ValuingPasses.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valuing passes using Football Tracking Data\n",
    "\n",
    "Passing is the most frequent event in a football match. This project scratches the surface how we can calculate different parameters of a 'pass' event using tracking data. Traditional metrics to value passes in football mostly utilises the widely available on-the-ball event data.\n",
    "\n",
    "Tracking data captures much more insights as it includes every player position at each instant. Even though there is very [limited public open source tracking data](https://github.com/metrica-sports/sample-data#other-tracking-data-sources), this project discusses the algorithms for calculating parameters of a pass. As more tracking data would be available, we can compute and analyse how various aspects and scenarios of a pass influence the pass's impact. The pass parameters can itself be features to a trained model.\n",
    "\n",
    "We also implement the Pitch Control x EPV and the VAEP metric for valuing passes.\n",
    "\n",
    "All the output files generated has been moved to the output folder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Acknowledgement</b>\n",
    "- Metrica Sports: For publishing tracking + event data to the football analytics community<br>\n",
    "  Dataset Link: [Metrica Sports sample tracking and event data](https://github.com/metrica-sports/sample-data)<br><br>\n",
    "- Laurie Shaw and FriendsOfTracking: For lectures and code to understand and get started with tracking data<br>\n",
    "  Repo: [Laurie's code for Metrica tracking data](https://github.com/Friends-of-Tracking-Data-FoTD/LaurieOnTracking)<br>\n",
    "  Youtube: [Friends of Tracking](https://www.youtube.com/channel/UCUBFJYcag8j2rm_9HkrrA7w)\n",
    "\n",
    "<b>References/Further Reading</b>\n",
    "- [Valuing passes in football using ball event data](https://thesis.eur.nl/pub/41346/Bransen.pdf)\n",
    "- Pitch Control: [Lecture](https://www.youtube.com/watch?v=X9PrwPyolyU&t=215s), [Presentation](https://www.researchgate.net/profile/William-Spearman/publication/334849056_Quantifying_Pitch_Control/links/5d434d0aa6fdcc370a742d04/Quantifying-Pitch-Control.pdf)\n",
    "- EPV: [A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes](https://arxiv.org/pdf/1408.0777.pdf), [A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions](https://arxiv.org/pdf/2011.09426.pdf)\n",
    "- VAEP: [Actions Speak Louder than Goals: Valuing Player Actions in Soccer](https://arxiv.org/pdf/1802.07127.pdf)\n",
    "- [A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains](http://nessis.org/nessis11/rudd.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import Metrica_IO as mio\n",
    "import Metrica_Viz as mviz\n",
    "import Metrica_Velocities as mvel\n",
    "import Metrica_PitchControl as mpc\n",
    "import Metrica_EPV as mepv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load event data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up initial path to data\n",
    "DATADIR = r\"C:\\Users\\adnanazmat\\Downloads\\PROJECTS\\sample-data\\data\"\n",
    "game_id = 2 # let's look at sample match 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the event data\n",
    "events = mio.read_event_data(DATADIR,game_id)\n",
    "\n",
    "# Bit of housekeeping: unit conversion from metric data units to meters\n",
    "events = mio.to_metric_coordinates(events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 Get home, away shots and goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get events by team\n",
    "home_events = events[events['Team']=='Home']\n",
    "away_events = events[events['Team']=='Away']\n",
    "\n",
    "# Get all shots\n",
    "shots = events[events['Type']=='SHOT']\n",
    "home_shots = home_events[home_events.Type=='SHOT']\n",
    "away_shots = away_events[away_events.Type=='SHOT']\n",
    "\n",
    "# Get all passes\n",
    "shots = events[events['Type']=='PASS']\n",
    "home_passes = home_events[home_events.Type=='PASS']\n",
    "away_passes = away_events[away_events.Type=='PASS']\n",
    "\n",
    "# Get the shots that led to a goal\n",
    "home_goals = home_shots[home_shots['Subtype'].str.contains('-GOAL')].copy()\n",
    "away_goals = away_shots[away_shots['Subtype'].str.contains('-GOAL')].copy()\n",
    "\n",
    "# Add a column event 'Minute' to the data frame\n",
    "home_goals['Minute'] = home_goals['Start Time [s]']/60.\n",
    "away_goals['Minute'] = away_goals['Start Time [s]']/60."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 View events, passes, shots and goals data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PASS              543\n",
       "CHALLENGE         160\n",
       "RECOVERY          135\n",
       "BALL LOST         120\n",
       "SET PIECE          34\n",
       "BALL OUT           27\n",
       "SHOT               13\n",
       "FAULT RECEIVED      9\n",
       "CARD                3\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_events['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <td>Home</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>504</td>\n",
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       "      <td>534</td>\n",
       "      <td>21.36</td>\n",
       "      <td>Player3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.72</td>\n",
       "      <td>-28.56</td>\n",
       "      <td>4.24</td>\n",
       "      <td>-34.68</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Home</td>\n",
       "      <td>CHALLENGE</td>\n",
       "      <td>AERIAL-WON</td>\n",
       "      <td>1</td>\n",
       "      <td>1084</td>\n",
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       "      <td>Player6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.06</td>\n",
       "      <td>-17.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Home</td>\n",
       "      <td>RECOVERY</td>\n",
       "      <td>INTERCEPTION</td>\n",
       "      <td>1</td>\n",
       "      <td>1084</td>\n",
       "      <td>43.36</td>\n",
       "      <td>1084</td>\n",
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       "      <td>NaN</td>\n",
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       "    Team       Type       Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "8   Home  CHALLENGE    GROUND-WON       1          504           20.16   \n",
       "9   Home   RECOVERY  INTERCEPTION       1          504           20.16   \n",
       "10  Home   BALL OUT           NaN       1          504           20.16   \n",
       "18  Home  CHALLENGE    AERIAL-WON       1         1084           43.36   \n",
       "19  Home   RECOVERY  INTERCEPTION       1         1084           43.36   \n",
       "\n",
       "    End Frame  End Time [s]     From   To  Start X  Start Y  End X  End Y  \n",
       "8         504         20.16  Player3  NaN    12.72   -28.56    NaN    NaN  \n",
       "9         504         20.16  Player3  NaN    12.72   -28.56    NaN    NaN  \n",
       "10        534         21.36  Player3  NaN    12.72   -28.56   4.24 -34.68  \n",
       "18       1084         43.36  Player6  NaN     1.06   -17.00    NaN    NaN  \n",
       "19       1084         43.36  Player6  NaN     1.06   -17.00    NaN    NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>28</th>\n",
       "      <td>Home</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1467</td>\n",
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       "      <td>1489</td>\n",
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       "      <th>36</th>\n",
       "      <td>Home</td>\n",
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       "      <td>HEAD</td>\n",
       "      <td>1</td>\n",
       "      <td>1672</td>\n",
       "      <td>66.88</td>\n",
       "      <td>1693</td>\n",
       "      <td>67.72</td>\n",
       "      <td>Player6</td>\n",
       "      <td>Player7</td>\n",
       "      <td>19.08</td>\n",
       "      <td>14.96</td>\n",
       "      <td>19.08</td>\n",
       "      <td>8.84</td>\n",
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       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Home</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2414</td>\n",
       "      <td>96.56</td>\n",
       "      <td>2440</td>\n",
       "      <td>97.60</td>\n",
       "      <td>Player3</td>\n",
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       "      <td>-29.92</td>\n",
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       "      <th>53</th>\n",
       "      <td>Home</td>\n",
       "      <td>PASS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2465</td>\n",
       "      <td>98.60</td>\n",
       "      <td>2493</td>\n",
       "      <td>99.72</td>\n",
       "      <td>Player8</td>\n",
       "      <td>Player4</td>\n",
       "      <td>2.12</td>\n",
       "      <td>-29.92</td>\n",
       "      <td>10.60</td>\n",
       "      <td>-31.96</td>\n",
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       "    Team  Type Subtype  Period  Start Frame  Start Time [s]  End Frame  \\\n",
       "28  Home  PASS     NaN       1         1467           58.68       1489   \n",
       "29  Home  PASS     NaN       1         1489           59.56       1578   \n",
       "36  Home  PASS    HEAD       1         1672           66.88       1693   \n",
       "52  Home  PASS     NaN       1         2414           96.56       2440   \n",
       "53  Home  PASS     NaN       1         2465           98.60       2493   \n",
       "\n",
       "    End Time [s]      From        To  Start X  Start Y  End X  End Y  \n",
       "28         59.56   Player1  Player11    46.64    21.08  53.00  10.20  \n",
       "29         63.12  Player11   Player9    53.00    10.20   9.54  24.48  \n",
       "36         67.72   Player6   Player7    19.08    14.96  19.08   8.84  \n",
       "52         97.60   Player3   Player8    16.96   -24.48   1.06 -29.92  \n",
       "53         99.72   Player8   Player4     2.12   -29.92  10.60 -31.96  "
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_passes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>12202</td>\n",
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       "      <td>-3.40</td>\n",
       "      <td>-54.06</td>\n",
       "      <td>-2.04</td>\n",
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       "      <td>Home</td>\n",
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       "      <td>ON TARGET-SAVED</td>\n",
       "      <td>1</td>\n",
       "      <td>16484</td>\n",
       "      <td>659.36</td>\n",
       "      <td>16499</td>\n",
       "      <td>659.96</td>\n",
       "      <td>Player10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-41.34</td>\n",
       "      <td>11.56</td>\n",
       "      <td>-48.76</td>\n",
       "      <td>0.68</td>\n",
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       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>HEAD-OFF TARGET-OUT</td>\n",
       "      <td>1</td>\n",
       "      <td>27345</td>\n",
       "      <td>1093.80</td>\n",
       "      <td>27360</td>\n",
       "      <td>1094.40</td>\n",
       "      <td>Player3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-43.46</td>\n",
       "      <td>-6.12</td>\n",
       "      <td>-54.06</td>\n",
       "      <td>-6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ON TARGET-SAVED</td>\n",
       "      <td>1</td>\n",
       "      <td>29754</td>\n",
       "      <td>1190.16</td>\n",
       "      <td>29777</td>\n",
       "      <td>1191.08</td>\n",
       "      <td>Player4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-31.80</td>\n",
       "      <td>-13.60</td>\n",
       "      <td>-53.00</td>\n",
       "      <td>-2.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Team  Type              Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "83   Home  SHOT       OFF TARGET-OUT       1         4419          176.76   \n",
       "198  Home  SHOT       ON TARGET-GOAL       1        12202          488.08   \n",
       "252  Home  SHOT      ON TARGET-SAVED       1        16484          659.36   \n",
       "431  Home  SHOT  HEAD-OFF TARGET-OUT       1        27345         1093.80   \n",
       "472  Home  SHOT      ON TARGET-SAVED       1        29754         1190.16   \n",
       "\n",
       "     End Frame  End Time [s]      From   To  Start X  Start Y  End X  End Y  \n",
       "83        4443        177.72   Player5  NaN   -39.22    18.36 -55.12   2.72  \n",
       "198      12212        488.48  Player10  NaN   -47.70    -3.40 -54.06  -2.04  \n",
       "252      16499        659.96  Player10  NaN   -41.34    11.56 -48.76   0.68  \n",
       "431      27360       1094.40   Player3  NaN   -43.46    -6.12 -54.06  -6.12  \n",
       "472      29777       1191.08   Player4  NaN   -31.80   -13.60 -53.00  -2.72  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_shots.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Type</th>\n",
       "      <th>Subtype</th>\n",
       "      <th>Period</th>\n",
       "      <th>Start Frame</th>\n",
       "      <th>Start Time [s]</th>\n",
       "      <th>End Frame</th>\n",
       "      <th>End Time [s]</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>Start X</th>\n",
       "      <th>Start Y</th>\n",
       "      <th>End X</th>\n",
       "      <th>End Y</th>\n",
       "      <th>Minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ON TARGET-GOAL</td>\n",
       "      <td>1</td>\n",
       "      <td>12202</td>\n",
       "      <td>488.08</td>\n",
       "      <td>12212</td>\n",
       "      <td>488.48</td>\n",
       "      <td>Player10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-47.70</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>-54.06</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>8.134667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>HEAD-ON TARGET-GOAL</td>\n",
       "      <td>2</td>\n",
       "      <td>73983</td>\n",
       "      <td>2959.32</td>\n",
       "      <td>73995</td>\n",
       "      <td>2959.80</td>\n",
       "      <td>Player9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.70</td>\n",
       "      <td>-1.36</td>\n",
       "      <td>54.06</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>49.322000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1723</th>\n",
       "      <td>Home</td>\n",
       "      <td>SHOT</td>\n",
       "      <td>ON TARGET-GOAL</td>\n",
       "      <td>2</td>\n",
       "      <td>121027</td>\n",
       "      <td>4841.08</td>\n",
       "      <td>121055</td>\n",
       "      <td>4842.20</td>\n",
       "      <td>Player12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.74</td>\n",
       "      <td>-5.44</td>\n",
       "      <td>54.06</td>\n",
       "      <td>-2.72</td>\n",
       "      <td>80.684667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Type              Subtype  Period  Start Frame  Start Time [s]  \\\n",
       "198   Home  SHOT       ON TARGET-GOAL       1        12202          488.08   \n",
       "1118  Home  SHOT  HEAD-ON TARGET-GOAL       2        73983         2959.32   \n",
       "1723  Home  SHOT       ON TARGET-GOAL       2       121027         4841.08   \n",
       "\n",
       "      End Frame  End Time [s]      From   To  Start X  Start Y  End X  End Y  \\\n",
       "198       12212        488.48  Player10  NaN   -47.70    -3.40 -54.06  -2.04   \n",
       "1118      73995       2959.80   Player9  NaN    47.70    -1.36  54.06  -2.04   \n",
       "1723     121055       4842.20  Player12  NaN    30.74    -5.44  54.06  -2.72   \n",
       "\n",
       "         Minute  \n",
       "198    8.134667  \n",
       "1118  49.322000  \n",
       "1723  80.684667  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_goals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Load Tracking Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading team: home\n",
      "Reading team: away\n"
     ]
    }
   ],
   "source": [
    "# READING IN TRACKING DATA\n",
    "tracking_home = mio.tracking_data(DATADIR,game_id,'Home')\n",
    "tracking_away = mio.tracking_data(DATADIR,game_id,'Away')\n",
    "\n",
    "# Convert positions from metrica units to meters \n",
    "tracking_home = mio.to_metric_coordinates(tracking_home)\n",
    "tracking_away = mio.to_metric_coordinates(tracking_away)\n",
    "\n",
    "# reverse direction of play in the second half so that home team is always attacking from right->left\n",
    "tracking_home,tracking_away,events = mio.to_single_playing_direction(tracking_home,tracking_away,events)\n",
    "\n",
    "# Calculate and add player velocities to tracking data\n",
    "tracking_home = mvel.calc_player_velocities(tracking_home,smoothing=True,filter_='moving_average')\n",
    "tracking_away = mvel.calc_player_velocities(tracking_away,smoothing=True,filter_='moving_average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 View tracking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Period</th>\n",
       "      <th>Time [s]</th>\n",
       "      <th>Home_11_x</th>\n",
       "      <th>Home_11_y</th>\n",
       "      <th>Home_1_x</th>\n",
       "      <th>Home_1_y</th>\n",
       "      <th>Home_2_x</th>\n",
       "      <th>Home_2_y</th>\n",
       "      <th>Home_3_x</th>\n",
       "      <th>Home_3_y</th>\n",
       "      <th>...</th>\n",
       "      <th>Home_6_speed</th>\n",
       "      <th>Home_7_vx</th>\n",
       "      <th>Home_7_vy</th>\n",
       "      <th>Home_7_speed</th>\n",
       "      <th>Home_8_vx</th>\n",
       "      <th>Home_8_vy</th>\n",
       "      <th>Home_8_speed</th>\n",
       "      <th>Home_9_vx</th>\n",
       "      <th>Home_9_vy</th>\n",
       "      <th>Home_9_speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frame</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>-0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>-3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.08</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>-0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>-3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>-0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>-3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.16</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>-0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>-3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>0.20</td>\n",
       "      <td>46.9315</td>\n",
       "      <td>-0.28084</td>\n",
       "      <td>15.70072</td>\n",
       "      <td>14.5486</td>\n",
       "      <td>18.81712</td>\n",
       "      <td>4.89396</td>\n",
       "      <td>21.07492</td>\n",
       "      <td>-3.81208</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Period  Time [s]  Home_11_x  Home_11_y  Home_1_x  Home_1_y  Home_2_x  \\\n",
       "Frame                                                                         \n",
       "1           1      0.04    46.9315   -0.28084  15.70072   14.5486  18.81712   \n",
       "2           1      0.08    46.9315   -0.28084  15.70072   14.5486  18.81712   \n",
       "3           1      0.12    46.9315   -0.28084  15.70072   14.5486  18.81712   \n",
       "4           1      0.16    46.9315   -0.28084  15.70072   14.5486  18.81712   \n",
       "5           1      0.20    46.9315   -0.28084  15.70072   14.5486  18.81712   \n",
       "\n",
       "       Home_2_y  Home_3_x  Home_3_y  ...  Home_6_speed  Home_7_vx  Home_7_vy  \\\n",
       "Frame                                ...                                       \n",
       "1       4.89396  21.07492  -3.81208  ...           NaN        NaN        NaN   \n",
       "2       4.89396  21.07492  -3.81208  ...           0.0        0.0        0.0   \n",
       "3       4.89396  21.07492  -3.81208  ...           0.0        0.0        0.0   \n",
       "4       4.89396  21.07492  -3.81208  ...           0.0        0.0        0.0   \n",
       "5       4.89396  21.07492  -3.81208  ...           0.0        0.0        0.0   \n",
       "\n",
       "       Home_7_speed  Home_8_vx  Home_8_vy  Home_8_speed  Home_9_vx  Home_9_vy  \\\n",
       "Frame                                                                           \n",
       "1               NaN        NaN        NaN           NaN        NaN        NaN   \n",
       "2               0.0        0.0        0.0           0.0        0.0        0.0   \n",
       "3               0.0        0.0        0.0           0.0        0.0        0.0   \n",
       "4               0.0        0.0        0.0           0.0        0.0        0.0   \n",
       "5               0.0        0.0        0.0           0.0        0.0        0.0   \n",
       "\n",
       "       Home_9_speed  \n",
       "Frame                \n",
       "1               NaN  \n",
       "2               0.0  \n",
       "3               0.0  \n",
       "4               0.0  \n",
       "5               0.0  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_home.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Period', 'Time [s]', 'Home_11_x', 'Home_11_y', 'Home_1_x', 'Home_1_y', 'Home_2_x', 'Home_2_y', 'Home_3_x', 'Home_3_y', 'Home_4_x', 'Home_4_y', 'Home_5_x', 'Home_5_y', 'Home_6_x', 'Home_6_y', 'Home_7_x', 'Home_7_y', 'Home_8_x', 'Home_8_y', 'Home_9_x', 'Home_9_y', 'Home_10_x', 'Home_10_y', 'Home_12_x', 'Home_12_y', 'Home_13_x', 'Home_13_y', 'Home_14_x', 'Home_14_y', 'ball_x', 'ball_y', 'Home_1_vx', 'Home_1_vy', 'Home_1_speed', 'Home_10_vx', 'Home_10_vy', 'Home_10_speed', 'Home_11_vx', 'Home_11_vy', 'Home_11_speed', 'Home_12_vx', 'Home_12_vy', 'Home_12_speed', 'Home_13_vx', 'Home_13_vy', 'Home_13_speed', 'Home_14_vx', 'Home_14_vy', 'Home_14_speed', 'Home_2_vx', 'Home_2_vy', 'Home_2_speed', 'Home_3_vx', 'Home_3_vy', 'Home_3_speed', 'Home_4_vx', 'Home_4_vy', 'Home_4_speed', 'Home_5_vx', 'Home_5_vy', 'Home_5_speed', 'Home_6_vx', 'Home_6_vy', 'Home_6_speed', 'Home_7_vx', 'Home_7_vy', 'Home_7_speed', 'Home_8_vx', 'Home_8_vy', 'Home_8_speed', 'Home_9_vx', 'Home_9_vy', 'Home_9_speed']\n"
     ]
    }
   ],
   "source": [
    "print (list(tracking_home.columns), sep=', ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Utility methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot positions at a given time frame\n",
    "def plot_at_pass_frame(frame, include_player_velocities=False):\n",
    "    fig,ax = mviz.plot_frame( tracking_home.loc[frame], tracking_away.loc[frame], include_player_velocities=include_player_velocities)\n",
    "    return fig, ax\n",
    "\n",
    "# Get the end frame of a PASS event\n",
    "def get_end_frame(df, start_frame):\n",
    "    return df.loc[df.index[df['Start Frame'] == start_frame][0]]['End Frame']\n",
    "\n",
    "# start_frame: Starting frame of pass\n",
    "def make_movie_of_pass(tracking_home, home_passes, tracking_away, start_frame, fname, Include_player_velocities=False):\n",
    "    end_frame = get_end_frame(home_passes, start_frame)\n",
    "    mviz.save_match_clip(tracking_home.iloc[start_frame:end_frame],tracking_away.iloc[start_frame:end_frame],'.',fname=fname, include_player_velocities=Include_player_velocities)\n",
    "\n",
    "# Method returns start and end coordinates of a pass\n",
    "# Input: df (dataframe containing passes), start frame of the pass event\n",
    "def get_pass_start_end_coordinates(df, start_frame):\n",
    "    end_frame = get_end_frame(df, start_frame)\n",
    "    row = df.loc[df.index[df['Start Frame'] == start_frame][0]]\n",
    "    return row['Start X'], row['Start Y'], row['End X'], row['End Y']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Calculate Pass Scenarios / Parameters\n",
    "\n",
    "We calculate different parameters of a pass using event and tracking data. Here we have tried to calculate the following:\n",
    "- Distance\n",
    "- Trajectory\n",
    "- Opponents beaten\n",
    "- Players pressing\n",
    "- Pass errors leading to shots/goals\n",
    "\n",
    "This is an inexhuastive list and more pass parameters can be added"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 Distance\n",
    "\n",
    "- Distance is the most basic parameter of a pass\n",
    "- Vision to identify and execute short and long decisive accurate passes can create transition from a low to high threat possession value\n",
    "- Defensive ball clearance can turn into goal scoring opportunities\n",
    "\n",
    "<br><br>\n",
    "Calculation\n",
    " - We are aware of the pass start and end coordinates\n",
    " - Use the distance formula to calculate the distance of the pass\n",
    " - Strictly speaking, displacement would be the more accurate term here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to calculate distance\n",
    "def calculate_distance(start_x, start_y, end_x, end_y):\n",
    "    return np.sqrt(((end_x-start_x)**2) + ((end_y-start_y)**2))\n",
    "\n",
    "# Calculate distance of a pass\n",
    "# Input: df (dataframe of passes), start frame of the pass\n",
    "def pass_distance(df, start_frame):\n",
    "    start_x, start_y, end_x, end_y = get_pass_start_end_coordinates(df, start_frame)\n",
    "    calculate_distance(start_x, start_y, end_x, end_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 Trajectory\n",
    "\n",
    "- Balls do not always travel in straight lines\n",
    "- Even though we do not have the data for the spin on a ball, we can get similar information using the ball’s trajectory\n",
    "- Beautiful curving and bending passes can cut through or go round the defence and create spaces for attackers\n",
    "- Missing z-axis data; 3D trajectory of aerial balls cannot be calculated\n",
    "\n",
    "<br><br>\n",
    "Calculation\n",
    " - Metrica tracking data provides the positional information of the ball at 24 fps.\n",
    " - Map the trajectory: plot the position of the ball at every frame\n",
    " - Join the points to get the trajectory of the ball and the actual distance covered by the ball\n",
    " - Calculate the curve (bend) of the pass using the ratio (Ball displacement / Actual distance of ball path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To calculate the bend/curve of a pass, this method calculates the ratio of displacement to actual distance covered by the ball\n",
    "# Input - team_passes: passes dataframe, index: index of pass of interest\n",
    "# Input - team_tracking: tracking dataframe, plot: option to plot the trajectory, makeMovie: option to generate movie of pass\n",
    "\n",
    "def calculate_displacement_to_distance_ratio(team_passes, index, team_tracking, plot=False, makeMovie=False):\n",
    "    # Let's retrieve the essential position and time values\n",
    "    x1 = team_passes.loc[index]['Start X']\n",
    "    y1 = team_passes.loc[index]['Start Y']\n",
    "    x2 = team_passes.loc[index]['End X']\n",
    "    y2 = team_passes.loc[index]['End Y']\n",
    "    start_frame = team_passes.loc[index]['Start Frame']\n",
    "    start_time = team_passes.loc[index]['Start Time [s]']\n",
    "    end_time = team_passes.loc[index]['End Time [s]']\n",
    "\n",
    "    # Calculate the ball displacement and curved distance\n",
    "    ball_displacement = calculate_distance(x1, y1, x2, y2)\n",
    "    curved_distance = calculate_curved_distance(team_tracking, start_time, end_time)\n",
    "\n",
    "    # Ratio of ball displacement to actual distance covered by ball (curved distance)\n",
    "    ratio = ball_displacement/curved_distance\n",
    "\n",
    "    if (ratio < 0.96 and ball_displacement >= 12):\n",
    "        if plot:\n",
    "            fig,ax = plot_at_pass_frame(start_frame)\n",
    "\n",
    "        time_window = np.arange(start_time, end_time, 0.04)\n",
    "\n",
    "        for time in time_window:\n",
    "            ballx = team_tracking.loc[team_tracking.index[team_tracking['Time [s]'] == time]]['ball_x']\n",
    "            bally = team_tracking.loc[team_tracking.index[team_tracking['Time [s]'] == time]]['ball_y']\n",
    "\n",
    "            if plot:\n",
    "                ax.plot(ballx, bally, 'ro', markersize=5)\n",
    "        \n",
    "        if makeMovie:\n",
    "            filename = \"pass \" + str(index) + \" \" + str(round(ratio, 2)) + \" \" + str(round(curved_distance,2))\n",
    "            make_movie_of_pass(tracking_home, team_passes, tracking_away, team_passes.loc[index]['Start Frame'], filename)\n",
    "\n",
    "    return ratio, ball_displacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some times the ball position may be missing at the first and last frame of the time_window\n",
    "# Method to calculate the first and last index when ball is visible during the time_window\n",
    "# Input- time_window: Range of time of interest, team_tracking: Tracking data of team in possession\n",
    "\n",
    "def find_ball_visibility_indexes(time_window, team_tracking):\n",
    "    ball_visiblity_start_index = None\n",
    "    ball_visiblity_end_index = None\n",
    "\n",
    "    for time in time_window:\n",
    "        time = round(time, 2)\n",
    "        x = team_tracking.loc[team_tracking.index[team_tracking['Time [s]'] == time]]['ball_x']\n",
    "        y = team_tracking.loc[team_tracking.index[team_tracking['Time [s]'] == time]]['ball_y']\n",
    "        \n",
    "\n",
    "        if x.size > 0 and not(x.isnull().values.any()):\n",
    "            if y.size > 0 and not(y.isnull().values.any()):\n",
    "                if ball_visiblity_start_index is None:\n",
    "                    ball_visiblity_start_index = team_tracking.index[team_tracking['Time [s]'] == time][0]\n",
    "                ball_visiblity_end_index = team_tracking.index[team_tracking['Time [s]'] == time][0]\n",
    "    \n",
    "    return ball_visiblity_start_index, ball_visiblity_end_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to plot every position of the ball and calculate distance by joining these points\n",
    "# Input: team_tracking: Tracking data of team in possession, start_time, end_time: start and end time of pass\n",
    "\n",
    "def calculate_curved_distance(team_tracking, start_time, end_time):\n",
    "    time_window = np.arange(start_time, end_time + 0.04, 0.04)\n",
    "    \n",
    "    # Remove NA ball position values\n",
    "    team_tracking = team_tracking[team_tracking['ball_x'].notna()]\n",
    "    team_tracking = team_tracking[team_tracking['ball_y'].notna()]\n",
    "\n",
    "    # Find next start (from start_time) non-null ball_x\n",
    "    ball_visiblity_start_index, ball_visiblity_end_index = find_ball_visibility_indexes(time_window, team_tracking)\n",
    "\n",
    "    dist = 0\n",
    "    # Loop through every ball positions and calculate distance travelled by ball\n",
    "    for index in range(ball_visiblity_start_index + 1, ball_visiblity_end_index + 1):\n",
    "        ballx1 = team_tracking.loc[index]['ball_x']\n",
    "        bally1 = team_tracking.loc[index]['ball_y']\n",
    "\n",
    "        ballx2 = team_tracking.loc[index - 1]['ball_x']\n",
    "        bally2 = team_tracking.loc[index - 1]['ball_y']\n",
    "\n",
    "        ballx1 = round(ballx1, 2)\n",
    "        bally1 = round(bally1, 2)\n",
    "\n",
    "        ballx2 = round(ballx2, 2)\n",
    "        bally2 = round(bally2, 2)\n",
    "\n",
    "        dist = dist + calculate_distance(ballx1, bally1, ballx2, bally2)\n",
    "\n",
    "    return round(dist, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ratio = []\n",
    "\n",
    "# Iterate through all passes of home team to calculate the curve ratio \n",
    "for index, home_pass in home_passes.iterrows():\n",
    "    ratio, displacement = calculate_displacement_to_distance_ratio(home_passes, index, tracking_home)\n",
    "    all_ratio.append((index, ratio, displacement))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1451, 0.9391049755421733, 14.997506459408509),\n",
       " (1722, 0.9568054328892973, 13.83444975414635),\n",
       " (780, 0.9568559735220069, 12.74914899120722),\n",
       " (307, 0.9572291169115631, 12.102247725112893),\n",
       " (784, 0.9611056056023161, 12.285812956414405),\n",
       " (1749, 0.9631713271080687, 12.102247725112884),\n",
       " (1916, 0.9633728490717367, 12.594173255914814),\n",
       " (1443, 0.9636260175261416, 16.60809441206305),\n",
       " (848, 0.9639775815605591, 15.087213129004311),\n",
       " (1416, 0.9655949952426802, 24.502938599278256)]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's view the least ratios of passes which are atleast 12m long\n",
    "sorted(all_ratio, key = lambda x: x[1] if (x[2] > 12) else float('inf'))[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating movie...done\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.9568054328892973, 13.83444975414635)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise and generate movie of pass indexed 1722\n",
    "calculate_displacement_to_distance_ratio(home_passes, 1722, tracking_home, plot=True, makeMovie=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Few trajectory plots were not as desired. This can probably be due to\n",
    "# missing z-axis data (aerial balls) or inconsistency and NA values in ball tracking data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.3 Opponents Beaten\n",
    "\n",
    " Finding gaps and spaces between opponent’s is key to switch plays, penetrate defences and counter attacks. Creative passes which find spaces between opponents are risky to execute but can create a great attacking opportunity\n",
    "\n",
    "\n",
    " <br><br>\n",
    " Calculation\n",
    "- Draw the line of pass from passer to the receiver\n",
    "- Drop a normal to this line from the position of opponents\n",
    "- Calculate distance of opponents from this line\n",
    "- Alternatively, calculate estimated time taken by opponents to block line of pass\n",
    "- Use a normal distance / time to reach threshold to filter opponents nearby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to find point of intersection (x4, y4) from point (x3, y3) to the line joining (x1, y1) and (x2, y2)\n",
    "\n",
    "def get_point_of_intersection_of_normal(x1, y1, x2, y2, x3, y3):\n",
    "    k = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)**2 + (x2-x1)**2)\n",
    "\n",
    "    # x4, y4 is the point of intersection\n",
    "    x4 = x3 - k * (y2-y1)\n",
    "    y4 = y3 + k * (x2-x1)\n",
    "\n",
    "    return x4, y4\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to iterate through every opponent and determine how many opponents are likely to intercept the pass\n",
    "# Input- team_passes: passes dataframe of team in possession, tracking_opponent: tracking dataframe of opponent team (not in possession)\n",
    "# Input- start_frame: starting frame of pass, distance_threshold: distance in meteres (how far an average player is likely to intercept the pass), plot: Option to plot\n",
    "# TODO: Introduce time to reach / intercept in place of distance\n",
    "\n",
    "def find_intercepting_players(team_passes, tracking_opponent, start_frame, distance_threshold = 3, plot=False):\n",
    "    count = 0\n",
    "    \n",
    "    x1, y1, x2, y2 = get_pass_start_end_coordinates(team_passes, start_frame)\n",
    "\n",
    "    # X and Y position of column names in tracking data\n",
    "    x_columns = [c for c in tracking_opponent.keys() if c[-2:].lower()=='_x' and c!='ball_x'] # column header for player x positions\n",
    "    y_columns = [c for c in tracking_opponent.keys() if c[-2:].lower()=='_y' and c!='ball_y'] # column header for player y positions\n",
    "\n",
    "    if plot:\n",
    "        fig, ax = plot_at_pass_frame(start_frame)\n",
    "        ax.axline((x1, y1), (x2, y2), color='blue')\n",
    "\n",
    "    # Loop every opponent player\n",
    "    for i in range(0, 12):\n",
    "        distance = None\n",
    "\n",
    "        x3 = tracking_opponent.loc[start_frame][x_columns[i]]\n",
    "        y3 = tracking_opponent.loc[start_frame][y_columns[i]]\n",
    "        \n",
    "        x4, y4 = get_point_of_intersection_of_normal(x1, y1, x2, y2, x3, y3)\n",
    "        \n",
    "        # If point of intersection lies on pass line segment, calculate distance of player along normal\n",
    "        # (Case when nearby players may intercept the pass) -> Else calculate direct distance of players\n",
    "        if min(x1, x2) <= x4 <= max(x1, x2) and min(y1, y2) <= y4 <= max(y1, y2):\n",
    "            distance = calculate_distance(x4, y4, x3, y3)\n",
    "        else:\n",
    "            distance = calculate_distance(x3, y3, x1, y1)\n",
    "\n",
    "        if distance <= distance_threshold:\n",
    "            count = count + 1\n",
    "\n",
    "            if plot:\n",
    "                ax.axline((x3, y3), (x4, y4), color='red')\n",
    "\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "opponents_beaten = []\n",
    "opponents_beaten_index = []\n",
    "\n",
    "for index, passes in home_passes.iterrows():\n",
    "    \n",
    "    start_frame = home_passes.loc[index]['Start Frame']\n",
    "    count = find_intercepting_players(home_passes, tracking_away, start_frame, distance_threshold = 3)\n",
    "    \n",
    "    opponents_beaten.append(count)\n",
    "    opponents_beaten_index.append(index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 999),\n",
       " (2, 1197),\n",
       " (2, 1376),\n",
       " (2, 1564),\n",
       " (3, 71),\n",
       " (3, 197),\n",
       " (3, 737),\n",
       " (5, 193),\n",
       " (6, 430)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id = zip (opponents_beaten, opponents_beaten_index)\n",
    "\n",
    "# View the passes that beat the most opponents\n",
    "sorted(id, key=lambda x: x[0])[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.3.1 Plot and generate movie of pass indexed 430"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating movie...done\n"
     ]
    },
    {
     "data": {
      "image/png": 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UBt+/t5y50xYyYMshVCXSj/RhlZUoSV3ZjXpexzB1v6jrcIQQQghRDTzykIJ8YwOWBHgzeuZE3n9tENdNDHjl962ET5xFyJ+7saoj9aBXLYxYENyRkXMm8fkL/dAr0/L2DxtZGjKbx5fuw+x6oa5DrHEytrajONtCVl2FEEIIcUuF9XnVKPXY2rUpW7t40PrwRUaviOOJpfsYszyWTT2aERbgzek6UA9aolaxtk9L1vZuQYf4swSviOWpBbsZF7aXtX1bsWRoW1Id6uk6zGpPq1FwcUVPTBpfwKLNCV2HI4QQQohqouKHFCgUJLV0JqmlM86pVxgZGc+gzYfw33yIvd6u6Gl7UtarV4WfttpRKNjn3ZB93g1xO32ZUZFxDF2fROCaBHZ2cicswJvk5o5SF3sXWXtbUXDRlqavLZBvkRBCCCFueeSygXu54GjJ98/0ImhuCDMe98P99GUMhw7FsGNHBm4+hLqktDJPX22cbFSfz1/sz8jZk1g4ogNtk8/z2+tL+OPVUHruPIZSU6brEKsVrbZ8FKyhfSY2fkm6DkcIIYQQ1UilJq83XTM34s/gjoycPYmiP/4ArZa3ftzI0kmzeWLJPsyvFVRFGDqXZWXKzHGdCZo7me+e7onFtQI++moti6bMZWRkHEb5xboOsVq4dtCN3OOuOAVGoVBKYi+EEEKI/1clyetNJWoVmieeoHDfPl7+cDgnG9Znyp+7WTZxFi/9vhWn1JyqDEdnCg3VrPD3YuzvT/LWW0O4bGPKtFnbCZ84i2fm7qB+5nVdh6hTF8J7oba4jm3v/boORQghhBDVTMXXvN4PhYL93q7s93al0dlMgiPiGLzxIAHrEtnV0Y3QAB+SW9T+etAypR47fd3Z6etO8+PpBK+IJTgijlGR8Wzt4kFYgDfH3e10HWaVyjvtwJXY5jR4fC1Kg7rRbk0IIYQQ9083yettTrva8MUL/ZgxrjPDVycQsC6JbntOctjDnrAAb7b7NUGjrNIFYp044mHPB6/7Y3/p6q3hD/22HyXO05mwAB9i2jWq/cMfgAvLe6FnWITDoGhdhyKEEEKIaqjaZIXZlibMeqIzI+aE8O3TvTC7XsiHX61l8ZS5jIqIwzi/SNchVol0Owt+CenO8LmT+XVCV5zScvjy40j+nDqfoeuS0C+qvZvcCi9ZcnmHF/b9Y1Cb5es6HCGEEEJUQzpfef2nQkM1Ef5tWDnAE7/9pwmOiOX52duZsDiGVf09WTakLRn1zXQdZqXLMzEgdHg7lg5tS89dJxgdEcurv21h8sJoVgxqzXJ/L3LqGes6zIdinZ1Lt5gUukWnYJpXRK6JATv83Pn15NsoFOA0bIeuQxRCCCFENVXtktebypR67Orkxq5ObjQ7ns7oiFhGRsYxMjKObV08CA30qRP1oBqVks09mrG5e1O8Dl4kOCKWCaF7eSz8AJt6NCcswJszDax1HeZ980i5xLQZUahLNeQa65NlaYy6REOnjWm8fdoXt3a7Maifo+swhRBCCFFNVdvk9XZHPez54DV/7DKuMWJVPEM2HKTvjmPEt3ImNNCbmHaNa389qEJBgqczCZ7OuFzIZtTKeAZuOcTgTQeJ8WlIWKAPsa1dqvUmN+vsXKbNiEKjVLC8UMP0I6lkFGuw1VfSzPhTCrXGfJH1Pn9mNyHLylTX4QohhBCiGqo2Na/345KtOb9O6k7Q3BB+mdgNx/SrfPnxShZOnc+wdYkYFNaN3ennna34dmpvguZMZtZYX5qezOCHd8KZ/eJf9N96GFWJRtch3lG3mBTUpRoi84r48mwWl4o1aIFLxQZsz5lEE6MNtFAeoWtMiq5DFUIIIUQ1VaOS15vyTAwIC/QheOYEPnh1EHlG+vzvt60smziLSQujsbySp+sQq8RVCyPmj+7EyNmT+OL5vqhLNLzz/QaWhMxm7NJ9mOYW6jrEv+kWnUKusT7TL16hqEx721cmAjZklnzGdWN9ukvyKoQQQoi7qBFlA3ejUSnZ0q0pW7p60OZQeT3ouCV7GbP8Rj3osLaccbXRdZiVrlhfxZp+rVjTtyUdY88QHBnH0wt2M27JPtb2acmSYW1Js6+n6zAxzSsiy9KYjOLbV4aVwCvAbnJKd1CibohVjnQaEEIIIcSd1ejk9RaFgsRWziS2csbl4hVGRcYxYMthBm86yB7vhoQGehPbpkG1rgetEAoFe9s1Ym+7RridvkxwRCzD1icRuDaRnR3dCAv04WBzR52Fl2tigLqkvMb10q0EdhTQEJiGrb4SdYmGPBMDncUohBBCiOqtRpYN3Mt5J0u+ndqbEXNCmPm4H01OZfDDu8uZO20hA7Ycqrb1oBXtZKP6fPbSAEbOmsSioHZ4J5/n99fC+P1/ofTYfRw9TVmVx7TDzx3T/GKecrLE4NYGu9eAw+gr1vCUkyVm+cVs93Wv8tiEEEIIUTPUuuT1pqsWRiwI7sio2ZP4fFpf9Mq0vP3DRpaGzObxpfswu1696kErS5a1KTPGdWHEnBC+f6onllfz+fiLNSx+ai5BK+Mxyi+uslh2+LpTolIyzMSA112tqacaBHhhrvyWNxpaMczEgGKVkp2SvAohhBDiLmpt8npTsb6KtX1bMf6XJ3jlw0BONbDmqQW7CZ8wkxenb8MxLUfXIVaJAiN9lg/24rE/nuTtNweTZWXCizOjCJ84i6fn7aR+5vVKjyHLypSfpvRAqdEyWqWkscHb1FensbZFFKNVSpQaLT9N6SFtsoQQQghxV7Wj5vV+KBTs827IPu+GN+pB4xi6PonANQns6OTOkgBvkps71vq62DKlHjv8mrDDrwktjqYRHBHL6BWxBEfEsaVr+fCHlMa2lXb+4+52vP+GP64RKuJO+PGq9fsY6JWwtncLdvq6S+IqhBBCiHuqO8nrbcrrQfszfVxnhq9JJGBdIj1iUjjU1J7QAB92+rqjUdb6RWkON3Pg/TcG45B+lRGr4hm86SD9o44S29qFsABv9vg0qpThD1lWpuy6NB6lST47fysmxnhEhZ9DCCGEELVT7c/Q7iHL2pSZ4zoTNHcy3z3dE4trBXz85RoWTZnLyMi4Kq0H1aU0ewt+ntyDoDkh/DahKy4Xr/DVR5EseHYBQzYko19UWqHnK7hYn6wYTxwG7UZlXFShxxZCCCFE7VYnV17/qdBQzQp/LyIHtKbzvlMER8QybdZ2Jizew6r+rVg2pC2Xbcx0HWaFs87OpVtMCt2iUzDNKyLXxIAdfu4881UwbQ5fZPSKOF77ZTOT/9zNikFtWDGoDTn1jG/dvzjbjMyY1mRFe1KaZ4TKpABrv2RsfJPQt7p7De2FFT1QqDQ4DtlZFQ9TCCGEELWIJK+3KVPqsdPXnZ2+7jQ/nk7wjVrQUZHxbO3qQWiADyfcKq8etCp5pFxi2owo1KUaco31ybI0Rl2iYcCWw/TefoyfpvRg0g+P4XXwAqNXxDJx8R7GLtvPhl7NCRvmw+Gi1pycEYC2VInSuBC15TW0JSoytrTj8va2uE2JwNT9wr/OW5xtRsaW9tj12Ye+Za4OHrkQQgghajKdJa8ben2iq1Pfn17A01B89iyqX3+l7/z59Is6iqZbN0qmTaOsf3/Qq6FVF2lpGHz2BNi4safw/xPMEn0VV/RVGBUUM21GFO+/4U+CpwsJni40OJ9dPvxh22GGbjjIetOe/GJTn2ibtrc2uSn0S9HTz0VToM/JGQE0f2P+v1ZgU1d2Q1umh1NgVFU+YiGEELVYtc8pRIWqodlX1dG6ulLy1VcUHDtG8aefojh5EsMRIzBs1w7lnDlQUKDrEB+YcuVKKC4G0zvv7C8w0ke/VEPXmJRb/3fOxYpvnuvDiDkh/NBuFD4FSaw+M45th0cxMnMVqrKS/z++UTFlpUoyYzz/dtzSfAPS1vlh45uEkWNm5Tw4IYQQQtRqOlt57b/1HV2d+uG1BuUvQfTcdYLREbE0ff55ct5+jRWDWrPc3+tv9aDV2ffzltHW+t6DAK4b69M9JoUIf6+//X+OhTEfFL/MFy3eIvh6BFPTFzD99Ju8f+EHptuNZX79EVxTmaMyLiQrpjWO/tG37pu+3g9NvhHOI7ZWxsMSQghRx9XI3KIWqaoVcFl5fUAalZLNPZoR8v1jPP/ZCA42c2BC6F6WTZzFaz9vwvV8lq5D/E+meUVgYHDP25SolZjk3bkTQGmeEUUGKv6sPwK/VhGMavIbJwwb8uGF7ziY2JvPz36Oa9kZNHmGt+5TVqIkNbIbFm2O37EWVgghhBDifsiGrYelUNyqB3W5UF4POnDrYYZsPEiMT0PCAn2Ibe1SLYce5JoYQFERGBre9TbqEg15JndOcFUmBWhLVCj0S9Eq9Nhcrxub63WjVf5RpqbPZ+LlMEIyFrPKoi/rjlpyuJkDGdvaUZxtQZMXFlfWwxJCCCFEHSArrxXgvLMV3z5bXg86a6wvTU9m8MM74cx+8S/6bz2MqkSj6xD/ZoefO1y7ds/bmOUXs933zqUF1n7JaPL/nfgeNG7G1Maf49V6Az9YTqFnwS6mvxrKr6+G0e6vPMwanaVe2+MV8hiEEEIIUTdJ8lqBciyMmT+6EyNnT+KL5/uiLtHwzvcbWBIym8eW7cc0t1DXIQKww9cd9PUh986tqowKiilWKdl5l+TVxjcJhUqDpkD/jl+/oHHhQ/tXGfbz8/wwuQeW6cUszp7Ewez2jFgdj1FB3Rj+IIQQQoiKJ8lrJSjWV7GmXyvG/zKO/70fwFkXK56Zv4vwCbN4Yfo2HNJzdBpflpUpxV9/DaWlWOXkoy4uBa0WdXH5v5UaLT9N6UGW1Z27EehbXcdtSgRajZLiHFPKilVotVBWrKI4xxStRonblAg0jkUsG9IWT9u9jLaYy1V7FS/OiCJ8wiyemr8Lmyzp8yqEEEKIByM1r5VIq6dgb7tG7G3XCPdTGYyKjGPY+iQC1yays5MboYE+HGrmqJvY2ral6M8/WfvNZLrHpGCVk0+eiQFre7dgp6/7XRPXm0zdL9D8jflkxniSFdOa0hwzlCaF2PXej41v8q3+rtcONebq8cYceDqOqf6jaXk0ldErYhmz/ADBEbFs6dqU0EAfTjaqXxUPWwghhBA1nCSvVSSlsS2fvTSAGeO6ELQ6gWHrkugRncLBpg6EBXqzo5M7ZcoqXgh3cCDC3+tf7bDul77VdRz9o//WDuufLoT3Qm1xHds++wE41MyRd990xCE9h5Er4/HfdIgB245woI0LYQE+7PVuiFav+m1yE0IIIUT1IGUDVSzT2pTp47sQNDeE75/qieXVfD7+Yg2Ln5pL0Mp4jPJrTz1o3hkHrhxogcPgXSgNSv72tTT7evw0pSdBc0P4fXwXXM9n8/WHESx4bgGDNySjX1yqo6iFEEIIUZ1J8qojBUb6LB/sxWN/PMnbbw4my8qEF2dGET5xFk/P21kr6kEvhPdEz7AIB//dd71Nrqkhi0a0Z9SsSXzyUn9KVEpe/2UzSyfN5snFe6h3Nb8KIxZCCCFEdSdlAzpWptRjh18Tdvg1ocXRNEZHxDJ6RSzBEXFs6epBaKAPKY1tdR3mAyvMsOTyjrY4Dt6F2uy/E9BStZINvVqwoWdzvJPOMzoijkmLYnh82T7W9WrBkmHenHe2qoLIhRBCCFGdSfJajRxu5sB7bwzGIf0qI1bFM3jjQfpHHSW2tQuhgTWrHjQ1sjsKBTgFbH+wOyoUxLVpQFybBjQ8l1U+/GHLYQLWJ7O7fSNCA31IaOVcLYc/CCGEEKLySdlANZRmb8HPk3uU14M+2QWXi1du1YMOqQH1oCXXjEnf0JH63eMwqJ/z0Mc508Car57vy4g5IcwZ04mWx9L5+a1lzHppEX2jjqAsrV7DH4QQQghR+SR5rcZyTQ1ZFNSe4JkT+eiVARSrVbz2y2aWTZzFk4tjqJdTPetB09Z0oazIAKfh2yrkeDn1jJn7mC9Bc0L46tneGBaW8N636wmbPIcxyw9Um+EPQgghhKh8UjZQA5SqlWzq0ZxN3ZvRNvkCo1fEMmnRHh5fup8NvZoTNsyHcy7Vox5UU6gmdXUXLNsdwsQ1vUKPXWygYtWA1qzu50mn2NMER8Qxde5Ongzdw+q+rVg6tC3pdhYVek4hhBBCVC+SvNYkCgXxrV2Ib+1Cg/PZjIqMY8C2wwzdcJDoduX1oPGeuq0HvbS5I6XXTHEesbXSzqHVUxDTvjEx7RvjkXKJ4Mg4hq9JJGh1AlF+TVgS4M3hpg6Vdn4hhBBC6I4krzXUORcrvnmuD7Oe8CNgbSLD1yTy09vLON7YltBAb7Z28UCjUlZpTFqNHhdXdMes2WnMW5yuknMed7fj41cG8sf4LoxYFc+QDQfpves4Sc0dCQ30YXeHxlU//EEIIYQQlUbe1Wu4HAtj5o3xZcTsEL56rg/6xaW89+16loTM4bHw/VVaD5q5qw1FGdY4B22t8sXfyzZm/D6hG0FzQvhxcndssnL57LNV/PXMPIavTsCwsOS/DyKEEEKIak9WXmuJYgMVq/p7srpvKzrGnWH0iliembeL8WF7Wd23FcuGtCXN/v/rQdPSYOVKJcnznqY0zwiVSQHWfsnY+Cahb3X9gc+v1ZaPgjVyvoRVh8MV+dAeSIGxPsuGerPC34uuMSmMjojlpenbmPRXNJEDWxPu70WWtanO4hNCCCHEo5HktZbR6inY064Re9o1osnJDIIjYm/Vg+7wdSc0wId9qvY88ZkBxcWAQova8hraEhUZW9pxeXtb3KZEYOp+4YHOmxPflLzTTrhPC0Whp62cB/cANEo9orp4ENXFg1ZHUgleEcvYZfsZvSKWzd2aERbgzclG9XUdphBCCCEekCSvtdgJN1s+eWUgfzzZlaBV8Qxbn0zP3SfYY3Sc1Q0bkOA6lFNZ5T1jFfql6OnnoinQ5+SMAJq/Mf+BVmAvhPdC3+oqtj1iK+vhPLSDzR052NwRx7QcRq6MZ9DmQwzceph9Xg0IC/Bhn7erDD0QQgghagipea0DMq1Nmf5kV4LmhvBR5wnYlVzmkyMjmbGjBSGXFmGi+f9+sUqjYspKlWTGeN738a8fd+FqUhMch21HT119BwekOtTjx6d6EjQ3hD/GdabxuSy+/WAF85/7k0GbDqIuqd7DH4QQQgghyWudUmCkz3fX38CnxXo+9Qrjqr4NX537jKTEPrx7/nvsizMAUBkXkhXT+r6Pe2F5T5QmBdgPiKms0CtUrqkhf43swMhZk/jkpf6U6Sl486dNLJ04m/Ghe7C4WqDrEIUQQghxF5K81jGleUZo9bXsdgjiFd/d9G++kJ3mHZmWPpeEpH78euotPEsOoskzvK/jFaTakBXdGodBu1EZF1Vy9BWrVK1kQ68WTPjpcV78eDjH3WwJ+SuGZRNn8cpvW3C5eEXXIQohhBDiH6TmtY5RmRSgLfn/p32/qRdPunvhWniepy/9ydjMFYzJWkmUqS+rD9ix16fhPetBLy7viUKlwXHIziqIvpIoFMR6uRLr5UrDs5kER8YzaNMhhq5PIrp9Y8ICfEho5SR1sUIIIUQ1ICuvdYy1XzKa/H+vqp41dOFN17fwbLOZd61fp1nZUb75MIIFzy5g8IZk9Iv/XQ9afMWMS1vbYdfrAPqWD95eqzo642rDl9P6MmLOJOYHd6TV0TR+fmspM19eRO/tR1GWVt+aXiGEEKIukOS1jrHxTUKh0pCbe+evZ5fU50fbKQT9PJVPXupPqUrJ679sZumk2Ty5+O/1oKkru6EtVeI0fFsVRV91rliaMGesH0FzQvh6am+MC0r44Jt1hE2eS/CKWEzyalaJhBBCCFFbSNlAHaNvdR23KRGULnmBS5egTKNCoS5FW6KiNN8QPZUGtykR6Nnms6FXCzb0bI530nlGR8QxaVEMjy/bx7peLVjcvxMx6/yw9kvCyDFT1w+r0hQbqFg5sDWr+nvie+AUo1fE8dycHUxYvIfV/VqxdGhbLtma6zpMIYQQos6Q5LUOMnW/wLw/i1i5UsmH8xWU5pihNCnErvd+bHyT/97fVaEgrk0D4to0oOG5LEZFxjFwy2EC1iezknRWtnHmmNag1teDavUURHdwI7qDGx4plwiOiCNoVTxBq+LZ3rkJoQE+HPWw13WYQgghRK0nyWsd5eAATz2lYXmTP+77PmcaWPPV832ZPrornaaa8XTJTIb+doVjG2wJDfBhW5cmaFTKSoy6ejjubsfH/xvI9PFdGLEqniEbkum98ziJLRwJC/Bhd4fGlCmlIkcIIYSoDPIOKx7YsYRuvFv4Jf3f/JCvnu2NYWEJ73+7jrDJcxiz/ECdqQfNqG/GbxO7ETQ3hJ9CumObmctnn61i4TPzCViTiGFhia5DFEIIIWodWXkVD0RbpuBieE9MGl/AqMNpVilas7qfJ74HTjMqMo6pc3fyZOgeVvctrwdNt7PQdciVLt/YgKXDvFk+2Itu0ScYHRHHK39sJeSvaCIHtGb54DZkWZnqOkwhhBCiVpDkVTyQ7L0tKbhoR9P//XmrzLW8HrQx0R0al9eDRsYxfE0iQasTiPJrQligD0fqQD2oRqnHtq5N2dbFA8/DqQRHxvH4sn2MXhHL5u5NCQvw4VRDG12HKYQQQtRokryK+6bVwoXwXhjYZWHTJfGOtznubsfHrwzkj1v1oAfpvauO1YMqFCS3dCK5pRNOqTmMXBnHoM2HGLTlMPvauhIa4MP+tg1q/SY3IYQQojLU8ixCVKRrhxtx/VhDnAKjUCjL7nnbyzZm/D6hG0Fz/l0PGrgmoc7Ug150rMcPT/ciaO5kZjzRGbczl/nu/eXMe/5PBm0+hLrk38MfhBBCCHF3kryK+3ZhWS9U5rnY9d533/cpMNZn6TBvxsyYwLuv+3PN3JCX/9hG+ISZTFmwC+vsu0xLqGWumxny56gOjJw9iU9f7AcKBW/+uJGlk2bzRNhezK8V/PdBhBBCCCFlA+L+5J2158qBljR4bD1KwwdfNdUo9Yjq4kFU5yZ4HkllVEQcY5ftL68H7daMsABvTjaqXwmRVy8lahXre7dkfa8WtEs4x+iIWKYsjGbckn2s692CJQHeXHC01HWYQgghRLUlyau4LxfCe6JnUISD/65HO5BCQXILJ5JbOOGYlsOolfEM2nSQgVsPs8+rAWEBPuzzdq399aAKBQfaunKgrSuNzmYyKjIO/02HGLY+id0d3AgN9CaphVPt/z4IIYQQD0iSV/GfCjPqkbnDGwf/3ajN8yvsuKkO9fjhqZ7MfsyXoeuTGLE6gW8/WMGpBtaEBXizqUczStS1/0f0tKsNX07rx8wnOhO4JpHAtYl03XuSw03sCAv0YbtfEzS1fZObEEIIcZ/kHVH8p9TI7gA4DouqlONfNzPkr5EdGDlrEp+81J8yPQVv/rSJpRNnM64O1YNmW5ow+3E/guaE8O0zvTDLK+LDr9YSOnkOwRGxGOfXjeEPQgghxL3U/mUt8UhKrhuTvrETNt3iMLTNqdRzlaqVbOjVgg09m+OTeI7RK+KYvDCaJ5bsY33vFiwZ5s15p9pfD1pkqCZiUBsiB7TGb/8pRq+I5bnZO3hy8R5W9fNk2RAvMmzNdR2mEEIIoROSvIp7SlvTmbJCA5yHR1XdSRUKYr1cifVypeHZTIIj4xm06RBD1yexu0NjwgJ8SGxZ++tBtXoKdnd0Y3dHN5qeSGd0RBwjV5b/ieriQWiAN8ea1P7hD0IIIcTtJHkVd6UpVJO6qiuW7Q5j0jBNJzGccbXhy2l9mfGEH8PXJBKwNpGue09xxN2OsABvojo3QaNS6iS2qnSsiT0fvjqI38d3YcSqBIZuSKbPjmMktHQiNNCH6PaN0erV7mReCCGEAKl5FfdwaUsHSq+Z4hy0VdehcOVGPeiIOSF8PbU3JgXFfPDNOsImzyV4RSwmeXWjHjTD1pzfJnVj+LwQfpnUDfuMa3zxyUr+emYeAWsTMagjwx+EEELUXZK8ijvSavS4uKIHZk3PYN7ylK7DuaXIUM3Kga15/LfxvP7uUFLtLXhuzg7CJ8zi2dnbsc24pusQq0S+sQFhAT6MnjmRD14dRK6xAa/8vpVlE2cxaWE0VlfydB2iEEIIUSmkbEDcUebu1hRdsqbxpMhqWVqq1VMQ3cGN6A5ueKRcYvSKWEasjGfEyni2d25CaIAPRz1qfz2oRqnHlm5N2dLVg9aHLzJ6RRzjluzlsfADbOzZjCXDvDntaqPrMIUQQogKI8mr+BetFi6E98LI6RJWHQ/pOpz/dNzdjo9eHcQf47swYnUCQzYk03vncRJaOhEW4EN0+0aU1fY+qQoFSS2dSWrpjMvFK4yMjGPglsMM3nSIvd6uhAb4cMCrQa3f5CaEEKL2q+Xv6OJh5MR7kHfKGafhUSj0tLoO575l2Jrz28RuBM0N4edJ3bG7fJ3PP13JwmfmE7Cm7tSDnney5LupvQmaG8KMx/1wP3WZ799bztxpCxmw5RDqklJdhyiEEEI8tAdKXpWassqKQ1QjF8J7oW91FdueB3QdykPJNzZgSYA3Y2ZM4L3XBnHdzJBX/thK+MRZhPy5u87Ug14zN+LP4I6MnDOJz1/oh0Kr5e0fNrIkZA5PLNmH2fVCXYcohBBCPLAHKhtwO5PJ/37ZzJJh3pxzsaqsmIQOXT/hzNUkDxpOWIWeWqPrcB6JRqnHtq5N2dbFA8/DqYyOiOWJpfsYszyWzd2bEhroUyfqQUvUKtb2acna3i1oH3+O0RGxTPlzN08s2cvaPi1ZOtSbi471dB2mEEIIcV8eKHm9ambIgK2HGbYhmeh2jQgN9CHe01nq6GqRi+G9UJoUYD8gWtehVByFguSWTiS3dMI59QojVsYzaPMhBm05zL625fWg+9vWgXpQhYL93q7s93al8ZlMgiNiGbohmcC1iezs5E7YMG+SWzjW/u+DEEKIGu2BktdLtuaMeD+QgHWJDF+TyE9vL+N4Y1tCA73Z2sWjTjSLr80KUm3IjGmN8/BtqIxrZ9/UC46W/PB0L2aP9SNgXRJBq+P57v3lnGxoQ9gwbzZ3b0qJuvbvYzzV0IbPX+zPjHGdGb46kYB1iXSPSeGwhz2hAd7s8GuCprZvchNCCFEjPfC7dE49Y+aN8WXR8Pb0jzrCqIg43vt2PU/P20X4EC9W9vck19SwMmKt84qzzciMaU1WtCeleUaoTAqw9kvGxjcJfavrj3z8iyt6oNArw3HIjgqItnq7bmbIn6M6EBroTZ/txwiOjOOtHzfy1IJdhPt7ETmwNdfMjXQdZqXLsjJl5rjO/DmqAwO2HGZUZBwffbWWNFtzlg1py6p+rSgw1td1mEIIIcQtD73EVGygYlV/T1b3bUXHuDOMXhHLM/N2MT5sL6v7tmLZkLak2VtUZKx1Wm6KMydnBKAtVaI0LkRteQ1tiYqMLe24vL0tblMiMHW/8NDHL75ixqUt7bHtvb9CEuGaokStYl2flqzr3YJ2CTfqQRdGM27JvvJ60GFtueBoqeswK12hoZoI/zasHOBJ532nGBUZx/OztzNhcQyr+nuybEhbMuqb6TpMIYQQ4tH7vGr1FOxp14g97RrR5GQGwRGxDF+TSNDqBHb4uhMa4MPhZg4VEWudVZxtxskZASiUGlSmBbf+X6Ffip5+LpoCfU7OCKD5G/MfOvFMXdUVbakS58CoCoq6hlEoONDWlQNtXWl0NpPgiDgGbzxIwLpEdndwIzTQm6QWTrW+HrRMqcdOX3d2+rrT/Hg6wRGxjIyMY+TKeLZ28SAswJvj7na6DlMIIUQdVqHFfSfcbPnklYFMH9+FoNUJDF2fTM/dJ0hu7kBogA+7OrpV5OnqjMyY1mhLlX9LXG+nNCqmOMeUzBhPHP0ffKNVab4BaWs7Y+2bjJHT5UcNt8Y77WrDFy/0Y8a4zgSuSSRwbSJd957kcBM7wgJ92F5H6kGPeNjzwWv+2GVcY+TKeAZvPEi/7UeJ83QmLMCbmHaN0erV7mReCCFE9VMp78CXbcz448muBM0N4YfJPbDOzuPTz1fz1zPzUP3xB+TmVsZpa62saE+UxvfuyakyLiQrpvVDHT99Qyc0eUY4B219qPvXVtmWJsx+3I+gOSF8+0wvzPKK+PCrtSyeMpdREXEY59fOTW3/dMnWnF9CuhM0N4RfJnbDKe0qX368kj+nzmfouiT0i2TogRBCiKpTqctHBUb6hA9ty5jpE3j3DX9yzI3Rf+UVjJo146n5u7DOkiT2fpTmGaFQ3ztBUKhL0eQ9+Ea5shIlqZHdsWh9AjOP8w8bYq1WZKgmYlAbxv7+JG++PZRL9c14fvZ2wifMYursHdhmXNN1iFUiz8SAsEAfgmdO4IP/DaTASM2rv20hfOIsJi2MxrKODH8QQgihW1XSE6hMqUdUZw+iOnuwyWQIqp9+YszySIIjYtncrSlhAT6cbFS/KkKpkVQmBWhLVCj0757AaktUKE0efGLS5e3eFGfVo8nzSx4lxDpBq6dgVyc3dnVyo9nxdIIj4xi5svzPthv1oMea2Os6zEqnUSnZ0r0ZW7o1xevgRUZFxjJuyV7GLD/Aph7NCQvw5kwDa12HKYQQopaq8oaWZR07UvzXXzy56DlGRZY3ix+49QgH2rgQGuDDXp+GtX5TzIOy9ksmY0s79PTvvlJdmm+IXe/9D3TcsrLyUbAmjS5Sz/voo4ZZpxz1sOfDVwfxx/gujFgVz5ANB+m74xgJLZ0IDfQhun0dqAdVKEjwdCbB0xmXi1cYFRnHwC2HGLzpIDE+DQkL8Ca2TR0Y/iCEEKJK6WzXSZp9PX58qidBc0P4fXwXXM9n882HESx4dgH+Gw+iXyx1dDfZ+CahUGnQFNy536amQB89lQYb3+QHOu7atUoKLtjhNHyb5BcP6ZKtOb9O6s7weSH8Mqkb9hnX+OKTlSycOp9h6xIxKCzRdYhV4ryTJd9O7U3QnMnMGutL05MZ/PDucua88Bf9tx5GVVKzRw0LIYSoPnS+ZTrX1JBFI9ozatYkPnmpP6UqJW/8vImlk2bz5OI9WFy98w77ukTf6jpuUyLQapQU55hSVqxCq4WyYhXFOaZoNUrcpkQ8cJus779XYWCbRf2uCZUTeB2Sb2xAWIAPo2dO5INXB5FnpM//ftvKsjpWD3rVwoj5ozsxcvYkPp/WF6WmjHe+38CSkNmMXboP09wHL20RQgghbqfQarX3fWOzJnZar+/HPtIJN/T65N430GrRi4pC/fPPKDdsQGtoSOnYsZQ+9xxaD49HOvft+m99p8KOVVXKJ2x5khXTGk2eIUqTQqx9k7DxTX7gxPV9w8/o29eQxk8tx3HwrkqKuA7Tamlz6CLBEXF03neSUqWSjT2bETbMmzOuNrqOrupotXSIO8voiFjaJ5yjwEDF2r6tWDK0LakO9XQdXYUy0FPjaGSFo7E1TkbWOBpb07peQ5yMy5/vdakHSM3P4mJBFqn5WaQWZFNUVjdW5oWobDdzi5r43l6R/jPHqiIV8TzsGvJ9rFarbXenr1W/Ie4KBWU9e1LUsyeKI0dQ//ILqoULUc+eTemgQZQ+/zxlXbvWyTo6favrOPpHP1Qv13/67js11tZa7PrsrYDIxL8oFCS2ciaxVXk96MjIOAZuOczgTYfY411eD3rAqw7UgyoU7PNpyD6fhridvkxwRBxD1ycRuCaBHZ3cCQv04WBzR11Hed8MlfrlCeqN5NTJyPpWwmpjYH7P+w50/Pfv4Myia7cS2f9Pasv/XagprqyHIYQQNZrOktf7zsqDLLHsNZ7AtYkErt1GvbVrOeZmS2iAD9u6NEGjUj7QeavLpxJdyjtrz651St5+u5jthrLyU9nOO1ny3dTezB7rx7D1SQxfncD37y0npaENYQHebO7WjFL1g/0c10QnG9Xns5f6M31cZ4LWJDBsXRI9YlI42NSBsABvdvq6V7vhD+5mjvS0a00zc2ccjKyxNrj7iNzislLSC7JJLci+tcKaXnCFT73GA/DzsZW3VmQdjaxwMLLCxsAcGwNzWls2+tfxsoquk1aQxdFrF9h2KYmU66mV9jiFELWLrlagqyrHqn4rr3dwxdKEOWP9WDiiA/23HSY4Io73v13H0/N3smxIW1b19yTPxEDXYdYYF5f3xNhYy1NPlbI9UdfR1B1XLYxYENyRxcN96Lv9GKMiYnn7h408tWA34YO9iBzQmutmD96rt6bJsjZlxrguLBjVkYFbDjEqMo6Pv1xDqq05S4e1ZU2fVhQY33lzYlVwMLKil10betq1xsXk7y38istKSbuRnN5cIb144++XC69Sxt3LsFZf3Pe3f+uhoL6hBY5G1jjdSGhvrug6GFlhbWCGtYEZreo1ZESDLpzPu8zWS4lsu5REWkF2pTx2IYSoCWpE8npTsYGKVQNas7qfJ74HTjMqMo5n5+5kQugeVvdtxdKhbUm3s9B1mNVaYUY9Lm/35ukppVhLK06dKFGrWNunJWt7t6BD/FmCI+J4asFuxoXtZW2fliwd6s1Fx3q6DrPSFRqqWeFfnrR33neK0StieWHmdiYu2sOq/q1YNqQtl23uvtJZkfT1VPRz8KaPfVuaW7jc+v8rxblsv5TM3syjnM/PJKvo2j0T1AdRhpZLhTlcKswh/srJv31NDwXWBua4GNvQ0aYZPew8cTGpz/jGfRjfuA9Hrp5nc3o8G9PiKC6TzixCiLqlRiWvN2n1FER3aEx0h8Z4pFwiODKO4WsSCVqdQJRfE8ICfTjiUfubxT+M1JXd0Gph2jR5w9M5hYJ93g3Z592QxmcyCY6IZeiGZALXJrKzkzthAd4kN3es9XWxZUo9dvq6s9PXnRbH0gheEUtwRByjIuPZ2tWD0AAfTrjZVsq5jZUGDHbuyHAXPyz1TQHILy0i+vJhtl1KIu7KScq0ZZVy7nspQ8vloqtcLrpK3JWTTE9Zh7elGz3t29DZpjnNLVxobuHC4416sfzcblZf3Ee+pm6MKxZCiBqZvN7uuLsdH78ykOnjuhC0urxZfO9dx0ls4UhYgA+7OzSmrJrV0elKyXVj0jf4Ur9bPA0atNB1OOI2pxra8PmL/ZkxrjPDVycSsC6R7jEpHGpqT9gwb3b4Nal29aCV4XBTB95/YzAO6VcZsSqewZsO0i/qKLGtXQgL8GaPT6MKGf5gpjIi0MWPYc6dMFUbAXDs2gWWn48m5vKRatcFoExbxoHsExzIPsFPemp86zdnuIsfTc2dmeTen1Gu3Vh5YQ8rzkdzvVTaCwoharcan7zelFHfjN8ndGNecCcGbzrIyJXxfPbZKs471GPpsLas692SQkO1rsPUqbS1fpQVGuA8fBsgyWt1lGVlysxxnflzVIdb9aAffbWWVFtzlg1ty+q+uq0HrSpp9hb8PLkHc8d0YsjGgwStiuerjyI542zFkmFt2dCzBcUGD/7rSw8Fg5zaM75xH8zVxgAkXjlN6Jko4v5x6b66KiorIepSElGXkvC2cmeMa3daWzZibKOeDHHuyLyTm1iXeqDCyhuEEKK6qTXJ600FxvosHebN8sFedI1JYXRELC//sY2QhdFEDmwNzdPAwUHXYVY5TZGatFVdsfQ5gkmjNF2HI/7DP+tBgyNimTZrOxMXxbCqvyfLhrQlo37V1IPqUq6pIYuHt2PJ0Lb03HWC0RGxvPbrFiYvjGbFoDasGNSGnHrG93WslhauTPXwx92svDVXwpVTzD+1mcNXz1XmQ6hUcdkpxGWn0NLClXGNe+Nl2ZhpzYYxyKk9vx1fw6GrZ3UdohBCVLhal7zepFHqEdXFg6jOTfA8ksqoiDjGLtuPIqI5mlGjcOuo4GSj+v99oFoiY3N7Sq6a4Ry0VdehiAdwez1o8+PpBEfEMjIyjpEr49naxYOwAG+Ou9vpOsxKp1Ep2dyjGZu7N8Xr4AVGr4hl4uI9jF22n409mxMW4M1ZlzvvQDTQUzPZfQBDnDsCcKkwhxkn1rLr8uGqfAiV6tDVs7weP4cu9VsypclA3M0c+c5nMisv7GFWyoZqVwYhhBCPotYmr7coFCS3cCK5hROOaTksjDNAtWAB8/7KZ59XA8ICfdjX1rVWb4rRavS4GNEDs6ZnMW9VMy6Nin874mHPB6/5Y3/pKiNWJTB440H6bT9KnKczYQHexLRrXCH1oNWaQkGCpwsJni40OJ/NyJVxDNx6mCEbDxLTrhGhAd7EtXa59Xpuau7May1G4GxsQ0lZKWFnd7Dk7M5am8ztunyI/VnHCXbtxijXrgx17oS3lTtfHV7GsWsXdB2eEEJUiNq/A+Q2qQ71KPn2WwqOHWP6uM40PpfFt++vYP5zfzJo00HUJbVzB35mdGsK021wCtqKQlG+WeUmb0s3HUYmHka6nQW/hHQnaG4Iv07oilNaDl9+vJI/p85n6Lok9Itq58/xP51zseLbZ/swYk4Isx/zpdmJdH58J5zZL/5Fv21HGO3oy/fek3E2tuFUbjrTDvzBn6e31trE9aaishIWnN7CtAN/cDo3HWdjG773nswo1266Dk2ISuFt5X7r77e/v4naq04lr7dYWbFwZAdGzprEJy/1p0xPwZs/bWLpxNmMC9uL+bXas1tXq4ULy3ph5JSBdceD//r6p17jGePaHQW1fMWuFsozMSB0eDuCZ07kg/8NpMBIzau/bSF84iwm/hVNvZx8XYdYJXIsjJk3phMj5oTw5XN9MCgp493v1vPMkBcx+P4HIpPWMu3AH5zKTdd1qFXqVG46zx/4g6Vnd6LUUzLJrR9vtxqNobL2b/gTdYMCBWMa9uDTNuN0HYqoYnUzeb2hVK1kQ68WTPjpcV78eDgnGtsyeWE04RNm8cpvW3C5eEXXIT6ynAQP8k454xS4DYVe+e7jf7bSedKtL6+3HIlar/ZXkdRGGpWSLd2bMfm7x3j+s5EcbObAhNC9LJs4i9d/2kTDc1m6DrFKFOur2D+sK3n79lK4fDmlTdzRf+89hvV8jGf+2IRD+lVdh1jlSspKmXVyA+8l/kleaSHdbFvxo89T2BnW03VoQjwStZ6KN1qO5MnGff72/9Iqrm6QbAVAoSDWy5VYL1cans0kODKeQZsOMXR9Ers7NCYswIfElk41si72Qngv9K2uYtvrwB2//n7SQt5sGUxPu9bUNzDng6S/5MVfUykUJHg6k+DpjMuFbEatjGfglkMM3nSQGJ+GhAX6EHtbPWht08jEjk+9xmNtYM7pLko+NO+H0XAXgiPiCFybxPA1iezs5EZooA+HmjnqOtwqtTfrGNMO/MH7no/R0NSO73ym8HbCfM7kXdJ1aEI8MDOVER+0fpxW9VzJKy3k80NL+ERWX+sUSV7/4YyrDV9O68uMJ/wYviaRgLWJdN17iiPudoQFeBPVuQkalVLXYd6X3BRnriZ60PDJVeipNXe8zb6s47wSN5OPWj9Bq3oN+artRN5MmEdOSV4VR1szWWfn0i0mhW7RKZjmFZFrYsAOP3d2+LqTZWWqs7jOO1vx7dTezBrrR8C6RIavSeSHd8I50ag+YQHebOnalFJ1zfg5vh+tLFz5sPXjmKqNSLxymg+SFpZPnGpsy6cvD2D6+C4ErU5g2LokekSnkNzMgbAAH3Z2cqszQ0wu5GfywoHpfND6cdpYNuJb7xDeT1rIQWmnJWqQemoTPm87gcam9lwuvMq7iQs4LR/C6py68Vv7IVyxNGH2436MmBPC11N7Y1JQzAffrCN0ylyCV8Riklf9RzFeCO+J0rgA+wEx97zdqdx0Xoydzvm8yzQ2c+Ab7xCs9HWXeNUUHimX+PCLNQzYcpgyBWRZGlOmgAFbDvPhF2vwSNH9L9SrFkbMH92JkbMn8cXzfVGVanjn+w0sCZnN2KX7MM0t1HWIj6ylhSuftBmHqdqIXRmHeDtx/r9GpWZam5YnsHND+GFKD6xy8vnki9UsfmouQSvjMSoo1lH0VStfU8TbifPZlXEIU7URn7QZR0sLV12HJcR9sdI35RvvEBqb2nM+7zIvxk6XxLWOeqDkVb/4zqt3tVmRoZqVA1vz+G/jef3doaTZWfDcnB2ET5jFs7O3Y5txTdch3lFBmjWZ0W1wGBiNyuS/E5TMomu8EjeLU9fTcDGpzxdeE7BQ31/z97rIOjuXaTOi0CgVXKlnTIm+ChQKSvRVXKlnjEapYNqMKKyzc3UdKlBeD7qmXyvG/TqOVz4I5EwDa55esJvwCbN4Yfo2HNJzdB3iQ2lu7sInbcZhpDJgU1o8nx4MpaTs7t0WCoz0CR/Slsf+eJJ33hhMlqUJL86MInzCLJ6atxObrOrxfFWmkrJSPj0Yyua0eIxUBnzSZhzNzV10HZYQ92ShNuYLrwm4mNTn1PU0XombRWZR9Xz/FZXvgZLXRuey+OqDFfgknC3fxl6HaPUURHdwY9rnIwn5bgzR7RsxYmU8YZPn8MFXa2h2vHrtZL64ogcKvTIch+647/tcLcnjjYS5nM29hKupHZ+0GS87k++iW0wK6lINBUZ3/v4UGOmjX6qha0xKFUf2HxQK9vk05OWPg3jyp8fZ7ufOsPVJLH5qHh9/topWR1J1HeF9a2Bcn4/bjMNYZcDW9ES+O7L8vkeilin12N65CVO/Hs3TXwdzwKsBY1bEsnTSbN7+bj3upzIqOXrdKkPLt0eWsy09EWOVAR+3GUcD47oztEXULIZKfT5tMx5XUzvO5l7i9YS5XJXStjrtgWpeM61M8DiZwQ/vLq+1tXP341gTez56dRB/jO/CiNUJDNmQTO+dx0lo6URYgA/R7RvptI6u+IoplzZ3wLbXAfStrj/Qfa+W5PNGwly+9ZmCh7kTb7UM5oPkvyjTlt37nNlmZMa0Jivak9I8I1QmBVj7JWPjm/TAMdQE3aJTyDW+d2J/3Vif7jEpRPh7VU1QD+hko/p89tIApo/rQtCaG/WgMSkcbOpAWKA3Ozu5o6mm9aBW+qZ84jUeM7URuy8f5usj4feduP7ToWaOvPeGIw7pOYxcGY//pkMM2HaE2NYuhAb6sNe7Ya0c/lCGlq+OhKOvVNO5fgs+bjOOl2Knk11c+1efRc2hp9DjrZbBNDF3IrUgmzcS5nKtpG60ARR390DvTGa5Rcx83Jevnu2NUlPGO99vYGnIbB5fug+z6zW/du5BZdia89vEbgTNDeHnSd2xu3ydzz9dycJn5hOwJhGDQt00Q09d1RVtqRLn4dse6v7Zxbm8kzCfq8V5dLRpymS3/ve8fW6KM0e+GE/Glnag0KK2vAYKLRlb2nHki/Hkpjg/VBzVmWleESX/8aGtRK2sEbXRWdamzBjXhRFzQvj+qZ5YXs3n4y/WsOipuYxYGYdRfvWqB1Xrqfig9ePYGdbj8NVzfHlo6X9+uLofafb1+GlKT4LmhvD7k11wuXiFrz+MYMFzCxi8IRn94to3/KFMW8aXh5Zy5Op57I0s+aD149IyT1Qrk93609GmKVeL83g7Yb58uBLAA2/Y0vLGL1uYuHgPm7s15d3X/TnVwJqnFuwmfMJMXpy+Dce0nEoJtDrLNzZgSYA3Y2ZM4L3XBnHdzJBX/thK+MRZhPy5G6srVXd5ozTfgLS1nbHudBAjp8sPfZyLBVl8mLyIkrJShjfoTG97rzverjjbjJMzAlAoNajr5aKnX4pCAXr6pajr5aJQajg5I4DibLOHjqU6yjUxQF1y7xpwdYmGPBODKoro0RUY6bN8sBeP/fEkb701hEwrU16YuZ3wibN4et5O6mdWjxX05z2G0NTcmbSCbD5IWljhE7NyTQ1ZFNSe4JkT+fjlARSrlbz+y2aWTZzFk4tjqHe1dq36FJWV8H7Sn6QXXKGpuTPPeQzRdUhCANDb3ovhDTpTUlbKh8mLSC2oGz2rxX97oOT1TANrXv5wOKdcbZiyMJq3ftjAeSdLXnt3GFGdPRi6PonFT82tcbVzFUWj1GNb16Y89c1opn4xisSWTjyxdB9LJ87mjR830uhsZqXHcGljJzR5xjiP2PrIxzp09Sy/HV8DwLSmQ+9YE5cZ0xptqRKl0Z1X55RGxZSVKsmM8XzkeKqTHX7umP7HiqRZfjHbfd3veZvqqEypx05fd579KpinvhnNfq8GjF4Ry5KQObz77TqanNRdPegABx/6O/pQqCnmo+RFXK3Ey4elaiUbezZn0g9jmfbpCI40sWfSoj0smziL//2ymQbnsyvt3FXtakk+Hyb/RZGmhAGOPgxw8NF1SKKOczWxZVrToQD8enw1h6Slm7jNA18f2u/tyn5vVxqfySQ4IpahG5IJXJvIro5uvPeaPy2OpzNs/W21cwHe7PStvrVzlUKhILmlE8ktnXBOvcLIyHgGbT6E/+ZD7GvrSmiAD/vbNqjwZvFlJUouRnTHwjMFM49zFXLMtan7aWHRgL4ObXm71WieO/D733ZzZ0V7ojS+d8mIyriQrJjWOPpHV0hM1cEOX3d6bz+GUUHxHTdtGRUUU6xSsrMGJq+3O9zUgfffGIxD+lVGrIpn8KaD9Is6SmxrF8ICvNnj06jK6kGdjW14xsMfgJ+Oray6ca8KBfGtXYhv7YLr+SxGRcYxYOthhm1IZnf7RoQF+BDv6Vzjhz+cyk3nx2ORvNZiBM94+JOcc4aLstIldEBfT8VbLYMxVOqzMS2Odal3HrIj6q6HzihPNbTh8xf7M3L2JP4c2YE2hy7y+Wer8Ek6z48h3flhSg/qXcvn4y/XsGhK9aydqwoXHC35/pleBM0NYcYTnXE7c5nv3l/OvGkLGbj5EOqSiquju7zdm+KsejgNf/RV19v9cnwV5/Mu09DUjvGNev/ta6V5RijU934MCnUpmjzDCo1J17KsTPlpSg+UGi1WOfmoi0tBq0VdXIpVTj5KjZafpvTQ6aCCipRmb8HPk3sQNCeE3yZ0xeXiFb76KJIFzy5gyIZk9Isqtx5UqdDj9RYjMVTqszk9gS3pCZV6vrs562LN18/1ZcScEOaM6UTLY+n89PYyZr20iL5RR1CW1ux2gltufG8Nlfq83nIkSkUdWnQQ1cb4xn1oaGrH+bzL/Hp8ta7DEdXQI/9myrIyZdYTnRkxJ4Rvn+6F2fVC3v1hI8ERcUQMaMOHrwwk0/r/a+fUb7+N4uLFioi9RrlmbsSfozowcvYkPnuhHwBv/biRpZNm80TYXsyv/X0ka3G2GalrOpP89tPEv/gSyW8/TeqaznetHdWWKbiwvCfGDVOx9DlaobEXaor5+vAyNNoyghp0/ltPSJVJAdqSey/ga0tUKO+j12xNc9zdjvff8Gdt7xboAVY5+egBa3u34P03/DnubqfrECtcrqkhi4e3Y9SsiXz0ygCK9VW89stmlk2axYRFMdTLqZzL+EENuuBh7kR6wRV+PbaqUs7xIHLqGTP3MV+C5oTw1XN9MCgq5b1v1xM2eQ5jlh+o0cMffjm26lb9a5BLZ12HI+qYFuYuDHfxQ1Om4evDyyjU1L1FL/HfHqhswOpKPqa5heSa/nsVrdBQTYR/G1YO8KTzvlOMiozjubk7yDXWZ1V/TxYNb0ff7Ufp9dNPqH75hXe7uBMa4MMJN9sKezA1QYlaxbo+LVnXuwXtEs4xOiKWKQujGbdkH2v7tGTpsLYczffk5IyA8lpS40LUltfQlqjI2NKOy9vb4jYlAlP3C387bvb+FhSct8fjlYWVcvXy2PWLLD27k9ENu/NCs2E8u/83NNoyrP2SydjSDj39u+8ALc03xK73/ooPqhrIsjIlwt+r2rbDqiwalZJNPZqzqXszvA5eYPSKWCYu3sPYZfvZ0Ks5S4Z5c9bFukLO5WhkxeMNewLw49GIf03P0qViAxWr+nuyum8rOsWeJjgijqlzd/Jk6B5W923FsiFtdR3iA8vXFPHjsUg+93qSxxv1YtflQ6QW1J76XlF9KRV6TGs2DD2FHqFnt3Pset1b6BL354GS1/pZuYRPmMXaPi1ZMqwtafb1/nWbm5s9dvq60/x4OsERsYyMjGPkyni2dvGgKCwMZVQUXebMoF/UUeI8nQkL8CGmXdXVzlULCgUH2rpyoK0rjc5mEhwRx+CNBwlYl8hakz78Ut+efdatb9XRKfRL0dPPRVOgz8kZATR/Y/7f+qdeDO+JgW029bsmVFrIf53ZRjc7TxqZ2hPg7Ev4+d3Y+CZxeXtbNAX6d9y0pSnQR0+lwcY3udLiEjqkUJDg6UKCpwsNzmeX14NuO8zQDQeJadeI0ABv4lq7PFI96NNN/DFQqtmcFk/clZMVGHzF0eopiGnfmJj2jWlyMoPgiFiGr0kkaHUC2nU5lE6bpusQH0hcdgqb0xPoY+/F000G8V7SQl2HJOqAAGdfGpnak5qfxV9nHq7Vo6gbHqzbgIvV3yfyfL6Klkfv3lXgiIc9H7zmz+iZEwkf7EXnfacwHDkSvaQkvnyuD7+N74JTWg5ffnyjdm59UqXXzlVHp11t+OKFfoycM4nf2gbhl3+A9afHsunIGAKz1qHU/v/35E67968dbsi1I41xCohCoXz0fpd3U1xWyu836o8ea9gDC7Ux+lbXcZsSgVajpDjHlLJiFVotlBWrKM4xRatR4jYlolYOKhB/d87Fim+e68OIOSHMfsyXZifS+fGdcGa/+Bf9tj1cPWg7qyZ0tGlKXmkhM1PWV0LUFe+Emy2fvDKQUbMmEhrog3LLFgx79ODX18LoFn0CPU3lvUYr0swT68grLaSjTTN8rGr2xkNR/VmojRnbqPwKy28n1lB8jzHPQjxQ8lpkoOKzlwYwatYkFg9vh0/ief54NYzf/xdKj93H7/pL+ZKtOb+EdCdobgjFn32G4vRpPvx6Hf6bD7EwqD2fvtCPIgMVr/26pdJr56qzbEsT3in9jJYtt/GK6ztYlF5n9qlXiU0axDPpCzDTlF+av7l7/6YL4b1QmeVi13dfpce4L+s4B7KOY6o2YrRrdwBM3S/Q/I35N0oDFJTmmAEK7Hrvp/kb8/9V4iBqtxwLY+aN6cSIOSF8+Vwf9Es0vPvdepaEzOGx8P33XQ+qQMEk9/IBGYvORJFTw8ZBXrYx448nu1Jw/DjFX3+NTXYun36+mr+emcfw1QkY6miIyf3KKclj8ZkoAELcB6CgDl0ZE1VuTMMemKgMOZB1nP1Zx3UdjqjmHmqUSqa1KdPHd2HBqA4M3HKYUZFxfPzFGlLtzFky1Ju1fVv+q31QRJ8PsDY0L//H//4Hy5bh+u23vDJ9GznmRkQM8GT+qA74bz50q3ZuY8/mhAVUXO1cTVCaZ0ShZQlzbUczr/4oBuRE8Wz6fD49/xWvp/7GApsgpts+zpkCDwDyztqRva8VLmM2oDSsmsL22Sc30s7ag8HOHVl+PprLRVfRt7qOo390rWqHJR5Nsb6K1f09WdO3FR3jzjB6RSzPzNvF+NC9rOnbkqVDvUmzt7jr/bvZtqSxqT0ZhTlEXthThZFXMFNTSqdO5bEmF+i6J4XgFXG8NH0bk/6KJnJga8L9vciyrp5dKSIu7GGYsy+NTe3patuSHRkHdR2SqIXqG1jg79SBMm0Zs1I26Doc8RD+luMBOwd/B0BW4TUCNn9Q4ed7pG4Dt0/kefvNwWRZmfDizCjCJ8ziqXk7scn6/008tz8o1GoYMwb274eoKJKbOzBu6T7e/2YdV+qZ8Nq7w1jXuwX9oo6wcOoCvvowAu/Ec6B9uNnlNcntu/e1Cj3WWfZicPP59G6+mE0WXXn60kLikwcwJ/NZmp5I5+LynujpF+Pov6vKYjyVm862S0no66kY0aBLlZ1X1ExaPQV72jXixU9HMOHHsezwdSdwbflAk4++WE2Lo2n/uo8CBWMb9QLKV11LasElRI1Sj6jOHjzzzWie+SqYuNYuPBZ+gKUhs3nr+/W4nX74iXiVpaSslEU3Vl8fb9hTVl9FpRjZoAv6eiq2ZxzkdN4lXYcjHsLfcrz7+P9HVSFDrMuUeuzwa8IOvya0OJrG6IhYxqyIZXREHFu6ehAaeJdpLQoFdO/OW+8Mw+XiFUZFxjFwyyEGbzpIjE9DPnp5AI3PZRG4Nokf3wnneOP6LBnmzZauTSn9j7nyNdXddu/Hm3oy2fRrPnR+iZBzS5iQ9xcjXy5gOxeY224gp8xy0VbhG0vome30tGvNQMd2LDqzrVInHYnaI6WxLZ++PIDp47sQtCqeYeuT6bn7BMnNHAgL8GFnJzfKlHp0sPbA1cSWjMIcNqXF6zrsCnewuSMHmzvimJbDyJXlQ0wGbj3Cfq8GhA3zZq9Pw2oz9GBjWhyPNeyBq6kd7a2bsE8u6YoKZKE2YYBjO4BbZSpC/JcK70B9uJkD770xmDHTJ7Dcvw1d95xk7gt/Qe/esHYtlN25Lva8kyXfTu1N0JzJzBrrS9OTGXz6xRq6xZxk+hN+fD21N+oSDe98v4GwyXN4bNn9187VJDa+SShUGjQF/57aBHC2rCHvOLzJsJ+n8WGrZ2jIWeYdeIuFU+czbF0iBlVUR3cm7xJ7M49ioFQzyLF9lZxT1B6Z1qZMf7IrQXND+GFKD6yv5PHJF6tZ9PQ8glbFM9LaG4AV56Mp1dbsxv/3kupQjx+f6knQ3BD+GNeZhuey+ObDCBY8uwD/jQfRL9b9inOpVsOK8+XlQHKlRVS0QU7tMVCq2ZN5lLN5uhs9LWqWShufcmsiz9wQfn+yCxw7Bv7+0KoVzJwJhXdOPK9aGDF/dCdGzp7E59P6otSU8ebPm3kydA8bejTj3dcHcc7Zkmfm7yJ8wiymzdiGQ/rVynoYVe5+d+8XWCj5+OR3dOq2hA9eHUSekT7/+20ryybOYtLCaCyvVP7mlhXnYwAY7NwRPZnEIx5CgZE+4UPaMmb6BN55YzDZ9Yx5cUYUHTr2g7ffYn9SlK5DrBK5pob8NbIDo2ZN4pOX+lOqUvLGz5tYOmk240P3YHG14L8PUonWpR4gv7SINpaNaWBcX6exiNpDT6HHYKcOALc+IAlxPyo948g1NWRRUHs4dQoWLgRDQ5gyBRo0gA8/hMt3rvMq1lextm8rxv/yBK98EMjpBtY8/Wc0b/2wkbMu1rz27tC/1c79V9uumuR+du+nremMpsAQ+xE72dKtKVO+G8Nzn48kuYUT45bsZdnE2bz+00Yans2stDjjr5zkfN5lbAzMaW/VpNLOI2q/MqUe2zs3YerXo9mw+Ec0PXpg9NPPzJ/4O29/Vz3rQStDqVrJhl4tmPjjWF78JIhjbraE/BVD+MSZvPLrZlwu6GZYQL6miG2XkoDylTIhKkIHaw9sDMw5n3eZhCundB2OqEEqpOb1fmSVFWI9diw89hhERcG338IHH6D94gv+170JS4Z5c87F6t93VCjY59OQfT4NcTt9meCIOIauTyJwTQI7Ornzzhv+tDqWzrB1SfSITvlX7VxNda/d+5oiNamrumLpfQTTRjcSdoWCxFbOJLZyxuXiFUZGxjFwy2EGbzrEHu+GhAV4c8CrQYXX0a1Pi2Wy+wD6OXizN+tYhR5b1D1KhR6eA8ZQPDSEDyI/pv3i9fhvOsSAbUc40MaFsAAf9no3rP0DTRQKYts0ILZNAxqey7qxH+AwAeuT2dWhMWEBPiS0cqrSuti1qfvxd2pPT7vWzExZj0ZbM/rViuqrr335BLr1abE6jkQ8qqzCa3fcnJVVeK1SzldlyevNVgkben0CHTvS/2kPXP2tyyfybD3MsA3JRLdrRGigD/Geznf8pXyyUX0+e6k/08d1JmhNQnnCGpPCwaYO/DClB+bXCxmxOoFPvlh9z7ZdNV3GlvaUXDXDacTWO379vJMl303tzeyxfgxbn8Tw1Ql8/95yUhraEBbgzeZuzSoslm3piUxy60cHaw+MlQbVanSnqHm8rdypp2/C2bwMYsyKiJnSkzmP+TJ0fTIjVsXz9YcRnHGxImyYNxt7NqdYv8p+henMmQbWfPV8X2Y+7kfg2kQC1ybR5a2lHHW3JWyYD9u6NEGjqvwNrCnXUzmXl0EDE1vaWrpxIPtEpZ9T1F7GSgM6WHtQpi1ja3qirsMRj+hvOR7Qf+s7lXo+nS5NnnWx5uvn+t6YyNOJ5ifS+entZcx+cRF9o+4+kSfL2pQZ47oQNHcy3z3dk3rX8nn3+w2MXBlPuH8bPnx5wD3bdtVkWo2Ciyt6YOpxFotW9x6VedXCiAXBHRk5ZxKfv9APhVbL2z9sZGnIbFRffw3Zj34JMqv4Osk5Z9FXqulo0/SRjyfqtm62rQCIunGJGm6UHo1oz6hZk/j45QEUq5W8/stmlk6azZOL91Dvat3odHHF0oQ5Y/0ImhPC11N7Y1RQwvvfriNs8lxGLz+ASV7lf3C8WTpw83kS4mF1tGmGvlJNcs5ZsotlAqN4MNXiunpOPWPmjfFlxOwQvnquD/rFpbz37X9P5Ck0VLPC34uxvz/JW28NIdPalGmzd/Dy9G0kN3fkjXeGcsCrAWNWxLJ00mze/m49isSa/QkvM7o1hek2OAdtve8rhiVqFWv7tOTJn5/glQ8DOeVqg/4HH2DUtCkv/rEVx7ScR4op+vJhAPxsmj/ScUTdpoeCDtblwzd23/iZul2pWsnGns2Z9MNYXvgkiGPudkxaFMOyibP43y+baXBeN/WgVa3YQMXKga154rfxvP7uMC46WPDs3J2ET5jFc7O2Y5dROZfp4P+fl442TdGTnq/iEfjVL3+/2H35kI4jETVRtbrmVmygYlV/T1b/cyJP2F5W923FsiFt7ziRp0ypx05fd3b6utPiWBqjIuIIjohjVGQ8W7t68ObbQ2mfcBb/TYcw8vND0707nXpY1rjaOa22fBSsoWMG1h0fYtKNQsE+74bs827IRtvRqH/+maGhiwhcm8jOTu6EBXiT3Nzxgevo9mQe5RkPf7yt3NFT6FEmtXDiITQxd6KevilpBdn3bpmjUBDXpgFxbRrgej7rb6VHu9uXlx4ltLpz6VFtotVTEN2hMdEdGuORcongiDiCVsUTtCqe7X5NCAv04YiHfYWe82xeBmkF2TgYWdHE3Ilj12T0s3hwego9vC3dANiTKXslxIOrVsnrTTcn8uxp14gmJzMIjohl+JpEglYnsMPXndAAHw43c7jjfQ83deCD1/2xv3SVkSvjGbzpIP2ijhLb2oUvpvXlHdNOqP74g6+3p9a42rmriU3IO+mC+3NLUCgfbdqYtlUriqdP57G+RgxfnUjAukS6x6RwqKk9YcO82eHXBM19bnhLL7zCxfxMnIxt8DBz5Ki8oYmH4GXZGIDYrPuvpbxZejTz8c4Erk1k+JpEfn5rGcfcbAkL8GZrF48qqQfVtePudnz8v4Hlwx9WxzN0fTK9dx0nsYUjYQE+7O7QuMI2sMZlp+Dv1IE2lo0lea0k1tm5dItJoVt0CqZ5ReSaGLDDz50dvu5kWVXPUcIPoqmZE6ZqIy7kZ3Kp8IquwxE1ULUoG7iXE262fPLKQEbNmkhooA/tEs4x/dVQfnstlG7RJ9DT3HmVL93uRp/ZOSH8NqErzqlX+Oirtaj++ouSV1/l82l9b9XOLZs4iycXx1T72rkL4b1QW17DtueBCjtmlpUpM8d1vlU/bHGtgI++WsuiKXMZGRmHUX7xfR0n4cppADzrNayw2ACKs81IXdOZ5LefJv7Fl0h++2lS13SmONusQs8jdK/NjeT1YVrm5NQzZu5jvgTNCeGrZ3tjWFjCe9+uJ2zyHMYsP1ArB5rcSUZ9M36f0I3hcyfzU0h3bDNz+eyzVSx8Zj6BaxIwrIAhJjefn5sfNkTF8ki5xIdfrGHAlsOUKSDL0pgyBQzYcpgPv1iDR0rNH596830iUdpjiYdU7ZPXmy7bmPHHzYk8k3tgnZ3Hp5+v5q9n5jF8dQJGBXdOsnJNDVk8vB3BMyfy4SsDwcgIg5de4un5u9jVoTFvvTmYI03smbRoT7WunctNcSYnoSlOQ3egp1/xU3f+WT982caUabO2s3zCTKbO2YHt5XsX1B/MOQNASwvXCospN8WZI1+MJ2NLO1BoUVteA4WWjC3tOPLFeHJTnCvsXEK3FChoZl7+fB66eu6hj1NsoGLVgPJ60NfeG8YFR0umzt1J+MRZPD8zCvtLtWegyb0UGOuzdJg3Y2ZM4L3XBnHdzJCX/9jGsomzmLxgN9bZD7+B9WDOWaB89Uwhda8Vyjo7l2kzotAoFVypZ0yJvgoUCkr0VVypZ4xGqWDajKhHev6qgxYWDYD//1kS4kFV/2vl/1BgpE/40Las8G9Dtz0pBK+I46Xp25j0VzQrB7Rm2WAvsqz/fVlFo1KyuUczXv1wKXo7d3L4valMDN1L0Y1NIK+9O5Qu+07dd9uuqnZheU+UxgXYD6zcKSS31w83P55OcEQsIyPjGLkynq1dPAgL8Oa4u92/7nfz8qG7mWOFxFGcbcbJGQEolBpUpv8/XUihX4qefi6aAn1Ozgig+Rvz0beSnao1nbOxNSYqQy4XXq2QncdaPQUx7RsT0/5GPWhk3K3So+2+7oQF+nC46Z1Lj2rTJVuNUo9tXZuyrYsHnkdSCY6I4/Fl+xiz4gCbuzUjLMCbk40ebGJWdvF1Lhdepb6hBc7G1pzPr7xBKHVNt5gU1KUack2N7/j1AiN9rHLy6RqTQoS/V9UGV4GamDsBSImZeGg1Lnm9qUypR1RnD6I6e9DyaCqjV8QyZvkBgiNi2dytKWEBPnf+paxQUNatG2+8F0CD89mMXBnHwK2HGbLxIDHtGvHRKwNofDaL4WsT+ent6lE7V5BmTebuNjgFRqEyqbrLn0c87PngtfL64RGrEhi88SD9th8lztOZsAAfYto1urXhLbUgm7zSQuobWmCuNuZayaOVYGTGtEZbqvxb4no7pVExxTmmZMZ43nGQg6hZGpuWbyxKuV7xU/KOu9vx8SsD+WN8F4JWJzB0fTK9dp8gqbkjoYF/rwf1SLnEtBlR5QmEsT5ZlsaoSzQM2HKY3tuP8dOUHnf88FbtKRQkt3AiuYUTTqk5jFwVz6BNBxm49TD7vBoQFujDvrau9/1BPeV6KvUNLWhkai/JawXqFp1CrvG9+5JfN9anew1OXi3UxtgYmJNXWkhaQfW7yilqhhpTNnAvh5o58u6bQxgz/UkiB7Sme3QK86Yt5Pt3ltHxwOnybfp3cM7Fim+f7XOjz6wvTVMu8ekXa+i69yTTx3Xmm2d6YVBUqvPauYsreqDQK8NxyI4qPzeU1w//EtKdoLkh/DqhK05pOXz5cSR/Tp3P0HVJ6BeVokXLubzyEZ6uJraPfM6saE+Uxvf+XquMC8mKaf3I5xK652pSnhDes8vAI7pVejQnhB8nd8cmq7we9GbpkWPalTpxyfaiYz1+eKonQXMnM31cZxqfy+Lb91cw7/k/GbT5EOqS/y5Luvk8VcRrXfw/07wiStT3XiQpUSurpKdvZbn9ta7l0TYei7qrRq68FmebkRnTmqxoT0rzjFCZFGDtl0yxbxI/PlWP2WP/fyLPNx9GcNrFirAAHzb1uPNkqRwLY+aN6cSioHb0jTpKcEQsb/y8mQxrU8L925BmZ8GwDclMnbuTJ0P33LNtV4U/1hxTMrZ0wLbnAQysK69/4/3IMzEgdHg7lg5tS89dJxgdEcurv21h8sJoVgxqzSXbnjS3cMHJyJrkGzWwD6s0z6i8xvUeFOpSSnNk41Zt4GRsDcC5/MuVfq4CY32WDfVmhb8XXW8rPSo0UJFrrM/pBtZ3vl8tuWR703UzQxaO7EBYgDe9dxxjdEQcb/64kSnzd7F8sBcRA1tzzdzojvc9f+N5cja2qcqQa71cEwPUJZryD053oS7RkGdiUIVRVaybr/ULsmIvHkGNS15zU5w5OSMAbakSpXEhastraEtUZGxpx+XtbXGbEgHuF1g0oj1LhnnTe+cxgiPieOPnTUz5czeq5ywonTz5jscu1lexpl8r1vZpSYe4M4yOiOWZBbvJN1Szpm9Lwm4c72+1c/do21UR0lZ1paxEidPwbZV2jgd1s354c/emeB28SHBELBNC91K6PAjGPEbz4b6sf8Q1fZVJAdoSFYp7bE7TlqhQVmEZhag89oaWAKQXVF3bHM1tpUetjqTy2Scrsb6Sj1VOPpetTEm1tyD/H5dwa/ol2zspUatY37sl63u1wCfxHKNXxDF5YTRPLNnHut4tWBLgzQVHy7/d5+bzZGdoeadDioe0w8+dAVsOc+UeyatZfjFre7eowqgqli5e66L2qVHJ64Nu4ilVK9nQqwUbejbHJ+k8wRFx+H7yCepvvuGVHk1YMsyb885W/zqPVk/B3naN2NuuEe6nMgiOiCNwbRLD1ySys5Mb77zhj+fRtFu1c8nNHQgN8GFXR7cK66UIoCnQJ3VNZ6w7HcTYufJXpB6YQkGCpzMJns64XMjm5c3n8QkLY/j8+Tj4NCQs0IfY1i4PteHN2i+ZjC3t0NO/+yXa0nxD7Hrvf5RHIKqJ+oblVzEyCnN0cv6DzR25VN+MUw2scMi4jl3mdeyycskxN+SivQU55kblJQRqJVY51bul3kNTKIj1ciXWy5WGZzMJjozDf9Mhhq1PYneHxoQF+JDY0gkUCi7deJ5sDSv/6lNdssPXnd7bj2FUUEyB0b9rX40KiilWKdnp666D6CqGjYE5AJeL6kbnD1E5alTy+tCbeBQKYts0ILZNAzY6jEX9yy8M/OtPAtYns6tDY8ICvO86kSelsS2fvjzgRvPvBIatS6JHdArJzRz4/qme1LtWwIhV8Xz6+WouOFiwdKg3a/u0pNBQ/bfj3K3UwcY36a675dM3dEKTZ4xz0NaH/6ZVkfPOVvz1ckdafDeXjJ++pOnsefzwTjgnGtUnLMCbLV2bUvoftVy3s/FN4vL2tmgK9FEa/bsNmqZAHz2VBhvf5Ip8GEJHLNQmAOQU666eNNfEgDIFnHa15pxTPewvX8fh0jVaHr9EvqG6PIk1M6zRl2zv1xlXG76c1o+ZT3QmcE0iAWsT6br3FEfc7QgL9GFX1/LRnubqO++KFw8ny8qUn6b0YNqMKKxy8rlurE+JWom6RINZfnni+tOUHjWu68XtLA3KS72yi6RLjHh4NWrDVkVs4tE2b07xr78ycvYk5o7uSKujafz81jJmvbSIPlFHUZZq7ni/TGvT8gR2bgg/TOmBVU4+7323nhGr4lk6uC0fv9yfHHNjXpq+jfAJM5myYBfWWeVvxA/Tr7SsRMnFyO6Yt0rBrOnD972sStdLC8DGhiv/e4GRsyfxxfN9UZVqeOf7DSwJmc3Ypfvue8ObvtV13KZEoNUoKc4xpaxYhVYLZcUqinNM0WqUuE2JkDZZtYCx0gCVnpL80iJKtHd+/VWFHX7umN4YyqFRKbnoUI/Y1i4cb2SDVqGgyZlM2h5OpUhfhcXVO3+Arm2yLU2Y/bgfI+aE8M3UXpgUFPPB12tZOGkGZT98hzo3H2Nl7U/mq9Jxdzvef8Oftb1boAdY5eSjB6zt3YL33/Cvmd0ubmOmKq+jvv6IHWlE3VajVl4rchPPFUsT5oz1Y+GIDvTfdpjgiDje/3YdT8/fSfiQtqzs73nHFZYCI33Ch7RlxaA2dNl7ktERsbwwezvXTQyIHODJwpHtGbD1MI+FH2D0ilg2dmzFp6mfoTB5sH6ll3e0pTjTEvdnlz3Ad0i38kvLE1MTleGt+uE1fVvSIe4soyNieXrBbsYt2cfaPi1ZMqwtafb17nk8U/cLNH9jPpkxnmTFtKY0xwylSSF2vfdj45ssiWstYawqf50VaHS7g/pOl2y1egou25hx2doU28zr2F3OpfP+0yybOIv1vVuUlx451f66zyJDNZED27Cyf2v89p9i9IpYvN5+F+0XXzG1b0sW+Lckw9Zc12HWGllWpkT4e9Wq2uqbbr7e83X8ehc1W41KXitjE8/NiTyr+3nie+A0wRGxf+sqsHRoW9Lt/l3XVabUY4dfE3b4NaHF0TRGR8QyZkUs2og4NndryptvD6FD/DkGrj+Kf+lgosw78ZvdeLZYdMHR2AZTtRG5JQWkkkXRP0odtGUKLob3wrhhKpY+Rx78G6UjRWXlz4u+3m0/VgoF+3wass+nIW6nLxMcEcuw9UkErk1kZ0c3wgJ9ONj87oMN9K2u4+gfLb1cazEDvfISmyLNo48ufRT/dck238iAlz7pT7G+iuCIOAZtOsTQ9UlEty+vB01o5fSv0iO92yZQjXbtTtjZHTW6PZBWT8Hujm7s7ujGIsNeOMxYwODwcPwj9hLVxYPQAG+ONbHXdZiiGrv5ei/U8eu9oumhYJRrN5pbuHDk6vka/1qv7mpU8lqZm3i0egqiOzQmusO/J/JE+TUhLNCHIx53/qV8uJkD770xGIf0q4xcWb7JYcC2I8S2duFZh89pVHKOyVl/seTEM6QYe7C68f/Y5vgYlvrlQxTOF18lK6b1rQQt+0Bz8s/b4/HyX9VhuNd9Ky0rv+SrVNy5GuVko/p89tIApo/rQtCaG/XDMSkcbOpAWKA3Ozu5o6nADW+iZtC78UOu0ZbpOJL/v2TbNSaF7jEpWOXkk2diwNreLdh524StL6f1ZcYTfgSuTSRwbRJd3lrKUXdbQgN8iOrc5NZAk1Gu3W4de4JbXwBCz26v+gdWCfLbeFI8bx6vBzbEb8lWhmxMps+OYyS0dCIsoHz4w80hJkLcVJ1e7xVplGu3W6/xTjblbTlry2u9OqpRyWtVbeK5OZFn+rguBK2OZ8iGg/TedZzEFo63finfqatAmr0FP03pyZzHfBm6IZmgVQnMy3qJo4aN+cZxCiUKfZ7PXMKLB6cw/vg7rG7wDH85jOPCP0odLob3wqB+NjZd4x/pcVS1+32byrI2Zca4Lvw5sgMDtxxmVGQcH3+xhlQ7c5YObcuaPq0o+I8pM0JUlvu9ZHt76dGArYcJjozjg2/WcWneLpYN8WJVf0+aW7j87T7//HdtkGVXj98mdWPemI4M3niIkSvj+PzTlZx3rMeSYd6s69WCon9sYBWitn2sqQuv9eqkRiWvNzfxnJwRQHGOKSrjQhTqUrQlKkrzDdFTaSp0E09GfTN+n9CNecGdGLzpICNXxvPZZ6s471CPpcPasq73v7sKAOSaGrIoqD1LhnrT5AVnnrs8l+/OfsJllRVLHCewxP09+l2cyxMpHzLy1JcsshzGr/UmkbqmM5c2tSfvZAMMHTNI3+B7z24Ed6LLuezqG+UCJWX3t+mmwEj/VjP0zvvK6+hemLmdiYv2sHKAJ+GDvbhsI0MIarvSGyswKj3djF9+VMUGKlYObM2q/p74HjhFcEQcz87dyZOhezkx4gKKt75E26ABAEeuntdxtBXnn6/3fGMDlgR4Ez7Ei+7RJwheEcsrv28lZGE0EQNbs3ywF9mWJroMWVQDN39eVHo1Kv34T0eunr+14nrz36Ly1LifHl1s4ikw1mfpMG+WD/aia0wKoyNiefmPbYQsjCZyYGvCB3vdMTEsVSvZ4t+YxZvX0lO5jWfT5/HsuW8pvGDIevvRPNtyAR0vRfJYZgQTs8JYm96Hb7TGROk5oG+V87fBC6buF/4zTl3PZTdQlifyxWUPVstUptRjp687O33daXEsjeAVsYxeEUtwRBxbu3oQGuDDCTcZQ1lbFWnKr6IYKmv2ant56ZEb0R3cykuPIuLo9dcqlIvWoAkIYMXwjoSZp+s6zApjeOP1XvSP17tGqcfWrk3Z2sUDz8OpjI6I5Yml+xizPJZNPZoRFuDNaVeZzFVX3Xx/uPnzU1uEnS0f3357zauoPDUueQXdbeLRKPWI6uJBVOcmeB5JZVREHGOX7Wf0ilg2dyv/pXyyUf2/3edmqcN2ZRd2eXTAo+Akz1z6k+C0RQSkzmO9SU8eV8/FxyyayVf/YqtmELHqNvyR/xiRlv0oKjK5YzeCf7LOzr01lz3X9P97L5boq7iir8KooJhpM6J4/w3/SluBNb3RAiW39OGnXh1u6sD7N+qHR6yKZ/Cmg/SLOkpsaxfCArzZ49NI6uhqmbzS8l3HJrWo5dJxdzs+/t9Apo/vQmiSKao5cxgZHo7Hf5Qe1SQ3W2Tll95l17hCQXJLJ5JbOuGceoWRkfEM2nwI/82H2OvtSmiADwe8GjzUEBNRc+Xe1pWmNtGilRrXKlSzf3vqikJBcgsn3n1rCGOmT2DlgNb02H2cedMW8u274XSIOwPa8l2Gt/cr1WYak33FkZkFTxNkEMqvqqdol5/IspLHGFi4lTcMP2IqP2Omd42Zp14nLnkgL+RMx7Qoj8wYz3uG1C0mBXWp5o5TWaD8Er1+qYauMSkV/d245WbD8msV0L8vzd6Cnyf3IGhOCL9N6IrLxSt89VEkC55dwJANyegX3b3jhKhZispKKNQUo69UY1TDV1//KaO+GSWffkrB8eP8FNId28xcPvtsFQufmU/AmkQMC2vmjmtjpQH6SjWFmuJ/rbzeyQVHS75/phdBc0OY8bgf7qcv8/17y5k3bSEDNx9CXSKv57riZn9XCxlwIR6BJK+PKNWhHj881ZOguZOZPq4zjc9l8e37K5j/3J8M2nQQdUkppu4X8A/+lmfyZ2CdnU+WxoZL6vqctbNmiH44n+v/D5OSAmblPc9bep+zyCaAiY2/5rSBCx9d+JZjZzrxyrLV2F+6+zi9btEp5P7HJqebc9kri3UlTE7JNTVk8fB2jJo1kY9eGUCxvorXftnMskmzmLAohnq1dVRnHZNTnAeAlX4trXE2M2PpMG/GzJjAe68N4rqZIa/8sZVlE2cxecFurLN1N1nsYVjql1+9ufm83a9r5kb8GdyRkbMn8dkL/UCr5a0fN7J00myeWLIP82t1Y/hDXZZ9Y4perX2tiypRI8sGqqPrZoYsHNmB0AAfeu88xugVsbz50yamLNjN+l7NaZN8keL6SvwbhP3tfiuvjGatXj9+L3wGTw7zutEnfHDxe67rGfNX/eH8ajeO4dnrmZz5F09N0dy1bZdpXhFZlvf+JFvZc9ltDesBlTOzWqNSsqlHczZ1b4bXwQuMXhHLxMV7GLtsPxt6NWfJMG/OulhX+HlF1bhUmIO9kSV2hvW4WJCl63AqjUapx7auTdl2ox40ODKOx5ftKy896t6UsAAfTjWs/vWgdkblgxkuFV55qPuXqFWs69OSdb1b0D7+HKMjYpny526eWLKXdb1bsmSYNxcd61VgxKK6yCjMAf7//UKIhyHJawUrVSvZ0KsFG3o2xyfxHKNXxDF2eSxlCrhkY0aqvcXfOhSYKq+RqzUltbQBV43NiGkxG8+8I0y9NJ9JGaFMvrSIlRb9GOf6I0N85ty1bVeuiQHqEg0l+nd/StUlmkqdy+5gZAVAWsHDvaHdF4WCBE8XEjxdaHA+m1GRcQzYdpihGw4S064RoQHexLV2kTq6GqY8CWpU/jN05aSuw6l8t9WDOqXmMHJlHIM2H2LQlsPs82pAWKAP+9q6VtufYwfD8uQ1/VFf6woF+71d2e/tSqOzmQRHxDF440EC1iWyq6MboQE+JLdwrLbfB/Hgbr4/ON54vxDiYUjyWlkUCmK9XIn1cuX3VxaXj5bMvI795etk1zMm1d6ca6aGdLPcwJ9pz6BBRWPFSc5iRrJJc55p/AUfO7/I5EuLeDJjKYFX15N7WZ+rJgZcMTek4bnsv7Xtim7fiD47jnHlHsmrWX4xa3u3qLSH3MD4xtCF/MuVdo7bnXOx4pvn+jDrCT8C1iYxfE0CP74TzvHGtoQFeLOlq8etZvGiejuXlwFAA5O611XiomM9fni6F7PH+jFsfRJBqxP49v0VnHS1ZkmAD5u6N6VEXb1+VbveeJ7OVeBr/bSrDV+80I8Z4zozfHUCAeuS6LbnJIc97AkL8Ga7XxMZYlILXLjxM+NiUv8/binE3clvgiqg0pRxonF9DrRpwAXHepjnFuJ5NJ3Wh1MZqV1CbokFZsoc6mmv/e1+qfr2fGHyMgP1V5Ji5YCqVIPT5evYZuWRb6jirGM9ivWVvPzHNp4M3Yt1dh5md6kZMyooplilZKeve6U8RrVCiZOxNWXaMs7nVU3yelOOhTHzxnRixJwQvnyuD/olpbz73XqWhMzhsfD9mOY+fPcDUTXO3kheG5pWXiu36u5m6dGoWRP59MV+oFDw5s160LC91aoe9ObzdPN5q0jZlibMeqIzI+aE8O3TvTC7XsiHX61l8ZS5jIqIwzj/Lt0NRI1wLu8yZdoynI1tUCtkcUE8nOr1cb6Wuv2S/jknSy7YW2CblYvjpWv4nj/GUZqziGAiyoZQVqy6NXhBcU0f+/TLPOv0JZesDLmkdcH6Sj6O6VdxyMylVKkgy8KYT6f1pcv+03Tdk0L9rFyumRly3sGCa+ZGt+ayF6uU/DSlR6W1yWpkao9aT8XZvIz72n1cGYr1Vazu78mavq3oGHeG0StieWbeLsaH7mVN35YsHepNmr2FTmIT95ZyPRWAJmaOKFDU6ZngJWoV63u3ZH2vFrRLuFEPujCacUv2sa53C5YEeHPB0VJn8SlQ4G7mCMDJG89bZSg0VBPh34aVAzzx23+a4IhYnp+9nQmLY1jV35NlQ9qSUV82/dQ0RWUlXMjPpIGJLQ1N7ThRiT9DovaS5LUK7PBzZ8CWw7cu6Zcp9Ui3NeeijQVzEt9katkfvKn5mhf4iTmpj/Gr8WTS6lnTyymSacxBZXVjJ7JCQZaVCVlWJphdL8Tx0lVss/N445fNbOzRjDfeGUb3mBP0izqK5/FL5BqpSbW3YFW/Vuz0a1KpE7aa3RiFd/zaxUo7x/3S6inY064Re9o1wv1UBsERcQSuTWL4mkR2+LoTGuDD4WYOug5T3Ca7OJfLhVepb2iBs7FNlZWeVGsKBQfaunKgbXk96KjIOPw3HWLY+iR2d3AjNNCbpBZOVV4P6mJsg4nKkIzCnFs7xytTmVKPXZ3c2NXJjWbH0xkdEcvIyDhGRsaxrYsHoYE+lTp8RVS849cu0sDElubmLpK8iociyWsV2OHrTu/txzAqKP5bH9btOQOYXxpCS5d91FOeJd3egqf3zePprHlsb9AEh7Qccs0NKPnH05RZXJ/w/P7sKO2PlUEOk8rmMnjHGgZuPcJ+rwZ89PIAnNNyCFqTiMfpTALXJVNsoGZTj2aVVjvnaeEKwKGrZyvl+A8rpbEtn748gOnjuxC0Kp5h65PpufsEyc0cCAvwYWcntxrfLL62OHz1HN0NPWldr6Ekr/9w2tWGL6f1Y+YTnQlck0jA2kS67j3JEXc7wgJ9iOpcdfWgrS0bAeXPV1U76mHPB6/5Y5dxjRGr4hmy4SB9dxwjvpUzYQHeRLdvLENMaoCDV8/Sx6EtnvUasvLiXl2HI2ogedeuAllWpvw0pQdKjRarnHzUxaVoy7QsSn0KV/VJepus48sX+vH220MJnjmRpcO86Rh7hhYpGTQ9mYHVlbxbQw+O5rXi3ZO/sC4zCAVl5BoY8qPqOZo12Mv79V/F9XQOH3+1ln5RR5kzuhNfPN+HMj0Fb/60iaUTZzOuEmrn9FDgZeUGQNKV0xV67IqSaW3K9Ce7EjQ3hB+m9MD6Sh6ffLGaRU/PI2hVPEYFxboOsc5LuHIK4NbPkvi3bEsTZj/ux4g5IXwztRem+UV88PVaQifPITgitkrqQdtYNgb+//nShUu25vw6qTtBc0P4ZWI3HNOv8sUnK1k4dT7D1iViUEOHP9QVN98n2lg2Rg/5sCEenCSvVeS4ux3vv+HP2t4t0AOOp/lwtKA17X0j+OjNgbcue2XYmvPbxG4EzQ3hvEM9DItKaZ6SgXfyBUxTy5h+9lWUlGKlzkRfrxglWpTqUvKslfxgN5nmDnv5YEoApSolr/22hSl/RrPd15233xjMica2TF4YTfiEWahffBFFSsUMLGhi7oS52pj0givVvkdngZE+4UPaMmb6BN55YzDZ9Yx5cUYU4RNm8dT8Xdhk1axm8bVJ/I0WWW0t3VAq5FfTvRQZqokc2Iaxvz/JG+8MJd3WnOdm72D5k7N4dvZ2bDOu/fdBHoJSoYe3ZfmHC10mrzflmRgQFuhD8MwJfPDqIPKM9Pnfb+XDHyYtjMbyyoMNURBV42JBFukFV7DQN6HJjfppIR6ElA1UoSwrUyL8vYjw9+Lgu0+hzr9G6guX0NP/dy1qvrEBS4e1ZcDmQ+hptTilX6PNxbNsZAgRBv6EKwLI1rNCpSnjkk35pgWlUTFFOab8qTeWLT82xCfpPMERcUxavIcifSXrerXg1XeH0n3PSfznz0c1axafdWhEWIAPiS0fvnbO16YZAPuyjj38N6eKlSn12N65Cds7N6Hl0VRGr4hlzPIDBEfEsqVrU0IDfTjZSFq5VKW0gmzO5WXQwMSWVhauJOZUz1X86kSrp2B3Rzd2d3Sj6Yl0RkfEMWJlPCNWxhPVxYPQAG+ONbH/7wPdp1b1GmKqNuJsXgZpBdkVdtxHpVEp2dKtKVu6etDm0EWCI2IZt2QvY5YfYFOP5oQNa8sZ1+o//KEu2Zd1jKHOnehUvznHrut+r4SoWSR51YHcFCdyEpriOm41evp3n+l9s1ZWo1SQ1NyBOYde5fGSUMYVLeaxoqVsUvViqXo4Ryw73rqPyriQrJjWOPpHE9umAbFtGtDwXBajIuMYuOUwAeuT2dWhMUXz5qFMTMTztx/puvfU/9fO+bk/cG/ULvVbAhB9+cjDfUN07FAzR9590xGH9BxGrozHf9MhBmw7woE2LoQF+LDXu2GNraOzzs6lW0wK3aJTMM0rItfEgB1+7uzwda/UDXwPKybzKA1MbOli21KS1wd0rIk9H746iN/Hd2HEqgSGbkimz45jJLR0IjTQp0LqQbveeK3vyTxaESFXPIWCxFbOJLZyxuXilfIhJlsOM3jTQfZ4NyQ00JvYNg1k6EE1EHP5CEOdO9G1fkvmn9qs63BEDSPX5nTgwoqeKI0KcRgYfc/b/a1W9moB0XThbeP3GWsyh7WqAfQujWJewVMsOjWVnld3g1aLQl2KJs/wb8c508Car57vy8jZk5g7uiOtjqZh+Nhj6G3cyK8TuvHdUz0xKSgur52bMpfgFbGY5N1f7Zy7qQMuJvXJKc6t8clGmn09fprSk6C5Ifw+vguu57P5+sMIFjy3gMEbktEvvvsHjerII+USH/4fe/cdV+V9/n/8dTjsoSiKslRkbzjHvbeyDwIe2qxmt+m3aftNR5omXUnTtP2l6Uj7bZrVkSagKIchoLj3OoeNgnshIiB7H87vj6PGJIqowIFzPs/Ho4+2iee+Lznc9/lw8/5c11ubWbO9gj4J1I+zpU8Ca7ZX8Mu3NuN76qqhS/yKXVdLAFjkHCKiAw+o1nkMf3t6EWv/+QzvPr2IybXNvPVGFv/91j9R5D54HlQqMWORczDw+fs0kl10G8fbLywn6aNneP/ReficqeWPr23i4xc/Yc32csx7tIYu0aQVN56lsbsVD7uJeNmL7i/C/ZHodAPvp+jgM0kX/s4jD3Qi1Ypf4GQ95iv/vL6zGcW2XzzQMR/ElmVvALB6x6vDds7bddaM59jzr+Cm2IXnkzkDeo1TQysLD54i7ZNfYa7tRWrRS/04O3QO3Xz9+iaerf2UyT11lNv48NcJT5LuGIvfmx/d9XiWXb3kVftg/u67mFVVcXWCA+kxYdROHIMir4SIsku02ViSvTqYDbER1Dp/9X276Zs+USR4zCPz4kH+dnLzfX89RjLzHi3L9lWhVKnxPXONBkdbMqLCUEWF0jjW1tDl9cupoZVfvrUZrVTyhQ4XN9l0dCPV6vj5y9Ej7gnsP2a/yFQ7Z35e8snIfcJ3Hwx9z5Fq+1h84CTKDDWBJ6/S6GCNKiqMjOgwGsbZDfg4cyf484vQRznfepXnjvxlCCseGpbdvazYfQKlSsP0C/XUjbdjY0w4mWtCaXGwvvcBhEH3bd8Y4tznkHHxAH8/mftQxzL0dTZSGOrrMBRrvH2x76h1Ot2MO/27YXu0cae/VH//3FhdzliCxKwP17i9A37Nzaxs/aNXKXX3pNzPhRrnMVy1mcA7rs8RHrqVb3vqv2H/dvEVyk4v7HciT7eVOb1PP02nWs2PX4vnsstYvv3Pffz43QJOTp/Ij1+N48BMT5KyCkl79iN+8bvN+FfVfOU4lmbmrJgcDsCWK5r7/2KMcL0WUrYuDeDpPz7Cd99IpNJ7Ek9/epD0pz7gB+9uw+PSyMn8fdmig6ew6NXeceEK+o1rlr1aFh4cnE17g2lrtRqAKNc73rOE+6SVmrFjoR/Pv/01vv1WMqWBbjy+/jAbnvqQl/+0Fc/zdQM6TqTrTGD0XuvdlubkrgzmiXcf46VfJnBmihPP/3s/G598n++9txPXK42GLtHkbLlxra+YHI6lmUgxjmbDvcYT3y3DqLvRnqvbZuO8VI2VU9N9v37C3BKu7Y5A22GJ1Obz1k7dZpZ8NkHBJ7bJLGk+xMuOv7o1kSd3RRAb4iPuPJHHzIwDs6ZzYNZ0fE9dRZmpYe3mYhJzitg1z4dXX4klrPwysVtKWb63iqIgN9IUcvbP0mfnlk4Kw8HClsrmS5xuvfIwX5qRTSJBEzYFTdgUpl7U54fX7Kggfksp+2d6kpogpyjYfUTl6BYdOEWr7Z0Xrje12Fqy+OApVNHhw1PUAG2tKeQJr5XMdPJlsvU4ajqvG7ok4yCRUBLkTkmQO+7V10nOLCRqWznR28o5LJtKqkLOsfA750EnW49jppMP3X29FNQUGqD4QSSRcEQ2jSOyaXidvYZSpSEuv4SEzUXsmePNeoWM0gDXEXU9G6tTrVeobL6E3xh3lkwKZeso/cFIGH4iVDaMruQsoK9HilvCzgd6veX4FryeU6HTSulutKev2xydDvq6zelutEfXZ86F/63jR2/G8fi7j7F9kR8xW8v47zf/yZtvZBFafulWv9gvq/KexOsvRaJ8/ynSFDJmFZ7nrTeyCKy8wtvfWsa7Ty1kcm0zv/n1zexcCWudwgFQXTz4oF+SUee8hxO//5+VJH30DB99bQ5BlTX85ZV0Pvj+p6zcdRxp78jI0dm3ddFj0f/Gux4L6YCzzcOpuaedXVdLMJOYkeAxz9DlGKVLruN451vLSPz4Gf7x6Dy8z1zjnZ99nge16PlivnutxzzMJGbsulpCc0+7gaoefKc9J/Lm91eT/MHTfJI0C1npRf724/X8/YepLNlXhVTbZ+gSjV7mpUOA/ntMEAZKLF6HibbDkiubFzB+djm2HrUPfBx770sEvPwvJi0/CkjobXQAJExafpSAl/+FvfclQD+R563vriL5o6f5T/IsQisu89eXN/DeS5+xbG8l9N5581HtRAf+78lFJH70DH9+ZjHOda38/O184vNK+SxBxhvfW02LnRUv/d92AuSL6HntFUorTW9CSqOjLR9/fS6JHz3D7769HOvOHn72dj5pz37E1zYdw76106D1tdpZYXGPDSkWPVra7KyGqaL7s/HCfgDWuMoZazGy88WjWfMYG/6jnE3yR0/zm++uQqLT8dM/bmX9Mx/x2PojOLR0MtbCltWucgDSL+wzcMVDo97Jnvcfn0/ix8/yh28uZWxzB6//djOfPvcxyZkabNrFEJOhsudqKXVdzXjaT2a2k5+hyxFGCREbGCY1W+fQ22qLe+KOhz6W5fgWXKMP4Brdf7cC0E/k+eCx+fwneRaROypYl6nhl7/LpS81hN4XXsDWs4t2268uYDpsLdkQL2NTTDiLDpwkRaXh+//YTZODNZmrQ7B+5WdM2ZTPmHf+TKrUjIIl/qQpZJw1sV6K3VbmZK8JJWdVCHPUZ1GqNLzw8V6+kXqInJXBbIiLoGbS2GGva888b9Zsr+C65d0vcYf2bnKXBw5jVQN3ru0qh+tOMHuCP8lTFvLB6S2GLsmo9ViYk7siiNzlgcwsvECKSs1z/9nPY+sPcz4xEhvXixwc28P5tgf/wXs06LS2ICNav4lr/pEzKFVqXvxgN09+dojs1cGkx0Zw7UZfbWFw9Oi0pF/Yxzd9onjEcymHR1G/cMFwhm3xWt/ZfNedaMaur9eMatVixgSdZoz/eYPU0GVtgSoq7NZN+Y1d9Vi+/DIbbS3JXhVCemz4HbsKaKVm7Fzox84FvoQcr0ap0vDoxmNIMotoT0rgzdcSkB87fSs7dyRCn507GmFavRR1ZhIOzpzOwZlfzQ/vnutNWoKcCr/hawdzs0ewTUf3XbsNdJtL2TvXe9hqul//PruD2RP8iXWfzaaLB2jobjF0ScZPIuGobCpHZVOZfq6Ox7NKWLYhHz7LYez8QEJigigNNP48aJ/UjL1zvdk715uAqhqUGWqUKg3rMgvZscCXNIXs1lRE4eHlXj5K8pSF+I1xZ/7EQPZfqzB0ScJ9Gu413rC1yrrJ0O0sDHH+qztmcPKdrxP4s/cZP3NkNPLfsuwNzNRq9r36LEv2VQGw88ZNub+JPBYSKR9NjMXt48/Q/fNjLDu7OBI+hezVwXhcbiRxcxFO19s5PW0CafEyti32o8fCNB/wT6xrITGniLj8UhzauigJcCU1Qb/hrU869Ikd31NXefEfu7Ds1dJia0mPhRSLHi0O7fqF65+fWzLiP4BfC/4aC5yD2FKt5g8nMgxdzgMx9D3vYbwUsJZVuFDzzq9x+vdnjG3ppMJ3MqkKGXvm+aAdhu/jkWLy1SaSsouI3VqKbUcPmhB30hRyDs7wHLVDTEaSWLfZ/I9fLJfa63j+8F/o1d3f/oHRfJ0NJkN/HQbz/COiVZap0vVJuLxxKbZTrzBuxshYuN7UJ5fzyx9GkfL+U6THRTDv6Fk++N/P+MvL65l/+DSSvq/+YBPvMRfn4Bmc/tWPSfz4ad57fD7TL9Tz+m9zWbGnkg+/Npff/s8K0Ol45U9b2fD0hzy2/shd23YZs2sTHPj7NxaS+NEz/OnZxUyob+XNN7P577f+ydqcIqwfsFn8QFV5T+LnL0eTuzwQM2B8YztmQO7yQH7+cvSIX7gCfHh6Kz19vax0iRAz0IeZj4Mrq1xk9EyayE8SppP00TO8/c1lOLR08qvf5fLZcx+zTmU6edCaSWN595nFrP34Wf765ELcrjTy29cz+fe3/01sfgmWXaNriMlIk1t9lItt13C3nYDCfY6hyxFGONN8JDaMrh8LoP2CC77f/++I/U3bVecx/PXpxfwzZQ4xW8tIzirkrTeyuOA2jvXxEeQvDaTL2gJna0ce81wGwHsn82i0t+KT5FmkKuQs31tJSoaaH/1tO/WOtmyKDuPjlPHEbS27lZ3LWx7E+ngZl10dDfsXHmYdtpakx8nIiA5n4aFTKDM0fP+9nTz93wNkRoayMTqceqehGRRws0fwSGuHNVDVHfWoLh4keepCvusXz4vq9+jTiR3gQ81MYsZ3/RWAvptIdUcDWFugig4ja00I846eRalS850Pd/PkZwfJXh1CemwEtRONPw/aZmdF6toZbIiLYOm+k6So1Pzor9t57j8HyIgKZVN0OI2OYpPh/dLq+vj7yVx+Hf4Ej3ouY3dtGde67r+lpGAaxOJ1iF3atBSriQ1MWDTyeyO22VmRliAnPTacJQdOocxQ84O/7eCZ/xxAFRWGzw/fwFpqyZ6rpRxrOHnrdb0WUrYsC2TL0gDkxRdIydDw7H8P0mlpTt7yQH74s3iWHDhFzNYyFHnF7JvtRZpCRkmgm9Fn526nlZqxa74vu+b7Eny8GmWGmkfSj5KSoWbbIv2Gt9OeEw1d5ojz33M7WTQpBJ8xbiS4z2Xjxf2GLsnorXWfi4+DK1c7G/nk3Bdb+/VJzdg3x4t9c7z0eVCVmuRMDclZppUH1ZpL2bbEn22L/Qgvu4RSpeHJ1MN8feMxti4NIE0h47yHk6HLHFWONZxkT20Zi5yD+Y5fHD8r+Y+hSxJGKLF4HULNJ6bSXO6F57MZmJmPnqdFWnMp2xf5sX2hL2Hll1Gq1Dyx/ghkRNO5LpmchQ7gavPVF0okqMOnog6fyrTzdSgzNUQXlBOfX8L+WdP5+Y+i8D91FUVeCYsOnabCZxJpCXJ2m1h2DqAswJWyAFdcrzSSnKVvFh+5o4Kj4VNIVcg5IptqUgv7/nRou/lLZRZvhD3OE9NXcLS+igvt1wxdltGaYjuRx6evAODPJzLp1N49FnDcdzK/+FE0k2qbSc4qJGZrGat2n6Aw2J3UBBkHZ0w3/jyoREJRiAdFIR54XGpgXaaGyB0VxG4t46B8GmkJctShHuJ6HqD/q9pMxDgvZk/wY7WLnC1X1IYuSRiBTGvFMMwupS/D3L6dyStHaR9UiYTiYHf++uunaDx2mN4nnkC6YQPvPv93fv/zDGYUnr/r0INzUyfw2xf1fWb/vW42IRXV/ObNbGZpzvPu04v5w/NLcGjr4pe3Zeds20dew/yhVu3iyJ+eX0rix8/w98fnM+1CPW//IoN/f/vfRG8tw7Jb5OgAjtZXsaVajZXUgh8HJWMh6X8Ag/BgLCRSXg5ah5XUgvxq9Rd+w9Kfq85jePeZxSR+/AzvPrUI15omfvt6Fp+88C/i84qxGuJ890hx0X08b397BUkfPcMHj8zF73Qtf3x1Ix9+77+s3lGB+T16LwvQ0N3C36pyAPimTxSuNuLptfBVYvE6RNovOtNwOASX6H1fGOU62liYmfNKsBIr/yAKfvIUaz98ivcfnYfPmdovTOS52025YZwdHz46j6SPnuH/vbAMu45ufvrHLXx9k5rM1SH84geRXJ3owHc+3M3GJz/ghQ/34HzN9Foitdpb89/kWaz74Gne+P5qtFIzXv5LARue/pAnUg8xtsn0Nrx92f+d3Ex1ez3eDq485xNp6HKM0nM+kXg5uFDdXs//ndx836+/GT1Svv8kv/hBJO02FvzgbztIf+oDnv7kAOOutw1B1SNP41hb/pUyh+QPn+at76zEokfLq+9sYf0zH/L19KMGH2Iy0u24WsyuqyXYmlvxSrASCzPxS2Lhi8R3xBC5tGkpZpbduMbuNXQpD+VFvzi8HVypbq/nTycyaR9rw7+Vs/lsrZyVuytZp1Lz0z9u5fl/72djjL65d4uD9VeO02VtQWZkGFmrQ5l77AwpGRr+5+O9tNlYkr06mE8T5KzefYLkLA3JWRp2LvAlNUFuEtm5230hP1xyEWWGmmf+e5BHNxwlf3kg6+NlXHQbZ+gyDaJD281vytfztvxZ4tznUNl8iW01RYYuy2ismBxBnPscuvt6+U35+n7jAveiNZeyfbE/2xf53YoePb7+MF/bdIyCJfo86Lkpxv9ErdvSnM2rgtm8MojZ6nOkqNR861/7eCLtMLkrglgfH8GVyY6GLnNE+tOJTHzHuOPj4MqLfnG8fXyToUsSRhCxeB0CXXVjubZLzuTVh7AYO3qfNCR4zGOVi4xObTevl31Gu/bzX+vfPpFnVuF5lBlqnv/3fh6/dVOWUe3i+JVj6swkHJjlxYFZXvidrEGp0pCUVUhSViG7FvjyyiuxyMouEbuljJV7KikMdidNIePATBPIzt1OIkEdNgV12BSmXahHqdIQVVBOXH4JB2ZOJ00hpyjYtDa8AVS1XOZvVTl8z1/Bi37xXGqv40TzJUOXNeoFjPHgRb84AP5amU1Vy+XBOfCN6FFxsDsel6/r86Dby4kpuJEHVchQh5nAQBOJhMMzPDk8wxPvM7Wsy9QQn19CQm4xe+d4kZogp9xftIK7Xbu2i9dLP+Ud+XOscpFxprWGjIv3niopmAaxeB0C1VmL0PVJcFPsMnQpD2zBxECe814DwNvHN3GmtebOf1Ai4YhsGkdk0/A6e411mRritpTeuCl7k6aQURpw54k8lT6T+dUPo/j7EwtuNf9esaeSoiA3fv/CciY2tJKUXXTHtl2m5NwUJ3774kr+8dg8EnKLScgtYcErGzjh7UyqQs6u+T5ozU0nA5pXfQwfB1ei3Wbxy9DH+L76PX0rJ+GBuNo48cvQR7GSWrD58hHyh2iDzEW3cbz9wnI+eGQeirxi1m4u5o+vbeKk50TSFDK2L/Sj18L4v49PTXfmze+v4R+PLyAxp4j4vBKWHDhFmZ8LaQky9szxHpYhJqPBmdYa3j6+iZ8Gp/Cc9xpqOxvF9C0BEJnXQdfbak1N/lwmLijGevLo/EANGzedHwcmYyYx48PTW9lTWzag1532nMhvvrea5A+f5pOkWUSUXuRvP17P33+YytK9lUi1d+64UOs8hr89vYjEj5/hL08vZnJtM794O4+4LaV8ulbOG99bRZuNpUlm5253fZwdHz0yj8SPnuH3LyzHtqOHX/y/PNKe/ZiUTcewazOdDW/vVuVwtL4KR0s7fh32BOMtjb+/6FAYb+nAr8MeZ6ylHYfrKnn3xkaZodQ01uZWHvQ3L65Equ3j1Xe2sOGZD3l0wxEcWkwjD1rnZM97Tywg8eNneOf5pYxrauf1tzbz2fMfk5hVaDLDH+5lT20ZH53eipnEjB8HJhPm6GnokoQRQCxeB9mVvPloO6xxW7vz3n94BAocO4VfhjyCpdSC7EuHWX9+z30fo368Pe8/Pp/Ej5/lD99cytjmDn71u1w+fe5jkjPvPpGn3daK9QoZKe8/xc9/FEWLnRXf/8cuvvPhHo5ETOEnr8RSGujK4+sPk/7Uh/z4z1uZdr7uYf/Ko063lTlZkaE8+rcn+PFrcVx2Gcu3P97Lxic/4H8+2M2k2qGZJT2S9On6eKMslarmy7jaOvHbiCdxtLAzdFmjyjhLe34X8RSutk5UNV/mzfK0YR0A0W1pTu7KYJ549zFe+kUCZ6Y46bPzT77P997bieuVxmGrxZA6bCzZFBPO1//+DX76kxjqx9vxvfd3sfGpD/jmP/cyob7V0CUaXNr5PWRfOoyV1IJfhj5K4Ngphi5JMDARGxhEfd3mVGctxDHiBPZeg5QZG0ahjp78KvRRbMytKLhSyF8f8ilMp7UFGdH6TVzzj5xBqVLz4ge7eerT/ifyaKVm7Fjox44FvoRUVJOiUvNY+lF6pVIKlvjz8qvxzD12lsjtFcQUlHNINo3UBBPJzt3m9vyw76mrKFUaErMLScwuZPc8H9IS5Bz3nWzoModMp7abV4r+ye9kTzPdfjK/i3iKl4s+pqFbfNjfy3hLe94KfxIPu4mcabnCK0X/fKgNWg9FIuGIfBpH5ProkVKlIS6/hITNReyZ401agpyyAOPPg/ZJzdgzz4c983wIPHEFpUpNSoYapUrD9oX6DaynpjsbukyD+WtVDjZSS1a4RPDrsCf4ecknlDSeNXRZgoGIxesgurp9Jj2NY3BP/MTQpdy3WU6+vBr8NaykFmyvKeIPJzLQcecerverT2rG3rne7J3rfX8TeSQSSoPcKA1yw736OsmZ+mb+0dvKORIxlV9/bzVTLjfcys6dmjaBNIWMbYv8TSI7d7sq70m8/oNI/a8hcwqJyy9l+b4qigNdSVPI2T9rulHm6Fp6O3i58CN+F/E00+wn8Y78eX5S9E+qO+oNXdqI5WbjxJvh32CyzTjOtV7l5aKPaekdGa3YTntO5M3vr+a9x+eTuPlGHvTgjTyoQsbeud4mMdCkwt+Fn78cg0tNE0nZ+uEPq3edQB3qQWqCnMOyaaa1gRXQoePtExmYScxYNjmMN8Ie542yzzhSX2Xo0gQDkOju0mT+Thx8JunC33nkoU64ZdkbAKze8epDHWeknV+nlaD+1suY23UQ9oc/jvgHgLd/HWLcZvGCbwxSiRmbLx/lL5VZg7ZwvZvJV5tubdKy7ehBE+JOmkLOwRme/d6UxzR3EJ9fSmJOIU7X2zk9bQLp0WFIgKTsIqZfqKduvF2/bbtMgU17NzEFZSRnFeJS28xFF0fWx8vIXx5IpxFueBtrYcuvQh/Df6wHjd2t/Kz4P1QO1o75h2Toe97t/Bzc+FXY4zha2nGi6SKvlfyH5p52Q5d1V9adPURuL2ddpgb3K01UO49hQ3wEm1cE02Fraejyho19aydxW0pJzC7Cub6Vcx7jWR8vY8vSALotTesZlBkSvuMXR5TbTLS6Pv5WlUPO5SMj6jozJEN/HQbz/Pti31HrdLoZd/p3YvE6SOr2h3LirW/g//I/mTC/ZFCPPRRufh0yLh4gwWMeAP89u5N/n90+rHXYtXURu6WUpOxCJtW1ct5tnH6RtSyQbqu735QtenpZsbsSpUqN1/l66sfZsjE6nItu44jdUsqsogt0WOkzdevjIu7YtssUSLV9LDpwkhSVhsCqGpocrMlcE8qmmDDqx9sburxBZS215LXgFGY4+dKt7eFPlVlsqyk0dFkGv+fdtHJyBC/6xWEpteBofRWvl35GV9/omHxlpu1j/pEzpGSoCT1eTYudFdmrg0mPjeDaBNPZrGfeo2Xp/ipSMjT4nqnl+lgbNkWHoYoMo9HR1tDlDavHPZfziOdS4IufY4a+zgzN0PcbsXgdIkNxfp0Oiv/3e/S22SD/v7eQSIf2qeVguPl1AOjp6+VPJzIpMOAHvbRXy9J9J0lRqfE7XUvjGBsyokLZFB3e/01Zp2Nm4QVSVGpmFZ6nw8qcvOVBHJJPY8mBk6zcfQKptu+ebbuMnk5HSEU1ykwNCw+dolcqZdtiP9IUcs5Mm2Do6gaNuUTKt3yiiHGfDUDmxYP841Q+vTrDjeU09D3PXCLlOe81xHvMBSDn0mH+dnIz2mHcnDWYAiuvsE6lYcmBk+gkEnYs9CVVIeeklwnlQXU6IkovkZKhZt6xs3RZSNmyLIC0eDkXPMYburphs3JyBN/1j//CBC6xeDWeNVZ/i1fT+n3DEGkq8ab11BS8XtgwKhauk60/n9B0rbOJX5elcrz5ogEr0k/k2bbEn22L/Qgv00/keTL1MF/feI+JPBIJR2VTOSqbiuf5OpQqDTFby1DkFbNvthc/+1EUASevosgrYfHBU5T7TSYtXsaeeT4mkZ275bb8sFt1I8lZGqK2lRO1vYIj4VNIS5BzJGLqqF/Y9+q0/KUqm5Mt1XzbL5Z4j7kEOk7l9xXpnG+rNXR5w26qnTM/CkzC28GV7r5e3q3MZssQ9XEdLhV+Lvzix9FMvtpEclYhMQVlrNp1YsDRI6MgkVAY6kFhqAdTLjawLlPDmp0VxG0p48AMT1IT5BSGuI/66/leCmoKudRex6shX2OC1RgAJlmP42rndQNXJgw18eR1EJT97Dnazroy88M3MLPsHbTjDhUzJOQtex2AdXt/Q1PPyOyZ6nGpgXVZhURuL8eqW6ufyJMgRx3q0e9Nefz1NtbmFKHIK2FsSycVPpPYFB2GfXs3SdmFt7Jz6XER5Kw0rezc7RxaOonPLyExp4gJDW2cnurEeoWcgsV+9FiM/p9r/ce483LQOlxsxtOt7eGjMwWoLh4c8jz3lxninidBgsJjLk9NX4ml1IIrHQ38pnw9lUY4jcy+tZOYrWW3okfn3MezPj6CLUv7jx4ZG8emdhS5+uEP45o6qJruTGqCjB0LfI1+iImjhR1pC38CwJodrw37NT6SGNMaS8QGhvD8rafdKPreS0x9fDMeycObF30Yhn4f7sfYpo5bE3nGN7YPeCKPVWcPkTsqWJepwaO6kSvOY9gYHU6dky0JuSWEVZhudu52Fj29LN9TSYpKg9e5ulv54czIUJrH2Bi6vIdiI7XkeZ8oIl3197/jTRf5W1XO4I0/HYDhvtZ8Hdx4wTeGgLEegH4i2Xsnc+kwVCusYfLl6NH1sTZkRIWREWVaeVDLrl5W7zrOOpWGaZcaqHWyZ2NsOFmrQ2i1N94NrKPpM20oGfrrIGIDo8TlTUuR2nTiErnf0KUYrZsTeT5bO4OVu06gVKl59Z0tPP+vffquApGhd7wpd1lboIoK+0Kf2f/5eA+ttpZkrw7h07UzWLXrBEqVhnWZ92jbZcR6LMzJXx5E/rJAZhTp88PPfXKAx9cfIW95IOsVMi65jrv3gUagDm03fzyh4nDdCb7jF0/AWA/+NON5tlzR8PHpghH7W4cHMdbCjqe8VrHGVQ5AfVczf67M4lDdCQNXNjy+GD3S50Gf+uwQj6QfZetSffTovMcdokdGptvKnOzVIeSsDGa25hwpGWq+9c99PJF2mJyV+h/Ur0wea+gyBeGhiMXrQ+isGc+1feG4xe/G3N40RhoaUrelOZtXBbN5ZRCz1edQZmr45r/38/j6I+SuCGJ9fARXJjt+5XU6Mwn75nixb44X/lU1pNzsM5upYecCX37y01jkJZeI3VrKqt0mlp27nUTCsYipHIvQ54fXZWqILignPr+E/bO8SE2QURLoNipzdAfrTlB0/Qxfn7aUBI+5RLrOYJFzMJmXDpFx8cCIbhd1L2MsbEnwmEe8+xzszK3p6esl4+IBPj23y+iftt6RREJRiAdFIfo8aHKWhsgdFcRuLePgDE9SFTI094geGQOdmYRDMzw5NMMTn9O1KFVq1m4uJjGniD1zvUlVyKnwdzF0mYLwQMTi9SFcVi1BYtaHa/z9j1AVHoJEwuEZnhye4XljIo+a+PwSEnKL2Tvbq9+JPCd8J/OLH0UzqbaZpOxCYreUsXJPJYXB7vz22yuYVNdKYk4hv309c8Btu4zR2akT+O2Lq3j/sfkkbC5GkVvMwsOnOe49ibQEObvmj74Nbx3abj48vYUtV9R80yeKmU6+fH3aEtZ6zGPz5aNsvLCP+u4WQ5c5YE6WDiRNWUCU20yspfrc9tH6Kv5+MpdL7aY3NvlOLniM5+1vr+DDR+ehyNXfI/706kaqpk9kffy9o0fG4qSXM2+8FMnfv7GQxOxC4vNLWbr/JKUBLqQq5Oyb7WWUQ0wE4yUyrw+op8mOo0+9xoRFhfh+N20wShtWhn4fBptTfeutiTxjWrv0E3kSZOyd0/9EHru2LmK26pv5T6pr4YLbONJjwui0siBxc/H9te0yYladPazZWYFSpc8P10x0ID0uguxVwbTbWhm6vAcSOHYKX5u6mFkT/ADo7utlz9VStl8tpuj6GfoGqZXUYF5rZhIzwsdNZ/mkMBZNCsHyRougw3WVpJ7fTUXThYc+hzGz7O69FT3yvNjAtfF2pMdGkLXGuPOgX2bT0U1UQTnrsjS4Xm3m8uSxbIiLIHdFEB02o3MDq7F9pj0oQ38dxIatITJY5z//yRoupq1C9re3sPUYfS14DP0+DBWbjm4it+s3abnVNFE9aQwb4u49kUfaq2XJgVMoM9QEnLpKo4M1qshQKr2did5WwYIjZ+iykJpUdu5OJH065h3VN4sPL79Mm40l2auD2RAbQa3zGEOX90C87F1ImbaYBRMDMZPof9Bp6Gphd20pO2qKH3pz12Bca74ObiybHMZi5xDGW+k3Fvbp+thXW07q+T2cbr3yUDWaGkmfjlmac6So1Mwovki7tUW/0SNjZabtY9GhUygzNARXXqHZ3oqsNaGkx4RT7zS6hpgY62fa/TL010Fs2BrBtB2WXNm8gPGzS0flwtWYddhYsikmHFVkKAsOnyZFpea77+/mqU8PkbUmhI0x4XfsKqA1l7J9kR/bF/oSVq7vM/v4hiP0SqVsXerPD1+LY/7Rs59n5wbYtsvY6Mwk7J/txf7ZXvidrEGp0pCUVUhSViG7FviSqpBR6TPZ0GXel9OtV/h1WSouNuNZNimMZZPDcLedQILHPBI85nGx7RpH6iu52F5HdUcD1e311HU1D0k7HgkSJliNwdXWCVeb8XjYTmD2BH/cbT8fJHGpvY4dNcXsuFrMlY6GQa/BFOjMPo8eeZ+pRanSoMi7ET2a40Vqgpxy/ztHj4xJn9SMXfN92TXfl6AT1aRkqPnapmMoVWq2LdIPMTntOdHQZQrCV4jF6wOoKZhNb6st7kk7huwcTg2tLDp4ikUHTmHf1kWrnRV75nmzZ6630Y31HAp9UjP2zPNhzzwfAk9cQalSk5KhRqnSsH2hL6kJck5Nv8NEHomE4mB3ioPd8bh8neRMDZHbK4gpKOeQbBq/+t81eF5oYG1uMX80wezc7Sp9JvOrH0bx9ycWkJRdRNyWUlbsqaQoyI3UBDkHZk4fVRvernQ08N9zO/nvuZ1feNLpYTcRD7svfoB3a3u40tGgX8x21HO5vf7W/77W2URfPwtbMyRMtB6Lq40TbjcWqa42+v92sRmPpdTiK68ZzCfBwhedmu7Mr/93De89sYDEHH30aMmBU5T6u5CmkLN3jmnkQcv9XXntJ6641DSyLrOQqG3lRO44zrEwD1IVcg7Lp5nUD+rCyCZiA/epr9cM9XOvYDWxkdDfvjuYpd3ie+oqL/5jFxa9WlptLemxkGLRo8W+vZsecyl/fm7JQ7dzMvT7YAguNU0kZesn8th29KAO9SBNIeOQvP+uAmObOojPL2FtThFOje2cmjaB9JgwpH2QlF14KzvXX9suU2Db3kXs1jKSsgqZfK2Fi66OrI+XkbcskC7rry7IRgMziRnhjp74jnG/tdB0sXHCyeruPYG7+3qpubGwbevtZPnkcAAO1Z3AzcaJyTbjvjDO8svqu1q40qFfDF9ur6eq+RJFjWcHLYMr9M+mo5uobeUkZxXeih6tj5ORu3L05kEfhH1rJ3H5pSRlFzKxoY2zHuNZHy9j69IAui1H3nMvU/xMuxNDfx2MPvNqaA/6ha3dIafqnUcIfO0Dxs+qGOSq9E9cf/nWZrRSyR1vlDYd3Ui1On7+cvRDPYE19De4Idm3dt5aZDnX35jIo5CxZUlAv10FLLt7WbFb3xd2+oV66sbbsTE6jMsujsRuLWNm0QWTzc7dTqrtY/GBkygz1ASevJEfjgojIzqMhnF2hi5vUNhILXG58cRUv6i98QTV1unWmMr+1HU1U33jae3ljvob/7ueKx0NptneagQy0/bdih6FHL9Ci50VmWtC2BgbQd0oy4M+DPMeLcv3VqJUafA5e40GR1syosJQRYXSOHbkbGA15c+02432NdbtxOL1Dh7kC6vTQeF3fgg6iPjL/0NiNviZt4TNRazZXsH1fna1j29sJ3d5IKro8Ac+j7jQ9Zu0lu2rIiVDg++Z+5jIo9Mxq/A8ygw1s4ou0GFlTu7KYA5HTGXp/ipW7KnErE93z7ZdRk+nI7TiMikZGuYfOU2vVErBEn/SFDLOTp1w79ePUlZmFrcWsrOcfFlzY7rXr0o/vbVg7errMXCVwv0IPHGFFJWaRQdPoZNI+o8eGSudDlnJRVJUGuYeO0uXpZS8ZYGsj5dx0X28oasTn2k3jOY11peNyMXraPwGazgaQMWvnsXn+58yadmxITnHOz9Np08CPf38Wsaiuxcz4PtvJD3weUbz+zDodLpbE3nmHz1Ll4WULcsCWB9/764CXmevsS5Tw8rdJ5Bq+9g7x5v8ZQEEVtYQn//Ftl175nibRHbuTtyrr5N8I0dn3d3LYdlUUhVyjoVPMfocnbjWjMet6NHWMmw79dGj1AQ5h2XTRlW++2FNu1DPukwNq3Yex6pHy75Z00lTyCgKdjfY9Syus5FBdBsYgS5tXIbVhOtMXKQZsnPYt3VRP67/X8X0WEgZ3zh6JwKNOF+ayLMuU8OanRXEbbn3RJ7TnhP5zfdW84/H57M2pxhFXjGLD56i3G8yf3p2CWNau0jKLuT1tzZ/np1bEdRv2y5jdMl1HO98axkfPjKX+LwSEjcX8c7PNnFq2gTSFDK2L/Kjx0LcjoSR7crksfzl2SV8/LU5xG0pJTG7iN//UsU5j/GkjeA86GA7N8WJ331nJe8/Oo+E3GIScktY8Eo6lV7OpCrk7Fzgg9bctDawCsNLPHkdoOYTUyn54XfxfEaF2xBO1BJPXkcGx6Z2FLnFrN1czLimDqqmO+sXWQt9+70pW3f2ELm9nHWZGtyvNFHtPIaNMeE0ONqyNq/4Vnauv7ZdpsCip5eVuytZp1LjdV6fH94UrW9x1uJgXBvexLVmvMx79NEjpUqN75lrXB9rw6boMFRRYSMqDzrULLt6WX1jiMnUy9e5OsGejbERZK0Ooc1ueIaYiOtsZDD6DVuj7Rus4tdP0lzmxcyPfoXUZug2VIjM68hi2dXLql3HUWZqmHaxgVonezbGhpO1uv+JPGbaPuYfOYNSpSasopoWOyuyVwdTEujGqp3HWWzK2bnb6XTMLLxAikrNrMLz+vzwiiA2xMm47Opo6OoGhbjWTIBOR0SpPno070YeNH+pPg96wcPwedDhIunTMffYWdZlapCXXKTdxoKclcFsiIugZtLYIT23uM5GBhEbGEHaLzrTcDgIj3XbhnThCrBnrjfLd1di09F9124D3eZS9s71HtI6BL1uK3NyVoeweWUwszXnSMlQ861/7uOJ1MNsXqlfZF2Z/NWbcp/UjL1zvdk715uAqhqUN3rMrsssZMcCX155JRZ5yUViCspYvevEgNt2GR2JhKOyqRyVTWX6uTqUKjVxW0pJyC1m32wvUhVySgNdjT4XK4xyEgmFoR4Uhnow9aI+D7pmRwXxW0o5MMOT1AQ5hSGGy4MOF52ZhAOzpnNg1nR8T11Fmalh7eZiEnOK2DXPh7QEOcd9R9cQE2FkEk9eB+Dkn5Vc2y1jxoevY+nYOuTnu9nn1bJXS8ttfV4d2vUL17v1ee1ucKDuYCj1B0LobbPB3K4Dp3mlTJhbguX4li/82dH4PowUNyfyrNhTiUSnY89cb1IVcir8Xfp93eSrTSRlFxG7tRTbjh40Ie5krgllUm0zSTlFX2zbZSLZuTtxamglYXMxirwSxrZ0UuE7mTSFjN3zfNCOwg1v4lozTY6N7Sjyvhg9Sk2QsWNB/9EjYzOxroWk7EJit5Th0NZFcaAraQo5+2dNH9QNrOI6GxlEbGCE6Kofy7FnfsrkVYfw+tamYTuvU0MrCw+eYvHBU9i1ddFmZ8XuG0/y7tTftfWUO6f/oUDXK0Vq24nEohddjznadmsk5lq8nlNh733p1p8fbe/DSDShvpXE7ELi80txaOsa8EQeu7YuYrfom39PqmvlvNs40mPC6bKyICmn6Fbbrk3RYagi79G2y4hZd/awZnsF6zI1eFxp5IrzGNJjI8heFTyqNryJa820WXb1snrXcdapNEy71HArD5q9KtikBprYtHcTva2M5MxCXGubuejiyIb4CPKWB9E5CENMxHU2MojF6whx9uMYLquWMOO9N7GePDLniHc3OHD8rSeQSLV3jDVoOyzRaaUEvPyvW09gR9v7MJLZdHQTVVDOuiwNrlebuTx5LBviIvRdBfqZyCPt1bJ030lSVGr8TtfSOMaGjMgQTk53JqagXJ+du9G2Ky1eblLZuduZafuYd/QsSpWa8PLLtNpakr06hPTYCGonjvwNb+JaE0CfB52jPotS9cU8aHpsxB2jR8ZKqu1j4cFTpKjUBFXW0GxvRWZkKBtjwsXgHSMgMq8jQG+rNTV585gwv3jELlwB6g6GouuVYm7fccd/L7XpprvRnrqDIbhGHxjm6oxfh40lG+MiyIgOY+Gh0yhVar73j108/d+Dt27Kd5rIozWXsm2JP9sW+xFedhmlSs2TaUfospCydWkAP/xZPAsOn7nVtsuUsnO365OasW+OF/vmeOnzwyo1yZkakrP0+eE0heyhxyULwlDTmUk4OHM6B2dOx+d0LUqV+lYedPdcb9IGED0yBlqpGbsW+LJrvg8hx6tZp9LwSPpRUjLUbFukH2Jy2nOiocsURjixeO3Hlbx5aDuscU/cYehS+lV/IASpbWe/f8bctpP6g6Fi8TqE+qRm7J7vw+75PgSdqCYlQ83XNh1DqVKzfaEfqQnyO9+UJRKKQtwpCnHH45K+z2zkjgpit5ZxUD6NX70UyfRzdazNLebPP00fcNsuY3TcdzK/+FE0k2qbSc7SN4tftfsEhcHupCbIODhjumlteBNGpZNezrzxUiTvPbGAxJwi4vJLWbb/JKUBLqQq5Oyb3X/0yChIJJQGulEa6IbrlUbWZRUSVVBG5I4KjoRPIU0h54hsqkn9oC4MnIgN3EVftzlHn34Vu2lXCH79PUOX06/C730fi3HN/V7jOh30NjoQ/s4fgdHzPox2LjWNJGcVEl1Qjm1nD8fCPEhT3Hsij2NTO/F5JazdXMz4xnaqpk9kU3QY0t4+knOK7qttlzGza+siZmsZyVmFTKpr4YLbONbHR5C/NJCuQcjRDQZxrQn38uXo0SWXsWyIk5G3PLDf6JGxcWjpJC6/hKScIiY0tHFmihNpChkFS/zvOcREXGcjg8i8GlhN/hxO/XUdwW/8H45hJw1dTr9Kf/pNkOgws+y965/p6zYHJIS88Xdg9LwPxsK+tZO4fP0mrYkNbQOeyGPZ3cvKXSdQqtR4Xmzg2ng7NkaHUe3iSHx+qT47Z23Rb9suUyDt1bJkvz4/7H+qlkYHa1RRYWyKDuP6ODuD1iauNWGgzLR9LDp0CmWGhuDKKzTbW5G1JpT0mHDq7xA9MlbmPVqW760kJUON97k66h1t2RSjH2LSPMbmjq8R19nIIDKvBqTTSriUsQR774uMDR3ZC1cAp3ml1G6fgZnl3dt49bZbM2n50WGsSrhdq701nybNZH28jOV7K1GqNPz43W08+8kBMqLCUEWF3nEiT7elOZtXBbN5ZRCz1edQZmr45n8O0G5tQe6KIFLjI1i+7yQJufqntANt22VstOZSti/2Z/siP8LK9fnhx9cf5mubjlGwJIA0hYxzU5wMXaYg9KtPasau+b7smu/7lejRtkV+pCnuEj0yMr0WUrYsC2TL0gDkxRdIydDw7CcHeGz9EfKX64c/XHQbZ+gyBQMSi9c7qD8UQme1M/4//teoiNtMmFvCtd0RaDss79ptwMxcy4S5pQaoTrjd7TdlWclFUlQanv70II+mHyF/aSBpChkX3e/QVUAi4fAMTw7P8MTr7DWUKjXx+SUk5Bazd44Xr/4kmuDjV4jPL2WpqWXnbieRUBzsTnGwOx6Xr+vzw9vLiSnQ54fTFDLUYVNEjk4Y8cr9XXntJ664XtFHj6K2lRO54zhHw6eQFi/jsHya8X8fSySow6eiDp/KtPN1KDMLiSooJy6/hP2zppOmkFMc5Gb8XwfhK0Rs4Et0Oih+6Xv0ttoi/7/fIJEO/OtjSDf7vPb1SjG/rc9rb7s1ZqLP64g27YJ+Is+qncex6tGyf6a+q0BRcP9dBZzqW0nKKSI+rwSHti7K/FzIiA5lbHMnSdmF99W2y5iNbeq41Sx+fGM7Jz0n3tjw5kevxdBveBPXmjAY7Fs7ic8rITGniIkNbZz1GE+aQk7BEn+TGmgy7nobazcXo8gtxrGlk+Pek0hTyPjRq5+BhYW4zgxMZF4NpLHEm7KfvoDXCxtwiTxo6HLui37CVgj1B0PRtlkjtevEaW4JE+aWiglbo4BjYzsJufpFlmNzB5Ve+q4C95rIY9PRTeSNZv5uNU1UTxrDhpgImsbakJBbTMiJK7TYWfXbtssUWHb3smL3CZQqDdMv1FM33o6NMeFkrgmlxWHoNryJa00YTDfzoEqVBp+z12hwtCUjKoyMqDCaxt45D2qMrDp7WL3zOMpMDVMuX6fP3Z3eF14gbvol2uysDF2eyRKLVwMp+/lztJ1xY8YHbyC16jF0OUNmpL8Ppsyyq5fVOytQqjRMvXx9wBN5zLR9LDh8mhSVmpDj+gVr1poQSv1dWLPzOAsPnabPTNJ/2y5ToNMxS3MepUrNrKILdFiZk7symPVxEVS7OA766cS1JgwJnQ55yUWUKg1zj52ly1JK3rIbedA7RY+MlKRPx9xjZ3hz13Wke/fSZmNJzqpgNsRFcNV5jKHLMzliw5YBtJ5xpVHjz9THNhv1wlUY2bqtzMleE0rOqpBbE3le+Hgv30g9RM5K/U25ZtJXuwr0Sc3YM8+HPfN8CDxxBaVKTUqGGqVEwvaFvvzkp7HMLLpAdEE5a3Ye51iYB6kKuWlk524nkXBEPo0j8mk38sMa4vJLSNhcxJ453qQlyCkLcDV0lYLQP4kEddgU1GFTbkWPIrdXoMgvZd+NPGhRsPHnQXVmEg7M8qLr5TeQFBay/6fPkJhdSGJ2Ibvn+5CqkHPCd7KhyxQGmXjyepvK//cIDUeCmPnR63edVmUsRvL7IHyV76mrKDM1LNtbhUSn00/kSZBT4dd/VwGXmiaSsguJKSjDtqMHdagHWauDcbna/IXs3PoBtO0yZk71rfocXV4xY1r1+eE0hYy9c73RPuSGN3GtCcNl3PU2EnKLScgtwbG5gxPezqTFy9m5wMfoB5rcfp05X2shKbuQ2C2l2Ld3UxTkRppCzoGZnqa1gdUARGxgmHVeHcex517BNW4v05/OMnQ5Q26kvg9C/ybW3bwpl+HQ1kVJgCupCXL2z5re703ZvrWT2K1lJGUV4lzfyjmP8aTHhNNjLiUpp+gL2bm7te0yBdadPURuL2ddpgb3K01UO49hQ3wEm1cE02H7YBvexLUmDDfLrl7W7NDn4PXRIwfSY8PJXh1itHnQO11ntu1dxGwtJym7EJfaZi66OLI+Xj/8YaQMMTE2YvE6zE6/l0BN/lxmvP9rrCY0GbqcITdS3wdhYGzau4neVkZyZiGutZ9P5MldEURnPzdlaa+WZfuqSMnQ4HumlutjbdgUFcqZqROJ2VZu0tm525lp+5h/5AwpGWpCj1fTYmdF9upg0mMjuDbB4b6OJa41wVD0edCzKFVqZKWXaLex6Dd6NJr1d51JtX0sOnCSFJWGwKoamhysUUWGsikmnAYDDzExNiLzOox6muy4unU2ExdrTGLhKox+HbaWpMfJyIgOZ+HBU6So1Hz/vZ08/d8D+q4C0XeeyKM1l1KwJICCxf5ElF4iJUPN058dpstCypZlAfzotXgWHj59Kzs30LZdxqZPasbeud7snetNYOUV1qk0KFUa1mUWsmOhL6kKOSe9nA1dpiD0S58Hnc6BWdNvRY/Wbi4mMaeIXfN8SEuQc9wE8qBaqRk7F/qxc4EvIRXVpKjUPLbhCF/bpGbbYv3whzPTJhi6TOE+iMUrUJ2zgL5uS9wTdxi6FEG4L1qpGbsW+LJrgS/Bx6tRZqh5JP0oKRlqti3yJ00hu3NXAYmEwlAPCkM9mHKxgXWZGtbsrCBuSxkHZnjyyx9E4n32GmtzS/jLK+kDbttljCr8XPjFj6OZfLWJ5Cx9fnjVrhNoQtxJU8g5OMMTnZnpLOyF0anKexKvvxTJe48vIDFHHz1avq+K4kBX0hT3jh4ZBYmE0iA3SoPccK++TtKN4Q9R2ys4EjGVVIWcoxFiiMloYPKxAW2nJUefeo0xgWcJfPUjQ5czbEba+yAMntsn8th29nA0fAqpCjlHZFP7vSk7NrWjuNFndlxTB1XTndkYHYZFr5bkrMIvtO3KMuLs3L3Yt3YSs7WMpOxCJtW1cs59POvjI9iyNJBuq68+DxDXmjAS2bR3E1NQRnLW53nQDfER5C3vP3o0Uj3odebQ0okir4TEnEKcrrdzetoE0uJlbFvsR4+FeL53v0TmdZhUZy3kzPsJhP7uz4wJOGfocobNSHsfhMF3cyJPUk4RExraODPFiTSFjIIl/v3elC27elm1S9/8e9rFBmqd7NkYE8YV57HEbylFXnLRqLNzAyXt1bJ030lSVGr8TuvzwzebxTc6fr7hTVxrwkgm1fbdih4FVerzoJlrQtkUE0b9+NEz0ORhrzOLnl5W7K5EmanB61wd9eNs2RgdTmZkKM1jTGf4w8MSi9dh0Ndrhvq5V7Ca0Ejo7941dDnDaiS9D8LQujmRJyVDjfe5OuodbcmIDkMV2f9EHkmfjtmac6RkqL+wYFWHerB8X9Wttl275vmwXiG7Z9suo6XTEV6mzw/PP3qWLgspW5cGkKaQcd7DSVxrwuig0xFyvBqlSsPCQ6fQSs36jx6NMIN2nel0zCi6QIpKzWzNeTotzcldEcSG+AguuY4bhEqNm9iwNQzq9kbQdW0807+5ydClCMKQ6bWQsmVZIFuWBiAvvoBSpeGZ/x7k0Q1HyV9+o6uA21dvyjozCYdmeHJohifeZ2pJyVDf2uyxZ643P305hrDjl29l5wbatsvoSCQUhXhQFKLPDydnaYjcUUHs1jIOzvDEzGwFfYsXG7pKQeifREJpoBulgW64VTeSnF1IVEEZkTsqOBI+hbQEOUci+o8eGQWJhGMRUzkWMRXP83UoVRpitpahyCtm/ywvUhNklAQa//CHkc5kn7zqdFD44g9Aa0bEu79HYjbwr4MxGCnvg2AY087XocwsZNXO45hrtRyYObCJPBPqW0nMLiQ+vxSHti5KA1zYFBnGuKZ2krKL7qttlzHT54dLSMgtZnxjO32hoby53IXtC/3otTCtDW/C6OXQ0kl8fgmJ9xk9MoSh/Ewbf72NhM3FJOQWM7alkwqfSaQlyNk9z+ehh5gYGxEbGGINxwKo+OWz+HzvUyYtP2bQWgxhpLwPgmHdaSJPqkLOrvn9T+Sx6egmqqCcdVkaXK82c3nyWNJjwmh2sGFtbjFBlTU021v127bLFFh295J7NQCLv/wFs+PHuTbejvTYCLLWhNBqb23o8gRhQO4UPdoUE45qBOVBh+Mzzaqzh8gbwx88qhupmehAelwE2auCabc1zQ2sXyYWr0Os5Ccv0FnjxIx/vImZhdagtRjCSHkfhJHh5kQeZaaGKfcxkcdM28fCQ6dRqtSEnLhCi51+wVrm58KaHRUsGoXZucG2Zdkb0NfHz/7fY6So1Mwovki7tQW5K4JYHx/BlcmOhi5REAZGp0NefIGUDA1zNOfotDQnb3kg6xUyg+dBh/MzTdKnY/6RMyhVasLLL9Nqa0n2qhDSY8OpdR4z5OcfyUTmdQi1VE6hucwbz6dVJrlwFYQv67YyJysylOzVIcw9doaUDA3f/ngv30g9TM4qfVeBq3e4KfdJzdg934fd830IOlFNSoaar206Rp+ZhO0L/fjxq/HMLjxP1Lbyz7NzA2jbZXTMzDg8w5PDN/LD6zI1xOfrYwV753iRmiCn3N/V0FUKQv8kEtThU1GHT70RPdIQXVBOfH4J+2fpo0fFQcafB9WZSdg3x4t9c7zwr6pBmakhOUv/n50LfElTyKj0Mf7hD4Zkkk9ej7/5DRpLvJn50euY23YZrA5DGgnvgzCy+Z66ilKlYdneSgB2z/chVSHnxD0m8rjU6PvMRhfo+8weC/Mgc3UIbjVN9922yxjc7VqbUN9KYk4R8Xkl+vywvwtpCjl753iZ1oY3YVS7mQdV5Bbj2NLJcW99HnTXPO9hHWhi6M+0SbXNJGXrhz/YdXRTFORGaoKcAzOnm9QQExEbGCLtlyaieeHHeCRvZ+pjeQapYSQw9PsgjB7O11pu3JRLsW/vHvBEHvvWTuLyS0nKLmRiQxtnPcaTHhOO1tyMpOyi+2rbNZrd61qz6egmals5yVmFuNU0UT1pDOvjZOSuDKLDxnI4SxWEB2bV2cOanRWsyyxkyuXr+jxobAQ5q4KHZaDJSPlMs23vInZrGUlZhUy+1sIFt3Gsj48gf2kgXSawgVUsXofIyT+v49puOTM+fB1Lx1aD1DASGPp9EEYf2/YuogvKvzCRZ328jPzlgf12Fbi52UOp0uBz9hoNjrZkRIZydqoT0QXlzFXrs3P9te0azQZ6rZlp+1hw+DQpKjUhx2/kh9eEsDE2gjoT3fAmjD6SPt2t6FFE2SXabCzJXh3MhtiIIc2DjrTPNKm2jyX7T6LMUBNw6iqNDtaoosLYFB3G9XF2hi5vyIjM6xDoqh9D7c4ZTFp52KQXroLwINptrdgQL2NTTDiLDpwkRaXhpb/v4Jn/Huh3Is/tfWZlJRdJUWl4+rNDdFlKyVsWyA9fi2PxoTNEFZQTl18y4LZdxqZPasaeeT7smedD4IkrpKjUfC1DTYpKw/aFvqQmyDk13dnQZQpCv3RmEg7M8uLALC/8TtagVGlIyiokKatwwNEjY6CVmrF9kR/bF/oSWnGZlAwNj68/zNc3HmPrUn/S4mWcmzrB0GWOWia1eK3OXoSuzwy3hF2GLkUQRi2t1IydC/3YucCXkIpqUlRqHk0/QkqGmm2L/UhTyDkz7Q43ZYkETdgUNGFTmHahnnWZGiK3V6DIL2X/TE9+8cNIfM5eIyG3hAWvbBhw2y5jVOHvws9ejsGlpomk7EJitpaxetcJ1KEepCbIOSybZlI5OmF0qvSZzK9+GMXfn1hAUk4RsVtKWb63iqIgt1vRI6P/PpZIKAlypyTIHY/L10m+cd+LKSjnkGwaaQoZx8KnmNQP6oPBZGIDvW3WHH3qNcbJTuD/4/8M67lHopH2KxZhdHOvvk5SViFR28qx6eod8EQex8Z2EnKLWbu5GMfmDiq9nEmPDse6u4fk7KJbbbs2xA1fdm6wDca1Zt/aSdyWUhKzi3Cub+Wcx3jS4mVsXRpAt6VJPYMQRjHb9i5itpaTnKVh8rUWLrrqo0d5yx4+DzqaPtPGNnUQn1/C2pwinBrbOTVtAmkKGdsW+Y/6ISYi8zrILqUv49y/Ygh/523svS8P67lHotF0oQujh0NLJ4q8EhJzCnG63s7pqU6sV8gpWOzXb1cBy65eVu+sQKnSMPXyda5OsGdjTDhXJ45BkVdyKzvXX9uukWowrzXzHi3L9lWhVKnxPXON62Nt2BQdhioqjMaxtg99fEEYDlJtH4sP6POggSev0uRgjSoylE0x4TQ8YB50NH6mWfT0snJ3JetUarzO11M33o6NMeFkrgmlxWF0DjERi9dB1NdtztFnXsVu6hWCX39v2M47ko3GC10YPSx6elm+p5IUlQavc3XUj7NlY3Q4mfeYyCPp0zFHfRalSoO85CLtNhbkrAxGHerB8r1V9922ayQYkmtNpyOi9BIpGWrmHTtLl6WU/KX6DW8XPMYP3nkEYSjpdLeiRwsOn6ZXKqVgiX6gydn7zIOO6s80nY5ZhedRqjTMKjxPh5U5uSuC2BAn47Kro6Gruy9iw9Ygqt05g57rY3D/3/8auhRBMAk9FubkLw8if1kgM4oukKJS89wnB3h8/ZF+J/LozCQcnDmdgzOn6/vMZmpYu7mYxJwids3z4dVXYggrr76VnRto2y6jI5FQGOpBYagHUy/q88NrdlQQv6WUAzM8SU2QUxjiLnJ0wsgmkVAa5EZpkBvu1ddJztRHj6K3lXMkYiqpCjlHI0wgDyqRcEQ2jSOyaUw/V4dSpSZuS+mNISbepClklAa4Gv/X4T4Y/ZNXnVaC5ts/xsy6i/B33hHv/Q2j+qdUYVTyPF/HukwNq3aewFyrZf8sL1ITZJQE9t9VYGJdy63m3w5tXZQEuJIRFcr46+0k5RTdV9suQxiua82xsR1Fnj4/PK6pg6rpzqQmyNixwNfkNrwJo9eY5g7i80s/jx5Nm0BavIxt94geGdtnmlNDK2tzilHkFTOmtYtyv8mkxcvYM88H7Qj+QV3EBgZJ3YEQTvzmSfx+9C8mLiwelnOOBsZ2oQujx82JPAm5xYy9fSLP/P5vyjbt3URvKyM5sxDX2mYuuYwlPSacFntrEjcXE1hVQ5ODdb9tuwxhuK81y65eVu86zjqVhmmXGqh1smdjbDhZq0NotR+dOTrB9Fj09LJidyXKzNuiRzERZK4JuWP0yFg/06w7e1izvYJ1mRo8rjRS7TyG9LgIclYG02E78oaYiMXrINDpoPgH36W32Q75399CIu0b8nOOFsZ6oQujh1VnD5E7btyUqxv1E3niIsheFUy77d27Cki1fSw8eIoUlZqgyhqa7fXN/Cv8XFiz4zgLD52iVyrtv23XMDLUtSbp0zFbc46UDPUX8sPpsRFcmTx2WGsRhAem0zGzUB89upkHzVsexPr4L+ZBjf0zzUzbx/wjZ1Cq1IRVVNNqa0n26hDSYyOonehg6PJuEZnXQdBU5kVr1VS8vpUuFq6CMMJ0WVugigojc00o846eISVDzf98uIdvfHaI7FUhpMeG33Eij1Zqxq4Fvuxa4Evw8WqUGWoe2XgMrdSMbYv8efnVeGZrzhG1rZyo7RWmlZ27jc5MwqEZnhya4YnP6VqUKvWt/PDuud6kKeRU+LsYukxB6J9EwlHZVI7KpuJ5vg6lSkPM1jIUecXsm+1FmuJG9MjI9UnN2DvXm71zvQmoqkGpUpOcqSE5q5AdC3xJU8io8p5k6DKHjVEvXi+nL8PCsQXn5UcNXYogCHehM5Owf7YX+2frJ/KkqDQkZ+n/s2uBL6kKGZU+d+4qUBbgSlmAK65XGlmXVUhUQRmROyo4Ej6FX393NVOqr5O4uYg//HzTgNt2GaOTXs688VIk7z2xgMScIuLyS1m2/ySlAS6kKuTsm+1lWhvehFHp7NQJvPXdVfzj8fmszSlCkVfCokOnqfCZhPTVCLQKhaFLHBbHfSfzix9FM/lqE0nZRcRsLWPV7hNoQtxJU8g4OMP4hz8YbWyg9awrRS/+gKmP5uKh3Dak5xqNjP1XLMLo5lzbTFJ2EXFbSrHr6KYoyI3UBDkHZvZ/U3Zo6SQuv4SknCImNLRxZooT6THh9EklJGcX3VfbrsEyEq81m45uogrKWZelwfWqPj+8IU5G3vJAOmxGXo5OEO7ky9GjvilT+OtKT3JWBfUbPTI2dm1dxG4pJSm7kEl1rZx3G6ffwLoskG6r4f1BXWReH1Ll249QfyiYWR//CnP7jiE912g0Ej9QBeHLbNu7iN1aRlJW4X1N5DHv0bJ8byUpGWq8z9VR72hLRlQo5zyciC0oY7bmPJ2W5v227RosI/laM9P2sejQKZQZGoIrr9Bsb0XWmlDSY8KpdxoZG94E4V4kfToKOudi/uc/I92/X58HXRVCetzIyoMONWmvliX7T5KiUuN/qpbGMTZkRIWyKTqcRsfhGWIiFq8PofPqOI499wqusXuZ/kzWkJ1nNBvJH6iC8GVSbR9L9usn8gScukqjgzWqqDAyosP6n8ij0yEvvoBSpWGu+hydlubkLw/kwAxPFh06dd9tux7EaLnWgk5Uk5KhZuGh0/SZSdi2SL/h7bTnREOXJgj3dPM6++7fnyFFpWbx/pMA7FzgS2qC3KTyoOh0hJddRqlSs+DIGbospBQsCSBNIePcFKchPbXYsPUQLquWIJGAW/weQ5ciCMIg0ErN2L7Ij+0LfQmtuExKhobH1x/m6xuP9T+RRyJBHT4VdfhUpp2vQ5lZSFRBOXH5JeyfNZ1f/DAK39NXUeSVsPCwPjuXliBn9wjvpTgUyv1dee0nrrjUNLLuRrP4yB3HORo+hbR4GYfl00xqw5swOp24kQedVNt8qz/0yj2VFAbr86D3ih4ZBYmEohB3ikLc8bjUwLqsQiK3lxNTUMZB+TTSEuSoQz1G9fVsdE9ee5rsOPrUa0xYUITv91OH5BzGYLQ8DRKEu/G4fJ3kTA2R2yuw7u7lsEzfVeBYeP9dBcZdbyMht5iE3BIcmzs47j2JTdFh2HR2k5xddF9tuwZitF5r9q2dxOXrc3QTG9o46zGeNIWcgiX+dFsa5XMPYRS723Vm19ZFzNYykrMKmVTXwgW3cayPjyB/af/RI2Mztqnj1hCT8Y3tnPScSJpCxvaFfvRaDN4QExEbeEDnP13Nxc9WI/vrb7GdcnVIzmEMRusHqiB82ZjmDuLzSkjMKcKpsZ1T0ybob8qL+u8qYNnVy5odFSgzNUy5fJ2rExzYEBtOnZM9irwSwssvf56du0vbroEY7dfazfywUqXB5+w1GhxtyYgKIyMqjKaxQ7/hTRAG4l7XmbRXy5IDp74SPdoUHcb1/qJHRsayu5eVu06gVKnxvNjAtfF2bIzRb2AdjCEmYvH6ALSdlhx96lXGBJwj8LWPBv34xmS0f6AKwpdZ9PSycncl61RqvM7XUzfejk3R4agiQ2lxuPtNWdKnY+6xM6RkaIgou0SbjSU5q4LRhLqzcnclS/ZVAdyzbdfdGM21ptMhL7mozw8fO0uXpZS8ZYGsj5dx0X28oasTTNyArzOdjrByfR50/pEz9EqlbF3qT1q8jHN3ih4ZK52OWZrzpKjUzCy6QIeVObkrg1kfF0G1i+MDH1ZkXh/A1YJZ9LbY4564w9ClCIIwzHoszMldEUTu8kBmFZ5HqdLw3H/289j6w+SuCGJD3Bcn8tykM5NwYJYXB2Z54XvqKkqVhsTsQhKzC9k934efvhJLeNll4raUsmJP5YDbdhkdiQR12BTUYVOYdqGedTciG4r8UvbNmk6aQkZRsPuoztEJJkAioTjYneJg9y9Ej2IKyjkkm0Zqggx1mAkMNJFIOCKfxhH5NLzOXkOpUhOXX4Iit5i9s71IS5BTFuBq6CrvymievPb1mqF+/hUsnZoI+91fBvXYxshongYJQj+mn6tDqVKzcvcJpNo+9s32IlUhpzTQtd8PJ+drLTc2e5Ri395NcaArqshQJjS0kZhTdF9tu4z5WvtyfrjSy5lUhZydC3zQmg9ejk4Q7uVhrrOxTR3E55ew9kvRo22L/Ac1DzrSOdW3kri5iPi8Esa0dlHm50Jagoy9c7wHvIFVxAbuU+0uGVVvP0rAqx/iNLt8UI9tjIz5A1UQvsypoZW1OcUo8ooZ09pFhe9kUhUy9tyjq4BtexfRBeUkZxXiUtvMRRdHNsSG025rxdrNRQSevHfbLlO41iy7elm9swKlSsPUG/nh9NhwsleH0GZnOs3iBcMZjOvMsruXFbtPoFRpmH5BHz3aGBNO5pr+o0fGxqajm8jt+uEPbjVNVE8aw4a4CDavCKbDtv8hJmLxeh90Oih88QegNSPi3d8jMRv438lUmcIHqiB8mXVnD2tu3JQ9rjRyxXkM6bH6rgL93ZSl2j4WHThJikpDYFUNTQ7WqNaEcMJ3MlHbKph/5PSt7Nz6+C+27TKla02fHz6LUqVGVnqJdhsLclYGsyEugppJYw1dnmDEBvU60+n00aMMNbMGMQ862php+5h/5AwpGWpCj1fTYmdF1poQNsaEc23CnYc/iMzrfbiu9qf9nCs+3/1MLFwFQbirTmsLVNFhZK0JYf6RM6zL1PCdD3fz5GcHyV4dQnrsnSfyaKVm7Fzox84FvoRUVJOiUvNY+lF6pVK2Lfbj5VfjmXvs7K3s3BfadpkQfX54OgdmTdfnhzM1rN1cTGJOEbvm+ZCWIOe47/1teBOEYSeRcEQ2jSMyfR50XaaGuPwSEjYXsXeON2kKGaUB/UePjEGf1Iy9c73ZO9ebwMorKDPUpGSoUao07FjoS6pCzkkvZ4PUZhRPXktfeYGO6gnMeP/XmFloB+24xsyUngYJQn8CqmpQ3pzII5GwY4EvaQrZPSfyuFdfJylL38zfpquXIxFTyVoVzJTL17/QtsvjJ79Gm5zM6v2vD9PfaGRxvtZCYo6+WbxDWxfFga6kKeTsnzWdPhMbBCEMnaH+TPty9KjcbzKpCjl75w48D2oMXGqaSMouJKagDNuOHtShHqQpZBySe6Izk4jYwEC1VE2h+KXvMe2pTNwTdg/KMY2dU0MrafUBmGdmcvLScVrtrNgzz5s9c72pHy/mmQumaVJtM8lZhcRsLcOuo5vCYHdSE2QcnNF/VwGHlk4UeSUk5hTidL2d01Od2BgTAehIzCnC63w9fZMn88FKr3u27TJmNu3dxBSUfTE/HB9B3vIgOk2oWbwwNIbrgYx1Zw+R28tZl6nB/UoT1c5jSI+LIGflvfOgxsS+tZPYrWUkZRXiXN/KOffxrFfI+J9fpIKNjVi83svxN79BY4k3Mz96HXPbrkE5pjHzPXWVF/+xi2BbFxgzhkMtZ7Ho0WLf3k2PuZQ/P7fEtGZAC8KX2LV1EbtFP1lqUl3rgCfyWPT0snxPJSkqDV7n6qgfZ8vGqDAej/w2Fh9/jHT7dn12rp+2XaZAqu1j4cFTpKjUBFXW0GxvhSoyjE0xYeKHZ+GBDfdvE2/mQZUqNWEV+jxo9upg0mMj7poHNUbSXi3L9lWRkqHB90wtugkT6H3uOdYG1NPoaPtQxzbaxWv7pYloXvgx7knbmfZ43kMfz9g5NbTyy7c2o5VKCPMIBeBQ3Ylb/96moxupVsfPX44WHyKCyZP2almy/yQpKjX+p2oHPpFHp2NG0QVSVGpma86js7Gh97HH+OnkqyzZf/JW2669c7xJi5fds22X0dLpCDlezTqVhkWHTqGVmrFtkT9pChmnPScaujphlDFkFC6gqgZlhpolB06iu4/okVHR6Qgvu8Qf97Yhzcujy0LKlmUBrI+Xcd7D6YEOabSL15N/WUftTjkzP3wdy3GtD308Y5ewuYg12yu47mjLnAn+wBcXrwDjG9vJXR6IKjrcABUKwgik0xFedpl1mfqJPD3mUgqWBJCmkHFuSv83Zc/zdXx0GKRpaeh6utk/y4vc5QH4n6pFkVfC2JbOAbftMmauVxpZl1VIVEGZPj8cPoW0BDlHIqaa5sJeuG8jYR/H5KtNJGUXEbu1FNuOHjQh7qQp5Byc4WkyA022LHsDSWUlW3/yBGt2VmDVreXgDE9SFTI0oR73dT2PyMXrw7pyBQIDbXj88V7+9KeeQTmmsbOKigIzM7D+PHP35cWrRXcvZsD330ga5uoEYeTzuHz9xmSpcv1NWT6NNEX/E3m2LHsDampY/8rXScgt1i9YfSaxKToMu/ZukrKL7qttlzFzaOkkPr+ExJwiJjS0cWaKE2kKGQVL/OmxMIrmOMIQGay1xaBoasL8448x/9vfMLt8mT5fX3r/53/o/frXwcbG0NUNi9U7XsWxqR1FbglrNxcxrqmDqukTSVPI2bHAd0DDH4xy8fraaxb88Y/mFBV14uUl2mMNhNX8+TB58hc+ZL+8eEWnY3xjO8895PssCMZsbFMHirxi1m4uZnxjOyc9J5KmkLF9od9Xbsq3PxGy6uwhcseNPrPVjdRMdND3THSyQ5FXSnj5ZVptLftt22UKzHu0LN9bSUqGGu9zddQ72rIpJhxVZCjNY0zjw1+4PyNq8XpTTw/STZuw+POfMSsq0udBn32WnueeA2fDtJgaLrc/Abfs7mXVzuMoMzVMu9hArZM9G2PCyVoTQqv93TewjsjF68M82u9ts+boU68xLqIS/5f//VD1mJJ3fppOnwR6LO/+BEM8eRWEgbs5kSclQ43nxYY7TuS50z1P0qdj3lF98+9bC9ZVIRQFubFqz4n7bttltHQ65MUXSMnQMEdzjk5Lc/KXB7I+XsZFt3GGrk4QBuZG9EipUrPgyBm6LKRsXaqPHj1oHnSk6m+NJ+nTMVtzjpQMNfKSi7RbW7B5pX4D65XJXx1iYnSL10sbl3Lun7GEv/MH7L0vPVQ9puT2zOvdiMyrIDwAnY5ZmvOkqNTM/NJEno8feRe4+z3P72QNKSoNS/ZVAbBrgS9bF/khL710q22XPjt377Zdxmza+TqUmYWs2nkcc62W/bOmk6aQUxzkJnKxwqjhcalBHz3aUfF59ChBjvo+86Aj1UDXeN5nalGqNKzYU4lEp2PPXG9SFXIq/F1u/RmjWrz29Ug59syr2HhcJeSNvz9ULabm9m4DHTZfzdSJbgOC8PC8zl67cVPWdxXoi4un98UXWdme0+/rnGubScouIm5LKXYd3RQFuZERGYpzfStJ2UVMqmvhvNs41sfLyF8WSLeVaWZAx11vY+3mYhS5xTi2dHLcexJpCXJ2zfNGa37vHJ0gjASOTe3E55Xcih5VTZ/I+vg7R49Gk/td402obyUxu5D4/FIc2roo9XchTSFn7xwv9ij+ZDyL15otszn1rpKgX/2dcRFVD1WLKbrZ59WyV0uLrSU9FlIserQ4tHfTLfq8CsKgcapvJXFzEY9uO4Xk+nXK/FxIU8juOZHHtr3rVvPvyddauOA2jvSYMDqsLUncXKRv2zXGBlVk6L3bdhkxq84e1uysYF1mIVMuX6dmogPpsRHkrAqmzc7K0OUJwoBYdveyctcJlCp99OjazehRZGi/edCR6kHXeDYd3URt0w9/cL3azOXJY3GvaTKOxauuT4LmhR9jZtVN+B//YAxP2A3CqaGVhQdPsfjgKezaumizs2L3jfnF4omrIAyuLbN/gvknn1D7+1/emsizIT6CzSv67yog1faxZP9JlBlqAk5d1feZjQylytuZyO0V9922y1hJ+nTMPXaGlAwNEWWXaLOxJHt1MBtiI6h1HmPo8gRhYHQ6ZqvPoczUMLPoAu3WFuSuCGJ9fARXJjsauroBe9gHlGbaPhYeOo1SpSb0xBXjWLzWHQzhxJtP4vfDfzNxUdFD1SEIgjAcbt7zIgteYf4R/Sat0OP3MZFHpyO04jIpGRrmHzlNr1TK1qX+7JnjzbxjZ++rbZex8ztZg1KlYemN/PDu+T6kKuSc8J1s4MoEYeD00SM1K/ZUYtanY+9sL9IS5JQFuBq6tHsazH67RpF51emg5IffpbvRnhnv/QaJtO+h6hAEQRgOd7rnBVZeQZmhZvHBU/qJPAt9SVXIOenVf/scj8vXSc7UELm9AuvuXg7LppK9MphpFxtYm3vvtl2mwrm2maScImK3lGLfrs8PpynkHJjpSZ+JDoIQRp+b0aP4vBLGtHbpo0cJMvbM8R6x38di8folTaVelL7ybby+mY5L9IGHqkEQBGG49HfPc6lpIim7kJiCsvuayDOmuYP4PH0zf6fGdk5Nm0B6TBhmOkjKLmL6hfpRn50bDLbtXcRsLScpuxCX2mYuujiyPl5G3vJAuqwtDF2eIAyITUc3kdv1/aHdapqonjSG9XEyclcEjbiBJmLx+iXlv3iW1lPuzPjwDaRWYqKWIAijw0DuefatncRuLSMxu5BJda2ccx/P+vgItiztv6uARU8vK3dXsk6lxut8vb7PbHQYl1zHEbel9Cttu6pdHAf7rzcqSLV9LDpwkhSVhsCqGppu5Ic3xYTTYKIb3oTRx0zbx4LDp0lRqQk5foUWOyuy1oToB530Fz0aRmLxepu2sy4UvvhDpjyayxTltoc6vyAIwnC6n3uetFfL0n0nSVGp8Ttdy/WxNmREhZERFUZjP/2Z0emYVXgepUrDrMLz+gXriiAOR0xj6YGTt9p27ZnjPWqyc0NCpyOkopoUlZoFh/X54YIl/qQpZJydOsHQ1QnCgAWeuIJS9Xn0aPtCX1IT5JyabtjJXcO1eB0VjQIvbVqKmXUXLlEiLiAIgvHSmkvZtsSfbYv9CC+7REqGmqc+O8Qj6Uf7n8gjkXBENo0jsmlMP1eHUqUmbkspCbnF7J3jzc9+FE1gVQ3x+SUsOXhqwG27jI5EQmmQG6VBbrhXXyc5s5CobeVEbyvnSMRUUhVyjkaY7oY3YfSo8Hfh5y/HfCF6tHrXCdShHqQpZByS9x89Gu1G/JPXztpxHHv2FVxj9jH92cyHOrcgCMJwe9gnEVMu6ifyrNl5YyLPDE9SFTI095jI49TQytqcYhR5xYxp7aLCdzIbo0JxaO8mKbvwvtp2GbMxzR3E55eSmFOI0/V2Tk+bQFq8jG2L/eixGBXPdwThVvQoKasQ5/ob0SOFjC1LA+juZyT8YBOxgRtO/0NBTe58Zrz/a6wmNj7UuQVBEIbbYN3MHZvaUeSWsHZzEeOaOgY8kce6s4c1NzZ7eFxp5IrzGDZGh1M/3o6E3OL7a9tlxCx6elmxuxJlpgavc3XUj7NlY7R+w1vzGBtDlycIAyLt1bJsXxUpGRp8z+ijR5uiw1BF3iN6NEjE4hXoabbl6FOvMWF+Cb7f/+yhzisIgmAIg3kzB/1EnlU7j6PM1DDtxkSe9NgIstaE9NtVwEzbx/wjZ1iXqSG8/DKttpZkrw6hONCNlbtPsOTAyftq22W0dDpmFF0gRaVmtkafH85bHsT6eBmXXR0NXZ0gDIxOR0SpPno079hZuiykbFkWQFq8nAse44fstCLzClzZvIC+Livc1u4wdCmCIAgjQrelOTmrQ9i8MphZmnOkqNR861/7eCLtMJtXBrEhTsaVyWO/8ro+qRl7b0zSC6iqQalSk5ypITmrkB0LfPnJT2OZUXyRmIIyVplQdu4rJBKORUzlWMRUPM/XoVRpiNlahiKvmH2zvUhTyCgJdBO5WGFkk0goDPWgMNTjC9GjuC1lHJjhSWqCnMIQ91H7fTxin7xqOy04+tRrOPifJ+hnHz7UOQVBEAxlsJ+83on3mVqUKg0r9lQi0enYO8eL1AQ55f79dxWYVNtMclYhMVvLsOvoRhPiTuaaECbVtpC4uei+2nYZs/HX21ibU4Qir4SxLZ1U+EwiLUHO7nk+prXhTRjV9NGjYtZuLr4RPXK+MdDEF6354Aw0MfnYQHXOAs68t5aQt/7C2KCzD3VOQRAEQxmOxetNE+pbSczRT+RxaOui1N+FNIWcvXO8+p3IY9fWReyWUpJu9Jk97zaO9JhwOq0tSMopur+2XUbMqrOHyB038sPV+vxwemwEOauCaLe1MnR5gjAgll29rNr1efSo1smejbHhZK3uP3o0ECa9eNVpzTj23E+wHN9M6O/+MlqfaguCIAzr4vUmm45uoraVsy5Tg+vV5s8n8qwMosPm7l0FpL1aluzX95n1P1VL4xgbVGtCqPJyJnpbOfOP6rNz/bbtMgGSPh3zj5xBqVJ/nh9eFUJ6bDi1zmMMXZ4gDIikT8dszTlSMtTISy7Sbm3Rb/RoIEx68Vq7S0bV248S8NMPcZpT/lDnEwRBMCRDLF5vMtP2sfDQaZQqNSEn9BN5MteEsDE2gjon+7u/UKcjvOwy6zLVzD9yhh5zKQVLAtgzx4t5R88QueP+2nYZM/+qGpSZGpbsqwJg5wJ9s/gq70kGrkwQBs77TC0pGWqW761CotOxZ643qQo5Ff4u93Uck1286nRQ9N2X6OuVInv390jMBl6fIAjCSGPIxevtgk5Uo1RpWHSfE3k8Ll9nXaaGyO3l+gWrfBo5q4KZfr6OhNwSxje2UzV9ImkKOTsW+PbbtsuYTaptJim7kNgt+vxwYbA7aQoZB2ZON60Nb8KoNqG+lcTsQuLzS/XRowAXUhVy9s3uP3p0k8kuXq+r/Sn/xXP4fPczJq04+lDnEgRBMLSRsni9yaWmkeSsQqILyrHt7EEd6kFqgpzDsmn9LrLGNnWgyNNv9hjf2M5Jz4mkx4Rh3ttHck7R59m5mPB7tu0yZnZtXcRsLSM5q5BJdS1ccBvH+vgI8pcG0mVtYejyBGFAbDq6iSooZ12WPnp0efJYNsRFkLui/+iRyS5eS3/6LTouOTPjgzcws9A+1LkEQRAMbaQtXm+yb+0kbkspidlF+ok8HuNJi5ex9R4TeSy7e1mx+wQpGWo8b/SZ3RgdxmVXR+LzS5lRPDjZudFO2qtlyYFTKDPUBJy6SqODNaqoMDZFh3F9nJ2hyxOEAblj9CgylI0x4XeMHpnk4rWlyoPil77PtCezcF+766HOIwiCMBKM1MXrTeY9+ok8SpUa3zPXPp/IExVG49h+ugrodMzSnCdFpWZm0QU6rMzJXRnM4YgpLNt38r7bdhktnY6w8ssoVfr8cK9Uytal/qTFyzg3dYKhqxOEAQs6UU1KhpqFh07TZyZh+0I/UhPknPaceOvPmOSQgksblyG162DymoOGLkUQBMEk9N7oHrB1iT+ykoukqDQ8/ekhHk0/Sv7SQNbHy+48kUci4Yh8Gkfk0/A6ew2lSkNcfgkJm4vYM8ebV1+OJriyhvi8EpYcODXgtl1GRyKhONid4mB3PC5fJzlTQ+T2CmIKyjkkm0aaQsax8Ckmu+FNGD3K/V157SeuX4gerdl5nGNhHqQq5ByWTxu2WkbMk9eOyxNRf+vHuCftYNrjuQ91DkEQhJFipD95vZOpF+tZl6lh9Y7jWPVoBzyRx6m+lcTN+j6zY1q7KPNzISMqlDEtnSTlFOFW0zTgtl3GbGxTB/H5JazNKcKpsZ1T0yaQppCxbZG/yW54E0Yf+9ZO4vL1/aEnNrRx1mM8ri+/jjYlhdUH3njo44+K2MDJd5Op3TGDmR++juW41oc6hyAIwkgxGhevNzk2tpNwYyKPY7N+Ik9qgowdC/qfyGPd2UPkdn2fWfcrTVQ7jyE9NpzrY21Zm1dMyPH7aNtlxCx6elm5u5J1KjVe5+upG2/HxphwMteE0uJgmhvehNHHvEfL8r2VKFUafM5eQzdxIh+t9EYVFdp/9OgeRvzitbvBgaNPv8akFUfw/nb6Qx1fEARhJBnNi9ebLLt6Wb3rOOtUGqZdauDqBHs2xkaQvSq4364CZto+5h85Q0qGmtDj1bTYWZG1JoTSAFdW7zx+q23XtkV+pClk92zbZbR0OmYVnkeZoWbWbfnh9XERVLs4Gro6QRgYnY4C6Uos/vxnpFu20GUpJW+ZPnp00f0O0aN7GPGL13P/iubSxqXI//4WNq51D3V8QRCEkcQYFq83Sfp0zFGfRanS6Cfy2FiQszKY9NiIe3YVCKy8gjJDzeIbC9YdC33ZvsCXGcUX7rttlzHzOnuNdZkaVu4+gVTbx9453qQpZJQGuIpcrDDi3bzfPf/Pb7EuU8Oqnfro0f6Z+uhRUXD/0aPbjejFa2+7FUef+hmO4ZUEvPzvhzq2IAjCSGNMi9fb+ZyuRan6fCLP7rnepA1gIo9LTRNJ2YXEFJRh26FfsGauCcHlajOJOffXtsuYOTW0sjanGEVeMWNauyj3m0xavIw983zQmtKGN2FU+fL97svRo0ovZ1IVcnYu8Ok3egQjfPF6adNSzn0cS9gf/oCDz6WHOrYgCMJIY6yL15sm1rWQmFNE3H1O5LFv7SR2axmJ2YVMqmvlnPt40mPC6LE0JzGn6P7adhmxO+aH4yLIWRlMh61pbngTRq673e8su3pZvbMCpUrD1MvXb0WPslaH0GZndcdjjdjFa1+PlGPPvIqN+1VCfv33hzquIAjCSGTsi9ebbNq7id5WRnJmIa61zVxyGcuGOBl5ywP77Sog7dWydN9JUlRq/E7Xcn2sDRmRoZzynEhMQTnzjp2ly1Laf9suE3AzP6xUqQmr0OeHs1frIxvXJjgYujxBAO59v7tb9GhDXAQ1k74YPRqxi9earbM59RclQb/8O+NkVQ91XEEQhJHIVBavN0m1fSw8dAplhobgyis021uRtSaU9Jhw6vvrKqDTEV52iZQMNfOPnqXrRv/ZvbOns+DImVttu/bP9CRNce+2XcYsoKoGpUrN4v0nQSJhxwJf0hQyqrwnGbo0wcTdz/3O99RVlJkalt2IHu2a58N6hYwKP330aEQuXldtew3NCz/CzKqH8D/+wVTvQYIgGDlTW7zeLvh4NUrV5xN59F0FvjiR506mXGwgOUtD5I4KrLq1HJzhSc6KQLzO1bE2t+RWdi5Nce+2XcZs8tUmkrKLiN1aim1HD5oQd9IUcg7O8DTZDW+CYT3I/W5iXQtJ2YXEbinDoa2LkgBXUhPk/ObN7JG3eJ3x61SOv/kUfj/4DxMXFz7UMQVBEEYqU1683uR6RT+RJ2qbvqvA0fAppMXL9BN5+nly4djUjiK3hLWbixjX1EHV9IlsjA7HoldLUnbRfbXtMmZ2bV3EbtE3i59U18p5t3Gsj5eRvyyQbivT3PAmGMbD3O++HD2SwMhavOp04CKvpbvRgRnv/QaJtO+hjikIgjBSicXr5+xbO4nPKyExp+jWRJ40hZyCJf79dhWw7O5l1c7jKDM1TLvYQK2TPRujw7gyaSzxW0rvu22XsfpyfrhxjA0ZUaFsig6n0dE0N7wJw2sw7ndSbR8LD57ijd9uHlmL1337zFi92prp39yIa/T+hzqeIAjCSCYWr191cyJPSoYa73N1NDjakhEVRkZUGE1jbe76Okmfjlmac6So1Mwovki7tQWbVwZxNHwqy/dW3nfbLqOl0xFedhmlSs2CI2fospBSsCSANIWMc1OcDF2dYMQG837XX+bVIL9P+MMfLDAf08qk5UcMcXpBEATBgHotpGxZFsiWpQHISy6izFDz9KcHeTT9SL8TeXRmEg7P8OTwDE+8z9SiVGlIyC1h7eZi9s7x4tWXowk5cYW4/FKW7T854LZdRkcioSjEnaIQdzwuNbAuq5DI7eXEFJRxUD6NtAQ56lAPk93wJox+w/7k9W3nXzN7tg1THsljSkrBQx1LEARhpBNPXgdm2oX6L0zk2TdrOmkKOUXBbv0usibUt5KYU0R8Xom+z6y/CxlRYTg2d5CUXYjr1c/bduWuCKLT2mIY/1Yjx9imDhR5+mbx4xvbqZo+kfXxMrYv9KPXwjQ3vAmDb7ievA774tXj09+RmSkl5IPXsHBof6hjCYIgjHRi8Xp/xl1vIyG3mIQbXQVOeDuTFn/viTw2Hd1EbSsnOasQt5omLk8eS3pMOM1jrEnYXHKrbVdmZCgbo+/RtsuIWXb3snLXCZQqNZ4XG7g23o6NMeFkRoaa7IY3YfAY5eK1s9aRoudf47nneimJ/PEDH0cQBGG0EIvXB2PZ1cuaHRWsy7w5kceB9NhwsvuZyAP6Zv4LD51GqVITcuIKLXZWZK4JoczflTU7K+67bZfR0umYrT6HMlPDzKILtFtbkLsiiPXxEVyZ7Gjo6oRRyigXr2feV1Cbu5Cysk6eOfnTBz6OIAjCaCEWrw9H0qdj7rGzKFVqZKWX+p3I82VBJ6pRqjQsOngKnUS/YN2xwIdZhRfuu22XMfM6ew2lSs2KPZWY9enYO9uLtAQ5ZQGuhi5NGGWMbsNWT7MtNVtmo0zW4uGhg5PDdWZBEARhtNKZSTgwazoHZk3XT+RRaVibU0RiThG75vmQliDnuO/kO7623N+Vn73sikuNvs9sdEE5a3YeRx3qwVsvrsTtShNrNxfx/36pGnDbLmN02nMib35/De89voDEzfr88JKDpyjzcyEtQcbeOd5oTWnDmzDiDduT1wufreLCp2s4cqSDoCCdeAohCIJJEE9eB5/ztRYScwqJyy/Fvr2b4kBX0hRy9s+a3m9XAfvWTuK2lJKYXYRzfSvnPMazISacXnMpSTlF+Jy9RoOjLZuiw1BF9t+2y5jZdHQTuV0f2XCraaJ60hg2xEWweUUwHbaWhi5PGMGMKjag7bTg6NOv4eB7nku7PQFxIxcEwTSIxevQsWnvJqagjOSsQlxqm7no4siG+AjylvffVcC8R8uyfVUoVWp8z+gXrBmRoZyZ6kTMtgrmHjtLl6W037ZdpsBM28f8I2dIyVATeryaFjsrstaEsDEmnGsTHAxdnjACGVVs4Or2WfQ22+OeuBPwHI5TCoIgCEauw9aSDfEyNsWEs+jASVJUGv737zt5+r8HyVwTyqaYMOrHf7WrQK+FlK1LA9i6xB9ZyUVSVBqe/uwQXZZS8pcG8qPX4ll4+DSR2ytQ5JcOuG2XsemTmrF3rjd753oTWHkFZYaalAw1SpWG7Qt9SU2Qc2q6s6HLFEzQkC9edVozLm9agoPfOcYEnRnq0wmCIAgmRis1Y+dCP3Yu8CXkuH6T1qPpR/haxjG2LfInTSG7c1cBiQRN2BQ0YVOYelHfZ3bNjgrit5Syf6Ynv/pBJN5nr5GQW8KCVzYMuG2XMarwc+HnL8fgUtNEUnYhMQVlrN51AnWoB2kKGYfknujMTGdhLxjWkC9e6/aF0VXrxPRnMk3pB1ZBEARhuEkklAa6URrohlt1I8lZGqK2lRO5o4Ij4VNIS5BzJGLqHZ+envdw4vf/s5L3H51PQq6+mf/8o2ep9HLmb99YgFV3L0nZRfz87Ty++a99A2rbZYyuTB7LX55dwsdfm0Ps1jKSsgr53a8yOec+nvUKGVuWBNBtZVob3oThN6SZV50Oir77En095sj++jskZjqR/xIEwaSIe55hObR0Ep9fQmJOERMa2jgzxYk0hYyCJf70WNx9kWXZ1cvqXcdZp9Iw7VIDVyfYszEmnKsTHYjPL73vtl3GStqrzw+nZGjwPVPL9bE2ZESFkREVRqOjraHLE4aZUWReGwv9aDvrhveLqUjMBr5IFgRBEITB0OJgzSfJs0hTyFi+p5IUlYaf/LmA5/69n00x4agiQ2ke89WuAt1W5mSvDiFnZTBz1GdRqjS88M99txasqQoZK/ZWsXZz8YDadhkrrbmUgiUBFCz2J7zsEikZap767BCPpB9ly7IA0uLlXPAwzQ1vwtAZ0sXrpY1LsRzfhPMS9VCeRhAEQRD61WNhTv7yIPKXBSIvvkBKhoZnPznAY+uPkLc8kPUKGZdcx33ldTozCQdnTufgzOn4nK5FqVLfWrDunuvNqz+JJrSimtgtZSzfVzXgtl1GRyKhKMSDohAPplxs0OeHd1YQt6WMAzM8SU2QUxjiblIb3oShM2SL15aT7jSV+DLtyWzMLLRDdRpBEARBGDiJBHX4VNThU5l2vg5lpobognLi80vYf6OrQHHQnbsKnPRy5o2XInnviQUk5hQRl1/Ksv0nKQlw5Q/PL2V8YztJOUW8+Wb2gNt2GaMLHuP5f/+zgg8em4fiRn74zz9Np2q6M2kKGdsX+prchjdhcA1Z5vX4W4/TWOTHzI9+hblt161/LvJfgiCYEnHPG/nGX28jYXMxitxiHFs6Oe49ibQEObvmefe7yLJp7yZ6WxnJmYW41jZzyWUs6THhtNhbk7i5mMCqGpocrPtt22UKLLt6WbXrOMpMDdMuNlDrZM/G2HCyVofQam9t6PKEQTSqM68d1ROoPxCKe+LOLyxcBUEQBGGkaRhnx4ePzuOTpJms2VmBUqXhF7/PpWaiA+mxEeSsCr5jV4EOW0vS42RkRIez8NAplBkavvf+bprtrchcE8K/k2cSueP4wNp2GbFuK3NyVoeweWUwszXnSMlQ861/7uOJ1MNsXhnEhjgZVyab5oY34cEMyeL18qalSMy1uMbtGYrDC4IgCMKg67K2IDMyjKzVocw7qp8s9T8f7eHJzw6RvTqYDbER1DqP+crrtFIzds33Zdd8X4KPV6NUqfn6JjV9ZhK2LfLj5VfjmV14nqiCsgG17TJWOjMJh2Z4cmiGJ95nalGqNCTklrB2czF75nqTqpBT4e9i6DKFUWDQF6/d1x24umMGk5Ydw3Jcy2AfXhAEQRCGlM5Mwv7ZXuyf7YXfyRqUKg1JWYUkZRWye74PqQo5J+7SVaAswJWyAFdcrzSSnFV4o8/scY6GT+HX312Nx5XrJG4u5u2fZwy4bZcxOjXdmV//7xp9fji7kPj8UpbuP0mpvwtpCjl753iZ1oY34b4Meub13L+juJS+DPnf38LGte4r/17kvwRBMCXinmccnGubScouInZrKfbt3RQFud3qKtDfZCn71k7i8/R9Zic2tHHWYzwbYiPok0pIyi7C+1wd9Y62/bbtMgU2Hd1EbStnXaYG16vNXJ48lg1xEeSuCKLDxtLQ5QkDNFyZ10FdvPa2W3H0qZ/hGFZFwE/+dcc/I27kgiCYEnHPMy627V3EbC0nOUvD5GstXHR1ZH28jLxlgXT101XAvEfL8r2VKFUafM5eo8HRlk1RoZydMoHYrWXM0Zyj09K837ZdpsBM28fCQ6dRqtSEnLhCi50VmZGhbIwJp87JNDe8jSajcsNWTf5ctG02uCfuGMzDCoIgCMKI0G5rxXqFjI2x4Sw+cBJlhpqX/m8Hz3xyAFVkKJtiwmkYZ/eV1/VaSNmyLJAtSwOQl1xEmaHmmU8P0WUpJW9ZID98LY7Fh04PuG2XseqTmrF7vg+75/sQdKKalAw1X9t0DKVKzfaFfqQmyE1uw5vwVYO2eO3rkVKduZixoSdx8L04WIcVBEEQhBFHKzVjx0I/dizwJbTiMikZGh7bcISvbVJTsETfVeDs1AlffaFEgjpsCuqwKUy7UM+6TA2R2ytQ5Jeyb9Z0fvHDKHzP1KLILWbh4TMDbttljMr9XXntJ6641Ojzw9EF5azZeZxjYR6kKeQclk3rN7IhGK9BW7zW7pLT3TAWn++mDtYhBUEQBGFkk0goCXKnJMgd9+rrJGfqN2lFbyvnSMRUUhVyjkZMuePT03NTnPjdd1by/qPzSMgtJiG3hAVHznDC25m/PrkIm64eknKKBtS2y5hdmezIn59bykdfn0tcfilJ2YX8/pcqznmMJy1extalAXRbmtaGN1M3KJlXXZ8Ezbd/hJlFL+F/ervf33CI/JcgCKZE3PNMz5jmDv0mrc1FOF1v5/S0CaTFy9i22K/frgKWXb2s2VHBukwNUy9f5+oEB9JjwqidOAZFXgkRZZdos7Hst22XKTDv0bJsXxVKlRrfM/r8cEZUGKqoUBrH2hq6PJM2qjKvDUcC6bg0Cd+XPjGlaI4gCIIgfEXzGBv+o5xN6lo5K3ZXolSpeeVPW3n+3/vYGBNB5pqQO3YV6LYyJysylOzVIcw9dhalSs23/7mPNhtLclYFk5ogY8Xuylttu3Yt8CVVIaPS585tu4xVr4WUrUsD2LrEH1nJRVJUGp7+9CCPph8hb1kg6+NlXHQfb+gyhSH00ItXnQ4ubVyGlXM9ExcWDUJJgiAIgjD69ViYk7ciiLzlgcwsvECKSs1z/9nPY+sPk7c8iPXxMi67On7ldTozCQdmTefArOn4nrqKUqUhMbuQxOxCds/z4dVXYgkru0zs1lJW7KkccNsuoyORoAmbgiZsClMvfjE/vH+mJ6kJcoqC3U1qw5upeOjFa3OFJy0nPJn+/CYk0r7BqEkQBEEQjIdEwlHZVI7KpjL9XB1KlZrYraUo8orZN9uLNIWMksA7dxWo8p7E6z+I1DfzzykkLr+U5fuqKA505e1vLcPpehtJ2UX85tdZA27bZYzOezjx+/9ZyfuPzicht5i1m4v5yyvpVHo5k6aQsWOBr8lteDNmD515Lf/l07RUTWXmh68jte655zFE/ksQBFMi7nnCnTg1tJKwuRhFXgljWzqp8NF3Fdg9zwdtP5OlbNq7iSkoIzmrEJfaZi66OJIeG0abrTVrNxcRePIqTQ7W/bbtMgWWXb2s3lmBUnUzP2zPxtgIslcF02pvbejyjNaoyLy2nZ/M9WNBTPl6/oAWroIgCIIgQP14ez54bD6fJM9izXb9Jq1f/i6XK85jbnQVCKLd9qtdBTpsLdkQL2NTTDiLDpwkRaXh+//YTZODNZmrQ/jPullEbq8YWNsuI9ZtZU72mlByVoUwR30WpUrDCx/v5Ruph8hZGcyGuAhqJo01dJnCA3qoxevlTUsxs+rCJXrfYNUjCIIgCCaj09oCVXQYWWtCmHdUv0nrOx/u5snPDpK9KoT0uAhqJzp85XVaqRk7F/qxc4EvIcerUao0PLrxKFqpGdsW+fPyq/HMUZ8bcNsuY6Uzk3Bw5nQOzryRH87UsHZzMYk5Reye601agpwKPxdDlyncpwdevHbWOnJtt4zJUfuxGNM+mDUJgiAIgknpk5qxb44X++Z44V9VQ4pKTXKWhuQsDTsX+JKaIKfKe9JXXyiRUBroRmmgG27VjSRnFxJVUEbkjgqOhE/h199fhcflRhI3F/GHn28acNsuY1TlPYnXX4rk708sIDGniLj8UpbtP0lJgCupCfoNb339RDaEkeOBM69n3o+nOmcBM95/E2vn6wM+hsh/CYJgSsQ9T3hQk2qbScouJHZLGXYd3RQGu5OmkHFgZv9dBRxaOonPLyExp4gJDW2cnurExpgIdBJIyi7E63w99eNs+23bZQps2ruJ3lZGcmYhrrXNXHIZy4Y4Gbkrgug0sQ1vg2W4Mq8PtHjtabHl6FOv4TSnFL+XPr2vYsSNXBAEUyLuecLDsmvrImarfpPWpLoWLriNY318BPlL++8qYNHTy/I9laSoNHidq6Pe0ZZN0WGcdx9P3NYyZhWep8PKvN+2XaZAqu1j4cFTpKjUBFXW0GxvRWZkKBujw6l3sjd0eaPKiN6wdWXzfPo6rXBP3PlwlQmCIAiC0K82OyvSEuSkx4azZP9JUlRqfvC3HTzznwOoosLYFB3G9Tt0FeixMCd/eRD5ywKRF18gJUPDs/89SKelOXnLA/nhz+JZcuAUMVvLBtS2y1hppWbsWuDLrgW+BB+vRpmh5pH0o6RkqNm2SL/h7bTnREOXKdzmvhev2i4LqrMXMm5GBXbTrgxFTYIgCIIgfInWXMr2xf5sX+RHWPlllCo1j68/zNc3HmPrUn/S4mWcu1NXAYkEdfhU1OFTmXa+DmWmhuiCcuLzS9g/azo//1EU/qeuosgrYdGh0wNu22WMygJcKQtwxfVKI8lZhURtKydyRwVHw6eQqpBzRDbVpBb2I9V9L15rt82kt9ke97XiqasgCIIgDDuJhOJgd4qD3fG4fJ11mRrWbK8gpqCcQ7JppCbIUIfduavAuakT+O2Lq3j/sfn6PrO5xSw8fIbj3pN49+nF2HR0k5xdOKC2Xcas2sWRPz2/lA8fmUt8nj4//PYvMjgzxYk0hYyCJf4mt+FtJLmvzKu992Rdb2s5lo6thP7+Tw/0w4fIfwmCYErEPU8YDmObOojPL2FtThFOje2cmjaBNIWMbYv86bW4+2Qpq84e1uysYF1mIVMuX6dmogPpseHUjbdHkVdCePllWm0tyV4dQnrsndt2mQLzHi3L91aSkqHG+0Z+OCM6DFVkGE1jTXPD252MyA1b1pM9dV1Xz+L/ysdMmFt634UEj53K2/JnAfhrVQ67r5bQ1CPabAmCYLzE4lUYTpbdvazYfQKlSsP0C/XUjbdjY0w4mWtCaXG4+2QpSZ+OeUfPkJKhJrz8Mm02lmSvDkYT7M7q3SdYvP8kQP9tu0yBToe85CLKDDVz1efotDQnf3kg6+NlXHQbZ+jqDGashS2LJ4Xybd8YAF5Sv09Z0/mHOuagLV6lVqE6q4kFyP72OyRmA3/dTZGuM/iev+LW/+/UQxTMAAAACtpJREFUdvPR6a1kXjp038cSBEEYDcTiVTAInY5ZhedRZqiZVXSBDitzclcGsz4ugmoXx35f6neyBqVKw9J9VQDsWuDL1kV+yMou3XfbLmOmzw8Xsmrnccy1Wg7MnE6aQk5RsGlteIt3n8NTXquwllre+mfvHM8g/4r6oY47aItXiWSGzvs7P2TyqsMPVMh4S3s+W/AyAEfqKpk1wQ+A/57dyb/Pbn+gYwqCIIxkYvEqGJrX2WsoVRpW7DmBVNvH3jnepClklAa49rvIcq5tJim7iNitpdi3d1MU5IZqTSgTG1pJyi66r7Zdxmzc9TYScotJyC3BsbmDE97OpCrk7Jrvg9b87pENY/C453Ie8VwKfHFd97V9b9HQ3fpQxx60xauZeYRu7oYVmFloH7iY22/ki5yDeTkwGamZlJc0H1DWeO6BjysIgjASicWrMFI41beydnMxirxixrR2Ue43mbR4GXvu0VXAtr2LmK3lJGdpmHythYuujqTHhNNma0liTjEBp67S6GDdb9suU2DZ1cuaHRUoMzVMuXydqxP0+eHs1SG02Rnfhrdgx2m8LXsGbZ+Wtyo2sKe2bGRmXq0m+Ohm/TP2oYr58l/sMc9lPOq5jCN1lbxW8p+HOrYgCMJIIxavwkhj3dlD5PZy1mVqcL/SRLXzGNLjIshZGUyHreVdXyfV9rH4wEmUGWoCT16lycEa1ZoQTvhMJmp7OfOPnKHHXErBkgDS4iPu3LbLBEj6dMw9dgalSoOs9BJtNpbkrApmQ1wEV53HGLq8QfN66GPMmuDHJ2d38J+zO4AROqTAYuzDPQK+k8xLh3jUcxnh472QSszQ6voG/RyCIAiCIOh1WluQEa3fxDX/yBmUKjUvfrCbJz87RPbqYNJjI7g24atdBbRSM3Ys9GPHAl9CKqpJUal5LP0ovVIpBUv8efnVeOYeO0vk9gpiCsru2bbLWOnMJByY5cWBWV74nrqKUqUhMbuQxOxCds/3IVUh54TvZEOX+VCkEjPCx3sBGGTf0gONh30YN1flgiAIgiCMDGbHjmH+5z8jzcgAMzO0a9fS8+KL6CIi+n2d5NQpzP/6V8z/8x8kHR1oV6yg5xvfwKyyEov33kNSW0tfcDA93/kO2nXrwPLuT3aNmeTSJcz/9jfMP/4YSXMz2nnz6H3xRbRRUSA1vlzsUD95Na3RGYIgCIIgfEXfjBl0//vfdJaV0fv880hzc7FZsACryEjM8vKg786/FdV5e9Pzzjt0VFbS/fOfY1ZaivWjj2K+aRPdr71G15//DFotVs8/j3VAAOa//z00NAzz387wdO7u9Lz5pv7r9NvfIrl0CauUFKwjIjD/xz+grc3QJY4qw/7kVRAEQRCEkc2+tZOYrWUkZRcyqa6V827jWB8vI39ZIN1Wd08cWvT0smJ3JUqVGq/z9dSPs2VjdDgX3cYRu6X0vtt2GSupto9FB06SotIQWFVDk4M1mWtC2RQTRv14e0OXNyIM2oYtsXgVBEEQBNMh7dWydN9JUlRq/E7X0jjGhoyoUDZFh9PoaHv3F+p0zCy8QIpKzazC83RYmZO3PIhD8mksOXCSlbvvr22X0dLpCKmoRpmpYeGhU/RKpWxb7EeaQs6Zaaa54e0msXgVBEEQBOHB6XSEl11CqdKw4MgZuiykbF0aQJpCxnkPp35f6nm+DqVKw8pdJzDX/v/27uyp6TyN4vAHkUVQRFRQkUEBQUCIJLQ6ao/iQouAJIKQ/3CA0iYIDQ1qi9pqq5MQItBsiqIsAyooe1gyF/boVE2bqsyoMeE8t6lv1Zu7c/G+57fCr4dTaTqVRVb/GOZm14farmqziTt/TfNa2xXMEkemuHTVwfnrXWxYXOZhXjLVZhOP8tbWwdu/KbyKiIjIZ5H08g2V9Q6Kfukmwr3CfdMeaiwm7LlJXkNW3OQsFxudmJtdbJ5eoDt9Bz+ez2XjnJuKhnafaruC2abpBcp+dlHe6GTbm1meJG+l1mzi2okMlsJ8KokKaAqvIiIi8lnFvp3D3OTC0tRB3NQcfSnbqS0zcuP7DJbDPn1BH7mwxLkb3VTWO0ganWI0PoYrxQd5tTUKS5MLQ/cI09ERXmu71oKwpWVO3+7FanOQ+uzVh/3h+qJc3sVs8Pd4X5zCq4iIiHwR4e5lzrb1UGWzs/fFGybiorlS8j5kzWyM/OS7dSurHH00SJXNzsGuYWaiwmn4IQdndiKFbT2cvNePJySEX46nU2M20peW8BX/1TfE4yHf+X5/+LDjOQvh62k+nUWt2cjLXVv8Pd0Xo/AqIiIiX1TIqodDjmdYbXbyO14wFxlG05lsasvyGN0R6/Xt/r4xrDY7J+72A3DzeDrXTmRgcr2ktPUxUfNLOHJ2U2M2cT9/L551a28HFN7vD1fWOyi8+X5/+O6hVKotRlxZiUG3F6vwKiIiIl9N2tNxKusdnLndy7pVD3cOp1JjMdGZucvru4Txd1Q0tFPa0kn0vJv2A7uxFeWQMDFDeePH2q4as5GWAu+1XcEsbnIWy08dmJs6iJ1e4Pe0BGosJtqO7QuagzeFVxEREfnqtr2eobzRSVmzi02zi3Rm7KTGYuT2kTRWvYSs6NlFSlo7uXS1nYRX0wwlbuFyiYGFiDDKf+rwrbYriEUsLHHuZjdVNgdJI1OMbd/E5Qt5NBQeYC4qwt/j/V8UXkVERMRvNsy7KfrjSCtx7C0jCTHUXjDSdCbba6tA6PIKJ+8NUFVnJ3Pgn0xtisRWlEtvWjzF17t9ru0KViGrHo4+eoq17j/2hwtzuFx6kPH4GH+P9z9ReBURERG/W7eyyvEHT7Da7OT8Psp0dARXz+VwpeSg91YBjwdD1zBVNjvHHj5laX0o105mcutICsceDfpc2xXMMvrHsNocnPy1D4C24+lUm4307tvh58l8o/AqIiIi35SsnlGsNjt/uz+AJySEG9+nU20xMZAS7/Vd0vAklfUOzt3oJtK9zG/GPTQUZrN36A0XfaztCmbx4++oaHByoeUx0fNunNmJVFtM3PsuJSAO3hReRURE5Ju0c+wtFQ3tlLR2ErWwhD03iRqzkd9M3lsFNr+dp+xnFxcbnWydmmNgzzYulxgIXYWKhnafaruCWdTcIqWtnVRcbWfHxDQvdsVSW2ak+VQWi5Fh/h7vkxReRURE5Ju2cWaBCy2PKW9wEv96hme746g1G2kpyMQd/ulWgXD3Mmdu9VBlc5Ay9JpXcdFcKTYwvDOW0tZOvnMO+VTbFaxCV1Y5ca+fqjo7Wf1/7A+fN1BXbODNlmh/j/dfFF5FREQkIKxfWqHgbh/WOgfpT8eZ3LyBH4sN2IoM3lsFPB4OtT+nqs7OIecQ8xHraTp7gAd5yRTc7ftY23UklWqLia793mu7gpbHQ273MNY6B8cePmE5NJTWgv3UlhkZTN7m7+k+UHgVERGRwOLxkPf4JdY6O0f/MchiWCgtpzKptuTzItH7l6VSByeorHdw9lYPoSur3DmSRtPpLA70jlLW7CJm5mNtV9vRfWv2uGv3yCSX6ts5f72LSPcyD4zJ/P1iPnbDX/w92ucLryEhIRPA8881mIiIiIjIn0j2eDzb/+wHn8KriIiIiIg/Bcc3xERERERkTVB4FREREZGAofAqIiIiIgFD4VVEREREAobCq4iIiIgEDIVXEREREQkYCq8iIiIiEjAUXkVEREQkYCi8ioiIiEjA+BfE5hVb1Dp5qAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start_frame = home_passes.loc[430]['Start Frame']\n",
    "\n",
    "find_intercepting_players(home_passes, tracking_away, start_frame, 3, plot=True)\n",
    "\n",
    "# We see in the plot that pass 430 was able to beat 6 opponents\n",
    "\n",
    "# Make a clip of the brilliant pass\n",
    "make_movie_of_pass(tracking_home, home_passes, tracking_away, home_passes.loc[430]['Start Frame'], 'pass 430-6')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.4 Pass under press\n",
    "\n",
    "Players good with the ball, attract opponents when in possession\n",
    "Inviting press:\n",
    "- breaks defensive structures\n",
    "- frees up other players on the field\n",
    "- create chances of getting fouled\n",
    "    - booking opponents\n",
    "    - winning set pieces (freekick, penalty and corners)\n",
    "\n",
    "<br><br>\n",
    "Calculation\n",
    "- Position of all 22 players are available at 24 frames/sec \n",
    "- Instantaneous velocity of each player can be calculated using ds/dt ( here, dt = 24/60 sec )\n",
    "- Calculate D = distance of all opponents from the player making the pass (P)\n",
    "- Calculate T<sub>i</sub> = time needed for each opponent to reach P = ( D / V<sub>i</sub> )<br>where V<sub>i</sub> is the velocity component of opponenti towards P\n",
    "- Estimate the pressure on the player P using T<sub>i</sub> of the pressing opponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine opponent pressing by calculating the number of players pressing and the\n",
    "# time needed for each player to reach the player getting pressed\n",
    "\n",
    "# Input- frame: frame value of interest\n",
    "# Input- passes_df: passes data frame of team with ball (in possession)\n",
    "# Input- opponent_tracking: Tracking data of team without the ball\n",
    "# Input- time_threshold: Threshold value within which opponent is expected to be called a pressing player (default: 2sec)\n",
    "\n",
    "def calculate_opponents_press_info(frame, passes_df, opponent_tracking, time_threshold=2):\n",
    "    x, y, _, _ = get_pass_start_end_coordinates(passes_df, frame)\n",
    "\n",
    "    # column headers for player positions\n",
    "    x_columns = [c for c in opponent_tracking.keys() if c[-2:].lower()=='_x' and c!='ball_x'] \n",
    "    y_columns = [c for c in opponent_tracking.keys() if c[-2:].lower()=='_y' and c!='ball_y']\n",
    "\n",
    "    # column headers for player velocities\n",
    "    vx_columns = [c for c in opponent_tracking.keys() if c[-3:].lower()=='_vx']\n",
    "    vy_columns = [c for c in opponent_tracking.keys() if c[-3:].lower()=='_vy']\n",
    "\n",
    "    frame_of_interest = opponent_tracking.loc[frame]\n",
    "    \n",
    "    pressing_players = []\n",
    "\n",
    "    # Iterate through all 11 opponent players\n",
    "    for i in range(0, 12):\n",
    "        x1 = tracking_away.loc[frame][x_columns[i]]\n",
    "        y1 = tracking_away.loc[frame][y_columns[i]]\n",
    "\n",
    "        # Calculate distance between opponent (presser) and passer\n",
    "        dist = calculate_distance(x, y, x1, y1)\n",
    "        \n",
    "        # Displacement vector from presser towards passer\n",
    "        displacement_vector = [x1 - x, y1 - y]\n",
    "\n",
    "        # Calculate magnitude of velocity of presser along displacement_vector\n",
    "        velocity_projection_magnitude = (((x1 - x)*(tracking_away.loc[frame][vx_columns[i]])) + ((y1 - y)*(tracking_away.loc[frame][vy_columns[i]]))) / dist\n",
    "        \n",
    "        if velocity_projection_magnitude == 0:\n",
    "            # avg acceleration assumed = 2 m/s^2\n",
    "            time = np.sqrt(dist)\n",
    "        else:\n",
    "            # Estimated time to reach the passer\n",
    "            time = dist / velocity_projection_magnitude\n",
    "        \n",
    "        # If time is negative, the presser needs to change direction, avg acceleration assumed = 2 m/s^2\n",
    "        # We also assume initial velocity as 0 instead of negative for sake of easier calculation\n",
    "        if  time < 0:\n",
    "            time = np.sqrt(dist)\n",
    "\n",
    "        # Players who can reach passer within time_threshold (default: 2sec)\n",
    "        if time <= time_threshold:\n",
    "            pressing_players.append(time)\n",
    "    \n",
    "    # Returned value: Pressing players who can reach passer within time_threshold (default: 2sec)\n",
    "    return pressing_players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating movie...done\n",
      "Generating movie...done\n",
      "Generating movie...done\n"
     ]
    }
   ],
   "source": [
    "# Iterate through all home passes\n",
    "for index, _ in home_passes.iterrows():\n",
    "    start_frame = home_passes.loc[index]['Start Frame']\n",
    "    \n",
    "    # Calculate info of pressing players for every pass\n",
    "    pressing_players = calculate_opponents_press_info(start_frame, home_passes, tracking_away)\n",
    "\n",
    "    # Also make movie of pass where number of pressing opponents are more than 2\n",
    "    if len(pressing_players) > 2:\n",
    "        filename = 'pass under press ' + str(index)\n",
    "        make_movie_of_pass(tracking_home, home_passes, tracking_away, start_frame, filename, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We estimated passes under press and created movie clips for the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.5 Pass error leading to shot/goal\n",
    "\n",
    "- Players are bound to lose possession at some point in the match\n",
    "- Loss in possession can be due to high-reward pass, opponent press,   defensive mistake, careless pass, duels etc\n",
    "- Hence while valuing passes it is necessary to consider and quantify passing errors that lead to loss in possession\n",
    "\n",
    "Pass errors calculated here are meant to used when determing the overall performance of an individual player/team\n",
    "\n",
    "\n",
    "<br><br>\n",
    "Calculation: Ball loss leading to dangerous situations<br>\n",
    "Find the ball possession transition state (Loss of possession)\n",
    "- Start from frame of goal / shot / high EPV * PC value / high xG situation\n",
    "- Find the most recent ‘BALL LOST’ event of opponent team before the above frame\n",
    "- Above event is a transition state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.5.1 Ball lost events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PASS              543\n",
       "CHALLENGE         160\n",
       "RECOVERY          135\n",
       "BALL LOST         120\n",
       "SET PIECE          34\n",
       "BALL OUT           27\n",
       "SHOT               13\n",
       "FAULT RECEIVED      9\n",
       "CARD                3\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BALL LOST is an event type\n",
    "home_events['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type       Subtype               \n",
       "BALL LOST  CLEARANCE                  1\n",
       "           CLEARANCE-INTERCEPTION     1\n",
       "           CROSS-INTERCEPTION         7\n",
       "           FORCED                     5\n",
       "           GOAL KICK-INTERCEPTION     2\n",
       "           HAND BALL                  1\n",
       "           HEAD                       6\n",
       "           HEAD-INTERCEPTION          9\n",
       "           INTERCEPTION              58\n",
       "           OFFSIDE                    1\n",
       "           THEFT                     15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sub types of ball lost event\n",
    "home_events[home_events.Type=='BALL LOST'].groupby(['Type', 'Subtype']).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.5.2 Ball lost events leading to goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to search last index/frame of ball lost event before given time_frame\n",
    "# df: dataframe of ball loses\n",
    "# frame: frame before which search needs to be made\n",
    "def get_ball_lose_event_before_frame(df, frame):\n",
    "    time_frames = df['Start Frame']\n",
    "    \n",
    "    ball_lose_frame = None\n",
    "    index = None\n",
    "\n",
    "    # TODO: Use sorted search\n",
    "    for idx, time_frame in time_frames.items():\n",
    "        if frame < time_frame:\n",
    "            break\n",
    "        ball_lose_frame = time_frame\n",
    "        index = idx\n",
    "    \n",
    "    return index, ball_lose_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating movie...done\n",
      "Generating movie...done\n"
     ]
    }
   ],
   "source": [
    "# Ball losing is a common activity and quite common for creative players but can be very risky\n",
    "home_ball_lose = home_events[home_events.Type=='BALL LOST']\n",
    "\n",
    "# Ball lost events leading to goals\n",
    "for index, away_shot in away_shots.iterrows():\n",
    "    start_frame_of_shot = away_shots.loc[index]['Start Frame']\n",
    "    index, home_team_ball_lost_event_leading_to_away_team_shot_start_frame = get_ball_lose_event_before_frame(home_ball_lose, start_frame_of_shot)\n",
    "\n",
    "    isGoal = '-GOAL' in away_shot['Subtype']\n",
    "\n",
    "    if isGoal:\n",
    "        # Save match clip (from ball lost to goal)\n",
    "        mviz.save_match_clip(tracking_home.iloc[home_team_ball_lost_event_leading_to_away_team_shot_start_frame-10:start_frame_of_shot+30],tracking_away.iloc[home_team_ball_lost_event_leading_to_away_team_shot_start_frame-10:start_frame_of_shot+30],'.',fname=\"pass error at frame \" + str(home_team_ball_lost_event_leading_to_away_team_shot_start_frame),include_player_velocities=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pass errors are not perfect and can be in many other scenarios\n",
    "# More work and ideas are needed for identifying and quantifying pass errors\n",
    "\n",
    "\"\"\"\n",
    "Idea: categorizing and quantifying pass errors\n",
    "Analyse sequence of few passes before transition (ideas)\n",
    "- Categorize bad pass scenario (press, defensive clearance, poor decision making, inaccurate pass)\n",
    "- How dangerous was the loss in possession? \n",
    "- Assign values to the errors\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Valuing Passes\n",
    "\n",
    "Implement existing metrics for measuring pass impact/value\n",
    "\n",
    "- Pitch Control x EPV\n",
    "- VAEP value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Pitch Control x EPV\n",
    "\n",
    "<b>Pitch control</b> at a given location is the probability that a player (or team) will gain control of the ball if it moves directly to that location.\n",
    "\n",
    "The <b>Expected Possession Value</b> (EPV) is essentially an estimate of which team will score the next goal, given all the spatiotemporal information available at any given time. Let G ∈ {−1, 1}, where the values represent one or the other team scoring next, respectively;\n",
    "\n",
    "EPV = P<sub>poss</sub> ( G | situation ) = P<sub>poss</sub> ( G | ball, team, opponents, match state)\n",
    "\n",
    "We will see why Pitch Control x EPV is a good metric for valuing passes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first get pitch control model parameters\n",
    "params = mpc.default_model_params()\n",
    "\n",
    "# find goalkeepers for offside calculation\n",
    "GK_numbers = [mio.find_goalkeeper(tracking_home),mio.find_goalkeeper(tracking_away)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" *** GET EPV SURFACE **** \"\"\"\n",
    "home_attack_direction = mio.find_playing_direction(tracking_home,'Home') # 1 if shooting left-right, else -1\n",
    "EPV = mepv.load_EPV_grid(r\"EPV_grid.csv\")\n",
    "\n",
    "# plot the EPV surface\n",
    "mviz.plot_EPV(EPV,field_dimen=(106.0,68),attack_direction=home_attack_direction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# home team value added\n",
    "home_pass_value_added = []\n",
    "for i,pass_ in home_passes.iterrows():\n",
    "    EEPV_added, EPV_diff = mepv.calculate_epv_added( i, events, tracking_home, tracking_away, GK_numbers, EPV, params)\n",
    "    home_pass_value_added.append( (i,EEPV_added,EPV_diff ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# away team value added\n",
    "away_pass_value_added = []\n",
    "for i,pass_ in away_passes.iterrows():\n",
    "    EEPV_added, EPV_diff = mepv.calculate_epv_added( i, events, tracking_home, tracking_away, GK_numbers, EPV, params)\n",
    "    away_pass_value_added.append( (i,EEPV_added,EPV_diff ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "home_pass_value_added = sorted(home_pass_value_added, key = lambda x: x[1], reverse=True)  \n",
    "away_pass_value_added = sorted(away_pass_value_added, key = lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 home team passes by expected EPV-added\n",
      "[(1753, 0.09134393894242654, 0.2252), (1478, 0.0725662469979875, 0.36379999999999996), (197, 0.061713985330662274, 0.2444), (430, 0.04848923317187011, 0.10749999999999998), (851, 0.04528996595787534, 0.1001)]\n",
      "Top 5 away team passes by expected EPV-added\n",
      "[(1663, 0.07350679988214434, 0.0378), (961, 0.07103134497967961, 0.1555), (1901, 0.06363375695912961, 0.2474), (822, 0.0527647290646007, 0.0664), (1735, 0.0318116338246547, -0.005600000000000001)]\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 5 home team passes by expected EPV-added\")\n",
    "print(home_pass_value_added[:5])\n",
    "print(\"Top 5 away team passes by expected EPV-added\")\n",
    "print(away_pass_value_added[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>, <AxesSubplot:>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_number = 1753 # home team assist to header off target\n",
    "EEPV_added, EPV_diff = mepv.calculate_epv_added( event_number, events, tracking_home, tracking_away, GK_numbers, EPV, params)\n",
    "PPCF,xgrid,ygrid = mpc.generate_pitch_control_for_event(event_number, events, tracking_home, tracking_away, params, GK_numbers, field_dimen = (106.,68.,), n_grid_cells_x = 50, offsides=True)\n",
    "fig,ax = mviz.plot_EPV_for_event( event_number, events,  tracking_home, tracking_away, PPCF, EPV, annotate=True )\n",
    "fig.suptitle('Pass EPV added: %1.3f' % EEPV_added, y=0.95 )\n",
    "\n",
    "mviz.plot_pitchcontrol_for_event( event_number, events,  tracking_home, tracking_away, PPCF, annotate=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 TODO: VAEP metric\n",
    "- See: [Actions Speak Louder than Goals: Valuing Player Actions in Soccer](https://arxiv.org/pdf/1802.07127.pdf)\n",
    "- Implementation: https://github.com/ML-KULeuven/socceraction\n",
    "\n",
    "Note: VAEP model only uses event data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculable Scenarios\n",
    "\n",
    "# Distance of opponents along line of pass (opponent's beaten) -> Done\n",
    "# Opponents within 'x' distance from the line of pass (opponents beaten) -> Done\n",
    "# UCL broadcast data -> Abandoned (Event Data not available cannot be used)\n",
    "# Wrong pass that led to goal/opportunity -> Find a way to identify scenarios where pass errors were made that led to dangerous situations -> Done\n",
    "# Velocity and number of pressing opponents -> Done\n",
    "# Pass EPV added -> Done\n",
    "\n",
    "\n",
    "# TODO: Future work\n",
    "# Disguising intention of pass\n",
    "# Vertical and diagonal passes (blog images) (space behind defenders)\n",
    "# VAEP (Scisports)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}