data_explorer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "98da2fb34e9f01c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:09.780949Z",
     "start_time": "2024-09-09T13:24:09.543607Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import fastf1 as f1\n",
    "\n",
    "from streamlit_app import current_season"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "ebc7631e41158f31",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:10.469538Z",
     "start_time": "2024-09-09T13:24:09.785657Z"
    }
   },
   "source": [
    "circuits = pd.read_csv('data/circuits.csv')\n",
    "constructor_results = pd.read_csv('data/constructor_results.csv')\n",
    "constructor_standings = pd.read_csv('data/constructor_standings.csv')\n",
    "constructors = pd.read_csv('data/constructors.csv')\n",
    "driver_standings = pd.read_csv('data/driver_standings.csv')\n",
    "drivers = pd.read_csv('data/drivers.csv')\n",
    "lap_times = pd.read_csv('data/lap_times.csv')\n",
    "pit_stops = pd.read_csv('data/pit_stops.csv')\n",
    "qualifying = pd.read_csv('data/qualifying.csv')\n",
    "races = pd.read_csv('data/races.csv')\n",
    "results = pd.read_csv('data/results.csv')\n",
    "sprint_results = pd.read_csv('data/sprint_results.csv')\n",
    "status = pd.read_csv('data/status.csv')\n",
    "hungary = f1.get_session(2024, 13, \"Race\")\n",
    "belgium = f1.get_session(2024, 14, \"Race\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "req         WARNING \tDEFAULT CACHE ENABLED! (474.58 MB) /Users/jestonlewis/Library/Caches/fastf1\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "aac19bc2b4270a52",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.225180Z",
     "start_time": "2024-09-09T13:24:10.587875Z"
    }
   },
   "source": [
    "hungary.load()\n",
    "belgium.load()"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "core           INFO \tLoading data for Hungarian Grand Prix - Race [v3.4.0]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n",
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['81', '4', '44', '16', '1', '55', '11', '63', '22', '18', '14', '3', '27', '23', '20', '77', '2', '31', '24', '10']\n",
      "core           INFO \tLoading data for Belgian Grand Prix - Race [v3.4.0]\n",
      "req            INFO \tUsing cached data for session_info\n",
      "req            INFO \tUsing cached data for driver_info\n",
      "req            INFO \tUsing cached data for session_status_data\n",
      "req            INFO \tUsing cached data for lap_count\n",
      "req            INFO \tUsing cached data for track_status_data\n",
      "req            INFO \tUsing cached data for _extended_timing_data\n",
      "req            INFO \tUsing cached data for timing_app_data\n",
      "core           INFO \tProcessing timing data...\n",
      "req            INFO \tUsing cached data for car_data\n",
      "req            INFO \tUsing cached data for position_data\n",
      "req            INFO \tUsing cached data for weather_data\n",
      "req            INFO \tUsing cached data for race_control_messages\n",
      "core           INFO \tFinished loading data for 20 drivers: ['44', '81', '16', '1', '4', '55', '11', '14', '31', '3', '18', '23', '10', '20', '77', '22', '2', '27', '24', '63']\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "2c6a0794f43f48f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.287484Z",
     "start_time": "2024-09-09T13:24:16.241841Z"
    }
   },
   "source": [
    "final = pd.merge(results, races, on=\"raceId\")\n",
    "final = pd.merge(final, circuits, on=\"circuitId\")\n",
    "final = pd.merge(final, drivers, on=\"driverId\")"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "id": "38d1aa263b0e6a48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.331977Z",
     "start_time": "2024-09-09T13:24:16.313788Z"
    }
   },
   "source": [
    "final.drop(columns=[\"resultId\", \"number_x\", \"positionText\", \"positionOrder\", \"points\", \"laps\", \"time_x\", \"milliseconds\", \"fastestLap\", \"rank\", \"fastestLapTime\", \"fastestLapSpeed\", \"statusId\", \"round\", \"name_x\", \"url_x\", \"fp1_date\", \"fp1_time\", \"fp2_date\", \"fp2_time\", \"fp3_date\", \"fp3_time\", \"quali_date\", \"quali_time\", \"sprint_date\", \"sprint_time\", \"name_y\", \"location\", \"country\", \"lat\", \"lng\", \"url_y\", \"number_y\", \"code\", \"forename\", \"surname\", \"dob\", \"nationality\", \"url\", \"alt\"], inplace=True)\n",
    "\n",
    "final = pd.merge(final, constructors, on=\"constructorId\")"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "5f5c6bc15ae7cee0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.353309Z",
     "start_time": "2024-09-09T13:24:16.349454Z"
    }
   },
   "source": [
    "final.drop(columns=[\"name\", \"nationality\", \"url\"], inplace=True)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "c0d1b1d673a9e40c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.372717Z",
     "start_time": "2024-09-09T13:24:16.370673Z"
    }
   },
   "source": [
    "final.rename(columns={\"time_y\":\"time\"}, inplace=True)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "93b702591b828cb6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.394612Z",
     "start_time": "2024-09-09T13:24:16.390086Z"
    }
   },
   "source": "final.drop(final[final.year < 2010].index, inplace=True)",
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "id": "6cbbf43482aec61e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.414064Z",
     "start_time": "2024-09-09T13:24:16.411858Z"
    }
   },
   "source": "new_races = [belgium, hungary]",
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "6c8e23aba1b19f11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.443210Z",
     "start_time": "2024-09-09T13:24:16.431596Z"
    }
   },
   "source": [
    "for race in new_races:\n",
    "    race_results = race.results\n",
    "    race_results.drop(columns=[\"DriverNumber\", \"BroadcastName\", \"Abbreviation\", \"TeamColor\", \"FirstName\", \"LastName\", \"FullName\", \"HeadshotUrl\", \"CountryCode\", \"ClassifiedPosition\", \"Q1\", \"Q2\", \"Q3\", \"Time\", \"Status\"], inplace=True)\n",
    "    race_results = pd.merge(race_results, drivers, left_on=\"DriverId\", right_on=\"driverRef\")\n",
    "    race_results[\"year\"] = race.session_info[\"StartDate\"].date().year\n",
    "    race_results[\"date\"] = race.session_info[\"StartDate\"].date().strftime(\"%Y-%m-%d\")\n",
    "    race_results[\"time\"] = race.session_info[\"StartDate\"].time().strftime(\"%H:%M\")\n",
    "    if race.session_info[\"Meeting\"][\"Circuit\"][\"ShortName\"] == \"Spa-Francorchamps\":\n",
    "        # TODO come up with better way to add circuit names\n",
    "        race_results[\"circuitRef\"] = \"spa\"\n",
    "        race_results[\"raceId\"] = 1134\n",
    "    else:\n",
    "        race_results[\"circuitRef\"] = \"hungaroring\"\n",
    "        race_results[\"raceId\"] = 1133\n",
    "    race_results = pd.merge(race_results, circuits, on=\"circuitRef\")\n",
    "    race_results = pd.merge(race_results, constructors, left_on=\"TeamId\", right_on=\"constructorRef\")\n",
    "    race_results.drop(columns=[\"DriverId\", \"TeamName\", \"TeamId\", \"Points\", \"number\", \"code\", \"country\", \"lat\", \"lng\", \"alt\", \"url_y\", \"name_y\", \"nationality_y\", \"url\", \"forename\", \"surname\", \"dob\", \"nationality_x\", \"url_x\", \"name_x\", \"location\"], inplace=True)\n",
    "    race_results.rename(columns={\"Position\":\"position\", \"GridPosition\":\"grid\"}, inplace=True)\n",
    "    final = pd.concat([final, race_results], axis=0)"
   ],
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.465565Z",
     "start_time": "2024-09-09T13:24:16.461691Z"
    }
   },
   "cell_type": "code",
   "source": "final.replace(to_replace=\"\\\\N\", value=np.nan, inplace=True)",
   "id": "157fcd2e3a183e79",
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "id": "308c23e99b482b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.486937Z",
     "start_time": "2024-09-09T13:24:16.484364Z"
    }
   },
   "source": [
    "final.drop(columns=[\"driverId\", \"constructorId\", \"circuitId\"], inplace=True)"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.508377Z",
     "start_time": "2024-09-09T13:24:16.506313Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def update_names(local_table, column):\n",
    "    refs = local_table[column]\n",
    "    real = np.array([])\n",
    "    for ref in refs:\n",
    "        if ref.find(\"_\") != -1:\n",
    "            ref = ref.replace(\"_\", \" \")\n",
    "        ref = ref.title()\n",
    "        real = np.append(real, ref)\n",
    "\n",
    "    return real"
   ],
   "id": "224af710ff469e2b",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.895627Z",
     "start_time": "2024-09-09T13:24:16.526579Z"
    }
   },
   "cell_type": "code",
   "source": [
    "final[\"circuitRef\"] = update_names(final, \"circuitRef\")\n",
    "final[\"constructorRef\"] = update_names(final, \"constructorRef\")\n",
    "final[\"driverRef\"] = update_names(final, \"driverRef\")"
   ],
   "id": "56a5f59efc2e93ed",
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "6bc233a665d321ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.918640Z",
     "start_time": "2024-09-09T13:24:16.914158Z"
    }
   },
   "source": [
    "final[\"circuit_code\"] = final[\"circuitRef\"].astype(\"category\").cat.codes\n",
    "final[\"driver_code\"] = final[\"driverRef\"].astype(\"category\").cat.codes\n",
    "final[\"constructor_code\"] = final[\"constructorRef\"].astype(\"category\").cat.codes"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "5b9d2d44eeac3431",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.940976Z",
     "start_time": "2024-09-09T13:24:16.938318Z"
    }
   },
   "source": [
    "final[\"position\"] = final[\"position\"].astype(float)"
   ],
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.962812Z",
     "start_time": "2024-09-09T13:24:16.960144Z"
    }
   },
   "cell_type": "code",
   "source": "final = final[final.position <= 20.0]",
   "id": "fa5d59dbbf6895ad",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:16.984610Z",
     "start_time": "2024-09-09T13:24:16.982249Z"
    }
   },
   "cell_type": "code",
   "source": "final[\"pos_delta\"] = final[\"grid\"] - final[\"position\"]",
   "id": "bfe0402bc290b44b",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.010742Z",
     "start_time": "2024-09-09T13:24:17.003742Z"
    }
   },
   "cell_type": "code",
   "source": "final",
   "id": "4695a227ec9f7bda",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       raceId  grid  position  year        date      time   circuitRef  \\\n",
       "20320     337   3.0       1.0  2010  2010-03-14  12:00:00      Bahrain   \n",
       "20321     337   2.0       2.0  2010  2010-03-14  12:00:00      Bahrain   \n",
       "20322     337   4.0       3.0  2010  2010-03-14  12:00:00      Bahrain   \n",
       "20323     337   1.0       4.0  2010  2010-03-14  12:00:00      Bahrain   \n",
       "20324     337   5.0       5.0  2010  2010-03-14  12:00:00      Bahrain   \n",
       "...       ...   ...       ...   ...         ...       ...          ...   \n",
       "15       1133  12.0      16.0  2024  2024-07-21     15:00  Hungaroring   \n",
       "16       1133  14.0      17.0  2024  2024-07-21     15:00  Hungaroring   \n",
       "17       1133  19.0      18.0  2024  2024-07-21     15:00  Hungaroring   \n",
       "18       1133  18.0      19.0  2024  2024-07-21     15:00  Hungaroring   \n",
       "19       1133   0.0      20.0  2024  2024-07-21     15:00  Hungaroring   \n",
       "\n",
       "      driverRef constructorRef  circuit_code  driver_code  constructor_code  \\\n",
       "20320    Alonso        Ferrari             2            3                 5   \n",
       "20321     Massa        Ferrari             2           42                 5   \n",
       "20322  Hamilton        Mclaren             2           23                13   \n",
       "20323    Vettel       Red Bull             2           73                17   \n",
       "20324   Rosberg       Mercedes             2           60                14   \n",
       "...         ...            ...           ...          ...               ...   \n",
       "15       Bottas         Sauber             7            7                19   \n",
       "16     Sargeant       Williams             7           64                22   \n",
       "17         Ocon         Alpine             7           50                 2   \n",
       "18         Zhou         Sauber             7           77                19   \n",
       "19        Gasly         Alpine             7           17                 2   \n",
       "\n",
       "       pos_delta  \n",
       "20320        2.0  \n",
       "20321        0.0  \n",
       "20322        1.0  \n",
       "20323       -3.0  \n",
       "20324        0.0  \n",
       "...          ...  \n",
       "15          -4.0  \n",
       "16          -3.0  \n",
       "17           1.0  \n",
       "18          -1.0  \n",
       "19         -20.0  \n",
       "\n",
       "[5171 rows x 13 columns]"
      ],
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raceId</th>\n",
       "      <th>grid</th>\n",
       "      <th>position</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>circuitRef</th>\n",
       "      <th>driverRef</th>\n",
       "      <th>constructorRef</th>\n",
       "      <th>circuit_code</th>\n",
       "      <th>driver_code</th>\n",
       "      <th>constructor_code</th>\n",
       "      <th>pos_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20320</th>\n",
       "      <td>337</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Alonso</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20321</th>\n",
       "      <td>337</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Massa</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>337</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>Mclaren</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20323</th>\n",
       "      <td>337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Vettel</td>\n",
       "      <td>Red Bull</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>17</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20324</th>\n",
       "      <td>337</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Rosberg</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1133</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Hungaroring</td>\n",
       "      <td>Bottas</td>\n",
       "      <td>Sauber</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1133</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Hungaroring</td>\n",
       "      <td>Sargeant</td>\n",
       "      <td>Williams</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>22</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1133</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Hungaroring</td>\n",
       "      <td>Ocon</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>7</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1133</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Hungaroring</td>\n",
       "      <td>Zhou</td>\n",
       "      <td>Sauber</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>19</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2024</td>\n",
       "      <td>2024-07-21</td>\n",
       "      <td>15:00</td>\n",
       "      <td>Hungaroring</td>\n",
       "      <td>Gasly</td>\n",
       "      <td>Alpine</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>-20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5171 rows × 13 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.050531Z",
     "start_time": "2024-09-09T13:24:17.048254Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def rolling_finish_avg(group, cols, new_cols):\n",
    "    group = group.sort_values(\"raceId\")\n",
    "    rolling_stats = group[cols].rolling(3, closed=\"left\").mean()\n",
    "    group[new_cols] = rolling_stats\n",
    "    return group"
   ],
   "id": "9dfee56579a5cc89",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.083950Z",
     "start_time": "2024-09-09T13:24:17.079468Z"
    }
   },
   "cell_type": "code",
   "source": [
    "cols = [\"grid\", \"position\", \"pos_delta\"]\n",
    "new_cols = [f\"{c}_rolling\" for c in cols]"
   ],
   "id": "cc8111747748ea26",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.229848Z",
     "start_time": "2024-09-09T13:24:17.119959Z"
    }
   },
   "cell_type": "code",
   "source": "final_rolling = final.groupby(\"driverRef\").apply(lambda x: rolling_finish_avg(x, cols, new_cols)).droplevel(\"driverRef\")",
   "id": "202265af2f0ab480",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/ffpx1y0s2cn133v15g3yks080000gn/T/ipykernel_2135/536465426.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  final_rolling = final.groupby(\"driverRef\").apply(lambda x: rolling_finish_avg(x, cols, new_cols)).droplevel(\"driverRef\")\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.306981Z",
     "start_time": "2024-09-09T13:24:17.296846Z"
    }
   },
   "cell_type": "code",
   "source": "final_rolling = final_rolling.sort_values([\"raceId\", \"position\"])",
   "id": "8acc4b8ee0fb3114",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.391956Z",
     "start_time": "2024-09-09T13:24:17.374468Z"
    }
   },
   "cell_type": "code",
   "source": "final_rolling.index = range(final_rolling.shape[0])",
   "id": "93d396454bd5ed9",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.462753Z",
     "start_time": "2024-09-09T13:24:17.429085Z"
    }
   },
   "cell_type": "code",
   "source": "final_rolling.to_csv(\"data/final_rolling.csv\", index=False)",
   "id": "dc15105d29295",
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "id": "54257b95ae6fae4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.515101Z",
     "start_time": "2024-09-09T13:24:17.510953Z"
    }
   },
   "source": [
    "single = final_rolling.loc[final_rolling[\"raceId\"] == 1134]"
   ],
   "outputs": [],
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "id": "3ce73ee5b780db07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.553055Z",
     "start_time": "2024-09-09T13:24:17.548247Z"
    }
   },
   "source": [
    "single[\"constructorRef\"].values"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mercedes', 'Mclaren', 'Ferrari', 'Red Bull', 'Mclaren', 'Ferrari',\n",
       "       'Red Bull', 'Aston Martin', 'Alpine', 'Rb', 'Aston Martin',\n",
       "       'Williams', 'Alpine', 'Haas', 'Sauber', 'Rb', 'Williams', 'Haas',\n",
       "       'Sauber', 'Mercedes'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "id": "9f930c7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.633255Z",
     "start_time": "2024-09-09T13:24:17.620019Z"
    }
   },
   "source": [
    "dutch_gp = pd.DataFrame(\n",
    "    {\n",
    "        \"raceId\": [1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135, 1135],\n",
    "        \"grid\": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],\n",
    "        \"position\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20],\n",
    "        \"pos_delta\": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],\n",
    "        \"driver_code\": [343, 617, 461, 519, 580, 699, 607,  20, 583, 670, 756,  14, 290,\n",
    "       425, 102, 798, 705, 379, 855, 693],\n",
    "        \"driverRef\": ['hamilton', 'piastri', 'leclerc', 'max_verstappen', 'norris',\n",
    "       'sainz', 'perez', 'alonso', 'ocon', 'ricciardo', 'stroll', 'albon',\n",
    "       'gasly', 'kevin_magnussen', 'bottas', 'tsunoda', 'sargeant',\n",
    "       'hulkenberg', 'zhou', 'russell'],\n",
    "        \"constructor_code\": [136, 131,  69, 165, 131,  69, 165,  11,   5, 162,  11, 208,   5,\n",
    "        79, 168, 162, 208,  79, 168, 136],\n",
    "        \"constructorRef\": ['mercedes', 'mclaren', 'ferrari', 'red_bull', 'mclaren', 'ferrari',\n",
    "       'red_bull', 'aston_martin', 'alpine', 'rb', 'aston_martin',\n",
    "       'williams', 'alpine', 'haas', 'sauber', 'rb', 'williams', 'haas',\n",
    "       'sauber', 'mercedes'],\n",
    "        \"circuit_code\": [39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39],\n",
    "        \"circuitRef\": [\"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\", \"zandvoort\",\"zandvoort\"]\n",
    "    }\n",
    ")"
   ],
   "outputs": [],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "id": "3c7d2110",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.699507Z",
     "start_time": "2024-09-09T13:24:17.688717Z"
    }
   },
   "source": "with_single = pd.concat([final, dutch_gp])",
   "outputs": [],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.745376Z",
     "start_time": "2024-09-09T13:24:17.736201Z"
    }
   },
   "cell_type": "code",
   "source": "with_single",
   "id": "dc97114854143035",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       raceId  grid  position    year        date      time circuitRef  \\\n",
       "20320     337   3.0       1.0  2010.0  2010-03-14  12:00:00    Bahrain   \n",
       "20321     337   2.0       2.0  2010.0  2010-03-14  12:00:00    Bahrain   \n",
       "20322     337   4.0       3.0  2010.0  2010-03-14  12:00:00    Bahrain   \n",
       "20323     337   1.0       4.0  2010.0  2010-03-14  12:00:00    Bahrain   \n",
       "20324     337   5.0       5.0  2010.0  2010-03-14  12:00:00    Bahrain   \n",
       "...       ...   ...       ...     ...         ...       ...        ...   \n",
       "15       1135   NaN      16.0     NaN         NaN       NaN  zandvoort   \n",
       "16       1135   NaN      17.0     NaN         NaN       NaN  zandvoort   \n",
       "17       1135   NaN      18.0     NaN         NaN       NaN  zandvoort   \n",
       "18       1135   NaN      19.0     NaN         NaN       NaN  zandvoort   \n",
       "19       1135   NaN      20.0     NaN         NaN       NaN  zandvoort   \n",
       "\n",
       "        driverRef constructorRef  circuit_code  driver_code  constructor_code  \\\n",
       "20320      Alonso        Ferrari             2            3                 5   \n",
       "20321       Massa        Ferrari             2           42                 5   \n",
       "20322    Hamilton        Mclaren             2           23                13   \n",
       "20323      Vettel       Red Bull             2           73                17   \n",
       "20324     Rosberg       Mercedes             2           60                14   \n",
       "...           ...            ...           ...          ...               ...   \n",
       "15        tsunoda             rb            39          798               162   \n",
       "16       sargeant       williams            39          705               208   \n",
       "17     hulkenberg           haas            39          379                79   \n",
       "18           zhou         sauber            39          855               168   \n",
       "19        russell       mercedes            39          693               136   \n",
       "\n",
       "       pos_delta  \n",
       "20320        2.0  \n",
       "20321        0.0  \n",
       "20322        1.0  \n",
       "20323       -3.0  \n",
       "20324        0.0  \n",
       "...          ...  \n",
       "15           NaN  \n",
       "16           NaN  \n",
       "17           NaN  \n",
       "18           NaN  \n",
       "19           NaN  \n",
       "\n",
       "[5191 rows x 13 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raceId</th>\n",
       "      <th>grid</th>\n",
       "      <th>position</th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>circuitRef</th>\n",
       "      <th>driverRef</th>\n",
       "      <th>constructorRef</th>\n",
       "      <th>circuit_code</th>\n",
       "      <th>driver_code</th>\n",
       "      <th>constructor_code</th>\n",
       "      <th>pos_delta</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20320</th>\n",
       "      <td>337</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Alonso</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>20321</th>\n",
       "      <td>337</td>\n",
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       "      <td>2010.0</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Massa</td>\n",
       "      <td>Ferrari</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20322</th>\n",
       "      <td>337</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>Mclaren</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20323</th>\n",
       "      <td>337</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Vettel</td>\n",
       "      <td>Red Bull</td>\n",
       "      <td>2</td>\n",
       "      <td>73</td>\n",
       "      <td>17</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20324</th>\n",
       "      <td>337</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>2010-03-14</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Rosberg</td>\n",
       "      <td>Mercedes</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>tsunoda</td>\n",
       "      <td>rb</td>\n",
       "      <td>39</td>\n",
       "      <td>798</td>\n",
       "      <td>162</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>sargeant</td>\n",
       "      <td>williams</td>\n",
       "      <td>39</td>\n",
       "      <td>705</td>\n",
       "      <td>208</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>hulkenberg</td>\n",
       "      <td>haas</td>\n",
       "      <td>39</td>\n",
       "      <td>379</td>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>zhou</td>\n",
       "      <td>sauber</td>\n",
       "      <td>39</td>\n",
       "      <td>855</td>\n",
       "      <td>168</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>russell</td>\n",
       "      <td>mercedes</td>\n",
       "      <td>39</td>\n",
       "      <td>693</td>\n",
       "      <td>136</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5191 rows × 13 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:17.997496Z",
     "start_time": "2024-09-09T13:24:17.829120Z"
    }
   },
   "cell_type": "code",
   "source": "dutch_rolling = with_single.groupby(\"driverRef\").apply(lambda x: rolling_finish_avg(x, cols, new_cols)).droplevel(\"driverRef\")",
   "id": "1d9f0e60212198db",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hk/ffpx1y0s2cn133v15g3yks080000gn/T/ipykernel_2135/64606415.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  dutch_rolling = with_single.groupby(\"driverRef\").apply(lambda x: rolling_finish_avg(x, cols, new_cols)).droplevel(\"driverRef\")\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.038572Z",
     "start_time": "2024-09-09T13:24:18.034484Z"
    }
   },
   "cell_type": "code",
   "source": "dutch_rolling = dutch_rolling.loc[dutch_rolling[\"raceId\"] == 1135]",
   "id": "9f481dbc5d16e52d",
   "outputs": [],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.063460Z",
     "start_time": "2024-09-09T13:24:18.049153Z"
    }
   },
   "cell_type": "code",
   "source": "dutch_rolling",
   "id": "c9ab51fd110fcb2a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    raceId  grid  position  year date time circuitRef        driverRef  \\\n",
       "11    1135   NaN      12.0   NaN  NaN  NaN  zandvoort            albon   \n",
       "7     1135   NaN       8.0   NaN  NaN  NaN  zandvoort           alonso   \n",
       "14    1135   NaN      15.0   NaN  NaN  NaN  zandvoort           bottas   \n",
       "12    1135   NaN      13.0   NaN  NaN  NaN  zandvoort            gasly   \n",
       "0     1135   NaN       1.0   NaN  NaN  NaN  zandvoort         hamilton   \n",
       "17    1135   NaN      18.0   NaN  NaN  NaN  zandvoort       hulkenberg   \n",
       "13    1135   NaN      14.0   NaN  NaN  NaN  zandvoort  kevin_magnussen   \n",
       "2     1135   NaN       3.0   NaN  NaN  NaN  zandvoort          leclerc   \n",
       "3     1135   NaN       4.0   NaN  NaN  NaN  zandvoort   max_verstappen   \n",
       "4     1135   NaN       5.0   NaN  NaN  NaN  zandvoort           norris   \n",
       "8     1135   NaN       9.0   NaN  NaN  NaN  zandvoort             ocon   \n",
       "6     1135   NaN       7.0   NaN  NaN  NaN  zandvoort            perez   \n",
       "1     1135   NaN       2.0   NaN  NaN  NaN  zandvoort          piastri   \n",
       "9     1135   NaN      10.0   NaN  NaN  NaN  zandvoort        ricciardo   \n",
       "19    1135   NaN      20.0   NaN  NaN  NaN  zandvoort          russell   \n",
       "5     1135   NaN       6.0   NaN  NaN  NaN  zandvoort            sainz   \n",
       "16    1135   NaN      17.0   NaN  NaN  NaN  zandvoort         sargeant   \n",
       "10    1135   NaN      11.0   NaN  NaN  NaN  zandvoort           stroll   \n",
       "15    1135   NaN      16.0   NaN  NaN  NaN  zandvoort          tsunoda   \n",
       "18    1135   NaN      19.0   NaN  NaN  NaN  zandvoort             zhou   \n",
       "\n",
       "   constructorRef  circuit_code  driver_code  constructor_code  pos_delta  \\\n",
       "11       williams            39           14               208        NaN   \n",
       "7    aston_martin            39           20                11        NaN   \n",
       "14         sauber            39          102               168        NaN   \n",
       "12         alpine            39          290                 5        NaN   \n",
       "0        mercedes            39          343               136        NaN   \n",
       "17           haas            39          379                79        NaN   \n",
       "13           haas            39          425                79        NaN   \n",
       "2         ferrari            39          461                69        NaN   \n",
       "3        red_bull            39          519               165        NaN   \n",
       "4         mclaren            39          580               131        NaN   \n",
       "8          alpine            39          583                 5        NaN   \n",
       "6        red_bull            39          607               165        NaN   \n",
       "1         mclaren            39          617               131        NaN   \n",
       "9              rb            39          670               162        NaN   \n",
       "19       mercedes            39          693               136        NaN   \n",
       "5         ferrari            39          699                69        NaN   \n",
       "16       williams            39          705               208        NaN   \n",
       "10   aston_martin            39          756                11        NaN   \n",
       "15             rb            39          798               162        NaN   \n",
       "18         sauber            39          855               168        NaN   \n",
       "\n",
       "    grid_rolling  position_rolling  pos_delta_rolling  \n",
       "11           NaN               NaN                NaN  \n",
       "7            NaN               NaN                NaN  \n",
       "14           NaN               NaN                NaN  \n",
       "12           NaN               NaN                NaN  \n",
       "0            NaN               NaN                NaN  \n",
       "17           NaN               NaN                NaN  \n",
       "13           NaN               NaN                NaN  \n",
       "2            NaN               NaN                NaN  \n",
       "3            NaN               NaN                NaN  \n",
       "4            NaN               NaN                NaN  \n",
       "8            NaN               NaN                NaN  \n",
       "6            NaN               NaN                NaN  \n",
       "1            NaN               NaN                NaN  \n",
       "9            NaN               NaN                NaN  \n",
       "19           NaN               NaN                NaN  \n",
       "5            NaN               NaN                NaN  \n",
       "16           NaN               NaN                NaN  \n",
       "10           NaN               NaN                NaN  \n",
       "15           NaN               NaN                NaN  \n",
       "18           NaN               NaN                NaN  "
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       "      <th>raceId</th>\n",
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       "      <td>693</td>\n",
       "      <td>136</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>zandvoort</td>\n",
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       "      <td>zandvoort</td>\n",
       "      <td>tsunoda</td>\n",
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       "      <td>39</td>\n",
       "      <td>798</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1135</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>zandvoort</td>\n",
       "      <td>zhou</td>\n",
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       "      <td>39</td>\n",
       "      <td>855</td>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.138296Z",
     "start_time": "2024-09-09T13:24:18.132108Z"
    }
   },
   "cell_type": "code",
   "source": "dutch_rolling.to_csv(\"data/dutch_rolling.csv\", index=False)",
   "id": "7efa367ac83352c0",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.203888Z",
     "start_time": "2024-09-09T13:24:18.196613Z"
    }
   },
   "cell_type": "code",
   "source": "circuit_refs = pd.merge(left=circuits, right=races, on=\"circuitId\")",
   "id": "508255551306a97",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.225662Z",
     "start_time": "2024-09-09T13:24:18.220984Z"
    }
   },
   "cell_type": "code",
   "source": "circuit_refs.drop(circuit_refs[circuit_refs[\"year\"] != 2024].index, inplace=True)",
   "id": "ebd55d3dc11a2313",
   "outputs": [],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.310074Z",
     "start_time": "2024-09-09T13:24:18.304530Z"
    }
   },
   "cell_type": "code",
   "source": "circuit_refs[\"circuitRef\"] = update_names(circuit_refs, \"circuitRef\")",
   "id": "7d3705c21c9abae8",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.428726Z",
     "start_time": "2024-09-09T13:24:18.417604Z"
    }
   },
   "cell_type": "code",
   "source": "circuits24 = circuit_refs[\"circuitRef\"]",
   "id": "14ba1b1dbd1a9db",
   "outputs": [],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.437210Z",
     "start_time": "2024-09-09T13:24:18.435125Z"
    }
   },
   "cell_type": "code",
   "source": "circuits24.index = range(circuits24.shape[0])",
   "id": "ecc9431843379dd3",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.487535Z",
     "start_time": "2024-09-09T13:24:18.477686Z"
    }
   },
   "cell_type": "code",
   "source": "circuits24",
   "id": "eead5f4da7b185d1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Albert Park\n",
       "1           Bahrain\n",
       "2         Catalunya\n",
       "3            Monaco\n",
       "4        Villeneuve\n",
       "5       Silverstone\n",
       "6       Hungaroring\n",
       "7               Spa\n",
       "8             Monza\n",
       "9        Marina Bay\n",
       "10         Shanghai\n",
       "11       Interlagos\n",
       "12            Imola\n",
       "13           Suzuka\n",
       "14            Vegas\n",
       "15       Yas Marina\n",
       "16        Rodriguez\n",
       "17        Zandvoort\n",
       "18         Americas\n",
       "19    Red Bull Ring\n",
       "20             Baku\n",
       "21           Jeddah\n",
       "22           Losail\n",
       "23            Miami\n",
       "Name: circuitRef, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:24:18.553898Z",
     "start_time": "2024-09-09T13:24:18.548838Z"
    }
   },
   "cell_type": "code",
   "source": "circuits24.to_csv(\"data/circuit24.csv\", index=False)",
   "id": "493b662fd9b5e0a2",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:25:29.774170Z",
     "start_time": "2024-09-09T13:25:29.745425Z"
    }
   },
   "cell_type": "code",
   "source": "import seaborn as sns",
   "id": "13e0b578d0cbb23c",
   "outputs": [],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:29:19.564311Z",
     "start_time": "2024-09-09T13:29:19.543947Z"
    }
   },
   "cell_type": "code",
   "source": "current_season = final_rolling[final_rolling[\"year\"] == final_rolling[\"year\"].max()]",
   "id": "7719bf03df35663e",
   "outputs": [],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:29:29.971399Z",
     "start_time": "2024-09-09T13:29:29.964175Z"
    }
   },
   "cell_type": "code",
   "source": "driver_names = current_season[\"driverRef\"].unique()",
   "id": "77fdd8d49050df33",
   "outputs": [],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:29:32.120743Z",
     "start_time": "2024-09-09T13:29:32.115920Z"
    }
   },
   "cell_type": "code",
   "source": "driver_names",
   "id": "e2a9be8db6acfd25",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Max Verstappen', 'Perez', 'Sainz', 'Leclerc', 'Russell', 'Norris',\n",
       "       'Hamilton', 'Piastri', 'Alonso', 'Stroll', 'Zhou',\n",
       "       'Kevin Magnussen', 'Ricciardo', 'Tsunoda', 'Albon', 'Hulkenberg',\n",
       "       'Ocon', 'Gasly', 'Bottas', 'Sargeant', 'Bearman'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:33:33.254791Z",
     "start_time": "2024-09-09T13:33:33.245484Z"
    }
   },
   "cell_type": "code",
   "source": "current_drivers = final_rolling[final_rolling[\"driverRef\"].isin(driver_names)]",
   "id": "16fc4e66ce39eba4",
   "outputs": [],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:41:28.501002Z",
     "start_time": "2024-09-09T13:41:28.485646Z"
    }
   },
   "cell_type": "code",
   "source": "hamilton_result = final_rolling[final_rolling[\"driverRef\"].isin([\"Hamilton\"])]",
   "id": "77d7a7fd3145bb77",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:37:03.342559Z",
     "start_time": "2024-09-09T13:37:03.169370Z"
    }
   },
   "cell_type": "code",
   "source": "current_drivers[\"position\"].hist(grid=False, bins=20, legend=True)",
   "id": "4d4faa31e53966b6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:42:02.058257Z",
     "start_time": "2024-09-09T13:42:01.796482Z"
    }
   },
   "cell_type": "code",
   "source": "hamilton_result[\"position\"].hist()",
   "id": "f32ba36915cfc1d5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-09T13:43:28.419615Z",
     "start_time": "2024-09-09T13:43:28.168618Z"
    }
   },
   "cell_type": "code",
   "source": "sns.relplot(hamilton_result[[\"grid\", \"position\"]])",
   "id": "db6f96bfe7156a30",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x169ccb790>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 597.875x500 with 1 Axes>"
      ],
      "image/png": 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punAV5zOgY1u6Z8Q1Wx5Ew8qIt5ERb2N4t9TmzkqroNPpaJtgo22CjfN7pTd3doQQ4oTUOaD5+uuvCYVCvPLKK7zyyisRy7Zs2cLLL7/MX/7yF8aNG0fHjh156aWXmmVQPSGEEEKcfOoc0EyZMoUpU6bUurxjx47MnDmzQTIlhBBCCFEfMjmlEEIIIaKeBDRCCCGEiHoS0AghhBAi6klAI4QQQoioJwGNEEIIIaKezNDYmrgKwGgFi0O9Ls8FgwnsSc2bLyFEi1Hk8lPo9FHs9pMUYyYpxkJSjPnYbxSihZOAprUo3AEzxsIFj0H3i8BbCu9NhG4XwLA7JagRQrC/2MP97//Kit3FFWmndUrk2WsG0kZGiBZRTpqcWgNPCSz+J5QdgDm3wK8zVTCTsxp++A+4C5o7h0KIZlbi9vPgh6sjghmAFbuLefCD1ZS4/bW8U4joIDU0rYEtAS58HMoPwp4l8NkfVLpODxPfh/j2zZo9IUTzK3D6WbazqMZlP+0sosDpJ8EuTU8iekkNTWsRmwETXo9MO2U8dDwdTFKVLMTJzukLHH259+jLhWjpJKBpLcoOwLtXR6ZtmA3bvgKfs3nyJIRoMeKspqMvtx19uRAtnQQ0rYGnBD7/s+ozo9PDuNeg03DQNJhzM5TnNHcOhRDNLNlh5ryeNc9Uf36vNJId0twkopsENK2BLQFG/QOSu6g+M70ugfGHgppRj0JMzRcxIcTJI95m5vEr+nLhKRnodCpNp4MLeqfz+OV9ibdJQCOim3QKbi0SO8LNX4I5RvWZMdlUnxqDRQU8QoiTXma8jakT+vEnZw/KfUFiLUaSHRZpbhKtggQ0rUlMSuRrR3rz5EMI0WLF2UwSwIhWSZqchBBCCBH1JKARQgghRNSTgEYIIYQQUU8CGiGEEEJEPQlohBBCCBH1JKARQgghRNSTgEYIIUSL53A4mjsLooWTcWiEEEJUKtwBRivEt1WvS/dD0KtGIm/qrDh97C1y8+XGXEw6jdFxLjITbMTLODqiBhLQCCGEUAp3wPTREJMGE2eptFkTofwgTF4AKd2aLCt55V7+8tE6vtqYV5H2/OKd3HVuF24bnkVijEzVICJJQCOEEAJCASjNBle+CmDemaAmu81dD3oDlO5TU6wYmiaQ+H5LfkQwc9jL3+5gZK90CWhENdKHRgghBBhM0H4YXPuhCmDyNlYGMxM/gA7DmiyYKXT6eO2HXbUun750F/5guEnyIqKHBDRCCCEUkxXSe0fOAxeTBumnqAlvm0gwrFHqCdS6vMgVIBiWgEZEkoBGCCGEUrYf3r0ayg5UppXnwLtXqc7BTSTeZuLs7im1Lr+4bwZ2s/SYEJEkoBFCCKH60OxdDjlrVDPTtR/CpDnq/wfXwp6lap0mYDUZuPOcLthMhmrLMuKsnNs9rUnyIaKLhLhCCCFUH5qu58Po/0BiJ+h0FqBTgU3hdug2Sq3TRNon2fn47jP558JNfLctH6Nex6X92/Cbkd1pm9h0zV8iekhAI4QQQrHGQ9+rwGiu7DPT8UxoOwhsCU2aFaNBT/eMWF64diBlngBut5u2KfHYLTIGjaiZNDkJIYSoZIuP7ABssjZ5MHOkWKuJjDgLZQf3YDHKT5aonRwdQgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGi3nEHNH6/n7Fjx7J8+fKKtMcee4wePXpE/M2cObNBMiqEEFHBWwbO/MrXQR+4C0HTTmizmqZxsNTL+v2l/LKnmOwiNx5/8AQzK0TrYTyeN/l8Ph588EG2bdsWkb5jxw4efPBBrrjiioo0h8NxYjkUQoho4S2DDR/Bj8/CDR+DIwOyl8HsW+CGTyCtF+h09d5sKKyxfn8pt7/9MwfLvACYDDruOrcrN57RiaQYcwN/ECGiT71raLZv385VV13F3r17qy3bsWMHvXv3JjU1teLPZrM1SEaFEKLFC7jgs99D0U546zJYPxtmjgdXPix4ANxFx7XZAyUeJr62rCKYAQiENJ77ehvfbc1rqNwLEdXqXUOzYsUKhg4dyv3338+AAQMq0p1OJ7m5uXTq1OmEMhQKhU7o/UduoyG21dpJWdWNlFPdndRlZU1Cf8Mn6N66VAU18+4EQEvtBeNeI2xNgCPKpa5l9cO2fNz+mtd55qttnNklmeRWXEvT3MeUwWBolv2K+tFp2vE37Pbo0YO33nqLoUOHsmbNGq6++momTJjA999/T0JCAjfddFNE89PRhEIhVq9efbxZEUKIFiEzNZH0zTMwLH22Ii1w+1K2FGr4fL56by8uLo431np4b2V2ret8de9Qyg7uOZ7sijoYNGhQc2dB1MFx9aGpyc6dO9HpdGRlZTFp0iRWrlzJI488gsPhYNSoUXXeTt++fU84Gg6FQqxbt65BttXaSVnVjZRT3Z3UZRXyo9+3At3ylyKSTR9O4pTr5xGO6xW5eh3LamDJ/loDms4pMcTHOsjKGHDC2W+pTupjStRZgwU0l19+OSNGjCAhIQGAnj17snv3bt577716BTQGg6HBDtiG3FZrJ2VVN1JOdXdSlpW7FN6ZAKEApPaCEQ/B3FuhaCe6ubdimDgL7MnV3nassjqjSwpxViNl3upPNf3+gh6kxVkb9GO0VCflMSXqrMHGodHpdBXBzGFZWVnk5uY21C6EEKJlMzvgspdUMDNpNvS4WD3dFNcWLn2pxmCmLtok2Jg1ZRgdk+0VaVaTnofH9OL0rse3TSFamwaroXnuuef49ddfmT59ekXa5s2bycrKaqhdCCFEy2ZxQI/RkDUCHKkqrc0guOOH4w5mAPR6Hb3bxPPhHadT5PTjC4ZJdphJdViwmKTGQghowIBmxIgRvPrqq7z++uuMGjWKJUuWMG/ePN56662G2oUQQrR8Fof6O8xoAmPD1KKkxVpJiz05mpeEqK8Ga3Lq168fzz33HB9//DFjx47l7bff5qmnnmLgwIENtQshhBBCiBqdUA3Nli1bIl6PHDmSkSNHnlCGhBBCCCHqSyanFEIIIUTUk4BGCCGEEFFPAhohhBBCRD0JaIQQQggR9SSgEUIIIUTUa7BxaFq0kmzQwpDYUb0uzwVfGSR3BZ2uefMmRBTKKfWw6UAZK3YX0Sk5hjO6JJORYMUsw9I3idwyL9vynCzZlk9mvI1zuqeSHm/FJoPsiZNY6w9oSrLh7cshHIIbPgajFebdBftXweQFkH6KBDVC1MOeQhfXvrac/SWeijSzQc/0m4YwpFMSejmdGtWBEg+T31zB1lxnRZpeBy9deyojeqZhlaBGnKRad5OTpoEzD0r3QfEueOtS+PBG2LEIvCWQtwkC7ubOpRBRo8wT4JGPN0QEMwD+UJhb31pFbrm3mXJ2cvD4gzy3aGtEMAMQ1uCe934lr0zKX5y8WndAo9NBZn+48RNVM1O8G/b+pJZd/gr0uAjMMc2aRSGiSZHLzw/b8mtc5vaH2JHnrHGZaBiFLj8f/XqgxmWhsMayXUVNnCMhWo7WHdAAGIyQ2BmSOlemmR3Q+WywxDZfvoSIQoFQGE2rfXmxJ9B0mTkJhcIa/lC41uUF5b4mzI0QLUvrD2jKc+GjO1Xz0mF+J8y4BIr3NF++hIhCDquR1FhLrct7ZcQ1YW5OPjFmI13THLUuH9alYSbBFCIate6ARtOgYKvqMwNw2ctwy1eq+aloJ2z4CPyu5s2jEFEkPdbKw2N61bjsolPSSYurPdgRJy4l1sLfLz2lxmWDOybSPtHexDkSouVo3U856XTQZiCMfx1Cfuh1CRisqk/Nls/h1OulD40Q9aDX6xjRI43XbxzMPxduYke+i3ibiVuHd+aaIR1ItJsJhULNnc1WbUD7BGZNGcY/5m9kY04ZMWYD15/ekclndD5q7ZkQrV3rDmgALA7ofhGgVfaZyTwVkruDPbFZsyZENIqzmTi/Vzr92sXjDYQxGnSkOSwYDK27wreliLEYGZaVzNu3nIbbH8Ko15ESa8YkYwCJk1zrD2hABTVHMprAKMGMECciNdba3Fk4qSU7LEiPGSEqyS2VEEIIIaKeBDRCCCGEiHoS0AghhBAi6klAI4QQQoioJwGNEEIIIaKeBDRCCCGEiHoS0AjRCji9QQqdPvzB2uf5Ea1I0H/0140kEApR6PRR7j32nF2BUJhCp48ymd9LNJGTYxwaIVqpYrefjQfKeOXb7eSV+xjaOYmbz+pM+0Q7RhnornVyF8HmBdD1fIhro6Zv2fUdpPaKnIS3AYXDGtnFbmYu28P3WwtIsJu4/ews+rdPINkROTqxpmlkF3t4f8VeFm3KI9ZqZMrZWZzaMZEUh4xkLBqPBDRCRKlyT4A3luzihW+2V6RtzXXywap9zL7zDPq2jW/G3IlG4S6CFa/Ct09A2yFwzTuwdynMvgni2sLkBZDYqcF3u7PAyeUvLcXpC1akLd9VxMTT2vOHi3qSaDdXpO8udHP5Sz9SekTNzKq3f+byAW34v7G9SZKgRjQSuYUTIkrlO30RwcxhvmCYh+auo8jla4ZciUZltEJGP9DpYf9KePVsFcxoGsR3AEPDBwtlngCPfbopIpg57L0V2Rws9Va8dvmCTP1ic0Qwc9i81QfYV+Jp8PwJcZgENEJEqZ/3FNe6bN3+Ukrd1X+ARJQz2yHrXLjqbfW6/KAKZtqdBhPegLjMBt9lmTfAd9vya12+eHNexf9LPQG+2JBb67qfrTvYoHkT4kgS0AgRpfS6oy/XHWO5iFYahKp0Ag4FVHoj7e5odFUOtKMddnJMisYkAY0QUerUjkm1/kAM7JBAvM3UtBkSjc/vhG1fwpyb1euEDqr5KedXeP96KDvQ4LuMt5s4r2darcuPXJZgMzG6b0at647u2/A1SEIcJgGNEFEqxWHmwVHdq6XbzQYev7wviTHmGt4lolooAMV7VDNTxzPh1q9Vx2CdHtwFh2pqGlas1cRDF/cizlb9GZKbzuhEelxlvx27xcgDo3qQVMOxd/WQ9rRNsDV4/kTDuv7663nhhRdqXd6jRw+WL1/ehDmqO3nKSYgoFWs1MWlYR4ZmJfPq9zvJLfNyRlYy15zWgfZJ9ubOnmgMtkQYdCPEt4NOZ4IjDTqfC9d9CMldIbFjo+w2KzWGT+8dztxf9vHN5jySYszcNjyLXpmxJNgjg5dOKTF8cveZfLx6P19szCXeZuLW4Z3p0yZegmwgu8jNrJV72VvkoUOSjWuGtKzz9YUXXsBkis7aXQlohIhiCXYzQzolcUpmHP5QmBiLEZOMP9O62RKh9+VgOHT5NttVUGNovMu5TqejQ5Kde0d0ZfIZnTAb9Ngtte+vXZKdO87tynXDOmIy6Ik5yronk9k/7+OPc9YSCld2TPrfdzt5cnw/Jgxq14w5q5SQkNDcWThucuUTohWwW4wk2M0SzJwsqgYvjRjMROzGoCfBbj5qMFOxrl5Hgt0swcwh2UXuasEMQDCs8ac5a8kucjfevrOzmTx5Mv379+eSSy7h9ddf57zzzmPu3Llcc8013H333QwaNIhPPvmkWpPTiy++yOmnn87QoUP58MMPGy2PDUGufkIIIUQjm7Vyb7Vg5rBgWGPWyr2Nst9gMMjtt99OXFwcc+bMYcqUKbz44osVy3/99Ve6du3KBx98wFlnnRXx3vfff5+33nqLf/7zn0yfPp05c+Y0Sh4bioTOQgghRCPbW3T0QQWzj7H8eC1btoycnBw++OADHA4HXbt2ZevWrSxYsABQzYl33nknVqu12ns/+OADbrzxRkaMGAHAY489xpgxYxolnw1BamiEEEKIRtYh6ehPeLU/xvLjtWXLFjp37ozD4ahIGzBgQMX/k5OTawxmAHbs2EGvXr0qXnft2hW7veV0YK5KAhohhBCikV0zpAPGWkbDNOp1XDOkQ6Ps12AwoGmRTV1HvrZYjj5dRtX3Go0tt2FHAhohhBCikbVPsvPk+H7VghqjXse/xvdrtEe3u3Xrxu7du3E6nRVpGzZsqPN7161bV/F63759lJWVNXgeG0rLDbWEEEKIVmTCoHYM7ZzErJV7yS7y0L4JxqE5/fTTyczM5JFHHuGee+5h27ZtvPXWW8THxx/zvZMmTeLvf/87vXr1onPnzjz++OPo9S23HkQCGlF37iKwJ9X++hBPIEiR008wrOGwGEl2NPwMwEIIEY3aJ9n5/YU9m2x/er2eF154gUceeYTLLruMrKwsxo0bx/fff3/M91522WUUFxfz6KOP4vV6mTJlCps3b26CXB8fCWhE3RTugC8fhjFPQVwbKN0PCx6Ei/4JSVkVq+0vdvP0V1v5ZM0BAiGNU9rE8bdLT6FPmzhsZjnchBCiKRUWFnLgwAHefffdirRp06aRlpbGuHHjGDduXMT6b7/9dsTryZMnM3ny5IrXU6ZMadT8noiWW3ckWg5XAcwYC1sWwqxJULoPZk2ErZ/BW5ep5UBOqYfrpi1nzi/7CYRUR7INB8q4+n8/sSXXebQ9CCGEaCR33nkn7777Lvv372fp0qXMmDGDiy66qLmz1eAkoBHHZo6BMU+rCfAO/AzPnAI5a0BvgDHPqOXAhv1l7C6sPtplWIPHF2ykxO1v6pwLIcRJLTk5mWeffZb33nuPiy66iL/85S9MmjSJa6+9trmz1uCkDUAcm8kGWefC2Odg/r2V6Ze9DJ3OApMaw+DrzXm1bmLVnmLc/hAJLXcIAyGEaJVGjhzJyJEjmzsbjU5qaETduItg1bTItOWvgqeo4mVmfM2DMwEk2EzUMgSDEEIIccIkoBHH5sxVfWYONzOdemNl89OsSWo5MLpvJrpagpabz+xMqjztJIQQopFIQCOOzWCBPuNUMDPxA7jwn3DNeyqo6XelWo6qofnPhP7VamLO6JLEVUPaY5CZoIUQQjQS6UMjjs2WAKdOhl6XQ2z6oT4158B9v4I1QS0HYixGLu6bwaCOiSzZXkCpO8CZ3ZJpl2AnJVZqZ4QQQjQeCWhE3dgSKgIXQAU1iZ2qrWY3G+mUYqRTSkxT5UwIIYSQJichhBBCRD8JaIQQQghRL+eddx5z584FwOl0Mm/evBqXNSVpchJCCCGawuIn1L8j/lzz6ygye/Zs7HY1sNj06dNZvnw5l19+ebVlTUkCGiGEEKKxLX4CvnsyMu3I11EW1CQlVU5MrGlarcuakjQ5CSGEEE3puyerBzeNZN++ffTo0YP58+czfPhwBg8ezGOPPUYwGARg8eLFXHHFFfTr14/Ro0fz5ZdfVrx38+bNXHPNNfTv35/hw4fz4osvViw73Kw0d+5cXnzxRVasWEGPHj0ilgGEw2GmTZvG+eefT79+/bj++uvZsmVLxXZ69OjBxx9/zNixY+nTpw/XXnst2dnZx/VZJaARQgghGtuIP8M5f6qefs6fmqR25sUXX+SZZ57hxRdf5Msvv+SFF17gp59+4t577+Wyyy7j448/5sorr+T+++9n/fr1APzhD3+gV69efPrppzz++ONMmzaN7777LmK7o0eP5uabb2bgwIEsWbKk2n5feukl3njjDR566CE++ugj2rZty6233orbXTnv3wsvvMBf/vIX5s6dS3FxMc8+++xxfUYJaIQQQohW7ve//z2DBw9m2LBh/OY3v+GDDz5g5syZXHjhhUyePJnOnTtz0003ccEFF/DGG28AsH//fhISEmjbti1nn302b775Jr17947YrtVqxW63YzKZSE1NjVimaRozZ87kN7/5Deeffz5dunTh0UcfxWAw8Mknn1Ssd9NNN3H66afTvXt3Jk6cWBFQ1VerDGgCoTCxGR1Zt7+MdftKOFDiIRTWjv1GIYRoBu5AiD2FLn7ZW8ymnDLyyr3NnaUmEwprHCjxsHZfCWuyS9hf7MYfCjV3thpeTX1oQKUd7hzciE499dSK//fp04eioiJ27txJv379ItYbOHAgO3bsAOD222/nlVde4ayzzuKhhx7C7/dXC1qOprCwkJKSEvr371+RZjKZ6NOnT8U+ADp27Fjxf4fDQSAQqPfng1bYKbjME2Dhuhz+8elG3H51UiTFmHnumgGc1ikJi8nQzDkUQohK8WnteOrLbby9bE/FjVenZDuvXj+Y7hmxzZy7xuUNhFi2s5Dfvr+aErf6EYsxG/jHZX244JR0Yq2mZs5hIznc9NRE/WhABRKHhcNhAHw+X7X1wuFwxfIpU6Zw8cUXs2jRIr755htuvPFGHn30Ua688so67dNiqXmE+FAoVLGPqnk7Ea2uhmbTwTL+NHddRTADUOTyc9ObK9lX7GnGnAkhRKRQWOPzrSVMX7o7ohZ5d6Gbia8t40BJ675mZRe5uWXGqopgBsDlD/Hgh2vYlutsxpw1gsN9aA73man6upFt2rSp4v/r168nLS2N/v37s2bNmoj1fv31Vzp37ozP5+Oxxx7DbDZz00038fbbb3PVVVfxxRdfVNu2rpZZiWNjY0lJSWH16tUVaYFAgA0bNtC5c+eG+WBHaFUBTZknwLNfba1xWTCsMWvlXml6EkK0GAUuP9N+3FPjskKXn805ZU2co6YTCIUjaqWqeu7rbTi9wSbOVSM7HMjU9roRPf7446xbt46lS5fy3HPPcd111zF58mS++OILZsyYwe7du5k+fTpfffUVEydOxGKx8Msvv/Doo4+yc+dO1q1bx6pVq6r1oQGw2Wzk5eWxb9++assmT57M888/zzfffMOOHTt45JFH8Pl8jB49usE/Y6tqcvIEQuwscNW6fGNOGf5gGJtZmp2EEM3PFwxH1E5UtTXPyXm90pswR03HGwix6SgB2458J+5AEIe1Vf1MNZvRo0dz++23Ew6HmThxIlOmTEGv1/Pvf/+bF154galTp9K5c2eeffZZTj/9dACeeeYZ/vGPfzBhwgSMRiMXXXQRd911V7Vtjxo1ilmzZjFmzBi++eabiGU333wzTqeTRx55BKfTycCBA3n77bcbZayaVnWk2EwGuqY6yC2r3i4I0KdNPGZjq6qUEkJEMYtRT6LdRHEtQU2PVtyHxmoy0KdNPCt3F9e4vHuaA7vcfDaYMWPGcPvtt9eYPmbMmBrf07FjR15//fUalx0ZuHTo0IGvvvqqxmUGg4H777+f+++/v8btHDkmDcC4ceMYN25c7R/kKFrVr3uczcT9o7rXuMxk0HHVkPYY9DW39QkhRFNLdVi44+ya+xKkxlrokd56AxqTQc91wzpirOWafN/IbjgsrbRTsGgUrSqgAeieEcvTV/Un1lJZ+ZQaa+Gtm4fSPtHWjDkTQohIeh2c3yWW28/OwmSo/GHvlubgvduG0SahdV+z2ifamH7TEFIc5oq0OKuR568ZQNdURzPmTESjVtXkBBBnNTGmTwZ90q14w0YMBh3JMWbS46y19sQWQojmUpq3n9+c34/rhnWk2OXHatKTHGMhJbbmR15bE4vJwBldUph/71kUOv1omkayw0JarAWjodXdbzeLdu3aVWvWaa1aXUADYDToKD+4hwEDBmAwSBusEKJlsxj1dEiy0yGp6Wcobm56vY7MeBuZ8a27Nko0PgmBhRBCCBH1JKARQgghRNSTgEYIIYQQUU8CGiGEEEJEPQlohBBCCBH1JKARQgghRNQ77oDG7/czduxYli9fXpGWnZ3N5MmTGTBgAKNHj2bJkiUNkkkhhBBCiKM5rnFofD4fDz74INu2batI0zSNu+++m+7duzNnzhwWLVrEPffcw8KFC2nTpk2DZTjalHoC5JZ6WbQpl0AozHk902ibaCMpphkHzfK7ofwA2JPBHIfdZkVfvBOs8eBIa758RTFPIERuqZfvtuWTV+bjrK7JdEtzEAhpbDpYxs97SuiYbGdYVjKZcVZMMqeYOIr9xW7W7Ctlw/5SemTGMahDAm0MJeiCXkjKUis588FbAomdwRB9Q4ppmsb+Eg+rs0vYdKCM3m3iGNA+kTYJMgiqOD71Pgu2b9/Ogw8+iKZFTvm+bNkysrOzmTVrFna7nS5duvDTTz8xZ84c7r333gbLcDQpdvl59fsdvPLdzoq0ZxZt49L+mTwy9hRSm2MkUL8bdnwNH1wP5/8N3aDJdLGVo/vfFdDrErjgcXCkNn2+opgnEOLbzXnc/e4vhA+dFm8v283/Jg3idx+uZX+Jp2Jdi1HPWzefxqCOiTISqqjR1oPlXP3qTxUTVtpMBj68rhNtltwHJbth8gKwJcGiv8GGOXDDfGgzMOqCms0Hy7nm1WWUeion5kywm3h/yjB6ZMQ1Y85EtKr3FXXFihUMHTqU999/PyJ9zZo19O7dG7u9cqTLQYMGsXr16hPOZLTanu+MCGYO+2RNDst3FjZDjoCACzYvBE2DRX9F9+n9mN+9AgJu2LMUQjXPVC5ql1fm5Z73fq0IZgCuPa0DL3yzPSKYAfAFw9wyYxW5Zd4mzqWIBvnlPu5855eI2beTHWZSjF50+ZvAmQvTx8C8O2D1TAh4YO9P6ryOIrllXm57a1VEMANQ4g5wx8xfyCuX80PUX71D+muvvbbG9Pz8fNLSIpsrkpOTOXjwYL22HwqF6pulWrfRENs6Xv5gmDd/3FXr8ld/2MnpXZJIsDXxbLLWJHQj/4ZOC6NbOwvdhrkqPb492o3zCTsyoRnLraU62jH14/YCQuHIGstBHRP5bw3BLIDTF2RXgYuMuNY5V09LOP+iRdWyKnL52JHvjFhnX7GHmxeU8vq4uWTOHaeCmm1fAqCd/39oAyahmRxRdd4WOn3sK/bUuGxXgYtCp59ke+W1sbmPKZlCJzo0WB2lx+PBbDZHpJnNZvx+f722s27duobKUoNuq75iE1MoctX+2YvdfvIKithdmNOEuVJibDa6DLgB09pZFWmhrhdQ6AqRvWt1k+cnmlQ9pmJiYsgt1aqtFwhVTztSfqmbDRtyCAQCR10vmjXn+RdtDpdVOKF9jcs3HXSyw5dJRmZ/dLt/UIk6PcEel7J55378/tpvnlqiYHy7oy4vd3tYvXpbtfTmOqYGDRrULPsV9dNgAY3FYqGkpCQize/3Y7Va67Wdvn37nnA0HAqFWLduXYNs63hpmsbIXiGW7SyqcfnZ3VLp1CYVc/v0Js5YCP3BdehmTIhINvz8OqmJHUgZeAOaNb5p8xQFjnZMlVtKeO6byNoYTyBEcoyZwlqC2lPaJ9El5egX9WjVEs6/aFG1rA6UeLEY9fiC4Yp1dDp45fIOnLr5qcpgBkALY3prDH0mLyCc2LsZcn/89pd4Mep1BMPVA3+zQU9GgoO2nQZUpMkxJeqiwQKa9PR0tm/fHpFWUFBQrRnqWAwGQ4MdsA25reNxUZ8M/vvdDgqckT9qdrOBW4dnYTM3cXMTgKsYFv1V9ZmJb49202eEF/0dw/oP0f3wH3T9rgJDUtPnK0rUdEx1SolhQPsEVmeXVKTNWrGXu0Z04dFPN1Xbxui+GaTFWlr9hbm5z79ocrisUuOs3D2iK09/tbViWVaKgyEpAexffKgSzvs/6HEhvHExOHPRrXgVw4iHwRY9NyIpsRZuG57FK9/tqLbsznO7kBZnrfHYkWNKHE2DPWbRv39/NmzYgNdb2Znr559/pn///g21i6jTLtHO7DvOYHTfDAx6HTodDO+Wwkd3nUn7RFvzZComBca9Cr0ugxvnE45tQ/npf0QbfLN6esKR0Tz5imKpsVZemXQqt5zVCZtJXWxdvhDDOifzyqRT6ZSsOsrH20w8MKo7f7v0FBLs5qNtUpykrCYDk4Z25F/j+5IRp2q3y7wB1nlSCE/8AEb+DQbfDKm94abP4NTJcPbvoyqYAbCbjdwyvDOPXlb5tGd6nIUnxvXlhtM7YjVJ0CLqr8FqaE477TQyMzP585//zF133cXixYtZu3YtTzzxREPtIip1Solh6oT+/GW0Hw2Is5qIa+qOwFXFtYFLnwNbIoRC7DhYxsARD6OzJYJeHiU+HpnxNv54UU9uOSuLUFjDbjaQ7LBwStt4BnVMxBcIYzToSHNYMMjj2uIokhxmrhrcnnO7p+EPhTEZ9KTHWdAF06DdYLAlqBXTesPIv4I9OmtUUxwWJg3ryKjeGQRCYUxGPemxFhmDRhy3BgtoDAYDL7/8Mn/5y18YN24cHTt25KWXXjqpB9U7LMZiJMbSwsaIsCVGvNSsCRLMnCCz0UCbhOo1b2mx9etHJoROpyM9vspxY7Kqv8P0+qgNZg7T6XRkVP2cQhynE/qV3bJlS8Trjh07MnPmzBPKkBBCCCFEfcktuRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqCcBjRBCCCGingQ0jaTE7afYXb95rJpKKKxR7PJXm+n2aDyBIIVOHx5/8NgrB6tsN1TLfqqm17JeKBSmyOWj3Nt65z1qFrWU/+FjV9OOPh9VkwmHIFTluAu1zHMralQ9RzWtelpTCQUhrKZ68Hq9lLm9+P1edTyG/BXfdUJsjFovHD2TcIqm1cIGR4l+B0u9fL81n3eW7yGswZWD2zGqVzqZNYxP0hz2Fbv5ZPUBFqzLwWoycNMZneiRXvOEeAAuX5CdBS7+9+0Otuc76ZkZy+1nd6Fjsh27uYbDJ3+Lmg24/VAwWqAkG/athK4jwRpXuV75QTVjcM+xaiwNVwFs/hS6XwyxlfNbZRe5mf3zPr7amIvDauS2szozsEMiKbGtc6bqJuMugi0Locv5EJcJfhfhnd/jiu/K/Z+XkO/0MmFQey48JZ3M+GY8dsMhOLgOtBBk9AeDEYp2QsE26DQczPbmy1u0Cvph3wqwp0BaTxXM5G9Wx0S7IWBswlGsQ0HIWU1Yb2KvsQNJJRuJsdvQTHbCJXvREUaX0B6dow0dyUaf6wSdHjL7g15GExaRJKBpQAdLPdz21irW7S+rSFu3v5QZS/cw85bTmj2oyS5yM+G/S8kt81Wk/bynmHO7p/DvCbGkxUXmzx8K8c3mPO5979eKtM0Hy/lk9QFeu2Ew5/ZIw6A/YlTPgm3w5sXgLYVJcyC5G7wzQV0sL3keTrlCBTXluTB3Cuz6Dgq2w/D74dsnYeVr0G0BXPYiONLZXeBi3CtLI2YtX7GriEv7Z/K3S08hKUaCmuPiLoIVr8K3T0D7YXD124R2/4hhzk3Exrfnr2Pf57J3ivnbJxuYsXQ37946tHmOXU2D3PUwfQxoYZj8KdiSYMYlULYfrn4HuowAkwQ1dRYMqGDm7cvBEguTPwM0dd76XXDDx9DuNBU4NrZwCHJWw4yx6DMH0v68hzF8MA7O/5uakdMSA5/cB7EZ6K55D6MWgLcuVe+dvFAFNTKqsDiCNDk1oJ92FkYEM4ftyHfyxYaDzVqF7w+GeOPHXRHBzGHfbi1ge56rWnp+mY8/z11XLT2swe9nryWvzBu5wGQDRzqEgzBzPLw2QgUzBhMkdVY1NqDurNIOzQ689Dl45UwVzACk9wG9Cbc/yLNfb40IZg77ZE0Oe4vc9SsAUclogfRT1I9B9jJ49RwMc24CTSPgaEuxT4c3oKr1dxW4mL/2AOEaZkVudDodWOLUqNYBN7w5GqadB6X7VBAT2wYMEtTWi9GkamYssSqwfX2U+vMUqzR7ctMEM6CuA7ZEsMZD2T4MQa/6f/4msB7Ki8kOZQfQvX05zL5ZBV22RHVjJMGMqEICmgZS7g3w3orsWpe/vyq7xh/nplLkCvDx6gO1Ln9/VXa1H628ch9OX819Zopcfgqrfp74dnDdh6pmJhxUTU86HUyaW9kEBWqCzLN/B0OmqNdl+9W/Z9wHZ9wL9iRK3QEWrj1Ya34/WVP7ZxHHYI6BLufBhBnqddkBFcy0GcL6059h4qzd+ILhitU/WLWPoubqD5bUGW6cD7GZEPSqH2GjBW5cAJn9pNnheKT1VDUzljjwlYGvHKwJarLL1B5Nm5fkLnium68C1gUPwOUvw+b5kL0SXIUw/jUwWlXAFfCoG6Yb50NSVtPmU0QFCWga0NHuF3TomveG4hj7rmnxsSaJq3GxpgFV7ua1cA0rovpFHKkenf30x/pA4tiqlL9OC6PTqWM1Ip1jHj6NrMoxdfgYaymdlqNNxTl6ZPk1Y3lqh/at0wM69X+94Yj8VcmnELWQgKaBxFpNXHtah1qXXz2kPYn2JuxsV0WS3cQVA2ufKPTqIe3R6yN/tlIdFmJrmVQzOcZMUtXPU5Kt+swUblfNTLGZ6uI0czzsXQbBQ81drgL47l+w6nX1OuFQuS17CZY8C+5C4u0mxvbLrDW/l/SXSU+Pm98J276COTer1/HtQafDmPMzfX78De9N7IjFWHlpaNZjt2in6jNTflA1P8SkqKdepo+Bg2urP/0kju5wB+A3L66smbHGq35v0y+GvM1Nm5+C7djeuUQ1d415Cj66HU4ZB21OVd/1nFvVdcOerGoWnXnquy/a2bT5FFFBApoGNDQrmQHt46uld0tzcEHv9GPWeDQms9HATWd2JrOGmW3P75FKl1RHtfS0OAtPju9XrSZGr4OpV/YnPa7KtoJecBeoYGbSXLjta9VXJhyEkr2VAU04pDoDA5z1ANz+PQy9U70u2AahIHazkd+M7EZyTPUf0isGtqV9Yst4aiwqBf0q6NQ06Hgm3PYNoQlvqaDGnUucScNiUpeGLqkxjOmbWS3YbRKapvpMeEvVj9nkhXDL1yoADnpVk2ZYHt+ul1BQNdv5XSpIuOVLuPlL9aSh3wXekqYLEsPhyiav2DaEDDYVbCd1Uf+6i1QzU3w7tOs/gvGvg9kB3kPvkRo6UYVOayGDTYRCIVavXs2AAQMwGE6sXbwht1VfB0s9/LijkHeW7SWsaVw9pD3n9kht3kdfj7C/xMPCtTl8suYAdrOBm87sRJd4PVltUmosK5cvyO5CF69+t1M9tp0Rx21nd6Zjcgw2Uw1lW7AVnPnQbrDq61C6D/b/DFkjqj+2veMb6H5R5WPbW79Qj3cf8dj2viI381bv5/MNB4m1mrj1rM70b5fQ5I9tN+cx1SjcReqx+axzVE2a30V414+44rL47ZfFFDr9XDmoHef1TKv3E04NWlbhMOSuU/8e7jNTtAsKd0DHM6L+se1mOa6Cfti/SgU0h/vM5G1WwUzbU8HQxI9tH1xDWG8i29CexLLNxNhsaAYr+vJ96NDQxbUlHJtJeP8vGOyJ6HR6yOgr/adENRLQNJJSTwA0jfhmbGaqTTisUeYJYDDosJv0dSorbyCExx/CZjZgrSmQOVIwoJ6mOCwUULU2VYWCkU9UVH19ODmsUe4JYDTocVibZ6SBlnBMNbiq38uh16WeAJqmkXCcx26Dl1U4DGiRP2C1HVNRptmOq5rOtVrOvybJi04Pej1en49ASIfVCCaDHtW3B0I6Pbt3bKNT5y4YDHoJZkSNZByaRhJva7kXW71eR8KhppxQqG4dca2mOgQyhxmrfPbafniqXjxruZgajsivaEBVv5dDr1vcsauvoWW8FQQzzaqmc605gpkq+7VaLFRvFAdCIUrKXepYkGBG1EL60AghhBAi6klAI4QQQoioJwGNEEIIIaKeBDRCCCGEiHoS0AghhBAi6klAI4QQQoioJ49t10Gx24/TG0Svg0S7Gfvh6QDcRWpQuMM8xWom2MYUDqmRU0FNLmcwgrtYTbZjMBMKeMkN2AmGw9hNBlKMnsbPk2gxQmGNvHIvgVAYs8FAepylWUeoFk0gFCTkKSU3qM77GLORZL2L/JAdTyCEUa8nxWHGbGykx52rXveOuC46fQFK3AE0DeJsRuJt5mO+z+kNUuLxo2lqCIE4m4lyb5CU9l3YW+wBIMZsJK3qSOXipCcBzVH4gyE2Hyznb/M38MueEox6HWP6ZfKni3qSGdgL3z4JFz0BsRlqHqPP/gAX/6tybqKGFg5B7no1C+2mT+DUG9R8Jzu/BZ0eLaU7rHqTXZ3v5fef5fD+VZloKx4jfPG/MSS0a5w8iRajwOlj7i/7+e93Oyhy+UmPs/DAqO6M6p1OUkzTjqwsmkgoSGj/r4RXv8vGNrfy929ymT8xk/CP/6Tw1Ie54u1d6HQw8bQO3Da8MxkNPWJ5yV747I9w8b8hob0aAfzzP8O5fyLb0I5HP93Eok25hDU4PSuJv156Cl1THRhL98AXf4ExUyGurZrx/bM/Ehjxfzz6vZsPf95HWINJQztw7dAO5Jf7+GF7AR+szKbMG6Rdoo0/XdyTs7qmHPcAkKL1kSano9iZ72L8K0v5ZU8JAMGwxvw1BzD5imD6aNgwF+beDqX71aSMWxbCO1eqYfwbgzNPTQ+w9TP4+U01SVv+FijbD5n90c0cj2HNuwze+E/m39CR9vOvRrdlAfrZN6M5GylPokUo9wZ4btFW/rlwE0UuNb9RbpmPP85Zx7vL9+IL1H0mcxE9wuU5GN6+BNMvb3DGzueZf107Ej64HP3mT+i67CH+O74Tbn+I15fs4p53f6Wg3NdwO3cVqOvdloXq+le6X00uuWEuvHkx1kAJX2/OI3xoLPqfdhZx+Us/orkK4O3LYcsCeG+iCmY+nAybPsE081J+c0YyYQ1MBh0XnJLBok15vL1sD9N+2EWZV80zta/Ywz3v/srnGw4SCreIwe5FCyABTS3KvQGe+moLgVDkyRLW4IXv9+E7/3GVsOtbeKa3msHWYILRU9XMsY3BZANHupr/KL49BNzqQvLtk7D6XbSz7gfAsnU+ya8NUndPJhv5Z/6VwnDLmEtKNI5Cp593lu+tcdmLi7eT72zAHzLRYhT6TZQO+Q0A9o2zSHhtiKolscSRP/SP/Pv73Ip1V+0pJrvY3XA7t8TB6P+o617+ZnUd3PktAJ7zH+OlH/ZVCza8gTCz15WgXTxVjfibswae7gXZy0Gno2D4Yzz9bTYAo3qns3BdDn3axrNoU16NWfjXZ5vJLfM23GcSUU0Cmlo4fUGW7SiqcdmMnwtYGDgVbdQ/IhdcOQPaD1WTMjYGWwKccgVkr4Arp0cM/67tW8XBrAk4B98V8ZaCy9/j3u80ijzhxsmTaBEOlnmp7UbVGwhT7Ao0bYZEk/hsh5cv7Jfg6TspIr1gwhymfOllwwFnRPrPe4obbudGs7reXfV2RHJ45N/52DuQ6atqrhV++cd9lGeeDpe/EpFePnIqz+/pwOy16rrbOzOeHXlO9hS6as1CsTtAmVeObaFIQFMLg05HQkzN88XE20yc0UaHbvW7kQuWv6o6ujUmd6G6kGxZqCboO0TnyifV6MKx/dOI1R2rp3H3aYmYDfJVt2Yx5qN3+LSY5Ptvjdon2BiWHsa2e1FEuv3X17lnaBJV+4OnOBr4ZstTBMv/G5GkW/0uZ7czkmCv+fqZHmvFHiqDla9HpMese5ur+zhwHHrowuULYjEZjjm3mKWxOjuLqCNXuVqkxlq4bXhWtXS9DuZMyiLtk0mVzUwDrlMLd32r+tQ4a64ePWGl++DnGRAOwg9PqbSscyG5K1z2Asb3rqpoZgqcchUA1m2fMmzLv2hrrv0uR0S/1FgLGbU89dErM5ZkmdyzVRqe7qXDJ1dWNDMFe48HVPPT8L2v8PTY9hXrmgw6Tu3YgE88OvNUn5lDzUz0vxYMJnQFW8icfx2zr8tCX8MDdtPGd8A456aKZiYGXAd6A/qDa+i5+HY+uE5ddxesy+HiPhmYjXrirDU/v3JW1xSS5NgWh0hAUwudTsdFfTI4r2daRHpYg5DehNbjYhXMTJqreviPe02dnH0ngLGRHic0WsBghoz+YHZA36vg/L+q/Zdko3U4HUw2csfNYV7b+yk+53HQ6TD0G4/JGtM4eRItQnqclWk3Dq524U91WHhh4qkkN/SduWgR9CYrofZngCWOnHFz+Kzj7wmc+xfQG3B1HcNX21WTk1Gv43/XDyIjrgGPA6MV+kxQ/x/3Goz+N0z6CAzq+ugOGao1g14xsC1mi001net0qrnq4n/DxPdBb8Df4xKW7ikDYG+RG38ozOacMp4c3w+bKbImpl2ijX9e0aflzQ4vmo1O07QW0UU8FAqxevVqBgwYgMFwYlWIDbmtQqeP/SUevtuSj8Nq5JzuqaTFWnCEy1XzUnx71QTkc4IrD+ypYG2kTsEArnzwuyEcAEu8Cqq8paCF0PQmwgEvSwod/LqvnMt7Oehg9aKPTQdzzQFNQ5ZVaxYN5RQOaxwo9bAmu4StuU76to2nd5s42iQ0bYfwaCirlqIhyipcnkvYW843eXY25Ti56hQHaSYPO7xxfLa5mIx4K8OyksmIs2IxNfD34S0DdwHEpIHFAUE/lGaDLZEyXSy5ZV6+3ZKPPxRmRI9UMuJtqkbFU6KaqxwZYLZDwAPlOXhNcex1W/h2Sx7BkMaInmkk2E24fUHKvUE25ZRzoNTDqR0T6ZUR2/CPoYuoJuPQHEOyw0Kyw0K/dglVliRFDqpncai/xhaTClVjE1sCcGhsPeCcVDinZ2bj50W0KHq9jnaJdtol2hnT3JkRTUYfm44+Np0LUuGCUyrTuwPd26U27s6tcervMKMZkrsAEAfE2Ux0S6/hBs+WUHHdAtQTnElZWIHusdC9yntCoRDbtm3jqsHdJEgWtZImJyGEEC2eyyX9AMXRSUAjhBBCiKgnAY0QQgghop4ENEIIIYSIehLQCCGEECLqSUAjhBBCiKgnAY0QQgghop6MQ9PSuArUKMCmQ6MNO/PUiJzWOErcfgqdfsq8AeJtJpJjzMTbo2jYb58Tgh41lg5AyA++crAlUW3SmWjiKoBwCGLT1WtvGQS94Eg7+vsagrcMgj5wHCrT4KEyjUlu/H23RD6nGqStojwC4C+r/RhzF6k50Q5/d36XmsU+5jjGb/G7CPnc7PbaKPMESHeYyLD40NsSQd/494555V4KnX68gRDJMWZSHBasJgO5h9JDYY1kh5m0WAvmY8x/VOzyU+b14wtolHoD2M0GUmMtpMXWPAp6uTdAgdNPidtPjMVISoyFJEfltanE7afI5afUEyDOaiLZYSbBTOWx6ndDwAV646FBQPWEvaUc9FvJLfdjNxuIT2uLzl1AWcjIQZ+ZMneA7rEeTAYD1oR0dNF8DRENQgKalqRgK8y4FK74L3Q4XY0K/M5VcNqtlHW9jHtmb2fJ9soZbM/tnsoT4/uSGQ2jZfqcsHkBfP8vuH4exGbCvp/hwxvV67Re0RnUuArg2yeg9ABc+iwYbbB+Dqx4Fa77EOLbNd6+vWWw8WP46UW4bjY40tX8OHNvO1SmPRtv3y2RzwlbPoNv/6k+f1wbOPALfHA9XP8RpPWOPMbcRbDiNdj5DUyYDtZ42LYIFv0f3PAxJHaq+779LkLbFmH46hH0Y97j1WUu/jY4gH7BZELXzsaQ0bdRg5ptueXcMfNnduSrsVqMeh2PjO1Ft7RYfjNrNflOHwB2s4FHxvZmdN8M4m013wztL3azZl8JP24v5P2V2QQPzV/QJTWG/04aVG2gvNwyL/9cuIlP1hzg8LjzfdvG89J1p9IhyU5OqYeH5q5j8Zb8ivfcPbwDD/QqxvDRbXDjQsjfCF/8BS59HmLSCAfc6GdNRD92Jo986WNvkYfFU7qhm3k9pn438GXpYPpnWjH/+DhhezLFZ/2ZuOQMjDIJ70lNvv2Wwl0EC34H5TkwczxsnAfvXAl5G2Dh79H5nWzMKYt4y7db8/nL3HWUuv3Nk+f68Ltg/n1QuAPevhw2fgJvXwbOXPjkHvX5o1Hhdlg5DbYuhE9+C7/MgE9/C3kbYdl/1Y9sY/G7YMEDapLUdyao4GbmFeoYWvAAuAobb98tUcCljqWineoY2zQf3rpEHWMf36Nmqj+SKx++ewL2LoPZk2HdBzD7RijeDd88pobnr6Ow343h47ugZA+dF1zDEz13kT53AjjzMHx8J+FGPL73l3iY+NqyimAGIKxpdEqOYfKbKyuCGQC3P8Sf565j44HyGrdV6PTxv+93sCmnnHeW760IZgB25LuY+NoyDpR4KtI8gSAvfL2Nj1dXBjMA6/aXctObK8kp8fDwR+sjghmA6wclY5h7y6HzPgQf3QEle9T3tG8F+hmXgDOPjG9+y3/GtOfBEe1IWD8dXe56bF/9gUmmxQzZ8BjW7Quwr30LQ+FWCo74nOLkJAFNS2FPgstfVneR4SDMnaJ+FPVGgtfO4f5P91Hkqh64fLMln8Ia0lsce6K66zVaVFAz52bVVJLcBa6cHr1NJGm94ZLn1P+3LoQvH1b/7zkWzry3cafDsCeryVENJhXUzL1VNZ+k9oQr/he9ZXq8rElwwyfqGCvaCbNvUsdYUhZcNR1iUiLXj28HV76lam32LoP5vwFNU7Wjox6NHJr/GHY6TRy84gMw2aFkLwkLpqimq4QO7B75GjkBe4N+1CNtySmjwBl5DTizawqLD82hVJOnv9pCSQ03QoVOP/3bJfL2sj01vq/A6Wfzwcobq/xyPx+s2lfjujvynZR6Any9Oa/asknvbmX/pe+r5qV5d8K4V1XZlWar7+FQ2e0aNY0b399BadBE/im34O8+FoCEHx/Fun0hAMUjnmTa9hgWrDtYYz7EyUMCmpYkvh1c825k2mm3U5DQn0VbS2p9W5k32Lj5aggGM2QOgDPvj0y/aiYkdGiWLDUIaxz0vhw6nVWZZrTApS+oJqDGZDRDu8Ew7O7I9KtnQkL7xt13S2Q0QZsBcNaDkelXz4SEjtXXN8dA1rnQ+4rI9CtnQFz95kLbXuDlv5tjcJ35x4j0otGvcu/nxY16jm7JrV7bkhlvY1dB7VMF7Mx34Q1UD3YKXT5sZgMl7kDt+ztYuT+3L1hr0ATUup3teS7+sTxEcPTTsG8VbF8EZ/02Yp2ii//Hb74oJrfMR3qcjZ8LTZSPekrN+3RYh2FsTh7FCz/msSa7uNZ8iJODBDQtSek+mHVtZNqK/5FSspqR3RNqfVucNQq6QoX8cOBX+PGZyPT3r4OSvc2Tp4bgLYMNH8HuJZVpQR/Mu1s1dTSmoF/9GCx7KTJ91nVQkt24+26JggE4sBqWPBWZ/v4kKK6hxsHvgh2LYeNHkekf3gBlOfXaddcUK3f0dBHz478i0pMWTuGFixKJs5rqtb366JFRffLHnFIPWalVZ7Gt1CUtBpup+uU/OcaCxx8i0V57fo/cn91ixHyUfisJtWyna1oM/3eaAePCB1RQ3vV8WPJsxDpJC6fw3IUJpMdZyC3zMCg5QOyXD6hO34ftXUbPgi+598w0BrRPrDUf4uQgAU1L4S6CeXdVNDMx7rWK5ifjuxN4ZkxbkmKqd+I7r2cayTWktzjuYtVn5nAz04Q3KpsGPpwcvf098jaqPjMAPcbABY+p/29dCD++0Lh9aNyFqs/M4WamcdNU81PBFtVkGa1lery8RfDWpZXNTBPerHKMFUSuX7pP9ZnRNOgwTDUdHm5++vLhevWhyXIEyPjoyoqmkuIxr1U0P3X66lYyTY13HPTMiCPVYYlI+3F7Aed2T6012HhgVI8an5BMiTWzbn8xk4bVUKMFpDjM9MyonF071WHmqsE1d3zvmuYg3m5iZK/qT/vNvLY7bedfrYLKy1+Bubersotvr76HQ81Pnb+6lelXZxFvDJC64XXM2xYAUHLmI3i7jgYg8ds/c2tXF6P7ZtSYD3HykICmpbAnwZj/qCczJs1RzRjXfQhpp8DoqWiWWE5pExfxlvN6pPL4FX2i49Ftcwxc+qIKZq6fB70uhes/Vs0yl72oPn80Su4KQ25Vwcwlz8Cpk2Hss+p7G3ZH4/ahMcfAmGdUMHPdbOh9mXqaJzYTLnn25OtDY4pRx1JSFtwwD3pdAjfMP+IYq1IejjQ49yEVzEyYDn2vUs1NiZ3g/Efq1YdGb7YTuuy/kNCRXWNm8dDmThwcNxscaYQu/x/6qvtuQG0SbLx721C6pFYea3qdjj1FLmbcPITU2MpgJ8Zs4MlxfemdGVfTpkiKsXDb2V3pmRHHpKEdMOornwrrkurgvduG0SahssnHZjZy3/ndGDewbcQDZP3axfPG5CFkxtt49PI+nNczMqiZ+WshoXFvqO9EZ1RPdiZ0VN9Tu9MI3/gpONLJOe85frcgm6cW76Okz2S09D54Rk1lZnAEK055GG/XMbj73UA4pTspVYI6cfLRadqRfdObTygUYvXq1QwYMACD4ehjJDTltpqcq1D9CBoPnZy1jEMTd2gcmoQTDGaatKyqjUMTAN9RxghpQY5aTq4C0EKVfWaaexwaf3n1H+8m1KznX9VjrC7j0IQDld+d36WaNKp2IK4Lv5uQ38Uej41ST4AMh4n0Y4xD05BllV/uo9DpwxsIkVTLODQpDjOpdRmHxu2nzBPAGwhTXjEOjTUiODpSuTdAodNPsduPw2Ik2WEmKaZy3VKPunZVG4fm8LHqd6saGr2h1nFoDH4XWYl6nCEjuT4zpe4APWI9GI1GbAmN3F9NRIUo6Hxxkql6V33Ej2KC/cQDmGZlcUTWWBhMzfrD22Cq/vhZ44Ca74AbnLXKfoxmMLaCMj1eVY8xo+no5VG1ZtAcc+gH9TiY7RjMdrIiKuUasYauitRYS40BR2a8rd5jVSXazSTW41oTazURazXRiZrLLt5mrnncm8Pfjdmu/o6gj0mmTQy0SYw5FPhtQ2s7gDiDoanOLhFlpMlJCCGEEFFPAhohhBBCRD0JaIQQQggR9SSgEUIIIUTUk4BGCCGEEFFPAhohhBBCRL1WG9CYTI031LgQQoimJdd0cSytchwafdk++mRaCZUfZJsnljStkDizRhgdvph2xNoiT4z8ch+7C10s2VZAisPM8G6ppMVZsJuPo3iKd4PeBPFt1euyHDXQV1LWiX+waBUOQ/FOsMRVjqtTtZyOwukLklfm5but+ZR5ApzdPZX2SfaGHRnUlQ+eUjVKrMGohr13F0Fs+vGPSyKiWoHTx/5iD99tySfGauDcHmmkx1pwNOK8THXmLSPszOdgOJ6NBSHKnE5GdwjhNydgjk3Gajr2IH15ZV52FbhYuqOAtFgrZ3VLIT3OWv29rkI1AF44CLZEdd56S9WAjhYHxFaZciAUVLNmgxprKr4dFO4CgwFCAYos7dlV6OKHbfkk2s2c3T2V9KNdbwt3ojeY6JVmVnlw5kA4pP4caY07GreIKq0voCnYga50L7qPbkeXlEXHsS9jK98DHz8MfifuCbMpTexYMV3AwVIPd77zC7/uLanYhF4Hz1w1gFGnpNcvqCneDTMuAWsCTJwFOj3MvknNJXPTQjVM/skmHIbc9TB9NHQdBaP/rUZzfetSdXGc+B7E1R7UlHsDzP1lP3/9ZENF2jOLtjG8WwpPXdmftDjriefRlQ9fPwbr3ocb50NKD1j7Pnz+R5gwA7qdD4b6DUwmolteuZc/zF7Lt1vyK9Ie/XQTf72kN+NPbUecrRmDGm8ZobUfYvjsQWIu/i8F/n6cH7sX66vX4R98D3n9biMtPfOoQc2BEg+3vbWKDQfKKtIMeh0vX3cq53RPrXyvqxDWfagCh49uhyG3Qf9r1MSrH02BzFPh8pcqg5pQEA6uBU8xLPwdGCxwzTtqfrGlL0HBZkIT5vHA7AL2FLoBNYDz1PH9uLhvJjGWKtfbgm2w5yfY+xOmDXPRrp+nJrMt2w/f/APGvw7dLpSgRgCtrcnJ71YHe+k+cOWh2/sTtnm3wBd/Vj+qpdlQspeDJepECoRCTF+6OyKYAQhr8NsPVpNb6q37vkNBNaNv2X51Qr97lZrld+9P4MqD/K3qjuZkE3DBvpXgK4cNc9UkdDMuUd9TwdbqEwZWcaDEExHMHPbDtgI+WXOAcLgBZu4I+mHnYjXk/YxLYMGD8Nkf1KSFGz6KnN1XtHqapvHp2pyIYOawv8/fyP6S5j0eAj63Opc0jfiFtzP+4POkfnwdhIM4dn2J1+clt6z2a5cvGOLlxdsjghmAUFjjrnd+iXxvyK+m9djzo/r/spdg8eMqmHEXQc4varqIw/xONWGr36XO8fxN8N418MNTsGcJuAowF22hX0bljYimwe9mr+Vg1TwHvFC4HZI6odv7EwS96N6+HLZ9AV//Xb1x/Vw5P0WF1hXQmO3QfjDhuLYw+j8q9D/wC+RtAr2B0ive5c8rrbz47U78wTAF5X5mLttb46Y0DRZtyqv7vg1GaD8UJn6g5iPJXQ/7V6k8TJgBWedUzs90MrHEQp/xcNGT6vWOr1VgabLBjZ+qSRyPYvbP+2pd9vqSXRQ4GyBIjG8LN3yiJscLeGDdByq912WqRunwvEDipJBf7uONJbtqXf7hquwmzE11u70xlF78CoH2ZwJgWv8ehIOEU3uzZeQbTJi5g6Xba59pvcDp58NazqtQWOOnHUe8Ny5TTfKZ3hdOvVGlbf1cBTP2JJj8mZpw9jBbAvQYreYYu/wV0BvVjcu+VQCUjX6Fp3a2Z/6Gomr7/nxdTmSCyQqdz1Y1umP+A4md1Rxp6+eo5T3HwNinK+cxEye91hXQAFhi0VJ6QkZfsB8xx058e/JjurF4RxkFTj+BUJiQpuH0BWvdVF55PWpoQJ2AGX0hrl1lmj0FOpx2cvfDsCVAr7GRAV1ar8r+KrXQNK36XdsRit1+Qg01t6o9Sc1WfaSht6tJDcVJJaRpFLv8tS4/WOZtmJrB4+QNhtntMlPe76aI9PJeV/PRJjdlnuBRr13BUBhfMFzr8vzyKjcJtiQ1gWf/qyPTO5yp+phVZU+CTmepZqrEjkdsJ5GyjKHM21hS435zq+4X1HWzzUA4sAa6jYpcdtrtYDuJ5y0T1bS+gKZkH4bCLfDpA6pvxGHFu8lafDezrunERX0ysJsN2M1G+rWLr3VTZ3evZ+RflgMf3ggleyrTXPnwzlVQur+eH6QVKdoFb46ObHLb/4tqY3fWXgum0+m48JSMWpefnpWMo2qb+/HwlMCa92Dp85Hp74yHnF9Vc6I4aTgsRoZ1qf2H8qI+mej1zTc7fGYM9AysJ+mzOyLS47//K7dnbOG6U1M4o0vts4XHWIx0S6u9z0nEZ/eWwfav1c3H+5MiV9w8H77/D7ir1AYVbFfNTt/9Cwp3VKZ7imk7/zo+vKYDcbbq5+2InjXMTl+6D1a/C2iw4tXIZe9eBQd+lvNTVGhdAU3AA7nrVHNP7nrQGwhf9TaMngo6Hfrsn+ge3MyYPmnodDqSYsz839je6Gq4NvXIcNA9Pbbu+w6FYN8K1WdGp4MrZ8B1s1Xz08G1sH2R6qtxsvGVw69vqfZ0kx1u/QYufEIt2zhPdaQ+ikEdEmmfVL1DrlGv4/cX9iS2IZ44CXrhm8fU/3tdBr9dX9n89MVfwFty4vsQUSPWauJ3F/TAZKh+YWiXaGNIx8RmyFWlZKMf+6I/VTQzFd2+Bn/7s9SyJX/lgXPa0SnFXuv7UxwW/npJ7xqX9W8XT6fkI94b9Knjf/U7lc1MN39R2fy04lV1Q3CYpwS2fameitqzVKWNfQ7GvQYGE7r8TbTPX8zlfSIDxi6pMfTKqDKHdtAH27+B7hfA0hcA0HqMhtu/r2x++uxPcn6KCjpNa6g6+xOjpodfzYABAzAYjv3IYa3chWj7VsH+nwm3HcIST0cGm/Zg8xcSdJfg6jmexKTKmhe3P8iG/WX8bf4GNhwow2LUc+Wgdtw1oittEur5ZIunFNbOUj3+u44EnQF2L4Gc1TDkFvVUTwvTYOV+NK58dbfWbyJk9lcdB9fMgrg20PX8YzbH7St288xXW/lkzQECIY2BHRL42yWn0DMjFksdHk89pnBY3VEuewVG/lVVlRfthi8fgounQnzbpimnVqI1lJU/GGLzwXL+Nn8Dv+wpwWTQcUm/NjwwqjvtkmoPFurreMsqVLQb/ZcPUXbu49zxSS6/OyOefuufxHnGHwgmdiU19uj99Vy+IGuyS/jb/A1szXViNem5Zkh77jinKxnxRzw5qGmQv0U92PDLWzD8d2CNh73L4MCv0P1CaDcEjObK9zhzYeuXqhOxJRayzoVti8CRirZ3Ofl9buHmD7azfr+63o4b2JZ7z+9W8/XWXQTrP0JrMwBtxWvoRv4fupw16trx3b/g4n+rx8KFoDUGNEDYVUhJcRHxqW1xhk2EPWWYdUGMRhOW2Jr7RBQ6fbj8IQx6HSkOMxbjcebBU6LGXjj8Ix3wqjt9e8sLZqAJf3zcRWocmsN9ZqqW0zF4AkGKnH7CmmoSSIwxH/tN9REOqzs9+xHHh7u44ntrDT/STaU1lVWJ20+5N4hepyMxxnR8Y1MdxYmUleYupjBkx+UPEgprZJg8GB3JmI11r3gvcPpwH7rupTosNb9X09Rj2FB5fniKVbrZERnMHOYuUsNW6I3qkWp3sfp/OAj2RIpcPly+EHq9juQY89HHzXEXETJYKck/QEJmZwwhD4TC6umrFnpdFc2j9Y1DA2jWBHbl7mZAZhbxBkOdOo4lOyw0SPcyW0Lka5NV/Z3s7FUCyarldAw2k5G2iY14uOr11fMoF8uTXoLdTIK9gYPnBqKzJ5ICpHC4Nqb+Y7HUaXBKna6G8/cY58YxzqWkGAtJdX1Owp4EoRC7c0sZkIkKooSoQevqQyOEEEKIk5IENEIIIYSIehLQCCGEECLqSUAjhBBCiKgnAY0QQgghop4ENEIIIYSIeg0a0Hz11Vf06NEj4u++++5ryF0cv1CAUFij2O2n3BtQw2XXZQieUKDK6zq+ry7CITX+yZFDd4cCBAM+ilx+XEeZZ6rWvAQDNa97xPYj8xDEaqny6GbVocSrzhIekd8q6x7efiiAz+ej2O3HHzz0GY/Irz8Ypsjlp8wToMjlwxsIHT3f0aq28mmSfUW+9gVDFLv8uP3HMVR8tfOgET8HHN+xjRoQr+pnLHH7KXG37FG6dVqV47+G8nX6ghS5/PgD6jOq61jN34s/GKLI5cdd0zXk8HFRca5WeX0oze3xVpZbKIjXp177g2HKPAFKXH4OD2N2+NjyBGo+tjxer5p77fAcWIf26Q2EKHL5an1fXXgCQYpdfryBEOXeAMUuP6FQ7XNVidarQQf22L59OyNGjODRRx+tSLNU/bFsDj4n4R3fUhrfk3sW5tMr3cYf+rgx2hMwpPeixrkPDr2P3T9AWm81yVooqGbvNtkh/ZTa31cX4RDkrFGjbu5eAj1Ho3nL0HxOwgc3kB87mP/8VMrNZ2bRMzOWxKpjYdSUl4JtUJ4D7U6reeybGj6P/uAausWbKn88ynJg57dqBFB7EpRkw45v1MjH8W3BVQBbPoNuF6hpHQ7/PzZdzfuy63u09FMIl+xD5ynDZe9Ckc5KJ88G9DEpYI5hn6kzP2wvoE2CjQ9XZbOrwE2PdAdTzulCp2Q7tgYevKzZHC6r7heAI12NJL3zGzWyakOPblp+UM250+MisCeDqxC2LIRuo/BZU9lb5GbaDztZt7+MTsl2bj+nC1mpMXWbOqJopxo5uePpapb0sgOw+0c1WWA9xxOqk+M4tgPBMNnFbmYs3c2qPcV0SYvhvvO6s2xnIR+uykav03Hd0A6c1S01ciTcFqB9igPd+jlqRN24TPC71DUhtQckdqLI5WP9/jLmr9nPuFPb8cO2Ar7bms8dpyVzkXUtdByOKbEt+N1oe5YSTOjMi6uDfL0pj/Q4K7efnUWPjFg1nk75QTUNS5fzYdf36jvd8yN0PhdyN0ByV/w6E/rSvZgKd7IvfgiB2BiSSzdiDAUxJXbj0wNW3lm+F38wzJ3ndKFzagzTf9xV47GVX+7D4M7DtnsRv5qGsrHExOSBccTsXoSnw7n8bXEB6/eX0TXNwe1nZ9E5JQZ7Hednc/qC7Cpw8ep3OzineypGo573V2ZT5gkwslc6Ewa1o30DjuosWr4G/eXYsWMH3bt3JzW1BU3n7nMS2rQAw7wpJCV35Z+j38XuycHy7lVgshOa/JkKaqq9zwXbv4LZN6l5Q274WF0MZlyiZo2+6XNIr3k+lGMKh9X8TnkbYfMC9cOTvRzdaVPQbZiL+cfn6NJuKHef8Tzjpi3jrnO7MuXsLOJsh358QkE17PiMS8BohckLVJ6mj1YjdE6aAx3OiBzB0+dSc6zMuTni8+hmXILZaEW7aaH6IZx9M+xdCiP+AgOuhXl3qIvrsLtg+INquPEVr8KEN2HPElj5OvQcC5c8D5s/VRfk5a9iSOmKYeGDtEvoRHjca+gJw9uXgcGM/cqPcVjSuGn6yoo4amNOGR+vOcCrNwxmRI80DM04+V+DcBXA4sdh1RvQ6xI1n83GebDgAUjvA9d9AHFtG2Zf5bnw0R2wczGcdT+ccR98PxWWvwLdL8Iz8j9c/Nw6gofujjfmlLFw/UH+PaEfl/Zvc/RRWot2wfQx6tif+D5k9oNZk9SkgBf+EwZc17BBzfEc24c+01X/+6liFul7z+vGnTN/Zlues2KdX7NL6N8unv9dP7jFBDU6bwlJO+ai+/5J9bmumgF7foLZN0J8B0I3fML/lrl5e9le/jtpEHe98wvF7gAPnJXG2c4FmD5/jFDbIYSunonhwC/o3r8OU1wbxo39gLd+8rDhQBnfbM7jwVHduXNwLMb596hAqSQbvntSBdej/q5uYj6+GybOwlS8C11pNix9gW4dTofR/0FXlo1+wQMY4zswcMws/pZXTpt4G8FwmEteWFLt2Jo6oR/ndE+lKG8fnZc+hGXnF5w24FayBt2P/qeX0K14DmPHc7hyyL/4YFUZG3PKmL/2AC9deyqjeqdjMhy98cAfDPH1plx+M2s194/sxndb85m/Nqdi+YYDZcz4aTcf3XUGnVNkIL6TRYMHNGeccUZDbvKE+XVGvLZM4gxmKNxOp48uUcN2h/wQ3w6/3kqNMzYZzGq+EINF3aFOOx+8par5JTazzkP210ivV9MAuPIhuYtKW/2Oqj0p2QtAMCGLg+UBwhq8uHg7V5zatjKgMRjVj4glVm3jzYvUsOKeYlXjE5OqphWo+nni2x7l8zhUjUtyFxXQLH4cHBmQ0EG9f9nLKmA5lD90Okg7Rf1/86eqtqlsP1z+isqfI03tq3gX+lkT1SSVQS840rHExPH0nK3VWhTCGvxh9lo+vfes+s+j1dLoTZXls2k+HFgNpdnqdWIn9X012L4M6kdq52JY8gysn1PxPYVTevDd9pKKH5wjPTJvPad3SaZ94lHuYo0WiElTtTLvXa2OlbL9alj75C5qeUM6jmO7wOnjD7PXVgQzp3ZIYPPBsohg5rA1+0pZtrOQywc2UDB5ogxmtORu6nzauxReG6HKV9Mgrg1+zcj0pXsYf2o73lm+l2K3ahbaXRIg0LMr6PQY9q+EaeepGiwtTDAmg/KgPqIZ9+lFW5k8cDCxKd3VDNZdRqh97lsJc26reK/mK0dDjy4pC3Q6dHt/Ut/7oTyFYzMpDejwBcJMPrMTT3y2ucZj61+fb6ZTcgzb9zlpm9Qdy84vcKyehmP3lxXHpj+hC/tKK5u5NA3+OHst/e8/m7bHOP/zyn38ee46bCYDPTPjeGbRtmrrlLgDPPXlVv41vh8xdaz1EdGtwfrQaJrGrl27WLJkCRdeeCEjR47kP//5D35//dquQ6FQg/wd3lahW+P3P5nIu+J9dRF25qpgJrET2y+cydRl7pq3odMTyhiAdsMn6oLqzFM//vHt0G74mFBcuxPLY0IntF6XqlqRYXeqD3/oRPf0vpLvOtzDnfP2VpTLip2Fke9PzFK1KrZEFSx4isEcgzZ5IaHkHoTC4Tp+nvZ4r36fQEwmIWsS4fP/D63/tWqn8++FzP5oAyZF5E874z7Cnc4mfMp4tIunqmWl2aCFCa3/iJK+N+HbswLGT1P7cuWrYCa+HTtHv8uKEge7i9w1fv9FLj9FLn+DHQcN+Vev49PsINxnAtrF/64sH0DrfjHhMU8TsqU0XN6siYSH/x5t8C2R39Pp91B66l385pO91coZwBcMs6+oluP/8F9MOto176Kl9wUtrH7YAO2adwl1HE5Ibz7xsqr6V89ju8TtZ0tuecXnGt4tlS835NZ6jXlvxV7KPC3jGAvqLRyw90Yb/8ah42QfaBpau9PQJrzJzA0+fMEwZ3RN5utNlZ9p7vpintySQcHoaSqhbL8KZjIGsGn4i1z93h68gcp+JJoGi/YECJ/1AJojHXZ+B2OejnivljmQ7ba+fKU7Ha8/oCZmPSJP4baDWXv6s0x8bw++YJgEm5mcUm+NZdynbTyfrNnPnz/fz4LYCZSdekfEsenseyOL0m7i/vnZEe8r9wUpKPcd85jKLfXi9ocY0D6Bn3YU1vpdf7b+ICXuE/+uRXRosLD1wIEDeDwezGYzzz77LPv27eOxxx7D6/Xy8MMP13k769ata6gssW7dOmLT2hPWQKNqE4YODQiFw+zatYvS0tJq74+xW8myBDFXeZ/X62Xr3vUEg8ffkc1kMtE91YRV06CGvIWrVF8EgkE2b96M16suIGazme5JOqreHwcCAbZt3ozXV6Ujb62fR13sNm3aRDAYJCs9jjhNw3Dkwip9hUKhMMWFBfgCYTJCoSoHkU51FKyl3/RRFlXw+X2sXr39GGs1j/ocnxmJdtKDwYjyCWsazvJytm/d16D56pQWS3xYi9hXKBQ+5sXYHwiwdu1awuGaO1Hq9Xq6Z8Rir3IMBIMhsvfsobjcU+u2j/dcru+xbUntUG0b2lGOMg3IycnBWVxwXPlraMlxNoLBEEfWO2ka+DwegsHK76/qJwrVcG4efm9Nzy0EAgFKSr3EhTWMWphq1x2dur8N17YBdIfO37o9FHF4E6oCJ3Jf2qHrb018fj+rV6+ucdnhY8pfj+Zat9vN6p2b6rx+TQYNGnRC7xdNo0Fn2y4pKSE+Ph7doZPsiy++4Pe//z2//vrrMWeSDYXUrLN9+/Y94Rl6Q6EQ69ato2/fvmjhIL49q4j7cIKqkXCkqZmeQ35I7orn6tmYk6tfEAmrDrO6GZeo2oWYVPCVqW0kdka7YR7huPbHnUd9yW50mz5R/QKWPq8S49upuyHA0+davm1/F3d+pO5ovn7gbDolVzYN6It3oJs+RtW0mB2qGt5TDNYEtMkLCKf0jLzYhYPoc1aje+vS6p8nKYvwpI/AbEf3zWPofpmh3nPJc5C9QjWHVcmfNmE6eIrQLXigclnZfrjsZbS8jejaD4W5t6mZxmNSDjU5qbJzXj2HMW/tZU8NtTSJdhML7juL9NgW0Jn8CEceU3U5PnW+MnQb5kaWz+Gy63UJ2uin0OwpDZI3nacY3Q9T0S3/b7V9hc/4LfNjJvCb+dVraSxGPYseOJs2R+lPoncehPcnoTvwi/rBc6Sr5gmdHm3iLMIdzwRjZPNAfcuq2j7reWwXuwNcN21FRS3NqR0SObt7Cs/W0AwB8OzVA7ikX0a989UYNJ8LbduXmObdWtHMpJp/NLQOZ+C97DUGPLOOCYPaUeD08cWhmqfL+yTycK88Uj69SdWcxbVR/Zy0MME2g9l41otc9d7uiloanQ7W/n4wjpXPoyvZqzr5f/rbI957aJ/j30BzF6E3GFR/L01Tfb3KD4CmEWp/OmuHPcM17+3mH5f14dlFW2uspUlxmHnlukFs3b2HS91ziV1Z/RrnHHArX6bcwAOfVgb3sRYjn/92OBlxked/1WMqp9THBc9+j6bBs9cM4Pa3f66xfMf0zeRf4/tgO1o/sTqI9lnjTxYN+th2QkJCRTAD0KVLF3w+X421H7UxGAwN8nd4WxZdmBjPAfVjmtyF3Vd8qpqfDGZwF2LWvDVvIxxEV7Zf/fgndobbvoEb56uOip4idH7X8edPp0PnK1N9EwoP1UT0vxaufgfOuBcAc8kO2sSZ0Ovg7nO7khprrXw/GjpPqXqqyBoPN38Bt3ylghS/E527AAPhyH1qR/k87kJ0ARd6LYyuYIvKz7l/hq6joHiPej30TpjyHQy5FVD1W7rc9WpZ99Ew5XsY+6zKQzikmvaCXkjsRPiaWTBuWkXZ+dxlPHBB92o3l3odTJ3Qn/Q4W4MdBw35V5/jUx8Oojt4qIai51hVPqP/o8qucCd6Ldhg+dJrIXT5h763M34Dty+BoaqKX1+wiXO6JWKsoZP1Py47hbQjj6sa/nQhP7pDAQwT34Mp30LmANDC6Aq2YQjX/DmO+1w+jmM7JdbKvyb0xWJUl7Nf9hbTKzOOrmnVO4P2axvHsKykZj+WKr+7gDrnNA06DFPlO2G66r9Stg+zLsiNZ3Rk3q/7uW5oRxLsqh6nS6IJU9FW1czbZjChWxbD1TNBp8foOki8ORzxI37/yO7YjBq6vM3qCcfSfSqYaTsIxr8Ol74EOj06a6yqOynccSioGoZ27Ycw+inQ6TCU7yfBomEx6Zm+dBd/urhnjR34/3BRTzom2xnUPhZz4WYAnP1vZs+VX+A+TQ3lYS7aSvuEyvpinQ6eHN+XtLiaj8kjj6m0OAuPX9EXTyDE5oPljO2XWS0PCXYTv7uwOw6ruUHOfdHyNVgNzQ8//MDvfvc7vv32W2w2dcc2f/58HnvsMZYvX37M9x+uoRkwYMAJH0DVtuUrJ7R9MaUJvbl7YQGnpNv4/SlOjDEJGNJ7H/2x7V3fq0dHKx7b/hlMMSf+2HYoqJ50ssYdemx7TMVj26Gc9eyMG8LUn0q5+YzO9MqMIzGmpse2q+SlYJvqvNl+aO2PbR/5eYIBtJxfCWDEkNkPg9GoHtve8Q30uLjyse3ti9Sj2Ycf2968QD0erDOoDsHdL658bHvnd2gZfQiXZBNyl5IX0w2/3kon13p0jsrHtpdsLyTz0GPbOwtc9EyPZco5XeiYbMfeAh/bPq7j05V/qKwuVjUb3lLYtgg6DG2cx7a3fQU9x6jvzVUAmxdC9wvxWlPIPvTY9tr9pXRMtnPnOV3r/th24Q4o3g0dzzjise0lhx7bTqy2+gmfy8dxbB9+bPvNpbv5eXcxXdNiuPe8bizbWcSHP1c+tj28hT22HQqFKNy3ndSiVei6nFf52Pau7yGtl3ps2+ln/YFSPlm9j/GD2vPd1ny+31rAXcOSudC0Gjqfc8Rj2z8STMjihdVBFm3MIy3Owh3ndKHnkY9tb/1SfXe7voOOZ6rvMutcyF0Hyd3x60wYSvcSLtjOroShJMY6SC7dQDjox5fUgy8PWHl72V78oRB3ntOFrFQHb/64i3X7S+mcHFPlsW0vBlc+tt1fscx8OuuKjdx8ahwxu77E03EEf/2mgPUHyuiaFssdZ2fRKSWmxg68NR1TTm+AXQUu/vf9Ts7tkYpRrx7bLvUEGNVbPbbdLtEWcZMtWrcGC2icTiejR49myJAh3H333WRnZ/Pwww9zww03cNtttx3z/Y0a0IAaWE9npMwTwKDXEWdCPVFxrIM9FIh8qiIUVE+VNMRJEgqq7Wjhyn2E/ATDGuV+HWajjhjLUX5waspL1fxWe0/k8lDAx+Yt2+jZq1dEWUVsI+CN/BE5cnm18jn0OhTAGwjhCeuIMZsw60KATt3p6/X4g2GcvgBGvZ5gOIzdbDz648PN7LiPz9rKpzEcY1++YAiXL4jVZKh/0Bjyq1rN2vZ15KoNcS4fz7GNepzX6QtiMRoqfhhL3H50QHzVsZxagMNlNbBfH/SmI5pZavisTm+QQCiM3WzA7Q9hNOiINVHlO1ffkz8YwuULYTbqqwcIR5yjkf8e8R2HArh8QQIYVCBU5Xz2BkKENI0EmwmdTnfMY8vj9eIN6YmzmVSNzqF9egMh3P4gNrMBm6n2Y/Jox5QnEMTjDxFjNuIPhQmGNGKtRozHePRbtD4NdivscDh4/fXX+ec//8n48eOJiYnhmmuu4dZbb22oXZwYgwkDVK/pqMP7Il83YO1BxbaOOEENZowGSKzLb15NeTnWj2XV5Xpj9Q7EVdepekd85PJq5WOq+NdqMFH5zsiLi9moJ6mhH/ltiWorn2bYl8VowGI8zgDDUOW8aczPAcd3bANmo4GkKp8xoQUGMlVpVR/jr+GzOqyV61hqC/4PfU9mowFzbd/1Eedo5L/miHVi7JHn+ZHns9kYeT4f69iyWasMj3Fon1aT4YRvZGwmY0UwVGu5iJNCg9btd+vWjTfffLMhNymEEEIIcUxSJyeEEEKIqCcBjRBCCCGingQ0QgghhIh6EtAIIYQQIupJQCOEEEKIqNfyRjA7QaGwRm65n5i0DuQ7/WTEH33WVk8gSNhVTDkxWExG9Vi3pxhM9oiZhF3lJQSDIYqCFgx6HUk2HQ68ahAzdxHlWCnxqtFuU8x+LEYjhANq8DmdHiwONWidxQHhoJqYxWDEHwiSH7Bg19xYzGbsjrhGLqEjuIvVrN8Vr4vU60OPzOaVe/EGwpj0OlJjLWq0WU8J2BPBW66mZwkH1eBqQb8aEMxefaC1IwVCYYKuIgpDdsxGPWmxVrVfW2LDjO1zsnAXUxS24/SHMOp1pJvcGKzxaooJeyK4i3BiRR9wU6rZSTIF1Ei6lthq24n4zjylhMMhcgM2AmENm1lPqsGD3xRPgdNHKKxhNxtIdtTxkfuq23cXqzzUYfgDpy9AiTsAGsTZTJWzzUPEMeP2BQm5i/FhxmSxVY434ylW0zIcHnbA71Lj29ji65b3BqK5i8kP2PCGwpgMOjJMXnRHlIkjMYUDpT50Ooi3mY4+2GHVc8VdrAY71BvBV0bIkkCe04emQZLehdcYR7k3eOi6pGa2LvCbCGuQaKXyGlZXPidoGn6jnQKnH4MWJE7vxRafCp5SNXZQyK+2GfBCyAfh8DGvC/XiLlKjSOsN6vPrdOqRc7Ndfec1DPYoTg6tqoamoNzH6z/sZOwLS7jopRWMf+UnPvp1H8Wummf8zi5yk713D8ZFjxDO3cTf52+gMP8g2vL/wd6fIOjDFwiRk5ePeceXGDbNY/ZPm/jT7F/x7/2Z0PdPES7dT+j7pwjs/Zk/ffgLM79fT2DjQrSSPbBuNqyfA1s/U0ONr3oddv8IRTvhuyfRDqwmuH0xu/eodYObPyenoIBgqOaJAhtUWQ58ej/64p2YzWZ0niL49l+Qsxqnx8sXGw5y1X9/4ux/L+bCZ7/n3eV7COVuhC//ot676WPY9Cmsfk+N4LpvOfz0orrY1CKvzEtJzm7MC3+DN38Xd7/zC4W5+9C+fBjyNtYyIZ6oKlR6gPCCB/DlbmXStOWs2bKD4PLX0PI3w8IH0Ur3Efrxeaw5K7Gs/C9G536C6z8htPETFfAcVrgdPr1fjR4LUHYQ7eBadF8+RM7+PVz536V48najfXwPRTk7ufylJQz/92JueGMFy3cW4vbVPjmrTqdD78qDhQ+qUX5BjV78/b/hwK8qsDiKXQVOHvxgLWf/ezHDpy7m3vd+ZVtuOaGwpuZ4+vIRyN1ATombVZt3oK14DW3PUl77djMbDpQSdBbCyjdgz4/qh9Xvgu1fw9r31YjNTSRUtBvtk3sozdvN+JeXkn9gD+FP7yeUvxVN09hd6OZf3+Vy9tTFDP/3Yn77/mp25DmpcbzTIz43mqbOtRWvwsF1sP9nwt//hx179jLlrVUYirZj/uL35B7Yw8inv+Oj5VvQb/6UwPqPeXPxeh6e8wuBPSsI/fg8YVcdJ+n0OWHzp4Q2fsz363fxyNxfYf8qtCXP4szfh7b9a8hZra4j5bmw/+fKY6wsp0HKU+cphG8eh7zN6rha+KAaxXrvUjWq+cf3VE7XIk46DTo55Yk40dFFyzwBnli4ifdWZldb9sjY3txwegdMR2w3p8TDz1t2MWr/y1jWvg3WBPKv+hjL5nnErXhG3fHc8SPZxg6kF6/C/PYlABSfN5VAcg/SProSBk1GsyWj+/afYLSQd8WHGIu2ktSuB6z9EDqfBXOnQL+r1JDtCx5UczUZberCbrLB+GmEi/ei/+LPoNNRfPUnuNMH0zbRXu1zNBhXIXx8twq0HOkEbliAcflL6H5+E0w2vFN+4sz/bqfwUCBoMepZcvcppM4YDpn9YfDN8OGNalsXPq6G8Z9zqxr988InYfBk9dmOUOTyU5J/gE5f3oz+wM+QlEXRhDnYv3kY6/YFYE2Au1eoKRRaoIYcyfqEuAsJf3wv+i0LwJFG4VXzsa58hZgzboF3JkDPsWixmegWP6buWq/4n5p48IuH1OeYNBdDl/PAXQj/PUst63gmjJ+G5i1D9+ZF4Ckm3P1iSs79J0mzx0PRTsKZA1l37jQue1PNGaXTwazbhjE0K7laFkOhEGW5e0j4/mF0m1U+ufkLWPqiCupNNrhruZp+owbZRW4ufXEJxe5ARHqM2cBP9w8i7scn1Has8RRe9THGrZ8Sv+w/oDdSOOlrpm8x8Nu0XzHMv1fdxV/7AfhcMPtGFQhc9Tb0GAON/D2Gnfno37wICrcTSutD6bh3iV1wB6bspRCbQXDKEgY9/QulnsjgLs5q5NP7htMh6YhrgKcEFv0Nfn7z0BxXn8OGj9Xra96F6WMg6MXZ/2Z8g6aQ/P4l4Mon0H0svw58jAGGnZjfHQdA4ajnCMV3IO2jqyHkJzzqMfRDblE1HLXRNDVVwluXAVA08hmCCZ1Jm3c1ZJ2L1v86dAntYfpoNSHtwBtg0A0w6zo1r1v3i+Dyl8Fe/Xipi1AoRP6+naRvfRvd0ufh9h9g5ni17Q6nw5j/wNzbIXc9pHSDmz5T83+Jk0qrqaEpdPmZtap6MAPwzFdbySuLrKVZtaeYqd/nktfnNjUbtLeE1LfOUcEMoA28Ab81iWnf7yTP3AFf1oUAJH7ze9I+vFRNdrlvFaFTxkFSFgR9pH14KUlf/w6WPIvW/QIVuHQ8A7Z8rn700/uoO8QOQyGxkzrxZ12nghnAm3URGzzJvPjNNryBUOMVli0RzvuLal5y5mJ6ebAKZoDw6fcyf3N5RTAD4AuGmbEyl+LT/wzZy1Rip7PUv1/8BT64UQUzKT2g19hqwQyo2pmHv9hH/pl/VU15RTtJenWgCmaA8IiHa55/SkQoDNspGPyg+lFz5pH8xlBi1k2Hn14kfN4jsHEeujYDILmrqvqffVNFMOPvdB4Ftq4qGjHZ4KIn1P/3/AhP90K3alrF5Kj6rZ+R9OpAVZtotJB/5t/497cHK/KhafCPTzdS6PTVkEvIKXarCU4P5ZPnB6ogBGDY3dWbvg4JhTU+Wb2/WjAD4PKHePOXYrRhd6ggyVtK8lvnqmAGcPW+huV5BmYsP0BO8uloGf3URKkzx8OHN6hMtx8G7YY0ejADUBw0U3D2Y6A3YMhbT9J/+6lgRqej7Jx/8M32smrBDECZN8j7K/ZG1tTaEuD0u9SPtLcUXj4dvntS/aCXHyQ89E4AHGveIPmNYWoeMWs8+YPu5701xeTbuuDvdB4AyV/9hrTZV6jjI7kree0vwqM7xmjKOh2k9iTcdRQASYvuJ2325eo6WLoPX2pfwjlrYdBktf6vb8G0kSp/ljg472GwnlhTUF6pB06drGb//ulFGP6gWrD3J3jlTBXM6I1w0b/BXPPxJVq3VhPQZBe5a22xcPqClHoqf6DDYY3P1+ewp9DNpI+LKLjq04j1g1kj8Z3zFwq1WLzBEH9fXMDm0x5XAclh5hhcl7/JxNn55Fz+IZiPmNnXeZA9Mf0oD+jgnD+CIxXm3KbuIjQN5t0J46dF/vBn9mfTkMeY/OEevt9WSJmn+gW9wej1kHYKTF4Qmd5vIuUDp/D7BdUDwxeX5vG+bxiBoXfDR1PUxSSlW+UK1gS4YR4ktK9xlxtzyli6q5TfLTVSeNk7EcvKT7uPkm7j1I+fOKoyb5jJC50UTPgoIt0XDLMh9kyCZ/1OfT+XvxJZnqk92HbGVP70ZS5ufxDMMWrC0Yv+XbnOitfwtD0L7+kPRGy74NK3+cMyMz/uimyq2XCgDLe/5sDb4/URTulZwzF2tQqaaum34fIF+Xpzfq2f/5PVByi2dkS7+cuIPlehTuewqut93P3xXsq8Qa79YB+hie+rwOewhI5w1VtqAsgmcMCl8ei6RIoueiUivfTsf/CLZSjP/XCg1vcu3pJPubdKsJPSHW75MjIt6zzoeAa5fe/A12t8xKKCK+dx82curGYjDy/KY9sZUyG1R+UK1ngOXPYBN8w5QFkNgVU1sRkUjXxWTZp5mCWWA2Pe5rzXd7EjaTik9YY+kflg8gJ1vdGf2M9NIBAgnNBR1b6snwMFW9X19Ujjp6kaR7k5Oim1moDmyHlOanLkPCN6vY70OHXA3zY4EfOG9yPWNR5Yhcmdi0mvQwMGtoshI5Sj2oMP87vQ7/ia3w9PwrL3O/A7K5eVZNPG4sV+cKXqN1CyV81um7cJ3AXQ+3I1m27AU/megq1khnMY1D6WBLup8SdW8xTCqjci03Z+gz1QQvuk6jUsyTFmRnYyYdo0DzqcoTrfFe+uXMFbombt9TmrvRfUHFpGvY4pgxOJWRM5PUbs9k+J1cprfJ+IZDLouHtoEra1b0WkW3J/pUd8GMOad6HnWNi3IrKvSNEuUv17GdoxDtPhY8tTAqtnVq4Tk4JVH8S6ZV7Eth1r3uS2wQmqU/gRbCaDmmiwFjpvCayqMhXKzm/VOVHb5zPqSDrKfGvxNjMx4XJV03nEHYzh4Gq62crpka5uLC7pFY9u3ypVU3FY2T51Fx/w1rr9hmQ1GrhhQDyx62ZEpDs2fUDPhBBtjjLrd2KMqfJ7OsxdBGsir1Uc+AXcBSRTiiX7h8j9r32bO09LxOULMaS9g1T/XijaVbmCtxTrviWMP6X6d1ujkB+Hc7eqtTvMV441+3sePDuNZG+2qj3Z82Pk+1a9AZ7a+9bVh85XBpvmq4c2ul0AmyNvRln1JniLG2RfIvq0moAmM85KiqPmC2G/dvHVJqW8cnB7/nBOOpf6F1Q0MwW6XHioSrcEw/TRJPuyGdEjlVu6uEj/6EoIeiGxM/72qrnFtv9HhvhWkLRI3dH6258JiZ3h0ucxr/wfhvaDYd4dkHWuCmgWPAD9rlHrfP0PlZGOZ6o7x4CHjI+u5IXzzDx4QfejXtRPmKsQvn1Stb8D4V6XVjY/vTWGjya2I/6IJ0osRj0fXteJrp9PUlXfQ26Fj25XzUzJXVUVPsDc22D7V5GB2iHd0hxMm9CJIRsfr2hmCvSeAEYrFO3E9M4VqnOxOKoMo4uReTOIWTsdgECPS1Xt2Oj/YJ5zI7qUrpDRTzUFArQ/DZK7QMhP2kdXc0tWCSa9TnXanDURctaofiYDJ8GEN9F9ck9FM1Og9wQArDs+Y/D6R3ltQmSfl4mnta/1nOuY6kD3/b8rm5l6X3ao+SlX9bM4Mhg+gs1k5NazOtf6+aeO7YhlzQx03z4BgL/LBRXNT20+GseblyRwzanp3JuVg2H2oWamtoNVDUE4BO9eqYK9Y3RKbgidrC76r/gdpuwfQacj0OfqQ81P60j9dDLPjGlT64N9tw3PirxJ85SowOC7J9Xr7hepz63XgzMP88yxqmnPmkCwh+rv51jzBiPz3+KyHnZu6VJW0WeG5C4E2p0OQNKX9zE5bQvJ5mM0cWsa5KzFOmucamZKysLf7gy1ja0fcEV6Pkl2I3x8l+pkbolTgTWo68zif6p+WycgLcGGbv2H6sGEK2fA139XHaT1Rug7QdXY7fpO9Vt05p3QvkR0ajUBTXqclddvHEKMObJtPC3WwrNXD6gWILRLsNG5TTqedmeB0YKr74181/vvFFw5TwU16X3QWRwM6piE25SEltgZkrLYMfpdfhnyH9WnpmiP6iRrTcCXdQG/nvYU2y9+F3I3oKX1VJ3s4ttDKAQZ/SEmDbSw+pGxxqN1uwCG3YV/4mxI7EQ4IYuANYl+bRu56cUcA90vBr0B7ew/UDL8H2iTF6iLUPuhxDhiSTxipl1fMMymPC/BTueoi1VKN/W5UnrARU/C2OdU0GZPVlXONfShSY+10iE9CVeXMQAUj3iSj9s9SMH42Sqo6XxO48/g3AoYrbEYe6nvruy03/JFl4c4cMVsOLgWrfPZkLsJrd1gsCWiZY2AYXcTGvemCjwTOqCPb3PoMVdTxTHAtR/ARU+iWeLUsWm04LpqNh+3+x3OUVMBcHcZw+IdlbVvfdvGMeXsrFpndC7xhCq3P/x3cMnzMHmhCmraDVH9y2rRPSOW24ZXD2omnNqO5KRE6DQcjFbCp07mwLlPs/fS2eBII5DSm4NeE2azBWNqN3W+dRgGV8+E6z5UgV5cW3Xs1uGx8RNlstjQ9b4U/r+98w6zojof/2fKbXvv9r5LWXqTjhTBrqAgFhTEgmKviUmM8RtTjDGWaGKMsWHFhoiiKIgFVBQFpK/0zrK93929/d6Z8/vjbIVFNz8xCs7neXjYO+XMmfecOeed877zvopC9VlP8XbWr6g89zUpk17j0Wx2Lht1qGP05aO7MvDgMcDugW7yvhl+lTQpzlwsf0f8iJzh4EyidMrbfNTjD0TG/ApUnUDeGSza5qXBlgZJXSC1J7smvMrG0f+SPjWuZPSsge0+s21QFPBkNpbRg91nzWHDqEeIdDsd/FWY8bly5S17iBxHrlwozYujb5b322ci2NzfS56+sCn7js0tV4h7TZDKzPlPQc/xcMGzsp79JrcJuWHx8+GY+coJpENhiTfI+oJatpfVMaxrCsflJJKd1P7D2hCKUu8PkhIp5UDATtCeRJeUOFJCxWB3Qby0tVc3hHGHy0CYvLpd4AtFuWaohzgRREvtjlG9l6Di4sV8P4lxdqb3Al1TUYUJCDl4ujPkW6nuBBQwI5iqnZhhsGCfzpk5IVwOO0FnFskdjfHxfYiGoL4I05nMhu37GTJ4EFpdgRyMPBmU1QXZUdbA2oJauqTEMap7Krl2P1q4DhK7gq9UvrUJUyqAEZ/8NDa1x2EvGTEMfPV1uCPVrKlUMezxHJcVR2q0TE50P+GvEn4yXzkBREOY3kIqDDevfdNAvyw347ODqPY41FgQEjpj1BUSUxyIaIhtDXb6Jwt0TUVL7tJSTqBGmgISOkmfg4gf4a9ExCJ8We1hdUEDF/SPp5sriFdN5v0d9VQ0hBnXK41uqW4yEto3mTTLamA/tIYScKVInxnThNq90mHzO75m8wYiVNSHWbqtHMMUnN4vg+xEl1xpNWLyWXIlUq8mUeuPkBIpwmfYqFJTyElykep2NK40uVp8ZuqKZWyo5Lwj0w4dIVSH6atkuy+OxTsaOLlHPMOSAqiuFBR3CjX+MEVVDSzfW4sQcHq/TLISnSTHtbPyZcSgdp9cJW16Vqr3SmVEURGRBsq1HFbsraGHJ8KAJIMDRgoLt1ST5LJxSV8VIQRzdwgaQjFmDonDJYJoqT06HgPKW4gpBMUila92VzE2yyTDEcXv7kJKtEzG3DIikNxdrsYZYRmrqqmP/X/S3KcGD0LzFoAzQSozwRpQ7XKMbVxlxp1m+eP9TDmmFJrWZe3evZuePXv++JPPT5yf1ET9E8aSU8exZNVxDMMgPz+fwYMHW7L6Fqw+ZdERjhmT08H4fO07p1pYWFj8lPiJvFNaWBz1HLMKjYWFhYWFhcXPB0uhsbCwsLCwsDjqsRQaCwsLCwsLi6MeS6GxsLCwsLCwOOqxFBoLCwsLCwuLox5LobGwsLCwsLA46vnhw2X+L4kGZXC3pqyuwoSAF+FIpMwXpcYXwRCCVLcdj1OnNhDFG4gQZ9dJddtJ/f8IaFdeH6LGHwEBcQ6NQMQgEjNJcdtxOzTqAjHqQhE8DhupHnu7AbPqg1GqfZH2j/NXyhQDWmPdhAAzApoNoahURx2U+QxMIUj1OMiMd3QoD5Q/HMMbiOALG5gpeewo95ER7yQt3kFdIEKcUU8sEiKq2FBcKcSbXhk6HlVmKo5LbSksUN0YUCvakgwwFoFwA7jlcZGYQUVDmBpfBE1TSHU7yPqWXDaHYBoyX5QRk1FAXUkyMqkZA0WTCUA7XJYp8704k2QUU5BytrlldOf/tk7O5JbEe/4qIpqL8oBCjT+CXVdJddsPDULX+txQLZGIbFNFmIRMFbduonoyZAh33fGDBAoLRGJU+SLU+MO4bBp5zgAOmy4DkwEEahGxEF7DQaFPxa6rdHf5sdsdhySXrKgPUe2PEImZJMfZSElLRw17CanxVPijNISj9PJECOMkqjmp8YfxhWMkuuykeezEO789SnR9UD6ruoiSao+wx+ckMc5GttaAakaoUVMo98VI1YIk2U0Kwh78kRhJcbJ8j+M7olA3lMtAbY39lUCN7M+tAgCaDeU0xDQKfDqaptDDJeWl/Dd9L1BDJBJmb9BNOGbS2W1yXPfvSJYZ9MoAdZ7GujSNc03t1EQkINMSKMjnwu6WATRNA4QhA/A19qOYYVLREMYfjmLTVCKGIBCJIQSkxzvIiHdg1zXCUYPKhjA1fvncprkdZDY+t2ZDBfUxjfKQTndPBGGa2HWNahFPvFlHWUAh2W4Spwu0hCxZx7BP1v+/kVk7HNwWfVwNaCo02DOp8oXRjSBZLkHAlkRSe0EKLY5Jjh2FJhqEfcth8e2oMxaQmJCAWrkV5kwjMnUOdy2J8NmuGlQFHpk2hBV7qnhrXRFmYwiIATkJPHnZMLqmdiw8t2EKtpbUc9Nr63DaNP4wsR/3LNzC/uoAdk3lsUuGsmBDMR9tLWvOoXd8XjKPXjyE3OSWSbO8PsQ9C7fwweaW40Z2S+bfFw8hW1TKXCUHVkHXMTIX1PQ5ULwBkdYL5e3r4OxZPLBc46t9dSS4dO6/YCCn9snA7Th801Y2hNhe2sDizaW8ubaIWKMQhnRO5PFLhuH07sIWKURf/RykD8IcPhMKl8vBKLU7LPo1zFggM/dW7oANr0JaT1j1NFz+loyKXLQG3r4WZrxLvSePdzaU8OAH2wlGZc6Y7EQnT1w6jEGdEr9bATMNKNsklY5lD8KAC6DfOVC4GnZ9DJodzrinY4OkaUL5JpgzDS6ZK8Phew/Ay+fB2X+XKRg6otSYBpR9A69Ph0vflJnYvfvh5fOInfkQj2xI5p3NMndN5xQXT18+nH5ZCaiqIs8tzYe5lyCuXIS5/lVsvjKUMbfC3OloZz+Muv8LxMgbUOZdAcOuhEFTj6hSU+UL88wXe3nxq32YAl64qCt5O/6O6U5BPf2PoOqI2v0o71xH/NjbWXygF6d2i4MNf8DIHoQy9peo7lSEEGwva+DGV9dRUB0AYFjnBF6aFIfy1JlELniVB76C3412YZ93KZz5AC8c6MS/Pi8CZIDayYOy+cOk/s0JYw+mrC7Em+sOMCLXTe/QNzg+/S2ec16nss5Fzo5HIeTFfeqf2VMQo6e5HHXT64RPeprL5x4gEIkxdXgnfjuhD+nxh1GgG8ph4W2Q0g1O+i2gwIr/QOk3cP6TEJ+J4S1GeeNSRJ+pLPOP4bhMBz12PYwZlwin/wmtA31P+GswVz6BUrga3/EPsWiHj1902oNj9UPELnsbUvIOPSnohQ2vwJYFcPEr4EqW+YoW3wFXvAsp3eVxkYDMUaU7Zc65d2+RKUncaVJx/vhPMPxKGDiVgOrmy11VbCmpJzfJRYJL5+73tlBeHwYgzq5x18R+nNk/k/e/KeWhj7YTipoA5CQ6mTVjBP3c9WivT0MdfQfZyXnY5lyCOPcJjLpinLljsM+bRsb5zyHWvwtVW4ic+wR2VzzsWAxfPASXvwNJnb9TZu1heItQ5l6K6Hsxn/lHMeW4JLQtr0Lh16iTnmT28kpu67If+6oHqDr3dYJJXchO+i9eVCyOWo4dk1M0ICdQ7wGUlyaTF9qMMnsSNJThePc6/nh6DgBnH5fNuoJa5q1tUWYAtpTUM+P51ZTXdywTb4k3yPRnVlJUG+S34/vw63kb2d84oF98fGcW5pfw4Zay1gmBWbO/lpvnrKfaJwcOfzjK/e9vY/Gmtset3lfLpr2FiF1LZaj4wRfDe7+QyRtfuwh0G8q8GdBQStqHN/LQ5DwA6oMxbp2zgT2Vhw8qGIkZfLqtgpV7q3l9dWGzMgNw1oBsAt4K0va/DzX7UPZ+huvrf+Ne/R8oXAXdT4RFv4KGUph9tsx0+/p0GHIJLP4tVG6DVy+C7QvhlfNlfRf+EsNfw93vbWlWZgBK60Jc+twqir2HJrI8hGCtTHpZsAKK18rkdGtegG3vwaY3YeNrULqxTfblw5dVI5NoNpTB7Emw/X14aTJ4C2D+tbIfdYRgLcy/prGcibDjfZh9DngPELfwBv44vmtzJurCmiDTZ7W616Zz41JR6orRVjyK0uNUWHo31BWhzpshE5bOu0IqTR/+rm3m7O+JYQoWbCjmmS/2EjUEp/VJY6jYimPHu6jrX8T85K9QsgFl7fNQtQv93Rv5Zepahq7/A/Z9n6KteBThPQBAsTfIxc+sbFZmAB44uxPx78skhQlzz+P+fgV0WzQdvAXY372eGcPTsWlSNkLAe/mlPPLxDgLhQxNGNoSi3L94K8flJNErWSX1w5ugrogu6/7OIG0fysZXYfsiHJ/ew8ToR9g+vhOtdCM9D8zj2lGZmALeWFvErC/2EomZ7QgjBns+gZ0fwKonYdnf4ct/wZePwJ6lcgIONcDa51FLN5K07A/MsH3CmJ0P4dyxAG3DS1C2pUNyN+tL0L78B7aCLxi0+nfclr6e1A9vhOo9qJ//vf02DtbAkj/Lfv/GDNjyDrx+iVTCP/4TBBozS0cDUqkJ1cFX/4a6Inj9Yvli8fEfZT/64HcQqqewJsDf3t9KbrKLjAQHt83d2KzMAAQiBs9/uY91BbX8ddHWZmUGoKQuxI4Dpagrn0Cp2EpCYjIJH/4CGspQ5s1AUwTueVPBHocz1oBr9WNoez9Be/dmRP5c+exV7YIv/gGh+g7JrTVKLAhrX0Atyydp2V1clbSRTpoXZcWjKAdW4H7/Fn6b/Q2pH1wPNXvJWPMPtu8vah5zLY5tjh2FxpkEV7wnk7jVF6PPnylNHvFZFJz1Ele+sQeAiQOzeWtdUbtFHKgJUFjTsQnti52V+CMGnVNclNWH8AaizftO6p3OB5tL2z0vv7COysaHq8oXYeE37WeY/sXbewn1mACeLNj8tnxTdCZJM8u7t8pJMS6FonPmMGPO7jbnPvHZbvztTA4AFQ1hnHaNV78uOGRfr0wPl87ZxZ5OjZmRh1wqd6x9ATbOgbeullluXclySf6Ny2Vumc8egEvmyYSHldvkcUYE0noRO/8Zbn57X7t1CUVNPtpS1u6+NrjTYMAUyOwPfWVyS1b8G7YtlH+fcQ90GtGxfDTuNLkyk5Arl+3nzYC6QrDFwZXvSRl3BHeaXJmJz5L97I3Lob4Y7B5Kz5/H1W/swmilLDaEY3y9r6bl3Mvekqa6LfNh/N/kZDPqBsgcIM0FH/4fStk30iR2yTy56nWEqKgP8cRnLX1mybZK3q3rSf3IXwOgrn9JToY9z0DknQhA3NI7se//FICaMx9lo1+aZtbtr6U+2LavzXxjL74przTLOHnRNc0yLrvgTa57cw9Ro63yOX99MVXtTDpVvgiGCYu+KeGK13dSev48mbNn+yLU/cvhpDvkgTveR/3kHgBCvc7h6/SL+M+XLc/Wq6sK2n9Z0XToPQHG3Cp/r54FKx6Tf4+8HvpNxms6+Cb3YkK9ZPbo5BX34dr5LgDeE+9hbbRLh6L9bvQlUjPhCQDsB74g+dPfgRBEO5/Atv6/ojLazgqSJwsufk2adIvXwoKbpCk9eyhMfBjiGs3rziRIyJHmyRNuk0lizZh80Sjb1NiP3iDsTOGJz/Zw0fDOrN1fw8o91YTbUfSmjejEv5fuavc+ysMam7tfTaTbGbDgRjjzrzJpZTQgV7pq90PNXur1FOonSFlq+z5FWXy7LKDbKXDq72U+pv8Sb8zGptzpzW2RsOR2lNWz4OyHQFFQDnxFwpLbQQhiuaPZ1P92bpq/hypf5L++lsXRx7Gj0KiaXPY/869tNjdMmsVvP2mgqFa+HSsKbVYKDqb1m+a3sbHIC0BOoou9B62IRA2zzerPwVQ2vg01hGKHPS4cM9nobcxC3e8c2LYITv9zm2O8Z/yLP30VZW+Vv832XeU+ApH27zEYMdBV9ZBJyGlT8QaiVPki3PR+FdWJx8HIG+Qg2YQRlRPu5MfaFjrwIugyRmbWbc3016mxZbCx8PCrCxsOeDG/TVhNpPaA+BwpA0ergTB7MAy7QipZHSWlO0yd3XbbaX+S5qf/Jgtzag+48Pk2mxpO+jP/3uQgv7jhkMM3F7eSQ2oPmPIcrH9Z+iN1Ol6aFM97os055rCZ0PWE75XY72BCMZPaVgo4wJ8+LqGk31UykzpIX4yld2Ne+EKLnxFg9DiDda6xzP1GrgxsKj60bUvrQuT7UqiZ+Eyb7dFT/sgT2+JYV3jom3nMFPjCh/ZZXyhKTpKLvZV+tpb6eGiDRv3Jjc/4iscgdxjkDm85we7Gd+Y/uW7+gTYKZShqHv65j0uFE2+Xq2JNJOTAKb8HdxqhqMH1bx/Ad+Y/pV9KE7nD2ZU9mVlfVx+ioLXHO5u9rNSPx+g9qWWjouCb/ByXzN1P1GhnBckeB91OgsGXtN1+yZy2z6amy2fTWwSO+EP6EcOvgq4n4Ivp7ChrIDfJhRAcMnY0kZPkYm9V+yu9WYkurnhjPw0TH5cvNp/cA5MeaXOMf8wdPLoRVjlGY/Q4s2WHqsFFz8sXgf8PwlGT6+Y3toWt0Yz0jVT4zb7ntTm24bznuWp+IaGo2a6ybHHscewoNKYJFVvksn0r4hffxD9OTyCn0ZFNCDl5H44uqR2ztQ7Klf4MpXUhuqW19buxaSrqtywWpMVLB1+PQz/scQ5dZXBSCPZ+Btveh36T4dN72xyT9Mnt3DvWTt5Bde6Z4SHO3n4CN5ddI2aaJDjbTtyhqElSnI1Ut50nJ6WT6t0Ea56TZqMmVA3Kt0qzU2s2zZP2+1VPtd3+xuWkRCsYmHt434/BnZKkX8l3UbMX6ovg0/sg3GpCLM2XJqeg97vLaC5rn1xFas1nf4PyzY2Ozx0tZ69cQm9F/PJ7uW1ghAE5nkMOH5DTShGr2QvvXA9DLpNmgsLVMOmf0rTYCnX9S9LcF+2YKbQjOHWVpLi2TrJ3n5FD9o5XpDkApF/S6XejvnNdG5loe5YyPPw10wYmAXBc7qFv2ZkJDgZ7akn54MY2222f38fN/YIM6RR/yDm6quB2HNpnPU6dsrog3dLd9Mtyc8dQk4QvGp/x0TdLP5fidS0nRPy4l97J0xd0bjb5gXyeXLbDJDUM1EjlyNtq1bK+BL54GPzVOG0aT5/fBffSO+WqXhPF6+hZ/gHXHZ/abEL7Ns7rn8RocwParsUtG4XAvegGXrs4D729MqJBKPgS8l9vu/2NGW2fTdOAiq2QlCufj4P6EetmQ+HXeHSDXpkeSutCqIpyyNjVRKn30HGtiYr6EC9O7Yrnw1/JF4nT/yxXglrhXvkPbhuqMDq6Fm3Pkrb1fPt66bf0/4FDV3l6SmfZFk3m4eMuhEA16vZ32xzrWXgdL1zYGYeukuaxHIN/Dhw7Ck2wBl46t9HMlE3s/GcbzU8ldP3wSl6+pAcAH2wu5aJhndotonOKi84pHVNoTu6TQZxd40BNgJxGx7omlu+qZMKA9t9ABuYmkN6o0KTF25k0sP0vHB67oDuuvR9JR9jjzod3b242MzH533Ig8VfRadGlvHpprzbn3nJqz8M6BWfEOwhEDC4d1eWQfbsrfLx+WS96Fr0jB8UNr8gdw66EQdPgotnw5pXSVOJKlqscSV3h1Lukb0+jmYkLnmk0P21Hf/dGZl2Y125dHLrK2QM78Kbmr4JNb0kH5G3vyW2jb4Y+E+XfH/8RitZ2zIfGXyX9fprMTBe90GJ+emmylHFH8FfBa1PlpGL3wEUvQnw2hBvIXjCNl6b3ajOhehw6o7untjr3IinDgdPgw/+DiQ/B6mdazAPj/4ZoMj+9NhX8FR2rVwfIiHdw88k9mn+f0S+dC5P3kLjqYQDMwZfB9Ndhz6coe5cBEDjtPiJdTwYg5aNbGeaR5rMReSmHKMcvT++B5+3Lm2XsnTirWcbZC6bywrSehygA5w3JbX4uWpPW+OXhOYNyePmS3uQsmCoVwL6TMHucBssekAf2PgvzlD8A4Nq5gBNq3ubWcS0rGJeN6kJGQjtfMZox2PmR9JsBGHENjLpJ/r3qSdi+kCQ1xJCyebh2LgDAO+ZOQj3lKkvy539gpPMASgfMncMSvNJnptHMVHvK/QDYDyxnwPZ/k2FrZxWhoVT6zDSZmSb/p8X8tPh3UhkD2W/riiAalg7N5ZtlPzrrwRYz5mtTcYSquPXUnry1vpBhXZM5oUcqDv3QaWDe2kJ+eVqvQ7YDpNujDCqYjWPPh3DBLFhytzQz2VxSKU/qAindSIxWk/CBXLU18k5BnPWgLGDPJ/D5g/9fPjRJtihDSt5obov60x9CjLoJFkszk+g8moYzpPnJVriCQdse4ckLezT3I4tjm2NHobHFwQVPQ0IuYub7FHiGImYuAnc64XOf5oFPpU/L4k2ljOyWwpRhuW1WR/pmxfPK1aPIOsyXFgeTm+Ti9etGk53o5J8f7+DRi4fSOcUFwNzVhZw/NJcz+rX1exjWNYmnLhve/HB5HDb+MKk/4/tnHnLcwB6dUXqeIc0j+fPgnEflMu1l88EwENNeBk8mVROe4K7F+wGId+j8e/oQeqQf/kstu65xer8MTuiRyrQRbd9iF28qxZWYTlXeREjpgcg7keDxtxIY/SvIOwn2fQGT/iE/H73qA+h3Hlz6hqzfWQ9AWm/59cKAC+Dy+fK4SY+geVL50zn92qyMZcQ7eO3aUeQmub5b2M4k6HmmNGtlD5E+MyNvkG9mA6bAoIul6akjPjSuZNlP3Okw833odz7MXASJneH8p1qWsTtSpynPtJTT/zz5f0IugXOe5MGlLSaP3CQXc68f3XKvziS44FnwVSASczFH34rYswxO/wsk5GBe9JKcyC5+TZpRx98HjiP3hZOmqUwZ1omrx+ahqQrLdlSRr/Yn3GsS5pAZqOPvgdwRiBFXQ2oPouc8zpN1Y8gf8SDRvJOJjboVJUkqxDmJLl6/fjSdklva8a4Pi2g4+wlwp1M/fQF/3NWTgsnzILEz0clP8sq6ymYTjaLAxIFZ/O6sPsTZD1XC4502/jCpH5uLvez2mlRPeAIScigc/n9sNvIQgy+F3mcROe2vfOQ+h+gZf8PIHMTeLlN54etyVAUuHJbLjSf3wKG3s0Kj6tDjVOg1XvapU++Ck++AE34B3U+B3mdJX4/hV2FmDsJ70j3MMc9idf8/EOo9GWPQpSiZ/TskdyUhB2PMr4h1Gcum0Y/wRO1Ias56ApK7YZ74u/a/YnMlw+l3S2Xmkjnya7eLX4PETnDmPS2fz9tcoDllGSf8UirX0+fIZ2bC/bIfTZD9qEtKHHdO6EtZXZDyuhCPTBvcRpl02lSuOKErI/KSuWtivzYKT2aCg16dsxGjbkKk96XeW0XDWY+BOx0x9WUMdAIXvwVhHyE9ntCImzDyTsE8/ymUoTPkM5baA8b9+v/Lh0boLhh+dWNb/JVXfCMoESmI0bciOo0kMPlp/lUxjOqznobkPCqG/4Z+3Tr/f4XksDj6UMRPJHe9YRhs3LiRIUOGoGmHWRr+LiIBiAYwnMmyrMGD0CINmPYEyn1Rqv0RDFOQ6rET79CpDUap9bfEoUlr5w3xuyivC1Htb/rcUScQMQjHDFI9Dtx2jbpglLpgFI9DJ9XjIMV96NJnXTBKtS+MNxgl3qmT6m51XFMcGtUuR/+mODSqHaEoVMcclDZIX5w0j4x3YutAHJqGUBRvIEogEqMuGMVt18lIcJLeGIfGbdQTjYaJooMrhQTD2xjzRZX2eldKS32CNYAKZus4NGH5mXdjXI9w1KDSF6aqIYyuqaR5HGQmODr0ZgvIr1HC9XIVSHc0r1BhRhvj0PwXTrOmId/yHQktPjP+KqnMHOaT7Xb7Z1OdnImt4tlUEVadVARVqnxhHLoq4wMdrCi3PjfoJRqNIDQ7KiYhA+J00RiHplxOVK4fIA5NOEaVL0y1P4LTptHd5W+MQ9P4CXJjHJo6w87+BhWHrtIjLoDdZm+J19JIeX2Ial9E9n23HdNXTdd0NyHVQ4U/RkMoSu/4CGEcRDUn1b5IYxwaG2keBwmub48TUxeMUB+MopkR0uwxdvvsJLpsZOsNqGaUGiWFMl+MND1Akl2wP+zGHzZIjrPL5/074tzIODRaS2wXf7Xs7wfFofHFVPb5bGiqQk93AIf+38ahqSYSibAnEEc4ZtIl3sRDGC0h8/DjXrBWxnVqqkvjOHdoHBq/XKFpCg2gu+TzYsbkP1tccz+KGSblDWEC4Rg2TSESM/FHjDZxaBw2GYemoiFMtS+MTVNJ9djJTHCiKEpLHJqgRvf4GMKMYdN1qoWHBLOesoBCit3E1SYOTYM0n/6XcWgOfv4Obou+cTIOTb09k2pfGM0IkBMHPj2p3dhfFscmx5ZC8wOUdaxjyapjWHLqOJasOo4lq45hycmiIxw7JicLCwsLCwuLny2WQmNhYWFhYWFx1GMpNBYWFhYWFhZHPZZCY2FhYWFhYXHUYyk0FhYWFhYWFkc9lkJjYWFhYWFhcdRjKTQWFh0kNTX1uw+yAMDtPnxwR4u2OBxW0DcLiyPBf5GJ7+jC4XCg1hXIYFKNidCiNQeIxgxWlcPYbIGu6iiYqEmdoXY/QndgRsMsr3KTX1TPkC5JDMiOJy1SAo4EhKJhhhtQzQimaqc04iJd9xMzhIyCaxpoaY0h5QPVMr9QYhdCQqW8PkRpXRBVUVi7v4YhnZNx2TW+2l1F12QnZ3cKgyMePSGTyoYQnmAx9RGVlZV2hnVNJivBgV3XiMZMyupDrNlfQ1FtgNP7ZWKaglV7qxnaJYmYCav31pAWb2dszzQyEpyHz2HTiMuTQGFtiJV7qqnyhRnTI5WuqXGkx7cEgxNCUFYfIhA2CERi7KrwsbfSR/+cRAZ3TiQ36dCAdNW+MMXeIMt3VuF2aJzUO52MeAeeg4KcxQyTsroQ6w/Usq/Kz5l9kunjqscr3Dhj9dQLF+m2MIozES3qk4ka41tSRtQHo1Q0hPh8RyW9M+MZkRpCM6PsjqZiBLz0T4phCpU6ZxYpRjVKLMhOn5PlB6Jc1V/GIVOSu8rAag1lMn9OSjeqGgMBKkC23U/neAH1xcRiYRTNgWqEiWlOVBFFVTWUWFimgrA5ZPuH6mREV00G9jJNQWldkIqGMKGoQUlNAxM7R4naE1FcKcS7bBhVe/ALBy9/EyQ9wcHo7qlkJThxROsg5AVnEkbQS3nESYoWpCrmItsRQnUloTRFjW2PUL0Mk+9Jk6kaoiFoKAHdhWFEMaNhCgM6DcLJwEQZjC0gdF7eHCEr0cXxeSlkJTixibCMYuxKAVeSTP0Q8cuItXGphOvKUcP1bPDaWL26kBN7yTZ3O/RG2QYgpTsV9UFSIyWoqsqeSBJf7K7lot46DqLM3a1imHB63wwMIft2MGJwaR+w2exUkkKNP0I3lw/VCPPKduiTqjM2y8BwJGH3pMjAhd4CGY22KVCgEcGsLaTacPH6Zj890t1MyA6iONxoCVkEIjEq6sN8va+avFQ3MUOwZn8NWYlOxvRIJTPBibPxWSqvD5EQLCQgHJQZiaw/UMvELgZuh46wx2M3fIhYGJ9wsaIoxpldQVcEpmGwMxBHb0+YoNAhEiDkTIG6QkwzhtDsGNEIHxQ7GZMZI91hAAqflNjYWxvh8gFOHITRUrt/6zMNyHQEAlBVmY7AWwgIKZuUbjIoprdABstEwTQNIkJBARQh0HUNxYigOBNbAuHV7peBLI2IjGKuKLJfhepasn1/G+21S80+0J2Q0H4amO+ioj7Evio/q/ZWy7bqLtvK8R3jnsWxxzEbWC9Wtg3HnPMgazCc/xTRaBht7sWoXccR634Kuqohlj2AEvIiLpmHUr4JsfU9lL2fUTLlba79MICqKLxwlpOM+VMQk/+DqC9BzegD79yIyOgPE+5H8ZUiTANl5ZNQsYXoFe9ji8+E5f+ANc8iZrzLWqM7m0sDbCz08u7GEv45bTALN5awbGclg3LjmXWmney3LyTWZzLR0++hpqaK3HenEXPnsHXcY1zxZiFPXT6MoZ2TyC+q44oXVhOOmfzt/OP4ancVH28t5+nLh/H4p7vJL2rJfqypCo9NH8ppfdNxtRNWHsAfjvLJ1jJ+/eamNtmJh3RO4qnLh5GdKEPa762UGcuFgF/Py6fGH2k+NjnOxtzrx9AnqyXpYEVDiLve3sTSbW1zEP15cn+mDutEfGNkWMMU5Bd5ufy5rwlEDM7qm8y/x4ZxfPEA5il3oc69BPO4i1C7jUMkdkF56yo5kF70PMRn4w1EeOHLfTz26W7uObc/k/Ig5f3rUH1leKctwL3/I2xL/wjnPY7Z5QSUt29AqS/CmD4X0xaH7fWpYHMhpr6MYvfAghugYiuxK97nxe06nVJcHJ9ukFy8DM2MQmKujKC77T2U/ctlqgfNhvjkHpSS9fJ3YmdY+R9Y+QRc8S50GoFQbWwurmNrST2f7qjEFwjyyNgYmQum0TDoKsqH/II8hw/95UlE0gexYdh9zHijAIHg81sGk731ORQzhvBkoiz5E+aU51EbijGT8lDfuhLjlD+hHT+z/azjoXrY/JbM5n3RS9DzdJkLaPeniNQeKPGZMP86oj3HI8b9Fru/GBb9BtOVwtaTnuLi1wuImYKFNw2nl38DyuvTYfzfoP+58NY1MofQRS9iJHYmuuRenDvfpWzK29y4NEJ+cQN/nzKQC3vraAtugsqtRGd+AKbANudCcCURveA5AsKBe9EN6HUFFEyex1PfCI7LTeTu97aQHGfjjWk59PjgMoQzAd8Fr2Ci4F50I3rtHqqmLUSr2EzyomsIn3E/6pBLsHn3wuxJMlfW6X8EZyLGgTVor55Pw9Dr+CJjBoOTQnRaMAUj93jEOf/m/X0mv3srnycuG86/luxkS0lLriFdVXjqsmGc1DudYm8Qp3c3Oe9MIZYznLWD7iHFqdDz45nQ/1yU9L4ozgR4+zqMTqNg/N/QMOCdG6HTSESn41HeuQ5x5n0oug0jeyhaxAcRH2LtiyilG4jMeB+bKlDmzQDNQezCF9nboJK3/HfYqzZjXPk+Wkafww+ClTtku3x0l4wSfOmbULkd1j4vk3leu1RG864vgZKNMmL2J/fABc8gNAeK3SUT/r59LaLPJJQJ98kov6X5kD8XDqyEqxZDQieZlHb5wzLdSZcxh1dqjBiU5cPsc2S6ktP/JF/6Zk+ClB5w4XOHVWoONz+UeINc+9Iatpa2ZLfXVYVnZgxnbM80S6n5mXFsrtDEQmiVW2U4+z2fIN66Gi1QhVq5HUbfhFrwFfQ4FaVqB0SDKHOmQkq35kR87vK1nNBpGDZNwVm8EiI+FHeqfIjtLvBXouz7HN7/NWgOlJN/B2XfQLAW28uTEF3GomyTmV/Fzo/Y75yCw57IuxtLGNwpkVJviGU7KwEY18VJXMlXEPGhb3odgtXkVm6FuiL0YC2ZtgDJbjtXz17L4tvGcfXsNYRjJj3SPUQNkw82lzFhQBbLdlS2UWZAKgu/eH09n95+Cnlp7Td1ZUOEX837BvMgtXZjoZfnlu/jzrP64A1E2VPpp7IhzLPL97VRZgBqA1FueGUt824cQ0a8EyEEH28uP0SZAfjrwq2M7p5K/0aFprw+xNWz1xCIyIzOM0ekwdZZ0GkE6r5lEA2gbngZGkpQyrfI1YGITw6E8dnsqfTz2Ke76ZTsIs3jIN6sQq3YAtEASaEDiK3zZWK/5Y+gnNsdpWIzRPxonz+IOmwGeA/IAf+NyxCuFJQDK2QXKt3MCV3HUthgEqcaaPEZULReplrI6I9StEa+lb5zPVzwtFRmAjXw6gWQdyJsbcz8u/19SO9LWdTFve9v4+zjsvhoSxl/Oj2buIK3IRYmfv3TuAIl6IUrwF+F3dxAF49JsttGlS9CMBRE2b4Qxv8N5fO/gzBR374GBl2MuvRuGeZ+69tEB1+Crb3UWLEQfPOmfCN/60oYcrnM3nzJ61CSLxNfhuuxbXkLglUyE7j3AKq/ggzNR3aSi90VPvYUVdCz5B0UYcpM7DlDoXyLbI+3rkLNGohz54cAuIq/5MSuJ7OxqIFHl+5iSpdOULoBQnXYlv4FMWiaTF7pLUCffzUJuhOleA0oCvENe7l8xBjOfXoNpoCe6S6SwiVQdwCl1sA9/zKELQ6t6GtQFBLUMOx6F4SJY8n/YZZvlElMowGZCPHkOzBCDcS2vY9mRIhf+wTj+xRiK/wSAjVoRauJhv3ct3g/4/vL9mmtzADETMGNr63nk9+czJurdnFDxlYIVKPv/phh4QB6oAK1eidk/xFRvgUc8RCsRdv1oVzJGjxdKhmjbkD55g0QAuXju2DgVLTdn8rkr+402Y+Ctdg/+SMcfx3UFoAZQ3vrSno4U9AKZf80SjejJXeh3QaP+KF6LyhCtqURgTkXgScLCr6Sx9QVy7QqjniZvPKrf8n+8c71KIMuBnu8TG8RbkD5Zq5Mv1BfAmc/AMVr5GrhixOlcrx5vixz20LIHHh4hSbigz2fyXZZ96JM61GyoeWZDtcBHV+lCUUNHvtkVxtlpqmtbnh1HZ/cfgpdOphs2OLY4Nj0odGdlHoGIs5/GgCl4EupzKg6lXo2W7vNJLxvBUx5Vg4I3gJoVGbqTrqHV33H89yaKp5aVcnrkXHUjf0DzLsCpdcZctn2rMYMv4WrYf9y+OD/MC57uzm/UJMyw5hb2d/7KnY32Ji/vhiAcwbn8Oa6wuaqPrmyknmxk6kbe5es+u6P5ZuV3U3ZBfO54aMA+6r8uOwaO8t9+Bsn/nMGZfPWuiJAJvd7Z0Nxu6IwBXzeqDy1xxe7Kg9RZpp4ffUBqnwRagMRfGGD9Hgn+6r87R67vzpAtU8qOlW+MM8s33vYa76x+kDz34U1AbyBaPPvKtPDJ5kz8fvqZduMltl62b1UDnyOeJkEMq0PkZjJi1/tA2DSwGzW7q9l3gE3wcvek6bGNy5HOfF2yB0G1btRPrpLvgXa3bDjfZS1L8D5T4Kqo1Rub1ZmjAue4Y2a3qwo8BMzBUuKdQLx3SF3qFQOKrbKJHueTKjeDQtukhnG41KkUtOkzIy6USbhi0uhvD7M2B6pzW127yelfJQwlYZhNwKgb39PKuCuZIrPn88VC6oorw9jmIKLXt1L/UXz4LP74MTfyvsRplRKTAMjcxA7TnmGPYHDDN6eDJlVvMsJctLa8AqYMcRnDxLqdyGGzQ0n/VYeu3eZVPJsLiqmvMUtS0PsrvABcMuCAmpO+CMMvFhOlB/dBRc2ZbUvRmlUZurG3Mlb4jT+85VUaEvqQry6S4eZi2Xuqu0L5aR+rswcrZRvalZmqs9+htcq8liy09vcL1ftq+PuDW7C0+aCqqGWb2pWZqrPepp7v/KzsvfvCPWaDID6zVw5aSZ2hisXQmInyqJxfJR0Cb4h1wJg2/GebCtXMpVTF/D6bo2K+jDjB2SxML+kXTEapqDKF+apr0r4xBiK/6x/A2Av/FIqM5qNqCOZ2r7TMUxTJpUE+GaubLsLn4PFd8Lgi6UiALDpTdj5ARSsxIxG5JjkSobdn8jVlFb9s0mZqZnwOLMre1IbOczqg90NeWOluWnKM9LkWb2nRZk551GpjPY8Q67eCQFjfwWdR8q/8+fK1WVHPMZpf5bn7PoIyjfJ+s9YIPNIhbwtyszwq+CU3x+S46sNriQYcTWcfKf8vWNxq2d6MaT2Pvy57VDlCx923IsagrX7a/6r8iyOfo5NhQbpv0H3U9ouwad0o8rVnb99VklD59Ok/TejVaZc3UGo92Rmb/A2b3phXS2RvlPkG8TOj8AZD7kj2pZrhAk4M4jmjm5biRFX83mRgcep4w3Iyd7j0KkNtF3heH5tLeF+F8rMv42YmYPYF0sjv1i+KbodGlUN4eb9HqdObeNKiU1Tm1c42qOiPnTYfeV1h98XiBgYpiAcM/EGIkQN87DHgkxACfINyXvQPbamrD4kB3zk6k5rFBQeXVlDcOi1sP4VmVFbaemmZs4wOVGpKhHDoLJRJh6njikEe6tC1NqzMTMHyTfVzfPl8jbIFYJYGJE9RP7e97lUSpK7tlQgLgWRdyLL9gfwR2IEIwal3hDlhodYXSnEpUGOVJDodpI8p2oX1Bcjuo5re6MjrmlOINgQjOJx6m1Wtx5fVUN48JVtTonmjmZTg5tdFS2KY20gyvZ6G7H+F7a9n0bqj5vB65t939oHcKXAqBvabDIGTmPJvij1tgzoM7HZ1wfAzBhAgZnJusKWlQpTwMZqDUbf2CjPjdJs0SRPAFUjMmAaz62pbXOtTSUNkJCL6DxSbtjziZR7YueWg9wZ+DJGsL40fMgq4Bd7GwinDYDELi0b49LwZ43knS11PL6qBt+w69vec//zmuVvmCaPrqgmNOSqNodEO5/IvnACO8ul0qapCqHo4ft5sFHGGyoMQl1PbZshO6UH4aQebK7VafB0l/3D0WiGLV4HuhOR3gfWzYahM9qUG3IkE07tK5WsLmPkxn1fNPbPvJYDXckEcsfyzpY6oua3PI/OBMgZIrO0p/Vq2e6Ih94T5H5XEnQdI81NBStg8CVtivDhJthnCmi2tud7MqDH6W2vN/I6iOuA03xcCgy5TI69TeQMl75m6n83HcUMOTYdjopW46XFz4NjVqHpneWGV6fIpdImqnbRc/vTvHy2jbRIEWycIweaJmJhMudP4Y2p2aTHy2zQc6dmkf7meTDgAsgaBBkDYOFtbcsdPhPPyn9i2/1B20q8MJ5Luoep8oUZkScdNreV1nN8XovzZlaCk7lTM8l48zxp+mhELVzJ4G0P8/QFXVAUKK8Lc1xuy+C5rbSe47vJcgqq/QzISTisLMb2TDvsvnG9Dr+vX3Y8cXaNJJeNrqlu7LqKTWs/O7ZNU5ozhMc7dUZ3P/zgNmFAFlrj4NUzw9NmXyQW45XJ8aR9dAtM/je8cblcjWhE3fc5LL0H/FW47Tpn9JcZiLeW1BPv0Ll6qIeMlfeiFq2C3mfJt9CP7pID6KR/wdb3UAq+ksviFzwDXzws316bCNSgzZnKvacl0zvTQ6rHzpk9nHSuWYkelywH8wMr5SrapjflOWf8Bco2oWx7r+2NvjgBKrYD0Ckljq0lLW2flxrHq+clkzZvcls57v6AsSUv8PDETs3brhiexlD/CvTClS3304rkT+/ktrwiOh+uC0RDULgS5l/TZrO+/3POSS4gOSkJ3rxKmiaa5Fy8lsGb7+fZC7uiNjb5cTnxnJxQDi+f2yjPR6SZoeDLlkJNg/R55zL3okyyE1ucym8flwbL7kfZtUSuvE15Fj75q1wdbcJXTu6HV3Hn2ARG5LW8MDh0lRcv6oxn4fVQu6/leH8luYtn8t6VPXj8NJ20t9sqeqx8XD7joTri7TZevyDpUHnvfI/+e57nxpHyesXeIL0O6pOtyUhwkOaxc+1AOylvT5NmxyYqtxO37hlOcOwnKc4B79wg/U4UFSY/Butmy+zyo26Uq3pN2N04Ow/BWfAZFH4tVy50B1wwq7F/7m45NlhLzsLLeeGCHBK+LYO4txCK18Oqx6VZsIlwA7w0Wfbf2v1QtAYK10D2YFh8R8tx8dnEp3fBM2cyGK1eOnqeBiseh2/eaHu9FyfKlcvvcsms3gMvTGh73L5lsi/4D7+S3B5xDo3uaYf/mm5kq3HW4ufBManQKLEwjj0foVRsBVVHzHiH8DlPAGDreRK2jS/LFZYdiwEQp9wF01+TA23tfnL2zmPm8DSuOT6N7J1zoL4ITrhNOtWVb5YPrqojpjwH5z4GnUagbHwFgOjImxE3r2o2PzlW/otTO9uYNDCbOLvGO+uLmTG6a7NicO3INLJ2vCoHGFscoas+wXvC7wGI2/YmxycH6JsVz+juKWTEOzilj/wy4P1vSrlwWCecNpU5qw9w08k92rz0NNE3K54e3zJAd09z06+VM29r7p48gFSPgzSPA8Mw2Vvp45KRXdo99uqx3UiLl7Zzj8PG7eN7t6v8ZCc62yg7aR475wxqsZtn6EHS1/wDjpsCWxfIJWm7By57C0Y3TgIbXoaGUhRF4ewBWaS47SzZWs6pfTPIFFXom+Vga4y/Dz7/u/Qx6TsZkT1Ylglw9sMIVYP9cjIWkx6R11B1lPLNZJd+SrpLodQbopMb9MQcqN4lJ4m8cbD6GVnOqBvl6sb6l+XvYVdAU/sHauDzhyBQQ6rbjkPXuGh4Jxy6yi/HppO58fFmM1PdVcuJjpTmtfj8Fzitq06nZBeaqvDrUzpjW/J7aRZa9qC8n6yBcOPyZvNT2hd3kWo7zApNxCcdgs2YXJH89RZpfjrpt6gVW2HXEqlY2FyIS9+UZi3Avv0dhsXX0b9RWf77xE7on/xZTopdT4TsobDlHXmNE36BuO4zae6oLyZz+2yuHymV5THdU8nUGmDtCwBEz/oHIhaWJltFQUz+D8bFc0BR0cs2klu9gmyPRu9M2W9P7JlCr/A21IIvQVEIn/dss/lJK8+nS1yI9K/+0mhm6oTxy3zod56s1yd/hWiAZNVH+vrHms1MVVcubzY/eTbMItseYkz3VOZ8fYBbTu3ZrhiPy0kgOc7G38/tTfqB91Eqt4Nmo2LKm9Sc/k8A1F6nS1+oAyulIqLZEVe8K78q2r4QTvotYvk/5Zd0yXlw/eeIE28HfzVKem/ZtwHzjL+Camvpn2c/TGj6m6DqqJVbyCxeipNou/UkEpDmJWFI0xXAhAfgohcbzU+7pT/PrqXyd6/T4fMHW/rHDcvh5N9JU1hdoTTfXvcpnP2wVKhXz2rs6zPh1nUt5qdP74Pgt5h5gl6pZDaZmW5YDic1mp82vAy+Q33uvo2MeCd/nty/3X1DOifROaU9hzKLY5lj9iun2uI9pO6cg9L9FOgyhmAoiLbjfYLhCKFO40gvWAxJudBQhnrcFNjwGiJvHEb+G3yecx3Xzt9PpyQXb87oRebah1FGXo+h2qTDaek30HUMweQ+OHctQqT1RhEmYv9XxEb/AkdCOlRsk186TbifUjORd9YX0T8nkVlf7EVVYOYJeTy1bA8bC708f1EeYwueRD3+akJpA6iqriJ1+xzCSb34554cEhMSuGpsNzITnJTXh3hpxX5eXllA78x4bjujJ08t20NGvJMz+mXwxGd72FHegENXuWhYJ249rSfZSYd/sA3DYH9FHS+sKuatdUWEYyZ9MuP5y7n9GdQpSX5ui7RXF3uD1AUi5BfV8dKK/VT5IqR57Nxyak/OHZxDqqfFGTASM9he1sDd721hwwEvuqpw9nFZ3HFW30Mc9SoaQry26gAvfrWPcMzkk+v7kLPyHqKn/hnli4cxjr8Wff9ytL4TULctlPb/bic2O0Tuq/LxwOLtlNWF+Of5vcmrWwU1e9iYfj6dXUEyV/wVMe7X+N2d8VSsh5L1NPS7hM8KokxWvkDRbIjeZ6PGpcCBFbD/K2KjbmJrrYY/bOBxKPRRi7GZIenIWLNfKhKb3oKhlyP81Sg2O8rW92DcbyCpk2z/zx+S/laNYQMqG8Is21FBmsfBs8v3ctuoBIZtfYj6Ub8ilNibDN2PsvI/1OeeyB1fx7FifwPTj+/Mzaf2JD24D1bPQoy+GT7+M5Ez78O2/CGiJ/4O29I/Is74K1p6+xMxIN/GP7gTJv1TLu/Xl8KyB2V5tXsROz6kutc0fK4s8qq/QgRraXDl8sDOTizaXseMMV25+oQ80qmB938L4++Vn87v/QyKN8CgqUQSukLFVsTqZ1mRdws3LShk0sBsfn1mbzp5VGnWKPiK8PE3UuWLkrPvTXClUJ4xjvlbG7i2UzGxog28qZ3FYyuqeWbGcOavL+Lt9cXcOiadmZ5VuJKz2eYZjd1uI69hA7EDa1jonMzgLDt91t2DMf4+bGk95OS45C8w+gbpqKqqGHWlKB/9nsrhv+Kyd738YnQqZ3nnoPediNZlFMU+g8eW7sQUcELPNJ78bDe7Knw4bSpTR3Tm5lN6kJ3ootoXpraqjJzNs9D7jGfW/gz8fh/Xp23G7nDh6j4KtXwTFKygrttEQvF5ZPi2o4TrEL4KYr0mYlvye4zT7yG27jWCQ68isXK9/Ow/IYvYzqVszptJX7cPZ9laUDQOpJ/CQ8sruW9YA/GlK1HG3ILqOfzqKv4q6XcWC8sVzr7nyNUY3SG3n3i7XCXZIf2aRHIeLHsA87Q/Q6geRVVQQvXS32n4TPm1aKRBmt3js2H7IukL406Dyp3w6b1w9kPf/em1rwKW/Fn6xmUeJxWh1c9IM26rZ7q9caq9+aEhFGX9AS9/XbiVPZWyraYf35kbT+5BVqKl0PzcOGYVmo0bNzK0bx6qw9PidR9uwBeMUGM6SdeC2BwOdGFIW3KgBuxuzLCPsmgcUcPEadPITHDKfY4E0HTMQA2xcJCg4sSPi0T8hA0Vpy5w2dSWWCBCSLNU4+9wzKDGHyFmmMQMgUPX0DWFUMxEVSDLFpAmDVXeu99bSUDoRBUn6fF2bK1kEjUMKhukT0ucQ8OuqtSFYthUBUWBcMxE11RS3fbmuBnfJqv8/Hz6DhhIbTBGzDBxO3TSWiknTZimoNofJhIzCcdMhJDLvpnxTlS1fVNUbSBCQyiGqkBKnJ04R/tfW8UMk4qGcIvc9QA1pht7tI6A5iFVC6I5EuRqg815yMDnC0fx+qMoCnjUKKoRoU5xY5iCDN0PqLgS0yASwAh5qRTJRGKCdFsQlxKD+MxG4Ybkm35ju9X4I4QiBrpi4iGIooBhGkRVF27hI4YNXdexaxpEfTIWRxOBmuZyWuRtUuWTMoyYgmxbEFxJxDlszed4DTt1EQVdU0j3yPhDrcsTgRoqYy48wodP8ZCuB789Bk1zfWohrpXvV6vygpEY9SIOVI1MWxBFUamPQG3Mhk1T2/bB1uVEAtLs4k6XvhimQdhXw946iHe7SfHYiWsKGXCQbAPeSjRiVJgJRA1BghbDqUaoMT2oCiTH2dFUhWpfmJgpSNNDuJ0ODJubal8YzQyjGyEqDTdOm0qWPYTuTml7f86ktr4ZgRpqTTf14Ri6qpJl86M5E0CX5tJgJNbGfydmCnRVJS1errA1YZqCYH0lfuHEVG3EDEEcQZw2hbj4FEx/DYFIDJ9w4I+p5DhCKEIgzBi1ajJZtiB1woke8VEagB5JmvTfMVWMaISKWBweLUKiGkFVoSyWgACSHSYeJXJIv2q/vWsARY4pzoQWB2BhtJwfqJG+e0IghIEvKjAE2DBw2XW5hN849kkBeWV5RrRtHdrp699ar9btEqyVcWgOo8zAd88PlQ0hAhGj8ZlxYtePSeODxXdwTCs0R6KsYx1LVh3DklPHsWTVcSxZdQxLThYdwVJjLSwsLCwsLI56LIXGwsLCwsLC4qjHUmgsLCwsLCwsjnoshcbCwsLCwsLiqMdSaCwsLCwsLCyOeiyFxsLCwsLCwuKo59jMtn04zJiMuWDE8EUBM0qc04kqYi35SoxYS8yF1uccjoOPN6Jtc58c9riDfiNjW9SHojhVcDrtrQ6NEohC2DCwaxoJLtvhyzFNQIAQmIrWWJ5A1yAQU7BpGi5NtDnHZrPJ6LOtP4dsdR91wSgKtL1uIw2hKKqIEUMjapjoqoouDBxOBzatg/qyESUck/F5dJuOiIaJoQGipX1QDpUfMr5P1BDEBMTpoCIIoWFD4FAFqBpRUxCOmqjEMNEJmwpxOtgUQdBUEaYBigYi1hjmXyFmCDRNwWnTsCkChEKcy9Hm2mg6gUiMSNTEQBBn13DZ9Ob9QtWoD0ZxqgaqphONRDEVBQ0Dh9Mt70tRMEwIRGKomi4DGZoGoBAIR4mZMqaQMAwECqpiEkUDU+B2SnkEwzHsdjuxWJSYUNEVAwUFQwicdgdGNERUqAhUXJpBzBQIRcUfVXDrgjiHHRCYqNSHouiqikduIiIUAhEDlyZwqOA3VaKGwK1DLBYD3UHUNNCNCIqiomoaIQO6dsklHI0SC4XRVRVNMYmaOlEhcCgGqCoOTUXTbc0pFyKmQsgAl2pgoBIzBS4NtMZ2b4gKVGEQ53SgmIasYNOzFovKdAxNfSQaJih0zFgUXVMQQLQx74+qqthVGUcpYGjoSgxN1TCEIBYzcOgamiqIxBScNhVdFQSiAhNwO+woRoSQoRIyId6uEolE0VQQup1QxMClg11TD/u8+0Kx5rEHM4bDcVDMp6b+Bc3PsEMVuGyajAHTXkjwnxD1wSgCSGxnvPhBaD3mmjGZlLNJ9u2MsxbHLj+flg7UQMFXiOTuRAL1fFWbylj7Hszs3ojK7WjdT4Zwvcyl0uM0mXTOVyHDwvee0Jzkrg1VO2VEztwRMjBXXRGUbIRuJ8sklk3U7JORWruMlsGj6ktk1NSeZ8igfkBRbYBF+aX08IQZa66jpusp4M5EjfrwlK4gmtSP33xQjS8c45KRnTmxVzpZcUDhKkjqAindpTJTsRlUO2bVLmqT+lNQLxgc+BrRaQRBX4Tl1Q4mxG1HyRmKliwTA/ZOt6Ee+Kpt/Q6spKHTyXywK8jcNQdQFIVLR3ZhbM9UshJdlNeH2FFaT+8kk7TyL4lljmR9lZ09JdVckl1MJLErfndnshOd6N+i2BjV+1GMEHrEj62+BCWlG4Tq0FUbwl+DmdUfJVCNYkRkNFHdBnXFiFAdIhJAD9ejO+JRXMko1bvAFofbk4nQbIhwA5RtRs8bh167T4bsT+0BigdHzItSsQV313EY9aXgTEYnKsPNl28mlnEcmrcAJbUbwoihVmylV2pv1LoCCPsQpfl4u5zOznoHb64rZHtZA3mpbv54ei4ZgV0osSA+dx5VYZ3uwW8gpSe6bsP0FqEKE5I6I0yBqN6NiM8hHIrxn60OpgzNZYBaiOpMwFFfjEPVUW0OqXDpLqjeiTO9P0bYj7Z7M2bucOJ8VRjOZOyBShxJXdGKVxPNGootWI3uSkIXBvbi9ZB3IkpdIboRAXcG9SIRI1SG6dAxBSwsT2X2ykJ+PTaNk9J9KBXbKEwey9rSENN6CER9KZH4XpiqDa1+B3qkgWjWULRoCJtqyOfFiGKPz4JANcKnQVwKircAnAlojiQ03Y0zXAPlmzE6n4BphFA0G3gL0DU78fFZ4D2A4kjG4XRDfSkiIQezaA1K7jhE8VqqskeRGtyPGq6XfSIWlCH6XakyfYOqQelGQqnDSKjfhWp3QXwm9pKNGBkD0aN+GWjuwApc3U7HXrsL4nPBCMv2jzRAxI9NCBl4MNKAXXOjqBqoCkKzoR9Yg9H1TKgtxelwQ8EKfJ1Pxef347KFMau2I7qdjJaYLQPH7VpKtMsJfFGisb2okstzigmk98EVLKN3cqvUJJU7pMLiLcaMhahP6M26shhjHXsw41MRdg9aZv+fpFJTVh9i5Z4qXl11ACEE00Z05uQ+6WT/kBF7A7WwewnknSiVvV0fy0jidrccw3ctlalKviuCscUxwc9DofFXw7qXIGcQysvn4Ij4OXP6XFTNDXMvhtr9iOu/QFlws8zIfPbDMPBC+PAu2PymzFly+h9lNNQmqnbBi2fL6JszFshcLa9NlXmezn8a+p0j85XU7pfJ4OqL4ZLXZf6bN2dKxWn8fTD0cgqDdqY8tYJfjklhdOlbxK17Ar37mZjnPoG67xP0924iKa0vfxz/Eue9sp873vqGa0ZncdeAWrTXp4EnC2YuklF0Vz8HGX1RP/w9KRP/SUrNXpRVT0CP08g85fecpW5Ae/MXiPR+GJfPRzUiOF+/EHxlcMkbMkfQGzOgeC2ceh8Ftcez/oAXgHUFtQzKTeCJy4bx8Ec7uG1sJqm7F6AvuYv43OPpecYshqbuJentKyE+m4bJ89gd6UzfrPazJho1+9G8e6G2QCbTyzsBXp4MET/69LngSYHavVJe0QBc8Z7MzlyzB2X3JygpPcCTBlUlsj2yB8Hb18G0l1HqimU+my6jZZh2VYUPfw/pfXFPexllx/uwawmKKwX1w/+TiTDNGCz9C6T1wmbGZHt7C2Wo9rJv0M9+SJa37EGUHYtJHH41pckzeWtdMQCjcuy4y75GNevhvV8QP/5veKIhlJwhcsLd+ylazlB49xbZZhe9iKJpqK+eS5oQ/GL6YjyiAD1QBZvegM6jwe6Ct2bKRIUfXAtVu1CuXIi6+xM4sALNmQRvXonmSoIrF8E714Nuwz42RQ7wJXthxwcoDo/sj7Fg8/UzL52HEquBN65DFQbjLnoH+4hExroKUNcugI1z6DZkBt3H/gLlpckQqCbp6o+hfDNKNADL/4lt6myUaFDmLkrOk2kVXj5Xhs+/ZqlMPJjYCV69CH3qbLRgLcruJVC0Bv38J+WzWbweMvqjeNJlQtlYBP2Kd+Tfp/4B1r2Itu093IMvRTn+auLKV6CGvfDFP2DqbNnmvnIpo+zBMOcSlD5nkxSoQ4lLAhGDj/+AEg2gnvNvaCiBDa9CLIzdnYay+A75zApDpgdIzAVFl/m4XpoMRhj9ivdAccrkiutfQvcWkJLWHaUpSemuj/GMvI74IZdD/uvw9dOY3U7BPP8J1E3zYemfUTuNJu/0pxicuofEVU/A2F/C/GvQ7B7EVY2Jbcs2yWu401AX305ScjdOuWg2Wp0Br5wHuhPjysVoWe3nMPqxKK8PceMr69hY6G3etv6Al96ZHl66euQPo9QEamHdi/DJPXDxK7BlQWM2+mkw+hbZLp/+DbqOhYteaE5BYnHs8vNQaDQbJOTKv51JEKhBnTtdDlgNZaA5MFDRkjqjlG6AD+6QSdSaMgGn9pRvbq3Rnc0JKHnlPIhLk0nXVF2+DWj2lmu70+WE9volMjR+XZF8w0ruSgSdJz/bTWVDmAPeKNGMHgDY9y6B2Wc21yHmzsIbNokactl89QE/4aHJxOlOqSw9dwbEQtBpJHQ/GTQbSsUWSO4q67HnU6jejeY9AEDUnY1pqDg0OyIuDaW+GF6/WOZpqSsCRSXs6UTRgVib264JRNlYWMfATknsqQmT7u6ETVFRi9fQfcG5UgamgeFMpSGqct97W3jisuHNmbhbExE6jmgINRYCT3qb9mHudLnqNP5vUs7BWjlRejJhwv1ymdmVKOUcrJGTtd0j2ylYCxG/vPf6Etk2cSmyLSq3o6x+BtL6yDwyjakmMCJSfvGZcqVBc8jfQsgM2wAf/A6Suja3SSixOwXeljD5xfUxFFcymJrsH5U7ULIGIm03DbJMR4LcV18Mcy+R14j4IT4LQ3Ni6CbU+6T5z+GW9dOdjekFMqTi9s08yOgnw9Brunwb9VVIxSA+szE7s5AKWrBOyqF8q0wZoTuar6+8fG6b64fVOPbXRiHVLu8TULe9CwOnSPn5ylG+eUO+ATs8YMZQIn5ZBqp8IzZN2Yb+Klj7PHQeKVf9bC7wV0hTUVJX2R+jQdkGukOWZ0QgLlVO6HVF4EqR/akxLYWSPwcKvkJN7grDZ8r7i/jkcfUl8O6tUkn0ZEB9CUrmALDHSRnEZ0FpvlSMg7VSySpag6I0tn8sJHMeRUNgi5fPsdJ4T94CmHclnP+kbIeEHCjfIhW5QI1cIQWU1c/Czo+b+0ckvguKUHGk5IGioBWtoseCc2Xbp3RHOBJQbHEQqEZ5frysx7hfSXnEpcq+XbMXbc5Ued1YCDyZRFQHP7UsRav2VrdRZprYWe5jydZyZozuKjONH0k0m1SiFUVmFPc0KizfzIMDX7eM30l5coXT4pjn5+EU7EyAvhPx15TCxIflgBoLS2VGUak4fy6//LCWyISHZBI3aHkYzrgHhs1oNg01k9QZLn9HTrpGVA68igKXz4cuY1ryRyXkwvRXIWuQHDDriuT2aS9Dj9Opjmi8m18CwLOrq5hVOZCa0x5uU4dI5xPZOOIBZrxRQMyUmSo2lTRwz9cK5pXvywkqUC0npvJNVCYOpPr81+Xbi7cQTvtTY3lSmYl0OYmNw+9naaHA9GQRPP8FROZx0nejsX7ec1/kb9syeHeLt81tj+uZxvx1RXROjiOmuXi8oBOBC2bLnXVFUpnJOI5tp8xi+twDrNxbQ32w/azAb+6IsdnshkjqIhWHyp2N7dNFtk/FNnjnJmLT5rRs8x6At2/AHHYlxoE18p4z+ktlofQbuOBpWPQbqSA5k+Vbd7BGTrDnPy3baM1zULJOZsr+8E55zfd/K006Q6+QA+Xa5+UEq6gw4mrofmqbNomdcS/PNYzhX1+2ZAj+cIeXVeEu1CkJcOGz8k29fItUQlzJcgWpYAVMeVb2j6Y2c6dTcO5bvLxdcP/qKL6U/pA9RCZ+NE0451/w8R9g5PXQeRRseEWaJoZcBh//CaY8IxW6d2+GXuPlIL/0L/JeU3vIPpjeG1Y/K+/nwufa9hl3GkXnvcX0+ZU8/Hkpe+29CWQdDyfdIc2wb14FZ9yDSOstMy2XbpDKyMR/wNvXy7KyBkj5RwNw1oPyuVj/klyJrCuGKbNkXVVNPjvDroB3bpTKQWIulGyQz8cpd0kFbP51cPqf5Bt3fDaMuKZF/vu+IFixj/CU2fD2DfK49L5ywn9rJhx/HaK+VCZhDNTKZ3fAFMgcAAt/CcndZLLFnKHy7X7SI3K7qkHn4+X9mVHZ/uc8IuXXUAJvXyvr2ul4aZZ+9xZI6wWpvaSC1ap/hI67lKXZ17G13gU9z6Bh8nONz0ghCJOY7mankUvJlHekkhvyyn/L/4nZdzLB2hK44BlZJ1+5vLfETuw9ew4PrgzyE8lYA0g/ujlfHzjs/rmrC9vkxzpiODyyv095XmahdyXCyOvkvqbxe+DFcOY9LS9MFsc0Pw+FBkABodnkxKkepK2rGhFT7jpkJUazH95erSiHav6qTV7s4Isf7CistpTbemyKGAKhHbSaoeoYAsyDBrGYibx+6/opKiYKosmR2Ygccm2h6sRMgYksT8Ah9yFUG40+lIfccmuippD30vpcRUPQUt/DDb0CMARy0m7dLm3qIuQk3N42YTZuV+QEpGpyO0jlTJiy/Yyo3K7qNLeNEUWoNnltRW2sjZDnqLp8+1c0+VuIQx0LNTsR49A7MwUItbG+itpYL5n8Tzpsq23rCaCoCBQQYJg0nmfKujY6KaMoLXUDWa7WVE+15R7MRgd3YTTeq2iRg2m0HNtOnzEalWVTNF6vqR+KRkfz5tWsWIs8hWiRETQeI1rur0n2TffQ1C6q3lhuY1spSss9NO0TQsrRiBwq/+brH9R3hCnvXWt0zm7absbks2kaLb9by6S9+2i6h+YyjMZuYraSMfL/gz4cMDV7q+dHOXRcUW2YiiLb/aC+gKIgmvoJh2+nowWB+OFcfhRV+tVBYz85aOzUbD9JfyOLH4afh0ITboBdS/AkpMJHv4eavS2mIGGS8c40npqYguPTP8HWd+Q5TVmTP/o95L8hl3xbU1ckfWaqd8nBzJMhB8NXzpd2+FjjG0l9iXzDLV4nH6wmO+7cS2DfF6TYDSYPkg5rM4en8ovcnaQuua1NHewFnzFs4928fHEeemNW6/7ZHu49QUF9aZJ8W3Ylgy0O0vqQ2bCVtLenwZDLpWliyZ/blOfY/ynD8//CmV1UVF8ZcQuvRynLl4NDY/2S353B3QNrmdivVXZm4Kvd1VwwLJcSbxDNCPOrHuXEvX2Z3BmfBYqKXp5Pv+W3MveSPI7PSz7s1w7T+uoMVveg1BeBzSFXEz78P6jdJ9sntRdc8DT6m5c1brPLt+ULnkZdPxut2wnSlFG2SU58WQPlW//Eh+TqQywkV3UScmT7vHO9nIyGXQl541BW/Bsm/RMW/xYm/UOuLnzzhjRjjL6pZYLd+Jp0Dm8lQ/2jO7k5dQM3j2l58zu9VxJj4opJMhtg/rVw3BTpMJ6cJ80cFVuh21i5r7nNXOArJ2/Rxczop/DHMQ48VflQvhl6nCrr8P5v4Iy/yhWPgq9g8HTpi7X2BTj779KHJFQHk/8D+5dD5XbpnyUMaX4KVEtn9TE3y23zr2vbZ3wVdHlvGm9MzeS2cVn0jO0irmIjfHafNONNnQ2f3odSsU2uVnUZLc9b9Gu58hILQcV26aCr2eVKTPVuGHwpdDtFymzBjXD63VJQ/kq5SjblGfAWyd+5w2TbfPGQPPfC5+Hzv0PfSRD2wdezWuTf9QRcmT1wvDVDlrHsQbkSptnhwhdg/SvS9NdvsjQZBaphxwfyGTzvCWnSqt4lV8vG/w0W/QomPyaf3+J1kDtcPtOqDovvkKuC7gzph1F3AMo2y/LOf1qulNUVyvtp1T/i8mczofpl+sWHYd8y4t+d2fiMZIOioIdr6KuVkLvgQrky44iXKzVjbkXd/j5xqbmyXc2Y/CBBs4P3AD0/vIy7xnqOvPnmexDvtDF9ZOfD7p86ojPJcYeanL83Eb80Xc67Ak78rVQ4Vz4h9zWN3xtflX40/qojf32Lnxw/D4UmFpbL/ooCvkrQ7BjTX5cDVEp3EAY6BkrlDnn8+L/BjV9Cv/Pk75KNLQpKExG/dKRVdWlmuu4zufxsROSAHAu3XLvugLz2tFfh+s/lwC9MKN+Cgyi3ntaTNI+dvukO7BX5sviupxK6ehmRSY8B4KjbQ7pLxa7LJhvTJR5HsFxOvvFZcO1SuGqx9Pep3S/rkTsMUb1X1qPbSTDtFYyJjzSXZ1dNiIWl/4yiwvTX4Lpl0rHSNHDW7qB3Sts3T49DY1iXJNYV1NIzRSfOuwtMAzN7CPsvXEzVOS9KpaahmGS7wV/PO65d/xkAu4iiOjxycqxtXLJubB+mz5E+C6resm3GArj6I+lfoDvAVyWdXOOzIVgtlYZGPwMcCVIpSOkOYb+cdGJhSO2BGHOLdER2p8ltonGVyZEgZZfYWZpabHHSP6V2X0u/uHQe9DoTAGflRvpnOJvvp1eKjuKrAH8FRP2QNRjRUC5l60qWSoS/stlnhUvmyonb7oZADTYjgC1SL481o9LhPBZu9BNJbjYZMvgyqZR7MiESkAp7XJo0R9Xsk/JoWpFwp0nTXXJXCPnkwN7YZ8QV78lnwO6BYA1xRgMDMh0oRggqtgBg9jsXkZjb6B+mIYZejogEpDw1mzzXmSgVpUClvFdfudw36gYIVMn7DfsgMRdhc0t/HleyNFV50qWsgt5G355SsMdL0059CaT0kMoHIAZOhelzMIfMkLLQdHn9hlJ53XP/IxX4ugOQ0gMRrJEyjEuVbRiXIvuNJ0N+oRifhYiFpExtLnl93SXrEqyRz6i/Sp5z8SvSV8edAVU7wJWM0B3Sv6dx3BBDr4BL5sHx18pnrHordiUKZVtAmBg5w9l/4WKqJz4LngyUYI1URF3JiGuWwDUfyzEkGpD3Hg1Cch7mpW+1MVM6DH+7z9OPyZjuaQzMPdT5v0e6m7OPy/phFLBYWL7MCCEV4qo9cvuAKfIDh5N+J39XbJXPg8UxjyJ+IsbYI5kevt2y/NWw93NEWi/Cfi9f1mcwRtuBI6c/asVm1B6nQ7gO9n8lP9N2JUlHy+2LoO/k9m2wFdvl5NV5lBxsvIVydabnGdJvp4nqPVCzB7qOk06K9cWwZxn0mQhxcgWkqDbA/HVFHJcc44TICoLdxiM8GehRP+6iz6lPGcSti6toCMWYfnxnTu+XQZZLyLfMlDypyJgGlH0DmhOzYhu1KYMo8sFx9V8guoyl2hdiXY2DMx1bUDsfj5bcBcMwiJXvwO4vQmmqX10x7PsCX94ZvLPNzxtrC1FQuHRUF07tk0FWopOy+hDbiuvon2KSVvwp/txxfFluo6C8mksyCzGTuuH3dO3AZ9v7UGJBRCSI4i1ASesJwVpM3SWVgaxBaMFKlFhY+jdodvAWIUJeRCSACHnBkYjiTkWp3C4npvgchGZDCddL34wep0n5B72Q3peAFo8r6kUpWY/ocRqmtxgRl4IuGj/bLt1ILGsYWu0eWR8zBqXfYKT3Q3O4UcI+RPFa6vLOZlu9nddXF7KjrIGuqXHcM6ETWf7tEA3SEN+d2qiDLnVrIbMfqDpmTYGMP5PSHRBQuR0jvhPeYIzHtsYxZVguxykFqHGJcvVCVVFsTqnY2VxQsRWRNQgjEkIvWYvocgKioRTTlQa+ctTU7qgFXxLLHQWBKnR3MpgG4sAq6HE6ine//Dw5PptKkYIrUIS78bPtBeXpPL/iAL8/OYNxqT6Uso3sTTuVDWURLuwWg7pi6pL6YSo2kuq2oYRqieaOhGgIm2JKRSYagsQc8FchFBu4U1Gq94ArEcOZTNQWjzNcDSXrMbudihrxS2fl2n0IxYaSmAO1+zBdqagOt+yLSV0Q+78k0PV0lIIVBDuPIyWwBzXohS6jIBIE736p1Nk9CEWB4nXUZ4zE492O6oiD+GxE0RrMrGFo0Ualce8yYr3ORq/eIZVYIyLNPGGf/IcplZhwA4bNI79IUxTQHRj7vqSh+zkkhIqlUr5vGYG8CdT5/aTpIfTybxC9xqMl5khFe/v7RPNO4ZNinT0lFVyeeQAtsz+uQDGGPREtq78cqyq3y9XGugJEJIg3sR8bK03G6Duxu1MQzgS0zAHyq72fGGV1QZbtrGTO1wcwTPnZ9vj+mWQnfX8X5sPOD4Ea2LFYPuOqDtsWyvG46bPtpn1NKzYWxzQ/H4UG5CqLZgMjRl0UVDNCnNOFJmIyjkzTMbq97Tn6tyyXdvT4DhxnGCZ1oShO1STO1TIIGNEw/phCMGJit6ltl28PLsc0mv0BDEVvLs+mqviiYLOpeDTRfI5hGGzdupX+fXqi2V2HlCuEwBuIgkK7y8beQARdxIgIDcM0UVUVGzGcTid2vYPtGIsQjBmNgfVsiFgIQzQF1nOhmTIAXbOtvKl+QDDaFFhPwa2bKAjC2LBh4tREs39UOGo0BtbTCBkabpvAppj4DR3FjCFUHUVEUVBQFGRgPRVcdh0bBoaisXv3Hnr26oNGo3OEbscXihGOGhhC4HHqxNn15rqZqg1vMEKcEkXT7UQiEQxFQcfA4fTIfgdEhYI/HEPTdeKdtmYflUAoJIMV6hrCiCEUFVUYRIUdiOFxyNUzf9jA7rBjRKNE0HAohvRPMgVOhwMRDRIxVUxFw6XGME0TU7Hhi0KcLoh3OUAIDEWjLhhBU1USbXJYCKPhC8Xw2AQOxaTesBE1TOJtAiMWwdCcmEKgGmE0RUFRNUKmhhmoxeFOwDBNNEVBVwRh7EQNgVMJg2bDoanoNrtciUAhZEJYqMQpEWLoRA2FON1Eb/Sf8UZAJ0ac04VqRpvboKU/NAbWUxSIBPAJGyIWxa6CqahEYjIYn6poODUwhUmDoeMkhqIqmEAkJnDpoKkQiYHTrmJD4I9Jn7A4hwPNjBAwFEKmSoJdJRwOoWsqpu6QgfU0E6euHfZ5rw9GURrHHowI23bupl+/fnKsEkL6HTViqDbqghE5Jth0qXD9BJWZ1ngDEYSApDjbEVuZ+db5ofUY2LSSfrjx3OKY5ufx2XYTTR1bt5GoAzRNkPZDjznc78OV+V3Hd+A4TVNJcTsO3W5zkGCDhPZedA4up5XDswZtyks+tGgAIpF2nOkay1UUheTDmIwAkuLstJEfAIe50OHQ7bha90R7O47Z7ZwD4NI55BPWg4+2A/ZWG1uFPCQJaKnvt9TbMPAFQm2dZQGPU8fjPOgxaqybSpP8Zbk2e3s1k70wydbqnhsn8Li4uMPXpxWJTcUeLLcmbPE429nsPuj3wf0FZM0dnpY+1cao4HAedGTj5QyDjduLGTIkvc3k42jnL1lveZ/Oxn/gwMah7ZqkQ3Prqt/xPNnj8AA4WmTS3uPjaK8+7dSy7SOgEwc0tY7equ1ctsMMqa3qJyNuy3MMbITD4ZbjFKXNse21yU+dpB/CX+bb0L/H+G1xTPHTVvUtLCwsLCwsLDqApdBYWFhYWFhYHPVYCo2FhYWFhYXFUY+l0FhYWFhYWFgc9VgKjYWFhYWFhcVRj6XQWFhYWFhYWBz1HFGFJhwOc9dddzFixAjGjRvHCy+8cCSLt7CwsLCwsLBolyMah+ahhx5i8+bNvPTSS5SUlHDnnXeSk5PDWWeddSQvY2FhYWFhYWHRhiOm0AQCAd58802effZZBgwYwIABA9i1axevvfaapdBYWFhYWFhY/KAcMZPT9u3bicViDB06tHnb8OHDyc/PxzTNI3UZCwsLCwsLC4tDOGIrNJWVlSQnJ2NvFd49LS2NcDiM1+slJSWlQ+UYhvG969JUxpEo61jHklXHsOTUcSxZdRxLVh3jx5bT980vaPG/4YgpNMFgsI0yAzT/jkQiHS5n06ZNR6pKR7SsYx1LVh3DklPHsWTVcSxZdYwfS07Dhw//Ua5r8d9xxBQah8NxiOLS9NvpbC81Xluakn7379//iGTb3rp16xEp61jHklXHsOTUcSxZdRxLVh3jx5aTYRioqnrEsodb/DAcMYUmMzOT2tpaYrEYui6LraysxOl0kpCQ8B1n0+xns3Xr1iNVpSNa1rGOJauOYcmp41iy6jiWrDrGjymnIUOGWErnT5wjptD069cPXdfZuHEjI0aMAGDdunUMHDgQVf1u32Nd15uPtbRgCwsLC4ufEh2Zxyx+XI6YQuNyuTj//PP5y1/+wv33309FRQUvvPACDzzwQIfOV1X1EB8cCwsLCwsLC4uOoIgm55UjQDAY5C9/+Qsff/wxHo+Ha665hpkzZx6p4i0sLCwsLCws2uWIKjQWFhYWFhYWFj8GllHQwsLCwsLC4qjHUmgsLCwsLCwsjnoshcbCwsLCwsLiqMdSaCwsLCwsLCyOeiyFxsLCwsLCwuKox1JoLCwsLCwsLI56jjmFJhwOc9dddzFixAjGjRvHCy+88GNX6X9OJBLhnHPO4euvv27eVlhYyMyZMxkyZAgTJ07kyy+/bHPOihUrOOeccxg8eDBXXHEFhYWFbfbPnj2bE088kaFDh3LXXXcRDAb/J/fyQ1BeXs4vf/lLRo4cyYknnsgDDzxAOBwGLDkdTEFBAddccw1Dhw7llFNO4bnnnmveZ8mqfa6//nr+7//+r/n31q1bmTp1KoMHD+bCCy9k8+bNbY5ftGgRZ5xxBoMHD+aWW26hpqameZ8Qgn/84x+MHj2akSNH8tBDDzWniTlaWbJkCX369Gnz75e//CVgycrieyKOMf7617+KyZMni82bN4uPP/5YDB06VHzwwQc/drX+Z4RCIXHLLbeI3r17i1WrVgkhhDBNU0yePFncfvvtYvfu3eLpp58WgwcPFsXFxUIIIYqLi8WQIUPE888/L3bu3Cluu+02cc455wjTNIUQQnz44Ydi+PDh4tNPPxX5+fli4sSJ4p577vnR7vH7YJqmmDZtmrj22mvFzp07xZo1a8SZZ54pHnzwQUtOB2EYhhg/fry4/fbbxb59+8SyZcvEsGHDxHvvvWfJ6jAsWrRI9O7dW9x5551CCCH8fr8YO3asePDBB8Xu3bvFvffeK0444QTh9/uFEELk5+eLQYMGiXfeeUds27ZNXH755eL6669vLu/5558XJ598slizZo1YuXKlGDdunHjuued+lHs7Ujz55JPihhtuEBUVFc3/6urqLFlZfG+OKYXG7/eLgQMHNk/kQgjxxBNPiMsvv/xHrNX/jl27dolzzz1XTJ48uY1Cs2LFCjFkyJDmgUEIIa688krx2GOPCSGEePTRR9vIKBAIiKFDhzaff+mllzYfK4QQa9asEYMGDRKBQOB/cVtHlN27d4vevXuLysrK5m0LFy4U48aNs+R0EOXl5eK2224TDQ0NzdtuueUWcffdd1uyaofa2lpx0kkniQsvvLBZoXnzzTfFaaed1qzImaYpzjzzTDF//nwhhBB33HFH87FCCFFSUiL69OkjDhw4IIQQ4uSTT24+VgghFixYIE499dT/1S39INx+++3in//85yHbLVlZfF+OKZPT9u3bicViDB06tHnb8OHDyc/P/1ksPa5evZpRo0bxxhtvtNmen59P//79iYuLa942fPhwNm7c2Ly/KaEoyLxcAwYMYOPGjRiGwaZNm9rsHzJkCNFolO3bt/+wN/QDkJ6eznPPPUdaWlqb7T6fz5LTQWRkZPDoo4/i8XgQQrBu3TrWrFnDyJEjLVm1w9///nfOO+88evbs2bwtPz+f4cOHNyfcVRSFYcOGHVZO2dnZ5OTkkJ+fT3l5OaWlpRx//PHN+4cPH05xcTEVFRX/m5v6AdizZw95eXmHbLdkZfF9OaYUmsrKSpKTk9skuUxLSyMcDuP1en+8iv2PuPTSS7nrrrtwuVxttldWVpKRkdFmW2pqKmVlZd+5v76+nnA43Ga/ruskJSU1n380kZCQwIknntj82zRNXn31VUaPHm3J6Vs47bTTuPTSSxk6dCgTJkywZHUQK1euZO3atdx8881ttn+XnCoqKg67v7KyEqDN/iZF/GiVkxCCffv28eWXXzJhwgTOOOMM/vGPfxCJRCxZWXxvjli27Z8CwWDwkIzdTb8jkciPUaWfBIeTS5NMvm1/KBRq/n24849mHn74YbZu3cpbb73F7NmzLTkdhscee4yqqir+8pe/8MADD1h9qhXhcJi7776bP//5zzidzjb7vktOoVDov5LT0T6elZSUNMvk0UcfpaioiL/97W+EQiFLVhbfm2NKoXE4HId03qbfBw80PyccDschK1SRSKRZJoeTW0JCAg6Ho/n3wfsPXgk62nj44Yd56aWX+Ne//kXv3r0tOX0LAwcOBOTk/dvf/pYLL7zwkK+Sfq6yevzxxznuuOParPw1cTg5fJecXC5Xmwn5YJkdjXICyM3N5euvvyYxMRFFUejXrx+maXLHHXcwcuRIS1YW34tjyuSUmZlJbW0tsViseVtlZSVOp5OEhIQfsWY/LpmZmVRVVbXZVlVV1bw8e7j96enpJCUl4XA42uyPxWJ4vV7S09N/+Mr/QNx77728+OKLPPzww0yYMAGw5HQwVVVVLF26tM22nj17Eo1GSU9Pt2TVyPvvv8/SpUsZOnQoQ4cOZeHChSxcuJChQ4d+rz6VmZkJ0GxOaf330SinJpKSkpr9ZAB69OhBOBz+Xn3qWJWVxX/HMaXQ9OvXD13Xm53IANatW8fAgQNR1WPqVv8rBg8ezJYtW5qXZUHKZfDgwc37161b17wvGAyydetWBg8ejKqqDBw4sM3+jRs3ous6ffv2/d/dxBHk8ccfZ+7cuTzyyCNMmjSpebslp7YUFRVx6623Ul5e3rxt8+bNpKSkMHz4cEtWjbzyyissXLiQBQsWsGDBAk477TROO+00FixYwODBg9mwYQNCCED6kKxfv/6wciotLaW0tJTBgweTmZlJTk5Om/3r1q0jJyfnEF+So4Xly5czatSoNqt727ZtIykpieHDh1uysvh+/JifWP0Q/OlPfxKTJk0S+fn5YsmSJWLYsGHio48++rGr9T+n9WfbsVhMTJw4UfzqV78SO3fuFLNmzRJDhgxpjhlSWFgoBg4cKGbNmtUcM2Ty5MnNn08uWrRIDBs2TCxZskTk5+eLSZMmiXvvvfdHu7fvw+7du0W/fv3Ev/71rzZxMCoqKiw5HUQsFhNTpkwRV199tdi1a5dYtmyZOOGEE8Ts2bMtWX0Ld955Z/PnxQ0NDWL06NHi3nvvFbt27RL33nuvGDt2bPPn7uvXrxcDBgwQ8+bNa46tcsMNNzSXNWvWLDFu3DixatUqsWrVKjFu3Djxwgsv/Cj3dSRoaGgQJ554ovjNb34j9uzZI5YtWybGjRsnnnnmGUtWFt+bY06hCQQC4ne/+50YMmSIGDdunHjxxRd/7Cr9KLRWaIQQYv/+/eKyyy4Txx13nJg0aZL46quv2hy/bNkyMX78eDFo0CBx5ZVXNsd2aGLWrFlizJgxYvjw4eL3v/+9CIVC/5P7ONLMmjVL9O7du91/QlhyOpiysjJxyy23iGHDhomxY8eKp556qlkpsWTVPq0VGiFkQLjzzz9fDBw4UFx00UViy5YtbY6fP3++OPnkk8WQIUPELbfcImpqapr3xWIxcf/994sRI0aIUaNGiYcffrhZ/kcrO3fuFDNnzhRDhgwRY8eOFf/5z3+a78mSlcX3QRGicX3PwsLCwsLCwuIo5efrWGJhYWFhYWFxzGApNBYWFhYWFhZHPZZCY2FhYWFhYXHUYyk0FhYWFhYWFkc9lkJjYWFhYWFhcdRjKTQWFhYWFhYWRz2WQmNhYWFhYWFx1GMpNBYWFhYWFhZHPZZCY2FhYWFhYXHUYyk0FhYWFhYWFkc9lkJjYWFhYWFhcdTz/wCwJdQF/0Q92gAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "f2d6e936e658c23b"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}