05_OBP_to_SLG_ratio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pybaseball\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"font.family\"] = \"Arial\"\n",
    "\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# Define colors\n",
    "red  = np.divide((232, 24, 40),255)\n",
    "blue = np.divide((0, 45, 114),255)\n",
    "grey = (0.5,0.5,0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load statcast data or download directly. NOTE: THIS TAKES A WHILE TO GET ALL DATA\n",
    "load_statcast_data = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gathering player lookup table. This may take a moment.\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>playerid</th>\n",
       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>MarApr_PA</th>\n",
       "      <th>MarApr_AB</th>\n",
       "      <th>MarApr_H</th>\n",
       "      <th>MarApr_HR</th>\n",
       "      <th>MarApr_R</th>\n",
       "      <th>MarApr_RBI</th>\n",
       "      <th>MarApr_SB</th>\n",
       "      <th>...</th>\n",
       "      <th>MarApr_IFFB%</th>\n",
       "      <th>MarApr_HR/FB</th>\n",
       "      <th>MarApr_O-Swing%</th>\n",
       "      <th>MarApr_Z-Swing%</th>\n",
       "      <th>MarApr_Swing%</th>\n",
       "      <th>MarApr_O-Contact%</th>\n",
       "      <th>MarApr_Z-Contact%</th>\n",
       "      <th>MarApr_Contact%</th>\n",
       "      <th>FullSeason_OBP</th>\n",
       "      <th>key_mlbam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15998</td>\n",
       "      <td>Cody Bellinger</td>\n",
       "      <td>LAD</td>\n",
       "      <td>132</td>\n",
       "      <td>109</td>\n",
       "      <td>47</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.407</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.860</td>\n",
       "      <td>0.406</td>\n",
       "      <td>641355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11477</td>\n",
       "      <td>Christian Yelich</td>\n",
       "      <td>MIL</td>\n",
       "      <td>124</td>\n",
       "      <td>102</td>\n",
       "      <td>36</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.279</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.566</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.429</td>\n",
       "      <td>592885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17975</td>\n",
       "      <td>Scott Kingery</td>\n",
       "      <td>PHI</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.222</td>\n",
       "      <td>0.402</td>\n",
       "      <td>0.745</td>\n",
       "      <td>0.535</td>\n",
       "      <td>0.543</td>\n",
       "      <td>0.854</td>\n",
       "      <td>0.711</td>\n",
       "      <td>0.315</td>\n",
       "      <td>664068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7927</td>\n",
       "      <td>Eric Sogard</td>\n",
       "      <td>TOR</td>\n",
       "      <td>49</td>\n",
       "      <td>43</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0.505</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.939</td>\n",
       "      <td>0.978</td>\n",
       "      <td>0.962</td>\n",
       "      <td>0.353</td>\n",
       "      <td>519299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14130</td>\n",
       "      <td>Daniel Vogelbach</td>\n",
       "      <td>SEA</td>\n",
       "      <td>92</td>\n",
       "      <td>71</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.337</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.827</td>\n",
       "      <td>0.757</td>\n",
       "      <td>0.341</td>\n",
       "      <td>596129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   playerid              Name Team  MarApr_PA  MarApr_AB  MarApr_H  MarApr_HR  \\\n",
       "0     15998    Cody Bellinger  LAD        132        109        47         14   \n",
       "1     11477  Christian Yelich  MIL        124        102        36         14   \n",
       "2     17975     Scott Kingery  PHI         35         32        13          2   \n",
       "3      7927       Eric Sogard  TOR         49         43        17          3   \n",
       "4     14130  Daniel Vogelbach  SEA         92         71        22          8   \n",
       "\n",
       "   MarApr_R  MarApr_RBI  MarApr_SB  ...  MarApr_IFFB%  MarApr_HR/FB  \\\n",
       "0        32          37          5  ...         0.057         0.400   \n",
       "1        26          34          6  ...         0.118         0.412   \n",
       "2         5           6          1  ...         0.000         0.222   \n",
       "3         8           9          2  ...         0.000         0.200   \n",
       "4        15          16          0  ...         0.074         0.296   \n",
       "\n",
       "   MarApr_O-Swing%  MarApr_Z-Swing%  MarApr_Swing%  MarApr_O-Contact%  \\\n",
       "0            0.226            0.660          0.407              0.811   \n",
       "1            0.279            0.724          0.448              0.566   \n",
       "2            0.402            0.745          0.535              0.543   \n",
       "3            0.273            0.505          0.373              0.939   \n",
       "4            0.237            0.488          0.337              0.661   \n",
       "\n",
       "   MarApr_Z-Contact%  MarApr_Contact%  FullSeason_OBP  key_mlbam  \n",
       "0              0.884            0.860           0.406     641355  \n",
       "1              0.878            0.757           0.429     592885  \n",
       "2              0.854            0.711           0.315     664068  \n",
       "3              0.978            0.962           0.353     519299  \n",
       "4              0.827            0.757           0.341     596129  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_mlb_id(temp,_players):\n",
    "    temp[\"key_mlbam\"] = _players.loc[_players.key_fangraphs.isin([temp.playerid]),\"key_mlbam\"].item()\n",
    "    return temp\n",
    "\n",
    "# Load data as bd -> short for batting data\n",
    "bd = pd.read_csv(\"./data/batting.csv\")\n",
    "\n",
    "# Convert and % str to float\n",
    "cols = [col for col in bd.columns if '%' in col] + [\"MarApr_HR/FB\"]\n",
    "for col in cols:\n",
    "    bd[col] = bd[col].str.rstrip('%').astype('float') / 100.0\n",
    "\n",
    "# Get data for matching IDs    \n",
    "_players = pybaseball.playerid_reverse_lookup(bd.playerid.tolist(),key_type='fangraphs')\n",
    "_players.dropna(subset=[\"name_last\",\"name_first\"],axis=0,inplace=True)\n",
    "\n",
    "# Get MLB id\n",
    "bd = bd.apply(lambda temp: add_mlb_id(temp,_players),axis=1)\n",
    "bd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>player_name</th>\n",
       "      <th>batter</th>\n",
       "      <th>game_date</th>\n",
       "      <th>year</th>\n",
       "      <th>launch_angle</th>\n",
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       "      <th>TA</th>\n",
       "      <th>OBP</th>\n",
       "      <th>Slg</th>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143897</th>\n",
       "      <td>0</td>\n",
       "      <td>Allen, Greg</td>\n",
       "      <td>656185</td>\n",
       "      <td>2019-09-29</td>\n",
       "      <td>2019</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>86.516667</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143898 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index      player_name  batter   game_date  year  launch_angle  \\\n",
       "0           0  Bellinger, Cody  641355  2017-04-25  2017     16.250000   \n",
       "1           0  Bellinger, Cody  641355  2017-04-26  2017     63.285714   \n",
       "2           0  Bellinger, Cody  641355  2017-04-27  2017     55.333333   \n",
       "3           0  Bellinger, Cody  641355  2017-04-28  2017      2.750000   \n",
       "4           0  Bellinger, Cody  641355  2017-04-29  2017     20.200000   \n",
       "...       ...              ...     ...         ...   ...           ...   \n",
       "143893      0      Allen, Greg  656185  2019-09-25  2019           NaN   \n",
       "143894      0      Allen, Greg  656185  2019-09-26  2019     15.500000   \n",
       "143895      0      Allen, Greg  656185  2019-09-27  2019      8.666667   \n",
       "143896      0      Allen, Greg  656185  2019-09-28  2019           NaN   \n",
       "143897      0      Allen, Greg  656185  2019-09-29  2019     40.000000   \n",
       "\n",
       "        launch_speed  release_speed       xBA     xwOBA  ...  SF  K  FieldOut  \\\n",
       "0          76.000000      86.725000  0.268667  0.247000  ...   0  0         2   \n",
       "1          80.914286      88.581481  0.090000  0.108000  ...   0  3         2   \n",
       "2          67.133333      90.427778       NaN       NaN  ...   0  2         0   \n",
       "3          66.475000      85.346667  0.313333  0.285333  ...   0  0         1   \n",
       "4          96.180000      91.423077  0.608250  1.039000  ...   0  0         2   \n",
       "...              ...            ...       ...       ...  ...  .. ..       ...   \n",
       "143893           NaN      96.600000       NaN       NaN  ...   0  1         0   \n",
       "143894     70.975000      83.592308  0.391333  0.352000  ...   0  0         1   \n",
       "143895     82.933333      88.187500  0.553333  0.510000  ...   0  0         2   \n",
       "143896           NaN      85.740000       NaN       NaN  ...   0  0         0   \n",
       "143897     87.000000      86.516667  0.013000  0.020000  ...   0  0         1   \n",
       "\n",
       "        FieldErr  H  AB  OB  TA       OBP       Slg  \n",
       "0              0  1   3   1   3  0.333333  0.333333  \n",
       "1              0  0   5   0   5  0.000000  0.000000  \n",
       "2              0  0   2   1   3  0.333333  0.000000  \n",
       "3              0  2   3   3   4  0.750000  0.666667  \n",
       "4              0  2   4   2   4  0.500000  2.000000  \n",
       "...          ... ..  ..  ..  ..       ...       ...  \n",
       "143893         0  0   1   0   1  0.000000  0.000000  \n",
       "143894         1  1   3   1   3  0.333333  0.333333  \n",
       "143895         0  1   3   1   3  0.333333  0.333333  \n",
       "143896         0  0   0   1   1  1.000000       NaN  \n",
       "143897         0  0   1   0   1  0.000000  0.000000  \n",
       "\n",
       "[143898 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add data to dataframe\n",
    "def get_statcast_data(temp,start_dt='2015-04-05', end_dt='2019-11-30'):\n",
    "\n",
    "    # Get player data\n",
    "    data = pybaseball.statcast_batter(start_dt=start_dt, end_dt=end_dt,player_id=temp.key_mlbam)\n",
    "    \n",
    "    # Keep only regular season data\n",
    "    data = data.loc[data.game_type.isin([\"R\"]),:]\n",
    "\n",
    "    # group by date\n",
    "    grouped = data.groupby(by=\"game_date\")\n",
    "    \n",
    "    # Create empty dataframe\n",
    "    out = pd.DataFrame()\n",
    "    for name, group in grouped:\n",
    "        # Create empty dataframe\n",
    "        temp = pd.DataFrame()\n",
    "\n",
    "        # Get name\n",
    "        temp[\"player_name\"] = [group.player_name.unique()[0]]\n",
    "\n",
    "        # Get batter MLB ID\n",
    "        temp[\"batter\"] = [group.batter.unique()[0]]\n",
    "\n",
    "        # Save date\n",
    "        temp[\"game_date\"] = [name]\n",
    "        temp[\"year\"]      = [pd.to_datetime(group.game_date.unique()[0]).year]\n",
    "\n",
    "        # Some more advanced metrics\n",
    "        temp[\"launch_angle\"]  = [group.launch_angle.mean(skipna=True)]\n",
    "        temp[\"launch_speed\"]  = [group.launch_speed.mean(skipna=True)]\n",
    "        temp[\"release_speed\"] = [group.release_speed.mean(skipna=True)]\n",
    "        temp[\"xBA\"]           = [group.estimated_ba_using_speedangle.mean(skipna=True)]\n",
    "        temp[\"xwOBA\"]         = [group.estimated_woba_using_speedangle.mean(skipna=True)]\n",
    "        temp[\"wOBA\"]          = [group.woba_value.mean(skipna=True)]\n",
    "        temp[\"BABIP\"]         = [group.babip_value.mean(skipna=True)]\n",
    "        temp[\"ISO\"]           = [group.iso_value.mean(skipna=True)]\n",
    "\n",
    "        # Get counts\n",
    "        counts = group.events.value_counts()\n",
    "\n",
    "        # Get values for computing OBP\n",
    "        which_events = [\"single\",\"double\",\"triple\",\"home_run\",\"walk\",\"hit_by_pitch\",\"sac_fly\",\"strikeout\",\"field_out\",\"field_error\"]#,\"grounded_into_double_play\",\"force_out\"]\n",
    "        event_names  = [\"B1\",\"B2\",\"B3\",\"HR\",\"BB\",\"HBP\",\"SF\",\"K\",\"FieldOut\",\"FieldErr\"]#,\"GIDP\",,\"ForceOut\"]\n",
    "        for (event,event_name) in zip(which_events,event_names):\n",
    "            temp[event_name]  = counts[event].item() if event in counts else 0\n",
    "\n",
    "        # Compute new OBP\n",
    "        temp[\"H\"]   = temp.B1 + temp.B2 + temp.B3 + temp.HR\n",
    "        temp[\"AB\"]  = temp.H +  temp.K + temp.FieldOut + temp.FieldErr# +temp.GIDP + temp.ForceOut\n",
    "        temp[\"OB\"]  = (temp.H + temp.BB + temp.HBP)\n",
    "        temp[\"TA\"]  = (temp.AB + temp.BB + temp.HBP + temp.SF)\n",
    "        temp[\"OBP\"] = (temp.OB / temp.TA) if (temp.TA > 0).bool() else 0\n",
    "\n",
    "        # Add slugging percentage \n",
    "        temp[\"Slg\"] = (temp.B1 + 2*temp.B2 + 3*temp.B3 + 4*temp.HR)/temp.AB\n",
    "\n",
    "        # Concatenate to outer dataframe\n",
    "        out  = pd.concat([out,temp],axis=0)\n",
    "    return out\n",
    "\n",
    "#######################################\n",
    "#                                     #\n",
    "#        LOAD/DOWNLOAD STATCAST       #\n",
    "#       (very slow to download!)      #\n",
    "#                                     #\n",
    "#######################################\n",
    "if not load_statcast_data:\n",
    "    sd = pd.DataFrame() # <--- sd: statcast data\n",
    "    for row in bd.itertuples():\n",
    "        temp = get_statcast_data(row)\n",
    "        sd = pd.concat([sd,temp])\n",
    "\n",
    "    # Reset index\n",
    "    sd = sd.reset_index()\n",
    "\n",
    "    # Save dataframe\n",
    "    sd.to_pickle(\"./data/forecasting_data_2015-2019.pkl\")\n",
    "else:\n",
    "    sd = pd.read_pickle(\"./data/forecasting_data_2015-2019.pkl\")\n",
    "\n",
    "sd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pybaseball import batting, pitching\n",
    "\n",
    "# Add player's name\n",
    "def add_name(ID):\n",
    "    _player = _players.loc[_players.key_bbref.isin([ID]),:]\n",
    "    if not _player.empty:\n",
    "        try:\n",
    "            fname = _player.name_first.item().capitalize() #if type(_player.name_first.item())==str  else \"NaN \"\n",
    "            lname = _player.name_last.item().capitalize() #if type(_player.name_last.item())==str  else \"NaN \"\n",
    "            name = fname + ' ' + lname\n",
    "        except:\n",
    "            print(_player)\n",
    "\n",
    "    else:\n",
    "        name = \" \"\n",
    "    return  name\n",
    "\n",
    "\n",
    "# Get the data\n",
    "batting_data = batting()\n",
    "pitching_data = pitching()\n",
    "\n",
    "# Remove unneeded columns\n",
    "rows_to_keep = (batting_data.AB > 0) & (batting_data.yearID < 2019) #& (batting_data.lgID == \"NL\") \n",
    "# cols_to_keep = [\"AB\",\"H\",\"BB\",\"HBP\",\"SF\",\"HR\",\"playerID\",\"yearID\"]\n",
    "\n",
    "# Add the player's name for ease of reading\n",
    "batting_data = batting_data.loc[rows_to_keep ,:]\n",
    "batting_data.dropna(inplace=True)\n",
    "\n",
    "# Add batting average\n",
    "batting_data[\"Avg\"] = batting_data.H / batting_data.AB\n",
    "\n",
    "# Add OBP\n",
    "batting_data[\"OBP\"] = (batting_data.H + batting_data.BB + batting_data.HBP) / (batting_data.AB + batting_data.BB + batting_data.HBP + batting_data.SF)\n",
    "\n",
    "# Add on base (OB), no base (NB), and total appearances (TA) -- not quite PA since we are only counting those considered in OBP\n",
    "batting_data[\"OB\"] = (batting_data.H + batting_data.BB + batting_data.HBP)\n",
    "batting_data[\"TA\"] = (batting_data.AB + batting_data.BB + batting_data.HBP + batting_data.SF)\n",
    "batting_data[\"NB\"] = (batting_data.AB + batting_data.BB + batting_data.HBP + batting_data.SF) - (batting_data.H + batting_data.BB + batting_data.HBP)\n",
    "\n",
    "# Add singles and compute slg\n",
    "batting_data[\"1B\"] = batting_data.H - (batting_data[\"2B\"] + batting_data[\"3B\"] + batting_data.HR)\n",
    "batting_data[\"Slg\"] = (batting_data[\"1B\"] + 2*batting_data[\"2B\"] + 3*batting_data[\"3B\"] + 4*batting_data.HR)/batting_data.AB\n",
    "\n",
    "# Change order\n",
    "# batting_data = batting_data.loc[:,[\"playerID\",\"yearID\",\"AB\",\"H\",\"BB\",\"HBP\",\"SF\",\"HR\",\"Avg\",\"OBP\",\"OB\",\"NB\",\"TA\"]]\n",
    "\n",
    "# Get names of players\n",
    "_players = pybaseball.playerid_reverse_lookup(batting_data.playerID.unique().tolist(),key_type='bbref')\n",
    "_players.dropna(subset=[\"name_last\",\"name_first\"],axis=0,inplace=True)\n",
    "\n",
    "# Group by player\n",
    "grouped = batting_data.groupby(by=\"playerID\",as_index=True).agg({\"AB\": \"sum\", \"H\": \"sum\", \"BB\": \"sum\", \"HBP\": \"sum\", \"SF\": \"sum\", \"yearID\": \"mean\",\"OBP\": \"mean\", \"Slg\": \"mean\"}).reset_index()#.sum().reset_index()\n",
    "grouped[\"yearID\"] = grouped[\"yearID\"].astype(\"int\")\n",
    "\n",
    "# Is pitcher?\n",
    "grouped.loc[:,\"Pitcher\"] = grouped.playerID.isin(pitching_data.loc[pitching_data.G > 5,\"playerID\"])\n",
    "\n",
    "# Add name\n",
    "grouped[\"Name\"] = grouped.apply(lambda row: add_name(row.playerID),axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped[\"OPS\"] = grouped[\"OBP\"] + grouped[\"Slg\"]\n",
    "grouped[\"OBP2SLG\"] = grouped[\"OBP\"]/grouped[\"Slg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mb/lkph6zw14x3fgj_tmmjpq0nr0000gn/T/ipykernel_24332/1130895298.py:40: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n",
      "  cbar = plt.colorbar(splot)#,ticks=cbar_ticks)\n"
     ]
    },
    {
     "data": {
      "image/png": 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uo3v37qEv+8UXX0xUVBTR0dF4PB7++c9/Eh8fz8KFC8O2ve222zBNk3bt2lFaWkogECAxMRFN07jqqquYN28ef/vb31i5ciVOp5PNmzdTVFR0zNDuu+66i0GDBvHNN9+E7AG46qqrePTRR/nTn/4UmntYt24dKSkp1bqhrr32Wn766Sfee+899uzZQ0pKCmvXrmXWrFlMmjSJzz77jPfee4/s7GySkpLYvn07K1euZNOmTTW6hhUuwPXr1/PII4/Qs2fP0IiuoKCAp59+muzs7LAn57S0NAYPHsySJUu48cYb6d27d+j6VzBu3Dhee+210DxZ69at2bNnD0uXLuXbb78NeyipID4+nlGjRvHNN99QUFDA6aefHrJv5cqV3H///fTr14+EhARWrFgR2iYSTz31FAcOHKBfv360aNEi5P4bPHhwlRF5PXr0YMWKFTz++ON07NiROXPmhC0fP348Cxcu5NVXX2XPnj0sW7YsbHlNrktdONZxq2Pu3Ln87ne/48CBAxw6dIhWrVpx+umnY1kWbdu2Zf369UyePJnMzExycnJYsmQJr7/+eph770h69erFwIEDWbZsGRMnTmT48OHk5eUxYsQIrrnmGsaOHcvXX3/Nb37zG84880yKiopYvnw5V1xxBVOmTKmRzenp6WRnZ/Pyyy8zd+5cbrzxRlq0aAEEhWjv3r2he0kFF198MdOmTWP69Ol4vV62bNkSJoaKonDllVfy5ptvcuONNzJq1Cg8Hg+rVq1i8ODBPPPMMzW+ppJjc9KMzACuvvpqXnnlFU477bSwG9if//xnnnvuuXo5Rnp6OhdffDE///wzSUlJ3HvvvZx99tnY7Xb++te/kpGRERpVnHfeeWHbDh48mAMHDjBr1izmzZtHnz59+Pvf/46iKCF/fvfu3Zk3bx7ff/89qqrWKDdoypQpLFiwAIfDwY033shVV10FwOTJk3niiSdo06YN3377LfPmzSMpKanKUUoFQ4cO5fXXX6d///4sX76cb775JnSD7969O2+//Xbo5jJ79mzcbje33HJLja/hoEGDuOiii1BVlY8//pg5c+bQv39/brvtNhISEliwYAEXXnhh6Mm3gueee46hQ4eyf/9+9uzZw/XXXw8cfopPTU3l/fffZ9SoUWzcuJGZM2eSl5fHlVdeWcnFfCRHzl1UBIVU7K99+/YsXLiQzz77DK/XyxVXXMGkSZMi7ufyyy+nQ4cOrFixgmnTplFaWsoll1wSGqFF4qGHHiIzM5ONGzeSm5sbCoY50rY77riD6Ohofv7557DrXHHex7oudaEmx62KK6+8kkAgwMaNG+nVqxevvvoqLpeL6Oho3nnnHS666CL279/PjBkz2LlzJ+PHjw/NoUVCVVVeeeUVrrnmGmw2GzNmzGDHjh2hkd6TTz7JLbfcgqqqfPHFF6HApeHDh9f4fO+66y7at2/PqlWreO+998jPz+f++++nV69ela5rBYMHD2bq1Km0bNmSX375hXHjxoVc1RVu+XvuuYd7772XhIQEZs6cyaJFi+jYsSNjx46tsW2SGtJkcZSS4yJSuPXJTklJSdjrhx56SGRmZop77723iSxqeAzDEG63O/R6xYoVIjMzU/To0SMUIt4Q16Umxz2aihD4ivSOU4Ejr31OTo7o3r27yMzMFLt3725Cq05NTho3o+Tk5/PPP2fOnDkMHjyYvXv3MnPmTFRVDY1ET0bcbjcXXXRRaG6vIqR70qRJoRFSQ1yXmhxXEhzJjxw5ksTERGbPno1lWYwcOZJ27do1tWmnHFLMJCcMHTt2JD8/nzfeeIOoqCgGDhzIHXfcERZ1ebLhcDho3749n3/+Obqu07p1a6666ipuvPHG0DoNcV1qclxJcD5v9uzZeDweMjIyuPHGG7njjjua2qxTEkWIKpJmJBKJRCI5QTipAkAkEolEcmoixUwikUgkJzwn3JzZqlWrwkom1RS/31+n7ZqKE8neE8lWOLHslbY2HM3VXr/fX2/zwN8vXo1dqf1MUuukmLBqPicCJ5yYOZ3OUFma2rBx48Y6bddUnEj2nki2wollr7S14Wiu9lbUjKwP7IpguTdykn91pMb5682GxkK6GSUSiURywiPFTCKRSCQnPFLMJBKJRHLCI8VMIpFIJCc8UswkEolEcsIjxUwikUgkJzxSzCQnHbppUeTVMSxZqU0iOVU44fLMJJKqsITgp+2HWJtTigKgwOntEhnUNjFid2GJRHLyIEdmkpOGX3YWsDanFMMS6JZANwULdxexPre0qU2TSCQNjBQzyUmBJQSr9pVUci0almDxnqKmMUoikTQaUswkJwWGKTCq6GbkDpiNbI1EImlspJhJTgrsmkKMQ4u4LDWu+RWTlUgk9YsUM8lJgaIojOqcgk0ND/SwqQojOiU3kVUSiaSxkNGMkpOGzJaxuGwaC3cXUuTVaRXr4MwOyXJkJpGcAkgxk5xUtEuKol1SVFObIZFIGhnpZpRIJBLJCY8UM4lEIpGc8Egxk0gkEskJT4OJ2f33388ZZ5zBRRddFHG5EIInnniCMWPGMG7cONavX99QpkgkEonkJKfBxGzChAm8+eabVS6fP38+u3bt4rvvvuPxxx/nkUceaShTJBKJRHKS02BiNmjQIBISEqpcPmfOHC655BIURaFfv36UlJRw4MCBhjJHIpFIJCcxTTZnlpeXR1paWuh1WloaeXl5TWWORCKRSE5gmizPTESoo1eTNh1+v5+NGzfW+ng+n69O2zUVJ5K9J5KtcGLZK21tOE40eyXV02RilpaWRm5ubuh1bm4urVq1OuZ2TqeTHj161Pp4GzdurNN2TcWJZO+JZCucWPZKWxuO5mpvfQqsbgnyyvTab3iMW/H999/PTz/9REpKCv/73/8qLZ85cyZvvPEGADExMTzyyCN079699nbUgiZzM44ePZoZM2YghGDVqlXExcXVSMwkEolE0rQcK8CvTZs2fPDBB8yaNYvbb7+dhx56qMFtarCR2R/+8AeWLFlCYWEhI0aMYMqUKRiGAcDkyZMZOXIk8+bNY8yYMURFRfHUU081lCkSiUQiqUcGDRrE3r17q1w+YMCA0N/9+vUL88I1FA0mZi+88EK1yxVF4eGHH26ow0skEomkGTBt2jRGjBjR4MeRhYYlEolEEkZBQQETJkwIvZ40aRKTJk2q9X4WLVrEtGnT+Oijj+rTvIhIMZNIJBJJGMnJyUyfPv249rFp0yYefPBB3njjDZKSkurJsqqRtRklEolEUq/s37+fKVOm8Oyzz9KxY8dGOaYcmUkkEomkVhwrwO+VV16hqKiIRx99FABN0457pHcspJhJJBKJpFYcK8DvySef5Mknn2wka4JIN6NEIpFITnikmEkkEonkhEeKmUQikUhOeKSYSSQSieSER4qZRCKRSE54pJhJJBKJ5IRHiplEIpFITnhknplE0gzIdwc4UOYn3mmjdYKrRo1qJRLJYaSYSSRNiGkJ/rchj12FXir0K85h4zf90olxyJ+nRFJTpJtRImlClu0tYlehF8MS6GbwX6FX5+tNB5vaNInkhEKKmUTShKzZX4phibD3BJBd5MVvWE1jlERyAiLFTCJpQo4WsgoUgi5IiURSM6SYSSRNSJcW0agRYj0SXHaiHVrjGySRnKBIMZNImpChHZKJcWjYyxVNU8CuKZzfvWUTWyaRnFjIcCmJpAmJdmhcP6gtG3JL2VfsIynaTlZ6PLFO+dOUSGqD/MVIJE2MQ1Pp1zqBfq0TmtoUyUmGYQoKyvQ6bHniubilm1EikUgkJzxSzCQSiaSeMU0Ty5KpFY2JFDOJRCKpJyzLoqSkhP379+Pz+ZranFMKOWcmkUgkx4kQAq/XS1FREUIE8wNlfc3GRYqZRCKRHAd+v5+ioiJ0XcfhcKBpGl6vt6nNOuWQYiaRSCR1QNd1iouL8Xq9OBwOoqKimtqkUxopZhKJRFILTNOktLSUsrIyNE0jOjq6qU2SIMVMIpFIaoRlWXg8HoqLiwFwuWTfueaEFDOJRCKphorgjuLiYkzTxOl0oqoyELy5IcVMIpFIqiAQCFBUVEQgEMDhcOBwOJraJEkVSDGTSIBt+R6WZhehW4J+GfH0TotFlS6kUxbDMCgpKcHj8WCz2WRwxwmAFDPJKc/Xmw7yy65CdDOYH7TloJvle6O5bmBrKWinGJZlUVZWRklJCaqqShE7gWhQx+/8+fM577zzGDNmDK+//nql5aWlpdx2222MHz+eCy+8kM8//7whzZFIKlHg0fl552EhAwiYgu2HPGzN9zShZZLGRAiB2+0mJyeH0tJSXC4XTqezqc2S1IIGG5mZpsljjz3G22+/TWpqKhMnTmT06NF06dIltM6HH35I586defXVVykoKOD8889n3Lhx0i8taTS25ruJNPgKmIL1uaV0axnT+EZJGhWfz0dxcTG6rsvgjhOYBvvU1qxZQ/v27Wnbti0Oh4MLL7yQOXPmhK2jKAputzv0VJSQkIDNJj2fksbDZVMj/ghUBaLsJ14bDEnNMQyD/Px8Dh48CEBUVJQUshOYBvvk8vLySEtLC71OTU0lLy8vbJ2rrrqK7du3M3z4cMaPH88DDzwgv0ySRqVHaiyRhmaaojCwjewvdjJimiaFhYWh0Vh0dLR8iD4JaLBPsKLY5pEcnWD4yy+/0KNHD9577z327NnDDTfcwMCBA4mNja1yv36/n40bN9baHp/PV6ftmooTyd4TyVaobO/ZreCHXIWKr6wFnJ5ikZ+9nfwI2+sW7HGDz4L0KEhuQK/4iXRtm7utlmXh8/nwer0oioJlWWRnZzfIsQKBAPn5+SftvNv999/PTz/9REpKCv/73/8qLRdC8OSTTzJv3jxcLhfPPPMMvXr1alCbGkzM0tLSyM3NDb3Oy8ujVatWYetMnz6dW265BUVRaN++PW3atGHHjh1kZWVVuV+n00mPHj1qbc/GjRvrtF1TcSLZeyLZCpXt7QEMswQ7DnkwLEGnlGhctsgegt2FXt5cshchBJYQKIUKfTPiuDwrrUGqQZxI17a52npkRXvLskLzYtu3b6dz584Nckyv10tKSkqdoiGb8wNBBRMmTODqq6/mvvvui7h8/vz57Nq1i++++47Vq1fzyCOP8NlnnzWoTQ3m0+vTpw+7du0iOzubQCDA7NmzGT16dNg66enpLFy4EID8/Hx27txJmzZtGsokiaRKbKpCZssYeqbGVilklhC8u2wffsMiYAoMC3RLsCanlLW5ZY1ssaQm+P1+Dh48yKFDh0L5YnIq4/gZNGgQCQlVu+HnzJnDJZdcgqIo9OvXj5KSEg4cONCgNjXYyMxmszF16lRuvvlmTNPksssuo2vXrnz88ccATJ48mTvuuIP777+fcePGIYTg3nvvJTk5uaFMkkiOi+wiH7pZuXtwwBQs3lNEVnpcE1gliYRhGBQXF+PxeLDb7bIYcCNzdMxEWlpaRO9cfdKgs54jR45k5MiRYe9Nnjw59HdqaipvvfVWQ5ogkdQblhDlwSKV54OtCHPEksbHNE3KysooLS2VFe2Pg4KCAiZMmBB6PWnSJCZNmlTj7WsSM1HfyBAeiaSGtE2MItLP0aEpnCYjH5sUIQQejyfU6VlWtD8+kpOTmT59ep23PzpmIjc3t0FHZdDAFUAkkpMBIQS7CrxsOeBmYp807KqCTQ3eKB2aQoekKPpnxDexlacmQgh8Ph+5ubkUFBSEmmRKIWtaRo8ezYwZMxBCsGrVKuLi4hpczOTITCKphoNlAV6ct4tin4GqgGEJLurZkhinhjtg0rVFDF1aRMsajk1AIBCguLgYn8+H0+mUlYMakT/84Q8sWbKEwsJCRowYwZQpUzAMAwhOJY0cOZJ58+YxZswYoqKieOqppxrcJilmEkkVCCF4cd4uDpYFwmbJZm84yO9HdqCrLHXVJBiGEer03FyDO7xeb1ObAIBpCQrK/HXYsvpr+sILL1S7XFEUHn744Toct+5IN6NEUgW7C30U+4xK4R4BUzB366EmselUxrIsSkpKyM3Nxev1EhUVhd1ub2qzwtiwYQMTL7uMbt268e3MmU1tzimFHJlJJFXgDpioVXgPc0oD+A0LZxU5aZL648ik5+Ya3JGXl8czTz/Nhx99hGVZxMXEkJok04waE/lLlEiqoGNyFIYVOeQ+r8TPH2ZsZNHuosY16hTD7/dz4MABCgoKsNlszU7IvF4vL7zwAoMHD+b9Dz5AQeGGSybw04ef0HfgwKY275RCjswkkiqIdmhc1ieV6WvzCJjhoqaXi9y7S/fRLslFRryrKUw8adF1neLiYrxebyhCsTlhWRbTpk3jySeeYN/+/QCMOeNMHrrjLroNGYTf6UCJkt+JxkSKmURSDed0a0G75Cimr85lxyEvR9f/MC3Bz9sLmdQ/vUnsO9kwTZPS0lJKS0ux2WzNMrhj4cKFPPTgg6xavRqAXl268PCdUxhx7hi05CQUux2lmQSAnEpIMZNIjkFmyxjO6pJCdvF+/Ea4nFkCSv1GE1l28mBZFh6Ph+LiYoBmmSu2c+dOHn3kEf43ezYAqSkp3Hfjb5k06Tc40tNQnDI1oCmRYiaR1IDMVjGYEebPnDaVfq1lwnRdqQjuKC4uxjTNZtnpuaioiOeff54333wTXdeJcrm4fdIV3HHDDcR37Iga0/xGj6ciUswkkhqQHG3n3G4p/LDlUGj+zKEptElwSTGrI4FAgKKiIgKBAA6Ho9klPeu6zttvv83fnn2WwqIiFEXhN+edz19uv4PWfXqjxsc1u9HjqYwUM8kpi25aHPLoxDttRDu0Y64/ISuNzJYxzNtegM+wGNwugTPaJ4ZKW0lqhmEYlJSU4PF4Qm1ZmhNCCL7++mseefhhduzcCcDQfv15+K4p9B0xHDUhHkU79vdF0rhIMZOckny76SAz1x8EgkEcQ9olcPXADOxaZBdXXqmfL9bmseWAmziXjbHdWzKkfYJ8Mq8FlmVRVlZGSUkJqqo2OxEDWL16NQ89+CC/lvdZ7NSmDQ/dfifnjxuHrUUySjMbPUoOI8VMcsqxqcji+30HwsLtl2QXo6kK1w5qXWn9fHeAJ77fjs+wEAJK/CbvLdvHQXeAcb0atnhqU7KrwMviPUX4DIs+6XH0TY9Dq8Mo9MiK9kCzyxUDyMnJ4YknnuDTTz9FCEFSXDx/uO4Grr/malytM1BczqY2UXIMpJhJTjkWHxAEzPD3dFOwcFcRV/RPx3FUVY+vNhzEXy5kFQRMwVcbDzImMwWX/eRzOc3bfojvthzCsAQC2JrvZsmeIn47pG2tBM3n81FcXIyu680yuKOsrIx//vOfvPLPf+L1+bDbbNx46WXc89vfktKtK2qMrL95oiDFTHLK4darXubVzUpitjXfQ6RCIJqikFsaoENy83OXHQ9lfoNvy4WsgoAp2FvsY11uKX1r0O6muSc9m6bJzJkzefONN8g7cACAi0aM5IE776LTaQOC82LNbPQoqR4pZpJTjvRo2FFa+X2XXSXOVfkn0TLGQU5J5crjhiVIijr5fkI7CrxoKhyVUkfAFKzNqV7MTNOkpKQEt9vdbDs9z58/n4cefJD1GzYA0K9bNx6Z8jvOOOcc1KTEKoM7DMtCVRTZ7qeZcvL9EiWSYzA8XWWfFwKGFaqI79AUJvVPj3ijGtujBZsOlIXNsdlVhd7psSRENa+q7fVBsHiyAkf1C1CAqCqiPiuSniu6CzfHebGtW7fy8NSpfPf99wCkJqfw0O13MuHyy7C3alllcEdeqZ8FOwso9hkoqkLXlGgGt0usMlhI0jRIMZOccrRwKTwwpiMz1x1gxyEvLWLsXNSzFT3TYiOu37VlDDcObs2HK3JCQSAD2sRHDBY5GeiSEo0WQYhsqsKQdolh7x1Z0b7Cpdjc5sXy8/N59tlneffddzFNk5ioKH531dWMPWcMPYaeWW0NxSJvgG82HwwlzAtLsDXfjVc3OSezZWOdgqQGSDGTnJJkxLu47cx2NV5/YLtEBrRNoMhrEG1XT/igDyEEliBiMIemKtw8pA3/WbIXwxSgBNMXLuzRkjYJh2/8fr+f4uJi/H5/qNNzcxIyv9/P66+/zgsvvEBpaSmqqnLNReP40+13kNa7Jztzc49ZDHhdThnWUROmloC9JX7K/DqxzpNvZH6iIsVMIqkhqqKQHB28eRX7DBbvKqTYb9KjVQw902JPiLkU0xJ8vzWfBbuK8BsWrWIcXNy7FV1bhEfttUlw8eDZndlZ4MFvCjolRxFVLuCGYVBcXIzH42mWnZ6FEMycOZPHHn2U3Xv2AHDWwEE8/Lvf0fOMM1ET4lBqKLqFXr1Sc1YATYESnynFrBkhxUwiqSWbDpTx8vzdCIIh/T9tK6B9kos/jOyArZnPo3y5Po/le0tCLWwOuAO8s3Qft53RjraJ4aMUTVXocoTImaZJWVkZpaWlzTa4Y9myZTz00EMsXboUgG7tOzD1zrs4Z9xFweAOW+1ueS1jHeR7AmFpGQCmBQknYfDPiYz8NCSnFH7D4rvdAV5YtQYLGN4picv7ZxBTg3JWAJYleO3X7LBgEL9hsavAy887ChnVNaWBLD9+vLrJsr0llRqO6pZgztZ8rh/UJuJ2RyY9N9dOz3v27OHxxx/niy++AKBFYiJ/vvFmrrzqKpzpqXWu3NE7LY6t+W70Iz5vTVXomBxFjEPePpsT8tOQnDIIIXjk6y1sP2hglN+bvt5wkJV7S3jh0p41SgbOLvJVatQJwbD1X3cVNWsxK/IaaKoSsXt2Xlmg0ntCCPx+P4WFhRiGgcvlalZzYgAlJSW8+OKLvP7a6/gDfpx2O7dM/A2/u+VWErt0RDnO/LZYp42LerRiyZ4icssCODSFHq1iyapBrp2kcZFiJjllWJdTxu4Cb0jIIDgqOVgWYOmeIk7vkHTMfagKVPI5leM7OjGrmZEUbY/YxkYBMuLDyzUFAgGKi4vx+Xyh4I7mhGEYvP/++/z1mWfIP3QIgEtHn839d/2ODgP6ocRE19voMSnawXndT96yZScLUswkpwzbD7nRrcqC4zMsth5010jM2iS6sGkqftOstCzfHcAwrWY7b+ayqZzZPpGFe4rC3GY2TeGc8hGlYRiUlpZSVlbWbIM75syZw8NTp7J5yxYABvXqzaO/u5vTRp+FmpBQ4+AOycmFFDPJKUOrWCcOTcV7lKA5bQppcTUrJKsoCq1iHews8FZapgLbD3np1qr51vO7oEdL4l025u0owBMwaZ3gYnzPVqTGOigpKQmraN/c5sU2bNjA1Ice4qd58wBon57OA7fezriJE4MV7WsZ3CE5uZCfvuSUYVC7BJw2FZ9+uPKHAtg1lWGdkmu8n6qCRRRFobm3NlMVhRGdkhlRfr4VSc+5ubnNNrgjLy+PZ555hg8//BDLsoiPieGea67jphtvJLp1BoqzeblAmxOGaVFYVk0x0ippXiPymiDFTHLKYNdUnh7Xnae/2sB+T1DO2iW5uHtkxyrLNEViWKckthx0VwoEsakKnVKOfRMwLMHs9Qf4cVuwyWeXFtFc0T+dNonVJ/DWN36/n6KiInRdx+FwoDWzhpMej4dXX32Vl156CbfbjU3TuP6SS/njnXfSslsmajNzgVZgmiZWBHe2pGGRYiY5pUiNc3Jrlou2nboiBBELC1cghGBvkQ9FgdYJh0csp7WJZ83+BJZlF2MKgU1RQFG4c1i7GkVEvrNkH8v3FofmrTYdcPPMnB08cn4XWsTU7ygjr9TP2pwy7JrCgNbxxLlsESval/h08ot8JEfbSYxq2pGOZVlMmzaNJx5/nP05OQCce8aZPPS7e+h2+mCU2JhmN3qEoN1+vx9VVWnRogUuV+M+nJzqSDGTnJLEOqv/6m896OZvc7ZT6g8GesS7bPz57E50bhG8kd44pA1jMlPYkOcmxqFxWtv4UIWM6ij06CzLLq6c62VafL85n8kDMup+Ukfx5bo8vtmYD4CiwCcr9nFVnwQ6xKqhpGfTEkxbncua/aXYNAXDFHRrFcNv+qU1SSHdhQsX8tCDD7Jq9WoAenXuzKNT7mb4BWNRE5tncEdFCgNAQkICMTExzS6F4VRAiplEchRuv8EjX2/Bqx92FR0sC/Dw11t5Y1KfkEuybVIUbZNql8eUW+rHrlXO9TIFEYNK6sqOQx6+3ZQfrPQhBKrhRzM8fLqkkD+d0w1HecLvT9sOsS6nFFMIzPKchS0H3Xy76SAX9UqtN3uOae+OHTz66KPMnj0bCFa0/8tvb+E3V12Jo2ULFHvzLBsVCASCxYtjYoiPj292rtpTCSlmEskRmJZg5rq8iPlYliX4dVchZ2e2qPP+W8U6gsV7j0JVqFRO6nhYtLsI3bBQzACa7kERFkKzg01ja76Hvq3jy9crDpW2qsCwBMuyS7iwZ6sGd+cVFRXx3HPP8Z///Add14lyurjjiiu445Zbie/QvtkGdxiGQSAQICoqihYtWmBvpmJ7KtGgYjZ//nyefPJJLMvi8ssv55Zbbqm0zuLFi3nqqacwDIOkpCQ++OCDhjRJcgpQ6NH5ZuMBth700CE5igt6tqJF7LFviqv2lfDSTztxB8yIVTICpkWRt3JkWInP4IfN+ewu8NC5RQyjM1OqdGOmxDjonR7Lutyy8FwvVeHcbnUXyaPR/QG0QAmKaSA0G0IJ2qObFpYIL8UVcXtLIETQPdkQBAIB3n77bf727LMUFRejKAqTzjuf+6ZMoU2fPqgxzTO4o2JezG6307JlSzkv1oxoMDEzTZPHHnuMt99+m9TUVCZOnMjo0aPp0qVLaJ2SkhIeffRR3nzzTTIyMjhUnskvkdSV/cU+7pu5iYBhoVuCdTmlfLvpII9f0I1OLaq+QR4o9fPXH7ZXeXMHcGgqPVPjwt7bW+Tj/lmbCJgWuilYsruIz1fn8LeLe7A2p5RPVuyn0KOTFufk2sFtGNw+kVvOaMtnq3L5ZWchAVPQLtHF1QMzSK1hrlt1GIZBSUkJtkBxcDRmCxdxATjth+dz2iVGsbPQU2k/6XFO1AbIMxBC8PXXX/Pwww+zc+dOAIb27ccj9/yerBHDUePjmmVwx5HzYklJSURH11+FEUn90GBitmbNGtq3b0/btm0BuPDCC5kzZ06YmM2aNYsxY8aQkRGc9E5Jab517SSNQ6EnwPxtBQQMi9M7JtV6Tuo/i7LxBMxQHplhCQxL8Nqve/jr+O6V1hdCEDAF3206iFlNOLXTptI9NZbuqeEJ0a8t2I07cLgaiN8U6JbJ099vI7c0EBLH/SV+XvhxBzcMacM53VpyaZ9U4p0aa/eXkhrvRDcsPli2nzU5pUTbVcZ0a8GZHRJrfN6WZVFWVhZKerZUO0Kt/PNWgEK3EXp9Ya+WvLEwOzhiI5j4rakK43vXf/mmVatWMXXqVH799VcAOrVuw8N33sV5Ey5FS0pEaYbzTUIIAoFAML8tPp7Y2FgZ3MGxvW6lpaX86U9/Yv/+/ZimyY033shll13WoDY1mJjl5eWRlpYWep2amsqaNWvC1tm1axeGYXDNNdfgdru59tprueSSS6rdr9/vZ+PGjbW2x+fz1Wm7puJEsre+bF2Z6+eddcFRghDwxoJdjG7v4uKuNRe0tfs8EftPbTvoZt2GDWiKErJ3c6HJrB0BivzlXYSr2e/AlgrntdXZtGlT6L2DHoP1uZUL9FoCdhf6Kr0fMIMV999dnI2qQMAEI9j7ktnrDxDnsmOzqRQA7y/dx+od+zgjWa/22laMGDweD0II7HZ7cMTgtdCAo4tu2RTwlxxi+/aC0Hvj28O6AjjohWQX9E5W0Av2c8QqNcLv97N9+/ZK7+fl5fHqq6/y1VdfAZAQG8stEy7j0ssuQ0tJZndxERQX1e5g9UBV9lZgGAamaeJ0OomOjqa0tLQRrWu+1MTr9uGHH9K5c2deffVVCgoKOP/88xk3blyD1vhsMDETEYqxHj0sN02T9evX88477+Dz+bjiiivo27cvHTt2rHK/TqeTHj161NqejRs31mm7puJEsrc+bC3zG/x+7jL0IwdHAn7KDjB+UFd6psdVue2RuJavosxfuW6iTVPo1aMHiqKwceNG1JS2/HfxVvwRgjEiEbDH0rvX4R+raQme/GBVjbY9Gt9R5lVYUOrXSdQcKIqCIWBNgcLgllV/330+H8XFxei6jtPpDBsxtNZNVvy0C59uhlU7iXbaGJnVsVI+XL86nUk427dvp3PnzqHXZWVl/OMf/+Bfr7yC1+fDbrNx06UT+P2dd5HcpTOK6/jdqsfD0fZWYJomgUAAh8NBYmJioxdZbu4PsTXxuimKgtvtRgiB2+0mISEBWwOXG2uwvaelpZGbmxt6nZeXR6tWrSqtU+F/jo6OZuDAgWzatKlaMZOcnCzeVRixFFTAtPhh88Eai9m53Vryv/V5YdU57KrCyM7JYQ9Tn63MwW/WvErDyr0lfLpyPwoK+4t9uOwqHr3q7e2aEhbgUROEAEsItHI77ZrCgcoDvIhJz0fjsmvcekZbPl+Ty76i4E46pkQzISu1Rondx4Npmnz88cc89dRTHDhwAICLho/ggbvvofOg01BjmmftyorgDk3TSE5Obpb1KZsDNfG6XXXVVdx+++0MHz4ct9vNiy++2ODu2QYTsz59+rBr1y6ys7NJTU1l9uzZPP/882HrnH322Tz22GMYhoGu66xZs4brr7++oUySNGMiBA8GEUQMk6+KSQPS2VvkZdW+EjRVwbQE3VJjufH0tmHr7SuOoBLVYFiCT1bkoARNwnaM3+Xl/dL5fHVutQElkVA4fPM0LUGcPfg6YFj8uuMQew8U0NJp0bt1QqiivU83WZpdzNYDHuKjbJzRPpHWiS5axjq47cx2+HUTRVFwHMvoemDevHlMfegh1m/YAED/bt155O57OP3cMagJ8c1SHI4M7khMTCQ6OvqUnxcrKChgwoQJodeTJk1i0qRJQM28br/88gs9evTgvffeY8+ePdxwww0MHDiQ2NjYBrO5wcTMZrMxdepUbr75ZkzT5LLLLqNr1658/PHHAEyePJnOnTszfPhwxo8fj6qqTJw4kczMzIYySdKMGdIhMaJoOW0qZ3drWeP92DWVv4zpwv5iH3uLfGQkOGmTWHnk0rlFDAdKA5XmyVQlOJKryv14OLCkahui7RrbDpbRIdnFviI/ZYHKbs9I2FQlFEGoKZCR4KKFK0BusZcHZ6zB9LvxGxZ2h4PkrcX8YVRHQOGVX4JBKLolUIB1OaVMyEoNNZB01qAyyfGyZcsW/vSnP7FgwQIAkhJTuGLidUy57Tpatk1rlsEdEEwRMAyDuLg44uLiZNJzOcnJyUyfPj3ispp43aZPn84tt9yCoii0b9+eNm3asGPHDrKyshrM5gZ1Yo4cOZKRI0eGvTd58uSw1zfffDM333xzQ5ohOQHwGxYXZ6UxY01uyN1m11TO79WKPhk1czEeSUaCi4yEqnOAhnVKYuHOwrD37JrC+N6ptE+O4rUFe8KiFGtDwDRZsqcEBXBoCv1bx7Ihz3PMUVqcy45DU7AEZLaM5qYhrfll2Vq+XboMn9uPodhA1fAbwYais9YdIDnGQanfxBSHg1h0SzBj3QF6psVha2CXYn5+Ps8++yzvvvtuebCEi2EjxtH73EkYLVrx6hYvd7U0SY5pXiJhGEYoX0wmPdeOmnjd0tPTWbhwIQMHDiQ/P5+dO3fSpk2bBrVLVgCRNCmWELwwZzvfbzqIXVNRgJRYB6O6pnBWZgsyW9XMLbHzkIdZa3Mp8Oic2TGJUZktqqwt6DMEr/y8O8KoTOGyvmm47Bq7C7x8uTYvYvL0kTg1BbtNxW9YJLhsFHn10KhNEAzVX5/rZlL/dD5blRvqRm0r1xhDBEXUrio8dG5nYpw2XHYV1TJ44dvVbMstxi80UMNvtoYlWJ5dTJvEqJCQhSGCuXPVCfrx4PP5eOONN3jhhRcoLS1FVVUGDRrF6Rddi9W6I77yoAnLsPhpewETstKOscfGoSK4w263k5CQQIsW9ZeofqpQE6/bHXfcwf3338+4ceMQQnDvvfeSnFzzNkt1sqtB9y6RHIMvVuXww+Z8AqYgUN69+VBZgH3FvhoL2Q+bD/LcD9vLq1vA4p2FfL4qh5cv743TVnlEsPZQ5AofCrB4dxEju6RwbveWzF5/4JhiltU6nvvHBKO4nvh2K/nuyhVCAqbgw2X7OXJcZhyx27aJLu47pwstYx0YhkFxcTE/rN/Ltjw3fuxQxeDKq5sU+SL3qjKFwNUAc2RCCGbMmMHjjz3GnuxsAEYNHMSUW6fwi9IatyPcpWsBuwrrr+ZkXamYF1MUJRTcUVBQy9wDSYhjed1SU1N56623GtUmKWaSJmX66pxK7jfdEvy6oxCvbh6zEr3fMHlhTnjlDp9hsbvAy9frD3BJ3/RK2xT7rYguv4BpcahcjFrGOnjovK68PH8nBR4dyxLBkPmjxG31vhLmbc3Hq1uszy2r0s7qHIw7Dnl59vut3Hl6K1bsyMOmafya7cEvqncRWgIK3DrRTi1sAl4FUmMdJNdzO5lly5bx0IMPsnTZMgC6tW/Pw1Pu5uyLL8briuL7H3dF3C7J1XQuvIqkZyEEcXFxMun5JEaKmaRJcUfICYNgTUC/bh1TzDbmlkWMkPMbFj9uORRRzBIcCh6/jl+3gvNadpUohw2HTaNry8Nh4z3SYvnX5b0p8Og4bCr3fL6eQq8Rtq+AKXhjYTamEPiN2oXiAyAEmhlgf04RD3yRg6nYalUQUTctfLqCy67ismkIIUiMsnPVafXXSmbPnj089thjzJgxA4AWiYn8+cabuOqGG4IV7W02YoE2MbDfo2CIo9Mikmp1vFKfzuaDbhQUuqfGEOOo222qIrgjNjaWuLi4Bs9zkjQt8tOVNCmD2icyd0t+pdD8lrEOEqKO/fWMsmsRQ4UBYpzhQrhgRwGv/ryLvUWHw/IFQdG0LJ1eaXH0Tg93bSqKQkqMAyFEJSGroLp8syoRAlUYOAwPijDLRaxuIwa/bqIiGN0lhR6psWQkOOslBL6kpIS///3vvPbaa/j9flwOB7dMvJwpd95FYsf2KEclE4/MUFldFsP63DJUBWyayoU9WtCpRc3zyhbvLuKrDQeDOYeKwsz1B5jQJzVU5b8mmKYZtNflIiUlpdGTniVNgxQzSZNy05ntWLK7CK9uopsCrfwmeO/ZXWp0Q85sFUNClB2f7q8U0LH1gJsfNh3gnO6t+GX7IZ74ZmvVVeJNQakvwIacMnJLfaTEOMhqHY8QwWr6uwu8xDltlPojC1ptUC0Dm+FFEzqWYsNSj/9mawro3iqG1vXQRsYwDN577z3++swzHCqfV5ow+mz+7/d/oH2/LJQISdoQHIVN6p+ON2Di1U0Souy1StDOdwf4auPB4MhOQEUixPQ1eXRqEU3cMRqqHpn03LJlS5zO+hF1yYmBFDNJk5IW7+Kda/oxY3Uua/aX0C4pisv6Z9CuhgWGFUXhmYt78Mfp6ynzmWFVPfLdAZ6bs4Myv8nnqyrPzR3Nmn2l3PXZWlQlWI3DaVNpnRyFR7fQDeu4q8grwsRu+tBMP5aiYdaDiEEwPiQpynbcQiaE4IcffuDhhx9my5YtAAzu1ZtHfv8HBp5zNkpMzSrFRzm0UAPT2rB2fylWpMAcBTbkljGkfWKVdvt8PhRFkRXtT2GkmEmanKRoBzec0a7O27dPjua/Nw7ktx+tYueh8Mg5v2Hx1sI9Vc7NRaLifuozLHble4iPDoqOWcvyVCGEhc0MYDe9CMBU7MfVKMxlVwgYIsw1W+Iz2Vfko00tuwxUsH79eh5++GF++uknANqnp/Pg7Xcy7orfoCUmojRC0IQpRMRiz6KaKjAVnZ4rgjtk0vOpiwzrkZwUaKrCwbLKFewhKEot6hjZZ1oi4mihRgiBZviJ0kuwW15MxYalHp+QQfBHazsih04QDNN/7dc9Vc4fVkVeXh733HMPo0aN4qeffiIhNpaHb7+D+V99wyW33IwtOblRhAygZ2pslUne3VuFz7vpuo7H48HpdJKWlkZCQoIUslMcOTKTnDSkx7vYetBd6X1LwKCOifywKb/WtRIBguOF2gmQaunYDS+aMDEUrc7BHZHw6IJIDWvcgZqPzjweD//+97956aWX8Hg82DSNGy6ZwB/vuYeWXbugOBs/aCIjwcXgdgks2VMcSoGwqQpndU4OpRlUBHc4nU5atWqF09m0lfclzQcpZpJmQcCwsGkK6nGMWm44vS2Pfr2lkmCZlmDu5nyi7Coum0qxz0BVqilufASKQq1sUoQZFDFLx1JUDLXxcqwUhUrVQLILvfy09RAF3gA9U+M4o0MiX838gieeeIL9+/cDcN4ZZzL1D3+k65BBqNFVd+NuDC7o2Yo+6XGszS1FVRT6pseRnuAKBXeoqkpKSoqsaF9DTEtQUuJvajMaBSlmkiZl8a5CXv5pJ7klPpw2lQn90rnh9HZ1alNyRqdk7j2nM6/+vJtD7nCXo1e30E2BQrAYb009h0cGMgghqryBKsJCM33YTT8CBbOBRcyuKuhHnYRNUcKKKi/PLuKDpfvQzeDYcvGvC/nd5//i4K7NAPTu3IVH7r6H4RddiBoX22zEoW1SVKjDeEVwB0BCQgIxMTEy6VkSESlmkiZj3f4SHp69OTSS8uoW01bm4PGb/G5Upzrt85xuLenXOp7Jby+vVNn+WKWpIuHxm+iGQNOU8tJYhwVNCIGwLOxCxxEK7qhd0nNdaZccxd4iX7CKvqagKAo3nN429BBgWIJPlucQMAWlB/ayevpr7Fv9MwDJCUlMvf0OJl1/LbbkpEabE6stFcEdMTExxMfHyzkxSbXUWMyEEOTk5IReJycn43I1TBFTyanBu4uzK7kE/YbF7PV53Dy0PdF1CO8GygsMV3QeO35000I3wRcwcdk17DYVYQl0vw+X5cXh1BpNxACi7Cpje7bEp1tsz/cQ79IY3C6RxOjD81y5xT48ZcWsnPkO2+bNwDIN7HYHg868gDMvvparJp+BUo+V4vPK/OwosXAW+Wh9nEnbFf0NXS6XrGgvqTHVitlLL73EvHnz+Oijj/B6vZx99tmhZTfddBP33ntvgxsoOXnJrqIAraYq5LsDtHPULcw8IcpOeoKT7MLaNeCsCT7dRA/4cVo+HJaBqTaukEFwBPvmwmwsAVeflsFp7RLDlgcCAf77/lt88dxzBDylgEKfvsPoP/YazNadcKQl1JuQGabFxytz2JbvASH4NS+bFrEObhjcptYPI0dWtG/RooV8WJbUimr9C19//TWDBw8OfamEEGRlZdG5c2fmzZvXKAZKTl66tIyNGCNoCWgVe3zRdEnR9R+NpwoTl+kh2ixDERaGasdEDc5d1TIk/njx6RYBw+KDZfvIKw1O8AshmD17NkOHDuXpxx4m4CmlffvuTL7jafrd+BD+9t1QXU5Gd02pNzt+2l7AtnwPhiUwBAQsQW6pnxlr82q8j4p5MV3XSUpKolWrVlLIJLWm2pFZTk4OXbt2Da5os9G9e3ceeOABduzYwWOPPdYoBkpOXq4/vS3L9hSFuRpdNpXLB2TgOs7uyFo9DpQUYWG3/DhFMLjDUMJHNV6/gWVTD3d0rhilCdHgIzbTEizcWUgHK5f77n+A5UsXA9C5dRv+dNud7EwbxH4RLCtlt4L1G2tT5/BYLN1TUmku0hKw8YAb3bSq7CkHhyvaW5ZFfHy8rGgvOS6qFTOHw8HWrVsBiIuLC1XN/u6775pN5JPkxKVLyxhemNCLf/28i60HykiIsnPlwNZcXMdGjgHDYsGOAnJL/HWutB6GENitAE7hQxEEOz1X8b33G8G2Ms4jRe0Y+xYEm5NqFfs8at81SR8oLTjAc1OfYdMv3wAQFRXL0FET6Hz2pQw4vx/jkqPZU+jDb5p0SI4m9hj1DWuLblWRtyfKk82ruBS6rqPreii4Q1a0lxwv1X6DBgwYwIcffkhKSgrDhw9HURR+/fVX3nvvPYYMGdJYNkpOYnqmx/HP3/QJvS716ew46CEj0VWr+n77i31M+XQtXt0kYFrH7fXThI7L8qFaZlDEapgqUCFqEJz7czm0kFgJBIYJumkiAGEJnHYbmkYlIdMUmDQgnW/W5eC2VAK6FRbOovs8bPruYzb/8Cmm7kfTbPQffA69zrsKvUUGRZrGS7/sCa2fHG3n6oGtiW1Zv6KR2SKadblllUJtUuOcEUX9yKTn1NRUWdFeUm9U+82+++67Wbx4MS+88AIvvPBC6H2Xy8Xvf//7BjdOcuqgmxZPz97M1+vysGkqpiW49ox23DqywzG9AKYleOLrLRR69eMWMVWYOC0vNmFgoh1X0rNpCdy+6qvs2zQl4mhPUxV6psbRyirEF92K/SU+dh3ysiG3hC0/f8Xamf/BVxKsaN+t+0AGXngdSofu+GyH7T3S/XegLMA/f9nN/40JdrSuL87v0YIdBV4ChoVuBUeZmqpwSZ/UsPVkRXtJQ1OtmPXs2ZNp06bx2muvsWHDBgB69+7Nb3/7W7p06dIoBkpODV76YTvfrD9AwBQEzGBR4PcX7aFVvIMJA1pH3OZgqZ+nZm9mwfYComPsx3VzVISFw/LhEAEs1ErzYg1FmJvxCAKm4NOV+zkt0SKthZ3l2SWsXPwLv3z0T3J3BV3/ndp3ZvB51+LoNQS/49gBE6ZlMW/bISb2q9ywtK4kRjm4Z0QHlu0tZlN2Ph3TkhjcLoGEqOD1E0Lg9wcDVBITE4mOjpbzYpIG4Zg+hy5duvC3v/0t7L3t27czZ86csFB9iaSuGJbFFyv3V8o58+kW7yzYE1HMDMvixndWkFfsow6tMQ9TPi/mEj4EYNC4YfY+3STGcdToTAh0U/DL9gJ+BcTcH1g1/TX2r1sEQEJCMg/cdgfDL7mcl1YW4K+hvaYFuQ1Q2ijaoTGiUzKtRSGdO7cIvV/R6TkuLo64uDiZ9CxpUOrkQJ8xYwZvvvkmGzdurG97JKcgPt3CqKK9SqFHj/j+Z0v3kVvsCwVI+P0GDodW86d+IbBZOi7hC4bZN3KuWAWmKfDpwWTsinLGhiXwBgz8pYVs++59shd9hbAsHA4XQ4ZdROdzJqF3b89u01Ermx2aQpeWDV970TAMAoEA0dHRMun5FKawsJBnn32WH3/8kbKyMjp37sxtt93G2LFj+ctf/sIXX3wBBCPl27Vrx5QpU7jgggsAePHFF/n8888pLi6mRYsWnH/++dx3333VHk+GEJ3kmJZg9Z4iAoZF//aJNYu0a2RiHBot4xzkFFceNfSOEEa+M9/NS3O2h0X6BQIWgYCFokBMjKPaRpqaMHBaXjTLxFQ0RCMWA45EwDjc/FMIgR4IsOvnL9gx52MMnwdFUeg7YBR9L7gGI609Xk1ja4GPUr12NU4cNo3hnZIb7Dwsy8Lj8eBwOGRFewlTpkxh6dKlXHbZZfTq1YvXX3+de+65h8TExNA6v//974mLi+OFF17gz3/+M3369GHLli28+uqrnHPOOZxzzjns3buXvLxj5y1KMTuJWZtdzC1vLcerm6HiTs9O6sO5feoW+t5QKIrCn8/P5C+frw+5GhUlmHN2zzmdK63/5s+7qqyzKAT4fAZRUbZKc2jB4A4fNqEH58WaWMSORBCsppG7ah6bZ/8Hb2Hwx9u+Yy/6nnsNUd374Xc6QyOxKIdGjLPmDyYOTeEvZ3cipp5D8yEoYhV1FGVFewnA4sWLWbp0Kf379+epp54CoF27dtx8883861//onXr4NTB0KFD6dOnD0uXLuXrr79m/fr1ZGdnA9CjRw8uvPDCGke8HjM0PxK6Htn1I2k++HST615fSulR0XR//HgNX7WOp21y07b6OJoRmS3499X9ePPnXewp8NAjPY4rB7dlxZ4ipi3fR8/0OM7vnUaUQ2PdvpJqoxYNw8IwLGw2FUVRyoM7/DiqSHpuDhTu2sCmma9RtDvouk9pkcGgsycT1XckPqcL3QK8OnabgtOu4VBgWKckthx0E6hBj7bkaAdJdWxQWhUVSc9CCOLi4kIBHhLJli1bAOjfv3/ovYq/t2zZEhKzsrIydu7cydq1awHIyMigVatWKIrCP/7xD15//XUGDRrEbbfdxqBBg6o9ZrViduRwUHJi8ePGg1gR7vimJZi+dB93n9e1Cayqnr5tE/jHlX2BoCvxxrdXEDAsfIbFV7Y8/jl3B9ec0Q57Dcp7+Mp7lrlUgxjVj01Tm2xerDo8h3LY8tVb5KwKloeLio5j4PBLaTF0PP7oOHxH2asbAt0w2OE3WLyrkLE9WvDVhoPYVAVLgGUJDEuEuR8dmsrQTkn1andFcEdsbCxxcXHYbDYZpSipNddffz0Q9M5MnjyZrKwsAN566y0+/vhjFi9ezC+//MLy5cv58ccfSUqq+ntcrZjNmTMHIUToSzp//ny2bdtGZmYmw4YNq6fTkTQExR6dSMUZdFNU6vXVGJT5DWatzmHVnmI6pEQz4bQMUuOrDid/4n+bKfUZoZuyr1zUXvlxx7HniYRAEybRwoeKhQcNoQjsdguHQ63kArPKXZbVzbPVN7rXzfY5H7N7/nQs00Cz2ek7cAwdRk/Gn9QS3zGEwbDg1x0FXDMoi6GdktlV4CXWoRHj0Hhp3k48uhV8mBHQIy2WkV3qpx5jRdKzy+UiJSVFJj1LIpKZmQnAqlWrQu9V/F2xDGDq1Kl07dqVdu3akZYWnP4IBAKceeaZnHnmmRiGwc0338zChQvJzs6uu5jdcMMNKIrC22+/zWeffcbUqVNDy6ZMmcIdd9xR65OUNA6nd06OODKLdmic1b1lo9pysNTPNW8uo9Rv4NOD/bc+WrKXf1/dL2KAR26xj1XZxRH3dazyTpowiRI+bJgYqMGK9uXouomqKmha+QjGsLAsK0z0nU4t5J5sCCzTJHvRbLZ9+z4Bd/AcM3sOodeYq9Fbd8ar1W5Oy6Ob6KYg1qHROsGFTVN5ZGw3Nh0oo8hj0CE5itaJx1+0tyLp2WazyaRnyTEZMmQIgwYNYunSpTz44IP06NGDN998E4A77riDL7/8EoCsrCz69OkTtu3HH3/MTz/9xPDhw4mNjWXPnj24XC7at29f7TGr/eVs2bKFW265BYBPPvkkZMjSpUv57LPPpJg1Yzq0jOE3Q9owbek+vIFgEnKUQ6NvuwRGNrKY/evHHRR4ApjloqGbAt00eXTmRj67PbwsWnaBl0mvLa71MVRh4RR+nOiYKOhK5K+23199RQ6/38Q0LZzOygEkx4MQgoMbl7B51huUHQiWmcpo3YUB516D1u00fPbaj3BcNo0X5u5gf7EPDQVFhRtOb8fpHZPolRZXb3b7fD4URSEpKYno6GgpYpIa8Y9//INnn32W77//nhkzZtCpUyf+/ve/c8YZZ4TELBK9evXihx9+4LXXXsPj8dCuXTv+9re/kZCQUO3xqhWz0tJSEhMTKS0tZePGjaSnpzNlyhS++OILHn744bqdoaTReOjiHgzNbMF/F2Xj103GD8hg/ICMUDfixmL+1kMhITuSHfkebnh7ObeO7Mjp5SHjL36/Fb9R84BzRQgcIkAUfgSgox33vJhhCGw2gc1WP9epZP92Ns18nUNbVwKQkNiSgaN+Q/zAc/E5XZh1sFdTwaYq7CnwhI1W3/h1N+kJTtrXQ4CP3+/Hsizi4uKIjY2VSc+SWpGUlMTTTz8dcdkzzzzDM888E3HZwIEDef/992t9vGrFrHXr1rz11lt88803WJbF8OHDAdi/f78MDjkBUBSFs3u24uyerZrUDpet6vmfNXtL+ON/1/J/F3bjwqw0Fu0oqNlOhcAudKLxoyDqRcSOxOczcLlsaJoS6uRS2xGJr+QQW79+l71LvwUhcDqjGXDmRaSPnIAvNqlScEd1VMS8WEDreBfn9GjJx8v2VXK76qbFf37dw8R+6fRuHY9ah2siK9pLTkSOWWj4z3/+M1u2bCExMZEbb7wRgK+++op+/fo1hn2Sk4AJp2Xwn593VypXVYHPsHjh+22c3zsVl03Fb5jV7s8mDFyheTENoTTMiMF3RFpDVLQdFVEjQTMDPnbOm8aOuZ9iBnyoqkav/mfRZcyVBFq0xlvLqD9Vgb9P7I0nYJIa70JTFVbuLYooVELAzkMe/jFvJy3jnDxwftcat8Op6PQsk54lJyLVfsvHjh3L6aefTnZ2Np07dyYmJgbDMHj++edp0aJFdZsCwejHJ598EsuyuPzyy0Pzb0ezZs0aJk2axIsvvsj5559ftzORNFuuPaMdG/eX8uv2gioFzRswKXAHuHRAa975dU/EdVRh4iqfFzPQ0BsxX8w0TLRjiIKwLPavmMOWr97GV5wPQMcufck67zqs9t3x1TK4owIFSIx2kFjuOTQtQatYZ9W9xAg+IOSU+PhsxX6uP71dtfuvCO5QVZXk5GSZ9Cw5ITnmryspKSksHLKi4/SxME2Txx57jLfffpvU1FQmTpzI6NGjK1XbN02T5557Tob6n8TYNZW/TuzN+r3F3PzeSiKVYRRCEOuycdtZHflocTaBI1ZSyoM7XAQr2geaIOlZ1y3s9sojM1EeMVqwYy2bvnyVkn3bAGjZqi0Dx1yNo8+ZBOzHN8KxBFz77grSE5x0axXLol1FQSETAk1VMKsI8TRMwaKdRVWK2ZEV7RMSEoiJiZG5YicZpmlR0gDFpZsjDeYMX7NmDe3bt6dt27YAXHjhhcyZM6eSmL3//vucd955oQxwyYnHxjwf0zZvQFEULuqfTr92iaFlliX499ztvPnTLgKWhcsVOXG5R3ocUXaNIo9++OZ8RHAHKOiNXNH+SISoELTDYftCCNwH97Fx1hvkb1gIQExsIgNHTiT59AvwRcUQqAd7K6Rqf7Gf/UfVr7Sp0CrWwYGyyLmDooqsvIryUxVJzzK4Q1IbauJ1W7x4MU899RSGYZCUlMQHH3zQoDY1mJjl5eWFkuAAUlNTWbNmTaV1fvjhB959910pZicoT83axEe/BvuQKcB/F2dz/fAO/HFsMDHyX3O38/rcHXjLhaCiMvzRZKYGQ8l/3X4Ih6ZgBPxEEUDFqvfgjroSCJhYlsDu0NA9JWz77kP2LpyFsExsNgf9Tx9Lm9GT8Mel4GukiFHDEgRMix6psWw+UBYWEKKpCkM6hCeZVlS0j4qKkhXtJXWiJl63kpISHn30Ud58800yMjI4dOhQg9vVYGImIiTsHu2iefLJJ7n33ntr9VTo9/vr1HrG5/OdUC1rmtre3BKdvDKDdkkOkqIifz47DwX4aEEu/nKXoCDYzuWteTvJSvSRHm/j9bnZePXgcqvKjGdBjFXKopXr2LyrjGirDDNC0nNzIODzs3v+bPbM+wTDVwYodO1xBl3PuRq1XSd8auOPcIq9Btf1hD0FCrolCJjg0CDOoTAoyc/27duxLAtd17HZbMTExFBWVsbBgwfr1Y6m/s7WlhPN3uZCTbxus2bNYsyYMWRkZACQklI/FWiqo8HuFGlpaeTm5oZe5+Xl0apVeIj4unXr+MMf/gAEe9/MmzcPm83GOeecU+V+nU4nPXr0qLU9GzdurNN2TUVT2ev2G9z13kqW7ijEYVPxGxYTB7Xm4Ut6Vir39OOc7RGr1wtgbZGLIjWKwBGBiaYpME2BphHmqhNCMGtDKV+sOoBdBAhYClYzEzEhBIc2LWTnd2/jK8gBIL1NN3qNuhKrYxalmp0ooaIdtU1jBFK0inNyWq+u9OlmsWRPEXklPtolRTOgbQKaquD3+1EUhYSEhAZNepa/sfqhOQhsQUEBEyZMCL2eNGkSkyZNAmrmddu1axeGYXDNNdfgdru59tprueSSSxrU5ga7Y/Tp04ddu3aRnZ1Namoqs2fP5vnnnw9bZ+7cuaG///KXv3DWWWdVK2SShufh6etZsr2QgGmFIg+/WLaPTq1iuG5Yh7B1HZqKpqqYR2VEW5bg9Z924rRVDk7weHScTg17eV810zBRAh5KvQGcUQ68ii2yH7IJKd23lR3fvknJ7vUAJCSl0nf4b3D0GYnfcbhUlN9vEB3d+G67g2V+pny2lqsGtmF45+ATcEVFe92yiI+PJzY2VgZ3SGpMcnIy06dPj7isJl430zRZv34977zzDj6fjyuuuIK+ffvSsWPHBrEXGlDMbDYbU6dO5eabb8Y0TS677DK6du3Kxx9/DMDkyZMb6tCSOuLXTb5enUfgKHHy6hbvzN9dScwu6JvG37/dWmk/pgCEwBOI7Fb0+038fgMnOtFKAAXwCw1NaDRycZJq8RcfZNcP73FgzY8AuFyxZA0ZR8Lp4/FFxeKvomAxRB6VVdwE6ntkZImgq/HNhXuIcdro0SpKJj1LGoyaeN3S0tJC5c+io6MZOHAgmzZtOjHFDGDkyJGMHDky7L2qRKyq0iaSxsOrm1VGv5X4Kvewy0iK4vHLevHgtHXYNRWhgNdvHrOqvR2DGMWPpljoQkNQ+abflHlOht/D3l8+Z9+vX2AZAVTVRo9+o2gz4gp8ia2qrNyhqhVu02Dko7O8eaZpCfw+IyR2NpuKw6HVe5V+XTf47+IdTBjQjgFdMkhOiKnX/UskUDOv29lnn81jjz2GYRjous6aNWtC7V4aCvnIJgmREGUnPdHFnkPesPdVBc6sooXIpQNbk6EVss9IxBMw+XzZPtbtLYm4roZJtOLHqZjoQiUgwr9+um6iKAp2e2V3mBAiFAnZUEInLJPcFd+ze+4H6O4iADp0GUDXs67CaN0FzzGCOywL3O7Dom+zqyiA1xP+IBCs1i8idsOum+ECmzCwFJXtZTZeXnyIwMJ8rhnchmsGtz3+/UskR1ATr1vnzp0ZPnw448ePR1VVJk6cGNb6pUHsatC9S04oFEXhiYm9ufWt5fgNC0uAXVOIsmvce0G3KrdbtNvDByvz8AZMLCHQFMISo1UsopQAbeMVDpRa+EXkr52uWzgcVQtGQwpZ4faV7PjmP3gO7AKgZWoH+oy6EqXrIHy2us2DCcvCiJQhTtAlaVlwXOldQqBhoKDgU6PQVQcoCoYedBN/tHQf3VJjGdy+fhtzSiQ18brdfPPN3HzzzY1mkxQzSRhndEnh89+dwX/m7WLHQTcDOiRy/fAOpCVE7on1/bo83lhUGArPh8PxGwoCpxKcF0NATolKt4xENu4vrfL4hmFFHJlVLLOVFy2uL1FzH9jDzu/+Q+HW5QDExiWTNfQyok87Lyy4oy4YhkWEufIQliXQatA1OxKaMFAQBBQnAdWJUCpfM59h8cWq3EYVs4Nlfn7cko8nYDKkQxLdU2NlaSxJoyDFTFKJrmlxPDOpz7FXBP7x3bYwIYNg1QkHBjFKAIXgvFiFxFUnZBC8wVdU2jgSv98MiVl93BwDZUXs/vFD8pZ/ixAWdruL3oPGkjLsMvzRCZWCO+qCcYxWNoYR7J0WPKdgp+tI56ZwuAqIIkw0LAzFTkB1EeNyopgWAdOK2Li09Bj92+qTlbl+3pu7EksIDFPw3+X7OatrCn8e00UKmqTBkWImOS72F/vCXtswiVH82MrnxUQdvmKBQFC4NC0oaIZhVjvCgZoHjVh6gH2LZpI9/7+YAS+KopDZZwTtR15JoEUG/ggjnIbCDIZ9lp+rEiz1FYGMBBeHynwYuo6panjU2FAy+eAOifzx7M5MeGMpXj08CtVpUxjeObmhTwMIFop+d52HwBEm+AyLn7Yd4qzMFpUqkUgk9Y0UM0kl/LqJXVMjRtsdKvPzxk87mbcpn1ZxDjISXRR7dFSs8uAOAzNCcEdtCc4pVW4Fc3R9RIic93I0QggOrpvPru/fxV98AIDW7XvRY9RVmO174lOb9qdgVjG3hhBkxKrkl1h41WgM1R4q7eWyqwxsn4jLrjFlZCde+mkHATPo2nTaVNLinYzvkxZ5v/XMir3FEdMqfLrF9xsPSjGTNDhSzCQhFm07xMNfbGDXQTcOm8pvBrfhzxd1x1k+T3WozM9Fzy+g2Kujm4JteWBTLWJUnSgCmEI5bhE7FoGAiaoqaBqh6MZgVZGqR2Ul2RvZ8c2blO7dDEBSSgZZIyZh6z0Cfx2DOxoCv98MhfMHgzssVAV6tGtFdFJLftpagFGeyO7QFFLjnIzqGmzFNLZXKzq1iGbGmhwK3DpndEzi/J6tcNkbp7xWdeNZ6WGUNAZSzCQAbNxfwm/fWo6v3FXl0y0+XbKXQrfOC1f1BeA/83ZR4jXQy91jTnRiRLBau5/D82INjc9nhOaYLCuY1xWsvBHuavQV5rLz+3fJX/8zAK6oeLLOvISEwRfhc0UTuc5802EYFqZp4dBAUyzKhB17TCz9O6Zzeato+rbJZ8aaHHy6xajMFlzePwPHEV28u6XGct+YrjU61s58N5sPuElPcJKVEX/cc1r92yZEzC902VTOb+JO55JTAylmEgBem7uDwFGNM326xXfr8jhY4sNnWHy9JoeAaWIvnxfTyivaH5303BgIEe6a83p1oqLtIASGt4y9v3zGvkUzEaaBptnpOWAMacN/gz8+pcqk56ZGFRaaMAlYNrxKFM5oJ7rP5Pq3lpGa4OLhcd159Yq+x3UM3bR4ePZmVmQfdgu2jHXy4mW9SI5x1Hm/LrvGzVmxvLnWg0Kwmr+mKozt1YoBbROOy2aJpCZIMZMAsC2vLGI0nKoqjP/7r5R4dUzDIF7xY1dMDKESaEZfHyHAXeKlYO337P/lEwxvMGqyU7chdBp1FXpaB7xEjhZsahQhsGFiolKmxIBmI9ppwxcwy0fBkF3gZcpHq/n0tsFkJEbV+VifLN/Hiuwi/EdEWu4r9vLM99t49pKex3UevVra+e+NpzFv2yF8usmg9kl0TIk+rn1KJDWl+dyNJE1Kn7YJbMsrq9QF2hsw8Qd0opQAcapeL8Ed9Y0QgpIdy9n307v4C/YB0Cq9C73PvgrRuT9+1VajIJHGwq4pQZESAhvB0bAHFwHFjk1T+NdV/bj7k7UhIatAtwSfLtvHPed0ibTbGjFrbV6YkAGYFqzMLsYTMImuJmm9JiRE2Rst6EQiOZLmdVeSNBm3jurEV6tz8RzRs8WuChxCx6UEsAQEROPNi9UUz4Gd7PvxXcr2BFtQxMa1oOfplxI96DwCDieKojQrIQPQFAVL6KiADyd+xYEoHzEO7JBEgUePGBlomKJSqbHaoh9VRLryMtlxWnJiIsVMAkCHljF8cucQnpq1iVW7C0lxQnoM7DgQICBUmpuI6WUF7P/lYwrWzgUEdkcUPQZcSMygi/E5oykxFGwE87eCidhmk7RnORpVmAjDILN1Eiv365hH5LU5bSqPX9IDt9+K2CfOaVMZ0P745p+Gdkrmm40HOFrT2iS6SIhq+usjkdQVKWaSED0y4nnz+r4UFRWh6zpfrsxjU56nUk6XZVqYhgmKgs1hQ23EeSgz4OPAspkcWPwFluFHUVQ69xxJ6pmX401ICwV3BCvXm+hH1Pj1+QycLtvhcluNaLciLGxYGGjojmguP7M7kzWF1+btosgT4MwuKdw1uhNJ0Q6SY+Csbi2YtyU/FF2qqQpxLhsX98s4LjtuOrMdS3YXUeoz8BkWDk1BUxX+cm7NoiAlkuaKFDMJALquU1xcjNfrxeFwEBUVxf5CX3nlDRFy1/m9AcQRowZTN3G47Kha/ZSZqgohLArWzyPn5w/RywoAyGifRcdhV+BPz8RTg8odpinwuHVUVcFu18o7XlNJrCvOtV6q9JcHdwhU3LiwVDsto1yM7NYCu6ZyVreWAKzOLuZPn61jW56b9EQXvx3egR4ZcXz06y4MVEZmtuC2szoSV0WVkJqSFO3g3Wv6892mA6zbX0rbpCgu7J1KynFEMkokzQEpZqc4pmlSWlpKWVkZmqYRHX04+iynwI3f48fmsKHZNfSAHiZkFQTKe52p5TlPDmfQXVVf4la6Zx37fnoHb94OAJJS2tB96CTMrkPw1KFyh2UJ/OU1C10uW1jlessSmKaFZVkoiorDUcfyVuXBHXYNEhMT2VlkgaIwtEsy/3dBN+za4f2uyi7izg9W4ytPjSjNK+OhGRv4ywWZvDC2JT169KibDVUQ5dC4OCudi7PS63W/EklTIsXsFMWyLDweD8XFxQC4XK4w8dmSU8KBYj8ARsDACBy7YK1VfjP2m34c0U44ziabvoL97J/3HsXblgAQFZ1Aj8EX4+h3Pj778VW0Dx3DV/V5qapF9bUtIqMJExULHw6SkxL56PYzDi+LENnx8pwdISEL2WVYvPzDdv4+tnFqK0okJzpSzE4xhBB4vV6Ki4sxTROn04mqht+wv1q5j+dnbcSvVx35Vv0xgv+vq5AZ3lJyf/2U/FXfICwTzeYgs++5JA6egC+mfira14TgKE2UuyOPfUxVWNgwCWDDrURjKho7C/y8v3A31w/tUOV2W/PKIr5f4jPw6uEj4fwyP2v3lpAS66BP6+Ov3CE5ubFMC3eJp6nNaBSkmJ1CBAIBioqKCAQCOBwOHI7K8yTegHFcQlaB7tdxumo3D2OZOvkrvyH3108x/W5AoUPmGWQMm4w/uXWTVO7w+QxsdhW7TQuV0DoapVzETDRKlRgMJfxn9cb86sUsLd7JjvzKNxyHTcVlqwhoEfxz7g4+WpyN3aYiBCTH2Hn16v6kJ9bPKFUiOZGRYnYKYBgGJSUleDwebDYbUVHhFSQOFPv4Ykk2W3NLSIi210t0omVYGLqBaVoIS6BqCja7DUWpXIVDCEHxlkXsm/8+gaJcAFplZNJ52JXo7XrjbcS2LJEwdAtDt1AUiDkyUEII7JgIFNxEoSv2iFV1fYbFvkIvrZMiV+64dWRHHp65MRS5CMGK+FcOaYOmBl29P27O579L9xIwBQEzmAvo003+8OkaPr5lcD2erURyYiLF7CTEsgQ5RV5inBprt+fy6fxN5JcFOL1bGpcMbov9iHSibbml3PHmEgKGhVFefb7KdiS1RD+iMaRpCUw9gDPKgXJEhXt3zlb2/fgO7n0bAYhLSKXnGROxegzHZ2teEXaKUh7tSLBvmwJ4cRI4Iuk5EjZV4Zt1eXRoEc1p7ZNIPCrf7ZyerSjx6fxz7g68ARNNDQrZrSM7snnTJgA+WbK3Ur8yS8CeQ172HPLQTpaNkpziSDE7yfh+TQ5/+mAFfq8XJ34QAr8VTHpeu28bb87djhAQ47IxcUhbFm45iMd/uOpHfQlZVfh9AZzRTozSQ+z/+UMKN8wDwOGKpftpFxJ12jgCzuZ5Y7asYKdnmyLQceDFgVU+aqxuLGtYgrcX7EZVgmWsppzdiSuHtA1bZ8KA1lzcL4P8Mj/bD7iJcmhhDUlLqwhU0VSFskbsJi2RNFekmJ1ErMsuYsqbC7GZXhyKIGCpiCOi8YLeqeAdssSj88Evuwgc59xYbTH9XvYv/Yz8lbMRRgBVtdGl9yhSzrgcb1wKgWYa0KBiYVMs3B4NvxaFYrOXB4jo4a7HKjhyVPXK3B1ktUmgd+v4sHXmbjrAo7M2B0tZCXDaVe4eEk8P4OweLdl1yFOps4GiKHRNja2PU5RITmikmJ0k6LrOG7NXEi3c+IVaXkexehpTyIRlUbzpJw4u+QzTG0wHyGjfj/TBl+NP7YTH6UD36zic9sNJy+Ujk0hBF42FQnBezESlxIpCRwNDAcNAVSE6urKQqUpwpOawaeimyVH6g9+0mL5if5iYZRd4ePjLTfiPWNkdMPnrzwWcM9jkisFt+GpNLnmlfny6haYo2DWFBy/KDMtZk0hOVaSYneCYpklJSQllZWXsP1SK12x+H6k7ew0Hfv0Qf0E2AEkp7ek4eCL+9n1xKzYwBYbHH4qdMA2LgD9QMYhEURUcUY5GLZsFAnt5Rfsy4cSPnaOdiZqmHp5HK7dNU4KV4+8Y2YFol50nZ2/GOKJ4MwRTF8qOchvOWp2LGSEh3RKwYNshzu7Rig9/O4j/rcnhl60FtIp38JuBbeSoTCIpp/nd+SQ1wrIs3G43JSUlAERFRdG3U0s253nRjx4KNBH+gr0c+PVD3NmrAYiKSaLrgPHQfRSeCMEdQgTPq6KiSOh9SxDwBHBEO46/vFQNsGGiKQK3cOAT9jBX7ZHouoVhBLDbtfKyWMEU613Ffh75YiNJMfaI3ayj7Cpn92gZ9l6RR49YXNgSgpJy4YtyaFw+sA2XD2xzvKcokZx0SDE7wahIei4qKkIIgcPhQFVVDhR72bavkJKSMgRgs9mwO+woTVDt3vAUk790GkUb5wbLOtlddO49hqh+4/C5qh9JHC1kFQghEKaF0oAutYp5Mb+wUWI5MctFLCXGToFbJ1JojBAQOGrkBeAJmARMi4GdktlxyIPftBAiKGS9MuI5u2e4mA3rmsJXa3MrRSwKAYM6JNXbOUokJytSzE4g/H5/KOnZ6XSilRcVdPt1rnlxHodKfaF5JlM3sClgczpQVQXDFMG5FaXh5sosI0Dhmm84tGIGlu5DUVTaZp5BysCJeBNS8dVEWKsJplQaqJixgoVdsTCERrEVjXFUT69ib2Uhq5jGi9SduwLDFGzeX8Lbtwxi+or9lPoMzunRktE9WmI7qurK0C4p9GmTwNq9xSFBi7KrnNXBRZsq8tMkEslhpJidAOi6Hkp6djgcYcWAAWYs2s2hUl/YjTVY8V3wzOS+5JUEKPbq/LrpAOv3loSW1hdCCEq3LeTA4k8wSvMBaJnRg7ZDLseX2hWvUk8NHy0RnJSqN4LzYgKFUstFABuRrktQQMNVyxIQH2WjxFt9WLxdC47EemXEV7uepir848osvl13gK/W5hJl17h0QAZJgbzanpREckoixawZY5omZWVllJSUYLPZKolYBZ/8vCPiCEE3LXYfcLNs+0HmbcjD4XKWux3rTxA8uVs4sOADfAe2ARCXmE7ngZdidD69ThXtq0PXDRyqvR5GZ+XBHQp4Q/NiVe9Tj5B7p8AxhcxpU7lsYOsaW2VTVS7MSuPCrLTQexs3Hqjx9hLJqYwUs2aIEAKfz0dubi5CCKKioqq8gQcMk5zCyIVEDVPw7283UurVsdur7iIsELWeWwuU5HFw0SeUbl8MgNMVR5e+F6L1OQ+f3VmrfdUUy7DQFR27o+4tZrTy4A6fsOO1HFh1qIoP1XpDgWA5qj5tErjjnM512r9EIqkdUsyaEUII/H4/hYWFbN9XwKZDCm1axNKjTXghWd2wgsEfdg3DrHr+SwFKvZUDKiwrGPpuWcHRic0WrJkY7OGlYLPZKlXSr8D0uzm0fAaFa79FWAaqZqdjz1HEDbgEX3QilUMh6hdTtzB1P85oR63ETC2fF/MLjVLLiUndXJ/B+BMlYhg9gF1TmDSkLeP7p9OvfaKsai+RNBJSzJoJFRXt3R4fj322ll825eGw7cW0oFNqHP+85QwE8ORnq5i3PgchoFe7JKZO6k9m6wQ27S2utM+kGAcF7mBwuGEa2LBhCQuf13d4JQGGfthd5nA4UCIkKQvToGjDHPKXfY7pC7Ysad1xIC0HXx6saF/HEU5d0OwqwhIIJSgo1QqGsHCoFqZQKbai0I/zKx/tsNGzdRyLtxdWWubQVF68Kotz+6RF2LJmHCrz88GCPSzfVUiX1FiGphvUb2tOieTkpEHFbP78+Tz55JNYlsXll1/OLbfcErZ85syZvPHGGwDExMTwyCOP0L1794Y0qdlRUdHe7XZjt9v576Jsft18kIAhCBhBkdmaU8zjn65k90E3ew6WYZTP4azdVcANL8/nb9cP4o9vLUE3LQKGhdOuEu20MahrK75duRcI5mpV1Sn6SDS7FuZyFEJQtnsFBxd+RKAoB4DkVp3oMOhyfG1746uv4I5aEBydBUekzmgnEKkJqMCBhYmgxLATUBwEx6rH1zA0I9HFWd1bsmR7YSVXo92mMLJ7y4jb1YS9BR4ufWkhnoBBwBAs3VHIpyq83bINgzrJJp0SSXU02OO0aZo89thjvPnmm8yePZv//e9/bNu2LWydNm3a8MEHHzBr1ixuv/12HnrooYYyp0lZvauA/3t/Gfe+vYSfNwTnwSzLoqSkhNzcXHw+H1FRUdjtdj7/dRc+PdxZFzAs5m3IJafAg36EW1EQdDmu3lnA328aQo+2icRHO+jYKo6/XjuI60aFz9cEW7LU3BHoy99F9qyn2Pf18wSKcoiOa0G/kTeRfsnDeNr1xWoCITsa3RcceQbLXwXlxYaJQzFxCzt5HjtFZRaeUl9YpY66EGXXuP3sznyyKDvinNnd53bBaa/7NfnbV1so8eoEjODeDUvgNwQPTFtf531KJA3B/PnzOe+88xgzZgyvv/56leutWbOGHj168M033zS4TQ02MluzZg3t27enbdtgdfALL7yQOXPm0KVLl9A6AwYMCP3dr18/cnNzG8qcJuPFL9fx1y/W4NdNhIAP52/j8sEZ3De+KwAulyvsBuuNkIALwZGVpVa+hfp0k39/synsvRJPgFv+taBO9vq9flTDzaFl0yjeNB8Q2B1RdOpzPo5+F+JzNK+K9pYl8Ln9qDYVl0PDrgh8wobHcuDzmRjG4espLFHl45tdUxg/IAOnTaXEq3N2z1Ys2HqIWStz0FQFRYG7z+1KRlIUB0sr1/VQgJ0Hj6+j7y9b8iNGpWYXeCj26CREVx3EI5E0FhUDlbfffpvU1FQmTpzI6NGjw+7tFes999xzDBs2rFHsajAxy8vLIy3t8NxBamoqa9asqXL9adOmMWLEiIYyp0nYe8jNM9PXhEZaDtUiWvj5adV2xg1IY0CXyi6poT1S+X5VcK7sSFonx5Bf6m9Qey3dT/GGbylZ9w3CDKAoKu26DSdx4AT8cS3Rm6CaSE1QEdisAF6vRr7lwAQsq/K1CpS3n4kU3KKbAgT87tyupMQGS21d1D+DB8b34FBZgLREF06byvfr8iLWiBTA/iLvcZ1HrDNy3ppCMDpSImkO1GSgAvD+++9z3nnnsXbt2kaxq8HETIgIuTlVuHgWLVrEtGnT+Oijj465X7/fz8aNG2ttj8/nq9N2x8MXyw9CeYmkOJuBU7PQLYUSH8z4dTMJSkmlbS7uHcvCjTa8uknAENg1BU1VuH1UKh/8mse2A2bIDVVfCGHh3r6Q4pVfYHqLAGiR0Yv0gRPxpXbCb2ueIwIFgV0xsVApNpzlnQLCE5yPfGVZAm+ZD1e0E80W7g4UQvDZot3MWLyLawclc37PRKIchwVkx8Hg/6P9Bn6jsuDYNchMMI7rO3ZuVxcfr/DjPyKvzabCkHZR7Ni2pc77bSya4jd2PJxo9jYXajJQycvL44cffuDdd9898cUsLS0tzG2Yl5dHq1atKq23adMmHnzwQd544w2Sko5dg87pdNKjR+3juzZu3Fin7Y6HFXkaCfZdKGoAQ4DPDN4cbZpCaosUOneunIPUGZjRtRP/+WoFe0uhU1o8E8/oQFpSNMMH6PzuzV9ZuaNyJF1d8eVuonDZp+gFewCIS2xN234X42vbj2JFBb9OlM1WZR6aCBOOxhq5CeyKhaJAqenAb9kiJj1XVaHD5/HjctqwUFBUBUVR8HsDwbqXwOu/HOSjZYXMuu8sOraqXEvy2hw7HyzYE9auRTdh2lo3wpXINUPbk1HLElSGadE/kMfcHVvYW+Alyq5hCkHnZDsv33gmpmVh11RinM03ALkpfmPHQ3O1tzkIbEFBARMmTAi9njRpEpMmTQJqNlB58sknuffee0Ml9xqDBvtl9OnTh127dpGdnU1qaiqzZ8/m+eefD1tn//79TJkyhWeffZaOHTs2lCmNjmVZeDweBrS241R1SvTwqhuaqnLBaVVXPk+IcXDxgJZhYieE4LH/rmT1rqJ6sVEvzqVo+TS8e1cB4HTF077PWMzMsyjTwkdiVQVOCERwHkoBVWkcN5hNsdAUC49px2PZqk16Hta9FT+sza3U0BLAV0135oBhUeQO8Mf3ljP93pGVlv9pbCbTluwLEzOAQo/OW/N38dHCbN69dRD92iXW6JwChsV1ry9lw74SPAETmwoB0+KPYzNJoZiJ/1hI9qHgfNzQzBb8dVIfkmvQEFQiqSvJyclMnz494rKaDFTWrVvHH/7wBwAKCwuZN28eNpuNc845p8FsbjAxs9lsTJ06lZtvvhnTNLnsssvo2rUrH3/8MQCTJ0/mlVdeoaioiEcffRQATdOqvIAnAhUV7YuLizFNkxaJsTx29en85b2laKqCEGAIwe/H9aJjalyt9r1k60EWbDyAdYzQ+mNh+sooXjOLss0/gTDRNAdtu52Fo88F+JyRK9qbuoniUMJD9stHZAF/ALvTXp8VsiKiYWFTLfyWRpERFapoXxUOm8LY/hn8vPFARDE7FpaApdsL8OkmrqMiFLfmucOiSo/ezhMw+b9P1/HVvTWb+P5yxX7W7y3BWz63GjRX8NK327AsE/8RMUG/bMnnuteWMvP3Z8qEbEmTUJOByty5c0N//+Uvf+Gss85qUCGDBs4zGzlyJCNHhj/ZTp48OfT3k08+yZNPPtmQJjQaFUnPgUAAh8OBwxF8ch7RK41vHzmPXzYcwLAszuzeipS48IoeB4s9rN6eT3K8i/6dW0a8Sc1amo0nUH0twOoQpk7pprkUr52NCHgAhfQOg0joezGe+DR81dwXdV1H0ZQwl4EQIph8LcA0TFSHGlHsjtf1qCCwqxamUCg0XOhHdNCuXP73MN0yEhiTlc6f3l9xHMcmYrBHTTRk50E3JV6d+KhjzzfOWrk/JGRHYlhWpUAgwxTsOeRh1Z5i+rdPPLYhEkk9U5OBSpPY1SRHPYmoSHr2eDzYbDaioirPlcRFORgbwa0ohOClL1by/pxNOGwqQkBSnIs3fx/+BPPat5v4dmV2newTQuDds4Ki5dMwyoJRDEmtupDe71I8qZl4avh0H/AFUFUVVVWxhIV1xF3W0A1URUU7YgQjLIHf58cZ5ayjC1LgUEwECiWGHb+oXNF+aPeWLN9eEFEINu8vwbIEUyf24fHP1+HXzWrbtRyNqsCInq1w2CrbnpkWS3yUDU8VaRQQFLxI20YiyhF5XsESkVvMKArsK/RKMZMcE8u0cJe6632/xxqoHMkzzzxT78ePhBSzOmJZVqiivaqqEUXsWMxZmc1HP24moFuhHmPegJspr/zIveO68PaHy1m4KY9Cd6R+xcfGn7+DomWf4T+wFYDouFZ0yLoIb8chlKm1n5i1LCtYzzECgUAARVdQtWCpqYr1DN0IjVJrRkVwh8BtOvBWEdwB4LSrJMTY8RZVFhVFAU/A4PqzOtO3fRLvzd9BYVmA7XllbM8rO6YViqLw7NX9q1z2z2v7c93rS/EbVqgiSwV2TeGsHi0ruSerYvLp7Vi4raBSjmGUQyMQMAgcdclNS9Azo3ZuaonkZEeKWS0RQuDxeEKdno9Oeq4NH/+4Ga8//AZmWYJt+4r53TtrsTRHneaijLJDFK2cjmdnsKK93RFDu17novQ4hzJbw1S0h+C1MY2jzqeaQshHo2GhqRY+04bbsh+zov3irYcY2z+DzxbuqbQsJdZJemLwAaN/x2T6dwyWg3L7DM74v6/Idx+7fcumfSVkJEVOEu/bLpGfHziL/63K4f0Fu9mV78GuKQgUOreK4anLe9fklAEY2b0FV53RlvcX7EFTFVQFbJrKv6/rzx3vLMfyWxjlQzSXXWVEt5Z0ihBlKZGcykgxqwU+n4/i4mJ0XcfpjJx8WxsiVbSH4DyQ3xcAAtiio1FqeBxL91Gy7itKN3yPMHUUVaNNl+FEZ43DG51wXLbWFVU7tu1qeXCHLjRKjCgMUbPz9esm23Ir5+oBDOiYFPEhI8Zl49Fxbfjj53vwVdNx2xMw+XF9HqN7V100OC7KzuQz2jH5jHbsPOhmU04pbZOj6NU6vlYPOIqicN9F3bnqzHYs2VFIQrSd4ZktcNhUXrw4jf9thzkbDhBl15h8RltuGnnyRP5KJPWFFLMaoOs6xcXFeL1eHA5HnVyKkThnQBt25BThr+amang8YNOwO6MQwsIyDBCgOQ6P2oRl4t72C0WrZmD5SgFo1SaL5L6X4Eluh7cJg95s9qq/YgoCh2oGK9obLgJCpTZDUd0UrNxZFHHZgs35VW7XPS2Kj343jEenrWHdnmLMCHkzAF8syeax32TVSJg6toyhY8uYGtldFW2So2mTHD4STI628fRvml8ulETS3JBiVg2maVJSUkJZWVm1nZ7ryuSR3Xn/+0349WPMiRkmulF5nkdzOvDuW0fR8k/Ri/YDEJ/cjjb9LsbTOqvGwR0NhcNVVc+x4LwYCIoNOz7LBkIJtqkp7612vFQVOl/B6Zkt+Pr/RuPTTTJ/NzPkxjsSj99g5wE3nVKlS08iae5IMYtARXBHaWlwlFNdp+fj4dP5W/BHiMSrCf5DuyldPwt/zgYAXNFJtO9zAXrXEXUK7mgIdL+OsAlUTUXVgqH7NsVEVQRuw0apX0GoKgFfeUV7NdgY9HjRVIVzs9JrtK7LrtEpNYYtOZUfFoSoOtJQIpE0L6SYHUFF0nNFcIfD4TjuebEKfAGDLxZsY8aCHXj9OlmdWvDDyuwqq+RXhekrpWzj13h2LgQEms1J2x5nY+91Pt5mVtFeCIGu66CD06ES5dDwWTbcph2v38Aw9LBEsWP1WqsOp6bgNwXRDo24KDsPTax5AMZNo7vwyGdrwz4LVYHureNJr2VZKolE0jRIMSvH7/eHkp6dTme91hQL6CbXPvsdW/cWhtxZO/NKa7UPYQZwb5tH2eYfEIYfFIW0tgNJOG0C3thW+JtpMQgVgUMT6AHBPo+KLkygagEP+AJHuScFx5pHa50cxc2jOrMlt5R+HZKYMLgdMa5jf7WXbj/Ev7/bwt5DHtokR7En341NU1EUhcQYB2/cenrNT1QikTQpp7yY6boeSnp2OBz1Pi8GMOPX7WzdVxhxXuZYCGHh27uS0nX/w/QGCwwntexKi54XUJbSgTKHE60mQnZsTahXFAROVWACRQEbPquiov1hHJpK4Ki5LcMwsLwWDoc9mCwmgv+LcWicf1obZi3bS0V1KrumkJYYxcd3D6v1vNYXi7O594MV+AImArBrwe7cf7iwO91bJzC0W0tUtZk+IUgkkkqcsmJmmialpaWUlpY2SHBHBQeLPfz106WVEmtrQuDQDkrWzEAvDOZRRcel0qbneXha96OsPAfLMgw0+zHy0UTwP3qZB9VhP/b6x4XAoQbFrMSw4TU1BMHcKYdN4/KhHckr8pJf4uO8fhk8O2Md5lEib5kWPm94PzLVsjE8swXPXjWArbkl5BT6SE100b9D5BD86jAtwQOfrApzK+qmhdsn2JZbym/P6Vr305dIJE3CKSdmQgjcbjfFxcUIIRosuKOCl6evIqDXTsiMsnxK18/Ct281AHZnLG27jcboNIxS9ahqGlVU5AhDAYQCCliBAJahY4+KqXdBsykWNgXcpobbCLZYARjRM5VRfdIZP7g9XdLj+XVTHv+YvYGPf9lZ4327fQYLN+dxxfBO9OuQTL8Odbdzf1Gg0ogQwLAEP204UPcdSySSJuOUETMhBD6fj8LCQkzTrJek5+qwLEGJN8D3K3bXfJuAh7LN3+PeNh+EiarayOh0Jo7u5+J1VO1Gs4wAqq0Woy1LYAb8aM76qQaiKcFQe7+lUaTbKiU9b80p4Yv7z8GmqXw4bxt/fHtJrQNf7JpS604DVRHn0qocKbeIa7gKKRKJpOE4ZcTM7/dz4MABXC5XLWsF1p4vFmzjxc9XUObT0WvQFVpYJp6dCyjd+C0iECwK2rJ1XxJ7jqUsLg3vMbY3/QFMw0DVbCiqiqJp4aNNETwGRyQHW4Zx3GKmIHBqAsNSKNCdBKzIDwdF7gA/rcthRK807ntvWa2FDIKmXzWicjPTupAYbWN495b8vOlgWHuYaIfGHedl1ssxJBJJ43LKiJllWdhstgbvfPrDij08/fHSGt2whRD4c9ZTsu5LzPKK9vHJ7UnteQHuVpmU1cYPaFpYZnnytaIEy2BxOCLQ8Plqtp8Kvav20MHgDgEU6TZ8ZuXgjiPxBkzW7ykiNTEqYpfamnDliM60TKi/MPl/3jSIm19dzPIdh7DbVHRDcNf53bhwQOt6O4ZEImk8Thkxa0hKPQFyCtxkpMTwr1mrayRketFeStZ+SeBgsKK9KzqZtj3G4G03iDLlOD8WITDcblS7HYQIlsCq0XZgmQYgULVIDTcFdkWgKlBmaHjMqivaH832vBIuj+tYbSCMTVOwLFGp7Um0U+PPE7Jqdg41JCHawWd/GM7eQx5yi7x0y4gn7qjeY5Yl2LS/BLtNpUtqrGyGKZE0Y6SYHQemafG3z5Yz7eet2DQVw7SwjjHyML1FlK7/Cu+epYDAZnfRuutZ0HUkZVr9JuhaeuRCxhEpFzKzYgTnIkzQNMXCroLX1CjTNcwaFgOuoMSjk5EczaCuLfh5Q17EdRRg4TMXcfXf57PvkBtFVYiya7x+5zDatji+uodV0SYlmjYplSNZF245yO1vLMHtN7AEpCW4eOuOM+iWEd8gdkgkkuNDitlx8OY365j+yzb8ulVtsWAAy/Dj3jIX99YfEWYARVFJaz+EqB7n4Y1KaiSLD6PYtKOqb5iY/sPh8KbPj3AI7A47DtUiYKkc8tvRayliECwJNbJXsPr8e3ePpNOtn0bsEG1Ygq6tE1j63Hi25ZTg0016tk1Ea8BAnUjkFXu55h+/hjXf3HnQzWXPz2f5M2Nx1rBPmUQiaTykmB0H7/+w6ZguRSEsvLuXUrrhKyxfMQDJqT1I7nUB7sQ2eBszk/lIu3QD3XCjaCpYVqVSUgoCpwigqDYKdTv+CEnPNaVVgovfDA22LUmOc3J2VgY/rNlfab1BXVqGhKtrRtO0rAGYtnBPpdw3gIBu8f2aXC46Tc6rSSTNDSlmx0GJJwAIMA0wdbC7QDk8ivAf2ELJ2i8xivcBEBOfTuteY3Gn98Z9jMaTjYIQCONoMQ5GKKqqgjM2nkLDgc806zxfZNcU7p/YlxhXcD5q3yE3V5/VmSVbD+A3giNah03FYdN48aYhx3lC9UNusQ+/ESkPzeJgSQ0DaSQSSaMixew46No6gS3b9oFRPjflCM55GaV5lKybhT9nXfBtZxxtu40m0GkYpaq9qt01OXZVoKkCt65is7s4p3db/jtvK7puomgamisKtZbRoAoKh0r8lHp1bvzHfOatz8Vp0/AbFoM6tyDKaaNPh2RuObcb6VV0dT4S3bDILfKSEuck2tkwX98zu7XkkwW7cB/VBVxBYXCXlAY5pkQiOT6kmB0H5/fLYMumw0nRlr+U0k3f49mxAISFqtnJaDcYW8cz8diiwesOJkw5XMF/TeRiPBpNCRYD9hoKBT4bowe0J2BYfDp/a6hFjTBNDHcZ9ti4Gne+BrDbVbI6JHPna78yb31u2Pziip2HePnmM7h8aM06J7/5/WYe++9KAoaFEHDNWZ15+ppB2G31O8od0yeNbhnxbNxbgrf8/KMdGudkpdGrbWK9HksikdQPUsxqgWVZLF6/l+y8Yrp3aMnGHXmAQFgG7j1LKNvxM8LwAQqt0nsT32UU7qjkwzXiK4TM7qQ5CFlFRXvDUsj3aphoPH/rcMac1p7T7vwIX4T5QDPgx+Y6HHUpystpVQicqhAKrXfaNXq0SSSrQxJfr9gblqAM4PGb/H3W+hqJ2ZdLdvPQh8vDgjI+mLcdVVV59rpBNT7njXuL+MPHW1m3bzXJcU5+d2FPbjwnM8yNatNUpv1xBO/+tINpi/fg0FSuHtGR35zRvsbHkUgkjYsUsxqSX+Tm+sc+J6eglECg4qYs8OZupHTrD6GK9glJ7UnNHENpQgbuSIIV8EHAD7EJNJWg2TVQsVBVBbdh58ax/RjVry0jstrgtGt8vXQXDpsWUcxUETx3YZroXs/h2pCqSlxCLKP7tmXZtnxUVeGKYZ24b0IfDpX60aqoQJ9fWrM5qGenrw0TMggmY7/341YemzwAVw2aaG7PLeGcqV/j9hkIoMSr8+D/t3ffYVGd2QPHv1OAgaEIKGBBjajYQOyyFpRIUBHBGlyX3Zj4i2m2GI1u7LGHaFw1m0Rj2Vg2iatgw0RNxBgVu4iCLRbUCCoinWFm7u8PwsSRNkjX9/M8eR6cee+dMwO5Z95733vO5lPcfJDB3BHtjcaqzBSM8WvGGD9RdFgQagKRzEwgSRLjPt3FzXuPDY9pHt8h9dIP5KYkAGBp5YBr016kO7ciTVbSgfXZm1CWhblSxlv9WtGkri2H4h5iZ2vN3/3a4O5qfGuAewN7NAUWhoC5Uk5TVwfupmpJeZhqVB4LvZ6s1HS+eOsv1LI2LpNlaa5ErVIWWPkpl2FYsl+Su8kZhT4uSZCSkYOLCY1Jl0bEkvVHy5d8mTk6vtwbz6SgNthZVWyZM0GobJJOhyb1cckDnwMimZVAkiQmr4jk/LW8auq6rMekXjlA9r3zACjNLGnw0l/Qu3YhTVGKxR2aHDB/+nRjxTUdU/5xXaxzK1d6tndjuH/Rv/omde3o064h+8/cMpqdWZgpCJ/ely8jL7J82+lCqnlITP36MMvf6WV0L5ZcLuPT1zrz1hdHnugfJkOtMuOfQ9uaFH+7JrU5UMhyfiuVkjp2KpP2cfLag0KX3Jsr5fx2L412TZ6fxR3//ve/2bVrF3K5HLlczty5c2nbti2hoaFMmTIFDw8Po/ExMTF88sknJCYmolarqVOnDpMmTcLd3b1McSxbtozw8HBSU1M5c+ZMoWMePXrEuHHjiI2NZdCgQcycOdPwXGhoqKGmKsDatWtxdHREkiSOHz8OQOfOnQ2nib/99lvWrVsHgLW1NVOnTqVjx44F9qXRaPD396dly5YA+Pr6olarkclk1K5dm8WLF1OnTp0yvXehcolkVoxHqZnMWn2An0/dQK/NIf36YTJuHgW9DplcgUuD9qia9CDb/BmquWuy8rpOmj05Gyj/RCb/I4nl6GTcS1cwcukhftvghrKE3/zGqX2Z9Z+jrN17gcwcLd1a1+XTMT1xdbLFXl34DEark9j8UzxH434nKmyY0QwtuGtj6juqWbbjAjfup9GthTMTAltT39G0yh6zQ9px9FISWTlaw8zK0lzBvL+2N/mm6mZ1bbl0+3GBeXGOVk/9QqqA1FRnzpzh4MGDbN++HXNzc5KTk8ktphrMgwcPmDBhAmFhYbRvn3e69eTJkyQkJJQ5mfXu3ZuRI0fi7+9f5BgLCwvGjx/PlStXuHLlSoHnw8LCjJJvdnY2s2bNMjy2bds25syZw9GjR/n222/ZvHkzDg4OXLhwgXfffZfvv//ekJjy95WSkoKvry/vvPOOofD4hg0bcHBwYOnSpXz55ZdMnz69TO9dqFwimRXh15ibTFi6m6zsXDLvnCb92kH0f1S0d3Ryx75pbzKsnSnTXUc5maBQglZTyCytbGRImCsl9HpIzlaQo5MBMrRaHXtO3GBI96bFbm9hpmDRG91Z9Eb3As91bO6EhZkCbSE9wXJ1EjcTU1n07YkC23ZqVofNk3o90/vxbOzAj7P9mffdWU7/9hDX2mo+HOyJf7sGJu9j4sA27D931+h0p8pcQf/2DXAqxyLGVe3+/fvY29sbDtIODg7Fjt+4cSPBwcGGRAYYZjNl5eXlVeIYKysrOnbsyK1bt0zap0qlYvbs2YwcORKATZs2oVKpWL16NZMnTza839atWxMcHMymTZuYMGGC0T4yMzNRqVSFFh7v2LEj33zzjUmxCNXHC5/MdDo9Zy7fJSM7l/bN62GjtiBHk8uk5ZGk3L1E2qUf0WbkVbS3tq1Lvaa9SavdtPDFHaUll0NWWt6FHzOLZ85lMp68Cpd30zPA4xw5WVo5T+5Yo9XzMLWkpjLF6+lRn7ZNanP6ShLZuQWvreXk6vnfL1cLTYRl4dHIgW8n+z7z9h3cavPNBB/GfnmYB+laFHIZI3o0YVGo6ashSysgIIA9e/aU6z779+/P7t27i3y+W7durFq1Cn9/f7y9venfvz+dO3cucvzVq1cJDg426bWPHTvGwoULCzxuaWnJrFmzTNpHaf3zn/9ELpfzyiuv8M4775CTk8PcuXMZPHgwAHPnzmXWrFlcvXqVNm3aGG3bpk0bwsPDDf/+4IMPMDc35+bNm7z++uuFJrODBw/SvLloBVTTvNDJLP7mfd5evIPMLA25Oh1arYSrix2dGltw9+gGMpPyTnlYqGxp4NaTnHpeJizuKIUnu0RnZ4BlfgPO/PRUcnYzU8oY2qMZxy7+TrYmh5S0bFI1cjK1cvRSwe11ej0+HqbPZgojk8nYPS+I+ZuPE7b1dBFxVYMKJ4Xw86rPtrEe1GvUFCsLBebK56/OolqtZtu2bZw8eZLo6GgmTpzIpEmTDAf/kgwbNoz09HS6detW4FRb165diYiIKHS7uLi4Msf+tLCwMJydnUlPT2fcuHFEREQQHBzMggULDNfMRo4cWWyFmiefyz/NmJyczODBgxk2bBj16+eVJ/vHP/6BXC7H3d29wExOqP5e2GSm1eoYszCc5CfKE+ly0jn/006ib58BJBQKc+o17oqskTeZStMWGTwznRYyHqOwUOFob4uZUkZSmga9nkIXLeRzrWPD5KHtyM1tw+Xf0xmx5CDphcyW8rVwdSiwevFZqMyVfPzaXzgce5fjl+4ZtW2xtFDyun/rMr9GRZHJZNQq4rpfeStuBmWKuLg4wyKF0lAoFHTp0oUuXbrQvHlzwsPDi0xmTZs25eLFi/Tp0weA77//nr1793Lw4MECYyt7Zubs7AzkLeYYMGAAMTExBAcHI5PJ6NLFuPyZm5sbsbGxeHt7Gx67cOECbm4Fm7o6ODjQpEkTzp07Z0hm+dfMhJrphUxmt+494rsDsWRm510Ul3S5ZNw8Rvr1w0g6DchkONXzwtrNhyxVrUqJydJcQbsW9Qjt50X3to25du0a1g4ubDt8ja/3xhZald/KXM7YAa2Qy+U4OTnh6urK5OEZzN9ywlC540nmSjnffFj0hfhnsX7KK/hM/JZMjR6tTo9MJqNb63qMC/Yq19cRTPfbb78hl8tp3LgxkJcQ69WrV+T4kSNHMnz4cLp37264bpZdRDPXypyZabVaUlNTcXBwIDc3l4MHDxolqqeNHj2asLAw1qxZg729PXFxcWzfvp3vvvuuwNisrCyuX79Ow4YNyzVmoeq8UMlMkiTmfv0TO36Jz+vyrNGRfS+W1Cv70WenAlDLsQl1mvVGW7sROknCQpZXLT3/lqr8ChcKhQxdMY0mn2ZuJkdTTJsYPfDFh0FGp0Sc7dW8HeiJtaUZK8LPopckNLl6ZEg0cVHzXlA7BnRvhaWlpWG7ycM78rc+LVn3wwWWbz+LXq9HLpeTq9Oz4p1etGxYvsvPGznZsnPaX7iTpSbhfjodmjvRvqlTub6GUDqZmZnMmzeP1NRUFAoFjRo1Yu7cuYbnx4wZg/KP5axeXl7861//YtmyZYSFhZGYmIijoyO1atXi3XffLXMsS5YsYdeuXWRlZdGzZ0+GDRvG2LFjOXDgALGxsYwfPx7IWxqfnp5Obm4u+/fvZ+3atdSrV4/Ro0eTm5uLXq/H29ub4cOHF/laL7/8MomJiYSEhCCTyVCr1XzyySc4Of359/jBBx8Ylub37t27wDU2oeaSSc/ax94Ehw4dYv78+ej1eoYNG8abb75p9LwkScyfP5+oqChUKhWLFi2ideviT08962mXU6dOEXs7g4+/PkRmjhbNo1t5Nz2n5t27ZGldB1e3XmQ4t0CPHHNzOZtmD+fExdukZ2no1rYRbvUc2HUknuMXb6PVSvwac4McTfF9zCCvPuEgn1Zs+/lCkZ2WlQoZ/1v4V5o0yDvNce3aNaPTI5k5uVxKSMbOSkE9B2tsbW1Rq9XIi1mWrtXpOXrxd8PSemvLijm19qy/k6pSk+IVsVac6hpveca1aUcUo786X+rtTn/ycrExlHRs37FjB6tXrwbyruHOnj2bFi1alDqO0qiwmZlOp2Pu3LmsW7cOZ2dnhg4diq+vL02b/rkk/NChQ9y4cYMff/yRc+fOMXv2bL7//vuKContP18k9VESaZf3k52Ud0rEzFxN/Sbd0DXoaKhob2Ymp2trV1o0rkOLxsY3Tr7ax5NX+3jyOCMb33e+LvE1zczkzBzVm7+0bcS+Y1d4lJ5T6DilQo5UTGUQpUyiRf28BGZra1voKqzC9tnDQ/TeEgSh/JhybG/QoAEbN27Ezs6OqKgoZsyYUaHHdqDimmrFxMTQqFEjXF1dMTc3JyAggAMHDhiNOXDggOFirpeXF6mpqSQlJVVIPKmpqZw7+D33f11FdlIccrmSeo270qDb22gaeSMpzTE3V2BuJucvHg1Z/G7x15bs1CpmjOqNmXnRH6GN2pwZo3oR3KsVTvZqvl/8VzybORc61t7Gkib1C1581mq1ZGZmYmZmhrOzM/b29iYlMkEQhIpgyrG9ffv22NnlNdj18vLi3r17FR5Xhc3MEhMTcXH5s+6es7MzMTExxY5xcXEhMTHR6Bx3eThy5AiDBg3i8eO8GmWOLq2xd/MhQ12HbMBObc6Py0dx92Ea9jYqHO1Mq0oR3KsVXu512fVLPLeTUnFvVJvG9eyRy6CDe32s1cY1Cp3trVk3fTBvL97B+Wv3yMrRoTJXoFDICRvfz+h6mV6vJysrCzMzM+rUqWMo5yMIglCVTDm2P2nr1q307NmzwuOqsGRW2KW4p+8FMWXM03Jyckq9aioiIoLHjx/TtlUrajXsSYKyLhkaPWYKGXK5jLHBnvx+NwEZkJIDKQ9KtXv6dXAC8hNw3ntKvHebxCLGTwvxJOa3+sTdTKaWjQU92tRDLcvg2rVrSJJEbm4uubm53L9/HwsLC5KTk0sXUCXLzs6ukHuMKkpNilfEWnFqWryVKf8+vHyvvvoqr776KlC64/axY8fYunUrmzdvrphAn1BhyczFxcVoalnYjOvpMffu3StxVmZhYVHqi6OLFi2im7c37du3R2mh4sCJaxyJuUUdezWDfFrRwNmuVPsrD0+eX4a8PxCNRoNer8fW1paEhIQSF8NUF9X1QnpRalK8ItaKU13jrQ4J1sHBgW3bthX6nCnHdoD4+HimT5/O6tWrsbcv+72tJamwa2YeHh7cuHGDhIQENBoNu3fvxtfXuBSRr68v4eHhSJLE2bNnsbGxKfdTjJD3raG+qytypRIzpYK+3s2ZO6YPY4d7V0kie1pubi5ZWVmoVCpcXFywtbUtdpWiIAhCVTHl2H737l3Gjh3LkiVLeOkl0zrJl1WFHTGVSiUzZ85k9OjR9O/fn379+tGsWTO2bNnCli1bAPDx8cHV1RU/Pz9mzJhRYbXdqiudTkdWVhZyuRxnZ2ccHBwM9/8IQlncv3+fiRMn0qdPH/r378///d//cf369Wfa18mTJwkICCAoKIjExETGjRtX6LiPPvqI8+dLvwy8rKZOncrevXuLHRMdHc3p04WXXitP0dHRjBkzpsJfpyqZcmxftWoVKSkpzJkzh6CgIJNLqZUprorcuY+PDz4+PkaPjRgxwvCzTCZ74RIY5C3uyMnJQaFQ4ODgYHTTs/D80V66ij6j8OaiJVFcv4kmS2P0mFytRuledNcDSZJ47733CA4OZtmyZUDeqauHDx8+07fkHTt28PrrrzNkyBAA/vWvf5V6H1Xt+PHjWFlZGXUGKIlWqxVfLotQ0rF9/vz5zJ8/v1JjEr+pSiRJEjk5efeZ1apVCysrK3E68QWgz8hAbmf7TNtKNuoC2+ofpxa7zbFjx1AqlUYHl/xrQ5IksWTJEn755RdkMhlvv/02/fv3Jzo6mpUrV2Jvb8/ly5dp3bo1YWFhbN26lb1793L48GGOHDnCxIkTeeutt9i1axfZ2dlMmzaNq1ev4ubmhkbzZ9I9fPgwK1asQKPR4OrqysKFC1Gr1fj6+hIcHMzPP/+MVqvls88+w83NjYyMDObNm0dsbCwA7733Hv7+/kXupyiF7d/CwoL//ve/yOVyduzYwYwZM2jSpAmLFi0iPT0dyKvM36FDB1asWEFSUhJ37tzB3t6eW7dusWDBApo1awbkNfj88MMP0ev1LFiwgOzsbFQqFQsWLKBJkyZGsRw/ftxwQJfJZGzcuBFra2uEiiGSWSXRaDRotVpsbGywsbER94oJFebKlStFLh768ccfiY+PJyIigkePHjF06FBD77KLFy+ye/dunJycGDFiBKdOnWLYsGGcOnWKXr160bdvX27fvm3Y15YtW1CpVOzcuZP4+HgGDRoE5K2E+/e//826deuwsrLiq6++Yt26dbz33nsA2Nvbs337djZt2sTatWuZP38+n3/+OdbW1uzcuROAx48fl7ifohS2/5CQEKysrHjjjTcAmDRpEgMHDmTIkCHcvXuXN954g8jISCCvOPHmzZtRqVSsX7+eyMhImjVrRlJSEklJSbRp04b09HQ2btyIUqnkyJEjLFu2jBUrVhjFsXbtWmbOnEmHDh3IyMjAwsKiQKxC+RHJrIJptVo0Gg1WVlbUrl0bMzOzqg5JeIGdOnWKgIAAFAoFtWvXplOnTpw/fx5ra2s8PT0N9w+1aNGCO3fuFNuk88SJE4SGhhrG5xc2PnfuHFevXjXMDHNzc42adL7yyitAXq+xffv2AXD06FGWLl1qGGNnZ8fPP/9c7H6KUtj+n3bkyBFiY2P5z3/+A0B6erphlubr62u4r7Nfv36MGjWKcePGERkZSd++fQFIS0vjww8/5ObNm8hkskI7ebdv355FixYRGBjIK6+8UuyMUig7kcwqiE6nQ6PRYGZmhpOTk/hWJlSaZs2a8cMPPxT6XHGlWPM7U0NeCxmdruhWQvkKu9YrSRLdunUzSk5Pyv9CJ5fLDa8hSVKh96EWt5+iFLb/p+n1ehYvXlxocrS0/LPruLOzM7Vq1SI+Pp7IyEjmzJkDwPLly+nSpQurVq3i9u3b/P3vfy+wnzfffBMfHx+ioqIYPnw469atK7QdjVA+xAWbcqbX68nOzkar1eLg4CASmVDpunbtikajMWp9EhMTw/Hjx+nUqRORkZHodDqSk5M5efIknp6ez/Q6nTp1MpwWvHz5Mjdu3ADyyhedPn2amzdvAn+2WylOt27d2Lhxo+Hfjx8/fqb9FEWtVpPxxCKc7t27G3UAL+7eroCAANasWUNaWhru7u5A3swsv9fa9u3bC93u1q1buLu78+abb9KmTZtnjl0wjUhm5SR/cYdGo8HGxgYXFxesrKzEKkWh0slkMlauXMmvv/5Knz59CAgIYOXKlTg5OeHn50fz5s0JCgriH//4B5MnT6ZOnTol77QQI0aMIDMzk8DAQNasWWNYJOHg4MDChQt5//33CQwMZPjw4fz222/F7uvtt98mNTWVAQMGMHDgQKKjo59pP0Xp3bs3+/btIygoiJMnT/LRRx9x9epVAgMD6d+/v2FJeWH8/f3Zs2cP/fr1Mzw2evRoli5dSkhISJGzvw0bNhjej0qlqpSSTi+yCm0BUxHK0gKmbt26FTJLyl/cYW1tjY2NTbks562u1QkKU5NihcqPtyxL829cv0njlxoZPVbS0vyqIv4OykdNaAFTHYlrZmWg0+nIyclBpVLh6OhodM1BEPKVJfHoLM0xr2EHFaEa0WshtZTFZmsokcyewZM3PdepUwcLCwtxOlEQBKEKiWRWCpIkkZ2djUwmw97eXlwTEwRBqCZEMjORRqNBp9NhY2ODtbW1uOlZEAShGhHJrAT5vcXUajW2traiVpsgCEI1JI7MRchf3GFhYSHuFRMEQajmxH1mT9Hr9WRlZaHVanF0dDQs8BCEmqRly5YEBQUxYMAAxo0bR1ZW1jPv68kWK/n3ZxXl6VYrW7ZsITw8/JlfO9/t27cZMGBAieO++OKLMr+WKUJDQ6uk3Y1QNDEz+8OTFe3t7OxQq9Wior1QLu7fv2/42yqthw8fGhX3hbxu6yXd6KxSqYiIiADyiur+97//ZdSoUYbndTrdM133Lamtx9OtVp6s3F8ZvvzyS956661SbfOsn4VQvYhkxp+LO/Kvi4k/bKE85eTkGNX7Kw1zc/MC25Z2ltWxY0cuXbpkaPPi5OREXFwcO3fuJCwsjOPHj6PRaBg5ciQhISFIksTHH3/MsWPHaNCggVE9x9DQUKZMmYKHhweHDh1i2bJl6HQ67O3tGTVqVIFWK0ePHjVUq4+Li2PWrFlkZWXRsGFDFixYgJ2dHaGhoXh6ehIdHU1aWhrz588vtsDxtm3b+Omnn8jKyiIhIYE+ffowZcoUwsLCyM7OJigoiKZNm/Lpp58SERHBN998Q25uLm3btmXWrFkoFAratWvHgAEDiI+Pp1evXly+fJnly5cDebPLdevW8cUXXzBr1izOnz9PTk4O/v7+BRqT6nQ6PvroI2JjY5HJZAwZMoTXXnutVL8foXy80MlMq9WSm5uLSqUSFe2F55JWq+XQoUP06NEDgPPnz7Nz505cXV359ttvsbGx4X//+x8ajYaQkBC6detGXFwc169fZ+fOnTx48ICAgABDY858ycnJzJgxg40bN+Lq6kpKSgq///57gVYrR48eNWwzZcoUZsyYQefOnVm+fDkrV67ko48+AvKSwtatW4mKimLlypWsX7++2PcVFxdHeHg45ubm9O3bl9DQUD744AM2bdpkmJFeu3aNyMhItmzZgpmZGbNnz2bnzp0EBweTmZlJw4YN+fjjj9FqtfTp04fMzEysrKyMSldNnDiRWrVqodPpeO2114iPj6dFixZGcSQmJrJr1y4AUlOL7zUnVJwXMpk9WdG+du3ahnYPgvC8yJ+hQN7MbOjQoZw5cwYPDw9cXV0B+PXXX7l06ZKhwn5aWho3b97kxIkThjYxzs7OdO3atcD+z549S8eOHQ37qlWrFr///nuR8aSlpZGWlkbnzp0BGDRoEOPHjzc87+fnB0Dr1q25c+dOie/P29sbGxsbANzc3Lhz5w5169Y1GnP06FFiY2MZOnSo4TNxdHQE8roCeHt7A6BUKunRowc///wz/v7+REVFMXnyZAAiIyP57rvv0Gq13L9/n2vXrhklM1dXVxISEvj444/x8fGhe/fuJcYuVIwXKpnl3/QMiJuehefak9fMnmRlZWX4WZIkpk+fbpi15YuKiirx/4vCWraURX4puOLathQ2HopuVyNJEoMGDWLSpEkFnrOwsDC6nNC/f382bdqEnZ0dHh4eWFtbk5CQwNq1a9m6dSt2dnZMnTq1wLVPOzs7IiIiOHz4MJs3byYyMpKFCxea/L6F8vPCrHCQJMlw03PdunVRq9UikQkvtO7du7NlyxZDY8nr16+TmZlJp06d2LNnDzqdjqSkJKKjowts265dO06cOEFCQgIAKSkpQMFWK/lsbGywtbXl5MmTAERERNCpU6dyf09KpdLwfry9vfnhhx94+PChIcaiZn2dO3fm4sWLfPfdd4ZTjBkZGVhaWmJjY8ODBw84dOhQge2Sk5ORJAl/f3/Gjx/PxYsXy/09CaZ5YWZmKpWKevXqiZueBeEPw4YN486dOwwePBhJkrC3t+fzzz/Hz8+PY8eOERgYSOPGjQtNOg4ODsydO5exY8ei1+txdHRkypQp9O7dm3HjxnHgwAFmzJhhtM3ixYsNC0BcXV0rZAYzfPhwBg4cSKtWrfj000+ZMGECr7/+Onq9HjMzM2bOnEn9+vULbKdQKOjVqxfbt29n8eLFQF737FatWhEQEICrq6thheaTkpKSmDZtGnq9HoD333+/3N+TYJoXpgVMdW33UJSaFG9NihUqP96yLM2/ceMGjRs3NnrMlKX5VUH8HZSPcm0BE36A0Ut/KfV2p798tVp+NsUR0xRBqGBlSTxpaWk0aNCgHKMRhOfTC3PNTBAEQXh+iWQmCIIg1HgimQmCIAg1nkhmgiAIQo0nkpkgCIJQ44lkJgiCINR4IpkJgiAINZ5IZoIgCEKNJ5KZIAiCUOPVuHJWZ8+excLCoqrDEARBqBA5OTl4eXmVy76uXLmCVqst9XZKpZJmzZqVSwyVpcYlM0EQBEF4mjjNKAiCINR4IpkJgiAINZ5IZoIgCEKNJ5KZIAiCUOOJZCYIgiDUeM9dMjt06BD+/v74+fnx1VdfFXhekiTmzZuHn58fgYGBXLhwoQqizFNSrNeuXePVV1+lTZs2fP3111UQobGS4t2xYweBgYEEBgYSEhJCfHx8FUSZp6RY9+/fT2BgIEFBQQwePJiTJ09WQZR/KinefDExMbRs2ZK9e/dWYnTGSoo1OjqaDh06EBQURFBQECtXrqyCKPOY8rlGR0cTFBREQEAAf/vb3yo5QqHcSM8RrVYrvfzyy9KtW7eknJwcKTAwULpy5YrRmIMHD0pvvPGGpNfrpTNnzkhDhw6ttrE+ePBAOnfunLR06VJpzZo1VRJnPlPiPXXqlJSSkiJJUt7nXJ0/2/T0dEmv10uSJElxcXGSv79/VYQqSZJp8eaPCw0NlUaPHi1FRkZWQaSmxXrs2DHpzTffrJL4nmRKrI8fP5b69esn3blzR5KkvP/nhJrpuZqZxcTE0KhRI1xdXTE3NycgIIADBw4YjTlw4ADBwcHIZDK8vLxITU0lKSmpWsbq6OiIp6cnSqWy0uN7minxtm/fHjs7OwC8vLy4d+9eVYRqUqxqtRqZTAZAVlaW4eeqYEq8AN988w3+/v44OjpWQZR5TI21OjAl1p07d+Ln50e9evUAqvSzFcrmuUpmiYmJuLi4GP7t7OxMYmJisWNcXFwKjKkMpsRanZQ23q1bt9KzZ8/KCK0AU2Pdt28fffv2ZcyYMSxYsKAyQzRi6t/t/v37CQkJqezwCsRhymd79uxZBg4cyOjRo7ly5UplhmhgSqw3btwgNTWV0NBQBg8eTHh4eCVHKZSXqv/KX46kQoqZPP2N25QxlaG6xGGq0sR77Ngxtm7dyubNmys6rEKZGqufnx9+fn6cOHGC5cuXs379+kqIriBT4p0/fz4ffPABCoWissIqlCmxtm7dmp9++gm1Wk1UVBTvvvsuP/74Y2WFaGBKrDqdjgsXLrB+/Xqys7MJCQmhbdu2vPTSS5UVplBOnqtk5uLiYnRqKzExEScnp2LH3Lt3r8CYymBKrNWJqfHGx8czffp0Vq9ejb29fWWGaFDaz7ZTp07cunWL5ORkHBwcKiNEI6bEGxsby/vvvw/Ao0ePiIqKQqlU0qdPn2oXq7W1teFnHx8f5syZUyWfranHA3t7e6ysrLCysqJjx47Ex8eLZFYDPVenGT08PLhx4wYJCQloNBp2796Nr6+v0RhfX1/Cw8ORJImzZ89iY2NTJUnElFirE1PivXv3LmPHjmXJkiVVejAwJdabN28avrlfuHCB3NzcKku+psT7008/Gf7z9/dn1qxZlZ7ITI31/v37hs82JiYGvV5fJZ+tKbG+/PLLnDx5Eq1WS1ZWFjExMbi5uVV6rELZPVczM6VSycyZMxk9ejQ6nY4hQ4bQrFkztmzZAsCIESPw8fEhKioKPz8/LC0tq+xaiSmx3r9/nyFDhpCeno5cLmfDhg3s2bPH6JtvdYp31apVpKSkMGfOHAAUCgXbtm2rlrH+8MMPREREoFQqUalULFu2rMpO85oSb3Vh6me7ZcsWFAoFKpWKpUuXVslna0qsbm5u9OjRg4EDByKXyxk6dCjNmzev9FiFshNV8wVBEIQa77k6zSgIgiC8mEQyEwRBEGo8kcwEQRCEGk8kM0EQBKHGE8lMEARBqPFEMhNeSI8ePWLatGl07dqVNm3aEBQURGRkJABTp07F3d0dd3d3PD09GTBgAPv37wfg9u3bhufc3d3p3LkzEydO5NGjR1X5dgThhSeSmfBCGjt2LNu2bcPX15dp06aRkpLChAkTOHr0qGHMxIkTmTJlCrdv32by5Mno9XrDc61ateLTTz+lS5cu7Nmzh08++aQq3oYgCH8QyUx44URHR3PixAnatWvHggULGDlyJPPmzQPg888/N4xr27Yt3bt3x8bGBkmS0Ol0huecnJwYMGAAEyZMAPIqXQiCUHWeqwoggmCKy5cvA9CuXTvDY/k/X758mfr16wPw2muvGZ4fP348ZmZmhn9rtVqSk5MNpx/r1q1b0WELglAMkcwEoQgzZ87ExcWFJUuWsGHDBqOyUocPH8bb2xvIay2SXwRYEISqIZKZ8MLJr7139uxZw2P5Pz9Zl8/T0xMPDw9Onz7NmjVrjIrQtm3blokTJ2JnZ0fTpk0xNzevtPgFQShIJDPhhdOlSxc6derEiRMnmD59Oi1btmTNmjUAvPPOO0RERADw66+/cvnyZcMqx/zTjwD29vaGmZkgCFVPLAARXkgrVqxg8ODB7Nu3j4ULF2JjY8Nnn31mlKCWLVvGjBkzAJg+fTpNmzatqnAFQSiBqJovCIIg1HhiZiYIgiDUeCKZCYIgCDWeSGaCIAhCjSeSmSAIglDjiWQmCIIg1HgimQmCIAg1nkhmgiAIQo0nkpkgCIJQ4/0/O60wYA+8Fe8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl \n",
    "\n",
    "# OLS using statsmodels\n",
    "def run_regression(xx,yy):\n",
    "    # X = sm.add_constant(xx)#, prepend=False)\n",
    "    ols = sm.OLS(yy,xx)\n",
    "    ols_result = ols.fit()\n",
    "    return ols_result\n",
    "\n",
    "\n",
    "min_atbat = 50\n",
    "\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "    fig = plt.figure(figsize=(7,5))\n",
    "    ax = fig.add_subplot()\n",
    "\n",
    "    # Get colormap\n",
    "    cmap = mpl.cm.Blues_r(np.linspace(0,1,20))\n",
    "    cmap = mpl.colors.ListedColormap(cmap[1:12])\n",
    "\n",
    "    # Plot scatter \n",
    "    splot = ax.scatter(grouped.loc[(grouped.AB > min_atbat),\"OBP\"],grouped.loc[(grouped.AB > min_atbat),\"Slg\"],c=grouped.loc[(grouped.AB > min_atbat), \"OPS\"],cmap=cmap,norm=mpl.colors.Normalize())    \n",
    "    \n",
    "    # Run regression\n",
    "    model = run_regression(grouped.loc[(grouped.AB > min_atbat),\"OBP\"],grouped.loc[(grouped.AB > min_atbat),\"Slg\"])\n",
    "    xvals= np.linspace(0,.655,1000)\n",
    "    # xvals2 = sm.add_constant(xvals)\n",
    "\n",
    "    # Plot Confidence and Prediction intervals\n",
    "    CI = model.get_prediction(xvals).summary_frame(alpha=0.05)\n",
    "    ax.plot(xvals,model.predict(xvals),color='k',lw=2,label=\"SLG = {}*OBP\".format(round(model.params[\"OBP\"],3)))\n",
    "    ax.fill_between(xvals,CI.mean_ci_lower.to_numpy(\"float\"),CI.mean_ci_upper.to_numpy(\"float\"),color=red,alpha=0.15,label=\"Confidence Intervals\")\n",
    "    ax.fill_between(xvals,CI.obs_ci_lower.to_numpy(\"float\"),CI.obs_ci_upper.to_numpy(\"float\"),color=grey,alpha=0.15,label=\"Prediction Intervals\")\n",
    "\n",
    "\n",
    "    # Clean up\n",
    "    ax.set_xlabel(\"OBP\",fontweight=\"bold\")\n",
    "    ax.set_ylabel(\"SLG\",fontweight=\"bold\")\n",
    "    ax.set_title(\"On-base percentage vs Slugging percentage\",fontweight=\"bold\")\n",
    "    cbar = plt.colorbar(splot)#,ticks=cbar_ticks)\n",
    "    cbar.set_label(\"OPS\",rotation=0,fontweight=\"bold\")\n",
    "    \n",
    "    ax.legend(frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So basically see slugging percentage carries approximately 15% more weight than OBP. Let's try to dive a little deeper into this...\n",
    "\n",
    "TO DO..."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.10.4 ('phillies-project-forecastML')",
   "language": "python",
   "name": "python3"
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  "language_info": {
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