4_EDA_time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xXYQlI6ZZlLl"
   },
   "source": [
    "# EDA of Accident Data (2013-2017)\n",
    "In this notebook we will be looking at the accident data that we previously cleaned in order to explore further into traffic accidents that happened in the UK from 2013 to 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JJn0DkgjZlLo"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import urllib, os\n",
    "import glob\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder, StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, auc, roc_curve, classification_report\n",
    "import scipy.stats as stats \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WJRllLqhZlL5"
   },
   "source": [
    "# Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>Accident_Index</th>\n",
       "      <th>Location_Easting_OSGR</th>\n",
       "      <th>Location_Northing_OSGR</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Accident_Severity</th>\n",
       "      <th>Number_of_Vehicles</th>\n",
       "      <th>Number_of_Casualties</th>\n",
       "      <th>Date</th>\n",
       "      <th>Day_of_Week</th>\n",
       "      <th>...</th>\n",
       "      <th>Urban_or_Rural_Area</th>\n",
       "      <th>LSOA_of_Accident_Location</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Two_Hour_Groupings</th>\n",
       "      <th>Time_of_Day</th>\n",
       "      <th>Was_Daylight</th>\n",
       "      <th>Was_Bad_Weather</th>\n",
       "      <th>Was_Road_Dry</th>\n",
       "      <th>log_Number_of_Casualties</th>\n",
       "      <th>log_Number_of_Vehicles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201501BS70001</td>\n",
       "      <td>525130.0</td>\n",
       "      <td>180050.0</td>\n",
       "      <td>-0.198465</td>\n",
       "      <td>51.505538</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>Monday</td>\n",
       "      <td>...</td>\n",
       "      <td>Urban</td>\n",
       "      <td>E01002825</td>\n",
       "      <td>18</td>\n",
       "      <td>6pm-8pm</td>\n",
       "      <td>Rush hour</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201501BS70002</td>\n",
       "      <td>526530.0</td>\n",
       "      <td>178560.0</td>\n",
       "      <td>-0.178838</td>\n",
       "      <td>51.491836</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>Monday</td>\n",
       "      <td>...</td>\n",
       "      <td>Urban</td>\n",
       "      <td>E01002820</td>\n",
       "      <td>7</td>\n",
       "      <td>6am-8am</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201501BS70004</td>\n",
       "      <td>524610.0</td>\n",
       "      <td>181080.0</td>\n",
       "      <td>-0.205590</td>\n",
       "      <td>51.514910</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>Monday</td>\n",
       "      <td>...</td>\n",
       "      <td>Urban</td>\n",
       "      <td>E01002833</td>\n",
       "      <td>18</td>\n",
       "      <td>6pm-8pm</td>\n",
       "      <td>Rush hour</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201501BS70005</td>\n",
       "      <td>524420.0</td>\n",
       "      <td>181080.0</td>\n",
       "      <td>-0.208327</td>\n",
       "      <td>51.514952</td>\n",
       "      <td>Slight</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>...</td>\n",
       "      <td>Urban</td>\n",
       "      <td>E01002874</td>\n",
       "      <td>7</td>\n",
       "      <td>6am-8am</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201501BS70008</td>\n",
       "      <td>524630.0</td>\n",
       "      <td>179040.0</td>\n",
       "      <td>-0.206022</td>\n",
       "      <td>51.496572</td>\n",
       "      <td>Serious</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>Friday</td>\n",
       "      <td>...</td>\n",
       "      <td>Urban</td>\n",
       "      <td>E01002814</td>\n",
       "      <td>7</td>\n",
       "      <td>6am-8am</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.693147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Location_Easting_OSGR  Location_Northing_OSGR  Longitude  \\\n",
       "0  201501BS70001               525130.0                180050.0  -0.198465   \n",
       "1  201501BS70002               526530.0                178560.0  -0.178838   \n",
       "2  201501BS70004               524610.0                181080.0  -0.205590   \n",
       "3  201501BS70005               524420.0                181080.0  -0.208327   \n",
       "4  201501BS70008               524630.0                179040.0  -0.206022   \n",
       "\n",
       "    Latitude Accident_Severity  Number_of_Vehicles  Number_of_Casualties  \\\n",
       "0  51.505538            Slight                   1                     1   \n",
       "1  51.491836            Slight                   1                     1   \n",
       "2  51.514910            Slight                   1                     1   \n",
       "3  51.514952            Slight                   1                     1   \n",
       "4  51.496572           Serious                   2                     1   \n",
       "\n",
       "         Date Day_of_Week          ...           Urban_or_Rural_Area  \\\n",
       "0  2015-01-12      Monday          ...                         Urban   \n",
       "1  2015-01-12      Monday          ...                         Urban   \n",
       "2  2015-01-12      Monday          ...                         Urban   \n",
       "3  2015-01-13     Tuesday          ...                         Urban   \n",
       "4  2015-01-09      Friday          ...                         Urban   \n",
       "\n",
       "  LSOA_of_Accident_Location  Hour Two_Hour_Groupings Time_of_Day Was_Daylight  \\\n",
       "0                 E01002825    18            6pm-8pm   Rush hour           No   \n",
       "1                 E01002820     7            6am-8am     Morning          Yes   \n",
       "2                 E01002833    18            6pm-8pm   Rush hour           No   \n",
       "3                 E01002874     7            6am-8am     Morning          Yes   \n",
       "4                 E01002814     7            6am-8am     Morning          Yes   \n",
       "\n",
       "  Was_Bad_Weather Was_Road_Dry log_Number_of_Casualties log_Number_of_Vehicles  \n",
       "0              No          Yes                      0.0               0.000000  \n",
       "1              No          Yes                      0.0               0.000000  \n",
       "2             Yes           No                      0.0               0.000000  \n",
       "3              No           No                      0.0               0.000000  \n",
       "4             Yes           No                      0.0               0.693147  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import cleaned accident data from csv created at end of accident cleaning notebook\n",
    "accidents_df = pd.read_csv('data/Accidents/Cleaned_accidents.csv')\n",
    "accidents_df = accidents_df.drop('Unnamed: 0', axis=1)\n",
    "accidents_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 648205 entries, 0 to 648204\n",
      "Data columns (total 28 columns):\n",
      "Accident_Index               648205 non-null object\n",
      "Location_Easting_OSGR        648205 non-null float64\n",
      "Location_Northing_OSGR       648205 non-null float64\n",
      "Longitude                    648205 non-null float64\n",
      "Latitude                     648205 non-null float64\n",
      "Accident_Severity            648205 non-null object\n",
      "Number_of_Vehicles           648205 non-null int64\n",
      "Number_of_Casualties         648205 non-null int64\n",
      "Date                         648205 non-null object\n",
      "Day_of_Week                  648205 non-null object\n",
      "1st_Road_Class               648205 non-null object\n",
      "Road_Type                    648205 non-null object\n",
      "Speed_limit                  648205 non-null int64\n",
      "Junction_Detail              648205 non-null object\n",
      "2nd_Road_Class               648205 non-null object\n",
      "Light_Conditions             648205 non-null object\n",
      "Weather_Conditions           648205 non-null object\n",
      "Road_Surface_Conditions      648205 non-null object\n",
      "Urban_or_Rural_Area          648205 non-null object\n",
      "LSOA_of_Accident_Location    648205 non-null object\n",
      "Hour                         648205 non-null int64\n",
      "Two_Hour_Groupings           648205 non-null object\n",
      "Time_of_Day                  648205 non-null object\n",
      "Was_Daylight                 648205 non-null object\n",
      "Was_Bad_Weather              648205 non-null object\n",
      "Was_Road_Dry                 648205 non-null object\n",
      "log_Number_of_Casualties     648205 non-null float64\n",
      "log_Number_of_Vehicles       648205 non-null float64\n",
      "dtypes: float64(6), int64(4), object(18)\n",
      "memory usage: 138.5+ MB\n"
     ]
    }
   ],
   "source": [
    "accidents_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seems Date column saved as an object when should be datetime, so need to convert this\n",
    "# Change Date to datetime\n",
    "accidents_df['Date'] = pd.to_datetime(accidents_df['Date'], format='%Y/%m/%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accidents_df.isna().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a serious dataframe to later compare accidents that are serious and fatal to the whole dataset\n",
    "serious = accidents_df[accidents_df['Accident_Severity']!='Slight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set UK flag colors\n",
    "c_blue = '#00247d'\n",
    "p_red = '#cf142b'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the overall trend like for accidents in the UK from 2013-2017?\n",
    "Let's first take a look at accident totals by month and plot it as a time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "\n",
    "accidents_df.set_index('Date').resample('M').size().plot(label='Totals per Month', color='grey', ax=ax)\n",
    "# Use 10-months moving average which is similar to a popular 200 day moving average\n",
    "accidents_df.set_index('Date').resample('M').size().rolling(window=10).mean()\\\n",
    "                    .plot(color=c_blue, linewidth=5, label='10-Months Moving Average', ax=ax)\n",
    "ax.set_title('Accidents per Month', fontweight = 'bold', fontsize=16)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.legend(frameon=False, bbox_to_anchor=(1, 1))\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=False, left=False, right=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 2015, it seems that accidents have been steadily decreasing. We can confirm this with a Dickey Fuller Test, which is a statistical test that tests for stationarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  0.613133\n",
      "p-value                         0.987935\n",
      "#Lags Used                     11.000000\n",
      "Number of Observations Used    48.000000\n",
      "Critical Value (1%)            -3.574589\n",
      "Critical Value (5%)            -2.923954\n",
      "Critical Value (10%)           -2.600039\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "dftest = adfuller(accidents_df.set_index('Date').resample('M').size())\n",
    "\n",
    "# Extract and display test results in a user friendly manner\n",
    "dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value (%s)'%key] = value\n",
    "\n",
    "print ('Results of Dickey-Fuller Test:')\n",
    "\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the p-value is much greater than a typical critical value of 0.05, we can reject the null hypothesis that this time series is stationary meaning that there is a trend!\n",
    "\n",
    "<br>\n",
    "Now let's take a look at the accident totals by month for serious and fatal accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "\n",
    "serious.set_index('Date').resample('M').size().plot(label='Totals per Month', color='grey', ax=ax)\n",
    "# Use 10-months moving average which is similar to a popular 200 day moving average\n",
    "serious.set_index('Date').resample('M').size().rolling(window=10).mean()\\\n",
    "                    .plot(color=p_red, linewidth=5, label='10-Months Moving Average', ax=ax)\n",
    "ax.set_title('Serious and Fatal Accidents per Month', fontweight = 'bold', fontsize=16)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.legend(frameon=False, bbox_to_anchor=(1, 1))\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=False, left=False, right=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately it seems that serious and fatal accidents have been steadily increasing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  0.215999\n",
      "p-value                         0.973145\n",
      "#Lags Used                     11.000000\n",
      "Number of Observations Used    48.000000\n",
      "Critical Value (1%)            -3.574589\n",
      "Critical Value (5%)            -2.923954\n",
      "Critical Value (10%)           -2.600039\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dftest = adfuller(serious.set_index('Date').resample('M').size())\n",
    "\n",
    "# Extract and display test results in a user friendly manner\n",
    "dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value (%s)'%key] = value\n",
    "\n",
    "print ('Results of Dickey-Fuller Test:')\n",
    "\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And again we can confirm that this time series is not stationary.\n",
    "<br>\n",
    "Let's now look into these time series' further, we saw how there are obvious positive and negative trends, but there also seems to be some seasonality as well. Below we will decompose the time series to get a better sense of these trends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KoqQQQuecR1EURUkkoRm0OOrE/DepSVutKF80IcRKNAlPTkgpq5g7nuRECFEczYUZSzRZGKfpKRN14UntP0VJ4VTLl6IoiqIopvYTmtadykKI2nEV/hIIIVoLIW6iyXhoiabF+5Msh0KIwkAH7cuhSRehoiimYBV3EUVRFEVRlISTmsGvDU1t/6VIj2YsvWA044MNkFJ+Mii3tvurOl9TlM+E6naoKIqiKIqiKIqSBFS3Q0VRFEVRFEVRlCTwWTVjZ8mSRTo4OJg7DEVRFEVRFEVRvhBnz571k1LGOZA8fGaVLwcHBzw9Pc0dhqIoiqIoiqIoXwghxH1Dy6puh4qiKIqiKIqiKElAVb4URVEURVEURVGSgKp8KYqimNC7wFCmLjrBpetPzR2KoiiKoihm9lnd86UoipLczFvtyXdjdgJQ2ikH3VuXpnMLZ7JlTmvmyBRFURRFIywsDB8fH4KDg80dSrJmZ2dHnjx5sLa2TvA6PqtxvlxdXaVKuKEoSnJSse0cgkLC6dOhHIs2nOfclSdYWVnQpGYRurcuTdNaRbC1VdfBFEVRFPO5d+8e9vb2ZM6cGSGEucNJlqSUvHjxAn9/fwoUKPDRPCHEWSmlqyHrUd0OFSUaKSUvXweaOwzlM3Dn/ktOX3pEt5YuDPqqEmc3fY3XtoEM6V6Z05d8aPvNKnJVG883v23lzKVHfE4XwxRFUZSUIzg4WFW84iCEIHPmzIluHVSVL0WJZtrik2QuP5ZCdSbT66dNLN10gQePX5s7LCUFWrXNC4COTUu+n1aySHbG/9iQh4eHsd2jK/WrFsJjzTkqtJ1Dicb/Mm7OER49fWuukBVFUZQvlKp4xc0Y+0h1O1QUHRERkTjWm0oqOyuKFMjCodPevHoTBECBPBmpVdGBWhULUKuiA/lyZTBztEpyV7LJv2RMb8eRlb1jLff6bRBrtl9h8cbzHD/3EEtLC7bM6UzjmkWSKFJFURTlS3bt2jWcnJzMHUaKoG9fqW6HipJAu47c5p7PK0Z/W5uNM93wOz2CC/8NYMrPjXFxysHmfdfp/sMG8tecRMHak+kxYiOLN5zn4ZM35g5dSWYu33zGlVvP6dS0VJxlM6RLRd9Orhxb3YebewaTP1d6fpt+UHVDVBRFUb4oPj4+tGzZEkdHRwoVKsTgwYMJDQ39pNzjx49p165dnOtr0qQJr18nrPfS6NGjmTBhQoKWjY2qfCmKjhnLTpEja1pa1dNc0bCwsMDFKQeD3SuzcaYbvqdGcHHLAKaObEJppxz8t/867iM2UqjuFM5cemTm6JXkZOVWLywtLWjfuES8lnN0yMywXlU5ddGH4+cemCg6RVEURUlepJS0adOGVq1acevWLW7evElAQAA///zzR+XCw8PJlSsX69ati3Od27dvJ0OG5NVTSVW+FEXr7oOX7Dh8m74dXbG2ttRbxsLCAudiOfi2eyU2aCtjF/4bgJ2NFdOWnEziiJXkSkrJqq1e1K1cIEEp5d3blCZThlRMmH/cBNEpiqIoSvKzf/9+7Ozs6NGjBwCWlpZMnjyZBQsWMHPmTNq3b0/z5s1p0KAB3t7elCypuZ86MDCQDh064OzsTMeOHalYsSJRtyE5ODjg5+eHt7c3Tk5O9OnThxIlStCgQQOCgjS3lcybN4/y5cvj4uJC27ZtCQw0bdI1ld9YUbTmrPLEwkLQt6NBXXaBDy1j3Vq5MH/tOab83JjMGVObMEolJTh90Ye7D18xamCtBC2fOpUNX7uVZ8zsI9zyfoGjQ2bjBqgoiqIoMRjy53YuXHtq1HWWdsrBlJFNYi1z5coVypUr99G0dOnSkS9fPsLDwzlx4gSXLl0iU6ZMeHt7vy8zc+ZMMmbMyKVLl7h8+TKlS5fWu/5bt26xcuVK5s2bR4cOHVi/fj1du3alTZs29OnTB4CRI0cyf/58Bg0alLg3HAvV8qUoQHBIGPPXnaNVvWLkzpEu3sv36+RKSGg4SzZdMEF0iqHOXn7M/hN3zR0GK7d6YWNtSesGCb95+ZtuFbG2smDyQtX6pSiKonz+pJR6swlGTa9fvz6ZMmX6ZP7Ro0fp1KkTACVLlsTZ2Vnv+gsUKPC+YlauXLn3FbjLly9TvXp1SpUqxfLly7ly5YqR3pF+Zmv5EkIMBvoAApgnpZwSQ7nywEmgo5Qy7s6dSoryxj8YSwtB2jS2Zo1jzfYrvHgVyIAuFRK0vHOxHFQqnYc5qzwZ4l5ZpWtNYlJKpi46wfB/dmMhBNd2DqJgvk9/oJNCREQkq7dfpkmtIqS3t0vwenJktadrSxcWbbjAH0PqqhZVRVEUJUnE1UJlKiVKlGD9+vUfTXv79i0PHz7E0tKSNGnS6F3O0ORUtrYfzjUtLS3fdzt0d3dn06ZNuLi4sGjRIg4ePJiwN2Ags7R8CSFKoql4VQBcgGZCCEc95SyBccCupI1QSQpSSmp2XkDrASvNHQozl5+mWMEs1K5UIO7CMejvVp4bd/04dNrbeIEpcfIPCKHj4DV8N2YnDasVxsrKghHj95gtnsNnvHnqG4Bbs7izHMZlaM8qBAWHMWvFaSNEpiiKoijJV926dQkMDGTJkiUAREREMGzYMNzd3UmdOuYLkNWqVWPNmjUAXL16FS8vr3ht19/fn5w5cxIWFsby5csT/gYMZK5uh07ASSlloJQyHDgEtNZTbhCwHnielMEpSWPXkdtcvP6UvcfvcvWW+T7is5cfc+qiDwO6VEhUi1WHJiXJkM6OOSvVWHNJ5cqt55RvO4cNu6/xzw8N2DK3CyP6VGPdziscNlMleOVWL9KmsaFZ7cSP0VXCMRuNazoyfekpgkPCjBCdoiiKoiRPQgg2btzI2rVrcXR0pEiRItjZ2TFmzJhYlxswYAC+vr44Ozszbtw4nJ2dSZ8+vcHb/eOPP6hYsSL169enWLFiiX0bcTLLIMtCCCdgM1AZCAL2AZ5SykE6ZXIDK4A6wHxgq75uh0KIvkBfgHz58pW7f/++6d+AYhT1uy/C6+ZzXr0Jor9beaaOMk8zd6+fNrFqmxePjw1PVDcxgMF/bGfWyjP4HBmWoCx3iuFW/HeJPiM3Y5/GltVT2lOzoqbVMjAolKINppE9S1pOr++LhUXSXWMKDQ0nR5XxNKnpyLKJcY8/Yoh9x+9Qr/tiPMa0pFf7cnEvoBhNaGg4T3wDyJ87eaUpVhRzunbbl4Xrz/H39/WxtFSpAz4XKXmQ5YiICMLCwrCzs+POnTvUrVuXmzdvYmNjY5LtpchBlqWU19B0J9wD7AQuAuHRik0BRkgpI+JY11wppauU0jVr1qwmiVcxvovXNC1eQ9wr0a5RCRZvvMC7wE8H0TO1V2+CWLHlEl1buCS64gWaxBthYREsWn/eCNEp+oSGhjPo9210GbaOsiVycn7z1+8rXqDJFDj2+/qcvfyYpZsuJmlsu4/e4dWbIKN0OYxSp3JBSjvlYOL840RGRhptvUrswsMjaNZ3OcUaTuP5iwBzh6MoycYP/+xivMcxDp/xNncoigJoUs1Xq1YNFxcXWrduzaxZs0xW8TIGs12ykFLOl1KWlVLWAF4Ct6IVcQVWCSG8gXbATCFEqyQOUzGRSQuPkya1Df06lae/mytv/INZve1yksexaMN5gkPCE5xoI7rijtmo7pqfuavPqhNlE3j45A01Oi/g36WnGNarCvuX9CBnNvtPyrk1L0UF59z8b9JeAt6FJFl8K7d6kTF9KupXLWS0dQohGNarKtfu+LLz8G2jrVeJ3fBxu9lz7A7BIeFm+W1SlOTo6q3nbD1wE4B1O6+aORpF0bC3t8fT05OLFy9y6dIlGjdubO6QYmW2ypcQIpv2bz6gDfBR1gUpZQEppYOU0gFYBwyQUm5K8kAVo3v87C0rt3rRs20ZMqZPRTXX/JRwzMaslUmbVCAyMpJZK85QtVw+XJxyGG29/d3Kc+fBS/YdN3/K88/JnqO3KdNyFldv+7Juekcm/Ngo1sGwJ//cmMfP/Pln3tEkiS8wKJTN+67TrlFxbGyMm0i2Y5OS5M6ejokLjhl1vYp+C9edY8qiEwxxr0xppxws3Zy0LaiKklxNXHCcVHbW1K5UgPW7rhIRoS4yfk7McStSSmOMfWTOzrrrhRBXgS3AQCnlKyFEfyFEfzPGpCSBf5edIiIikiHulQHNlf3+buXx9HqMp9ejJItj77G73PJ+wYDOxmn1itK2YXEyZ0zNnFUq8YYxREZG8ueMgzTsuZScWdPiubEfbRuViHO5KmXz0bFpSSbMP87DJ29MHufWAzd5Fxhq1C6HUaytLRncvRL7T9zj/JUnRl+/8sHxcw/o/8sW6lctxPgRDejWyoUzlx5x466fuUNTFLN68tyfZZsv0qNtGfp1cuWZXwDHzj4wd1iKkdjZ2fHixQtVAYuFlJIXL15gZ5e421TMNs6XlLK6nmmzYyjrbvKAlCTxLjCU2Ss9aV3f6aNxmLq1cmHE+N3MXnkGj1K5kySWmStOkzVTGto2LG7U9draWuHepjRTF5/kyXN/vd3iFMO8fB1It+/Xs/3QLbq0cGbOHy1Ik9rwftzjhjdg057r/DRhj9ESYMRk5VYvcmazp0Z5B5Osv0/Hcvw+4yATFxwz+Xv5Uj188oY2A1eRL1d6Vk1pj5WVJW7NnBk+bjdLN13gz6H1zB2iopjNtCUnCY+IZGiPKmTPkgY7WyvW7rxCjQoO5g5NMYI8efLg4+ODr6+vuUNJ1uzs7MiTJ0+i1mG2ypfyZVq4/jyv3gQxrFeVj6ant7ejc3Nnlv93iQk/NiRDulQmjePB49ds2X+DEX2rYWtr/K9B346uTJx/nAXrzvHzgJpGX//nLiQknJVbvRg9/QCPn/szc3Qz+ncuH++hAPLnzsCwXlUYM+sw335ViQouifvBjMnrt0FsP3iTAV0qmCz7V4Z0qejdvhzTl57i7+/rkzen4Wl0lbgFBoXS6usVBAaFcWBpDzJl0IwpkzObPfWqFGTZf5f4fUidJM2eqSjJhX9ACLNWnKFNg+IUyq+5cNqkZhHW77rK1JGN1ffiM2BtbU2BAgkf61QxnPq2KEkmIiKSyQuPU6l0HiqXyffJ/P5u5QkKDkuSDHVRXQL7dSpvkvUXKZCFOpULMG/NWdUnPh78Xr7jzxkHyV9rEj1+3Eh6e1uOruzF14kYg+3HvtXJkTUtQ/7aYbLuFBt3XyM0LIJOTUuaZP1RBnevBMC0xSdNup0vjZSSXj9t5vzVp6yY1A6nwh9nzu3WqjT3H71WXayUL5bH2rO88Q9meO+q76e1a1ScJ8/9OX7uoRkjU5SUR1W+lCSzee917j58xbCeVfXOL1cyF+WdczNrxRmT9jkOCQnHY81ZmtUuatLxe/p1Ks/9R6/ZdURlqIvLjbt+fP3LFvLVnMSoKfspWyInexZ158J/AxLdWmWf1pY/v6vLifMPTZa1buVWLwrmzWiylrUoDnky0q5Rceau9uStf7BJt/UlGTvnCKu2efH3sHo0q1P0k/mt6zuRJrWNSryhfJHCwiKYvPAENcrn/+g3rlntotjaWLF2xxUzRqcoKY+qfClJZtLC4xTIk5HWDWIexO9rt/Jcu+PLkTOmGyx7w+6rPH/xjgFdTNPqFaVVvWJky5xGJd6IgZSSg6fu0bzvMoo1nMbC9efp0sKZy9u/YbtHN+pVLZTg1q7o3NuUobRTDkaM301QcJhR1hnl+YsA9p24S6dmpYwWb2y+71WVtwEheKw9a/JtfQm27LvOz5P20bm5Mz/0raa3TJrUNrRp4MSa7VcIDjHu8RMf3j6v+H7sTvJWn0CZFjPp9v16xs45zNb9N7j38JUa3kIxiTXbL/PwyRu+7/XxhVP7tLY0qlGY9buuqmNPUeJBVb6UJHHqwkOOnX3AEPfKsd4T07FpSTKks2PWijMmi2XmitMUzp/JqGMx6WNjY0XPdmXZeuAGPkmQbS+lCAuLYPnmi7i2nk3trgs5ecGHXwfV4v6hocz7qyUlHLMZfZuWlhZM+l8jHjx+w+SFx4267rU7rhAZKU2S5VAf11K5qVE+P1MXnyQ8PNYx6JU4XLn1nM7awbo9xrSMtfLcraULb/yD2br/ZhJG+OEiRZsBKylUdwpTFp2kbIlc5MgIH6p9AAAgAElEQVSaloOn7vHThL0077ecgnUmk67MGCq0nUOPERuZOP8YOw/fwufJG5W9TEkwKSXjPY5RrGAWmtYu8sn89o1K8OjZW05e8DFDdIqSMqmEG0qSmLjgOBnS2dGzXZlYy6VOZUP31qWZueIMz18EkC1zWqPGcen6U456PmDiTw2T5AbhPh3KMXbOETzWnmX0t3VMvr3kLDQ0nMkLTzB96SkePXtLsYJZmPtnC7q2dCGVnbXJt1+7UkFa1ivG33OO0LNdWXJkNU4WypVbvShZJBsli2Q3yvoMMaxXVVr2X8G6nVfplESVvs/Ni1eBtOi3nLSpbdg00y3OY7BO5YLkzGbP0s0Xadc47qEOEis4JIwV/3kxbclJLl5/SuaMqRnRtxoDOlcgj06ylTf+wVy9/ZzLN59z5dZzLt96zs4jt1i04fz7Munt7Zjyc2Pc28b++6so0e09doeL15/iMaal3v+ZzeoUxcbakrU7rlCl7Kf3ciuK8inV8qWY3L2Hr1i/6yr9OrmSNo1tnOX7dSpPWFgEC9adM3osM5efxs7WCvc2SXMSUjBfJhpWL4zHmnNffCvFhPnH+HHCHooVysJ2j65c2fENfTq6JknFK8r4EQ0JCY1g5OR9Rlnfg8eaJAydmiZtBahZ7SIUKZCZCfOPqVaNBAgPj6DjkDX4PH3LxhluH1VmYmJpaUHn5qXYfugmfi/fmSy2R0/f8vOkveStPpFe/9tEpJR4jGnJw8PDGDOs/iexpre3o3KZfPTp6MqUkU3Yu9idJ8d/wO/0jxxa3pOZo5tRvHBWvv51C1duPTdZ3MrnacL8Y+TImpauLVz0zk9vb0fD6oVZt/OK6nqoKAZSlS/F5KYuPoGFheCbrhUNKu9UOCu1KjowZ5WnUX/M3/gHs+y/S7g1K/U+jXRS6NfJlUfP3rL90K0k22ZyExkZyfy156hdqQB7F7vTuGYRs6QmdnTIzKBuFVmw7rxRBitetdULIMkrXxYWFgztUYWzlx9z+LR3km77czDs713sO36XuX+2oFKZvAYv161lacLDI1m93biJW6SUnDj/ALcha3GoPYm/Zx+harl87F/qzsUtA+jVvly8L1JkzpiaGhUc+LpLBTbOdMM+jS1dh60jJCTcqLErn6+L156y++gdvv2qUqxDsrRvXAKfp285ffFREkanKCmXqnwpJvX6bRDz152jU9NSBl1djtLfrTzePsbNFLh000XeBYYy0MBKoLE0q12UnNnsmb3SdPexJXeHz9zn7sNX9GxX1tyhMGpgTTJlSMXQvxOfen7VtstUcM79ftybpPRV69JkyZiaCfOPJfm2UzKPNWeZtuQkQ3tWoXs8W8BdnHJQqmh2lhkx6+GBk3ep2G4uVTp4sOPwLb79qhK39w5m06zO1K5U0ChJXLJnSYvHmJZcuPaUX6buN0LUypdgwvxjpEltQ3+32JNTNa9TFGtrS9btUlkPFcUQqvKlmNS81WcJeBfKsJ5V4i6so3V9J7JlTsPslcbJFCilZOby01Rwzk25krmMsk5DWVtb0rt9WXYevo23z6sk3XZysWDdOdLb29G2YXFzh0KGdKn47dvaHDzlzea91xO8nht3/Th/9UmSJdqILpWdNQO7VmDrgZtcv+NrlhhSmqOe9xkweisNqhVi3PD6CVpHt5YunLzgwy3vF4mOx+/lO9oMXIXvy3f8+2tTfI4MY+JPjSiYz/iV+RZ1i9GnYznGexzj0Kl7Rl+/8nl5+OQNq7Z50adDOTKmTxVr2QzpUtGgaiHW7byqukErigFU5UsxmbCwCKYtOUmdygUoXTxnvJa1sbGiV3tNpsAHj18nOpaDp+5x7Y4vA7pUSPS6EqJ3h3IIobnq/qV54x/Mup1XcWtWKknv74pNv06uOBXKyvBxuwgNTVg3rJVbLyGEoEMT0w6sHJsBXSpga2PF5IUnzBZDSvHU15+236zCIXcGVk3pgJWVZYLW07m5M0IIo7R+jZqyH/93oWyb25WBXSsadE9sYkz6qRGF8mXkqx828EaNE6fEYsqiE0gJQ9wrG1S+XaMS3H/0Gk+vxyaOTFFSPlX5UkxmzfbL+Dx9G+OgynHp29EVKY1TYZm5/AyZMqQy24lyvlwZaFzDkfnrzhEW9mUl3li11Yug4LBk0eUwipWVJZP+14jb91/y77JT8V5eSsnKrV7UrJCfXNnTmSBCw2TLnJburUuzeOMFnr8IMFscyZ2Ukl4/beJtQAibZrrFeSU/NrlzpKNu5QIs23wxUVf5L157ytzVngzsUoHiJhheQZ+0aWxZOr4tj575881v25Jkm0rK8/ptEHNXe9KxSUny585g0DIt6xXDysqCtTtMM5C98nlL6EXQlEpVvhSTkFIyccFxihXMQqMahRO0Doc8GWlcozAeaxNXYXn09C0b91yjV7uyZm156e9Wnqe+Afy3L35d3V6+DmT0tP0UqD2JPUeNdw9cUlmw7hwli2TDtVTSdveMS6MajjSqUZjf/z3Ew3iOw3b+6hNu3nuBWzNnE0VnuO96VCYkNJyZy0+bO5Rka+4qT7YfusU/PzQwSkWna0sX7j58xfFzDxK0vJSSb//YRsb0qRj9be1ExxMflcrkZeSAGizbfJHV27ySdNtKyjBnpScB70IZ3tvwC6cZ06eiXpWCrN15RXU9VOJl/c4r2Jf+i0vXn5o7lCSjKl+KSRw8dY/zV58wtGeVRGW1+7pzBZ489493hUXXvDWeREZK+nWK/aZhU2tc05G8OdMbnHjjyXN/ho/dRb6ak/ht+kGePA/g32Up6wT78s1nnL70iF7tyholcYCxTfyxEYHBYTjUmkTdrxYyWzu+XFxWbvHCysoiWdzDVqxQVprVLsLkhSfYf+KuucNJdm55v2Do3zupX7UQA7sap9txmwbFSWVnzdJNCet6uHbHFQ6fuc9f39VNVCtcQv38dU0quuSh/y9b1ADwykdCQsKZuuQk9aoUjPftAu0blcTb5zXnjJBJVvkyPPMLoN8vWwgNi0jw72lKpCpfiklMWnCcrJnS0K2V/rFBDNW4piP5cqVnVgIzBXrdeMasFWdoVKOwWTLS6bK0tKB3+7LsPX6X2/djvlnf2+cVA37dQoHak5m08Dit6hXDa9tABnWryI7Dt3j5OjAJo06cBevOYW1tSZcYxogxt+KO2bi0ZQD/61+DR8/8+frXLeSsMp463RYya/lpnvl9WhGLjNSkGm9YrTCZMybdkAWxmf5LU3JnT0eDHkuYufy0uvKsFR4eQbfv12NrY8XCsa2NNryBfVpbWtd3Ys2OK/FO3R4YFMr3Y3dR2ikHvTuUM0o88WVtbcnSCW0JDYug+4gNanwm5b0VWy5pLvz1rhbvZVvVV10PFcNJKek78j8C3oVSpnhOVm+//MX8FqnKl2J01+/4svXATQZ2rYCdbeK6+VlaWtC3oyv7jt/l5j0/g5eLiIhkgsdRXFvPRgjBH0PqJioOY+nVvhyWlhbMW/3pfWzX7/jS/YcNFK43FY+15/iqtQs3dw9m2cR2lCySnS4tnAkLi2DtDuOn833+IoAqHeZx+qKP0dYZGhrO0k0XaVGnKFkzpzHaeo2tWKGs/PFdXa7tHMSlrQP5+esaPHnuz4DRW8lVdTy1uy5g5vLTPPX1B+D4uYc8fPLGbFkO9XHIk5GTa/vQuEZhBo7eSv9RW764PvT6jJl1mFMXfZj9e3Ny5zDuvXndWrnw6k0Q2w/djNdy/8w7ysMnb5g2qgmWlub7F+zokJkpPzdm/4l7TF180mxxKMlHZGQkE+Yfw7lYdupXKxTv5TNlSE2dSgVU1kPFIEs3XeS/fdf5a2hdhvaswsMnbzh5wXjnIMmZqnwpRjd54QnsbK0Y0Nk4XXx6tS+LlZUFc1YZlnb+/qPX1P1qEcPH7aZprSJ4bR2Y5OnlY5I7Rzqa1ynKgnXn3l8xP3flMe2+WUXxxv+ydscVBnWryN19Q5j7Z8uPWutcnHLgVCgry/+7ZPS4Zi4/zYnzDxn6906j/dPceuAmfq8Ck1WijdgIIShVNDu/D6nL1Z2D8No2kJEDavLM7x0DR28lV9UJ1OqygJ8n7cXO1ooWdYuZO+SPpLO3Y9OszvzUvzpzV3tSr/tifF+8M3dYZnPm0iN+n3GILi2cTZJop16VgmTPkjZeXWXuP3rNuLlH6di0JNXLOxg9pvjq3aEcLeoW48fxe/C68czc4XwR/F6+S7YXRnYcusXV275836tqgruJt29ckjsPXnLh6pdz/44Sfz5P3vDtH9up5pqPIe6VaVGnKLY2Vqze9mW0mqrKl2JUvi/esWTTBb5qVdporR05strTur4TizacJyg4LMZyUkoWbzhPqaYzOHflCQvHtmb9jE7JrtWlXydX/F4F8vuMgzTutYRyrWaz59gd/te/OvcPDmXyz431DkgthKBLC2eOeN7n/qPEp9+PEhISzqwVZ8iYPhXHzj5gx6FbRlnvgnXnyJ09HQ2rJyzhijkJIShZJDu/Da7D1Z2DuLz9G0YNrMnzF+84fOY+res7YZ/WtGnBE8LS0oIxw+qzYlI7zng9onzbOVy89uWdBAUGhdL1+3XkzJqWf39tapJtWFlZ0rl5KbYevGlwV+Dh43YhhGD8iIYmiSm+hBDM+6sFGdLZ0WXYOoJDYv59VRLv8bO3ONafStlWs7lx1/CeHEllvMcx8uRIR6emCW/Vb1WvGJaWFqzd+WWcRCvxJ6Wk1/82ERYewaKxbbC0tCCdvR1NajqyZsdlIiI+/66HqvKlGNXMFacJDgnnux6GjQ1iqP5u5Xn5OijGLnd+L9/R7pvVuI/YSGmnHFzaOgD3tmWSZZKHBtUK4ZAnA2NmHebs5SeMGVaPB4eH8efQenFWFDs312TXW7XVeFnKVm3z4vmLdyyb0JaCeTMycvK+RPe7fvT0LTsO36J7m9Jm7VplLCUcs72viN3aO5g5f7Qwd0ixcmvuzJGVvQgPj6RKx3ms32n8rqrJ2fBxu7l57wWL/2lDhnSmS2jRrWVpwsIiWLM97v178NQ91u64wo99q5FXz8UVc8mWOS0L/m6F141njJy8z9zhfNaG/r2ToOBwnvkF4NpmNmu2J58KyplLjzh02psh7pWxtk7YGHgAWTKloXZFB9buUFkPFf3mrT7L7qN3GD+i4Ue9ezo1K8VT3wCOeN43Y3RJw2xnRUKIwUKIy0KIK0KIIXrmtxRCXBJCXBBCeAoh4n/3p5KkgkPCmLHsNE1rFaFYoaxGXXftSgUoUiCz3kyB2w7coGTTGWw9cIN/fmjAgWU9cMiT0ajbNyYLCwuW/NOG2b83x/vgd/zUvwbp7e0MWrZA3oxUKZuX5VuM0/VQSsnUxScpXjgrjWs68tvgOpy/+oT1u64mar1LNl0gMlLSo20Zo8SZnBTOnzlZtnpF51oqN2c29MO5aHbaDVrN6Gn7v4ibmXccusnM5af5rkdl6lQuaNJtlS6eg+KFs7J084VYy4WHRzD4z+3kz52B4X2S37+yprWL0t+tPJMWqIyZprLn6G1Wb7vM//pX59zmrylVJDsdB6/h29+3JYtuiOM9jpIurS19jJAEpl2jEty+/5JL11VXVuVj9x6+YujfO6lbpSD93Vw/mte0VhFSp7L+IroemqXyJYQoCfQBKgAuQDMhhGO0YvsAFyllaaAn4JG0USrxERgUSo8Rm/B9+Y5hvaoYff1CCPq7lefE+Yfvu1EFvAuh/6j/aNZ3Odkyp+HMhn4M71MtRbS0VC/vQD+38qROZRPvZTs3d8brxjOj3KNx1PM+568+YXD3ygghcGtWiuKFs/LLlP2EhydsbDUpJQvWnaNmBQcK58+c6BiVhMuZzZ4Dy3rQvXVpfpt+kPaD1hDwLsTcYZnMi1eB9PxpEyUcszFmWD2Tb08IQbdWLhw/95A791/GWG7e6rNcuv6MCSMamnWswdhM+LEhjg6Z6P7DBl69CYqzvJSSa7d9mbvKk27fr6dg7ckMH7srCSJNeYJDwhgweiuF82fiB23L58FlPRjiXpnpS09Ro/MCHjw2Xlfy+Lr74CXrd12lv1t50hl4ITA2rRs4YWEhWPeFtbgrsYuMjKTHjxuxEIIFf7f6JPtsmtQ2NK9TlHU7ryT4/COlMNdZqhNwUkoZKKUMBw4BrXULSCkD5Ic26zSAar9Opu49fEWVDh6s3n6ZMcPqUbuSaa42d29dGjtbK2avPMPJ8w8p03IWc1efZXjvqpxZ3w/nYjlMst3kpkPjklhaWrD8v8SPiTF18UkyZUhF15aa7oyWlhb8+V1drt/1Y9nmhLWuHfW8z+37L1NMoo3PnZ2tNQvHtWbiTw3ZtPcaVTt54O3zytxhGZ2Ukn6j/uPF6yCWTWib6EyrhurSwgUhRIzfx5evAxk5ZR+1KjrQtpH5x4WLSZrUNiyb0I6nfgEMHL31k/nh4RGcvfyYyQuP02bASrJX+ofijafTb9R/7Dl2BxsbS6YtOWnQOHlfmvHzjnH7/ktm/Nrs/XFpY2PF5J8bs256R67e9qVMy1nsPGyc+231CXgXwuWbz9iy7zrTFp/ku7920OrrFbg0n4FLi5lYWlowuHslo2wrW+a01KrooAZcVj7y79LTHDrtzZSfG5MvVwa9ZTo1LYXfq0D2n7iXxNElLSszbfcy8JcQIjMQBDQBPkllJ4RoDfwNZAP03jUthOgL9AXIly+fqeJVYrD7yG3chq4lMlKybV4XGtcsYrJtZcqQmo5NSrJg3TnmrvYkb870HFjqTs2KBUy2zeQoa+Y0NKxWiBVbvBgzrF6Cxy66/+g1G/dcY3jvqh+1wLWq74RrqVyMnn4At2alsLWN38/E/LXnsE9jmywGIFY0hBAM7VmVEo7Z6Dh4LeXbzGHDjE7JIuOesSzbfJH1u64y9vv68R4cNjHy5kxPrYoOLN18kVHf1PrkPtNfpx3g9dtgpo5skizvQdVV3jk3v35Ti1FT9lO/aiEK58/E4TP3OeJ5n2PnHhDwLhSAgnkz0qSmI9XL56dGeQcK58/Ejbt+ODWaztxVnowcWMu8byQZuXP/JX/NOkyHJiVpoCf5UNtGJShVNDvtv11Nk97LGDmgBr8Oqp3gHhw+T96w6+htbnm/4J7Pa+75vMLb5zW+Lz/OfJo6lTUF8mSkQJ4M1CzvQLPaRcmV3XjDMbRrWIIBo7dy+eZzShXNbrT1Kh+8Cwzll6n7KemYjR7J/GLnzXt+/DhhD01qOtKjXcy3IzSqUZh0aW1Zvf2y3u/L50KY66qEEKIXMBAIAK4CQVLK72IoWwP4RUoZaz8SV1dX6elpWDpyJXGklPwz9yj/m7SXEo7Z2DjDLUkGMT57+TGVO8yjS3Nnpo5sbJQuEinRiv8u0WXYOg6v6JngE+jhY3cxedEJ7h347pMEALuP3KZhzyX8+2tTBnataPA63/oHk7PqeLq0cGbuny0TFJdiWjfv+dGi/wruPHjJxhluNKtT1NwhJdr9R69xbjYD52LZObisZ5J3PV647hw9f9rEiTV9qFQm7/vpl28+o3SLWfTr5MqM0c2SNKaECg+PoEbnBZw4//D9tJJFslHdVVPRqu6aP8Yx0xr1XMKlG8+4f3BoopI2fC6klDTpvZSjZx9wfee3sY41FxgUysDR21i04Tx1qxRkxaR2ZMuc1qBtXLvty6a919i45xqeXo8BsLG2JH/uDDjkzqCpZOXV/s2TEYc8GciaKY1JLwY88wsgZ5XxjBpYk98G1zHZdr5UF689peOQNdy460emDKl4dPT7JGvtj6+IiEiqdfLgxr0XXN42MM5K/lfD17Nl/w2enfgBGxtztRHFnxDirJTSNe6SaL645n4AY4ABcZS5B2SJrUy5cuWkYnr+AcGy3TerJIVHyY6DV8uAdyFJuv3AoNAk3V5y5B8QLFOX+l32G7k5QcsHvAuRGcr+JdsPWqV3fmRkpKzZeb7MUXmcfBdo+Oc7b7WnpPAoefL8gwTFpSSNV28CpWO9KbKGm4e5Q0m08PAIWcPNQ9q7/CnvPnhplhjevA2SdiV+kwN+3fJ+WmRkpKzTbYHMWG6M9Hv5zixxJdSDx6/lr1P3yc17r8Ur9m0HbkgKj5Irt1wyYXQpx/qdVySFR8nJC44ZvMz8NWelXYnfZK6q/8ijnt56y0RERMjj5+7LH8btkkXqT5EUHiUpPEpWajdHjp19WF699VxGREQY620kWM3O86VTw2nmDuOzEhkZKWcsOyVti/8mc1QeJ3+dui/Zf+fGzj4sKTxKLt980aDyUb8jW/ZdN3FkxgV4SgPrPebMdphN+zcf0AZYGW1+YaG9LCOEKAvYAC+SOk7lY7e8X1Cp/Tw27L7K+BENWDm5PWlSxz9pRGIk1xvWk1LaNLa0qufE2p1XEpQpa8nGC7x+G8wQd/1DAggh+GtoXZ76BvDv0lMGr3f+2rMUL5yVCi554h2TknQypEtFt5YuHPF8gM+TN+YOJ1EmLzzO4TP3mTaqCQXymifLaTp7O1rWK8aqbV7vv48bd19j/4l7/DGkDpkzpjZLXAmVN2d6Rn9bhxZ1i8Ur9kY1ClM4fyamLj5hwuhMJzAoFP8A4ySkCXgXwuA/t+NSLAffdDO890DP9mU5saYPqWytqdllIRPnH0NKSWhoOLuO3KL/qP/IXW0CVTp4MGnhcRxyZ2DWb815dPR7Tqzty4h+1XEqnDXB3dGNqX3jEly748vVW8/NHcpn4eXrQNoOXMXA0VupU7kAF7cM4JdvapE/dwY81p41d3h6Xb75jF+m7qdNg+K4NTds/Lh6VQqSMX0qViejoRiMzZzfzvVCiKvAFmCglPKVEKK/EKK/dn5b4LIQ4gIwA+iorVkqZrLtwA3Kt5nDU78Adi34iu97V0v29zB8zrq0cObl6yB2Hrkdr+UiIyOZtuQkrqVyUVmni1R0Vcvlp0lNR8bOPcob/+A413v11nNOXvChZ7uy6rhIATo2LYmUkrUpOCPZpetP+XnSPlrXd6J7m9JmjaVbSxdevg5ix+FbBAWHMWzsTkoVzU6/Tob1QvkcWFhYMKhbJU5e8OH0RR9zhxMvkZGR1Ou+GMf6U42SSfa36QfxefqWWb83w8oqfl0wSxfPydlN/WlRpyjfj91F+TZzyFpxHI16LmXZf5eoVi4/yye2w/fUCHYt7E7/zuWNer+WsbRpUBwhRIr+jUkujp29T5mWs9hy4AYTfmzI1rldyJY5LRYWFvRqV5Z9x+9y90HMGVfNISwsgq+GbyC9vR2zfm9m8HmBjY0VbRo4sWnvNYKCP8+B381W+ZJSVpdSFpdSukgp92mnzZZSztY+HyelLCGlLC2lrCylPGquWL90kZGR/D79AM37raBg3ox4buhHvaqFzB3WF69+1UJkyZiaFfEc82vPsTtcv+v3Pr18bP78rh6v3gQxacHxONe7cP15rKws6NbKJV7xKOZRpEAWypbImWLHVAkKDqPr9+vJmD4Vc/5oYfYKf4NqhcmaKQ1LN11k4vxjePu8ZurIxvE+8U7p3NuUxj6NLdPj0WKeHCzecIET5x/yLiiUWl0X4On1KMHr8rrxjMmLTtC7Qzkql0lYIrD09nasn9GJiT81JDAojPaNS7BlThf8To9g7fSOdG7hbNIBxI0hZzZ7qpXLp1LOJ0JERCRjZh2iZpeFWFlacHx1H4b1qvpRy6Z72zJYWAgWrj9vxkg/NWb2Yc5ffcLs35sbdP+irk5NSxHwLpQdh0yXAdSczN8urSRrb/yDaT1gFb9OO0DXls4cW907WQ9g/CWxtrakQ5OS/LfvRry6ykxZdIIcWdPSoXGJOMuWKZGT9o1LMGnhcXxfvIuxXFhYBEs2XaB57aLx/pFVzKdjk5KcuujDvYcpK/V8YFAoLfotx+vGM+aPaUnWzGnMHRLW1pa4NSvFlv03+HvOEdo2LG6yYTeSs3T2dvRoW4bV2y/z1Nff3OEY5I1/MD9O2EPlMnm5sHkA6dPaUafbIo6c8Y73uiIjI/n61y1kSGfH2O/rJyquqCylV3cOwmNMK5rVKZpskyrEpH3jEly++Zzrd3zNHUqK8+S5Pw16LObnSfto36gE5zd/TXnn3J+Uy5szPY2qF2bh+vPJZnysc1ce8+fMQ3Rp4UybBGQ+rlXRgayZ0ny2XQ9V5UuJ0d0HL6nYbi7bD91k2qgmLP6njbrfKpnp0sKZoOAwNu65ZlD563d82Xn4Nl+7lTc4i9Dvg+sQGBTGuHlHYiyz7eBNnr94p8b2SmE6NCkJwOrtXmaOxHD+ASE07rWU/SfvsWhca5rWTj7ZGru1ciE0LILISMmEHxuaOxyz+aZbRcLCIpizKmVkH/5jxkF8XwYy/ZemFMqficMrepIruz0Ney5ldzy7dS/ZeJFjZx/wz/AGKe5eP1No00Bz4r12h2r9io8dh27i0nwmJ877MH9MK1ZMbhdrdufeHcrx6NlbdsXzeDWFkJBwuv+wgayZ0jD9F72jRMXJysqSdo2Ks2X/DQLeGec+zOREVb6UGP08aR9Pnvuzb7E7g76qZPZuPcqnKpfJS4E8GQ0ecHn60lPYWFvSv3N5g7dRrFBWvmpVmn+Xno4xOcOCdefImc2eRjU+33E5PkcOeTJSuUxeVm1NGVcX3/gH07DnEo6de8iyCW3p3ibm8WLMoVzJXLSsV4y/h9X7onsIODpkpklNR2atOJOghEBJ6fodX6YuPkmv9mUpVzIXAHlypufwip44OmSieb/lbN5r2MWtF68CGT5uF1XK5sW9rXnvQUwucudIR9Vy+Vi3S1W+DBEaGs7wsbto0nsZObKm5ezG/vRsH/d91M1qFyVb5jR4rD2XRJF+EBgUysFT9/hzxkEa9VxCtkrjuHzzOR5/tSRj+oR3je3UtBRBwWFsPXDTiNEmD6rypegVFBzGlgM3cGtWihoVHMwdjhIDIQSdm5di7/G7cXbxef02iMUbL9C5uXDaEOMAACAASURBVHO8uwb+OqgWkVLy58xDn8x78tyf7Ydu0b116S/u/pbPQccmJbl4/Wmy7xb08nUgdb9ahOflx6yZ2gG35s7mDukTQgg2zerMkB5VzB2K2X3bvRLP/AKSdYuHlJIhf+0gTSob/hpa96N52TKn5cDSHpR2ykHbb1az0oB7a/83cS+v3gYz67fmySLbYHLRrmFxLl1/xs17fuYOJVm7dP0p1d3mM2H+MQZ0qcCpdX1xKpzVoGWtrS3p3ro0W/bfMHl33+cvAti4+yrD/t5JpXZzSV92DLW7LmTUlP08evaWzs2d2Tq3C01qFUnUdqq55iNXdvvPsuthyhm9TElSOw7d4l1gKO0NuC9IMa/OzZ35a9Zh1my/wrfdK8VYbv7ac7wLDGVwLGVi4pAnI307lmPOKk+G96720YDaSzddICIikh5tk1crhGKY9o1L8N2YnazefplfB9U2dzh6+b54Rz33RVy/48eGfzt9FgNDf+7qVy1E0YJZmLbkJF1aJs8kPFv332DXkdtM/l8jvRekMmVIzd7F7jTvt5wuw9bzLiiM3h3K6V3XyfMPmbfmLN/1qIxzsRymDj1FadtI8xvT86dN5MuZnpDQCIJDwggJiyA4JJzgkHBCQqP+Rrx/bW1tyfHVvSlSIIu534JJXb/jy+jpB1i97TIZ06di/b+dEnSfVK/25RjvcYzFGy4wol91o8X38Mkb9h67w9GzDzh69j4372lGfbK1saKCc26G965K1bL5qFI2X6JauqKzsLCgQ+OSzFx+mjf+waSPpdtlSiM+p+ztrq6u0tMzZfQxT+7chqxl7/E7PDk+XLVmpABlWszExtqSU+v76Z0fHh5B4XpTyZ8rPYdW9ErQNp4896dQ3Sm0bVicpRPaAporx8UaTiNb5jQcWdk7wfEr5lW76wKe+gZwdeegZNe9+Mlzf+p1X8Q9n9dsmulGg+qqa2tKMWPZKb75bRsn1/ahYumYh7Uwh+CQMEo0/hdbGysubhmAtXXM/+cCg0Jp+80qdh7WVNSit2yGh0dQvs0cfF8Gcm3nIOzT2po6/BSn2/frOXTaG1sbS+xsrbC1scLOVvOwtbbEztb6o3nWVhZMX3qKP7+ry88Dapo7fJO49/AVv/97kCWbLpDKzpoh3SsxrFfVRFVgarjN56lfADd2f2uU3/I791/i3HwGgUFhZMqQiqpl81GtXD6queanXIlc2Nqatg3n5PmHVO4wj8X/tOGr1sm7K68Q4qyU0qCxRVTLl/KJqC6HXZo7q4pXCtGlhTPDx+3mlvcLHB0yfzL/v303uP/oNZN+apTgbeTMZs+gbhUZ73GMH/tVp4RjNo6fe8DNey/4sa/xrrIpSa9jk1J8/esWvG48S1ZX7X2evKHOV4t4/Nyf7R5dqVWxgLlDUuLhq1al+d/EvUxbcorlyazyNXnhCe4+fMXuhV/FWvECSJ3Khk0zO9N56Dq+G7OTd0FhH1UIZi4/w4VrT1k7raOqeMUg6oJdfJy66MOW/Tc+u8rXo6dv+XPmITzWnsXSwoIh7pX5sW91o2Rt7d2hHN1/2MCRM/eNcsvIiPG7EUJwdlN/SjvlSPLutBVL5yF/7gys3uaV7Ctf8aE6JSufiOpy2KGJ6nKYUrg1c0YIEeOYX1MXn8AhTwZa1iuWqO380Kca9mlsGDV5HwAL1p0nbRob1T01hWvbsDiWlhas2pZ8sh56+7yiRucFPPMLYPfCr1TFKwWyT2tLz3ZlWbPjMo+fvTV3OO89evqWv2YdplV9J+pXM6wl1dbWitVT29OtlQsjJ+/jpwl7kFLy+NlbRk7eR8PqhWnbKP5dxZSYNa9TlNOXHvHML8DcoRjF8xcBDB2zg0J1p+Cx9ix9OpTjzr4hTPypkdGGy2jXqDjp0trisfZsotd15Iw363dd5ce+1fg/e/cdH1WV/nH8c9I7CRCSkAChBEKv0kEpIlIUsGBFXbHs2l1dUXfXdV3b2tGfYm+riGChCYKAAkpHeq8SICFAAultzu+PGWKQAAEmmYR836/XvObOLWeei9fJPHPOfU6HlnU9ch+jMYaRg1ox6+ftHErLrvD3Ly9KvuQEE2esp3ZEEBeq0EaVERsdxkVd4vlsyhr+OJR41Yb9zF+2m7tv6IK397n9L18rIoi//qk738zeyI9LdjLhu3WMHNSKkGD92luVRdYKpn/3Rnwxbd0J148nbN11iN7XfUDa0Rx++Phmunc4u4lqxfPuuqEzRUWWceOXeTqUYo+8MIvCQgcvneF0AD4+3nz0/HDuvPYCnnt7Afc+9R0PPjuT/IIi3nhicKUbslvVDenTDGst3/1YtavdpR3J4fGXf6BR31d57ePFXDukNVtm3cebTw4lNjrMre8VFOjH9Ze1YeKM9aQfzTnrdhwOBw8+O5PYqDAe/JNnCwiNHNyKwkJHmafUqQqUfMlxjg05HDGghYYcVjHXDW3D1l2HWLFu33HrX/t4McFBftx6lXvm4Hrglu7Ujghi+F/Gk5Wdr7m9zhMjB7ViZ1Iay9fuO/3O5WjjtlQuvP4DsnMLmPfpLaVOKipVR5MGtRh8UQLjxi8nL8/zZed/XrGbz6as4aFbu9Oofs3TH/AHXl5evPnkEB66tQdvfLqECdPX8eidvWjS4MTh3nJu2jaPpl5MDabO3ezpUM5Kdk4+//m/H2nY5xWeeWs+Q/o0Zf13d/Ph88NpWK/8pqIYfXVHcvMK+Xzq2Y9k+HzqWpav3cezD/UnKNDPjdGdufYtYmjSoCYTKtHIjHOl5EuOM3O+qhxWVVcObIGfrzefTfl96GHKwUw+n7qGm4a3IzzMPVWIQkP8GXNHL9KP5tKsUW26ta9c93LI2Rk+oDm+vt4eHXq4ZlMyF17/AQ6H5afP/kS7FjEei0Xc595RXUk9nOXxktFFRQ7ufeo7YqPCePTO3mfdjjGG/z4ygGcf6s/A3k145PaeboxSjjHGMKRPU2b9vL1SJO5n6r7/zOAfr87lws7xrJryF7549WoSG5etdPy56NCyLu2aR/Pel2c39DA7J59HX5xNx1Z1uf4yz0/pYYzhmsGtmbt453kzBFUFN+Q4x4YcXtQl3tOhyBkKDwtk8EVN+WL6Wl4ccwne3l68PX4Z+QVF3DvqzMvLn8pfru/MhO/Wcec1F2iozXkiPCyQS3snMGH6Ol54ZMA5j++fu2gHO/ak4e1l8PHxwtvLy/X8+2tvb4OPtxfe3l4cychl9OOTCfT3Ze6nN5/35aWrk/49GtO8cSRjP1nMjcPaeuwz44NJK1m5fj/jX7mK4KBz+zXfGMOYO3oz5o6zT+Lk9Ib0acZbny/jx6U7uaRXgqfDKbPUQ1l8+u1q7rimE+OeuqzC33/01R25+8nprFy/jw4t657RsS9/8AtJyUf57KUrK818dSMHt+I/b/7EV99v4C/Xd/Z0OOdMyZcUy8ktYMpcVTmsyq6/rA3fzN7I3EU7uLBzPG+NX8alFybQrJF7v8gGBviy9CRl7aXqGjmoFVPmbOKXlXvo2anBWbfzzawNjLjrizM+rkFsOHM/ufmshoNJ5WWM4d5RXfnzE1NZ9Osej9zDl3Ykh8de+oFenRowcnCrCn9/OTt9uzUkKNCXqXM3V6nk650Jy8nLL3T7D59ldd3QNjz03Pe8P3HlGSVf+w9k8Nw7CxkxoIVbqiW6S6umUbRoEsmE6WuVfMn5RUMOq77BfZoSFuLPZ1PWkHIoi+TUzLOaVFmqp8v6NSMwwJcvpq896+Rr5540bhnzLRe0iWXi2JFYaykqshQWOSgqcrieLUUOB4WFjhLPlvYtYtw6SadUHjcOa8uYF2cz9pPFZ5V8bdl5kHVbDjDoogQC/H3P+PgnX5/H4SM5jP3HIPXWVyEB/r5c3KMx0+Zt4fV/Wrf9t1u7OYU/PzGVL1+7mrpR7i16UVBQxJufL+XiHo1pkVDHrW2XVUSNQK4c2JLPpqzhhUcGlPm+rX+8Oof8giKe/9vF5RzhmbtmcGueGDuPvclH3V6opKJVjv5EqRQ05LDqC/D35YpLWvD1rI289P7PJDaqzYAyllIWCQn2Z0ifpkycsZ7CwqIzPj4/v5CR938JwIRXr6ZBbDjxcRE0blCTZo1q0yKhDm0So2nfMoZOrWPp2r4ePTo24MIuDenbrZESr/NYcJAfo6/uwKSZG0jaf6TMxy1ZtYcr7vqCxEte54q7vyD+old4/u0FHM3ILXMb67ce4I3/LeW2qzvqPsIqaEifZuzem866LQfc1ubTb/3Ezyt+46UPfnFbm8d89f0G9qVkePyHz9FXdeBIRi5ffb+hTPuv3pjMB5N+5Z4bu1TKAjIjB7fCWsvEGZ69d9QdlHwJ8HuVw+EDmmvIYRV3/WVtyMjKY9XGZO4d1VW/8soZGTmoFQcOZfHjkl1nfOyYF2ezbM1ePnh2WLlW85Kq6a7ru+BwnL7svLWWGT9t4aLrP6DrVe8yd/EOHruzF9PeuZ42zaIY8+Js6l/4Mo+//AMHDp36BnxrLff95ztCg/34zwP93Hk6UkEGX9QUgGnz3FP1MGn/ESbN3EBggC9vf7Hc7fNHjf1kMY3r1+TSCz07TLJ353iaNKhZpsIb1lr++txMImoE8PdKOql104a1adc82uOFe9xByZcAziGHmVn5XH2pxsJXdRd1aUhMnVDCwwLOqxnhpWIMuqgpIcF+Z/wHbvIPG3nlw0Xcc2MXRlyiyWblRA3rRXBZv2a8/cVycvMKTtheUFDE/yavpu3QNxk0+n9s/+0wLz82kN9++iv/ebA/g/s0Y9ZHN7Hs6zvo370Rz45bQIMLX+aef09n9970Ut/z29kbmfPLDp66vx+1a7pnIlupWDF1QunUuq7bSs7/32dLnT0oY68mKzuf//vfEre0C7BszV4W/bqHe27s4vFiFcYYRl/VkfnLdrNl58FT7vvdj1uY88sO/nVPn0o9AuGawa1ZvCqJXUlpng7lnCj5EkBDDs8n3t5efPDsMD594Ypzrugl1U9ggC/D+jfnq+83kJ9ftvLOu5LSuPmRb+jYqi4vPHJmE9dK9XLvqK4cTMtmfIk5iLKy83nto0U06f8qNz70FQ6H5eP/jmD7nPt54JbuhIYcP4l7p9axTHrjGjbMuJtrh7Rm3PhlNO73KqMe/ooNW38fmpaTW8CDz86kVdM63Hltpwo7R3G/oX2asXhV0ml7Ok8nOyefdyYsZ/jFzRncpxlD+jRl7KdLyMrOd0ucr3+6mJBgP265or1b2jtXN41oh7e3F+9PXHnSfQoKinjoue9p2rAWd157QQVGd+auHuTsIPiyivd+eSz5MsbcZ4xZZ4xZb4y5v5Tt1xtj1rgevxhj2noizupAQw7PPwN7JzCkbzNPhyFV1MhBrUg7ksPsn7efdt/8/EKuuX8iDodlwqtX4++vOk5ycn26NqRlQh3GfrKY1ENZPPHaXOpf+BL3Pz2D+nVrMPXt61kz7S+MGt4OP79TX0uJjSP54Lnh7Jj7AHff0Jmvvt9Ay0FvMOzPn7Nk1R5efO9ndiWl89rfB+lvWxU3tG+iazjq1nNq53+T13A4PYf7b+4GwKN39uZQWvZZz4lVUnJqBl9MX8ctI9oTFhpwzu25Q3RkKEP6NOXjb1ZRUFD6fbzvTFjOph0HefGRS/D1rdz/nzSsF0HnNrFVfuihR5IvY0wr4DagM9AWGGKM+ePg2J3AhdbaNsBTwDsVG2X18f2CbWRm5XPVQFU5FBEY0LMx4WEBZfoD99jLP7BkdRLvPzOMxg1UIl5O7VjZ+VUbk4nr9SL/fuNHenVqwM8TRrNg/GiG9G12xsO16sXU4NW/D2L3jw/yz7svYv6y3XS96l3+9fo8rhzYkr7dGpXT2UhFadcimtiosHMaemit5bWPF9GhZQw9OjorbnbvUJ/eFzTgxfd/LnNP/8m8/cVyCgqKuPvGLufUjruNvqojKQczmf7jlhO2pR/N4Ymx8+jTtWGV+cH2miGtWbl+P1t3HfJ0KGfNUz1fzYHF1tpsa20h8BMwvOQO1tpfrLXHBnUuBuIqOMZqY+KM9dSKCKJP14aeDkVEKgE/Px+uuKQF387eRE7uiffmHDN1ziZeev8X7rqhM1dqigopoxsub0OPjvW5/rI2bJhxD9++dZ1b5v6qXTOYJ+/ry+4fH+TFMZfQp2tDXnpUw2DPB8YYhvRpyvcLt5GXd3ZJ0g8/b2fDtlTuv7nbcYWoxtzRyzmp8JQ1Zx1ffn4hb32+jEEXJlS6CeIH9m5C3ajQUnv3nn5zPofTc3hpzMAqU5zrWEfBhOlrT7Nn5eWp5Gsd0NsYU8sYEwQMAuqdYv9bgRkVElk145xYeRMjNORQREoYOagVGVl5Jx3m89u+dG565Bvat4jhxTH6gitlFxTox8IvRvPBc8Np3iTS7e2Hhvjz11t78MPHN1O/brjb2xfPGNKnGZlZ+cxftuusjn/1o0VE1Q45obDYwN4JtGsezfPvLsThcJxV21/OWE/KwUzurYTzavr4eHPLiPbMmL/1uGketu8+zNhPFnPziHa0b1l1pmCIi6lBr04NqvTQQ48kX9bajcDzwGxgJrAaKPWnDGNMH5zJ1yMn2X67MWa5MWZ5ampqOUV8/tKQQxEpTZ+uDYmsGVzqH7iCgiKuuX8ihYUOvhx79VlNeisicib6dW9EYIDvWQ093LLzIN/9tJW/XHfBCfelGmMYc0cvNu84yLezN51x28eGMzZrVJuLezQ+4+Mrwp+u7IDDYfno61+L1415cTY+Pl5VcgqG//5tAJ+9dKWnwzhrHiu4Ya1931rbwVrbGzgMnPDzqjGmDfAecLm1ttTBndbad6y1nay1nSIj3f8L2vlOQw5FpDQ+Pt5cdWlLps7dTGZW3nHbHn/5Bxb9uod3n76sUk7GKSLnn8AAX/p3b8S0eVuw1p7RsWM/WYyfrzd3nKSa3xWXtKBx/Zo8+/b8M257yaoklq/dx72junq8vPzJNKpfk37dG/H+pJU4HA4WLt/NpJnreeS2ntSNCvN0eGesa/t6tEmM9nQYZ82T1Q7ruJ7rAyOA8X/YXh/4GrjRWnviXYJyznLzCpg6dzPDL9aQQxE50chBrZzVUEv80jx93mZeeO9n7rz2AkYObu3B6ESkuhnSpxk7k9LYsK3sI53Sj+bw0deruG5oG6Jqh5S6j4+PN3+7rSfL1+5j7qIdZxTT2E8WExbiz6hhlbso9+irOrIrKZ05v+zgwWdnEhsVxl9v7eHpsKolT6boXxljNgBTgbustWnGmDuNMXe6tv8TqAW8aYxZZYxZ7rFIz1PfL9hGRlYeV+tGeREpRc9O9akbFVo89HDP/iOM+tvXtE2M5pXHB3o4OhGpbgZf1BSAqXPLPjzw/YkrycrO577T3I81anhboiNDePbtBWVue2/yUSbOXM+tV3UgJNj/9Ad40LCLE6kZHsifHv2WZWv28sxf+2suUA/x5LDDXtbaFtbattbaOa5146y141zLo621Edbadq6HZkh0sy+/05BDETk5Ly8vRg5qxYyftnLwcBbX3P8l+QVFTHx9pO7zEpEKFxsdRoeWMUybV7YBUYWFRbz+6RIu7BxPuxanLioR4O/Lg7d0Z84vO1i2Zm+Z2h83fhlFRZa7b6hc5eVLE+Dvyw2XtSUp+SgdWsZww+VtPB1StVU5B6dKudOQQxEpi5GDWpFfUET/mz7ml5V7eOepy0iI131eIuIZQ/s2Y9Gvezh4OOu0+06Zs5nde9NP2+t1zB3XdCI8LIDnytD7lZtXwNtfLGdo32Y0ql815jj883UXEBsVxth/DKq096dVB/qXr6aODTlUlUMROZXObeOIjwtn9aZkbh/ZiWuH6tdSEfGcoX0TcTgsM+aXPg1GSa9+tIj4uHAu65dYprbDQgO46/rOfDN7I5u2n/q+si+mrSP1cBb3jqr8vV7HJDaOJGnhQ/To2MDToVRrSr6qKVU5FJGyMMbw4C3d6d+9Ea/+/VJPhyMi1Vz7FtHE1Ak9bcn5lev3sWD5bu65sQve3mX/unvfTd0I8Pfhv+8uPOk+1lrGfrKYlgl16NutUZnbFgElX1XamZZDPSY3r4Apc5xDDn19NeRQRE7tnlFdmf3xzQQG6D4vEfEsLy8vhvRpysz528jPL3WKWABe+3gxIcF+3HpVxzNqP7JWMKOv6sCnk1ezp8SkxCX9vOI3ft2wn3tHdcUYc0btiyj5qqIm/7CR8A7P8MxbP1FYWHRGx2rIoYiIiFRVQ/s0IyMrjwXLd5e6PTk1g/HT1nLLiPbUCA044/aPlWB/6f2fS93+2seLiagRyPWXaRi2nDklX1VQcmoGtz42GWMMj788h17Xvs/WXaXOQV2qiTPWUzM8UEMORUREpMrp170RAf4+Jx16OG78MgoLHdwzqmyFNv6oQWw41w1tw7tfrjihsMdv+9L5ZvZGRl/VQaXa5awo+apirLXc9vhksrLzWfTlbYx/5So27ThIu8ve5M3Plp52KOKxIYcjBrTQkEMRERGpcoIC/ejXrRFT524+4XtPXl4hb32+jMEXJZxTZda/3daT7JwCXv90yXHr3/p8GdZa7qoC5eWlclLyVU627T7Epbd+QsrBTLe2+/7ElUybt4XnHrqY5k0iuWZIa9ZNv5ueHetz17+mMfBPn7A3+ehJj5+1cLuGHIqIiEiVNqRPM3bsSWPT9oPHrf9i+loOHMrivpu6nVP7LRPqcHn/RF7/dAmZWXkA5OQW8M6E5Qzr35wGseHn1L5UX0q+yklObiE/Ld3NtQ9MpKjI4ZY2d/x2mAeemUHfbg25p0Rp09joMGZ+MIo3/zWEhSt+o9XgNxg/dU2pvWBffrdOQw5FRESkShvSpykAU+duKl5nreXVjxbRMqEO/bqfexXCMbf3Iu1IDu9MWA7AZ1PWcDg9p0qVl5fKR8lXOWndLIq3nhzCvMU7+edrc8+5vaIiBzc98jVexvDhc8NPmBzPGMOfr+/Mqil/IbFRba57cBLX3D+RQ2nZxfuoyqGIiIicD+JiatC+RQzT5m0pXjd/6S5WbUzmvpvcU4Wwa/t6XNQlnpc++IW8vELGfrKYtonR9O4cf85tS/Wl5Ksc3TSiPaOv7sgzb81n+rxTz0dxOi+9/zMLl//GG08Mpn7dk3d1J8TXYsH4W3nmr/35ZvZGWg1+g+9+dH4wFQ85vFRDDkVERKRqG9KnKT+v/K34h+bXPl5MrYggbri8rdve49E7erMvJYPRj09m7eYUlZeXc6bkq5yN/ccg2jWP5saHv2ZXUtpZtbFmUzL/eHUuIwa0KNMHio+PN4/e2Zulk26ndkQQg2/7H3f8fQoff72KmuGB9O2qCQFFRESkahvaNxGHwzJj/lZ27knj2x82cfvIjm6dk/Dino3p0DKG/01eTa2IIK4d2tptbUv1pOSrnAUG+DLp9WtwOCxX3TuBvLyTTwhYmry8Qm58+CsiagTy9lNDz+jXlnYtYlj29R08PLoH7365gq9nbdCQQxERETkvdGwVQ3RkCNPmbeaN/y3B29vwl+s6u/U9jDGMuaMXAHdc00mTzcs5U/JVARo3qMlHzw9n+dp9PPjszDM69omxc1mzKYX3nr6c2jWDz/i9A/x9+e8jl/DTZ7fQv3sj7rnx7Oa8EBEREalMvLy8GHxRU2b8tJX3vlzBlZe0JC6mhtvf54pLWvDxf0cw5vZebm9bqh8lXxVk2MXNeejWHrz52VI+n7KmTMcsXL6b/777M6Ov7siQvs3O6f17XRDP7I9vpm3z6HNqR0RERKSyGNq3GUcz8ziamcf9N5fPD8xeXl6MGt6O0BD/cmlfqhclXxXomb/2p2en+tz+jyls2HrglPtmZOYx6uGviY8N5+VHB1ZQhCIiIiJVR//ujfH386FL2zi6tKvn6XBETkvJVwXy9fVmwqtXExzoy5X3TCietK80f31uJrv2pvPJCyP0S4uIiIhIKYKD/PjqjZF88OwwT4ciUiZKvipY3agwPn/5SjbvPMjtf59S6kTI0+Zu5t0JK/jbbT3o2amBB6IUERERqRoG92lGi4Q6ng5DpEyUfHlAv+6N+fd9fRk/bS3jPl923LbUQ1mMfnwybRKjePLevh6KUERERERE3M1jyZcx5j5jzDpjzHpjzP2lbE80xiwyxuQZYx7yRIzl6dE7e3HphQnc//QMlq3ZC4C1ljv/OZW0Izl8+sIV+Pv7eDhKERERERFxF48kX8aYVsBtQGegLTDEGJPwh90OA/cCL1ZweBXCy8uLT1+4gujIEK66dwKH07P53+TVfD1rA0/d35c2iapKKCIiIiJyPvFUz1dzYLG1NttaWwj8BAwvuYO19oC1dhlQ4IkAK0KtiCAmjh3JvgMZXHnPBO5+cjo9O9Xnr7f28HRoIiIiIiLiZp5KvtYBvY0xtYwxQcAgoFrWB+3cNo5XHhvIvMU7cVjLx8+PwNtbt+KJiIiIiJxvPHJTkbV2ozHmeWA2kAmsBgrPpi1jzO3A7QD169d3W4wV6S/Xd+ZoZh5tE6NpVL+mp8MREREREZFyYEordV7hQRjzDJBkrX2zlG3/AjKttae996tTp052+fLl5RChiIiIiIjIiYwxK6y1ncqyr8fK6Rlj6lhrDxhj6gMjgG6eikVERERERKS8ebKW+VfGmFo4C2rcZa1NM8bcCWCtHWeMiQaWA2GAw1WOvoW19qjnQhYRERERETk7Hku+rLW9Slk3rsRyMhBXoUGJiIiIiIiUk0pxz5e7GGNSgd3l+Ba1gYPl2L5UL7qexN10TYk76XoSd9L1JO5U2a6nBtbayLLseF4lX+XNGLO8rDfTiZyOridxN11T4k66nsSddD2JO1Xl60kTSomIiIiIiFQAJV8iIiIiIiIVQMnXmXnH0wHIeUXXk7ibrilxJ11P4k66nsSdquz1pHu+REREREREKoB6vkRERERERCqAki8REREREZEKRVp/6QAAIABJREFUUK2TL2NMPWPMPGPMRmPMemPMfa71NY0xs40xW13PEa71icaYRcaYPGPMQyXaCTDGLDXGrHa186Snzkk8y13XVIn2vI0xvxpjplX0uYjnufN6MsbsMsasNcasMsYs98T5iGe5+XoKN8ZMMsZscrXXzRPnJJ7jxu9QzVyfS8ceR40x93vqvMRz3PwZ9YCrjXXGmPHGmABPnFNpqvU9X8aYGCDGWrvSGBMKrACGATcDh621zxljxgAR1tpHjDF1gAaufdKstS+62jFAsLU20xjjCywE7rPWLvbAaYkHueuaKtHeg0AnIMxaO6Qiz0U8z53XkzFmF9DJWluZJqWUCuTm6+ljYIG19j1jjB8QZK1Nr+hzEs9x9987V5vewF6gi7V2d0Wdi1QObvxeHovzu3gLa22OMeZL4Dtr7UcVf1YnqtY9X9ba/dbala7lDGAjEAtcDnzs2u1jnP9RsdYesNYuAwr+0I611ma6Xvq6HtU3q63G3HVNARhj4oDBwHsVELpUQu68nkTcdT0ZY8KA3sD7rv3ylXhVP+X0+dQP2K7Eq3py8zXlAwQaY3yAIGBfOYdfZtU6+SrJGBMPtAeWAFHW2v3gvBCAOmU43tsYswo4AMy21i4pv2ilKjjXawp4Ffgb4CinEKUKccP1ZIFZxpgVxpjbyytOqRrO8XpqBKQCH7qGRb9njAkux3ClknPD59Mx1wDj3R2fVD3nck1Za/cCLwK/AfuBI9baWeUZ75lQ8gUYY0KAr4D7rbVHz6YNa22RtbYdEAd0Nsa0cmeMUrWc6zVljBkCHLDWrnB7cFLluOMzCuhhre0AXArcZYzp7bYApUpxw/XkA3QA3rLWtgeygDFuDFGqEDd9PuEavnoZMNFdsUnV5IbvUBE4e8saAnWBYGPMDe6N8uxV++TLdY/WV8Bn1tqvXatTXONOj40/PVDW9lxDL34EBro5VKki3HRN9QAuc92n8wXQ1xjzv3IKWSoxd31GWWv3uZ4PAN8AncsnYqnM3HQ9JQFJJUZ4TMKZjEk14+bvUJcCK621Ke6PVKoKN11T/YGd1tpUa20B8DXQvbxiPlPVOvlyFcp4H9horX25xKYpwE2u5ZuAyadpJ9IYE+5aDsT5H32T+yOWys5d15S19lFrbZy1Nh7nMIy51tpK86uNVAw3fkYFu25exjU8bACwzv0RS2Xmxs+nZGCPMaaZa1U/YIObw5VKzl3XUwnXoiGH1Zobr6nfgK7GmCBXm/1w3j9WKVT3aoc9gQXAWn6/r+YxnONLvwTq4/wPeJW19rAxJhpYDoS59s8EWgDxOG8A9MaZ0H5prf13xZ2JVBbuuqZKdrMbYy4CHlK1w+rHjZ9RtXH2doFzyNjn1tqnK+o8pHJw5+eTMaYdzmJAfsAO4BZrbVpFno94lpuvpyBgD9DIWnukYs9EKgs3X1NPAiOBQuBXYLS1Nq8iz+dkqnXyJSIiIiIiUlGq9bBDERERERGRiqLkS0REREREpAIo+RIREREREakASr5EREREREQqgJIvERERERGRCqDkS0REREREpAIo+RIREREREakASr5EREREREQqgJIvERERERGRCqDkS0REREREpAIo+RIREREREakASr5EREREREQqgJIvERERERGRCqDkS0REREREpAL4eDoAd6pdu7aNj4/3dBgiIiIiIlJNrFix4qC1NrIs+55XyVd8fDzLly/3dBgiIiIiIlJNGGN2l3VfDTsUERERERGpAEq+REREREREKsB5NexQpLrLzy/kSEYe6Rm5pB/NJf1oTonlXNIzckk7klO8nH40l6OZefj7eRMa7E9YiH+JZ78T14X4Fb8uuZ+Pj7enT11ERESk0lPyJeIGRUUOMrPzcTgsDofFWovD2uLXDofztbUc9/rYcpHDQXZOAVk5+WRm5ZOZnU9WTgGZWXm/L2c7t2Xl5J+wnJGVz5GMXLJzCk4Zp7e3F+FhAUSEBRAeFkB4aADRtUPILygiIyuPPfuPkJGVz9HMPDKy8sjNKzztuXt7e9EyIZJOrWLp1KounVrH0qZZFP7++ngRERE5nxQUFJCUlERubq6nQ/GIgIAA4uLi8PX1Pes29O1IBLDWkpGZR9rR43uM0o4c33tU+vZcMrLyyjU+X19vQoL8CA70JSTIj5BgP4ID/YiJDCUk2I+QIL/iZCo8LLDEcsBxy8FBfhhjyvy+Ba6kzJmM5ZdYdj1n5nHgcBa/btjPlDmb+GDSyuJ4Wzet40zIWtelU6tYWiZE4uenjxwREZGqKikpidDQUOLj48/o+8T5wFrLoUOHSEpKomHDhmfdjr4JyXmvqMjBgUNZ7E05SlLyUfamHD1u2fmcQVZ2/inbCQvxd/UaOZObhnERtG8eQEQN5+uQID98vL0wxuDl5XqUWDaG414fWz62f3CgL8FBzkQqJMiveDk40NdjSYuvrzc1w4OoGR502n2ttfy27wjL1+5l+bp9LF+7ly9nrOOdCc4KpP5+PrRNjKJT6997yJo3rq0hiyIiIlVEbm5utUy8AIwx1KpVi9TU1HNqR8mXnBcyMvNYuyWFNZtS2LzzIEnJR0lKPsLelAz2p2ZQWOg4bn8fHy/q1gklNiqMNs2iGXRhU+rWCaVmeGBxcnWsxyiiRiBhIf54e6s+zakYY2gQG06D2HCuGNgScCZkO35LY/m6vSxfu4/l6/by6berefOzpQAEBvjSuU0swy9uzohLWlAvpoYnT0FEREROozomXse449yNtdYNoVQOnTp1sprn6/zmcDjY/lsaazYls2ZzCms2p7B6YzI7k9KK9wkO8qNedBix0WHERoUR53qOjQolLroGsVGh1KkVjJeXkilPcDgcbNt9uDgZm7NoB2s2pQDQtV0cVw5syRWXtCA+LsLDkYqIiEhJGzdupHnz5p4Ow6NK+zcwxqyw1nYqy/FKvqTSSj+aw5pNKa4kK5k1m1JYuyWluKiEl5ehaXwt2iRG0zYxijbNommTGEW9mBrV+leZqmjLzoN89f0GJs1cz8r1+wHo1LouV17SkisGtqBJg1oejlBEREQ8nXwdOnSIfv36AZCcnIy3tzeRkZEALF26FD8/P7e9V1xcHOvWrSM8PPy49Uq+SlDyVbVlZuXx1fcb+HrWRlZt3M9v+44Ub6sZHkjbxGjaNHMmWW2bR9OiSSSBAWdfbUYqpx2/HS5OxJau2QtAu+bRXDmwJVcObEmzRrU9HKGIiEj15Onkq6R//etfhISE8NBDDx233lpn1elzHeFUXsmX7vkSj3I4HCxYtpuPvl7FxJnrycrOp2FcBD07NqDNdVG0cfVo1Y0KVW9WNdGofk0evq0nD9/Wk9170/l6ljMR+/src/j7K3No1bROcSLWMqGOp8MVERERD9u2bRvDhg2jZ8+eLFmyhGnTprFmzRr+/e9/k5eXR0JCAh988AHBwcHExcUxevRoJk+eTFFREZMmTaJp06akpqZy3XXXcejQIbp06UJ5dVAp+RKP2LknjU++XcXHX69iZ1IaocH+XDukNTePaEf3DvWVaAkADWLDeeCW7jxwS3f2Jh8tTsSefP1H/jV2Hs0a1eaizvF0a1+Pbu3rkRBfS9eOiIhIBbj/P9+xamOyW9ts1zyaV/8+6KyO3bBhAx9++CHjxo3jwIEDPPfcc8yZM4egoCCefvppXnvtNR577DEAoqKi+PXXXxk7diwvv/wy48aN44knnqBPnz489thjTJ48mXHjxrnz1Iop+ZIKc2xY4Udf/8qPS3ZhjKFft4Y8dX9fhg9oTlCg+8bpyvknNjqMe0Z15Z5RXUlOzeCbWRuZPGcT46et5e0vnMONa0UE0bVtXHEy1rlNLCHB/h6OXERERMpb48aNueCCCwD45Zdf2LBhA927dwcgPz+fnj17Fu87YsQIADp27Mh3330HwPz584uXL7/8ckJDQ8slTiVfUq5KG1bYpEFN/vNAP24c1pb6dcNP34jIH0RHhvLn6zvz5+s743A42Lj9IIt+3VP8mP7jFsBZlKV106jiZKxb+3o0aVBTvWMiIiLn6Gx7qMpLcHBw8bK1loEDB/Lpp5+Wuq+/v/OHWW9vbwoLC4vXV8T3A7ckX8aY+4DbAAO8a6199Q/bLweeAhxAIXC/tXahMaYd8BYQBhQBT1trJ7iO+Qi4EDhWdeFma+0qd8Qr5U/DCqWieHl50TKhDi0T6jD66o4ApB3JYcnqpOJk7LMpaxg3fhkAtSOC6Nru996xji3rEhYa4MlTEBERETfq3r079913Hzt27KBRo0ZkZWWxb98+EhISTnpM7969+eyzzxgzZgxTp04lIyOjXGI75+TLGNMKZ+LVGcgHZhpjpltrt5bYbQ4wxVprjTFtgC+BRCAbGGWt3WqMqQusMMZ8b61Ndx33sLV20rnGKBXDWsvM+Vt58f2fmbtop4YVisdE1AhkYO8EBvZ2fsgWFTnYsC31996xVXuYNs/ZO2aMoVnDWlzQJpYLWjsf7VpEE+CvSpoiIiJVUVRUFO+//z4jR44kPz8fgGeeeeaUydeTTz7Jtddey5dffkmfPn2IjY0tl9jOudS8MeYq4BJr7WjX638Aedba/55k/27AB9baE+pUGmNWA1e6krGPgGlnknyp1LxnFBYWMXHGep57ZwFrNqVQL6YGt4/syKjh7TSsUCqtw+nZLF2zl2Vr9rJsrfORnJoJgI+PF62bRjmTMVdS1jIhEh8fbw9HLSIi4jmVqdS8p1SGUvPrgKeNMbWAHGAQcEIGZIwZDjwL1AEGl7K9M+AHbC+x+mljzD9x9pyNsdbmlXLc7cDtAPXr1z/nk5Gyy80r4KOvVvHCewvZsSeN5o0j+ej54Vw7pDV+frqdUCq3muFBx/WOWWvZm3y0OBFbtmYvX85YxzsTnB9ngQG+tG8RXdw7dkGbWFVXFBERkTPilkmWjTG3AncBmcAGIMda+8BJ9u0N/NNa27/EuhjgR+Ama+3iEuuScSZk7wDbrbX/PlUc6vmqGEczcnnr82W88tEiUg5m0rlNLI/e2ZvL+jU75wntRCoTay3bdh8u7h1bvm4vK9fvJzunAIC6UaEM6NGES3o1oX/3RtSuGXyaFkVERKou9XxVjp4vrLXvA++73vwZIOkU+843xjQ2xtS21h40xoQB04G/H0u8XPvtdy3mGWM+BB4qrT2pOCkHM3nt40W8+dkyjmTkMqBnY8bccSUXdWmoX//lvGSMISG+FgnxtbjusjaAc5jtxu0HWbxqDz/8soMpczfx0de/YoyhQ8sYBvRszICeTejevp56gEVE5Lxjra223/vc0mnlpp6vOtbaA8aY+sAsoJu1Nq3E9iY4e66sMaYDMBWIA3yBGcDUUiokxlhr9xvnf91XgFxr7ZhTxaGer/Kxc08aL77/Mx9MWklefhFXDmzBmDt60aFlXU+HJuJxRUUOVq7fz6yF25i1cBu//LqHwkIHwUF+XNQ5ngE9mzCgZ2OaNapdbf9YiYjI+WHnzp2EhoZSq1b1G3ZvreXQoUNkZGTQsGHD47adSc+Xu5KvBUAtoAB40Fo7xxhzpyvQccaYR4BRru05OKsYLjTG3AB8CKwv0dzN1tpVxpi5QCTO8vWrgDuttZmnikPJl3ut3ZzC8+8s4Ivp6/DyMtw0vB1/u60nCfG1PB2aSKV1NCOXH5fuciVj29m66xAA9WJqMKBnYy7p1YR+3RpRMzzIw5GKiIicmYKCApKSksjNzfV0KB4REBBAXFwcvr7HV0Su8OSrslDy5R7rtqQw5oXZTP9xCyHBftxxTSceuLk7sdFhng5NpMrZuSeN2T9v5/sF25izaAdHMnIxxtC+RTRNGtQiPjacBrHhNKhbw/UcTmiIv6fDFhERkTJS8iVnJSe3gKf+70deeO9nwkL8eeDmbvzl+s76hV7ETQoLi1i2di+zFm5n4fLd7ExKZ8/+I+QXFB23X0SNwOOSsQaxNVzPzte1awZVu+EeIiIilVWFF9yQqm/2wm3c+c+p7NiTxs0j2vPCIwNUuU3EzXx8vOnWvj7d2v8+LYbD4SA5NZPd+9LZvfcIu/emu5bT2bb7MHMW7SAzK/+4doICfWlcvyYdWsTQsVVdOrWuS9vEaE1kLiIiUskp+armDhzK5MFnZvLZlDUkxNdi7qc306drI0+HJVJteHl5UTcqjLpRYXRrf+J2ay1pR3KciZkrKdu9L51NOw4yY/5WPv5mlasdQ4smkXRsWZeOrZyPds2VkImIiFQmGnZYTVlr+WDSSh5+fhaZ2fk8ekcvHr2zFwH+vqc/WEQqhWMTQ69Yv48V61yP9ftJOeisTeTlZWjeONKZjLWsS8dWMbRrHkNwkBIyERERd9E9X3JKm7ancsc/pjB/2W56dWrA209dRvMmkZ4OS0TcwFrLvpQMVqzfx/K1e09IyABCg/2pGR5IrfBAaoUHuZaDSn1d0/UcHhagSdRFRERKoXu+pFS5eQU8O24Bz769gJAgP9575nJuuaK9vlCJnEeMMcRGhxEbHcZl/RKB4xOytZtTOJiWzaH0bA6lZXP4SA679qZzKD2btCO5J51A0svLEFEjkMiIIOLjwmkYF0HDuIjjlmuGB6oQiIiIyCko+aom5i3ewZ3/nMqWnYe4bmgbXnl8IHVqhXg6LBGpAKUlZKVxOBykH83lUHpOcWJWMkk7lJ5Dcmomu/amsXTNXg6n5xx3fGiwf3EyFh8bTsO4cBrWiyA+NoKGceGEhQaU96mKiIhUakq+znMHD2fx0HPf8/E3q2hUL4LvPxjFgF5NPB2WiFRCXl5e1AwPomZ4UJkmUz+SkcuupHR27U1j5540dials2tvOjuT0pi7+MQqjbUjgrisXyKjhrWl1wUN1OsuIiLVju75Ok9Za/n029U8+OxMjmTk8tCtPfjHXReq8pmIVAhrLYfTc9iZlOZ87Elj7ZYDfPvDRjKz8omPC+fGy9ty47B2ZUr0REREKisV3KjmdiWlMfrxycz5ZQfd2tfj7acuo3WzKE+HJSJCVnY+387eyCffruKHX3bgcFi6d6jHqGHtuHpQKyJqBHo6RBERkTOi5KuaOlY+/v6nZ2AwPP/wxdxxbScN7RGRSmlv8lE+m7Kaj79ZxYZtqfj5ehcPSxzYOwFfX29PhygiInJaSr6qoeTUDG57fDLT5m3hws7xfPT8cOLjIjwdlojIaVlr+XXDfj75ZhWfT11L6uEsImsGc93Q1owa3o72LWJURVFERCotJV/VzKQZ67nzialkZuXz3EP9ufemrurtEpEqqaCgiJnzt/LJt6uZMmcT+QVFtGpah1HD2nHd0DbERod5OkQREZHjVHjyZYy5D7gNMMC71tpX/7D9cuApwAEUAvdbaxe6tt0E/N2163+stR+71ncEPgICge+A++xpgq1uyVfakRzufnI6n09dQ6fWdfnkv1dosmQROW+kHclhwvR1fPLtKhb9ugeAtonRXNyjMRf3aEyvCxoQGODr4ShFRKS6q9DkyxjTCvgC6AzkAzOBP1trt5bYJwTIstZaY0wb4EtrbaIxpiawHOgEWGAF0NFam2aMWQrcByzGmXyNtdbOOFUs1Sn5mrVgG3969FuSD2byj7su5LE7e+v+CBE5b23ddYiJM9Yx++ft/LxyDwUFRfj7+dCzU30u7t6Yi3s2pl3zaPX6i4hIhavo5Osq4BJr7WjX638Aedba/55k/27AB9ba5saYa4GLrLV3uLa9Dfzoesyz1ia61h+338lUh+QrKzufh5//nrc+X0bzxpF88sIIOrWO9XRYIiIVJis7n/nLdjH75+3M/nk767YcAJzziPXr3qi4Z6x+3XAPR3pquXkFpB/N/f2RUXI5p3g5Mzuf8LAAImsGux5Bxy3XCg/C21tJp4iIp5xJ8uWOSZbXAU8bY2oBOcAgnL1ZfwxqOPAsUAcY7FodC+wpsVuSa12sa/mP66u1X1b+xk1/+5rtv6XxwC3dePrB/hpyIyLVTnCQH5de2JRLL2wKwP4DGfzwy/biZGzC9HUANG1YqzgR69OlIWGhAeUaV1GRg5SDmexNyWBvytHix76UDFIOZR6XZKUdySUvv/CU7fn5ehNRI5CgAF/XMTml7meMoWZ4IJERQaUmaLUjgggN9iMsxJ/QEH9Cg/2LXwcG+KqYiYhIBTrn5Mtau9EY8zwwG8gEVuO8r+uP+30DfGOM6Y3z/q/+OO8RO2HXU6w/gTHmduB2gPr165/NKVR6eXmFPPnGPJ5/ZyH1Ymow99ObuahLQ0+HJSJSKcTUCeXGYe24cVg7rLWs33qgOBH78Ktf+b//LcUYQ0SNAGqEBFAj1J/wsABqhDof4aHOdTVCA5zri/cJLF4f4O9DcmrmcUnV3pQM9ib/vpx8MJOiIsdxsXl7exETGUJ0ZAgRYYHUi6lBeFgAEWGBhIc53zs8rMQjNLB4OcDf57jEqKCgiEPp2Rw4lEXq4SxSD2e7no9f3rg9lfnLsjiUnsPpRrd4eZniZCw02N+ZoLmWQ0P8CQ8NoG1iNF3bxZHYuLaGdYqInCO3Vzs0xjwDJFlr3zzFPjuBC4CL0bDDU1qzKZkbH/6KNZtS+NOVHXjlsYHl/uutiMj5Ii+vkEWr9jB/6S4OHM7iSEYe6UdzOJKRx5FMZ0/UkYw8jmbmnTZR+aMaoQHERoUSGxXmeriWo8OoW8e5XKdWsMeGBBYVOTicnsPBtGwysvJcj/zi56OZuc7XmSXXO/8tjr0+fCSHzKz84vPt0jaWru3q0a19Pbq0jdOk2CIieKbaYR1r7QFjTH1gFtDNWptWYnsTYLur4EYHYCoQB0TgLLLRwbXrSpwFNw4bY5YB9wBLcBbceN1a+92p4jifkq+iIgcvvLeQf742j5o1Ann3P5cxtF+ip8MSETkvORwOMrLyOZKRe3yClpHLkcw8cnILiKodclyyFRzk5+mwy53D4WDLzkMsXpXEol/3sHj1HtZtOYDD4fzu0KxRbbq1q0fXdnF0a1+Plgl1dP+ZiFQ7nki+FgC1gALgQWvtHGPMnQDW2nHGmEeAUa7tOcDDJUrN/wl4zNXU09baD13rO/F7qfkZwD3VpdT8b/vSueGvX7Fg+W6uuKQF4/49lNo1gz0dloiICBmZeSxbu5fFq/Y4E7JVSRxMywYgJNiPzm2cvWNd2zoTMv39EpHznSZZrsImzVjPbX+fTGGRg/97Ygg3Dmurm6FFRKTSstay/bfDv/eOrdrD6k0pFBU58Pb2Ylj/RO65sQu9O8fr75mInJeUfFVBWdn53Pef73h/4ko6t4nl85evonGDmp4OS0RE5Ixl5+SzfO0+ps7dzPuTVpJ2JIc2iVHcfUMXrr+sDUGB5/+QTRGpPpR8VTEr1+/j2gcmsnXXYcbc0ZMn7+2rCZNFROS8kJ2Tz+dT1/L6p4tZsymFiBqBjL6qA3+5vjPxcRGeDk9E5Jwp+aoiHA4Hr3y4iEdf+oE6NYP59MUR9OnayNNhiYiIuJ21lgXLdvP6p0v4ZvZGrLUM7duMe27sQt9ujTQkUUSqLCVfVcD+Axnc9Levmf3zdoZd3Jz3nr6cWhFBng5LRESk3O3Zf4Rx45fxzhfLOZiWTYsmkdx9QxduHNaWkGB/T4cnInJGlHxVctPnbebmR74hK6eAVx4byO3XdNIvfiIiUu3k5hUwYfo6xn6ymJXr91MjNIBbrmjPXTd0pkmDWp4OT0SkTJR8VVK5eQX87flZvP7pEtokRjH+5atokVDH02GJiIh4lLWWRb/u4fVPljDp+/UUFVkGXZjALVe0p32LGOLjwvHy0vxhIlI5KfmqhNZvPcC1D0xk7eYU7rupK889fDEB/r6eDktERKRS2ZdylLe/WM7bXywn5WAm4Jw/rGWTOrRuFkXrplG0blaH1k2jNIeYiFQKSr4qEWst4z5fxoPPziQ02J+Pnh/OoIuaejosERGRSi0/v5BfN+xn7ZYDrN2cwprNyazdcoBDrgmdAaIjQ2jTLKpEUhZFiyaR+nFTRCqUkq9K4uDhLEY/PpnJP2zikl5N+Oj54URHhno6LBERkSrJWktyaiZrN6ewdktK8fOGbank5hUC4OVlSIivRZP6NYmODCEmMrTU58AAJWgi4h5nknz5lHcw1dXS1UkMv2s8qYezefmxgdx3U1eNVxcRETkHxhhi6oQSUyeUAb2aFK8vLCxi2+7DJRKyA+zem86K9fs4cCgLh+PEH5prhAYQExlSamJWt04oHVrWJaJGYEWenohUA0q+ykndOqE0jItg2ts30L5ljKfDEREROW/5+HiT2DiSxMaRXHVpq+O2FRU5SD2cxf4DmSQfzPj9OTWT5NRM9qdmsHTNXvanZpCdU1B8nLe3F93ax3Fp7wQu7d2Udi2iVZlYRM6Zhh2WI2utPqhFRESqAGstmVn57E/NYM/+I8xbvJMZ87eycv1+wHl/2cBeCVx6YQIX92isXjERKaZ7vkRERETcIDk1g+8XbGPG/K3MWridtCM56hUTkeMo+RIRERFxs8LCIpasTmLGT1vVKyYixSo8+TLG3AfcBhjgXWvtq3/Yfj3wiOtlJvBna+1qY0wzYEKJXRsB/7TWvmqM+ZerzVTXtsestd+dKg4lXyIiIlJRTtYr1rh+BBFhgYSHBVAjNIDw0ADCwwJO+zoo0Pe0PWjWWoqKHBQVWQqLHM5lh6Ww0EGRw0FosD/BQX4V9C8gIlDByZcxphXwBdAZyAdm4kyutpbYpzuw0VqbZoy5FPiXtbbLH9rxBvYCXay1u13JV6a19sWyxqLkS0RERDyhZK/Y1t2HST+aQ/rRXI5k5pF+NJf0o7nk5Reesg0fHy9qhAbg7WWOS6hKJlqlVW4syRhDkwY1aZsYTdvEKNomRtOuRQxx0WEaGilSTiq61HxzYLG1Ntv15j8Bw4H/HtvBWvtLif0XA3GltNMP2G6t3e2GmEREREQqjI+PNz06NqBHxwYn3SeuLY+IAAAgAElEQVQ3r4AjGceSsRznckbu769diVpRkQMfHy+8vbzw9jb4eHvh7e2Ft5fB29vL9drg7eXl2s+53tvbi4OHs1i9KYWV6/cxaeb64veOqBFYnIy1TYymbfNoTUgt4gHuSL7WAU8bY2oBOcAg4FTdT7cCM0pZfw0w/g/r7jbGjHK191drbZob4hURERGpcAH+vgT4+xJVO6RC3u9oRi5rt6SwelMKqzcms3pTMu9+uaK4pL63txeJjWoX95K1ax5Dl3Zx1AgNqJD4RKojd93zdStwF877uTYAOdbaB0rZrw/wJtDTWnuoxHo/YB/Q0lqb4loXBRwELPAUEGOt/VMpbd4O3A5Qv379jrt3q+NMREREpDRFRQ62/3aYVa5k7FhSlpR8FHAOfezVqQFD+zZjSJ9mJMTX8nDEIpWfR6sdGmOeAZKstW/+YX0b4BvgUmvtlj9suxy4y1o74CRtxgPTrLWtStt+jO75EhERETlzh9KyWbVxPz/8soOpczezfusBAJo1qs2QPk0Z2qcZ3TvUx9fX28ORilQ+nqh2WMdae8AYUx+YBXQrOUTQtX4uMOoP938d2/4F8L219sMS62Kstftdyw/gLMRxzaniUPIlIiIicu527klj+o9bmDp3Ez8u2UV+QRHhYQEM7JXA0L7NGNi7CTXDgzwdpkil4InkawFQCygAHrTWzjHG3AlgrR1njHkPuAI4Niaw8FiAxpggYA/QyFp7pESbnwLtcA473AXccSwZOxklXyIiIiLulZGZxw+/bGfq3M1M/3ELBw5l4e3tRY8O9RjSxzk8MbFxbVVTlGpLkyyLiIiIiNs5HA6Wr93H1LmbmTZvM6s2JgPQuH5NhvRpSs+ODejQMoaG9SKUjEm1oeRLRERERMrdnv1HmD5vC9PmbWbOoh3k5jnnMouoEUiHljF0aBFDx1Z16dCyLo3rR+Dl5eXhiEXcT8mXiIiIiFSovLxC1m5JYeX6/axYt4+VG/axZlMK+QVFAISF+NPelYx1bFmXDi1jaNqwlhIyqfIqepJlEREREanm/P196NQ6lk6tY4vX5ecXsn5rKis37HMmZOv38+ZnS4t7yEKC/WjXPJqOLevSNjGalgl1aN44ktAQf0+dhki5Us+XiIiIiFSYwsIiNm4/6ErG9rFi/T5WbUwunvwZoH7dGrRoEkmLJnVo2aSOazmSME0ALZWQhh2KiIiISJVRVORgZ1Ia67ceYMO21OLnjdtTi3vJAOKiw2jRJJKWCXWciVlCJM0bRxIeFujB6KW6U/IlIiIiIlVeUZGDXUnpbNh2gPXbfk/MNm4/SE7u7z1ldaNC6dmxAZf2TuCSXk2IqRPqwailulHyJSIiIiLnLYfjWFKWyoZtB1izOYW5i3ey/0AGAG0ToxnYuwmX9k6gW/t6+PmpzIGUHyVfIiIiIlKtWGtZuzmFGT9tZeaCrSxc8RuFhQ5Cg/3p170RA3s1YWDvBBrEhns6VDnPKPkSERERkWrtaEYu85bsdCVj29i9Nx2A5o0jGdi7CQN7JdC7cwMC/H09HKlUdUq+RERERERcrLVs3nGQmfO3MmP+Vn5aupu8/EICA3y5qEs8nVrVJSzEn9Bg/+Ln0GA/wkICCA3xK14f4O+DMcbTpyOVjJIvEREREZGTyM7J56elu5g5fxsz5m9l665DZTrO29vLlZT9nqCFBvvj4+OFtRZrOf4ZTljvcFgsv6/z9vKiR8f6jBjQgk6t6yq5q4KUfImIiIiIlJHD4SAru4CMrDyOZuaRkZVfYtn5+mhmrnN95on7FDkcGGMw4Hw2pTxT+vqc3EKWrtlLUZGDejE1GH5xc0YMaE7PTg3w9vby9D+NlIGSLxERERGRKuJwejZT527m61kbmbVwG7l5hUTWDOby/on/z959x0V55A8c/wzdXsDeEFABG/aGXRZL1ECKmmg0vRiTXC65y/0u1ZRLubskxiTGNEuMNWrUWLB3UFAsYMMKimIXVPr8/thdjyAgZQvl+369eAnPPvvsMA67M8935juEGvwY0N0LV1fJ2FhayeBLCCGEEEKIMijlZhqrt8SxOCyWFRuPknwzjepVXbmvfytCDX4M7tOCKpVd7F1MkYPNB19KqZeBpwEFfK+1/iLX448Cfzf9mAI8r7XeZ3rsFJAMZAGZ5oIrpWoD8wFP4BTwsNb6akHlkMGXEEIIIYQoL9LSMlm/8wSLw2L5fd1hLl29hZurE8G9fQg1+DN8QCtq1ahk72JWeDYdfCml2gDzgK5AOrAa4+DqWI5zegKHtNZXlVJDgHe11t1Mj50COmutL+W67qfAFa31x0qpN4BaWuu/UwAZfAkhhBBCiPIoMzOLbVFnWLwmlsVhhzh74QbOzo4M6unFg8GtuT/Il9o1K9u7mBWSrQdfDwHBWuunTD+/BaRprT/N5/xawEGtdSPTz6fIe/B1BOintU5USjUANmmtWxVUFhl8CSGEEEKI8i47O5vIA+dYtDqGhatjOJVwDScnBwb28OLBwa25f5AvHrWr2LuYFYatB19+wO9AD+A2sB6I1FpPyuf81wDfHIO1k8BVQAPfaa2nm45f01rXzPG8q1rrWnlc7xngGYCmTZt2On36dIl+HyGEEEIIIcoKrTV7YhJZuOogC1fFcCL+Ko6ODvTv5slDQ9oQEuRHHXcZiFmTPdZ8PQlMxLieKxa4rbX+Sx7n9Qe+AQK11pdNxxpqrc8ppeoCa4FJWusthR185SSRLyGEEEIIUVFprYmOPc/C1caBWNzpKzg4KPp18+ShwW0IMfhRz6OqvYtZ7tg126FS6iMgQWv9Ta7j7YAlwBCt9dF8nvsukKK1/rdMOxRCCCGEEKJ4tNbsP3zBGBFbHcPRk5dxcFD06dKMh4a0IdTgR/061exdzHLBHpGvulrrJKVUUyAM6JEzM6Hp+AbgMa31jhzHqwAOWutk0/drgcla69VKqc+AyzkSbtTWWv+toHLI4EsIIYQQQog/01pz8GiScY3YqhgOHb+IUgrvprVo07IerX3q0KZlPdq0rEtLT3dcXGRPsaKwx+BrK+AOZACvaq3XK6WeA9BaT1NK/QA8AJgXZGVqrTsrpbwwRsMAnIBftdYfmq7pDiwAmgJngIe01lcKKocMvoQQQgghhChYzLEklq49RPSh88QcS+LoqctkZWUD4OTkQEtP97sGZd5Na+Po6GDnkpdOssmyEEIIIYQQolDS0jI5cvISMceSOHgsiYNHk4g5lsSJ+KuYxwquLk74eXsYB2Ut6tDRvyHdAhpTo5qbnUtvfzL4EkIIIYQQQpTIrdvpHDp+iYNHLxgHZHHGgVl84nUAlFK0bVWXnh2a0qtTU3p2aELzJrVQStm55LYlgy8hhBBCCCGEVVxPTiXywFm2R51h+54zhEcncCMlDYD6daqaBmNN6NmhKR38G+DqWr7XkMngSwghhBBCCGETWVnZxBxLYseeeLbvOcOOPWc4EW/Mvefq4kSXdg3vRMd6BDQpd/uOyeBLCCGEEEIIYTeJScns3GscjG2POsOe2EQyMrIAaNncnUE9vRkxsBX9ujYv85ExGXwJIYQQQgghSo3bqRlEHjjLjj3xbI08zYbwk9xOzaBqFReCA30YMdCXoX1b4FG77EXFZPAlhBBCCCGEKLVup2awYecJlq0/wvKNR0hMSsbBQdGzYxOG92/FiIG+tPLyKBPJO2TwJYQQQgghhCgTsrOz2ROTyLL1h1m+4QjRh84D4NOsNiMG+jJiQCt6dWqKk5OjnUuaNxl8CSGEEEIIIcqkM+eusWLjUZatP8zG8JOkZ2RRq0YlhvZtwfABrRjcp0Wp2l9MBl9CCCGEEEKIMi85JY2wbXEs23CEPzYd5fLVW7Ty8uDwmpfsXbQ7ijL4KtupRYQQQgghhBDlVrWqrjwwuDUPDG5NVlY2O/fGc/X6bXsXq9hk8CWEEEIIIYQo9RwdHQjs3MzexSgRB3sXQAghhBBCCCEqAhl8CSGEEEIIIYQNlKuEG0qpi8BpC1/WA7hk4WuKe5N6ty2pb/uQerctqW/7kHq3Palz25M6t73SVOfNtNZ1CnNiuRp8WYNSKrKw2UuE5Ui925bUt31IvduW1Ld9SL3bntS57Umd215ZrXOZdiiEEEIIIYQQNiCDLyGEEEIIIYSwARl83dt0exeggpJ6ty2pb/uQerctqW/7kHq3Palz25M6t70yWeey5ksIIYQQQgghbEAiX0IIIYQQQghhAzL4EkIIIYQQQggbKHeDL6VUE6XURqXUIaVUjFLqZdPx2kqptUqpY6Z/a5mO+yqldiql0pRSr+W4jptSapdSap/pOu8V8JrjTdc9ppQabzpWTSkVnePrklLqC2v//vZiqXrPcT1HpdRepdSKAl7zrno3Hf9QKRWvlEqxxu9aGpSW+pZ2Xvx6V0qdUkodMNVbZAGvOVgpdUQpFaeUeiPH8RdNx7RSysNav7M9lbL63pqjnZ9TSi211u9tbxau95pKqUVKqcOm6/XI5zUrbDuHUlfnFaKtW6rOlVKtcn0O3lBKvZLPa1bYdl7K6tu+bVxrXa6+gAZAR9P31YCjgD/wKfCG6fgbwCem7+sCXYAPgddyXEcBVU3fOwMRQPc8Xq82cML0by3T97XyOC8K6GPv+int9Z7jeq8CvwIr8nm9fOsd6G4qT4q966Ui1Heu86SdF7LegVOAxz1ezxE4DngBLsA+wN/0WAfAszDXKatfpam+c533G/CYveunjNT7TOAp0/cuQM2i1HtFaOelrc5znVdu27ol6zxXvZ7HuOGutPNSWt+5zrN5Gy93kS+tdaLWeo/p+2TgENAIGInxDQnTv/ebzknSWu8GMnJdR2utzZETZ9NXXtlJgoG1WusrWuurwFpgcM4TlFItMDairSX/DUsnS9U7gFKqMTAM+KGAl8y33rXW4VrrRIv8YqVUaarvHNeRdm5UqHovpK5AnNb6hNY6HZhnei201nu11qeK+7uUBaWpvs2UUtWAAUC5jAaA5epdKVUd6AP8aDovXWt9LY+XrNDtHEpXnee4Vrlu61Z6fxkIHNdan87jsQrdzktTfZvZq42Xu8FXTkopT4x3EyKAeuYOuenfuoV4vqNSKhpIwtjxjMjjtEZAfI6fE0zHchoDzNemIXZ5V9J6B74A/gZkF3BOYeq9QihF9S3tnCLVuwbClFJRSqln8jlH2rlJKarvEGC91vpG4UtfdpWw3r2Ai8DPyjit+QelVJU8zpN2nkMpqvMK09Yt8P5iNhqYm89j0s5NSlF926WNl9vBl1KqKsZQ4ivFrVStdZbWOgBoDHRVSrXJ66XyemqunwtqHOVKSetdKXUfkKS1jrrXqXkcqxCd/pxKWX1LOy+aXlrrjsAQYKJSqk9eL5XHMWnnxWOp+h6DtPPCcgI6At9qrTsANzFOK7rrpfI4VuHaOZS6Oq8Qbd1C7y8opVyAEcDC/E7J41iFa+elrL7t0sbL5eBLKeWM8T92jtZ6senwBaVUA9PjDTBGswrFFLLfBAxWSnXLsUhvBMaRdJMcpzcGzuUoS3vAqRCd2zLPQvXeCxihlDqFMUQ8QCn1S1HrvSIoTfUt7bzo7y9a63Omf5OAJRhv8DTJUe/PIe28VNW3Usod41SWP0r+m5VuFqr3BCAhx6yRRUBHaed5K011XlHauoX7i0OAPVrrC6bnSjvPpTTVtz3beLkbfCmlFMa5zoe01v/N8dAywJwRbzzw+z2uU0cpVdP0fSVgEHBYax2htQ4wfS0D1gAGpVQtZczQYjAdM6sod44sUu9a639orRtrrT0xRlI2aK3HFqPey7VSWN/Szov2/lLFNNcc05QgA3BQax2fo96nAbuBFkqp5qa7fKNNr1UhlML6fghjUppUS/x+pZUF31/OA/FKqVamQwOBWGnndyuFdV7u27ql6jyHP30OSjv/s1JY3/Zr47oUZECx5BcQiDGsuB+INn0NBdyB9cAx07+1TefXxzg6vgFcM31fHWgH7DVd5yDwdgGv+QQQZ/p6PNdjJwBfe9dLWan3XNfsRz7Z9wqqd4yZcxIwrmFKAN61d/2U5/o2PSbtvGjvL14YMy/tA2KAfxbwmkMxZoU6nvM84CXT9TIx3s37wd71U57r2/TYJmCwveulrNS76bEAINJ0raXkkSW1orfz0lbnFaWtW7jOKwOXgRr3eM0K285LU32bHrNbG1emAgghhBBCCCGEsKJyN+1QCCGEEEIIIUojGXwJIYQQQgghhA3I4EsIIYQQQgghbEAGX0IIIYQQQghhAzL4EkIIIYQQQggbkMGXEEIIIYQQQtiADL6EEEIIIYQQwgZk8CWEEEIIIYQQNiCDLyGEEEIIIYSwARl8CSGEEEIIIYQNyOBLCCGEEEIIIWxABl9CCCGEEEIIYQMy+BJCCCGEEEIIG5DBlxBCCCGEEELYgAy+hBBCCCGEEMIGnKz9AkqpwcCXgCPwg9b641yP9wG+ANoBo7XWi3I8Nh540/TjB1rrmQW9loeHh/b09LRg6YUQQgghhBAif1FRUZe01nUKc65VB19KKUfgayAISAB2K6WWaa1jc5x2BpgAvJbrubWBd4DOgAaiTM+9mt/reXp6EhkZadlfQgghhBBCCCHyoZQ6XdhzrT3tsCsQp7U+obVOB+YBI3OeoLU+pbXeD2Tnem4wsFZrfcU04FoLDLZyeYUQQgghhBDCKqw9+GoExOf4OcF0zGLPVUo9o5SKVEpFXrx4sdgFFaK4tNZcuXbL3sUQ5ciVa7fQWtu7GKKcuJ6cSlZW7vubQhRPdnY2127ctncxRDlREftQ1h58qTyOFbZHUajnaq2na607a60716lTqKmWQlhMzLEkBj8xC4+unxB54Ky9iyPKuITE64x77Tfcu3zMgpUH7V0cUcZdvX6bVz9ahUfXj/nXtC32Lo4oB7bsOkXnkO9o3Ps/XLx8097FEWXcgSMXMEyYSZ1unxAdm2jv4tiMtRNuJABNcvzcGDhXhOf2y/XcTUUtQEZGBgkJCaSmphb1qaKY3NzcaNy4Mc7OzvYuitVcunKTd7/ayLS5kVSr4oJS8Pu6w3RuW9jArhD/c+t2Op/9sJ1Ppm8jO1tTpbILS9YeYtSwtvYumiiDMjOzmD4/ire/3MCVa7epWd2NJWsP8ebEfvYumiijTsZf5W+fhrFodQwetSpz81Y663YcZ8zwdvYumiiDLl6+yTtTNvDdvEhqVHNDa1i2/jAB/g3sXTSbsPbgazfQQinVHDgLjAYeKeRz1wAfKaVqmX42AP8oagESEhKoVq0anp6eKJVXME1Yktaay5cvk5CQQPPmze1dHIvLyMjimzm7ePerjSTfTOe5MZ15d1J/Rj7/K2Hb4nj/LwPtXURRhmitmbv8AH//LIyE8zd4eGgbPnk9iHenbGT5xiNkZWXj6Cg7gojCW7stjr98tJqYY0n06+bJ5/83hD82HeXNz9dz8fJN6rhXsXcRRRmSnJLGR9O28N+fduDk5MD7rwzglQk98Oz/OWHbZPAliiY9PZOpv0QweepmUm6l8+LYbrwzqR9DnpxN2PbjvD2pv72LaBNWHXxprTOVUi9iHEg5Aj9prWOUUpOBSK31MqVUF2AJUAsYrpR6T2vdWmt9RSn1PsYBHMBkrfWVopYhNTVVBl42pJTC3d2d8rb+TmvNyk1H+evHazhy4hKGQG/++39DaN2iLgCGQB/e+2oTl6/ewr1WZTuXVpQFEdHxvPLhKsKjE+jUpiFzP3+IwM7NAGN7mrkkmj0xiXRpJ9FUcW9HTlzitY9Xs2LjUbyb1mbJN2MYOcgXpRQZmdm8+fl61m4/ziMjpLMs7i0rK5uZi6P5v/+u48KlFB4LCeCjVwfRqH51AAb19CJsexxaa+lfiXvSWrNiwxH++vEajp26zJC+LfjPG4Px8zEuFzIE+vCv77ZyPTmVGtXc7Fxa67P6LVWt9UqtdUuttbfW+kPTsbe11stM3+/WWjfWWlfRWrtrrVvneO5PWmsf09fPxS2DvDHYVnmrb/O6rvuemWN8A5n+KKt/euzOwAvAEOiN1pr1O0/YsaSiLDCv6+r+0PecOnuNnz8OYddvz9wZeAEM6uUFQNi2OHsVU5QR5nVdbYZNZfOu03z6NwMxK1/k/iC/O+/FHVs3oHbNStKeRKFs2XWKLqHf8eT/LcW7aS12/fYsMz8NvTPwAjD08uHchWRijiXZsaSiLDCv6xrx3K84OihW/jCWlT+MuzPwAmMfKisrmw0VpA9l9U2WhSircq/r+uKfQ3jh0a44OzvedW6Xto2oWd2NsG1xPDy0jR1KK0q73Ou6/vl8H954tjdVq7jedW5d96p0bN2AsG1x/POFvnYorSjtcq/revrhTkx+ZQD1PKreda6jowNBvbwJ235cIhUiXznXdTVpUIO5nz/EqGFt8mwvhkBvANZsjaNNy3q2LqooA3Kv65ry1lCeG9Mlzz5U94AmVKviSti244QY/O1QWtuSxQQ28uGHH9K6dWvatWtHQEAAERER9i7SXfr163dnk+qhQ4dy7do1rl27xjfffGPnktlWZmYWX87YSYugL5k2N5LnxnQmbt0rvDyhR55vGgBOTo4M7OHFmq1xkiJc/InWml+X7aeVYQrvTtnI8AGtOLxmEh+8OijPgZeZIdCHHXvjuZEsyYLEn63bfpyAEd8y8d0VtG1Zl72/P893H4zIc+BlZujlQ2JSMgePSqRC/FnKzTT+8e+1+AZPYeXmo7z/ygCOhL3E6Pva5jtQb9ygBv4+dSSaKu6SkZHFf3/aTougL5k+P4oXx3Yjbt3LTHqse759KGdnRwb0aF5h+lAy+LKBnTt3smLFCvbs2cP+/ftZt24dTZo0ufcT7WjlypXUrFnT6oOvIycucTL+qtWuXxwff7eVVz5cRdd2jdi3/AWmvnNfodZxGQJ9SDh/g8PHL9mglCIvZ8/f4ODRC/Yuxp8sWHmQR/+6iHoeVdk690nmf/kwno1r3fN5hkBvMjOz2bTrlPULKfJ0PTmV8L3x9z7RhiIPnMXw+CxS0zJZ8s0YNsx+nPZ+9e/5PHOkQjrL9hV18ByXrpSuFO3PvrWcj7/bypj72nI07GXenNiPSm73zlZsCPRhy+7T3E7NsEEpRV4OH7/I6bPX7F2MP/nw28389V9r6NWxCQdWTOTLt4ZSu2bh+lAnE65y/EyR0zuUORVq2uErH6wk+tB5i14zwK8+X7w5tMBzEhMT8fDwwNXVeJfbw8MDgKioKF599VVSUlLw8PBgxowZNGjQgO+//57p06eTnp6Oj48Ps2fPpnLlyixcuJD33nsPR0dHatSowZYtW0hNTeX5558nMjISJycn/vvf/9K/f39mzJjBsmXLuHXrFsePHyckJIRPP/0UgOeff57du3dz+/ZtHnzwQd577727yuzp6UlkZCRvvPEGx48fJyAggKCgIM6fP8+DDz7IyJEjAXj00UcZNWoUI0aMKHLdZWRkMWj8DBrWrUbEb88W+fnWYMzEs4shfVvwx/djizQ953/TMI79aS6zsJ0xf1nI4ROXOLf9NZyc8r7DZmuf/7yTls3diVj0TJEyF/bs0JQqlV1YszWOEQN9rVhCkZ9XP1rNzCXRnN32WoFRJVv6cmY4VSu7ELX0uSItTDdHKtZsjeOvT/ayYglFfs5fTKbnw98z5r62zPg01N7FAYxrUOevPMirT/TkP/8YXKTnBvf24YsZO9m6+zSG3j5WKqHIT3p6JgMem0HzxjXZPv9pexcHgNS0DL6Zs5vhA1qx7LtHi/Tc4EBjG1qzNQ6fZu7WKF6pIZEvGzAYDMTHx9OyZUteeOEFNm/eTEZGBpMmTWLRokVERUXxxBNP8M9//hOA0NBQdu/ezb59+/Dz8+PHH38EYPLkyaxZs4Z9+/axbNkyAL7++msADhw4wNy5cxk/fvydPc2io6OZP38+Bw4cYP78+cTHG+/gfvjhh0RGRrJ//342b97M/v378y37xx9/jLe3N9HR0Xz22Wc89dRT/PyzMffJ9evX2bFjB0OHFjz4zM/SdYdIOH+DXfvPEp94vVjXsLQFq2K4cCmFl8d3L/K6CM/GtWjZ3J2w7cetVDpRkL0xiWyNPM3FKzfZFnXG3sUBjBkNI/YlMGlc9yKnjHd1daJfV0+JVNjJpSs3mbNsP1lZ2Sxbf9jexQEgMSmZ+SsP8vgDHYqVEUwiFfY1fV4k6RlZLNtwhIyMLHsXB4Bv5+5Ga82kcd2K/Nw+XZrh4uxI2HZ5j7KHRatjSUxKZseeeBKTku1dHADmrTjIxSs3eWVCjyI/17tZbbya1CJsW/nvQ1WoyNe9IlTWUrVqVaKioti6dSsbN25k1KhRvPnmmxw8eJCgoCAAsrKyaNDAuLncwYMHefPNN7l27RopKSkEBwcD0KtXLyZMmMDDDz9MaKjxrtm2bduYNGkSAL6+vjRr1oyjR48CMHDgQGrUqAGAv78/p0+fpkmTJixYsIDp06eTmZlJYmIisbGxtGtXuPTDffv2ZeLEiSQlJbF48WIeeOABnJyK14ymzArHo1ZlLl29xdK1h5j0WPdiXcdStNZ8OXMnvl4eGAKLdxfPEOjDjwv3kJaWiatrhfrzsruvZodTuZIz2dmaxWti6dfN/vvMTZkVQfWqrowPCSjW8w2BPvyx6SgnzlzBq2ltC5dOFOT7BVGkpWfiXqsyi8NieXpUZ3sXie/m7SYzM5tJjxW9owzG6LxEKuwjPT2Tb+fuvvOZt3nXKQb18rZrmW6nZvDdvEhGDPQt1FTo3CpXcqF3l2YVorNcGuXuQz3/aFe7lkdrzZRZ4bRuUZf+3Yv3+WsI9OGXZfvIyMjKd31YeSCRLxtxdHSkX79+vPfee0ydOpXffvuN1q1bEx0dTXR0NAcOHCAsLAyACRMmMHXqVA4cOMA777xzJ5I1bdo0PvjgA+Lj4wkICODy5csFLkw0T3M0v5bYUnwAACAASURBVH5mZiYnT57k3//+N+vXr2f//v0MGzbszvULa9y4ccyZM4eff/6Zxx9/vBi1AXtizrEt8gz/93wf/LzrsGTtoWJdx5IiohOIPHCOSY8VPeplFhzow+3UDLZFnbZw6URBLl6+ya/LDzA+JABDoA9L1x22+6LdcxdusGDVQZ54sCPVquafWKMgwaYOsnRubMu8mfqgnl5MCA1g/c6TXLdz4pO0tEymzY1kaN8WxZ6S07erJy7OjqyRaKrNLVody/mLKXz3/ggquTmXis+8eSsOcPnqLV4q5mAejJ95B45c4NyFGxYsmbgX86yKtyb2o4Wne6loT9ujzrA3NpGXStKH6u1Dys10dpaytbaWJoMvGzhy5AjHjh2783N0dDR+fn5cvHiRnTt3ApCRkUFMTAwAycnJNGjQgIyMDObMmXPnecePH6dbt25MnjwZDw8P4uPj6dOnz51zjh49ypkzZ2jVqlW+Zblx4wZVqlShRo0aXLhwgVWrVhVY9mrVqpGc/Odw9oQJE/jiiy8AaN26dV5Pu6evZkVQpbILjz/QgVCDH1t2n7b7IuQvZ4ZTo5obj93fvtjX6NfNE2dnR+ks29j3CyJJS8/kxbHdCDX4EZ94naiD5+xapmlzd5OVpXlxbPE7Ni2bu9O0YQ2Zemhj5inRLz3WnVCDPxkZWazcdNSuZVqw6iAXLqXwUglmCPwvUiHtydamzAqnZXN37g/yZUifFixdd4js7Gy7lcc40yOctq3qlWiWgHmWyFqZbm9TX82OoFoVVyaEBhBq8GNjxEmuXr9t1zJ9OTOcWjUqMXZk8Tdy79+9OY6ODuX+PUoGXzaQkpLC+PHj8ff3p127dsTGxjJ58mQWLVrE3//+d9q3b09AQAA7duwA4P3336dbt24EBQXh6/u/hfavv/46bdu2pU2bNvTp04f27dvzwgsvkJWVRdu2bRk1ahQzZsz4U8Qrt/bt29OhQwdat27NE088Qa9eBS+8dnd3p1evXrRp04bXX38dgHr16uHn51fsqFfS5RR+Xb6f8SEB1KxeiRCDP1lZ2SzfcKRY17OEs+dvsGhNDE8+1LHA9N/3UrWKKz07NCn3bxyliTFKsZugXt74t6jL8AGtcHR0YHFYrN3KZI5S3Ne/Jd7Nij9dUCmFIdCH9TtPkJlZOtaIVARTZoXj1aQWQ/u1pHtAY+rXqcriMPvdWTZ3lP286xAUWLKpaoZe3hw8miSRChvKufbTwcGBEIMf5y4ks2vfWbuVaevu0+w7fL5EUQqAtq3qUs+jKmu2ymeerSQmJbNgVQyPP9CB6tXcCAnyIzMzmxUb7deHOnPuGkvWHuLphztRuZJLsa9To5ob3QMal/+181rrcvPVqVMnnVtsbOxdx0TJ3Lx5U3t5eelr167le05B9f7B15s0Pm/p2GNJWmuts7OzddM+/9bDn/nF4mUtrDf/u06rFm/r46cvl/haH327WePzlj5/MdkCJRP3Mv+PAxqft/Ty9YfvHBsw7ifta/jSbmWauXivxuctvXZbXImvtXDlQY3PW3pb5CkLlEzcS9TBsxqft/R/f9p+59izb/6uq7R7X9+6nW6XMu3Yc1rj85b+5peIEl8rOjZR4/OW/nnRHguUTBTGo68u1NXaf6BvJKdqrbW+cu2WdvJ9R//tkzV2K9MDE+fq2p0/0jdvpZX4WuNeW6Q9uvxLZ2VlWaBk4l7e+XK9xuctffTkJa211llZWbphr091yPO/2q1Mb3wWph1avq1PJVwt8bUmf7VRqxZv64uXUyxQMtsBInUhxysS+RJFsm7dOnx9fZk0adKdZB5FkZGRxTe/7sIQ6H0nHbtSipAgP8K2HSflZpqli3xPqWnGRcfDB7SySFIDmYZhW1NmhePdtDZD+7W4cyzU4M/hE5c4FHfR5uXRpsQt/j51GNjTq8TXG9CjOQ4OSqay2kjOKdFmoQZ/bt5KZ52d/qbNU6LHlWBKtJk5UiHRedswRymeeLDDnbWftWpUYkD35ixZe8gua1NPnzVGKZ4Z1blEUQozQ6APl67esvhWPuJuOdd+tvA0rv10cHAgJMiP1VvjuHU73eZlunU7nenzIwkJ8qNZo5olvp4h0ButNet3nrBA6UonGXyJIhk0aBBnzpzhlVdeKdbzF4fFcu5C8l3rFkKC/EhLz2TVlmP5PNN6zKlRXx5vmWyLHfzr41GrsnRubCDq4Dm2R53hxbFdcXD439vZ/YP8AFiy1vZTD3fsOcOemJItOs6pds3KdGnbSNqTDeSeEm3Wr5snNaq52WXqYULidRatjuWph0s2JdrMwcGBoF7erN1+3K5rjioKc4bKF3Olcg8J8ufYqcvE2uEG0TdzdqGU4vlHuljkekG9zHtcynuUtS1cbdwO56Xxd/ehbqdm2OUm3a/LD3Dl2u0SrUfNqXPbRtSqUalct6cKMfiyx52liqyg+jZHKYb0bfGn44Gdm+FRqzJLbNy5MUcpSpIaNTdz5yZsm3RurO2rWeHGKMWDHf90vFH96nRt18guGaC+nBlOzepujB1Z8iiFWXBvH3btP2v3BdXl3ffzo0jPyLorSYqLixP39W/J8o1HbL727tu5u8nO1kx8tPiJW3IL7m2MVOyNlUiFNRWUoXLkIF+UUixeY9sbRDdvpfP9gihCgvxo2rDkUQqAeh5VCfCrLzeIrMzcX2nl5XFnwGvWp4sntWpUsvlaZ3OZ2vvWp3eXZha5pqOjA4N6ehG27Xi57b9bffCllBqslDqilIpTSr2Rx+OuSqn5pscjlFKepuOeSqnbSqlo09e04ry+m5vbPVOyC8vRWnP58mXc3O7eADTywFl27Iln0rhuf4pSgPGPbcRAX/7YdJS0tExbFZdtkaeJPlTyRce5GQJ9uHAphQNHkix2TfFnSZdTmLviABNCA/LccDbE4EfkgXOcOXfNZmWKT7zO4rBDPPVQJ6pULvl0HjNDoDfZ2Zr1O8rvNAx7y2tKdE4hQX5cvnqLrZG220bif/swtaJ5k6Lvw5SfQabpsNJZtq47UYo8IgIN6laje0Bjm98gmrNsP1ev37bYTA8zQ6AP2/fE22XpQEVxZzucPPpQzs6ODB/QiuU23sB7U8RJDh5N4uXxlu9Dnb1wwy5LB2zBqrvAKqUcga+BICAB2K2UWqa1zjk0fxK4qrX2UUqNBj4BRpkeO661Lt7upCaNGzcmISGBixfL539gaeTm5kbjxo3vOv7V7AiqVnFhQo61FDmFGvz4adEeNoSfYEjfltYuJmDcBLekqVHzYr4rFbYtjvZ+9S16bWE0fV5knlEKs1CDP//49zqWrj181xQNa/n2111orZk41rKbXXZt15jqVV0J2x7Hg0OKt72DKJh5SvT090fk+fjgPi1wc3Vicdgh+ncv+Vq+wjDvw2TpjnL9OtVo72uMVPzjuT4WvbYwMkcEfL088s1QGWrw4/VPwjiVcLVYmxwXp0xTZoXTwb8BvTo1tei1DYHefPr9NjZFnOK+AflvdyOKb8qscKpXdeWx+/PuFoca/Ji1JNqmG3hPmRWBR63KjBne1qLXNZj+ZtZsi8O/RV2LXrs0sOrgC+gKxGmtTwAopeYBI4Gcg6+RwLum7xcBU5UFh8/Ozs40b26Z6WSi+C5cSmHeigM8M7pznlEKgIE9vahaxYUlaw/ZZPBlTo361yd6WmTRcU6N6lenTcu6rNkWx+tPB1r02sIcpdhNcG8ffL3vjlIAtGzugb9PHZasjbXJ4Ot2agbT50cxcqCvxTtSzs6ODOzhxZqtcWitLXqHURjlNyXarEplF4J7+7B07SGmvDXU6v8H2pRevk3LuiXahyk/wb19+HzGTlJupllkLZn4M3OU4ut378u3rYQE+fP6J2EsWXuIvzze0+pl2rDzBDHHkvj54xCLt9/ATs2o5OZM2LY4GXxZwbkLN1i4OoYXx3a7k7glN0OgD5UrObM4LNYmg6+T8Vf5fd1h/vFcb9xcnS167aYNa+Lr5UHYtjib/G3YmrWnHTYCcm5TnWA6luc5WutM4DpgnhzdXCm1Vym1WSnVO68XUEo9o5SKVEpFSnSr9LpXlALAzdWZoX1b8vu6w2RlWX+t1DdzjFGKFx61bJTCzBDow9bdp+2Sfai8+21NLIlJdyduyS0kyHYbeP+6fD+Xr96y2kDPEOjDmXPXOXryslWuX5EVNCU6p5AgPxLO3yDygPU38LbUPkz5MQR6k5GRxaaIUxa/tsgZpch/7ad3s9q0bVXPZmudp8yKoE7tKoy+r43Fr+3q6kS/bp6skamsVjFt7m6ysgqeVVHJzZnBvVuwdN1hm6w3/3pOBA4OihcesV4favOu06SmZVjl+vZk7cFXXp8YuRdf5XdOItBUa90BeBX4VSlV/a4TtZ6ute6ste5cp07ed8CFfaWnZ/Lt3N0M7uNDKy+PAs8NNfiRdPkmO/acsWqZbt3+36JjS6RGzUtwoA/pGVls3nXKKtevyKbMCsenWW0G9/Ep8LzQYH+yszXL1lt380nzdJ52vvXo29XTKq8R3Nv4u5bnDFD2cq8p0Wa23MB7yqxwatesxKMjLDsl2swcqZDOsuWZoxRPPnTvDJWhBj+2RZ3hwqUUq5bpxJkrLN9whGdHd7Z4lMIsONCHoycvcyrhqlWuX1GZE7cM63d34pbcQg1+JCZZfwPvlJtp/LBgDw8O9qdR/bu65hYR3NuH26kZbIu0bn/QHqw9+EoAmuT4uTGQ+5bhnXOUUk5ADeCK1jpNa30ZQGsdBRwHbLMQSFhUYaMUAEP6tMDF2dHqi5DnLNtv0dSoeendpRmuLk6yP5OF7d5/lp1745k0rnuBUQqADv4NaNaoptVTzm/edYr9hy9YLUoB0LxJLXya1ZYkCRZmnhI9IbRDvlOizWrXrEy/bp5Wf38y78P09MOdLD4l2swcqZD2ZHl3ohSFyFAZEuSP1ppl6w9btUxTf4nA0dFy6eXzIntcWsf8lcbtcArTXxnWvyVOTta/QTR76T6uJ6dafD1qTn27euLs7Fgu36OsPfjaDbRQSjVXSrkAo4Fluc5ZBow3ff8gsEFrrZVSdUwJO1BKeQEtAEn1VQZ9NTuCFp7ud+7cF6R6NTcG9fSy6uaT5iiFJVOj5qWSmzN9ujQrl28c9vTV7HBjlCL03rl4lFLcP8iXtdtPkJxivSxcU2aG416rMo8Mt06UwswQ6MPGiJM2zQha3hVmSnROIUF+HLHyBt7mfZisNSXazNDLWyIVFmaOUtzXvyXezWrf8/x2vvVo3riWVQf0KTfT+HHhHh4a3JqG9awTpQDw9fagcf3qEp23IHN/xc+7TqHWcdWsbv0NvM1l6ty2Id0Dmtz7CcVUpbILgZ2alssb2FYdfJnWcL0IrAEOAQu01jFKqclKKXNKqR8Bd6VUHMbpheZ09H2A/UqpfRgTcTyntb5izfIKyzNHKV4cW/BaipxCg/05lXCNaCvtQWOt1Kh5Ce7tQ2zcReITr1v1dSqK8xeTmffHQSaEdqD6PaIUZqEGf6tu4H0q4Sq/rz/MM6M6UcnNOtN5zIJ7+3DrdgY79pa/aRj2UJQp0WbmDbytdWc555RoS+3DlJ/g3sbkIuWxc2MvRYlSgPEGUWiwH+t3nOBGcqpVyjRr6T5upKRZdaYHGH+X4N4+rN95wub74ZVX4dHxRB00ppcvbH8l1OBP3OkrxByzzlY367Yf5/CJS1ad6WEWHOjDvsPnOX8x2aqvY2tW3+dLa71Sa91Sa+2ttf7QdOxtrfUy0/epWuuHtNY+Wuuu5syIWuvftNattdbttdYdtdbLrV1WYXlFiVKYjRjgi4ODstpUsS9nhlslNWpe7kzDkM6NRUyfF0lGEaIUAL06NaVO7SpWW9T+9S/GKMXzVlp0nFO/rp44OTlIZ9lCijIl2qxR/ep0a2+9/Zl++d24D9NLj1luU+X8mCMVEp23jJxRioE9C78dQUiQH+kZWazcbPkbRNnZ2UyZFU7Xdo3o3sF6UQozQ6AP126ksvuAddccVRRTZkZQo5ob4wpI3JKbeQNva33mfTkznHoeVXl4iOUTt+RWXqeyWn3wJSouc5Ti8SJEKQDquFchsFNTq3RuTsZfZdn6IzxjxUXHObVpWZcGdasRtl06NyVVnCgFmDfwbmWVDbxv3krnh4VRhBr8aNKghkWvnZfq1dzoEdBEOssWMmVWeKGnROcUEuRH1EHLb+Bt7rwH+NUnsLP1pkSbKaUwBEqkwlJ27jVGKYoaEejRoQn1PKpaJZq6dvtxjpiiFLYwsIcXSim5QWQBZ8/fYNGawiVuyal+nWr06NDYKu3p2KnL/LHpKM+N6Yyrq7V3q4L2fvWoU7tKuZvKKoMvYTV3ohTjin4HN9Tgz8GjSRw7Zdm02tZOjZqbUgpDL2/Wbj9uk/T55dmi1bGcv5hSrE5EqMGf5JtprN9p2WWjv/y+j2s3rLvoOLfg3j7siUkk6bJ1s6OVd7v2JRAenXDP9PJ5CTEYpx5a+s7yxvCTxBxL4uXxPWy2l1twb4lUWMqUWeFFjlIAODg4cP8gX1ZtOWbxtNpTZoZTv05VHrLR5uzutSrTpW1DuUFkAf9L3FL0/kqowZ/oQ+c5GW/Z9Zxf/xKBs7Mjz42xXuKWnBwcHDAEGvtQtkifbysy+BJWYY5SDOnbgpbNCx+lMLs/yNy5sdydG1ukRs2LIdCHK9dusycm0WavWR4VN0oBxrux1aq4WjSaao5SdGzdgJ4dm1rsuvdinoaxbrvkHyqJr2ZHUK2KK+NDCj8l2qxlcw9at6hr8ei8eUq0NfZhyo9EKiwjIfE6i1bH8tTDHalSuegZKkMMfqTcTLfo3/XRk5dYufkYz4/pgouL9aMUZoZAHyL2neXajds2e83yJjUtg+/mRTJ8QCu8mt47cUtuIaY+1NJ1lnuPupGcyk+L9jJqaBvq16lmseveiyHQh6TLN9l/+ILNXtPaZPAlrKIkUQqAZo1q0rF1A4t2bsypUW01/cJsUC/j3H+5E1h8EdHxROwrXpQCjGm1h/ZrYdENvNfvOEFs3EWbLDrOqWPrBtSuWUnaUwmcv5jM/JUHefyBok2JzikkyI+tkae5eNkyG3jbYh+mvLjXqkxniVSU2LS5u8nOLlx6+bz079acGtXcLDpVbOpsY5TiWRtFKcwMgd5kZWWzYedJm75ueTL/D3PiluK1J6+mtWnnW8+i7WnmkmiSb1o/cUtuQaYsj+Vp6qEMvoRVTJkVTsvm7hgC750aNT+hBn/CoxM4e/5GicuTMzVqDxssOs6prntVOrZuUK7eOGytJFEKs1CDPxev3GR7lGUyBU6ZFU5d9yqMHmb9xC05OTo6ENTLm7Dtx62WSri8+64EU6LNQg2mDbw3WGZ/pq/n7DLtw2SbKdE5BUukokTMUYoRA1vRvEmtYl3DxcWJ+/q3ZNmGIxZZf3cjOZWfF+9l9LA21POoWuLrFUX3gCZUq+IqA/pi0lrz5cxw/H3qMKBH4RO35BZq8Gd7VLxFNvDOzs7mq9kRdA9oTJd2jUp8vaJoULca7Xzrlau18zL4Ehb3vyjFvTfBLYg5bP67BTafXLvNdqlR82II9GFndLzVUgmXZ4lJySxYFVOiKAUYN/B2dXGySDT1+OkrrNh4lGdH22bRcW6GXj4kJiVz8Kh1UgmXZ+npmXz7626G9m1BC0/3Yl8nwL++cQNvC6z7Mu/D9GBwa5tOiTaTSEXJzFtxkEtXb5U4IhAS5Mflq7fYZoEbRD//tpeUm+m8PL5Hia9VVM7Ojgzo0Zw12+LkBlEx7Nhzhr2xiSXur4QE+VlsA+/VW+I4duqyXdoTGD/ztkWe4eatdLu8vqXJ4EtYnCWiFAB+PnVo2dzdIuu+psyyXWrUvBgCvcnMzGbTrlN2ef2y7Lt5u0scpQCoVtXVYht4T/0lAkdHZbNFx7mZI8pyZ7noFq6K4cKlFF4qYZIUpRQhQX6s3X68xBt4zzJPiR5v/fTyeZFIRfGZZ1W0blGX/t2bl+hag/u0wM3VicVrSvaZZ45S9OzYhE5tGpboWsVlCPThVMI14k7L9qxFNWVWBDWruzF2ZNESt+TWtlU9vJrUssjUwymzwmlYrxoPBPuX+FrFYQj0Jj0ji83lpA8lgy9hUeYoxRMPdqBa1cKnRs2LUopQgz8bI05x5dqtYl/H1qlR89KzQ1OqVHaRqYdFlJaWybS5kSWOUpiFGvw5ffYae2OLn/wkOSWNnxbt4eEhbWhYz/ZRCoDGDWrg71NH2lMxTJkVTisvjzvrCEoi1GDen+losa+RnZ3NV7PC6dKuEd0DbDsl2kwiFcW3PcoyUQqAKpVdCO7tw9J1h0v0/7Bq8zGOn7li87U5OQWbEgPJe1TRJCRe57c1sTz1UKdiJW7JydyHWr/zJNdLMOvm8PGLrNkax/NjuuDs7FiiMhVX7y7NcHN1Kjc3iGTwJSzqu3m7yczMLnGUwiwkyI+srGxWbCx+5+bOouPR9olSgDHhQ7+unuXmjcNWFq62TJTCbPiAVsYNvEswVWzmkmhupKTZZBPcghgCfdiy+zS3Uy2bmro8i4iOZ9f+s8VO3JJbz46mDbxLMJV17Xb7Tok2k0hF8UyZFU6tGpV4dEQ7i1wvJMiP+MTrRB08V+xrfDkznEb1qhNqsE+UAsC7WW28mtSSz7wi+nbubrTWTBxrmbWfIQY/MjKyWLmp+H2or2ZH4OriZNc+lJurM327epabrKwy+BIWkzNK4dOs5FEKgM5tG9KoXvVid2527Uvgx0V7eHhIaxrUtV1q1LwYAn2IO32FE2ekc1MYxkXHOy0WpQDjBt69Ozcrdns6GX+VT6ZvpWu7RnSzU5TCzBDoTVp6Jlt2n7JrOcqSKbMiqF7VlcfuL9mUaDNHRwdGDvJl5aZjxdrA+8q1W/zj3+uo51GVhwbbZh+m/JinskqkovDiE6+zOOwQTz1UvPTyeRk+oBWOjg7Ffo/6bXUMa7cf54VH7RelMDME+rAx4iTp6Zbd3L68up1qTtzii2fj4iVuya17QGPq16la7Pa0c+8ZZizey5j72lLHvYpFylRcwb19OHT8IvGJ1+1aDkuQwZewiEtXbvLY3xZz4VIKkywYEXBwcCDE4MearXFFXmi5bvtxBjw2g3ruVfjw1UEWK1NxmfenKi93bqzp1u10/vqv1UQeOMeLYy0TpTALNfgTcyyJoycvFel5B45coNfoH7h5O4Ov373PYuUprr5dPXFxdpT2VAiZmVl89v02Fqwyppcv6ZTonEINfsXawPvchRv0ffQnYo4lMf2DEXabEm3m08xdIhVFcPz0FR55dSFaa14oxia4+aldszL9unkWa93XDwuiePjlBfTs2IRJ4+w35dAsuLcPKTfT2bk33t5FKfUuXr7JuNd+4/LVW0yy0MwhMG/g7cfKzceKPEtizdZjDBo/k4Z1q/H+KwMsVqbiMu9xWR7eo2TwJUps4aqD+A+ZypK1h3j/lQF3/kAsJSTIj9upGUW6I/vb6hiGPf0LXk1qsW3eUzRrVNOiZSqOls3dadqwRrl447CmzREnaXffN3z+806eG9OFZ0Z1suj17w/yBSjS1MOde8/Q55EfUQq2zn2Szm1tm2o3L5UrudC7SzNpT/dw8OgFeo76gb99GsZ9/VvxzqT+Fr3+gO7GDbyLsqg97vRleo3+gVNnr7Hqx3GMGOhr0TIVl0Qq7i0rK5svft5B2/u+Zv/hC8z4JMRiUQqzkCA/Dp+4xOHjFwv9nE+nb+Xpf/6OIdCbsJ/HW/QGQ3H1794cR0cHwrbLDaL8aK2Zt+IA/kO+YvmGI/zrtUElTtySW0iQHzdvpbOuCP8P8/84wPBnf6Wlpzvb5j1F4wY1LFqm4vD3qUOjetXLRXReBl+i2M5fTObBF+fx8EsLaNaoBlFLnuPNif0svm6hT5dm1K5ZqdBh8+/nR/Lwywvo3LYhm+c8YffphmZKKQyBPqzfeYKMjJLv41LeJKekMfHdFfQb+zNaazbMnsC3k4fj4mLZiEDThjXp1KZhoduT+e6fR63KbJ/3FK1b1LVoeUrC0Mubg0eTLLIXXnmTkZHF+1M30fH+aZyMv8r8Lx9m8dejqVWjkkVfx9XViWH9WrJs/ZFCbeC979B5Akf/SPLNdDbMerxE+/hYmiHQWyIVBTh8/CK9x/zIXz5azcAezYldNYlxFprCmtP9g4zbrBTmPUprzd8+WcPfP1vLmPva8vu3j1hsCmRJ1ajmRveAxuWis2wNiUnJhLwwlzF/WYhXk1rs+f153ni2j8X7UP26eVKjmluhP/Om/bqbMX9ZRPf2jdk05wmb7xOXH2Mfypt1O04U6r22NLP64EspNVgpdUQpFaeUeiOPx12VUvNNj0copTxzPPYP0/EjSqlga5dVFI7Wml9+30froVNZsfEon7wexM4FT9O2VT2rvJ6TkyPD+7dixcYj97wj+8l3W3nmzWV37v5ZuqNVUsGBPtxISWPX/gR7F6VUCdsaR5thU/n21928MqEH+1dMpH9363VKQ4L8iNh37w28c9/9s/Qd7pIK7t0CMCZtEP+zNyaRLqHf8faXG3jA4E/sqkk8PLSN1RJahBj8CrWB97bI0/R99CecnRzYNvdJm29Wei8DenhJpCIPmZlZfPzdFgJGfMuRk5f45d8PsOy7R622J1uj+tXp2q7RPaOpmZlZPP3P3/nsh+288GhXfvnPAxa/WVVSwYE+7IlJ5OLlm/YuSqmhtWbGb3vxH/IVa7bG8dnfDexY8LTVbuwVdgNvrTUffrOZ599ZzrB+LVjz82PUKMHemtZgCPTh6vXbJUpIUxpYdfCllHIEvgaGAP7AGKVU7vQ7TwJXtdY+wOfAJ6bn+gOjgdbAYOAb0/WEHSUkXmf4M3MY99pv+Hp5sG/5C/ztmd44OVn3vyY02J9rN1LZFHEqz8fNd//e+Hfpu/uX08CeXjg4KFmnWYrUhgAAIABJREFUY3Ltxm2e/MdSgp+YRSU3Z7bNe5LP/znE6v935ixgS9flfyewtN79y6ltq7rU86gqUw9N0tIyefO/6+jywHdcuJzCkm/GMPeLh6y+UNy8gXdBneWVm45ieHwW9etUZfv8p/D1rmPVMhWHOVIh7el/9h8+T/eHvucf/17Hff1bErtqEo+ObG/1zJShBn8iD5zLN7lAaloGo15eyI8L9/D2i/2Y+s4wi66NtRRDoDda6yKviSyvzpy7xpAnZ/P4G0to07Iu+5a/wGtPBeLoaN3/u1CDP5ev3mJr5Ok8H8/Ozuav/1rNm5+vZ+zI9iz+egyV3JytWqbiGNTTC6VUmY+mWvsvtSsQp7U+obVOB+YBI3OdMxKYafp+ETBQGd/VRgLztNZpWuuTQJzpesIOtNb8sCCK1kOnsjHiJF++OZQtvz5JKy8Pm7x+UC9vqlR2yTNsXhbu/pnVqlGJru0alfk3DktYseEIrYdOZeaSaN54tjfRy56nZ8emNnltP586tPLyyLM9lYW7f2YODg4YAr1Zu/042dllexpGSUVEx9Px/m/58NstjBvZnthVk7g/yM8mr12tqitBvfLfwHvO7/sY+fyv+PvUYeuvT9K0of3XoOYnONCHqIOJXLpSsSMV6emZvDtlA51CphGfeJ2FU0axaOpom92ECTEY2+7SPN6jklPSGPb0LywOi+WLfw7hvZcH2HWbgoJ0btuIWjUqVfjPvOzsbKb9upvWQ6eyLeoMX709jM1znqBlc9v0oYJ7++Dm6pTnWufMzCyeeGMpn/+8k5ce687MT0PsnikzPx61q9CpTQPCtpft9mTtwVcjIOfk8QTTsTzP0VpnAtcB90I+V9jAyfirBE2YydP//J2OrRuwf/lEXhrf3ep3anKq5ObM4N4+LF136E+dzJx3/96a2LfU3v3LyRDow+4DZ0u0cXRZdvnqLcb+dRHDn52De83KhC98mn+9FoSbq23vsoUE+bEp1wbeZeXuX06GQB8uXb3F3tjz9i6KXdy6nc5rH6+m56gfSL6Zzqofx/HzJyE2n3IcEuTPmXPX79rAe+rsCMa+9hu9Ozdjw6wJdk/XfC/mSMW6HRU3UhF54CydQ7/jva82MWpoG2JWvsiDQ2y7FUDL5h74+9S5K5p66cpNBo6fweZdp5n1WSgvT+hh03IVlaOjA4N6ehG2veJu4H389BUGjZ/J8+8sp1v7xhxYMZEXLbTXYGGZN/DOfYMoNS2DB16cz8wl0Ux+eQBfvDmkTPShdu5N4EYJNo62N2vXcF63YnL/9eV3TmGei1LqGaVUpFIq8uLFwmcGEveWnZ3N1NkRtL3va3btO8u0ycNZP2sC3s1q26U8oQZ/zl9MITzauF4q992/ya8MLLV3/3IyBHqTna3ZsPOkvYtic4tWxeA/5CvmrzzIO5P6Ebn4WbtlDgw1+JOVlc3yDUeAsnX3LyfzHmgVcarYll2naD/8G/7z4w6efrgTB/+YyOA+LexSlhEDjRt4m1OEa61576uNTJr8B/cH+bHyh7FUL6UR1JzMkYqK2J5S0zJ447Mwuj04ncvXbrFs2iP88p8H8ahtnwFzqMGfLbtP34lCJiRep88jP3HgyAWWfD3aKsk+rMEQ6MO5C8nExlWsPlpWVjZfzthJu+FfE3XwHN9/OJK1M8bTvIl91g6HGvxJOH+DyAPG9VI3klMZ/MRslm84wtR3hvHWi5ZPmGYNhl7eZGVlsyG87PahrD03KwHIuRNpYyD3KjnzOQlKKSegBnClkM9Faz0dmA7QuXPnUnNbJfLAWf76r9V8/+FIm4WVLe2jb7fw1hcbCO7tw/QPRth9qsyw/i1xdnZkSdghWnq6M/TpX9gTk8isz0LLzIcQQLf2jale1ZVp83ZzLimZtPRM01dWgf8CfPo3A/6lKNteUcz/4wCjX1lIx9YNWDtjPO1869u1PJ3bNqRxfeMG3qOGtWH0Kwv5fd1hJr88gDcn9i0TH0IA9TyqEuBXn7krDlC1skuh2lJaeiaZWdm8/lRgqcq2VxS795+l39if8WxUk/WzJtj99/CoXYU+XYwbeE9+ZQCvfLCKr2ZHMCG0A99/OMLq62ItxRypWLn5GFNnRxT6/Sk9I4txI9vz6Mj29v4Viu3Jf/zOr8v38+RDHfn3G8HUrG7fhE0hBj8++GYzyzccoWfHphgen8m1G6ms+ekx+nT1tGvZisK8gfe/pm2hS9tGhX6PcnVx4vsPR+Jeq7Kdf4Pief/rTbz31SaG9m3BtPdH0MTO6drv69/StIF3LM0a1WDIk7PZf+QCc/7zAGOGt7Nr2YqiR4cmVK3iQti2OJtNLbc0aw++dgMtlFLNgbMYE2g8kuucZcB4YCfwILBBa62VUsuAX5VS/wUaAi2AXVYur8VUqeTClt2n2bEnvkwOvrTWzFq6j/7dm7Pqx3GloiNao5obA7o3Z8Gqg/yx6Sgn4q+y5OvRDC8le+QUlpOTIyMG+vLL7/tYn2Nqj1IKVxdHXF2c8vw3+tB52vvVLxUbRhfH7KX78Gxck4hFz5SKjqhSivuD/PhhQRTBj89iy+7TTH1nGBPHWm6DS1sJCfLjnSkbeen9lX86nl9bcnVx4sjJS1Sr4mr3QUtx/bp8Py7OjkQtfa7UZDUNCfLn5Q9WMuzpX1i9JY5Xn+jJZ383lPppPLmFBPmxcFUMkyb/8afjLs75vz+dSbxO4sXkMjv4SrmZxuKwWF54tGup2EQdoIN/A5o1qsnUXyL4+2drAdj0yxN0aN3AziUrGvP2HnOW7WfOsv13jjs4qHzbk4ODYv/hC4QG+zO2DLYprTWzl+4jqJc3K74fWyr6UOYNvOetOMhva2KJT7zB798+wtB+Le1dtCJxcXGif7fmbJTIV9601plKqReBNYAj8JPWOkYpNRmI1FovA34EZiul4jBGvEabnhujlFoAxAKZwEStdZnZHKmVlzs1q7uxc288Ex7oYO/iFFls3EWOnbrMXyb0KBVvGmahBn+efWsZ1au6suancfTtZtnNCG3l54/v55PXg/70gePk5FBgXXcOmVZm99+5kZzK2u3HmTi2a6kYeJmFGvyYOjuCHXvjmfOfB3lkRNm5+5fTWy/24+lRnXF2crjTppydHQtsT2NeWci2qLwzX5V2WmsWh8US1Mur1Ay8wBipePmDlazeEsdHfx3EG8/2LlXvn4U1Zng7Bvb0QvG/G0IuLgW3p3enbGDy1M1cT04ttQlqCrJ6SxypaZk8ZOO1XQVRShES5McXM3bStGEN1s4YXyZv5gJsm/ckV6+n/mmAVdBnQVZWNjU7fcTOvfFlcvC1//AFTsRfLXXvAaEGfya+u4Ia1dwI+/kxAjs3s3eRimXqO8Nwr1k2I6Jg/cgXWuuVwMpcx97O8X0q8FA+z/0Q+NCqBbQSBwcHurVvTPi+stlZXhIWeycyUJqMGtaGXfsTmPhotzJ39y8nJydHGtYr2h4x3QMaM2NxNJmZWaVqAFMYq7YcIz0j606K99Kid+dmTBrXjWH9W97ZM6ssUkoVeTPx7gGNmffHARISr9PYztNhimpvbCJnzl3n3Un97V2UP2nSoAbvvtQfrya1ytRU6LzUdS9aVr9eHZuitSY8Or5M/i0tWXsIj1qVCexkm4yrhTVpXDeuJ6cy+eUBZe7vNCc3V2ca1C188iJHRwe6tmtUZm84Lllr7EONHFS6ZuaMua8te2LOMWlcd9r72Xfqf0nYexlMSZWtuRBlTI8OTTh4NInklDR7F6XIlqw9RPeAxkXu0FlbjWpu/PDR/WV64FVcPQKacPNWOgePJtm7KEW2ZO0h6rpXoUeHJvc+2YacnByZ8vawMtlZLCnz/8XO6LLXuVkSdggHB8XwAa3sXZS7vDOpf5kfeBVH94AmODgoduwpe+0pPT2TFRuPMGKgb6m7seXVtDY/fRxSpgdexdUjoAn7j1zg5q10exelyBaHHSKwU9Mi38Swtlo1KvHDR/eX6YFXeSCDLyvqEdCE7GzNrv0J9i5KkZxKuMqemERCDaUr6lXRmTvL5myPZUVqWgZ/bDrK/YP8bLo9gShYgF993Fydylx7AlgcFkvfrp52y0In7latqivtWtVj+54z9i5KkW0I/3/27jssqmvrA/Bv07sUUURAUcQCSlUZsHfR2KImRmNNuyZG0/Plpt1000xiqhpLbFER7AUsWAGlSgfFQpMu0uv+/pgZRaUM0wfW+zw86MyZwyLZzpx99tpr3cT98hr6zFMzAg97NDQ0IjI+W9WhtMv120WIT82j8URaRFdCCjTMTVhCW9OWzcVNHWdPVK8Usc7O0d4C3ayMNW6l4vTlDJRX1D5oGkrUg56eDrxcbTXu/Sk1oxBJ1wswW81Sogng6+mA8Ngs1NdrzPZsAMLJvImxHsb7ambxmY7Kx10zV+fFjYzVbdsGUR80+VIgczNDDHKy1rg7y4HByRgyoLvK+nmR5jHG4ONur3EXy4HByTAz0cc4H80sjtKR+bjbISohBzU19aoORWJBIcI+WnRho378PB1QUVmLa6l5qg5FYg0NjTh4KgXTxjgrvdE7aZ2VhRH69bZCWIxmXUMFhSTD06UHetuppp8XUX80+VIwgYc9wuOyNKaze15hOS5G3aG7ympK4GGH9FtFD5puqrv6+gYcOp2C6WP7Q09P4fV9SDsJ3O1RW9eAmKRcVYcisaDgZAwd0lPlPXPIk/xExSo0ad/X5eg7yC+qoM88NSXwEN5w1JRrqJy8+wiLyaTxRFpFky8FE3jYo6ikEum3ilQdikQOnU4B51ztqtIRIYEoDSMiTjPuBF6MuoPCkkrKfVdTD/cRasbFclZuKa5cy6bxpKYcbLugZ3czXIrSnH1fQSHJ0NPVhv9ozep11FkI3O1RUFyBm5klqg5FIgdPpQAAXUORVtHkS8Ee5CxrSKpYUEgy+thbYHD/7qoOhTTDe3BPaGtrac54Ck6Ggb4OpozqfNUENYFtdzM42HZBmIakRh8QXdjQflT1xBiDr6e9xhTdEPaLS8ZEv74wNdFXdTikGQ+qsmrIZ15gcBKcHa0w0Mla1aEQNUaTLwUb2LcrupgaaMS+r9Kyapy6nIE5kwapVVNA8pCxkR6G9O+uERuQOecICknG5JFOMDbSU3U4pAWatI8wMDgJg5ys0b+PZjaa7Qz8PB2QmVuKzNxSVYfSptiku7idfY9WKdSYSz9rGBvpacRnXvG9SoReuUXXUKRNNPlSMGGzZc1oFHgsNA11dQ1UlU7NCTzsceVaNhoaGlUdSquiEnKQmVtKue9qTuBuh8zcUmTfva/qUFpVWFyB81dv03hScw/3fan/6ldgcJLa9osjQjo62hrTbPnI2TTU1zfSexRpE02+lMDH3R7xaXlq32w5MDgZNtYm8HG3U3UopBUCD3uUV9QiMV29my0HBidBW1uLLmzUnKbs+zp8JhUNDY2YM5lWKdSZ2wAbGBnqasS+r6CQZIwa2gvWVtQvTp0JPOwRl5KHyir1brYcFJIMOxszeA+2VXUoRM3R5EsJBB7CZstX1bhRYFV1HY6fT8esCQOhpUXDQp0JNGQfYVBwMsYM7w1LcyNVh0Ja4TGoB/T1dNQ+rScoJBm9eprDY1APVYdCWqGrq43hbnZqv+8r7WYhEtPzaf+gBhC4i5st56g6lBZVVNbixPl0zJpI11CkbTRClGC4m3AlSZ3vLIdcuoGKylqqIqYB+jhYoKuFkVpPvpKvFyAlo5D2UmgAPT0deLr0UOvxVFZeg+CLNzB74kDaS6EBfEUrFeUV6pvtIW6ES2n26k98DaXO71EnL1xHdU09XUMRidDkSwksuhhiYF9rtX7jCApOhrmZAcYMp0a46o4x9qB/nLoSN8KdOX6AiiMhkhB42CMqIRe1terZbPnEhXTU1NbTXgoN4eflgIaGRly5pr7ZHoHBSfAebEv94jSAtZUxnHpZqvVnXmBwEqwsjDDSu5eqQyEagCZfSuLjbofwWPVstlxf34BDZ1Lw1Lj+0NXVVnU4RAICD3ukZhSi+F6lqkNpVmBwMnzc7dDTxkzVoRAJCNztUVNbj9jku6oOpVmBJ5NhbWn8oJgDUW8CD3swxtR239fDfnG0Mq8p1LnZcm1tPY6cTcOMcf2ho0PXUKRtCpt8McYsGWMhjLF00XeLFo5bIjomnTG2pMnjoYyxVMZYrOirm6JiVQaBhz0KSypx/XaxXM53v6wa32+6iOqaOpnPdf7qbRTfq6K7yhrkYZEE+dwJrK9vwI+bL6GwuELmc93JuYeohBwaTxpEEb10Nvwbieu3ZW8uX1NTj6OhaZg5YQC0tel+oSYwNzOESz9rue77upVVgt93XpHLxffDfnH0HqUpBO72yCssx62se3I53737Vfh+00XU1Mi+2n824iZKy6ppPBGJKfKT7H0Apznn/QCcFv39EYwxSwCfABgOYBiATx6bpC3knLuLvtS7tFsb5F1R7LedV/DO2mB8u+GizOcKDE6CoYEuJo90kkNkRBm8XW2hpcXkdrF8ICQFb319Em98dULmcz3cS0F3lTVFTxsz2NmYya3oRlzyXbz80SE8//Z+NDbK1hLhdFgGyipqaC+FhvHzdEBYTKbcWmJ8/PMZvPrpEQScSJT5XEEhSRjY1xoD+lIjXE3hI+dCU+v/icA7a4Pxw+ZLMp8rKDgZxkZ6mDiirxwiI52BIidfMwFsE/15G4BZzRwzGUAI57yYc14CIATAFAXGpDKDnKxhZqIvlzcOzjm27I8BAHyz4aJMzSwbGxtxICQFU0Y6wciQGuFqChNjfQzp311uk/kt+6MBADsOxiEsRra71UEhyXB17oZ+va3kERpREoGHvdxWUsXjKTw2C7sOx8t0rqCQZJga62OcTx95hEaUxNfTAffLa5B0vUDmc90vq0bACeE+0re/OYmqaukzPopKKnHuym0qtKFhBvfvBmMjPbl85jU2NmJroPAa6qs/L8jU47ChoREHTqXAf3Q/GOjryhwb6RwUOfnqzjnPBQDR9+bSBnsCaPovKUv0mNgWUcrhR6yFEleMsZcYY5GMsciCAtnf5BVFS0tLbo0CL0XdQfqtInz6+lhwzvHu2mCpz3X1Wjay8+5T7xwNJPCwR0Sc7M2Ws+/ex4kL1/H6Yh/06GaK1V8cl3q1oqCoAhcib9NeCg0k8LDH7ex7yM0vk+k8tbX12HHoGp6ePAjeg23x3nfBUle9a2hoxMFTKZg+1hn6+joyxUWUy89TuD9PHvu+9hxLQFV1Hda+MxF3ckrx3UbpMz4e9Iuj9yiNoqOjjaGDbeWyOn/+6m1kZJbgf6+PRV1dA97/PkTqc4XHZiKvsJzGE2kXmSZfjLFTjLGEZr5mSnqKZh4TJ3Qv5JwPBjBS9PV8cyfgnG/gnHtzzr2trdU7hUDgYY9rqbKX392yPwbGRnp4a7kv3nnBD/8ejceFq7ekOldQSDJ0dLQwbYyzTDER5RN42KOsQvY7y9sPxqKxkeO154dj7TsTcfVaNv4JipPqXIfOpKCxkVPuuwaSV/+4w2dSUVRSiRXzPPHLR/7IySvD139dkOpcl6LuoKC4glYpNFAfBwt072oil31fW/bHYGBfa7zz4gjMneIiU8ZHYHASHGy7wNOF+sVpGoGHPWKT78q08gkIx5OZiT7efsEPb63wlSnjIzA4GXq62vAf3U+mmEjnItPki3M+gXPu2szXQQB5jLEeACD63tyerSwA9k3+bgcgR3TubNH3MgC7INwTptHEzZYjE6RvFFheUYO9xxPwjL8rTIz18d5LI2FnY4bVXxxv9woI5xyBwckYO9wRFl0MpY6JqIY8cuDFKawjvXuhX28rLJwxBD7udnj/+xDcL6tu9/kCTyaht5053AbaSB0TUQ2PQT2gp6st8+Rry/4Y9OxuhkkjnCDwcMCimW744e/LyLjT/mJDgcFJ0NfTwdRRdGGjaRhj8PN0kHnlK+VGAcJiMrF8rgcYY/juvUngnOO9b9uf8VFeIewXN2sC9YvTRD7u9qivb0RkvPQtDIQprIl4dtpgGBnq4YNXRkmd8cE5R1BwMsb79oGZqYHUMZHOR5Fph4cAiKsXLgFwsJljTgKYxBizEBXamATgJGNMhzHWFQAYY7oApgNIUGCsSiGPRoEBJ5JQXlGLZU97AACMjfTw7buTEJOU+2AfmKSSrhcg/VYRLZdrKKdeluhqYSRTDvzl6DtIu1n0YDxpaWnh5w/9kVdYji//ON+uc90vq8apyxmYM2kQXdhoIH19YbPl8Djpx1NO3n0cP5+OxbPdHlQm/ObtidDR0cLb35xs17k45wgKScakEX1hYqwvdUxEdfy87HEzq0SmVNYt+2Ogra2FRTPdAAC97Szwzgt+2H0kHhcjb7frXMfPC/vFUfEWzeTjLryGkmVv6t7jiaisqnvwmWdirC91xkdc8l3czCqhayjSboqcfH0DYCJjLB3ARNHfwRjzZoxtAgDOeTGAzwFcFX19JnpMH8JJ2DUAsQCyAWxUYKxKYWluhP59uso0+dqyPxr9els90u/m2emD4eflgA9+OIV796skPlfgySQwxjBzAjXC1USMMfi428k4noQprPOmujx4bJibHZbO8cC6rWFIvyV5qfBj59JRW9dAKYcaTOBhj8j4HKmbLW8/EIfGRo6lczwePNbTxgwfvDISQSHJOH35hsTnik7MxZ2cUhpPGszXQ/g5dVnK1MP6+gb8cyAW/qP7wcba9MHj4oyP1z8/1q6Mj6DgZHS1MMIIaoSrkbpZmaCvg6XM11AD+nTFcNFEDoDUGR9BIcnQ0mKYMb6/1PGQzklhky/OeRHnfDznvJ/oe7Ho8UjO+QtNjtvMOXcSfW0RPVbBOffinA/hnLtwzldzzhsUFasyCdylbxR4/XYRzl+9jWVPezyyssAYwy8f+aOwpBKf/3ZO4vMFhSRD4GGHHt1M2z6YqCUfd3ukSNlsuaKyFnuOJWD+VJcnVha+emsC9PW08dbXkpeeDwpJRveuJg/aKhDN4+Nuj+qaesSl5LX7tZxzbAmMwQhvBzg7dn3kuTeX+8LRzgKrvziO+nrJ3sqDgpOgra2Fp8bRhY2m8nTpAQN9Han3fZ28cB13C8qxfK7nI49Lk/FRUyNshEv94jSbj7sdwmKlu4ZKzSjE5ehMLJ/r+cg1lLQZH4HBSRjh5YBuVibtjoV0bvQOpGTiZssZd0ra/dqt+2OgpcWweJb7E895utgKN7j/E46UG20XYLiZWYKYpFy6q6zhxBOdiLj2p2EEnEh8JIW1qR7dTPHRytE4fCYVJy+kt3mu6po6HDuXhpnj6cJGk8nSbDksJhOpGYVYNsfziecM9HXxw/9NRmJ6Pv7cHSnR+QKDkzFqaC90tTRudyxEPejp6WDo4J5S7/vaHBADa0vjZgtCNc34KJVgteJMuLBfHH3maTaBhz3uFpTjdnb7my1v2R8NbW0tPD/L7Ynn2pvxkX6rCAlp+TSeiFToKknJxDnL7S2X2tDQiG1BsZg0oi962pg1e8yXb46HkYEu3pRgteLAKVEj3ImUq6zJhg3pCS0tJlUO/Jb9MXDqZdliCs7qJQI49bLEmi+Oo66u9dWKU5cyUF5RizmT6YNIk9n36IKe3c2k2ke4ZX8MjAx1H0lhbWrWxIEYJ3DExz+fQVFJ6yu1KTcKkHyjgPZSdAB+Xg6ITspFZVVtu15XWFyBw2dTsWjmEOjqaj/xfNOMj89+DW3zfIHBwn5x432pX5wmE98gau9nnjCFNQ5TRzk9ksLaVHsyPoKChX3nZtN7FJECTb6UzKVfN5gat7/Z8unLGci6e/+J9IumulmZ4JNVY3D8XDqOnk1t9XyBwUkYMqA7+vaybFccRL2YGOtjsHP3do+nG7eLce7KrSdSWJvS19fBj/83BSkZhfhtR0Sr5wsMTkIXUwOMHe7YrjiI+hF42Lf75lBFZS32HE3A/KmuMDVpvjgGYww/f+iP0rJqfPzzmVbPFxQivDk0i/ajajxfD3GFuvZV+d156Brq6hpa/cxrmvGRmlHY4nHifnH+Y6gRrqYb0r87DA102/2ZF3zxBnLzy1odT+3J+AgMToanSw/06mnerjgIAWjypXTa2loY5tb+Zsub90fD0twQM8a1fjHy2qLh6N+nK9746kSLm+bzCstxKSqT7ip3ED7udoiIy2pXmdytgS2nsDY1fVx/TBrRF5+uD0VBUUWzx9TXN+DQmVRMH+sMPT1qhKvpfNztcCvrHu4WSF6hbv/JJJRV1DSbwtqUq3N3/Oe5ofhz91XEp7a8rywoJBnDhvSEXY8uEsdA1JOvuNlyO/Z9cc6xeX80vAfbwtW5e6vHijM+3vjqeIvHiPvF0Wee5pO22fLmgGh0tTBqs6epOOPjjS9PtJjxkX33PiLismg8EanR5EsFBO7CZssVlZKlYZSUVuFASAqee2oI9PVbv7jV09PBug+mIP1WEX75J7zZYw6eSgHn1Ai3oxB42ON+eQ2Sb7R857cpcQrrRL++bV7cMsbw03/9UV5Ziw/XnW72mAuRt1FUUknjqYOQJq1ny/5o9HWwxMihbVeR+2z1OJibGWD1F8ea3TSfmVuKq9eyqbFyB2FlYYQBfbq2a99XTFIurqXkYdnTLa9SiDXN+DgWmtbsMdQvrmMReNgjJilX4mbLhcUVOHQmFYtmurV5g1Cc8ZF8owC/77zS7DEPt23QexSRDk2+VMDH3Q4NDZI3Ctx9JB41tfVYLsEHEQBMHe0M/9H98Nmv55BXWP7E80EhSejrYInB/Vu/o0g0Q3uLJJwJy0Bmbmmr6RdNDXSyxmuLhmHj3ijEJuU+8XxQcDIM9HUwhS5sOgRPlx7QbUez5Yw7xQiNaD2FtSlLcyN8tnoczobfRFBw8hPPHxClHNJd5Y7Dz8sBl2MyJV6d37I/Bvp6OlgwfbBExz/M+Dj+RMaHuF/cRL8+LaZfpUlWAAAgAElEQVTEEs0iEKWyRidKlsq663B8mymsTYkzPj755WyzGR9BIcno36crBjpZtytuQsRo8qUCPu6ii2UJl803B0TDbYANPFx6SPwz1v13Kqpq6vDBD6ceeby0rBqnw25i9sSB1Ai3g+jX2wqW5oYSXyxvDoiBRRfDdvUm+WTVWFiZG+L1zx9drRBf2Ewe6QRjI712x07Uj4G+LjwH9ZB4PG0NjAFjDItnt57C2tTLz3rD1bkb3vrmxBN3rwODkzDIyfqJcvVEc/l5OqCktAqpGW1XkauuqcPOQ9cwe+JAWHQxlOj84oyPtJtFWL/90f2pD/vF0WS+o3hwDSXxZ140vFxtJb7h3DTj46OfHs34KCqpRGjELbqGIjKhyZcKWFkYwdnRSqK0nvjUPEQl5Eh8x0bM2bErVi/xwZb9MY+ssB09m4a6ugaqSteBMMbg4yZZs+WS0ioEhSRj4Ywh7dp4btHFEF+8MR4XIm9j3/HEB49Hxucg6+59WqXoYHzc7RCZkNNmlcvGxodVWO3bsT9LR0cbP3/oj1tZ9/Dj5ssPHi8srsD5q7dpPHUw7dn3deh0KkpKq7B8buv7Bx/3MOMj9JGMj6DgJGqE28F072oCRzsLhMW0fQ0Vk5iLuJS77b6GEmd8bNjzaMbHkbOpaGhopPcoIhOafKmIj4TNlrfsj4aurjaee0qy9IumPnp1DKwtjR7ZWxEYnIQe3Uwx3M2ujVcTTSLwsEfyjQLcu1/V6nH/ilJY2yqM0JwX5nvBbYAN3v7m5IOy0YGiRrjTx7a+iZloFoGHPaqq63CtlaIYAHAm7Cbu5JRKNZ7GCfpgzqRB+OrP88jKLQUgvPBubOS036uDcXa0QlcLI4n2fW3ZHw37Hl0wTtD+kvDr/jsVldV1+O+PDzM+qF9cxySuyirJNVR7UlibEmd8PHoNlQw7GzN4D7aVKm5CAJp8qYzA3R75RRW4mdlys+Xa2npsPxCHGeP6S/XB0cXUAF+9NQGXozOx+3A8qqrrcPx8OmZNGAAtLfpf35FI2mx58/5oDBnQHR6DJE9hFdPW1sIvH/kjM7cU3226BM45AoOTMHZ4b1iaG0kVN1FPku4j3BwQDXMzA8yUsiT89+9PRkMDx/vfhwAQ7kft1dNcqvFJ1BdjDL6eDm2ufGXlluLkhRtYMttdqmbt4oyPzQExiErIoX5xHZjAwx65+WXIFN24aU5NTT12Hr6GWRMHSJzC2pQ44+P81dsIOJGI8ooaBF+8TimHRGZ0Ba4iklzcHA1NQ2FJZbuXy5ta9rQHvFxt8e63wQgKTkZlVR1V6OmAhg2xA2Os1fEUn5qHyPgcLH/aU+oPjlHDemO+vyvWbriIkxeuI+1mETWZ7IDse3SBbXfTVsdTSWkVAoPbn8LalKO9Bd5e4Yudh67h5IV0BF+8QRc2HZSfpz3SbxW12LICAP45EAvOOZbOaf9KqljTjI9AUSPcWfSZ1+FIcg116EwKiu9VSVysrDlNMz6CQpJRXVNPK/NEZjT5UhFX524wNtJDeCsrFZsDotGjmykmjegr9c/R0tLCzx9ORXbefbz88SGYmxlgDDXC7XBMTfTh6tyt1Q8icQrrwhlDZPpZ3747CZxzzHt9DwBqhNsRCfcR2rc6nh6msEp/YQMA7788ErbdTTF31R7U1jVgDl3YdEh+XsJ9X5djml/94pxjy/4YjB7WG317WUr9c8QZH5ei7uDrvy5g6JCe7dqPSDSDJM2WNwdEw87GDON925/CKibO+LiTU4qVnx6BlYURRnq33VKDkNbQ5EtFtLW1MGxIy82W7xaU4fj561g8yw06Otoy/Sw/r1547qkhKK+oxVPj+kNXV7bzEfUkcLdHRFx2s+Wc6+oasOPgNTw1VroU1qZ69TTHey+NQHlFLXzc7WDb3Uym8xH1JPCww82skmbbVQDAlsAYDO7fHZ7tqMLaHBNjfXz7ziSUV9TC2tL4QXEG0rF4udpCT1e7xX1fFyNv4/rtYqn2Dz5OnPFRXlFLmR4dlK6uNrxdbVssXJZ99z6CL0qfwtqUOOOjvKIWM8b1l/majBCFTb4YY5aMsRDGWLrou0ULx51gjN1jjB157HFHxliE6PV7GGMdro61wMMecSl3HxQvaGr7gTg0NDTKfFdZbO07E+Hq3A0vzPOSy/mI+hF42KO0rBopzTRbPhqahoLiinZXEGvJuy+OgK+nPVYuHCaX8xH187DZ8pM3iBLS8nD1WjaWS9jbqy3PzRiC2RMH4rVFw2S+UCLqyUBfF16uti3u+9qyPwYmxnqYO8VF5p+lpaWF3z+djgF9ukpVaIFoBoGHPaKTclFd82Sz5X8OxKKxkWOpHCbzAPDde5Pg0q8bVsyTzzUZ6dwU+Sn3PoDTnPN+AE6L/t6c7wA838zjawGsE72+BMAKhUSpQgJ3YaPAyPhHGwVyzrE5IBq+nvbo30c+vW7senRB/NHXMGpYb7mcj6ifBznwzVwsi1NYJ490ksvPMjLUw6U9L+L5WZL3diKaxdPFtsVmy1v2x0BHRwsLZ7jJ5WcxxhD4+wJ8vGqsXM5H1JOfpwMi43NQU/NoI+TyihrsPZ6IZ/xd5dYvcJibHZJPvo7eds3e9yUdgMDDHnV1DYhOzH3kcfE11KihveDUy0ouP8vB1hwJx16DnxelHBLZKXLyNRPANtGftwGY1dxBnPPTAMqaPsaEt1LHAQho6/WazMddWO798TvLEbFZSMkolNuqF+kc+vW2hEWXJ5st3y0ow7Fz6Xh+puwprKTzMDTQhftAmyfGkzCFNQ5Pje0Paysq300k5+flgNq6BkQlPnrDcd/xRFRU1sol5ZB0HuJrqMffoy5F3RGlsNI1FFFPipx8deec5wKA6Hu3drzWCsA9zrn49lgWgJ7NHcgYe4kxFskYiywoKJApYGXramkMp16WCHssZ3nL/hgYGepi/lTZ0y9I56GlpQUfd7sncuB3HBSnsNKFDWkfgbs9rsbnoL7+YbPlY+fSkF9UIVMVVtI5+XoKV+cf3/e1ZX8MnB2taL8faRcba1P0tjN/4jPvYQorVeIl6kmmyRdj7BRjLKGZr5kyxtXcJoJmO+lxzjdwzr05597W1tYy/ljlE3g82my5sqoWu4/EY+4UF5iZGqg4OqJpBO72SLpegNKyagDi9IsYCDzsMaCv5v37IKrVXLPlzQExsLE2wZRR8klhJZ1HNysTOPWyfGTfV/qtIlyIvI1lcto/SDoXgfujVVnLK2qw51gC5k91hYmxvgojI6RlMk2+OOcTOOeuzXwdBJDHGOsBAKLv+e04dSEAc8aYjujvdgByWjleYwnc7ZFXWI5bWfcACLunl1XU0CoFkYrAwx6cc0SI7gReictC8o0CGk9EKo/30skrLMfR0DRKYSVS8/N0wOXohzcctwbGQEuLYTHtHyVSEHjYIzvv/oNmywEnkiiFlag9RaYdHgKwRPTnJQAOSvpCLnxXPgtgrjSv1yQ+7o9WFNuyPxp97C0waiht6iTtN2xITzDGmoynGBga6OIZf1cVR0Y0kYNtF9hYmzxI63mYwkoph0Q6fl4OKCiuwPXbxWhoaMS2wFhMGelELSuIVJq7hurX2+pBXzlC1JEiJ1/fAJjIGEsHMFH0dzDGvBljm8QHMcYuANgHYDxjLIsxNln01HsA3mSMXYdwD9jfCoxVZQb3FzZbDovJxM3MEpwJu4mlczygpUXllkn7mZkawKWfNcJiM5uksA6iFFYiFcbYg7QecQUxH3c7DHSiFFYiHT/Rvq5LUXcQcukGsvPu02SeSM1tQHcY6OsgLCYT128X4fxVSmEl6k+n7UOkwzkvAjC+mccjAbzQ5O8jW3h9BoAO30RIR0cbQwfbIiw2E5ZBMWCMYckcSr8g0hN42GPf8UQEBifjfjmlsBLZCDzsERSSjGOhaUi6XoC/Pp+h6pCIBhvQtyvMzQxwKfoO7pfXwNLcEE+N66/qsIiG0tPTgfdgW4TFZMLIQJdSWIlGoOUVNSDwsEds8l1sDojBBN8+cLA1V3VIRIMJ3O1x7341Pvn5DBztLDCaersRGYj3fb3++TFhCus0SmEl0tPS0oKvhz2CL17HgZBkLJrhBn19hd0HJp2AwN0e0Ym52BoYi8kjndDThlJYiXqjyZca8BE1W87MLaVVCiIzcQ58RmYJls5xpxRWIhMvV1vo6GghI7MET08ehC6Uwkpk5OflgDs5paita6DPPCIzH3d71NY1iFJYaTwR9UdXZWpA3Ciwi6kBZk0cqOJoiKbr38cK5mYGohRW+iAishE3WwZAFzZELsT7vtwH2sB9UA8VR0M0nXh13tLcEDPGDVBxNIS0jdb61UA3KxOM8HbASO9eMDTQVXU4RMNpaWlh1oSBKK+sRa+elMJKZDdrwkA0NnKMGd5b1aGQDmCYmx3se3TBG8t8VR0K6QB6dDOFj7sdJvj2pRRWohGYuNdGR+Dt7c0jIyNVHQYhhBBCCCGkk2CMRXHOvSU5ltIOCSGEEEIIIUQJaPJFCCGEEEIIIUpAky9CCCGEEEIIUYIOteeLMVYA4LYCf0RXAIUKPD/pXGg8EXmjMUXkicYTkScaT0Se1G089eKcW0tyYIeafCkaYyxS0s10hLSFxhORNxpTRJ5oPBF5ovFE5EmTxxOlHRJCCCGEEEKIEtDkixBCCCGEEEKUgCZf7bNB1QGQDoXGE5E3GlNEnmg8EXmi8UTkSWPHE+35IoQQQgghhBAloJUvQgghhBBCCFECmnwRQgghhBBCiBJ06skXY8yeMXaWMZbMGEtkjK0WPW7JGAthjKWLvluIHh/AGAtjjNUwxt5uch4DxtgVxlic6Dz/U9XvRFRLXmOqyfm0GWMxjLEjyv5diOrJczwxxm4xxuIZY7GMsUhV/D5EteQ8nswZYwGMsRTR+QSq+J2I6sjxGqq/6H1J/HWfMbZGVb8XUR05v0e9ITpHAmNsN2PMQBW/U3M69Z4vxlgPAD0459GMMVMAUQBmAVgKoJhz/g1j7H0AFpzz9xhj3QD0Eh1Twjn/XnQeBsCYc17OGNMFcBHAas55uAp+LaJC8hpTTc73JgBvAGac8+nK/F2I6slzPDHGbgHw5pyrU1NKokRyHk/bAFzgnG9ijOkBMOKc31P270RUR96fd6JzagPIBjCcc35bWb8LUQ9yvC7vCeG1+CDOeRVjbC+AY5zzrcr/rZ7UqVe+OOe5nPNo0Z/LACQD6AlgJoBtosO2Qfg/FZzzfM75VQB1j52Hc87LRX/VFX113lltJyavMQUAjDE7ANMAbFJC6EQNyXM8ESKv8cQYMwMwCsDfouNqaeLV+Sjo/Wk8gBs08eqc5DymdAAYMsZ0ABgByFFw+BLr1JOvphhjvQF4AIgA0J1zngsIBwKAbhK8XpsxFgsgH0AI5zxCcdESTSDrmALwE4B3ATQqKESiQeQwnjiAYMZYFGPsJUXFSTSDjOOpD4ACAFtEadGbGGPGCgyXqDk5vD+JPQtgt7zjI5pHljHFOc8G8D2AOwByAZRyzoMVGW970OQLAGPMBMB+AGs45/elOQfnvIFz7g7ADsAwxpirPGMkmkXWMcUYmw4gn3MeJffgiMaRx3sUAD/OuSeAqQBeZYyNkluARKPIYTzpAPAE8Afn3ANABYD35Rgi0SByen+CKH11BoB98oqNaCY5XENZQLha5gjAFoAxY2yRfKOUXqeffIn2aO0HsJNzHih6OE+UdyrOP82X9Hyi1ItQAFPkHCrREHIaU34AZoj26fwLYBxjbIeCQiZqTF7vUZzzHNH3fABBAIYpJmKizuQ0nrIAZDXJ8AiAcDJGOhk5X0NNBRDNOc+Tf6REU8hpTE0AcJNzXsA5rwMQCMBXUTG3V6eefIkKZfwNIJlz/mOTpw4BWCL68xIAB9s4jzVjzFz0Z0MI/6enyD9iou7kNaY45//HObfjnPeGMA3jDOdcbe7aEOWQ43uUsWjzMkTpYZMAJMg/YqLO5Pj+dBdAJmOsv+ih8QCS5BwuUXPyGk9NLAClHHZqchxTdwD4MMaMROccD+H+MbXQ2asdjgBwAUA8Hu6r+QDC/NK9ABwg/B84j3NezBizARAJwEx0fDmAQQB6Q7gBUBvCCe1ezvlnyvtNiLqQ15hquszOGBsD4G2qdtj5yPE9qiuEq12AMGVsF+f8S2X9HkQ9yPP9iTHmDmExID0AGQCWcc5LlPn7ENWS83gyApAJoA/nvFS5vwlRF3IeU/8D8AyAegAxAF7gnNco8/dpSaeefBFCCCGEEEKIsnTqtENCCCGEEEIIURaafBFCCCGEEEKIEtDkixBCCCGEEEKUgCZfhBBCCCGEEKIENPkihBBCCCGEECWgyRchhBBCCCGEKAFNvgghhBBCCCFECWjyRQghhBBCCCFKQJMvQgghhBBCCFECmnwRQgghhBBCiBLQ5IsQQgghhBBClIAmX4QQQgghhBCiBDT5IoQQQgghhBAloMkXIYQQQgghhCiBjqoDkKeuXbvy3r17qzoMQgghhBBCSCcRFRVVyDm3luTYDjX56t27NyIjI1UdBiGEEEIIIaSTYIzdlvRYSjskhBBCCCGEECWgyRchhBBCCCGEKAFNvhQkM7cU73xzEmXlNaoOhRBCCCGEEKIGaPKlILn5Zfj+70vYGhij6lAIIYQQQkgLrqXcRURspqrDIJ0ETb4UZJibHXzc7fDLP+FobGxUdTiEEEJIp9DQ0Ig5K3fjk5/PqDoUoiFWfXYU017aicqqWlWHQjoBmnwp0JqlAly/XYxjoemqDoUQQgjpFL7+8zyCQpLx5+6rdPOTtIlzjvi0fBSVVGJbYKyqwyGdAE2+FGjOpEGwszHDT9vCVB0KIYQQ0uGFxdzBp+tD4WDbBflFFYhKyFV1SETN5eaXoaS0CowxrNsahoYGmrATxaLJlwLp6mrj1UXDcPpyBhLS8lQdDiGkibSbhXh37UnU1TWoOhRCiByUllXjuTcDYN/DDGf+WQbGGI6dS1N1WETNJaTlAwBeWeCN9FtFOHwmVcURkY6OJl8K9uJ8bxga6OLnbeGqDoUQ0sTmgGh8t+kStgVRmgkhmo5zjv98fBiZufex68e56NvLEsPdeuJoKE2+SOsS0oU3xz9+bQx69TTHD5svqTgi0tHR5EvBrCyM8PxMN+w4GIfC4gpVh0MIEQmPzQIAfP5bKGpq6lUcDSFEFtsPxGH3kXh8umoMBB4OAAD/0c64ei0beYXlKo6OqLOEtHx072oCG2tTrFnig4uRd6jyIVEomnwpweolPqiuqceGPZGqDoUQAqC+vgFX47PhNsAGd3JK8XdAtKpDIoRI6frtIrz6vyMYNbQX/u+VUQ8enzbWGQBw4jwVvSItS0zPh6tzNwDAinle6GJqgB82X1ZxVKQjo8mXEgzq1w2TRvTFbzuvoLZW/e+wRyfmYMbLO6lBNOmwEtLyUVlVh3dfHIER3g748o9zqKquU3VYhJB2qq2tx3NvBkBHWws7fpgLbe2HlzXuA21gY22CY+do8kWa19jYiMTrBXDtJ5x8mZro4+VnvbH/ZBJuZpaoODrSUdHkS0lWLxEgJ68MASeSVB1Kmz5cdxqHz6TSXhjSYYlTDn3c7fD5mvHIySvDX7uvqjgqQkh7ffLLWVy9lo2NX8yEfY8ujzynpaUF/9HOOHnhOhXWIc26nV2KispauIgmXwDw+mIfaGkx/LSVKlUTxaDJl5JMGeUEZ0cr/LQtDJxzVYfTomspd3H8XDq0tbXw644ItY6VEGmFx2bC2tIYjvYWGDPcEeMEjvj6rwuoqKQGm4RoijNhGVi74SJemO+FuVNdmj3Gf3Q/lJZVIyyG9vCQJ4krUYvTDgGgp40ZFkwfjL8DolFSWqWq0EgHRpMvJdHS0sLqJQJcvZaNcDXeyPn935dgbKSHH96fjNSMQpy+nKHqkAiRu4i4LPi424ExBgD4fM145BdV4LcdESqOjBAiicLiCjz/zn7hTc3/Tm3xuIl+faGjo0VVD0mzEq8Ly8y7OHV75PG3lvuhorIWf/1LGRFE/mjypUSLZ7mhi6kBftqqnmXn7+Tcw+4j8XhxvhdeXuANa0tj/EoXo6SDKSmtQkpGIXzc7R485uvpgCmjnLB240XcL6tWYXSEkLZwzvHCfw+isLgSu3+cB2MjvRaPNTM1wEjvXtTvizQrIS0fDrZdYGZq8MjjbgNtMMG3D375J0Ij9uoTzUKTLyUyMdbHi/O9sP9kEjJzS1UdzhN+2hoGzoE3lglgoK+LF5/xwuEzqbiVRZtOScdx5Zp4v5f9I49/tno8iu9VUU8+QtTcn7uu4uCpFHzzzkR4uPRo8/hpY5yRkJaPOzn3lBAd0SQJafmP7Pdq6u0X/JCbX4bdR+KVHBXp6GjypWSvPT8cnHO1S28qKa3Chj1RWDB9MBxszQEArywYCgD4kwoRtCngeCI8ZvyO8gqqEKnuwmOzwBjD0ME9H3l86JCemDF+AH7YfJny/InaSLtZiIGTf0HazUJVh6IWEtPz8ebXJzB5pBNWL/GR6DX+o4Ul54+FUtVD8lB9fQOSbzysdPi4SSOc4OrcDT9svkz734lcyWXyxRibwhhLZYxdZ4y938zz+oyxPaLnIxhjvUWPT2SMRTHG4kXfxzV5TajonLGir+b/dWiYXj3NMXviQGzYE6VWm/v/2HUFFZW1eOcFvweP2ffoglkTBmLTvmgqw92Kmpp6vPXNCcQm38XeY4mqDoe0ITw2E67O3WBqov/Ec5+tHofSsmr8SD1eOpz6es2sdrf7SDxSMgqxNTBG1aGoXHVNHRa8sQ9mJvrYunY2tLQku4QZ0LcrHO0scDQ0VcEREk1y404Jausa4OrcvdnnGWN4a7kf4lPzEHLxhpKjU7za2noseTcQUQk5qg6l05F58sUY0wbwG4CpAAYBWMAYG/TYYSsAlHDOnQCsA7BW9HghgKc454MBLAGw/bHXLeScu4u+8mWNVV2sWSpASWkVdhyMU3UoAIQfaD9vC8eUUU4YMsDmkedee34YikoqsedogoqiU38b9kTiTk4pzM0MsHEvNdJWZ42NjYiIy35kv1dTbgNtMHeKC37aFobC4golR0cUJfvufVh4f43ZK3cjSw1Tvltz5KxwwrD3WGKnv/v+7tpgxKfmYcs3s2FjbSrx6xhj8B/TD6fDbqK6hm4kEqHmKh0+bsH0wbCxNsEPmy8pKyylCTiRhH+CYvHD3x3vd1N38lj5GgbgOuc8g3NeC+BfADMfO2YmgG2iPwcAGM8YY5zzGM65eMqdCMCAMfbk7egOxs/LAV6utvhpaxgaGxtVHQ7+CYpDflEF3n1xxBPPjRnuCJd+3bB+e3in/+BvTkVlLb784zzG+jjiw5WjER6b9eANnaif9FvFKCmtgo+bfYvHfPr6WFRU1uG7TfSB1FEcOp2C8opaHAtNw6Cpv2L9P+FoaFD9e29bcvPLEBmfgwF9uuLGnWLEJOWqOiSVOXo2Feu3R2D1Eh/4j3Fu9+unjXFGVXUdzl25Jf/giEZKSM8HYwwD+nRt8Rh9fR28vtgHwRdv4FrKXSVGp3jrtwv3Nx86k4rKKvXJxOoM5DH56gmgae30LNFjzR7DOa8HUArA6rFjngYQwzlvumlmiyjl8CMmrgndATDGsHqJD1IyChFySbVL2Q0Njfj+70vwHmyLMcMdn3ieMYbXFg1HdGKuWpfIV5X128ORV1iOL98cj8Wz3KGrq42Ne6JUHRZpgXgMt7TyBQAu/bphwfTB+HVHBPIKy5UVGlGgo6Fp6GNvgaTjq+DjbofXPz8G3/kbEZes3hdT4gp9f3z2FHR0tDptBkJufhmWvhcEtwE2WPvOJKnOMWa4IwwNdHH0LFU9JEIJafno62ABI8OWq2UCwMvPesPIULdDpaNHxmcjPDYLsyYOREVlLY6fo/2QyiSPyVdzk6LHl0haPYYx5gJhKuLLTZ5fKEpHHCn6er7ZH87YS4yxSMZYZEFBQbsCV6X5U11hY22i8spqB0+lIP1WEd59cQRamt8umjkEZib6+HX7FSVHp97u3a/CtxsvYdoYZwg8HGBtZYzZEwdi+8E4Sm1RU+GxWTAz0ceAvi3f6QSAT1aNQXVNPb7564KSIiOKUllVi9NhGZg2xhl9e1ni5JbF2PH907iZdQ9es//Eu2tPqtX+26aOnE2Dg20XjB7WGxN8+2Dv8YROl4HQ2NiIJe8GoqKqDrt+nAt9fR2pzmNooItxPo44GprW6f4bkuYlpOW1uN+rKUtzI6yY64ldR+KRk3dfCZEp3vrtETAx1sPmr2ehm5Ux9hzrnDd2VEUek68sAE1zeOwAPL5778ExjDEdAF0AFIv+bgcgCMBizvmDZSDOebboexmAXRCmNz6Bc76Bc+7NOfe2traWw6+jHPr6Olj53DAcP5eOlBuqmTRyzrF2wwX0sbfAnEmPb9N7yMRYH8ue9sC+E4m4W1CmxAjV2w9/C6viffHG+AePvTjfCyWlVQg8mazCyEhLwmMzMWxIzzY36js7dsXiWe74Y9dVZN/tGB+2ndXZ8JuorqnH9LH9AQhX8xfOdEPKyVVYMtsd3226BNdpv+LEefW681tdU4eQSzcwfWx/MMYwf6orbmXdQ2R859ocv25LGEIu3cC6D6ZgUAtV6STlP8YZGZklSLtZJKfoiKaqqalH+u3iFisdPm7NUgEaGhqxfrt6VaqWRn5ROf49Eo8ls91h0cUQc6e44MjZNKrWrETymHxdBdCPMebIGNMD8CyAQ48dcwjCghoAMBfAGc45Z4yZAzgK4P845w82WDDGdBhjXUV/1gUwHUCHm5a/vMAberra+OUf1ax+Xbh6G1euZePtFX7Q1m59KKxcOAx1dQ2UUieSX1SOdVvDMN/fFe6DHvaZGSdwhKOdBRXeUEMVlbW4lpr3RH+vlnz82hg0NDbiqz/PKzgyokhHQ9NgbKSH0UApploAACAASURBVMN6P/K4pbkR/v56FkJ3LIO+ng6mrtiOBWv2qU2q6bkrt1BRWYvpY4X7m2ZNHAhdXW3sOdZ5eg5FJeTg/344hdkTB+KlZ71lPp//6H4AQA2XFejoWc3oDZp6sxANDY0t9vh6XB8HS8yeOBB/7r6q8ZOUjXuiUFvXgFcXDgcAPOPviqrqOhwNpX8XyiLz5Eu0h+s1ACcBJAPYyzlPZIx9xhibITrsbwBWjLHrAN4EIC5H/xoAJwAfPVZSXh/AScbYNQCxALIBbJQ1VnXTzcoEC2cMwbagWJX0Ffp240VYWxpj6dMebR7r7NgVk0c64c9/r6KuTjNLNsvTN39dQFV1HT5bPe6Rx7W0tLBinidCI24h/ZZy767W1TWgtKwaufllyLhTjIS0PFyJy0JoxE0cC03D/hOJ2H4gFn/tvop1Wy7jy9/P4cMfT+G9b4PVsum3vEUl5KCxkbe636spR3sLLH/aExv3RuF2NjVn1USccxwNTcME3z4tpquNHu6IuEMr8enrYxEYnIQBk3/Bxj2RKi+GdORsGowMdTHWR7gX16KLISb59e00VQ9z88swe+VudLcyxsYvZ7aYFt8eve0sMMjJWuEXmRGxmQi+cF2hP0Mdpd8qwvSXdmLA5PV4/7tg3C+rVnVILUpIExbQbq3S4ePeXuGHe/ersTlAc9s+1Nc34I9dVzHBtw8GOgmzxfy8HNCjm2mn3VOqCtIlTz+Gc34MwLHHHvu4yZ+rAcxr5nVfAPiihdN6ySM2dbd6iQBb9sdg094ovNNMtUFFSUjLw9HQNHy2ehwMDXQles1ri4bjqZd34sCpZMyb6qrgCNVXVm4pft95FUtmu6N/M1WSlj3tgU9+OYu/90XhGyk3h7fHyQvpmLdqL8qkuBunpcXQ2MhxK/se9vw8XwHRqQ9xsY3hbpJNvgDgw1dHY2tgDL74/Rw2fvl4EVei7hLT83EnpxQfrhzd6nH6+jr4ZNVYPOPvilc+PoyXPjyEfw7EYsPnMx9coCgT5xxHzqZigm9fGOg/fH+e7++Ko6FpCI/NhMDDQelxKUtFZS2eenknikurcGHXClhZGMnt3NPGOOOnbeEoK69pttefrKpr6jBr5W7cLSjHoplu+OUjf1h0MZT7z1FHZ8IyAACTRzph7YaL2BwQg8/XjMOKeZ7Q0dFWcXSPSkjPg46OFpx7P177rWU+Hvbw9bTHT1vDsHLhULX7nSRxICQF2Xn38fv/pj94TFtbC/OmuOCvfyMV9u+CPEouTZaJ9NwG2mDM8N5Yvz1CqU1Av990CUaGuli5sNmtdM2aOrofHO0s8GsHyHmWxee/nUMj5/hk1dhmn7ftboZpY5yxNTBW4auE9fUNWPPFcXSzMsYXb4zHjx9MwR//ewrbvp2Dfb88gyMbFuLM9qUI3/cirh15FemnViP74tsoifo/1CR+jPqUT/HBf0Zh3/HEDl8iPzw2C069LNHV0lji19j36IKXnvXGlv0xuHG7WIHREUUQV7bzHy1ZafIBfa1xdscy/P3VLCSmF8Btxu/4+KfTSi+gk5iej1tZ9x6kHIrNnDAAerraHbqZe0NDIxa+FYCYpFz8u24ePFx6tP2idvAf44y6ugacuqyYSsP/BMXhbkE5Fs4Ygt1H4tVyP6GinI24CdvupjjwxwJcDXwZA/p0xSsfH4b7jD9w8oJ6/TdITC9Af8eu0NNr3xrE2yv8cDOrBEEhmrmve/32cPS2M8e0x9o1zPd3QU1tPQ6dTlFRZJ0LTb7UwJqlAmTmluJAiHIGfVZuKXYevoYX5nm1646itrYWVi4civNXb3e4fheSun67CJv3R+PlZ73Rq6d5i8e9MM8TeYXlOHwmVaHx/BMUh5SMQnz77iT8d+VovLHMF688NxSLZ7tj7lQXTBvbH2N9+mC4uz0G9+8Op15WsO1uBnMzQ+jp6YAxhjeX+cLESA+f/Rqq0FhViXOOsNhMifd7NfXBK6Ogq6OFz34LlX9gRKGOhKbCfaANetqYSfwaxhiWz/NEyslVmD/VBZ//dg5uT/2O0IibCoz0UeLGyo9fIHUxNcCUUf2w73iiytMiFeWdtSdx8FQKfvrvVEwf11/u5/fzdICZiT6OKaC0dkNDI77bdBFerrbY/v3TiAh4CeamBpi6Yjte+egQyso1e69QazjnCI24hTHDHMEYg/fgnji3azn2//osqqrrMGX5dkxd8Q+S0vNVHSoAcaXD9hdwmTF+APo6WOL7TZc0Lv33WspdnL96G68uHPbEPn+Bhz3sbMyo6qGS0ORLDUwf2x997C3w07Ywpfy8dVvDwDnwxjJBu1+7fK4nDPR18NuOzll2/tNfzkJXRxv//c+oVo+bOrofbLubYtM+xRUoqa6pw6frz2LokJ6YPWmg1OexsjDC64uHY9/xRMSndszVr8zcUtwtKJd4v1dTPbqZYuXCYdhxME5ulUlraurV5iKkoyq+V4nL0ZkPqhy2VzcrE+z4YS5Obl6M+vpGjF+8VWk3nY6cTYOXqy1suz85aZw/1QXZefdxObrj9V38fecVrNsShtcX+2DVYh+F/AxdXW1MGuGEY+fkX3I+KDgZ128X472XhK1bvFxtEXXgFby9wg8b9kTB7anfcb6DNnlOuVGIvMLyB3sUAeGNjDmTByHp+Cr88H+TERaThSFP/Y6VnxxGQVGFymKtqKxFRmYJXJzaP/nS1tbCm8t9ceVaNi5F3VFAdIqzfnsEDA10sXyu5xPPaWlpYd5UF5y8cB337iu/BkFnQ5MvNaCtrYVVzw/Hpag7iIzPVujPKimtwoY9kXjG3xW97Sza/XpLcyMsnDEEOw7FqaRIiColpOVh1+F4vL54OGysTVs9VkdHG8uf9sSJ89dxJ0cxxRr+3HUVmbml+PqtCTJvRn9zuS9MjfU77OpXeGwWAEi18gUA7700AoYGuvjf+lCZ4igrr8H3my7Ccdw6uPj/isvRmvXhLYv6+gZs+DcSlVXK6al18sJ1NDbyJ1aP2mvSSCdcDXwZerra+HP3VTlF17LC4gqExWQ+kXIoNmP8AOjr6WBvB7tDfSw0Das+O4qnxvXHjx9MUejPmjbGGTl5ZXJtss05x9qNF+DUy/KR1i0G+rr47v3JOL9rORgDxizagje/Oo6q6o7VC/KsaGV47HDHJ57T19fBm8v9cP3UavznuaHYsCcKThN+wrcbLqikJ2bSdeFNNGlWvgBg6Rx3WJob4gcNarpcfK8SOw9dw6KZQ2Bp3nzG0zP+rqita8DBU5R6qGg0+VITy+d6wtRYX+FNl4VlUmvxzgt+Up/j1YXDUVlVh62BmlvxRxof/3QGpsZ6eFfCwigr5gnvLm0OiJZ7LGXlNfjyj/OY4NsH4337ynw+S3MjrF7ig4ATiR0ypTQ8NhMG+joY0r/thprN6WZlglXPD8eeYwlS7Y0rLK7Axz+dRq8xP+KdtcEY5GSNLqYGnWr/5OEzqXj5o0P4Y5fiJzCAsMR8VwsjDB3SU+ZzWZobYb6/K3YcvKbwhswnRJPGllbsTE304T+6HwJOJqGhoWOkHsYl38Uza/bCbYANdv04t83WJ7KaMsoJAORa9fBs+E1Exue02LplhHcvxB1eif88NxTrtoTBc+YfuHpNsTdblSk04ibsbMzQx6Hlm7pdLY2x/uNpSDj6KkZ698J734Vg0JRfsU/JzcMT08WVDqX7PDAy1MPK54bh4KkUpVc1ltbf+6JRVV2HVc+3vKI8zM0OvXqaa8SNnZy8+xr974cmX2rCzNQAy+d6YM+xBIV1UK+uqcPP28IxaUTfR3pTtZeHSw/4eTngtx1XOuy+g8ddvZaNoJBkvL3Cr8W7Ro/rbWeBCb59sDkgRu4XST9uuYzCkkp89dZEuZ3zjWUCmJnoy7y6o47CY7PgPdgWurrSV6d6e4UfTIz08MnPZyV+TWZuKVZ/fgwOo3/E57+dw5hhvRER8BJObVuKZU97IOBkUqdpXH5EVPxi094ohV9oNTQ04vj5dEwd3U9uF/IvPeOFsooahZdjPnI2FTbWJvBspdDEfH9X5OaX4WLkbYXGogzZd+9j2os70MXEAIf/WggTY8VXWrOxNoX3YFu59vtau+ECunc1wZI57i0eY2Ksj98+nY6TmxejvLIWgvkb8dG606itrZdbHKog3u811sdRoiyMAX2tcWTjIgRvWQwTYz3Mf30vRjy7CVfispQQrbDSoYG+DvrYtz/7R+zVRcOgq6OFdVvUf/WroaERv++6gtHDemNwKzcghc3cXRB86QaK71UqMULJcc6x82AcXKf9hkVvB2jsDSiafKmRVc/7oL6+UWF3hrcfiENeYbnEKzeteW3RcNy4U4yTnaSXyYfrTqGrhRHWLG3fPrkXn/FCZm4pgi/K779TQVEFfvj7MuZMGiSXu/pi4tWvwOAkuabjqFpNTT2iE3Ph4yZdyqGYlYUR3lwuQGBwEmISc1s9NuVGAZa9F4Q+49bh911XMH+qC5KOr0Lg7wswTFTqvjM1Lm9sbMTR0DSYmxkgJaNQ4emW4bGZKL5XJXPKYVO+ng4Y5GSNDXsU10C9rq4BJ85fx7QxztDSavnjefpYZxga6GLvcc2uelheUYOnXt6J0vJqHN24qF2FUWTlP9oZ4bFZKCqR/SIzJjEXwRdvYM1Sn0daA7Rk0kgnxB99FYtmDMEXv5/D8LkbNHq/bdL1AhQUVzSbctiaiSOcEHPwP9j45UzcuFOC4XM34Ju/FN/UPiEtHwP7Wst0Y8bG2hTPz3LD1sBYFBarbv+aJI6cTcWtrHtY9fzwNo99Ztpg1Nc3qmU1x/yicjz96r9Y9PZ+DOjTFYf/WqjwVXJF0cyoO6i+vSzx1Lj++HP3VbnnQTc0NOL7vy/B06UHxgn6yHy+OZMGwsbapFOkTZ2LuIngizfw/ssj293/Yub4AbC2NJbrBfbXf51HRVUtvnhjvNzOKfbGMl90MTXA/9ZLvrqj7uJS7qKmtr5d/b1a8sYyX1h0McTHP59u9vnI+Gw8/eq/GDT1V+w5loD/LBiK66fWYOu3c57oF9Wvt1WnaVweGZ+DvMJyfPP2RJgY62HTXvmn4jZ1NDQN2tpamDzSSW7nZIzhpWe8ERGXpbCbE5ei76C0rLrNIiEmxvqYNsYZAScSNfbOb0NDI557MwBxKXex56f5cBtoo9Sf7z+6HxobuVxuIK7deAGmxvp4ZcFQiV9jbmaIrd/OQdDvC5CTXwav2X/im7/Oa+T/z7Phwv1eY9o5+QKEe95fmO+F9JDVGOvjiF+3X1H4ynhCWr7U+72aenOZL6qq65SWSi2t9dsjYN+jC2ZOGNDmsZ4uPdDH3kLtUg8DjifCZeqvOBqahm/fnYQLu1fA2fHJPquagiZfambNUh8UllRi16F4uZ730OkUpN0swrsvjpC5OAMA6Onp4OVnvXH8/HVcv60ZOc/S4Jzjv+tOw7a7abt6oonp6elgyWx3HD6bKpf0sswmDZ4V0fzVoosh1iz1QVBIMmKTWl/d0RQRceJiG7JPvrqYGuDtFb44cjYNEaKmzZxznAnLwMQlWzF0zl84HZaBD14ZiVuhb+CXj6e12pLg1YXDkJNX1uE3OB85mwotLYa5U1ywYPpg7D2egNKyaoX9vKOhaRjh5QBzM/k2t31+lhv09XSwca9iVr+OnE2Fnq42Jvi2fYPsGX9X5BdV4JyGVs976+sTOHwmFb985A9/Oa5QSmrokJ6wtjSWed/XjdvF2Hc8Ea8s8JZqvM2aOBAJR1/DjHH98X/fn8KIZzch7WahTDEp29nwm+jV0xyOMqTxmZro49lpg5Gddx+pGYr7/e/dr0J23n249pN98jWoXzf4j+6HX3dEqKRwiCSS0vNx+nIG/vOcZE2hGWN4ZporTofdVGlFSrGikkosWLMP817fg9525og5+B+88+IIjV3xEtPs6DugMcMdMWRAd/y0LUxud38451i74SIc7Szw9ORBbb9AQi8/OxTa2kzt7/rI4vi5dFyKuoOPVo6BoUHb6STNWTHPE/X1jdgWGCtzPP9bfxacc3z6evMNnuVhzVKBcPWrg1Q+DI/NQs/uZrDr0UUu53t9sQ+6Whjho5/O4EBIMnzmbsD4xVsRn5aPte9MxJ1zb+KLNyegm5VJm+fyH+OM3nbm+HVHx15BPnwmFX6eDrCyMMIL87xQWVWHf4/I9waTWGZuKa6l5Mk15VDM0twI86a6YPuBOIVUbTxyNg1jfRwl2vfkP6YfjAx11e4OtSTW/xOOn7eFY81SAV5d1HYqlCJoaWlhyignnLiQLtNq0w+bL0FHW6vdKelNWVsZY9/6Z7Dzh7lIySiE+4w/NKYxc2NjI85dvYUxw3rLfK7xoqycU5czZD5XS2QttvG4t1b4Ib+oAjsOXpPL+eTtt51XoK+ngxfmeUn8mmf8B6OhQfWph4dPp8B12q/YH5yEz9eMw+U9L2KQHCbN6oAmX2qGMYbViwWIT82TW1PPi5G3ERGXhbdW+Ep050NSPbqZYu5kF2wOiFZ4BTBVaGxsxIfrTqOPvQWWz/WQ+jwD+lpjpHcvbNonW6GBlBsF2LI/BisXDoODbcurKbIyNzPEG8sEOBCS3ObeJk0QHpspl1UvMRNjfbz30giEXLqB2St3o6C4En/87yncCn0D7740EmamBhKfS1tbC/9ZMBTnrtySqoqiJsjKLUVs8t0HpdOHDumJwf27K6wH3jHRSoYiJl+AsPDG/fIa7D0m3/1W6beKkJpR+P/t3XdYVEfbwOHf0IuiiF1U7AVUFAEroth7omKPxhYTTa+mvOnJ+6Z9KZpiTIyJGmuMBRMraCygoojYABEVG4qV3ub7g8WgAgJ7toBzXxcXcPbsObPjcdk5M8/zlLgumYO9DUN6tWD1pmNkZ5efZatBwSd57sO/GBrQks9e62fStgzyb861G2l3ZsdLKzEpmYWrDzFxeLtCa7KVhhCCcUPbcnTjbKo7O/DtkvJRSzMqOpGk66l31fcqq8YNnHFzrcq2vYYbfEVF5w2+3DX6EN+zUyPat67DFz/vMbsEZDdvp7NoTQRjBnlQw8WxxM9r27IWzRu5sHyjYW6QPciNW2lMfuUPhs5cSk0XR/avfoI3Z/nrlTDL3FiZugHK/cYNbcOrn27m85/24O9bsuxBxfnkx11Ud3bg8RFlH0AUZfZEH5YFHWHp+kimj+6o+fFNafWmYxw6dpFfP30UGxv9/qtMH+3FYy//QUjYaXp2KlvM3VtfbsfB3prXZxZf4FkLz07qxJe/7OXducH8+d04g5/PUBKTkok7d50nx5U8FqMknhrvQ/z5G3Rp34DAge563dSYOsqLt78OZt7ifXz33hANW2ke8rMcDumVF28ghGDaKC+e/WAjEccu6pV5tTBBIdG4uVY1yLJcyEsZ3rJxdeYvP8BkDd9Tg4JPAqUbNI4e6MHyoCiCQ0/Tp5t28W2GEnHsIqOfW4lnK+OklH+Qvt2aYmlpwcaQaLp0aFDq53+9KJSMzBxenqZ/Eqt8dWs50a97U1b9fZTc3NxiE6+YA33ive4lhKB3lyas/Oso2dk5mt4szhcVk0glRxsa1NVmJYQQghendGHCS6t59oO/cHayIz0jm7SM7Lzv6Vmk5/+ckaXbdvfv6RnZCCH45q2BjB/WTpN2Afyy+hApqZmlLlguhGD0QA8+/G4nl68mU6v6g1dxaGXTPzFMnbOWS1eTefOpHrw1q4fen7/MkXn/r35I2dlaM3uiL0Eh0bQb8i0/rzxY5vXER2MS2RAczdMTfXGwt9G4pXkZwDxb1Wbu4jCj1ukwtOzsHP7z5XZaN63BuCFt9T7eyP7uVKlsV+ZEAweOnGfV30d54fEupbqDVVZVnex5fnJn1m49wcGjFwx+PkMJ07O4clEc7G2Y+/Zgxg1tq/cHBBdnB8YMasNvaw8bNA7KVDYEn6RxfWdaNvk3OHrCsLbY2ljxk8Y18NLSs9i6J45B/s01iW0tjBCCGWM6svfQOU0z1G0Iica9Wc1Sxc3092tGJUebcpH1MOHiTQbNWIyzU15KeUcH7f8elZZzFXu6tK/Pxh2lX+J3OzmDeUv2Mbx3S1o01jbw38+7ITdupd+ZpTFnIfviaeTqXGxsa2kEdG7MzdvpHDTQqouo6Mt4NKup6ftD4EAPmjdyYe5vYbw/bwdzF+9jybpIgkKi2XvoHMdPXeHS1WQyMnNwsLOmXq3KuDerSfeODRnSqwWPDfekacNqTJnzJ3sPaZMJNjc3l7mLw+jcvj5eHnXL9JpycyWrNx3TpD0Pcjs5gxlvrqX/lN9wqmTL3hXTef/5gAo58AI1+DJbbzzpx88fDwdg6ut/0sDvC97+ajuXryaX6jifLdiNg721wdbVCyGYPdGXyBOXK0TNmXyL10ZyIu4q7z8XoMndWXs7ayYMa8vqTcfKVD/j9c+34uLswItTuujdlpJ6dnJnqjrZ8c7X5TfzYejhBKysLIqtmWQOZk/0JSU1U5O4QHOSmpbJtr1xDOnV4q4PO9WqOvBo31YsXnuYtHTtAtVDwk6Tlp5V4qV7ZfXYcE9srC35UaO08zdvp7NjX/ydpZklZW9nzdBeLflj8zGzzph5Ozkvpfyt5AyCfpyg9xI9LQ30b8ahYxdLXV/zxxUHuHErnVdnaDfrlc/P2w2AnfvN+29qbm4uO/bFa7LkMF+vznnH2rrnlGbHLOhozBXN4r3yWVtbcuyvp0mP+g+50e+SeuQtrh2Yw4XdL3Nq+/Mc/etpDqyZya5l09iyaDLr509g5Tej+fXTEcz/YBhfvTWQvxZMpH6dKjzy1DLOXbypd5s2/RNL7JlrJUovXxiP5rVo3bQGy4MMv/QwODSONoPnsWDFQV6e1pWDa2dqWkbHHKnBl5mysrLk8ZEdOLz+KbYumoRP23q8NzeEBn6f8/ira0qU6jjh4k2WrI9k6sgOuDiXrDBwWYwd3AbnKvbMXVw+1qg/SEZGNu98E4yXR10e6dtKs+NOD+xIRmY2v/15uFTPCw6NY8vuU7w+s3TxRPqqUtmOF6d0Yf32k4RHlc/Zr9CIc7RrWdsgs75a8vKoi287V+YtCTO7uAF9bNsTR3pGdqGDoWmBXty4lc4fGt5ZDQqJxsHeGn9fN82OWRgXZwdG9nfnt7XaJN7YvCuW7OxchvQq/aBx9CAPrt1IM2icjD6ys3MY+/xKjkQnsvLr0bRtadyU8g8yyD+vz/8qRYKLzMxsvvh5L/6+bvhqPKsO0LBeVRrUrcLO/fGaH1tLh49f5vrNNE0HXzVdKtGuZW2DJN1ITErmyrUU3A2wJNnS0gJbW6syz6i5ODuw9vtxpKZlMfzJpXq/r3zzWxi1a1TSK8na6IEe/HPgbKlvTJRUSmomT78XRK+Jv2BjbcmuZVP55NV+JaqVV96pwZeZE0IQ0KUJG36cwMnNzzAt0IsVf0XhOfRbAh5byPptJ4r8sPbVolBycyUvGHi2xMHehqkjO7B60zHOXzLMf1JjWrAynDPnb/DhCwGaLk1o16o23m3r8eOKkifekFIy57OtuNZ2KlOqe30981gnnKvY887X241+bn3l5OSyL/K8psk2DGn2RF+iTyexzYCZvoxtffBJKjva4ufd8L7H/H3daFzfWbOlh1JKgkKiCejc2Ch/vGeMzhs8rvpb/8HjhuBoqlW1L9Py2H7dmuJUydZssx6+8NHfBIVE881bA+nv18zUzbmPR/OauNZ2Iii45Cnnl6yL5PzlW7w6o7vB2uXn7caOffFmvZw/ZJ8u3kuDTIcFBXRpzO7ws5pnFM1fxqn1zJdW3JvVZOkXIzl07BJT5vxZ5n/7mPgk/toRw8yx3not2wsc6IGUUpP3uHvduJWG96M/MPe3MJ55rBMR654sU9xleaUGX+VI80bVmffOYM7tfJH/vtSHk3FJDJ25lJb9vmHe4jCSUzLu7HvjVho/LDtA4AB33FzLXnujpJ4c501uruSHZeU77XxqWiYffLuD7h0b0tcAAezTA704GpN4JxbpQdZuPUHY4QTeeaanSe4GOelmvzYER7M/8rzmx0+4eJPtBrpjfyz2CskpmZrHexnKqP7u1KjmWGFmkKWUbAiOpl/3poV+ALCwsGDqqA4Eh57WpFbg8dgrxCfcMFiWw3v5+bjRvJEL8/VcepiTk8vGkGgG9mhepiXOtrZWDO/TijVbjpOZma1XW7T2x6ZjfPNbGC9M6cKTJrh5VBJCCAb5N2fLnlMl6r/c3Fw++XEX7VrW1rSI9738vBuSmJRC9GnzraMZHHqapg2raVbGI1/vLo3JzMphd7g28U/5/h18mW+68sG9WvDxi71ZHhTFh9/uKNMx5i0Ow9rakifG6JcErWWTGrRtWUvzrIdSSqa/sY7o+CT+/nkiX7010OxXp2hNDb7KoWpVHXj1ie6cDn6e3/9vFM5Odsx+N4j6fp/z6iebOXfxJj/8foDbKRmaZmEqTuMG1Rjk34wflh0gI8O8PgCUxtzfwrh0JVnzWa98Ywa1wdHBhh9XPDjNdk5OLm98sZUWjasz6RFPzdtSUk9P9KVaVXve/Ubb2K89B8/SYfj3BDz2i0GKaobqiiCXl5kvW1srpo/2YkPwSeITrpu6OXo7ePQiFxNvFxvHNPnR9lhYCH7WYPZrgy5boLEK9gohmDG6I7vDz96pHVQW+yITuHo9tdTxXgUFDnDnxq10tuw2TJxMWS1YGU79OlX45JW+pm5KsQb6Nyc5JZN/ShC3vH77SU7EXeWV6d0MltQFoIduNslclx7m5OSyc/8ZTZcc5uvesSHW1paaLz08GpOIi7ODUbP3lcUrM7oxYVg73vpyO2s2l27WKTklg4WrDzGqvzu1a1TWuy2BAzzYc/CcJnFo+X74/QCr/j7KRy8E0K+7+c2GG4Mmgy8hRH8hxEkhRKwQ4rVCHrcVQizXPR4mhHAr8Ngc3faTQoh+JT2mYijYoQAAIABJREFUkhfkOWZwG0JXzWD38mn07tKEz37aTaOe/8d780Lo07UJ7Y2YaGD2RF8Sk1KMlh2nMAePXuDZ9zfyyfx/+GPTMQ4fv3TXjGBxbt5O538/7qK/X1O66wKetVa5ki1jBnmwLOgItx6Q2W7x2sMci73CB88FGCTlbknlz34FhUSzr4z1cO61dF0kvSb+QmVHG2ysLZlngCLDYYcTcHF2oEmDapof21Dy71R+/3v5nkGGvMGQEIKBPYoeVNSt5cQg/+YsXH1I71pVQSHRtGtZm/oa34UvzqRH9E+8sSE4GktLC71mUfp0bUJVJzuzynqYmJTM5l2nGDekjclTyj9IQOfG2FhbPjDroZSS/83fhZtrVQIHuhu0Tc3cXKhVvZLZJt2IOH6Jm7fT8ffRfvBVydGWTu1cNY9jjIq5jHvTGgYdNGtBCMGPHw7Fp209Jr78B5EnHhzjn+/XPw9zKzmjzIk27jV6kAcAK//SZlnz4eOXeO7Dv+jXvSkvTeuqyTHLI73fEYUQlsA8YADQGhgrhLg3wm8qcF1K2RT4P+B/uue2BsYA7kB/4FshhGUJj6noCCHo0qEBK78Zzaltz/Hc5E7UcqnEO8/0NGo7+nRtQjM3F+Ya4IN0ScSeSaLv47/y7dJ9vPrpFkbMXobn0G+p7PkhtTt/QrcxC5j0yh+8PzeEpesi2Xc4gaTrqXfWVf/fwj1cu5HGB8/3Nmg7pwd6kZqWxbKgot/MMjKyefvrvKQfI/qb/tJ/emInXJwd9J79ys3N5T9fbmP8i6vwbVePfaufIHCgB7/8EcHt5JINkksqNCIB37b1zP4PbUEN6lZlWEBLFuhRXsJcbAg+SSdP1weWRpgW6MWlK8llSved7/rNNHYfPMcgPWaPyqJ6NUce7duaX/8se9bGDcEn6d6xAVWd7MvcDhsbKx7p04o/txw3m+tmeVAUOTm5TNCwbpGhODrY4O/rRlBI8XFfuw6cYe+hc7w4pavBb4gJIfDzbmi2M1/BoXkDI0PMfEHe0sODRy+SdL302YELI6UkKjrRbOO97mVna82ab8dSpbItQ2cu5UpSygOfI6Vk7m9hdGxTF1+NVnw0behCB/c6mhSVT07JYPRzK6hW1Z5fP33U7GvYGZIWr9wHiJVSxkkpM4FlwLB79hkGLNL9vAoIEHmfiIYBy6SUGVLK00Cs7nglOaZSCDdXZz57rT9xwc8bPXjRwsKCWeN92HvoHGG6JV/Gcv1mGoNnLEFKyfG/n+bGwdcJ/3Mmy78K5KMXezPIvzlWlhZs2xPHf77anvfhf+R8qvv8l2odP6bjI9/z6YLdjOjXukw1MUrDp50rbVrU4scVRd8tn7/8AGfO3+CjF3qbxeChciVbXprahY07Ysr8b5uWnsXY51fx/rwdTBnZgS2/TMLF2YHZE3y4nZLBr39ql2b95u10jsVeKTfxXgXNmuBD0vVUlhczODd3Fy7f4sCRCyXK3jewRzPq1KzMghIsxS3K5l2x5OTkGi3eq6AZY7y4fjOtTDP+Zy/cIPLEZU1S4wcO9OBWcgabd5nH0sMl6yJp27JWufmwO8i/BSfjrnLqzLUi9/nf/F1Ud3ZgykjtimsXx8/bjbMXbnLm/A2jnK80gkNP06JxderU1H9pW2F6d22ClPJOEWd9JVy6xa3kDDyamW+8173q1nLiz2/HcflqMiNmL3tgTOL2vXEcP3WF2RN8ta1jNsCDsMMJei+Hn/VuENGnk1jy+Qhqupj30k9D02LwVQ8o+GksQbet0H2klNnATcClmOeW5JgACCFmCCEOCCEOXLlyRY+XoWhh8oj2eelNZy/n9DnjxK1kZeUQ+Mxy4s5d54+5Y2ja0IUqle3o4F6XwIEezJnpx08fDydkyRQSdr1E6pG3iNo4m7Xfj+PzOf0YN6QtLlXtadm4Oh+9aNhZL8i7ozltlBcHjlwg4tj9hSSTUzJ4f94OenZqRJ9uTQzenpKaNd4XF2cH3inD7NfFxNv4j/+ZlX8d5ZNX+rLgo2F3kjD4etbHu2095v6mXaHu/ZHnkVKWm3ivgnp1bkzLxtWZt6T8Jt7In8UqyaDCysqSyY96EhQSXeZsqUEh0bg4O+Dbzvj/3v6+jWjasBrzl5V+6WF+hj0tBl8BnRtTraq9WWQ9jD2TRNjhBCYMNf9Zr3wD/fNiTzbuKHz268jJywSFRPP0RF+jJQfIzxK6Y1+8Uc5XUtnZOfxz4KzmWQ4L8m5Tj0qONpotPYyKziuIbs7JNgrj3bYeP300nH8OnGH2e0HF/o385rcwqjs73FkqqJXAgXnH0+e9ZdEfh/h1TQT/md2Dnp0aa9W0ckuLwVdhw+t7r46i9int9vs3SjlfStlRStmxRg3tazcopVOlsh2bF04iNT2LgMd+MXjqeSklT78XxNY9ccx/fyg9fB+8BMLezhr3ZjUZGtCSF6Z0Zd47g9m0cBLhfz5J80bVDdrefBOGtcXWxooFK++/2//lL3u5ci3FbGa98lWuZMvLU7vy985YQg+VfPYr4thFfEb8QFRMImu+HcPLhQSqz57gy4m4q5qlWQ+NOIcQAh8TfBjXlxCCWRN82R95XrMYO2Nbv/0kDetVLfEHnSkjO5CbK1m05lCpz5WTk8tfO2Lo372pSWKL8hNv/HPgDMdjS3cDcEPwSZo2rEbzRi56t8Pa2pJH+7Zm7bYTmhauLosl6yIRQjB2cBuTtqM0mjZ0oXkjlyIHX5/8uAtHBxtmTdAmlqYkPJrXxLmKvdktPTx49CK3UzIMtuQQ8q5nfx83zYot5yfFcS9HM1/5xg1ty5yZ3flxeThzfys8rCM+4Trrt59kxpiOmmdGblTfGe+29cocU3ri1BWeemcDPXzceGuWv6ZtK6+0+EuVABRc2+MK3FuR9c4+QggroApwrZjnluSYiplq06IWf/80kSvXU+gzeVGJ1iqX1Ve/7OWHZQd47YnuTB5hnKUgWqhW1YGR/VuzeG3kXbVMkq6n8umC3Qzr3ZJO7c1vydysCT5UL8Xs17ptJ+g29icAdv0+jWG9Cy9aHTgwP826NvGCoREJtGpSnSpGLEqtpceGt6OSo025nP1KS89i655TDO7ZvMQ3D5o2dMHf142fVh4sdZHp/GyBplhymG/So55YW1sWu5T4XimpmWzbe5rBPVtodpMlcIA7ySmZ/F2KgsFak1KyZF0k/r5umqcgN7SBPZoTHBpPSurd9aXOnL/B7xuOMD3QCxdnB6O1x8LCgu4dG5pd0o2QMF19rxLc7NRH7y5NiD1zTZNll1HRidSpWZlqVY3376elD54PYEivFjz/0d9sLSSr6bdL9iGE4Mmx3gY5/+iBHoRHXSh1WZC09CwCn12Bg501Sz4fafbJd4xFi17YDzQTQjQSQtiQl0Bj3T37rAMm6X4eCWyXeXOn64AxumyIjYBmwL4SHlMxYz7tXNnww3hOJ1yn35RfuXErTfNzbNh+khc+3sQjfVrx4QsBmh/f0KYHenHz9t1FWv/7wz/cTsnkwxcMv/yxLCo52vLytK5s+ieWvYeKrsEipeSzBbsY/uTvtG5ag32rnyg286adrTXTR3uxfrv+adallIQeTiiX8V75nCrb8dhwT5ZtOGLQmxeGEBx6mtS0rBLFexU0bZQXceeuExIWX6rnBWmQLVBfNV0q8UifVixaE1HihBfb98aRkZmtV4r5e/Xs1Ijqzg4sN+HSw/2R54mJT2L80LYma0NZDfJvTkZm9n1xRl/8vAch4IUpXYzeJj/vhsTEJ3Ex8bbRz12U4LDTtGpSw+Ap2wO65C1P02JFRFRMYrmK97qXhYUFSz4fScvG1Ql8dgUx8f8OglLTMlmw8iCP9GllsBseowbkZfdcWcrZrxc++psjJy/z66ePUq+2kyGaVi7pPfjSxXDNBjYBx4EVUsqjQoj3hBBDdbv9BLgIIWKBF4DXdM89CqwAjgF/A7OklDlFHVPftirG1cO3EX/MG0NUTCKDpi++726iPiJPXGLsCytp37o2v302olxmzfHzcaOZm8udu+XnL91i7uIwJg5vZ9ZLI2ZN8KVGNUfe+brw2a/MzGymv7GWl/+3mZH9WxOy+PESBWXPHOuNEILvluqXZv3U2WskXU8tl/FeBc0a70NmVg4/rSp7IgpT2BB8EkcHmzt1ikrq0X6tqepkV+rEG0Eh0XRpX9/kd7RnjPbi2o00/th0vET7rw8+iVMlW7p3bKhZG6ysLBnRrzXrt5+8a0bdmJasi8TWxooR/UyfpbW0undsiKODzV1ZD5Oup7JgZTjjhrQ1ahmDfH66siclqUFmDFlZefFehlxymM+9WU1qVa+k99LD3NxcjsVeKXfxXveqXMmWdd+Pw8JCMPSJJdzUlatZuv4I12+maZZevjAN6lalc/v6pUoEtfKvKL7/fT8vT+vKgGJKjjyMNPnEKqXcKKVsLqVsIqX8ULftP1LKdbqf06WUo6SUTaWUPlLKuALP/VD3vBZSyr+KO6ZS/gzo0ZzfvxhJaEQCw59cqkka5EtXbjN4xhKqVLJj/Q/jcXQon5XR8xJvdGDXgbOcOHWF9+aGkJMredfIJQJKy9HBhlemd2XzrlPsOXj37Ne1G6n0m/IrP608yFuzerDsy1ElDk6vX6cKw3u3ZMHKcL1iVkIj8uKkyvPMF0DrZjXp1bkR3y3dT05O6ZbimYqUkg3B0fTp2qTUcQf2dtZMGNaO1ZuOlTi99PlLt4g4fsmkSw7z9ezUiCYNqjG/BDW/8vupX/emdxLPaCVwoAepaVlsDDH+0sPs7ByWBR1hcM/meqXONxVbWyv6dG3Cxh3RdxIbzF0cRmpaFq9M72aSNrVvXRtHBxuzifsKj7pASmomPQ285BDy/kb27tKYbXvj9ErGdPrcDdLSs8z6pmZJNW5QjVXfjCb27DXGPr+SnJxcvvktlLYta9HdW7sbOYUZPdCDwycucTLu6gP3jTt7jWmvr8W3navZruQxpfI3XaCUOyP6u/Pzx8PZuieO0c+uJCur7MVU09KzGDZzKUk3Uln3wzjq1irf09iTHm2PlZUFcz7fyk+rDjJzTEfcXJ1N3awHenKcDzVdHHn7q+13tkWfvkqnUT+y5+A5Fn82gveeCyj1jOTsib5cu5HG7+uPlLltoRHnqORoQ+um5T8Bz6zxvpy9cJMNwSdN3ZQSiTxxmXMXbzKkjNn7po3yIjMrhyXrIku0f35yBGPX9yqMhYUF0wO92LEv/oEfTg4du8jFxNuaZDm8Vw8fN2q6OJpk6eHWPXEkJqWUi9peRRnYoxlnL9zkaEwiKamZfPNbGIN7NjfZB3crK0u6dqhvNnFfwbp4r9LObJdVQOfGJCalEBWdWOZjRMXoMh1WgMEX5MXaff3mQP7aEcOg6YuJPHGZpyd2MniCrpH93RFCPDDrYWZmNqOfW4EQgmVfjsLa2rA18cojNfhSjGLSo+2Z+/Yg1m07weRX15TpTn5ubi6TX13D/iMXWPL5SDq4G7YelzHUql6Job1a8ueW49jZWvHGUz1M3aQSyZv96sbWPXHsOnCGbXtO4TtyPjdupbP9t8mML+OHrx4+bng0r8k3v4WW+U5n2OEEfNrWqxCBvUMDWuBa26nIDFfmZv32E8C/abtLq12r2nRsU5cfVxwo0b9/UEg0DepWMZs72pNH5N1MeVDijQ3bTyKEYIBf2fqpOJaWFozs705QSDTJKdoWLn+QJesiqepkZ5DXZSwDdcujNoZE8/OqgyRdT+XVGd1N2iY/bzeOnLysWcFhfQSHnsajec0HFk/XSn7clz5LD/MHbq2bmsf7hBaeHO/Dk+O82fRPLM5V7Bk3xPCZRevVdqKbV4MH3tiZ8/lWDhy5wM8fDy8XN5NNofx/OlHKjVkTfPn4pd4sXR/Jk2+vL/WH63e+DmbFxij+93IfhvcpPGteeTRjjBcAz03qZPAAZi09Oc6bmi6OTH51Df2n/ka9Wk7sWz2Drl5lX/oghGD2BF8ijl+6b0ljSaSlZxFx/JJJ6j0ZgpWVJTPHerN1TxwnTpl/HcMNwdH4tK1H7RplL7w6bZQXUdGJ7I88X+x+6RlZbNl9StNsgfqqVb0Sw3u34pc/IsjIKLog6oaQaDp5uhrsA+zogR6kpWfdFbtkaCmpmazZcpxRA9yxtdV2KaUx1avtRLuWtVm77QSf/bSbLh3q003DuLyyyK/3tSvctLNfmZnZ7D541ihLDvM1qFuV5o1c9Eq6ERWdiJtrVSpXstWwZab31ZsDmfxoez5+sbfRas+NHuTB0ZjEO6n777Vh+0m++HkPsyb48Gg5jPs0FjX4UozqtSf8eP1JP35cHs5L/91U4gHYkrWHeX/eDqaM7MBL07oauJXG1bdbUzbMH1/u6l842Nvw6oxunDp7jT5dm7Bn+TRN7nJNGNaOKpXtmPtb6dOsHzx6gezs3HIf71XQ9NFe2Fhb8q2Zp52/fDWZfZHn9V5KN3ZwGxzsrflp5cFi99uxL57UtCyziPcqaMZoL5Kup7JmS+GJNy5duc3+yPOaZjm8V1evBtSpWblUwfH6Wrv1BCmpmeWqsHJRBvVszp6D5zh74SavmXjWC8CnrSu2NlYmX3q4/8h5UtOyDJ5i/l4BnRsTsi++zCELR2MTca9As175rK0tWfi/R3jCQOnlCzOiX2ssLApfephw8SaTXv0Dz1a1+ey1fkZrU3mkBl+K0X3wfABPT/Tli5/38N7ckAfuvzv8DFPm/Im/rxvfvTvYbO5ya0UIwaCeLcrl3eJnJ3Vm26+TWff9OJw0qqnl6GDDlJHtWbXpaKnTK+cn26goM1+Ql8Z81AB3Fq2J4HaycZeRlcbGkLwkBaVNMX8vp8p2BA7wYOmGyGKXzQUFR2NvZ22UrGulEdClMY1cnYtMvJGfCMMQ8V75LC0tGNmvNRt3xBjtmlm87jD161ShW8cGRjmfIQ3skbdssnXTGmYRT2hra0UnT1eTJ90IDj2NEMJo8V75endpQkpqJmFlKDqflZXDibir5T7TobmoXaMyPXzcWL4x6q6b59nZOYx9YSUZmTks/ypQ80LPFY0afClGJ4TgyzcHMPnR9rzzdTBf/Ly7yH3jzl5j+JO/07BeVVbPHaN5ZjBFP5aWFvTq3BgrK20Dap8a70NOjuSHZaVLOx8akUAjV+dytXyzJGZP8OVWcgaL1x42dVOKtCE4GtfaTrRrVVvvY00L7EBySmaRNWWklASFRNOrUyPs7czrj7yFhQXTR3sRHHqa6NP3J97YEHyS+nWq0KZFLYO2Y/QgDzIys1m/3fDJWhKTktm86xTjh7Ytl2U/7tXJsz5DA1ry6av9zOb1+Hk35ODRiya9AROyL562LWoZtdA05GUSFUKwbW/plx7GxCeRlZWDR3PD/n97mAQO8OBk3FWOnLx8Z9u734Sw68BZvn9vCM0bVTdh68oH83hXUR46FhYWLPhoGKMGuPPix5uYv+z+u8Q3b6cz5Ikl5ORKNswfb/I6PorxNG3owgC/pvyw7ACZmUXHztwrNOJcua/vVRhfT1c6uNdh3pJ9eqVcNpSMjGw2747VLP6qS4cGtGxcnQUrC6/5dTLuKnHnrpvdksN8j+sSb9xbsyyvn04xuGdzg8/gd25fn3q1nIyS9XB5UBQ5ObnlsrByYSwtLVj7/TgGmtH15eftRm6uZE8xxe0NKSMjm93hZ/H3dTP6uZ2r2OPlUadMSTeidLFJFSXToTkY0a81lpYWd95btu4+xYff7eTxEe3LdaZTY1KDL8VkLC0tWPzZCAb2aMbM/6xnaYH00tnZOQQ+s5zo+CRWzx2t7qQ8hGZP9OXSlWRWbzpWov0TLt4k4dKtChXvlS8/EcnRmER27Is3dXPus2NfPMkpmZrFMQkhmBboxZ6D5zhWSGB3fup9c1gSVpjaNSoztFdLFq4+dFfijR374klJzTToksN8FhYWjBrgzt87Y+4UYzWUJesiadeytppdMKDO7etjZWVhsv//YYcTSM/INtky395dmhAakVDqDJ5R0ZexsBC0bKI+Q2ilhosjvTo1YsXGKC5duc2El1bTsnF1vvnPIFM3rdxQgy/FpGxsrFg1dww9fBry2Ct/sHZrXpD6cx/+xeZdp/j+vSH07NTYxK1UTKFf96Y0bViNuYtLlmY9Px6gIs58AYwZ3IZqVe2Zt9j8Em+sDz6JvZ01vTpr93914vB2WFtb8tOq+xNvBIVE06ZFLRrUrarZ+bQ2Y4wXV6+nsnbbiTvbNuj6yVgfYEcP9CAzK4e1W088eOcyij2TRNjhhAoz62WuHB1s8HKva7KkGyFhefFeft5uJjl/7y6Nyc7OLfXrPxpzhaYNq6kYJI0FDvQg9sw1ek9axM3b6Sz/KhBHB+NkXKwI1OBLMTl7O2vWfT8eL/c6BD6zgulvrGXe4n28PK0rU0d5mbp5iolYWFgwa7wPew6e4+DRCw/cPzQiAVsbKzw1iDkyR/Z21kwd2YE1W46TcPGmqZtzh5SSDcEn6d2lsabxVzVdKjEsoCW/rrk7bfvN2+nsCj9rtksO8/Xp2oSG9areSbyR10/RmvdTcXw9XWlQt8oDi6LqY8m6SIQQjB1s+DpDD7sePm7sizxPWnqW0c8dHHYaz1a1ca5ib/RzQ95SZFsbq1IvPYyKuYxHMzUjq7VH+rTCysqCozGJfPXmQIPHsFY0avClmIXKlWz566eJtGxSnQUrwhnWuyUfv9TH1M1STGzyiPY4OtiUqMhw6OFzdHCvU6GTsjw5zofcXFlkJj1TOBqTSHzCDYMspZsW2IGr11NZt/3fmZvNu2LJzs41+8GXhYUF0wO92LYnjtgzSRyPvcLphOtGWXKYTwhB4AAPNu8+VWjyD31JKVmyLhJ/Xzdc61TR/PjK3fy8G5KVlVOmrH/6SM/IYu+hBJNmFrW3s6ZbxwZsLUW9r7T0LGLPXFOZDg3AxdmBJ8Z0ZOZYb6aPVjfJS0sNvhSzUa2qA1t+mcRnr/Vj8WcjsLRUl+fDrqqTPROHtWPp+iMkXU8tcr+srBzCoy5W2CWH+RrVd2aQfzPmLw8vVSISQ7oTf2WAwVDvLk1oULfKXYkrgoKjca5iXy7+rR8f0R5Ly7zEG/lZB409aHxhShcc7a2Z9vpacnNzNT32/sjzxMQnVYjaXuVBV68GCCHYaeS4r9CIBDIys/E3cor5e/Xu0pgjJy9z+WpyifY/ceoqublSDb4MZO7bg/nuvSEVrvyPMahPt4pZqelSiRendqWSY8WqRK+U3eyJvmRkZt+XOa6gI9GXSUvPqlD1vYoya4Ivl6+WPBGJoa3ffpIO7nWoV9tJ82NbWlowZWQHtuyOIz7hOrm5uWzcEUP/7k01L29gCHVrOTGkVwsWrj7Emi3HDdZPxalTszJfzOnPPwfO8N3S0pVueJAl6yKxtbFiRP/Wmh5XKVxVJ3vatazFzgPGjfsKDj2NhYXAz8SDr4DOTQDYXsKU80dj85L1VMQCy0r5pgZfiqKYNfdmNenZqRHfLt1HTk7hd+7ziytXxEyH9+rbrQlNG1bjzf/bxvMf/sXH3+9kwYpw1m07wd5DZzl15hq3kzOMkpL+6rUU9h5K0LuwcnEeH9EegIWrD7E/8jxXrqUYdemevmaM9iIxKYWwwwkma/fkEe3p260Jr366hfiE65ocMzs7h2VBRxjcszlVNCqwrjyYn7cbew6eM+rMd0jYaTq41zH5v3MH9zpUdbIrcb2vqOjLWFtb0szNxcAtU5TSqbjBEYqiVBizJ/gyYvYy1m8/yfA+re57PDTiHLVrVKJB3Yofd2JhYcH7zwXwxhdbWbAynOSUzEL3s7WxoqaLIzWqOei+O1LTxZGGdasydVQHHOz1z0z1184YpJQGHVQ0qFuVvt2a8POqg2Tn5GJhIejv19Rg59Na325NaVC3Cmcv3DTZ4EsIwfwPhuExaC7T31jL5l8m6b1UaOueOBKTUlRdHyPz827I17+GEn70Ap3bNzD4+dLSswiNSOCZxzoZ/FwPYmlpQa9Ojdmy+xRSygdew1HRibRsXB1ra/OfJVceLmrwpSiK2Rsa0IL6daowd3FYEYOvBDp51n9o1p6PGdyGMbrscmnpWVy5lkJiUt5X/s93tl3L+/lY7BUSk1JIz8jm1z8jWPvdOOrW0m8J3PrtJ6lTszId3Oto8bKKNG2UF6OeWc5Xi0Lp3L5+uSq4bmlpwaszuvPrmgi8PAzbT8VpWK8qn7zcl6fe2cDCVYeYMqqDXsdbvPYwVZ3sGODXTKMWKiWRn+p9574zRhl87Tl4lsysHJMm2ygooEtj/th8jFNnr9G0YfEzWlExiXRpX/FXQyjljxp8KYpi9qysLHlynDevf76VYzGJtG727xr+pOupxMQnMXWkfh8myyt7O2sa1K1aoppXUkrWbTvB+BdX4z3iB9Z+N46ObeqV6byZmdls+ieWwIEeWFgYdgX70IAWVHd24Or1VLPPcliYp8b78NR4H1M3gyfGdmRZ0BFe+Phv+nVvWub4s5TUTP7ceoLxQ9pia6s+RhhTDRdHWjWpwc798bz6RHeDny8kLB5LSwu6eRl+oFcSvbvk1RLctieu2MHX7eQMzpy/wfRAlYlPMT96/cUUQlQTQmwRQsTovjsXsd8k3T4xQohJum0OQoggIcQJIcRRIcR/C+w/WQhxRQgRofuapk87FUUp/6aN8sLWxop5S+4uMlzRiytrSQjBsN6t2LN8GlaWFnQf+1OZa0D9c+AMt5IzDBrvlc/GxopJj3oCxs8WWJFYWFjw08fDyczKYeZ/1pU5LnDt1hOkpGaqwsom4ufdkF3hZ4uMgdVScNhpvNzr4GQmcX3N3Fxwre30wJTzx3TJNlSmQ8Uc6Xu78jVgm5SyGbBN9/tdhBDVgLcBX8AHeLvAIO0zKWVLoD3QVQgxoMBTl0spPXVfC/Rsp6Io5VwNF0fGDPJg0ZoIbt4+U+aIAAAYbUlEQVROv7M9NOIcFhaizDM4D6O2LWuz/48n8PKoy+hnV/DO19tLnYZ8Q3A0tjZWBHRubKBW3u2tWf78MW8MbVtWzCLaxtK0oQsfPN+LDcHR/L7+SJmOsXjdYRrUrUK3juYxG/Kw8fN241ZyBpEnLhv0PCmpmeyLPG82Sw4h7wZS7y5N2B4aV+x7VlSMbvClCiwrZkjfwdcwYJHu50XA8EL26QdskVJek1JeB7YA/aWUqVLKYAApZSZwEFC3rhVFKdLTj3UiJTWTRX9E3NkWGpFA2xa1cHTQP4HEw6SmSyW2LZrMpEc8efebEMY8t5LUtMKTd9xLSsn67Sfp1bmR0fq9SmU7HumrUppr4dlJnfFt58ozH2wkMalkNZPyJSYls3nXKcYNaWvw5aZK4bp7NwRg5/54g55nz8GzZJlRvFe+3l0ac+1GGhHHLxW5T1R0IvZ21jSq/+Dl2IpibPq+c9aSUl4E0H0vbH63HnCuwO8Jum13CCGqAkPImz3LN0IIESmEWCWEKDJiUggxQwhxQAhx4MqVK2V9HYqilANeHnXp5OnKvCVh5ObmkpubS9jhhIcixbwh2NpasfB/j/Dpq31Z9fcxuo/9iYSLNx/4vJNxVzl19hpDylHKd+VflpYW/PzxcG4nZ/D0extL9dzlQVHk5OSqLIcmVL9OFRq5OrNzv2HrfQWHncbKyoKuHcxrhrOXbrZ96+5TRe4TFX2Z1k1rqBsEill64FUphNgqhIgq5GtYCc9RWPqxOwvNhRBWwO/A11LK/EW86wE3KWVbYCv/zq7dfyAp50spO0opO9aoUaOETVIUpbyaPcGX6NNJbNl9ihOnrnIrOUPFe+lBCMFL07qx7vtxRMcn4TNyPvt0cXRFWb/9JACDeqr4q/KqdbOa/Ge2Pys2RvFHKQp2L1kXSbuWtXFvpmJpTKmHjxs798cbtJ5fcOhpvNvUo5KjrcHOURZ1albGvVnNYuO+jsZewUNdo4qZeuDgS0rZW0rpUcjXWuCyEKIOgO57YiGHSAAK3pZ2BS4U+H0+ECOl/LLAOZOklBm6X38EVLoaRVEAGDXAnVrVKzH3t7ACyTbUzJe+BvdqwZ7l07G1saTH+J9ZtqHoeKANwSdp17J2iTIsKubrlend8GxVm6fe2cC1G6kP3D8mPomwwwlMGKYSbZian3dDrl5P5XisYVb8JKdksP/IBbNbcpivd5fG7Ao/S3pG1n2PJV1P5WLibZVsQzFb+s7HrgMm6X6eBKwtZJ9NQF8hhLMu0UZf3TaEEB8AVYDnCj4hf0CnMxQ4rmc7FUWpIGxsrJgx2ougkBiWro+kqpMdzdyqmbpZFUKbFrXYt+oJvNvUY+zzK3nr/7bdF9R+7UYquw+eY7Ca9Sr3rK0tWfjfR0i6kcoLH/39wP2Xro9ECMHYwWrwZWp36n0ZaOlhfjZFfx83gxxfXwGdG5OWnsXeQ+fue+xofrKN5irZhmKe9B18/RfoI4SIAfrofkcI0VEIsQBASnkNeB/Yr/t6T0p5TQjhCrwBtAYO3pNS/hld+vnDwDPAZD3bqShKBTJzrDeWloKte+Lwbeeq1vVrqIaLI1sXTWLKyA588O0OAp9ZQUrqv4k4/t4ZS05OrlFSzCuG59m6Dq9O78aiNRH8tSO6yP2klCxZF0nPTm5lrg+maKdxA2fq1qpssKQbwaGnsba2pKuZ1Pe6Vw8fNywtLdhWyNLD/EyHammsYq70+sSiWx4YIKVspvt+Tbf9gJRyWoH9fpZSNtV9LdRtS5BSCillq3tTyksp50gp3aWU7aSUPaWUJ/Rpp6IoFUvdWk48qst8p+K9tGdjY8WCj4bx+Zx+rNlynO5jf+KcLhHHhuCT1HRxxLutSu1fUbw1y59WTWow48113CpQxqGg/ZHniYlPYvwQlWjDHAgh8Ovoxs79ZwwS9xUSdhrfdvVwsDfPLLJOle3wbVev0LivozGJOFWyxVXdJFDMlLpdrChKufT85M5YWlrQu0sTUzelQhJC8MKUrqz/YTyxZ67hM+IHdoef4a+dMQzyb65mGysQW1srFv53OBcSb/PKJ5sL3WfJukhsbawY0V+l+zcXft4NOX/5FqfPXdf0uLdupxN+9CL+PuYZ75UvoHNj9h85z41baXdtj4q+jEfzmghRWL43RTE99ddTUZRyqVP7+iTtf41uHRuauikV2kD/5uxdOR17W2v8xv3MjVvpDFYp5iscX8/6PDe5Mz8sO0Bw6N2zCdnZOSwLOsKQXi2oUtnORC1U7tVDF4+1Y1+8psf958AZcnJyzTbZRr7eXZqQmyvvev1SSqJiElVxZcWsWZm6AYaWlZVFQkIC6emFL6VQSs7Ozg5XV1esra1N3RRFAVAfBI3EvVlN9q2ewYjZyzgSnUifrmq2sSJ6/7lerN16gmmvryVyw6w7BbS37okjMSmF8UNVog1z0qppDao7O7Bz/xkeH9lBs+OGhMVjY21J5/bmnUW2k6crDvbWbN0Tx7DerQC4dCWZazfSVLyXYtYq/OArISGBypUr4+bmpqag9SClJCkpiYSEBBo1Mu+7YYqiaK96NUeCFz/O7ZRMKlcyr7o/ijYc7G346aNh+E9YyJv/t43/e2MAAIvXHsa5ij0D/JqZuIVKQUIIundsqHnSjeCw03TydMXezrxvtNrYWOHn3fCuYsv/ZjpUgy/FfFX4ZYfp6em4uLiogZeehBC4uLioGURFeYhZWFio2cYKrodvI54a78NXi0LZc/AsKamZ/Ln1BKP6u2NrW+Hv15Y7ft4NiTt3nQRdQhx93biVxqFjF81+yWG+3l2acCLuKucv3QLy4r1ADb4U81bhB1+AGnhpRPWjoihKxfffl/pQv44TU+b8yfKgKFJSM1VhZTPlp4v7+ueANvW+/tl/htxcSU/f8jP4Ati2Ny9OMSomkRrVHKnpUsmUzVKUYj0Ugy9FURRFUUqmciVb5n8wlJNxV5n17gYa1K1itvWeHnbtWtbGqZKtZsWWg8NOY2tjhW85KeHRpkVNqjs7sHVP3tLDqOhE3JvVMHGrFKV4avBlBJaWlnh6euLh4cGQIUO4ceNGmY4zbdo0jh07dt/2X375hdmzZ5e5fZUqqTtEiqIoyr/6dW/G4yPak56RzfihbVVpATNlaWlBN68GmsR9nb1wgzVbjtOlQ33sbM073iufhYUFAV0as21vHLm5uRyNTcSjucp0qJg39W5qBPb29kRERBAVFUW1atWYN29emY6zYMECWrdWNVYURVEUw/vi9f7MHOvNrPG+pm6KUgw/bzeOxV7hSlJKmY/x984Y2g/7jqTrabzxpJ+GrTO83l2acOHybTbvOkVySiYeKtOhYuYequjZ5z7YSMTxS5oe07NVbb58c2CJ9+/cuTORkZF3fv/0009ZsWIFGRkZPPLII7z77rukpKQQGBhIQkICOTk5vPXWW4wePRp/f38+++wzOnbsyMKFC/n444+pU6cOzZs3x9Y2L/vY5MmTGTx4MCNHjgTyZrWSk5NJTk5m2LBhXL9+naysLD744AOGDRt2V9suXrzI6NGjuXXrFtnZ2Xz33Xd0795dg15SFEVRypuqTvZ8994QUzdDeQA/n7xah/8cOMOj/Up3gzYnJ5f35obw/rwdeDSvyeq5Y2jm5mKIZhpMQOfGAHz5y15AJdtQzN9DNfgytZycHLZt28bUqVMB2Lx5MzExMezbtw8pJUOHDmXnzp1cuXKFunXrEhQUBMDNm3dnMbp48SJvv/024eHhVKlShZ49e9K+fftiz21nZ8eaNWtwcnLi6tWrdOrUiaFDh96VRGPp0qX069ePN954g5ycHFJTUzXuAUVRFEVRtOTlXhd7O2t27o8v1eDrSlIK419cxZbdp5j0iCffvjsYB3sbA7bUMBrVd6ZxfWc2/RMLoGp8KWbvoRp8lWaGSktpaWl4enoSHx+Pl5cXffr0AfIGX5s3b74zcEpOTiYmJobu3bvz0ksv8eqrrzJ48OD7Zp/CwsLw9/enRo28oNLRo0cTHR1dbBuklLz++uvs3LkTCwsLzp8/z+XLl6ldu/adfby9vZkyZQpZWVkMHz4cT09PLbtBURRFURSN2dhY0bm9a6mSbuw5eJbAZ1Zw9XoqCz4axpSRHcp1RuPeXZowf/kB6tVyoqqTvamboyjFUjFfRpAf83XmzBkyMzPvxHxJKZkzZw4RERFEREQQGxvL1KlTad68OeHh4bRp04Y5c+bw3nvv3XfMot4kraysyM3NvXP8zMxMAJYsWcKVK1cIDw8nIiKCWrVq3Vezy8/Pj507d1KvXj0mTpzIr7/+qmU3KIqiKIpiAH7ebkQcv8TN28XX4pRS8uXCPfQY/zO2NpbsXTGdqaO8yvXACyCgS97SQ7XkUCkP1ODLiKpUqcLXX3/NZ599RlZWFv369ePnn38mOTkZgPPnz5OYmMiFCxdwcHBgwoQJvPTSSxw8ePCu4/j6+hISEkJSUhJZWVmsXLnyzmNubm6Eh4cDsHbtWrKysoC8pYs1a9bE2tqa4OBgzpy5/w7ZmTNnqFmzJtOnT2fq1Kn3nVdRFEVRFPPTw8cNKSW7w88Wuc+t2+kEPrOC5z/6m0H+zQn/cybt3esYsZWG06tTIywsBG1bqEyHivl7qJYdmoP27dvTrl07li1bxsSJEzl+/DidO3cG8pJjLF68mNjYWF5++WUsLCywtrbmu+++u+sYderU4Z133qFz587UqVOHDh06kJOTA8D06dMZNmwYPj4+BAQE4OjoCMD48eMZMmQIHTt2xNPTk5YtW97XtpCQED799FOsra2pVKmSmvlSFEVRlHLAt50r1taW7NgXz0D/5vc9fuTkZUbMXkbcuet88kpfXprWtdzPdhVUvZojW36ZRBuVZl4pB4SU0tRt0EzHjh3lgQMH7tp2/PhxWrVqZaIWVTyqPxVFURTF/HQbs4CcnFz2rpxx1/Zf10Qw8z/rqVLZluVfBuLn42aaBipKBSaECJdSdizJvmrZoaIoiqIoSjnn592QA1EXSEnNi/VOz8hixptrmfTKH/i2q8ehtU+qgZeimAE1+FIURVEURSnn/LzdyM7OJTTiHHFnr9ElcAE/Lg9nzszubPllErVrVDZ1ExVFQc+YLyFENWA54AbEA4FSyuuF7DcJeFP36wdSykW67SFAHSBN91hfKWWiEMIW+BXwApKA0VLK+LK2U0pZodY2m0pFWqKqKIqiKBVJl/b1sbAQfPLjLsIOn0cIWP/DeAb3amHqpimKUoC+M1+vAduklM2Abbrf76IboL0N+AI+wNtCCOcCu4yXUnrqvhJ126YC16WUTYH/A/5X1gba2dmRlJSkBg56klKSlJSEnZ2dqZuiKIqiKMo9nCrb0cG9Dpt3naJJA2cO/vmkGngpihnSN9vhMMBf9/MiIAR49Z59+gFbpJTXAIQQW4D+wO8POO47up9XAXOFEEKWYQTl6upKQkICV65cKe1TlXvY2dnh6upq6mYoiqIoilKIt2b5Ex51gTkzu2Nna23q5iiKUgh9B1+1pJQXAaSUF4UQhVW3qwecK/B7gm5bvoVCiBxgNXlLEmXB50gps4UQNwEX4Oq9BxdCzABmADRo0OC+k1tbW9OoUaMyvDRFURRFUZTyY2hAS4YG3F9KRlEU8/HAwZcQYitQu5CH3ijhOQoLtsqfwRovpTwvhKhM3uBrInmxXsU95+6NUs4H5kNeqvkStklRFEVRFEVRFMWoHjj4klL2LuoxIcRlIUQd3axXHSCxkN0S+HdpIoArecsTkVKe132/LYRYSl5M2K+659QHEoQQVkAV4FpJXpCiKIqiKIqiKIo50jfhxjpgku7nScDaQvbZBPQVQjjrEm30BTYJIayEENUBhBDWwGAgqpDjjgS2lyXeS1EURVEURVEUxVwIfcY0QggXYAXQADgLjJJSXhNCdARmSimn6fabAryue9qHUsqFQghHYCdgDVgCW4EXpJQ5Qgg74DegPXkzXmOklHElaM8V4EyZX1DhqlNIrJlicKrfjUv1t2mofjcu1d+mofrd+FSfG5/qc+Mzpz5vKKWsUZId9Rp8PQyEEAeklB1N3Y6Hjep341L9bRqq341L9bdpqH43PtXnxqf63PjKa5/ru+xQURRFURRFURRFKQE1+FIURVEURVEURTECNfh6sPmmbsBDSvW7can+Ng3V78al+ts0VL8bn+pz41N9bnzlss9VzJeiKIqiKIqiKIoRqJkvRVEURVEURVEUI1CDL0VRFEVRFEVRFCOocIMvIUR9IUSwEOK4EOKoEOJZ3fZqQogtQogY3Xdn3faWQoi9QogMIcRLBY5jJ4TYJ4Q4rDvOu8Wcc5LuuDFCiEm6bZWFEBEFvq4KIb409Os3Fa36vcDxLIUQh4QQG4o55339rtv+oRDinBAi2RCv1RyYS3+r67zs/S6EiBdCHNH124FiztlfCHFSCBErhHitwPbZum0yv2B9RWNm/f1Pgev8ghDiT0O9blPTuN+rCiFWCSFO6I7XuYhzPrTXOZhdnz8U17pWfS6EaHHP38FbQojnijjnQ3udm1l/m/Yal1JWqC+gDtBB93NlIBpoDXwCvKbb/hrwP93PNQFv4EPgpQLHEUAl3c/WQBjQqZDzVQPidN+ddT87F7JfOOBn6v4x934vcLwXgKXAhiLOV2S/A5107Uk2db88DP19z37qOi9hvwPxQPUHnM8SOAU0BmyAw0Br3WPtAbeSHKe8fplTf9+z32rgMVP3Tznp90XANN3PNkDV0vT7w3Cdm1uf37Nfhb3Wtezze/r1EnkFd9V1bqb9fc9+Rr/GK9zMl5TyopTyoO7n28BxoB4wjLw3JHTfh+v2SZRS7gey7jmOlFLmz5xY674Ky07SD9gipbwmpbwObAH6F9xBCNGMvIvoH/1foXnSqt8BhBCuwCBgQTGnLLLfpZShUsqLmrwwM2VO/V3gOOo6z1Oifi8hHyBWShknpcwElunOhZTykJQyvqyvpTwwp/7OJ4SoDPQCKuRsAGjX70IIJ8AP+Em3X6aU8kYhp3yor3Mwrz4vcKwKfa0b6P0lADglpTxTyGMP9XVuTv2dz1TXeIUbfBUkhHAj725CGFAr/wO57nvNEjzfUggRASSS98EzrJDd6gHnCvyeoNtW0FhgudQNsSs6ffsd+BJ4BcgtZp+S9PtDwYz6W13nlKrfJbBZCBEuhJhRxD7qOtcxo/5+BNgmpbxV8taXX3r2e2PgCrBQ5C1rXiCEcCxkP3WdF2BGff7QXOsavL/kGwP8XsRj6jrXMaP+Nsk1XmEHX0KISuRNJT5X1k6VUuZIKT0BV8BHCOFR2KkKe+o9vxd3cVQo+va7EGIwkCilDH/QroVseyg+9BdkZv2trvPS6Sql7AAMAGYJIfwKO1Uh29R1XjZa9fdY1HVeUlZAB+A7KWV7IIW8ZUX3naqQbQ/ddQ5m1+cPxbWu0fsLQggbYCiwsqhdCtn20F3nZtbfJrnGK+TgSwhhTd4/7BIp5R+6zZeFEHV0j9chbzarRHRT9iFAfyGEb4EgvaHkjaTrF9jdFbhQoC3tAKsSfLgt9zTq967AUCFEPHlTxL2EEItL2+8PA3Pqb3Wdl/79RUp5Qfc9EVhD3g2e+gX6fSbqOjer/hZCuJC3lCVI/1dm3jTq9wQgocCqkVVAB3WdF86c+vxhudY1/rw4ADgopbyse666zu9hTv1tymu8wg2+hBCCvLXOx6WUXxR4aB2QnxFvErD2AcepIYSoqvvZHugNnJBShkkpPXVf64BNQF8hhLPIy9DSV7ct38Ny50iTfpdSzpFSukop3cibSdkupZxQhn6v0Mywv9V1Xrr3F0fdWnN0S4L6AlFSynMF+v17YD/QTAjRSHeXb4zuXA8FM+zvUeQlpUnX4vWZKw3fXy4B54QQLXSbAoBj6jq/nxn2eYW/1rXq8wLu+juorvO7mWF/m+4al2aQAUXLL6AbedOKkUCE7msg4AJsA2J036vp9q9N3uj4FnBD97MT0BY4pDtOFPCfYs45BYjVfT1+z2NxQEtT90t56fd7julPEdn3iut38jLnJJAXw5QAvGPq/qnI/a17TF3npXt/aUxe5qXDwFHgjWLOOZC8rFCnCu4HPKM7XjZ5d/MWmLp/KnJ/6x4LAfqbul/KS7/rHvMEDuiO9SeFZEl92K9zc+vzh+Va17jPHYAkoMoDzvnQXufm1N+6x0x2jQtdAxRFURRFURRFURQDqnDLDhVFURRFURRFUcyRGnwpiqIoiqIoiqIYgRp8KYqiKIqiKIqiGIEafCmKoiiKoiiKohiBGnwpiqIoiqIoiqIYgRp8KYqiKIqiKIqiGIEafCmKoiiKoiiKohjB/wMtzESrF+asxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import seasonal_decompose\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "ts = accidents_df.set_index('Date').resample('M').size()\n",
    "decomposition = seasonal_decompose(np.log(ts))\n",
    "\n",
    "# Gather the trend, seasonality and noise of decomposed object\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "# Plot gathered statistics\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(411)\n",
    "plt.plot(np.log(ts), label='Original', color=c_blue)\n",
    "plt.title('Decomposition of Accidents (Monthly)', fontweight='bold', fontsize=16)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend', color=c_blue)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label='Seasonality', color=c_blue)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals', color=c_blue)\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the negative trend is very clear, as well as a seasonal trend.  It is interesting how accidents seem to spike right around the fall time and then decrease during the winter. Let's now decompose the serious and fatal accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = serious.set_index('Date').resample('M').size()\n",
    "decomposition = seasonal_decompose(np.log(ts))\n",
    "\n",
    "# Gather the trend, seasonality and noise of decomposed object\n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "# Plot gathered statistics\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(411)\n",
    "plt.plot(np.log(ts), label='Original', color=p_red)\n",
    "plt.title('Decomposition of Serious and Fatal Accidents (Monthly)', fontweight='bold', fontsize=16)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend', color=p_red)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label='Seasonality', color=p_red)\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals', color=p_red)\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see the positive trend happening since around July of 2015.  Also notice how the seasonal trend is a bit different with serious and fatal accidents increasing and spiking during the summer, where before total accidents had two peaks, in summer and during the holidays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a certain time of the year where there are more accidents?\n",
    "Now let's take a look at what months have the most accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "totals_by_month = accidents_df['Date'].dt.month.value_counts().sort_index(ascending=False)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "colors = ['lightblue',c_blue,'lightblue','lightblue','lightblue','lightblue',\n",
    "          'lightblue','lightblue','lightblue','lightblue','lightblue','lightblue',]\n",
    "\n",
    "ax.bar(totals_by_month.index, totals_by_month.values, color=colors)\n",
    "\n",
    "ax.set_title('Total Accidents by Month', fontweight = 'bold', fontsize=16)\n",
    "ax.set_xticks(range(1,13))\n",
    "ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May','Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=False, left=True, right=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "serious_totals_by_month = serious['Date'].dt.month.value_counts().sort_index(ascending=False)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "colors = ['salmon','salmon','salmon','salmon','salmon',p_red,\n",
    "          'salmon','salmon','salmon','salmon','salmon','salmon']\n",
    "\n",
    "ax.bar(serious_totals_by_month.index, serious_totals_by_month.values, color=colors)\n",
    "\n",
    "ax.set_title('Serious and Fatal Accidents by Month', fontweight = 'bold', fontsize=16)\n",
    "ax.set_xticks(range(1,13))\n",
    "ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May','Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec'])\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=False, left=True, right=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day with the most accidents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11/04    2268\n",
       "11/28    2201\n",
       "11/25    2171\n",
       "12/16    2169\n",
       "01/14    2150\n",
       "12/09    2148\n",
       "11/11    2120\n",
       "11/18    2105\n",
       "12/08    2092\n",
       "07/18    2079\n",
       "Name: Date, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use strftime to get month/day with most accidents\n",
    "accidents_df.Date.dt.strftime('%m/%d').value_counts().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting how the month with the most accidents is November, and the day with the most accidents is November 4th. This might have to do with the national holiday [Guy Fawkes Night](https://en.wikipedia.org/wiki/Guy_Fawkes_Night) which is on November 5th. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"GuyFawkes.jpeg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What day of the week has the most accidents?\n",
    "Now let's keep going further and look at accidents by day of the week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get daily time series and convert to a df\n",
    "total_weekday = pd.DataFrame(accidents_df.set_index('Date').resample('1d').size().reset_index())\n",
    "total_weekday.columns = ['Date', 'Count']\n",
    "weekdays = total_weekday['Date'].dt.weekday_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Avg_Accidents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>406.042146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>354.265385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>322.954023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>272.977011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>377.858238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>376.042146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>374.762452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Avg_Accidents\n",
       "Weekday                 \n",
       "Friday        406.042146\n",
       "Monday        354.265385\n",
       "Saturday      322.954023\n",
       "Sunday        272.977011\n",
       "Thursday      377.858238\n",
       "Tuesday       376.042146\n",
       "Wednesday     374.762452"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by weekdays and get daily average\n",
    "weekday_avg = total_weekday.groupby(weekdays)['Count'].mean().reset_index()\n",
    "weekday_avg.columns = ['Weekday', 'Avg_Accidents']\n",
    "weekday_avg.set_index('Weekday', inplace=True)\n",
    "weekday_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Need this for labels later (be careful of the order!)\n",
    "days = ['Sunday', 'Saturday', 'Friday', 'Thursday', 'Wednesday', 'Tuesday', 'Monday']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "colors = ['lightblue','lightblue',c_blue,'lightblue','lightblue','lightblue','lightblue']\n",
    "\n",
    "weekday_avg.reindex(days).plot(kind='barh', color=[colors], ax=ax)\n",
    "\n",
    "ax.set_title('Avg Accidents by Weekday', fontsize=16, fontweight='bold')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.legend('')\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=True, left=False, right=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Avg_Accidents</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weekday</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>64.429119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>54.269231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>57.168582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>52.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>57.712644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>57.593870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>57.440613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Avg_Accidents\n",
       "Weekday                 \n",
       "Friday         64.429119\n",
       "Monday         54.269231\n",
       "Saturday       57.168582\n",
       "Sunday         52.724138\n",
       "Thursday       57.712644\n",
       "Tuesday        57.593870\n",
       "Wednesday      57.440613"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get daily time series and convert to a df\n",
    "serious_total_weekday = pd.DataFrame(serious.set_index('Date').resample('1d').size().reset_index())\n",
    "serious_total_weekday.columns = ['Date', 'Count']\n",
    "serious_weekdays = total_weekday['Date'].dt.weekday_name\n",
    "\n",
    "# Group by weekdays and get daily average\n",
    "serious_weekday_avg = serious_total_weekday.groupby(weekdays)['Count'].mean().reset_index()\n",
    "serious_weekday_avg.columns = ['Weekday', 'Avg_Accidents']\n",
    "serious_weekday_avg.set_index('Weekday', inplace=True)\n",
    "serious_weekday_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "colors = ['salmon','salmon',p_red,'salmon','salmon','salmon','salmon']\n",
    "\n",
    "serious_weekday_avg.reindex(days).plot(kind='barh', color=[colors], ax=ax)\n",
    "\n",
    "ax.set_title('Avg Serious and Fatal Accidents by Weekday', fontsize=16, fontweight='bold')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.legend('')\n",
    "\n",
    "sns.despine(ax=ax, top=True, bottom=True, left=False, right=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Friday was the day of the week with the most accidents on average for both total, and serious/fatal accidents."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time of day with the most accidents?\n",
    "Now let's look at what part of the day has the most accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['6pm-8pm', '6am-8am', '8am-10am', '2pm-4pm', '10pm-12am',\n",
       "       '12pm-2pm', '10am-12pm', '4pm-6pm', '12am-2am', '8pm-10pm',\n",
       "       '2am-4am', '4am-6am'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accidents_df.Two_Hour_Groupings.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make list to order x labels later\n",
    "times = ['12am-2am','2am-4am','4am-6am','6am-8am','8am-10am','10am-12pm',\n",
    "         '12pm-2pm','2pm-4pm','4pm-6pm','6pm-8pm','8pm-10pm','10pm-12am']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group accidents by two hour groupings column and reindex by the time groupings listed above\n",
    "time_groups = pd.DataFrame(accidents_df.groupby('Two_Hour_Groupings').size()).reindex(times)\n",
    "time_groups.plot(kind='bar', color=c_blue, figsize=(14,5))\n",
    "plt.title('Accidents by Time Periods', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.legend('')\n",
    "sns.despine(top=True, bottom=False, left=True, right=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "serious_time_groups = pd.DataFrame(serious.groupby('Two_Hour_Groupings').size()).reindex(times)\n",
    "serious_time_groups.plot(kind='bar', color=p_red, figsize=(14,5))\n",
    "plt.title('Serious and Fatal Accidents by Time Periods', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.legend('')\n",
    "sns.despine(top=True, bottom=False, left=True, right=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both plots show similar time distributions and that the time period with the most accidents and serious/fatal accidents is from 4pm-6pm. This could be due to rush hour traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a significant difference in the age of female casualties versus male casualties?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Accident_Index</th>\n",
       "      <th>Age_Band_of_Casualty</th>\n",
       "      <th>Age_of_Casualty</th>\n",
       "      <th>Bus_or_Coach_Passenger</th>\n",
       "      <th>Car_Passenger</th>\n",
       "      <th>Casualty_Class</th>\n",
       "      <th>Casualty_Home_Area_Type</th>\n",
       "      <th>Casualty_IMD_Decile</th>\n",
       "      <th>Casualty_Reference</th>\n",
       "      <th>Casualty_Severity</th>\n",
       "      <th>Casualty_Type</th>\n",
       "      <th>Pedestrian_Location</th>\n",
       "      <th>Pedestrian_Movement</th>\n",
       "      <th>Pedestrian_Road_Maintenance_Worker</th>\n",
       "      <th>Sex_of_Casualty</th>\n",
       "      <th>Vehicle_Reference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016010000005</td>\n",
       "      <td>5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016010000006</td>\n",
       "      <td>7</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016010000008</td>\n",
       "      <td>5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016010000016</td>\n",
       "      <td>9</td>\n",
       "      <td>59.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016010000018</td>\n",
       "      <td>6</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Accident_Index  Age_Band_of_Casualty  Age_of_Casualty  \\\n",
       "0  2016010000005                     5             23.0   \n",
       "1  2016010000006                     7             36.0   \n",
       "2  2016010000008                     5             24.0   \n",
       "3  2016010000016                     9             59.0   \n",
       "4  2016010000018                     6             28.0   \n",
       "\n",
       "   Bus_or_Coach_Passenger  Car_Passenger  Casualty_Class  \\\n",
       "0                       0              0               1   \n",
       "1                       0              0               1   \n",
       "2                       0              0               1   \n",
       "3                       3              0               2   \n",
       "4                       0              0               1   \n",
       "\n",
       "   Casualty_Home_Area_Type  Casualty_IMD_Decile  Casualty_Reference  \\\n",
       "0                        1                  4.0                   1   \n",
       "1                        1                 10.0                   1   \n",
       "2                        1                  8.0                   1   \n",
       "3                        1                  4.0                   1   \n",
       "4                        1                  6.0                   1   \n",
       "\n",
       "   Casualty_Severity  Casualty_Type  Pedestrian_Location  Pedestrian_Movement  \\\n",
       "0                  3              2                    0                    0   \n",
       "1                  3              9                    0                    0   \n",
       "2                  3              9                    0                    0   \n",
       "3                  3             11                    0                    0   \n",
       "4                  3              1                    0                    0   \n",
       "\n",
       "   Pedestrian_Road_Maintenance_Worker  Sex_of_Casualty  Vehicle_Reference  \n",
       "0                                   0                1                  2  \n",
       "1                                   0                2                  1  \n",
       "2                                   0                1                  1  \n",
       "3                                   0                2                  1  \n",
       "4                                   0                1                  2  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import casualties data \n",
    "casualties_df = pd.concat([pd.read_csv(f) for f in glob.glob('data/Casualties/Casualties*.csv')], ignore_index = True)\n",
    "casualties_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 916713 entries, 0 to 916712\n",
      "Data columns (total 16 columns):\n",
      "Accident_Index                        916713 non-null object\n",
      "Age_Band_of_Casualty                  916713 non-null int64\n",
      "Age_of_Casualty                       733043 non-null float64\n",
      "Bus_or_Coach_Passenger                916713 non-null int64\n",
      "Car_Passenger                         916713 non-null int64\n",
      "Casualty_Class                        916713 non-null int64\n",
      "Casualty_Home_Area_Type               916713 non-null int64\n",
      "Casualty_IMD_Decile                   538566 non-null float64\n",
      "Casualty_Reference                    916713 non-null int64\n",
      "Casualty_Severity                     916713 non-null int64\n",
      "Casualty_Type                         916713 non-null int64\n",
      "Pedestrian_Location                   916713 non-null int64\n",
      "Pedestrian_Movement                   916713 non-null int64\n",
      "Pedestrian_Road_Maintenance_Worker    916713 non-null int64\n",
      "Sex_of_Casualty                       916713 non-null int64\n",
      "Vehicle_Reference                     916713 non-null int64\n",
      "dtypes: float64(2), int64(13), object(1)\n",
      "memory usage: 111.9+ MB\n"
     ]
    }
   ],
   "source": [
    "casualties_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36.24473461993362"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_df.Age_of_Casualty.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "casualties_df.Age_of_Casualty.hist(bins=20, figsize=(12,5))\n",
    "plt.title('Distribution of Casualties by Age', fontsize=16, fontweight='bold')\n",
    "plt.vlines(casualties_df.Age_of_Casualty.mean(), ymin=0, ymax=120000, label='Mean age: 36.24')\n",
    "plt.xlabel('Age')\n",
    "plt.legend()\n",
    "sns.despine(top=True, bottom=False, left=True, right=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you can see the distrubtion of casualties by age. Majority of the accidents seem to be from age 20 - 30 but the average age is around 36. Now we will break up the data by male and female and explore further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    0.591797\n",
       " 2    0.408035\n",
       "-1    0.000168\n",
       "Name: Sex_of_Casualty, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1 = Male, 2 = Female, -1 = missing data and will not be used\n",
    "# More male drivers than female drivers\n",
    "casualties_df.Sex_of_Casualty.value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_casualties = casualties_df[casualties_df['Sex_of_Casualty']==1]['Age_of_Casualty']\n",
    "female_casualties = casualties_df[casualties_df['Sex_of_Casualty']==2]['Age_of_Casualty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male mean: 35.574289365523434\n",
      "female mean: 37.230572160546544\n"
     ]
    }
   ],
   "source": [
    "print('male mean:', male_casualties.mean())\n",
    "print('female mean:', female_casualties.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_casualties.hist(bins=20, color=c_blue, figsize=(12,5), label='male', alpha=0.6)\n",
    "female_casualties.hist(bins=20, color=p_red, figsize=(12,5), label='female', alpha=0.6)\n",
    "plt.title('Distribution of Casualties by Age', fontsize=16, fontweight='bold')\n",
    "plt.vlines(male_casualties.mean(), ymin=0, ymax=120000, color=c_blue, label='Mean male age: 35.57')\n",
    "plt.vlines(female_casualties.mean(), ymin=0, ymax=120000, color=p_red, label='Mean female age: 37.23')\n",
    "plt.xlabel('Age')\n",
    "plt.legend()\n",
    "sns.despine(top=True, bottom=False, left=True, right=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above you can see the distribution of casualties by age for both male and female. You can see how the average age for males is less than the average age for females. Below we will set up our hypothesis statements to see if there is any statistical significance between this difference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "Null Hypothesis: The average age of male casualties in accidents is at least the same as the average age of female casualties in accidents.\n",
    "<br>\n",
    "\n",
    "$H_0: \\mu_m >= \\mu_f$\n",
    "<br>\n",
    "\n",
    "Alternative Hypothesis: The average age of male casualties in accidents is less than the average age of female casualties in accidents.\n",
    "<br>\n",
    "\n",
    "$H_a: \\mu_m < \\mu_f$\n",
    "<br>\n",
    "\n",
    "Will use an $\\alpha$ = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance Male: 344.63602140263055\n",
      "Variance Female: 402.0328765651826\n"
     ]
    }
   ],
   "source": [
    "# Check to see if variance is the same between the samples\n",
    "print('Variance Male:', np.var(male_casualties))\n",
    "print('Variance Female:', np.var(female_casualties))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 9.306860441282675e-281\n"
     ]
    }
   ],
   "source": [
    "# Since the variance for both samples are not the same, will set equal_var to False in order to run a Welche's t-test\n",
    "# test will be a two sample t-test (one tail)\n",
    "t_stat, p_val = stats.ttest_ind(male_casualties, female_casualties, equal_var=False, nan_policy='omit')\n",
    "print('P-Value:', p_val/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a p-value < 0.05 which means that we can reject our Null Hypothesis and accept the Alternative Hypothesis that there is a statistical significance between the samples.  From our alpha, we can be 95% sure that there is a statistical significance that shows the average age of male casualties is less than the average age of female casualties without this being due to random chance.\n",
    "<br>\n",
    "\n",
    "Now we can use Cohen's $d$ in order to measure the effect size or represent the differentiation between our two sample groups.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Cohen_d(group1, group2):\n",
    "\n",
    "    # Compute Cohen's d.\n",
    "\n",
    "    diff = group1.mean() - group2.mean()\n",
    "\n",
    "    n1, n2 = len(group1), len(group2)\n",
    "    var1 = group1.var()\n",
    "    var2 = group2.var()\n",
    "\n",
    "    # Calculate the pooled threshold as shown earlier\n",
    "    pooled_var = (n1 * var1 + n2 * var2) / (n1 + n2)\n",
    "    \n",
    "    # Calculate Cohen's d statistic\n",
    "    d = diff / np.sqrt(pooled_var)\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d = 0.08633256563223733\n"
     ]
    }
   ],
   "source": [
    "d = Cohen_d(female_casualties, male_casualties)\n",
    "print('d = {}'.format(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An effect of 0.086 is considered a very small effect.  Although it is very small, it still means that there is a an effect happening but it is not so obvious to see without investigating like we have with our hypothesis testing.\n",
    "<br>\n",
    "\n",
    "It's interesting how average age of males in accidents is slightly less than the average of females in accidents. Let's now explore this again but just for serious and fatal accidents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a significant difference in the age of female casualties versus male casualties for serious and fatal accidents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter male and female dataframes for severity not equal to 3 (slight)\n",
    "male_serious_casualties = casualties_df[casualties_df['Sex_of_Casualty']==1][['Age_of_Casualty', 'Casualty_Severity']]\n",
    "male_serious_casualties = male_serious_casualties[male_serious_casualties['Casualty_Severity']!=3]\n",
    "\n",
    "female_serious_casualties = casualties_df[casualties_df['Sex_of_Casualty']==2][['Age_of_Casualty', 'Casualty_Severity']]\n",
    "female_serious_casualties = female_serious_casualties[female_serious_casualties['Casualty_Severity']!=3]\n",
    "\n",
    "male_serious_casualties = male_serious_casualties['Age_of_Casualty']\n",
    "female_serious_casualties = female_serious_casualties['Age_of_Casualty']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "male mean: 37.82895240254496\n",
      "female mean: 43.13229584786471\n"
     ]
    }
   ],
   "source": [
    "print('male mean:', male_serious_casualties.mean())\n",
    "print('female mean:', female_serious_casualties.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_serious_casualties.hist(bins=20, color=c_blue, figsize=(12,5), label='male', alpha=0.6)\n",
    "female_serious_casualties.hist(bins=20, color=p_red, figsize=(12,5), label='female', alpha=0.6)\n",
    "plt.title('Distribution of Serious and Fatal Casualties by Age', fontsize=16, fontweight='bold')\n",
    "plt.vlines(male_serious_casualties.mean(), ymin=0, ymax=12000, color=c_blue, label='Mean male age: 37.82')\n",
    "plt.vlines(female_serious_casualties.mean(), ymin=0, ymax=12000, color=p_red, label='Mean female age: 43.13')\n",
    "plt.xlabel('Age')\n",
    "plt.legend()\n",
    "sns.despine(top=True, bottom=False, left=True, right=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "Null Hypothesis: The average age of male casualties in serious/fatal accidents is at least the same as the average age of female casualties in serious/fatal accidents.\n",
    "<br>\n",
    "\n",
    "$H_0: \\mu_m >= \\mu_f$\n",
    "<br>\n",
    "\n",
    "Alternative Hypothesis: The average age of male casualties in serious/fatal accidents is less than the average age of female casualties in serious/fatal accidents.\n",
    "<br>\n",
    "\n",
    "$H_a: \\mu_m < \\mu_f$\n",
    "<br>\n",
    "\n",
    "Will use an $\\alpha$ = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance Male: 386.63026291892015\n",
      "Variance Female: 556.4916443507184\n"
     ]
    }
   ],
   "source": [
    "# Check to see if variance is the same between the samples\n",
    "print('Variance Male:', np.var(male_serious_casualties))\n",
    "print('Variance Female:', np.var(female_serious_casualties))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value: 2.2803460923896326e-256\n"
     ]
    }
   ],
   "source": [
    "# Since the variance for both samples are not the same, will set equal_var to False in order to run a Welche's t-test\n",
    "# test will be a two sample t-test (one tail)\n",
    "t_stat, p_val = stats.ttest_ind(male_serious_casualties, female_serious_casualties, equal_var=False, nan_policy='omit')\n",
    "print('P-Value:', p_val/2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a p-value < 0.05 which means that we can reject our Null Hypothesis and accept the Alternative Hypothesis that there is a statistical significance between the samples.  From our alpha, we can be 95% sure that there is a statistical significance that shows the average age of male casualties is less than the average age of female casualties in serious/fatal accidents without this being due to random chance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d = 0.2534486692996894\n"
     ]
    }
   ],
   "source": [
    "d = Cohen_d(female_serious_casualties, male_serious_casualties)\n",
    "print('d = {}'.format(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An effect of 0.253 is considered a small effect, much bigger difference than what we found for all accidents.  Although it is small, it still means that there is a an effect happening but it is not so obvious to see without investigating like we have with our hypothesis testing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What age band of drivers has the most accidents?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>1st_Point_of_Impact</th>\n",
       "      <th>Accident_Index</th>\n",
       "      <th>Age_Band_of_Driver</th>\n",
       "      <th>Age_of_Driver</th>\n",
       "      <th>Age_of_Vehicle</th>\n",
       "      <th>Driver_Home_Area_Type</th>\n",
       "      <th>Driver_IMD_Decile</th>\n",
       "      <th>Engine_Capacity_(CC)</th>\n",
       "      <th>Hit_Object_in_Carriageway</th>\n",
       "      <th>Hit_Object_off_Carriageway</th>\n",
       "      <th>...</th>\n",
       "      <th>Sex_of_Driver</th>\n",
       "      <th>Skidding_and_Overturning</th>\n",
       "      <th>Towing_and_Articulation</th>\n",
       "      <th>Vehicle_IMD_Decile</th>\n",
       "      <th>Vehicle_Leaving_Carriageway</th>\n",
       "      <th>Vehicle_Location-Restricted_Lane</th>\n",
       "      <th>Vehicle_Manoeuvre</th>\n",
       "      <th>Vehicle_Reference</th>\n",
       "      <th>Vehicle_Type</th>\n",
       "      <th>Was_Vehicle_Left_Hand_Drive?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>201301BS70003</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2402</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>201301BS70003</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>201301BS70005</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>201301BS70006</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>201301BS70007</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>124</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1st_Point_of_Impact Accident_Index  Age_Band_of_Driver  Age_of_Driver  \\\n",
       "0                    1  201301BS70003                   8            NaN   \n",
       "1                    1  201301BS70003                   7            NaN   \n",
       "2                    1  201301BS70005                   6            NaN   \n",
       "3                    0  201301BS70006                   8            NaN   \n",
       "4                    4  201301BS70007                   7            NaN   \n",
       "\n",
       "   Age_of_Vehicle  Driver_Home_Area_Type  Driver_IMD_Decile  \\\n",
       "0              10                      1                  8   \n",
       "1              -1                      1                  8   \n",
       "2               5                      1                  4   \n",
       "3              -1                      1                  1   \n",
       "4               7                      1                  4   \n",
       "\n",
       "   Engine_Capacity_(CC)  Hit_Object_in_Carriageway  \\\n",
       "0                  2402                          0   \n",
       "1                    -1                          0   \n",
       "2                   124                          0   \n",
       "3                    -1                          0   \n",
       "4                   124                          0   \n",
       "\n",
       "   Hit_Object_off_Carriageway              ...               Sex_of_Driver  \\\n",
       "0                           0              ...                           1   \n",
       "1                           0              ...                           1   \n",
       "2                           0              ...                           2   \n",
       "3                           0              ...                           1   \n",
       "4                           0              ...                           1   \n",
       "\n",
       "   Skidding_and_Overturning  Towing_and_Articulation  Vehicle_IMD_Decile  \\\n",
       "0                         0                        0                 NaN   \n",
       "1                         0                        0                 NaN   \n",
       "2                         0                        0                 NaN   \n",
       "3                         0                        0                 NaN   \n",
       "4                         0                        0                 NaN   \n",
       "\n",
       "   Vehicle_Leaving_Carriageway  Vehicle_Location-Restricted_Lane  \\\n",
       "0                            0                                 0   \n",
       "1                            0                                 0   \n",
       "2                            0                                 0   \n",
       "3                            0                                 0   \n",
       "4                            0                                 0   \n",
       "\n",
       "   Vehicle_Manoeuvre  Vehicle_Reference  Vehicle_Type  \\\n",
       "0                  6                  1             8   \n",
       "1                 18                  2             1   \n",
       "2                 18                  1             3   \n",
       "3                 14                  1            11   \n",
       "4                  4                  1             3   \n",
       "\n",
       "   Was_Vehicle_Left_Hand_Drive?  \n",
       "0                             1  \n",
       "1                             1  \n",
       "2                             1  \n",
       "3                             1  \n",
       "4                             1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import vehicles data \n",
    "vehicles_df = pd.concat([pd.read_csv(f) for f in glob.glob('data/Vehicles/Vehicles*.csv')], ignore_index = True)\n",
    "vehicles_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1270711 entries, 0 to 1270710\n",
      "Data columns (total 23 columns):\n",
      "1st_Point_of_Impact                 1270711 non-null int64\n",
      "Accident_Index                      1270711 non-null object\n",
      "Age_Band_of_Driver                  1270711 non-null int64\n",
      "Age_of_Driver                       1017798 non-null float64\n",
      "Age_of_Vehicle                      1270711 non-null int64\n",
      "Driver_Home_Area_Type               1270711 non-null int64\n",
      "Driver_IMD_Decile                   1270711 non-null int64\n",
      "Engine_Capacity_(CC)                1270711 non-null int64\n",
      "Hit_Object_in_Carriageway           1270711 non-null int64\n",
      "Hit_Object_off_Carriageway          1270711 non-null int64\n",
      "Journey_Purpose_of_Driver           1270711 non-null int64\n",
      "Junction_Location                   1270711 non-null int64\n",
      "Propulsion_Code                     1270711 non-null int64\n",
      "Sex_of_Driver                       1270711 non-null int64\n",
      "Skidding_and_Overturning            1270711 non-null int64\n",
      "Towing_and_Articulation             1270711 non-null int64\n",
      "Vehicle_IMD_Decile                  749271 non-null float64\n",
      "Vehicle_Leaving_Carriageway         1270711 non-null int64\n",
      "Vehicle_Location-Restricted_Lane    1270711 non-null int64\n",
      "Vehicle_Manoeuvre                   1270711 non-null int64\n",
      "Vehicle_Reference                   1270711 non-null int64\n",
      "Vehicle_Type                        1270711 non-null int64\n",
      "Was_Vehicle_Left_Hand_Drive?        1270711 non-null int64\n",
      "dtypes: float64(2), int64(20), object(1)\n",
      "memory usage: 223.0+ MB\n"
     ]
    }
   ],
   "source": [
    "vehicles_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 6     0.207363\n",
       " 7     0.173664\n",
       " 8     0.156528\n",
       "-1     0.113333\n",
       " 5     0.110275\n",
       " 9     0.087863\n",
       " 4     0.071877\n",
       " 10    0.043862\n",
       " 11    0.026875\n",
       " 3     0.006245\n",
       " 2     0.001889\n",
       " 1     0.000225\n",
       "Name: Age_Band_of_Driver, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vehicles_df.Age_Band_of_Driver.value_counts(normalize=True, dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop age band of driver where data is missing\n",
    "vehicles_df = vehicles_df.drop((vehicles_df[vehicles_df['Age_Band_of_Driver']==-1].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "drivers_df = vehicles_df.groupby('Age_Band_of_Driver').size().reset_index()\n",
    "drivers_df.columns=['Age_Band_of_Driver', 'Count']\n",
    "\n",
    "# replace numbers with correct labels from data dictionary\n",
    "drivers_df.Age_Band_of_Driver = drivers_df.Age_Band_of_Driver.replace([1,2,3,4,5,6,7,8,9,10,11], \n",
    "                                                    ['0-5','6-10','11-15','16-20','21-25','26-35',\n",
    "                                                    '36-45','46-55','56-65','66-75','Over 75'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+MneWMR7xvvrqq+fOJEmSJPVkbC+uXLpsx7pt3S8WuhmSJEkaf3fvEW9JkiRpMTHwliRJknpg4C1JkiT1wMBbkiRJ6oGBtyRJktQDA29JkiSpBwseeCc5KMklSS5P8pYZ8qxPsqJdvtR3GyVJkqTNtaD38U6yBLgUeAawGvge8OKq+sG0fDdW1b3vSt3ex1uSJEk92SLu470fcHlVXVlVtwKfBg5e4DZJkiRJQ7fQgfeDgJ90Xq9u06a7R5LJJN9N8tyZKktyRJtvcsP6dcNuqyRJkrTJli7w/gcNyw+a+/LgqvpZkt2AbyW5sKquuFPBquOA46CZajLcpkqSJEmbbqFHvFcDu3Re7wxc3bmQ8jkAVfWz9u+VwBnAvn03VJIkSdocCx14fw/YI8lDk2wDHAqcXFX7tMuXkmyXZFuAJDsATwJ+MEudkiRJ0qKzoFNNquq2JK8Cvg4sAY6vqoumZXsE8M9JNtB8UThm+l1PJEmSpMVuQW8nOEreTlCSJEk92SJuJyhJkiTdLRh4S5IkST0w8JYkSZJ6YOAtSZIk9WBsA+999tpl7kySJElST8Y28JYkSZIWEwNvSZIkqQcG3pIkSVIPDLwlSZKkHoz1kyuX737IndKvW3nsArRGkiRJY8wnV0qSJEmLhYG3JEmS1AMDb0mSJKkHBt6SJElSDwy8JUmSpB70FngnuV+Sk5NcnGRVkicOyHN8krVJVk5L3z7J6Ukua/9u11e7JUmSpGHoc8T7A8DXqurhwKOBVQPynAgcNCD9LcA3q2oP4Jvta0mSJGmL0UvgneQ+wFOAjwFU1a1V9avp+arqLODaAVUcDHy8Xf848NwRNVWSJEkaib5GvHcDfgmckOT8JB9Ncq+7UH7HqloD0P59wCgaKUmSJI1KX4H3UuAxwD9V1b7AbxjBdJEkRySZTDK5Yf26YVcvSZIkbbK+Au/VwOqqOrt9fTLwn5KsaJcj5yj/iyQ7AbR/1w7KVFXHVdVEVU1stWTZ0BovSZIkba5eAu+q+jnwkyQPa5OeDpxXVfu0y4fnqOJLwGHt+mHAF0fUVEmSJGkk+ryryauBk5JcAOwD/M/pGZJ8CvgP4GFJVif5i3bTMcAzklwGPKN9LUmSJG0xlva1o6paAUzMkefFM6RfQzNKLkmSJG2RfHKlJEmS1AMDb0mSJKkHBt6SJElSDwy8JUmSpB4YeEuSJEk9SFUtdBtGYmJioiYnJxe6GZIkSRp/mU8mR7wlSZKkHhh4S5IkST0w8JYkSZJ6YOAtSZIk9WBsL65cumzHWr77IUOp67qVxw6lHkmSJI0lL66UJEmSFgsDb0mSJKkHBt6SJElSDwy8JUmSpB4YeEuSJEk9GGrgneT4JGuTrOykvSjJRUk2JJmYpezAfEl2TbIuyYp2+fAw2yxJkiT1Ydgj3icCB01LWwk8HzhrjrKz5buiqvZplyM3u5WSJElSz5YOs7KqOivJrtPSVgEks9/ecL75JEmSpC3RljLH+6FJzk9yZpInL3RjJEmSpLtqqCPeI7IGeHBVXZPkscCpSfaqqhumZ0xyBHAEQLZe3nMzJUmSpJkt2Ih3khPaiyW/Olu+qrqlqq5p188FrgD2nCHvcVU1UVUTWy1ZNvxGS5IkSZtowUa8q+pl88mX5P7AtVW1PsluwB7AlSNtnCRJkjRkw76d4KeA/wAelmR1kr9I8rwkq4EnAv+a5OszlJ0p31OAC5J8HzgZOLKqrh1muyVJkqRRS1UtdBtGYumyHWv57ocMpa7rVh47lHokSZI0luZ1W74t5a4mkiRJ0hbNwFuSJEnqgYG3JEmS1AMDb0mSJKkHBt6SJElSD8b2riYTExM1OTm50M2QJEnS+POuJpIkSdJiYeAtSZIk9cDAW5IkSeqBgbckSZLUg7G9uHKYj4yXNLfrVh670E2QJGmheHGlJEmStFgYeEuSJEk9MPCWJEmSemDgLUmSJPXAwFuSJEnqwVAD7yTHJ1mbZOW09FcnuSTJRUnePUPZ9yS5OMkFSU5Jcr/Otrcmubyt48BhtlmSJEnqw7BHvE8EDuomJHkacDCwd1XtBbx3hrKnA4+qqr2BS4G3tuUfCRwK7NXW/Y9Jlgy53ZIkSdJIDTXwrqqzgGunJR8FHFNVt7R51s5Q9htVdVv78rvAzu36wcCnq+qWqvohcDmw3zDbLUmSJI1aH3O89wSenOTsJGcmedw8yrwcOK1dfxDwk8621W3anSQ5IslkkskN69dtVqMlSZKkYVra0z62A54APA74bJLdaoZHZiZ5O3AbcNJU0oBsA8tW1XHAcdA8uXIz2y1JkiQNTR8j3quBL1TjHGADsEOSE5KsSPLVqYxJDgOeDbykE5ivBnbp1Lcz8LMe2i1JkiQNTR+B96nAAQBJ9gS2Aa6uqpdV1T5V9ax220HAm4HnVNVNnfJfAg5Nsm2ShwJ7AOf00G5JkiRpaIY61STJp4D9aUa0VwN/BRwPHN/eYvBW4LAZppl8CNgWOD0JwHer6siquijJZ4Ef0ExBeWVVrR9muyVJkqRRywxTrbd4S5ftWMt3P2ShmyHdbVy38tiFboIkSQtl0DWJd+KTKyVJkqQeGHhLkiRJPTDwliRJknpg4C1JkiT1YGwvrpyYmKjJycmFboYkSZLGnxdXSpIkSYuFgbckSZLUAwNvSZIkqQcG3pIkSVIPxvbiSp9cKUmbzyeSStK8eHGlJEmStFgYeEuSJEk9MPCWJEmSemDgLUmSJPXAwFuSJEnqwVAD7yS7JPl2klVJLkry2jb9Re3rDUkmZin/niQXJ7kgySlJ7tem75pkXZIV7fLhYbZbkiRJGrVhj3jfBryhqh4BPAF4ZZJHAiuB5wNnzVH+dOBRVbU3cCnw1s62K6pqn3Y5csjtliRJkkZqqIF3Va2pqvPa9V8Dq4AHVdWqqrpkHuW/UVW3tS+/C+w8zPZJkiRJC2Vkc7yT7ArsC5y9iVW8HDit8/qhSc5PcmaSJ29m8yRJkqReLR1FpUnuDXweeF1V3bAJ5d9OM23lpDZpDfDgqromyWOBU5PsNb3uJEcARwBk6+WbcwiSJEnSUA19xDvJ1jRB90lV9YU58p7QXiz51U7aYcCzgZdU+zz7qrqlqq5p188FrgD2nF5fVR1XVRNVNbHVkmXDOyhJkiRpMw11xDtJgI8Bq6rq/XPlr6qXTSt/EPBm4KlVdVMn/f7AtVW1PsluwB7AlcNsuyRJkjRKw55q8iTgpcCFSVa0aW8DtgU+CNwf+NckK6rqwAHlP9TmPb2J4flueweTpwDvTHIbsB44sqquHXLbJUmSpJFJO5tj7CxdtmMt3/2QhW6GJG3Rrlt57EI3QZK2BJlPJp9cKUmSJPXAwFuSJEnqgYG3JEmS1AMDb0mSJKkHBt6SJElSD8b2riYTExM1OTm50M2QJEnS+POuJpIkSdJiYeAtSZIk9cDAW5IkSeqBgbckSZLUg7G9uHLPZcvrg7vvu9DNkCRJ0ogduPKshW6CF1dKkiRJi4WBtyRJktQDA29JkiSpBwbekiRJUg8MvCVJkqQeDDXwTrJLkm8nWZXkoiSv7Wx7dZJL2vR3z1D+r5NckGRFkm8k+d02ff8k17fpK5K8Y5jtliRJkkZt6ZDruw14Q1Wdl2Q5cG6S04EdgYOBvavqliQPmKH8e6rqvwMkeQ3wDuDIdtu/V9Wzh9xeSZIkqRdDDbyrag2wpl3/dZJVwIOA/w84pqpuabetnaH8DZ2X9wLG8ybjkiRJutsZ2RzvJLsC+wJnA3sCT05ydpIzkzxulnLvSvIT4CU0I95Tnpjk+0lOS7LXqNotSZIkjcJIAu8k9wY+D7yuHcVeCmwHPAF4I/DZJAOf8FNVb6+qXYCTgFe1yecBD6mqRwMfBE6dYb9HJJlMMnn9+t8O9ZgkSZKkzTH0wDvJ1jRB90lV9YU2eTXwhWqcA2wAdkhyQnux5FcHVPVJ4AXQTEGpqhvb9a8CWyfZYXqBqjquqiaqauK+S7Ye9qFJkiRJm2yoc7zbUeyPAauq6v2dTacCBwBnJNkT2Aa4uqpeNq38HlV1WfvyOcDFbfoDgV9UVSXZj+YLwzXDbLskSZI0SsO+q8mTgJcCFyZZ0aa9DTgeOD7JSuBW4LCqGnTh5DFJHkYzIv4jNt7R5IXAUUluA9YBh85QXpIkSVqUMq7x657LltcHd993oZshSZKkETtw5VkL3YSB1y5O55MrJUmSpB4YeEuSJEk9MPCWJEmSemDgLUmSJPXAwFuSJEnqwbBvJ7ho3Gevh3Hg5IJf4SpJkiQBjnhLkiRJvTDwliRJknpg4C1JkiT1wMBbkiRJ6sHYPjJ+6bIda/nuhyx0MyRJ0hi4buWxC90ELW4+Ml6SJElaLAy8JUmSpB4YeEuSJEk9MPCWJEmSemDgLUmSJPVgqIF3knskOSfJ95NclOR/tOlJ8q4klyZZleQ1c9TzwSQ3dl4fnuSXSVa0yyuG2W5JkiRp1JYOub5bgAOq6sYkWwPfSXIa8AhgF+DhVbUhyQNmqiDJBHC/AZs+U1WvGnJ7JUmSpF4MdcS7GlMj1Vu3SwFHAe+sqg1tvrWDyidZArwHeNMw2yVJkiQttKHP8U6yJMkKYC1welWdDewOHJJkMslpSfaYofirgC9V1ZoB216Q5IIkJyfZZdjtliRJkkZp6IF3Va2vqn2AnYH9kjwK2Ba4uaomgI8Ax08vl+R3gRcBHxxQ7ZeBXatqb+DfgI8P2neSI9rgfnLD+nXDOSBJkiRpCEb6yPgkfwX8BngFcFBVXZUkwK+q6r5Jvg7sCEwCpwAfA25uiz8YuLKqfm9anUuAa6vqvrPt20fGS5KkYfGR8ZrDvB4ZP9SLK5PcH/htVf0qyTLgD4G/A04FDqAZ6X4qcClAVR04rYoHduq6cSroTrJTZ/rJc4BVw2y3JEmSNGrDvqvJTsDH21HprYDPVtVXknwHOCnJ64EbaUbA74rXJHkOcBtwLXD4ENssSZIkjdxIp5osJKeaSJKkYXGqieYwr6kmPrlSkiRJ6oGBtyRJktQDA29JkiSpBwbekiRJUg/G9uLKiYmJmpycXOhmSJIkafx5caUkSZK0WBh4S5IkST0w8JYkSZJ6YOAtSZIk9WBsL670yZWSJC0Mn/KouyEvrpQkSZIWCwNvSZIkqQcG3pIkSVIPDLwlSZKkHhh4S5IkST0w8JYkSZJ6MPTAO8mSJOcn+Ur7OkneleTSJKuSvGaGcicm+WGSFe2yT5u+f5LrO+nvGHabJUmSpFFbOoI6XwusAu7Tvj4c2AV4eFVtSPKAWcq+sapOHpD+71X17OE2U5IkSerPUEe8k+wM/Gfgo53ko4B3VtUGgKpaO8x9SpIkSVuCYU81+QfgTcCGTtruwCFJJpOclmSPWcq/K8kFSf4+ybad9Ccm+X5bfq+ZCic5ot3P5Ib16zbvSCRJkqQhGlrgneTZwNqqOnfapm2Bm6tqAvgIcPwMVbwVeDjwOGB74M1t+nnAQ6rq0cAHgVNnakNVHVdVE1U1sdWSZZt+MJIkSdKQDXPE+0nAc5JcBXwaOCDJ/wZWA59v85wC7A2Q5OvtxZIfBaiqNdW4BTgB2K9Nv6GqbmzXvwpsnWSHIbZbkiRJGrmhBd5V9daq2rmqdgUOBb5VVX9GM0J9QJvtqcClbf4Dq2qfqnoFQJKd2r8BngusbF8/sE0jyX5tm68ZVrslSZKkPoziribTHQOclOT1wI3AK2bId1KS+wMBVgBHtukvBI5KchuwDji0qmrEbZYkSZKGKuMawy5dtmMt3/2QhW6GJEl3O9etPHahmyD1LfPJ5JMrJUmSpB4YeEuSJEk9MPCWJEmSemDgLUmSJPVgbC+unJiYqMnJyYVuhiRJksafF1dKkiRJi4WBtyRJktQDA29JkiSpBwbekiRJUg/G9uJKn1wp6a7yaXuSpE3kxZWSJEnSYmHgLUmSJPXAwFuSJEnqgYG3JEmS1AMDb0mSJKkHQw+8k1yV5MIkK5JMdtJfneSSJBclefcs5e+UL8muSda1da5I8uFht1uSJEkapaUjqvdpVXX11IskTwMOBvauqluSPGBQoTnyXVFV+4yovZIkSdJI9TXV5CjgmKq6BaCq1m5mPkmSJGmLMoosPfrzAAALsUlEQVTAu4BvJDk3yRFt2p7Ak5OcneTMJI+boexs+R6a5Pw2/ckjaLckSZI0MqOYavKkqvpZO03k9CQXt/vZDngC8Djgs0l2qzs/NnNgPmAN8OCquibJY4FTk+xVVTd0C7eB/hEA2Xr5CA5NkiRJ2jRDH/Guqp+1f9cCpwD7AauBL1TjHGADsEOSE9qLJb/aFh+Yr6puqapr2nrPBa6gGR2fvu/jqmqiqia2WrJs2IcmSZIkbbKhBt5J7pVk+dQ68ExgJXAqcECbviewDXB1Vb2sqvapqme1VQzMl+T+SZa06bsBewBXDrPtkiRJ0igNe6rJjsApSabq/mRVfS3JNsDxSVYCtwKHDZhmAnD8oHxJngK8M8ltwHrgyKq6dshtlyRJkkYmg+PfLd/SZTvW8t0PWehmSNqCXLfy2IVugiRpy5T5ZPLJlZIkSVIPDLwlSZKkHhh4S5IkST0w8JYkSZJ6YOAtSZIk9WBs72oyMTFRk5OTC90MSZIkjT/vaiJJkiQtFgbekiRJUg8MvCVJkqQeGHhLkiRJPRjbiyt9ZLxm4mPBJUnSkHlxpSRJkrRYGHhLkiRJPTDwliRJknpg4C1JkiT1wMBbkiRJ6sHQA+8k90tycpKLk6xK8sQ2/dVJLklyUZJ3z1D2M0lWtMtVSVa06bsmWdfZ9uFht1uSJEkapaUjqPMDwNeq6oVJtgHumeRpwMHA3lV1S5IHDCpYVbff/y/J+4DrO5uvqKp9RtBeSZIkaeSGGngnuQ/wFOBwgKq6Fbg1yVHAMVV1S5u+do56AvwJcMAw2ydJkiQtlGFPNdkN+CVwQpLzk3w0yb2APYEnJzk7yZlJHjdHPU8GflFVl3XSHtrWeWaSJw+53ZIkSdJIDTvwXgo8BvinqtoX+A3wljZ9O+AJwBuBz7aj2jN5MfCpzus1wIPbOv8r8Ml2dP0OkhyRZDLJ5Ib164ZyQJIkSdIwDDvwXg2srqqz29cn0wTiq4EvVOMcYAOwQ5IT2oslvzpVQZKlwPOBz0ylVdUtVXVNu34ucAXNKPodVNVxVTVRVRNbLVk25EOTJEmSNt1Q53hX1c+T/CTJw6rqEuDpwA9oAuUDgDOS7AlsA1xdVS8bUM0fAhdX1eqphCT3B66tqvVJdgP2AK4cZtslSZKkURrFXU1eDZzU3tHkSuBlNFNOjk+yErgVOKyqaobyh3LHaSbQXLD5ziS3AeuBI6vq2hG0XZIkSRqJzBz/btmWLtuxlu9+yNwZdbdz3cpjF7oJkiRpvMx27eLtfHKlJEmS1AMDb0mSJKkHBt6SJElSDwy8JUmSpB4YeEuSJEk9GNu7mkxMTNTk5ORCN0OSJEnjz7uaSJIkSYuFgbckSZLUAwNvSZIkqQcG3pIkSVIPxvbiSh8ZPzw+Yl2SJGlWXlwpSZIkLRYG3pIkSVIPDLwlSZKkHhh4S5IkST0w8JYkSZJ6MGfgnWTnJF9MclmSK5J8IMk2w25IkoclWdFZbkjyunbb0Ul+2tn2rGHvX5IkSRqlWQPvJAG+AJxaVXsAewL3Bt61uTtOsrT7uqouqap9qmof4LHATcApnSx/P7W9qr66ufuXJEmS+jTXiPcBwM1VdQJAVa0HXg+8PMk9k5ydZK+pzEnOSPLYJPdKcnyS7yU5P8nB7fbDk3wuyZeBb8yy36cDV1TVjzbr6CRJkqRFYq7Aey/g3G5CVd0A/Bj4PeDTwJ8AJNkJ+N2qOhd4O/Ctqnoc8DTgPUnu1VbxROCwqjpglv0eCnxqWtqrklzQBvTbDSqU5Igkk0kmN6xfN8ehSZIkSf2ZK/AOMOjRllPpnwVe1Kb9CfC5dv2ZwFuSrADOAO4BPLjddnpVXTvjDpv548/p1AXwT8DuwD7AGuB9g8pW1XFVNVFVE1stWTbHoUmSJEn9WTrH9ouAF3QTktwH2IVmKshNSa5JsjdwCPBfprIBL6iqS6aVfTzwmzn2+UfAeVX1i6mE7nqSjwBfmaMOSZIkaVGZa8T7m8A9k/w5QJIlNKPNJ1bVTW2eTwNvAu5bVRe2aV8HXt1enEmSfe9Cm17MtGkm7TSWKc8DVt6F+iRJkqQFN2vgXVVFE+i+KMllwKXAzcDbOtlOppmT/dlO2l8DWwMXJFnZvp5TknsCz6C5k0rXu5NcmOQCmjnjr59PfZIkSdJikSa2Hj9Ll+1Yy3c/ZKGbMRauW3nsQjdBkiRpMct8MvnkSkmSJKkHBt6SJElSDwy8JUmSpB4YeEuSJEk9GNuLKycmJmpycnKhmyFJkqTx58WVkiRJ0mJh4C1JkiT1YGynmiRZWVWPWuh2aHSSHFFVxy10OzQ69vH4s4/Hn308/uzj+RvnEe+bF7oBGrkjFroBGjn7ePzZx+PPPh5/9vE8jXPgLUmSJC0aBt6SJElSD8Y58Hau0fizj8effTz+7OPxZx+PP/t4nsb24kpJkiRpMRnnEW9JkiRp0Ri7wDvJQUkuSXJ5krcsdHs0tyRXJbkwyYokk23a9klOT3JZ+3e7Nj1Jjm3794Ikj+nUc1ib/7Ikh3XSH9vWf3lbdl5Pl9KmS3J8krVJVnbSRt6nM+1DwzdDHx+d5KftubwiybM6297a9tclSQ7spA/8NzvJQ5Oc3fblZ5Js06Zv276+vN2+az9HfPeTZJck306yKslFSV7bpnsuj4lZ+thzeVSqamwWYAlwBbAbsA3wfeCRC90ulzn77Spgh2lp7wbe0q6/Bfi7dv1ZwGk0j2Z9AnB2m749cGX7d7t2fbt22znAE9sypwF/tNDHPO4L8BTgMcDKPvt0pn249NbHRwP//4C8j2z/Pd4WeGj77/SS2f7NBj4LHNqufxg4ql3/S+DD7fqhwGcW+r0Y1wXYCXhMu74cuLTtS8/lMVlm6WPP5REt4zbivR9weVVdWVW3Ap8GDl7gNmnTHAx8vF3/OPDcTvonqvFd4H5JdgIOBE6vqmur6jrgdOCgdtt9quo/qjm7P9GpSyNSVWcB105L7qNPZ9qHhmyGPp7JwcCnq+qWqvohcDnNv9cD/81uRz0PAE5uy0//vEz18cnA0/0VazSqak1Vndeu/xpYBTwIz+WxMUsfz8RzeTONW+D9IOAnndermf0DpMWhgG8kOTfJ1E34d6yqNdD8wwA8oE2fqY9nS189IF3966NPZ9qH+vOqdprB8Z3pAXe1j38H+FVV3TYt/Q51tduvb/NrhNppAPsCZ+O5PJam9TF4Lo/EuAXeg74peduWxe9JVfUY4I+AVyZ5yix5Z+rju5quxcM+HR//BOwO7AOsAd7Xpg+zj+3/niW5N/B54HVVdcNsWQekeS5vAQb0sefyiIxb4L0a2KXzemfgZwvUFs1TVf2s/bsWOIXmJ6tftD9D0v5d22afqY9nS995QLr610efzrQP9aCqflFV66tqA/ARmnMZ7nofX00zTWHptPQ71NVuvy/zn/KiuyjJ1jQB2UlV9YU22XN5jAzqY8/l0Rm3wPt7wB7tFbTb0EzW/9ICt0mzSHKvJMun1oFnAitp+m3qyvfDgC+2618C/ry9ev4JwPXtz5BfB56ZZLv2J7FnAl9vt/06yRPauWN/3qlL/eqjT2fah3owFSi1nkdzLkPTL4e2dzF4KLAHzUV1A//Nbuf7fht4YVt++udlqo9fCHyrza8ha8+vjwGrqur9nU2ey2Nipj72XB6hhb66c9gLzVXVl9JcXfv2hW6Py5z9tRvN1c/fBy6a6jOaeV7fBC5r/27fpgf4X23/XghMdOp6Oc2FHpcDL+ukT9D8o3EF8CHaB0e5jLRfP0Xz8+RvaUY1/qKPPp1pHy699fG/tH14Ac1/qjt18r+97a9L6NxZaKZ/s9t/G85p+/5zwLZt+j3a15e323db6PdiXBfgD2h++r8AWNEuz/JcHp9llj72XB7R4pMrJUmSpB6M21QTSZIkaVEy8JYkSZJ6YOAtSZIk9cDAW5IkSeqBgbckSZLUAwNvSZIkqQcG3pIkSVIPDLwlSZKkHvw/IGc9CJAnHt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seaborn barplot\n",
    "plt.figure(figsize=(12, 5))\n",
    "sns.barplot(y='Age_Band_of_Driver', x='Count', data=drivers_df, palette=[c_blue,c_blue,c_blue,c_blue,c_blue,\n",
    "                                                                         p_red,c_blue,c_blue,c_blue,c_blue,c_blue])\n",
    "plt.title('Distribution of Drivers by Age Band', fontsize=16, fontweight='bold')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "sns.despine(top=True, right=True, left=False, bottom=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seems like the age band with the most drivers is from 26-35 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We found many interesting findings by exploring the UK traffic accidents dataset. Here is a short summary of the findings:\n",
    "- The number of total accidents have been steadily decreasing in the UK since 2015\n",
    "- However, number serious/fatal accidents have actually been steadily increasing\n",
    "- The months of the year with peak accidents are November and July\n",
    "- The day of the week with the most accidents on average is Friday\n",
    "- Time of the day with the most accidents is from 4PM-6PM\n",
    "- Majority of casualties are in their early 20s, while average of casualties is around 36\n",
    "- Female casualties seem to be younger than male casualties with regards to statistical significance\n",
    "- Age band with the most accidents is 26-35 years old"
   ]
  }
 ],
 "metadata": {
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