKgrWLHwZGso0850IhG347AsyzIdIqkICQkhICCA4OBg/HUWWSROfv8dnn8eQkOhenV7ObK0aU2nEpEk784dey3DLVsgRw77zSRvXtOpRFIkHQPLo9Dvi4j8k+/3XGTgwn3EOi0al87BmJdK4+2p9sQibs3phLmt4Pg6yFQEum8AH534kZTFFcfA+msoIo9t0yZ7JkxoKNSsac+EURFGROJk0iS7CJMhA6xbpyKMiIiIiJubu/0cAxbsJdZp8WL5XIxtVUZFGJHkYMt4uwjj5Wv3hVERRuSx6C+iiDyWmTPtmTC3b0OdOrBiBfipR5uIxNXrr9tfK1ZAiRKm04iIGBcdHc348eNNxxAReSxTNp/mnSUHsCzo+HRehrd8Ek8P9f0TcXvnt8P6D+1x/eGQVZ/dRB6XesSIyCO5cwd694Zp0+zb9evD4sWQOrXRWCLiDizL/vLwsL9GjzadSEQkUZw5c4ZPP/2UrVu3cufOnfsetyyLy5cvExMTQ58+fQwkFBF5fBM3nGDk2qMA9KhegP/UL4bDoSKMiNsLC/q7L0zJF6BcJ9OJRNyaCjEiEmf790OrVnDkiH0O9f334b33wFN9F0UkLoYNgwMH7EpuqlSm04iIJIpz585RsWJFbty4wb+15/T19U2kVCIi8WdZFmN+PMb4n08A0L9OYfrVLqwijEhyYFmwrBcEn4eMBaHxWNC/bZF40dJkIhInu3dD5cp2ESZHDvj5Z/jgAxVhRCSOJk+GQYNg7lxYvtx0GhGRRPPJJ59w/fp1SpUqxcKFC6lcuTLe3t78+OOPbNiwgfr16wPQvHlzLl68aDitiEjcWJbFJysP3y3CvFO/GP3rFFERRiS52PolHF0Fnj7/7QuTznQiEbenQoyI/KuwMGjXDsLDoWZN2LsXatQwnUpE3MaSJdCjhz1+5x1o2dJsHhGRRLR+/XocDgfz58+nZcuW9OvXj5iYGHx9falRowbLly+nbNmyfP/99xw8eNB0XBGRf+V0Wrz3/UG+23wagI+alqBHjYKGU4mIy1zYBT9+YI+fHwrZnzSbRySZUCFGRP7V22/bM2GyZ4eFCyFzZtOJRMRtbNgAbdqA0wkvvwyffmo6kYhIorp06RKenp4ULVoUgKpVq2JZFrt27QLAw8ODwYMHY1kWY8eONZhUROTfxcQ6eWPRPmZvO4fDASNaPknHp/OZjiUirhJ+CxZ1Bmc0PNEMnupmOJBI8qFCjIj8ozVrYMIEezx1KgQGms0jIm5kzx5o2hSioqB5c5g0SesKi0iK4+/vT2xsLFFRUQDkypULf39/jhw5cnebOnXqAPDbb78ZySgiEhdRMU76zdvLkt0X8fRwMLZVGV6qkNt0LBFxlb/6wtw6BxnyQZMv9PlNxIVUiBGRh7p+Hbp0scd9+kC9embziIgbiYyEZs0gNBSefRbmzAEvL9OpREQSXYECBQBYtWrV3ftKlCjBnj177t728fHB4XAQEhKS6PlEROIiIjqW12btYuWBy6Ty9ODLduVoWian6Vgi4krbv4EjK8Azld0XxjfAdCKRZEWFGBF5IMuyWzpcuQLFi8Pw4aYTiYhb8fGBadPshlLLloGvr+lEIiJGtGvXDsuy6NSpE1OnTgXg6aefZseOHaxduxaAKVOmYFkWuXPrynIRSXrComJ4efpOfjpyFR8vD77t9BT1SmQzHUtEXOnSHlj3nj2u+wnkKGs2j0gypEKMiNzHsux+2kuW2Bewz5oFqVObTiUibqdmTbtHjL+/6SQiIsZ0796d4sWLExoayvLlywHo0KEDlmXRoEEDMmbMSM+ePXE4HDRq1MhwWhGRe4VGRNNpynY2n7hOmlSeTOtSkRpF1DRUJFmJCIaFnSE2Coo3hoqvmE4kkiypECMi9xk69O8ZMBMnQrlyZvOIiJsID4c2beDQob/v05rCIpLC+fr6snnzZl555RUyZ7ZPXpYuXZr33nsPy7K4desWlmWRI0cO3nrrLcNpRUT+dissivaTt7HjzE3S+Xox6+VKPF1QTUNFkhXLgh/6ws0zkD4PNJmgz3AiCcRhWZZlOkRSERISQkBAAMHBwfjr6l1JocaOhQED7PHo0fD660bjiIi7iImBFi1g+XIoUACOHAFvb9OpRCQOdAxsztatW/n1118JCAjgpZdeIkOGDKYj/Sv9voikDNdvR9J+8jaOXAklQxpvZnarRMmc6hchkuzsmAwrB4KHN3RdC7nKm04kkiS54hhYXXNF5K5vv/27CPPhhyrCiEgcWRZ0724XYXx97d4wKsKIiPyrypUrU7lyZdMxRETucSU4gnaTt3Ly2h0yp/Nh9suVKJI1nelYIuJql/fDmkH2+LkPVYQRSWBamkxEAJgzB3r0sMdvvgmDB5vNIyJu5O237eKLpycsWADVqplOJCKSpIWFhXHlyhWioqJMRxERucf5oDBe+noLJ6/dIUeALwt6PK0ijEhyFBn6374wkVC0AVTuaTqRSLLnskLMpk2b+Pzzz/nggw8AOHnyJEOGDOHcuXOu2oWIJADLgvHjoWNHe/zaa3Z/GC0JKiJxMnKk/QUweTI0bmw2j4iIYdevX2fq1KmMHj2aZcuWERMTc/ex48ePU6tWLfz9/cmZMyepU6fmySefZPz48TidToOpRUTg9PU7tPp6C+eCwsiTMQ0LXn2a/Jn8TMcSEVezLFjeH4JOQkBuaDpRJ4FEEkG8e8Ts3r2bdu3acezYMQB8fHwICwtj27ZtPP300/j5+TF79myaNGniksAJSesdS0oTGQk9e8KUKfbtbt3gm2/AQ3PlRCQuliyBli3t8YgR9nQ6EXE7OgZ2nUWLFtGtWzdu3759974SJUrw008/YVkW5cqV4/Lly/z/j2AOh4P27dszffr0xI78yPT7IpI8HfszlHaTt3EtNJKCmf2Y/XJlsgX4mo4lIglh1zRY3g88vKDLashd0XQikSTPeI+Y8+fPU6dOHW7duoXD4bjnA4W/vz/p0qUjNDSUNm3asH//fgoWLBif3YmIC12+bJ8/3bLFLryMHGn3h9FFECISZ7VrQ/XqULGiijAikuLt27ePdu3aER0dfc/9f/zxBz179iQwMJBLly6RNWtWmjZtSqZMmbh06RIrVqzg+vXrzJo1i44dO1K7dm1DP4GIpFQHLwbT4btt3AyLpnh2f2Z2q0imtD6mY4lIQrhyEFa/bY9rv68ijEgiitd175999hm3bt2icuXKnDp16p7HihcvzqVLl6hZsybh4eGM/GvZEhEx7vBhqFTJLsKkTw+rV8Prr6sIIyKPKCAA1q2z1zMUEUnhRo8eTXR0NE8++SR79uwhNDSUDRs2kD9/fpYuXcqsWbMoVqwYhw8f5quvvuKTTz5hypQpHD9+nFKlSgEwdepUwz+FiKQ0u87epM23W7kZFk3pXAHM7V5JRRiR5Crytt0XJiYCCteFp/uYTiSSosSrELN69WocDgcTJ04kb9689z3u5+fHuHHjAFizZk18diUiLrJ1KzzzDJw/D0WLwo4dULeu6VQi4jb277cbS/3Fx0frGYqIYPfMdDgcfPXVV5QuXRo/Pz9q1KjBpEmTcDqdhIeH8/7775M+ffp7nhcQEMCQIUOwLIutW7eaCS8iKdLvJ6/T4btthEbEUDFfRma9XIn0aVKZjiUiCcGyYOVAuHEc0uWAZl/pc5xIIovX0mSXLl3C4XBQunTph27zxBNP4HA4uHz5cnx2JSIusGoVvPAChIfbM2JWrIBMmUynEhG3cfo01KsHV67YBZhXXjGdSEQkybh8+TIeHh5UqFDhnvtr1KiB47/TjmvWrPnA5z7zzDOA/flKRCQxbDx6lR4zdxEZ46Ra4Ux83aE8aVLF6xSRiCRle2fD/nng8IQXpoBfoOlEIilOvEqf6dKlw7IsoqKiHrrNn3/+iWVZpE2bNj67EpF4mjEDmjSxizD168NPP6kIIyKP4M8/7elzV65AqVLw0kumE4mIJClRUVF4e3vj6el5z/2pUqUiVSr7CvOsWbM+8LmZM2fG29ubyMjIBM8pIrLm4BW6z9hJZIyTOsWz8G3Hp1SEEUnOrh6GlW/Y41rvQt6nzeYRSaHiVYgpU6YMAOvXr3/oNrNnzwagbNmy8dmViDwmy4KRI6FTJ4iNhQ4dYNky8PMznUxE3EZIiF3BPXEC8uWDtWvtBlMiIuIyHloeREQSwbK9F+k1ZzfRsRYNS2VnUvvy+Hp7/vsTRcQ9Rd35b1+YcChYG6oOMJ1IJMWK19F+ly5dsCyLvn37cvjw4fseX7RoER9++CEOh4POnTvHZ1ci8hicTnjjDXjrLfv2G2/AtGng7W00loi4k4gIaNYM9uyBLFlg3TrInt10KhERERF5RPN3nKP//L3EOi1alMvJuNZl8PZUEVgkWVv1Flw7AmmzQfOv1RdGxKB4zT1t27YtM2bM4Mcff6R06dI4HA6ioqKoVKkSJ0+e5ObNm1iWRZ06dWjfvr2rMotIHERFQdeu8N9JaYwcaRdiRETizLKgXTvYsAHSpYPVq6FwYdOpRESSrMjIyPuWJvtf//SYZVl3e8mIiLjatN9OM2T5IQDaVcrDx01L4uGh9xyRZG3XdNg7Cxwe8MJ3kDaz6UQiKVq8yqAOh4Pvv/+eNm3aEBMTg2VZOJ1OduzYQVBQEJZl0bp1a5YuXeqqvCISB7dv2/1gZs8GLy+7P4yKMCLyyBwOqFEDfH3tNQ3LlTOdSEQkSbMs67G/REQSyqSNJ+8WYV5+Jj+fNFMRRiTZO70JVr5uj2sOgnzPmM0jIvGbEQOQOnVqZs+ezaBBg1i1ahVnzpwBIF++fNSvX5+SJUvGdxci8giuXYOGDWHHDkiTBhYtsls7iIg8lr59oWVLyJnTdBIRkSTtgw8+MB1BROQelmXx+frjfPHTcQD61irEgOeKaPadSHIXdAoWdABnDJRsCdV0Za5IUhDvQsxfSpQoQYkSJVz1ciLyGM6ehbp14dgxCAyElSuhUiXTqUTE7SxYAM89Bxky2LdVhBER+VcqxIhIUmJZFkNXHebbTacBeOv5ovR8tpDhVCKS4CKCYU5rCL8JOctD04n2SgciYpzLCjEiYtaBA/D883DpEuTJA2vXQrFiplOJiNuZPRvat4dSpeC33+zeMCIi4jLh4eGcPn2akJAQAPz9/SlQoAC+vr6Gk4lIcuF0Wrz/w0FmbT0HwAeNn6BL1fyGU4lIgouNgYVd4PpR8M8JreeAd2rTqUTkvxK8ELN//37OnDlDsWLFKFKkSELvTiRF2rQJGjeG4GAoWRLWrNEF7CLyGFavhs6d7XGtWpA2rdE4IiLJyY0bN3j55ZdZuXIlsbGx9zzm6elJo0aNmDx5MhkzZjSUUESSg1inxduL97No1wUcDviseSlaV8xjOpaIJIZ178HJn8A7DbSZC+mymU4kIv8jXoWYGTNm4OnpSbt27R66zYEDB+jYsSMNGjRg+fLl8dmdiDzA999D69YQGQnPPAM//PD3akIiInG2ZYvdCyYmBtq2hTFjNIVdRCQOoqOj+c9//kPPnj0pWLDgQ7d75ZVX+OGHHyhfvjxVq1bF398fgJCQEDZv3syyZcvw9PRk4cKFiRVdRJKZ6FgnA+bvZcX+y3h6OBj9YmmaldUVeiIpws4psG2SPW7+NWQvbTaPiNwnXoWYzp07kzp16n8sxNSrVw/Lsti2bVt8diUiD/Dtt/Dqq+B0QpMmMG8epNasUxF5VH/8AQ0bQni4vcbh1Kng4WE6lYiIW7Asi3z58lG/fn2KFy/OgAEDePbZZ+/bbsOGDQD8+OOPBAQE3PPYrVu3yJgxIz///HNiRBaRZCgiOpbec/aw/vCfeHs6GN+mLM+XzG46logkhlO/wKo37XGtwfBEE7N5ROSB4r00mWVZ//h4pkyZ8PT05NatW/HdlYj8l2XBp5/C4MH27W7d4KuvwEtdn0TkUZ09C/Xqwc2bULkyLFoEqVKZTiUi4jZSpUpFnz596N27N0uXLuX999/nzp07DBgwgNatW+P13wO05s2bM3XqVMqUKUP58uVJ998eXKGhoezcuROAFi1aGPs5RMR9hUfF8srMnWw6fh0fLw++al+emsWymI4lIonhxklY0BGcMVDqJag20HQiEXmIBD9te+fOHWJjY+9OvReR+ImNhX79YOJE+/a778LHH2sFIRF5TBER9uyXJ56AlSvBz890IhERt+RwOGjRogUtWrRgx44dfP7557z33nv06NGDV199lfHjx+Pr68uSJUtYsmTJPc/NmjUrPXv2ZMSIEYbSi4i7uh0ZQ9dpO9h+Oog0qTyZ3PEpqhTKZDqWiCSG8FswpxVE3IJcFaDJeJ0cEknCErQQExoaygcffADAU089lZC7EkkRIiOhQwdYuND+2zpuHPTpYzqViLi1okXht9/sNxU1iBYRcYkKFSowZ84cLl68yBdffEGpUqVo0qQJAwYMYOLEiYSGhhIaGgqAv78/adOmNZxYRNxRcFg0HaduZ9/5W6Tz8WJa1wqUz6vjOZEUITYGFnaGG8fBPxe0ngPevqZTicg/iHMh5sMPP+Sjjz667/7IyEg8PT3/8bkOh4O333770dOJyF0hIdC8Ofz8M3h7w8yZ0KqV6VQi4pYiI2HfPqhY0b6dO7fZPCIiyYRlWezZs4czZ84QHh5OmjRpaNOmDe+++y4zZsygUaNGFC1alNdff/2BfWREROLqxu1IOny3nUOXQ0ifxpuZXStRKlfAvz9RRJKHte/AqQ3g7Qdt50FaLUcoktQ90oyYB/WD+bceMbly5WL48OE899xzj5ZMRO66cgUaNIA9eyBtWli6FOrUMZ1KRNxSbKw9tW7ZMpg9G154wXQiEZFk4Y8//qBRo0acO3cOsD8nOf67PEj+/PlZtWoVvXr1YtmyZXzwwQeEhIQwYMAA2rZte7ePjIhIXPwZEkG7yds4cfU2mdL6MOvlihTLpuXgRVKM7d/C9m8AB7T4BrKVMp1IROLAYf1bJeW/goODuXXr1t3blmVRoEABfHx8OHLkyAOfkzp1arJkcZ+KbEhICAEBAQQHB6unjSQZJ09C3bpw6hRkyQKrV0O5cqZTiYhbsizo3Ru+/NKeWrdyJehCCZEUT8fArvHMM8+wZcsW2rZtS5UqVUidOjXh4eH88ssvLFiwgDp16rBu3bq72+/atYvPP/+czZs388orr/Dqq6+S0Q2WiNTvi4hZF26G0W7yNs7eCCObvy+zu1eiYGYtbyiSYpzcALNaghULdYbAMwNMJxJJEVxxDBznQsyDeHh44OvrS1hY2OO+RJKiDxWS1ISFQcmScPo0FCgAa9dCoUKmU4mI2xoyBD780O4HM28evPSS6UQikgToGNg1AgMDuXXrFlevXiUwMPDu/RcvXiR37txkz56dixcv3ve8S5cu8cUXXzBr1izatWvH8OHDEzP2I9Pvi4g5Z67foe23W7kUHEHujKmZ83JlcmdMYzqWiCSW68dhcm2ICIbSbaDZJPuznYgkOFccA3u4OJOIuNAnn9hFmNy57V7aKsKIyGObONEuwgBMmKAijIiIi7Vu3RrLsihSpAhVqlShZs2aPP3005QsWRKANm3aPPB5OXLkYNiwYRw7doxGjRolZmQRcSPH/wzlpa+3cCk4ggKZ/FjQ42kVYURSkrAgmNPKLsLkrgyNx6kII+Jm4rUYcZ48eUidOrWrsojI/zh8GEaNssfjx0O2bGbziIgbmz8f+vSxxx98AD17ms0jIpIMjR49mnTp0rFixQr2799PREQEqVOnJl++fPTu3ZvBgwf/4/PTpElDtWrVEimtiLiTgxeD6ThlO0F3oiiWLR0zu1Uiczof07FEJLHERsPCzhB0EgLyQKtZ4KX3ABF3E69CzJkzZ1wUQ0T+l9MJvXpBdDQ0agRNmphOJCJubd06uz9Mz552IUZERFzO19eXYcOGMWzYMNNRRCQZ2XPuJp2mbCckIoYncwUwvUtFMvilMh1LRBKLZcHqt+D0L5AqLbSZC2kzm04lIo8hXoUYEXE9pxN69IANGyB1avjiC802FZF4+vZbePZZaNtWbygiIiIibmLrqRt0m7aDO1GxPJU3A1O6VMDf19t0LBFJTNu/hZ1TAAe0nAzZSppOJCKPyWWFmBs3bnDnzp377rcsi9OnTzN+/HgWL17sqt2JJEuxsdCtG0yfDh4e9rnT/PlNpxIRt3Txor2moaen/YbSoYPpRCIiIiISR78cu0aPmTuJiHZSpWAgkzs9RZpUupZWJEU58ROsedseP/cRFK1vNo+IxEu8/4r/8MMPDBw4kFOnTrkij0iKFRMDHTvC3Ln2edNZs6B1a9OpRMQtXbgAVapApUr2m4mP1g8WERERcRfr/rhC7zl7iIp1UrNoZia1L4+vt6fpWCKSmK4dhYVdwHJCmfZQpY/pRCIST/EqxGzevJkWLVrgdDr/cbtUqVJRunTp+OxKJFmLjrZXDFq0CLy8YN48aNnSdCoRcUs3bkDdunD+PKRJA7dvqxAjIiIi4iaW77tE//l7iXVa1C+ZjXGty5LKy8N0LBFJTGFBMKcVRAZDnqeh0RgtMS2SDMTrr/moUaNwOp20a9eOa9eu0a5dO7y9vQkODsbpdPLpp58C8Nxzz7F161aXBBZJbiIj4cUX7SKMtzcsXqwijIg8pjt3oGFDOHwYcuaEtWshMNB0KhERERGJg4U7z9Nv3h5inRbNy+ZkfBsVYURSnJgoWNARbp6G9Hmg1Szw0oV1IslBvP6ib926FYfDwZgxYwgMDKR9+/bExMSwefNmAN555x1efPFFVq1axYwZM1wSWCQ5iYiwiy7LltkXrH//PTRpYjqViLilqCj7DWXbNsiQwS7C5M1rOpWIiIiIxMHMLWd4c9F+nBa0qZib0S+WxstTRRiRFMWyYNUbcGYTpEoHbeaDXybTqUTEReL1V/3mzZt4enqSOXNmACpWrIhlWezbt+/uNv3798eyLKZMmRK/pCLJTHg4NG0KK1dC6tSwfDk0aGA6lYi4JacTunSxiy9p0thvLCVKmE4lIiIiInHwza8nGbzsDwC6VM3H0Oal8PDQMkQiKc62r2D3dHB4wAtTIOsTphOJiAvFqxCTNWtWYmNjuXXrFgAZMmQgW7ZsHDly5O42Tz31FA6H457ijEhKd+cONGoE69aBnx+sWgXPPWc6lYi4rT/+gCVL7CZTixbB00+bTiQiIiIi/8KyLMatP87QVfY5lF41C/J+oydwqBeESMpz/EdYO8ge1/0EitQ1m0dEXC5ehZjixYsDMHHixLv3lSpVii1btty9HR4ejmVZWJYVn12JJBu3b0P9+vDzz5A2LaxZA88+azqViLi1UqXsyu7MmfYbjIiIiIgkaZZlMXzNUT5ffwyAN+oW4c16xVSEEUmJrh6GhV3AckK5jlC5p+lEIpIA4lWI6dWrF5Zl8f777zNw4EAAatasyYkTJxg0aBD79+9nwIABABQsWDD+aUWSgbfegk2bwN8ffvwRnnnGdCIRcVvh4X+Pq1WD1q3NZRERERGROJu7/Txf/XISgMGNnqB3rcKGE4mIEXduwJxWEBUKeZ+BBqNBBVmRZClehZgmTZrQunVrLMvi9OnTAHTp0gU/Pz+GDx9O2bJlmTZtGg6Hg44dO7oksIg727EDvvrKHi9dCpUrm80jIm5syRIoUgT27zedREREREQewR+Xghmy3O4J89bzRen2TH7DiUTEiJgoWNABbp2FDPngpRnglcp0KhFJIPEqxADMmTOHuXPnUrNmTcDuGzNnzhz8/f3vLklWr149evbUtDpJ2WJj4bXXwLKgQweoVct0IhFxWxs2QJs2cOECTJ1qOo2IiIiIxFFoRDS9Zu8mKsZJ7WJZeLW6Vg8RSZEsC1YOgLO/gY8/tJkPfoGmU4lIAvJyxYu0atXqntuNGjXi8uXL7N+/n4CAAIoWLeqK3Yi4ta+/hl27ICAARo40nUZE3Nbu3dC0KURFQfPmekMRERERcROWZfHOkgOcuRFGjgBfRr1YGg8PLUEkkiJtmQh7ZoHDA16YClmKmU4kIgnMJYWYB/H19aVixYoJ9fIibuXPP2HQIHv86aeQNavZPCLipo4fh+efh9BQePZZmDMHvBLsT7mIiIiIuNCsbedYsf8yXh4OJrQrRwY/LUEkkiIdXQPr3rPH9T6DwnXM5hGRRBHvpclE5N+99RYEB0O5cvDqq6bTiIhbunQJ6taFa9egbFlYtgx8fU2nEhFJ8UJDQ/n666+JjY01HUVEkrCDF4P5ePkhAP5Tvxjl8mQwnEhEjPjzECzuBlhQvgtU6mE6kYgkkiRdiDl48CAVKlQgQ4YMvPnmm1iWFafnOZ1OqlSpwujRoxM4oci/W7cOZswAhwMmTQJPT9OJRMQtvfMOnDkDBQvC6tXg7286kYiIALt27eLtt9+mRIkSLF++PNH3r89MIklfSEQ0vebsJirWSZ3iWen2TH7TkUTEhNvXYG4riLoN+apBg5H2ySIRSRHiVYgZOnQoH330EaGhoa7Kc1dkZCSNGzemfPny7Ny5k0OHDjFt2rQ4Pferr74iODiYvn37ujyXyKM4fx7atrXHPXuCVusTkcc2YYL9hrJundY3FBFJQp599llOnDhBrVq1aNmyJbVq1WL37t2Jsm99ZhJJ+izL4j+L93P2Rhg506dm9IulcejEq0jKExMJ89vDrXOQsQC8NAM8vU2nEpFEFK9CzJdffsmHH35ITEyMq/LctXr1aoKDgxkzZgwFCxZk6NChfPfdd//6vEuXLjFo0CDGjx+Pt7fe0MScCxegWTO4ccNekmzUKNOJRMTt/O9VzenSwezZUKCAuTwiIvJAmTJl4ssvv2T//v2kTZuWihUr0rFjRy5cuJCg+9VnJpGkb8aWs6w6cAVvTwcT25UjII3+zYmkOJYFKwbA+a3gEwBt5kOajKZTiUgii1chplixYgBcuXLFJWH+1759+6hcuTJp0qQB4Mknn+TQoUP/+rz+/fuTN29ezp8/z++///6P20ZGRhISEnLPl4grLF8OpUvD7t2QIQMsWqRWDiLyiJxO6NIFhg27tyAjIiJJVrFixfjhhx/48ccfOXToEEWLFuW9997j9u3bCbI/fWYSSdr2X7jFJyv/6gtTnDK505sNJCJm/P4F7J0NDk94aRpkLmI6kYgYEK9CTKtWrbAsiyVLlrgqz10hISHkz//3uqkOhwNPT09u3rz50Ods2bKFhQsXkitXLk6ePEmnTp3o3bv3Q7f/7LPPCAgIuPuVO3dul/4MkvJERkK/ftCkCQQF2TNhtm2D/FoCWEQe1dtvw/Tp8N57EIeTaiIiknTUrFmTnTt3MmnSJGbMmEGhQoX4+uuvcTqdLt2PPjOJJF3B4XZfmOhYi3olstK1aj7TkUTEhCOr4McP7HH94VCwltk8ImJMvAoxnTt3pnTp0gwbNow9e/a4KhMAXl5e+Pj43HOfr68vYWFhD33Ot99+S6VKlVixYgUfffQRP//8M19++SVHjx594PbvvPMOwcHBd7/Onz/v0p9BUpZjx+Dpp+GLL+zbAwbA779D4cJmc4mIGxo58u/1DKdMgRIlzOYREZHH0rFjR44dO0afPn146623KFWqFKtWrXLZ6+szk0jSZFkWby3ax/mgcHJnTM2IF9QXRiRFunIQFr8MWFDhZajY3XQiETEoXoUYb29vlixZQp48eahZsyazZs1yVS4yZszItWvX7rkvNDSUVKlSPfQ5Fy5coEGDBncPcHLnzk3mzJk5efLkA7f38fHB39//ni+Rx3H1KlSvDnv2QGAgrFgBY8bA//tcLCLy76ZOhbfessejRkHHjmbziIhIvPj6+vLuu+9y4sQJqlWrRtOmTXnuuefYv39/vF9bn5lEkqapv51h7R9/2n1h2pYjILX6woikOLevwtzWEH0H8teA54eZTiQihnnF58ldu3YFoHjx4hw5coROnToxevRoypYte9+2DocjTo0j/1KhQgW+/fbbu7dPnz5NZGQkGTM+vJlVrly5CA8Pv3v79u3bBAUFkTNnzjjvV+RRWRZ07w5//gnFi8OPP4J+5UTksfzwg/2GAnYxZuBAs3lERMRlMmfOzFdffUW/fv148803KV++PB06dODTTz8le/bsj/Wa+swkkvTsPX+Lz1YfBuDdBsV5Mld6s4FEJPFFR8C8dhB8HgILwUvTwVMFWZGUzmFZj98B2MPDA4fDQVxewuFwEBsbG+fXjomJIUeOHAwfPpwuXbrQvXt3rly5wvLly7l16xbp0qXD09Pznuf8+OOPtGnThvnz51OoUCEGDx7Mnj172L9/f5ymAYeEhBAQEEBwcLCu9JI4mzIFunWDVKlg+3YoXdp0IhFxS+fPQ5EiEBEBXbrAd9+BlrAQkUSgY2Azfv75Z9544w2OHTvGwIEDeeutt/Dz83uk19BnJpGkJTgsmgZfbOLirXAalMrGxLbltCSZSEpjWbD0Vdg/D3wD4OWfIVMh06lEJJ5ccQwcr0LMhx9++Ejbf/DBB4+0/Q8//ECbNm1InTo1Hh4ebNy4kSeeeAKHw8GePXsoU6bMfc/57rvvGD58OOfPn6dMmTJMmzaNokWLxml/+lAhj+raNShaFG7ehBEj4M03TScSEbc2aRKsWwcLF4JXvCatiojEmY6BzbEsi+nTpzN48GBiY2P5+OOP6dq16yOduNVnJpGkwbIsXpm5ix8P/UmejGlY0fcZ/H11BbxIirNpDPz0ITg8ocMSKPCs6UQi4gLGCzGJ4cqVK+zatYvKlSsTGBiYoPvShwp5VN262TNinnwSdu3SeVMRcQHL0kwYEUlUOgY2Lzw8nFGjRjFy5Ejy5cvHuHHjqFmzZpyfr89MIuZN3nSKT1YeJpWnB0t6VqFkzgDTkUQksR1eDvPb2+OGY6BCN7N5RMRlXHEM7OHiTC6XLVs2GjZsmOAfKEQe1W+/2UUYsC9iVxFGRB7Zn39Cu3YQFPT3fSrCiIikOKlTp2bw4MEcP36cKlWqcPHixUd6vj4ziZi1+9xNhq0+AsDgRsVVhBFJiS7vgyWv2OOKPVSEEZH76NSxyGOIiYGePe1xt25QpYrZPCLihoKD4fnnYe9ee7xihelEIiJiWNasWfnqq69MxxCRR3ArLIo+c/YQ47Ro+GR22lfOazqSiCS20D9hbhuIDoOCtaDeUNOJRCQJSvIzYkSSogkTYP9+yJgRhg0znUZE3E5EBDRtahdhsmSBsWNNJxIRERGRR+R0WgxcsI+Lt8LJF5iGYS1KPVKPJxFJBqLDYV5bCLkIgYXhhangqeveReR+KsSIPKLTp+H99+3xsGGQKZPZPCLiZmJioE0b+OUX8PeHNWugUCHTqURERETkEU3efIqfjlwllZcHE9uVI52vt+lIIpKYLAuW9YaLO8E3PbSdD6nTm04lIkmUCjEijyAiAl54AUJDoWpVe1kyEZE4syx49VX4/nvw8YFly6BsWdOpREREROQR7TobxPA1RwH4oPETlMihvjAiKc6mUXBwEXh4QauZEFjQdCIRScJUiBF5BO+9B7t327Ng5s4FD/0LEpFHMXQofPed/eYxdy48+6zpRCIiIiLyiILuRNF7zh5inRZNSuegbcU8piOJSGI7tAx+/sQeNxwN+aubzSMiSZ5OI4vE0dat8Pnn9njKFMid22weEXFDL70E+fPD119D8+am04iIiIjII3I6LV5fsJfLwREUyOTHUPWFEUl5Lu2FJT3sceWeUL6zyTQi4ibUPUokDiIioGtXcDqhfXto3Nh0IhFxS4ULw8GDkCaN6SQiIiIi8hi+/vUUG49ew+e/fWHS+ui0ikiKEnIZ5raBmHAoVAee+9h0IhFxE5oRIxIHH30Ehw9D1qwwbpzpNCLiVlavhjVr/r6tIoyIiIiIW9pxJohR6+y+MB82KUHx7P6GE4lIoooKg3ltIPQSZCoKL0wBTxVjRSRu9G4h8i927YIRI+zxpEmQMaPZPCLiRrZsgZYtIToaNmyAZ54xnUhEREREHsON25H0nrObWKdFszI5aFVBa1WLpCiWBct6waU9kDojtJ0HvgGmU4mIG9GMGJF/EBUFXbpAbCy0aqWWDiLyCP74Axo2hPBweO45qFTJdCIREREReQxOp8WABfv4MySSgpn9+LS5+sKIpDi/jIA/loCHN7SaBRkLmE4kIm5GhRiRfzB0KBw4AJkywfjxptOIiNs4exbq1oWbN+Hpp2HhQvD2Np1KRERERB7DpF9O8uuxa/h6e/Blu/L4qS+MSMpycAlsHGqPG42BfFXN5hERt6RCjMhD7NsHn35qjydMgMyZzeYRETdx7ZpdhLl0CUqUgBUrwM/PdCoREREReQxbT91g9H/7wnzUtCRFs6UznEhEEtXFXfD9a/b46d5QrqPZPCLitlSIEXmA6Gh7SbKYGHs5spdeMp1IRNzC7dvQoAEcOwZ58sDatWosJSIiIuKmrt+OpO/cPTgtaFEuJy+Wz2U6kogkppBLMLctxERA4Xrw3EemE4mIG1MhRuQBRo6EPXsgQwb48kvQ8r8iEie+vvDkk/Z6huvWQc6cphOJiIiIyGOIdVoMmL+Xq6GRFM6Slk+alVRfGJGUJCoM5raG21cgc3FoORk8PE2nEhE3poVNRf6fP/6ADz+0x+PGQbZsZvOIiBvx8oLJk+H8eXtGjIiIiIi4pYkbTrDp+HVSe3vyZbtypEml0yciKYbTCd+/Cpf3QZpAaDsPfP1NpxIRN+fyGTHR0dEAREVFERkZ6eqXF0lQMTHQtStERUHDhtC+velEIpLkWRbMnm2/gYA9hU5FGBERERG39fvJ64xdfwyAj5uVpHBW9YURSVF+GQaHloGHN7SaDRnymU4kIsmASy7pmDNnDt999x07d+4kJiaGO3fusGfPHmrWrEm3bt0YMWIEqVOndsWuRBLU55/D9u0QEABff60lyUQkDoYMgY8+gsWL7S+9cYiIpEi1atWK92s4HA5++uknF6QRkcd1NTSCvnP34rTgxfK5eEF9YURSlgOL4Jfh9rjxOMj7tNk8IpJsxLsQ06lTJ2bNmoVlWQD4+vrefSwiIoIvv/ySY8eOsXbt2vjuSiRBHT0Kgwfb4zFj1NpBROJgwgS7CAPw3HMqwoiIpGAbN278x8f/6i3x1+emB92v/hMiZsU6LfrP28v125EUyZqWj5qWNB1JRBLThZ3wfU97XKUvlG1nNo+IJCvxKsTMnj2bmTNn4unpSYcOHZg2bdrdx5544gmGDBnC0KFDWb9+PVOnTqVLly7xzSuSIGJj7SXJIiOhbl3Qr6qI/Ku5c6FvX3v84Yfw2mtm84iIiFFTp0596GM///wzM2fOJHXq1LRt25ZixYrh5eXF0aNHmT17Nrdv36Zhw4a88MILiZhYRP6/L346zu8nb5Amld0XJnUqNeYWSTGCL8C8thAbCUXqQ50hphOJSDLjsP7/JVmPoEaNGmzevJkxY8bQr18/PDw88PX1JSws7O4233zzDa+++ipVq1Zl06ZNLgmdUEJCQggICCA4OBh/fzXhSknGjYP+/SFtWvjjD7V3EJF/sXYtNGpk94Xp3Ru++EKzYUTEbekYOGFt3bqVGjVqUKpUKZYvX0727NnvefzatWs0btyYHTt2sHLlSp5//nlDSeNGvy+SXP124jrtv9uGZcHnrUrTvKyWJBNJMaLuwJR6cOUAZCkB3daCj3pDicjfXHEM7BGfAPv27QPs5ckepm3btgDs378/PrsSSTAnT8I779jjkSNVhBGRf7FtG7RsaRdhWre2K7kqwoiIyEMMHjyYmJgYpkyZcl8RBiBz5sxMnjwZy7IYOnSogYQicjUkgn7z9mBZ0LpCbhVhRFISpxOWvGIXYfwyQ9t5KsKISIKIVyEmMjISDw8P0qdP/9BtvL29cTgcREVFxWdXIgnCsuCVVyA8HGrWtMciIv/o1i37zaNuXZg+HTzi9adURESSue3bt+Ph4cGTTz750G1KliyJh4cHe/bsScRkIgIQE+uk77w9XL8dRbFs6RjSpITpSCKSmDZ8AkdWgGcqaDUb0uvqXBFJGPE6e5QnTx6cTidnzpx56Da7d+/Gsixy584dn12JJIjZs+Hnn8HXF779VudTRSQO6tWDTZtg8WJIlcp0GhERcQNOp5MLFy489PGLFy/idDrx0MGoSKIb99Nxtp4Kwi+VJxPblcPXW31hRFKM/Qtg02h73GQ85KlkNo+IJGvxOtKvW7culmUxevToBz4eGxvLf/7zHxwOB/Xq1YvPrkRc7uZNeP11ezx4MBQsaDaPiCRhN27AiRN/3y5Xzm4qJSIi8i/Kly8PwKBBgx66zfvvvw9AuXLlEiWTiNh+PXaNCRvsY7yhLUpRMLOO70RSjPPbYVlve/zMACjd2mweEUn2vOLz5DfeeINp06bx5ZdfcuPGDcC+2mv16tUcP36cqVOnsm/fPvz8/Bg4cKBLAou4yttvw7Vr8MQT8MYbptOISJJ1+zY0bAinT8OaNVC2rOlEIiLiRgYOHMjGjRuZPXs2O3bsoEuXLhQqVAhPT09Onz7N1KlTOXjwIA6Hgz59+piOK5Ji/BkSwYD5e7EsaFspD03L5DQdSUQSy61zMK8txEZCsUZQ633TiUQkBXBYlmXF5wWWLVtGmzZtiIyMvO8xy7Lw8fFh7ty5NGvWLD67SRQhISEEBAQQHByMv7+/6TiSgFauhEaN7PEvv0D16mbziEgSFRUFTZrA2rWQMaO9JNkTT5hOJSLiUjoGTniDBw/m008/BcDhcNzz2F8fx/r27cvYsWMTO9oj0++LJAcxsU7afruN7WeCKJ7dn6U9q2hJMpGUIvI2TKkHfx6ErKWg6xrw0Ww4EflnrjgGjvcixE2bNmX79u00b94cPz8/LMvCsiz8/Pxo2bIl27dvd4sijKQcV69C1672eMAAFWFE5CGcTujc2S7CpEljV3BVhBERkcfw8ccfs2LFCmrVqoWPj8/dz0xeXl5UrVqV+fPnu0URRiS5+Hz9MbafCSKtjxdfqi+MSMrhdMKS7nYRxi8LtJmrIoyIJJp4LU32l5IlS7Jo0SIsy+L69esAZMqU6b6rvUSSgt697WJMyZIwdKjpNCKSJFkW9O8Pc+eClxcsXgyVK5tOJSIibqxBgwY0aNCA2NhYgoKCcDqdBAYG4uXlko9kIhJHG49eZeKGkwB81qIU+TP5GU4kIonmpw/h6Crw9IHWcyB9btOJRCQFcelRv8PhIHPmzK58SRGX2rwZFi4EDw+YORN8fU0nEpEk6dNPYfx4ezx9Ojz/vNk8IiKSbHh6euozk4ghl4PDGTB/LwDtK+ehcekcZgOJSOLZOxd+G2uPm06E3BWMxhGRlCdehZhjx45RpEgRV2URSVB37tgXuAN06wZlyphMIyJJVlQUrFhhj8eNg7ZtzeYREZFk4/Tp0/zwww+cOHECgEKFCtGkSRPy589vOJlI8hcd66TPnD3cDIumRA5/3muoJWdFUoxzW2F5X3tc7Q148kWzeUQkRYpXIaZ48eJUqVKFLl268NJLL5E2rdZVlKQpOBgaNIBdu8DfHz780HQiEUmyUqWCn36ylyPr2NF0GhERSQacTidvvPEGEyZMIDY29p7H3nzzTfr06cPIkSPx8Ih3C08ReYjR646x8+xN0qkvjEjKcvMszGsHsVFQvDHUfNd0IhFJoeJ1pG9ZFr/99hvdu3cnW7ZsdO7cmY0bN7oomohrOJ32Be2//w7p09t9t7NnN51KRJKcS5f+Hvv5qQgjIiIu07lzZ8aNG0dMTAxp0qShbNmylC1bljRp0hATE8PYsWPp2rWr6ZgiydbPR/7kq1/svjDDX3iSvIHqCyOSIkSGwtzWEHYdsj0Jzb+216oXETEgXu8+p06d4oMPPiB//vyEhYUxY8YMateuTcGCBfn44485e/asq3KKPLZPPoFVq+x+MOvXq9+2iDzArl1QtCh89BFYluk0IiKSjPzwww/MmjULDw8PRowYQVBQEDt37mTnzp0EBQUxcuRIHA4HM2fOZPny5abjiiQ7F2+F8/qCfQB0ejovDUrpqjyRFMEZC4tfhquHIG1WaDMPUqkIKyLmxKsQky9fPj744ANOnDjBr7/+SteuXUmXLh2nT59myJAhFCxYkDp16jBnzhwiIiJclVkkzlavhiFD7PFXX0H58kbjiEhSdOwY1K8Pt2/Dxo0QE2M6kYiIJCOTJk3C4XDwzjvv8MYbb+Dt7X33MW9vbwYOHMiAAQOwLItJkyYZTCqS/Nh9YXZzKyyaJ3MFMKhhcdORRCSxrP8Ajq0BL19oPRcCcppOJCIpnMOyXHvpb0REBEuWLGH69On89NNP/PXy/v7+3Lx505W7crmQkBACAgIIDg7G39/fdByJp9On7cLLzZvQo4ddiBERucelS1ClCpw9C+XKwYYNdiMpEZEURMfACSswMJBbt25x9epVAgMDH7jNhQsXyJMnDxkyZODGjRuJnPDR6PdF3MnQVYf55tdTpPP1YmWfauQJTGM6kogkhj2zYFkve/zCFCjZ0mweEXF7rjgGdvnCiL6+vrRt25bVq1fz7bff4nA4sCyLkJAQV+9K5KFiY6F1a7sIU6ECjBtnOpGIJDk3b0K9enYRplAhewqdTiiJiIiLhYWF4eHh8dAiDECmTJlwOByEhYUlYjKR5G39oT/55tdTAIx8obSKMCIpxdnfYXl/e1zjbRVhRCTJcHkh5siRI7zzzjvkzZuXl19+GafTCUClSpVcvSuRh5ozB7Zvt8+pLloEPj6mE4lIkhIWBo0bw8GDkD07rFsHWbKYTiUiIslQ3rx5cTqdbNq06aHbbN68GcuyyJs3byImE0m+LtwMY+BCuy9Ml6r5eL5kNsOJRCRRBJ2Gee3AGQ1PNIMa/zGdSETkLpcUYm7evMmXX35JpUqVKFGiBCNGjODixYtkzZqVN998k0OHDvH777+7YlcicTJ5sv39rbcgTx6zWUQkCVq1Cn77DdKnh7VrIX9+04lERCSZatSoEZZl0bdvX0JDQ+97PDQ0lH79+uFwOGjSpImBhCLJS1SMk95z9hAcHk3pXAG8U199YURShIgQmNsawoMgexloNgk8XH79uYjIY4tXj5jly5czffp0Vq5cSVRUFJZl4e3tTePGjenSpQvPP/88np6ersyboLTecfJw4gQULgwOB5w7B7lymU4kIknSlClQpAg884zpJCIiRukYOGFdvXqVEiVKEBQURJ48eXj11VcpWbIkAAcPHuTrr7/mzJkzZMqUiYMHD5Ilic/Q1O+LJHUfrzjEd5tP4+/rxcq+1cidUUuSiSR7zliY0wpO/AjpskP3n8E/h+lUIpKMuOIY2Cs+AZo2bXq3B0zp0qXp0qUL7dq1+8f1j0USUnAwvPCCPX7uORVhROR/WBZERf29VmHXrmbziIhIipAlSxYWL15M06ZNOXv2LIMGDbrnccuy8Pf3Z/HixUm+CCOS1K394wrfbT4NwKgXS6sII5ISWBasftsuwnilhtZzVIQRkSQpXnP0MmbMSO/evdm9ezd79uyhb9++KsKIMZGR0KwZ7Ntnt3r48kvTiUQkSRk50p79cu2a6SQiIpLCVK9end27d9OhQwf8/f2xLOtuAaZDhw7s3r2batWqmY4p4tbOB4Xx5n/7wrz8TH7qllBfGJFkz+mElQNhx7f27eaTIGc5s5lERB4iXjNiLl++jLe3t6uyiDy22Fho3x42boS0aWH1aihY0HQqEUkypkyBt9+2x99/D927G40jIiIpT/78+Zk+fTqWZXHjxg0AAgMDcTgchpOJuD+7L8xuQiJiKJsnPW/XL2Y6kogkNGcsLO8He2YCDmjyBZRobjqViMhDxasQoyKMJBX9+8OiReDtDUuXQjldACEif1m27O/Cy1tvqQgjIiJGORwOMmXKZDqGSLIydNVh9l0IJiC1N+PblMXbUw26RZK12Bj4/jU4sAAcHtDsKyjdynQqEZF/FK9CjEhSsHEjTJhgj2fMgDp1jMYRkaTk11+hVSt7ynqXLjBsmOlEIiKSgp0+fZoffviBEydOAFCoUCGaNGlC/vz5DScTcV9rDl5m2u9nABjzUmlyZVBfGJFkLTYaFneDQ8vAwwtaTtZMGBFxCyrEiNsbOdL+3qMHtG5tNouIJCH79kHjxnYDqSZN4JtvQMu/iIiIAU6nkzfeeIMJEyYQGxt7z2Nvvvkmffr0YeTIkXh46Cp+kUdx7kYYby7aD0CP6gWoXTyr4UQikqBiImFhZzi6CjxTwYvToVgD06lEROJEhRhxa3/8AatW2edW33jDdBoRSTIsy54BExIC1avDvHngpT95IiJiRufOnZk9ezaWZeHn50fRokUBOHr0KHfu3GHs2LHcuHGDadOmmQ0q4kYiY2LpNWc3oRExlM+bgTfqFTUdSUQSUnQ4zGsHJ38CL19oNRsKa0kUEXEfuuRK3FZwMPTsaY9btIBChczmEZEkxOGwG0e1aGH3iEmd2nQiERFJoX744QdmzZqFh4cHI0aMICgoiJ07d7Jz506CgoIYOXIkDoeDmTNnsnz5ctNxRdzGpysPc+BiMBnSqC+MSLIXeRtmv2gXYbzTQNsFKsKIiNvRkYq4peBgeO45u/2Djw+8957pRCKSJFjW3+MCBWDxYkif3lgcERGRSZMm4XA4eOedd3jjjTfw9va++5i3tzcDBw5kwIABWJbFpEmTDCYVcR8r919mxpazAIxpVYYc6XXRjUiyFRECs1rCmU2QKh20XwIFaphOJSLyyOK0TkutWrUAyJs3L1OnTr3v/rhwOBz89NNPjxhP5H7BwfD887BjBwQGwpo1UKaM6VQiYlxEBDRvbk+Va9zYdBoREREAtm/fDkC/fv0euk2/fv0YPXo027ZtS6xYIm7rzPU7vL3Y7gvz2rMFqVk0i+FEIpJgwm/aRZiLu8A3ANovhVzlTacSEXkscSrEbNy4EYBixYo98P64cKhBsrjA2bPQsKHdGyZjRli/XkUYEQFiYqBNG7syu307nD4N/v6mU4mIiBAWFoaHhweBgYEP3SZTpkw4HA7CwsISMZmI+4mIjqXn7N3cjoyhQr4MDHyuiOlIIpJQ7tyAmU3hygFInRE6LIUcZUynEhF5bHEqxPw1CyYgIOCB94skhqtX4emn4fJlyJ4dVq1SEUZEsJcje/VV+P57e63CJUtUhBERkSQjb968HD9+nE2bNlGtWrUHbrN582YsyyJv3ryJnE7EvXyy8hCHLoeQ0S8V49uUw0t9YUSSp9tXYXoTuHYY/DJDxx8g6xOmU4mIxEucCjGdOnV6pPtFEsInn9hFmKJF4ccfIXdu04lEJEkYNAi++w48PGDePKih9YJFRCTpaNSoEWPGjKFv3778+uuvpEuX7p7HQ0ND6devHw6HgyZNmhhKKZL0Ld93iVlbzwHweasyZAvwNZxIRBJEyCW7CHPjOKTLbhdhMmv2m4i4P4dl/W9n45QtJCSEgIAAgoOD8dfV1EnK6dN2ASY6Gn76CR6hPZGIJGdjxsDAgfb4u++ga1ezeURE3JCOgRPW1atXKVGiBEFBQeTJk4dXX32VkiVLAnDw4EG+/vprzpw5Q6ZMmTh48CBZsiTtfhf6fRETTl27TePxm7kTFUuvmgV5s16xf3+SiLifW+dgemO4eQYCckOnHyBjAdOpRERccgwcpxkxIiZZFvTtaxdh6tRREUZE/mvDhr+LMMOGqQgjIiJJUpYsWVi8eDFNmzbl7NmzDBo06J7HLcvC39+fxYsXJ/kijIgJEdGx9JqzhztRsVTKn5EBdXRlvEiyFHTKngkTfB4y5INOyyF9HtOpRERcRguqSpK3YAGsWAHe3jBunOk0IpJkVK8OPXrA66/DW2+ZTiMiIvJQ1atXZ/fu3XTo0AF/f38sy7pbgOnQoQO7d+9+aP8YkZTuw+WHOHw5hEC/VHzRpqz6wogkR9eOwdQGdhEmsBB0Wa0ijIgkO5oRI0laUJA9Gwbg3XfhCfVmE5G/eHrCpEn2tDmHw3QaERGRf5Q/f36mT5+OZVncuHEDgMDAQBz6GybyUMv2XmTu9nM4HDC2dRmy+qsvjEiy8+chmNEE7lyDzMWh4zJIl9V0KhERl9OlJJKkvfEGXL0KxYvDf/5jOo2IGHfwIPTuba9VCHYBxkN/ykRExH04HA4yZcpEpkyZVIQR+Qcnrt7mnSUHAOhTsxDVCmc2nEhEXO7SXpjW0C7CZCsFnVeqCCMiyVaCz4iJiIggKiqK1KlT4+3tndC7k2Rk8WKYOtU+zzp5Mvj4mE4kIkadOQP16sGlS+DvD0OHmk4kIiLyrzJkyICnpycnT54kICDgH7e9evUq8+bNw9/fn9atW+Prq6v/JWUKj4ql1+zdhEXF8nSBQPqpL4xI8nNhJ8xqARHBkLM8tF8MqTOYTiUikmDidRnxuXPnOH/+/D9uM3HiRDJkyMArr7wSn11JCuJ0wiefwIsv2rd79oQqVcxmEhHDrl6FunXtIkyJEvZ0ORERETeQJk0agH8twly+fJly5coxYMAAunXrRu3atXE6nYkRUSTJGfLDHxz9M5RMaX0Y16YMnh6aPSaSrJzdAjOa2UWY3JWhw/cqwohIshevQky+fPkoVqzYP27TsmVLLMvi559/js+uJIUIDbULMIMH220fevaEMWNMpxIRo0JDoUEDOH4c8uaFtWshY0bTqUREROKkQ4cOREREMH/+fJo3b06jRo0YPnw4d+7cuWe7zz//nEuXLlGlShUKFy7M1q1bWbJkiaHUIuYs2X2B+TvP43DAuNZlyJJOM8NEkpVTv9gzYaJCIV81eyaMr7/pVCIiCc5hWZb1uE/28PDA19eXsLCwf9zO09MTLy8vIiMjH3dXiSIkJISAgACCg4Px99cfgcQWEQFVq8Lu3eDtDV9+CS+/bDqViBgVGWkXYX7+GTJnhs2boYiWphARcSUdAyesW7dukSNHDiIjI/nro5fD4eDJJ59k06ZNpE2bFoDKlSuzY8cOjhw5woULF6hduzatW7dmzpw5JuPfR78vkpBOXA2l8fjfCI+OpX+dwvTXkmQiycvx9TC/HcREQMHa0Ho2eKc2nUpE5F+54hg4wTscx8TE4HA48PT0TOhdiRu7cAGqVbOLMBkywC+/qAgjIkC3bnYRJm1aWL1aRRgREXE7Y8eOJSIighw5cjBs2DCGDx9Orly52L9/Px9//PHd7c6ePQvYqw5UqlQJgF27dhnJLGJCWFQMPWfvJjw6lqqFAulTq7DpSCLiSkdWwrw2dhGmSH1oM1dFGBFJUbwSegdLlizB6XRStGjRhN6VuKnQUHj2WTh5EtKnh7lz4emnTacSkSShc2dYswYWLIDy5U2nEREReWQzZszA4XCwdOlSnnrqKQBq1qxJxYoVWbhwIcOHDwe4u1SZt7f33a8///zTWG6RxPb+sj849udtMqfzYWyrsuoLI5Kc/LEUFr8Mzhh4oim0mAxeqUynEhFJVHEuxEyfPp3p06ffd39UVBS1atV64HOCg4PZt28fDoeD7t27P35KSdZ69bKLMHnywK+/2i0gREQAqFMHTp+GdOlMJxEREXksFy9exOFwUP5/LigoX748DoeDS5cu3b0vIiKCNGnS3PPcf1sCWiS5WLjzPIt2XcDDAV+0LkvmdD6mI4mIq+ybD9+/CpYTSr0EzSaBZ4JfFy4ikuTE+Z3vzJkzbNy48b77nU7nA+//Xz169KBnz56Pmk1SgFOnYOZMezxrloowIgJMnWpPiytWzL6tIoyIiLixLFmycOnSJY4fP06R/y6xefz4cSzLIleuXACEhoYSExNDnjx5AHt55+joaPVgkRTh2J+hDF52EIABdYrwdMFAw4lExGV2z4Af+gIWlG0Pjb8AD7UuEJGUKc6FmGbNmpEvX767ty3LomvXrnh7e/PNN9888DmpU6emXLlyFCpUKN5BJXmaPNn+Xreu3SNGRFK4uXOha1cIDIR9+yBnTtOJRERE4qVx48Z89dVXtGrVinfffReAoUOH4nA4eP755wH45Zdf7nnOmTNnAMiRI0eiZhVJbHci7b4wEdFOqhXORM+aOncgkmxs/xZWvWGPn+oGDUaBR4K3qhYRSbLiXIgpXbo0pUuXvue+rl274unpSadOnVweTJK//fth7Fh73KOH0SgikhSsXQsdO9rjtm1BJ59ERCQZ+Oijj1i1ahX79u2jVatWgH1RW+7cuZk9ezZXr15lw4YNAJw9e5aJEydy5MgRgLs9ZUSSI8uyGPz9QU5cvU1Wfx8+b1VGfWFEkovfJ8A6++IDKveCep+CQ/++RSRl06KMYkRICLRrB+Hh8OST0Lix6UQiYtS2bdCyJcTEQJs2dpVWB+oiIpIMZMqUiW3btjFkyBA2bdqEr68vbdq0oV+/fjRo0IBFixYBUKtWLZo3b06fPn1wOBw4HA46d+5sNrxIAlq48wJL9ly82xcmU1r1hRFJFn4dBT9/bI+feR1qv6/PdiIiqBAjBliWXYQ5eBCyZIE1a8Db23QqETHm8GFo2BDu3LHXKZw2TVPWRUQkWcmaNSuTJk267/5Vq1axZs0afHx8qF27Nh4eHty4cYOFCxfSqVMnatWqZSCtSMI7ciXkbl+YgXWLUqmA+sKIuD3Lgg1D4dcR9u2a70L1N1WEERH5L4dlWdbjPvns2bM4HI67TSXdXUhICAEBAQQHB6sxZgL68kvo1Qt8fGDzZtCKCyIp2IULUKUKnD8PFSvCTz9B2rSmU4mIpCg6BpZHod8Xia/bkTE0mbCZU9fuUKNIZqZ2roCHliQTcW+WBT++D79/Yd+u8yE8099oJBERV3LFMXC8ZsTkzZs3Pk+XFMay4D//gRH/vThi6FAVYURSPD8/yJPH/r5ypYowIiIiIsmYZVm8u/QAp67dIZu/L5+3KqMijIi7syxY/TZs/9q+/fxwqPyq2UwiIkmQliaTRPPVV38XYV5+Gfr1M5tHRJKADBlg3Tq4dQsyZTKdRkREREQS0Lwd51m29xKeHg7Gty1LRr9UpiOJSHw4nbByAOyaBjig0efwVBfTqUREkiQtwi+J4sQJeP99ezxiBHz7LXh6ms0kIoZERcGSJX/fTpMGcuQwl0dEREREEtyhSyF88MMfALxRtygV8mU0nEhE4sUZC8t62kUYhwc0+1JFGBGRf6BCjCS4P/6A6tXh+nUoVQr69zedSESMcTqhY0do2RI+/th0GhERERFJBKER0fSas5uoGCc1i2amR/UCpiOJSHzERsOS7rBvLjg8ocW3UKat6VQiIkmaliaTBHXoENSqBVev2kWYH38Eb2/TqUTECMuy1yScP99+I6hUyXQiEREREUlglmXxzpIDnL5+hxwBvox5SX1hRNxaTCQs6gpHVoCHN7w4FYo3Np1KRCTJ04wYSTC//GKfZ716FcqUgY0bIWtW06lExJhPPoEJE8DhgOnToW5d04lEREREJIEt2HmeFfsv4+XhYHzbcmRQXxgR9xUdAfPb20UYTx9oPVtFGBGROIpTIcbT0xNPT09KlCjxwPvj8uXlpck3Kcn169CmDdy+Dc8+a/fizqglgEVSrkmT/m4UNW6c/QYhIiIiIsna7cgYRqw5CsAb9YpSPm8Gw4lE5LFF3YG5reD4OvBKDW3nQZF6plOJiLiNOFVHLMu65/v/v/9RXkOSP8uCrl3h8mUoVgxWrAA/P9OpRMSYhQuhVy97PHgw9OljNo+IiIiIJIrvNp3mxp0o8mfyo9sz+U3HEZHHFRkKc1rB2d/A2w/aLYB8z5hOJSLiVuJUiDl9+jQA3v+vucdf94v8r0mTYPlySJUK5s1TEUYkxbt40a7Q9ugBH35oOo2IiIiIJIIbtyP55teTAAysWwRvT62MLuKWwm/B7Bfgwg7w8Yd2iyCP+n2KiDyqOBVi8ubN+0j3S8p18CAMHGiPR4yA0qXN5hGRJKB/f/vNoHp1uz+MiIiIiCR7Ezec5E5ULKVyBtCgZHbTcUTkcYQFwczmcHkv+KaHDkshZznTqURE3JIuSRGXCQ+32z5ERED9+tC3r+lEImLMqVMQHPz37Zo1wdPTXB4RERERSTQXboYxa+tZAN56vigeHroYR8Tt3L4G0xvbRZg0maDzChVhRETiQYUYcZk337RnxGTNCtOm6cJ3kRTr4kWoVQtq1IArV0ynEREREZFENnb9caJinVQpGMgzhTKZjiMijyrkMkxrCH8ehLRZofNKyFbKdCoREbcWp6XJRP7NkSMwcaI9njEDsmQxm0dEDAkKgnr14OxZu1GUh+r9IiIiIinJsT9DWbL7AgBvPV8Mh67QE3EvwRfsmTBBp8A/J3RaDoEFTacSEXF7KsRIvMXEwODB9rhRI6hb12weETEkLAwaN4Y//oDs2WHdOlVlRURERFKYkWuP4rTg+RLZKJM7vek4IvIobp6xizC3zkH6PHYRJkM+06lERJIFFWIk3t56CxYtssc9epjNIiKGREfDiy/C779D+vSwdi3ky2c6lYiIiIgkol1nb/LjoT/xcMAb9YqYjiMij+L6CZjRBEIuQsYCdhEmIJfpVCIiyUaCrRkTFRXFnj17uHjxYkLtQpKAQ4fgiy/s8fjx9owYEUlhnE7o2hVWrYLUqWHFCiil9YNFREREUhLLshi+5ggAL5bPTaEs6QwnEpE4u3oEpjWwizCZikKX1SrCiIi4WLwLMevXr6ds2bK8++6799yXM2dOnnrqKfLmzUvfvn3juxtJogYMgNhYaNYMevc2nUZEjLhyBTZsAE9PWLgQqlY1nUhEREREEtkvx66x/XQQqbw86FensOk4IhJXVw7YRZjbf0LWktB5JaTLZjqViEiyE69CzJEjR2jYsCH79+8nOjoagDt37tCqVStu3LiBZVk4nU4mTpzIihUrXBJYko516+wvb28YNcp0GhExJkcOe0myBQugYUPTaUREREQkkTmdFiPWHAWg09N5yZE+teFEIhInF3fDtEYQdgOyl7GXI0ub2XQqEZFkKV6FmM8++4zo6GjatWvH66+/DsD333/PzZs3effddwkKCqJz585YlsWkSZNcEliShqNH4YUX7HHXrlCwoNk8ImLA1at/j/PkgRYtzGUREREREWOW77/EocshpPPxouezhUzHEZG4OLcNZjSFiFuQqyJ0XAZpMppOJSKSbHnF58lbtmzB4XDw2WefkS2bPW3x119/JWPGjAwZMgRPT08+/vhjpk2bxsGDB10SWMxzOqFNGwgNhWrVYMwY04lEJNF9/z20awczZ6oAIyIiApw7d84lr5MnTx6XvI5IYomKcTJ63TEAXqlegAx+qQwnEpF/dWYzzH4Jou9A3qrQdj74qK+TiEhCilch5vz583h4eJAzZ8679x04cICnn34aT09PAHLmzImHhwdXrlyJX1JJMpYtgz17ICAA5s+HNGlMJxKRRLVxI7RuDZGRsGaNCjEiIiJA/vz54/0aDoeDmJgYF6QRSTzzd57nXFAYmdL60PWZ+P87EJEEdvJnmNsWYsKhwLPQei6k0okdEZGEFq+lyVKlSoXT6eTOnTsAWJbFoUOHKFWq1N1twsLCcDqdpNHZ+mTBsmDYMHvcpw9kz242j4gksj17oEkTuwjTtCl8+aXpRCIiIkmCZVnx/nI6naZ/DJFHEhYVwxc/HQegb+1C+PnE61pPEUloR9fAnNZ2EaZwXWgzX0UYEZFEEq9CTNGiRQGYP38+AMuXLyc0NJTSpUvf3eb3338HoEiRIvHZlSQR338P27dD6tTQt6/pNCKSqE6cgOeft9clrF4d5s4FL33YFhERAXA6nS75EnEnU387w7XQSHJnTE3rClpWTyRJO/QDzG8PsZFQrBG0mg3evqZTiYikGPEqxLz00ktYlsWrr77KU089RatWrfD19aVu3boALF68mF69euFwOGjUqJFLAos5N27Aa6/Z4wEDIHNms3lEJBFdvgz16sHVq1C6NPzwg12RFREREZEU6VZYFF/9chKAgc8VJZVXvE4viEhCOrAIFnYGZzSUbAkvTgMv9XMSEUlM8TpS6tu3L1WqVCEmJobdu3cTHR3NZ599RoYMGQB4+eWXOX78OHnz5qVfv34uCSxmxMTAq6/Cn3/CE0/A+++bTiQiiWrSJDh1CgoUsPvCBASYTiQiIiIiBk3aeJLQiBiKZUtHk9I5TMcRkYfZMxsWvwxWLJRuCy2+BU9v06lERFKceK0pkypVKjZs2MCcOXMIDg7m2Wef5cknn7z7eLFixXjyySf54IMP8Pf3j3dYMSMyEho0gJ9/Bg8PmDYNfHxMpxKRRDVkiN0kqnNnyJbNdBoRERERMehycDjTfj8DwFvPF8XDw2E2kIg82M6psKK/PS7XCRqNtU/siIhIoov34v7e3t506tTpgY9t2bIlvi8vScDChXYRJm1amD4dKlQwnUhEEkVMjH2Q/tfXxx+bTiQiIiIiScAXPx0nMsZJxXwZqVk0i+k4IvIgW7+CNW/b44o9oP5wcKhoKiJiisrg8q9mzrS/DxwILVqYzSIiicSyoEcPaNcOoqJMpxERERGRJOLktdss2HkBsGfDOHRiVyTp2Tz27yJMlb4qwoiIJAEuL8TcuHGDGzduuPplxZDLl2H9envcoYPZLCKSiN55B6ZMgQULYNs202lEREREJIkYs+4YsU6LOsWz8FS+jKbjiMj/sizYOBzWf2DfrvE2PPeRijAiIkmASwox586do2vXrgQGBpIlSxayZMlCYGAg3bp14+zZs67YhRhgWTBqFDidUKUKFCxoOpGIJIrRo2H4cHv87bdQrZrZPCIiIinAmTNnGDhwoOkYIv9o/4VbrDxwGYcD3qhX1HQcEflflgU/fQQbh9q3aw2GmoNUhBERSSLiXYj57bffKFu2LNOnT+fmzZtYloVlWdy8eZNp06ZRrlw59YpxU+++C2PG2OPevc1mEZFEMmMGvPGGPR4+HLp2NZtHREQkGdixYwfPPfcc6dKlw9PT84FfBQsWZOzYsaajivyjEWuOAtC8TE6KZfM3nEZE7rIsWPsubP7vSZy6n0L1N8xmEhGRe3jF58lXr16lefPm3Lx5k1y5cvHqq6/y5JNPArB//36+/vprzp8/T/PmzTlw4ACZM2d2SWhJeBs2wGef2ePRo6FNG7N5RCQRrFz5d+Fl4EB4802zeURERJKBgwcPUqNGDSIjI7Es6x+3zZYtWyKlEnl0m49fZ/OJ63h7OhjwXBHTcUTkL04nrHoDdn5n324wCip2N5tJRETuE68ZMaNGjeL69euUKlWKffv2MWjQIBo1akSjRo0YNGgQe/fupWjRoly7do1Ro0a5KrMksNhYeP11e/zaa3+PRSQZCwmxG0HFxkLHjjBihKawi4iIuMDQoUOJiIigdu3a7Nixg+effx5vb28OHTrE6dOn6d7dPlnWokULLly4YDityINZlsWItUcAaFcpL7kzpjGcSEQAcMbC8j7/LcI4oMl4FWFERJKoeBVifvjhBxwOB+PHjydDhgz3PZ4hQwbGjRuHZVksW7YsPruSRDR9OuzdCwEB8NFHptOISKLw94clS+zpb5Mng4dLWoiJiIikeL/++isOh4PvvvuO8uXL06NHD2JiYrh48SJ58+bl66+/platWixdupQVK1aYjivyQGsOXmH/hWDSpPKkd61CpuOICEBsDCztAXtmgcMDmn8N5TqaTiUiIg8RrzNtZ8+excPDg2r/0Mi5evXqOBwOzp49G59dSSI5evTvGTCDB0OmTGbziEgievZZmDMHvL1NJxEREUk2rl27hqenJ7lz5wbg6aefxrIs9u7de3ebt99+G8uymDRpkqGUIg8XE+tk5Dq7N8zL1QqQKa2P4UQiQkwULOoCBxaChxe8MAVKtzKdSkRE/kG8CjFp0qTB6XQSFBT00G2uX7+OZVmkSaOpy0ldbCy0aAHBwVClCvTpYzqRiCSoq1ehZk04eNB0EhERkWQrMDCQ2NhYwsLCAMiSJQsZM2bkyJEjd7f568K2HTt2GMko8k8W7brAqWt3yJDGm+7V8puOIyIxkbCgIxz+ATxTwUszoURz06lERORfxKsQ89RTTwEwYcKEh27z12MVKlSIz64kESxbBocOQcaM9gpFqVKZTiQiCSYkBOrXh40boVMn+JfmwSIiIvJ4ChcuDMCcOXPu3leqVKn7ii4Oh4OIiIhEzSbybyKiYxm7/jgAvWoWIp2vZk6LGBUVBnPbwLHV4OULredCsQamU4mISBzEqxDz2muvYVkWn376KePGjSMmJubuYzExMXzxxReMHj0ah8PBa6+9Fu+wkrDGjrW/v/YaZM1qNIqIJKSICGjWDHbvhsyZYd48cDhMpxIREUmWXn75ZSzLolevXgwfPhyAZ555hgMHDvD1118TEhLCp59+imVZ5MuXz2xYkf9nxpYzXAmJIGf61LSvnNd0HJGULToc5rwEJ38C7zTQdgEUrmM6lYiIxFG8CjHNmjWjdevWxMTE8PrrrxMYGEiFChWoUKECgYGBDBgwgNjYWNq0aUPTpk1dlVkSwPHjsGkTeHpCz56m04hIgomNhfbtYcMGSJcO1qyB/16pKyIiIq7Xtm1bqlevTnR0NNu2bQOga9eueHl50bNnTzJkyMDQoUNxOBy89NJLhtOK/C04PJqJG04C0L9OYXy9PQ0nEknh1g6CM5sgVTpovwQK1DCdSEREHkG8CjEAM2bMoFevXjgcDkJDQ9m1axe7du0iNDQUh8NBr169mDZtmguiSkKaPdv+Xrcu5MhhNouIJBDLsiutixfbaw9+/z2UK2c6lYiISLLm6enJunXr+OyzzyhRogQA+fPnZ8KECXh5eWFZFpZlUapUKfr37282rMj/+PbXUwSHR1M4S1palMtlOo5IynZ4OeycYo9fmgZ5nzYaR0REHp3DslzTGODkyZMsW7aMU6dOAVCgQAGaNGlCoUKFXPHyiSIkJISAgACCg4Px9/c3HSfRWJZ9QfzJk3ZBpm1b04lEJEFMnAi9e9vLkC1cCC1bmk4kIiJJQEo9Bk4Kzp8/z5YtWwgICKBWrVp4eyf9/hv6fUkZroZEUGPkRsKjY/m6Q3nqlchmOpJIyhV8Eb6qCuE3oUofqPuJ6UQiIimOK46BvVwVpmDBgrz++uuuejlJRNu22UUYPz/QCnIiyVj79rBoEbRpoyKMiIhIEpA7d25y585tOobIfcb/fILw6FjK5klP3SfUQFTEGGcsLHnFLsJkLwO13jedSEREHpPLCjHivmbNsr83b24XY0QkmQoIgPXr7WZQIiIiIiIPcPbGHeZuPwfA288Xw+FwGE4kkoJtGgNnN4O3H7wwBbxSmU4kIiKPySWFmPDwcObNm8dvv/3G5cuXcTgc5MyZk+rVq/Piiy+SKpX+UCRVoaF/F2LatzebRUQSwNq18Mcf8NeMRRVhREREkpyNGzeyf/9+KlasSOXKlU3HkRRuzI/HiHFa1CiSmcoFAk3HEUm5zm+HjZ/Z44ajILCg2TwiIhIv8S7E/P7777zwwgv8+eefAPzVcsbhcDB58mTeffddFi1axFNPPRXfXUkCmDYNgoOhSBF47jnTaUTEpbZuhRYtICwMcuSA1q1NJxIREUlxPvroI7y8vBg0aNBDtwkODqZ///7UqlWL9evXJ2I6kXv9cSmYZXsvAfBmvaKG04ikYOG3YFE3sGKh5AtQuo3pRCIiEk/xKsScOXOGhg0bEhwcDECJEiUoUKAAsbGxnDhxgmPHjnHu3Dmef/559uzZo/WPkxinE8aNs8f9+oGHh9k8IuJChw5Bw4Z2EaZePbsgIyIiIoluyJAh+Pr6/mMh5tlnnwVgz549iZRK5MFGrj0KQOPSOSiZM8BwGpEUyrJgxQAIPgfp80KjMaAlAkVE3F68Tr1/8sknBAcHU6hQIbZs2cKBAwdYtmwZK1as4MiRI2zYsIG8efNy8+ZNPvnkE1dlFhdZvx5OnoT06aFTJ9NpRMRlzp2ziy9BQVCpEixeDFoiUkREJMkKCAjA09OTkJAQ01EkBdt66gYbj17Dy8PBwOeKmI4jknLtnQ1/LAGHJ7T8DnxVFBURSQ7iVYhZs2YNDoeDb7/9lkqVKt33eI0aNZg8eTKWZbFq1ar47EoSwIIF9vfWrcHPz2wWEXGR69ehbl24cAGKF4eVK/UPXEREJIm7efMmsbGxBAToZJuYYVkWI9YcAaBVhdzky6TjRxEjrh+HVW/Z45qDIHcFs3lERMRl4rU02fXr1/Hw8KB69eoP3aZmzZp4eHhw/fr1+OxKXCw6GpYutccvvWQ2i4i4SHQ0NGgAR49C7tywdi0EqsGqiIhIYgkJCeHWrVv33W9ZFufPn7/bT/N/BQcHM3ToUBwOB1WrVk2ElCL3W3/4KrvP3cLX24N+tQubjiOSMsVEwqKuEH0H8lWDZwaYTiQiIi4Ur0JMjhw5OHv2LKGhofj7+z9wm9DQUJxOp/rDJDGrVtmrFmXJAv9QRxMRd+LtDR07wpkzsG6dXYwRERGRRPP555/z0Ucf3Xd/VFQU+fLle+jzLMsiVapUDB48OAHTiTxYrNNi5Fp7NkzXqvnJ4u9rOJFICvXTR3BlP6TOCC2+AQ9P04lERMSF4rU0WevWrbEsi7Fjxz50m+HDhwPQtWvX+OxKXMiy4L//W+jSBTz1t10k+ejdG44dg2LFTCcRERFJcSzLuu/rYff/9eXj40PVqlVZt24dTz31lOGfQFKipXsucuzP2wSk9qZHjYKm44ikTMfXw5YJ9rjpBPDPYTaPiIi4nMN60Pz4OIqMjKRp06asX7+ezp07079/fwoXLozT6eTw4cOMGjWK+fPn065dO6ZPn47D4XBldpcLCQkhICCA4ODgh87wSQ62b7f7d/v42BfOZ8tmOpGIPDbLgi++gA4dIGNG02lERMQNpZRjYFM8PDzw9fUlLCzMdBSX0O9L8hIZE0utUb9w8VY4/6lfjFdViBFJfLevwqQqcOcaVOgODUeZTiQiIv+PK46B47U0WYYMGe5eyTV16lSmTp2Kw+G4b+3jRYsWsWjRonvuczgc3LlzJz67l8c0bZr9/YUXVIQRcXsffwwffABTpsCOHZAqlelEIiIiIuImZm89x8Vb4WT196HT0/lMxxFJeZxOWPqqXYTJ8gTU/dh0IhERSSDxWposIiKCyMjIe6bWO53O+6bbR0REPPBLEl9kJMybZ487dTKbRUTiadIkuwgD0L27ijAiIiIiEme3I2OYsOEEAP3rFCF1Kq1Z/TBOp5PBgweTLVs2cubMyRdffPFIzw8NDeXTTz/lpZdeonfv3hw4cOCex7du3cpzzz1HmjRpyJs3L6NHj77nAtfbt2/TrVs3MmTIgI+PD8888wxHjx695zWuXLnCu+++y4svvsgbb7zBmTNnHvvnlUS09Us4+RN4+cILU8A7telEIiKSQOI1I2bDhg2uyiGJZO5cuHkTcuWCWrVMpxGRx7ZwIfTqZY/ff9/uDSMiIiJJTvXq1fHx8TEdQ+Q+kzedIuhOFAUy+fFi+Vym4yRpQ4YMYeTIkYwcOZKcOXPSo0cPsmbNSqtWrf71uZGRkVSvXh2Hw0GDBg345ZdfqFixIjt37qREiRLs37+fZ599lo4dOzJkyBB2797Nm2++yc2bN/nkk08A6NGjB2vWrOHNN98kU6ZMfPLJJzRu3JgjR47g4eHB9evXqVChAgULFuSZZ55hxYoVTJs2jUOHDpElS5aE/s8jj+vSHlg/xB7XGwpZihuNIyIiCStehZgaNWq4KockAqcTRoywx336gKcueBJxT+vXQ7t2dn+YV1+FIUNMJxIREZGH2Lhxo+kIIve5cTuSb389BcDAukXx8ozXYhnJWkhICCNHjuSjjz6iT58+ANy6dYuPPvooToWYCRMmEBYWxt69e0mdOjWxsbE88cQTTJ06lVGjRjFs2DBKlSrFN998A0DVqlW5evUqY8aM4YMPPuDUqVPMnz+frVu38tRTTwGQN29enn/+efbv30+ZMmX48MMPKVSoED/99BMeHh688847ZM+enQULFtBbF2wlTZG3YVE3cEZDsUbwVFfTiUREJIHpaCsFWbQIDh+G9OmhRw/TaUTksezcCc2aQXQ0vPgiTJgADofpVCIiIhJHN2/eZNeuXezatYubN2+ajiMp1IQNJ7gTFUupnAHUL6nGof/kt99+IyIignbt2t29r1mzZhw6dIhLly796/PLlSvHlClTSJ3aXnLK09OTbNmyERkZCcDOnTupU6fOPc8pVqwYYWFhXLt2jVy5crFr1667RRiAwMBAAGJjYwGoU6cOX3zxBR4e9ikePz8/MmTIcHcfkgStfhuCTkK6HNBkvD7TiYikACrEpBBOJ3z0kT3u3x8CAozGEZHHlTYtBAZC7dowc6amtomIiLiJnTt3UqtWLTJnzkzFihWpWLEimTNnpnbt2uzcudN0PElBzgeFMXvrOQDeer4oHh46AfxPLl68SMaMGcmZM+fd+zJmzEhAQAAnTpz41+fXrFmTqlWr3r29adMmfv/9d5o3bw7YhZkbN27c85yDBw/i7e1N5syZ8fPzo3Tp0vc8vnr1atKmTUupUqUAaNq06d0xwLx587h48SKNGzd+9B9YEt6BRbB3FuCAlt9CmoymE4mISCJQISaFWLwY/vjDLsD062c6jYg8tmLF4PffYelS0HrzIiIibmHp0qVUq1aNX375BafTiWVZWJaF0+lkw4YNVKtWjWXLlpmOKSnE2PXHiYp1UrVQINUKZzYdJ8kLDw8nffr0992fNm1arl27FufXOXbsGHXq1KFmzZoMHTqUWv9t2lqjRg1mzZrF5s2bAdi2bRsTJkygYcOGeHt73/c6N2/eZOzYsfTq1YtUqVLd89hvv/1G1apVadu2LbNnz6ZIkSKP8JNKorh5BlYMsMfV34B8zxiNIyIiiUeFmBTA6YQPP7THAwbYS5OJiBsJCoJffvn7ds6ckC6duTwiIiISZ6dPn6ZDhw5ERkZSpkwZZs2axf79+9m/fz+zZs2iXLlyREZG0r59e86cOWM6riRzR6+EsmTPBQDeqlfMcBr34OPjg+cDZqE7HA7Cw8Pj/DoZMmSgVq1a5M6dm++++47Lly8DMGTIEAoUKEC1atXImDEjlStX5vbt2w/t7dK7d29SpUrF22+/fd9jOXLkuDvzbty4cdy+fTvO+SQRxEbD4pchMgRyVYQa/zGdSEREEpEKMSnAsmWaDSPitu7cgYYN4bnnYMkS02lERETkEY0YMYKwsDCqV6/Oli1baNu2LSVLlqRkyZK0bduWLVu2ULFiRcLCwhg5cqTpuJLMjVp3FMuC+iWzUTp3etNx3EKWLFm4ePHiffcHBQXh5+cX59fJnDkzgwYNYt++fYSHh/P+++8DkC1bNvbu3cuqVasYPXo0fn5+VK9endq1a9/3GnPnzmXu3LlMmTKFDBky3Pd4/vz5+fjjj9m1axcHDhxg3Lhxj/CTSoLbOAwu7AAff2g5GTy9TCcSEZFEpEJMCjBliv391Vc1G0bErURHwwsvwNatdm+YokVNJxIREZFHtHbtWhwOB59//vl9ywgBeHt78/nnn2NZFqtXrzaQUFKKXWeD+PHQn3g4YGBdHVfGVenSpQkLC2P37t137zt8+DBhYWHkyJHjX59/7do1goOD79729/fn2Wef5fDhw3fv8/Lyon79+vj5+XHnzh2GDh163+scOnSI7t27M3DgQOrXr3/PYxcvXrxndk6uXLkoV67cPfsQw05vgk2j7XHjsZAhr9E4IiKS+FSISeauXoW/Ps917mw0iog8CqcTunSBNWsgdWpYsQJKlDCdSkRERB7RpUuX8PDwoGzZsg/d5qmnnsLhcHDp0qVETCYpiWVZDF99FIAXy+emUJa0hhO5j/z581OuXDmGDRt2975x48aRIUMGypcv/6/Pb9WqFe+888499x0/fpx8+fLdc59lWXz22Wc0a9aMqlWr3vPYlStXaNCgAeXKleOzzz67bx/Vq1dn/Pjxd2/HxMRw+vTp+/YhhoQFwZJXAAvKtIeSLU0nEhERAzQPMpmbOxdiY6FiRbvHt4i4AcuC11+H2bPBywsWL4YqVUynEhERkccQEBDA9evXuXjxIjlz5nzgNufOncOyLAICAhI5naQUG49dY/uZIFJ5edCvTmHTcdzOqFGjqFu3LjVr1iR16tSsXr2aMWPG4OXlxaVLl7h69SplypR54HP79+9Ps2bNSJ8+PXXr1mX58uXs2rWLMWPG3LPdggUL+OOPP5g/f/59r9GhQweuXbvGxIkT2bt379378+fPT2BgIP379+fNN9/E4XDw1FNPMWXKFEJDQ+natasr/zPI47As+KEPhF6CwEJQf7jpRCIiYohmxCRzM2bY3zt2NJtDRB7BsGHw13rOU6fC/1t6QERERNxHlf9eTPGgq9j/8tcyRP//KngRV3A6LUassWfDdK6SjxzpUxtO5H5q1qzJr7/+io+PDzdu3GDy5MkMGDAAgG+++YZnn332oc9t0qQJM2bM4Pvvv6dhw4b89ttvrFmzhsqVK9/dJiYmhvfee48ePXpQpEiRe54fFBTE+vXrCQsLo1GjRlSoUOHu1/LlywHo06cPn332GZMmTaJJkyZcvnyZDRs2UKBAAdf/x5BHs/M7OLICPLyh5Xfgo9loIiIplcOyLMt0iKQiJCSEgIAAgoOD8ff3Nx0n3g4ehFKlwNsbLl2CTJlMJxKRf2VZ0LUrTJsGn38O/fubTiQiIslccjsGTmp++eUXatasicPhoG/fvrz99ttky5YNsJcbGjFiBGPHjsXhcLBhwwaqV69uOPE/0++L+1m29yL95u0lnY8Xv75Vkwx+9/cqEpEE8Och+LYmxERA3U+hSm/TiURE5DG54hhYM2KSsZkz7e8NG6oII+I2HA6YMgVWrVIRRkREJBmoUaMGAwcOxLIsvvjiC3LmzEnmzJnJnDkzOXPmZNx/Z8EOHDgwyRdhxP1ExTgZve4YAD1qFFARRiSxRIfD4m52EaZQHajc03QiERExTIWYZCo2FmbNssdalkzEDRw+bP/DBbsYo+XIREREko2RI0cycuRI0qZNi2VZ3Lhxgxs3bmBZFmnTpmXkyJGMGDHCdExJhubvOMe5oDAypfWhS9X8puOIpBzr3oOrh8AvCzSbBB46/SYiktJ5mQ4gCePnn+3lyDJmhAYNTKcRkX+0Zw/UqAF169oVVF9f04lERETExQYOHEj37t1Zv349p06dAqBAgQLUqVNHS3xJggiLimHcTycA6Fu7EH4++vgvkiiOrIQdk+1x80mQNovZPCIikiToSCyZmjHD/t66Nfj4mM0iIv/gxAl4/nkIDYXr102nERERkQTk7+9PixYtTMeQFGLqb2e4fjuSPBnT0LpCHtNxRFKGkEuwrJc9frq3vSyZiIgIWposWQoNhSVL7LGWJRNJwi5ftmfBXL0KZcrAsmWaDSMiIiIi8XbzThRfbTwJwMC6RUjlpY/+IgnOGQtLXoHwm5C9NNT+wHQiERFJQlxyNPb777/TrVs3SpcuTcGCBQHYtm0b+fLlY/To0ViW5YrdSBwtWQJhYVCkCFSsaDqNiDzQrVv2TJjTp6FgQVizBgICTKcSERERA77//ntef/11VqxYYTqKJBOTfjlJaGQMxbKlo/GTOUzHEUkZNn8OZzaBtx+0nAL/x959h1dRrW0c/u30HgghoffepQgqvTeRZoUjKKIo9u6xHgv2Loo0QUVFpCO9SVOp0qQFCB1CgPS+M98fA0E+QYGUtXfy3Nc116zsNs/hBJzZ76x3efmYTiQiIi4k14WYl156iVatWjFhwgS2bt3KsWPHcp47ePAgTz/9NIMGDbqqz962bRvNmjWjePHiPPXUU1dU0ImLi6N06dJER0df1bHd2bm2ZHfeaa/5LSIuJjUVevWCLVugVClYuBAiI02nEhERkXxw9913M2zYsH98TVBQEB999BFvvfXWVR1D103yV8fiU5mwJhqAZ7rWwsNDF4Ui+e7QOlg2wh53fxfCq5nNIyIiLidXhZh58+bx+uuvY1kWbdq0ueC5qlWrMnjwYAAmTZrEjBkzruiz09PTufHGG2nSpAnr16/nzz//ZMKECZf9/qeeeorjx49f0TELg4MHYdkyezxwoNksInIJmzfDunUQEmLPhKlSxXQiERERyScTJkzg63N3Sl3CddddB9gFlSul6yb5/z5evIeMrGyurRRG25olTccRKfzS4mHq3WA5oV4/aHSH6UQiIuKCclWI+fjjj3E4HDz33HMsXbr0gufCw8MZP348r732GpZlMWrUqCv67Hnz5hEfH88HH3xA1apVGTFiBOPGjbus965YsYJZs2ZRokSJKzpmYTBpElgWtG0LFSuaTiMiF9WiBSxeDLNnQ8OGptOIiIiIYYGBgXh6epKcnHzF79V1k/xVVEwSP64/BMAz3WriUIsEkfxlWTDncYg7CMUqQM8P1ZpEREQuKleFmHXr1gHw8MMPX/I156bhr1279oo+e/PmzbRo0YKAgAAAGjRowJ9//vmv70tPT+e+++7jk08+ISgo6F9fm5CQcMHmzizrwrZkIuJiTp06P77hBmjd2lwWERERcRnHjh3D6XReVUEkv6+bCts1U2H3waJdZFvQsXYkTSqGmY4jUvht/h62/QQOT+g3Dvy07qeIiFycV27enJycjIeHB5H/sLZBSEgIDoeDlJSUK/rshIQEKleunPOzw+HA09OTM2fOULx48Uu+b8SIEdSoUYNbb72VZ5555h+P8eabb/K///3vinK5snXrYOdO8PeHfv1MpxGRC7z7Lrz/vt2KrFEj02lEREQkHxw8ePCia61kZ2ezcuXKi67dEh8fz4cffojD4aB9+/ZXfMz8vm4qbNdMhdnmQ3HM3XochwOe6lLTdBz5N04nrFwJx45B6dLQqhV4eppOJVciNgp+ftIet3sOyl9rNo+IiLi0XBViSpcuzcGDBzl+/DilSpW66Gv+/PNPLMu65POXDOblha+v7wWP+fn5kZKScskLih07djBq1Cg2bdp0Wcd47rnnePzxx3N+TkhIoHz58leU05W8956979vXXnpCRFzEhAnw9NP2eNkyFWJEREQKqa+++opXX331b49nZmbStm3bS77PsixCQ0OvquCR39dNhe2aqTB7Z8FOAPpcU5aapYINp5F/NG0aPPIIHD58/rFy5eDjj+0LenF9WRkwdQhkJkOlVtDy8X9/j4iIFGm5KsS0bduWr7/+mtGjR/PSSy9d9DWvvPIKDoeDdu3aXdFnh4WF/W2xysTERHx8fC76esuyuPfee3n99dcpU6bMZR3D19f3bxct7uqPP2DKFLsV6b9MBBKRgjR7Ntxzjz1+8kl47DGzeURERCTfFCtWjAoVKlzw2IEDBwD+9vg5/v7+NGnShBdeeIHq1atf8THz+7qpMF0zFWar9sSyOuoU3p4OHutYw3Qc+SfTpkH//nZv8b86csR+/KefVIxxB0tfhWN/gH9x6PMleGg2k4iI/DOHdbH58Zdp27ZtNG7cGMuyeOqpp3jrrbfw9fVl06ZN7N69m9GjRzN37ly8vLzYsGED9evXv+zPXrp0Kffeey9RUVEA7N+/nzp16pCUlITnRabrHjhwgEqVKhEaer4fZ0JCAkFBQYwaNYo77rjjX4+ZkJBAaGgo8fHxhLjZlJI+fWDGDLj9dvjuO9NpRASAVaugUydIS4NBg+Crr7Rwo4iIuBx3Pgd2Bx4eHjkzVPJDQV836ffF9ViWRa/PVrP1SDyDr6/EK73qmo4kl+J0QqVKF86E+SuHw54Zs3+/2pS5sqjF8O3ZfvC3fQe1epjNIyIi+S4vzoFzNSOmXr16fPLJJwwfPpy3334bh8NBRkYGdevaJ36WZeFwOPjoo4+uqAgD0Lp1axISEvjqq6+46667GDFiBB07dsTT05O4uDiCg4MvuLAoW7Ys+/fvv+AzWrZsyQ8//ECjQt4GKDERfv7ZHj//vNksInLWli3Qs6ddhOnZE8aOVRFGRERE8pyum2TetuNsPRJPoI8nD7avZjqO/JOVKy9dhAF7lsyhQ/br/qGdoRiUdBKm32+Pmw5REUZERC5brgoxAMOGDaNy5co8//zzbNy48YLnmjRpwmuvvUbXrl2vPJiXF2PHjuX222/nqaeewsPDg+XLlwNQvHhxNm3adMGFgpeXF5UqVfrbZ5QrV46goKArPr47+eUXyMyEKlWgrm5+EnENL74I8fHQsiVMngxeuf7nVkRERORvdN1UtGU5s3lvwS4A7mlVhfAgtZFzRYkZiWyK2UTS6nF0v4zXT1r6HgnFdlC5WGWqhFahUkglfDwv3m5QClB2NswYBskxULI2dHnDdCIREXEjefLNYJcuXejSpQsxMTE5PZArVqxIRERErj63V69e7N27lw0bNtCiRQtKlCgB2DNtLkd0dHSuju8uvv3W3nfqZDaHiPzFt9/Cc8/Ba69BQIDpNCIiImLInXfeecn1WvKKrpuKrikbDrMvNpmwQB/uaVXZdBw563TaaTae2MiGExvYcGIDu87sItvKpmli0mUVYhZnbGf95gM5P3s4PCgfXJ7KoXZhJmcrVoVA78D8+x8iF/r9C7stmZcf9B8P3v6mE4mIiBvJ1RoxhY079jtetAg6dwYPD/jtN2jWzHQikSIsK0szX0RExO244zmwmKPfF9eRlumkzbvLOJGQzos96zCkpQoxphxPPp5TdNlwYgP74vf97TUVgivQNPwanr11FH4nTuO4yFcxlsNBeqmS/DBvBHuTotkXv4/9cftJzEy85LEjAyJzijJVQqvkFGvC/MJwqDVy3jm2GcZ0gOxM6P4eXDvUdCIRESlAxteIEfNmzLD3Q4aoCCNiVFoadO9uT0179lmtByMiIiIi+WrimmhOJKRTtpg/A5pXMB2nyLAsi0OJh9hwYgPrT6xnw4kNHEk68rfXVStWjSaRTWga2ZTGkY2JCDjbMWRkY+jf375e+GsxxuHAAfh99gWDG/a94HgnU0+yL34f++L22cWZ+P3si99HbGosJ1JOcCLlBL8e+/WC44f6hlI1tOr5WTRnCzWlAkvh4fDIjz+awisjGX4aYhdhavaAZveYTiQiIm4oV4WYKlWqXPZrHQ4He/fuzc3h5CJWr7b3aksmYpDTCQMGwLJlsH49DBwI5cubTiUiIiIihVR8aiafL7evrx/tWB0/b0/DiQqvbCubvXF7L5jxcjL15AWv8XB4UDusNk0im9AksgmNIxpTzK/YxT+wb1/46Sd4+GE48pcCTrly8NFH9vN/4XA4iAiIICIgghalW1zwXHx6fE5R5lyRZl/8Po4mHSU+PZ6NMRvZGHPhWr7+Xv5/a3FWuVhlygeXx9vD+2r/mAq3ec/AqT0QXAZu+kw33YmIyFXJVSHm33oJOxyOnL7EmhKb95Ysgc2b7XOAG24wnUakiLIsuP9+mDYNfHxg5kwVYUREREQkX41esZf41EyqRwTRt3E503EKlazsLHad3pUz22VjzEbi0+MveI23hzf1w+vnFF4almxIkE/Q5R+kb1+46SZYuRKOHYPSpaFVK/C8soJaqG8ojSIa0Sii0QWPp2alciDhAHvj9p6fQRO3jwOJB0jNSuXPU3/y56k/L3iPl4cXFYIrULVY1QsKNZVCK+HvVYTXQtk2DTZ9Azig75cQEGY6kYiIuKlcFWKWLVv2t8eysrI4ePAgs2bNYtasWZQtW5aRI0cSGhqam0PJRXz6qb2/7z4oU8ZsFpEi68UXYcwYe6Gm77+Hdu1MJxIRERGRQiwmIY1xq/YD8FSXmnh66KbH3MhwZrAtdlvObJdNMZtIyUq54DX+Xv40LNkwp/BSP7w+fl5+uTuwpye0bZu7z7gEfy9/aoXVolZYrQsez8zO5HDi4b/NoNkfv5/UrNScn//KgYMyQWWoHFqZqqFVL1iLJtS3kH/Pc+YAzH7Uu22IpQAAvXBJREFUHrd6Aiq3NhpHRETcm8OyLrJCXB5ZuHAh/fv3p3bt2qxcuRIfH5/8OlSecKeFJ48ft2cuO52wbRvUrWs6kUgR9PHH8Oij9nj0aBiqBRtFRMT9uNM5sJin3xfzXpixlW9/O8g1FYox7f7r1X3iCqVkpvDHyT/YeGIjG05sYMvJLWRkZ1zwmmDvYBpHNs4pvNQuUbtQt+3KtrI5kXyCffH7LpxFE7+PuPS4S76vhF+JnMLMX9ehKelf0v1/L51ZMKE7HPodyjWDu+aBZ+H9HRARkX+WF+fAuZoR8286d+7MhAkT6N+/P++++y7PP/98fh6uSPnwQ7sI07y5ijAiRmzfDo89Zo/feENFGBERERHJd9Gxyfyw9hAAz3St5f5fdheA+PR4NsVsypnx8uepP3FazgteE+YXllN0aRrZlGrFquHpkc/r7qSmQuuzMyxWrAB/c+2/PBwelA4qTemg0txQ9sK+56fTTl8we+bc+ETKCU6lneLU8VOsO77ugvcEewdTObSyPYumWNWcQk2ZoDL5/+eaV3552y7C+IZAv7EqwoiISK7l64yYc6pWrYqfnx/bt2/P70Plirvc3XXmDJQta5+3zZ4NPXuaTiRSRH35JezaBe+/rwUbRUTEbbnLObC4Bv2+mPXw95uYtfkobWqUZOLd15qO45JiU2Nzii4bTmxgz5k9WFz4tUfpwNI0jWyaU3ypGFKx4ItayckQdHZdmaQkCAws2OPnUnJmcs6smX1x+9gbv5f98fs5lHiIbCv7ou/x9fSlYkjFv82gqRhSER9PF+qgEr0aJvYEKxv6jYP6/U0nEhERw1x+Rsw5oaGh7N69uyAOVSRMmmQXYRo0gB49TKcRKcLuu890AhEREREpIrYfjWfW5qMAPN21puE0ruNo0tELCi/RCdF/e02lkEo5RZcmkU0oE6RFVnMr0DuQeuH1qBde74LHM5wZHEg4cH79mbj97I3fS3R8NOnOdHaf2c3uMxd+P+Tp8KRccDkqh1bOKdLUCqtFzTADv+cpp2HaULsI02iAijAiIpJn8r0Qs23bNrZv346fXy4XspMc331n7++6SzfhixSoP/+EJ5+Er7+G8HDTaURERESkCHl3wS4AejUsQ90yhXyR9EuwLIvohOgLCi/Hko9d8BoHDmoUr5FTdGkc2Zhwf527FxQfTx+qF69O9eLVL3jcme3kaNLR8y3O/tLmLCkziQMJBziQcIDlh5bnvKdZqWYMbzScJpFNCia8ZcGshyDhCIRVhW7vFMxxRUSkSMhVIebrr7++5HNOp5Ndu3YxevRosrKyuP7663NzKDlr3z749Vfw8IBbbzWdRqQIOXgQOneGI0fgiSdg4kTTiURERESkiPht3ymW7zqJl4eDxzvVMB2nwDiznUTFRbH+xPqcwsvptNMXvMbT4UndEnVzCi+NIhoR6ls0C1WuzNPDk/Ih5SkfUp425dvkPG5ZFidTT15QmNkXv48/Yv5g3fF1DJ4/mBalWzC80XAaRTTK35AbvoKdc8DDG/qPA9+g/D2eiIgUKbkqxAwePPhf+6halkVISAgjRozIzaHkrO+/t/ft20Pp0maziBQZJ0+eL8LUqQMffmg6kYiIiIgUEZZl8fb8nQDcdm15KoW711oiVyIzO5Mdp3bkFF02xmwkMSPxgtf4ePjQoGSDnMJLw5INCfAOMJRYcsvhcBAREEFEQAQtSrfIefxY0jHGbB3D9Kjp/HbsN3479hs3lLmBBxo9QIOSDfI+SMwOmP+cPe74MpS5Ju+PISIiRVquCjGtW7f+x0JMQEAA9evX55FHHqG0qga5Zln2+jAAd9xhNotIkZGYCN27w65dUKECLFgAYWGmU4mIiIhIEbHozxNsOhiHn7cHD7ev/u9vcCNpWWlsjd2aU3jZfHIzqVmpF7wmwCuAayKuySm81Auv51oLu0u+KB1Umpeue4kh9YcwZssYZkTNYPXR1aw+uprW5VrzQKMHqFuibt4cLDMNfhoCWWlQtT20GJ43nysiIvIXuSrELF++PI9iyOXYsgV27ABfX+jb13QakSIgPd3+y7Z+vb0mzMKFUK6c6VQiIiIiUkQ4s62ctWHuvqEyESHuvfZqUkYSf5z8I6fwsi12G5nZmRe8JtQ3lMYRjWkS2YSmkU2pGVYTL498X97WDK07+a/KBpXlletfYUj9IYzeMprZe2ez4vAKVhxeQdvybXmg4QPULlE7dwdZ9CLEbIfAktB7lN0LXkREJI8V0rOZwum77+x9jx4Qqpa3Ivnvscdg8WIIDIS5c6FmTdOJRERERKQImb7pCHtikgj19+a+NlVNx7kqR5OOMmnHJNafWM/O0zvJtrIveL6kf8mc2S5NIptQtVhVPBxF4IvwwEC7BbJclvLB5Xnthte4p/49fLn5S37e/zPLDy1n+aHldKzQkfsb3U+N4lexftKuebB2tD3u/QUER+ZlbBERkRwqxLiJ9HS1JRMpcI8/DsuWwSefQLNmptOIiIiICzt58iTHjh0jPDycMmXKmI4jhUBappMPF+0G4IG2VQn19zac6ModSDjA3fPvJiY1JuexskFlc2a7NIlsQvng8v+69qzIORVDKjKi1QiGNhjKqM2jmLd/HosPLmbxwcV0rtiZ+xveT7Xi1S7vwxKOwowH7HGL4VC9U/4FFxGRIk+FGDcxbpy9Tnjp0vaMGBEpANWqwdat4KV/KkVEROSfPfTQQ0yZMoXx48czaNCgnMdXrFiBh4cHLVu2NJhO3NGk3w9yJC6VyBBfBl1fyXScK3Yw4SB3L7CLMFVDqzK0wVCaRDahVGAp09GkEKgcWpm3W7/NvQ3u5YvNX7AgegELDyxk0YFFdK3clWENh1EltMqlPyDbCdPvg9TTUKoBdHy54MKLiEiRlKv5vt27d6dDhw6cOnUqr/LIJUyebO+ffhr83LstsIhrGz3abkN2joowIiIichlWrVoFQLdu3S54vG3btnTp0sVEJHFjiWmZjFwWBcCjHWvg5+1pONGVOZhwkLsW3EVMil2EGddlHD2q9FAR5q9SU6FtW3tLTTWdxm1VLVaV99q8x9ReU+lUsRMWFvP2z6PPzD48t/I5DiQcuPgbV38M+1eAdwD0Hw9evgUbXEREipxcfcO4c+dODhw4oGnEBeDgQXt/7bVmc4gUaj/+CMOGgacnrF8PDRuaTiQiIiJuIiYmBg8PDyIiIv72nGVZBhKJOxu7cj+nkzOoEh7IzU3KmY5zRQ4lHLJnwqTEUCW0CmO7jKWEfwnTsVxPdjb88sv5seRKjeI1+KDtB+w8vZPP//icZYeWMWffHObtn0fPKj25r+F9lA8ub7/48HpY+ro97vYOhFc3F1xERIqMXM2IaXZ2zYSoqKg8CSMXt2QJREeDh4fdKUlE8sHixTBwIFgW3HsvNGhgOpGIiIi4kWLFimFZFpmZmaajiJuLTUpn7Mp9ADzZpSZenu6zcP2hxEPcvfBuTqScoHJoZcZ1GUe4f7jpWFKE1AqrxSftP+GHnj/QplwbnJaTmXtncuP0G3l5zcscObULfrobLCfU7QPXDDQdWUREiohcndENGjQIy7L48ssv8yqP/D/p6TB8uD0ePhwucoOdiOTWunXQuzdkZsItt8Ann4Bm+omIiMgVqFWrFpZlMXbsWNNRxM19tjSK5Awn9cuG0q2e+7TyOpRoz4Q5nnycyqGVGd9lvIowYkzdEnX5rMNnfNf9O24oewNOy8m0PdPoOedm/ueZwLHiFaDnR7ruExGRAuOwcjlP/rbbbmPKlCmMHj2aIUOG5FUuIxISEggNDSU+Pp6QkBDTcQAYORIefBAiI2HXLggNNZ1IpJDZuRNatoRTp6BjR5gzB3zVH1hERIoOVzwHdkfTp0+nX79+OBwOwsPDCQwMBCA6OhqHw0HFihX/9TMcDgd79+7N76i5ot+X/HXodArt319OptPi2yHNaVndPQoZhxMPc/eCuzmWfIxKIZUY32U8JQNKmo7l2pKTISjIHiclwdl/MyR//BHzB5+vfIlfk/YD4OXwpF+N/txT/x6tXSQiIv8qL86Bc70K9YQJE/D29ubee+/l119/5Y033iAyMjK3HytnrVtn7x94QEUYkTx34gR06WIXYZo1g2nTVIQRERGRq9KnTx+ef/553n77bU6ePMnJkydznrMsi+jo6H/9DK29KR8u3k2m0+KGaiXcpghzJOkIQxYMURFGXFojz2BG79rABkcmn1dpwNq0E0zeNZnpe6bT/2xBRr+3IiKSn3JViKlSpcoFP3/11Vd89913lCr197sJ3OHuLld07o+salWzOUQKpfBw6NwZVq6En3+G4GDTiURERMSNvfbaazzxxBPs2LGDjIwMLMuiffv2+Pj4sGDBAtPxxMXtOp7I9E1HAHi6Sy3DaS7PkaQj3D3/bo4mH6VSSCXGdRmnL7PF9WRlwNQhkJFEk4o3MO7m2ayL2cjIP0ay4cQGvtv5HVP3TOWWmrdwd7271VJPRETyRa5ak3l4XP4SMw6HA6fTebWHKhCuNs0+PR2KFYO0NNixA2q5x7m4iHuxLDhzBsLCTCcRERExwtXOgQsbDw8P/Pz8SElJMR0lT+j3Jf/cM3E9i3ecoFu9UnwxsInpOP/qaNJR7l5wN0eSjlAxpCLju4wnIkCLml625OTzi8DGxKg1WX5a9BKs/hj8isH9qyG0HGDPVvz9+O+M3DSSP07+AYCfpx+317qdwfUGE+ana0QREbEZb022bNmy3Lxd/sXSpXYRpmRJqFnTdBqRQiIjA774AoYPBy8ve3FGFWFERERExKANB06zeMcJPD0cPNnF9S/+jiUdu6AIM67zOBVhrlRgoF2Mkfy1d6ldhAG46bOcIgzYNwy3KN2C5qWa8+vRXxn5x0i2xG7hq+1f8cOuH7ij1h0MrjuYYn7FzGQXEZFCJVeFmDZt2uRVDrmI996z9x062N8Vi0guZWfDXXfBd9/Bb7/B99+bTiQiIiIiRZxlWbw9bxcANzcpR9WSQYYT/bNjSce4a8FdHEk6QoXgCozrPI7IQK0TKy4oORamD7PHTe+G2jde9GUOh4Pry17PdWWuY+WRlXz+x+dsP7WdcdvG8f3O7xlQewCD6g4i1FcL94qIyNW7/N5iUqDWrbNnxHh5wSuvmE4jUghYFjz2mF2E8fKCO+80nUhERESKgOzs7ELTlkzyx/JdJ1kbfRofLw8e6VjddJx/dDz5eM5MmPLB5RnXRUUYcVGWBTPuh6QTULIWdH7jX9/icDhoXa413/f4nk/bf0qtsFqkZKUwZusYuk7tyud/fE5CRkIBhBcRkcJIhRgX9dZb9n7AALUlE8kTb74Jn3xijydMgG7djMYREREREcnOtnh7/k4ABl9fidKh/oYTXdrx5OPcNf8uDicdplxQOcZ3GU+pwFKmY7mvtDTo0cPe0tJMpyl8fh8FexaCpy/0Hw8+AZf9VofDQdvybfmx54981PYjahSvQVJmEl9s/oKuU7syavMokjKS8jG8iIgURpfVmuzrr7/G09OTAQMG5HceAXbuhOnT7fHTT5vNIlIojBkDzz9vjz/6yK5wioiIiBSwpKQklixZQlRUFADVqlWjQ4cOBAW5disqyT+ztxxl5/FEgn29uL9NVdNxLuncTJhzRZivun6lIkxuOZ0wd+75seSdY1tg0Uv2uMsbEFn3qj7G4XDQoWIH2lVox+IDi/li8xdExUUx8o+RfLvjWwbXHczttW4n0DswD8OLiEhhdVmFmMGDB+Pv769CTAF5+217Fm3v3lCnjuk0Im5u2jQYdrYv8H//C488YjaPiIiIFEmffPIJL7/8MgkJF7a1CQkJ4dVXX+Whhx4ylExMycjK5v2FuwEY1rYqxQN9DCe6uBPJJxiyYAiHEg9RNqisZsKIa8tIhqlDwJkBNbtDs3ty/ZEeDg86V+pMx4odWRi9kM83f87++P18vPFjvt7+NYPrDea2mrcR4H35s25ERKTouezWZJZl5WcOOevgQfj2W3v87LNms4gUCoGB4OcHQ4fC66+bTiMiIiJF0PPPP89jjz1GfHw8lmVRvHhxihcvjmVZxMfH8+ijj/L8udm7UmT8sO4gB0+nEB7ky103VDId56JOJJ/g7gV3czDxYE4RpnRQadOxRC5t/rMQuxuCS0Ovz8DhyLOP9nB40LVyV6b3ms6brd6kYkhFzqSf4cMNH9JtWjcmbp9IalZqnh1PREQKF60R42I++ACysqBdO2je3HQakUKgSxdYtw6++CJPT8JFRERELsfKlSt58803AXjggQc4fPgwsbGxxMbGcvjwYYYPH45lWbz11lusWrXKcFopKMnpWXyyxG5R90iHagT4XFazigIVkxLDkIVDLijClAkqYzqWyKVtnwEbvwYc0OdLCCyRL4fx9PCkZ5WezLhpBm+0fINyQeU4nXaa99a/R/dp3fn2z29Jy9K6PyIicqHLPtuzLItDhw7lamZMhQoVrvq9RUFsrL2UBcBzz5nNIuLW9uyx+/vVqGH/rB5/IiIiYshHH30EwL333stnn312wXNlypTh008/JTExka+//pqPPvqIli1bGkgpBe2r1fuJTUqnQlgAtzZzvevkmJQYhiwYwoGEA5QJLMO4LuNUhBHXFncQZj9sj1s+BlXa5PshvTy86FW1F90qd2PO3jl8ueVLjiQd4e11b/PVtq8YUn8I/Wv0x8fTNdsOiohIwXJYl1FZ8fDwwJHLO8kdDgdZWVm5+oz8lpCQQGhoKPHx8YSEhBT48V96CV57DZo0sW/g1837Ilfh6FG44QZIToZFi6BhQ9OJREREXJrpc+DCLjIyktjYWA4ePEjZsmUv+pqoqChq1KhByZIlOXHiRAEnvDL6fcm9M8kZtH5nGYnpWXx8WyNuanTx3wtTTqac5O4FdxOdEE3pwNJ81fUryga5VsZCITkZgoLscVKS3VJZro4zCyb0gEO/QdmmcPd88PQu8BiZzkxm7p3J6C2jOZZ8DIDIgEjubXAvfar1wdtAJhERyRt5cQ58Ra3JLMvK1SaXlp4O526Qe/ZZFWFErsqZM3YrsuhoCA2FUlpEVERERMyKj4/Hw8PjkkUYgIoVK+JwOIiPjy/AZGLKF7/sJTE9i9qlQ7ixgWvNMvn/RZjxXcarCCOub8W7dhHGJxj6jTVShAHw9vSmf43+zOkzhxeav0BEQAQnUk7w2m+v0WN6D6bunkpmdqaRbCIiYt5ltybz8vJi9OjR+ZmlSNu61f4OuUQJ6NPHdBoRN5SSAjfeCNu22QWYhQshMtJ0KhERESniypQpw4EDB9i8eTMNLzFTd+PGjViWRenSWgS9sDsal8qENdEAPN21Jh4ernMHXmxqLEMWDiE6IZpSgaUY12Uc5YLLmY5VeAUG2u2UJXcOrIEV79jjnh9CWGWzeQAfTx9urXUrvav3ZuruqYzdOpZjycd45ddXGLN1DPc1uI8bq96Il4frrQ0lIiL557L/1ff09GTQoEH5maVI27zZ3jdqBJ6eRqOIuJ/MTLj1Vli92p4Js2ABVDZ/Ai4iIiLSuXNnRo8ezeOPP86CBQvw8rrwEszpdPL444/jcDjo2rWroZRSUD5evIeMrGyurRxG2xolTcfJEZsay5AFQ9gfv59SgaUY32U85YPLm44l8s9Sz8DUoWBlQ8PbocHNphNdwNfTlztq30Hf6n2ZsnsK47aO40jSEV5a8xJjt45lWMNhdKvcTQUZEZEi4opak0n+OVeI0XIWIlcoOxvuuQfmzAE/P5g9Gxo0MJ1KREREBICnn34af39/li9fzg033MDkyZPZvn0727dvZ/Lkydxwww38+uuv+Pv78/TTT5uOK/koKiaJKRsOAfBM15q5Xoc1r5wrwuyL30dkQCTjO6sII27AsmDWw5BwGMKqQPd3TSe6JD8vP/5T5z/M6zePJ5s+SZhfGAcTD/LfVf+lz8w+/LzvZ5zZTtMxRUQkn6ns7gKcTpg2zR5fe63ZLCJuJyUF9uyxp5L9+CO0amU6kYiIiEiOKlWqMGHCBAYOHMj69eu54447Lnjesiy8vb2ZOHEilTWjt1B7f+Eusi3oWDuSJhXDTMcB4FTqKe5ZcA/74vcRERBhz4QJURGmQKSlwX/+Y4+/+ca+qUwu38aJsGMWeHhDv3HgG2w60b/y9/JnUN1B3FzjZr7f+T1fbf+K6IRonl35LKO3jOb+hvfTuVJnPBy6Z1pEpDC6rH/dW7duTSt9uZlv9u2DI0fA3x969zadRsTNBAXBokUwd669RoyIiIiIi7n55ptZvnw5LVu2BOzii3V2bYhWrVrxyy+/0K9fP5MRJZ9tPhTHvG3HcTjgqS41TccBzhZhFt7D3vi9RARE8FWXr6gQUsF0rKLD6YSffrI3p2ZDXJGYnTDvWXvc4UUo29hsnisU4B3AkPpDWNBvAQ9f8zAhPiHsi9/HUyueot+sfiw6sIhsK9t0TBERyWMOy9LqcOckJCQQGhpKfHw8ISEhBXbc6dOhb19o0gTWry+ww4q4t127oKZrXMSKiIi4M1PnwEXVqVOn2L9/PwCVK1emRIkShhNdGf2+XJ0BY39jddQp+jUux/u3mO9HfTrtNEMWDCEqLooI/wjGdx1PxZCKpmMVLcnJ9k1lAElJEBhoNo+7yEyDsR3gxDao0g4GTgMP955BkpiRyKQdk/h6+9ckZiYCULN4Te5vdD/ty7d3mTaGIiJFWV6cA7v3f60KiS1b7H29emZziLiNWbOgbl145RW7N7CIiIiImyhRogRNmzaladOmbleEkauzcs9JVkedwsfTg0c7Vjcd54IiTEn/kirCiHtZ/LJdhAkIhz5fun0RBiDYJ5hhDYcxv/98hjUcRqB3ILvO7OLRZY9y65xb+eXQL+geahER9+f+/8UqBNassfdNmpjNIeIWVq6EW2+1p+9HR5tOIyIiIiJySdnZFu/M3wXAgBYVKB8WYDTPmbQz3LPwnvNFmC4qwogb2TUffh9lj3t/AcGRZvPksRCfEIY3Gs6CfgsYWn8oAV4B7Di9gweXPsgdP9/BysMrVZAREXFjKsQYlpV1vhDTpo3ZLCIub/Nmex2YtDTo2RPGjgVN0xYRERERFzVv23G2Hokn0MeT4e2qGc1yrgiz58weSvqXZFyXcVQKrWQ0k8hlSzwOMx+wxy0egBqdzebJR6G+oTzc+GHm95vP3fXuxt/Ln22ntvHAkgcYOG8gSw4sIdOZaTqmiIhcIRViDFuxwm4HGxam1mQi/2jfPujaFeLjoWVL+PFH8PIynUpERERE5KIyndm8t9CeDXNPqyqEB/kayxKXFsfQhUPZfWY34f7hjOsyjsqhlY3lEbki2dkw/T5IOQWl6kPHV0wnKhDF/YrzWJPHmNd3HoPrDsbP048tJ7fw6PJH6TClA2+vfZtdp3eZjikiIpdJhRjDFi+29127ForWpiL548QJ6NwZjh+H+vVh9mzw9zedSkRERETkkn7acJj9scmEBfowtHUVYzni0uK4Z+E97DqzixJ+JVSEEfez5hPYtxy8A6DfePAyV9Q0oYR/CZ5o+gTz+s3j7np3E+4fzpn0M3y741v6z+7PLbNvYdKOSZxJO2M6qoiI/AN99W/YggX2vksXszlEXNr8+bB3L1SqZP+lKVbMdCIRERERkUtKzXDy0eLdADzYrhpBvmZmcsenxzN00dCcIsz4LuOpEmquKCR/ERBgt8dISrLHcnGHN8DS1+xxt7ehZA2zeQwK9w/nsSaPsaj/IkZ2GEmnip3w8vBix+kdvLX2LdpPac9jyx7jl0O/kJWdZTquiIj8P+rrY5BlQVSUPW7a1GwWEZc2aJA9ZaxFCyhd2nQaEREREZF/NPHXaE4kpFO2mD8DWlQwkiE+PZ6hC4ey8/ROwvzCGNdlHFWKqQjjMhwOCAw0ncK1pSfC1CGQnQV1esM1/zGdyCV4eXjRulxrWpdrTVxaHHP3z2VG1Ax2nN7B4oOLWXxwMSX8SnBj1Ru5qepNVCtudn0qERGxOSzLskyHcBUJCQmEhoYSHx9PSEhIvh/v9GkoUcIep6So05LIBbKyIC0NgoJMJxERESnUCvocWNybfl/+XXxKJq3eWUpCWhbv3dyQ/k3KFXyGs0WYHad3EOYXxvgu46larGqB5xDJlWn3wZYfILQ8DFsF/sVMJ3Jpu07vYubemfy872dOp53OebxeiXrcVO0mulXuRqhvqMGEIiLuKy/OgdWazKC9e+19mTIqwohcwLLg/vuhTRt7fRgRERERETfx5Yq9JKRlUT0iiD7XlC3w4///Isy4zuNUhHFF6ekweLC9paebTuN6Nk+2izAOD+g3VkWYy1AzrCZPN3uaxTcv5uN2H9O+fHu8HF5sO7WNN35/g/Y/tufJX55k1ZFVOLOdpuOKiBQ5ak1m0L599r6KZoeLXOj552HsWLsd2fr10KOH6UQiIiIiIv8qJiGN8av3A/BUl5p4ejgK9PgJGQncu+jenCLM2M5j1ZbIVWVlwcSJ9njkSPAtWgvQ/6PT++Dnx+1xm2ehQguzedyMt4c37Su0p32F9pxKPZXTumz3md0siF7AgugFRPhH2K3Lqt1E5dDKpiOLiBQJKsQYdG5GjAoxIn/x4Yfw5pv2eNQoFWFERERExG18vGQPaZnZNK5QjE51Igv02AkZCdy78F7+PPUnxX2LM7bzWKoXr16gGURyLS0eJv8HMpKgwvXQ+knTidxaCf8S/KfOfxhYeyA7Tu9gZtRMft7/MzGpMYzbNo5x28bRsGRDelfrTZdKXQj2CTYdWUSk0FIhxqBzM2Kqapa4iO3bb+Hxs3c+jRgBQ4eazSMiIiIicpn2xybzw7pDADzTtRYOR8HNhknISOC+hfex/dR2uwjTRUUYcUNZ6fDDADixDYIiod8Y8PA0napQcDgc1ClRhzol6vBE0yf45fAvzIiaweojq9l8cjObT27m7bVv06FiB26qehPNSzfHw6HVDERE8pIKMQZpRozIX8ydC3fdZY8ffRSefdZoHBERERGRK/HBot04sy3a1ixJ8yolCuy4iRmJDFs0jG2ntlHMtxhjOo+hRvEaBXZ8kTyRnQ3Th0H0SvAJhgFTILSc6VSFko+nD50qdqJTxU6cTDnJnH1zmBE1g33x+/h538/8vO9nSgWWolfVXvSu2pvyIeVNRxYRKRRU3jZIa8SInJWZCY88YvdJHjgQ3n8fCvAOQhERERGR3DgSl8rszUcBe22YgnKuCLM1divFfIsxtvNYaoYV3PFF8szCF2D7NPDwhlu/gdINTScqEkoGlOSuencx46YZfNf9O26teSvBPsEcTz7O6C2j6T69O4PmDWL6nukkZyabjisi4tY0I8aQjAw4ZM9aV2syEW9vWLQI3n0XPvoIPFQjFhERERH3sXTHCQCaVixO3TKhBXLMpIwkhi0expbYLYT6hqoII+5rzafw20h73PtzqNrObJ4iyOFwUL9kfeqXrM9TzZ5i2cFlzIiawZqja9gYs5GNMRt5c+2bdKrYid7VetMksolal4mIXCEVYgzZvx8sCwICICLCdBoRQ5xO8Dzb87dSJRg50mgcERERkfw0c+ZMfvzxR/bs2UNCQgIAISEh1KhRg1tuuYVevXoZTihXa8nOGAA61I4skOMlZSRx3+L72HJSRRhxc1t/smfDAHR6FRrcYjaP4OvpS9fKXelauSvHk48zZ98cZkbNJDohmll7ZzFr7yzKBpXlpqo30ataL8oGlTUdWUTELah8bcjWrfa+Th11YJIi6uRJaNIEZs0ynUREREQkV7aeO7n/Bx9++CF9+/bl+++/Z8+ePaSmppKamsqePXv47rvv6NOnDx9//HEBpJW8lpKRxZq9pwDoUDv/77JLzky2Z8Kc3EKITwhjOo2hVlitfD+u5LGAAIiJsbeAANNpzNi33F4XBqD5/XD9w0bjyN+VCizFPfXvYVbvWXzT7Rv6Ve9HoHcgR5KO8Pnmz+k6tStDFgxh9t7ZpGSmmI4rIuLSVIgxZPNme99QbU+lKEpMhO7d7b8Ijz0G6emmE4mIiIhclcTERAYPHkyrVq2YPn06lmVd9HXvvPMOAJs2beLMmTMcOHCAAwcOcObMGTZs2IBlWbz11lsFGV3yyOqoU2RkZVOuuD/VI4Ly9VjJmckMWzSMzSc320WYzmOoXaJ2vh5T8onDASVL2ltRvDvz2Bb4YSBkZ0LdPtBlRNH8c3ATDoeDRhGNeOX6V1h2yzLebPUmzUs3x4GDtcfX8t9V/6X9lPa8vOZlNp7YeMn/FoqIFGVqTWbI9u32vkEDszlEClx6OvTpA+vXQ3g4zJ0Lvr6mU4mIiIhcleDgYDZs2MDy5cv58MMPeeaZZ3jwwQcZMmQIgYGBOa8rW7YsMTExTJo0iUOHDhEcHAzYhZxffvkFgHLlyhn53yC5s3SnvT5Mh1oROPLxi+TkzGTuX3w/f5z8g2CfYMZ0HkOdEnXy7Xgi+ebMAZjUHzISoWJL6D1K64S6EX8vf3pW6UnPKj05mnSUWXtnMTNqJoeTDjNtzzSm7ZlGheAK3FTtJnpV7UWpwFKmI4uIuASHpTJ1joSEBEJDQ4mPjyckJCRfj9W6NaxcCT/+CDffnK+HEnEdTifcfjtMmQJBQbBsGTRtajqViIhIkVaQ58BFQVRUFB999BEzZszg9ttv5+GHH6Z8+fJs2rSJzp07c+rUqb99WW9ZFuHh4SxYsIBrrrnGUPLLo9+XC1mWRfMRS4hJTGfi3dfSpkbJfDlOcmYyDyx+gI0xG3OKMHVL1M2XY0kBSU+Hxx+3xx98UHRuTks5DeM6w6k9EFEX7poL/sVMp5JcsiyLDSc2MHPvTBZELyA1KxUABw5alG5B72q9aV+hPX5efoaTiohcnbw4B1Yh5i8K8qKiYUPYsgUWLoROnfL1UCKuwbLgwQfh88/B29ueCdOxo+lUIiIiRZ6+WM8fZ86c4csvv+TLL7+kRYsWPPbYY9SvX58VK1awe/duEhMTAQgJCaF69eq0bt0af39/w6n/nX5fLrTtSDw9P11FgI8nG1/shJ+3Z54fIyUzhfsX328XYbzPFmHCVYRxe8nJ9s1pAElJ8JcZdIVWRgp8fRMcXgsh5eCeRRBSxnQqyWMpmSksOrCIGVEzWH9ifc7jwd7BdK3cld7VelM/vH6+ziAUEclreXEOrNZkhsTF2ftixUymEClAP/1kF2EcDvj2WxVhREREpFArXrw4zz77LE8++SSTJ0/mgQcewM/Pj8cff5wHH3xQX0AVEkt2xADQslp4vhVhHljyQE4RZnTn0SrCiHtyZsHUIXYRxi8UBk5VEaaQCvAO4KZqN3FTtZs4lHiIWXtnMStqFkeTjzJl9xSm7J5CldAq3FTtJnpW6UlEQITpyCIiBUJNOA1wOuHkSXtcvLjZLCIFpm9feOABGDkSbrnFdBoRERGRPJWRkcGLL75IgwYNCAkJwdvbm9DQUJo1a0ZUVBS//vorb7zxBt988w01atTgk08+ISkpyXRsyaWc9WFq5/0XieeKMBtObCDIO4gvO31JvfB6eX4ckXxnWTD3Sdg1Fzx94fbJEFHLdCopAOWDyzO80XDm9ZvH2M5jubHKjfh5+rEvfh8fbviQTj914oHFD7AgegEZzgzTcUVE8pVmxBiwcyekptozjytXNp1GpIB4etpFGBEREZFC6IknnuDzzz8nODiY2rVr4+/vT2pqKjt37uTVV18lOTmZd955hzZt2hAVFcXHH39MrVq1uO2223j44YepUKGC6f8JcoViEtPYfDgegHY187YQk5KZwoNLH8wpwozuNJr6Jevn6TFECsyKd2HDV4AD+o2FiteZTiQFzMPhQfPSzWleujn/bf5fFh5YyIyoGWyK2cTKIytZeWQlIT4hdK/cnd7Ve1MnrI5mjopIoaMZMQasW2fvmzSxv5sWKbQWLoS77oLMTNNJRERERPLVd999B0BUVBS///47y5cv5/fff2f79u1YlsWkSZNyXlutWjU+/fRTtm/fTkREBG3atOHWW2/l999/NxVfrsLynXabgwblQokIybsFqFOzUnlo6UOsO76OQO9Avuz0pYow4r42fgPL3rDH3d+FOr3M5hHjgnyC6Fu9L193+5o5feYwtP5QIgMiSchI4IddP3DbnNvoO6svE7dPJDY11nRcEZE8oxkxBqxda++bNTObQyRfrV1rtyNLToa6deHJJ00nEhEREck3tWrV4rfffuOpp57iuuuuw9fXl7S0NFauXAlA7dq1//ae0NBQnn76aR5//HGmTJnC5MmTad68eUFHl6u05Gxbsva18m42TGpWKg8ueZC1x9fmFGEalGyQZ58vUqB2L4DZj9jjlo/DtUPN5hGXUzGkIg83fpjhjYbz+7HfmbF3BksOLCEqLor31r/HRxs+omW5lvSu2pvW5Vrj7eltOrKIyFVzWJZlmQ7hKhISEggNDSU+Pp6QkJB8O06zZrB+PfzwA9x6a74dRsScnTuhZUs4dQo6d4bZs8HHx3QqERERuYiCOgcu7LZu3Uq3bt04evTo356rUKEC8+fPp1Yt918TQb8vtvQsJ9e8uoiUDCdzHmpJvbKhuf7M1KxUHlryEL8f/51A70BGdRxFo4hGuQ8rrik5GYKC7HFSkt27vDA5vAEm9oTMFGh4B/T+HNRqSi5DQkYC8/fPZ2bUTLbEbsl5vLhvcXpU6UHvar2pGVbTYEIRKYry4hxYhZi/KIiLivR0CA62OzXt3QtVquTLYUTMOXwYrr8eDh2Ca6+FJUvOX2CIiIiIy9EX63nH6XSyadMmoqOjSUtLw9/fn0qVKnHNNdfg4VE4ukLr98W2YvdJ7hy/lsgQX357rkOu1zJIy0rjwaUP8vux3wnwCuDLTl+qCFPYZWfDwYP2uEIFKCT/RgAQGwXjO0PKKajaAe6YDJrJIFdhX9w+Zuydwey9sy9oU1YrrBa9q/Wme+XuFPcrbjChiBQVKsTksYK4qFi3zv5uukQJOHlSN4RIIXPqFLRqBTt2QM2asGoVhIebTiUiIiL/QF+sy5XQ74vtlVnbmbAmmtuvLc+bfXPXOiwtK42Hlj7Eb8d+I8ArgFGdRnFNxDV5lFSkgCXFwNiOEHcASjeCwT+Dr27Mk9zJys5izdE1zIiawfJDy8nMtteh9fLwom25ttxU7SZalm2Jl4dWYBCR/JEX58D6F6qArV9v75s1UxFGChnLgn797CJM2bKwcKGKMCIiIiJS6FiW9Zf1YSJz9VlpWWk8vPRhfjv2G/5e/nzR8QsVYcR9pSfCpP52EaZ4ZRgwRUUYyRNeHl60Ltea1uVaE5cWx9z9c5m5dyZ/nvqTxQcXs/jgYiL8IxjWaBh9qvVRQUZEXFIhmvvqHg4dsvc1apjNIZLnHA546SWoWNEuwlSoYDqRiIiIiEiei4pJ4tDpVHy8PLihWomr/py0rDQeWfYIvx77FX8vf0Z1HEXjyMZ5mFRcWkYGPPWUvWVkmE6Te1kZ8OOdcGwzBITDwKkQFGE6lRRCxfyKcUftO5jcczJTe03lzjp3EuYXRkxqDK/++ir9Z/VnxeEVqAGQiLgaFWIKWEyMvS9Z0mwOkXzRvj3s3g116phOIiIiIiKSL5bstC/qrq9aggCfq7vrOt2ZzqPLHmXN0TU5M2FUhCliMjPhvffsLTPTdJrcsSyY9RDsXQreAXDHj1CiqulUUgTUKF6Dp5o9xeL+i3n22mcJ9Q1lb/xehi8Zzj0L72H7qe2mI4qI5FAhpoDt32/vy5Y1m0MkT1gWvPaa3Y7sHB8fc3lERERERPLZ0h12IaZDrau72z/dmc4jyx5h9dHV+Hv583mHz2kS2SQvI4oUrCX/gy0/gMMTbvkayun3WQqWt6c3A2oPYG7fudxV7y58PHxYe3wtt825jWdXPsuRpCOmI4qIqBBT0LZts/f165vNIZInRoyw25G1bg1xcabTiIiIiIjkq7iUDNYfOA1Au6soxJybCbP6iF2EGdlhJE1LNc3rmCIF5/fRsOpDe9zrU6jeyWweKdJCfEJ4vMnjzOkzhxur3AjAz/t+5sbpN/L++veJT483nFBEijIVYgpQZub51mQVK5rNIpJro0fDCy/Y4xdfhGLFjMYREREREclvv+w+SbYFtUoFU654wBW9N8OZwWPLHmPVkVU5RZhmpZrlU1KRAvDnTJj3tD1u/wJcM8BsHpGzSgeVZkSrEUzuOZnmpZqTmZ3JhO0T6DG9B19v/5oMZyFYl0lE3I4KMQXo1Cl773BAWJjZLCK5MnUq3H+/PX7+eXj4YbN5REREREQKwJKzbcnaX+FsmAxnBo8tf4yVR1bi5+mnIoy4vwNrYOpQwIKmd0OrJ00nEvmbOiXqMKbzGEZ2GEm1YtWIT4/n3fXv0mtGL+bvn49lWaYjikgRokJMAYqNtfdhYeDpaTaLyFVbuhTuuAOys+Hee+01YkRERERECrksZzbLd51dH6b25RdiMpwZPL78cVYcXoGfpx+fdfhMRRhxbzE74PvbwJkOtXpC9/fsO05FXJDD4aB1udZMuXEKr1z3CiX9S3Ik6QhPrXiKAXMHsOHEBtMRRaSIUCGmAJ08ae9LljSbQ+Sqbd4MN90EGRnQty98/rlOuEVERESkSNhw4AwJaVkUD/CmUfnil/WeDGcGTyx/gl8O/4Kvpy+fdviU5qWb53NSkXwUfwS+7Qdp8VC+OfQbCx6601Rcn5eHF/1q9GNOnzkMbzQcfy9/tsZuZfD8wTy89GH2x+83HVFECjkVYgrQuUJMeLjZHCJXrWJFuOYaaNcOJk3S1C4RERERKTKW7rRnw7SrGYGnx7/fjJTpzOSJX55g+eHl+Hr68lmHz2hRukV+xxR34e8P27bZm7+/6TSXJzUOJvWHhCMQXgNu/wG83SS7yFkB3gEMaziMuX3nckuNW/B0eLLs0DL6zOzD67+9TmxqrOmIIlJIqRBTgI4etfelSpnNIXLVihWDBQtg5kzw8zOdRkRERESkwCw5W4hpfxltyXKKMIfsIsyn7T9VEUYu5OEBdevam4cbfDWTmQY/3AExf0JwaRg4FQK0+K24r3D/cF687kWm9ZpG2/JtcVpOJu+aTI9pPfhy85ekZqWajigihYwb/Ne+8Ni2zd7XqmU2h8gVOXMGJk48/7O/PwQHm8sjIiIiIlLADpxKJiomCS8PB62q/3Ov6UxnJk/+8iTLDi3Dx8OHT9p/wnVlriugpCL5IDsbpt8LB1aDbwgM+AmKVTCdSiRPVClWhU/bf8r4LuOpV6IeKVkpfPbHZ/Sc1pPpe6bjzHaajigihYQKMQVoyxZ737Ch2Rwily0lBW68EQYPhnfeMZ1GRERERMSIc23JmlUKI9Tf+5Kvy8zO5KkVT7H00FJ8PHz4tP2nXF/m+oKKKe4kIwNeecXeMjJMp7k0y4IFz8GfM8HDG279FkrVM51KJM81K9WMST0m8U7rdygbVJaY1BheWvMSN8+5mVVHVmFZlumIIuLmVIgpIE7n+RkxDRqYzSJyWTIz4ZZbYPVquyVZt26mE4mIiIiIGHGuENPhH9qSZWZn8vQvT7Pk4JKcmTDXl1URRi4hMxP+9z97y8w0nebSVn8Mv4+yx31GQZU2ZvOI5CMPhwfdKndjVu9ZPNn0SUJ8QthzZg/3L76fexfdy87TO01HFBE3pkJMAdm/H1JT7a5OVauaTiPyL7KzYcgQ+Plney2Y2bOhfn3TqUREREREClxSeha/7TsFQPtaFy/EWJbFcyufY/HBxfh4+PBx+4+5oewNBRlTJO9tngyLX7bHXUZA/f5m84gUEB9PHwbVHcTcvnMZVGcQ3h7e/HbsN26ZfQvPr3qe48nHTUcUETekQkwBOXjQ3lesCJ6eZrOI/CPLgqeegm++sX9Zp0yBli1NpxIRERERMWLVnpNkOi0qhwdSpWTQRV+zJXYLC6IX4OXhxUftPqJlWZ0/i5vbuxRmPmCPr3sQrhtuNo+IAaG+oTzZ7Elm9Z5Ft8rdsLCYtXcWPaf35KMNH5GYkWg6ooi4ERViCsiRI/a+XDmzOUT+1bvvwgcf2OPx46FnT7N5REREREQMWrLDbkt2qdkwAPP3zwegc8XOtCrXqkByieSbo3/A5P9AdhbU6w+dXjOdSMSocsHleKf1O3zf43uaRjYl3ZnOuG3j6DGtB5N2TCLT6cLtBUXEZagQU0AOH7b3KsSIy/PxsffvvQd33mk2i4iIiIiIQdnZFst2nV0f5hKFmGwrm4UHFgLQtVLXAssmki9O74dJN0NGElRuDb0/Bw99dSQCUC+8HuO7jOfT9p9SObQyZ9LP8Nbat+g9szcLoxdiWZbpiCLiwvRf0wKiQoy4jUcfhQ0b4IknTCcRERERETFqy5F4YpMyCPb1ommlsIu+ZlPMJmJSYgj2Dta6MOLekk/Bt/0gOQYi68Otk8DL13QqEZficDhoW74t03pN48UWL1LCrwQHEw/yxC9P8J95/+GPmD9MRxQRF6VCTAFRIUZc2vr1EBd3/ufGjY1FERERERFxFUt3nACgdY2S+Hhd/PL5XFuydhXa4ePpU2DZRPJURjJ8dwuc3guhFWDAFPALMZ1KxGV5eXhxS81b+LnvzwxrOAx/L382n9zMf+b9h8eWPcaBhAOmI4qIi1EhpoCcK8SULWs2h8jf/PEHdOgAbdpATIzpNCIiIiIiLmPJzn9eHyYrO0ttyeTq+PnB2rX25udnNoszC366G46sB//iMHAqhJQ2m0nETQR6BzK80XDm9JlDv+r98HB4sPjgYnrP6M2I30dwOu206Ygi4iJUiCkgR47Ye82IEZeydy907QoJCRAaCsHBphOJiIiIiLiE4/FpbD+agMMBbWuWvOhr1p9Yz+m004T6htKiTIsCTihuzdMTmjWzN09PczksC35+DHbPBy8/uH0ylKxhLo+Im4oIiOCV619h6o1TaVW2FVlWFt/v/J4e03owdutY0rLSTEcUEcNUiCkASUlwwp7RrkKMuI7jx6FzZ/uXs2FDmDUL/P1NpxIRERERcQlLz86GuaZ8MUoEXXydjHNtyTpW6Ii3h3eBZRPJM8vfgo1fg8MD+o+HCs1NJxJxa9WKV+Pzjp8ztvNYaofVJikziY83fkzP6T2ZGTWTbCvbdEQRMUSFmAKwdKm9r1IFSpQwm0UEgPh4eybMvn32L+b8+VCsmOlUIiIiIiIuY+lO+266DrUjL/p8ZnYmiw8uBqBrZbUlkyuUkQHvvmtvGRlmMqz/Cn55yx73eB9q9TCTQ6QQal66OT/0/IERLUdQOrA0J1JO8MLqF7hl9i2sObrGdDwRMUCFmAKwb5+9b9oUHA6zWURIS4NevWDzZoiMhIULoVQp06lERERERFxGWqaTVVGxwKXXh/n92O/Ep8cT5hdG08imBRlPCoPMTHj6aXvLzCz44++aBz8/bo9bPw1N7y74DCKFnIfDgxur3sjsPrN5rMljBHsHs+vMLu5bdB/DFg1j1+ldpiOKSAFSIaYAREXZe7UlE5dw7JhdHQwJgXnzoGpV04lERERERFzKmr2xpGVmUybUj1qlLr6O4rm2ZJ0qdsLLw6sg44nkzqF1MOUusLLhmoHQ7r+mE4kUar6evtxd727m9p3LwNoD8fLwYvXR1dw8+2ZeXP0iJ5JPmI4oIgVAhZgCsHGjvW/SxGwOEQAqV4Y1a2DuXLjmGtNpRERERERczpId9vow7WtH4LhIW4MMZwZLD9o9qLtWUlsycSOxe+C7WyArFap3hp4fqXWHSAEp5leMZ659hlk3zaJzxc5YWMyImkHP6T35ZOMnJGUkmY4oIvlIhZgCsG2bvW/UyGgMKerO9cgDKF8ebrjBXBYRERERERdlWRZLd9qFmA61Lr4+zJqja0jMTCTCP4LGkY0LMp7I1Us8Dt/2hdTTUKYx3DwBPL1NpxIpcsqHlOf9tu8zqfskGkc0Js2ZxpitY+gxvQc/7PyBzGwD7QpFJN+pEJPPMjMhMdEeaxkOMeaDD6B2bZg61XQSERERERGXtuNYIsfi0/Dz9uC6qiUu+pr50XZbss6VOuPh0GW1uIG0BJjUH+IOQlgVGDAFfAJNpxIp0hqUbMCErhP4qN1HVAqpxOm007zx+xv0ndmXJQeXYFmW6Ygikod0xpjPzpw5Pw4NNZdDirBvvoEnnoCMDNi923QaERERERGXtnSn3au/ZbVw/Lw9//Z8WlYayw4uA6BLpS4Fmk3kqmRlwOSBcHwrBJaEgdMgMNx0KhEBHA4HHSp0YNpN03i++fOE+YURnRDNo8seZfD8wWw5ucV0RBHJIyrE5LPoaHtfqhR4/v0cXiR//fwz3HWXPX7sMXj2WbN5RERERERc3JKzbcnaX6It2aojq0jJSqF0YGkalmxYkNFErlx2NswcDvt/AZ8geyZMWGXTqUTk//H28Oa2Wrfxc5+fGVp/KL6evmyM2ciAuQN48pcnOZRwyHREEcklFWLy2bp19r5+fbM5pAhaswZuvhmcThg4EN57T4swioiIiIj8g9ikdP44FAdA+1oRF33NubZkXSp1waHza7lafn6wbJm9+fnl33EWvwxbfwQPL7jlayhzTf4dS0RyLcgniIcbP8ycPnO4qepNOHCwIHoBvWb24u21bxOXFmc6oohcJRVi8tmiRfa+XTuzOaSI2bYNevSA1FTo1g3GjwcP/XUXEREREfkny3edxLKgbpkQSoX+/cvxlMwUfjn0CwBdK3Ut6HhSmHh6Qtu29pZf7TN++wLWfGKPe30G1Trkz3FEJM+VCizF6y1fZ8qNU7ihzA1kZWfx7Y5v6T6tO+O3jSfdmW46oohcIX0zm4+ysuybWwA6dTKbRYqY8eMhLg6uuw6mTAFvb9OJRERERERc3rn1YTpcYjbML4d/Ic2ZRvng8tQpUacgo4lcmW3TYP5z9rjDy9DodrN5ROSq1AyryahOo/iy05fULF6TxMxEPtzwITdOv5E5++aQbWWbjigil0mFmHx08CAkJNizjK/R7F8pSO+9B2+/DXPmQGCg6TQiIiIiIi4vIyubFbtjAWhf++Lrw8zfb7cl61qpq9qSSe5kZsLIkfaWmZm3n71/JUy/D7Dg2nuh5WN5+/kiUuCuL3M9k3tO5vUbXicyIJJjycd4buVz3DbnNtYeW2s6nohcBhVi8tGpU/Y+LCz/ZhqL5EhOtteDAbsN2dNP2798IiIiIiLyr9ZFnyYpPYvwIB8alA392/NJGUmsOrIKsNeHEcmVjAx48EF7y8jIu889sR1+GADODKjdC7q+pbVCRQoJTw9Pbqp2E3P6zOGRxo8Q6B3IjtM7GLJwCMOXDCfqTJTpiCLyD1SIyUdbttj72rXN5pAiID0devWCO+6wxyIiIiIickWW7IgBoF3NCDw8/v7F9bJDy8jIzqByaGVqFK9R0PFE/l3cIfi2H6THQ4Xroe8Y8NBdoSKFjZ+XH/fUv4e5fedye63b8XJ4seLwCvrN7scra17hZMpJ0xFF5CJUiMlHGzfa+8aNzeaQQs7phIEDYelSmDsX9u41nUhERERExK1YlsWSc+vD1L74+jDzo9WWTFxYymm7CJN4DErWgtu/A28/06lEJB+F+YXx3+b/ZfpN0+lYoSPZVjZT90ylx/QejPxjJCmZKaYjishfqBCTj3butPf165vNIYWYZcHw4fDTT+DjAzNnQh0tGioiIiIiciX2xSZz4FQK3p4OWlYv+bfn49PjWXN0DWAXYkRcSmYq/HAHxO6C4DIwcCr4FzedSkQKSKXQSnzY7kO+7vY1DUo2IDUrlVGbR9F9Wnd+3PUjWdlZpiOKCCrE5KsYe2Y7pUqZzSGF2Msvw5df2j1/J02C9u1NJxIRERERcTtLz7Yla1GlBEG+Xn9//uBSsrKzqF68OlWKVSnoeCKXlu2EqffAwV/BN9QuwoSWM51KRAy4JuIavu32Le+3eZ/yweU5lXaK1357jaELhxKfHm86nkiRp0JMPjp5tiVjyb/fUCWSe59+Cq+9Zo8//xz69zebR0RERETETZ1rS9a+1r+3JRNxGZYF856GnXPA08duRxapDgkiRZnD4aBzpc7MvGkmz177LIHegaw/sZ47593J0aSjpuOJFGkqxOST7GyIjbXHKsRInjt+HJ55xh6/+ioMG2Y2j4iIiIiIm4pPzWRd9Bng4oWY02mn+f3Y74AKMeJiVn0A68YCDug7Giq1NJ1IRFyEt6c3A2oPYGLXiUQERLAvfh8D5g7gz1N/mo4mUmSpEJNP4uLsNdQBwsONRpHCqFQpmD8fnn0WXnjBdBoREREREbe1YvdJnNkW1SKCqFgi8G/PLz6wGKflpHZYbSqEVDCQUAolX1+YM8fefH2v/P1/fAdLXrXHXd+Cun3yNp+IFAo1w2oyqfskqhevTmxqLIPnD2bF4RWmY4kUSSrE5JNzbclCQq7unErkorKzz49bt4Y337TXhxERERERkauydKe9PkyHS7QlWxC9AICulTUbRvKQlxf06GFvXn9fl+gf7VkMMx+0xzc8Ai3UIUFELq1UYCkmdp1Ii9ItSM1K5eGlDzNl9xTTsUSKHBVi8onWh5E8t2MHNGoEW7eaTiIiIiIiUig4sy2W7bILMRdrS3Yy5STrjq8DoEulLgWaTeSijmyEH+8EywkNboUOr5hOJCJuINgnmM87fE6vqr1wWk5e/fVVPtn4CZZlmY4mUmSoEJNPtD6M5KlDh6BLF7sI89RTptOIiIiIiBQKmw6eIS4lkxA/L5pULP635xceWIiFRYOSDSgbVNZAQim0MjNhwgR7y8y8vPec3gff3QKZyVClHfT6DDz0tY6IXB5vT29ev+F17m94PwBjto7h2ZXPkuHMMJxMpGjQf7HziWbESJ45dcouwhw6BLVqwbffmk4kIiIiIlIoLDnblqxtzQi8PP9+eZzTlqyS2pJJHsvIgLvusreMy/gSNOkkfNMXkk9CqQZw6zfg5ZP/OUWkUHE4HDzQ6AFevf5VvBxezN0/l2GLhxGfHm86mkihp0JMPlEhRvJEUpLdM3jHDihXDhYsgPBw06lERERERAqFpTvOrg9T++9tyY4nH2dTzCYcOOhcsXNBRxM5Lz3JnglzZj8UqwADfgLfYNOpRMSN9aneh5EdRhLoHci64+sYNG8QR5OOmo4lUqipEJNPzhVi9J25XLWMDOjfH37/HcLCYOFCqFDBdCoRERERkULh0OkUdp1IxMMBbWr8/Q66c7Nhrom4hsjAyIKOJ2JzZsKUwXB0I/iHwcDpEKzfRxHJvevLXs/ErhOJCIhgb/xeBswdwJ+n/jQdS6TQUiEmn2iNGMm111+3Z8AEBMDcuVC7tulEIiIiIiKFxrJd9myYphXDKBbw9xZPOW3JKqstmRhiWTD7EYhaBF7+MGAKhFcznUpECpGaYTWZ1H0S1YtXJzY1lsHzB7Py8ErTsUQKJRVi8sm5QoxmxMhVe/JJ6NwZpk2D5s1NpxERERERKVSWnG1L1v4ibckOJx5ma+xWPBwedKrYqaCjidiWvQF/TAKHB9w8Aco1NZ1IRAqhUoGlmNh1Ii1KtyA1K5WHlj7ET7t/Mh1LpNBRISafqBAjuRYSAvPnQ5cuppOIiIiIiBQqyelZ/Lr3FAAdav29EHNuNkyzyGaE++uiTgxYNw5WvGuPe34ENTUzS0TyT7BPMJ93+JxeVXvhtJz879f/8cnGT7Asy3Q0kUJDhZh8okKMXJUvv4R33jn/s8NhLouIiIiISCG1OiqWDGc25cP8qRYR9LfnzxViulTWTVFiwI45MPdJe9z2OWgyyGweESkSvD29ef2G1xnWcBgAY7aO4blVz5HpzDScTKRw8DIdoLBSIUau2E8/wf33232AGzbUTBgRERERkXyydKfdlqxDrUgc/+/mpwMJB9hxegeeDk86VuhoIp4UBb6+8OOP58fnHPwNpg4BKxsaD4I2z5jJJyJFksPhYHij4ZQJLMOrv77Kz/t+5mTKST5s9yEhPiGm44m4Nc2IyQdpaZCUZI9ViJHLsmQJDBhgF2Huu89eG0ZERERERPJcdraVU4hpf5G2ZPP3zwegRekWFPcrXqDZpAjx8oKbb7Y3r7P3yJ7cBd/dCllpUKMb9PhAXRJExIg+1fswssNIArwCWHt8LXfOvZNjScdMxxJxayrE5INTdqthPD0hNNRsFnEDGzZA796QkQH9+8PIkTrZFhERERHJJ9uPJhCTmE6AjyfNq4T97fn50XYhpkslzVCXApRwDL7tB2lxUK4Z9B8PnmpiIiLmXF/2eiZ2m0iEfwR74/cyYO4AdpzaYTqWiNtSISYf/LUtmb5Pl3+0ezd062ZPoWrfHr791q7giYiIiIhIvliy8wQAraqH4+t14bl31JkoouKi8PLwon2F9ibiSVGRlQVTpthb0imY1B/iD0GJanD7ZPAJMJ1QRIRaYbWY1GMS1YpV42TqSQbNH8TKwytNxxJxSyrE5AOtDyOXJSnJbkF28iQ0aQIzZlzYG1hERERERPJczvowtSP/9ty52TAty7Qk1FftDSQfpafDLbfY26QBcGIbBEXCwKkQWMJ0OhGRHKUCS/F1t69pXro5qVmpPLT0IX7a/ZPpWCJuR4WYfKBCjFyWoCB46imoWRPmzoXgYNOJREREREQKtZiENLYcjgegXc0L14exLIsF0QsA6FJZbcmkAB1YAz7BMGAKFK9kOo2IyN8E+wTzRYcv6FW1F07Lyf9+/R+fbPwEy7JMRxNxGyrE5IN9++x96dJmc4gbGD4c/vgDIv6+SKiIiIiIiOStZbvs2TANyxejZPCFs9F3ndlFdEI0vp6+tCvfzkQ8Kao8vOHWb6B0Q9NJREQuydvTm9dveJ1hDYcBMGbrGP676r9kOjMNJxNxDyrE5IOVZ1sltmhhNoe4oMxMeO45OH36/GN+fubyiIiIiIgUIUt2nG1LVuvvN0LN32+3JWtVthWB3oEFmkuKoDWfnR/3/BCqqvgnIq7P4XAwvNFwXr3+VTwdnszZN4dhi4eRkJFgOpqIy1MhJo9ZFqxZY49btTKbRVxMdjbcfTe89RZ06WL/LCIiIiIiBSIt08mqKLuPdPtaf29Ldm59GLUlk3y38WtY9sb5n+v3M5dFROQq9Kneh5EdRhLgFcDa42sZNG8Qx5KOmY4l4tJUiMljsbEQHw8OB9SpYzqNuAzLgiefhG+/BU9PeOUV8NBfPxERERGRgvL7/tOkZDiJDPGlbpmQC57bfmo7R5KO4O/lT+uyrQ0llCJhx2yY/YjpFCIiuXZD2RuY2G0iEf4RRMVFMWDuAHac2mE6lojL0jfBeezc+jBly6rjlPzF22/Dhx/a4/HjoUcPs3lERERERIqYpTtOANC+ViQOh+OC5861JWtTrg0B3gEFnk2KiP0r4Ke7wcqGhrebTiMikmu1wmoxqcckqhWrxsnUkwyeP5hVR1aZjiXiklSIyWPnCjFVqpjNIS5k7Fh7XRiA99+HO+80m0dEREREpIixLIslOy++Pky2lc2CAwsA6Fqpa4FnkyLi6Cb4/g5wZkDtG+Gmj+Crr+zNx8d0OhGRq1YqsBQTu02keanmpGSl8OCSB5m6e6rpWCIuR4WYPLZ3r71XIUYAmDUL7rvPHj/7LDz+uNk8IiIiIiJF0J6YJA6fScXXy4MbqoVf8NyWk1s4nnycQO9AWpZraSihFGqxe+DbfpCRCJVaQd+x4OcPgwfbm7e36YQiIrkS4hPCFx2/oFfVXjgtJ6/8+gqfbvoUy7JMRxNxGSrE5LE9e+x91apmc4iLqFULypeHu++GESNMpxERERERKZKW7LBnw1xftQT+Pp4XPDc/2m5L1q58O3w9fQs8mxRy8Ufgmz6QcgpKN4LbvgNv9TEXkcLH29Ob1294nfsa2Dckj94ymudXPU+mM9NwMhHX4GU6QGHz55/2vk4dsznERdSoAWvXQlgY/L8+1CIiIiIiUjCW7jy7PkztyAsed2Y7WRCttmSST1JOw7d9If4QlKgGA6eCX4j9XFYWLLB/9+jSBbz09YyIuD+Hw8GD1zxImaAyvPrrq8zeN5sTKSf4sN2HhPiEmI4nYpRmxOSh7GzYscMeqxBThO3dC4sWnf85IkIn1SIiIiIihpxJzmDDgTMAtP9/68NsjNlIbGoswT7BXF/mehPxpLBKT4JJN8PJnRBcBv4zHQL/0hYvPR169rS39HRzOUVE8kHf6n0Z2WEkAV4BrD2+lkHzBnE8+bjpWCJGqRCTh44eheRk+zt3tSYroo4dg86doXt3mDPHdBoRERERkSLvl90nybagVqlgyhbzv+C5+fvttmQdK3TE21PrdEgeycqAH/8DR9aDf3G7CFOsgulUIiIF6oayNzCx20RK+pckKi6KAT8PYOfpnaZjiRijQkweOnXK3pcoobX2iqS4OOjWDfbtgwoVoGlT04lERERERIq8JTvt9WE61L5wNkxWdhaLDy4G1JZM8lC2E6bfB3uXgncgDPgJImqZTiUiYkStsFpM6j6JasWqEZMaw6B5g1h9ZLXpWCJGqBCTh+Li7H2xYiZTiBGpqXDTTbB5M0RGwsKFUKqU6VQiIiIiIkVapjObX3bZhZj2tS5cH2bt8bWcTjtNcd/iXFv6WhPxpLCxLJj7JGyfBh7ecOs3UE436IlI0VY6qDQTu03k2lLXkpKVwvAlw5m2Z5rpWCIFzqULMdu2baNZs2YUL16cp556Csuy/vU9//vf/wgLC8PX15c+ffqQmJhYAElt5woxxYsX2CHFFWRlwe23w4oVEBIC8+erN52IiIiIFAh3u2YqaBsOnCEhLYuwQB8alS92wXMLou2F0jtW7IiXh9Z0lDywbASsHw84oO9oqNbBdCIREZcQ4hPCqI6juLHKjTgtJy+veZnPNn12WectIoWFyxZi0tPTufHGG2nSpAnr16/nzz//ZMKECf/4nkmTJjFp0iTmz5/P9u3b2bFjB2+99VbBBEYzYooky4L77oOZM8HXF2bNgkaNTKcSERERkSLAHa+ZCtrSs23J2tYsiaeHI+fxTGcmiw+oLZnkod9GwYp37HGP96FeX7N5RERcjLenN2+0fIN7G9wLwJdbvuT5Vc+T6cw0nEykYLhsIWbevHnEx8fzwQcfULVqVUaMGMG4ceP+8T2HDh1i4sSJXHvttVSrVo1bb72VTZs2FVBiFWKKJKcTsrPBwwN++AHatDGdSERERESKCHe8ZipoS3acAKDD/2tL9uuxX0nISCDcP5wmkU1MRJPCZMuPMP8Ze9zuBWg2xGweEREX5XA4eOiah3jlulfwdHgye99s7l98P4kZhXd2rsg5Ljv/evPmzbRo0YKAgAAAGjRowJ9//vmP73n22Wcv+HnXrl1Ur179kq9PT08nPT095+eEhIRcJFYhpkjy8oLx4+H+++Fa9ZUWERERkYLjjtdMBSk6Npm9J5Px8nDQqkb4Bc+da0vWqWInPD08TcSTwmL3Qphxvz1ufj+0fvLy3ufjA599dn4sIlKE9KvRj8jASJ5Y/gS/H/+dO+fdyRcdv6BUoNZblsLLZWfEJCQkULly5ZyfHQ4Hnp6enDlz5rLev3v3bqZPn8699957yde8+eabhIaG5mzly5fPVeaTJ+19WFiuPkbcwerV9towAA6HijAiIiIiUuDc8ZqpIJ1rS3Zt5TBC/LxzHk93prP04FJAbckklw7+Bj/eCdlZ0OBW6DLCvj68HN7eMHy4vXl7//vrRUQKmZZlWzKh6wRK+pckKi6KAT8PYOfpnaZjieQbly3EeHl54evre8Fjfn5+pKSk/Ot7s7Ozufvuu7nnnnuoW7fuJV/33HPPER8fn7MdOnQoV5lP2LPeKaXibeE2Z47dguzmm+EvdweKiIiIiBQkd7xmKkjnCjHta0Vc8PjqI6tJykwiIiCCRhGNDCSTQuHEdvjuFshKhepd4KaRdstqERG5bLVL1GZS90lUK1aNmNQYBs0bxOojq03HEskXLnuWEBYWxslzU0zOSkxMxOcypuy+9tprnD59mnffffcfX+fr60tISMgFW24cP27vVYgpxFatsgswTieEhOjOJRERERExxh2vmQpKYlomv+8/BUCH2heuDzM/ej4AXSp1wcPhspfE4spO74dv+kJaPJRvATdPAM8rvDZ0OmH5cntzOvMhpIiIeygdVJqJ3SZybalrSclKYfiS4UzbM810LJE857Jnnc2aNePXX3/N+Xn//v2kp6cT9i99v2bPns0HH3zA1KlTc3olFxQVYgq5rVvhxhshLQ169ICxY3XHk4iIiIgY447XTAVl1Z5YMp0WVcIDqRwemPN4alYqyw8tB9SWTK5S4gn4pg8kHYeIunDHD+BzFX+P0tKgXTt7S0vL+5wiIm4kxCeEUR1H0bNKT5yWk5fXvMxnmz7DsizT0UTyjMt+i9y6dWsSEhL46quvABgxYgQdO3bE09OTuLg4nBe5Y2THjh3cfvvtfPrpp5QvX56kpKTLmpafV1SIKcT274cuXSAuDq6/Hn78UbNhRERERMQod7xmKihLLtGWbMXhFaRmpVI2qCz1w+ubiCbuLDUOvu0HZ/ZDsYrwn2ngX9x0KhGRQsHb05sRLUdwbwN77bovt3zJC6tfINOZaTiZSN5w2UKMl5cXY8eO5cEHHyQ8PJyZM2fy9ttvA1C8eHG2bt36t/eMHj2a5ORkBg0aRHBwMMHBwdSpU6dA8iYnQ1KSPVYhppCJiYHOneHYMahXz14jppDeOSgiIiIi7sPdrpkKSna2xbJzhZjaFxZiFkQvAOy2ZI7LXVRdBCAzFb6/HU5shcAIuHMGBOviX0QkLzkcDh665iFevu5lPB2ezNo7i/uX3E9iRqLpaCK55rBcfI7X8ePH2bBhAy1atKBEiRL5eqyEhARCQ0OJj4+/4t7H+/ZB1arg728XZXROX4gsXw7du0NEBKxZA2XKmE4kIiIikmdycw4srsFdrpkKyqaDZ+jz+RqCfb3Y+FInvD3t+w+TM5NpM7kN6c50fuz5I7VL1DacVNyGMwsmD4Td88A3FO76GUrlckZVcjIEBdnjpCQIDPzn14uIFDErD6/kyV+eJCUrhWrFqvFFxy8oFagCuJiRF+fALjsj5pxSpUrRo0ePfL+gyK2/tiVTEaaQadsWliyBhQtVhBERERERl+Mu10wFZenZ2TCta5bMKcIALD+0nHRnOhVDKlIrrJahdOJ2srNh1kN2EcbLz14TJrdFGBER+VetyrViQtcJhPuHExUXxYCfB7Dr9C7TsUSumssXYtyF1ocpZJxOOHz4/M/XXQc1apjLIyIiIiIil2XJDrsQ0+H/rQ8zP3o+oLZkcgUsCxa+AJu/A4cn3DwRKl5vOpWISJFRu0RtJnWfRNXQqsSkxjBo/iDWHFljOpbIVVEhJo+oEFOIWBYMHw5Nm8KmTabTiIiIiIjIZToWn8qfxxJwOKBtzfOFmISMBFYfWQ1A10pdTcUTd7PqA/htpD2+aSTU1O+OiEhBKxNUhq+7f02zUs1IzkzmgSUPMH3PdNOxRK6YCjF5RIWYQuTll+HLLyEmxl78R0RERERE3MK5tmSNKxQnLNAn5/FlB5eRmZ1J1dCqVC9e3VQ8cScbJsCSV+1xlzeh0e15+/ne3vDOO/bm7Z23ny0iUsiE+IQwquMoelTpgdNy8tKalxj5x0hcfOlzkQt4mQ5QWJw4Ye8jI83mkFz65BN47TV7/MUX0K+f2TwiIiIiInLZlp5tS9b+Um3JKncp8EzihrbPgDmP2eNWT8B1D+T9MXx84Kmn8v5zRUQKKR9PH95s+SZlAsswZusYRm0exdGko7xy3St4e6qgLa5PM2LyiGbEFALffQePPGKPX3sN7rvPbB4REREREblsqRlOVkXFAtCh9vlCTFxaHL8d/Q1QWzK5DPuWw7ShYGVDk8HQ/kXTiURE5CyHw8HDjR/m5etextPhyay9s3hgyQMkZiSajibyr1SIySMqxLi5+fNh0CB7/NBD8PzzZvOIiIiIiMgV+XVfLOlZ2ZQt5k/NyOCcx5ccXEKWlUXN4jWpHFrZYEJxeUc2wA8DwJkBdW6CHh+Aw5E/x3I6Yd06e3M68+cYIiKFVP8a/fm0/af4e/nz27HfGDR/EMeTj5uOJfKPVIjJIyrEuDHLsvvyZmXB7bfDRx/l38m2iIiIiIjki8V/aUvm+Mv5/Lm2ZF0razaM/IOTu+Hb/pCRBJXbQN8x4OGZf8dLS4Nrr7W3tLT8O46ISCHVqlwrJnSdQLh/OHvO7GHA3AHsOr3LdCyRS1IhJg9Ylgoxbs3hgFmz4KWXYMIE8NBfCxERERERd2JZ1vn1Yf7Sliw2NZa1x9cC0KWS1oeRS4g/DN/0htTTUKYx3DYJvHxNpxIRkX9Rp0QdJnWfRNXQqsSkxDBo/iDWHFljOpbIRekb5zwQHw8ZGfY4MtJsFrkCKSnnx0FB8L//2QsmioiIiIiIW/nzWALHE9Lw9/bkuiolch5ffGAx2VY2dUvUpXxweYMJxWUln4Jv+kDCEQivAQN+At/gf3+fiIi4hDJBZZjYbSLNSjUjOTOZ4UuGM33PdNOxRP5GhZg8cPq0vQ8IAD8/s1nkMsXGQpMm8Prr9pQmERERERFxW+dmw9xQLRw/7/PtpM61JetWuZuRXOLi0hNhUn+I3Q0h5eA/0yGwxL+/T0REXEqobyijOo6ie+XuZFlZvLTmJT7/43MsfecnLkSFmDyQkGDvQ0LM5pDLlJQEPXrAzp0wejTExZlOJCIiIiIiubBkp12I6fCXtmQxKTFsPLERUFsyuYisdJg8EI5uBP8wuwgTWs50KhERuUo+nj681eothtYfCsAXm7/ghdUvkOnMNJxMxKZCTB6Ij7f3oaFmc8hlyMiAvn1h7VooUQIWLoTixU2nEhERERGRq3QyMZ3Nh+MAaFfzfCFmYfRCLCwalWxEqUAt5il/ke2EaUNh33LwDoSBP0HJGqZTiYhILjkcDh5u/DAvXfcSng5PZu2dxQNLHiAxI9F0NBEVYvKCZsS4iexsuPNOWLQIAgNh7lyoVct0KhERERERyYXlu2KwLKhXNoRSoed7RZ9rS9a1cldT0cQVWRb8/Dj8ORM8feC2SVC2ielUIiKSh26ucTOftP8Efy9/fjv2G4PmD+J48nHTsaSIUyEmD2hGjBuwLHjkEZg8Gby9Yfp0uPZa06lERERERCSXlp5tS9a+VmTOY8eSjrH55GYcOOhUsZOpaOKKlr4OGyYADug7Bqq2M5PD2xteftnevL3NZBARKcRal2vNhK4TCPcPZ8+ZPQyYO4Bdp3eZjiVFmAoxeUAzYtzA6tXw2WfgcMA330AnXYyJiIiIiLi7jKxsVuw+CUCHWufbki2IXgBAk8gmRAREXPS9UgT9OhJWvmePe34IdXuby+LjA6+8Ym8+PuZyiIgUYnVK1GFS90lUCa1CTEoMg+YPYs3RNaZjSRGlQkweOFeI0YwYF9ayJYweDZ9+CrfeajqNiIiIiIjkgbX7T5Oc4SQ8yJf6Zc9fkOW0JauktmRy1h/fw4L/2uMOL0HTu8zmERGRAlEmqAxfd/uappFNSc5MZvji4cyImmE6lhRBKsTkgXOtyTQjxgVZ1vnx0KEwfLi5LCIiIiIikqeW7DwBQPtaJfHwcABwKOEQ209tx8PhQceKHU3GE1exax7MPHst2GI4tHzcbB6w1zDdvt3esrNNpxERKdRCfUP5stOXdK/cnSwrixdXv8gXf3yB9dfvDUXymQoxeUCtyVzUkiXQujXExppOIiIiIiIiecyyLJbs+Pv6MAsO2G3Jri11LSX8SxjJJi7kwBqYMhgsJzS8HTq/bresNi01FerVs7fUVNNpREQKPR9PH95s9Sb31L8HgM83f87nmz83nEqKEhVi8sC5GTFqTeZC1q+H3r1h1Sp46y3TaUREREREJI/tPZnMwdMp+Hh60LJ6eM7j8/erLZmcdXwrfHcbZKVBjW7Q61Pw0NcgIiJFlYfDg0caP8IzzZ4BYNTmUUzYNsFsKCkydAaSBzQjxsXs2gXdukFSEnToAG+8YTqRiIiIiIjksaVn25I1rxJGkK8XAPvi97HrzC68HF5qS1bUnd4H3/SF9HiocD3c/BV4eptOJSIiLmBgnYE8fM3DALy/4X1+3PWj4URSFKgQkwdO2Of/lNCsd/OOHIHOne12ZE2awPTp4OtrOpWIiIiIiOSxc23JOtSKyHlswX67LVmLMi0I9VXLgiIr8Th83RuSYyCyPtz+PXj7m04lIiIuZGiDoQypNwSA1397ndl7ZxtOJIWdCjG5ZFn2BAyA6tXNZinyTp+GLl3g4EGoUQPmzYPgYNOpREREREQkj8WnZLL+wBng/PowlmUxP9puS9atcjdj2cSw1DP2TJi4A1C8MgycCv7FTKcSEREX9EjjR7it5m1YWLy4+kWWHFhiOpIUYirE5NLJk/YaMQ4HVKtmOk0Rd889sH07lCkDCxZAyZKmE4mIiIiISD74Zc9JnNkW1SOCqFAiAIA9cXvYF78Pbw9v2pVvZzihGJGRYq8JE7MdgiLhP9MhONJ0KhERcVEOh4Pnmj9Hr6q9cFpOnlrxFGuOrDEdSwopFWJy6dxsmAoVwF8znc16911o2tQuwlSqZDqNiIiIiIjkk6U77P7Q7Wufb0s2f789G6Zl2ZYE+2hmfJHjzIQpg+DQb+AXCgOnQVhl06lERMTFeTg8+N/1/6NTxU5kZmfyyLJH2Hhio+lYUgh5mQ7g7s4VYmrWNJtDgKpVYe1ae3qSiIiIiIgUSlnObJbvPglAh7+0JVsQba8P07VSV2PZxJDsbJg5HPYsBC9/uONHKFXPdKp/5u0NTz55fiwiIsZ4eXjxdqu3Sc1KZdWRVQxfMpyxXcZSt0Rd09GkENGMmFzau9fe16hhNkeRZFnw3HMwZ875x1SEEREREREp1DYdiiMuJZNQf28aVygGwI7TOziYeBA/Tz/alm9rNJ8UMMuCBc/Blsng4QW3fA0VWphO9e98fOyuDu++a49FRMQob09vPmz7IU0jm5KUmcSwRcOIOhNlOpYUIirE5FJsrL2PiPjn10k+ePtteOst6NsXoqNNpxERERERkQKwZEcMAG1rlsTL076knR9ttyVrVa4VAd4BxrKJASveg99H2ePeX0CNzmbziIiI2/Lz8uPT9p9Sr0Q94tLjGLpoKAcTDpqOJYWECjG5dOaMvQ8LM5ujyBk71p4NA/DOO1oTRkRERESkiFi68+z6MLXsu+Esy2Jh9EJAbcmKnHXjYNnr9rjr29DgFrN5rkR2tn1DYXS0PRYREZcQ5BPEqE6jqF68OrGpsQxdOJTjycdNx5JCQIWYXDpXiCle3GyOImXGDLjvPnv83HPw6KMm04iIiIiISAE5dDqF3SeS8PRw0KZGSQC2xm7lSNIR/L38aVWuleGEUmC2TYOfn7DHrZ+GFsPM5rlSqalQubK9paaaTiMiIn8R6hvK6E6jqRhSkaPJRxm6cCixqbGmY4mbUyEml46fLYiWLGk2R5GxfDncdpt9x9CQIfDGG6YTiYiIiIhIAVm6025L1qRicYoF2OtqnGtL1rZ8W/y9/I1lkwK0dylMuxewoOkQaPdf04lERKSQCfcPZ0ynMZQOLE10QjT3LbqP+PR407HEjakQkwuWdX5pksqVjUYpGqKi4KabID0deveGUaPA4TCdSkRERERECsiSs4WYDmfbkmVb2SyIXgCoLVmRcXg9/DAQsjOhbl/o/q6uC0VEJF+UDirNmM5jKOFXgt1ndvPA4gdIzkw2HUvclAoxuRATAykp9jlf+fKm0xQBlSvbs2HatIHvvwcvL9OJRERERESkgCSnZ/Hb3lMAdKhtF2I2xWwiJiWGIO8gWpZtaTKeFISYnTCpP2QmQ9X20OdL8PA0nUpERAqxiiEVGdN5DKG+oWyJ3cJDSx8iLSvNdCxxQyrE5MK+ffa+bFnw9TWbpUjw9LRnwcybB35+ptOIiIiIiEgBWhUVS4YzmwphAVQtGQTA/P12W7L2Fdrj4+ljMp7kt7iD8E0fSD0DZZvCLd+Al/4/FxGR/Fe9eHVGdRxFoHcg646v4/Hlj5PpzDQdS9yMCjG5sGOHva9Vy2yOQi0uDl55BTLP/uPmcIC/+j6LiIiIiBQ1S3fYbcna14rA4XDgzHay6MAiQG3JCr3kWLsIk3gUwmvCgCngG2Q6lYiIFCH1wuvxWfvP8PP0Y+WRlTy78lmysrNMxxI3okJMLpwrxNSubTZHoZWaCjfeCP/7HwwbZjqNiIiIiIgYkp1tsXSXXYjpWDsSgPUn1nMq7RShvqG0KNPCZDzJT+mJ8G0/OBUFoeXhP9MhIMx0KhERKYKalmrKR+0+wsvDi4UHFvLKmlfItrJNxxI3oUJMLvz5p72vU8dsjkIpKwtuvRVWrYKQEHj4YdOJRERERETEkG1H4zmZmE6gjyfXVra/hJ8fbbcl61ihI94e3ibjSX7JTIMf7oBjf0BACfjPDAgtazpV3vDyggcesDetfyoi4jZuKHsD77Z+F0+HJzP3zuTttW9jWZbpWOIGVIjJBc2IySeWBUOHwuzZ9lows2dDw4amU4mIiIiIiCFLzrYla12jJD5eHmRmZ7L4wGIAulTqYjKa5BdnFkwdAvtXgE8QDJwK4dVMp8o7vr4wcqS9adFZERG30rFiR1674TUAvtv5HZ9u+tRwInEHKsRcpawsOHjQHlevbjZLofPMMzBhAnh6wuTJ0Lq16UQiIiIiImLQ0p3n14cBWHtsLXHpcYT5hdGsVDOT0SQ/WBbMeRR2zgFPH7j9eyhzjelUIiIiOW6seiPPN38egDFbxzB261jDicTVqRBzlY4cAacTfHygVCnTaQqR99+Hd9+1x2PGQK9eZvOIiIiIiIhRJxLS2HokHocD2ta0CzHn2pJ1qtgJLw+1dSp0lvwPNn0DDg/oPx4qF8Kb8ywLTp60N7W0ERFxS7fVuo3HmjwGwMcbP+b7nd8bTiSuTIWYqxQdbe8rVAAP/Snmnfr1ITAQ3n4b7rrLdBoRERERETFs2dnZMA3LFaNksC+ZzkyWHFwCqC1ZobTmU1j1oT2+8WOofaPZPPklJQUiIuwtJcV0GhERuUp317ubexvcC8CI30cwM2qm4UTiqnTr0FU6cMDeV6xoNkeh07kz/PmnXeESEREREZEib8nZQkyHs23J1hxdQ2JGIiX9S9I4orHJaJLXNk2ChS/Y447/g8Z3ms0jIiJyGR5s9CDJmclM2jGJl9a8hL+XP50rdTYdS1yM5nJcpdhYex8ZaTZHobBmDezadf5nFWFERERERARIy3Syao998dW+9oVtyTpX6oynh6exbJLHdv4Msx6yx9c/BC0fNRpHRETkcjkcDp5u9jR9qvUh28rmmZXPsPLwStOxxMWoEHOVzs0cDgw0m8PtbdkC3btDy5b2TBgREREREZGzftt3itRMJ6VC/KhTOoS0rDSWHVoGQNdKXQ2nkzwTvQqm3AWWExoNhE6vmU4kIiJyRTwcHrx83ct0rdSVrOwsHlv+GOuOrzMdS1yICjFXKTnZ3qsQkwv790PXrhAfDzVrQqVKphOJiIiIiIgLWXq2LVn72hE4HA5WHVlFcmYypQJL0aBkA8PpJE8c2wzf3w7OdKjZw14XxuEwnUpEROSKeXp4MqLVCNqUa0O6M50HlzzI1pNbTccSF6FCzFU6t0aMWpNdpRMn7PVgjh2DevVg9mwICDCdSkREREREXIRlWSzZceH6MOfaknWt1BUPhy5n3d6pvfBtP0hPgIotof948NRStiIi4r68Pbx5r817XFvqWlKyUhi2eBi7z+w2HUtcgM5cr9L27fa+Xj2zOdxSQgJ06wZRUfYsmAULoHhx06lERERERMSF7D6RxJG4VHy9PLi+ajgpmSmsOLwCUFuyQiHhGHzTG5JPQqkGcPt34O1nOpWIiEiu+Xn58Wn7T2lQsgEJGQncu/BeouOjTccSw1SIuQpZWbBzpz2uW9dsFreTlga9e8OmTVCyJCxcCGXKmE4lIiIiIiIuZsnOEwDcUC0cfx9PVhxeQWpWKuWCylGnRB3D6SRXUk7Dt30h7iCEVYGBU8Ev1HSqguXlBYMG2ZuXZgGJiBQ2Ad4BfN7hc2qF1eJU2imGLhrK0aSjpmOJQSrEXIWoKMjIsDtpVaxoOo2bSUuD9HQIDob586F6ddOJRERERETEBS0925as/f9vS1a5Kw6tIeK+MpLhu1sh5k8ILg3/mQFBEaZTFTxfX5gwwd58fU2nERGRfBDqG8qojqOoFFKJ48nHGbpwKCdTTpqOJYaoEHMVtm2z93Xrgof+BK9MsWKwaBEsWwaNG5tOIyIiIiIiLuh0cgYbD54B7EJMUkYSKw+vBNSWzK1lZcCPd8LhteBXDAZOg+K6u1FERAqvEv4lGNN5DGWDynIw8SD3LrqXuLQ407HEAJURrsK59WHUluwK/Pbb+XFAADRpYi6LiIiIiIi4tF92x5BtQe3SIZQp5s+yQ8vIyM6gUkglahSvYTqeXI3sbJhxP0QtBu8AGDAFIotwiznLguRke7Ms02lERCQflQosxZhOYyjpX5KouCiGLR5GUkaS6VhSwFSIuQrnCjH16pnN4TY+/hiuuw5eesl0EhERERERcQNLzrYl63C2LdmC6AWA2pK5LcuC+c/Atp/Awwtu+QbKX2s6lVkpKRAUZG8pKabTiIhIPisfUp4xncdQzLcY209tZ/iS4aRmpZqOJQVIhZirsHevva9Z02wOtzBpEjz6qD1W31sREREREfkXmc5sftlt909vXzuC+PR4Vh9dDagtmdv65R1YOxpwQJ8voXpH04lEREQKXNViVfmy05cEeQexMWYjjy17jAxnhulYUkBUiLkKp0/b+5IlzeZwefPmweDB9vjhh+G//zUaR0REREREXN/66DMkpmVRItCHhuWKsfTgUrKys6hWrBpVi1U1HU+uhGXB6k9g+Qj75+7vQv3+ZjOJiIgYVKdEHT7v+Dn+Xv6sPrqaZ1Y8Q1Z2lulYUgBUiLkKp07Z++LFzeZwab/+Cv36QVYW3HEHfPghqIWAiIiIiIj8i6U7TwDQtmYEnh6O823JNBvGvWQ7Yf6zsOhF++c2z8K1Q81mEhERcQHXRFzDx+0+xtvDm8UHF/PS6pfItrJNx5J8pkLMFcrMhMREexwWZjaLy9q+HXr0gNRU6NoVvvoKPPSrJiIiIiIi/27JzrPrw9SO4EzaGX479htgrw8jbiIjBX68E34fZf/c6TVo+6zZTCIiIi7kujLX8V6b9/B0eDJ732xG/D4Cy7JMx5J8pG/Hr9Dx4/be21uFmEtatQrOnIHmzeGnn8DHx3QiERERERFxA/tjk9l3MhkvDwetqoez6MAinJaT2mG1qRhS0XQ8uRxJJ2HijbBzDnj6Qv+v4IaH1SFBRETk/2lfoT1vtHwDBw4m75rMhxs/VDGmEPMyHcDdHDli70uX1iSPS7rvPihRAtq1g8BA02lERERERMRNLD07G6Z5lTCC/bzPtyXTbBj3EBsFk/rBmWjwLw63fQ8VrzOdSkRExGX1qNKDlKwUXv31Vb7a9hVB3kHc2+Be07EkH6gQc4XOFWLKljWbw+UkJUF2NoSE2D/31wKMIiIiIiJyZc6tD9O+ViSxqbGsP7EegC6VupiMJZfj4O/w/W2QehqKVYSBUyG8uulUrsvT8/x1s6en2SwiImLUzTVuJiUzhffWv8enmz4lwCuAgXUGmo4leUyFmCt09Ki9L1PGbA6Xkp4OffrAqVMwbx5ERppOJCIiIiIibiYxLZPf950GoEOtCBZGzyTbyqZBeAPKBulOOJe2fQZMuxec6VCmMdwxGYIiTKdybX5+MGWK6RQiIuIiBtUdREpmCp9v/py3171NoHcgfar3MR1L8pCaa12hc4UYzYg5y+mEQYNg8WLYvRsOHTKdSERERERE3NDKPbFkZVtUKRlIpfDAnLZkmg3jwiwL1nwGUwbbRZia3WHwHBVhRERErsKwhsO4s86dALy85mXm759vOJHkJRVirtBp+wYtwsPN5nAJlgWPPAKTJ4O3N0yfDk2bmk4lIiIiIiJuaMkOe32YDrUiOJ58nI0xGwHoXKmzyVhyKdlOmPcMLHwesKDZULj1W/DROqEiIiJXw+Fw8GTTJ+lfoz8WFs+tfI5fDv1iOpbkERVirlBcnL0vVsxkChfx2mswciQ4HPDtt9Cpk+lEIiIiIiLihpzZFst32YWY9rUiWRi9EIDGEY0pFVjKZDS5mIwU+PFOWPul/XPn16H7u+ChtU4uW3KyfS3tcNhjERER7GLMC81foHvl7mRZWTy+/HF+P/a76ViSB1SIuULnCjGhoUZjmPfFF/Dyy/b4s8/gllvM5hEREREREbe1+XAcp5IzCPbzomml4mpL5sqSTsLEG2HnHPD0hf5fwfUP2QUFERERyTVPD09eb/k67cq3IyM7g4eWPsTmk5tNx5JcUiHmCsXH2/siPSMmORlGjLDHL78MDzxgNo+IiIiIiLi1pWfbkrWpUZKY1GNsid2Ch8NDbclcTWwUjOsIR9aDf3G4cybU62s6lYiISKHj7eHNu23epUXpFqRmpXL/4vvZeXqn6ViSCyrEXCG1JgMCA2HlSrs12blZMSIiIiIiIldpyc6z68PUjsiZDdM0sinh/lqc02Uc/M0uwpyJhmIVYcgiqHid6VQiIiKFlq+nLx+3+5hrIq4hMSOR+xbdx774faZjyVVSIeYKFelCTFra+XGlSvDCC5p+LiIiIiIiuXI0LpUdxxLwcECbGhHM3z8fUFsyl7L9/9q79/ic6/+P449r543NNmMYNoQQhYicJedjhXT4IuJHvh1I6lsqnaQSkQiFfKOjlL5EDkORCjmfwpwPw2xms+P798fVrqxtbLPtc2173m+3z+26rs/1OTyv7W0+7+v1+bw/i2Fed4iPggoNYPBKCKpudSoREZEiz8fdh2l3TaNWYC0uXLnAoyse5fil41bHklxQISYHjCnG94jZuxeqV4fFi61OIiIiIiIiRcjqv66GaVA5gEspp9hzYQ+uNlfuDr3b4mSCMbDhffhyAKQkQM3OMOB7KFnW6mQiIiLFhq+HLzPunkHVUlU5G3eWR1c8ytm4s1bHkhxSISYHzp+HhAT787LF6bjz+HHo0MH+OGECpKZanUhERERERIqItEJM21p/Xw1zR/k7CPAKsDKWpKbAsjGw4nnAQKNHoe9/waOE1clERESKnUCvQGa1n0XFkhU5HnucISuGEHUlyupYkgMqxOTAuXP2R39/8Pa2NErBuXDBXoQ5ehRq1oTvvgMXNRsREREREblx8Ykp/PynvaN1183B/BBhL8R0DOtoZSxJjIPPH4ZfP7S/bv8adH4bXFytzVWUuLpC5872yVU/VxERub6yPmWZ3WE2ZX3KcjD6IEN/HMqlxEtWx5Js0jfqOZCYaH/08LA2R4G5fBm6dIHduyEkBJYvhzJlrE4lIiIiIiJFxIaD50hITiXE3xtXzzP8efFP3FzcaFu5rdXRiq/YSJjXFfb9D1w9ofdcuPPfuj9oXvPygv/9zz55eVmdRkREComQkiHMaj+LQK9A9lzYw2OrHiMuKc7qWJINKsTkQNr9YXx9LY1RMJKS4L774JdfICDAXoQJDbU6lYiIiIiIFCGr/hqW7K5aZVl+ZDkAzSo0o5Rncbspp5M49yd81A5ObAbvAPjXt1Cnl9WpRERE5CpVS1Xlw7s/xNfDl61nt/LkmidJSEmwOpZchwoxOXDokP2xShVrcxSI2bPhhx/Ax8d+hk6dOlYnEhERERGRIsQYw+o99kJMm5plHPeH6RDWwcpYxdfRX+xFmKgI8A+FQT9CaFOrU4mIiEgmbg68mentpuPt5s3GUxsZvXY0SalJVseSa1AhJgcOH7Y/Vq1qbY4CMXQoPPEEfPUVNNXBt4iIiIiI5K1dJ2M4HXMFb3dXSgeeJyImAg8XD9pUamN1tOJn12KY1x3io6BCAxi8CoKqW52qaLt8GUqUsE+XL1udRkRECqFby9zK1LZT8XDxYM2xNbzw0wukpKZYHUuyoEJMDhSLK2KMsT+6uMDkydCpk6VxRERERESkaFr917BkzasHseb4jwC0qNiCkh4lrYxVvBgDG96HLwdASgLU7AwDvoeSujdogYiLs08iIiK5dEf5O3i39bu42dxYengpr216DZP2/a44FRViciCtEFNkr4iZPRseeggSE61OIiIiIiIiRVza/WHaXjUsWcewjlZGKl5SU2DZM7DiecBA4yHQ97/gUcLqZCIiIpIDrSq1YnyL8bjYXPhq/1dM/H2iijFOyM3qAIVJkR6abNEi+3Bkqalw990wYIDViUREREREpIiKvJTAtmMXAQgpd57je4/j7eZNy4otrQ1WXCTGwdeDYd//7K/bvwZNR4DNZm0uERERyZWOVToSnxzPixteZN7ueZRwL8Gw24ZZHUuuokJMNsXFwalT9udFrhCzZg3062cvwjz6KPTvb3UiEREREREpwtbss18NUzekFL9FrgGgZcWW+Lj7WBmreIiNhIV94cRmcPWEez6EOr2sTiUiIiI3qFf1XlxOusyE3ybwwbYP8HH3oX8dfc/rLFSIyaaICPujnx8EBFgaJW9t3Qo9etiHI7vnHpg+XWdBiYiIiIhIvlq9x16IaVOzDD9ELAc0LFmBOPcnfHovREWAdwD0+wwqN7E6lYiIiOSRh2o/xOWky7z/x/u88/s7+Lj70LtGb6tjCbpHTLZdfX+YIlOnOHAAOnaES5egdWv49FNwdbU6lYiIiIiIFGEJySmsPxAJQGhIJKcun8LHzYfmIc0tTlbEHf0FPmpnL8IEhMGglSrCiIiIFEFD6g1h4C0DAXh146v879D/LE4koCtisq3I3R8mORm6d4ezZ6F+ffj2W/DysjqViIiIiIgUcb8evsDlxBTK+HqyP3Y9AG0qt8HLTf2RfLPrG1g0FFISIKQh9PscSpaxOlXx5uICrVr9/VxERCSP2Gw2nmrwFHFJcXy+73Oe/+l5vN28aVu5rdXRijX9b59NV18RUyS4ucEHH0CDBrBsmX3MNRERERERkXy2Km1YshpB/HjkRwA6hXWyMlLRZQxsmApfDrAXYWp2gf7fqwjjDLy9ITzcPnl7W51GRESKGJvNxn/u+A/dqnYjxaTw9Nqn2Xhyo9WxijUVYrIprRBTpYq1OfJUmzbw228QHGx1EhERERERKQaMMazaewaAsEpniIyPxNfDlzsr3GlxsiIoNQWWPQMrXrC/bjwE+s4HDx9rc4mIiEiBcLG58EqzV2hXuR1JqUk8seYJtp7danWsYkuFmGwqElfEJCfD0KGwa9ff83QJtIiIiIiIFJCDkbEcuxCPh6sLZ1I2AXBX5btwd3W3OFkRkxgHnz8Mv860v27/OnR6C1x0T1AREZHixM3FjQktJ9AspBnxyfEMXzmc3ed3Wx2rWNK38Nl07Jj9MTTU2hy5Zgw8+ijMnAnt28OVK1YnEhERERGRYiZtWLI7qvkTfnwVAB3DOloZqeiJjYR5XWHf/8DVE3rPhTtHgM1mdTK52uXLUKaMfbp82eo0IiJShHm4ejCp9SQalG1AbFIsQ38cysGLB62OVeyoEJMNly9DdLT9eUiItVlybcwYmDsXXF1hxgzw0o0wRURERESkYK3aay/EVK98hgtXLuDv6U/j8o0tTlWEnDsAH7WDE5vBOwD6fwd1elmdSrJy7px9EhERyWfebt5Mu2sadUrX4WLCRR5d8SjHLh2zOlaxokJMNpw4YX8sUQJ8fa3Nkitvv22fAGbPhm7drM0jIiIiIiLFzsW4RDYfiQIgxvV3ANqFtsPdRcOS5Ymjv8BHd0NUBASEwaCVULmJ1alERETESZT0KMmMdjO4yf8mIuMjeXTFo5y+fNrqWMWGCjHZcPKk/TEkpBBezT1nDjzzjP3522/DgAGWxhERERERkeJp7f5IUlIN1YO9+eVMOKBhyfLMrm9gXneIj4KQhvYiTNBNVqcSERERJ+Pv5c+s9rOo7FuZE7EneHTFo5yPP291rGJBhZhsSLsiptANS7Zypf2+MACjR8PTT1ubR0REREREiq3Vfw1LdnOVM0QnRFPaqzS3B99ucapCzhjYMBW+HAApCVCzC/T/HkqWsTqZiIiIOKkg7yBmtZ9FuRLliIiJYOiPQ4lOiLY6VpGnQkw2FNpCzO23Q7Nm9qtgJkywOo2IiIiIiBRTySmphO+LBCDJawsAd4fejauLq5WxCrfUFFj2DKx4wf668VDoOx88fKzNJSIiIk6vQskKzLp7FqW9SrMvah/DVw0nLinO6lhFmgox2ZBWiKlQwdocOebvD8uXw6xZhXBMNRERERERKSq2HL1IdHwSpXxsbLvwEwAdq2hYslxLjIPPH4ZfZ9pft38dOk0AFbZEREQkm8JKhTGz/Uz8PPzYHrmdx1c/TkJKgtWxiiwVYrKhUF0Rc+gQfPDB36+9vMDNzbo8IiIiIiJS7K3aewaAW6qdJDYplrI+Zalftr7FqQqp2EiY1xX2/Q9cPaH3PLhzhE6+K2xcXOyjWNx+u/25iIiIBWoE1GBGuxn4uPmw6fQmRoWPIik1yepYRZL+t8+Gkyftj05fiDl9Gtq3h8cegylTrE4jIiIiIiICwOo99vvDUHIbAO1D2+NiU3c0x84dgI/awYnN4B0A/b+DOj2tTiW54e0Nv/1mn7y9rU4jIiLFWN0ydXn/rvfxdPVk7fG1DFs5jPPx562OVeToyDcbDh60P1aubG2Oa4qOhk6d7GGrVIHeva1OJCIiIiIiwtHzcRw4G4uraxIHLm0CoFOVThanKoSObISP7oaoCAgIg0EroXITq1OJiIhIEdCoXCMmt5mMt5s3m05toveS3mw+s9nqWEWKCjHXERkJZ/86eat2bWuzZOnKFejRA/74A8qWhRUroHx5q1OJiIiIiIiw+q9hyWqEHSc+JZ6QkiHUDaprcapCZtc38EkPiI+CkNvtRZigm6xOJSIiIkVI85DmLOyykKqlqhIZH8mg5YP4eOfHGGOsjlYkqBBzHbt22R+rVoUSJazNkqnkZOjXD9auBV9f+OEHuEkH5CIiIiIi4hxW7bWf2eblvwOA9mHtsel+JtljDGyYCl8OgJQEqNkF+i+BkmWsTiY3Ki4OwsLsU1yc1WlEREQAqOZfjYVdFtK5SmdSTAqTNk/i8TWPE50QbXW0Qk+FmOvYudP+eMst1ubIlDEwbBgsXgweHvDdd1BfN7wUERERERHnEJuQzKZDF8CWwNEr9uEtOoZ1tDhVIZGaAsuegRUv2F83Hgp954OHj7W5JG8YA0eO2CedaSwiIk7Ex92HN1u8ydgmY3F3cSf8WDh9v+/LrvO7rI5WqKkQcx1OXYix2aBuXXBzg4ULoXVrqxOJiIiIiIg4/HTgHIkpqZQvf4jE1AQq+1amVmAtq2M5v8Q4+Pwh+HUmYIMOb0CnCeDianUyERERKQZsNht9avZhfuf5hJQM4UTsCR5e+jCf7/1cQ5Xlkgox1+HUhRiAxx+HffvgnnusTiIiIiIiIpJO2v1h/ILsZ1B2COugYcmuJzYS5nWFfUvB1RN6z4Wmj9lPxBMREREpQHVK1+Hzrp/TplIbklKTeG3Ta4xZP4a4JA2rmVMqxFyDMU5aiFm2DKKi/n5dtap1WURERERERDKRmmpYvTcSXK5wNnkbAB2raFiyazp3AGbfBSc2g3cg9P8O6vS0OpWIiIgUY6U8S/Fem/cY1XAUrjZXlh1exv3/u58/o/60OlqhokLMNVy4ANF/3YeoenVrszgsXQrdu0OrVvaAIiIiIiIiTmjHiWjOxSZQMmAvySaJqqWqUt3fWTpWTujIRvjobrh4BALCYNCPULmJ1alEREREsNlsDLhlAHM6zqGsT1kORx/mgaUPsOTgEqujFRoqxFzD6dP2x8BA8PKyNgsAGzfCffdBcjLUqwf+/lYnEhERERERydSqvWcBCAzeDUDHsI4aliwru76BT3pAfBSE3A6DVkLQTVanEhEREUmnftn6fNntS5qWb0p8cjz/+ek/vLzhZRJSEqyO5vRUiLmGtEJMuXLW5gBg1y7o0gXi46FTJ5gzB1z06xMREREREee0eu8ZcIkj2vx1f5gqHSxO5ISMgZ+nwJcDICUBbu4K/ZdAyTJWJ5P8ZrNB7dr2SQVKEREpRAK9ApnebjrDbx2ODRtfH/iah5Y+xLGYY1ZHc2r6Jv8aIiLsj5YXYo4cgQ4d7PeFadIEvvwS3N0tDiUiIiIiIpK509FX2HkiBne/naSSQo2AGlQtpXtbppOaAsuegR/H2l83Hgp9PgEPH2tzScHw8bGfcLlrl/25iIhIIeLq4sqw24Yx4+4ZBHgGsPfCXvp834eVR1ZaHc1pqRBzDatW2R+bWDksb2QktG8PJ07Yz5T53/+gRAkLA4mIiIiIiFzbmn32YckCytqHJetUpZOVcZxP4mX4/CH4dSZggw5vQKcJ4OJqdTIRERGRbLuzwp182e1L6petT2xSLE+FP8Vbv71FUmqS1dGcjgox17B/v/3R0kJMVJR9OLLKlWH5cvsNa0RERERERJzYqj1nsbnGcsXV3qnqEKZhyRxiz8LcrrBvKbh6Qu+50PQxDU8lIiIihVJwiWA+6vARA+oMAGD+7vkM/GEgpy+ftjaYk1Eh5hqiouyPpUtbGKJGDfj5Z1ixAipWtDCIiIiIiIjI9V1JSuHnP8/h5rsTQyp1Stehkm8lq2M5h3MHYHY7OLkFvAPt94Op09PqVGKFuDioU8c+xcVZnUZEROSGuLu4M+r2UUxuMxlfd1+2RW6j95Le/HTiJ6ujOQ0VYq4hrRDj71/AO05JgT/++Pt1pUpQs2YBhxAREREREcm5jYfOE5+Ugk/gDgA6hnW0OJGTOLIRProbLh6BgDAY9CNUvsPqVGIVY2D3bvtkjNVpRERE8sRdle/i826fUyuwFhcTLjJ85XDe3/o+KakpVkeznAoxWUhNheho+/OAgALcsTEwYgQ0bgxffFGAOxYREREREblxq/ecxeYWQ6rnIUDDkgGw6xv4pAfER0HI7TB4FQTdZHUqERERkTxXybcS8zvPp3eN3hgMH27/kKErh3Iu/pzV0SylQkwWLl2yF2OggAsx48bBjBmQnFyAOxUREREREblxxhhW7z2Lm+8OwHBrmVspX7K81bGsYwz8PAW+HAApCXBzV/twZCWCrE4mIiIikm88XT15semLjG8xHm83bzad2kSfJX3YfGaz1dEso0JMFtKGJfPysk8FYto0eyEm7XmfPgW0YxERERERkRu378wlTlyMx6PUdqCYD0uWmgJLR8OPY+2v7/g/6PMJePhYm0tERESkgHSt2pWFXRZStVRVIuMjGbR8EB/v/JhUk2p1tAKnQkwW0goxBXY1zGefwb//bX/+8sswbFgB7VhERERERCRvrNpzFpvbRVy8j2DDRvuw9lZHskbiZfj8IfhtFmCDDm9Apwng4mp1MhEREZECVc2/Ggu7LKRL1S6kmBQmbZ7EE6ufIDoh2upoBUqFmCykFWL8/QtgZytWwL/+Zb9s/bHH4MUXC2CnIiIiIiIieWv13rO4+dmvhmkQ3ICyPmUtTmSB2LMwtyvsWwquntB7LjR9zOpUIiIiIpbxcfdhfPPxjG0yFncXd8KPh9P3+77sOrfL6mgFRoWYLJw9a38MKoihe7/5BpKSoG9fmDIFbLYC2KmIiIiIiEjeuXA5kS1Ho3D3K8bDkp07ALPbwckt4B1ovx9MnZ5WpxJnZLNBaKh90ncAIiJSDNhsNvrU7MN/O/+XkJIhnIg9wcPLHuazvZ9hjLE6Xr5TISYLJ0/aH0NCCmBn06bBhx/CJ5+Ai34lIiIiIiJS+ITvOwtu53H1Po6LzYV2oe2sjlSwjmyAj+6Gi0cgoAoMXgmV77A6lTgrHx+IiLBPPrpvkIiIFB+1S9fmi25f0LZSW5JSk3h90+uMWTeGy0mXrY6Wr/StfxZOnbI/VqiQTzs4dw5SUuzPXVxgyBDw8MinnYmIiIiIiOSvVXvPOq6GaVSuEUHeBTG8gMVSU2H/Cph/D8zpBPFREHK7vQhTuprV6URERESckp+HH5PbTObp25/G1ebKsohl3P/9/RyIOmB1tHyjQkwW0q6IyZdCzPnz0LIl9OsHCQn5sAMREREREZGCk5SSyrp9kY77w3QK62Rxonx2JRp+mQ7vN4QFveHgKsAG9e63D0dWohgUoURERERugM1mo3+d/szpOIeyPmWJiInggf89wHcHv7M6Wr5wszqAs8q3Qszly9C1K+zZAzEx9itjCmT8MxERERERkfzxW8QFYs0pSnqdws3mVnSHJYvcD7/OhG0LITHWPs+zFDR4GBoNhsAq1uaTwiM+3n6CJsC6deDtbW0eERERi9QvW58vu33Js+ueZeOpjTz/0/NsObOFZxs/i5ebl9Xx8owKMVnIl0JMYiLcey/88gsEBMCKFSrCiIiIiIhIobd6z1nc/bYB0KRCE0p5lrI4UR5KTYU/f4RNM+Dg6r/nB9WEO4ZCvb7gWdK6fFI4pabC77///VxERKQYC/QKZHq76czcPpPp26bz9YGv2XV+FxNbTaSyX2Wr4+UJFWKykFaIKV8+jzaYmgoDB8Ly5fYb8S1dCrVr59HGRURERERErLN671nHsGQdwzpanCaPXImGrZ/ar4CJOvzXTBvU7GQvwFRpBTabpRFFREREigpXF1eG3TaMW8veyrPrnmXvhb30/b4vrzZ7tUhcba1CTCYuXYLYv64yz5NCjDHw1FOwYAG4ucHXX0OTJnmwYREREREREWsdiowl4tJBSpQ5i7uLO20rt7U60o2J3GcvvvyxEJIu2+dp+DERERGRAnFnhTv5stuXjF43mq1nt/JU+FM8VOshRjYciburu9Xxck2FmEycOWN/9PW1Tzfszz9h5kz783nzoGMROUNMRERERESKvauvhmkW0gxfj7zoRBWw1FQ4sMI+/NihNX/PL3Pz38OPeZSwLp+IiIhIMRJcIpiPOnzElC1TmLtrLv/d8192nNvBO63eoVyJclbHyxUVYjJx6pT9Mc/uD1O9uv1+MDt3wgMP5NFGRURERERErLdq7xncfQvpsGTxF+GPT+HXWf8YfqzzX8OPtdTwYyIiIiIWcHdxZ9Tto7it7G2M/Wks2yK30XtJb8a3GE/zkOZWx8sxFWIykWeFmMRE8PCwP2/Rwj6JiIiIiIgUETFXkvj95E68ws7h4eJJ60qtrY6UPZH7YNOHsO2zv4cf8yoFDf5lH34sIMzSeCIiIiJid1flu6jRrQajwkex58Iehq8czpB6Qxh26zBcXVytjpdtLlYHcEanT9sfb6gQs2YN1KwJ27fnSSYRERERERFns37/OWwltwHQqlJLSrg78fBdqSmwbxl80hOmNYbfP7IXYcrUgq6TYeQeaP+aijBScIKC7JOIiIhcUyXfSszvPJ8+NfpgMHy4/UOGrhzKufhzVkfLNl0Rk4kbviJmyxbo0QMuXYJJk2DOnDzLJiIiIiIi4ixW7jmN+1/3h+kQ1sHiNFmIvwhb/wu/zYKoCPs8m4t9+LHGQzT8mFijRAmIjLQ6hYiISKHh6erJ2KZjaRDcgHEbx7Hp1Cb6LOnD263epmFwQ6vjXZcKMZk4c8b+WL58LlY+cAA6drQXYdq0genT8zSbiIiIiIiIM0hJNayJ2IxLuSg8XbxoWbGl1ZHSO7sXfk0bfizOPs+rFDTo/9fwY6HW5hMRERGRHOtStQu1AmsxMnwkB6MPMmj5IB5v8DgD6gzAxea8A4CpEJOJXF8Rc/IktG9vP6ulQQNYvBi8vPI6noiIiIiIiOX+OHaRePfNeACtK7XG283b6kj24cf2L7cXYA6F/z2/bG371S/1+oCHEw+fJiIiIiLXVdW/Kgu6LODVX17l+0PfM2nzJLae2cprzV+jlGcpq+NlSoWYTOTqHjFRUdChA0REwE03wbJl4OeXH/FEREREREQst2rPKdz+Gpasc9VO1oaJj7IPP/brLLh4xD4vbfixO4ZCWAsNPybOJT4eOv3172bZMvB2gkKmiIhIIeLj7sMbzd+gQXAD3tz0JuHHw+mzpA8TW0/klqBbrI6XgQoxmcjVFTHPPw87d9rHM1uxAsqWzZdsIiIiIiIizmDZgV9w8YvB08WH5iHNrQmR6fBj/tDgXxp+TJxbaiqsXfv3cxEREckxm81G7xq9qVO6DqPCR3E89jj/WvYvRjcazf0178fmRCfiqBCTifh4+2OO7hEzYQKcPQsvvQRVquRLLhEREREREWdw8mI8J5N/+WtYsjZ4uHoU3M5TU2D/D7DpQzi89u/5ZevAHUOgbh/w8Cm4PCIiIiJiqdqla/N5t88Z+9NYVh9bzRub3mDrma28dOdLlHB3jmFpVYjJgr8/+OTk2N3XF776Kr/iiIiIiIiIOI3w/adx89sJQPebOhfMTh3Dj82Ei0ft8xzDj/0fhDXX8GMiIiIixZSfhx+T20zmk92fMHnzZJZFLGPPhT282/pdqgdUtzqeCjFZydawZM88A4GBMGaMDvhFRERERKTYWLr/F1xKXMLTpSRNyzfN352d3WO/+mX7538PP+Yd8PfwY/6V83f/IiIiIlIo2Gw2+tfpT70y9Xh67dNExETwwP8e4IUmL9Djph6WZlMhJgvXHZbs7bftE8Bdd0GjRvmeSURERERExBnsi/0ZjxLQvEIb3F3d834HjuHHZsDhdX/PD74FGg+Bur01/JiIiIiIZKp+2fp82e1Lnlv/HBtObuCFn19gy9ktPNf4ObzcvCzJpEJMFtyv1ZeYM8d+NQzAO++oCCMiIiIiIsWKzWc3AH1u7pq3G46Pgi3z4bdZ6Ycfu7mLffix0GYajUBERERErivQK5AP7vqAmTtmMv2P6Sw6sIhd53YxsfVEQv1CCzyPCjFZGDQoize++w4GD7Y/f+YZGDWqwDKJiIiIiIg4Axe3ODxtATQu3zhvNnhmN/z6IWz7HJLj7fO8A6BBf2g0SMOPSdGUoxvTioiISE65urgy7NZh3FrmVp5b/xz7ovbR9/u+vNrsVe4OvbtAs6gQk4Wbbspk5rp10LcvpKbCwIHw5psFnktERERERMQZNCrbCjeXG+hSpqbAvmX2Asw/hx+7Y6h9+DF37xsPKuKMSpSAy5etTiEiIlIs3FnhTr7o+gWj141m69mtjAwfyUO1HmJkw5H5M8xuJlSIycKtt/5jRmQkdO8OV67YH2fO1CXxIiIiIiJSbD10S/fcrRh3AbbOh99m/2P4sa5/DT92p/paIiIiIpKngksE81GHj5iyZQpzd83lv3v+y/Zz25nYaiLlSpTL9/2rEJOJli0zOe4vUwbGj4cvvoDPPgM3/ehERERERKR4csePJhVyeK/MM7tg04ew/Yv0w481HAC3DwL/SnmeU0REREQkjbuLO6NuH0X9svV54acX2B65nd5LejO+xXiahzTP13275OvWC6lq1bJ4Y9gwWLkSvHV5vIiIiIiIFF91A5rh6uJ6/QVTU2DPEpjbFabfCVvm2YswwXWh+/swcg+0e1lFGCl+rlyBLl3s05UrVqcREREpVtpWbsvn3T6nVmAtLiZcZPjK4UzdOpWU1JR826cKMZkIDPzrSXQ0DB4M58///aZrNjobIiIiIiIiRdijDe659gJxF+CnyfDebfD5QxCxHmyuULsHDFwG/7ceGjyse8BI8ZWSAkuX2qeU/PvSR0RERDJXybcS8zvPp0+NPhgMM7fPZOiPQzkXfy5f9qfxtTIRGMjf94JZtw4OHYLVq62OJSIiIiIi4hTqB9fN/I1Mhx8LtA8/1mgQlKpYYBlFRERERK7F09WTsU3H0iC4AeM2jmPT6U30WdKHt1q+xe3lbs/TfakQk4nSpZKhX397EcbPDyZOtDqSiIiIiIiIc0pJhn1L4deZ9itf0pSrC3f8H9xyr658Efmnq6+CWbcO2rfXCBwiIiIW6VK1C7UCazEyfCQHow8yeMVg/l3/3wy8ZSAutrwZVMyphybbuXMnjRo1IiAggNGjR2OMue46X331FaGhoVSoUIGFCxfmar/Nv34SFi8GT0/49luoXz9X2xEREREREclvVvWbHMOPTbkNvnj4quHHesLAH2Doeqj/kIowIv+0aBHUrv33686dISzMPl9EREQsUdW/Kgu6LKBr1a6kmBQmb5nM46sfJzohOk+277SFmISEBLp160bDhg35/fff2b17N3Pnzr3mOjt37uTBBx9k7NixLF++nBdffJF9+/bleN+V1swHFxdYuBBat87dBxAREREREclnlvWblo6Gd2vBypcg+ph9+LEWo+DJ7dBnHoQ2BZst9x9MpKhatAjuuw9OnEg//8QJ+3wVY0RERCzj4+7DG83f4MWmL+Lh4sHa42vps6QPu8/vvuFt20x2TpeywOLFi3nkkUc4fvw4Pj4+bNu2jccee4yffvopy3WefPJJ9u7dyw8//ADAe++9R2RkJK+99lq29hkTE0OpUqWIBvxmzYLBg/Pio4iIiIiIOC3HMXB0NH5+flbHkRwq6H6To70864ufpw3K1btq+DGvPPtcIkVSSor9ypfjxzN/32aDihXh8GENUyYiImKx3ed3Myp8FMdjj+OS4ML2odtvqM/ktPeI2bZtG02aNMHHxweAevXqsXv3tStP27Zto1OnTo7XjRs35pVXXsly+YSEBBISEhyvo6PtlxmdfnwM9OkDMTE38hFERERERJxezF/HvE56fpZcR373m7LqM8WEdoDmQ6FSI/uXx/GJ9klEsrZ+fdZFGABj4Ngx+OEHaNGi4HKJiIhIBhXdKzKr1Sxe/+V1Vh9YDdxYn8lpCzExMTFUqVLF8dpms+Hq6kpUVBQBAQHZWsfPz4+TJ09muY/x48czbty4DPNrTpkAUybcQHoRERERkcLl/PnzlCpVyuoYkkP53W/Kqs9UadhXwFc3Fl5EMte1q9UJREREJBM30mdy2kKMm5sbnp6e6eZ5eXkRFxeXZYfin+ukLZ+V5557jpEjRzpeX7x4kdDQUI4ePapOqGRLTEwMlSpV4tixYxrKQ7JFbUZySm1GckptRnIqOjqaypUrExgYaHUUyYX87jepzyQ3Sv8vSU6pzUhOqc1ITqnNSE7lRZ/JaQsxgYGB7Ny5M928S5cu4eHhcc11IiMjs728p6dnhk4LQKlSpfSPUHLEz89PbUZyRG1GckptRnJKbUZyysXFxeoIkgv53W9Sn0nyiv5fkpxSm5GcUpuRnFKbkZy6kT6T0/a2GjVqxMaNGx2vDx8+TEJCwjWrTv9cZ+vWrYSEhORrThEREREREauo3yQiIiIi4vycthDTsmVLYmJimDNnDgBvvPEG7dq1w9XVlYsXL5KSkpJhnXvvvZfPPvuMHTt2EBsby5QpU+jQoUNBRxcRERERESkQ6jeJiIiIiDg/py3EuLm5MXv2bEaMGEFQUBDffvstEyZMACAgIIAdO3ZkWOfWW2/liSee4PbbbyckJARXV1eGDx+e7X16enry0ksvZXrpvUhm1GYkp9RmJKfUZiSn1GYkp9RmCreC7jepvUhOqc1ITqnNSE6pzUhOqc1ITuVFm7EZY0weZspzp0+fZvPmzTRp0oTSpUtna53du3dz4sQJWrVqdc2xkUVERERERIoC9ZtERERERJyX0xdiRERERERERERERERECiunHZpMRERERERERERERESksFMhRkREREREREREREREJJ+oECMiIiIiIiIiee7bb7+latWquLm5cdttt7Fnzx6rI0kh0rFjR+bOnWt1DCkkxowZQ7du3ayOIYXA7NmzqVSpEj4+PrRu3ZpDhw5ZHUmKiWJViNm5cyeNGjUiICCA0aNHk53b43z11VeEhoZSoUIFFi5cWAApxZnkps2MGzeOwMBAPD096dWrF5cuXSqApOIsctNm0ly8eJHy5csTERGRfwHF6eS2zaSmpnLnnXcyceLEfE4ozianbcYYw7BhwwgMDMTf358BAwYQHx9fQGnFGZw7d44qVapk+/+XtWvXUqtWLYKCgnj33XfzN5wUKjdynCPFz8GDBxk4cCBvvvkmJ06coEaNGgwePNjqWFJIfPrppyxfvtzqGFJIbN++nQ8++ID33nvP6iji5A4ePMgrr7zCt99+y969e6lWrRoDBgywOpY4ocz6UDd6LFxsCjEJCQl069aNhg0b8vvvv7N79+7rnlmxc+dOHnzwQcaOHcvy5ct58cUX2bdvX8EEFsvlps18+umnfPrpp/zwww/s2rWLPXv28OabbxZMYLFcbtrM1UaPHs3p06fzL6A4nRtpMzNmzCA6OprHH388f0OKU8lNm5k/fz779u1j69atrF+/nl27djF+/PiCCSyWO3fuHF27ds12ESYyMpLu3bvTr18/Nm7cyKeffsqaNWvyN6QUCjd6nCPFT1pfqE+fPgQHBzNs2DC2bt1qdSwpBC5cuMCoUaOoWbOm1VGkEEhNTWXIkCE89dRTVK1a1eo44uS2bt1KkyZNaNCgAZUrV+aRRx7hzz//tDqWOJnM+lB5cSxcbAoxy5YtIzo6mnfffZdq1arxxhtv8NFHH11zndmzZ9OmTRsGDx5M3bp1GTFiBPPnzy+gxGK13LSZY8eOMW/ePBo3bsxNN91E37591dkoRnLTZtKsW7eO7777jtKlS+dzSnEmuW0zJ0+e5D//+Q9Tp07F3d29AJKKs8hNm/n111+57777CA0NpW7duvTs2VOdjWLk/vvv54EHHsj28p9++ikVKlRg7NixVK9enRdffDHb/5dJ0XYjxzlSPHXt2pUhQ4Y4Xu/bt4/q1atbmEgKi1GjRtGrVy+aNGlidRQpBGbMmMGOHTsICwvju+++IzEx0epI4sRq167N6tWr+eOPP4iOjuaDDz7g7rvvtjqWOJnM+lB5cSxcbAox27Zto0mTJvj4+ABQr149du/efd112rZt63jduHFjNm/enK85xXnkps08++yzNG3a1PFanY3iJTdtBuxV9aFDhzJlyhRKliyZ3zHFieS2zTz55JOEhoZy7NgxNmzYkN8xxYnkps3UqVOH//73v5w5c4YjR47w2WefqbNRjMyaNStHV85t27aNNm3aYLPZAB3/yt9y+3+WCEBiYiITJ07k//7v/6yOIk5uzZo1rFq1irfeesvqKFIIxMbG8tJLL1G1alWOHDnCpEmTaN68uYbhlSzVrl2b++67j/r16+Pv78/GjRt55513rI4lTiazPlReHAsXm0JMTEwMVapUcby22Wy4uroSFRWV7XX8/Pw4efJkvuYU55GbNnO1/fv3880336Q7C0yKtty2mTfeeIMaNWrQt2/f/I4oTiY3bWbjxo18+eWXVKxYkYMHD9K/f39GjBhREHHFCeSmzQwePJjY2FjKlStHWFgYVapUoX///gURV5zA1e0lO3T8K1m50WNjKd5eeuklSpQooXvEyDVduXKFoUOHMn36dHx9fa2OI4XAokWLuHz5MmvWrGHcuHH8+OOPXLp0SaPZSJZ+/fVXlixZwi+//MLFixfp168fnTt31n3vJJ3M+lB5cSxcbAoxbm5ueHp6ppvn5eVFXFxctte53vJStOSmzaRJTU3lkUceYfDgwdSpUye/IoqTyU2b2bNnDzNmzGD69On5HU+cUG7azKxZs7jjjjv4/vvveeWVV1i9ejUffPCB7mFWTOSmzbz33nv4+/tz5MgRjh49SnJyMqNHj87vqFJI6fhXsnIjx8ZSvK1evZpp06axYMECDakq1/Tqq6/SqFEjunTpYnUUKSSOHz9OkyZNCAoKAuz/V9WrV0/D8EqWFi5cyP33388dd9xBqVKleO211zh48CDbtm2zOpo4ubw4Fi42hZjAwEAiIyPTzbt06RIeHh7ZXud6y0vRkps2k+bVV1/lwoULvP322/kVT5xQTtuMMYYhQ4bw2muvUaFChYKIKE4mN39njh8/TufOnR3DBlWqVIkyZcpw8ODBfM0qziE3bebTTz9l9OjRVK5cmUqVKjF+/Hjd10GypONfycqNHBtL8XX48GH69evHtGnTqF27ttVxxMktWLCAb7/9Fn9/f/z9/VmwYAHDhw9n+PDhVkcTJ1WxYsUMw5AdOXKEkJAQixKJs0tNTeXs2bOO15cuXSIuLo6UlBQLU0lhkBfHwsWmENOoUSM2btzoeH348GESEhIIDAzM9jpbt27VH/NiJDdtBmDJkiW8++67fP31145xA6V4yGmbOXr0KD/99BOjR492dDaOHj1KvXr1WLBgQUHFFgvl5u/MPzsbsbGxXLhwQf8/FRO5aTP/7GycPn1aHQ3Jko5/JSu5PTaW4is+Pp6uXbvSo0cPevXqRWxsLLGxsRr+RbK0fv16du7cyR9//MEff/xB9+7deeWVV3jllVesjiZOqkuXLuzevZsZM2Zw/PhxpkyZwrZt27jnnnusjiZOqkWLFixatIhJkyaxYMECevbsSbly5ahXr57V0cTJ5cWxcLEpxLRs2ZKYmBjmzJkD2O/J0K5dO1xdXbl48WKmX0jce++9fPbZZ+zYsYPY2FimTJlChw4dCjq6WCQ3bWbPnj3069ePqVOnUqlSJWJjYzVcQzGS0zYTEhLC4cOHHR2NP/74gwoVKrB06VK6d+9uxUeQApabvzP9+vVj1qxZrFq1iiNHjjB8+HBuvvlmHTgWE7lpMy1atODNN99k7ty5zJw5k+HDh+tvjBATE0NSUlKG+d27d+fnn39m5cqVJCUl8dZbb+n4V4Br//0RycyKFSvYvXs3s2bNwtfX1zEdOXLE6mjipCpWrEhYWJhjKlmyJEFBQY5hp0T+qXTp0ixdupR58+ZRo0YN3nvvPb744gsqVapkdTRxUvfeey/PPvsskydPZsCAAVy8eJFvvvlGQ2fKdeXFsbDNFKPTUb777jv69euHt7c3Li4uhIeHU7t2bWw2G1u3buW2227LsM7zzz/PO++8g5eXF9WrV2f9+vV4e3sXfHixRE7bzFNPPcXkyZPTzQsNDSUiIqLAMou1cvN35mphYWGEh4cTFhZWIHnFerlpMx999BETJkzg2LFj3HbbbcydO5eaNWsWfHixRE7bzMWLF3n88cf54YcfuHTpEh06dGD27Nn6UqOYsdlsHD582PH/S1hYGJMnT6Znz54Zlp0xYwaPP/44JUuWxN/fn40bNxIcHFywgcUpZfX3R0RERESkqPlnH+pGj4WLVSEG7MNxbN68mSZNmlC6dOlsrbN7925OnDhBq1atNAZyMZSbNiPFm9qM5JTajOSU2ozkt8OHD7N3715atGhByZIlrY4jTkR/f0RERESkuLqRY+FiV4gREREREREREREREREpKMXmHjEiIiIiIiIiIiIiIiIFTYUYERERERERERERERGRfKJCjIiIiIiIiIiIiIiISD5RIUZERERERERERERERCSfqBAjIiIiIiIiIiLZFhMTY3UEERGRQkWFGBGxVOvWrbHZbNmaBgwYkOf7f/nll/Nt25I94eHh2Gw2wsLCCmR/+p2LiIiIiNyY1q1bs23bNqtjiIiIFBoqxIiIU7DZbHh6el5zcnd3tzqmOInw8HBefvllFi9ebHWUYisiIoKXX36ZyZMnWx1FRERERPLQ9u3badasGYGBgYwYMYIrV66kez88PBxvb29uvfVWixJm7ZtvvqFly5aUKVMGd3d3QkJC6Nu3L2fOnLnhbWd2sqCLiwsBAQG0bNmS6dOnk5SUlAefwjmkfcaIiAiro2RJJ9mJSGGiQoyIOIWWLVty5cqVa06zZs2yOqbkAx8fH2rWrEm1atWyvU54eDjjxo3LVSEmKCiImjVrUr58+RyvK3+LiIhg3LhxKsSIiIiIFCHJycl07tyZDRs2EBUVxbRp03j11VfTLfPuu+8ycuRIixJmbezYsdxzzz2sX7+e8+fPk5qaysmTJ/niiy84depUnu3H1dXVcbIgwMWLF1m/fj3Dhw+nTZs2xMbG5tm+igOdZCcixYUKMSIiYqnGjRuzd+9eVq1aVSD7GzFiBHv37mX8+PEFsj8RERERkcJi69atnDhxgocffpgzZ87g5+fHt99+63j/wIED7N69m549e1oXMhNRUVG8+eabAEydOpX4+HhSUlI4deoUH3/8MUFBQXm2r6efftpxsmB8fDx79+5lzJgxuLq68vPPP/PEE0/k2b6KA51kJyLFhQoxIiIiIiIiIiLiGIbqzjvvpGzZstSsWTPd0FSTJ09mxIgRuLq6WhMwC3v27CE5OZmgoCBGjBjhuFqlXLlyDBw4kIoVK+bLfj09PalZsyZvvvkmzz77LADz5s3j/Pnz+bI/SU8n2YlIYaJCjIiIiIiIiIiIOO4Hk1bI8Pb2Jj4+HrBfdfLNN98waNAgy/JlJTExEYASJUpYlqFbt24ApKSk8Ouvv1qWQ0REnJMKMSJSqB08eJChQ4dSu3ZtfHx8KFmyJLfffjuTJk0iOTk5z/f31Vdf0b59e8qUKYOHhweVKlWiY8eOLF++/Jrr/fzzz/Tt25eQkBA8PDwoW7Ys99xzDxs3bsyzbHPnzsVms3HzzTcTFRXF8OHDCQkJwdPTkypVqvDkk09y9uzZLNdPSUlh7ty5tGzZklKlSuHj40O9evV49dVXiYmJyXK9mJgYXnzxRerVq0fJkiUpUaIEtWrV4rHHHuPYsWPXzR0eHo7NZiMsLOy6y6RN48aNA+xnm/3zhpnXk50bOp45cwZ3d3dsNhvbtm27ZiZvb28uXryY4f0dO3YwcOBAwsLC8PT0JDAwkA4dOrB06dLrZsypAQMGYLPZePnllwH772TSpEk0b96ccuXK8cknn2S6XnJyMvPnz6dly5aUL18eDw8PQkJC6NevHzt27Miw/NU/5zZt2gBw5MiRDL+D8PDwTPeXkJDAtGnTaNasGf7+/nh5eVGzZk2eeeYZnTUoIiIi4gS8vb2Bvwsy8fHx+Pj4APDhhx/Sr18/fH19LcvnzEqXLu14HhUVZWESERFxSkZExEKtWrUygGnVqlWO1/3222+Nh4eHAQxgXFxcHM8B06tXr+tu46WXXjKA6d+//3WXffrpp9Nt/5/7e+mllzJd7/nnn89yPRcXF/Phhx/m8JNnbs6cOQYwlStXNrfeeqsBjM1mMzabzbG/ChUqmD179mRYNzo62tx1113pcl69XtWqVc3u3bszrHf+/HlTo0aNLD+fn5+f2bZt2zVzr1mzxgAmNDQ0y2XWrl1rPD09HZOrq6tjX1fP9/T0vO7PKbu/827duhnAPPXUU5m+P2DAAAOYfv36ZXhv+vTpxs3NLcu28vzzz183Z07079/f0QZ///13U6VKlXT7mzNnToZ1rly54vj3lzal/VwB4+XlZX755Zd061z9c3Z3d3cs+8/fwdq1azPs7/Tp06Zhw4bp2tfVbaxixYrm8OHDefpzEREREZGc+e233xzHuEePHjW+vr6mbt26JjEx0YSGhpqIiIh82/eff/5phg4dasLCwoyHh4cpU6aM6dWrV6bHlsaYdMex15rWrFmTJ/nStjdmzJhM39+4caNjmWXLlmV4Pzw83PTs2dOUKVPGeHh4mLCwMDN06FBz8ODBDMum9ZE8PT1NfHy8ee655xw/lwoVKphHHnnEHDp0KMN6hw8fdmTIytV9h+x83uwco0dFRZlXX33V1K9f3/j7+xtPT09TvXp18+STT5pz585l+tmyM11PTvrzxuS8jaX1lyZNmmSMMWbBggWmSZMmxs/Pz/j4+JhmzZqZlStXZmvfIiIqxIiIpf75RXBmU9pBz9UuX75sAgMDDWBatGhhdu3aZVJSUsyVK1fMwoULHV8or1u37pr7z+6B26FDhxx5hg8fbk6ePGlSU1NNVFSUmThxouNL7LNnz6Zbb+bMmY4vtd977z0TGRlpUlNTzeHDh83AgQMNYNzc3DI9iM6ptEIMYNzd3c37779vLl++bBISEszixYtNmTJlDGDq1q1rUlNT063btWtXAxhvb28zefJkEx0dbRITE823335rKlWq5CiUXLx4MdOfX8mSJc3XX39t4uLiTGpqqtm/f79p166dAUz37t2vmTs7hZh/yukBd27WXbRokQFM2bJlTVJSUrr3YmNjTcmSJQ1gVqxYke69H374wdhsNuPi4mJefPFFc/z4cZOammpOnjxpnn32WcfvaP369TnOnpW0ztTgwYNNcHCwKV26tJk0aZLZv3+/OXfunLl06VKGdcaPH+9ofzNmzHAsExER4SjKtW3bNst95uT3lpqaapo1a+Zof6tXrzZXrlwxCQkJZu3ateamm24ygLnrrrty/TMQERERkRuXnJxsKleunK4/9uKLL5r58+ebPn365Nt+v/jiC+Pt7Z3liUzPPvtshnVu5CSh3LheISbtWN/Nzc2cP38+3XvXOqnPx8fHfP311+mWTzvW9vDwMO3bt890vVKlSmXoU1hRiDl48KAJCQlJd8LV1TmrVatmLly44FjeipPsjMldG7u6EPPEE0+k+4xpz93c3K578qGIiDEqxIiIxdIObGw2W4aDrrRp6tSpGdbbsWOH6dGjh+nRo0emZxB16dLFAOatt9665v6ze+D2xx9/OA60oqOjM7z/wgsvmFGjRpkjR4445iUnJ5vg4GADmOnTp2dYJzU11XHlwssvv3zN/WfH1YWYzLb32WefOd6/+qyd1atXO+b/swNgjL0IVaJEiUwP1p988kkDmV99tGPHDjNq1CgzYcKEa+Z21kJMYmKiCQoKMoBZsmRJuvc++eQTA5hKlSqZlJSUdO+lXfWRVQetdevWBjADBgzIcfaspHWmbDabqVChQrYKe88//7zp0aOHef311zO8l3YmpI+PT5br5+T3tmTJEgP2K6TOnDmT4f3w8HBHG8zPsyxFRERE5Pp27dpl2rRpY8qVK2dGjhxpEhISTIMGDczGjRvN5s2bTcOGDU1wcLCZOHFinuxvw4YNjkJKq1atzObNm01qaqo5evSoGTJkyDVP0EuTmz5FTmVWiImJiTHbt283I0eOdBQUHn300XTrvfPOO451/+///s8cPXrUpKammt9//920aNHCUXDZtGlThs+Tdoz/4osvmqioKJOcnGxWr15tqlWrZgATHBxsYmNjHetZUYjp0KGDAUy5cuXMqlWrTEJCgklJSTG//vqro0DzyiuvZLl+QfTtctvG0r6vSBtx4oEHHjAHDx40iYmJZsmSJaZUqVIGMH379s1xdhEpflSIERFL3cjQZP+UmJhoNm/ebN566y3j7++frYPL7B64JSQkmPLlyzvOCIuLi7tunk2bNqU7w+ZaU9euXXPwSTN3dSHmn1euGGNMSkqK8fPzM4AZN26cY/6IESMMYGrUqJHltgcPHmwAU69evXTzv/vuOwOYgICAXF/h4ayFGGP+LjTdd9996eanXTHywgsvpJt/+vTpbP/Ob7nllhxnz0paZwpubOiF06dPm8WLF5tOnTpdtwOXk9/bsGHDsv1z+eqrr3KdX0RERETyXnh4uGnatKm5fPmy40SltGnRokU3vP2WLVsawDRs2NAkJCRkeH/48OGOk3oyOynOmIItxFxratmyZbrCyMWLF42vr68BzL///e8M27xy5YqpX79+hj7x1YWYzE7gunoYtNmzZzvmF3QhJjk52dx7772mR48e5ttvv83w/ttvv20A07lz5yy3URB9u9y2satH8BgyZEiG9V577TUDmNq1a+c4u4gUPy6IiBRSycnJfPHFFzz44IPUqlULHx8fGjZsyDPPPENsbGye7svDw4OFCxcSHBzMK6+8gr+/P/Xq1ePBBx9k6tSpHD9+PMM6ERERjueenp7XnGJiYvIsa7ly5ShVqlSG+S4uLoSGhgJw6tQpx/xDhw4BcMstt2S5zXr16gHw559/ppvfrVs3nn76aWJiYmjRogVBQUG0bNmSf//73yxatIiEhIQb/jxWGjhwIABLlixx3HDz2LFjrFmzBoABAwakW/7IkSOO5x4eHtf8nV+6dCnP83br1o3WrVtne/lDhw4xduxYWrduTenSpSlXrhw9e/bkxx9/zNNcaf8WXFxcrvtvQTc2FREREXEukyZNYuTIkaxbt45z584xcuRItmzZAsAXX3xxQ9s+d+4c69evB2DUqFF4eHhkWOY///kPADExMaxateqG9pcXXF1dHceu3t7eBAcH07ZtW2bNmsWqVasoUaKEY9mVK1c6jvufffbZDNvy9PRk1KhRAKxbt47z589nWOapp57KMK9JkybUqVMHgJ9//jlPPlduuLq68tVXX7F48WK6d+8OgDGGP//8k7lz5/Lxxx8DcPnyZcsy5kUbCw4OZvLkyRnmN2zYELD284lI4eFmdQARkdw4f/48HTp0YPPmzfj6+tKyZUu6d+9O7dq1ufPOO3n99deZN29enu6zVatWHDlyhNWrV/Pbb7+xa9cufv75ZxYsWMBTTz3F66+/zpgxY7LMe/UBeX5KSUm57ns2my3P9vf222/z1FNPsWLFCnbs2MH27duZM2cO77//PhUrVmTJkiXcdttteba/glSvXj0aNGjAli1b+Oyzzxg2bBjz588nNTWVli1bUq1atSzX3bJli6NzVFAaNGiQ7WVnzZrFY489RlJSEnXr1qVnz57cfPPN3HbbbYSEhORL9nvuuYcvv/wyz7crIiIiIvnjzz//ZMeOHfTq1Ytp06YB9n5R/fr1KVWqFAcOHLih7R8+fBhjDJD1iWEhISEEBgZy4cKFDCeGWeHpp5/mzTffzNayaSe9BQYGUqFChUyXSTvpzRjDoUOHKF26tOM9m81G9erVM12vatWq7Nq1K91JdtmRnJyco+Wz46effmLhwoVs2rSJPXv2EBcXB4Cbm/VfO+ZFG+vZsyfe3t4Z5nt5eeVtWBEp0qz/iygikgv/+c9/2Lx5MzVq1OCXX34hICCgQPbr6elJp06d6NSpk2Pe4sWL6d27N8899xytW7fmjjvuACAsLMyxzIEDBzItRhw/fpzjx4/j4eGRoy/RryUyMpJz584RFBSUbn5SUpLjio2QkBDH/KpVqwKwc+fOLLe5fft2AG666aZM369QoUK6q0Pi4uIYPnw48+bN44EHHmD37t25+izOYODAgWzZsoV58+YxbNgwR4Ev7WqZq6VdcQT233lmxYzIyEgOHjwIwB133JGnRbHsOnr0qKMIM23aNIYPH57u/auv5soLaf8WrtVR37p1KwkJCYSEhFCpUqU83b+IiIiI5M7kyZMZMWIErq6ujis7fH19AfDz88vTK/slI2MMqampmb6X25Psjh49esO50hhjGDRoEHPmzMHV1ZVmzZrxyCOPUKtWLRo1asSOHTsYNGhQnu3PKtc6AU9EJLs0NJmIFErr1q0DoFOnThmKMHv37mXx4sV5ur/Ro0fj7+/PE088keG9nj17Urt2bYwx/PTTT475DRs2JDg4GICZM2dmut1//etfNG3alCeffDJP877zzjsZ5i1YsMBxyXTLli0d83v16gXA/v37M/25RURE8Nlnn6VbNk1ISAj+/v5s2rQp3XwfHx/Hz2rPnj2ZXmJ/I9LORoqOjs7T7WbmgQcewNPTk02bNjF37lz2799PyZIl6d27d4Zlg4ODHZenZ/U7HzNmDE2bNqVPnz6WFGEANm7cSFJSEmD/fP80fvz4624j7XeQnc53586dAdi2bVuGtgL2Nnb77bfTtGnTPB8WTURERERy5+LFiyxatChfv0ivUqWK45g4qxPDTpw4wYULF4CsTwxzVmknvV24cIGTJ09mukzaSW82m82x/NX27NmT6XppV25cfZLd1TIbKeHMmTOZHo/n1meffcacOXPw8PDgt99+Y+3atUydOpXhw4fTqFEjXFys/9oxL9qYu7t7/gUUkWLD+r+IIiK5kHYPlMWLF7Nz506MMRw5coQJEybQuHFjxxf0aZcg36j69esTHR3NRx99xMcff8y5c+cAeyHgvffecxzQpRVewD5e7rhx4wCYPn06jz76KPv27SMlJYWTJ0/y+OOPO+41MmLEiDzJmeadd95h8uTJxMXFkZiYyKJFixxjC99+++20aNHCsWzbtm3p0qULYP9S/u233+bMmTPExsby1Vdf0axZM2JjY6lcuXKG8Ynr1KlDdHQ0//73v9mwYQNXrlwhNTWVAwcOMHbsWMBelClZsmSefr60Ysfy5ctZs2YNKSkppKSksGfPHt5666083VdgYKBjvOO0Tmjv3r2zHGru1VdfxWazsWzZMnr16sW2bdtISkri/PnzvPHGG8ydOxfI+995Tlx9D6F33nmH+Ph44uPj+fHHH2nXrl26IlJW/4bq1KmDl5cXUVFRTJw4kStXrgD2TsyMGTPSnWnXpUsXmjZtCkDXrl2ZPXs2kZGRJCcn88svv9CzZ09SU1MJDg7mvvvuy4+PLCIiIiI59OGHH3L//ffj5+cH4HhMuzImJibGMS+3goKCaN68OWC/F03ayUJXSxsGzM/Pj7vuuuuG9lfQ2rVr57iCKLN+SmJiIu+++y5gP1nu6mHJ0mR2kt3atWvZv3+/Y700Pj4+juf79u3LsN6oUaNITEzM4afIWtoJkrVq1aJ+/frp3rt8+TJTpky57jby+yS7ot7GRKQQMSIiFmrVqpUBTKtWrXK03sKFC43NZjOAAdI9L1OmjGnWrJkBzMMPP3zN7bz00ksGMP3797/mcsnJyaZDhw6OfQDGxcUl3euGDRua+Pj4DOuOGTMm3XJXZwXMmDFjcvTZszJnzhwDmKpVq5rWrVs79nX1/ipVqmQOHDiQYd3o6GjTtm3bLHNWqVLF7N69O8N627dvNwEBAdf8ubz11lvXzL1mzRoDmNDQ0Gx/1tTU1HS/j6v3WapUqWuum93f+dWWLl2a7jOtW7fumstPnTrVuLq6Zvk7f+CBB0xycnK29389/fv3N4B56aWXsrV8QkKCqVevXpb5evXq5Xh+8ODBLLfzxhtvpNvG1b+HrVu3plv21KlT5rbbbsuyjQUEBJiff/75Bn4KIiIiIpJXEhMTTWhoqDl8+LBj3rJlywxgRo4cabZs2WIA069fvxve14YNG4y7u7sBTNOmTc3GjRtNQkKC2b9/v+M4FzCTJk3Kchu56VPkVG77b2+//bZj3YEDB5oDBw6YhIQEs2HDBnPHHXcYwHh4eJhffvnFsU7a50mbnn32WRMVFWWSk5PNypUrTWhoqAFMxYoVM/RBy5YtawDTpUsXExcXZ4wxJjY21jz66KMGMOXLl89W3yFt31e3gX9K6+u6ubmZxYsXm+TkZHP+/Hkzf/58U61aNcc2WrZsmeU2fvzxRwMYb29vs3r1apOcnGySk5PN7t27zYQJE66ZMbt9u9y2sbTvK7JqewXR7kSk6FAhRkQsldtCjDHGrFq1ynTu3NkEBwcbT09PU716dTNy5Ehz6tQpM23aNAMYX19fc+zYsSy3kZMv5ZOTk83s2bNN27ZtTZkyZYybm5sJCgoyTZo0Me+//765fPlyluuuXbvW3HfffaZcuXLGzc3NlC5d2nTr1s2sWrUqx587K2mFmJo1a5q4uDjz9NNPm4oVKxoPDw8TFhZmnnrqKRMZGXnNz/fxxx+b5s2bG19fX+Pl5WVuueUWM27cOBMdHZ3leidOnDBPP/20qVu3rilZsqTx8PAwoaGh5t577zVr1qy5bu7cHrxeuXLFvPLKK6ZmzZrG3d3deHh4mKpVq5onnnjimuvlphCTnJxsKlSoYABz0003ZWudP/74w/zrX/8ylSpVMu7u7qZUqVKmXbt25quvvsr2frMrp4UYY4yJiooyL730kqlVq5bx9vY2AQEB5u677zaLFi0yycnJply5cgYwTz755DW38+mnn5omTZoYHx8f4+rqaoKDg829995rTp8+nWHZK1eumClTppimTZsaX19f4+7ubm666Sbz5JNPmlOnTuX0Y4uIiIhIPvn000/Nfffdl27e5cuXTVBQULoCwaJFi/Jkf1988YXx9vbO9clrzlyIMcaYUaNGZXlCko+Pj/n666/TLZ/2eTw9Pc2DDz6Y6QloAQEB6Yo3aaZOnepYJjAw0DRu3Nj4+fkZwLz66qvZ7jtkpxCzf/9+4+vrm+nncnV1Nd26dXOcEJiVgjrJLjdtTIUYEclLKsSIiBQRVxdiREREREREcuuJJ54wGzZsyDD/999/Nw0aNDBly5Y1EydOzNN9/vnnn2bIkCGmcuXKxt3d3QQFBZmePXua8PDw667r7IUYY+wZe/ToYYKCgoy7u7sJDQ01Q4cOzfQK9KsLMcnJyeb111831apVMx4eHqZChQpm0KBBJiIiIst9TZkyxdx8883G3d3dBAYGmrZt25rvv//eGJP9k7iyU4gxxl6MeeSRR0xoaKjx8PAw5cuXN/369TNbt241O3fudGxn8eLFWW6joE6yy2kbUyFGRPKSzZg8uoGCiIhYau7cuQwcOJCaNWuyd+9eq+OIiIiIiIhILoSHh9OmTRs8PT0d92IUEZHCzcXqACIiIiIiIiIiIiIiIkWVCjEiIiIiIiIiIiIiIiL5RIUYEZEiYsCAARhjNCyZiIiIiIiIiIiIE9E9YkRERERERERERERERPKJrogRERERERERERERERHJJyrEiIiIiIiIiIiIiIiI5BMVYkRERERERERERERERPKJCjEiIiIiIiIiIiIiIiL5RIUYERERERERERERERGRfKJCjIiIiIiIiIiIiIiISD5RIUZERERERERERERERCSfqBAjIiIiIiIiIiIiIiKST/4fAWd3KAU1BBAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>No.</th>\n",
       "      <th>fpr</th>\n",
       "      <th>tpr</th>\n",
       "      <th>thresholds</th>\n",
       "      <th>ks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    No.  fpr  tpr  thresholds   ks\n",
       "0     1 0.00 0.01        0.82 0.01\n",
       "1     2 0.04 0.32        0.51 0.28\n",
       "2     3 0.12 0.49        0.31 0.37\n",
       "3     4 0.22 0.61        0.20 0.39\n",
       "4     5 0.32 0.70        0.17 0.38\n",
       "5     6 0.45 0.78        0.15 0.33\n",
       "6     7 0.54 0.83        0.14 0.30\n",
       "7     8 0.65 0.89        0.12 0.25\n",
       "8     9 0.77 0.94        0.10 0.17\n",
       "9    10 0.91 0.98        0.08 0.07\n",
       "10   11 1.00 1.00        0.04 0.00"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算auc 和 ks，并返回 ks 结果数据\n",
    "plot_roc_ks(data_train,scorecard,return_data=True,precision=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1032b71f",
   "metadata": {},
   "source": [
    "## AUC 计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c19ee8f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c9b4ea9419f4af59a97a3ca5754d43d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据训练集和评分卡计算auc\n",
    "get_auc_by_card(data_train,scorecard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2d920b7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据训练集预测得分，计算auc\n",
    "get_auc(data_train_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e45fe0",
   "metadata": {},
   "source": [
    "## KS 计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "93e68693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "263dbebb23844babbd4914f062b77949",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.39"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据训练集和评分卡计算ks\n",
    "get_ks_by_card(data_train,scorecard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e5a5a479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据训练集预测得分，计算ks\n",
    "get_ks(data_train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "83058354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>No.</th>\n",
       "      <th>Proba</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>%CumBad</th>\n",
       "      <th>%CumGood</th>\n",
       "      <th>KS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(0.039, 0.08]</td>\n",
       "      <td>2834</td>\n",
       "      <td>187</td>\n",
       "      <td>2647</td>\n",
       "      <td>12.60%</td>\n",
       "      <td>3.79%</td>\n",
       "      <td>15.07%</td>\n",
       "      <td>6.60%</td>\n",
       "      <td>3.79%</td>\n",
       "      <td>15.07%</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(0.08, 0.1]</td>\n",
       "      <td>2360</td>\n",
       "      <td>206</td>\n",
       "      <td>2154</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>4.17%</td>\n",
       "      <td>12.26%</td>\n",
       "      <td>8.73%</td>\n",
       "      <td>7.96%</td>\n",
       "      <td>27.33%</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(0.1, 0.12]</td>\n",
       "      <td>2245</td>\n",
       "      <td>223</td>\n",
       "      <td>2022</td>\n",
       "      <td>9.98%</td>\n",
       "      <td>4.52%</td>\n",
       "      <td>11.51%</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>12.48%</td>\n",
       "      <td>38.85%</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(0.12, 0.14]</td>\n",
       "      <td>3076</td>\n",
       "      <td>437</td>\n",
       "      <td>2639</td>\n",
       "      <td>13.67%</td>\n",
       "      <td>8.85%</td>\n",
       "      <td>15.03%</td>\n",
       "      <td>14.21%</td>\n",
       "      <td>21.33%</td>\n",
       "      <td>53.87%</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(0.14, 0.15]</td>\n",
       "      <td>2080</td>\n",
       "      <td>292</td>\n",
       "      <td>1788</td>\n",
       "      <td>9.24%</td>\n",
       "      <td>5.92%</td>\n",
       "      <td>10.18%</td>\n",
       "      <td>14.04%</td>\n",
       "      <td>27.25%</td>\n",
       "      <td>64.05%</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>(0.15, 0.17]</td>\n",
       "      <td>1260</td>\n",
       "      <td>213</td>\n",
       "      <td>1047</td>\n",
       "      <td>5.60%</td>\n",
       "      <td>4.32%</td>\n",
       "      <td>5.96%</td>\n",
       "      <td>16.90%</td>\n",
       "      <td>31.56%</td>\n",
       "      <td>70.01%</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>(0.17, 0.2]</td>\n",
       "      <td>2296</td>\n",
       "      <td>490</td>\n",
       "      <td>1806</td>\n",
       "      <td>10.20%</td>\n",
       "      <td>9.93%</td>\n",
       "      <td>10.28%</td>\n",
       "      <td>21.34%</td>\n",
       "      <td>41.49%</td>\n",
       "      <td>80.29%</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>(0.2, 0.31]</td>\n",
       "      <td>1894</td>\n",
       "      <td>488</td>\n",
       "      <td>1406</td>\n",
       "      <td>8.42%</td>\n",
       "      <td>9.89%</td>\n",
       "      <td>8.01%</td>\n",
       "      <td>25.77%</td>\n",
       "      <td>51.38%</td>\n",
       "      <td>88.30%</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>(0.31, 0.511]</td>\n",
       "      <td>2205</td>\n",
       "      <td>844</td>\n",
       "      <td>1361</td>\n",
       "      <td>9.80%</td>\n",
       "      <td>17.10%</td>\n",
       "      <td>7.75%</td>\n",
       "      <td>38.28%</td>\n",
       "      <td>68.48%</td>\n",
       "      <td>96.05%</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>(0.511, 0.82]</td>\n",
       "      <td>2250</td>\n",
       "      <td>1556</td>\n",
       "      <td>694</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>31.52%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.          Proba  #Total  #Bad  #Good  %Total    %Bad   %Good %BadRate  \\\n",
       "0    1  (0.039, 0.08]    2834   187   2647  12.60%   3.79%  15.07%    6.60%   \n",
       "1    2    (0.08, 0.1]    2360   206   2154  10.49%   4.17%  12.26%    8.73%   \n",
       "2    3    (0.1, 0.12]    2245   223   2022   9.98%   4.52%  11.51%    9.93%   \n",
       "3    4   (0.12, 0.14]    3076   437   2639  13.67%   8.85%  15.03%   14.21%   \n",
       "4    5   (0.14, 0.15]    2080   292   1788   9.24%   5.92%  10.18%   14.04%   \n",
       "5    6   (0.15, 0.17]    1260   213   1047   5.60%   4.32%   5.96%   16.90%   \n",
       "6    7    (0.17, 0.2]    2296   490   1806  10.20%   9.93%  10.28%   21.34%   \n",
       "7    8    (0.2, 0.31]    1894   488   1406   8.42%   9.89%   8.01%   25.77%   \n",
       "8    9  (0.31, 0.511]    2205   844   1361   9.80%  17.10%   7.75%   38.28%   \n",
       "9   10  (0.511, 0.82]    2250  1556    694  10.00%  31.52%   3.95%   69.16%   \n",
       "\n",
       "   %CumBad %CumGood   KS  \n",
       "0    3.79%   15.07% 0.11  \n",
       "1    7.96%   27.33% 0.19  \n",
       "2   12.48%   38.85% 0.26  \n",
       "3   21.33%   53.87% 0.33  \n",
       "4   27.25%   64.05% 0.37  \n",
       "5   31.56%   70.01% 0.38  \n",
       "6   41.49%   80.29% 0.39  \n",
       "7   51.38%   88.30% 0.37  \n",
       "8   68.48%   96.05% 0.28  \n",
       "9  100.00%  100.00% 0.00  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据训练集预测得分，计算ks，并返回数据\n",
    "ks,ks_data = get_ks(data_train_score,return_data=True)\n",
    "ks_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c74253",
   "metadata": {},
   "source": [
    "## 查看评分卡分数分布 和 提升度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "886f3ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Score Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>%BadRate Random</th>\n",
       "      <th>%CumBad</th>\n",
       "      <th>%CumTotal</th>\n",
       "      <th>Lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 503.9]</td>\n",
       "      <td>2250</td>\n",
       "      <td>1556</td>\n",
       "      <td>694</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>31.52%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(503.9, 567.0]</td>\n",
       "      <td>2276</td>\n",
       "      <td>863</td>\n",
       "      <td>1413</td>\n",
       "      <td>10.12%</td>\n",
       "      <td>17.48%</td>\n",
       "      <td>8.04%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>53.45%</td>\n",
       "      <td>20.12%</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(567.0, 610.0]</td>\n",
       "      <td>2346</td>\n",
       "      <td>600</td>\n",
       "      <td>1746</td>\n",
       "      <td>10.43%</td>\n",
       "      <td>12.16%</td>\n",
       "      <td>9.94%</td>\n",
       "      <td>25.58%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>43.93%</td>\n",
       "      <td>30.54%</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(610.0, 624.0]</td>\n",
       "      <td>2232</td>\n",
       "      <td>433</td>\n",
       "      <td>1799</td>\n",
       "      <td>9.92%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>10.24%</td>\n",
       "      <td>19.40%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>40.46%</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(624.0, 635.0]</td>\n",
       "      <td>2615</td>\n",
       "      <td>395</td>\n",
       "      <td>2220</td>\n",
       "      <td>11.62%</td>\n",
       "      <td>8.00%</td>\n",
       "      <td>12.64%</td>\n",
       "      <td>15.11%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>32.83%</td>\n",
       "      <td>52.08%</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>(635.0, 642.0]</td>\n",
       "      <td>1823</td>\n",
       "      <td>267</td>\n",
       "      <td>1556</td>\n",
       "      <td>8.10%</td>\n",
       "      <td>5.41%</td>\n",
       "      <td>8.86%</td>\n",
       "      <td>14.65%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>30.38%</td>\n",
       "      <td>60.19%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>(642.0, 653.0]</td>\n",
       "      <td>2216</td>\n",
       "      <td>292</td>\n",
       "      <td>1924</td>\n",
       "      <td>9.85%</td>\n",
       "      <td>5.92%</td>\n",
       "      <td>10.95%</td>\n",
       "      <td>13.18%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>27.96%</td>\n",
       "      <td>70.04%</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>(653.0, 666.0]</td>\n",
       "      <td>2364</td>\n",
       "      <td>210</td>\n",
       "      <td>2154</td>\n",
       "      <td>10.51%</td>\n",
       "      <td>4.25%</td>\n",
       "      <td>12.26%</td>\n",
       "      <td>8.88%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>25.47%</td>\n",
       "      <td>80.54%</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>(666.0, 686.0]</td>\n",
       "      <td>2748</td>\n",
       "      <td>231</td>\n",
       "      <td>2517</td>\n",
       "      <td>12.21%</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>14.33%</td>\n",
       "      <td>8.41%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>23.22%</td>\n",
       "      <td>92.76%</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>(686.0, inf]</td>\n",
       "      <td>1630</td>\n",
       "      <td>89</td>\n",
       "      <td>1541</td>\n",
       "      <td>7.24%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.     Score Range  #Total  #Bad  #Good  %Total    %Bad   %Good %BadRate  \\\n",
       "0    1   (-inf, 503.9]    2250  1556    694  10.00%  31.52%   3.95%   69.16%   \n",
       "1    2  (503.9, 567.0]    2276   863   1413  10.12%  17.48%   8.04%   37.92%   \n",
       "2    3  (567.0, 610.0]    2346   600   1746  10.43%  12.16%   9.94%   25.58%   \n",
       "3    4  (610.0, 624.0]    2232   433   1799   9.92%   8.77%  10.24%   19.40%   \n",
       "4    5  (624.0, 635.0]    2615   395   2220  11.62%   8.00%  12.64%   15.11%   \n",
       "5    6  (635.0, 642.0]    1823   267   1556   8.10%   5.41%   8.86%   14.65%   \n",
       "6    7  (642.0, 653.0]    2216   292   1924   9.85%   5.92%  10.95%   13.18%   \n",
       "7    8  (653.0, 666.0]    2364   210   2154  10.51%   4.25%  12.26%    8.88%   \n",
       "8    9  (666.0, 686.0]    2748   231   2517  12.21%   4.68%  14.33%    8.41%   \n",
       "9   10    (686.0, inf]    1630    89   1541   7.24%   1.80%   8.77%    5.46%   \n",
       "\n",
       "  %BadRate Random %CumBad %CumTotal  Lift  \n",
       "0          21.94%  69.16%    10.00%  6.92  \n",
       "1          21.94%  53.45%    20.12%  2.66  \n",
       "2          21.94%  43.93%    30.54%  1.44  \n",
       "3          21.94%  37.92%    40.46%  0.94  \n",
       "4          21.94%  32.83%    52.08%  0.63  \n",
       "5          21.94%  30.38%    60.19%  0.50  \n",
       "6          21.94%  27.96%    70.04%  0.40  \n",
       "7          21.94%  25.47%    80.54%  0.32  \n",
       "8          21.94%  23.22%    92.76%  0.25  \n",
       "9          21.94%  21.94%   100.00%  0.22  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度\n",
    "score_dist(data_train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "41f87167",
   "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>No.</th>\n",
       "      <th>Score Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>%BadRate Random</th>\n",
       "      <th>%CumBad</th>\n",
       "      <th>%CumTotal</th>\n",
       "      <th>Lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 567.0]</td>\n",
       "      <td>4526</td>\n",
       "      <td>2419</td>\n",
       "      <td>2107</td>\n",
       "      <td>20.12%</td>\n",
       "      <td>49.01%</td>\n",
       "      <td>12.00%</td>\n",
       "      <td>53.45%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>53.45%</td>\n",
       "      <td>20.12%</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(567.0, 624.0]</td>\n",
       "      <td>4578</td>\n",
       "      <td>1033</td>\n",
       "      <td>3545</td>\n",
       "      <td>20.35%</td>\n",
       "      <td>20.93%</td>\n",
       "      <td>20.18%</td>\n",
       "      <td>22.56%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>40.46%</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(624.0, 642.0]</td>\n",
       "      <td>4438</td>\n",
       "      <td>662</td>\n",
       "      <td>3776</td>\n",
       "      <td>19.72%</td>\n",
       "      <td>13.41%</td>\n",
       "      <td>21.50%</td>\n",
       "      <td>14.92%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>30.38%</td>\n",
       "      <td>60.19%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(642.0, 666.0]</td>\n",
       "      <td>4580</td>\n",
       "      <td>502</td>\n",
       "      <td>4078</td>\n",
       "      <td>20.36%</td>\n",
       "      <td>10.17%</td>\n",
       "      <td>23.22%</td>\n",
       "      <td>10.96%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>25.47%</td>\n",
       "      <td>80.54%</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(666.0, inf]</td>\n",
       "      <td>4378</td>\n",
       "      <td>320</td>\n",
       "      <td>4058</td>\n",
       "      <td>19.46%</td>\n",
       "      <td>6.48%</td>\n",
       "      <td>23.10%</td>\n",
       "      <td>7.31%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.     Score Range  #Total  #Bad  #Good  %Total    %Bad   %Good %BadRate  \\\n",
       "0    1   (-inf, 567.0]    4526  2419   2107  20.12%  49.01%  12.00%   53.45%   \n",
       "1    2  (567.0, 624.0]    4578  1033   3545  20.35%  20.93%  20.18%   22.56%   \n",
       "2    3  (624.0, 642.0]    4438   662   3776  19.72%  13.41%  21.50%   14.92%   \n",
       "3    4  (642.0, 666.0]    4580   502   4078  20.36%  10.17%  23.22%   10.96%   \n",
       "4    5    (666.0, inf]    4378   320   4058  19.46%   6.48%  23.10%    7.31%   \n",
       "\n",
       "  %BadRate Random %CumBad %CumTotal  Lift  \n",
       "0          21.94%  53.45%    20.12%  2.66  \n",
       "1          21.94%  37.92%    40.46%  0.94  \n",
       "2          21.94%  30.38%    60.19%  0.50  \n",
       "3          21.94%  25.47%    80.54%  0.32  \n",
       "4          21.94%  21.94%   100.00%  0.22  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度，分为5组，查看提升度\n",
    "score_dist(data_train_score,qcut=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9a0dabbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style type=\"text/css\">\n",
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       "  background: linear-gradient(90deg, #007bff 18.8%, transparent 18.8%);\n",
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       "</style>\n",
       "<table id=\"T_00828\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_00828_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_00828_level0_col1\" class=\"col_heading level0 col1\" >Score Range</th>\n",
       "      <th id=\"T_00828_level0_col2\" class=\"col_heading level0 col2\" >#Total</th>\n",
       "      <th id=\"T_00828_level0_col3\" class=\"col_heading level0 col3\" >#Bad</th>\n",
       "      <th id=\"T_00828_level0_col4\" class=\"col_heading level0 col4\" >#Good</th>\n",
       "      <th id=\"T_00828_level0_col5\" class=\"col_heading level0 col5\" >%Total</th>\n",
       "      <th id=\"T_00828_level0_col6\" class=\"col_heading level0 col6\" >%Bad</th>\n",
       "      <th id=\"T_00828_level0_col7\" class=\"col_heading level0 col7\" >%Good</th>\n",
       "      <th id=\"T_00828_level0_col8\" class=\"col_heading level0 col8\" >%BadRate</th>\n",
       "      <th id=\"T_00828_level0_col9\" class=\"col_heading level0 col9\" >%BadRate Random</th>\n",
       "      <th id=\"T_00828_level0_col10\" class=\"col_heading level0 col10\" >%CumBad</th>\n",
       "      <th id=\"T_00828_level0_col11\" class=\"col_heading level0 col11\" >%CumTotal</th>\n",
       "      <th id=\"T_00828_level0_col12\" class=\"col_heading level0 col12\" >Lift</th>\n",
       "      <th id=\"T_00828_level0_col13\" class=\"col_heading level0 col13\" >Lift.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_00828_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_00828_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_00828_row0_col1\" class=\"data row0 col1\" >(-inf, 567.0]</td>\n",
       "      <td id=\"T_00828_row0_col2\" class=\"data row0 col2\" >4526</td>\n",
       "      <td id=\"T_00828_row0_col3\" class=\"data row0 col3\" >2419</td>\n",
       "      <td id=\"T_00828_row0_col4\" class=\"data row0 col4\" >2107</td>\n",
       "      <td id=\"T_00828_row0_col5\" class=\"data row0 col5\" >20.12%</td>\n",
       "      <td id=\"T_00828_row0_col6\" class=\"data row0 col6\" >49.01%</td>\n",
       "      <td id=\"T_00828_row0_col7\" class=\"data row0 col7\" >12.00%</td>\n",
       "      <td id=\"T_00828_row0_col8\" class=\"data row0 col8\" >53.45%</td>\n",
       "      <td id=\"T_00828_row0_col9\" class=\"data row0 col9\" >21.94%</td>\n",
       "      <td id=\"T_00828_row0_col10\" class=\"data row0 col10\" >53.45%</td>\n",
       "      <td id=\"T_00828_row0_col11\" class=\"data row0 col11\" >20.12%</td>\n",
       "      <td id=\"T_00828_row0_col12\" class=\"data row0 col12\" >2.660000</td>\n",
       "      <td id=\"T_00828_row0_col13\" class=\"data row0 col13\" >2.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00828_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_00828_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_00828_row1_col1\" class=\"data row1 col1\" >(567.0, 624.0]</td>\n",
       "      <td id=\"T_00828_row1_col2\" class=\"data row1 col2\" >4578</td>\n",
       "      <td id=\"T_00828_row1_col3\" class=\"data row1 col3\" >1033</td>\n",
       "      <td id=\"T_00828_row1_col4\" class=\"data row1 col4\" >3545</td>\n",
       "      <td id=\"T_00828_row1_col5\" class=\"data row1 col5\" >20.35%</td>\n",
       "      <td id=\"T_00828_row1_col6\" class=\"data row1 col6\" >20.93%</td>\n",
       "      <td id=\"T_00828_row1_col7\" class=\"data row1 col7\" >20.18%</td>\n",
       "      <td id=\"T_00828_row1_col8\" class=\"data row1 col8\" >22.56%</td>\n",
       "      <td id=\"T_00828_row1_col9\" class=\"data row1 col9\" >21.94%</td>\n",
       "      <td id=\"T_00828_row1_col10\" class=\"data row1 col10\" >37.92%</td>\n",
       "      <td id=\"T_00828_row1_col11\" class=\"data row1 col11\" >40.46%</td>\n",
       "      <td id=\"T_00828_row1_col12\" class=\"data row1 col12\" >0.940000</td>\n",
       "      <td id=\"T_00828_row1_col13\" class=\"data row1 col13\" >0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00828_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_00828_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_00828_row2_col1\" class=\"data row2 col1\" >(624.0, 642.0]</td>\n",
       "      <td id=\"T_00828_row2_col2\" class=\"data row2 col2\" >4438</td>\n",
       "      <td id=\"T_00828_row2_col3\" class=\"data row2 col3\" >662</td>\n",
       "      <td id=\"T_00828_row2_col4\" class=\"data row2 col4\" >3776</td>\n",
       "      <td id=\"T_00828_row2_col5\" class=\"data row2 col5\" >19.72%</td>\n",
       "      <td id=\"T_00828_row2_col6\" class=\"data row2 col6\" >13.41%</td>\n",
       "      <td id=\"T_00828_row2_col7\" class=\"data row2 col7\" >21.50%</td>\n",
       "      <td id=\"T_00828_row2_col8\" class=\"data row2 col8\" >14.92%</td>\n",
       "      <td id=\"T_00828_row2_col9\" class=\"data row2 col9\" >21.94%</td>\n",
       "      <td id=\"T_00828_row2_col10\" class=\"data row2 col10\" >30.38%</td>\n",
       "      <td id=\"T_00828_row2_col11\" class=\"data row2 col11\" >60.19%</td>\n",
       "      <td id=\"T_00828_row2_col12\" class=\"data row2 col12\" >0.500000</td>\n",
       "      <td id=\"T_00828_row2_col13\" class=\"data row2 col13\" >0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00828_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_00828_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_00828_row3_col1\" class=\"data row3 col1\" >(642.0, 666.0]</td>\n",
       "      <td id=\"T_00828_row3_col2\" class=\"data row3 col2\" >4580</td>\n",
       "      <td id=\"T_00828_row3_col3\" class=\"data row3 col3\" >502</td>\n",
       "      <td id=\"T_00828_row3_col4\" class=\"data row3 col4\" >4078</td>\n",
       "      <td id=\"T_00828_row3_col5\" class=\"data row3 col5\" >20.36%</td>\n",
       "      <td id=\"T_00828_row3_col6\" class=\"data row3 col6\" >10.17%</td>\n",
       "      <td id=\"T_00828_row3_col7\" class=\"data row3 col7\" >23.22%</td>\n",
       "      <td id=\"T_00828_row3_col8\" class=\"data row3 col8\" >10.96%</td>\n",
       "      <td id=\"T_00828_row3_col9\" class=\"data row3 col9\" >21.94%</td>\n",
       "      <td id=\"T_00828_row3_col10\" class=\"data row3 col10\" >25.47%</td>\n",
       "      <td id=\"T_00828_row3_col11\" class=\"data row3 col11\" >80.54%</td>\n",
       "      <td id=\"T_00828_row3_col12\" class=\"data row3 col12\" >0.320000</td>\n",
       "      <td id=\"T_00828_row3_col13\" class=\"data row3 col13\" >0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_00828_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_00828_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_00828_row4_col1\" class=\"data row4 col1\" >(666.0, inf]</td>\n",
       "      <td id=\"T_00828_row4_col2\" class=\"data row4 col2\" >4378</td>\n",
       "      <td id=\"T_00828_row4_col3\" class=\"data row4 col3\" >320</td>\n",
       "      <td id=\"T_00828_row4_col4\" class=\"data row4 col4\" >4058</td>\n",
       "      <td id=\"T_00828_row4_col5\" class=\"data row4 col5\" >19.46%</td>\n",
       "      <td id=\"T_00828_row4_col6\" class=\"data row4 col6\" >6.48%</td>\n",
       "      <td id=\"T_00828_row4_col7\" class=\"data row4 col7\" >23.10%</td>\n",
       "      <td id=\"T_00828_row4_col8\" class=\"data row4 col8\" >7.31%</td>\n",
       "      <td id=\"T_00828_row4_col9\" class=\"data row4 col9\" >21.94%</td>\n",
       "      <td id=\"T_00828_row4_col10\" class=\"data row4 col10\" >21.94%</td>\n",
       "      <td id=\"T_00828_row4_col11\" class=\"data row4 col11\" >100.00%</td>\n",
       "      <td id=\"T_00828_row4_col12\" class=\"data row4 col12\" >0.220000</td>\n",
       "      <td id=\"T_00828_row4_col13\" class=\"data row4 col13\" >0.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171def2ff10>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度，分为5组，查看提升度\n",
    "view_score_dist(data_train_score,qcut=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "af017bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style type=\"text/css\">\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f5904_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_f5904_level0_col1\" class=\"col_heading level0 col1\" >Score Range</th>\n",
       "      <th id=\"T_f5904_level0_col2\" class=\"col_heading level0 col2\" >#Total</th>\n",
       "      <th id=\"T_f5904_level0_col3\" class=\"col_heading level0 col3\" >#Bad</th>\n",
       "      <th id=\"T_f5904_level0_col4\" class=\"col_heading level0 col4\" >#Good</th>\n",
       "      <th id=\"T_f5904_level0_col5\" class=\"col_heading level0 col5\" >%Total</th>\n",
       "      <th id=\"T_f5904_level0_col6\" class=\"col_heading level0 col6\" >%Bad</th>\n",
       "      <th id=\"T_f5904_level0_col7\" class=\"col_heading level0 col7\" >%Good</th>\n",
       "      <th id=\"T_f5904_level0_col8\" class=\"col_heading level0 col8\" >%BadRate</th>\n",
       "      <th id=\"T_f5904_level0_col9\" class=\"col_heading level0 col9\" >%BadRate Random</th>\n",
       "      <th id=\"T_f5904_level0_col10\" class=\"col_heading level0 col10\" >%CumBad</th>\n",
       "      <th id=\"T_f5904_level0_col11\" class=\"col_heading level0 col11\" >%CumTotal</th>\n",
       "      <th id=\"T_f5904_level0_col12\" class=\"col_heading level0 col12\" >Lift</th>\n",
       "      <th id=\"T_f5904_level0_col13\" class=\"col_heading level0 col13\" >Lift.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f5904_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f5904_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_f5904_row0_col1\" class=\"data row0 col1\" >(-inf, 567.0]</td>\n",
       "      <td id=\"T_f5904_row0_col2\" class=\"data row0 col2\" >4526</td>\n",
       "      <td id=\"T_f5904_row0_col3\" class=\"data row0 col3\" >2419</td>\n",
       "      <td id=\"T_f5904_row0_col4\" class=\"data row0 col4\" >2107</td>\n",
       "      <td id=\"T_f5904_row0_col5\" class=\"data row0 col5\" >20.12%</td>\n",
       "      <td id=\"T_f5904_row0_col6\" class=\"data row0 col6\" >49.01%</td>\n",
       "      <td id=\"T_f5904_row0_col7\" class=\"data row0 col7\" >12.00%</td>\n",
       "      <td id=\"T_f5904_row0_col8\" class=\"data row0 col8\" >53.45%</td>\n",
       "      <td id=\"T_f5904_row0_col9\" class=\"data row0 col9\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row0_col10\" class=\"data row0 col10\" >53.45%</td>\n",
       "      <td id=\"T_f5904_row0_col11\" class=\"data row0 col11\" >20.12%</td>\n",
       "      <td id=\"T_f5904_row0_col12\" class=\"data row0 col12\" >2.660000</td>\n",
       "      <td id=\"T_f5904_row0_col13\" class=\"data row0 col13\" >2.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5904_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f5904_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_f5904_row1_col1\" class=\"data row1 col1\" >(567.0, 624.0]</td>\n",
       "      <td id=\"T_f5904_row1_col2\" class=\"data row1 col2\" >4578</td>\n",
       "      <td id=\"T_f5904_row1_col3\" class=\"data row1 col3\" >1033</td>\n",
       "      <td id=\"T_f5904_row1_col4\" class=\"data row1 col4\" >3545</td>\n",
       "      <td id=\"T_f5904_row1_col5\" class=\"data row1 col5\" >20.35%</td>\n",
       "      <td id=\"T_f5904_row1_col6\" class=\"data row1 col6\" >20.93%</td>\n",
       "      <td id=\"T_f5904_row1_col7\" class=\"data row1 col7\" >20.18%</td>\n",
       "      <td id=\"T_f5904_row1_col8\" class=\"data row1 col8\" >22.56%</td>\n",
       "      <td id=\"T_f5904_row1_col9\" class=\"data row1 col9\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row1_col10\" class=\"data row1 col10\" >37.92%</td>\n",
       "      <td id=\"T_f5904_row1_col11\" class=\"data row1 col11\" >40.46%</td>\n",
       "      <td id=\"T_f5904_row1_col12\" class=\"data row1 col12\" >0.940000</td>\n",
       "      <td id=\"T_f5904_row1_col13\" class=\"data row1 col13\" >0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5904_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f5904_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_f5904_row2_col1\" class=\"data row2 col1\" >(624.0, 642.0]</td>\n",
       "      <td id=\"T_f5904_row2_col2\" class=\"data row2 col2\" >4438</td>\n",
       "      <td id=\"T_f5904_row2_col3\" class=\"data row2 col3\" >662</td>\n",
       "      <td id=\"T_f5904_row2_col4\" class=\"data row2 col4\" >3776</td>\n",
       "      <td id=\"T_f5904_row2_col5\" class=\"data row2 col5\" >19.72%</td>\n",
       "      <td id=\"T_f5904_row2_col6\" class=\"data row2 col6\" >13.41%</td>\n",
       "      <td id=\"T_f5904_row2_col7\" class=\"data row2 col7\" >21.50%</td>\n",
       "      <td id=\"T_f5904_row2_col8\" class=\"data row2 col8\" >14.92%</td>\n",
       "      <td id=\"T_f5904_row2_col9\" class=\"data row2 col9\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row2_col10\" class=\"data row2 col10\" >30.38%</td>\n",
       "      <td id=\"T_f5904_row2_col11\" class=\"data row2 col11\" >60.19%</td>\n",
       "      <td id=\"T_f5904_row2_col12\" class=\"data row2 col12\" >0.500000</td>\n",
       "      <td id=\"T_f5904_row2_col13\" class=\"data row2 col13\" >0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5904_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f5904_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_f5904_row3_col1\" class=\"data row3 col1\" >(642.0, 666.0]</td>\n",
       "      <td id=\"T_f5904_row3_col2\" class=\"data row3 col2\" >4580</td>\n",
       "      <td id=\"T_f5904_row3_col3\" class=\"data row3 col3\" >502</td>\n",
       "      <td id=\"T_f5904_row3_col4\" class=\"data row3 col4\" >4078</td>\n",
       "      <td id=\"T_f5904_row3_col5\" class=\"data row3 col5\" >20.36%</td>\n",
       "      <td id=\"T_f5904_row3_col6\" class=\"data row3 col6\" >10.17%</td>\n",
       "      <td id=\"T_f5904_row3_col7\" class=\"data row3 col7\" >23.22%</td>\n",
       "      <td id=\"T_f5904_row3_col8\" class=\"data row3 col8\" >10.96%</td>\n",
       "      <td id=\"T_f5904_row3_col9\" class=\"data row3 col9\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row3_col10\" class=\"data row3 col10\" >25.47%</td>\n",
       "      <td id=\"T_f5904_row3_col11\" class=\"data row3 col11\" >80.54%</td>\n",
       "      <td id=\"T_f5904_row3_col12\" class=\"data row3 col12\" >0.320000</td>\n",
       "      <td id=\"T_f5904_row3_col13\" class=\"data row3 col13\" >0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f5904_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f5904_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_f5904_row4_col1\" class=\"data row4 col1\" >(666.0, inf]</td>\n",
       "      <td id=\"T_f5904_row4_col2\" class=\"data row4 col2\" >4378</td>\n",
       "      <td id=\"T_f5904_row4_col3\" class=\"data row4 col3\" >320</td>\n",
       "      <td id=\"T_f5904_row4_col4\" class=\"data row4 col4\" >4058</td>\n",
       "      <td id=\"T_f5904_row4_col5\" class=\"data row4 col5\" >19.46%</td>\n",
       "      <td id=\"T_f5904_row4_col6\" class=\"data row4 col6\" >6.48%</td>\n",
       "      <td id=\"T_f5904_row4_col7\" class=\"data row4 col7\" >23.10%</td>\n",
       "      <td id=\"T_f5904_row4_col8\" class=\"data row4 col8\" >7.31%</td>\n",
       "      <td id=\"T_f5904_row4_col9\" class=\"data row4 col9\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row4_col10\" class=\"data row4 col10\" >21.94%</td>\n",
       "      <td id=\"T_f5904_row4_col11\" class=\"data row4 col11\" >100.00%</td>\n",
       "      <td id=\"T_f5904_row4_col12\" class=\"data row4 col12\" >0.220000</td>\n",
       "      <td id=\"T_f5904_row4_col13\" class=\"data row4 col13\" >0.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171dfb02110>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度，分为5组，查看提升度\n",
    "view_score_dist(data_train_score,qcut=5,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "7bfd8289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_62376_row0_col13 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_62376_row1_col13 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 35.3%, transparent 35.3%);\n",
       "}\n",
       "#T_62376_row2_col13 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 18.8%, transparent 18.8%);\n",
       "}\n",
       "#T_62376_row3_col13 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 12.0%, transparent 12.0%);\n",
       "}\n",
       "#T_62376_row4_col13 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 8.3%, transparent 8.3%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_62376\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_62376_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_62376_level0_col1\" class=\"col_heading level0 col1\" >Score Range</th>\n",
       "      <th id=\"T_62376_level0_col2\" class=\"col_heading level0 col2\" >#Total</th>\n",
       "      <th id=\"T_62376_level0_col3\" class=\"col_heading level0 col3\" >#Bad</th>\n",
       "      <th id=\"T_62376_level0_col4\" class=\"col_heading level0 col4\" >#Good</th>\n",
       "      <th id=\"T_62376_level0_col5\" class=\"col_heading level0 col5\" >%Total</th>\n",
       "      <th id=\"T_62376_level0_col6\" class=\"col_heading level0 col6\" >%Bad</th>\n",
       "      <th id=\"T_62376_level0_col7\" class=\"col_heading level0 col7\" >%Good</th>\n",
       "      <th id=\"T_62376_level0_col8\" class=\"col_heading level0 col8\" >%BadRate</th>\n",
       "      <th id=\"T_62376_level0_col9\" class=\"col_heading level0 col9\" >%BadRate Random</th>\n",
       "      <th id=\"T_62376_level0_col10\" class=\"col_heading level0 col10\" >%CumBad</th>\n",
       "      <th id=\"T_62376_level0_col11\" class=\"col_heading level0 col11\" >%CumTotal</th>\n",
       "      <th id=\"T_62376_level0_col12\" class=\"col_heading level0 col12\" >Lift</th>\n",
       "      <th id=\"T_62376_level0_col13\" class=\"col_heading level0 col13\" >Lift.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_62376_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_62376_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_62376_row0_col1\" class=\"data row0 col1\" >(-inf, 567.0]</td>\n",
       "      <td id=\"T_62376_row0_col2\" class=\"data row0 col2\" >4526</td>\n",
       "      <td id=\"T_62376_row0_col3\" class=\"data row0 col3\" >2419</td>\n",
       "      <td id=\"T_62376_row0_col4\" class=\"data row0 col4\" >2107</td>\n",
       "      <td id=\"T_62376_row0_col5\" class=\"data row0 col5\" >20.12%</td>\n",
       "      <td id=\"T_62376_row0_col6\" class=\"data row0 col6\" >49.01%</td>\n",
       "      <td id=\"T_62376_row0_col7\" class=\"data row0 col7\" >12.00%</td>\n",
       "      <td id=\"T_62376_row0_col8\" class=\"data row0 col8\" >53.45%</td>\n",
       "      <td id=\"T_62376_row0_col9\" class=\"data row0 col9\" >21.94%</td>\n",
       "      <td id=\"T_62376_row0_col10\" class=\"data row0 col10\" >53.45%</td>\n",
       "      <td id=\"T_62376_row0_col11\" class=\"data row0 col11\" >20.12%</td>\n",
       "      <td id=\"T_62376_row0_col12\" class=\"data row0 col12\" >2.660000</td>\n",
       "      <td id=\"T_62376_row0_col13\" class=\"data row0 col13\" >2.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62376_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_62376_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_62376_row1_col1\" class=\"data row1 col1\" >(567.0, 624.0]</td>\n",
       "      <td id=\"T_62376_row1_col2\" class=\"data row1 col2\" >4578</td>\n",
       "      <td id=\"T_62376_row1_col3\" class=\"data row1 col3\" >1033</td>\n",
       "      <td id=\"T_62376_row1_col4\" class=\"data row1 col4\" >3545</td>\n",
       "      <td id=\"T_62376_row1_col5\" class=\"data row1 col5\" >20.35%</td>\n",
       "      <td id=\"T_62376_row1_col6\" class=\"data row1 col6\" >20.93%</td>\n",
       "      <td id=\"T_62376_row1_col7\" class=\"data row1 col7\" >20.18%</td>\n",
       "      <td id=\"T_62376_row1_col8\" class=\"data row1 col8\" >22.56%</td>\n",
       "      <td id=\"T_62376_row1_col9\" class=\"data row1 col9\" >21.94%</td>\n",
       "      <td id=\"T_62376_row1_col10\" class=\"data row1 col10\" >37.92%</td>\n",
       "      <td id=\"T_62376_row1_col11\" class=\"data row1 col11\" >40.46%</td>\n",
       "      <td id=\"T_62376_row1_col12\" class=\"data row1 col12\" >0.940000</td>\n",
       "      <td id=\"T_62376_row1_col13\" class=\"data row1 col13\" >0.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62376_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_62376_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_62376_row2_col1\" class=\"data row2 col1\" >(624.0, 642.0]</td>\n",
       "      <td id=\"T_62376_row2_col2\" class=\"data row2 col2\" >4438</td>\n",
       "      <td id=\"T_62376_row2_col3\" class=\"data row2 col3\" >662</td>\n",
       "      <td id=\"T_62376_row2_col4\" class=\"data row2 col4\" >3776</td>\n",
       "      <td id=\"T_62376_row2_col5\" class=\"data row2 col5\" >19.72%</td>\n",
       "      <td id=\"T_62376_row2_col6\" class=\"data row2 col6\" >13.41%</td>\n",
       "      <td id=\"T_62376_row2_col7\" class=\"data row2 col7\" >21.50%</td>\n",
       "      <td id=\"T_62376_row2_col8\" class=\"data row2 col8\" >14.92%</td>\n",
       "      <td id=\"T_62376_row2_col9\" class=\"data row2 col9\" >21.94%</td>\n",
       "      <td id=\"T_62376_row2_col10\" class=\"data row2 col10\" >30.38%</td>\n",
       "      <td id=\"T_62376_row2_col11\" class=\"data row2 col11\" >60.19%</td>\n",
       "      <td id=\"T_62376_row2_col12\" class=\"data row2 col12\" >0.500000</td>\n",
       "      <td id=\"T_62376_row2_col13\" class=\"data row2 col13\" >0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62376_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_62376_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_62376_row3_col1\" class=\"data row3 col1\" >(642.0, 666.0]</td>\n",
       "      <td id=\"T_62376_row3_col2\" class=\"data row3 col2\" >4580</td>\n",
       "      <td id=\"T_62376_row3_col3\" class=\"data row3 col3\" >502</td>\n",
       "      <td id=\"T_62376_row3_col4\" class=\"data row3 col4\" >4078</td>\n",
       "      <td id=\"T_62376_row3_col5\" class=\"data row3 col5\" >20.36%</td>\n",
       "      <td id=\"T_62376_row3_col6\" class=\"data row3 col6\" >10.17%</td>\n",
       "      <td id=\"T_62376_row3_col7\" class=\"data row3 col7\" >23.22%</td>\n",
       "      <td id=\"T_62376_row3_col8\" class=\"data row3 col8\" >10.96%</td>\n",
       "      <td id=\"T_62376_row3_col9\" class=\"data row3 col9\" >21.94%</td>\n",
       "      <td id=\"T_62376_row3_col10\" class=\"data row3 col10\" >25.47%</td>\n",
       "      <td id=\"T_62376_row3_col11\" class=\"data row3 col11\" >80.54%</td>\n",
       "      <td id=\"T_62376_row3_col12\" class=\"data row3 col12\" >0.320000</td>\n",
       "      <td id=\"T_62376_row3_col13\" class=\"data row3 col13\" >0.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_62376_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_62376_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_62376_row4_col1\" class=\"data row4 col1\" >(666.0, inf]</td>\n",
       "      <td id=\"T_62376_row4_col2\" class=\"data row4 col2\" >4378</td>\n",
       "      <td id=\"T_62376_row4_col3\" class=\"data row4 col3\" >320</td>\n",
       "      <td id=\"T_62376_row4_col4\" class=\"data row4 col4\" >4058</td>\n",
       "      <td id=\"T_62376_row4_col5\" class=\"data row4 col5\" >19.46%</td>\n",
       "      <td id=\"T_62376_row4_col6\" class=\"data row4 col6\" >6.48%</td>\n",
       "      <td id=\"T_62376_row4_col7\" class=\"data row4 col7\" >23.10%</td>\n",
       "      <td id=\"T_62376_row4_col8\" class=\"data row4 col8\" >7.31%</td>\n",
       "      <td id=\"T_62376_row4_col9\" class=\"data row4 col9\" >21.94%</td>\n",
       "      <td id=\"T_62376_row4_col10\" class=\"data row4 col10\" >21.94%</td>\n",
       "      <td id=\"T_62376_row4_col11\" class=\"data row4 col11\" >100.00%</td>\n",
       "      <td id=\"T_62376_row4_col12\" class=\"data row4 col12\" >0.220000</td>\n",
       "      <td id=\"T_62376_row4_col13\" class=\"data row4 col13\" >0.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171e42d0c70>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度，分为5组，查看提升度\n",
    "view_score_dist(data_train_score,qcut=5,color='#02B057')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ebf116bb",
   "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>序号</th>\n",
       "      <th>分数区间</th>\n",
       "      <th>#合计</th>\n",
       "      <th>#坏</th>\n",
       "      <th>#好</th>\n",
       "      <th>%合计</th>\n",
       "      <th>%坏</th>\n",
       "      <th>%好</th>\n",
       "      <th>%坏件率</th>\n",
       "      <th>%随机坏件率</th>\n",
       "      <th>%累计坏</th>\n",
       "      <th>%累计合计</th>\n",
       "      <th>提升度</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 503.9]</td>\n",
       "      <td>2250</td>\n",
       "      <td>1556</td>\n",
       "      <td>694</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>31.52%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(503.9, 567.0]</td>\n",
       "      <td>2276</td>\n",
       "      <td>863</td>\n",
       "      <td>1413</td>\n",
       "      <td>10.12%</td>\n",
       "      <td>17.48%</td>\n",
       "      <td>8.04%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>53.45%</td>\n",
       "      <td>20.12%</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(567.0, 610.0]</td>\n",
       "      <td>2346</td>\n",
       "      <td>600</td>\n",
       "      <td>1746</td>\n",
       "      <td>10.43%</td>\n",
       "      <td>12.16%</td>\n",
       "      <td>9.94%</td>\n",
       "      <td>25.58%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>43.93%</td>\n",
       "      <td>30.54%</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(610.0, 624.0]</td>\n",
       "      <td>2232</td>\n",
       "      <td>433</td>\n",
       "      <td>1799</td>\n",
       "      <td>9.92%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>10.24%</td>\n",
       "      <td>19.40%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>40.46%</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(624.0, 635.0]</td>\n",
       "      <td>2615</td>\n",
       "      <td>395</td>\n",
       "      <td>2220</td>\n",
       "      <td>11.62%</td>\n",
       "      <td>8.00%</td>\n",
       "      <td>12.64%</td>\n",
       "      <td>15.11%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>32.83%</td>\n",
       "      <td>52.08%</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>(635.0, 642.0]</td>\n",
       "      <td>1823</td>\n",
       "      <td>267</td>\n",
       "      <td>1556</td>\n",
       "      <td>8.10%</td>\n",
       "      <td>5.41%</td>\n",
       "      <td>8.86%</td>\n",
       "      <td>14.65%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>30.38%</td>\n",
       "      <td>60.19%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>(642.0, 653.0]</td>\n",
       "      <td>2216</td>\n",
       "      <td>292</td>\n",
       "      <td>1924</td>\n",
       "      <td>9.85%</td>\n",
       "      <td>5.92%</td>\n",
       "      <td>10.95%</td>\n",
       "      <td>13.18%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>27.96%</td>\n",
       "      <td>70.04%</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>(653.0, 666.0]</td>\n",
       "      <td>2364</td>\n",
       "      <td>210</td>\n",
       "      <td>2154</td>\n",
       "      <td>10.51%</td>\n",
       "      <td>4.25%</td>\n",
       "      <td>12.26%</td>\n",
       "      <td>8.88%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>25.47%</td>\n",
       "      <td>80.54%</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>(666.0, 686.0]</td>\n",
       "      <td>2748</td>\n",
       "      <td>231</td>\n",
       "      <td>2517</td>\n",
       "      <td>12.21%</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>14.33%</td>\n",
       "      <td>8.41%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>23.22%</td>\n",
       "      <td>92.76%</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>(686.0, inf]</td>\n",
       "      <td>1630</td>\n",
       "      <td>89</td>\n",
       "      <td>1541</td>\n",
       "      <td>7.24%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   序号            分数区间   #合计    #坏    #好     %合计      %坏      %好    %坏件率  \\\n",
       "0   1   (-inf, 503.9]  2250  1556   694  10.00%  31.52%   3.95%  69.16%   \n",
       "1   2  (503.9, 567.0]  2276   863  1413  10.12%  17.48%   8.04%  37.92%   \n",
       "2   3  (567.0, 610.0]  2346   600  1746  10.43%  12.16%   9.94%  25.58%   \n",
       "3   4  (610.0, 624.0]  2232   433  1799   9.92%   8.77%  10.24%  19.40%   \n",
       "4   5  (624.0, 635.0]  2615   395  2220  11.62%   8.00%  12.64%  15.11%   \n",
       "5   6  (635.0, 642.0]  1823   267  1556   8.10%   5.41%   8.86%  14.65%   \n",
       "6   7  (642.0, 653.0]  2216   292  1924   9.85%   5.92%  10.95%  13.18%   \n",
       "7   8  (653.0, 666.0]  2364   210  2154  10.51%   4.25%  12.26%   8.88%   \n",
       "8   9  (666.0, 686.0]  2748   231  2517  12.21%   4.68%  14.33%   8.41%   \n",
       "9  10    (686.0, inf]  1630    89  1541   7.24%   1.80%   8.77%   5.46%   \n",
       "\n",
       "   %随机坏件率    %累计坏    %累计合计  提升度  \n",
       "0  21.94%  69.16%   10.00% 6.92  \n",
       "1  21.94%  53.45%   20.12% 2.66  \n",
       "2  21.94%  43.93%   30.54% 1.44  \n",
       "3  21.94%  37.92%   40.46% 0.94  \n",
       "4  21.94%  32.83%   52.08% 0.63  \n",
       "5  21.94%  30.38%   60.19% 0.50  \n",
       "6  21.94%  27.96%   70.04% 0.40  \n",
       "7  21.94%  25.47%   80.54% 0.32  \n",
       "8  21.94%  23.22%   92.76% 0.25  \n",
       "9  21.94%  21.94%  100.00% 0.22  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看训练集评分卡分数分布 和 提升度,  标题显示为中文\n",
    "score_dist(data_train_score,language='cn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "01dc469c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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bjf/97381nmP//v1G586djYkTJ56xvtb82nrhhReMtWvXGgcOHDBWr15tjBs3zhg3bly1uiUZS5curfYeUVFRUefzeuu98EzjtW/fPuPtt982tm7dahw4cMD44osvjKioqGp1vfPOO0ZwcHC1c83IyKi3ntYcrxMefPBBz9fODz/84GlvzGvyhLY6XidUVFQYI0eONKZPn17j/f/66683Xn75ZeOHH34wduzYYdxyyy2Gw+EwDh06VGc9rTlecXFxxty5c6u9noqKijzbG/KaPF1bHa+GvFd8++23xn//+18jOTnZ8/o80+e+NcfrP//5jxEaGmq88sorxq5du4ykpCTjk08+adTnoTZ333230bVrV2PZsmXGpk2bjLFjxxrjx4+v0e/xxx83hgwZ0uBzOpuvrQceeMDo16+f8cUXXxgpKSnGpk2bjG+++aZanzP97Hi6pv4sdrqcnBwjLi7OuOWWW4z169cbKSkpxuLFi429e/d6+tx5551Gr169jG+//dbYv3+/8dprrxkWi6XaeTbmPfWEjz/+2PD19TXefvttIykpybjzzjuNkJAQz885J3z77bcN+tmlqZo6tikpKYbNZjPmzJlj7N2719i8ebMxadIkY9iwYdWO462xbYoRI0YYjz32WI32d955p9bfCQA0DaEtPD799FMjMjLyjP1OfyM+8YPMv/71LyMuLs4IDg42rrnmGqOgoMAwjKpf1CV5bnFxcQ2u6Uw/1H711VeG2Wyu9ovbK6+8YgQHBxvl5eV17vfTn/7UuPHGG+vcft111xk/+9nPqrX97W9/M7p06WK43e5G1dgUdY2FJOPzzz+vc7+HH37YSEhIqNZ2zTXXGNOmTav3+UJDQ40333yzzu0xMTHGP/7xj2ptV155pXHDDTfUe9zTnelzlZOTY/j5+RlLly5t8DG3bt1qSDJWrFhRZ5/GvE6aMp5NHa+vv/7acDgcRnZ2doOf68UXXzSCgoKq/XJ5uqa+Dk43ZswY49FHH21Q37p+QGvK12hycrIhydi4caOn7euvvzZMJpNx+PDhan0bGwTWNlaNfd3V9ZxN+bz/85//NEJDQ6t9Lh555BGjX79+Darl1Oep7z2tNv/+978NX19fw+l01qjp/PPPN5YtW3bGX3xacqwMo3nG64RLL73UuPXWW6u1VVZWGuPHjzfefPPNBtXnja+tE7744gvDZDJ5gpYTQcypweCZeOO9sKnj9atf/cqYMGGC53FTfgls7fH66quvjP79+xtJSUkNGpvaXpOnH68tj9fDDz9s3HjjjQ0am8rKSiMoKMh499136z1ea41XXFyc8eKLLzbquKe/Jk/XVserKe8VhmEYw4YNq/fz2Frj5XQ6jc6dO9f7c2pT3mfy8vIMq9VqfPrpp562HTt2GJJqBHKNDW2bOlbJycmGj4+PsXPnzjr7NOVnx6b+vnS6Rx55pN6vAcMwjISEBGPu3LnV2oYPH278/ve/9zxuyvfA0aNHG/fdd5/nscvlMmJjY41nnnmmWr+WDm2bOraffvqp4ePjY7hcLk/bggULqn1f9+bYnv6eFBcXZzz99NPGrbfeagQGBhpdu3atNqHg1N/tJRmPP/64ZxuhLdC8WB4BHitXrtSIESOatO++ffs0f/58LVy4UAsXLtT333/v+Tezv/71r5o7d666dOmi9PR0bdy4sVHH/uCDDxQREaGBAwdqzpw5Kikp8Wxbu3atBg0apOjoaE/btGnTVFBQoKSkpFqP98MPP2jNmjU6//zz63zO8vLyGv926efnp0OHDik1NbVR9TdFfWNx3333KSIiQqNHj9bbb78twzA829auXaspU6ZU6z9t2jStXbu21mO5XC59/PHHKi4urvdf9er6fKxataqhp+RR33guWbJEbrdbhw8f1oABA9SlSxf9/Oc/18GDB+s83ptvvqm+fftq4sSJdfZpyuukMZo6XgsWLNDIkSP17LPPqnPnzurbt68eeuihev897K233tK1115b73IQjX0d1CYrK0vr169XVFSUxo8fr+joaJ1//vmNHvOmfO7Xrl2rkJAQjRw50tM2ZcoUmc1mrV+/vlHPf7raxqopr7u66m7s533t2rWaNGmSfH19q+2za9cu5ebmNuh53W63vvzyS/Xt21fTpk1TVFSUxowZo/nz59e7X35+voKDg+Xj4+NpS05O1ty5c/Wvf/1LZvOZf0RoybGSmne88vPzFRYWVq1t7ty5ioqK0u23396gerz1tZWTk6MPPvhA48ePl9VqrbbtiiuuUFRUlCZMmKAFCxacsf7Wfi9synjt3btXixYtqvF9uqioSHFxceratatmzJhxxppbc7wyMzN155136r333pO/v3+Djl3ba/L0+tvqeC1fvlyffvqpXn755QY9V0lJiZxO5xnPtzW/vv785z8rPDxcw4YN03PPPVfvvxPX9Zo8vf62Ol5Sw98rDMPQsmXLtGvXLk2aNKnOfq01Xlu2bNHhw4dlNps1bNgwxcTEaPr06dq+fXuTPg8nbN68WU6ns9o59O/fX926dWvUOdSmqWP1v//9Tz179tTChQvVo0cPde/eXXfccYdycnI8fZrys2NzvTZPPPfVV1+tqKgoDRs2TG+88Ua1PuPHj9eCBQt0+PBhGYahb7/9Vrt379bUqVMlNe17YEVFhTZv3lxtrMxms6ZMmXLWY9VYTR3bESNGyGw265133pHL5VJ+fr7ee+89TZkyxfN93ZtjW5u//OUvGjlypH744Qfde++9uueee7Rr1y5JVcsgJCQk6Ne//rXS09P10EMPndVzAagboS08UlNTFRsb26R93W635s2bp4EDB2rixIm66aabPGvbOBwOBQUFyWKxqFOnToqMjGzwca+//nq9//77+vbbbzVnzhy99957uvHGGz3bMzIyqn2TkuR5nJGRUa29S5custlsGjlypO677z7dcccddT7vtGnT9N///lfLli2T2+3W7t279Ze//EVS1TepllbXWMydO1f//ve/tWTJEl111VW699579fe//92zva7PR0FBQbVv+ImJiQoMDJTNZtPdd9+tzz//XPHx8XXWM23atP/f3p0HRXHlcQD/IuMM4ohHUEFQFFECeIFGFu8jRo3XGq1VNqVoFDHGMhuN8TYeJeK9ETUmXmjhiuK9xgML8Y5mJYyiYkBALa+NR0mAELl++4dFL8MMMAx3/H6qrHJ63nS/19/XPT2vm26sXbsWCQkJyM3NxenTp3Hw4MESr4vi8kxKSkJubi4CAwPxz3/+E/v378fLly/Rr18/ZGZmGszvjz/+wO7du4sdcClJPzGHuXklJSXh4sWLuHnzJg4dOqS0ecqUKUaX89NPP+HmzZtF9l3A9H5QlKSkJABv7iHs7++PkydPwsvLC3379kVCQoJJ8yiqLnnvFfaZRo0a6U1TqVRo0KBBqfMyllVJ+11hzFnvZdE3f/31V6SlpSEoKAgDBgxAREQEhg8fjo8++gjnzp0z+pnnz59j6dKlmDRpkjLt9evX8PX1xapVq9CsWTOTll2eWQFll9e+ffvwn//8B+PHj1emXbx4Edu2bTP4wVmUit62Zs2ahdq1a+Odd97BgwcPcOTIEeU9rVaLNWvWIDw8HD/88AO6deuGv/71r0UOxlTGvrAkeXXp0gVWVlZo1aoVunfvjiVLlijvubq6Yvv27Thy5AhCQ0ORm5uLLl264OHDh4XWp6LyEhGMGzcOkydP1juBURRjfdLU+ue9V1rm5vXixQuMGzcOISEhsLGxMWlZs2bNQpMmTQwG+fKryO1r2rRpCAsLQ1RUFAICAhAYGIivvvrKYH5F9UlT65/3XmmZm5ep+4qUlBRotVqo1WoMGjQIwcHB6NevX4nbW9Z55S8zf/58HDt2DPXr10evXr2UwUxzvheePn0KtVptcD/mxo0bV9qxRlJSEu7fv4/w8HDs2rULISEhiI6OxsiRI/XmU5Jjx7y2lkXfTEpKwrfffotWrVrh1KlT+PTTTzFt2jTs3LlTKRMcHAx3d3c4OjpCrVZjwIAB2Lhxo3ICwJzjy+fPnyMnJ8doG8pi2yoJc7Nt0aIFIiIiMHfuXGg0GtSrVw8PHz7Evn379OZTWdka8+GHH2LKlClwcXHBrFmzYGtri6ioKACAnZ0dVCoVtFot7OzsoNVqS7UsIiqcqvgi9LbIyMgwuJrSw8NDubK0e/fuhT6wqnnz5qhTp47y2t7eHr/++mup65R/QKFt27awt7dH3759kZiYiJYtW5ZoXhcuXEBaWhquXLmC2bNnw8XFBb6+vkbL+vv7IzExEYMHD0ZWVhZsbGzw+eefY9GiRSZdfVZaxrIAgAULFij/9/T0RHp6OlatWoVp06aVaP6urq7Q6XRISUnB/v374efnh3PnzhU6cPvNN9/A398f7777LiwsLNCyZUuMHz++2BvjF1Rcnrm5ucjKysL69euVM/J79uyBnZ0doqKi0L9/f735HTp0CKmpqfDz8ytRPcqauXnl5ubCwsICu3fvRt26dQEAa9euxciRI7Fp0ybUqlVLb37btm1D27Zt0blz53JsDZS6AUBAQIAyqODp6YnIyEhs374dy5cvL/c6lAdjWZW031U1eVkNGzYMX3zxBQCgQ4cOuHz5MjZv3mxwZdhvv/2GQYMGwd3dHYsWLVKmz5kzB25ubnonUipbWeQVFRWF8ePHY8uWLfDw8AAApKamYsyYMdiyZQtsbW0rpjH56g+Ytm3NnDkTEyZMwP3797F48WKMHTsWx44dg4WFBWxtbTF9+nSl7HvvvYfHjx9j1apVGDp0aIW2KU9p89q7dy9SU1Nx/fp1zJw5E6tXr1YG0nx8fPT+IqRLly5wc3PDd999V+KHp5SEKXkFBwcjNTUVc+bMMWmexvpkZTA3L39/f/z9738v8irM/IKCghAWFoazZ8+a9PC40jB1+8q/7bRr1w5qtRoBAQFYvnw5NBqN8l5RfbKimZuXqfuKOnXqQKfTIS0tDZGRkZg+fTqcnZ3Rq1evcmuTKXnllZk3bx5GjBgBANixYwccHR0RHh6OgICAKvc9bm5Wubm5eP36NXbt2oXWrVsDeHPs17FjR/zyyy9wdXUt8bFjWcrNzUWnTp0QGBgI4E1WN2/exObNm5Vj8eDgYFy5cgVHjx6Fk5MTzp8/j88++0w5aVPdjy/Nzfbp06fw9/eHn58ffH19kZqaioULF2LkyJE4ffo0LCwsKjVbY9q1a6f838LCAnZ2dmXy+56ISoZX2pLC1tbW4M9xjx8/Dp1OB51Oh61btxb62YJ/rpn3xVPWvL29Abz5MzXgzVm+/E95BaC8trOz05veokULtG3bFv7+/vjiiy/0BisKsrCwwIoVK5CWlob79+/j6dOnykCZs7NzWTWnUMayMMbb2xsPHz7E69evARS+PmxsbPS+6NVqNVxcXNCxY0csX74c7du3xzfffFPocho2bIjDhw8jPT0d9+/fx507d6DVaku9LgrmaW9vDwB6g8cNGzaEra0tHjx4YPD5rVu3YvDgwQZnmAsqST8xh7l52dvbw8HBQTkwAwA3NzeIiMHVY+np6QgLCzPpz7hN7QdFMZZFXv2MZVHSuuS9V9hnCh4UZmdn4+XLl6XOy1hWJe13hTFnvZdF37S1tYVKpTIpq9TUVAwYMAB16tTBoUOH9PbdeX/urFKpoFKp0LdvX2X+X3/9daH1L6+s8pZdmrzOnTuHIUOGYN26dRg7dqwyPTExEffu3cOQIUOU9u7atQtHjx6FSqVCYmKi0fpU9LZla2uL1q1bo1+/fggLC8Px48f1nvhekLe3t7I/LUn9894rrdLm1bRpU7i7u8PX1xdBQUFYtGgRcnJyjC6rZs2a8PT0NKu9ZZ3XmTNn8OOPP0Kj0UClUsHFxQUA0KlTJ4OTioX1yZLUP++90jI3rzNnzmD16tXKtjNhwgSkpKRApVIZnMxdvXo1goKCEBERoTcAYExlfnd5e3sjOzvb4EnuJemTVTUvY4ztK2rUqAEXFxd06NABM2bMwMiRI4scQKuovIyV0Wg0cHZ2LrJMcevBzs4OmZmZePXqlUEbKutYw97eHiqVShmwBd6sCwB6ZUw9dsxTVn3T3t6+yKwyMjIwd+5crF27FkOGDEG7du0wdepUjBo1CqtXry50PRScT0G2trawtLQ02oay2LZKwtxsN27ciLp162LlypXw9PREjx49EBoaisjISOV2UpWZrTEV9fueiIrGQVtSeHp64vbt23rTnJyc4OLiAhcXFzg4OFRSzf5Pp9MB+P+Xo4+PD2JjY/UGDU6fPg0bG5si/9w/70x2cSwtLeHg4AC1Wo09e/bAx8enRLd3MJexLIzR6XSoX7++clWIj4+PcluKPKdPny7yfrWA6evDysoKDg4OyM7OxoEDBzBs2LBiP1OUgnl27doVAJT7JQFv7uX4/PlzODk56X02OTkZUVFRJg1imttPTGVuXl27dsXjx4+RlpamlImPj0eNGjXg6Oio99nw8HC8fv3apKsgze0H+TVv3hxNmjTRyyKvfgWzKK4uJV33Pj4+ePXqFaKjo5VpZ86cQW5urjLQby5jWZWk3xXFnPXu4+OD8+fPIysrS+8zrq6uqF+/vknLVavVeO+994rN6rfffsMHH3wAtVqNo0ePGlwpcuDAAVy/ft3gRN2FCxfw2WefFVr/8soKKF1eZ8+exaBBg7BixQq9q/yBN/ctjI2NVdqq0+kwdOhQ9O7dGzqdDk2bNi20vZW1beX9UCpqX63T6ZT9qTGVsS80d/vKu3KpsB+IOTk5iI2NLba9FZHX+vXr9bad48ePA3hzleayZcuUzxTVJwurf1XM68cff9TbdpYsWaJcpTl8+HDlcytXrsTSpUtx8uRJk24bUZnbl06nQ40aNQxu95JfcX2yquZlTHH7CqD4Y8OKyqtjx47QaDR6ZbKysnDv3j2ljDnroWPHjqhZs6ZeG3755Rc8ePCgRG0wxtysunbtiuzsbL0Th/Hx8QCgV8bUY8c8ZdU3u3btWmRWWVlZyMrKMvirREtLS2W7MWcbVavV6Nixo15Wubm5iIyMLHVWJWVutr///rvR9QL8//u9MrMloiqs8p6BRlXNjRs3RKVSycuXL4ssV/CJkMaeqLpu3TpxcnIq9LUp7t69K0uWLJFr165JcnKyHDlyRJydnaVHjx5KmezsbGnTpo188MEHotPp5OTJk9KwYUOZM2eOUmbDhg1y9OhRiY+Pl/j4eNm6davUqVNH7ymmwcHB0qdPH+X1s2fP5Ntvv5W4uDiJiYmRadOmiZWVlVy9etWgnuY8Abg4xrI4evSobNmyRWJjYyUhIUE2bdok1tbWsnDhQqVMUlKSWFtby8yZMyUuLk42btwolpaWcvLkSaXM7Nmz5dy5c5KcnCw3btyQ2bNni4WFhURERChlxowZI7Nnz1ZeX7lyRQ4cOCCJiYly/vx56dOnj7Ro0aJET2Y1JU8RkWHDhomHh4dcunRJYmNjZfDgweLu7q48WTXP/PnzpUmTJpKdnW2wrIMHD4qrq6vy2pR+ksecPM3NKzU1VRwdurXvwgAADNVJREFUHWXkyJFy69YtOXfunLRq1UomTpxosIxu3brJqFGjjC5/9uzZMmbMGOW1Kf3AFOvWrRMbGxsJDw+XhIQEmT9/vlhZWcndu3eVMvfv35eYmBhZvHixaLVaiYmJkZiYGElNTRUR09b91atXxdXVVR4+fKhMGzBggHh6esrVq1fl4sWL0qpVK/H19TWoo5+fnwwbNszkNhW2nzOl3926dUtiYmJkyJAh0qtXL6WteUxZ7wX3Na9evZLGjRvLmDFj5ObNmxIWFibW1tZ6T+g1xcGDB6VmzZry/fffS0JCggQHB4ulpaVcuHBBRERSUlLE29tb2rZtK3fv3pUnT54o/4xtQyLGn8BckVmJmJ/XmTNnxNraWubMmaPX1qKeyGysfpW1bV25ckWCg4MlJiZG7t27J5GRkdKlSxdp2bKl/PHHHyIiEhISIv/6178kLi5O4uLiZNmyZVKjRg3Zvn27spyqsC8UKT6v0NBQ2bt3r9y+fVsSExNl79690qRJE/n444+VeSxevFhOnToliYmJEh0dLaNHjxYrKyu5deuWUqYy94X5JScnCwC9/YMpfbK65FWQsSeFBwUFiVqtlv379+u1N++7QaTy8rp8+bKsW7dOdDqdJCYmSmhoqDRs2FDGjh2rzMOUPlld8jJlXxEYGCgRERGSmJgot2/fltWrV4tKpZItW7YoZSpz+/r888/FwcFBTp06JXfu3JEJEyZIo0aN9NZFcevh4cOH4urqqncsP3nyZGnWrJmcOXNGrl27Jj4+PuLj42NQR2O/dYpiblY5OTni5eUlPXr0kJ9//lmuXbsm3t7e0q9fP2Uephw7lqZvFuWnn34SlUoly5Ytk4SEBNm9e7dYW1tLaGioUqZnz57i4eEhUVFRkpSUJDt27BArKyvZtGmTUsaUzPv06SPBwcHK67CwMNFoNBISEiK3b9+WSZMmSb169eTp06d6dTR27FKWzM02MjJSLCwsZPHixRIfHy/R0dHSv39/cXJykt9//11EKjfbgvskJycnWbdunV6Z9u3by9dff13o6zzGvhOIyHwctCU9nTt3ls2bNxdZpqwGbXfs2CFFnTd48OCB9OjRQxo0aCAajUZcXFxk5syZkpKSolfu3r17MnDgQKlVq5bY2trKjBkzJCsrS3l//fr14uHhIdbW1mJjYyOenp6yadMmycnJ0WtD/vo9e/ZM/vKXv0jt2rXF2tpa+vbtK1euXDFaz/IYtBUxzOLEiRPSoUMH0Wq1Urt2bWnfvr1s3rxZrx0ibw5WOnToIGq1WpydnWXHjh1673/yySfi5OQkarVaGjZsKH379tUbsM1rk5+fn/L67Nmz4ubmJhqNRt555x0ZM2aMPHr0SO8zZZVnSkqKfPLJJ1KvXj1p0KCBDB8+XB48eKBXJicnRxwdHWXu3LlGl2WsLsX1k/xtNydPc/OKi4uT999/X2rVqiWOjo4yffp05eAtz507dwSAQU55/Pz8pGfPnnrTiusHxeWVZ/ny5eLo6CjW1tbi4+OjDALmXzYAg39RUVFKmeLWfd4BdnJysjLtxYsX4uvrK1qtVmxsbGT8+PF6P/bzL7+kA4HG9nOm9DsnJyejbc2vuPVecF8jInL9+nXp1q2baDQacXBwkKCgIIN5Flw/xmzbtk1cXFzEyspK2rdvL4cPHzaYh7F/hc3X2A+fis5KxLy8CuuXBbeT4upXWdvWjRs3pHfv3sr+snnz5jJ58mS9wfKQkBBxc3NTvts6d+4s4eHhxdaloveFIsXnFRYWJl5eXsr+0t3dXQIDAyUjI0Mp849//EOaNWsmarVaGjduLB9++KH8/PPPesupzH1hfsYGbU3pk9Ulr4KM/UAvbH+Z/wd+ZeUVHR0t3t7eUrduXbGyshI3NzcJDAxUToiImNYnq0tepuwr5s2bp3x/1K9fX3x8fCQsLEyvTGVuX5mZmTJjxgxp1KiR1KlTR95//325efNmidZD3naZ//gkIyNDpkyZIvXr1xdra2sZPny4PHnyxKB+JR20FTF/23r06JF89NFHotVqpXHjxjJu3DiDE47FHTua0zeNrR9j/v3vf0ubNm1Eo9HIu+++K99//73e+0+ePJFx48ZJkyZNxMrKSlxdXWXNmjWSm5urV664zJ2cnAwGBIODg5Xvgc6dOxv9bVbeg7Yi5me7Z88e8fT0lNq1a0vDhg1l6NChEhcXp1emsrLloC1R1cVBW9Jz7NgxcXNzMxhYKg8LFy4s8gd0dVFeg7YVmUVZeNvzZF6Vw5yBwOqW1fbt28XFxaXQq9yqC3MHbatbXn+WbYv7wuqFeVUvzKt6MWfQtrpldebMGalXr16xf3FZ1VXEoC2zLRoHbYnKlqqsbrNAfw6DBg1CQkICHj16VOg9/crKiRMnsGHDhnJdRnnavXs3AgICkJGRgQ4dOpT5/Csyi7LwtufJvCrWhQsXMHDgQLx+/RqDBg0q0WerW1bHjx9HYGCgwQMhqovSZAVUv7yq+7bFfWH1wryqF+ZVvTx48ADu7u7IzMws8f1Bq1tWx48fx9y5c02+n35V5OHhgaSkpHJfDrMtnFarRXZ2tsFzE4jIfBYiIpVdCaLqKDU1VXk6Z7169WBra1vJNaLSYJ7VS0ZGBh49egTgzQFiRT89mEzHrKoX7gurF+ZVvTCv6iU7Oxv37t0DAGg0mmoxQPc2u3//vvJgV2dnZ4MHf1H5u3v3LoA3D1lr0aJFJdeG6M+Bg7ZEREREREREREREVQhPPxERERERERERERFVIRy0JSIiIiIiIiIiIqpCOGhLREREREREREREVIVw0JaIiIiIiIiIiIioCuGgLREREREREREREVEVwkFbIiIiorfEs2fP8Omnn6JZs2bQaDSws7ND//79cenSpcquGhERERER5aOq7AoQERERUcUYMWIEMjMzsXPnTjg7O+O///0vIiMj8eLFi3JZXmZmJtRqdbnMm4iIiIjoz4xX2hIRERG9BV69eoULFy5gxYoV6N27N5ycnNC5c2fMmTMHQ4cOVcoEBASgcePGsLKyQps2bXDs2DFlHgcOHICHhwc0Gg2aN2+ONWvW6C2jefPmWLp0KcaOHQsbGxtMmjQJAHDx4kV0794dtWrVQtOmTTFt2jSkp6dXXOOJiIiIiKoZDtoSERERvQW0Wi20Wi0OHz6M169fG7yfm5uLgQMH4tKlSwgNDcXt27cRFBQES0tLAEB0dDT+9re/YfTo0YiNjcWiRYuwYMEChISE6M1n9erVaN++PWJiYrBgwQIkJiZiwIABGDFiBG7cuIG9e/fi4sWLmDp1akU0m4iIiIioWrIQEansShARERFR+Ttw4AD8/f2RkZEBLy8v9OzZE6NHj0a7du0QERGBgQMHIi4uDq1btzb47Mcff4xnz54hIiJCmfbVV1/hhx9+wK1btwC8udLW09MThw4dUspMnDgRlpaW+O6775RpFy9eRM+ePZGeng4rK6tybDERERERUfXEK22JiIiI3hIjRozA48ePcfToUQwYMABnz56Fl5cXQkJCoNPp4OjoaHTAFgDi4uLQtWtXvWldu3ZFQkICcnJylGmdOnXSK3P9+nWEhIQoV/pqtVr0798fubm5SE5OLvtGEhERERH9CfBBZERERERvESsrK/Tr1w/9+vXDggULMHHiRHz99df48ssvy2T+tWvX1nudlpaGgIAATJs2zaBss2bNymSZRERERER/Nhy0JSIiInqLubu74/Dhw2jXrh0ePnyI+Ph4o1fburm54dKlS3rTLl26hNatWyv3vTXGy8sLt2/fhouLS5nXnYiIiIjoz4q3RyAiIiJ6C7x48QJ9+vRBaGgobty4geTkZISHh2PlypUYNmwYevbsiR49emDEiBE4ffo0kpOTceLECZw8eRIAMGPGDERGRmLp0qWIj4/Hzp07sWHDhmKv0J01axYuX76MqVOnQqfTISEhAUeOHOGDyIiIiIiIisArbYmIiIjeAlqtFt7e3li3bh0SExORlZWFpk2bwt/fH3PnzgXw5kFlX375JXx9fZGeng4XFxcEBQUBeHPF7L59+7Bw4UIsXboU9vb2WLJkCcaNG1fkctu1a4dz585h3rx56N69O0QELVu2xKhRo8q7yURERERE1ZaFiEhlV4KIiIiIiIiIiIiI3uDtEYiIiIiIiIiIiIiqEA7aEhEREREREREREVUhHLQlIiIiIiIiIiIiqkI4aEtERERERERERERUhXDQloiIiIiIiIiIiKgK4aAtERERERERERERURXCQVsiIiIiIiIiIiKiKoSDtkRERERERERERERVCAdtiYiIiIiIiIiIiKoQDtoSERERERERERERVSEctCUiIiIiIiIiIiKqQjhoS0RERERERERERFSF/A+ePVgaVnAutgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制提升度图    \n",
    "plot_lift(data_train_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c0961315",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>No.</th>\n",
       "      <th>Score Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Bad</th>\n",
       "      <th>#Good</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Bad</th>\n",
       "      <th>%Good</th>\n",
       "      <th>%BadRate</th>\n",
       "      <th>%BadRate Random</th>\n",
       "      <th>%CumBad</th>\n",
       "      <th>%CumTotal</th>\n",
       "      <th>Lift</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(-inf, 503.9]</td>\n",
       "      <td>2250</td>\n",
       "      <td>1556</td>\n",
       "      <td>694</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>31.52%</td>\n",
       "      <td>3.95%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>69.16%</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(503.9, 567.0]</td>\n",
       "      <td>2276</td>\n",
       "      <td>863</td>\n",
       "      <td>1413</td>\n",
       "      <td>10.12%</td>\n",
       "      <td>17.48%</td>\n",
       "      <td>8.04%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>53.45%</td>\n",
       "      <td>20.12%</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(567.0, 610.0]</td>\n",
       "      <td>2346</td>\n",
       "      <td>600</td>\n",
       "      <td>1746</td>\n",
       "      <td>10.43%</td>\n",
       "      <td>12.16%</td>\n",
       "      <td>9.94%</td>\n",
       "      <td>25.58%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>43.93%</td>\n",
       "      <td>30.54%</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(610.0, 624.0]</td>\n",
       "      <td>2232</td>\n",
       "      <td>433</td>\n",
       "      <td>1799</td>\n",
       "      <td>9.92%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>10.24%</td>\n",
       "      <td>19.40%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>37.92%</td>\n",
       "      <td>40.46%</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(624.0, 635.0]</td>\n",
       "      <td>2615</td>\n",
       "      <td>395</td>\n",
       "      <td>2220</td>\n",
       "      <td>11.62%</td>\n",
       "      <td>8.00%</td>\n",
       "      <td>12.64%</td>\n",
       "      <td>15.11%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>32.83%</td>\n",
       "      <td>52.08%</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>(635.0, 642.0]</td>\n",
       "      <td>1823</td>\n",
       "      <td>267</td>\n",
       "      <td>1556</td>\n",
       "      <td>8.10%</td>\n",
       "      <td>5.41%</td>\n",
       "      <td>8.86%</td>\n",
       "      <td>14.65%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>30.38%</td>\n",
       "      <td>60.19%</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>(642.0, 653.0]</td>\n",
       "      <td>2216</td>\n",
       "      <td>292</td>\n",
       "      <td>1924</td>\n",
       "      <td>9.85%</td>\n",
       "      <td>5.92%</td>\n",
       "      <td>10.95%</td>\n",
       "      <td>13.18%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>27.96%</td>\n",
       "      <td>70.04%</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>(653.0, 666.0]</td>\n",
       "      <td>2364</td>\n",
       "      <td>210</td>\n",
       "      <td>2154</td>\n",
       "      <td>10.51%</td>\n",
       "      <td>4.25%</td>\n",
       "      <td>12.26%</td>\n",
       "      <td>8.88%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>25.47%</td>\n",
       "      <td>80.54%</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>(666.0, 686.0]</td>\n",
       "      <td>2748</td>\n",
       "      <td>231</td>\n",
       "      <td>2517</td>\n",
       "      <td>12.21%</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>14.33%</td>\n",
       "      <td>8.41%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>23.22%</td>\n",
       "      <td>92.76%</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>(686.0, inf]</td>\n",
       "      <td>1630</td>\n",
       "      <td>89</td>\n",
       "      <td>1541</td>\n",
       "      <td>7.24%</td>\n",
       "      <td>1.80%</td>\n",
       "      <td>8.77%</td>\n",
       "      <td>5.46%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>21.94%</td>\n",
       "      <td>100.00%</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.     Score Range  #Total  #Bad  #Good  %Total    %Bad   %Good %BadRate  \\\n",
       "0    1   (-inf, 503.9]    2250  1556    694  10.00%  31.52%   3.95%   69.16%   \n",
       "1    2  (503.9, 567.0]    2276   863   1413  10.12%  17.48%   8.04%   37.92%   \n",
       "2    3  (567.0, 610.0]    2346   600   1746  10.43%  12.16%   9.94%   25.58%   \n",
       "3    4  (610.0, 624.0]    2232   433   1799   9.92%   8.77%  10.24%   19.40%   \n",
       "4    5  (624.0, 635.0]    2615   395   2220  11.62%   8.00%  12.64%   15.11%   \n",
       "5    6  (635.0, 642.0]    1823   267   1556   8.10%   5.41%   8.86%   14.65%   \n",
       "6    7  (642.0, 653.0]    2216   292   1924   9.85%   5.92%  10.95%   13.18%   \n",
       "7    8  (653.0, 666.0]    2364   210   2154  10.51%   4.25%  12.26%    8.88%   \n",
       "8    9  (666.0, 686.0]    2748   231   2517  12.21%   4.68%  14.33%    8.41%   \n",
       "9   10    (686.0, inf]    1630    89   1541   7.24%   1.80%   8.77%    5.46%   \n",
       "\n",
       "  %BadRate Random %CumBad %CumTotal  Lift  \n",
       "0          21.94%  69.16%    10.00%  6.92  \n",
       "1          21.94%  53.45%    20.12%  2.66  \n",
       "2          21.94%  43.93%    30.54%  1.44  \n",
       "3          21.94%  37.92%    40.46%  0.94  \n",
       "4          21.94%  32.83%    52.08%  0.63  \n",
       "5          21.94%  30.38%    60.19%  0.50  \n",
       "6          21.94%  27.96%    70.04%  0.40  \n",
       "7          21.94%  25.47%    80.54%  0.32  \n",
       "8          21.94%  23.22%    92.76%  0.25  \n",
       "9          21.94%  21.94%   100.00%  0.22  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制提升度图，并输出提升度表格\n",
    "plot_lift(data_train_score,return_data=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "51bf2861",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看训练集分数分布\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('default')\n",
    "fig=plt.figure()\n",
    "ax1=fig.add_subplot(1,1,1)\n",
    "ax1.hist(data_train_score['Score'] )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b029d0bc",
   "metadata": {},
   "source": [
    "## 模型稳定型 PSI计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d13af529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9c7df2bcba24cf6b01e35ae4dbe7703",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d4067d07264044b88e2af7a076dfb5c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Bins Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Actual</th>\n",
       "      <th>#Expected</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Actual</th>\n",
       "      <th>%Expected</th>\n",
       "      <th>PSI</th>\n",
       "      <th>Total PSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Score</td>\n",
       "      <td>(-inf, 503.9]</td>\n",
       "      <td>3018</td>\n",
       "      <td>2250</td>\n",
       "      <td>768</td>\n",
       "      <td>10.06%</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>10.24%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Score</td>\n",
       "      <td>(503.9, 567.0]</td>\n",
       "      <td>3026</td>\n",
       "      <td>2276</td>\n",
       "      <td>750</td>\n",
       "      <td>10.09%</td>\n",
       "      <td>10.12%</td>\n",
       "      <td>10.00%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Score</td>\n",
       "      <td>(567.0, 610.0]</td>\n",
       "      <td>3018</td>\n",
       "      <td>2346</td>\n",
       "      <td>672</td>\n",
       "      <td>10.06%</td>\n",
       "      <td>10.43%</td>\n",
       "      <td>8.96%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Score</td>\n",
       "      <td>(610.0, 624.0]</td>\n",
       "      <td>2945</td>\n",
       "      <td>2232</td>\n",
       "      <td>713</td>\n",
       "      <td>9.82%</td>\n",
       "      <td>9.92%</td>\n",
       "      <td>9.51%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Score</td>\n",
       "      <td>(624.0, 635.0]</td>\n",
       "      <td>3065</td>\n",
       "      <td>2615</td>\n",
       "      <td>450</td>\n",
       "      <td>10.22%</td>\n",
       "      <td>11.62%</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Score</td>\n",
       "      <td>(635.0, 642.0]</td>\n",
       "      <td>2706</td>\n",
       "      <td>1823</td>\n",
       "      <td>883</td>\n",
       "      <td>9.02%</td>\n",
       "      <td>8.10%</td>\n",
       "      <td>11.77%</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Score</td>\n",
       "      <td>(642.0, 653.0]</td>\n",
       "      <td>3054</td>\n",
       "      <td>2216</td>\n",
       "      <td>838</td>\n",
       "      <td>10.18%</td>\n",
       "      <td>9.85%</td>\n",
       "      <td>11.17%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Score</td>\n",
       "      <td>(653.0, 666.0]</td>\n",
       "      <td>3151</td>\n",
       "      <td>2364</td>\n",
       "      <td>787</td>\n",
       "      <td>10.50%</td>\n",
       "      <td>10.51%</td>\n",
       "      <td>10.49%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Score</td>\n",
       "      <td>(666.0, 686.0]</td>\n",
       "      <td>3555</td>\n",
       "      <td>2748</td>\n",
       "      <td>807</td>\n",
       "      <td>11.85%</td>\n",
       "      <td>12.21%</td>\n",
       "      <td>10.76%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Score</td>\n",
       "      <td>(686.0, inf]</td>\n",
       "      <td>2462</td>\n",
       "      <td>1630</td>\n",
       "      <td>832</td>\n",
       "      <td>8.21%</td>\n",
       "      <td>7.24%</td>\n",
       "      <td>11.09%</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.   Name      Bins Range  #Total  #Actual  #Expected  %Total %Actual  \\\n",
       "0    1  Score   (-inf, 503.9]    3018     2250        768  10.06%  10.00%   \n",
       "1    2  Score  (503.9, 567.0]    3026     2276        750  10.09%  10.12%   \n",
       "2    3  Score  (567.0, 610.0]    3018     2346        672  10.06%  10.43%   \n",
       "3    4  Score  (610.0, 624.0]    2945     2232        713   9.82%   9.92%   \n",
       "4    5  Score  (624.0, 635.0]    3065     2615        450  10.22%  11.62%   \n",
       "5    6  Score  (635.0, 642.0]    2706     1823        883   9.02%   8.10%   \n",
       "6    7  Score  (642.0, 653.0]    3054     2216        838  10.18%   9.85%   \n",
       "7    8  Score  (653.0, 666.0]    3151     2364        787  10.50%  10.51%   \n",
       "8    9  Score  (666.0, 686.0]    3555     2748        807  11.85%  12.21%   \n",
       "9   10  Score    (686.0, inf]    2462     1630        832   8.21%   7.24%   \n",
       "\n",
       "  %Expected  PSI  Total PSI  \n",
       "0    10.24% 0.00       0.07  \n",
       "1    10.00% 0.00       0.07  \n",
       "2     8.96% 0.00       0.07  \n",
       "3     9.51% 0.00       0.07  \n",
       "4     6.00% 0.04       0.07  \n",
       "5    11.77% 0.01       0.07  \n",
       "6    11.17% 0.00       0.07  \n",
       "7    10.49% 0.00       0.07  \n",
       "8    10.76% 0.00       0.07  \n",
       "9    11.09% 0.02       0.07  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "\n",
    "# 按照等频分箱，分为10组，计算模型得分 Score 的PSI\n",
    "get_psi(data_train_score,data_test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "faee4b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_401f0_row0_col11, #T_401f0_row1_col11, #T_401f0_row2_col11, #T_401f0_row3_col11, #T_401f0_row6_col11, #T_401f0_row7_col11, #T_401f0_row8_col11 {\n",
       "  width: 10em;\n",
       "}\n",
       "#T_401f0_row4_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #007bff 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_401f0_row5_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #007bff 25.0%, transparent 25.0%);\n",
       "}\n",
       "#T_401f0_row9_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #007bff 50.0%, transparent 50.0%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_401f0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_401f0_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_401f0_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_401f0_level0_col2\" class=\"col_heading level0 col2\" >Bins Range</th>\n",
       "      <th id=\"T_401f0_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_401f0_level0_col4\" class=\"col_heading level0 col4\" >#Actual</th>\n",
       "      <th id=\"T_401f0_level0_col5\" class=\"col_heading level0 col5\" >#Expected</th>\n",
       "      <th id=\"T_401f0_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_401f0_level0_col7\" class=\"col_heading level0 col7\" >%Actual</th>\n",
       "      <th id=\"T_401f0_level0_col8\" class=\"col_heading level0 col8\" >%Expected</th>\n",
       "      <th id=\"T_401f0_level0_col9\" class=\"col_heading level0 col9\" >PSI</th>\n",
       "      <th id=\"T_401f0_level0_col10\" class=\"col_heading level0 col10\" >Total PSI</th>\n",
       "      <th id=\"T_401f0_level0_col11\" class=\"col_heading level0 col11\" >PSI.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_401f0_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_401f0_row0_col1\" class=\"data row0 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row0_col2\" class=\"data row0 col2\" >(-inf, 503.9]</td>\n",
       "      <td id=\"T_401f0_row0_col3\" class=\"data row0 col3\" >3018</td>\n",
       "      <td id=\"T_401f0_row0_col4\" class=\"data row0 col4\" >2250</td>\n",
       "      <td id=\"T_401f0_row0_col5\" class=\"data row0 col5\" >768</td>\n",
       "      <td id=\"T_401f0_row0_col6\" class=\"data row0 col6\" >10.06%</td>\n",
       "      <td id=\"T_401f0_row0_col7\" class=\"data row0 col7\" >10.00%</td>\n",
       "      <td id=\"T_401f0_row0_col8\" class=\"data row0 col8\" >10.24%</td>\n",
       "      <td id=\"T_401f0_row0_col9\" class=\"data row0 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row0_col10\" class=\"data row0 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row0_col11\" class=\"data row0 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_401f0_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_401f0_row1_col1\" class=\"data row1 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row1_col2\" class=\"data row1 col2\" >(503.9, 567.0]</td>\n",
       "      <td id=\"T_401f0_row1_col3\" class=\"data row1 col3\" >3026</td>\n",
       "      <td id=\"T_401f0_row1_col4\" class=\"data row1 col4\" >2276</td>\n",
       "      <td id=\"T_401f0_row1_col5\" class=\"data row1 col5\" >750</td>\n",
       "      <td id=\"T_401f0_row1_col6\" class=\"data row1 col6\" >10.09%</td>\n",
       "      <td id=\"T_401f0_row1_col7\" class=\"data row1 col7\" >10.12%</td>\n",
       "      <td id=\"T_401f0_row1_col8\" class=\"data row1 col8\" >10.00%</td>\n",
       "      <td id=\"T_401f0_row1_col9\" class=\"data row1 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row1_col10\" class=\"data row1 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row1_col11\" class=\"data row1 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_401f0_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_401f0_row2_col1\" class=\"data row2 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row2_col2\" class=\"data row2 col2\" >(567.0, 610.0]</td>\n",
       "      <td id=\"T_401f0_row2_col3\" class=\"data row2 col3\" >3018</td>\n",
       "      <td id=\"T_401f0_row2_col4\" class=\"data row2 col4\" >2346</td>\n",
       "      <td id=\"T_401f0_row2_col5\" class=\"data row2 col5\" >672</td>\n",
       "      <td id=\"T_401f0_row2_col6\" class=\"data row2 col6\" >10.06%</td>\n",
       "      <td id=\"T_401f0_row2_col7\" class=\"data row2 col7\" >10.43%</td>\n",
       "      <td id=\"T_401f0_row2_col8\" class=\"data row2 col8\" >8.96%</td>\n",
       "      <td id=\"T_401f0_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row2_col10\" class=\"data row2 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row2_col11\" class=\"data row2 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_401f0_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_401f0_row3_col1\" class=\"data row3 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row3_col2\" class=\"data row3 col2\" >(610.0, 624.0]</td>\n",
       "      <td id=\"T_401f0_row3_col3\" class=\"data row3 col3\" >2945</td>\n",
       "      <td id=\"T_401f0_row3_col4\" class=\"data row3 col4\" >2232</td>\n",
       "      <td id=\"T_401f0_row3_col5\" class=\"data row3 col5\" >713</td>\n",
       "      <td id=\"T_401f0_row3_col6\" class=\"data row3 col6\" >9.82%</td>\n",
       "      <td id=\"T_401f0_row3_col7\" class=\"data row3 col7\" >9.92%</td>\n",
       "      <td id=\"T_401f0_row3_col8\" class=\"data row3 col8\" >9.51%</td>\n",
       "      <td id=\"T_401f0_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row3_col10\" class=\"data row3 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_401f0_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_401f0_row4_col1\" class=\"data row4 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row4_col2\" class=\"data row4 col2\" >(624.0, 635.0]</td>\n",
       "      <td id=\"T_401f0_row4_col3\" class=\"data row4 col3\" >3065</td>\n",
       "      <td id=\"T_401f0_row4_col4\" class=\"data row4 col4\" >2615</td>\n",
       "      <td id=\"T_401f0_row4_col5\" class=\"data row4 col5\" >450</td>\n",
       "      <td id=\"T_401f0_row4_col6\" class=\"data row4 col6\" >10.22%</td>\n",
       "      <td id=\"T_401f0_row4_col7\" class=\"data row4 col7\" >11.62%</td>\n",
       "      <td id=\"T_401f0_row4_col8\" class=\"data row4 col8\" >6.00%</td>\n",
       "      <td id=\"T_401f0_row4_col9\" class=\"data row4 col9\" >0.040000</td>\n",
       "      <td id=\"T_401f0_row4_col10\" class=\"data row4 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row4_col11\" class=\"data row4 col11\" >0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_401f0_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_401f0_row5_col1\" class=\"data row5 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row5_col2\" class=\"data row5 col2\" >(635.0, 642.0]</td>\n",
       "      <td id=\"T_401f0_row5_col3\" class=\"data row5 col3\" >2706</td>\n",
       "      <td id=\"T_401f0_row5_col4\" class=\"data row5 col4\" >1823</td>\n",
       "      <td id=\"T_401f0_row5_col5\" class=\"data row5 col5\" >883</td>\n",
       "      <td id=\"T_401f0_row5_col6\" class=\"data row5 col6\" >9.02%</td>\n",
       "      <td id=\"T_401f0_row5_col7\" class=\"data row5 col7\" >8.10%</td>\n",
       "      <td id=\"T_401f0_row5_col8\" class=\"data row5 col8\" >11.77%</td>\n",
       "      <td id=\"T_401f0_row5_col9\" class=\"data row5 col9\" >0.010000</td>\n",
       "      <td id=\"T_401f0_row5_col10\" class=\"data row5 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row5_col11\" class=\"data row5 col11\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_401f0_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_401f0_row6_col1\" class=\"data row6 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row6_col2\" class=\"data row6 col2\" >(642.0, 653.0]</td>\n",
       "      <td id=\"T_401f0_row6_col3\" class=\"data row6 col3\" >3054</td>\n",
       "      <td id=\"T_401f0_row6_col4\" class=\"data row6 col4\" >2216</td>\n",
       "      <td id=\"T_401f0_row6_col5\" class=\"data row6 col5\" >838</td>\n",
       "      <td id=\"T_401f0_row6_col6\" class=\"data row6 col6\" >10.18%</td>\n",
       "      <td id=\"T_401f0_row6_col7\" class=\"data row6 col7\" >9.85%</td>\n",
       "      <td id=\"T_401f0_row6_col8\" class=\"data row6 col8\" >11.17%</td>\n",
       "      <td id=\"T_401f0_row6_col9\" class=\"data row6 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row6_col10\" class=\"data row6 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row6_col11\" class=\"data row6 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_401f0_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_401f0_row7_col1\" class=\"data row7 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row7_col2\" class=\"data row7 col2\" >(653.0, 666.0]</td>\n",
       "      <td id=\"T_401f0_row7_col3\" class=\"data row7 col3\" >3151</td>\n",
       "      <td id=\"T_401f0_row7_col4\" class=\"data row7 col4\" >2364</td>\n",
       "      <td id=\"T_401f0_row7_col5\" class=\"data row7 col5\" >787</td>\n",
       "      <td id=\"T_401f0_row7_col6\" class=\"data row7 col6\" >10.50%</td>\n",
       "      <td id=\"T_401f0_row7_col7\" class=\"data row7 col7\" >10.51%</td>\n",
       "      <td id=\"T_401f0_row7_col8\" class=\"data row7 col8\" >10.49%</td>\n",
       "      <td id=\"T_401f0_row7_col9\" class=\"data row7 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row7_col10\" class=\"data row7 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_401f0_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_401f0_row8_col1\" class=\"data row8 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row8_col2\" class=\"data row8 col2\" >(666.0, 686.0]</td>\n",
       "      <td id=\"T_401f0_row8_col3\" class=\"data row8 col3\" >3555</td>\n",
       "      <td id=\"T_401f0_row8_col4\" class=\"data row8 col4\" >2748</td>\n",
       "      <td id=\"T_401f0_row8_col5\" class=\"data row8 col5\" >807</td>\n",
       "      <td id=\"T_401f0_row8_col6\" class=\"data row8 col6\" >11.85%</td>\n",
       "      <td id=\"T_401f0_row8_col7\" class=\"data row8 col7\" >12.21%</td>\n",
       "      <td id=\"T_401f0_row8_col8\" class=\"data row8 col8\" >10.76%</td>\n",
       "      <td id=\"T_401f0_row8_col9\" class=\"data row8 col9\" >0.000000</td>\n",
       "      <td id=\"T_401f0_row8_col10\" class=\"data row8 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row8_col11\" class=\"data row8 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_401f0_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_401f0_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_401f0_row9_col1\" class=\"data row9 col1\" >Score</td>\n",
       "      <td id=\"T_401f0_row9_col2\" class=\"data row9 col2\" >(686.0, inf]</td>\n",
       "      <td id=\"T_401f0_row9_col3\" class=\"data row9 col3\" >2462</td>\n",
       "      <td id=\"T_401f0_row9_col4\" class=\"data row9 col4\" >1630</td>\n",
       "      <td id=\"T_401f0_row9_col5\" class=\"data row9 col5\" >832</td>\n",
       "      <td id=\"T_401f0_row9_col6\" class=\"data row9 col6\" >8.21%</td>\n",
       "      <td id=\"T_401f0_row9_col7\" class=\"data row9 col7\" >7.24%</td>\n",
       "      <td id=\"T_401f0_row9_col8\" class=\"data row9 col8\" >11.09%</td>\n",
       "      <td id=\"T_401f0_row9_col9\" class=\"data row9 col9\" >0.020000</td>\n",
       "      <td id=\"T_401f0_row9_col10\" class=\"data row9 col10\" >0.070000</td>\n",
       "      <td id=\"T_401f0_row9_col11\" class=\"data row9 col11\" >0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171df1c10c0>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照等频分箱，分为10组，计算模型得分 Score 的PSI\n",
    "view_psi(data_train_score,data_test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2ebffed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a5875_row0_col11, #T_a5875_row1_col11, #T_a5875_row2_col11, #T_a5875_row3_col11, #T_a5875_row6_col11, #T_a5875_row7_col11, #T_a5875_row8_col11 {\n",
       "  width: 10em;\n",
       "}\n",
       "#T_a5875_row4_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, green 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_a5875_row5_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, green 25.0%, transparent 25.0%);\n",
       "}\n",
       "#T_a5875_row9_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, green 50.0%, transparent 50.0%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a5875\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a5875_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_a5875_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_a5875_level0_col2\" class=\"col_heading level0 col2\" >Bins Range</th>\n",
       "      <th id=\"T_a5875_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_a5875_level0_col4\" class=\"col_heading level0 col4\" >#Actual</th>\n",
       "      <th id=\"T_a5875_level0_col5\" class=\"col_heading level0 col5\" >#Expected</th>\n",
       "      <th id=\"T_a5875_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_a5875_level0_col7\" class=\"col_heading level0 col7\" >%Actual</th>\n",
       "      <th id=\"T_a5875_level0_col8\" class=\"col_heading level0 col8\" >%Expected</th>\n",
       "      <th id=\"T_a5875_level0_col9\" class=\"col_heading level0 col9\" >PSI</th>\n",
       "      <th id=\"T_a5875_level0_col10\" class=\"col_heading level0 col10\" >Total PSI</th>\n",
       "      <th id=\"T_a5875_level0_col11\" class=\"col_heading level0 col11\" >PSI.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a5875_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_a5875_row0_col1\" class=\"data row0 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row0_col2\" class=\"data row0 col2\" >(-inf, 503.9]</td>\n",
       "      <td id=\"T_a5875_row0_col3\" class=\"data row0 col3\" >3018</td>\n",
       "      <td id=\"T_a5875_row0_col4\" class=\"data row0 col4\" >2250</td>\n",
       "      <td id=\"T_a5875_row0_col5\" class=\"data row0 col5\" >768</td>\n",
       "      <td id=\"T_a5875_row0_col6\" class=\"data row0 col6\" >10.06%</td>\n",
       "      <td id=\"T_a5875_row0_col7\" class=\"data row0 col7\" >10.00%</td>\n",
       "      <td id=\"T_a5875_row0_col8\" class=\"data row0 col8\" >10.24%</td>\n",
       "      <td id=\"T_a5875_row0_col9\" class=\"data row0 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row0_col10\" class=\"data row0 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row0_col11\" class=\"data row0 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a5875_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_a5875_row1_col1\" class=\"data row1 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row1_col2\" class=\"data row1 col2\" >(503.9, 567.0]</td>\n",
       "      <td id=\"T_a5875_row1_col3\" class=\"data row1 col3\" >3026</td>\n",
       "      <td id=\"T_a5875_row1_col4\" class=\"data row1 col4\" >2276</td>\n",
       "      <td id=\"T_a5875_row1_col5\" class=\"data row1 col5\" >750</td>\n",
       "      <td id=\"T_a5875_row1_col6\" class=\"data row1 col6\" >10.09%</td>\n",
       "      <td id=\"T_a5875_row1_col7\" class=\"data row1 col7\" >10.12%</td>\n",
       "      <td id=\"T_a5875_row1_col8\" class=\"data row1 col8\" >10.00%</td>\n",
       "      <td id=\"T_a5875_row1_col9\" class=\"data row1 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row1_col10\" class=\"data row1 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row1_col11\" class=\"data row1 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a5875_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_a5875_row2_col1\" class=\"data row2 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row2_col2\" class=\"data row2 col2\" >(567.0, 610.0]</td>\n",
       "      <td id=\"T_a5875_row2_col3\" class=\"data row2 col3\" >3018</td>\n",
       "      <td id=\"T_a5875_row2_col4\" class=\"data row2 col4\" >2346</td>\n",
       "      <td id=\"T_a5875_row2_col5\" class=\"data row2 col5\" >672</td>\n",
       "      <td id=\"T_a5875_row2_col6\" class=\"data row2 col6\" >10.06%</td>\n",
       "      <td id=\"T_a5875_row2_col7\" class=\"data row2 col7\" >10.43%</td>\n",
       "      <td id=\"T_a5875_row2_col8\" class=\"data row2 col8\" >8.96%</td>\n",
       "      <td id=\"T_a5875_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row2_col10\" class=\"data row2 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row2_col11\" class=\"data row2 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a5875_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_a5875_row3_col1\" class=\"data row3 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row3_col2\" class=\"data row3 col2\" >(610.0, 624.0]</td>\n",
       "      <td id=\"T_a5875_row3_col3\" class=\"data row3 col3\" >2945</td>\n",
       "      <td id=\"T_a5875_row3_col4\" class=\"data row3 col4\" >2232</td>\n",
       "      <td id=\"T_a5875_row3_col5\" class=\"data row3 col5\" >713</td>\n",
       "      <td id=\"T_a5875_row3_col6\" class=\"data row3 col6\" >9.82%</td>\n",
       "      <td id=\"T_a5875_row3_col7\" class=\"data row3 col7\" >9.92%</td>\n",
       "      <td id=\"T_a5875_row3_col8\" class=\"data row3 col8\" >9.51%</td>\n",
       "      <td id=\"T_a5875_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row3_col10\" class=\"data row3 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_a5875_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_a5875_row4_col1\" class=\"data row4 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row4_col2\" class=\"data row4 col2\" >(624.0, 635.0]</td>\n",
       "      <td id=\"T_a5875_row4_col3\" class=\"data row4 col3\" >3065</td>\n",
       "      <td id=\"T_a5875_row4_col4\" class=\"data row4 col4\" >2615</td>\n",
       "      <td id=\"T_a5875_row4_col5\" class=\"data row4 col5\" >450</td>\n",
       "      <td id=\"T_a5875_row4_col6\" class=\"data row4 col6\" >10.22%</td>\n",
       "      <td id=\"T_a5875_row4_col7\" class=\"data row4 col7\" >11.62%</td>\n",
       "      <td id=\"T_a5875_row4_col8\" class=\"data row4 col8\" >6.00%</td>\n",
       "      <td id=\"T_a5875_row4_col9\" class=\"data row4 col9\" >0.040000</td>\n",
       "      <td id=\"T_a5875_row4_col10\" class=\"data row4 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row4_col11\" class=\"data row4 col11\" >0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_a5875_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_a5875_row5_col1\" class=\"data row5 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row5_col2\" class=\"data row5 col2\" >(635.0, 642.0]</td>\n",
       "      <td id=\"T_a5875_row5_col3\" class=\"data row5 col3\" >2706</td>\n",
       "      <td id=\"T_a5875_row5_col4\" class=\"data row5 col4\" >1823</td>\n",
       "      <td id=\"T_a5875_row5_col5\" class=\"data row5 col5\" >883</td>\n",
       "      <td id=\"T_a5875_row5_col6\" class=\"data row5 col6\" >9.02%</td>\n",
       "      <td id=\"T_a5875_row5_col7\" class=\"data row5 col7\" >8.10%</td>\n",
       "      <td id=\"T_a5875_row5_col8\" class=\"data row5 col8\" >11.77%</td>\n",
       "      <td id=\"T_a5875_row5_col9\" class=\"data row5 col9\" >0.010000</td>\n",
       "      <td id=\"T_a5875_row5_col10\" class=\"data row5 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row5_col11\" class=\"data row5 col11\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_a5875_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_a5875_row6_col1\" class=\"data row6 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row6_col2\" class=\"data row6 col2\" >(642.0, 653.0]</td>\n",
       "      <td id=\"T_a5875_row6_col3\" class=\"data row6 col3\" >3054</td>\n",
       "      <td id=\"T_a5875_row6_col4\" class=\"data row6 col4\" >2216</td>\n",
       "      <td id=\"T_a5875_row6_col5\" class=\"data row6 col5\" >838</td>\n",
       "      <td id=\"T_a5875_row6_col6\" class=\"data row6 col6\" >10.18%</td>\n",
       "      <td id=\"T_a5875_row6_col7\" class=\"data row6 col7\" >9.85%</td>\n",
       "      <td id=\"T_a5875_row6_col8\" class=\"data row6 col8\" >11.17%</td>\n",
       "      <td id=\"T_a5875_row6_col9\" class=\"data row6 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row6_col10\" class=\"data row6 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row6_col11\" class=\"data row6 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_a5875_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_a5875_row7_col1\" class=\"data row7 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row7_col2\" class=\"data row7 col2\" >(653.0, 666.0]</td>\n",
       "      <td id=\"T_a5875_row7_col3\" class=\"data row7 col3\" >3151</td>\n",
       "      <td id=\"T_a5875_row7_col4\" class=\"data row7 col4\" >2364</td>\n",
       "      <td id=\"T_a5875_row7_col5\" class=\"data row7 col5\" >787</td>\n",
       "      <td id=\"T_a5875_row7_col6\" class=\"data row7 col6\" >10.50%</td>\n",
       "      <td id=\"T_a5875_row7_col7\" class=\"data row7 col7\" >10.51%</td>\n",
       "      <td id=\"T_a5875_row7_col8\" class=\"data row7 col8\" >10.49%</td>\n",
       "      <td id=\"T_a5875_row7_col9\" class=\"data row7 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row7_col10\" class=\"data row7 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_a5875_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_a5875_row8_col1\" class=\"data row8 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row8_col2\" class=\"data row8 col2\" >(666.0, 686.0]</td>\n",
       "      <td id=\"T_a5875_row8_col3\" class=\"data row8 col3\" >3555</td>\n",
       "      <td id=\"T_a5875_row8_col4\" class=\"data row8 col4\" >2748</td>\n",
       "      <td id=\"T_a5875_row8_col5\" class=\"data row8 col5\" >807</td>\n",
       "      <td id=\"T_a5875_row8_col6\" class=\"data row8 col6\" >11.85%</td>\n",
       "      <td id=\"T_a5875_row8_col7\" class=\"data row8 col7\" >12.21%</td>\n",
       "      <td id=\"T_a5875_row8_col8\" class=\"data row8 col8\" >10.76%</td>\n",
       "      <td id=\"T_a5875_row8_col9\" class=\"data row8 col9\" >0.000000</td>\n",
       "      <td id=\"T_a5875_row8_col10\" class=\"data row8 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row8_col11\" class=\"data row8 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5875_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_a5875_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_a5875_row9_col1\" class=\"data row9 col1\" >Score</td>\n",
       "      <td id=\"T_a5875_row9_col2\" class=\"data row9 col2\" >(686.0, inf]</td>\n",
       "      <td id=\"T_a5875_row9_col3\" class=\"data row9 col3\" >2462</td>\n",
       "      <td id=\"T_a5875_row9_col4\" class=\"data row9 col4\" >1630</td>\n",
       "      <td id=\"T_a5875_row9_col5\" class=\"data row9 col5\" >832</td>\n",
       "      <td id=\"T_a5875_row9_col6\" class=\"data row9 col6\" >8.21%</td>\n",
       "      <td id=\"T_a5875_row9_col7\" class=\"data row9 col7\" >7.24%</td>\n",
       "      <td id=\"T_a5875_row9_col8\" class=\"data row9 col8\" >11.09%</td>\n",
       "      <td id=\"T_a5875_row9_col9\" class=\"data row9 col9\" >0.020000</td>\n",
       "      <td id=\"T_a5875_row9_col10\" class=\"data row9 col10\" >0.070000</td>\n",
       "      <td id=\"T_a5875_row9_col11\" class=\"data row9 col11\" >0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171df565600>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照等频分箱，分为10组，计算模型得分 Score 的PSI\n",
    "view_psi(data_train_score,data_test_score,color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bd9ce941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_ce22d_row0_col11, #T_ce22d_row1_col11, #T_ce22d_row2_col11, #T_ce22d_row3_col11, #T_ce22d_row6_col11, #T_ce22d_row7_col11, #T_ce22d_row8_col11 {\n",
       "  width: 10em;\n",
       "}\n",
       "#T_ce22d_row4_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 100.0%, transparent 100.0%);\n",
       "}\n",
       "#T_ce22d_row5_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 25.0%, transparent 25.0%);\n",
       "}\n",
       "#T_ce22d_row9_col11 {\n",
       "  width: 10em;\n",
       "  background: linear-gradient(90deg, #02B057 50.0%, transparent 50.0%);\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ce22d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ce22d_level0_col0\" class=\"col_heading level0 col0\" >No.</th>\n",
       "      <th id=\"T_ce22d_level0_col1\" class=\"col_heading level0 col1\" >Name</th>\n",
       "      <th id=\"T_ce22d_level0_col2\" class=\"col_heading level0 col2\" >Bins Range</th>\n",
       "      <th id=\"T_ce22d_level0_col3\" class=\"col_heading level0 col3\" >#Total</th>\n",
       "      <th id=\"T_ce22d_level0_col4\" class=\"col_heading level0 col4\" >#Actual</th>\n",
       "      <th id=\"T_ce22d_level0_col5\" class=\"col_heading level0 col5\" >#Expected</th>\n",
       "      <th id=\"T_ce22d_level0_col6\" class=\"col_heading level0 col6\" >%Total</th>\n",
       "      <th id=\"T_ce22d_level0_col7\" class=\"col_heading level0 col7\" >%Actual</th>\n",
       "      <th id=\"T_ce22d_level0_col8\" class=\"col_heading level0 col8\" >%Expected</th>\n",
       "      <th id=\"T_ce22d_level0_col9\" class=\"col_heading level0 col9\" >PSI</th>\n",
       "      <th id=\"T_ce22d_level0_col10\" class=\"col_heading level0 col10\" >Total PSI</th>\n",
       "      <th id=\"T_ce22d_level0_col11\" class=\"col_heading level0 col11\" >PSI.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ce22d_row0_col0\" class=\"data row0 col0\" >1</td>\n",
       "      <td id=\"T_ce22d_row0_col1\" class=\"data row0 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row0_col2\" class=\"data row0 col2\" >(-inf, 503.9]</td>\n",
       "      <td id=\"T_ce22d_row0_col3\" class=\"data row0 col3\" >3018</td>\n",
       "      <td id=\"T_ce22d_row0_col4\" class=\"data row0 col4\" >2250</td>\n",
       "      <td id=\"T_ce22d_row0_col5\" class=\"data row0 col5\" >768</td>\n",
       "      <td id=\"T_ce22d_row0_col6\" class=\"data row0 col6\" >10.06%</td>\n",
       "      <td id=\"T_ce22d_row0_col7\" class=\"data row0 col7\" >10.00%</td>\n",
       "      <td id=\"T_ce22d_row0_col8\" class=\"data row0 col8\" >10.24%</td>\n",
       "      <td id=\"T_ce22d_row0_col9\" class=\"data row0 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row0_col10\" class=\"data row0 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row0_col11\" class=\"data row0 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ce22d_row1_col0\" class=\"data row1 col0\" >2</td>\n",
       "      <td id=\"T_ce22d_row1_col1\" class=\"data row1 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row1_col2\" class=\"data row1 col2\" >(503.9, 567.0]</td>\n",
       "      <td id=\"T_ce22d_row1_col3\" class=\"data row1 col3\" >3026</td>\n",
       "      <td id=\"T_ce22d_row1_col4\" class=\"data row1 col4\" >2276</td>\n",
       "      <td id=\"T_ce22d_row1_col5\" class=\"data row1 col5\" >750</td>\n",
       "      <td id=\"T_ce22d_row1_col6\" class=\"data row1 col6\" >10.09%</td>\n",
       "      <td id=\"T_ce22d_row1_col7\" class=\"data row1 col7\" >10.12%</td>\n",
       "      <td id=\"T_ce22d_row1_col8\" class=\"data row1 col8\" >10.00%</td>\n",
       "      <td id=\"T_ce22d_row1_col9\" class=\"data row1 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row1_col10\" class=\"data row1 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row1_col11\" class=\"data row1 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ce22d_row2_col0\" class=\"data row2 col0\" >3</td>\n",
       "      <td id=\"T_ce22d_row2_col1\" class=\"data row2 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row2_col2\" class=\"data row2 col2\" >(567.0, 610.0]</td>\n",
       "      <td id=\"T_ce22d_row2_col3\" class=\"data row2 col3\" >3018</td>\n",
       "      <td id=\"T_ce22d_row2_col4\" class=\"data row2 col4\" >2346</td>\n",
       "      <td id=\"T_ce22d_row2_col5\" class=\"data row2 col5\" >672</td>\n",
       "      <td id=\"T_ce22d_row2_col6\" class=\"data row2 col6\" >10.06%</td>\n",
       "      <td id=\"T_ce22d_row2_col7\" class=\"data row2 col7\" >10.43%</td>\n",
       "      <td id=\"T_ce22d_row2_col8\" class=\"data row2 col8\" >8.96%</td>\n",
       "      <td id=\"T_ce22d_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row2_col10\" class=\"data row2 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row2_col11\" class=\"data row2 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ce22d_row3_col0\" class=\"data row3 col0\" >4</td>\n",
       "      <td id=\"T_ce22d_row3_col1\" class=\"data row3 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row3_col2\" class=\"data row3 col2\" >(610.0, 624.0]</td>\n",
       "      <td id=\"T_ce22d_row3_col3\" class=\"data row3 col3\" >2945</td>\n",
       "      <td id=\"T_ce22d_row3_col4\" class=\"data row3 col4\" >2232</td>\n",
       "      <td id=\"T_ce22d_row3_col5\" class=\"data row3 col5\" >713</td>\n",
       "      <td id=\"T_ce22d_row3_col6\" class=\"data row3 col6\" >9.82%</td>\n",
       "      <td id=\"T_ce22d_row3_col7\" class=\"data row3 col7\" >9.92%</td>\n",
       "      <td id=\"T_ce22d_row3_col8\" class=\"data row3 col8\" >9.51%</td>\n",
       "      <td id=\"T_ce22d_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row3_col10\" class=\"data row3 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ce22d_row4_col0\" class=\"data row4 col0\" >5</td>\n",
       "      <td id=\"T_ce22d_row4_col1\" class=\"data row4 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row4_col2\" class=\"data row4 col2\" >(624.0, 635.0]</td>\n",
       "      <td id=\"T_ce22d_row4_col3\" class=\"data row4 col3\" >3065</td>\n",
       "      <td id=\"T_ce22d_row4_col4\" class=\"data row4 col4\" >2615</td>\n",
       "      <td id=\"T_ce22d_row4_col5\" class=\"data row4 col5\" >450</td>\n",
       "      <td id=\"T_ce22d_row4_col6\" class=\"data row4 col6\" >10.22%</td>\n",
       "      <td id=\"T_ce22d_row4_col7\" class=\"data row4 col7\" >11.62%</td>\n",
       "      <td id=\"T_ce22d_row4_col8\" class=\"data row4 col8\" >6.00%</td>\n",
       "      <td id=\"T_ce22d_row4_col9\" class=\"data row4 col9\" >0.040000</td>\n",
       "      <td id=\"T_ce22d_row4_col10\" class=\"data row4 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row4_col11\" class=\"data row4 col11\" >0.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ce22d_row5_col0\" class=\"data row5 col0\" >6</td>\n",
       "      <td id=\"T_ce22d_row5_col1\" class=\"data row5 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row5_col2\" class=\"data row5 col2\" >(635.0, 642.0]</td>\n",
       "      <td id=\"T_ce22d_row5_col3\" class=\"data row5 col3\" >2706</td>\n",
       "      <td id=\"T_ce22d_row5_col4\" class=\"data row5 col4\" >1823</td>\n",
       "      <td id=\"T_ce22d_row5_col5\" class=\"data row5 col5\" >883</td>\n",
       "      <td id=\"T_ce22d_row5_col6\" class=\"data row5 col6\" >9.02%</td>\n",
       "      <td id=\"T_ce22d_row5_col7\" class=\"data row5 col7\" >8.10%</td>\n",
       "      <td id=\"T_ce22d_row5_col8\" class=\"data row5 col8\" >11.77%</td>\n",
       "      <td id=\"T_ce22d_row5_col9\" class=\"data row5 col9\" >0.010000</td>\n",
       "      <td id=\"T_ce22d_row5_col10\" class=\"data row5 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row5_col11\" class=\"data row5 col11\" >0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ce22d_row6_col0\" class=\"data row6 col0\" >7</td>\n",
       "      <td id=\"T_ce22d_row6_col1\" class=\"data row6 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row6_col2\" class=\"data row6 col2\" >(642.0, 653.0]</td>\n",
       "      <td id=\"T_ce22d_row6_col3\" class=\"data row6 col3\" >3054</td>\n",
       "      <td id=\"T_ce22d_row6_col4\" class=\"data row6 col4\" >2216</td>\n",
       "      <td id=\"T_ce22d_row6_col5\" class=\"data row6 col5\" >838</td>\n",
       "      <td id=\"T_ce22d_row6_col6\" class=\"data row6 col6\" >10.18%</td>\n",
       "      <td id=\"T_ce22d_row6_col7\" class=\"data row6 col7\" >9.85%</td>\n",
       "      <td id=\"T_ce22d_row6_col8\" class=\"data row6 col8\" >11.17%</td>\n",
       "      <td id=\"T_ce22d_row6_col9\" class=\"data row6 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row6_col10\" class=\"data row6 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row6_col11\" class=\"data row6 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ce22d_row7_col0\" class=\"data row7 col0\" >8</td>\n",
       "      <td id=\"T_ce22d_row7_col1\" class=\"data row7 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row7_col2\" class=\"data row7 col2\" >(653.0, 666.0]</td>\n",
       "      <td id=\"T_ce22d_row7_col3\" class=\"data row7 col3\" >3151</td>\n",
       "      <td id=\"T_ce22d_row7_col4\" class=\"data row7 col4\" >2364</td>\n",
       "      <td id=\"T_ce22d_row7_col5\" class=\"data row7 col5\" >787</td>\n",
       "      <td id=\"T_ce22d_row7_col6\" class=\"data row7 col6\" >10.50%</td>\n",
       "      <td id=\"T_ce22d_row7_col7\" class=\"data row7 col7\" >10.51%</td>\n",
       "      <td id=\"T_ce22d_row7_col8\" class=\"data row7 col8\" >10.49%</td>\n",
       "      <td id=\"T_ce22d_row7_col9\" class=\"data row7 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row7_col10\" class=\"data row7 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ce22d_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_ce22d_row8_col1\" class=\"data row8 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row8_col2\" class=\"data row8 col2\" >(666.0, 686.0]</td>\n",
       "      <td id=\"T_ce22d_row8_col3\" class=\"data row8 col3\" >3555</td>\n",
       "      <td id=\"T_ce22d_row8_col4\" class=\"data row8 col4\" >2748</td>\n",
       "      <td id=\"T_ce22d_row8_col5\" class=\"data row8 col5\" >807</td>\n",
       "      <td id=\"T_ce22d_row8_col6\" class=\"data row8 col6\" >11.85%</td>\n",
       "      <td id=\"T_ce22d_row8_col7\" class=\"data row8 col7\" >12.21%</td>\n",
       "      <td id=\"T_ce22d_row8_col8\" class=\"data row8 col8\" >10.76%</td>\n",
       "      <td id=\"T_ce22d_row8_col9\" class=\"data row8 col9\" >0.000000</td>\n",
       "      <td id=\"T_ce22d_row8_col10\" class=\"data row8 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row8_col11\" class=\"data row8 col11\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ce22d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ce22d_row9_col0\" class=\"data row9 col0\" >10</td>\n",
       "      <td id=\"T_ce22d_row9_col1\" class=\"data row9 col1\" >Score</td>\n",
       "      <td id=\"T_ce22d_row9_col2\" class=\"data row9 col2\" >(686.0, inf]</td>\n",
       "      <td id=\"T_ce22d_row9_col3\" class=\"data row9 col3\" >2462</td>\n",
       "      <td id=\"T_ce22d_row9_col4\" class=\"data row9 col4\" >1630</td>\n",
       "      <td id=\"T_ce22d_row9_col5\" class=\"data row9 col5\" >832</td>\n",
       "      <td id=\"T_ce22d_row9_col6\" class=\"data row9 col6\" >8.21%</td>\n",
       "      <td id=\"T_ce22d_row9_col7\" class=\"data row9 col7\" >7.24%</td>\n",
       "      <td id=\"T_ce22d_row9_col8\" class=\"data row9 col8\" >11.09%</td>\n",
       "      <td id=\"T_ce22d_row9_col9\" class=\"data row9 col9\" >0.020000</td>\n",
       "      <td id=\"T_ce22d_row9_col10\" class=\"data row9 col10\" >0.070000</td>\n",
       "      <td id=\"T_ce22d_row9_col11\" class=\"data row9 col11\" >0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x171df559840>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照等频分箱，分为10组，计算模型得分 Score 的PSI\n",
    "view_psi(data_train_score,data_test_score,color='#02B057')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "90daaab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b55297e394f44ca889cba164fc302f39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8cd12c7ebd348028f9b0235041ab8b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Bins Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Actual</th>\n",
       "      <th>#Expected</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Actual</th>\n",
       "      <th>%Expected</th>\n",
       "      <th>PSI</th>\n",
       "      <th>Total PSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(-inf, 50000.0]</td>\n",
       "      <td>7676</td>\n",
       "      <td>5855</td>\n",
       "      <td>1821</td>\n",
       "      <td>25.5867%</td>\n",
       "      <td>26.0222%</td>\n",
       "      <td>24.2800%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(50000.0, 100000.0]</td>\n",
       "      <td>4822</td>\n",
       "      <td>3593</td>\n",
       "      <td>1229</td>\n",
       "      <td>16.0733%</td>\n",
       "      <td>15.9689%</td>\n",
       "      <td>16.3867%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(100000.0, 180000.0]</td>\n",
       "      <td>6123</td>\n",
       "      <td>4548</td>\n",
       "      <td>1575</td>\n",
       "      <td>20.4100%</td>\n",
       "      <td>20.2133%</td>\n",
       "      <td>21.0000%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(180000.0, 270000.0]</td>\n",
       "      <td>5421</td>\n",
       "      <td>4035</td>\n",
       "      <td>1386</td>\n",
       "      <td>18.0700%</td>\n",
       "      <td>17.9333%</td>\n",
       "      <td>18.4800%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(270000.0, inf]</td>\n",
       "      <td>5958</td>\n",
       "      <td>4469</td>\n",
       "      <td>1489</td>\n",
       "      <td>19.8600%</td>\n",
       "      <td>19.8622%</td>\n",
       "      <td>19.8533%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.       Name            Bins Range  #Total  #Actual  #Expected    %Total  \\\n",
       "0    1  LIMIT_BAL       (-inf, 50000.0]    7676     5855       1821  25.5867%   \n",
       "1    2  LIMIT_BAL   (50000.0, 100000.0]    4822     3593       1229  16.0733%   \n",
       "2    3  LIMIT_BAL  (100000.0, 180000.0]    6123     4548       1575  20.4100%   \n",
       "3    4  LIMIT_BAL  (180000.0, 270000.0]    5421     4035       1386  18.0700%   \n",
       "4    5  LIMIT_BAL       (270000.0, inf]    5958     4469       1489  19.8600%   \n",
       "\n",
       "    %Actual %Expected  PSI  Total PSI  \n",
       "0  26.0222%  24.2800% 0.00       0.00  \n",
       "1  15.9689%  16.3867% 0.00       0.00  \n",
       "2  20.2133%  21.0000% 0.00       0.00  \n",
       "3  17.9333%  18.4800% 0.00       0.00  \n",
       "4  19.8622%  19.8533% 0.00       0.00  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "\n",
    "# 按照等频分箱，分为5组，计算PSI\n",
    "get_psi(data_train_score,data_test_score,col='LIMIT_BAL',qcut=5,precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3308dae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f88d00c48734427a8e3eb8cc2ce56179",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4bb1900f73ee432287708ba74a5af407",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Bins Range</th>\n",
       "      <th>#Total</th>\n",
       "      <th>#Actual</th>\n",
       "      <th>#Expected</th>\n",
       "      <th>%Total</th>\n",
       "      <th>%Actual</th>\n",
       "      <th>%Expected</th>\n",
       "      <th>PSI</th>\n",
       "      <th>Total PSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(-inf, 30000.0]</td>\n",
       "      <td>4111</td>\n",
       "      <td>3109</td>\n",
       "      <td>1002</td>\n",
       "      <td>13.7033%</td>\n",
       "      <td>13.8178%</td>\n",
       "      <td>13.3600%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(30000.0, 50000.0]</td>\n",
       "      <td>3649</td>\n",
       "      <td>2746</td>\n",
       "      <td>903</td>\n",
       "      <td>12.1633%</td>\n",
       "      <td>12.2044%</td>\n",
       "      <td>12.0400%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(50000.0, 100000.0]</td>\n",
       "      <td>4794</td>\n",
       "      <td>3593</td>\n",
       "      <td>1201</td>\n",
       "      <td>15.9800%</td>\n",
       "      <td>15.9689%</td>\n",
       "      <td>16.0133%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(100000.0, inf]</td>\n",
       "      <td>17446</td>\n",
       "      <td>13052</td>\n",
       "      <td>4394</td>\n",
       "      <td>58.1533%</td>\n",
       "      <td>58.0089%</td>\n",
       "      <td>58.5867%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.       Name           Bins Range  #Total  #Actual  #Expected    %Total  \\\n",
       "0    1  LIMIT_BAL      (-inf, 30000.0]    4111     3109       1002  13.7033%   \n",
       "1    2  LIMIT_BAL   (30000.0, 50000.0]    3649     2746        903  12.1633%   \n",
       "2    3  LIMIT_BAL  (50000.0, 100000.0]    4794     3593       1201  15.9800%   \n",
       "3    4  LIMIT_BAL      (100000.0, inf]   17446    13052       4394  58.1533%   \n",
       "\n",
       "    %Actual %Expected  PSI  Total PSI  \n",
       "0  13.8178%  13.3600% 0.00       0.00  \n",
       "1  12.2044%  12.0400% 0.00       0.00  \n",
       "2  15.9689%  16.0133% 0.00       0.00  \n",
       "3  58.0089%  58.5867% 0.00       0.00  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "\n",
    "# 按照指定的切分点，计算PSI\n",
    "get_psi(data_train_score,data_test_score,col='LIMIT_BAL',bins=[-inf,30000,50000,100000,inf],precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "65a414d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>名称</th>\n",
       "      <th>分组</th>\n",
       "      <th>#合计</th>\n",
       "      <th>#实际</th>\n",
       "      <th>#期望</th>\n",
       "      <th>%合计</th>\n",
       "      <th>%实际</th>\n",
       "      <th>%期望</th>\n",
       "      <th>PSI</th>\n",
       "      <th>PSI 合计</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(-inf, 30000.0]</td>\n",
       "      <td>4111</td>\n",
       "      <td>3109</td>\n",
       "      <td>1002</td>\n",
       "      <td>13.7033%</td>\n",
       "      <td>13.8178%</td>\n",
       "      <td>13.3600%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(30000.0, 50000.0]</td>\n",
       "      <td>3649</td>\n",
       "      <td>2746</td>\n",
       "      <td>903</td>\n",
       "      <td>12.1633%</td>\n",
       "      <td>12.2044%</td>\n",
       "      <td>12.0400%</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(50000.0, 100000.0]</td>\n",
       "      <td>4794</td>\n",
       "      <td>3593</td>\n",
       "      <td>1201</td>\n",
       "      <td>15.9800%</td>\n",
       "      <td>15.9689%</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>(100000.0, inf]</td>\n",
       "      <td>17446</td>\n",
       "      <td>13052</td>\n",
       "      <td>4394</td>\n",
       "      <td>58.1533%</td>\n",
       "      <td>58.0089%</td>\n",
       "      <td>58.5867%</td>\n",
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      "text/plain": [
       "   序号         名称                   分组    #合计    #实际   #期望       %合计       %实际  \\\n",
       "0   1  LIMIT_BAL      (-inf, 30000.0]   4111   3109  1002  13.7033%  13.8178%   \n",
       "1   2  LIMIT_BAL   (30000.0, 50000.0]   3649   2746   903  12.1633%  12.2044%   \n",
       "2   3  LIMIT_BAL  (50000.0, 100000.0]   4794   3593  1201  15.9800%  15.9689%   \n",
       "3   4  LIMIT_BAL      (100000.0, inf]  17446  13052  4394  58.1533%  58.0089%   \n",
       "\n",
       "        %期望  PSI  PSI 合计  \n",
       "0  13.3600% 0.00    0.00  \n",
       "1  12.0400% 0.00    0.00  \n",
       "2  16.0133% 0.00    0.00  \n",
       "3  58.5867% 0.00    0.00  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "\n",
    "# 按照指定的切分点，计算PSI，标题显示为中文\n",
    "get_psi(data_train_score,data_test_score,col='LIMIT_BAL',bins=[-inf,30000,50000,100000,inf],precision=4,language='cn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "d7f6c95c",
   "metadata": {},
   "outputs": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>PSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Score</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PAY_AMT3</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LIMIT_BAL</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Proba</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PAY_AMT2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PAY_AMT6</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PAY_AMT4</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PAY_AMT5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAY_AMT1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PAY_AMT2_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PAY_AMT5_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PAY_0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY_0_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LIMIT_BAL_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PAY_AMT3_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>PAY_AMT6_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PAY_AMT4_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PAY_AMT1_Score</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  PSI\n",
       "16            Score 0.07\n",
       "8          PAY_AMT3 0.00\n",
       "4         LIMIT_BAL 0.00\n",
       "17            Proba 0.00\n",
       "6          PAY_AMT2 0.00\n",
       "14         PAY_AMT6 0.00\n",
       "10         PAY_AMT4 0.00\n",
       "12         PAY_AMT5 0.00\n",
       "2          PAY_AMT1 0.00\n",
       "7    PAY_AMT2_Score 0.00\n",
       "13   PAY_AMT5_Score 0.00\n",
       "0             PAY_0 0.00\n",
       "1       PAY_0_Score 0.00\n",
       "5   LIMIT_BAL_Score 0.00\n",
       "9    PAY_AMT3_Score 0.00\n",
       "15   PAY_AMT6_Score 0.00\n",
       "11   PAY_AMT4_Score 0.00\n",
       "3    PAY_AMT1_Score 0.00"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 批量计算所有特征的PSI\n",
    "data_train_score = get_predict_score(data_train,scorecard)\n",
    "data_test_score = get_predict_score(data_test,scorecard)\n",
    "\n",
    "get_data_psi(data_train_score,data_test_score,precision=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e200d5e",
   "metadata": {},
   "source": [
    "## 模型应用分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfbfccbc",
   "metadata": {},
   "source": [
    "### 决策树分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "45e305ed",
   "metadata": {},
   "outputs": [
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       "<text text-anchor=\"middle\" x=\"65\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.422</text>\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1191</text>\n",
       "<text text-anchor=\"middle\" x=\"65\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [361, 830]</text>\n",
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       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>4</title>\n",
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       "<text text-anchor=\"middle\" x=\"213\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.433</text>\n",
       "<text text-anchor=\"middle\" x=\"213\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1161</text>\n",
       "<text text-anchor=\"middle\" x=\"213\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [368, 793]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
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       "<title>2&#45;&gt;4</title>\n",
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       "<title>7</title>\n",
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       "<text text-anchor=\"middle\" x=\"522\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Score &lt;= 610.5</text>\n",
       "<text text-anchor=\"middle\" x=\"522\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.353</text>\n",
       "<text text-anchor=\"middle\" x=\"522\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5774</text>\n",
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       "<!-- 6&#45;&gt;7 -->\n",
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       "<title>10</title>\n",
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       "<text text-anchor=\"middle\" x=\"691\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">Score &lt;= 653.5</text>\n",
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      "text/plain": [
       "<westat.model.tree.graphviz.sources.Source at 0x171e42d36d0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_tree(data_train_score[['Score','y']],max_depth=3,criterion='gini')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "81dbcd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制决策树，并保存图片到本地\n",
    "# plot_tree(data_train_score[['Score','y']],max_depth=3,criterion='gini',out_file='test.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "07ce7e52",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制决策树，并保存图片到本地\n",
    "# plot_tree(data_train_score[['Score','y']],max_depth=3,criterion='gini',out_file='test.pdf')"
   ]
  }
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