kbo-ops-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "zdQl77V80Scn"
   },
   "outputs": [],
   "source": [
    "# Load libraries\n",
    "from matplotlib import font_manager, rc\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import platform\n",
    "\n",
    "if platform.system() == 'Windows':\n",
    "    font_name = font_manager.FontProperties(fname=\"c:/Windows/Fonts/malgun.ttf\"\n",
    "                                           ).get_name()\n",
    "    rc('font', family=font_name)\n",
    "else:\n",
    "    rc('font', family='AppleGothic')\n",
    "\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NISwJz8C0qYo"
   },
   "source": [
    "## Preseason Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 598
    },
    "id": "dPNZE1040n5M",
    "outputId": "71426ad1-05b2-4e46-978c-586ad7766a54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1393, 29)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\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>batter_id</th>\n",
       "      <th>batter_name</th>\n",
       "      <th>year</th>\n",
       "      <th>team</th>\n",
       "      <th>avg</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>E</th>\n",
       "      <th>height/weight</th>\n",
       "      <th>year_born</th>\n",
       "      <th>position</th>\n",
       "      <th>career</th>\n",
       "      <th>starting_salary</th>\n",
       "      <th>OPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>가르시아</td>\n",
       "      <td>2018</td>\n",
       "      <td>LG</td>\n",
       "      <td>0.350</td>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.409</td>\n",
       "      <td>1</td>\n",
       "      <td>177cm/93kg</td>\n",
       "      <td>1985년 04월 12일</td>\n",
       "      <td>내야수(우투우타)</td>\n",
       "      <td>쿠바 Ciego de Avila Maximo Gomez Baez(대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2011</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2014</td>\n",
       "      <td>한화</td>\n",
       "      <td>-</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2015</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.130</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.286</td>\n",
       "      <td>2</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2016</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.188</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.281</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   batter_id batter_name  year team    avg   G  AB  R  H  2B  ...  GDP    SLG  \\\n",
       "0          0        가르시아  2018   LG  0.350   7  20  1  7   1  ...    1  0.550   \n",
       "1          1         강경학  2011   한화  0.000   4   2  2  0   0  ...    0  0.000   \n",
       "2          1         강경학  2014   한화      -   4   0  2  0   0  ...    0    NaN   \n",
       "3          1         강경학  2015   한화  0.130  10  23  3  3   0  ...    0  0.130   \n",
       "4          1         강경학  2016   한화  0.188  14  32  4  6   1  ...    0  0.281   \n",
       "\n",
       "     OBP  E  height/weight      year_born   position  \\\n",
       "0  0.409  1     177cm/93kg  1985년 04월 12일  내야수(우투우타)   \n",
       "1  0.500  0     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "2    NaN  0     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "3  0.286  2     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "4  0.212  0     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "\n",
       "                                   career  starting_salary    OPS  \n",
       "0  쿠바 Ciego de Avila Maximo Gomez Baez(대)              NaN  0.959  \n",
       "1                       광주대성초-광주동성중-광주동성고          10000만원  0.500  \n",
       "2                       광주대성초-광주동성중-광주동성고          10000만원    NaN  \n",
       "3                       광주대성초-광주동성중-광주동성고          10000만원  0.416  \n",
       "4                       광주대성초-광주동성중-광주동성고          10000만원  0.493  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load preseason dataset\n",
    "preseason_df = pd.read_csv(\"data/Pre_Season_Batter.csv\")\n",
    "# Load regular season dataset\n",
    "regular_season_df = pd.read_csv(\"data/Regular_Season_Batter.csv\")\n",
    "# Print data size\n",
    "print(preseason_df.shape)\n",
    "# Display top 5 data\n",
    "display(preseason_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "Nj3UKf8u0555",
    "outputId": "daf91ed8-8766-44fe-b911-6daec3c723da"
   },
   "outputs": [
    {
     "data": {
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       "\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>batter_id</th>\n",
       "      <th>year</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>TB</th>\n",
       "      <th>...</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SO</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>E</th>\n",
       "      <th>OPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1364.000000</td>\n",
       "      <td>1368.000000</td>\n",
       "      <td>1393.000000</td>\n",
       "      <td>1364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>173.434314</td>\n",
       "      <td>2013.014358</td>\n",
       "      <td>8.705671</td>\n",
       "      <td>19.201723</td>\n",
       "      <td>2.679828</td>\n",
       "      <td>5.021536</td>\n",
       "      <td>0.954774</td>\n",
       "      <td>0.119885</td>\n",
       "      <td>0.391960</td>\n",
       "      <td>7.391960</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629576</td>\n",
       "      <td>0.291457</td>\n",
       "      <td>1.877961</td>\n",
       "      <td>0.330223</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>0.447236</td>\n",
       "      <td>0.361012</td>\n",
       "      <td>0.317912</td>\n",
       "      <td>0.381910</td>\n",
       "      <td>0.676924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>94.716851</td>\n",
       "      <td>4.166757</td>\n",
       "      <td>5.562686</td>\n",
       "      <td>13.395946</td>\n",
       "      <td>2.637212</td>\n",
       "      <td>4.232584</td>\n",
       "      <td>1.196904</td>\n",
       "      <td>0.379976</td>\n",
       "      <td>0.748557</td>\n",
       "      <td>6.538787</td>\n",
       "      <td>...</td>\n",
       "      <td>1.146854</td>\n",
       "      <td>0.595522</td>\n",
       "      <td>2.053392</td>\n",
       "      <td>0.642204</td>\n",
       "      <td>3.180884</td>\n",
       "      <td>0.723364</td>\n",
       "      <td>0.269892</td>\n",
       "      <td>0.151489</td>\n",
       "      <td>0.729521</td>\n",
       "      <td>0.386933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.217000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.472000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>178.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.344500</td>\n",
       "      <td>0.333000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>254.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>344.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>183.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         batter_id         year            G           AB            R  \\\n",
       "count  1393.000000  1393.000000  1393.000000  1393.000000  1393.000000   \n",
       "mean    173.434314  2013.014358     8.705671    19.201723     2.679828   \n",
       "std      94.716851     4.166757     5.562686    13.395946     2.637212   \n",
       "min       0.000000  2002.000000     1.000000     0.000000     0.000000   \n",
       "25%      99.000000  2010.000000     6.000000     9.000000     1.000000   \n",
       "50%     178.000000  2014.000000     9.000000    18.000000     2.000000   \n",
       "75%     254.000000  2017.000000    11.000000    28.000000     4.000000   \n",
       "max     344.000000  2018.000000   119.000000   183.000000    35.000000   \n",
       "\n",
       "                 H           2B           3B           HR           TB  ...  \\\n",
       "count  1393.000000  1393.000000  1393.000000  1393.000000  1393.000000  ...   \n",
       "mean      5.021536     0.954774     0.119885     0.391960     7.391960  ...   \n",
       "std       4.232584     1.196904     0.379976     0.748557     6.538787  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       2.000000     0.000000     0.000000     0.000000     2.000000  ...   \n",
       "50%       4.000000     1.000000     0.000000     0.000000     6.000000  ...   \n",
       "75%       8.000000     2.000000     0.000000     1.000000    11.000000  ...   \n",
       "max      51.000000    11.000000     4.000000     5.000000    68.000000  ...   \n",
       "\n",
       "                SB           CS           BB          HBP           SO  \\\n",
       "count  1393.000000  1393.000000  1393.000000  1393.000000  1393.000000   \n",
       "mean      0.629576     0.291457     1.877961     0.330223     3.714286   \n",
       "std       1.146854     0.595522     2.053392     0.642204     3.180884   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     1.000000   \n",
       "50%       0.000000     0.000000     1.000000     0.000000     3.000000   \n",
       "75%       1.000000     0.000000     3.000000     1.000000     5.000000   \n",
       "max       9.000000     4.000000    21.000000     4.000000    36.000000   \n",
       "\n",
       "               GDP          SLG          OBP            E          OPS  \n",
       "count  1393.000000  1364.000000  1368.000000  1393.000000  1364.000000  \n",
       "mean      0.447236     0.361012     0.317912     0.381910     0.676924  \n",
       "std       0.723364     0.269892     0.151489     0.729521     0.386933  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.217000     0.250000     0.000000     0.472000  \n",
       "50%       0.000000     0.344500     0.333000     0.000000     0.675000  \n",
       "75%       1.000000     0.478000     0.400000     1.000000     0.867000  \n",
       "max       5.000000     4.000000     1.000000     5.000000     5.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Describe preseason statistical data\n",
    "display(preseason_df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "id": "j9WrDgRM0_CY",
    "outputId": "18cca920-ae4a-46c7-8011-f2f1504fc7a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization\n",
    "preseason_df.hist(figsize=(10,9))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "id": "CW1Ja6SC1XyV",
    "outputId": "884e8c77-5cac-405a-998c-8d0df0c347e1"
   },
   "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>year</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>regular</th>\n",
       "      <td>43.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>73.00</td>\n",
       "      <td>85.00</td>\n",
       "      <td>98.00</td>\n",
       "      <td>115.00</td>\n",
       "      <td>124.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>151.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>194.00</td>\n",
       "      <td>186.00</td>\n",
       "      <td>207.00</td>\n",
       "      <td>213.00</td>\n",
       "      <td>217.00</td>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preseason</th>\n",
       "      <td>12.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>72.00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>104.00</td>\n",
       "      <td>117.00</td>\n",
       "      <td>134.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>167.00</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ratio</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year        2002   2003   2004   2005   2006   2007    2008    2009    2010  \\\n",
       "regular    43.00  54.00  68.00  73.00  85.00  98.00  115.00  124.00  130.00   \n",
       "preseason  12.00  19.00  28.00  37.00  36.00  43.00   61.00   66.00   72.00   \n",
       "ratio       0.28   0.35   0.41   0.51   0.42   0.44    0.53    0.53    0.55   \n",
       "\n",
       "year        2011   2012    2013    2014    2015    2016    2017   2018  \n",
       "regular    151.0  174.0  194.00  186.00  207.00  213.00  217.00  227.0  \n",
       "preseason   75.0   87.0  104.00  117.00  134.00  153.00  167.00  182.0  \n",
       "ratio        0.5    0.5    0.54    0.63    0.65    0.72    0.77    0.8  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group number of players by year in regular season (Starting from 2002)\n",
    "regular_count = regular_season_df.groupby('year')['batter_id'].count().rename('regular')\n",
    "\n",
    "# Group number of players by year in preseason since 2002\n",
    "preseason_count = preseason_df.groupby('year')['batter_id'].count().rename('preseason')\n",
    "pd.concat([regular_count,preseason_count, np.round(preseason_count/regular_count,2).rename(\n",
    "        'ratio')], axis = 1).transpose().loc[:,2002:] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QpfzJa2h1w8v"
   },
   "source": [
    "Note that there is not much preseason data compared to regular season data in the past."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7XPpTeEk2A9K",
    "outputId": "31f023e7-d7cd-4f55-c8e0-074ceb0b49f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1358, 30) (1358, 30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1358"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new index from batter's name and year\n",
    "regular_season_df['new_idx'] = regular_season_df['batter_name'] + \\\n",
    "                               regular_season_df['year'].apply(str)\n",
    "preseason_df['new_idx'] = preseason_df['batter_name'] + preseason_df['year'].apply(str)\n",
    "\n",
    "# Use set() to get intersection of both regular season and preseason\n",
    "intersection_idx = list(set(regular_season_df['new_idx']). \\\n",
    "                        intersection(preseason_df['new_idx']))\n",
    "\n",
    "# Load data in the intersection\n",
    "regular_season_new = regular_season_df.loc[\n",
    "    regular_season_df['new_idx'].apply(lambda x: x in intersection_idx)].copy()\n",
    "regular_season_new = regular_season_new.sort_values(by = 'new_idx').reset_index(drop=True) \n",
    "\n",
    "# Sort data by new_idx\n",
    "preseason_new = preseason_df.loc[preseason_df['new_idx'].apply(\n",
    "    lambda x: x in intersection_idx)].copy()\n",
    "preseason_new = preseason_new.sort_values(by = 'new_idx').reset_index(drop=True)\n",
    "\n",
    "# Print output to check validation\n",
    "# Now the size of both regular season and preseason is same\n",
    "print(regular_season_new.shape, preseason_new.shape)\n",
    "sum(regular_season_new['new_idx'] == preseason_new['new_idx'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "JCfIjf6a2w8A",
    "outputId": "6a430623-c249-4caf-bf48-d814511196cb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/likelion/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the correlation between preseason and regular season and visualize the graph\n",
    "correlation = regular_season_new['OPS'].corr(preseason_new['OPS'])\n",
    "# Plot scatter graph\n",
    "sns.scatterplot(regular_season_new['OPS'], preseason_new['OPS'])\n",
    "plt.title('Correlation: '+str(np.round(correlation,2)), fontsize=20)\n",
    "plt.xlabel(\"Regular season OPS\",fontsize=12)\n",
    "plt.ylabel(\"Preseason OPS\",fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5jmflusX4DOk"
   },
   "source": [
    "The scatter plot of regular season and preseason is not linear, and also the correlation value is just 0.15.\n",
    "\n",
    "That is to say, **the regular season OPS and the preseason OPS are hardly correlated**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q39XcSQY5FHe"
   },
   "source": [
    "## Regular season Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 878
    },
    "id": "eXHMA5e33e6x",
    "outputId": "98b31ce7-2ece-42fd-db7f-dc6c3bd44aad"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2454, 29)"
      ]
     },
     "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>batter_id</th>\n",
       "      <th>batter_name</th>\n",
       "      <th>year</th>\n",
       "      <th>team</th>\n",
       "      <th>avg</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>...</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>E</th>\n",
       "      <th>height/weight</th>\n",
       "      <th>year_born</th>\n",
       "      <th>position</th>\n",
       "      <th>career</th>\n",
       "      <th>starting_salary</th>\n",
       "      <th>OPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>가르시아</td>\n",
       "      <td>2018</td>\n",
       "      <td>LG</td>\n",
       "      <td>0.339</td>\n",
       "      <td>50</td>\n",
       "      <td>183</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.383</td>\n",
       "      <td>9</td>\n",
       "      <td>177cm/93kg</td>\n",
       "      <td>1985년 04월 12일</td>\n",
       "      <td>내야수(우투우타)</td>\n",
       "      <td>쿠바 Ciego de Avila Maximo Gomez Baez(대)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2011</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2014</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.221</td>\n",
       "      <td>41</td>\n",
       "      <td>86</td>\n",
       "      <td>11</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.337</td>\n",
       "      <td>6</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2015</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.257</td>\n",
       "      <td>120</td>\n",
       "      <td>311</td>\n",
       "      <td>50</td>\n",
       "      <td>80</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.325</td>\n",
       "      <td>0.348</td>\n",
       "      <td>15</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>강경학</td>\n",
       "      <td>2016</td>\n",
       "      <td>한화</td>\n",
       "      <td>0.158</td>\n",
       "      <td>46</td>\n",
       "      <td>101</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.232</td>\n",
       "      <td>7</td>\n",
       "      <td>180cm/72kg</td>\n",
       "      <td>1992년 08월 11일</td>\n",
       "      <td>내야수(우투좌타)</td>\n",
       "      <td>광주대성초-광주동성중-광주동성고</td>\n",
       "      <td>10000만원</td>\n",
       "      <td>0.489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   batter_id batter_name  year team    avg    G   AB   R   H  2B  ...  GDP  \\\n",
       "0          0        가르시아  2018   LG  0.339   50  183  27  62   9  ...    3   \n",
       "1          1         강경학  2011   한화  0.000    2    1   0   0   0  ...    0   \n",
       "2          1         강경학  2014   한화  0.221   41   86  11  19   2  ...    1   \n",
       "3          1         강경학  2015   한화  0.257  120  311  50  80   7  ...    3   \n",
       "4          1         강경학  2016   한화  0.158   46  101  16  16   3  ...    5   \n",
       "\n",
       "     SLG    OBP   E  height/weight      year_born   position  \\\n",
       "0  0.519  0.383   9     177cm/93kg  1985년 04월 12일  내야수(우투우타)   \n",
       "1  0.000  0.000   1     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "2  0.349  0.337   6     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "3  0.325  0.348  15     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "4  0.257  0.232   7     180cm/72kg  1992년 08월 11일  내야수(우투좌타)   \n",
       "\n",
       "                                   career  starting_salary    OPS  \n",
       "0  쿠바 Ciego de Avila Maximo Gomez Baez(대)              NaN  0.902  \n",
       "1                       광주대성초-광주동성중-광주동성고          10000만원  0.000  \n",
       "2                       광주대성초-광주동성중-광주동성고          10000만원  0.686  \n",
       "3                       광주대성초-광주동성중-광주동성고          10000만원  0.673  \n",
       "4                       광주대성초-광주동성중-광주동성고          10000만원  0.489  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "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>batter_id</th>\n",
       "      <th>year</th>\n",
       "      <th>avg</th>\n",
       "      <th>G</th>\n",
       "      <th>AB</th>\n",
       "      <th>R</th>\n",
       "      <th>H</th>\n",
       "      <th>2B</th>\n",
       "      <th>3B</th>\n",
       "      <th>HR</th>\n",
       "      <th>...</th>\n",
       "      <th>SB</th>\n",
       "      <th>CS</th>\n",
       "      <th>BB</th>\n",
       "      <th>HBP</th>\n",
       "      <th>SO</th>\n",
       "      <th>GDP</th>\n",
       "      <th>SLG</th>\n",
       "      <th>OBP</th>\n",
       "      <th>E</th>\n",
       "      <th>OPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2428.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2428.000000</td>\n",
       "      <td>2430.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>2428.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>178.079462</td>\n",
       "      <td>2011.614507</td>\n",
       "      <td>0.237559</td>\n",
       "      <td>72.535045</td>\n",
       "      <td>201.514670</td>\n",
       "      <td>29.912388</td>\n",
       "      <td>55.988183</td>\n",
       "      <td>9.863488</td>\n",
       "      <td>0.957620</td>\n",
       "      <td>5.504075</td>\n",
       "      <td>...</td>\n",
       "      <td>5.290139</td>\n",
       "      <td>2.335778</td>\n",
       "      <td>20.943765</td>\n",
       "      <td>3.424613</td>\n",
       "      <td>38.596985</td>\n",
       "      <td>4.603504</td>\n",
       "      <td>0.343826</td>\n",
       "      <td>0.306684</td>\n",
       "      <td>3.676447</td>\n",
       "      <td>0.649939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>97.557947</td>\n",
       "      <td>4.992833</td>\n",
       "      <td>0.098440</td>\n",
       "      <td>45.093871</td>\n",
       "      <td>169.537029</td>\n",
       "      <td>28.778759</td>\n",
       "      <td>52.253844</td>\n",
       "      <td>9.871314</td>\n",
       "      <td>1.647193</td>\n",
       "      <td>7.989380</td>\n",
       "      <td>...</td>\n",
       "      <td>9.088580</td>\n",
       "      <td>3.194045</td>\n",
       "      <td>21.206113</td>\n",
       "      <td>4.132614</td>\n",
       "      <td>31.801466</td>\n",
       "      <td>4.713531</td>\n",
       "      <td>0.163335</td>\n",
       "      <td>0.111778</td>\n",
       "      <td>4.585248</td>\n",
       "      <td>0.261634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>101.250000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.203000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.267454</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.546000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.360124</td>\n",
       "      <td>0.328592</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.688637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>265.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>357.500000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.436000</td>\n",
       "      <td>0.367000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.797234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>344.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>161.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         batter_id         year          avg            G           AB  \\\n",
       "count  2454.000000  2454.000000  2428.000000  2454.000000  2454.000000   \n",
       "mean    178.079462  2011.614507     0.237559    72.535045   201.514670   \n",
       "std      97.557947     4.992833     0.098440    45.093871   169.537029   \n",
       "min       0.000000  1993.000000     0.000000     1.000000     0.000000   \n",
       "25%     101.250000  2008.000000     0.203000    28.000000    38.250000   \n",
       "50%     183.000000  2013.000000     0.255000    79.000000   163.000000   \n",
       "75%     265.000000  2016.000000     0.291000   115.000000   357.500000   \n",
       "max     344.000000  2018.000000     1.000000   144.000000   600.000000   \n",
       "\n",
       "                 R            H           2B           3B           HR  ...  \\\n",
       "count  2454.000000  2454.000000  2454.000000  2454.000000  2454.000000  ...   \n",
       "mean     29.912388    55.988183     9.863488     0.957620     5.504075  ...   \n",
       "std      28.778759    52.253844     9.871314     1.647193     7.989380  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000     8.000000     1.000000     0.000000     0.000000  ...   \n",
       "50%      21.000000    40.000000     7.000000     0.000000     2.000000  ...   \n",
       "75%      49.000000   100.000000    16.000000     1.000000     8.000000  ...   \n",
       "max     135.000000   201.000000    47.000000    17.000000    53.000000  ...   \n",
       "\n",
       "                SB           CS           BB          HBP           SO  \\\n",
       "count  2454.000000  2454.000000  2454.000000  2454.000000  2454.000000   \n",
       "mean      5.290139     2.335778    20.943765     3.424613    38.596985   \n",
       "std       9.088580     3.194045    21.206113     4.132614    31.801466   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     3.000000     0.000000    10.000000   \n",
       "50%       2.000000     1.000000    14.000000     2.000000    33.000000   \n",
       "75%       6.000000     3.000000    34.000000     5.000000    60.000000   \n",
       "max      84.000000    21.000000   108.000000    27.000000   161.000000   \n",
       "\n",
       "               GDP          SLG          OBP            E          OPS  \n",
       "count  2454.000000  2428.000000  2430.000000  2454.000000  2428.000000  \n",
       "mean      4.603504     0.343826     0.306684     3.676447     0.649939  \n",
       "std       4.713531     0.163335     0.111778     4.585248     0.261634  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       1.000000     0.267454     0.272727     0.000000     0.546000  \n",
       "50%       3.000000     0.360124     0.328592     2.000000     0.688637  \n",
       "75%       7.000000     0.436000     0.367000     5.000000     0.797234  \n",
       "max      24.000000     3.000000     1.000000    30.000000     4.000000  \n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load regular season dataset and display data information\n",
    "regular_season_df = pd.read_csv(\"data/Regular_Season_Batter.csv\")\n",
    "display(regular_season_df.shape, regular_season_df.head(),regular_season_df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 657
    },
    "id": "-2qB8-1h56y_",
    "outputId": "12614025-8200-4236-9f91-d01d76775e92"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize regular season data\n",
    "regular_season_df.hist(figsize=(10,9))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "id": "lfEyAcBC6vXp",
    "outputId": "50928b98-995f-497d-d723-6dd2f2452b8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xRKQoXAXcZmYnAcuA84Erh2ipAl5z+B5gV7wv3mYWvaFYz54kKdZgN+eifoxXi/Rp4LfOH63RzIw984r24VKfg68N7zN2KNHn0vH5+CPgbjObi/fd5FTgM4PE4xJMcJI+XyT3+ZhIvINyzi0zs5eAD5vZD/2Ea1DmjW59FvD3Ya7BlDjnnjazLeizMW9oegVJmZmNwatFW+GvetT/+Z4hyh+M1+Th0X5PXYrXl+BivDfUE81sURJxTMerzXl4kKdHGlMyHsFrxnHGEM+/Ay/xSuUYAPgfNIuBj/hv3qfjTwcRI3qcdw21HzOLvhmfw54BAmKTvBK8PnbZiHc41+LdDf6pnzAPyrzhx88BfumcG67md0hmdqzf/HM4e+P1RVgRp5yIjF534PW3+xLeCM/deN0cBuW8+ceWAO/0k4hB+e/bT/gPh5pG5vhh4joPuNU59yvXd0j+NwGJDrufrEeAE6zftD4x3oPXZ+7ZNBzrb3jvzZ8E3ouXPPefpuBR4LjhphmI+XzM9PlKJN7hfBPvO9iQU3r4fgxU4Y0/MCI2yBQf/Z4fj1czvWykx5D0UqInKfHnUfk/vLuKvwSveRteDc5X/AQqtnw9XufjLcRU65vZoXgTyH7dOdfinLsHr83+VWY2rd9h32T9JsP2m1Zcj3cX7f/1j3MkMeH1C8PMauOfCcCb7uEJ4Af9YzZvPraf4L35/SXB/cWzGK+PwU14CWb/D4YH8b40XOknwX2Y2bF4HzDgjRBWBkzpV+wLeCPIZSPeITnngnh3Ig8G/jTYh7OZvQ2vr8GzeE2kRup9wL9siPmhzOxMvC8lNw/WV1REBHprwf4POBGvpcGvnXNb4mx2ObAvg0x+bWZVZnYdcLpz7hm8yda/4fcdji33DrxaoaGEgBn9tqnG66c1ZI1Qin6AlxT9vH8Sa2YfxHtP/WG8PtOJ8M/7T/FG87wIuH2QLhXReH5mMXP4+fGUmdm32VOrltHzlWC8w23/F7zBza42sw/1f95/Pdfg1Sh/zTk3YM7bJNxq3vy44wY5TgXePLblDHNDQ7JLTTclUbPN7IyYx5V4o36dj3cn6Sa8N5qoi/AGH/mXmf0M78v3DOBTeFX674w2J/TfHG7E6/e0OGYfn8VLjH5F335mewPLzOwGvJEjG4CPAfvhjSS1ZojXkHBMvugdqR+b2YN4c9383xD7xjnnzOx9eDWKz5jZT/Dm+5uHV0MZBt7jnOseah/JcM5tMbM/4E0DcFP/5qIx8TwCPGdmvwSewztf78Q7p9GBZ36FV5P6oJn9CK9Zy7vx+tL9HPi0mX0RbwCWlzIRbwLb/9vMTsHrx7DczH6N9zcaC5yM90XhH3jz4e0aSYy+f+JNZP+M30Tzb3jNgSfjnbeT8AZZiHf3VETkN3g1KBPw5l4blnPuXjP7Ct4NuqOB24AOvBtuH8UbWTr6OfRhvPerJ/z37VfxBiP7JN5n6cVDHOYa4Idmdgfejcd6f5tlePOQHuMnDH9M8rUO97pe8BO6m4BZZvZbvKkF3oXXCuM24Mp0HQ9vioTv4A2Ydekg8Txv3gTsNwIHmNlNeDd75+A106zCu7kI2Tlfw8abgM/ifcf4rZl9mD3z+O2NN37Cvnijiv8whRjB+zz8AnCRmf0e72ZyN94cwh/BG+zl8+noEy9pkulhPbUU/sLQQ++G8Aa8+NAQ25XjvSG8gFfTtgVvctTZ/cp9F69z8MGD7OPz/rE+6j9+DO+N+f14w92H8IYhvqP/9gwydUGiMfllDa/t/Bb/OPcmeL5q8e7Gvoo3GEoL3ofugOH4B4sxyb9NdMqG44YpMxbvLvFrfjwb/OP2P19z8ZKoLXgfEHfiJan74XX472DPpOhDDntNv+kVko03gdfcgDclwsv+32U7XjOcDxEzF16/c7w9yWPU493NfQCv/0uXf04e8o+jefS0aNGCc32mSLhgiOe/BfwxzrbH9Vv/Nrwbk9vxRg1+Gu+GYWW/ctP897jNwE68idVPYs+UQccNFh9ec8Rn/H2vxEsyqoD/8d/r1/qPL2CYaYZIfhqig/Ba17TifQY/h9d3e7D37qSnV+i3/R140zQN2HdMmcPwkswteInni3iTnI/tVy6p8wXMHORYxw33+ZdIvAm85qPwBvJpwfu8Xw/8FjhwiPKOISZMj/M3vNr/2+3wj7MK76bGwdn4n9OS+GL+H02kIJjZY3iJ0QU5DiUvmNmv8PpizHEF8M9caPGKiEjh8fsCrgZ+7Jz7To7DiavQ4pXCoaabIgXGzN6ON2dOtInJonxOmgotXhERKUzmzcXbhvdZU0rfLiV5p9DilcKjRE+k8MzGG3BmJ3C5c+6GHMcTT6HFKyIihelsvLntXgVOdc4NN/F8Pii0eKXAqOmmiIiIiIhIkcna9Apm9jUz+0ScMgEza/EnjRQREREREZERyHiiZ2ZTzOxJvJEO47kKWJ7hkERERERERIpaxvvoOec2AEf685WMGaqcmX0Mb4LnkxLZbyAQcDNnzkxHiCIikueeeeaZVudcY67jKBT6jBQRGR2G+3zMi8FYzGwfYKFz7iIzGzLRM7NFwCKA6dOns3Tp0myFKCIiOWRma3IdQyGZOXOmPiNFREaB4T4fs9ZHbyhmVgZcCVwSr6xz7jrn3ALn3ILGRt3YFRERERERGUzOEz3gMLz5te70J8M+BbjJzD6a06hERESyZLgBy8ys1Mx+YWZP+8ux2Y5PREQKT04SPTM70My+DuCce8o5d7Bz7jjn3HHA/cCHNNeWiIgUuwQHLFsErHDOvQk4GfiemZVnJUARESlYuarRmwDMzNGxRURE8oJzboNz7kjgS8MUOwf4uV9+K3AvXusXERGRIWVtMBbn3I0xvz8OPD5EuQuyFJKIiEghKHXOdcY8fh2vy0Mf/QcsExGR0S0f+uiJiIhIctyAFRqwTEREYijRExERyW/OzCpiHu8LrMhVMCIiUhiU6ImIiOSR2AHLfHcAH/efGwucCDyQi9hERKRw5MWE6SIiItKr/4BlPwMW+6NzRoBLnHNduQhMREQKhxI9ERGRHBtuwDI/qbsoB2GJiEgBU9NNERERERGRIqMaPYlr8eLFNDc3A9DS0gJAU1MTALNnz+biiy/OWWwiIlL4VrcGmTxuDJVlpbkORUSkaKhGT5Kya9cudu3aleswRESkSOzuinDKj//BLU+ty3UoIiJFRTV6Eldsjd0ll1wCwNVXX52rcEREpIh07O5md1cP67aGch2KiEhRUY2eiIiI5Ewo3A1AWzCc40hERIqLEj0RERHJmWBnBIDWnZ05jkREpLgo0RMREZGc6a3R26kaPRGRdFKiJyIiIjkTDHs1em1B1eiJiKSTEj0RERHJmVDnnhq9nh6X42hERIqHEj0RERHJmWiNXnePo313V46jEREpHkr0REREJGeiffQAWtVPT0QkbZToiYiISM5ER90EaNPImyIiaaNET0RERHImtkZPc+mJiKSPEj0RERHJGdXoiYhkhhI9ERERyZlQuJuGmgrM1EdPRCSdynIdgIiIiIxewXCEsVXlOKBVNXoiImmjRE9ERERyJtTZTXVFKWUlRptq9ERE0kaJnoiIiORMMNxNTUUZdWOgLagaPRGRdFEfPREREcmZUDhCdWUpDbWVqtETEUkjJXoiIiKSM6FwhJqKMhprK9VHT0QkjZToiYiISM5E++g11FTQvrubcHdPrkMSESkKSvREREQkZ4LhCDWVZTTUVgKwVZOmi4ikhRI9ERERyZlQ2K/Rq60ANMWCiEi6KNETERGRnAh399AVcdRUlhFQoiciklZK9ERERCQnQuFuAL+Pntd0UyNvioikhxI9ERERyYlgOAJATUVZb9NNzaUnIpIeSvREREQkJ0Kdfo1eZSm1lWVUlpWoRk9EJE2U6ImIiEhOxNbomRmB2kpaleiJiKSFEj0RERHJid4avYpSABpqK9R0U0QkTZToiYiISE701uhVlgHQUFOhppsiImmStUTPzL5mZp8Y4rmpZvZbM3vEzJaY2SXZiktERERyIzrqZlVvjV6lplcQEUmTjCd6ZjbFzJ4EvjBMsTrgMufc8cBRwIlmNj/TsYmIiEjuBDv39NEDv+nmzjDOuVyGJSJSFMoyfQDn3AbgSDO7ABgzRJllMb/3mNk6oCLTsYmIiEju9M6jV+nV6AVqKglHeujo7GbsmPJchiYiUvDyro+emZ0FVDjnnhvkuUVmttTMlm7ZsiX7wYmIiEjaRGv0qsv3DMYCmjRdRCQdMl6jlygzKwe+A4SACwcr45y7DrgOYMGCBWrXISJSoBYvXkxzczMALS0tADQ1NQEwe/ZsLr744pzFJtkTCndTWVZCWal33zlQWwlA285OZgVqchmaiEjBy4tEz8wqgVuA/+ec+0eu4xERkezZtWtXrkOQHAmGu3tH3IQ9NXqaS09EJHU5SfTM7EDgVOfcFf6qjwPXK8kTERkdYmvsLrnEG2j56quvzlU4kiOhzkjvHHoQU6OnufRERFKWqxq9CcDMmMdvBs40sy/FrPuZc+7PWY1KREREsiYY7u4dcRNgfLVfo9ehGj0RkVRlLdFzzt0Y8/vjwOMxj8/OVhwiIiKSH0LhSO+ImwAVZSXUV5WrRk9EJA3ybtRNERERGR1C4UifGj3YM5eeiIikRomeiIiI5ESws7tPHz3w5tJr3akaPRGRVCnRExERkZwIhSN9Rt0ECNRV0BZUjZ6ISKqU6ImIiEhOhMIDa/QaaippU42eiEjK8mIePRERSV10EvJkJyDX5OWSK8HOgTV6DbUVbAt10R3p6Z1IXUREkqdET0SkyKQyAbkmL5dsifQ4dnVFBtbo+XPpbQ2G2WvsmFyEJiJSFJToiYgUiWjNW7ITkGvycsmFXV0RgAGjbgZq/Ln0dirRExFJhdpEiIiISNaFOrsB+syjB3tq9DSXnohIapToiYiISNYFw4PX6DXUejV6mktPRCQ1SvREREQk64LRGr3+8+j5NXqaS09EJDVK9ERERCTrQtEavX6jbo4dU0Z5qWkuPRGRFGkwFhGRPKKpDmS0CIa9Gr2qfjV6Zqa59ERE0kCJnohIntJUB1LMQp2D99EDr59eq/roiYikRImeiOS9QqvlSiXeQpvqoND+NpI/ojV6/fvogTfypmr0RERSo0RPRApKodVyFVq8qRhNr1VSF51eoX8fPfDm0mvevDPbIYmIFBUleiKS9wqtlqvQ4k3FaHqtkl7R6RUGr9GroC3YiXMOM8t2aCIiRUGjboqIiOSAmU00s/vMbImZPWhmTYOUmWBmfzKzR/3luOxHmhmhcDelJUZl2cCvIoHaSnZ39fSOzCkiIslTjZ6IiEhuXANc5pxbYmYLgKuBc/uVuRr4tXPuATOrB+40s2XOuU3ZDjbdQuEI1RWlg9bYNfhz6bXtDA/atFNEROJTjZ6IiEiWmdk4oME5twTAObcUqPfXx5rvnHvAL7MD+CnwgSyGmjGhzsigI26C13QToDWoAVlEREZKt8kkb2k0PxEpYrOAFf3WrfTXPxu7zsxOd87dZWZTgAuBNYPt0MwWAYsApk+fnv6I0ywY7qa6cmD/PIBAjVej19qhRE9EZKRUoycFYdeuXRrRT0SKiQFukPX9130OOMvMHgW+A/we2D7YDp1z1znnFjjnFjQ2NqYz1owIhePX6LUFNZeeiMhIqUZP8pZG8xORIrYamNNv3Wx/fS/n3Bbg/OhjM7sKeCLDsWVFsLN70BE3ASbU+Ime5tITERkx1eiJiIhkmXNuKxAys8MAzOwgoBWYZmZfj5YzsylmNsb//XDgSODO7EecfqFwZMiBVsaUl1JXWUbrTtXoiYiMlGr0Coz6rYmIFI3PAzeYWS2wA6//3VxgZkyZBuBmM6vEa7J5vnOuJ7thZkYw3M2Miuohnw/UVarppohICpToFTD1WRMRKVzOufXAyf1WbwIejynzAvC2bMaVLcONugnQUFOhppsiIilQoldg1G9NRESKwXCjboI3IMvq1lAWIxIRKS5K9ERGoVw1AVbTYxEBcM4NO+omeJOmP7NmWxajEhEpLkr0REa5XDUBVtNjkdGrs7uHSI8btkYvUFPB1mCYSI+jtMSyGJ2ISHFQoicSY7TUOOWqCXAujjta/qYihSQUjgDErdHrcbAtFCZQW5mt0EREisaoT/T0JVCGohqnwRXy/4z+piL5IdjZDTDkPHoQM2n6TiV6IiIjMeoTvVj6Eph+hZYUaLCb5BTC/4z+piL5J1qjVz3sqJtecueNvFmXjbBERIrKqE/09CUwewohKZD49D8jIqkKhv0avWH66DXWeTV6rZpLT0RkREZ9oieZpaRARET6C3Um0EevT42eiIgkqyTXAYiIiMjo0lujN0wfvfqqckpLjLadqtETERkJJXoiIiKSVSE/0aupHLpGr6TEmFBTQatq9ERERkRNN0VEhjDUYEL5OJCQSCEJ9jbdHLpGD6ChpoJW1eiJiIxI1mr0zOxrZvaJIZ4rNbNfmNnT/nJstuISEUnErl27NKCQSJrsio66OUyNHkCgtpK2oGr0RERGIuM1emY2BbgdmA1cOkSxRcAK59wnzWwCcKeZneCc68p0fFJ8Cm1Kh1SMpteaCxpMSCQzon30qsrj1OjVVrB2bSgbIYmIFJ2MJ3rOuQ3AkWZ2ATBmiGLnACf55bea2b3AKcA9sYXMbBFeUsj06dMzFbIUkdFUAzOaXquIFLZQOEJVeSmlJTZsuUBtpUbdFBEZoXzpo1fqnIt9J38dmNO/kHPuOuA6gAULFrgsxZZ2qoXJrNFUCzOaXquIFI9gZzc1w8yhF9VQW0EwHGFXOEJVnP58IiLSV74keoPJy0Ru8eLF/P3vfwcgFArh3OBhmhnV1dUAnHTSSUMmb6qFERGR0SYUjlA9zBx6UYHoXHrBTqZWVGc6LBGRopIviZ4zswrnXHRorX2BF3MZUCapFkZEREazYGf3sHPoRTXUVgDQujPM1PFK9EREkpGTRM/MDgROdc5d4a+6A/g48FMzGwucCORl5nPxxReraaWIiEgKQuHIsHPoRTXU+jV66qcnIpK0XNXoTQBmxjz+GbDYzJ4EIsAlGnFTRESkOAXD3dQmkujVeDV6bZpLT0QkaVlL9JxzN8b8/jjweMzjLuCibMUiIiIiuRPqjDCxbqiBuPcI+DV6rZpLT0QkaVmbMF1EREQEvBq96gRG3ayqKKWmolQ1eiIiI6BET0RERLIqFI5Qk8Com+D101MfPRGR5CnRExERkawKdiZWowfeyJttQdXoiYgkS4meiIiIZE13pIfO7p7Ea/RqKtnSoRo9EZFk5cs8eiKj1uLFi2lubgagpaUFgKamJgBmz56t6TxEpKiEuiIACc2jBxCoreD59dszGJGISHFSoieSJtGELZVkbdeuXRmLT0QkH4Q6o4leon30KtgaDNPT4ygpsUyGJiJSVJToiaRZsslabBJ4ySWXAHD11VenNSYRkXwRDHcDUJNoH72aSiI9jh27uhjvz6snIiLxKdETSZNowqZkTURkaMnW6AXqvLn02oKdSvRERJKgwVhEREQka3pr9BLto+cnd62aS09EJClK9ERERCRrQn6iV12Z+Dx6gCZNFxFJkppujiIa3VFERHItFPaabiZao9dQG63R0xQLIiLJUKI3Sml0RxERyYXePnoJ1uiNr67ADNqU6ImIJEWJXhbF1qhFRR9HB/CIle5atmRGdxwsVshuvCIiUnyS7aNXWmJMqK6gNaimmyIiyVCil0XNzc28tuwFmur3zANUGnEA7Gx5sU/Zlh0uq7H119zczLJlLzB+fN/1PT3ezzfeeKHP+m3bshSYiIgUtGjTzURH3QSv+aZq9EREkqNEL8ua6o1PHBt/eOhr/5n7O5fjx8OJb0+s7EMPZjYWEREpDsHObspLjYqyxMeDa6ip1GAsIiJJ0qibIiIikjWhcCSp2jzw5tJrU9NNEZGkKNETERGRrAl2difcPy+qoaZCo26KiCRJiZ6IiIhkTSgcSXjEzahAbQUdu7vZ3RXJUFQiIsVHiZ6IiIhkTTA8gho9f9L0rWq+KSKSMCV6IiIikjWhzuT76DXUeIOYaUAWEZHEKdETERGRrAmGu6mpHFmNXmtQ/fRERBKlRE9ERESyZkSjbtaqRk9EJFlK9ERERCRrgp3J1+gF/Bo9TZouIpI4JXoiIiKSNaFwhKry5Gr0qitKGVNeorn0RESSkNw7reTE4sWLaW5uHrA+uu6SSy4Z8Nzs2bO5+OKLMx6biIhIopxzI+qjZ2Y01FRqLj0RkSQo0SsAzc3NvLrsBSaOsz7rS3ocANs3vthn/abtLmuxpVuySa0SWpGR0Q0kyYXdXT04R9J99MDrp9eqPnoiIglTolcgJo4zzntbYn+u3z/aneFoMqe5uZmXl79AdUPf9WE/d1215YXedaG2LAYmUmSam5tZ8cprTK+b0md9Rbf3PhNeF+yzfm3HhqzFJsUrGPY+n5Kt0QNv5M1N7bvTHZKISNFSoid5p7oB5r47fvfR5X/tyUI0IsVret0Uvr7wUwmVvWLJzzMcjYwGoc4IMLIavYaaCl5paU93SCIiRUuDsYiIiEhW9NboVYysRq8t2Ilzhds9QUQkm1SjJyIiWaF+gRIK+zV6lSPro9cVcbTv7qa+qjzdoYmIFB0leiIikhVev8DlTK9v7LO+wvvuT3hD3463a3dsyVZokiWhFGr0YufSU6InIhKfEj0RySiNpCqxptc38o1jzkyo7OX//nOGo5FsC6bSR6+2AoC2YJi9G+MUFhERJXoiklnNzc28sPw1Shsm9Vnf47wuwi9v2TO4QqTtjazGJiLZFUpl1M0ar0avtUNz6YmIJEKJnohkXGnDJKpPuyhuudDd12chGhHJlWB45DV6Ab9GrzWoufRERBKhRE9ERJIy0kFVpC8zmwjcCEwA2oEPO+da+pWpAH4B7ANUA39zzl2W3UjTJ9Q58hq98TV+082dqtETEUlExhO90fhBJtmn0fykkBXa9esNqvIq08f2bY5b0e01xw2v39Fn/dp2NckdwjXAZc65JWa2ALgaOLdfmY8AK51zF5lZKfA3MzvcOfdMtoNNh2A4ghmMKUs+0SsvLWFcdTltO1WjJyKSiGzU6I26D7LRLhdfWr1+YC9AYPDnX2h9oe+K1hEfSrKk0JKfVDQ3N/PashVMqZ/eZ31ZxKvBCLb0/WK7YcfarMU2lOljJ/G/R384obLf+89vMxxN4TGzcUCDc24JgHNuqZnVm9k459z2mKKlwHq/TMTMNgObsx1vuoQ6u6kuL6WkxEa0fcCfS09EROLLaKI3Wj/IRrvm5mZeWfYCYyf0Xd/tz3G7flPfpKt9a5oOHICS9yZ2Sffc0Z2mg0qmNDc38+Ly1yltmNZnfcR5w6q/smV33/Vt67IWWyZMqZ/OJ4/7RkJlf/HY5b2/D5YQF2MyXIRmASv6rVvpr382Zt31wC/MbDYwE6/Fy6AXu5ktAhYBTJ8+fbAiORcMR0Y0h15UQ00FrarRExFJSKZr9NL6QVYIH2LiGTsBjnpHYndsn7jPZTgaKVSlDdMYe/oXEyrbftc1GY4mP3nNKFcwvXbPe2JFl1cTGF7b9wvx2p25rwmUXgYM9ubXf90pwEbgZ0Al8F0z2+qce3DAhs5dB1wHsGDBgrx8Yw2Fu0c0h15UoLaS5W+0xy8oIiIZT/TS+kFWCB9i+UaDJogUv+m10/nagq/FLff9pd/PQjSSoNXAnH7rZvvrY33FOXd09IGZfR2vT/uARK8QBDsjIxpxM6qhVjV6IiKJynSit5pR+EGWT5qbm1m+7AUC4/o90eP9aN3Ytxll6/ZsRCUiMro557aaWcjMDnPO/dfMDsLrPTzNzD7pnLvCL1puZnOdc8v9xx+ib4uYghIKd49oxM2ohppKduzqItzdQ0VZSRojExEpPhlN9EbrB1m+CYyD009I7IP1rocjmQ0mD42mQT9GSudIJCM+D9xgZrXADuBCYC5eF4aoC4GfmFk5Xn/2/wCJdebMQ8FwhHFV5SPevsGfS29bKMzEsWPSFZaISFHKxqibnyfPPsj0pVVieSN2vgyBqn7PeM2DXmhd2Xd1667eX0fLQBjeOXqVkoa9+qzvcV4/zJe2bOu7vk1jKYnE45xbD5zcb/Um4PGYMi8BJ2UzrkwKdXbTVD/yBK130vSdnUr0RETiyHiil48fZM3NzaxY9grT6+v6rK+IeCMxhlv6jgOzdkdHtkKTXAlUUXbG3ISKdt+5vPd3LwF6BWvYcy05511HL27pex25tj3XUSHebChp2Isxp56TUNnd99yS4WjyTyH+TUWyLRROrY9eoLYSQHPpiYgkIBs1enlpen0d33jLwoTKXv6PJRmORgqZNdRRdnr8a6n7rj3XkZcgLsMaxvUp45zXefLFLRv7rm/bnnKcklnNzc28umwFEyfM6LO+xHk1ENs3dfVZv2nrmqzFJpIvgqn20YsmeppLT0QkroJO9BYvXszf//53AEKhEM4NHIjTzKiurgbgpJNO0t1zyRvWMI6y045LqGz33Y9lNJZik6vatYkTZnD+yYm1Or/5gcvjFxIpMqE0jLoJqtETEUlEQSd6IpKc0dSn8JXlK6gL9J1vsxvvS+K61r5fEjtaNb+cSKZ1RXoIR3pSmkevrrKMitIStuxUjZ6ISDwFnehdfPHFBfklVCRXvCajy7GGQO+6aEX4i1ta+5R1bX0fF5q6wHQWnv71hMouueuK+IVEJCWhsDeqc3XlyL96mBkNtRWq0RMRSUBBJ3pSfFpaWgi1w/K/9sQtG2qDlq6WLERVXKwhQMWpp8ctF77nrixEIyKjRSjsDVSVSo0e4Cd6qtETEYlHs42KiIhIxgU7U6/RA2/S9LagavREROJRjZ7klaamJjrLW5n77vj3IJb/tYemxqYsRJVfNIy/iBSidNbordi8Mx0hiYgUNSV6WdTS0sLO7Y5r/xn/TmTLdkctapYoA+2ZmmFCn/XRUWdf3LKp7/q2rVmLTURkKL01eimMugnQWFtJ685OnHOYWTpCExEpSkr0RAqQNUyg/NRTEirbdc/9GY5GRCS+3hq9FObRA69Gr7O7h2A4Qm2KzUBFRIqZ3iGzqKmpiZ208YljK+KWvfafYWqbRl+zRBHJjpaWFoIdO7liyc8TKr+mYwM1LbUZjkqKWTCcnhq9hhpv0vTWjk4leiIiw9BgLCIiIpJxoc701egBtAU18qaIyHB0K0wG1dLSwo4d8NCDiZXftg16etSnUGQkvP67QX7x2OUJld+wfQ211KR0zKamJsKRIF9f+KmEyl+x5OdUNKV2zJaWFoI72rn8339OqPyaHVuoMX2ZLxbpqtEL1Po1eppLT0RkWEr0CkBLSwsdOxy/f7Q7ofKbtjtCTkmXyEgkO6rpaBzRtKWlhWB7B9/7z28TKr+m/Q1qWoIZjkryXbRGrzoNo24CmjRdRCQOJXoyqKamJkpKWjnx7YmVf+hBmDRJfQql8DU3N7N8+QoaGmb0We+c9+Vyy5au3nVtbWvScsympiaChPnkcd9IqPwvHrucmqb4fX3zTVNTE2FXyTeOOTOh8pf/+89UNDVkOCrJlmA4QkVZCeWlqfUamVATTfRU2ysiMhwlegWgqamJ7dbGeW9L7M/1+0e7GTdZSZfISDU0zOC00y6NW+7uu7+bhWjyT1NTE+GeHfzv0R9OqPz3/vNbKprqMxyVpNMV9y6jY3cX33/vQWnbZyjcnfIcegCVZaWMHVOmSdNFROJIOtEzs4OAvYEnnXNvpD8kESkmLS0tRNo7CN19fdyykbaNtHRpImSRXFu3NcTraZ6UPNgZSbl/XlTAn0tPRESGNuw7rpl9BQg5537qP/4A8D3gduBrZvZ159zDmQ9TZHgtLS3QDj13JNaPkVZoCasfYz5raWmhuz1I+13XJFS+u20dLV2pDRZSiLxpEoJ8f+n345Zd07GGmpbRd44keYHaSp5Y2ZbWfYbC3SmPuBnVUFuhPnoiInHEu7X2PuCYmMdfAd7hnHvVzMYDdwBK9ERkSE1NTWwrb6f6tIvilg3dfT1NjWOzEJWIDCdQW8n2UBddkZ6U+9RFBcMRqtJUo9dQU0nzFtX+i4gMJ947btA51wVgZocA25xzrwI457aZWU+G4xNJSFNTE60VrZS8N7EvET13dNMUUD/GfNbU1MT28t2MPf2LCZVvv+samhrHZDiq/NPU1ES4O8zXFnwtbtnvL/0+FQU4iItkX3Rky63BMBPHpuf/KtSZnj564MX31GrV6ImIDCfet+IOM5vtnGsGvgTcGH3CzGqA8gzGJiJSMLxpUELc/EBic+Ft2rqGUKQ6w1GJjEx0rrotHZ1pS/SC4QjjqtNzo6GhtpJtoTDdkR7K0lTjKCJSbOK9O34e+IWZPes//l3Mc/+v32MREREpAo11/hQGaRzZMp199AK1FTgH20Jd8QuLiIxSw9boOedWmtl5wDTgFedcbFPNi51zkYxGJyJSIJqamthe2sX5Jyc2F97ND1zOuIlqFCH5qaHGq9Fr7UjfyJbpHnUToC3YSWNdZVr2KSJSbOKNunk+8C3geWCumZ3nnHseQEmeFAtvxM4Q3XcuT2yD1tCoG7GzpaWFnvYOdt9zS0Lle9o209K1K8NRDa2lpYWO9iBL7roiofIdrWtoCWs0SpGogJ88pXMKg11pmkcPoKF30nT10xMRGUq8W2ufBRY453aY2QHA94FTMx+WSGFoaWnBtXfQfdeSuGVdWwctXaMrQRSRwlRTUcqY8pK0JXo9PY5QV4TqyjSNulmb/kRURKTYJDLq5g4A59xLZlaXhZgkjVpaWmjfAXc9nFgFbOt2CLsWmppGz4iU3oiduyk7Y25C5bvvXJ7yiJ1egriD7rsfS6i8a9tOS5frs23XPfcnuO1WWrpSq4Bvampia/k2xpx6TkLld99zC02N41M6ZiqampqIVIRZePrXEyq/5K4raApoNEqRKDMjUFuZthqz3d0RnCNtNXoBf1TQVtXoiYgMKV6i5/o91nQKIjGamppoK49QdvrCuGW771pCU2NuE2gvSWwnfM9dccu6tlZauvQlSmS0aqitZEuaasyCnd7NpnTV6I0dU05ZidGmGr0BHn11M4dOG5e2EU5FpHDFe8c93Mz+45ebDZSbWQh4CegGcM4dndkQJRVNTU1UWCunn5DYXdS7Ho4QmDx6avNyxUsQjbLTjkuofPfdj9HUODlm21LKTz0loW277rmfpsaJIw1VREapxtoKNmzfnZZ9hcLdQPpq9EpKjAk1Feqj18+2YJiP3Pg0718wjSvfd1CuwxGRHIs36ma9mX0M+AzwV6AFmAIcDPzSOXdD5kMUkXTxksQKKk49PW7Z8D130dQYyEJUIpKPArWVvLB+R1r21Vujl6ZRN8GrcWwLqkYv1srWIM7BXc+18PV3zWPsGI3sKzKaxRt182Jgf2Chc25XzPoq4BozK3PO/TLDMYqIiEiWNdRW0BYM09PjKCmxlPbVW6OXpnn0wOunpz56fa1uDQKwqyvCX/67gQ8fPTO3AYlITsWbMP1c59ynY5M8AP/xZ4APZywyERERyZlAbSWRHsf2XalPSh4Mp79GL6AavQFWtQYpLTHmTx7LzU+uwbn+Qy2IyGgSL9Eb8h3Cn0cvtVt8IiIikpcCaZzCINSZ/hq9BvXRG2BVW5Bp46u44OiZvL55J0+t2prrkEQkh+IleivM7MTBnjCztwMJzjAtIiIihaShdwqD1BO9aI1eTZr76IXCkd5moQKrtgSZGajh1IObGDumjJuXrM11SCKSQ4lMmP57MzsbuANvMJZJeJOmzwISm1RLRERECkpjb41e6rVm0WSsOk2jbsKeRLRtZ5jqCelLIAuVc47VbUEW7j2BqopS3nf4VG5+cg1bOubTWFeZ6/BEJAeGrdFzzu10zp0O/AE4Fvg0cDxwt3Pu3c65nVmIUURERLKst+lmRxpq9PxRN2vSNI8exE6arn56AJs7OgmFI+wdqAHgvIXT6Yo4bl26LseRiUiuxGu6CYBz7jHn3P865xY5577qnHsw04GJiIhI7tRX+ZOSp2HAk1C4mxKDyrKEvnYkZPoEL6H58cOvE+xU882VW7wRN2f6id6cveo4cu8J/GHJWiI9GpRFZDTKeFsHM5sI3AhMANqBDzvnWgYpdy7wKaATaHXOnZ3p2ERE+mtpaaG9PcTdd383btm2tjV0dVVnISqR7ItOSt7akXrTzWBnhJqKMszSN4bbnL1queI9B3LpXS/x/uue4NcXvIm96sakbf+FZpU/tcIsP9EDOP/IGXz6D8/yj9e28La5e+UqNBHJkfTdWhvaNcBlzrmFwNeAq/sXMLNjgDOBE5xzxwNfyUJcIiIiMoxAbWV6Rt0Md1OVxv55UecunM71H1rAyi1B3vPz//D6po60H6NQrG4LUlFWQlN9Ve+6k+ZPIlBbyc1PrslhZCKSKxmt0TOzcUCDc24JgHNuqZnVm9k459z2mKJfBT7tnNvtl1uVybgK0abtjt8/2rdpyradXlOM8bU2oOy4yVkLbYCWlhbad8AT9yXWVKR9K7REBlTyShGJtL1B6O7r+6zr2dEGQEl9Q59yNI7Namz9NTU1UV7exWmnXRq37N13f5fGxvIsRCWSG4G6SlqDaajRC0fS2j8v1tvm7sWfFh3FhTc+zfsW/4frPrSAI/duiL9hkVm5JcjMhuo+k9tXlJVwzpum8fPHVrB+W4ip49UCQWQ0yXTTzVnAin7rVvrrn41ZNwHYx8yuBWqAO5xzP+q/MzNbBCwCmD59eibizUuzZ88edH1bczMA4yb3fX7c5KG3KQShNlj+154+63bv8H6Oqe9bjsbsxSUjM9S12Ny+xXs+NrFrHFvQ165IsQnUVNC8OfVx10Kd3WkdcbO/A6fW85dPHs2FNz7Nh254iqvOOojTD5mSsePlo9VtQWY31gxY/4GF0/nFYyv441Nr+dLJc3MQmUhhWL8txJKVWzn14CYq0tifOJcynegZg0+63n/dLOAY4Az/uT+Y2UvOuYf6bOTcdcB1AAsWLBg1PYsvvvjiQddfcsklAFx99YDWsDnV1NRET2krR70jsb4YT9znaJrYBAyXFHhJ7azGmOcbCzuhHS0K7fqVxKxtf4Pv/ee3fdZtCnqTM0+smTCg7BzqkcITqPOabjrnUupfFwx3p3UOvcFMm1DN7Z84mkW/W8rnbnmODdt3cfFbZ6e1X2C+ivQ41rQFOWHewH54U8ZVcfzcvfjT0+v43An7Fs0XWJF0Cnf3sOimZ3hlYzs/f3QFl757flH0a810orcamNNv3Wx/faxm4PvOuTCAmd0CHAI8hIwqSgpE8t9QN1jCza0AVEztm9TNoZ7Zs2fT7LdCkMIRqK2gs7uHnZ3d1I0ZeTPlUDjChJqKNEY2uPrqcm766BFcctsL/PD+V9mwbRffPm1/ykqLO7nZsG0XXRHXO7VCf+cdOYOHlm3mgZff4NSDm7IcnUj++9mjK3hlYzufOX4Of3thIxfe+DRv26+Rb7x7PrMba3Md3ohlNNFzzm01s5CZHeac+6+ZHQS0AtPM7JPOuSv8or8FvgX8r3m33k4Frh9ityIikgZrOzZwxZKf91m3KeQlaxOrAwPKzmFfYOQ3ZKLPS+FoqPHm0mvbGU450Zs2PjuTmleWlfLj9x/C1PFVLH6smY07dvPTDxyasT6C+WBVW3TEzcG/kL5ln0amjq/i5ifXKNET6eelDTv4+aMreM+hU/jiSfvxmeP34aYnVvPjh17n5P/3Dy48ZiafOWEfxqbwHpgr2XjX+zxwg5nVAjuAC4G5wMyYMjcA3zSzf+E13bzNOffPLMSWdS07HNf+c0/H9lZ/QJVAvwFVWnY49s3xe/G2bfBQvxkTO/wBzerqBpadNCk7ccnoEWlbR/td1/Rdt2MzAKX1ew0oS+M+WYut0A1dK/cGABXT+tYMzGFfNZUehQJ1/qTpOzt752cbiUz30euvpMT4yilzmTKuim/e9RLnXPckN1ywoGinX1i1xetHOTMw+GArpSXGuQun88P7X+X1TR3sM7Fu0HIio01nd4Qv3vo8DTUVXHbq/oA3iNFFx+7N6YdM4Zq/v8r1/1rFX57dwJdO3o+zDp/WZ8CjfJfxRM85tx44ud/qTcDjMWUc8G1/KVqDfUna5Ddlqm3q+9y+TbntfzbUsYNBL95Jk/o+P2mS+stJeg3dX7PLe76x3xe2xn0K+hrcsGMtv3js8j7rWnduAiBQO3FA2X2b+reKT46aSUsiArVec8tUp1jI5Kibwzn/yBlMrh/Dp//wLO/5+X/47UfexJy9spvkdEd6MDNKM/jlcHVbiNrKMhprK4csc/aCafy/B1/j90vWctlp+2csFpFC8uOHXufVTR38+oIF1Ff3rbFrrKvkyvcdxHkLZ/Dte17mK7e/yM1PruVbp85nwcwJQ+wxce27uwh1RphUn7kbUMXbjiEPDfbFKl+/VOlLoOTaaLoGh0pQ32j2av9rmvr2bdq3aU5BJ7VSOKKJQ+vO1KZYCIWzW6MX64R5E/nTx4/kIzcu5b2/+A+/+tACFmZx+oXP/+k5OnZ389uPHJGxY6xsDTIzUD3swDOB2kreccBkbv/ver58yn5UZ3hwHJF899y67Vz7eDNnHT6V4+dOHLLcgVPrue0TR3H38y18/97lnHntE5x+SBNffcdcJsfMWzmcLR2dvNSyg1da2nm5ZQcvt7Szpi3EGYc08aNzDk3XSxpA/+UiIjk2mpLatTu2cPm//9xn3abgdgAm1owbUHbOlNE3H1o+GV+Teo1euLuHrojLaR+5g6aO4y+fPJoLfvMUH7zhKa4++2BOy0Jftc3tu7n3xY1UlpUS6XEZq9Vb1bqTQ6aNj1vu/CNncPfzLdzzfAvvf9PomaZKCtO2YJir/v4qHzpqBnMnpXeO3d1dEb5463NMHDuGS0+dH7e8mXH6IVN4+/yJXPtYM9f+YyV/f3kTnzxuNh97y96MKfduZDnnWL9tFy+37OClDXuSus0de95Dp02o4oCmes46fCpHzMrsZ5wSPSkerdBzR99J5fHn3xswsnsrEECKWEfrWpbcdUWfdaEdXlPI6vqJA8oSSK0pZC6t3bmW7y/9fu/jTSHvdU6snjig3JwBAyFnz9D9ArcDUNEvqZszpUE1lzlWXlrC+OrylBK9UNh7X85VjV7UtAnV3H7x0Sz63TN87pZnmTepLuN91e54dgM9DnZ1RVjTFmTvDIze19kdYcO2Xbzn0Klxy75p5nj2nVjLzU+uVaInee+bd7/MPc+3cN+LG/nDx45k3uT0JXv/9+BrNG8JctNHjkhqkJXqijK+cNJ+nLVgGlfcu4xrHnyNPy1dx4nzJrL8jXZeaWmnfbf3nldaYsxurOHNcwLMbxrL/k31zG8aS31V9gZ1UaInRWHI/lw7vD6FswP9ng+oT2ExG/p68JqfTQv0G+Y9ULhNIQeLO+w3+ayY3vd1ziG3r3M01VwWk0BtJa0dI2+6GQxHADI+j14ixlVXcO35h3PkFQ9nvK+ac47blq4jUFtB684wy9/oyEiit25riB4Hs4YYiCWWmXH+kTP45l0v8/y67Rw8bVza4xFJh3tf3Mg9z7dw3sLpPLJ8M+f+6kl+f9GRzG9KPdl7Zs1WfvXPlZy7cDpv2bdxRPuYNqGaxecfzn+aW/nuX5fxx6fWMndSHe8+uIn9/aRu7qS63pq+XMn9u65IGhTiF0jXtp3uux/ru26HN3Ka1dcOKEvj5CxFVvgK8XoYqULq+yuFqaG2grZgCjV6nX6NXmVuv/BETaip4OQDJnHHf9fz1XfMzdgXsWfXbad5S5DvnL4/377nFZZtbOedB6b/fXxVawgYemqF/t5z6BSuvG85Nz+5Rome5KXWnZ18486XOHBKPZedtj+L3rI3H7juSc69/kl+f9FC9m/q30wrcbvCES657QWmjKvi6++cl3KsR88OcN/njqWnx+XlaJzFPYOoSJ6aPXs2B82dx4GNk/ssNVZCjZUMWH/Q3HkFW+MkIoUtUFuZ0mAs+VSjF3XuEdNp393N317YmLFj3LZ0HVXlpbz3sKnsHahh2cb2jBxnVat3g3BWQ2LTX9SNKef0Q5q454UWdoS6MhKTyEg557j0zpfYububa84+mPLSEmY01HDLoqOoLi/lvOuX8NKGHfF3NIQfPrCcVa1BfnjmQdSmsd9wPiZ5oBo9kZwYTTVOIlLYvKabaajRy3EfvVhH7j2BvQM1/PGptbzv8Ph925K1Kxzhnuc38s4DJ1NbWca8yWN5Zs22tB8HYFVrkAk1FQOGhh/OeQtn8Men1vHn/67no2+elZG4REbinhc2ct9Lb/CVU+ayb0wf2ukN1dyy6Cg+8KsnOe/6Jfz+ooUcMCW5mr0nV7bxm3+v5sNHzeDo2aNjoAbV6ImIiMiQArUVdHR2s7srMqLte2v0cjjqZn9mxgeOmM7SNdt4bVNH2vd//8sb2dnZzVkLvCRy7uQ6NmzfxY5d6a9BW9UaZFaSk9kfMKWeQ6aN4/dL1uBNZSySe5s7dvPNu17ikGnj+NixA29AeMnekdRWliVdsxfs7OZLf36eGQ3VfOUdc9MZdl5ToidSgFzbVrruub/PEr75NsI33zZgvWvbmutwRaSABfy59NqCI2u+mS+jbvb3vsOnUlFawh+WrE37vm9bup7pE6pZOMubVDk6WuDyDDTfXNUaZGaCzTZjnX/kDFZuCfLEyra0xySSLOccX7/jRXaFI1xz9sGUlQ6eokyb0DfZe3F9YsnelfctZ/22XVx15sGjag7J0fNKRYrEkCNKtnv9NGY39pv0s3Gi+vdlyaata7j5gcv7rNvW8QYA4+smDSg7bmLhTukgqTOzicCNwASgHfiwc66lX5mfAgfGrJoAfMQ5tzRbcUYTvdaOTqaMS2xy4FjBzvyr0QNvUJZTMjAoy7qtIf7T3MYX3r5v7wTm8/1Eb9nG9rRO1h7s7GZTeyd7Nyaf6L37oMl896+v8Psn146aZmySv/7y7AYeWraZb7xrHrPjjE4bTfa8ZpxPcvNFCzlo6rghy/97RSu/e3INH33zLI7wb76MFvn1risicaXav8+1tRK+5649j3d4d8Osvn5AORr3fPj3tG1m9z239CnTs8Prc1JS33ei3p62zdAYf/LeYjJUMt3mD2IxbmLf/jPjJhbulA6SNtcAlznnlpjZAuBq4NzYAs65z0R/Ny9reBB4IZtBBuqiNXoj66eXrzV6AB84Yjp3P9/CX1/YyJlp6qv352fWY0afvn971VUyvrqc5W+kt5no6rYgwIhq9MaUl3LW4VO58T+r2dy+m73GjklrbJKc3V0R/vl6K4fPGM+Emor4GxSRN3bs5lt3v8yCGeO58JjE+oxGk71zrnuS869fMmSy17G7iy//+QX2DtTwpZP3S3Pk+W9UJnotLS0Et3dw+T+WJFR+zfYOamiJX1Akzw2WWDS3e4ne7MZ+d3QbA73lh65F3Opv2y+paxw/6pIYDbAjyTCzcUCDc24JgHNuqZnVm9k459z2ITZ7J/Cwc27kQ2COQIP/pXOkc+lFa/TysblU7KAs6Uj0enocf35mPcfMDvSp/TQz5k0em/aRN1e1eolesn30os47cgbX/2sVf3p6HZ85YZ90hiZJ+v69y/jtE2soMVgwYwJvnz+RE+dPHPHftlA45/jqHS/QFenh6rMOpjSJ0Sunjo+t2VvCzR9dOGDKkO/9bRkbd+zizxcfnfM57XIh/951RSRjRjrnmpIYkbSbBazot26lv/7ZIbb5BPDhoXZoZouARQDTp09PQ4ieRr9Gb8vOkdfoVZaVJPUFLluig7J8795lvPpGB/tNqou/0TCeXNnGhu27+PIpA2sO5k0ey++XrCHS49J2Llb7id7MBCZLH8ysQA1vnhPgj0+t5ZNvm5OXf6PR4Jk127jpyTW899ApTJ1QzYOvbOJ79y7je/cuY85etV7SN28ih04bl7fD+I/UbUvX89irW7js1PnMHEFS6yV7R3HOdU9w/g1L+N1HF3KIn+w99upmbnl6HZ9462wOmz66WhlFjcpEr6mpiTARvvGWhQmVv/wfS6hoaspwVCIiMooYMNhwh4MOgWhmhwKrnXNDjq7knLsOuA5gwYIFaRtKcUx5KbWVZbSOMNELhrvzrn9erPcdPpWrHniVPz61lstO2z+lfd32zHrqxpRx8v6TBjw3b/JYdnf1sLotGLcPUqJWtgaZNHZMSrWl5y2czsW//y+PLN/M2+dPjL+BpFW4u4ev3v4Ck8eO4TtnHEBtZRlfePu+rNsa4uFlm3hw2SZ+9Y+VLH6smUBtJSfO24sT503kzfsECr6GasP2XXznr69w5N4T+NBRM0e8nynjqrypF657kg9ev4SbPnoEezfW8tXbX2SfvWr5/Imjt7Y6f995ZVCLFy+mubkZoPdntFZl9uzZQ9a8iIhIXlkN9B+NZ7a/fjD/A3wng/EMq6G2ore/abJCnZG87J8XFR2U5fb/rucrp8ylaoSxtu/u4r6XNvK+w6YO+gV8rl9buGxje9oSvZFMrdDfifMnslddJTc/uUaJXg4sfqyZ1zfv5NcXLOgzgfe0CdVccMwsLjhmFjt2dfHYq5t58JVN/O2Fjdzy9Dqqyks5dp9Ab23f+ALr1+ec4yt/foEe57jqzINTrqn0kj2vGeeHbniKQ6aPY8vOTq770OEFnxCnQoleAauqSn70MxGJr61tDXff/d0+63bs8EbPrK+f1KdcY6NGzpTkOee2mlnIzA5zzv3XzA4CWoFpZvZJ59wV0bJmNhWodM71b+qZNYHaytRq9PKwf16scxd6g7L87cWRD8rytxc2srurh7MWTBv0+X0m1lJWYizb2M67D0pPK6HVrUFOOWBySvsoLy3hnCOm89NHXqd5y860JaES34rNHfz80RWcenATx88dOsmuryrn9EOmcPohUwh397BkVRsPvrKJh17ZxN9f2UR9VTl3fuqYgurP94en1vKvFa1cfsYBTJswsqbH/TX5yd451z3JP19v5TPHzxl2NM7RIL/feWUA1diJZNZQg8i0t3u1GY2Ne0bPbGzUyJmSks8DN5hZLbADuBCYC8zsV+6zwM+yGlk/gdqK3oE/khUKR6iuzO876gtnTWDvxhr+sGTNiBO9W5euY5+9ajl4av2gz1eWlTK7sZZlG9Mz8ub2UJhtoS72TsOX+/MWTufGf6/i0394ljsuPnrEtZqSuJ4ex1dvf5GqilK++e75CW9XUVbCsfs0cuw+jXz7tP15bt12PvrbpXzspqXc+alj+tQK5qt1W0N872/LePOcAOctTF9/YoDJ9VX8adFR3PfSRs5bOCOt+y5E+X81iIhkkQaekWxxzq0HTu63ehPweL9yX85aUENoqK1k6eptI9o2FI7kfY2emXHuEdO5/G/LWP5GO3MnjU1q+xWbO3h27Xb+953zeufOG8zcyXU8vWrIbpZJSXXEzVgTx47hJx84lAtvfJov3/4CPznnkGFfh6TuD0+tZemabVx15kG9Ax4ly8w4dPp4fnbuoXzwhqf4wp+e49rzD8/rAVt6ehxf+vPzlJjxgzMPysh1Nql+TMLTNBS7waedFxEREfEFaivZGgrTHelJettgZ3de99GLet9hU6koLeGPS9Ymve1tz6yntMQ449Apw5abN3ksLTt2sz2U+gwZq3pH3ExPc73j9tuLL588l3ueb+G6f6xMyz5lcG/s2M2V9y3nmDkNaZnW4+jZAf73nfP4+yub+NmjOWvhnZDfPbmGJ1du5dJ3z+szBYlkhhI9ERERGVZjbQXOwdYRJCihcCSvR92MGl9TwTsOnMQdz25gVziS8HbdkR7u+O8G3rbfXnFrZuZN9moK09F8c1VrkBKD6Wnq3wTwibfuzbsOmswP7l/O469tSdt+pa9v3vUSXZEernjPgWmr0brwmJm897Ap/N+Dr/HQK5vSss90W90a5Mr7lnPcfo2cPURfVkkvJXoiIiIyrECtl8CMZNL0ULgwavQAzj1iOh27u/nrCy0Jb/OP17ewpaOTsxbEr5mZN9kbeXP5G6lPnL6qNcjU8dVUlKXvq5yZcdWZB7HvxDo+84f/9s7TJ+lz/0sb+fsrm/ift+/LjIb0DZ5iZlzxngM5cEo9//On51ixeWfa9p0OkR7HJbc9T1mpceV7M9NkUwZSoiciIiLDavATvbZg8iNvBjsLo0YP4IhZE5jdWMMfnkq8+eatT6+noaaC4+fuFbdsY20lDTUVLNuYnkQvE6MsVleU8asPLaCkxFj0u6UEO7vTfozRaseuLi6962X2bxrLRW9Ofx+yMeWl/PKDh1NRVsKi3y2lfXdX2o8xUr/59yqWrtnGZafuz6T6MbkOZ9RQoicC0LqL7juX911++7y39FtP665cRysiklWBWm+OrmSnWIj0OHZ15fc8erHMjA8cMZ1n125PKBnbGgzz8PJNvOfQKZSXxv9KZWbMmzw25aabzjlWZyjRA28Ot5994DBWbN7JF299HudcRo4z2lx533LadnZy5XsPoiyB62UkmsZV8YvzDmNtW4gv/Ok5enpy/7fb2dnN1X9/lRPn7cV7Dxu+H6uklxI9GfVmz57NQXP356DA3n2WGiqooWLA+oPm7q8h9UVkVAnUjazp5q4ur69bvo+6Get9h02loqyEPyZQq3fnsxvoirgh584bzLzJdby6qWNEA9tEbenoJBiOZHTetDfvE+Dr75zH/S+/wc/zfICPQvDkyjb++NRaLjp2bw4cYgqOdFm4dwPfPHU+Dy3bzI8efj2jx0rEP1/bwu6uHj527N5qspllhfPOK5IhGk5fRGR4dZVlVJSW0Jpk082Q3+wv3+fRizW+poJ3HjCJv/x3A197x7xh55S77Zn1HDS1nv0m1SW8/7mTxhLu7mF1W5A5eyW+XayVaZxaYTgfffMsXtqwg2sefI15k8dywryhJ/WWoe3uivD1O15k2oQq/ufEfbNyzA8eOYMX1+/gJw+/zv5NYzl5/0lZOe5gHl6+mfqqcg6fMT5nMYxWSvREUuTaOui+a8mexztCAFh99YByNGY1NBGRtDAzArUVSdfoBcOFV6MH8IEjpnPncy3c80LLkKMDvrRhB8s2tvPd0/dPat/RkTdf2dgx4kRvdZYSPTPjyvcdxIotO/n8Lc9x56ePYXZjbUaPWYx+9sgKVrYG+d1Hj8jaZPRmxnfPOIDXNu/kC396jjs/dQz7TBzZ9ZaKnh7Ho8s3c9x+jRlrripD0xkXSYHX7HM+BzZO611qrIwaK+uz7sDGaRw0d76afIpIwQrUVSbdRy86kEeh9NGL6h2UZZg59W5buo6KshJOOzi5Pkdz9qqlvNRSGpBlVWuQitISmrIwD5k3wMcCKspK+NhN+TXARyFY/kY71z7ezHsPm8Kx+2T3bu+Y8lJ+ef7hVFWUseh3z7BjV/b/ds+t305bMJzQYEWSfoV1i00kzwzW7FNNPkWkGDXUVLAlyUQvFK3RK5BRN6Oig7Jc/rdlvNLSzvymsX2e7+yOcNfzLZy8/yTqq8uT2ndFWQmzG2tZnkKit7I1yIyGakpLstPfacq4Kn5+3mGcd/0SvvCn57jug96onDK8SI/jK7e/SH1VOZe+a35OYphUP4Zrzz+MD/zqST53y7Pc8OE3Ze26AXhk2WZKS4zj9lWilwuq0RMREZG4ArWVI2i6WZg1egBnHj70oCwPvbKZ7aEuzjo8/tx5g0l15M3VrUFmZrjZZn9H7t3AN9+dPwN8FIKbnljN8+u2881T5zO+piJncSyYOYFvnbo/j726hf978NWsHvvh5ZtZMGN80jdEJD2U6ImIiEhcgbpK2oKdSQ21H+oszBo9gHHVFbzrwMnc+ewGQuG+c8nd9sw6JteP4Zg5gRHte97kOt5o3822YPIT0Ed6HGvaQuyd5UQP4ENHzeDMw6fyk4df5/6X3sj68QvJ+m0hrnrgVY7br5HTDm7KdTict3A657xpGj9/tJl7X9yYlWNu2L6LZRvbOWGeavNyRYmeiIiIxBWoraQr4pLq5xOt0asqL7waPfAGZeno7Oavz+/5YvzGjt3847UtvO+wqSNuAhcdkGXZG8k332zZvotwpCfjA7EMxsy4/IwDOHhqPV+89Tle35TafIDFyjnHpXe+BMDlZxyQF1MKmBnfPn1/Dp0+jktue55X38j83+6R5ZsBNFprDinRExERkbj2TJqeeC1UdHqFQqzRA3jTzPHM2auWP8Q037zj2fX0OK9p50jNneQneiNovrnKH3Ez2003o8aUl3LtB70BPj5209KcDPCR7+5+voVHX93CJSftx9Tx1fE3yJLKslKuPf9waiq9v932UPI1ysl4eNkmZjZU56T2WTxK9ERERCSuQK0/aXoSA7JEp1coxD56sGdQlufWbeeVlnacc9y2dD1HzJqQUqLVWFdJoLZyRCNvRhO9XH55nlxfxbXnH8aG7bv45O+f4V+vt46oGWoxen7ddr5zzyscPG0cHz56Zq7DGWDi2DFce/7hbNyxi6/e/mLGjhMKd/Of5jaOnzsxL2o0R6vCvMUmIiIiWTWSRC8U7qa0xKgsK9z7yu87bAo/uH85f3hqDWccMoVVrUE+eVzqU+XMm1w34kSvpqKUxrrKlGNIxYKZE/ju6Qfw9b+8yL9XtAHe6JwHTqnngCljOWBKPQdMqe+9bord1mCYqx5Yzi1PryNQW8lVZx6U1dEtk3H4jPF84q2z+dmjK1i/LZSRWsd/r2gj3N3Dieqfl1NK9ERERCSuBr/pZlsSTTeDnRGqK0oL+o7+nkFZWtixq5vqilLeeeDklPc7b/JYbvz3arojPUlNJL3KH3EzH87pOUdM55QDJvFySzsvbdjBixt28HJLO/e/vGeglkljx/hJ31g/Caxn4tgxOYw6vSI9jlueXstVD7xKx+5uPnrMLD534j7UjcnvUSbPXjCNnz26gtuf2cDnTtwn7ft/eNkm6irLWDBzQtr3LYnLeKJnZhOBG4EJQDvwYedcyxBlA8ALwPHOueWZjk1EREQSM766ghJLrkZvVzhCTUXh31M+d+F0/vLsBu55voWzDp+alj6H8ybXEY70sLI1yL4T6xLebnVbkAOn1Kd8/HQZV13BMXMCfUYgbd/dxSt+8vfShh281NLOw8s3ER2wtbGukjmNtdSOKaOmopTqSu9nTWUZNRVlVFeWej8rSqmtLOt9vrqyjAnVFVTlSVPg59Zt55t3vcQL63ewcNYEvnP6Aew3KfG/ZS5Nm1DNMbMD3PbMOj5z/Jy0zovY0+N4ZPlm3rJvIxUFXJtfDLLx7nsNcJlzbomZLQCuBs4douxVgBK8NGvdDnc9HOmzbsdO72d97cCygdRvVIqISJEpLTEm1FQm2Uevm+rK/PhSnooFM7xBWVZs3slZC6alZZ+9I29ubE840Qt397Buaygvhusfztgx5Ry5dwNH7t3Quy7Y2c2yjdGav3ZWtwVZtzVEKBwhFO4m2BlhV1dkmL3u0VhXyYwJ1UxvqGbGhBpmNFT7Sw3jq8szXtu5NRjmh/cv509L19FYW8mPzzmE0w5uyota1mSctWAqn7vlOZ5c2cbRI5wqZDAvt7SzuaNT0yrkgYwmemY2Dmhwzi0BcM4tNbN6MxvnnNver+zHgAeBkzIZ02gze/bg/Qh2NDcDEJjc9/nA5KG3ERGR0S1QW8GWJCZNDxVJjZ6Z8cW378t9L73Bm2aOT8s+ZzfWUl5qLNvYwemHJLbN2q0hehw5mVohVTV+M77hmvJFehy7uiIEO7sJdnYTCkf2/Ax767Z0dLKmLcSarSH+s6KNO9o39NlHXWWZlwA2VDM9mgROqGafiXUp92uM9Dj++JTXTHNnZzcXvXkWnz0h/5tpDuXk/ScxdkwZty5dl9ZE76FlmygxOG4/JXq5lul331nAin7rVvrrn42uMLN9gIXOuYvMbMhEz8wWAYsApk+fnv5oi9DFF1886PpLLrkEgKuvvjqb4YiISAEL1HqTpicq2NldsCNu9veOAyfzjjT0zYsqLy1hzl7JDciyOsdTK2RaaYlRW1lGbRJNY3d3RVi3NdSb/K1tC7Jma4hlGzt48JVNdEVcb9lJY8dw4NR6DpxS3/sz0cFinl27jW/e9TIvbtjBkXt7zTSTaXKbj8aUl3L6IVO4dek6vr2ri/qq9CSsjyzfzGHTxzOhpiIt+5ORy3SiZ4AbZH3vOjMrA64EPhpvZ86564DrABYsWDDYfkVERCRDArUVrFkbTLh8KBzJ+eiQ+Wze5Dr+9XprwuXzYWqFfDOmvJR9JtaxzyBJV6TH0bJ9F2vaQix/o50X/QFjHnxlU2+ZyfVjvMRvSj0HDJL8te3s5If3v8qflq5j4thKfvKBQzn1oMkF10xzKGcvmMbvnlzDPc+3cP6RM1Le36b23by4YQdfPmW/NEQnqcp0orcamNNv3Wx/fdRhfpk7/X+aucB8M/ulc+6GDMcnIiIiCQrUVtKaRNPNYLibGRX5M2F0vpk/eSx3/HcDbTs7aUigZmlla5Dx1eWMq1ZNSSJKS4xpE6qZNqGaN++zp2lix+4uXm5p58X1XuL30oYd/D0m+Wuq90YKnT6hmtueWU+ws5tFb9mbz56wT1K1jYXggCljmTupjtuWrktLovfI8s0AnDB3Ysr7ktRl9Gp1zm01s5CZHeac+6+ZHQS0AtPM7JPOuSucc08BB0e3MbMbgSs16qaIiEh+aaitZFeXN3hGdQJ970KdxdFHL1PmTvIGZFn+RgfHzImf6K32p1aQ1NQNMlhM++4uXt7QzosbtvPihvbe5O+ovRv4zun7D1pjWAzMjLMXTOM7f32F5W+0916TI/Xwss1MHV/FvhNr4xeWjMvGu+/ngRvMrBbYAVyIV2s3MwvHFhERkTQJ+HPptXaEmd4Q/ytEsYy6mSnzJnvJw7KN7X2mJxjKqtYgR89piFtOkjd2TDlHzW7gqNl7zm9nd4TKsuK/fs84dArfv28Zty1dz6Xvnj/i/ezuivCvFVt4/4JpRdO0tdBlPNFzzq0HTu63ehPw+BDlL8h0TCIiIpK8gN/fbsvOTqY3DN8k0zlXNKNuZkpDbSV71VXySgIDsoTC3bzRvptZDarRy5bRkOQBTKip4O3zJ/KXZzfwlVPmjnjuuyea29jd1cPx89RsM1/o3TcHFi9eTLM/vUH0Z3QUzNmzZw85UqaIiEguBWq8RK8tgbn0Ort7iPQ41ejFMW/yWJZv7IhbbnVrCIBZjUr0JP3OXjCNe198g4eXbRrx6LIPL99EdUUpR+499BQakl2arj7HqqqqqKqqynUYIiIicQXq/KabO+MPyBIKe5Nfq0ZveHMn17Fi8066Ij3DlouOuFmIc+hJ/jt2n0YmjR3DrUvXjWh75xyPLNvMsfsERk1NaCHQu28OqMZOREQKUYNfo9eaQI1esLMboGjm0cuU+ZPHEo700Lxl57ADYaxu8+fQU9NNyYDSEuPMw6fyi8dW8MaO3UyqH5PU9ss2dtCyYzefP3HfDEUoI6EaPREREUlIRVkJ9VXlCSV6vTV6RTYcfbrNm+yPvBmn+ebKLUEmjq3U+ZSMOfPwqfQ4uP2/65Pe9uFl3vQUb5u7V7rDkhQo0RMREZGENdRW0JZA081gWDV6iZgVqKGitIRlcQZkWd0WVG2eZNTMQA0LZ03gtqXrcM4lte3Dyzdz8LRxNNbFnyZEskeJnoiIiCQsUFvJlkRq9Dq9Gr1E5tsbzcpLS9hnYm3ckTdXtQbZWwOxSIadvWAaq9tCPL16W8LbbOno5Pn12zlBtXl5R4meiIiIJKyxtjKxPnqq0UvYvMljWTZM080doS62BsMaiEUy7h0HTqK2siypQVkefXUzzsEJ85To5RsleiIiIpKwRJtuhvxET33K4ps3eSytOzvZ0jF4Ar1KA7FIllRXlHHqwZP52wsb2ekPqBTPI8s2M2nsGOZPHnowIckNJXoiIiKSsEBtJTt2dRHuHn46gGBndHoF1ejFM29SHQDL3xi8+eaq1p0AaropWXHWgmns6orwtxda4pbt7I7wz9e3cPy8vTCzLEQnyVCiJyIiIgkL1PqTpgeHb765yx91s1o1enFFR94cakCWVa0hSgymTajOZlgySh06bRxz9qrl1qXxR99csnIrwXCEE9VsMy8p0RMREZGENdT6k6Z3DN98M9pHr6pcNXrxjK+pYNLYMUNOsbCqNciU8VWaiFqywsx4/4JpPLNmGys27xy27CPLNzOmvISjZweyFJ0kQ4meiIiIJCxao9cap0YvFI5QVV5KaYmacyVi7uS6IUfeXNW6k1mB2ixHJKPZGYdOoazEuO2ZoQdlcc7x0LJNHDM7wBjd0MlLSvREREQkYY3RRG+IgUOigp3d1FTqy1+i5k0eS/OWnQP6PjrnWN0aYlaDmm1K9jTWVXL83L24/ZkNdEUG74/7+uadrN+2ixPmTcxydJIoNZyXvLV48WKam5sBen9ecsklAMyePZuLL744Z7GJiIxWgTq/6WackTdD4Yjm0EvCvMlj6Yo4VmzeyfymPaMXbtnZyc7Obk2tIFl39oJp/P2VTTz26hbePn9gMvfwss0AHK/58/KWavSkIFRVVVFVVZXrMERERr3qijKqyktpizOXXrCzW3PoJWH+5MFH3ly1xZ9aQYmeZNlx+zXSWFc55Jx6Dy/bxP5NY5lUPybLkUmidKtN8pZq7CRKtbsi+SVQVxF30vRQOKI59JIws6GGirKSASNvrvbn0NtbffQky8pKS3jvYVO4/p+r2Nyxm73q9iR0W4Nh/rt2G58+fp8cRijxqEZPRAqKandFci9QWxm36WYwrBq9ZJSVlrDfxDqW9Rt5c2VrkPJSY8p4ve9J9p11+DQiPY47n93QZ/3jr22mx8EJaraZ13SrTUTynmrsRPJLQ00l67eFhi0T6owwsU5NupIxb3Idjyzf3Gfd6tYg0ydUa/RSyYk5e9Vy+Izx3Lp0PR87du/eSdEfWraZxrpKDpxSn+MIZTiq0RMREZGkNNZVJFajp1E3kzJ30lhad4bZ3LG7d92q1qCmVpCcOnvBVFZs3smz67YD0BXp4R+vbuH4/faiRDcg8ppq9ERyTP3PRKTQBGor2RrsJNLjhqxpCoUj1GjUzaTMm+yNtrlsYwd71Y2hp8exui3EW/dtzHFkMpq966Amvn3PK9y2dB2HTR/P06u20tHZzfHz1Gwz3+kdWCSPqO9Z+imRFkm/hpoKehxsC4V7J1DvL9ipGr1kzfcTveUb23nrvo207NhFuLtHNXqSU7WVZbzrwMnc8/xGLn33fB5evpmK0hLePCeQ69AkDiV6IjmmRCN7lEinnxLp0SlQ5yV3bTsHT/S6Iz10dveoRi9J9dXlNNWP6R15c1WrN+Km5tCTXDv7TdO47Zn13PfiGzyyfDNHzW7QqLoFQH8hEcmaXCQFSjSyR4n06BFN7lp3drIfdQOeD3VFADTq5gjMnTy2d+TN1Ur0JE8smDGeWYEafvLI66xpC3HhMTNzHZIkQImeiOSEkoLioER6dIpN9AYT6vQSPd3xT968yXX847UtdHZHWNkapKq8lIljB28eK5ItZsZZC6byw/tfBeB4TatQEPQOLCJZo6RApDgEaisAhhx5MxjuBlSjNxLzJo+lu8exYvNOf8TNmt4h7UVy6X2HTeXqB15l34l1TB1fnetwJAGjNtFbu6ODy/+xpM+6TTu9OYEm1lYPKDunKWuhiYiI5LX6qnLKSy1ujV61+uglbe6kPSNvrm4Nsn+T5imT/DBx7Bi++o65TJ+gpsSFYlS+A8+ePXvQ9WG/z1BF07Q+6+c0Db2NiIjIaGNmNNRU0toxeKIXrdGrUY1e0mYFahhTXsKL67ezbtsu3n2Q7jRL/lj0Fn0fLiSjMtEbqvlYdFCIq6++OpvhiIiMOhqts/A11FYMXaMXbbqpPnpJKy0x9ptYx4OvbCLS45ipgVhEZIRKch2AiIiMblVVVaNycB4zm2hm95nZEjN70MwGrboxs3PN7N9m9oiZ3ZrtOIcSqK2kLTh4H71Q2B+MRTV6IzJv8lhaduwGNOKmiIycbrWJiEjWqcYOgGuAy5xzS8xsAXA1cG5sATM7BjgTOME5t9vMZuUgzkEFait5fVPHoM/19tFTjd6IzJ20Z8qKvZXoicgIqUZPREQky8xsHNDgnFsC4JxbCtT762N9Ffgf59xuv9yqbMY5nEBtBa07wzjnBjynPnqpmTfZG5Clvqqc8TUVOY5GRAqVbrWJiOQR9V0bNWYBK/qtW+mvfzZm3QRgHzO7FqgB7nDO/WiwHZrZImARwPTp09Md7wCB2krCkR46OrsZO6a8z3PRppsadXNk5vqJnpptikgqVKMnIpKnRmvftVHCgIFVYQPXzQKOAc4ATgTebGYnDrZD59x1zrkFzrkFjY2N6Yx1UIE6fy69QUbeDHZ2U15qVJTpa8ZI1FeVs89etRwwZWyuQxGRAqZbbSIFTLU/xSeVv1n0etC1UBBWA3P6rZvtr4/VDHzfORcGMLNbgEOAhzIbXnyB2krAmzR97355ZSgcUW1eim79+FGMKVfTVxEZOd1qEykSqv2RKF0L+c85txUImdlhAGZ2ENAKTDOzr8cU/S3wLb+MAacCS7Ic7qAaarxEr22QKRaCnd3qn5ei8TUVVOkcikgKdLtNpICplkZi6XooOJ8HbjCzWmAHcCEwF5gZU+YG4Jtm9i+8Zp23Oef+meU4B9XbdHOQRC8UjmjETRGRHMv4u7CZTQRuxOtQ3g582DnX0q/MVOB7wDS8zua3Oec0a7mIiBQt59x64OR+qzcBj8eUccC3/SWvTKiuwAy27Bw4l14wrBo9EZFcy0bTzeg8QQuBr+HNE9RfnV/meOAo4EQzm5+F2ERERGQEykpLGF9dMXiNXqf66ImI5FpG34UHmyfIzOrNbJxzbnu0nHNuWczvPWa2DtDEMTIiGqBEJDn6n5GRCtRWDN5HL9zN5PoxOYhIRESiMl2jN9w8QYMys7OACufcc4M8t8jMlprZ0i1btqQ1UClOGpRCJDn6n5FkBGoraR2k6aZG3RQRyb1MvwsnOk8QZlYOfAcI4XVIH7iRc9cB1wEsWLBgsP2KqPZBJEn6n5GRaqit5IX12wesD3Z2U1OpPnoiIrmU6URvNQnME2RmlcAtwP9zzv0jwzGJiIhIGnhNN1WjJyKSjzL6Luyc22pmITM7zDn3337zBH3SOXeFX/TjwPVK8kQknwzVd0391kQ8gdpKdnZ2s7sr0ju5t3NOo26KiOSBbNxu+zzx5wl6M3CmmX0pZt3PnHN/zkJ8IiJxqd+ayECNtd6k6Vs6Opk2oRqA3V09OAdVqtETEcmpjL8LJzhP0NmZjkNEJFmqtRMZXkPtnknTo4leMNwNoD56IiI5ptttIqOQhtPPLJ1fGS0Cfo1ebD+9UGcEQH30RERyTO/CIqOcmiRmls6vFLNAnZfoxU6a3lujpz56IiI5pURPZBRSjVJm6fzKaNFQs6fpZlQo7NfoVeorhohILuldWESSomaJIhI1pryUusqyPpOmh1SjJyKSF5ToiciIqVmiiATqKvs23VQfPRGRvKB3YRFJimrsRCRWQ01Fv6abGnVTRCQflOQ6ABERESlcgdrKPqNuBsOq0RMRyQdK9ERERGTEAnX9avQ6VaMnIpIPlOiJiIjIiAVqK9kW6qIr0gN4NXpmMKZMiZ6ISC6pXYXEpVEWRURkKA3+pOlbg2Emjh1DqLOb6vJSSkosx5GJiIxuqtGTpFRVVWmkRRER6dVY23cuvWA4ojn0RETygN6JJS7V2ImIyFACfo1edC69ULhbc+iJiOQB1eiJiIjIiEWbbrZ2+DV6nRGNuCkikgeU6ImIiMiIBfymm21BL9ELhbs14qaISB5QoiciIiIjVltZRmVZSW/TzWBYNXoiIvlAiZ6IiIiMmJkRqK3sbboZ6lSNnohIPlCiJyIiIikJ1FawZWe06aZq9ERE8oESPREREUlJoLaStt6mm91Ua9RNEZGcU6InIiIiKQnUVvbOoxfSqJsiInlBiZ6IiIikpKG2grZgmM7uCOFIj+bRExHJA0r0REREJCWB2koiPY6N23cDUF2pGj0RkVxToiciIiIpCdR5k6av3RoCUI2eiEgeUKInIiIiKQnUeJOmr/ETPdXoiYjknhI9ERERSUm0Rm+davRERPKGEj0RERFJSaDWS/TWtAUBNOqmiEgeUKInIiIiKRlXVU5pibGmza/Rq1SNnohIrinRExERkZSUlBgTaip6B2NRjZ6ISO4p0RMREZGUBWorCYUjgGr0RETygRI9ERERSVmgtqL3d9XoiYjknhI9ERERSVl0QBaAao26KSKSc0r0REREJGXRGr2KshLKS/X1QkQk1/ROLCIiIimL1uhpDj0RkfygRE9ERERS1uAneuqfJyKSH5ToiYiISMqiTTc14qaISH5QoiciIiIpC6hGT0Qkr2Q80TOziWZ2n5ktMbMHzaxpkDKlZvYLM3vaX47NdFwiIiKSPo11fh891eiJiOSFbNToXQNc5pxbCHwNuHqQMouAFc65NwEnA98zs/IsxCYiIiJpMKHGa7pZVa4aPRGRfJDRd2MzGwc0OOeWADjnlppZvZmNc85tjyl6DnCSX2armd0LnALck8n4ABYvXkxzczNA789LLrkEgNmzZ3PxxRdnOoSsyeZrbd8KT9znCHZApGvwMqXlUFPnlWVi2g6dklTOUXTbYr+OREQGU15awrjqctXoiYjkiUzfdpsFrOi3bqW//tmYdaXOuc6Yx68Dc/rvzMwW4dX+MX369PRGClRVVaV9n/kqk6919uzZvb+3RFrYtWvXkDE0TWyCiX23yRcjPUej6ToSEYn1meP3YZ+9anMdhoiIAOacy9zOzQ4DLnDOfTZm3U+BG5xzz8Ws+5dz7s0xj98HTHPO/WiofS9YsMAtXbo0I3GLiEh+MbNnnHMLch1HodBnpIjI6DDc52Om++itZmDN3Gx/fSxnZhUxj/dlYE2giIiIiIiIJCCjiZ5zbisQ8mv2MLODgFZgmpl9PaboHcDH/TJjgROBBzIZm4iIiIiISLHKxtBYnwduMLNaYAdwITAXmBlT5mfAYjN7EogAlzjnhhjCQ0RERERERIaT8UTPObceb8qEWJuAx2PKdAEXZToWERERERGR0SAb8+iJiIiIiIhIFinRExERERERKTLZ6KMnIiIi/ZjZROBGYALQDnzYOdfSr8wtwGQgOhfSM865L2YzThERKUxK9ERERHLjGuAy59wSM1sAXA2c26/MJOBk59zurEcnIiIFTU03RUREsszMxgENzrklAM65pUC9v15ERCRlSvRERESybxawot+6lf76/r5nZveZ2UNm9rahdmhmi8xsqZkt3bJlSzpjFRGRAqSmmyIiItln7Ol3F6v/uiuBVc65V81sEvA3M3u7c27rgA2duw64DmDBggWD7VtEREYRJXoiIiLZtxqY02/dbH99L+fc/TG/v2FmTwIzgQGJnoiISCw13RQREckyv0YuZGaHAZjZQUArMM3Mvh4tZ2YLzazE/30ycCiwLAchi4hIgTHnCrN1h5ltAdYMUySA96GZrJFuV2jHTGVbxZufx0xlW8Wb2W1HyzFT2TbedjOcc40jCyk/mdlU4AagFtgBXAjMBc5zzi3yy3wEOAcoBcLAV51zzyew73ifkfkoleuuWOmcDKRzMpDOyUCj6ZwM+flYsIlePGa21Dm3IFvbFdoxU9lW8ebnMVPZVvFmdtvRcsxUtk3lmFIcdA0MpHMykM7JQDonA+mceNR0U0REREREpMgo0RMRERERESkyxZzoXZfl7QrtmKlsq3jz85ipbKt4M7vtaDlmKtumckwpDroGBtI5GUjnZCCdk4F0TijiPnoiIiIiIiKjVTHX6ImIiIiIiIxKSvRERERERESKjBI9ERERyRgz+5qZfSLm8fvN7F9m9qiZ/cLMqvz1ZmbfN7N/+MunY7a5xcweN7PH/OWaXLyWVJjZ0WZ2j/+6/2NmJ/vrjzKzJWb2tJn9yszKYrZZZGbPmNl/zexTMetL/XP3tL8cm4vXlA5pPi8Ff53AyM6J//xYM3uh37qiuFbSfE6K4jpJRFn8IiIiIiLJMbMpwO3AbOBSf91s4CLgeOdc2Mw+BHwT+BrwQQDn3FvMrAT4pZmd4py7H5gEnOyc252Dl5IupcC5zrkOMxsPPG5mjwA/AN7pnGszs88BHwd+bmbzgXcA0bnAbjWzx51zLwGLgBXOuU+a2QTgTjM7wTnXlf2XlbJ0npdiuE4gyXMCYGb/C5wP1PXbV7FcK+k8J8VyncRVtDV6/p3BcSlsf+EIthlnZoeYWeUItj3ezL5gZsfEKXdY/7sVSR7nUP/DFzM70czuMLNv+B+q8bZtNLPPmdlPzOw7ZnZGgscsNbN3+dt+1szeaWalI30NCR5zrpkdNMj69yaxj6xfQ/52I7qOEr2G/LI5uY5Geg352xbkdeSXH/G1lO3ryN8279+PJP855zY4544EvhSz+lDgfudc2C9zExC9zo4A/uyv78FL/hZlL+LMcs790znX4T/cDuwCTsE7H23++sXAmf7vHwWucj7gGiD6fnAO/pdZ59xW4F5/XwUnzeelKIzgnOCc+55zbh6wvt/uiuJaSfM5GTWK5sPUzPY3r0r3SjPbD/gv8Hsze8TMZsTZdt9+y37Ax6OPh9nuqpjfjwMewrtT+bCZHRjnmLdE4/LvQHwCWAN8zGKaqwziN8C9ZvaVZL84mtk3gSuA75nZWcAHgE8CYeDyONu+FbgFWAvMBF4Cjjav6nvaMNu9GXgE2B9Yh/fPdhBwv5ktTCb+RJnZF4D/B3zazH7T7+khz20uriF/2xFdRylcQ5CD62ik15C/bcFcR/62I7qWcnEd+eUL6v1ICtrLwKlmVuPfALkA2N/MAsALwLn++mrgy3jvFVHfM7P7zOwhM3tb1iNPE/9GxtXADcAc4NXoc34CXO4/7PMc8Lq/DqDUOdc5xHMFKU3nBYrkOoGkzslwiupaSdM5gSK6ToblnCuKBXgUmI+XyS8D9vXXzwf+EmfbDXhfin6N98XlN8BG/+evh9nukZjf/w5M9H+fDPwtzjEfivn9MaDM/92Ax4d7nf7PdwJ3Az8G9k7wHC1hz5QaNwJTYmOIs+3DwFj/9ybgV/7vhwN/jXPM8YOsHwv8c5jt/gj8YZDlj8Af4sT6RMzvZwL/2//85cs1lMp1NNJrKFfX0UivoUK7jlK5lnJxHaVyLeXiOtJSeAtwAfCJmMfnAo8D9+M1q3oQqMC7+XwF8A/gDuDomGvsFGA///dJwDPAhFy/thGci73895/T/cdfAN7br8w//Z93x75GoAG40//9X/22eR/w+Vy/vjw4L0VxnSR7Tvqte7Lf46K5VtJ4TormOom3FFMfvR7n3CvAK2Z2mXPuNQDn3CsJ3Gk+GPgh8Hfn3C0AZvaocy5eUwCL+b3bObfJP+ZGi99caoyZlTuvjXQoui/nnIvTbMn55e7Fu5N+CPAtM6sAfuac+/cw2+5y/lUNrAZi2yZH4sRrzrl2/9gtZra3//szcc6vOee2DXgRzrXb8E2+fgL8HjiOvuc5EaGY4/zZzK4ws2P8c+OG2S4X1xCM/Doa6TUEubmORnoNRbctlOsIRn4t5eI6gsJ7P5IC5pyL3nDBvD5Dn3V+U07g69FyZvYu4EV/m/tjtn/DzJ7Eq+3bmqWwU+a/5/0U+LhzLtqUbCUwN6ZMBdAd89y+wJP+432BFf7vzswqYs7bvvjnqtCk87wUw3UCIzonwymKayWd56RYrpNEFE3TTWK+lAFbor+YmQFVw23onGt1zn0EqDWz68xsLPG/yEX3X+5fWC+b2T4xx4z3ReUXwB/9JmvXAF81szIzuxzv7uaQh+wX+3POuQ8DlwCnxTnm62b2VTM7H5gKfM6Pdz/i/2NsNrO3++VPA1r83yvxmloN5Wkz+7yZ9Val+6/z03hN9wblnHsCeNY5t9Y5t6b/EifWdf2ax30T+KKZvYPhr/mcXEP+MUZyHY30GoLcXEcjvYagsK4jGOG1lKPrCArv/UgKmJnN8X+W4TWPvtJ/XGVmTf7vAeAbeDc+MLOF0ZsOZjYZr6/fsuxHn5LLgI/EfEkFr1bzVPMGlwD4CHCr//tNeO855v8ffxqvth+8Gs+PgzeqIHAi8EBmw8+Yy0jTeSmS6wSSPyfDKZZr5TLSdE6K6DqJK9pspuD5X/hedM49bmYznXOr/fVnAtOdc/+X4H7m4H3ozHbOHRqn7B+Biez5svN959zfzewIYH/n3G/ibH8M8BVgAjAL747Ub51zvx5mm5845z6byGsZZNtyvH+CUuB6vKrrr+CNvnqhXwMx1Lbj8dpEzwea8ar9W/0P5Qbn3KB3h/x/pEV4nYGjA2f04L3x/Nw5l/YvdOYN7lDnnFses64C743uzc659w+xXdavIb/8iK8jMzsa+CpJXEP+dlm/jkZ6Dfnbxl5HJXjnKi+vI79cytdSNq8jf/ukr6U0XUcleH0tEn4/ksJiXj+8Mc65a/3HVwMLgRrgN865n/rra/CaZY3H62fzNefco/5zH2HP50gY+Kpz7vksv5SUmNkKBg4KcRHejY4f4N3geB74jHMu4m/zWbzRSCN4zbev89eX4w08cYD/3CX+ja2Ck+bzUvDXCYzsnMRs+6TzBkGKPi6KayXN56QorpNEFE2il07mjeR3mHPu6VzHIoVJ15Ckg64jERERGali6qOHmZ2Ed7d9BrADrw3yD51zLSPZ1sw2xNs23cdMZNsMHPMHzrmNGYx3Pt7Qx3PwmqGtwLsDtzyB7T6CNwdTwtulsm26j2lmHUlsO9JzlPR2Q8Wb6W1TiXc08u9KKskTERGR5Lk8GBEmHQvwKeCXeKMv/QA4Em8kuP8Ab8nEtqPlmCnGezHwJ7xmOhP85UjgZuCidG9XaMf0t/1kDuId0TFzdX61aNGiRYsWLVq0JLcUTdNNM3vMOXec/3sV8Dvn3Jl+h+67nXNHp3vb0XLMFONd6pxbMMh6A/6d7u0K7ZiKN+FjPo/Xb6d/+3zDGxxyqGOOaLtcbat4E9tWRERE4iumppu9o78553aZWaP/e6uZxRuoYaTbjpZjprrtAM45Z8MPi5/W7QrtmKlsW6TxHos3D06yX/5Hul2utlW8IiIikhbFNL3Cv83se2Z2qD+i17+A6LCpA+bfStO2o+WYqWz7SzO73cyONLPxZjbOzI4ws1uB32Vgu0I7puJN4JjOm3/v5jhxpW27XG2reEVERCRdiqbpJoCZfRA4AngBuN6vKRgDlDjnQpnYdrQcM8V45wEfxRuAw/CG1v+dc+7ZOMcb0XaFdkzFm9gxRURERCRxRZXoRZnZBKDdjWBurZFuO1qOmeq2IiIiIiKSeUWT6JlZJfAt4Bi8CZn/C9QDVzrn7szEtqPlmCnGWwJ8AjgTr0+ow5vU8lbgOjfEBTjS7QrtmIp3xMcE6BrBMRPaLlfbKt7EthUREZH4iqmP3jXAK865twJfAn4LvAM4w8w+laFtR8sxU9l2MVAKnOyce4u//Sn+cz/NwHaFdkzFO7JjvmWEx0x0u1xtq3gT21ZERETicXkwx0M6FuDhfo/v9X+WAUszse1oOWaK8S4Z5rl/pnu7Qjum4i2+YyrezMerRYsWLVq0aIm/FFONXoWZlQKYWTVQDeC8fmTDDjKSwraj5ZipbOvMbFz/lf668gxsV2jHTGVbxZufx0xlW8Wb2LYiIiISRzHNo/dT4F4zux84FbgWwMxmA0sytO1oOWYq2/4PcJeZ3Qe8itcvaz/g3cCXM7BdoR1T8RbfMRVv5uMVERGROIpmMBYAM9sbOBR42Tm3PBvbjpZjphhvGV7fm+iQ+iuB+4EpzrmV6d6u0I6peIvvmIo38/GKiIhIHLluO6qluBfgJOB54Angon7PPZLu7QrtmIq3+I6peDMfrxYtWrRo0aIl/lI0TTfNbNFwzzvnrkv3tqPlmClu+03grcAO4CozO9U5d090t8PscqTbFdoxFW/xHVPxZj5eERERiaOYBmOZAHwX2A10DrJkYtvRcsxUtg0757Y75xzetAwfMLPJ/nPDtRse6XaFdkzFW3zHVLyZj1dERETiyXWVYjoX4IlsbztajjnSbYG7gZqYx3sB9wCNDN8kbETbFdoxFW/xHVPxZj5eLVq0aNGiRUv8pdgGY2lyzrVkc9vRcsyRbmtmRwElzrl/x6ybBVwGHOWc2zed2xXaMRVv8R1T8WY+XhEREYmvqBI9ERERERERKa4+eiIiIiIiIoISPRERERERkaKjRE9ERERERKTIKNETEREREREpMkr0REREREREiowSPZE8YGZXmtnbYh7famYzzOwvZvawmf3RzKrNrMzMfmNmD5nZE2a2v1/+RjP7rpk9bWbH5+6ViIiIiEg+UKInkh9+DZwPYGaVwDjgWuAy59wJwL3AZ/H+Z690zp0IfA74Vsw+Zjnn3uSceySbgYuIiIhI/inLdQAiAs6518ysycyqgOOAf+Elcj82M4BSYLlzLmxmnWb2WeBAYFrMbq7LctgiIiIikqeU6Inkjz8DpwNvBb4PHOuce3tsATNbCHwN+DZwE3B/zNObsxSniIiIiOQ5Nd0UyR+3Au8DJjrn1gKdZnYIgJnVm9m5wFHAXc65Z4EP5CxSEREREclrqtETyRPOuQ4z2wm85K/6GPBzM5sABIH/BR4CbjGzC4C7gWozOycX8YqIiIhI/jLnXK5jEBGfmd0EXOqcW5PrWERERESkcKnppkgeMLMJZvYEsE1JnoiIiIikSjV6IiIiIiIiRUY1eiIiIiIiIkVGiZ6IiIiIiEiRUaInIiIiIiJSZJToiYiIiIiIFBkleiIiIiIiIkVGiZ6IiIiIiEiR+f/M33mv/fpDNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1,2,1)\n",
    "g = sns.boxplot(x=\"year\", y=\"OPS\", data=regular_season_df, showfliers=False)\n",
    "g.set_title('Boxplot of yearly OPS', size = 20)\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(regular_season_df.groupby('year')['OPS'].median())\n",
    "plt.title('Median of yearly OPS', size = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "showfliers : Print outliers out of box range if True, and not print outliers\n",
    "out of box range if False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>year</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>...</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>col_0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>43</td>\n",
       "      <td>...</td>\n",
       "      <td>124</td>\n",
       "      <td>130</td>\n",
       "      <td>151</td>\n",
       "      <td>174</td>\n",
       "      <td>194</td>\n",
       "      <td>186</td>\n",
       "      <td>207</td>\n",
       "      <td>213</td>\n",
       "      <td>217</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "year   1993  1994  1995  1996  1997  1998  1999  2000  2001  2002  ...  2009  \\\n",
       "col_0                                                              ...         \n",
       "count     1     2     1     7     8    10    14    20    32    43  ...   124   \n",
       "\n",
       "year   2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "col_0                                                        \n",
       "count   130   151   174   194   186   207   213   217   227  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(regular_season_df['year'],'count').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior to 2000, there were only few data recorded, mostly less than 20 number of games.\n",
    "\n",
    "That is why *the change of OPS was very volatile before 2000*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HCHUOxURuYvCcMNcw1Do1p3JPdaFkRv7LSJLEhoQNhLmG4WPj0+Z4W1Nbvpv4HZ5Wnjyy6xFO5JzoeiEboYqMwqR7dxSOjlf82v8ljArOiEEUVxVzrvBck/5vbTHAdQACYcyHM9JlRGZHcqH0QpPgktZwMHPgu4nf4WLhwkN/PcSZvDNdKGFDJJ0O1fHjxvJcVwCjgjNiEJHZkUhIDHJrvsByS9iY2NDToacx0KQLUGdlkfbIo2jy8q62KFeVDQkbsFJaMcFnQrvmOVs4s3TiUmxNbXlg5wOcLTjbRRI2pCoxEV1xsbHB6RXAqOCMGMSxrGOYyc3o49Sn3XPDXMM4mXMStVbdoWtXnDpFzocfImm1HZp/rVKydStlu3ZRuPK3qy3KVaO0upSdqTuZ0n1Ks62b2sLN0o3vr/8eC6UF9++4n4TChC6QsiEVxganVwyjgjNiEMeyjhHq0j7/Wy3hruFUaiuJyY/p0LWz3nqL/O+Wkv/9Dx2af61SHnEIgKLVq5HUHXt4+Lez7fw2KrWV7TJPNsbTypPvJ36PQqbgvh33cb74fCdK2BRVVDQKFxeUXl5deh0jhpfqMvIfprCykHOF53is/2Mdmj/AVR8pFpkdSahLaLvmVpyJofLkKeROTuR+/jlWI4ZjFhzcITmuJXRVVagiIzEN8KcqIZHS3Xuwub7ZQkLXNOsT1hNoH0iw46XvhCRJfH/gPOmFFe1aK1T5AvtKX2fu73cw2voVrORuLY4d4GPPjSEeHZJZFRWFedgAY4PTK4BRwRlpk1r/maEJ3o1xMHPAz9aPqOwo7u17b7vmFq5YgbCwwPe3laTOX8DF55+n+7p1yEzbrqRyLVMRHY1UVYXzU0+R/eZbFK5c+Z9TcPEF8cTkx/DCoBcaKIuM4kre3BqHuVKOUt4+JSIp7wX3r9he8CpkPozQODQZo9FJLItIwcZMwZieLu1aX33xIprMTCzuuadd84x0DKOC+weRUJiAQOBv37i70NXlWNYxzBXm9HFsv/+tljDXMLae34pGp2kxEbcxmsJCSrZuxXbGTZh4eeH+f/9H2n33kfvRx7gueqHDslwLlEdEgFKJ5eDB2M2bR+7HH1OVlISpn9/VFu2KsSFxA0qZkqndpzY4HpdRAsCv9w4izKepgmqL2PxB3LvjXmx7/cyySctwtXRtcL5SreWm/x3k2TUn+eOJkbhYt9w2qjGqaGOD0yuJ0Qf3D0GSJJ7Y8wSP7X4MnaS72uI04GjWUUKdQ1HKO96zKtwtnHJ1OfGF8QbPKV6/HqmqCvsFCwCwGjkC+wULKPjpJ8oPHeqwLNcC5QcjsAgJQWZpid3sWaBUUvjbqqst1hWjSlvF5qTNjO82Hjszuwbn4jL1Cq6nm02H1g52DOab676hsKqQe3fcS15FwyhVM6Wcz+f3p6xKwzOrT6LTNdfPuXlUkVHIrKwwDQjokGxG2odRwf1DOJl7krTSNNLL0jmccfhqi1NHQWUBiUWJDHJvX3pAY8Jc9U+shpbtkrRaClf+hkV4OHJfH4qyMgFwee5ZTHx9yVi0GG1JyWXJ9G9FU1hIZVwclsOHAaBwdMTm+usp3rgRnUp1laW7Muy+sJuS6pJmK5fEZZXQzcECK9OOG6j6Ovflq+u+IluVzb3b76WgsqDB+QBXa16+oTf7E/JYeiDZ4HVVUZGYD+iPkMs7LJsRwzEquH8IW5K3YCY3w87UjjXn1lxtceqoVUjhru1L8G6Mi4UL3ay7GZzwXbZ/P+r0dOxvWcDen7/np+cfpbK8DJm5OR7vv4cmN5esN968LJn+ragOHQJJwnLYsLpj9vNvRldaSvHWrVdRsivH+oT1eFh6MMR9SJNzcZml9HK3vuxr9HfpzxfjviC9LJ0Hdj5AcVVxg/PzB3kzuY8b7/0Zz8m0ojbX0xQWUp2YhEXY5f0tGTEco4L7B6DWqdmesp2x3mOZ4T+DPWl7yFHlXG2xgEv+t95OvS97rXC38LqGqW1RuGIFCmdnlEOHELNvF5qqKhKP6s2S5n374vTwQ5Rs3kzJH39ctlz/NsoiIpDZ2GDW55JP1HzAAEwDAylcuRJJMtxk9m/kYtlFDmce5ib/m5CJhrcwVbWGlPxyerl3zDzZmEHug/h07KckFSXxwM4HKK0urTsnhOCdmf1wsTbl8d+OU1alaXWtiuPHAZo0OK3UVJJSnMLhzMNsTNzI1ye/5mLZxU6R/7+OUcH9A4i4GEFRVRFTe0xlduBstJKWDQkbrrZYgD60f4DLAJSyjvvfaglzDaOkuqTNZNrq1FTK9+3Hbt48YvbvQVNVhZmVNXEH6/rr4vTAA5iF9CPztddRZ2dftmz/FiRJojwiAsvBgxuYuYQQ2C+YT1VsHJWnru26nxsTNyIQ3OR/U5NzZ7NKkSQ6TcEBDPcczsdjPia+IJ6H/nqIcnV53TlbCyWfzu9PWoGKlzc2Lfel1qpJL00nMiuSM7tWo1PI+US1lcd2PcaczXMY+dtIBi4fyLSN07hvx328dPAl/nfifyQWJnaa/P9ljFGU/wC2Jm/FztSOYZ7DUMqUDHEfwrqEddzb917ksqtnq8+vyCexKJGpPaa2PdgAas2cUdlR9HTo2eK4wpW/gUKBzexZnHjrRTyDeuPduy9H1q+mvKgQSzt7hEKB57vvkjxjJpmLFuO99DuE7Np/XqtOSUGTkYnl/fc3OWdzwzRy3v+AwhUrr9ku0Vqdlo2JGxnqMRR3K/cm589m6ndYwZ2o4ABGe4/m/dHv8+zeZ3l016N8ed2XmMhMyK3IRWmRxbShuWw+u4/SLWuxsiwnqzyLLFUW+RX5SOh31G8e0pDpBlsubsfN0g03Czf6OvXV/1zz2s3SDVdLV4MaChtpG6OCu8qUq8vZk7aH6f7T63ZJcwLn8MzeZziYcZBRXqOummy1/rL21p9sCQ8rD9wt3YnMjmRBrwXNjtFVVFC0fj3WE64j/WIqxTnZjFxwF07ePhxe9xvxh/YzYPKNAJj4+uK6cCFZr75K4YqVONx6S6fI+U+mNnq0vv+tFrmVJbbTb6Ro7TpcXliIwt7+SovX5RzOPExWeRbPhD/T7Pm4zBKsTRV42be/bFdbXOdzHW+PfJsX9r/A+DXjUalVaKVL5ePMXOFQrgnele5423gQYB9wSXnJ7LF/7zFs7ryNiPkLO102I81jVHBXmV0XdlGpreSGHjfUHRvbbSyOZo6siV9zVRXcsaxjWCgs6OXYq9PWDHcN52DGQSRJaraSQ8nWrehKSnBYsIA//tyElYMj/gOHIFcocPH14+yBvXUKDsBu3lxK9+wm5/33sRw65JrPAyuPiEDp6YnS27vZ83Y330zhipUUr1+P4zWYTLw+YT12pnaM8x4H6E22uuJiNHl5aPLyUfwdwV2qEvI+j8d25ixMvDw79fqTu0/GRG7C7gu7cbVwrVNgrhauoLFjzlcnUDpa8sVNwzBRXLIolB8+wgWtFttBTYNirgRRUVEuCoViKdCHa8s1pQPOaDSae8PCwpoELhgV3FVmS9IWvKy8CHG+ZFJSypTMDJjJ92e+J6s8CzfLlksGdSXHso7R37V/p/jfaglzDWNz8mbOl5ynh22PBuckSaJg+QpMAwNRubuSeuo4w+fdhlyh/5oGDR/FvuU/UpSViZ2b3jwlhMDjzTdJnnYjGc89j+9vKxEm7a+X+W9A0mhQHT6CzeTJLZZ5MgsMxCI8nMLfVuFw113/SrOtJEnoSkvR5OWjyctFm5+PJjePsux0/CK3cwPuXFyzAE1+Ppr8fKhXh3Nuzf95+6A6LR3P99/rdPnGdxvP+G7jmz333qwQHvw1ig93xLNoyqUHQ1V0FAiBef/+nS6PISgUiqVubm69nJ2dC2Uy2TUThaTT6URubm5wVlbWUuDGxueNCu4qkqvK5UjWEe7re1+TG9aswFksPb2U9QnreTj04SsuW15FHsnFyUz3n96p69b2k4vKjmqi4CqOn6AqLg63V18lavtW5Eol/a6bVHe+5zC9gjsbsY8hM+fVHVc4O+P2+mtcfPwJcr/6CpcnnuhUmf8pVJw+ja6srC7/rSXs5t9MxjPPUn7wIFYjR14h6dqHtqycki2bUWdno83L1+/A8msUWl4+UnV1kzmSTBBiIeHgYYLczQHTwEAUTk4onByROzlRoLTi9t+TeGzWIEZF/E7hqlW4LnwehZPTFXtfk/q4ceuQbnyzL5nh/k6MCnQGoCIyCtPAQOQ2nesbbAd9rjXlBiCTySRnZ+firKysZsssGRXcVWTb+W3oJF2zQRyeVp4M8xzGunPruL/f/QaXt+osavPfBrp2rP5kS3Sz7oazuTORWZHMCZzT4FzhihXIrKwwGz+O2GceImjYKCxsbOvO2zg54xnUm7gDfzN4xtwGDwU2EydSNmMG+d98i9WoUVhcpSflrqQ8IgKEwGLw4FbH2UyYQLajI4UrVv5jFVzWyy9R8sc2kMmQOzjoFZWjI6bduyN3ckTh5IzCyRGFkxNyR70Cm7f3bkyV5qy8YWWzax46k0WadRkB/p7Y+86n8NdfKVq7FqcHH7yi723J1GCOni/g6dUn2fbESJzM5VScOIHtTTddUTkaIbvWlFstNe+rWVPFv89+cQ2xJXkLvR170922e7Pn5wbOJacih33p+66wZHrzpKXSslP9b6A3KYa5hukbqNbL19Lk5VGyfTu2M2YQdywCdVUl/SdNazK/14jRFFxMIze1aUsT1xcXo3RzI2PhC+jKy5uc/7dTHnEIs9692wweESYm2M2ZTdnff6O++M/Lp1JFH6fkj204PvAAQadPEXhgPz02bqDb90vxePcdXJ97Dse77sR22jQshw7FLDCQs9qLJJQkMTOw5bY4cZklCAE93awx7dEDy2FDKfxtFZKm9fy0zkZfymsApZVqnl1zkoq4s+hUqv90/clZs2b5Hj9+vK5o52uvveayYMECn5CQkKD6495//32n8ePHd5oj3ajgrhLJRcnEFcQ1CC5pzCivUbhYuFyVyiZHs44ywGVAl+wcw13DyVHlkF6WXnesaO1aUKuxmzeP49u34B4YhGuPpkWnAwYPRyaXczaiqdKXW1nh8e47qNPSyH63830vVxNtWTkVJ082Gz3ZHPZz54IQFK7+51TFAZB0OrLfeQeFszNO999ncMmq9YnrMVeYM9l3cotj4jJL6O5oiYWJ/jtrv2ABmqwsSnfv7hTZ20NPN2teuiGYvedy2bN2BwDmYf9dBVefFStW2O7Zs8fmp59+Sm187o8//rCrqKiQFRcXd4puMiq4q8SW5C3IhIxJ3Se1OEYhUzAzYCYHLx68opUNclW5pJSkdLg9Tls0rkspaTQU/rYKy2HDyCwtpCgrs9ndG4CFjS0+/fpz9uBeJF3TiigWAwfieM/dFK1eTemePV0i/9VAdfQoaDQGKzilhwdWY8ZQtHYtumb8WVeLkq1bqTx1CuennkJmaWnQHJVaxbbz25jgMwErE6sWx8VllTRI8LYaMwaFhzuFK5o3aXY1twzuxvW9XcnafxjJzQOlq2vbk65xIiIizD/44AP3DRs2JCuVDYPXMjIyFCYmJtKNN95YtHr1atsWlmgXRgV3FZAkiT/O/8FQ96E4mbfuAJ8VMAshBOvOrbtC0nV+/ltj/Oz8sDe1r+szV7p7N5qsLOxvWUD0n5uxtLMncHDLN/Jew0dTmpdLxrmzzZ53evxxTIOCyFzyEpqCgmbH/Nsoj4hAmJtjPsBw36L9/Plo8/Mp3bGzCyUzHF1FBTkffoRZcDC2NxkevLQzdSfl6vJWu3aXVqpJK6hoUINSKBTYz7sZ1eHDVCUlXZbsHUFfyqsvvQtSOGLl3WYpr2udtLQ05cyZMwOWLl2aYm9v3+TpdPny5XbTpk0rmjVrVvHGjRs7JYnTIPuTEMIVWAY4ACXAHZIkZTQa8znQt94hB+BuSZIihRDxQGa9c99LkvTL5Qj+b+ZE7gkull3kkdBH2hzrZunGKM9RrE9Yz0OhD3VqyH5LHM06ipXSqtVqI5eDEIIBrgPqFGnhipUoPNxR9wwg5duPGTbnFuSKlt+n38AhKExMiTu4F8+gpt29ZSYmeLz3LimzZpP58st4ff75v757cnlEBBbh4cjakQJhOXwYym7dKFy5Etsb2l+N5kJxGramNtiadcrDNPk//ogmKwvP999rV/rC+oT1+Nr4MsBlQItj4rP0FUwal+iymzObvC++oHD5Ctxefqljgl8GFjkZ2FaWctTKmzO/x/Dh3KtfYea5tSe9z2WVWnTmmoFu1qr3Z4ektTbmmWee6TZ06NCSb775xumbb75Jb3x+8+bN9uvWrUt2dnbWZmVlKcvKyoSVldVlBcYY+i37EHhVkqTBwCLgg8YDJEl6TJKkMZIkjQHGAjlAbVG8wtpzNf/+s8oN9Llv5grzFnNpGjOn5xzyK/PZc+HKmNwisyIJcw3r0sjNcNdwLpZd5OKZI6gOH8Z+3s2c+GsbMrmiQWpAc5iYmeMXNohzh/ajbSGAwCwwEOenn6bsr10Ur/9n1PXsKOqsLKqTk7EcOrRd84RMhv3NN1MRFUVl/Ll2zS2sKOaG9bOYtGYOhZWF7ZrbHOrsbPK/W4r1xIlYDDTc9J1SnEJ0TjQ3+d/U6kNKbQ+4xgpO4eCAzZTJFP/+O9qyKx94VBGlt1L0nzqWddHpbDz+zwv6uVK8+uqr6b/88kvqjh07bKOiohp0ic3IyFCcOXPGYurUqf6DBg3qmZmZabJhw4bLfrJq8w4mhLADHCVJOgJQsyOzFULYSZJU1MK0KcAuSZL+Ocb/fwhqrZrtqfrOARZKwx6ihnsMx93SnTXn1jDRd2KXypejyiGlJIXZgbO79Dq1fri0Zd9ip1RiMW0qMYufpOfQEVjatW2dCBoxhvhD+7lw5iTdQ5t33jvccTtlf/9N9ltvYTF4ECZeXp36Hq4U5RE15bnayH9rDtsZN5H7yScU/rYS91deMXjeS39/jk5UUqbN5Yk9T/DdxO8uqz5i7sefgEaDy3PPtmve+sT1yIW8zXzM2MxSbM2VuNs27a5tv2ABxb9vonjT7zgsaL5EXFehiopGbm/PXfNHs3PpEZZsPEP/bnb4OBrmf+wK2tppdRXBwcFVVlZW0jvvvJP+8MMPdzty5EjdU9fy5cvtFi5cmLFw4cJcgF27dll+/vnnLrfddlvR5VzTkB1cd6BxaevkmuMt8SDwTb3XTkKI74UQu4QQa4UQvs1NEkLcL4SIFEJE5ubmGiDav48DFw9QXFXcrgLGcpmcWQGzOJx5mAslF7pQOn16AFxKyO4qAu0DcdZZYfHXUWymTCb+9HGqKypaDC5pjG/IAEwtLTl74O8WxwiZDI+3/w9kMjIWvoCk1bY49p9MeUQEcicnTAMD2z1XYW+PzZQplPy+CW1ZmUFzcspz2Je9Hk1JCBUZ8ziRc4LF+xd3uNN8xZkYijduxP722zBpocRYc6h1ajYlbmKk18g2fdVxmSX0crdudpdn1q8fZr17U7h8xRVvJaSKisI8bABKhZxPbu6PTMDjK49TrenYZ3ktMG/evGIbGxvtV1995VB7bMOGDfbz5s0rqn09ZsyY8hMnTlhWVlZelm/BEAUngOa+Fc1+U4QQ/YEUSZLqe/dfA56TJGk8evPmj83NlSTpW0mSwiVJCnd2djZAtH8fW89vxcHMgaEe7TM3zQiYgVzIWXtubRdJpudY1jGsldYE2Qe1PfgykMvkzE1xRVmpwW7+zRz/cwtufgG4Bxjm91MolQQOHk7CscOoqypbHKf08MDt5ZeoiIoi/4cfOkv8K4ak01F+6BCWQ4d22I9ov2A+OpWK4k2bDBr/xsHP0KFlkO18NCV9meX7IDtSd/Bx1MftvrYkSWS/8zZyB4d2J1zvT99PfmU+M/1bDi4B0Ook4rNKW2yRo28ltIDqpCRUR462S4bLQZ2Tg/rChboGp5525rw7qx8n04v5aGf7TMbXGl999dWFt99+20OtVovMzExFVVWVzNfXt67mmlwuZ+DAgWW///77ZZV+MUTBpQCNE5L8ao43x1PAp/UPSJL0S63CkyTpMND5pb7/BZRVl/F32t9c73t9u4NFXCxcGOs9lo2JG6nWdp3l91jWMcJcw7q8TY8kSQw8mEeSGyRUF1GYkU7/yU1KybVK0PAxqCsrSI5uvUu4zbRpWE+aRO5nn1MZF3c5Yl9xqs6dQ5ufb3B6QHOY9e2LWXAwRSt/a3MHk1aSxt6MLcjLhvDeTfqCxk66Cdzc82aWxSzjt7O/tevapTt2UhEZhfPjjyG3bl+X7Q0JG3Ayd2KkV+vVWFLzy6lQa+nl1vK90GbqFOS2thSuWNEuGS6HiuhooGGD08l93VkwuBtf701if8K1aaVqjnXr1qX079+/7knU399ffeHChTOxsbFx7u7umqioqPjGc9asWZMyb9684sbH20ObCq5GMamEEAMAhBD9gDzAWwixuP5YIYQXYCpJUmKj48Pq/Twa6Fo72z+Uvy78RZW2qtXk7taYEziHwqpCdl3Y1cmS6ckqz+JC6YUuy3+rj+roMSzS89keJuPIH+uwsLUjcMiIdq3hFdwbS3sHzh78u9VxQgjcXnkZhZ0dGc8/j66q6jIkv7KUH4wAwHJY+3b89alrhpqQUBf00BJvH/4EnSRjfuDduNma0d3JkpNpxbww6AXGeI3h7aNv83fa3wZdV1ddTc4HH2Aa4I/d7Pb5dHNVuey/uJ8b/W5sM9gpLrP5CMr6yMzMsJ09i9Jdu1BnZbVLlo6iioxCmJtj1qthNaCXpgYT4GLF06tPklf27/ku/hsxNIrySeBtIcRB4B3gOfRpAL6Nxj0OfNHM/HlCiO1CiN3AfcBDHZLWAFQlxVRXVnTV8pfFluQteFt709epb9uDm2GIxxC8rLxYHb+6kyXTU+t/uxIKrnD5cmS2tpz1s6YkLoV+101CoWzfrlYmkxM0bCTnj0dSWd66f0lhb4/7//0fVQmJ5H7UflPb1aI8IgITf7/LThK2mToVmbV1q0nP8QXx7M/ciVQ8ggeG6/PtQrxsOZFWhEzIeHfUu/Ry6MXz+54nJi+mzWsW/vIL6rQ0XF54gcrKCtLjznBy5x/s+uFrVr++mFWvvdBsyTWA35N+RytpmeE/o83rxGWWIJcJAlxbTgIHfV4gOh2Fq1a1uWZnoIqKwjw0BNHoe21uIufzBf0prtCX8tLprskSkf8IDFJwkiSlS5J0vSRJwyVJmiJJUrYkSXslSbq/0bjnJUna38z8J2rmj5Mk6VZJkvI76w005vD63/jiznn8vPBx/lr6JbH791CUlXnFncuNyS7P5mjmUW7ocUOHfSkyIWN24GwisyNJLk7uZAn1Cd7WJtYE2rc/mKE9qLOyKN21C7vZsxiU74skIOS6lkswtUbQ8DFoNRoSjka0OdZq5AjsFyyg4Kef6hqH/pPRVVWhioy8LPNkLTJzc+xmzqBk5040eXnNjvng6CdIWjNu9FmAvaU+3y7U246c0iqySiqxUFrwxfgvcDBz4JFdjzRbXaeyvIyLZ2M5/vta/l69nMiwYH79+Wu+vGc+q159Qf83uW8XmqoqCi6ms+LFZzjz918N1pAkiY2JGxngMgBfW98239vZrBJ6OFlipmzdrG7i5YXV6NEUrVnbbLeCzkRbWkpVfDwWA5qP8A1ys+Glqb34Oz6XHyNSulSW/zLXXDeBoGGjMbO0IuPcWeIO7OHkzj8AsLC1wyMwCPeAIDx69sK1hz9KkyvXFv7PlD+RkNoVPdkcN/nfxBcnvmDtubU8P/D5TpJOz7GsY4S7hne5/61o9WrQ6bCcOQOH1w5y3q0cjUXHrunawx87N3fOHthL37Ftp1C4PPcs5YcOkbFoMT02/X4125e0SUV0NFJVVbvz31rCbt7NFPz0M0Vr1+H04AMNzh3POc7h7AOo86/n4Zv61R0P7aZP2ThxoQj3vuY4mTvx5fgvuXPTrSz87SEe874bVVYu+ekXyE9LpazwUmyZ3NoMRzdXfP0CcfTuhpNXNxy9u2Ht6IwQgvKiQrZ+9j7bv/qEi2djGXf3AyhNTInKjiK1JJX7+t5n0PuKyywlzMewwhf2tyyg7L77Kdmxs0PJ74ZSceIE6HStFli+dYgP+xLyeGdbHIO7O9DHs3MS6o1c4ppTcB6BQXgE6iMAdTot+elpZMTHkZlwloxzcSQeOwyATK7ApXsPPGoUnkdgL6wdu65v1JbkLfR16ouPjc9lreNo7sj4buP5PfF3Hu//OGaKpnk/HSGrPIu00jTmB83vlPVaQqqupnD1GqxGjSIp+RxUaYj1LSU6J5px3ca1ez0hBEHDx3Bk/SrKCguwsndodbzM3ByP994jZf58st54s0saYnYW5RERoFBgMbBzSqaZ9uiOxdAhFK5aheN999YVOpYkiQ8jPwaNNWM9ZuHtcCk/089egac6l1O7d2J+Ql2jyC4wPd8B0HGApShMTHD06ka3vqE4enXDRigof+kVPGbPwf2lJS3KY2lnz+wlbxCxegVHNqwiOzmBaU8vYkPiBiyVlkzwmdDmeypWqblYVMGtQwz7u7IcPhylTzcKly/vUgWniooCuRzzkJYrlwgheG9WPyZ/up/HVx5n82MjsDS95m7JV5Vr+tOUyeQ4d/PFuZsvIRP0JjBVSbFe2cXHkZFwllO7thO9TR8+beXohEdgrxqlF4SLb49WS0YZSmJhImcLzvLCoBcuey3Qt9HZnrKdnak7meZnWN5YW9T637qq/mQtJTt2os3Lw27BfHZtWoWzbw9KHLOIzI7skIIDfafvw+tWcu7QfgZMabvGoXnfPjg9/BB5n32O9dgx2EyZ0qHrdjXlEYcwDw1BbtV5ScH28+dz8fEnKNu7F+tx+s/7wMUDnMw9TmXedB6+pRc6nZb4QweI3LSenJQkZgKkwwmlEgdPb7x69cbRqxvnlTl8duF7RvWZyOMj30EIgSRJpN1zL8LMHOdH2y5FJ5PJGXHzbXgEBrHtiw/55YUniO2dyeQRkw0qhBCXVVvBxLAITSGTYT9/PjnvvEtlXFyTAJDOoiIyCrPgYGQWrb8He0sTPrk5lPnfHebVTTG8P+fql/K6lrimFVxzWNjY4hc2GL8wfdNIrUZD3oUULtbb5Z07pHcjKpQmuPr54xHYC/fAIDwCggyqstGYree3IhdyJvm2XoLKUAa6DcTXxpc159Z0moI7mnUUGxMbAuwDOmW9lihcsQJlt24U2NuQn36B6x96kn5VZXWFlzuCo6c3Lr5+xB3ca5CCA3C6/37K9u4l87XXMQ8L+8dVetcUFlIZG4vTY4926rrW48ahcHGhcMVKrMeNQyfp+CT6U4TGkTDbCYiEo/z4/hqKsjJx8PRm+Nxb+StLxuYLOva/OQuTegETg4Gy02Z8Gv0pHlaePD7gccr27qU8IgLXRS+02beuPj0GDOS2dz/jp7efY0SkHT42pmgHapAr2oqg1Cu44FYiKBtjN2MGuZ98SuGKFbi/8YbB8wxFV11NxalT2BtYNWVID0ceG+vPZ7sTGRHgxPRQz06X6Wrz9NNPewwbNqxs9uzZJfWP79ixw/Lll1/21Ol0oqqqSsyYMaPw1Vdfze6s6/7nFFxj5AoFrj389b3HJuuVRVlBPhn1dnnRf/yOdpO+mr+9uwfTn12Co1c3g9bXSTq2Jm9lqMdQHM0dO0VmIQSzA2fzQeQHJBQmdIpSqvW/yUTXNZioPHuWiuhoXBYu5MCOrZhb2xA0bBThsef45tQ3lFaXYm3SvlypWoJGjGbfrz9QlJWJnZt7m+OFQoHnu++SPGMmmYsW4730u3YVAO5qVIcPgyRh1QkBJvURCgV2c+eS98UXVF+4wC5tDEl55+gRM4phZd+zY08BLt39uPHpxfgPHIKQycg6fpGlaSdIzK0g2KOhReOePveQXprOd6e/w8PMlf7v/oSJj48+YrGd2Di7cGh0Fe6RMth9gNXp+dzw1EKsHVp2HcRlluBoaYKzteH+dLmtLbbTbqB48xZcnn0WuW3n+r4qz8QgVVe3q8Hp4+MDOJiUz5INZ+jvbU83x06thfyP5ODBg+YvvPCC9+bNmxM9PT01arWad955x6Uzr/HP+Yv+B2Hl4Ejg4OGMuf1eFrzxAY/+uJr5b7zPqFvvpjArk/hDBwxe63jOcTLLMy87uKQxN/rdiInMpFOaoWaUZXCx7CKD3LvWPFm4fAXCzAwxagRJkUfpO/56FCYmhLmGoZN0HM853uG1ew7VJwOfPbjX4Dkmvr64LlxIeUSEwVU+rhTlERHIrK0x69On09e2mzMH5HKyfv2ZTSu/YPYeb0ZeTMHZzZWZL7zKrW9/QsDgYXUKP9TbDoCT6UVN1hJCsGTIEoZ7DufYV29Sff48LgsXItrR9aCWc4XnOF0UQ//5c5n6+HPkpp7nl4VPkHrqRItz4jJLCWqhRFdr2C9YgFRZSVEXFOJWRekLD5gPaLn7QWMUchmf3hwKAh7/7Thq7bVfymvhwoVe//vf/1I9PT01AEqlkpdeeimnM69hVHAGoDAxwSOwFwOnzcTFpwfpcWcMnrslWd85YJx3x/xLLWFvZs8E3wlsSdqCSq26rLXq6k+6dl39SW1xMcVbtmBzw1ROH9oHAkIm6H1f/Zz7oZApLstMaePkjFevPsQd3NuulBC7eXMx7dmTgu9/uOqpJLVIkkT5wQgshwxGtGGi6whaSwsuDBnAhugI/E8K8kzscb35aW5+7T269w9voix8HC2wt1By4kJRs+spZAreC32ZuQclYrrLSQ9x65BcGxI2oJQpuaHHDQQNH80t//cxFja2rP2/lzi0dmWTBrcarY747NJWK5i0hFmvXpgPGEDhyqbrXi4VkVGY9OiBwqH1gKfGeNlb8M7MfpxIK2JHTKdZ6f6RqFQqUVxcrBg6dGiXJi3/502U7cUruA+ndm5Do1a3mZhcra1mR8oOxncbb3DngPYwJ3AOW5O3sj1lOzMC2k6IbYljWcewM7XrUv9b0YYNSBUVWM+ZzZlP3yFg4FBsnPT1Rs0V5vRx7FPXH66jBA0fzV9L/0du6nlcfHsYNEcIgcOdd5K5aBHlBw5iNbJ91VS6AnVqKuqMDBzvu7dT11UVFxG5dSMnd2yluqICl7IKzvjLOOTxJO9NG9PiLkgIQYi3HSfSilpe+9tlWFRKbJ7qxFe7HmX51OW4WRqu6Kq11WxO3sy4buOwM7MDwNHLm1ve+oidS/9HxJrlZJyLY/Kjz2Bhozcpns8rp1qja76CSVkuWDhAKykv9gsWkPHss5QfOIDVqFEGy9oakk6H6vhxbK6/vkPzp/Zzx91uGAO6dUq/z+bZ+Ig3ObGde0NyCVZx0/8M7lKQnZ2tcHFxqQZ9l+8nn3yyG0BhYaFi48aNib179+6UEi/GHVw78Q7ui0ZdTVZik9JpTdh/cT8l1SWdbp6sZYDLAPxs/S7bTNnV/jdJp6Nw5UrM+/fnfF42leVlTboGhLuFE5sXe1m70cAhw5HJ5e0yUwLYTp2CwtmZgmXLOnztzqQsorY8V+f430rz89i97Bu+e/Qejm1aR/fQcGzuG49baRZDjmu5Z0R3TBSt/+5Dve04l1PabFfqquTzFK5Yid2sWSy5ZSkqjYqH/nqI0upSg2Xcnbab4qriJoWVlWZmTH7kaSbc9yhpMaf45YUnyDinryca10KTU7Jj4OPesOwGKGvZ4mUzcQJyJycKl7evPmV2SgkJkdnNViCpSkhEV1KCeZjh5snGdKly+4fg6OiozcnJMQEYNmxYxdGjR+OPHj0a36dPn/Lq6upO605s3MG1E89evUEI0mPP4NWrdf/I1mR954Ah7kO6RBYhBHN6zuGdo+8Qlx9HL8f2hzxfLLtIRnkGt/e+vQsk1FN+MAJ16gWcHnmUfX9uxrmbr/5zrEeYaxhLTy/lZO7JdndaqMXc2gbfkAGcjdjHyPl3GBw0IkxMsL/1VnI//pjK+HOY9ezaSi5tUR4RgdLDA2U3wwKZWqIoK5Ojv68hZu9uQKLXiLEMumk2CicbJq+bzHUDPLl550VGWbXdRifU2w5JglPpRQzzaxj0kfP++8hMTXF+4nHc7Z34eOzHPLTzIZ7++2m+vO5LgwqLrz+3HndLd4Z4NP1bEULQ77pJuPbwZ/PHb7Pq1RcYfevdxIlAlHKBv0u9El06LWx6DJTmkHEcvhkN834Br6bmd2Figv3cOeR99TXVaWlttvLR6SSit6dydPN5JJ2Eo2cqw2b60a33peCxWv+bRXjXtpu6LNqx0+oqbGxsdGZmZrrjx4+b1S/CnJaW1qnVN4w7uHZibmWNczdf0trww5VUl7A3bS+Tu0/u0s7YN/S4AVO5aZu7uLLCKg6sTqC6suET+JXIfytcsQK5oyMlvl7kXkghdNK0Juaw/i79kQt5s364vLIqzlw0rKh40LBRlOblcvFc+7oG2M+bizA3v+q7OEmjQXX4CJbDh3W4pFt++gX++PwDfnjyAWL376Hv+Ou559PvmPTwkzh4eLHszDJKqkvYaH0jGhNTqta3bQEI8bIDaGKmLD90iLI9e3B84AEUTnrFN8R9CK8Oe5XDmYd5LeK1Nn2bGWUZHM48zE3+N7VqRXDt4c+tb39K9/7h7PnpO8q3/UCgvbLh7vPIN3AxCqZ+CPfuBLkSfpwMUT81u6bdvHkgk1G4svUuCeXFVWz+7ARHfk/Gf4Az190VjLpKw+bPT7Lp0+Pkpet3kxVR0ShcXVF6Xnuh/p3N66+/fvGBBx7wyc/PlwNs2bLF+tSpU53aCda4g+sAXsF9OL1rB1qNusVE8L9S/6JaV93hzgGGYmtqyyTfSWxN3soz4c9gqWz++3H673RO7k6jSqVm/J3BdcePZR3D3tQePzu/LpGvOj2dsr//xvGB+zn81zbMrKzpNWJ0k3GWSkt6OfRq4IdTVWtYuv883+xNokKtZeV9Qxjco/VUC7+BQ1CYmHL2wF68gnq3OrY+cjs77GbMoHDNGpyfehKlS6dGKxtMxenT6MrKOmSezE5O5MiG1SQcjUBhasqAqdMJv2FGg+oueRV5/Br3K17KoSQKfywmT6Fky1Zcn3uu1XB5e0sTfB0tOFlPwUlaLdnvvIvS0xOHOxpaAKb7TyejLIMvT36Jp7UnD4W0XF99Y+JGQF+Gri3MrKyY/uwSIjev5+/lyxhVnkNuqj/OPt2hMBV2vwEBE6HPLBAC7v8b1t0Dmx/X7+gmvwuKS5sEpasr1uPHU7xuHc6PP4bMrGlloNSYfHYti0VdqWXsbUH0GuaOEAL/AS6c2XeRY3+cZ9Vbx+g52A3Xkwk4hA3o8MPJtczixYu93nvvvbquw8uXLz//2GOPZY8ZMybQzMxMFxQUVHHzzTc3Xyi1gxh3cB3Au1dfNNVVZCU1bnR+ia3JW/Gx8aG3o+E32Y4yp+ccVBoVf5z/o9nzkiSRGJWNwkTG2cNZnDuWVXf8WNYxwt26zv9W9NtvIASKiRNIPHaYvuMmojRtvrxYmGsYp3NPo1JXsurYBcZ+8Dcf7TzHyABnfBwtefy34+S30V7ExMwcv/DBnDt8AK2mqb+oNRzuuB00mivaM6wx5YcOgRBYDDHcrH3xbCzr3n6FXxc9yYUzJxkycx73ffEDY267p0npsu9OfUe1tpqUxJHcGOKJx+23IFVWUvz7721eJ7RRoEnRunVUxcfj8tyzyEybWpYeDHmQ6X7T+fLEl/ye2Pz6Wp2WjYkbGeI+BA8rD4PerxACv/HT2OB2I0qdWl+wec9O2PIUIGDqR3rlBvpAk1vWwoinIOpHvV+uJLPBeva33IK2uJiSrQ3/frQaHQfXJbLl85NY2JgwZ9FAgod71CkvuVJGyHhvbntjKP2v60bisWwOdH+IeIcxVFW077t3rfPRRx9lnDt3LrbW13b06NH4gICA6ttuu63o9OnTcceOHYv/5ZdfLvz8888X6pssLxejgusAtf6j9NjTzZ7PKs/iWNYxpvaYekWe5Po59SPQPpA18WuaNQflpJZSklfJiDkBuPWwYe/yeEryKkgvSyezPLPL2uPoqqooWrsO6/HjiTkZCRKETmw54CbMNYxqXTWTv17OwnWn8bQzZ+2DQ/n6tjA+n9+fwnI1zxjQXiRo+GgqSku4cPpEu+Q18fHBavw4ilb+hk51eakXHaU8IgKz4GCDqoBUlJWy+vXF/PbK82QnJTDi5tu5738/MHzebXWRhvVJL01n9bnVBFiMo0LlwP2jemDeuzfmISEUrljZpikx1NuO7JIqMosr0JaVkfvpZ5iHhWHdQsSgEIJXhr7CYPfBvBqhN1k25kjmETLLM5kZ0HrX7sbEZZaQYe5B/8ffwD0wiO1ff8r2/RdQj14Cdo18aTI5XPcqzPlJH4Dy7Wi4cEkWi0EDMQ3wp3D58rrPoDi3gvUfRHNi5wX6jPJk9sJwHDyat46YWigZNsufaaPKcM49QVy6Fb++dIhTe9LQaq79fLZ/MkYF1wEsbGxx8vYhrQUFt+38Nn3ngO5dV8y1PkII5gbOJa4gjpj8pn26EqNykMkFfgNcmHC3Xjnv/CGWYxl6c+BA165RcCV/bENbVIT1vLmc3rUdv/BB2Dg3b/o7c7GYr7frkCRBhewcX94ygHUPDSPcV78D6eNpy5Ib9O1Flh5ovVVQ99ABmFlaEdfOaEoAx7vu0ufsGbCj6Wy0ZeVUnDhpsHkydu8u0mJOMfrWu7nvix8YPGMuphYtuzC+OvkVciEn6dxQxvZ0pqebvmqM/YL5VKek6KuntEL9zgL533yLNj8f1xcWtvoQp5Qr+XjMx/ja+vLUnqdIKExocH594npsTW3bXYe0tkRXvwAvZj/1JIPdcjlT7MbKTXEUZjZt4wNA75vgvl1gYgXLpsLR70CSEEJgN38+lbGxVJ48SUJkNqvfOkpRtopJ9/dh9IKeKEza7nYhi4mk78X1zFk4AEdPK/avSmDla0dIis75x+RY/tcwKrgO4hXcl4z4uGbNYFuSt9DPuR/dbC4vCq49TO0xFXOFeZNgk1rzpHewA2aWSmyczBl9S0+ykouJ/ysfBzOHLvO/FS5fjomfH2nqCipKS5qkBgBcLKrgqVUnuOHzA5zL1OJi6ks//wKm9HVvcuO8bYgPk/u48d6f8URfKGzxunKFkoAhw0k8dhh1VfusHeYDBmDWrx8Fy37q9ATgtlAdOwoaDZbDDVNwSZFHcPL2IXzaTJTN+I7qk1CYwOakzYTYTqWgxIL7R136nVtPmoTczq7VZqigL2hsIpdx7kQ8BcuWYTv9Rsz7tt2819rEmq+u+woLhQUP73qYHJU+dL+osojdF3ZzQ48bMJG3r/JJbGYJztamOFqZItvxIiMck5jxwN2U5uXy66KnSDjSQn9Al15w327wGw9/PAu/PwrqSmxvnI5kbceu70+yY2kMDh6WzHtxIH4DDPfFqqKiMB/QH5fudkx/MpSpj/RDrpTx57dnWP9+NJlJhgVKGek8jAquHaiqNSxce4rM4gq8g/ugrqokO7mhH+5c4TnOFZ7r8uCSxliZWDGl+xS2nd/WIP8o+3wJZQVVBIRd+kMNHOhGz8FuWJ/2ZbhsQpeYUStOnaLyzBnsbr6Z439uwdGrG969L/UZK65Q8/a2OMZ+8Dd/nM7koTF+7H1+LBN6DOVU3knUOnWTNYUQvDOrH262Zjy24jjFqqZjagkaNhp1ZQXJ0cfaJbcQAsc776A6NZWyPXvaNfdyKY84hDAzw7x//zbHVpSVkn42Br/wwQat/fnxz7FUWpJ4bhD9vGwZ0uOSb05maortrJmU7t6NOrvlChqmCjm9PGzwWP09yOU4P/WUQdcGcLN043/X/Y+SqhIe2fUI5epytiRvQa1TG9S1uzFxmaX6/LeEnXB6NYx8hh7jZnLbu5/h4OHJpo/+jyMbW4gONbeD+b/B6IVw4lf4cRJF6VlEDn6R1GpPQke7cNMzA7BxMjdYHk1hIdVJSViE6dMDhBD49nVi3osDGXtrECX5Fax/P4o/vzlNUfbVMX//FzEquHawPyGPVZFpbDudVZcD17hs19ZkfeeA6307VsngcpjTcw4Vmgq2JG+pO5YQmY1cIaN7iHODsX5TLSkxLcD76GCqWlEUHaVw+QpkFhao+vQiJyWJ/jWpAdUaHT8cOM+Y9/fw7b5kbujnzu5nx7BwUhA2ZkrC3cKp0FQQmx/b7Lq25kq+WDCA7JJKnl93skXTj1dwb6zsHYg70H4zpfXEiSg9PCj4cVm7514O5RERWISFNRuw0ZiU45FIOl1dV4zWOJl7kj1pexjuPJvUXLh/VI8mDzX28+aBTkfR6tZTBiZoMumTEIn93XejdGtfSa4ghyA+HPMhCYUJPLP3GdYlrKO3Y296OvRs1zpqrY7EnFL6ucj1gSVOPWHk04C+YPO8194jaPhoDqz8icjN65tfRCaDsYuR5q3gzHlv1nyagEZhRuip/9Gz+CByeftujRXR0QBYNErwlsllBI/w4NbXhzJoWndSYwtY+doR9q06R0VZ13YVN2JUcO3i6Hl9t+IzGcVY2Nrh6NWtQaCJTtLxx/k/GO45HAez9tWh6wx6O/Ym2DGYNef0wSaSTiIpKoduvR0wMW+YERJdFMmugJ+QyuX8vTy+U30EmsJCSrZtw/am6Zzc+xemlpb0GjGGracymfDxXl7fEktvD1s2PzqCj+aG4ml36Ul5gIv+BhGZ1XLZrlBvO16YHMT2mGx+PpTa7BiZTE7PYSNJORFJZVnbicz1EQoF9rffhioykorThtcdvRzUWVlUJyUZ7H9LjDqKha0dbn6tl1eTJIlPoz/FwcyBhIRQvB3MmdS7qWIy6dYNy5EjKFq9Gknd/AOPpNMxbPsv5JnZUHDDXIPkbMwIzxEsGbKEgxcPkliU2O7gEoCk3DLUWokZBT9AcTrc+HmD0H+FUsnkR54mcMgI9v76A9HbNje7TpVKzfb93dhbeBcelincbHMfXm5lFK76DamdEbiqyCiEiQlmLZhslaZyBk7tzq2vD6HXcHfO7L3Ir0sOEfVnCppqbbNzjFw+RgXXDo6czwcgNkPv4Pbq1Yf0s7HotPovaFR2FFnlWVcsuKQ55gTOIaEwgZO5J8lMKqa8uBr/8KZ+hGPZx9A6lzP4xh4kRuVw9lBmM6t1jKK1a5Gqq1FOnULC0QicBoxk7g/RPLIiGjOFnGV3DeSXewbRx7NppJ+juSM9bHu0WXj5nhHdGR/kwltb41pMAg8aPgatRkPC0Rb8Ma1gN3s2MisrCn78sd1zO0J5xCEAg/xvWo2alBOR+IUNarNay6GMQxzLOsb1nrdw8kIl947ogaKF3Yn9zfPR5OZSurt502zJ5s2YJ5/jx+CpnMjreKnA2YGzeTjkYbysvJjcfXK758dlltBfJNAjeTkMvBe6Nd3FyuRypjz2LP4Dh7Bn2Tec3NkwBSAruZhVbx7j/Ik8hs7wY9rr87EIHoa94yk0mVmU7vyzXTKpoqMw69cXWRtdFCxtTRlzSxA3vzQIj0B7Dm9MZvkrhzl7KBOpjejgfzNarZYHH3zQa9CgQT379esXNHny5B6LFi1ye++99+pMS3/99ZflgAEDgkpKSjpNLxkVnIGUVKqJzSjBXCknIaeMSrUWr+A+qCsryDmfBOjNkxYKC8Z2G3vV5JzSfQqWSkvWnFtDYmQ2CqUM374NSyvV5r8NdBtI/4k+ePa0Y9+qhE7xDUhaLUUrf8Ni0CAOnTqFVqvl/847kllcwXuz+vHHEyMZ09OlVb9fuGs4x3OOo9W1/GQrhOCDOSE4Wpnw6IpoSiub7jpce/hj7+7R7tqUAHIrK+zmzKFk+3bUGRntnt9eyiMikDs6YhrYdpmwtNgzVFdUtOl/kySJT49/ioelB4lJfbG3UDIn3KvF8VajR6HwcKdwZdNgE51KRc5HH2PWpw/Hew5qsbOAoTwU+hB/zPyjQ/3/4i8W8K7Jd2DjDuNfbnGcXKHghicX0mPAQP5a+iWn9+xAqim3tf6DaBAw47kBDLjeB2FhC3N/wfqWp1FYaCn8aDEUphgkj06lojImFosBhvd/c3C3ZOrD/bjp6f5Y2Jiw66c4Vv3fMdJiCwxe49/EJ5984mRra6s5evRo/KlTp87ef//9eWZmZnVRXPHx8SZPPvlktw0bNiTZ2Nh0WnSXUcEZSFRKIToJZod5odVJnMsuxTtYb45Iiz1NlbaqrnOAucJw53RnY6G04IYeN7Dj/A4SorPx6euIiVlD8+SF0gvkqHIY6DYQmUxw3Z3ByBWCHd/HXHbeTtnevagzMvjTbyjHd24jzdKX+6aEs+fZMcwd6I1c1nZAS5hrGGXqMs4Wnm11nL2lCZ/N709aYQWLN5xpYmYVQtBz2GguxJyirLD5G0dBZQFfnfyKi2VNQ8sdbrtVP+aXX9uU+XKQdDrKDx3CcuhQg+pnJkUeQWFiSrc+Ia2O25m6k9j8WGb1uJvdcQXcNtQXC5OWixcJuRz7eTejOnyYqqSkBufyf/gRTXY2roteoJ+3Q6udBQylo8FN/ue+I1CkI6Z+DGatt8qRK5RMe2oRviED2PHN5yx/aRmHNiTRI9SZeS8OxK17PSuCTIYYuxD7mTeiSlNT9d5YSNrdpjwVp06BRtOuBqe1eAbaM3thOBPv6U11hYZNn51g82cnKMwqb/da/2SOHz9uMW7cuDpfwYwZM0rs7e21APn5+fJ58+b1+OGHH1J8fHw6NSDAqOAM5Mj5ApRywa1DfACIySjB0s4eew8v0uPOsD99P6Xq0isePdkccwLn4FDoTWWpBv8w1ybna+tP1iZ4W9mbMe7WXuReKOXIptZzzFqjolrLyc+Xkm9uy6acciy0Fdx9/+08Nj6g1RtrY8Jc9TeKqKy2+8MN9HXg6QmBbD6ZwapjTWvIBg0fBZJEfMT+BsclSeL3xN+5ceONfHniSx7f/TiVmoYpBUoPD2yuv56iNWvQttOP1x6qEhLQ5ucb5H+TJImkqCP49OvfYkUYAI1Ow+fHP8fP1o+k5J6YKmTcMdSnzfXtZs8CpZLC31bVHVNnZZH//fdYT5qERVhYq50FupzceG4sWcEJm3HQc5JBUxQmJgyY+jAK025kJ66n56ASrr+vN6YWzZfZs3toEUKpoDDJBn6dBQc+gVZ81KqoKBAC89DQDrwhEDJBwEBXbnl1CMNn+5OdWoK66tryy910001FDzzwgO/SpUvtKyoq6p5sNBoN06dP77FgwYL8QYMGdXpvOGMtSgM5cj6ffl52BLpaYW2mICZD7/fxDu7D2YP7SEoswMncqcu7YhtCT4eeDCofj0ZeTbc+TYNdjmYdxcncCV8b37pjPfo703ukB8d3XMC7lwPevdoXJLPlVAbfrfibd+KOc3DULGZaXEDm4UW/we1PIne1dMXb2pvI7EiDuhw8NNqPw8n5vLIphv7d7OsSmAEcPb1x6e7H2Yi9hE2dDkBaSRqvH36dw5mHCXUOZUqPKfzfkf/jnaPv8OqwVxus7XDXXZT88QdFa9fieOed7X4vhlB+sKY9jgH+t9zU85Tm5TJ09vxWx21K2kRKSQqvDX6PF37JZE64F45WbUdnKhwdsZk4keKNG3F56klkFhbkfvwxaDS4PPsMAKHd9J0FTqcXM9Sv9dqgnYpOR/WGRymXzIgNXUKoAVO0Wh1HNyUTvf0CTt1vRqraxKmdP+DT24WAwc1/3gpHR6wnTaZ4926cb7wB+V+vQOYJuPELMLVqMr4iKgrTnj2R27S/8Wp95EoZodd1o/coT5QGJJZ3lJcOvuSdWJjYqf3g/O39VW8Mf6PFLgWzZ88ucXR0TP7qq6+cX3nlFa/x48cX9+rVq+LTTz91nzRpUuGvv/7q+Oyzz+bKDOwAYijGHZwBqKo1nE4vZnB3B4QQBLvbcOZiTaBJcF+qK1SciT3MJN9JXdo5wFB0Wh0eOT05b3eaU4UnGpyTJInIrEgGug5sYiIaPicAezcL/loW264Q5qTcMp747QRTkyOQFAom3XcDRReSCJ10Q4fNUOGu4UTnRKOT2jaZymSCj+aGYm2m5JEV0aiqG+4sgoaPJivxHHkZafxw5gdmbprJ6bzTLBm8hJ8m/8T8oPnc1/c+1iWsY1PSpgZzzfv2wTw8jMKff2l3ZJ2hlEdEYOLnh9K16W67MUmRR0AIevRv+cGhSlvFlye+pK9TXxJTfFHrdNw70rAGsKCvbKIrLaV461YqTp+m+PdNONx5ByZeev9daAudBbqcqB8wyTjKm+pb8fVpezdaklfBhg+iid5+geCRHsxdPIy5L7+Gu39Ptnz6LklRR1qca79gPrryckpkU2DC6xD7O3w/AfIbmm4ljQbViZNYhLXfPNkSXancriZjx45VrV69OjU+Pv6MSqWSvf32257Tpk0r+Omnn9J8fHyqPv74Y6e2V2kfV/9u/C8gOrUIjU5iUHf9rqa3hy0rjqai1Ul41+TDOebJucHv6psnAdLjC9FVyMjofpbV53IJd7vUmyq1JJXcilwGuje9QSpN5Ey8tzdr3olk989nmfJQX4MU1Mc7zxFEBqOSj2A9cSInDx/AxNyc3qPaV36pPmGuYWxI3EBiUSKB9m0HXjhbm/LpzaHc+v0RXv49hg/mXPJPBQ0bxb7lP/Lmj4+zyyuRcd7jWDx4Ma6WlxTKw6EPczznOG8efpNgh2D87f3rzjnedRfpjzxK6Y4d2EyZ0uH31By6qipUkZHYzZlj0PikqCO4B/TE0q7lWpWrzq4iW5XNksGv8/gPF7g+2I3uToZ3ITEfMADTwEAKf1uPzMwEuaMjjg88UHe+trPAibSWq8l0OsUXYeerpNsPZl3mSF5qrot3PZKO57D757MgSUy8tzcB4bW/awtmLnqVtW8uYfNHbzP92SV079+0d5t5aChmwcEUrlyB3aZNCLd+sPYu+G4szFwKgRMBqIw7i6RSNcl/axZJAp0GNJWgqar3f1XTY17hYNnp93sAWttpXQmsrKykhQsXZv3555/2gYGBVQBffPFF+vDhw4NuueWWQjc3t06zzxoVnAEcOZ+PXCbq6iL29rChUq0jObeMAFdHqmzk9ChxINghuI2VrgyJkTmYmMkZEB7I6qRVFFYWYm+mvyEezToKtFx/0snLmmEz/DmwJoGYfRfpM7rlqDvQp0zsPZXE7ow3yCuzwHRUCPGr1xMycTIm5h23gtQq5ajsKIMUHMBwfyceGxfAZ7sSGNrDkVlhXqjUKr5JXka2fQX250346JaPmOA7oclchUzBe6PeY87mOTy992l+m/obFkq9/FZjx6L06Ub+j8uwnjy5Uyu/VBw/jlRZieXQtpu8lubnkZ2cyIj5d7Q4pqy6jO9Of8cQ9yEkXXCnpLKQB0YbvnsDffCH3c03U3bIDEmnxvEhGXKrhqa5EG87Difnt2vdDiNJsPUZ0Gn40f4J3FVm2Fm0HI5fklfB9m/P4NzNmuvv69OkIomphSWzFr/Bmjde5PcP32LG86/g0y+0wRghBPa3LCDzxSWojh7DcvBYuH8vrLoFVszVt+NRmKLaq/dZm6d+Dcu+bF1paavAAIsEALesg4Dr2vUx/ZN57rnn3GfPnl00ePDgCoBly5Y5Lly4sC6yy8fHR/3AAw9kP/nkk16//fZb88mtHcAgBSeEcAWWAQ5ACXCHJEkZjcZ8DtTPcnQA7pYkKdKQ+f9kjpwvoI+HDVam+o+rt6f+6TEmowQryzJSbYsIzHVAknQIcXXNC1qNjuQTuXQPcWZc8GyWJ/zK74m/M7eqL1mvvoZP4UX+p5XQLL+fRATU3KtF3Q8CS8DJdRb7l6vRfLQYa3X+pfYj+sE1QwX5hRV8py6mQGWGqYOOc7s+Qaf1oP/1l7eb9bTyxN3SncisSOYHte5vqs8T4wM4kpzPS7+fQTKLZ+nZ97lYdpH5oaMx3Z1CCC3X3XS2cOa9Ue9x3877eO3Qa7wz8h2EEAiZDIc77iD79TeoiI7uVHNU+cEIUCiwGNS27zY5Wv9w4t9KesDPsT9TVFXEIyGP8ciP5xnk60D/bm13JmiMae/RqE4lIUk6qi+ao8mvQOF4SVGEetvx+4kMMosrcLft4qjhmA1wbhtMfJMDR6zp5d769WIP6G8tkx7oi7VD84E4ZlZWzF7yBqtfX8zG999g5guvNCglB2AzZQrZ771P4YoVWA4eBPY+cPcO2PY8nPsTFGZUxIPSVoZSUQaSmb6Qs4WTPvFcYdbof9N6rxufMwO5yaXXjl1TH/ZqMXPmzKIlS5Z4FBcXK7RaLUOGDCmztrbWVldX17nJFi1alBMSEtJr165dluPHj++UMFJDd3AfAq9KknRECBEOfAAsqD9AkqTHan8W+kfcncApQ+d3FpJaTXVKCqYBrVd4MJRKtZYTaUUNItD8na0wVciIySgmX3GILIcqAtPU5F1IxcW3fU/LnU1aXAFVKg3+YS742jkxwGUAW078xvDvloFMRpyHDidzN/xcQhtGhtX+XPN/uJTIngpPTnnMZLTpfuTomowrVFVztiyPcVZZmDqbYfP4G+z45Wu6WxZgn7AC3J5tqBjbSZhrGIcyDiHVVHw3BLlM8NoMX+auXcRrkVH42nRn2aRlBFsE8PXe2zh7cG+rv6NB7oN4JPQRPj/+OWGuYcztqa/YYTdjBnmffkb+jz92roKLiMA8JAS5VdsmxKTII9i5uuPg6d3s+YLKAn6K+YkJPhNIzXTkYlEar93YsX6ElbElCBOB/TQvirflkPP1KZzv7YPSVS9nqLcdACfTirpWwakK9ArFPZSq8PtJ2rKL64JbLoCs1eqIi8jEp49ji8qtFnNrG+YseZPVry9mw7uvM3Pxaw2a5MrMzbGbOZOCn39GnZ2t95GaWMD0LwC9P1u1ZgRWY0bCfe90zvu9Rhk6dGjFzp07k1obo1AoiImJievM67ap4IQQdoCjJElHAGp2ZLZCCDtJkopamDYF2CVJUnV75gsh7gfuB+jWrWOV+DOXLKHswEH8tm836KbRFifTiqjW6Bjc/VK0mEIuI8jNmtMXi4nUbMExwA9OFpMWc/qqK7jEqBxMLRR4B+vNqXMCZ5PzzELUeTJMvv+QD+Of5ZWhD+IZOLvNtRQx+Wz5/CSpQx9g1LyGZkJJknjm28OY+pzgPu0OmPohcdW2lFfL6D/YD3a/CTlxMP1/oOzYDTDcNZwtyVtIKUmhu233NsdLksSW5C28d+w9ZFZlqHLG09v6dsJc9f4R35ABnD24j5Hz72g13+zevvcSnR3NO0ffoY9TH4Idg/U3u/k3k//Nt1SnpmJiQJBDW2gKC6mMjcXp0UfaHFtdWcGFMycJvb7lHoPfnfqOSm0lj4Q8wmM/J+PnbMm4oPZ3JtdVaKiIycNyoBuWA/0w8XYj9/vT5H5zCqe7+2DiZU2whw0mchnH04qY1Me93dcwmB0v6ZXcretJyK1Eo5P0RZZbIPVUPqqSaoJHehq0vIWtHXNeeotVr77AhndeZdbiN/AIDKo7bz//ZgqWLaNo1SqcH3+8wdzq8yloCwow70D+m5ErgyFRlN2Bxq2rk2uOt8SDwDftnS9J0reSJIVLkhTu7Ozc+LRB2C9YgDY/v9NKLB05X4AQ+nyr+gR72BKTd5bEokSu73sjtq5upMc13x/uSqFRazl/Ipfuoc7IFfpf7ZDjFQw9K3Fsmj/R9vrUhkFuhqUy+PR2JGScN6f3pJNyumEn+QOJeRw5X8AStyOgtIC+czn+52bs3T3wfWgZjH8FzqyHHydDSces0bX5cJHZLdelrCWtNI0Hdj7A4gOL8bHxYe20NdzV+wF+O5rJllP66wcNH01pfi4X45sv5FyLTMh4e+TbOJg58Mzfz1BSrY+YtV+wAKFQUPDTzx16P41RHT4MkmRQ/lvqyeNoNZoWiytnlmWyKn4V0/2mk5lnS2xmCfeP6oHMgMT6JnKdygWNhOUAfWCG0s0SlwdCECZycr87TdX54rrOApdb0aRVkvboq/0Pfxzc+9X1gGtNwcUcuIiVvSk+vQ1Pc7G0s2fOy29hYWPH+rdfISvpUs86k27dsBw1ksLVa5CqG0YWV0Tr8zRrOwgY+edhiIITQHNZjs1mPgoh+gMpkiTVlo5o1/zLxTwkBOtJk8j/8UfUOTmXvd7R8wUEudlg2ygptLeHDdVmkciFnIm+E/EO7kt6XMwV7yFWnwsxBVRXauta41SnppL/f++SH+TGp0Ep/JnyJy4WLnhbN2/iao6hM/xw9LJi109xlBfr6w9KksT72+MJsJUIyNkBfWaRnZFD5rmzhE6cipDL9dXdb14BeQnw7VhIbztpuzE+Nj44mTu1WpdSo9Ow7MwyZv4+k1N5p1g8eDE/T/4Zf3t/np3Yk/7d7Fi07jSp+eX4hQ9GYWLK2YP72ry2vZk9H4z+gKzyLF4++DKSJKF0ccHmhhso2rABbVFRu99PY8ojDiGztjaop1pS1BHMLK3wDGre5PjVya8AeCjkIb7Zl4SztSk39TdsF9MYVXQOChdzlF6XAksUTuY4PxiC3NqEvB/OUHmukP7edpy+WIy2K2ooVqtgy5Pg4Kdva4O+RY6ZUoavY/OWmZK8Ci7EFtBrmDuydnYDsHZwYs7Lb2FqacW6t14iJ+VSwQOHBQvQ5uVRsnNngzmqyCjkDg6YdPdt17WMXDkM+RakAP6NjvnVHG+Op4BPL2P+ZePy1JNI1dXk/e/Ly1pHrdURlVrI4O5NnwZ7uVuisDlBT5uB2JvZ49WrD5VlpeSldVoAULtJjMrBzFKJZ5A9klrNxeefB4UCj/feRS20dfUn2xMFKFfKmHhPbzRVWnb9FIekk9gek82p9GLeDoxHqMvRhd7OsU3rUJqZ03tMvcivoClwzw5QmOh3cqdab8XSGCEEYa5hRGZFNtvtIDY/lgVbF/Bh1IcM8RjCxukbmR80H5nQf62Vchmfz++PEPDoiuNIChP8wgcTf/hAs41qGxPqEsqTYU+y68Iufon9BQCHO+9EqqigcNXqdr2XxkiSpG+PM3gQQtG6p0Cn05IUfYzuAwYikzcNYkouSub3pN+Z13MeBSUW7E/I467hvpgq2h/wpM6roDq1BIsBrk2+Jwo7U5wf7IfCyZy8n2IYIxSoqrWcyy5tYbXL4O+39bUgb/yszsQdl1lCTzebFsu9xUVkIoBewz06dEkbJxfmvvwWSjNz1ry5hLwLKQBYjhyJ0tubwuUrGoxXRUVhETagS/opGukc2lRwNTsxlRBiAIAQoh+QB3gLIRbXHyuE8AJMJUlKbGt+K/67y8bExwf7m2+maO1aqpI7Xnrq9MViKtTaZhWcSpaITFmCs9CHd1+qS3ll2qs0RlOtJeVUHj36OyOXy8j76isqT57C/bVX6RE4qM4saah5sj4O7paMmBtAWmwBx3dd4KOd8fRwtiQwbRMRqoF8987/iD+0n37XTcLUotHTtWtvuG+PPq9n/b3w16vQjl1uuGs42arsBrUiKzQVfBj5IQu2LiC3IpcPR3/IZ2M/w82yaRsYL3sL3p8TwumLxby7LZ5eI0ZTWVpC6unjBl3/9uDbGec9jo+jPuZEzgnMegZiOWwYhb/+2sRk1R7UFy6gvnjRIPNkxrmzVJaWtGie/OLEF5jJzbiv3318ty8ZSxM5twzumI9QFZ0NAiz7N++7k1uZ4HxfX0w8rfCLyGESys5P+M44AYe+gAF3gO8IQP9AEJdVQrB788WZdVodsQcz6GZAcElr2Lq4Meflt5ArFKx5cwn56WkImQz7+fOpiI6m8qy+Pqo6Owd1WhrmnRhwZKTzMXQf/yTwthDiIPAO8Bz6kH/fRuMeB74wcH6X4vTwQ8jMzMj56KMOr3EkWW9lHdiMgtt54Q+EZEZZoT74wsbZBRtn1wb94a4kqWfyUVdp8Q93QRUVRd7X32B7003YTNa3I7k9+HbMFeYM8zCs31hjgkd40CPUmYj1CZglxnJLznqWHrLiUKoZTt18ufHZFxl1y53NT7Z0gts26m9YBz7W5xJVGfbU39gPF5ERwYzfZ7AsZhk3+d/Exukbmeg7sdWn6Ot7u3HnMF9+OHieRBMvzCytDDJTgn4X+caIN3CzdOPZvc9SWFmIw113ocnNpfiPP9peoAXKI/TluawMUHBJkUeQyRX4hjRNJj6Td4adqTu5o/cdVFSas/lUJjcP6oatefN1FltD0kmoonMwDbBHbttyWS+ZhRKne/pi1sOWJZiji2y5C3i70aph06Ng6ayvIFJDVkklRSo1QW7N+99STuejKq6m94iO7d7qY+/mwZyX3gJgzZsvUph5EbuZMxCmpnW7uEv+t85XcNryzm9A/F/FIAUnSVK6JEnXS5I0XJKkKZIkZUuStFeSpPsbjXtekqT9hszvrDfQEgoHBxzvu4+yv3bpi6F2gKPn8/F3scKpUQ2/Km0VO1N34iYPJy7jUoFe7+A+pMc1rWp/JUiIzMHcWombm4KM555H6emJ65IX686P9h7NofmHmt3lGIKquAgLm9NUFv3A6LytVGWmMtA5i3ve/5hZi14jYOBQZLJWTGIKE5j2KUx+D85th+8nGtSOxM/ODztTO/Zc2MPi/Yt5YOcDKGVKfrj+B14d9iq2pk17yjXHoilB9PG04fn1MXj0H0zi0UOoqyrbngjYmNjw4ZgPKagsYNGBRZgPH4ppgD8FPy7r8O+6PCIChYc7SgOiMZMij+Dduy+mFk0T5z+N/hQ7UztuD76dHw6cB+DuEW1HnDZHVXIx2qIqLAe0HXkpM5XjdGcf4ixljL5QScnfnVQc49AXkHUapnwA5nZ1h89m6h+IWgowiT2QgaWtCT59Oqc2pqOnN3NeegudRsPqN16ktKoSm2k3ULx5M9riYn2DUwsLzHr16pTrAWgKKincmEjm20epOt98j8NrDZ1OR0VFhdBqWy9e0tb5lrima1E63HE7ChcXct57v903Iq1OIjKlsK48V332pu2lTF3GEJcJZJdUkVemD77w6tWHitIS8tMvdIr8hlJdqSH1dB5+A1zIeetN1NnZeLz3bpPqE/LWFFAzSDodqadPsPmjt/n24TuJ/H0FeUprlJZT6enUh5HXDcGuWzvyDYWAwQ/Areug5KI++CTlQKtTZEJGmGsYu9N2s+38Nu7vdz9rb1xb1wnBUEwVcr6YPwCtTmJTqRvqqkqSoo4aPD/YMZgXBr3AwYsH+f7M9zjceSdV8fGoDh1qlxygr19YfvgIlsOGtem/KchIpzDzYpPeb2qdms+iP+Nw5mHu7XsvWo0pK49eYFo/9wYd0tuDKjobYSrHvLdeSSRF55B9vqTF8UIpI2aQEztRU/JnCsV/plzew11+Evz9DgTdAME3NjgVWxNBGdSMibK0oJLUmHx6Dfdod3BJazh5+zB7yZtoqqpY88ZiFFMmI1VWUrRhA6roaCxCQ9r0nxqCOrucglXxZH1wjPJjWViEOre6g/430717997Dhg0LHDp0aOCwYcMCR44cGTBx4kT/2NjYBm947NixDeI2JkyY0DiOwyCu6VJdMnNznB9/jMwlL1G6Yyc21080eG5sRgmlVZpm/W9bkrfgbO7MRL/h/LwnkpiMEkYHOuPd+1J/OCfvy8+TMpTUM/lo1Drcq5Io2bwZp8cexaJ//w6vpyopJmbvLk79tY2irEzMrKzpd/003kqyxcHdk+lkE32yJz4WZk2ihwzCb6zeL7diHvw8Xf+0Hn5Xi8PnBuqTrR8Ofdjgsl3N4etkyTuz+vLY8ih6W9hw9uA+goaNMnj+nMA5RGZH8r8T/yN09JfYOTmRv2yZQX60+lSeOYOutNRg8ySAX9gl32lycTKL9i8iNj+WG/1uZH7QfL7bl4qqWsv9ozpWAUNXpaXiTB4WoS4IpZzKcjU7f4jFysGUBa8OaTHdIMTXnntIZGiQM/ydhq5Kg900P0R70xMkCTY/oa/mMeWDJqfjMkvwsjfHxqyp6TX2oD4NpNfwzs/Hc/HtwewX32DNmy+y6bcfGRbSj8Kff0GdmYn1I23nL7ZGdVopJXvSqIzNRyhlWA31wGqUF4prVLkBBAUFVWzbtq3VwAi1Wk1KSoqpTqfjcrsLXNM7OADbm27CNMCfnI8+RFIbbts+cl5fZ69+gjdAcVUx+y/uZ3L3yfT10Cu/2tY5Ns6uWDs6k36FA00SI3OwsJKj++JVzPv3x6leYVxDkSSJ9LgzbP3sfb596A72/foDlnb2TH70GR746ieSuo8ludqC567vySDxGS7m6fy9TUNpgWFmviY4+sG9f0GPMfpw8D+eB23zkY3DPIfxydhPLku51XJDPw/mD/HlhKI7ycePUdmOPm9CCF4d+io+Nj48f/hFTOfeRPm+/VQlNk7zbJ2yiAgQAgsD6k8mRR3B2bcHNk4u6CQdy+OWM3fzXDLKMvhozEe8NeItJEnOsogURgY4EezRsZYtFafzkKp1WNSYJ5Oic9BqdBTnVJDYio8txMsOHbDHxxyrUZ6UH8qkcO05JG07d3LHf4GU/Xq/m01TRRWXWdKseVKn1RF3IINuwY7YOHZNRRXXHv7MWvw6FSXFHLRWUJqTDZLUoQankiRRmVBI7nenyPnfCarOF2M9vhtuLwzCbprfNa3cAE6dOmU5duxY/7Fjx/qPGzfOf9y4cf6jR4/23717d1102o8//mivUCj49ttv29ezqxmu6R0cgFAocH7mGdIffIjC1atxuOUWg+YdOV+Aj6MFbrYNI7K2p2xHo9NwQ48bsLVQ4mVvTkyG3nwihMA7uA8pp463q7zU5VBdoSH1TB7eqjMISYfH+++1y2xSWVZG7P7dnNy5jYKLaZhaWNJ3/CRCrpuEUzdfAEor1Xz5dxKjAp0ZbHYBsqKZOOU2Vm2R+OvHWKY/1b9DCcWY28GC1bDzZb3vJe8czPkRzNtfO7E9vHxDMLfH9EM6fZKovXsZPnWqwXMtlBZ8OFofwfme5wkeMzMjf9kyPN580+A1yiMiMOvVC4V96+9TVVJMRvxZBs+cR3Z5Ni8dfIlDmYcY6TmS14a9hrOFvhjCxuMXyS2t4uO5oQbL0ORa0dkoHM0w8dErkbOHsrB3s0DIBJHbUgkId212V+ZgaYKPowUn04t58NYByEwVlOxMRarS4jA/CKEw4Bm6NAt2LAGfEfpApEZUqrWczytnar+mASSpZ/IpL65m1PzLDy5pDXf/nsxc9Drr3nqJo4HeDE7KwLxfv7Yn1iDpJCpj8yn5Ow11ehkyaxNsp3THcrAbMtMrexvOWPyid1VCQqf2gzMNCFB5/N9bbTpiz549G6NQKCSlUikpmrlPnTt3zmTp0qUu+/fvPztjxgy/sLAw1cCBAyurq6vF0KFDAxcuXJh50003GZyXcs0rOACr0aOxGDSIvP99ie306U18U43R6SSOpRQwMbhpf66tyVvpYduDIAd9OZ8+HrbEZlzyU3gF9yV2/x4KLqbj6GV4QnVHOX8qD61GwvHMNtxeebmuZ1drSJJEVuI5Tu7cRnzEPjTqatz8A5n44OMEDR2F0qyhUl+6/zxFKjXPTewJUa+AwhzbETMZbV3BX8viiP4zlfApvh17AzI5XP8WuPSCzU/Cd+NhwSpw6pxaos1hppTz1n1T+OWZTWzfvI0hk6e0mFvVHAH2ASwZsoQlB5cwfXgg3TZtxuXJJ1E4td3eRFtWTsWJkzjedWebY88fj0SSdGS7a3hu0ww0Og0vDXmJOYFz6h6edDqJb/clE+xuw3D/jgVYaAoqqUouxmaCD0IIinJUZCUXM3SGH9YOZuz4Poak47n4hzUffBLqbceR5AKEENiM74YwlVO8JZm8n2NxvLUXsrb6m217HtSV+iCkZkxS8Vml6CSaTRGIOZCBha0JPn27vvGqZ89ezHzhVda++SKRoT0JVFdjQet6QtLqUJ3IpXRvGpqcCuQOZtjN8MdygCtCec0b0OpYtWqV7VdffeUsk8mQyWQSgFqtFjqdTqjVaqFSqWTTpk0r+vPPP21//vnnFDc3N+2mTZuS7rrrLp+FCxdmyeVy6cCBAwltXacx/wkFJ4TA5blnSZkzl/zvv8fliSdaHX8up5QilZpBjcyT6aXpROdE83j/x+tuML09bPgzJovSSjXWZkq8gvX94dLjTl8RBRe/JxHTqkK8RgRjM21aq2OrK1TEHfibkzu3kZt6HqWpGcGjx9Hvusm4dm/ed1NQXs33B84zuY8bfZ3lcHoN9JkJ5nYEDrYlNaaAo1vO4xVkj1sPwyIam6X/reDoD6tu1Su5OT+Af9e1CwlwtabbwBEURWzl002RPH1T+4JWpvtPJzonmg/y1/HpLi2FK1bi/Phjbc5TRR4DjcYgv93ZowfQWip4JekD+rn04/9G/B8+Ng19u7vP5pCUW86nN4d22GKgOq6v+FNrnow/kgUCAge5YmFrit2W80RuS8FvgHOz16jtLJBVXImbrRnWIzyRmcopXJ9A3g9ncLqzNzKzFm41cVv0zUTHvwxOzXt0WyrRVVpQyYUz+QyY5IO8E4NLWsMruA8zF7/OhndfZ/Xri5n78v9hYWvXZJyk1lIemU3p3nS0RVUo3SxwuLkn5n2dEfKrmxhuyE6rs5kyZUrpmDFjyszMzCQhBHv27LHct2+f1YcffphZO0an0/HMM8/kODg46ACcnZ21W7ZsSa4516EP7T/zCGHety82U6ZQ8OMy1Nmtl/A6el6f/9Y4wGR9wnpkQsY0v0uKpLZ1TlxNGLOdqztWDo6kxXR9PlxFXjHp5ytwK4/H/dWXW73BZSbG881Dd/DXUn11l+vufZgHvv6ZCfc92qJyA/h6bxKqag1PTwiEM+ugugzC7gT0Dw6jF/TEyt6UnT/EUF1xmR2vuw2B+3aDXTdYPgcOfdmw40EnM3PONASwb8dOIlMK2hzfmEWDFmHt35MTgUryVyxHV9m2P7I8IgJhaor5gNYbZB5I3Ufi8aMkOhbxaP9H+WnST02UG8A3+5LwtDNnSt+OBVhIkkR5dDamPWxR2Jsh6STiD2fh1dMOq+MfIttwP2Gex8hPLyNlw1qI/1Nfdq0wVV9Oi0udBeo3QLUc6IbDzUFUXygl97vTzed2VRbDH8+Cax8Y9njT8zXEZZZgaSLH277hbinuYAYSENzByiUdpVufEGYsfJninGxWv76Y8qJL71tXqaFkTxqZ7x6j6Pck5LamON4RjMsTA/QBPFdZuV0trK2tda6urlpbW1udjY2NTqFQSPJGVXlkMhkODg66Bx98sIkZavDgwYY7y+uv2UF5/5U4P/UkklZL3heftzruSHIBHrZmeNlfclqrdWo2JG5gpOfIBrlkvT30u5baQBMhBF69rkw+3Im3f0EScvreOR65TevBBUc3rkWmULLgzQ+57d3PCJkwpdm8qvpkFVfyU0QKN/X3JMDVGqKWgUsweF3a7ZiaK5h4T29KC6rY+uUpKso6Xt0D0Cu3u/+EoKmwfZE+6VdTdXlrtoCDhxdOvj0IViXx3vb4dv++zBRmfDTmI/4cYoJUVEzBhg1tzimPiMAiLAyZafPBBBWaCt4+8jZvrn4GhVawYNrjPBDyAApZ0x1QVGoBx1IKuXtEd5Qd3MFUp5agza/EIkxvjs9MKqY0v5IgzW+w7z24cIjAnHexkWcRubsAacU8WDoOPu0H/+cOb3kQsn40G01exu+ve+H3R+Gv1+DQl1iIv3GcoEGdVUbuV9FoC1UNL77zFSjL1pfjkrecmB6XWUpPN+sGfl5dTVucbr0cmjQ0vRJ06xPCzBdeoThXr+RKL+ZS/GcKmW8fpWR7CkoPK5zv74fzg/0w7+VoLOfVCJ1OJ3QtVDRKSkpq8sfxwQcfZDY3ti3+EybKWky8vXFYMJ+CX37F4fbbm+0ZJ0kSR84XMDLAqcGXcm/aXvIq8pjdqM2Mi7UpTlYmdYEmoC/bdfbgXgozM3Dw6FjB27Yo+XM7KRckLN2q6DZ5bKtjS/PzSIo8Qvi0GbgH9DT4Gp/vTkAnSTx1XSBknoSMaH2idqM/Vrcetlx3Vy92/3SWte9EMuWhfjh6tu7nbBVTK5jzM+x9B/a+q8+PmvsLWHWsw0Rr9B4xhrxff2DruWQOJQcwzK9tP1p9fGx8WDD/TZJ3PEXZt5/iOG9ei6141NnZVCcmYTdjRrPnY/JiWHRgEeeLz3ObejBKsyJGDG3Z7PzJXwk4Wppw88COm8JVUTkIExnmffTvO37vORSyKrqXLYebvoLQBciAsL/Ps+e381wYtx0ftyIoz635l4esPBepPAFlWQYkJIIqD3T63bw54CTrS37eS+S8twVn6/dR2AiwcIQLETD0UfBsORqxtkTXjSENd2kXYgooK6xi5NzLj6ztKN69+zHz0ZdJ+nkfBZ+dQS4UmPd1wnqMNyaX8/3/D6DT6dBqtc1q/djYWIthw4Y1+MVWV1eLDz/8MG306NGq5ua0xH9KwQE4PvggRes3kPPhR3h//VWT88l55eSVVTVJ8F57bi2uFq6M8BzR4LgQQt86p1GgCUB67OkuUXDqrCxSX3+Hwr6LCR3t0+bT4ald25GQ6HfdZIOvkZpfzqpjacwf1A1vBwvY8pO+23C/uc2ODxzohq2TBX98fYp170Ux4e5guodchkKSyWDsYnAOgo0Pw/fXwQP7waxjYfAt4RYQDmIZo1VxfPJXj3YrOIDru0/il5vW0ePrAxxc8xkj5j3Z7LjyCH1SeGP/m0anYenppXxz8hsczB345rpvOP3G17iHDEChbH5nc/R8AfsT8nhxSi8sOxiFp6vWojqVi3kfJ2SmcjRJh0iMysfP8jgmd66qqwMJ0HOED8d2ZBB5REm3565v8p3b+PsZ1kSlc3rx9ciRoLIIyvOgPBez8lyc03PJPeBBbuVbOHltRqlNAv8J+t9xK1wsqqC0UtPE/xZzIAMLGxN8+nV9cElzqHNUlP6dhjhRir9NKCmlMaQrk7hhyiJM7I3KrS2mT59eOn369GajIVNTU08LIRBCIJPJ0Gq1SJLUoZy4/5SJEkBhb4/j/fdR9vfflB9pWsmiOf9bemk6ERkRzAqY1aypqI+HDQnZpVRp9OVk7N09sLSzJ60L6lJKOh0ZC18gxzoIScgJGNS670Wr0XB61590Dw3DztXwMl2f/JWAQi54bJw/VJXBqdXQe0arIfyu3W2Y88JA7N0s+OPr00RdbmUL0Ae03LpO7/PZ9Xrb4w1EkiRiD2aw9ctk5Mpe+BSeJibhAoeS8ju03twHP6HYVknOj9+TVtK8D788IgK5gwOmPS/tolNLUrlj2x3878T/mOA7gfU3rqdHpTNlhQUtFlcG+HjnOZysTLl1SMcLClTG5iNVafXmyVOrOb/0baolC3rOndFAuQHIFTIGTPQhK7mYi/GFTdYK7WaHqlpLQk6p/uHEwgGcA8F3OPS+CZPrb8XlkcFIJjbkpi6geuJquHUtmLTelDiumRJdZYWVpJ7Oo9cw9ysWXFKLTqWmYHU82R9HUXE6D6uh7rg9Pwjfh0eQk3ee1a8toqygY98hI3oUCgVyubxOocnlchQKhVHBgT5suii79V2sw223oXBzI+eDD5rcgI8k5+NkZUp3p0t/eOsT1iOEYEZA86al3h62aHQS57L0flAhBF7BfbvED1fw44+ojhyhKOwmbJ3NcfJu/Wkx8dhhyosKCZ1oeK5XfFYpG09c5I5hvrjYmEHMeqgurQsuaQ0re1NmPDOAgDAXDm9M5q8fY9GoO1ZHrg7f4TD4QTi2FC4cvry1gCqVmh3fx7Dnl7O4drfBJ3QySBKjy0/wyV/nOrSmqZklLnfcSa8UDe+veJgqbUO/oSRJlB86hOXQoQiZDEmSWB2/mjmb53C+5DzvjXqP90a9h62pLUlRRxBCRvf+zTfSjEjK41ByPg+P8cO8rRD8ViiPykZuZ4pp6lew/j7ipRuxslPiGd58z7lew92xsDUhcltKk3Oh3voHn9YaoCrdLHF+MAShlJP73SmqUtqutxiXWYIQEOR2KUUgLiITSdIXAL+SVMTmk/VxNKoTuViP8rqUnG1nilevPsxc/BplhQWsfn0RpQV5bS9opMu55hTcnp/j2PBhNDptyy1ZZGZmOD/xBJWnT1P65591x2v9b4N7ONSZYGqDS0Z5jmqxUHHvmuoRtYEmoC+8XFaQT1F2h3yjzVIRE0POJ59iMuEGsguV+Ie7tGmePLljKzbOLviGth61V5+PdsZjZaLgwdqyT1HL9KZC75Z3FFUXStCW6gNMFCZyJtzTm8E39uDc0Ww2fHi8rllqhxm3BGy9YNPjlxV0kplUzKo3j5EUncuQm3pw4xP96T+xD3LTvvgVxhB3LqXDuzifW+9FZ25Kn51JvHv03Qbnqs6dQ5uXh+WwYeSqcnlk1yO8cfgNQp1D2XDjBiZ3v2Q+Too8gkfPXljYNE27kCSJT3Ym4GpjyoLB3TokJ4C2uIqqxCIszI8i9r1Dea+7uVDSg8DBHi0m7SuUcgZM9OFifBGZiUUNzvk6WmBr3nbrHKWTOc4P9kNuZULu0tMU70hBV93yA1BcZgk+DhZ1ZlidTiL2QAbewVcuuESnUlOwKp78n2ORWypxeSQU28ndkVs2NB97BfVm1qLXKCssZM3ri41K7h/ANafgfEOcUJVUcyG29bBv2xunYRoYSM5HH9f19UovrCCzuLKBefLvtL+bDS6pTzcHC6xMFQ39cL1q/XCdU7ZLV1FBxrPPobC3p2zKfUgSBIQ3TUSvT376BdJiT9PvusmtV/qvx8m0IrbHZHPfqB7YW5pA5im4GKXfvbWgTHXVWvK+O03e96frblZCCMKn+DL5gb4UZJaz5u1IclJbLtzbJqZWcMPHkBcP+9vfAkmnk4j8I4UNH0aDgJnPDiBski8ymcA72AELh2EIIWNU+fEO7+LkNjY4zZ3H8LOwK3I1W5K31J0rP6hvjxPto2HmppkczTrKokGL+HrC17haXvo9luTmkJt6vklx5VoOJuZzNKWAR8b6Y6a8jN3bkfMggWX+FzD+FRIcn0LSQc/BrZuxg0d6YG6tJPKPlAbHhRCEeNsZ1BtOYWemjy7s40Tp7jSyP4pCdTq3WWtH4xJdF2LyKSus6pS2OIag37VFoTqZi/X4brg8GtpqAIlnUDCzFr9OeVEhq19bRGm+UcldTa49BdfXCTMrJWcjWt85Cbkcl+eeRZ2WRuFvqwA4nNy0/uTac2txs3RrElxSH5lMEOxu02AH5+DphYWtXaf54bLffZfq8+fxePcdzseVYe9mgYNH6/6Lkzu3IVco6Dt2gsHX+WBHPA6WJpdarkT/BHJT6DevxTlViUVIah3qLBVFm5IanOvR35lZz4UhkwnWfxBNwuX0DguYAH3nwv4PISfO4GllhZVs+uQ4RzYl4z/AmXlLBjVISpfLZQQM9ENhFkL3ojji45M6vIuzv+02ZJLgzjgXXj/0OslF+rqyxQf2UexmzRMxb+Bp5cnqaatZ0GtBXffxWpKiaosrN1VwkiTx0c543G3NmHcZkZNSdhyqvacwkcWhmPcOjHya+CNZuPhYt/mdUprICb2uGxdiC5p0Ggj1tuNcdinlVW3nQ8qtTHC8OQjnB/ohM1dQsPwseUtPo84urxtTXqUhtUDVQMHFHsjA3MYE35D2BwO1B51KTcFvZ/W7NisTXB4NxXaCj0Glxzx79mLW4tdRFRex+rVFlOTldqmsRlrmmlNwcoWMwEGunD+V12ZOluWIEVgMHULel1+iLS3l6PkC7CyUBLjon9DSStOIyIhgZsDMNlvN9Pa0IS6zFK1O/xRalw8Xe/l+uNLduyn6bRUOd98NwQO4mFCEf1jr5snqygpi9u4icMiIZistNEdEUh77E/J4eIwfVqYKqC6/FFxi0XLd08qzBQhTOVajvFBFZlMe3VCJOXlZMfuFcFx8rNmxNIYjm5KRdB38TCa9DabWsOkx0LXt20s+kctvbx4lO7WUcbf3YsI9vTE1bxooFBDuikw5ELlcyajy43y6q2O7OBMvL6wnTmTo0VLstKY8/ffT7EveRemxIxzyUvFgyIP8MuUXetj2aHZ+UtRR7D28mo2+3Xsul+gLRTw6zh9TRQd3b0l7UH/7EBqtOxajQ6D3TeSll5GXVkbPIYYFIfUZ7YmppaKJL66/tx06CU5fNLyXmWl3W1we7Y/ddD+qM8rJ/jSaos1J6Co0nM0qRZIuBZiUFVaRcjqfXkO7NrikIiafrI+iUJ3Kw+a6brg8EoqJR/siIz0CezFr8RuoSopZ/foiSvJaLy7xX0HdTMH7lvLhOoNrTsEB9Brmjk4rce5o67sFIQQuzz6LtqiI/O+WcuR8AYN8Hep8ELWVS2b4Nx9cUp/eHrZUqLWcz7uUcO8d3JfS/FxKcju+a1Hn5JD54hJMe/XC+cknSIrOAQn82zBPnj2wl+oKFSEGBpdIksQH2+NxszG7FJkXswGqSloNLpEkiYqzBZgF2mN7vS8m3W0p2pDY4EkcwMLGhOlP9qfXcHci/0jhz2/PUF3Zgconlk4w6R1IP6YPOmkBTbWWfSvj2fb1aawdzJi3eCC9hrm3+FDgEWCHpb0ddp5D8Sk6R8LZhLodfXtxvOtOpLIy3i6ZQHJxMl/++jgmaonrZj3NI6GPoJQ1H/pfpSonLeZ0g9Y4tUiSxMc7z+FpZ86csA7u3iJ/hF9nUS4mg0JgMVqffxZ/JAuZTLRp8q7FxExByDhvUk7lkZt2KdI7pK6iSVG7xBJygdVQD9yeDcdyoBtlERlkfRBJ4aEMBNCrpgZlXEQGkk4ieETnt8UBfSft/N/Okv9LLHJr/a7N5jrDdm3N4REYxOwX36CipKRmJ/ffVnIajYbhw4f3HDZsWMDIkSMDRo4cGTBixIgAd3f3VqtWjxkzxh9g+PDh7S5Qe00qOCcva5y7WXP2UNsBHua9e2MzbRr5P/2E6mJGXf6bWqdmQ8IGRnm1HFxSn0uBJvXz4fR1KQ0p2yVJEsU7Uijdl37pmE5H5uIX0alUeH7wPjITExIjc3D0tMTBvWVTkiRJnNi+BeduvngEBrV5bdDXNIy+UMTj4wMu+XailoFTT30JrRZQZ5SjK6nGLMgBIRc4zu+JMJGTv/xsk+ABuULG2FuDGDEngPMnc1n/fjQl+RUGydeAfnPBb7y+YkZR05D8goxy1r4byem9Fwm5zpvZz4dj59p61RaZTOAf5kJFWR+U5uaMLI3i07/aXdsVAPOQEMz798d2435eGvQit5b1A7mc4AnN5xDWknIyGp1Wg394089799kcTqYX8/h4f0zae8PVaWH7i7DlSaTuE1BpRmLe2wmZmQKdVse5I1l06+OIubWJwUv2G+uFiZmcqHq+uNrOAq1FUraG3FKJ/YwAXB7tj8LJnKATBXwvLHEu0eiDSw5m4BVkj61zpxbCB6AiJk8f+l+7a3u0/bu25nAP6MnsJW9QWVamV3K5/10lJ6uJHpYkSUiShEajEVVVVbL6Fi5vb+8+tQ1Rly1bZgdgZmamq/9/u67ZWcL/0wga6k5eWlmDJ8yWcH7iCXRaHbfFbWdID73/bc+FPeRX5jMncI5B1/N3scJEIWug4By9umFubUN6XNuBJuVHsyjdnUbJ7jSkmgjQwl+XU37gAK4vLMTUz4+ywkoyk4rxD2v9STvj3FlyL6QQMnGqQSWCdDqJ97fH4+NowZzwmjJwWWf0u6RWgktAb55EgFlPfZi43MYUh5t7oslVUfR7UpPxQghCxntzw6MhlBZUsvadSDIaReS1iRD6gBMk2Pp0Xb1KSZI4s+8iq98+hqqkmhseC2HE7ADkBlZtDwh3RaczxTdkPN4lSSTGne3wLs7hrjtRp6dzfaodvZM1mIeEtNnFIinyCObWNrgHNqw2o/e9ncPH0YKZ/9/eecfVXV////m+k8veM4RNWNlkJ2aaOOKsu9qvtdZObTpsa3e/rbX9Vtvqz9pa27pnnNFoNK6oGSSQHSDMEAiby4a7378/PkAYF7gQssjn+XjwCHzWPZB7P+dzzvuc15kz+rSIAdg64eXblHFE8++ie+YjSIsTn573UFVhM11tNtI8TE/2YvTWM2NVLKX7GmiqPpm1mDnFs0KTkTDE+BL2zRk8EyKI0Ghp+McBqv59CHuzlcxlEyuc4Oy00/RiIU3PFqD17xe1TWAKNCp5Gtf9XHFyL//2PlrrT2Ed+jxGo9Hg7e3tfPnll8teeOGF8pdeeqn8pZdeKvvoo4+OWq1WARAdHW3bsWNH0c6dO4tuv/32lt7z+v87Fiatkknq/Ai2v1ZM4Y4awm4cOmajP4YpMRQsWseazzeT0F4LBPQVlyyJXuLR6+m1GqZF+A0oNFH64bKoHKWS0lrRRsumUrSBRpwtVqxlreBqoP7BB/FdsYLAm24CoCRPefobbmxJLwe2vovBZCJ92QqPbN98qIbC2nYevmnWSU3D3uKSmTeNeG53QROGWD+0vief/r1SgvBbGUv7x5UYEwLwcZP6mpoZwnU/mcvmxw7y1l/3sfyWaWMTzQ2Kg1W/VPQqD7+GJfEqPnmukLJ9DcRmBLP6f9LxGePwyIgEf/yCvXAxC6Pvp1zUnsvDH6ax8K6xq2X4rV6NPjaWxscew1pcTOgo05+dDgdl+/aQnL1oSMXr+0fqOFLdxkPXzxyb5mRbtTI1ve6wIrG24Bt0PXUEjb8BY3IgAIW7ajF664ifPvaijZmrYjnwUSV571Ww9mtK79ys2EA2HTg5WWC8SAnPt3fgmhvD3SZfWrdVsdpfT3CHFel0TYgD6j7cSPObJbi6HfhfHIffiikT6tj6E5mcynW/+D2v3v8LXvnf+7jhVw8QEO5ZSnii+eiZgljziY4JDYODY3y7Vn8lfdQpBVdffXXL66+/HpCXl+fjcrm46KKLOhwOB1OnTrUbjUap1+slwJ49e7zuueeeqQC928bDpI3gvHz0JMwI4+juWpz20SPbp+NXYDOaaPzLQ1S2VbKzZqdHxSX9yYz250h124Cikinp02lrqBs2NeFst9H0fAHaACPhM7YhNA66DtZT/aMfofH3J+r+3/dFYSV59YTG+o6Ybutqa6Vo5+dkXLQag9fofUIOp4u/bC0iLdKPK3oHStq64MDLkHHViMUlznYb9qoOvNKHHuO/Jg5jYgAtb5Vgr+10czYERfpw3U+yiUkN5JNnC/nileIR+xeHsOAbEDOX6tef4OX/3cWxg40svjaZK747c8zODZQHkpR54dQUdzF73VVEtx+jPP8IOeOI4oRWS/BXvoK1qAikHHU8TvXRfKydnSRlD1x/c7kkf/uwiMRQH66aNYYHgJoD8MQqMJfBzS/Dgm/gbLdhKTLjPTscoRHYuh2U728gJTvC4yi3P16+erKWx1CSW9cnrjBraiAwcLLAeKhs7qLT5iR5SgC6RdF80uHAEWik/b1j1D28F0vx+K/fF7U91/O5++5sZY7daVZFiUxK4fpf3I+tq4uXf/tTWutrT+vrnUv897//DVq0aFHq66+/HvTWW28F7d+/3ycvL8/32WefDXnuuedCV69enfLwww+H9I7FmTdvnmXnzp1FO3fuLPLz83MC4yrWm7QRHCjFJqV76yk/2Dhi1NPYYeVQO5xYfxNer/2XPW88jFZouTb52jG9XmZMAC/tqaS61UJMoOJcYnvX4fIPkbl89YDjpcNF0/MFyG4HobcEo335frz4KV05M7EWlxD7xOPoQpTooa2xm7ryNhZdM/xoG4DDn2zF6XAwa+1lHtn82t4qyhs7eeIr2ScbfI+8AdbWUZVLLIVKr6FX2tAIR2gEwTenUffwXpqeLyD8u7PRGIc+LHj56Fn/3Zlsf62EAx9X0lzbydo7MzF6D68u34tLCnL9HiC3ph1/bzNf+vFKwuNOTasyZV4Ee98/jm/ofEz+bytR3EeZvJA49igu8NpraPh//w9cLkwzpo94bGleDlq9nrgZswdsf+9wbV90rfP0Blz4Lrz2NTAFwx3vQ6TyHuza3wAu8OmZ+1a6rx6H3eVx9aQ7Zq2ZyqFPqsh77xirb88gI8ofvVawv7KVS7LGXwzSfwZcwY4aOhyS0Dsy8Wqy0PJOGY3/OYxXZgiBlyeiC/Y8UhwQta2Nw2/56Yva3BGRmKxEcr//BS//5j5u+PUDY5LQmwg8ibQmmv/5n/9pvv3225t704zPPvtsoF6vlzfddFNrj/AyQgj+8Y9/RLz11lt+FotFc+LECf2GDRsae6ssxzMTbtJGcACxGcH4BBhGLTbp1Z+M/upt6KKiCH/qfS6KXjagAdcTegtNDvcrkw6NjcPL189tP1zL5jJsx9oIui4FQ9GjoDMi9GbQ+BK4/ip8ly3rO9aT9KTL5eTA1veYkpFFyJTRVS4sdicPf1jMrNhA1qT3u27eUxCSAnEjRx3dhWa0AUb0ke4jSq2fgeCb0nA0dtPyZsmwT2AarYZlN6Sy8tY0qo428+qf8kaVW2tr6ubNv+xjz6edpMY1coPvXYTb94x4jieExPgSFOlN+f4WFlx9AxEdlVQcPjiuKE7j40PkL39J2A++j9AN/ywppaQkN4epWTMHRN3OnugtOdyX9TM8iN6khB2Pwku3KMozX/+oz7kBdO2tQz/FF32EUqB0dFctAeEmIhLG/1Dg7W8gc1kMR3fX0drQjZdeS0aU/ylHcPk17WgEpIb7kv+FUlwSFOGDKSOEyO/PxX9dHNaiZmr/kkfr1ooR1VAAnB02ml4o6IvaIu6ejf+q0x+1uSMiMZnrfnk/dquFV357Hy21E6d2dK7SX1uyrq5O+8QTT4Q9+eSTofn5+QaNRoNer0ej0XDZZZe1HDp0yNTZ2amZN29el0ajwW63CxhfBDf5HNzBjfDO9wGlMm7awiiOH2mis2V4eafd5WZMei3TE8KpvXU1cTUOvnxi7CK26ZH+aMTASkqh0TAlPXNIoUlnXh2dO2vwXRqDd5wVDryEI/VGGj/di3Q5MXnZlEKPHkry6gmP9x9RnujY/r20NdR5rDv5Qs5xqlst3Ltu2slilLojULV71OIS6XBhLW7GKz14xEIWr+RA/FdPpWtfPV17Rl5cz1gazVUbZmPptPPqn3KpHEaNpiSvnlfu30PjiQ7WfDWDNT+6GkNYLLyzQSmqOAWEECRnRyi9hvNX4RMUwrK2XB4ep7pJwBXrCb7llhGPaao6Tmtd7ZDm7ncOVlNc38GGNSloh5HP6sNpV973H/wc0q+A2zeD38nIwFbdgb2ms6+4pK2pmxNFLaQtjDzlWWWz105FoxHsfb8CUNbhDlW19vWEjoeCmjYSQn1oKGml3WwZoDsp9Br8V04l4ofZmDKCaf/oeI8aSqPbm2DXoQbq/rqX7iNN+K+NI/zbM9FHjtzQfrqJSEji+h4n9/L/3kdzbfVZtedM4HQ6ef755wNuvfXW+Mcee+z4Sy+9VP7zn/88+oc//GHUwYMHjRqNhr/85S/Vv/jFL+rvuOOO5mXLlnUBfQ5uPP1yk8/BtVRA7n+VqcMoaUoplV6f4dhV1sTcuCD0Wg3PxJRTFaUn+On3cNnGNrzTZNCSGOZLfr9CE1D64VrravsUDWwnOmh+owRjYgABlyYgtz9CR42B48+V42puwBClxSIXIl+4GTobaanvouF4u0fFJT6BQSTPG76sv5dOq4O/f1LC4qQQliT3KzDIexq0Bph584jnW8takTYXXmnDr9H14rdqKsbkQJo3lWKrGdkBRacEcv1Ps/ENMvL2owc48HFl303LbnPyyXOFvP/EYQLCvbnx5/MUaSm9lzI0s+U4fPKHUe0ZjZTscJBw7GALi750I2Fd1VQd3t8X6U80pbmKekni3JODZB1OFw9/WMy0CD8uGy3VZ2lVJqDnPQlLvw/XPw2GgVF1V14daAWmGcoIo6Ic5WEjdf6pp8d8Ao2kL4micGcN7WYLM2MD6eydLDBOeiW6jnxRjclPT+KsoaOXdIFGQm5JJ+yu6Wi8tJifL6DxP4f7ejCdHcr6tvn5QrSBZzdqc0d4fCLX//J+HDYbr/x28ju5jRs3BlRUVBg2bdpUlpWVZQ0KCnK98cYbx9asWdN+4sSJYdckHn300UpQU5QKC76pDFP85H4AAiO8iUwM6FEgH/p019Jl42hdOwsSgjnedpxddTk03bEeR3U1zc+/MOaX7y006U/ffLiCw8oC97OKaGvQzWl0fvwuFQ+8SeUngTjbO4l+8M/4LEzC4YzE0WGAl2+lZI+SwhjJwbXW11K2L5fpq9eh1Y2+fvXk9nKaOm38aF2/knRbFxx8SSku8Rl5zclSaEboNXglDRUEHozQCIJvmtYjyVSAaxQpJ/9QE9feO5f46SF88Uoxnz5/lPqKNjb+YQ/526uZsy6Oa++dM7AfKm4xZN8Bux5TtDNPgaBIH0JjfSnOrSNr5cX4hYaztC2Xhz88ekrXHY7SvBwiElPwCz75oLHpQDVljZ18/+KUYcWPAWg+Bv9ZC8c+h6v+Dmt+o4yr6Yd0uuja34ApLRitjx4pJYW7aohOCZwwweI565SMx773K5jV2/A9zn64NoudquZu0gN9OHagkbSFUWhH6P0zJgYSfvccAq9MwlbVQd3DezFvLFL62vKb8F8XT/i3Z531qM0d4fGJ3PDL+3Ha7bzym5/SXHPibJt02rjppptaf/aznzWYTKYBN+JLL72049JLL+0Y7rzp06dbAT7//PMxN6Z65OCEEBFCiPeEEDlCiK1CCLcLAkKIW4QQ24UQHwshXum3/agQ4tN+X7eN1VCPMfrCkg1Q+lHfaJX0xVG01HUN0c4D2HOsGSlhfkIwrxW/hlZoWfWlDfgsWULjP/+Js9Vz2SFQHFxNqwVz58noL3RqHEYfH6qOHML8YiHODhumTAuVd95G5d0/wt4FkT/6Nknvb8F/7VpMGYpz6U79PRzfScmnB4hMVErYh+PAh1sQQjBj9SWj2tjaZefxz8pYkx7BnKn95rvlv6VEA6MUl0gp6S5owpgciPBQ8FfrayDk5mk4mrppfn349bheDF46Lv3GdOZeGkf+F9VsfCAXa5eDK++ZxaJrktxLNa35DfhGKBMHnEMlgcZCSnYEdeVtdLY4WHzdzYR011F9MI89xyY2iutsaaampGhA9aTD6eLhj4rJiPJnbcYIEVbLcXhiNbTXwm1vwOxb3R5mOdqMq9OuzH0D6srbaK3vPqXiksH4BXuRtjCS/O01hOt1BJj0HKhqGde1jtYqkV9Yox2XS3o0FkdoBb6Lo4m8Nxuf7Ei69tahDfJSoraVsQjtqaVhTydhcQlc/6s/4HQ4eOW392GunrxO7kzjaQT3EPAbKeUC4D7gwcEHCCGWANcBq6WUq4Cf9NvdLKVc0e/r2VM1fETm3anc6D7+PaBEPjq9hgI3Asw5ZU0YdBoyon14s+RNlk9ZTrh3OOE/+iGutjaannhiTC+dFa1ENP374TQaLVPSszAV67GWtOCo/pCaH38DZ309kQstJN+7iKA770ZjUHrJtAFGDLF+dDdG0jzrVzS1B5IcOvzDi8Nm4/DHH5A0dz5+IaP3Mz3+WSkdVgc/XJs6cEfeUxCSDHEj9/456rtwNls9Sk/2x5gYiP/FcXQfaKBz9+gl0kIjWHhVEuu+nkXGsmhu+uV8Yt20JPThFQCXP6T0fW1/eEy2DaY3Wi7OrSPjolUEREYrUdzW8a3FDUfZ3j0g5QD1ktf3naCiqYvvX5w6cvS2/wXoaoI7tkDCRcMe1pVXh8ZH39eMf3RXLVq9huQ5I6e8x8qcS+JxuST7t1YyMzaQfeOM4Apq2kBCZ2ErMdMCR1Wh6Y/WR0/QtSlE3beA8G+dm1GbO8KmxnPDr/6A0+nklf+9D3N11egnqYzKqA5OCBEIhEgpcwCklLlAQM/2/vwU+L6U0tJzXPnEmjoGDN6w9AdK2qZsGwaTjqQ54RTn1mEfVG21+5iZWbGBbK/9FLPF3DcWxys9nYArr8T8zLPYqz3PjWf0VVKejBallMQ5o0nQZ9F9bBuOii+I/N3/kvTLdQTFmxErfjTkOl6ZIdirOihzXgFIko/9HIo/dPuaRTnb6W5v80h3sr7dwpPbj3HlzOgBKu3UF0DlrlGLSwC6C5QoxjRGBwfgtyIWY2oQLW+XYjsxbFZiAMlzw1n55TTPpKTSLldSrNv+Dxo9y2i4iyb9Q01EJvpTnFuPRqtlyQ1fJtDSSN2BnAmN4krzcvAPCyd0ajwAdqeLRz4qZnpMwMDKVnfkb1Jk1MLThz3E2Wmnu9CM96wwhFaD0+6iOLeOxFlhGNyITp8KAWEmUudHcOSzE8wK8/N4ssBgCmrayNQa6DJbyVw6PuUSrb/hnI7a3BE6NZ4bfnk/Bi8Ttu5xSNipDMGTCC4BKBm0raxne3+CgZSeVOZnQogN/faFCiH+I4T4SAjxqhAi3t0LCSHuEkLkCiFyGxpOccTE3NvBP0ZZi5OStMVR2C1OyvadvG67xc7hE60sTAjm1aJXifaJZnH0ydL4sO/dA0DDw494/LKB3gZiAk0cqW5FSkn7x59w7JZvEtIYR6PlBG3zjCRteY+gqy5D7HkcUtaCG61RU6aSpmzf30h0kj8+UdHw6lehYWgEceCDdwmKiiYua+ao9j32SSk2p4vvrxkcvfUWl4xc8QfK+ps+xhfteJqpNYLgG1LReusxv1CAazyCy6Nx6Z+VwpO3vwcjVF5Jp4u2Tyup/s1Oug4Mfb8lZ0fQVNWBuaaTtEXLCI6ZyuK2XB7+YGLW4uxWCxUH95M0d0FfJePG3Cqqmrv5wcWpI1c3NpZA/RHFmY9A98EGcMq+9OSxw41YuxwTmp7sz9xL4nA4XETX2cc8WaCX/Jp25rsMePm4Ly6ZzIROjef2hx4jMmnMusIqbvDEwQnA3YLJ4G0JwBLgamANsFQIsaZn32+Be6WUq1HSm0+6eyEp5b+klNlSyuywsFN8Y+u9YNkPoTIHSj4iJiUQvxCvAWnKvIpmXBLiIy3k1OQMUS7RR0cT/JXbaN20CUuB5/PHMqP80O38nGNfuo6q7/0QbcRlaLx07Gl7H7OfCaHXKw6l2wzLhkZvAPowb0SQFwFddpLnRcHNL4LOCC/eCF0nI4j6Y2VUFxUw8+LLEKNotVU1d/F8TgU3ZMcSH9ovdWPvhgMvKuXloxSXODvt2Craxpye7I/W10DwLWk4mi00v1Z8yuOEhuAXAWvvh4rtiuSYG6zH26j/f/to23IMJLS+f6xPA7QXZSQRlOTWITQalt54K/7WZhoP7CR3AqK444cP4LBZ+9oDrA4nj35czOypgayYNsr7v+At5d/0K0Y8rDOvDn2UT59w8NFdtXj7G4hNCxrxvPESFOlD8txwWg+Z8XKNfbKA0yWprG4jpNVJ2qLIcSmsnO9otOMfZKsyEE/ePceA5EHbknq296cUeEBKaZVS2oCXgFkAUspnpZTmnu93AWdm1vzs2yBwKnzye4RQBJhPHG2mrVEJ/3PKzeg0ghLLh2iFlmtSho7FCbnrLrT+/tQ/+NCoLyddLtq2buVrz/6ab2z9J472dgK//Gc03iGE3Tmb0GnxVBYcBocVdjwCcUthqvvJzQBt3jpCdIKEtCAIjIUbn4fWKth4e18RxYEP3kVnMJK5fM2w1+nl4Q+LEUJwz+pB/535b4GlZdTiEgBrUTPI8aUn+2OMD8B/bTzdhxrp3HUaGl1n36qsS239FbSdvL7L4qD5zRIa/nEAV7eDkNsyCL55Gk6zha59A+XUfAKMRKcGUZxbj5SS5PmLCI1LZFFrLg9vLTxlE0tzczCYvJmSoeg4vtKjgjNq9AZKejJmLgQML75sr+vEXtWB9xwleuvusFFxqInU+RFoTmOpfPal8TisLlZqTGOupDzW1ElypwYh8ai4REVlJEZ9l/c4pi4hxBwAIcQMoBGIFUL8rN+hTwO/7jlGAFcAOT0/9+X9hBDLgeMT9QuMiM4AF/0YqvfB0ff6FNMLdykFDrvLzUyf4sPm8k19xSWD0fr7E/Ktb9K5fTsdX2x3+zLS5aLtgw8ov+ZaTtx9D95OOw/OuQnn1/+Gs8VA4BWJGOMDmJIxnebqKjq3PwntNXDRD4c1XUpJUX03GiEQvWtVUxfAFQ9D+TbYch/Wrk7yv/iEtCUX4TWKUn1ZQwev7a3itoVxRAUMer7IewqCEyF+mdtz+9NdaEbjq0cfc+qjRPwumoLXtCBa3inDVjX+nim3CAHr/wZOG7z7I6Xy83AjtX/JozOnBt9F0UT8YC6mzBC80oLRT/EdMMmhl5TscFrqumis7EAIwbKbb8PX3oZ53/ZTiuKky0Vp3m4SZs1Fq9NjsTt59JMS5sUHsTR5lEKh5gqo2Q/pV454WOfeetCAd0+ar3hPPS6XZNrC0zNPrZeQGF8SZ4WR3i7IH6ODy69uZaZNS0CcL0HnSYGIypnhdDZ6bwAeEEJsB/4I3Iuy5hbf75j/ADYhxBfAZ0CelPLznn03CiHeF0J8DHwd+NaYLR0vM29Wbt6f/AH/YCMx04Io3FlDl8XBwaoWIqNKMVvMXD9t+LE4Qbfcgj4mhvoHH0T2+yNLl4u2Le9TfvU1nLjne0irlej/+xOhr76BZepCfPc24T0nHJ+eG0pseo8u5YdPQ/RsSFw57Gs2VnVQ1WDB5aWl+0g/mahZt8Die2DPExx59vc4rFZmXjy67uTGvCqEEHxz+SAty/pCOL7To+IS6XRhOWpWZr+NpqzhAUIjCLphGlpfA00vFE78elxIEqy4D0fBLpr+/rEi0+SjJ/zbswi8MgmNUSmyEELgvyZOieL2DozikmaHo9EIinOVxuiEWdlEJE9jQVsej3zgedp6MDUlRXS1tpCUrUTwL+4+Tl2ble97Er0VbFL+zRjewUmXpGtfPV6pwWh7inOO7qohZIovoVNO/eFkNLIvi0frlEQ3OKhrs3h8XtHBRgJdGmavGONYIJXzhr/+9a8DnuDWrVuXZLFYBrzpV6xYkdze3t7nnx577LHgv/3tb2MeeeGRg5NSVkkp10kpl0gpL5NS1kkpt0kp7+p3jJRS/lZKuVRKuUxK+Ui/fd/rOX+VlPJWKeX4hmyNB60Olv8U6g5BwSbSF0fR3mTh8x1V2J2Sej4dUlwyGI3BQNj3v4+1sJC2t9/ucWxbKL/qak5s2IC024n+8/+RuPkdAq68khCnhl9hos5bQ9A1yX03rPCEJAwGPVX1VmXtbYQbWUluPUIj8J4eiqWoeaDW3prfIJPXcuCLHCKnRI66IC2lZPPBGpYkhxLmN6gwZO/ToNF7VFxiPdaGtDhPOT3ZH62PnuBb0nC2WGneWDSh63HSJWmXX6LO9jjWKknAxZGEf3c2htih45O8pgUpUdwnA6M4L189sZnBFOfWIaVUoribbsPH3kHLvs/JqxhfFFeal4PQaEiYlU23zcljn5ayMDGYxUkefIbzN0HkdOXBbRisJS242mx9xSXmmk7qK9rHPPdtvIRN9SMw2Z+5Vh15JZ7/jboKWrFqYZqH08VVzi8cDgePP/74gFSZEEL2H4nT2tqq2b9/v29nZ2ffDVKj0Yxr4OmknibQx/Tr4POH4NMHSPza5xi8tEovkKGRorZ93D37bjRiZF/vf9mlmJ98kvqH/kLTv/+NtbgEQ2Ii0Q8+iP+llyB6FoZdVgfm5wqQWsFffVw8168RWiMEMb6dVFnCYdrwUZeUkpK8OmLTgvCdFY5lTx3WomZMWT03P42WqswfYn7796zT7IGmUiVaGYbDJ9o4bu7iuysHrb3ZLUovVfp68B29qMdSaAatwJgysQUKxjh/Ai6Jp/Xdcjp2VOO35NSHWtqqO2h+vRh7VQfGOG+Cau9A17kWtO4rYnujuKanjtC1tx6feScdQUp2BBWHmqgtayMqKYCpWTOJTs9ifvFeHn4/n2fuWjpm+0pzc5iSnoWXry///ryMhnYrj948e/QT26oVrdCVvxjxsM68OoRJh6mnb/BoTi1CI0iZd+Ycx7Krknj7oX0UfFHNZXNGX0/rarMRaLbTOsULnYcCAipj5/1//C22sbJiQufBhcbGda371oZRpxT84x//CBFC8MYbb/hfc801A5Q3nE4nWq2WX/ziF1FpaWldf/zjHyNuvfVW87e//e24+vp6/b333jtmLbMLo0RJo4UVP4WGQvRFb5E8LwIqu5gSdUQpLkkeWlwyGKHREH7vvTgaGpBSEv3QgyS+vYmA9Zf3OTcpJc2vFuNo6GJnZgA55nZsjn4PHUXvMUV7gqYuHV3tQ1VVemk43k5bo4Xk7HCM8QFovHUD05TA/k8/xcvbm2nB7fDizYoCyTC8c6ganUawNnPQza1gU09xyVdH/f1BcXDGpEC3Y29OFd9lMXilB9P6bjk2D6awD4fL5qTl3TLqH92Hs8VK8M3TCP3mInRLrlei1fLPhz13uCguYUYoWr2mL03ZG8WZHF207/10zFFcS20NTVXHSZq7gC6bg398WsrS5FAWeDKSp+Ad5d8R2gNcFgfdR5rwnhmG0GmQLklRTi1TM4LHNSdvvExNCaLRR6ApahvSf+qOfZ9VoUEQMnPso4lUzn3ee+893y1btgTk5eUVPPbYY2Hbtm3zBnA6nWLJkiWpr732mv+f/vSnsGPHjhm2b99eVFFRYdi5c6fP7t27j/74xz+udjqdY14XuTAiOICMqyFCieKS131A/ufVRHd0MzttBWHenrUk+CxcQPJHH6KLiOhzav1p31ZF96FGAi5LIDRAg/1gJUV17WTFBCijTD5/iCkRJmhQdClTF7p/8leaiwUJM8MQWoFXWjDd+ea+ScYd5iZK9uxk9qVXol96Nzx7Nbz6NbjlZcWZ96N/ejLQe1Cj9BiKS+yN3TgauvFddHoq24QQBF+fSt0j+2h6oYCIu2ej8WAmXH8sR800v1mCs9mKz7xIAi6NP3mNFfdBwdvw9j3wrR2gH1rIO1wUZzDpiM8KoSSvnqXXK9qQU9IyiZ0+m+z8/Tyy5TBPf2N4JZHBlObtBiApewHP7KygqdPG9y/2sO8p/y1lFE5Y6rCHdB1sAIerb3LAiaJmOpqtLP7S4GLo048mKwBDTguHPzvB7DXDj3CSLknBF9VUap1cOk11cKcTTyKtiebBBx8MPXjwoPfGjRvLvb295euvv15+1113xQYGBtY6nU6xa9euoqNHjxp2797t8+abb5ZrtVpee+21Y//973+D7HalYlydJjASGo1ykzOXYqt4nSZdN8n1M7k+dfjiEnfoo6PdOjdLcTNt7x/DNCMU32UxilMD8nuFl8u3wYk8ItZ+E73Ry+18OOiXnswIxstHuTmbMkORFgfWMiVKO/TxB7icTmauuQQSlsFlD0LJVqUkfhCHTrRS1dzN5TMGVc41FCl9YnP+Z4g4rztODjeduPW3wWi8e9bj2myYX/W8P87ZbqPpxUIanzyC0GkI+8YMgr6UMtBBGryVClRzGWz707DX6oviPj4+IIpLzo6gu81GddHJOWcX3XQbXk4LnXs/Ja/C8/lnpXk5hMbGoQsM5fFtpSxPDWNunAd/144GOL5j1OrJrrx6dOEm9FNO9r4ZvLQkzBjzGv0pkzkznONaJ3nvV+CwDx/FnShqxtpi44DRSXrU0DVSlfObDRs2ND7zzDPHDQaDBPDz83O9+OKLFTNnzrT+4Ac/qAOYNm2a7cEHH6yprKzU33333TFr1qxJfuqpp0K//OUvx/n6+rruvvvuMdduXDgODhQZp6hZhO79G0dDc4lsTyRNM7r6x2g4zBbMLxaiC/cm6DqlCi4u2Btfo+6kJuVnD4JvJNq5txE9LZ2q/MNur1VX3kaH2UpKv8kBXqmBCL2G7iNNuJxODn60hbgZswmK6lmryv4qzP8G7HwU9j034HqbD9ag1wrWDRbt7S0umfVlj35HS6EZXYT3mKYnjwfjVH8CLk3Akt9Exxcjp9ylS9K5u5bah/LoPtyI/5qpRHxvDsaEYSYcJC5X+uO2PwI1B90e0ldR2WwdUFEZPz0EvVFLce7JbZHJqcTPmc/ctv08usX99QZj6eigquAwSdkLeHrHMZq77Hz/4uGjsQEUvgPSNWL1pKOxG1tFG95zIhBCYLc6KdnXoOixGs78utas2EB2ejmwttspdKMF28uRL6px6gTmIC3hfqf3PaZy5tH1DPyNjIycuWDBgtRly5alLF++PHnJkiUpt99++4BqqWuvvTZp+fLl7W+++WbZRx99VPKb3/ym5oUXXgh+5ZVXRh9dMogLy8EJASt/TrOjjpKYD0BIinaNPIRzNKTdSdNz+UgXhN6WgabnJqLRCNKj/Dhc3QaVuxVdzMV3g85IbMZ0Gisr6Gobum5WkluPVqchYebJtKnQa/FKDaI7v4nS3F10mJuGDjVd9wdIXAFvb+iboiCl5J2DNSxNDiWgfzTTW1ySdrlHxSWunuhxIqsnR8J3STRemSG0vleO9bj7tUp7fRcN/zpI8+vF6KN8iNgwB/81cYgRxqoAcPHvlHFKm+4Gp/u2BK9pQehj/ZQormcNVWfQkjArlNK99Tj7ratedNNtGFw2uvM+Yu/x0aO48v25SJeLyOlz+ddnZaxOC+8bLzMq+W8pKeWIrGEP6dxbBwJ8ZisPSGX7G3BYnae99204EkJ9aPHVYA3Qkfd+xYC/XS/d7TbK9jVw3F+QGj3+6eIq5z7Z2dntOTk5RZ9//nnxtm3bSrZv316cnZ09YNHdbreLFStWdPr7+7uMRqPMyMiwLVy4sLO+vn7MS2oXloMD7ImrecwvFqu+lag0X47uqsE1zsnDUkqaXy/BXtNJ8E3T0A2arZUZHUBBTRvyswfBFNynFNI7H+5EwZGB13NJSvbWMzUzeIgQrldWKK42GyVbduAXEkbinHkD9qPVwfVPKcotL30ZWo5zoKqVEy3dXD5j0LpZ4TuKTJgHyiUAlqJmcEm8RlLyn0CEEARfl4o2wID5hUJcXSdH30i7i9atFdQ9vBd7XRdBX0oh7K7p6MM8LArzDobL/k9plM75x7Cv779m6pAoLiU7AmuXg8qCk0UlYXEJJM1fwqz2Qzz67v5RX740NwfvgEDertbS2j2G6K3LrDwkpV85bHuJdEm69tZjTA7s0wk9uqsG/1AvojyY23c6EEIwc2og+/1cdJitbgcPF+6sxeWUfOGyDBQAV5l07N6922/ZsmUpq1atSl61alXy4sWLU3fv3j0gJ/3AAw9UXX311YmLFi1KXbRoUerixYtTT5w4ob/rrrvG3JNz4RSZ9LCvqpEPfHWs7GpnZmQJWwqiqMw3E5c19oXtzh3VdO2rx3/NVLfRTUa0P3H2MkTx+7Dy58qsOiAyKRmdwUhlwSFSFpzsv6spbaWzxUryl4aW/JumBdGsAV0NzFh7iXu9OlOQUmjyxGp48WY+iHkYvVZwccag6sncJyEoHhKWe/R7WgrNaLx1GGLP3M1HY9IRcks69f88gPmVIkK+koG1vJWWN0pwNHbjPSuMgPWJaH09mDAwmIyrlTaNj++HtPUQPFg3HLxSe6K4T47jPSccodMQmx6M0VtHcW4d8dNPrmctu/FWSvfswLr3Q/YenzVwxl4/nA475fvzSJi3mPu+qGBdZkTfWu2oHH0PXI4R05PW8lacLVYCLokHoKPZSmVhM9mXxU9IY/54mR0byKPFjVwWG0bee8dIWxjZJxUmpeTIFycInOpLbVuDuv42iXG5XLz22mvFy5cv79L0W/ffunXrAMma9evXt69fv35CpI0uuAju5SPv4dRZudo4lfjjv8fLR+d2TtxoWMtbadlcjld6MH6r3FeHZUb7823dW9h1PjD/633btTq9sg53ZGChSfGeOnR6zYCbZy8abz1dpk5ifFKYvmrt8IaFpsD1TyLr81mw/2dclBxCgKlferKxGCq+8Li4RLqkol6SGnTGx48YYv0IvCwBS6GZhn8eoPGJQ0iXJPSOLIJvShufcwMlArrsQdDo4J0NSoXrkEOGRnFanYakOeGU72/E0a/sPWRKLKmLVzCj7TB/37x32Jetyj+CrbuLo8aptFsdbBg80WEkCjZBQCxEzxn2kK68OoRR2zeJomh3LUiYtuDMNHcPx6ypgbiAwOxQ2hotFO85uSxwoqiF1vpuRLLy8KdGcJMXjUbDypUrBzg3gIsvvrjztL3m6brwucrOhs1onCEsW/07tJ3VpMbWUX6wAUuH5xOgna1Wmp4vQBfsRfCN04Z9Ok7R1nG5JofcsGuV6KofselZNFRW0N2hPKhUF7dw5ItqkudFYPAaGljbrRaKa/fgrw/BYBullyl5NSfm/5Llrhx+ZHh14L68p5Qbu4fFJbbKdlydDrzSz07pts/iaEzTQ7FVteO3fAoRG+bglToBjeYBMXDxb6DsU2WSghu8UoMwDFqLS8kOx251cuzQwIKuZTfcgk5I7Hu3sm+YtbjSvBy0BgNPH9Nz+fQoz2/mljYo/XjE9KTL6qT7cCPeM8IQei1SSgp31RKZGEBg+IT29I6ZmVMCAThmdBES40PuexV9ywL5n5/A6K3juLdArxUkhZ1+GTGVC4cLysGVNpfRLgpJNa1GE78UEleS1v4oLoekaM/oE6YBpMNF03MFSJuLkNvS0bhxRr0Ydj2CXeh5lvVD9k3JyAIpOVFwhM5WK+8/cRj/UC+WXe++H6pwx2dUNCtrdt1HGke18ynHOl52rSK9+F9wcKOy0WFVikumXaaMlPEAS6EZNEyMUxkHQgiCb0oj6r4FBFya0FfEMyHMvQOmLoIt90FH/ZDdfVFci1Up3gCiU4Mw+RsoyR1YnBQYGUX6RavJas/nsXdyh1xLSklJ7i7sESm0OzV8b80Y5n0Vva+IRo+Qnuw+3Ii0ufDuqb5trOyguabztM19GwshvkamBntzoKqVuZfG01LXReneerrbbZTub2DagkgK6ttJDvdDfxqnHKhceFxQ76Yn9r+IlBquTLpa2bDqF4Q59xMa1OVxmrJlUym2ynaCrk9FHzGC2nlrFRx4idzg9eys0wzp6YpMnoZOb+D4kYN88O8j2CwOLv3GdLdTlqWU7H9/M95Rwehj/YaomgzG5ZK8e7iWjxPvhbgl8NZ3oCpPaXTuNittBR5iKTAraioTPP15LAit6BMMnlA0GrjiEbC2w/aH3R5i7Ini2j+uRDpcaDSC5LnhHDvchK17YBXm0utvRisEjr0fDIniGirKaW9sYLstgitmRJMaMYa1poK3wDcSpswf9pCuvDq0IV4Y4pSosHBnDRqdYuu5wMzYQPZXtpA0J5ygSG9y3z1Gwc4aXA5JxtJoCmra1PU3lQnngnFwVqeVj6o242jPYHVqj6LDlGxIWUeaeIPGyg4aRxnZ0nWgns7dtfitmIK3m3WyAez4f4CkNusumrvs1LQOVFTX6fVEpaZRtHMv1cUtrPhyGiHDjKCpLS2ivryUWWsvx5QZgr2qA0eLddiX3lfZQnWrhUtmToUbnlWitZduUWwKjIOEFSPb3oOjxYK9tvO0NnefdcJSIfMa2PuMkgocxIAoLk+J2lLnReC0uyg/ODCS9g8NJ2v1JWS0F/KPTTkD9pXm5SARFBtjuWf1GKI3WycUf6gMNh1mzdTRbMFa1opPT++b0+miaE8dCTNC+8QCzjazYgOpabXQ0GFl7qXxmKs72fNOOZGJARCgp77dSoa6/qYywVwwDu6jio+wuNoJdC4jOrBfOf/Kn5Gqex+NxjViFOdstdL8RimGqX74Xxw/8ot1NCgTu2fcRHzSNACOVA+9efoEJdLZUkX6ouARCwEOfPAueqMX6ctW9hUQWEZIU24+WINBp2FNeoQyofvml8HWoZTGz/WsuASU6A04Y+0BZ41F3wFr25Am+V76orhPlCguIsEfv2CvAcUSvSz50o1odDrkvg8GTLM+unsX9V4RrMtOITl8DOtMxVvB0T1ierK3CMa7p/ft+BEzlg77Wet9c0dvr9++4y2kZIfjH2bCYXOReVE0BTXKg6VaYDL5+clPfhK5ZMmSlK997WuxvdtWrVqVbLVa+xaX//nPfwY/+OCDoc8//3xfifHSpUtTHI6xj9O6YBzcxqKNYA9hSfSigTuiZ2HKWE6CVx5FOTVuG1GlS2J+tQicLoJvmDZ6NeGux8BhgaUbSIv0RwhOKpr00FLXRUW+otgQmzb8vKzu9jYKd3xGxkUrMXp7ow/zRhduojvffZrS5ZK8d7iGi1LC8PPqeXqPyIDrnlTWm2bfNrLt/bAUmtGFeA3p75t0xMxR/jY5/3Db/C2EwP/iuL4oTghBcnY4lfnmIcVJvkHBzLj4cqZ1FPHPN5UBue3mRpqOlVLmHc/dY4neQKme9A6Bqe7HOUkp6dpbhzExoE9l5uiuGrx89UzNPHceTDKj/dFrBQeqWtBoNSy5NpmIBH+S5oRTUKM8/KkObvJSXFxs+PWvfx0RGhrquOGGG8xpaWndv/71ryO2bNniq9FopNFolP/+97+DlixZkrJx48agrVu3Bvz3v/8NXbZsWcozzzwTqNFo+tRQxsIF4eDKWsvIrcvF2jyPBYluUosrf0aa4QMsnU6OHRoaGXXuqsFa3ELA5Ymj3+y7W2DPvxW199AUfIw6EkN9BkRwdquT9x4/hMEUg1an50TRkWEvd+TTD3Ha7QOGmpoyQ5Wep86hlZ/7KpupabWwfrD2ZOpauGML+Hq2JuOyObGUtijDTUcbwDkZWPQdaDmuNMG7wZgSiGHqySguJTsCl0tSum9occqSa69H6I3QE8Xt374DgOTsBSSEjmFKtd2iFJikrVca+d1gq2jD0WTBe45SNGTptFN+sJHUeRFoz6GCDS+9lvQof/b3TPhOnB3GdT/JRm/QUlDTRoS/kWCf07DOqnJOkJKSYrv33nvri4uLvV577bXg4uJirw0bNjRccsklHS6XSwDceeedze+8807pkiVLOkJCQhzZ2dmdr776atlXvvKVFq1WOy41jgui0fu1otfQoMXeMpeFCW7K3SMymTo7Du9tzRR+XkHS7JNOwF7fRet75XhNC8LHk36iPU8o6a5lP+zblBkdQO4xJd0npeTTFwox13Ry5d2z2LFxGlXDCS+7XBzY+h4xaRmExZ1sRjZlhtD+SSWWQnOfYnwv7/SkJ1enn1pxgbWkBRxnTr3krDPtMqX5feffIfPqIbt7NSob/3uYzrw6QudHEhjhTXFuPZnLBs6v8/YPYM6lV8KmjTz+xufElW+jS+fPt64afqiuW0o/VlLLo6QnhUGDqWdNuCSvHpdDnhPVk4OZFRvIa3lVOF0Sbb/WmvyaNjV6O4OYXy2Ktdd2TmjviD7Spyv4utQRpxTcf//9ETNnzux6+OGHT2zcuDHgvvvui/7nP/9ZBbB27dqke+65p/4///lPyIYNG+p/9atf1e3evdu0YcOGKS+++GLFeB3cufOId5qwOq28VfoWwWIOET5hxAa7j8A0q37KNNOnVBS00tmqFHBIpwvzK0cReg1BX0odPZKxdcKuf0DKWoia0bc5M9qf6lYLzZ02jnx2gqKcOhZckUBsRjBT0qdTX16GtWtor2PFwX201NUwc5DupD7GF22AcUg1pcslefdQDStS+6Unx4ml0IwwajHGnx2JpzOORgsLv60ME63c4/aQviju40pwSlKywzlR1Nz3funPoqu/BAYThrzNiJoSDInTiRtL9AZKetIrAOLdj+KRdiddBxowZYX2zeg7uquWoCgfwqaeexWJM6cE0mlzUlLf0bfN5nBR2tChOrgLAIPBIHU6HUIIfHx8XHa7XYDy0L9ly5bS9evXt6ekpFh37drlc+TIEeOBAwdM8fHxVgCXyyUWLVqU+vLLL4/phjTpI7gPKz6k1dqKoWEuSxNHSLeFpZI+y8i+7RqOflbCnCsyafu4EntVB8FfTkPr70H6JO9p6GqCZT8asDkzWvk/2ZNXS+krpcRlhTC3R04pNmM6u157kRNH80mcPVBfcv/Wd/EOCCRl/sAnfyEEpswQOnbX4rI5+3rD8o43U9dmHToaZ4xIKeku6FEvGU28eDIx68uKfNeuv0PsU0N2D47ikrMj2LP5GCV59cxcFTvgWC8fX+ZdeS28+jwAF1++Zmy2OGxw9F0lstQNfe9Jl6T5jRKk1Yl3TxTfUt9FbVkri65JOifTyrOmBgKwv7KZaZGKAy5t6MDulKqDO4OMFmmdLn7yk5/U//jHP45eu3ZtSHx8vPXPf/7zCVA+V73qJv/3f/9Xs23bNu+bbrop6Zvf/GbdNddc0/rBBx/4CCHkzp07i8b6mpP+7rWxaCOR3jE0NU5lfsLI6bagy75FpP4ohdtKsR5vo/2T43jPDsd7ugcDUR1WpQw/bilMXTBgV2a0PyYXFL9Zjk+gkTVfzehTP4lKnYZWp6NykGxXW2M9ZXl7yFp5MTr90GjMKyMEHC6s/eaTbe5LT3rWxD0c9upOXO22yd0e4A6jL2Tfrqj2N1e4P6RfFBcUZiJkiu+Qpu9eFq6/Cq3JF42XNzOzZ4/NlmOfKVPa3Uzulk6J+ZWjdO2tx//iOLySAgEUIWMBqfPPvfQkQEKID/5eugHVpb0FJhlqD9ykx9vbW8bFxVk/++yz4meeeeZ4RESEEyAmJsYG8Oijj4YsW7Ys5U9/+lNkVlZW1759+7w3btwYWFFRYXA4HON6YpvUEVxZSxl5dXmsDLudYjQscLf+1p+QJNKmdfL5YV8anj2M1s9I4FVDhY/dcuAlaK+Gq/8+ZFeASc+1Ni9cVieXfG/2gN4kvcFIZHIqVQUD58Md/PB9JJKZay51+3LGhAA03jq6jzRhygrtS0+unBaGr/HU/lstBU0glLExFxzzv6Gsw+3+F6y7f8juAVFcbh0p2eHserOMtsZu/AcVIBlM3lz9g59g6+5CO9YKsPy3wOALiSsHbJZOF+aXj9J9sBH/S+LxX6FEjtIlObqrlti0IHyDRpFyO0toNKKn4ftkRXFBTRsGnYb4kDGmb1XOS958882ge++9d0Al37PPPnsc4Lvf/W7Td7/7Xbfl4U899VSoy+VisI7laEzqCO7V4lfRCR3OtmxCfQ0khY3+IUq57jqyTBLanQRdnzqiFFcfTgd88VeInj3khgSw551yoi2C/WGC8LihqZjYjOnUlZVg6+7quZydQx+/T+KcefiHuS8WEVqBV1ow3QVmpNNFbkUz9e3WoaNxxkF3oRlDrN/4xYzPZwJilMbvvKfdNn5DTxQX50/7J5Ukz1Ki+5K8odWUAPEzZpO6YMnYbHA6oHAzpK4D/cnhn9LhwvxCId0HGwm4LKHPuYEyiaK9yXJO9b65Y3ZsIEdr2+iyKe0YBTXtTIvwQ3cOVXyqnD6KiopMixcvTr3oootSVqxYkbxkyZKURYsWpf7xj38cMU0mpRQu19AWrtGYtBGc1WllU+kmVk1dxa5dTuYneFbu7moLIN5ooNxqIdK/GQgc/cXy34Tmclj73BAx3GOHGsl99xiueG8+bmmi0+rAZ1CENSV9Ortef5kTRwtImDWX4pwddLW2DB1qOghTZihde+uxlrWyOb8ao07D6rRTq550ttmwV3Xgvy7ulK5zXrPw23BoI+x7VmkfGESvuknjfw6jPdZGRII/xbl1zJmov9nxHcpabr/0pHS4aHq+AEuBmYD1ifgtHVi5eXRXDTqjlsRZHqTTzyKzpgbiknCoqpX5CcEU1LSdcsWvyvlDQ0PDwcHbPGng3rFjx5jX32ASR3BbK7bSam1lRfSVnGjpZn786OtJri670tDtr+Nwt5ay114e/YVcLvj8IQhLg2kDHVJbYzcfPplPaKwvqZfFIYHC2qFRQXRqGhqttq9dYP8H7xIQEUn8jJHXbbxSAxF6DV2Hm3j3cC2r0sKHOM+xYjnao16SdnamB5wTxMxRGqt3/XPYqd/G5N4o7jgpc8IVcePaCZr6kb8JdCZIVgpTpN1F07P5WArMBF6VNMS5OWxOSvLqSZ4dht44gWLUp4HeyQL7K1toaLfS1GlTC0wucHQ63biauD1h0jq4jUc3EusXi6VN6R9bkDj6Dbv5zRJcHXbCvpKFj3c3BQVGaCod+aSiLVCfD0t/MEACy2F3suVfyrraJXdNZ3pPBZk7yS69lxeRSalU5h+i4fgxThQeYeaaSxGj5JuFXotXahDthxpobD/16kmA7gIz2kAj+sizO2LlrLPoO9B6HArfdru7T6Oy1UasFhBQnOs+TTkmXC5FFDtlDRh8kHYnjc8cwXK0mcBrkvFdNDQFXX6wEZvFeU72vg0mxNdIbLCJ/ZUt5KsKJiqnmUnp4EpbStlbv5frUq8j91gLASY900ZRb+/aX68s3K+ZinGKH2lLplJlm077e48Mf5KU8PmDioBx1pcG7Pr85WIajrez+vYMAsJMRAV4EeSt58gJ9+s6UzKyqCsrIe+dN9Dq9WSu8Kys3CsrFG2XgxlaHatOMT0p7S6sJc0XjnrJSEy7FIISlIKTYeiN4qw5NUxJCaAkt27I1IgxU7UbOmoh42pcNieNT+djLWkh6LoUfBe4f4A5uqsW3yAjMWdppNFYmRUbxP7KlpMalJGqgzsDuHoVQyYbPb+X2wW6SengXi16FZ1Gx1VJV5FT3sS8+GA0wwwlBXC0Wml+UxFS9luuLNynLU8GNBTu74L6Qvcnlm+DE3mwdMMAKaWCHdXkf1HN3EviSJihKEwIIciKCeBITavbS8VmTMfldHJk20dMW7QMb3/P+hkNqUE4kHw52B9vw6mF+dbyVqTNdeGol4xEX+P3Hqjc7faQ/lFcRrg3zbVdNFZ1uD3WY/I3gdaAa+pqGp88grW0haDrU/HJdh+ddbZaOZ5vJnVB5LCDd881eicLbCuqJybQRID3uTHxYJJzuKGhIWCyOTmXyyUaGhoCgMPu9nt0RxRCRABPAcFAG/A/UspqN8fdAnwHsAKNUsobxnL+RGBxWNhUuonVU1fjsPtwrKmLWxcOv/gvXZLmjUXgGiik7B9qIibZl8Jjq8j+5AHEjU8PPfnzh5Q5Xf2mYzdUtrPtxSKmpAUx/8rEAYdnRPvz5BfHsDlcGAY1UEdPS0doNEiXa9Tikv7k1rZRi5M51lOveOwuaELoNXglBp7ytSYFs26BT36vRHGx7mex9UZxmoo2dBpBSW4dYbHj7OmSEgo24YpfR+OLx7EdbyP4xml4zxo+Mi/eU4d0yRGnUZxr9E4W2FVmZo1aYHJGcDgcd9bW1v67trY2i8kV2LiAww6H4053Oz195H8I+I2UMkcIkQ08CNzS/wAhxBLgOmC1lNIihEgYy/kTxdaKrbTZ2rg+9XpyypWCiZEavDt3VmMtaSHwmuQhQsrpS2P5sKSD6gPFxCw/BJHTT+6s3APln8Ha+0Gn9B1Zu+xsefwQXj56Lr4jc0jUmBkdgM3pori+vU/dpBeDl4mYtAwcViuRyake/76bD1Vj0ziZ12bHXt+FPnx8a2dSSmW4abJSuKKC0vg996uw4xGl8Tto6INS/4rKGbG+FO+pZ+HV41QSqd6Lq6WJRsft2JrbCb4pDe8ZI1dFHs2pJTzOj+Co86ePLDPaH51G4HCpCiZnirlz59YDw4uaTlJGvZMJIQKBECllDoCUMhcI6Nnen58C35dSWnqOKx/j+RNCflM+cf5xzIucx+7yJnyNumEHKdrru2h575gipOxG/SFxTjh6o4ZC6yXwyQMDd37+EJiCYO7tgBIJfvhUAR1mK5fclYW3G2mvzGjFDneFJgBX/vDnXPuz//X45uh0SbYcrkVMCwSge4QZcaPhqOvC2WJV05ODmX8XCA3kPD7sIcbkQAzx/sRY7XSaLdSVu///HQ3XgXdpsP8eW7MXIbeM7NxcTheHPq2isbLjnO99G0zvZAGANHX9TeU04smjegJQMmhbWc/2/gQDKUKI94QQnwkhNozxfIQQdwkhcoUQuQ0NDR6YNpSfzP8JL69/GY3QkFNmZm5ckNsm0l5FCI1BQ9B17oWU9QYtKdkRlFgWYSv4CE7sVXbUHoai95Q1GqMyvHLvBxUcO9jIkuuTlSnFbkgI8cHboCV/GAdn8vXD5Ot5eiunvInGDhsr5sRgiPUbIr48FroLlWjXNE11cAMIiIHMa3smfrtfP+2N4jQWJ/FeWoqHke4aCWeHjYadSdhlIiG3pmPKcj8xXkpJxeEmXr5/D5+9VERUcsB5UT05mN40Zboq0aVyGvHEwQnAXWnY4G0JwBLgamANsFQIsWYM5yOl/JeUMltKmR0WNv6GVR+9D00dVorrO1iQ6P6G3fZxJfYTHQRek4LWb/j1q7TF0TicWkpcF8Mnf1A2fvEXMPjB/K8DUFloJuetMlKyw5m+Ysqw19JoBOlR/kOGn46XzQdrMOm1rJgWjldmCPaqDhwtQ5XtPcFSYO6bUqAyiEXfBls77H122EOMSUoUl+ajpSy3DpfL82pKZ4eNxn/uwW6PIGRxPaYM9y0tDZXtbHp4P+88egCn3cUl38jimh/OwWg6//QabpwXy20L41SJLpXTiicO7hiQPGhbUs/2/pQCD0gprVJKG/ASMGsM508oe3rmry1ws/7WJ6Q8Jxzv6e6flHuJTPQnMMKbQnEDlGyF/S/CkTdg3tfAFERHs4Wt/zlCYIQ3K25NGzW9mBXtT35125hugO5wOF1sOVzL6vRwTAYtpkzlpmgZZtL3SDg77diOt1144sqeEj0b4pZAzvCN371RnN4pCbM4qS5u8ejSzg4bDU8cwm62E2r4PaaVq4Yc09Fs5aOn83nlD3toqGxn6Q0p3PzrBSTNDj9v2zmyYgL43dVZI1Y3q6icKqM6OCmlGegSQswBEELMABqBWCHEz/od+jTw655jBHAFkDPc+VLKlon8RQazq8yMl17D9JjAAdtdNifNrxSh9TcSeOXoQspCCNIWRVLT4EuLPhPe+jZoDbDoOzgdLt5/4jAOm4tLvzkdgwe6lZnRAXTanBxrOjXVi5xyM02dtr7J3fowb3ThpnGtw1mKmkGCSV1/G55F34HWSmVG2zAYkwLRx/mTYtJQsrt21Es622w0/OsgTrOF0JAn8Er0Ad+TmQubxUHOpjKe/9VOivbUMXvNVG773SJmropFeyGNMVJRGSeefko2AA8IIbYDfwTuRVlzi+93zH8AmxDiC+AzIE9K+fkI559WdpebmTM1aEg5fuu75Tgauz0XUgbSFkYhBBQEbgDpgjlfAd9wdrxWQm1ZG6u+kk5QpGeploxRCk085Z2DNXgblPRkL6bMUKzlrTg77WO6lqWgCY2fHn207ynZNKlJvQSCE2Hno0o5vxuEEARcHIdJCGwHG3A6hxeHdbZaFefWYiX0Wn+82t+BdKXIzeV0cfizEzz3y53kvnuMhJmhfPk3C1n8pWSMas+YiorHeHSHl1JWAesGba4DtvU7RgK/7fny5PzTRmuXnYLaNjasHlhubzlqpnNXDb5LY/pmaHmCT6CR2IwQjlYaWLD2Z2gWfI3iPXUc/KSKmatjSZ7reS9PaoQfeq3gSHUbV8wcn/K/w+ni/SO1rEmPwEt/UnvQlBlC+yeVWArN+Mz1bCacdLqwHG3GND30vGkUPiv0Nn6/+yOl8XvQzL9ejEkByDATCXVdVB5qJN5ND5ujxULDE4dwddgJvSMLY8W/AJBpl1NxqJEdr5fSXNNJVHIAl307mciEC2SquorKBDMp8xy5FWakHNj/5uy0Y361GF2ENwHr4sd8zfTFUXS22KiMuBNzq4mPnyskKimARdd6OC+uB4NOQ0q43ykVmuwqM2PutA3RnuwtEhlLNaX1WBvS6lTTk54w6xbwClSiuGEQQhCyPhGTRtD48dDByQ6zhYbHDyrO7WtZGOMDoOAtGkKuYNOT9Wz++0FcDheXfmM61/xwjurcVFROgfOv/MoDcsrNGLQaZvcIHEspaXmrBFeXndDbM8fVyJwwIxSjj45Dn1bR1tCN3qBh3dez0I5jjlVmtD8fF9YjpRxXkcDmQ9X4GLQsTx1YaSqEwJQZQueeWlw2JxrD6MrylgIzaAXG5PNDx/CsYvCB7K/C9oeh+RgExbs9zJQaRJe3jsC6TuyddvQ9A24dTd1K5GZxEvb16Rim+NFRXkRO4QoKLSsx+igFJFkXxahrbCoqE8Ck/BTllJuZGRvQl77rPtDQI6QchyFmfOtMWr2G1PmRVBxqoqWui3V3ZuETOL6S+qyYAJo6bdS1jb2k395TPbkmY2B6shevjBBFNLmo2aPrWQrNGJMC0ZzjY1bOGTxo/BZCYFo2BS8hqHqnDAB7YzcNjx9E2hTnRqiJXW+V8vyDxymyLGP2RUFqAYmKygQz6T5JHVYHh0+0siBBKZt3tFhpfrMEQ5w/fsuH71HzhIwl0Wh1GhZdk0zMtPFHPL2KJodPjD1NubO0ieYuO5dPd69eYUwIQOOt8yhNaW/owtHYjUltD/Ac/2hlcsQIjd8A0RfFYJbAwQZs1R2Kc3O6CLkji6LSVp775U7y3qsgwT+fL2c+xuJb5qgFJCoqE8ykc3B7K5pxuiQLEoMVIeVXi8AlCb4h9ZSLKEKn+HLHg0uZvXbqKV0nPcofIcZXSbn5YA2+Rh0XpbpvhBdagVdaMN0FZuQIVXwAlkIlylP738bIwm+DrUNxcsOg1WqwJAehd0rqH90PUmJbEctr/z7CtheOEhjhzXXfjWWt8Zf4z1p+5mxXUbmAmHQOLqe8Ca1GMGdqEB09QsoB6xPRhZhGP9kDPOl1Gw0fo46EEJ8xF5rYnS7ez6/l4mHSk72YMkORFgfWspGvbylsQhfhjS7Ya0x2XPBEz4K4pSNO/AaIXTmFersLp1Zw0Khn8/NFuJyyr4AkouND5cCMq86M3SoqFxiTzsGlRvjxP4viMbTaaH3vGF5pwfjMO/e0+jKi/cccwe0obaKly85lw6Qne/FKVSYCjJSmdFkcWMvb1OrJ8bLoO9BWBQVvDXtIZGIAR/Ra3muwcqK2i2U3KgokibPDlOKi/LcgPBNCxlaJq6Ki4hmTzsFdNSuGX16WdlJI+Usp56ScUWZ0ACdaumnpsnl8zuaD1fgZdSxLGVleTOi1eKUGYclvQg4jCWYpagaXVNOT46W38XvHCI3fGsGSm6YxZ30Ct/5uITNW9isgaa+D47vU6E1F5TQy6RwcQNtHx7Gf6CDo2pGFlM8mWTFKoclwkwUGY3O4eP9I3ajpyV68MkNwttmwn3A/YdpSYEbjrcMwVR1XMi40GmUtrnovVOYMe1jirDDmr08YWkBS+DYgIeOCG9GlonLGmHQOThFSrsR7TviwI0fOBXoHnh72cB1ue2kjrd32Ic3dw2FKCwaNcKtNKV0Sy1EzXtOCVfWSU8GDxu9hyX8LQlIgLG3CzVJRUVGYdA6uK7cObYBnQspnk2AfA1EBXh6vw20+WIOfl46lo6Qne9F46zEmBrhdh7NVtuPqcqjpyVPF4APZd0DhZjCXe35eZxMc266kJ8/B9LmKymRh0jm4wKuTCfvmTI+FlM8mmR4WmtgcLj44UsvajEiMOs8bsk2ZITgaurHXdw3Ybikwg0bglaqql5wy8+8CoR2x8XsIRzeDdKrpSRWV08ykc3BCI9CNU2HkTJMRHUBZQwfdNueIx20vaaTN4uDyGWOrBu0dnDk4irMUNmGM90dzHg7KPOfwj1Iav/c9C90tnp2TvwkC4yByxmk1TUXlQmfSObjzicxof1wSCmpHjuLe6U1PJo9tyrk2wIgh1m/AOpyj2YK9tgsvtT1g4lg0euN3H90tUPapEr2p6UkVldOK6uDOIlkxSqHJSGlKq8PJB/m1rMuMHDLbzhO8MkOwV3XgaFV0Ly2FyqRzdf1tAomaCfHLlDSlc5RZfEVbwGWHjKvPiGkqKhcyqoM7i0QHeBHorefICJqUXxQ30m5xeFw9ORhTppKmtPSkKbsLzOhCTejDvMd1PZVh6G38zh++8RtQ0pP+MRA958zYpaJyAaM6uLOIEGLUQpPNB2sIMOlZkjS+lgd9mDe6cBPdRxpxWZ1YS1vU6O10kLIOgpNGnPiNtR1KPoT0K5Q+OhUVldOK+ik7y2RGB3C0th27G2Fkq8PJ1vw61mVGjCs92YspMxRreSvdBxvAqaqXnBY0GmUtrnqfolDijuIPwGlV1UtUVM4QqoM7y2RG+2NzuiipH6o48nlRI+1Wx6jak6NhygwBF7S+fwxh1GKMV9VLTgszbwZT0PCN3/mbwCccYhecWbtUVC5QVAd3lulVNHGXptx8qCc9mXxqiiz6GF+0AQZcHXa8UoMQ6kDN08OAxu+ygftsXVC8FdLXg0YdLquiciZQ73RnmYRQH0x67ZDRORa7kp68JDMSvfbU/puEEHj19MSp7QGnmXlfB41uaON36Udg74R0tblbReVMoTq4s4xWI0iP8uPIiYER3GdFDXRYx189ORjfBVEYkwMxpYdMyPVUhsE/CqZfB3sHNX7nbwJTMMQvPWumqahcaKgO7hwgMzqA/Jo2XP1G27x7qIYgbz2LkibGIekjfQi7c7qqXnImWPhtJVrb+7Tys8Oq9L+lXQZa/cjnqqioTBiqgzsHyIz2p8Pq4LhZ0YzsTU+um4D0pMpZIGrGwMbvsm1gbYN0tXpSReVMot49zwEGF5psK2qg0+acsPSkyllg0Xeh7YTS+J3/FhgDIHH52bZKReWCQnVw5wCpkb7oNKKv0GTzwZ70ZKK6XnbekrIWQpJhxyPK9IBpl4Du/BABV1GZLKgO7hzAqNOSEuHH4eo2LHYnHxbUcUlWFDo1PXn+0jvxu+YAdDer1ZMqKmcBj+6gQogIIcR7QogcIcRWIUS0m2NeEkJsE0J82vP1UL99R/tt/1QIcdtE/hKTgcxof/KrW/n0aD1dNifr1fTk+U9v47feB5JXn21rVFQuODwtqXsI+I2UMkcIkQ08CNwy6JhIYJ2U0uLm/GYp5Yrxmzn5yYz259W8Kv67/RghPgYWJKj9auc9Bm9Y/1ewtILedLatUVG54BjVwQkhAoEQKWUOgJQyVwgRIIQIlFK2nGb7Lhh6C012l5u5ZcFUNT05Wci85mxboKJyweLJXTQBKBm0raxn+2Du70llfiiEWNlve6gQ4j9CiI+EEK8KIeLdvZAQ4i4hRK4QIrehocGjX2CykB7l1/f9+lPUnlRRUVFR8SxFKQB38z8Gb/sjUC6lPCqEiAQ2CyEullKagd8Cm6WUZiHEQuBJYOWg85FS/gv4F0B2dvYwM0cmJ35eehJCfWi32JmvpidVVFRUThlPHNwxIHnQtqSe7X1IKbf0+75WCLELiAfMUspn++3bJYRQFyTc8MO1qQBqelJFRUVlAhj1TtoTgXUJIeYACCFmAI1ArBDiZ73HCSEWCCE0Pd9HAbOBgp6fF/c7bjlwfCJ/icnC+hnRrJ8xpEBVRUVFRWUceFpFuQH4jxDCF2gFvgqkoURovWQCvxNCaAEb8C0pZXfPvhuFEL8G9EA18K1TN11FRUVFRWV4hJTn5lJXdna2zM3NPdtmqKioqJxXCCHypJTZZ9uOcwF1sUdFRUVFZVKiOjgVFRUVlUmJ6uBUVFRUVCYlqoNTUVFRUZmUqA5ORUVFRWVSojo4FRUVFZVJyTnbJiCEaAAqzrYdwxCK0ux+vnG+2g2q7WcL1fazw6nYHielDJtIY85XzlkHdy4jhMg9H/tMzle7QbX9bKHafnY4n20/l1BTlCoqKioqkxLVwamoqKioTEpUBzc+/nW2DRgn56vdoNp+tlBtPzucz7afM6hrcCoqKioqkxI1glNRUVFRmZSoDk5FRUVFZVKiOjgVFRUVlUnJBe/ghBCLhRBvCyE+EULsEEKs69m+SAiRI4TYI4R4Qgih63fOXUKIPCHEXiHEdwZd7xYhxHYhxMdCiFfOF9uFEAYhxL+FENt6zvvNuWZ7z35/IcTBQdu0QojHes7ZI4RYdh7ZPkUI8XTP+yVHCPGj88HufvtChRDVQoi002X36bD9XP+cDmf7mf6cnvdIKS/oL2AZ4NfzfRBwEGXy+GdASM/27wHf6fk+A3gDED1fG4Gsnn1LgNcBr56fE84j278J/Kzney2wBZh7rtje8/PPgQKgatC1vgX8oOf74J5r6M8T29N73ycoD5xbgIxz3e5++58EPgbSzqX3+ih/83P6czqK7Wf0c3q+f511A86lr56bfg5wRe+bqGe7Afik5/uHgMX99i0EHur5/m0UmZzz0fbvAF/pt+8ZIPZcsX3Q8bsG/bwNMPb7+afAFeeD7W72PwHMOh/sBr4O3AI8dbod3AS/X87pz+kotp+1z+n5+DUgJL6QEUJogD8D/wGSgaO9+6SUNiGEvufHAfuA4p5toEQPKUKIfwI+wOtSyr+dZtMnyvZ/A48JIZKAeGCzlLLyNJs+FttHQiultPb7uf/vddqYINv7X+96wCCl3D+Rdrp5nVO2WwiRAiyQUt4phFh72owd+roT8Tc/1z+nI3FWPqfnKxf8GhyAECIceB74TEr5L5SnrMENgr0/j7QvASX9cTWwBlgqhFhzOmzuZQJtvwSoAR5FSY9cIoS4+LQY3WvM2GwfK6e1wXMibRdC6IUQDwBpwFcn1NChr3XKdvesFf0ROG3rhcO87kT9zc/1z+lInPHP6fnMBR/BCSESgf8HfENKWdWzuQzlZtN7jAFw9NuXCuzq+TkVKOn5vhR4QEpp6znvJWAW8OF5YPtPpJSL+533M+AxYOs5YvtISCGEoffvjvJ7HZpIe/szkbYLIYzAS8BfpZSfnQZz+7/WRNk9ByUCeVMIQc/5GUKIx6WU/5lww5nw98u5/jkdiTP6OT3vOds50rP9hZLDjhi0zQvYDgT1/PxN4Fs9389BKc7oLdR4np6iAOBO4P6e7wXwNLDsPLF9D/3WUYCfAL86V2wfdNzgdYnvA3f3fO8PfMTpLTKZSNvvAS4/3e/zibZ70L6nOP1FJhP5Nz+nP6ej2H5GP6fn+9cFL9UlhCgBqgZtvhOYAvwJ5anqAMoN1Nlzzj3AbYAT+K9UUg4I5XH2V8DFKOmGjVLKR84T27OAv6BUd2mBHcAvpJSePFWeEdv7nbtLSrmw38964B9AVs/v9SMp5c7TYfdpsP0VIBJw9TvsUSnlq+ey3YP2PQX8UUpZONE293uNifybn/Of0xFsP6Of0/OdC97BqaioqKhMTtQiExUVFRWVSYnq4FRUVFRUJiWqg1NRUVFRmZSoDk5FRUVFZVKiOjgVFRUVlUmJ6uBUVFRUVCYlqoNTUVFRUZmU/H/PfEeBU0QQfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the median of each team's yearly OPS\n",
    "med_OPS_team = regular_season_df.pivot_table(index=['team'], columns='year',\n",
    "                                             values='OPS', aggfunc='median')\n",
    "# Only check teams without NaN after 2005\n",
    "team_idx = med_OPS_team.loc[:,2005:].isna().sum(axis=1) <= 0\n",
    "\n",
    "plt.plot(med_OPS_team.loc[team_idx,2005:].T)\n",
    "plt.legend(med_OPS_team.loc[team_idx,2005:].T.columns,\n",
    "           loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.title('Yearly record of each team')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the yearly data, each team's standing and record vary by year."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Now the correlation between player's height and weight and their record will be\n",
    "examined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "177cm/93kg 177.0 93.0\n"
     ]
    }
   ],
   "source": [
    "# Get player's weight and height separately by splitting string data\n",
    "import re\n",
    "\n",
    "regular_season_df['weight'] = regular_season_df['height/weight'].apply(\n",
    "    lambda x: int(re.findall('\\d+',x.split('/')[1])[0]) if pd.notnull(x) else x)\n",
    "\n",
    "regular_season_df['height'] = regular_season_df['height/weight'].apply(\n",
    "    lambda x: int(re.findall('\\d+',x.split('/')[0])[0]) if pd.notnull(x) else x)\n",
    "\n",
    "print(regular_season_df['height/weight'][0], regular_season_df['height'][0],\n",
    "      regular_season_df['weight'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/likelion/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/opt/anaconda3/envs/likelion/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate weight per height\n",
    "regular_season_df['weight_per_height'] = regular_season_df['weight'] / \\\n",
    "                                         regular_season_df['height']\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "\n",
    "# Calculate the correlation between regular season and preseason\n",
    "correlation = regular_season_df['weight_per_height'].corr(regular_season_df['OBP'])\n",
    "sns.scatterplot(regular_season_df['weight_per_height'], regular_season_df['OBP'])\n",
    "plt.title(\"'Correlation between weight/height and OPS: \" + str(np.round(correlation, 2)), \\\n",
    "          fontsize=15)\n",
    "plt.ylabel('Regular season OBP',fontsize=12)\n",
    "plt.xlabel('weight/height', fontsize=12)\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "#  Calculate the correlation between regular season and preseason\n",
    "correlation = regular_season_df['weight_per_height'].corr(regular_season_df['SLG'])\n",
    "sns.scatterplot(regular_season_df['weight_per_height'], regular_season_df['SLG'])\n",
    "plt.title(\"'Correlation between weight/height and SLG: \" + str(np.round(correlation, 2)), \\\n",
    "          fontsize=15)\n",
    "plt.ylabel('Regular season SLG', fontsize=12)\n",
    "plt.xlabel('weight/height', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scatter plot is nonlinear, and the data is evenly plotted.\n",
    "\n",
    "In other words, weight per height does not significantly affect neither OPS nor SLG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "내야수(우투우타)    643\n",
       "외야수(우투우타)    230\n",
       "외야수(좌투좌타)    201\n",
       "포수(우투우타)     189\n",
       "외야수(우투좌타)    184\n",
       "내야수(우투좌타)    141\n",
       "내야수(좌투좌타)     36\n",
       "포수(우투좌타)      14\n",
       "외야수(우투양타)      7\n",
       "내야수(우투양타)      7\n",
       "Name: position, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regular_season_df['position'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "내야수(우투우타) 내야수 우타\n"
     ]
    }
   ],
   "source": [
    "# position\n",
    "regular_season_df['pos']=regular_season_df['position'].apply(\n",
    "    lambda x: x.split('(')[0] if pd.notnull(x) else x)\n",
    "\n",
    "# Right side, left side, and both side\n",
    "regular_season_df['hit_way'] = regular_season_df['position'].apply(\n",
    "    lambda x: x[-3:-1] if pd.notnull(x) else x)\n",
    "print(regular_season_df['position'][0], regular_season_df['pos'][0],\n",
    "      regular_season_df['hit_way'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the correlation of position and batter direction\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "ax = sns.boxplot(x='pos', y='OPS', data = regular_season_df, showfliers=False)\n",
    "\n",
    "# Median OPS by position\n",
    "medians = regular_season_df.groupby(['pos'])['OPS'].median().to_dict()\n",
    "\n",
    "# Value counted by position\n",
    "nobs = regular_season_df['pos'].value_counts().to_dict()\n",
    "\n",
    "for key in nobs: nobs[key] = \"n: \" + str(nobs[key])\n",
    "\n",
    "xticks_labels = [item.get_text() for item in ax.get_xticklabels()]\n",
    "\n",
    "for label in ax.get_xticklabels():\n",
    "    ax.text(xticks_labels.index(label.get_text()),\n",
    "            medians[label.get_text()] + 0.03, nobs[label.get_text()],\n",
    "            horizontalalignment='center', size='large', color='w', weight='semibold')\n",
    "\n",
    "ax.set_title('OPS by position')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "ax = sns.boxplot(x='hit_way', y='OPS', data = regular_season_df, showfliers=False)\n",
    "\n",
    "# Median OPS of batting direction\n",
    "medians = regular_season_df.groupby(['hit_way'])['OPS'].median().to_dict()\n",
    "# Value counted by batting direction\n",
    "nobs = regular_season_df['hit_way'].value_counts().to_dict()\n",
    "\n",
    "for key in nobs: nobs[key] = \"n: \" + str(nobs[key])\n",
    "\n",
    "xticks_labels = [item.get_text() for item in ax.get_xticklabels()]\n",
    "\n",
    "for label in ax.get_xticklabels():\n",
    "    ax.text(xticks_labels.index(label.get_text()), medians[label.get_text()] + 0.03,\n",
    "            nobs[label.get_text()], horizontalalignment='center', size='large',\n",
    "            color='w', weight='semibold')\n",
    "ax.set_title('OPS by batter direction')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Player's positions (내야수, 외야수) do not affect player's records, except for 포수.\n",
    "\n",
    "Also, player's batting direction (좌타, 우타) does not have meaningful result as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    쿠바 Ciego de Avila Maximo Gomez Baez(대)\n",
       "1                         광주대성초-광주동성중-광주동성고\n",
       "2                         광주대성초-광주동성중-광주동성고\n",
       "3                         광주대성초-광주동성중-광주동성고\n",
       "4                         광주대성초-광주동성중-광주동성고\n",
       "Name: career, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regular_season_df['career'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['도미니카', '캐나다', '미국', '네덜란드', '쿠바']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foreign_country = regular_season_df['career'].apply(\n",
    "    lambda x: x.replace('-', ' ').split(' ')[0])\n",
    "\n",
    "# Find foreign countries\n",
    "foreign_country_list = list(set(foreign_country.apply(\n",
    "    lambda x: np.nan if '초' in x else x)))\n",
    "\n",
    "foreign_country_list = [x for x in foreign_country_list if str(x) != 'nan']\n",
    "foreign_country_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foreign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>korean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>korean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>korean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>korean</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country\n",
       "0  foreign\n",
       "1   korean\n",
       "2   korean\n",
       "3   korean\n",
       "4   korean"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change oversea players to \"foreign\" and korean players to \"korean\"\n",
    "regular_season_df['country'] = foreign_country\n",
    "regular_season_df['country'] = regular_season_df['country'].apply(\n",
    "    lambda x: x if pd.isnull(x)\n",
    "                 else ('foreign' if x in foreign_country_list else 'korean'))\n",
    "regular_season_df[['country']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = sns.boxplot(x='country', y='OPS', data = regular_season_df, showfliers=False)\n",
    "\n",
    "# Median OPS by korean players and foreign players\n",
    "medians = regular_season_df.groupby(['country'])['OPS'].median().to_dict()\n",
    "# Value counted by korean players and foreign players\n",
    "nobs = regular_season_df['country'].value_counts().to_dict()\n",
    "\n",
    "for key in nobs: nobs[key] = \"n: \" + str(nobs[key])\n",
    "\n",
    "xticks_labels = [item.get_text() for item in ax.get_xticklabels()]\n",
    "\n",
    "for label in ax.get_xticklabels():\n",
    "    ax.text(xticks_labels.index(label.get_text()), medians[label.get_text()] + 0.03, \\\n",
    "            nobs[label.get_text()],\n",
    "            horizontalalignment='center', size='large', color='w', weight='semibold')\n",
    "ax.set_title('국적별 OPS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the number of foreign players are far lower than the number of korean\n",
    "players, foreign players tend to have much higher OPS than korean players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000만원     177\n",
       "6000만원      117\n",
       "3000만원      105\n",
       "9000만원       97\n",
       "5000만원       91\n",
       "8000만원       89\n",
       "30000만원      74\n",
       "4000만원       62\n",
       "12000만원      62\n",
       "18000만원      54\n",
       "7000만원       53\n",
       "11000만원      49\n",
       "13000만원      48\n",
       "20000만원      46\n",
       "25000만원      45\n",
       "15000만원      41\n",
       "16000만원      28\n",
       "14000만원      26\n",
       "28000만원      20\n",
       "43000만원      17\n",
       "45000만원      16\n",
       "27000만원      15\n",
       "21000만원      13\n",
       "23000만원      12\n",
       "33000만원      10\n",
       "6500만원       10\n",
       "100000달러      4\n",
       "300000달러      3\n",
       "50000달러       2\n",
       "17000만원       1\n",
       "Name: starting_salary, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regular_season_df['starting_salary'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/likelion/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/envs/likelion/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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s4ihXikhtv2NWYbV7eMwY82y/bd8Rkb/2K74yHOH2ALfaiVof9vPcijXLx6mNgkpjY34KZT83AfcYYx6zr0hdA3xukP1/KCKHYJ0Q/9AYM9yrVGrsuBurmaaht2BHD71rv4LAycCpWElHuBBJ9LqvXcCU6IPaVxm/CfzIGONUyasBq1jJUyKyFbhNRK62/6BH2GsWHjuI1xWO82CE1+5Fx/EaQ2gmaie187AqgAUYOIq5xP73VBGpo3f9XwbWIvFs+997jTFOzb+VUmpCMsb8W0TOwCqP/7ZYFZRfwlq/PA8rMVmIXbTEGNMlIu8H/gm8LiI/A97FWtv8Bax1X59IQlx7ReSDwMPAahG5wz52tX38Gqy+Yv3dB/wv1sXMm7A+G0+0H3MDVlXLz4vIg8aY/9qPWWffdyXWZ3DQfg+i43nTTtTuBqaLyO+w1mK/Dzgfa+bKj4f7urES6nOwLs4O9fPqa1jTW58TkVuwXtds4PNYn58XOzzmW1gXl5dgVYscFmPMahG5DLuQjf0+bcL6TJ6FdRG5CPhoEpJ8NcakWwJ3GjBLekuidgyy74+BrcaYd+zqSI+KyKkO1QdVenofVsLWebBVD8WqCtlfN1YSmMiUwwx7X3+8HYfABbglwXYG/XgBIyJTBtknXIkyE/AbY7b023421nz6cF+dcEXOcDIXLsRyLdZ7FF3YJbx+z4s1gqkJnFJKRbGTuNlYjbTPxUp23FjJz/PAt7Ead4f3XysiS+z7L8VqK7Abq8n1dcaYoVZ4jBXXk/YFvP/FWrpSbMf0IPBTe711/8c0itVo/Af26/FhrYc7FSuROMOOeR+9CcvVWAnhd7E+U26iXwJnH/s+EVlnxxMukb8eK3H9VZKqHb8IzDyYi43GmA4ROQErSf0E1ijkfqxm6N8zxjQ5POxh4DBg0DX3Q4zj53ahki/T23YiiFVI5n7g//W/wKzGBxnrFb9F5GKsxbW/FJEXjTHv6bf9JKxeKGAVajgnxnF+DtwRLrGqVCwi8lWs9Qp/wCrRuxVrCmK48MhUrNG9zwDXGmN+kMTn/grW9IscY0znEB97JX2nuYSibuEEK3qqyIP9e+DY02dcDCMxVkoppZRSIyfdErhfAX+1p1BmAhcYY+6K8bhlwCvGmJA9J/lB4OShnhSriUlEFmFNPzgKa1pEAdbIVSvWmoQXgLuMMauS/LzLsK7Q/Wao8/vFqnzlAkLGmJgFXKL208IjSimllFJpJt0SuDysaQPTsKbP/cgY82SMx12CNVfajTUt7pvGmDdGJ2qllFJKKaWUSr4xn8AppZRSSimllLKM2TYCSimllFJKKaX60gROKaWUUkoppdLEmGsjUFpaampqalIdhlJKqRH26quv7jfGlKU6jnShn49KKTVxDPYZOeYSuJqaGlatSmphP6WUUmOQiGxPdQzpRD8flVJq4hjsM1KnUCqllFJKKaVUmtAETimllFJKKaXShCZwSimllFJKKZUmNIFTSimllFJKqTShCZxSSimllFJKpQlN4JRSSimllFIqTWgCp5RSSimllFJpQhM4pZRSagSIyHEisivGNreI/EJEXrFvx412fEqlu1DIsKW+jRc372dLfRuhkEl1SEqNijHXyFsppZRKdyJSAFwK7ImxywpgkzHmCyJSDDwsIicbY3pGLUil0lgoZHhi7V4uv381XT0hsrwubv7IEpbXVuJySarDU2pE6QicUkoplXw3AP8LBGJsPx/4OYAx5gDwGLB8dEJTKv1ta2iPJG8AXT0hLr9/Ndsa2lMcmVIjTxM4pZRSKolE5GPAq8aYjYPs5jbG+KO+3wjMcjjWChFZJSKr6uvrkx2qUmlrX0tXJHkL6+oJUdfalaKIlBo9OoVSJeSel3ckvO8Fy6pHMBKllBq7RKQaONMYc+FBPHzAAh5jzO3A7QBLly7VBT5K2Srys8jyuvokcVleF+V5WSmMSqnRoSNwSimlVPKcC8wRkadF5GngEPvrw/vtZ0QkI+r7OcCmUYpRqbRXU+Lj5o8sIctrncqG18DVlPhSHJlSI09H4JRSSqkkMcbcAtwS/l5EXjLGnCgiC0Xk28aY6+xNfwE+C9wqIvnAKcCNox+xUunJ5RKW11Yy77LjqGvtojwvi5oSnxYwUROCJnBKKaXUyCsGaqK+/xlwm4i8BASBK7QCpVJD43IJM8pymVGWm+pQlBpVmsAppZRSI8QYc5T970pgZdT9PcBnUhWXUkqp9KVr4JRSSimllFIqTWgCp5RSSimllFJpQhM4pZRSSimllEoTmsAppZRSSimlVJrQBE4ppZRSSiml0oQmcEoppZRSSimVJjSBU0oppZRSSqk0oQmcUkoppZRSSqUJTeCUUkoppZRSKk1oAqeUUkoppZRSaUITOKWUUkoppZRKE5rAKaWUUkoppVSa0AROKaWUUkoppdKEJnBKKaWUUkoplSY0gVNKKaWUUkqpNKEJnFJKKaWUUkqlCU+qA1Dx3fPyjoT3vWBZ9QhGopRSSimllEolHYFTSimllFJKqTShCZxSSimllFJKpQlN4JRSSimllFIqTWgCp5RSSimllFJpQhM4pZRSSimllEoTWoVSKaWUShIR8QE/BGYBBUAd8CljTEu//e4FqgBj3/WqMeZroxmrUkqp9KQJnFJKKZU8WcBdxpg3AETki8AK4MZ++1UCpxtjukY5PqWUUmlOEzillFIqSYwxDUADgIh4gZnAf1IalFJKqXFF18AppZRSSSQiHxCRlcBmYA7wfIxdfygij4vIv0XkvTGOtUJEVonIqvr6+pEKWSmlVBrRBE4ppZRKImPMQ8aYE4wx1cCdwC8ddvsxcLsx5gzgE8CNIlLscKzbjTFLjTFLy8rKRjZwpZRSaUETOKWUUmqEGGP+AtQ43P+EMeYd++u9wEtO+ymllFL9aQKnlFJKJYmIlIjIKVHfXwg8LyILReTbUfcvExGX/XUVcCiwbtQDVkoplXa0iIlSSimVPB3AR0TkOqAdeAe4HDiCviNstcAPRMQNdAOfN8Z0jnKsSiml0tCIJ3AiUg3cBviAHOBHxpiHRvp5lVJKqdFmJ2ErHDattG/h/e7EWh+nlFJKDclojMD9BPihMeYFESkBXhSRh40xJt4DlVJKKaWUUkr1Go01cG5gp/11G7BFkzellFJKKaWUGrrRGIG7Evg/EXkOOAlrLUAfIrICe8pJdXX1KISklFJKKaWUUulnNEbgLgCewFoHdzXwIxGZHL2D9rlRSimllFJKqfhGNIETkfnATGPMr40xXcaYVcC9wHkj+bxKKaWUUkopNR6N9AhcC7BQRPIARCQT+AiweoSfVymllFJKKaXGnRFdA2eM2WX3wnlCRLqxEsbfGGNWxnmoUkoppZRSSql+RryIid3zTfu+KaWUUkoppdQwjUYRE6WUUkoppZRSSaAJnFJKKaWUUkqlCU3glFJKKaWUUipNaAKnlFJKKaWUUmlCEzillFJKKaWUShOawCmllFJKKaVUmtAETimllFJKKaXShCZwSimllFJKKZUmNIFTSimllFJKqTShCZxSSimllFJKpQlN4JRSSimllFIqTWgCp5RSSimllFJpQhM4pZRSSimllEoTmsAppZRSSimlVJrQBE4ppZRKEhHxichPReTvIvKsiDwoIvkO+7lF5Bci8op9Oy4V8SqllEo/msAppZRSyZMF3GWMeb8x5jjgSWCFw34rgE3GmCOA04Efioh3FONUSimVpjSBU0oppZLEGNNgjHkDwE7IZgLrHHY9H/i5/ZgDwGPA8tGKUymlVPrSBE4ppZRKIhH5gIisBDYDc4DnHXZzG2P8Ud9vBGY5HGuFiKwSkVX19fUjE7BSSqm0ogmcUkoplUTGmIeMMScYY6qBO4FfJvpQh2PdboxZaoxZWlZWltQ4lVJKpSdN4JRSSqkRYoz5C1DjtElEMqK+nwNsGpWglFJKpTVN4JRSSqkkEZESETkl6vsLgedFZKGIfDtq178An7X3yQdOAf4xqsEqpZRKS55UB6CUUkqNIx3AR0TkOqAdeAe4HDiCviNxPwNuE5GXgCBwhTGmZ5RjVUoplYY0gVNKKaWSxBjTiXPbgJX2LbxfD/CZ0YpLKaXU+KEJ3AQVChnW7W1hf1s3uZluaicVkOV1pzospZRSSiml1CA0gUuRe17ekZLnbfMHuP2ZLdzz8g72t/VWsC7M8XL+EdVcdvIscjL010IppZRSSqmxSM/UJ5Cn3qnj639+k/1tfk6ZX8EZCyqZVpJDQ3s3D7++i189s5nnNtXzm4uOoLIgK9XhKqXUqBGRKcAyY8yDUfddCNwAeIHfAl83xoRSE6FSSill0QRuAjDG8NN/b+SWJzcytyKPX190OIdWF/XZ5/TaSp5cv49L73mdD932Ao986RhKcjNTFLFSSo26HwAvhb8RkWqs/m2XAquBq4CvYSV0SimlVMpoG4FxLhQyfOfhNfy//2zkA4dO5qEvHDMgeQs7aV4F9/zPUdS3+fnKfasJhgb0lFVKqfHqaKym22EfAx4wxtxpjHkNuAj4eEoiU0oppaJoAjeOGWP45l/e5I8v7+BzJ8zkpg8vJjtj8EIli6cWcvXZtTy7cT+3Pa09ZZVSE4a/Xxn/jwK/D39jjGkGZNSjUkoppfrRBG4c+8k/3uH+VTu57KRZfGP5XEQSO/c4/4ipvG9hFbc+uYndTZ0jHKVSSo0JLSIyHUBElgFTgSfDG+1m25rAKaWUSjlN4MapP7y0ndue3swFy6r56qlzEk7eAESEb505DwPc+I93Ri5IpZQaO74PPCEiNwAPAjf0K1jyTeCRVASmlFJKRdMEbhx6ZdsBvv/IWk6aV84PzlkwpOQtbEpRDpccM52/vL6Lt3Y2j0CUSik1dhhj/g18CNgNfMEY85PwNhHxAR3AjSkKTymllIrQBG6cqWvp4vN/eI2pxTn830eX4HYd/IyfL7x3JvlZHn75zOYkRqiUUmNWFlZ15iIRKQvfaYxpN8Zca4xpSllkSimllE0TuHEkZAxXPPAmbf4efnXh4RRke4d1vPwsLx87spon1uylqaM7SVEqpdTYIyKfAB4FqoHjgVUiUpvaqJRSSqmBNIEbR17a0sAzG+r5zvsOYU5FXlKOedHRNQC8uKUhKcdTSqkx6nLgZGPMpcaYTwOfBX6Y4piUUkqpATSBGyf2t/l5Ys1eTplfzseXVSftuJMLs1m+oJJXth3AHwgm7bhKKTXGZBhj1oS/McY8AdSkLhyllFLKmSZw44Axhodf34XHLVz3gYUHVbRkMJ98Tw1dPSHe3t2S1OMqpdQYEkrwPqWUUiqlPKkOQA3fq9sb2bK/nXOXTKY8Pyvpxz+ipoiiHC+v72ji0OqipB9fKaXGgGwRWUhvrzdxuA9jzJupCE4ppZQK0wQuzXX1BPnH2r1MK85hac3IJFciwqHVRTy1vo7mzp5hF0dRSqkxaCdwS7/79va7zwAnjVpESimllANN4NLc0+/U0d4d5OJFk3AleepktCVTC3lyfR1vvNvE8XPK4j9AKaXSiDHmveGvRWQqMNf+9h1jzLupiUoppZQaSBO4NHagvZvnNzdw6NRCJhdlj+hzleZmMrUom9WawCmlxim7bcAvgUlAuKBJrYjsAv7HGLMhZcEppZRSNk3g0tiT6/chwGm1lZH77nl5R8KPv2CI1SoXTSnk0bf2sL/NT2lu5pAeq5RSY5mILAD+AlxpjHm437YPAY+IyDnGmHdSEZ9SSikVplUo09T+Nj+v72jiqBklo7Ym7ZBJ+QBajVIpNR79BLiof/IGYIx5EPgMcPNoB6WUUkr1pwlcmnpqfR0et3Dc7NJRe86inAwmFWTx9h5N4JRS4061MealWBuNMc+hfeGUUkqNAZrApaGGNj+r321i2fQS8rJGtyLkIZPy2XGgg5aunlF9XqWUGmEuid9EU/vCKaWUSjlN4NLQC5sbcIlw7KzRG30LO2RSAQDrdBROKTW+/Au4NNZGEfm0vU9cInK0iPxNRJ4SkRdE5HSHfe4VkZUi8rR9u+ngQ1dKKTWRjHgRExFxAdcBxwJB4DFjzPUj/bzjVWd3kFe3N7JoSgH5KejHVpGXSbEvg/V7Wlk2vWTUn18ppUbIt4HHRWQJ1lq39Vh932YBX8FqK/C+BI/lBi4wxrSKSBGwEvhHv30qgdONMV3DD10ppdREMhojcN8EGo0xxxpjTgDuH4XnHLdWbT9AdzDEMSkYfQOrqfecijy27G+jJ6iziZRS44Mxph2rSfdq4C7ggH37E7ARK9nqTPBYzxpjWu1vm4DOBKZnKqWUUgkZ0RE4EckAPggcEb7PGLN1JJ9zPAsZw4tbGphe6mNS4cj2fRvMnIpcXtrSwPaGDmaV56YsDqWUSiZjTAC4xb4Nmz0D5QbgDmOMcdjlhyJyCOAFfmiMecrhGCuAFQDV1UNr/aKUUmp8GukRuOnAm8AXReRJ+3ZK/51EZIWIrBKRVfX19SMcUvraXNdGU0cPy6YXpzSOGaW5uF3Chn2t8XdWSqkJSETKgT8CzxhjbnfY5cfA7caYM4BPADeKyIA/7saY240xS40xS8vKykY2aKWUUmlhpBO4XOBUYJ8x5iTgfOAGe01AhH5AJeaV7Y3kZLg5pCo/pXFkeFxML/FpAqeUUg5EZAbWNMyvG2P+6rSPMeaJcFNwY8xe4CW0TYFSSqkEjHQCtxlYZ4z5M4Axpg54Hpg5ws877rT5A6zb3cKhUwvxuFNfPHR2RS51rX6aOrpTHYpSSo013wcuMcbsDN8hIgtF5NtR3y+zp1giIlXAocC60Q5UKaVU+hnRTMAY0wTsFZHlACKSCxwGvDOSzzserX63iaAxHF6T2umTYbMr8gDYVNeW4kiUUmrMORq4L6pFwNNAMX1H2GqBJ0TkP8CdwOcTLZKilFJqYhvxNgLAZcCtIvJN+/vvRFXnUglavaORyYXZVOZnpToUwGon4Mv0sGV/O0vHSFKplFLJYBcWuQuYHX03YIwxcf/gGWNmxdi0MmqfO7ESN6WUUmpIRjyBs0fhLhzp5xnP6lv97G7u4syFVakOJUJEmFHqY0t9G8YYtEK2Umoc+Q1wvTHmL6kORCmllOov9YupVFxv7mpCgIWTC1IdSh8zyny0dAU40K7r4JRS40qBJm9KKaXGKk3gxjhjDG++28y0Eh8F2d5Uh9PH9FIfAFv2t6c4EqWUSqot9jRKpZRSaswZjTVwahj2tnRR3+bn6FklqQ5lgLLcTPIyPWypb+MIXQenlBo/fo5VhOR3wGtAILzBGPNMyqJSSiml0ARuzFu7uwUBaieNremTYK2Dm17mY+v+dl0Hp5QaTz4KrMKqFHkIVgETAANoAqeUUiqlNIEb49btaaG6JIfczLH5o5pe6uPNnc00tHdTmpuZ6nCUUmrYjDGfsnu0nQRMA7YATxtjTGojU0oppXQN3JjW2NHNnuYuDqnKT3UoMc0szQVgS72ug1NKjQ8iMhlr6uQXgTnAl4H/2g23lVJKqZQam8M6CoD1e1oAmF85dhO4ktwM8rI8bNnfxpHTdR2cUmpcuA242hjzUPgOEfkgcDtwVsqiUkoppdARuDFt3Z5WynIzKc0bu1MTRYTppT621lvr4JRSahyYHp28AdhtBaanKB6llFIqQhO4MaqrJ8iW/W3Mr8pLdShxzSzNpdUfYH+b9oNTSo0LXSIyJfoOEZkENKUmHKWUUqpXwgmciIzdYaBxaHN9GyEDcyrHfgI3vSzcD64txZEopVRSfBd4UkQuFpFlIvJJ4HHgyhTHpZRSSg1pBO5xEfmDiJyrydzI21jXRobHRXVxTqpDiavEl0F+lkcLmSilxgVjzBPAh4AjsJK5Q4HzjDEvpDQwpZRSiiEUMTHGnGRPITkb+IOItAGPAI8ZY/wjFeBEZIxh475WZpb68LjG/izX8Dq4LboOTik1fqwzxnwRQEQ+DEwFNqY2JKWUUmqIa+CMMbuNMb8EbsRqbHop8KCIXD4SwU1UB9q7aezoYXbF2J8+GVZT6qPVH+BAu66DU0qlNxH5BfB5++uvA18Avi0iX05pYEoppRRDGIETkRrgQuB44AXgGmPMFnvbf4GbRyLAiWhDnbWWbHZ5boojSVxNibUObltDR4ojUUqpYTsRqwccwEeBU4BO4BXg/6UoJqWUUgoYWh+464A7gWvNwHlyFyctIsWmfa0U+zIoyU2fpYZleZlke91sa9B1cEqptBc0xhgRmQG0GWOaAEQklNqwlFJKqaFNoXzbGPPv6ORNRL4FYIx5O+mRTVDBkGHL/nZmlqXP6BuAS4RpJTls268JnFIq7b0tIt8FbgHuAxCRWUBrSqNSSimlSCCBE5FqETkB+KCIHB91OxX4+MiHOLHsae7EHwgx0y7Nn05qSnw0tHdT36o1bZRSae0zQAbwb2PMbfZ9xwHXpC4kpZRSypLIFMrpWOsBiux/xb4/BHxuRKKawLbaI1jTS9MwgbNjXrXtAGcsrEpxNEopdXCMMa1Y7QOi77tLRH4E/Cs1USmllFKWRBK4540xK0XkNeAfIx3QRLelvp2y3EzysrypDmXIJhVm4XUL/9UETimVxkSkBPgBcBjWSByAGygEvpWisJRSSikgsTVw/2v/eznwhMNNJUkwZNjW0M70NJw+CeBxuZhSlMOqbY2pDkUppYbjLmAf8CvgKeCrwE7gf1IZlFJKKQUJjMAZY662vzwHcBtjGkXkCGApVlVKlSTh9W8z0nD6ZFhNiY+VG+po8wfIzRxKkVOllBozZhhjzhaRScBxUbNQngL+meLYlFJKTXBDqUJ5D1AuIsXAT7DWxP12JIKaqNJ5/VtYTUkOIQOvbddROKVU2uoUEY8xZjcwS0RcQAeQn8iDReRoEfmbiDwlIi+IyOkO+7hF5Bci8op9Oy7Jr2HUhUKGLfVtvLh5P1vq2wiF+nccgkAgxBvvNvLEmj288W4TgYB2ZhirEvl5KqVSYyhDJCXGmHdE5ALgF8aYP4vI0yMU14S0dX87pbkZabn+Lay6OAeXwCvbDnD8nLJUh6OUUgfj18BHsC5cPoo18hYAHkrw8W7gAmNMq4gUASsZuIZ8BbDJGPMF+8LowyJysjGmJymvYJSFQoYn1u7l8vtX09UTIsvr4uaPLGF5bSUul1X7LBAI8fAbu/jOw2si+1x77gLOXTwZj2co15PVSEvk56mUSp0h/cW0m5r+D71r3/RyTJIYY9hxoINpxek7+gaQ6XVTO6mAV7YdSHUoSil1UIwxtxtj7rG/vh6rfcC1xphvJPj4Z+1KlgBNWCN6/c96zwd+bu9/AHgMWJ6E8FNiW0N75GQfoKsnxOX3r2ZbQ29v0LV7miPJW3if7zy8hrV7mlMSs4otkZ+nUip1hpLAfQW4Cbjdvqp4PPD7EYlqAqpv89PRHWRaSU6qQxm2pTVFvL6jiW6dGqOUSkMi4hWRb4rI/fZdLwMX2GvihnIcF3AjcIcxpv8FT7cxJrpp5kZglsMxVojIKhFZVV9fP5SnH1X7WroiJ/thXT0h6lq7It/vaXbeZ29zF2psSeTnqZRKnYQTOGPMy8DtQK6IrADmMbQpmGoQOxo6AKgeBwnckTXF+AMh3tqlV1WVUmnpJqyWAfMBjDFtwJ+wkrGEiEg58EfgGWPM7Qk+bMCsFns0cKkxZmlZ2didll6Rn0WWt+8pRZbXRXleVuT7qoJsx30qC7JQY0siP0+lVOoknMCJyK+Bs4BuwB91U0mwvaGDnAw3ZbmZqQ5l2JbWFANWQ2+llEpDJxhjvom17g0AY8yTwKJEHmwvN7gL+Lox5q8xdjMikhH1/Rxg00HGm3I1JT5u/siSyEl/eM1UTUnvsoDaqnyuPXdBn32uPXcBtVUFKYlZxZbIz1MplTpDGUGbb4w5dsQimeC2H2inujiHgcsk0k9ZXibTS328su0Anz1hZqrDUUqpoersf4eIJFSB0vZ94BJjzL6oxy8EzjLGXGff9Rfgs8Ct9rFPYQgjfGONyyUsr61k3mXHUdfaRXleFjUlvj4FLzweF+cunszs8lz2NndRWZBFbVWBFjAZgxL5eSqlUmcoCdwBESkyxmh9+CRr8wfY39bN4dOKUx1K0hxRU8Q/1u4jFDL6B18plW7uF5GbALGLjxyGNa3y1gQffzRwX78LclcBNVHf/wy4TUReAoLAFelagTLM5RJmlOUyoyw35j4ej4vFU4tYPHUUA1MHJZGfp1IqNYaSwD0JPCMivwPqwncaY+5OelQTzLsHrPVv04rTf/1b2BE1xdy/aieb6tuYU5GX6nCUUiphxpibReSzwGlYVSS3ATcbY36X4OMHFCOxrYzapwf4zPAiVUopNRENJYF7HfgS1iJrHVJJoncbO3AJTCrMTnUoSXOEvQ7uv1sPaAKnlEo7xphfAb8CEJFKoC21ESmllFKWoUw8fxurb82VxpiVwHQ0kUuKnY2dVORnkTGO1gFMK8mhLC9T+8EppdKOiHzOHoFDRM4GXgPeEJGTUxuZUkopNbQE7g6sksjheX73AN9KekQTjDGGXY2dTCkaP6NvACLCkTXFrNqmSyaVUmnnC1ifcQBfBt4LHA9cF/MRSiml1CgZSgKXZYx5DnvUzRjTDWQM/hAVz4H2bjp7gkwpGj/r38KW1hSxq6mTXU0DCroppdSYZoxpFZEioMgY844xZhf6maeUUmoMGMoauH0ishy70aiInAFsH5GoJpB3G63kZjyNwN3z8g4AGtq6Afh//97AkqlFjvtesKx61OJSSqkENYrIccBy4HEAESnEodG2UkopNdqGksDdAPwUmCsizwNvYhU1UcOwq7EDr1soz8tKdShJV1mQRabHxbaGjpgJnFJKjUFfAH4J7Acutu/7H+D3qQpIKaWUCoubwInI54FLgY3AXiATKAdeMsZoVa5herexk0kF2bjHYa80lwjVxTls29+e6lCUUiphxpi1wHH97rshReEopZRSfQyawInIV4FlwJHRyZqITAF+LSJdxpj7RjjGcSsYMuxp7uTImvHTwLu/mlIf/3p7Hx3+ADmZQxnwVUoppZRSSvUXr4jJ+cBF/UfajDE7gY9gTTNRB2lfSxc9QTMuC5iE1ZT4ANhuNytXSimllFJKHbx4CVynXW1yAGNMKxBIfkgTx65xWMCkvylF2bhF2Nag0yiVUkoppZQarnhz2opE5LQY2wQoSXI8E8q7jR1ke90U+8ZvZWqv28XkomxdB6eUShsicrMx5vJUx6GUUko5iZfA/QV4zyDbH0piLBPOriargbfI+CtgEq2mxMdzm+rpDoTI8Ayl9aBSSqXEOSLyzVgzUNTBC4UM2xra2dfSRUV+FjUlPlzjsIiXUkqNpEETOGPM1aMVyETTHQixr6WLeXPKUh3KiKspyeGZjbCzsYMZZbmpDkcppeK5BnhURK4F1gKRRM4Y05KyqNJcKGR4Yu1eLr9/NV09IbK8Lm7+yBKW11ZqEqeUUkOgwyEpsqe5k5BhXBcwCZtW4kNA18EppdLFVcAs4LfAK8Ab9m116kJKf9sa2iPJG0BXT4jL71+tnw1KKTVEWtc9Rd6dAAVMwrIz3FTkZ7GtQStRKqXGPmPMDKf7ReSo0Y5lPNnX0hVJ3sK6ekLUtXbp7AyllBoCTeBSZGdjBwXZXvKyvKkOZVRMK8nh9XebCIbMuGxarpQaX0TkSOAwIFxlyg1cCVSlLKg0V5GfRZbX1SeJy/K6KM/LSmFUSimVfkZtCqWIHCciu0br+ca6XY2dTC4c/6NvYTUlProDIfY2d6U6FKWUGpSIfBP4NXAy8D7gUODbwDdTGVe6qynxcfNHlpDltU49wmvgwv1ClVJKJWZURuBEpAC4FNgzGs831rV29dDQ3s1h04pSHcqoqSm1PqC3NbQzeQJMG1VKpbWLgaXANOB/jDFfEZFlwNeB36UysHTmcgnLayuZd9lx1LV2UZ6nVSiVUupgjNYI3A3A/6KNvwFYt6cVgEkFE2faSEG2l2JfBlvq21IdilJKxdNujGkD1gPzAIwxL4e/VgfP5RJmlOVy1IxSZpTlavKmlFIHYcRH4ETkY8CrxpiNsfqdicgKYAVAdXX1SIeUcmt2NQMwKcVTKO95eceoPt/Mslze3Knr4JRSY94aETnBGLNSRBrtz6geYH+qA1NKKaVGdARORKqBM40xvxpsP2PM7caYpcaYpWVl478v2trdLeRleiZMAZOwWeW5+AMhdjVqNUql1Jj2ZWC7/fVXgeOAE4FPpCogpZRSKmykR+DOBeaIyNP294fYX3/NGPPqCD/3mLV2dzNVhRNn+mTYzFKrH9ym+jaqddG6UmoMEZGzHe5bZH/5Z0CwqlLuHMIxvwU0GmN+6bDtXqyKlsa+61VjzNeGGrdSSqmJZ0QTOGPMLcAt4e9F5CVjzIkj+ZxjXVdPkI11bRw3uzTVoYy6nEwPkwqz2VTXxknzKlIdjlJKRftAAvsY4JF4O4nIZOBBYCbw3Ri7VQKnG2PSpjRvKGTY1tDOvpYuKvK1AIlSSqWK9oEbZe/sbSUYMkwqmJiVGGeW5fLcpnr8gSCZHneqw1FKKQCMMZ8Kfy0iJ9A7MnYwx9oFHCUiFwPjYrpFKGR4Yu1eLr9/NV09oUgLgOW1lZrEKaXUKBu1PnAAxpijRvP5xqK1u1uA1BcwSZVZ5bmEDGzb357qUJRSKpav2LevYrUOuBv4O3B+kp/nhyLyuIj8W0Tem+RjJ9W2hvZI8gbQ1RPi8vtXs61B/5YrpdRo0xG4UbZmdzP5WR6KciZWAZOwaSU5eFzCpro25lbmpzocpZQawBjTZzqliLiAa5L8ND8Gthpj3hGRSuBRETnVGHOg33OPiSrN+1q6IslbWFdPiLrWLmaU5aYoKqWUmphGdQROWSNwtZMKiNVSYbzzul1MK8lhc71etVVKpQdjTMgY8x3gg0k85hPGmHfsr/cCLwE1DvuNiSrNFflZZHn7njJkeV2U542LGaJKKZVWNIEbRYFgiPV7WqidNLFHnmaV5bK3pYvWrp5Uh6KUUgkRkSLgoBfuishCEfl21PfL7JE9RKQKOBRYN+xAR0hNiY+bP7IkksSF18DVaEVhpZQadTqFchRtrm/HHwixYHIBHd3BVIeTMjPLc+HtfToKp5Qak0Tk/+hbxCQLOBX4yTAOW0zfEbZa4Aci4ga6gc8bYzqHcfwR5XIJy2srmXfZcdS1dlGel5oqlFoJUymlNIEbVWt2NQNQOymfV7Y1pjia1JlUmE22183murZUh6KUUk5W9/u+C7jZGLNpKAcxxvw26uuVwMqo7+8E7jz4EEefyyXMKMtN2Zo3rYSplFIWnUI5itbubiHL65rwC75dIsws87Gpvg1jDrpSt1JKjZQlwOKo2zLg8yLyTRE5MpWBTWRaCVMppSyawI2iNbubmV+Vj1uvFDK7PI/mzh427NNROKXUmOMDZgBvAG8CbcC5gBe4SUT+J3WhTVyDVcJUSqmJRKdQjpJQyLBudwvnHjo51aGMCXMq8wB4cn0dc+2vlVJqjFgGLDPGRDIDEfk91hq45cBzwK9TFNuEFa6EGZ3EaSVMpdREpCNwo2THgQ5a/YEJX4EyrCDby6SCLJ5cvy/VoSilVH++6OQNwBizEZhrjGkHdBpFCmglTKWUsugI3ChZu7sFgNpJBSmOZOyYW5nHyg31NHV0U5iTkepwlFIq7A0R+Yox5qfhO0TkE8AeEckGJm4VqhQaK5UwlVIq1XQEbpSs3d2MxyXMqZzYBUyizavMJ2Rg5Yb6VIeilFLRPg0cJSIbROQxEXkb+DhwIXAm8KOURjeBhSthHjWjlBlluZq8KaUmJB2BGyVrd7cwqzyXTM9B94EddyYXZVPiy+A/6+o4Z4muDVRKjQ3GmCbgfBEpBaYDu40xu+zND6YsMKWUUgodgRs1a3e36PTJflwinDSvnKfeqaM7EIr/AKWUGgUiki8ivwQeMca8AjSLyIMiMi/VsU10oZBhS30bL27ez5b6NkIhbUWjlJp4NIEbBXUtXexv82sBEwfLF1TS2hXgxS0NqQ5FKaXCbsNqIZADYIxpA64FbkhlUBNduJH3mbc8y8d+/TJn3vIsT6zdq0mcUmrC0QRuFPQWMNEErr9jZpXiy3DzxJq9qQ5FKaXCDjHG3AZEMgNjzOtY0ylVimgjb6WUsmgCNwrW7m4G4BBN4AbI8ro5cV45/3p7H0G9iqqUGhu6RKTPGnERqQICKYpnTEj19EVt5K2UUhZN4EbB2t0tTCvJIS/Lm+pQxqTltZXsb/Pz2g6tzK2UGhN+BvwR8IpItYh8CPgncHVqwxqe4SRgY2H6YriRdzRt5K2Umog0gRsFVgETHX2L5b3zysnwuHj0zT2pDkUppTDG/BG4A9gGPAZcBFxqjHkolXENx3ATsLEwfVEbeSvlLNWj42r0aRuBEdbS1cOOAx189IipqQ5lzMrN9HDS3HIefWsP333/Ibi1r49SKsWMMf/EGnWLEJFSY8z+FIU0LLESsHmXHceMsvj9SQebvpjI45NhrDTyDoUM2xra2dfSRUW+NhNXqRW+OBP+/x2+sLG8tlJ/L8cxHYEbYW/bBUx0/dvgzl4yifpWPy9pNUqlVAqIyCwReVFEtonIt6LurxaRy0TkaWBj6iIcnuGuHxsr0xdT3ch7LEwlVSra1v3OF2e27tfiPuOZjsCNMK1AmZiT5pWTm+nhkdW7OWZWaarDmZDueXlHwvtesKx6BCNRKiVuBH4HPADcJiIfAD4NzAXuB64CXkhdeMMTTsCik7ihJGDh6Yv9r/KP9vTFeKNfIz06NtyRTKWSbfuBdseLMzsOtDOzXH8nxytN4EbY2t3NlOVl6iLrOLK8bk6rreCxNXu45txaMj3uVIeklJpY5hhjzgUQka8AG7D6vp1ljEn74ZVEErDBkh+XSzhtfgX3rTiKPc1dVBVkU1uVP6ojYPGmio3GVLKxMJV0rNCppIkZ6ffJl+FxvDiTk6Gn+OOZ/nRH2NtawCRh5yyZzF9e28WT6+o4Y2FVqsNRSk0skRYBxphdItIMXD0ekjeIv34skeTon+v2pXSdTbzRr9EYHRvuSGaixnpypOuuEjMa75Mv082XT57N//vPxshzfPnk2fgy9UL4eKZr4EZQV0+QjXVtmsAl6NhZpVTmZ3H/qndTHYpSSu0dL8lb2GDrx+JVmRwLVSjjreMbjT5xyaqEOVjVwHRYZzcWfh/SwWi8T509QXK8blYcP4MvnTSLFcfPIMfrpqsnmLTnUGOPjsCNoA37WgmGDLWTClIdSlpwu4TzDp/CL57exN7mLioLdNqpUmrUVInIzVHfT+n3PQDGmMtHMaZRE29qYKzt+1pGb+pgvNGv0RgdS0YlzHijMumwzk6nkiZmNN6nEl8md76wlfcvmowIBENw5wtbueviI5NyfDU2aQI3grSAydCdd/gUfvbUJh58bSdffO+sVIejlJo4ruj3/RspiSJF4iU/OTHX2YzeNK3qohxuOG8RG+vaCBlwC8wqz6W6KAcYvUIr4ZHMgz0Bj1U1cO6lxzGz3EqW55Tn8pnjZ9LpD5CT6eHXz2weU8lReW6m4+9DqS8zhVGNPaNxUaGmxMc3ls9PeYEhZRmt6c+awI2gtbubycv0MNX+cFHx1ZT6WDa9mPtXvcvnT5ipc+mVUqPCGPO7VMeQSvGSn+5gkG8tn0dDR3ckeSrOyaA7GIpz5OTZ2dTBnuYubn9mSyTGy0+dw86mDmpKc8dEoZVExKsaOKkwi48fNY0rH3gj8jqvPruWqiHMShnpk8jOQNBx3VVXQKftRRutiwoZHmHF8TMIGXCJ9b0afaO5NlQTuBG0dncL8yeNvQ+Pse6CZdV8+d7VPLOxnhPnlqc6HKWUOih2P7lGY8wvHba5gVuBI+y7LjfGPDuS8QQCIdbuae6T3Hg81lqueFMDs71u/MHQgOQp29s7ApeMpGGwY+xr8XPzvzb0Gbm6+V8bWDylkJrSXEIhw9Mb63hzZzMhA+v2tFDf1sVJcyvG1Odw7NFM65SsqaOHqx5Z2+d1XvXIWu5bcRTTSuIffzROInc2dvL4W3v4yXmL+4wSzij1sWByYVKeYzwYjebz2xra+dI9rw/4fXpsDE25nShGc/qzJnAjJBgyrN/TyvlHTk11KGOaU++xQChEXqaHHz22nt1NvYvPtfeYUiodiMhk4EFgJvDdGLutADYZY74gIsXAwyJysjGmZyRiCgRCPPzGLr7z8JrISf215y7g3MWT+yRxsaYGdnQHHZOnQ6da62xCIcOT7+yLJE9ugYVTCoaUPIVChsfX7OVrf+5NPG768BLOWGAlHu3dAceRq45uq4DojgPtdPh7WDqtiAPtPRT7vDR1+NlxoJ2a0t7XNFgim8j24Saq2R6X4+hVpv0ce2OuN/QndPxtDe1c/8Q6Pn3sDMQO6/on1jGvMi9pJ5FTirI57/ApbKprjfy8zzt8CpOLspNy/PEkEAhxoL2b/W3deF0uphRkk5HEqce6HnHsGM2fhSZwI2Tr/jY6e4Is0AImQ+ZxuThiejFPra+joc1PSa7OqVdKpQ9jzC7gKBG5GIg17+184DR7/wMi8hiwHPjbSMS0dk9zJHkD66TiOw+vYXZ5LounFsV9fJvfOXlq8/cmT7sbO/ts393YOSB5GsyW+rZI8hY+/tf+vJq5FccyqyKPacU+x5Gr6mJrOlpbV4DOHsOVv3+1z9TDtq5Ih4i4iWy87ckY3Wro8EeqBoanvOV43TR2WAlafpbzCF1ugmXhG9r9XHL09D7TXS85ejoN7f6knUTmeN20dwf7jMh++eTZ5Hi1dH207u4gT22soydgaPcH2EMX+9v9vHd2edKSuNFqbaHiG82fhbYRGCGRAiaTtYDJwThyejEi8OKWhlSHopRSI8FtjIkeUtkIjFjlpj3NXRTlZPDF987iSydZt6KcDPY0985yGKy0/bRiH9NKsvs8flpJdiR5amjrdnzeWPc72dbgvDYsXHJ9WnEO1567oE8J/2vPXcC0YmudeXt30HHqYXt377qstXua+efa3fzqwsP56UeXcPuFh/PPtbtZu6c5st0p0Q1vT0ZZ+BJfFne+sJXw8sFw1cBiuwCI1+3i22fM47KTrff5yyfP4ttnzCPDk9gpm9flioy8hYlY9ydLfVt3ZAQRrPfh//1nI/VD+Hknw2C/s6Px+HjeqWvlQHsPVzzwBt/4y1tc8ec3ONDewzt1rUl7jmS1tlDDN5o/Cx2BGyFrd7eQ4XExU4evD0p+lpdFUwpZta2Rk+aWk5Opv6pKqXFvwNmjiKzAmm5JdfXBTyOvKsjiovdMGzBtL1wYI97I0tTCbL544my+90jvyNQ1Zy9gaqE1ZS5gQo4jMgGTeJETX5aHaSXZkXLoAH97Yxc+++//zqYOOvyBPiNXHf5ApIhJfavfMQGsb+vNkzt7enjfosm8ur3RGp3aD+9bNJmuHmvm6p5m5ylQe5u7WDw19hSpobRTqK3K59KTZg8Y5autsmbstPkDdPb0XW/41VPm0O4PxDmyxR8IOv4s/EksMNLS1eP4PrR0jcgMYEfDHQ0djbWCbf4AV/+t70WFq/+2lrsuPiLOIxM3sHhPFrVVBWNq3edEMRprHsP0rHiErN3dzLzKPLxuHeQ8WCfMKWP1u028sKWBU+ZXpDocpZRKJiMiGcaY8JDFHOCtATsZcztwO8DSpUsPenigJxhyHDE5osaaPhlv8f26vS2R5C28/XuPrGFuRS6Lq4sIBI3j8e/45NI+cQy2fsyEQnzu+Flc/fe1kRPqq95fi7GTwH0tfq57fP2A6UlzK/OpKc2lqsB5+lJlfu/0pQy3h91NLQOSm3C16KqCbOdjFMRppzCEqYMej4uzF06ipsTH3pYuqvKzWDipILLOzpfp4f/+3Xe94f/9ewO/vySxvl49MX4Wv7loaZxHJq4g2+v4PhRmexM+xnDXEg63YMRoFJxo7Oh2THQbO5I3UhkKGf65bt+oVD4c75JRiGm4bUYSpQncCDDGsHZ3C2csqEx1KGmtIj+L+VX5vLi5geNmlaY6HKWUOmgishA4yxhznX3XX4DPAreKSD5wCnDjSD3/vhbn0alwYYx4i+93NXU6bt/V3MliiujoDsYoMNI76hNvxCPD4+GXz2zqU3zjl89s4qYPLwGIW8QkJ8PFTz+6mEAQ2v0BfFkePC7wZfReSG3zBxyTm9svPBywRseuPXdBzNGxzp4erjqrNjKqkuV1cdVZtXQFekfH4p0EhkKGf62vi1mspb7VT1FOBh88bErkfXjw1Z3sb0usiEmsn0Vnd/JG4CryM/n+WbV8P+p9+P5ZtZTnJ7ZmPRmjX/taunjP9GIuPnY6jXbRmrue25pwwYjRKDhRnpflOKpcnpe8tf3p0Pg9HYxmC4Bk0ARuBOxu7qKpo4dDtIDJsJ04p4zb9rTw0tYDfOrY6akORymlDlYxUBP1/c+A20TkJSAIXDFSFSgBSvNiNF62i0TFW3xfEuPxJfa6rdyYhTd6TzPinWg2d3bz0aXV3PJk7zTPy06aTUuXNVoRr4hJe3eQpo7AgKSiNLc3cenqCTmetIfv83hcvL+2imnFOext8VOZn8nCqt7RMbe4ePDVHX3K5//uhS18Y/l8ILGTwHjFWirynae7JloIIT/b+WeRl528U76egOG2lb3JtjFw28pNLJ2W2NTAZCQdk4uyWL6wis/2K1ozqTCx96kiP1ZylbyCE92BgOOocncSp7NqFcrk2Lrf+Xdy7qXHMbN87L2POr9vBKzdZS12rp2kBUyGa2pxDnMr8li5oY7mjtGbW6+UUsNljPltuAecMWalMWZF1LYeY8xnjDFHGWOOMca8OJKxZLmtkaLoxfVXnVVLlp2YxFt878twc/XZfR9/9dm1+OzKiDleN9d/aGGfwhvXf2ghvqhKe/tanAup1LVahVTysryR5A2sE6hbntxIboY1LS9eEZNAyESSt/Djv/+3tQSiClOU5WVEHh95b7wuyvIyrGMEQvx97R4uvPO/XPqn17nwzv/y97V7CASsY3Z0BzhpXiVX2kUpvv7AG5w0rzIyCphIkZOtMYq1bLX3MQbHUcJE58929QT56ilz+rxPXz1lDl09Q0saBivwsaOxg+0Nnfz8qU387MlN/PypTWxv6OTdxo6Ejj1Y0pGoupZux6I1dS2JTU+sLsrh0pNmc8dzW/jZk5v4zbNbuPSk2VTb02nDhlPoRMQVSd7CMV7997WIJO/0O3zxJZpWoRy67Qec/1/uOJB4gSIY+cI4YToCNwLW7G7BJTCvMi/VoYwLp9VW8LMnN3Hbys1884x5qQ5HKaXSTn2bnz+9vN0aOeoOkJ3h4TfPbOayk2cD8QshNHX08OdVO/o8/u4XtjD5lLmR52hs7+mztuyby+dRE9V4OlYhlfAatdYYhTFa/dbFux2NHdz73+0DRr8Oqy5iRlkuDW3O642iK2H2BEN8+4x57G/vLbFf4sugxy4JuXZ3jHYLZdZav2yvxzHJvPtT1vq0fS1dzCnP5TPHz+zT4Dq6yEmW1+04QpZlr6OrG2Zyk5vpJtvr6lPsJbvfaGg88UYSfXGakceTjNGv+tYux6mm9W2JvU87Gjscf9bh3ycY/rS61q4Y7Te6EitIk4jwxZf+MWoVyqEZ7u80jO40TE3gRsCbO5uYXZ43pB+6iq2qIJvFUwu56/mtXPSeaUwq1EahSik1FAXZXpq7enhnb2vkZLe5q4cCu+hEvEIIbf4Aq7Y3s2r7632OG+4D19Ed4MdPrO9zMvzjJ9Zz18W9hTOCIeeRpdMOsdaL52U5F8bIy7JibGj3875Fk7nygTciMX5j+TwO2P3NymJM8yyL6iXa0R1E+tXYF5HIWr1dzZ2OCVh4rV/MdXg91vtQmOPh40dN6xPj1WfXUpjTez5QlOPl8lPnRBqjZ3ldXH7qHIrsn0XM6a6+xNZNGSOOxV4SLYIC8aeT+TLdjs3IfQn2qguPfvVfa9h/9GswMSurJpgEJjL1cLhTPbMzYiTrSWzkDZDhkT4Je4Zn7K3ZGusq8jMdL+5UJLiuE6zfl+ufWNdnHe/1T6xjXmWeNvIe64wxvLmzmZPnlac6lHHl1PkVrNvTwg8fXcfPP35YqsNRSqn0IobPnTBrQPENxJreE++EPda6qvws6zQi1khDa1fvtL26VucT5vq2LmaW59LY0cNlJ80esAau0Z4+n+lxcX2/JPH6J9Zz/2ePAiAQDPKDcxbw3b/2JgU/OGcBgVBvDLmZHtr8gQFVKMOjU5MLs/nUsdPZVNcaOYn71LHTmWxfOMzP8jqOHOVmWslXW1eQXzzdtxDLL57exE3nLY7EUJDjYVpxNjeetzhSbMXrggKfFUNFfibXnF3L9x7p/Vldc3YtFQWJnUgm0k4hnsGmk80sz6WzJ+jYjDzRaZrbDziPfh06tSjh9Ub+gHO1zcM+lViiWp4bP1Ee7vqy/EyPY6Kbl8TWSNsa2vnSPa8PeB2PaRGTIZlSmIMvyxu5+JHldfHDDyxkSmHiFxUa2v2O63jDF5mSSRO4JNvZ2MmB9m4WTy1MdSjjSpEvgy+9dxY3/WsD52+s57jZZakOSSml0oa/x/DLlf0qPK7cxA/PXQjEP2HPcLscR43CzaVjTwvsXZsTr1BKUY6X+1bt6FMY475VO7jBTn72NceopNnshyngy/SS4ekeMBLhy/D22X+wEvvBkGFvc9eABC+8zq6jp8exKEW4j1yLv8e5EIu/dw13fXM39W3d1LX6I0liWV4m9c3dTC2C5s4e7l81sFDKvKrElmVUxminUDGE6YnxppOV+DK584WtkUQ23Iz8rosTS57i/b4lor7V7zhammii2hkIOiZXXVEFRuL9zsYTMgZfRt9E15fhxpjkrYvSIibJsf1AB//70Ft9/jb870NvsWRKYcK/kxlul+MU6/tWHJX0eDWBS7I3d1oFTBZPKUxtIOPQ/xw/gwdf28n3/rqWx798XGS9gFJKqcEFQkEuOHJapL9YuLBFIGSdaORmDl5Fst0fINPdd11VptsVaS6dneE8pS47w82W+jb2tXQxqTDLcYQsPG2uIs/t2Cy8PM/6W5+b5WHptAIuOnpGn8Qm1x4F7OwJ8o0H3xrwGn77qd7KiLFHCq3X0eYP8PqOBn514eGR0vR/eGlrpChZttfL1X9/bUBRij98ehlgJT5OJ3DRMQSM1V4hOkm8/NQ5kabn+9v8kUIp0Ulg9Fq+wWTb72v/9zknI/HCGQU5bn70wYVs3d8eSTJrSn0UZFs/i5oSH99YPv+g1135Mpybtg9l6cnUYufR0qlFiS2z2NnYyd0vbu9zweDuF7czvdTHgsmFkdc5nPVlO5u6uG3llsg6vWAIblu5hW+fOZ9DpyX8Ugc13CRTWZJxUaGjO+i4LrMjiS08wjSBS7I3djaR4XYxVwuYJF2W180PP7CQj//mZa5/Yr01/UcppVRcORlex+bQd9vronK8MRIw+0JZbqbXccQl3KOtMNvN5MKsPgne5MIs8rPcnHnLs3T1hLjs5Fn8dfWuPifMP3tqI0umFjK7Io+G9hBet7GmFnYH8GV46AkGONAeoqYUXK4QFx3d94T9oqOn43JZoxmJFDEpzc2I0U7BqkKZ6YX3L57Cq9sbrefYb32f6bXOxrp7nNsQdIerVPpj9GDz957ABYImMpIZ3n7zvzZwp9303JfhXI3zdwlODWzrDuIPBPv8LPyBIG1DOIns9Ieob/UPSDJrinunkw1n3VVlQabjlN7KBKeJgrVkxWm0dHpJYlPeKvIzaezo5udPbYrcZyU+fWMYzuuM9RwVSewDp0VMkiMZRUxirsssSH4yrQlckr3xbhPzJ+VHppWo5DpmVikXH13DXc9v49T5FRwdo8H3PS/vSPiYFyyrTlZ4Sik1JjV1OFd4bLLXl9W3+fnvlr4jT3c9t5X59sXIoAnxueNnsKfFmvbnccHnjp9ByB41auoIcveL26zRsagqlV8/fX7keUOGSOn5aFv3tzO7Io82f4BvPbR2wAlUuMl2pttDU3vfkt5N7d1U2yMupbHWNEUVMcm22yFcFbW+7Oqza8m2i0pkuDzsamwZkBRMtdfA+WL0uwsX78jPjlGIJbt3GmdLnPWCMatxdiXWSqezO8i1j66L+T4motUf4I8vb+8z5faPL2+PVNfeut953dWjCfbMCgSJJG9gj2T+bS2PXnpcwjG2dDk3Za+dlNjrNAbHixbRtjW084O/vx25cBEy8IO/v82M0tyEpifmZbr5yXmL2FTXFrnoMLM8l7ys5M0gcrmE5bWVzLvsOOpauyjPG9g8XsVXnpfp+PswlKbrwRDc+8qOPv9v7n1lR6RQUzJpApdEwZBhza5mzjt8SqpDGde+sXwez2yo5yv3rebvlx2r0wSUUiqObK/bccpaOHEpy83ktAV9myJfdVYtJXbyYwjhcbv7JDY/OGcBxu5OtrfF71ilsr6173ok5yqTVgxdMUa3/OET9ECI9n5TD601S9b27mCA759VO6CRd3ewt2T77qZO/vjSwHYKXzppNgsmQ6s/6JgULLjQGh0TMVx77oIB1RPD58pNnd189ZQ5A6aqNnX2jgLGmq4a6akXo3JhdoKVC3uCzu9juFVCIkLGOK7lC9lrt3YMc7pZvII2iejscR7t7H9fLHtbunj8rT2R34WcDGsN3bSoEbz61i4uOXo6DR29lQkvOXo6+xNcXxbCSqijf2evPruWxH8SiXG5hBlliSWVKjan9YpDcaDDuYhJY4cf0CImY9aW+jbau4Ms0vVvIyo7w80vPnEY5/78eb50z+v88TPL8Lp1xFMppWLJznA5TlnLtouMdPQEHUdEwlMsxbgja6rC27/71zWR/mfleZmOCWJZ1NXrB1/d6VimO8MdHr1yTmzysq1TlUDQOF7dXji5wHp8ZgbbQ519TsACoRB5mRlRx3Ozoa6Ny/70ep/nCBdbafc7j4612426PS7B6+47pc7rFtx2BleQ5eWe//ZdV3XPf7dzY1QVSq9bHK/0e93WMTI9ztNZszyJnUzGbMeQ6R3kUX3lxOl354uVhA6hD9xw122Vx2wbkTHIo3pVF+dwxsKqPmsNv3xy30beXreLjp6BFw08CZ5ztHQGHJuN33XxEXEeqUbb1v3tPLW+jouPnd5nFsLU4hymJ5gYaxGTNLX63SYAFk8tSG0gE8C8ynx+/MFFfOW+1Xzvr2u47gMLB/T2UUopZQmFnKeshU/I45We3xdje509wpaf7eaLJ84aUPo+P6t3NCnDI2R6+47iffWUOTTao1Pt/oDj6FWHnTx1B4NceFQNN/7zncj2K06bS0/QmnrYFXCeOnh3VAERX4ZzWfdw4lGRP3gvudYu50Ip4fVpHd0Bxyvw4dfQG0fsK/0luRmO20sSTEwaO3r41vJ5fUaNinMyaOxMbAomWCOJTsUYmuxj+ANBx5YP3cHE1tnVlPi46cNL+Nqfe9dt3fThoa3bEnCcDpvoqUCWx+042nrq/IrIPm1+52maiU5HPdDuvC6zsSOxgjRq9BTmeBxnIeRnJ54qdXQ7jwqnXRETETka+BbWuGEmcLUx5h8j+Zyp9ObOZnIzPcwo1SHs0XDuoZPZWNfKz5/aTGV+Nl8+ZXb8Byml1ATU3Om8rqrZPiGPlbiU24lLzO12k9umjp5I8hY+9vceWctdFy+N9DurKsxihX1yFN4nupBKiS+DQGmIuy4+gvpWP2V5mbR0dVOcbSUu+VneSPIWfvyN/3yHP3zaenxjjHV+4T5yYLUBqMzvW2ylMj+Ljp7wPiZGUmBNHayLlejaiazL5XJshXCt3a4BrH52/dcnuVxChj2qU13sY1ZFBzVBHwfskQCP27o/EWW5GWxvaB9QgKTMl1gCCNbollMxhnI7iZxUkMN3Hl4z4HWeXptYoZVAIETQhPr8HIImRCAQIiPBaWstXQH+7NBu4bKT5yT0+Jg/yzY/syqstX7DPSGP1Vw+el1mMoRChm0N7exr6aIiX9fAHYxArItclyT2Ow2JrcNNlpEegXMDFxhjWkWkCFgJjOMEromFkwv0P80ouuK0uexr8fN//95AltfFZ0+YmeqQlFJqzCmJUX2x2D6pDxrjODIVste4+WIU/wiPHLV1OZ/otnUFucKeonbZybMc92mwR/ncLqGpI8CX732jz3NMKrA+U2ONZhxot5Kv3Ezn0vS+qKbJOV4vf3x544BiK9884xAA2rtDfRpxG2M14g4nYDETWXuqaFG2l6+cMidSft/jgq+cMofCqCImmR4XkwuzqC7OiSRowVAoMo0zEAixs9HP96LaAFxzzgICUxNLboIh5yqXvx/CiWh30LlJ9uH2MaaX+rjy9PkDRtCmlyaWZL65u5krH3hzwPs46dPZLK0pTugYARPirEWT+1QlPWvRZIKhxHqsZXpcjj/L6CUZsRKwsgRPyFv9zs3p2/yJj4bGEwoZnli7d0AVyuW1lXo+OgTxLs4kot0fcPw7Gm63kkwjmsAZY56N+rYJ6BQRMcnsYDhG+ANB1u1p5VPH1qQ6lAlFRPjxBxfiD4T40ePrIycJSimlevljNC0OT3lr6uhx7Ik10177sf1ABy9v3s+dFx9BQ5ufktxMHnhlB3lZHmonF1KQ47zuqiDbG7kvZJyLmJT4rJPhjn7JE1jJ008+ZK0fy45R5jtc3CM7w8XnT5g1oIhJdlT/s0yPi/OPnNZn3dO15y4gy64c3dkdcKyU2WmPuASCQa7/0EI21/f2R5tR5iMQsrYHQ4YD7d19Rr++fvpcQiW9pz3+QIi9Ld0D1iMW5VjJ9Ju7myPJG9ijmX9dw4xSX0LJTSIjkfHsb3M+mW2IapKd6e27FjDcaiERe2M0n97b0pXwMYqyMninp23A+rToZHkwzV3djslVa1fv9MaungDXnF07YGpwVyCxE/K8TC9Prt87YJRw6bT5Cb/OeLY1tEeSNyvmEJffv5p5lx2nRU2GoCJGsj6UKpQ7m5x7C1YX57Ckuiip8Y7KGjgRcQE3AHeMx+QNYP2eVrqDIW3gnQIet4uffnQJXrfwf//ewO6mThZMLogsKldKqYmu3R90PLGYXW7108zxuh37VYWLnFTmZzFvUgGX/PaVPtPyKu2iE6HQ4CN4AM+8U8dV76/l6r/3HcUzYrci6OxxXD8WXneVE6NZuM8bHpWSSPIG1ons9/+2lj9+ZlkkhrxsD8U+L7dfeDiNHT0U5XitQif2OpfcLOdRvFy7QmSmx01je0+fpOGby+cx2W4z0N4d4IZ/9J3mecM/3on0eAPotKdm9Z+qFS5ssbely3H92b4Ek5tYVSxzhlBRL9ZUsHBV0m0Nzm0EHkswaYhVgGQoJ8sdPUHHojaLpiRWh6DEl8V9q94eMA30lvMPjezjcbu4PzxNM2rE9srT5yX0HMU+Nx9ZWt3ngsE1Z9dSnJu8NgL7Yvy+1CVYKVNZ8rLdjlVs87IT/1lVFWT16RModt/Ayvw07AMnIuXA/wPuNcb8NcY+K4AVANXV6dmT682dTQAJ/+FQyeV2CTeet5gphdnc8uQmXtrSwEeOmBq5oqmUUhNZfrbXMUHLt0crcjKdi3uEm9h2B4OO0/J+axcIae0KOCaIs6JOII+bU84vnxk4PTE8wlaU49zAOrwGpScYcizu0ROy9o9ZiCVqClQoBPtb/eRkeOnqDtLiEjr8PUwvseLM9rqdq3XayY+I8OMn1veJ8cdPrOceO0mM1eOtpat3xCb2VFBr5GdqYbbj+rNwkhiP1y1cc/Yh5GR4afcH8GV56PD3RKpcJqKjO+A4OtVpF2PZF2MELdGkIWSCjlNyQybxYg89waBjwp9ou4S5Zbl87dS5bKpvi0x3/dqpc5lblhfZJ9Pt5uT5lQMqVWYkWBG0oS3ouDb0d586klnlCb/UQcVqHj0SScN4Vt/SjduFtWa3O4Avw0NnT4D9rd2QYBu30twMx2JOpXnJPxcd6SImM4Bbgc8aY3bG2s8YcztwO8DSpUvTcoTujZ3NlOZmJPwHViWfyyVcftpcZlXk8fU/v8GtT27k3CWTta2DUmrCi1mZ0J5WJzhXRgxf0W/qcE5Mwo3A87I9jgliXraHy06eRchAdVG24/TE8DHiNbBu6ezhtpVbIiMNwRDctnILV59tjyImMPLU0tlD0EhkXV44QWuxR/k6u53bKYRH0BranJOvhjYr+cqLUV4/N2od3pSiHMd9wucPORkex/Vnp8zrrY44GGsEoO9rvObs2j4jA/G4RByLsfzgnAXA8NsAeF0e/rNuD7+68HCaOnoozPHyx5e2suL4xIuR5WQMnvDHs6mhjfo2/4Dprpsa2lgwuRCw+gY6XZiYVpLDEuJPiUvGuqp4giEcf19Gonn0eJbldfPdv64d8Ds9lLWj+1r8jgn73ZccybSS9OoD933gEmPMvhF+npR7490mFk0p1FL2Y8DZiyexo6GD+17Zwb2vvMuGfW28b2FVzCao97y8I+FjX7AsPUeIlVITW7zKhM0xkqPrPmCdsMdqPh1OTGKN2HR0ByLP+eWTZzn3J7MbeefG6F+Wa/cvK8zJcJyeFF7z5IsxihjdmyxWv7vw9MX2GFUH2+01cIOt9QMo9HkdYyjK6V2XVVuVzw/OWRDpq5fltZqiL5hkzeB5t6nDMYadTR3Mrswjnp4gMU8iE5Wd4ebjy6ZFRl3Dvy/hz9GaEh83f2TJgMIZibYBcLngjAWTeXV7Y+SCwhkLJuMZQkvXAx3DK9Hf2NHjON311xf1Tnctzs1wvDBRnGBFz3jVW5MhGU3RlTWF2+l9bB5K+40Y60+bhrD+NFEjncAdDdzXL6n5jDFmU4z901JLVw+b6tt4/6JJqQ5F2Yp9Gaw4fib/Wb+Ple/Us7GulXMWT+KQSWNziqsmkUqpkRSvMmF1UY5jcjTVbmrs9QqXnzpnwAm9135MUU6G44jNDectityXneF2PEY4wersdu4D19UTsF9DyHF6Y8hYryknw6ruGD2KOLkwi5zM3qygob2bOeW5fOb4mZGiEr9+ZnNk+mJRtnOCFk4SGzucqwqGe6y1dvUwqV8Mkwqz+lQd9HhcnLNoEtNLfext6aIyP4tFkwrw2NlLvGQ5nlhTSfe3JT7q48twU5Gf2ed1VORnRqqOulzC8tpK5l12HHWtXZTnDa10fVFOBq91NA0Y/To0pzDhGAtjNH4vSLCIib/HOVn39/RO48zwCD/64MJIVVG3QE2pL+HpqG5xbkvhluRNNktGU3RltSlxvsCU2O8TWP9vnNbQDmX9aaJGugrlhCgH+PqOJoyBw6clt8KMGh63SzjtkEoOqcrnL6/t4g8v72DBpHzOWjxpSP8hlVIq3TXEWHfVYCcuXq84rt3IsCsLehDK8vqe0JflZeKxz1IE+NJ7Zw8YVfK6XdzxXO9J+reWz+M3n1zKS1sOWJUL3a5II+/cTA/ZXlef58j2usixExePy8UvV/atUvnLlZu48TxrDV1TR4C7X9w2oEXA16IKTtSU5PCxZX2rUF51Vi3Tiq1E1cQ44cY+4S7K8cZIVK0YXLj4fVQMORlW1cErT++tOhgKGZ7ZXM+bO5sJGdi4r5Wmzm5OmluByyUxi7XEmkXSX8wpmgWJL/Fo81trHsMnoiEDN/9rQ+R1gpXEzSjLPahCGcEQjqNfJ85JfGGY1+X8PnldiQ3jleU5Jz7RPbvcuOgJhPokmtecXYtHEnsOEZfjVNFZZcnrWzvc0VBlKc/LdPx9GkphnYJsr+NFpkQvKgzFqFShHO9e3WZ9EC2pLkx1KMrBlKIcvvjeWTy7sZ4n19exqX4DZy6o4vBpRTrlVSk1IcSq+hfuZ7Wv2c/P+5Xw//nTm5heupjq4lyCBr71l7cGPD48La+zJ0hnd6BP8tXZHaAzapSjqyfEnS9s5Xq7aEnIwJ0vbOXmDy8BrPL61z2+fsBz3GGvP2v1O1epDI9uNbR3s2p7M6u2v97ntR9o651SFwgaxymU9/7PUYCVWPh7gn1eh78nSLguRl6W2zHRDU8D7Q4GHXuThds1AGxvaGfjvoHl72eW5jK9LDdmQZglUwsT+ll7XDieiHqGUMTEH3AuENIdSLzIyGCSMe1vb0tXzJLtiegOBh1HUwOh3rhEnKej3r/iqISe40BHN4dPK+WzdgP78Khy+KJFsmR4+rZ0GMp6R2URGXwdcCJiTdEeyvTlRGkClwSrtjcyvyo/4ekNavS5XcKJc8upnVTAQ6/v5C+v72L1ziY+sGRypCyyUkqNZ04n9eGTk+Yu5+SoOaqAiNMJd7j4RyCEY/L16wt71xNVFWTx0aXVXHzXf/tWNrSnSLZ0OhdKabUrOOZkeByLVoQrYZbFaFZektu7XilW/7Fwif5A0Di+jt/Y66J2NXVRkOPhrouPYH+bn9LcTFq6utnV1MX8KijMzqCjp8WhN1lvDLubOx2LTiyaUsD0slwq8rMc111VJFhVcFeMwhszy3KpnZzQIcjPdC4QEk50hysZ0/6mFOU4vk+JFpMr8WU6jqYuX9Bb/CPemsh48rO83PPfvj+Le/67nRs+tDj+gxM03JYOyrKnuctxHfDsijxqShN7H9v8zn/D0q6R90QQCIZY/W4THz58SqpDUQkoy8vkM8fN4JVtB3hizV5ueXIjp8yv4OiZpdo3Tik1bu1uHny0ItYJ+92fsq4cl+YOPt2sM8aJbmvU2q8PHjbF8Tl+Zz9HSYwELFwwojVGif5wgufL8HDVWbUDpi9FFzGZVJDt+ByVBVbiEOsErM0+ASvLzeSNd5tYu9saYdtY10apLyPSA7a9O+iYnB0W1cQ31uto67KSguFOiSvxZQ6r8AbELhDSlKSRo2RM+4tXDCaRGL6xfP6gMcRKNBNNpivyMx2n1FUUJO/CsfaBS46cDOdKukNZvzat2Of4+1JdnPzprJrADdP6va10dAc5vKY41aGoBLlEWDa9hHmV+fx19S4eX7OXN3c284FDJzNJ20AopYZJRCqA3wLFQAvwSWPM7n773AtUQaTT9avGmK+NVEwlPucKjuGT+sbOwU/YW/3djtPNwtMXS3wxRr98vVM33S5irMOzimv4Mtxcc3btgOmJ4cIZeVnORSvysuxKmD09FPu8ffo4eT1CZ09vEumONb3QvoBXluf8OkrtUTzBeaQxPKWuvds5Oevo7r0CP6nQOYmssk/qh1sgpLPHuSJouBhMImK1Q4hOhodjuK8xLD/bM+DnncwYhptoTi3yMamwvU+MedluphYl74Re+8AlR6wptYn2FQSYVpzDtecu4DsP915UuPbcBZE1tsmkCdwwrdp2AIClWsAk7RRke7nwqGm8tauZv725h188vYlT5ldwwpwyXRunlBqOm4DvG2NeFpGlwI3ABf32qQRON8Z0jUZABdkevnDirAHFOQqyrdOAohznxKUwx0pcMj1ux+lmR9RYU8Fa/d2OFSSbO3sia0rmVOQ5j2bY0+YOdPT0WYdnjLUO74fnLgSsNXWOiYk9nS3T42Ffcxv727sja1hKfRlU5hdGnu/dRufphTNKfdROLsQjLudqm3ZhjN3NzqMde5q7WDQVpsUoIBKu5glwSGW+40neIVW9I0fDKRDidTv/rA6flvi0vUDIOCa6QZP8Vr0He8i1e5r58r2rB7zX9604isVTEzsni/c+DzfRdLmEY2aUsXZPM3uau6gqyKK2qmDIiepgtA9cciQypTaeHY0d3Prkxj7HuPVJawQ+2aOhmsAN06rtjVQVZOnITZoSERZNKWRWeS5/Xb2bf769j52NnXz48ClkepNf9lUpNb6JSCFQYox5GcAYs0pECkSk0BjTlKq4eoKhSPIG1kneVY+s5b4VywCrD5xTchTugZSX5XacCpZrF+/IzvA4rvU5/4hqbvmPNSWpqiBrQFJwzdkLOKTC6m3WHQg6Nvr224UzMr3Oicl1H7ASvNauwKDr16wYsh2nSYVHK3Y1dXLX89v6PMddz29jSlE2iyliSlG242hHeN2V2y18/fS5kQqL4fL40QVEPB4X5y6ezOzyXPY2d1Fpn9R7htIEbRAV+Zmcf0T1gBgrhtB7rKWrJ+Y6umQIhQxPrN07YGRreW1lwsnNnhjJ9N7mLhZPTUqYwPCS6VDI8PTGukjF0XV7Wqhv80cqjiaD9oFLjkSm1Mazr6XL8W/YSExn1QRuGIwxvLz1AMfMLEl1KGqYcjI8nH/EVKYW5/DEmj38YuVmPrFsGmVDKB+rlFLAdKB/r9Mt9v2v97v/hyJyCOAFfmiMeWqkgtrb7NwbbF9zN0yFwmzn8vjhEv0tnUGeWm+VQ29st6Yq/uGlrcwotU5uMlwux8bPwVDv8Moeex3eTR9ezLq9rfYI20bmVuayeGoReXH6MPm8HsfEJNee1hdv/RpAYY7zOrlCn/UcpXnO68dKfdZnQcgYx9GOY2ZZ5wH1bX480rcioEeE/W3+PoUQPB4Xi6cWxUw0QiHDtoZ29rV0UZE/tFGfKYU5TCnK7hPDlKJsphQmPo0rN8t5PVBuZnIubG5raI+cKIP1Pl5+/2rmDaHwRrxkeizYccC54uisstyEC2PEo33gkiMZ03pH82ehCdwwbNnfTn2rn6NmaAI3HogIx84qpaogiz/9dwe3rdzEJ5ZN00XASqmhEHrXtUXrf9+Pga3GmHdEpBJ4VERONcYc6HMwkRXACoDq6uqDDio7w+14YpGZYY36xCuPb0yI986t6lMO/ar31wLW8Ro6uinMstcj+QP4sjx0dPUMqNbX2NFN0MDPnuxNDMIjJo0dPXxr+TwaOrojJfiLczJo7LDbBHT4yc309ElMcjM9HOiw1tCV+DIcm+iWRBXv2N3UxZ9e3s5Pzlsc6RX3m2c2M700h2kluVQWZDr2gasszIw83ilJ3NPUxaIpkOF28aMnBo4C3tev7PxgCVoio1ODPX5HYwc3/vOdyPsQDMGN/3yH2kkFCX+euXBu3J6sUaN9MaqBDmWkIl4yPRbsa/Hzn3V7rd83u3H8717YwmHVRUlL4LQPXPIMZ7QVRvdnoQncMLy4uQFAE7hxZmZZLl987yx++8I27nphGx8+fAqL7ApjSikVxzZgVr/7Ztr3Rxhjnoj6eq+IvATUAAf67Xc7cDvA0qVLD3oBUm6mx3FNU57d/qaxI8D9q3b0SWzufmELU4utJthul4ur/96vv9Hf1/J7u79RcU4GG/e18b2/vd3n+EtriiKJY3ha5u6mjkhc0RUgy3Iz2N7Q3me04vJT51BmJ2DleVnsb/UzpzyvN0n091CWaz2+oyfAV06Zw9b97ZEE8CunzKEjqnhHRX4WG+rauOxPr/eJIXyFvLvH9DZebu+h0Gc1Xj7criJZFaeKZUeMapwdUYlsvAQt3uhUvMfva+miO9D7qyIC3QEztGlcAlmevk3Vs5I0xROSM1IRL5keCwwhPr6spk9fwI8vqyF84SMZklUQRg3faP4sNIEbhpe2NFCZn8W0kuRXl1GpVZSTwWePn8HvX9rOva+8S2tXgGNmlaY6LKXUGGeMOSAiHSJymDHmNRFZBOwHporIF4wx1wGIyDLgFWNMSESqgEOBdSMV19xyHzsbO/uckE8uzGZuuXVlOGYT7HarCuW+Fj9zynP5zPEzIyMJv35mM/tardGvNn/AcTTktk8cPmBa5jlLrGZk4eIdtXbxDo9bIiM+4WPc/K8N3P9Za/RKBBrae/jeI32TRLHzirxML2/vbh0wXW1aVAGReFfI97R0smRqSZ+RxstOms3elk5mVeQxvyKPa85ZwPeiStdfc84C5lfkA4mVnd/W0M71T6zr0zT9+ifWMa8yjxlluXFHp+IleFUFWXz+hBmRYi5ugc+fMGNIVQnzsjwDTjpdLokk/MOVjJGKeMn0WJDl8bC3pXnA7+T00uSOyAx35Eglz2j9LDSBO0jGGF7acoBjZ5VoxcJxKifDwyXHTOf+Ve/y6Ft7aOnq4WNHTk37n3dHd4DnNu7n1R2NvL27hfpWP109QYIhQ0luJjNKfczTxvRKDcdXgDtEJBdoBj4FzMMaYQurBX4gIm6gG/i8MaZzpALyZWdy0twSKguy2NfipyI/k0MqffiyramBVQVZjtMPwyf9NSU5fGzZNK584I0+a8fC5bG7epxHnroDIe54rvfk9aYPL6aqIIvZ5XlU5GeysDI/Uryjoc25lUFDm5VEHmjvcUwSF0y2EsBYPdh+HVXEJN4V8sx+FRzBSjqX2hUc323q5OdP9a0y9/OnNnJ4dREzy3MTSkwa2v1ccvT0PlNFLzl6Ogfa/cywG3kPNjoVL8Ezxnov+icNQ6n26MvwUOzLoDA7IzLa6RLwJelzIRkjFbVVztU8a6OqeaZaa4wLG+HfWTX+DGf96lDoGdpB2lzfzv42Xf823nndLj52ZDV/e2M3z27cz9cfeJMff3AhHnfyppKMhmDIsHJDHQ+8upP/rKvDHwjhdQtzK/OYUpRDdoabdXta2FzXxup3m/C8sZtDqws5eV4F+dneVIevVFoxxuwETu939z5gZdQ+dwJ3jlZMoZDhpW1NkWp4m+paaenqiVTDO6Q8jy+eOJvvPRI1shRVIbKzJxgp/AH2FMq/9U6hjNWGoDjHy2P2SXqJL5N1e1u44Dcv9xm9OnfRJDIy3ORkOPcey7GLlLT7A45VB9vtIiWxmol39luHF+aU0HQHg3x0afWAapzdQesY2w+0O1aZ23GgPVLxL8PTt4hJRr/eZNleN/5gaMBU0Sy78nG8JDBeglfX6o/ZTHx6gqMCDW3dbG/oGFjJMi+LmiRNRhnuSMVIV/NMhlgXNrp6nH8nVXpLRnXVRGkCd5Be3KLr3yYKlwhnL55EbqaHB17dSVNHDz+74NDIh+1Y1tzRw/2r3uV3L25jZ2MnJb4MPnZkNacdUsHhNUVkenpfwz0v78AYw96WLl7acoDXdzTy1q5mzqitYmlNUdqPPCo1kcWrhrdhf1skeQPrJPN7j6yJVIjc2+JcxXJvizWFsiNGj7aO7gBHlJUwoyyXVdsOcOUDb/Z9jr+uYUapj6U1xVTkZzqu0wuXv58ao+rglCKr6mCxz7mKZVFO70WoeCdYeZneyGsIx3jLkxu573+saZy+OEnmtoZ2vnTP6wO2PxZVXbGjO+g4VXTJVCsZjjc6FS/BS6SZeDxdAefRzDs+uTTOI0dXvGqeqTajxOf4+zJdC4yMS8morpooTeAO0jMb6plSlK3r3yYIEeHk+RWcMLeMqx5Zy0V3/pfffHIp+Vljc3RqU10bv31hKw++uovOniBHTi/m22fO55T5FWQMcnVSRKgqyOYDh07muNmlPPT6Lh5avYsdBzo4Z8mktBt5VEpZ9rXEHpWpKc1lT7PztLxwhciK/MwYa7us5MqX6XFsQ3DTh3ubR++NMfVvX4vVy7y62Mfsitw+o1ezK3KpLrZbFXjcjq/hlPkVgLU+zqmZeLiKJcQ/weqIMWLSYY+YxEsyE6mu2NzZ47hPuOceDD46FS/Bm1bsnDSE38dE+HtCMafEqsRNL8vlpg8v4Wt/7k22b/rwEl2rNk4lo7pqojSBOwg9wRAvbm7g7CWTdFRigrnoPTUU5mRw+X2r+eivXuJ3lxwxZnqt9ARD/Ovtfdzz8g6e27SfDLeLs5dM4uKjaw5qvn1pbiafPnY6/1m3j6feqedARzeffE/NoAmgUmpsijcqU1WQ7bwGzi4IsbAyn2vOXjBgiuXCSqt4R5bX7dgHLnqmQlWcAh8ul3DS3ApmlOY6JibvNnY4voadjR3Mrsij2OfluscGNhMP97ID6wTLaRrmvhbrBKs8b/DpifGSzESqK+bH6Hc3lAuCgyV400t9jknDUApnTIsxcjSUJFBZP6czFlQyv0orRE4E2gdujHtteyNt/gDHzy5LdSgqBc5ePImCbC+f+/2rfODnL/CrCw9P6YLkdw90cO8rO7h/1U7qW/1MKsjia6fO4WPLqinNHV4jcpcIpx5SSWluJg+8upPfvrCNi4+uSU7gSqlRE29UZn5FHl987+yY1RUzMz3Mrcrh7k8dyb7WLiryssjMsO4Ha1pgprtv2flMt4vOqLU+tZX5/OCcBXw36jl+cM4CFthJIAyemMSbvriwssBxHd/CqKIWeVkex2mYeVnWMdwuHEfYwpMP4iWZiRQxiTeKlwyZ3r7r8DK9Q0sYppc6v45kV09MB8MtSqEVIicO7QM3xq3cUI/bJRw9hppFqtF1wpwy7v/se1jx+1Wc98sXuP5DiyKlsUdDc2cP/1y7l0fe2M1zm/YjwHvnlvPxo6o5YU457iRf3Tu0ugiXCPevepc/vrydjx9VjVenUyqVNuKdkO9s7owkb9C7Pm3ptCJmlOWy40A7L2xqHJB0FGRlUlOai0uE5zfX8fGjptPY3kOxz8sfXtpKbdTFrb1tXQSCwT6JRSAYZG9bFzVZ8U9uy/OcE5/yPCvxycrycPbCKqaX5kQqbS6sKiArq/dUpyNGpcql06w+b3uau7j7xb6jeHe/uJ1DqwsjjZeHM70R4o/iDVci6/Di0d5iltEsSqHSn/aBG+Oe2VjPYdWFY3b9kxodC6cU8MiXjuWLf3yNL9+7mrW7W/j66XNHLLHpDoRYt7eFp96pY+U79XQHQ1QX53DpSbP56BFTmVyYPSLPG7Z4aiE9wRB/eX0X33loDT/+0EKdQqxUmoh3YhFv7Ua8NXRBE+Dk+VV9+qddfXYtQdM7Arevxc/Vf183ILH43aeOjCRHgxEBX4a7T+Ljy3AT/WcoI8NNSW4mAbstSkZG32JTbX7nqaRtdiXLivwsGju6+1SZHOoUqHgjLi6XcOLscspyM9nT3EWVXT0xWSd5yVqHoyNHo1uUQo0P2gdujKpv9bNmVwtXnDYn1aFMOPe8vCPVIQxQlpfJHz6zjB/8/W1uf2YLz2/azw3nLeaQSfnxH5yA7kCIDftaWbO7mXV7WugJGiryM7nwPdM4a/EkFk8pGNUkamlNMY0dPdy36l1mV+TymeNmjNpzK6WGZ7ATi9Jc5yIlJT5rdKuj27mEf3gNneDmqkf6thm46pG13G23GQgfYzjVEfc0d3Hbyi2RGIIhuG3lFuZU5lFTmpvQaEm8qaTVRTmOvcWqi5JXsCwUMjy9sS7S0mHdnhbq2/yRlg7DNZrrcMa70SxKodRQaAI3RE+u3wfAiXPLUxyJGisyPC5+cO4Cjp1dyv8+9Bbvv/VZPnrEVC47eTZVBUMfFWvt6mH93lbW7WlhU10bgZAhJ8PNodVFLJ5SyLfOmJfSqRunzC/H6xZ+/Ph6lkwtZGlNccpiUUolbrC1PF09Qb5/Vi3ft3u9ZXldfP+s2ki/qrK8TD51TM2AIiVl9jrbulbnNgN1rf7I98U+5ySxOCcjoRgr8rNYODmXI6cXRaZpbtzXFElMEhktiTeVdEdjB7c+2bdR961PWiON4WMEAiHW7mm2R8+yqa3KH1LvsXgtHYZrNNfhjHfJSIZHq7Gzmlg0gRuif67dx+TCbGqTNMKixo/Tays5sqaYW57cyO9f3M6fV+3k9AWVnLVoEsfOLiU30/m/W2N7N+v2tLCtoZ1t+9vZ2diJAQpzvBw5vZj5VfnUlPgi69pS/YdfRLjhw4s5+2fP8cV7XuPRy44bdrEUpdTICoUMT76zLzLq4xZrGnh41Ke1q4fbVm7qk7jctnITP/rAQsBK8Jx6lx32aWuELVaFycr83hPd7mDQsVdcTygUiXGwEbRJeVmcMn9Sn2ma15y9gEn2yXSs0ZJwhUlIbCqpU6Pu8IhLIBDi4Td2DRihO3fx5ISTuHjTUYdL168lz3CTYV1Dp0aKJnBD0O4P8Oym/Xx8WbWu/VGOinwZXHVWLZccM527X9zG/at28uibe3CJVZZ5SlE2uZkeeoKG5s5u3j3QyV67B5JbhMlF2Zw0v5xDqvKpzM8as79nBdlefvHxw/jgL17gsj+9zu8/vSzphVOUUskTb9Sn1R9wTFza/NYIXGOHc++ycI+1hZMKHNsMLJrUW8SkxJfp2Ctu+YJKwBpBu/6JdZHtANc/sY55lXnMKMtlzd4Wx2bjM8qsRuA5MatU9l0HN9hU0ngjLmv3NEeSt3AM33l4DbPLrYbniUhGo+14dP1acgw3GdY1dGqkaAI3BCs31NMdCHF6bWWqQ1Fj3NTiHP73fYdw5fJ5rNrWyItbGtiwt5U9LV3sbe7C43aRn+XhmFmlzCz30djew5Si7LSq7Fg7qYAfnLOAKx98k//71wauOH1uqkNSSsUQb9Qndol+K/mJ17vM5RLK8r3cfuHhNHb0UJTjJWhCfU50a0p8fGP5/JijGQ3tfi45ejoNHd2RUcJLjp7OgXY/M8py4zYC7w4GHRt59wQTbz4db8QlXsPzRCSj0bYaPcNJhnUNnRopmsANwT/X7qUoxxspN6xUPF63i/fMLOE9MwdvOTEWC7Qk4iNHTOWVbQf4+dObeM/MEo6ZVZrqkJRSDuKN+uRlehxL9IenfsfrXbZ2TzOf/8PA0vX3rTgqMjIVbzQj2+umoyc4YJQw3Aw8XiPwstxMqgoyrCSyvYcin5emDv+QpnjHi7GqINt5qmhB4muitMfaxFGRn8W0kmzev2hyZFT5b2/s0oIyatg0gUtQV0+Q/6yvY3ltJZ40GiVRaqRdfU4tr+1o5Cv3reaJLx9Hia6HU2rMmVbsY+m0Ai46egad/gA5mR5+98KWyKhPdyhEbqanT4n+3ExPZH1avN5lQx2ZMmbgfe1+5x5th1VbCWBtZT4/+dAiNtW3RUboZpbl9mkE3t5tuPLB3jVyV51VO+T3arARl9qqfMcqlbVRzcITOb6uUZsYqousVj8jWdVUTUyawCXoqfV1tHYFOHvJpFSHotSYkpPh4daPHca5v3ieK/78Bnd88gg9EVFqjJlamM1HjpjGlQ+80btG7ZwFTLX7R5b6MjGmpc9jjDGU2m0EXC7hpLkVzCjNPeiRqXgFHeKNEu5t62JvS1efEbrLT50TaQRe1+Ln6r/1bWVw9d/WcneCfeYS4fG4OHfxZGaX57K3uYtKu4fbUKpQgq5Rmyh2NHY4rpmMrmqq1MHQBC5BD72+i/K8TI6eqVPElOrvkEn5/O+Z87nqkbXc+fxW7Q+n1Bizbl8L3/trvwIgf13D3Aqr+EZrVw/XPb5+QAJ2/9TCyPfDHZmKV9Ah3tqwfS1+x0qYi6cUUlOaS32bcyuD+jY/yeTxuFg8tSjhNW+pkIzS9Vr+fvh0DZwaKZrAJaCpo5un3qnj4qNrtNKeSiujubbuovdM47lN+7n+ifUsm17CwimJTylSSo2sPU3OJ5J77CmOu2NMgdzT3MUiO1EZ7IQ+kZGpeCez04qdm2hPK7amm8UboSvLc+4zV5Y3saZ1J6N0vZa/Tw5tqq5Gii7mSsDf39xDT9Bw7qGTUx2KUmOWiPCTDy2iNDeTS//0Gm3+5JXEVkoNT3FuBlnevh/5WV4XxT6riXZ4CmT/7eEpkOET+jNveZaP/fplzrzlWZ5Yu5dQqHcxW3hk6vQFVSyeWjRgWmH4ZLb/c4RPZqObaH/ppFl8+tgZ3PrkRnY0dgC91Rv7Pz48QleRn8lVZ9VG9gmvgQsXWpkoYo10bmtoH9VjjIZQyLClvo0XN+9nS31bn9/HsSBc1TT6d1Kbqqtk0AQuAfevepe5FXkcUqXNu5UaTJEvg59+dAk7DnTwvYfXpDocpZStszvAVe/vl9y8v5ZOe/QqPAUyenv0FMhETujjnUzHO5mNbqL9syc38fOnNrG9oZO6VqtNQLh6Y//Hh6s3Ti7Iodjn5cbzFnP9hxZy43mLKfZ5mVwwsQpGDDbSOZrHGGmJXFRItXDBmscuO457VyzjscuO01FMlRQ6hTKO1e828ebOZn5wTu2Ybaqs1FiybEYJl55klRs/dnYpHzxsSqpDGlFDmaZ6wbLqEYxEqdhyMjw8+NpGfnLeYjq7A2RneLj7hS18Y/l8IP4UyFgn9PtarOmPiUy5i1d9Md50s3iP39HYwXWPrYuUbDcG/v7mLmaX5yV1vdFYXxuWjGl76TD1L12aZGvBGjUSNIGL4+4XtpGb6eED4/wkVKlkuvSkWby4uYHvPryGQ6uLtL+RTZM9lSoet/Chw6r7VKG86v21eNy9icdgxTly4jT6TvRkerCT2XhNtOM9PnoEL1oyC0akw9qwRN7H0TjGSNMCIWoi0wRuEPvb/Pz9zT187MipkWamSqn4PG4XPz1/CWf8v2f5/B9e5cHPH41P/w8plTIF2Rk8+NqOASNwPzlvSUKP7w4Gueyk2dzyZG8j78tOmk1P0DqBTsbJ9HD7o43GqFE6jPoko89cOvSqS4dRQqVGip5RDeLuF7fTHQxx4XumpToUpSJGs7LkcEwqzObWjx3KxXf9l6/et5pffuLwMfXhr9REUlPi45JjZx70iEqJL5P7Vu3g08fOiExPvG/VDpYvqASSdzI9nOlmozFqlC6jPsmYtjfWp/6lwyihUiNFE7gYmjt7uOv5rSyvrWRWeV6qw1EqLR0/p4zvvv8Qrv7b29z8rw1ccfrcVIek1IgTkQrgt0Ax0AJ80hizu98+buBW4Aj7rsuNMc+OVEwul3D8rCLuvuRI9rX4qcjP5JDKviMqbZ1dvL23vc/23GwrAasp8XHDhxcSDAr1rX7K8jI5YW5x5GS5psTHjR9ezBV/7p2ieeOHFw84mR7sORLZ3tTZxYao7XMqfRRm966RO6bfa5xbOXDUKN4atsG2JytR7eoK8NaeZva2+KnMz2RhVQFZWck7JQsEQqzd08ye5i6qCrKprcofcrPxkY5xuJIxSjjW1zMqFcvY+Z84xtz53FZauwJcdvLsVIeixhBdwzR0Fx9dw4Z9rfzsqU3MrsjlnCXajkONezcB3zfGvCwiS4EbgQv67bMC2GSM+YKIFAMPi8jJxpiekQiovdPPY2vq+d4jvT3Wrjl7Ae9bUI4vO5O2zi7H7WcuKCM3Owu/P8C2+q4B2xdWBMjO9tLu9xMKGVYcP4OQAZdYJ8ftfj95doIV7znibW/q7OKfDttPW1BGYXYWLZ1d/MNh+/IFZeRn922HEGsNW7ztyRj16eoK8MhbewbEefbCqqQkSIFAiIff2DWgn965iycnnMSNdIzJMpxRwnRYz6hULGPnf+EY0tjezZ326Nshk7R1gDo46TLVcaSJCFefvYDN9e18/YE3qczPYtmMklSHpdSIEJFCoMQY8zKAMWaViBSISKExpilq1/OB0+x9DojIY8By4G8jEdfavW2Rk3Gwpv1975E11JQeyZHTM3l7b/sg27N4a29LjO05HDm9hHV727nywTcHjEzdfYn1eCDuc8TbviHO9vVxtkP8NWzxtidj1OetPc2OcU4vzeGI6cP/27h2T3MkeQsf/zsPr2F2eS6LpxaNiRjHgnRYz6hULNoHzsEN/3yHju4gXz11TqpDUWpcyPC4+OUnDqe6OIdP/24Vb+1sTnVIKWWMoTsQorM7SCAYwpix07dIDdt0YFO/+7bY90dzG2P8Ud9vBGb1P5iIrBCRVSKyqr6+/qCD2tfij9EGwD8q28dODIP3N0uk/1l41OeoGaWRpG4o9iYQ53DsaXZ+DXubE+/hNtIxjgXp0OtOqVh0BK6fN95t4k//3cElx0xnbqWufVNquKJHIj902BRuf2YzH/nVi3zqmBqmFA1ssDvepp529QTZUt/G9gMd7Grq5EBbN82dPUSnbBkeF4XZXirys5hSlM2cijyMMdp7Mj0J4JSRJ5KlD9jHGHM7cDvA0qVLDzrTr8jPdFy7VZGfOSrbx04Mg69hG43KhpUJxDkcVQXZjsevLEj8NYx0jGOBVrFU6UxH4KJ0B0L878NvUZqbyVdO0bVvSiVbQbaXTx87gyyvizue28rW/e2pDmlE+ANBXt/RyF3Pb+XaR9/mDy/v4IXNDfh7QtSU+jhhThnLays5c0Elpx1SwdJpRZT4Mni3sYPH1+zl//1nI8f95Cm+99c1vLylQUfo0ss2Bo6kzbTvj2ZEJCPq+zkMHLlLmtrKXK45ewFZXutjP7ymqbbSmip2SKXPcfshldbaroWV+Y7bF1Zaywzmx3j8/MretWHxniPe9jkxts+xt8+LsX1eVAzhNWzR+0SvYYu3PRkWVhU4v5dVBUk5fm1VPtee2/f41567gNohHH+kYxwLRuNnrdRIkbF2YrB06VKzatWqlDz3tX9/m988t5VffuLwSGnkkaLro1Q6G8oomdPvenNnD3c8t5XGjm4+dNgUlkwtPKhjjwXRr29nYwcvbm5gze5meoKGwhwviyYXMKcyj+qiHDzu+NfMmjt72LC3lVZ/gOc21dPVE2JGmY/zj5jKhw6bQknu+LkCLiKvGmOWpjqOZBORB4DrjDGvicgi4ArgBuAsY8x19j5fBQLGmFtFJB94CFg+WBGT4X4+tnf6Wbu3LVKhsbYyF1927+9TvAqQnZ09vLW3JbJ9YWU+2dneyPbWzi7WRT1+fqUvUsAk0ecYThVKgJbOLtZHbZ9X6YsUMAkLVx6MtYYt3vZkCFd4jLyXI1SFcm9zF5UFWdRWFRx0FcqRinEsGI2ftVIHa7DPSE3gbP9+ex+fuXsVF71nGtecs2DEn08TOJXOhpvAAXT4A/zh5R1sa2jn+NmlnHpIJW6XpF0C9/sXt/P2nhZe2LSf7Qc6yPS4WDSlgCVTi5hWkoPrIKdBXrCsms7uII++tYd7/7uDVdsbyXC7eP+iKj55dA2Lo5LedDWOE7gpwB1ALtAMfAqYB3zcGLPC3scL3AYsAILAFcaYFwc7biovcCqllBpdg31Gjq9LKQfptR2NXPqn11kwOZ9vnzk/1eEoNSHkZHq45Jga/vbmHp7ZuJ/tDR2cd/iUVIeVsOaOHu5btYNfPL2Zpo4ein0ZvG9hFYdPKyLL607Kc2RnuDnv8Cmcd/gUNuxr5Y8vbeeBV3fyl9d3cWh1IRcfXcMZC6rIGOKVdTWyjDE7gdP73b0PWBm1Tw/wmdGMSyml1Pgw4RO4Nbua+dRdr1CRn8ldFx+ZtBMvpVR8HreLDxw6memlPv66ehe3PLmRTK+Li4+ePmaTkk11rdz1/Db+8touOnuCTC/18f6Fk5hXlXfQo22JmFORx9XnLOCK0+fywKs7ufvF7Xz53tVcm7eOTyybxseWTdXF90oppdQEMKETuH+s3ctX7l1NsS+D3396GWV542dtiVLpZMnUwkgSd91j67nn5R1cftpc3rewCvcYWI8QDBlWbqjjrue38ezG/WR4XJy7ZBKfPLqGN94d3ZYIeVlePnXMdD75nhpWbqznt89v4//+vYGfPbWR9y+yYloyDqZXKqWUUsrZhEzg2vwBfvz4Ov7w0g4WTy3k1xcdrleulUqxgmwvF72nhkmFWVz32Dou+9Pr/PRfG7hgWTXnHjqZ0hQU71i3p4WHX9/Fw6t3RRbyf/30uZx/xNRIMZHRTuDCXC7hvXPLee/ccrbUt3H3i9b0yode38XiqYV8dOlUTq+tGFdFT5RSSik1wRK4zu4gf/rvDm5buZn9bX4+fex0vn76XJ02qdQYcuLcco6fXcYTa/dy+zNbuPbRdfz48fWcOLecsxZXcfTM0hEbLfcHgry6rZGnN9Tz1Po6Nta14XEJJ84t43vvn8JptRV4E6gkOdpmlOXy/bNr+dppc3jw1Z3c/dJ2vv3QW3z3r2s4akYxyxdUcczMEqaX+rS3nFJKKZXmRjyBE5EK4LdAMdACfNIYs3uknzeszR/gxc0N/PvtfTz61h7a/AHeM6OEX114OIdVF41WGEqpIXC5hDMXVnHmwio21bXy51d38tBru/j3un0AzC7P5T0zS5hflc+s8lxml+dSmJMR56h9tfkD7GrsZP3eFt7a2cxbu6xbR3cQr1s4oqaYTxw1jfcvqkqbUay8LC8XHzOdTx5dw9t7WnjsrT089tZevvvwGsBqdHz0zFIWTylgXlU+8yvzKcjxxjmqUkoppcaS0RiBuwn4vjHmZRFZCtwIXDBST7Zq2wGeXF/Hhn1tbNjXyruNHRgDORluzlhQxflHTuWImuKRenqlVJLNKs/jW2fM58rT57FmVzMvbG7gxS0NPPDqTjq6g5H98jI9lORmUJKbSVFOBpkeF26X4HEJPSFDhz9Amz9AS1eA3U2dNHf2ttvK9LiYX5XPhw+fwnGzy3jPzBJ8mek7QUFEqJ1UQO2kAq44bS5b97fz4pYGXtjcwLMb63no9V2RfSvyM5lcmM3kohwmFWZR4ssgP8tLQbZ1y8/2kpPhJsPjIsPjItNtfe11S0J97ZRSSimVXCN6hiIihUCJMeZlAGPMKhEpEJFCY0zTSDznS1sauP2ZLUwv9bFwSgEfOmwKR9QUcXhNEZkenSqpVLpyu4TFUwtZPLWQz584k1DIsKupk011bWysa2V3UxcN7d00tPnZ2dhBIGQIhgw9wRBetwtfphtfhofJhVksnVbE5KJsJhdmM6s8l1nluWNyamQyiAgzynKZUZbLx5dNwxhDXaufdXtaWL+3lU11bexq7OSNd5t4Yk0nPcHEeoNOKcrmuW+cNMLRK6WUUqq/EW3kLSKHApcYYy6Nuu9W4E5jzOtR960AVtjfzgXeGbGghqcU2J/qIFJEX/vEpK99Yhqt1z7NGFM2Cs8zLohIPbA91XGkiYn8/zfZ9L1MDn0fk2MivY8xPyNHeo6QAE4ZYp/7jDG3A7ePcCzDJiKrYnVEH+/0tetrn2j0tU/M1z6WabKbOP0dTh59L5ND38fk0PfRMtJzhrYBs/rdN9O+XymllFJKKaXUEIxoAmeMOQB0iMhhACKyCNg/UuvflFJKKaWUUmo8G40ya18B7hCRXKAZ+NQoPOdIGfPTPEeQvvaJSV/7xDSRX7saH/R3OHn0vUwOfR+TQ99HRriIiVJKKaWUUkqp5BmfdbOVUkoppZRSahzSBE4ppZRSSiml0oQmcAkQkQoReVxEXhaRf4nIpFTHNBwi8i0R+VzU9++xX9srIvJrEfFEbVshIq+KyGsi8sWo+90i8gv7Ma+IyHFR28bU+yUiR4vI30TkKRF5QUROt+8f16/bjsknIj8Vkb+LyLMi8qCI5Nvbxv3rDxOR40RkV9T34/61i8g7IvJ01O3CqG3j/vWr8WOifWYl20T+DEwm/TxNvon42Zw0xhi9xbkBfwCW2V8vBe5JdUwH+TomAy8B9cDn7Pu8wDNAif39l4Ev2l8fAjyE1c9PgD8DC+xtnwcut78uto/hHYvvF3AckGd/XQS8ORFetx1HCbA46vsvAldMlNdvx1IA3A+smii/83YcL8W4f0K8fr2l/40J+pk1Au/jhP0MTPL7OOE/T5P8fk7Iz+akvX+pDmCs34BC4PF+9z0KFKY6tmG8povp/TA8C/h21LYM4Cn765uAo6O2HQXcZH+9EsiM2vZN+1hj+v2y/+O/PNFetx2PF7gZeN9Eev1YFatmYyc0E+W1EzuBmxCvX2/j58YE/swagfdywn4GJvl9nJCfp0l+DyfkZ3OybjqFMr7pwKZ+922x7x8PZgHvhL8xxnRj/WEasA3YSG9jdrcxxu+wbcy+XyLiAm4E7mBive4PiMhKYDMwB3ieCfL6ReRjwKvGmI1Rd0+I1w6UisgdIvIfEXlARGrs+yfK61fjk/7+HqSJ+hmYTBP58zSZJvhnc1JoAhefAMbhfqf70pHT6zMJbHNiYjwm3uNGnIiUA38EnjHG3M4Eed0AxpiHjDEnGGOqgTuBXzIBXr+IVANnGmN+1X8T4/y1264Gvm6MORnrpO0u+/6J8vrV+KS/vwdhIn8GJtNE/TxNJv1sTg5N4OLbRm+WHzbTvn882IJ1FQkAEckAAk7b7K/DVzOMvW//bdsYY++XiMzAOnn9ujHmr/bd4/51OzHG/AWoYWK8/nOBOWIX8QAOsf8Vxv9rxxjze2PMAfvr/9/encfYWdVhHP8+tJSiyKaIsgshYllCWQQkSjEQIgFFKASU3YV9lU0gsQrKEi0tWAVll1plaVEQCyK0bBaLQIGWfYcGlBJAgUApj3+cM+nNdWZ6a2c6nbnPJ5nMve95l9+Ze3N/83vfc947DVi2NrXDax8DV96/Cyk5sHe0WT7tSbvRxrm5p6SAW4D6D9A7kjYDkLQJ8JrtN/o0sJ4zGdhV0kr1+cGUSaUAVwLfUwUcSTnjBDAROASg3oVpB+DmJfTvNQo42PZLDcvaod9I+rikHRqe70cZ8jHg+2/7fNtb2R5hewQwq/4exwDve43jCw2PtwNeqE8H/GsfA1revwtvFG2aA3tSO+fTntTuubmnqE7qi25IWoMyZnw54E3gINuv9m1U/z9JBwJDbV9Yn48AzqGc6ZgBHGV7Xm07GtgPmAdcWodeIGlp4JfARrXtBNt/q21L1N9L0lPAS02Lvw2swQDud41pWWAssCnwNmUM+fG23xnor3szSdNsb10fj2CA913SWGADyhyC2cAxtufUthEM8P7HwNFuOauntXMO7EnJp72j3XJzT0kBFxERERER0U9kCGVEREREREQ/kQIuIiIiIiKin0gBFxERERER0U+kgIuIiIiIiOgnUsBFRERERET0EyngInqYpJ0lfasPjz9K0rF9dfyIiOh/JO0g6W5Jj0h6UtIVktbqheOcLmnTHtzf5pJulfRwjfs6SRu3sN0bPRVDxOKWAi6ikvTcImw7RdI6ALZvsn1JT8UVERHRm2rB8wvgANsbUb5TaxqwVW1fR9KURdj/cx2PbZ9p+8FFibdhv6sC1wDft70x5bsvfw98qSf2H7GkGtzXAUREREREn9oRuMH2UwC236N8OfKSblvgAdvTAeqXPl/dtyFF9L5cgYu2Iml5SVfXISKPSbpQ0lKS/g6sVpePlTRE0gWS7pf0hKQxkpaq+5gi6dC6/BpJk4AtgZslXSvpwLr+KEn7ShovaaakGZK+2BDLMZKekfSQpKskzeoi5sNrXDMlTZW0UV1+iKT7JM2SdKOkVZq2W5g+nCjp9Kbt75E0rEdfgIiIWBI9DewtaevmBkkbAjcDW9ZctK+ktSRNkvRAzaUHNKz/vKSTJT0r6SRJjzA/v54s6XJJu9U8tLWk2yU9Kukvklav+1hG0sV1Hw9J+rmk6zuJ+xlgR0k7d9ap7uJsWGc7SXfUIZj3SdqiLh8haUL9n+Hput4zkj7SsO1OklIwxmKXAi7azUnAzDpEZEPgUWCw7c8Ds21vZPsYYCgw1fZmlKEkw4C9GvZzBLCN7T1tfx2YDuxke2TT8X4C/Nr2hsB3gQkqvgLsCWxqexNgMrBac7CS1gcOB7as+zgBWLU2vw1sa3sYcB9wRtPmLfcBuAz4hiTV464BDLXdaVEZEREDh+0/ABcAN0p6UNIpktaubTOBnYDpNUdeBXwSGGV7OLAd8GNJa9bdrQYMB9azfW7Ntx359ZymQ/8M2N/25yh58Ly6/EeUHLcusBkwqIu4HwSOBn6lMv/tjKYTj93F2WF5YGQdgnki8JuGtj2AO2yvZ3sqMImSuzuMBMZ3FltEb0oBF+1mDrCVpPVsz7M91vb7zSvZfsv2tfWK1TrAE5Qk0uE823NaON6NtqfUfd4LvElJbkcAp9l+q7Zd1cX2/wZWBLaXNMj2dNt/7djG9nv1jOULTfEtVB9svwY8AGxf20YCv22hfxERMQDYPhv4FOVE5zDg0c6uWNV177M9Q9JQSk57Dui4cchg4FTbH7Zw2LG2X6yPrwSG1xOJBwCnuPiAMq+tq7gvB9YEDgJWAqZLOrWFODu2v8H2PyWtCLxPuVr40do8GxjXsPqF9ThIGkQpCv/cQj8jelTmwEW7GUP5gL5U0scoSeFc225cqbaNBtYDHgc+AbzcsMojLR7v/qbnc4GlKWcVH284njrb2PYrkkYCRwGjJd0LnG77RUknA7tQhr68DgxZxD5cCBwC3AbsDuzTYh8jImIAqMXSLcAtkqYC5wJXNK8naYPaNpgykmUo83PQu7afafGQjTmyIz+uArxh+93GQy4gbgN3AXepTGuYLOkCYPVu4uzoyy7A8ZQ8+hgwr8YBMKvx/wPbT0r6oI6OWRu4vbOTwBG9LQVctJX6QTwOGCdpZWAi8Cz/O+n5ZGCO7e9AuTU/5UpYh1Y/sD/oYvlsyof/K/X5ut3EPA2YVs/2HQlcJGk0pXj7su25kkYAX16UPti+U9LoOt/hfduNxV5ERAxQkpaz/Z+mxQ/QVOw0uBg40/bkuv2UhraFKWg6y5FzgJUlLW17bl3WaY7sJu5B9ae7OJG0HHAJsIntVyUtQ5mq0F1fLgIOBlYmwyejj2QIZbQVSQfXM2dQxtfPo5x1A5graYV6NcyUs4Ed89D2p4sx+B3bAh3btuIS4AcqNxpZGjitHrM53uGSRkpaqt5d650arymJz/VK20mdxLewfYAyhGU8MKHFfkRERP93nKSzJK0AUH+PYv7Vt7lAR1tzjvwasAXd55dlVG4YtsAcWXPdJOCUuv9VgcPoJEcCe9Sbnaxa1x0KnAX80fYbLcT5Yf2ZV2M7s27TXV+up8wJ3By4c0H9iegNKeCi3bwMXCZpBvAPyhy1W2vbJZQzd2OAn1LG4j9KuZXyCcA+knbvYr9XUj7UW7oble0JlKEjTwB3AzcB/+oi3t2AhyQ9TEkax9Z5cNMpQ0KmAucDK0pqnCC+sH2AMnl7feC6VvoREREDwjhKIXNXzTV3Avcw/2rUbOCFmk/2pYwGOVvl7sm7Aj8Exkj6TBf7n0gZyn9Si/GcSMlfT1NOKl5J5znyGkoenSzpIcoNvd4Evlnbu43T9juU4ZN3UaYVPEeZ0/anrgKrVwVvB25tnn4Rsbgo772IxU/SNsBrtp+sz48DPmv70D6Oa3vgMNt7LXDliIiIXiBpV+Bu269LGkIp4q6z/bs+Dq3jCuRtwJH1Dp0Ri13mwEX0jeeBs1S+020IMINyK+Q+I+lOylcUfLUv44iIiLb3CjBe0qcpo8Um0s2dKBcXSXsDZwPXpniLvpQrcBEREREREf1E5sBFRERERET0EyngIiIiIiIi+okUcBEREREREf1ECriIiIiIiIh+IgVcREREREREP5ECLiIiIiIiop/4L2BhFvtDWGbcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NaN value is 0 and unify unit\n",
    "regular_season_df['starting_salary'] = regular_season_df['starting_salary'].apply(\n",
    "    lambda x: x if pd.isnull(x)\n",
    "                 else(int(re.findall('\\d+',x)[0]) if '만원' in x else np.nan))\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "b=sns.distplot(regular_season_df['starting_salary']. \\\n",
    "               loc[regular_season_df['starting_salary'].notnull()], hist=True)\n",
    "b.set_xlabel(\"starting salary\",fontsize=12)\n",
    "b.set_title('Distribution of starting salary', fontsize=20)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "# Calculate the correlation between regular season and preseason\n",
    "correlation = regular_season_df['starting_salary'].corr(regular_season_df['OPS'])\n",
    "b = sns.scatterplot(regular_season_df['starting_salary'], regular_season_df['OPS'])\n",
    "b.axes.set_title('Correlation: '+str(np.round(correlation,2)), fontsize=20)\n",
    "b.set_ylabel(\"Regular season OPS\",fontsize=12)\n",
    "b.set_xlabel(\"Starting Salary\",fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting salary and the performance do not have high correlation.\n",
    "(correlation: 0.18)\n",
    "\n",
    "\n",
    "## Regular season EDA Conclusion\n",
    "In general, variable that directly affects OPS record is not enough.\n",
    "\n",
    "Although foreign players tend to have higher OPS, there are only a few players\n",
    "exist, and thus it is hard to generalize from such a small data.\n"
   ]
  }
 ],
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