ahead_of_their_time_2024.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2e7d119a",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "This notebook analyzes the performance trends in the men's and women's **100-meter and 200-meter dash** in the Summer Olympic Games. The goal is to understand:\n",
    "\n",
    "* How fast each medalists' time is compared to the model's **expected result** for that Olympic year\n",
    "* Which medalist **overperformed** their expected result the most compared to all other competitors in the same event\n",
    "* To **predict** the expected result for Gold, Silver, and Bronze in the 2024 Olympic Games 100-meter and 200-meter dash\n",
    "\n",
    "This notebook was forked and based on Omri Goldstein's work on [Kaggle](https://www.kaggle.com/code/drgilermo/ahead-of-their-time). The updates to his notebook are the following:\n",
    "\n",
    "* Added results from the Summer 2020 Olympic Games\n",
    "* Plotted the data and fitted an exponential curve for **each medal**\n",
    "* Removed outlier results from early Summer Olympic Games to better fit model\n",
    "* **Predicted** the results for the 2024 men's and women's 100-meter and 200-meter dash\n",
    "\n",
    "## Data Loading and Initial Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05806962",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b8bd43f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Event</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>G</td>\n",
       "      <td>Mohamed Farah</td>\n",
       "      <td>GBR</td>\n",
       "      <td>27:05.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>S</td>\n",
       "      <td>Paul Kipngetich Tanui</td>\n",
       "      <td>KEN</td>\n",
       "      <td>27:05.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>B</td>\n",
       "      <td>Tamirat Tola</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:06.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>Kenenisa Bekele</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:01.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>2008</td>\n",
       "      <td>S</td>\n",
       "      <td>Sileshi Sihine</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:02.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender       Event Location  Year Medal                   Name Nationality  \\\n",
       "1      M  10000M Men      Rio  2016     G          Mohamed Farah         GBR   \n",
       "2      M  10000M Men      Rio  2016     S  Paul Kipngetich Tanui         KEN   \n",
       "3      M  10000M Men      Rio  2016     B           Tamirat Tola         ETH   \n",
       "4      M  10000M Men  Beijing  2008     G        Kenenisa Bekele         ETH   \n",
       "5      M  10000M Men  Beijing  2008     S         Sileshi Sihine         ETH   \n",
       "\n",
       "     Result  \n",
       "1  27:05.17  \n",
       "2  27:05.64  \n",
       "3  27:06.26  \n",
       "4  27:01.17  \n",
       "5  27:02.77  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the CSV file\n",
    "df = pd.read_csv('results_updated.csv')\n",
    "\n",
    "# Display the first few rows of the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e40f2182",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2526 entries, 1 to 131\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   Gender       2526 non-null   object\n",
      " 1   Event        2526 non-null   object\n",
      " 2   Location     2526 non-null   object\n",
      " 3   Year         2526 non-null   int64 \n",
      " 4   Medal        2526 non-null   object\n",
      " 5   Name         2293 non-null   object\n",
      " 6   Nationality  2526 non-null   object\n",
      " 7   Result       2526 non-null   object\n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 177.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display information about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd938b3a",
   "metadata": {},
   "source": [
    "# Data Cleaning and Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5c6e186",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Event</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Name</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>G</td>\n",
       "      <td>Mohamed Farah</td>\n",
       "      <td>GBR</td>\n",
       "      <td>27:05.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>S</td>\n",
       "      <td>Paul Kipngetich Tanui</td>\n",
       "      <td>KEN</td>\n",
       "      <td>27:05.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Rio</td>\n",
       "      <td>2016</td>\n",
       "      <td>B</td>\n",
       "      <td>Tamirat Tola</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:06.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>Kenenisa Bekele</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:01.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>M</td>\n",
       "      <td>10000M Men</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>2008</td>\n",
       "      <td>S</td>\n",
       "      <td>Sileshi Sihine</td>\n",
       "      <td>ETH</td>\n",
       "      <td>27:02.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>W</td>\n",
       "      <td>Javelin Throw Women</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2020</td>\n",
       "      <td>S</td>\n",
       "      <td>Maria Andrejczyk</td>\n",
       "      <td>POL</td>\n",
       "      <td>64.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>W</td>\n",
       "      <td>Javelin Throw Women</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2020</td>\n",
       "      <td>B</td>\n",
       "      <td>Kelsey-Lee Barber</td>\n",
       "      <td>AUS</td>\n",
       "      <td>64.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>W</td>\n",
       "      <td>Heptathlon Women</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2020</td>\n",
       "      <td>G</td>\n",
       "      <td>Nafi Thiam</td>\n",
       "      <td>BEL</td>\n",
       "      <td>6791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>W</td>\n",
       "      <td>Heptathlon Women</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2020</td>\n",
       "      <td>S</td>\n",
       "      <td>Anouk Vetter</td>\n",
       "      <td>NED</td>\n",
       "      <td>6689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>W</td>\n",
       "      <td>Heptathlon Women</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>2020</td>\n",
       "      <td>B</td>\n",
       "      <td>Emma Oosterwegel</td>\n",
       "      <td>NED</td>\n",
       "      <td>6590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2483 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender                Event Location  Year Medal                   Name  \\\n",
       "1        M           10000M Men      Rio  2016     G          Mohamed Farah   \n",
       "2        M           10000M Men      Rio  2016     S  Paul Kipngetich Tanui   \n",
       "3        M           10000M Men      Rio  2016     B           Tamirat Tola   \n",
       "4        M           10000M Men  Beijing  2008     G        Kenenisa Bekele   \n",
       "5        M           10000M Men  Beijing  2008     S         Sileshi Sihine   \n",
       "..     ...                  ...      ...   ...   ...                    ...   \n",
       "127      W  Javelin Throw Women    Tokyo  2020     S       Maria Andrejczyk   \n",
       "128      W  Javelin Throw Women    Tokyo  2020     B      Kelsey-Lee Barber   \n",
       "129      W     Heptathlon Women    Tokyo  2020     G             Nafi Thiam   \n",
       "130      W     Heptathlon Women    Tokyo  2020     S           Anouk Vetter   \n",
       "131      W     Heptathlon Women    Tokyo  2020     B       Emma Oosterwegel   \n",
       "\n",
       "    Nationality    Result  \n",
       "1           GBR  27:05.17  \n",
       "2           KEN  27:05.64  \n",
       "3           ETH  27:06.26  \n",
       "4           ETH  27:01.17  \n",
       "5           ETH  27:02.77  \n",
       "..          ...       ...  \n",
       "127         POL     64.61  \n",
       "128         AUS     64.56  \n",
       "129         BEL      6791  \n",
       "130         NED      6689  \n",
       "131         NED      6590  \n",
       "\n",
       "[2483 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean and filter the data\n",
    "df['Result'] = df['Result'].str.replace('h', ':')\n",
    "event_data = df.copy()\n",
    "event_data = event_data[event_data.Result != 'None']\n",
    "\n",
    "# Convert 'Year' to int\n",
    "event_data['Year'] = event_data['Year'].astype(int)\n",
    "\n",
    "# Remove rows with NaN or inf values\n",
    "event_data = event_data.replace([np.inf, -np.inf], np.nan).dropna(subset=['Result'])\n",
    "\n",
    "# Display the cleaned DataFrame\n",
    "event_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5bc8c2e",
   "metadata": {},
   "source": [
    "## Function to plot sprint results with exponential fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d67a839",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the exponential function\n",
    "def exponential_func(x, a, b, c):\n",
    "    return a * np.exp(b * x) + c\n",
    "\n",
    "def plot_sprint_results(df, event_name, exponential_func, initial_params=(12, 1e-6, 1), maxfev=10000, omit_years=None):\n",
    "    # Filter the dataframe for the specified event and clean the data\n",
    "    sprint = df[df.Event == event_name]\n",
    "    sprint = sprint[sprint.Result != 'None']\n",
    "    sprint['Year'] = sprint.Year.apply(lambda x: int(x))\n",
    "    sprint['Result'] = sprint.Result.apply(lambda x: float(x))\n",
    "\n",
    "    # Remove rows with NaN or inf values\n",
    "    sprint = sprint.replace([np.inf, -np.inf], np.nan).dropna(subset=['Result'])\n",
    "\n",
    "    # Omit specified years if provided\n",
    "    if omit_years is not None:\n",
    "        sprint = sprint[~sprint['Year'].isin(omit_years)]\n",
    "\n",
    "    # Prepare for plotting\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    # Colors for medals\n",
    "    colors = {'G': 'gold', 'S': 'silver', 'B': 'brown'}\n",
    "    medals = ['G', 'S', 'B']\n",
    "    labels = {'G': 'Gold', 'S': 'Silver', 'B': 'Bronze'}\n",
    "\n",
    "    # Dictionary to store the fitted parameters for each medal\n",
    "    fitted_params = {}\n",
    "\n",
    "    # Plotting the data and fitting exponential curves for each medal\n",
    "    for medal in medals:\n",
    "        # Extract data for the current medal\n",
    "        medal_data = sprint[sprint.Medal == medal]\n",
    "        x = medal_data.Year.values\n",
    "        y = medal_data.Result.values\n",
    "\n",
    "        # Plot the data points\n",
    "        plt.plot(x, y, 'o', color=colors[medal], markersize=10, alpha=0.4, label=labels[medal])\n",
    "\n",
    "        # Check if there is enough data to fit the curve\n",
    "        if len(x) > 2:\n",
    "            try:\n",
    "                # Fit the exponential function to the data\n",
    "                popt, _ = curve_fit(exponential_func, x, y, p0=initial_params, maxfev=maxfev)\n",
    "                fitted_params[medal] = popt  # Store the fitted parameters\n",
    "                yy = exponential_func(np.unique(x), *popt)\n",
    "\n",
    "                # Plot the fitted curve\n",
    "                plt.plot(np.unique(x), yy, color=colors[medal])\n",
    "            except RuntimeError as e:\n",
    "                print(f\"Could not fit data for medal {medal}. Error: {e}\")\n",
    "        else:\n",
    "            print(f\"Not enough data to fit for medal {medal}. Data points: {len(x)}\")\n",
    "\n",
    "    # Add labels and legend\n",
    "    plt.xlabel('Year')\n",
    "    plt.ylabel('Result [sec]')\n",
    "    plt.legend()\n",
    "    plt.title(f'{event_name} Results by Medal')\n",
    "    plt.show()\n",
    "\n",
    "    return fitted_params, labels  # Return labels along with fitted_params\n",
    "\n",
    "def predict_result(year, params):\n",
    "    return exponential_func(year, *params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ded1d12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate fitted parameters without plotting\n",
    "def calculate_fitted_params(df, event_name, exponential_func, initial_params=(12, 1e-6, 1), maxfev=10000, omit_years=None):\n",
    "    # Filter the dataframe for the specified event and clean the data\n",
    "    sprint = df[df.Event == event_name]\n",
    "    sprint = sprint[sprint.Result != 'None']\n",
    "    sprint['Year'] = sprint.Year.apply(lambda x: int(x))\n",
    "    sprint['Result'] = sprint.Result.apply(lambda x: float(x))\n",
    "\n",
    "    # Remove rows with NaN or inf values\n",
    "    sprint = sprint.replace([np.inf, -np.inf], np.nan).dropna(subset=['Result'])\n",
    "\n",
    "    # Omit specified years if provided\n",
    "    if omit_years is not None:\n",
    "        sprint = sprint[~sprint['Year'].isin(omit_years)]\n",
    "\n",
    "    # Colors for medals\n",
    "    medals = ['G', 'S', 'B']\n",
    "    labels = {'G': 'Gold', 'S': 'Silver', 'B': 'Bronze'}\n",
    "\n",
    "    # Dictionary to store the fitted parameters for each medal\n",
    "    fitted_params = {}\n",
    "\n",
    "    # Calculate fitted parameters for each medal\n",
    "    for medal in medals:\n",
    "        # Extract data for the current medal\n",
    "        medal_data = sprint[sprint.Medal == medal]\n",
    "        x = medal_data.Year.values\n",
    "        y = medal_data.Result.values\n",
    "\n",
    "        # Check if there is enough data to fit the curve\n",
    "        if len(x) > 2:\n",
    "            try:\n",
    "                # Fit the exponential function to the data\n",
    "                popt, _ = curve_fit(exponential_func, x, y, p0=initial_params, maxfev=maxfev)\n",
    "                fitted_params[medal] = popt  # Store the fitted parameters\n",
    "            except RuntimeError as e:\n",
    "                print(f\"Could not fit data for medal {medal}. Error: {e}\")\n",
    "        else:\n",
    "            print(f\"Not enough data to fit for medal {medal}. Data points: {len(x)}\")\n",
    "\n",
    "    return fitted_params, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a157e03e",
   "metadata": {},
   "source": [
    "## Function to calculate expected result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9db81ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the expected result for a given event, medal type, and year\n",
    "def get_expectation(fitted_params, medal, year):\n",
    "    if medal in fitted_params:\n",
    "        return exponential_func(year, *fitted_params[medal])\n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2573421a",
   "metadata": {},
   "source": [
    "## Functions to calculate top performers for each event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11903866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate top performers\n",
    "def calculate_top_performers(df, event_name, fitted_params, exclude_years=None):\n",
    "    # Filter the dataframe for the specified event and clean the data\n",
    "    sprint = df[df.Event == event_name].copy()\n",
    "    sprint = sprint[sprint.Result != 'None'].copy()\n",
    "\n",
    "    # Convert 'Year' and 'Result' columns to int and float respectively using loc to avoid the warning\n",
    "    sprint.loc[:, 'Year'] = sprint['Year'].astype(int)\n",
    "    sprint.loc[:, 'Result'] = sprint['Result'].astype(float)\n",
    "\n",
    "    # Reset index after filtering and conversion\n",
    "    sprint = sprint.reset_index(drop=True)\n",
    "    \n",
    "    # Exclude specific years if provided\n",
    "    if exclude_years:\n",
    "        sprint = sprint[~sprint.Year.isin(exclude_years)]\n",
    "\n",
    "    # Initialize 'expectation' and calculate it for each row\n",
    "    sprint['expectation'] = sprint.apply(lambda row: get_expectation(fitted_params, row['Medal'], row['Year']), axis=1)\n",
    "\n",
    "    # Calculate ratio\n",
    "    sprint['ratio'] = sprint['expectation'] / sprint['Result']\n",
    "\n",
    "    # Sort and return the top 20 performers\n",
    "    top_performers = sprint.sort_values('ratio', ascending=False).head(20)[['Name', 'Year', 'Medal', 'Result', 'expectation', 'ratio']].reset_index(drop=True)\n",
    "    \n",
    "    return top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c5e4056d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to get top performers by medal without plotting\n",
    "def get_top_performers_by_medal(df, event_name, top_n=10, exclude_years=None):\n",
    "    # Get fitted parameters and labels\n",
    "    fitted_params, labels = calculate_fitted_params(df, event_name, exponential_func, omit_years=exclude_years)\n",
    "\n",
    "    # Calculate top performers\n",
    "    top_performers = calculate_top_performers(df, event_name, fitted_params, exclude_years=exclude_years)\n",
    "\n",
    "    # Display the top performers by medal\n",
    "    for medal, label in labels.items():\n",
    "        top_performers_by_medal = top_performers[top_performers['Medal'] == medal]\n",
    "        print(f\"Top {top_n} {label} Medalists:\")\n",
    "        print(top_performers_by_medal.head(top_n))\n",
    "        print(\"\\n\")\n",
    "\n",
    "    return top_performers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23794295",
   "metadata": {},
   "source": [
    "## Function to calculate predictions for future results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "783190f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_event_results(event_name, year_to_predict, fitted_params, labels):\n",
    "    predictions = {}\n",
    "    medals = ['G', 'S', 'B']\n",
    "    for medal in medals:\n",
    "        if medal in fitted_params:\n",
    "            predictions[labels[medal]] = predict_result(year_to_predict, fitted_params[medal])\n",
    "        else:\n",
    "            predictions[labels[medal]] = None\n",
    "\n",
    "    print(f\"Predicted results for {event_name} in {year_to_predict}:\")\n",
    "    for medal, result in predictions.items():\n",
    "        if result is not None:\n",
    "            print(f\"{medal} Medal: {result:.2f} seconds\")\n",
    "        else:\n",
    "            print(f\"{medal} Medal: No prediction available\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b048cf",
   "metadata": {},
   "source": [
    "## Analysis and Visualization for 100m Men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "15bc4fce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 100M Men in 2024:\n",
      "Gold Medal: 9.79 seconds\n",
      "Silver Medal: 9.91 seconds\n",
      "Bronze Medal: 10.03 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '100M Men', exponential_func)\n",
    "predict_event_results('100M Men', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8249c426",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stanley Rowley</td>\n",
       "      <td>1900</td>\n",
       "      <td>B</td>\n",
       "      <td>11.20</td>\n",
       "      <td>11.863708</td>\n",
       "      <td>1.059260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>William Hogenson</td>\n",
       "      <td>1904</td>\n",
       "      <td>B</td>\n",
       "      <td>11.20</td>\n",
       "      <td>11.605025</td>\n",
       "      <td>1.036163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Walter Tewksbury</td>\n",
       "      <td>1900</td>\n",
       "      <td>S</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.496807</td>\n",
       "      <td>1.035748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Donald Lippincott</td>\n",
       "      <td>1912</td>\n",
       "      <td>B</td>\n",
       "      <td>10.90</td>\n",
       "      <td>11.190943</td>\n",
       "      <td>1.026692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ralph Metcalfe</td>\n",
       "      <td>1932</td>\n",
       "      <td>S</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.572544</td>\n",
       "      <td>1.026461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frank Jarvis</td>\n",
       "      <td>1900</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.287772</td>\n",
       "      <td>1.026161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Justin Gatlin</td>\n",
       "      <td>2012</td>\n",
       "      <td>B</td>\n",
       "      <td>9.79</td>\n",
       "      <td>10.038000</td>\n",
       "      <td>1.025332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Reggie Walker</td>\n",
       "      <td>1908</td>\n",
       "      <td>G</td>\n",
       "      <td>10.80</td>\n",
       "      <td>11.042554</td>\n",
       "      <td>1.022459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2012</td>\n",
       "      <td>G</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.829849</td>\n",
       "      <td>1.020753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Eddie Tolan</td>\n",
       "      <td>1932</td>\n",
       "      <td>G</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.507868</td>\n",
       "      <td>1.020181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yohan Blake</td>\n",
       "      <td>2012</td>\n",
       "      <td>S</td>\n",
       "      <td>9.75</td>\n",
       "      <td>9.933562</td>\n",
       "      <td>1.018827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Maurice Greene</td>\n",
       "      <td>2004</td>\n",
       "      <td>B</td>\n",
       "      <td>9.87</td>\n",
       "      <td>10.047656</td>\n",
       "      <td>1.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jim Hines</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>9.90</td>\n",
       "      <td>10.068813</td>\n",
       "      <td>1.017052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ato Boldon</td>\n",
       "      <td>1996</td>\n",
       "      <td>B</td>\n",
       "      <td>9.90</td>\n",
       "      <td>10.060699</td>\n",
       "      <td>1.016232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Arthur Jonath</td>\n",
       "      <td>1932</td>\n",
       "      <td>B</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.567141</td>\n",
       "      <td>1.016071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>9.69</td>\n",
       "      <td>9.843792</td>\n",
       "      <td>1.015871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Charlie Greene</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.154343</td>\n",
       "      <td>1.015434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Alvah Meyer</td>\n",
       "      <td>1912</td>\n",
       "      <td>S</td>\n",
       "      <td>10.90</td>\n",
       "      <td>11.060198</td>\n",
       "      <td>1.014697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Archie Hahn</td>\n",
       "      <td>1904</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.160139</td>\n",
       "      <td>1.014558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Andre De Grasse</td>\n",
       "      <td>2020</td>\n",
       "      <td>B</td>\n",
       "      <td>9.89</td>\n",
       "      <td>10.030852</td>\n",
       "      <td>1.014242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Year Medal  Result  expectation     ratio\n",
       "0      Stanley Rowley  1900     B   11.20    11.863708  1.059260\n",
       "1    William Hogenson  1904     B   11.20    11.605025  1.036163\n",
       "2    Walter Tewksbury  1900     S   11.10    11.496807  1.035748\n",
       "3   Donald Lippincott  1912     B   10.90    11.190943  1.026692\n",
       "4      Ralph Metcalfe  1932     S   10.30    10.572544  1.026461\n",
       "5        Frank Jarvis  1900     G   11.00    11.287772  1.026161\n",
       "6       Justin Gatlin  2012     B    9.79    10.038000  1.025332\n",
       "7       Reggie Walker  1908     G   10.80    11.042554  1.022459\n",
       "8          Usain Bolt  2012     G    9.63     9.829849  1.020753\n",
       "9         Eddie Tolan  1932     G   10.30    10.507868  1.020181\n",
       "10        Yohan Blake  2012     S    9.75     9.933562  1.018827\n",
       "11     Maurice Greene  2004     B    9.87    10.047656  1.018000\n",
       "12          Jim Hines  1968     G    9.90    10.068813  1.017052\n",
       "13         Ato Boldon  1996     B    9.90    10.060699  1.016232\n",
       "14      Arthur Jonath  1932     B   10.40    10.567141  1.016071\n",
       "15         Usain Bolt  2008     G    9.69     9.843792  1.015871\n",
       "16     Charlie Greene  1968     B   10.00    10.154343  1.015434\n",
       "17        Alvah Meyer  1912     S   10.90    11.060198  1.014697\n",
       "18        Archie Hahn  1904     G   11.00    11.160139  1.014558\n",
       "19    Andre De Grasse  2020     B    9.89    10.030852  1.014242"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '100M Men', fitted_params)\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "25a447fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "             Name  Year Medal  Result  expectation     ratio\n",
      "5    Frank Jarvis  1900     G   11.00    11.287772  1.026161\n",
      "7   Reggie Walker  1908     G   10.80    11.042554  1.022459\n",
      "8      Usain Bolt  2012     G    9.63     9.829849  1.020753\n",
      "9     Eddie Tolan  1932     G   10.30    10.507868  1.020181\n",
      "12      Jim Hines  1968     G    9.90    10.068813  1.017052\n",
      "15     Usain Bolt  2008     G    9.69     9.843792  1.015871\n",
      "18    Archie Hahn  1904     G   11.00    11.160139  1.014558\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                Name  Year Medal  Result  expectation     ratio\n",
      "2   Walter Tewksbury  1900     S   11.10    11.496807  1.035748\n",
      "4     Ralph Metcalfe  1932     S   10.30    10.572544  1.026461\n",
      "10       Yohan Blake  2012     S    9.75     9.933562  1.018827\n",
      "17       Alvah Meyer  1912     S   10.90    11.060198  1.014697\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                 Name  Year Medal  Result  expectation     ratio\n",
      "0      Stanley Rowley  1900     B   11.20    11.863708  1.059260\n",
      "1    William Hogenson  1904     B   11.20    11.605025  1.036163\n",
      "3   Donald Lippincott  1912     B   10.90    11.190943  1.026692\n",
      "6       Justin Gatlin  2012     B    9.79    10.038000  1.025332\n",
      "11     Maurice Greene  2004     B    9.87    10.047656  1.018000\n",
      "13         Ato Boldon  1996     B    9.90    10.060699  1.016232\n",
      "14      Arthur Jonath  1932     B   10.40    10.567141  1.016071\n",
      "16     Charlie Greene  1968     B   10.00    10.154343  1.015434\n",
      "19    Andre De Grasse  2020     B    9.89    10.030852  1.014242\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '100M Men', top_n=10, exclude_years=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5736f762",
   "metadata": {},
   "source": [
    "## Note about the Model and Early 20th Century Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873eb1f8",
   "metadata": {},
   "source": [
    "In his original work, Goldstein mentions the following:\n",
    "\n",
    "```\n",
    "It seems that this metric is biased in favor of runners from the early 20th century. Donovan Bailey broke the world record in the 1996 Olympic games in Atlanta, but is ranked way below many athletes from the very first Olympic games.\n",
    "```\n",
    "\n",
    "It's not that I want to discount the results from the early 1900s, but I'm interested to see how the model adjusts itself if those early marks were removed to better improve the model's accuracy for the present and future.\n",
    "\n",
    "The below is the same foundational code except the following years have been excluded: 1896, 1900, 1904, 1908, and 1912."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6733a6f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 100M Men in 2024:\n",
      "Gold Medal: 9.70 seconds\n",
      "Silver Medal: 9.80 seconds\n",
      "Bronze Medal: 9.81 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '100M Men', exponential_func, omit_years = [1896, 1900, 1904, 1908, 1912])\n",
    "predict_event_results('100M Men', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f49ebeb7",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ralph Metcalfe</td>\n",
       "      <td>1932</td>\n",
       "      <td>S</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.590591</td>\n",
       "      <td>1.028213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arthur Jonath</td>\n",
       "      <td>1932</td>\n",
       "      <td>B</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.675172</td>\n",
       "      <td>1.026459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Charlie Greene</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.261650</td>\n",
       "      <td>1.026165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Eddie Tolan</td>\n",
       "      <td>1932</td>\n",
       "      <td>G</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.534831</td>\n",
       "      <td>1.022799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jim Hines</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>9.90</td>\n",
       "      <td>10.123262</td>\n",
       "      <td>1.022552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Lennox Miller</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.196931</td>\n",
       "      <td>1.019693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jesse Owens</td>\n",
       "      <td>1936</td>\n",
       "      <td>G</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.481994</td>\n",
       "      <td>1.017669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Bob Hayes</td>\n",
       "      <td>1964</td>\n",
       "      <td>G</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.162476</td>\n",
       "      <td>1.016248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2012</td>\n",
       "      <td>G</td>\n",
       "      <td>9.63</td>\n",
       "      <td>9.775944</td>\n",
       "      <td>1.015155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ralph Metcalfe</td>\n",
       "      <td>1936</td>\n",
       "      <td>S</td>\n",
       "      <td>10.40</td>\n",
       "      <td>10.539956</td>\n",
       "      <td>1.013457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yohan Blake</td>\n",
       "      <td>2012</td>\n",
       "      <td>S</td>\n",
       "      <td>9.75</td>\n",
       "      <td>9.866412</td>\n",
       "      <td>1.011940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Martinus Osendarp</td>\n",
       "      <td>1936</td>\n",
       "      <td>B</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.623184</td>\n",
       "      <td>1.011732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>9.69</td>\n",
       "      <td>9.801969</td>\n",
       "      <td>1.011555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ato Boldon</td>\n",
       "      <td>1996</td>\n",
       "      <td>B</td>\n",
       "      <td>9.90</td>\n",
       "      <td>10.012439</td>\n",
       "      <td>1.011358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Justin Gatlin</td>\n",
       "      <td>2012</td>\n",
       "      <td>B</td>\n",
       "      <td>9.79</td>\n",
       "      <td>9.892996</td>\n",
       "      <td>1.010521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Harry Jerome</td>\n",
       "      <td>1964</td>\n",
       "      <td>B</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.301987</td>\n",
       "      <td>1.009999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Frank Fredericks</td>\n",
       "      <td>1996</td>\n",
       "      <td>S</td>\n",
       "      <td>9.89</td>\n",
       "      <td>9.971057</td>\n",
       "      <td>1.008196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Maurice Greene</td>\n",
       "      <td>2004</td>\n",
       "      <td>B</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.950824</td>\n",
       "      <td>1.008189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Dave Sime</td>\n",
       "      <td>1960</td>\n",
       "      <td>S</td>\n",
       "      <td>10.20</td>\n",
       "      <td>10.273224</td>\n",
       "      <td>1.007179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Francis Obikwelu</td>\n",
       "      <td>2004</td>\n",
       "      <td>S</td>\n",
       "      <td>9.86</td>\n",
       "      <td>9.916758</td>\n",
       "      <td>1.005756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Year Medal  Result  expectation     ratio\n",
       "0      Ralph Metcalfe  1932     S   10.30    10.590591  1.028213\n",
       "1       Arthur Jonath  1932     B   10.40    10.675172  1.026459\n",
       "2      Charlie Greene  1968     B   10.00    10.261650  1.026165\n",
       "3         Eddie Tolan  1932     G   10.30    10.534831  1.022799\n",
       "4           Jim Hines  1968     G    9.90    10.123262  1.022552\n",
       "5       Lennox Miller  1968     S   10.00    10.196931  1.019693\n",
       "6         Jesse Owens  1936     G   10.30    10.481994  1.017669\n",
       "7           Bob Hayes  1964     G   10.00    10.162476  1.016248\n",
       "8          Usain Bolt  2012     G    9.63     9.775944  1.015155\n",
       "9      Ralph Metcalfe  1936     S   10.40    10.539956  1.013457\n",
       "10        Yohan Blake  2012     S    9.75     9.866412  1.011940\n",
       "11  Martinus Osendarp  1936     B   10.50    10.623184  1.011732\n",
       "12         Usain Bolt  2008     G    9.69     9.801969  1.011555\n",
       "13         Ato Boldon  1996     B    9.90    10.012439  1.011358\n",
       "14      Justin Gatlin  2012     B    9.79     9.892996  1.010521\n",
       "15       Harry Jerome  1964     B   10.20    10.301987  1.009999\n",
       "16   Frank Fredericks  1996     S    9.89     9.971057  1.008196\n",
       "17     Maurice Greene  2004     B    9.87     9.950824  1.008189\n",
       "18          Dave Sime  1960     S   10.20    10.273224  1.007179\n",
       "19   Francis Obikwelu  2004     S    9.86     9.916758  1.005756"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '100M Men', fitted_params, exclude_years = [1896, 1900, 1904, 1908, 1912])\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "816ecf18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "           Name  Year Medal  Result  expectation     ratio\n",
      "3   Eddie Tolan  1932     G   10.30    10.534831  1.022799\n",
      "4     Jim Hines  1968     G    9.90    10.123262  1.022552\n",
      "6   Jesse Owens  1936     G   10.30    10.481994  1.017669\n",
      "7     Bob Hayes  1964     G   10.00    10.162476  1.016248\n",
      "8    Usain Bolt  2012     G    9.63     9.775944  1.015155\n",
      "12   Usain Bolt  2008     G    9.69     9.801969  1.011555\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                Name  Year Medal  Result  expectation     ratio\n",
      "0     Ralph Metcalfe  1932     S   10.30    10.590591  1.028213\n",
      "5      Lennox Miller  1968     S   10.00    10.196931  1.019693\n",
      "9     Ralph Metcalfe  1936     S   10.40    10.539956  1.013457\n",
      "10       Yohan Blake  2012     S    9.75     9.866412  1.011940\n",
      "16  Frank Fredericks  1996     S    9.89     9.971057  1.008196\n",
      "18         Dave Sime  1960     S   10.20    10.273224  1.007179\n",
      "19  Francis Obikwelu  2004     S    9.86     9.916758  1.005756\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                 Name  Year Medal  Result  expectation     ratio\n",
      "1       Arthur Jonath  1932     B   10.40    10.675172  1.026459\n",
      "2      Charlie Greene  1968     B   10.00    10.261650  1.026165\n",
      "11  Martinus Osendarp  1936     B   10.50    10.623184  1.011732\n",
      "13         Ato Boldon  1996     B    9.90    10.012439  1.011358\n",
      "14      Justin Gatlin  2012     B    9.79     9.892996  1.010521\n",
      "15       Harry Jerome  1964     B   10.20    10.301987  1.009999\n",
      "17     Maurice Greene  2004     B    9.87     9.950824  1.008189\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '100M Men', top_n=10, exclude_years = [1896, 1900, 1904, 1908, 1912])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8be80bf",
   "metadata": {},
   "source": [
    "## Analysis and Visualization for 100m Women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44a7d25f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1K7fccgu33norXbt25dhjj2XBggXMmDGDCy+8kMGDB+8xnmeeeaZV5zN+/Hjuv/9+3nnnHa677rpG7x1xxBEA/Pvf/+awww4jJiam4b177rmHY445hvHjx3PdddfhcDiYNWsWy5cvZ/bs2fv0A3t3Tj/9dF5++WV+97vfceWVV/Kb3/wGu91OUVERc+fO5cQTT9znxLK57rzzTl577TVuvfVWPvvsM6666ireeustjjzySK6++mqGDx9OMBhk06ZNfPLJJ1x77bUccsgh3HbbbRQVFXH00UeTmZnJzp07eeihh7Db7Q3TLw8++GAGDBjAddddh9/vp0uXLrzzzju7rDHWlGHDhjFv3jz+85//kJaWRlxcHAMGDOCQQw7hhBNOYPjw4XTp0oUVK1bw4osvMnr06Ibv6J78+OOPXHjhhfzxj3+ksLCQm2++mYyMDC699NJG+1mtVqZNm8aNN95ITExMi+/7+6XmfJeb+9kfe+yxHHnkkdxwww3U1NRw0EEH8e233/Liiy/u0uYJJ5zA888/z8CBAxk+fDgLFy7kb3/7G5mZmft8LiIiDSJcUENEpEPLzs42gd0+fl0h7ccffzSPPvpoMzo62oyPjzdPOukkc+3atbtt96GHHjL79+9vOhwOs0ePHubtt99uer3eRvv8ssLanjS3WqBpmmZlZaVps9lMwJwzZ84u7+fk5JiAefPNN+/y3tdff20eddRRZkxMjBkVFWUeeuih5n/+859G+9RXn/t1dbemzuWcc84xY2JiGm3z+Xzm/fffb44YMcJ0uVxmbGysOXDgQPPiiy8216xZ07Bfdna2efzxx+8S59ixY82xY8fu9bMAzGnTpu32veuvv94EzC+//NI0TdOsrq42b7nlFnPAgAGmw+EwExISzGHDhplXX321WVJSYpqmac6ZM8c87rjjzIyMDNPhcJjdunUzf/e735lff/11o7ZXr15tHnvssWZ8fLyZkpJiXn755eb777/frGqBeXl55pgxY8zo6GgTaDjP6dOnmwcddJDZpUsX0+l0mr179zavvvpqs6ysbI+fQf31+uSTT8yzzz7bTExMNKOioszf/e53jT7rX9qwYYMJmP/3f/+3x7Z/qTXf5eZ89qZpmjt37jTPP/98MzEx0YyOjjaPOeYYc+XKlbtUC9yxY4d5wQUXmN26dTOjo6PNww8/3Pz66693+d6oWqCI7AvDNH9eJEJERERkLx5++GGuuOIKli9fzpAhQyIdjohIh6LkSkRERPZq8eLFFBQUcPHFFzNmzBjefffdSIckItLhKLkSERGRverZsyclJSUcccQRvPjii7stcy4icqBTciUiIiIiIhIGKsUuIiIiIiISBkquREREREREwkDJlYiIiIiISBhoEeHdCAaDFBcXExcXF7aFMUVEREREpPMxTZOqqirS09OxWPY8NqXkajeKi4vJysqKdBgiIiIiItJBFBYWkpmZucd9lFztRlxcHBD6AOPj4yMcjYiIiIiIREplZSVZWVkNOcKeKLnajfqpgPHx8UquRERERESkWbcLqaCFiIiIiIhIGCi5EhERERERCQMlVyIiIiIiImGge65ERERERCLMNE38fj+BQCDSoRyQ7HY7Vqu11e0ouRIRERERiSCv18uWLVuora2NdCgHLMMwyMzMJDY2tlXtKLkSEREREYmQYDBIQUEBVquV9PR0HA5Hs6rSSfiYpklpaSlFRUX069evVSNYSq5ERERERCLE6/USDAbJysoiOjo60uEcsFJSUtiwYQM+n0/JlYiIiIhIZ2axhKnOXNANpg8MO1hc4WnzABCu0UIlVyIiIiIinZnpBe9qcC8C30Yw/WDYwJ4NrpHg6A+GI9JRHhCUXImIiIiIdFa+Iqh6B3zrAQtYu4IlOpRguZeAezHYe0PcyWDPjHS0+z2tcyUiIiIi0hn5iqDipVBi5egDzgFgSwFrl9B/nQNC233rf96vKNIR79aMGTPIycnZ4z7nnnsuJ510UrvE0xpKrkREREREOhvTGxqxCmwFx4Cmp/0ZjtD7ga2h/U1vWMMoKSnhyiuvpG/fvrhcLlJTUzn88MN5/PHHD8jS8poWKCIiIiLS2XhX/2/EytjLeIlhCe3nXR86zjk0LCGsX7+eMWPGkJiYyMyZMxk2bBh+v5/Vq1fz7LPPkp6ezuTJk8PSV2eh5EpEREREpLNxLwKM5heqqN/PvShsydWll16KzWbjxx9/JCYmpmH7sGHD+P3vf49pmgBs2rSJyy+/nM8//xyLxcJvf/tbHn74YVJTU3fbbiAQ4Prrr+fZZ5/FarVywQUXNLTV0WlaoIiIiIhIZxJ0h6oCWpNadpw1KXRc0N3qEMrLy/nkk0+YNm1ao8TqlwzDwDRNTjrpJLZv386XX37Jp59+yrp16zjttNOabPvvf/87zz77LM888wzffPMN27dv55133ml1zO1BI1ciIiIiIp2J6QtVA7S0cNFhwwbB2tDxtG4NrLVr12KaJgMGDGi0PTk5Gbc7lLxNmzaNCRMmsHTpUgoKCsjKygLgxRdfZMiQISxYsICDDz54l7YffPBBbrrpJn7/+98D8Pjjj/Pxxx+3Kt72opErEREREZHOxLCHEiXT37Lj6te/MuzhC+VXi+/+8MMP5OXlMWTIEDweDytWrCArK6shsQIYPHgwiYmJrFixYpf2Kioq2LJlC6NHj27YZrPZOOigg8IWc1vSyFVnoJW2pY356urwe73YHA7sUVGRDkdERET2xOIKLRDsXhIqud5cgXJw5YTl92Tfvn0xDIOVK1c22t67d28Aon7+PWGa5i4J2J62d3ZKrjoqrbQtbczn8VC2ahUleXlUb96MGQhgWK3EZmTQPSeH5AEDsDudkQ5TREREdsc1MrRAsOlt3m/C+hLsrpFh6T4pKYljjjmGRx55hMsvv7zJ+64GDx7Mpk2bKCwsbBi9ys/Pp6KigkGDBu2yf0JCAmlpafz3v//lyCOPBMDv97Nw4UJGjgxP7G1JyVVHpJW2pY3t2LSJVe+9R01REYbFgiMhAavdTjAQYMfKlWzPzycmM5MBkyfTpUePSIcrIiIiv+boH/o96F338zpXe7jbxwyG9rP3Dh0XJrNmzWLMmDEcdNBBzJgxg+HDh2OxWFiwYAErV65k1KhRTJgwgeHDh3PWWWfx4IMP4vf7ufTSSxk7dmyTU/2uvPJK7r33Xvr168egQYN44IEH2LlzZ9jibktKrjqa+pW2A1t/XrfgV3+JsKX8PKq1LrRfwp+UYEmL7Ni0ifzXX8ddVkZsVhZWR+PvmKtrVwJeL9WFheS//jqDTz1VCZaIiEhHYzhCf2iveAm8q3b/uxH+97vRmhraP4wzn/r06cPixYuZOXMmN910E0VFRTidTgYPHsx1113HpZdeimEYvPvuu1x++eUceeSRjUqxN+Xaa69ly5YtnHvuuVgsFs4//3xOPvlkKioqwhZ7WzHMCBaN/+qrr/jb3/7GwoUL2bJlC++88w4nnXQSAD6fj1tuuYUPPviA9evXk5CQwIQJE7j33ntJT09vss2nnnqKf/3rXyxfvhyAUaNGMXPmTH7zm980O67KykoSEhKoqKggPj6+VefYIqYXdjzx84JwzfkLxKrQXyC6XKwpgtIsPo+HhU89RU1REXG9emGxNP0dCwaDVBUUEJOZyaipUzVFUEREpA243W4KCgro1asXLtc+3AvVMOOpIPTamvS/YheB8tA2ey/NeNqLPV2HluQGEa0WWFNTw4gRI3jkkUd2ea+2tpZFixZx6623smjRIt5++21Wr16911We582bxxlnnMHcuXP57rvv6NGjB8ceeyybN29uq9MIn31ZadtXEDpOpBnKVq2ipqiI2KysPSZWABaLhdisLGo2b6Zs1ap2ilBERERaxJ4Z+kN74vmhYhUEQ+XWCYZeJ54fel+JVbuI6LTA4447juOOO2637yUkJPDpp5822vbwww/zm9/8hk2bNtGjiWlKL7/8cqPXTz31FG+++Saff/45U6ZMCU/gbaUDrLQt+7eSvDwMw9hlKmBT6vcrycsjbfjwtgxNRERE9pXhCP0WdA5VlekI61T3XFVUVGAYBomJic0+pra2Fp/PR9euXZvcx+Px4PF4Gl5XVla2Jsx9E46VtvUPSPbAV1dH9ebNOFrw7wfAmZhI9ebN+OrqVKZdRESko7O4aO0CwbLvOs0iwm63m+nTp3PmmWe26D6o6dOnk5GRwYQJE5rc55577iEhIaHh8ctFztpN/UrbRgvz3fo5taavbeKS/Ybf68UMBLBYrS06zrBaMQMB/F5vG0UmIiIisn/oFMmVz+fj9NNPJxgMMmvWrGYf99e//pXZs2fz9ttv7/EGwZtuuomKioqGR2FhYTjCbpkOtNK27J9sDgeG1UowEGjRcfXrX9maOZVQRERE5EDV4ZMrn8/HqaeeSkFBAZ9++mmzR63uv/9+Zs6cySeffMLwvdwr4nQ6iY+Pb/Rod/UrbQe2t+y4QHnoOE0JlL2wR0URm5GBt4VlTD07dxKbkaEpgSIiIiJ70aGTq/rEas2aNXz22WckJTXvfqS//e1v3HnnnXz00UdNLk7WIblGAsH/raC9N2FeaVv2f91zcjCDQQLNnOJXv1/3nJw2jEpERETCJRgM4vf7CQaDkQ7lgBTRghbV1dWsXbu24XVBQQF5eXl07dqV9PR0/vCHP7Bo0SLmzJlDIBCgpKQEgK5du+L4eYrSlClTyMjI4J577gFCUwFvvfVWXnnlFXr27NlwTGxsLLGxse18hi3UAVbalv1b8oABxGRmUl1Y2Kx1rqoLC4nJzCR5wIB2jFJERERaIhgMUldXR1VVFW63u2G7y+UiLi6OqKiovS7BIuER0U/5xx9/JDc3l9zcXACuueYacnNzue222ygqKuK9996jqKiInJwc0tLSGh7z589vaGPTpk1s2bKl4fWsWbPwer384Q9/aHTM/fff3+7n12L1K21bU0MLBDc1gmV6Q++3wUrbsn+zO50MmDwZV3IyVQUFTY5gBbxeqgoKcCUnM2DyZC0gLCIi0kF5PB6Ki4vZsmULNTU1GIaBxWLBMAxqamrYsmULxcXFjSpjS9sxTNM0Ix1ER9OSVZjbhFbalja2Y9MmVr33HjU/L67tTExsqAro2bkTgJiMDAZMnkyXJtaUExERkdZzu90UFBTQq1evPRZg2x2Px8PWrVvxer24XK7djk4Fg0HcbjcOh4PU1FSc7fgHU8MweOeddzjppJPYsGEDvXr1YvHixeR0wNsN9nQdWpIbdKp1rg4Y9Stte1eHFgj2bQyttG3YQittu0aGpgJqxEr2UZcePRg1dSplq1ZRkpdH9ebNBH0+DKuVroMG0T0nh+QBAzRiJSIi0kEFg0FKS0vxer1ERUVhGMZu97NYLERFRVFXV0dpaSnp6elhmyK4bds2br31Vj788EO2bt1Kly5dGDFiBDNmzGD06NFs2bKFLl26hKWvzkLJVUellbaljdmdTtKGDydt+HB8dXX4vV5sDoeqAoqIiHQCdXV1uN1uXC5Xk4lVPcMwcLlcuN1u6urqiImJCUsMv//97/H5fLzwwgv07t2brVu38vnnn7N9e6j6dffu3cPST3P5fD7s9sguT6Q72zoDiwuscUqspM3Yo6KISkhQYiUiItJJVFVVNdxf1Rz1+1VVVYWl/507d/LNN99w3333MX78eLKzs/nNb37DTTfdxPHHHw+Ekrp33313l2ODwSCZmZk8/vjjjbYvWrQIwzBYv349ABUVFVx00UV069aN+Ph4jjrqKJYsWdKw/4wZM8jJyeHZZ5+ld+/eOJ1OIn3Hk5IrEREREZFOpP4+KputZZPQ7HY7brc7LGXa6ytxv/vuuy0ulmGxWDj99NN5+eWXG21/5ZVXGD16NL1798Y0TY4//nhKSkr44IMPWLhwISNHjuToo49uGBkDWLt2La+//jpvvfUWeXl5rT6v1lJyJSIiIiLSidQnR3ubDri341vDZrPx/PPP88ILL5CYmMiYMWP485//zNKlS5t1/FlnncW3337Lxo0bG2J69dVX+dOf/gTA3LlzWbZsGW+88QYHHXQQ/fr14/777ycxMZE333yzoR2v18uLL75Ibm4uw4cP3+fPJFyUXImIiIiIdCL1U/z2dQpcuApa/P73v6e4uJj33nuPiRMnMm/ePEaOHMnzzz+/12Nzc3MZOHAgs2fPBuDLL79k27ZtnHrqqQAsXLiQ6upqkpKSGkbJYmNjKSgoYN26dQ3tZGdnk5KSEpbzCQcVtBARERER6UQsFgsul4uampoWFXDw+XzExsaGdUFhl8vFMcccwzHHHMNtt93GhRdeyO233865556712PPOussXnnlFaZPn84rr7zCxIkTSU5OBkIjWWlpacybN2+X4xITExueh6s4R7ho5EpEREREpJOJi4vDNM1mT/Gr3y8uLq4tw2Lw4MHU1NQ0a98zzzyTZcuWsXDhQt58803OOuushvdGjhxJSUkJNpuNvn37NnrUJ2AdkZIrEREREZFOJioqqqG8+t6mB5qm2VC2PSpMlYHLy8s56qijeOmll1i6dCkFBQW88cYb/PWvf+XEE09sVhu9evXisMMO44ILLsDv9zc6bsKECYwePZqTTjqJjz/+mA0bNjB//nxuueUWfvzxx7CcQ1vQtEARERERkU7GYrGQkpLC1q1bqaurw+Vy7Xa6X31lQYfDQUpKStimBMbGxnLIIYfwj3/8g3Xr1uHz+cjKymLq1Kn8+c9/bnY7Z511FtOmTWPKlCmNEj/DMPjggw+4+eabOf/88yktLaV79+4ceeSRpKamhuUc2oJhRroYfAdUWVlJQkICFRUVxMfHRzocEREREdlPud1uCgoK6NWrFy5Xy9c09Xg8lJaW4na7ARrdg+Xz+YDQfVEpKSk4nc7wBL0f2tN1aEluoJErEREREZFOyul0kp6eTl1dHVVVVQ1JFoRGl+Li4oiKigprEQtpmpIraZK/tpbKggK6DhkS6VBEREREpAkWi4WYmBhiYmIIBoMEg0EsFosSqgjQJy675S4v59MpU/j8gguo+MVaAiIiIiLScVksFmw2mxKrCNGnLrtlj4vD5nLhq6riy2nTcO/YEemQREREREQ6NCVXsltWh4Mj/vlPYjIzqS4s5OsrriDg9UY6LBERERGRDkvJlTTJ1bUr4x59FHtsLKWLFvHD7bfvdR0FEREREZEDlZIr2aOEvn05/IEHMKxWCt57j/ynnop0SCIiIiLShIDHg6+mhoDHE+lQDkiqFih7lTZmDAf9+c8suPNOljz0EHHZ2fSYODHSYYmIiIgIEPB6qdq4kR35+VQXF2MGAhhWK7Hp6XQZPJi47GysDkekwzwgKLmSZul3+ulUFBSw+qWX+O6mm4hJTydp2LBIhyUiIiJyQKstKaHws8+oLioCiwVnQgJWu51gIMD2VavYvmIFsZmZZE2YQHT37pEOd7+naYHSbCNvuIH0I48k4PHw5WWXUbNlS6RDEhERETlg1ZaUUDBnDtVFRcRmZZHQsyeuLl1wxMfj6tKFhJ49ic3KorqoiII5c6gtKYl0yPs9JVfSbBarlTF/+xsJ/frhLivjy2nT8NXURDosERERkQNOwOul8LPPcJeVEd+rF1a7fbf7We124nv1wl1WRuFnn4W1+vO5556LYRgNj6SkJH7729+ydOnSsPXR2Si5khaxx8YybtYsXElJ7Fy1ivk33EAwEIh0WCIiIiIHlKqNGxtGrAzD2OO+hmGERrA2b6Zq48awxvHb3/6WLVu2sGXLFj7//HNsNhsnnHBCk/v7fL6w9t/RKLmSFotJT+fIhx/G4nCwed488v7+90iHJCIiInJA2ZGfD4bR5IjVr9XvtyM/P6xxOJ1OunfvTvfu3cnJyeHGG2+ksLCQ0tJSNmzYgGEYvP7664wbNw6Xy8VLL71EMBjkL3/5C5mZmTidTnJycvjoo48a2qw/7u2332b8+PFER0czYsQIvvvuu4Z9xo0b12jUrP6xYcMGACoqKrjooovo1q0b8fHxHHXUUSxZsiSs5747Sq5knySPGMHomTMBWPnCC6x9/fUIRyQiIiJyYAh4PFQXF+NMTGzRcc7ERKqLi9usTHt1dTUvv/wyffv2JSkpqWH7jTfeyBVXXMGKFSuYOHEiDz30EH//+9+5//77Wbp0KRMnTmTy5MmsWbOmUXs333wz1113HXl5efTv358zzjgDv98PwNtvv90wYrZlyxZOOeUUBgwYQGpqKqZpcvzxx1NSUsIHH3zAwoULGTlyJEcffTTbt29vk3Ovp2qBss+yjzuOyoIClj36KAvuvpvYHj3ofuihkQ5LREREZL8W9PsxA4Fmj1rVs1it+H0+gn4/VqczLLHMmTOH2NhYAGpqakhLS2POnDlYLP8bw7nqqqs45ZRTGl7ff//93HjjjZx++ukA3HfffcydO5cHH3yQRx99tGG/6667juOPPx6AO+64gyFDhrB27VoGDhxI165dG/b7xz/+wRdffMH3339PVFQUX3zxBcuWLWPbtm04fz7P+++/n3fffZc333yTiy66KCznvjsauZJWGXrJJWQffzym38/XV11Fxfr1kQ5JREREZL9msdkwrNYW3/ce/Hn9K4stfOMr48ePJy8vj7y8PL7//nuOPfZYjjvuODb+4t6ugw46qOF5ZWUlxcXFjBkzplE7Y8aMYcWKFY22DR8+vOF5WloaANu2bWu0z4cffsj06dN57bXX6N+/PwALFy6kurqapKQkYmNjGx4FBQWsW7cuPCfeBI1cSasYhsGhd95JzebNlOXl8eWll3Ls7Nm4unSJdGgiIiIi+yWr00lsejrbV61q0W8uz86ddB04MGyjVgAxMTH07du34fWoUaNISEjgqaee4sILL2zY59d+XYTDNM1dttl/MTJX/14wGGzYlp+fz+mnn869997Lscce27A9GAySlpbGvHnzduk3sYVTKVtKI1fSalankyP/+U9iMjKoLizk6yuvDGuZTxERERFprMvgwRAMEmhm9b36/boMHtyWYWEYBhaLhbq6ut2+Hx8fT3p6Ot98802j7fPnz2fQoEHN7qe8vJxJkyZxyimncPXVVzd6b+TIkZSUlGCz2ejbt2+jR3JycstPqgWUXElYuJKSGDtrFvbYWEoXLuSHGTMwTTPSYYmIiIjsl+Kys4nNzKS6sHCvv7lM06S6sJDYjAzisrPDGofH46GkpISSkhJWrFjB5ZdfTnV1NZMmTWrymOuvv5777ruP1157jVWrVjF9+nTy8vK48sorm93vKaecQlRUFDNmzGjov6SkhEAgwIQJExg9ejQnnXQSH3/8MRs2bGD+/Pnccsst/Pjjj+E47SZpWqCETWLfvoz5+9/58pJLKPj3v4nv1YshU6dGOiwRERGR/Y7V4SBrwgQK5syhsqCA2Kys3Ra4CPh8VBcW4kpOJmvCBKwOR1jj+Oijjxruh4qLi2PgwIG88cYbjBs3rqEs+q9dccUVVFZWcu2117Jt2zYGDx7Me++9R79+/Zrd71dffQVAz549G20vKCigZ8+efPDBB9x8882cf/75lJaW0r17d4488khSU1P36TybyzA1vLCLyspKEhISqKioID4+PtLhdDqrX3mFH+++G4DD//EPevxiDqyIiIiI/I/b7aagoIBevXrhcrlafHxtSQmFn31G9ebNQKjcuuXnYheenTsBiM3IIGvCBKK7dw9n6PuVPV2HluQGGrmSsOt/5plUbtjA6pdf5rubbiImPZ2koUMjHZaIiIjIfie6e3f6nnoqVRs3siM/n+riYvw+H4bVSteBA+kyeDBx2dlhH7GS3VNyJW1i5A03ULVpE1u+/pqvLruMia++qr+WiIiIiLQBq8NBYr9+JPbrR8DjIej3Y7HZwloVUJpHBS2kTVhsNg6//34S+valrrSUL6dNw1dTE+mwRERERPZrVqcTe0yMEqsIUXIlbcYeG8vYWbNwJSWxY+VK5t94Y4sXuxMRERER6SyUXEmbis3I4Ih//hOLw8HmuXNZ8o9/RDokERERkQ5HNeYiK1yfv5IraXMpOTkcetddAKx47jnWvvlmhCMSERER6RjsP5dPr62tjXAkBzav1wuA1WptVTsqaCHtoufxx1O1cSPLHn2UBXfeSWxmJt0PPTTSYYmIiIhElNVqJTExkW3btgEQHR2NYRgRjurAEgwGKS0tJTo6GputdemRkitpN0MvuYTKggI2fvABX199NRNnzyb+Vwu/iYiIiBxouv9cUbk+wZL2Z7FY6NGjR6sTWy0ivBsdbhHhYA1YYiIdRVgEPB4+P/98yvLyiO3Rg4mzZ+NMTIx0WCIiIiIRFwgE8Pl8kQ7jgORwOLBYdn/HVEtyAyVXu9Ghkquqf0PJVEh/DWLGRzaWMKkrK+OTM86gpriYbgcfzPgnn9TCdiIiIiLSIbUkN1BBi46u+j8QKIUtZ0OgPNLRhEVUcjJjZ83CFhPDtgULWPCXv6hCjoiIiIh0ekquOrrUh8AxAPybYcsFsJ8kIYn9+nH43/+OYbGw/p13WPHss5EOSURERESkVZRcdXSWGEifDYYDqv8NOx+PdERhk37EEYycPh2AvH/8g8LPPotwRCIiIiIi+07JVWfgyoWUe0PPt10DnuWRjSeMBpx1Fv3OOANMk/k33sj2n36KdEgiIiIiIvtEyVVn0eVKiPktmG4oPgOCdZGOKGxGTZ9O2pgxBNxuvrzsMmq3bo10SCIiIiIiLabkqrMwLJD2PFhTQyNX266PdERhY7HZGPP3v5PQpw9127bx5bRp+LVKuYiIiIh0MkquOhNbKqS9EHq+81Goei+y8YSRIy6OsbNm4ezalR0rVjB/+nTMYDDSYYmIiIiINJuSq84mdiJ0vTb0vOR88G2ObDxhFJuZyZH//CcWh4Oizz9n8f33q0S7iIiIiHQaSq46o5SZ4BwZWvdqyxQwA5GOKGxScnM59M47AVj5wgvkP/10hCMSEREREWkeJVedkeGAjNlgxEDtF7D9b5GOKKx6nnACudeH7ilb8uCDrH7llQhHJCIiIiKyd0quOitHf0h9OPS89Fao+yGy8YTZoHPPZej//R8AP959NwX/+U+EIxIRERER2TMlV51ZwrkQdxrgD5VnD1RGOqKwGnbZZfT/058A+O/NN1P0xRcRjkhEREREpGlKrjozw4Duj4MtG3zrYeu0SEcUVoZhMOrGG+l14omYgQDfXHMNJf/9b6TDEhERERHZLSVXnZ01EdJfAaxQ+RJUvBS2pgMeD76aGgIeT9jabCnDYuGQv/yFrAkTCPp8fHXZZZQtWRKxeEREREREmmKYqnW9i8rKShISEqioqCA+Pj7S4TRP2Z1QdhtYYqFnHjj67FMzAa+Xqo0b2ZGfT3VxMWYggGG1EpueTpfBg4nLzsbqcIQ39mbG9eW0aZTMn489Pp4Jzz9PlwED2j0OERERETmwtCQ3UHK1G50yuTIDsOkoqPsKXL+B7G/AsLeoidqSEgo/+4zqoiKwWHAmJGCxWgkGAngqKiAYJDYzk6wJE4ju3r2NTqRp/tpavpg6lbK8PFxJSUx48UXis7PbPQ4REREROXC0JDfQtMD9hWGF9JfA0gXcP0DpbS06vLakhII5c6guKiI2K4uEnj1xdemCIz4eV5cuJPTsSWxWFtVFRRTMmUNtSUkbnUjTbNHRjHvsMboMHIi7vJwvLriAmi1b2j0OEREREZHdiWhy9dVXXzFp0iTS09MxDIN333234T2fz8eNN97IsGHDiImJIT09nSlTplBcXLzXdt966y0GDx6M0+lk8ODBvPPOO214Fh2IPQvSfl50d/t9UNO86noBr5fCzz7DXVZGfK9eWO2hES8TCASDDftZ7Xbie/XCXVZG4WefEfB6w30Ge+WIj2f8k08S17MntVu2MHfqVNzl5e0eh4iIiIjIr0U0uaqpqWHEiBE88sgju7xXW1vLokWLuPXWW1m0aBFvv/02q1evZvLkyXts87vvvuO0007j7LPPZsmSJZx99tmceuqpfP/99211Gh1L3CmQeBFgwpY/gb9sr4dUbdzYMGJlGAaEjmZrSQmFhYX4A4GGfQ3DCI1gbd5M1caNbXQSe+ZKSuKop54iunt3KgsKmHvRRXgr968y9CIiIiLS+XSYe64Mw+Cdd97hpJNOanKfBQsW8Jvf/IaNGzfSo0eP3e5z2mmnUVlZyYcfftiw7be//S1dunRh9uzZzYqlU95z9UvBWthwEHhXQOwkyPh3qGx7Ewr+/W+2r1xJQq9eDdt8fj+bNm0iEAhgt9vJzMzEbrM1vF9RUEDXgQPpdeKJbXoqe1K5YQOfTZmCu7yclNxcxj/1FLaoqIjFIyIiIiL7n/32nquKigoMwyAxMbHJfb777juOPfbYRtsmTpzI/PnzmzzG4/FQWVnZ6NGpWaIhfTYYDqj+D+yc1eSuAY+H6uJinL/6TO02G1mZmdhsNnw+H0WFhXh9vob3nYmJVBcXR7RMe3zPnox/8knscXGULl7M11deGZGpiiIiIiIi0ImSK7fbzfTp0znzzDP3mDGWlJSQmpraaFtqaioleyjAcM8995CQkNDwyMrKClvcEeMaASl/Cz3fdi24l+52t6DfjxkIYLFad3nP4XCQlZWF3WbD5/dTVFSE9+fkxWK1YgYCBP3+NjuF5ugycCDjHnsMa1QUW779lvk33BDxmERERETkwNQpkiufz8fpp59OMBhk1qymR2HqGb+aAmea5i7bfummm26ioqKi4VFYWNjqmDuELpdDzPFgeqD4jNB0wV+x2GwYP5db3x27zUZmVhYOhwO/309hUREer5fgz+tfWX4xVTBSUnJzOfKf/8Rit1P46af8MGMG5i8KcYiIiIiItIcOn1z5fD5OPfVUCgoK+PTTT/c6z7F79+67jFJt27Ztl9GsX3I6ncTHxzd67BcMA9KeA2t38OaHRrB+xep0EpueHlrHqgl2m43MzEwcDgeBQIDCwkKqtm0jNj0dq9PZlmfQbGmHHcaY++/HsFpZ/847LLrvPjrI7YQiIiIicoDo0MlVfWK1Zs0aPvvsM5KSkvZ6zOjRo/n0008bbfvkk0847LDD2irMjs2WAun/Cj3f+ThU7VqWvsvgwRAMEvjFPVW7NGO1kpWZicvpJOD1Ulpair2DTZ/MmjCBQ+68E4BVL73E8scei3BEIiIiInIgiWhyVV1dTV5eHnl5eQAUFBSQl5fHpk2b8Pv9/OEPf+DHH3/k5ZdfJhAIUFJSQklJScN9PwBTpkzhpptuanh95ZVX8sknn3DfffexcuVK7rvvPj777DOuuuqqdj67DiTmGOh6fej5lgvBV9To7bjsbGIzM6kuLNzjaI/VaiU9IwNLZSWWrl1ZX1HBjh072jLyFut94omM+vOfAVj26KOs/Ne/IhyRiIiIiBwoIlqKfd68eYwfP36X7eeccw4zZsyg1y9Kg//S3LlzGTduHADjxo2jZ8+ePP/88w3vv/nmm9xyyy2sX7+ePn36cPfdd3PKKac0O65OX4p9d0wvbBwD7h8haiz0+ByM/xWxqC0poWDOHNxlZcRmZTUsJPxLAZ+P6sJCnF274unXj2qLBYvFwuDBg5s1qtielj/+OEsffhiAQ+68kz4tuP4iIiIiIvVakht0mHWuOpL9MrkC8K6BglwwayD5Lki+udHbtSUlFH72GdWbNwOhcuuWn4tdeHbuBCA2I4OsCRNwdetGfn4+5eXlGIbBoEGDSElJae8zapJpmuT9/e+seO45DIuFMfffT4+JEyMdloiIiIh0MkquWmm/Ta4AKl6ALecCVsj+GqJGN3o74PVStXEjO/LzqS4uxvy5KmBsejpdBg8mLjsbq8MBQDAYZOXKlZSWlgIwcODAPRYOaW+mafLD7bez7q23sNhsHPnII6QfcUSkwxIRERGRTkTJVSvt18mVacKWs6ByNth7Qs88sCbsdteAx0PQ78diszVZFdA0TVatWsXWrVsB6N+/P2lpaW0UfMsFAwHm33gjmz78EKvLxfgnn6TbqFGRDktEREREOomW5AYdulqgtAHDgNTHwN4LfBtg6yWhhGs3rE4n9piYPZZbNwyDAQMGkJ6eDsDq1aspKipqcv/2ZrFaGT1zJulHHknA7ebLSy9le35+pMMSERERkf2QkqsDkTUB0l8BrKERrMrWVdQzDIO+ffuSmZkJwLp169i0aVMYAg0Pq8PB4Q88QLeDDsJXXc3ciy6iYv36SIclIiIiIvsZJVcHqqhDIfkvoecl00LFLlrBMAx69+5NdnY2ECqrX1BQ0GEW8rVFRTH20UfpOmQInh07+OLCCxsKd4iIiIiIhIOSqwNZ0o0QPS5UPbD4jFC59lYwDIOePXs2lNDftGkT69ev7zAJlj02lnFPPEF8797Ubd3KFxdeSN3PxThERERERFpLydWBzLBC2ktg6QruhVB6S1ia7dGjB3379gWgqKiINWvWdJgEy9WlC0c9/TQxmZlUb9rEF1OnNpSZFxERERFpDSVXBzp7BqQ9G3q+/W9Q82lYms3IyKB///4AbNmyhVWrVnWYBCs6NZWjn36aqJQUKtasYd4ll+CrqYl0WCIiIiLSySm5Eog7ERIvCT0vngL+8EyVS0tLY+DAgQBs3bqVFStWEAwGw9J2a8VmZTH+qadwJCRQvnQpX11+OQGPJ9JhiYiIiEgnpuRKQrr9HRxDIFACW85rsjx7S6WmpjJkyBAMw6C0tJT8/PwOk2Al9uvH+CeewBYdzdbvv+fb664j6PNFOiwRERER6aSUXEmIJQrSZ4PhhJr3YcfDYWs6OTmZIUOGYLFYKC8vZ/ny5QQCgbC13xpJw4Yx9tFHsTgcFH3xBf+99VbMDpL8iYiIiEjnouRK/sc1LDSCBVB6PbiXhK3ppKQkhg4disViYceOHSxbtgy/3x+29lsj9Te/4Yh//APDZmPDf/7Dj3ff3WHuDxMRERGRzkPJlTSWeCnETg6VZS8+HYK1YWu6S5cuDB8+HKvVSkVFBUuXLsXXQabhZYwbx+iZM8EwWPPqqyz95z8jHZKIiIiIdDJKrqQxw4Duz4AtHbwrYdvVYW0+ISGBESNGYLPZqKqqYsmSJXi9rVtfK1x6Hn88B992GwA/Pfkk+c88E+GIRERERKQzUXIlu7IlQ9qLgAE7n4SKV8LafFxcHDk5OdjtdmpqaliyZAmeDlKpr9+pp5JzzTUA5D3wAGtefz3CEYmIiIhIZ6HkSnYv5ihIujn0vGQqeJaHt/mYGHJycnA6ndTW1pKXl4fb7Q5rH/tq8AUXMOSiiwBY8Je/sOH99yMckYiIiIh0BkqupGnJMyDmWDBroegUCFSEtfno6GhycnJwuVy43W7y8vKorQ3fPV6tMfyKK+h3+ulgmnx3000UzZ0b6ZBEREREpINTciVNM6yQ9jLYeoBvDWw5N2zrX9VzuVzk5OQQHR2Nx+NhyZIl1NTUhLWPfWEYBgfdfDM9J03CDAT45ppr2PrDD5EOS0REREQ6MCVXsme2ZMh4EwwHVL8L2/8a9i6cTicjRowgJiYGr9dLXl4eVVVVYe+npQyLhUPvuovMo44i6PXy5bRplC1dGumwRERERKSDUnIlexd1MKQ+Enpe+meo+TzsXTgcDkaMGEFcXBx+v58lS5ZQURHeaYj7wmKzMeb++0k95BD8tbXM+7//Y+eaNZEOS0REREQ6ICVX0qSgvxa/ZydBfy0kXAgJ5wFBKD4DfIVh789utzN8+HDi4+Pxu93k/fADWzdvDns/LWV1Ojny4YdJGj4cb0UFc6dOpWrTpkiHFV5BNwSqQv9tRwGPB19NDYEOUi1SREREpDUM0wzzTTT7gcrKShISEqioqCA+Pj7S4bSrYMBNXeVKqnasw+2uBjMIhgWXK5a4hCyiKi7F4l0IrkOgx5dgcYat74DXS9XGjZQvX07BkiXU1dSAxULG4MH0Puww4rKzsTocYeuvpTw7d/L5eeexc/VqYjIyOObFF4lOTY1YPK1mesG7GtyLwLcRTD8YNrBng2skOPqHpoOGWf113pGfT3VxMWYggGG1EpueTpfBgyN+nUVERER+qSW5gZKr3ThQkytP9QZKi+fjdtdhADabFcNiwQwG8fsDmIDLESTFvBensR4SL4Hus8LSd21JCYWffUZ1URFYLDji4ynfsYOqykqCNTXExcSQPmQIPSZMILp797D0uS/qSkv5dMoUqjdtIr53byb861+4unSJWDz7zFcEVe+Abz1gAWvXUGJl+iGwHQiCvTfEnQz2zLB1++vr7ExIwGK1EgwE8FRUQDBIbGYmWRG+ziIiIiL1lFy10oGYXHmqN7C18Cu8Ph8uVxQWq3WXfYKBAG53HQ7LTlKN+3BaSyHtBUiY0qq+a0tKKJgzB3dZGbFZWVjtdgBMoLy8nO3bt2P6/Thqakjr25fekydH9Id3TXExn559NrUlJXQdMoSjnnkGR1xcxOJpMV8RVLwEga3g6LP70SnTC951YE2FhD+FJcFq6jr/UsDno7qwEFdyMr1OOEEJloiIiERcS3ID3XMlBANuSovn4/X5iIqK2W1iBWCxWomKisEbTKTUvIigaYOSi8G9ZJ/7Dni9FH72Ge6yMuJ79Wr0g9sAkpOSSE1NxbDZ8MbHU7RqFRs++YSA17vPfbZWTHo64596CmeXLmz/6Se+nDYNf11dxOJpEdMbGrEKbAXHgKan/RmO0PuBraH9zdZ93nu6zr9ktduJ79ULd1kZhZ99FtHrLCIiItJSSq6EusqVuN11uFxRGBZjj/saFgOXKwp3sBd1tslgumHzKRDYsU99V23cSHVREbFZWRjG7vtOiI8nIyMDq8VCID6eDXl5lK1evU/9hUtC796Mf/JJ7LGxlC5cyNdXX905EgHv6tBUQEcfMPbyz9+whPbzFYSOa4XmXOeGbg2D2Kwsqjdvpmrjxlb1KyIiItKelFwJVTvWYUCTI1a/Vr9flfUUsPcM/VgvnhIqftFCO/LzwTCaHMmoFxMdTWZWFjanE5/Px5KPP6a6urrF/YVT18GDGTtrFlaXiy1ff813N91EMBCIaEx75V4EGM0vVFG/n3tRq7pt7nWuV7/fjvz8VvUrIiIi0p6UXB3ggv5a3O5qbLbmJVb17DYrbrefYOrLYDihZg6U39OiNgIeD9XFxTgTE5u1v8vppEePHri6dMGzbRuLFyxgx459GzELl26jRnHEgw9isdnY9NFHLLjjDjrsbYxBd6gqoDWpZcdZk0LH7WOZ9pZe53rOxESqi4tVpl1EREQ6DSVXB7hgwAtmEMPSwq+CxQJmkKB9MKQ+FtpWdivUfNL8vv1+zECg2SNmAHabjfSMDJx2O36vl2XLlrF169aWxR5m6UccwWF//SuGxcK6t95i8f33d8wEy/T9r9x6S9RXETR9+9TtvlxnCI2QmoEAQb9/n/oVERERaW9Krg5wFqsDjFC59RYJhta/slgdkHgeJEwFTCg+MzTK0Zy+bTaMn8twt4QBdE9PJyU1FdM0WblyJZs2bYpoQtNj4kR+c8cdAKx8/nl+euKJiMXSJMP+v0SpJeoTMqN5U/p+bV+vc/Dn9a8sthYmgyIiIiIRouTqAGexReNyxeL3t+yHr88fwOWKxWKLDm1I/Se4DoJAOWz+Q7OmkFmdTmLT00PrG7WAZ+dO4jIyGDJiBJmZoRLhBQUFrFmzJqIJVp9TTmHkjTcCsPThh1n18ssRi2W3LK7QAsGB7S07LlAeOs7i2qduW3OdY9PTsTrDt1C1iIiISFtSciXEdemDCc0eWajfL65Ln/9ttLgg402wdAX3j7Dtyma11WXwYAgGCfiaN+Wsfr8ugwdjGAZ9+vShb9++AGzZsoWffvqJQASLSgycMoVh06YBsHDmTNa/+27EYtkt10gg2PzS6vX7uUa2qtvWXGcRERGRzkLJlRAVPzBUXt1dhxnc88iPGTQbyrZHxQ9s/KY9GzJmAwbsfBJ2PrvXvuOys4nNzKS6sHCvo06maVJdWEhsRgZx2dkN2zMyMhgyZAgWi4Xy8nKWLFmCN4Jl0YdecgkDzj4bgO9vvZXCTz+NWCy7cPQHe+/QAsF7q+5oBkP72XuFjmuFcFxnERERkY5OyZVgsbpIST8Mh91OXV1NkyNYwUCAuroaHHY7KemHYbHuZppYzLGQ/JfQ862X7rWEt9XhIGvCBFzJyVQWFDQ5shHw+agsKMCVnEzWhAlYHY1LiScnJzN8+HBsNhtVVVUsXryY2travZ98GzAMg5E33EDvk0/GDAb59vrr2TJ/fkRi2YXhgLiTwZoK3lVNj2CZ3tD71tTQ/s0t3d6EcF1nERERkY7MMDtkWbPIqqysJCEhgYqKCuLj4yMdTrvxVG+gtHg+bncdECq3jsUCwSC+n+/JcrmiSEk/DGdsz6YbMoNQdGKoPLu9J/RcCNaue+y7tqSEws8+o3rzZiBUhtvycxEEz86dAMRmZJA1YQLR3bs33U5tLcuWLcPtdmOz2Rg2bFjErmEwEODb666j8JNPsEZFcdRTT5GSmxuRWHbhK4Kqd0ILBEOo3Hp9sYtAeWibvVcosbJnhq3bcF1nERERkfbSktxAydVuHKjJFUAw4KauciVVO9bhdleHEiXDgssVS1yXPkTFD9z9iNWvBXbChlGhBYZjjoPMOWDseaA04PVStXEjO/LzqS4uxvy5WlxsejpdBg8mLju7WSMZXq+X5cuXU1VVhcViYdCgQSQnJzfzEwivgNfLV5ddxpZvv8UeF8eE55+ny8CBez+wPZhe8K4OjS76Nv6vKqA9O3SPlaN/q0esdidc11lERESkPSi5aqUDObn6paC/lmDAi8Xq+F9VwJZwL4GNh4LphuQZkHx7sw8NeDwE/X4sNts+VYsLBALk5+ezfXuoMl7fvn3JyMhocTvh4K+tZe5FF1G6eDGupCQm/OtfxPfsGZFYmhR0h9axMuz7XBVwX7T2OouIiIi0tZbkBrrnSppksUVjcybuW2IF4BoB3X9e76nsDqj+sNmHWp1O7DEx+/yD22q1MnToUNLS0gBYu3Yt69evj0ipdlt0NGMfe4wugwbhLi/niwsuoKa4uN3j2COLC6xx7ZpYQeuvs4iIiEhHouRK2lbCFEi8hNACw2eBt6DdujYMg379+tHz51GiwsJCVq5cSbClCyaHgSMujvFPPEF8r17UlpTwxdSp1JWVtXscIiIiItJ2lFxJ2+v2D3AdAsEdsPn3EKxrt64NwyA7O5sBAwZgGAbbtm1j6dKl+P3+douhnispiaOefpqY9HSqNmxg7kUX4W3hwroiIiIi0nEpuZK2Z3FCxhtgTQbPYtg6Ddp5el737t0ZOnQoVquViooKFi9ejNvtbtcYAKK7d2f800/jSkpi56pVzLv0UvwRKhkvIiIiIuGl5Erahz0L0l8FLFDxHFQ83e4hdO3alZycHBwOB7W1tSxevJjq6up2jyM+O5vxTz2FPT6esrw8vrrySgIRXPRYRERERMJDyZW0n5ijIeXu0POtl0HdgnYPITY2ltzcXKKjo/F6veTl5TVUFGxPXQYMYPzjj2OLiqJk/ny+vf56ghGYqigiIiIi4aPkStpX1xsh9qTQGkub/wD+9i/q4HK5yM3NJSEhgUAgwPLlyykpKWn3OJJHjODIRx7BYrdT9NlnfH/bbZgRKLYhIiIiIuGh5Eral2FA2vNg7wf+TVB8JpiBdg/DZrMxfPhwunXrhmmarFq1io0bN7Z7qfbuhx7K4Q88gGG1UvDvf7Pw3nsjUi5eRERERFpPyZW0P2sCZLwFRjTUfgplMyIShsViYeDAgWRlZQGwYcMG1qxZ0+7JTeZRR3Ho3aHpkqtffplljzzSrv2LiIiISHgouZLIcA2D7k+FnpffBVX/iUgYhmHQu3dv+vbtC8CWLVtYvnw5gUD7jqb1mjSJg265BYDljz/Oiuefb9f+RURERKT1lFxJ5CScCV0uDz3fcjZ410YslIyMDIYMGYLFYmH79u0sWbIEbztX8Ot/xhmMuOoqABb/7W+sffPNdu1fRERERFpHyZVEVrf7IWo0BCt+XmA4cms+JScnM2LECOx2O1VVVSxevJjadl6DavCFFzLo/PMB+GHGDDZ++GG79i8iIiIi+07JlUSW4YD0N8DaDTxLoeT/2n2B4V+Kj48nJycHl8uF2+1m8eLFVFRUtFv/hmGQc8019D31VDBN5k+fzuavvmq3/kVERERk3ym5ksizZ0D6a4AVKl+EnY9HNJzo6Ghyc3OJi4vD7/ezdOlSSktL261/wzA46JZbyP7d7zD9fr656iq2/fhju/UvIiIiIvtGyZV0DDHjIOXe0POtV0Ld9xENx+FwMGLECJKSkggGg+Tn51NUVNRu/VusVkbPnEn62LEEPB7mXXop23/6qd36FxEREZGWU3IlHUfXayHu94Dv5wWG22+0aHesVitDhgwhPT0dgHXr1rFu3bp2K9Vusds5/IEH6Hbwwfhraph70UVUrI1c0Q8RERER2TMlV9JxGAZ0fxYcA8BfFEqwgu4Ih2TQt29fevXqBUBRURErVqwgGAy2S/82l4uxjz5K16FD8ezcyRdTp1LdjiNoIiIiItJ8Sq6kY7HGQ8bbYImHuq+g+Cww23fNqV8zDIMePXowcOBADMOgtLSUpUuX4vP52qV/e0wM4594goS+fanbto0vLryQuna8B0xEREREmkfJlXQ8zsGQ8W6okmD127D1sohWEKyXmprKsGHDsFqtVFRUkJeXh9vdPiNrzsRExj/1FLFZWVQXFvLFhRfi2bmzXfoWERERkeZRciUdU8x4SHsZMELVA8vvjHREAHTp0oWcnBwcDge1tbUsXryYqqqqduk7uls3jnr6aaK6daNi7VrmXnwxvpqadum7rQSDQfx+f7tNsxQRERFpS0qupOOK/wOkPhJ6XnY77HwysvH8LDY2lpEjRxITE4PX62XJkiVs3769ffrOzOSop57CmZjI9uXL+XLaNPztNHoWLsFgkJqaGkpKSti0aRNFRUVs2rSJkpISampqlGiJiIhIp6XkSjq2LpdC0i2h5yWXQNW7EQ2nntPpJCcnh8TERAKBAMuWLaOkpKRd+k7o25dxTzyBLSaGbQsW8M011xBsp/u/Wsvj8VBcXMyWLVuoqanBMAwsFguGYVBTU8OWLVsoLi7G4/FEOlQRERGRFlNyJR1f8l8g4UIgCMWnQ+1XkY4IAJvNxrBhw+jWrRsAq1atYsOGDe1Sqj1p6FDGPvooVqeT4i+/5Ls//5lgILKFP/bG4/GwdetW3G43LpeLqKgo7HY7NpsNu91OVFQULpcLt9vN1q1blWCJiIhIpxPR5Oqrr75i0qRJpKenYxgG7777bqP33377bSZOnEhycjKGYZCXl9esdh988EEGDBhAVFQUWVlZXH311e1WeEDagGFA98cgdjKYHiiaDO5lkY4KAIvFwsCBA8nKygJg48aNrF69ul2mtqUefDCHP/gghs3Gxg8+4Me77263NbhaKhgMUlpaitfrJSoqCotl9//rsVgsREVF4fV6KS0t1RRBERER6VQimlzV1NQwYsQIHnnkkSbfHzNmDPfee2+z23z55ZeZPn06t99+OytWrOCZZ57htdde46abbgpX2BIJhg3SX4WowyFYAUW/Bd/GSEcFhEq19+7dm379+gFQUlLC8uXLCbTDSFLGkUdy2L33gmGw9rXXWPKPf7R5n/uirq6uYcTKMIw97msYRsMIVl1dXTtFKCIiItJ6tkh2ftxxx3Hcccc1+f7ZZ58NwIYNG5rd5nfffceYMWM488wzAejZsydnnHEGP/zwQ6tilQ7AEgWZ78HGI8D7ExROhB7fgC050pEBkJ6ejsPhYMWKFezYsYO8vDyGDRuGw+Fo036zjzsOX3U1P8yYQf4zz2CPi2PI1Klt2mdLVVVVNdxf1Rz1+1VVVRETE9OWoYmIiIiEzX53z9Xhhx/OwoULG5Kp9evX88EHH3D88cc3eYzH46GysrLRQzooaxfI+ghsWeBdBUXHQ7DjlCNPTk5mxIgR2O12qqurWbx4MbW1tW3eb98//pHc668HYMmDD7J69uw277O5gsEgbrcbm61lf8ux2+243W5NDRQREZFOY79Lrk4//XTuvPNODj/8cOx2O3369GH8+PFMnz69yWPuueceEhISGh71989IB2XPhKyPwdIV3D/A5j+C2XGq5cXHx5Obm0tUVBRut5vFixdTUVHR5v0OOvdchlx8MQA/3nUXBf/5T5v32Rz1ydHepgPu7XgRERGRjm6/S67mzZvH3XffzaxZs1i0aBFvv/02c+bM4c47m16E9qabbqKioqLhUVhY2I4Ryz5xDoKs98GIgpoPYcuF0IGKOURFRZGTk0NcXBx+v58lS5ZQWlra5v0Ov/xy+p91FgD/vflmir74os373Jv6KX77WmyjuVMJRURERCJtv/vVcuutt3L22Wdz4YUXMmzYME4++WRmzpzJPffc0+RfwJ1OJ/Hx8Y0e0glEHQoZbwBWqPwXlDY9OhkJDoeDESNGkJSUhGma5OfnU1RU1KZ9GobBqOnT6TV5MmYgwDfXXEPJf//bpn3ujcViweVy4ff7W3Scz+fD5XIpuRIREZFOY7/71VJbW7vLjzGr1Yppmh22TLW0Quzx0P3p0PPtf4XtHatantVqZciQIaSnpwOwbt061q5d26bfRcNi4ZA77yTz6KMJ+nx8ddlllC1d2mb9NUdcXBymaTZ7il/9fnFxcW0ZloiIiEhYRTS5qq6uJi8vr2H9qoKCAvLy8ti0aRMA27dvJy8vj/z8fCC0SGteXh4lJSUNbUyZMqVRmfVJkybx2GOP8eqrr1JQUMCnn37KrbfeyuTJk7Fare13ctJ+Es+FlJ/L9W+7Bipejmg4v2YYBn379qV3794AbN68mfz8/Da9l8hiszHm/vvpPno0/ro65l18MTtWrWqz/vbmlwsE7y2xNE2z0ULDIiIiIp2FYUZwOGfevHmMHz9+l+3nnHMOzz//PM8//zznnXfeLu/ffvvtzJgxA4Bx48bRs2dPnn/+eQD8fj933303L774Ips3byYlJYVJkyZx9913k5iY2Ky4KisrSUhIoKKiQlMEOwvTDCVWOx4EbKH7sWKOjXRUu9i2bRsrV67ENE3i4+MZOnQodru9zfrz19byxdSplOXl4UpK4pgXXyQuO7vN+tsTj8fD1q1b8Xq9TU73q68s6HA4SE1Nxel0RiBSERERkf9pSW4Q0eSqo1Jy1UmZQdjyJ6icDUYM9JgLUQdHOqpd7Ny5s2GR4aioKIYNG9amIzTeigo+O+88dq5aRUx6OhP+9S9i0tLarL898Xg8lJaW4na7ARollj5fqOKjy+UiJSVFiZWIiIh0CC3JDfa7e67kAGZYIO15iJ4AZg0U/Q68qyMd1S4SExPJzc3F6XRSV1fH4sWLqaqqarP+HAkJjH/ySeKys6kpLmbu1Km4t29vs/72xOl0kp6eTlpaGrGxsQ33QpqmSWxsLGlpaaSnpyuxEhERkU5JI1e7oZGrTi5QBYXjwb0Q7D0hez7YIjNSsycej4dly5ZRU1ODxWJh8ODBJCUltVl/NcXFfHr22dSWlNBl0CCOfu45HBEuGBEMBgkGg1gsFlUFFBERkQ5JI1dyYLPGQeYHYO8Lvg1QeBwE2n4R35ZyOp3k5OSQmJhIMBhk+fLlbNmypc36i0lP56inn8bZtSs7Vqzgy0svxV9X12b9NYfFYsFmsymxEhERkf2CftHI/snWDbI+BmsqeJbA5pMg6I50VLuw2WwMGzaM1NRUAFavXs2GDRvarFR7fK9eHPXkk9jj4ihdtIivr7ySgNfbJn2JiIiIHGiUXMn+y9Ebsj4ESxzUzoMtZ4MZiHRUu7BYLAwYMIAePXoAsHHjRlavXt1mpdq7DBrEuFmzsEZFseXbb5l/440EAx3vcxERERHpbJRcyf7NlQsZ74LhgKo3YesVobLtHYxhGPTq1Yv+/fsDUFJSwvLly/H7/W3SX8rIkRz50ENY7HYKP/mEH2bMwGzDdbdEREREDgRKrmT/F3MUpL0EGLBzFpTfFemImpSWlsbQoUOxWCzs2LGDJUuW4PF42qavMWMY87e/YVgsrH/7bX6cOVMJloiIiEgrKLmSA0P8HyH1n6HnZbfBzqciG88eJCUlkZOTg91up7q6msWLF1NTU9MmfWUdcwyH3HknAGtmz+aHGTM0RVBERERkHym5kgNHl8sg6ebQ85L/g6p3IxrOnsTFxZGbm0tUVBQej4e8vDx27tzZJn31PukkDp05E8NiYd1bb/HdTTcR/HlBXxERERFpPiVXcmBJvhMSLgCCUHwG1H4d6YiaFBUVRW5uLvHx8fj9fpYuXcq2bdvapK/eJ57ImPvvx7DZ2Pj++3xz7bWqIigiIiLSQkqu5MBiGND9cYidDKYbiiaDZ3mko2qS3W5n+PDhJCcnY5omK1asoLCwsE1KtfeYODFU5MLhoOjzz/nq8ssjvg6WiIiISGei5EoOPIYN0mdD1BgI7oTCieDbGOmommS1Whk8eDAZGRkArF+/nnXr1rVJgpUxbtz/yrR/8w3zLrkEXxvd7yUiIiKyv1FyJQcmSzRkvgeOIeAvDiVY/rJIR9UkwzDo06cPvXv3BmDz5s3k5+cTaIPiE91Hj2b8E09gi4lh24IFfDF1Kt7KyrD3IyIiIrK/UXIlBy5rV8j6CGxZ4F0FRSdAsOOO0hiGQVZWFoMGDcIwDMrKyli6dCm+Nig+0W3UKI5+5hkc8fGUL1nC5+edh3v79rD3IyIiIrI/UXIlBzZ7JmR9DJau4P4eNp8KZseulNetWzeGDx+OzWajsrKSxYsXU9cG90YlDRvG0c8/jyspiR0rV/L5uedSV1oa9n5ERERE9hdKrkScgyBrDhhRUPMBbJkKbXA/UzglJiaSk5OD0+mkrq6OxYsXU1VVFfZ+ugwYwIQXXiAqNZWKdev49OyzqSkuDns/IiIiIvsDJVciAFGjIeN1wAqVL0DpTZGOaK9iYmLIzc0lNjYWn89HXl4e5eXlYe8nvlcvjvnXv4jJyKC6sJBPzz6bqo0dtwCIiIiISKQouRKpF3sCdH8q9Hz7fbD9wYiG0xxOp5MRI0bQpUsXgsEgy5cvp7gNRpZiMzM55l//Iq5nT2pLSvjsnHOoWLs27P2IiIiIdGZKrkR+KfE8SLkn9Hzb1VA5O7LxNIPNZmPo0KF0794dgDVr1lBQUBD2Uu3R3bsz4YUXSOzfn7rSUj4791y2r1gR1j5EREREOjMlVyK/1vVG6HJl6HnxOVDzSWTjaQaLxUL//v3Jzs4GYNOmTaxatYpgMBjWfqKSkzn6uefoOmQInh07+Py88yhbsiSsfYiIiIh0VkqupEMKBoP4/f6wJwfNYhjQ7QGIOx3wQdEpUPdj+8fRQoZh0LNnT/r37w/A1q1bWbZsGX6/P6z9OBMTOeqZZ0jJzcVXVcUXF17I1h9+CGsf7cFXV0ddRQW+Nqi0KCIiIgcmwwz33KH9QGVlJQkJCVRUVBAfHx/pcA4YwWCQuro6qqqqcLvdDdtdLhdxcXFERUVhsbTj3wOCntDaV7WfgTUFsr8FR7/2678VysvLyc/PJxgMEhMTw7Bhw3A6nWHtw19by5eXX87W//4Xq9PJEQ89RPoRR4S1j3DzeTyUrVpFSV4e1Zs3YwYCGFYrsRkZdM/JIXnAAOxh/pxERESkc2tJbqDkajeUXLU/j8dDaWkpbrcbwzCw2WwYhoFpmvj9fkzTxOVykZKSEvYkYY8CVbBpHHgWgb0XZM8HW/f2678VqqqqWLZsGT6fD6fTybBhw4iJiQlrHwGPh6+vvpriL7/EYrMx5oEHyDr66LD2ES47Nm1i1XvvUVNUhGGx4EhIwGK1EgwE8FZUYAaDxGRmMmDyZLr06BHpcEVERKSDUHLVSkqu2pfH42Hr1q14vV5cLtduR6eCwSButxuHw0Fqamr7Jlj+rbBxDPjWgXME9PgSrAnt138ruN1uli5dSl1dHVarlaFDh5KYmBjWPgJeL99Nn86mjz/GsFoZfc899Dz++LD20Vo7Nm0i//XXcZeVEZuVhdXh2GWfgNdLdWEhruRkBp96qhIsERERAVqWG+ieK4moYDBIaWkpXq93j9P+LBYLUVFReL1eSktL2/deLFsqZH0M1m7gWQKbTwpNGewEXC4Xubm5xMfHEwgEWLp0Kdu2bQtrH1aHg8P++ld6TZ6MGQgw/8YbWffWW2HtozV8Hg+r3nsPd1kZcb167TaxgtB5xPXqhbusjFXvvYfP0zmusYiIiHQcSq4kourq6nC73bhcLgzD2OO+hmHgcrlwu93UtXcRAkcfyPoILHFQOw+2/AnMQPvGsI/sdjsjRowgOTkZ0zRZsWIFmzZtCmupdovNxqF3303f004D0+T7225j1csvh6391ihbtYqaoiJis7L2es+exWIhNiuLms2bKVu1qp0iFBERkf2FkiuJqKqqKgzDaHahivr9qqqq2jKs3XPlQsa7YDig6k3YeiV0klm1FouFwYMHk5GRAUBBQQFr164Na4JlWCwcfOutDDznHAAWzpzJT089Fbb291VJXh6GYTQ5YvVr9fuV5OW1YVQiIiKyP1JyJRFTfx+VzWZr0XF2ux232x2ZMu0xR0Hai4ABOx+F8pntH8M+MgyDvn370qdPHwCKi4v56aefCATCNwJnGAa511/P0EsuAWDJgw+y9OGHw76gcXP56uqo3rwZRwvvM3MmJlK9ebPKtIuIiEiLKLmSiKlPjvY2HXBvx7e7+FOh20Oh52W3wM6nIxPHPsrMzGTw4MEYhkF5eTlLlizB6/WGrX3DMBh+2WXkXH01AMsff5zFf/tbRBIsv9eLGQhgsVpbdJxhtWIGAvjD+LmIiIjI/k/JlURM/RS/ff3R3a5rXv1a18sh6c+h5yUXQ9W/IxfLPkhJSWHEiBHYbDaqqqrIy8sL+31sgy+8kFF/Dn1GK194gQV33onZzgmxzeHA+LncekvUr39la+ZUQhERERFQciURZLFYcLlc+P3+Fh3n8/maLNnerpLvgoTzgSAUnw6130Q2nhZKSEggNzcXl8tFXV0dixYtYseOHWHtY8BZZ3HInXeCYbD2tdf47uabCbbwereGPSqK2IwMvBUVLTrOs3MnsRkZ2KOi2igyERER2R8puZKIiouLwzTNZk/xq98vLi6uLcNqHsOA7k9A7CQw3VA0CTzLIx1Vi0RHR5Obm0tcXBx+v5+lS5dSVFQU1il8fU45hcPuuw/DamXDe+/x7fXXE2jH6Xbdc3Iwg8Fm91m/X/ecnDaMSkRERPZHSq4koqKiohrKq+/tB71pmg1l26M6yoiCYYP0VyHqMAjuhMLfgm9TpKNqEYfDQU5ODt27dwdg3bp1rFq1KqyFLnoefzyH/+MfWOx2Cj/5hK+vuopAO60jlTxgADGZmVQXFu41iQ8Gg1QXFhKTkUHygAHtEp+IiIjsP5RcSURZLBZSUlJwOBzU1dU1+eM3GAxSV1eHw+EgJSUl8lMCf8kSDZn/Acdg8G+GwokQKI90VC1isVjo378/ffv2BWDr1q0sWbIETxgToKyjj+bIRx7B6nRS/OWXzLv0Uvy1tWFrvyl2p5MBkyfjSk6mqqCgyRGsgNdLVUEBruRkBkyejN3pbPPYREREZP9imJGqkdyBVVZWkpCQQEVFBfHx8ZEO54Dg8XgoLS3F7XYDoXLr9Xw+HwAul4uUlBScHfVHr68QNh4G/iJwHQo9PgNLTKSjarEdO3aQn5+P3+/HbrczZMgQEhISwtb+1gUL+PLnxColN5exjz2Gox2mee7YtIlV771HzebNQKjcen1VQM/OnQDEZGQwYPJkuvTo0ebxiIiISOfQktxAydVuKLmKjPrRqaqqqoYkC0JJVVxcHFFRUR1rxGp3PPmw8XAI7oCY4yHzHTDsez+ug6mrq+Onn36ipqYGwzDo168faWlpYWu/bMkS5v7f/+GrrKTrkCGMe+IJXF26hK39pvg8HspWraIkL4/qzZsbqgLGZmTQPSeH5AEDNGIlIiIijSi5aiUlV5EXDAYJBoNYLJaOn1D9Wu18KJwAZh3EnwNpz4WKX3QygUCAVatWUVpaCkB6ejp9+vQJ2/XYvmIFc6dOxbNjB/G9ejH+ySeJSU8PS9vN4aurw+/1YnM4VBVQREREmtSS3KCT/WqVA4XFYsFms3W+xAog+jBIfw2wQuULUPrnSEe0T6xWK4MGDaJnz54AFBcXs3Tp0rAtONx10CAmvPAC0d27U1lQwCd/+hM7164NS9vNYY+KIiohQYmViIiIhE0n/OUq0gnETYLuT4Web78Xtj8U2Xj2kWEYZGdnM3ToUKxWKxUVFSxatIiqqqqwtJ/Qpw/HvPQSCX36ULd1K5+efTalixaFpW0RERGR9qbkSqStJJ4HKTNDz7ddBZWvRjSc1khKSmLkyJFERUXh8XjIy8tj27ZtYWk7Ji2NCf/6F8k5OfgqK/niwgspmjs3LG2LiIiItCclVyJtqet06HJF6HnxFKj5NLLxtEJ0dDQjR46ka9euBINBVqxYwfr168Oy4LAzMZGjnn6a9LFjCXg8fH3llax7550wRC0iIiLSfpRcibQlw4Bu/4C40wAfbD4F3AsjHdU+s9lsDB06lKysLAAKCwtZtmxZQ7n8VrUdFcWRDz1E75NOwgwE+P6WW/jpqafCkryJiIiItAclVyJtzbBA2gsQfTQEq6HwOPCuiXRU+8wwDHr37s2gQYOwWCzs2LGDxYsXU1NT0+q2LXY7h9x1F4POPx+AJQ8+yKL77sNsYnFpERERkY5EyZVIe7A4IeNtcOZCoBQKJ4K/JNJRtUq3bt3Izc3F6XRSV1fH4sWLKSsra3W7hmGQe+215F5/PQCrXnyR+dOnEwhTlUIRERGRtqLkSqS9WOMh60Ow9wFfQWgEK1AZ6ahaJTY2lpEjR5KQkEAgEOCnn35i48aNYZnKN+jccxl9770YNhsb33+fry67DF8YRsdERERE2oqSK5H2ZEuFrI/B2g08ebD5ZAh6Ih1VqzgcDoYPH076zwsAb9iwgfz8fAKBQKvb7jVpEmMfeQRrVBRbvv2Wz88/H/f27a1uV0RERKQtKLkSaW+OPqERLEss1H4BW84Gs/WJSCRZLBb69etH//79MQyDsrIyFi9eTF1dXavbTj/iCI5+9lmciYlsX76cT88+m5ri4jBELSIiIhJeSq5EIsE1EjLeBexQ9QZsvQr2g6p4aWlpjBgxAofDQU1NDYsWLWLHjh2tbjd5+HAmvPgi0WlpVG3YwCdnncXO1avDELGIiIhI+Ci5EomUmKMh/UXAgJ2PQPk9kY4oLBISEhg5ciRxcXH4/X6WLl1KUVFRq+/DSujdm2NffpmEfv2o27aNT885h20LO29ZexEREdn/KLkSiaT406DbQ6HnZTfDzmciG0+YOJ1OcnJySE1NBWDdunWsWrWKYCtLqkenpnLMCy+QkpuLr7KSuVOnUvTFF+EIWURERKTVlFyJRFrXyyHpptDzkoug6r3IxhMmFouFAQMG0KdPHwC2bt1KXl4eHk/rCng4EhIY/9RTZIwbR8Dj4esrr2TdW2+FI2QRERGRVlFyJdIRJN8NCecDQSg+DWq/jXREYWEYBpmZmQwfPhybzUZVVRWLFi2ioqKiVe3aoqI44qGH6H3KKZjBIN/fdhs/PfVUWErAi4iIiOwrJVciHYFhQPcnIOYEMN1QdAJ4fop0VGHTpUsXRo4cSUxMDF6vlyVLlrBly5ZWtWmx2TjkL39h8NSpACx58EEW3nsvZiunHoqIiIjsKyVXIh2FYYOM1yBqNAR3QuFvwVcY6ajCJioqitzcXJKTkzFNk9WrV7NmzZpW3YdlGAY5V13FyOnTAVj90kvMv+EGAl5vuMIWERERaTYlVyIdiSUaMueAYxD4i6BwIgT2n0VzrVYrgwcPpmfPngAUFxezdOlSvK1MhgaefTaH/fWvWGw2Nn74IV9Om4avpiYMEYuIiIg0n5IrkY7G2hWyPgZbJnhXhKYIBmsjHVXYGIZBdnY2Q4YMwWq1UlFRwcKFC1t9H1bP449n7KxZ2KKiKJk/n8/POw/39v0nMQ0HX10ddRUV+MKwuLOIiIjsyjB1B/guKisrSUhIoKKigvj4+EiHIwcqz0+w8QgI7gjdi5X5Tmjq4H6kpqaG/Px8amtrMQyDPn36kJ6ejmEY+9xm+bJlzLvkEjw7dhCXnc34p54iNiMjjFF3Lj6Ph7JVqyjJy6N682bMQADDaiU2I4PuOTkkDxiA3emMdJgiIiIdVktyAyVXu6HkSjqM2vlQeHSoyEXCedD9mVDxi/2I3+9n9erVlJaWAtCtWzf69++P1Wrd5zYrN2xg7tSp1BQXE5WSwrgnnqDLgAHhCrnT2LFpE6vee4+aoiIMiwVHQgIWq5VgIIC3ogIzGCQmM5MBkyfTpUePSIcrIiLSISm5aiUlV9KhVP0HNp8MBELrYaXMjHREYWeaJps3b2b9+vWYpkl0dDRDhgwhOjp6n9us3baNeRdfzM7Vq7HHxTH2kUfodtBBYYy6Y9uxaRP5r7+Ou6yM2KwsrA7HLvsEvF6qCwtxJScz+NRTlWCJiIjsRktyg4jec/XVV18xadKkhmlA7777bqP33377bSZOnEhycjKGYZCXl9esdnfu3Mm0adNIS0vD5XIxaNAgPvjgg/CfgEh7iJsUKtMOUH4PbP9nZONpA/XrYY0YMQKHw0FtbS2LFi1qGM3aF9HdujHhhRdIGTUKX1UVX0ydSuHnn4cx6o7L5/Gw6r33cJeVEder124TKwCrw0Fcr164y8pY9d57+Fq5wLOIiMiBLqLJVU1NDSNGjOCRRx5p8v0xY8Zw7733NrtNr9fLMcccw4YNG3jzzTdZtWoVTz31FBkH8D0Xsh9IvCC00DDAtqug8tWIhtNWEhISGDVqFAkJCQQCAfLz81m3bt0+Lw7siI9n/JNPknnUUQS9Xr656irWvvlmmKPueMpWraKmqIjYrCwslj3/b95isRCblUXN5s2UrVrVThGKiIjsnyJ6d/xxxx3Hcccd1+T7Z599NgAbNmxodpvPPvss27dvZ/78+djtdgCys7NbFadIh5B0EwRKYMfDUDwFrMkQMyHSUYWdw+Fg+PDhFBQUUFRURFFREVVVVQwePBhHEyMwe2JzuTj8H/9gwV/+wrq33uKH22/HXV7OkIsualXhjI6sJC8PwzCaHLH6tfr9SvLySBs+vC1DExER2a/td6XY33vvPUaPHs20adNITU1l6NChzJw5k0Ag0OQxHo+HysrKRg+RDscwoNuDEHcq4Avdh+VeFOmo2oTFYqFPnz4MHjw4LOXaLTYbv7njDoZcfDEAS//5TxbOnInZigWMOypfXR3VmzfjSExs0XHOxESqN29WmXYREZFW2O+Sq/Xr1/Pmm28SCAT44IMPuOWWW/j73//O3Xff3eQx99xzDwkJCQ2PrKysdoxYpAUMC6T9C6KPgmA1FB4H3nWRjqrNpKSkMHLkSKKjo/F6vSxZsoSioqJ9miZoGAYjrriCUX/+MxgGq195hW+vu47Afnafkd/rxQwEsLSw2qJhtWIGAvhbuaCziIjIgWy/S66CwSDdunXjySefZNSoUZx++uncfPPNPPbYY00ec9NNN1FRUdHwKCwsbMeIRVrI4oSMd8CZC4FtUHgs+LdGOqo2Ex0dzciRI0lJScE0TdatW8eKFSvw+/371N6As85izN/+hsVmY9PHH/P5+efj3rEjzFFHjs3hwPi53HpL1K9/ZduHqZciIiISst8lV2lpabuskTNo0CBKSkrwNvEXWafTSXx8fKOHSIdmjYesD8DeG3zrQyNYgf13OqvVamXQoEH07dsXwzAoLS1l8eLF1NTU7FN72ccdx/gnn8QeH09ZXh6fnHEGlS24t7Mjs0dFEZuRgbeFUyg9O3cSm5GBPSqqjSITERHZ/+13ydWYMWNYu3YtwV/cS7F69WrS0tL26WZ4kQ7L1h2yPgZrN/AsDt2DFdy/prj9kmEYZGRkNCrXvnjx4n0u1556yCEc+9JLxGRmUl1YyCdnnMG2H38Mc9SR0T0nBzMYJNDMKX71+3XPyWnDqERERPZ/EU2uqqurycvLa1i/qqCggLy8PDZt2gTA9u3bycvLIz8/H4BVq1aRl5dHSUlJQxtTpkzhpptuanh9ySWXUF5ezpVXXsnq1at5//33mTlzJtOmTWu/ExNpL46+oREsSyzUfgFbpoC5/xVp+KXdlWv/9R9Umt1Wnz5MfOUVkkaMwFtZyRcXXkjBnDltEHX7Sh4woCFp3NvnEgwGqS4sJCYjg+QBA9opQhERkf1TRJOrH3/8kdzcXHJzcwG45ppryM3N5bbbbgNClf9yc3M5/vjjATj99NPJzc3l8ccfb2hj06ZNbNmypeF1VlYWn3zyCQsWLGD48OFcccUVXHnllUyfPr0dz0ykHblGhe7Bwg5Vr4fWwdrHdaE6C4fDwYgRIxqKz2zevJklS5bg2YfiFK6kJI5+9lmyjj2WoM/HdzfeyLJZs/Z5ba2OwO50MmDyZFzJyVQVFDQ5ghXweqkqKMCVnMyAyZOxO53tHKmIiMj+xTA78y+INlJZWUlCQgIVFRW6/0o6j8pXofiM0POUmaF1sQ4AZWVlrFy5kkAggN1uZ/DgwSS2sAw5gBkMkvePf7Di2WcB6HXiifxmxoxmrxXVEe3YtIlV771HzebNQKjcen1VQM/OnQDEZGQwYPJkuvToEcFIRUREOq6W5AZKrnZDyZV0Wtv/CduuDD3v/gwknh/ZeNpJbW0t+fn5DQUuevfuTWZm5j4tErz29ddZcNddmIEA3Q4+mCMfeghHQkK4Q243Po+HslWrKMnLo3rz5oaqgLEZGXTPySF5wACNWImIiOyBkqtWUnIlndq2m2D7vYA1NF0wblKkI2oXgUCA1atXs23bNgCSk5MZMGAANputxW0Vf/MN31xzDf6aGuJ792bcrFnE7gfr3/nq6vB7vdgcDlUFFBERaaaW5Ab7XbVAkQNeykxIOA8IQPGpUDs/0hG1C6vVysCBAxvKtZeVlbFo0aJ9KteefvjhHPPii0R3707l+vV8fOaZlC1Z0gZRty97VBRRCQlKrERERNqIkiuR/Y1hQPcnIeZ4MN1QdAJ4fop0VO2ivlx7Tk4OTqeTuro6Fi1a1DCa1RJdBgzg2Nmz6TJoEJ7t2/n8vPPY9PHHbRC1iIiI7C+UXInsjwwbZLwOrkMhuAMKfwu+wkhH1W7i4+MZOXIkiYmJBINBVqxYsU/l2qO7dWPCCy+QMW4cAY+Hb665hvxnnunUlQRFRESk7Si5EtlfWaIhaw44BoG/CAonQmB7pKNqNw6Hg+HDh9Pj5yp4+1qu3R4TwxH//Cf9//QnAPIeeIAFd9xB0OcLe8wiIiLSuSm5EtmfWZMg62OwZYB3BRRNgmBtpKNqN4Zh0KtXL4YMGYLVaqWyspKFCxeyY8eOFrVjsVo56KabGHXTTRgWC2vfeIN5l16Kr7q6jSIXERGRzkjJlcj+zp4VSrAsiVA3HzafBqY/0lG1q+TkZEaNGkVMTAw+n4+lS5eyadOmFk/vG/CnP3HEP/+JNSqKkvnz+fTss6n5xSLmIiIicmBTciVyIHAOgcw5YLigZg6UXAwH2H1DUVFR5ObmkpqaCkBBQQE//fQTvhZO78scP55jXngBV3IyO1ev5pMzzmD7TwdGwRARERHZMyVXIgeK6DGQ/hpggYpnoeyWSEfU7qxWKwMGDKBfv34YhkF5eTkLFy6ksrKyRe10HTKEibNnk9i/P3WlpXx6zjkUzZ3bRlGLiIhIZ6HkSuRAEjc5VKYdoHwmbH84svFEgGEYpKenk5ubi8vlwuPxkJeXR2FhYYumCcakp3PMiy/S/bDDCNTV8dXll7PqpZfaMHIRERHp6JRciRxoEi+A5LtCz7ddCZWvRTaeCImLi2PUqFGkpKRgmibr169n+fLlLZomaI+NZdysWfT5wx/ANFl4zz38OHMmwUCgDSMXERGRjkrJlciBKOnPkHgZYELx2VDzeaQjigibzcagQYMapglu376dhQsXUlFR0ew2LHY7v5kxg5xrrwVg9csv8/UVV+CrqWmrsEVERKSDUnIlciAyDEh9EOL+CPhg88ngXhjpqCKifprgyJEjiYqKapgm2JJqgoZhMPj88zn8gQewOp1snjePz849l9pt29o4ehEREelIlFyJHKgMK6S9CNFHQbAKNk2Auv9GOqqIiY2NZeTIkXTr1g0IVRNctmwZXq+32W30mDiRo597DmfXruzIz+eTM85g5+rVbRWyiIiIdDBKrkQOZBYnZLwDUWMguDOUYNUcuFXvbDYbAwcOpH///lgsFnbs2NHiRYeTR4xg4iuvEN+7N7UlJXzypz9R/M03bRi1iIiIdBRKrkQOdNb40CLD0RPArIGi30H1+5GOKmIMwyAtLY2RI0cSHR2N1+tl6dKlbNiwodnTBGOzsjj2pZfodvDB+Gtq+PLSS1n7+uttHLmIiIhEmpIrEQFLDGT+B2Ing+mGopOg8o1IRxVRMTExjBw5ku7duwOwceNGli5disfjadbxjoQExj/5JL0mT8YMBPjhjjtY/Pe/YwaDbRm2iIiIRJCSKxEJsbgg402IPwPwQ/HpsPP5SEcVUfWLDg8cOBCLxcLOnTtZuHAh27dvb97xDgeHzpzJsGnTAFjx7LN8c801+N3utgxbREREIkTJlYj8j2EPFblImAoEoeQ82PFIpKOKuNTUVEaNGkVMTAw+n49ly5ZRUFDQrGmChmEw7NJLGX3vvVjsdgo//ZTPzzsPd3l5O0QuIiIi7UnJlXRMQTcEqkL/lfZlWKH7E9Dl6tDrrZdD+T1t01ckr3ML+46OjiY3N5e0tDQANm3aRF5eXrOnCfaaNImjnn4aR3w85UuX8tFpp7FjxYp9Dl9EREQ6HsNs7h3aB5DKykoSEhKoqKggPj4+0uEcOEwveFeDexH4NoLpB8MG9mxwjQRHfzAckY7ywGGaUDYDyv8Sep10EyTfHVojq1XtRvA6h6nvbdu2sXr1agKBQEOFwaSkpGaFUFlQwJeXXUbVhg1YXS5Gz5xJj4kTW3tmIiIi0kZakhsoudoNJVcR4CuCqnfAtx6wgLVr6Eev6YfAdiAI9t4QdzLYMyMd7YGl/H4ovT70vMvl0O1BMPZx0DuS1znMfdfV1ZGfn091dTUAmZmZ9OrVC4tl75+Nt7KSb6+7ji3ffgvAkIsvZvhll2E041gRERFpX2FPrt57770WB3HMMccQFRXV4uM6AiVX7cxXBBUvQWArOPrsfuTA9IJ3HVhTIeFPSrDa244nYOslgAkJ50L3p0PTB1sikte5jfoOBoOsX7+ezZs3AxAXF8fgwYNxuVx7PzYQIO+BB1j5/PMAZB51FKPvvRd7TEyLTk1ERETaVtiTq+b8JbZRo4bBmjVr6N27d4uO6yiUXLUj0xv64e5bD44Bex4RMYPgXRUaXehysaYItreKl2DLuUAA4v4I6S81/xpE8jq3Q9+lpaWsWrWqYZrggAEDSE5Obtax6//9b36YMYOg10tC374c+fDDxPXo0axjRUREpO21JDdodtZUUlJCMBhs1iM6OrrVJyEHCO/qn3/09tn7VDPDEtrPVxA6TtpXwp8g4w3ADlVvQNHJEKxr3rGRvM7t0HdKSgqjRo0iLi4Ov9/PTz/9xNq1awk2Y02r3ieeyIQXXiAqJYWKtWv5+PTTKfnvf5vdt4iIiHQczUquzjnnnBZN8fvTn/6kER9pHvciwGj+6ET9fu5FbRaS7EHcyaHFho0oqPkAin4Xqri3N5G8zu3Ud1RUFDk5OWRmhqYTbt68mcWLF1NXt/cENHn4cCa+/jpJw4bhrahg7kUXserll5tV6l1EREQ6jmYlV8899xxxcXHNbvSxxx5r9pQYOYAF3aGKbdbmVVlrYE0KHacy7ZEROxGyPgZLHNTOg8JjILCj6f0jeZ3buW+LxUKfPn0YMmQINpuN6upqFi5cSGlp6V6Pje7WjQkvvEDPyZMxAwEWzpzJD7ffTsDrbVnsIiIiEjEtLk1VUVHB9u3bd9m+fft2KisrwxKUHCBM3/9KYbdEfYU309c2ccneRR8BWV+ApSu4v4dN48C/dff7RvI6R6jv5ORkRo0aRXx8PIFAgPz8fNasWbPXaYJWp5PRM2eSe/31GBYL6956iy/OP5+6srJ9ikNERETaV4uTq9NPP51XX311l+2vv/46p59+eliCkgOEYf/fj9iWqP+xbNjbJi5pnqiDIPvLUHU9z1LYdCT4CnfdL5LXOYJ9u1wuRowYQVZWFgDFxcUsWrSI2traPR5nGAaDzj2XsbNmYY+Lo3TxYj4+7TS25+fvcywiIiLSPlqcXH3//feMHz9+l+3jxo3j+++/D0tQcoCwuEKLtwZ2HQndo0B56DjL3stdSxtzDoXsr8HWI1QAYuMRoXLmvxTJ6xzh75jFYqF3794MGzYMu91OTU0NCxcuZOvWJkb5fiH9iCOY+OqrxPfqRW1JCZ+efTYbP/ywVfGIiIhI22pxcuXxePD7d/0rsM/na9aN2yKNuEYCwVC57Oao3881ss1CkhZy9AslWPZ+4N8Im44Az69GWSJ5nTvAd6xr166MGjWKhIQEgsEgK1euZOXKlbv9f+kvxffsybGzZ5N2xBEE3G6+ve46ljz0EGYzqhCKiIhI+2txcnXwwQfz5JNP7rL98ccfZ9SoUWEJSg4gjv6hNYW860JrDO2JGQztZ+8VOk46DnsPyP4qNJLl3xKaIvjLanuRvM4d5DvmdDoZMWIE2dnZAGzdupWFCxfu9V5VR1wcYx99lEHnnw/AT08+yVeXX46vujqs8YmIiEjrNWsR4V/69ttvmTBhAgcffDBHH300AJ9//jkLFizgk08+4YgjjmiTQNuTFhFuZ76i0AK1ga0/r0W0m5LZpjf0o9eaGlpvyZ7Z/nHK3gW2Q+Fvwb0ALPGQ+QFEjwm9F8nr3MG+Yzt37mTlypV4PB4AevbsSY8ePTAMY4/HFfznP3x/222hBYf79AktOPxzsiYiIiJtoyW5QYuTK4C8vDz++te/smTJEqKiohg+fDg33XQT/fr12+egOxIlVxHgK4Kqd0KLt0KoFHZ9IYJAeWibvVdonSUlVh1boBKKJkHdV2BEQ+a7EHNM6L1IXucO9h3z+XysWbOmoUx7QkICAwcOxOXa831e5cuW8dUVV1C3bRuO+HgOf+ABuo8e3ebxioiIHKjaPLna3ym5ihDTGyqK4F4UWmOovmKbPTt0/4ujf/MXgpXICtbC5t9DzUeha5b+OsSdGHovkte5g33HTNNk69atrF27lkAggNVqpX///nTr1m2Px9WVlvLVlVdSvmQJhsVC7g03MOBPf9rryJeIiIi0XJsnV+vWreO5555j/fr1PPjgg3Tr1o2PPvqIrKwshgwZss+BdxRKrjqAoDu0xpBhV1XAzirogeIzofptwArpL0L8Gb/aJ4LXuQN9x+rq6lixYgVVVVUApKam0rdvX2y2ptfnCng8LPjLX1j/7rsA9D75ZA6+7TasDv0BQkREJJxakhu0uKDFl19+ybBhw/j+++956623qP75puqlS5dy++2371vEIr9mcYE1LuI/eqUVLE7IeA3izwYCUHwW7HzqV/tE8Dp3oO9YVFQUOTk59OjRA2hesQur08khd93FyBtuwLBYWP/OO3x+3nnU/TzNUERERNpfi5Or6dOnc9ddd/Hpp5/i+MVfSMePH893330X1uBEpJMzbJD2PCReAphQchFsfyDSUXVIFouFXr16MWLECJxOJ263m8WLF7Nx40aammBgGAYDzzmHcY8/jj0+nrK8PD4+/XS2//RTO0cvIiIisA/J1bJlyzj55JN32Z6SkkJ5eXlYghKR/YhhgdRHoesNodfbroWyv4Bu99ytxMREDjroIFJSUgDYsGEDeXl5uN3uJo9JGzMmtOBw794NCw5veP/99gpZREREftbi5CoxMZEtW7bssn3x4sVkZGSEJSgR2c8YBqTcC8l3hl6X3Q6lNyjBaoLNZmPQoEEMHDgQq9VKZWUlP/74I9u2bWvymPjsbCbOnk362LEEPB7m33ADeQ88QDAQaMfIRUREDmwtTq7OPPNMbrzxRkpKSjAMg2AwyLfffst1113HlClT2iJGEdkfGAYk3wLd/hF6vf1+2HrJ3hf2PUAZhkFqaiqjRo0iLi6OQCDAihUrWLlyJX6/f7fH2GNjOfLhhxl84YUA5D/zDF9dfjnenwtliIiISNtqcbVAn8/Hueeey6uvvoppmthsNgKBAGeeeSbPP/88Vqu1rWJtN6oWKNLGdj4DJVMBE+LPgrTnQlX7ZLdM02Tjxo1s3LgRAJfLxaBBg/b4/6cN77/P97feSsDjIb53b478f/buOzyO6uz7+He2r7TqvUvuNgbbGGM6IfRgOhhCDIYQQmgBAuSFJCSEEErgCSQhBELohGK66QESejFuFNuSm/pq1aXVrrbOzPvHyGsby7ZklVW5P9e1F8/OzuzeY+uJ9dtzzn3uvZdk2XBYCCGE6Ldh2edq8+bNrFy5Ek3TmDNnzpjZQBgkXAkxLLzPgPtcIAqJxxl7YZmT4l3ViNbZ2cm6desIhUIAlJSUUFJSstP9rVq//dbYcLixEWtyMofcdRd5Bx88nCULIYQQo96wbiKsqirffPMNJSUlpKWlDeStRgwJV0IME99rUL8Q9ADY50DR62DJi3dVI1o0GmXDhg2x9VfJyclMnz4dh6P3lvKB5mY+uuoqWlavNjYcvvZapp53nmw4LIQQQvTRkO5zddVVV/HQQw8BRrA6/PDD2XfffSkqKuL999/fo4KFEOOUawEUvw/mLAitgqoDIbQu3lWNaDtrdtHY2Njr+c6sLI585BEmnHYauqax8k9/4tPrriPi9w9z5UIIIcTY1+9w9fzzzzNr1iwAXn31VTZv3kx5eTlXXXUVv/71rwe9QCHEGOfcH0o+A+tkiFZD9UHQ/WG8qxrxtjS7SE5ORlVVysvLWbduXa/NLsw2G/Nvvpm5v/oVisVC9Ztv8p8f/pDOzZvjULkQQggxdvU7XLW0tJCbmwvAG2+8wcKFC5kyZQoXXngh33zzzaAXKIQYB2wToeRTcB4IWgfUHg3eZ+Nd1YjndDqZPXs2JT2NKpqamlixYgWdnZ07nKsoClN/9COOeuQRnNnZdG7axNtnnUXN228Pd9lCCCHEmNXvcJWTk8PatWtRVZW33nqLo446CoDu7u4x0SlQCBEnlkwoeg9cp4IeBvfZ0HqX7IW1G4qiUFpayuzZs3E4HASDQVavXk1VVRW9LanN2ndfjnvuObLnzSPa3c3Hv/gFK//0J7RIJA7VCyGEEGNLv8PVBRdcwMKFC5k5cyaKonD00UcD8MUXXzBt2rRBL1AIMY6YnFDwHKT93HjefB00XQm6bIS7OykpKcydO5fs7GwAqqurWb16NYFAYIdznZmZfP9f/2LGhRcCUP7YY7x34YUEmpuHtWYhhBBirNmjboHPP/88tbW1nHnmmRQWFgLw2GOPkZqaysknnzzoRQ436RYoRJzpOrTfDU3XGM9dp0L+v43wJXarsbGRDRs2oKoqZrOZyZMnk5OT0+u5te+9x+e/+hURnw9HRgaH/PnPZO+33zBXLIQQQoxcw9qKfSyScCXECOFdAg3nGtMEHQdA4avG9EGxW8FgkHXr1uH1egHIzs5m8uTJWCyWHc71Vlfz0ZVX0rlhA4rZzOyrr2ba+edLu3YhhBCCIWjF/te//pVgMNjnAu6//366urr6fL4QQvQqeSEUvQumNAh+bnQSDG+Kd1WjgsPh6HOzi+SSEo596ilKTzwRXVVZddddfHz11UR8vuEuWwghhBjV+jRyZTab8Xg8ZGVl9elNk5OTWb16NRMmTBhwgfEgI1dCjDChdVB7vNGq3ZwFha8ZLdxFn3R2dlJeXh77kqyoqIjS0lJMpu2/X9N1nY3PPsuK225Di0ZJKi3l0HvuIXXy5HiULYQQQowIgz4t0GQyMXPmzF6nk/Tmm2++oaKiQsKVEGLwRBugdgGEVoLihPxnIOmkeFc1akSjUTZu3BjbbDgxMZFp06bhcrl2OLfl66/5+Oqr6fZ4MDudzL/pJkoXLBjukoUQQogRYdDD1e9///t+F3HllVeSmpra7+tGAglXQoxQmg/qzwT/W4AJcv4GaZfGu6rRQwvS3NzEho21RKLRWBv3oqKiHdZXBdvb+fSXv8Tz6acATDnnHOZcdx1mmy0elQshhBBxIw0tBkjClRAjmB4BzyXQ+ZDxPP3/QdatoPR7Z4nxQQ9DeD0EV0KkGvQoYdXJ+oZZtHoTAGMq99SpU0lISNjuUk1V+ebvf2fNAw8AkDFrFof++c8k9GwkL4QQQowHEq4GSMKVECOcrkPrLdDyW+N58jmQ+zCY7PGta6SJ1EHXSxDZDJjAnA6KBfQoerSNRm8BGxtno2pmTCYTEyZMID8/f4dRrPoPPuDT668n4vViT0vj4LvuIveAA+JzT0IIIcQwk3A1QBKuhBglOh+Dhp8AUUj4HhS8BObUOBc1QkTqoPNJUBvBNhGUXqbz6WGCfjcVnvl0+FMBSE1NZerUqTgcju1O9dXW8tHVV9O+bh2KycQ+V1zBjJ/8BMUkI4ZCCCHGtkFvxS6EECNSymIoegNMSdD9PlQfApGaeFcVf3rYGLFSG8E2tfdgBaDYcCQWs0/h+0zKq8dkMtHR0cHy5cvxeDxs+92bq6iIo598kgmnnYauaXz1l7/w4RVXEO7ZR0sIIYQQcQ5XH374ISeeeGJsGsrLL7+83esvvvgixx57LJmZmSiKwurVq/v1/s888wyKonDKKacMWs1CiBEm8Wgo/ggs+RBeA9UHQnB1vKuKr/B6YyqgbeLu16IpJhT7RApSljN372SSkpJQVZWKigrWrFlDOByOnWpxODjgD39g/s03Y7LZqH//fd5auJD2deuG+IaEEEKI0aHf4ermm2+mu7t7h+OBQICbb765X+/l9/uZNWsW9957705fP/jgg7n99tv7WybV1dVce+21HHroof2+VggxyjhmQclnYNsLom6oOQz878S7qvgJrgSUnY9YfVfPeQnKV8yZM4fS0lIURaG1tZXly5fT0tKy3ekTTz+dY558ksSCAny1tfznRz9i80svDfJNCCGEEKNPv9dcmc1mGhoayM7O3u54a2sr2dnZqKq6Z4UoCi+99FKvo0xVVVWUlZWxatUqZs+evdv3UlWVww8/nAsuuICPPvqIjo6OHUbFthUKhQiFQrHnXq+XoqIiWXMlxGijdkD9qcYUQSyQ+yCknh/fmoabFoS2OwETWPq28TsA0WZAg/TrwOTA5/NRXl6O3+8HICcnh0mTJm2332Goo4PPbrgB94cfAjDxjDPY71e/wmyXxiJCCCHGjiFdc6Xr+g6dpAC++uor0tPT+/t2Q+Lmm28mKyuLCy+8sE/n33bbbaSkpMQeRUVFQ1yhEGJImFOh8C2jeyBR8FwALX8wuguOF3oE9KjRFbA/eroIokcAcLlc7LvvvrH/PWxsbGT58uW0tbXFLrGnpnL43//O3pdfDorCpuef551zz8VXXz9otyOEEEKMJn0OV2lpaaSnp6MoClOmTCE9PT32SElJ4eijj2bhwoVDWWuffPLJJzz00EM8+OCDfb7mhhtuoLOzM/aora0dwgqFEEPKZIe8JyD9euN5y2/B89NYaBjzFOvWoNQfWwKZYo0d2tKeffbs2TidTkKhEN988w0bNmyIzVJQTCb2vuQSjnjgAeypqbStWcNbZ56J+6OPBvOuhBBCiFGhz19t3nPPPei6zo9//GN+//vfk5KSEnvNZrNRWlrKgQceOCRF9lVXVxeLFi3iwQcfJDMzs8/X2e127DKNRYixQzFB9m1gLYbGy6HzXxCtg4LnwOSKd3VDy+QAawkEv+rftEC1FRyzjeu/IyUlhblz57J582bcbjdut5v29namTp0a+7cg7+CDOe655/jo6qtp+/Zb3r/kEva+9FJm/uxn0q5dCCHEuNHncLV48WIAysrKOOigg7Barbu5Yvht2rSJqqoqTjzxxNgxTdMAsFgsVFRUMHHixHiVJ4QYbmmXgKUA3GeD/y2oPhyKXgdLbrwrG1qOfSG4ymjJ3pemFnp463U7YTabmTx5MhkZGaxfv55AIMDq1aspKiqitLQUk8lEYn4+Rz/xBCtuv52Nzz7LN3//Oy1ffcVBd9yBPTV1cO5NCCGEGMH69HWi1+uNPebMmUMgENju2LaPeJo2bRrffPMNq1evjj1OOukkjjjiiNgvAUKIcSbpJCh+H8xZEFoJVQdAaIy3DrdNAesECG8CXdv1ubpmnGctM67bjfT0dPbbbz9ycnIAqK2tZeXKlfh8PgDMNhv7//a3HHDrrZgdDho+/pi3zjyT1m+/HfBtCSGEECNdn0auUlNTe21isa0tjS760y3Q5/OxcePG2PPKykpWr15Neno6xcXFtLW1UVNTg9vtBqCiogKA3NxccnONb57PO+88CgoKuO2223A4HMycOXOH2oEdjgshxhHn/kar9trjILIRqg+GwlcgYYxu1aDYIOlU6HwSwhU9+131MoKlh41gZc4xzu9j63aLxcK0adPIzMxk/fr1+P1+Vq5cSWlpKUVFRSiKwoSTTyZt2jQ+uuoqfDU1vLNoEXNvuIFJCxfu9t8TIYQQYrTqUyv2Dz74oM9vePjhh/f53Pfff58jjjhih+OLFy/m0Ucf5dFHH+WCCy7Y4fXf/e533HTTTQB873vfo7S0lEcffbTXzzj//PN324r9u/rTblEIMYpEm6HuJAh+bgSJvMch+ax4VzV0InXQ9RJEKo3n5oytzS7UVuOYtcwIVtbCPfqIcDjM+vXraW013i8pKYlp06aRkJBgvO718vmvf03df/8LQPGxx7L/TTdhk/9tFUIIMUr0Jxv0e5+r8UDClRBjmNYN7h+B72XjecaNkHmT0QRjLNLDEF5vbCwcqd7aFdBaYqyxsk3p+2bDO/sIXaexsZGNGzeiqmqsy2B+fj6KoqBrGuWPPcbqe+5Bj0ZJLCjg4LvuInOffQbpJoUQQoihM6Th6sOezSJ35rDDDuvP241IEq6EGON0FZqug/a7jeeuU4xRLHNSXMsaclrQaEmvWHvtCjhQwWCQiooKOjo6AGNa9tSpU3E4jM9q+fprPrnuOvx1dSgWC7OuvJLp558v3QSFEEKMaEMarky9/CO47fz5/qy5GqkkXAkxTnQ+1rMHVhjsM6HgFbBNiHdVo5qu67jdbjZv3oymaZjNZiZNmkROTg6KohDu6mLZ735HzdtvA5B3yCEceOutODIy4ly5EEII0bv+ZIN+f13Y3t6+3aOpqYm33nqLefPm8Z///GePixZCiGGXshiKPwBzLoS+hap54P9vvKsa1RRFoaCggLlz55KUlISqqlRUVLBmzRrC4TC2pCQO/r//Y/+bbsJst9Pw8ce8cdppeD7/PN6lCyGEEAM2aGuuPvzwQ66++mpWrFgxGG8XVzJyJcQ4E6mH+lMh+CVghuy7Ie1ykK52A6LrOrW1tVRVVaHrOhaLhcmTJ5OVlYWiKHRs2MAn11xD56ZNoCjsddFF7H3ZZZgsfd6CUQghhBhyQzpytTNZWVmxVulCCDGqWAug+ENIPhdQoenn4LkItFC8KxvVFEWhuLiYfffdl8TERKLRKOvWrWPNmjWEQiFSJ0/m2GefZeIZZ4Cus+af/+S988/H37P9hhBCCDHa9Hvk6uuvv97uua7rNDQ0cPvttxOJRPjkk08GtcB4kJErIcYpXYe2P0PzLwENnAdBwYtgyYl3ZaOepmnU1tZSXV2NruuYzWYmTpxIbm4uiqJQ/eabLLvpJiI+H7bkZObfcgtFRx4Z77KFEEKIoW9ooSgK373sgAMO4OGHH2batGn9r3iEkXAlxDjnewvcZ4PWCZZCKHwZHHPjXdWY4Pf7qaiooKurC4C0tDSmTJmCw+HAV1vLx9deS9u33wIw5ZxzmHPttZjt9niWLIQQYpwb0nBVXV293XOTyURWVlas1e5YIOFKCEF4vbHhcLgCFAfkPQzJP4x3VWOCruvU1dVRVVUV6yhYVlZGfn4+WiTC13/9K+seeQSAtGnTOPiuu0guK4tz1UIIIcarYd9EuKOjg9TU1IG+zYgh4UoIAYDaCe5zwP+G8Tz9esi6BRRzfOsaI7q7u6moqMDr9QKQkpLC1KlTcTqduD/6iM9uuIFQezsWp5P9bryRCSefHOeKhRBCjEdD2tDijjvu4Nlnn409X7hwIenp6RQUFPDVV1/1v1ohhBipzClQuBTS/5/xvO12qDvZCF1iwBISEpg9ezaTJk3CZDLR2dnJ8uXLqa2tJe+QQzj+xRfJ2X9/ooEAn//qV3x6ww1E/P54ly2EEELsVL/D1QMPPEBRUREA77zzDu+88w5vvfUWxx9/PNddd92gFyiEEHGlmCH7dsj7tzE90P86VB8A4Q3xrmxM2LIv1n777UdqaiqaprF582ZWrVqFnpjIEf/6F/tccQWKyUTV0qW8deaZtK1bF++yhRBCiF71e1qg0+lk/fr1FBUVceWVVxIMBnnggQdYv3498+fPp729fahqHTYyLVAI0avAcqg/BaL1YEqF/GfAdWy8qxozdF3H4/GwadMmVFVFURRKSkooKiqiZdUqPv3lL+n2eDBZrcy57jqmnHMOiuxFJoQQYogN6bTAtLQ0amtrAXjrrbc46qijAOMfRVVV96BcIYQYJZz7QelycB4IWgfU/cBo3T44e7GPe4qikJeXx7x580hPT0fXdaqqqli1ahUJU6dy/AsvUHDEEWiRCCtuvZWPfv5zQh0d8S5bCCGEiOl3uDrttNM455xzOProo2ltbeX4448HYPXq1UyaNGnQCxRCiBHFkgtF/4OUHwMaNF0DDeeDFox3ZWOG3W5n5syZTJs2DYvFgs/nY+XKlbjb2znkL39h7q9+hclqpe6//+XN00+nacWKeJcshBBCAHsQru6++24uv/xyZsyYwTvvvIPL5QKgoaGBSy+9dNALFEKIEcdkh9x/QfZfATN4H4eawyHijndlY4aiKOTk5DBv3jwyMzPRdZ2amhpWrlxJ3okncsxTT5FUUkK3x8N755/Pt/ffjyazJ4QQQsTZoLRiH2tkzZUQos/870H9QtDawJIHBS+Bc368qxpzmpub2bBhA5FIBIDCwkIKsrJYeeutVL36KgA5++/PgXfcQUJ2djxLFUIIMcYM6ZorgCeeeIJDDjmE/Pz82KbC99xzD6+88sqevJ0QQoxeiUdC6TKw7QXRBmMEq/PxeFc15mRlZTFv3jyye4JTXV0dX5eXM+P66zng1luxOJ00LlvGm6efjvujj+JcrRBCiPGq3+HqH//4B7/4xS84/vjj6ejoiDWxSE1N5Z577hns+oQQYuSzTYSSz8B1MughaFgMjdeAHo13ZWOK1Wpl+vTpzJw5E5vNRiAQ4KuvvkKdMYOjn3mG1KlTCbW18f7Pfsaqu+5CDYfjXbIQQohxpt/h6m9/+xsPPvggv/71rzGbzbHj++23H998882gFieEEKOGOQkKXoSMG43n7X+GuhNAHf3bU4w0GRkZzJs3j9zcXADcbjcVLS3sf999TDnnHADWPfII7553Hr6e7rZCCCHEcOh3uKqsrGTOnDk7HLfb7fj9/kEpSgghRiXFBFk3Q/4SUBLA/x+omg8h2fR2sFksFqZOnco+++yDw+EgFAqxpqKCpDPO4OC778aWnEzrN9/w5hlnUP3WW/EuVwghxDjR73BVVlbG6tWrdzj+5ptvMmPGjMGoSQghRrfkM6HkE7CUQGQDVM8H3+uxlyOBAIHOTiKBQByLHBvS0tLYb7/9yM/PB8Dj8eBOSuKAhx4ia84cIj4fn1xzDZ//5jdE5AtAIYQQQ8zS3wuuu+46LrvsMoLBILqus2zZMp5++mluu+02/vWvfw1FjUIIMfo4ZkPpl1B/JgQ+ILL5VFq8N+DZPBFffT26qqKYzbgKCsidPZvMqVOx2u3xrnpUMpvNTJ48maysLNavX08gEGBTczMF119Pxn/+Q/nDD7P5pZdo/PJLDrz1VrLnzo13yUIIIcaoPWrF/uCDD3LLLbdQ2zOXvaCggJtuuokLL7xw0AuMB2nFLoQYNHqE9lVXUfH2GvyNNhRrOraMfTBZbGiqSrizE13TSCwsZOpJJ5FWXBzvikc1VVWpqqqirq4OMJpgZAYCrLv9dvxuN4rJxPQLL2TvSy/FbLPFuVohhBCjQX+ywYD2uWppaUHTtFhr3Pr6egoKCvb07UYMCVdCiMHSXlPD2iVLCDaW48rYjNmigjkdEg4HUyIAajiMr7YWR2YmMxYulIA1CLxeLxUVFXR3dwOQ6nTie/FFqnv2xEqbPp2Dbr+dlEmT4lmmEEKIUWDI97naIjMzk+zsbDweD1dccQWT5B8pIYSIiYRCVCxdSrClhaRJ8zCnHAkmO6ht4H8DovUAmG02ksrKCLa0ULF0KZFQKM6Vj37JycnMnTuX4uJiFEWhIxBAX7CA6b/5DbbUVNrXreOthQupePJJdE2Ld7lCCCHGiD6Hq46ODn70ox+RlZVFfn4+f/3rX9E0jd/+9rdMmDCBzz//nIcffngoaxVCiFGlpaICf10drqIiTCYTWLIh8QdgzgAtBP7/QWg1oGMymXAVFeGvr6eloiLepY8JJpOJsrIy5s6dS3JyMqqq4s3Pp/gPfyD7wANRQyFW3HYb/7v4YrobG+NdrhBCiDGgz+HqV7/6FR9++CGLFy8mPT2dq6++mgULFvDxxx/z5ptv8uWXX/LDH/5wKGsVQohRxbN6NYqibL+2x5QIiceAfZrxPPgt+N8BLRA7z9NLR1ax5xITE5k9ezaTJ0/GbDYTMJuxLFpE6c9+htnhwPPpp7xxyilUv/lmvEsVQggxyvU5XL3++us88sgj3HXXXSxduhRd15kyZQr//e9/Ofzww4eyRiGEGHUigQC++npsqak7vqiYwbEfJBwKJitEm8D/OkQ92FNT8dXXS5v2QaYoCvn5+cybN4+srCwURSGyzz7k3XgjydOmEfZ6+eTaa/n0//0/wl5vvMsVQggxSvU5XLnd7tg+VhMmTMDhcPCTn/xkyAoTQojRLBoOo6sqJrN55ydZS3qmCaaBFgT/eyhqJXo0SjQcHr5ixxG73c6MGTOYOXMmdrsdPS0N12WXkXP66WAyUfXaa7xx6qk0fvFFvEsVQggxCvU5XGmahtVqjT03m80kJiYOSVFCCDHaWWw2FLMZTVV3faIpCRKPA9tkQEcPbkSJfoPF1DksdY5XGRkZzJs3j8LCQhSzGcuRR5Jz3XU48vPp9nh478ILWXnnnajSXEQIIUQ/9LkVu8lk4vjjj8fes8nlq6++yve///0dAtaLL744+FUOM2nFLoQYDKsef5z28nKSy8r6dkF4M52bVpNe6mXODyoh/xlIOGxoixR0dXWxfv16fD4fWjBI4NVX6fjf/wBInTKFA2+/nbSpU+NcpRBCiHgZklbsixcvJjs7m5SUFFJSUli0aBH5+fmx51seQgghDLmzZ6NrGmofp/ipFIJ1GrnTLBBtgJrvQ+sdoEur8KGUlJTEvvvuy8SJE7EkJJB45plk/OxnWFJS6Fi/nrfPOou1Dz+8+1FIIYQQ496ANhEeq2TkSggxGCKhECsefBB/XR1JZWVGO/ad0DSNrspKEgsLmXvhOVjbrwLvE8aLiT+A/MeNFu5iSAWDQTZu3EhraytqVxfep5+mu6d7Y/a8eRx4660k5ufHt0ghhBDDatg2ERZCCLFzVrudqSedhCMzk67Kyp2OYKnhMF2VlTgyM5l60klYnemQ9xjk/gsUh7HhcOUcCHw+zHcw/jgcDvbaay9mzJiBMyOD1IsuIvVHP8LkcND05Ze8ceqpVPZ0zBVCCCG+S0aueiEjV0KIwdReU0PF0qX46+sBsKemopjN6KpKqKMDgMSCAqaedBJpxcXbXxz8CurPhMgGwALZf4K0q0BRhvUexqNoNEplZSVut5toUxPtjz9OePNmAIqPPZZ5v/0t9t5a7QshhBhT+pMNJFz1QsKVEGKwRUIhWioq8Kxeja++Hl1VUcxmXAUF5M6eTebUqVh7GgbtQPWC5yLoWmI8d50KeQ+DOXXY6h/PvF6v0fDC66XrP/+h6403QFVxZmVxwB//SN7BB8e7RCGEEENIwtUASbgSQgylSCBANBzGYrNhdTr7dpGuQ8c/oOlq0MNgLYOC58Axd2iLFYCxJq6uro7q6mqClZW0P/oo0cZGAKaccw6zf/ELLH39uxRCCDGqSLgaIAlXQogRK7Ac3GdCpAoUG2TfA6k/k2mCwyQQCLBhwwZaGxvxvvQS/g8+ACB5wgQOuv120vfaK84VCiGEGGwSrgZIwpUQYkRT26HhAvC9YjxPOgtyHwRzUnzrGid0XaepqYlNmzbRtXo17U8+idbZiWKxsPellzLjwgsxWSzxLlMIIcQgkXA1QBKuhBAjnq5D+z3Q9EsgCrYpkP8cOPaJd2XjRiQSYfPmzdRv3EjH008TXLUKgMzZsznwtttI+m5zEiGEEKOStGIXQoixTlEg/Woo+RAshRBeD9XzoeNhI3iJIWe1Wpk6dSr7HnwwBVdcQdp556E4HLSsXs2bp5/OphdekJbtQggxzsjIVS9k5EoIMapEW6DhPPC/aTxPXgy5fwdTYnzrGgHUUAgtGsVksWDeWTfGQaBpGjU1NWxetYq2Rx8lvHEjAAXf+x7733QTzqysIftsIYQQQ0umBQ6QhCshxKija9B2BzT/BtDAtpfRTdA+Pd6VDTs1HKarupr2tWvxud1b297n55M2YwZJJSWYbbYh+ezu7m4qysupf+EFvK++CqqKNSmJ/X79a0oXLECRxiNCCDHqSLgaIAlXQohRq/sDqD8bVA8oiZB7P6QsindVw6bb46H23Xfx1dWByYQ9JQWT2YymqoQ6O0HTcBUWUnTUUSTk5g5JDbqu4/F4KP/gA1oefZRIbS0AeYcdxvzf/56E7Owh+VwhhBBDQ8LVAEm4EkKMatFGcP8Iut8znqdcBDl/AdPY3oep2+Oh8rXXCLa04Coqwmy1ouk6uq6jKAomRUGNRPDV1uLIzKRswYIhC1gA4XCYzRs2sOGJJ2IbD1tcLubecAMTTj5ZRrGEEGKUkHA1QBKuhBCjnq5Cyx+g9WZAB/ssY5qgbXK8KxsSajjMxiVL8NXV4SotJRqNEgwGiUQisXOsVisOhwOLxYKvqgpXYSGTFi4csimCW3R2drLm/fepv/9+IjU1AGQdeCAH33LLkIY7IYQQg0O6BQohxHinmCHrJih6G8xZEPoKquaC97l4VzYkuqqr8dXV4cjLw+v10tnZSTgUio1YKYpCOBSis7MTr9eLIy8PX309XdXVQ15bSkoKB550Egc+8AApp54KFgvNn33G0hNPZP2SJdJRUAghxhAJV0IIMZYlHg2lq8F5KGhd4F4IDT8BzRfvygZV+9q1qJqGPxAgEolgtVqx2myYzWZMZjNmsxmrzYbVaiUSieAPBFBVlfa1a4elPkVRKCwu5ujf/pbpd92FtbQUrbub5b//PW8tXozP7R6WOoQQQgwtCVdCCDHWWfOh+L+QcQOgQOdDUDkbAp/Fu7JBoYZCdNXXE7VaiaoqVqt1p+uZFEXB2nNe1Gqlq74eNRQatlptNhtzjj6aox5/nMyzzgKLhfYVK3jtxBP55vHHZRRLCCFGOQlXQggxHigWyLoViv8HlmKIbILqQ6D5d6BHdn/9CKZFo0RCIaKatstgtUUsYGkakZ59sIZbWkYGR994I/vdfz+2iRPRgkG+ueMOXjvnHDqqqoa9HiGEEINDwpUQQownCYdD2deQvAjQjIYX1QdDeH28K9tjJouFcDQKqtrnDnyKoqCrKuGeDYbjQVEUphx4IAueeYaC889HsVrp+vpr3jztNL68/340VY1LXUIIIfachCshhBhvzCmQ/wTkPw2mVAh+CZVzoOOfMAqnpSlWK+b0dNTu7n5dp/n9mNPTUazWIaqsbxwJCRx+3XUc9uSTOKZMQQ+F2PC3v/HKwoV41q2La21CCCH6R8KVEEKMV8lnG6NYCd8HvRs8F0P9yRBtindl/aJpGokTJ6LoOlqkb1MctUgEBUicOBFN04a2wD4qmDmTk597jomXXopisxEoL+d/55zDx3ffTSQcjnd5Qggh+kDClRBCjGfWIih6B7L/DxQb+F6Fyr3B91q8K+szk8mEs6AAe24uQbd7t00hdF0n6HZjz83FWVCAyTRy/ik0WyzMv+wyjnn+eVx77YUeDlPzr3+x9Mwz2bx8uTS8EEKIEW7k/IsihBAiPhQTpP8CSr4E+0xQm6DuRPD8DDR/vKvbLZPJREJSEin77481PZ1ATc1OR7C0SIRATQ3W9HRS5s0jISlpRIWrLTImTuTEZ55hxjXXoDgchDZu5POf/IT3br2Vrs7OeJcnhBBiJxRdvgbbQX92YRZCiDFFC0Lzr6H9z8Zz62TI/zc458W3rt3w+/00NDSgdHXR+umnBDweAKwpKSgmE7qmEekJJc7cXDIOOgg9KYm8vDwSExPjWfpueWtr+fiGG+hYtQoAW1kZU6+9lumHHIIlTs04hBBiPOlPNpBw1QsJV0KIcc//HjQshmg9YIHM30HG9UZL9xFI0zTcbjfBYBC7xUKgvh7fxo0EGxvRVRXFbMaRk4Nr0iScBQWEolEcDgf5+fkjcuTqu3Rdp/zpp/nqz39GCwTAYiHtlFOYc/HF5OTl9blLohBCiP6TcDVAEq6EEAJQ28BzCXQtMZ47D4S8J8A2Mb517UQoFKKxsZFwOIzD4cBkMqGGw+jRKIrFgtlmQ9M0gsEgNpuNnJwc7HZ7vMvuF7/bzSe//jUty5YBYC0tpfiSS5h5xBEjfgROCCFGKwlXAyThSggheug6eP8NjZeB5gWTC7L/AikXwAgcLQmFQjQ3NxMMBgGwbtNmPdKzDsvhcJCVlTXqgtUWuq6z6cUXWXHHHah+P1gsJP/gB0y74AJKJ0yQqYJiWKg9G3CbLBbMo/T/l4Toq/5kg7jOhfjwww858cQTyc/PR1EUXn755e1ef/HFFzn22GPJzMxEURRWr1692/d88MEHOfTQQ0lLSyMtLY2jjjqKZT3f8AkhhOgnRYGURUbLdudhoPnAcyHUnwHRlnhXtwO73U5+fj55eXm4XC50XY89XC4XeXl55Ofnj9pgBcbmw5NOP50TX32V3EMOgWgU79KlrLrySj556SWampqkq6AYEmo4TMeGDVS+8gprH36YdY88wtqHH6bylVfo2LABVbYMECK+4crv9zNr1izuvffenb5+8MEHc/vtt/f5Pd9//31++MMf8r///Y/PPvuM4uJijjnmGOrr6werbCGEGH+sJVD8X8i6HbCC78Welu1vx7uyHZhMJhITE8nNzaW4uJjCwkKKi4vJzc0lMTFxVKyx6ouEnByOuP9+Drj1ViwuF5GaGur/8Ae++L//46uVK/H7R36nRzF6dHs8bFyyhE0vvEBbRQWKyYTF4UAxmWirqGDTCy+wcckSunuayQgxXo2YaYGKovDSSy9xyimn7PBaVVUVZWVlrFq1itmzZ/frfVVVJS0tjXvvvZfzzjuvT9fItEAhhNiF4Cpw/wjC64znqZdD9p/A5IxvXeNYoLmZZTfdRP377wNgLSoiddEiyg44gJKSku2mRwrRX90eD5WvvUawpQVXURHmXn6e1EgEX20tjsxMyhYsICE3Nw6VCjE0Rs20wOHQ3d1NJBIhPT19p+eEQiG8Xu92DyGEEDvhmAOlKyDtCuN5x71QNReCK+Nb1zjmzMrisHvv5aA77sCakkKktpbmO+5g7d/+xucffYS7D5srC9EbNRym9t13Cba0kFxWFgtWmq6jahpaz8+V2WoluayMYEsLte++K1MExbg15sPV9ddfT0FBAUcdddROz7nttttISUmJPYqKioaxQiGEGIVMTsj5KxS+BeZcYxSr6gBovQN0Nd7VjUuKolC6YAELli6l+NhjQdPwvfsu7t/9jm9eeokVK1bQ3t4e7zLFKNNVXY2vrg5XURE6EAqH6fR6aWtro729nba2Njq9XkLhMDrgKirCV19PV3V1vEsXIi7GdLj605/+xNNPP82LL76Iw+HY6Xk33HADnZ2dsUdtbe0wVimEEKOY61go+wZcpwERaL4eao6AcFW8Kxu3nJmZHPLnP3P4ffeRmJ+P2tZG6333UfuXv7Dqo49Ys2YNgUAg3mWKUaJ97VpQFDRFif2eFA6FUBQFk6KgKArhUCj2mtbTRbR97do4Vy5EfIzZcHXXXXdx66238p///Id99tlnl+fa7XaSk5O3ewghhOgjSyYUPA+5Dxut2gMfQdUs6HzCaOU+TqmhEBG/HzUUisvnFxx+OCe88grTzj8fxWwmsHIljTffTPWLL7Lsiy+orKxEVWWUUeycGgrhc7sxu1x0eb1EIhGsVitWmw2z2YzJbMZsNmO12bBarUQiEbq8XswuFz63O24/+0LE05jcDOPOO+/klltu4e2332a//faLdzlCCDH2KQqkXgAJh0PDuRD4FBrOA99rkPsPMO983etYoobDdFVX0752LT63G11VUcxmXPn5pM2YQVJJCWabbdjqsSQksO9111G6YAHLfvc72tasoeOZZ+hetozIOefg8XgoKysjJycHZQTuWybiS4tG0aJRApEIut2O1Wrd6c+JoiixgBUNBkm02dCiUdkDS4w7cQ1XPp+PjRs3xp5XVlayevVq0tPTKS4upq2tjZqaGtxuNwAVFRUA5ObmktvThea8886joKCA2267DTCmAt5444089dRTlJaW4ulpCepyuXC5XMN5e0IIMf7YJkDxB9B6O7T8HrqWQOATyHsMEo+Md3VDqtvjofbdd/HV1YHJhD0lBbPViqaqtFVU0LZuHa7CQoqOOmrYO6mlT5/OMU8/zYann+arv/yF8ObNNN12G66jjiJ0/PG43W4mTZokMzfEdkwWC6qmEQ4GSXC5dhvAtwSs7s5OVIsFk2xoLcahuE4LXL58OXPmzGHOnDkA/OIXv2DOnDn89re/BWDp0qXMmTOHE044AYCzzz6bOXPmcP/998feo6amhoaGhtjz++67j3A4zBlnnEFeXl7scddddw3jnQkhxDimWCDzN1DyKdimQLQeao+CxqtAG5t7L21pVb1l4X9KaSmOtDRsyck40tJIKS01FvrX1VH52mtx2QvIZDYzddEiTli6lMLvfx9UFd/bb9P0xz/S/OWXrFq1ivLyckIylUv0MNvtKGlpqD5fn0c2FUUh2tWFkpYmo1ZiXBox+1yNJLLPlRBCDBLND03XQkfPl2LWMsh9cEyNYqnhMBuXLMFXV0dyWdkufwnVdR1vZSWuwkImLVw4rFMEv6v2vfdY/sc/EmhsBMC5//6knH461pQUiouLKSoq6veGy2oohBaNYrJY5BfrMUDTNNZ9+CFN77xDYkEBpj7sl6ZFInTX15N19NFMP+ywMbNptxjf+pMNZLxWCCHE0DElGmuuXCeD56cQqTRGsVJ+Atl3gjk13hUO2LatqvsybWrLCFZXdTWpkycPU5U7KjrySHLnz+erv/2N9f/+N4FlywivXUvSKadQeeCBeDweJkyYQGZm5i7va6StMxODR9M0nAUFOHNzCbrdOIuLd/vlQdDtxp6bi7OgAE3TJFyJcUd+4oUQQgw913FQtgZSLzWed/4LKveCrqXxrWsQbGlVbe7Dt/pA7LyR0Kra6nKx3w03cOzTT5M2bRqqz0fHk0/S9pe/0FVVxdq1a/nqq6/w+Xy9Xt/t8bBxyRI2vfACbRUVKCYTFocDxWSiraKCTS+8wMYlS+IyDVIMnMlkwmS1kn7QQVjT0wnU1KBFIr2eq0UiBGpqsKank3HQQZisVglWYlySaYG9kGmBQggxhLo/hIafQGSD8TzpbGNDYktWfOvaA2ooxNqHH0YxmXCkpfX5umB7O7qmMePHPx4x0+e0aJSKJ5/k63vvRQ0EUCwWko49Ftcxx6BYreTl5VFWVoa1JxxuWWcWbGnBVVTUa7hUIxF8tbU4MjMpW7Bg2Bt5iIHzeDz4/X4Un4+Wjz8m0BOUrSkpKCYTuqYR6ewEwJmbS+Yhh6AlJuJyuWLNx4QY7fqTDeQrBSGEEMMr4TAo+wrSfwmYoOsZqJwOnU+Nun2xtGgUXVUxmc39us5kNqOrKlo0OkSV9Z/JYmH6+edzwiuvkH/YYejRKN7XX6f19tsJrV9PQ0MDy5Yto66ujkgoRO277xJsaSG5rGyno3Zmq5XksjKCLS3Uvvsuajg8zHclBiopKQld17FlZJC/YAF5xx1H0qRJoGlooRBoGkmTJpF33HHkL1iALSMjdp0Q45GMXPVCRq6EEGKYBJaD50IIfW08T1xgrNGyFsa3rj4aSyNX29J1nZq332bFbbcRbGkBIPnQQ0k48UTMLhfmtjaUr74ic/LkPk2HVCMRfHV1TDzttLiuMxP9p2kabrebYDCI0+mMrblSw2H0aBTFYomtqdN1nUAggMPhID8/X6YFijFDRq6EEEKMDs79oPRLyLwZsIL/NWMtVsc/R8Uoltlux5WfT6hnWlRfhTo6cOXnj8hgBUbjjZLjjmPBq68y6ayzAPB+9BGtf/wjoeXL6ayooKW1FU9TE+E+jEaNpHVmon9MJhNZWVnYbDYCgQCapgFgttmwJCTEgpWmaQQCAWw2G1lZWRKsxLglP/lCCCHiS7FB5o1Qtgoc80HzgudiqD0SwpviXd1upc2YAZqGupOF/t+15by0GTOGrCY1FCLi96MOcM8qW3Iy+//2txz95JOkTJpEpLOTlocfJvif/wDg7+6mqrqa5uZm1J5funfGnpqKz+0ecE1i+NntdnJycnA4HASDQbq7u4lEIrFHd3c3wWAQh8NBTk4O9hH6pYEQw0GmBfZCpgUKIUSc6Cq0/xWafw16ABQnZN0CaVeC0r91TcNlpOxzNdQt0dVwmPLHHuObf/wDLRRCMZtJmj0bZdIkFLMZs9lMZkYGySkp9PYnEPZ6iQaDTL/gAqyJiXt+oyJutoxOdXV1EQwGY8cdDgdJSUk4nU4ZsRJjUn+ygYSrXki4EkKIOAtvBs9F0P1f47ljf8h7GOx7xbeunYh357xuj4fad9/FV1cHJhP2lBRMZjOaqhpTFjUNV2EhRUcdNeDP7dy4kQ8uvxxfbS0A9owMnPvvDz1rzux2O5mZmSQmJGx33UhfZyb6R9O02D5WEqjEWCfhaoAkXAkhxAig68Z+WE3XGlMFsULmbyDjemMq4QgTCzj19YAxDS4WcDo6AHAVFAxKwPnu5w53sNv88stUv/UWzStWEO3uBiB55kxM06ZBz+hYQkICmZmZOHqCVGdlJenTplF28skD+mwhhBhuEq4GSMKVEEKMIJE68FxiNLsAsO8NuQ8bzTBGmG2n5nXV16NFo5gsFpIKCgZlal5vnxePKYkdGzaw6YUXcGRkUP/++7SsXg2AJTGR1AMPJJKVFaslOSmJtJQUgh6PdAsUQoxK/ckGlmGqSQghhNgz1kIoXGrsh9X4cwh9A9XzIf0ayPw9mJzxrjBGsViw5ufjTEpC93pjraqdyclYk5JQLIP7z25XdTW+ujpcRUW7DFZgdAB0FRXhq6ujq7p6QCEnqaQEV2Ehvro6Sk48kZS99qL2jTcIt7fT8u672PPzcc2bh5qYSKfXS9uGDWRMnIizoGCPP1MIIUYDmSQrhBBi5FMUSP4hlK01/osGbXdC5T7Q/WG8qwMgFArhdrtpaGjA7/djttuxulyY7Xb8fj8NDQ243W5Cg9gtr33tWlCUPu01BYPXEt1ss1F01FFYUlNp+PZbIgkJ5C9cSNr++6OYzYTcblqXLiW8ciVafT1KUhKh0lKWr1pFTU0NqqoO6POFEGKkknAlhBBi9LBkQf5TULAULPkQ2Qg1h4PnUlC9cSsrFArR2NgYa0ftdDqxWq1YLBasVitOpzPWxrqxsXFQApYaCuFzu7GnpvbrusFqiW5OSyNh3jwsmZmoLS1EmptJ2WsvCk4/nYSSEtB1usvL8X/xBS6nE1duLtFolMrKSr788ks8Hg+yMmH00jSNaDQa2/dKCGGQNVe9kDVXQggxCqgd0PRL6HzQeG4pgtwHwHX8sJahaRput5tgMIjT6dztuqdAIIDD4SA/P39AXdYifj/rHnkEi8OBrR//Vg1GS/Rt79lusRCor8e3cSPBxsZYC3g1GKTl448J9DT4SCgpYfJll9GVkRELl4mJiZSVlZGenr7baY0i/qQVuxivpKHFAEm4EkKIUcT/X/D8BCKVxvPkcyHnbjBnDM/H90z5czgcffrFUtM0gsEgeXl5JA5gvyc1FGLtww+jmEw4etqg98VgtETf2T2r4XBsnZnZZkNTVRreeIPKRx4h2tUFQOGRR5K7aBHNkQjRaBSAlJQUJkyYIP/mjmChUIjm5maCwSCKomCxWFAUBV3XiUaj6LqOw+EgKytLNhEWY05/soF8vSCEEGJ0S/w+lH0DaVcDCnifgM0zwPuc0c59iHV1daEoSp+/sd9yXldP2NhTZrsdV36+sY9VP4Q6OnDl5w9or6md3bPZZsOSkBDrRGgymyk48UTmP/ooOSecgGIyUffee6y8+GKcX3xBXkYGiqLQ2dnJqlWrWLt2LYFAYI/rEkMjHtNehRitJFwJIYQY/UyJkPNnKPkUbDNAbQL3Qqg/HaINQ/axW0ahLP3sAmi1WgkGgwNer5I2YwZoGmok0qfzt5yXNmPGHn/mntyzNTmZSZddxj5/+xs58+ejhcOUP/QQa664gtyWFrKzswFobm7myy+/ZMOGDYTD4T2uUQweTdNobm4mHA7vctqfyWTC6XQSDodpbm6WtVhi3JJwJYQQYuxwHgClKyHjRsACvpeMUaz2B0Af/A51W36B3NP1QgP9BTTWEr22drfNIXRdx1dbi6uggKSSkj3+zIHcc2JpKYc98ACH/vWvJBYWEmhqYvmNN1J7yy1McDpJT09H13XcbjfLli2jqqpKOgvGWSAQiI1Y9aXd/5YRLBmBFOOVhCshhBBji8kOWTdD6XJwzAWtAxp/BtUHQmD54H5Uz7f4e7p8eaCL/7e0RHdkZuKtrNzpCJYaieCtrMSRmUnRUUcNaAPhgd6z2Wym6MgjWbB0KbOuugqL00nL6tV8dMEF+J95hqkFBSQlJaGqKtXV1XzxxRe43W4ZCYmTeE17FWK0knAlhBBiyEUCAQKdnUSG89tsxywo+Ryy7wFTEgS/hOr9wXMJqG2D8hEmkwmHwxFrzNBXkUikzw0wdichN5eyBQtim/p2VlYSbG8n7PUSbG+ns7LS2Gi4sJCyBQtIyM0d0OcN1j2b7Xb2uugiFrz+OqUnnQTA5pdf5sNzzsGxahVTJ07E4XAQiUTYsGEDy5cvp7m5Wdq3D6N4T3sVYjSSboG9kG6BQggxcJFQiJaKCjyrV+Orr4+16HYVFJA7ezaZU6diHa6uYtEGaLoOvP82npszIesOSDkflIEFnHh1C/wuNRymq7qa9rVr8bndW/+88/NJmzGDpJKSAY1YbWso7rnlq69YfuuttH37LQCu4mLm/PKXKJMnU1NTQ6RnVC4pKYkJEyaQ2s/9vUT/RaNR6urqMJlM/QpYkUgEXdcpLCzsdzATYiSSVuwDJOFKCCEGpr2mhoqlS/HX1aGYTNhSUjCZzWiqSrizE13TSCwsZOpJJ5FWXDx8hfnfh8bLILzWeO48EHLuA8fsPX7LeO1ztStqKIQWjWKyWAbUFXBnhuqedU2j8pVXWH333QRbWwHIO/hgZl13HZ0WC3V1dbHRkIyMDMrKygY1oIrtaZpGTU0NiqJgtVr7fN2WcFVcXCz7XokxQcLVAEm4EkKIPddeU8PaJUsItrTgKirqdbREDYfx1dbiyMxkxsKFwxuw9Ai0/QVabgLdD5gg7TLIvBnMqXv0lltaVYfD4Z2O5mwZvbHZbOTk5Iz6vYCG8p4jPh/fPvAAFY8/jtazb9aUH/6QqRddhLu1lYaGrR0gc3NzKS0tHfV/niOVx+PB7/fjdDr7fE13dzcul4vcAU5BFWKkkHA1QBKuhBBiz0RCIVY8+CD+ujqSysp2+a21pml0VVaSWFjI3IsuGr4pgltE6qDpGuhaYjw350D2nZC8CPagE962m6wC233Tv2VK21jbZHWo79lbXc2qP/2J+vffB8Celsasn/+cvOOPp6qmhpaWFsBYB1ZQUEBxcbFMQxtkI2XaqxDxJOFqgCRcCSHEnmn4+mvWPvXUTkesvksNh/HV1THjhz8kb599hqHCXvjfhcbLIVxhPHceCjl/B8fe/X4rTdMIBAJ0dXXFAgcYASMpKWmX+wSNVsNxz+6PP2blHXfg3bwZgLRp05j7q19hnzSJzZs34/V6AbBYLJSUlAzplMvxZiROexViuEm4GiAJV0IIsWdWPf447evWkTxhQp+v6dy8mfTp05lz3nlDWNluaCFo/zO0/AH0AGCGtCsh83dg3rN/BzRNQ9M0TCbTuPklcyjvWYtEWP/MM3zz978T6WnzXXz88cz+xS8I2mxUVlbS3d0NGMGutLSU7OzsPd6DTGw1Hqe9CrGt/mSD8fG/9kIIIYZcJBDAV1+PrZ9d3Oypqfjq64e3Tft3meyQcQNMKAfXqYBqhK3KaeB9Bvbge0gTYSxKABPhwa93hNrSVW4owqTJamXauedy4htvMGnhQlAUat58k9dPPJGGJUuYvddeTJkyBZvNRjAYpLy8nJUrV9LWNjht98czu91OTk5ObIPg7u5uIpFI7NHd3R3baFiClRjvZOSqFzJyJYQQ/Rfo7OTLv/0Ni8OBLSWlz9eFOjtRg0HmXXEFzn5cN6R8b0LjFRDZZDxP+D7k3Av26bu+Tg9DeD0EV0KkGvQoKBawloBjX7BNAWVw2qGPd+3r1rH8tttoXrECgIS8POZcey0FRx2F2+2mpqYGVVUBSE1NZcKECSQlJcWz5FFvPE57FQJkWuCASbgSQoj+iwQCfH7PPSgmE4709D5fF2xrQ9c0DrjqKqz96Eg25LQgtN0JrbeCHgQskP4LyLwRTK4dz4/UQddLENkMmMCcbgQrPdqzabEG1gmQdCpYC4f5ZsYmXdepefttVt15J90eDwDZ++3H3OuvxzVpEtXV1bjd7tjGw9nZ2ZSWlvar853o3Xic9irGL5kWKIQQYthZnU5cBQWEOzv7dV2oowNXQcHIClYAJocRpMrWgutEIAptf4LN08H7wvZTBSN10PmkEaxsE8E+FSxZYE4z/mufahyPbO45ry5utzWWKIpCyXHHseC119j7ssswOxw0LV/OWwsXsurWWylMT2fevHlkZ2cD0NTUxJdffsnGjRtj3QzFnhnKKaBCjGby/xFCCCEGTe7s2eiahhru2zqjLeflzp49NAVpQVC7jP/uKVsZFC6FgqVgLYVoHbjPgLrjILzBmArY9RKojWCbuvNpf4rNeF1tNM7Xh24tlqZpRKPR2Ia7Y53F6WTvSy9lwauvUnz88eiaxsYlS3j1hBOofv55pk6axL777ktqaiq6rlNfX88XX3xBdXV1bOqgEEIMBpkW2AuZFiiEEHtmROxzNZTrnrQAtN4GbXcYn6PYIHmx8V/7tL69rx6G8GZI/THYZ+5ZHb2VJuthYpqWL2fFbbfRXl4OQPKECcy9/nryDj6YtrY2Kisr8fl8ANhsNkpLS8nNzZXOgkKIXsmaqwGScCWEEHuuvaaGtUuWEGxp2el+V2o4jK+2FkdmJjMWLiStuHhwPny41j2FNxoNL/xvGc9NyZD8I7DP6tsGxKFycMyGlMFpP7/tZr6KomCxWFAUBV3XiUaj6Lo+5jYw3h1NVdn8wgt89de/EmpvB6DgiCPY97rrcBUX09TURGVlJaFQCICEhATKysrIyMiQkCWE2I6EqwGScCWEEAPTXlNDxdKl+OvrAaPdumI2o6sqoY4OABILCph60kmDG6w6n+yZnjex91EkPQzhTWDOgZRFAwtYug7eJdB4MWg968zse0PSWcY6q12JNgMapF9nrO0agF73INKjgAqYQbGM6z2Iwp2dfPOPf7D+6afRo1Gjpft557HXxRdjdjpxu91UV1cTjUYBSE5OZsKECaSMlM6VQoi4k3A1QBKuhBBi4CKhEC0VFXhWr8ZXX4+uqihmM66CAnJnzyZz6tTBnQrY/kBPQ4mpoOxi+puuQbjCGMFKu3hgrdHVLqObYOBzCHyEEWgs4DoOEo8DxbqT69pB64b0a8G85+3BNU3D7XYTDAZxOm0oahNEakBtBTSM0bsMsBajm7MJBIwAlp+fP26mCG7RuWkTK26/Hc+nnwLgyMxk9tVXU3bSSaiaRk1NDfX19bF1apmZmZSVlZGQkBDPsoUQI4CEqwGScCWEEIMrEggQDYex2GxD0xUw9C10PLTzEavvGqx1T1vatWMCVPA+DWFjnQ/mTEg6Gxx773jdII1c+f1+GhoacFhDmCKrQW02alESQDGDroLebXyWOQvNOptgxE5eXh6JiYl7/Lmjla7ruD/4gBV33IGvpgaAjL33Zu6vfkXmPvsQCoWoqqrC09PWHSAvL4+SkpJxNdonhNiehKsBknAlhBCjTOfjEFxtNJXoq8Fa99T5OAS/Mtqt6zoEV0DXc6B1GK/bZkDSGWAtGPTP9ng8+L0enKwE3QvmLGON2XfpUSN4Kcl0sy+u5Fxyc3MH9NmjmRoOU/HEE3x7//1Eu7sBKD3pJGZffTUJ2dn4/X4qKytpbW0FjLbjhYWFFBUVYbH08ucrhBjTZJ8rIYQQ44cWNLoCmjP6d505w7huIG3awehAiNbTPVAB536Q+XtIOBowQ3gttP4BOp8AtXNrC3bHvgP6WGMdlR+LVt4TrHJ7D1ZgHDfngu7FqpUTDPrHTZv23phtNmZceCEnvvEGE045BYCqpUt57Qc/YM2DD+KwWJg5cyazZs0iKSkJrWfa4LJly7abOiiEEN8l4UoIIcTopke2tlvvjy1dBPUBbiZrm2Ks3wpvMtZzgTHVL/kMI2TZ9wV0CHwMLTdC51NgKTCuGwBN0yDaiqK19oxY7abDnaIY52ltEG2VgAA4s7I44I9/5NhnniFj1iyigQBf3XMPr598MnX//S8pKSnMmTOHGTNm4HQ6iUQibNy4kS+//JKmpiZk8o8Q4rskXAkhhBjdFOvWoNQfWwLZzppO9PnzbUZrd3OO0Shj282BLVlG04z068BSAnoIgp9B2/+B99mtYWwPmEwmUN3ouqnvwVKxgK6A6h53DS12JWPvvTnmySc58PbbcWZn46ut5cMrruB/F11E56ZNZGVlsd9++zF58mSsVivBYJB169axatUq2nvavAshBEi4EkIIMdqZHMYGwWpb/65TW43rBtgKHTBauqcs6hnB2mysqYo2G10Bo81GkHOdamw4bCkEtQEazoPq/aH7gz36SBNhHOZGonr/1gZH9GQc5kZMhHd/8jiimEyUnXgiC157jb1++lNMViuezz7jzdNOY/mttxLt6iI/P5/58+dTUlKCyWSiq6uLr7/+mm+++Sa2KbEQYnyTcCWEEGL023bdU18M0rqn7VgLjVGq1B8bzSrQjHbraMbztAsh758wYQNk3Q6mJKP5Rc33oO5UCK/v3+fpEZLsnei6mb7O8DPOM5Nk7xz4dMgxypqYyKwrr2TBq69SeOSR6KrK+n//m1d/8AM2PPMMClBaWsr8+fPJz89HURTa2tpYsWIFa9aswe/3x/sWhBBxJN0CeyHdAoUQYpSJ1z5Xu6IFjQCjWHsfHYs2QctN0PFPYvtjpV0Kmb/tW3MOLYjWchfu9nyC0TSc9vAul13pOgRCNhyWdvLT3Jgyrx2cUbsxzvPZZ6y4/XY6N24EIHXKFObecAM5++8PQHd3N1VVVTQ3N8euycrKorS0VPbIEmKMkFbsAyThSgghRqFIHXQ+CWrjzve70sNG4wlzTs80vsLhr/O7Qmuh6Zfgf914bkqFzBsh9TIw7WZvpc7HCXVV0OifTThqwWEL09tSKk2DYNiGzRIlJ3EV9qRpA29BP45o0Sgbnn2Wb+69l7DXC0DRMccw59prcRUYLfb9fj9VVVW0tLTErsvOzqa0tBTnUOztJoQYNhKuBkjClRBCjFKROuh6CSKVxnNzxtZmF6qxZxHWMqMBxUgIVtvyvwtN10Doa+O5dQJk3QFJp++8E2DP5skhptLsTScYNppzWC0qoAMKkagZAIctQlZyG3bWD3zz5HEq2N7ON/fey8YlS9A1DbPdzvQLLmDGhRdi6Rml8vl8VFVVxfbIAsjNzaW4uFhClhCjlISrAZJwJYQQo5geNtYvBVca+1ht6QpoLTHWWNmmDN1UwIHSVeh8DJp/DarHOOY8GLL/DM79ezl/63RIzTKVQNhBV8BOMGIFTQGTjsMaIckZwmkLYooOw3TIcaC9ooIVt91G05dfApCQm8usK6+k5IQTMJmNMNvV1UVVVRVtbUajFUVRYiHL4ZDpmEKMJhKuBkjClRBCjBG7W/c0Umk+aL0T2u4EPWAcSz4Hsm41QuK2epkOqWkKmg4mBUwmfWROhxzldF2n9p13WHXnnfjdbsBYjzXrqqvIP+wwlJ7RRq/XS1VVVaxlu6Io5OXlUVxcjN2+m2mfQogRQcLVAEm4EkIIMSJE6qD5N+B9HNBBsUPa1ZBxA5iTtz9vtE6HHOWiwSAVTzzB2oceItLVBUDW3LnM/sUvyJo9O3ZeZ2cnVVVVdHR0AEbIys/Pp7i4GJtNRhGFGMkkXA2QhCshhBAjSnCVsR6r+3/Gc3MWZN4MqT/ZuoHwaJ4OOQaEOjpY+69/UfHvf6OFjVb/hd//PrOuvJKUSZNi53V0dFBZWYm3pzGGyWQiPz+foqIiCVlCjFASrgZIwpUQQogRR9fB9yo0X7d1TyzbDMi+ExKP377pxWidDjkGdHs8fHPffWx+6SV0TTM2Jz7lFPa+9FIS8/IAY0phe3s7VVVVdPWMdplMJgoKCigqKsJqtcbzFoQQ3yHhaoAkXAkhhBix9Ah0PGDskbVlyl/C0ZB9Fzj2iWtpYqvOTZv46i9/oe699wAw2WxM/dGPmPGTn2BPTQWMkNXW1kZ1dXUsZJnN5ljIslgs8SpfCLENCVcDJOFKCCHEiKd2QOsfof2vxpRATJByAWT9ASx58a5O9GhevZqv7r6bpuXLAbAmJTHjwguZumgRlp7W7Lqu09raSlVVFX6/HzBCVlFREQUFBRKyhIgzCVcDJOFKCCHEqBHeDM3XQ9dzxnMlETL+H6RfA6aE+NYmACM8uT/6iK/uvpuO9caUTmd2NntfcgkTTjsNU0940nWdlpYWqqqq6O7uBsBiscRClrmnzbsQYnhJuBogCVdCCCFGne5PjaYXwc+N5+ZcyPw1pFwEJmn5PRJoqkr166/z9d/+FmvfnlRayqyf/5yiY46JtW/XdZ3m5maqq6tjIctqtVJUVER+fr6ELCGGmYSrAZJwJYQYUuOx2cB4vOd40HXoWgLNN2xty24phszfQcp5WzsLirhSw2E2PPssax54gFDP/lfpM2cy5xe/IGf+/Nh5uq7T1NREdXU1gYCx35nVaqW4uJi8vDwJWUIMEwlXAyThSggx6MZjm+zxeM8jhR6Gjoeg9RaIGiMk2KZA5u8haSEopvjWJwCI+Hyse/RRyh99lGhPeMo7+GBmXX016dOnx87TdZ3Gxkaqq6sJBoMA2Gy2WMgymeTvU4ihJOFqgCRcCSEGVWyD182ACczp22zw2gZoYJ0wtjZ4HY/3PBJpAWi/D9puB7XFOGbfBzL/AK4Tt2/fLuIm0NLCmgceYMOSJejRKAAlP/gB+1xxBUnFxbHzNE2LhaxQKASA3W6nqKhIQpYQQ0jC1QBJuBJCDJpIHXQ+CWoj2Cb2PlKjhyG8Ccw5kLJo9IeN8XjPI53aBe33QNtdoBmb1+LYH7L+CAlHSsgaIbpqavj6b3+j+o03AFAsFiadeSYzf/YznJmZsfM0TaOhoYGamhrCPRsW22y2WMiS6YJCDK7+ZIO4fsXx4YcfcuKJJ5Kfn4+iKLz88svbvf7iiy9y7LHHkpmZiaIorF69uk/v+8ILLzBjxgzsdjszZszgpZdeGvzihRBid/SwMXqjNoJt6s6nwCk243W10ThfDw9vnYNpPN7zaGBOgswbYWIlZNwASgIEl0Ht0VBzBHR/Eu8KBZBUXMzBd97Jcc8/T97BB6NHo2x4+mlePe44vr73XiI+H7B1w+H58+czadIk7HY74XCYTZs28cUXX1BTU0O0ZwRMCDG84hqu/H4/s2bN4t57793p6wcffDC33357n9/zs88+46yzzuLcc8/lq6++4txzz2XhwoV88cUXg1W2EEL0TXi9MS3ONnH3a1wUk3FepNK4brQaj/c8mpjTIetWmLgZ0q40Qm7gA6g5BGp/YKyPE3GXPn06R/zznxz58MOkz5xJNBDg23/8g6XHHUf5E0+g9oxWbQlZ+++/P1OmTMHhcBCJRKisrOSLL76gurpaQpYQw2zETAtUFIWXXnqJU045ZYfXqqqqKCsrY9WqVcyePXuX73PWWWfh9Xp58803Y8eOO+440tLSePrpp/tUi0wLFEIMis7HIbga7NP6fk2oHByzjc5uo9F4vOfRLFILLX+AzocB1TiWdDpk3gz2GXEtTRh0Xaf2P//hq7/+la6qKgASCwrY5/LLKTnhBEzbTAHc0viipqYm1l3QbDZTUFBAYWEhVqs1HrcgxKg3aqYFDoXPPvuMY445Zrtjxx57LJ9++ulOrwmFQni93u0eQggxIFrQ6JBnzujfdeYM4zotODR1DaXxeM+jnbUI8v4JE8oh+UeAAl0vQOXe4D7P2KBYxJWiKBQfeywnvPwy+//udzizsvDX1/PZDTfw1hlnUP/BB2z5nlxRFHJzc5k3bx7Tp08nISEBVVWpqanhiy++YPPmzbE1WqL/otEowWBQRgPFLo25cOXxeMjJydnuWE5ODh6PZ6fX3HbbbaSkpMQeRUVFQ12mEGKs0yNbW4/3x5aOenpkaOoaSuPxnscK2yTIfxLKvgbXqYAG3idg81Tw/MxoUiLiymS1MmnhQk58801mXXUV1qQkOtav54NLL+WdRYvwfPbZdiErOzub/fbbjxkzZuByuVBVldraWr744gs2btwY6zYodi0ajdLS0kJFRQXffvst5eXlfPvtt1RUVNDS0iJBS+xgzIUrILbD+Ra6ru9wbFs33HADnZ2dsUdtbe1QlyiEGOsU69bQ0B9bwokyCqfvjMd7HmvsM6HwRSj5EhKPBaLQ8QBsngSNv4BoU7wrHPcsTid7XXQRJ731FtMvuACz3U7L6tX89yc/4d3Fi2lctix2rqIoZGVlse+++zJz5kySkpLQNI36+nq++OILNmzYENs3S+zI6/VSXl5OVVUVPp8PRVFiD5/PR1VVFeXl5TLjSWxnzIWr3NzcHUapmpqadhjN2pbdbic5OXm7hxBCDIjJYWyWq7b17zq11bjO5BiauobSeLznscq5HxS9BcUfgvNQ0EPQfjdsmgDNvwG1I94Vjnv21FTmXHstJ739NlMWLcJks9G8YgXvXXAB7/34xzStWBE7V1EUMjIymDNnDnvvvTfJycnouo7b7WbZsmVUVFTE1mgJg9frpaqqimAwiNPpJCEhAZvNFnskJCTgdDoJBoNUVVVJwBIxYy5cHXjggbzzzjvbHfvPf/7DQQcdFKeKhBDjlmNfQOt7m/Et5zn2HbKShtx4vOexLOFQKP4ACt8Cx1zQ/dD6R9hUBi23gubb/nwtaOypJevnho0zK4v9briBk958k8lnn43JYqHxiy9497zz+O9FF9Hy1VexcxVFIT09ndmzZzNr1ixSU1PRdR2Px8OyZcsoLy+nu7s7jnfTN2ooRMTvRx2iqY3RaDS2h5jT6dzp5swmkwmn00k4HJb29yKmnxPjB5fP52Pjxo2x55WVlaxevZr09HSKi4tpa2ujpqYGt9sNQEVFBWCMTuXm5gJw3nnnUVBQwG233QbAlVdeyWGHHcYdd9zBySefzCuvvMK7777Lxx9/PMx3J4QY92xTwDrB2CzXNnXXrcl1zTjPOsG4brQaj/c81ikKuI6FxGPA9zI03wjhNdDya2Nj4vTrIOH7xrFI9dZpntYSIzTbpux8vzMxaBJyc5l3443MuPBC1vzzn2x66SU8n36K59NPyT/sMPa+7DIyZs4EjJCVmppKamoqnZ2dVFdX097eTmNjI42NjWRlZVFSUkJiYmKc72orNRymq7qa9rVr8bnd6KqKYjbjys8nbcYMkkpKMNsG5+eso6MjNmK1s2C1xZaAFQgE6OjoIHObzZ7F+BTXVuzvv/8+RxxxxA7HFy9ezKOPPsqjjz7KBRdcsMPrv/vd77jpppsA+N73vkdpaSmPPvpo7PXnn3+e3/zmN2zevJmJEyfyxz/+kdNOO63PdUkrdiHEoInUQeeTPZvqTuz9l0w9bIQMcw6kLAJr4fDXOZjG4z2PJ7oK3meg5XcQ2WQcU1zgPMCYQmiyGwFLbQM0IzwnnSp/x8PMV1fHtw88QOUrr6CrRpv9giOOYJ/LLiNt+vQdzvd6vdTU1NDa2ho7lpmZSXFxMUlJScNWd2+6PR5q330XX10dmEzYU1Iwmc1oqkqosxM0DVdhIUVHHUVCz5fvA1FRUUFXV1e/wqXf7ycpKYmpU6cO+PPFyNOfbDBi9rkaSSRcCSEGVaQOul4yNssFo/X4lsYPas8vMtaysfUL6Hi85/EmXAlN14D/HdB7pgeaM8F1IjjmgWKWED3E1FAILRrFZLFgttt7PcdbXc23999P9WuvoWsaAEVHHcXel19O6uTJO5zv8/moqamhubk5diw9PZ2SkpK4/E7U7fFQ+dprBFtacBUVYbZa0XQ91qzMpCiokQi+2locmZmULVgwoIAVjUb59ttvURQFWz9GwsLhMLquM3PmTCyWuE4ME0NAwtUASbgSQgw6PQzh9RBcOX6mTo3Hex4v9DC0PwCRzcbIVOBj8L8JWpfxujkTEo6ChIMBC4QrjPPSLpa/8wHa0+lxnZs38+0//kH1m2+CroOiUHzccex9ySWkTJy4w/l+v5+amhqamrZ2iExLS6O4uJjU1NShvMUYNRxm45Il+OrqcJWWxvaZikS2bttgtVpxOBxYLBZ8VVW4CguZtHDhHk8RDAaDlJeX73G4mjZtGg6HNOcZayRcDZCEKyHEkNKCxp5OinX8dMgbj/c8loW+hY6Htp/2qYWg+3/bj2QpLkj8PjgPgmgjpP7YaPcu9shgTI/r2LiRb++7j5q33zYOKAqlJ5zAzEsuIbm0dMfP7O6mtraWxsbG2D5aKSkplJSUkJqausutbgaqY8MGNr3wAo68PLpDISKRCApgMptRAB3QVBUdI2Ql2O0EPR4mnnZar6NyfSEjV6I3Eq4GSMKVEEIIsQudj0NwNdin7fiaHobuT6D7na1TQBU72GZAyrmQfvWwljpW9DY97rv6Mz2uvaKCb+67j7p33wVAMZkoO+kkZv7sZ7iKinY4PxgMUlNTg8fjiYWspKQkiouLycjIGJKQVfnKKzSvWQPp6URVFavV2uvn6LpOJBLBYjZDWxtZe+1F2ckn7/HnVlRU4PP5SEhI6PM1suZqbJNwNUASroQQQoid0ILQdidgAkvWzs/TVQiuAP/bEK3rOWiCpLMh8wYZweqHbafHJZeV7TLI6LqOt7Kyz9Pj2tau5et778X9wQcAKGYzE045hb0uvhhXQcEO54dCIWpra2loaEDrWcOVkJBAUVER2dnZu+2u11dqKMSahx7C5/dDQsJOg9UWWwIW3d24EhPZ68ILd7oObXdaWlqoqqrqU7dAAE3TCAQClJaWSrfAMao/2WDM7XMlhBBCiCGkR7aun9sVxQzO/SHjN5D2c2PNFRp0PQWVe0PtAuj+0Fj/I3apq7raWHdUVLTbESJFUXAVFeGrr6erunq3750+Ywbfu+8+jnn6afIOOQRdVdn0wgu89oMfsOzmm/E3NGx3vt1uZ9KkScyfP5+ioiLMZjPd3d1UVFSwbNky6urqUHu6Ew6EFo0SCYWIatpugxUY9221WolqGpGeRh97KjU1FYfDQSAQiAXIndbZE6wcDsewrUUTI5uEKyGEEEL0nWLd2vmxT+crYN8LUn4MKReD61RAAf/rUHM4VB8EXS8b+56JXrWvXQuK0utUwN5sOa997do+f0bmPvtwxAMPcPSTT5J74IFo0Sgbn32WV48/nuV//CPd2zS2ALDZbEyYMIEDDjiAsrIybDYboVCITZs28fnnn1NVVbVd44n+MlkshKNRUNU+TzlUFAVdVQn3dFDcUxaLheLiYmw22y4D1pZgZbPZKC4ulrVWApBwJYQQQoj+MDmMjo9qW/+uU1sh4SAofBEmVEDqxcZarODnUH8qVO4FHQ8bjTFEjBoK4XO7sfcyKqJpYdRoAE0L7/CaPTUVn9uNGurfn2fWnDl8/1//4qjHHiN73jy0SIT1Tz3Fq8cdx4rbbyfQ0rLd+VuCyPz585kyZQpOp5NoNEp1dTWff/45GzduJBgM9qsGAMVqxZyejtrd3a/rNL8fc3o6Sh+D6M4kJydTWloaG8Hy+/2Ew+HYw+/3x0asSktLZRmJiJFwJYQQQoj+cewLaEbzir7Ycp5jX+O/tsmQez9MrIKMG8CUAuFy8FwImydA652geoei8lFHi0bRVRWT2Ww816OEuuvobFlFW+MK2puN/3a2rCLUXYfWM6JoMpvRVXWPp8dl77cfRz7yCN9/6CGy5sxBDYWoeOIJlh57LKvuuotg2/bh2mQykZeXx7x585gxYwYulwtN06ivr2fZsmWUl5fj9/v7ft+aRuLEiSi6jtbHETCtp5tg4sSJu53O1xfJyclMmzaN0tJSkpKS0Hv219J1naSkJEpLS5k2bZoEK7EdaWjRC2loIYQQQuzCtvtc2aaCsovvanVt9/tcqV7o+Ce03w1Rt3HMlAKpl0D6z8GSNzT3MQqooRBrH34YxWTCnKDh69y0tSW5yWRMhdN1NE2LtSR3pUxE7TahaxozfvzjPW7ssIWu63g+/ZSv772X1q+/BsDidDLlnHOYtngxjoyMXq/p6OigpqaGjo6O2PGMjAyKiopISUnZ5WdqmkbVpk00vvUWkcZGnMXFu21oEaipwZqTQ85xx1E6ceKgNdfYIhqNEo1GsVgsMgVwnJFugQMk4UoIIYTYjUgddD4JauP2+11tSw9DeBOYcyBlEVgLd/2eWgi8T0Hbn4yRLDDeN3kxZFxnjHiNQ5WvvELzN19Ccvc2Lcl3DA66rm1tSe51krX3/gNqSb7j++u4P/qIb+69l7Y1awAwOxxMOuMMpl9wwU5bv3u9Xmpra2nZZkphSkoKRUVFpKen7zQ0eTwe2qqr6fzkEyJtbTjy8zH1Mt1Pi0QIut1Y09NJOegg0ktLyd1FG3oh+kvC1QBJuBJCCCH6IFIHXS9BpNJ4bs7Y2uxiyx5X1jJIOnX3wWpbuga+V6HtDgh81nNQgaTTIP3/gXPeoN7GSNdW/i1fP/p/WDISsTsT2FV/B12HUKAbtdXP3udfQ/q0wW95r+s69f/7H98+8ABt334LGA0oJpx6KjMuvLDXfbKg9w2JExMTY23cvxuy/H4/DQ0NKF1dtH76KQGPBwBrsgvFZIzMRbzGhtXO3FwyDjoIPSmJvLw8EhMTB/2+xfgl4WqAJFwJIYQQfaSHIbwegishUr21Tbu1xFhjZZvS+6hWn95bh8DH0Pon8L+29XjC94yQlXgsu0waY0RX03K+evQRoq1+Eouydjs9zl/bjCUjkVnnX0BS9n5DVpeu63g++4w1DzxA0/LlgLFPVskJJ7DXT35CysSJvV4XCoWoq6ujoaEh1rbd4XBQWFhIbm4u5i3ryzQNt9tNMBjEblEI1HyDr2IFQU8DuqaimMw4cvNwTZ2Ls3hvQlEdh8NBfn7+oE8JFOObhKsBknAlhBBC7AEtaOyDpViNroKDKfSt0ejC+xTQ06TBPgvSfwnJC3e/79Yo5tn8Am3VtXQu20Sk3Y8jNw2T1bzDeVpEJehpx5qWSMr+E0kvKSJ3wunDUmPTihWs+ec/afj4Y+OAolB09NHs9dOfkj59eq/XRCIR3G439fX1sbbtVquVgoIC8vPzsVqthEIhGt3rCfsrcFjqMSkKasSGrikoJh2zNYym6wSjBdgSp5KTPwX7ANeYCfFdEq4GSMKVEEIIMUJFaqDtbuh4EPSe7nPWUki/xthLy5QQ1/IGmxbtpmb9cyjoqF0RWj5bR6CxAxSwuhJQzAq6qhPxdYMOzpxUMg+cjjnJio5C8ZQzMVmG78+k9dtvWfPgg9S9+27sWP5hh7HXxReTNXt2r9eoqorH46Guri7Wtt1sNpOXl0dhjhm6XqS5w0JQywfMWC0qoAMKkagZUHGY3GSlRrFnLuzfFFQh+kDC1QBJuBJCCCFGOLUN2u+D9r+C2mwcM2dA2hWQdrnxf48B0VAHdRtfwmQ2Y7Ha0SIq3e5WfJsaCDZ1omsaismEIzsF18Q8EvIzMFnNRCIhdFWlcNKpWOypw153x4YNrHnwQWrefBO9py16zv77s9fFF5Mzf36vUxt1Xae5uZmamppY23ZF0chJrqUgO4qimOgK2AlGrKApYNJxWCMkOUM4bUFM0d10pRRiD0m4GiAJV0IIIcQooQWg8xFou2trYw0lAVIvNEazrCXxrW+Ath25stqd272mhqPoURXFYsZs235aZCQUiMvI1Xd5q6tZ99BDbH7lFfSePbcyZs1i5k9/Sv7hh+80ZLW1tVFbXU5n15Z9unQyU3wUZbfhcoTQdDApYDJt82usHobwZkj9MdgHv5GHGL8kXA2QhCshhBBilNGj0PW80fwitKrnoBmSzzbWZTn2iWt5A+HZ/AJ+XwfORFefr+n2+3C5UodtzdXu+N1u1j3yCBuffx4tbGwqnTZtGnv99KcUHX00Sm8NKDofp7OtmtqOObR6k2KHU11+CrPaSU/y79jPJFQOjtmQct7Q3YwYdyRcDZCEKyGEEGKU0nXofhda74Du97YeTzweMv4fOA8bdR0G/e2raahdgcPhxGTesZHFd2mqSjAYIK9oLolps4e+wH4INDdT/thjbHjmGaKBAADJEyaw10UXUfKDH2DasjmvFoS2OwETWLLwB23UNqXT1J6MjvH3l2APUZjVTk6ad+sIVrQZ0CD9usFvqiLGLQlXAyThSgghhBgDgiuMkNX1AmCs+8GxvxGyXCeDsvugMhJoahD3phcJBgM4nYkopl20Ytd0AgE/DoeT/ImnYTKPzIAR6uig4sknqfj3v4l4vQAkFhYy48ILmXDKKZjNIWOqpykBzGmx64JhC3XNaXjaUlA14+/PaomSn9FBQWYHVqUFtG5IvxbMSb1+thD9JeFqgCRcCSGEEGNIeCO0/Z+xNksPGcdsU4zRjeRzwTTyW3eHfFU01n5IOBLZ6QjWlhErm9VKTtFh2F2lw19oP0V8PtY//TTljz9OqK0NAGdODtMXL2LSkdVYHDawZO1wXVQ10dCaQn1LGqGIFQCTopGT2khhRh0JeZfLyJUYNBKuBkjClRBCCDEGRRuN7oLt94HWYRwz50L6VZD6MzCnxLO63Qr5qmh2f0owaEyns1rMYDKBphGJbtmM10lW/kGjIlhtKxoIsPH551n38MMEmpoAsKcmMO2MAqYsPBJrYu8BWNOhpSOJ2uZ0fIGtYSojI4PCwkJSUlJ2uemyEH0h4WqAJFwJIYQQY5jaBZ0PQtufIVpvHDMlGwEr/Sqw5MW1vF3R1CABbzld7ZsIBn2ga6CYcDhcJKVNxJk8bcROBewLNRym8uWXWfPQQ/jr6gCwumxMPWM2UxfOxp7i7PU6XYdOn5W6Jietvq1/fy6Xi8LCQrKysjD11jRDiD6QcDVAEq6EEEKIcUAPQ+dT0PYnCK8zjik2Y6pg+nVgnxrf+nZDi3ajqWFMZltc260PBS0apfr1pax54P/wVncAYHFamXzK3kz74b44MxK3v0DXIGzsc9VtP4+6+kYaGxvRevbYstvtFBQUkJeXh8ViQYj+kHA1QBKuhBBCiHFE18D3OrTdAYFPeg4q4Dqlp8Pg/HhWN67poRpqX72NNU+son1jFwAmm5kJx09n6sI5pJSm9+xvtQnMOZCyCKyFAEQiEdxuN/X19UQiEQDMZjO5ubkUFhbicIzeET4xvCRcDZCEKyGEEGKc6v7ECFm+V7cecx4OGb802rnL+p3hF6lD976I++Mv+fbJTbSu7Yy9lD8/k2lnlpKz/1yU5NNiwWpbmqbR2NhIXV0d3d3dseNZWVkUFhbK73pityRcDZCEKyGEEGKcC60xWoF3PglEjWP2vY0NiZPPAsUa1/LGHT0M4fXogRU0r1hO+ZJy6j5xQ89vsalTpzBt8fmUHH88Zput97fQddrb26mtraWjoyN2PDk5maKiIjIyMqT5heiVhKsBknAlhBBCCAAiddB2N3T+EzSfccxSDOm/gNSfgClx19eLwacFQY/QVeuh/Mln2fzyy6g9GxI7s7KYcs45TFq4EHtq6k7fwufzUVdXR1NTE1t+FXY6nRQUFJCbm4u5D5s1i/FDwtUASbgSQgghxHbUdqOFe/tfQG02jpnSIe1ySLsCLJnxrW8cC3V0sPG551j/1FOxNu5mh4MJJ5/M1PPOI7m0dOfXhkLU19fT0NBANGqMUFosFvLz88nPz8duH/l7oImhJ+FqgCRcCSGEEKJXWgA6HzOmDEY2GccUJ6ScC6mXgmNWfOsbx9RwmJq336b80UdpLy83DioKBYcfzrTFi8meN2+n0/5UVcXj8VBXV0cwGOy5VCE7O5vCwkJcLtdw3YYYgSRcDZCEKyGEEELskq5C1wvQegeEVm497jzICFlJZ4BJRj3iQdd1mr78kvLHHqP+/fdjx9OmT2fa4sUUH3vsLtdltba2Ultbi9fr3XptWhqFhYWkpaXJuqxxSMLVAEm4EkIIIUSf6DoEPjSmDHa9SKz5hTkLUi6E1IvBVhrPCsc1b2UlFU88weZXXkHtGZFy5uQw9ZxzmHTmmdhSUnZ+rddLXV0dzc3NsWOJiYkUFBSQnZ0t67LGEQlXAyThSgghhBD9Fm2Ajn9BxwMQre85qEDiCZB2KSQeC4opriWOV8H2djYuWcL6p54i2NICgNnpZOKppzJ10SKSSkp2em0gEKC+vh6Px4OqqoCxLisvL4/8/HzZL2sckHA1QBKuhBBCCLHH9KixT1b7fdD97tbj1gmQ+jNI/TGYM+JX3zimhsNUv/EG5Y89Rsf69cZBRaHw+99n2uLFZO27706n/UWjUTweD/X19bF1WQCZmZkUFBSQkpIiUwbHKAlXAyThSgghhBCDIlQBHfdD5yOg9Wx+q9gh6WxjNMsxTzYmjgNd12n84gvKH3sM94cfxo6n77WXsS7rmGMwWXvfy2zLuqz6+vrt9suSKYNjl4SrAZJwJYQQQohBpfnB+wy0/x1Cq7Yed8w1GmAknw2mhPjVN451btpExRNPULl0KWooBEBCbi5TfvQjJp1xBrZd/C7o9/upr6+nsbERTdMAmTI4Fkm4GiAJV0IIIYQYEroOwWU9DTCeBd34ZR5TKqRcAGk/A9uUuJY4XgXb2tjwzDNseOYZgq2tAFicTiacfjrTFi3CVVS002sjkUhsymCoJ6CBMWWwsLCQ5OTkUTllUA2F0KJRTBYL5nG855eEqwGScCWEEEKIIRdtMaYLdvwDIpVbjyccbUwZdC0AxRK/+sYpNRSi6vXXKX/sMTo3bgRAMZkoPPJIpi1eTObs2TsNSjubMuhyuWJTBk2mkd3URA2H6aqupn3tWnxuN7qqopjNuPLzSZsxg6SSkp22sh+rJFwNkIQrIYQQQgwbXQP/28Zolv91oOdXM0uh0co99SdgyY1rieORrut4Pv2U8sceo+GTT2LHM/bZh2mLF1N01FGYLDsPvz6fD7fbvd2UQavVGpsyaB+BI0HdHg+1776Lr64OTCbsKSmYzGY0VSXU2QmahquwkKKjjiIhd/z8TEq4GiAJV0IIIYSIi3CV0cq981+gtvQctEDS6cZolvNQaYARBx0bNsTWZWmRCAAJeXlMXbSIiaefji0paafXRiIRGhoacLvdsSmDiqLEugyOlCmD3R4Pla+9RrClBVdREeZeGnqokQi+2locmZmULVgwbgKWhKsBknAlhBBCiLjSQtD1PHTcB4FPtx637WWErORFYJbfUYZboKWFDc8+y4annybU3g6AJTGRiaefztRFi3AVFOz0Wl3XaWlpob6+ns7OztjxkTBlUA2H2bhkCb66OpLLynYZ9nRdx1tZiauwkEkLF46LKYISrgZIwpUQQgghRozgamNdVueToHcbx0wuSD4XUi8Bx95xLW88igaDVL36KuWPP45382bAWJdVdPTRTDv/fDL32WeX1/t8vliXwS2/isdzymDHhg1seuGFnY5YfZcaieCrq2PiaaeROnnyMFQYXxKuBkjClRBCCCFGHLUTOh83RrPC5VuPO+ZC8mJI/iFYMuNX3zikaxoNn3xC+WOP4fnss9jxzNmzmbZ4MYVHHolpF3tebZkyWF9fTzgcBowpg1lZWbEpg8Oh8pVXaCsvJ6WsrM/XdFZWkj5tGmUnnzyElY0MEq4GSMKVEEIIIfpq2NtV6zp0v2+ErK6XgWjPCxZwnQApi43/KmN/utZI0l5RQcXjj1P12mtoUePvJLGw0FiXddppWBMTd3qtpmm0trZSV1eH1+uNHU9KSqKgoICsrKwhmzKohkKsffhhFJMJR1pan68LtrejaxozfvzjMd+mXcLVAEm4EkIIIcSujJh21dEW8D4N3scguGLrcXMGJP0QUs4Dx37SBGMYBZqbWf/002x89llCPe3YrS4Xk848kynnnENifv4ur+/q6qK+vp6mpqbtpgzm5+eTl5c36FMGI34/6x55BIvDscsNk78r7PUSDQaZfsEFuwyOY4GEqwGScCWEEEKInRmx7apDa4xpg94nIereetw23RjNSl4E1p03XBCDKxoIULl0KeWPP05XVRUAitlM8THHMG3xYjL23vVauXA4HOsyuGXKIBgbE+fn55OamjooXQZl5Gr3JFwNkIQrIYQQQvSmt3bVmq6j6zqKomBSlPi3q9ZV8L9rjGZ1vQR6sOcFBRKOMoJW0qlgShjeusYpXdNwf/gh5Y89RuOyZbHjWfvuy7Tzz6fge9/b5bosTdNoaWnB7XZv12XQ6XSSn59PTk4O1j40odiVyldeoa2igpTS0u0/+zs/29uSNVe9k3DVCwlXQgghhPiubdtVu0pLiUajBINBIj37HoExfcvhcGCxWPBVVcW/XbXqha7noPMxCHy09bgpCZLONKYNOg8FJT4twMebtnXrKH/8carfeAO9Z12Wq6iIqeeey4RTTtnt9Dq/3x/bmFhVVQBMJhPZ2dnk5eXt8e+t23YLVCwWIpHITn+2rVYrejQq3QJ3QsJVLyRcCSGEEOK7tvwC6sjLozsUIhKJoAAmsxkF0AFNVdExfhFNsNsJejwj5xfQ8GbwPmFMHYxs3nrcWgrJ50HKuWCbFLfyxpPuxsbYuqxwTwMLa3Iyk3vWZe1utFNVVRobG3G73fj9/tjxpKQk8vLyyM7OxryL0bAd3q/ni4OO6mqUzEyi0ehOf7YtFgt6SwupJSWyz1UvJFz1QsKVEEIIIb6r8pVXaF6zBtLTiaoqVqu11zUvuq4TiUSwmM3Q1kbWXnuNrKlTug6Bj43RrK4loHVtfc15cM+0wYVgTolfjeNEtLubza+8Qvnjj+OrqQFAsVgoOe44pi1eTPqMGbu8Xtd1vF4vbreb5ubmWAMMi8VCTk4O+fn5JCT0bfpne00Na5cs2TrltZfQpIbDsSmvMxYuJK24uJ93PDpJuBogCVdCCCGE2JYaCrHmoYfw+f2QkLB9sNJVjO/2FVCM0YItAYvublyJiex14YUjc9G/1g2+V4yg5X8H0IzjigNcpxjTBhOPBsUSzyrHPE1VcX/wAeWPP07Tl1/GjmfPm8e0886j4HvfQ9lNK/ZwOIzH46GhoYFgMBg7npqaSn5+PpmZmTttgKFpGm63G29dHV0rVhD0eACwpqSgmEzomkakZ72XIzeXpLlzSS4sJD8/f8haxI8kEq4GSMKVEEIIIbYV8fv5+p//xB8O40xLQ1F0Yz2T2ga6n63hKhHM6WBORtcVAu3tJNps7PPTn478dtURN3j/bQSt8Jqtx825kLLImDro2HWHOzFwbWvWsO6xx6h5++3YuqykkhKm/OhHlJ10ErakpF1er+s6bW1tuN1u2traYsdtNht5eXm9tnP3+/00NDTgcDhAVemuq8O3cSPBxsbYNgOOnBxckyaRUFgIZjPBYJC8vDwSR/rP9SCQcDVAEq6EEEIIsS01FGLZX/9KJBIhISMRorU90+mUns16TYAGehjQjYYRliL8rX5sViv7//znI3Pkqje6DqGVRsjyPgVq69bX7HN62rqfA5as+NU4DvgbGlj/1FNsfO45Il3G1E2z00npCScw5eyzSZs+fbfvEQwGcbvdeDye7ZpTfLedu8fjwe/343Q6t7teDYfRo1EUi2WHaYLd3d24XC5yh7sbZhxIuBogCVdCCCGE2JamaSx/5BH8mypIyg+DHjACFL1NidKM4KU46XJbSZw4jf0uuGB0Tp/Sw+B70whavteALb+gW8B1PCQvBtcCMI2S4DgKRfx+Nj3/PBufew5vZWXseMasWUw5+2yKjz12t8F9Szv3+vp6vD0NNMBo556Xl0ckEsFsNverpXskEkHXdYqLi0fnz3Y/SLgaIAlXQgghhNhWNBql4qMPaX7rCRJyzJjs6bu9Rgu10d2okXXcIqYeehgWyyhft6S2gvcZI2gFt64LwpQOyWcbI1qOeTAIG9sKY9Soq7qa9rVr8bndaNEo3Y2NeDdupG3tWvSeVuz21FQmnHoqkxYuJKkPDSZ8Ph8NDQ3btXNXFIXExETS0tKMqYF9sCVcFRYWjv6f7d2QcDVAEq6EEEIIsS1N06ha/zmNrz1OpCWIszBrp80BwFj3EqhrxprpIGfBeZROOWBsfbsfWme0dPc+AdH6rcdt04wmGMmLwFoUv/pGuW6Ph9p338VXVwcmE/aUFExmM5qqEursJOr3E2hqor28nEBTU+y6vIMPZvLZZ5N/2GGYdhN4otEoTU1N1NfX093dHTtut9tJTU3F5XLt8mdWRq56J+GqFxKuhBBCCPFdns0v0FZdQ+eyzUTa/Thy0zBZd9xLSIuoBD3tWNMSSdl/IuklReROOD0OFQ8DXYXu//a0dX/RmC4JgAIJ3+9p634amMZ+04PB0u3xUPnaa1tbovcyVU+NRPDV1mJPT8eZmUnN22/T8Mknxno5ICE3l0lnnsnE00/HmbXrtXG6rlNZWUlLSwuBQCB23GQykZycTEpKCrZe2rLLmqveSbjqhYQrIYQQQmxHC+J3/52G9jwUn5/WL9YRaOwABayuBBSzgq7qRHzdoIMzJ5WM+dPRXYnkpTWQmH8ZmPo23WrUUrug63kjaAU+2Hrc5IKkM4z1WQmHgTK2RzkGYstmvr66OpLLynY7OuqtrMRVWMikhQvp9njYuGQJm196iVBHB2DsmVV01FFMPusssufN2+n7bekWaLFY8Pl8dHZ2Eu3pVAjG2qyUlBQSExMxmUxomibdAndCwlUvJFwJIYQQYjtqF1rr/+FuLyUYTcFuChBoaMW3qYFgUye6pqGYTDiyU3BNzMOZl0FIc+KwdJKfVoUp4xow77qF9pgSrgTvk0bQimzaetxSAinnGlMHbZPjV98I1bFhA5teeGGnI1bfpUYi+OrqmHjaaaRONv481VCImv/8hw3PPEPL6tWxc5MnTGDy2Wf32s59yz5XwWAw1jHQ7/fT2dm53ZTBLaNZNpuNpKQk2eeqFxKueiHhSgghhBDb0YLQdiehiINGbynhqAWHLYzJBGo4ih5VUSxmzDYLmgbBsA2bJUpOchV2axDSrxv7I1e90XUIfNozbfBZ0LZ2qsN5oDGalXwWmFPjVuJIUvnKK7SVl5NSVtbnazorK0mfNo2yk0/e4bX2devY8OyzVL32GtGeKX9b2rlPPvts0rdp5x4KhWhsbCQcDuNwOGKhKRKJ4PV68Xq9241mbQlXWVlZmM07To8dSyRcDZCEKyGEEELsoPNxCH5FSNmL5k4XwbAxsmC1qGzZRDgSNX7JdNgiZKX4sOvfgmO2MVIz3mkB8C01gpb/bUAzjit2cJ1srM9KPAaUsd15bmfUUIi1Dz9sjICmpfX5umB7O7qmMePHP95pS/ZwVxdVr77KhmeeoXPT1pHEjFmzmHzWWZQcdxxmu51QKERzczPBYBBgu9bs4XCYYDBIMBjcbjTLbDaTk5NDXl4eLperv7c9KvQnG8R1HO/DDz/kxBNPJD8/H0VRePnll7d7Xdd1brrpJvLz83E6nXzve99jzZo1vb/ZNu655x6mTp2K0+mkqKiIq6++OvZDIoQQQgixRxz7Ahp2azf5GR3kpXficgbRAV0zoQMuZ5C89E7yMzqwW7u3uU5gchqjVEVvwKQ6yLoL7HuDHoKuJVB3AmwshMZrIPh1vKsddlo0iq6qmPo5CmQym9FVFW2bUaXvsiUlMeWcc/jBK69w1GOPUXL88ZgsFlq/+orPf/UrXv7+91l1112EGxvJz8+PBSVd12OPpKQkJkyYwNy5cznggAMoLS3Fbrejqiput5sVK1awcuVKGhoaYi3ex6O4fjXg9/uZNWsWF1xwAaefvmMXnT/96U/8+c9/5tFHH2XKlCnccsstHH300VRUVJCU1Pu85X//+99cf/31PPzwwxx00EGsX7+e888/H4C77757KG9HCCGEEGOZbQpYJ0B4EybbVBKdYRKdYTRNQdPBpIDJ1DMhSNcgvMk43zYlvnWPRJY8yLgG0n8BodXGaJb3KVAbof3PxsM+yxjNSj4HLDnxrnjImSwWlJ526/2hqSqK2bzb1utg7GeVvd9+ZO+3H4GWFja/+CIbliyhu6GBdY88wrpHHtmunTs9zStMJtN2a6vsdjslJSUUFxfT3t5OQ0MDra2tdHV10dXVxaZNm8jOziY3N5ekpKRdNuYYa0bMtEBFUXjppZc45ZRTAGPUKj8/n6uuuor/9//+H2DMBc3JyeGOO+7g4osv7vV9Lr/8ctatW8d7770XO3bNNdewbNkyPvrooz7VItMChRBCCNGrSB10PmmEANtEUHZsUY0eNoKVOQdSFoG1cPjrHI30CPjeAu9j4HvV+HMEwAyJxxtTK10njum1a5WvvEJbRQUppaV9vmZXa676QlNV3B9+yIZnn6Xh44/3qJ07GNMGPR4PHo9nu5buiYmJ5OXlkZOTM2o3Gx410wJ3pbKyEo/HwzHHHBM7ZrfbOfzww/n00093et0hhxzCihUrWLZsGQCbN2/mjTfe4IQTTtjpNaFQKLZQb8tDCCGEEGIH1sKewDQBwpshVA7RZlDbjf+Gyo3j1gkSrPpLsULSiVDwPExqgJz7wDEfUMH/GrgXwsY88FwC3R8Ye2wNJy1otJvXhm6pSdqMGaBpqJHI9h+t66iahvadMZEt56XNmLHHn2kymyk84giOuP9+TnrzTab/+MfY09Lo9nj4+m9/4+WjjuKjK6/E/dFHuxxVs9lsFBcXM2/ePGbNmkV2djaKouD3+9m4cSOfffYZ5eXldHZ2MkLGdobEiI2PHo8HgJyc7YeBc3JyqK6u3ul1Z599Ns3NzRxyyCHouk40GuWSSy7h+uuv3+k1t912G7///e8Hp3AhhBBCjG3WQki7GMLrIbgSItWgdRuNGByzjTVWtim9j2qJvjGnQ9olxiNUDt4noPMJiNZCx/3Gw5xjbFCcdCYkHDo0jTD08PZ/z3rU+BxryZD8PSeVlOAqLMRXW4urtJRoNEowGCSyTdiyWq04HA5jT6raWlyFhSSVlAzK57uKiphzzTXsc/nl27Vzr333XWrffZeE3FwmnHoqE049FVdBQa/voSgKqamppKamMmnSJBobG2loaKC7u5vGxkYaGxtJSEiIjWZZ+9ByfjQZsdMCP/30Uw4++GDcbjd5eXmx8y666CJqa2t56623en2f999/n7PPPptbbrmF+fPns3HjRq688kouuugibrzxxl6vCYVChEKh2HOv10tRUZFMCxRCCCHE7mlBY0qbYh3TU9biTteg+3/g/Td0vQxa+9bXzFk9QesMSPje4AStSB10vQSRzYDJCHyKxQhYahugGSOUSacO6ghlt8fDhpdfpr2uDktmJmarFZPZjILRk1JTVdRIhGhLC2mFhUw+5RQScnMH7fO/q2P9eja98AKVr75KuLPTOKgo5B54IJPOOIOCI47AbNt1wNR1Ha/XS0NDA83NzWia1vM2CllZWeTm5pKamjpi12aNylbs3w1XmzdvZuLEiaxcuZI5c+bEzjv55JNJTU3lscce6/V9Dj30UA444ADuvPPO2LEnn3ySn/70p/h8vj5tdCZrroQQQgghRjA9DP7/QddzRgDS2ra+Zs4E16mQfAYkHGGE3v6K49q6UChEzbff0vjR/2/v3qOrKu/8j7/3OTmXXE8IkJsJAUEBDVLBKUqhoiBCC4qwOh21aKcuh3HQUVn9jeMqa7TLn2A7Ler8aGfUjtrW1aoFUWbZoeBwsSgiKqkoIxhuEUm4hJD7ybns5/fHTg6EJBDMSc6BfF5r7cXKPs/eZ2/9inx49v4+fyZ67BhYFp5AAMvlwtg24dpaMAb3oEHkTZ7MkNJSfF20YI+naEsLB9evp3zFCg6/915sv2/AAIbddBPD584lMGLEWc8TiUQ4cuQIlZWVNDQ0xPanpqaSn59Pfn4+3rOEtb52QbxzNWzYMPLz81m3bl1sXygUYtOmTUycOLHL45qamjoEKLfbHWsjKSIiIiLnOcsLGTdCwa/gkiooXguBu51gFT0Gtc/BFzfC5/lQeZfTKCPWIOMsTMgJbNHD4B3Z9WN/ltf5PHrYGd/d85+BbdscPXoUKxCgZM4cCmbOJHPECLBt7JYWsG0yR4ygYOZMSubMwQoE2s0E9Sa3z0fJzJlM/c//5KY1a7h8wQJSc3Npqanhs1//mjdvvpm1t9/OnlWrCDc2dnmelJQUCgsLGT9+POPGjaOgoAC3201zczP79u3jvffe49NPP+X48ePn5Z/dEzpz1dDQQHl5OQBXXnkly5Yt47rrriMnJ4chQ4bwk5/8hKVLl/LCCy9wySWXsGTJEjZu3NiuFfsdd9zBRRddxNKlSwF49NFHWbZsGc8++2zsscB77rmH8ePH88orr3TrujRzJSIiInIeMhGn2UX9Cqh/DaJHTn7mGgCZNzvvaKVP6zo0tXwCJ/6z6xmrDt8ZcpqYZP8AfKU9uvzGxkYqKyvx+/3tJguioRAmEsFKSWn3CJ5t2wSDQQoKCkhPT+/Rd38VdiRC5TvvsGflSr7cuBHT2vAiJT2dkpkzGT5vHgPHjDnr437RaDQ2m1VfXx/b7/P5uPzyy7tcgqmvnDePBW7cuJHrrruuw/4777yTF198EWMMP/7xj3nmmWeoqalhwoQJ/OIXv6C09GThTpkyhaFDh/Liiy8CzlTj448/zm9/+1u+/PJLBg8ezOzZs3n88cfJzs7u1nUpXImIiIic50wUmt5uDVornRmmNq4AZNzsvKOVPh1cpzxWV/sbCJaBb1T3v6vlM6eZSeCOHl1yVVUVjY2NpKamdvuYpqYmMjIyyO/F9666o/noUfa98QblK1fSUFER25996aUMnzePobNm4evGn8UbGhqoqqri8OHDGGO4+uqrE97C/bwJV8lK4UpERETkAmKi0PwO1P0BGlZCpPLkZ64sZ/2szO9A6rVw4v8BLkg5+9pOMZGjgA05/+crNzWxbZuKigosyzqnDnrhcBhjDEOGDOlWb4HeZozhyAcfsGflSr5Yu5Zoa9M4l9dL8bRpDJ83j7yvfx3rLNcaamqiobaWQE4O7j54p+xMFK56SOFKRERE5AJlbGh+t7UZxkqIfHnyMysDPEOdWajUCd1vsx6tcdrx5/wQ3F/tEbZIJMLBgwdxuVznNFPTFq6KiooSPsNzulBtLfvffJPyFSs4sWtXbH9GcTHD585l2Jw5pOXmxvZHQyHqDxygZudOGg4dwkSjWG43GYWFDLjsMjJLSs7ambA3KFz1kMKViIiISD9gbGh+r/XRwRXOOlptLB/4xjjrWfnGnDloaebqjIwx1OzcSfnKlRx4803CrV0CLbebwsmTGT5vHtkjR/Llhg00HDwILhe+QACX240djdJSWwu2TUZREcXTpvVq6/nOKFz1kMKViIiISD9jbAi+D8d+7Mxs2XUnP7O84C1tDVql4DrtnajefufKRIAo4O6whleyvHPVXZGmJirWrmXPypUc/eij2P6U9HSyhg2jdBMmHwAAHhVJREFUYNIk0vPyOhwXDYdp+OIL/IMGMWzWrD4NWApXPaRwJSIiItJPtXwCNb8ClwdaPobghxCtPmWA22nB7rsC/GPBldFL3QINRA5DuKL1+22cxYwHgmcIpORh21ZCuwX2VO3evZT/4Q/sWbGCSFNTbH/m0KEMHjeOnNGjcZ3yqKMxhrp9+8goKmLEX/91nz0ieC7ZILkezBQRERERSSTvpU4b9vBeZzHijLkQqXBCVrDM6ToY2uls9S+DezD4xjvvXBkDZ2k7fiapqan4/X6CTcdItT7Fso8CLrDSnNkzE4XIQYhUYFyDCZrL8acNOqfugskkcPHFDJ87FxOJEAkGqf74Y2rLy6nfv5/6/fs54Pczcv58MgoLAbAsi4ziYhoOHqT+wAGyL7kkwXfQkcKViIiIiEgbywuZt0DtSxDa5QQtT4mzZc6FSBW0/MUJWuF9ED0KTWvgwBpIKYKMm5z1tNKmdL8hRiuXy8Xg7AiH6z+mORzG78/F5XafNioTOxol2NyM1/Mxg7O/mbTvWnVHzc6dWCkpDBozhkFjxtBSW8ux7ds5WlZGtKWFtMHtuza6W99Hq9m5U+FKRERERCTpeYog8D2oX+U88gfO43hWCuCGlBLIKAH3ILD80LwZGv/kzCqd+KWzuTIhfaYTtjK+Be4BZ/9eE8IX+i/yMg9xtPlrBENeCIMnJQoYwCIcccKWP9XN4NQyfKFaMAvOOcglg2hLCw2HDrVb/8oXCHDRlCnkT55MsLoaOumA6MvOpuHQIaItLQlv0346hSsRERERkdN5imDAAgjthuBHED7gPPpnpTjNK/zjnEcILS+wCOxmaFoP9W9Aw39BtArqX3U23JD2zdagdRN4L+78O0O7IbwXX/pwCtNqaW7xUt/sIxj2gO0ClyEjNUhmagupvhAu6yIn/IV29/h9r0SwIxFMNBqbjbKNIRwOEwwGCYfD4HbTfPw4Ho8Hv9+Px+PBZVm43G4i4TB2JKJwJSIiIiJyXrC8TmjxlYIdBBMGy9N5u3VXKmR829mMDcFt0LDa2Vo+gaYNznbkQed8GTdBxs3gvwqs1sf6gh8BFlheXBakp4ZITw1h2xa2AZeF0+gixnvyuPMwXLlSUrBa262HIxEaGhoIh8NYgMvtxsKZrwu1tNDS0oLH4yEjIwO7df0rV5Kt6wUKVyIiIiIiZ+fyA91cw8pyOYsQp06AwY9DaI8zm9XwBjT92QlbLZ9A9RJIKYCM2ZA2A0LlzuOHp3+1y9DlW1Xuga2zasGvvMZWorh9PjIKCzm6cycYQyQaxePxYJ3WFMTtdmNaZ7Xq6+rg+HEGX3550s1aAV3/exIRERERkTjwDoecB2DIBrjkCBT8FjK/47Rxj1TCiWfh0FyofgLqfg9N74Ld0L1zWynOOlgm3Ku30FsCo0bR1NhIKBjsNFi1aVtcORQM0tzURGDUqD6+0u7RzJWIiIiISF9x5zjNMgLfA7sFmja2Pj74BkS+hNCnzlZngWe4s5aWbyykdFxYF3CClZXiPK54HvLk5ZEyaBCRY8fwDRly1vGRY8dIGTQITycLDScDhSsRERERkURw+SDjRmczy6H6MWj4bydkRb6AcLmz1a8Edy74LnferYo10sBZYNj/tfPukcA2jcEgOddcQ+0779BcUYG/sBCXp2NQtMNhgocO4cvJIXD11TQGg2Qm4HrPRuFKRERERCTRLMtZRyta4zxGaDdA8C/OmlqhXRA9Ak1HnKYYeJyA5RsFVib4rkz01X8ltm0TDAZJz8/HP3UqxzZvprmyEgBPIIDlcmFsm3BtLQCpBQUMmjQJd3Y2wWAQ27aTbo0vhSsRERERkWTgvRQ8FzsNMLwjIf06Z7ObIfRZayOMT8GuOfn4IEDzJkj/FmTMgLTrwJWe2PvoJtu2Aed9Kv/gwRTOmkXTwYM0lJcTPHwYOxzGcrvJHDGCjBEjSCsqwuXxOG3aW49PtnBlGWPM2Yf1L3V1dQQCAWpra8nKykr05YiIiIhIfxE+CLUvQfSwM4N1+uLAxkCkwuk6GDkE4QrglGYWlg9SvwkZM51FjL0jnVmxJGTbNhUVFbFmFaeKhkKYSAQrJQW3t/0/g3A4jDGGIUOG9Em4OpdsoJkrEREREZFk4Slyml3Ur3IWCAan3XpbV8BotbMv8zuQeQu4As6jgo3/3fq+1gFoWudsLALPUEif4QSt9OudDoVJwuVy4ff7aWxs7BCu3F4vnBaq2oTDYTIyMpJu1go0c9UpzVyJiIiISEKZEIR2OwsEhw+c7AroKQH/uPZNLWLHGOf9rLag1bzJOU+MB9ImO0ErYyZ4LzvzrNbZFk6Og8bGRiorK/H7/d0KS23vaRUUFJCe3jePP55LNlC46oTClYiIiIgkja8acuzG1lbv/+0ErvDe9p+nFDuzWhkzIW0quLO+Wqjrya3ZNocOHSIYDJKamtrlOlcAxhiam5vx+/0UFhb22cyVwlUPKVyJiIiIyAXFGAh/Dg1rnKDVtBFM8JQBKeC/CtyDwZXltH5POfVxxOOA7TTcyLzFeXwxTlpaWjh8+DChUKjLGay2GSuv10teXh4+ny9u3382Clc9pHAlIiIiIhc0uxmaNp18hDD8efvPXdmt62pdDt5RTgdCE3I6GbrznPfC4hywjh49SjDoBL5T38Fq6w7o9/sZPHhwnwYrULjqMYUrEREREek3TAiO/l9o3gzRo857W6d2IMRyHiH0jQbPSMB2Hg8csCDujwg2NzdTX18fC1nghKrMzExSU1MT0sRC3QJFRERERKR7QrvB1EPWXCcsmTCEPnfW1QrthEil0/49UgH8CUiBlAIIl0PWbeAf7zw+2EMul4v09HTS09OxbTu2jlUydgXsisKViIiIiEh/FvwIsE7OQlke8F3mbADRE84ixqHPoOUzZxHjyBdQ82/O5spyFi9Omwrp05zHCHu4ttb5FqraKFyJiIiIiPRXdtDpCuge2PUYdzakXu1sxkD0CDRvg/AeZyFj+wQ0vOFs4MxqpU2D9KlO4Irju1nJTuFKRERERKS/MmGnG6ArrXvjLQtS8iDtG2BfCQMedB4PbPofaHzLeW8rUgl1v3U2AO/Ik7NaaVPAPaDXbifRFK5ERERERPory3Oy3fq5aFv/yuWH1KucbeBDzkxY87snw1bwA6dBRmgXnPgl4HLWy2qb2Ur9BrhSe+XWEkHdAjuhboEiIiIi0m/U/gaCfwHfyO4f0/IZ+L8GgTvOPC56wllTq/F/oOkt572tU1k+J2ClT3Nmt/zjwXKf4w30LnULFBERERGR7vGPg+B2pyV7d1qrm9DJ487GnQ2Zc5wNIPxl66zW/zi/Rr6EpvXOBuAKOM0x0qc6s1vekT1ujtGXFK5ERERERPoz76XgudhZINg7EqwzdOkztjPOc7Fz3LnyXOTMdgXucJpjhHadfISwaQPYtdDwurMBlGxzHjk8TyhciYiIiIj0Z5YXMm+B2pecsOMd3vkMlgk5wcqd54zv6QLClgW+Uc42YKHzHlfwo5Nhq2Un+K/s2Xf0Mb1z1Qm9cyUiIiIi/U74INSvgvA+52f3wJPNLqLVzj7PMCdY9UV79bamGQmmd65EREREROTceIpgwAII7XZmkMIHwG5yAo7/a847Vt5Lez5j1V1JEKzO1fl3xSIiIiIi0jssL/hKnc0OOutgWR6n5bqclcKViIiIiIh05PIDClXn4gytQERERERERKS7FK5ERERERETiQOFKREREREQkDhSuRERERERE4kDhSkREREREJA4UrkREREREROJA4UpERERERCQOFK5ERERERETiQOFKREREREQkDhSuRERERERE4kDhSkREREREJA4UrkREREREROJA4UpERERERCQOFK5ERERERETiQOFKREREREQkDhSuRERERERE4kDhSkREREREJA5SEn0BIiIiIiIi7dhBMGGwPODyJ/pquk3hSkREREREEs+EILQbgh9B+ACYCFgp4CkB/zjwXgqWN9FXeUYKVyIiIiIikljhg1C/CsJ7ARe4c8CV5gSs4F8guB08F0PmLeApSvTVdknhSkREREREEid8EGpfguhh8A7vODuVMrh1VmuPMy7wvaQNWGpoISIiIiIiiWFCzoxV9DB4R3b92J/ldT6PHnbGm1DfXmc3KVyJiIiIiEhihHY7jwJ6h4N1lmhiuZxx4X3OcUlI4UpERERERBIj+BFgdb9RRdu44Ee9dkk9kdBw9fbbbzN79mwKCwuxLIvXX3+93efGGB599FEKCwtJTU1lypQpfPrpp2c974kTJ1i4cCEFBQX4/X5Gjx7NH//4x166CxEREREROWd20OkK6B54bse5BzrH2cHeua4eSGi4amxsZOzYsSxfvrzTz3/605+ybNkyli9fzrZt28jPz+eGG26gvr6+y3OGQiFuuOEG9u/fz4oVK9i1axfPPfccF110UW/dhoiIiIiInCsTPtlu/VxYKc5xJtw719UDCe0WOHPmTGbOnNnpZ8YYnnrqKX70ox8xd+5cAH7961+Tl5fH7373OxYsWNDpcc8//zzHjx/n3XffxePxAFBSUtI7NyAiIiIiIl+N5TkZlM5FWyCzPL1zXT2QtO9c7du3j6qqKqZPnx7b5/P5uPbaa3n33Xe7PG716tVcc801LFy4kLy8PEpLS1myZAnRaLTLY1paWqirq2u3iYiIiIhIL3L5nQWCo8fP7bhotXOcy98719UDSRuuqqqqAMjLy2u3Py8vL/ZZZ/bu3cuKFSuIRqP88Y9/ZPHixfz85z/n8ccf7/KYpUuXEggEYltxcXF8bkJERERERLrmHwfY3W+t3jbOP67XLqknkjZctbEsq93PxpgO+05l2za5ubk8++yzjB8/nr/5m7/hRz/6Ef/+7//e5TEPP/wwtbW1se2LL76I2/WLiIiIiEgXvJeC52JngWBjn3mssZ1xnmHOcUkooe9cnUl+fj7gzGAVFBTE9h85cqTDbNapCgoK8Hg8uN3u2L7Ro0dTVVVFKBTC6+3Y5tHn8+Hz+eJ49SIiIiIiclaWFzJvgdqXILSrdb2rTtqym5ATrNx5zvjutm7vY0k7czVs2DDy8/NZt25dbF8oFGLTpk1MnDixy+O+8Y1vUF5ejm2fTL67d++moKCg02AlIiIiIiIJ5CmCwPdaZ7D2QstnEDkK0Rrn15bPnP2ei1vHFSX6iruU0HDV0NBAWVkZZWVlgNPEoqysjIqKCizL4oEHHmDJkiWsWrWKTz75hO9///ukpaVx2223xc5xxx138PDDD8d+vueee6iurub+++9n9+7dvPnmmyxZsoSFCxf29e2JiIiIiEh3eIpgwALI/gH4vwbYYDc5v/q/5uwfsCCpgxUk+LHADz74gOuuuy7286JFiwC48847efHFF/mnf/onmpub+Yd/+AdqamqYMGECa9euJTMzM3ZMRUUFLtfJjFhcXMzatWt58MEHueKKK7jooou4//77eeihh/ruxkRERERE5NxYXvCVOpsddNaxsjxJ2RWwK5YxxiT6IpJNXV0dgUCA2tpasrKyEn05IiIiIiKSIOeSDZL2nSsREREREZHzicKViIiIiIhIHChciYiIiIiIxIHClYiIiIiISBwoXImIiIiIiMSBwpWIiIiIiEgcKFyJiIiIiIjEgcKViIiIiIhIHChciYiIiIiIxIHClYiIiIiISBwoXImIiIiIiMSBwpWIiIiIiEgcKFyJiIiIiIjEgcKViIiIiIhIHChciYiIiIiIxIHClYiIiIiISBwoXImIiIiIiMRBSqIvIBkZYwCoq6tL8JWIiIiIiEgitWWCtoxwJgpXnaivrweguLg4wVciIiIiIiLJoL6+nkAgcMYxlulOBOtnbNvm0KFDZGZmYllWoi9H+lBdXR3FxcV88cUXZGVlJfpy5AKlOpPephqT3qYak96WTDVmjKG+vp7CwkJcrjO/VaWZq064XC6KiooSfRmSQFlZWQn/D1kufKoz6W2qMeltqjHpbclSY2ebsWqjhhYiIiIiIiJxoHAlIiIiIiISBwpXIqfw+Xw88sgj+Hy+RF+KXMBUZ9LbVGPS21Rj0tvO1xpTQwsREREREZE40MyViIiIiIhIHChciYiIiIiIxIHClYiIiIiISBwoXImIiIiIiMSBwpVccN5++21mz55NYWEhlmXx+uuvt/v88OHDfP/736ewsJC0tDRmzJjB559/3um5jDHMnDmz0/PU1NQwf/58AoEAgUCA+fPnc+LEid65KUkq8aqxLVu2cP3115Oenk52djZTpkyhubk59rlqrH+LR51VVVUxf/588vPzSU9PZ9y4caxYsaLdGNVZ/7R06VL+6q/+iszMTHJzc5kzZw67du1qN8YYw6OPPkphYSGpqalMmTKFTz/9tN2YlpYW7rvvPgYNGkR6ejo33XQTBw8ebDdGNdY/xaPGjh8/zn333cfIkSNJS0tjyJAh/OM//iO1tbXtzpNMNaZwJRecxsZGxo4dy/Llyzt8Zoxhzpw57N27lzfeeIPt27dTUlLCtGnTaGxs7DD+qaeewrKsTr/ntttuo6ysjDVr1rBmzRrKysqYP39+3O9Hkk88amzLli3MmDGD6dOn8/7777Nt2zbuvfdeXK6Tvy2rxvq3eNTZ/Pnz2bVrF6tXr2bHjh3MnTuX7373u2zfvj02RnXWP23atImFCxfy3nvvsW7dOiKRCNOnT29XPz/96U9ZtmwZy5cvZ9u2beTn53PDDTdQX18fG/PAAw+watUqXn75ZTZv3kxDQwOzZs0iGo3GxqjG+qd41NihQ4c4dOgQP/vZz9ixYwcvvvgia9as4a677mr3XUlVY0bkAgaYVatWxX7etWuXAcwnn3wS2xeJRExOTo557rnn2h1bVlZmioqKTGVlZYfz7Ny50wDmvffei+3bsmWLAcxnn33Wa/cjyeer1tiECRPM4sWLuzyvakxO9VXrLD093fzmN79pd66cnBzzq1/9yhijOpOTjhw5YgCzadMmY4wxtm2b/Px888QTT8TGBINBEwgEzH/8x38YY4w5ceKE8Xg85uWXX46N+fLLL43L5TJr1qwxxqjG5KSvUmOdefXVV43X6zXhcNgYk3w1ppkr6VdaWloA8Pv9sX1utxuv18vmzZtj+5qamrj11ltZvnw5+fn5Hc6zZcsWAoEAEyZMiO27+uqrCQQCvPvuu714B5LsulNjR44cYevWreTm5jJx4kTy8vK49tpr29WgakzOpLu/l02aNIlXXnmF48ePY9s2L7/8Mi0tLUyZMgVQnclJbY9Z5eTkALBv3z6qqqqYPn16bIzP5+Paa6+N1caHH35IOBxuN6awsJDS0tLYGNWYtPkqNdbVebKyskhJSQGSr8YUrqRfGTVqFCUlJTz88MPU1NQQCoV44oknqKqqorKyMjbuwQcfZOLEidx8882dnqeqqorc3NwO+3Nzc6mqquq165fk150a27t3LwCPPvood999N2vWrGHcuHFMnTo19s6MakzOpLu/l73yyitEIhEGDhyIz+djwYIFrFq1iuHDhwOqM3EYY1i0aBGTJk2itLQUIPbvPy8vr93YvLy82GdVVVV4vV4GDBhwxjGqMfmqNXa66upqHnvsMRYsWBDbl2w1pnAl/YrH42HlypXs3r2bnJwc0tLS2LhxIzNnzsTtdgOwevVq1q9fz1NPPXXGc3X2LpYxpst3tKR/6E6N2bYNwIIFC/jbv/1brrzySp588klGjhzJ888/HzuXaky60p06A1i8eDE1NTW89dZbfPDBByxatIjvfOc77NixIzZGdSb33nsvH3/8Mb///e87fHZ6HXSnNk4foxqTeNRYXV0d3/72t7nssst45JFHzniOM52ntylcSb8zfvx4ysrKOHHiBJWVlaxZs4bq6mqGDRsGwPr169mzZw/Z2dmkpKTEpp3nzZsXe5QmPz+fw4cPdzj30aNHO/wNjPQ/Z6uxgoICAC677LJ2x40ePZqKigpANSZnd7Y627NnD8uXL+f5559n6tSpjB07lkceeYSrrrqKX/ziF4DqTOC+++5j9erVbNiwgaKiotj+tkfiT/+b/yNHjsRqIz8/n1AoRE1NzRnHqMb6t57UWJv6+npmzJhBRkYGq1atwuPxtDtPMtWYwpX0W4FAgMGDB/P555/zwQcfxB4B/Od//mc+/vhjysrKYhvAk08+yQsvvADANddcQ21tLe+//37sfFu3bqW2tpaJEyf2+b1IcuqqxoYOHUphYWGHlrS7d++mpKQEUI1J93VVZ01NTQDtOlCC825W2+yp6qz/MsZw77338tprr7F+/fpYKG8zbNgw8vPzWbduXWxfKBRi06ZNsdoYP348Ho+n3ZjKyko++eST2BjVWP8VjxoDZ8Zq+vTpeL1eVq9e3e5dU0jCGuvzFhoivay+vt5s377dbN++3QBm2bJlZvv27ebAgQPGGKfLzIYNG8yePXvM66+/bkpKSszcuXPPeE5O69RljDEzZswwV1xxhdmyZYvZsmWLGTNmjJk1a1Zv3ZYkkXjU2JNPPmmysrLMH/7wB/P555+bxYsXG7/fb8rLy2NjVGP9W0/rLBQKmREjRpjJkyebrVu3mvLycvOzn/3MWJZl3nzzzdg41Vn/dM8995hAIGA2btxoKisrY1tTU1NszBNPPGECgYB57bXXzI4dO8ytt95qCgoKTF1dXWzM3//935uioiLz1ltvmY8++shcf/31ZuzYsSYSicTGqMb6p3jUWF1dnZkwYYIZM2aMKS8vb3eeZK0xhSu54GzYsMEAHbY777zTGGPM008/bYqKiozH4zFDhgwxixcvNi0tLWc8Z2fhqrq62tx+++0mMzPTZGZmmttvv93U1NT0zk1JUolXjS1dutQUFRWZtLQ0c80115g///nP7T5XjfVv8aiz3bt3m7lz55rc3FyTlpZmrrjiig6t2VVn/VNntQWYF154ITbGtm3zyCOPmPz8fOPz+cw3v/lNs2PHjnbnaW5uNvfee6/JyckxqampZtasWaaioqLdGNVY/xSPGuvq90HA7Nu3LzYumWrMMsaY3pwZExERERER6Q/0zpWIiIiIiEgcKFyJiIiIiIjEgcKViIiIiIhIHChciYiIiIiIxIHClYiIiIiISBwoXImIiIiIiMSBwpWIiIiIiEgcKFyJiIiIiIjEgcKViIiIiIhIHChciYjIBc8Yw7Rp07jxxhs7fPbLX/6SQCBARUVFAq5MREQuJApXIiJywbMsixdeeIGtW7fyzDPPxPbv27ePhx56iKeffpohQ4bE9TvD4XBczyciIslP4UpERPqF4uJinn76aX74wx+yb98+jDHcddddTJ06la9//et861vfIiMjg7y8PObPn8+xY8dix65Zs4ZJkyaRnZ3NwIEDmTVrFnv27Il9vn//fizL4tVXX2XKlCn4/X5eeumlRNymiIgkkGWMMYm+CBERkb4yZ84cTpw4wbx583jsscfYtm0bV111FXfffTd33HEHzc3NPPTQQ0QiEdavXw/AypUrsSyLMWPG0NjYyL/8y7+wf/9+ysrKcLlc7N+/n2HDhjF06FB+/vOfc+WVV+Lz+SgsLEzw3YqISF9SuBIRkX7lyJEjlJaWUl1dzYoVK9i+fTtbt27lT3/6U2zMwYMHKS4uZteuXVx66aUdznH06FFyc3PZsWMHpaWlsXD11FNPcf/99/fl7YiISBLRY4EiItKv5Obm8nd/93eMHj2aW265hQ8//JANGzaQkZER20aNGgUQe/Rvz5493HbbbVx88cVkZWUxbNgwgA5NMK666qq+vRkREUkqKYm+ABERkb6WkpJCSorzv0Dbtpk9ezY/+clPOowrKCgAYPbs2RQXF/Pcc89RWFiIbduUlpYSCoXajU9PT+/9ixcRkaSlcCUiIv3auHHjWLlyJUOHDo0FrlNVV1fzv//7vzzzzDNMnjwZgM2bN/f1ZYqIyHlAjwWKiEi/tnDhQo4fP86tt97K+++/z969e1m7di0/+MEPiEajDBgwgIEDB/Lss89SXl7O+vXrWbRoUaIvW0REkpDClYiI9GuFhYW88847RKNRbrzxRkpLS7n//vsJBAK4XC5cLhcvv/wyH374IaWlpTz44IP867/+a6IvW0REkpC6BYqIiIiIiMSBZq5ERERERETiQOFKREREREQkDhSuRERERERE4kDhSkREREREJA4UrkREREREROJA4UpERERERCQOFK5ERERERETiQOFKREREREQkDhSuRERERERE4kDhSkREREREJA4UrkREREREROLg/wPGXtZNCTjS4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 100M Women in 2024:\n",
      "Gold Medal: 10.67 seconds\n",
      "Silver Medal: 10.75 seconds\n",
      "Bronze Medal: 10.73 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '100M Women', exponential_func)\n",
    "predict_event_results('100M Women', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7716495b",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Helen Stephens</td>\n",
       "      <td>1936</td>\n",
       "      <td>G</td>\n",
       "      <td>11.50</td>\n",
       "      <td>11.827443</td>\n",
       "      <td>1.028473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wilma Rudolph</td>\n",
       "      <td>1960</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.308920</td>\n",
       "      <td>1.028084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Irena Kirszenstein</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.383195</td>\n",
       "      <td>1.025513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Barbara Ferrell</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.367857</td>\n",
       "      <td>1.024131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Juliet Cuthbert</td>\n",
       "      <td>1992</td>\n",
       "      <td>S</td>\n",
       "      <td>10.83</td>\n",
       "      <td>11.059110</td>\n",
       "      <td>1.021155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Giuseppina Leone</td>\n",
       "      <td>1960</td>\n",
       "      <td>B</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.521708</td>\n",
       "      <td>1.019620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stanislawa Walasiewicz</td>\n",
       "      <td>1936</td>\n",
       "      <td>S</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.922165</td>\n",
       "      <td>1.018988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Irina Privalova</td>\n",
       "      <td>1992</td>\n",
       "      <td>B</td>\n",
       "      <td>10.84</td>\n",
       "      <td>11.045467</td>\n",
       "      <td>1.018955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Dorothy Hyman</td>\n",
       "      <td>1960</td>\n",
       "      <td>S</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.489411</td>\n",
       "      <td>1.016762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wyomia Tyus</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.182036</td>\n",
       "      <td>1.016549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Marlies Oelsner-GãHr</td>\n",
       "      <td>1980</td>\n",
       "      <td>S</td>\n",
       "      <td>11.07</td>\n",
       "      <td>11.203723</td>\n",
       "      <td>1.012080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Wilhelmina VON Bremen</td>\n",
       "      <td>1932</td>\n",
       "      <td>B</td>\n",
       "      <td>12.00</td>\n",
       "      <td>12.139636</td>\n",
       "      <td>1.011636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KÃ¤the Krauss</td>\n",
       "      <td>1936</td>\n",
       "      <td>B</td>\n",
       "      <td>11.90</td>\n",
       "      <td>12.036795</td>\n",
       "      <td>1.011495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Renate Stecher</td>\n",
       "      <td>1976</td>\n",
       "      <td>S</td>\n",
       "      <td>11.13</td>\n",
       "      <td>11.256142</td>\n",
       "      <td>1.011334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Hilda Strike</td>\n",
       "      <td>1932</td>\n",
       "      <td>S</td>\n",
       "      <td>11.90</td>\n",
       "      <td>12.005556</td>\n",
       "      <td>1.008870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Elaine Thompson-Herah</td>\n",
       "      <td>2020</td>\n",
       "      <td>G</td>\n",
       "      <td>10.61</td>\n",
       "      <td>10.694948</td>\n",
       "      <td>1.008006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Inge Helten</td>\n",
       "      <td>1976</td>\n",
       "      <td>B</td>\n",
       "      <td>11.17</td>\n",
       "      <td>11.258553</td>\n",
       "      <td>1.007928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gail Devers</td>\n",
       "      <td>1992</td>\n",
       "      <td>G</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.898338</td>\n",
       "      <td>1.007240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Raelene Ann Boyle</td>\n",
       "      <td>1972</td>\n",
       "      <td>S</td>\n",
       "      <td>11.23</td>\n",
       "      <td>11.310821</td>\n",
       "      <td>1.007197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Silvia CHIVAS Baro</td>\n",
       "      <td>1972</td>\n",
       "      <td>B</td>\n",
       "      <td>11.24</td>\n",
       "      <td>11.319230</td>\n",
       "      <td>1.007049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Name  Year Medal  Result  expectation     ratio\n",
       "0           Helen Stephens  1936     G   11.50    11.827443  1.028473\n",
       "1            Wilma Rudolph  1960     G   11.00    11.308920  1.028084\n",
       "2       Irena Kirszenstein  1968     B   11.10    11.383195  1.025513\n",
       "3          Barbara Ferrell  1968     S   11.10    11.367857  1.024131\n",
       "4          Juliet Cuthbert  1992     S   10.83    11.059110  1.021155\n",
       "5         Giuseppina Leone  1960     B   11.30    11.521708  1.019620\n",
       "6   Stanislawa Walasiewicz  1936     S   11.70    11.922165  1.018988\n",
       "7          Irina Privalova  1992     B   10.84    11.045467  1.018955\n",
       "8            Dorothy Hyman  1960     S   11.30    11.489411  1.016762\n",
       "9              Wyomia Tyus  1968     G   11.00    11.182036  1.016549\n",
       "10   Marlies Oelsner-GãHr  1980     S   11.07    11.203723  1.012080\n",
       "11   Wilhelmina VON Bremen  1932     B   12.00    12.139636  1.011636\n",
       "12           KÃ¤the Krauss  1936     B   11.90    12.036795  1.011495\n",
       "13          Renate Stecher  1976     S   11.13    11.256142  1.011334\n",
       "14            Hilda Strike  1932     S   11.90    12.005556  1.008870\n",
       "15   Elaine Thompson-Herah  2020     G   10.61    10.694948  1.008006\n",
       "16             Inge Helten  1976     B   11.17    11.258553  1.007928\n",
       "17             Gail Devers  1992     G   10.82    10.898338  1.007240\n",
       "18       Raelene Ann Boyle  1972     S   11.23    11.310821  1.007197\n",
       "19      Silvia CHIVAS Baro  1972     B   11.24    11.319230  1.007049"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '100M Women', fitted_params)\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7d59748",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "                     Name  Year Medal  Result  expectation     ratio\n",
      "0          Helen Stephens  1936     G   11.50    11.827443  1.028473\n",
      "1           Wilma Rudolph  1960     G   11.00    11.308920  1.028084\n",
      "9             Wyomia Tyus  1968     G   11.00    11.182036  1.016549\n",
      "15  Elaine Thompson-Herah  2020     G   10.61    10.694948  1.008006\n",
      "17            Gail Devers  1992     G   10.82    10.898338  1.007240\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                      Name  Year Medal  Result  expectation     ratio\n",
      "3          Barbara Ferrell  1968     S   11.10    11.367857  1.024131\n",
      "4          Juliet Cuthbert  1992     S   10.83    11.059110  1.021155\n",
      "6   Stanislawa Walasiewicz  1936     S   11.70    11.922165  1.018988\n",
      "8            Dorothy Hyman  1960     S   11.30    11.489411  1.016762\n",
      "10   Marlies Oelsner-GãHr  1980     S   11.07    11.203723  1.012080\n",
      "13          Renate Stecher  1976     S   11.13    11.256142  1.011334\n",
      "14            Hilda Strike  1932     S   11.90    12.005556  1.008870\n",
      "18       Raelene Ann Boyle  1972     S   11.23    11.310821  1.007197\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                     Name  Year Medal  Result  expectation     ratio\n",
      "2      Irena Kirszenstein  1968     B   11.10    11.383195  1.025513\n",
      "5        Giuseppina Leone  1960     B   11.30    11.521708  1.019620\n",
      "7         Irina Privalova  1992     B   10.84    11.045467  1.018955\n",
      "11  Wilhelmina VON Bremen  1932     B   12.00    12.139636  1.011636\n",
      "12          KÃ¤the Krauss  1936     B   11.90    12.036795  1.011495\n",
      "16            Inge Helten  1976     B   11.17    11.258553  1.007928\n",
      "19     Silvia CHIVAS Baro  1972     B   11.24    11.319230  1.007049\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '100M Women', top_n=10, exclude_years=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ca508d9",
   "metadata": {},
   "source": [
    "## Note about the Model and Early 20th Century Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13b638a",
   "metadata": {},
   "source": [
    "Again, the model is favoring results from earlier Olympic races. So in an effort to improve the overall implentation of the model, I have the same code below except with the years 1932 and 1936 excluded from the function.\n",
    "\n",
    "However, when those years were excluded from the model, the predicted results for 2024 seem *less accurate* for Silver and Bronze while result for Gold remained the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "00bff05a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 100M Women in 2024:\n",
      "Gold Medal: 10.68 seconds\n",
      "Silver Medal: 10.91 seconds\n",
      "Bronze Medal: 10.88 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '100M Women', exponential_func, omit_years = [1932, 1936])\n",
    "predict_event_results('100M Women', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4a68c89",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wilma Rudolph</td>\n",
       "      <td>1960</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.334702</td>\n",
       "      <td>1.030427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Giuseppina Leone</td>\n",
       "      <td>1960</td>\n",
       "      <td>B</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.527010</td>\n",
       "      <td>1.020089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dorothy Hyman</td>\n",
       "      <td>1960</td>\n",
       "      <td>S</td>\n",
       "      <td>11.30</td>\n",
       "      <td>11.498129</td>\n",
       "      <td>1.017534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wyomia Tyus</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.191274</td>\n",
       "      <td>1.017389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Irena Kirszenstein</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.289746</td>\n",
       "      <td>1.017094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shelly-Ann Fraser-Pryce</td>\n",
       "      <td>2020</td>\n",
       "      <td>S</td>\n",
       "      <td>10.74</td>\n",
       "      <td>10.909359</td>\n",
       "      <td>1.015769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Barbara Ferrell</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>11.10</td>\n",
       "      <td>11.267888</td>\n",
       "      <td>1.015125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Juliet Cuthbert</td>\n",
       "      <td>1992</td>\n",
       "      <td>S</td>\n",
       "      <td>10.83</td>\n",
       "      <td>10.981950</td>\n",
       "      <td>1.014031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carmelita Jeter</td>\n",
       "      <td>2012</td>\n",
       "      <td>S</td>\n",
       "      <td>10.78</td>\n",
       "      <td>10.919630</td>\n",
       "      <td>1.012953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Irina Privalova</td>\n",
       "      <td>1992</td>\n",
       "      <td>B</td>\n",
       "      <td>10.84</td>\n",
       "      <td>10.977443</td>\n",
       "      <td>1.012679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shericka Jackson</td>\n",
       "      <td>2020</td>\n",
       "      <td>B</td>\n",
       "      <td>10.76</td>\n",
       "      <td>10.889202</td>\n",
       "      <td>1.012008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Veronica Campbell-Brown</td>\n",
       "      <td>2012</td>\n",
       "      <td>B</td>\n",
       "      <td>10.81</td>\n",
       "      <td>10.902417</td>\n",
       "      <td>1.008549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Elaine Thompson-Herah</td>\n",
       "      <td>2020</td>\n",
       "      <td>G</td>\n",
       "      <td>10.61</td>\n",
       "      <td>10.695435</td>\n",
       "      <td>1.008052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tori Bowie</td>\n",
       "      <td>2016</td>\n",
       "      <td>S</td>\n",
       "      <td>10.83</td>\n",
       "      <td>10.913883</td>\n",
       "      <td>1.007745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gail Devers</td>\n",
       "      <td>1992</td>\n",
       "      <td>G</td>\n",
       "      <td>10.82</td>\n",
       "      <td>10.889895</td>\n",
       "      <td>1.006460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Daphne Hasenjager</td>\n",
       "      <td>1952</td>\n",
       "      <td>S</td>\n",
       "      <td>11.80</td>\n",
       "      <td>11.869629</td>\n",
       "      <td>1.005901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Renate Stecher</td>\n",
       "      <td>1972</td>\n",
       "      <td>G</td>\n",
       "      <td>11.07</td>\n",
       "      <td>11.128854</td>\n",
       "      <td>1.005317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Elizabeth Robinson</td>\n",
       "      <td>1928</td>\n",
       "      <td>G</td>\n",
       "      <td>12.20</td>\n",
       "      <td>12.261933</td>\n",
       "      <td>1.005076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Shelly-Ann Fraser-Pryce</td>\n",
       "      <td>2016</td>\n",
       "      <td>B</td>\n",
       "      <td>10.86</td>\n",
       "      <td>10.895079</td>\n",
       "      <td>1.003230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Merlene Ottey</td>\n",
       "      <td>1996</td>\n",
       "      <td>S</td>\n",
       "      <td>10.94</td>\n",
       "      <td>10.962945</td>\n",
       "      <td>1.002097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name  Year Medal  Result  expectation     ratio\n",
       "0             Wilma Rudolph  1960     G   11.00    11.334702  1.030427\n",
       "1          Giuseppina Leone  1960     B   11.30    11.527010  1.020089\n",
       "2             Dorothy Hyman  1960     S   11.30    11.498129  1.017534\n",
       "3               Wyomia Tyus  1968     G   11.00    11.191274  1.017389\n",
       "4        Irena Kirszenstein  1968     B   11.10    11.289746  1.017094\n",
       "5   Shelly-Ann Fraser-Pryce  2020     S   10.74    10.909359  1.015769\n",
       "6           Barbara Ferrell  1968     S   11.10    11.267888  1.015125\n",
       "7           Juliet Cuthbert  1992     S   10.83    10.981950  1.014031\n",
       "8           Carmelita Jeter  2012     S   10.78    10.919630  1.012953\n",
       "9           Irina Privalova  1992     B   10.84    10.977443  1.012679\n",
       "10         Shericka Jackson  2020     B   10.76    10.889202  1.012008\n",
       "11  Veronica Campbell-Brown  2012     B   10.81    10.902417  1.008549\n",
       "12    Elaine Thompson-Herah  2020     G   10.61    10.695435  1.008052\n",
       "13               Tori Bowie  2016     S   10.83    10.913883  1.007745\n",
       "14              Gail Devers  1992     G   10.82    10.889895  1.006460\n",
       "15        Daphne Hasenjager  1952     S   11.80    11.869629  1.005901\n",
       "16           Renate Stecher  1972     G   11.07    11.128854  1.005317\n",
       "17       Elizabeth Robinson  1928     G   12.20    12.261933  1.005076\n",
       "18  Shelly-Ann Fraser-Pryce  2016     B   10.86    10.895079  1.003230\n",
       "19            Merlene Ottey  1996     S   10.94    10.962945  1.002097"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '100M Women', fitted_params, exclude_years=[1932, 1936])\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1b7ec469",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "                     Name  Year Medal  Result  expectation     ratio\n",
      "0           Wilma Rudolph  1960     G   11.00    11.334702  1.030427\n",
      "3             Wyomia Tyus  1968     G   11.00    11.191274  1.017389\n",
      "12  Elaine Thompson-Herah  2020     G   10.61    10.695435  1.008052\n",
      "14            Gail Devers  1992     G   10.82    10.889895  1.006460\n",
      "16         Renate Stecher  1972     G   11.07    11.128854  1.005317\n",
      "17     Elizabeth Robinson  1928     G   12.20    12.261933  1.005076\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                       Name  Year Medal  Result  expectation     ratio\n",
      "2             Dorothy Hyman  1960     S   11.30    11.498129  1.017534\n",
      "5   Shelly-Ann Fraser-Pryce  2020     S   10.74    10.909359  1.015769\n",
      "6           Barbara Ferrell  1968     S   11.10    11.267888  1.015125\n",
      "7           Juliet Cuthbert  1992     S   10.83    10.981950  1.014031\n",
      "8           Carmelita Jeter  2012     S   10.78    10.919630  1.012953\n",
      "13               Tori Bowie  2016     S   10.83    10.913883  1.007745\n",
      "15        Daphne Hasenjager  1952     S   11.80    11.869629  1.005901\n",
      "19            Merlene Ottey  1996     S   10.94    10.962945  1.002097\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                       Name  Year Medal  Result  expectation     ratio\n",
      "1          Giuseppina Leone  1960     B   11.30    11.527010  1.020089\n",
      "4        Irena Kirszenstein  1968     B   11.10    11.289746  1.017094\n",
      "9           Irina Privalova  1992     B   10.84    10.977443  1.012679\n",
      "10         Shericka Jackson  2020     B   10.76    10.889202  1.012008\n",
      "11  Veronica Campbell-Brown  2012     B   10.81    10.902417  1.008549\n",
      "18  Shelly-Ann Fraser-Pryce  2016     B   10.86    10.895079  1.003230\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '100M Women', top_n=10, exclude_years = [1932, 1936])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c83e88",
   "metadata": {},
   "source": [
    "## Analysis and Visualization for 200m Men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bce4f636",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 200M Men in 2024:\n",
      "Gold Medal: 19.37 seconds\n",
      "Silver Medal: 19.63 seconds\n",
      "Bronze Medal: 19.79 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '200M Men', exponential_func)\n",
    "predict_event_results('200M Men', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "472d0402",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Archie Hahn</td>\n",
       "      <td>1904</td>\n",
       "      <td>G</td>\n",
       "      <td>21.60</td>\n",
       "      <td>22.189015</td>\n",
       "      <td>1.027269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>John Carlos</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.536355</td>\n",
       "      <td>1.026818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tommie Smith</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>19.80</td>\n",
       "      <td>20.306726</td>\n",
       "      <td>1.025592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Michael Johnson</td>\n",
       "      <td>1996</td>\n",
       "      <td>G</td>\n",
       "      <td>19.32</td>\n",
       "      <td>19.775207</td>\n",
       "      <td>1.023561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nate Cartmell</td>\n",
       "      <td>1904</td>\n",
       "      <td>S</td>\n",
       "      <td>21.90</td>\n",
       "      <td>22.389290</td>\n",
       "      <td>1.022342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Peter Norman</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.440992</td>\n",
       "      <td>1.022050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jesse Owens</td>\n",
       "      <td>1936</td>\n",
       "      <td>G</td>\n",
       "      <td>20.70</td>\n",
       "      <td>21.109420</td>\n",
       "      <td>1.019779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yohan Blake</td>\n",
       "      <td>2012</td>\n",
       "      <td>S</td>\n",
       "      <td>19.44</td>\n",
       "      <td>19.758058</td>\n",
       "      <td>1.016361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>19.30</td>\n",
       "      <td>19.586362</td>\n",
       "      <td>1.014837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Frank Fredericks</td>\n",
       "      <td>1996</td>\n",
       "      <td>S</td>\n",
       "      <td>19.68</td>\n",
       "      <td>19.965154</td>\n",
       "      <td>1.014490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ato Boldon</td>\n",
       "      <td>1996</td>\n",
       "      <td>B</td>\n",
       "      <td>19.80</td>\n",
       "      <td>20.085714</td>\n",
       "      <td>1.014430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>William Applegarth</td>\n",
       "      <td>1912</td>\n",
       "      <td>B</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.313226</td>\n",
       "      <td>1.014238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Donald Lippincott</td>\n",
       "      <td>1912</td>\n",
       "      <td>S</td>\n",
       "      <td>21.80</td>\n",
       "      <td>22.054788</td>\n",
       "      <td>1.011688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Valery Borzov</td>\n",
       "      <td>1972</td>\n",
       "      <td>G</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.222102</td>\n",
       "      <td>1.011105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2012</td>\n",
       "      <td>G</td>\n",
       "      <td>19.32</td>\n",
       "      <td>19.527936</td>\n",
       "      <td>1.010763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Kirk Baptiste</td>\n",
       "      <td>1984</td>\n",
       "      <td>S</td>\n",
       "      <td>19.96</td>\n",
       "      <td>20.149346</td>\n",
       "      <td>1.009486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Carl Lewis</td>\n",
       "      <td>1984</td>\n",
       "      <td>G</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.986250</td>\n",
       "      <td>1.009407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ralph Craig</td>\n",
       "      <td>1912</td>\n",
       "      <td>G</td>\n",
       "      <td>21.70</td>\n",
       "      <td>21.888422</td>\n",
       "      <td>1.008683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Larry Black</td>\n",
       "      <td>1972</td>\n",
       "      <td>S</td>\n",
       "      <td>20.19</td>\n",
       "      <td>20.362648</td>\n",
       "      <td>1.008551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pietro Mennea</td>\n",
       "      <td>1972</td>\n",
       "      <td>B</td>\n",
       "      <td>20.30</td>\n",
       "      <td>20.459327</td>\n",
       "      <td>1.007849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Year Medal  Result  expectation     ratio\n",
       "0          Archie Hahn  1904     G   21.60    22.189015  1.027269\n",
       "1          John Carlos  1968     B   20.00    20.536355  1.026818\n",
       "2         Tommie Smith  1968     G   19.80    20.306726  1.025592\n",
       "3      Michael Johnson  1996     G   19.32    19.775207  1.023561\n",
       "4        Nate Cartmell  1904     S   21.90    22.389290  1.022342\n",
       "5         Peter Norman  1968     S   20.00    20.440992  1.022050\n",
       "6          Jesse Owens  1936     G   20.70    21.109420  1.019779\n",
       "7          Yohan Blake  2012     S   19.44    19.758058  1.016361\n",
       "8           Usain Bolt  2008     G   19.30    19.586362  1.014837\n",
       "9     Frank Fredericks  1996     S   19.68    19.965154  1.014490\n",
       "10          Ato Boldon  1996     B   19.80    20.085714  1.014430\n",
       "11  William Applegarth  1912     B   22.00    22.313226  1.014238\n",
       "12   Donald Lippincott  1912     S   21.80    22.054788  1.011688\n",
       "13       Valery Borzov  1972     G   20.00    20.222102  1.011105\n",
       "14          Usain Bolt  2012     G   19.32    19.527936  1.010763\n",
       "15       Kirk Baptiste  1984     S   19.96    20.149346  1.009486\n",
       "16          Carl Lewis  1984     G   19.80    19.986250  1.009407\n",
       "17         Ralph Craig  1912     G   21.70    21.888422  1.008683\n",
       "18         Larry Black  1972     S   20.19    20.362648  1.008551\n",
       "19       Pietro Mennea  1972     B   20.30    20.459327  1.007849"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '200M Men', fitted_params)\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "65019f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "               Name  Year Medal  Result  expectation     ratio\n",
      "0       Archie Hahn  1904     G   21.60    22.189015  1.027269\n",
      "2      Tommie Smith  1968     G   19.80    20.306726  1.025592\n",
      "3   Michael Johnson  1996     G   19.32    19.775207  1.023561\n",
      "6       Jesse Owens  1936     G   20.70    21.109420  1.019779\n",
      "8        Usain Bolt  2008     G   19.30    19.586362  1.014837\n",
      "13    Valery Borzov  1972     G   20.00    20.222102  1.011105\n",
      "14       Usain Bolt  2012     G   19.32    19.527936  1.010763\n",
      "16       Carl Lewis  1984     G   19.80    19.986250  1.009407\n",
      "17      Ralph Craig  1912     G   21.70    21.888422  1.008683\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                 Name  Year Medal  Result  expectation     ratio\n",
      "4       Nate Cartmell  1904     S   21.90    22.389290  1.022342\n",
      "5        Peter Norman  1968     S   20.00    20.440992  1.022050\n",
      "7         Yohan Blake  2012     S   19.44    19.758058  1.016361\n",
      "9    Frank Fredericks  1996     S   19.68    19.965154  1.014490\n",
      "12  Donald Lippincott  1912     S   21.80    22.054788  1.011688\n",
      "15      Kirk Baptiste  1984     S   19.96    20.149346  1.009486\n",
      "18        Larry Black  1972     S   20.19    20.362648  1.008551\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                  Name  Year Medal  Result  expectation     ratio\n",
      "1          John Carlos  1968     B    20.0    20.536355  1.026818\n",
      "10          Ato Boldon  1996     B    19.8    20.085714  1.014430\n",
      "11  William Applegarth  1912     B    22.0    22.313226  1.014238\n",
      "19       Pietro Mennea  1972     B    20.3    20.459327  1.007849\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '200M Men', top_n=10, exclude_years=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "22aab324",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Carlos</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.536355</td>\n",
       "      <td>1.026818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tommie Smith</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>19.80</td>\n",
       "      <td>20.306726</td>\n",
       "      <td>1.025592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Michael Johnson</td>\n",
       "      <td>1996</td>\n",
       "      <td>G</td>\n",
       "      <td>19.32</td>\n",
       "      <td>19.775207</td>\n",
       "      <td>1.023561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Peter Norman</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.440992</td>\n",
       "      <td>1.022050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jesse Owens</td>\n",
       "      <td>1936</td>\n",
       "      <td>G</td>\n",
       "      <td>20.70</td>\n",
       "      <td>21.109420</td>\n",
       "      <td>1.019779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Yohan Blake</td>\n",
       "      <td>2012</td>\n",
       "      <td>S</td>\n",
       "      <td>19.44</td>\n",
       "      <td>19.758058</td>\n",
       "      <td>1.016361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2008</td>\n",
       "      <td>G</td>\n",
       "      <td>19.30</td>\n",
       "      <td>19.586362</td>\n",
       "      <td>1.014837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Frank Fredericks</td>\n",
       "      <td>1996</td>\n",
       "      <td>S</td>\n",
       "      <td>19.68</td>\n",
       "      <td>19.965154</td>\n",
       "      <td>1.014490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ato Boldon</td>\n",
       "      <td>1996</td>\n",
       "      <td>B</td>\n",
       "      <td>19.80</td>\n",
       "      <td>20.085714</td>\n",
       "      <td>1.014430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>William Applegarth</td>\n",
       "      <td>1912</td>\n",
       "      <td>B</td>\n",
       "      <td>22.00</td>\n",
       "      <td>22.313226</td>\n",
       "      <td>1.014238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Donald Lippincott</td>\n",
       "      <td>1912</td>\n",
       "      <td>S</td>\n",
       "      <td>21.80</td>\n",
       "      <td>22.054788</td>\n",
       "      <td>1.011688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Valery Borzov</td>\n",
       "      <td>1972</td>\n",
       "      <td>G</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.222102</td>\n",
       "      <td>1.011105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Usain Bolt</td>\n",
       "      <td>2012</td>\n",
       "      <td>G</td>\n",
       "      <td>19.32</td>\n",
       "      <td>19.527936</td>\n",
       "      <td>1.010763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kirk Baptiste</td>\n",
       "      <td>1984</td>\n",
       "      <td>S</td>\n",
       "      <td>19.96</td>\n",
       "      <td>20.149346</td>\n",
       "      <td>1.009486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Carl Lewis</td>\n",
       "      <td>1984</td>\n",
       "      <td>G</td>\n",
       "      <td>19.80</td>\n",
       "      <td>19.986250</td>\n",
       "      <td>1.009407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ralph Craig</td>\n",
       "      <td>1912</td>\n",
       "      <td>G</td>\n",
       "      <td>21.70</td>\n",
       "      <td>21.888422</td>\n",
       "      <td>1.008683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Larry Black</td>\n",
       "      <td>1972</td>\n",
       "      <td>S</td>\n",
       "      <td>20.19</td>\n",
       "      <td>20.362648</td>\n",
       "      <td>1.008551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pietro Mennea</td>\n",
       "      <td>1972</td>\n",
       "      <td>B</td>\n",
       "      <td>20.30</td>\n",
       "      <td>20.459327</td>\n",
       "      <td>1.007849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Matthew Mackenzie Robinson</td>\n",
       "      <td>1936</td>\n",
       "      <td>S</td>\n",
       "      <td>21.10</td>\n",
       "      <td>21.226959</td>\n",
       "      <td>1.006017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>James Gathers</td>\n",
       "      <td>1952</td>\n",
       "      <td>B</td>\n",
       "      <td>20.80</td>\n",
       "      <td>20.897356</td>\n",
       "      <td>1.004681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name  Year Medal  Result  expectation     ratio\n",
       "0                  John Carlos  1968     B   20.00    20.536355  1.026818\n",
       "1                 Tommie Smith  1968     G   19.80    20.306726  1.025592\n",
       "2              Michael Johnson  1996     G   19.32    19.775207  1.023561\n",
       "3                 Peter Norman  1968     S   20.00    20.440992  1.022050\n",
       "4                  Jesse Owens  1936     G   20.70    21.109420  1.019779\n",
       "5                  Yohan Blake  2012     S   19.44    19.758058  1.016361\n",
       "6                   Usain Bolt  2008     G   19.30    19.586362  1.014837\n",
       "7             Frank Fredericks  1996     S   19.68    19.965154  1.014490\n",
       "8                   Ato Boldon  1996     B   19.80    20.085714  1.014430\n",
       "9           William Applegarth  1912     B   22.00    22.313226  1.014238\n",
       "10           Donald Lippincott  1912     S   21.80    22.054788  1.011688\n",
       "11               Valery Borzov  1972     G   20.00    20.222102  1.011105\n",
       "12                  Usain Bolt  2012     G   19.32    19.527936  1.010763\n",
       "13               Kirk Baptiste  1984     S   19.96    20.149346  1.009486\n",
       "14                  Carl Lewis  1984     G   19.80    19.986250  1.009407\n",
       "15                 Ralph Craig  1912     G   21.70    21.888422  1.008683\n",
       "16                 Larry Black  1972     S   20.19    20.362648  1.008551\n",
       "17               Pietro Mennea  1972     B   20.30    20.459327  1.007849\n",
       "18  Matthew Mackenzie Robinson  1936     S   21.10    21.226959  1.006017\n",
       "19               James Gathers  1952     B   20.80    20.897356  1.004681"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '200M Men', fitted_params, exclude_years=[1900, 1904])\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56c56bd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "               Name  Year Medal  Result  expectation     ratio\n",
      "1   Michael Johnson  1996     G   19.32    19.762365  1.022897\n",
      "2      Tommie Smith  1968     G   19.80    20.242322  1.022340\n",
      "3       Ralph Craig  1912     G   21.70    22.175042  1.021891\n",
      "5       Jesse Owens  1936     G   20.70    21.132809  1.020909\n",
      "8        Usain Bolt  2008     G   19.30    19.614216  1.016281\n",
      "12       Usain Bolt  2012     G   19.32    19.570849  1.012984\n",
      "14    Valery Borzov  1972     G   20.00    20.159861  1.007993\n",
      "17       Carl Lewis  1984     G   19.80    19.942014  1.007172\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                          Name  Year Medal  Result  expectation     ratio\n",
      "4                 Peter Norman  1968     S   20.00    20.418287  1.020914\n",
      "6            Donald Lippincott  1912     S   21.80    22.192058  1.017984\n",
      "7                  Yohan Blake  2012     S   19.44    19.771608  1.017058\n",
      "11            Frank Fredericks  1996     S   19.68    19.958862  1.014170\n",
      "13               Kirk Baptiste  1984     S   19.96    20.132252  1.008630\n",
      "16                 Larry Black  1972     S   20.19    20.340162  1.007437\n",
      "18  Matthew Mackenzie Robinson  1936     S   21.10    21.246853  1.006960\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                  Name  Year Medal  Result  expectation     ratio\n",
      "0          John Carlos  1968     B   20.00    20.528845  1.026442\n",
      "9   William Applegarth  1912     B   22.00    22.347251  1.015784\n",
      "10          Ato Boldon  1996     B   19.80    20.084941  1.014391\n",
      "15       Pietro Mennea  1972     B   20.30    20.452261  1.007501\n",
      "19          Noah Lyles  2020     B   19.74    19.837406  1.004934\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '200M Men', top_n=10, exclude_years=[1900, 1904])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "646d169f",
   "metadata": {},
   "source": [
    "## Analysis and Visualization for 200m Women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eae837f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted results for 200M Women in 2024:\n",
      "Gold Medal: 21.73 seconds\n",
      "Silver Medal: 21.92 seconds\n",
      "Bronze Medal: 22.01 seconds\n"
     ]
    }
   ],
   "source": [
    "fitted_params, labels = plot_sprint_results(df, '200M Women', exponential_func)\n",
    "predict_event_results('200M Women', 2024, fitted_params, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "221089d2",
   "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>Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Medal</th>\n",
       "      <th>Result</th>\n",
       "      <th>expectation</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Valerie Brisco</td>\n",
       "      <td>1984</td>\n",
       "      <td>G</td>\n",
       "      <td>21.81</td>\n",
       "      <td>22.147644</td>\n",
       "      <td>1.015481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nadezhda Khnykina</td>\n",
       "      <td>1952</td>\n",
       "      <td>B</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.515551</td>\n",
       "      <td>1.013039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Merlene Ottey</td>\n",
       "      <td>1984</td>\n",
       "      <td>B</td>\n",
       "      <td>22.09</td>\n",
       "      <td>22.377247</td>\n",
       "      <td>1.013003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Merlene Ottey</td>\n",
       "      <td>1980</td>\n",
       "      <td>B</td>\n",
       "      <td>22.20</td>\n",
       "      <td>22.481491</td>\n",
       "      <td>1.012680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Florence GRIFFITH Joyner</td>\n",
       "      <td>1984</td>\n",
       "      <td>S</td>\n",
       "      <td>22.04</td>\n",
       "      <td>22.266887</td>\n",
       "      <td>1.010294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Elaine Thompson-Herah</td>\n",
       "      <td>2020</td>\n",
       "      <td>G</td>\n",
       "      <td>21.53</td>\n",
       "      <td>21.747043</td>\n",
       "      <td>1.010081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BÃ¤rbel Eckert-WãCkel</td>\n",
       "      <td>1980</td>\n",
       "      <td>G</td>\n",
       "      <td>22.03</td>\n",
       "      <td>22.250845</td>\n",
       "      <td>1.010025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marjorie Jackson</td>\n",
       "      <td>1952</td>\n",
       "      <td>G</td>\n",
       "      <td>23.70</td>\n",
       "      <td>23.927064</td>\n",
       "      <td>1.009581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Raelene Ann Boyle</td>\n",
       "      <td>1972</td>\n",
       "      <td>S</td>\n",
       "      <td>22.45</td>\n",
       "      <td>22.662571</td>\n",
       "      <td>1.009469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Irena Kirszenstein</td>\n",
       "      <td>1968</td>\n",
       "      <td>G</td>\n",
       "      <td>22.50</td>\n",
       "      <td>22.711212</td>\n",
       "      <td>1.009387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bertha Brouwer</td>\n",
       "      <td>1952</td>\n",
       "      <td>S</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.420850</td>\n",
       "      <td>1.009126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gwen Torrence</td>\n",
       "      <td>1992</td>\n",
       "      <td>G</td>\n",
       "      <td>21.81</td>\n",
       "      <td>21.992229</td>\n",
       "      <td>1.008355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Jennifer Lamy</td>\n",
       "      <td>1968</td>\n",
       "      <td>B</td>\n",
       "      <td>22.80</td>\n",
       "      <td>22.986010</td>\n",
       "      <td>1.008158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Marlene Mathews-Willard</td>\n",
       "      <td>1956</td>\n",
       "      <td>B</td>\n",
       "      <td>23.80</td>\n",
       "      <td>23.992853</td>\n",
       "      <td>1.008103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Natalia Bochina</td>\n",
       "      <td>1980</td>\n",
       "      <td>S</td>\n",
       "      <td>22.19</td>\n",
       "      <td>22.369145</td>\n",
       "      <td>1.008073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Christa Stubnick</td>\n",
       "      <td>1956</td>\n",
       "      <td>S</td>\n",
       "      <td>23.70</td>\n",
       "      <td>23.887250</td>\n",
       "      <td>1.007901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Raelene Ann Boyle</td>\n",
       "      <td>1968</td>\n",
       "      <td>S</td>\n",
       "      <td>22.70</td>\n",
       "      <td>22.870160</td>\n",
       "      <td>1.007496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Gabrielle Thomas</td>\n",
       "      <td>2020</td>\n",
       "      <td>B</td>\n",
       "      <td>21.87</td>\n",
       "      <td>22.025409</td>\n",
       "      <td>1.007106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Marilyn Black</td>\n",
       "      <td>1964</td>\n",
       "      <td>B</td>\n",
       "      <td>23.10</td>\n",
       "      <td>23.247928</td>\n",
       "      <td>1.006404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Renate Stecher</td>\n",
       "      <td>1976</td>\n",
       "      <td>B</td>\n",
       "      <td>22.47</td>\n",
       "      <td>22.612736</td>\n",
       "      <td>1.006352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Year Medal  Result  expectation     ratio\n",
       "0             Valerie Brisco  1984     G   21.81    22.147644  1.015481\n",
       "1          Nadezhda Khnykina  1952     B   24.20    24.515551  1.013039\n",
       "2              Merlene Ottey  1984     B   22.09    22.377247  1.013003\n",
       "3              Merlene Ottey  1980     B   22.20    22.481491  1.012680\n",
       "4   Florence GRIFFITH Joyner  1984     S   22.04    22.266887  1.010294\n",
       "5      Elaine Thompson-Herah  2020     G   21.53    21.747043  1.010081\n",
       "6     BÃ¤rbel Eckert-WãCkel  1980     G   22.03    22.250845  1.010025\n",
       "7           Marjorie Jackson  1952     G   23.70    23.927064  1.009581\n",
       "8          Raelene Ann Boyle  1972     S   22.45    22.662571  1.009469\n",
       "9         Irena Kirszenstein  1968     G   22.50    22.711212  1.009387\n",
       "10            Bertha Brouwer  1952     S   24.20    24.420850  1.009126\n",
       "11             Gwen Torrence  1992     G   21.81    21.992229  1.008355\n",
       "12             Jennifer Lamy  1968     B   22.80    22.986010  1.008158\n",
       "13   Marlene Mathews-Willard  1956     B   23.80    23.992853  1.008103\n",
       "14           Natalia Bochina  1980     S   22.19    22.369145  1.008073\n",
       "15          Christa Stubnick  1956     S   23.70    23.887250  1.007901\n",
       "16         Raelene Ann Boyle  1968     S   22.70    22.870160  1.007496\n",
       "17          Gabrielle Thomas  2020     B   21.87    22.025409  1.007106\n",
       "18             Marilyn Black  1964     B   23.10    23.247928  1.006404\n",
       "19            Renate Stecher  1976     B   22.47    22.612736  1.006352"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_performers = calculate_top_performers(df, '200M Women', fitted_params)\n",
    "top_performers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5346a5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Gold Medalists:\n",
      "                      Name  Year Medal  Result  expectation     ratio\n",
      "0           Valerie Brisco  1984     G   21.81    22.147644  1.015481\n",
      "5    Elaine Thompson-Herah  2020     G   21.53    21.747043  1.010081\n",
      "6   BÃ¤rbel Eckert-WãCkel  1980     G   22.03    22.250845  1.010025\n",
      "7         Marjorie Jackson  1952     G   23.70    23.927064  1.009581\n",
      "9       Irena Kirszenstein  1968     G   22.50    22.711212  1.009387\n",
      "11           Gwen Torrence  1992     G   21.81    21.992229  1.008355\n",
      "\n",
      "\n",
      "Top 10 Silver Medalists:\n",
      "                        Name  Year Medal  Result  expectation     ratio\n",
      "4   Florence GRIFFITH Joyner  1984     S   22.04    22.266887  1.010294\n",
      "8          Raelene Ann Boyle  1972     S   22.45    22.662571  1.009469\n",
      "10            Bertha Brouwer  1952     S   24.20    24.420850  1.009126\n",
      "14           Natalia Bochina  1980     S   22.19    22.369145  1.008073\n",
      "15          Christa Stubnick  1956     S   23.70    23.887250  1.007901\n",
      "16         Raelene Ann Boyle  1968     S   22.70    22.870160  1.007496\n",
      "\n",
      "\n",
      "Top 10 Bronze Medalists:\n",
      "                       Name  Year Medal  Result  expectation     ratio\n",
      "1         Nadezhda Khnykina  1952     B   24.20    24.515551  1.013039\n",
      "2             Merlene Ottey  1984     B   22.09    22.377247  1.013003\n",
      "3             Merlene Ottey  1980     B   22.20    22.481491  1.012680\n",
      "12            Jennifer Lamy  1968     B   22.80    22.986010  1.008158\n",
      "13  Marlene Mathews-Willard  1956     B   23.80    23.992853  1.008103\n",
      "17         Gabrielle Thomas  2020     B   21.87    22.025409  1.007106\n",
      "18            Marilyn Black  1964     B   23.10    23.247928  1.006404\n",
      "19           Renate Stecher  1976     B   22.47    22.612736  1.006352\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "top_performers = get_top_performers_by_medal(df, '200M Women', top_n=10, exclude_years=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e78205",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af4e010",
   "metadata": {},
   "source": [
    "### Overall Model\n",
    "Based on the historical data, the model performed pretty well in terms of ranking which athletes overperformed their predicted result. In all four events, the model did favor results from the early and mid 1900s, which may be because there was no data or races prior to those dates.\n",
    "\n",
    "It's really interesting to note that the sprinters that overperformed their expected result the most weren't gold medalists, but silver and bronze medalists. My thinking is that even though some gold medalists like Usain Bolt or Michael Johnson ran all-time great races, it was the sprinters behind them that were trying to catch these legends that overperformed *more* than those running from the front."
   ]
  }
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