{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change this cell to change season and player name\n",
    "season = ['2017/2018','2018/2019','2019/2020']\n",
    "ssn = '0506'\n",
    "length = int(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import StatsbombPitch as sb\n",
    "#from pandas.io.json import json_normalize\n",
    "import codecs\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import codecs\n",
    "import StatsbombPitch as sb\n",
    "\n",
    "main_df = pd.DataFrame(data=None)\n",
    "path_match = \"/home/kirugulige/Documents/Football-Analytics/open-data-master/data/events/\" #location for play by play events\n",
    "for root, dirs, files in os.walk('/home/kirugulige/Documents/Football-Analytics/open-data-master/data/matches'):\n",
    "     for file in files:\n",
    "        with open(os.path.join(root, file), \"r\") as auto:\n",
    "            with codecs.open(root + str('/') + file,encoding='utf-8') as data_file:\n",
    "                data = json.load(data_file)\n",
    "                df = pd.DataFrame(data=None)\n",
    "                df = pd.json_normalize(data, sep = \"_\")\n",
    "            #for x in df.competition_country_name:\n",
    "            #    if x == 'Spain':\n",
    "            #        print(df.match_id)\n",
    "            for i in range(len(df)):\n",
    "                if df.iloc[i]['competition_country_name'] == 'Spain'  and df.iloc[i]['season_season_name'] in season :\n",
    "                    match_no = df.iloc[i]['match_id'] #gets match with Spain as country\n",
    "                    match_no = str(match_no) # from int to str \n",
    "                    #print('match list \\n',match_no)\n",
    "                    with codecs.open(path_match + match_no + str(r'.json'),encoding=\"utf8\") as event_file: #open the respective file\n",
    "                        df_match = json.load(event_file)\n",
    "                        df_match2 = pd.DataFrame(data=None)\n",
    "                        df_match2 = pd.json_normalize(df_match,sep=\"_\")    \n",
    "                        df_match2 =  df_match2[(df_match2['type_name'] == \"Shot\") & (df_match2['team_name'] == 'Barcelona')]\n",
    "                    main_df = main_df.append(df_match2,ignore_index=True,sort=False)                    \n",
    "#print('total matches ',len(match_no)) \n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#main_df['player_name'].value_counts()\n",
    "df = main_df.loc[:,['location','shot_body_part_id','shot_end_location','shot_outcome_id','player_name','player_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['player_name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['shot_outcome_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>shot_body_part_id</th>\n",
       "      <th>shot_end_location</th>\n",
       "      <th>shot_outcome_id</th>\n",
       "      <th>player_name</th>\n",
       "      <th>player_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[99.1, 44.1]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 36.6, 2.2]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>[94.7, 51.6]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 42.4, 0.4]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>[106.7, 36.8]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 37.9, 0.1]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>[114.1, 41.1]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 39.0, 1.8]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>[109.3, 32.0]</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[120.0, 40.9, 0.2]</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>5503.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location  shot_body_part_id   shot_end_location  shot_outcome_id  \\\n",
       "3    [99.1, 44.1]               38.0  [120.0, 36.6, 2.2]             97.0   \n",
       "48   [94.7, 51.6]               38.0  [120.0, 42.4, 0.4]             97.0   \n",
       "49  [106.7, 36.8]               38.0  [120.0, 37.9, 0.1]             97.0   \n",
       "54  [114.1, 41.1]               38.0  [120.0, 39.0, 1.8]             97.0   \n",
       "62  [109.3, 32.0]               38.0  [120.0, 40.9, 0.2]             97.0   \n",
       "\n",
       "                       player_name  player_id  \n",
       "3   Lionel Andrés Messi Cuccittini     5503.0  \n",
       "48  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "49  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "54  Lionel Andrés Messi Cuccittini     5503.0  \n",
       "62  Lionel Andrés Messi Cuccittini     5503.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_goal = df[(df['shot_outcome_id'] == 97) ]\n",
    "#df_shot = df[(df['shot_outcome_id'] != 97) ]\n",
    "\n",
    "df_goal = df[(df['shot_outcome_id'] == 97) & (df['player_id'] == 5503.0)] #for messi\n",
    "df_shot = df[(df['shot_outcome_id'] != 97) & (df['player_id'] == 5503.0)] #for messi\n",
    "df_goal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 748.8x489.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import StatsbombPitch as sb\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"horizontal\",\"full\")\n",
    "\n",
    "for i in range(len(df_goal)):\n",
    "    xe = df_goal.iloc[i]['location'][0]\n",
    "    ye = df_goal.iloc[i]['location'][1]\n",
    "    g = plt.scatter(xe,ye,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "for i in range(len(df_shot)):\n",
    "    xe = df_shot.iloc[i]['location'][0]\n",
    "    ye = df_shot.iloc[i]['location'][1]\n",
    "    s = plt.scatter(xe,ye,color=\"blue\",edgecolors=\"black\",zorder=5,alpha=0.6) \n",
    "plt.legend((g,s),('goal','shot'),scatterpoints=1,loc=0)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zP8KxwihjPeEne1jzivmGXUcQVisi2AShzag187ScBSM6bvPl3Z/ji7F422dS1kIzxM9KSmaoBf/gJLGffA+4FAwvOOPAKGbwWqJj0w1tsZZc8IKKkJw9gTJuRbkuQDtHSEzOkpjaK1Y2QSiDCDZBWCWUyzDV+x2+GIsvER/vHlvDFz71962adltz/X2/y7XonMeuBUJRxejt96Hchd+3krBm3KC7QU+BCqLM96XF2024euJsPW8rTx5/su7ruDrjxJ87gDJuQ5mD6UfXo9xDRMeebUn/XUFYSYhgEwTqa8bdLpTLMC2WSTn2/Mo+KLWVap+kbQNlOhg+q2FCanBdH3smppZkcg4OrMNeCGB2hle8aDPcoJ0AKrsDhk6gzFRCwJPHn2T00dG6r+MfnCR69AjK1Q9agxNFO0dw9Xa1bRa0ILQTItiEs57VEgydX7IBcjNMi5XgCPo1b73rrQ2dy3IJ4Ox7l52k0ah7Fx202TFJTjLDsPJwmtfxunftxfDuzCt3sfJo9h5m8PQbeNZOkpx9CG2fh+GycfVEUcYkhrc9s6AFoZ0QwSac9TS7WO1yUS7DtJmZlNkspwBu9r3LJDMM7VWEHZuAaw2JNTdjBq9JlcVoM8tQLUK5WS3WChG4ZIrQgQeWiMN2zYIWhHZCBJtw1tOsYrXL7Wb19Bv4zt9H5NAUdsyL6YvTseUYnv50L9EmZFIWWmP4yR7s0HqSzs8x3AHMwOZ0ba/GC+BaKvdXi3+TydxkH974bdie9WR2q93q49UjlBvdYq3UdZZLHArCakMEm3DWU86VWAutcLMmphxix67E1T2Euz9lvYgdG8Xdc+aajcykLLTG07/YhR1ag+F5L8q1EW0fxZq9D1fvuThNsEbVWrm/WlZCfbx6rY3L9QNjucShIKw2RLAJZz3NOIwb5aordYjmP2fH9LK6dguuceYFoK9DsR6lTDAHgbdiz+3Ee07jrVH59y5Vuf81uHq8KKUatgflrJftQD2W4tUSxykIqxkRbMJZTzPcNI1ws5Y6RIElzyVOPYC7fyBHYjazD2mhNWrHAWMA7RwBNoLhR+t+dPII/sHyAnjhsE3k0Pk5oqhzc3FRVKxyv+lTi69plHu7nPWyljEbadGqx1K8WuI4BWE1I4JNEGi8m6YRbtZShyiw5DnDvRF7bh6Xv7fma2ZTTlAUWqMyDEzjeVw9cezQQqrVkTqOe215MbJw2Ca0/xUo83aU5yIc69eE9n8GeLisaMuu3O/EJ4HGubeh8YKmGRateizFzYrjFAShcYhgE4QmUMnhWU4QlTtE858zu7dhTY3iJG6s27VbiaAotEa8z2CoCRzrDWi9Ee08D8Y38Z4zCZR2H0YOnY8yb8dwb009YGzF4XYih8bo3Hyyonk3K9as0YKmHgFYrLdpPZbiZsRxCoLQWESwCUITKHd4ViKIyh2i+c8ZZgeegUMY3mfrdu1WIigKrbHn4kms05rwEzHQ6zE8vRi+FxE7lkBHH8d4VhfMUE1MOSTDHlDfQCf/A+V+GYZrG5gXYccqF0XNykJstKCpVQCW621aq6W4nNBth8LS7TAHQWglItgEoUmUOjwrEUSlD1EKPhfcdjrvEKvtQKtUUBRaY3SsD0//WzA867Gj49jhwyQjUXonYISlQsMMKEIHrgDjreAMofUsOv7V1HDaxPRVJ4qakYXYaMtdrQKwWb1NSwnddkhIaIc5CEKrEcEmCC2gEkFUzlrUzHpW9ViUMmuzo+NYs8dQxq102v+VEXRBoRHdsB7DPYS7twtr+inQlwLDOPGdKPMYHZcdo5w7tdlk3wtr1oO2FNodJzo2AFS/77UKwGLtxeYjLJZqKeYyrWSNhYRuNe7bZlnBJClCEESwCUJLqFQQlbIWNbOeVT0Wpcza7PBhlHEryhxkQU8UFRrutMBzexTwHMnTIXQSMPYSvPxwyYSD5SQlPCZJhq/A7EjtS62WnnIWrWKip1h7sa60WivnMq2FSq2tzbSCSVKEIIhgE4SW0O6FWOuJBcuszUn0gOt8sCN0qj726KVN1Ls6wMoSr+4uhbtrAScxgeGdbxuxlqGRlp5Cgruc6CnXXqwZLtNKf1w00womSRGCIIJNEFrCSijEWqsFLyP25vaeh534VwzPOuLdb2B47iuM6MQSoeEPLBWvybkv0JGYYOJwY1poNYqMpcea/hd8cz8krCMEVAcLHS66GzB+OdFTrr1YJS7Taqn0x0UzrWDt/gNHEJYDEWyC0AIWDtuE//MatPMmDO8AyneE2LEH6yrE2k54+g26rzlO6MB96UbfVzNjzDF0+luEncQSoZFtzVP2LP3heXY5jXPrNQpXZ5z48w/QH/p3RrBT89MRhhcMwgc0gW31/ZNaiegp1V6snMu0Fiq1tjbTCiY9SAVBBJsgLDuJKYfwE5cAf4rhvgScKHYoiBlkVQVR5x+y7jVxPC+2Wd/vLvjazLojoxa7nMZnQjYC/+AkxjP/wQh2zvxGcBh6yoBt9Y1fr+gp5zLNUG1iQsmM5/RY0cgEAeMjxHpuwtXz6oaXBpEepMLZjgg2QVgGsg8rZwG0vQ7DuwWlFJgdwEacaIik2dwg6mIHda2ZheXw9BvY4VO4TzrMPwfuk2BfrkuO3Qy3XqPw9BvMaKvg/MJJh0Cd2az1uv7KuUyhsYkJS8ZyEgzPfp1p+yE8A+62Kg0iCCsdEWyC0GTyD6vk7D+j7XNwrKOYnsHUiww/jjWJt4lB1MUO6vlJh+AYFR/g1VhKahEHzXDrNZKAy2BP0lkyv4CrfuHRiNjGUi5TaGxiQqGxRjTcbE3Tsd1NLaVBBEEojPy0EYQmk31YKWVgePrAvAxtPYC2x9DaRicPooxvpBukN57ElIP9KIuHq5szB7X5TOHH9X6n4DihA1fgxG/D9N+BE7+N0IErSEwtfS3kHujlxs6gLjfYYcKPAYvUn4Xcei3jYs0wKmd+wyi4WNc99Jkm8+/Cd94HcXW/i9ixK4vuby2UsmA2a6zkghfllrIcglAPYmEThCaTH0huBjbjWP+Jo3vB/CI6MQtqgsClB5uSJZoRWVbyFwUP15Cu3AVZraWkFvdmJW69VhLY5iJMkqGnjJQb1GXAxfUnHMDyWKIaacGsdKxSsXnSckoQKkMEmyA0mfzDyvRvwklOY8RGMTrBtTZzSBUWJPUeaNGxAbRzFQF1kD06suRwDSrYoys7wKst3RD0wZ7Y0rGDvtJzLufWq5d6Y/YC21ywDTxTiujYAPHnIHrUi3Jr3L2JmkXHchSIrTQxIUOpvap0rGKxeZ71pwgduFJi2wShAuQbIQhNxj84iWON4iQm0NrBSUygjMfovuY4fdedpHv7dNHDKTHlMLf3fMyxKNZzRzDHosztPb8qF1l8FuxQhAX3bQzjW+JqtF9AxS5IV2ccbZ3KeaxUFmO8q4th5c51Hyo38a6uiuffaDJxdbsjEAd2R8D3iEN03K5qnEXL5fwQOvJqHOt/k1z4JNb8e0u6iUtR7f7Wgn+TSexqg5s7wAvc3AGxqwsL1nJ7VelYmYxhw7sTO3onhncnwW2/JDm7Ni9cINWmLNXySxCEbMTCJghNpp4aUuH9nfSFTjCSzkzcY88xHIpwen8na26IVnR9ZXnRDGH4tjJlrGUofhdhPUFAGZhXa7o2mUQH7IpckNVmMWqzl9n+NzM0O0o4OUPAtYZ47xCYPwIqm3+jaVTQfcZ9mZx7YrEFl7Yj6FgMs7s2N+ZyFYit1IJZyV5VOlahshyhJ6TllCBUigg2QVgGau4aMLXAiM6r+aUthqacisdQbo1OOGg7Au7XETFfBfazxDo/RP+m54DqDt1qxGfKHTyIvfHj+AEbMBITGN7WtRRqVNmQjPvSscIo1wWpBw0/TtIsKTpKubjbrUBss0usSMspQagcEWyC0MaEHbtwzS/HJpAWbOVi3Ny9CTAPomMxnKSJ4bJxdY7h7kpUNIdCMUzd2ysTn+3YUqhYoHzAMJl7tK/i+LOM2DDcAbR9FMxBcKIYLjtHdGTvX9AHEc8mXN3vLBqz1U4FYptdYqUdPx+C0K6IYBOENqZc0H4lBUn9g5MkDzyI2Z19KD6YLiFS+lAsVEdt+OcOk0/24hlwlxU3pSxGjSzWW81YhQLlh5WbeN/bMOKDFQe9Z8SG8l2FE7oP7QyhtYOrcwzHepDOiyeJjuvc/YvBcPwEp31HUnFbbV6PrNoEhWppN4vi2YBk5a5cRLAJQhtjXGmwY69zpq8msMNIPQ6VlYGo51AsVhh1aEFjdd1WkbgpZDFqarX9MmPllw0JmN0k+n4XV/Ca9CsqE1DZ++rYoKyHU1miXWeyRCM/sQrsn8XQzG7s9PXaOWZrOUqstJNFcbUjHSdWNiLYBKGNKXdgVloGotZDMT+GaQT4KKQSCJ67m1jX9TVZh5pdbb/cWJmYvehDG0j678BUZ/ajGgG1dF8zpMYrFgMWTs7gz8y/zWO2ml1iRVg+pOPEykYEmyC0OaUOzEYHbee7S4K+iUWX7AjwP4B7SFuykjMMz36LaTtY9XUaGcxez1jNDnovGi9ndpPUjsRsCcvKctT5E5qH/AshCCuYQjXeHGu0phZXhdpORTyb2GGk6qd9lJRYy24zNaIT+Bdmi44392gf0w9tYO7Rvpy6ZF0dKeGSTV3V9mscq5H7V4iCbbYMSPTpnHpk4o4SloPlqPMnNA+xsAnCCqaRQduF3CWu7ncy6/skN4fnS7j3HALkuh7Lxco0Mpi9nrGaHfRezKW9ZlOUM3XoRKwJy4Nk5a5sRLAJwgqnUHxaLZlgxdwlTrKXjqEojFoVl3goFyvj32QSDmmGnlK5/TjTMWfVZH3WGxjv6TcI7ZvANwUhwHUYQv02fa92V/T+ckgMWHWspCzGlTRXkKzclY4INkFoM+o9BGrNBCsXz1WNJatcrExiyiE+9RsY5wzhc6/Fsk7hTI3imfoldlhXnPWZL+y6X1p9BuPk/RadTkqsbQXeCNwzBdM/sKoSbY0sU3K2spKyGFfSXLORrNyVi9wpQWgjCsWRVduXMtu6VU1/xnLxXNX0oCwXK1NqjtlZn5lYuV12Khs0Z50N6Aka+Z6LPgd2AzHg/wC7gHcBvqnC74mO20RGLSZGLCKjFtFxu2H9Sc92av3stoKVNFdhdSAWNkFoIxqRdl9rJlgl7pJK3XvlYmVKzTFaYdZnI0qD6NkoI+SOcQ/wx6Qsbp15ry9W8y1i0rAyJWczKymLcSXNVVgdiGAThDaiEYdALaUqlpTzuOx4WqjVZoQvJ/5KzdFfYTukWsp55K8zykTBMQ4ChYqVFBOJN9jN7bl5trCSeouupLkKqwNxiQpCC8kvfaGMcN1p99WWqijnhi1VnqMUnv5Uz9G+607SvX0611JXYo4FS2EUiJWrtpxHoXUGKTxGEIj1Lx2jmEjsLDJOo3putiu1fjaK0ewyK41kJc1VWB2IhU0QWkShoGUnNoqjd2HEh3GiG3GsSZTxXQKXngIqz3osZN0CmHu0b0kyQyk3LEyWDayuJtg+28KlzHG0/r84UV+uBa6firI+qy3nUWidUdd6diQn2EVWX1FgwYCBAgkHxQrhOm7Y4VBwLsuZSbjc12p00P1KymJcSXMVVgci2AShRRQTSsQ+gx16APR6DE8vhu9FxI4lcPecOQjLHcz5mWCJKYoeriXjycrE1FXTxzP/gM/EtZ054KuLlau2nEehdbou+DDTR97HG+0EIVKWNdXrZ+DGZMExiolEz1UGsQJzMQNq2TIJlztrsVltjlZSFuNKmquw8hHBJggtophQsqe78PS/ZfEgBHAS5y4ehLUczKUO11KxOOVi6qoJ/K/1gM+I08SkhX9hlnDSyRVnFdY4K7ZO9+AaOrdPZyUYFBZrUIFIzJvL3KN9y9a7cbn7RErQvSAsL/JzQBBaRH7pCzs6TuL5b+HETKy5X2FHxxefyz4IayknkFzwotyFD9dSsTiFynMkw8/jLMD0QxtKBv5XM4diZMSpNfOb9M2HGE06NZfNaFTMkX+TSceQm/XDbjqG3CVrrdWy5lopdq34LA2NM8sgbY4EYXkRC5sgtIjs0heOHcGafhZ4Dcr7ZrTVhTU7CoDp35RzENZi2ShlRSsdi5PjDloiAAAgAElEQVRbniMZfp7k6R/j6rkR038lXvMv2GPPVdT9oNAckuF96FhK/BVy7WbEqXvqbkZ0ouayGRkrnbYmSUbuQbvjeHtpeszRcmYSFt3f6CBOx2vrcpMWcsFLmyNBWF5EsAlCi8gWSomTHRiu9+Dq8QIW1owChkiG70OZvpyDsBYRUO5wLRaLky/mdAxcPTfiDr4YgETf7zI8+WVGtFU28D9/DsnwPpKnD+HqeSemf11BMZERp+HkTM1lM7JdyK7uM2v3Dza/Iv1yippC17JDezCDt9TlJi3lgpege0FYPkSwCUILyQil5ENeTH83SqnUE2umSM57cWK/wli7J+cgrEUElMtoK5XEkC3mph/agOm/cnFcM3gNs9phaPrLhB27aOB/IQuXsry4et6JO3gOUFhMZMRpwLWGPcmZspa8QutY7tiubJqRSVjsXnn6DXzn7yNyaAo75sX0xVHuGK7Aupz3V+uSLbV/qXIthYPuV1qfTUFod0SwCUIbkG81M30KZRzF8M7TvX2a7IOwVhFQzIpWTRJD/jzt0F68M98oK9YKWbg0k2XFREacxrquZ3j2W4zoRFFLXrF1aGsSV3frguMbmUlY6l4BxI5diat7CHd/ap8T098lGd63aBGF6l2ytbjgV0KfTRGUwkpDBJsgtAHVWs0aKQKqsUDlxN3Fxuid+soZd2i6pEc4pLFj6xYPQjumC46fjNyDtkq7drPF6bQdZKhQlmiZdVRynZVC6Zp5LHnODNyIHXoA03tuzS7ZWlzwrbRqVsJKEJSCkI8INkFoA1pZhLOcBSXfEuE7fx/J2RO4T0wyou0liQBDT7pxzr1t8SBMnHoAd/9AjjxQ7rVodxzHKi9Sc8WpSaBIAeFi66j0OiuBcvdqSZ25wDqwQxjenTV/rmpxwbd7yY92F5SCUAgRbILQJrSqCGcpC0ohS0TsWMoSMXPYLpgIEHYS+LMOQsO9EXtuHpe/N2d8by/4BxsjUhNTDs4CJGf/GcPThxnYvJhdW891srs4BBWEdPkCvc2knLWrYJ253kTarZ6huv2t5cdEK/tsVuLqbHdBKQiFEMEmCCuIZsTdlLKglLJEdHVMFGzTFHCtIbs6mtm9DWtqFCdx45LxqxGpxdaeEZXK9wZ04hIcy8CZHcVJTqOMx6q+ToZMF4d32bALuEeX7+bQbEpbu2haRmq1+9eqkh+VujqlcbuwEhHBJggrhGbF3Xj6Dbz9j8NT+wgnHQIuAy7WePpdhJ4obonwFGjTNKwg1nU92V04DbMDz8AhDO+zNVvSSq09W1Qa7lnskB878SJ07DN0XXO87HWKCcFMF4c/Bu6hsm4Ozaactatdymy0ysVfqatTasgJKxERbILQRBppEWtW3E103KbzoGaXrVPCK+mw4yBEg3ZJS0ShNk3OhQo1NY6TmMg5CIPbTuetu7o9KLX2bPeW6VOYvhhaB7GjVCTWignBTBeHgxTv5lBJS6xGU8ra1U69LVsxl0pdndK4XViJiGAThCbRaItYs+JuSvUD9f9maUtERrR1ZUTbsxrvhY9jxxp7EJZaezXurXwBXSyDNdvlewkp62El3RyE1lLNZ6GdxK0gVIJ8QgWhSdTS87MUzerdWKofaMYSYXh3YkfvxPDuzBGcmTiv3REWe3x2HtR4Bk7Rd93JdGHV+v+ZKbX2SnuEZgS0E78N038HTvw2EqcGcOzc+5ERgupygx0mvBF4F/BjwEr/Waybg9BaGtUvVhDaEbGwCUKTaLRFrFlxN10dFEweyFiQSlkiSlnnGhnfVWrtlbq3CrlVi2WwZrt8P5O2Hr6xDbJEhdKIq1NYzYhgE4QmUcw9o8wYc4/2VR3X1qzDSBVIHqjUglTKOtdIj2G5tVfi3iokoEtlsGZcvmwyF9fS2cA1Cc1BXJ3CakUEmyA0iYLNuCOjOLoXpXbUFNfWjMOoUPJApRakcta5RlLv2gsJ6EZksAqCICwHItgEoUkUsgppx8I0/qRkpmepzNJm9T80A4rohvW4F7xYnXH8gcpifuqxzi03xdyq3v4ZjGc10Qj4O8AeMIiGzxTMFReoIAjtgAg2QWgi+Vah6Yc2YHhLt4Eq1dy7GXXY6slmrdQ6V0hoxsZszGdScWFBBfYLoGu7u/CFGkAhAe3ufj5d0uRMUdzhvQ5xDbtbVCg3u7uCiEVBEDKIYBOEZaRc2YFqm3vXU4ctI6JiJwMotR7VHcPwGFWPm4nzcmVE2biXxOTSTgTZgvD0f3yagdgUu0iLIg07DsM8VtNFm6d/enHtPGmwy8nthzriwLtpTaHcTNZttoDcsddh5pkAjt2DthTaHU+32xLXrSCcTYhgE4RlpFymZ3ZgvB3ai2dmN9HkDAHDJBrswd1XWdZpOddptojSehBUL9bs/QCY/k1VZ7NW2okAUkKzMzbNLvJEEfDGZ4DtFV+2JrLnGnX+qmDSxFiBx5ajUG7BrFsHhqYUYc8nUcoAaxTL7CB54LG6rauCIKwcRLAJwjJSLtsxY4Fz4kfombyXEZ1IWVocm+H5aWbMH+Dufc3ieIXqsFXi4swWUaZbo+31YLwVO/ylxabplRSezQjBSjsRZAihCwqlkK4+E7PauL7suQZca9iTnFmSNDGY957lKpRbLOs27MxhmBeizA607UfHvoTZXX+Xi0bTrBhLQRAk31kQaiIx5TD3aB/TD21g7tE+ElNOxe/19Bt0b58uWFg2U/jTM/01RnSC6wE3aVedBt/pb5UtClpJwd7kghflTrdzCkbRzhG07sexQlUVng0duILElJMzXobsTgT5RW+DKPbk7cseUrFs1VBqTsXInmtizc0MK09OUdxhA6ZVbqHcYQUL7r6q73W1dHVQcF86VT8Y/tQDxgU4VrghXS4aSS33QhCEyhHBJghV0syDKWOBC9tzRSwtiaJdBzKUEk8ZskWU6VO410yh1EOgDhUdt5QQrLYTwYKvjx3kdQ8glXhQDbV0k8hZe/AaTg+8nSGzGy9wcwfErzHgJQY3d4AXGDI8zPS+GXffR5suQjLdFXLFopuo93fBiaZe5BzFcAca0uWikTS6s4cgCLmIS1QQqqRZTdgzePoNujrsovXNOraXrkVWST/F/Fg6ZZzCDI4S3HYkLdQqKzybEYLBy45X1Ymg5+WzhMZSMWshDQEgaULsMLhOWjmZkYXcbJC6D9GjXRjeC3B1xTF9KmdOxViydu9G4gObWZMlUhNTDtEN61EnO4io9+Dy96CUavi9XjK3vKzboA8innMxfOeQnH8W5RjAKEZnb0O6XGRohCuzWb1uBUFIIYJNEKpkOQ6meuqbVdLCqpauCaWEYC2dCDz9BmwHo1BmZLqMhhlQS+Lx5h8fJZmYwnAuQ9unsCP/hh2/DO9AD6ZPlbU8efoNfOfvI3JoCjvmxfTF6dhyDE//GYGYuSb6Z2iuw5o5DmumMH2q6SIkv7uCe+oY0bHPgu1ZzBJ1dzUuS7Sesi7ZVNN4XRCE6hHBJghVshwHUz3dByoVY9V2DignBGvtRFCqH2l0w/ol1sz4qZeirafBfxvK04uO/xqSD5OYOQ9vv6us5Skx5RA7diWu7iHc/al1xI6N4u755ZIECsMdRNtToDZihxYwfbFlFyFL9zVDYyJaGmUxblavW0EQUohgE4QqWa6DKd/SUg3NaGHVrF6mpfqRugtYM3VyGngTyhzENMFRl+AkbHT84xjeUNk5lRMo2RZUM7AZa/Y+MN6CY6nFhIzVJEIaZTGWxuuC0FxEsAlClazUg6kRcUr1CMFi1y/WjzTgMliYM7Hn53D19CzGqKGnQG04MwNXELgM0HRvny47p3ICJduCavo3pd4z91kcZz/JORPl1ulA+va/55VQymJc7WdGGq8LQvMQwSYINbDSDqZGxSk14/qF4vWGUcS6bsbleyHW9C9ITF2Lu8+NYU5iuE/iOE+j7XNSpS6cKNrei2cgVNFcyrm0lyQlmD7wPoPbtw6zI/XYcu9fMylmMfasP0XowJUt+8wIgpCLCDahrdl63lbu+8B9rZ7GikZbkJz3ovW5KOVBmTbKUGj9OpRxAjNoNX0OdsiNds4F7aDtKOgtwMtRrnlcPUl++L2HGf7kPSzMh1kANnR18efv2c4NN7wO7cTQyTAQwfCeC2ordrQLdDqezzBQxhZcwZ2oCrpaaQvshQCoflDu1AN6CrPz0sX3awucmBttGyhzA9q5Akjt3+I4y7h/zSZ/vYbv0tTfndW75lay9bytPHn8yVZPQ1hhiGAT2pbRR0dbPYW2I/dgdTB8VkmRsihOtAul1qQEUzIGLguM1Dj1jF/xvG0DsNHJOLAOlA90DMdKoq15AHwJixHSVrb5Od7xdx8G4IYbXgceLzgLGD4LeyGIcgXAmQedBBKY/kjF81RuMDvDOLF40XUqN5juM6IkedoLhnvJQPn71wiadQ9Kkb9eAL1gLNuaG0Ur9q4Wnjz+pPz7JlSNCDahbRnZM8LInpFWT6Mky9mKJ9utmO26KuWimnu0Dyd+G8m5J9D2rShzEG1HUOaTuLrHMbw703FftY2f4f4P3M9LLnoJz59+nt/+X7/N6cjpJfOIP/di4DaU2QcsoO0I8Djec+7CfXKC3bHcTNF/iscY+sT/z10PrMFJTGB4dwLgxG9bTBgAFp/r3t68Fk2ZfWzkdaPjNjovCzi7jEml9+Dll7ycL972RVymi49+/aN8/t8+X9N88mnGmptJPZ9fQVgJyKdYEGpkuVvx1FJJPtP1wAxsRjv3oe0xtPJgJ55f0n6qnkr1b7nrLXxn33dY272Wn370p1y84eKc51PdDlLtr7TWaDuCdo5gdneRXPAW76GZnMlplVVJF4dmUKhbQ/7+VdOuLJquPbc7AnFgdwR8jziE93dWdQ/+4JV/wJf/+MuYhslffOUvGibWKl1zOyGdFoTVjljYBIHaLGXN7niQTy3lFzIB9plsRzv8JXRiGtNzmOC24zlrLDZ+fDZlbSm3N+/9h/fyZ2/4M/7oxj/iO3/5HT745Q/yzUe+CaSTNNZOkpx9CG2fh+GycfVEUcYkhjeOv1imqGFie3cuZuG2qjhrscxgSFsPZ0FHBzEDN+IKrCsboO/sK1x7bmhqgeSmyu7x3936d7zpmjdh2RZv//Tb+fnTP1/ymkJWvEpq+ZVac7taq6TTgrDaEcEmnPXUmkG53AdELWIlOwPQ8F2AMn1pN9HxJWsrNH4yvA8dHcTpeG1Fe/OJBz/BwRMH+dR/+RR3v+NuXnbRy3j/3R9KieEoaP3NRVGTXb/OHijc2cG8WhPYdKZUR6mMxrlH1zbVNZ2fGZyY4kxHBOtXoN+KHVIY7ilMX3HxnphyCMWKWBQdG69V+h53dXTxtT/9Gls2bGEmNMNNf3sTx6ePL5lvtEQHiVJtv7L3bSVlQ0unBWG1I4JNOOup1VK23AdENQV7sw9i5TqOdj6FEw2UtJIUGt8O7cEM3lJ2bxJTDuGD/VgzQe4Z3c83Pnstuz79SS70beGy+Hv5T3UhuruDpLkPO/QA2CHcvYkzc+mnos4O+VYfZYTRtklo/1YM90bM7m048Y6CorLR8YbZnxtt/Rzl2gJOfLEjQka851/XjloEzCR77LklFsWgD2JW8Xv85mvezMff9nE8Lg/7x/fzpk+8iaSTLDi/Uh0k2GS2vNRLo5FOC8JqRwSbcNZTq6VsuQ+ISl1U+Qextk7hJLMP4sJzKzS+2eHBFViX87r8vUlMOcw/fgXJ8K0o8xpQk8wc/zKv+4O/5upLz8fD2+k3nsKa3ouV1JjBa3F3jS4pcltpZ4eM1WcxhjByC6jt2PFx7In7MXw2ZuCqHFFZSJzMPz6K4RtHO4GaBFz258ZwB9D2UTA24SRTIlNbp1BmbMl1E5Pfww72MTz3r4zoRE7tOeNKRTCw9B53re/gi7d/kZde9FIc7XDn7jv53Pc/lzOffGEYjUwU7SDRwfK79JvNSnPhCkK1iGATznpqtZS14oCoxEVVz0GcP34qU7D03kTHBnBit2C4XoYyO4AgmlvRsZ38dO8+OrwPs7b3j1nf90IWogeZmP0Ucbum5Rdcp5U8B53UKHUD2tiKTvw9diiCY+e+VjtXkZx7AscKg9GJE78OM/YUnnU3VWVdWhRGc27s+W/h6r78TAsrZwjlsnASEyTnvoB/YZKw/TwB1xiJNTdjBq/BcA+hrYc5PfB2hmZ2E07OEDC7SfRp1myKpu/DmXtw3aWvYue7d9Lp7eTo1FGG7x5e4gItJEgDxkfY4ySWWPG60op4NcZ8rSQXriBUiwg24aynHktZOx4QjTyIK9mb5IIXx9mA4fKfeaNxATrpgN1BzHoNJ0572NBr0dlxGev0e1Cup1DGXFF3XlXrdExgE6gg6A60TqC4GWU9vPja+CmNvRAD3gnqAtBHQH8L25pFKaNiUZstjFw9A1jTFonpPbj7LsQMdmCH/hrDE0LHT9MfnmeXk44fS84wPHkvpwGz+yoSzx8EwHJfg+E1iHifoefyp8n+/JzXdx6fe/fnuOyCy9BoPv/Dz/ORr3+k4LwKifRYz00Mz36dEZ0bF6guT10j/4eKHdMkT8+htcnco31NLVEjCEL1iGATznpWmyvF1RnHmt+Hjs3iWGEMdwDl68XdVX1sXSV74+qMY8+fBOcSMNPmG+coyjBQJqmSEMkwx6eP0ekx6XQn2XjuuXz/rhHu+vZdS1x71azTiZ9CGQG0o9FODJxfA26cuIVyayAlsuyFHtBvQ5kvBp1E2x4w3ozS+xbHc2JjuE9MMnHYLhpDly2MDED1GyRPv5jk6b/HtyFC1+WpvYmMWuxycuPHRnSCoZndxNa4wFiLHb8O7A1gnsQ0n1u8hs/l46533sWNV9yIQvHsqWe57R9u46mTTxXdi0Ii3dXzaqbth7jZmi4YF5gtxh07JT7hF7j7bikaB7gSKRe7uJy1FAWhHkSwCQLtaSmrFVfvKaJHD6HM28G8CMf6NTr2GfwbTwGVlXTIptze+AcnsWYfIBn2o3Qqhk0nv4zR+Qye3hBO4iA6FsNJmkQsm6g5xuaeGC7TxR0338G7b3g3d+y6g+/v/35V88oIDuW6BZxpsMaBb4L5OpSp0FaQxNQJomMDKLMfndSgQ6ACoBLgWOBOWR3t0F56p77CiLaLZlTCUmFk+hTGum7sqJ0Tk1eqrpya2wO8C9P7inQv1C04cS/hgx/lzk+8iz989R/icXk4vXCaO756B9/Z952ye1HMre8ZcNOx3V0wLjBbjCdOBjBcW1Lu3XQJmJUcz5ahXGLFaku8EFY3ItgEYZWRnF2Lq+d6dCyCkzyA4bZRwetJzj4FNP7w9fQbdP3GLwkfPI41EwTt4OmfI7A11e0gdOBBzO5sl+qDPKl/yeUfupxP/ZdPccO2G/j8f/s8U/NT7PzXndzzo3sqvm5wW+q69smvgnk5yvM7GEYH2rkPM3gt0bFpkgteDN85OInTYP8a7Vhgakg+jTIgPvl9/PP/wghW0YzKDJXGOwZcBnuSztK6csokTAyMXpzEvWhnCsPsp6vzlQT1Fq5Zey0f+PP/w8O/eIxjC4cqtvbU6tbPiPHkQ15M/00odea1Kz2eDcrHc662xAthdSOCTRBWGckFL67AOlQwtviY1uuaevh6+g3WvHwGmMl6NHX4F3OphmNh3vXZd7FxYCN37riTl170Uv7q9/6KPxv6Mx746QN87OsfI5aMFbxe/nVPfV+BFUVb96LMAK7uzRi+C0gufA9XZxxt96Gtb6E8b03H1x3CUbtwnCgq8ToW+FrRjEorK56rUmEU7exleD6UmwWqPES7guhYFKyfosw/pMO3FaXGSUQ/R8iZ5D0fupexmctwzGG0Vbm1p5TruhKX32qtYVYunnM1Jl4IqxcRbILQJjQqlqbdDt9yLtUjk0fY8akddHV08eHf+zA3XXUTt153K297+dt47NnH+Nz3P8ePnvhRyWt4e8GJv2hJ38vMPiYPPIYZvAon9kV0YhbUBK6e53D53o/h6cY8soY9yZmlFjHXGqz4bTnCqZJ4R8+AmxnzJobmf5zKAnWtIdZ1Pd6+nxKY7SUy/0q8ngsxDA+2s45E8iqiyQeJT12C4RlAUb21p9A+V+ryW2k1zCr9rpT7LrTbd0UQSiGCTRDagEbG0jTz8G1mgPZ8ZJ4PfumDfOjeD/G+176Pd1z3Dq7efDXXvPAaookoPzv0Mz79nU+zb3zfkveWWnOOyDK9uNam5h164rzFvqSJNTczPHnvEotYYs3NmHnCqZJ4R//gJMnwONY578fnXsu6bi8D/p/xt3/zD/z5//gih58ZIBp/goWF0zh2LD2Gn9hz5+HqcGEGo5g+Vbe1p1KX30pKvKnmu1Luu7DShKpwdiOCTRBaSEYAxU52oNQtqJ4eDKVwnHXYoVuY/cksvg2RosKomIBqxuG7XAHajuNw97fv5u5v383FGy7mT17/J7zyklfyqm2v4lXbXkUoGuJXR37Ftx//Nrv37iaSiJyJZ3tynMREN2DgXhNaHLOQyMq2rpjBazgNDE1/jbA9R8C1ZrFuGlTvJht84fm87BWXcfKJI8xPHWZw0zrePjzEBZvPwd0bJ5T8R9zhH6GZIcA5hPlvwFpI2tixc1O14tZMoYz6rD3VuPyakXhTTy/TomNWEXdW7rtQrkesZI4K7YQINkFoEdkCCP0zNNdhzRzHCUxhh/tBXQH6IZz4S4u2WioloBp++LYgQPupk0/x3n94LwDXvPAabr/xdrZv3s7LtryM37z4N/no8EeZDc+y/8h+/vEbX+LbT86h+l63aC0pJSjzrSvKu5H4wGY8ruNY6o/SlrUU5dxk5/Wdxxu3v5HfetFvsWXDFvweP0optNbMLszyg/0/4P3f+CNOzJ4gfCJJzxyMoNPWvJMM8z95nt8HHkNbcZRrO8nThzGD9Vl7Wunyq6SXaS1UG3dW7rtQqkesZI4K7YQINkFoETk1vdxBtD0FaiPJ0zEMz0bgeZQrmBZIS4VRrQKqVrdmqwO09z69l71P7wVg48BGdly7g1dufSUXrruQ6y+7nm988Ze8dvswXV2bSVhxwrEwx09dxkL44wReeIoTsydyxitlXSnkJuvaOsvm9Zu5evPVXPWCq9hy7hbO7T2Xro4uTCPdjkprQrEQe5/ey3d/+V2+/rOvE4qFcq5rPKsZIa9GG0newL8RNu4B54Ogv43mEMFtR+oSCa10+ZXrZVorzRahkjkqtCsi2AShRWQLoMXWRsZb0EnQnglw7sfVvRkoLIxqEVD1uDXbKUD7yOQR7tx9J3fuvhOAi865iMnH13Hhxj8kbsXwun30e/z0BW/gxMkH+dbHvonWGkc7JJIJYokYoViISDyC7dg4joOjHQzDwDRMDj5xmO/8y684efIQF25cxx/s+BuuvupKlFJn1q410USUY1PHeOb5Z9j79F7u/8n9nI6cLjn3YjXaFjgByo1yd2EGL0DHniT0xHl1ueRaGZtWbJ2ZXqa10mwR2uofJoJQDBFsgrBM5Fu2lBFONQj3rF8sVpqc+ywYT6N4Ia7eM0VMCwmjWgRUPdaDdg7Q/vVzv2YuNM2zvxhZXJvLdLPGm2TjudN8f//36Q/209PZQ9AfpMPTwYbeDRiGgUItGe/iDRdz02/dSMyKEYlHmIvMsffpvUyFpjj83GF+cugnPPbsYziOU3Zu+fc96JtgT4wlGamdDBDWfwceL8nTh3D1vBPTv65ul1yrikJ3daTcoMV6mdZKs0VoO/0wkS4MQjYi2ARhGShk2XJiozh6F252pGKoTB9mcILOrYeJHQuizGvQ2ikqjGoRUPVYD9o9kzB/PxLRY5ycHyW0/pe8+3Pfa8mcCt33iOcL7EiMn+kzCgyjWDA0KvAzTAKoznfiDp4DrFyXnLrcYEd2DBu5vUzroZkitF1+mEgXBiEfEWyC0ECK/SIuZtky9P/F8O7ME0Am7p4Kan3VIKDqtR6kxp5cXGN0bABorGir1arQjoKy0H13db+TafOTDIXChJMOAZcBF2vWnDNDdGyA6FEvBgewXfFFC+tKdMn5N5lEScWsNTJLtB4q+Wy1y+dIYumEfESwCUKDKPWLuJhly4n66N5+MuvRM+UGylkQahE29VoPqv3VX+0cK+n9mD8eUOIarbVEFLrvjj1AcuECrHNuwedei2Wdwjq5i9hznZgdQxjeC9BWAGt2FADTv6lql1y7uNL8m0zYZNYVs9YoqvnsNsKCV+89kFg6IR8RbILQIEr9Im50XEyt7pJ6rQfV/OqvZY6lxofJJeOd/sUuDJUSOu3oNip03+25eQz3xtw1zrwAh1fg7unF1RVP9WRliGT4PpTpa6qoPltYTotVI+5BO8XSCe3B2fvtFYQGk1zwLlbOz5D5RewfnMSxRnESE2jt4CQmcKzRRQtRtWQfPkoZ6dIgQ2kXZWk8/Qbd26fpu+4k3dunq/7VX2yNjZhjqfELjUf8BTixW2rah+Wg2H03u7flvE47Do6zAQDTp3CvmUK5wjixX2F4d1Z10Nfz2VjNVPPZrZdG3ING/5shrHzEwiYIDaLUL+JGx8Ush7ukkEunml/9yQUvJMdwP3f3Yj/NZO8QTrL4HEuNX2jN2nHQbACii4+1k9uo0H33DFgo48ac1ynDQHES6AVSok0ZRzG883Rvn6aa39bFPhvx2bO7ev9yWqwa8f1sl1g6oX0QwSYIDaJcfFgjM9uaffgUc+n4zt9H7FhlMXDKnqVn6iuMaCuVJZicYXjqK5we8Be9bqk9jI4NLFlzvtBp9D6Uo9Ig9txK+s6SNeJ9BkNN4CTqz0ws9NlIhveho4M4Ha89a92kjcz+LHffG/X9bFVJFqE9EcEmCA1iOX8RN7v0QLF4n+TsiYrX6J2fZ0TnVfTXFm+ctwB3wesWtEitP0V0bC3WrAc7cj9m8FpcgSsbLnSqpdI4pUKHe/4aey7OJDXKtkUAACAASURBVE8U72mpjDAoF9r2lbSQFfps2KE9mMFb2j7jcOGwTeTQ+dgxL6YvTseWY3RubkxWaaO+n5Xc93Lfz3ZJChFWFiLYBKGBLNcv4maLw1IunUrXGIoVrnQfikFniWtnj586HK/EcA/h6l4L5vPY4e/h2KN4eykqdJbj8KskiL3U4Z5ydWbIZAcX7mmJipCYeha4FjPoJv7cPNGjo3gGDhHYejpnvYU+G2aHB1dgXc7828l1DCmxFtr/CpR5O8pzEY71a0L7PwM83FDRVu/3s5L7Xur7KUkhQq2IYBOEFUozxWEjXDrVVrqPjtvovJpdicm1OYejO3gOpve1GN6dObFdrXAbVRKnVE9mYvZ7k5M/xHDdirYDJE/PYHq3o9yXkjz9WUIHJpYc9vmfjblH+9o+4zBy6HyUeTuGe2vqAWMrDrcTOTRG5+aTpd+8jFQan1bs+yn11YRaETkvCMISGpGhpi432GHCjwGL1J/FKt1Hx218jzjsjkAc2B0B3yMOiUlr2TL7qsXVGU+5ZbPIF0H1ZCZmv9exwmBcgLbjoAdRZgfK2Ih2dEXZh/n30wo9R2L6u4uJCImp8i22mo0d84J5Ue6D5kWpx9uISu57KZYzW1VYXYiFTRCEJTTC5VpNpXu9P9XCKDvebZcNQwuzWF3NtQxlxxMpexbv/DyhWPnK/JXEEdZjqcx+r+EOoO2jaMdBGT7AAecohjtQ0WGffT9TsYDBdCzgrW3jkjN9cRzr12BsPfOg/WtMX/tYAaH++FGprybUigg2QRAK0giXa6WV7ucjhePdwkkHt9W85IrseCKSY+ms1nTvywjseMQhmllHHp5+A9/5+4gcmsoJkvf0n3ltPYd79nuNwIVY018C/VIw1qLtKbRzH67uzQUP+2JB7Z7+aeYe7cPseEvbueQ6thwjtP8zONyesrTZv0bbn6HjsmNA69pZ5VPvj5l26VUqrDxEsAmC0HJKxbu5SgRv15tplx1P5H7ubka0tcTKd/N+BwoItsSUQ+zYlbi6h3D3pw7e2LFR3D1nLFW1HO7ZsXxB3y+Idz0NZi+e/jBO4iGSc2vRzkY0LyYxE0QZ3yBw6SkyoqZcUHu7tjxKJRY8TOTQ2BkBfFnjskQbST0/ZqS+mlArItgE4SynHUoMqMsNdjyScoteS0qsZeLdCh2Ojcq0yxYv4eRMQSvffISCFsLo2ADauYrk3BM4VjjlnvRdtcRSVc3hnonlW9yHGOyw5oldHV608i0cniL8hEbpGMrTi+F7EbFjiUWhmB/U7jjrsEO3MPuTWXwbIigzhrba0yXXudnMSzBoP7HWCKS+mlALItgE4SymXUoMVBPvBksz7Rw7hh1az+xPBvFtCFcsOrPjiQKuNexJzlSc1RqfBR2JoIxbUa4L0PZRnNB9OHZ1a8+mWCxftpUvObsWT/8ZlyaAkzh3UShmi1A7prFm+kFdAfohnPhLSUZ2Yaj2ccm1ww8GQVgJiGAThLOYdioxUGm8G+RaxuzoONbsMTDei9azOPGxxa4Mydm1JYVAdjxRsneI4ezODBTPagVQlhfNEMocTD1gDqKdIZT1cMVrzi9lUiyWL9vKV86lmS1C7ZAfZWwEnke5ginXLzvQzqcwvDtb7pKr9QeDiDzhbEQ+4YJwFrNSSwxkl1aww4dRxltRrMd0awzPerTzBsJPXIITvw3TfwdO/DZCB67IKV+ROfS1NUly7h5s57ucHvDzRh94gZs7IHZ1cSufcmu0dtB2BK11+k8H5dYVrSE6buPbm1vKpIuUUMwm38pXrqxEdgkP21JoJtDOfZiBzSmL2+kLSEz2ARC87Djd26dbJnZqaZKeEXml7q0grEbEwiYIZzErtcRAtmXMsUKg+kEfwdUTBRROdCPo9UUth9mWHVf3GbdgYNsv8fS7S3ZiyODuTYB5EB2L4SRNDJeNq3MMd1eiojU4+xx2Obnuz9uBYWCEpbF8hdZerGdtJqhdqQCKLbh6Lwe1MeUeRWP4XoQTf1HLy3nUkgDRTlZhQVhORLAJwllMJSUG2tH9lC1KUB0o9RCu3h5MnwLAsSYxPL0578kWAo049P2DkyQPPIjZnb13D6aLC5fen8SUU7B1118DHyNl3SsWy5dZe/jJcRIT3YCBe01oyf7AJHZMkzh1hOQcOI4LRR8wiitwUXrt9Qmdej8btfxgaNcsV0FoNiLYBOEsplyJgWIxRpXEhy3H3D390/inJgkdeABlDKF1Sjgp47sYvhflvD71eJi5R/uIHu0CM4kyfgDOAoY7gBG4EKeKQ7/a8gwZcZMpXNvJUfYQWpLkEDBMOoaMsrF82t6Ep++MWMy2lmXfN3f/APbcPDr2L+Dfg6fnJZj+TUB9QqcRCSu11CRbqVZhQagXEWyCIBSlkCXKjr+B8BMxPP1vaVhmaT2WmkLCKXDpKWLHEjiJcxeFgLWwC+JBPBOzYE/Qyf8mzPtQ/h1o+yjW9Jfw9Iermnel5RmyxY2TuAAIEFafYljvZYTEovtzWLlJ9PuBaMnrlrMQZj9vAC5/L/GJ1wMPL4o1qE/oLMnUjR/BO32YmR/YdHXYJbN8M9RSk0wKzwpnKyLYBOEsppYiq+Xiwxo9h0pYKpxM3D25QsAIR1gTiWZlgS4wzN2ciq8B//XAtaD+ver5V0K2uNG2G+W6BOX97zwfv5MhDhPWz9OJl1hwPd2XH6Oc8CjnFiz0vNndhTV1BCcxUZPQyc9oTbgt3H3pTN3QXnom72Xk/7V35/FR1ff+x9/nzEwme0ggLGFXFlGsK6Ti0rpcLSgCaqmiLW4UqVq1Wm9t9Vpr3WjvT4tIwbbeqkWo1kKgAqW3ahWrEAFRhB8YDMhiJBuQkG1mzrl/hIQQEkgyMzknM6/nPzwcMzOfySQ57/l8N7uuTadENNXePcnYeBbxisAGdHHhdKeO16lpafjpePPD2itak8ibB4HKP1taYIeOmOS/QNW60npKNZ5e8mX4ZIcSO/x8x9I0QJnekOxQtUzfAFl2mqr9M6W6r1STMFsZuTvbvX9cqHq7QpUFsupKZSbU/zy09L6ZnmIl9Czu0HYeR23oWyVdZ5SqzPMP+TIvU0LZIi2w69p8SkS42HgW8YjABnRh4XanjtepaWn4qbX5YR0dWovUJPLjBddKK9TiHmcHVaKk7BpZdcUy/dGZB9U0QHnSqhUo2yFZtkx/irwZ22UG8pR26q52z/0K1Z6tUEWVpMmyZclI3KyKT5Yosf961ew8etgw7dSSZs/RtudraUPfBbY0Yd9SBVJOU3UbT4lw4wIWoKsgsAFhcvIiFG536ngTuNs6PyycOUSRmETeluCallh/1NNRk/w9WQrUFUV1HtQRB7n7e8qTtlGhyhWSr1Cmf1W7hvQOFoRUtaW/gtX7pdA/JfNWeZOr5Emrliexj6y6CQqW747osGFrG/pWWnVK8s9RqunRKivU6ikRdSWWKjf3UN3ebJm+gfJknCqrNtnxbUWAroTABoTB6aOdwu1OtWUCd1vmh4UTBiIxifx452cmDS6WeYapKavr9z47PMlfqk41lOCfE9V5UM2Dry+9VumnNX2+toe1ig0XyPDcIdM/TFbVQ1JouAz/3sYtTSK9xUVdiaVUM0GrrLoWA1nyqFJVZ9uaskYtngXb8DsSqpgsw/tNSSUKli+UL7P/oU1y2T8NaAsCGxAGpzfxDLc71dEJ3JGcQxSJSeTHOz+zIUTX5JpHnVeaPehAq68hkt3TSHzPqrb0rw9rvpMlSbZnkOxQqoL7quRLP1h/W2CvDE9NxD5IVBdmK9DtXF1XvvTwogLVh92GDX2PdRbs/vzuMn0TFLT7yfCkyjDSJF2rUOWL8vW46KhwybAp0DICGxAGpzfxjER3yg0TuMOtoWlwDexLlB04Qbb9lQyzm2yrV2MnJ2NUaZvPK3W6e9qSUI1fRsKwxv82fGNkW0tkBwfJtlMa33/DE4zYB4ngQb+83f5D+7zdNKFskSqDZUr1Zqk6xWgSdls/C7bhd6RhsYU8yZI5QFag8qgPF278ngNuQWADwuD0Jp5u3uKgYd5SoCxNkiVf5n6lnrwvKrUdnoR/payq4ZJZJWmZZH5LgbJUeTOtdm2KK3V+97QtnSVPYq2swFbJrO+wmd5TZQULZZjPKVQdanz/Kzb2k+mPzAeJhp9xT1quQmm5SpIUqCtSgn9OO+/fq36xhQbKVpEM0zjqw4XTHWvAzQhsQBjcsImnGzpkzdWVWDqw7nQFK6fK8ORKRrHqSl7SvrUfq9tZn0U8tDUE1/2rSySjhwzjazISviXTe6rsUJVC+wvk79O+EN2Z3dO2dpaSh+9UxYbZsnSH5BkmhbbK0Aqlnb5dKUMats8wI/pBItyf8Yb7m74J8mZaCu0vkB3Iky97y1EB/oih7YbtSgIVkpGspBJ3fBABnEJgA8Lg5g6Xk6oLs2XVTJbpHSPDkywpTbamSrVzVF24LyrdkoQepswUKSH1PxQsrz/43LZDslUkO5DXpjM+m+rM7mlbO0v1oewdVW0pVKjGL09irZJH7mwS1upF8oNEuD/jTe9vHfTL36dp9/DIx2j4nluhGgXKd8owpx4K4G+r4pNXGRpFXCOwAWHq7A5X8x3n23IEUGdoOqQX3O+RFayRx590+AvMAbKDVlTn99Vf8JPly+yvUOWLsgKVMkxDvuwtx7zQtzQcGU7oae971J5uXsoQj1KG7Glyy9GPG+kPEuH+jLf1/o1D2xW9JXOGpF6SvUPezG4yTIZGEd8IbEAX0tKO8209Aiiamg/phQ7sl4LrZRkfyuMfVf9F1hcyTDOq8/uaDr/5elzUGLJST96ntpzz2Xw4siOhpyPvUTS6eW4cKj+ehqBZ/t5g2Xa5TF+ZvN2q5Uk0ZNuRGY5mFSq6KgIb4JCOXDha2nE+mkcAtVXzIT1vt26y6sbIDuTJ8pwkGcWygy/JTNnW7qHJ9mitsyRJZe9mtbgA4ljDkRmjStsdejryHrlhLqRbJPQwlZhTKau28NB7Ur+/XCSGo1mFiq6MwAY4oKMXjtZ2nG9+BFBnaz6k50k05Mv2KVD6saTrJdtSQo/orRJtqnlnqa5Ex1wAEemjsaqritr9HjEX8kjRCrCsQkVXRmADHNDRC0d6cv0QW2tHADmltcPGk/pX1u99dvjWqNbRUtfyeAsgGie6W70UqkiSFfTIMHbJm1nTrudtCOCp3me0KljW7veopaC5P797TA7dHa+7HK0A6/S+iUA4CGyAAzp64TBOMzWl6fwoHT4CyEktdURCVXmyrYBK386R4amR7KBsKzVq4aO1rqUdKJZl5cj0trwAIrHfTlV+/GdZdf0kzwmSZ6gMrVWwKlN1Jc0PS29Z0wBelzVJ1xW/fMSpAO19j2J56K6try0ac/Cc3jcRCAeBDXBARy8cxzoCyEnNOyKGp0aWnSmPeZdsO1t1xQFJq+TrfkLUDv1urWsZrPqDTHOPZI2o32VfalwAYZiVqtl5hmzjGskcKVlfSvZiebr1lDd5iqoL5yihR+lxO0JNA7gnLVf7pMZTATryHsXy0J2Tr425gujKCGyAA8K5cLR2BJDTmnZE9ud3l2FMkZnQW8HiRJnekyWdKKvyRXmzL1E0LtCtdS1tX61M36sKVibJsHOPWAAhwyvTN0GGOUBm8gkyjBNlh/pKgRcbO55t6Qg1D+CetFwF/AOV5J+j5FHtf42xPHTn5GtjriC6MgIb4IBYv3A0vShbQY8MT5Kk+vMjpehcoFvrWvozpaTBH6ly8676VaJNFkBUbOwn8zjnXLalIxTpzo0bh+4itR2G06+tK253AkgENsAxbrxwROOi3BiG9JVMX6qk6FygjxWaEnqYyjq/TFJZk3scPsLpWOdcVmzsd9yOUKQDuNuG7iI5p85trw3oKghsACRF76JspmYrUPpvSavkyzhBVl1RVC7QHQlNbTnnsq0doUgGcLd1YCM578xtrw3oKghsACRF76JsHfQrIbthlehqmf7oXaDbG5racs5lZ3eEmnc500buavHczc4U6XlnbuwuA25HYAMgqTMuypJ04NC/7rlAHy88dGZHyK3beTg97wwAgQ3AIVyUW9dZHSG3bufBvDPAeQQ2AJLaf1Hu6odou7F+t27nwbwzwHkENgCS2ndRduvQXVsdLAipcuMIye4tIyFTdqi7gp982KH6Ixn83NzlZN4Z4CwCG4BGbb0ou3Xori3qSixVfpor6V4ZvuFS6AuFKhbKk3Z2u+s/VnCV1O4gx9AjgNYQ2AC0m1uH7tqiujBbtnWVTN8IGYYheQZLulZWzf8o6Glf/a0F18pN22WHBh0R5A6sy5OZuP2Y56m6fejRjcPIQLwgsAFoNzcP3R1P8KBfpi9bsqoPny1qDpBdVy5vz2PX3zywBMoT5M04OrjWFWUoofvhIGdZvRSsnCqz5u9K6DX+mEPIbh167OrD4EBXR2AD0G5dceiuMWzt98i2iyQrUaaGSGaS7OAWyShS0uDW628psISq/ix5vpIvrU/j19mBvZJMGb7DQS5UkSTDc7psa4UMwzxiCFkqjkjXKtrdr648DA7EAgIbgHZz+9Bdc03DlrdblQKl6yQrWZZVJYVKZZh/Veopm5XQw9PqY7QUWDxp5ylUuUIe/9gjgqsvq0J24HAH0gp6JKNYpje18fEMX0/VlkvByvC7Vp3R/erKw+BALCCwAXEsnK6MW4fuWtI0bJmSjO6mgvv/LtveosS+lYded+thTWo5sHhTz5AVypPpn3NEcJV0RAfSMHbJCubJkzGk8b52YK+MgF9mcvhdq87ofnXlYXAgFhDYgDgVT3OSmoctT9IgmYkDFKreqIxRpWpL2GwtsPgzdegxGtQ/1hEdyMwaBasyZXhGyratxk6c4bOPGDqVOta1qi2XFPhYduADmb5UeVKHyEwcENHuV1ccBgdiCYENiFPxNCcpEt2h9gaW5h3IupISVRce2YmrLswOu666Ekt29WDJvlaGd7js0BcKlC+UJ61UvvTIdb+62jA4EGsIbECciqc5SZHoDoUbWFoeQg6/rurCbHlSv6VQhSFZtZI5SLImKFTxsNJPi2z3q/61Hl4kUV2YLYnQBnQGAhsQp+JpTlKkukORnrcXibqCB/3ypvaS6StRqOKgrKBHhjcgM6Ei4kEq3GF09nEDOo7ABsSpWJiTVL09JHuDpQNVUnqyZJxmKmlQy4sH3LpIIty6GoK3J7G3PIk1kiSrrkimvy6CVdYLZxg9nuZMAtFAYAPiVFefk1S9PaTENZZeCUnnSVpVJU1ZY6laajW0xaLODN5Nh9FD1dsVqiyQFaiQjGQllRz7Z6d52LOsXgpVTFb5e+VKzKmi2wYcB4ENiGNu7Tq1hb2hPqxdeOi/L5T0SkiatMGS4iiwdWbwbujmWaEaBcp3yjCnSkYPGcbbqvjk1WN2y44IezW2AmU9JON0yX5bVu05dNuA4yCwAeiSDlTVd9aaOu/Q7clRek63zsHqrODd0M0LVfSWzBmSekn2Dnkzu8kwjz002nTOZKgiSYY5UNJXMrxph7pusblCGYgU5//SAEAHpCdLq5rdturQ7dHQMAfLqv2BPEkPyKr9gSo+OV11JVZ0ntCFGrp5tr1FtlUuw7NJvqwSeRKN464wThpcLCuQJ6uuSKGAIVtFsq2F8qTWbyYcqyuUgUihwwagSzJOMzWl6Rw2SVM89bdHQzztW3csCT1MJeZUyqotPPS9MCQdf4Vx06Fbw0iVoeHyZp4mT9KgNt0fiHd02AB0SUmDPKoZbWpSsuSXNClZqhnd+irRcAUP+iNyKkEsaNots21LVl2RrECekgYXH/N+CT1MZYwqVea5hfKkFcnwJLbr/kA8o8MGoMtKGuSRBnmiNmetqXjat+54IrGJcFdeoQw4gcAGADr+goJjbZ9RVyJXLkaIpnAXOnTlFcqAEwhsAOJeWzZ1ba0rJIkNYQFEHYENQNxr64KClrpC+/O7sxgBQNTx8Q9A3AtnQQGLEQB0BjpsAOJeOAsKIr0Ywa2b8wJwFn8FAMS9jm5TEe59m2NzXgCtocMGIO6Fs81EJLeoYHNeAK0hsAGAwttmIlJbVDQ9IL0B8+EASAyJAoBreFNqZQf2HnFbvG7OC+BIBDYAcIlIzocDEFsYEgUAl+DIJgCtIbABgItwZBOAlvCXAAAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOUIbAAAAC5HYAMAAHA5AhsAAIDLEdgAAABcjsAGAADgcgQ2AAAAlyOwAQAAuByBDQAAwOW8ThcAAJ3B7/OrR1oPZaVmqXtad3VP6660xDR5TI88pkemaSoUCilkhxQMBbXv4D6VVpSqtLJUZRVlKqssU9AKOv0yAMQpAhuAmJHgTdAJvU7QsD7DNCxnmIb2GaqhfYaqd0ZvpSSmhP34+w7u067SXdr65VZt3bO1/t8vt2pX6S7Zth2BVwAALSOwAeiyuqd119eHfl1jho9R7tBcDe45WF5P/Z+1YCio7Xu3a8vuLXrrk7dUUlGissqyI/6trK5UMBRUyA7JsqzGbpvP61O35G6Nnbjuad2VlZql7PRsDegxQF8f+nVdlXtVYx1VtVXavGuz3t/6vv699d9au22tagI1Tn1bAMQgAhuALsPv8+uCERdozPAxGjN8jE7qe5IkqaK6QvkF+Vq+fnlj96twb6HqgnUdfq7SilJt+2pbq/8/LTFNQ/sM1bCc+m7e6QNP122X3qY7xt6h2kCt1hWu0/tb39e7m97V+u3r6cABCAuBDYCreU2vzhtxniaMmqBLT7tUqYmpqq6rVn5BvhavWaz3t76vT774RCEr1Kl1VdRUaF3hOq0rXNd4W4o/RaOGjNKY4WN0zrBzdPe4u/WjK36kXaW7tPTDpVry4RJt2rWpU+sEEBsIbABc6ewTztbE3Im6/MzLlZWapf1V+/W3tX/Tkg+XKL8gP6zuWbQcrD2otz99W29/+rYkKT05XRePvFhXnn2lpl0yTTMum6GCLwuU92GeFq1epJ2lO50tGECXQWAD4Bpe06vLz7pc0y6ZplMHnKrqumr94+N/aEn+Ev1r079cGdKO5UDVAS1as0iL1ixSZkqmxp05TleefaXuHX+v7r78bv39o7/rd//7uyO6dADQEgIbAMelJabp2nOv1U0X3aS+WX1VUFSgB+Y/oMX5i1VVW+V0eRFRfrBc89+dr/nvzldOZo5uuOAG3XDBDRp35jit3bZWz//v81q5YaUs23K6VAAuRGAD4JgUf4pmXDZDN37zRqUlpen9Le/rwQUP6q1P34rpSfp7yvdoZt5MzV4xW5PPmaxbLr5F86bP0/a92/XMG89ocf7imH79ANqPwAag03lMjyaPmax7x9+r7PRsLf1wqeb+Y642frHR6dI6VVVtlf749h/10r9e0mWnX6bbv3W7nrnpGd104U169PVHlV+Q73SJAFyCwAagU50/4nw9ePWDOqnvSVpTsEY3z7lZH+/42OmyHGXZlpavX64VH63QxFETdf+E+/WXe/+i5euX64lFT2hH8Q6nSwTgMAIbgE7RK6OXHp/yuC752iXaUbxDtz1/m5avX+50Wa5i27YWrVmk5euX168qvXSGLj71Yj3/j+f1zBvPKBAKOF0iAIdw+DuAqBt/9nitfGilzj3pXD3+18d1yS8uIawdQ02gRs8uf1bffPibysvP0x1j71Def+ZpeM5wp0sD4BACG4CoyUjO0KybZ2n2LbP1+d7PNfaxsZr3j3ldbnsOp+w9sFf3vXSfbp5zs3pm9NTSnyzV9P+YLtPgTzcQb/itBxAV5510nlY+tFLjzhynX+X9Stf8+hoV7i10uqwu6Z+f/FOXPnqp3tr4ln561U+18J6F6pfVz+myAHQiAhuAiLvt0tv08p0v60DVAU18aqJmr5jd6UdHxZqyyjJNf3667vnjPRrRb4SWPrBU5ww7x+myAHQSAhuAiPH7/PrNTb/RA5Me0N/W/U3jnxyvjTvja6uOaPvr6r9q/BPjVVpRqj/98E/67gXfdbokAJ2AwAYgIjJTMvXKXa/oyrOv1FOLn9Kdf7hTNYEap8uKSduLt2vizIl6+9O39cvrfqmHrn5IhmE4XRaAKCKwAQhb/x79tej+RRrZf6R+8PsfaM7f5zhdUsyrrKnUtLnT9MKbL+jWS27Vb6f9VgneBKfLAhAlBDYAYemb1VcL716obsnddN0z17FdRyeybEuPvPaIHnntEY09x/7OmwAADJtJREFUY6yeu/U5+Tw+p8sCEAUENgAd1rtbby24e4HSktJ0w6wbtK5wndMlxaUX3nxBDy54UJeedqlm3TxLHtPjdEkAIozABqBDeqb31Ct3v6Ks1Cx9d9Z3WVzgsJffeVmPvPaIxp05Tk/f+DR7tQExhqOpALRbVmqW5t81X70zeut7z35PG3ZscLokqL7TluBN0AOTHlBdsE4/fvnHsm3b6bIARACBDUC7eE2v5n5/rgb0GKCps6fqw88/dLokNDF35VwleBN07/h79UXxF5q1fJbTJQGIANcGtoykTCV7e8tj+PXijCVOlxNxlm1rR/E2Pb3sUe2vLne6HKDNfnr1T5U7NFd3vXCXPvjsA6fLQQtmLZulwT0H654r7tEnX3yitz59y+mSAITJtYHtnnEPqVfPHGVkpMvc63Q1kWfbUvfuWbpHD+nnr//I6XKANpk0epJuuegW/eHNP2hx/mKny8ExPDD/AQ3PGa7f3PwbjX9yvHYU73C6JABhcO2s1IHZJyojIz1mN4M0DMmb6NXA7BOdLgVok5P7nawnrn9CH2z9QI+//rjT5eA4agI1mj5vuizL0rzvz1NSQpLTJQEIg2sDm2kYMRvWGhhG/esE3C7Zn6x50+dp38F9uv33tytoBZ0uCW2ws3Sn7nzhTg3LGaZHr33U6XIAhMG1gS2W/PY38/Ti7152ugygw+694l4N6DFAd75wp0oqSpwuB+3w7uZ39dyK5/Ttc76t80ec73Q5ADqIwAbgmE4dcKpuuugmvfzOy8ovyHe6HHTA7OWzVVBUoMenPK5EX6LT5QDoANcuOmiv5XnL9cKvn9O2L7/SiX166eb7btfYCWPDftznn/293shbpsysTPXu00sjRo5Q7rmj9dhDT6imukb9BvTTI0/9l9Iz0vX6wkV6feEiBQMB9R/YT7/870eVlMQfR3RdHtOjp254SiUHSjRz8Uyny0EH1QZr9dNXfqpXf/Sq7r78bj25+EmnSwLQTjHRYVuet1xzf/aY5u0pUo1ta96eIs392WNanhfemYYbP/5U//v3N/XqGwv03Auz9OknmyVJD933sO66/069tmyhhg4formzfidJuviyC/XK4pf06hsLNPjEwVr8Kqvo0LXdctEtOqX/KXr41Yd1oPqA0+UgDKs/W60FqxZo2iXTdEq/U5wuB0A7xURge+HXz+l/qmt0oSSfpAsl/U91jV749XNhPe5Hazfom5d8Q36/XympKfrGxeerurpaFQcqdHbuWZKk8VddoXX59ecnFmzdppu+c6uuGfsdLVuyQts++zy8FwY4KCs1S/dccY9WbljJge4x4vFFj6v8YLkenvyw06UAaKeYCGzbvvxK5zW77bxDt3em/7r/Ef3k5/frL8v/rOl3TlNtbV2nPj8QSbdefKsSfYl6YtETTpeCCDlQdUCzl89W7tBc5Q7NdbocAO0QE4HtxD69tKrZbasO3R6O0886Te+8+Y5qa2tVdbBK77y5SklJSUrPSNe6/PWSpL8tfkNnjT5TklR18KCys3soEAhq2RI6Eui6uqV009RvTtXStUv1+Vd0imPJgvcWaO/+vbpr3F1OlwKgHWIisN183+26KSlRb0kKSHpL0k1Jibr5vtvDetyRXztF37j4An173HW6/eYfaujwE5Walqpf/OrnevrJ3+jb467Vlk1bNf3OaZKkH9wzQzdcfaNunHyzBp8wKNyXBTjmlotuUXJCsp5d/qzTpSDCagO1mrtyrs496VydfeLZTpcDoI1iYpVow2rQ6U1Wid4WoVWiU2/9rmbcNV3V1TW65dppOnnkCJ108nC9/Pofj/rayddfo8nXX3PU7TPumh52HUBnSU9O140X3qhl65fpsy8/c7ocRMH8d+drxmUz9MNxP9T3nv2e0+UAaIOYCGxSfWiLREBr7hc/e0yfFxSqrrZW46+6QiNGnhTx5wDc5DvnfEfpSemavXy206UgSmoCNfr9P3+vByY9oKF9hhLMgS4gZgJbtDz5zGNOlwB0qkm5k7S+cL02797sdCmIotfef00/vvLHmjR6kmbmscce4HYxMYcNQGQMzxmuU/qfokWrFzldCqKstKJU72x6RxNHT4z5c5uBWEBgA9Bo0uhJCoaCWrp2qdOloBMsWrNIfbP6KncIW3wAbkdgAyBJMgxDE0dP1L82/UtllWVOl4NOsHLDSlXWVGpS7iSnSwFwHAQ2AJKkMwefqT6ZfbR4DUeqxYuaQI1WfLRCY88YK9PgcgC4Gb+h7TT2gvEqL9vX5q/P/+BDfbR2QxQrAiJj9JDRkqR3N7/rcCXoTO9uelcZyRk6qS8r4AE3I7BF2Yer12rDuo+dLgM4rtFDRuuzLz9T+cFyp0tBJ8rfli9JGnXiKIcrAXAsMbOtx8YNn2rJ66u1e1eZ+vbL0pVX52rkaaeE9ZjVVdX68Z0/0d6ivQqFQvr+HbdKkha89Ge98+Y7CgaC+tXspzT4xEHav2+/Hv7PX2j3zt1KTErUQ4/9TCmpKfrLK3+V6TH1Rt5y/eThH+vMUWdE4uUCEWUaps468Sy9sfYNp0tBJ9tdtlu7y3Zr1JBRevFfLzpdDoBWxESHbeOGTzXn6TXaV36DevV6WvvKb9Ccp9do44ZPw3rc9975t7J7ZuvVNxbo9RWvasw3xkiSMjMztHDJfH37+mv00u9fliT99pl5OumU4Xpt2ULdee/tevC+h9W3X46umXKVbrhpil792yuENbjWsJxhykjO0JqCNU6XAgfkF+Q3DokDcKeYCGxLXl+t1LTrlZY+RIbpUVr6EKWmXa8lr68O63GHDh+iD95brWeemqV1+euVlpYqSbrososkSSePHKE9u76UJK1f+5GumDhOkjR6zCjtL9+vyorKsJ4f6CwNZ0rmF+Q7XAmckL8tX7269VL/7v2dLgVAK2IisO3eVabU1MFH3JaaOli7d4W3NcHAwQO1cMmfNHT4ED33/36rec/+TpKUkJAgSTJNU8FQMKznANxgUPYgVdVWaVfZLqdLgQO27tkqSRqYPdDhSgC0JiYCW99+WaqsLDzitsrKQvXtlxXW4+79qliJSYm6fOI4TZ32XW3e+P9b/dozzz5Dy/JWSKpfGdotK0OpaalKSUnRwYNVYdUBRFvfrL7aU7bH6TLgkIb3vl/3fg5XAqA1MRHYrrw6V5UV81VxoEC2FVLFgQJVVszXlVeHt3t3wZYC3TBpqiZfMUVzZ/1O026/pdWvve2u72vTxs369rhrNetXs/Xorx6RJH3j4vP11sq3NPmKKVqXvz6seoBoycnKobsWx4r2FSlkhZSTmeN0KQBaEROrREeedop+cI+05PU/Na4S/d6t4a8SHXPBORpzwTlH3Lb8ncNH9pzytZP1h1eelyRldMvQM/P++6jHGDh4oF5btjCsOoBoy8nM0aadm5wuAw4JWkF9te8r5WQR2AC3ionAJtWHtnADGhCP/F6/emb01J5yhkTj2Z7yPeqb1dfpMgC0IiaGRAF0XEZKhiSptKLU4UqcMWHUBL33y/dUOKdQ7/3yPU0YNcHpkhxRUlGizJRMp8sA0IqY6bAB6BifxydJCoQCDlfS+SaMmqAnr39Syf5kSfWT7p+8/klJUl5+npOldbpgKNj4swDAfVzbYbNsW7ZtO11GVNl2/esEnOT11H9ui8ctau6fcH9jWGuQ7E/W/RPud6gi54SsUOPPAgD3cW1g21G8Tfv3H4jZ0GbbUrAmqB3F25wuBXGu4XfMMAyHK+l8rU2yj9fJ97Zi8+8tEAtc+3Hq6WWP6swTzlRJcalKKoqdLifiLNvWjuJtenrZo06XgjjX0FmLx+GwPWV7Wtx7LB73pPN5fHHZZQW6CtcGtv3V5aoKFkmSpv72WoerAWJXbbBWkpToS3S4ks43M2/mEXPYJKmqtkoz82Y6WJUz/D6/6oJ1TpcBoBWuDWwAOkdZZZnqgnXq2a2n06V0uoaFBfdPuF85WTnaU7ZHM/Nmxt2CA0nq3a23ivYVOV0GgFYQ2IA4Z9u2ivYVqW9mfO7BlZefF5cBrbmczByt+3yd02UAaIVrFx0A6Dy7y3arb/f4DGyoXxmbmZqp3WW7nS4FQCuMWF2FCQAAECvosAEAALgcgQ0AAMDlCGwAAAAuR2ADAABwOQIbAACAyxHYAAAAXI7ABgAA4HIENgAAAJcjsAEAALgcgQ0AAMDlCGwAAAAuR2ADAABwOQIbAACAyxHYAAAAXI7ABgAA4HIENgAAAJcjsAEAALgcgQ0AAMDlCGwAAAAuR2ADAABwOQIbAACAyxHYAAAAXI7ABgAA4HL/B2/i8VypwkcZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 748.8x489.6 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import StatsbombPitch as sb\n",
    "sb.sb_pitch(\"#195905\",\"#faf0e6\",\"vertical\",\"half\")\n",
    "\n",
    "for i in range(len(df_goal)):\n",
    "    xe = df_goal.iloc[i]['location'][0]\n",
    "    ye = df_goal.iloc[i]['location'][1]\n",
    "    g = plt.scatter(ye,xe,color=\"red\",edgecolors=\"black\",zorder=10,alpha=1)\n",
    "for i in range(len(df_shot)):\n",
    "    xe = df_shot.iloc[i]['location'][0]\n",
    "    ye = df_shot.iloc[i]['location'][1]\n",
    "    s = plt.scatter(ye,xe,color=\"blue\",edgecolors=\"black\",zorder=5,alpha=0.6) \n",
    "plt.title('Messi in Valverde era') \n",
    "\n",
    "#ax = plt.subplot()\n",
    "plt.legend((g,s),('goal','shot'),scatterpoints=1,loc=0)\n",
    "plt.savefig('MessiValverdeEraScatter.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#type(df_goal.iloc[0]['location'])\n",
    "dfj = pd.DataFrame(df_goal['location'].values.tolist(), columns=['x','y'])\n",
    "dfj.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "hmap = dfj.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdraw_pitch(\"#195905\",\"#faf0e6\",\"vertical\",\"half\")\n",
    "#plt.gca().invert_xaxis()\n",
    "sns.kdeplot(dfj.y,dfj.x, cmap=\"YlOrRd\", shade=True,shade_lowest = False,zorder = 20)\n",
    "plt.title('Barca Valverde era')  \n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('BarcaEnriqueeraheatmap.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "fig=plt.figure() #set up the figures\n",
    "fig.set_size_inches(12, 8)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "\n",
    "ax.set_facecolor('grey')\n",
    "draw_pitch(ax) #overlay our different objects on the pitch\n",
    "plt.ylim(-2, 82)\n",
    "plt.xlim(-2, 122)\n",
    "plt.axis('off')\n",
    "plt.gca().invert_yaxis()\n",
    "sns.kdeplot(dfj.x,dfj.y, cmap=\"YlOrRd\", shade=True,shade_lowest = False,n_levels=40)\n",
    "#plt.hexbin(dfj.x, dfj.y,gridsize=(100,100))\n",
    "#sns.jointplot(x=dfj['x'], y=dfj['y'],  kind=\"kde\", space=0, color=\"r\",dropna='True');\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}