{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color=\"darkblue\"> ML models to discriminate between fishing trips from federal verse state waters using catch compositions on U.S. West Coast landing receipts</font>\n",
    "\n",
    "Version: 2<br>\n",
    "Date: 01/26/2023<br>\n",
    "Author: Robert T. Ames\n",
    "\n",
    "### Study:\n",
    "This code was used to train and test two machine learning models that could discriminate between fishing trips from federal verse state waters using the catch compositions on the landing records (i.e., fish tickets). The landing records for training and testing the models were linked to observer data to create the labels. The labels are binary, where 1 denotes trips where the vessel fished exclusively in Federal waters, and 0 denotes trips where the vessel fished exclusively in state waters. Trips that included fishing in both federal and state waters were excluded. The landing records were from open access fixed gear fishing vessels where the plurality of the species landed were groundfish and where the landings occurred on the U.S. west coast (northern Washington to southern California) between 2002 and 2019. The delivery ports, dates, and vessel ID information has been withheld and some initial data preprocessing was performed to protect confidentiality. \n",
    "The notebook includes:\n",
    "* Initial data preprocessing steps, which have been comment out to render those code blocks inert. This code was included to show the complete process, but is unnecessary to run the provided code using the data in this repo. The preprocessing steps comprised of (1) pulling data from database, (2) pivoting landings by species or species group, one row per landing record, (3) splitting the data into training (80%) and testing datasets (20%) and stratifying by the label, and (4) normalizing the training data and applying that transformation blindly to the testing data\n",
    "* Forward stepwise subset selection process on the training data to determine the best variables for the random forest and gradient boosting models. The subset selection process was executed separately for each model and used 10-fold cross-validation. Final selected variables for each model were those that produced the highest mean accuracy with the lowest standard error using the fewest number of variables \n",
    "* plot the difference combinations of variables selected during the stepwise process along the model accurancy scoring and standard error for each subset selection\n",
    "* Variable of importances scoring was performed on the variables chosen by the sequential forward selection process. The second variable selection process was to decrease the complexity of the models by reducing the number of variables and prevent overfitting. The aim was to reduce the selected variables to the most relevant variables for each model, the group of variables that together provided greater than 98% of the overall score for making predictions. The other variables were determine to be irrelevant to the models and were discarded \n",
    "* The models were tuned to determine the best hyperparameters using the all the training data, but only the best variables from the stacking of the to subset selection processes. The models were fit to the training data and 10-fold cross-validation with accuracy scoring to evaluate the fits\n",
    "* The optimal random forest and gradient boosting models with best hyperparameters were refit to all the training data, but only using the subset variables. The models were then fit to the testing data to evaluate the model performances. This last step provided an indication on how the model would perform on out-of-sample data\n",
    "* To ensure persistence of model efficacy despite such inter-annual variability, the selected subset of variables were also used to fit the models whose training data iterated through all but one year, and were tested on the remaining year\n",
    "\n",
    "\n",
    "Note: The sequential forward selection process will provide slightly different results each time it is executed. However, the subset selection of the top 5 variables is consistent and will provide about the same level of model fit and performance (i.e., ~97% accuracy on the testing data). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.9.12\n"
     ]
    }
   ],
   "source": [
    "# Python version used in this study\n",
    "from platform import python_version\n",
    "print(python_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading packages\n",
    "import time\n",
    "#import cx_Oracle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# suppressing warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timing the durations to run the code\n",
    "start = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Initial data preprocessing steps:\n",
    "These steps have been comment out to render those code blocks inert, and are not necessary to execute the below scripts and models. Non-confidential training and testing datasets are provided in this repo, which were used in this study. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from getpass import getpass\n",
    "# password = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# user = \"XXXX\"\n",
    "# host = \"pacfin...\"\n",
    "# port = \"9999\"\n",
    "# sid = \"XXXXX\"\n",
    "# pswd = password\n",
    "# dsn = cx_Oracle.makedsn (host, port, sid)\n",
    "\n",
    "# connection  = cx_Oracle.connect (user, pswd, dsn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pulling in landings linked to observer trips from master table \n",
    "# sql = \"SELECT keyid, pacfin_year, species_name, area_code, kg, origin_catch, total_receipt_kg, dist_202m_depth FROM enf.ole_fedwater_viol_manuscript  WHERE pacfin_year > 2001 AND fishery = 'GRND' AND wcgop_trip_id <>  28716 AND pacfin_group_gear_code <> 'TLS' AND wcgop_trip_id IS NOT NULL AND kg > 10\"\n",
    "# wcomp = pd.read_sql(sql, con=connection)\n",
    "# pd.options.display.max_columns = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "# wcomp.to_csv('wcomp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#wcomp = pd.read_csv('wcomp.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot on species to create a wide dataframe with species separated into columns, and one row per ticket\n",
    "# wcomp_pivot = wcomp.pivot_table('KG', ['KEYID', 'PACFIN_YEAR', 'AREA_CODE', 'ORIGIN_CATCH', 'DIST_202M_DEPTH', 'TOTAL_DAY_KG', 'TOTAL_RECEIPT_KG'], 'SPECIES_NAME') \n",
    "# wcomp_pivot = wcomp_pivot.fillna(0)\n",
    "# wcomp_pivot = wcomp_pivot.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some variables, the database provide the headers in uppercase\n",
    "# wcomp_pivot.rename(columns={\"AREA_CODE\": \"Label\", \"PACFIN_YEAR\": \"Year\", \"DIST_202M_DEPTH\": \"Distance 202m Depth\", \"TOTAL_DAY_KG\": \"Total Daily Kg\", \"TOTAL_RECEIPT_KG\": \"Total Receipt Kg\", \"ORIGIN_CATCH\": \"Origin Catch\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy of the data for analysis\n",
    "# wcomp_pivot_ml = wcomp_pivot.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unnecessary columns\n",
    "# wcomp_pivot_ml.drop(['KEYID', 'Origin Catch', 'Year'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pulling out the label from dataframe\n",
    "# X = wcomp_pivot_ml\n",
    "\n",
    "# y = X.pop('Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape: (10630, 116)\n",
      "y shape: (10630,)\n"
     ]
    }
   ],
   "source": [
    "# number of rows and columns\n",
    "# print('X shape:', X.shape)\n",
    "# print('y shape:', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train and test splits, test is only 20%, and stratified by area\n",
    "# from sklearn.model_selection import train_test_split\n",
    "\n",
    "# x_train_plot, x_test_plot, y_train_plot, y_test_plot = train_test_split(X, y, test_size=0.20, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training_df_plot = pd.concat([x_train_plot, y_train_plot], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4524\n",
       "0    3980\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training_df_plot.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#decoding for plot presentations\n",
    "# Replace = training_df_plot.loc[:, \"Label\"] == 1\n",
    "# training_df_plot.loc[Replace, \"Label\"] = \"Federal Waters\"\n",
    "\n",
    "# #decoding for plot presentations\n",
    "# Replace = training_df_plot.loc[:, \"Label\"] == 0\n",
    "# training_df_plot.loc[Replace, \"Label\"] = \"State Waters\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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OOgHAo5gJAgAAABR/BQpBsrKy9MsvvygxMVGJiYnatm2b0tLSJEmmaapMmTK68cYbFR8fr7Zt26pWrVoeKRoA3I2ZIAAAAEDx51QI8tZbbykxMVG7du1Sdna2pEuhR1BQkFq2bGkLPZo2baqAgACPFgwAnuDO4MKdgQoAAAAA93EqBJk8ebIkyTAM1a9fX9ddd53atm2rVq1aKTQ01KMFAsDVwGBUAAAAoPhzKgTp2bOn4uPjFR8frwoVKni6JgC46ixuDC4sdIIAAOCTTNOUYRjeLgOAFzkVgrz22muergMAvMqd3RumG4esAgAA98nJyVJQUIi3ywDgRQzwAABd+mTIXZgJAgCAb3Lnz3sARRMhCADIvZ0gzAQBAMA3mfyMBvweIQgAyN2DUfmUCQAAAPBFhCAAIPcORmU7DAAAvsnkZzTg9whBAEDu3cLizkAFAAC4D1tWARCCAIAkq1sHo7IdBgAAX0QnCACnjsjdsGFDoV4kPj6+UI8HAE9jMCoAAMUfg1EBOBWCDBo0SIZhuPQChmFo165dLj0WAK4Wt3aCMBgVAACfxBG5AJwKQVq1auXpOgDAqxiMCgBA8We1WrxdAgAvcyoEmT59uqfrAACvcucnQ8wEAQDAN7FlFQCDUQFAbp4JQicIAAA+iZkgAJzqBMnPsWPHlJKSoqysLLtPUU3TVGZmpk6ePKmVK1fqk08+KXShAOBJ7gwu+JQJAADfxOkwAFwKQbKysvT0009r2bJl7q4HALzCvUfk8gsWAAC+iE4QAC5th5k8ebKWLl2qwMBA1a9fX1WrVpUktWzZUnXr1lVgYKBM09S1116rDz74wK0FA4AnuHUwKr9gAQDgk/igAoBLIcgPP/yggIAAffHFF1qwYIGefPJJSdLLL7+sr7/+WitWrFCTJk108OBBW0ACAL7Mne2xDEYFAMA30QkCwKUQ5ODBg2ratKlatGghSWratKlM01RiYqIkKTIyUu+//74kacqUKW4qFQA8x2J143YYfsECAMAnMRMEgEshSHZ2tqpUqWL7ulq1agoKCtLvv/9uuxYVFaW4uDjt2rWr8FUCgIe5sz3WnVtrAACAe2zbtk0vvTJa27Zt83YpALzIpRCkYsWKOn36tO1rwzAUHR2t5OTkPOuOHj1aqAIB4Gpw7xG5bIcBAMCXmKapadOm6cjRY5o2bZrdyZYA/ItLIUizZs2UmJioQ4cO2a7Vrl1bv/zyizIyMmzXkpOTVbp06cJXCQAe5tbTYegEAQDApyQlJdk+sE1OTlZSUpKXKwLgLS6FIL1791ZWVpZ69uypGTNmSJISEhKUkZGhYcOGaeXKlRo5cqT279+vRo0aubVgAPAE93aCEIIAAOArTNPUjBkzFBBw6Z8+AQEBmjFjBt0ggJ9yKQSJj4/XiBEjdP78eVuK2rlzZzVp0kTr1q3Tww8/rLlz5yowMFDDhg1za8EA4AnunONBBgIAgO9ISUlRcnKy7QMPq9Wq5ORkpaSkeLkyAN5QwtUHDho0SHfccYdOnjwp6VKiOm3aNH3++efatm2bKlasqL59+6px48ZuKxYAPMWdnwYxGBUAAN8RHR2tOnXqaN++fbJarQoICFCtWrUUHR3t7dIAeIHLIYgkRUREKCIiwvZ1yZIl9cgjj9i+vnjxopKTk1WnTp3CvAwAeBzbYQAAKJ4Mw1Dfvn318ssvS7r0M79v374yDMPLlQHwBpe2wzRo0EAjRoy44rrhw4erf//+rrwEAFxV7h2Myh5jAAB8SVxcnO2D2Tp16iguLs7LFQHwFqdCEKvVavtjsVhkmqbdNUd/0tPTtX//frvTYgDAV9EJAgBA8WUYhvr376+qVSLVv39/ukAAP+bUdpjevXtrx44dtq8Nw9DixYu1ePHiKz6W02EAFAUWNwYXhCAAAPie2NhYvfryc6oSU8vbpQDwIqc6QV544QWZpmn7I8nua0d/goODVadOHf3f//2fJ+sHALdwZ3DBYFQAAHwTHSAAnOoEadq0qXbv3m37un79+urataveeustjxUGAFeTO+d4uPOkGQAA4D5GgEsjEQEUIy6dDjNq1ChVq1bN3bUAgNfQCQIAQPEXEBDo7RIAeJlLIcidd95p+98XLlzQ9u3bdeLECQUHB6tSpUpq0qSJQkJC3FYkAHgag1EBACj+AugEAfyeSyGIJGVnZ2vcuHGaPXu2MjMz7e6VLFlSPXr00NNPP63g4OBCFwkAnmZx43YYdwYqAADAfQyDEATwdy6FIBaLRUOGDNH69etlGIaaNm2qmJgYmaapQ4cO6ddff9X06dO1d+9eTZ482d01A4Dbme48HYYQBAAAn8RMEAAuhSBffvml1q1bpyZNmmjcuHF55oMcPHhQTz75pDZs2KC5c+eqZ8+ebikWADzFncEFg1EBAPBNdIIAcOm7wIIFC1S6dGl9+umnDgekVq9eXRMnTlSpUqX03//+t9BFAoCnWd0YXFgIQQAA8EnMBAHg0neBffv2qXXr1qpYsWK+aypWrKjWrVtr3759LhcHAFeLO4/IZTsMAAC+yTAMb5cAwMtcjkKdafc2TVM5OTmuvgQAXDXuPNbWnYEKAABwH2aCAHDpu8C1116rzZs3Ky0tLd81aWlp2rx5s2rWrOlycQBwtbhzjgdH5AIA4JuYCQLApe8Cd955p86fP68hQ4bo8OHDee4fPnxYDz30kDIyMvTvf/+70EUCgKdZOB0GAIBij5kgAFw6HaZPnz5aunSpNm/erE6dOqlhw4aKjo6WYRg6fPiwdu3aJYvFolatWqlPnz7urhkA3Mo0TfeeDqNLg1YD2HcMAIBPoRMEgFPfBW6++WaNGTPG9nVgYKA+//xzDRgwQEFBQdq5c6d++OEHff/999q5c6eCg4M1YMAAffbZZypRwqWcBQCuGk8caUs3CAAAAOB7nEooUlJSdOrUKbtrwcHBeu655/TUU09px44dOn78uCSpcuXKaty4sUJDQ91fLQB4gDu3wuSyWq1SYKDbnxcAALiO02EAFLpNIzg4WC1btnRHLQDgFaYHTnOxeqC7BAAAFA7bYQDwXQCA3/NYJwgAAPApdIIAIAQB4Pc8EVh4IlgBAACFRAgC+D2nt8Ns27ZNzz33XIFfwDAMvfnmmwV+HABcLZ4IQegEAQAAAHyP0yHIoUOHdPDgwQK/ACEIAF/nifkdzAQBAAAAfI/TIUjdunV1yy23eLIWAPAKOkEAAAAA/+B0CFKvXj098sgjnqwFALyCwagAAACAf2AwKgC/Z/XAEbkMRgUAAAB8DyEIAL/HdhgAAPwEM7sAv0cIAsDvWT2xHYZfsgAA8DkmnZqA33MqBGnVqpVq1arl6VoAwCvoBAEAwD+YfEgB+D2nBqNOnz7d03UAgNdYPHFELiEIAAA+hxAEANthAPg9TwQWDEYFAMD3EIIAIAQB4Pc8MROEX7IAAPA9Jp2agN9zajsMABRnHjki189/ydqyZYv69eun2NhYzZ49O8/9lStX6qGHHsr38QkJCXr33Xftru3cuVOfffaZtm7dqtOnTyssLEwtWrTQ4MGD1bRp0yvWdODAAd15550KCwvT6tWrC/6mAABFntVq8XYJPunQoUMaP3681qxZo9OnT6ty5cq64YYbNGTIEEVGRuZZn5OToxkzZmjhwoXav3+/SpUqpcaNG2vQoEFq27atw9fYv3+/Pv74Y23cuFFpaWmqUKGCrrvuOj300EMO509arVbNnz9fs2bN0oEDBxQUFKQWLVro4YcfVqNGjdz+3wD+gxAEgN9jMKp7nTt3TsOHD7/sf4Ndu3ZJujR4u0qVKnnux8XF2X393Xff6ZlnnlFOTo7q1q2r2NhY/fnnn1q2bJlWrVqlt956S7fffnu+r5eTk6Phw4crIyNDYWFhLr4zAEBRRwiS159//qm77rpLp0+f1rXXXqvY2Fjt379fs2bN0rfffqsZM2aobt26tvVZWVkaPHiwNmzYoBIlSqhx48YqW7astm/frkGDBmnYsGF65JFH7F5j586d6t+/vzIyMlS7dm3Fxsbqjz/+0DfffKMVK1bo888/V/Pmze0e83//93+aM2eOypUrp7Zt2+rkyZNasWKFVq9erYkTJ+YbtgBXQggCwO95Yn6HJ7pLiorXX39dKSkpl12TG4K8+OKLql+//mXXnj59WiNHjlROTo7GjBmjbt262e4tWLBAzz77rF588UXFx8erYsWKDp/jk08+0fbt2wv2RgAAxY4/f0iRn2eeeUanT5/Www8/rGHDhskwDEnSRx99pA8//FAjR47UnDlzbOsnTJigDRs2KCIiQuPHj7d1Y6alpenRRx/Vhx9+qCZNmuiGG26wPeb//u//lJGRoaeeekqDBw+WdGnr8CeffKL3339fI0eO1Lfffmtbv2rVKs2ZM0d16tTRtGnTbD/flyxZoieeeELPPvusli1bppCQEI//90Hxw0wQAH6Pwajus2TJEi1YsECtWrW67Lpdu3YpJCREtWvXvuJzLl26VOfOnVOnTp3sAhBJuvPOO3XTTTcpIyNDK1eudPj47du3a8KECVesCQBQ/Jl0gtg5cOCAtm/frujoaLsARJKGDh2qUqVKadu2bTp9+rTt+ldffSVJevnll+22o1aoUEFvvfWWSpQoYbel9fTp0/rll19UqlQp3X///bbrhmFoyJAhKl26tPbu3au//vrLdu/zzz+XJA0fPtzuA45OnTqpS5cuOnbsmL7//nv3/YeAX6ETBIDfs3pgiKnpgRDkww8/1EcffaSPP/5YQUFBmjhxonbt2qWAgADFxsZq6NChatGixRWfZ9OmTerfv79Trzlt2jS1adPGqbXHjx/XSy+9pIYNG+qhhx7S5s2bHa5LS0vTkSNH1LRpU5UoceUfQzk5OWrUqJHatWvn8H6NGjUkSceOHctzLyMjQ8OHD1d4eLheeOGFPCEKAMC/WCw5XnttX/w5XqNGDW3YsEHp6el2AYgkZWdnKzs7W5IUGBgoSfrrr7904sQJlShRwq7TI1fVqlVVvXp1/fbbbzpx4oQiIiJsj7148aJOnz5tF2qcO3dOmZmZKlGihEqXLm27tnXrVpUqVcrhlpdbbrlFCxcu1I8//sjPdbjEpRCkfv36CggI0Lfffqtrr73W4Zo//vhDt99+uwzDsLU9A4Av8kgniAe3wyxcuFDLli1TjRo11K5dOyUnJ2vt2rXatGmTZsyYodjY2Ms+Pjw8XF26dHHqtcLDw51aZ5qmnnvuOWVkZOitt96y+zTnn3J/JlStWlVvv/22VqxYodTUVIWHh6tTp04aMmSIypUrZ1vfp08f9enTJ9/n27lzpyQ5nC0yevRo/fnnn5o8eTKzQAAAysnJ9nYJPvdzvGLFinm2k164cEGvvfaasrOz1aFDB9vP0NzfmUJCQhQcHOzw+XI/4Ni3b58iIiIUFham2NhYbdu2TQ8//LBGjhypmjVr6sCBA3r99deVk5OjAQMG2La27N27V1arVTVr1nT4YUnuENU9e/Y49d8A+CeXO0GcOf6RIyIBFAVFbTDqsmXL9PTTT+v++++XYRiyWq164okn9MMPP2jy5Mn64IMPLvv4WrVq6Z133nFrTTNmzNDatWs1YsQI1alTR5s2bcp37a+//irp0taZ0qVLq3Xr1qpSpYp27typyZMna+XKlZoxY4YiIiKu+Lpr167Vli1bFBISkucTqZUrV2rOnDnq27ev2rZtq8OHDxfuTQIAijyLD4QgvvhzPNfy5cv15Zdfavv27Tpz5oxuvPFGjRkzxna/YsWKKleunNLT0/Xrr7/mOaXl1KlT2r9/vyTZfSDyzjvvaMiQIUpMTNSdd95pux4UFKQXXnhB/fr1s107ceKEJOX7e0Du9ZMnTxby3cJfuTQTZNq0afriiy8UFRWV75qoqCjbOgDwZR4ZjOrBmSANGjTQAw88YGtbDQgIUN++fSV551ORffv26Z133lGrVq00cODAK67/7bffJEk33nijfvrpJ02YMEFTp07VsmXLdN111+nAgQMaOXLkFZ/nzz//1IgRIyRJDz74oN2nXadOndKLL76oGjVq6Omnn3btjQEAih1fCEF87ef4323YsEFr1qzRmTNnJEmZmZk6ePCg7X5AQIC6du0qSXruuefsBqGfPXtWzz33nG0LTVZWlu1e5cqV9Z///EchISGqU6eObr75ZtWoUUPZ2dmaOXOm7QMSSTp//rwkqWTJkg5rDA0NlXRpyyvgCpc6QVq3bn3FNaGhoU6tAwBv80TXmidPh3HUJlu5cmVJl/bbXk3Z2dl65plnFBgYqNGjRysg4MrZ+pgxY/TYY4+pSpUqtl9kpEufLr311lvq3LmzVq1apcOHDysmJsbhc+zbt0/33nuvTp48qZtuukkPPfSQ3f0XXnhBp0+f1ieffJLvL1EAAP/jC9thfOnn+D8NGTJEI0aM0LFjxzRjxgxNnTpV/fv319y5c23bUB5//HElJibq119/VUJCgpo1a6bQ0FBt375dQUFBuvXWW7V06VIFBQVJuvS7wn333actW7bozTff1H/+8x/b682YMUOvvfaa7r33Xn3//feqVKmSbYbIP2eU/BO7DuAqTocB4PcsntgO48FOkL/Py8iV+wuDM9twNm3apHr16jn153LbWqRLQ95+/fVXPffcc/kGFv8UHBysGjVq2AUguSIjI9WwYUNJsvtU6O82b96sPn366OjRo7rxxhv1/vvv24Uvc+bM0apVqzR48GA1a9bMqZoAAP7BkpN15UUe5ks/x/8pIiJCwcHBqlatmp577jn16tVL58+f16RJk2xrypQpo5kzZ2ro0KGKjIxUYmKidu/erYSEBH399dcqX768JKls2bKSLh1nv3nzZt155512AYgk9e3bV127dlV6erpmz54tSSpVqpSk/AOh3Ot8yAFXFep0mMTEREVHRysyMlLSpeF0H3zwgY4ePaqmTZtq2LBhDgfVAYAv8cxgVN89ItddA9VOnjypSZMmKTg4WJs2bbL7RevUqVOSLh299/TTT6tixYp6/vnnC/SaFy5cyHNv4cKFevHFF5Wdna0777xTr7/+ep6haaNHj5ZhGLbXzpX7fGfOnLFd99SeagCAb8rO9n4IUlieGHCen3//+9+aM2dOng8mSpYsqccee0yPPfZYnsfs27dPkmyjEzZu3ChJat++vcPXuPHGG7Vo0SLt3r1b0v+6YvKb+XGlmSHAlbgUgmRmZmrIkCHauHGjRo0apW7duunEiRMaOHCgMjIyZJqmbcrxggUL8kwbBgBf4omuDU8ORi0sdw1Uu3jxoqxWq7KysvTNN984XPPXX3/pm2++UXR0tJ5//nllZWXplVdeUVpamt555x3bpz1/d+jQIUl5T3uZNGmSre5HHnlEw4YNc/iauXuEv//+e4f3L1y4YKuXEAQA/EtOMQhB3DkYdevWrVq4cKEaNWqku+++O8/93BNgcnL+d7RwcnKyUlNTdd1119lOdMl1/vx57dy5U2FhYbYj7NPT0yX9r9vln3K7OXNfo3bt2goICNAff/whq9WaZ6vt3r17JUl169Yt6NsFJLm4HeaLL77Qhg0bFBMTo6pVq0qSZs+erfPnz6t9+/ZauHCh7r33Xh07dkwTJ050a8EA4G6eOM7Wk9thfEVMTIx+//13h3+mTZsmSWrevLl+//13rVy5UtKlX6bWr1+vFStWaPXq1Xmec/fu3dq9e7ftOL1cs2bN0jvvvKPAwEC9+eab+QYgkvKtacWKFZIubbnJvQYA8C/Z2ZneLsGnpKen66uvvtLkyZNlsVjy3M/9Wd24cWPbtQkTJmjw4MHasGFDnvXz589XVlaWbr31VlvoUbNmTUnSTz/95LCG9evXS5Lq1asn6VKXSatWrXT27FmH23mWLVsmSXlOhQOc5VII8sMPP6hs2bKaO3eu2rRpI+nS/zMahqEnn3xS9evX1/Dhw1WjRg2tWrXKrQUDgLuZHukEYVhXfnr16iXp0raVv0+cP3nypJ5//nlZLBbdd999tpkhycnJevPNNyVJr7zyirp37371iwYAFAs5WYQgf9e+fXtFR0frzz//1Ntvv20XhKxcuVKffvqpAgMD7U5/u/nmmyVJH3zwgc6dO2e7vmXLFo0bN05BQUF2A8t79uypwMBALVy4UIsXL7Z7/YULF2revHkKDQ21/X4gSffcc48k6bXXXrNtf5GkpUuXavHixapcubLuuOMO9/xHgN9xaTvMgQMH1Lp1a9vQm2PHjik5OVnh4eFq0KCBbV3dunXzTfwAwFcUtcGoRd29996rzZs3a+3atbrjjjvUsmVL21yRjIwMderUSYMHD7atHz9+vLKzs1W6dOk8s0f+rmPHjurcufPVehsAgCJm27ZtGj95noLL1XB4Qos/Cg4O1rhx43TvvfdqypQpWr58uerXr69Dhw5p9+7dKlGihF555RU1atTI9piEhAR98803WrlypW699VbFxcUpLS1NiYmJCgwM1NixY1WtWjXb+rp16+qll17SK6+8oqeeekqffvqpatSoof379ys5OVlBQUEaPXq0bYaIJHXq1EldunTRN998o86dO+u6666zvUZQUJDeeecd21YdoKBcCkEMw7Db05XbJnXdddfZrTt//rxTxyUCgDd5omvDlwejeltwcLAmTpyomTNnasGCBdq6dasCAgJUp04d9ezZUz169LA7Fu/nn3+WdOlnSn6zRyQpOjqaEAQA4JBpmpo2bZpO/ZWuadOmqVmzZlc8gtVfxMbG6uuvv9b48eO1du1arVq1SuXKlVPnzp11//33q0mTJnke8/7772vChAn65ptv9NNPPyk8PFydO3fW4MGDbae8/d3dd9+t+vXr6/PPP9fWrVv1xx9/qHz58rrtttv04IMP2n2QnmvMmDFq1qyZvvrqK61Zs0Zly5ZVhw4dNGzYMIfrAWcZpgsHLP/nP//RX3/9pZUrVyogIED33Xef1q9fr7ffftvWlnTy5EndcsstuvbaazV//ny3F+6LMjIy9Ntvv6lBgwYOh/0B8E0/bNmgRet+0qHl6yRJx567W2ZwUIGew8jKVuSoLyVJ1Tq20x3x/1JC63ZurxUAABRcYmKiXn75ZdvXr7zyipo3b+7FigC4m7P/HnepTaNDhw46evSoBg0apCeffFLr1q1TWFiYOnToIElatGiR+vbtq4sXL+q2225z7R0AwFXiiZNcrAXPlwEAgAeYpqkZM2bYOtQDAgI0Y8YMufBZMIBiwKXtMIMHD9bvv/9um8ybu1csN2157733lJqaqltvvdVuiA4A+CJPBBa+fEQuAAD+JCUlRcnJybavrVarkpOTlZKSopiYGC9WBsAbXApBgoOD9eGHH2rLli06ceKE4uLiVKVKFdv9/v37q1q1arbJwQDgyxiMCgBA8RUdHa06depo3759slqtCggIUK1atRQdHe3t0gB4gUshSK6WLVs6vE73B4CixBNdGwxGBQDANxiGob59+9pmglitVvXt25fBqICfcioEKew/EDghBoAv80TXhidOnAEAAK6JjY1VSEiIMjMzFRISwhG5gB9zKgT5+7nQBWUYhnbt2uXy4wHA0zwRWLAdBgAA37Ft2zZlZmZKkjIzM7Vt2zZOhwH8lFMtGqZpXvZPiRIlFBMTo2uuuUalSpWyXa9UqRLDhgD4PM90ghCCAADgCzgdBsDfOdUJsnv3bruvT58+rb59+yojI0PPP/+8brzxRpUo8b+n2rx5s1555RVlZmZq+vTp7q0YANzMM4NR+cUKAABfwOkwAP7OpWEd48aNU0pKiqZNm6aOHTvaBSCS1KpVK02ZMkV//fWXxowZ45ZCAcBTGIwKAEDxlXs6zN87QerUqcPpMICfcikEWbZsmdq0aXPZ5DQiIkLXXXed1q5d63JxAHA1eKJrw2QmCAAAPiH3dJjcDz04HQbwby6FIFlZWbJYLFdcd/78eVeeHgCuKs90grAdBgAAXxEbG6ugoCBJUlBQEKfDAH7MpRCkTp062rRpk93eun/aunWrNm/erMaNG7tcHABcDR4ZjEonCAAAPiMpKUnZ2dmSpOzsbCUlJXm5IgDe4lIIMmDAAGVlZalfv36aPn26/vzzT2VmZiozM1P79u3Tp59+qgceeECmaeqBBx5wd80A4FYeGYzKTBAAAHyCaZqaOHGi3bWJEydyOgzgp5w6HeafbrvtNu3Zs0cTJkzQm2++qTfffNPuvmmaCgoK0ksvvaT4+Hi3FAoAnmL1wNYVBqMCAOAbDh8+rNTUVLtrqampOnz4sKpVq+alqgB4i0shiCQ99thj6tChg7788ktt2bJFx48flyRVqVJFbdu2VZ8+fVSrVi23FQoAnuKJrSt8ugQAAAD4HpdCkGXLlqlRo0Zq0qSJmjRp4u6aAOCq8kQIQicIAAC+ISYmRlFRUXbdIFFRUZc96RJA8eXSTJBXX31Vffv2dXctAOAVnpjfwWBUAAB8g2EYGjx4sN21wYMHc0Qu4KdcCkHS09M59QVAseGJ42wZjAoAgO9o3ry5oqOjJUnR0dFq3ry5lysC4C0uhSCxsbFKTEzUmTNn3F0PAFx1bIcBAKB4MwxDQ4YMUYVyZTRkyBC6QAA/5tJMkJEjR2rYsGHq1q2b/v3vf6tu3boqV65cvt9MOCEGgC/zRNcGg1EBAPAtsbGxGtDrFsXGxnq7FABe5FII0qVLFxmGIdM0NWHChMuuNQxDu3btcqk4ALgarB4ILOgEAQDA9wQFhXi7BABe5lII0qpVK3fXAQBew2BUAAD8Q4mgYG+XAMDLXApBpk+f7u46AMBrPNG1YfXAsFUAAFA4QcF0ggD+zqXBqABQnHhifgedIAAA+B46QQC41AmS68SJE5o9e7Z+/vlnnThxQsHBwapUqZLatGmjbt26qWrVqu6qEwA8xjOdIIQgAAD4mqCgUG+XAMDLXA5BfvrpJz311FM6f/683aeoycnJ2rRpkz777DO9/fbb6tChg1sKBQBP8cwRuWyHAQDA19AJAsClEGTfvn167LHHlJmZqW7duikhIUExMTEyTVOHDh3Sd999p0WLFumpp57S/Pnzde2117q7bgBwGwajAgDgH0qUKFQjPIBiwKXvAp9++qkyMzP1xhtv6D//+Y/dvZo1a+qGG25Q69at9cILL2jKlCl69dVX3VJsfqxWq2bNmqWvvvpKBw4cUIUKFRQfH68nnnhCkZGRtnWpqal67733tH79ep07d05169bVgw8+qJtvvjnPcyYnJ+vdd99VUlKSLl68qKZNm+rRRx9VixYtPPpeAFx9bIcBAMA/BJYI8nYJALzMpcGoGzZsUL169fIEIH/XvXt31a9fX2vXrnW5OGc9++yzeu211xQTE6Pnn39enTt31uLFi3XPPffozJkzki7NL+nbt6+WL1+u7t27a/jw4crJydHQoUP1zTff2D3fvn371KdPH+3YsUP9+vXT448/rqNHj2rAgAH6+eefPf5+AFxdnhmMynYYAAB8TWAgIQjg71zqBElLS1PLli2vuO7aa6/VihUrXHkJpy1fvlxff/21+vTpo5dfftl2vX79+nr22Wf15ZdfavDgwfroo4+UmpqqWbNmqXnz5pIuBTV33XWX3njjDd18880qVaqUJGnUqFHKysrS/PnzVa1aNUlS165d1bVrV73yyitavHixDMPw6PsCcPXQCQIAgH+gEwSAS50gFSpU0P79+6+4bv/+/SpXrpwrL+G02bNnq3Tp0nrqqafsrt9+++0aPHiwatSoIYvFokWLFik2NtYWgEhSSEiI+vfvr7S0NP3444+SpJMnT2rNmjXq2LGjLQCRLr3nnj17au/evdqxY4dH3xOAq8szg1EJQQAA8DWBzAQB/J5LIch1112n33//XQsXLsx3zcKFC7V79261adPG1dquyGKxaPPmzWrdurXKlCkjSbp48aKysrIUHBysp556SrfeequSk5OVkZGh2NjYPM/RrFkzSdL27dvt/q8zawEUD1YPnOTCYFQAAHxPQECgt0sA4GUuRaEPPviglixZoueff16bNm1S586dFRMTI0k6fPiwlixZooULFyo4OFiDBw92a8F/d/jwYWVmZiomJkZLlizRRx99pD179igwMFDx8fF64YUXVLNmTR07dkySVLVq1TzPUaVKFdtzSdLRo0fzXZs7ZDV3bX4sFossFovrbwzAVeWJwMJqmnwfAADAx5j8fAaKLWf/brsUgtSuXVvjxo3T008/rQULFuTpCDFNUyVLltRbb72lunXruvISTklPT5ckrVu3TnPmzNGgQYP02GOPaffu3Zo0aZJ69+6tefPm6ezZs5Jkm/nxd6GhoZKkCxcuSJLOnTsnSSpdunSetSVLlrRbm589e/a4+I4AeIPF4pmZIElJScwPAgDAh1w8n6bDR/7ydhkAvMjlTXEdO3bU8uXL9eWXX2rLli06fvy4TNNU5cqV1apVK/Xs2dPueFpPyMrKkiT98ccf+vDDD3XrrbfaamvYsKEeeughvf/++7rhhhvyfY7cUyECAgLsvnZ0WsQ/1+anbt26DgMXAL7HNE2ZG1d65LmbNWt2xe8XAADg6vnrRKoqRkR5uwwAHpCRkeFUQ0KhJgOFh4frkUcesbuWO4/jasgNGiIjI20BSK4OHTqoatWqWr9+vRISEiQ57uC4ePGiJCksLEzS/zpAnFmbn8DAQAUGst8QKAo8OsA0wOB7AQAAPiSwBL+nA8WVs3+3Xf6I8ty5cxo1apRGjBhhd3316tVq3769Ro8ebevU8JTceR7h4eEO74eHh+vs2bO2eSW58z7+Lvda7nM5s9bRvBAARZMnj7LlmFwAAADAt7gUgqSnp6tXr1764osvlJiYaHfvwoULOn36tL744gvdfffdOn/+vFsKdaRixYqqXr26Dhw4oMzMTLt7VqtVhw8fVkxMjGrWrKmwsDCHR9vmnvSSe3RukyZNFBAQcNm1cXFx7n4rALzEk6e4WB1sqwMAAF7Ez2bA77kUgkyaNEn79u3T7bffrlmzZtnd69KlizZu3Kjbb79du3bt0oQJE9xSaH66d++u8+fP67PPPrO7/tVXXyktLU233367SpQooYSEBG3ZssUutMnMzNS0adMUHh6u66+/XtKl7pG2bdtqyZIlOnTokG1tWlqa5s6dq/r166thw4YefU8Arh5PHI/7v+emEwQAAF/iaO4fAP/i0kyQlStXKiYmRm+//bbDoX9lypTRqFGjlJiYqB9++EFPPfVUoQvNz7333qsff/xRH3zwgf744w+1bt1au3bt0ldffaW6devqvvvukyQNGzZMK1eu1AMPPKBBgwapUqVKmjdvnvbs2aNx48YpJCTE9pwjRoxQr1691Lt3bw0cOFDBwcGaOXOm0tPT9d5773nsvQC4+iye7AQhBAEAwKeY/GwG/J5LIUhqaqpuuOGGy556EBQUpEaNGmnVqlUuF+eM4OBgTZkyRZMmTdI333yjJUuWqFKlSrrnnnv0+OOP2461jYiI0OzZszV27FhNmzZN2dnZqlevnj799NM8p8fUrVtXs2bN0rhx4zR+/HgFBASocePGGjNmjGJjYz36fgBcXZ4MKix82gQAgE+hEwSASyFIuXLllJqaesV1p06duuJJKu5QsmRJPfroo3r00Ucvu65atWpOd3I0aNBAkyZNckN1AHyZJ+d28GkTAAC+xfRgByiAosGlmSAtW7bUL7/8ohUrVuS7ZsOGDUpKSlLLli1dLg4APM2znSD8ogUAgC+xWC3eLgGAl7nUCXLfffdp6dKleuyxx9SrVy/ddNNNtmNjjx07ptWrV2v27NkKDAzUAw884NaCAcCdPHtELi23AAD4Eqslx9slAPAyl0KQhg0b6o033tDLL7+smTNn5jkhxjRNhYSEaNSoUWratKlbCgUAT2AwKgAA/sNqoRME8HcuhSCS1LVrV7Vu3VoLFizQ5s2bdeLECeXk5CgiIkItWrRQz549FRUV5c5aAcDt2A4DAID/sNAJAvg9l0MQSapSpYoeeughPfTQQ+6qBwCuKgajAgDgPwhBABQqBJGk7Oxs/fbbbzpy5IjCw8PVokULpaam0gUCoEjgiFwAAPwHM0EAuByC5OTk6OOPP9bMmTN19uxZSVKXLl3UokULjRgxQufPn9e7776ra665xm3FAoC7eXYwKp0gAAD4im3btunDDz7RsEdDFBsb6+1yAHiJS0fk5uTk6MEHH9SECRN04cIFNW7cWObfPvE8f/68du3apXvuuUcnT550W7EA4G4MRgUAoPgzTVPTpk3T8ROnNG3aNLt/uwDwLy6FIDNnztS6devUrl07rVixQnPnzrW7P2vWLHXr1k0nT57U559/7pZCAcATTA8eY+vJeSMAAMB5SUlJSk5OliQlJycrKSnJyxUB8BaXQpAFCxaofPnyev/991W5cuU890NDQ/Xaa6+pcuXKWr16daGLBABPoRMEAIDizTRNzZgxQwEBl/7pExAQoBkzZtANAvgpl0KQAwcOqGXLlipdunS+a4KCgtS0aVOlpqa6XBwAeBpH5AIAULylpKQoOTnZ9jPfarUqOTlZKSkpXq4MgDe4FIKUKFFCZ86cueK606dPq0SJQh9AAwAeY/Xkdhg6QQAA8Lro6GjVqVPHrhOkTp06io6O9nJlALzBpRCkQYMG2rlz52XT00OHDmnnzp2qX7++y8UBgKexHQYAgOLNMAz17dvXrhOkb9++MgzDy5UB8AaXQpDevXvrwoULGjp0qHbt2pXn/u+//65hw4YpKytLPXr0KHSRAOAppiePyGWvMQAAPiEuLk516tSRJNWpU0dxcXFergiAt7i0VyUhIUHr16/XvHnz1L17d4WFhckwDK1Zs0Y33nijjh07JtM0dfvtt+vf//63u2sGALehEwQAgOLPMAz1799f494Zrf79+9MFAvgxlwd2vP7664qNjdXnn3+u/fv3S5LS0tIkSVFRURowYID69+/vnioBwEM8OROEwagAAPiO2NhY9eoSr9jYWG+XAsCLCjW1tEePHurRo4dOnDihI0eOyGq1KiIigiFDAIoMT3Zr0AkCAIBvyc7K9HYJALzMLUe3REREKCIiwuG9LVu2qGXLlu54GQBwO6snt8MwEwQAAJ9htVpltVpktVptJ8UA8D8F/tt/8OBB7dixQ2fPnr3surS0ND333HPq16+fy8UBgKdxRC4AAP4hKXGr5i9JUlLiVm+XAsCLnO4E2bx5s1555RXt27dPkhQYGKh+/frpmWeeyZOkzp07V2PHjlV6erp7qwUAN/PoYFRmggAA4BNM09T0GTOVfvaCps+YqeYtWjIcFfBTToUgu3fv1qBBg5STk6PAwECFhYXp9OnTmjp1qiRpxIgRkqRDhw7p2WefVWJiokzTVMWKFTV8+HCPFQ8AheXJbg2LB7tMAACA85KSkmwf5u7bt09JSUlq3ry5l6sC4A1ObYf59NNPlZOTo9tvv12bNm3Sxo0bNWvWLFWqVEnTp09XWlqaNm7cqP/85z9KTEyUJN111136/vvv1a1bN0/WDwCFwmBUAACKN9M0NWPGDFvnh2EYmjFjhkxmdwF+yakQZPv27Spfvrxee+01lSlTRpLUvHlzDR8+XBaLRbNnz9bQoUN19uxZ1axZU7NmzdKrr76qcuXKebR4ACgsTw4vZTsMAADel5KSouTkZFvoYZqmkpOTlZKS4uXKAHiDUyHIyZMn1aBBA5UqVcruetu2bWWapsaPH6+MjAwNHDhQCxYsUFxcnEeKBQB382wnCJ8wAQDgbdHR0YqKirK7FhUVpejoaC9VBMCbnJoJkpWVpfDw8DzXK1SoIEmyWCwaOXKk7rnnHvdWBwAexmBUAAAAwH84fUSuo+nJgYGBkqRatWoRgAAokjw7GJUQBAAAb0tJSVFqaqrdtdTUVLbDAH7K6RDkcurVq+eOpwGAq86TM0EYuAYAgPdFR0erTp06dtfq1KnDdhjAT7klBMntCAGAooZOEAAAijfDMNS3b1+7a3379nXY6Q6g+HNLCAIARZUn53ZwRC4AAL4hLi5O1atd6vyoXi2agxwAP+bUYFRJ2rZtm5577rkC3zMMQ2+++aZr1QGAh3myW4PBqAAA+AbDMJTQ+SbN/vK/Suh8E10ggB9zOgQ5ePCgDh48WOB7hCAAfJknj7FlOwwAAL7DarHY/V8A/smpEOSRRx7xdB0A4BWe7NZgMCoAAL7BNE1998MqpZ+9oO9+WKU7uvWmGwTwU4QgAPwag1EBACj+kpKSdDj1qCTpcOpRJSUlqXnz5l6uCoA3MBgVgF/z5BG5DEYFAMD7TNPU9OnT7a5Nnz6djk3ATxGCAPBrDEYFAKB4S0lJ0d69e+2u7d27VykpKV6qCIA3EYIAAAAAKLaioqIUEhJidy0kJERRUVFeqgiANxGCAPBznmuFpcsWAADvS01NVWZmpt21zMxMpaameqkiAN5ECAIAAACg2IqOjlbt2rXtrtWuXVvR0dFeqgiANxGCAPBrnu3WoBUEAABvMwxD8fHxdtfi4+M5IhfwU4QgAPwc22EAACjOTNPUunXr7K6tW7eO02EAP1XClQc5s3/OMAyVKFFCZcqUUcmSJV15GQAAAAAolJSUFP3xxx921/744w+lpKQoJibGS1UB8BaXQpAOHToUqH2sUqVKuvXWW/Xkk0+qTJkyrrwkAHiEJz8E4vMlAAC8r2rVqjIMw67zwzAMVa1a1YtVAfAWl7bDXHfddapevbpM05RpmgoPD1ezZs3UtGlTVa5c2XY9NDRUZcuW1alTpzRr1iz16dMnz2RmAAAAAPCUpKSkPFtfTNNUUlKSlyoC4E0uhSCvvvqqTp8+rVq1amn27Nlas2aNvvzyS82ZM0c//fST/vvf/6phw4YqU6aMFixYoI0bN6pbt25KTk7WF1984e73AACF4Ml+DXpBAADwtubNmyskJMTuWkhIiJo3b+6ligB4k0shyDvvvKOcnBxNmTJFcXFxee43atRIn332mS5evKixY8eqXLlyeuONN1SpUiV9//33hS4aAAAAAJwREBCgHj162F3r0aOHAgI4IwLwRy79zd+4caPatGmjypUr57umYsWKatOmjdasWSNJCgwMVOPGjXXo0CHXKgUAD/DoTBAaQQAAAACf4lIIYpqmLl68eMV1GRkZys7Otn0dHBzMUVQAAAAArhqr1ap58+bZXZs3b56sVquXKgLgTS6FIPXq1dPPP/+snTt35rtm165d+vnnn1WvXj3btT179qhKlSquvCQAeIRnY1lCXwAAvC0xMTHP4QyZmZlKTEz0UkUAvMmlEOS+++5TTk6O7r33Xk2ePFkHDhxQVlaWLl68qP3792vKlCkaNGiQLBaL+vfvL6vVqrFjx+rPP//U9ddf7+73AACF4LmggsY3AAC8j8GoAP6uhCsPuummm/Tss8/qnXfe0dtvv6233347zxrDMPToo48qISFBhw4d0qRJk1SuXDkNGDCg0EUDAAAAgDMCAgJ01113afr06bZrd911F4NRAT/l8t/8gQMHatGiRbrnnntUq1YtlSxZUkFBQapWrZp69Oih+fPn66GHHrKtHzJkiObPn892GAA+xbPdGrSCAADgC3r06KESJQIlSSVKBOY5LQaA/3CpEyRXzZo19eKLL15xXbVq1fT4448X5qUAAAAAwCUBAQFq36apVm/YpvZtmtIFAvgx/vYD8HMenAnisWcGAAAFVTUyXGGlQ1U1MtzbpQDwIpc7QVJSUjRz5kwlJyfr4sWL+R4xZRiGZsyY4XKBAAAAAFAYpmlqyapNSj97QUtWbVLvgaYMw/B2WQC8wKUQZO/everdu7fOnTsn8wob6vnmAsCXeXImyJW+PwIAgKsjMTFRf6WdkST9lXZGiYmJatGihZerAuANLoUgH3zwgc6ePavmzZvrrrvuUmRkpAIDA91dGwAAAAAUimmamjhxot21iRMnasKECXxgC/ghl0KQTZs2qWrVqpo6daqCg4PdXRMAXEV0awAAUJwdPnxYqampdtdSU1N1+PBhVatWzUtVAfAWlwajZmZmqkmTJgQgAIo8T0YgbIcBAAAAfItLIUitWrV08OBBd9cCAAAAAG4VHR2toKAgu2tBQUGKjo72UkUAvMmlEKRfv37avXu3vv32W3fXAwBXFd0aAAAUb6mpqcrOzra7lp2dnWeLDAD/4NJMkPr16+uWW27R8OHDtWTJEjVr1kxly5bNd7BQjx49ClUkAAAAALgiOjpatWvX1t69e23XateuTScI4KdcCkG6desmwzBkmqaWLl2qZcuWXXY9IQgAf0STCQAA3mcYhvr166eXX37Zdq1fv36cDAP4qUKFIAAAAADg6+Li4hQTFanDqccUExWpuLg4b5cEwEtcCkFGjx7t7joAwCs8ORPE5PhdAAB8gmEY6nzr9Zr738XqfMv1fKAL+DGXBqMCAAAAQFFi/P9/+hCAAP7NqU6QefPmSZI6d+6sMmXK2L52FjNBAAAAAHiLaZr6ftlPSj97Qd8vW60u/+lLGAL4KadCkBdffFGGYahFixYqU6aM7WtnEYIA8FWeHF7K8bsAAPiGpKQkHU45Kkk6nHJUSUlJat68uZerAuANToUguYNQw8LC7L4GAAAAAF9mmqZmzJhhO93SMAzNmDFDcXFx/JsG8ENOhSD/HITKYFQAxQXDSwEAKN5SUlKUnJxs+9o0TSUnJyslJUUxMTFerAyANzAYFQAAAECxFR0drdq1a9tdq127tqKjo71UEQBvcqoTZMOGDYV6kfj4+EI9HgCKImaCAADgfYZhKD4+Xnv37rVdi4+PZysM4KecCkEGDRpUqG8Sv/32m8uPBQAAAABXmaaZ50PdDRs2qGfPngQhgB9yKgRp1apVnmspKSlKTU1VUFCQmjdvrpiYGAUGBur48ePasmWLzp07p0aNGqlBgwZuLxoA3IVuDQAAireUlBS7LhBJ2rt3LzNBAD/lVAgyffp0u69///139e7dW9dff71Gjx6tihUr2t0/f/68Ro4cqRUrVuiFF15wX7UAAAAAUADR0dGqU6eO9u7dazsdhpkggP9yaTDqO++8o1KlSumDDz7IE4BIUunSpTVmzBhVrFhR7777bqGLBICiiJNnAADwPsMw1LdvX1v3p2ma6tu3L1thAD/lUgiydetWtWjRQqGhofmuCQoKUtOmTfXLL7+4XBwAAAAAFFZcXJwqVSgnSapUoZzi4uK8XBEAb3EpBAkNDdWxY8euuO7gwYMqU6aMKy8BAFeFJ2eCMG4EAADfkZWdY/d/Afgnl0KQuLg4bd++XYsXL853zRdffKHffvtNbdu2dbk4ACjaSEEAAPAFiYmJOnvuvCTp7LnzSkxM9HJFALzFqcGo/zR06FCtWbNGzzzzjJYsWaLrr79ekZGRki5NX16+fLnWr1+vcuXKaejQoW4tGAAAAACcZZqmJk6caHdt4sSJmjBhAnNBAD/kUgjSqFEjffjhh3rhhRe0bNkyLV++3O6+aZq65ppr9Pbbb+uaa65xS6EA4AmeHF7KdhgAALzv8OHDSk1NtbuWmpqqw4cPq1q1al6qCoC3uBSCSNINN9ygpUuXavny5dq8ebOOHz8uwzAUGRmptm3b6qabblJwcLA7awUAAAAAAHCZyyGIJJUqVUpdu3ZV165d3VUPAFxVnu3WoBUEAABvi4mJUVRUlF03SFRUlGJiYrxYFQBvcWkwqiNWqzXfPwAAAADgDYZhaPDgwXbXBg8ezDwQwE+53AmydOlSTZw4UcnJycrKysp3nWEY2rVrl6svAwAexkwQAACKu7i4OAUGGLJYTQUGGIqLi/N2SQC8xKUQZNWqVXrsscdkOvEbvjNrAAAAAMBTtm3bJov10r9LLFZT27ZtU/Pmzb1cFQBvcCkE+eyzz2SapgYNGqT+/furcuXKCgwMdHdtAOBx5LQAABRvpmlq+vTpdtemTZumuLg4tsQAfsilmSC7du1SgwYNNGLECFWtWpUABEAR5sHtMAxGBQDA61JSUrR37167a/v27VNKSoqXKgLgTS6FICVKlGCaMgAAAACfFxUVpaCgILtrQUFBioqK8lJFALzJpRCkWbNm+vXXX2WxWNxdDwBcVZ7cDsNWGwAAvC8lJUXZ2dl217Kzs+kEAfyUSyHIo48+quPHj2vMmDEEIQAAAAAAoEhwaTDqtm3b1L59e02fPl3ffvutGjdurLJlyzocLGQYhsaMGVPoQgGg6KEVBAAAb4uJiVFkZKSOHTtmuxYZGcn2fsBPuRSCvPnmmzIMQ6Zp6tSpU/rpp5/yXUsIAgAAAMBbDMPQv9rFa978hbZr/2oXz8kwgJ9yKQQZNWqUu+sAAK/w5AkuzAQBAMD7TNPU6rXr7a6tXrte/QfeSxAC+CGXQpA777zT3XUAAAAAgNsdPnxYx48ft7t2/PhxHT58WNWqVfNSVQC8xaXBqAXB4FQAvsyz3Rq0ggAA4G1mPj/s87sOoHhzqRNEkjIyMrRy5UodPnxYWVlZdt9ErFarsrKydOLECa1bt04bNmxwS7EA4H4e3A7jsWcGAADOym/LC1thAP/kUghy7Ngx9e7dW0eOHLG7bpqm3TeTf34NAAAAAFdTdHS0AgMD7TrUAwMDFR0d7cWqAHiLS9thJkyYoNTUVFWtWlX9+/dX69atZRiGHnzwQfXt21e1a9eWaZqqU6cOXSAAfJpHO2FpswUAwOtSUlLybNG3WCxKSUnxUkUAvMmlTpC1a9eqZMmSmjt3ripVqqSffvpJP//8s66//nq1aNFCVqtVL774ohYsWKCNGzeqc+fO7q4bAAAAAACgQFzqBDl+/LhiY2NVqVIlSVLDhg1lmqZ27Nhx6UkDAvTSSy+pTJkymjt3rvuqBQC3YyYIAADFWUxMjCIrR9hdi6wcoZiYGC9VBMCbXApBAgICVK5cOdvXERERKlWqlJKTk23XQkNDFRcXp/379xe+SgAAAABwgWEYatf2OrtrbePbMLsQ8FMuhSCVK1fOs4euevXq2rNnj9214OBgnTp1yvXqAMDDPDsShF4QAAC8zTRNJW3bYXdt2/ad/JwG/JRLIUibNm30yy+/aPny5bZrDRo00O7du3XgwAFJ0sWLF7Vt2zZFRETk8ywAAAAA4FkpKSnaf+BPu2v7D/zJYFTAT7kUgtx7770KCQnRsGHDNHLkSElSz549lZOTo3vvvVejR4/WXXfdpVOnTik+Pt6tBQOAW/EpEAAAxVp0dLSurXGNcne/GIZ0bY1rOCIX8FMuhSA1atTQZ599ptq1a9uOm2revLl69+6t1NRUTZ06VXv27FFkZKSGDRvm1oIBwJ04IRcAgOLNMAzd2a2L7eeyaUp3duvCTBDAT7l0RK4ktWzZUt98840yMjJs115++WUlJCQoKSlJFStWVOfOnVWmTBm3FAoAAAAArmjYoN5lvwbgP1wOQXKVKlXK7utWrVqpVatWhX1aALgqPDsUjVYQAAB8wXffL83z9aD7BnupGgDe5NJ2GGd17txZbdq08eRLAAAAAEC+rFarFn+3xO7a4u+WyGq1eqkiAN7k0RDk3LlzOnPmjCdfAgB8Fn0gAAB4X2JiorKysuyuZWVlKTEx0UsVAfAmj4YgAAAAAOBNzZs3V3BwsN214OBgNW/e3EsVAfAmQhAAfs2TI0E8O28EAAA4IyAgQAmdO9pdS+jcUQEB/FMI8Ef8zQcAAABQrHXscP1lvwbgPwhBAPg5ujUAACjuZsyae9mvAfgPQhAAfs2jB+SyHQYAAK+zWCza+PNWu2sbf94qi8XipYoAeFMJZxalpqa69OR8YwEAAADgTcuWLcv3eufOna9yNQC8zakQ5Oabb/Z0HQDgFXRrAABQvHXs2FEff/yxw+sA/I9T22FM03T5DwAAAAB4y9GjRwt0HUDx5lQnyO7duz1dBwAUOwTBAAB4X3R0tKKjIpWSeux/16IiFR0d7cWqAHgLg1EBAAAAFFuGYeiWG+Ptrt1yY7wMw/BSRQC8iRAEgF+jWwMAgOLv2muiVKlCaUlSpQqlde01UV6uCIC3EIIAAAAAKNYslmxdG11JAQGGro2uJIsl29slAfASQhAA8BBTdJkAAOALsrMy9fuB47JaTf1+4LiyszK9XRIALyEEAeDXPLkdhp02AAD4hj17D+jsuYuSpLPnLmrP3gPeLQiA1xCCAAAAACi2TNPU8tVb7K4tX72FuWCAnyIEAQAAAFBsHT58WOlnMuyupZ/J0OHDh71UEQBvKnYhiMViUZ8+fVSvXr0891JTUzV8+HC1b99esbGxuuuuu7RixQqHz5OcnKyhQ4cqPj5ecXFxGjBggLZu3erp8gEAAAC4UX4dH3SCAP6phDOLhg8f7vILGIahMWPGuPz4gpowYYLDsOLEiRPq27evTp8+rX79+ikyMlLz5s3T0KFD9c4776hLly62tfv27VOfPn0UEhKifv36qXTp0po1a5YGDBigyZMnq3Xr1lft/QDwLE8OL+WXKwAAvM8wjAJdB1C8ORWCLFq0yOUXuJohyI4dOzR+/HgFBwcrKyvL7t5HH32k1NRUzZo1S82bN5ckde/eXXfddZfeeOMN3XzzzSpVqpQkadSoUcrKytL8+fNVrVo1SVLXrl3VtWtXvfLKK1q8eDHfNAEAAIAioGrVqjIku489jP9/HYD/cSoEGTVqlKfrKLTz58/r6aef1r/+9S+dP39eP//8s+2exWLRokWLFBsbawtAJCkkJET9+/fX888/rx9//FEJCQk6efKk1qxZozvuuMMWgEhShQoV1LNnT3388cfasWOHmjVrdlXfHwDPoFkDAIDi7eDBA3n6Ps3/f71mzdreKAmAFzkVgtx5552erqPQ3njjDZ09e1avv/66nnjiCbt7ycnJysjIUGxsbJ7H5YYZ27dvV0JCgrZv3y5JV1xLCAIAAAD4vpx/dIhf6TqA4s2pEKQw/vrrL1WsWNGjr7F06VL997//1ccff6zw8PA8948dOybJcctblSpVJMk2Hfro0aP5ro2MjLRbmx+LxSKLxVKAdwDAWzw5t8M0Tb4XAADgZVlZF/O9zs9poPhw9u+zyyHIsWPHtHDhQh0+fFhZWVl2/5CwWq3KysrSiRMntHPnTv3yyy+uvoxTdYwcOVI9evRQx44dHa45e/asJNlmfvxdaGioJOnChQuSpHPnzkmSSpcunWdtyZIl7dbmZ8+ePU5WD8DbLl50/IuRu55727ZtHnt+AABwZYf+3Ovw+t69ycrMZl8s4G9cCkH279+vXr166ezZs7bwwzAMu/8tXfoUtGzZsm4qNS/TNDVixAiFhYXp+eefv+y6K90LCAiw+9rRY/65Nj9169Z1GLgA8D3f/75dunDeI88dEhricGsdAAC4ek4eT3F4vWTJkvycBoqRjIwMpxoSXApBPv30U505c0ZNmjRRly5dlJiYqKVLl+qll15SZmam1q5dq7Vr16pOnTpauHChKy/hlClTpmjjxo36+OOPlZmZqczMTElSdna2pEtbcQIDA21dHY46OHI/BQ4LC5OkAq3NT2BgoAIDA115SwCKGb4XAADgXRUrlMv3Oj+ngeLD2b/PLoUgmzZtUrly5TRlyhSVKVNGderU0ZIlS1SjRg3Fx8dr4MCBeu+99/Tpp59q/vz56tmzpysvc0WrVq2SaZoaOnSow/vx8fGKjo7WhAkTJP1v3sff5V7LnQ0SExNzxbUcpwUAAAAUDeGVyhfoOoDizaUQ5OTJk2rTpo3KlCkjSWrQoIFM09TOnTsVHx8vSXrkkUc0d+5cff311x4LQUaMGKEzZ87kuT569Gj9/vvvmjJlikJCQlSzZk2FhYVpx44dedbmngaTe3RukyZNFBAQoB07duiee+5xuDYuLs7dbwWAl5h5Ds1z43OzzRgAAK87ciTvh5u516+tw4mPgL9xKQQJCgqym3lRvnx5lS1bVsnJyf974hIl1LRpU/3222+FrzIfjRs3dni9XLlLLW9t27a1XUtISNBXX32lxMREW+CRmZmpadOmKTw8XNdff70kKTw8XG3bttWSJUv0yCOPqFq1apKktLQ0zZ07V/Xr11fDhg099p4AAAAAuE/F8mGqVKG0TqX9bwZYpQqlVbH85be4AyieXApBoqOjtX//frtr1atX1++//253zTRNnT592uXi3GnYsGFauXKlHnjgAQ0aNEiVKlXSvHnztGfPHo0bN04hISG2tSNGjFCvXr3Uu3dvDRw4UMHBwZo5c6bS09P13nvvee9NAHA/j3Zr0AoCAIC3WXKyFdcgRsvX/+/fKnENYmTJyfZiVQC85fLHnOSjffv22rt3r7744gvbiSmNGzdWcnKybcvJX3/9pa1btyoyMtJ91RZCRESEZs+erX/961+aNm2a3nrrLQUFBenTTz9VQkKC3dq6detq1qxZatCggcaPH68PPvhAVatW1fTp09WyZUsvvQMAAAAABZWTk6VTpzPsrp06naGcnCwvVQTAm1zqBBk0aJAWLlyo0aNHa+vWrfrggw/Uu3dvffnll7r//vvVpk0bbdu2TefOnVOPHj3cXfMVTZ8+3eH1atWqOd3J0aBBA02aNMmNVQHwRcwEAQCgeMvKvKgdv9sfk7vj9xR1ybzopYoAeJNLnSCVK1fWzJkzdcMNNyg8PFySVK9ePT399NM6d+6cli1bphMnTqhBgwb5ntwCAAAAAJ62Z+8BWSxWu2sWi1V79h7wTkEAvMqlThBJqlmzpu3o2Vz333+/OnfurB07dqhChQpq06aNAgJcylkA4KrwbLcGrSAAAHhbtahKDq/HVK14lSsB4AtcDkHyExMTo5iYGHc/LQB4iAe3w3jsmQEAgLNOnPzL4fWTp9KuciUAfIFLIcjmzZsLtL5Vq1auvAwAAAAAFIol2/EA1PyuAyjeXApB+vXrJ8MwnF7/22+/ufIyAOBxHt0OQysIAABeV6Z0sIKDApWVbbFdCw4KVJnSwV6sCoC3uBSC5NfZYbValZ6erv3798tiseimm25SjRo1ClMfAAAAALgsOytTOTkWu2s5ORZlczoM4JdcCkHyO4I21+nTpzVy5Eht3rxZL774okuFAcDV4cmZILSCAADgTaZp6s/Dx2T9x49kqyn9mXJcpmkWqMMdQNHnkaNbypcvr7FjxyowMFDjxo3zxEsAAAAAwGVZcrJVJTzM4b0q4WGy5GRf5YoAeJvHzq8NDg5W8+bNtX79ek+9BAAUmkdHgtAIAgCAV+XkZOnIiTMO7x05cUbZDEcF/I7HQhBJOnnypC5cuODJlwAAAAAAh3Kys1SmVIjDe2VKhSgnO/MqVwTA2zwSglgsFs2cOVNJSUmqU6eOJ14CANzDo90atIIAAOBN2dlZKhdW0uG9cmEllZNDJwjgb1wajHrjjTfme89isejMmTPKysqSYRjq16+fq7UBgMd5cngpEQgAAN6Vk52l9LOOO9PTz15QTjYzQQB/41IIcvTo0SuuqVKliu6//3517drVlZcAAAAAgELJyc7UmXOOQ5Az5y6wHQbwQy6FICtWrMj3XkBAgEqVKqVy5cq5XBQAXC0eHV7KZFQAALzKYsnJ9whcwzBkseRc5YoAeJtLIUh0dLS76wAAAAAAt7LkZCuqsuMPZ6Mql+OIXMAPefR0GADwfcwEAQCguLJYci57RC6dIID/caoT5HKDUK/EMAytWrXK5ccDAAAAgCssOTmXPSLXkkMIAvgbp0IQZwahBgYGKiAgQNl/m7AcEBCQ7x48APAFnh0JQi8IAADeZLHkqHzZUioZGqQLF//375SSoUEqX7YUnSCAH3IqBPnnINSLFy/qscce0/Hjx/Xwww/r1ltvVdWqVSVJp0+f1o8//qixY8cqKipKn332mfurBgAAAIArsFovDUatUyNCO3an2q7XqREhwzBkJQQB/I5TM0Gio6Pt/syfP18HDhzQ1KlTNWDAAFsAIknly5dXt27d9MUXX+i3337T22+/7bHiAaDQ6NYAAKDYslqsMk1Tu/fad7bv3ntUpmnKarV6qTIA3uLSYNRvvvlGbdq0UcOGDfNdU7NmTcXHx2v58uUuFwcAnsZ2GAAAii+r1aK09PPKyrEPO7JyrEpLPy+rlU4QwN+4FIKcPXtWISGOBwz9nWmayszMdOUlAAAAAKBQrFaLjp866/De8VNnZbVYrnJFALzNpRCkRo0a2rhxo44dO5bvmn379mnjxo2qW7euy8UBgKfRrQEAQPFltVpUs1olh/dqVqvEdhjAD7kUgtx9993KyMhQ//79tXLlSmVlZdnuXbhwQYsXL9bAgQOVnZ2tgQMHuqtWALg6snJkZGUX6I+yaKcFAMDXWC0W/XHolMN7fxw6xXYYwA85dTrMP/Xq1Utbt27VokWL9PDDDyswMFDlypWTdOl0GKv10gCiBx98UJ06dXJrwQDgCX/vCIkcO88tz0WPCQAA3mW15KhypTCH9ypXCmM7DOCHXApBJOmtt97SzTffrNmzZ2vr1q06depSwhoSEqL4+HgNGDBA8fHxbisUAAAAAArCYrGofNlSMmT/4YQhqXzZUrJwRC7gd1wOQSSpU6dO6tSpk0zTVFpamgzDUIUKFdxVGwB4XG7XhmEYtmvHnuohBRfw22NWjq2DxPZczBsBAMCrrNYcnT2fmac705R09nymrFY6QQB/U6gQJJdhGKpYsaI7ngoAvC+4hMzgoAI9xLjyEgAAcJVZLRaVLROqsNKhOnv+ou16WOlQlS0TSicI4IecCkGGDx8uwzD0zDPPKDw8XMOHD3f6BQzD0JgxY1wuEACKKvpAAADwrtyQIyfHPuzI/dpKCAL4HadCkEWLFskwDA0ZMkTh4eFatGiR0y9ACALAl3kyqOD4XQAAvMuSk630sxd0IdM+7LiQmaP0sxdUrmK2lyoD4C1OhSCjRo2SJEVERNh9DQAAAAC+Kjs7K98PJUzTVE521lWuCIC3ORWC3HnnnZf9GgCKLLo1AAAotnKyMwlBANgJcOVBjz32mFatWiUL52oDAAAA8FE52Vk6f8Fx0HH+QpayszOvckUAvM2l02GWLFmipUuXqnz58rr99tvVtWtXNW3a1N21AYDHeXQmCKNRAQDwGoslR1arRVUjyjq8XzWirEyrVRZLjgID3XJoJoAiwKVOkHfffVcdOnTQ+fPnNWPGDPXq1UsJCQn69NNPlZqa6u4aAQAAAKBAsrMudXkcOXHG4f3c67nrAPgHlyLP2267TbfddpvOnTunH374Qd9++61+/vlnvfvuu3r//ffVokULdevWTZ06dVKZMmXcXTMAuI1HT3ChEQQAAK/JyrwgSSpdMtjh/dzrWZkXFFqy9FWrC4B3Farvq0yZMurRo4d69OihU6dO6dtvv9V3332nLVu2aMuWLXrttdfUoUMHjRs3zl31AgAAAMAVZV7MkCQFBDhufs+9nrsOgH9waTuMI5UqVVL//v315Zdf6v3331dkZKQuXryo77//3l0vAQBFCjNBAADwnqz/H26UKeW4EyT3ehYhCOBX3DYBKCkp6f+1d+fhUdX3Hsc/k31lCSD7DglKWAz7Im6gFotSRbQq0ta2ru1jvVXL1dv2XmvVW/VaXEptRcUVVFQQXMCAhH2HACEsgSQkJCRk3ybJzLl/xIyMmUAImTmZnPfreXhMzjlz5jtROZPPfH/foxUrVujrr79WXl6eDMNQz549dcMNN7TUUwBAi/NqUEEGAgCAaeo7PI5m5HvcfzQjX3EDutEJAljMBYUgBw4c0IoVK/TFF1/o5MmTMgxD0dHRuvnmm3XjjTdqzJgxLVUnAAAAADSZ3V4Xbgzq20Ubd6Y12D+obxe34wBYQ7NCkPnz52vlypVKT0+XYRgKCgrSlClTdOONN2rq1KkKCfHccgYArY5XG0FoBQEAwCz2ynJJks1m87i/fnv9cQCsoVkhyKuvvipJuuSSS3TDDTdoxowZ6tSpU4sWBgAAAADNVVlRJknKyi3yuD8rt0i9u8e4jgNgDc0KQe6++2795Cc/0aBBg1q6HgDwKW92a3jz7rsAAODsqipLJUlREaEe99dvrz8OgDU0KwR55JFHWroOAAAAAGgxVd8tc2kXFeZxf/32KpbDAJbSpBDk73//u2w2m+666y516NBBf//735v8BDabTb/97W+bXSAAeBXdGgAAtDmGYaiqCcth+vTopKqKMhmG0ejsEABtS5NCkH/84x+y2WyaMWOGOnTo4PreOEuvd/1+QhAAAAAAvmSvqpBhOJt0rGE4Za+qUFh4pJerAtAaNCkEeeCBB2Sz2dSxY0e37wHA33l1JghtJgAAmKKyosT1da9uHT0ec+b2yooSQhDAIpoUgvzmN7856/cAAAAA0FpUln8/7PRct8itP75jp+5erwuA+QLMLgAAzOTVO7jQCAIAgCkqK74PQU4Xer77y5nbzzweQNvWpE6Qjz766IKeZNasWRf0eADwRyyHAQDAHJXl3y+H2ZOS6fGYPSmZunrS0AbHA2jbmhSCPPHEE82aAVI/GJUQBAAAAICvnNnZcVGnaGWcLG5wzEWdor8/nhAEsIwmhSAzZ85sEILs2rVLx48fV+fOnTVlyhT16tVLQUFBOnXqlJKSkpSenq6RI0fq8ssv90rhANASvNqtQSMIAACmqCj7PvRoFx3h8Zgzt1cQggCW0aQQ5JlnnnH7fuvWrVq+fLluvvlm/elPf1JISIjbfqfTqb/97W9666239Otf/7rlqgUAAACAczgz1GjK3WEIQQDraNZg1Pnz56tbt276n//5nwYBiCQFBATo0UcfVd++ffXqq69ecJEA4DVebQShFQQAAF9zOhxuy2HKKqo9Hnfm9sqKUjkdDq/XBsB8zQpB9u/fr/j4eAUGBjZ6jM1mU1xcnI4cOdLs4gAAAADgfFRWlLjd/i06MlQ/nG5o+267i2HUPQ5Am9esECQqKkrHjh076zGGYejgwYPq2NFz+xkAtAbe7NagDwQAAN8rL3MfglpUUtHgmmx8t/1sjwPQNjUrBJkwYYIOHTqk1157zeN+wzD07LPPKj09XVddddUFFQgAAAAATVXxgzDj1OlSj8f9cHt5aZG3SgLQijRpMOoPPfDAA1q7dq3+7//+T19++aUuu+wydevWTZKUlZWlxMREHTt2TN26ddN9993XogUDQIvyZruGQS8IAAC+VlZS6PZ9/14x2rSrYRd7/14xbt+XlxY2OAZA29OsEKRv375auHChHn/8cR04cEAHDhxw3ULX+O5N/+jRo/X000+rc+fOLVctALQwlsMAANC2lJUWuH2fcjTX43EpR3M18pI+3z+uhBAEsIJmhSCSFB8fr08//VQ7d+7Utm3bdOrUKdlsNnXt2lUTJ05UfHx8S9YJAAAAAOdU/oMwIz62h3YdONHguPjYHu6PoxMEsIRmhyBS3R1gRo0apVGjRrVUPQDgU17t1mA5DAAAPmUYhsp+EGYEBHgeg/jD7WWlhTIMw9XhDqBtuqAQRJJOnz6tyspK1zIYT3r37n2hTwMAAAAAZ1VVWSZHbY3btqzcIo/HZuUWqXf37+eCOGprVFVRpvDIaG+WCMBkzQ5B3nzzTf3rX/9SQUHBWY+z2Ww6cOBAc58GALzLi90a9IEAAOBbZSUNfzeJCAv2eKyn7WUlBYQgQBvXrBBk6dKleuaZZyRJISEh6tixowIDA1u0MAAAAAA4H55CkPLKao/HlldWq1NH922lJQXq0r2vN0oD0Eo0KwR55513ZLPZ9MQTT2j27NkKDvacrgJAa0e3BgAAbUdpccMQJDoyzOOxnraXlZxu8ZoAtC7NCkGOHj2qkSNH6o477mjpegAAAACgWTx1gkSGe/7A1tN2T48H0LZ4HpV8DuHh4erYseO5DwSA1s7Ld3A529BoAADQsjyFGNuT0z0e62k7IQjQ9jUrBBk7dqz27t2rysrKlq4HAHzK2xEFEQgAAL7hdDpVXlrUYPtFnaI8Hu9pe3lpkZxOZ0uXBqAVaVYI8rvf/U6VlZX6z//8TxUXF7d0TQAAAABwXiorSmQYDQOMohK7x+M9bTcMQ5UVJS1eG4DWo1kzQT744AMNHz5cX375pVatWqV+/fqpXbt2stlsDY612Wx65513LrhQAPBLhiF5+LsRAAC0rIpSzx/OhoR4/ty3se0VpcWKjOrQUmUBaGWaFYK89dZbrq9ra2t15MiRRo/1FIwAAAAAQEsqLyvyuD0oMPC8tpeXFamLuE0u0FY1KwRZtGhRS9cBAD7ni6GlzAQBAMA3GgtBLuoUfV7bGzsPgLahWSHI2LFjW7oOAAAAAGi2ijLPy2HKKjzPBCmrsKtzTNPPA6BtaNZgVABoC3zSpcEtcgEA8ImqijKP2yuqas5re2PnAdA2NKkTZNu2bRf0JGPGjLmgxwMAAADA2VRVlnvc3rWRZS+NbW/sPADahiaFIHPmzGn2gFObzaYDBw4067EA4FXMBAEAoM2wV3nu4GhsBlhj2xs7D4C2oUkhSI8ePbxdBwD4nC8CCoMYBAAAr3M6HKq2V3ncl5Fd0Oj2zjHtGmyvtlfJ6XAooJG7xwDwb00KQRITE71dBwAAAAA0S1VV40tYenXroD0Hsz1uP9v5IiIbBiQA/B+DUQFYly+GltIIAgCA19nPEoLkF1Wc13ZJsjMXBGizCEEAAAAA+LXqqspG94WFeF7W0th2Saq2N34+AP6NEASAZTETBACAtsFe1XhXR6W99ry2n+t8APwbIQgAAAAAv1Ztbzy0CA/xPAaxse3nOh8A/0YIAsCyfNKlQSMIAABeZz/LcpjCYs/7Gtt+rvMB8G+EIAAAAAD8mv0snRvBIZ5/5Wlsu8RyGKAtIwQBYF0+aQShFQQAAG+rLC9tdF9QoOcBqI1tl6TKisbPB8C/EYIAsCyWwwAA0DZUVpQ0uq9r53bntf1c5wPg3whBAAAAAPi1yrLGQ4vARjo+Gtt+rvMB8G+EIACsi0YQAAD8Xk21XTU19kb3R0WEnNd2SaqpsaumuvFzAvBfhCAAAAAA/FZl+dm7NlKP5Z7X9qaeF4B/IgQBYFm+mAnCYFQAALyrtKTA43bDMFRT61Bpuefb3ZaWV8owGr9ON3ZeAP4tyOwCAAAAAKC5igpyGmwzDEMr1+7TqdON3+Ul5cgpnS6s1PQr4mWz2Tyet2ffuBatFYD56AQBYF2+aNKgEQQAAK8qOn32ZS2t7bwAzEUnCAAAAAC/5akTxGazafoV8ap1OGUYht5fvk1O5/efTAQG2HTbjDEKDgr02AXS2HkB+D86QQBYlm8aQWgFAQDAWyorymSvLPe4z2azKTgoUCHBQbps9EC3fZePG6yQ4KBGAxBJsleWq7Ki8eU0APwTIQgAy/JJQEEGAgCA1xSdPtmk43p1j3F9HRwUoD49OjXpcYX5dIMAbQ0hCAAAAAC/dCr7WJOOO7PjY8rY2LN2gLid/2TTzg/AfxCCALAunzSC0AoCAIC35GSlnfdjul/UvsnH5mYdPe/zA2jdCEEAAAAA+J2ykgKVlxZ69TnKS4tUVlLg1ecA4FuEIAAsyxddGvSBAADgHbnN6AJpzc8DwDcIQQAAAAD4neYshWne87AkBmhLCEEAWJdP7pFLLwgAAC2tptquvJx0nzxX3sl01VTbffJcALyPEAQAAACAXzmZeVhOR61PnsvpdOhk5iGfPBcA7yMEAWBZzAQBAMA/ZR474OPnS/Hp8wHwHkIQAAAAAH7DXlWhU9nHfPqcp7KPyV5V4dPnBOAdhCAALMsnXRrMBAEAoEVlZxySYTh9+pyG4VR2BktigLaAEASAdfkgoCACAQCgZZ3w8VIYs58XQMsiBAEAAADgFyrKS3x2V5gfystJV0V5iSnPDaDlEIIAsCyWwwAA4F/M7sYw+/kBXDhCEAAAAACtnmEYyji6z9QaMo7uk8EHHIBfIwQBYF3MBAEAwG8UF55SSVGeqTWUFOWpuPCUqTUAuDCEIAAAAABavUyTu0DqtZY6ADQPIQgAy/JFlwadIAAAXDjD6VRmK5nHkXnsgAynb2/RC6DlEIIAAAAAaNXycjNUVVlmdhmSpKrKMuXlZphdBoBmIgQBYF2+GGzG8DQAAC5YZtp+s0tw09rqAdB0hCAALIvlMAAAtH4OR62y0lPNLsNNVnqqHI5as8sA0AyEIAAAAABarZzMI6qtsZtdhpvaGrtyMo+YXQaAZiAEAWBZPunSYDkMAAAXJCOtdd6NpbXWBeDsCEEAAAAAtEqVFWXKOdE6Oy5yThxRZUXrGNYKoOkIQQBYlw+6NOgDAQCg+TKOJstopV2VhmEo42iy2WUAOE+EIAAAAABaHcMwdPzwbrPLOKvjh3e32pAGgGeEIAAsi5kgAAC0Xvk5GSovLTK7jLMqLy1Sfk6G2WUAOA+EIAAAAABandbeBVLvmJ/UCaAOIQgA62ImCAAArVJ5aaFOHD9odhlNknX8oMpLC80uA0ATEYIAsCxfBBQGMQgAAOctZc96GYbT7DKaxDCcStmz3uwyADQRIQgAAACAVqOkKF8ZafvNLuO8ZKTtV0lRvtllAGiCNhGCpKam6re//a3Gjx+v+Ph4XXXVVXrqqadUWlrqdlx2drYeffRRTZ48WSNHjtTs2bP1zTffeDzn4cOHdf/992vChAm69NJLNXfuXO3YscMXLweAr/hiaCmNIAAAnJeUPev9b7C4YdANAvgJvw9B0tLSdNttt2njxo269dZb9cQTT2js2LF65513dPvtt6uiokKSlJeXpzvvvFOrV6/WzTffrEcffVS1tbW6//77tXz5crdzHj16VLfffrv27t2rOXPm6KGHHlJOTo7mzp2rrVu3mvEyAQAAgDavqCBXWcdTzC6jWbKOp6ioINfsMgCcQ5DZBVyop556SjU1NVq8eLFiY2MlSbfddpsuueQSPfXUU3rvvff0y1/+Ui+//LKys7P13nvvKSEhQZJ08803a/bs2Xrqqad09dVXKyIiQpL09NNPq7q6WkuXLlXv3r0lSTfccINuuOEG/fd//7c+//xz2Ww2c14wgBbDTBAAAFoPwzC0b3ui2WVckH3bEzVp2m38rgC0Yn7dCVJdXa3t27dr1KhRrgCk3syZMyVJ27Ztk8Ph0LJlyzRy5EhXACJJoaGhuuuuu1RYWKi1a9dKkvLz85WUlKSpU6e6AhBJ6tixo2655RYdOXJEe/fu9fprAwAAAKwkLXWnTp08bnYZF+TUyeNKS91pdhkAzsKvQ5CgoCB9/vnnevLJJxvsy8+vG0wUEBCgw4cPq6KiQiNHjmxw3IgRIyRJe/bscftnU44F4N980qVBIwgAAOdUWnxayX7eBVIveXuiSotPm10GgEb49XKYgIAAt26NMy1cuFCSNG7cOOXm1q3N6969e4PjunXrJkk6ceKEJCknJ6fRY7t27ep2bGMcDoccDkdTXgIAEzkd3r/1nsPJ3wcAAJyN0+nQ1nWfyemoNbuUFuF01Grrus805bo7FRAQaHY5gGU09T23X4cgjfn000/14Ycfqnv37rrlllu0Zs0aSXLN/DhTWFiYJKmyslKSVFZWJkmKjIxscGx4eLjbsY05dOhQ84sH4DOlVWf/f7klHDiQovbhDf/uAQAAdYpyj6i4jQ0ULS7I1bpVn6hD10FmlwLgB9pcCPLJJ5/o8ccfV0REhObPn6/IyEgZZ7nFVv2+gIAAt+89PeaHxzYmNjbWY+ACoHXJLy6Sdm/y6nNcfPHFuqhDR68+BwAA/qogL0vp+1eZXYZXFOcf0/CECYrp0tPsUgBLqKioaFJDQpsKQV555RXNnz9f0dHRWrBggYYPHy7p+64OTx0cVVVVkqTo6OjzPrYxgYGBCgyk9Q1o7QICvT8WKSAwgL8PAADwoKKsWFvXfSqd5QNLv2YY2rruU105fa4iotqbXQ3Q5jX1PbdfD0atV1NTo3nz5mn+/Pnq2rWr3nnnHY0ePdq1v1evXpK+n/dxpvpt9bNBmnKsp3khAPyQb+6RCwAAfqCm2q6NiR/KXlludileZa8s18bED1VTbTe7FADf8fsQxOFw6D/+4z+0dOlSxcXF6cMPP9SQIUPcjhkwYICio6M93tq2/k4v9bfOHTZsmAICAs567KWXXtrSLwMAAACwBKfTqa3rPlVJYZ7ZpfhESWGetq77VE6n9weyAzg3vw9B/v73v+urr77S8OHD9e6777ru4HKmoKAgTZ8+Xdu3b9fOnd/ft9tut2vRokXq3LmzpkyZIknq3LmzJk6cqK+++kqZmZmuYwsLC10ByyWXXOL9FwbA63zTCEIrCAAA9QzD0N6tq5SblWZ2KT6Vm5WmvVtXnXVWIQDf8OuZINnZ2Xr99ddls9k0bdo0JSY2vLd4586dNWnSJP3mN79RYmKifvWrX+nnP/+5OnXqpI8++kiHDh3SCy+8oNDQUNdjHnvsMd1666366U9/qp/97GcKCQnRu+++q+LiYr344os+fIUAAABA23E0ZZvSUnee+8A2KC11p6LaddSgS8aaXQpgaX4dgmzdulW1tXX3E3/++ec9HjN27FhNmjRJXbp00fvvv6/nn39eixYtUk1NjeLi4vTPf/5Tl19+udtjYmNj9d577+mFF17Qq6++qoCAAMXHx+vZZ5/VyJEjvf2yAPiIT7o0+MAHAABJ0onjKdq77RuzyzDV3m3fKCwiWr36XWx2KYBl+XUIMnPmTM2cObPJx/fu3bvJnRwXX3yx/vWvfzWvMAAAAAAuJzMPa9u6ZWaX0SpsW7dMgYFB6t57sNmlAJbk9zNBAKDZfNIIQisIAMDacrPStGXtJzIMBoNKkmE4tWXtJ5abiwK0FoQgACyL5TAAAHhXXk6GNq/5WE6nw+xSWhWn06HNaz5WXk6G2aUAlkMIAgAAAKDFnT51Qhu/WSKHo9bsUlolh6NWG79ZotOnTphdCmAphCAArIvlMAAAeEXh6ZPauHqJHLU1ZpfSqjlqa7Rx9RIV5p80uxTAMghBAAAAALSY/JwMJX31vmpq7GaX4hdqauxK+vp95bM0BvAJQhAAluWLLg36QAAAVnLyxBGtX71YtQQg56W2xq71qxfr5IkjZpcCtHmEIAAAAAAuWGbafm1O/FhOZoA0i9NRq82JHyszbb/ZpQBtWpDZBQCAaXzRpmHQCwIAaPuOHtyhPVu+NrsMv2cYTm1LWqbq6ioNHDLK7HKANokQBAAAAECzGIah1L0bdWD3OrNLaVP2bPlaNfYqxQ2fKJvNZnY5QJtCCALAspgJAgBA8zmdDu3dulppqTvNLqVNOrB7naoqyzR87FQFBASaXQ7QZhCCALAsnwQULIcBALRB9qoKbf32U+XlpJtdSpuWlrpTpcWnNfbymQoNizC7HKBNYDAqAAAAgCYrKczT2hVvEYD4SF5OutaueEslhXlmlwK0CYQgAKzLB10a9IEAANqSk5mHtfaLRSovKzK7FEspLyvS2i8W6WTmYbNLAfweIQgAAACAszIMQ6nJm7Qp8SPV1lSbXY4l1dZUa1PiR0pN3iSD5bZAszETBIBl8fYBAIBzq62p1q5NXyjz2AGzS4Gk/TvXqqTwlC6d8CMFBYeYXQ7gdwhBAAAAAHhUXHhKW7/9VKXFp80uBWfIPHZARQW5Gnv5TLXveJHZ5QB+heUwAKzLFzNBaFcFAPghwzCUlrpLa1a8RQDSSpUWn9aaFW8pLXUX7zeA80AnCAAAAACXmuoq7dz0pbKOp5hdCs7B6ajV7s1fKi8nXQkTrlNwSJjZJQGtHiEIAMvyxWcmfC4DAPAnBfnZ2vbtZ9z9xc9kHU9RUf5Jjbn8RsV07mF2OUCrxnIYAPAm2lMBAH7AMAwd3r9V337xNgGInyovK9K3X7ytw/u3sjwGOAs6QQAAAAALq6wo086NK5SblWZ2KbhAhtOp5O3f6NTJY0qYeL3CI6LMLglodegEAWBZvviUhM9hAACtWVZ6qr5Z9m8CkDYmNytN3yz7t7LSU80uBWh16AQBAAAALKamxq69W1cr/ches0uBl1TbK7Vl7VL1HTRcw8dOVXBwqNklAa0CIQgAy/JJlwZrcgEArczpUye0LWm5Kpj9YQnpR/YqLydDYy6boU4X9TK7HMB0LIcBAAAALMDpdGj/zm/17ZfvEIBYTEVZkb798h3t3/mtnE6H2eUApqITBIB1MRMEAGARpcWntS1pmYpO55hdCsxiGEpN3qjc7DSNuewGRbfvZHZFgCkIQQDgB2w1tT55DAAA3mYYhtIO7lDyjjVyOrhWQSo6naNvli/UsFFXasCQUbLZbGaXBPgUIQgAy2qsS+Oi5z5qweegFwQAYI7KilLt2LBCp7KPmV0KWhmno1Z7tq7SyRNHNGrS9QqPiDa7JMBnmAkCAAAAtDEnjqdo9Wf/JgDBWZ3KPqbVn/1bJ46nmF0K4DN0ggCwrjNmgtgCA9R76qRGD3U6HMpas1mS1PPK8QoIDGz0WFvgGfkyjSAAAB+qrq7Sni1fKzNtv9mlwE/UVFdp67ef6mTmYY0Yd41CQsLMLgnwKkIQAJZ1Zj5hs9lkC2o82DhTQGCgApp4LMthAAC+kncyXds3fK7K8hKzS4Efykzbr/zcTI2e9GN16d7X7HIAryEEAQAAAPyYw1GrA7vW6fD+LWaXAj9XWV6ipK/f0+Ch43TJpVMUGMivi2h7+K8aALyJRhAAgBeVFOVrW9IyFRfkml0K2pDD+7fo1MnjGnPZDWrXobPZ5QAtisGoACwrwAe3hAsI4K9ZAEDLq7v17U4lfv4GAQi8orggV4mfv6GjB3fIMPhUB20HnSAALMsXAYUvghYAgLVUVZZr58aVyjlxxOxS0MY5HbXas+Vr5WYdVcLE6xUWHml2ScAF4yNKAJZFJwgAwN/knDiqb5a9TgACn6r77+7fyjlx1OxSgAtGJwgAy/JJJwghCACgBTgctdq3PVFHD+4wuxRYlL2qQhu/WaIBQ0Zp2KgrFRgUbHZJQLMQggCwLF8EFIE2QhAAwIUpLszTtnWfqaQoz+xSAKUd3KH8nAyNmXKj2nfsYnY5wHnj3TkAy/JFQEEnCACguQzD0NGU7Vrz+RsEIGhVSorytObzN3Q0ZTtDU+F36AQBYFk+mQnCYFQAQDPUDT9dwQwGtFpOp0N7tq5STlaaRk1iaCr8ByEIAMtiJggAoDXKyTqqHetXyF5VbnYpfsMwDNU6nI3ur611ePzak6DAANn4EKPJcrPqhvWOmny9uvUcaHY5wDkRggCwLEIQAEBr4nDUav+OtTqSss3sUvxOrcOpdz7d0qRjP/h8+1n33zlznIKDAluiLMuwV5Vr4+olGnTxGA0ddYUCA/k1E60X/3UCsCzfDEblkyQAwLkVFeRqe9JyZn/Arx1J2aZTJ49r9GUz1CGmq9nlAB4RggCwLAajAgDM5nQ6dSh5k1L2rJdhNL6cA0337JwghZznbznVtdJjb9d6pyCLqRua+qYuHjFZscMm8F4IrQ4hCADL8s1gVC78AADPSotPa/v65SrMP2l2KW1KSJAUGny+13jucNKSDMOpA7vX6eSJwxo9eYai23cyuyTAhXfnACzLNzNBWA4DAHBnGIaOHNimb5YvJABBm1aYf1LfLF+oIwe2cStdtBp0ggCwLAajAgB8raKsWDs2rFBeTrrZpQA+4XTUau+21TqZeVijJl2viKj2ZpcEi+PdOQDL8sVyGF/MHQEAtH6GYejYoV1avezfBCCwpLycdK1e9m8dO7SLrhCYik4QAJZls9kUYLPJ6cULsY27wwCA5ZUU5mnX5i91+tQJs0sBTFVbU61dm75UxtF9unT8dWrXsYvZJcGCCEEAWFqALUBOw+Glc9sIQQDAwhy1NTq4d6MO7d8sw8mdX4B6p0+d0DefL1Ts0PEaMnyiAoOCzS4JFkIIAsDSAgICJKeXQhDmgQCAZZ3KPqZdm79SeWmh2aUArZLhdCo1eaNOHE/RpeOv1UU9+ptdEiyCEASApXnz7i3cHhcArMdeVaG921YrM22/2aUAfqG8tFDrV32g3gOGaviYqQoNizC7JLRxhCAALM2bQUUgnSAAYBmGYSj9yF7t25GoanuV2eUAficzbb9ys44qftRV6jtoOEuK4TWEIAAszZtLVrh4A4A1FOaf1J6tq1SQl2V2KYBfq7ZXaefGlTp+eI9GjJ2mjp27m10S2iBCEACW5s3b5NIJAgBtW1VluQ7s+lbHD+8xuxSgTSnIy9KaFW+q3+ARuuTSyxUWHml2SWhDCEEAWJo3gwoGowJA2+R0OpR2cKdSdieppsZudjlAm3X88B5lHT+oi0depgFDEhQQEGh2SWgDCEEAWJo3gwoGowJA23Pq5HHt2bJKpcX5ZpcCWEJNjV17t63WsUO7NWLsVO4igwtGCALA0rw7GJWZIADQVpSXFSl5W6KyM1LNLgWwpNLifK1f9YF69InTsDFXKTKqg9klwU8RggCwNG/eItdGJwgA+D1HbY1S923WoX2b5XTUml0OYHnZGanKyTqq2PhxioufoMCgYLNLgp8hBAFgadwiFwDgiWEYOpl5WHu3rVZFWbHZ5QA4g9NRq4N7Nijj6D4NHzNV3XsP5q58aDJCEACW5t3BqFyMAcAflZUUaM/WVcrNSjO7FABnUVFWrM1rPlbXngM0Yuw0RbWLMbsk+AFCEACWxmBUAEC92ppqpSZv0uH9W+R0OswuB0AT5WalafVn/9bgoeMUN2yCgoJDzC4JrRghCABLC/Bi6yTLYQDAPxiGoez0VO3d/o0qy0vMLgdAMzidDqUmb1RG2j4NH321evSNY4kMPCIEAWBp3uwE4cILAK1fafFp7dnytU6dPG52KQBaQGV5ibZ8+4ku6t5PI8Zdo+j2ncwuCa0MIQgAS2MwKgBYk9Pp0KF9W3Rwz3qWvgBt0KmTx/XNstc1ZMRkxcaPU0BAoNkloZUgBAFgaYFeHF7qzS4TAEDzFRXkaueGlSoqyDG7FABe5HQ6dGDXt8pKP6hRk65Xh5iuZpeEVoAQBIClMRgVAKzD6XDoYPIGpe7dJMNwml0OAB8pLsjVms/fVNzwCRoybJICAukKsTJCEACWxnIYALCGwvyT2rFhhUqK8swuBYAJDMOpg3s2KDv9kBImTVdM5x5mlwSTEIIAsDRvhiAMRgUA8zkctUrZnaRD+7dIhmF2OQBMVlKUp7UrFyl26DhdPGKyAoOCzS4JPkYIAsDSArw4E4ROEAAwV0F+trYnLVdZSYHZpQBoTQxDh/ZtVnbGIY2+bAZdIRbDO3QAlubNoILBqABgDqezru3925VvE4AAaFRZSYG+XblIKXvWy+lkTpBV0AkCwNK8uRyGwagA4HtlJYXavn65CvKyzC4FgB8wDEMpu5OUm5Wm0ZNnKKpdR7NLgpfxDh2ApXmzW8Obt98FALgzDEPHD+9R4vKFBCAAzltBXpYSly/U8cN7ZDA/qE2jEwSApQV4cXipjU4QAPAJe1WFdm78QiczD5ldCgA/VltbrZ0bV+pk5hElTPyRQsMizC4JXkAIAsDSvNsJQggCAN6Wc+KIdmxcKXtludmlAGgjTmYe0uplWRo1cbq69RpkdjloYYQgACzNq4NRuUUuAHhNZUWZkret1onjKWaXAqANsleWa+M3H6pXv4s1bMxUhUdEmV0SWgghCABL8+pgVDpBAKDFGU6n0lJ3av+udaqtsZtdDoA27sTxFOVkpWnopVM0IC5BNt7f+T1CEACWFuDF4aUshwGAllV4Oke7Nn2hotM5ZpeCVsxec/5DLZvzGFhHbY1de7auUvrRZF064Ufq2Kmb2SXhAhCCALA0b3aC2FgOAwAtoqbargO71+nowR0Sd22AB2fezeMP7zha7FzAmYpO52jNijc1cMgoXTJyioJDQs0uCc1ACALA0hiMCgCtl2EYyk5P1Z6tq1RVWWZ2OQAgGYaOpmxX1vGDGjF2mnr0jeODLz9DCALA0rw7GJUQBACawzAM5edm6ODejco7edzscuAHzvwl9Jk7AxUafH6/lNprDFcHCb/QoimqKsu05dtP1KVbXw0ZMUmdu/bhvx0/QQgCwNK8eQcXBqMCwPkxDEMnM4/o0L6NKsjLNrsc+KnQYNt5hyBAc+XlpCsvJ10xXXooNn6iuvceRBjSyhGCALA0bwYV3CIXAJrG6XTqxPEDOpS8WSVFeWaXAwDnrSAvW5vXfKR2Hboodth49ep3CR+ItVKEIAAsjVvkAoB5HI5apR/Zq0P7tqiirMjscgDggpUU5Wl70nId2JWk2Phx6jtouAID+bW7NeHfBgBL4xa5AOB7NTV2HUvdpcMHtspeWW52OQDQ4irKirR781dK2bNegy8Zq/5xlyo4mLvJtAaEIAAsjcGoAOA7lRWlOpa6S0cP7lBNdZXZ5QCA19kry7VvxxqlJm/SgCEJGhCboPDIaLPLsjRCEACWxnIYAPAup8OhkycO6/jhvcrNTpMMw+ySAMDnaqqrlLp3o1KTN6lrj/7qN2iEuvUexFIZE/ATB2Bp3lwOw2BUAFZWVJCr9CN7lZm2X9X2SrPLAYDWwTCUm5Wm3Kw0hYSGq/eAoeo7aLg6xHQ1uzLLIAQBYGl0ggBAy6m2Vyrz2AGlH96rooIcs8sBgFat2l6poynbdTRluzrEdFPfQcPUe8BQhYSGm11am0YIAsDSvBlUMBgVgBUYTqdOnTyu9CN7lZ1xSE6nw+ySAMDvFBXkqGhrjpK3J6p7n1j1GzRcF3XvJxvvJ1scIQgAS2MwKgA0T1lJgdKPJCvjaLIqK0rNLgcA2gSn06Gs4ynKOp6i8Iho9Rk4TH0HDVNUuxizS2szCEEAWJp3l8MwEwRA21JTY1fW8YPKOJqs/NxMs8sBgDatsqJUqckblZq8UZ279lafgcPUs98QbrV7gQhBAFiaN4eX0gkCoC0wnE6dyklXxtFkZaenyuGoNbskALCc/NxM5edmas+Wr9Wjb5z6DBymi7r1ZblMMxCCALA0b84EYTAqAH9WWnxa6UeTlXl0H8tdAKCVcDhqlZm2X5lp+xUeEa3eA+PVd+AwRbfvZHZpfoMQBICleXcwKsthAPiXanulThxPUfqRZBXmZ5tdDgDgLCorSnUoeZMOJW9Sx8491HfQMPXqdzF3lzkHQhAAlhbozZkgLIcB4AecTqdys9OUcSRZJzMPc3cXAPBDhfnZKszP1t6tq9W992D1GTRMXXsMoDPZA0IQAJbmzeGlXHQAtGYV5SU6emCbMtL2y15VbnY5AIAW4HQ6lJV+UFnpBxUaFqk+A4Zq4CVjFBHZzuzSWg1CEACW5tW7w3hx6CoANFdtTbVS923Skf1bGXIKAG2Yvapchw9sVVrqTg0aOlZx8RMUFBxidlmmIwQBYGkMRgVgFYbTqeNH9urA7nWyV9L5AQBW4XDUKnXvRh0/vEeXjJyifoOGW/quMoQgACzNm8thAi18cQHQuuRmH1Py9m9UUphndikAAJPYK8u1a9MXOnpwu4aNvlpde/Q3uyRTEIIAsDQGowJoy0oK85S8I1G5WWlmlwIAaCVKCvO0YdUH6tpzgIaNukrtOnYxuySfIgQBYGkshwHQFlVVlitld5KOHd4tGYbZ5QA+VV0rSef3330143FgQblZacrNPqb+g0fq4pGXKSw80uySfIIQBICleXN4KYNRAZjl8P4tOnZol9llAKZ47G0SDaDJDEPHDu1SUHCIho2+yuxqfIKPKQFYGp0gANqi3v0vMbsEAIAfsdJ1g04QAJbmzaDCm/NGAOBs2sd0VXT7ziotzje7FMAnggIDdOfMcY3ur6116IPPt0uSbvvxaAUFBZ71XICVRLfvrPYxXc0uw2cIQQBYmlcHo9IJAsAkNptNfQYO1f6d35pdCuATNptNwWcJNs4UFBTY5GMBK+gzcKhsFlrGzTt0AJZm8+ItcpkJAsBMvfsPNbsEAIAfsNr1ghAEgKXRCQKgrYqIaq/Y+PEKC48yuxQAQCsUGh6p2Pjxiohqb3YpPsVyGACWxmBUAG1Z/KgrNTThChUX5Con66hyThxVQX42t80FACuy2RTTuYe69Rqobj0Hqn1MV0stg6lHCALA0rw7GNV6FxUArY/NZlOHTt3UoVM3DRk+SfaqCp3KPqacrKPKzUpTtb3S7BIBAF4SEhqurj0HqFvPgbqoR3+FhkWYXZLpCEEAWBrLYQBYTWhYhHoPGKreA4bKcDpVePqkck7UdYkUFeSYXR4A4AJ1iOmmrr0GqHuvQerYqbtsvCd1QwgCwNK8OxiVCw6A1s0WEKCYLj0V06WnLrl0iiorypSblabcrKPKzT6m2hq72SUCAM4hKDhUXXv0V9eeA9W15wCFRzAL6mwIQQBYGp0gAPC98Igo9Rs8XP0GD5fT6dDpUyeUc+KocrOOqqQo3+zyAADfadehs7r2HKhuvQaq00W9FBDAbZ+bihAEgKV5dzAqM0EA+K+AgEB16dZXXbr11bDRV6naXqmykgKVFp/+7k+BykpOq6ykUIbhNLtcAGhzbLYARbXrqKh2MYpu30nR7Tu5vg4JDTe7PL9FCALA0rw7GJVOEABtR0houGvpzJmcTofKS4tcoUh9SFJWclrV9iqTqgUA/xESGqaodp3OCDo6Kbp9jCKjO9Dh4QWEIAAsLcBmk02SN24WyRAqAFYQEBDoeuMuDXbbZ6+q+EH3SF04Ul5aJIPb9AKwEJvNpsjoDj8IO+q6Orhji28RggCwPFtAgAxny7Zy21QXsACAlYWGRSg0LEKdLurltt3pcKistPC7UOT7kKSs+LRqGMYKwI8FB4cqqn190BHjCj2iojsqIJCujtaAEASA5QXaAuRUy4YgDEUFgMYFBAaqXYfOatehs9t2wzBkr6pwdYy4Zo8Un1Z5ebFE9wiAViIyqsN3YUfMGUtY6ro6bHwQ1qoRggCwvIAAm+Ro6XMSggDA+bLZbAoLj1RYeKS6dOvjts/hqFVZSaHKik+rtKRAFWXFqigrUkV5sSrKSuR0tvBf5AAsLSAgUBFR7RQR2V4RUXV/ott1UlT7Topq11GBgfwq7a/4NwfA8rwRWDAUFQBaVmBgkNp37KL2Hbs02GcYhqoqy74LRopVUV6s8vqvv/tDSALgTHUhx/cBR2RUe7fAIyw8io6ONooQBIDleSOwoBMEAHzHZrMpPCJa4RHRDeaPSPXLbMpVUfaDcKS82LXN6ag1oXIA3hIQGFQXbPwg3KjfFhoWSchhUYQgACzPGxdAhqICQOtRt8wmSmHhUQ1u8St9P4vEPRgp+i4sKVFFebEctTUmVA6gMYFBwd+FG+2+Czc6uAUezOZAYwhBAFheYCNdG4ZhyHDUDUx1Or5voz7za1tggMcLLJ0gAOA/zpxFEtOlR4P9hmGo2l7pHoy45pHUdZIQkrQ+hmGo9rvreG3t99fuM78OauQ6DvMFBgV77OSoDzxCQsP5d4dmIQQBYHmeAgvDMJS7ZY/sRSUN9mWt2ez6OrRDO3UdN6LBRZgQBADaDpvN5rrdb0znxkOS+lDEbdlNebEqSotVW1ttQuXWZRiGVq7dp1OnSxvs++Dz7a6vL+oUrelXxPPLtAmCgkIazuQ4I/Qg5IC3EIIAsLwAL8wEYTAqAFjHmSFJx07dG+x3dZKUFqm8rMj1z4rSuq8rKkq4/S/aHptNERHtFBndQRHRHRQZ1UGRZ/yTkANmIQQ5i8LCQr388stKTEzU6dOn1a9fP911112aNWuW2aUBaEGelsPYbDZ1HTfCtRxGqnsTW7/PdVyjy2G4qAMA6rh1knhYbuN0OOruaPODkKT8u5CktsZuQtX+zWazafoV8a7lMJLn6zjLYS5MUHBoXbDhIeSIiGyvgMBAs0sEGiAEaURFRYXuvvtuHTp0SLfffrsGDBigL7/8Uo8//rjy8/N17733ml0igBbS2Jsfm80mW1DzLt7e6C4BALRNAYGBimoXo6h2MQ32GYahmuqqBsFI/deV5cWuX+7hzmazKbiZ13HUsdlsCo9s7x5ynPF1cEgYIRL8DiFII9555x3t379fL7zwgq6//npJ0q233qpf/epXevnll3XjjTeqe/eG7Y4A/E9jg1EvBDNBAAAtwWazKSQ0XCGh4erYueF7T6fTocrykjOCkeLv/lmoirIiVdurTKga/iQkNEwRUZ5DjvDIdgoIIEhC20II0ohPP/1UXbt2dQUgUt1F6Je//KWSkpK0fPly/frXvzaxQgAtxRuBBbfIBQD4QkBAoCKjOyoyuqPH/dXVVXWzRzx0kVSUFcswnB4fh7bDZguoGzzqoZsjIrqDQkLCzC4R8ClCEA9KS0uVlpamadOmNdg3YsQISdLevXt9XRYAL/HG0hU6QQAArUFISJhCOnVTh07dGuxzOp2qqihVeWmRykoLVVKUp+KCUyouPKWaajpI/E1wSJjad7xI7WMuUrsOXRQV3VGR0R0UFhHN+xLgDIQgHuTm5sowDI/LXcLDw9W+fXudOHGiwT6nsy5JLy8vl8PhaLAfQOvULjRMnSKiWvic4SotbXhbPgAAWpcAhUXFKCwqRp27D5T03d1sqipUVlqgspLCuj+lBaoqb3jbeJgjLLKdoqJjFNWuo6LadVRkdIxCwyIazOdwGHW/mwBWUFVVF97W/17eGEIQD+p/cYmIiPC4PywsTJWVlQ222+11k7szMjK8VxyAFje6Z3+pZ/8WP++hQ4da/JwAAPhWmIKjuqtjFLPwWiO7U7IXVep0UZbZpQCtht1uV1RU4x9wEoJ4UD9hu7FJ24ZheGwpa9++vfr166fQ0FBazgAAAAAA8BGn0ym73a727duf9ThCEA8iIyMlfd9O80NVVVUel8oEBQWpU6dOXq0NAAAAAAA0dLYOkHq0K3jQq1cv2Ww25eTkNNhXUVGhkpISdevWcLgUAAAAAABovQhBPIiMjNTAgQOVnJzcYN+ePXskSQkJCb4uCwAAAAAAXABCkEbccMMNysrK0ooVK1zbDMPQ66+/rpCQEE2fPt3E6gAAAAAAwPkiBGnE3LlzNWjQID322GN69tlntWTJEv3iF79QUlKSHnroIV100UVmlwj43NKlSxUXF3fOPy3pD3/4g+Li4jzeltpX4uLiNGfOnEb3Z2RkKC4uTnPnzvW4/7nnnlNcXJwSEhJUU1PTYP+KFSsUFxenl1566bxrs9vtHpfuAQBwIRwOhz788EPdeeedGjt2rOLj43X55Zfr4Ycf1s6dOz0+prS0VAUFBRf0nJmZmc1+/A9dccUVGj58uKqrqxvsW7dunet9S32n95ny8/PPef0/m/T09GY9DoD3MRi1EWFhYXr77bf1wgsv6LPPPlN5ebn69++vZ599VjNnzjS7PMBU06ZN07Rp08wuo9Xo06ePevXqpb1796q2tlZBQe5/tSYlJSk4OFjl5eXauXOnxo0b57Z/27ZtkqRJkyad1/MeOHBADz74oB588EHddNNNF/YiAAD4jtPp1IMPPqg1a9boyiuv1H333aeoqCidOHFCn3zyiVasWKF58+bpZz/7mesx69ev1yOPPKIXX3yxwXWuKbKzs3XPPffommuu0W9+85sWeR0TJkzQ0qVLdeDAAY0cOdJtX/21uaamRuvWrdOIESPc9m/dulXS+V+by8rK9Otf/1p9+vTRM888c0H1A/AOQpCziImJ0V/+8hezywBanbi4ON14441ml9GqTJgwQR9++KEOHjyo+Ph41/a8vDylpqZq9uzZWrx4sZKSkhq8Ody+fbuio6MbvAE7l4MHDyorK6tF6gcAoN5XX32lxMREPfzww7rnnnvc9v3qV7/SzTffrOeee07Tpk1Tz549JUm7du26oC6QzMxMHTp0SNdcc80F1X6m+hBk586dHkOQcePG6fjx40pKSmoQvGzfvl3S+YcgRUVF2rFjh/r06XNBtQPwHpbDAEALmDhxoiQ1aBFOSkqSYRi6+eab1b9/fyUlJbntLyws1JEjRzR+/HgFBgb6rF4AABpTHwBcccUVDfZFRUXp9ttvV21tbaPLYlqLxq7NWVlZOnbsmMaNG6dJkyYpOTlZRUVFbsds27ZNHTp00NChQ31VLgAfIQQB4FVpaWl6+OGHNWHCBMXHx+uaa67Riy++qKqqqgbHLl68WDNmzNDw4cM1depULVq0qNHzLl++XLNnz9bIkSN16aWX6o477lBiYqLbMfUzTFasWKFZs2YpPj5e1113nex2uyRp1apV+sUvfqFx48Zp6NChGjdunO69917t27fvvF/n+PHjZbPZtGPHDrftSUlJ6tChg4YNG6bJkycrNTVVeXl5rv07duyQYRhunzSlpKTo4Ycf1pQpUxQfH6+EhATddtttWrlypeuYP/zhD5o3b54kad68eW6zWJxOp95++23deOONGj58uEaPHq1f/vKXDWp76aWXFBcXp2+//VY/+tGPFB8frzvvvFNS3SdyDz30kK688krXOvB58+YpOzv7vH82AAD/EhUVJUl6//33VVtb22D/nXfeqf3792vGjBmSpDlz5ujll1+WJN1111266qqrXMdmZGToiSee0FVXXaX4+HiNHDlSM2fO1Lvvvus65qWXXtJdd90lSXr55ZcbzAJryjXfk86dO2vw4MENQpB169ZJki677DJNnjxZTqdTGzZscO0vKirS4cOHNXHiRAUE1P26lJ+fr7/+9a+69tprNXz4cA0fPlzTp0/XK6+84voZLV26VFdffbUk6ZNPPlFcXJy2bNniOm9SUpLuuusuJSQkaMSIEbrpppu0dOlSt9q2bNmiuLg4vffee/rFL36h+Ph4TZkyRbm5uaqsrNTTTz+t6667TsOHD9e4ceN0zz33NLi+Azg7lsMAOG+VlZWNtrzGxMS4vt67d69+9rOfKSoqSnfccYdiYmK0e/duLViwQJs2bdKiRYsUGhoqSXr++ef12muvKSEhQb///e+Vn5+vF1980eNz/O1vf9O///1vTZo0SQ8//LDsdrtWrFih++67r8EaZUl64oknNHXqVM2aNUsVFRUKDQ3VW2+9pb/+9a8aO3asHnzwQQUHB2vfvn369NNPtWvXLiUmJioyMrLJP5OYmBjFxcW5vdFyOp3auHGjJkyYoICAAE2ePFlvv/22kpKSXDM86tccT548WVLdbbjvvPNOde/eXXfeeac6duyozMxMLV68WL/73e/UrVs3JSQk6NZbb1VISIgWL16sW2+9VaNGjXI97+9//3utWLFC1157rWbPnq3i4mItXbpUc+bM0QsvvKDrrrvOrfaHHnpIs2bNUr9+/RQSEqKSkhLdddddcjqd+ulPf6pOnTrp8OHDeu+997RlyxatXLlSYWFhTf7ZAAD8y0033aRFixbp/fff1+rVq3X11Vdr7NixGj16tLp27dqgc/Hee+9V+/bttWrVKt17770aNmyYJOnEiROaNWuWQkJCdNttt6lr1646deqUPvzwQ/3P//yP2rVrpxkzZmjatGmqra3VggULXHPH6t9PnO81/4cmTJigRYsW6fjx4+rXr5+kuvklnTt31pAhQ9SrVy8FBQUpKSlJ119/vaS6TpgzP6AoLS11XU9/+tOfqm/fviosLNRnn32m+fPnKzAwUPfee6/GjBmjefPm6emnn9bo0aM1e/ZsDRw4UJL07rvv6sknn9SwYcP04IMPKiAgQN98843mzZunlJQUPf744251/+1vf9OYMWP0X//1X8rJyVHXrl113333acOGDbrjjjs0YMAA5efn691339XcuXP10UcfaciQIRf07x2wDAMAmujjjz82YmNjz/qnntPpNK6//nrj8ssvNwoLC93Os2TJEiM2NtZ47bXXDMMwjPT0dOPiiy82br/9dqO2ttZ13IEDB4yhQ4casbGxRmZmpmEYhrFnzx4jNjbW+POf/+x2zurqamPOnDnG0KFDjZMnT7rVO2vWLMPpdLqOra2tNcaNG2fMnDnT7fkMwzCeffZZIzY21vjqq69c22JjY40777zznD+fZ555xq3W3bt3G7GxscaHH35oGIZhlJeXG0OHDjUeeugh12N+8pOfGFOnTnV9/8ADDxjx8fFGbm6u27nXrl1rxMbGGk8++aRrW/3r+/jjj13bVq5cacTGxhr/+te/3B5fVlZmXHfddca4ceOMiooKwzAMY/78+UZsbKzx29/+1u3YL774woiNjTVWrFjhtv2NN94wfvzjHxvJycnn/FkAAPzbjh07jGnTpjW4zk+fPt345z//adjtdrfj668pmzdvdm3761//asTGxhr79u1zO/bw4cNGbGyscc8997i2bd682YiNjTXmz5/v2nY+1/zGJCYmul0rq6urjYSEBOORRx5xHXPrrbcakyZNcr1XePrpp43Y2FjXud96660G7w0MwzCKi4uNoUOHGj/+8Y9d2zIzM43Y2Fjjsccec207efKkMXToUOOee+5xez/idDqNRx55xIiNjTX27Nnj9nO44oorjMrKStexp0+fNmJjY40//elPbjXs3r3buOaaa4wlS5ac9ecA4HsshwFw3m688Ua98cYbHv/US01N1eHDh3X55ZfL6XSqoKDA9efKK69UaGioVq1aJUlKTEyUw+HQ3Llz3T5duvjii3XZZZe5PfeKFSskSdOnT3c7Z2lpqaZPn66amhqtWbPG7TFXXnmlbDab6/vAwECtW7dOb775ptvzVVRUKDg42PX1+Ro/fryk79ce18//qO/yiIiIUEJCgjZt2iTDMFRWVqaDBw+6LYWZP3++1q5d63Yb7traWjmdTklSeXn5WWuo//lce+21bj8fu92ua665RoWFha670dQ7s21Zkrp37y5JWrBggRITE10/i5/97Gdavny52+BXAEDblJCQoC+++EJvvvmm7r77bg0bNkyBgYE6cuSInn/+ec2aNavBHI0f+sMf/qANGza4zdVwOp2u5SPnutY255r/Q2PGjFFQUJDr2rxr1y6VlZW5XXsnT56svLw8HT58WFLdPJCBAweqW7dukuqW+GzcuFFTp051O3dhYaGio6PP+Tq+/vpr1dTU6Ec/+pEKCwtdr6OwsNDVffL111+7PWbixIluXZdRUVGKjo7WF198oSVLlriW1o4YMUJfffWVbrnllrPWAOB7LIcBcN569+7tGjbWmGPHjkmSPvjgA33wwQcej6m/s0lGRoYkeZykPnjwYLd1v/XnrZ9dcbbz1uvSpUuDY0JCQrRjxw598cUXysjIUGZmprKzs2UYhiS5QofzMWbMGAUHB2vnzp264YYblJSUpEGDBrneREl1b7S2bNmiQ4cOKScnRw6HwxWSSFJAQICKioq0cOFCHTlyRCdOnFBGRoZqamokyVVfY+p/Pj98o3amc/18RowYofvuu0+vvfaa7rvvPgUHB2vEiBGaMmWKZs6cqa5duzbtBwIA8GuBgYGaMGGCJkyYIKluWciqVav08ssvKzU1Va+88kqDZRxnstlsqq2t1UsvvaSUlBTXNa2yslJS069p53PN/6GoqCgNGzbM7QMKm83WIAR56aWXtGXLFvXs2VMpKSm644473M4TEBCg119/XcnJyTpx4oTS09NVVlYmSQoPD2/S63j00Ueb/Do6d+7s9n1ISIieeeYZzZs3T//1X/8lSYqNjdXkyZM1Y8YMXXLJJWetAcD3CEEAeEV9iHDHHXc0+gt5UNC5/wr6YRjhcDgkSa+++mqjbzrqOxnqebrryh//+EctXrxYgwYN0ogRI3T55ZdryJAhOnbsmP77v//7nHV5EhERoZEjR2r37t0qKSlRcnKy5syZ43bM5MmT9fzzz2vr1q06deqUgoKCXB0kkrRs2TI99thj6tSpk8aMGaPp06crLi5OXbt21axZs85Zg8PhUGRkpGtAnSf9+/d3+97Tz+ehhx7SHXfcobVr12rDhg3aunWrtm/frgULFuiNN95ocKtBAEDbUFFRoX/+85/q0aOHbr31Vrd90dHRuummmzR+/HhNmzbNbeinJ5s2bdI999yj0NBQjR8/XldffbUGDx6sUaNGacqUKeespTnXfE8mTpyoV199VcXFxdqwYYMuvvhit5Bh2LBhat++vbZu3ap+/fo1+IDi0KFDmjNnjux2u8aNG6eJEydq7ty5SkhIaHCdP9vrePLJJ9WrVy+Px5w5U03y/B5p6tSpmjRpkpKSkrR+/Xpt2bJFCxcu1BtvvKH//M//dA2XBXB2hCAAvOLMi/wPu0acTqe++uor9e7dW5LUt29fSdKRI0caDPVKT0/3eN6LLrrINXjtzGPT0tIUERFx1tq2b9+uxYsX68c//rGee+45t6Uyu3fvbsKra9yECRP06quvasOGDQ3eREl1S3w6deqkAwcOKCMjQ8OHD3dN4bfb7frTn/6kPn366OOPP3Ztl9Tkye+9evXSsWPHNGTIkAZvqFJSUnTq1KlzfmJV3xI8evRo3XLLLbrllltkGIaWLVumRx99VAsXLtT8+fObVA8AwL+EhYXpjTfeUKdOnTRr1iyPQXmPHj0UFRV1zuvJH//4R4WFhWnFihVuXYe5ublNqqUlrvlS3fuQV155RZs3b1ZKSop++ctfuu0PDAzU+PHjtW/fPu3cuVPBwcEaM2aMa/9f//pXlZSU6PPPP3cNOpWkmpoaFRYWqlOnTk16He3atWvwnujUqVPau3ev6z1RY8rKypSamqpevXrpmmuu0TXXXCNJOnjwoObOnatXXnmFEARoImaCAPCK+Ph49ezZU5988olruUu9xYsX66GHHtLHH38sSZo2bZqCg4P1+uuvq7q62nVcWlpag7W+9Xc2eemll1yfrEh1b0TmzZune++995xvrurXMMfGxroFIAUFBfroo48kye3c52PChAmqra3Vu+++q9DQULc3UVJda/DEiRO1b98+7d+/360dt6qqShUVFerVq5dbAFJbW6uFCxe6vq5Xf9u+M7tlrr32WklqcGedsrIyPfTQQ3rggQdctwhuzMcff6yf//znWr16tVvdCQkJkjx3jgAA2oaAgADNnDlT2dnZeu655zwuD12xYoWKiorc7jbm6ZpUWFiomJiYBks7/vWvf0lyv6bVX1vOfHxLXPOlumWeERERevvtt+V0Oht8QCHV3S43KytL69evV0JCglu4UlhYqPDw8AZBxdtvv62qqqpzvo5rrrlGAQEBWrBggaqqqtzO8cwzz+iBBx7Qvn37zvoaDh06pNtvv12vvvqq2/ZBgwYpOjq6Sd21AOrwfwsArwgMDNRf/vIX3XPPPbrpppt02223qU+fPkpOTtbHH3+sPn366P7775dU94nS7373O/3v//6vZs+erZkzZ6qsrExvv/222rVrp9OnT7vOO2HCBM2aNUsfffSRZs+erenTpyskJETLli3T3r17dfvtt2v48OFnrS0hIUEdOnTQggULXKHDiRMn9PHHH6u0tFSSVFJS0qzXXd/ZsW3bNk2ePNnjrWQnT56s5cuXu76u1759e40ZM0br16/XvHnzlJCQoKKiIi1fvlxpaWkKCAhw1Sd9v1542bJlMgxDM2fO1E033aQvv/xSixcvVkZGhq666irV1tbqww8/1PHjx/XII4+cc6bHLbfcog8++ECPP/64du/ercGDB6uwsFBLlixRcHBwk1p/AQD+67HHHtPhw4e1cOFCrVu3Ttdee6169OihiooKbd68WYmJibrsssvcZnXUX5Pef/99nTp1SjfeeKOuvvpqffrpp3rggQd0+eWXq7KyUl9//bV27typkJAQt2tafTdFYmKievTooWnTprXINV+SgoODNXr0aK1bt04RERG69NJLGxxTfz3eu3ev/uM//sNt39VXX61XXnlFv/jFLzR9+nQZhqF169Zp7dq1CgsLc3sdHTt2VGBgoLZu3aolS5Zo0qRJ6tevn37zm9/o73//u2688Ub95Cc/Ubt27fTNN99o/fr1uvLKK12dHY1JSEjQ5MmT9cEHH6ikpERjx46Vw+HQV199pczMTD322GPn/DkAqEMIAsBrJk6cqCVLlugf//iHK2Do1q2bbr/9dt1zzz1urbF33323unfvrtdff13/93//pw4dOmju3Lmy2+1asGCB23n/8pe/aOTIkVq8eLFeeuklBQYGql+/fvrLX/7SpLkZMTExWrhwoV544QV98MEHqq6uVteuXXXttdfq5z//ua677jolJSXp7rvvPu/XHBQUpDFjxmjNmjVuXR5nmjRpkmw2m6Kjoxu097744ot6/vnntX79en3++efq0qWL4uPj9b//+7/685//rO3bt6uyslLh4eEaP368ZsyYodWrVys5OVmjR49W//79tWDBAr311lv67LPP9Nxzzyk8PFwDBw7USy+9dM43WVLdG9G3335b//jHP7R69Wq9//77ioiI0KhRo/TCCy806Q0nAMB/RUZG6p133tHSpUv15ZdfasmSJSoqKlJERIQGDx6sJ598UjfffLOr+0OSrr/+eq1atUpr167Vpk2bNG3aNP3xj39Uhw4d9PXXXyspKUkxMTGKjY3VokWLtHjxYq1cuVLZ2dnq0aOH+vfvr5///Of66KOP9NRTT6lXr16aMGHCBV/z602cOFHr1q3TuHHjFBIS0mB/9+7dNWDAAKWlpTW4ft9///0KDAzUp59+qqefflrt27dX//799corryg5OVkLFizQ9u3bNXr0aIWFhen3v/+9XnvtNT355JP685//rJtvvln333+/Bg0apEWLFum1116T0+lU79699eijj2rOnDlN6rKcP3++Fi5cqJUrV2rt2rWSpCFDhui5557TjBkzmvyzAKzOZpxrLDMAAAAAAEAbwEwQAAAAAABgCYQgAAAAAADAEghBAAAAAACAJRCCAAAAAAAASyAEAQAAAAAAlkAIAgAAAAAALIEQBAAAAAAAWAIhCAAAAAAAsARCEAAAAAAAYAmEIAAAAAAAwBIIQQAAAAAAgCUQggAAAAAAAEv4f9qiSSZqDrylAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1100x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.style.use('seaborn-whitegrid') \n",
    "# fig = plt.figure(figsize=(11, 9)).gca()\n",
    "\n",
    "# flierprops = dict(markersize=4,\n",
    "#               linestyle='none')\n",
    "\n",
    "# ax = sns.violinplot(data = training_df_plot, palette='turbo', x = 'Label', y = 'Total Receipt Kg', inner=None, linewidth=0, saturation=0.2)\n",
    "# sns.boxplot(data = training_df_plot, x='Label', y='Total Receipt Kg', palette='turbo', width=0.032, flierprops=flierprops,\n",
    "#             boxprops={'zorder': 2}, ax=ax)\n",
    "# ax.set_xticklabels(ax.get_xmajorticklabels(), fontsize = 18)\n",
    "# plt.xticks(fontsize=14)\n",
    "# plt.yticks(fontsize=14)\n",
    "# plt.ylim(0,1200)\n",
    "# plt.text(0.06,650, 'n = 4524', fontsize=16) #4524\n",
    "# plt.text(1.06,650, 'n = 3980', fontsize=16)\n",
    "# plt.ylabel(\"Individual Landing Records: Total Weight (Kg)\", fontsize=16)\n",
    "# plt.xlabel(\"\", fontsize=13) #Origin of Fishing\n",
    "# plt.tight_layout()\n",
    "# #plt.savefig('PlotManuscript_violinfnlRA.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing min/max scaler to normalize all weight columns as well as vessel length\n",
    "# this is not necessary for decision trees, but b/c these data are going to be provided to GITHUB it's necessary\n",
    "# for confidentiality\n",
    "# from sklearn.preprocessing import MinMaxScaler\n",
    "# scaler = MinMaxScaler()\n",
    "\n",
    "# # normalizing data\n",
    "# x_train.loc[:,'Distance 202m Depth':'Yellowtail Rockfish'] = scaler.fit_transform(x_train.loc[:,'Distance 202m Depth':'Yellowtail Rockfish'])\n",
    "\n",
    "\n",
    "# #Same scale as the training data. \n",
    "# x_test.loc[:,'Distance 202m Depth':'Yellowtail Rockfish'] = scaler.transform(x_test.loc[:,'Distance 202m Depth':'Yellowtail Rockfish'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "# x_train.to_csv('x_train.csv')\n",
    "# x_test.to_csv('x_test.csv')\n",
    "# y_train.to_csv('y_train.csv')\n",
    "# y_test.to_csv('y_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### End of initial data preprocessing steps:\n",
    "The above steps have been comment out to render those code blocks inert. The below 4 non-confidential datasets have been provided in this repo. Those dataset have been split into the training and testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 non-confidential datasets\n",
    "x_train = pd.read_csv('x_train.csv', index_col=0)\n",
    "x_test = pd.read_csv('x_test.csv', index_col=0)\n",
    "y_train = pd.read_csv('y_train.csv', index_col=0)\n",
    "y_test = pd.read_csv('y_test.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging the datasets to create the violin plot below\n",
    "training_df = pd.concat([x_train, y_train], axis=1)\n",
    "testing_df = pd.concat([x_test, y_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1131\n",
       "0     995\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test data record counts\n",
    "testing_df.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    4524\n",
       "0    3980\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training data record counts\n",
    "training_df.Label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Distance 202m Depth</th>\n",
       "      <th>Total Daily Kg</th>\n",
       "      <th>Total Receipt Kg</th>\n",
       "      <th>Albacore</th>\n",
       "      <th>Arrowtooth Flounder</th>\n",
       "      <th>Aurora Rockfish</th>\n",
       "      <th>Bank Rockfish</th>\n",
       "      <th>Bat Ray</th>\n",
       "      <th>Big Skate</th>\n",
       "      <th>Black And Yellow Rockfish</th>\n",
       "      <th>...</th>\n",
       "      <th>Unsp. Squid</th>\n",
       "      <th>Vermilion Rockfish</th>\n",
       "      <th>White Seabass</th>\n",
       "      <th>Widow Rockfish</th>\n",
       "      <th>Wolf Eel</th>\n",
       "      <th>Yelloweye Rockfish</th>\n",
       "      <th>Yellowfin Tuna</th>\n",
       "      <th>Yellowtail</th>\n",
       "      <th>Yellowtail Rockfish</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8851</th>\n",
       "      <td>0.003198</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.004376</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>0.053466</td>\n",
       "      <td>0.001811</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6060</th>\n",
       "      <td>0.029206</td>\n",
       "      <td>0.004243</td>\n",
       "      <td>0.005742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>0.053466</td>\n",
       "      <td>0.001162</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>0.051074</td>\n",
       "      <td>0.003243</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 117 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Distance 202m Depth  Total Daily Kg  Total Receipt Kg  Albacore  \\\n",
       "8851             0.003198        0.003234          0.004376       0.0   \n",
       "521              0.053466        0.001811          0.002450       0.0   \n",
       "6060             0.029206        0.004243          0.005742       0.0   \n",
       "635              0.053466        0.001162          0.001573       0.0   \n",
       "668              0.051074        0.003243          0.000402       0.0   \n",
       "\n",
       "      Arrowtooth Flounder  Aurora Rockfish  Bank Rockfish  Bat Ray  Big Skate  \\\n",
       "8851                  0.0              0.0            0.0      0.0        0.0   \n",
       "521                   0.0              0.0            0.0      0.0        0.0   \n",
       "6060                  0.0              0.0            0.0      0.0        0.0   \n",
       "635                   0.0              0.0            0.0      0.0        0.0   \n",
       "668                   0.0              0.0            0.0      0.0        0.0   \n",
       "\n",
       "      Black And Yellow Rockfish  ...  Unsp. Squid  Vermilion Rockfish  \\\n",
       "8851                        0.0  ...          0.0                 0.0   \n",
       "521                         0.0  ...          0.0                 0.0   \n",
       "6060                        0.0  ...          0.0                 0.0   \n",
       "635                         0.0  ...          0.0                 0.0   \n",
       "668                         0.0  ...          0.0                 0.0   \n",
       "\n",
       "      White Seabass  Widow Rockfish  Wolf Eel  Yelloweye Rockfish  \\\n",
       "8851            0.0             0.0       0.0                 0.0   \n",
       "521             0.0             0.0       0.0                 0.0   \n",
       "6060            0.0             0.0       0.0                 0.0   \n",
       "635             0.0             0.0       0.0                 0.0   \n",
       "668             0.0             0.0       0.0                 0.0   \n",
       "\n",
       "      Yellowfin Tuna  Yellowtail  Yellowtail Rockfish  Label  \n",
       "8851             0.0         0.0                  0.0      1  \n",
       "521              0.0         0.0                  0.0      0  \n",
       "6060             0.0         0.0                  0.0      1  \n",
       "635              0.0         0.0                  0.0      0  \n",
       "668              0.0         0.0                  0.0      0  \n",
       "\n",
       "[5 rows x 117 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dataframe for below violin plot\n",
    "training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#decoding for plot presentations\n",
    "Replace = training_df.loc[:, \"Label\"] == 1\n",
    "training_df.loc[Replace, \"Label\"] = \"Federal Waters\"\n",
    "\n",
    "#decoding for plot presentations\n",
    "Replace = training_df.loc[:, \"Label\"] == 0\n",
    "training_df.loc[Replace, \"Label\"] = \"State Waters\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1100x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# violin plot of the distribution of total weight landed on landing record by Federal and state waters\n",
    "plt.style.use('seaborn-whitegrid') \n",
    "fig = plt.figure(figsize=(11, 9)).gca()\n",
    "\n",
    "flierprops = dict(markersize=4,\n",
    "              linestyle='none')\n",
    "\n",
    "ax = sns.violinplot(data = training_df, palette='turbo', x = 'Label', y = 'Total Receipt Kg', inner=None, linewidth=0, saturation=0.2)\n",
    "sns.boxplot(data = training_df, x='Label', y='Total Receipt Kg', palette='colorblind', width=0.032, flierprops=flierprops,\n",
    "            boxprops={'zorder': 2}, ax=ax)\n",
    "ax.set_xticklabels(ax.get_xmajorticklabels(), fontsize = 14)\n",
    "#plt.title('Violin Plot of Total Ticket Lbs. by State and Federal Waters \\n', fontsize=14)\n",
    "plt.grid()  \n",
    "plt.ylim(0,0.025)\n",
    "plt.text(0.04,0.02, 'n = 4524', fontsize=13)\n",
    "plt.text(1.05,0.02, 'n = 3980', fontsize=13)\n",
    "plt.ylabel(\"Total Receipt normalized kg\", fontsize=13)\n",
    "plt.xlabel(\"\", fontsize=13) #Origin of Fishing\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset shape: (8504, 116) (8504, 1)\n",
      "Testing dataset shape: (2126, 116) (2126, 1)\n"
     ]
    }
   ],
   "source": [
    "# loading feature selection package\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "\n",
    "print('Training dataset shape:', x_train.shape, y_train.shape)\n",
    "print('Testing dataset shape:', x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Label\n",
       "1        0.531985\n",
       "0        0.468015\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stratified split for training\n",
    "y_train.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Label\n",
       "1        0.531985\n",
       "0        0.468015\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stratified split for testing\n",
    "y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Classification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   26.9s\n",
      "[Parallel(n_jobs=-1)]: Done 116 out of 116 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 11:21:58] Features: 1/116 -- score: 0.8926389714522708[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   36.2s\n",
      "[Parallel(n_jobs=-1)]: Done 115 out of 115 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 11:24:01] Features: 2/116 -- score: 0.9557840602751089[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   31.5s\n",
      "[Parallel(n_jobs=-1)]: Done 114 out of 114 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 11:25:42] Features: 3/116 -- score: 0.9637799129052326[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   53.5s\n",
      "[Parallel(n_jobs=-1)]: Done 113 out of 113 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 11:28:29] Features: 4/116 -- score: 0.9696603304071335[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   46.9s\n",
      "[Parallel(n_jobs=-1)]: Done 112 out of 112 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 11:30:57] Features: 5/116 -- score: 0.9715419921199973[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   53.0s\n",
      "[Parallel(n_jobs=-1)]: Done 111 out of 111 | elapsed:  2.6min finished\n",
      "\n",
      "[2023-02-04 11:33:31] Features: 6/116 -- score: 0.9724830303449229[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   44.6s\n",
      "[Parallel(n_jobs=-1)]: Done 110 out of 110 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 11:35:50] Features: 7/116 -- score: 0.9737771479919818[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   46.1s\n",
      "[Parallel(n_jobs=-1)]: Done 109 out of 109 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 11:38:12] Features: 8/116 -- score: 0.9755407479090342[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.6s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 11:40:42] Features: 9/116 -- score: 0.9755407479090344[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.5s\n",
      "[Parallel(n_jobs=-1)]: Done 107 out of 107 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 11:43:08] Features: 10/116 -- score: 0.9761284302205018[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   47.5s\n",
      "[Parallel(n_jobs=-1)]: Done 106 out of 106 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 11:45:32] Features: 11/116 -- score: 0.9762460772793254[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   46.8s\n",
      "[Parallel(n_jobs=-1)]: Done 105 out of 105 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 11:48:00] Features: 12/116 -- score: 0.9761285684661644[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.0s\n",
      "[Parallel(n_jobs=-1)]: Done 104 out of 104 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 11:50:17] Features: 13/116 -- score: 0.9764809566599848[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   45.5s\n",
      "[Parallel(n_jobs=-1)]: Done 103 out of 103 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 11:52:36] Features: 14/116 -- score: 0.9764809566599848[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   45.6s\n",
      "[Parallel(n_jobs=-1)]: Done 102 out of 102 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 11:54:49] Features: 15/116 -- score: 0.9767166655146197[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.0s\n",
      "[Parallel(n_jobs=-1)]: Done 101 out of 101 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 11:57:17] Features: 16/116 -- score: 0.9764812331513099[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.3s\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 11:59:33] Features: 17/116 -- score: 0.9767165272689569[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.2s\n",
      "[Parallel(n_jobs=-1)]: Done  99 out of  99 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 12:01:49] Features: 18/116 -- score: 0.9767163890232945[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.4s\n",
      "[Parallel(n_jobs=-1)]: Done  98 out of  98 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 12:04:04] Features: 19/116 -- score: 0.9767166655146194[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   47.6s\n",
      "[Parallel(n_jobs=-1)]: Done  97 out of  97 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 12:06:18] Features: 20/116 -- score: 0.9767165272689569[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.8s\n",
      "[Parallel(n_jobs=-1)]: Done  96 out of  96 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 12:08:29] Features: 21/116 -- score: 0.9768341743277805[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   47.3s\n",
      "[Parallel(n_jobs=-1)]: Done  95 out of  95 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:10:36] Features: 22/116 -- score: 0.9768343125734431[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   47.1s\n",
      "[Parallel(n_jobs=-1)]: Done  94 out of  94 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:12:42] Features: 23/116 -- score: 0.9768340360821179[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   46.9s\n",
      "[Parallel(n_jobs=-1)]: Done  93 out of  93 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:14:47] Features: 24/116 -- score: 0.9775399184350592[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   51.0s\n",
      "[Parallel(n_jobs=-1)]: Done  92 out of  92 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:16:56] Features: 25/116 -- score: 0.9770689154627774[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.0s\n",
      "[Parallel(n_jobs=-1)]: Done  91 out of  91 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:19:02] Features: 26/116 -- score: 0.9768341743277803[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.8s\n",
      "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:21:07] Features: 27/116 -- score: 0.9768341743277805[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.1s\n",
      "[Parallel(n_jobs=-1)]: Done  89 out of  89 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 12:23:16] Features: 28/116 -- score: 0.9771867007672634[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.5s\n",
      "[Parallel(n_jobs=-1)]: Done  88 out of  88 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 12:25:21] Features: 29/116 -- score: 0.978127462500864[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.5s\n",
      "[Parallel(n_jobs=-1)]: Done  87 out of  87 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 12:27:32] Features: 30/116 -- score: 0.9775396419437341[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   50.3s\n",
      "[Parallel(n_jobs=-1)]: Done  86 out of  86 | elapsed:  2.0min finished\n",
      "\n",
      "[2023-02-04 12:29:35] Features: 31/116 -- score: 0.9774217183935855[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.3s\n",
      "[Parallel(n_jobs=-1)]: Done  85 out of  85 | elapsed:  2.0min finished\n",
      "\n",
      "[2023-02-04 12:31:36] Features: 32/116 -- score: 0.9775389507154214[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   50.0s\n",
      "[Parallel(n_jobs=-1)]: Done  84 out of  84 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:33:33] Features: 33/116 -- score: 0.9778918918918919[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   50.4s\n",
      "[Parallel(n_jobs=-1)]: Done  83 out of  83 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:35:30] Features: 34/116 -- score: 0.9776568742655698[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.3s\n",
      "[Parallel(n_jobs=-1)]: Done  82 out of  82 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:37:26] Features: 35/116 -- score: 0.9774217183935855[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   57.7s\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 12:39:43] Features: 36/116 -- score: 0.9774218566392479[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.6s\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:41:40] Features: 37/116 -- score: 0.9774217183935855[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  79 out of  79 | elapsed:  2.0min finished\n",
      "\n",
      "[2023-02-04 12:43:42] Features: 38/116 -- score: 0.9775392272067464[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   51.9s\n",
      "[Parallel(n_jobs=-1)]: Done  78 out of  78 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:45:38] Features: 39/116 -- score: 0.9774213036565979[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.0s\n",
      "[Parallel(n_jobs=-1)]: Done  77 out of  77 | elapsed:  2.0min finished\n",
      "\n",
      "[2023-02-04 12:47:39] Features: 40/116 -- score: 0.9775389507154213[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.4s\n",
      "[Parallel(n_jobs=-1)]: Done  76 out of  76 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:49:31] Features: 41/116 -- score: 0.9776561830372573[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   53.4s\n",
      "[Parallel(n_jobs=-1)]: Done  75 out of  75 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:51:23] Features: 42/116 -- score: 0.9776567360199074[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.3s\n",
      "[Parallel(n_jobs=-1)]: Done  74 out of  74 | elapsed:  1.8min finished\n",
      "\n",
      "[2023-02-04 12:53:14] Features: 43/116 -- score: 0.9780094007050529[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.5s\n",
      "[Parallel(n_jobs=-1)]: Done  73 out of  73 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:55:06] Features: 44/116 -- score: 0.9780095389507155[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.3s\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  1.8min finished\n",
      "\n",
      "[2023-02-04 12:56:51] Features: 45/116 -- score: 0.9782446948226999[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.9s\n",
      "[Parallel(n_jobs=-1)]: Done  71 out of  71 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 12:58:43] Features: 46/116 -- score: 0.9782445565770373[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   53.0s\n",
      "[Parallel(n_jobs=-1)]: Done  70 out of  70 | elapsed:  1.8min finished\n",
      "\n",
      "[2023-02-04 13:00:29] Features: 47/116 -- score: 0.9783623418815234[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   52.9s\n",
      "[Parallel(n_jobs=-1)]: Done  69 out of  69 | elapsed:  1.8min finished\n",
      "\n",
      "[2023-02-04 13:02:15] Features: 48/116 -- score: 0.9778917536462293[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.0s\n",
      "[Parallel(n_jobs=-1)]: Done  68 out of  68 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:04:00] Features: 49/116 -- score: 0.9777743830787309[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.3s\n",
      "[Parallel(n_jobs=-1)]: Done  67 out of  67 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:05:44] Features: 50/116 -- score: 0.9780092624593903[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.2s\n",
      "[Parallel(n_jobs=-1)]: Done  66 out of  66 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:07:29] Features: 51/116 -- score: 0.9780089859680652[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.3s\n",
      "[Parallel(n_jobs=-1)]: Done  65 out of  65 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:09:13] Features: 52/116 -- score: 0.9778918918918919[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.1s\n",
      "[Parallel(n_jobs=-1)]: Done  64 out of  64 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:10:53] Features: 53/116 -- score: 0.9781267712725514[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.1s\n",
      "[Parallel(n_jobs=-1)]: Done  63 out of  63 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:12:35] Features: 54/116 -- score: 0.9784792977120343[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   56.1s\n",
      "[Parallel(n_jobs=-1)]: Done  62 out of  62 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:14:15] Features: 55/116 -- score: 0.9781266330268888[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.2s\n",
      "[Parallel(n_jobs=-1)]: Done  61 out of  61 | elapsed:  1.6min finished\n",
      "\n",
      "[2023-02-04 13:15:53] Features: 56/116 -- score: 0.9781267712725514[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.9s\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  1.6min finished\n",
      "\n",
      "[2023-02-04 13:17:27] Features: 57/116 -- score: 0.9783616506532107[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.5s\n",
      "[Parallel(n_jobs=-1)]: Done  59 out of  59 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 13:18:59] Features: 58/116 -- score: 0.9785972212621828[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   56.0s\n",
      "[Parallel(n_jobs=-1)]: Done  58 out of  58 | elapsed:  1.6min finished\n",
      "\n",
      "[2023-02-04 13:20:33] Features: 59/116 -- score: 0.9781273242552014[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.6s\n",
      "[Parallel(n_jobs=-1)]: Done  57 out of  57 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 13:22:05] Features: 60/116 -- score: 0.9782442800857123[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.9s\n",
      "[Parallel(n_jobs=-1)]: Done  56 out of  56 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 13:23:36] Features: 61/116 -- score: 0.9782446948226999[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   56.8s\n",
      "[Parallel(n_jobs=-1)]: Done  55 out of  55 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 13:25:04] Features: 62/116 -- score: 0.9785970830165203[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   55.6s\n",
      "[Parallel(n_jobs=-1)]: Done  54 out of  54 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 13:26:31] Features: 63/116 -- score: 0.9784792977120343[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  53 out of  53 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 13:28:14] Features: 64/116 -- score: 0.9781270477638764[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  52 out of  52 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 13:29:39] Features: 65/116 -- score: 0.978127462500864[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   57.5s\n",
      "[Parallel(n_jobs=-1)]: Done  51 out of  51 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 13:31:03] Features: 66/116 -- score: 0.9782445565770376[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.5s\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 13:32:28] Features: 67/116 -- score: 0.9781271860095391[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  49 out of  49 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 13:33:59] Features: 68/116 -- score: 0.9784794359576967[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 13:35:17] Features: 69/116 -- score: 0.9785973595078454[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  47 out of  47 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 13:36:35] Features: 70/116 -- score: 0.9783617888988733[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  46 out of  46 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 13:37:53] Features: 71/116 -- score: 0.9780096771963779[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   59.5s\n",
      "[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 13:39:12] Features: 72/116 -- score: 0.9782445565770373[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done  44 out of  44 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 13:40:26] Features: 73/116 -- score: 0.978362203635861[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.6s\n",
      "[Parallel(n_jobs=-1)]: Done  43 out of  43 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 13:41:37] Features: 74/116 -- score: 0.9784795742033594[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 13:42:49] Features: 75/116 -- score: 0.978244418331375[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.5s\n",
      "[Parallel(n_jobs=-1)]: Done  41 out of  41 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 13:44:00] Features: 76/116 -- score: 0.9782445565770373[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.5s\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  1.1min finished\n",
      "\n",
      "[2023-02-04 13:45:06] Features: 77/116 -- score: 0.9785973595078454[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  39 out of  39 | elapsed:  1.1min finished\n",
      "\n",
      "[2023-02-04 13:46:11] Features: 78/116 -- score: 0.9782445565770373[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  38 out of  38 | elapsed:  1.1min finished\n",
      "\n",
      "[2023-02-04 13:47:17] Features: 79/116 -- score: 0.9782440035943873[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  37 out of  37 | elapsed:  1.1min finished\n",
      "\n",
      "[2023-02-04 13:48:22] Features: 80/116 -- score: 0.978362203635861[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:  1.0min finished\n",
      "\n",
      "[2023-02-04 13:49:23] Features: 81/116 -- score: 0.9784798506946843[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  35 out of  35 | elapsed:  1.0min finished\n",
      "\n",
      "[2023-02-04 13:50:24] Features: 82/116 -- score: 0.978244418331375[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 out of  34 | elapsed:  1.0min finished\n",
      "\n",
      "[2023-02-04 13:51:25] Features: 83/116 -- score: 0.9783619271445358[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 out of  33 | elapsed:  1.0min finished\n",
      "\n",
      "[2023-02-04 13:52:26] Features: 84/116 -- score: 0.9784794359576969[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  32 out of  32 | elapsed:   55.8s finished\n",
      "\n",
      "[2023-02-04 13:53:22] Features: 85/116 -- score: 0.9780092624593903[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  31 out of  31 | elapsed:   54.8s finished\n",
      "\n",
      "[2023-02-04 13:54:17] Features: 86/116 -- score: 0.9781269095182139[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:   54.6s finished\n",
      "\n",
      "[2023-02-04 13:55:12] Features: 87/116 -- score: 0.9784797124490219[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  29 out of  29 | elapsed:   54.5s finished\n",
      "\n",
      "[2023-02-04 13:56:06] Features: 88/116 -- score: 0.9780091242137278[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:   48.1s finished\n",
      "\n",
      "[2023-02-04 13:56:55] Features: 89/116 -- score: 0.9782445565770373[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:   48.2s finished\n",
      "\n",
      "[2023-02-04 13:57:43] Features: 90/116 -- score: 0.9783616506532109[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 out of  26 | elapsed:   47.9s finished\n",
      "\n",
      "[2023-02-04 13:58:31] Features: 91/116 -- score: 0.9783619271445358[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:   48.0s finished\n",
      "\n",
      "[2023-02-04 13:59:19] Features: 92/116 -- score: 0.9781270477638765[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:   41.4s finished\n",
      "\n",
      "[2023-02-04 14:00:01] Features: 93/116 -- score: 0.9781270477638764[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  23 | elapsed:   41.8s finished\n",
      "\n",
      "[2023-02-04 14:00:43] Features: 94/116 -- score: 0.9780089859680652[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  22 out of  22 | elapsed:   41.7s finished\n",
      "\n",
      "[2023-02-04 14:01:25] Features: 95/116 -- score: 0.9784792977120343[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:   41.3s finished\n",
      "\n",
      "[2023-02-04 14:02:06] Features: 96/116 -- score: 0.9781269095182139[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:   35.1s finished\n",
      "\n",
      "[2023-02-04 14:02:41] Features: 97/116 -- score: 0.9783617888988733[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  19 | elapsed:   34.9s finished\n",
      "\n",
      "[2023-02-04 14:03:17] Features: 98/116 -- score: 0.9783619271445358[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:   34.9s finished\n",
      "\n",
      "[2023-02-04 14:03:52] Features: 99/116 -- score: 0.9778913389092416[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:   35.4s finished\n",
      "\n",
      "[2023-02-04 14:04:27] Features: 100/116 -- score: 0.9776564595285823[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:   28.9s finished\n",
      "\n",
      "[2023-02-04 14:04:56] Features: 101/116 -- score: 0.9781270477638765[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:   28.7s finished\n",
      "\n",
      "[2023-02-04 14:05:25] Features: 102/116 -- score: 0.978009124213728[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:   28.6s finished\n",
      "\n",
      "[2023-02-04 14:05:54] Features: 103/116 -- score: 0.9777741065874059[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:   28.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:   28.7s finished\n",
      "\n",
      "[2023-02-04 14:06:23] Features: 104/116 -- score: 0.9782441418400498[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:   22.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:   22.2s finished\n",
      "\n",
      "[2023-02-04 14:06:45] Features: 105/116 -- score: 0.9780092624593903[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  11 out of  11 | elapsed:   22.1s finished\n",
      "\n",
      "[2023-02-04 14:07:08] Features: 106/116 -- score: 0.9776563212829199[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:   21.8s finished\n",
      "\n",
      "[2023-02-04 14:07:30] Features: 107/116 -- score: 0.9776564595285823[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of   9 | elapsed:   16.7s remaining:    4.7s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:   23.9s finished\n",
      "\n",
      "[2023-02-04 14:07:54] Features: 108/116 -- score: 0.9781273242552014[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   8 | elapsed:   15.8s remaining:    5.2s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   16.0s finished\n",
      "\n",
      "[2023-02-04 14:08:10] Features: 109/116 -- score: 0.9776567360199072[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   7 | elapsed:    7.5s remaining:    5.6s\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of   7 | elapsed:   14.6s finished\n",
      "\n",
      "[2023-02-04 14:08:25] Features: 110/116 -- score: 0.9775386742240961[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:    7.4s remaining:    7.4s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:   14.6s finished\n",
      "\n",
      "[2023-02-04 14:08:40] Features: 111/116 -- score: 0.978009124213728[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   14.4s finished\n",
      "\n",
      "[2023-02-04 14:08:54] Features: 112/116 -- score: 0.9778918918918919[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:    7.5s finished\n",
      "\n",
      "[2023-02-04 14:09:02] Features: 113/116 -- score: 0.9770689154627774[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:    7.5s finished\n",
      "\n",
      "[2023-02-04 14:09:10] Features: 114/116 -- score: 0.9770689154627774[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:    7.3s finished\n",
      "\n",
      "[2023-02-04 14:09:17] Features: 115/116 -- score: 0.9771867007672634[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:    7.3s finished\n",
      "\n",
      "[2023-02-04 14:09:25] Features: 116/116 -- score: 0.9756581184765329"
     ]
    }
   ],
   "source": [
    "# The random forest classifier\n",
    "from sklearn.ensemble import RandomForestClassifier \n",
    "\n",
    "# Build RF classifier to use in feature selection\n",
    "clf = RandomForestClassifier() \n",
    "\n",
    "# Build step forward feature selection using 10-fold cross-validation and accuracy scoring\n",
    "sfs1 = sfs(estimator=clf, \n",
    "           k_features=(1, 116), \n",
    "           forward=True, \n",
    "           n_jobs=-1,\n",
    "           floating=False, \n",
    "           verbose=2,\n",
    "           scoring='accuracy',\n",
    "           cv=10)\n",
    "\n",
    "# Perform SFFS\n",
    "sfs1 = sfs1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_rf_df = pd.DataFrame.from_dict(sfs1.get_metric_dict()).T.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 26, 27, 30, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 47, 49, 50, 52, 54, 57, 60, 61, 63, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 82, 83, 84, 90, 91, 94, 98, 99, 101, 102, 103, 107, 108, 109, 111, 115]\n"
     ]
    }
   ],
   "source": [
    "# best model accuracy score included these variables (the number is the index, e.g., 69 is sablefish)\n",
    "# However, not all of these variables were used b/c the model overfit\n",
    "feat_cols = list(sfs1.k_feature_idx_)\n",
    "print(feat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe to process the forward selection results\n",
    "sfs_rf_df_maxmin = sfs_rf_df.loc[: ,['feature_idx', 'avg_score', 'std_err']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_rf_df_maxmin['idx_len'] = sfs_rf_df_maxmin['feature_idx'].str.len()\n",
    "sfs_rf_df_maxmin['avg_score_rank'] = sfs_rf_df_maxmin['avg_score'].rank(ascending = 1)\n",
    "sfs_rf_df_maxmin['std_err_rank'] = sfs_rf_df_maxmin['std_err'].rank(ascending = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final selected variables for each model were those that produced the highest mean accuracy \n",
    "# with the lowest standard error using the fewest number of variables\n",
    "sfs_rf_df_maxmin['total_score'] = sfs_rf_df_maxmin['std_err_rank'] + sfs_rf_df_maxmin['avg_score_rank'] + sfs_rf_df_maxmin['idx_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_rf_df_opt = sfs_rf_df_maxmin[sfs_rf_df_maxmin.total_score == sfs_rf_df_maxmin.total_score.min()]\n",
    "\n",
    "x_train_rf = x_train.iloc[:,list(tuple(list(sfs_rf_df_opt[\"feature_idx\"])[0]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Std Error')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AaDTi7e1tN0k7Pj6eoUOHkpycjMFgwNfXl9dff51HHnmE2NhYZs+ezaJFizh58iQjR47kq6++csuTEMfUpklERERERERERESyQvXq1TN8fffIkSPpblO2bFmGDx+eqZgMBgNt27albdu2mdrfljPxmtWoUSND29t68MEHefDBBzO9f1ouXrwImKpaMsK8/YULF9wSh1PJiLi4OL7++mvAlKnp06cPXbt2pWjRolbbLVy40CqwTz/91GpIScOGDSlRogRTp05l8eLF9OzZ06mp4pI5d/IA614P9+Ja3DUKFyzs6VBEREREREREREREPM7c8Sij2ycmJrrl/E4lI1asWMGlS5cwGAx8+umntGvXzuF2S5YsAUwZqJo1azqclt6vXz8WLlzIlStX+PPPP5WMyELZMjMih1ZGfNTmI0+HICIiIiIiIiIiIuJxJUuW5NSpU+zbt482bdo4vd++ffsAxzM+MsPLmY02bNgAmAZlpJaIiI2NtfSeAlLdLn/+/Dz66KMYjUY2b96cmZjFSdnSpkkzI0RERERERERERERyrHvvvRej0cj8+fO5fPmyU/tcvHiR+fPnYzAYaNiwoVvicCoZcfDgQQwGA82bN091m+3bt5OYmGgp3WjatGmq29aqVQtwX68psZecDPHx1svupDZNIiIiIiIiIiIiIgLt27cHTAUFvXr14vz582luf+7cOV599VViYmIAHHZAygyn2jRFREQAUL58+VS32b59u+Xz0qVLp7ltkSJFALhy5Yozp5dMcJQTuJPaNImIiIiIiIiIiIgI3HfffbRo0YKVK1dy+PBhnnrqKZ5++mkeeughKlSoQMGCBYmLi+PkyZNs2rSJJUuWEBcXh8Fg4JFHHuGBBx5wSxxOJSOio6MBKJjGrfU7duwAcKps48atK+VpHU9ck23JiBxaGRE0KIizkWcpW6QsZ74+4+lwRERERERERERERDzm888/59KlS+zZs4e4uDjmzJnDnDlzHG5r7n5Uq1Ytvv76a7fF4FSbJnMlQ2xsrMP1cXFx7N+/3zIvIiQkJM3jmdszmY8r7ucoJ5AtbZpUGSEiIiIiIiIiIiKSowQEBPDzzz/To0cP8uXLh9FoTPUjf/78dOvWjZkzZ+Lv7++2GJyqjChZsiRXrlzh+PHjDtf//fffJCYmAqbKiAcffDDN423atAmAUqVKZSRWyQDb4dWgNk0iIiIiIiIiIiIid6oCBQrw3nvv0atXL/7++2927drF5cuXiYmJwc/PjxIlSlCvXj2aNm1KsWLF3H5+p5IRISEhHDx4kFWrVvHWW2/ZrV+6dClgSkRUrlyZcuXKpXqs48ePs3PnTgwGA40aNcpk2JKeO71Nk4iIiIiIiIiIiIjAwoULKVy4MM2aNcNgMFCsWDGeeeYZnnnmmWyNw6k2TY899hgAYWFh/PDDD1br9u7dy4oVKywtmtJ6AklJSXzyySckJSUB8PDDD2cqaEmfbU4gf3649SVyL1VGiIiIiIiIiIiIiORY06ZN47XXXuPll1/2aBxOVUY0atSIBx54gC1btjBq1Ch2797Ngw8+yKlTp/jtt99ISkrCaDQSGBhIp06dHB7j3LlzfPLJJ2zevNky5LpBgwZufTJym22bpiypigBVRoiIiIiIiIiIiIjkYGfOnAGgadOmHo3DqWQEwPDhw+natSuXLl1i1apVrFq1yrLOaDTi7e3N8OHDKVy4sNV+S5cuZdasWezatctSEZEvXz4+/PBDNz0FccQ2J5Alw6tBA6xFREREREREREREcjDzvOfixYt7NA6n2jQBVKhQgVmzZnH//ffbTdcuVqwYY8eOpUWLFnb7/fvvv2zfvt1SPeHn58fkyZOpUaOGW5+IWMu2ygi1aRIRERERERERERHJserWrQvA33//7dE4nK6MAChbtiw///wzhw8fZt++fURFRVGhQgWaNm1Kvnz5HO4TGBgIgJeXF82aNWPgwIHcc889rkcuabKtjFCbJhEREREREREREZE7z5AhQ+jUqRMLFy6kTJky9OrVCz8/v2yPI0PJCLMaNWo4XdnwxBNPULNmTapXr85dd92VmdNJJmRbmybbLEd8PBiNWTQtW0REREREREQk+yxZsoSBAwcC8OKLLzJ8+HAPRySpWbNmDUuWLGH37t1cvnwZLy8vAgMDqVevHk8++SRPPPGEp0MU8Zj4+HgGDx7M559/zuTJk/npp5+oVasW99xzDwEBAakWGqTUv39/l+PIVDIiI0qVKkWpUqWy+jRiw2MDrMGUkHC0PBv98sov3Ey4SX5fz8YhIiIiIiIiIrnX3LlzLZ8vWrSIQYMG4e/v78GIxNbNmzfp27cv69atA6BEiRJUqVIFMA3tXbJkCUuWLKFhw4ZMmjTJbt6tyJ2gY8eOGFLcPH7jxg127drFrl27nD5GrkhGiGd4bIC1+eQeTkY8Uv0Rj55fRERERERERHK3c+fOsWXLFooUKULFihXZvXs3ixcvplOnTp4OTVIYNmwY69ato3Llynz11VcEBwdb1hmNRjZu3MjgwYPZsWMHb7zxBr/88osHoxXxHKPRmObjtBjc1AVHyYg8KttmRjg6sIZYi4iIiIiIiEguN3/+fJKTk2nQoAENGjRg9+7dzJo1S8mIHOTs2bMsWrQIgPHjx9vNqTUYDDz88MOMHj2abt26sX37djZv3syDDz7oiXBFPOaLL77wdAiAkhF5lkfbNGmItYiIiIiIiIjkYkajkYULFwLQtGlTmjRpwjfffMPhw4fZvXs39evX92yAAsDBgwdJTk7G39/fLhGRUkhICBUrVuTEiRPs2bNHyQi54zz33HOeDgFQMiLP8mibphxQGbH2yFrLzAi1bBIREREREZE7XVISRESkvu7qVR8uXQJv7+yNKzNxFCuW9XEePHiQM2fO4OXlxWOPPUapUqWoW7cue/fuZebMmWkmI/755x9mzpzJzp07uXTpEv7+/tSvX5/u3bs7vAgeExPDzJkz+eOPPzh16hTx8fGUK1eOFi1acN9991lt261bN7Zt20afPn1466237I717bffMn78eEJCQggNDbUsb968OWfPnmXhwoX8/vvvLFy4kMTERCpVqsSPP/5IkSJFMBqNrF69moULF7Jv3z6uXLkCQGBgIJUrV6Zfv37Uq1fP4XM+fPgwM2bMYPPmzYSHh1OgQAFq1apFp06daNmyJQDHjh2jVatWAMybN4/atWs7PNYTTzzByZMnGT16NK1bt071dQbw9fW1vIb//POP3euV0vfff4/RaKREiRIO169evZo5c+Zw4MABIiIiKFKkCPfddx+vvPIKderUsds+PDyc0NBQ1q1bx9mzZ/Hy8qJ8+fI8/vjj/N///R8BAQFW25u/Nr169aJ+/fp8/fXXnD17lpIlSzJw4ECeeuopwDRoeObMmSxbtoz//vuPhIQEypQpwyOPPELPnj0pWbJkmq+JiCNffvklAB06dKBy5coei0PJiDwq29o0OZq0ngOSEV1/6MrZyLOULVKWM1+f8XQ4IiIiIiIiIh4zZw68+SZcvJjaFt6A44vM2cu5OEqWhPHjoUOHrItk7dq1gOmO+lKlSgHw9NNPs3fvXv744w/ef/99h4OQv/nmG8tF7yJFilC9enXOnz/PmjVrWLNmDZ988gkvvPCCZfuwsDD69OnDqVOnMBgM3HPPPXh5eXHs2DEmT55M2bJlmTt3LkWLFnXL8xo+fDg7d+6kSpUqxMXFkS9fPksi4p133mHJkiUAlCpViqpVqxIZGcm5c+cs8zMmTpxIs2bNrI45Y8YMvvjiCxISEihUqBBVq1blypUrbNmyhS1btlgSJ5UrV6ZBgwbs2rWLhQsXOkxG7Ny5k5MnT1K4cGFatGiR7vO599578fPz4/r16/Tu3Ztu3brxzDPPWAZYp1ShQgWHx0hKSmLIkCGWSpgSJUpQrVo1Tp8+zR9//MGKFSvsnvf+/ft59dVXiY6OxtfXlypVqpCYmMjRo0c5fPgwv//+O9999x3Vq1e3O9/27dv58ccfKVy4MPfccw9hYWHUrFkTgIsXL9K7d28OHTqEwWDg7rvvpkiRIvz3339MmzaNBQsWMHHiRBo2bJjuayOS0urVqzl16hRHjx5l6tSpHovDy2NnliyVbW2aDAb76gi1aRIRERERERHJMXr1SisRkftcvGh6TlklOjqa7du3A9CmTRvL8qeffhofHx9u3rzJ/Pnz7fZbunQp3333HV5eXrz//vts2rSJuXPnsmHDBgYMGACYkgFhYWGA6Q74t956i1OnTlGnTh2WL1/O0qVLWbx4McuWLaNChQqcPXuWTz/91G3PbefOnYwePZqlS5eyevVqJk6cCJjmYyxZsoQCBQrw/fffs379eubOncuqVatYtGgRQUFBJCYmMm7cOLvjffbZZyQkJNC7d282b97MvHnzWLduHV988QVeXl5MnjyZjRs3AtC+fXvLa5WYmGgX34IFCwB46qmnyOfoBlgbAQEBDBkyBIDY2FgmT57MU089RdOmTRk4cCAzZ87kxIkTaR5j6tSpLFy4kIIFC/LNN9+wYcMG5s2bx8aNG+nUqROJiYkMGDCAa9euAaY5Fd988w3R0dE0b96ctWvXsmDBApYsWcJff/1FgwYNOH/+PH369CE6OtrufLt376Z58+asW7eOhQsXsn79eipXrozRaKRfv34cOnSIhg0bsmzZMlavXs28efP4+++/ad++PZGRkbzxxhtcunQp3ddGJKXw8HAAnnzySY/GoWREHpVtbZrAPtORAyojREREREREREQyY+nSpcTHx5M/f36rC3eBgYGWNkuzZs2y22/8+PEA9OjRg+7du+N9q5eUt7c3r732Go0bNyYpKclywX3lypUcOXKEQoUK8d1331nduV+hQgU+//xzAFasWOHwonZm3HfffVatj4oVKwbA33//jY+PD507d7arfKhcuTLPPPMMAEePHrVaN3HiRJKTk2nVqhUDBw4kf4obVtu1a8fzzz8PmNoyAbRu3Ro/Pz8uX77M33//bXWsmzdv8scff1j2dVbHjh2ZNGkSZcuWtSwLDw9nyZIlfPzxxzz55JO0bt2aefPmYTQarfaNj4/n+++/B+Ddd9/lqaeewmAwAJA/f36GDRtGpUqVuH79uiW2KVOmcOPGDapWrcrYsWMpXry45XjlypXju+++o0SJEpw7d86qVVZK7733niXZYv4arFq1il27dlGyZEl++OEHq1Y6d911FyNGjKBevXpcvXqVadOmOf36iACW/5s+Pp5tlKRkRB6VbZURoMoIERERERERkRxsyhRTa6O8omRJ03PKKuaqh0cffRR/f3+rdeZKiePHj7NlyxbL8pMnT3Ls2DEAXnzxRYfHHTFiBCtXrrTMeli9ejUALVq0sLqgbdagQQNGjBjBhg0buOuuu1x8ViaptfcZNWoUe/fudTiHAm5fyIyPjyc5ORmAuLg4y2uQsvVUSgMGDODPP/+09KsvVKiQZYaEuS2S2apVq4iKiqJatWoEBwdn6Hk1b96cFStW8OOPP9KtWze7Nk1hYWEMGTKE1157jfj4eMvyf/75x9JqyVECxMvLi++//561a9danuO6desA6NSpk8PqjcKFC1sqQFauXGm3vkSJEpQrV85uuXnbFi1a4OfnZ7feYDBYvv/WrFnj+IUQScUjjzyC0Wjk999/t/wf9gTNjMijsm1mBNgnI1QZISIiIiIiIpJjdOgA7dqlNcA6if3791OnTh3L3fye4GwcWTnA+r///mPfvn0AlmqAlB5//HHLjIKZM2fywAMPAKZkBICfn5/DC80AZcqUsXp86tQpAGrUqJFqPJUqVXJbIgJIdXgzmCo44uPj2bx5M8eOHeP06dOcOHGCQ4cOceHCBct2ycnJeHl5ce7cORISEtJ8DoGBgQQGBlota9++PfPmzWPVqlXExMRYEj7mipGMVEXYxt+4cWMaN24MQEREBFu3bmXt2rUsW7aM+Ph41qxZw7fffsvAgQOB21+3SpUqUSCVi2fly5e3fB4TE2Npd1OrVq1UYzHPwzh+/LjdutQGUJurTtasWcPhw4cdbhMVFQXAiRMn7Ko8RNIyZMgQ9u/fz65du+jRowevvfYaDRo0sKpmyg4uJSMef/xx2rVrR9u2be1+oIpnqU2TiIiIiIiIiJh5e0Nq16GTkqBo0URKlMi6i/zOyAlx/P7775bP33jjjTS3XbVqFZcvX6Z48eJERkYCpjv/nWXex9Fd8FkltQvuCQkJTJgwgZkzZ1riAtMF/qpVq1K+fHm2bdtmtU/K7TLyvO+77z4qVqzIiRMnWL58Oe3bt+fSpUts3LgRHx8fqzkdrihWrBitWrWiVatWDBgwgN69e3P06FF+/fVX3n77bQwGQ4a/BrGxsZbP00oSmRMs169fx2g0Wlo/Aale/I2JiQHg/PnznD9/Ps04kpKSrGIRSc+cOXNo0aIF06ZNY9u2bWzbtg1vb2/Kli1LQEBAujNaDAYDv/zyi8txuJSMOH36NOPGjePbb7/lgQceoH379jz++ONODZiRrKU2TSIiIiIiIiIizktISGDRokWA6eJ0ahebjUYjFy9eJCEhgd9//50+ffpYLmZn5AJxwVt3jrrzovL169cztd+wYcOYN28e3t7evPDCCzRq1IiqVatSsWJFfH19CQ0NtUtGpLyAHxMTY5l94Ix27drxzTffsHjxYtq3b8/SpUtJSkriscces6ukSE1SUhIvvvgily9fZuTIkdx///2pblumTBnee+89evbsSUxMDJcuXaJkyZIZ/hqkTLqkNcfDPOzaz8/PKhGRFnMsQ4cOpWvXrulun5SU5NRxRcDUii3l96LRaCQxMdFSoZUW24SaK1yaGREYGIjRaCQ5OZnNmzfzzjvv0LhxYz7++GP27NnjlgAlc1QZISIiIiIiIiLivHXr1nHlyhUA3n//fdasWcP69evtPjZs2EC1atUA+O2330hOTqZixYqAKRlw5swZh8dftWoV3bp146uvvgKw7PPvv/+mGtP//vc/3nzzTfbu3QtgaV+Vcu5BShcvXszYk8Y07Nk8J+PTTz/lk08+4ZlnnqFGjRqWSgrz65JSuXLlLPGk9hz27dtHp06dGDJkiFVbobZt2+Lt7c327duJjIxkxYoVQMZaNHl7e3P58mXOnTvH2rVr093e3KLKy8uLIkWKAKb2TGBq13QzletZM2fO5KWXXmLq1Kn4+/tb2iwdPHgw1XPt378fuP01doY5lrS+H86fP8/u3bsz9XUWMRqNlg9Hy1L7cCeXkhEbNmzgu+++48knn8TX1xej0Uh0dDSzZ8/mxRdfpHXr1kydOpVLly65K15xkmZGiIiIiIiIiIg4z9yiqVq1alSuXDnNbc1Dqs+ePcv69eu55557KFu2LABz5851uM/8+fPZtm0bEbeGdzRr1gwwJSmuXr1qt/2RI0fYuXMna9asoWjRogCWf83DslOKjY1l8+bN6T5PW+fOnbNccDTPOkgpOTmZ9evXWx6b78j39/e3DMRO7TkvWbKEnTt3cubMGas7q0uVKsXDDz9MYmIi8+bNY9euXQQGBvLII49kKHZzS6dZs2Zx5MiRNLddunQpAA899JClq0vDhg3x8/MjPj6exYsX2+2TnJzM77//zubNmy1VJ+av28yZMx0mha5du2aZf9G0aVOnn8ujjz4KwLJlyxwmf8CUJHvhhRd4++23nT6uCJh+zrjy4WgYe2a4lIzw8vKiWbNmjB07lo0bNzJ06FDq1q1ryZocP36c//3vfzz66KP06dOHFStWkJiY6JbAJW1q0yQiIiIiIiIi4pxLly6xYcMGwLm785999llLm6JZs2ZhMBh4/fXXAZgyZQpz5syxXOBPSkri+++/Z8WKFfj4+PDSSy8B8PTTT1OxYkWioqJ48803rYZEHzt2jEGDBgHwxBNPWIZimy/+b9iwgb/++suy/cWLF+nXr1+qF7HTUqFCBUuFw5QpU4hLcVHp3LlzvPXWW1YDlVOuf/311zEYDCxcuJDJkydbXfdbsGABoaGhALzyyit2523fvj0A3377LUlJSbRp0wYfn4x1lO/RowcVK1bk+vXrdOvWjdDQUEuLJLOIiAhGjRrF999/T4ECBawu5Pv7+1u+Hl988QWrV6+2rLtx4wYjRoxg//79+Pv788ILL1ieS8GCBfn333/p37+/1Wt++vRpXn31VS5fvkypUqXo3r2708+ldevWVKtWjaioKHr27GlVIRETE8PHH3/Mpk2bMBgM9O7dO0Ovk0jZsmVd/nAHl2ZGpBQQEECXLl3o0qULx44dY968eSxevJjw8HASExNZt24d69ato3DhwrRp04bnnnuOmjVruuv0YuNOb9N05mvHJZEiIiIiIiIiIrYWLFhAYmIivr6+PPPMM5w8eTLN7f39/XnmmWeYPXs269at49y5czz//PP8999//PTTT3z44YeMGTOG0qVLc+bMGSIjI/H29ubjjz+mRo0aAOTLl48JEybwyiuv8M8//9C8eXOqVq3KzZs3OXXqFElJSVSsWJGPPvrIct7nnnuOX375hePHj9O3b1/Kly+Pn58fYWFheHt706dPHyZPnpyh516sWDF69OjBDz/8wJIlS1i3bh3ly5cnNjaWkydPYjQaqVWrFkePHiUxMZELFy5Y2hw9+OCDDBkyhJEjRzJ69Gh+/PFHypUrx4ULF7h8+TJgGgRuriZI6dFHH6VYsWKWSpGMtGgyK1KkCD/99BNvvfUWu3fv5rPPPmPkyJEEBQUREBBAVFSU5TkEBgYycuRIu+qPN954g+PHj/PHH3/w2muvUaZMGYoVK8aJEyeIjY2lQIECfPPNN5b2TOXKlaN///6MHz+e1atX06xZM6pUqUJSUhL//fcfycnJ3H333YwfPz5DczR8fX2ZOHEir7zyCocOHeLpp5+mUqVKFCxYkBMnTlgqM4YMGULTpk01M0JyJZcqI1JTuXJl3nnnHdauXctPP/3ECy+8QIkSJTAajURGRhIaGkq7du1o27YtM2bMSHPgi2SOKiNERERERERERJxjnpnQvHlzSyuk9HTq1AkwtfL57bffABg8eDA//fQTjz32GEajkcOHD+Pt7U3Lli2ZPXs2HTp0sDpGlSpVWLhwIa+//jr33HMPJ06c4Ny5c1StWpWBAwcyfPhwy4V/MA1Qnj17Nj179qRChQqcP3+ey5cv8+STTzJ//vw0hzinZdCgQYwdO5aGDRvi6+vLkSNHiI6O5sEHH+TLL7/kgw8+oH79+gCsWbPGat/u3bsze/Zsnn76afLnz8+RI0dITEykWbNm/PTTT/Tr18/hOX19fXn66acBqFOnjmUOR0bdfffdzJo1iwkTJtCuXTvKly9PZGQkBw8eJDo6mgYNGvDOO+/wxx9/OGyb5OPjw+jRoxk9ejSNGzcmLi6OI0eO4O/vT7t27ViwYIFdMqVu3bosWrSIl156iaCgII4fP8758+epWbMmAwcOZOHChQ5bXqWnXLlyzJ8/n3fffZd69epx6dIljh49SqFChXjyySf55ZdfMlRtIeKqpKQkYmJiiImJccvx3FYZ4YjBYODBBx/kwQcf5L333mPixIlMmzaNpKQkjEYjR44c4bPPPuN///sfbdu25dVXX6V06dJZGdIdI1tnRuTAyggREREREREREWctW7bM8rmzd5zXrFnT4ZyChx56iIceesjpcxcuXJj+/fvTv39/q+VJSUns3r3b4fbvvvsu7777rt26ypUrO4wpZfuh1LRs2ZKWLVvaLTfHMX36dEs7J1t169Zl1KhR6Z7Dlrk1VWaqIlIyGAy0aNGCFi1aZHr/1q1b07p1a6f3KV26NEOGDGHIkCFObd+3b1/69u2b7nZ+fn707NmTnj17Oh2LiNkXX3wBwPPPP0/VqlVdPt6aNWvo27cvBoMhzaHtzsrSZMT169dZsWIFK1as4O+//+bGrSvk5p55hQsXJjIykri4OGbNmsWiRYv46KOPLMNnnHXkyBF++OEHtm7dSkREBEWKFKFOnTp07tw5Q4NibG3dupUZM2awc+dOIiMjCQgIoEGDBnTq1IkmTZqkuW9MTAyzZ89mxYoVhIWFERcXR0BAALVq1aJt27a0bt0aL68sKUwBsrlNkwZYi4iIiIiIiIhIBly6dIk1a9ZQsGDBDF8LFBHHfv75ZwwGAyEhIWkmI958800AevfuTd26ddM8pvlavju4PRlhNBr5+++/WbhwIStXrrRLQBQvXpxnn32W559/nkqVKrFlyxZmz57N8uXLiY2NZfDgwRQtWpSHH37YqfOtXLmSAQMGkJCQYFlm/mG2Zs0aunXrxocffpih55CcnMyIESP45ZdfrJZfuXKFlStXsnLlSl544QU++ugjhxnhsLAwevfuzZkzZ+z237BhAxs2bGD+/PmMHz+eglmQJUhIANsk/p3Wpmn4ouFci7tG4YKF+ajNR+nvICIiIiIiIiIiWSo8PJwbN25w/fp1hg8fTkJCAi+88AJ33XWXp0MTuaOsXLkSg8HAc889l63ndVsy4vDhwyxcuJAlS5ZYBtSYExA+Pj40bdqU559/nmbNmlldwH/ggQd44IEHmDNnDkOHDiU5OZlJkyY5lYw4cOAAb7/9NgkJCQQHB/Puu+9StWpVzpw5w6RJk1i1ahWhoaFUqlSJLl26OP1cvv32W0sionLlygwcOJB7772XmJgYFi1axKRJk5g9ezaJiYl8/vnnVvtev36dXr16cfbsWQoUKMAbb7zBE088wV133cXx48eZOnUqq1evZuPGjbz//vuMHj3a6bic5SgXcKcNsJ6yYQpnI89StkhZJSNERERERERERHKA7du3M3DgQMvjEiVKWO7QFpG8z6VkRHh4OIsXL2bhwoX8999/gHXZRuXKlWnXrh3PPfccgYGBaR6rQ4cO/PDDD5w8eZLDhw87df4xY8Zw8+ZNypcvz88//0yhQoUAKFq0KBMmTKBv376sWLGCcePG8eyzz+Lv75/uMc+dO8eUKVMAqFatGr/++qslO1usWDHefPNNKleuzFtvvcXcuXNp27YtISEhlv1nzJjB2bNnAZg4cSKNGze2rAsMDOS+++7js88+IzQ0lGXLlvHyyy8THBzs1PN1lqNkxJ1WGSEiIiIiIiIiIjnLPffcQ9myZYmIiKB+/foMGzbM6YHhIpL7uZSMePTRRy3JB/O/fn5+tGrViueff54GDRpk6HiFCxcGIL/txW0HwsLCWL9+PWDqbWVORJgZDAYGDx7MypUriYyMZPny5bRv3z7d4y5dutTS8umjjz5yWCbWunVrfv31V7Zv386UKVOskhHLly8HICQkxCoRkVLfvn359ddfSUpKYt26dW5PRsTF2S/L1mREDqiMEBERERERERGRnKVmzZpODdMWkbzJpWREcnKy5fN7772X9u3b06pVK/z8/DJ1vLi4OOrXr5/ucGjAkogwGAw0b97c4TZBQUFUr16dw4cPs2rVKqeSEQcOHABMsy3uu+++VLdr0qQJ27dvZ/PmzcTHx5MvXz4AIiMj8fLyol69eqnuW7hwYQoXLkxERAQXL15MN6aMUpsmEREREREREREREclJXEpGlChRgmeffZb27dtTqVIll4NZvHix09seOnQIgNKlS6fZAqpWrVocPnzYkmRIT1RUFAB33313mtsVK1YMgISEBI4dO0aNGjUA0/CPxMREq4Hatq5du0ZkZCQAAQEBTsWVEbaVEV5e4OP2UeUpqE2TiIiIiIiIiIiIiKTBpUvUa9eutRpGnZ3OnTsHQNmyZdPczpxUCA8PJyEhAV9f3zS3N1d1xMbGprndtWvXLJ+Hh4dbkhFgGtjtk8bV/9mzZ1uqSu699940z5MZtrmAAgXAYHD7aaxPkJIqI0REREREREREREQkBZeSESkTETt27GDRokV07dqVqlWrWm0XExNDixYtaNy4MV26dHHLBfirV68C6VcWmGc+GI1GoqOjLRUNqalatSorVqzg+PHjhIeHU6pUKYfbbdu2zfJ5eomLlI4fP86kSZMAKFeuHA8//HC6+yQnJ5OUlOT0OUzh3P7aFCxoJCkpOdXt02M+d2oxGHx98Urx2HjjBskZiNddcaS3n6fjcDfFoTgUh+JQHO6TU2JRHIpDcSgOxaE4skpOiUVxKA7FoTgUR86PI2VrfhF3crl5T3x8PIMHD+aPP/4AoGHDhnbJiDNnzhAZGcmyZctYtmwZXbp04cMPP3TpvDdv3X1fIJ3JzCmHYd904o79li1bMmnSJJKTkxkxYgRjxozBy8vLaptNmzaxYcMGy+P4+HinYr506RJ9+vTh+vXrGAwGhg4dmm6lBphevzNnzjh1DoADBwKA218Db+8Edu/e5/T+qdm3z/Exil24QMomXXFXr3Jo926Xz5fROFIyt8lKSEhgdxbF4kwc2UFxWFMc1hSHNcVhTXHYyymxKA5risOa4rCmOKwpDmuKw15OiUVxWFMc1hSHNcVhTXFYyylxiDjD5WTEW2+9xerVqzEajQCcPHnSbpubN29SqlQpwsPDAZgxYwYGg4EPPvgg0+c1V2UY0uk/ZI4LsEsqOFK9enXatWvH3LlzWb58OT179uS1116jWrVqREdH8+effzJ+/HhKlixpeT7m4dVpuXDhAi+//DInTpwA4M0336RZs2bp7gemQdzFixd3alsA2y9BQIAv9evXd3p/W0lJSezbt4/g4GDHbbn++8/qYUFvb5fOl+k4UvCdZUry+Pq69txdjSMrKQ7FoTgUh+LIe7EoDsWhOBSH4lAceT0WxaE4FIfiUBw5P46IiAhOnTrl1mOKgBtmRqxatQqDwUDx4sV57733ePLJJ+22q1evHuvWrWPLli18/PHHnDhxgl9++YVnnnmGunXrZurc5tkON9IZlpyyasGZpAHARx99xNWrV1m9ejWbNm1i06ZNVusrVarERx99xEsvvWQVS2r+/fdfevfubZlz8dJLL/Hmm286FQuYkigZ+aFiW6hRoIDBLT+UvL29HR+nYEGrh4YbN7L0h3GqcaTQrFozLsdcprh/8SyLxZk4soPiUByKQ3EoDvfJKbEoDsWhOBSH4lAcWSWnxKI4FIfiUByKI+fG4cwN3SKZ4VIyYt68eQAUKlSI3377zTIsOjUPPPAAP/30E61bt+bGjRv8+uuvmU5G+Pv7AxAdHZ3mdlFRUYDpP1HhwoWdOnb+/PmZOHEiixcvZs6cORw8eJDk5GTKly/PU089Rbdu3fj3338t25coUSLVY23YsIEBAwYQExMDwOuvv07//v2diiOz4uKsH6fTycp1OXCA9YxeMzwdgoiIiIiIiIiIiEi2S6+bUEa3cxeXkhF79+7FYDDwwgsvpJuIMCtTpgwdO3bk559/5p9//sn0uStVqsS2bds4f/58mtuZ15cpUyZDWT2DwUCbNm1o06aNw/WHDx+2bFehQgWH2/z2228MHz6cxMREvL29GTZsGC+++KLTMWSWbbGITeGC+6WYywHkiGSEiIiIiIiIiIiIyJ3ojTfeSHcbo9Ho1Hbu5FIy4sqVKwDUrFkzQ/uZt7948WKmz129enUAzp49S1RUFAEBAQ63O3DgAAA1atRw+thGo5HIyEiKFi2a6jYbN24EoGrVqpYqjZQmT57M6NGjAVMbp9GjR/PII484HYMrsr0ywjYZkU7rLBERERERERERERHJOilnKdtKWRHh7Hbu4FIDMPNF+PTmNqTG2RkOjjRt2hSA5ORk1q5d63Cb06dPc/ToUQAefvhhp467efNmgoODeeCBBwgLC3O4zaVLl1i3bh0ALVq0sFs/ffp0SyIiMDCQ0NDQbEtEgH0u4E5s0yQiIiIiIiIiIiJypzEajWkmGFJu48x27uRSZcTdd99NZGQkmzdv5vnnn3d6P3N7pjJlymT63OXKlaNhw4bs2LGDCRMm8Oijj3LXXXdZ1huNRkaOHInRaKRo0aI8++yzTh23du3alozPr7/+ytChQ63WJycnM3z4cG7cuIGfnx+dOnWyWr9nzx6+/PJLAIoVK8aMGTOoVKlSpp9nZni8TVNiIiQlgQeH+DT/X3PCo8IpFVCK1e+s9lgcIiIiIiIiIpK7HDlyhJ9//pmhQ4dy8eJFbt68SbFixahatSqPPPIIzz//PAXSuPPzwoUL+Pv7O+ykkZp58+YxZMgQSpUqxfr1653aZ+vWrfzf//2f0+cAeOyxx5g4cWKG9hGR3MM8WiCncikZ0bRpUw4cOMCff/5Jt27dqF+/frr7HDlyhIULF2IwGHjggQdcOT1DhgyhY8eOnDhxgs6dO/Pee+9Rq1Ytzp8/z4QJE1i1ahUAffv2xc/Pz2rfli1bAlC3bl2++uory/KAgAA6dOjAjBkzmDFjBvnz56d9+/YULVqUI0eOMHHiRLZt2wbAoEGDKFmypNVxP/nkExITEzEYDHz66aeULFmS2NjYVJ+Dr6+vSxUijni8TROYqiNsXvPsdDT8KGcjz3It7prHYhARERERERGR3GXcuHFMmjSJ5ORk/P39KV++PL6+vly6dIkNGzawYcMGfvjhByZMmEDt2rWt9o2Pj2fSpEn8+OOPLFq0KEPJCFfVqVPHqetLVapUyYZoREQccykZ0b59e77//nuSk5N59dVX+eCDD3j66adTHRT9119/8cknn5CQkICPjw+dO3d25fQEBwczYsQIhg4dytGjR+nZs6fdNj169KBLly52y48fPw5AiRIl7NYNGjSIf//9l23btjF16lSmTp1qtd7b25sBAwbYxb9jxw72798POD8A5LnnnmPkyJHpbpcR2V4Z4Sjb4eFkhIiIiIiIiIhIRsydO5cJEyZQsGBBevfuzSuvvGJ1gT8sLIz333+f3bt307NnT5YtW0axYsUs6y9evOixqoOxY8cSFBTkkXOLiDjLpWREUFAQb7/9Nl9//TVRUVG89957fPnllzRo0IC7776bggULEhcXx/nz59m9ezeXL1/GaDRiMBjo3bu3W9oXtWvXjtq1azN16lS2bt3KlStX8PPzo06dOnTu3NnhTIf0FCxYkJ9++onZs2ezaNEijh49SkJCAiVLluT++++ne/fuDgdi79mzx+Xn4w7ZPjPCUWWEhliLiIiIiIiISC4yefJkwHSTao0aNfC2aT99zz33MGnSJJ5++mmuXLnC9OnTGTBggAciFRHJnVxKRgD07NmTq1ev8tNPP5GUlMSVK1cs7ZFsmQdevPTSS/Tr18/VU1tUr17dqtWSM44cOZLmeh8fH7p06eKwqiI1L7/8Mi+//HKG4sgK2d6mKbXKCBERERERERGRXCAqKopTp04Bppbe8fHxDrcrVqwYLVq0YPbs2ezduzc7QxQRyfUc91PKoHfeeYfZs2fTqlUr/P39raZxmz+8vb1p1qwZoaGhvPfee+44raTC4wOsQckIEREREREREck1fHxu36+7bt26NLft27cvS5cuZdSoUZZl3bp147HHHrM8fuKJJ6hevTpbt2612nfFihV0796d+++/nwYNGtCtWzc2btzopmeRcc2bN6d69eocPnyYzz77jEaNGtGgQQPatWtHZGQk3377LbVq1WLmzJmsXLmSJ598kjp16tC8eXOWLl1qOc6NGzeYNm0aHTt25N577yU4OJgWLVowbNgwTpw4YXferVu3Ur16dTp27EhYWBidOnUiODiYBx98kC+//DIbXwERyU4uV0aY1alTh9GjR5OQkEBYWBiXL18mMjKSAgUKULx4cWrWrEl+Rxetxe2yvTLCxwe8vCA5+fYytWkSERERERERyTmSkiAiItV1PlevwqVLYNOaKFs5G0exYm6P08/Pj3vvvZedO3cyfvx4du/eTa9evbjvvvvs2jWVKFHCbgZptWrVuH79umWWaO3atcmfPz933XWXZZvhw4fz66+/AlCmTBnKli3L3r176dmzJyEhIW59Phk1fPhwdu7cSZUqVYiLiyNfvnwUKVLEsv7QoUMsW7aMwoULc8899xAWFkbNmjUBuHDhAj169ODYsWMAVKxYkUKFChEWFsbs2bNZsGABI0eOpHXr1nbnjYiIoHv37sTExFClShVOnjxJxYoVs+Mpi4gHuC0ZYebr6+twnoJkn2yfGWE+yfXrtx+rMkJEREREREQkZ5gzB958Ey5edLjaG6iXvRE55HQcJUvC+PHQoYNbzz906FC6dOnC9evX2bBhAxs2bMDf35+GDRty3333cf/99xMcHIyXl32jkaFDh3LmzBlLdcTo0aOpUKGCZf2iRYv49ddf8fX15YsvvuCZZ54BTO2hhg4dyp9//unW55JRO3fuZPTo0ZaEQYRN4uq///6jRYsWjB49mnz58hEREUGxYsVISkqiT58+HDt2jEqVKjFmzBjLdcGYmBhGjhzJnDlzePfddylbtiz16ll/hU+fPk358uWZO3cupUqVIjY21qpKRUTyFv3vzoOyvU0TmFo1pUxGqDJCREREREREJGfo1QuuXfN0FO5z8aLpObk5GVGrVi3mzJnD0KFD2blzJ2C6oL5u3TpL66bAwEA6dOhAnz59KJiBCy6TJk0CoE+fPpZEBEBAQABff/01R44c4fjx45mOPWWLqLSkNkP1vvvus6pcKFasmN02gwYNIl++fFbr//zzTw4dOkT+/PmZMmUK5cqVs2zv7+/PZ599xoULF9iwYQOjR49m2rRpdsd99dVXKVWqFACFChVy6nmISO7k1mREREQEN27cICkpyW6d0WgkISGB+Ph4oqOjOXToEIsWLWLu3LnuDEHwQJsmRydRZYSIiIiIiIiI5DJVqlThl19+YcGCBZw5c4bNmzezf/9+EhISALhy5QqTJ09m2bJlhIaGUrp06XSPefr0aUsLo+eee85ufb58+Xj++ef5+uuvMx13nTp1LImCzGjYsGGa64sUKWKVaDBbvXo1YJo94Wg9QI8ePdiwYQPbtm0jOjraqnWVM+cWkbzD5WTE9evXGTduHAsXLiQyMtINIYmrPFYZkZKHkxHDnhlGzI0Y/Av4ezQOEREREREREY+bMiXNNk25jrlNUxaqWLEibdu2pX///sTFxbFz5042btzIwoULuXLlCqdOnaJ///7Mnj073WOZExGFChWibNmyDrcxz1/IrLFjxxIUFJTp/W1nYNhKOT8iJXM1R+3atVPd17wuKSmJkydPUqdOnQydW0TyDpeSEUajkT59+rB9+3aMRmOG93fUY09c55GZEbbJCA+3aerdtLdHzy8iIiIiIiKSY3ToAO3apTrAOikpif3791OnTh27Yc3Zyek4smCAdVoKFixI48aNady4Mf379+f9999n6dKl7N69mwMHDqR5IR5McyHANCQ7NQEBAW6NOaMKpHPxKLWqi5iYGAC7aoeU/P1v3ygaGxub4XOLSN7hUjJi+fLlbNu2DYPBAEDx4sUJCgri5MmTREZGUq5cOUqUKEFUVBQnT54kPj7esu3AgQNp2bKl689A7KhNk4iIiIiIiIhY8faG1O5AT0oisWhR03oPJiM8GcewYcPYsmULzz33HL17p36DY4ECBfjkk0/466+/SEhI4Pjx4+kmI8xVBY4uxJvdzKXXUcwzHqKjo1PdxpyMSbm9iNyZXE5GmH3yySd07NgRgHHjxjFx4kSCg4MZNWoUYGrnFBoayrfffktSUhKHDh3ilVdeceX0kooc0aZJA6xFREREREREJJe4efMmJ0+eZOXKlWkmI8B0p3+hQoWIjIx0OOjZVqVKlQDTtbHjx49bHqf077//Zi5wD6tcuTIHDx7kwIEDqW6zb98+AAwGA+XLl8+u0ETuSAsWLMiyY7dt29blY7iUjNi7dy8Gg4HmzZtbEhEA9913HwCbNm2yLPPz8+PVV1/F39+fTz/9lGXLltG1a1caNGjgSgjigEcqI3LYzIjzkedJSk7C28ubMkXKeDQWEREREREREcnZ2rRpw4IFC9i/fz/z5893mDAw27hxI5GRkRQpUoR69epZlqdsR56ynXlQUBC1a9fmwIEDzJw5k/fff9/qeMnJycydO9eNzyb7PProoyxZsoTVq1dz+vRph0Osp0+fDkD9+vU93o5KJK8bPHiwpTOROxkMBrckI1wa2mAeWP3QQw9ZLa9Ro4Zl/enTp63WdenShapVqwLk2h+0OZnRaJ8HuBPbNDUa0Yhy75Wj0YhGHo1DRERERERERHK+xo0b8+STTwKmlk3Tp0/n7NmzVtvcvHmTuXPnMmDAAAD69+9v1XYo5UyIc+fOWe379ttvAxAaGsq0adNITk4GIC4ujqFDh1qqB3Kbli1bUr16dW7evEmvXr04fPiwZV1MTAxDhw5l48aN+Pj48M4773gwUpE7h9FozJIPd3CpMsLcz8526n2xYsUoXLgwUVFRHD161C4r2rJlS7799ttc+4M2J3OUA1CbJhERERERERGRtP3vf//Dz8+PBQsW8Oeff/Lnn39y9913ExgYyM2bNzlx4gTx8fH4+voycOBAOnfubLV/kSJFKFu2LGfPnuWNN96gcuXK9O/fn6ZNm9KkSRPeeecdRo0axRdffMGUKVMoU6YMx44dIzY2lscff5wVK1ZkOvb+/funOmTa1rhx4+yu5WWWj48PEydOpFevXhw7doxnn32WihUrUqhQIcLCwrhx4wYFChRg+PDhlk4qIpJ1vvjii1TXXb16lbFjxxIfH0+BAgV46qmnuP/++6lQoQL+/v7Ex8cTHh7O9u3bWbBgAZcvX6Z48eJ89tlnlC5d2i3xuZSMKFy4MBEREcTHx9utCwoK4uDBgxw/ftxunTk5YZslFtfZtmiCO7MyQkREREREREQkI/Lly8fIkSPp1KkTP//8M8ePH+fixYscPnyYggULUqlSJZo0acLzzz9P5cqVHR5j7NixjBgxgkOHDnHixAlOnTplWderVy/q1avHjz/+yL59+/j333+pUqUKL7/8MqVKlXIpGbF//36nt3X3sOygoCDmzp3LzJkz+fPPPwkLC+PChQuUKVOGhx9+mC5dulCxYkW3nlNEHHvuueccLk9ISKBjx47Ex8cTHBzMuHHjHCYYatSoQbNmzejTpw9vvfUWGzZs4IsvvmDevHluic+lZESZMmWIiIggLCzMbl358uU5cOCAwwE8CQkJgKkUTdzLUUGCRyojlIwQERERERERkVyoTp06dOnShfr16+Pt7Z2hfYODg5k1a1aq60NCQggJCXG47siRIxk61/3335/hfWytXr06zfV9+/bl9ddfZ/fu3Wlu5+fnR8+ePenZs6dT53VH7CLivBkzZnDo0CGKFy/ODz/8kO78Fn9/f8aPH0/r1q05deoUP/74I3379nU5DpdmRtx3330YjUYWLlzI9evXrdbdc889AGzbts2up5Q5W1swW66S31kcJSM8MsBabZpEREREREREREREPG7x4sUYDAY6duzo9CD5/Pnz07FjR4xGI8uXL3dLHC4lI55++mkAzp8/z4svvsjGjRstiYdGjUyDgy9cuMD48eMt+/zzzz/MnTsXg8GQakmbZJ7aNImIiIiIiIiIiIiI2YkTJwCoUqVKhvYrX748AGfPnnVLHC4lI+rUqcMTTzyB0Wjk33//pVevXqxfvx4wlZ1VqlQJgIkTJ9KiRQvatGlD9+7dLTMmHn/8cRfDF1u2BQn58oGXS19lJ6kyQkRERERERERERCTHSUpKAiAqKipD+4WHhwNgMBjcEofLl6m/+uorHnnkEUtFRFBQEGAK8NNPP8XHxzSW4uzZs/z777+WJ16pUiW6du3q6unFhm1lRLZURTg6kSojRERERERERERERDzOXOHwxx9/OL1PcnIy8+fPx2AwUK1aNbfE4dIAa4ACBQowefJkNm/ezLJlyyzJCDDNlJg6dSoff/wxx44dA0xJiqZNm/LZZ59RINuulN85bAsSsu0l1gBrERERERERERGRO8aRI0f44Ycf2Lp1KxERERQpUoQ6derQuXNnmjZtmunjnjlzhilTprBx40bCw8Px9/enevXqdOjQwTI2IDXm+ca///47hw4dIiEhgdKlS9OsWTNefvllypQpk6FYpk2bxhdffEGfPn146623Ut0uOTmZhg0b2s1VdmTnzp0UKlTIbrkrzzs9jz76KEePHmXbtm1MmTKFXr16pbm90Wjk008/5ciRIxgMBlq2bOnS+c1cTkaYPfjggzz44IN2y0NCQli2bBknT57k6tWrBAUFUbx4cXedVmzYJiOybUa42jSJiIiIiIiIiIjcEVauXMmAAQNISEiwLLt06RJr1qxhzZo1dOvWjQ8//DDDx92zZw89evQgNjbWsuzq1ats2bKFLVu2sHz5ckaPHm3pxpNScnIy77zzDkuXLrVafvLkSaZPn878+fMZP348DzzwgFOx7N27lzFjxji17fHjx51KRKTGleftjO7duzNz5kyio6P55ptv2LFjB507d+bee+/F39/fsl1kZCSbN29m2rRp7N27F4PBQPny5enUqVOmn1tKLiUjBg8ezPXr13nhhRdo3LhxmttWqFCBChUquHI6cYLaNJmsGriKxKREfLzdlm8TERERERERERG54x04cIC3336bhIQEgoODeffdd6latSpnzpxh0qRJrFq1itDQUCpVqkSXLl2cPu758+fp3bs3sbGxVKxYkSFDhlC3bl0uX77M9OnTmTNnDn/99RejRo3ivffes9t/1KhRlkREjx49eOGFFwgICGDbtm2MHDmSCxcu0LdvXxYvXkzp0qXTjGXv3r307NmTONuLrak4ePAgAL6+vvz9999pJg1sqyJcfd7OKFasGOPHj+eVV14hPj6edevWsW7dOgD8/f0pUKAAcXFxVskQo9FI0aJFmTJlCvltb0TPJJdmRmzbto0VK1YwZ84ctwQjrlNlhEn10tWpXbY21UtX92gcIiIiIiIiIiIiecmYMWO4efMm5cuX5+effyYkJISiRYsSHBzMhAkTePzxxwEYN24cMTExTh/3u+++IzIykoCAAEJDQ3nkkUcoVqwY1apV47PPPqN79+4AhIaGcvr0aat9L1y4wM8//wxAr169GDx4MJUqVSIwMJBWrVoxY8YMChcuTFRUFOPHj08zjpkzZ9KlS5cMDXs+cOAAADVq1KBw4cIUKlQo1Q93Pu+MCAkJ4eeff6ZGjRoYjUbLR3R0NJcvXyYmJsZqeePGjfn9998t8ybcwaVkxOXLlwFc6gEm7qWZESIiIiIiIiIiIpIVwsLCWL9+PQC9e/e2u7huMBgYPHgwBoOByMhIli9f7tRxr127xty5cwHo0qULJUuWtNumX79++Pv7k5CQwPz5863WhYaGkpCQgJ+fH3369LHbNygoiP/7v/8DYMmSJQ5bKu3du5euXbvy8ccfEx8fT+3atZ2KHW4nI4KDg53eB1x/3hnVoEED5s+fz3fffceLL75I9erVCQwMxNvbm+LFi1OrVi26devGjBkzmDp1KmXLlnXpfLZcSkYEBAQAEB8f75ZgxHVq0yQiIiIiIiIiIiJZwZyIMBgMNG/e3OE2QUFBVK9u6layatUqp467detWyzXm1I7r7+/P/fff7/C45rgaNWpkNQMhpcceewyAuLg4Nm3aZLd+wIABbN++HYPBQJcuXfj111+dit1oNHLo0CEg48kIV593ZhgMBpo1a8bHH3/MwoUL+fvvv9m/fz8bN25k3rx5fPDBBzRs2NDl8zjiUjKiVatWGI1GZsyY4dKADnEftWky+XXrr/yw4Qd+3ercDw0RERERERERERFJm/mie+nSpQkMDEx1u1q1agG3KwacPa6Xlxc1a9ZM97j//vuv5SJ+fHw8x44dA0izmqFatWqWWQ779+93uE1ISAi//fYbw4YNo4CTd3mfOnWK6OhoAAIDAxk5ciQtW7akTp06NGrUiP/7v/9j4cKFGI1Gu31ded4ZsX37drZv305kZGSG9jt37hwzZszgu+++y/A5HXFpuu8777zDkSNH2LZtGx06dODll1+mUaNGbu0jJRmjygiTd39/l7ORZylbpCyd7+/s0VhERERERERERETygnPnzgGk277n7rvvBiA8PJyEhAR8fX2dOm6pUqXS3NZ83KSkJC5cuED58uUJDw8nMTEx3bi8vb0pVaoUZ8+e5ezZs3brp06dSqVKldKM05GUCZc33niDhIQEy+OEhAS2bt3K1q1bWbJkCWPGjLFqbeXK886Ibt26YTAYGD9+vKVCxBm7d+/m008/pUSJErz66qsZOqcjLiUjvvjiCypUqMCePXsICwvjww8/BExf2ICAAPLly5fm/gaDgTVr1rgSwh0hOTmZpKQkp7ZNTISUrdoCAsDJXVNlPneaMfj44J3iofHmTZJdPXFm4khjP0/H4W6KQ3EoDsWhONwnp8SiOBSH4lAcikNxZJWcEoviUByKQ3EojpwfR3Jycqrrrl69Ctxu35+au+66C8AyILlYsWJpbp/R44Jp3kLKfQEKFy6c5v7mFk6OhlNnJhEB1smIIkWK0LdvXxo3boyfnx+HDx9m0qRJbNu2jfXr1zNo0CAmTpxo2d6V550dzOdy1zldSkbMmjULg8EAYPnXaDSSmJho9U3giNFotOwjaTtz5gxnzpxxattnnzV9pLR7t3vi2LdvX6rr/E+fpnqKx0mxsexx14kzEIeZOQOZkJDAbg/GkR0UhzXFYU1xWFMc1hSHvZwSi+KwpjisKQ5risOa4rCmOOzllFgUhzXFYU1xWFMc1hSHteyO4+atbijptTDKn6Kl+00nOqi4ctyUbYvy27aSt2E+/g03tpiPi4ujcOHCFCpUiN9++40SJUpY1j300EPcf//99O/fnxUrVrBq1SrWrFnDo48+avUc3PV6rlixgiNHjqS6ftmyZZbWUGkxGo1cu3bNMjC7aNGi6e7jDJeSEYDDXldpLZeMCwoKonjx4k5t+9Zb8OOPtx+/8gqMGuXa+ZOSkti3bx/BwcF4e3s73ihF+RGAd2Ii9evXd+3EmYnjFt9ZprImX19fj8aRlRSH4lAcikNx5L1YFIfiUByKQ3Eojrwei+JQHIpDcSiOnB9HREQEp06dcrjOfK70bjJPeW3Yyyv9scWZOa5525THd3Z/Z2Jy1rBhwxg2bBiJiYmWmRQpeXt7M2zYMNauXUtCQgJz5861JCNced6OlC1blgEDBjisbjEajSxbtsyp55SSwWCwxOsql5IRhw8fdksQkjYvLy+nf6hER0Ns7O3HPj7grp9H3t7eqcfh52f10HDjBt5eXpAF1S9pxpHK9lkho3FkFcWhOBSH4lAc7pNTYlEcikNxKA7FoTiySk6JRXEoDsWhOBRHzo0jrQv1freuAaZXWZCyWiG9Vv6ZPa65WsAvxXXJ9KowzOvTq6DIDEeJCLOSJUsSHBzMzp072bNnj2W5K8/bkVq1atGpUyd++eUXh+szU0Bw//33884772R4P0dcroyQnMX2+9ZjA6yNRtMAi3SG04iIiIiIiIiIiEjuYJ65EB0dneZ25pkMXl5e6c5xgNszEZw9LtxuHZRynkJ6+5vXu6vtUEbcfffd7Ny502q8gSvPOzUDBw7kiSeesDw2Go10794dg8FA//79uffee9ON1cvLiwIFClC6dGmnO/Y4Q8mIPCYuzvqxx5IRAJs2QbNm2RSAiIiIiIiIiIiIZKVKlSqxbds2zp8/n+Z25vVlypRxqiVSxYoVAbh48SLJycmp7mM+ro+PDyVLlrSco0CBAty4cYNz586leo6kpCTCw8MBU2LA3dKbkWyecVuwYEHLMleed2oKFixISEiIw3VVq1ZNdV12cF9zLMkRbCsjUnxvZ62gIChd2nrZW29BUlI2BSAiIiIiIiIiIiJZqXr16gCcPXvW6m59WwcOHACgRo0aGTpuQkICR48eTfe4VapUwfdWRxYvLy+qVKkCkOZw5iNHjpB061plzZo1nYorPWfPnuXxxx+nQYMGTJo0Kc1t//vvP+B2AgJce94Z8cUXX/D5559Tu3btDO/rTi5VRgwZMsSlkxsMBj7//HOXjiHWPNamycsLPvwQ3nzz9rJdu+Cnn0xTtEVERERERERERCRXa9q0KQDJycmsXbuWNm3a2G1z+vRpy4X1hx9+2KnjhoSEULBgQeLi4lizZo3DJEZMTAxbt251eNxmzZqxf/9+tm7dyvXr163mSJitWrUKMM2wcFd1QKlSpbhy5QrXr19n/fr1vP766w63O3jwIGFhYcDt1xBcf97Oeu655zK1n7u5lIyYP39+upO+U2MuW1Eywr081qYJ4NVXYdIkuJWpA+CDD6BDB3CiN5w7lS5c2upfERERERERERERcU25cuVo2LAhO3bsYMKECTz66KNWMxuMRiMjR47EaDRStGhRnn32WaeO6+fnx+OPP86iRYuYNm0azz77rF0rpTFjxhAbG4uvry9du3a1WvfMM88wefJkoqKiGD9+PO+++67V+tOnTzN9+nQA2rdvT0BAQGaevh0fHx9at27NnDlz2LVrF0uWLOHpp5+22iY2NpYPP/zQ8jxffPFFtz1vd9m7dy8HDx7k6tWrlC1blqZNm1KkSBG3n8flmREZncBtMBgoUaKEU4NLJOM81qYJwMcHxoyBxx+/veziRfjsM/j662wMBP758J9sPZ+IiIiIiIiIiMidYMiQIXTs2JETJ07QuXNn3nvvPWrVqsX58+eZMGGCpQKhb9++dhUKLVu2BKBu3bp89dVXVuvefvttVq5cSWRkJF26dGHw4ME0atSIq1ev8tNPPzFnzhwAunXrRmmbdvGVKlWic+fOhIaGMnXqVGJiYujevTtFixZl27ZtfPHFF0RFRVGkSBF69+7t1tejb9++rFixgsjISAYPHsyxY8do1aoVgYGB7N69m9GjR1sqRT744ANKlCjhtuedlpiYGObPn8+2bdt4+OGH6dixo902YWFhfPDBB+zZs8dqeb58+ejRowf9+/fPdDGCIy4lI8zfWGm5efMm0dHRHD16lKVLl7JlyxZ8fHwYO3YslStXduX04oBHKyMAWrSANm1g0aLby8aOhd69oWrVbA5GRERERERERERE3Ck4OJgRI0YwdOhQjh49Ss+ePe226dGjB126dLFbfvz4cQC7C/JgGkQ9btw4+vbty7lz5+jXr5/dNi1btmTQoEEO4xo0aBCnT59m7dq1zJ49m9mzZ1ut9/PzY/LkyW4fXl2qVCmmTJnC66+/zqVLl5gwYQITJkyw2sbX15dBgwbx/PPP2+3v6vN2ZNeuXbz55ptEREQAULRoUbtkxMmTJ+nSpQvXrl2zKzi4efMm3333HceOHeObb77Bx8flmgbAxWRE2bJlnd62Xr16dOjQgXHjxjFx4kRef/11FixYQIFsv1qet3lsZkRKo0bBH3/ArQnxJCTAwIHWCQoRERERERERERHJldq1a0ft2rWZOnUqW7du5cqVK/j5+VGnTh06d+5MixYtMnXchx9+mKVLl/L999+zceNGwsPDyZcvHzVq1KB9+/a0a9cu1Tv18+fPz+TJk1m4cCFz587l8OHDxMXFUbJkSZo0aUKvXr0oV66cK087VXXr1mXJkiX88ssvrF69muPHj5OUlESpUqV48MEH6datG1XTuFHbledta+/evbz88svcuHHDkmSIjIy02+69996zWt64cWOaNWtGbGwsCxYs4OTJk6xYsYIffviBPn36ZOj1SI17UhoZ0K9fP1avXs2RI0f49ddfefnll7M7hDzNo22azKpUgQEDrFszLV4Mf/0FTzzhgYBERERERERERETEnapXr27Xaik9R44cSXebsmXLMnz48EzFZDAYaNu2LW3bts3U/racidesSJEivPnmm7z55puZOpcrzzulTz75hLhb7XMaNWpE9+7dadiwodU269evZ/fu3ZYER/fu3Rk8eLBlfc+ePXnttdf4+++/mTx5Mu3ataNkyZIux+bl8hEyoU2bNhiNRv744w9PnD5P83ibJrMPPwTbb9C33oLExGw5/auhr9JhcgdeDX01W84nIiIiIiIiIiIi4kkbNmxg//79GAwGOnXqRGhoKC1atKBo0aJW282fPx8wzYMuWbIkb7/9ttX6fPny8fnnn1OgQAFu3rzJ8uXL3RKfR5IR5kEbJ0+e9MTp87QcURkBEBAAn39uvezgQZg8OVtOv3TvUn7f8TtL9y7NlvOJiIiIiIiIiIiIeNK6desA0/X3999/3+E2iYmJrF+/HoPBgMFgoEOHDuTLl89uu1KlStG0aVOMRiNr1651S3weSUacPn0agATzTAFxi8RE+8IDj47keOklaNDAetmwYXDlikfCEREREREREREREcmrdu7cicFg4Mknn8TX19fhNnv27CE2NtYyT6JZs2apHu/ee+8F4NSpU26JL9uTEdeuXbNMMg8KCsru0+dptlUR4OFkhLc3jB1rvezqVfj4Y4+EIyIiIiIiIiIiIpJXXbp0CYBq1aqlus0///xj+fyuu+6iTp06qW5bvHhxAC5fvuyW+FwaYG2ucEhPYmIiMTExHDp0iB9//JFz585hMBjSzLpIxjlKRnisTZPZww9Dx47w22+3l02aBH36QO3anotLREREREREREREJA+JjIwETEmG1JiTEQaDgQYNGliGWDtirp5wF5eSEY8//niawabFz8+Pl156yZXTiw3b4dXg4coIs6++gkWLbmdLkpJMw6yXL4dMfv+IiIiIiIiIiIiIyG1+fn5ERUVx8+ZNh+uTk5MtrZwAGjVqlObxzBURtgOwM8vlNk1GozHDHwEBAYwdO9ZS5iHukePaNJlVqACDBlkvW7ECFi/2TDwiIiIiIiIiIiIieYz5evu5c+ccrt+1a5fVvIgHHnggzePt2rULgGLFirklPpcqI9LLnJh5eXmRL18+AgMDqVevHq1bt6Zw4cKunFocsE1GGAzgYBC6Z7z3Hvz4I5w9e3vZwIHw5JOQP7/n4hIRERERERERERHJA4KDgwkLC2PTpk307t3bbv2ff/5p+TwwMDDNeRHR0dFs3LgRg8FAvXr13BKfS8mI0NBQtwQh7mHbpqlAgRzUBalQIfjyS+ja9fay//6Db7+Fd97xXFwiIiIiIpL3nDzJXVu2wD33QJEino5GREREJFs0a9aMBQsWsHXrVjZv3syDDz5oWRcREcHChQstLZqefPLJNI81duxYrl+/jsFgoEmTJm6Jz+U2TZJz2FZGeHx4ta3OnSHFfwAAPvkEwsM9E4+IiORNcXEwcyZ8/TVcuuTpaEREJLtt2IBXcDDV3nwTrwYNICLC0xGJiIiIZIsWLVpQoUIFAN58801CQ0P5999/WbVqFd26dSMqKgqj0YiPjw9dU940buP777/nl19+wWAwULZsWRo3buyW+FyqjEjp3LlzLFmyhFatWlGuXDmrdTdu3KBz5840adKEjh07EhQU5K7TSgq2yYgcMS8iJYMBxo6FkJDby6Kj4cMPYcoUt56qU0gnrl6/SlE/9wxXERGRXMBohFmzYPBgOHXKtOzbb02VeDmmb6GIiGS5iRMxXL8OgOHECZg7F3r18mxMIiIiItnA19eXoUOH0qdPH65fv87nn39utd5cFdGnTx8qVapkte7AgQOsXr2aZcuWceLECcvyd999l3xu+pvaLZURo0aN4oknnmD06NHs27fPbv2ZM2c4ePAgU6ZMoXXr1kybNs0dpxUbjto05TiNGsH//Z/1sqlT4cgRt57m6w5f80P3H/i6w9duPa6IiORQmzaZqu86d76diAA4fRpWr/ZcXCIikv2OH7d+fPKkZ+IQERER8YAmTZowYcIE7rrrLoxGo91H9+7deeONN+z2W7ZsGRMnTuTEiROWAdf9+vXjiSeecFtsLldGfPzxx8yePdsS4HHbN36Y+lH5+PiQmJhIfHw8X375JQkJCfTS3SlulePbNJl98YXp7qTYWNNjoxFWrIDq1T0bl4iI5D7Hj5sqIX77LfVtTp/OvnhERMTzLl+2fqw2TSIiInKHadasGatXr2bu3Lns27eP6OhoypcvT9u2baldu7bDfYoXL265xl+sWDHeffdd2rZt69a4XEpG7Nixg1mzZmEwGChYsCCvvvoq7du3t9suJCSEHTt2sGTJEkaNGkVERARjx46lefPm3HPPPa6EICnkisoIgLvvhlat4Pffby87d85z8YiISO5z7Rp8/jmMGQPx8Wlve+FCtoQkIiI5hO28ICUjRERE5A7k7+9P9+7dnd6+YcOG9O3bl1q1atG4cWO3tWZKyaVkxOzZswFTL6rQ0FDq1KmT6rb58+enffv2BAcH065dO5KSkpgxYwbDhg1zJQRJIcfPjEipTBnrx7pQJCIizkhMNM0ZGjbM/s7X1Oh3jIjInSM+HqKirJddveqZWERERERykbp161K3bt0sPYdLMyN27tyJwWDg+eefTzMRkVK1atVo164dRqORTZs2uXJ6sZFr2jRBlicjagytQUDfAGoMreHW44qIiAf98QfUqwevv+44EVG0qKlS4t13rZeHh2dLeCIikgNcuWK/TJURIiIiIjmCS8mIS7fKXzOaMQkODgbggu5UdKtc06YJoHRp68fnz7v18DE3Yoi+EU3MjRi3HldERDzkjTegdWs4eNB+nY8PDBgA//0H/ftD+fLW6/V+Q0TkzmHbogmUjBARERHJIVxq01SgQAHi0+vTnMp+AAaDwZXTi41cVRlhm4zQhSIREUnN0aMwcaLjdc8+C199BdWq3V6m3zEiIncuR5VzatMkIiIikiO4VBlR5larnZ07d2Zov3379gFQsmRJV04vNnL1zIiLFyEpyTOxiIhIznbrfYOV+vVh9WpYsMA6EQFKRoiI3MkcVUZERupvDREREZEcwKVkREhICEajkYULF3LixAmn9jl//jxz587FYDAQEhLiyunFRq5u05Sc7PgPBxEREdv3GCEh8M8/8OijjrcvVcr6cWwsxKhtn4jIHcFRZYTRCNeuZX8sIiIiImLFpWREhw4dAIiPj+fll19mx44daW6/f/9+evToQWxsLAAdO3Z05fRiI1e1aSpRArxsvv1056qIiDhim4yoWRO8vVPf3jbhDfodIyJyp3CUjADNjRARERHJAVyaGVG1alVeeuklpk2bxvnz5+natSs1a9akUaNG3H333RQsWJC4uDjOnz/Pzp07Le2ZDAYDzz//vGWQtbhHrqqM8PY2JSTCw28v04UiERFxxDYZUbFi2tv7+0OhQqaKCLMLF6BKFXdHJiIiOU1q1daaGyEiIiLicS4lIwAGDRpEeHg4f/zxBwCHDh3i0KFDDrc1Go0APPHEE3z00Ueunlps5KqZEWCaG5EyGXH+vOdiERGRnCujyQgwVUeEhd1+rIS3iMidQZURIiIiIjmWS22aALy9vRk9ejTjx4+nXr16gCnp4OijSpUqjBw5knHjxuHj43IeRGzkqjZNoAGjIiKSPqMx88mIlPQ7RkTkzpBaZYSSESIiIiIe57aMQIsWLWjRogWXLl1iz549XLlyhcjISAoUKEDx4sWpW7cu5cqVc9fpxIFc1aYJdKFIRETSFxFhP3xayQgREUmNKiNEREREciy3lyeUKFGCFi1auPuw4oRcVxlRpoz1YzdeKJrcdTJxCXEU9M3pL4KIiKTJtirC2xuCgtLfT8kIEZE7U2rJCM2MEBEREfE4tyUjzp07x5IlS2jVqpVdBcSNGzfo3LkzTZo0oWPHjgQ5cxFBMizXzYywvVDkxpkRT9d72m3HEhERD7JNRgQFgTOtHpWMEBG58xiNatMkIiIikoO5PDMCYNSoUTzxxBOMHj2affv22a0/c+YMBw8eZMqUKbRu3Zpp06a547RiQ22aREQkz8nMvAjQ7xgRkTtRdDQkJDhep2SEiIiIiMe5XBnx8ccfM3v2bIxGIwDHjx+32yYiIgIfHx8SExOJj4/nyy+/JCEhgV69erl6eklBbZpERCTPUTJCRESclVqLJlAyQkRERCQHcKkyYseOHcyaNQuAggULMmDAADp27Gi3XUhICDt27GDEiBEUK1YMo9HI2LFjCQsLc+X0YiPXV0ZER0NsrFsOvePkDjaHbWbHyR1uOZ6IiHiIu5IR4eGQnOyOiEREJKdKrUUTaGaEiIiISA7gUjJi9uzZAPj6+hIaGkqfPn0oUaKEw23z589P+/btmTZtGj4+PiQlJTFjxgxXTi82cv3MCHDbnavPjn+Wh0Y+xLPjn3XL8URExEPclYxITNRdsSIieZ0qI0RERERyNJeSETt37sRgMPD8889Tp04dp/apVq0a7dq1w2g0smnTJldOLzZyXZumu+6CQoWsl6mNhoiImBmNmU9GlCxpv0y/Y0RE8ra0KiOUjBARERHxOJeSEZduvdmrW7duhvYLDg4G4IIuCriN0ZgL2zSB/Z2r5897Jg4REcl5IiIgJsZ6mbPJiHz5IDDQepned4iI5G1pVUZcvWr6o0lEREREPMalZESBTF7tNu9nMBhcOb2kEB9vvyzHV0aABoyKiEjqjh+3fuztDUFBzu+v3zHiDomJsGoVBQ8f9nQkIpKetJIRN2/a370lIiIiItnKpWREmTJlAFO7pozYt28fACUdtVCQTLFt0QS5pDLi1veQhS4UiYiImW2LpnLlwMfH+f2VjBBXGY3w9NN4P/kktbp2xTBmjKcjEpG0pNWmCdSqSURERMTDXEpGhISEYDQaWbhwISdsLxik4vz588ydOxeDwUBISIgrp5cUHN3kkyuSEWrTJCIiqcnsvAgzJSPEVfv3w/LlloeGzz+H5GQPBiQiaUqrMgKUjBARERHxMJeSER06dAAgPj6el19+mR07dqS5/f79++nRowexsbEAdOzY0ZXTSwqOKiPUpklERHI1JSPE08LCrB4aIiLgyBEPBSMi6UovGXH1avbEISIiIiIOZaDXgb2qVavy0ksvMW3aNM6fP0/Xrl2pWbMmjRo14u6776ZgwYLExcVx/vx5du7caWnPZDAYeP755y2DrMV1jioj8ufP/jgyTBeKRMQsJgZ27oRataB4cU9HIzmBkhHiaY4qNrdsgZo1sz8WEUmf2jSJiIiI5GguJSMABg0aRHh4OH/88QcAhw4d4tChQw63NRqNADzxxBN89NFHrp5aUrCtjPDxyVhbbY+xnRmhNk0id6bwcGjYEM6ehWLFYN06qFPH01GJpykZIZ6WWjKiR4/sj0VE0qc2TSIiIiI5mkttmgC8vb0ZPXo048ePp169eoAp6eDoo0qVKowcOZJx48bhkyuulOcetsmIXNGiCewvFF28CElJnolFRDxn8mRTIgJMFwq+/daz8YjnGY1KRojnpZaMEJGcJyHBrg1TfIkS1tsoGSEiIiLiUW7LCLRo0YIWLVpw6dIl9uzZw5UrV4iMjKRAgQIUL16cunXrUq5cOat9jh8/TqVKldwVwh3Ntk1TrhheDfYXipKS4MoVKFnSpcMe+vQQRqMRg8Hg0nFEJJts3Wr9eM8ez8QhOceVK3BrxpSFq8mIK1cgPh7y5XMpNLmDOEpG7N8P0dFw113ZH4+IpM5BouFm+fLkS9m6STMjRERERDzK7eUJJUqUoEWLFqmuT0pK4q+//mLWrFls376dgwcPujuEO1KurYwoWRIMBtMdsGYXLricjLirgC4QiOQqu3ZZPz582PRzQQnFO5dtVYS3N5Qtm7Fj2CYjwFSBFxSU6bDkDuMoGZGcDP/8A48+mv3xiEjqHLRoulG+PHft2HF7gSojRERERDwq23olnT17lt9++425c+dy5coV3bXuZrbJiFxTGeHjAyVKmC4OmZ0/D3Xrei4mEcle58/bt8+5ds20zHaujNw5bJMR5cplfBhSsWKmfRITby+7cEHJCHFearOstmxRMkIkp7EZXm0sUoTEokWtt1EyQkQkZzl3Dr7/3nQT0Suv5JLhpyLiiiz9X240Glm7di2zZs1i48aNJCcnKwmRRXJtmyYw/dJJmYxQT2+RO4ttVYTZ4cNKRtzJXJ0XAeDlBaVK3Z5HAqZh6SLOSEpK/fvFtrWciHiebWVE8eIkBgRYL1MyQkQk54iPh0aNTAkJMP39N2aMR0MSkayXJcmIS5cuMWfOHH7//XfO37qjzJiiDY+Pjw/Nmzenffv2WXH6O1KubdMEpouNe/fefuyGZMQ3f31D1I0oAgoE8PYTb7t8PBHJQjt3Ol5++LDuPL6TuSMZAaaEd8pkhBLe4qzLl00tmRzZskWt5ERyGpvKCIoXJ6lwYetlmhkhIpJzrF9/OxEBMGuWkhEidwC3JiM2b97MzJkzWb16NUlJSVYJCICaNWvSrl07nnnmGYoUKeLOU9/xcn1lREqptUTIgG9WfMPZyLOULVJWyQiRnC6tZITcudyZjEhJyQhxVlrvR8LD4eTJzH9fioj7qTJCRCR3OXXK+vHFi6b2qmrVJJKnufw/PDIyknnz5jF79mxO3fpBkjIJYTAY6Ny5M+3bt6dWrVqunk5SkWtnRoAuFInc6VJr03ToUPbGITlLTk1G3LwJv/8OAQHQsiX4+rp2PMm50rs5YssWJSNEchKbZISxRAmSlIwQEcm5UlZFgKnq9NIlteoVyeMynYzYuXMnM2fO5K+//iI+Ph64nYTw9vYmMDCQi7fmAAwdOtQNoUpacn2bppSUjBC5c0RE2F90NlNlxJ3LaHRfMqJUKevHrv6OeeYZWLHC9Pmbb8K337p2PMm5nElGvPhi9sQiIumzbdMUGGhfGREVpbtuRURyipStVM3Cw5WMEMnjvDKycUxMDDNmzKBNmzZ06dKFJUuWcPPmTYxGI0ajkSpVqvDOO++wZs0aunbtmlUxiwNq0yQiudLu3amvO30aYmKyLRTJQa5cgdhY62U5oTLiyJHbiQiACRPsy8sl73AmGSEiOYejNk22MyMAIiOzJRwREUmHo2SEbk4VyfOcuiXkwIEDzJo1i6VLlxJ366q3uQqicOHCPP3007Rt25bg4OCsi1TSlKsrI9SmSeTOldq8CLOjR+Hee7MnFsk5jh+3fuztDWXLZu5Y7k5GpGQ0wi+/wPvvZ/6YknPZJCNuli5N/pTfP7t2mdp25c+fzYGJiEO2yYgSJUi66y777SIioHjx7IlJRERSZ9umCUyVESKSpzmVjGjfvj0Gg8GSgPD19aVp06a0bduWRx55BF8P90s+cuQIP/zwA1u3biUiIoIiRYpQp04dOnfuTNOmTTN93K1btzJjxgx27txJZGQkAQEBNGjQgE6dOtGkSZM09zUajSxcuJDff/+dQ4cOkZCQQOnSpWnWrBkvv/wyZdxcdparZ0bYvhZRUXD9Ovj5eSYeEck+qc2LMDt0SMmIO5Fti6by5TPfUsOdyYhjx+yXTZ8OQ4aAwZD540rOZJOMuNa0KSXmzMFgno0WH2/6GfbAAx4ITkTs2LRpMgYGYsyfH2PBghhSlpFrboSISM6QWpsmEcnTMvSXfYkSJXjttddo06YN/v7+WRVThqxcuZIBAwaQkJBgWXbp0iXWrFnDmjVr6NatGx9++GGGjpmcnMyIESP45ZdfrJZfuXKFlStXsnLlSl544QU++ugjvL29He7/zjvvsHTpUqvlJ0+eZPr06cyfP5/x48fzgBv/eM1TbZrAdLGocuXsj0VEsld6lRGaG3Fncte8CLD/HRMTY/rIzPuYsDD7ZUeOwPbtEBKSufgk57JJRtyoUAFq1YIDB24v3LpVyQiRnMBodFgZAUCxYtYXvK5ezb64RETEsYQEx4kHJSNE8rwMzYy4fPkyn3/+Oa+88gpTpkzh5MmTWRWXUw4cOMDbb79NQkICwcHBhIaGsmXLFn7//Xcee+wxAEJDQ5kxY0aGjvvtt99aEhGVK1dmwoQJbN68mRUrVtC3b198fHyYPXt2qoO5R40aZUlE9OjRgz///JNNmzYxZswYSpcuTXR0NH379uWCG9sR5eo2TXfdZR+wWjWJ5H2xsfZtbxo3tn6sZMSdKSuTEZD5P3IcVUaAqTpC8h6b9yIJxYtjvP9+6200N0IkZ4iNtf+DyNyKqVgx6+WqjBAR8bzwcFMi2ZauBYnkeU4lI9555x0qV66M0WgkMTGRPXv28M0339CyZUs6dOjAzJkziYqKyupY7YwZM4abN29Svnx5fv75Z0JCQihatCjBwcFMmDCBxx9/HIBx48YR4+QQ1HPnzjFlyhQAqlWrxm+//UaLFi0oVqwY5cuX58033+Trr78GYO7cuWzbts1q/wsXLvDzzz8D0KtXLwYPHkylSpUIDAykVatWzJgxg8KFCxMVFcX48ePd9VLk7soIg0FzI0TuRHv2WL8B9fKCjh2tt1Ey4s7kzmSEv79927/M/o5xVBkBMHOmqWWP5B1Go11lRELx4qBkhEjOZFsVAbeTEUWKWC9XMkJExPMctWgCz1VGrF8PLVpAu3bw33+eiUHkDuFUMuKVV15h6dKlzJo1iw4dOlCoUCGMRiNGo5H9+/fzySef0KRJE/r378+6detITk7O6rgJCwtj/fr1APTu3ZtChQpZrTcYDAwePBiDwUBkZCTLly936rhLly61tHz66KOPuMvB0LPWrVvTqFEjAEviwiw0NJSEhAT8/Pzo06eP3b5BQUH83//9HwBLlizh+vXrTsWVnlw9MwLs50a4mIy4t8K9PFD5Ae6toF7zIjmWbYummjXt50McPQqJidkXk+QM7kxGuCvhnZxsP1jbLCICli3L+DGz07Zt8MMPqf/hJ9auXbN7c5VQvDhG23ZcJ07oBgrJfYxG+PlnePZZGDkyb/yetU1G+PpCQIDpc1VGiIjkPDkpGREba/qduGoVzJ8P3btnfwwid5AMtWmqX78+n376KRs3bmTkyJGWC/JGo5H4+Hj++usv+vTpQ9OmTfnzzz+zJGAzcyLCYDDQvHlzh9sEBQVRvXp1AFatWuXUcQ/c6gNcvHhx7rvvvlS3Mw+w3rx5M/Ep7oY0x9WoUaNU52qYW0jFxcWxadMmp+JKT65u0wT2F4ps7kbMqEVvLmLzkM0senORS8cRkSxkO7y6QQOoUcN6WXy8/YVpyduMRvcmI8A9yYizZ9OufrhVFZkjLVhguqO/Vy8IDlYvXmc4eB+SULy4aWaE7fu7rVuzKSgRN9mwAV56CRYtgiFDwObmqlzJZng1xYubktGA0TYZoZkRIiKed+6c4+WeeJ+6bRtERt5+vGlT6vGJiMsylIwwK1CgAG3btiU0NNSSgChTpoylWuLy5cscOnTIsv3kyZM5ffq024IGLMcvXbo0gYGBqW5Xq1Yt4HaSIT3mdlN33313mtsVu/WmNiEhgWO3ekjHx8dbPq9du3aq+1arVg0fH9Ps8P379zsVV3pydZsmUJsmkTuRbWXEvfeaLh7Y/kxXq6Y7y+XLYFs1mBOSEanNizBbutRxm5CcYOLE259fvQpz53oultzCJhlhLFIEY/784O1tP6xcrZokt7Gt5PrrL8/E4U62P3/NLZoAiha1XqfKCBERz0utMuLy5eyv2Pv3X/tlGzZkbwwid5BMJSNSKleuHAMGDGD16tX88MMPtG7dmnz58mFM0Qd87NixPPHEE7z44ovMnDmTq264G+XcrSxl2bJl09zOnFQIDw+3tF9Ki9+tvtKxsbFpbnft2jXL5+G3Mrfh4eEk3vqhmVZc3t7elCpVCoCzbmqXkOsrI9zcpklEcribN8E2SdyggenfmjWtlysZcWexrYrw8YF0bhBIlzuSEbbzIipUsM78JyTA7NkZP2522LfP+vGZM56JIzexrYxI+T7lgQes1ykZIbmN7c+AvHBx3jYZUaLE7c+VjBARyXlSuxZmNNpXu2W1o0ftl93qeiIi7ufjrgMZDAaaNGlCkyZNiIqKYtGiRcybN4+DBw9attmzZw979uxhxIgRNGnShDZt2tC6detMnc+c0Agw9wJNhXnmg9FoJDo62lLRkJqqVauyYsUKjh8/Tnh4uCVpYCvl4Gpz4iJlkqVw4cJpnsfcwsmZwd/JyckkJSWluc2NG16AwfI4X74k0tnFaeZzpxeDKwwlS1plxoznz5Nsc77siMMZikNxKA43xLF3L942CeKk4GBISsJQvTpeGzdalicfPIjRxeeR418PxXF74bFjeKfYxliuHMkGA678UnPH7xjDf/9ZH6NuXYx+fnilSEAYf/6ZZAfzojLC7V+bq1fxtkm+JIeHp/t/Kkd/j2QDw9mz1l/vW+8Hk5KSoFEj6+/R7dtJjo83VU1ksTv966I43BOH1+nTKf5qAOPVq3Y/E7MjDncyhIdb/Z9NDgy0nD+5SBHr/89Xrrj1+aYnp3x/pIzB07EoDsWhOBSH19mzVr+LrM517hyULJktcQB4HT1qF4txw4Y0f1fk1a9LStkxD1juTG5LRqQUEBBA165d6dq1K0eOHGHOnDksWbKEyFs92BITE1m7di3r16/PdDLi5s2bgKllVFry589vt09aWrZsyaRJk0hOTmbEiBGMGTMGLy/rApJNmzaxIUXJlnlmRMrZESnP64g57hu2JQ0OnDlzhjPp3MV47Vod4PY5z507xu7d6Sc6MmKf7Z2VblT4+nWqpHiccPo0+3bvznQcb/3xFpE3IilSoAijW412T5CZiCM7KA5risNaTo0jcNEiKqZ4fCMoiAO37ogvedddlEux7vqOHRxJ5eeBq3F4iuKwljKOUps2EZRiXXRgIP+6+PUvnpBAhRSPrx8/zuEM/o6ptGMHKW9nuHjXXUTdfz9VUyQjDNu3c2jBAm662lYqjTgyqtCePdhMYiHq338Jc/I1zYnfI9khaO9eUt6OcvXW+7Z9+/bhU6gQ9VKsM8TGcmTePOKqVs22+O7Ur0tqFIe19OKofewYKf+CSrh4MdX33VkZhzuVP3qUFLUQXAZO3zr/qZgYKqdYd+PCBQ5mwfNNT075/oCcE4visKY4rCkOa3ktjlrHjpFaQ49jmzeT3tUsd74etfbts4vFsG8f+9atIymdG43z2tdFJDtkSTIiperVq/Phhx/y3nvvsXLlSubPn8/ff/9NUlKSVSunjPK+dfeZwZBaLtUk5TlskwqpxduuXTvmzp3L8uXL6dmzJ6+99hrVqlUjOjqaP//8k/Hjx1OyZElLe6Z8+fLZHd/ZuJyJKSgoiOIp+546kJxsfZxatSpTv366h3ZKUlIS+/btIzg42PK6u51NFtc3IoL6detCitcnI3Ecm3WMs5FnKVukLPXd9UJkIo6spDgUR26OwzB1qtV2+e+///b/1bNnYcwYy7pCp09Tv149yyBKd8aR3RRH+nHYfm/416nj+s9xm7lVflFRdsdM7zXxsmkxWeL++yn+6qsYP/8cQ4pBe7X++Qdj27aZDtXdXxvDjh12ywrfvJnua5qTv0eyg8HmfUmRGqaUjjkOY+XKGFLMEakRGYnRze83HLnTvy6Kww1xGI142bS/8I2Jcev7ZU+8Hl42d28Wr1GDIsHB7Nu3j3L16lmtK3D9utv/PkhLTvn+yEmxKA7FoTgUh9eVK6muu6dQoVTfV7n99UhKwiuVllHBUVHQrFn2xJFJWRlHREQEp06dcusxRSAbkhFmvr6+tGrVilatWnHx4kXmzZvH/PnzM30882yH9CoLUlYrmJMG6fnoo4+4evUqq1evZtOmTWzatMlqfaVKlfjoo494S4HcWgABAABJREFU6aWXrGIx/wvpV2GY16dXQQGmhEV6P1RsB1gXKuTt9m4B3t7eWfdD1mbGhiExEe/ISOt+r5mMI6tiztLXQ3Eojrwex65dVusNDRveXl+njvW6q1fxvnrV4c8Dl+PwEMWRRhwnT1qt86pUyfX2N7a/Y8LD8TYYrBLeDmNJyWaAtVfVqpA/P3TpAt98c3v5jBkwYoTDY2eE2742DmauGC5dcvrYOfJ7JDukSDABGG7NLbHEcf/9Vt8TXtu2gYstujLijv26KA7X47h82TS3KQVDbCzeSUng5N9KbonD3WwuanmVLGk5t1dgoNU6Q0QE3l5eLt3kkBk55fsDck4sikNxKI47NI7oaNNHKrwuXUr3/b/bXo+TJ02z3xyd4++/IZ2bjPLU18WGMzdPi2SGR76zSpYsSZ8+fVi+fHmmj2GeuRCdxg8wuD2TwcvLK905Dmb58+dn4sSJfP3114SEhODv74+fnx81atRg4MCBzJ8/n0KFClm2L3HrApl5PoUzcZnXF7UdqJZJtjmZdLpX5TwlS9r/QaAh1iJ5U1IS7Nljvcw8vBpMg4FtE7UaYn3nsB1g7YaWR3YDrBMSwKbSIU3Xrtld6KLyraYf//d/1stPn4a1azMcYpZJMbvL4uLF7I8jt0lrgDVoiLXkXqm1fs3Iz8ScKK0B1rYzAxMTISYm62MSERHHzp1Le73NTSFZytHwarMU7dlFxH1ybZqrUqVKAJy3/WPRhnl9mTJlMpTVMxgMtGnThtDQUHbs2MGuXbtYuHAhvXv3pmDBghy+dWHMYDBQoUIFyznMsyDOpfHDNSkpydLi6e5bd9q5IinJPpFbMLXmezmVry/YtqJSMkIkbzpyxL6cK2UywtsbqlWzXn/oUNbHJZ5nNGZNMsLRALyM/I6xqYrAYLgdV716ULeu9frp0zMUXpZy9H8nNtb0IamzeX9ptE1o2SYjDh2CW7PRJJsYjbB2LQEbN9rflSOpy6vJCJvWU1Z/V9gmIyD3P1+R3CQyEtavT/NOeLnDpNIWySI7kxH//pv6uh079J5ZJAvk2mRE9erVATh79qyl+sGRAwcOAFCjhu34xtQZjUaupvMGdePGjQBUrVrVUqXh5eVFlSqmMcyH0rhwduTIEcuk+5o1azodV2oc/f2V6yojwP7O1XQSTSKSS9m0aOLuu6FUKetltj+zVRlxZ7h0Ca5ft17mjmRE/vz2F6NcSUYEBVlX73Tvbr3+999zxh8u0dGQWp9X2wt3cltcnKkaJiXbyoj69e0ruLZty9KwxMbgwXi3aEHVAQPw6tzZlJyQ9OXFZERSEkREWC9LmYy46y771nm224tI1vj3X1ML1mbNoHx5+/dUcmdKrzIiO29MTasyIjFR1a8iWSDXJiOaNm0KQHJyMmtTaYdw+vRpjt76wfLwww87ddzNmzcTHBzMAw88QFhYmMNtLl26xLp16wBo0aKF1bpmt4bbbN26leu2F1RuWbVqFWCaYRESEuJUXGnJs8kIVUaI5E07d1o/vvde+21sE7V5IRlx7hxejz5KvUcewdCvX6q9Se9otlURPj528x4yzZXfMbbvB+65x/px587WF7piY8GFuVhuk9b/G7VqSp2jmyFskxH58tn/7NIfq9nn+nUYPdry0LBoEWzd6sGAcpHUkhG5+eJ8RIR9MiplmyYvL7BtjZubn69IbpGcDD163L4LPjISPvnEoyFJDpFbKiPAVNUjIm6Va5MR5cqVo2HDhgBMmDDBbkaD0Whk5MiRGI1GihYtyrPPPuvUcWvXro3h1uyCX3/91W59cnIyw4cP58aNG/j5+dGpUyer9c888wze3t5ERUUxfvx4u/1Pnz7N9FvtG9q3b09AQIBTcaXFUTIi17VpAvs/9JWMEMmbbCsjHCUj8mJlxAcfYNiwAZ+YGLwmToRXXtGdvLZskxHly7s+vNrMlWSE7V185nkRKY/95JPWy3JCqyZH8yLMlIxInW0yomBBcPR+TXMjPGfHDvuE7sKFnoklt8mLlRG28yIAbIZW21XH5ebnK5JbTJkCf/9tvWzxYtPd5uJ+SUmm6txhw+xv/sppbJMRtu3LPTkzolw568eaGyHidrk2GQEwZMgQvLy8OHHiBJ07d2bjxo1ERERw4MAB3njjDVauXAlA37598fPzs9q3ZcuWtGzZknfffddqeUBAAB06dABgxowZfPXVV4SFhREREcHmzZvp3r07K1asAGDQoEGUtOlDXalSJTp37gzA1KlTGTZsmGX/P//8k65duxIVFUWRIkXo3bu3W14H29brkEcqI9SmSSTvMRrt3xynnBdhZpuMOHHC8Q+73CIhwf5O+enT4f33PRNPTpUV8yLMsrIyAuwHWa9cmfpFv+yS1qwVJSNSZ/v+o3Rp05wQW7bJiK1blWDMLo4SPwsWZHsYudKdkIwICLBvo2abjFBlhEjWOncObK61AKb/e+66uJucDDNmwPDh9u/V7iRGIyxbZppj1qEDfPopNG4M+/d7OrLU2bZpsr057fLl7Ela3bwJJ09aL+vRw/rx5s0QH5/1sYjcQXJ1MiI4OJgRI0bg4+PD0aNH6dmzJw8++CDt2rWztELq0aMHXbp0sdv3+PHjHD9+3OEA7EGDBhESEoLRaGTq1Km0bt2aBx98kJdeeolt27bh7e3NwIEDLUkHR/s/8sgjAMyePduyf//+/blw4QJ+fn5MnjzZLcOrwXFlhO3771xBbZpE8r7jx+17sTuqjLAdYG00pt3PM6fbtMn+eQOMHAkOqujuWDk1GZFeZQTAs89a3z1vNJr+QPYkVUZkju17Q9vKTbP777d+HBEB//2XNTGJNUctmQ4fzhtVdFktLyYj0hpebaY2TTnHN9/g9cADBI0apTvk87J+/SC12Z7uSh5//jl07QoffwxNmjh+r53X7dgBjz0GTz0Ft+alAqaLRF9+6bm40mNbGWH796DRmD3zzY4dMyW1UnrpJevHN26YXmcRcZtcnYwAaNeuHfPmzePZZ5+ldOnS+Pr6UrhwYRo3bsyECRMYPHhwho9ZsGBBfvrpJ4YNG0b9+vXx8/PD19eXsmXLWs6XVlVD/vz5mTx5Ml9++SUhISEEBARY9n/hhRdYtGgRDRzdCZxJtsmIAgUc38CX47kxGfH242/z0TMf8fbjb7sYlIi4lW2LpmLF7EthAQoVMrXoSSk3X2RasiT1df36mUqqJWcmIxIS7O+YclQZUbCg6W60lKZP9+yd8kpGZI7t90ZqyYjy5e2/r9SqKXukNh9CrZrSZjTmzWSEbWWEo2SEKiNyhhUrYOBADP/8Q6mZMzF88YWnI5KssHAhzJ2b+voFC1x/f3TjBnz99e3HFy7AnDmuHTM3OXECunSB++6DNWscbzNnDly5kq1hOc02GVGvnvX8NcieVk228yJKloRKlUxD11PS3AgRt/LxdADuUL16db766qsM7XPkyJE01/v4+NClSxeHVRXOMBgMtG3blrZt22Zq/4yw7VySK1s0gVtnRrz9hJIQIjmSo+HVqWVPa9aEU6duP86ryQij0XRXV8mS0LRp9sWUE2VnMsLZP3BOnzb14E3JUWUEmFo1TZ16+/HBg6bv+VszrrJVXJx9RUdKSkakztnKCIPB1Kop5R2eW7ZAt25ZFppgau2Q2gX1BQvgvfeyNZxcJSoKYmMdr8vNF+dtkxEph1ebaWZEzmBzgdowZQoMHeq++VDieVFR8MYb1ssKFbL+2XPqlOkGJUfV0c5atsy+8mLDBtNMtrzs6lVTRci4cem3Drp5E0JDYcCAbAnNacnJ9u+1ypUzJZJTvj/NjmSEbeW9uTq/aVPrNlcbNuj9hYgb5frKCLGvjMiVw6vB/kJRZGTu7hEvIvZsKyPSqhKznRuRVv/7nCwszC6Rkmx7B/3Nm9CmTc7u7ZrVjMacWRlh24O4cGH7i1pmTZqY7qZKyVODrI8eTfuuQyUjUudsMgI0xNoTUquKANPrr5ljqUtrjk1uvjjvTJsmVUbkDP/8Y/XQcO6chsNmRk6eT/Thh/Z3vU+cCFWqWC9ztVXTzJn2y/Lw3euG+HgMY8aYqnP/9z/HiYigIFOlRErffZfzvl8uXbJv0Va2LJQqZb3ME5UR5mTEww9bL9+40f7mJBHJNCUj8oA8Uxlhe6EIsucXkIhkH0eVEamxTUbk1sqIpUutHiYEBmKcMQNefdV6u2vXoGVL0534d6JLl+x/oWVlMuLyZVMLpvQ4mheRWjWPl5f9XfG//urcedwtrRZNoGREWlxJRuzZA9evuz8muS2tZATAokXZE0dulFeTEc60adLMCM+7eRP27rVf/uuv2R9LbrV1K9SubbpwGxrq6Wjsbd1qPwutRQvTeyPbjhHz52f+PFFRsHix/fITJ6yrqvMCoxHD7NnU7tABr3fecfyzOiAAvvjCdCNKytZVYPr7Kacl/GyTVV5epkSEbTIiO2aI2lZGVK1q+tc2GXHt2p1901g6jhw5wqBBg2jatCl16tShSZMm9OnTh/UuJgjPnDnDRx99xGOPPUadOnV44IEH6N69O0vS6jpwi9FoZMGCBXTt2pWGDRtSt25dnnjiCUaMGOFwdnB6pk2bRvXq1Rk9enS628bExDB16lRefPFFGjVqRJ06dXjooYd45ZVXWLJkCcm2c0pSmDJlCtWrV0/348ucPBPGCUpG5AGOZkbkSoUL2wefyV9A0TeiiYqLIvpGtBsCExG3OH/ePsGYkWTEkSP2A8ZyA5s3S9ceesj0pnvCBNPQ45TOnjUlJHLzRaHMsq2K8PGBu+923/FtkxHODsazrYxwNC8iJdtkxOXL8Oef6Z/H3WyTEfnzWz9WMiJ1GUlG3HefdY/jpCQNOcxqNskIo21yUHMjUpdXkxG2P8udadOkZET227/fcXL+99/TbzcjpjvJO3Y0/X4/fx5efz1nfR8nJECvXtZ34RcsCJMnm27isE1G7N8P//2XuXPNn29KbjmS0y68u+qtt/Dq0oX8thfwwfReuV8/03vVwYNNr3ezZlC9uvV2332XPbE669w568elS5tatWW2paorUquMKFvWvi1rHq68ccXKlStp3749ixYtIjw8nISEBC5dusSaNWvo1asXn332WaaOu2fPHtq0acOsWbM4c+YMCQkJXL16lS1btjBw4ED69u1Lom2FzS3JyckMHDiQ9957j+3btxMTE8PNmzc5efIk06dP55lnnmFLBqqZ9+7dy5gxY5zaNiwsjGeffZavvvqKXbt2ERUVRUJCAleuXGHDhg0MHDiQXr16EZdKF5iD6d1QlkcoGZEH5Jk2TQaD/S+gTJba1xxak8L9ClNzaE03BCYibmFbFeHvb1+ynVJNm/+/N27kvrudoqNh7VqrRdfMd9p4e5tKzB96yHqfgwdNSQrbH+55nW0yonx59/aQDgy0P54zCW9HlRFpqVLF/mv688/pn8fdbNuaPfig9eNLl3Jnci+rJSbaX9hMKxlRqBDUrWu9TK2ask5Skl2bl4gnn7TeZtUq+z7iYpJWMiInXdTMqMwMsM7NyZfcyub/rsXVq7B8efbGkhutWWP9Pjgmxr79qSf973+wb5/1so8/vn0TxwMPmOajpZTZ5LGjFk1meemC8bJlMHas43UdOpje640da/0zz2CA3r2tt/39d/ufk55km1gpW9b0b3a3aYqNtY/FXBkB9rP88lqiyw0OHDjA22+/TUJCAsHBwYSGhrJlyxZ+//13HnvsMQBCQ0OZMWNGho57/vx5evfuTWxsLBUrVuS7775j8+bNLF68mA632h3/9ddfjBo1yuH+o0aNYumt7gQ9evTgzz//ZNOmTYwZM4bSpUsTHR1N3759ueDE34J79+6lZ8+eqSYPUrp+/Tq9evXizJkzFChQgIEDB7J8+XI2bdrEjBkzaN68OQAbN27k/fffd3gMczKiT58+7Ny5M9WPt956K914cjIlI/KAPNOmCTLf01tEcj7bZET9+tZ3FNsqWRKKFLFeltvmRqxcaXUXoNHXl6j777+9vmBBU5m5bRXIhg3Qpcud1Zs0K+dFgOl7zfaPYGd+x2S0MgJMg6xTWrw4+y/02d5V06yZ9ePERNNsJrEWHm7fWzmtZATYt2pKr42QZN7Bg6YLcCmc790bo6/v7QXx8Z6pRsoN8mplRGYGWOfm5EtulVoyAtSqyRmOLsDb3jDhKf/+C8OHWy+rVw9SXizz9ravCM5Mq6aLF03vr1OTV5IRMTHw2mv2yxs3hs2b4bffUr+pq3t364rY+HjP3BiTmpySjHBUmZPyNbVt1bR+fc6bv+FhY8aM4ebNm5QvX56ff/6ZkJAQihYtSnBwMBMmTODxxx8HYNy4ccTYvH9Ly3fffUdkZCQBAQGEhobyyCOPUKxYMapVq8Znn31G9+7dAVOi47RNi+MLFy7w863v9169ejF48GAqVapEYGAgrVq1YsaMGRQuXJioqCjG27aVszFz5ky6dOlClJM3ucyYMYOzt76/J06cSO/evalYsSKBgYHcd999TJo0iW63quiXLVvGPpsEbkxMDCdPngTg3nvvpVChQql+5MuXz6mYciolI/KAPFMZAfZ/8CsZIZJ3ZGR4NZju7MntcyNs+1k2bUpyoULWy4oVM104s21JNG8e9O9/57zpzepkBGQ84W00ZrwyAkxtFFK+QYyPN/3RmF3i4+3Lzm2TEaBWTY7YVmR6ezu+yzqllAlGUGVEVrJt0VShAjfLl4dHH7XeztXBqHlVWsmIGzdyb0WeMwOsbWdGxMSoNVB2S6uF3cKFdolGSeHGDZg713657Q0TnmA0Qp8+1m2TDAaYMgVSJorBvlXTpk0Zv+A8Z07aN+scPpw33t98+KFdRXjy//5numHJ9iYIW4GB8Pzz1su+/z7n/E1h26bJ/DdQds+MsJ0XUa6c9cU028qI8PDMtxbLg8LCwiwzIXr37k0hm79xDQYDgwcPxmAwEBkZyXInK+CuXbvG3Fs/77p06UJJ25vJgH79+uHv709CQgLzbZKaoaGhJCQk4OfnR58+fez2DQoK4v9u3Ti2ZMkSrjuY9bZ37166du3Kxx9/THx8PLVr13YqdvNzDAkJoXHjxg636du3L963qvXXrVtnte7gwYMYb/0/rVOnjlPnzK2UjMgD8szMCHBbmyYRyYEyMrzaLDcnI5KT7YZXG596yvG2FSrAH3+YBtClNGGCaSDdnSAnJiOuXLFv9+JMZUTRotCmjfWy6dPT389d/vvPVPmQUoMG9t9feeGPdXez/Z4oVSrtCi6wvyhw9mzaF30l82wSPcZbiSCj7d22S5fqQrMj6X1f5sbqiOvX7YfGO9OmCXLn882tbtywb+GTUlyc5r2k5Y8/HLefywnJiOnTYfVq62X9+kGjRvbbNm9uatNqZjQ6HkSdFtsqmg4dTC0TU8rt7XS2bYNx46wWRd13H8b+/U2JHme8+qr146NH7VrHekxqlRHZPTMitXkRZvfcYx9TXqm8cQNzIsJgMFjaD9kKCgqi+q0ZJqtWrXLquFu3biX+1nu41I7r7+/P/bfeA9oe1xxXo0aN8E/58yYFcwupuLg4Nm3aZLd+wIABbN++HYPBQJcuXfjVyeq9yMhIvLy8qFevXqrbFC5cmMKFCwNw0eZvMXOLprJlyxIYGOjUOXMrJSPyALVpEpEcLyICbpUcWqRXGQH2cyNyUzJi5067N9HG1q1T375uXdPdvLYllx98kL131XtKTkxG2FZF+PiY7ppyxq3yYYvNm+3/6Mkqtu3M7r7b1PLM9s4iJSPsZWR4tVm1avYt5VQdkTVsW2CFhABgfOYZ6+VRUTnnoktOkheTEVeu2C9z1KbJtjICcufzza327rVKkhsNBqLr17feJq05AHe61C6EebpN08WL8Pbb1svKlYNPP3W8fYECYPteOCOtmk6cMFVTpPR//2dqXZRSbr5gnJAAr7xiVcVgzJ+fU++/73wiAqBJE/u/o3LKIGtn2zRdvmx/c4072VZGpJwXAabXW3MjUnXo1t8bpUuXTvPCea1atYD/Z++846Oo0z/+md1sGgRIqAktCNJBBEUsWFBPrHh2sZ+n51mwHZ7eqb9TT+/snMrZK9aznO2soNjFSu8dAoQSkpAAKbvz+2OyYeb5zu5O35nd5/16+ZKdbPnu7uzMd57P9/l8lHwJM88bCoUwiO7DOs+7bNmyVvGisbERK1uOi8m6Gfr374+cnBwAwPz583XvM3r0aPznP//BrbfeinyDRdbp06dj3rx5uOKKKxLep6amBtUtVrntyEKx+FiGDh2KDz74ABdffDFGjx6NoUOH4qijjsLtt9+ODbSzKKCwGJEBsE0TwzC+h1o05eYCLROIpNDOiCBlRlCLpgEDkgd2A4rViN4K+kmTMjuQVZb9KUbQFYe9eyuChBGOOUYsiH33nbHH2oXmRcQn8ixGpMaKGBEKsVWTF+zYAZALWblFjEBZmfgd8CprLXV1qXNigpijQC2awmGgZcWhhtxc7YpsIJjvN6jQvIgBA7D1t7/Vbvv4Y3+F7PqF2trE3QMrVqTXeue668Tf0aOPAkVFiR9DrZqmT1eO70Z49VXt7eJi4De/EQvGQRYjdILA5VtuUSwJzSBJYnfEW2/5Y+5Hi6mJxAhZdveYkKozAtDPjWAAoLUo3j3+/SWgrMWGq7KyEk2qLMVUz9u1a1dEqNWbzvNGo9HWIOrKyko0twhYycYVDofRtWV/q6DiGICnn34a06ZNw/Dhw1OOl5KTk4OCJEXZ1157DbFYDICSC6Em3hnx2Wef4dprr8XXX3+NmpoaNDU1Yd26dXjppZdw3HHHGe4y8TMGr6iZdBKLxRBN4ovY3KztTCwqcjbzNP7aycbgGJ07I6y6KW/ciBh5fbPjcHrcnn4ePA4eR4aMQyYWTfLw4YiFQqkPVnvvrTkmYMsWRDdvVrxQLYzDy88j9P77UK9fih13nLFxnHYapAceQEi90qyyErHbboN8zz2OjM1v+0d040aESZtftGdPxwO8pS5dNKswUp1jpOXLtffv06f1/ikJhRAaNgySyrogVlkJOcXjnfhupAULNOOODRoEORpFqHNn7T65aVPC8fhuH/FoHNKGDdrPrls3yNFoynFIo0cjpPLClb/7zvi+YoJs/V4AALNmIaxeKZqTg+jw4cDy5YhGo5BOOgkhVeeE/M47iE2ZktpmywEC8b2sXas9nwKQO3WCpCr0RLdudeS46+nnUVmpvXbo2BExWQZ0freh4mJIqlyC6JYtjp9n9PDL/qEeg9djkX78UXtsHTkS1YcfDjk/H1J8ZV1zM2L/+Q9kWkB1Eb98N8nGIb31FkLqPAY1NTXK71bPhszhcQh8/DHCL72k2RQ7/XTI48cn/10dcwxCkQikeFGysRHRDz7QZBwkGkfolVe085jTToMcDgMHH6w9DsyZg9i2bWLXokk83z+WLkXotts071EePhzN11wDLF5sfhznnIPQjTfu+Y01NSH2zDOQJ0+2NDxHPo/duxEmHW3Rrl2Vfaa4GCFJgqQ610crKoTFPU59L6GlSzWfdXSvvcR9l+xbWLUK0TVrgB49AnH8sEu8aK7H9pbuQrq6n1LUIk7KsowdO3agJMXxyuzzAkq3gfqxAFqtkBIRt3DSC6fu06dP0sdaZdWqVXj00UcBAD179sRYldi1c+dOrFq1CgDQ1NSE448/Hueeey7Ky8tRU1ODTz/9FI8++ih27tyJq6++Gi+99FJSOyi/w2JEAFi/fj3WJ2mrPvts5T81s2c7Pw6a9O4GhbW1UDdiyZs2YfYvv2guJI2MI664NjU1YbYbH4bBcXgBj0MLj0OLX8ZRO3Mm1NOOrT17Yq2R32ZzM/bNyUFI1aK77P33UW/xxOvV55GzdSv2IUGNywYMQF3L66ccx6GHou/YseigbgV+6CEsOvhgNPTu7dg4/bJ/rPjsM82xP5aTg9mbN+vbb9igePduqOOnG9auxQKyH6o/k94//gi18/jW9u2N7bct9AmHNft95aJF2GDw8Xa+m0G//IJC1e11bdti6+zZ6BUKQX05t3XxYqxLMR6/7CNejaPv4sXooLq9SZKwUfUZJRpHu86doW7ul3/+GXN+/BFykhVddsi27wUAur7zDnqobu/ce28sbgmSnDdvHvIGDIA66k+qqMDSV17BToOhg07g5++l6IcfoF7z2dy+PRpLSlCoEiPWzpmDKqNWdBbH4TQlP/8Mdclgd9u2WJjguD6ooEBzbFw7Zw6qevSAV/hl/wC8H8ugb77RfPYbSksRa9MG28eORcmnn7Zur3/qKSylXU4e4JfvRm8c/Z54AslKaks//NDx41yqzyO0axcGX3KJpkjb3LYtFlx8MZoNzHP67bcf2qu6RWuefRardLqH1ePIX74cQ+bO1fx92X77oW72bEi5uRiRm4tQi1WLJMtY+eKLqD3kkJRjMYIn+4cso/9ll6FIJTzJoRAWX3cddrbY1FoZR/mRR6KjKsOucepULDjySFtCvZ3PI3f9egwj2+ZWVSHWst8MLy5GRNVts/K775CoN9zOOMK1tRhBui4WRaNooPtvLIZ9ioqQo+reWfPii9g+frwj43ASr8fR0LKvprIwysvLEx7j1vM2qvLC1H/XI/78u6nVjEts2bIFl112GXbu3AlJknDLLbdoOj82btyIsrIybNq0CZMmTcIfVMJ8SUkJLr30UowePRrnnnsumpqacPvtt7cGfQcRFiMCQI8ePdBJL4ithcsu01psTpqU2KbRCtFoFPPmzcOwYcNaU99dg7zPUHMzRvTuDXTsaGockVeVH3UkEsEI6kdqE08/Dx4HjyNDxlFMLHg6HnUUSgz+NqX+/TW2M/1jMcgmf9defx7SM89obsvt2qHfBRcgGgoZH8cTT0AePrx15ViouRlDnn4asXfftT0+v+0fexPrI6l3b4wYNcr5FyQrX/K2b289R+h9JqGWVTZxOu6/v+H9FgCkfv0AVZGla04OuqR4vO3vJhpFaO1azaYeRx+NHiNGQBo0SOPN3DkWQ8cE4/HbPuLVOEIkCLfbiBHoOmJE6nH06qVMwOLP09CAfSQJSOccRJbN+Uu7NQ6HCK1bp7ldcPjhGDZs2J5xjBgBuX9/SCoP6AGLFkE+5xzXxxaE34tEinjh3r1RUFys8czu3a4dejmwz3r5eUjEMiO/R4+Ex/VQWZn2/RYVOfJ+U+GX/SNtY9m5EyGSbdDthBOwGUDRpZdqzpNFv/6KER07Gs9nsolfvpuE49i8GaEffkj62AGRiOl5selxEKRbbkGIWO2E7rsPQ486ytDrSOeeq7GuLP7uO7QfPLg1N01vHNIbb2ieQ+7eHf0uuqi1qC4dcIDGz7/v+vW2PxdPj2XPPIMQWcgkX3kl+p97rr1x3HADoBIj8tevx4iqKsDgd6XGkc9D1Z0GAHKbNhh+8MGt85VQ9+4a66++bdsK36Mj4yC/KzkcxqDjjgN0FpGEDj1U8xmWr1+P3kbmhh7h5jiqqqqwllxXxGn9baaYa8qqTpeQARHMyvPG76t+fqOPNzImu2zatAm/+93vsLqlJnLllVfisMMO09ynb9++mDFjBqLRaMLvccSIETjzzDPx4osvYv78+Vi8eDEGUlvrgMBiRAAIhUJJDyo1NUB9/Z7bOTmKXarThMNh9w+yOt7M4S1bND7XZsfh1pg9+Tx4HDyODBhHaOdOISAsNGqU8QPVwIEaMSK0ZInlg5xnn8cHH2huSuPHI5yf39r6a2gcAwcC114LqKyZpA8+QPiTT4Bjj3VkmH7YPwAIxXOpvNydcRHvUGnHDoR379Z4HWo+E1I8Ce29t7l9jwrsVVWGH2/5u1m1CiCrjsLDhimvSzIzpC1bUr6GX/YRz8ZBckRC3btrvrOE4+jcWfEaVh3rwj/9JOYYOETKz2P2bCVEfelS4NJLgSlTXBEmPPteZFkoHITGjGl97dZxnHyy5pgZevdd4B//cH98Lfj690IKh1KPHgBZNRiqrnb0IsKTz4P41Utdugiv2ToOYg3h9PtNhV/2D8Djscyfr7U+CYUQGjkSWLYMoeOOU6x0VHkm4ddfByzayFjFL9+NMI633tJ+doWFwD77aAr5oVWrHN+PU34eNGx87FiELrnE+Gr7k08GVAGvUm0twl99peQ/6I1DloHXXtP8TTrrLITVhePDDtOIEaGvv3bsc3F9/9i0SREN1PTujdCddxqbgyRj7FhgyBBN5lL4qaeUbDOL2Po8yDxLKitDWL0oqWtXTWZGaMuWhN+jrXGQXDipTx/lWk0PIkaEvvrK/vfiAm6MI1mhvrBQ6XdL1Vmg7lbIbREck2HleeNdEPHHAqm7MOJ/T9VBYZdly5bh0ksvbc3CuPDCC3HllVcmvH+q7/DII4/Eiy++CACYO3duYMUIDrDOADIqwDo3V/SCp2GSBnjnynfw7Y3f4p0rObyQYdJNwdKlGu9PhMOAmTAoeoJtaVX2LQ0NmlV+AIDjj7f2XH/9qxjmds01gGrylRGsWaO97UZ4NSAGWANAZaX+fXfvBmig2V576d83EfR85kUwJw2v7tRpj9cuDdT2Q4ihn5BlMdRcb59JxJgx2tvpCrGeNw848khg7lxlP37oIe/C091i/XpxPqgn9NBg1IULxYDKbIVavvbooYS/qlF5LQcGGmCdpJtc8NXnAGtvoOHVgwbtWQSQlweceqr27y+/7M24ggD9LE4+GRhGTG5IUdV1YjGArpS+6y5ztj9lZeI5U9W5KTBrlrLYQg31qaYh1j/9pF2x6WcmTdIIcgCUIPAWT3tb6AVZv/22ON/xCjq3piHD9LrHrXEaCa+OQ/etBQsct5INIvHMhR0pAujjmQyhUChljgOwJwvC6PMCQHHLfEadI5Hq8fG/F9O5kIN89dVXOOuss1qFiMsvvxw33XSTrecsVS3grgrwPIbFiAyAihEprNX8D+2OsHACGtV7FA7seyBG9XbB5oNhGFMULlmi3TBwoDnV1K4YsWsXpKuvxt5XXAE4YHGUki++0F78SJL1ToZ27YB//lO7belS4OGHrY/Ph0jExss1MaKoSNz3Ep1jVq9WitNq+vY193q0KObFhcuiRdrbgwfv+beqyxAAixGUbduAeKBmHJ2OzYT4QYxYuhQ4+mixyOpSfpZnqIKpASgrqffeW7zfAQeIhYx3eGEKgMwVI6jIa0aM8OP7nTEDOOEEpaOJCi1BhVjPYL/9tLcnTtTenj1bFNazkdWrgW+/1W47+2xxLkK6OF2nqkoRJNRYCXul4vE774jPG4d2YvTvD4wcqd124IHaFfTNzelbFGCG994DXn9du+3ssx3rggYAnHeedv7b3Aw8+6xzz28G0qUniBF0EUiiRUN2IV37unOKOCNHKl1Jar7+2vkxBYx4yPPGFIuH438vLS01ZIlU3nIduHnz5qQB2vHnzcnJQZeWa5zS0tLWLIgNdF9TEY1GUdmyb5WVlaUckxX+85//4LLLLkNdXR3C4TBuu+02XH311SkfJ9PrT0KT6lqlIMAr0VmMyAB27dLeDrwYQU9A6VLtGYZxhEIqHtCLh1RQMWLlSsGGJimXXorQ1KloN2sWQmecIa7mcpr339feHjNGXJFuhvPPB0aP1m67/Xb3JufpwKvOCEkyfo6hF/edOytihhloZ4QXYgQt4AxSRYNTMaKqSiy++4np0xE64wz0eOABIMXqJkfQ2xfsdEYsX+5NN0yc1auVjgi9Y4Mfi65moAWlAw7QX4UbCgETJmi3vf22a8MKFFSM6N49GMX5VNDfWLLzrd87IzZuBE46SbEDefJJTQ5NoKGdEVSMOOwwUfilxeds5NVXtbdLShQbI9ql6XVnhJ5IlkwETAQVIzZuBH78Ubxfc7Ng0YSzzxatB9u2BWjeGMmU8R21tcDll2u3lZQo1opO0qEDcOaZ2m1PPplY/HET2hlBC8F0QYFb1ztmOiNyc8U5nsoSLFsZMGAAAKCiokLTpUBZ0GIRZtROKP68TU1NWEpFI53n7devX2sQdCgUQr9+/QAAi+gCLRVLlixBtMUCb5D6WskhHnvsMdxyyy1obm5GYWEh/v3vf+Oss85K+ph77rkHBx98MEaNGqWxoKIsX7689d/lbl0zewCLERlARtk0AeKFvwWbJoZh/IPQGWFXjIjFlCKfEZYtA156qfWm1NwMfPyxudc3gyyLYoRVi6Y4oZBis6KmtlaxcMoEZFkpoqpxc2JlVIygF/dmLZoAfTEixWoX21AxIllnBOBtsdwosgzceSdw9NGQ3noLXV9+GdI117j/unS+0bFja5imIYYNEydhH31kf1xGqKhQhAhacI4TxCKzGtoZkSyLgxa4vv02s8Rbq2RqZ4QZmyb6fv0mRrzzDrBz557b77+fnmKhk9TViR17VIwIhwFapHnlFffPl36HWjSdfrpyTqKdEevXm1ukYxfaVdm+vZA/Y4gBA7QLJgB9q6aZM8VjOLVoikPtdPwuRvz1r+Kx+YEH9OdrdqFWTatWibayXmDWpsmN87csm+uMAIK3b3nAoS2fSSwWw8yZM3Xvs27dulZBYezYsYaed/To0a0r/j///HPd+9TV1WFWy9yQPm88GHrWrFnYqT6nqpgxYwYAJcNiNF30Z5MXXngBDz74IACgY8eOmDZtGg4//PCUj+vQoQO2bt2K+vp6/EBy0tS89957AJR8jFFUgA0QLEZkANwZIfL+nPfx+k+v4/0576e+M+M/YjElxKtXL+C3v2UrkSDT0IACWtTdd19zz1FUJE5Uk6x00PDAA+LFLJ0EO8nixaKn7Qkn2H/eAw5QOiTUPPOMaH0QQHKqqiBRVd0PYgTtjDBr0QSIYkRjo1KYcYtYLLlNU8eO4kpCvx1fd+9W9vWbb9Zslrwo6lMxwoxFEwDk5IgXq08+aW9MRtiyBTjqqORWHX4rupqhqUk81iUTI8aN0/psy7Jig5HN7N4tCo96YkQQ95NM6oz47DPt7bo697s53Wb2bK2gEg4rAcwUWlxesUJ/lXy2MH++JsQXwJ7PiC6O0FvU4SZ03mCncE7FY71ONirKjBypCBl60HPw9997K9SY4bvvgKlTtduOOkqc7zvFAQeImX2PP+7OayUjlU2TF5kRmzeLHbfJOiMAJQhczS+/uDunDwA9e/ZsLYZPnTpVyGiQZRn//Oc/IcsyiouLMYF2riagsLAQRx99NADgueee07VbmjJlCurr6xGJRHDuuedq/nbiiSciHA6jtrYWjzzyiPDYdevW4YUXXgAAnHrqqWjXrp2hcRlhzpw5uPvuuwEAJSUleOmllzB06FBDjz3uuOOQ0xLmfvfdd+t2R7z//vv4rGWucNZZZ7XmdgQRFiMyAM6MELnsxctwxuNn4LIXL3NoUIynfPwxcO+9wLp1yqT0pJP8O5FkkrNgAaSWFshWRoww/zxWciM2b9b3Q3VTjKBdET16mAvrTsY//rEn8BFQLj4nTQr8ysFcWgCORMwXgc3gZWeE3gpdN62a1q8XwxrVYkQ4LI7JT2LE5s3K6v4XXxT+JFVWun8esCtGAMDvfqe9/eWXAO0Oc5Lt25WMCHpMpKJTEFe8x5k/X1x5k2wVW14ecNxx2m3ZbtWk55ucCZ0RsZh4TA1qZkQspqwAp9CCdNCgQuKQIfpt/PvtB7RYa7SSzUHW1Kaqe/c9xdD27cXFDl7mRrgpRixZoj2f7d4NvPWW9j40Y0TNIYdoz3+7d4s2YX6gsRG45BLtHL6gAHjsMfH87RR6QdbvvuutC4Usp7ZpovP0rVsVqy4noV0ReXlAz57JHzNmjLLoJE40qghKWc5NN92EUCiE1atXY+LEifj6669RVVWFBQsW4IorrsD06dMBAFdddRUKSe7G+PHjMX78eNxwww3C81533XUoLCxEdXU1zjnnHHz88ceoqqrCihUrcPPNN2PatGkAgPPOOw/dyD7Tp08fTGw5Tjz99NO49dZbsWLFClRVVeGjjz7Cueeei9raWnTo0AGXXnqpo5/H7bffjubmZkiShDvuuANdunRBfX19wv/UgkOPHj3wu5briKVLl+LMM8/El19+ia1bt2LFihW477778Oc//xkA0LdvX1x11VWOjt1rWIzIANimick4aCDUrFmKn2bAi67ZiPTrr9oNffsqF1FmsSJGPPKIfvHSTTHif//T3j7+eOcuKsrKhNXi+PbbwPsq59EiWa9e2gBCp/GyM6KoSHvhArgrRlCLpnbtxIK6X0Os589XVu3RsE41bv52AWfEiAkTxGKoW90RO3YoAZdz5mi39+8P/OUv2m1+WwFuBmrR1Ldvan9yuvpu+vTsXsFIbUCKipTjg15x3u5cq7YW0uWXo/+ll0Jyu5hcXa0UhNSYESP0gnjTxYIF+l788+d7PxYnSZUXEUeSxCLza6+J3282IMvi3O7ss7U5OXRO4mVuBN1P7eSi7befuDJebdX00UdATc2e25IkZh+oKS5WLBPV+NFO5+67ld+8mr/9zdpc0wznnKMNYo5GlU5rr9i+XSxepeqMkGXnLUVpXkS/fvo5VGoKC4Xjl8Qh1hg2bBjuvPNO5OTkYOnSpbj44otx4IEH4pRTTmm1QrroootwzjnnCI9dtWoVVq1apRuAXVpaioceeggFBQXYsGEDJk2ahAMPPBDHHXccXm8JfB8/fjwmT56sO67Jkye3WiO99tprOO6443DggQfi6quvxqZNm1BYWIjHHnvM0fDqn3/+GfNbztmyLOOKK67AyJEjk/536623ap7j2muvxZktx7iFCxfikksuwcEHH4zjjjsOTz75JJqbmzFo0CA8++yzgrgTNFiMyADYponJOPQ8r595RmxlZfwPFSPM5kXEoZ6yqcSI+vrE+4tbBc3t20UhzQmLJjXXXiteqNxwg7gaPkDkUjHC7SAuI+cYWRbFCCudEZIkrl50M6NBLy+CimF+FCM++AA46KDUNhOJ8hCcwgkxIi8PuPBC7bbnn3e+q2PnTuX4Qgv15eXAjBniMdNPK8DNYiYvIs5xx2mFwIYGd/OC/I5eXgQgdkY0NooXFma59VaEnngCRb/8AumCC0SxzEn0jqdmMiNiMdGqI11Qi6Y4Qe+MMCpGAKJV06ZN+t0imc6sWaLlJ/1s6JwkqJ0RoZAoHqs62UJUlDn00D3Hr0T43dt/9Wrg73/XbhsxArjuOvdfu317cV968knvRD+9Lj061+rUSZy7Op0bYTYvIg7ZtyQOsQYAnHLKKXjrrbcwYcIEdOvWDZFIBO3bt8fBBx+MqVOn4sYbb7T0vGPHjsX//vc/nHXWWejRowcikQjatGmDUaNG4a677sKUKVMQSiAi5eXl4bHHHsPdd9+N0aNHo127dohEIujevTvOPPNMvPvuu9jXrHV0CuY4MN8JhUK4/fbb8dxzz+GYY45Bly5dEIlE0KFDB+y///7429/+hjfeeANdqWgXQHJS34XxOxnXGUFPSNu3KxeRdHUpk7kkKjhdcw0wdChgIACI8QdCZ4TVk75eZ0QslngVyzPPJF4J7JYY8fHH2sl8fr7iXe4keXlKDob6wq2iQrFwohc2ASGPFoD9IEZs2iQW5KyuVuvYUXsR5WVnhNqiKY6fxAhZBv71L+D668XVyb16Ked+9We3bp2746H7It1XjPL73wP33bfn9tatSnEl2WpOE0iNjQiddppYYCkrU4SITMkCiGNFjOjQATjiCG0459tvA6ee6uTIgoNRMQJQ5t12VtupwiYlWQZef10/I8AJ6ArtNm2SXwjRzghA+W1Y6dh0mkRiRJA7I2prRZu6ZGGbAwcq80T13PHllxX7vmyCdhQNGCDOn9PZGeGkGAEoVk3//vee2z/8AFRUIFRXJ3YcJ7NoinPooUp3dJxvvlFsfvxSS3j5ZUX4jRMKAU895d34/vAH4Omn99xeswb45BOl09Jt6DVY585KKLuanBxFkFAf3zdtcvY8QjsjUuVFxBk7Frjnnj23f/gBko6vfzYyYMAA3KP+bAywxICNaffu3XHbbbdZGpMkSTj55JNxMrWDs0iq8f7ud79rtVmyy4EHHogDDzzQkefyK9wZEXBkOQMzI/Qu/rk7IrtIJEZEo8DppyuTJsb/RKPA3LnabVY7I6gYUV+fWFRoblYK9onYtk08cDoBvWAaN85eQScRJ54I/OY32m333eftqjgHETIj0iFGUFsSelGfl2c9x4Ku0nVTjEgWXh3HL2JEUxNw2WVKtw8VIsaMUYoR1GrBazHC6nc+YABw2GHabU5ZNTU1oc9f/gLpk0+02zt3VoSI+GrZoGcBxKmpETvhjIgRgOhF/v77yn6XjSQSIzp0EO9rV7iixxSVOOE4tDMilX1X27Ziwc8Pv43m5sQdAIsXB3e//fVX7fk1Jyd1jhYtNr/5pjtzNr/S3KzYU6k5+2xxpTjtjAiyGHH44YIgKL37LjrMnAlJ/d3n5BgTlGnQ8I4d7nZomeXDD7W3f/e75CKd0+y3nyhueRVkTa/dqEVTHDpX90tnxMEHa36L0u7dKKQLgRiGMQSLEQGnqUm8hg+8GNGhg1L4UcNiRPagF2ylZutW4Le/VSwqGH+zZAkkurrcamdEWZnica0mkVXTG2+ktnvRaxO2QzSqWM2ocdqiKY4kAQ8+qM1VaGgAEnhm+p202zQ1NYnFKD2LplResomgNk1uiRGyLHZGUKsewB9iRFUVMH488MQT4t8mTlSKl127imGCbosRdK5hJ0j9kku0t2fMsF8sikYhXXQRimnRsrhY6QBQi7Z0BfjOne4HgLvBjz9qi5m5uYqdhRFOOkl7u7raf3YdXpFIjAiHlewINXaK87GY2K3www/u5XWY9a6XJP3ciHTz669KF4EeTU1i4Swo0PDqYcNSX6iedZa28F5TIxZvM5nPPxfPy9RWBxA7I1au9C5bz8nMCACIRIQ5s/TOOyihovv48eKcSo9u3cSV7n459ldXi6HHv/2tt2PQC7J+/333c7kA8forkRhBLWicFCNiMWD5cu02o50ROpkkbWfPdmZcDJNlsBgRcPQWigTepkmSODcim6mpEf3v999fe/vXXxUbDA609je//KK93b279dVTkmQsxFqWgXvv1W7bf3/Ibdpotzk94f7+e7Ggcfzxzr6GmsGDgSuv1G57663ENg9+RZa9t2nS89ik5xhaNLaSFxHHq8yITZuUi1w1fuyMWLpU6XzQ21fvuAN48cU9xSovxYi6OrFgakeMOPVUsTvhqaesPx8ATJqE0KuvarcVFSkBn9S+IJH9TtCgFk0jRogLVhLRo4c4f1B5kWcVicQIQD/E2irbt4ve483NYp6SU5jtjAD8aWGW6twdVKsmM3kRcXr0ED3/aW5ABiPRY/x+++kXSqkYsWuXd9fLTndGAGIn28yZaEeP/3qiTCL8mhsxfbr2GJmXlx774YkTlU6xONGo1rrJLej1V6LwYDfFiPXrxSKa0c4IQNi3iuj1LsMwhmAxIuDoZcwFvjMCEMUIWqxiMhc9i6b331dsL9S88orWk5vxH06FV8ehYgS1pAGUC3o6KbzhBnGy67QYQS2ahg1TPO/d5G9/EwsvV1+tFH6CQmUlQnS1tttiRF6eWIyiF/C0M8JqXgTgXWcE7YooLNTfB9MpRixZoggR1Ks3Px/4z3+Am2/Wroj1UozQm2fYESPy84Hzz9due/ZZ63Yrn3+u9dQGlNUn//sfMHq0eP9MESO+/157e8wYc4+nBa63387OhQzJxAgnLb3oiuk4blk1UTHCyAptP3ZGpPp8ghpiTcUIo1Y01KrpvfcSd45kEFJDA6T//le7MVEBvqxMFGa9sOtsahJ/M06IEePHa96P1NwMSV20LywUu92SQcWIr74S7STSAe3yOewwdyxdU1FUJP7O6L7nBkZtmqgY4aTQRuegbduaywgjNmBt58zxLgCcYTIIFiMCjl5nREaKEdwZkT3QC+bOnZVJ7jvviFYCN96ohAYz/oSKAlYtmuIY6YygXRF9+yrtz26LEe+/r73tlkWTmg4dgDvv1G6bPx947DH3X9spqJ1WJJJ4lZSTpDrHONkZ4VVmBBXnBg3St5ZKpxjx0ENiobO0VFmxePrp4v3TKUa0batdNWgFatVUWakU1czS3KwIjSrk3FylsE69seNEIuL4/VB0NYMsWwuvVkPFiPXrxXNTptPUJB7jkokRdvaTRMcTt8QIKn4Y6YxwohPkxx8VYWyffYB33zX/eDWNjUqhVA095wSxM6KmRiz6GemMAJTOMnW2x+7dWdHV1P6bbyCpRRdJAs48U//OoRDQp492mxe5EXpzGLs2TYByvjr66MR/P+kkc+dkKkZs26a/iMlLZFnpZFTjRWh0Is44Q3t7yRL3BRujNk1uZkZQ27v+/cVMlmSQeVe4vl7MSGQYJiUsRgScjLRpAsTViCbFiLb5bVGUX4S2+TYLCYz3JFq9N2AA8PLL2slCLKZ4y1LfRyb9xGLeixFz54ri1PXXA+EwZDrZdVKMWLNGXLXohRgBABdfLPqn33abUtwIABINo+/d23o2gxlSXeRkQmeEXl4EIIoRO3eK1nhuQcWnffZR/OSplU4cKkZs2+ZeXpBT4dVqhgwBDjpIu00vJyMVTzwhHGPke+8Vg+wpQQ+xXr1aLDabFSMGDRLtF955x9awAsfGjWI3iFudEYnEiJ9/VorTTpMOmyZZBi69VBHK5s5VVhfbWTT1ww/ice2KK7S3g9gZQeeAubnA0KHGHtuxo7JSXs3LLzszLh9TQgvVhx+euFgLiKKVF50R9DcuScZyHIxAxWM1dBV/Knr3FrtD023VNHeuWIxPpxhBr6t27XI+U4/iB5smKpKasWgClPlhv36aTZJbVoQMk8GwGBFwaM0mP1+7kCSw2OyMWHzHYtQ+XIvFdyQIuGX8SzIrgeOPB/7+d+3fq6uBCROAHTtcHxpjguXLxZZ6oyviEkGLqxs3aosbtCuiUyfgwguVf7vZGUEtmjp2NF8ws0o4rKw2V7N1a3BWUdLitNsWTXGSnWPq6sSLniBkRlAxQi8vAtC3U/CqO4IW/f74R+0xnkLFCEDfys8J3BAjAKVoqeaTT8T9PhlVVcAtt2g27ezXDzINn9TDj3Y0ZqBdEZ06mf8tSpIyR1CTBSusNdDfTEGBtiDvZGZEomNJLOZOITAdNk1btwLqwNL6esVmzio0L2L4cOCII7TbVq70TjR2CmrRNHy48bwXQCw+T5/ufcaRl9TUoD0taKbKSKALJbzojKDfQceOzhUfTjxRf0FKcTFwzDHmn89vuRFUbCovNx6c7AZlZeIqVjcX+DU1ifPrdARY63VGmIXsWxLtbmMYJiUsRgScfv208xB6vRVYODMie0kmRgDATTcBp52m3bZwIUIXXugPL1BGgVyEyt26JV/dZYS+fZXiu5p4d8TatQAN/bvqqj2TbDc7I6gYceyx4jjdZOxYcfWXF6vjnIB2RvhBjFi1Srw/tUIwQ7psmhKJEUVFYkEoXWJEqtWURUWQ27fXbvNKjDDjH5yM008H1O9Bls2FRN56q/C5rfvTn4wVf4LeGUHFiNGjzVkpxKGrbefN86Zo5xf05lXqz9GLzgjAHaumdNg00UIWYG/VPhUjxo1TVizTouyCBdZfIx1YzYuIc9JJWi/9aBR4/XX74/Ip0jvvIKTuao1EFLuqZFBxNh1ihBN5EernOvhgcftppymdNWbREyPSmRlE8yKOPdbaOc0pJElY4S90DThJZaX4+RsVI7ZscS4Tjx7DzXZGAKJF5tdfZ2ceFcPYgMWIgCNJyrHvppuAu+5SshEzAps2TUyASSVGSJKyow8bpt38zjsoNVPgYdxF7yLU7oQ7N1dcBRYXI6ZM0U5SCwqAyy9vvemaTVN9PTBjhnbb8cc789xmSEervgNIfuyMoJ+d3soxM9CCe329vseiHbZuFQsEiWyaJCl9uRFUiKFFQT3oOcCt3Ai3OiMKC4Fzz9Vue+YZYxfV8+YBjz6q2SSfeirqjHaZObniPR3YzYuIM2aMuM9TETmTSTWvCrIYYcWmyW5nhJ4YMWuWtULwrl3Ad99pt40bp5xzaIEsKB2Pceg80Gx3bJs24iq7l16yNyYfI73yinbD+PGpz5F0TuzF3I8KgE7kRajRs2pK1SGSCCpGbNiQvvlxbS3wzTfabem0aIpDjzNuihH02is3N/GiFDpPl2VnOoubmsRFR050RmzerH9uYBgmISxGZADduilCxE03ZUheBKBfKGK1OTtIddEMKAFmb78tTNLLHn/cfpAg4ww//qi5KZtdEZcIvdyI6mrgySe12y++WFuUoDZNGzY4c0z57DOgoWHP7XDYWiu5XdLRqu8EPuyMkJzMiwD0L7Sc7o6gXRG5ucntbNIhRsRiYpHTT2IEXfTglBgBiEHWGzYAH3yQ/DGyrIRWqzv+8vMRu+ce46/rZDCx1zQ2ip7zVsWIcBg48kjtNnrsyWTMihFuBFgDirWRk8e+hgbRotNIYdTu+12yRH87LSYb4dtvtRlPodCeIhfNVwiSGLF9u1j0tWLVSa2avvtOtADNBCorxQ4ZIxkJdH5SWalYTbqJm50RAHDKKUpXSAtyebkoKhilf39xfOmyapo+XbsIITdXER7TTTrFiLKyxAvVOnUS/+aEVdPq1eJiECudEX36iNeWdM7CMExSWIxg/AktFDU2mlqpNfn1yfj987/H5NcnOzwwxnWMiBGAUmh77TWhjT104YUcaJ1uolFhQuaYGEFXey9eDDz2mPbiKxQCrrtOez/aGdHY6MwKG7q69pBDxEKHF6SjVd8uspw+MYK2fyfrjLCTFwEo+wO9oHJajKB5EQMGJLfxSYcYUVsrWukZECNkmhsRtM4IQAnqHj1au40KqJS33hJXkv/5z0oop1GC3Bkxd65W6AXEz9AMtEjtRpiyX6HzKno+dLIzgq6apnzxhfXnpuidw9PVGQEoq/bNLnKgBej99ttj60bFiCCFWP/8s/Z2Xh4wZIj55zn6aPFcNneu9XH5lddfhxSN7rndpo2Sn5AKPQtJPatJJ3FbjCgvB6ZOhdy5M3b36oXYCy9Ytz6VJP/kRtC8iEMPVb7ndOOlGEHDsZPZ9+bkiMdzJ8QI+v46djS2MIYiSaId6tq11sfFMFkIixGMP6GFIsCUVdMrP7yCp79+Gq/8YGGVEpM+duwQCwTJwk2POkoILZZqaxVv0V27XBggY4jFi4GdO7Xb7IZXx6GdEXPmAP/6l3bb6aeLF2jdukGmBWG7Vk2yLIoR6bBoAtLTqm+XykpI1K4oXZ0RKi9axzsjwmGx2Od2Z0SivIg46RAj9Ap+qTIjgODbNMWhQdYffJA4/2LXLuD667XbevYEbrjB3GsGuTOCWjT1729P6KXZI9ksRtDflIsB1jLNPXDSqomKEaGQsX3E7vtN1BmxeLEyJzGDXl5EHGJHGqjOCGrRNGKEZrW7YfLyxPOZOjw8U6CZIxMmGCtUFxSIq7PdXozithgBAJdcglhFBRa89RZw0EH2nssPYoQsi3kR48d7Pw49qBixYoV7GYx6nRHJSLZwyCpO5EXEoYtlWIxgGFOwGMH4k7w88WKBcyMyH73icKrQ42uvBc45R7ttzhxg0iTnxsWYg1yENnbtqi8wWoGKEatWiceGyTodUZEImukxxa4YsX69WOTxixixdq3ii+pnaF5EJOJ8ATgRel608RW9TndGAGLR3e3OiER5EXHSIUbQ95ybqw0nTYQXnRGNjeL4nN4XzzxTsRiME4sp2RF63Hef2DV0333GPi81QQ6w/v577W2rFk1x2rXT3g6KGLF4MfD73ytClFUxyUpmhFUbQ3osOeII7W0nxQjahVFSYmwFNZ0L7NxpPMcnGk3efWsmyLq2VrC01IgRtDNi0yZnOjq9wG54tZoRI7S3M02MWL1azA0xYtEUx+vMMLczI+JQIdMqVIxYuTLxQgC3WLBAfE0/5EUAYoD17t3O5epR6POmusanc3U3OiOs5EXE6dVLe9utxTIMk6GwGMH4F3ICkliMyHzoRK24OPXKIEkCHnsMMl059dRTwPPPOzs+xhjk4ro+1SptMwwYkPzv48YlvOhtpAVYu5Nt2gpfUJC6COwW9GI0GvX/Ch0qRvTu7dzFZyo6dRJfa9Mm5XOj47LbGQGIYoTTBSUqRgShM6KkxFCovexFZ4Te/MJpMaJtW1E4f+opZZ9Ts24d8I9/aLcddpjS8WWWINs00c6IMWPsPR/tjAiC7/zu3cp3//TTShcozR4xQjQqWmOkEiOam635zjc1Cb9z+YwztPdZsMCZghJgLbwa0O+eMPrbWLNGm/FAeeUV46uKv/pK+/uPRICDD95zu18/ZXGWmqB0R9gNr1azzz7a25kmRkyfrrkpd+wI/OY3xh/vdWaYF50RTjJ0KNChg3bbV195OwbaFdGrV/quFyilpeK1tltWTWZsmgBxIZsT5w7ujGAY38BiBONfkgSMMhmK0bwIStu2iL32GqI0wf2PfwyWx26mQC5Cdzo54S4uTt5lodcV0UITXb1lV4ygRevyckPFVVcoKREvtvyeG6H3+XlFOCxeQG/ahNzNmyHRjhK/d0bU1Ij7chDECCMWTYB4sVdTI4bW2oVaNOXmWvMQTgUtJq9bB3zyiXbbDTdobQZDIcWKzsqxJag2TVVVYjHEbmdEEG2avv9e+9t8+22gvt7cc1RWioJXKjECsCZc6Yis8rHHAkVF2o0zZ5p/biOvZ3SFtt77NfrboIUsmmewfj3w9dfGnotaNI0Zo+1+CofFY3kQ5rRbt4qdXXbECNoZMX++GEAbZEi+hnzEEeYsrbzujAiaGBEOA2PHard5bdVE8yKOPTZ91wsUSRK7I9wSI+zaNHFnBMNkFCxGMP6FrkpkMSLzsSpGAMCgQVjz179qt+3apawmdbpwxSSmqUlYteZoZwSQeDXRsGHAMcckfJjrnRF6QYJeErQQa+q7bSaY1wlo911lJXLpPtG2rTMWBHTFrpNixOLF2tvhcOqVXn6waTJa7Nc7Dzh9wUfFiG7d3CkUjBoFjByp3fbEE3v+/dVXwKuvav9+6aXiymCj6HVGWLXf8ZIfftDezs8Hhg+395xBFCPoMTwWE4OBU0GPaZGIeExr317c362IETQvQpKUYhItBDpl1UTtYox2RuTkiLZdRt8vPW+NHCnaKRm1akqWFxEniLkRdB+12zVKj38NDYlzO4KIXUsrLzsjdu8Wr6n8LkYA6c2N2LFD7MTwS15EHK9CrM3aNDmdGbF7t9i94GRnRFWV+QUDDJPFsBjB+BfujMg+7IgRALaPH4/YZZdpNy5ZovgtB6EAkwksWKBcKKpwtDMCEHMj4kyenLSA6ElnRDoJWog19cqmRRe30TnH5NFj0F57OVOUdrMzglo09eunrOxPBi0ebNniXmBhHD2bJiMUFqKZFpKd9nvWEyPcgnZHvPee8vrRqJh11KEDcMcd1l+LrgBvagrGhTK1aBo50lr4rZogZkboHcPpZ5MK+lvp3l20qAuFxM46B8SI5vbtFXGU5kbQIrxVrNo0AeLxx2pnxIABor//668nt3IClHMADbvWEyOo0BEEMUIvvJp2kJihY0fxeiBTrJoaG4G5czWbZCpYp4LO/VavFruhnIIKgIB7mRFOQsWIhQv134sbfPaZNsMtEgGOPNKb1zaKF2LEjh2ikOV1ZsSKFWI9wEkxAuDuCIYxAYsRjH/hzIjsw6YYAQDy/feLq4r+8x9g6lQbA2MMQwrM8l57IUoLiXbREyN69gTOOivpw5poAZZ6l5rFb50RXvsG22HHDrGIbteGxSw6Fzl5VKByIi8CcDczwmxeBCCKEdGo+3kCVsUIAI10dZzTF3t0fuFmkPrEiVorlmgUePZZJReAFthuv91cgZWi9xkHITeCFtydODbQ89DOnf63enFDjEg0r3Ii7JyKEfH9jxbZly1zJiDVTpCuVQszuiK/f39x7lFVJdqvUb74QlsUKyjQ38/1xAi/L65xMi8iDrVqokKOVaZPh3TBBeg6bZr7grweCxaIwpVZMYJ2xTY1uRfQTLsoc3JEIdOP7LuvmItg1E7NLjQv4pBDROu6dEML8suXO/8aetdcXts0UTG5rEzpgLZKmzaQ6TyLcyMYxjAsRjD+hRYDnAq8Y/yLA2IE8vKUVWl0cnzddaL1g1Fk2f8Xf36BXITKZtvNjaAnRlxzTcqVs42Z3hnhtW+wHX7+WfObknNyxGKD2xjtjHACLzsjjIgRekU7t62arGZGwAMxgnZGuClGtGsnFi8ffxygNoNDhii5R3Zfi3b2+D03QpbFc7UbYgTg/xDrdIoRVvYTIg40x59zn33EOZkTVk1+6Yzo0wc46CDt9lRWTbQ75JBDxLBqQOwYrK31/8pbatPkhhjhRGfEkiXAscci9NJL6PGvf0G67Tb7z2kW8lk1dO+un2mSjM6dxYKqW4tR6Dyhc2ex08qPRCLibzSZVZMsK0LRlCmQzjkHve66y9ocSZb18yL8Bs2MWLHCeXGOXnN16KBdmKEHnftt3Wqv64d2fNjpiojDuREMY5kAnD2YrIVtmrIPOlGxIkYAyoXh889rtzU1KfkRRouAsRjwwQfA0Ucrk6VRo9zz0Mwk3FgRRxk1SvEQj9Ohg2h9ooPQGbFtm+IfaoXmZrHI48fOCL+KaLTYuM8+2u/UC3S671zrjHAzM2LRIu1tI2JEXp5YnHVbjLCaGYEMEyMAJQdCzdq1YmH1X/+yZ20COGe/4yUrVoj7iltihN+tmvTEiPXrzXX1pbEzoin+Gw+HgcMO097XDTHCTGeEXp5KKurrxWNPPPyUWjW9805ySzQjeRGAYmNC910/h1hXVoqfkRPzQJobMXu2/fnNK69ouqOkKVO8Pz4SMaLeiq2pJHm3GCVo4dVqUuVGbNoEvPgicMEFynFy6FDg2msReu01dH7rLYSOOML8NcPixWKYu9/yIgCxKN/Q4Pw8i563Ulk0AaIYEYvZs9eiYrKd8Oo49JzKnREMYxgWIxj/QgtF27ZBUnsuJuH44cfjtFGn4fjhx7sxMsYNdu8WLyytihEAcNJJwA03aLetXQucf37y1R67dimBokOGAMcfD0yfrozt11+B//s/6+PJBnbvFi6SXemM6NQJuP9+ZSVYp05KJ4yBlmchwBqwbtW0bp24OsdvnRF1dd554pqFiBHy/vt7PwY9m6agdUbU14sdOkaLGV6HWNuwaWrKNDFi9OjkGSmnnOKcp7SVoms6oSv/u3RxJty+TRtxBa+fxYja2sR2bma6PI2KEU7sJ9SmSS1wuJEbYTXAGrDWCaJnXRIv4p1+uiK6xNm5E3j3Xf3n2bhRFJETiRGSFKwQa9oV0aaN0j1iF9oZsWWLeNw2y3ffaW5K9fVKl5qXkM9rZ6JMtFR4ZdNpxxot3VAxYvZsRTT8058Usau0FDjvPOCFF3SvDaQlS4D77jP3mtSiqXt30XrND3TtKnbXOL0Ajy72SWXRBCj7F+3utOOU4UJnhMydEQxjGRYjGP+iUwzIMVjAefy8x/H6Za/j8fM8nlQy1tGzzLEjRgDAnXcCY8dqt33wAXD33eJ9N20CbrlFyR74wx+U1SyUr76yN55MZ+5cbUibJJn3vjXK5ZcrhZrNm4GjjjL0kFibNpCpZ6xVqyZaAC4qMlVcdYWePUWrKr9aNdFimh/EiFWrkENtW9zKjNi+3Rm/+iVLtKtDJcl44SdAYkTGdUZIktgdEScvz3zBIxlO2O9Qvv4aoTFjMPC88xTveyfRy4twIkReksQQaz/bNNFMIjVmrJr80BkBiMX21avF86gZZNl7myaaF9Gz5x6bkS5dlE5aNYmsmmhXSFFR8rkSLV76uTOCdsfuu69WpLHKXnuJxVI7uRGxmP7v6KGHlFXhXtDUJIRX77TSGQFwZ4QRRo8GcnP33I7FgJNPVhY3ke8hIXfdZW7lOxUjjj3WmfOZ00iS+yHW9HrLSGdETo54XLcjRnBnBMP4ChYjGP9SXCwU1iJOWlsw/oJeMBcViYUDs+TkAK++Kk6Wb74ZmDlT+ffcucBFFykrL//+9+Qrltev9/dKynRDL0IHDLD/HSYjL8/cpF6SxMmvVTGCForKy9N/gREOi90Zfgyx3rhRKCb7oTNC6LwLhUQvWKvo5SM4sUKdrq7t0ye1B2+cdIsRdjMjnLIgi0bFi1u3xQgAOPdcfWuyyZOdtXxzujMiGgXOPRfSTz+hzaJFCF1wgbNB0N9/r709Zoxzz03tbvx8Pk9WTDQqRsiyt5kRyTojhgwRi0p2rJpqasT9zm2bJr28CDXUqumjj/TnlLQr5LDDkluy6YVY+xU38iIA5XysZ9VklUWL9H//Gzcq9k1esGCBIHz4vjMiyGJEfr55y79evSCrO+p27QKuv97YY+vrRSsoP+ZFxHE7xNqKTRPgXIj1jh2i5TdnRjBMWmExgvEvkiQUiyKJWtaZ4ONEeLUeZWXKhYV6MhmLKQGiRx+tXNw89xzQ2Gjs+WhYLLMHL/Ii7ELbgp3qjEi3RVOcIIRY//ij5mbUKRsHs9DOCEqvXtpVdHbQK7w7Ia5bCa+O46UYEYs52xlRXw9UV9sfF6AfiOiFGNGhA3DGGdptPXoAN97o7Os43RlRUaHxwJbWr3eu8LV7t1hgdCIvIk6miBE//mgsxHPrVnFu42FnhEaMCIWAww/X3t+OVZPe9YDXnRF0Ve3JJ2sFxuZm4I03xOehIkwii6Y41KZp0SJtF6qfcHMe6KQYQSyaNNx/vzd5W0S4kfv0QVQv28YIbNNkDGrVRCkqAiZMAB55RPm9r14NmXYxvvEGMGNG6tf6/HPt8Tcnxzn7RTegIdZ+sGkCRDHCaoYofT+S5Ej3s6zXGeHXvD6G8RksRjD+hhQEuDMig6FihNEVE0YYNw647TbttspKJQ8iEePHA598IvqvsxiRmACIEbJbnRHpDq+O49UFqR3Iqt76QYOcsXEwS7t2yUOzncqLAJQuHmox4YS4To9HZiwevBQjamvFrB4zmRF6qy/pOcMq1KJJkrxb7fmXvyie6oDyG3j00T23ncLpzgi9/SSZpZAZZs8Wrf6c7JqinXpBFSPq6sSuKD3obyQcTizCum3TBIhF988/t160ocfP/HzjXWGANZEuVWdEURFw4onabdSqafVq8btNJUbQzojGRudXLTvBhg3i6mcn54E0N8KOTRPtwFIzf74y/3cbMme2lbFG5yvV1e7kAwW5MwIALrlEe5wIh4GDDlIyAb/+Wlkk8vbbwBVXKGKjJEG+7TY0U5HoqqtSC4LUoumgg0RB3E/40aYJ0M13swQ9fvfurczN7UI7I3bvdi4XjmEyHBYjGH9DOyMMHtz3+/t+6DG5B/b7u/+KoUwC3OqMiPOXv6Ruj83LA37/e+VC5MMPlc6JIUO091mwwNlxZQr19eJn40MxQljxnOmdEX4UI0hexE76G/MKne47DU7lRcRxI8Q6KJ0ReoU+E2KEnJsL2a3cCCpGdO6c3DLFSQYMUApqjzyiFMdOOMH513C6M4KujAWcEyOoF/7Agc5a/dFCkJ8zI1J1tRmxaqLzqtLSxMKvXdFq505lHqCime57NMS6osJ6UV1vhbYZq0SznRGynLozAhCtmr78UnOskuIWoXE6dkweZh8fK11F7EerJmrRVFTkjA1KHCpGLF0q7HOGSdYZATib25MI+nnZyVjr3VvbAQ64M/8LuhjRu7cyb3r0UeC//1XmYd98A/ztb8DBB4u5awDQsSMqLr9cu23RIuW8nQhZ1s+L8DP0t7pypbEOPCPEYuJcy2ubJiquOJEXAQBlZVorL4BzIxjGICxGMP7Gok3TpppNqKiuwKYai618jPe4LUaEQsC0aUrgIKVzZ2UiunYt8OSTWgGCFvdYjNBn9mztyudQSAku9BvZ1hnhN5umWEywaapPlxgBJBcjnOyMAEQLEbtiREODWGywI0boFZmdghb6cnPNdwDQc4JTYgRt+ffCoklN377KKky3xFsnVryrcVOMoMcrq/7piQiSTRP9bdMuLitiRLJ5ld39RGe/aKYF/wEDxGOu1dwIO+HVgChGVFeL3VtqtmwR9xc9e8FjjxX3s1df3fNv+n6POEIsIusRhBBr2h07cqSx92aUoUO1zyfL1j6H6mpByN86YYL2PtOn27OBSoVOeLVsR4yIRMQV2k7P/2Q5+GIEoAgSl12m2KoZ7FTYevLJ4vfzf/+X2DJo2TLxvBg0MaKx0bmi+pYtYsaPVZsmpzojnBJKc3LQRO3KODeCYQzBYgTjb8hFSw63vWUubosRgLIC7b339lxADhkCPPWUMtn6v//Tn1RzZ4Qx6EXokCHmLBM8whGbpoYG0YrAL50RVIzYsEEJ3PMLy5YJBR3fihF+74xYtkxcteZXmyb6XktKzAe+uyVG0NV6XosRbhMkmyb6PE4LgkERI6JRsfvu+OO1t70QI5IV5ylkv5AjEUSpNZ0kid0RVnMjqBhh1rue/i5kOfn+QLsi8vLE4m98+2mnabfFrZpkGZLZvIg4tHsiCJ0RTgusBQWiAGTFqon8duTcXKy79lrI9Jx4//3mn9soOuHVtjojAPdtOuvrFQsaNUHLjLBKOIzYlCnabTt2JM54ol0RpaXA8OGuDM0xOncWOxGdsoOj11rhsCgyJMKtzAinOiOgk2vGnREMYwgWIxh/w5kR2YMXYgSgBODNnatYCsyfD1x8cXLfeFoorajwbwEjnQQgLwKAuBJnwwbzntXr1omP8YsYodeh4VSh0AmIRZNcVoYmoxckbuBlZwQVI+xmRlCLph49FFsMo9DCy/btYtitU9gIr44j0642FiOMESSbJrqS1+nfYFAyIyoqRD/ys8/W3p4/P7VFjZksLrqfxGJKsc0oeium9QRHWnyfOdNabgTdD812RtD3CyT/bdBVtf36Jba8olZNs2cDCxcib80aSHQhAxVnEuH3zghZ9mYeSK2arHQv0LyIkSMRa9sW8hVXaLe/+qpz2UQUKtz06WPpvKiBHi+d7ozQE6KD2BlhlYMOAs4/X7vt+ef1Lb+oGDF+vPkFGF4jSe7lRlAxols341lxbmVGOGghJ4gR3BnBMIZgMYLxNxZtmpiA0dgoTi7cEiMAxSKkoMDYfffeW/QP5xBrEWK941sxghZjGhvNF4Vp4a1DB+U/P9C2rbiKyE+5EUSMcDSc1gpB7oywkxcB6BcR3DrH0iIf/SyMwJ0R1ghSZ4TbYkRQMiPo59CmjVLMUhdvYjGxoEkxs8hDrxBqZl8xat9Ci++VlcbCuCl2bZoKC5W5oJpkYoSRvIg4hx0mHEekV19FES3Wl5bqWz3pQcWIFSuURTV+oaJCnMfbCWROhBNiBCkey2PGKP+/7DLttUFzM/DQQ+af3wj0t+vEZ+V2ZwT9jefnK3PObOLuu8VFH1deqe1S3blTEVnV+N2iKU6/ftrbTokRVIQ1atEEiNc0W7eaz7LYtk08nznZGUGvJbgzgmEMwWIE42/0AqytrKBi/M3GjeL36qYYYYbcXHH1BFs1aamtFS/U011kTkS3buLqJLNWTdQ+wy95EXH8HGJNOyPSvZ8kEiOKi50XmJzOjKAFPLNiREmJ6OftllWTA50RQt4PixHGoCvAU3njp0KvM6Kqyn5hv6ZG/E1kq02TnijTpo1o1ZPKqsmMGFFUJK5UtSNGJLJv2Wsv8bdsJTdCL8DaDJJkTqijq2qTiQjhMHDWWdqXe/VVtKOLNsaNM75aevBg7X1l2V8LY6jQ0r6984I+oHQ3q5k711xhMhYTOiPkAw9U/tGxI/C732nv//jj7oiWbogRXndGJOp+ymS6dVMyBtX88gvw9NN7bn/xhdaCKxwGjj7ak+HZxqvOCKPh1YAoRsRi5hfO0ON3JKJkhzgEd0YwjDVYjGD8DSkKhBob/XvxyFiHXjDn59tvV3YSzo1Izi+/aG9HIv71Ro1ExImtWTGCrgL2i0VTHL+GWDc0CKsYfStGuFFEcbszwkxeBKAIEbSA55YYoZcZYRKZFlLXr3dmcUKmixFmvfFTkSjo3G53BH28JDlaLAAQbDECAA44QLs9mRghy+bECEkSBVgzll40MyKROOBUboTdzghA/G041RkBCFZN0sqV6EBXSxvNiwCUTg56XvJTbgTdF0eNcja8Og7tjNi509yCi8WLxd+9+nd17bXaAnttrZIt5yRNTWLWhRudEevWOWu9aFcAzBSuukqcb/3lL3uOH9Si6cAD/dM9nQo/ihGdO4uil9ncCPo+9tpLdD2wAXdGMIw1WIxg/I2elzgtHDDBR++C2U+rbagY4afVaH6ArogbNkwJcfQrdkOsaWeE38QIv3ZGzJ0rXhin284rkRjh9IpswNnMiOZmsThmtjMC8C7E2o3OiN277dtKybI4p0hm3RVEzHrjpyLRPkKPi2ahYkT37s6fR4KSGeGEGFFdLdr4pOo41QuxNoqZQqVeboTZbh27AdaA8TyV5mbxPJrKXmnUKKGwJ9EV/GbECMB/IdaxGPC//wHHHAP885/av7l1bu/aVTxGm7Fqov7+PXpofxd9+wK//a32PlOmiBkudnAjvBoQxYhYzP5xWY1RK7ZMJxIBHn5Yu23bNuCWW5R/UzEiKBZNgChGrFypHP/sYsemKSdHFJvN5kbQzggHLZoAHTFiwwZnPjeGyXBYjGD8TX6+uJrArBrO+B+vwqutQot83BmhJSh5EXHsihG0aOY3mya/dkbQwtnAgeJKZa8JameEXsDtwIHmnyddYoSVzIiyMnGlrd1w0ZoaRdRQk2mdEYWFSvFEjZ3cCLc6I9zOiwCCmxmRSIxYv14s8qj/RklVALIjRpgpVNLOiKoq84HMdgOsAeOdEatXi8fbVMUsSRKDrNX06WN+IYNfQqxra5UshQEDgBNOAD75RLyPm/NAatVkRoyg4dVxiyY1f/qT9va6dcDrrxt/jVTohVdbOSdS2rcX92kn538sRuzhyCOB007TbnvsMeCNN4Dly7Xbx4/3blx2oWJEc7Mzq/ztdEYA4uJUs2IE7YxwMLwaAJr0rKQSnZsZhmmFxQjG/9AQOLMnIMb/+F2MoJ0RFRXKqkNGgXZGpNt6JxWZ3hlBC+mrVtnziHcKGl49enR6xqFGr/sOcKcQSotlVVXWbYboMbOw0FoxwysxwgGbJuTkiEKBXV9evcUNmSZGmPXGT8bOnUB9vf7fgipG+DGHjH4W8WP6wIFid0ei7gh6XuvaVQxsptjZT8wUKnv1Er9fM1ZNTU1iV4sTYkSi90tX1ZaUGHu9s89O/DezXRGAKEZ43RmxdCkwaZIyh7r6arHoGqdPH0WkcAtq1UQtj5JBOyP0xIgDDwQOOki77b77nDtWuJEXEcfNEGsWI7Tcf7828DwWA847T3ufrl3F/dXPdOwoLgJ1wqop3WKEy50RzR06QM7P127k3AiGSQmLEYz/oStXDXRG3HPaPXjy/Cdxz2n3uDQoxlH8LkbsvbfoLUnDY7OVqiqxcJLJnRG7donHIL91RtAiT0ODecHFDfwoRuh13wHedEY0N1tfme2UtV2QbJoA50OsqUVT+/ba4kKmYNSOJhWJuiKAYIoRsRhQV+f869hhxw7xc45/FqGQKPYnECMkesw3Mq9ysDMiYWZEHFqMNxNirddVZsWmyWhnhNm8iDgDBiQuNNPuECNQm6YNG+xZrhkhFgM++gg47jjl/Tz8cOLfTEkJcOONSresm8dRWtw12hlRUyParOqJEYDYHfHrr9aC1vVwU4xwM8SaMyO09OoF3HSTdhvttBw/3p3sFLeQJKBfP+02u2LE7t3iccqMTRMgihFmXDJk2fXOCEiSOD/l3AiGSUmAjo5M1kLFCAOZERMPmIjfj/09Jh6QpEWa8Q9+FyNyc8WLT7ZqUqAXVfn5YieJ37AjRqxZI27zW2dEt25iISDdVk3V1WJBxw9iBKBv1eRFZgRgPfPAqWOmVwHWTokR9H06LUZkWldEHKc6I5LtH06LEW6IvLSrAPBfbgT9HGiQNz1uJuqMsHKMsCpaybL5VdO0GP/llwDNVEiEnihm5Zhi9P3SVbWp8iLUJLJqsiJG9Osndre42R3x6aeKTemxx4o++GqGD1dCntevB/7xD2csh5JBbZo2bDB27po1S9vdkJsL7Luv/n1POkksyt5/v7lx6uFWeHUc7ozwlsmTk88Xg5QXEcfpEGs9uyKznRF0nm6mM2LjRrGj0+HOCADOL5ZhmCyAxQjG/9DiANs0ZR5WVvB5DedG6EPzIkaMEP3J/YYdMYJaNHXqBLRta3tIjiJJ/guxplZeublKAcMPkIscORJx5xjUpo1YSLKaG+GUGOFFZ0Qs5kxmBOB+Z0SmihFedUZYtTHRC1p1QxDUEyP8lhtBxYju3RWRPw7NjfjpJ/0ivpedETU1YqaCWTGipkZZfW4EKuIWF4vdq0YwKtJZ7YwAgDPPFLvWBg40vzIYUOZWgwZpt7mVG7F+PXDyyeJ7jxMKAaecooSPz54NXHyxd11l/fuLr2XEqonmRYwcCeTl6d83HAauu0677YMP7M/9Fy50J7w6jpudESxGiOTnAw8+qP+3UAg4+mhvx+METosR9FzUpo3+uTgZdmya6PgLCqwdf1Mgc2cEw5iGxQjG/5BCkWSgM4IJENGouGrCj2IEXe3PYoQCLTL73aIJEMWIqirFfskIdPWv37oi4vgtxJpaNI0YkbgI4DV0xVV5uVKIcBpJEn3GnRIjzK4yi+OFGFFbK2aW+NWmKVPFCKc6I5KJEfX11jt9NmwAGhu129wQI3JylEKIGr93RtDPgYoRdXW6tpGSFcHS6n6id9xIZeFSWip2GBjNjXDKLsaoTZOdzoju3YHDD9dus9IVEcer3Ii77lIyYijFxcANNyj76ZtvAocdZs0i0A7hsGhZZUSMMJIXoeaCC0Th/IEHUr9OMmg3cXm5s50kenM/J7IuZFn83bEYoXDiifodEAccYH2uk06oGJEoG8Yo9Bq/e3fzxww7YgQ9fu+9tzvWWdwZwTCmYTGC8T+0OGBAjFiyaQkWVCzAkk0JVvQw/qGyUlzVFwQxgvrOZiuZIEYA+m3EetDVu37Li4jjZqu+FagYQQtq6YRe5LhRBI1Diw7ptmnSEyOcDvTVK/CxGOEtTnVGpBKrrFo10QJ8QUHicHm70NyIoIkR3bopXuVq9KyavOyMoPtFmzZAYWHqx1nNjaDHTSvh1YCx30VdnfhZmrX4uPVWyC3FLzkvTwl/tgoVI9zojFizRrFdUjNwIPDEE8q55+67tdZh6cBsbkQsJnZGpBIjCguByy/XbnvxRUPXoQlxMy8CEOd+9fXOOApUVys5V2qyPTMijiQBU6aIXeFBtGgCRDFi1SrxuzcDPX5a6UqwkxlBO+6czouIw50RDGMaFiMY/2PBUuXI+4/E0L8NxZH3H+nSoBjHoEW1SMSfE1xq01RRoUzOs5nKSrEYGAQxol07cXWsUaumoHRG+MmmSZbFYplf8iIAYOxYzU2ZrmJ1EipG+M2madcu0VvXLrTAF4mIvz+j0Iu9igqx68IMtKiklx+SCdgJJlaTrDMCcE6M6NPHvdXWQRcjAFHM1RMjnMiMMLqfWF0xTTsEvvpKtHvSg4oRbnZGUIsPvYDXVBx+OGLffIN111yD2E8/meusoNCOgPnznReQ//537feQlwdMnw5ccokxkckLaG5EKjFiyRJxzj5mTOrXueIKbRdnYyPwyCNGRqiP22JEWZloB+lEZ6yV7qdson9/4NZb99xu2xa48MK0DccW9PjW3CwuxDIDvb6y0slL52ZbtxrLGJJl4L33tNtcuk4VbJq4M4JhUsJiBON/yElLqqlxvljCpA89uxE32iftsvfeoidxtndH0K6INm2U1XN+R5Ks50YEtTMinTZNFRXiKiY/iRETJgA33AB5wABsPfFEyH/8o3uv5YQY0dwsFtGdEiMA562a9PIirBaa6cVeU5O9VZ/Z0hnhRYA1YF2MoI9zszuJelX7PTPCghgRqq9X5spq3AywtuolT4Xf+noxh0oPKn5Y7Yygv4uGBtGykWYm9OplLRth//2x+dxzxcwHs9DOiJoac7lXqVixAnj2We22yy6zbgXoFrQzYvFiYPfuxPenFk3du4vnEz26dgXOP1+77dFHlY4ZszQ1iaKJ02JEOCzOS51YjEJ/40VF3mWEBIW//hV46SXgppuAb781tn/5kY4dxXOBndwIPZsms9DOiFjMWGfxL7+IdYaTTjL/+kag3/e2bfpWdwzDtOLDih/DEPROWk5OvJn04tQKX7fJzRVb87M9N4KKESNHuuO17wZWxYigdkZs25a+FcDUoqlDB/MrS90kJwe4+27EFizAmv/7P3cDyZ3IjHDS2q5tW204LuC8GEHfox0P5a5dRVGYnkPMQEWyTBUjvAiwBpzrjHBTjPBzZ0Q0aizIm4oR8+driqMRve/JSAGI7ic1NcY6j6yKEZ07iyv9jeRGOGXTpHcsor8NO3kRbtCrl1IIVuOkVdMdd2jPLwUFwI03Ovf8TjFsmFbUjkaTz8nNWjSpoUHW27eLgo0R9MKrnRYjAHdCrOlvnLsiRCQJmDhRyVuhx7Wg4WSItRM2TZ07i4tYjCxEeecd7e1+/ewLwonQE5+4O4JhksJiBON/2rQRLx5ZjMgcgiJGAJwbQQliXkQcK2JEXZ1YBPGrGFFeLk7c09UdQS1E9t/fn91PXuBEZgQ9ZubmWi/GSZL7Ida0uGdHjAiHxd+u1Yu9XbtE245MFSNcCrBuoEUFFiPsYTTImwr/sZjG/iWXFmk6djS2ipnuJ7Js7POxKkYAolXTww+nFr2cCrCm+wIgHq9oZ4TZvAinkST3QqyXLgWmTdNuu+IKf9rXFRWJHaDJrJrMhlerGThQCSlW8+CDxmxi1LgdXh3HjcwwDq/OLpwMsXbCpiknR/ytGMmNoGLEhAnuWUC2bSsK+pwbwTBJydJqABM4rK5idpoNGyDdey86TJ/uvEdrthIkMYLmRmRzZ4QsZ5YYYSTAWs8z1a9iRF6euErH6gXpk08qBZijj7Z27KWdEX6yaPIaJ2yanLa2C5IYATgXYq13IZupYoRLAdb1VKC36ivNYoQC/RwKC/WLfoWFwPDh2m0q0TeX/oaNzqvofgIYE67siBHUMmPzZuCPf0w+x3aqMyIcVjr11Pi9MwIQV1071Rlx223aTpg2bYAbbnDmud3AaIh1TY04XzcjRgDAn/6kvb1qFfDuu+aew+28iDhedEawGJHZONUZIcvO2DQBgigqGbGNnDtXu23CBGuvbZRevbS3uTOCYZLCYgQTDPwgRtTXA/vui9BNN6HvjTdCuuce78eQiQRJjKCFl2wWI/RyAPbfPz1jsYKVYwottHXt6m/PXCdCrBctUgpDy5YpAZann25OiI1GRdEqm8UIJ2ya6L5q95jptRhhdyUofb9WL/ZoXkR+vv5K6UyAFpnr68UV+EYgq2MFMWLNGvOB4jt3iucSN7N4/JwZoSfKJFrFSY+jKjEiQjsjjB4j2rQRbdCMCFd2CpXjxgEnnKDd9uabwMsvJ36MU2IEkLxrSJb91xkBuNMZsXAh8Mor2m2TJvnbjoeKEXPm6N/vhx+085ZIBNh3X3OvNXasuOBmyhRzz+GVGOFGZwSLEdmFU2LE9u1ilosVmyZAzI1IZdNExcJOnYCDDrL22kahi2W4M4JhksJiBBMM/CBGfPSRZjImvfCC92PIRPRW+foVWnjZsEG0+cgWaIG5fXvxAsjPOCFG+DW8Oo4TIdbvvKO1IvjuO+DTT40/fvFiMegxSKKV07jRGeF3McLJzAjAuc4IvfBqt9r3043eZ27Wqqm+XghjrKdF0cZGY11mavS6Kdw8tvq5M4IWDZOdU2luhKoDTeiMMDqvkiRRuHK7M0KSlO47emy88kr987IsO2fTBIi/DbX4UlkJ7Nih/bsfOiPo727hQqC52d5z/u1v2oJ9UZHYDeA39Doj9MRQatE0cqSYlZQKSQKuuUa77csvlYBcIzQ3i2KJV2LEpk32g3Sd/M0x/ofmuq1erQSwm0XvGG61A9WsGEEtmk44wf1cw3R2RuzcqSxEYOcOJkCwGMEEAz+IEbSQt2YNH/DtEos5v8rXTfr1E1cNZmtuBBUjRo0KVg6Ank1Tqt9zUMKr4zjRGfH55+K22283fuyjFk09e2auFY4R3MiM8LsY4VebJj0xIlOxar+jRsfHf3ffvpBpd5jZ3Ag6t+raVVmh7xZ+FiPM2FVRMWL9+lYhKGLVpgmwli9it1DZrRvw6KPabdXVwMUXi+eaujqxq8dOZ0QyCzPaFZGfrx9S6jVUjGhosLf6fe5c4PXXtduuvdb+sdpt9tlHe3vHDn1x0054tZrTTxfPE//6l7HHLlworhB3S4zQE3PtWjVxZ0R2QTsjolFrmVB0cUKXLkrOmRXMiBFVVYpYqMZtiyYgfZ0Rv/6K0IABGH788Qgdd5x9cZphPCJAlSMmq/GDGLFmjeamtHu3tUISs4etW8WLSj+LEbm5Yot+tlo1UTEiaKvd6TGlsTH17znbOiMaG4GvvhK3f/MNMHOmsefgvAgtVIzYvdv8isWgixF2bZrcEiP8GNLqFLm5YoHfrBhB9gs5EkG0bVtRlLUrRriZFwFkjhgxcKBoOdVi1WQ5MwIw3xkRjYrnTiuFytNPB84+W7vt44+Bxx/XbtM7T7vVGUHzIvbe2x+LLjp3Fgtzdqya/u//tLc7dFDECL/Tvbt4PqHdB7GYc2JEbq4S6K3mlVfEc4ke1KKpd293wqsBJVOGiiZ2rZpYjMguiovF/dOKVROt11i1aALEzIhkYsQHH2i7uvPzldw7t3FqfmqWO++E1HIckj79FHjvPeee+9tvkXvxxc49H8Oo8MGMimEM4EMxAgB7AdqFFtVCIf8XhDg3Qlmp+OOP2m1BCq8GlAt5asmS6rgStM4IKkasXWuuzXrWLGDXLv2/3XGHsedgMUKL3gpes1ZNQRcjnO6M2LDB2iqwbOqMAOyHWNPV7126KMdQp8UIt0VeWsAPqhgRComLAOJihNXMCMC8GLFtm9i9YLVQ+cgj4u/wT3/SFlLpfpibC7Rta+31gOSdIH7Mi4jjVIj1zz8Db7+t3Xb99WKwtx+RpNQh1kuXivuwVTECAP7wB63FU1OT2NWjh143sZs4HWLNYkT2Qbsjli83/xz0usqOFTMVYJPNVWlexNFHu9txGYfaNK1d642LBj2+0OOgVWIx4KKLEF682JnnYxgCixFMMKAnr40btYo34ce//oh1d6/Dj3/9MeF9TKMnRnjpBZiJ0KJaaalog+Q3qBiRjTZNq1eLhaygiRGRiDixTSVGBK0zgl6MRqP6x7FEfPZZ4r99/jnw9dfJH79rl2L/oIZai2Qb7duLK2vNdNjpWdvZzdkJemZELGZsZSqFhiZnuhhhxX5HTQIrHjnonRF+CbDesUP8jFN9FvR42iIg51CBxY4YkUq00jteWLVNKikBnn5au62+Hrjggj1zfr3wajtZL2Y6I/yQFxHHqRBr2hVRUgJcfbW150oH1KqJFuFoXkRZmT0Bv1Mn4LzztNsefVS0YKJ4FV4dx8kQ62hUPI9zZkTm40SINbVpclKMSNQZ0dAAfPihdpsXFk2AOD/dtcv8wg+z7NolLo61Yqmlx4YN4nmQYRyExQgmGNCTVzSatGBS2qEUPUp6oLSDg8UFvS4IFiPs4fQKXy8YPFh7Oxs7I+gKjI4dlZbzoGGm46qmRize+b0zoqREXN1oZnWcXl6EmlTdEbNna1esh0LuX4D7nVBILH6Z6Yxww9qOihFbtuiHgFohFnO+M6JzZyAvT7uNnkuMwJ0R5h5P51zxYpRdMYLeP1ttmvQ+t1TnGCpG/PST/lzVzc4Iul+UlChiv1WOPVZZfa7mm2+ABx5Q/u10kK6ZzAg/dUZQMcJKZ8SsWcD//qfddsMNSnh1UEjVGaFn0WRHvAJEsWbrVuDllxPfXy+82u0FPE52RjjZ/cQEBxpinW6bJipGbNmivzD188+VbKE4kqSEV3tB9+7i8cVtF43ly8Xfp152jhWcEjUYJgEsRjDBoEsXyHTFvJdWTTU1+hesLEbYI4hiBO2M2LBBCVoMKrEYcOutwJgxykWokcKoXl6E3Yu7dGBGjKATO0kS23H9iNUQ6507xRWF48drb3/yiWjDpIb+bfBge3YamQL14TUjRtBjZjhs39qOFhX0BASr7NghCht2fbIlSTxXWDkXZ7sYYbMzQo53RtAOMTMXr7Kc/s6IxsbUq5q9gH4O3btr7WD0oGJEXR2k6dO129q1M1dcNttB44Z9y333ifvBzTcrq//1OiPskKgzoqlJ/E781BlBbZqWL09sq5iA0N/+pt3QuTNw5ZW2huU5VIxYu1a7z9J5jB2LpjhDhoj+81OmJLZj8TK8Oo6TnRFOdj8xwcGJzggnO3lpZkQshhy96+933tHeHjNGFDLcIhIR55Ju14qoaA44JyKwGMG4DIsRTDAIhcSDu5diRCJrE86MsEcQxYi99xatpILcHfHYY8oK91mzgHvvVS62n346+crooOdFxDEjRtAJWVmZuDrbj1gNsf72W+0K/HAYeO45cUKfrDuixb+8lWzPi4hDL+LtiBGlpcp3Ywe9lcVOWTXpvTe7nRGA/ZDA5mbxPWa6GJHMjsYI9POK78d09f769WL3TrLnpAHubosRNDMC8Ed3hBVRpmtXoStReust7X3MzqvsdkY4IUa0baucb9SLHBobgfPPF20/nBYj4u931Soxi8ZPnRG0SzcWAxYtMvzwNrNnK0Gnam680RtfdScZOFDJDVET70KorRXtq5wQIwDgmmu0t+fNS9xN6mV4dRw691u9Oqm9cVKc7n5iggEVI9asMX5uj+OkTVPnzsLCtwidx8RiYl6EVxZNcfRyI9xEz0apokKxq7ILixGMy7AYwQQH2tqXxJbhiS+fwAOfPIAnvnzCmddOJEZwZ4Q9gihGRCLiBWmQcyMef1x7e9s24Pe/Bw46CPjlF/H+sZh4YZUNYgTtjPC7RVMcq6vjaF7E/vsrha/Jk7Xb338f+PVX/efg8Gp9aBHCTGaEG8fM3FzRzsspMYJeKEYizhS77HZGbN4srmLNdDHC4c6I1qIz7YyQZeMX37QAH4nYs3EwAu2MAPyRG2G1Q4R2R3z1lfa2XTHCbGaEU/YtY8cqQcpqfv1VnLO4ZdNECzydOjkjpDpF27biPmIiN6Lssce0G7p1Ay67zIGBeUwkInYsx62afvhBe5yPRICRI5153fHjxWuBBx/Uv6/XeRGAuG80NlpfxOe0NRoTDKgYEYuZK043NYm5DnbO7zk5wvw5hy54+flnUQDxWoywu1jGLHqdEbLszOuyGMG4DIsRTHAwUTi8/b3bcf3r1+P292535rVZjHAH+h0GQYwAxAufoHZGLFwoBgzHmTVLERmuuEJbtFq+XCzcZIMYQSdkfg+vjmPVpomKEUccofz/ssvElah63RHbtomvxWKEgpM2TU4dM90KsdbLi3DC0s3uxR61aAqHM7/AYjfAOlFmRHGxWOA36ldMj6vl5fY7fVKRny+upA5qZwQgHFcl2tXodmeEm4XKO+4Q51v19drbTndG1NQoK8j9nBcRx2puxOefox212/zLX4DCQmfG5TXUqineGUEtmvbdN7X1mVFCITE74v339a1s0iFGdOkiCv9WrZrcEhwZf9O+vXg8N2PVtGmTuOjDTmcEIHRnC50R1KKpf3+le8pLvO6M0BMjAGeEBBYjGJdhMYIJDLKZwqHTJDqRVFSIbdyMMWQ5mJ0RQOaIEa+8kvzvsgz8+9+KddNzzymrYugFbGmp/cllusjGzoiVKxP7GseprRW/53HjlP+3aQNcd532b//9r1gEoVZe+fli4SRbyWYxwilrCqfFiK5dleJSJmM3wJpmRqj3Gau5EV7nRcTxY4g1LRRa7YygmD1G+CEzIk5+PvDCC6I1phq74odet0N1tdgZ4ae8iDg0N8JIZ4QsQ8iK6NEDuOQSp0blPYlCrPXCq53k/PPFrsKHHtLe1guv9kKMkCTnQqxZjMhe7ORG0A6F3Fz7c0CSGxGh82cqRnjdFQF42xkhy4nFCCdCrFmMYFwmw6+8mIyCtvb5ITMiFhOLGowxqqtFr+igihFBtGmSZeDVV7Xbxo8XJ56AUoS66CLFNuG117R/C2pXBCCKEVVViQMg6aQuqJ0RdXXiSlbKV19pvYVzcxXbrjhXXCEWNu+8U3ubWjSNHMkew3HsZEa41U3mlhhB35tTNidOixF2Q8CDgJ3OCFkWjxvq/ThoYgTNjUi3GBGNiucYKiQnYuTI5N0kdjsjamuTe827XagcORK45ZbEf7fbGUHfL6DMBYLYGTFnjvLfunVKB4newoNPP4X0zTfabX/9q3MdA+lgn320txcsUPzS3RYj2rYFLr1Uu+3ZZ5XrmziLFonzSi/ECMC5EGsWI7KXfv20t82IEXS+WlZmvzOWdEbkqBdVrFwpCrLpECO87IzYulV7vFFjV0hoaEhqic4wTsBiBBMc0tkZkUiMANiqySp6Jzi3vaKdggYHbtiQeDLgV37+WbFcUnPbbcoK9zvvBAoKxMd8+60YDJZJYgQgruQBlAt6PTuRINCzp7iqNNXqOGrRdOCBWvuGdu3E8Mb//AdYvHjPbc6LSIzfMiMAb22anICKEZWV5oIVqRiR6XkRgL3OiPp6saCWSZ0R6c6M2LBB3H+NfhaFhcDw4Yn/bleMAJLPb7woVN50U+K5hl0xoqBALMRXVQWjM4KKERs3Kl0CvXophfL8fOXYNmSIsphkwgQxF6J3b+B3v/NsyK5AxYimJmWuSo9xTosRgLI4Qy0G1tcDTz+95zbtMu3Vy/4+axSnOiM4MyJ7sdMZQes0TnTRU5sm9YIX2hXRuTMwZoz91zQLnZ9u2OCei4ZeeHUcu2LE2rWpO+kZxiYsRjCBQfZjZwTAYoRVaFGta1fRx9mv7L23uMo7aFZN1KJpr72UkOK8PMU7eNEi4Le/Tf08QRYj2rUTPXX1jivbtwM7dmi3BaUzIhwWhZNUq+MS5UWomTRJu7pYlvd0R8gyixHJsGrT5Ka1XdDFCFk2Nyegq+eyUYzYvt34haZeN5W6IOWUGOHVcdVvNk30cygsNFfUT2bV5IQYkUy48kKMiEQUuya91ftOFEbpcWnNGlGw9GNnRP/+yTsOGxsV3/aFC4Gvv1YK9PS3ecstwZl7J6JDB3Ge8+ij2ttlZeJ5wwl69QJOPVW77aGH9hQf05EXEYc7Ixi7UDGCLmJLhgdihKYzgooRJ57ofgaVHrQzIhp1z0UjkUUTYN+miS2aGA9gMYIJDvQktmOHWCB0g4YGZTKfCLeDiTKVoOZFAMrFH70wDZIYEYuJdktnnaVtn+3dG3jrLeCDD5LbRQRZjJAkYx1XdEIWCgVrfzVzQbptm+hvHM+LUNOhA3DVVdptL7+sXKisWSMWL1mM2INVMcJNa7ugZUYUF4thq0bbyRsagA8/1G7Lhv2TFlwbG8X9KRF0f8jNBYqK9ty2IkY0NorfWbZmRuh1iJixs3BSjCgoEIvTiSy9du8Wu0rcKlQOGgT84x/abZLkjJBIfxuzZomvQ+1K/EBuri0bEnmvvZTcg0yAdkd8/rn29pgx9i1iEkE7RdeuBd5+W/k3FSO8nDPrZYZZgcWI7IWKEWvXKnOoVDQ1Aa+/rt3mxHw1UWbEtm2KxayadFg0AYpAnpen3ebWwlU3OyNYjGA8gMUIJjjoKepedEekOoFwZ4Q1gixGAKJVU5ByI77+WvztnH22/n2PPVZZRXz77eKqxMGDg39RYkSMoKtLevYMVv6BmQvSL77QrpYuKEhc6LrmGm1nSSymFItoV0THjt4VGYMAtWiorVUu3FJBj5lOFeKA4GVGSJL13IjPP1eyU9TPdeKJzozLz+iteDeaG0HFxS5dtIU9KkZs3qzYlSRjzRqxMyNbMyPs2lUlOkYXFooBu6mQJOP5InodM27OCSZNAk46ac/tU05xxvKGvt/vvtPeLi8Xi0t+4V//UnIL+vVTzrUh45f2sXvuCdZcJhk0xJrihkVTnDFjREF7ypT0hVfHoceRqiprlrJ0PsA2TdkDFSNiMWOi1vPPi/c75hj746E2TfEFLx98oIwtTkEBcNRR9l/PCnrzU7cWribrjKisTJyDaAQWIxgPYDGCCQ6FhWimF5BeiBHJLJoAFiOsEnQxgoZYB6kzglo0DR0qeg+ryc9XWvkXLgROPllpe+3cWbnYCjpWOiOCkhcRh16QJuuMoBZNY8cmtnDo1EnxS1bzwgtKfoSa0aPdW5EYRPS6A4x0R9BjZpcuztlrBM2mCbAuRsRXrMYZMyY7Aqzbtxd/h1bFCFqM6t1bfEwqiwBaqCgpETsW3MJvmRF2xYgBA/Q/ux49rB179Sy99KD7RU6OefHDDKGQ0rH5+utKdyft8LQKfb+//KK97ce8iDhlZcDjjyte7lu3KsL29u3Kef6HH4CPPlK6Fh95RFlUcs01iF1xBZZNmaLM5zKFdIoRkiR2R3zzDTBtWvrCqwHluEzFKbPdEY2Nolgb9EVIjHGKigQBIKVVU0MDcMcd2m0HHAAcfbT98VCbpu3bFRskatH0m9+I3bNeYnV+apZkYgRgz6qJxQjGA3JS34Vh/ENT587IUV80shgRXFiMSA9NTcAbb2i3nXWWscf26QP897/KitdwWN+/OWhY6YwImhhhpjOCihF6Fk1qrr8eePjhPRfczc3Am29q75MNFjhm0CvIb9uWuiDu5jGTFhdqaoy14qfCLZsmQHz/Rs7FsZh40ZpJBblkhMNKwVq9MtZoiHUqm442bZRt6vutWiWeJ9WkKy8CCIZNkxlCISXzafp07XarxwijYed0v+jUydTKfEuEw8Bppzn7nPSYTI99fsyLSEQopAhCHTok3I/kaBS1s2d7OSr3SSZGRCLuiwCnnQZMnqydQ15/vfY+XoZXA8pihZ49tdexK1YAI0cafw6vu58Y/9Gvn7LKvgVp2bLk+StPPy12Atx+uzOLkogYIcViym/uo4+091N30KUDmhvhRmdENJpaGFq1SrE4tAKLEYwHsBjBBIrGLl1QoF7Vm0CM6N+1P9oXtEfXdl11/24KcgKRCwogqVe6+D0zorERmDoVmDt3Twtj3BpBlvX/nZur2POceaZ748o0MWLjRmU1Gu3e8RszZiir59QYFSPi0NDnIGOlMyIo4dVxaEFiwwb91t1Nm5TgcjV64dVqunQB/vCH5F0yLEZoiUSUYqi6AGqlM8JNMQIQjxNW8FtnxA8/iBlQ2SJGAMrnrxYjnOqMAJTjIhUjkkH/7qWVW6aJEYBynKVihNXAUKOdEZniJZ/quBQkMSJb6d1bPK/G2Xdf9xfPRCLAlVcCN920Zxv93XjZFRGnb19RjDAD/Y2HQs6exxn/s/feSqdPnOXLEy9U2rULuPNO7bZDDnGmKwJQ5h6SpLF4lF59VWsLKUnACSc483pW8aIzYvVqweK1oawMeRs2aO9jFRYjGA9gMYIJFE30AjiBGPHZnz7T3W4J2hkxerTiqx5nyxYlxM+vq8SvvRb497/NP+7555UT+hlnOD8mIPhiRL9+ysWHeiKwcKFi+eFnqEXT6NHJA6oznWzojNArbOl1R9DAx3btjK2gmzwZePTRxCvp998/9XNkGx07aosmRgr/bh4zi4uVFcfR6J5tmzfbW8kmy/4TI6hF06BB2VVoNLrinWKk6Nynjzb4N9WFrBMFeKv4KTOirk78fK18Fnq5EVaPEUYzI7JFjPCzTROjIElKiPWXX4p/82pefsklygrwRD7t6RAj+vXTdrzOn2/u8enofmL8BcmNkJKtxn/8cWXBk5o77nDOqjUSUebPqjmz9PTT2vscdFD6z0VedEaQ8Gq5uBj1gwdrxQirgkJdnTMLkhgmBXw2YQJFIz25pMGmST7oIPE+tEjkJ6h/uxlo4cYpamtFj+agiRGRiFjE8rtV065dis2SmkTB1dkCFSM2bNCGoMmyKEYErTOibVvR81VPjKAWTYcdpniAp6KsDPj97/X/1qcPhx3qQe2K0t0ZEQqJ35Pd3IjaWq24AfhPjMimrgjA+Ip3itHOCDV+FiP8lBmhdyy2Ing7KUZkW2eEXri7mmwSLINMIqsmN/Mi1HTsCJx/fuK/p0OM2Gcf7e1ffzX3eHrsD+pvnLEODbFOJEbU1wP/+Id227hxwOGHOzseatVEu30mTHD29azgRWcEzYvo3x+NZWXabVbFCO6KYDwiI8SIJUuWYPLkyTj00EMxdOhQHHLIIbjsssvwpd7qCBPMmzcP119/PQ477DAMHToU++23H8466yy88MILaGxsTPrY3bt345lnnsEZZ5yBUaNGYejQoRg3bhxuvPFGLKI2GIxhjHZGOArtjBg2DFFqU+PX3Ijt2+0p26nyMqyi971ZtRNIJ0HLjfjwQ2DHjj233ex8CQp0v2tq0v5mtmwBdu7U3idonRGAUOiTjIgRqfIi1Pz5z4pAR2GLJn38JkYAQpFB0vOKNoPeqnsnMyPoxd7WrYlXpALA4sXixVu2iRFUDLLaGWFEjEhmDyDLol1Itto00WNxWRlQUGD+ebp2Fb8Dq59ptokRyUTSgoLgLZbJVmjhPY5XYgQATJqU+G/pECP23Vd7e/FicU6bjEz5jTPWoWLEunWQ9Dqhp04V9xcaZO0EqfLV/CBG0M6IrVvN/e6MQDsj+vdHAxUjrNo0ETEi5mXWDZNVBN6mafr06bjmmmvQpLJK2bJlCz7//HN8/vnnOO+883DzzTebft4XXngB//znPxFVrepramrCr7/+il9//RVvvfUWnn76aXTUubDeunUrLrroIiwlB4mKigr897//xXvvvYe//OUvOOecc0yPK9tp8rozIhYThAa5Vy80duumza7wa24EXb0QCimBapK0p2VS/f+VK4HXXttzf7fECFpUKymxdvGdbgYP1t5euDA94zAKtWg67DCl8JHNdOum/C7U3RAVFXsuuOhELicnmMJZ377Ad9/tub1qlfL9x1mzRiyKpcqLUNOzJ3DhhcCTT2q3sxihD53YGxEj6PnOZTHCdmcELXRHIs7mzegFKFZUKLYUetCuiLIyYL/9nBtPEHCqMyKRTZOaZCvrtm8XuxGyNcDayQ6RyZOByy8HAMgDB0I68khrz2M1wDqohcpkYsTee7MtTVDQ64woLRULg24yeDBwzDHAxx9rt/fsmZ4u0eHDtR77sRgwb55+J5UemfIbZ6xDrHwlWUbe+vXafWjHDuCee7SPGz9esUxyGtrprWbgQH90sunNT9evd3Zsep0RdG7jUGeEXFpq7XkYJgWBnl0tWLAA1113HZqamjBs2DBMmzYN33//Pd544w0c2TIBnzZtGl566SVTz/vtt9/irrvuQjQaRd++ffHvf/8bX331Fd59912cd955CIVCWLRoEa655hrdx1999dVYunQpwuEwfve73+G9997DV199halTp6K8vBzNzc2444478J26MMQYopFO5DZtApqbhfud8+Q5OObBY3DOkzYFn02bhHAg9O6NRnoi9GtnxLJl2tvl5cpk4e67gX/+U/nvH/9Q/rvrLuDGG7X337BBfP9OEPS8iDhed0asXm3dTqK2Fnj/fe22bLdoAhRxgf6e1UVfOpHr1Uvx1g8atDOCrkqmeREdOwLDhpl7jRtvFD8bv2eopAu6kCFVB9uOHWLB1O9iBBVYSkqc8w0GFN9/6v2f7FxMxYgJE7KvyGg0C0CNLFuzaaqpSfz89LgaCnlbMKT7TX297lzSE6gYYSfD6Y9/RPTLL7HyzjsR+/57/W41IxgVrTLFwiWZTRPnRQSHwYNFa8kxY5w97xjh2mvFbekSvtu0EfdhM1ZNRo79TGZTVCR0I+TRuda//iXO+W6/3Z3xJBMj/NAVASifWYcO2m1OL1wlYoRuZ0RVlbW6AYsRjEcE+ipsypQpaGhoQK9evfD8889j9OjRKC4uxrBhwzB16lQcffTRAICHHnoIdXV1hp/3iSeegCzL6NSpE1588UUceeSR6NKlCwYMGICbb74Zl156KQDghx9+wI8//qh57E8//YSffvoJAHD55Zfjz3/+M/r3748uXbrgqKOOwrRp01BcXAxZlvHoo4869ElkD0JnRCwGVFYK9/ti6Rf4ZOEn+GLpF8LfTEE7A3Jzga5dgyNG0M4I2mpJocUAWXan+yRTxYiNG42vNDWDLANnnqkUe3r0AP73P/PP8e67StB6nJwc4NRTnRtjkEkWYh30vIg4tMBFC2DUoumII8wXavfaC7jllj23Dz3UW3uEIGHWpskLazt6fnXapsnJvIg4Rn15N2zQhisD2WfRBFgLsK6r0547AP2ic69e4jEj0ao8evzp1ct64dwKdPUgoLUw9BKnszMOOgjbjzlGyQqyitUA66AWKpMdm/ywypYxRn4+MGiQdls65iC/+Y04jv33934ccahVkxkxgjsjGECoH+Sr51rV1cD992vvP2GCe/t8EMQIwN3ciLo68bqgf3806okGVqyaqBiR7S4KjGsEVoxYsWJFaybEpZdeijak9V+SJNx4442QJAnV1dX4mLZLJmHu3LkAgHHjxqFEZ4J61llntf57zpw5mr/Nmzev9d+nn3668NguXbpgXIsPd/x1GOM0d+gAmV6wumnVRMWInj2BUCg4YgTtjEhkXxGnuFi8gHXDqilTxIh+/cQCihvdEZ99tieIfMcO4KyzBK/IlFCLpt/8xln/9iCTTIygxbQg5kUAohixatUeaypZtpcXoebWW4EvvgDefBP49FPvVyQGBbNiBD1mduzovLUdzYxw2qbJjeON0Yu9997T3m7XzvlQxSBgpTNCT5TSKzpHIuK53KgY4WVeBKAvRqTLqindn4UeRjojZDlzCpXJxAjujAgWagvkvDxlvuw1kgQ8+OCeTtEOHYDf/977ccRhMYKxCxEj8tSr/B94QBEk1LjVFQEkzozo2tW4/ZgX0AWeTnZG0PqOJAH9+kHOyxO7GKxYNXFnBOMRgRUj4kKEJEmtxX1Kjx49MKBlEjljxgzDzx1qWdnVnKBlO6IqPoaJJYWkKrykenwo2+wBnCAUEj3uvRQjevcGADTSE2FQMiNSdUZIkrsnzziZIkZEIsKqOcmNgHp6/KqrU4Knk4W1qtm2DfjkE+02tmjaAxUjNmzY8+9M6YygNk2NjYjELzKXLROPo1bFCElSOiJOOUXpJGP0MZsZ4cUx0+nOCD2bJqehn0MiMYJaNB1/fHbun1Y6I2gxKi8v8ap7o7kR6S7At20rCqXpECNiMfEz8qMYUVcnWnYa7ZgJAu3aJe4E5M6IYPGnPwFPPAFcc42Sk6Xn3e4FxxyjFP2fe05ZPJTOrqGRI7W35841bsHLYgQDiGJEfK61bRswZYr2vqefrmSVuEWizogTT/SX9aabnRF0QWKvXnsWKNFFc2Y7I2RZmKMZ7YxYsmQJJk+ejEMPPRRDhw7FIYccgssuu6y1bmuV9evX4//+7/9w5JFHYujQoRgzZgwuuOACvE/tp3WQZRlvv/02zj33XIwaNQrDhw/Hb37zG9x5553YuHGj6bE899xzGDBgAB588EHXX9vO+w4KPvrFmmNRS8GvW7duuiHScQa3BMwuMLFaeXjLAfSLL75Alc6F2n//+9/Wf48kJ/jhqoOv+n5xampq8HmLNzd9LGMQL8UIWohPJEYEpTMilRgBiGIEd0Ykh1o1uRFi/dVX4rY5c/R9afV4802tH3Z+vr9aWdNNsmNKpnRGdOsmrKTPi79PmhdRWsoFGLcxmxmRDjHC6c6IdNk01daKgm42WjQB1gKs9XIBEnU80eOjUTHCa5FXksTciHSIERs2AI2N2m1+FCMAcV/ROz4EtVAZCon+3nH4XBgswmHgkkuUzgTaEeA1w4YBF1yQfvsy+jk0NACLFxt7LGdGMEBim6Z779VaHEoS8Le/uTuWRGKE365r3VzcScOrVR18stF5WCK2blVytFQYESOmT5+OU089Fe+++y4qKyvR1NSELVu24PPPP8cll1yCv//97+bG0cKcOXNw0kkn4dVXX8X69evR1NSE7du34/vvv8f111+Pq666KuEC8Fgshuuvvx5//vOf8eOPP6Kurg4NDQ1Ys2YNXnjhBZx44on4/vvvDY9l7ty5mELFtwTYfW077ztIBFaM2NCycrV7Cs/kspYfT/xHYYRJkyYhLy8P27Ztw/nnn4/PP/8cmzdvxooVKzBlyhT861//AgD89re/xT777KN57MiRI3HUUUcBAP7973/j3nvvxbJly7B161Z8+eWXuOCCC1BZWYmioiL86U9/MvWeGQU5maWK09BCfMuJpYmeCGtrrQcLu8X27eLK1FQ2TUCr4NKKG50R9DvLIDFCctqmafdu4Icf9P/2+OOi/ZIe9D4nnKCEazEKiY4psZh4DAiqGCFJQpErL17g1rNoYnsld6FixPbte2yz9EiXGCHL1p/PL2LEhx9qV4FGIsD48c6PJQjQ76C6Ovl+B5jLBTDaGeGHbgBq1ZSOORwVZQoKkvthe4UVMaKgQAnLDSp6x6fOnZOHWzNMECgpEQujRqya6uuFomRgBUfGHqR+kFtZqay4f/hh7f0mTlSC5N1E7xxZWAgceaS7r2sWLzsj1KK53c4IOj8LhyGnECEXLFiA6667Dk1NTRg2bBimTZuG77//Hm+88QaObPlepk2bhpdeesnUUDZu3IhLL70U9fX1KC8vx+OPP47vvvsO7733Xqsd/ieffIL7aWZJC/fffz/+15KzedFFF+Gjjz7Ct99+iylTpqBbt27YsWMHrrrqKmzatCnlWObOnYuLL74Yuwy6Uth5bbvvO0jkpHsAVtneMiluR1c2EYpaCm6yLGPHjh26GRCUESNG4Nlnn8Wdd96JBQsW4LLLLtP8vUOHDvjjH/+I888/X/fxDz74IB588EG8/PLLeOqpp/DUU09p/n7ooYfiz3/+M/oZKQxDUdai0aih+7pB/LXTOQb161Pfutj69ZCTjM3OuENr1kBdkov17IloNIpGnclYdPVqcZW8i6T8XpYsgdpETA6HEevZE0jxeUg9emhUSnnNGsQMfL6GP+dduxAmIkm0tDTluFKRtv104EDN5xzvjHBsHN9/jzBdOalCvvRSxEaMEFbvtX4ea9ci9MUXmv04euaZtj9vo/jt+KE7jm7dtL+Vigpln9+4EeGGBu3z9Opl67NL5+cR2msvjViWt349ok1NCH3+ufY4d/jhSY+pTuKX/UM9Bk/G0qGD9rgRiyG6bRtQUqI7jtC6ddrvqLTU+e+oY0fNmKTduxHaudPy5xHatk075uJiS2NO+r2UlWl/u+vWCecr6b//1Z7Txo1DrE0b079jv+yrtsbRrp24323fnnhVOACpslL7+XXujFg0qjsOqXdv7X1XrRLnD83Nwtwq2ru35eOq1c8j1L69dv/cvt3Wb8rKOKTly7Wf1157IZZKHHJhHAKRCEIFBZBUF93RrVu1BalNm7S/vS5dNGMP2u8lVFwMKsHL/fsnnf+6MQ4v8MtYeBzejSM0YgQk1eKy2M8/Q1bna+iNg/zGASDasSNfP2TjOPr0EfYF6Q9/AHbubL0th8OI3Xyz+/tHSQlCkgRJtVhGPvpoxHJzPds31ST8Xrp3154j165FrLnZkcVeoSVLtPOXvfduff1Yr16p52FJkFas0D6+d2/EUox5ypQpaGhoQK9evfD888+3ZvkWFxdj6tSpuOqqq/Dpp5/ioYcewoQJE9A2kdUn4fHHH0d1dTXatWuHadOmoUtL/a2kpAR///vfUVhYiOeffx7Tpk3DxIkT0VMlAG3atAnPP/88AOCSSy7RLAI/9thjMWzYMJxyyimoqanBI488krRz45VXXsFdd92FxiT1GDV2X9vO+w4agRUjGloKRPn5+Unvl5eXJzzGCHV1dUIodpwdO3Zg7ty5qKio0P3y6+vrEQqFkJ+fj93UTxVK+PYvv/xiWIxYv3491tNVkWlAHc6dTjaGQlCvCa1bvBjLZs/W3CfeBdPU1ITZ5G+GkWWMWLVKcyJZ3tSEHfPmAXl5aCopQUS18nPlzJmoNerB6SCJvpfiGTOgXmvY2K0b5huwECqRZajXNu5euhQLDXyGRvePvHXrMJRsm1tVhZjV78niOJwiPxSCWoKSNm1CuKbGsXF0e/11qNftRwsKEFYVCKS6OjScfDIWP/MMZJ3jYeUjj6CnasIWbdMGc0pLITv0eRvFL8cPvXHk79ih/Q6rqjDn++9RuGQJBqq2x3JzMXvTJvv2NQnG4TY9ioqgXk+UV1GBZe+8gyGkDX9B165ozNL9A/BmLNLu3aBGjYu++goNqs409TgGLV+OQtV918Zi2ObwdxTatQvU1CJn+3bLn8eAdeugvtxYV1+PrTbGrDeOvLo6zflEqq7G3G+/RaxQ+bSkxkbsQ7xV144c6fg40oGVcYR27hS+44XffIPGJF3GPRYu1Bw3qsJhrFZ9fupxtG1uhjruV161CrN//VVz8Z27YQOGkdbyeTt3Impzfzb7efQPh6HuD1y/YAG2OPCbMjOOsm+/hXp5TU1JCVb4ZC40rG1b5KrmGit//hm1qjlGp59/hrqPdmebNlisM/ag/F76RSKgsebbOnbEGoePs375PAD/jIXHocWNcZR26wa10Ur9119jaYp9e8V332GQ6nYsEsHslSs975zN5O/FCukax7DOnZGrul4IEfvLbccdhzX19YAH1w/DS0oQUS1sXDNihONzYrPQ7yV3xw4MU92Wdu7EvC+/RJR2ZZpFljFi0SJtfSocVupTAFbKMtTLE2MrVgjzsGR0/fZbTZ1tR6dOWJvEJWPFihWtmRCXXnqpUD+VJAk33ngjpk+fjurqanz88cc49dRTU46jpqYGb775JgDgnHPOaS3Iq5k0aRLefPNN1NXV4b///S8mTZrU+rdp06ahqakJhYWFwsJyQMkWPv/88/Hwww/j/fffx1/+8hcUFhZq7jN37lzcc889+PHHHwEAQ4YMMWT9b+e17b7voBFYMSIeHC2l+GHJqiKc0cDoZ555BnfffTcAYPz48fjDH/6Afv36oa6uDl9++WVr280PP/yAF154AXup2ssrKytx4YUXYuXKlSgpKcGdd96JcePGoW3btlixYgWeeeYZvPvuu7jllluwYsUK3HTTTSnH06NHD3SiYZceEo1GMW/ePAwbNkwI7E7HOLqNGqXZXlRbixEjRmi2RV5VQsIjkYjwN8NUVyNM2lP7jhuHaHk55s2bh3B5ucaGom9uLmSrr2WBVN+L9N57mtu5Q4YY+yzU3o8A8jdvxoh99kl4EjO9f1RXa27K7dph+MEHp35cCtK2nw4ZAjkSgaQSovJXrcJe553nyDhCxMZB+t3vEKuvR+i551q3FS5dihEvvAD53/9u3Rb/PLqTvAnplFOwz5gxtsdlFL8dP3THoeNXvk/nzpBIq6pUXo4RNrN+0vl5SAccALz8cuvtvPXrMYBYpsnl5Rh83HGejckv+0c6xiKTlceDunQBRozQHUeIdJP1PPBA9HThfCMXFkJSrXSLVFVhwLHHWvo8QmQBSM999kEPC2NO+r3o+LkPLykBBrbIiB9/LJzHe1x+OXqQDkvb4/AQW+OQZcg5OZBUYsDgbt2AJN8LPfMXDxiADgn2UxrMHmpowIhu3ZQcmjjEvktu2xbDDjvMcoHL6ucRKi3VFE16FBWhu43flJVxqH9rANBu332tz1ltjEOPUJcuGr/4vsXFmjmu9OGHmvsXlpdrxh6034vUqxfw7beabSUHHohih46zfvk8/DQWHoeH4zj2WCXYu4W2K1YkvLaLj6MfKZpKXbpghIc5HFnxvQRoHKFBg8QMkRbknBwUP/AAij3Kf5LOOAN49FHltbt3R8+rr0bPFG4pbpHwexk8GDLp4BjWvn3S+ZYhNm0S61PHHotojx6YN28eyo84QvO3cH09RvTubdgqVSJz97bDhqFXr14JBYm4ECFJEsaNG6d7nx49emDAgAFYvHgxZsyYYUiMmDVrVmsnQqLnbdu2LQ444ADMmDEDM2bM0BTl4+Paf//9E3ZiHHnkkXj44Yexa9cufPvtt61W+3GuueYaVFRUQJIkTJw4ETfccINg0a+Hnde2+76DRmDFiLh6pNd5oEbdTpObm5vyeVetWoX77rsPAHDmmWfi9ttvb/1bSUkJTj75ZBxwwAE49dRTsWXLFtx2222tbTgAcO+992LlypVo27YtXn75ZfRRHZQHDRqEe++9F6WlpXj88cfx3HPP4eijj8Z+++2XdEyhUCjtE1dAEYD8MA6JeGVLFRUIh0IJL2Qtj5l2o0gSwr17K+FoAKSePYFffmn9c6iiovVvXpLwe6FF7L33NvZZkImEtHMnwjU1ose50XFQNm7UPn+PHo7uV57vp+GwEhw1f37rpoIVK5wZRzQqXByHDj1UyXz48UdApc6HnngCOPxw4OyzW7flrl+PUIua33q/iRP9tZ/6YRzFxYrXtWpyF960SciLkMrLHXsPafk8SABdXkUFwl98odkmjRuXlu/JL/sH4OFYOnbUnGfC1dWa32brOHbtEvJ/1OciR+nSReMvm1NVZf3zIEXnUOfOtsasO46iIuUCS/Va4Q0b9lgmElEeY8YgbDNvwy/7quVxFBdrCgrh2trk3wsJVw917aq/nwKKR3JuriaUObxunTbjhFzQSnvthXCO/csR058HsaYK1dU58psyNQ4ieIf69nXsd217PyVZCaGaGu3YyH4hdemi+3qB+b3ozHFDAwc6fpz1y+cB+GcsPA4PxkEW8kk1NQivXZs0r4da6ib6jbtNRn8vQRpH//5AS5GVIl18McIGHT8c4V//QmzvvVE5dy663HQTwj7I9hG+l4ICoFs3Td0jvGGD8Fs0zfLl2tv5+coC2RbRI1xeDoRCmjyw8Nq1xsPnScZEaK+9ki7oXrRoEQCgW7du6JikVjR48GAsXrzYUGeB+nlDoRAGDRqU8H6DBw/GjBkzsGzZMjQ2NiI3NxeNjY1Y2VIHG5LEQr1///7IyclBc3Mz5s+fL4gRADB69GhMnjwZw4cPNzRuu69t530HkcAGWMdVph1kFTeltiWQLhQKob2Btqg33ngD0WgUubm5mDx5su59SktL8Yc//AEA8P3332N1y4+2vr4eH3zwAQDg7LPP1ggRaq688srWH+srRsJnGS3UTqC+XggevGTsJbj2qGtxydhLrL8ODa7t1g1Q2X7JbgYTOcGyZdrbpBCZkO7dlZOYGvpZ2MGLIFavIWFd+YlCO80yb54YqnnIIUpQ13/+o/xfzaWXakKtSj75RPv3jh39F/DlByRJP8Sahn55tOLHNchFZ05NDUDarJFgFQbjAnTSTi78WyHdKwDcO26SduAIDa01iix7E2ANJA4JjMWAd97R/u3kk90ZQ5Cg3wP9nihmAqxDIaB3b+02ej6koc3pCK8GxADrmhrvx+CXz0IPup+kCrAOerCt3vFpwABxG8MEkR49xDlHqhDrTPuNM/ZIVEfIzQX++ldvxxKJQJ40CRuuvBLo29fb1zYDDY5PYndkGBpevffe2rpNJCJeI5gJsaZzthTXvhs2bAAAdE9i9wkAZWWKUVxlZWWrnbqR5+3atSsikUjK541Go61h0JWVlWhu6QBONq5wOIyuLYHoFTrXWk8//TSmTZtmWIhw4rXtvO8gEtjOiD59+uCHH37ARrLKmhL/e2lpqSGbpriw0K9fv9bwaz3233//1n+vXLkS5eXlWLduXWt4zL5J2hhzc3Oxzz774LPPPsMqA0VLDrAm4+jaVQzUWrtWUxC++fibhceZRVq1Shvg06uXJrQx1r27qaBnp0n1vYSWLdMGRPbpYyzYSZIQ6t4dkkpcia5eDSRoSzO7f0jr1mk+t1j37o4EsaZzP5UGDdK8p4KVKx0Zh/TFF2K4Zdeuyvc4YACkqVMRuuiiPXeoq4N8xhmIff01orm5KPn4Y83zxU47DXIo5GnAl++OH4l+L2VlkFQTvNi6dZBWrtQGhPXqZXtfTevn0bOnEPomkVbf6KGHZuX+oR6DV2MJdeyo3b82b4asFwy8dq02BK9dOyUTwY2Qy06dNGPKqaqy9nnU1CBMHhdt397SmFP+dnv0gDRnTuvt2Jo1yu901iyEyRwxeuKJngclO43dcYQ6dNDud9u2JT2uhbZs0c4lWgJME40jVF4OSbUYIrZiheb5aThirLzc8+BoAJCKirTjqK72dhx1dQiTYp+dIG/L40iA1KGD9vMh+0moslK7H3XqpPl70H4v9P3KoRBiDnwfZsfhBX4ZC4/D23GERoyApFqAEvv5Z8g6An389eXKSs12+ht3m2z5XgIzjr32EuovABC79FLIZWWeh0en/fMwMI5Qjx6QZs1qvd06P7WBtGiR9lzVv7+mPhWNRpV5mDqwnszDEhKNIrRmjXbO16sXYqouC8r2loUK7VLYZMXrqrIsY8eOHShJsUDJ7PMCSt6C+rEAUi5Gjy9wr6WLP4GEC8uTYfe17bzvIBJYMWJAy2qViooK1NbWJvzC4q1AAwcO1P07RR18nAy1sBG/r9oSyujjjSiDHGCtZd7y5dinfXtlVW8LK778EjsMJtwbpfvPP6Ob6vb2du2wSuUvvFaWNQHRDStWYEEawpP0vpdwbS1GkFW2i5qb0WBwfANKStBWJUZUfPcdttDVjgbGoUffBQvQQXV7U04ONjr4uaVjP+1QWAj1uoyCFSsw14Fx9Pnf/6A+VW8bPFgbpjhsGHqfeCI6qaxIpDlzUHXBBdh8xhkYsmKF5vmWjRqFujQFfPnm+JFgHOUFBVCvGdsyezbaL10KdST4aknCdp+Ei1plaNeuyEuwgmJXeTkWbt7sSEC3WfyyfwDejaVPOKz5fVcuWoQNOsHAJd9+C/V0eHenTljo0u+4dzgMteu/1QDr3IoKTXAfAMxdvx4xq50WSPy99MrPh3qtftWcOVgzezbKnnpKEw68q7wcC3ftsh2u6Jd91eo4+uXkaIJ6NyxciMpEn4ksY9/NmzUXpkurq7EzQYA1APQqKtJ+Hz//rDlvDZw/H+p4w/W5uZ4HRwNAt/p6qNes1a5b50h4tNFx5C9fDtrAP6e2FrJPzjE9mpq0weUrV2q+x0Fr10Ldm7lm1y5UBTjAuqSuTnOcbSwtxfwWuwQvx+ElfhkLj0OLW+PoXlamua7d8eWXWJ7keFO9bJl2XgxgvU+uc9NBto8jPxoVzlmxvDzMO+EENKcxPNrP30uP/HzNebR67lxNLckKfX/6SVtHaddOuHbo3a6dZi6/9aefsM7A60Y2bcJwUpucX1+P5iQdHQ0tGRP5+fkJ7wMAeSp3kQaSS+H086prsuq/6xF//lTW/0ax+9pufZ5+JbBixKGHHgpA6RqYOXMmTjrpJOE+69atw9KWla5jx4419Lx77bUXvvjiC6xcuRKbN2/WTTAHgJ9++knzGADo3bs3wuEwotEovv/+e4wfP173sU1NTZjTsorPiOLGAdY64+jVS7GwaaFfQYHj4dHqYFEA6LDPPhihCm3sedBBmr/nbdmSNOjZaZJ+LyQnQA6FMOjYY5VWSgNIgwcDqpWmPZqbEwY7mt0/QnV1mtvdRo1CVwe+u7Tup3l5wJ//3Hozsm0bhnXvjrBRf0Y9ZBkhVQ4FAJScdJIYpvjSS5APPBCSyoOx81tvoSOxDZO7d0e/Cy8ULbhcxpfHD73A96FDAVUgZ5fduwGyKqz3YYeht0/CRa0SGjAASCBG5I0fbzs81Szp/jzSORapb1/g009bb3fNyUEXnWBgidit5fft69r3JA0cqMlZiFRVWfs8yAosORLB8IMOsnR+TPnbHTECePPN1tsdd+5E8YgRCH3/veZ+eWecYetz88u+anccUu/ewDfftN4uy8tDaaLPpbYWIbLQo//BBwN9+iQch7TffsBbb7Xe7lhbqzlvhchxtfshh3geHA207Osq2gPe7h/k4l4uK8M+Y8ZYfn3L40iARMLhOwLa75HY5Pbabz/0CnCANbp2hXzHHa3h7pGJEx09zvrl8/DTWHgc3o5DOuYYYNq01tvtVq7U3cfj4ygmRcnOQ4agk4dzxGz5XgIzDnJOAABcfjmG6vjse0HaPw8D45BGjgReeqn1dnFdHdrb/A3ROVTXQw8Vrh1yRo4E3n+/9T6d6+rQ0cjrkkwQubAQQ8eNQ9X27QkDrOPvWUoxv5dVXflG3GqsPG/8vurnN/p4I2Mygt3XtvO+g0hgxYiePXti1KhR+PnnnzF16lQcccQRmnYVWZbxz3/+E7Iso7i4GBMmTDD0vMcddxyeffZZRKNR3HXXXXjggQeEnbOyshKPPfYYAKVDY+8WD7327dtj7NixmDlzJt58802cdtppGDp0qPAajz76KLa0hAeeeOKJKcfEAdbiOKTu3TViRGjjRufDPMlBN1RernmNUHm55u/S7t0Ib99uPCDIIXS/F2L/JZWXI1xQYPxJSRdEaP36lJ+v4f2DdPmEevVy9LtLy346YIDi0aiauIeXLEG4W7ckD0rBihVC2HfosMPEz6qoSMmP2H9/YOfOPfclgpR05pkIJ/EedBs/HT90x0F856WfftJ8nwCUcDa/hItapV8/gIRWxwkdeWRaws0B/+wfgIdjIeeKUFWVfjBwi39oHKlnT/fGR45ZOdu3W/s8SMuwVFJiO6Q44TjI+Upavx7h5csBsqo5dMop3gcUu4jlcRDfcCGYWI1OnkS4W7fEAdaA4N8srV695+87dgjBx+G9907P90ICL6XaWke+V8PjIB7O0l57Obpf2d5PyX4iVVfveb5YTPweS0t1v8fA/F569ABmzgSeeALo1w+hP/3JlfOhXz4PwD9j4XF4NA4aYr1xI8JbtwJdu+reXSK/8RA59ntFxn8vQRlHURFw7LGtC7fkjh0RuummtF03xPH190JrRevW2RtrU5OQNRUaOFCYk4VI/pRmHpYMUvuSyssRzslJWqgvbMmvTNVZoO4YMBK2bOV5490ChapMzVRdA/G/p+piMIrd17bzvoNIYAOsAeCmm25CKBTC6tWrMXHiRHz99deoqqrCggULcMUVV2D69OkAgKuuukqzYwDA+PHjMX78eNxwww2a7cOHD8dpp50GAPjwww9x4YUX4quvvsK2bduwadMmvPnmmzj99NOxZcsWRCIR3HrrrZrH33DDDWjTpg0aGxtx3nnn4fHHH8fKlStRXV2NuXPn4oYbbsDUqVMBKN0dRx99tFsfT2ajFzarosfkHpAukdBjso2QTxraTG2K9C68nAgmcgKr4dVx3AhcAoDGRmG1eUYEWEciQtChtHChvef86ivt7S5dEn+PgwcDjz6a/PnOPtveeDIdekyhgfQFBZ4Lja6QLCD18MM9GwYDMUySXPi3Qm0a3Txm0gDrVOHGiaBh3G6FVwP6AdZvv63dVlqqCLaMUIRPGmBNLdsKCoA2bfTvG4d2/K5dC7SsNheCEQHhYt0z0h1g7efwaiB5gPX27aI/eCaE2x58MPD888Attyj7OsNkEnvvDZB6SNIQa3r8z4Q5MGOPJ59E7JJLsO3YYxH75BPeJ1JB56cVFfayNVat2jOfiqPXsULnYatXA6qV9EmfP9nz6BDPPdhBuiUp8VyEUCiUMksB2JOJYPR5AaC4ZX6rXqCe6vHxvxfTubFF7L62nfcdRALbGQEAw4YNw5133olbbrkFS5cuxcUXXyzc56KLLsI555wjbI8HR3fWOYj+7W9/Q3NzM95++23MmjULs1TBM3HatGmDe+65B/vtt59me9++ffHEE09g0qRJ2LZtGx544AE88MADwuMPO+ww3e2MQVKIEbbRsWgRxIhwGCgr0xYt160TVp6kheXLtbf79TP3ePpeqTBjFbLCF0BmiBEAMGQIoLZVclqMOOSQ5BYn55+vrOp79lnxb337+mO/9DP0mEIpL/fMgs1VyKrlVvbZB0ijHWBWQsUIWsCPk0YxIsdqxgMtcHspRtTVAS+8oN02YYLnFnW+hV60JPuOW7p4W+ncOfVxkIoL0aiyD5eXiwX47t2BFL64rkGz5uyIEbW1kO64A72XLgVuuw0wYofgdzEi2X6ilyvERSmG8TfhsDLX++67Pdt+/RXQs5WWZfH4nwmCI2OP7t0hP/ooVs+erVhTM8mhizujUcX1wOo8fskS7e1OnfTn11RE2LVLOW8n6IJqxYIY0adPH/zwww/YSNwcKPG/l5aWGrJEKm+ZS27evBmxWCzhY+LPm5OT02qvX1paivz8fOzevRsb9GpPLUSjUVS21PvKyspSjskIdl/bzvsOIoG/MjvllFPw1ltvYcKECejWrRsikQjat2+Pgw8+GFOnTsWNN95o+jkjkQjuvvtuPPfcczj22GNbn7ewsBADBgzAJZdcgg8//BBHJfDI22+//fDBBx9g0qRJGDZsGNq2bYtIJILOnTtj3LhxePjhh/H444+3KomMBehB3Gkxgq6KBsQCPaC/ItMPON0ZUVmpCDR2oUW1wkKgQwf7z+sHBg/W3FRnOFji66+1t43k3jzyiCKKUM4+OzMK6W6SSowwMCELBInEiCOO8HYcTDDEiOpqa6u4qBhB36uT6H0eVAw++WT3Xj9o0AtXM50RRgrOnTqJ3RPxC1xagE/ncZWuzKutNbZyUI8//AGh++5Dp3ffRejAA8Xztx4rVmhv+12MUO8ndL9o395wJhnDMGlk3321txN0RoTq6yGRvCAWIxjGJJ07i+dGO7WilizcVogrQytlZYprgxq9zlSKBTFiQMsYKioqNKv1KQta6iIDSV5XqudtampqzQBO9rz9+vVDpOU9h0Ih9GtZiLuIWLaqWbJkCaIt1ziDBg0yNK5U2H1tO+87iAS6MyLOgAEDcM8995h6zBKqLOpw4IEH4sADD7Q0pg4dOuCKK67AFVdcYenxTArc7oygnQAdOoir6AClaP/tt3tu+0WMsNsZQcUIQHlvZkUNil5RLVOK5FQESHICSkllpTjhMCJGFBYK+RGyJEGaONH6WLKFbt2UldOxmP7f02Ul4jSJCl7jxnk7DkbsRNm2TSyG6lnbpRLO7ECKDVIsphQhzebfeNkZkZenjFtvtTagnLtZbNuDnc4II8UoSVIuYNWdgqtWKd8BvdBNZwGeihGxGFBfD5hdKCTLmtB3afdu4IQTlGyeRCtHYzF/fRZ60P1k1y6goUH5vdHfGhcpGSYYGBQjdC0aufuJYcwRCikLV9WLD9auBSzWF4XOiERiRDis1HLUr7t6NTBmTPLnt7Bg5NBDDwUAxGIxzJw5EyeddJJwn3Xr1rUW1scaqWcAGD16NAoKCrBr1y58/vnnuiJGXV1dq4MNfd7DDjsM8+fPx6xZs7Bz507Bsh8AZsyYAUDJsBg9erShcRnBzmvbfd9BI/CdEUyWQosxlZVC2KwtqBihV5wHxM4IP2RGVFeL3uNmRYR27cSOBSfem5crfL2GiBHSpk3Apk3WnouuqmzbNnFRgzJ4MPDGG5BLShDLzYV8552AQ2p/RpOTk7x9NVM6I0pKxN92KAS0TCYZD6HdAo2NisWQmo0bRYHCzeOmnlVXoiJ/MrzMjADEc7GaY4/lVdtqkmUBUPRsmoxAj5eJOiP8JEYA1qya6uoUEYM+zzHHiAtD4mzYoPze1fhNjND7zcb3FRYjGCaYUDFi+XKlK4wgWDQWFqbOC2IYRsRJFw26UFEvLyIOXUSXqjOioUG00zZw7duzZ0+MarGCnjp1qpB1IMsy/vnPf0KWZRQXF2PChAkpnxNQgpzj2brPPfecruXRlClTUF9fj0gkgnPPPVfztxNPPBHhcBi1tbV45JFHhMeuW7cOL7RYup566qlop7fo2CJ2Xtvu+w4aLEYwwYSKEbJsvfCrR6rw6jh+tGmiF7+hkLVV3W6EWFMxws0Vvl7Tt68YDPfaa9aei+ZFHHigUiw3yrHHIlZZidkzZ0K+4QZrY8hGku2PmdIZAYhFr/320y/MMe6iZ11Ei/j0mFlQIK5YdpLcXPH5rYgRXnZGAMnFCLZo0kK/3x07Ei/msFp01gtPBPwlRqhCBluxIkYk8kmurASOPlo/K4t+Dvn55ruP3EbvOMNiBMMEm6FDxeuJOXOEuwliBP/GGcYaTtZTjHZGAInnYYlYs0Zc/GRwId5NN92EUCiE1atXY+LEifj6669RVVWFBQsW4IorrsD06dMBAFdddZXQJTB+/HiMHz8eN+jUK6677joUFhaiuroa55xzDj7++GNUVVVhxYoVuPnmmzFt2jQAwHnnnYduZA7Vp08fTGxxhnj66adx6623YsWKFaiqqsJHH32Ec889F7W1tejQoQMuvfRSQ+/TKHZf2877DhoZYdPEZCEdOyqt4g0Ne7ZVVCQvSJiBniiCJEbQvIjycmurQnv1AubO3XPbiRDrTO6MiESUotfLL+/Z9vzzwNVXm38uKkZYacGTJMi8Gtgc3bsDP/2k/7dM6YwAgIEDgV9+2XObLZrSQ7t2SlGguXnPtm3btOeVdFjbdemiWS0v0dXxRvAyMwJIfO6PRJTOCGYPesJQdbV+14OTnRF+syaKRJQFBC2WhgCsiRHJFsKsXq10SHzxhfZz1xNl/GZZGYkoK6HVXR8sRjBMsMnLUzqo1dd3v/4qXGcINk38G2cYazhVK6qtFecbyTojEnWoJoL+vaTE8EK1YcOG4c4778Qtt9yCpUuX4uKLLxbuc9FFF+Gcc87ReVnldTvrzC9LS0vx0EMP4aqrrsKGDRswadIk4T7jx4/H5MmTdcc1efJkrFu3DjNnzsRrr72G18gi0cLCQjz22GOOhVc79dp233eQ4M4IJphIkhLOo8bJ3AijnRFU7a6o0BaW0gEVI8zmRcSh75ltmlJzwQXa27/+CsybZ+45duwAZs/Wbgu4H2BgyJbOiMsuaxWq5HbtAM42Sg+SJBbpqcVeOo6ZtOgQBJumRJ/LuHHc9UPRW/GeKMTaSoA1oH8RvHGjdgGJ3v28Ri/E2iyJOiPizJ+vZEioi/p+6hBJRqIQaytZIgzD+AMDuRE5LEYwjDM41RlBLZpCIcWVIRFmbZoshFerOeWUU/DWW29hwoQJ6NatGyKRCNq3b4+DDz4YU6dOxY033mjq+eKMHTsW//vf/3DWWWehR48eiEQiaNOmDUaNGoW77roLU6ZMQSikX9LOy8vDY489hrvvvhujR49Gu3btEIlE0L17d5x55pl49913sS89HjqE3de2876DBHdGMMGle3ftgdNNMcJoZkQsplyYOtWhYQVq02Q1dNoNmyb6HWWaGHHkkZC7d4ekfp/PPw/cd5/x5/juO22IciQCHHCAc2NkEpNIjCgqcr+Y6iVjxyL2yy9Y8/bb6H3RRQi7sCKEMUjHjtqAalrET8cx0wkxwi82TWzRJJKXp9h97dq1Z1ui3AirRWd6EbthA7BokXabH6yJ2rfXigkOdEbIQ4dCamjQLgz57jvglFOUoOvc3GCJEWpBlDsjGCb47Luvcm0SR0+MqK7WbuDwaoaxhlOdEdSiqbxcmc8lgs7D1qxR6guJCtg2xQgAGDBgAO655x5Tj1lC35cO3bt3x2233WZ6PAAgSRJOPvlknOzQ9YCR8Tr12nbed1AIvpzCZC+0cOiUGBGNiieKRJ0RnTopF9Rq0m3V5FZnhF2bpuZmcQVhpokR4TDkFo/AVl56yVy3DLVo2m8/pXDEuE8iMaK83H8WGnYZOBDbx49PHtrNuA/tjEiVGZEOMcKsTZMs+0eMOOkkd183qNDvQ68zQpad64wAgM8/F++T7lVdNLDQgcwIedAg4NNPxfPJJ58A55+vzDGpGJFsdWM6SRR2zmIEwwQXuhp3wQKha41tmhjGIejizi1btItBjEI7I5LlRQBiZ0RTk36GVRwHxAiGMQuLEUxwSSJGvPj7F/HR1R/hxd+/aP55N20Si8eJxAhJEotD6RYj3OqMWLdOu2LfLJWVykW4mkwTIwDI55+v3bBpk1KYMAoVIw45xP6gGGMkEyMYxg0CIEZIZjsjduwQj/XpyIw44ADRzpFRoPY7ep0RtbVisLXRglRRkfidz5ihve2HbgBq0+REZkRpqTJn/OQTsZj/2mvAlVcCK1Zot/vhs9Aj0X5iVaRiGCb9jBihvd3crNjJqeAAa4ZxCL35KZ3bG8FMeDWgdJ7SBbPJQqxZjGDSAIsRTHBJIkYcPuBwHDP0GBw+4HDzz0s7APLykk/C3LAzskp1teg57pQY0dBgfoWsGnrizc1VOksyjUGDUD94sHabuh06GQ0NwKxZ2m2cF+EdicQInpAxbkGPgX7MjEgW0KsHFVQA9zsjysrE7iW2aEpMohXvavTO92aKzvS4+eOP2tt+KMC7kRkR7zYbPBj48EMlBFrNY4+JxXw/fBZ66GVGNDaK+wsXKhkmOLRrJ3ZjEasm7oxgGIdo106ca1ipFdHOiGTh1YAyJzaTG8FiBJMGWIxggotbNk16eRHJrASc8gJ0AtoVEQpZX9VdWqrkFaixY9VEi2rdu6ffosEltp1wgnbD228rQlEqfvkF2L1bu+3gg50aFpMK7oxgvCZZZ0Q0KrZUeyFG0HPasmWKZY9RaBEjJwdo29b+uJIRiWitJ0Ih4NRT3X3NIJMomFgNLZgXFoqF9WTQC1naWemHC123OiPijB6tnP/pXIri13OMXmcEFUwBLlQyTNAYOVJ7m4gRnBnBMA5it1Yky+ZtmgDjYsSOHeJCIj/M0ZiMJzMrgUx2oCdGmCmYJMJoeHUcP4kRNC+ivFzpQLBCKCQWvux0faRjhW+aqPrNbyCriw8NDcB//pP6gdSiaejQzApO9jtFRfrFNp6QMW6RTIxIl7XdkCGam1JVlTZkOxW0sN2xozeZKw8/rPxW27UDHnzQeldgNmClM8JsMSpVgd0P3QBuZEbQHJ6jjgJefjnx4ovSUkXo8SN6+wkVqUIhnqcwTNCguRFqMSIWY5smhnESuy4aGzYA9fXabak6IwDx+jWRTZOeSJHIopxhHITFCCa4UDFi587W1eczl8zEx/M/xswlM80/Lz1BpDoY+0mMoJ0RVsOr4zhpQZVFYkS0QweAdkcYsWqiYgRbNHmLJOl3R/h11SoTfJKJEemytisvBwoKtNsWLjT+eK/Dq+McdJASDFxdDUya5M1rBhUrnRFmxYhUIq4fxAi7nRFNTWKngLozIs5ppyn2THr44XNIhF5nBN0vOnUCwmHvxsQwjH2oGDFnzp7FD1VVkGgnG4sRDGMdu7UimhdRWJi4m1+N0c4Iur2sTMybYBgXYDGCCS56wZQtVk3nPnUuxv9rPM596lzzz0s7I1KJEX7KjKCdEXZXhtL37qRNUwaLEQAQo0HW334rfj+aB8SAb77RbuPwau9hMYLxkmSZEdR6sKzMG2u7UAgYNEi7bcEC44+nrd5er5r2ogsj6BgJsKadEWaLUanECD90nNnNjNi8WezI7dZN/76XXAL885/i9qCJEXb3C4Zh0g8VI3bu3HONQgVHIDMz/hjGK+zWiqgY0b+/sbmu1c4IP8zPmKyAxQgmuOTliZMjJ3IjzIoRVO3eskX0/fcKWuzmzoj0MX68uJL0hRcS33/hQrEgxJ0R3kPFiA4dlP8Yxg2SdEZI6TxmEqsmU2JEujojGOPQ70SvM8KuTVOyi9nOnd3PETGC3c4Ikhchh8PJi3Y33AD86U/abRMmmHtNL9HroKGFShYjGCZ4dO0qdnHFrZrosb99e+Wam2EYa9jtjLCSFwGI87B164DmZvF+LEYwaYLFCCbYOB1iLcv2MyMAsfDuFdSmyW5nBH3vVjsjZBlYtCj5c2cakQgwcaJ22wsviCGecahFU+/e+vsW4y70mMITMsZNqBhRX79HzE6nGDF4sPa2HZsm+h6Z9GOkM8Ju0TnZQg6/dAPYzYwgeRFNJSXJu5ckCbjnHuDZZ5X5wVNPAaecYu41vcSITROLEQwTTBLlRnD3E8M4i97iTjM5p7QzwqgYQTv7o1F9IWTlSu1tvvZlPILFCCbYOC1GVFcDdXXabak6I9q1E1fXpSM3orpa9C622xlB37vVzoiKCtG6Y/hwa88VJC64QHt77Vrgiy/078t5Ef5gn320t+nFGsM4id4q6vixkp7P0t0ZYfTCiTsj/I8XAdb5+fp2moB/xAiHOyOajFiZSBJw4YXASy8BF1/sb1sxup80NIiLUszuFwzD+IMEYoTEgiPDOAtdXFhf35pzagjaGWEkvBpQFgPRLlQ9qybujGDSBIsRTLBxWoygF1mSZKwARE8y6ciNoF0RoZD9kwlV8rdtU06gZpkzR3u7Xbvs8OEfMQIYOlS7TS/IWpZZjPALp5wCHHWU8u+99gImT07veJjMprhYLEa2iBFSOsUI2hmhZ8+SiHRnRjCpsRJgbaUglWgO4lcxwmxmBO2MyLQuILqfAOIKTS5UMkww0RMjZFk89rPgyDD26N5dnOsbrRU1NIhigdHOCEkS52H0uWSZxQgmbbAYwQQbt8WIsjIgNzf14+x6AToBzYsoLzc29mTo2QRZEVpmz9be3mcfb4JY040kid0Rb7whdt+sWSNasnB4dXrIywM++US5GFu4EBg4MN0jYjKZcFjMJPFDZ0SfPpALCrTbjOZGcGeE/9Fb8b5rl3ab3c4IIHhiREOD8p9RrHRGBAm9vCQWIxgmM6BixLZtyrUI7bLn3zjD2CMvT8lpUWO0VrRypWjxbLQzAhAXf1LhYcsWJcBeDYsRjEdkQTWQyWjcFiNSWTTF8YMYQTsj7Fo0AUCbNqKNiFNiRLZwzjla4aW+HnjrLe19vv5ae7tjR2DQIPfHxugjSUrhjQP7GC+gq6mrqpSVSunMjAiFxGOQVTEi01aLZwJ6K97V35ssuytG+OVCl2ZGAOasmkhnRHOm7es5OUBRkXYb7Y7lQiXDBJM+fURB9tdf2aaJYdxALzfCCHQBQLdu+nOXRND5FrVpouJETo631xtMVsNiBBNs3BYjjIYs0/v5oTPCbnh1HKsnTzXUpmnECMvDCRylpcAxx2i3vfCC9ja1aDrkEH/7SDMM4xxE8JW2bkVOdTWkxkbt/Ty+OJCpGGE0xJo7I/yP3op3dW5ETQ3Q1KT9ezbYNAHmxIhM74wAUv9+uVDJMMFEksTrsV9/5ZB6hnEDqwtXqRhhpisCSN0ZQW/37q10bTOMB7AYwQQbKkZs3gzQAo4ZaKHdameEHzIjnOiMAMTPgAo2qairE8eWTZ0RgGjV9Nln2kkI50UwTPZCV1Nv24ZIZaV2WzisrIbyEpobYaQzQpZZjAgC4bBYiFd/b7QrAnCuM8JPq+7y84FIRLvNTG5EpmdGAPpdNGq4UMkwwUUvN8KJrjiGYbTQxZ0zZwLRaOrH0fBqo3kRccx2Rvilc5XJCliMYIINFSMA4eLQFEG2afJrZ8S8eUqBKk44DAwZYn9cQWLCBG3hR5aBadOUf2/dCixapL0/ixEMkz3oiBG5dGViaannK5VkPTFCfSzXY8cOoLlZu43FCH9Ci8zqzgi6/7VpAxQWmn8NuiIvvs0vq+4kSRRljHZGyHJ2dEawGMEwmYsRMYJ/4wxjn2HDtLe//x544IHUj6OdEXbFiA0btNlYLEYwaYTFCCbYlJQoK9vUVFRg/b3rIT8pY/296/UflwinxIjaWnOr6+xSXS0GjjnVGWFXjKB5EQMGADQYNdPJzwfOPFO77fnnlWIGzYsoLBQvDhiGyVx0MiMitBicjpXkVIyoqhKL1BTaFQFwZoRfoSKRWoxwamVsjx6i8OAXi6Y41HvZqBhRUwPs3q3ZlHWdEXl5YqYEwzDBgV5vrF0LiZ7HWYxgGPucfrpYV/rrX0UrawrtjLBr0yTL2noXixFMGmExggk2kuRcbsSuXWKhxWhmhF6hyMvuCGqDFAo5dzKxa9NExYhsyotQQ62ali4FZs0SxYgxY0TbCIZhMhedzIhcatOUDjGiTx/EaIh7Kqumbdu0t3NygLZtnR0X4wy0yKwuQNG5kFUxIidHnEf57ULXamcE6YoAslCM6NyZ860YJsgMHKiIislgMYJh7NO2rZIZqT5nNjUB55wjLGxoZft2cXGI2c6I9u3F87jaqonFCCaNsBjBBB+nxAg98cBoZ0R+vjhZ8zI3glo09e4N5OY689y0kLB+vTGPwzjZHF6t5sADReus55/nvAiGyXaM2DSlQ4wIhbCLXpSkEiP08iK4WOlPktk0OWnTQbs0neradAoqRhjtaiVihNy+PWTaqZsJJLNZ4yIlwwSbSES0j1EjSdzdyDBOceihwOTJ2m0LFgA33aR/f9oVkZNjTSxIFGIdjYr1KhYjGA9hMYIJPk6JEXTFf3GxufbzdOZG0M4Ip/IiAFGMaG7WXRGoSzQKzJ2r3ZZt4dVxJAk4/3zttldeAX75RbuNxQiGyS6M2DTp5SN5wG56UbJwYfIHUDGCixj+hRaZkwVY2wkwvfDCPf/OyxMtC9ON1c4Imk9WWurMePxGss4IFiMYJvgks4bt2NE/GT8Mkwncfru4MHPKFGD6dPG+NC9ir72suSckCrGuqFC6M5Ldl2FchMUIJvjoiBG3vXsbrnvtOtz27m3Gn8dqXkScdIoRtDPCyZWHXbqILbxGrZqWL1fsr9RkqxgBAOedp71dU6MNew2HFZsmhmGyB73OCD/YNAHY1bevdoOVzgjGn5gJsLZTdJ44EfjoI+Cuu5ROSTpXSjdWMyPoooyuXZ0Zj99gMYJhMptkYgT/xhnGWfLygBdfFGsrF14ozqGpGGE2LyIOFRjinRHUoqmw0N7iE4YxCYsRTPDRESOe/OpJPDj9QTz51ZPGn8euGEE7CDKlM0KSrIdY07yIbt0y94LdCL17A0cckfjvI0cCbdp4Nx6GYdIPzYzYvt03YoTQGbFggRJ+lwiaGcFihH/xqjMCAI45RrEhMOt17AUOdUbI3BnBMEwQYTGCYbxlyBDg7ru12yoqgD/+UTvHpjZNVudQiWya9PIi2FqV8RAWI5jg45RNEy2wGw2vjkNX+6UzM8JJMQIQPwujnREcXi1Cg6zVsEUTw2QfOlZGoYYG7Qa/dEZUVYmr5unf1bAY4V+86ozwOw5lRmTsQgvOjGCYzGb4cCCUoCTEq6QZxh2uugo4+mjttv/8B3jppT23aWeEVTEikU0Th1czaYbFCCb4uJUZERSbpupqYOtW7TanAyLpZ2FUaOHwapFTT03c/cBiBMNkH0ZyFcrK3B+HDo2lpZALCrQbk1k1cWZEcKBF5mQB1plckHIqM6JbN2fG4ze4M4JhMpvCwsRFTv6NM4w7hELAs8+K59grrlBqUrGYuNjUqk0T7YzYvBmorwdWrtRuZzGC8RgWI5jgQ8WI3buVA7hZnBYj1q9PbmfhFNSiKRRy/mTilE1TNudFxGnbVhEk9DjkEG/HwjBM+snLS27P1rUrkJvr3XjUhMPAwIHabclCrLkzIjjQC+D4dyfL2SVGOJUZwTZNDMMElURWTfwbZxj36N4dePxx7bbaWsVFYc0apaalximbJkB5fu6MYNIMixFM8NG7AIxGzT1HNKqIB2rsZkbs3i12LLiAtGKFdkPv3s4XrqzYNG3eLK4c5M4IBT2rpkGDBO94hmGyhGS//TRZNMWRBw/WbkjWGcGZEcFBz6YpFlO6LZubtX/L5IKUU5kRmWrTlEyMyGSRimGyiURiBP/GGcZdTj8dOP987bYvvgAuv1y7rajIuh1kmzbiPG7VKhYjmLTDYgQTfHJzxQNszKQYsXGjePFtNjOitFRZRarGi9wIt/MiAGs2TdSiqaDAnbEFkcMPF/cv7opgmOwlmZ1RmsUImBEjuDMiONDvJhYDduwQuyKAzC5IWcmMaGwUhbdM7Yygn4+aTBapGCabGDlSfzv/xhnGfR56SKy1fPSR9vaAAfbCpWl3xOLFwIYN2m0sRjAew2IEkxlQq6Zmk2IEXemfn29+AhYOi77eXuRG0M4Ip/MiALFwXlOTevUgtWgaNkwUa7KVUAi4+mrttnPPTc9YGIZJPz4WI3Q7IxJZEHJmRHDQW/G+fbsYXt22rbKYIFOx0hlRWSluy9TMiHAY6NBB/2+ZLFIxTDaRqHOdxQiGcZ/27YFp05KLDVbzIuJQoWHmzNT3YRiXYTGCyQyoGGG2M4KKEb16WVOfadHeAzFC8qIzguZhAKm7Izi8OjnXXAPcdRdw/PHA888Dhx6a7hExDJMufCxGCJ0RVVViwRpQBArujAgORUXiAoGqquzKiwDEzIi6utRWnzQvIhLJ7H1dT7gqKspskYphsomSEn17YhYjGMYbxo4Fbrgh8d+t5kXEoULDl19qb3fsKM6HGMZlWIxgMgMqRkRNBljbDa+OQ4v2XnRG0ABrNzoj8vLEVX+pxAgOr05OKATcdBPw/vuiVyTDMNmFjzMjUF4uFh31rJp27BDtDjO5QBt0JEk/N4IKTZlejNKzIUpl1UTzsLp2Vc7pmYqeGJHp+wXDZBt6uRGZLkYzjJ+4/fbEizftdkZQmyY6z+GuCCYNZPDMmckqiBhx2M72+M3g3+Cw/ocZe7xbYoTLmRHhHTsg0ZBst3IZaNdHsve2e7fiRaiGOyMYhmH08XNnRDgMDByo3bZwoXg/2hUBsBjhd+j3s3179nVG6IkRqayaaGdEpuZFxGExgmEyHyJGyDk5iS3aGIZxntxc4KWXFLtwitOdEWb/zjAuwGIEkxkQMeKlBZ3x8bUf46VLXjL2eFpYNxteHcfjzog8+vyhkHsnEyrQUAFHzYIFos3BsGHOj4lhGCYTSCZG0M6/dDBkiPa2XmcEFSNychQrF8a/0CKzngVXposRbduKtpxmOyMyNS8ijp6oyGIEw2QWNMS6rCyzO74Yxo8MHgzcfbd2W26u850RFBYjmDTAZxgmM6DFmooKc493qjPC48wIQYzo3Vs5YbmBmc4IatHUrx8XpRiGYRKRzKbJD2KEXog1RS8vwkr2EuMdejZNtDMi04vOoZDok8ydEVr0OiMyXaRimGzjN78B+vZtvSmzhSzDpIcrrwQmTtxz++qrgTZt7D1n797J5+QsRjBpICfdA2AYR6DFmq1bgYYGJesgFbLsnk1TRYXioZ3jzk9NECPcsmgCzIkRHF7NMAxjnESdESUlQGGht2PRQ68zQpa1FzbbtmnvwxZN/od+R9kYYA0oYoRagEglRmRbZwTbNDFM5pObC/z4I2LTpmFVQwPKr7023SNimOwkFAJefBG4/HLFKnXMGPvPmZendDslWrDLYgSTBrgzgskM9FaObthg7LFVVUB9vXabU2JELCZetDpIPhUj3AivjmPGponDqxmGYYyTSIxId15EHCpG6Nn56HVGMP6GA6wVaG4Ed0ZoYTGCYbKD4mLIV1yB6nHjuLORYdKJJAEHH+yMEBEnmVUTixFMGmAxgskMOnQACgpab447DhgydRzG3Tcu9WPpCv9QyLotRqdOYuiQi1ZNae2M2LABaGoS7yfL3BnBMAxjBr+LEeXl4rmNhlhTMSJZDgbjD7gzQoGKEZwZoYXFCIZhGIYJNokEB0myvhCXYWzAYgSTGUiSRkBY2h5YWLMaSyuXpn4sXeFfVgZEItbH4WGItSBGuNkZQcWIWEy/1W/1avFCnjsjGIZhEpMoM8IvYkQ4DAwapN1GcyPYpil46AVYsxiRvDNClrOvM4IDrBmGYRgm2CTqjCgrM2ZtzjAOw2IEkzlY7WZwKi8iDhUjkmUr2KGmBpHt27Xb3OyMKCkRw5P03hu1aCop8U9BjWEYxo+0aaP4NVP8dOzUy41QwzZNwYN+RytXAtGodls2FJ3NBFhv3w40Nmq3cWcEwzAMwzB+JlFnBFs0MWmCxQgmc/CrGOFWZ8Ty5drboZC7JxNJMhZirWfRxL6jDMMwiZEkfVsjP4kRgwdrb7NNU/ChRWa9czp3RmihXREAixEMwzAMw/gbFiMYn8FiBJM5WBUj6MV3QMQIiYoRvXvrr6x1EipG6IVYc3g1wzCMefwuRuh1RsjyntvcGRE8aJFZ/X0CQFGRmBWSiZjJjKB5EcXFmW9v0Ls3kJOz53ZxMYuNDMMwDBMkEtk0sRjBpAkWI5jMwWrRhhbUacHdLPTxbnVGLFumve1mXkQcKtQY7YxgGIZhkqOXG+FnMWLbNmDzZu1tNSxG+J9U31E2dEUA9jojMj0vAlCEh+uuU/4dCgF/+5tWnGAYhmEYxt/07KlkwFFYjGDSBIsRTObgV5smtzIjVqzQ3nYzLyJOKpum6molwFoNd0YwDMOkxu+dEeXl4ip5tVUTd0YEDz37HTXZYsVjJjOCdkZkukVTnLvvVuxBV60CJk1K92gYhmEYhjFDTo7+dQWLEUyaYDGCyRysiBE7dwJbtmi3OS1GbNkC7N5t7zl1kPzQGUGFHNoVEYkAgwa5OyaGYZhMgIgRcrt2ik2OXwiHxeN5PMRaljkzIoikEiO4M0IkGzsj4vTta797mGEYhmGY9KAnPOy1l/fjYBiwGMFkEnpiRCyW/DF6XQt2L7SoGAEA69fbe049/NIZofaYpmLEkCHu51gwDMNkArR476euiDg0xDouRtTVAc3N2r9xZ4T/KShIngmRrWKEmcyIbOmMYBiGYRgm2FAxIhIBysrSMxYm62ExgskcunUDJEm7LRpN/hgqRpSUAG3b2htHu3biha3TuRE1NZBoR0c6xIj6emD79j23ObyaYRjGGlRQt9ul5wY0NyJu00TzIgAWI4JCsu8pW2yauDOCYRiGYZhMh4ZY9+6tnyPBMB7AYgSTOUQiQNeuAIBbfwHu/x64de8zkj/G6byIOFZyIzZvBmbM0C/qUJYv194Ohbzx++veXXktNerPkMOrGYZhrHHKKRrv+tjvfpfGwSSAihELFuhbNOXk+MtiiklMMqumbOmMoJkRtbXark813BnBMAzDMEwQGTUq+W2G8RAWI5jMomVl6aVLgOvmA5fmDk9838ZG4JtvtNvcEiOSdUbMnQtccIFiyXHUUcp7+OMfRcFBDc2L6N3bGzskvVa++HtragLmz9f+jTsjGIZhjFFaCsybh9gjj2DxU08Bv/1tukckQm2atm1ThHS98Graqcj4k2RiRLZ2RkSjSuenHtwZwTAMwzBMEBk/Hjj9dOXfffoAN9+c3vEwWQ2LEUxmQW0uKirE++zeDTz6qGJr9Pzz2r85JUZQOyMqRsgy8PHHwG9+oxTsX3hBKeYDQEMD8NhjQP/+ysnixx/F56dChRfh1XHIe5PiXR+LFysCjxoWIxiGYYzTqxfkyy5DvV+7yvr0ETMGFi7UFyOYYJDsu8qWzggqRgD6uRG7d2utKQHujGAYhmEYJhiEw8B//qPYUS5ZAgwdmu4RMVkMixFMZpFMjNi5E5gyBejbF7j8cn3rpH33dWYciTojGhqA555TivTjxwOffpr4OWQZeOMNYPRo4IgjgA8/3GMbQDsjvMiLiEMFmxabJmnuXO32Xr24IMUwDJNJhMPAoEHabQsWiPaCfOwPDtwZIdo0Afq5EZWV4jbujGAYhmEYJki0a6c4XjBMGslJ9wAYxlFaxIiNBUBUAsIblqN0xw6lE+L++xU7iUQcd9yetjW7UDFi2TLgH/8AHnpIbPE3wsyZyn9DhwI33KB0IahJY2dEq9DC4dUMwzCZz+DBwK+/7rm9cKG4EIDFiODAnRHKBXlBAbBr155temIEnb/l5QEdOrg6NIZhGIZhGIbJNFiMYDKLloLI/icDFW2A7ju/wPryctFCQs3BBwO33KJYJjnlcU3FiOXLgb/8JfH9O3cGrrhCCTB96SVFPNGzCJg/Hzj/fHG7l50R1KYpUWeEX21GGIZhGOvohVgXFGi3dezo3XgYe3CAtUL79qnFCL3was5GYRiGYRiGYRhTsE0Tk1nQ1ZmynFiIGDcO+Owz4KuvgGOOcfaCknYPJGLgQOCJJxSro//7P2DYMOCf/1QspO65RwyLToSXnRHUpmndOuVznjNHu507IxiGYTIPPTGCbZqCSyIxol07ZeV/tkBzI/QWhNDOCM6LYBiGYRiGYRjTsBjBZBZUjNBj/Hjgm2+AGTOULAY3VrX16JH874cfDrz3nlLEueQScVVp+/bA5MnAypXAM8+IHt0q5FAI2Gsv+2M2Cu2M2LQJuRUVkLZu1d6POyMYhmEyj8GDtbe3bROtA1mMCA6Jvqts6ooARDHCSGcE50UwDMMwDMMwjGlYjGAyi2QiwIQJwI8/KkHQBx3k7jjy8oADDtBuC4eBs88GfvoJ+Pxz4IQTgFCKn2BeHnDRRYo907vvAoccIt5n1CggN9e5sadCp+uj/ddfazcUFQF9+ng0IIZhGMYz+vQB8vO1237+WXubbZqCQ6LOiGwJr45DQ6yNZEZwZwTDMAzDMAzDmIbFCCazaNcOOOkk7bYzzlAshN5+G9hvP+/G8u9/K4LEwIHA9dcrXQ4vv6yIB2YJhYATT1Qspb75BpgwAXJ+PhpKSxG77z7nx56M9u2FFYQdvvxSe5/hw1MLLQzDMEzwCIfFbr3mZu1t7owIDtwZocCdEQzDMAzDMAzjCRxgzWQeL70ETO4BNNcAXbsCD76WnnGMHAl8/73zz3vQQcDbbyPW3Iz5v/yCEVbEDbv06gXMm9d6s4iuimWLJoZhmMxl8GDg118T/53FiOCQqDMi28UIzoxgGIZhGIZhGFfgpctM5tG2rfIfAORksN4mScoK1XRAQqylaFT7dw6vZhiGyVxoiDWFxYjgkOi7yjabJu6MYBiGYRiGYRhPYDGCYRjz6ORGaODOCIZhmMyFhlhTODMiOHTooL892zojUmVGxGJAZaV2G3dGMAzDMAzDMIxpWIxgGMY8ycSIUAgYOtS7sTAMwzDewp0RmUNODlBUJG7nzgjt7aoqoKlJu407IxiGYRiGYRjGNCxGMAxjHmLTpGHAAKCgwLuxMAzDMN7Spw+Qn6//t3BYv7jN+Bc98SjbOiNSZUbQvAgg+wQbhmEYhmEYhnGADDbUZ7KZGdfPQHO0GTlh3sVdIVlnBFs0MQzDZDbhMDBwIDB7tvi3khIl04gJDsXFwJo12m3ZVmhP1RlB8yI6dQJyc90dE8MwDMMwDMNkIFypZTKSAd0GpHsImU0yMYLDqxmGYTKfIUP0xQjOiwge3BmROjOCdkZwXgTDMAzDMAzDWIJtmhiGMU9pqeIzrQd3RjAMw2Q+iXIjOC8ieBQXi9s6dfJ+HOnEbGcE50UwDMMwDMMwjCVYjGAYxjzhMNCjh/7fWIxgGIbJfAYP1t/OYkTwoN9Z+/ZAXl56xpIuqBjR0KD8F4c7IxiGYRiGYRjGEdimiclIXp71MnY27kRhbiEmHjAx3cPJTHr1Alav1m7r2lX5j2EYhslsuDMic6CdEdlm0QSIYgSghFjHPwvujGAYhmGY/2/vvsOiuNo2gN+z9KaiYgENWMEo1qixt2hURFREjWKPJZa8Gkv0tSTYNfaGJdhIFHvBHhU1Yu8KdlEQQZQOUhaY7w++nZeFpS2LrHj/ritXcGfmzM2Ky8x55pxDRKQRLEZQsTR131QERwXDqpQVixGFxdo662scFUFE9GWoUgUwNAQSE5Vf55oRn5/MxYgvbfFqIOuaEUD6VE2KYgRHRhARERERaQSnaSIi9ahaxJqLVxMRfRl0dAA7u6yvc2TE56d5c+U/t2hRNDmKkpFR1rWwMq4bwZERREREREQawWIEEalHVTGCIyOIiL4cqqZqYjHi89OqFTBnDlCjBtC7NzB1alEn+vQEIedFrDkygoiIiIhII1iMICL1qJqmiSMjiIi+HKoWsWYx4vMjCMCsWcDTp8DevUDZskWdqGhkLkbExKT/PyFBuTABcGQEEREREZGaWIwgIvVkmp5DLFECqFmziMIQEdEnp2pkBNeMoM9V5nUjFAWIzKMiAI6MICIiIiJSE4sRRKQea2tgyBAAgCgIEGfPzjrfMhERFV+cpomKk+ymacq8XoSRkeoFr4mIiIiIKFcsRhCR+rZsQer16/DbuxfihAlFnYaIiD6lKlWAcuX+92d9faBq1aLLQ1QQ2RUjVK0XIQifJhMRERERUTHDx5iJSH2CADRsiCQZ65pERF8cHR1g+XJg5Mj0efXd3ABz86JORaSe7NaMyDwygutFEBEREUmePHmCP//8E9euXUNERARKlSqFOnXqoH///mjdurXa7b558wabN2/GpUuX8O7dO5iamsLW1hYuLi7o1q1bjseKoojDhw9j3759ePToEeRyOSpUqIA2bdpg2LBhqJjL9Zw657a1tc3X92dlZYVz584pvbZ582YsXbo012OHDRuGX3/9NV/n0yYsRhARERGRegYMALp3B0SRU9fQ5y2va0ZwvQgiIiIiAMCZM2cwYcIEyOVy6bX379/Dx8cHPj4+GDhwIGbOnJnvdu/du4ehQ4ciPj5eei0yMhJXr17F1atXcerUKaxYsQK6KqYKT0tLw+TJk3Hs2DGl11+/fo0dO3bg4MGDWLt2Lb799luNnzs/TExMsrzm7+9foDY/FyxGULFUoWQFpf8TERFRITEzK+oERAWX1zUjWIwgIiIigp+fH3755RfI5XLY29tj6tSpqFGjBt68eQN3d3ecPXsWnp6eqFKlCgYMGJDndkNCQjBy5EjEx8fDxsYG06dPR926dfHhwwfs2LEDe/fuxenTp7Fs2TKVowOWLVsmFSKGDh2Kvn37okSJErh+/ToWLVqE0NBQjB8/Ht7e3qiQ6bquIOe+fft2rt+bm5sbDh8+DAMDA8yfPz/LdkUxYvTo0Rg5cmS27ejp6eV6Lm3GYgQVSzdn3izqCERERET0ucjrmhGcpomIiIgIK1euRFJSEr766its375detLf3Nwc69atw/jx4/HPP/9g9erVcHJygqmpaZ7a3bhxI6KiolCiRAl4enqi3P+vUVe6dGnMmzcPxsbG2L59Ozw9PdG/f39UrlxZOjY0NBTbt28HAIwYMQKTJ0+WtnXp0gX29vbo1asXoqOjsXbtWsybN09j51Y10iGj48eP4/DhwwCAX3/9FXXr1lXaHhcXh9evXwMAGjZsmGt7nzNO9E5ERERERF+2vK4ZwZERRERE9IV78eIFLl68CAAYOXJklo5zQRAwbdo0CIKAqKgonDp1Kk/tRkdHY//+/QCAAQMGSMWAjH7++WeYmppCLpfj4MGDSts8PT0hl8thbGyM0aNHZzm2UqVKGDRoEADg6NGj+Pjxo8bOnZPQ0FDMnj0bANCiRQuVI0X8/f0hiiIAoE6dOnlu+3PEYgQREREREX3Z8rpmBEdGEBER0RdOUYgQBAHt27dXuU+lSpWkRZ3Pnj2bp3avXbuG5ORkAMi2XVNTUzRt2lRlu4pcjRs3znYkRocOHQAACQkJuHz5ssbOnZMlS5YgNjYWBgYGcHNzU7mPYoomKysrlClTJs9tf45YjCAiIiIioi+bqmmaUlOBd++UX+fICCIiIvrCPXr0CABQoUKFHDvOv/76awDp60vkp12ZTIZatWrl2u6zZ8+kAkJycjJevnwJAKhdu3a2x9asWVNafPrhw4caOXdObt26pbSGRcapnTJSZKlTpw6OHz+O4cOHo0mTJqhTpw6+++47zJkzB2/fvs31fJ8DFiOoWBrlOQouG1wwynNUUUchIiIiIm2nqhgRHp5ekMiIIyOIiIjoC6foFLeysspxP0tLSwDAu3fvIJfL89xu+fLlc1ykWdFuamoqQv9/FOu7d++QkpKSay4dHR2UL18eABAcHKyRc+dk0aJFAIBSpUrluCi1YmTEuXPnMHHiRFy6dAnR0dGQy+UICgrC33//ja5du+ZrRIa24gLWn4G0tDSkZr4R+oQU5y7KDPnNcez+MQRHBcOqlJXGc3+O7wdzMAdzMAdz5ExbsjAHczBHEeUwNYVOxj/HxSE1MFDpNVEQkFamTJYCRbF8P5ij2OXImKGoszAHczAHczCH9udIS0vLdltkZCQAoETmaS4zMTMzAwCIoojY2FiULl06x/3z2y6QvtZDxmMBoGTmh0wyUUzhFKNYI6yA587OtWvXcP/+fQDp61Bktyj1x48fERAQAACQy+VwcHCAq6srbGxsEB0djX/++Qfu7u74+PEj/vOf/+Dvv/9GvXr1cjy3NmMx4jPw5s0bvHnzpqhj4MGDB0UdAUDecigqrnK5HHfv3i2yHJ8CcyhjDmXMoYw5lDFHVtqShTmUMYcy5lCmiRx6oaGom+m112fOoGqGP6eYm+N+huH8hZFDE5hDGXNkpS1ZmEMZcyhjDmXMoYw5lH3qHElJSQAAQ0PDHPczMDDIckxhtZtxyqSM21VRtJ+YmKiRc2dn69atAABjY2MMHDgw2/1CQkJgaWmJ0NBQ/Pzzzxg16n+zvJQuXRojR45EkyZN4OrqCrlcjjlz5kiLbX+OWIz4DFSqVAlly5YtsvOnpqbiwYMHsLe3h46OTu4HaEEOPa/0IVV6enqoX79+keUoTMzBHMzBHMxR/LIwB3MwRxHlUPFkm02GG1QA0K1USeV1ZbF8P5ij2OXQpizMwRzMwRzMof05IiIiEBgYqHKb4lyCIOTYhiiK0tcyWe4rBajTrmLfjO3n9fiMxxTk3KoEBATg/PnzAABnZ2eYm5tnu2+1atVw9uxZpKamZvv3WL9+ffTt2xd//fUXHj58iMePH8POzi7HrNqKxYjPgEwmK/ILVyD9H+bnmKOwMn+u7wdzMAdzMAdzZE9bsjAHczDHJ85RqhQgCEDGm+Znz5R2ESpWzPE8xer9YI5imwPQnizMwRzMwRzMob05cioeGBsbA1AeWaBKxtEK+vr6uZ5TnXYVIxUUxwK5j1hQbM84yqEg51bl2LFjUuGiV69eObapkNvfYYcOHfDXX38BAO7fv89iRFF68uQJ/vzzT1y7dg0REREoVaoU6tSpg/79+6N169Zqt/vgwQNs27YNN2/eRHh4OAwNDVG9enV07doV/fr1y/Ufkp+fH/766y9cu3YN79+/h76+PmrWrImePXuid+/eeaoKEhERERFRIZPJADMzIMPcwXjyRHmfChU+bSYiIiIiLaRYcyE2NjbH/RRrMshkslzXcQD+tx5DXtsFII04yLiWQ27HK7ZnHK1QkHOrcvr0aQCAjY0Nvv766xzbzKuKFStKX0dERGikzaLw2Rcjzpw5gwkTJiityv7+/Xv4+PjAx8cHAwcOxMyZM/Pd7o4dO7Bo0SKlRWDkcjnu3LmDO3fu4MCBA/Dw8ECZMmVUHu/u7o7Vq1crLfiSnJyM27dv4/bt2zh58iTc3d1znceMiIiIiIg+gRIlci5GZLgBJCIiIvpSValSBdevX0dISEiO+ym2V6xYMU8PZNvY2AAAwsLCkJaWlu0xinZ1dXVRrlw56RyGhoZITEzE27dvsz1Hamoq3r17BwCwtLTUyLkzCwoKwpP/v450cHDINktmoijmOPVTxr5vIyOjPLerbT7rR/P9/Pzwyy+/QC6Xw97eHp6enrh69Sr27duHDh06AAA8PT3x999/56vdy5cvY8GCBUhNTUW1atWwfv16/Pvvvzhy5AgGDhwImUyGR48eYcKECSqP9/LywsqVK5GWloYGDRpg27ZtuHLlCo4cOYLu3bsDAHx9ffHHH38U6PsnIiIiIiINyfzEXlyc8p85MoKIiIgItra2AIDg4GClkQKZ+fn5AUCepxNStCuXy/H06dNc261evTr09NLXjJXJZKhevToA4NGjR9ke++TJE+nB81q1amnk3JldvHhR+vq7777Lti2FJUuWoEWLFmjUqJHSNFCZPX/+XPpaUTz5HH3WxYiVK1ciKSkJX331FbZv344mTZrA3Nwc9vb2WLduHTp27AgAWL16NeIy30zkYNOmTRBFEWXLlsVff/2FDh06oFy5crC1tcXMmTMxcuRIAMD169dx48YNpWPDw8OxdOlSAMC3336L7du3o1mzZihdujRsbW3xxx9/4PvvvwcA7N69G1FRURp4J4iIiIiIqEBymz6AIyOIiIiIpCnx09LSpEWaMwsKCpI69Vu1apWndps0aSI98e/j46Nyn7i4OFy7dk1lu23atAEAXLt2DR8/flR5/NmzZwGkr2HRpEkTjZ07o9u3bwNIX4dCUeTISalSpfDhwwfEx8fj+vXr2e7n7e0ttduoUaNc29VWn20x4sWLF1KlaeTIkTAxMVHaLggCpk2bBkEQEBUVhVOnTuW57fv37wMA2rdvj9KlS2fZ3q9fP+nre/fuKW07dOgQYmNjoa+vj3nz5qmchmno0KEA0qt2iooaEREREREVodyKERwZQURERITKlStLneHr1q3Lss6CKIpYtGgRRFGEubk5nJyc8tSusbGx9GD5tm3bVE63tHLlSsTHx0NPTw+urq5K2xwdHaGjo4OYmBisXbs2y7FBQUHYsWMHAMDZ2RklSpTQ2Lkz8vf3BwDUqVMnTwuLd+3aFbq66SspLF68WOXoiKNHj+LcuXMA0vulFet2fI4+22KEohAhCALat2+vcp9KlSpJFShF5SsvFPOCpaSkqNyecRhO5h+q48ePAwA6deqEypUrqzy+QYMGuHfvHu7du4cWLVrkORfl3Q9NfsDwlsPxQ5MfijoKEREREX0OMtyQqsSREUREREQAgOnTp0Mmk+HVq1fo378/Ll26hIiICPj5+WHs2LE4c+YMAGD8+PEwNjZWOrZz587o3Lkzpk6dmqXdX375BcbGxoiKisKAAQNw6tQpRERE4MWLF5g5cyY8PT0BAAMHDkSFTA+KVKlSBf379wcAeHh4YPbs2Xjx4gUiIiJw8uRJuLq6IiYmBqVKlZJmvdHUuRVSUlLw+vVrAEDNmjXz9F5WqlQJw4YNAwA8ffoUffv2xcWLF/Hhwwe8ePECS5cuxa+//goAqFatGsaPH5+ndrXVZ7uAtWL+rwoVKmS7iDQAfP3113j8+HG+RiDUrVsXvr6+uHDhAiIiIrKMjjh48KD0dcOGDaWvk5OTpQVKvv32W6VjUlNTIQiCVOgwNDTMcx7Kvz9cuB4HEREREeUDR0YQERER5Ym9vT3mz5+PWbNm4enTpxg+fHiWfYYOHYoBAwZkeT0gIAAAYGFhkWVbxYoVsXr1aowfPx5v377Fzz//nGWfzp07Y8qUKSpzTZkyBUFBQTh//jx2796N3bt3K203NjbGhg0blBav1tS5AeDdu3fSmhTZFSxUmThxIqKjo7F79274+/tjxIgRWfapVasWNm7cmKW487n5bIsRiuEyVlZWOe6n+OF69+4d5HJ5touLZPTzzz/j5s2bCA8Px6BBgzBp0iTUrl0bsbGx8Pb2xp9//gkA6NmzJ+rVqycdFxAQIK1sbmNjg4SEBGzduhXe3t4IDAwEkF4Vc3FxQd++ffM0VIeIiIiIiD6BnIoRJiaAmdmny0JERESk5Xr16oXatWvDw8MD165dQ3h4OIyNjVGnTh30798/T4s3q9KqVSscO3YMmzZtwqVLl/Du3Tvo6+vDzs4Ozs7O6NWrFwRBUHmsgYEBNmzYgMOHD2P//v14/PgxEhISUK5cObRs2RIjRozIdiabgp4bgNKUVfkpRshkMsyZMwddunTBrl27cOfOHURGRsLExAQ1atSAg4MDXFxcpOmcPmef7XcQGRkJAErze6li9v83DaIoIjY2VuUaEJnVr18fW7duxfz58+Hn54fRo0crbS9VqhR++uknDBo0SOn19+/fS18nJiaiR48eePXqldI+/v7+cHNzw5kzZ7B27do8VbPS0tKkqlpRUJy7KDMwB3MwB3MwB3MUJm3JwhzMwRxFl0MwM8t2DluxQgWkZXOe4vp+MEfxypExQ1FnYQ7mYA7mYA7tz5GWlpan/WxtbbFkyZJ8ta2YVSYnVlZWcHNzy1e7CoIgoEePHujRo4daxxfk3HZ2dnn6/rLTrFkzNGvWTO3jPweCKIpiUYdQx3fffYegoCB07doVK1asyHa/Xbt24ffffwcAnD9/HhXzONfrhQsX8Oeff6pcxVxHRwedO3fGxIkTlappp06dkobwWFhYIDw8HD/++CP69OmD8uXLIzAwEBs2bJBWP+/WrRuWLVuWbYbw8PAsxQwiIiIiItI8Cy8vfLV0qcptsfXr4+n/j44mIiIi+lLY2NjkOD0+UX59tiMjFFMc5TQ0BkgfEaGgWK8hN1u2bMHixYsBpM8FNmrUKFSvXh1xcXG4ePEili1bhmPHjuH69evYsWMHqlatCgBISEiQ2nj//j3mzZsHFxcX6bXq1atj6dKlMDAwwL59+3D06FEMGTIE9vb2OeapVKkSypYtm6fshSE1NRUPHjyAvb19kU4tlZ8ctX+rjbfRb2FZ0hJ+bnlfL0TTOQoTczAHczAHcxS/LMzBHMxRdDmEBw+y3WZarRrq16//SXKoizmY43PJwhzMwRzMwRzanyMiIkKacp5Ikz7bYoRieqPExMQc90tOTpa+1tfXz7XdgIAALP3/J6L69u2LOXPmSNtKly6NHj16oGnTpnB2dsb79+/h5uaG7du3AwCMjIykfRVrQ6jyyy+/4ODBg0hNTcWpU6dyLUbIZLIiv3AF0gtAn0uOuKQ4xCbGIs4wrtAyf07vB3MwR1FjDub4HHIA2pOFOZiDOYogh7l5tpsES8tcz1Hs3g/mKJY5AO3JwhzMwRzMwRzamyOvD3QT5ddn+5NlamoKQHlhEFViYmIApP8jKpnTonT/b9++fUhNTYW+vn62q6NXrFgRo0aNAgBcvXpVmkrJxMRE2qdp06bZnqNMmTLSaIrnz5/nmomIiIiIiApZTvcK+ViAkIiIiIiIVPtsixFVqlQBAISEhOS4n2J7xYoV81TVUxQWqlevLi1+rUrjxo2lr1++fAkgfTolBUNDwxzPoyimJCUl5ZqJiIiIiIgKWYkS2W/L47pzRERERESUvc+2GGFrawsACA4OlkY/qOLnl75egJ2dXZ7alcvlSv/PTsbChmJfa2trafqo3OZVCw8PBwCUL18+T7mIiIiIiKgQcWQEEREREVGh+myLEa1btwYApKWl4fz58yr3CQoKwtOnTwEArVq1ylO7iumTXr58ibCwsGz3u3nzZpZjBEGQcv3777+Ii4tTeWxgYKBUrMhuITwiIiIiIvqEcipGcGQEEREREVGBfbbFiMqVK6NRo0YAgHXr1mVZO0IURSxatAiiKMLc3BxOTk55ardr164A0lekX7BgAdLS0rLs8+7dO2zYsAFA+giNGjVqSNv69u0LAPj48SMWLFgAURSVjk1LS8PChQsBpC943aVLlzzlIiIiIiKiQpTTNE0cGUFEREREVGCfbTECAKZPnw6ZTIZXr16hf//+uHTpEiIiIuDn54exY8fizJkzAIDx48dL0ycpdO7cGZ07d8bUqVOVXq9bty569+4NADhx4gSGDBmCf//9F+Hh4QgNDcX+/fvh4uKC9+/fQ09PD7Nnz1Y6vnnz5ujevTsAYP/+/fjpp59w69YtREZG4v79+xg1ahTOnTsHAJg0aVKeFtUmIiIiIqJCpq8PqFr3TSYDLCw+fR4iIiIiomJGt6gDFIS9vT3mz5+PWbNm4enTpxg+fHiWfYYOHYoBAwZkeT0gIAAAYKHixuL3339HSkoKDh06hGvXruHatWtZ9jExMcGSJUvwzTffZNk2f/58pKam4tixY/Dx8YGPj0+WfYYNGwZXV9c8fZ9ERERERPQJlCwJJCYqv1auHKCjUzR5iIiIiIiKkc+6GAEAvXr1Qu3ateHh4YFr164hPDwcxsbGqFOnDvr374/vvvsu323q6elh8eLF6NGjB3bv3o07d+4gPDwcenp6qFy5Mlq3bo2BAwdmu/i0vr4+li9fDicnJ+zZswf37t1DVFQUypYti7p166J///749ttvC/qtExERERGRJpUsCbx7p/wa14sgIiIiItKIz74YAaSv27BkyZJ8HfPkyZNc92nWrBmaNWumbiy0adMGbdq0Uft4Ut8G1w1IkCfASM+oqKMQERER0edC1RSqXC+CiIiIiEgjikUxgiizbvW6FXUEIiIiIvrcqFrEmiMjiIiIiIg04rNewJqIiIiIiEhjODKCiIiIiKjQsBhBREREREQEqC5GcGQEEREREZFGcJomKpZuvb6F5JRk6Ovqo5F1o6KOQ0RERESfA46MICIiIiIqNCxGULHktNYJwVHBsCplhTd/vCnqOERERET0OeCaEUREREREhYbTNBEREREREQEcGUFEREREVIhYjCAiIiIiIgJYjCAiIiIiKkQsRhAREREREQFZixFmZoCJSdFkISIiIiIqZliMICIiIiIiAoBatZT/bG9fNDmIiIiIiIohFiOIiIiIiIgAoHZt4Kef0r8uWxaYN69o8xARERERFSMsRhARERERESmsXw9ERACvXwPt2hV1GiIiIiKiYkO3qAMQERERERFpFXPzok5ARERERFTscGQEEREREREREREREREVKhYjiIiIiIiIiIiIiIioUHGaJiqWHs19BFEUIQhCUUchIiIiIiIiIiIi+uKxGEHFkpmhWVFHICIiIiIiIiIiIqL/x2maiIiIiIiIiIiIiIioULEYQUREREREREREREREhYrTNFGxtPz0csQkxqCEYQn80umXoo5DRERERERERERE9EVjMYKKpeX/LEdwVDCsSlmxGEFERERERERERERUxDhNExERERERERERERERFSoWI4iIiIiIiIiIiIiIqFCxGEFERERERERERERERIWKxQgiIiIiIiIiIiIiIipULEYQEREREREREREREVGhYjGCiIiIiIiIiIiIiIgKFYsRRERERERERERERERUqFiMICIiIiIiIiIiIiKiQqVb1AGICkND64aoXLoyLMwsijoKERERERERERER0RePxQgqlo6MO1LUEYiIiIiIiIiIiIjo/3GaJiIiIiIiIiIiIiIiKlQsRhARERERERERERERUaFiMYKIiIiIiIiIiIiIiAoV14ygYqn72u54H/seFmYWXD+CiIiIiIiIiIiIqIixGEHF0u3XtxEcFQyrUlZFHYWIiIiIiIiIiIjoi8dpmoiIiIiIiIiIiIiIqFCxGEFERERERERERERERIWKxQgiIiIiIiIiIiIiIipULEYQEREREREREREREVGhYjGCiIiIiIiIiIiIiIgKFYsRRERERERERERERERUqFiMICIiIiIiIiIiIiKiQqVb1AEoe6mpqQCA+Ph4yGRFVzdKS0sDAERERHw2OaqUqgJTXVNYmFogPDy8yHIUJuZgDuZgDuYoflmYgzmYgzmYgzmKexbmYA7mYA7m0P4ccXFxAP7XN0mkKYIoimJRhyDVXr9+jQ8fPhR1DCIiIiIiIiIiIvrClC1bFtbW1kUdg4oRjozQYuXLlwcAGBkZQUdHp4jTEBERERERERERUXGXmpqKhIQEqW+SSFM4MoKIiIiIiIiIiIiIiAoVF7AmIiIiIiIiIiIiIqJCxWIEEREREREREREREREVKhYjiIiIiIiIiIiIiIioULEYQUREREREREREREREhYrFCCIiIiIiIiIiIiIiKlS6RR2A0qWmpiI2NhZJSUkQBAEGBgYoWbJkUccCAERFReHNmzcwNDSEjY0NdHWL5scmJiYGiYmJsLCwgCAIhX6++Ph4vHv3DnFxcZDL5dDT04OJiQksLCxQokSJQj8/ERERERERERERUXHBYkQRiY+Px+nTp3H27Fn4+fnh3bt3EEVRaR+ZTIYKFSqgVq1aaNWqFZycnGBoaKjRHElJSXj58iVkMhmqV68OHR0dadv9+/exePFi3L59W3rNxMQEPXv2xPjx4zXWIf/x40ccPnwYDx48wIIFC5S2vX//Hhs3bsSpU6fw4cMHAICOjg7q1KkDZ2dn9O7dW6OFicDAQGzbtg0XL15EcHBwtvuZm5ujXr166NSpExwcHKCvr6+xDESaMn36dAiCADc3N+jp6WmkzUGDBsHU1BQzZ86EpaWlRtokKkwfPnzApUuXEBYWhpIlS6Jhw4aoUaNGrsfJ5XJs3LgRgiBg7NixhZbv2bNnOHDgAO7du4eIiAgYGxvDxsYGrVu3RteuXT/575fg4GDs3bsXL1++hFwuR9WqVdGtWzfUqlXrk+agovf48WP4+fkhODhYeiBEJpNBX18f5ubmsLKygp2dHezs7Iok35UrV3D27Fm8efMGBgYGqF27NpycnFC+fHmNnkcURbx+/Ro2NjZZtoWFhcHHxwfPnz9HUlISypUrhwYNGqB58+aF8uCM4vPsyZMnWR6YMTY2hoWFBapUqYL69evj66+/1vj5iYiIiIgKShAz94BTodu5cydWr16N6OhoAMhShMhMcTNjbm6O8ePH44cfftBIjo0bN8LDwwOxsbEAgIoVK8LNzQ2tWrXC3bt3MXToUCQmJmbJJwgCrK2tsWXLlgJ3Rj548ADjxo1DWFgYdHR08PDhQ2nb3bt3MWbMGERGRqp8jwRBQL169bBq1SqN3Hi6u7tjw4YNSE5OzvXvRHF+AChfvjzc3NzQpk2bAmcg0iQ7OzsIgoDbt2/DyMhIo22am5tj0aJFaN26tUba1QRt7zgD2Hn2KTvPRFHEihUrsHXrVqSkpCht++abbzBz5kzY2tpme/zHjx/RsGFDCIKAR48eqZXh22+/hUwmw7lz57I8TJCWlobFixfD09NT+p2j+L/i76FixYqYP38+mjVrptb5MwsICMCff/6J+/fvQyaToVGjRhg1apT08+fp6YnFixcjNTVV6ThBENCvXz/MnDkTMtmXO8Pnl1DgTUxMxPbt27Fnzx68ffs2T8dYWFhgyJAh6NevH4yNjTWSIyYmBl5eXrh79y50dHTQunVr9OzZE7q6ukhNTcXEiRPxzz//ZDnO0NAQkydPxoABAzSSY9u2bfDw8MDHjx9x69Yt6XVRFLFq1Sps2bIFcrk8y3EVK1bE7Nmz0bZtW43kiIyMxNKlS3Ho0CGkpaVl2Z75s0ORYfjw4fjhhx80/u+WRd78YZFXtS/hMzUjbb9O5TUqC7y5iY2NxcOHDxEREQEjIyPY2NigatWqRZYnKSkJgYGBkMvl+Oqrr2BqalpkWbSF4tpN2z7/iDJjMeITmz9/Pv766y+IoghBEGBra4tatWrBysoKpqamMDQ0hCiKSEpKQmxsLN6+fQt/f388ffpUOmbo0KGYOnVqgXL89ttv2LNnT5ZOd319fXh6esLNzQ3+/v4wMDBA9+7dUbt2bSQmJuLff/+Fr68vBEFArVq1sHfvXqXRFPkRFhYGR0dHxMTEQBRFVKlSBYcPH4a+vj7CwsLQvXt3REVFQSaToU2bNmjcuDHMzMzw4cMH+Pr64ubNm1KOXbt2wcDAQO33w93dHatXr4YoiqhUqRKaN28Oc3NzxMTE4MaNG3j+/DkqVKiAGTNmICUlBY8fP4avr69UPNHR0cHChQvRvXt3tTOowpu9/OHNnjJF4U8gBh8AAE3GSURBVODOnTsaG1WlaFPxedSnTx9MmTKlyC7+tKXjDGDnWWZF3Xk2ffp0HDp0KNtitoGBAdzc3ODk5KTyeE0UI3IqCGbOV758eVSsWBFyuRyvXr1CfHw8AEBXVxfLly9Hp06d1MqgcObMGUyaNEmp4C4IAipWrIhdu3bh/v37GD9+vHTOatWqQSaT4dmzZ0hJSYEgCHB2dsa8efMKlONzVtwLvM+fP8fo0aMRHBys9O9GR0cHRkZGSteoCQkJSkUrQRBQuXJleHh4oHLlygXK8eDBA/z0008IDw9Xev3bb7/Fxo0bsXz5cmzbtk16vWzZstJ1syLLr7/+iiFDhhQoR8Z/o4Ig4P79+1KH6dSpU+Ht7S29T8bGxjA1NUV0dDSSkpKkHP/9738xcODAAuUICgrCDz/8gPDw8Gw/z8qWLQsbGxtERETg1atX0t+NIAho2LAh1q9fr5HpX1nkzYpFXvUV989UQHuuU3mNqqyor1EzCwsLw6ZNm3D+/HmEhYWhRIkSaNiwIQYMGICmTZvmeKzic1Umk8Hf31+t8//9998AgH79+qns2wkODsaSJUtw5syZLO+XlZWV9L5oUkhICO7fvw8dHR3Y29srFcUiIiKwfPlyHDp0SPps1dHRQdu2bTFlyhRYW1trNMvnxM7ODjKZDLdu3dLY5ypRYWAx4hP6999/MWLECABAx44dMW3aNFhZWeXp2KCgICxatAhnz56FIAhYt24d2rdvr1aOq1evYsiQIRAEAe3atZM60P/++2/cuHEDlpaWePv2LUqVKgVPT88snd/Hjh3D1KlTkZaWhnnz5sHZ2VmtHAsXLsT27dthamqKpUuXKl1c/P777/Dy8oKJiQk2btyIb775JsvxJ0+exJQpU5CSkoKZM2eqfZF0//599OnTB4IgYNKkSRg+fHiWJy/27duH3377DXXr1sXOnTul7Y8fP8bs2bNx//59GBoa4ujRo6hUqZJaOTLizV5WX/rNniYLKoIgqH2xqrjJa9y4Ma5fvw5BEFCmTBlMnDhR7c8CdWlLxxnAzrPMirrz7OLFixg5cqT0e+4///kPrK2t8erVK+zatQv79u1DamoqBEHAjBkz4OrqmqWNwixGXLhwAaNGjZJG+M2aNQu1a9eWtqelpeHs2bNYuHAh3r59CxMTExw7dgwVKlRQK0dQUBC6d++OhIQE6Ovro2HDhtDT08PNmzeRmJgIR0dHPH78GE+fPoWjoyP++9//wtzcHAAQHR2NhQsX4tChQxAEAdu2bcv1xri4Ks4F3ujoaDg6OiIsLAy6urpwdHREhw4d8PXXX6N8+fJZOidSU1Px7t07+Pv74+zZs/D29kZKSgosLCxw+PBhlC5dWq0ccXFx6Nq1K8LCwiCTyaQnLp8/fw5BEDBw4EDs27cPHz9+RPfu3TFp0iTpOuDOnTuYN28e/Pz8oKurC29vb1SpUkWtHCdPnsSECRMAAHXr1sXgwYPRtWtXCIKA48eP45dffoEgCGjevDkmTZokPTGbmpqK69evY9myZXj48CF0dXWxZ88etZ+oTUxMRI8ePfDq1StYWlpi3LhxaNmyJUqXLo2YmBhcvXoVa9euRWBgIObNm4cePXogKSkJV69exd69e3HmzBkIgoBvvvkGnp6eamXIiEVeZSzyFkxx/kwFtOc6ldeoyor6GjWzu3fvYvTo0YiOjlbKo7indnBwwNy5c7PtWNbU52p2Hdj37t3DyJEjpQdIVREEAU2bNsX69esLXEBLSkrC77//jkOHDkmv6erqYvTo0Rg7diwiIiIwaNAgvHjxQuUMHmZmZti0aRPq169foByfq8Io8hIVBhYjPqHRo0fj/Pnz+O6777B27Vq12hgzZgzOnTuHFi1awMPDQ602fvnlFxw/fhxdunTBihUrpNdTUlLg7OyMJ0+eQBCEHAsNS5YswZYtW9C8eXNs2bJFrRzff/89AgMD8dtvv6Ffv35K21q1aoUPHz5g9uzZOVbZN2zYgJUrV6J+/frw8vJSK8fkyZNx9OhRDBw4EDNmzMh2v2XLluHPP/+Em5sb+vTpI72elJSEvn374smTJ3B1dc2xjbzizZ6yorzZ09QTOIrMPj4+ah2b8eZKEzkK+nNx8+ZN7Ny5E6tWrZLe46pVq2LEiBFwdHRUe8RUXmlLxxnAzrPMtKHzbPz48fjnn3/QqFEj6WmvjG7evImff/4ZEREREAQBc+fORe/evZX2KczP0VGjRuHChQuoVasWdu/ene0osvfv36Nv374ICQnBkCFD8Ouvv6qVY86cOdi5cycsLCzg4eGBmjVrAki/IR86dKi0RlKrVq2wadMmlW2MHDkSFy9ehIODA5YtW6ZWDm3CAq+ylStXYsOGDShZsiQ8PDxQp06dfB1///59jBgxAjExMQUawau4ritbtiw2bNgg5bh06RJ++ukn6QGNjh07YvXq1VmOT0hIQJ8+ffD8+XP88MMPmD17tlo5hg0bhsuXL6N169bYsGGD0sMM/fv3x+3bt9GqVSts3LhR5YMOycnJcHV1xYMHD9CjRw8sXLhQrRxbt27F4sWLYW1tjV27dqn8XfXx40f07dsXgYGBOHjwoNKUGadPn8Yvv/yC1NRULFmyBI6OjmrlAFjkzYxFXmX8TFWmLdepvEZVpg3XqBlFRETA0dER4eHh0NXVRbt27fDVV1/h9evXuHDhAuRyuTSbhoeHB8qUKZOljcL8XI2KikLXrl0REREBIP2+uEOHDrC0tIRcLsfz589x7NgxPHr0SPr7U7ePCkh/uHH48OG4cuWKykLDokWLcOfOHXh5ecHQ0BB9+/ZF48aNpXtTLy8vJCQkwMLCAsePH4eZmZnaWbSBOn2Ga9euhSAIGDlyZJbp78aNG6epaEQFxmLEJ9SyZUuEh4dj3759ShfH+eHv749evXqhZMmSuHbtmlpttGvXDqGhodizZw/s7e2Vth08eFCav/Py5cvSRXNmz58/R7du3WBubo4rV66olaNu3bqQy+W4ePEiLCwspNdTUlJQp06dXDMA6XPitW/fvkDvR5s2bRAWFoajR4+iWrVq2e738uVLdO3aVWXh49y5cxgzZgxsbGxw8uRJtXIo8GZPWVHf7DVs2BAfP35UK3tmBfn72L17NxYtWoSEhAQIgoAuXbrgq6++yvGYDRs2QBAEDB8+HLq6ukrbFDcE+ZX558LPzw9z587F3bt3lUbFDBkyBI6Ojjn++y0Ibek4A9h5lpk2dJ61bt0a79+/x5YtW7IdnfXixQsMGTIE79+/h46ODtatW6dUfCzMz9FmzZohKioKa9euRYcOHXJsw9vbG1OmTEG1atVw7NgxtXIoiv+LFi3KUsRWdBAIgoBdu3Zl+yTZzZs34erqiooVK6pdVAVY4M0uR1EXeLt164YXL15gzpw5cHFxUauNvXv3YtasWQW6FurVqxcePXqE+fPno1evXkrb3NzcsGvXLgiCgAMHDmTb+fnPP/9g/PjxBcrRtGlTxMTEYO/evVl+v9jb2yMlJSXX6/lr165h8ODBqFy5ssrpT/LCxcUFDx8+xJo1a/Ddd99lu5/iOtTFxQVz585V2rZ8+XJs2rSpQA8QASzyZqYtRV5+pqrOUdSfqdpyncprVGXacI2a0apVq+Du7g4jIyNs374ddevWlbaFhIRg1qxZuHTpEgRBQPXq1bFjx44s91aF+bm6bNkybN68Gbq6ulixYgU6duyo8vhNmzZh+fLlEAQBy5YtQ9euXdXKcejQIUybNg2CIMDBwQH9+vWDvr4+Dhw4AC8vL1haWuLDhw8wMDCAp6dnlvVVnjx5goEDByI2NhYTJkzAqFGj1Mqxb98+tY7LTubfhXml+HvJD1VTjCmo+/NBVBh0c9+FNCUqKgoACjSNT8WKFQGgQJ2jHz58AACVU0Q1bNhQ+jqn4ayKqrxi+KY6DAwMIJfLs0xDpKurC2NjYyQkJMDExCTHNhQdrMnJyWrnUAxZLVu2bI77Kba/ePEiy7Z69eoBQJ7nAs3J3r17AUAaAqpgZ2cHNzc3ODo6Sjd78+fPh6Ghodq/4NSxc+dOKc+OHTuy3OzJZDJ07NgR9evXl272tm/frvbN3tatW6UnHFTd7Hl7ewNI73z8448/lI4tWbIkFi1ahIiICFy8eBF79uzJdzHi2LFjmDx5Mm7duiXdyGS8UPxU+vbti2+//RZTp07FvXv34OPjk+scrhs2bAAAjB07ttCGadauXRteXl44cuQIli9fjtDQULx9+xYLFy7EkiVL8O2338LBwQEdOnRAiRIlNHZexZNJkydPzvcNHpBeDJ08eTJmzZqFc+fOFagYcfr0aQiCgF9++UUpS8uWLdG7d2+p8+ynn35SebyRkRF+/vlnjB8/HpcvX1Y7h5+fHwRBwM8//5zlRu7BgwcQBAETJkzIdroyfX19TJo0CYMHD8bNmzfVznH8+HEIgoApU6Zk+ySfsbExJk6ciDFjxmDr1q1KnWedOnXCsGHDsGnTJhw8eFCtGz3FE1w5rbFTrVo1eHh4YODAgYiOjsbEiRPh6emp1s9TfsXFxQEAGjRokOu+imJKQX6/hIaGAkifkiGzjL/3c3rSUPFeZp7mIb9iYmI0WuBVl5ubm0YLvAUlk8kwYsQING/eXCrwvnz5EtOnT8fq1asLvcCr6Dht166d2m0oji3Iz2pQUBAA1T+rTk5O2LVrFwCgevXq2bbRqFEjAMC7d+/UzqH4Gc18zZ6amipN16G4HsmOYjrNsLAwtXM8f/4cwP++p+woioi+vr5Ztjk5OWHTpk3w8/NTOweQPlWHIAjZPlmpeFJYUeT97bffULZsWY12lufk/v37Ur6c1iyzsLDAxIkTMWXKFFy8eFHt61PFGnqTJ09W+lmoXLkyJk+eLBV5x4wZk20bimLE7du31coA8DM1O0X9maot16m8RlWmDdeoGV24cEH6nMh8f1mxYkVs3rxZWnP0+fPnGDlyJHbs2PHJpt9RTBE+fPjwbAsRQPpnWVBQEPbu3YuDBw8WqBghCAK6deumdF9ft25diKKI3bt3S9OGqVro3dbWFmPHjsXChQtx9uxZtYsRM2fOLNDnYUaCIKjdV2NtbY3Xr1+rVejlM+ek7ViM+ITKlCmDsLAwPHv2TOUaCHmhGLKaW8d5TkxMTBAdHY03b95k+SVcrlw51K9fH+/evUNwcDBsbGxUthEQEAAABZorsXLlynj06BEuXryIvn37Km1r0KABLl++DD8/vxw7axSjMhRFGnWUKFECkZGRePz4cY4d1a9evQKALGsSAJCmJdLEUza82VNW1Dd7FStWxJYtWzBmzBj4+voiJCQEK1euzNNC4pqmeIpn/fr12LBhA+bNm4dz585hwYIFSot6FYXu3bujS5cu8Pb2xrZt2/D06VOkpKTA19dX+jusUqUK6tWrh5o1a6JChQqoUKGC2vN5akvHGcDOs8y0ofPM0NAQ8fHxuRaqa9asibVr12LYsGFISEjA6NGjsWfPHlhaWqp13rwqV64c3r59q3KRxswUv18KQnFzn5CQkGVbxk6s5OTkbD/XY2JiACDLkO/8YoE3Z0VV4FUoyMMdip+vnK4N8np+VTfRGTs24+PjUapUKZVtaOLfTJkyZfDu3TsEBgYqnUdHRweWlpYIDg5GVFSU0sjezBS/W3J7sCYnivchPj4+x05TxWeJ4oGjjBTHFfR9YZFXmbYUefmZmrOi+kzVlutUXqMq04Zr1Ixev34NIPsRToIgSB3jnp6eePjwISZMmAB3d/dPshai4uc4L0WXAQMGYO/evQV6+l5x7NChQ7Ns69+/P3bv3g0AOU4B3aFDByxcuFDqu1GHq6srdu7cCVEUi7RT//Dhw1iwYAH27NkDQRDQs2dPTJ8+Pdt1OURRlGYYuXHjBteMIK3GYsQn1KRJE3h7e+OPP/7Atm3b8v3hEBcXh8WLF0OxQJC66tSpA19fX2zbtg3Lly9X2mZkZJSntRc2b94MQRCyTPOUHx06dIC/vz+WL1+OevXqKVW3XV1d4evrKy2opaqTPzw8HCtXroQgCGjdurXaOezt7XHhwgWsXLkSnp6e2T6Zo1jYS9VN2KlTpwBA7Xk0M+LNnjJtuNkzMDDA2rVr4ezsjICAAEybNg179+4tkgWxZTIZxo0bh9atW2Pq1Knw9fWFo6MjZs2aVeCncwpKT08PvXr1Qq9evXDlyhUcOXIEPj4+iIqKgiiKePHiBV6+fCntX5A5gRWKuuMsYwZ2nqXThs4za2tr+Pv7499//81S7M6scePGmDNnDqZPn47w8HAMHz4cf//9t8YW0wSyPm3aokUL7N27F7du3cr16bHz588DKNioSmtrazx58gSnT5/GyJEjlbadPn1a+vrKlSvZPvl28eJFACjwQpos8ObNpy7wVq9eHQ8fPsTRo0ez/IzklWJag5ymvMyNlZUVAgICcO3atSzTNJUqVQpbtmxBSEhIllG1Gf37778ACvZvpmHDhjh+/Dg2bNigNEoVSO8E2bp1K44ePaqyw0Thr7/+AgC1p2YF0t+Ply9f4tixYzk+4an496nqM/fZs2fZbssPFnmVaUuRl5+pefOpP1MVivo6ldeoyrThGjUjxd9PuXLlctxvxowZiIiIwLFjx3Dx4kW4ubnBzc2twOfPjZ6eHpKTk3P8e1FQfA/R0dFqn0/xnqqa3jljhpz+7hQ/H6o+m/Nq5syZaNasGSZOnAi5XI7atWtjxYoVn/ze39DQEHPmzEH79u0xc+ZMHDx4EDdu3MCiRYtyLajp6OgU+nR0RAXx6XvSvmAjRoyArq4u7t+/jx49euDAgQNSx3NO4uLicPToUfTu3RtPnz6Fjo4Ohg8frnaO3r17QxRFnDhxAlOmTFHqHMzN+/fvMW3aNGk+UXXnFQaAwYMHo2zZsoiJicGAAQOwadMm6eK9Xbt26N+/P27cuIHhw4fj8ePH0nEfP37EgQMH0LNnT4SEhMDQ0BBDhgxRO4di2Nzdu3cxatQohISEKG1PTk7G4sWLpWGdGefbjo+Px7Zt27BmzRoIgoDOnTurnUNB0QGW15s9PT096WZPE9NE5UZxoaGNN3vZ0cTNnpGREVavXg1dXV34+/tjz549arelCXXr1sWhQ4fQr18/xMTEYOrUqfjPf/6DyMjIIs2l0KxZMyxcuBC+vr7YunUrhg0bhgYNGsDQ0FB6yqQgT5oonuA6evSo2m1oouMM+N+Ud6rWrVF0ns2bN++TdJ6Joig9aZiR4gmi3N4vTXWeAch1fYPC7Dxr06YNRFHEihUrpKfgctKzZ0+MHTsWoiji1atXcHV1zdNxeeXg4ICJEydiy5YtuHHjBpydnaV1KnK6Wbpz5w5Wr15d4KJ7hw4dIIoi3N3dceTIEaSlpSE1NRWHDh2Cu7s7dHV1YWhoiD/++EOaUjKjkJAQrFu3DoIgoFWrVmrnUFAUeKtWrYqEhARMmzYNaWlpBW5XHYoC786dO2FtbS0VeBVTABYlRYH3yJEj2Lp1K3r27ImSJUtCFEWkpaXhxYsXOHjwIBYvXoyJEyeif//+ap/L0dERoihizZo12LNnT74/n3ft2oU///wTgiAUqDDeunVriKKI5cuXS58DCjKZDM2bN4ezs3O2I4RfvHgh/ZspyBPJiiKmj48Ppk2bJj2IAaSvTVCmTBmsWrVK5bppaWlpWLduHQ4cOABBENCjRw+1cyjeD3d392zXaAsODsaqVasgCEKWNXLi4uKwYsUKacHVgrC2tgbwv99XOVEUeQFIRd683PPkh6oiLwDcunUr12M1VeQFlAu6CpmLvNnRVJGXn6l58yk/U7XlOpXXqMq04Ro1I8UsFYpZJ3KycOFCNGjQAKIoYs+ePdi4cWOBz58bxcOieeljUPQpFWSEkaJQpeoaPOM1gWLEjyqKKbWzK67lVYcOHaTfrX5+fvD29oaVlZVa/xVU27ZtceTIEbRr1w5BQUEYNGgQlixZUqBiJ1FRYzHiE6pZsyZWrlwJfX19vH79GjNmzEDLli3Rvn17DBw4EGPHjsWkSZMwadIkjBs3DoMHD0anTp3QtGlTTJkyBa9evYJMJsPcuXML9LRL586d0alTJ4iiiKNHj6Jbt264evVqrseNHz8e7dq1w+HDhwGkL3SV28KbOTE1NcWmTZtQokQJxMfHY8WKFWjevDlcXV0xY8YMGBkZwcLCAteuXUPPnj3RpEkTtGzZEo0bN8aMGTMQFhYGmUyG+fPnq704suL7ULwfly9fRseOHdGvXz/88ssvGDVqFFq1aiWNilCsgwCkV/2//fZbLF68GMnJyahevToGDx6sdg4F3uwp06abverVq8PV1RWiKGLdunU5Xrh/CoaGhvj999+xceNGlClTBqdOnYKjo2OBFpfVNB0dHTRr1gxTp07Frl27cPv2bZw5cwaenp5Yu3at2u1qS8cZwM6zzLSh88zV1RXm5uaIjo5G79698d///he7d+/O8aJ9/Pjx6NOnD0RRREBAAAYOHKjWuTMTRRHBwcE4ceIE/vjjDwwaNAg//PADgPQbt0GDBmXpNPL29sbkyZPh6uqK+Ph4mJqaFuj3y+DBg1GhQgUkJCTg119/RaNGjdCgQQNMnz4diYmJcHJygpOTEwIDA9GrVy/s2bMHT58+hZ+fHzw8PNCrVy9ERETA2NhYY+8LC7z5U9gF3v79++Obb76BXC7Hb7/9ho4dO2Lu3Lk4cOAArl27hsePHyMgIACvXr3C48ePcfPmTRw5cgSLFy9Gly5dMGfOHKSmpsLe3l76+VbH0KFDYWZmhvDwcLi4uGDSpEk5djoo3LhxA3PmzIGzszOioqJQokQJDBo0SO0cTZs2Rb9+/SCKIg4fPoxOnTph1apVuHr1KhISErB27VqYmZlh+PDhGDNmDLZv347du3fjjz/+wPfffy/9fmvatCkcHBzUzjF8+HCYmZkhKSkJI0aMwOzZs3Hp0iW8fPkSDx48wKZNm9C7d298+PABurq6GDFiBAAgMTERy5cvh6OjI+7duweZTFagB3cAFnkz07YiLz9T86ewP1O15TqV16jKtOEaNSPFupMeHh657quvr4/169fjq6++giiK0qwOmnT06FGl9TGdnJwgiiL279+f67GKz7OCzJxRv3596XvLeM2enJyMVatWSX9WFKZU2bFjBwRB0Mh0de3atcNPP/0k/czkpWhUWEqXLo3169dj7ty5MDAwwNatW+Hs7MxFqemzJYhc2eST8/Pzw8qVK7N0Nmfu9M38V9OoUSNMnz5dI1PxyOVyLF68GF5eXkhNTcX58+dzHT47YMAAqQPayckJc+fOLfD0JkD6aIuFCxfi1KlT0pyRub0XAGBpaYm5c+dKneMFkZSUhFmzZuHIkSPSa4oMinM3a9YMK1askKrsoiiidu3aSEtLQ9u2bbFgwYJsF8LKj9WrV2P9+vUoVaoU/vrrrxzn8FRYs2aNdAFQpUoVLFq0CH369IEgCGr/grKzs4MgCLC0tETdunVhb28Pe3t76OvrY8CAAbC2tsa+ffuynW7szp07+PHHH/Hx40cMGzYMU6ZMUSuH4nszMjKCm5sbunXrBlEU4e3tDTc3N8jlcujp6cHCwgJ79uzJ8hRESEgInJ2dERkZiR9//BGTJk1SK4dCTEwMduzYAQDo1atXoU87kFeRkZGYNWuWtGBez549pYv227dva2zOSMXPhSbbVEdKSgqGDBmCmzdvQhAEWFlZoU2bNqhduzasrKxQsmRJGBgYQBAEJCYmIi4uDm/fvsWjR49w/vx5vHr1CqIoom7duti1a1eBhrG+e/cO3bp1Q1xcHAwMDNChQwdMmDAh1+LXjRs3cOLECRw4cACJiYkoWbIkjh07VqA1gX7//Xd4eXlBEASYm5ujb9++aNq0KWxsbBAaGorx48cjPDwcbdu2RdOmTWFoaIjAwECcPn0ab968gSiK+Pbbb6UCrDo+fPiALl26IC4uDjo6OujVqxc6deoES0tLxMfH48qVK9i6dSsiIyOhp6eHQ4cOoVq1akhMTMT69evh7e2N0NBQyGQy7Ny5U7pRy6+bN2/ip59+QmxsLARBgCAIuH//fq6LZC5cuBDbt2+XFosryOdoREQE/Pz84O/vDz8/P/j5+Unz7yqYmppmWYyxfv36SEpKgiiKMDAwwJo1awrUaQakdyj89NNPCAwMVHq9Tp062LZtG2JiYtCnTx98+PBB5e9gXV1drFixIscFDNWxePFibN26FRYWFvDx8dH4IqbquHDhAmbMmIEPHz6gbNmymDt3Ltq1a1con38FbVNR7AoJCUFMTEyBHhJJTk7G1KlTcfLkSQB5X8xWcZ3Uvn17LFq0qMDzrl+5cgXjx49HXFwcBEHAgQMHUKtWrRyP6datG168eAFRFGFoaAh3d/csnUj5pejA27p1K1JSUlS+H4rPiMyvAekPjLi7u8PU1LRAOXx9fTFu3DhpYWBVGXR0dDB//nypky4hIUGaTlMQBMyYMQOurq4FyhEREQEHBwdERUXBwMAAXbt2Rb169dCzZ88c7wdmz54tzXetr6+PpKSkAl+fZqR4TwRBgEwmQ2pqKurUqYPdu3crTanh7e2NCxcu4MSJE0hNTYWZmRmOHTuW6/Qo2YmJiUH37t0RGhoKQRBgaGiI1NRUaeRwr169oKenBy8vL1haWmL06NGoX78+5HI5rl69ij///BORkZEwMTHBiRMn1M6RGT9TteMzVVuuU3mNqkxbrlEVLly4gFGjRkEQBHTv3h0TJkzIdS3MV69e4YcffkBkZKQ0M8OJEyc0ct+vYGxsjNq1a6NWrVrYuXMnUlNTsXTpUpVTiwYGBmLu3Ln4999/IQgCVq9erfa14pUrVzB06FAIgoDq1avD0dERcrkcJ06cwIsXL/DVV1/BxMQEjx49wqRJkzB8+HApt1wux4oVK7Bly5YC58hIFEU4OTnh2bNn6NixI1avXl3gNgsqKCgIU6ZMwd27d6Grq4uxY8di1KhRkMlkWnOvTpQbFiOK0IsXL/Dvv//i0aNHePv2LSIjI5GUlASZTAYTExOYmprCxsYGtra2aNOmTYGeLs9OaGgozp49m+PiYgqbNm1CWFgYHB0dC/yLV5Xw8HCcPHkSDx48wNOnT/H+/XvEx8cjMTERhoaGMDExQaVKlVCrVi20adMGLVu21Pg8eHfv3sXhw4fh5+eH6OhomJiYoEaNGujSpYvKhaUOHDiAevXqFXial4x4s6dMW2/2tNX+/fuxYMECfPz4Ueok0aabPE3Slo4zgJ1nmWlL59m7d+/g7u4ujaK6fPlyno47deoUFixYgHfv3hXoc1SVmJgYqTDh5+eH5ORkrFu3Tmmf9u3bIyQkRBpVlPnzWF3Jyck4efIk7t+/D5lMhrp166JLly7S79Lnz59j9uzZuH37ttJxNWrUwKxZs9CkSRON5MiIBV7t+UxVePDgATw8PODr64vY2Ngc9zU3N0ebNm3g5ORU4M+vjEJCQuDu7o6zZ8/Cx8cn14dfRo8ejYsXL6J58+b49ddfNTpf/vPnz7Fz506cPHkyT6NP7e3t0adPH/Tu3TvPv5dy8+TJEyxZsgSXL1/O8oBO7dq18euvv2b599mhQwfUrVsXQ4cO1diCxizyKtPGIi8/U7XnM1VbrlN5japMW65RFSZPnoyjR49KWcqWLYuTJ0/muDbG48ePMXz4cISHh2vkc/X333/Ho0eP8OTJEyQmJkqvZ3w4UzHdeMb7/s6dO0uLcIuiiPbt22dZZym/lixZIhUUFBR/J2vWrEFcXBymTp0KQRBQvnx51K1bF8nJybhz5w5iYmIgiiJatWqFzZs3FyhHRpcuXcKPP/4ImUyGEydOSDM3FKW0tDRpXavU1FTUrVsXixYtQpcuXbTmM5AoJyxGEGkh3uwp08abPW0WFBSEX3/9Fbdv39b4xYjiZ0AT819qijZ0nAHsPMtMWzrPFKKiovI1f6ziSaz79+9j5syZGs2Sm7t378LKyipPCwYWhhcvXuDVq1eQy+WoWrUqatasWSQ5tMGXVOBVJSgoSOmBGR0dHRgbG8PMzAw2NjZF9jOaWWBgIMzMzDQyh3dO3rx5g2fPnkkPzCQkJMDIyEh6YMbOzk4jo2SzExERgYcPHyImJgbGxsaoUaNGgaegzC8WeZVpY5FXm32Jn6nacJ3Ka1Rl2nSNqhh14OnpiZSUFBgZGeHOnTu5Hvf69WtMnDgR/v7+AKCRz9W0tDQ8f/5c6f7/8ePH+PjxIywtLXHu3Dml/Zs1ayZNvebg4IAFCxbAwMCgQBkAYO/evdi8ebN0729tbY3JkydL9/Lz5s2TpmrKPJtFixYtsGrVqgIXrT4XDx8+xJQpUxAQEABDQ0MkJiZq3WcgkSosRhBpKd7sKePNXv6IoigtNqZNhYPC9rl0nAHsPNM2in8vRf0UKXNonzdv3mDq1KlfTIGXKK9Y5M07Fnn/50t7aCajz+U6ldeon15YWBjOnDmDsLAwTJgwIU/HyOVy7N69Gzt27EBQUFChrB8giiJevnyJsLCwLAWySZMmwczMDN27d0fDhg01fu7o6Gjo6OioLCxcvnwZe/fuVfpcdXBwwPfff6/xHNouKSkJixcvxs6dOwGAxQj6LLAYQfQZ4M1e3hXWzV5YWBg2bdqE8+fPIywsDCVKlEDDhg0xYMAANG3aNMdjP378iIYNG0Imk0lPrzCHZnLkVVRUFN68eQNDQ0PY2NgU6RzK2pKFOZTZ2dlBJpPh1q1bRXrxzhzpYmNj8fDhQ0RERMDIyAg2NjaoWrVqkeaIiIhAixYtijxHUb4fCjExMUhMTISFhYXGnlRlDu3OcePGDZiYmODrr7/WaLvM8WklJSUhMDAQcrkcX331VZE9PZyUlITXr18jJSUFlStXhpmZWZHl+JTvR3x8PN69e4e4uDhp3TsTExNYWFhoZNpQ5vi8cxREfvsLqPi5dOkSjh07BgCYM2cO9PT0ijgRUfZYjCAiysXdu3cxevRoREdHKw3lVdzoOzg4YO7cudl22Ck63ws6UoU5VEtKSsLLly8hk8lQvXp1pbVk7t+/j8WLFyuNmDExMUHPnj0xfvx4jd9gaEsW5sg/bZnaobjn+PvvvwEA/fr1U7nuU3BwMJYsWYIzZ84gLS1NaZuVlRWGDx+OH374gTk0nEPh48ePOHz4MB48eIAFCxYobXv//j02btyIU6dO4cOHDwAAHR0d1KlTB87OzhqdQoM5tC+H4jNh2LBhmDRpktK84Z8Sc2QvJCQE9+/fh46ODuzt7VG+fHlpW0REBJYvX45Dhw4hNTUVQPrPSdu2bTFlyhSNzoHOHMoCAwOxbds2XLx4McuUtxmZm5ujXr166NixI7p165brVErMUTxyEBF9iViMICLKQUREBBwdHREeHg5dXV20a9cOX331FV6/fo0LFy5ALpdDEATY2trCw8MDZcqUydKGJjrfmUO1jRs3wsPDQ5qDt2LFinBzc0OrVq1w9+5dDB06FImJiVnmgxUEAdbW1tiyZYvGpqHRlizMAaxdu1atYwRBwMiRI7M8STRu3Djm0EAOIOcRF/fu3cPIkSOlBQhVEQQBTZs2xfr162FsbMwcGsoBpM9rPm7cOISFhUFHRwcPHz6Utt29exdjxoxBZGSkyiyCIKBevXpYtWqVUocfcxSfHIppNwVBQIMGDbB48eIimc6EObJKSkrC77//jkOHDkmv6erqYvTo0Rg7diwiIiIwaNAgaUHijARBgJmZGTZt2oT69eszhwZzAIC7uzs2bNiA5OTkbD/HM58fAMqXLw83Nze0adOmwBmYQ3tzqPL48WNprUbFaDeZTAZ9fX2Ym5vDysoKdnZ2GpsKmTmIqCiwGEFElINVq1bB3d0dRkZG2L59u9KCZSEhIZg1axYuXboEQRBQvXp17NixI8v8qprofGeOrH777Tfs2bMny02Evr4+PD094ebmBn9/fxgYGKB79+6oXbs2EhMT8e+//8LX1xeCIKBWrVrYu3evyieSP8cszJFO8cRqfiiyqjpO3Z9T5sg+S+YRF1FRUejatau0mGXbtm3RoUMHWFpaQi6X4/nz5zh27BgePXoEQRDQvHlzeHh4MIeGcoSFhcHR0VEqfFSpUgWHDx+Gvr4+wsLC0L17d0RFRUEmk6FNmzZo3LgxzMzM8OHDB/j6+uLmzZvSv9tdu3apvYAlc2hnDuB/P6uCICAtLQ1GRkYYO3YsBg8e/EmngmAOZaIoYvjw4bhy5YrKjvVFixbhzp078PLygqGhIfr27YvGjRtDEATcvHkTXl5eSEhIgIWFBY4fP672dEnMkZW7uztWr14NURRRqVIlNG/eHObm5oiJicGNGzfw/PlzVKhQATNmzEBKSgoeP34MX19fqeCoo6ODhQsXonv37mpnYA7tzZFRYmIitm/fjj179khrduXGwsICQ4YMQb9+/Qr8MAJzENGnxmIEEVEOevXqhUePHuGXX37BiBEjsmwXRRHz58/HX3/9BUEQUKdOHezYsUOpU0kTne/Moezq1asYMmQIBEFAu3btpBuCv//+Gzdu3IClpSXevn2LUqVKwdPTEzVq1FA6/tixY5g6dSrS0tIwb948ODs7q5VDm7Iwx/98//33eP36NQRByNMTb7l5/PixWscxR1bZdb4vW7YMmzdvhq6uLlasWIGOHTuqPH7Tpk1Yvnw5BEHAsmXL0LVrV+bQQI6FCxdi+/btMDU1xdKlS9G2bVtp2++//w4vLy+YmJhg48aN+Oabb7Icf/LkSUyZMgUpKSmYOXMmBgwYwBzFKAfwv5/VjRs3YtasWXj37h0EQUDlypUxYcIEdOnS5ZOsl8Ecyg4dOoRp06ZBEAQ4ODigX79+0NfXx4EDB+Dl5QVLS0t8+PABBgYG8PT0zPL08JMnTzBw4EDExsZiwoQJGDVqFHNoIMf9+/fRp08fCIKASZMmYfjw4Vl+Hvbt24fffvsNdevWxc6dO6Xtjx8/xuzZs3H//n0YGhri6NGjqFSpEnMUoxwZPX/+HKNHj0ZwcLDSNZqOjg6MjIxgaGgIURSRlJSEhIQEaVoxANJnjoeHR4FHZjHH/2T8XVtQgiDAx8dHY+0RFRsiERFlq2HDhqKdnZ349OnTHPebN2+eaGtrK9rZ2YkjR44UU1NTpW3x8fHSNubQTI6JEyeKtra24oQJE5Rel8vlYvfu3aX29+3bl20bixcvFm1tbcWhQ4eqnUObsjDH/yQkJIizZs2SzjV9+nQxJiZGTElJUfmfXC6X9o2Njc2yXV3MkZWi3Y8fPyq93qVLF9HOzk5cvnx5rm3MnDlTtLW1FX/88Ufm0FCOTp06iXZ2duKuXbuybGvZsqVoZ2cn7ty5M8c23N3dRVtbW7Fv377MUcxyiKLyz2pkZKQ4duxY0dbWVnq9Y8eO4u7du8WkpKQCnYc58mfw4MGinZ2dOHny5CzbZs+eLeX566+/sm1j27Ztoq2treji4sIcGsoxadIk0dbWVpw3b16O+y1dulS0s7MTd+/erfR6YmKi6OTkJNrZ2eXaBnN8fjkUoqKixFatWom2trZi7dq1xWnTpon//POPGBwcrPJ6KyUlRQwODhb/+ecfcdq0aWLt2rVFW1tbsWXLlmJ4eDhzaChHgwYNpM/zgv5XkPtdouKMIyOItMy+ffs02l7v3r2ZowA57O3tkZKSgqtXr6JkyZI57jtp0iQcO3YMgiCgT58+cHNzA6CZkQDMoaxdu3YIDQ3Fnj17YG9vr7Tt4MGDmD59OgRBwOXLl7NME6Xw/PlzdOvWDebm5rhy5YpaObQpC3Nkdf78ecycORPh4eGoVKkSFi1ahEaNGqnctzAXjmaO3NutV68ekpOT4e3tjerVq+fYxuPHj9GjRw+ULVsWly5dYg4N5Khbty7kcjkuXrwICwsL6fWUlBTUqVMn13+zAPD27Vu0b98eJUuWxLVr15ijGOUAVP+sXrp0CQsWLMDLly8BpD8BWqZMGTg4OKBr166oV6+e2udjjrxp2rQpYmJisH//fnz99ddK2548eQInJycIgpDlZyijN2/e4LvvvkOJEiVw/fp15tBAjjZt2iAsLAxHjx5FtWrVst3v5cuX6Nq1K+rXrw8vLy+lbefOncOYMWNgY2ODkydPMkcxyqGwcuVKbNiwASVLloSHhwfq1KmTr+Pv37+PESNGICYmBkOHDsXUqVOZQwM5QkJCMHnyZNy6dQuCIKBq1apKUxPn18KFC9U6jiM0qDjTLeoARKRs5syZGhvWLQiC2kUA5khXunRphIWFISAgINeF7BYuXIi3b9/izp072LNnDywtLdUe3s0cOfvw4QMAwMrKKsu2hg0bSl+bmppm24ZicW3FAsufexbmyKpt27Y4cuQIZs6ciXPnzmHQoEEYPHgwJkyYAH19/QK1zRyapaenh+Tk5Gw7hzIqV64cACA6Opo5NJTDwMAAcrkcKSkpSq/r6urC2NgYCQkJMDExybENXd3024rk5GTmKGY5stOyZUt4e3vDy8sL27ZtQ1BQED58+IAdO3Zgx44dsLS0RIcOHdCgQQPUr18fFStW1HiGLz1HfHw8AKBChQpZtmX8/MipYKX4GUpISGAODeUIDw8HAJQtWzbH/RTbX7x4kWWboniV1znzmePzyaFw5swZCIKAyZMn57vjHUgvWE+ePBmzZs3CuXPn1O58Zw5lFStWxJYtWzBmzBj4+voiJCQEK1euzDK1bGGLiYnBx48fNdLWp5g2kCg/ZEUdgIiUubq6Sr8sRFEs8H/MUbAcigvOvCwMqq+vj/Xr1+Orr76CKIpYuXIlPD09831O5sid4kbxzZs3WbaVK1cO9evXR4UKFRAcHJxtGwEBAQCQ6wiPzyULc6hWunRprF+/HnPnzoWBgQG2bt0KZ2fnAi3CzByap5i3Oy8394qnjkuUKMEcGsqhmFv54sWLWbY1aNAAAODn55djG4pRTAXpYGUO7cyREx0dHQwYMACnT5/GqlWr0LBhQ+maKzg4GJ6envjll1/Qvn17NGrUCN26dcOPP/7IHBrKUapUKQDpIwoze/bsmfR1UFBQtm0oOlwVbTFHwXMoPo9zW2Pp1atXAKA0772CorCio6PDHMUsh4LiWrhdu3Zqt6E4tiDFEebIysDAAGvXrkXVqlWRkJCAadOmIS0trUBt5texY8ekEcyCIKBatWro2bOnWv/16NHjk2Ynyg2LEURaZubMmVizZg309PSkBYD/+ecfnD17Nt//nTlzhjkKmMPZ2RmiKOLMmTP49ddfERISkuP+5ubm2LRpE8zNzSGKIhYsWIAZM2ao++0zRzYUT8ts27YtyzYjIyN4eXnBx8cHNjY22baxefNmCIKQZSqhzzULc+TMxcUFhw8fRr169fDs2TO4uLjA3d39k99YMEe6o0ePKj1x6OTkBFEUsX///lyPXbduncZ+PpgjXYcOHSCKIpYvX56lk8bV1VXapqpjBkh/2nTlypUQBAGtW7dmjmKWIy8EQcD333+PnTt34ty5c5g2bRoaNWoEQRCkzvj4+Hg8f/4cvr6+zKGhHPXr15ce+Mg4+iU5ORmrVq2S/vzXX39l28aOHTsgCEKBpiFhDmX29vZSjswjmzJSXCupeuL61KlTAIAqVaowRzHLkVlBRq4pRvBoYoQrcygzMjLC6tWroaurC39/f+zZs6fAbeaHYoRGixYtIIoiQkJCMGzYMCxcuFCt/4i0CYsRRFqoQ4cOWLVqFQRBgJ+fH7y9vWFlZaXWf8xRsBxt2rRBt27dIIoijhw5gvbt26NVq1bSUzGq2NjYYOvWrShTpgxEUSzwfKLMkVXv3r0hiiJOnDiBKVOmSE8G58X79+8xbdo0ad5MFxeXYpGFOXJXuXJl7Ny5Ez///DMAYPXq1fjhhx+kkRifCnMAs2fPRrdu3dCoUSMMGjQIz58/h56eHnbu3Injx4+rPCYwMBAjRoyQnvR2dnZmDg3lGDx4MMqWLYuYmBgMGDAAmzZtkqYGaNeuHfr3748bN25g+PDhSp3iHz9+xIEDB9CzZ0+EhITA0NAQQ4YMYY5iliO/LC0tMWTIEPz999+4fv06tmzZgkmTJqFHjx5o0qSJNPKHOQqeY8CAAQCAO3fuwNnZGZs2bcK6devQq1cv3LlzB9bW1qhVqxZ27tyJP//8U2mUsFwux5IlS3D69GkAKNCTs8yhTDEt7N27dzFq1KgsD+8kJydj8eLFOH78OARBgJOTk7QtPj4e27Ztw5o1ayAIAjp37swcxSyHgmJNqKNHj6rdhmJ9xZzWwGAO9VWvXl0q/q9bty7HIlZh0IYRGkSFgQtYE2mxNWvWYN26ddDT08ORI0c0+gQGc+Rdamoqli5dCk9PT6SkpMDIyAh37tzJ9bjXr19j4sSJ8Pf3B4ACLdjMHFn9/PPPOH36NARBgCAI2LJlC7799tscjxk/fjx8fHyQmpoKURTRqVMnrF69Wu0M2paFOfLu4cOHmDJlCgICAmBoaIjExMRCWziaOf7n999/x6NHj/DkyRMkJiZKr2ecjk9XVxf379+HTPa/Z2Y6d+6M169fS/u0b98e69evZw4N5QAAf39/DB06FNHR0RAEAQYGBqhTpw6sra1RqlQpHDlyRFoTxszMDPr6+oiMjERaWhpEUYRMJsPSpUvRtWtX5iiGOQprUXvmKLglS5Zgy5YtSnOCi6IIHR0drFmzBnFxcZg6dSoEQUD58uVRt25dJCcn486dO4iJiYEoimjVqhU2b97MHBrMkfFaSEdHB3Xq1IGlpSXi4+Nx9+5d6VwNGjTAX3/9BR0dHURHR6Nly5ZISUmBKIqoUaMG9u3bBwMDA+YoZjmA9FE4CxYsgJ6eHmbNmgUXF5d8ze2/a9cuzJs3D2lpaZgxYwZcXV2ZQwM5MouJicGOHTsAAL169YKlpaVG2s2P58+fo2fPnkhJScFvv/2Gfv36ffIMRJrEYgSRFhNFEU5OTnj27Bk6duxYqB1zzJG7sLAwnDlzBmFhYZgwYUKejpHL5di9ezd27NiBoKAgjczLzhz/a2vx4sXw8vJCamoqzp8/j/Lly+d4zIABA3Dr1i0A6dOgzJ07VyPDeLUlC3PkT1JSEhYvXoydO3cCQJF1LH2JOdLS0vD8+XP4+/vDz88Pfn5+ePz4MT5+/AhLS0ucO3dOaf9mzZohMjISAODg4IAFCxYUuBOAObJ6//49Fi5ciFOnTklT/2TuCFB162BpaYm5c+eiRYsWBc7AHNqZQ1s635lDtb1792Lz5s0IDAwEAFhbW2Py5Mno2LEjAGDevHnS1EQZi50A0KJFC6xatQqmpqbMocEcSUlJmDVrFo4cOSK9lvlczZo1w4oVK6T1KURRRO3atZGWloa2bdtiwYIFKF26NHMUwxwAkJKSgiFDhuDmzZsQBAFWVlZo06YNateuDSsrK5QsWRIGBgYQBAGJiYmIi4vD27dv8ejRI5w/fx6vXr2CKIqoW7cudu3apfY6FszxeVi8eDG2bt0KCwsL+Pj4QFdXt6gjEamNxQgiLXfp0iX8+OOPkMlkOHHiBKytrZlDC3KoIyoqqkCL4TGHaqGhoTh79qw0ND8nmzZtQlhYGBwdHaXFuDVJW7IwR/5cunQJx44dAwDMmTMHenp6n/T8zJFOFEW8fPkSYWFhaNasmdK2SZMmwczMDN27d0fDhg2Zo5BzhIeH4+TJk3jw4AGePn2K9+/fIz4+HomJiTA0NISJiQkqVaqEWrVqoU2bNmjZsmWh3PQzh/bkmDZtGgRBKNLPJubIXXR0NHR0dFR2pF++fBl79+7Fq1evIJfLUbVqVTg4OOD7779njkLMcffuXRw+fBh+fn6Ijo6GiYkJatSogS5duqBt27ZZ9j9w4ADq1aunsWlmmEO7cyQnJ2Pq1KnSNLZ5HQmg6MZr3749Fi1aJC3OzRyayaGNtGGEBpGmsBhBRERERERERERUBB48eAAPDw/4+voiNjY2x33Nzc3Rpk0bODk5ZXlYgTk0m4OICgeLEUREREREREREREUsKCgIb9++RWRkJJKSkqCjowNjY2OYmZnBxsYGFhYWzFEEOYhIc1iMICIiIiIiIiIiIiKiQsUVT4iIiIiIiIiIiOiLtm/fPo2217t3b422R1QccGQEERERERERERERfdHs7OzyvHB2bgRBgL+/v0baIipOODKCiIiIiIiIiIiIvmiurq7YuXMnRFFEUT67zREaVJxxZAQRERERERERERF98c6ePYuJEydCLpejdu3aWLFiBWQymVptWVlZqXUcR2hQccZiBBERERERERER0SeiLU++M4dqPj4+GDt2LERRxPjx4zFmzBgNJcubefPmaWyEhiAIePTokYaSERUcixFERERERERERESfiLY8+c4c2VuzZg3WrVsHPT09HDlyBFWqVNFAurzThhEaRIVBvZ9iIiIiIiIiIiIiyjdXV1ep813x9HtB/mMOzeTIaNy4cahZsyZSUlKwYsUKjbSZHx06dMCqVasgCAL8/Pzg7e0NKysrtf4j0iYcGUFERERERERERPQJacuT78yRvUuXLuHHH3+ETCbDiRMnYG1trZF286OoR2gQaRqLEURERERERERERJ9YUa9NwBzaTxRFODk54dmzZ+jYsSNWr15d1JGICoTTNBEREREREREREX1i7dq1w08//QRRFOHu7o6AgADm0IIc2kQQBEydOhWiKOLMmTN4/fp1UUciKhCOjCAiIiIiIiIiIioC2vLkO3MQ0afAkRFERERERERERERFQFuefGcOIvoUODKCiIiIiIiIiIiIiIgKFUdGEBERERERERERERFRoWIxgoiIiIiIiIiIiIiIChWLEUREREREREREREREVKhYjCAiIiIiIiIiIiIiokLFYgQRERERFTs///wzbG1tYWtri9mzZ6vVxt27d6U2OnbsCFEUNZwye2vWrJHOvXfv3kI5x7Rp06RzXL58We12FG20bt1ag+mIiIiIiKi4YTGCiIiIiIqd3r17S1+fPHkSycnJ+W7j0KFD0tfOzs4QBEET0YiIiIiIiL5ILEYQERERUbHTsmVLVKhQAQAQHR2Nixcv5uv45ORknDhxAgCgo6ODnj17ajwjERERERHRl4TFCCIiIiIqdmQymVIBwdvbO1/H+/j4ICoqCgDQunVrlC9fXpPxcjV+/Hg8efIET548gYuLyyc9NxERERERUWFgMYKIiIiIiqWMUyv5+PggLi4uz8cePHhQ+jrjlE9ERERERESkHhYjiIiIiKhYqly5Mpo0aQIASEpKwunTp/N0XEREBC5dugQAsLCwQNu2bQsrIhERERER0RdDt6gDEBEREREVlt69e+PatWsA0qdq6tWrV67HeHt7Qy6XAwB69OgBXd3/XTKHhoZi//79uH79OgICAhAVFQVBEFCiRAnUrFkTrVq1gouLC0xMTLK0e+3aNQwaNAgAsHz5ctSrVw8LFizA1atXIYoiLC0t0bNnT/z4449Ys2YN1q5dCwCYN29etlM1XblyBadOncLt27cRFhaG2NhYGBkZoUyZMqhfvz4cHR3RsmXLPL1XcXFx8PDwwKlTpxAcHAxDQ0PUqFEDXbp0gYuLC/T19fPUTnZu3bqF/fv34+bNmwgLC4MgCChfvjyaNGmCH374AbVq1crx+MjISOzevRsXLlzAixcvEB8fDzMzM1hZWaFZs2bo27cvKleuXKCMRERERERUeFiMICIiIqJi6/vvv8fcuXMRExODq1evIiwsDOXKlcvxmEOHDklfZ5yiyd3dHevWrZMKFRklJiYiLCwMly5dwtatW+Hh4YHq1atne47Q0FAsXLgQ79+/l157/vw5DAwM8vR9RURE4D//+Q+uX7+eZVtsbCxiY2Px6tUrHDp0CF26dMHSpUuViiqZBQUFYebMmQgODlb6nm7cuIEbN25g+/bt2LhxI6pUqZKnfBklJSVh5syZOHLkSJZtAQEBCAgIwJ49e+Dq6orp06dDR0cny3537tzB6NGjpXU8FCIjIxEZGYmHDx9i69atmDRpEoYNG5bvjEREREREVPhYjCAiIiKiYsvAwAAODg7YtWsX0tLScPz4cQwZMiTb/Z8+fQp/f38AQJMmTWBjYwMA2LJlC1auXAkAMDY2hpOTE+zt7WFqaoqYmBjcu3cPR48eRUJCAkJDQzFlyhSldScyW7VqFZKSktC2bVt06dIFsbGxOHXqFLp3757r95Samorhw4dLOatVqwYHBwfY2NhAJpPh3bt3OHv2rFSoOHHiBBo0aIDBgwdn2+bcuXMhl8tRuXJluLi4oHLlyggKCsKePXvw5s0bvH79GgMHDoS3tzfMzc1zzZgx64gRI6TRKWXKlEHPnj1hZ2eHtLQ0+Pv748CBA4iJiYGnpydiYmKwZMkSpTaioqIwduxYaRRKp06d0Lx5c5QqVQoRERG4evUqTp8+jZSUFCxevBg1atRAq1at8pyRiIiIiIg+DRYjiIiIiKhY6927N3bt2gUAOHLkSI7FiIwFBGdnZwDpIw3WrFkDIL0Q4eXlBVtbW6XjXFxc4OrqChcXFyQnJ8Pf3x9PnjzJsp9CUlISunXrhmXLlkmvDRw4ME/fz4EDB6RCRPPmzbFx48YsUygNGTIEy5cvx8aNGwEAhw8fzrEYIZfL0a5dO6xcuRKGhobS64MGDcK4ceNw6dIlvH//Hn/88QcWLFiQp5xA+mgSRSGiVatWWLlyJUxNTaXtTk5OGDVqFEaMGIGHDx/i8OHDaN26Nbp16ybtc+TIEYSHhwMAxo8fj7Fjxyqdo3///ti7dy9mzpwJANi2bRuLEUREREREWogLWBMRERFRsVanTh3Y2dkBAPz8/PDy5UuV+6WmpsLb2xsAUKJECXTu3BkA4OPjg48fPwJI7/jOrsBgZ2eH7777Tvrzixcvcsz1888/5+8b+X/Hjx+Xvp4xY0a2azn89NNPEAQhT1kqVqyIZcuWKRUiAMDIyAhLly5FqVKlACgXBnITHx+P7du3AwDKly+P1atXKxUiFEqXLo0VK1ZI34e7u7vS9ozZM76/Gbm4uODrr79GnTp1cp2Gi4iIiIiIigaLEURERERU7ClGOQCQCg6Z+fr6Sms4dOvWTeqY79q1K86cOYOtW7fmOLoAgNICyomJidnuV65cOVhbW+c5f0Zr1qzB4cOHsXnz5hzXpTAyMkLZsmWlLKIoZrvvgAEDVC66DQDm5uZwcnICkD6C4sKFC3nKefHiRcTExAAAHB0dYWxsnO2+X331FVq0aAEgfe2M169fS9tKly4tfb1lyxYkJyerbOPgwYPYv38/Fi5cmKd8RERERET0aXGaJiIiIiIq9rp3744//vgDycnJOHr0KP7zn/9k2Se7hat1dXVRuXJlpUJDRvHx8Xj27Bnu3r0LHx8f6fXU1NRs81SrVk2N7yKdqakp7OzspNEemb179w7+/v64efMmYmNjpdfT0tJULg4NpE/3lJNvvvlGGuVw9+5d9OrVK9ecd+7ckb6Oi4vDmTNnctw/4+Ld9+7dk4o1Xbt2xYYNG5CWloZDhw7B19cX7du3R6tWrdC0aVOUKFEi1yxERERERFT0WIwgIiIiomKvVKlS6NChA06cOIHAwEDcvXsX9evXl7bHxcXh7NmzAICvv/4atWvXVtnOs2fPcPHiRTx79gyBgYEIDAzEhw8fchx1oIomOtDlcjkuXbqEO3fuICAgAIGBgQgKCkJ8fLzK/XPKmF2hRcHKykr6WjF6JDehoaHS115eXvDy8srTcQAQEREhfV2jRg3897//xYIFC5CWlob3799j9+7d2L17N3R0dGBvb482bdrg+++/L1CRh4iIiIiICheLEURERET0RejduzdOnDgBIH2qpozFiBMnTkjTKmUcFaEQGhqKGTNm4NKlSyrbNjQ0RMOGDZGSkoLr16/nmiW7dR7y6syZM5g7d65Sh39G5cqVQ8uWLXHu3DlERUXl2l7mtSIyMzIykr5OSkrKU8aMozLyKy4uTunPAwcORL169fDnn3/iwoUL0t9Vamoq7t69i7t372LVqlVo164d3NzcUL58ebXPTUREREREhYPFCCIiIiL6IjRv3hyWlpZ4+/Ytjh8/junTp0NXN/1yWDFFk6GhIRwdHZWOi4iIQN++faWOfxMTEzRt2hS1atVClSpVUKNGDVSrVg16enpYuXJlnooRBeHt7Y0pU6ZIIx2sra3xzTffoEaNGqhatSpq1qyJihUrAgDatGmTpzaTkpJyLJAoFvAGADMzszy1mbHAsXfvXtStWzdPx2Wnbt26WL16NRISEnDt2jVcvnwZV69exdOnT6X3wsfHB69evcKhQ4dyLbAQEREREdGnxWIEEREREX0RZDIZevbsiXXr1iEiIgK+vr5o06YNgoKCcOvWLQBAp06dskyhtGrVKqkQ0bp1ayxfvjzbDnnFgs2FJTExEXPnzpU63+fMmYO+fftmu39e84SGhuZYZAgMDJS+trS0zFObFhYW0tcvX74scDFCwcjICG3btkXbtm0BAGFhYTh69ChWrVqFxMREBAQE4PDhwzm+L0RERERE9OnJijoAEREREdGn0qtXLwiCAADSlE0nTpyQOvddXFyyHHPu3Dnp69mzZ+fYae/v7y99nd91JPLi1q1biI6OBgA0btw4xw73169fK41oyCnPvXv3cjzv1atXpa/r1auXp6wZiw8Z38PsuLu7Y9q0aVi9ejUCAgIApC+6feLECaxfvx4rV65UeVy5cuUwbNgwTJ48WXot498DERERERFpB46MICIiIqIvRqVKlfDtt9/iypUrOHPmDJKTk3Hq1CkA6dMdNW7cOMsxkZGR0tempqbZtn3z5k2lTn25XK7B5PnLAgAeHh5Kf05JSYGenp7KfXfu3ImePXtCR0cny7bQ0FB4e3sDSJ+iqVWrVnnK2rZtW+jr6yM5ORlnzpzBo0ePUKtWLZX7hoSEYN26dZDL5ZDJZNK6HTKZDH/88QeCg4Mhk8kwYMAApREXGZmbm0tfm5iY5CkjERERERF9OhwZQURERERfFEVHd2xsLA4dOoSHDx9KrytGTWRUuXJl6eudO3eqbPPGjRuYMGEC0tLSpNeSk5M1GTtLlmvXruHFixdZ9klNTcWqVauwe/dupddzWnjaz88P8+bNU8oPAFFRURg3bpw0wmLgwIF57ugvW7asNNIkNTUVY8eOVZk3JiYGY8aMkYo3Dg4OSlNBOTg4AEgfJTFt2jSV72tCQgK2bNki/blp06Z5ykhERERERJ8OR0YQERER0RelY8eOKFmyJKKjo7F06VIAgK6uLnr06KFyf2dnZ/zxxx8AgNWrV+PBgwdo1aoVSpUqhbCwMFy4cAFXr17NMg1SbGysxrPb29ujZs2aePr0KT5+/Ig+ffqgT58+sLW1hSiKCAgIwNGjRxEcHJzl2NjYWJQqVUplu6VLl8bOnTtx79499OjRA2XLlsWLFy/g5eWFDx8+AADq16+PMWPG5CvvlClTcPPmTTx58gTBwcHo0aMHHB0d0bhxY8hkMjx79gx79+5FVFQUAKBixYr473//q9TG8OHDcfDgQbx//x6XLl1Cx44d0bNnT1hbWwNIn45q//79CAsLAwA0adIkzwt3ExERERHRp8NiBBERERF9UQwMDNCtWzf8/fff0voLrVu3Rrly5VTuP2TIENy7dw+nT58GAPj4+MDHxyfLfnZ2dhg3bhzGjRsHAHj06JHGs8tkMqxYsQJDhgzB+/fvERcXpzQiICNXV1fo6Ohg+/btAIDHjx8rjazIaM2aNRg/fjz8/Pzg5+eXZXurVq2wcuXKbKd5yo6RkRE8PT0xZcoUXLhwAcnJydi/fz/279+fZd9atWph7dq1KF26tNLrpUqVwp9//olRo0YhNDQUoaGhcHd3V3m+Fi1aYNWqVfnKSEREREREnwaLEURERET0xenduzf+/vtv6c+qFq5W0NXVxerVq3H06FEcOnQI/v7+iI6OhoGBAcqWLQs7Ozt07doVHTt2hI6ODqysrBAcHAxfX1+Eh4ejTJkyGs1evXp1HDlyBNu3b8eFCxfw+vVrJCcnw8TEBJUqVUKDBg3g4uICOzs7XLt2TSpGHDlyBB07dlTZZq1atXDkyBFs3LgRPj4+ePfuHczMzGBvbw9nZ2d8//33auctWbIkNm3ahCtXruDIkSO4ffs2wsLCIJfLUapUKdSpUwddunSBg4MDdHVV357Y2dnh+PHj2L9/P3x8fPDs2TNERUVBV1cXZcuWRYMGDeDg4IC2bduqnZOIiIiIiAqXIGYeT05ERERERERERERERKRBXMCaiIiIiIiIiIiIiIgKFYsRRERERERERERERERUqFiMICIiIiIiIiIiIiKiQsViBBERERERERERERERFSoWI4iIiIiIiIiIiIiIqFCxGEFERERERERERERERIWKxQgiIiIiIiIiIiIiIipULEYQEREREREREREREVGhYjGCiIiIiIiIiIiIiIgKFYsRRERERERERERERERUqFiMICIiIiIiIiIiIiKiQsViBBERERERERERERERFSoWI4iIiIiIiIiIiIiIqFCxGEFERERERERERERERIXq/wCv56J96aAEkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1700x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the sequential forward selection process for the random forest.\n",
    "# The optimal model fit is shown by the vertical dotted lines\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "y1 = sfs_rf_df.loc[:,'avg_score']\n",
    "y2 = sfs_rf_df.loc[:,'std_err']\n",
    "x = range(1,117, 1) \n",
    "\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(17, 10))\n",
    "\n",
    "plt.ylim([0.85, 0.99])\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xticks(np.arange(0, 117, step=3), rotation='vertical', fontsize=20)\n",
    "plt.xlim([0, 116])\n",
    "\n",
    "#plt.title('Random Forest', fontsize=22)\n",
    "ax2 = ax1.twinx()\n",
    "plt.yticks(fontsize=20)\n",
    "line1, = ax1.plot(x, y1, 'b-', linewidth=3, label='Accuracy Score')\n",
    "line2, = ax2.plot(x, y2, 'r-', linewidth=3, label='Std Error')\n",
    "plt.axvline(list(sfs_rf_df_opt[\"idx_len\"]), color = 'darkgreen', linestyle='dashed', linewidth=2)\n",
    "\n",
    "ax2.legend(handles=[line1, line2], loc='right', fontsize=18)\n",
    "\n",
    "ax1.set_xlabel('Variables', fontsize=23)\n",
    "ax1.set_ylabel('Accuracy Score', fontsize=23)\n",
    "ax2.set_ylabel('Std Error', fontsize=23)\n",
    "\n",
    "#plt.savefig('RandomForestForwardSelectionFNLRA.png', dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "#sfs_rf_df_maxmin.to_csv('sfs_rf_df_maxminRA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>feature_idx</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>std_err</th>\n",
       "      <th>idx_len</th>\n",
       "      <th>avg_score_rank</th>\n",
       "      <th>std_err_rank</th>\n",
       "      <th>total_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(69,)</td>\n",
       "      <td>0.892639</td>\n",
       "      <td>0.002318</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 69)</td>\n",
       "      <td>0.955784</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 1, 69)</td>\n",
       "      <td>0.96378</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 1, 69, 74)</td>\n",
       "      <td>0.96966</td>\n",
       "      <td>0.001488</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 41, 69, 74)</td>\n",
       "      <td>0.971542</td>\n",
       "      <td>0.001435</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 10, 41, 69, 74)</td>\n",
       "      <td>0.972483</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 10, 17, 41, 69, 74)</td>\n",
       "      <td>0.973777</td>\n",
       "      <td>0.000823</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 1, 10, 11, 17, 41, 69, 74)</td>\n",
       "      <td>0.975541</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 10, 11, 17, 41, 69, 74, 108)</td>\n",
       "      <td>0.975541</td>\n",
       "      <td>0.001092</td>\n",
       "      <td>9</td>\n",
       "      <td>8.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 10, 11, 17, 41, 69, 74, 108, 109)</td>\n",
       "      <td>0.976128</td>\n",
       "      <td>0.000825</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(0, 1, 4, 10, 11, 17, 41, 69, 74, 108, 109)</td>\n",
       "      <td>0.976246</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>11</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(0, 1, 4, 10, 11, 17, 41, 42, 69, 74, 108, 109)</td>\n",
       "      <td>0.976129</td>\n",
       "      <td>0.000824</td>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(0, 1, 4, 10, 11, 17, 30, 41, 42, 69, 74, 108,...</td>\n",
       "      <td>0.976481</td>\n",
       "      <td>0.001039</td>\n",
       "      <td>13</td>\n",
       "      <td>14.5</td>\n",
       "      <td>35.0</td>\n",
       "      <td>62.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(0, 1, 4, 10, 11, 17, 27, 30, 41, 42, 69, 74, ...</td>\n",
       "      <td>0.976481</td>\n",
       "      <td>0.001039</td>\n",
       "      <td>14</td>\n",
       "      <td>14.5</td>\n",
       "      <td>36.0</td>\n",
       "      <td>64.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(0, 1, 4, 7, 10, 11, 17, 27, 30, 41, 42, 69, 7...</td>\n",
       "      <td>0.976717</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>15</td>\n",
       "      <td>20.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(0, 1, 4, 5, 7, 10, 11, 17, 27, 30, 41, 42, 69...</td>\n",
       "      <td>0.976481</td>\n",
       "      <td>0.000945</td>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(0, 1, 4, 5, 7, 10, 11, 16, 17, 27, 30, 41, 42...</td>\n",
       "      <td>0.976717</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>17</td>\n",
       "      <td>18.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>120.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(0, 1, 4, 5, 7, 10, 11, 16, 17, 27, 30, 41, 42...</td>\n",
       "      <td>0.976716</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>18</td>\n",
       "      <td>17.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(0, 1, 4, 5, 7, 10, 11, 13, 16, 17, 27, 30, 41...</td>\n",
       "      <td>0.976717</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>19</td>\n",
       "      <td>20.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 16, 17, 27, 30,...</td>\n",
       "      <td>0.976717</td>\n",
       "      <td>0.001161</td>\n",
       "      <td>20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>64.0</td>\n",
       "      <td>102.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx avg_score   std_err  \\\n",
       "1                                               (69,)  0.892639  0.002318   \n",
       "2                                             (0, 69)  0.955784  0.002421   \n",
       "3                                          (0, 1, 69)   0.96378  0.002549   \n",
       "4                                      (0, 1, 69, 74)   0.96966  0.001488   \n",
       "5                                  (0, 1, 41, 69, 74)  0.971542  0.001435   \n",
       "6                              (0, 1, 10, 41, 69, 74)  0.972483  0.001113   \n",
       "7                          (0, 1, 10, 17, 41, 69, 74)  0.973777  0.000823   \n",
       "8                      (0, 1, 10, 11, 17, 41, 69, 74)  0.975541  0.000838   \n",
       "9                 (0, 1, 10, 11, 17, 41, 69, 74, 108)  0.975541  0.001092   \n",
       "10           (0, 1, 10, 11, 17, 41, 69, 74, 108, 109)  0.976128  0.000825   \n",
       "11        (0, 1, 4, 10, 11, 17, 41, 69, 74, 108, 109)  0.976246  0.000838   \n",
       "12    (0, 1, 4, 10, 11, 17, 41, 42, 69, 74, 108, 109)  0.976129  0.000824   \n",
       "13  (0, 1, 4, 10, 11, 17, 30, 41, 42, 69, 74, 108,...  0.976481  0.001039   \n",
       "14  (0, 1, 4, 10, 11, 17, 27, 30, 41, 42, 69, 74, ...  0.976481  0.001039   \n",
       "15  (0, 1, 4, 7, 10, 11, 17, 27, 30, 41, 42, 69, 7...  0.976717  0.000781   \n",
       "16  (0, 1, 4, 5, 7, 10, 11, 17, 27, 30, 41, 42, 69...  0.976481  0.000945   \n",
       "17  (0, 1, 4, 5, 7, 10, 11, 16, 17, 27, 30, 41, 42...  0.976717  0.001226   \n",
       "18  (0, 1, 4, 5, 7, 10, 11, 16, 17, 27, 30, 41, 42...  0.976716  0.001188   \n",
       "19  (0, 1, 4, 5, 7, 10, 11, 13, 16, 17, 27, 30, 41...  0.976717  0.001148   \n",
       "20  (0, 1, 3, 4, 5, 7, 10, 11, 13, 16, 17, 27, 30,...  0.976717  0.001161   \n",
       "\n",
       "    idx_len  avg_score_rank  std_err_rank  total_score  \n",
       "1         1             1.0         114.0        116.0  \n",
       "2         2             2.0         115.0        119.0  \n",
       "3         3             3.0         116.0        122.0  \n",
       "4         4             4.0         113.0        121.0  \n",
       "5         5             5.0         110.0        120.0  \n",
       "6         6             6.0          54.0         66.0  \n",
       "7         7             7.0           5.0         19.0  \n",
       "8         8             8.5           8.0         24.5  \n",
       "9         9             8.5          47.0         64.5  \n",
       "10       10            11.0           7.0         28.0  \n",
       "11       11            13.0           9.0         33.0  \n",
       "12       12            12.0           6.0         30.0  \n",
       "13       13            14.5          35.0         62.5  \n",
       "14       14            14.5          36.0         64.5  \n",
       "15       15            20.5           3.0         38.5  \n",
       "16       16            16.0          19.0         51.0  \n",
       "17       17            18.5          85.0        120.5  \n",
       "18       18            17.0          72.0        107.0  \n",
       "19       19            20.5          59.0         98.5  \n",
       "20       20            18.5          64.0        102.5  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sequential forward selection output for the random forest model\n",
    "sfs_rf_df_maxmin.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model to training data\n",
    "clf.fit(x_train_rf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wrVu3JEn79u2Tj4+P1dktb29vyxlIybbnbtIb+AIFClhChXT3OMqUKZMlVEhSxowZ7zuGHqRSpUrq2LHjfbe/9tprdteVJ08eS6iQ7r6GODg4KDg42OqsSEhIiFavXq2//vpLhQoVUqZMmdS+fXvVqFHDcq1LUhC1VVIATkhIsOt+SVq1aiVJunnzpk6cOKFjx47p119/lfTPYx0UFKTFixcrNDRUVatWVcWKFVWnTh3L67C3t7dSpUqlt99+WzVr1lRwcLD8/f3l5eX1WDXZ+vxI8u/XLryYCBYATKtRo4Y6duyoU6dOyd3dXf/73/8sn3rf69q1a/Lw8LjvuoukN7A3btzQtWvXJN1/gWDSOkmuXr2qv//++4FnGqR/3jTZqkSJEg9ddv36dSUmJmrGjBmaMWPGfctTpUol6e6c9yFDhmjVqlWKj49Xnjx55OvrK2dn52T7nvssWbJY/Xz16lWtX7/+vnnh0uNdZPnvTy0lKXXq1I+837+/JSZp29evX7c8pv9+DJ2dneXh4WH1BjNz5syP/FYce/r879odHR0t61y9etWq1n+z9XFv1aqV3N3dtWzZMg0fPlwREREqXLiwevfurdKlS//nvvybu7u75VgsUaKEfH19Va9ePbVo0UIrVqywqnX79u365JNPdPToUbm7u8vT09MyR//ePvy7B0n9TboG4dq1aw/8JP3ex8uW526Sxz2GHiRjxoz/+dy0p64HPXcMw5Cfn98Dx75w4YJef/11LViwQFOmTNH69eu1aNEipU6dWnXr1lWfPn0sx8CjJJ1VPXPmzEPPXp0/f15Zs2a9b0qSdHfq54ABA/TVV1/JwcFB+fLls5xBSXqsa9asqcTERH3xxReaOHGiIiMjlTt3bnXv3l21atVSnjx5NH/+fE2fPl1ffvmlPvvsM6VPn16NGjVSly5dHrjd/2Lr8yOJvdMy8XwiWAAwrUKFCkqXLp02bdqkdOnSKU+ePCpevPh962XIkEFRUVGKj4+3eiNw4cIFSZKHh4c8PDwkyWp+t/TPm8Ak6dKlU0BAwEM/OXR1dTWzS1bc3d3l4OCgZs2aqVatWvctT3rTNGzYMG3atEnjxo1TmTJlLJ+OPurNZdIbvYSEBMt0gnsvMP4v6dKlU5kyZe77Vi5J973ZepL+/fgkPX6ZM2fW9evXJUkXL160egMbFxenqKgoy2Nuq8ft87+lT59e0t03bUmfgEvS2bNn9ffff6t48eI2Pe6Ojo5q3LixGjdurMuXL2vbtm2aOnWqOnXqpB9++MHUsZg0Da1Tp04aNmyYRo8eLUk6ceKEOnbsqMqVK2vatGmWMxILFiywzOe3lYeHhy5dunTf7fc+prY8d1OCmbrSpUunNGnSaN68eQ9cnjQd77XXXtPIkSOVkJCgX375RatWrdLChQuVJ08ey0Xgj1KuXDlJ0rZt2x4YLBISEhQaGqoiRYo88Hfc9OjRQ0eOHNGcOXPk5+cnV1dX3bp1S0uWLLFar3bt2qpdu7Zu3LihHTt2aMaMGfroo4/k7++v7Nmzy8vLSxMnTlRsbKz27dunxYsXa+rUqfL09LzvrM+j2Pq6iJcL3woFwDRXV1dVrlxZmzdv1oYNGx74n4x0dw54QkLCfZ+uJ01dKlmypPLnz6+cOXNq48aNVuskTfG4d6xjx47p1VdfVYkSJSx/Vq9erSVLlljeoCeHtGnTqmjRojp69KjVtgoVKqSJEydafonVvn37FBgYqDfeeMPyZve3337TlStXrL6h5t+fDCZ9wnv27FnLbT/++KNNtQUEBOjw4cN6/fXXLXUVL15cn332mbZs2WJqv+3xzTffWP28adMmpU6d2vJNMZK0Zs0aq3XWrVunhISE+67F+bd/98vWPj+Kl5eXXFxc9PXXX1vdPnfuXHXp0kVubm42Pe4NGzbU0KFDJd0NAqGhoWrcuLFu3Lhh9Y08j6tq1aoqX7681q5da9nmb7/9pjt37qht27ZW05ySQoU9Z8iCgoK0f/9+nT9/3nLb4cOHrb5ZzJbnbkowU1dAQIBu3rwpwzCsHt+//vpLkyZNUnx8vDZu3KigoCBdvHhRTk5O8vX11cCBA5U+fXrLdRO2fNJfqFAhVahQQdOnT3/gN7bNnDlTly5dUv369R94/3379qlatWoKCgqyBNWkL8ZIOuY//PBDy+/HSZcunWrUqKEOHTooISFBFy5c0GeffaaQkBDFxsbK1dVVpUuXtlzcfu9rj61sfV3Ey4UzFgCSRc2aNdW2bVs5OjpaLrr+twoVKigwMFADBgzQhQsXVLRoUe3evVszZszQm2++afkkr0ePHurevbv69u2r6tWr66efftLChQutxmrWrJlWrVqlZs2aqUWLFvLw8ND69ev15Zdf6uOPP072/evWrZvatGmj7t27q27dukpISNDs2bP1888/Wy5I9vLy0oYNG7Rw4UIVKFBAf/zxh6ZMmSIHBwerqVnp06fX/v37tWfPHvn7+ys4OFiffvqp+vXrp9atW+vcuXOaOHHif371ZJIOHTqoYcOGatu2rd577z2lSpVKixcv1ldffWX3BexmbNy4UVmyZFFwcLB2796tBQsWqGvXrkqTJo0KFiyoN998UxMnTtTt27cVGBiogwcPauLEiQoMDFT58uX/c+z06dPrwIED2r17t7y8vGzu86NkypRJ77//vubOnStXV1cFBQXp119/1fz589WtWzc5Ozvb9LiXKlVKs2fPVpYsWeTr66vz589rzpw5CggIsExdOnz4sGJjY1W0aNHH6m/v3r1Vt25dDR06VCtWrFCxYsXk7OyskSNHqkWLFoqNjdXy5cst39pz8+ZNm8du2rSpli5dqpYtW6pTp05KSEjQuHHjrL7e1tbn7tNmpq7g4GDLV6526NBBBQoU0C+//KIJEyaoXLlyypQpk/z8/JSYmKiOHTuqTZs2cnd314YNG3Tjxg3L75tIOvO1du1aeXt7W11Lcq9BgwapadOmeuedd/T+++/Lx8dHMTEx2rRpk9auXat33nnnvuuQknh5eWnNmjUqVqyYcuTIof3792vatGlWx3xQUJAGDBig4cOHq0KFCrp+/brlm9KKFCkiFxcXjRo1Sh07dlSTJk3k5OSkRYsWydXVVZUqVXqs/tvy/MDLhWABIFmUKVNG6dOnV86cOR/6Va8ODg6aNm2axo8fr3nz5unKlSvKkyePunbtajWVp3bt2nJ0dNTkyZO1atUqFS5cWIMHD1a3bt0s62TPnl2LFi3S6NGjNXDgQN25c0f58+fXsGHDrL41KbmUK1dOs2bN0sSJE9W5c2e5uLioWLFimjNnjuUC4fDwcMXFxWncuHGKjY1Vnjx51L59ex0+fFjffPONZapTu3btNHnyZLVu3Vrr16/Xq6++quHDh2vKlClq06aNChQooCFDhlh9VebDFClSRAsWLNDYsWPVs2dPGYahwoULa9KkSfddAP8kdenSRbt379bixYuVM2dO9e/fX++9955l+bBhw5QvXz4tW7ZMs2bNUrZs2RQWFqaOHTs+8hPfFi1a6JNPPlHLli01Z84cm/psq48++khZsmTRwoULNXv2bOXJk0e9e/dWo0aNJNn2uHfp0kWurq5atmyZJk2apHTp0ikkJMTylbbS3TeVp0+fvu/Mjq1ee+01hYWFafbs2Zo/f76aNWum0aNHa+LEiWrfvr0yZMggHx8fff755woLC9PevXsf+LtRHsTDw0MLFy7UsGHDFB4eLnd3d7Vq1crqLICtz92nzUxdjo6Omj59uiIjIzVt2jRdvnxZ2bNnV7NmzSwXjGfLlk0zZ85UZGSk+vTpo1u3bqlQoUKaMGGCgoKCJN09o7Rq1SqFh4fr7bff1sCBAx+4vVy5cmnx4sWaO3eu1q1bpxkzZsjFxcUy1ephZ3olKSIiwuo1IX/+/Bo0aJBWr16tvXv3Srp75iwuLk6LFi3SF198ITc3N5UuXVofffSRXFxcVKRIEU2dOlWTJk1St27dlJCQoOLFi2v27NlWUwHtYcvzAy8XByO5rigEALx0Tp06pcqVK+vTTz9VaGhoSpcDAEhBXGMBAAAAwDSCBQAAAADTmAoFAAAAwDTOWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA055QuALiXYRi6ciVGiYlGSpfyTHN0dFCmTO70ykb0y3b0ynb0yj70y3b0ynb0yj729Ctr1nT2j/+4hQFPgoODgxwdHVK6jGeeo6MDvbID/bIdvbIdvbIP/bIdvbIdvbLPk+4XwQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYJpzShcA3KtO91UpXQIAAMAzZXZ4SEqXYBPOWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWDxDLl27ZoGDhyo4OBg+fj4qFy5curVq5fOnTv3yPueOnVKnp6eOnXq1AOXL1++XCEhITbVYRiGunfvLh8fH4WEhGjKlClq1arVI+83YcIEhYWF2bQNAAAAvFicU7oA/KNr165Kly6dli5dqqxZs+rSpUsaNmyYmjdvrjVr1sjZ+ek8XBcuXNDatWu1fPlyFStW7KlsEwAAAM83zlg8Q/bt26cqVaooa9askqQsWbKod+/e8vb21vXr13XkyBG1bdtWFStWlJeXl2rWrKlvv/3WaoyVK1fqjTfeUJkyZdS3b19FR0c/cFu///67wsLCVKpUKVWtWlWfffaZDMPQgQMHVK1aNUlS48aNNX78eKszEdHR0eratasCAwNVtmxZtWzZUkeOHLGMGxMTo759+6pcuXIKDAzU2LFjn0SrAAAA8IwhWDxDatWqpQEDBmjgwIFav369Tp8+raxZsyoiIkKZMmVSp06dVLhwYW3ZskV79+5VuXLlNHDgQKsx9u7dqy+//FKrV6/WoUOH9Mknn9y3nfPnz6tp06aqXr26fvjhB02ePFlffPGFFi9erKJFi2rt2rWSpLVr16pz585W9509e7aio6O1bds2ffvtt8qaNatGjRplWX7gwAGVKlVK27dvV2RkpKZNm6b9+/cnf7MAAABeEs7Ojsnyx8np7lt/J6dHr/tYdSbnTsOcoUOHKjAwUOvXr1f//v1148YN5c2bV506dVLdunU1bdo0Zc+eXYZh6PTp00qfPr3Onz9vNUZ4eLgyZcokSercubPat2+voUOHWq2zevVqFShQQI0bN5YkFSxYUC1bttT8+fPVsGHD/6zRzc1Nf/zxh1auXKmyZcvqk08+kaPjPwdfoUKFVK9ePUlSUFCQsmTJohMnTsjX19d0fwAAAF5GHh7uyTpe+vSpk3W8JASLZ4ijo6Pq1aunevXqyTAMHTlyRKtWrVLPnj2VNWtWRUdHq0OHDrp48aIKFCigTJkyyTAMqzHy5Mlj+XfOnDkVGxurq1evWq1z+vRp/f777/L397fclpiYKCcnp0fW2Lp1a7m6umrp0qUaPHiwXnnlFXXv3l1Vq1aVJGXMmNFqfVdXVyUkJNjZCQAAACSJiopJlnGcnByVPn1qXb9+SwkJif+57uOEGYLFM2L79u3q3Lmzvv32W2XMmFEODg4qWLCgunfvru+//1779u3T5MmTNXHiRMu3O23atEmbN2+2Guf8+fNKmzatpLvfFJUmTRrLGYwkOXLkUGBgoGbNmmW5LSoqSjExjz5o//zzT4WEhKhZs2a6ceOGvvjiC3Xt2lU7d+402wIAAAA8QHz8f4cAeyUkJCb7mBLXWDwzSpUqpcyZM+vjjz/Wn3/+qbi4OEVHR2v16tU6fvy43njjDSUkJCh16runrg4fPqxJkyZJkmJjYy3jjBw5UteuXdO5c+cUGRmpBg0a3LetOnXq6KefftLq1asVHx+vCxcuqF27doqIiHhknUuWLFHPnj11+fJlpU2bVmnTplWaNGnk6uqaTJ0AAADA84gzFs8INzc3ffHFF5o4caLat2+vy5cvy8XFRT4+PpozZ46KFCminj176qOPPtKtW7eUI0cOvfvuuxo5cqQOHTpkmYLk6+ur6tWry9HRUbVr11bXrl3v21bu3Lk1c+ZMjRo1SkOHDpWTk5MqVqyoPn36PLLObt26afDgwapVq5bu3Lmj1157TZMnT1aqVKmSuyUAAAB4jjgY/56kD6SgOt1XpXQJAAAAz5TZ4bb9kuNHcXZ2lIeHu6KiYh45FSpr1nR2j89UKAAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmORiGYaR0EcC9oqJiFB+fmNJlPNOcnR3l4eFOr2xEv2xHr2xHr+xDv2xHr2xHr+xjT7+yZk1n9/icsQAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAApjmndAHAvep0X5XSJQB4AcwOD0npEgDgpcMZCwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkEiyfgwoULunnzZkqXAQAAADw1BIsH6N+/v3x9feXr66sSJUqoSJEilp99fX21d+/eh9730qVLqlatmq5cuWLTtsLCwjRhwoQHLpswYYJef/11y3a9vLxUoUIFDRgwQFevXrV5f2rVqqXVq1c/cnv/JTw8XOHh4Va3nTx5UlWrVlXLli0VHR1t95gAAAB4cTindAHPosGDB2vw4MGSpOXLl2vixIn65ptvbLrv7du3k/Vshb+/vz7//HPLz0eOHNHAgQPVtGlTLVmyRK6uro8cY926dclWT5KDBw+qdevWqlChggYPHixnZw4lAACAlxlnLB7Dn3/+qdatWysgIEAVKlTQwIEDdePGDSUkJKh27dqSpNq1a2v9+vWKjY3V8OHDVaNGDfn6+qp06dIaMmSIDMN4rG0XKFBAEydO1IkTJ7RixQpJUnR0tPr27auqVavKx8dH5cuX19SpUy33CQkJ0fLly63GOX/+vIoWLaoff/zRctulS5dUrFgxnThx4j9r2Llzp8LCwtSwYUN98sknllCRkJCgcePGqWzZsipTpowGDBighg0b3rdtAAAAvHj4mNlOUVFRev/99xUaGqoJEyboxo0b6tGjh3r27KkpU6Zo7dq1qly5stauXas8efJoxowZ2r59u+bOnats2bJp//79atKkid544w2VLl36sWrIkCGD/Pz8tHPnTjVo0ECjRo3SqVOntHTpUqVLl06bN29W586dVaNGDeXLl++BY2TPnl1ly5bVqlWr5OfnJ0lavXq1fH19lTdv3odue8OGDerZs6c++OADtW3b1mrZrFmztHr1as2dO1d58+bVhAkTtH//fr377ruPtZ8A8LicnZ/852ZOTo5Wf+O/0S/b0Svb0Sv7POl+ESzs9PXXX8vFxUU9evSQk5OT3Nzc1K9fP9WqVUsXL168b/13331Xb775pjJnzqwLFy7o9u3bcnd31/nz503VkTFjRst1HJ06dZKTk5PSpk2rc+fOKVWqVJLuXkT+sGAhSW+99ZYGDBigPn36yNXVVStWrFCLFi0euv7//vc/ffvtt/Ly8tKaNWvUuHFjpU2b1rJ86dKlatOmjQoWLChJ+vDDDy1nVQDgafLwcH9q20qfPvVT29aLgH7Zjl7Zjl7Z50n1i2Bhp8uXLytXrlxycnKy3JYnTx5J0unTp5UlSxar9W/duqXBgwdrz549ypEjh4oWLSrDMJSYmGiqjitXrihTpkyWmoYNG6YDBw4oT548Kl68uCQ9chshISEaMGCAtm3bply5cun06dOqVq3aQ9c3DEPz589Xzpw59dZbb6lnz56aNGmSHBwcJElnz55V7ty5Les7OTkpV65cpvYTAB5HVFTME9+Gk5Oj0qdPrevXbykhwdxr+suAftmOXtmOXtnHnn49zgc0BAs75c6dW2fOnFFCQoIlXCRdk5A1a9b7rp3o27evMmTIoB07dihVqlRKTExUqVKlTNUQFRWl/fv3q1+/fpKkLl26KCQkRLNmzZKzs7OioqL05ZdfPnIcV1dX1alTR+vWrVOuXLlUo0YNpUmT5qHrlylTRoUKFZIkjR8/Xg0aNNCECRPUuXNnSVKuXLl05swZy/qGYejs2bNmdhUAHkt8/NN7g5GQkPhUt/e8o1+2o1e2o1f2eVL9YkKanYKDgyVJo0aN0u3bt3Xx4kUNGzZMQUFByp07t2UaUtLXr0ZHRytVqlRydHRUdHS0RowYoejoaMXFxT3W9v/880917txZhQsXVt26dSVJN27ckJubm5ycnHTlyhUNHTpUkmzaxttvv63t27dry5YtCg0NtbkOT09PDRw4UJMnT9aWLVskSQ0aNNDs2bN17NgxxcbGatKkSbpw4cJj7CUAAACeNwQLO6VLl05z5szRoUOHFBwcrNq1ayt37tyKjIyUJGXJkkVVqlRRgwYNtHDhQvXt21d//PGHAgICVL16dUVHR6t8+fI6dOiQTdvbu3ev5fdYlCxZUp07d5aXl5fmzJkjFxcXSdKnn36q9evXy8/PT6GhocqePbuKFi1q0zaKFCmivHnzytHRUSVLlrSrF/Xr11eDBg3Us2dPHTp0SE2bNlVISIgaNmyoihUr6urVq8qRI4elTgAAALy4HIzH/d5TvDA++OADeXl5qU2bNqbG+fnnn5U7d27LdSaGYSgoKEhjxoxR2bJlbRqjTvdVpmoAAEmaHR7yxLfh7OwoDw93RUXFMAXDBvTLdvTKdvTKPvb0K2vWdHaPzxmLl9jJkye1ZcsW/fDDD3ZNg3qYNWvWqGfPnrpx44bi4+M1Z84cSZKPj4/psQEAAPBs4+Ltl9jEiRP19ddfq3fv3vd9m9Xj+PDDDzV48GBVqVJFsbGxKlasmGbNmiV396f3tY8AAABIGQSLl9jw4cOTdby0adNqxIgRyTomAAAAng9MhQIAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBq/eRvPlDWj6ykqKkbx8YkpXcozzdnZUR4e7vTKRvTLdvQKAPC4OGMBAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwzTmlCwDuVaf7qpQuAcBzanZ4SEqXAAAvNc5YAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADDNOaULSGmenp5KlSqVnJycZBiGXFxc5O/vr/79+ytnzpySpJCQEH3wwQcKDQ197O2cOnVKlStX1tdff608efLYtG7q1Knl4OAgSUpMTFTq1KkVFBSkgQMHKmPGjI9diySFh4dLkiIiIu5bduHCBXXo0EGHDx/WG2+8oatXr8rf31/t2rX7zzHDwsIUEBCgTp06maoNAAAAz5+XPlhI0owZMxQYGChJio6OVo8ePfTRRx9p/vz5KVrX2rVrrULIX3/9pfbt22vYsGEaOXLkE9vuzp07dfr0ae3evVuurq5PbDsAAAB4cTAV6l/Spk2rd999V7/99tsDl58/f14ffvihQkJC5O3trcqVK2vp0qWW5SdPnlS7du1UsmRJlS5dWgMHDlRsbOx940ycOFHly5fX4cOHba6tUKFCqlKlig4ePGi5be/evWrcuLH8/f0VEhKicePGWW1v7ty5qlKlinx9fRUaGqr//e9/9417+vRpVa5cWZ988onmzp2rPn36KCoqSoGBgfrhhx8UFhamCRMmSLobbho3bqxSpUqpUqVK6tWrl6Kjoy1j/f3332rRooVKlSqlypUra+PGjTbvHwAAAJ5fnLH4l2vXrmndunWqWrXqA5f37dtXGTNm1Lp16+Tq6qp58+ZpyJAhqlGjhlKlSqWWLVsqMDBQ3333nW7fvq2WLVtqwoQJatCggWWMyMhIrV69Wl988YVeeeUVm+oyDEO///67Nm7cqBo1akiSjh49qubNm6tHjx6aM2eOzp49q06dOik6Olp9+/bV8uXLNXnyZE2dOlXe3t5atmyZ2rdvr61bt1rGPXnypJo2bar69eurc+fOkqR06dJp4sSJ+uabbyRJU6ZMsaw/aNAglS5dWvPnz1dUVJSaNm2qJUuWqHnz5pKk77//XjNnztTrr7+uKVOm6OOPP1blypXl4uJi+4MAAI/B2fnpflbm5ORo9Tf+G/2yHb2yHb2yz5PuF8FCUrt27eTk5KTExETFxMQoXbp0mjZt2gPXHTp0qNzd3eXi4qIzZ87I3d1dt2/f1rVr13Tq1CmdPn1avXv3VurUqeXu7q6JEycqMTHRcv/IyEht3LhRmzdvtlzD8TB169aVo6Oj4uLiFBsbq+LFi6tp06Zq0qSJJGnNmjXy9PRU06ZNJUn58uVT9+7d1blzZ/Xu3VsrVqxQgwYN5OvrK0l65513VKBAAbm5uUm6e6YiLCxMFStWtISKR0mVKpW2b9+uAgUKqHTp0lq1apUcHf85OGvWrKlixYpZ/j1+/HhdvnxZOXLksGl8AHhcHh7uKbLd9OlTp8h2n1f0y3b0ynb0yj5Pql8EC0lTp061XGNx+/ZtLViwQE2bNtXixYstb5KTnDx5UiNGjNDx48eVP39+5cuXT9Ldi6svXrwoDw8PpU79z4OVdI3EqVOnJN2dSpQxY0atWbNGbdq0+c+6Vq9erTx58ujKlSsaMmSIDh48qBo1asjZ+e7Ddvny5fvOeOTJk0e3b9/W5cuXdfHiReXKlctquZ+fn+Xfe/fuVdmyZfX111+ra9euypAhwyN7NW7cOE2YMEFjx45Vt27d5Ofnp4EDB6pQoUKSZHVRedJZivj4+EeOCwBmRUXFPNXtOTk5Kn361Lp+/ZYSEhIffYeXHP2yHb2yHb2yjz39epwPawgW/+Lm5qaWLVtq+vTp+uGHH6yCRVxcnNq2batu3bqpUaNGcnBw0G+//abVq1dLknLkyKGoqCjdunXLEi727t2r3377TW+88YYkaezYsTp+/Lg6d+6s4OBgeXp6PrKmTJkyacSIEWrWrJlatGihxYsXK23atMqdO7c2b95ste6JEyfk6uqqDBkyKGfOnDp79qzV8rFjx6pu3bqS7p5RGDFihN577z0NGjRIY8aM+c86EhMTdeDAAXXq1Em9e/fW2bNn9emnnyo8PFzLli175H4AwJMUH58ybyoSEhJTbNvPI/plO3plO3plnyfVLyak/Ut8fLyWLVum69evq2TJklbL4uLidPv2bbm5ucnBwUFnzpyxfDtTXFycvLy8lD9/fg0fPly3bt3SpUuX9Omnn+rKlSuWMVxcXFSpUiXVrFlTPXv2fOCF3Q/i4uKiMWPG6NKlSxo2bJgkqVatWjpy5Ijmzp2r2NhYnThxQmPGjFGdOnXk6uqq0NBQLV68WL/88osSExO1bNkyLViwQB4eHpYxnZyc9Omnn+qrr77S+vXr/7MGR0dHDR06VOPGjdOdO3eUKVMmpUqVyjIeAAAAXl4EC0mtW7eWr6+vfH19FRAQoAULFmjMmDFW04YkKU2aNPrkk080adIk+fr66v3331fZsmWVJUsWHTp0SC4uLpo6darOnz+vihUrql69eipVqtQDr1/o06ePrly5Yvm2JVtkz55dgwcP1vLly7VhwwblyZNHM2fO1KZNm1SmTBk1atRIZcuWVf/+/SVJderUUadOnfTRRx/J399fixcv1owZM5QpUyarcQsUKKBOnTpp0KBBOn/+/H/WMG7cOB05ckTlypVTmTJldOPGDQ0ZMsTmfQAAAMCLycEwDCOliwCS1Om+KqVLAPCcmh0e8lS35+zsKA8Pd0VFxTAFwwb0y3b0ynb0yj729Ctr1nR2j88ZCwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGnOKV0AcK81o+spKipG8fGJKV3KM83Z2VEeHu70ykb0y3b0CgDwuDhjAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMM05pQsA7lWn+6qULgFINrPDQ1K6BAAAnhrOWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEx7boJFQkKCTp48mdJl4F94XAAAACA9wWAREhKiEiVKyNfXV76+vvLx8VG9evW0ZMkSq/V8fX21d+/eR47XtWtXrVy58glV+/j++OMPNW/eXAEBASpbtqx69uypK1euWJb//PPPeuedd+Tr66uQkBCr/TcMQ5MmTVJISIj8/PxUp04dbdy4MVnq2rVrlzw9PS399/b2VkBAgNq1a6e//vorWbYhWT8up06dkqenp06dOpVs4wMAAOD58ETPWAwaNEj79+/X/v37tXv3bnXs2FERERGaPn26ZZ39+/fL39//kWNFRUU9yVIfy+3bt9WqVSv5+vpqx44dWrt2ra5evarevXtLkq5du6Y2bdqofv362rNnj4YNG6ZPP/1Uv/zyiyRp7ty5Wr58uWbMmKF9+/apa9eu6tmzp2V5ckjq/88//6wNGzYoV65ceu+993T06NFkGf9ZfFwAAADw9D21qVCurq6qWrWqevXqpYkTJyo6OlqS5OnpqV27dkmSNm3apFq1aqlkyZKqUaOGJk+eLEnq06eP9u7dq2nTpqldu3aSpG+++UYNGzZU6dKl5e3trSZNmuj48eOSpOXLl+u9997T0KFDFRQUpNKlS6tPnz6Ki4uTJMXHxysyMlLBwcHy8/NT48aN9ccff0iSYmNjFRkZqcqVKysgIECtW7fW33///cB9OnPmjIoUKaKOHTvK1dVVHh4eatCggfbs2SNJ2rx5szJmzKjGjRvL2dlZpUuXVp06dbRgwQJJ0vXr19WxY0cVKFBADg4OCgkJUYECBfTjjz9KunvWZ86cOapbt668vb313nvv6ffff1fr1q3l6+urmjVr2hVCMmfOrP79+6tgwYKaNGmS5fYffvhBb7/9tvz9/VWrVi2tXr3asiw8PFy9e/fW+++/Lx8fH9WoUUNfffXVQx8XSVqzZo1q1KghHx8fNWvWTOfPn7e5RgAAADyfnJ/2BitWrKh+/frpxx9/VIUKFSy33759Wx999JFmzJihwMBAHThwQI0bN1a5cuU0bNgwnThxQgEBAerUqZPOnTunLl26KDIyUiEhIYqKitIHH3ygSZMmaeTIkZJkGX/79u06ePCgmjZtqjJlyqhWrVqaMmWK1q5dq1mzZunVV1/VxIkT1bZtW33zzTcaO3asdu7cqc8++0zZsmXTjBkz1KJFC61fv16pUqWy2pfXXntNM2fOtLpt06ZNKlasmCTpr7/+UuHCha2WFyxYUEuXLpUkde7c2WrZkSNH9Ndff1nuL0lLlizR7NmzlTZtWr311lsKCwvTrFmzVKxYMfXs2VOjRo3SvHnz7HoMKlWqZLnPH3/8ofbt22vkyJGqXLmyfv75Z3Xo0EEeHh4qX768JGnFihUaPXq0Zs+erbVr1+rDDz/UqlWr7ntckqZA/f777/ryyy+VmJioZs2aadKkSRo8eLBdNQIvAmfn5+YyNgsnJ0erv/Fw9Mo+9Mt29Mp29Mo+T7pfTz1YeHh4SJKuXr163zI3NzctXbpUiYmJ8vPz0759++ToeP+OZ8qUSevWrVPevHkVHR2tc+fOycPDw+qTcTc3N7Vr104ODg7y8vKSp6enjh07JunuG+W2bduqYMGCkqT27dsrODhYiYmJWrRokcaPH69XXnlFktSxY0d9+eWX2rp1q6pVq/bQ/TIMQ+PGjdO3336r+fPnS5JiYmKUOnXq+/bx5s2b993/2LFjat26terWratSpUpZbn/rrbeUI0cOSZKXl5eio6Pl6+srSSpXrpymTJny0JoexsPDw9L/RYsWqXLlyqpataokyc/PT++++64WLFhgCRYVK1ZUzZo1JUn169fXokWLtH79enXq1OmB47dr107p0qWTJJUvXz5Zp3YBzxMPD/eULuGxpU+f+tErQRK9shf9sh29sh29ss+T6tdTDxZJFzZnzpzZ6nY3NzctXLhQkydPVvfu3RUdHa1q1aqpb9++ypAhg9W6Li4uWrt2rRYtWiQHBwcVLlxY0dHRcnb+Z3cyZ84sBwcHq/sYhiFJunjxonLlymVZ5urqKh8fH12+fFk3b95Uly5drAJNXFycTp8+/dB9io6O1scff6zff/9d8+fPl6enpyQpderUunHjhtW6t2/flru79ZuNb775RuHh4QoNDVWvXr2slmXMmNHybycnJ6teODo6WvbJHpcvX1amTJkkSadPn9bOnTutrnNJSEhQ3rx5LT/nz5/f6v45c+bUxYsXHzr+vTW7uLgoISHB7hqBF0FUVExKl2A3JydHpU+fWtev31JCQmJKl/NMo1f2oV+2o1e2o1f2sadfj/Ph2FMPFt98843SpEkjb29vq9ujo6N14cIFjR49WpJ08OBBdevWTVOnTr3vzfaGDRs0f/58LVy4UPny5ZMkDRkyRIcOHbKphpw5c+rs2bOWn+Pi4jRy5Ei1bNlSqVKl0uzZs+Xj42NZfvToUWXPnv2BY504cUKtW7dWrly5tHTpUssbdkkqXLiwvv/+e6v1Dx8+rEKFCll+njRpkmbOnKnBgwerTp06941/bzhKLt9++63KlCkjScqRI4fefPNNq6lKFy5csAos/75G4tSpUwoJCUn2uoAXTXz88/ufXEJC4nNd/9NEr+xDv2xHr2xHr+zzpPr11CakxcbGav369RozZoy6du2qtGnTWi2PiYlR69attWbNGhmGoWzZssnR0dEydcrV1dXy6f+NGzfk6OgoNzc3GYah7777TitXrrRcnP0ooaGhmjVrlo4dO6b4+HhNmzZNX331lTJlyqS3335bo0eP1rlz55SYmKgVK1aodu3aD7yA+9q1a2ratKn8/Pw0a9Ysq1AhSVWqVNGlS5f02WefKS4uTjt37tSaNWv01ltvSZLmzJmjOXPmaMGCBQ8MFcnt4sWLGjhwoE6cOKEPPvhAkvT2229r7dq12rFjhxITE3X8+HE1adJEs2fPttxvy5Yt+uGHHxQfH6+lS5fq0KFDql27tiTrxwUAAAAvryd6xmLAgAEaMmSIJClVqlR67bXXNGjQIMt8/Xtlz55d48eP17hx49S/f3+5ubmpZs2aatasmaS7c/sHDhyo3377TZ999pn27dunWrVqycnJSa+99pqaNm2qBQsWKDY29pF1tWrVSvHx8WrZsqWuXbumEiVKaMaMGXJxcVGvXr00YcIENWrUSFevXtUrr7yi8ePHq2jRoveNs3z5cp05c0YbNmy47/dP7N+/Xx4eHpo9e7aGDRum8ePHK1OmTOrbt6+CgoIsv8Pi1q1baty4sdV927Zta/UtS2YkXY/h4OCgdOnSKSAgQIsXL7ZcQ+Lt7a0xY8ZozJgx6tKli1KnTq3atWurW7duljH8/f01Y8YMffDBB8qfP7+mT59uuf+9j8uIESOSpWYAAAA8fxyMx5mkj5dGeHi4JCkiIuKpbK9O91VPZTvA0zA7/PmbMujs7CgPD3dFRcUwreAR6JV96Jft6JXt6JV97OlX1qzp7B6f7+YCAAAAYBrBAgAAAIBpT/1bofB8eVpToAAAAPB844wFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADT+M3beKasGV1PUVExio9PTOlSnmnOzo7y8HCnVzaiXwAAPHmcsQAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJjmnNIFAPeq031VSpeAJ2R2eEhKlwAAAJ4gzlgAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMey6CxfHjx1O6BAAAAAD/wa5gce3aNQ0cOFDBwcHy8fFRuXLl1KtXL507d86yTkhIiJYvX55sBS5YsED9+vUzPc7q1atVq1atZKjIWv/+/eXr6ytfX1+VKFFCRYoUsfzs6+urvXv3Kjw8XOHh4cm+7ccRFhamCRMmPLXtJffxAAAAgGeTXcGia9euioqK0tKlS/XTTz9p5cqVio2NVfPmzRUfH/9ECrxy5UqyjFO3bl2tW7cuWca61+DBg7V//37t379fgwYNUq5cuSw/79+/X/7+/sm+TQAAAOBZY1ew2Ldvn6pUqaKsWbNKkrJkyaLevXvL29tb169ft6z3+++/q2HDhvLz81OtWrW0e/duy7I///xTrVu3VkBAgCpUqKCBAwfqxo0bkqTly5crNDRULVq0kL+/v1asWKFp06Zp7969ljfomzZtUq1atVSyZEnVqFFDkydPtozt6empmTNnqmLFivL19VXbtm11/vx5y9ghISGSpF27dikkJERTpkxR+fLlFRAQoE6dOik6Otoy1rp161SnTh2VLFlSoaGh2rFjh12N/bfLly+rc+fOCgwMVLly5TR//nzLsqioKPXr10/lypVTYGCg2rZta5n+derUKXl6eioiIkKlSpXSoEGDNGHCBHXu3Fk9evSQv7+/KlSooNGjR0uSfvrpJ73++utWZ5F+/fVX+fj4WPbv77//VosWLVSqVClVrlxZGzdutKx76dIl9ejRQ2XLllW5cuXUv39/q74sXbpUoaGhCgwMtPQ4KfwZhqGpU6eqXLly8vf31/Dhw5WQkGCqbwAAAHg+ONuzcq1atTRgwADt3btXAQEB8vb2Vu7cuRUREWG13o4dOzRjxgzlypVLAwcOVL9+/bRp0yZFRUXp/fffV2hoqCZMmKAbN26oR48e6tmzp6ZMmSLpbiiJiIjQ1KlTlZiYqFOnTmn37t36/PPPdfv2bX300UeaMWOGAgMDdeDAATVu3FjlypWTl5eXJGnlypX6/PPPlTFjRvXs2VNdu3bVF198cd++nD59WufPn9eWLVt0/vx5NW7cWF988YXatGmjbdu2acCAAZoyZYr8/Pz03XffqVOnTvryyy9VqFChx2r0zp07NW3aNEVGRmrlypX6+OOPVaVKFWXPnl2dO3eWo6OjVqxYoXTp0ikyMlLNmjXT2rVrLfePiYnR999/r9u3b2vu3LnavHmzIiIiNHz4cO3YsUNt27ZV5cqV5ePjo9dee02rV69WmzZtLD2pVq2a0qZNK0n6/vvvNXPmTL3++uuaMmWKPv74Y1WuXFlOTk7q0KGD8ufPr02bNikuLk4ff/yx+vfvrzFjxuiXX37R0KFDNW/ePHl5eencuXNq2rSp5s2bpw8//FDLli3T3LlzNXPmTBUqVEgTJ060Cjh4uTk7p9wlXU5OjlZ/4+Hole3olX3ol+3ole3olX2edL/sChZDhw5VYGCg1q9fr/79++vGjRvKmzevOnXqpLp161rWa9CggfLmzStJql69umWO/ddffy0XFxf16NFDTk5OcnNzU79+/VSrVi1dvHhRkuTi4qJ69erJ0fHBO+zm5qalS5cqMTFRfn5+2rdvn9W6Xbp00SuvvCJJ6tmzp6pXr65Tp049cKyOHTvKzc1N+fLlU2BgoI4dOyZJmj9/vt577z2VKlVKklSpUiWFhIRo0aJFj329R9myZVWmTBlJdwNaeHi4Tp48qdjYWO3evVvr1q2znAnq0aOH1qxZo23btsnb21uSVL9+fbm6usrV1VWSlD9/ftWvX1+SFBwcrKxZs+r48ePy8fFRaGioVqxYoTZt2iguLk5r1661uq6iZs2aKlasmOXf48eP1+XLl3XhwgX9/vvvmjNnjtzd3SVJvXr1UvXq1dWvXz8VLlxYa9euVZ48eXTt2jVduHBBmTJlspwVWrVqld59913L2F26dNGXX375WP3Ci8fDwz2lS1D69KlTuoTnBr2yHb2yD/2yHb2yHb2yz5Pql13BwtHRUfXq1VO9evVkGIaOHDmiVatWqWfPnsqaNatKly4tScqYMaPlPi4uLpbpMJcvX1auXLnk5ORkWZ4nTx5Jd88gSFLWrFn/M1QsXLhQkydPVvfu3RUdHa1q1aqpb9++ypAhgyQpX758lvVz5colSZbQ8m9Jb+ST6jQMw1LL7t27tXDhQsvyhIQEBQUF2dClB7u3J0nhICEhQZcuXZIkSxiSJCcnJ+XMmVOnT5+2BIts2bI9tPak+hMTEyVJ9erV05gxY3TgwAGdOnVK6dKls4Skf9fi4uIiSYqPj9epU6eUkJCg4OBgq7FdXV118uRJFSlSRPPmzdOaNWuUJk0aeXp6Kjo62tK3CxcuKGfOnFb7kfQYAFFRMSm2bScnR6VPn1rXr99SQkJiitXxPKBXtqNX9qFftqNXtqNX9rGnX4/zgaDNwWL79u3q3Lmzvv32W2XMmFEODg4qWLCgunfvru+//14HDhywBIuHyZ07t86cOaOEhARLuDhx4oSku2+Ujx49KgcHh4fePzo6WhcuXLBcT3Dw4EF169ZNU6dOVa9evSRJ58+fV+HChSXJcqYiV65clrMRtsiRI4fq169vmUokSWfOnJGbm5vNY9gqd+7cku72IWmaVUJCgs6cOWMVHv6rL/+WJUsWVahQQevWrdOpU6cUGhpq0/1z5MghNzc37dq1y/L4xMbG6uTJk8qXL59mz56t77//XmvWrFGWLFkkSe3atbO6/8mTJy0/G4ahCxcu2Fw3Xmzx8Sn/gp+QkPhM1PE8oFe2o1f2oV+2o1e2o1f2eVL9snmCValSpZQ5c2Z9/PHH+vPPPxUXF6fo6GitXr1ax48fV8WKFR85RtIn4aNGjdLt27d18eJFDRs2TEFBQZY32P+WKlUqy6fiMTExat26tdasWSPDMJQtWzY5OjrKw8PDsv6kSZN08eJFXb9+XcOHD1f58uWVPXt2W3dTkvTuu+9q3rx5+uWXXyTdvfg5NDTU6pqH5JItWzYFBwdr6NChunjxom7fvq1Ro0YpISFBlSpVeuxx33rrLW3ZskU//PCD3nzzTZvu4+XlpXz58ikiIkIxMTG6ffu2PvnkEzVr1kwJCQmKjo6Ws7OzXFxcFB8fr1WrVmn79u2Ki4uTJL3zzjv68ssvtX//fsXFxWnKlCkPPVsEAACAF4vNwcLNzU1ffPGFsmbNqvbt28vf318VK1bU6tWrNWfOHBUoUOCRY6RLl05z5szRoUOHFBwcrNq1ayt37tyKjIx86H0qVaqkq1evqmTJkkqdOrXGjx+vGTNmyM/PT7Vr11ZQUJCaNWtmWb9YsWJq1KiRQkJClD59eo0aNcrWXbSoXr26unXrpt69e8vPz09dunRRs2bNFBYWZvdYthgxYoReeeUVvfnmmypTpoz+/PNPzZ0712rKkr0qVqyomJgYeXl5WU1P+i/Ozs6aNm2aLl26pKpVq6pcuXI6ceKE5syZo1SpUqlFixbKmTOnKlWqpPLly2v16tVq1KiRDh06JEmqXbu2OnfurK5duyogIEAnT56Up6fnY+8DAAAAnh8ORtIE+ReAp6en5s2bp8DAwJQu5Znw5ptvqnXr1qpZs2ZKl2KzOt1XpXQJeEJmh4ek2LadnR3l4eGuqKgYTpU/Ar2yHb2yD/2yHb2yHb2yjz39ypo1nf3jP25heHYdO3ZMu3bt0sWLF/XGG2+kdDkAAAB4CRAsXkD9+vXTkSNHFBERYfkGKgAAAOBJeqGCxZ9//pnSJTwT7v2t3gAAAMDTwK8pBAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABg2gv1m7fx/Fszup6iomIUH5+Y0qU805ydHeXh4U6vAADAM4MzFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANOcU7oA4F51uq9K6RLsMjs8JKVLAAAAeCZwxgIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAaweIZdOzYMfXq1UsVKlSQr6+v3njjDY0aNUoxMTGPvO+uXbvk6en5FKoEAAAA/kGweMb8+OOPevPNN5U7d26tXLlS+/fv14wZM/Tzzz+rRYsWSkhISOkSAQAAgPsQLJ4x/fv3V/369dW5c2dlypRJkvTqq69q7Nixypw5s06ePKkff/xR77//vsqVK6cSJUooNDRUP/30k9U406dPV3BwsCpUqKCRI0cqNjbWsmzdunWqU6eOSpYsqdDQUO3YsUOSdP78efn6+lr9ef3119WoUSNJUlRUlPr166dy5copMDBQbdu21fHjxyVJp06dkqenp5YsWaKQkBCVLFlSzZs317lz55580wAAAJDiCBbPkBMnTuivv/5S7dq171uWJUsWTZ48WTly5FD79u1VrVo1fffdd9q1a5fy5s2rESNGWK1/6NAhrV+/Xp9//rk2b96sGTNmSJK2bdumAQMGqH///tq9e7c6deqkTp066a+//lL27Nm1f/9+y5+xY8cqderUCg8PlyR17txZJ06c0IoVK7Rt2za99tpratasmaKjoy3b3bp1q1auXKlNmzbp0qVLmjx58hPsGAAAAJ4VzildAP5x5coVSXdDxMO4uLho8eLFypcvn+7cuaPTp08rY8aM+vXXXy3rODg4qH///nJ3d5e7u7tatWql2bNnq2PHjpo/f77ee+89lSpVSpJUqVIlhYSEaNGiRerXr59ljF9++UXdunXTiBEj5OXlpZMnT2r37t1at26dsmbNKknq0aOH1qxZo23btsnb21uS1Lp1a6VPn16SFBISov379ydvk54xzs4pk82dnByt/sZ/o1+2o1e2o1f2oV+2o1e2o1f2edL9Ilg8Q5LesF+8eFH58+e/b/mlS5eUJUsW7dq1S61bt9bNmzdVsGBBOTs7yzAMy3rp06e3vLmXpJw5c+r8+fOSpNOnT2v37t1auHChZXlCQoKCgoIsP//9999q27atunTpojfeeMOybUl65ZVXLOs5OTkpZ86cOn36tCVY3BuK/l3Xi8jDwz1Ft58+feoU3f7zhn7Zjl7Zjl7Zh37Zjl7Zjl7Z50n1i2DxDMmdO7cKFy6s9evXW84oJLl8+bIqVaqktm3baurUqVq0aJGKFy8uSZo9e7aOHTtmWTc6Olo3b95UmjRpJEknT55U7ty5JUk5cuRQ/fr11aZNG8v6Z86ckZubm6S7Z01atWqlWrVqqWnTpla1SXenaxUqVEjS3UBy5swZSyB6GUVFPfqbup4EJydHpU+fWtev31JCQmKK1PA8oV+2o1e2o1f2oV+2o1e2o1f2sadfj/PhKcHiGdOvXz+1atVKmTNnVuPGjZUxY0b98ccf6t+/v4oVKyZvb285OjpagsBPP/2kefPmKT4+3jJGQkKCIiIiFB4errNnz2rWrFlq3ry5JOndd9/V0KFDFRQUJC8vL/36669q3bq1OnTooLfffltt2rTR66+/rt69e1vVlS1bNgUHB2vo0KEaNWqU0qVLp8jISCUkJKhSpUpW11m8TOLjU/ZFLCEhMcVreJ7QL9vRK9vRK/vQL9vRK9vRK/s8qX4RLJ4xAQEBmj9/vqZOnapatWrp1q1bypIli6pXr662bdvK3d1djRo1UuPGjZWYmKg8efIoLCxMo0ePtkxXypgxozJmzKjg4GC5u7urYcOGaty4sSSpevXqunnzpnr37q0zZ84oY8aMatasmcLCwrRq1Sr9+uuvOnnypEqVKqXExH8OuP3792vEiBEaNWqU3nzzTd28eVM+Pj6aO3euMmbM+NIGCwAAANzlYLzok+DxXKnTfVVKl2CX2eEhKbJdZ2dHeXi4Kyoqhk9obEC/bEevbEev7EO/bEevbEev7GNPv7JmTWf3+FxCDwAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA055QuALjXmtH1FBUVo/j4xJQuBQAAAHbgjAUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA055QuALhXne6rUnT7s8NDUnT7AAAAzyvOWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWDxHPD09tWvXrgcumzp1qlq1avWUK3q08PBwhYeHp3QZAAAAeMKcU7oAJI927dqldAkAAAB4iREsXhATJkzQ7t279fnnn2v58uVasmSJihUrprVr18rBwUEhISEaOHCgXFxclJCQoAkTJmjJkiUyDENVqlTRn3/+qXfffVehoaG6cuWKPvnkE23btk2Ojo4qU6aMBg4cqAwZMuj06dMaOXKkdu3aJUdHRwUFBalXr17Kli2bJOnrr7/WmDFjdPr0aQUGBkqSPDw8UrI1AAAAeAqYCvWC+vHHH5U5c2Zt375d06ZN0/r167V582ZJ0qxZs7R69WrNnTtXW7duVfr06bV//37Lfbt06aLo6Ght3rxZX3/9ta5fv65BgwYpLi5OLVq0kJOTkzZv3qwNGzZIunu2JD4+XkePHlWXLl3Utm1b7d27V++88462b9+eIvsPAACAp4szFi8oNzc3tWvXTg4ODvLy8pKnp6eOHTsmSVq6dKnatGmjggULSpI+/PBDrVixQpJ0+vRp7d69Wxs3brScaYiIiNDVq1e1d+9enTx5UsuWLVPatGklSYMGDVJAQIB+++037dixQ8WLF1fdunUlSW+88YYqVar0tHfdFGfn5yNrOzk5Wv2N/0a/bEevbEev7EO/bEevbEev7POk+0WweEFlzpxZDg4Olp9dXFxkGIYk6ezZs8qdO7dlmZOTk3LlyiVJunjxoiRZLc+aNauyZs2qP//8Ux4eHpZQIUlp06ZVxowZdfr0aZ0/f94yTpK8efMqKioq+XfwCfHwcE/pEuySPn3qlC7huUK/bEevbEev7EO/bEevbEev7POk+kWweAnlypVLZ86csfxsGIbOnj0rScqZM6ck6cyZM8qfP78k6fDhw1q7dq2Cg4MVFRWl6OhoS7i4ceOGoqKilDVrVuXIkUNbt2612ta5c+eUKlWqJ79TySQqKialS7CJk5Oj0qdPrevXbykhITGly3nm0S/b0Svb0Sv70C/b0Svb0Sv72NOvx/mwlWDxnLly5YrOnTtndVuWLFnsGqNBgwaaPXu2AgIClDt3bk2fPl0XLlyQJGXPnl1ly5bViBEjFBERIUdHR40cOVJp06ZViRIlVLBgQQ0YMEADBw6UJA0cOFB58+aVn5+fcubMqenTp+vLL79UaGiodu7cqS1btqh27drJsu9PQ3z88/WilJCQ+NzVnJLol+3ole3olX3ol+3ole3olX2eVL8IFs+ZDz/88L7b1q9fb9cYTZs21cWLF9WwYUM5OTmpZs2aypEjh1xcXCRJo0aNUkREhGrUqKH4+HiFhISoT58+cnZ21rRp0xQREaFq1aopNjZWZcqU0Zw5c+Ts7KxXXnlFU6dOVUREhIYNG6ZixYqpSpUqybHbAAAAeMY5GEkT7/HS+Pnnn5U7d27LmQ7DMBQUFKQxY8aobNmyKVpbne6rUnT7s8NDUnT7tnJ2dpSHh7uiomL4hMYG9Mt29Mp29Mo+9Mt29Mp29Mo+9vQra9Z0do/PJfQvoTVr1qhnz566ceOG4uPjNWfOHEmSj49PyhYGAACA5xbB4iX04YcfKkuWLKpSpYoCAgL07bffatasWXJ3f76+EQkAAADPDq6xeAmlTZtWI0aMSOkyAAAA8ALhjAUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMc07pAoB7rRldT1FRMYqPT0zpUgAAAGAHzlgAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwzcEwDCOliwAAAADwfOOMBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWeKIuX76sDh06yN/fX4GBgRo2bJji4+MfuO62bdtUp04d+fj4qEaNGvr222+tls+YMUMVKlSQj4+PwsLCdPTo0aexC09NcvUqMTFRvr6+8vHxka+vr+XPzZs3n9auPBX29CvJpk2bVLly5ftu59i634N69TIcW/b0auHChapWrZp8fX1VrVo1LViwwGr5i35cScnXL46tfyQmJmrChAkKDg6Wr6+v6tSpo/Xr11ut86IfW8nVq5fhuJIe7zX+0KFD8vb21q5du6xuN31sGcAT1KRJE6N79+7GzZs3jRMnThi1atUyZsyYcd96x44dM0qUKGFs2bLFiIuLM9atW2d4eXkZ586dMwzDMJYvX26UL1/eOHTokHH79m3j008/NWrVqmUkJiY+7V16YpKrV3/++adRrFgx486dO097F54qW/tlGIYRGxtrTJ8+3ShatKhRqVIlq2UcW9b+q1cvw7Fla6+2bNli+Pv7G/v37zcSExONH3/80fD39zc2btxoGMbLcVwZRvL1i2PrH/PmzTNCQkKMv//+2zAMw/jmm2+MIkWKWH5+GY6t5OrVy3BcGYZ9r/GGYRg3b940ateubRQuXNjYuXOn5fbkOLYIFnhijh8/bhQuXNjyhtcwDGPdunVGxYoV71t3zJgxRvPmza1ua9mypREZGWkYhmE0bNjQmDJlimVZbGys4evra/zvf/97QtU/XcnZq6VLlxqhoaFPtuAUZk+/DOPui27Lli2NsWPH3vdmmWPL2n/16kU/tuzp1fz5841p06ZZ3daxY0djyJAhhmG8+MeVYSRvvzi2/pGQkGDExMQYhmEYd+7cMZYuXWr4+voa58+fNwzjxT+2krNXL/pxZRj2v8YbhmH06tXLGDdu3H3BIjmOLaZC4Yn566+/lDFjRmXPnt1yW4ECBXTmzBldv37dat3Dhw+rcOHCVrcVLFhQf/zxxwOXu7i4KH/+/Jblz7vk7NWvv/6qO3fu6K233lJQUJAaN26sH3/88cnvxFNkT78kaeTIkZo5c6by5s173zKOLWv/1asX/diyp1eNGzdWmzZtLD9fvnxZe/bsUfHixSW9+MeVlLz94tj6h6Ojo9KkSaMdO3bI29tbffr0UZcuXZQtWzZJL/6xlZy9etGPK8n+1/iVK1fq77//1gcffHDfsuQ4tggWeGJiYmKUOnVqq9uSfv73/MYHrevm5mZZ71HLn3fJ2Ss3Nzd5eXlp8uTJ2rp1q0JCQtSyZUudPHnyCe7B02VPvyQpR44cdo31sh5b0n/36kU/tuztVZKLFy+qdevWKl68uGrXrv3QsV6k40pK3n5xbN0vICBAv/76q+bMmaNx48ZZrh140Y+t5OzVi35cSfb168iRIxo7dqxGjx4tJycnm8ay99giWOCJSZMmjW7dumV1W9LP7u7uVrenTp1at2/ftrrt9u3blvUetfx5l5y9Cg8P1yeffKLs2bPLzc1NLVu2VK5cubRt27YnuAdPlz39ehSOLdu96MfW4/Tqp59+0ttvv61XX31VU6ZMkbOzs6QX/7iSkrdfHFv3c3V1lbOzs0qXLq169eppzZo1kl78Yys5e/WiH1eS7f26c+eOunbtqt69eytXrlwPHCs5ji2CBZ6YQoUK6erVq7p06ZLltiNHjihHjhxKly6d1bqFCxfWX3/9ZXXb4cOHVahQIctY9y6Pi4vT8ePH75sS9LxKzl6NHTtWBw4csFoeGxurVKlSPaHqnz57+mXLWBxbtnnRjy17e7V06VI1a9ZMTZs21ejRo+Xq6mo11ot8XEnJ2y+OrX9EREQoIiLC6rbY2FhlzJjRMtaLfGwlZ69e9ONKsr1fv/76q44fP64+ffrI399f/v7+kqR27dpp4MCBlrFMH1t2XiMC2OW9994zunbtaty4ccPyTQXjx4+/b73Dhw8bJUqUMNatW2f5pqMSJUoYR48eNQzDML788kujfPnyxsGDBy3fVFClShUjNjb2ae/SE5NcvWrXrp3RqFEj48KFC8adO3eMCRMmGEFBQUZUVNRT3qMny9Z+3WvZsmX3XZDMsfVgD+rVy3Bs2dqrjRs3GsWKFTO+++67B47zMhxXhpF8/eLY+seWLVsMb29vY/fu3UZCQoLx9ddfG97e3sa+ffsMw3g5jq3k6tXLcFwZxuO9xhuGcd/F28lxbBEs8ERdvHjR6NSpkxEQEGAEBQUZERERRnx8vGEYhuHj42OsWrXKsu53331n1K1b1/Dx8TFq1aplbN261bIsMTHRmDVrlhESEmL4+PgYYWFhljfSL4rk6lVUVJQRHh5ulC5d2tKrgwcPPvX9edLs6VeSB71Z5tiyvVcvw7Fla69q165tFClSxPDx8bH6069fP8MwXo7jyjCSr18cW9bPwyVLlhhVq1Y1/Pz8jNDQUKtA9jIcW8nVq5fhuDKMx3uNN4z7g0VyHFsOhmEY5k7CAAAAAHjZcY0FAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADAtP8DWcCTx/g43XYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variable importance plots for the random forest\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.figure()\n",
    "fig = plt.figure(figsize=(12,16)) # define plot area\n",
    "ax = fig.gca() # define axis\n",
    "#ax.set_xlim([0,1.0])\n",
    "plt.title('Model Feature Importances: Random Forest Classifier\\n', fontsize=12)\n",
    "feature_importances = pd.Series(clf.feature_importances_, index=x_train_rf.columns)\n",
    "feature_importances.sort_values(inplace=True)\n",
    "feature_importances.plot(kind=\"barh\", figsize=(8,6))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a dataframe of the variable importance output from the random forest.\n",
    "rf_feat_import_df = pd.DataFrame(feature_importances.sort_values(ascending=False))\n",
    "rf_feat_import_df = rf_feat_import_df.reset_index()\n",
    "rf_feat_import_df.columns = ['Variable', 'Importance_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "#rf_feat_import_df.to_csv('rf_feat_import_dfRA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Variable</th>\n",
       "      <th>Importance_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sablefish</td>\n",
       "      <td>0.387732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Daily Kg</td>\n",
       "      <td>0.182826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black Rockfish</td>\n",
       "      <td>0.180296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Distance 202m Depth</td>\n",
       "      <td>0.119692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shortspine Thornyhead</td>\n",
       "      <td>0.079255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cabezon</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Lingcod</td>\n",
       "      <td>0.021333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable  Importance_value\n",
       "0              Sablefish          0.387732\n",
       "1         Total Daily Kg          0.182826\n",
       "2         Black Rockfish          0.180296\n",
       "3    Distance 202m Depth          0.119692\n",
       "4  Shortspine Thornyhead          0.079255\n",
       "5                Cabezon          0.028866\n",
       "6                Lingcod          0.021333"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_feat_import_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the top variables that contribute to >98% of importance score\n",
    "def important_variables(numbers):\n",
    "    total = 0\n",
    "    for number in numbers:\n",
    "        if total > 0.98:\n",
    "            break\n",
    "        total += number\n",
    "    return number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top x_train_top_variables that are important, which are used in a grid search for best hyperparameters\n",
    "x_train_top_variables = rf_feat_import_df.loc[rf_feat_import_df['Importance_value'] > important_variables(rf_feat_import_df.iloc[:,1]), ['Variable','Importance_value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "#x_train_top_variables.to_csv('x_train_top_variablesRA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Variable</th>\n",
       "      <th>Importance_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sablefish</td>\n",
       "      <td>0.387732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Daily Kg</td>\n",
       "      <td>0.182826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black Rockfish</td>\n",
       "      <td>0.180296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Distance 202m Depth</td>\n",
       "      <td>0.119692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shortspine Thornyhead</td>\n",
       "      <td>0.079255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cabezon</td>\n",
       "      <td>0.028866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable  Importance_value\n",
       "0              Sablefish          0.387732\n",
       "1         Total Daily Kg          0.182826\n",
       "2         Black Rockfish          0.180296\n",
       "3    Distance 202m Depth          0.119692\n",
       "4  Shortspine Thornyhead          0.079255\n",
       "5                Cabezon          0.028866"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the final list of most relevent variables\n",
    "x_train_top_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# most relevent variables are used in a grid search for best hyperparameters\n",
    "x_train_rf_grid = x_train.loc[:,list(x_train_top_variables.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [63, 83, 103, 123, 143, 163, 183, 203, 223, 243, 263, 283, 303, 323, 343, 363, 383, 403, 423, 443, 463, 483, 503, 523, 543, 563, 583, 603, 623, 643, 663, 683, 703, 723, 743, 763, 783, 803, 823, 843, 863, 883, 903, 923, 943, 963, 983], 'max_features': ['auto', 'sqrt'], 'criterion': ['gini', 'entropy'], 'max_depth': [1, 3, 6, 9, 12, 14, 17, 20, 23, 26, 28, 31, 34, 37, 40], 'min_samples_split': [3, 5, 10, 15, 20], 'min_samples_leaf': [1, 2, 3, 4, 6], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators = list(range(63, 1000, 20)) #[int(x) for x in np.linspace(start = 100, stop = 1500, num = 35)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "criterion = ['gini', 'entropy']\n",
    "# Maximum number of levels in tree\n",
    "# Change to reduce overfiting \n",
    "max_depth = [int(x) for x in np.linspace(1, 40, num = 15)]\n",
    "#max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [3, 5, 10, 15, 20]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 3, 4, 6]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'criterion': criterion,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=10, estimator=RandomForestClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [1, 3, 6, 9, 12, 14, 17,\n",
       "                                                      20, 23, 26, 28, 31, 34,\n",
       "                                                      37, 40],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 6],\n",
       "                                        &#x27;min_samples_split&#x27;: [3, 5, 10, 15, 20],\n",
       "                                        &#x27;n_estimators&#x27;: [63, 83, 103, 123, 143,\n",
       "                                                         163, 183, 203, 223,\n",
       "                                                         243, 263, 283, 303,\n",
       "                                                         323, 343, 363, 383,\n",
       "                                                         403, 423, 443, 463,\n",
       "                                                         483, 503, 523, 543,\n",
       "                                                         563, 583, 603, 623,\n",
       "                                                         643, ...]},\n",
       "                   random_state=42, scoring=&#x27;accuracy&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=10, estimator=RandomForestClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;bootstrap&#x27;: [True, False],\n",
       "                                        &#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: [1, 3, 6, 9, 12, 14, 17,\n",
       "                                                      20, 23, 26, 28, 31, 34,\n",
       "                                                      37, 40],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 4, 6],\n",
       "                                        &#x27;min_samples_split&#x27;: [3, 5, 10, 15, 20],\n",
       "                                        &#x27;n_estimators&#x27;: [63, 83, 103, 123, 143,\n",
       "                                                         163, 183, 203, 223,\n",
       "                                                         243, 263, 283, 303,\n",
       "                                                         323, 343, 363, 383,\n",
       "                                                         403, 423, 443, 463,\n",
       "                                                         483, 503, 523, 543,\n",
       "                                                         563, 583, 603, 623,\n",
       "                                                         643, ...]},\n",
       "                   random_state=42, scoring=&#x27;accuracy&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=10, estimator=RandomForestClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [1, 3, 6, 9, 12, 14, 17,\n",
       "                                                      20, 23, 26, 28, 31, 34,\n",
       "                                                      37, 40],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 4, 6],\n",
       "                                        'min_samples_split': [3, 5, 10, 15, 20],\n",
       "                                        'n_estimators': [63, 83, 103, 123, 143,\n",
       "                                                         163, 183, 203, 223,\n",
       "                                                         243, 263, 283, 303,\n",
       "                                                         323, 343, 363, 383,\n",
       "                                                         403, 423, 443, 463,\n",
       "                                                         483, 503, 523, 543,\n",
       "                                                         563, 583, 603, 623,\n",
       "                                                         643, ...]},\n",
       "                   random_state=42, scoring='accuracy', verbose=2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestClassifier() #RandomForestRegressor()\n",
    "# Random search of parameters, using 10 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 10, verbose=2, random_state=42, n_jobs = -1, scoring='accuracy')\n",
    "# Fit the random search model\n",
    "rf_random.fit(x_train_rf_grid, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new model using the best parameters from gridsearch\n",
    "rf_best_model = RandomForestClassifier(**rf_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_depth=31,\n",
       "                       min_samples_leaf=6, min_samples_split=5,\n",
       "                       n_estimators=163)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(bootstrap=False, criterion=&#x27;entropy&#x27;, max_depth=31,\n",
       "                       min_samples_leaf=6, min_samples_split=5,\n",
       "                       n_estimators=163)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, criterion='entropy', max_depth=31,\n",
       "                       min_samples_leaf=6, min_samples_split=5,\n",
       "                       n_estimators=163)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model with best para and top variables to the training data\n",
    "rf_best_model.fit(x_train_rf_grid, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modify test data to only include top variables \n",
    "x_test_rf_grid = x_test.loc[:,list(x_train_top_variables.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model prediction on test data\n",
    "y_pred_rf = rf_best_model.predict(x_test_rf_grid)\n",
    "\n",
    "probas_rf = rf_best_model.predict_proba(x_test_rf_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\n",
      "Random Forest Classifier Performance Matrix:\n"
     ]
    },
    {
     "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>Predicted True</th>\n",
       "      <th>Predicted False</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual True</th>\n",
       "      <td>1101</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual False</th>\n",
       "      <td>36</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Predicted True  Predicted False\n",
       "Actual True             1101               30\n",
       "Actual False              36              959"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import *\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print (\"\\033[1m\\nRandom Forest Classifier Performance Matrix:\")\n",
    "pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_pred_rf, labels=[1,0]),\n",
    "    columns=['Predicted True', 'Predicted False'],\n",
    "    index=['Actual True', 'Actual False']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC analysis\n",
    "LW_r = 1.5 # line width for plots\n",
    "LL_r = \"lower right\" # legend location\n",
    "LC_r = 'darkgreen' # Line Color\n",
    "\n",
    "fpr_r, tpr_r, th_r = roc_curve(y_test, probas_rf[:,1]) # False Positive Rate, True Posisive Rate, probability thresholds\n",
    "AUC = auc(fpr_r, tpr_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the ROC curve \n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.figure()\n",
    "fig = plt.figure(figsize=(10,10)) # define plot area\n",
    "ax = fig.gca() # define axis\n",
    "plt.title('ROC curve: OLE Project Random Forest Classifier', fontsize=16)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.plot(fpr_r, tpr_r, color=LC_r,lw=LW_r, label='ROC curve (area = %0.2f)' % AUC)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=LW_r, linestyle='--') # reference line for random classifier\n",
    "plt.legend(loc=LL_r)\n",
    "plt.tight_layout()\n",
    "#plt.savefig('RandomForest_ROC_CurveFNLRA.png', dpi=800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Matrix for Random Forest Model\n",
      "\n",
      "Random Forest Classifier Accuracy rate: 0.969\n",
      "\n",
      "Random Forest Classifier Error rate: 0.031\n",
      "\n",
      "Random Forest Classifier Precision: 0.968\n",
      "\n",
      "Random Forest Classifier Recall: 0.973\n",
      "\n",
      "Random Forest Classifier F1 score: 0.971\n"
     ]
    }
   ],
   "source": [
    "print('Performance Matrix for Random Forest Model')\n",
    "\n",
    "AR = accuracy_score(y_test, y_pred_rf)\n",
    "print (\"\\nRandom Forest Classifier Accuracy rate:\", np.round(AR, 3))\n",
    "ER = 1.0 - AR\n",
    "print (\"\\nRandom Forest Classifier Error rate:\", np.round(ER, 3))\n",
    "P = precision_score(y_test, y_pred_rf)\n",
    "print (\"\\nRandom Forest Classifier Precision:\", np.round(P, 3))\n",
    "R = recall_score(y_test, y_pred_rf)\n",
    "print (\"\\nRandom Forest Classifier Recall:\", np.round(R, 3))\n",
    "F1 = f1_score(y_test, y_pred_rf)\n",
    "print (\"\\nRandom Forest Classifier F1 score:\", np.round(F1, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   11.9s\n",
      "[Parallel(n_jobs=-1)]: Done 116 out of 116 | elapsed:   36.3s finished\n",
      "\n",
      "[2023-02-04 14:21:23] Features: 1/116 -- score: 0.8979303241860785[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   23.9s\n",
      "[Parallel(n_jobs=-1)]: Done 115 out of 115 | elapsed:  1.3min finished\n",
      "\n",
      "[2023-02-04 14:22:43] Features: 2/116 -- score: 0.9573129190571645[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   29.6s\n",
      "[Parallel(n_jobs=-1)]: Done 114 out of 114 | elapsed:  1.6min finished\n",
      "\n",
      "[2023-02-04 14:24:19] Features: 3/116 -- score: 0.9680149305315545[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   44.7s\n",
      "[Parallel(n_jobs=-1)]: Done 113 out of 113 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 14:26:44] Features: 4/116 -- score: 0.9726004009124212[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   48.8s\n",
      "[Parallel(n_jobs=-1)]: Done 112 out of 112 | elapsed:  2.6min finished\n",
      "\n",
      "[2023-02-04 14:29:18] Features: 5/116 -- score: 0.9733062832653626[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   53.8s\n",
      "[Parallel(n_jobs=-1)]: Done 111 out of 111 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 14:32:07] Features: 6/116 -- score: 0.9741292596944771[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 110 out of 110 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 14:35:05] Features: 7/116 -- score: 0.9743642773207991[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   56.0s\n",
      "[Parallel(n_jobs=-1)]: Done 109 out of 109 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 14:37:59] Features: 8/116 -- score: 0.9749520978779291[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   56.5s\n",
      "[Parallel(n_jobs=-1)]: Done 108 out of 108 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 14:40:55] Features: 9/116 -- score: 0.9750700214280779[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 107 out of 107 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 14:43:54] Features: 10/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   58.2s\n",
      "[Parallel(n_jobs=-1)]: Done 106 out of 106 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 14:46:51] Features: 11/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   59.3s\n",
      "[Parallel(n_jobs=-1)]: Done 105 out of 105 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 14:49:50] Features: 12/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 104 out of 104 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 14:52:47] Features: 13/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 103 out of 103 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 14:55:45] Features: 14/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 102 out of 102 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 14:58:48] Features: 15/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 101 out of 101 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:01:49] Features: 16/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:04:51] Features: 17/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  99 out of  99 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:07:51] Features: 18/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  98 out of  98 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:10:52] Features: 19/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  97 out of  97 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 15:13:57] Features: 20/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  96 out of  96 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:16:58] Features: 21/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  95 out of  95 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:19:56] Features: 22/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  94 out of  94 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:22:55] Features: 23/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done  93 out of  93 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:25:58] Features: 24/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  92 out of  92 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:28:57] Features: 25/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  91 out of  91 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:31:56] Features: 26/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:34:57] Features: 27/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  89 out of  89 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:37:59] Features: 28/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  88 out of  88 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:40:59] Features: 29/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  87 out of  87 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:43:59] Features: 30/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done  86 out of  86 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:47:02] Features: 31/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  85 out of  85 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 15:50:09] Features: 32/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  84 out of  84 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 15:53:10] Features: 33/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  83 out of  83 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 15:56:13] Features: 34/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  82 out of  82 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 15:59:18] Features: 35/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:02:25] Features: 36/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done  80 out of  80 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:05:27] Features: 37/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  79 out of  79 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:08:29] Features: 38/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done  78 out of  78 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:11:38] Features: 39/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  77 out of  77 | elapsed:  3.2min finished\n",
      "\n",
      "[2023-02-04 16:14:49] Features: 40/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  76 out of  76 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:17:49] Features: 41/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  75 out of  75 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:20:48] Features: 42/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  74 out of  74 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:23:52] Features: 43/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done  73 out of  73 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:26:53] Features: 44/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:29:53] Features: 45/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done  71 out of  71 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 16:32:53] Features: 46/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  70 out of  70 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:36:02] Features: 47/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  69 out of  69 | elapsed:  3.2min finished\n",
      "\n",
      "[2023-02-04 16:39:14] Features: 48/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  68 out of  68 | elapsed:  3.2min finished\n",
      "\n",
      "[2023-02-04 16:42:28] Features: 49/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  67 out of  67 | elapsed:  3.2min finished\n",
      "\n",
      "[2023-02-04 16:45:39] Features: 50/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  66 out of  66 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:48:43] Features: 51/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  65 out of  65 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 16:51:48] Features: 52/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  64 out of  64 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 16:54:43] Features: 53/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  63 out of  63 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 16:57:37] Features: 54/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done  62 out of  62 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 17:00:32] Features: 55/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  61 out of  61 | elapsed:  2.9min finished\n",
      "\n",
      "[2023-02-04 17:03:28] Features: 56/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 17:06:27] Features: 57/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done  59 out of  59 | elapsed:  3.0min finished\n",
      "\n",
      "[2023-02-04 17:09:30] Features: 58/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  58 out of  58 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 17:12:33] Features: 59/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  57 out of  57 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:15:24] Features: 60/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.7min\n",
      "[Parallel(n_jobs=-1)]: Done  56 out of  56 | elapsed:  2.7min finished\n",
      "\n",
      "[2023-02-04 17:18:08] Features: 61/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  55 out of  55 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:20:58] Features: 62/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done  54 out of  54 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:23:47] Features: 63/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done  53 out of  53 | elapsed:  3.1min finished\n",
      "\n",
      "[2023-02-04 17:26:53] Features: 64/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  52 out of  52 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:29:42] Features: 65/116 -- score: 0.9755403331720467[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  51 out of  51 | elapsed:  2.7min finished\n",
      "\n",
      "[2023-02-04 17:32:25] Features: 66/116 -- score: 0.9754225478675608[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:35:10] Features: 67/116 -- score: 0.9754226861132231[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  49 out of  49 | elapsed:  2.8min finished\n",
      "\n",
      "[2023-02-04 17:37:59] Features: 68/116 -- score: 0.9754226861132231[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done  48 out of  48 | elapsed:  2.6min finished\n",
      "\n",
      "[2023-02-04 17:40:35] Features: 69/116 -- score: 0.9754225478675606[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  47 out of  47 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 17:43:06] Features: 70/116 -- score: 0.9753047625630746[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done  46 out of  46 | elapsed:  2.6min finished\n",
      "\n",
      "[2023-02-04 17:45:44] Features: 71/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  45 out of  45 | elapsed:  2.6min finished\n",
      "\n",
      "[2023-02-04 17:48:18] Features: 72/116 -- score: 0.9756578419852078[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.9min\n",
      "[Parallel(n_jobs=-1)]: Done  44 out of  44 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 17:50:42] Features: 73/116 -- score: 0.9755403331720469[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done  43 out of  43 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 17:53:10] Features: 74/116 -- score: 0.9755404714177093[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done  42 out of  42 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 17:55:43] Features: 75/116 -- score: 0.9757754890440313[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.1min\n",
      "[Parallel(n_jobs=-1)]: Done  41 out of  41 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 17:58:14] Features: 76/116 -- score: 0.9757754890440313[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done  40 out of  40 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 18:00:41] Features: 77/116 -- score: 0.9757754890440313[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  39 out of  39 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 18:03:04] Features: 78/116 -- score: 0.9757754890440313[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  38 out of  38 | elapsed:  2.5min finished\n",
      "\n",
      "[2023-02-04 18:05:32] Features: 79/116 -- score: 0.9757756272896938[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  37 out of  37 | elapsed:  2.4min finished\n",
      "\n",
      "[2023-02-04 18:07:56] Features: 80/116 -- score: 0.9757756272896938[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  36 out of  36 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 18:10:11] Features: 81/116 -- score: 0.9757756272896938[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  35 out of  35 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 18:12:24] Features: 82/116 -- score: 0.9755403331720469[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 out of  34 | elapsed:  2.3min finished\n",
      "\n",
      "[2023-02-04 18:14:40] Features: 83/116 -- score: 0.9756581184765329[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  33 out of  33 | elapsed:  2.2min finished\n",
      "\n",
      "[2023-02-04 18:16:54] Features: 84/116 -- score: 0.9756579802308704[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  32 out of  32 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 18:19:01] Features: 85/116 -- score: 0.9760106449160159[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  31 out of  31 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 18:21:05] Features: 86/116 -- score: 0.9758929978571924[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 18:23:09] Features: 87/116 -- score: 0.9758929978571922[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  29 out of  29 | elapsed:  2.1min finished\n",
      "\n",
      "[2023-02-04 18:25:15] Features: 88/116 -- score: 0.9760105066703533[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  28 out of  28 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 18:27:07] Features: 89/116 -- score: 0.9758929978571924[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  27 out of  27 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 18:28:59] Features: 90/116 -- score: 0.9761281537291767[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  26 out of  26 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 18:30:51] Features: 91/116 -- score: 0.9758929978571924[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:  1.9min finished\n",
      "\n",
      "[2023-02-04 18:32:44] Features: 92/116 -- score: 0.9758929978571924[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 18:34:24] Features: 93/116 -- score: 0.9757753507983689[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  23 out of  23 | elapsed:  1.7min finished\n",
      "\n",
      "[2023-02-04 18:36:09] Features: 94/116 -- score: 0.9757753507983689[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  22 out of  22 | elapsed:  1.6min finished\n",
      "\n",
      "[2023-02-04 18:37:47] Features: 95/116 -- score: 0.9757756272896938[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  21 out of  21 | elapsed:  1.8min finished\n",
      "\n",
      "[2023-02-04 18:39:37] Features: 96/116 -- score: 0.9761281537291768[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  20 out of  20 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 18:41:09] Features: 97/116 -- score: 0.9758928596115297[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  19 out of  19 | elapsed:  1.5min finished\n",
      "\n",
      "[2023-02-04 18:42:39] Features: 98/116 -- score: 0.9756575654938826[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 18:44:05] Features: 99/116 -- score: 0.9750701596737403[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 out of  17 | elapsed:  1.4min finished\n",
      "\n",
      "[2023-02-04 18:45:30] Features: 100/116 -- score: 0.9750698831824153[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  16 out of  16 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 18:46:41] Features: 101/116 -- score: 0.9753050390543997[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 18:47:51] Features: 102/116 -- score: 0.9755401949263843[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  14 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 18:49:02] Features: 103/116 -- score: 0.9757752125527063[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:  1.2min remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  13 out of  13 | elapsed:  1.2min finished\n",
      "\n",
      "[2023-02-04 18:50:12] Features: 104/116 -- score: 0.9751876684869012[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:   56.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:   56.3s finished\n",
      "\n",
      "[2023-02-04 18:51:09] Features: 105/116 -- score: 0.9750702979194029[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  11 out of  11 | elapsed:   53.9s finished\n",
      "\n",
      "[2023-02-04 18:52:03] Features: 106/116 -- score: 0.9747176332342574[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:   53.9s finished\n",
      "\n",
      "[2023-02-04 18:52:57] Features: 107/116 -- score: 0.9747172184972698[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of   9 | elapsed:   39.9s remaining:   11.3s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:   59.2s finished\n",
      "\n",
      "[2023-02-04 18:53:57] Features: 108/116 -- score: 0.9749522361235916[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   8 | elapsed:   40.5s remaining:   13.4s\n",
      "[Parallel(n_jobs=-1)]: Done   8 out of   8 | elapsed:   40.5s finished\n",
      "\n",
      "[2023-02-04 18:54:37] Features: 109/116 -- score: 0.9748347273104307[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   7 | elapsed:   22.9s remaining:   17.2s\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of   7 | elapsed:   43.1s finished\n",
      "\n",
      "[2023-02-04 18:55:21] Features: 110/116 -- score: 0.9744819243796228[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   6 | elapsed:   20.2s remaining:   20.2s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:   40.1s finished\n",
      "\n",
      "[2023-02-04 18:56:01] Features: 111/116 -- score: 0.9747170802516072[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   39.8s finished\n",
      "\n",
      "[2023-02-04 18:56:41] Features: 112/116 -- score: 0.9743646920577869[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   4 out of   4 | elapsed:   20.9s finished\n",
      "\n",
      "[2023-02-04 18:57:02] Features: 113/116 -- score: 0.9743641390751365[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of   3 | elapsed:   20.5s finished\n",
      "\n",
      "[2023-02-04 18:57:23] Features: 114/116 -- score: 0.9743641390751364[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   2 out of   2 | elapsed:   20.3s finished\n",
      "\n",
      "[2023-02-04 18:57:43] Features: 115/116 -- score: 0.974364277320799[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 out of   1 | elapsed:   20.4s finished\n",
      "\n",
      "[2023-02-04 18:58:04] Features: 116/116 -- score: 0.97448178613396"
     ]
    }
   ],
   "source": [
    "# The gradient boosting classifier \n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Build RF classifier to use in feature selection\n",
    "gbc = GradientBoostingClassifier()\n",
    "\n",
    "# Build step forward feature selection\n",
    "sfs2 = sfs(estimator=gbc, \n",
    "           k_features=(1, 116), \n",
    "           forward=True, \n",
    "           n_jobs=-1,\n",
    "           floating=False, \n",
    "           verbose=2,\n",
    "           scoring='accuracy',\n",
    "           cv=10)\n",
    "\n",
    "# Perform SFS\n",
    "sfs2 = sfs2.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_gb_df = pd.DataFrame.from_dict(sfs2.get_metric_dict()).T.sort_index(ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 33, 34, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 95, 96, 97, 98, 100, 103, 104, 105, 106, 107, 109, 111, 112, 113, 114]\n"
     ]
    }
   ],
   "source": [
    "# best model accuracy score included these variables (the number is the index, e.g., 69 is sablefish)\n",
    "# However, not all of these variables were used b/c the model overfit\n",
    "gb_feat_cols = list(sfs2.k_feature_idx_)\n",
    "print(gb_feat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe to process the forward selection results\n",
    "sfs_gb_df_maxmin = sfs_gb_df.loc[: ,['feature_idx', 'avg_score', 'std_err']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_gb_df_maxmin['idx_len'] = sfs_gb_df_maxmin['feature_idx'].str.len()\n",
    "sfs_gb_df_maxmin['avg_score_rank'] = sfs_gb_df_maxmin['avg_score'].rank(ascending = 1)\n",
    "sfs_gb_df_maxmin['std_err_rank'] = sfs_gb_df_maxmin['std_err'].rank(ascending = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# final selected variables for each model were those that produced the highest mean accuracy \n",
    "# with the lowest standard error using the fewest number of variables\n",
    "sfs_gb_df_maxmin['total_score'] = sfs_gb_df_maxmin['std_err_rank'] + sfs_gb_df_maxmin['avg_score_rank'] + sfs_gb_df_maxmin['idx_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs_gb_df_opt = sfs_gb_df_maxmin[sfs_gb_df_maxmin.total_score == sfs_gb_df_maxmin.total_score.min()]\n",
    "\n",
    "x_train_gb = x_train.iloc[:,list(tuple(list(sfs_gb_df_opt[\"feature_idx\"])[0]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Li549e9KsWTM0Gg2HDh1i0aJFZGVlMW/ePPz9/Rk7dqzi+LFjxxrmSthzvDOtXLmSjIwMatWqxYABA/Dy8mLbtm306dPHsM8bb7zB5s2bAQgLC6Nnz57cd999eHl5cf36dbZt22ZoXR4dHc3UqVN59913Ta719ddfM3PmTED3EHDnzp158MEH8fb2JiYmhvnz53P79m02btzIuHHjmDJlCgCRkZH8/PPPAAUmHfRxaDQannzySZu+F+3atSMkJITExETS09N55plneOyxx+jWrRsPPvig3TdI7X3doEsKjR49mg0bNgDg6elJjx49aNGiBV5eXhw/fpwlS5aQkJBAdHQ0/fr1Y9GiRQQHB5uN5dKlS/z4448EBQUxZMgQKlWqxOHDhwkODjbMNomPj6dfv35cu3YNgDp16tC9e3cqV67M7du32bNnD3/++Sepqam8/fbbZGVlKX5ehDAnMzOTcePG8dlnnzFjxgx++eUX6tevz3333UdAQIDFwgFjr732msNx2JV0sEX58uUpX758YV/mnlRSKh1+f+F3MrIy8Pb0dkJQQgghhBBCCCGEZQsXwquvwtWr1uztBzQp5Iis4fw4QkNh2jQo6nuUubm5LFu2DNDduH3iiScU23v16sWcOXMA3VPuzz//vNmn5Ddv3mxIOISFhTFr1ixF54wnn3ySyMhInn32WdLS0vjll1945plnqFy5MgAbNmwwJBzsOd7ZMjIyqFmzJvPnzzdUJzz11FOG7bt27TIkHOrUqcPcuXNNqhh69+7NtGnTmDVrFgDLly/nnXfeUXz//v33X8Og5YCAAGbMmEHz5s0N23v06MGAAQPo168f169f56+//mLgwIG0aNGCmjVr0qxZM/bv38+pU6c4duwY9evXN3ktycnJHDhwAIDWrVtTqVIlm74X3t7eTJgwgVGjRpGbm0t2djZ//fUXf/31Fx4eHjRs2JDmzZvTsmVLWrZsabGaw5gjrxt07b70CYewsDB+/vlnatWqZTi+W7duDB8+nFdeeYXo6GguXrzI+++/z/Tp083Gk5mZiZeXF1FRUdSpU8cQg7G33nrLkHAYMWIEr732mqLapk+fPgwePJhhw4Zx48YNPvnkE1q3bq1ozSSEWt++fRV/J6Snp3PgwAHD76w1nJF0KLy6MVHoSkqlw8PhD/N4w8d5OPxhx2MSQgghhBBCCCHyMWyYtQmHu9vVq7rvRVH7+++/iYuLA6BDhw4EBQUptjdu3NhwM/fcuXPs3LnT7HmMWyJ99dVXZlt1N2rUiBdffBHQPam+evVqw7affvrJoeMLw3PPPWfxBrrxtceMGWNxv0cffdTQyjwpKYlEVUnP//3f/xlaC7333nuKG+96lStX5u233zYsr1ixwvB1ZGSk4evly5ebjWHNmjVkZWUBykoWWzz22GNMnTrVpL1WdnY2Bw8eZNasWYwYMYJWrVoxaNAgFixYQEZGhsXzOfK6s7Oz+eGHHwBd5cbUqVMVCQe9gIAApk2bRmhoKKBLbMXExFiM6fHHHzckHNT++ecfQ1KtY8eOvPHGG2bbezVq1Ih33nkH0CWu9IkVIfKj1WoN/6mXC/rPWQq90kEUnpJS6SCEEEIIIYQQQoh7w5IlSwxfW+pB36tXL77++msA5s2bR5s2bRTbr169ypEjRwBdi++WLVtavF5kZCQ5OTlUr16dRo0aGY4/fPgwAPXr17f5+MKSXxzvvfcegwcP5uLFi7Rt2zbf81SqVMkwTyHN6OZQZmYm27ZtA3SDl7t162bxHI8//jhnzpyhWrVqiiHZXbp0YeLEiaSmprJ69Wreeust3FV9ulauXAmAv7+/YUi4PR577DFatGjB3LlzWblyJadPnzbZJzs7mz179rBnzx6mT5/OJ598Qrt27RT7ZGVlOfS6Dx06xPXr1wF48MEH850vERgYyLPPPsukSZMAXQLG0pDxiIgIi+cxTjL17dvX4n4A3bt3Z8KECaSkpLBx40bGjx+f7/7i3vb5558XdwiAJB1KtPR05bKrVjoIIYQQQgghhBBFZeZMW9or3b307ZWK0s2bNw0tasqUKWNyc1ivR48eTJ48mZycHDZt2kR8fLyiNffhw4cNT9w2a9Ys32uGhoby6quvKtYZH69vn2PL8YXBw8NDMUBZzdfXl9q1a1O7dm2z2xMSEjh06BDr16/n/PnzhvW5ubmGr2NjY0lJSQGgadOmeHhYvu3n7e1ttoVKqVKl6NKlC4sXL+batWv8/fffij/H2NhYQ0Lo8ccfNxnGbKvg4GBefvllXn75Zc6fP8/OnTvZu3cv//zzD1euXFHse+XKFV588UW+/PJLunfvblh/+vRph163cduZBx54oMCY27RpY0g6HDx40OJ+NWvWtLjN+JoXL140/N5YUr58eVJSUoiPjzf5fRHCmK0zVgqLJB1KsJJS6bDlxBbDTAdpsSSEEEIIIYQQojD16QO9e1s7SDqVU6dOUbt2bbuH1zpDYcRRHIOkV65cSWZmJqC7mZ9fK5gyZcpw9epVsrOzWbhwoeLGv/6pc9D117eV8fG2zhsoLKVLlzY7u0ItNzeX6Oho9u7dS2xsLOfPn+fixYvcvHnT7P7G7VCMX3fFihXtjvWpp55i8eLFgK7FknHSYenSpYavjW/8O0PVqlWpWrUqTz/9NABnz55l27ZtLF26lGPHjgG678+HH37IQw89ZBiIq6/6APtet/H3Lb/EkHGcesbXVgsMDLS4Td+CDGDChAkFXtNYQkKCJB2ERf/73/8A3UyQ/BJfhU2SDiVYSZnpMPDngVxKukSloEpc/OqiEwITQgghhBBCCCEsc3eHcuUK3i81Fa5fz6ZcOdOPwEXJVeJwlP5GNUBMTEy+/e6NLVq0iJdeesnQxicpKcmwzZ4n6R09vjDob5Dn559//uGDDz4w22YIICgoiLp163Lp0iUuXLhgst34dfs6cJOoWbNm1KxZk9OnT7Nx40ZSUlLw9/cnNzfXMAchLCyMJk0Kdwh79erVqV69OoMGDWLZsmV88MEHZGZmkpqayoIFCxg4cCCAIiFjz+vWV0lYe7zxPqn53CvL78/c+Jq2cuRYcffbtGkT58+f5+TJk4ah88VBkg4lWJEnHdLSIDcXzAy2EUIIIYQQQgghxL3r+PHjhqfRbRUXF8eWLVt49NFHAWWiIF3dW9oKjh5vi/yGG9siOjqa5557zjCguUKFCkRERFC7dm1q1qxJnTp1KFu2LDExMXz11Vdmkw7GN8PT1DeNbNS7d2++/vpr0tLS2LBhA7169WL37t3Ex8cD0L59e7vOe+XKFdatW0dCQgK1a9fOd/6CsV69ehEXF8eUKVMAFD9rxn/e9rxu4+oia46/bdQJxN7kjo+PDykpKfj6+ubbokkIW+l/Rx9//PFijUOSDiVYkbdX0l/U3HohhBBCCCGEEELcsxYtWmT4etSoUbzyyisFHjNhwgTmzJkD6AZK65MOZcuWNeyj7utvzp49ewgJCaFy5cr4+voqjr98+bLNxwOKVkg5OTkWj71161aB57fGRx99ZEg4vPTSS4wcOdJkgLP+qfrk5GSz5yhTpozha+P2PZYcPHgQHx8fKleujL+/v2Lbk08+yZQpU8jOzmbdunX06tWLtWvXAuDu7s5DDz1k/YszcuHCBSZOnAjo5m1Ym3QAeOSRRwxJhxs3bhjWh4SEGL6253WXMyqLOnfuXIHHG+9ToUIFa0I3UbZsWVJSUkhLS+Py5csOtcMSwpi3tzcZGRn5zjYpCvLIeglW5JUOYPdcByGEEEIIIYQQQtydMjMzWblypWHZ2l7/kZGRhq937NhheHq/cePGhvUFPQWenp7O888/T7du3ejfv7/J8fv377f5eFC2xsnv6feTJ0/me35rnD17ltjYWEA3g+K1114zSTgYx2tc5WA806FevXp4enoCcOjQIcWQaXNee+01evbsyaOPPqo4D+huiutnOfz999/cvn2bTZs2Abphy8HBwTa+Sp3w8HDDazt48CAXL1rfhtu4rZDxrI7w8HCHXvf9999v2LZr164C4/j7778NX9erV8/a8BWMW1Nt3rw5331zc3MZM2YMH374IT/++KPDVSzi7vbwww+j1WpZtGhRgb8LhUmSDiVYsVQ62DnXQQghhBBCCCGEEHenjRs3GuYJ3H///YpBu/lp0KCB4aZtbm4uCxYsAHSDevUDUI8ePcrhw4ctnmP16tWGCoEHH3zQcHytWrUA+Pfff20+HpRVA/k9/b569WrLL9BKxk/tlypVKt+B02vXrjUM6wbIzs42fO3j42N4DdeuXWPdunUWz7N7925DFckDDzxg9ppPPfUUoEt0fP/991y9ehWAHj16WPOyzAoICDC0ZsrOzmbChAmK15Af/c8HQMeOHQ1fO/q6mzRpYqh22LlzZ74/L8nJyfzxxx+GZX11jq06depk+HrWrFn5zmlYs2YNK1euZP78+SxatMiheR3i7vfOO+9w3333ceDAAYYOHcru3bud1gbOFg4lHR577DF++OEHq0qXhPMVetLB3Akl6SCEEEIIIYQQQggjS5YsMXxt6w3p3r17G75evHixIQEwZMgQw/qxY8eabZN08uRJvv76awA8PT15+umnDduGDRvm0PHGT6KvXr1aMawYdBUGU6dOZffu3Va9zvxUrlzZcNP/v//+Izo62ux+CxYsULSxAtOZEi+88ILh6wkTJnD8+HGT88TFxTF+/HjDsn4gs1r79u0NN+Nnz54N6IZZP/zww/m/oAK8+eabhjkKW7du5YUXXrA4PBt0SY+vvvqK5cuXA9C0aVMeeeQRxT6OvG4vLy/D8VqtllGjRhkqT4ylpKQwatQoQ/Klffv2NG3a1KrXrNahQwfq1KkDwKVLlxg5cqTZxMPx48cVMQ8fPtyu64l7x8KFC+nYsSNeXl5ER0czdOhQmjdvzuOPP06fPn0YMGBAvv9Z+vvAVg41d7pw4QJTp07lu+++o3Xr1kRGRvLYY4/lO51dOE+hJx3c3cHHB4yHLkl7JSGEEEIIIYQQQuSJj483tJvx8PCgS5cuNh3fvXt3vvzyS7KyskhISGD9+vV07dqV7t27s2rVKqKjozl79izdunUjMjKSRo0akZGRweHDh1m6dKkhSfHGG29QpUoVw3l79erFpk2bWLt2rV3Hd+3alcmTJ5OcnMyVK1d4+umneeaZZ6hSpQpXrlxh5cqVxMTEEBYWRk5OjuFGtD3KlStHu3bt2Lp1K7m5uTz//PNERkbSsGFDPD09uXDhAmvWrOHUqVMmx6pnSkRERDB06FB++eUXEhISeOqpp+jZsyctW7ZEq9USExPDwoULDfMhBgwYQIsWLczG5eHhQY8ePZg1a5ahGqF79+6GVkb2qlWrFt9//z3Dhw8nMzOTXbt20a1bN5o3b84DDzxAhQoV8PHxISkpiWPHjrF582auX78OQLVq1Zg+fbpJZYajr3vQoEHs3LmTrVu3EhcXR69evejRowctWrTAy8uLEydOsHjxYkMcFSpU4IsvvrD7e+Dm5saUKVPo06cPt2/fZufOnXTq1InIyEjCw8NJTU3l4MGDrFixwvAz2r59e0P1iRCWTJo0SfH7odVqyc7O5vz58wUeq9Vq8620soVDSYcyZcqQkJCAVqtl165d7Nq1C39/f5544gmefPJJRVZYOF+hJx1AN9fBOOkglQ5CCCGEEEIIIYTIs3TpUsOg5bZt2yqG+lojODiYDh06GIYUz507l65duwLw6quvsmTJEpYtW8bt27f57bffTI738PBg1KhRPP/88ybbvv76a0qXLs2iRYtsPj4oKIhvvvmGUaNGkZqaypUrV5g8ebJin2rVqvHDDz/w2muvOZR0APj0008ZNGgQZ86cITMzk7lz55rdr3Xr1kRERDB16lRA9yR869atFfu8/fbb+Pj48NNPP5GVlcWiRYtMKiRA96T/e++9l29cTz31FLNmzTIsP/nkk7a+NLMeeOAB5s+fz6effsq+ffvIyckhOjraYpUHQLdu3Xj33XcVra+MOfK63dzcmDZtGh9//DGLFi0iMzPT4vGtW7fm66+/tvlnXe2+++5j7ty5jBo1irNnz5KQkMBPP/1kdt9u3brx2WefOe2GsLi7qWe0WFpXmBxKOmzfvp3t27ezZMkSNm/eTGZmJrdu3WL+/PnMnz+fGjVqEBkZSY8ePRST4IVzFEnSoVQpSEy8syyVDkIIIYQQQgghhMizdOlSw9fWDpBWi4yMNCQdoqOjiY2NJSwsDA8PD8aPH8+AAQOYP38+e/fu5erVq+Tm5hIWFkbr1q0ZOHAgtWvXNnteLy8vJk6cSL9+/ew6/qGHHmLZsmVMnTqVY8eOERcXh4eHB9WqVaNz584MGDDA0CbIUaGhoSxZsoTff/+d9evXc/r0adLS0vD19aVixYo0atSIbt264e3tTUBAAN999x1arZYVK1YoWlEBaDQaXn/9dbp3787cuXPZtWsXcXFxZGZmEhoaSosWLejfv79igLIlNWrUICgoiKSkJMLDw2nQoIGhWsBR9evX548//mDfvn1s376dvXv3Eh8fT1JSEunp6YSEhFChQgUeeOABunTpQt26dfM9n6OvW//z8swzz7BgwQL27t3LlStX0Gq1hIWF0aBBAyIjI2ndurXTbv6Hh4ezevVqVq5cyfr16zl69Khhxkf58uVp2rQpTz31FBEREU65nrj7bdy4sbhDABxMOri5udG+fXvat29PcnIyK1euZPny5YaBK2fOnOHrr79m8uTJtG3blsjISB555BE8PBy6rMhTZJUOxqTSQQghhBBCCCGEEHn0yQJHtG/fnhMnTijWGd/Ybty4MY0bN7b7/I4cX65cOfr370+9evUsJhhWrVpl8Xj168qPn58fw4cPt9i3PzU1lZiYGCpVqmR2ZoHafffdx/vvv2/19c3ZvXu3YUh4YbX2ad68Oc2bN3fa+Rx93Q0bNqRhw4Z2Hfvzzz/bnIjy8PDgySefdFoVibi3VapUqbhDABxMOhgLCAgwDJw4ffo0S5YsYeXKlcTHx5Odnc3WrVvZunUrgYGB9OjRgyeffJJ69eo56/L3nOxs3X/GCq3SwZgdlQ4Xv7ropGCEEEIIIYQQQgghRFFZsGABAD4+PvTs2bOYoxFClBRuhXHSmjVrMmbMGLZs2cIvv/xCv379KFeuHFqtlqSkJKKioujduze9evVizpw5JkNvRMHUVQ4glQ5CCCGEEEIIIYQQwjkWLlzImjVrAOjZsyeBgYHFHJEQorDk5OSQkpJCSkqKU85XqH2ONBoNDzzwAA888ABvv/0233//PbNnzyYnJwetVsuJEyf49NNP+frrr+nVqxcvvvgiFSpUKMyQ7hpFlnRwQqWDEEIIIYQQQgghhHB93bt3x8fHh5s3b3Lu3DlA1/ZpxIgRxRyZEMLY559/DujanlmaS2OLzZs3M3LkSDQaDceOHXP4fIWadEhNTWX9+vWsX7+ev//+m/T0dODOtOzAwECSkpJIS0tj3rx5rFixgvHjx9OjRw+brnPixAl+/vln9uzZQ2JiIkFBQTRs2JD+/fvTrl07u+Pfs2cPc+bMYf/+/SQlJREQEEDTpk155plnaNu2bb7HpqSkMH/+fNavX09sbCxpaWkEBARQv359evXqRdeuXXFzs7/QRCodhBBCCCGEEEIIIYQz+fn5cfDgQcOyRqNh/PjxVKxYsfiCEkKY+PXXX9FoNEREROSbdHj11VcBGD58eIGzbfT37J3B6UkHrVbL33//zfLly9mwYYNJoqFs2bL07NmTp556iho1arB7927mz5/P2rVruX37NuPGjSM4OJiHHnrIqutt2LCB119/naysLMO6a9eusXnzZjZv3syzzz5r8/CY3NxcJk6cyO+//65Yn5CQwIYNG9iwYQP9+vVj/PjxuLu7mxwfGxvL8OHDuXjxosnx27dvZ/v27SxdupRp06bha2emoCRVOny84mNupt0k0DeQ8T3GOykwIYQQQgghhBBCCOFMbdu25ezZs2RmZhIeHs5LL71E+/btizssIYSdNmzYgEajKfJB5U5LOhw/fpzly5ezatUqrl+/DtxJNHh4eNCuXTueeuop2rdvr7hR37p1a1q3bs3ChQv54IMPyM3N5YcffrAq6XD06FFGjx5NVlYWjRo14q233qJ27dpcvHiRH374gY0bNxIVFUWNGjUYMGCA1a/lu+++MyQcatasyZtvvkmzZs1ISUlhxYoV/PDDD8yfP5/s7Gw+++wzxbGpqakMGzaMS5cu4ePjwyuvvEKnTp0oXbo0Z86cYdasWWzatIkdO3bw7rvvMnnyZKvjMqZOOnh46P5zOidUOszcPpNLSZeoFFRJkg5CCCGEEEIIIYQQLmrkyJGMHDmyuMMQQpRwDt2mjo+PZ+XKlSxfvpz//vsPUJZh1KxZk969e/Pkk09SpkyZfM/Vp08ffv75Z86dO8fx48etuv6UKVPIyMigatWq/Prrr5TKeyo/ODiY6dOnM3LkSNavX8/UqVPp2bMn/v7+BZ7z8uXLzJw5E4A6derwxx9/ULp0aQBCQkJ49dVXqVmzJm+88QaLFy+mV69eREREGI6fM2cOly5dAuD777+nTZs2hm1lypShRYsWfPrpp0RFRfHnn3/y3HPP0ahRI6terzF10qFQqhxAZjoIIYQQQgghhBBCCCGEsJr9QwWARx55hEmTJvHff/+h1WrRarX4+fkRGRnJ3Llz+fPPP3nhhRcKTDjoBQYGAuDt7V3gvrGxsWzbtg3Q9aQqpbo5rtFoGDduHBqNhqSkJNauXWtVDKtXrza0aho/frwh4WCsa9eutGzZEsCQoNDTXyciIkKRcDA2cuRIQ7XH1q1brYpLrciSDjLTQQghhBBCCCGEEEIIIYSVHKp0yM3NNXzdrFkzIiMj6dKlC37qG9VWSktL4/777y9wSDNgSDhoNBo6dOhgdp/KlSsTHh7O8ePH2bhxI5GRkQWe9+jRo4Bu9kSLFi0s7te2bVv27t3Lrl27yMzMxMvLC4CkpCTc3Nxo0qSJxWMDAwMJDAwkMTGRq1evFhiTOVLpIIQQQgghhBBCCCGEEMLVOJR0KFeuHD179iQyMpIaNWo4HMzKlSut3jcmJgaAChUq5FtJUb9+fY4fP25IJhQkOTkZgIoVK+a7X0hICABZWVmcPn2aunXrArrhHNnZ2YrB1mo3b94kKSkJgICAAKviUpNKByGEEEIIIYQQQgghhBCuxqGkw5YtWxRDoYvS5cuXAahUqVK+++mTB/Hx8WRlZeHp6Znv/voqjdsFPNF/8+ZNw9fx8fGGpAPoBmd75DPVef78+YYqkWbNmuV7HUuk0kEIIYQQQgghhBBCCCGEq3Eo6WCccNi3bx8rVqxg4MCB1K5dW7FfSkoKHTt2pE2bNgwYMMDuG+3Gbty4ARRcKaCfyaDVarl165ahQsGS2rVrs379es6cOUN8fDzly5c3u190dLTh64ISFMbOnDnDDz/8AECVKlV46KGHrD5WLyMjg5s3MwEvwzpv7xxSUzNsPpdeWl4WI02VzXB3d8d4wkZuSgrpNlY76IeLa7VaUq041lIsRU3iMOUqsbhKHMYxFHcsEocpV4lF4jDlKrFIHKZcJRZXicM4huKOReIw5SqxSBymXCUWV4nDOIbijkXiMOUqsUgcplwlFleJwziG4o5F4jDlKrEUZhwZGfbfdxT3HoeSDgCZmZmMGzeOv/76C4DmzZubJB0uXrxIUlISf/75J3/++ScDBgzg/fffd+i6+h90Hx+ffPczHkptzS9H586d+eGHH8jNzWXixIlMmTIFNzflvO2dO3eyfft2w3JmZqZVMV+7do0RI0aQmpqKRqPhgw8+KLDywpy4uDjOns0BqhjW5ebeJibmlM3nUjt79qxiOTAxkVpGy1lJSYbWVtbKzs42/N+WY9WxFBeJw5SrxOIqcYDrxCJxmHKVWCQOU64Si8RhylVicZU4wHVikThMuUosEocpV4nFVeIA14lF4jDlKrFIHKZcJRZXiQNcJxaJw5SrxOIqcYh7l8NJhzfeeINNmzYZnmY/d+6cyT4ZGRmUL1+e+Ph4AObMmYNGo+G9996z+7r6KguNRpPvfvq4AJPkgTnh4eH07t2bxYsXs3btWp5//nleeukl6tSpw61bt1izZg3Tpk0jNDTU8Hr0Q6Tzc+XKFZ577jnDL/2rr75K+/btCzzOnLCwMAICyirWlSlTinr16tl1PtBlQM+ePUv16tXxNerV5JbXxkrPKzvb5uvoW015eHhYdaylWIqaxOG6sbhKHK4Ui8ThurFIHK4bi8ThurG4ShyuFIvE4bqxSByuG4urxOFKsUgcrhuLxOG6sbhKHK4Ui8ThurEUZhxJSUnExcU59Zzi7uXwTIeNGzei0WgoW7Ysb7/9No8//rjJfk2aNGHr1q3s3r2bjz76iLNnz/L777/TvXt3GjdubNe19bMX0tPT893PuArBmuQAwPjx47lx4wabNm1i586d7Ny5U7G9Ro0ajB8/niFDhihiseTUqVMMHz7cMIdiyJAhvPrqq1bFYo63tzc5OcrX4u/vXmAc1vD19VWeRzWkW5OaavN1Hg5/mOsp1ynrX9amY01iKSYShylXicVV4gDXiUXiMOUqsUgcplwlFonDlKvE4ipxgOvEInGYcpVYJA5TrhKLq8QBrhOLxGHKVWKROEy5SiyuEge4TiwShylXiaUw4iju1lGiZHEo6bBkyRIASpUqxYIFCwxDmy1p3bo1v/zyC127diU9PZ0//vjD7qSDv78/ALdu3cp3v+TkZEBX5RAYGGjVub29vfn+++9ZuXIlCxcu5NixY+Tm5lK1alWeeOIJnn32WU6dutPKqFy5chbPtX37dl5//XVSUlIAePnll3nttdesiiM/RTZIWv0XVGoqaLVQQIWJsTnD5jg5KCGEEEIIIYQQQgghhLi3FdQFyNb9nMWhpMPhw4fRaDT069evwISDXlhYGH379uXXX3/ln3/+sfvaNWrUIDo6usCyHv32sLAwq9or6Wk0Gnr06EGPHj3Mbj9+/Lhhv2rVqpndZ8GCBXz88cdkZ2fj7u7Ohx9+yNNPP211DPkpsqRDqVLK5ZwcyMwEo1kZQgghhBBCCCGEEEIIIYrWK6+8UuA+Wq3Wqv2cyaGkQ0JCAoDNPf71+1+9etXua4eHhwNw6dIlkpOTCQgIMLvf0aNHAahbt67V59ZqtSQlJREcHGxxnx07dgBQu3ZtQ9WFsRkzZjB58mRA135p8uTJPPzww1bHUJBiq3QAXbWDJB2EEEIIIYQQQgghhBCiWBnPNFYzrnCwdj9nsP7RfzP0N9sLmqtgibUzFsxp164dALm5uWzZssXsPhcuXODkyZMAPPTQQ1add9euXTRq1IjWrVsTGxtrdp9r166xdetWADp27Giy/bfffjMkHMqUKUNUVJRTEw5gmnTw8XHq6e+wlHQQQgghhBBCCCGEEEIIUSy0Wm2+iQTjfazZz5kcqnSoWLEiSUlJ7Nq1i6eeesrq4/RtlcLCwuy+dpUqVWjevDn79u1j+vTpPPLII5QuXdqwXavV8sUXX6DVagkODqZnz55WnbdBgwaGzM4ff/zBBx98oNiem5vLxx9/THp6On5+fjzzzDOK7YcOHeJ///sfACEhIcyZM4caNWrY/TotKbb2SgC3b9t0ig5fdyA+OZ7yAeXZNGaTkwITQgghhBBCCCGEEEKIe4++9b+rcqjSoV27dmi1WtasWcPBgwetOubEiRMsX74cjUZD69atHbk877zzDm5ubpw9e5b+/fuzY8cOEhMTOXr0KK+88gobNmwAYOTIkSYT2zt37kznzp156623FOsDAgLo06cPAHPmzOHLL78kNjaWxMREdu3axeDBg1m/fj0AY8eOJTQ0VHH8hAkTyM7ORqPR8MknnxAaGsrt27ct/peZmWnXay+ypIOnJ7i7K9fZWOlwMv4kx+KOcTL+pBMDE0IIIYQQQgghhBBCCOFqHKp0iIyM5KeffiI3N5cXX3yR9957j27dulkc2Lxu3TomTJhAVlYWHh4e9O/f35HL06hRIyZOnMgHH3zAyZMnef755032GTp0KAMGDDBZf+bMGQDKlStnsm3s2LGcOnWK6OhoZs2axaxZsxTb3d3def31103i37dvH0eOHAGsH9Dx5JNP8sUXXxS4n1qRJR00Gl21Q3LynXU2VjoIIYQQQgghhBDi3pGZmcmGDRvYunUr//77LwkJCdy+fZugoCDKlStH8+bNadeuHQ899JBVfcSPHj3KpUuX6NSpk90xLVmyhHfeeQeAzz//nN69e9t8Dv18UXsNGjSI9957z6FzCCFESeBQ0qFy5cqMHj2ar776iuTkZN5++23+97//0bRpUypWrIivry9paWnExcVx8OBBrl+/jlarRaPRMHz4cKe0HerduzcNGjRg1qxZ7Nmzh4SEBPz8/GjYsCH9+/c3O3OhIL6+vvzyyy/Mnz+fFStWcPLkSbKysggNDaVVq1YMHjzY7GDqQ4cOOfx6rFVkSQfQzXUwTjrITAchhBBCCCGEEEKYsWPHDj755BPOnj1rsu3atWtcu3aNY8eOERUVRcOGDRk/fjyNGzc2e6709HS+/PJLFixYwEsvveRQ0kEIIUTRcSjpAPD8889z48YNfvnlF3JyckhISGDjxo1m99UPpBgyZAijRo1y9NIG4eHhfPnllzYdc+LEiXy3e3h4MGDAALNVEpY899xzPPfcczbFYa8iTTqo5zpIpYMQQgghhBBCCCFUtm7dyssvv0x2djYArVq14sEHH6RixYp4e3tz69YtTp48ydq1a7ly5QpHjhxh0KBBzJo1i+bNm5uc7/Tp08ydO7eoX4ZVpk+fbvMxVapUKYRIhBDC9TicdAAYM2YMnTt3ZtasWWzfvp2UlBTTC3l40KZNG1544QVatmzpjMve04q80sGYVDqYWrMGFi4slISMV04ONW7exCsw0HS+RhFzlVhcJQ5XikXicN1YJA7XjUXicN1YXCUOV4pF4nBCLJUrw7Bh4GBrDCGEEOalpKQwZswYsrOz8fPz47vvvqNt27Zm9x0zZgwTJ05k3rx5pKWl8eqrr7Ju3TpKly5dxFHbz57OGkIIca9wStIBoGHDhkyePJmsrCxiY2O5fv06SUlJ+Pj4ULZsWerVq4e3t7ezLnfPk0oHF7JjB3TpUmin9wBCCu3stnGVWFwlDnCdWCQOU64Si8RhylVikThMuUosrhIHuE4sEocpu2JZuBAOH4bAwEKISAgh7m2LFi0iOa8182uvvWYx4QDg5eXFxx9/zOnTp4mOjiYxMZElS5YwePDgogpXCCFEIXJa0kHP09PT7LwD4VxS6eBCZswo7giEEEIIIYQ1zp+HxYuhiFqiCiHEveTff/81fP3AAw9YdczgwYOJjo4GIDo6WpIOQghxl3B60kEUDal0cCE7dxZ3BEIIIYQQwloLFjgv6ZCejvu8eZQ9dQpGjjR9WEcIIe4ht27dMnx94cIFwq1oZ9esWTMee+wxgoODadCggWH9kiVLeOeddxT7Tps2jWnTpgHw22+/0apVK8X2hIQEfvvtNzZt2sT58+dxd3enevXq9OjRg/79+zvy0grNxYsXefTRRwF488036dOnD1988QWbN28mIyOD8uXL06FDB8aNG6f4nvz6668EBQXx+eefc+jQITw8PKhcuTJDhgyhV69eimvs3buXxYsXs3//fq5evQpAaGgozZs3p3fv3vm2QO/QoQOXLl2ibdu2/PTTT/zwww8sWrSI69evU7p0aRo3bsykSZPw9/cvnG+QuGucOHGCn3/+mT179pCYmEhQUBANGzakf//+tGvXzu7zXrx4kZkzZ7Jjxw7i4+Px9/cnPDycPn360K1bt3yP1Wq1LF++nEWLFhETE0NWVhYVKlSgffv2PPfcc4SFhdkUy+zZs/n8888ZMWIEb7zxRoH7Hz16lN9//509e/Zw7do1vLy8qFOnDk8++SRPPfUUbm5uNl3f1Tg16ZCYmEh6ejo5OTkm27RaLVlZWWRmZnLr1i1iYmJYsWIFixcvdmYI94z0dOWyVDoUkytX4MwZ5bqRIyEoyGmXyMrK4tr165QrWxZPT0+nnbckx+IqcbhSLBKH68YicbhuLBKH68biKnG4UiwShwOxnD0LUVF3ljdsgIQEKFPG8SBeegnv2bOpBuTs2AFbt0IJ/4AohBD2qlq1quHrSZMm0axZM0JC8m+EFxISYkgkOCI6OppXXnnF0N5J7+jRoxw9epRly5bRvXt3h69TmG7fvs2AAQOIjY01rDt37pzZfU+ePMnkyZNJNbo/ExMTQ0BAgGH55s2bvPfee6xfv97k+HPnznHu3DmWLFnC448/zueff04p9QOnKuPGjWPFihWG5cTERMNNXiHys2HDBl5//XWysrIM665du8bmzZvZvHkzzz77LO+//77N5z106BBDhw7lttHD0Tdu3GD37t3s3r2btWvXMnnyZDw8TG9/5+bmMmbMGFavXq1Yf+7cOX777TeWLl3KtGnTaN26tVWxHD58mClTplgd+w8//MDUqVPJzc01rMvMzGT//v3s37+fNWvW8MMPP5ToUQUOJx1SU1OZOnUqy5cvJykpyQkhiYJkZpquc+VKhw+7f0hKegr+PsX8D1FMDPz5J7RqBfn0lrTJrl3K5dKlYfJkpw5WzEpNJS4mhqB69fAs5qfnXCUWV4nDlWKROFw3FonDdWOROFw3FleJw5VikTgciOX2bVi06E6pcE4OLF0KL7zgWACxsTB7tmHRfccOiI4GKz+cCiEKWU4OJCYWvF9qKh43bsC1a8VbrVQYcYSEOPWzaUF69epFVF6S9/Tp0zz++OP07NmTzp0707RpU9xtiKV169Z888037Nq1i4ULFwLQtWtXnnjiCQBq165t2PfAgQM8//zzZObdLImIiKBLly4EBgZy6tQpFixYQExMDKdPn3bWSy0Uv/zyCxkZGTRt2pSnnnqK7Oxs1q1bR58+fUz2/frrr8nIyKBHjx60bduW69evs23bNtq3bw9AWloaw4cP5+DBgwCUKlWK3r1707BhQzQaDf/++y9Llizh9u3brF27lri4OObMmYOXl5fZ2Pbu3UtGRgZhYWEMGTIEX19fNm7caLieEJYcPXqU0aNHk5WVRaNGjXjrrbeoXbs2Fy9e5IcffmDjxo1ERUVRo0YNBgwYYPV54+LiGD58OLdv36Z69eq88847NG7cmOvXr/Pbb7+xcOFC1q1bx6RJk3j77bdNjp80aZIh4TB06FD69etHQEAA0dHRfPHFF1y5coWRI0eycuVKKlSokG8shw8f5vnnnydN3ZbGgnnz5hkSFE2bNuW1114jPDyca9eu8fPPP7NixQr+/vtvvvrqK7uSMa7CoaSDVqtlxIgR7N27F61Wa/PxJb1MpLgUedLBwUqH4e2GOzEYO506Bc2b3/mwuWIFOOMpB3VrpVativRNnRBCCCGEKECpUtCtm26ItN78+Y4nHX77zXTdpk2SdBDCFSxcCK++CnmtZPLjBzQp/IgKVChxhIbCtGlg5qZ1YWjYsCFDhgxhdl5CNjk5maioKKKioihVqhTNmjWjRYsWtGjRgiZNmuRbpVaxYkWCgoJINEoc1axZk44dOyr2y83N5eOPPzYkHEaPHs2LL76o2GfIkCG88MILipkTrigjI4OIiAh++eUXw5PZTz/9tMV91S1cnn/+ecPXU6dONSQcwsPDmTlzJuXLlzds79mzJy+88ALDhg3j5MmTHD58mK+//pp3333X4vVCQkKYP38+5cuXJzU1lVq1alGvXj1HX7a4y02ZMoWMjAyqVq3Kr7/+aqioCQ4OZvr06YwcOZL169czdepUevbsaXXlzI8//khSUhIBAQFERUURGhoK6KqnPv30U/z8/Pj111+Jioqif//+VKlSxXDslStX+PXXXwEYNmwYY8aMMWzr0qULjRo1onfv3ty8eZNp06bx6aefWoxj7ty5fPbZZ4a/gwqSkJDA119/DeiSqz/99JOhmiEkJISvvvqKjIwM1q5dy/z583n11VcJcmI3laLk0F3/tWvXGgb+AJQtW5b777+f4OBgNBoNVatWpXnz5tSuXduQLdVoNGg0GsaMGcO6desci/4eZVSNZODKlQ4uYfFi5SCMb791znnVlQ4PPuic8wohhBBCCOfp21e5vGmT7mlie+Xmmk86bNxo/zmFEM4zbJhVCYe73tWruu9FEXr77bcZOXKkSULh9u3bbN++ncmTJzNgwABatWrF66+/ztatWx263vbt24mJiQGgXbt2JgkHgKCgIL777jv8nFzJEh4ebtN/L7/8coHnfOmll8y2glHz9vZmmIU/28TERObMmQOAn58f33//vSLhoFehQgW+//57fPNuKM2bN49r+fzb2LdvX7PnEcKS2NhYtm3bBsDw4cNNWnhpNBrGjRuHRqMhKSmJtWvXWnXemzdvGtr1DxgwwJBwMDZq1Cj8/f3Jyspi6dKlim1RUVFkZWXh5+fHiBEjTI6tXLkygwYNAmDVqlWKNmZ6hw8fZuDAgXz00UdkZmYqZtLkZ9myZdy6dQsvLy8+/fRTs+2Thg4dCuge1j969KhV53VFDlU6GP8wTJgwgb55b+anTp3K999/T6NGjZg0aRKga8MUFRXFd999R05ODjExMbzg6NNF96iSVungEi5fVi7v3Kn7RlooHbRKZib8849y3QMP2H8+IYQQQghROLp21b2n1b+Pzc2FJUvAzM0pq2zfrpsVofb337rhaz4+docqhBAlmZubG6+++ipdunTh999/Z+3atSQkJJjsd/v2bf766y/++usvmjZtyhdffEH16tVtvt6mTZsMXw8cONDifmFhYXTt2pVFixbZfI2i4uHhQdOmTa3at27duhafCN+xYwcZGRkAdOvWjcqVK1s8T5UqVejRowfz588nIyODLVu2mG3nBLq2VULYQp9w0Gg0dOjQwew+lStXJjw8nOPHj7Nx40YiIyMLPO+ePXsMlQWWzuvv70+rVq3YuHEjGzduZNSoUSZxtWzZ0uLv0aOPPsp3331HWloaO3fuNKmyev3117l06RIajYb+/fvz1ltv0aRJwfVqf/75JwCdOnVSVF8Ya9q0KYcOHcKngPeTy5YtK/B69lIPpLeHQ0mHw4cPG35w+ho9PdSiRQsAdhq1nvHz8+PFF1/E39+fTz75hD///JOBAwda/RequMNc0qFQP9c4WOkQlxRHTm4O7m7uhAXZNvndadRvctLSYO9eaNPG/nMeOAB5/5AbSDm9EEIIIYTr8fPTtdacP//OugUL7E86mKtyAN17w507wcIHYCFEEZk50+r2Snc1fXulYnDfffcxfvx4PvzwQ/799192797N3r17OXDgALdu3VLse+DAAfr168ecOXOoVauWTdc5cOCA4euC7i+1atXKqUmH6dOn27R/uXLl8t1esWJFQ9VBQWrUqGFxm/H35AErHoxs27Yt8/P+fTxw4IDFpMN9991nVWxC6OmrkCpUqECZMmUs7le/fn2OHz9u9VP9+vO6ubnl2+Krfv36bNy4kVOnTpGZmYmXlxeZmZmG+S75VSfUqVMHDw8PsrOzOXLkiEnSAXSJuLFjx9K4cWOr4s7MzOTEiRMAJgOqc3Jy0Gg0hlEEBSUcAEOViLNpNJriTzroB0c/qGopU7duXcP2CxcuKDI3AwYMYN68efz3338sXrxYkg52UCcdfHygEH7G7nCw0qHlxJZcSrpEpaBKXPzqohMDs8H166brtmxxLOmgnudQvz6U0D5rQgghhBB3vb59lUmHLVsgPh5sbRWRmqqcD6G2cWPJSDrk5oLM2BN3qz59oHdvqwZJp6amcurUKWrXru309ju2KJQ4iniQtDkajYbGjRvTuHFjhg8fTk5ODkePHmXLli0sXLiQq3mJoaSkJN577z3DzW9rXc/7rO/v709AQEC++9pTSZEfczchHVFQ/Nbue93o/ke1atUKPFfVqlUNX5urSrHmmkKYczmv60ilSpXy3a9ixYoAxMfHk5WVle+8F+Pzli9fvsDZMKC7oX/lyhWqVq1KfHw82dnZBcbl7u5O+fLluXTpEpcuXTLZPmvWrHyTf+acOXOGrLye+dWrVyctLY1ffvmFlStXcv78eUCX7OjTpw/9+vXD3Yq/v+2ZsVxUHEo66Mu11NnakJAQAgMDSU5O5uTJkyblIp07d+a7775z+SE+riorK4Pw8Ds3/gMCHO94pJ+wbm7SuruHB8YdxnJv3SLdhgvqfwG0Wq3ZPmi2xGIvn6tXTQaY5GzcSIbR0CVb4/Davl3xC5QdEUFmIbSeKozvh71cJRZXicM4huKOReIw5SqxSBymXCUWicOUq8TiKnEYx1DcsUgcpmyOpX17fEuVQqOv2s3NJXPePLJt7HfuPm8e3qqndI3lbNhAxnvv2XROZ7Dl++Hx8894fvAB2lKlyJw5k9xHHim2WAqTq8RhHENxx3LPxaGu2jcXi5sb2cHBpPn7F3Lf4mKIQ12Zb00cRfBnU6tWLWrVqsWzzz7Ld999Z5g/cPDgQXbv3m14algdQ1ZWlsk9hZs3bwK6p4ILut9gfAMvMzPTqvsTxnGo47H2+Pykp6cbvvbw8Mj3nMaDajUajcV99d+TgvYz59atW4r9c3NzDV9nZ2cbtt1zf5dYwVViKcw4Mmz8O+XGjRtAwQmr0qVLA7r7hrdu3SIkJMSp54U7vxf6YwECAwPzPV7feik5Odlkm60JB0AxMyU9PZ1evXpxVtWu89ixY3z88cds2LCBadOm5ZuE/vzzzy1uu3HjBt9++y2ZmZn4+PjwxBNP0KpVK6pVq4a/vz+ZmZnEx8ezd+9eli1bxvXr1ylbtiyffvopFSpUsPm1meNQ0iEwMJDExESzE7orV67MsWPHOHPmjMk2fRLisrrPvrCKj08cc+bEKdblVRY5TP3DDhCUmIhxEV1mUpKhlMka+gxidna2TceZi8VeDa9cUSROADQ7d3L88GG0BWRQzcah1dJoxw7FqotVq5LgrD8Ia+MoJq4Si6vEAa4Ti8RhylVikThMuUosEocpV4nFVeIA14lF4jBlSyw12rYlxGguXUZUFCfbtrXperV/+knxvjIrKAjPvApwALd9+zixdy+5FnoEF7aCvh9ely7RcPRoNDk5aJKT4eWXiVmypFhiKSquEge4TiwShylXiaWkxvHff/9x4sQJbt68SevWralZs6ZVxz3xxBMcPnzY8FDqli1bLD65fO3aNZN7CqVKleLmzZvcvn27wPsN586dM3x9+fJlm+5PgOn3xNbjzTG+CZmamprvOY3vod28edPivjk5OYavjx07VuDNZ+PXpZ/Bqqd/Khvg+PHjJk9el9Sf18LkKrG4Qhz6JEVBrYKMhylbk9hw5LzG97DNDXE2pj+/cXLQEbeN2tW/8847JCQkMHz4cMOQ9vPnzzNjxgxWrlzJ33//zQcffGCYlWzOk08+aXZ9VlYWffv2JTMzk0aNGjF16lSziYS6devSvn17RowYwRtvvMH27dv5/PPPWeKk94UOJR3CwsJITEwkNjbWZFvVqlU5evQop06dMtmm/0uruLN/JdWNG2G8+mqQYblKFVi+3LFzpqWlcfbsWapXr27SQ9Atr8RHzys7O9+eaWoeHh6G/1tzXH6x2MvLTFbSLSOD+rdvk2uhx2F+cWguXMDL6M0BQPlevQgND3dKvNbGUdRcJRZXicOVYpE4XDcWicN1Y5E4XDcWV4nDlWKROJwTi/vQoWCUdPDfv596wcFg5RNdmsuX8YmOVqzLGD8e97fewi3vM44mJ4d616+T27Klla/EOaz9fnguWYLG6IaUz/nz1KtRw6lD4lzl58RV4nClWCQO142lpMexbds2Q8VCWFgYTzzxhNXHdu7c2ZB08PHxMdw3SEtL49ixY4b9ypUrZ3JPISwsjJs3b5KWlkaFChUIDg62eB3jtkMVK1a0+r6G8ffEmC33RSwxftLaz88v33Ma318LDAy0uG+NGjX4559/AOvuw1y4cMHw9X333afY3zgBVLduXcP9nZL+83o3x1KYcSQlJREXF1fwjnn0SaqC5g4Ytwhys6L1oz3n1e9rfH5rj7cmJmsY3we/du0an376qWKGSq1atfj666/x9vZm0aJFrFq1iiFDhtCoUSObrjNnzhxiYmIoW7YsP//8c4EVIf7+/kybNo2uXbty/vx5/u///o+RI0fa9uLMcCjp0KJFC44cOcLy5csZNmyYouRDP2AmOjoarVar+IM8cuQIQLH/hVBSpad7c+LEne+1p6fp2AV7+fr6mpbuqMqa3NLSbOoxqf+z12g0Nh1nNhZ7pKVZ7D/ls2sXPPqo7XEcPKhcDgnBt0mTQu2L67TvhxO4SiyuEge4TiwShylXiUXiMOUqsUgcplwlFleJA1wnFonDlE2x9OwJpUtDXnskjVaL359/6gbOWmPxYjDunVu6NG4DB3I7KorS+/cbVvvs2AGRkda+BKfK9/uRmwtz55qs9ktLM3nPX+ixFCFXiQNcJxaJw5SrxFJS42jSpInh6/Xr1zN69Girb9QZP3lcvXp1i9f19PQ02dayZUuOHz8OwKFDh+jatavF6xw+fNjwtZeXl83fZ/U9LGf8ORk/qe3m5pbvOb28vAxfm/te6LVo0YKFebOH9u3bV+BAWH2CAqBRo0aK8xr/Gfr5+RmSDnol9ee1MLlKLIURh60Pj+uvX1ClgPHfAcY/5848r76qwfh7UlBVhX57QRUR1jL+O0Q/u8Gc0aNHs3TpUnJycli7dq3NSYeVK1ei0Wjo27ev1bNYvL296du3L5MnT2bt2rVOSTo4dIe0W7duAMTFxfH000+zY8cOQxaoZd6TPVeuXGHatGmGY/755x8WL16MRqOxutxOKKm7WRV67kbdB9OoHKhEyGcQElu22HdO9RDp1q1lEJ8QQgghhKvz8dElHowtWGDdsVot/Pqrcl2fPuDnR3JEhHL9pk32x1iYtm8HM+1vMXr6Vwgh7NG2bVtDH/azZ8/y008/WXVcZmYmy/NaN3h5edGuXTvFduMHWI3nC+h17tzZ8PUvv/yiaC1k7NatWyxbtsyqmEq6du3aGW6Srlq1iosXL1rc9+LFi6xatQrQPT3+8MMPF0WI4h6hn4lwK59ZWHBnZoKbm1uBcxbgzqwGa88LGKqgjOc8FHS8fnt+FVS2KGV0f7VVq1YW9ytTpozhnvl///1n83X0rbVq1apl03H6ofLmBmfbw6G7pA0bNqRTp05otVpOnTrFsGHD2LZtGwARERGGoRrff/89HTt2pEePHgwePNiQaXrsscccDP/eVORJB3VmNCMDLPxD7pLy+xC1c6ddw7XYtUu5/OCDtp9DCCGEEEIUvb59lcs7doA1H67++cd0kNrgwQDcatFCuf7wYbh61YEgC4k6aaInSQchhIO8vLx4++23DcuTJ0/mk08+MTuAVS8+Pp6XXnrJcINs6NChJgNkjZ8wNneuFi1a0KZNG0BXyTBhwgSTxEN6ejqjR49WtFe6m4WEhNA379+61NRUXnnlFeLj4032i4+P5+WXXzY8vd6vXz/CwsKKNFZxd9PfFy6oJZN+e1hYmFUVUvpWZ1evXjWbjFSf18PDg9DQUMM19BVG+c0azsnJMfzeVKxYscCYrFG5cmXD1wXNo9AnbGwd3g135rrk9/evOfrXW1DbKWs51F4J4MsvvyQzM5MtW7ag0WgM30CNRsMnn3zC0KFDyc7ONmRJ9JUQNWrUYODAgY5e/p5U7JUOoGtZVEzD8WyW3xuLtDTYuxdsGSCYmgoHDijXSdJBCCGEEKJk6NQJAgJA/0FMq9W1TRo1Kv/jfvtNuVyjhu49ZHo6txs2ROvvjyYl5c72zZuhXz/nxu6I27chr92GiXvkRpwQonD16tWLy5cv8+233wLw+++/s2TJEtq2bUvTpk0pW7YsGo2GhIQE9u/fz9atWw3tUTp27Mhrr71mck7jJMSSJUu47777CAoKokmTJlSqVAmACRMmEBkZSVJSEvPmzePAgQP07t2b0NBQzp8/z4IFC7h06RJlypQhIb9OCDbasGGDXce1b9/e4rBsZxkzZgz79u3j2LFjHD9+nK5du/Lkk0/SqFEjNBoN//77L0uWLCEl79+tunXrMm7cuEKNSdx7wvPmnl66dInk5GSLrX6OHj0K6H4ObTlvVlYWJ0+etHic/ry1atUy/M65ublRq1Ytjhw5ku/g9hMnThhu3jtjfgtAtWrV8PPzIzU1lfOq+blq+r+rypcvb/N1qlatyqlTp/jrr794+umnrTomNzeXpUuXotFoqFOnjs3XNMfhpIOPjw8zZsxg165d/Pnnn4qsTYsWLZg1axYfffQRp0+fBnTJiHbt2vHpp58WmNUR5hV7pQPoPrTcDUkH0LVYsiXp8M8/kJ19Z9nNDYp4UKAQQgghhLCTtzf06qVMIixYkH/SITPTdBbCs8/eaa/p4UFumza4Gw2pZtMm10o6LF0KxkkRY5J0EEI4ycsvv0zNmjX58ssvuXTpEqmpqaxbt45169aZ3d/b25sXX3yRYcOGGYbDGgsKCqJly5bs3buX1NRUPv74YwDef/99nn32WUD39PD8+fMZPnw4586d48SJE3z++eeK81SpUoV3332Xl156yWmv9ZVXXrHruL179xZ60sHHx4fffvuNsWPHsnnzZlJSUoiKijK77xNPPMGECROc1rdeCD19u7Tc3Fy2bNlCjx49TPa5cOECJ0+eBOChhx6y6rwRERH4+vqSlpbG5s2bzSYdUlJS2LNnj9nztm/fniNHjrBnzx5SU1PNzr7YuHEjoKviilC30bST/p74mjVr2L59OykpKYaKBmPnz583JCXuv/9+m6/zyCOPcPLkSaKjo5k5cybDhg3Ld3+tVssnn3zCiRMn0Gg0irZ1jnBaE/oHHniATz75xOQvqYiICP7880/Wrl3LvHnz2L59Oz/++CPlypVz1qXvOVlZyuViSTpYGMzskqxJOthC3VqpSZOSk4ARQgghhBCmLZb+/hvy6XnN6tWmc8IGDVIs5rRvr9ye92HVZcyebXmbJB2EEE7UuXNn/vrrL77//nv69+9PgwYNqFixIr6+vvj4+FC5cmUefPBB3n33XdavX88rr7yS7/DYSZMm0a9fP0JDQ/H09KRcuXKGJ/T1qlevzvLlyxk3bhxNmjTBz88PHx8f7rvvPl566SWWLVt2z7UOKl26NDNmzGD27Nn06tWLKlWq4Ovri7+/P7Vr16Zfv34sWLCAb775xuyNTyEcVaVKFZo3bw7A9OnTTWYoaLVavvjiC7RaLcHBwfRUz92ywM/Pz9Cyf/bs2WbbJE2ZMoXbt2/j6elp0mmne/fuuLu7k5ycrJhDrHfhwgV+y3s4JTIy0uphzNbol/dASmpqKp999pmhI5Bebm6uIWnq6+tLly5dbL7G4MGDDTF/8803jBgxgm3btpn8vZmUlGSohpg3bx4ajYaqVavyzDPP2PPSTDhU6TBu3DhSU1Pp16+foYeeJdWqVaNatWqOXE7kKfJKB3MXsGGY9MY3N5Kdk42Hu8OFNfZRf4jy81MmTfRzHazN6quHSD/wgGPxCSGEEEKIovXYYxAYCDdv3lm3aBG8/rr5/dWzENq2hfvuU6zKUQ/fjI2Fc+fAFT4DXbiQ/3BrSToIIZzM29ubRx99lEcffdThc5UuXZoJEyYUuJ+vry9Dhw5l6NChZrfXq1ePEydOOBSLo8erVa5c2epz9u7dm86dOxMTE2NTu5cHHniAB+y8b7Epv387hLDCO++8Q9++fTl79iz9+/fn7bffpn79+sTFxTF9+nRDRcHIkSNNKg70T9w3btyYL7/8UrFt9OjRbNiwgaSkJAYMGMC4ceNo2bIlN27c4JdffmFhXkvJZ599lgoVKiiOrVGjBv379ycqKopZs2aRkpLC4MGDCQ4OJjo6ms8//5zk5GSCgoIYPny4U78fDz74ID169GDFihUsXryYxMREhg0bRs2aNblw4QLfffedYV7ym2++adVgbbWQkBCmTZvGCy+8QGZmJlu3bmXr1q2AblaEj48PaWlp3Da6t6tP/MycOdNpVU8O3QWOjo4mLi4ONze3ApMOwnmKPOng5qa7SN5wIcCmSofwCuGFEJQN1E+lde6sKy/XZxNtmeug1coQaSGEEEKIks7LC558Uvn0/4IF5pMO16/rKh2M5Q2QNqZt1AjKlFG+99y0CSzc/CpSUVF33vuaI0kHIYQQQhSCRo0aMXHiRD744ANOnjzJ888/b7LP0KFDGTBggMn6M2fOAJjtlhMWFsbUqVMZOXIkly9fZpSZNpmdO3dm7NixZuMaO3YsFy5cYMuWLcyfP5/58+crtvv5+TFjxgynDZE2NnHiRHJycli9ejWbN29m8+bNJvs899xzDs1CjoiI4Ndff+Xjjz/m+PHjhvW3bt0iJSXFpMKiTZs2TJgwwTArxxkcSjpcz3tzqu/RJYpGkScdQDdM2jjpYEOlQ7FTf4iqXRsaN4ZDh+6ss3auQ2wsXLumXCdJByGEEEKIkqdvX2XSYdcuOH8eqlZV7jd3rnKel48P9Oljej43N3jkEV3FhJ4rJB20WtPWSt7eukpfPScOVhVCCCGEMNa7d28aNGjArFmz2LNnDwkJCfj5+dGwYUP69+9Px44d7TrvQw89xOrVq/npp5/YsWMH8fHxeHl5UbduXSIjI+nduzcajcbssd7e3syYMYPly5ezePFijh8/TlpaGqGhobRt25Zhw4ZRpUoVR162RV5eXnzzzTf07NmTBQsWcOjQIZKSkihbtiyNGzemf//+tG7d2uHrNG3alKVLl7Jt2zY2b97MgQMHuH79Ojdv3iQoKIjQ0FCaN29O586dDW2wnMmhpENAQAAJCQlkqu+Ci0JVLEkH9VyHkjzToWxZePhh06TD++8XfC51a6Xy5aF6dQcDFEIIIYQQRe7RRyE4GG7cuLNu4UJ4803lfurWSr166VozmdOhgzLpsHGj7qa/hQ+8RWL3bjh1Srlu0CCYOfPOslQ6CCGEEKIQhYeHm7RIKog1rccqVapkGDBvK41GQ69evejVq5ddx6vZ2n6tffv2tFfPBHMyjUZTJNcxx6FB0l26dEGr1TJnzhxSS9JN6BKuyAdJg67SwZgNlQ5/7PmDn7f/zB97/nByUFaylHQwpp/rUBBzrZWK80OkEEIIIYSwj77FkrEFC5TLR4/Cvn3KdWZaKxmoe5fHxYFRSXuxUCdNateG7t2V6yTpIIQQQghxV9i7dy979+4lKSnJpuMuX77MnDlz+PHHH50Sh0NJhzFjxhAREcGpU6fo06cPixcv5vz5804JTFimvjfu6pUOby16i2G/DeOtRW85OSgrmUs6tGunTBbo5zoURIZICyGEEELcPfr2VS5HR8PZs3eWf/tNuT0sTDeE2pLatUHdC7c4h3Cmp8O8ecp1gwaBujeyJB2EEEIIIe4Kzz77LIMGDWKf+sGZAhw8eJBPPvmE33//3SlxONRe6fPPP6datWocOnSI2NhY3s9rT+Pu7k5AQABeXl75Hq/RaMwOyxD5K2mVDsXOXNIhJMT2uQ7JyXDkiHKdzHMQQgghhCi5OnQwHf68cCGMHQs5OaD+0DVwILi7Wz6fRqOrdjBOVmzcCK+84ty4rbV8Ody8eWdZo9ElHdT9WtPSdA8VqR80EkIIIYQQ94Sbee8Zbxq/d3SAQ0mHefPmGQZy6P+v1WrJzs7mhnFvVDO0Wq3FYR4ifzLTwQapqbonvIyVLav7v61zHaKjITf3zrKnJxTCoBUhhBBCCFFEPD2hd2/lfIMFC3RJhw0b4PJl5f6DBhV8zg4dlEmHLVt0CYz8khWFRd1a6ZFHdIOyzZXbX79uOkRbCCGEEEK4pPXr1+c7R+LPP/8kJiamwPNotVpu3rzJ0qVLAQgODnZKfA4lHUAXmC3rheOKJelQUisdzJWKlymj+//DD8O3395Zr5/r4O1t/lzqeQ7NmoGPj1PCFEIIIYQQxaRvX2XS4Z9/4PRp0xv2zZpBw4YFn0891+HGDTh4sOgfVomLg7Vrlev08ygCA3VJkJycO9sk6SCEEEIIUWJUqlSJ119/nVzjB6TzaLVa/vzzT5vPqdFoeOSRR5wRnmNJh+PFPRTtHiWVDjZQJx3c3XUfsuDOXAd9gkw/18FSiyX1PAdprSSEEEIIUfI9/LCuEtb4feOsWZD3tJdBfgOkjVWuDHXqwMmTd9Zt2lT0SYc5c5RVuqVK6ao6QPceuGxZiI+/s13mOgghhBBClBj169fnmWeesTiDwZ6CgFatWjFmzBhHQwOcUOkgip5UOthA/eGpTBlwy5ufHhICTZronjzTszTXITfXtNJBhkgLIYQQQpR8Hh4QGQk//nhn3ZdfQna2cp9nnrH+nB06KJMOGzfqWjYVFa0WZs9WruvTB/z97yxL0kEIIYQQokR788036dSpk2FZq9UyePBgNBoNr732Gs2aNSvwHG5ubvj4+FChQgXK6lvSO4EkHUqgYhkkfbdUOqh/eR5+2DTpYG6uw/HjyiF8IEkHIYQQQoi7Rd++yqSDccIB4IknoFw568/36KMwY8ad5e3bdU8OeXk5Fqe19u+Ho0eV69SVGur3xZJ0EEIIIYQoUXx9fYmIiDC7rXbt2ha3FQW3YruysJtUOtjAmqSDMf1cBzV1a6WqVXWl80IIIYQQouRr1w5CQy1vt7a1kp76PWZqKuzZY3NYdlPPo6hWTfcajUnSQQghhBDirvP555/z2Wef0aBBg2KNw6FKh3feecehi2s0Gj777DOHznEvMp73BlLpkK+EBOWy+sPVQw9ZN9dBWisJIYQQQty99C2WfvjBdFtICHTtatv5ypaF++9XVtRu3Kh771nYMjPhjz+U6wYNutNiVK9MGeWyJB2EEEIIIUq8J598srhDABxMOixduhSNRmPXsVqtVpIOTuLqlQ4VAiso/l+kCqp0sHaugwyRFkIIIYS4u/Xtaz7p8Mwz4O1t+/k6dFC+x9y0CT76yN7orOa+Zo3pgzfmKjWk0kEIIYQQ4p5z+PBhjh07xo0bN6hUqRLt2rUjKCjI6ddxeKaDrZOwNRoN5cqVIzAw0NFLizyuXunwz/v/ODkYG5gbJK1W0FyHxETdTAdjUukghBBCCHF3eeghKF9eOVwZbG+tpPfoo/DNN3eWd+/WPbijfpjHydznzFGuaNsW7rvPdEdJOgghhBBC3BVSUlJYunQp0dHRPPTQQ/Tt29dkn9jYWN577z0OHTqkWO/l5cXQoUN57bXX7C4uMMehpMPGjRsL3CcjI4Nbt25x8uRJVq9eze7du/Hw8ODbb7+lZs2ajlxe5HH1SodiVVClA+iSDlOm3FlWzXVwi45W7u/rqyuXF0IIIYQQdw93d3jqKZg+/c66evWgRQv7zvfQQ7q2Tfqh1FlZsGMHPP6447Fa4HHjhq7SwZilpIn6fbG6OkIIIYQQQri8AwcO8Oqrr5KYmAhAcHCwSdLh3LlzDBgwgJs3b5oUEGRkZPDjjz9y+vRpvvnmGzw8HK5RABwcJF2pUqUC/6tZsyZNmjShT58+zJ49m5dffpnLly/z8ssvk56e7pQXca9z9UqHYmVN0kE/10FPP9chj7t66F/LluDp6cQghRBCCCGES3jtNeXDNuPHK98n2qJ0aYiIUK6z4qEtR4SsWYNGn+QA8PGBPn3M7yyVDkIIIYQQJdrhw4d57rnnSExMRKvVotVqSUpKMtnv7bffJikpyZBwaNOmDe+++y6vvfYa1apVQ6vVsn79en7++WenxeZQ0sEeo0aNom7dupw7d44/1APOhM08PHT/Fbq7udJBP9fB2JYthi/d1EkHaa0khBBCCHF3ql1bV/U6YQKsWQP9+jl2vkcfVS5v2uTY+QpQZtUq5YrevcFSW1tzSQcbW+cKIYQQQojiM2HCBNLS0tBqtbRs2ZJp06bx8ccfK/bZtm0bBw8eRKPRoNFoGDJkCLNmzWLQoEG89NJLrFy5kjZt2qDVapkxYwZXr151SmxFnnQA6NGjB1qtlr/++qs4Ln9XKZIqB3Co0uHFqBfpM6MPL0a96OSgCqDVmpaJm0s6gK7FkjF90iE7G7d/VDMpZIi0EEIIIcTdq3Fj+OAD57RB6tBBubx/v25eWCHQHDmC34kTypX5zaNQvy/OzISUFOcHJoQQQgghnG779u0cOXIEjUbDM888Q1RUFB07diQ4OFix39KlSwHdXObQ0FBGjx6t2O7l5cVnn32Gj48PGRkZrF271inxFUvSoUKFCoCun5RwTJElHdSVDqmpVj8JtfrwahbtW8Tqw6sLIbB83L6tmM0AWJ90yJvr4Bsbi0Zd1dG6tdNCFEIIIYQQd7EHHtC1ONLTahUVtc7koR4gXamSaaWFMXPvi6XFkhBCCCFEibB161ZAd5/93XffNbtPdnY227ZtM1Q59OnTBy8vL5P9ypcvT7t27dBqtWxx0nvVYkk6XLhwAYCsrKziuPxdpdgqHXJzTW/ouxpzH5rKlDG/r5m5Dm779uF/+LByv1q1IDTUeTEKIYQQQoi7l7c3tG2rXFcYLZays/GYN0+5buBA3XBsS/z9Qf2hU5IOQgghhBAlwv79+9FoNDz++ON4Wpg9e+jQIW7fvm2Y5dC+fXuL52vWrBkA58+fd0p8RZ50uHnzJvPnzwegcuXKRX35u06xVTqA6w+TVn9o8vCAgADz+5qZ6+C2fTul1EkHaa0khBBCCCFsoa42KIxh0mvXolH3382vtRLoHriRYdJCCCGEECXStWvXAKhTp47Fff4xahlfunRpGjZsaHHfsnnvC6876f2gQyOI9RULBcnOziYlJYWYmBj+7//+j8uXL6PRaPLNrgjrFFulA+jaF4WEFFEAdjA3RNq4mkHt4Yfh4EHDovu2bfifOqXcR4ZICyGEEEIIW6jnOhw/DpcvQ8WKzrtGXq9eg4gIqFev4OPKltXFoidJByGEEEKIEiEpKQnQJRMs0ScdNBoNTZs2RZPPfVGtlW30reVQ0uGxxx7LN9j8+Pn5MWTIEEcuLyjmpENJq3SwNM9B7+GHYcoUw6Lb33/jrW4BJpUOQgghhBDCFs2aQWAg3Lx5Z92mTbr2R85i9OAMAH36WHecVDoIIYQQQpRIfn5+JCcnk2Gh/X1ubq6hBRNAy5Yt8z2fvsJBPYjaXg63V9JqtTb/FxAQwLfffmso2xD2K7Kkg6en7j9j6gHLrsbWpINqroNGnXAoXRoaNHBScEIIIYQQ4p7g4QHqCm9nznXIyYGjR5Xrmja17lhJOgghhBBClEj6++qXjatWjRw4cEAxz6F169b5nu/AgQMAhDipq41DlQ4FZUj03Nzc8PLyokyZMjRp0oSuXbsSGBjoyKVFniJLOoCu2sH4CS1Xr3RISFAuF5R00M91UD8ppteqVf7D+IQQQgghhDDn0UdhxYo7yxs3glabf+tPa/33H6SnK9c1bmzdsZJ0EEIIIYQokRo1akRsbCw7d+5k+PDhJtvXrFlj+LpMmTL5znO4desWO3bsQKPR0EQ189ZeDiUdoqKinBKEsF+RJh1KlVImHe62SgcwmeugIK2VhBBCCCGEPdRzHc6fh9hYqFXL8XMfPqxY1JYvj6ZcOeuOLVNGuSxJByGEEEKIEqF9+/YsW7aMPXv2sGvXLh4wmkObmJjI8uXLDa2VHn/88XzP9e2335KamopGo6Ft27ZOic/h9kqieBV5pYMxV690UH9oUn+oMufhhy1vkyHSQgghhBDCHg0aQPnyynXOarGkSjrk5vMUmwmpdBBCCCGEKJE6duxItWrVAHj11VeJiori1KlTbNy4kWeffZbk5GS0Wi0eHh4MzGeW2E8//cTvv/+ORqOhUqVKtGnTxinxOVTpYOzy5cusWrWKLl26UKVKFcW29PR0+vfvT9u2benbty+VK1d21mXveT4+RXixUqWUy1ZWOjwT8Qw3Um8Q7OecQSRWs6fSQT/XwdzE9gJ6nwkhhBBCCGGWRqOrdpg798667dvBTCm8zcwkHaxuCKp+f6xuTyqEEEIIIVySp6cnH3zwASNGjCA1NZXPPvtMsV1f5TBixAhq1Kih2Hb06FE2bdrEn3/+ydmzZw3r33rrLby8vJwSn1OSDpMmTeKXX34hJyeHypUrmyQdLl68yLFjx4iJiWH27NmMHj2aIUOGOOPS97ySUOnwVZ+vCiEYK9iTdLA016F+fQgKclZkQgghhBDiXtOmjTLpsH+/c84rlQ5CCCGEEPektm3bMn36dMaOHUtycrLJ9iFDhvDKK6+YrP/zzz/5v//7PwDDoOlRo0bRqVMnp8XmcNLho48+Yv78+YYAz5w5Y7JPYmIiHh4eZGdnk5mZyf/+9z+ysrIYNmyYo5e/5xX5TAdjd+NMBzA/10HmOQghhBBCCEc0b65cPn5c935a/R7bFsnJYPR0GkBugwbWH28u6eCsAddCCCGEEKLQtW/fnk2bNrF48WL+/fdfbt26RdWqVenVqxcNLLwvLFu2rOFefkhICG+99Ra9evVyalwOJR327dvHvHnz0Gg0+Pr68uKLLxIZGWmyX0REBPv27WPVqlVMmjSJxMREvv32Wzp06MB9993nSAj3vJJQ6VAstFrT8nBbkg5TpijXSdJBCCGEEEI4onFjcHOD3Fzdcm4uHDrk2PvMI0cUi1p3d7R161p/vPr9cU4O3LwpFb5CCCGEECWIv78/gwcPtnr/5s2bM3LkSOrXr0+bNm2c1lLJmEODpOfPnw/oekhFRUUxYsQIypUrZ3Zfb29vIiMjmT17Nh4eHuTk5DBnzhxHLi+QSgeLbt2CrCzlOmuTDu3a6T4QGpOkgxBCCCGEcISfH9Srp1znaIslVWul9GrVwNvb+uPNvT+WFktCCCGEEHe1xo0b88orr/DII48USsIBHEw67N+/H41Gw1NPPUVDK3uH1qlTh969e6PVatm5c6cjlxeUjEqHuh/UJWBkAHU/sOGpK0eZ+7BkbdIhOBhee82wmB0ZCeHhTgpMCCGEEELcs9Qtlvbtc+x8qqRDWu3ath3v52f6gUKSDkIIIe5GcXHw3Xewfn1xRyLEPcGhpMO1a9cAXXbEFo0aNQLgypUrjlxeUDIqHVLSU7iVfouU9JRCCMoC9YclLy/b+uV+9RXpa9ZwasoUMmfNcm5sQgghhBDi3tSsmXLZyZUOqbYmHUCGSQshhLj7/fOPrtpw1Cjo1AlmzizuiIS46zmUdPDx8XHoOI0MKHNYSah0KBbmhkjb8vPm7k7uQw+R3LYteHo6NzYhhBBCCHFvUicdjh6F9HT7zqXVwr//Klal1apl+3kk6SCEEOJuduwYdO6sm1mkN3168cUjxD3CoaRDWFgYoGuzZIt/894ch4aGOnJ5QcmodCgW5pIOQgghhBBCFKf771c+CJOTY1KtYLXz5yE5WbFKkg5CCCGEkdOnoWNHSEhQrj96FDIyiicmIe4RDiUdIiIi0Gq1LF++nLNnz1p1TFxcHIsXL0aj0RAREeHI5QVS6WCR+h8USToIIYQQQojiVro01KmjXGdviyVVskIbFERW+fK2n0eSDkIIIe5Gly/DY4/pZjmoZWfDkSNFH5MQ9xCHkg59+vQBIDMzk+eee459BQxCO3LkCEOHDuV23hPyffv2deTyAql0sEgqHYQQQgghhCty1lwHVdIht2FD29qJ6knSQQghxN0mIUGXcDh92vI+Bw4UXTxC3IM8HDm4du3aDBkyhNmzZxMXF8fAgQOpV68eLVu2pGLFivj6+pKWlkZcXBz79+83tFXSaDQ89dRThoHSwn5S6WCBJB2EEEIIIYQrat4c5s69s1zAg1sWmUs62EOSDkIIIe4mycm6GQ7HjuW/n71JfyGEVRxKOgCMHTuW+Ph4/vrrLwBiYmKIiYkxu69WqwWgU6dOjB8/3tFLC6TSwSL1h6UyZYonDiGEEEIIIYypKx3+/RcyM8HLy7bzqIZI2510UL9PlqSDEEKIkiotDXr0gH/+Ua6vUweeeAImT76zTiodhChUDrVXAnB3d2fy5MlMmzaNJk2aALrkgrn/atWqxRdffMHUqVPx8HA43yGQSgeLpNJBCCGEEEK4oqZNlctZWbb3lU5PhxMnFKu0DRrYF4/6fbJ6NpoQQghREmRmQp8+sHWrcn2VKrB+PTz+uHL9oUOQk1N08Qlxj3Hanf+OHTvSsWNHrl27xqFDh0hISCApKQkfHx/Kli1L48aNqVKlirMuJ/IUa6WDJB2EEEIIIYSwTVAQ3HcfxMbeWbd/v2kFRH6OHYPcXMWq3Pr14cIF2+OR9kpCCCFKupwceO45WL1auT40FDZsgKpVwcdHuS0tTZfAr1+/6OIU4h7i9HKDcuXK0bFjR2efVlhQrJUOVrZXmjFwBmlZafh6FmGw6ie0JOkghBBCCCFcRbNmpkkHW6jmOXDffeDvb18s6vfJiYm6mzfu7vadTwghhChKWi1er70G8+cr1wcGwrp1utZKoEtAVKoEly7d2efAAUk6CFFIHG6vpHf58mV++uknLph5uiY9PZ3evXvzzTffcPHiRWddUlDMlQ6ZmZCdXeBh3Zp0o0+LPnRr0q2QAlPRaqXSQQghhBBCuK7mzZXLtg6TVicdGje2Pxb1++TcXEhKsv98QgghRFHRaqk0dSoev/yiXO/nB3/+CXlt4A3ULQ5lroMQhcYpSYdJkybRqVMnJk+ezL+qgWYAFy9e5NixY8ycOZOuXbsye/ZsZ1xWYFodVqjUlQ7gmi2Wbt407csnSQchhBBCCOEq1K2UDh3SzXawlvozlyNJB/UgaZAWS0IIIUoEj6++okJUlHKllxcsWwYPPmh6gPrfX0k6CFFoHE46fPTRR/z8889kZ2ej1Wo5c+aMyT6JiYl4eHig1WrJzMzkf//7HzNnznT00vc8Hx/QaIrwgupKB3DNpIO5D0mSdBBCCCGEEK5CfdMjIwOOH7f+eHWlQ6NG9sfi42PamkmSDkIIIVzd0qV4ffyxcp2bG8ybB489Zv4YdaXD/v26bhlCCKdzKOmwb98+5s2bB4Cvry+vv/46ffv2NdkvIiKCffv2MXHiREJCQtBqtXz77bfEGvcxFTYr0tZKYL7SwYq5DvvO7WNX7C72nbOxbNxe6g9JPj7mYxdCCCGEEKI4lCkD1aop11nbYik+Hq5eVa5zpNIBZJi0EEKIkuebb0zX/d//wZNPWj5GnXRISoJz55walhBCx6Gkw/y8IS2enp5ERUUxYsQIypUrZ3Zfb29vIiMjmT17Nh4eHuTk5DBnzhxHLn/PK/Kkg7nSCisqHXpO68mDXzxIz2k9CykwFZnnIIQQQgghXJ262sHaYdLqKgc/P6hZ07FYJOkghBCipDlxQrn8+ecweHD+x1StCsHBynXSYkmIQuFQ0mH//v1oNBqeeuopGjZsaNUxderUoXfv3mi1Wnbu3OnI5e95RZ500GhMKwasqHQocgkJymVJOgghhBBCCFdjb9JBPc+hYUNwd3csFkk6CCGEKEmys03/rerateDjNBr7//0VQtjEoaTDtWvXAGhsYzlvo7yeo1euXHHk8ve8Ik86gGnSoSTMdJCkgxBCCCGEcDXNmyuXDxyAnJyCj1NXOjjaWgkk6SCEEKJkuXbNdBZDhQrWHatusSSVDkIUCoeSDj4+Pg4dpynSKch3n2JJOqiHSbtipYMkHYQQQgghhKtTP2mZmgonTxZ8nDOHSOtJ0kEIIURJonqIWevurpuXZA1JOghRJBxKOoSFhQG6Nku2+DevJDg0NNSRy9/zpNLBAkk6CCGEEEIIV1e+PFSqpFxX0DDp7Gw4elS5TiodhBBC3GvUnVPKlrW+1aA66X/5MsTHOycuF6A5exavixeLOwwhHEs6REREoNVqWb58OWfPnrXqmLi4OBYvXoxGoyEiIsKRy9/zpNLBAkk6CCGEEEKIksDWvtInT0JmpnKdVDoIIYS416grHcqXt/7Y2rVNH6i9W6od3nsP3wYNaPjkk3h89VVxRyPucQ4lHfr06QNAZmYmzz33HPsKeDLnyJEjDB06lNt5N6r79u3ryOXveVLpYIH6Q5K1JXZCCCGEEEIUJVuTDuoh0pUqOee9rvocknQQQgjhylSVCTYlHdzdoUkT5bq7IemwahV89hkAGq0Wzy++gJSUYg5K3Ms8HDm4du3aDBkyhNmzZxMXF8fAgQOpV68eLVu2pGLFivj6+pKWlkZcXBz79+83tFXSaDQ89dRThoHSwj5S6WCBVDoIIYQQQoiSQD1Mev9+yM0FNwvPhhXGEGkwfb+ckOCc8wohhBCFwZFKB9DNddi1685ySU863LwJI0YoVmnS0+H4cWjRopiCEvc6h5IOAGPHjiU+Pp6//voLgJiYGGJiYszuq82bLN+pUyfGjx/v6KXveVLpYIH6Q5IkHYQQQgghhCtSVzrcugWxsbrWD+YUVdLhxg3d/AgPhz8uCiGEEM7naNLB1kpDV/f223Dpkul6STqIYuRQeyUAd3d3Jk+ezLRp02iSV56k1WrN/lerVi2++OILpk6dioe8gXWYVDqYkZsrSQchhBBCCFEyVKwIoaHKdfnd+FAnHZxVOW7u/XJionPOLYQQQjibOumg/re0IE2bKpdjY3XVAiXRli3w44/mtx0/XqShCGHMaXf+O3bsSMeOHbl27RqHDh0iISGBpKQkfHx8KFu2LI0bN6ZKlSqKY86cOUONGjWcFcI9p6RUOsR8EoNWq0Wj0RRSUEaSknSJB2OSdBBCCCGEEK5Io9G1WMqrGgdg3z7o189036QkOH9euc5ZlQ7m5kJcv26aEBFCCCFcgaOVDg0a6Kr5srPvrDt0CNq1c0JwRSg1FV54wfJ2C51ohCgKTi83KFeuHB07drS4PScnh3Xr1jFv3jz27t3LsWPHnB3CPaOkVDqU9ildSMGYYW7onQySFkIIIYQQrqpZM2XSwVKlw5EjymVPTwgPd04Mnp4QGKh8ylOGSQshhHBVjgySBvD2hoYN4eDBO+sOHCh5SYcPP9RVaVgilQ6iGBVZj6NLly6xYMECFi9eTEJCQtE9+X4XKymVDkVK/eHIz6+YvlFCCCGEEEJYwVxfaa1WVwVhTN1aqV498PJyXhxly0rSQQghhOtLT9dV/xmxOekAuhZLxkmHkjbXIToaJk9WrqtQQVkFcuqUzGgSxcbhmQ750Wq1bN68mRdffJFOnTrx008/cV3evDpNSal0KFLqny9prSSEEEIIIVxZ8+bK5Rs34OxZ0/0Ka4i0nvp9s3xuE0II4YpUVQ7gQNLB2IEDdgakD0Lr2PG2yMyE559Xthf38oJFi5T7ZWXBmTNFF5cQRgol1XXt2jUWLlzIokWLiIuLA3QJCMNFPTzo0KEDkZGRhXH5e0ZJqXT4Zt03JKcnE+ATwOhOowspsDwyRFoIIYQQQpQkVatCSIhycPP+/aCefVdYQ6T1JOkghBCiJFDNc8j18tK1CLSVOulw7JiuisLHx7bzaLUwejS+v//OffXqwR9/mN47c7bPPzdtuzh+PLRpg7ZsWTTG/4YfPw61axduPEKY4dSkw65du5g7dy6bNm0iJydHkWgAqFevHr1796Z79+4EBQU589L3pJJS6fDN+m+4lHSJSkGVCj/pIJUOQgghhBCiJNEPk16//s66ffvA+AGt3Fz491/lcVLpIIQQ4l6kSjpkhYSYtiS0RpMmuuP09y5zcnT/1rZsadt5fv8dpkxBAwRt307WV1/Bt9/aHo+1jhyBiROV6+6/H8aOBSA3PBx343/DY2Kge/fCi0cICxxOOiQlJbFkyRLmz5/P+fPnAWVVg0ajoX///kRGRlK/fn1HLyeMuESlg7RXEkIIIYQQwjHNmimTDuq+0ufOQUqKcp0kHYQQQtyLVO2VssuUwa6JsaVL6yoATp68s+7AAduTDj/9pFh0N/733NlycnRtlbKyjC7oDrNmgacnANo6deDvv+9sl2HSopjYnXTYv38/c+fOZd26dWRmZgJ3kg3u7u6UKVOGq1evAvDBBx84IVShVixJB3WFiqt9GJGkgxBCCCGEKGkKGiatbq1UpgyEhTk3Bkk6CCGEKAnUlQ5lyuBl77maNjVNOtgiNhZ27FCs0pw4oWtFXhgtlr79VjdA2tjYsYr3Ebl16ii3S9JBFBObBkmnpKQwZ84cevTowYABA1i1ahUZGRlotVq0Wi21atVizJgxbN68mYEDBxZWzCJPsSQdKldWLl+7put55yok6SCEEEIIIUoa9TDpa9fg4sU7y+aGSNvTSiI/knQQQghREphJOthNnfS3NekQFWWySpObC4cO2R+TJf/9B++/r1xXp45uloMRs0mHohxyLUQeqyodjh49yrx581i9ejVpaWnAnaqGwMBAunXrRq9evWjk7GFmIl/FknSoUsV03cWLUKtW0cdijvrDkSP/+AghhBBCCFEUatbUDcG8efPOuv3777z3Npd0cDZJOgghhCgJ1EkHRx42VQ+TPnQIsrPBw4rbpVot/Pab+W379sEDD9gfl7lrDRsGefdkAd3DB7NmmQy+1tatqzz2xg3dwwyhoc6LRwgrWJV0iIyMRKPRGBINnp6etGvXjl69evHwww/jmdc3rLicOHGCn3/+mT179pCYmEhQUBANGzakf//+tGvXzu7z7tmzhzlz5rB//36SkpIICAigadOmPPPMM7Rt2zbfY7VaLcuXL2fRokXExMSQlZVFhQoVaN++Pc899xxhTiiHLpakQ0CA7r/k5DvrLlxwnaRDQoJyWSodhBBCCCGEq9NodDc+tmy5s27/fujZU/e1OulQGA97qR/WUb+vFkIIIVyBKumQHRJi/7nUSYf0dDhxAho0KPjYHTvgzBnz29SzmRw1c6byPQLAK6+AmXuT2ipVyPX2xi0j487KmBhJOogiZ9NMh3LlyvHSSy/Ro0cP/P39Cysmm2zYsIHXX3+dLKMhKteuXWPz5s1s3ryZZ599lvfV5UcFyM3NZeLEifz++++K9QkJCWzYsIENGzbQr18/xo8fj7u7u9njx4wZw+rVqxXrz507x2+//cbSpUuZNm0arVu3tikutWJJOoDuiaujR+8sX7hQTIGYIe2VhBBCCCFESdS8ufKGwr59uv+npupaKhgrikqH5GTIzAQvuztlCyGEEM6nGiTtUHulsmV1bcSNWxoeOGBd0uHXXy1v0/8b7gwXL+rmNhirWhU++8z8/u7upFetit+pU3fWHT8O7ds7LyYhrGDTTIfr16/z2Wef8cILLzBz5kzOnTtXWHFZ5ejRo4wePZqsrCwaNWpEVFQUu3fvZtGiRTz66KMAREVFMWfOHJvO+9133xkSDjVr1mT69Ons2rWL9evXM3LkSDw8PJg/f77FAdmTJk0yJByGDh3KmjVr2LlzJ1OmTKFChQrcunWLkSNHckWVnbVVsSYdjLlK0iEnBxITlesk6SCEEEIIIUoCc8OkAY4dg9zcO+s1GutuhtjK3PtmqXYQQgjhSrRa5850AMv//uYnLQ0WLLC8/ehRZSske2m18NJLym4jAD/9BKVLWzwsvXp15QoZJi2KgVVJhzFjxlCzZk20Wi3Z2dkcOnSIb775hs6dO9OnTx/mzp1LsvoXoAhMmTKFjIwMqlatyq+//kpERATBwcE0atSI6dOn89hjjwEwdepUUlJSrDrn5cuXmTlzJgB16tRhwYIFdOzYkZCQEKpWrcqrr77KV199BcDixYuJVk2Nv3LlCr/mZTuHDRvGuHHjqFGjBmXKlKFLly7MmTOHwMBAkpOTmTZtmkOv32WSDufPF08cajdumA7HkaSDEEIIIYQoCdQ3PeLidP+pWyvVrg1+fs6/fnCw6XBqmesghBDClaSk6CoAjTicdFC3WLJmmPSyZXDr1p1ljQat8b+hOTnw77+OxQXw11+wapVy3eDB8Pjj+R4mSQfhCqxKOrzwwgusXr2aefPm0adPH0qVKoVWq0Wr1XLkyBEmTJhA27Ztee2119i6dSu5xk/iFJLY2Fi2bdsGwPDhwylVqpRiu0ajYdy4cWg0GpKSkli7dq1V5129erWhVdP48eMpbSZz2LVrV1q2bAlgSFDoRUVFkZWVhZ+fHyNGjDA5tnLlygwaNAiAVatWkar6y9IWLpN0KKDSoVm1ZrSu2Zpm1Zrlu5/DzH0okkHSQgghhBCiJKhTB9QtbPfvL5oh0qAbmhkcrFwnSQchhBCuxEzHkGxnJx0OHjR9oFVN3VqpY0e0deoo1zmjxdLChcrl0FD45psCD0uvVk25QpIOohjY1F7p/vvv55NPPmHHjh188cUXhhvvWq2WzMxM1q1bx4gRI2jXrh1r1qwplID19AkHjUZDhw4dzO5TuXJlwsPDAdi4caNV5z2aN6ugbNmytGjRwuJ++kHSu3btIjMz0ySuli1bWpx7oW/9lJaWxs6dO62Ky5xiSzpUrapcLiDpsOLVFex6ZxcrXl1RiEFh+qHI3x+8vQv3mkIIIYQQQjiDmxvcf79ynbmkQ2EMkdZTVwlL0kEIIYQrUSUdtP7+5Dp6c0xdaZiUBGfPWt7/8mVYv165bvBgcs39G+6oHTuUy+PGgRWDs9Nr1FCuOHfOpEJEiMJmU9JBz8fHh169ehEVFWVINISFhRmqH65fv05MTIxh/xkzZnDByX3/9eevUKECZfLJatavXx+4k0woiL5NVMWKFfPdLyTvlzwrK4vTp08DkJmZafi6QT59VuvUqYOHh26G95EjR6yKS83DQ/dfsXDVmQ7qnrPSWkkIIYQQQpQkzZsrl/ftK7pKB5CkgxBCCNemGiKtLV/e8XNWrmzaJSO/hMGcOcpZS/7+0KuXadLB0UqH+Hj47z/lOiuHQZtUOmi1cPKkY/EIYSO7kg7GqlSpwuuvv86mTZv4+eef6dq1K15eXmiNSpG+/fZbOnXqxNNPP83cuXO5ceOGo5fl8uXLAFSqVCnf/fTJg/j4eEPbpPz45fVHvX37dr773bx50/B1fN5fevHx8WRnZxcYl7u7O+Xz/mK8dOlSgTGZU2xVDmCadLh5U9nLrrioPxRJ0kEIIYQQQpQk6qctN20yfbBGkg5CCCHuVepKB2ckHTQa6+c6aLWmrZX69IFSpchVn+PIEcjIsD8udWeUUqWsfg+g9fEhV92lRFosiSLmtGflNRoNbdu2pW3btiQnJ7NixQqWLFnCsWPHDPscOnSIQ4cOMXHiRNq2bUuPHj3o2rWrXdfTJy4CAgLy3U8/k0Gr1XLr1i1DhYIltWvXZv369Zw5c4b4+HhDckDNeIC0PkFhnEwJDAzM9zr61kv2DuD28dGSmppm17HmpKWlKf6fr5AQ1KPr0k6eRFuvXtHHYsTj8mW8jJZzgoPJcKB8zN44nM1V4jCOobhjcZU4jGMo7lgkDlOuEovEYcpVYpE4TLlKLK4Sh3EMxR2LxGHKVWJxZhyaevVQPFukerBH6+9PWmio2RYJzojDKyhI8QExKy6OLDveT9+NfzaOcpVYJA5TrhKLxGHKVWJxlTiMYyjuWIorDs8LF/A0Ws7OS5Y7Godnw4Z4bthgWM755x+z95M0Bw7gq+qkkt6vH7mpqaSHh+NjvCEri7R//kGrTkZYG9OWLYrXmhMRQUZmJhi1eDdH/73IrlULr/Pn74Tz779k9ehhVyx6GY4kUcQ9p1Aa9AQEBDBw4EAGDhzIiRMnWLhwIatWrSIpKQmA7OxstmzZwrZt2+xOOuh/0H18fPLdz9uop781vxydO3fmhx9+IDc3l4kTJzJlyhTc3JQFITt37mT79u2GZf1MB+PZDt4FzBLQx52enl5gTOZ4eGQqWlg5y9n8+tYZaRwUhGfenyfAxV27sJQ+eeOvN0hKTyLIJ4jJXSY7PRa9SqdOUcFoOcnDg7NO+B7ZGkdhcZU4wHVicZU4wHVikThMuUosEocpV4lF4jDlKrG4ShzgOrFIHKZcJRanxJGbS1Nvb9wsfG65XbMmJ06cKLQ4KoHi/XTymTMOvZ++q/5snMRVYpE4TLlKLBKHKVeJxVXiANeJpajjqHbiBMY1eTfz7q05GkdwuXLUNFrO/ecfs/fcKk+bpng4ICMsjKMhIZC3r1/VqvgY3ei/+uefXC/gvqUl4Zs3K5IO8bVqEWfDv8k3ypfH+DHqW3v3cqYQ7iMKYUmhTwUIDw/n/fff5+2332bDhg0sXbqUv//+m5ycHEULJlu5u7sDugqL/BhfQ508sBRv7969Wbx4MWvXruX555/npZdeok6dOty6dYs1a9Ywbdo0QkNDDW2VvLy8TM5vbVzWxGROQIAn9ZxUWQC6TOjZs2epXr06vlb0bnKvVk03XCdPVTc3cizEEzs3lss3L1MxsKJVMdsai56X6ucpoGZNh75H9sbhbK4ShyvF4ipxuFIsEofrxiJxuG4sEofrxuIqcbhSLBKH68bi7Di0TZqAUVW1Me8WLSy+v3VGHB516iiWg7Ky7Ho/fbf+2dwNsUgcrhuLxOG6sbhKHK4US3HF4a1Kyvvfdx+Aw3Fo3N3hvfcMy54JCdQLDoYKRqn4rCx8jaohANwGD6Ze3kzXtLQ0UuvWVSQdKl65Qjl77kulpeGraocU0r07QTbcUyvVvDnMnWtYHxQX5/B9xKSkJOLi4hw6h7h3FNkoYk9PT7p06UKXLl24evUqS5YsYenSpXafTz97oaBKAePqA31yoCDjx4/nxo0bbNq0iZ07d7JT1UetRo0ajB8/niFDhihi0f8fCq6q0G8vqCLCEj8/N8X1nMXX19e681arBocOGRa94+PBwnH6BIxGo7EpZqtj0TNKggB4hoXh6YTvkc1xFBJXiQNcJxZXiQNcJxaJw5SrxCJxmHKVWCQOU64Si6vEAa4Ti8RhylVicVocLVpYTDp4NmtW4Ptbh+LIm4en537jhkOv6a77s3ECV4lF4jDlKrFIHKZcJRZXiQNcJ5Yij0M1a8i9cmXnxNG4sW5mgtF8V78TJ6CmUf3DihUm1/d87jnFv8uJdesSsm7dne2HD9t3X+qff8B4Lq2bGz4PP2zxvps5Hg0bKpbd/vsPP29vyHuI2x7F3dZLlCwOD5K2R2hoKCNGjGDt2rV2n0M/E+FWAQOM9TMT3NzcCpyzoOft7c3333/PV199RUREBP7+/vj5+VG3bl3efPNNli5dSqlSpQz7lytXDrgzP8KauPTbg4ODrYpJrZiT66bDpI0yucVGPWSvTJniiUMIIYQQQgh7NW9ueVthDpEGGSQthBDCtRXGIGkANze4/37lOvUwafUA6QcfhNq1FatS1ZUEhw8XOIPBrL//Vi43bgxG9xytkauqXiQ93TXu3Yl7RpFVOjhbjRo1iI6OLrCsR789LCzMplZGGo2GHj160MPCkJXjeWVOGo2GatWqGa7h4+NDeno6ly9ftnjunJwcQ2umiqqniaxV7EmHqlWVyxcuFE8cxtQfitQfmoQQQgghhHB1zZpZ3taoUeFeW5IOQgghXFVuLuTdSzNwVtIBoGlT5c3+/fvvfJ2YCCtXKvcfPNjkFKl16ypXZGbCsWOmCY2CqJMObdrYdjxAaCgEBSm7ghw/DjVq2H4uIexQLJUOzhAeHg7ApUuXDNUM5hzNmypfV/2Lnw+tVsuNGzfy3WfHjh0A1K5d21B14ebmRq1atQDyHfJ84sQJcnJyAOzup1bsSQd1pYMkHYQQQgghhHBc/fpgri1s1aq6mweFSV0pnJoK0kpBCCGEK7hxQ9lyCCdWOoAu6WDMuNJh3jzltb29oW9fk1PklC5Nrvqm/r59tsWRmwuqNu+0bWvbOQA0GlDfC1XNiRCiMJXYpEO7du0AyM3NZcuWLWb3uXDhAidPngTgoYcesuq8u3btolGjRrRu3ZrY2Fiz+1y7do2tW7cC0LFjR8W29u3bA7Bnzx5SU1PNHr9x40ZAN2MiIiLCqrjUfHzsOsx5zCUdHBgM7rDsbN0/QMYk6SCEEEIIIUoaLy/zbZQKu7USmH//rG5hKoQQQhQHVWslAG1eu3OnUCcdTp++UyWgbq3Us6fFBwFy1eexNelw/Ljp/S17Kh3ANOmQzwPSQjhbiU06VKlSheZ5/U6nT59uMkNBq9XyxRdfoNVqCQ4OpmfPnladt0GDBobBx3/88YfJ9tzcXD7++GPS09Px8/PjmWeeUWzv3r077u7uJCcnM23aNJPjL1y4wG+//QZAZGQkAQEBVsWl5nKVDmlpunKz4mLu2pJ0EEIIIYQQJZG5FkuF3VoJdDdQ1C1ppcWSEEIIV6BurRQSYr4y0F4NGoCnp3LdwYO6JEB0tHK9mdZKernqVkrGbZqskddZxaBKFdN7cNZSd1eRSgdRhEps0gHgnXfewc3NjbNnz9K/f3927NhBYmIiR48e5ZVXXmHDhg0AjBw50mSKfefOnencuTNvvfWWYn1AQAB9+vQBYM6cOXz55ZfExsaSmJjIrl27GDx4MOvXrwdg7NixhIaGKo6vUaMG/fv3B2DWrFl8+OGHhuPXrFnDwIEDSU5OJigoiOHDh9v92os96VCxoq5Uy1hxtlgy92FIBkkLIYQQQoiSyNww6aKodHBzM30PLUkHIYQQ+EkxaAAA3LxJREFUrkBd6VChgnPP7+UFDRsq1x04AHkPDhuULw+dOlk8jUnS4dAhXXcOazljnoOetFcSxajEDpIGaNSoERMnTuSDDz7g5MmTPP/88yb7DB06lAEDBpisP3PmDADlzJRijR07llOnThEdHc2sWbOYNWuWYru7uzuvv/66Iblg7vgLFy6wZcsW5s+fz/z58xXb/fz8mDFjht1DpMEFkg6enhAWBsYDsy9csH04jrOoy74DA00z1EIIIYQQQpQE5iodiiLpALpq4WvX7ixL0kEIYcmaNTBxoi5ZOXmyDKgVhauwkw6ga7FkPMth3z7Ia69uMGAAeFi+nWqSdEhP1w2Ttvbf8cJMOly7prt/Jg/piiJQopMOAL1796ZBgwbMmjWLPXv2kJCQgJ+fHw0bNqR///4mMxes4evryy+//ML8+fNZsWIFJ0+eJCsri9DQUFq1asXgwYPzHUzt7e3NjBkzWL58OYsXL+b48eOkpaURGhpK27ZtGTZsGFXsLY0yxOjQ4c5RpYpp0sGM0Y+NJjk9mQAf+1pJWUX9YUj+AhVCCCGEECVV48a6ymL9e+1q1aBOnaK5trpFqSQdhBDmXL8OffpASopu+fZtyOsKIUShKIqkQ7Nm8H//d2d50SLIyFDuM2hQ/ucoU0b37/a5c3fW7d9vXdIhPh7U82XtGSKtV6OG7oFc4yHYJ07Agw/af04hrFTikw4A4eHhfPnllzYdc+LEiXy3e3h4MGDAALNVEtbQaDT06tWLXr162XV8QUqXLpTT2qZKFdiz586ypaRDp9GFH4v6w5DMcxBCCCGEECWVlxfMnQtjxuhamn7zTb5PVTqVJB2EENZYv/5OwgFgyxbIyQF392ILSdzliqrSwZg64dCkie6/gjRvrkw67NsHQ4YUfJy6yqF0acdmOnl6Qq1aygHSMTGSdBBFokTPdLiXPfFEcUeA6SCb8+eLJw6QpIMQQgghhLi7tGunG1y5Z49jrRVsJUkHIYQ11DdHs7MhLq54YhH3BvUg6fLlnX+Nxo1N55cay2eAtIJ6NpO1w6TVQ6Rbt3Y8kSfDpEUxkaRDCfTuu9YlVgtd1arKZVcaJC1JByGEEEIIIWwnSQchhDV27jRdV5wPIoq7X1FUOvj7W25n6O4OFma7mlDPZjp4UFcJVBBnznPQk2HSophI0qEEatCguCPIo650sJB0uJV+i+S0ZG6l3yq8WCTpIIQQQgghhOMk6SCEKMitW3DokOn64kw6ZGZKpcXdriiSDmCaMNDr3Nn66gp1pUNqasE3+1NTTSsiJOkgSjBJOgj7qZMOly5Bbq7JbvU+qEfgqEDqfVDPZJvTSNJBCCGEEEIIx0nSQQhRkD17zH72V/SwL0o7d0JoKFSsCEOHglZbPHGIwpOdDdeuKdcVVtJBPddBz9rWSgDlypneMyuoxdLevbrXqefuDq1aWX9NS9RJh9OnTWdVWMvc770QFkjSQdhP/RdoVpZpj72ikpCgXC5TpnjiEEIIIYQQoiSTpIMQoiDqFjB6xVXp8PLLcPOm7uvZs3VDrUsCrRY2bIBVq6xrvXMvu3bNNJlUlEmHoCDo3t2286grJvbty39/9e9Vkya6QdKOCg9XLufmwn//2X6emTPxsfV7IO5pknQQ9itfHjw9leuKa66DVDoIIYQQQgjhOHNJB3lqWAhhzNw8ByiepMOZM6atntasKfo47PHGG/DYY7qb2U8/XdzRuDb1A65uboX3sKm5pEO/fuDjY9t51C2WbE06OKO1EkBAgK4KyFhMjG3nOHsWXn4ZTVKS3WGcOHGCsWPH0q5dOxo2bEjbtm0ZMWIE27Zts/ucABcvXmT8+PE8+uijNGzYkNatWzN48GBWrVpV4LFarZZly5YxcOBAmjdvTuPGjenUqRMTJ04kzo52bbNnzyY8PJzJkyfb81LYsWMHdevWJTw8nIsXL9p1DlciSQdhPzc3qFRJuU6SDkIIIYQQQpRc6vfRGRm6PtNCCAG6J/J37TK/rTiSDqtXm67bvLno47BVZib8+OOd5UWLZCZFftTzHEJDde2HCkOZMtCwoXLdkCG2n0dd6XDggOX2RLm5psk8ZyUdAOqp2p3bOtdh7lxl6ycbbdiwgcjISFasWEF8fDxZWVlcu3aNzZs3M2zYMD799FO7znvo0CF69OjBvHnzuHjxIllZWdy4cYPdu3fz5ptvMnLkSLItxJ2bm8ubb77J22+/zd69e0lJSSEjI4Nz587x22+/0b17d3bv3m11LIcPH2bKlCl2vQ6ApKQk3nnnHbR30YMeknQQjrFymHShysq6U0qpJ0kHIYQQQgghbGfuyVFpsSSE0DtyRDdI2hxXSTrs22d6j8DVXL0K6enKdZcvF08sJUFRDZHWmzxZl9jw8IB33oHWrW0/h7rS4fZtOHnS/L7HjoG6isCZSQdHh0n/8Yfdlz569CijR48mKyuLRo0aERUVxe7du1m0aBGPPvooAFFRUcyZM8em88bFxTF8+HBu375N9erV+fHHH9m1axcrV66kT58+AKxbt45JkyaZPX7SpEmszvv7Y+jQoaxZs4adO3cyZcoUKlSowK1btxg5ciRX1D97Zhw+fJjnn3+etLQ0m16DsQ8//JCrV6/afbwrkqSDcIwrJB3U8xxAkg5CCCGEEELYIyBAd5PFmCQdhBB6luY5gO6maXJykYXC7dvmqxpyc2H79qKLwx7qocgAN24UfRwlRVEnHTp21CXRkpLgs8/sO0eFCqZtjSy1WFL/XlWtCpUr23ddcxxJOvz7ry7ZaKcpU6aQkZFB1apV+fXXX4mIiCA4OJhGjRoxffp0HnvsMQCmTp1KSkqK1ef98ccfSUpKIiAggKioKB5++GFCQkKoU6cOn376KYPzBn9HRUVxQXWv8sqVK/z6668ADBs2jHHjxlGjRg3KlClDly5dmDNnDoGBgSQnJzNt2rR845g7dy4DBgwg2YG/+5YtW8batWvtPt5VSdJBOEaddCiOJxvMfQgKCSn6OIQQQgghhCjpNBoZJi2EsMzSPAe9orwnsHGjrgWcOa7eYsncE82SdLCsqJMOAN7eUKqUY+dQt1jav9/8fuqkQ9u2jl1XzVzSwdo2PnPn2n3Z2NhYw8yG4cOHU0r1/dRoNIwbNw6NRkNSUpLVN95v3rzJ4sWLARgwYAChoaEm+4waNQp/f3+ysrJYunSpYltUVBRZWVn4+fkxYsQIk2MrV67MoEGDAFi1ahWpZtpMHj58mIEDB/LRRx+RmZlJgwYNrIpd7dKlS3zyySe4ubnRs2dPu87hqiTpIBxTtapy2RUqHYKDTZ/OEkIIIYQQQlhHkg5CCEvyq3SAok06mGutpCdJh7uLepB0+fLFE4etrB0mXVhDpPXUSYfbt8GaQcVarUNJB33CQaPR0KFDB7P7VK5cmfDwcAA2btxo1Xn37NlDZmYmgMXz+vv706pVK7Pn1cfVsmVL/P39zR6vb/2UlpbGTjPJ1tdff529e/ei0WgYMGAAf9jRgio3N5dx48aRkpLCkCFDaNmypc3ncGWSdBCOcYX2SuoPQeb60AohhBBCCCGsI0kHIYQ5ly/D2bPKdYGByuWiSjpotfknHQ4ehMTEoonFHpJ0sE1xVDo4gzXDpOPi4PRp5TpnJx0qVQL1zXVrWizt3m36O2+DmJgYACpUqECZfO7V1a9fH9DNf7DlvG5ubtRTD8k2c95Tp04ZkhSZmZmczvt+51edUKdOHTzyHmg+YqG9VEREBAsWLODDDz/Ex8fHqtiNzZo1i+joaGrXrs0bb7xh8/GuTpIOwjHqpENcnG6wc1FSfwiSeQ5CCCGEEELYT5IOQghz1E/7BgSA+injc+eKJpZDh+DSJeU6N6NbXFot5D3N7JIk6WCbkpp0UFc6JCdDbKxynbrKISAAGjZ0bhwajX1zHVRVDrnVqtl02ct5w9ErVaqU734V82ZfxMfHk2XFPUX9ecuXL4+np2eB583JyTEMhI6Pjyc7O7vAuNzd3SmfV1FzSf13DbqEQVRUFI0bNy4wXnOOHz/Ot99+i6enJ//73//w8vKy6zyuTJIOwjHqpINWq3v6wcjyV5ezc9xOlr+6vHBikKSDEEIIIYQQziNJByGEOeqbow88ANWrK9cVVaWDusqhZk145BHlOldusSSDpG1TUpMOFSuatoJSt1hS/161bg3u7s6PxdakQ3Y2zJ+vWJWTN/TZWjfyfqYDAgLy3a906dIAaLVabt265fTzgm4OhPGxAIHqSi0Vfeslc0Oia9SoUWCclmRmZjJ27FiysrJ46aWX7J4H4eqk8X0JlJGRYXaIiSPS0tIU/7eajw++vr5ojI5LP3WK3HLlDMv1yt0pdbImbltj8YyLwzivmR0URKYTvj92f0+czFXiMI6huGNxlTiMYyjuWCQOU64Si8RhylVikThMuUosrhKHcQzFHYvEYcpVYrkb4/AMDFS+v46Pt+n99d34PXGUq8QicZhylVhKQhze27djfCs0MyIC/P0xfj435+xZMpx0vyLfWFasUMSS9fjjaEND8TLq3Z67cSPpLnpvwDsuDvVt5ezr1wv8u7Yk/Jw4XXo6fklJyusHBqJNTXWZ74dxDOpYvJs0wX3dOsNy1p49ZPXocWe7+veqVSuyHfi5tRSHx333KX9Xjx7N93fVbeNGfFQVOWnt2tkUS0beoPeCWg95e3ubHFNY59W3WVJvN0d//vT09AJjssWkSZM4efIkjRo14sUXX3TquV2JJB1KoLi4OOLi4grl3Gft6NXWoFw5fIyeZri8Zw83goOLLJbqp09j3BnuukbDpbz+bs5gz/ekMLhKHOA6sbhKHOA6sUgcplwlFonDlKvEInGYcpVYXCUOcJ1YJA5TrhLL3RRHaHY2xvXMaefPc9KO99d30/fEWVwlFonDlKvE4qpxaNLTaXrwoHKfihVxv3WL+4zWZZ8+bei3XlixeNy4QeO9e5X71K9Pjp8fxs9yux09yqmdO8l2wv0Jc3E4ou7585RSrUu9eJFTVn7vXPXnpDB4XrmCuoHNiaQkcoy+V67y/QDTWCpWqUKY0XLa338b/pwt/V7dcsLvkDqOID8/xe9qztGj+f6uVps5E+Nb+rfr1eOSjRUY7nn7azSafPfTarWGr93cCm7KY8959fsan9/a462JyVq7du3i119/xcfHhy+//NIwN+JudPe+srtYWFgYQUFBTj1nWloaZ8+epXr16vj6+tp0rGfNmooSyspaLRXyGeTi7Fi8c3IUyyG1axPgwPXtjaOwuEocrhSLq8ThSrFIHK4bi8ThurFIHK4bi6vE4UqxSByuG8vdGId73uBFvVJpafkOaizMWBzhKnG4UiwSh+vG4upxuG3fjsbos7fW3Z3KvXujOXVKcbzXtWvUq10bnHAjzVIs7nPmoDG6magtVYpK/fuDuzvaUaPQ3L5t2FY3Pp6cBx8slDgc4ZOSYrKuVFZWgX/XuvrPSWFwM/rzBNB6eVEnIgI0Gpf5fkA+P6+PPQazZhmWS586Rb26dUGjwW3bNtPfq8hIKKVOSTkeh/HvDIDX9evUq1jRdBg8QHo6vlu3KlZ5DhpEWFiYTQ9B+/n55Z0u/0oB4+oDa2Yb2HNefVWD/lgouKpCv72gighrJScnM27cOLRaLaNHj6ZmzZpOOa+rkqRDCeTt7a34JXEmX19f28+t6uHodeUKXkbnWHVoFWlZafh6+tKtSTfnx6Lqe+gVFqa4vqPs+p4UAleJA1wnFleJA1wnFonDlKvEInGYcpVYJA5TrhKLq8QBrhOLxGHKVWK5q+JQDVZ0S0y065x31ffESVwlFonDlKvE4rJxqPrQa5o0wS80VDeg1nh9To6uFU7VqoUXy4YNymt27IhfSIhuoW1bWLvWsM17504YMKBw4nCEmVk57jdvWn1+l/05KQyq1kqaChXwU92Ud5XvB5iJ5YEHFNs1SUn4xcfr5pD8849y2/3342fUrtypcTRqpJsVYZTk8LtwAcLCTA9eu1Y39NoQmAavZ5+1+ea7fiZCQXMa9DMT3NzcCpyzAHdmNVh7XoDgvIon4zkPBR2v3x7spGqpjz76iCtXrtCqVSsGDRrklHO6Mkk6CMep30xcuKBYHPH7CC4lXaJSUCUuNrno/OvLIGkhhBBCCCGcx9wgaa3W5OaiEOIeoh5226aN7v9ly4KPDxg/cXz+vFOTDgpZWbBmjXJdN6OHGx95RJF0cMlh0rdvg7le+jJI2rySOkRar0oV3e+J8b2rfft0SQdLv1eFwdtbd03j6qTjxyEiwnTfP/5QLrdvr3sgISHBpkvWqFGD6OjoAqsj9NvDwsKsamVUPe/h56tXr5Kbm2vxGP15PTw8CA0NNVzDx8eH9PR0Ll++bPEaOTk5xMfHA1CxYsUCY7LG6tWrAdizZw911YO9VR599FEAIiIiiIqKcsr1i5rzmlKJe1eVKsplVdKh0EnSQQghhBBCCOdRv5/OzoYCngYUQtzFcnNh507lOv3NUY3GNMFg1H7Z6f7+W/kENkDXrne+fuQR5baYGNOb1sVNNZzXIClJ970WSiU96aDRQLNmynX79+v+rHftUq5v27ZwY1Hf6D5+3HSf5GRYtUq57pln7LpceHg4AJcuXVJUHagdPXo0L7z8b8Srz5uVlcXJkycLPG+tWrXw9PQEdNUUtWrVAsh3psWJEyfIyasKsaXFpLhDKh2E44oz6ZCRYfoBSJIOQgghhBBC2M/c++nr1yEgoOhjEUIUv+PHTVrcYDwnoVo1ML7xd+5c4cWS96SwQbNmYPwUcrNmULq08j7Bli3w9NOFF5OtLCUdtFrdDV8nz/As8fKeNjcoX7544nBE8+awbt2d5X374OhRuHlTuV9hVjqALumwcuWdZXM33ZctU1YueXpCZKRdl2vXrh0Aubm5bNmyhR49epjsc+HCBUPi4KGHHrLqvBEREfj6+pKWlsbmzZvNJitSUlLYs2eP2fO2b9+eI0eOsGfPHlJTU8225tq4cSOgmzERYa4axA779+/Pd/uyZcuYMGECoKuKCAsLMwzNLomk0kE4Tp10uH4d0tKK5trmSrsk6SCEEEIIIYT9/Px0bRiMmek/LoS4R6hbwFSporwPUJSVDuonsJ94Qrns4QF5NzoNXK3FkqWkA0iLJXNKeqUD6JIOxvbtgx07lOuqV1cm0AqDNZUOc+cqlx9/HMqUsetyVapUoXnea58+fbrJDAWtVssXX3yBVqslODiYnj17WnVePz8/HnvsMQBmz55ttk3SlClTuH37Np6engwcOFCxrXv37ri7u5OcnMy0adNMjr1w4QK//fYbAJGRkQQ46aGLUqVK5fuf8RBtHx8fSpUqhY+Pj1OuXRwk6SAcp046AFwshNkN5qiTDhoNOGnAixBCCCGEEPckjcb8XAchxL2poL7zRZV0iI01vUmqTjqAaYslV0s6XLtmeZskHUzdDUkHdXulxETTuQmFXeUAoG4T9N9/ujkpeteuwfr1yn3693foku+88w5ubm6cPXuW/v37s2PHDhITEzl69CivvPIKG/IGw48cOdKk4qBz58507tyZt956y+S8o0ePxs/Pj6SkJAYMGMDatWtJTEwkNjaW999/3zAH4dlnn6WC6memRo0a9M97XbNmzeLDDz8kNjaWxMRE1qxZw8CBA0lOTiYoKIjhw4c79PrvZdJeSTiudGkIDFSWhV24ALVrF/611R9+QkKgBJceCSGEEEII4RLKloVLl+4sS9JBiHuXqyQd1K2VypWDli1N91MnHU6d0v19VqlS4cRlK6l0sM3dkHSoXl33gKzxn6+60qEokg55sxAMsrPh9Ok76xcuhLw5BoCu8tFMSyRbNGrUiIkTJ/LBBx9w8uRJnn/+eZN9hg4dyoABA0zWnzlzBoBy5cqZbAsLC2Pq1KmMHDmSy5cvM2rUKJN9OnfuzNixY83GNXbsWC5cuMCWLVuYP38+8+fPV2z38/NjxowZThsifS+SSgfhHMU110H94cfOki8hhBBCCCGEEal0EEKA7gb5f/8p1xnPcwDdTAdj587p5hM4mzrp0LUruJm5rdWkielcBFeqdpCkg23uhqSDRmPaYkmtKJIOISEQGqpcZ1w9pK6+6NkTSpVy+LK9e/dmyZIl9OzZkwoVKuDp6UlgYCBt2rRh+vTpjBs3zq7zPvTQQ6xevZqnn36aypUr4+npSalSpWjevDmfffYZU6ZMwc3c3xGAt7c3M2bM4H//+x8REREEBATg6elJpUqV6NevHytWrKBp06aOvOx7nlQ6COeoUgWOHLmzXFxJB5nnIIQQQgghhOMk6SCEANi5U7lcqhQ0bqxcp650uHVL1wnBmQORU1J0A6GNdetmfl93d2jfnv9n777DmyrfPoB/T7pLoS27ZRVBhuwNvgIKooggW5GhDMGB8EMUXIACIqAyRLYiSzYKMhQUZI/KHmUXECilQEv3TvP+ERJyzknSNDlJTsr3c11eNs9Zd0uaJs99nvvG778/Gtu1C5DUdXcbJh1sl5oKpKeLxzwx6QDoSyw9LCUkExwM1Krlmjhq1BA/B8+f1ycX/vtPvqrp9dcVu2z16tXxzTffFOiYixcv5rtPuXLlMH78eLtiEgQBXbp0QZcuXew6XsqWeK3p2bMnevbsqUgsasCVDqQM6UoHZzaOMsWkAxERERGR8ph0ICJAPgnZvLm+WbOp8uXlxyk9J7BjB5Cd/eixtzfwsJGsWWru66DGng5arb43pzNWqDhCusoBAMqUcX0cSrC20qFFC9eVCrfUTHr1avF4aKi+iTSRnZh0IGVI72wwWekQ5B+Eov5FEeQfpPx1mXQgIiIiIlIekw5EBMhXOpgrAePnJ7/7XOmkg7S0UsuW+rvDLZEmHa5d09/JrQZqW+lw44a+wXCFCvrJb+nKAneSJh2CghQp9+MW0mbSplxRWsnAUtJh1SrxeI8egK+va2KiQolJB1KGlZ4OFyZeQPIPybgw8QIUFx8vfsykAxERERGR45h0IKLMTODoUfGYtJ+DgTObSet08qSDpdJKBrVry3s+qmW1g9qSDjNm6JttA0BkJDBliutjsKQw9HMwqFLFcqLMlUmHmjXFjy9cAM6dA06dEo/37u26mKhQYtKBlKGWRtJMOhAREREROY5JByI6dkxc0kgQ9OWVzDHXTFohwsmTQGysePDll60fpNEAzz4rHlND0kGnU195pePHxY+XLNGXW1KDwpR0EATzqx28vICmTV0Xh3SlQ1KSPvFkKjxcv5qIyAFMOpAypEmH5GT9f87GpAMRERERkfKYdCAiaT+HOnUs36ntxJUOXtu3iweqVgWqVcv/QHN9HdzdsyApCcjJsbzdHUkHaYLo5k1g507Xx2FOXJz4sScnHQDzSYeGDV1bMqpiRcDfXzy2eLH48Wuvua7HBBVaTDqQMsw1jnLFagfphx/p8kkiIiIiIio4adIhPh7Iy3NPLETkHrb0czBwZtLhzz/FAy+/rL9rPD/SpMPNm8DVq4rFZRdrpZUA1ycdcnP1DaSlfv7ZtXFYIl3p4KlNpA3MNZN2ZWklQL8KqHp18Zh0ZQtLK5ECmHQgZfj7A6VKicceJh1GrRuFt5a+hVHrRil/Xa50ICIiIiJSnvR9dV4ekJjollCIyA10OnnSwVI/B8BpSQfv+Hhojh0TD+bXz8GgZk35JLW7SyypLelw+7b5UkobNgAJCa6NxZzCVF4JUEfSAZCXWDJVtar5OIkKiEkHUo6Fvg6r/l2FRfsXYdW/q5S9XkYGkJYmHmPSgYiIiIjIceZWEMfHuz4OInIL4coVee8Ba5Oj0p4Ot29bLyNko+CDByGYlkQKCgJatbLtYEFQX18HtSUdLPXeyM4GVq50bSzmFLakQ9WqQLlyjx77+dn+fFaStJm0qd69bVtJRJQPJh1IOa5uJm3uQw+TDkREREREjgsIAAIDxWPs60D02NAcOiQeCAsDIiIsHyBd6ZCXB8TEOBxH8P794oF27QBfX9tPoLa+DtKkQ0CA+HFiomtL2Vlr+K2GEkuFLemg0QALFgDFi+v/7WfOBEqXdn0c1lY6vP666+KgQo1JB1KONOmgYA1Hs6RJB40GCAlx7jWJiIiIiB4XbCZN9NjyiowUDzz9tPW7n4sXlycqHZ0TyM5GscOHxWO2llYykCYdYmOBS5cci8sR0tUj0obYeXlASorr4rH2b3TihP4/d9HpCl8jaUDfkyQ+Xl+545133BODpaRDgwbWExJEBcCkAylHemeDs1c6mGsireFTmoiIiIhIEUw6ED22NNLJ/vzqzguC4n0dNAcPwktaUrlDh4Kd5MkngfBw8Zg7SyxJVzpIG/oCri2xZG2lAwAsXuyaOMx58EBeosvTG0mbcmcJoyefNH99rnIgBXGGlpTj6vJK5pIORERERESkDCYdiB5LXklJ0Fy4IB60pdmttK9DfhPa+cWxbZt4oHHjgt/pLgjmSyy5izTp8MQTgJeXeMydSQdp9YhffgEyM10Wjoi0tBLgnlJEhVFgoPz3FQB69XJ9LFRoMelAyjGXdHBmrUTphx72cyAiIiIiUg6TDkSPpaDTp8UDAQH6siv5UXilgyzp8PLL9p1ImnTYvdt9fR2kSYcyZeQT/e5MOowcKY9l0ybXxWNKmnQoXlzfeJmUIS2j1LKlfF6PyAFMOpBypC9OmZnmmz0rhUkHIiIiIiLnYdKB6LFURJp0aNIE8PHJ/0Alkw6XL0Nz+bJ4TKmkw927wLlz9p3LUdKeDqVLA6Gh4jFXJR10OnnS4dln5T8vdzWULmxNpNWmUyfxY3f1l6BCi0kHUk54uLyngjNLLDHpQERERETkPEw6ED2Wgk6dEg/YUloJUDbpsHWr+HGZMkCjRvadq3JleWzuKrEkXengzqTD/ftARoZ4rFIlYOBA8dhffzm/fLY5TDo411tvAR9/rE8qTprEfg6kOCYdSDne3kBYmHjMmX+YpKsomHQgIiIiIlIOkw5Ej5/sbBSJihKP2Zp0MNfTwd4yRtKkQ4cO8pscbaWWvg5arfx1tFQp9yUdpKscvLz0N5N26wYUK/ZoXKcDli617xoxMUDbtvrv8+OPC/Z8iIsTPy5MTaTVwNcXmDIF+Pdf4LPP3NvYmgolJh1IWWb6Orxc92X0aNQDL9e1cymkJVzpQERERETkPEw6ED12NKdPQ5OVJR5s0cK2g6WrCdLS7JtAT0kB9uwRj3XsWPDzmDLX1yEvz7FzFlR8vHzS3Z0rHaRJh3Ll9DeTBgbKGwovXlzwn1d2NtC5M/DPP/q/H998I08mWcOVDkQejUkHUpaZpMOCfguw7p11WNBvgbLXYtKBiIiIiMh5mHQgeuxoDh0SD9SsqW/ga4ty5eR3S9tTYmn3biAnx/hQ5+MDtGtX8POYkiYdEhKAM2ccO2dBSfs5APrXWbUkHUxXqkhLLF29CuzdW7Dzf/YZcOyYeGztWtuPZ9KByKMx6UDKUrKGozWxscC1a+KxEiWccy0iIiIioseRNOnw4AGQm+ueWIjIJTSHD4sHbC2tBOjLtUhLLtszJyCJIa9ZM6Bo0YKfx1TFisATT4jHXF1iSdrPoXhxfYNuNSYdmjYFnnpKvL0gDaW3bQOmTZOP//mnvsyULZh0IPJoTDqQssysdFBcXJy+JmBionhc+uaGiIiIiIjsJ0066HSumwwjItfT6eAlXelQkKQDYL6vQ0GZSzoowd19HaRJh1Kl9P9XY9JBEOSrHdavB5KS8j/vnTvAm2+a33b/PnDkiG3xMelA5NGYdCBlOTvpcP8+8PzzwPnz4vF69fT/ERERERGRMsytJI6Pd30cROQa169DkDbvLWjSwdHqB1qtvrGtibymTQt2DkukSYc9e2y/614J0qRD6dL6/6sx6QAAffvqezwYZGQAa9ZYP2deHvDGG/Lv1dSWLfnHZq7pNpMORB6FSQdSljTpEBODxhMbo/yo8mj8VWPHzp2QoK/jePasePzJJ4E//hD/MSQiIiIiIsf4+gLFionH2NeBqPA6cED8uFQpoGrVgp3D0aTDuXNAaqpoSNvYwbkEA2nSISkJOHlSmXPbwtOSDmXKyBt451diado04O+/xWP+/uLHtjSTvndP3ri6TJn8jyMi1WDSgZQlTTrk5uJOYgxiEmNwJ+mO+WNskZgIvPCC/A3BE08A//wDhIfbf24iIiIiIjJPutqhSxegfHmr//lXq4YqH3wAISbGLSETkZ02bRI/fvppeWPo/DiadJCUVsoKD1fuDvfwcKBaNfGYK0ssSRtJuzPpkJIiv4406QDISyxFRgJRUebPeeSIvnm0qbAwYMUK8djJk8CtW9bjk5ZW0mjkJf+ISNWYdCBllS6tb4RkKtfB5YrJyUD79sCxY+LxihX1CYfy5R07PxERERERmSed5ImPB2JirP6niYlByL598O3VS36nKhGp0/79wLp14rFWrQp+HunEtYNJh7TatQsegzXS1Q579yp7fmts7emQmKjvoeNM5nptSBNGAPDSS/Kkz+LF8v2Sk4FevYDc3EdjgqBPOLzyChASIt7/jz+sxydNOpQuDXh5WT+GiFSFSQdSlkYjTwI4UiMxNRXo0EGfTTdVrpz+jgRzmXgiIiIiIlLGE0/YfajX8ePA2rUKBkNETqHVAu+/LxrSBQcD/foV/FzSievYWCA72/bjpUmHOnUKHoM10v4Q+d1xryRbyytptfqVCM4kTTqULg0EBMj38/bW92gwtWwZkJPz6LFOB7z7LnD1qni/zz7TJ3m8vfU3kprKr68Dm0gTeTwmHUh50hJL9iYd0tP19QOldSXDwvQJBwc+ABERERERkQ1GjACCguw//vPPCzbhSESut2ABcOqUaChn7NhHd+IXhDTpoNPZPrGflAScPy8aSlU66SAtGeeq/gmA7UkHwPlx5dfPwdSAAeLH9+7Ba9u2R4+XLQNWrhTv06IF8MUXjx5Le0Ps3KlvTG2JtKE5kw5EHoedd0l5CiQdhMxM+L36KrBnj3hD6dL6kkpPPulAgEREREREZJPmzfXlUY4fF5fNsOTsWeCjjx49vnoV+PFHYOhQ58VIRPa7fx8YM0Y0lF61KjB4MHztOV9IiD5RadoM+sYN224aPHJEVFZI5+uLDGkPBkdJy/wkJip7fmss9XQoVkxfOsh07uTBA+dWdihI0qFGDX1/j4MHjUNey5YBEyZAuHxZ/voeHKxPQpiW3m7fXl8Zw1ByLz0d2L1bX77JHOlKBzaRJvI4TDqQ8qR3Nmi1BVtTk5mJKqNHw8vkDxoAfT3ZnTv1f/CIiIiIiMg1QkOBtm1t2/eFF6Bdtw5epuVRJ0zQl+coWtQ58RGR/T7/XHZX/c1Ro1DB287pIkHQT2CbNhu2ta+DpLRSXr160PnalfqwTLqqIClJPxGucXIhkOxseYLDkHQQBH0yJD7+0TZnr3SQ/pvkl+AYOFCcdNi+Hb7vvgu/MWOAtDTxvj/+CEREiMdKlNCvfjCtZLF1q+1JB650IPI4LK9EypOudLDljiiD7Gz49e2LYGnCoXhxYMcOQOkmUkREREREpBxBQM7EieKxu3eB6dPdEw8RWXbsmH6C2ERujx5IbdTIsfNKb0Q017TYHGnSoUkTx+IwR7rSQafTN0F2NukqB0BcvkqaDFFTeSUAePVVIDDQ+FDQalHt7behkZTlwuDBQM+e5s/x8svix1u3Wm6YzaQDkcdj0oGUZ295pbw84PXX4fXnn+LxkBDg77+BevUUCY+IiIiIiJwn7//+D4ktW4oHv/tOXqObiNwnL0/fPNp00jcwEDlff+34uaVJB1tWOuh08qSDtOmzEtzRPwGQ93PQaPQ3VxqoPelQtKg+8WDC7/Zt8T41awIzZ1o+hzTpcP06cO6c+X2ZdCDyeEw6kPKkSQdDzb78LFoE/PabeKxoUWD7dqBhQ2ViIyIiIiIip4sZOhQ6QXg0kJoKfPWV+wIiIrHly2WT/Bg7Frpy5Rw/tz1Jh+hocXkhOCnpEBQkL6Xkir4O0qRDqVLiOFyZdMjOBmJjxWO29I8YONDyNj8/YPVq0WoImTp15PNFW7ea35eNpIk8HpMOpDzJH5Fv/gV+7DAZ3/T4xvIxiYnAZ5+JhnRFigDbtgHOeKNBREREREROk1m1KrR9+ogHFyzQTywSkXslJQGjR4vHnnwS+OADZc4vncC2JekgTYCUKQOdNHmhBEP/BFOuSDpYaiJt4Mqkw82b8rJGtiQdnnkGqFrV/LZp04C6da0fLwjy1Q5btsj3y8qSf/9sJE3kcZh0IOWFhoqy272jgbdK/h96N+tt+ZgJE4D790VDWcuWAU8/7awoiYiIiIjIiXLGjNHf/WocyAHGjnVfQESkN368/M77778X/746wlxPB0u1+w2kSYfmzfWT1M7g6lJGgPmVDqZcGZO0tFKxYvJEjDmCAAwYIB/v3Bl47z3brt2xo/jxwYNAQoJ4zFwpPq50IPI4TDqQ8gRBvmTu5k3L+1+4APzwg2jowbPPIq99eycER0RERERErqCrUAEYNkw8uGoVcPy4ewIiIiAqCpg1SzzWqRPw0kvKXUOadMjIkJVOkjGXdHAWd6x0kCYd3LnSQZp0KMiKkjffFN1kmhceri+VbWuC6LnnAH//R4+1Wn1JbVPSfg6+vrYlRYhIVZh0IOewNemg0+mXcObmPhry9cWtESOcFxsREREREbnGp58CwcHisU8+cU8sRI87nQ4YPlw/0Wvg5wfMmKHsdcLD5X0TrJVYSk8HTp0Sjzkz6aCGlQ5qSjrYUlrJoFw5YPVqaBs0QHLTpsjauhUoUcL24wMDgbZtxWPSvg7mmkg7a9ULETkNkw7kHCZJh4vBQNR/J3HxzkX5fn/8oe/bYCJ3+HBkly/v7AiJiIiIiMjZiheXJxn+/hvYscM98ZD7ZWfD+9tvEfHFF9Ds2+fuaB4v69cD//wjHhs1CqhSRdnr+PjoJ6dNWUs6HD8uuhERGg3QuLGyMZl63Hs6OJJ0AIBOnZC1fz8uz50LXbVqBb++tK/Dn3+KE2FsIk1UKDDpQM5hsjyvbQegds5qtJ0myWZnZ8sbVYWFIeejj1wQIBERERERucTw4fo7n0198gmQl+eeeMi9Jk6E75dfosTWrfB75RXg6lV3R/R4SEsDPvxQPFahgn41kjOY6+tgibS0Up06QFCQ8jEZqGGlg5p6OhQ06eAoadIhIUH8HJCudGATaSKPxKQDOYe0vJI5s2YBly+Lx6ZOBYoWdU5MRERERETkeoGB+sa1po4dA9atc0885F4bNxq/FLKzgbVr3RfL42TyZHnZ4+nTRfX5FSVNOlhb6eDKfg4Aezq4O+lQsaI+sWRqy5ZHX5srr0REHodJB3KO/JIOcXHAhAnisWbNgD59nBcTERERERG5R//+QI0a4rHPP9evfqbHh04HXL8uHtu92x2RPF6uXAG+/VY81qYN0L27866p5qSDGlY62JJ00OmUjyMvT558cnXSAZCvdjDt68CkA1GhwKQDOYe5pIPpH8zPPwdSUsTbv/9e3myKiIiIiIg8n7c38PXX4rHoaODHH90TD7lHQgKQmioeO3BAXM+flPfBB+IEn7c38MMPzm3OK53ItpR0uHULiIkRjxW2lQ5pafpm2abySzpotfLfFSXExgI5OeIxdyQdOnYUPz5z5tFzhEkHokLB290BUCFlLulgaAx07Bjw88/ibW++qV/pQEREREREhVOXLvrJRNO7midM0H8WcGb9dnfLyYFw/TqErCx3R+J+0lUOgH5i9fhxoGlTl4fjsQ4fBjZskE9km5OSIi5dAwDDhgFPPeWc2Axs7ekgXeUQEgLY05y4IKQT/M5OOkibSAP593QA9KsdlC4/Lf138PV1T8+E5s2B4sX1iUiDrVuBd99lI2miQoJJB3KOoCD9mwXTP95arX61w/Dh4lUPQUH6+pJERERERFR4CYK+h1vr1o/G7t7V15UfN859cTlTYiLw7LMIOHUKtcqVQ96OHfIyU4+Ta9fMj+/ezaSDrf79F3j6aftL75QpA3zxhbIxmSNNOsTFAZmZgL+/eFyadGjWzPkVEKQrHZxdXklaWsnXFyhWTDxWtKj++87LE8cl/Tk6Spp0qFjRPRUnvLyAl14CVqx4NGZIOrCRNFGhwFo25DzS1Q5aLbB6NXDwoHj888+BsDDXxUVERERERO7RqpW8rMa338on5QqLH38ETp0CAPjFxMB71iw3B+Rm5lY6AMCePS4Nw6PNnOlYrf+pU4HgYMXCscjcZPmtW/IxV/dzAFy/0sFcPwdpaSuNxjXJEHc3kTYl7euwc6f+Z5WWJh7nSgcij8SkAzmPNOmQmwOMGiUeq1JFX1+SiIiIiIgeD5MniyfcUlOBr75yXzzOdOiQ6KHXgQNuCkQlLCUd9u9/VI6XrNu/3/5j27QB+vVTLhZrgoPlyQ1pX4fsbH35ZVOuSDpIJ/czMgBnlj/Lr4m0gSsaXKsp6fDii/oVDwaZmcCqVfL9uNKByCOxvBI5j/TOhtRUIEbSCGnaNMDPz3UxERERERGRe9WuDbzxBrB06aOx+fOB0aOB8uXdF5czPFzlYCBEReknOAMC3BSQm1lKOiQnAydPAo0auTIaz3PjBnDzpnisXz8gMDD/Y598Enj7bdeW0qlYUd8g2EA64X36tH6i2ZQrymxJkw6AfrWDsya3pT0dmHTQK15cXyps375HYz/9JN4nKKhw9/whKsSYdCDnka50kK4Aff554JVXXBYOERERERGpxIQJ+tKrhruLc3KAbduAt95yb1xKSk4Grl4VDQlaLXDihH6i7XFkKekA6Ps6MOlgnXSlTGgosGSJe2ry20KadJCudJCWVqpeXT8R7Wzmkg4PHjgv6SBd6SBtIm3giqSD9N/AnUkHQF9uzzTpcPaseDtLKxF5LJX+ZaJC4WHS4chG4OZK/f+NvLz0tSildQyJiIiIiKjwq1gR6NRJPCZZFeDxTp82P37kiGvjUAudznrSgX0d8idNOvzf/6k34QDIqx/kl3RwRWklQF9tQbrayJl9HdRSXkmnU9dKB0De10GKSQcij6Xiv07k8R4mHcIygPLp+v8bvfceUKuWe+IiIiIiIiL3q19f/LiwJR0sfT+Pa9IhPl7eINbUvn3s65AfaT+H//s/98RhK+mEtlqSDoBrVhUYqCXp8OCBvuy1KXcnHZ56ynoM7OdA5LGYdCDnkZZXMiheHPjyS5eGQkREREREKlOvnvjx6dP6O3ELCyYdxK5dEz3USVe9JyZaXh1CQFKSuFQRADzzjHtisZW1lQ737wPR0eLtrkw6SEssOXOlg1p6OkhXOWg07u+jIwj6EkuWcKUDkcdi0oGcx9Ifr4kTXVOnkYiIiIiI1EuadEhKkt8J7cksJR0uXXLuBKdaSUorZYeFIa9KFfE+LLFk2eHDQF7eo8e+vkDjxu6Lxxbmkg6GxGJkpHhbYKC+ybyruHOlg7t6OkiTDuHhgI+Pstewh7USS0w6EHksJh3Iefz8gNKlsbA6ML02sLA6gDp1gCFD3B0ZERERERG5W/ny8ruNC0uJJa1Wfle6qWPHXBeLWphLOkjv1GfSwTJpP4fGjQF/f/fEYitp0iEz89Fd/9LSSo0bA97erokLcN1KB51OPeWV1NbPweC55/RJJ3OYdCDyWEw6kHO1aYMJDYEPmwMTGkLfPNqVbySIiIiIiEidBEG+2qGwJB2uXAEyMixvfxxLLEmSDlnh4dBKkw5794rv5qdHPK2fAwCEhQFeXuIxw2omd/ZzAOQT/M5KOiQlATk54jG1JB2kSSF38fcH2rY1v41JByKPxaQDOdeUKY/uvggNBdq0cW88RERERESkHoU16ZDf98Gkg36lQ8uW4n0SEoCzZ10Xk6fIyZGXI1J7PwdAf8OhtOzyjRv6lUDS78fVSQfpSgdnlVeS9nMA1FNeSS0rHQDLfR3YSJrIYzHpQM5VqRJQooT+a0vL5YiIiIiI6PFkrpl0YXDypOihTiP56M2kA7LCw6GrUAGoXFm8H0ssyZ08CaSni8eeftotoRSY9G76//4DLlwAUlLE482auS4mwHUrHaSllYoUsTw3Io0pIeFRDwwlqDnp0KGD+XGudCDyWEw6EBERERERkXvUrSt+fOUKkJbmnliUJFnpoG3fXrz95k0gLs6FAbmZTidf6RAerv+idWvxvkw6yEn7OdSoAZQs6Z5YCspcM2lpaaWKFfVNjV3JVSsdbO3nAMiTDlotkJqqXCxqTjqULw/Ury8ft/bzIiJVY9KBiIiIiIiI3KNWLcB0FYBOZ70Bs6eQJh26d4c2IEC8z+O02uHePdmd+lnWkg5K3t1dGHhiPwcDW5IOri6tBLiukbQjSQdAuWRIWhpw/754TE1JBwB4+WXx4+LFAT8/98RCRA5j0oGIiIiIiIjcIyAAqF5dPObpfR3i44GYGNFQXr16SK9ZU7zfv/+6MCg3k6xy0Hl7I8dwp7406XD/PnDunGvi8gQ6nXylgyf0czCQTmyrJeng7P4JBgVJOhQrBgiCeEypuAwNvE2pLenQvbv4ccOG7omDiBTBpAMRERERERG5T2Hr6yBNmvj5Qffkk0irVUs8/jitdJAmHcqX1zcZBoCICPnd8Cyx9MjVq8CdO+IxT17pcPEiEBUlHivMKx2kjaStJR00GueVfZKWVipRQt9fQk0aNAAmTdLHVb068O237o6IiBzApAMRERERERG5j7Svg6evdJDGX7s24O2N9KeeEo8fOfL4lBGSJh1MJ6IFgX0drJGucihVCqha1T2x2EOadEhJET/vfXz0k82uZq6RdF6e8teRrnQoVcr6/s5agaHmfg6mPvtM/xy5cMF8jwci8hhMOhAREREREZH7mFvp4IzJP1eRJh0efn9p0qRDfLxsMr7QkiYdpBOe0qTD7t2PT0ImP9J+Ds88Iy/Bo2bSpINUgwaAv79rYjElXVGQl6ds02aDgpRXAph0ADzr+U1EFjHpQE5XrUw1PBX2FKqVqebuUIiIiIiISG2kSYeUFM+ejD95Uvz44feXHR4OXYkS4m2PS4klyb9nXkSEeLs06XD3rr4Mj7NdvQrv2bMRdOyYepMc0pUOnlRaCQCKFjXfINnAHaWVAOc2bTbFpAMRPaa83R0AFX7/fPSPu0MgIiIiIiK1Cg/X1xePj380dvo08MQT7ovJXtnZ8ibIhhIhgoC8hg3h9fffj7YdOQK8+qrLwnMba+WVAKBKFaBcOXED7j17gBo1nBfT7dtAs2bwvX8f1QFkBQQA/fo573r2SEiQP588qYm0QcWKlifP3ZV0KFpUf0e9abIpMVH5yfiC9HQAmHQgokKDKx2IiIiIiIjIfQSh8PR1uHAByMkRj5l8b3mNGom3PQ4rHXS6/MsrmevrsHu3U8PCvHnA/fvGhz7ffefc69nj4EHxY39/9/Q/cJS1EkvuSjqYa9qsdDNprVb0HAPgvp4ON26IHzPpQEROVihWOly8eBE//fQTIiMjkZCQgJCQENSuXRu9e/dGq1at7D7vmTNnsGTJEhw9ehTx8fHw9/dH1apV0aFDB/Tq1Qu+vr4Wj83MzMTKlSuxbds2REdHIysrC6VLl0bTpk3x5ptvombNmnbHRUREREREVKjUqwfs2vXosacmHaRxV6qkn9hMTwdgJulw7Jh+YtLLyzXxucPdu0BGhmhIV6mSvoyWqdatgZUrHz3es0efsHBGfXedDli3TjSkOX1aH2t+d6K7krS0UrNmgJV5CNWylHQoXRqQltpypZAQ8aS+0uWVEhLk/WncsdIhJ0e8ighg0oGInM7jVzrs2LED3bt3x6ZNmxAXF4ecnBzcu3cPu3btwuDBg/HVV1/Zdd5ly5bhtddew5YtW3Dnzh3k5OQgJSUFJ06cwKRJk/Dqq68i3nT5r4n79++jZ8+emDp1Kk6dOoXU1FTk5OQgJiYGGzZsQI8ePbBixQpHvm0iIiIiIqLCQ9rXobAkHSTfl7ZhQ/H21FTX9C5wJ2l/Dm9v6MLC5PtJVzrExgJXrjgnpnPnzP/cd+50zvXsJW0i7Wn9HAwsTXA3b+7epsHSCX6lVzpI+zkAQMmS1o9xRtIhJkae/GDSgYiczKOTDlFRURg5ciRycnJQp04dLF++HIcPH8b69evRtm1bAMDy5csLPMF/8OBBfP3119BqtahSpQrmzp2Lffv2YdOmTejXrx80Gg3Onz+PESNGmD3+f//7Hy5dugQvLy8MHDgQmzdvxr59+zBnzhxEREQgNzcXEydOxKFDhxz9EXiEPj/2wYszXkSfH/u4OxQiIiIiIlIjadLh6lUgOdk9sTgin6QDypYFypcXjxX2EkvSpEPFiuZXdlSrpv/5mNqzxzkxrV9vfty034a7ZWXJnxue2M8BsLzSwV2llQyk5ZWUXukgTTqEhua/UsUZSQdpP4ciRYDixR0/LxGRFR6ddJg5cyaysrJQsWJFLF26FE2bNkVoaCjq1KmDOXPmoF27dgCAWbNmITU11ebzLly4EDqdDiVLlsQvv/yCtm3bonTp0qhevTrGjBmDIUOGAAD+/fdfHJG8CTh69CiOHj0KAHjvvffw8ccfo1q1aihdujSef/55LF++HKGhodDpdJg3b55CPwl123NpD/469xf2XHLSG0YiIiIiIvJsNWvKJ6LPnnVPLPbS6fJPOgBA06bix//+67yY1ECadLBUTseVfR2sJR1MGwu707Fj+sSDgSAALVq4Lx5HeErSQemVDtIm0vn1cwBck3SoVMm9K0yI6LHgsUmH6Oho7N27FwAwZMgQFClSRLRdEAR88sknEAQBiYmJ2L59u83nPn36NACgTZs2KG4m+9urVy/j16ckbyrPnDlj/Lpnz56yY0uXLo02bdqIrkNERERERPRY8/cHatQQj3laiaXYWPkko7mkQ5Mm4seP20qHypUt7ytNOhj6OijpwgXLCa1bt9RT7kraz6F2bfkkuacwl3TQaIDGjV0fiylnNW02kK50sKVfiKuSDkRETuaxSQdDwkEQBOMkvlT58uVRvXp1AMDOAtRm1Gj0P5bc3Fyz2318fIxfe0nuxhFMssX5HW+4DhERERER0WPP0/s6SOMNCgKeeEK+nzTpcOoUkJ3tvLjczdaVDoA86XDrFnDtmrLx/Pqr9e1qKbFUWPo5AEBYGGAyjwJAn0QpWtQ98Rg4e6WDUkkHRxNv0qSDpZUnREQK8thZ7/PnzwMAypYtixIlSljc76mnngKg7/9gq7p16wIA9uzZg4SEBNn2DRs2GL9uKGkEZjhWup9BUlISdu3aZfZYIiIiIiKix1ZhSzrUrau/m1uqUSPx4+xsoDCvgpcmDawlHWrWlJegUbrEkqS0kk5aZkYNSQedTr7SwVP7OQD63wNpLxN3l1YCPGOlQ24ukJbmWBxc6UBEbuCxSYfbt28DAMqVK2d1v/DwcABAXFwccnJybDr38OHD4efnh/j4eLzxxhvYtWsX7t69i+joaMycORPff/89AKBr166oJ3lj3LBhQzz//PMAgLlz5+Lbb7/F5cuXcf/+fezduxdvvvkm4uLiULRoUXz00UcF+p6JiIiIiIgKLZMbuAAAZ84AeXnuicUetvRzAPR3V1erJh4rrCWWdDr5hKe1pIO5vg5KNpO+cgU4eVI0pH3zTfE+u3cDNs4dOM3Fi0B8vHjMk1c6AMDDG0KN1JBEcXVPB3uSDoDjyRAmHYjIDbzdHYC9Hjx80S1WrJjV/Yo+XK6n0+mQkpJitkeDVP369bF48WJMmjQJUVFReOedd0TbQ0JC8O677+KNN94we/yMGTMwY8YMrFy5Ej/99BN++ukn0fZWrVrh448/RtWqVfONxZysrCykp6fbdawlGRkZov8rSfdwKaBOp7MpbmfGUhCMQ04tsaglDtMY3B0L45BTSyyMQ04tsTAOObXEopY4TGNwdyyMQ04tsTAOObtjefJJBJo+TktDRlQUdFWquDYOO/mfPCm6qy+7Zk3kPvz8I43Ft0EDeF+6ZNw399AhZEsnv53A5c+TO3cQmJkpjqFMGatxeLdoAV+T1Qh5u3cjU6HPv96rVsHX5LGuZEkkf/ghii9Z8mgwJQWZe/Yg7+mnFbmmrUx/Jl67dsHPZFteeDgyS5UCFJ4HyC8OJWmGD4ff3r0QUlKgbdgQWZ065fv9OPv56hUYKP45JySYfa7ZG4ffnTswLcidHRxsfE2wyMcHAYIAwaSkUsbt29A9rPBR4Fjy8hBw4wZM1/NklimDPAefS2r5m6OWOExjcHcszowjy7S5PVE+PDbpYHii+/v7W93Pz+/Rn5CC/HKkpqbKmlMbpKSk4PTp04iJiUGFChVk29PS0qDRaODv749MyRssQN8E+/jx43YnHWJjYxEbG2vXsfm5Lq23qQBDb4vc3FxjWSx3xWIPxiGnlljUEgegnlgYh5xaYmEccmqJhXHIqSUWtcQBqCcWxiGnllgYh1yBY9HpUDc0FD4md/Xe/vNPJLZt69o47CBkZqKBSRIBAK4WLYo0yecfQyyly5eH6SfJnIMHC/RZyVGuep4UOXMGpu3B87y9ce7BAyA52WIc/uXKoZbJY82NG4jeuRPZD6sYOKLG6tWipMP9Vq1wIysL/k8+icDLl43jCWvXItbcHecucP36dVT680/RZHhirVq4duGCy+NQVIkS8P71V/jcvYuMJ58EoqPdF8tDxZKT8aTJ49z7963+HhY0jlq3bomSDreys/HAht/zekFB8E5JMT6+ceoUUr3F03e2xuJ9/z7qSebCLmVlIUeh1xu1/M1RSxyAemJRSxz0+PLYpIOhgbMgrb8ooTPJDtvauPnnn3/G1KlTAQDt27fH22+/japVqyI1NRV79+7FtGnTsHXrVvz7779YtmwZnjBpDhYXF4f+/fvj6tWrKF68OCZNmoQ2bdogKCgI0dHR+Pnnn7Fp0yaMHTsW0dHR+PTTTwv6rSMsLAwh0mWADsrIyMD169cRERGBgIAARc/t/fCPo7e3N2rWrOnWWAqCcag3FrXEoaZYGId6Y2Ec6o2Fcag3FrXEoaZYGId6Y2EcysaiqV8feNgDDwAqPniAMBs+QygdR0Fpjh2DYFIKSicIqPjyy8DDG9mksWg6dACmTTPu73/tGmpWrGjc31lc/TzxOntWPFCxImrWrm09jurVoStRAoJJeaFqsbHQOph8Em7cQMC5c6KxogMGICIiAsnNmomSDmVOn0aInc87e5n+TEIlE8JBL75o02dppePw5NcSW2geJr8MfNLSzP6c7Y3DT3L+8Pr1UdaGf0dNiRKASdIhIjgY2ofHFTQWjaR0m87bG1VbtgS8vCwcYRu1PE/UEoeaYnFmHImJiU67CZoKH49NOgQG6hfemltJYCo7O9v4ta+vr5U99a5du4bvvvsOAPDaa69hwoQJxm3FixdHly5d0KxZM3Tv3h337t3D+PHjsXTpUuM+3377La5evYqgoCCsXLkSlStXNm6rWbMmvv32W4SFhWHBggVYsmQJ2rVrh8aNG9v2TT/k5+dn/P6VFhAQoPi5DYkhQRAKdG5nxGIPxiGnlljUEgegnlgYh5xaYmEccmqJhXHIqSUWtcQBqCcWxiGnllgYh5xdsTRoIEo6+Jw/Dx8Hvx+X/EwuXhQ9FKpWRaC0IbJpLM2b6yf9tFr9/nl5CLxwAWjZ0rlxSuNwNsnklOaJJ0TXtRhHq1bAhg3Gh36HDwNDhjgWyx9/iB+XKAH/F19EXk4OEpo1Q9lffjFu8jp6FIE5OUBwsGPXtENAcjI0V66Ixnyfew6+Lv699vjXEluULSt6KKSlIdDHB/DxcTyO7GxZjwj/ihUBW44vXhwwuUvdLz1ddpzNscTFiR4KFSog8GEZciWo5XmiljgA9cTijDjcXTqKPIvHNpIOCgoCoC91ZE3yw8yyRqNBsA1vGNavXw+tVgtfX1+MGjXK7D5hYWF4++23AQCHDx82LllKS0vDHw/fyLz++uuihIOp999/HyUe1uNbtWpVvjF5usEtB+OD5z/A4JaD3R0KERERERGpmbT5srQ5s1rZ2kTaIDAQqF1bPFYYm0lLy3tYayJtyhnNpE36RAAAunQxTi6nNGgAnelNilqtKPnlSl6RkeKBoCB5k3VShrkSWko1k75/Xz5mJhFpljQuRxpJs4k0EbmJxyYdDBP6+S3rMWwPCwuzqbySIYFQtWpVYxNqc5o0aWL8+urVqwCAmzdvQvvwTpUGDRpYPNbX1xf1Hr4JvXbtWr4xebovXvkC01+bji9e+cLdoRARERERkZpJJ+uvXweSktwSSoFIkw716+d/TNOm4sf//qtYOKoh/bxrb9Lh6lXg5k3747h1Czh0SDzWo4fxS52/P/JatBBv//tv+6/nAM3Bg+KB5s0Bb48tUqFu5spWOzLBb+ruXfFjjUa/gsEWTDoQUSHgsUmH6tWrAwBiYmKMqxnMiYqKAgDUqFHD4j6mcnJyRP+3xDSBYdjXtJSTrcfntx8REREREdFjo2ZN+QTr6dPuicVWOp08xvxWOgCAyY1sALjSwVTduvKJV0dWO/z2m/hxSAjQpo1oSCt57Lakw+HD4oFnnnFLHI8Ff3/9f6aUWukgTTqULGl7HwUmHYioEPDYpEOrVq0AAHl5edi9e7fZfW7evIlLly4BAFraWBvT0BT66tWruCv9I2Hi6NGjsmMqVapkbHB9WPpGwUROTg5OPbwTxlIJJiIiIiIioseOr68+8WBK7UmH//6Tr8awJ+lw9Spg0jzZ4+XlySc8bU06aDTy/haOJB2kpZU6d9Y/10zIGlVfvixPmjiZkJkJzYkT4sH/+z+XxvDYka52cNZKh9KlbT+WSQciKgQ8NulQoUIFNGrUCAAwZ84cWW8HnU6HKVOmQKfTITQ0FJ07d7bpvB06dAAAaLVafP3118jLy5PtExcXh/nz5wPQr7h48sknAQDBwcHG5Mavv/6Ks2fPmr3GvHnzcO/ePQBAp06dbIqLiIiIiIjoseBpfR2k8YWGAuXL539crVryu6xNbm7zeHFxQFaWeMzWpAOgXF+H2Fhg/37xWPfust109eoBD3svGrl4tUORqCgIubmPBry8gGbNXBrDY0eadFBqpcPDOR8jW/s5AEw6EFGh4LFJBwD49NNPodFocP36dfTu3Rv79+9HQkICoqKiMHToUOzYsQMAMGzYMFnH9vbt26N9+/YYPXq0aLxu3bro8bC2459//on+/ftj3759iI+Px507d/Drr7+iZ8+euHfvHnx8fDBu3DjR8aNHj0aRIkWQnZ2Nfv36YcGCBbh69SoSExNx+vRpjB49GnPmzAGgX63Rrl07Z/14VKP8qPIQBgsoP8qGN95ERERERPR487Skw8mT4sf16gGCkP9xPj6AtBdgYSqxJF0l4OsLhIXZfrw06XD5MnD7dsHj2LBBXwLLoGhRwNzncI0GeP558ZiLkw5B5p5LVnpNkgKUnOA3pYaVDklJgLQcOZMOROQiHt2NqE6dOpg0aRLGjh2LS5cuYdCgQbJ9BgwYgD59+sjGDQ2cS5nJNn/55ZfIzc3Fxo0bERkZicjISNk+RYoUwTfffIPGjRuLxqtUqYKFCxdi+PDhiI+Px/Tp0zF9+nTZ8a1btzY7TkRERERE9FiTJh3OnAG0WtvrobuaNCliS2klgyZNxA2OC3PSoVIl/cS+rerXB4oVE0+a7tkDvP56weKQllbq1Em+wsSgXTtgzZpHj3fudOlzL0j6XGI/B+dz1koHNSQdpKscAKBCBfvORURUQB6ddACAbt26oVatWli0aBEiIyMRHx+PwMBA1K5dG71798bz0jsVbODj44OpU6eiS5cuWLNmDU6cOIH4+Hj4+PigQoUKaNWqFfr164cyZcqYPb5x48b4448/sGLFCuzatQvXrl1DVlYWQkJCUKdOHXTt2hXt2rWDYMvdL0RERERERI+TunXFjzMygOhooFo198STH0eTDqYKc9KhIKWVAP1Ef8uWwNatj8Z27ChY0uHePXlZpoeVDcySroBISABOnAAkNxs6RV4eikj7l7Cfg/NJJ/gLc9IhLAzw87PvXEREBeTxSQdA31fhm2++KdAxFy9ezHefFi1aoEWLFnbFFBISgqFDh2Lo0KF2HU9ERERERPRYKlNG/19c3KOxU6fUmXRITtY3gDblSNIhNhaIiQHKlXM8Nnd7WF3AqKBJB0BfYsk06bBkiT5p8NJLth2/caO+obVBkSJA+/aW969YUf88u3Tp0djff7sk6SCcOwfv1FTxIJMOzuesRtLSng6OJh10OtvKtpliPwciciOP7ulAREREREREhZCn9HU4c0b82NsbeOop249/8kl9CSFThWW1g6MrHQCgSxdxSaa8PKBXL+DcOduOl5ZWevllICDA+jHS1Q4u6uvgZVpmC9D/vApD8kntXLXSwZFG0jk5QHp6wWNg0oGI3IhJByIiIiIiIlIXT0k6SOOqUcNyvwBzNBr5XfRMOjzy5JPAl1+Kx5KT9X0Z7t+3fmx8vL4ngylrpZUMpEmHAwfsm/AtII006cB+Dq7hrJUOSpZXAuyLS5p0qFix4OcgIrITkw5ERERERESkLtK+Dp6SdChIaSWDpk3FjwtD0iEvTz7haU/SAQDGjNGvbjB19ao+gZCdbfm4TZv0TaANAgJsK8v07LPixtHZ2cDevQUK2R6ypANLK7mGM1Y6pKcDaWnisYIkHYKD5WNKJB240oGIXIhJByIiIiIiIlIX6eT9zZvK3YGspJMnxY/tSTqYayat09kdkircuSNPCNibdBAE4Oef5T+nPXuA99+3/LOSllZ66SUgKCj/6wUHA82aicecXWLp1i1obtwQj3Glg2s4Y6WDtJ8DULCkg5eXPPHApAMReRgmHYiIiIiIiEhdatQAfH3FY6dPuycWS7RaeU8HJZIOiYnAlSt2h6UK0tJKfn5A2bL2ny8gQN8UWtrj4McfgVmz5PsnJsoTBbaUVjJwdV+HAwfEj0NCCtYbhOznjJUO0tJKPj7y3i35MddMuiAyM4G4OPEYkw5E5EJMOhAREREREZG6+PjIJ13VVmLpyhUgI0M8Zk/SoXx5oEwZ8Zinl1i6dk38uFIlcUNoe4SHA7//Lm8EPXIksG2beGzzZn3zXQM/P30TaVtJkw5nzuhXbziLNOnw9NOO/7zINuZWOji60shcPwdBKNg5HE06SFfOAEw6EJFL8a8YOd0vb/2Cbf/bhl/e+sXdoRARERERkadQezNpaTxlysiTB7YQBPMlljyZEk2kzWnUCFi6VDyWlwe89hpw4cKjMWlppRdfLNid5k2bAkWLisd27ChYrLbQ6fTNrjdsEI+zn4PrSJMOWq28H0NBOdJE2sDRpIO0tFJISMFXWxAROYBJB3K6Z6s/ixdrv4hnqz/r7lCIiIiIiMhTSJtJq628kjTpUL++/edi0sF2PXsCX34pHktOBjp1AhIS9F9v3y7eXpDSSoB+pc1zz4nHlCyxpNPpkxgtWwLPPw/cuiXezn4OriOd3AccL7GkxqQDVzkQkYt5uzsAIiIiIiIiIhnpSoezZ4HcXMBbJR9jpUkHe0orGUiTDsePq+t7LShnJh0AYNw44Nw5YO3aR2NXrugTEm++CWRlPRr38dEnJAqqXTtg06ZHj//+W58sKGiZHFM6nf4848cDBw+a36VkSQhNm9p/DSoYc3f/P3igL3tmL2kj6VKlCn4OJh2okLl48SJ++uknREZGIiEhASEhIahduzZ69+6NVq1a2X3eW7du4ccff8T+/fsRFxeHoKAgVK9eHT179kTHjh2tHqvT6fD7779j/fr1OH/+PHJyclC2bFm0bt0aAwcORFhYWIFiWbJkCSZPnox33nkHH3zwgdV9U1NTsWbNGvz999+Ijo5GRkYGihUrhqeeegpdunRBhw4doPHwMnse+g6GiIiIiIiICjXpJH5mJnD5MlCzpnvikXJm0iEjQz+pLl3t4SmcnXQQBGDxYuDqVeDo0Ufj//wDHD4s3vf55+UldGwh7esQG6v/N6lVq+Dn0un0qy/Gj5fHZyK7TBnkLV4Mf3//gl+D7OPlBQQHA0lJj8a40oFIUTt27MCIESOQY9Jr5969e9i1axd27dqFfv36YcyYMQU+76lTpzBgwACkmZREe/DgAQ4fPozDhw9j+/btmDFjBrzNJPDz8vLw0UcfYevWraLx//77D8uWLcOGDRswe/ZsNG/e3KZYTp8+jZkzZ9q0b3R0NIYMGYJbklVu8fHx2LdvH/bt22e8foC0j5EH8eyUCXmE3Rd3Y/vZ7dh9cbe7QyEiIiIiIk9RsqS+ebAptfR1iI+Xl8RxJOlQsqR8Yt5TSyzl5cknPJVOOgBAYCCwcSMgvRM1PV38uKCllQyqVQMqVBCPFbTEkk4H/Pkn0KIF8NJLlhMOFSsie9YsnN2wAXmtW9sXL9nPXDNpRzDpQGQUFRWFkSNHIicnB3Xq1MHy5ctx+PBhrF+/Hm3btgUALF++HCtWrCjQeWNjYzFkyBCkpaUhIiICCxYswKFDh7B582b07NkTAPDXX39h2rRpZo+fNm2aMeEwYMAAbNu2DQcPHsTMmTNRtmxZpKSkYNiwYbhz506+sZw+fRqDBg1CRkZGvvump6dj8ODBuHXrFvz9/fHhhx9i+/btOHjwIFasWIE2bdoAAPbv34/PPvvM1h+HKjHpQE7X96e+aP99e/T9qa+7QyEiIiIiIk+i1r4O0uSHnx9Qvbpj5ywsfR1iYwGTu1kBOCfpAADlygG//w5YWhng5QV07mzfuQUBeOEF8VhBkg5//w00awZ06ABERprfp1IlYMEC4PJl5A4aBJ2vr32xkmOkE/xc6UCkmJkzZyIrKwsVK1bE0qVL0bRpU4SGhqJOnTqYM2cO2j1cVTZr1iykpqbafN4FCxYgMTERxYoVw/Lly/Hss8+iePHiqFatGr766iu8+eabAPQJjZs3b4qOvXPnDpYuXQoAGDx4MD755BNUrlwZJUqUwEsvvYQVK1YgODgYycnJmD17ttU4Vq1ahT59+iA5OdmmuFesWIGYmBgAwNy5czFkyBBERESgRIkSaNy4MebNm4d+/foBAP744w+cOXPG5p+J2jDpQEREREREROokXT2glpUO0jhq1XK8/4K0jv/Chfq7+RX8L6BkSdR8/XVoLE2CK+HaNfFjf3+gbFnnXa9JE32pJXPatAFKlLD/3NISS3v2ANnZ1o9JSND3lXjhBcuJo4gI4McfgUuXgCFDACYb3EvplQ7u7umQmytficWkA7lBdHQ09u7dCwAYMmQIihQpItouCAI++eQTCIKAxMREbN++3abzJiUl4ddffwUA9OnTB6XNJPaGDx+OoKAg5OTkYMOGDaJty5cvR05ODgIDA/HOO+/Iji1fvjzeeOMNAMCWLVuQLl1BB/3qhr59++LLL79EdnY2atlYes/wPTZt2hT/93//Z3afYcOGwcvLCwCwZ88em86rRkw6EBERERERkTp5StLBkdJKBtKVDjqdvreDgv8JGRkIvHwZfj16AHFxjsdsjrSfQ6VKjjVftkWvXsDYsfJxe0srGbRtK449LQ04dMjy/hs2AE89BSxbZn77E08Aixbpkw1vvcVkg1ooudJBp3P/SocbNwCtVjzGpAO5gSHhIAiCsWyQVPny5VH94UrBnTt32nTeyMhIZD9MAFs6b1BQEJo1a2b2vIa4mjRpgqCgILPHG0o/ZWRk4ODBg7LtI0aMwJEjRyAIAvr06YOVK1faFHtiYiI0Gg3qWXnfEBwcjODgYADAXenriQdh0oGIiIiIiIjUSfqhPCZG30/B3ZyRdGjUyHKZIIUJCQnAu+/qJ0iV5uwm0pZ8+SXw6quPHoeHA6+95tg5S5YEGjQQj/31l3y/u3f11+rWzXwyp0oV/WqMCxeAgQMBHx/H4iJlKbnSITlZvhpGqaSDrb+vps3VAf1qH3tWWxA56Pz58wCAsmXLooSVVWdPPfUUAH3/h4KcV6PRoGbNmvme9/Lly8YkRXZ2Nq5evQoAVlcnVKtWzdiA+uzZs2b3adq0KdauXYtx48bB38a/3zt27MCZM2cwdOhQi/skJSUh8WHys1ixYjadV42YdCAiIiIiIiJ1qlZN3y/BlLv7OmRnA+fOicfq13f8vEFBwMSJjp/HVhs2AGvXKn9edyUdNBpg5Ur9SoLx44EDB4CHd4o6RFpiybSvg04HrF6tL69l7mdZrBgwf74+2dC/P5MNaqXkSgdzdyUrUV4pO1u/YskW0rJeTZo4f7URkRm3b98GAJQrV87qfuHh4QCAuLg45Eh7Alk5b5kyZeBj5XXVcF6tVmtsCB0XF4fc3Nx84/Ly8kKZMmUAwNiDwdSiRYuwfPly1JX2nrKBt7c3AgICLG5fs2YN8vLyAAANGzYs8PnVwsGik+QOWVlZZuuJOcLQYd2WTusFpXuYjdfpdDbF7cxYCoJxyKklFrXEYRqDu2NhHHJqiYVxyKklFsYhp5ZY1BKHaQzujoVxyKklFsYhp3Qs/jVrQnPypPFx9pEjyH1YMsGVcRgIZ84gQHInc3rVqoCVzzo2x/Lee0D37tCYmeBwWGYmfHv1gsZkpYhu6FBkNGtm353YFvhFR8PL5HF2eDhyJT8bpz5fe/V69LUCnz81LVvCf+pU42Pd0aPIuHULyMqC74gR8N6yxexx2vbtkT1rFnTlyuknjPPpBaGW32G1xGEag7Nj8S5SBKaFrnLv30e2yXOnIHFobtyA6f3OuiJFkCEINj0XRfz8ECgZyrh9GxkPkxHWYvGLjBT9DubUr48cD5pD8sQ4TGNwdyzOjCMrK6tA+z94uGoov7v1ixYtCkA/b5iSkoLixYsrel5Av3rA9FgAxhJGlhhKL5lrEl25cmWrx9rr2rVrmDdvHgCgQoUKaNmypVOu4wpMOnig2NhYxMbGOuXc16V3pSjAkEHMzc01LoFyVyz2YBxyaolFLXEA6omFccipJRbGIaeWWBiHnFpiUUscgHpiYRxyaomFccgpFUulChVQ0iTpkLx/P/57WGvZlXEYFN++HaZTDVlly+L8nTvAw7soFYnFyh2QdgsIQMioUajyySfGISE+HllvvYWrJpPqjqp15YpowvOWtzceWPgc6AnPVyE0FPX9/KB5ONkm6HRIf/ddhOzZA++UFNn+ucHBuPnRR0ho315fasfMZJU9cbiaWuIAnB9LqYwMVDR5nBEbi0tmnrO2xBF88iSqmjzODg4u0DyIkVaLRpKhq8eOIbNqVeuxaLWof+yYaOi/0qWRZE8MNlDL80QtcQDqiUUNcRiSFPmVHvIzWdFoS2LDkfNmmySA/aQrKSUM58/MzMw3JiXcu3cP77zzDtLT0yEIAsaOHWt1JYfaMenggcLCwhAirTnooIyMDFy/fh0RERFWl/jYw1ADzdvb22qtNVfEUhCMQ72xqCUONcXCONQbC+NQbyyMQ72xqCUONcXCONQbC+NwfizeLVsCmzcbHxe/cQOBbvxc4SNpEuzVsGG+n3PU8u+TERGBB3//jVCTppqhO3ei9vnz0Hbr5vgFtFr4SXoahD/9NMpKfj5q+XnYGovumWcAk59ZSQurG3K7dEH29OkoU6YMyjghDldQSxyujMWrRg3R4yLZ2aLf6YLE4SVpNO4dHm7TPIg5umLFIJgkraoUL460iAirsQjnz8NLsqohvHNnhJcta1cMlqjleaKWONQUizPjSExMLNBN0F5e+hS0kE95L51JvxKNJv9OAPac17Cv6fltPd6WmBx1584dDBw40Jgsev/999G6dWunX9eZmHTwQH5+fggMlC60U0ZAQIDi5zb8EguCUKBzOyMWezAOObXEopY4APXEwjjk1BIL45BTSyyMQ04tsaglDkA9sTAOObXEwjjkFIulcWPRQ8358wj09QW8zXycvX9fPzn899/w//tvFL91CwCgaDXzh3WWDbwbNoS3jd+nGv59Ln/8MUJOnoRgUmbJb+RI4MUXHW82e/MmIKnH7V+zJmDhe1bDz8PAaizt24uSDjKlSgFz58K7Rw+HJ1nU8jNRSxyAC2IpI04RaZKSzF7Ppjgk/SC8ypa1P/bQUNFKGf+MDOQ9nES2GIu04W358gh84gn7rm8DtTxP1BIHoJ5YnBFHQUs2Ga6f30oB09UHvr6+Vva0/7yGVQ2mP5P8VlUYtue3IsJRly9fxpAhQ4y9Kvr374/333/fqdd0BSYdiIiIiIiISL3q1RM/zs4GLl7UN+/NzgYOHgT++kvf4PfYMX1zXwDOvy/RQnwql1u8OLKnTYNf//6PBu/dA4YN0zdFdoS0nIe/v6L9ItxG2kzaVJ8+wMyZQMmSLguHFObMRtKOPP9DQ4H//nv02KQWvUXSJtKSpC2RKxl6IqSYKUVnytAzQaPR5NtnAXjUq8HW8wJA6MPfc9M+D/kdb9geKn2NUNC+ffswYsQIpKamAgDee+89/O9//3Pa9VyJSQdyulvf3nJ3CERERERE5KmKFwfKlwdumXyumDxZPzG4ezeQluauyABBAJo2dd/17aTt0QP4/Xdgw4ZHg2vWAD17At27239iadIhIkL/M/J0deoAlSqJJ4DDw4H584FOndwXFylDWr46JQXIzTW/mio/9+6JHzuyekg60WlP0qFJE/uvT+SgypUr499//823JJNhe1hYmE2ljCIiIgAAd+/eRV5ensVjDOf19vZG6YcJwLCwMPj7+yMzM9O4ssAcrVaLuIflAsPDw/ONyR5r167F+PHjkZubCy8vL4wbNw69evVyyrXcwWU3fxARERERERHZRbqaYMUKYOtW9yYcAGDUKKBixfz3UxtBAObO1Sd0TL33nr5Elb3MJR0KA40G+PFHoGxZfamowYOBqCgmHAoLc3cxJyXZdy6lVzqYyi/pkJ0NnDwpHmPSgdyoevXqAICYmBjRqgOpqKgoAEANSX+V/M6bk5ODS5cu5XveqlWrGhsyazQaVH3YkN1ak/eLFy9Cq9UCgN19WayZP38+xo4di9zcXAQGBmLu3LmFKuEAcKUDERERERERqV3duvokgy0CA4HWrZH97LOILlsWETVqOKepZ5kynl06qGxZ4Icf9OWBDO7eBYYPB1autO+chTXpAOhLLN28CWi1gJPre5OLSVc6APoJ/hIlCn4udyYdzpzRJx5MsbwSuVGrVq0AAHl5edi9ezdeeeUV2T43b940Jg5atmxp03mbNm2KgIAAZGRkYNeuXWaTFampqYiMjDR73tatW+Ps2bOIjIxEenq62d4XOx/28fH19UVThVc0Llu2DDNmzAAAlChRAgsXLkTt2rUVvYYaMOlARERERERE6pbfB/5GjfSTwi+8ADz9NODnh9z0dKSfPw+dlUbGj73XXwfWrtWXWjJYtUpfZqlr14KfrzAnHQB9uR17Su6QugUEAL6+4gl7e/s6uDPpIC2tVLWq+VUcRC5SoUIFNGrUCMeOHcOcOXPw3HPPiXoq6HQ6TJkyBTqdDqGhoejcubNN5w0MDES7du2wadMmLFmyBJ07d5aVQJo5cybS0tLg4+ODvn37irZ16tQJ8+fPR3JyMmbPno3Ro0eLtt+8eRPLli0DAHTv3h3FihWz59s369SpU5g6dSoAoHjx4lixYgUqV66s2PnVhOWVyOnGbxqPkWtGYvym8e4OhYiIiIiIPFHHjkCLFo8ely8PDBignyC/exc4elTf5+G553gXekEIAjBvnnxi8t13gfj4gp+vsCcdqHASBPlqB1v6J0jl5cnLk7myp8PRo+LHLK1EKvDpp59Co9Hg+vXr6N27N/bv34+EhARERUVh6NCh2LFjBwBg2LBhshUH7du3R/v27WVJAQAYOXIkAgMDkZiYiD59+mD79u1ISEhAdHQ0xowZg+XLlwMA+vXrh7Jly4qOrVy5Mnr37g0AWLRoEcaNG4fo6GgkJCRg27Zt6Nu3L5KTkxESEoIhQ4Yo+vOYMGECcnNzIQgCJk6ciNKlSyMtLc3if9nS1UsehCl6crof9/2ImMQYlAsphy9e+cLd4RARERERkafx9gb27gWOHweCg4Fq1QpHg2I1CAsDZs0C+vV7NBYXB/zvf8Avv9h+Hq0WuHFDPFZI796kQig0VLxKwZ6VDgkJ+sSDKXeudGDSgVSgTp06mDRpEsaOHYtLly5h0KBBsn0GDBiAPqal/h66du0aAKCUmeRdWFgYZs2ahWHDhuH27dsYPny4bJ/27dtj1KhRZuMaNWoUbt68id27d2PNmjVYs2aNaHtgYCDmz5+vaBPpY8eO4ezZswD0qzyGDh2a7zFdu3bFlClTFIvBlZh0ICIiIiIiIvXz9s6/zBLZp08ffZmlzZsfja1YoS+zZGO5C8TEALm54jGudCBPocRKB2lpJcB1Kx3S0/XNzU0x6UAq0a1bN9SqVQuLFi1CZGQk4uPjERgYiNq1a6N37954/vnn7Tpvy5YtsXXrVixcuBD79+9HXFwcfH19UaNGDXTv3h3dunWDYOEGBT8/P8yfPx+///47fv31V1y4cAEZGRkoXbo0nnnmGQwePBgVKlRw5NuWOXXqlKLnUzsmHYiIiIiIiIgeZ4IAzJ8P7NsnvsP7nXeAli2B4sXzP4e0tFJAgGMTrkSuJJ3gt2elgzTpEBKi7xVhr4IkHU6c0K82MtBogAYN7L82kcKqV6+Ob775pkDHXLx4Md99ypUrh/Hj7SvnLggCunTpgi5duth1vFR+8Q4cOBADBw5U5FqegD0diIiIiIiIiB534eHAzJnisTt3gBEjbDveXD8HlsAiTyFd6aBE0sGR0kpAwZIO0tJKtWoBRYo4dn0iIgcw6UBEREREREREwBtvAB06iMeWLwcWL87/WDaRJk+mRHmle/fEjx1d6SNNOmRlARkZ5vdlPwciUhkmHYiIiIiIiIhIvzJh4UJ9s25TQ4YAO3daP5ZJB/JkSpRXun1b/FjplQ4ABEtxMelARCrDpAMRERERERER6ZUrB/zwg3gsNxfo3l3eqNYUkw7kyZRY6XD1qvhx5cp2hwNAHhNgPq7ERODyZfEYkw5E5GZMOhARERERERHRI/36AR99JB5LStKXXrpzx/wx166JHzs64UrkSkqsdFA66eDtDRQtKhoyu9Lh6FHxY19foE4dx65NROQgJh2IiIiIiIiISGzqVP3qBlM3bgCdOgFpaeLx3Fzg5k3xGFc6kCdxxkqHJ56wOxwjSTLEbNJBWlqpXj194oGIyI2YdCCna12tNV546gW0rtba3aEQERERERGRLTQafRPp5s3F40ePAr17A1rto7GYGPFjgEkH8iyOrnRISQHu3xePOSHpYDYZwn4ORKRC3u4OgAq/FYNXuDsEIiIiIiIiKqiAAOD33/WJB9PySZs2AR9+CMycqX8s7ecQGAiULOmqKIkcZ26lg06nb65uC2l5MUCZxJs9Kx2YdCAiFeBKByIiIiIiIiIyr3Rp4I8/5Hdcf/89MGuW/mtzTaRtnawlUgPp8zsnB8jIsP14aWml8HDA31/xuGRJhzt3gFu3xGNMOhCRCjDpQERERERERESW1agBbNgA+PiIx0eM0K96MJd0IPIk0pUOQMFKLDmjnwOQf3klaRPpIkX0v69ERG7GpAMRERERERERWde6NbB4sXhMpwNef12/EsIUkw7kaYKD5WMFaSYtLa/kpKSDbKWDtLRSo0aAl5cy1yYicgCTDuR0bb5rg1rjaqHNd23cHQoRERERERHZq08fYMIE8Vh6OvDvv+KxypVdFxOREry9gaJFxWMqXOmQb9KBpZWISCXYSJqc7lLcJcQkxiApI8ndoRAREREREZEjxozRT7AuWWJ5H650IE8UEgKkpDx6XJCVDs5KOhQvLn5smnTQ6Zh0ICLV4koHIiIiIiIiIrKNIAALFgBtrKxkZ9KBPJG0f4KtKx3y8uTllZRa7WNtpcN//wH374v3Z9KBiFSCSQciIiIiIiIisp2vL/Drr8BTT5nfzqQDeSJpM2lbVzrExgJZWeIxVzSSlq5yKFGCpc2ISDWYdCAiIiIiIiKiggkJAbZuBcqUEY8XKaKf/CTyNPaudJCucvD3B8qWVSQkqysdpEmHxo31K5GIiFSASQciIiIiIiIiKriICGDLFiAw8NFY166c+CTPZO9KB2k/h8qVAY1C023SpENmJgTDqgr2cyAiFWMjaSIiIiIiIiKyT+PGwL//At9/D5QuDXzyibsjIrKPvSsdzCUdlCKNCYBXSoq+j8SxY+INTDoQkYow6UBERERERERE9qtVC1i40N1REDlGqZUOSvVzAOQxAfBOToZw6RKQkiLe0LixctclInIQyysREREREREREdHjTamVDkomHby9gaJFRUNeycnQSFc5hIfr/yMiUgkmHYiIiIiIiIiI6PEmXVVgbyNpJZMOgCwZ4p2SAs2JE+J9WFqJiFSG5ZXI6cZ1GofUzFQE+Qe5OxQiIiIiIiIiIjnpSgdbyitlZAC3b4vHnJF0uHHD+NDsSgcmHYhIZZh0IKcb0mqIu0MgIiIiIiIiIrLMnpUO16/Lx5RsJA3IVzo8eADN6dPifZh0ICKVYXklIiIiIiIiIiJ6vEmTDklJgFZr/RhpP4dSpYAghas8SJIOQSdPQsjMFO/DJtJEpDJMOhARERERERER0eNNWl4JAJKTrR/j7H4OgCyuYkeOiLdXqQIUL678dYmIHMDySuR0sYmx0OZp4aXxQlhImLvDISIiIiIiIiISk650APR9HcqWtXyMdKWDC5IOXmlp4u0srUREKsSkAzldk0lNEJMYg3Ih5XDr21vuDoeIiIiIiIiISKxIEcDbG8jNfTSWmKi6pIMMkw5EpEIsr0RERERERERERI83QZCvdnjwwPox0qSD0k2kASYdiMgjMelAREREREREREQkneBPTLS8r07n/pUOGg3QsKHy1yQichCTDkRERERERERERNKVDtaSDvfvA9L+Cq5OOjz1lL4sFBGRyjDpQEREREREREREJJ3gt1ZeSbrKwdsbKF/e+TGZYmklIlIpJh2IiIiIiIiIiIgKstJBmnSoVAnw8lI6IutJh8aNlb8eEZECmHQgIiIiIiIiIiJyZKWDM0orAVzpQEQeiUkHIiIiIiIiIiKigqx0uHZN/NhZSQdpTAY+PkDdus65JhGRg5h0ICIiIiIiIiIiUuNKBx8fIChIPl6vHuDn55xrEhE5iEkHIiIiIiIiIiIiR3o6OCvpAJgvscTSSkSkYt7uDoAKv50f7kSuNhfeXny6EREREREREZFKSZMOllY6ZGcDN2+KxypXdkpIAPRJB+n1mHQgIhXjLDA5XfWy1d0dAhERERERERGRddIVBZZWOty4AeTlice40oGIyIjllYiIiIiIiIiIiGxd6SBtIh0SYj4xoBTJuXVFigA1azrvekREDmLSgYiIiIiIiIiISJo4yMoCMjPl+7mynwMgiyuvfn3Ay8u51yQicgDLK5HTrYxcifTsdAT6BqJ3s97uDoeIiIiIiIiISE660gEwX2JJmnRwZj8HQLaqIe+ZZ8CUAxGpGZMO5HSj149GTGIMyoWUY9KBiIiIiIiIiNTJTNJBsCXp4OyVDv37A2vXAkePIr1aNeC99+Dj3CsSETmESQciIiIiIiIiIiIfH6BIESAtzTgkJCUBxYqJ93N10qFUKeDwYaTfuoXzd++iZsmSzr0eEZGD2NOBiIiIiIiIiIgIkPd1MNdMWtpI2tlJB0Dfw6FUKUDDqTwiUj++UhEREREREREREQGyEktCUpJ4+4MH8kSEK5IOREQehEkHIiIiIiIiIiIiQLbSQdbTQbrKQRCAihWdGxMRkYdh0oGIiIiIiIiIiAiQN5OWrnSQ9nOoUAHw9XVqSEREnoZJByIiIiIiIiIiIkBeXklaSskd/RyIiDwMkw5ERERERERERESAvLxSfisdmHQgIpJh0oGIiIiIiIiIiAiQl1eS9nSQJh0qV3ZmNEREHsnb3QFQ4Vc2uKzo/0REREREREREqpRfI2mudCAiyheTDuR0R8ccdXcIRERERERERET5k/Z0MC2vpNUC//0n3p9JByIiGZZXIiIiIiIiIiIiAmQrHUTllWJigJwc8XYmHYiIZJh0ICIiIiIiIiIiAqyvdJCWVipSBChVyvkxERF5GCYdiIiIiIiIiIiIAPMrHfLy9F+bayItCC4Ji4jIk7CnAznd28vfRkJaAooXKY4F/Ra4OxwiIiIiIiIiIvOkKx10OmjS0/UP2ESaiMgmTDqQ0209vRUxiTEoF1LO3aEQEREREREREVkmXekAwDs5Wf/FtWviDUw6EBGZxfJKREREREREREREABAUBGjE02VeKSn6L7jSgYjIJkw6EBERERERERERAfoeDZISSxaTDpUruyYmIiIPw6QDERERERERERGRgaTEkndKCpCaCty9K96PKx2IiMwqFD0dLl68iJ9++gmRkZFISEhASEgIateujd69e6NVq1Z2n/fMmTNYsmQJjh49ivj4ePj7+6Nq1aro0KEDevXqBV9fX6vHR0VF4ZdffkFkZCTu3bsHX19fVKtWDV27dkWPHj2g0TDnQ0RERERERESkKmZWOgjXr8v3i4hwRTRERB7H45MOO3bswIgRI5CTk2Mcu3fvHnbt2oVdu3ahX79+GDNmTIHPu2zZMkyZMgVardY4lpOTgxMnTuDEiRP47bffsGjRIpQoUcLs8fPmzcOsWbOQl5dnHMvOzsbx48dx/PhxbNu2DfPmzYOfn1+BYyMiIiIiIiIiIieRrHTwSk6G5r//xPuEhQGBgS4MiojIc3j0rfZRUVEYOXIkcnJyUKdOHSxfvhyHDx/G+vXr0bZtWwDA8uXLsWLFigKd9+DBg/j666+h1WpRpUoVzJ07F/v27cOmTZvQr18/aDQanD9/HiNGjDB7/OrVqzFz5kzk5eWhQYMGWLJkCQ4dOoRNmzbhlVdeAQAcOHAA3377rUPfPxERERERERERKUy60iE1FcK1a+J9WFqJiMgij046zJw5E1lZWahYsSKWLl2Kpk2bIjQ0FHXq1MGcOXPQrl07AMCsWbOQmppq83kXLlwInU6HkiVL4pdffkHbtm1RunRpVK9eHWPGjMGQIUMAAP/++y+OHDkiOjY+Ph7fffcdAKB58+ZYunQpWrRogeLFi6N69er49ttv8eKLLwIA1qxZg8TERAV+EkREREREREREpAhpT4fkZHl5JTaRJiKyyGOTDtHR0di7dy8AYMiQIShSpIhouyAI+OSTTyAIAhITE7F9+3abz3369GkAQJs2bVC8eHHZ9l69ehm/PnXqlGjbxo0bkZKSAl9fX3z11VdmyycNGDAAAKDRaBAVFWVzXERERERERERE5GRmejpouNKBiMhmHtvTwZBwEAQBbdq0MbtP+fLlUb16dVy4cAE7d+5E9+7dbTq3ocFzbm6u2e0+Pj7Gr728vETb/vjjDwDACy+8gAoVKpg9vkGDBjh16hT8/f1tisfTvd70dTxIf4DQwND8dyYiIiIiIiIicidpT4eUFAj374v3YdKBiMgij006nD9/HgBQtmxZi82cAeCpp57ChQsXCrSioG7dujhw4AD27NmDhIQE2WqHDRs2GL9u2LCh8evs7GxcvHgRgL60kimtVgtBEIwJjccl4QAA3/Zk7woiIiIiIiIi8hCSlQ5myysx6UBEZJHHlle6ffs2AKBcuXJW9wsPDwcAxMXFIScnx6ZzDx8+HH5+foiPj8cbb7yBXbt24e7du4iOjsbMmTPx/fffAwC6du2KevXqGY+7du2a8RoRERHIyMjA3Llz8dJLL6Fu3bqoU6cOunbtipUrV0Kr1Rb4eyYiIiIiIiIiIieTrHTw/+8/CJmZ4n3Y04GIyCKPXenw4MEDAECxYsWs7le0aFEAgE6nQ0pKitkeDVL169fH4sWLMWnSJERFReGdd94RbQ8JCcG7776LN954QzR+794949eZmZno0qULrksy4efOncP48eOxY8cOzJ49G4GBgfnGI5WVlYX09PQCH2dNRkaG6P/upJZYGIecWmJRSxymMbg7FsYhp5ZYGIecWmJhHHJqiUUtcZjG4O5YGIecWmJhHHJqiUUtcZjG4O5YGIecWmJhHHLujEXj7w/T+hTeSUmi7TpfX2SEhAAKz83kRy3/PoxDTi2xODOOrKwsxc9JhZeg0+l07g7CHs8//zxu3ryJDh06YMaMGRb3W7VqFb788ksAwO7duxEWFmbT+ffs2YOffvoJ//77r2ybl5cX2rdvjw8++EDUt2H79u0YPnw4AKBUqVKIj4/HW2+9hVdffRVlypTBjRs3MH/+fGzevBkA0LFjR0ybNs3Wbxnx8fGyJAYRERERERERESkn8OxZ1Ozf3+L2zEqVEPXrr64LiEhFIiIirJa6JwI8eKWDoYGzIAhW9zPNqRj6KeTn559/xtSpUwEA7du3x9tvv42qVasiNTUVe/fuxbRp07B161b8+++/WLZsGZ54WMfPNIt47949fPXVV+jZs6dxrGrVqvjuu+/g5+eH9evXY8uWLejfvz/q1Klj2zf9UFhYGEIk9QUdlZGRgevXryMiIgIBAQGKnrvBpAaITYpFWHAYTnx+wq2xFATjUG8saolDTbEwDvXGwjjUGwvjUG8saolDTbEwDvXGwjjUG4ta4lBTLIxDvbEwDnXFIjycc7LEp3p11KxZ00XRPKKWfx/God5YnBlHYmIiYmNjFT0nFV4em3QwlCXKlNbUk8jOzjZ+7evrm+95r127hu+++w4A8Nprr2HChAnGbcWLF0eXLl3QrFkzdO/eHffu3cP48eOxdOlSABD9MlerVk2UcDA1cuRIbNiwAVqtFtu3by9w0sHPz8+usky2CAgIUPzcadlpSMlKQbHsYgU6tzNisQfjkFNLLGqJA1BPLIxDTi2xMA45tcTCOOTUEota4gDUEwvjkFNLLIxDTi2xqCUOQD2xMA45tcTCOOTcEks+VTK8nnzSrT8ftfz7MA45tcTijDjcXTqKPIvHNpIOCgoCAKSkpFjdLzk5GYB+lUNwcHC+512/fj20Wi18fX0xatQos/uEhYXh7bffBgAcPnzYWPKoSJEixn2aNWtm8RolSpQwro64cuVKvjEREREREREREZGL5Fddgk2kiYis8tikQ+WHL/D5LesxbA8LC7OpvJIhgVC1alVjE2pzmjRpYvz66tWrAIDy5csbx/z9/WXHmDIkTdiEhYiIiIiIiIhIRfz8AGulaR7eSEpEROZ5bNKhevXqAICYmBjjagZzoqKiAAA1atSw6bw5OTmi/1timsAw7FupUiXj0qUbN25YPT4+Ph4AUKZMGZviIiIiIiIiIiIiFwkNtbyNSQciIqs8NunQqlUrAEBeXh52795tdp+bN2/i0qVLAICWLVvadF5D2aOrV6/i7t27Fvc7evSo7BhBEIxx7du3D6mpqWaPvXHjhjEpUb9+fZviIiIiIiIiIiIiF7FWYonllYiIrPLYpEOFChXQqFEjAMCcOXNkvR10Oh2mTJkCnU6H0NBQdO7c2abzdujQAQCg1Wrx9ddfIy8vT7ZPXFwc5s+fD0C/4uLJJ580bnvttdcAAOnp6fj666+h0+lEx+bl5WHy5MkA9E1dXnrpJZviIiIiIiIiIiIiF7G00qFECaBYMdfGQkTkYTw26QAAn376KTQaDa5fv47evXtj//79SEhIQFRUFIYOHYodO3YAAIYNGybr2N6+fXu0b98eo0ePFo3XrVsXPXr0AAD8+eef6N+/P/bt24f4+HjcuXMHv/76K3r27Il79+7Bx8cH48aNEx3/9NNP45VXXgEA/Prrr3j33Xdx7NgxPHjwAKdPn8bbb7+Nf/75BwDw4Ycf2tTcmoiIiIiIiIiIXMjSSgeWViIiype3uwNwRJ06dTBp0iSMHTsWly5dwqBBg2T7DBgwAH369JGNX7t2DQBQqlQp2bYvv/wSubm52LhxIyIjIxEZGSnbp0iRIvjmm2/QuHFj2bZJkyZBq9Vi69at2LVrF3bt2iXbZ+DAgejbt69N3ycREREREREREbmQpZUOTDoQEeXLo5MOANCtWzfUqlULixYtQmRkJOLj4xEYGIjatWujd+/eeP755wt8Th8fH0ydOhVdunTBmjVrcOLECcTHx8PHxwcVKlRAq1at0K9fP4tNoH19fTF9+nR07twZa9euxalTp5CYmIiSJUuibt266N27N5o3b+7ot05ERERERERERM7AlQ5ERHbz+KQDoO+r8M033xTomIsXL+a7T4sWLdCiRQt7w0Lr1q3RunVru48vLOb3nY+MnAwE+AS4OxQiIiIiIiIiovxxpQMRkd0KRdKB1K1jvY7uDoGIiIiIiIiIyHaWVjpUruzSMIiIPJFHN5ImIiIiIiIiIiJSHMsrERHZjUkHIiIiIiIiIiIiU+bKK3l5ARUquD4WIiIPw/JK5HTH/juG7Nxs+Hr7olGlRu4Oh4iIiIiIiIjIOnMrHSpVArw5lUZElB++UpLTdZ7dGTGJMSgXUg63vr3l7nCIiIiIiIiIiKwzt9KB/RyIiGzC8kpERERERERERESmzK10YD8HIiKbMOlARERERERERERkytxKByYdiIhswqQDERERERERERGRqaJFAUEQjzHpQERkEyYdiIiIiIiIiIiITGk0QESEeKxWLbeEQkTkaZh0ICIiIiIiIiIikvrsM+g0+qmz3O7dmXQgIrKRt7sDICIiIiIiIiIiUp233kJm8+a4duoUIrp04SQakZ0uXryIn376CZGRkUhISEBISAhq166N3r17o1WrVnaf99atW/jxxx+xf/9+xMXFISgoCNWrV0fPnj3RsWNHq8fqdDr8/vvvWL9+Pc6fP4+cnByULVsWrVu3xsCBAxEWFlagWJYsWYLJkyfjnXfewQcffODSa6sRXy+JiIiIiIiIiIjM0D3xBDKysuT9HYjIJjt27MCIESOQk5NjHLt37x527dqFXbt2oV+/fhgzZkyBz3vq1CkMGDAAaWlpxrEHDx7g8OHDOHz4MLZv344ZM2bA21s+/Z2Xl4ePPvoIW7duFY3/999/WLZsGTZs2IDZs2ejefPmNsVy+vRpzJw506Z9lb62WrG8EhEREREREREREREpKioqCiNHjkROTg7q1KmD5cuX4/Dhw1i/fj3atm0LAFi+fDlWrFhRoPPGxsZiyJAhSEtLQ0REBBYsWIBDhw5h8+bN6NmzJwDgr7/+wrRp08weP23aNOOk/4ABA7Bt2zYcPHgQM2fORNmyZZGSkoJhw4bhzp07+cZy+vRpDBo0CBkZGTbFruS11YxJByIiIiIiIiIiIiJS1MyZM5GVlYWKFSti6dKlaNq0KUJDQ1GnTh3MmTMH7dq1AwDMmjULqampNp93wYIFSExMRLFixbB8+XI8++yzKF68OKpVq4avvvoKb775JgB9QuPmzZuiY+/cuYOlS5cCAAYPHoxPPvkElStXRokSJfDSSy9hxYoVCA4ORnJyMmbPnm01jlWrVqFPnz5ITk62KW4lr612TDqQ052feB5Js5JwfuJ5d4dCREREREREREREThYdHY29e/cCAIYMGYIiRYqItguCgE8++QSCICAxMRHbt2+36bxJSUn49ddfAQB9+vRB6dKlZfsMHz4cQUFByMnJwYYNG0Tbli9fjpycHAQGBuKdd96RHVu+fHm88cYbAIAtW7YgPT1dts/p06fRt29ffPnll8jOzkYtG5vMK3FtT8GkAzldUf+iKBZQDEX9i7o7FCIiIiIiIiIiInIyQ8JBEAS0adPG7D7ly5dH9erVAQA7d+606byRkZHIzs4GAIvnDQoKQrNmzcye1xBXkyZNEBQUZPZ4Q+mnjIwMHDx4ULZ9xIgROHLkCARBQJ8+fbBy5UqbYlfi2p6CSQciIiIiIiIiIiIiUsz58/qKJ2XLlkWJEiUs7vfUU08B0Pd/KMh5NRoNatasme95L1++bExSZGdn4+rVqwBgdXVCtWrVjA2oz549a3afpk2bYu3atRg3bhz8/f3zjVvJa3sCeftuIiIiIiIiIiIiIiI73b59GwBQrlw5q/uFh4cDAOLi4pCTkwMfHx+bzlumTBmr+xrOq9VqcefOHVSsWBFxcXHIzc3NNy4vLy+UKVMGMTExiImJkW1ftGgRKleubDVOKaWu7SmYdPBAWVlZitf0MnRYt7XTekHM2jULKZkpKOpfFMOfG+7WWAqCccipJRa1xGEag7tjYRxyaomFccipJRbGIaeWWNQSh2kM7o6FccipJRbGIaeWWNQSh2kM7o6FccipJRbGIaeWWNQSh2kM7o6FccipJRZnxpGVlVWg/R88eAAAKFasmNX9ihbVl2PX6XRISUlB8eLFFT0voO8DYXosAAQHB1s93lD+yFyT6IImHJS8tqdg0sEDxcbGIjY21innvn79uuLnnPH3DNxNu4vSRUqjXdl2bo3FHoxDTi2xqCUOQD2xMA45tcTCOOTUEgvjkFNLLGqJA1BPLIxDTi2xMA45tcSiljgA9cTCOOTUEgvjkFNLLGqJA1BPLIxDTi2xqCEOQ5Iiv9JDfn5+smOcdV5DmSXpdnMM58/MzMw3Jlu489ruwKSDBwoLC0NISIii58zIyMD169cRERGBgIAARc9tqEPm7e1ttdaaK2IpCMah3ljUEoeaYmEc6o2Fcag3Fsah3ljUEoeaYmEc6o2Fcag3FrXEoaZYGId6Y2Ec6o1FLXGoKRbGod5YnBlHYmJigW6C9vLyAqBvJG2NTqczfq3R5N9+2J7zGvY1Pb+tx9sSky3ceW13YNLBA/n5+SEwMNAp5w4ICFD83IZfJEEQCnRuZ8RiD8Yhp5ZY1BIHoJ5YGIecWmJhHHJqiYVxyKklFrXEAagnFsYhp5ZYGIecWmJRSxyAemJhHHJqiYVxyKklFrXEAagnFsYhp5ZYnBFHQUs2Ga6f3936pisAfH19nXJew8oC059JfqsqDNvzW5VgK3de2x08N11CRERERERERERERKpj6EuQkpJidT9D3wKNRpNvrwPgUa8GW88LAKGhoaJjbTnesN1wrKPceW13YNKBiIiIiIiIiIiIiBRjaLacX0kmw/awsDCbyglFREQAAO7evYu8vLx8z+vt7Y3SpUsbr2Hol3D79m2Lx2q1WsTFxQEAwsPD843JFu68tjsw6UBEREREREREREREiqlevToAICYmRrTqQCoqKgoAUKNGjQKdNycnB5cuXcr3vFWrVoWPjw8A/WqKqlWrAgDOnz9v8diLFy9Cq9UCgE39aW3hzmu7A5MORERERERERERERKSYVq1aAQDy8vKwe/dus/vcvHnTmDho2bKlTedt2rSpsUn2rl27zO6TmpqKyMhIs+dt3bo1ACAyMhLp6elmj9+5cycAfY+Jpk2b2hSXLdx5bVdj0oGIiIiIiIiIiIiIFFOhQgU0atQIADBnzhxZHwOdTocpU6ZAp9MhNDQUnTt3tum8gYGBaNeuHQBgyZIlZksVzZw5E2lpafDx8UHfvn1F2zp16gQvLy8kJydj9uzZsmNv3ryJZcuWAQC6d++OYsWK2RSXLdx5bVdj0oGIiIiIiIiIiIiIFPXpp59Co9Hg+vXr6N27N/bv34+EhARERUVh6NCh2LFjBwBg2LBhCAwMFB3bvn17tG/fHqNHj5add+TIkQgMDERiYiL69OmD7du3IyEhAdHR0RgzZgyWL18OAOjXrx/Kli0rOrZy5cro3bs3AGDRokUYN24coqOjkZCQgG3btqFv375ITk5GSEgIhgwZoujPw53XdjVvdwdAhV/DSg1RoXgFlCpayt2hEBERERERERERkQvUqVMHkyZNwtixY3Hp0iUMGjRIts+AAQPQp08f2fi1a9cAAKVKyecTw8LCMGvWLAwbNgy3b9/G8OHDZfu0b98eo0aNMhvXqFGjcPPmTezevRtr1qzBmjVrRNsDAwMxf/58pzRydue1XYlJB3K6Te9vcncIRERERERERERE5GLdunVDrVq1sGjRIkRGRiI+Ph6BgYGoXbs2evfujeeff96u87Zs2RJbt27FwoULsX//fsTFxcHX1xc1atRA9+7d0a1bNwiCYPZYPz8/zJ8/H7///jt+/fVXXLhwARkZGShdujSeeeYZDB48GBUqVHDk27bIndd2JSYdiIiIiIiIiIiIiMgpqlevjm+++aZAx1y8eDHffcqVK4fx48fbFZMgCOjSpQu6dOli1/FStsTrrGurEXs6EBERERERERERERGRIph0ICIiIiIiIiIiIiIiRbC8EjndK7Nfwb2UeyhVtBT7OxAREREREREREREVYkw6kNMd/+84YhJjUC6knLtDISIiIiIiIiIiIiInYnklIiIiIiIiIiIiIiJSBJMORERERERERERERESkCCYdiIiIiIiIiIiIiIhIEUw6EBERERERERERERGRIph0ICIiIiIiIiIiIiIiRTDpQEREREREREREREREimDSgYiIiIiIiIiIiIiIFOHt7gDIdlqtFgCQmpqq+LmzsrIAAImJicjIyFD03JVDKiPIOwilgkohPj7erbEUBONQbyxqiUNNsTAO9cbCONQbC+NQbyxqiUNNsTAO9cbCONQbi1riUFMsjEO9sTAO9cailjjUFAvjUG8szozDMB9pmJ8kskbQ6XQ6dwdBtvnvv/9w//59d4dBREREREREREREj6GSJUuiUqVK7g6DVI4rHTxImTJlAAABAQHw8vJyczRERERERERERET0ONBqtcjIyDDOTxJZw5UORERERERERERERESkCDaSJiIiIiIiIiIiIiIiRTDpQEREREREREREREREimDSgYiIiIiIiIiIiIiIFMGkAxERERERERERERERKYJJByIiIiIiIiIiIiIiUgSTDkREREREREREREREpAgmHYiIiIiIiIiIiIiISBFMOhARERERERERERERkSKYdCAiIiIiIiIiIiIiIkUw6UBERERERERERERERIrwdncApB6JiYm4desW/P39ERERAW9v9zw9kpOTkZmZiVKlSkEQBKdfLy0tDXFxcUhNTUVOTg58fHxQpEgRlCpVCsWKFXP69YmIiIiIiIiIiIgKCyYdHhNZWVm4evUqNBoNqlatCi8vL+O206dPY+rUqTh+/LhxrEiRIujatSuGDRum2MR7eno6fv/9d5w5cwZff/21aNu9e/ewYMECbN++Hffv3wcAeHl5oXbt2ujevTt69OihaALixo0bWLJkCfbu3YuYmBiL+4WGhqJevXp44YUX8PLLL8PX11exGIiU9umnn0IQBIwfPx4+Pj6KnPONN95AUFAQxowZg/DwcEXOSeRs9+/fx/79+3H37l0EBwejYcOGePLJJ/M9LicnBwsWLIAgCBg6dKhTY7x8+TJ+++03nDp1CgkJCQgMDERERARatWqFDh06uPzvTUxMDNatW4erV68iJycHTzzxBDp27IiaNWu6NA5ShwsXLiAqKgoxMTHGm0E0Gg18fX0RGhqKcuXKoUaNGqhRo4bbYjx06BB27tyJW7duwc/PD7Vq1ULnzp1RpkwZRa+j0+nw33//ISIiQrbt7t272LVrF65cuYKsrCyULl0aDRo0wNNPP+2UG2cMr20XL16U3TATGBiIUqVKoXLlyqhfvz6eeuopxa9PRERERGQrQafT6dwdBDnXggULsGjRIqSkpAAAwsLCMH78eLRs2RInT57EgAEDkJmZCelTQRAEVKpUCT///LPDk41nzpzB+++/j7t378LLywtnz541bjt58iTee+89PHjwQBaDIY569erh+++/V+SD5Lx58zB//nxkZ2ebvZ656wNAmTJlMH78eLRu3drhGIicoUaNGhAEAcePH0dAQICi5wwNDcWUKVPQqlUrRc6rFLVPjLlqUgzgxBig/xnMmDEDixcvRm5urmhb48aNMWbMGFSvXt3i8enp6WjYsCEEQcD58+ftjqN58+bQaDT4559/4O/vL9qWl5eHqVOnYvny5ca/QYb/G/4twsLCMGnSJLRo0cLuGExdu3YNP/30E06fPg2NRoNGjRrh7bffNj4Ply9fjqlTp0Kr1YqOEwQBvXr1wpgxY6DRPN4VOR+HpG5mZiaWLl2KtWvX4vbt2zYdU6pUKfTv3x+9evVCYGCgYrEkJydj9erVOHnyJLy8vNCqVSt07doV3t7e0Gq1+OCDD/D333/LjvP398dHH32EPn36KBLHkiVLsGjRIqSnp+PYsWPGcZ1Oh++//x4///wzcnJyZMeFhYVh3LhxePbZZxWJ48GDB/juu++wceNG5OXlybZLX0MMMQwaNAivv/664r+/TOwWHBO75jnjtRVQ3+urKbW/dwWY1GVS1zYpKSk4e/YsEhISEBAQgIiICDzxxBNuiycrKws3btxATk4OKlasiKCgILfFoiaG93Rqey2kxwOTDoXcF198gbVr18om1319fbF8+XKMHz8e586dg5+fH1555RXUqlULmZmZ2LdvHw4cOABBEFCzZk2sW7dOtDqiIO7evYtOnTohOTkZOp0OlStXxu+//w5fX1/cvXsXr7zyChITE6HRaNC6dWs0adIERYsWxf3793HgwAEcPXrUGMeqVavg5+dn989j3rx5mDVrFnQ6HcqXL4+nn34aoaGhSE5OxpEjR3DlyhWULVsWn3/+OXJzc3HhwgUcOHDAmCTx8vLC5MmT8corr9gdgzn88FZw/PAmZ0gQnDhxQjbR6eg5dTodBEHAq6++ilGjRrn1TZxaJsbUMikGcGLM4NNPP8XGjRstJrD9/Pwwfvx4dO7c2ezxSiUdrCUApTGWKVMGYWFhyMnJwfXr15GWlgYA8Pb2xvTp0/HCCy/YHQcA7NixAx9++KEo0S4IAsLCwrBq1SqcPn0aw4YNM16zSpUq0Gg0uHz5MnJzcyEIArp3746vvvrKoTg8XWFP6l65cgXvvPMOYmJiRL8/Xl5eCAgIgL+/P3Q6HbKyspCRkSFKUAmCgAoVKmDRokWoUKGCw7GcOXMG7777LuLj40XjzZs3x4IFCzB9+nQsWbLEOF6yZElkZWUZb64RBAEff/wx+vfv71Acpr+rgiDg9OnTxknR0aNHY/PmzcafVWBgIIKCgpCUlISsrCxjHJ999hn69evnUBw3b97E66+/jvj4eIuvbSVLlkRERAQSEhJw/fp147+PIAho2LAh5s6di+DgYIfiAJjYtYSJXfs547XV9LxqeH0F1PPeFVDP+1e+d7Xs7t27WLhwIXbv3o27d++iWLFiaNiwIfr06YNmzZpZPdbwOqvRaHDu3Dm7rr9ixQoAQK9evczOAcXExOCbb77Bjh07ZD+zcuXKGX82SoqNjcXp06fh5eWFOnXqiBJgCQkJmD59OjZu3Gh8nfXy8sKzzz6LUaNGoVKlSorG4mlq1KgBjUaDY8eOKfo6S2QLJh0KscOHD6N///4QBAHPPfeccaJ8xYoVOHLkCMLDw3H79m2EhIRg+fLlsknurVu3YvTo0cjLy8NXX32F7t272xXH5MmTsXTpUgQFBeG7774TvUH48ssvsXr1ahQpUgQLFixA48aNZcdv27YNo0aNQm5uLsaMGWP3G53Tp0/j1VdfhSAI+PDDDzFo0CDZXRLr16/HF198gbp162LlypXG7RcuXMC4ceNw+vRp+Pv7Y8uWLShfvrxdcZjihzfz+OENiiZPBEGw+02n4UNbkyZN8O+//0IQBJQoUQIffPCB3a8JjlDLxJhaJsUATowZ7N27F0OGDDH+zfvf//6HSpUq4fr161i1ahXWr18PrVYLQRDw+eefo2/fvrJzODvpsGfPHrz99tvGFXxjx45FrVq1jNvz8vKwc+dOTJ48Gbdv30aRIkWwdetWlC1b1q44bt68iVdeeQUZGRnw9fVFw4YN4ePjg6NHjyIzMxOdOnXChQsXcOnSJXTq1AmfffYZQkNDAQBJSUmYPHkyNm7cCEEQsGTJknw/6BZmhTmpm5SUhE6dOuHu3bvw9vZGp06d0LZtWzz11FMoU6aMbMJBq9UiLi4O586dw86dO7F582bk5uaiVKlS+P3331G8eHG7Y0lNTUWHDh1w9+5daDQa4x2TV65cgSAI6NevH9avX4/09HS88sor+PDDD43vDU6cOIGvvvoKUVFR8Pb2xubNm1G5cmW74ti2bRtGjBgBAKhbty7efPNNdOjQAYIg4I8//sDIkSMhCAKefvppfPjhh8a7XrVaLf79919MmzYNZ8+ehbe3N9auXWv3XbGZmZno0qULrl+/jvDwcLz//vt45plnULx4cSQnJ+Pw4cOYPXs2bty4ga+++gpdunRBVlYWDh8+jHXr1mHHjh0QBAGNGzfG8uXL7YrBFBO7ckzsOsYZr62m53X36yugnveugHrev/K9q2UnT57EO++8g6SkJFFMhs/dL7/8MiZOnGhx8liJ11lrk9SnTp3CkCFDjDeUmiMIApo1a4a5c+c6nDDLysrCl19+iY0bNxrHvL298c4772Do0KFISEjAG2+8gejoaLOVO4oWLYqFCxeifv36DsXhyZyV3CWyBZMOhdjIkSPxxx9/4KWXXsKMGTOM47m5uejevTsuXrwIQRCsJhS++eYb/Pzzz3j66afx888/2xXHiy++iBs3buCLL75Ar169RNtatmyJ+/fvY9y4cVaz4fPnz8fMmTNRv359rF692q44PvroI2zZsgX9+vXD559/bnG/adOm4aeffsL48ePx6quvGsezsrLw2muv4eLFi+jbt6/Vc9iKH97k3PnhTak7Zgwx79q1y+7jTT8sKRGLI286BUHA0aNHsXLlSnz//ffGn/MTTzyBwYMHo1OnTnavhCoItUyMqWVSDODEmKlhw4bh77//RqNGjYx3aJk6evQohg8fjoSEBAiCgIkTJ6JHjx6ifZz9mvr2229jz549qFmzJtasWWNxhdi9e/fw2muvITY2Fv3798fHH39sVxwTJkzAypUrUapUKSxatAjVqlUDoP+QPWDAAGNPo5YtW2LhwoVmzzFkyBDs3bsXL7/8MqZNm2ZXHGrDpK7YzJkzMX/+fAQHB2PRokWoXbt2gY4/ffo0Bg8ejOTkZAwYMACjR4+2OxbD+72SJUti/vz5xlj279+Pd99913iDRrt27TBr1izZ8RkZGXj11Vdx5coVvP766xg3bpxdcQwcOBAHDx5Eq1atMH/+fNGNDL1798bx48fRsmVLLFiwwOxNDtnZ2ejbty/OnDmDLl26YPLkyXbFsXjxYkydOhWVKlXCqlWrzP7dSk9Px2uvvYYbN25gw4YNotIWf/31F0aOHAmtVotvvvkGnTp1sisOgIldc5jYFVPLayugntdXtbx3BdTz/pXvXS1LSEhAp06dEB8fD29vbzz33HOoWLEi/vvvP+zZswc5OTkQBAHVq1fHokWLUKJECdk5lEo6mHuNTUxMRIcOHZCQkABA/9m5bdu2CA8PR05ODq5cuYKtW7fi/Pnzxn/DRYsW2ffDgP5Gx0GDBuHQoUNmEwpTpkzBiRMnsHr1avj7++O1115DkyZNjJ9dV69ejYyMDJQqVQp//PEHihYtancsajF79my7jhEEAUOGDJGVsXv//feVCo3ILCYdCrHnnnsOd+7cwdq1a1GnTh3Rtg0bNhhraB48eND4BljqypUr6NixI0JDQ3Ho0CG74qhbty5ycnKwd+9elCpVyjiem5uL2rVr5xsDoK9D16ZNGwQHByMyMtKuOFq3bo27d+9iy5YtqFKlisX9rl69ig4dOphNcPzzzz947733EBERgW3bttkVhwE/vMm5+8Nbw4YNkZ6eblfsUo7+m6xZswZTpkxBRkYGBEHASy+9hIoVK1o9Zv78+RAEAYMGDYK3t7dom+HNfUFJnxtRUVGYOHEiTp48KVrp0r9/f3Tq1Mnq77Gj1DIxppZJMYATY6ZatWqFe/fu4eeff7a46io6Ohr9+/fHvXv34OXlhTlz5oiSjc5+TW3RogUSExMxe/ZstG3b1uo5Nm/ejFGjRqFKlSrYunWrXXEYkv5TpkyRJa8NH/oFQcCqVass3gF29OhR9O3bF2FhYQ4lUpnUtRyHu5O6HTt2RHR0NCZMmICePXvadY5169Zh7NixDr8/6tatG86fP49JkyahW7duom3jx4/HqlWrIAgCfvvtN4sTnH///TeGDRvmUCzNmjVDcnIy1q1bJ/tbU6dOHeTm5mL9+vWi90RSkZGRePPNN1GhQgWzpUps0bNnT5w9exY//PADnn/+eYv7Gd6f9uzZExMnThRtmz59OhYuXOjQDUQAE7vmqCWxq5bXV7W8tprG4u7XV7W8dwXU8/6V710t+/777zFv3jwEBARg6dKlqFu3rnFbbGwsxo4di/3790MQBFStWhXLli2TffZyZtJh2rRp+PHHH+Ht7Y0ZM2agXbt2Zo9fuHAhpk+fDkEQMG3aNHTo0MGuODZu3IhPPvkEgiDg5ZdfRq9eveDr64vffvsNq1evRnh4OO7fvw8/Pz8sX75c1gfl4sWL6NevH1JSUjBixAi8/fbbdsUB6CtiKEn699FWhn+bgjBXIszAkddZIlt4578Lear79+8D0NfVk2rYsKHxa2vLTA3Zc8OSSnv4+fkhJydHVj7I29sbgYGByMjIQJEiRayewzCBmp2dbXcchmWkJUuWtLqfYXt0dLRsW7169QDA5lqc1qxbtw4AjMsyDWrUqIHx48ejU6dOxg9vkyZNgr+/v91/nOy1cuVKY0zLli2TfXjTaDRo164d6tevb/zwtnTpUrs/vC1evNh4N4K5D2+bN28GoJ9c/Pbbb0XHBgcHY8qUKUhISMDevXuxdu3aAicdtm7dio8++gjHjh0zfigxfbPnSq+99hqaN2+O0aNH49SpU9i1a1e+dVTnz58PABg6dKjTlk7WqlULq1evxqZNmzB9+nTcuXMHt2/fxuTJk/HNN9+gefPmePnll9G2bVsUK1ZM0Wsb7ij66KOPCvyhDdAnQD/66COMHTsW//zzj90f3P766y8IgoCRI0eK4njmmWfQo0cP46TYu+++a/b4gIAADB8+HMOGDcPBgwftisEgKioKgiBg+PDhsg9mZ86cgSAIGDFihMVyY76+vvjwww/x5ptv4ujRo3bH8ccff0AQBIwaNcriXXiBgYH44IMP8N5772Hx4sWiibEXXngBAwcOxMKFC7Fhwwa7PrgZ7rqy1g+nSpUqWLRoEfr164ekpCR88MEHWL58uV3PJ3ukpqYCABo0aJDvvobEiSN/b+7cuQNAXzZByvR9gLW7BA0/T2kphoJKTk5WNKnriPHjxyua1HWURqPB4MGD8fTTTxuTulevXsWnn36KWbNmOT2pa5gYfe655+w+h+FYR98f3bx5E4D552znzp2xatUqAEDVqlUtnqNRo0YAgLi4OLvjMDxXpaU0tVqtsbSG4T2KJYbymHfv3rU7jitXrgB49D1ZYkgaHjhwQLatc+fOWLhwIaKiouyOA9CX1BAEweJdkYa7fQ2J3S+++AIlS5ZUdEI8P6dPnzbGaK3XWKlSpfDBBx9g1KhR2Lt3r93vWw397z766CPR86FChQr46KOPjInd9957z+I5DEmH48eP2xUDoJ7XV7W9tgLuf31Vy3tXQD3vX/ne1bI9e/YYXzOkn0HDwsLw448/YtKkSfjll19w5coVDBkyBMuWLXNZyZydO3caf18tJRwA/evazZs3sW7dOmzYsMGhpIMgCOjYsaPos3/dunWh0+mwZs0aY8kvc43Xq1evjqFDh2Ly5MnYuXOnQ0mHMWPGOPz+00AQBLvndSpVqoT//vvPrgQv7zcnd2DSoRArUqQIkpKScOvWLdkf0tKlS6N+/fqIi4tDTEwMIiIizJ7j2rVrAOBQncIKFSrg/Pnz2Lt3L1577TXRtgYNGuDgwYOIioqyOgljWGURFhZmdxzFihXDgwcPcOHCBauT0devXwcAWb8AAMZSQkrcFcMPb3Lu/vAWFhaGn3/+Ge+99x4OHDiA2NhYzJw506am3s5guPNm7ty5mD9/Pr766iv8888/+Prrr0XNs9zhlVdewUsvvYTNmzdjyZIluHTpEnJzc3HgwAHjv2PlypVRr149VKtWDWXLlkXZsmUdqqeplokxtUyKAZwYM+Xv74+0tLR8k9PVqlXD7NmzMXDgQGRkZOCdd97B2rVrER4ebtd1C6J06dK4ffu22caIUoa/N44wfGDPyMiQbTOdoMrOzrb4Gp+cnAwAsuXYBcWkbv7cmdQFHLuxw/Acs/ZeoSAxmPtgbDp5mZaWhpCQELPnUOJ3p0SJEoiLi8ONGzdE1/Hy8kJ4eDhiYmKQmJgoWsErZfg7k9+NNdYYfg5paWlWJ0UNrymGG45MGY5z9OfCxK6cWhK7anl9VetrK+C+11e1vHcF1PP+le9dLfvvv/8AWF69JAiCcfJ7+fLlOHv2LEaMGIF58+a5pJ+h4flsS4KlT58+WLdunUN30huOHTBggGxb7969sWbNGgCwWt65bdu2mDx5snGOx159+/bFypUrodPp3Dp5//vvv+Prr7/G2rVrIQgCunbtik8//dRi7wydTmesLnLkyBH2dCCXY9KhEKtduzYOHDiAJUuWYPr06aJtAQEBNvVG+PHHHyEIgqw8U0G0bdsW586dw/Tp01GvXj1RFrpv3744cOCAsWmVucn8+Ph4zJw5E4IgoFWrVnbHUadOHezZswczZ87E8uXLLd5NY2ieZe5D1fbt2wHAoRrsBvzwJqeGD29+fn6YPXs2unfvjmvXruGTTz7BunXr3NaYWqPR4P3330erVq0wevRoHDhwAJ06dcLYsWMVuaPGET4+PujWrRu6deuGQ4cOYdOmTdi1axcSExOh0+kQHR2Nq1evGvd3tD6vgbsnxtQyKQZwYsxUpUqVcO7cOezbt0+W4JZq0qQJJkyYgE8//RTx8fEYNGgQVqxYoWgTS0B+x+j//d//Yd26dTh27Fi+d33t3r0bgPxDeUFUqlQJFy9exF9//YUhQ4aItv3111/Grw8dOmTxjrW9e/cCgMMNLJnUtZ2rk7pVq1bF2bNnsWXLFtnzxFaGsgPWylfaoly5crh27RoiIyNl5ZVCQkLw888/IzY2VrZ61tS+ffsAOPa707BhQ/zxxx+YP3++aDUqoJ/cWLx4MbZs2WJ2IsTgl19+AQCrJZjyU65cOVy9ehVbt261eoem4ffU3Ovv5cuXLW4rCCZ25dSS2FXT66uaX1sB99w0A7j/vatpDO5+/8r3rpYZ/o1Kly5tdb/PP/8cCQkJ2Lp1K/bu3Yvx48dj/PjxisRgjY+PD7Kzs63+2xgYvoekpCS7r2f4uZor3Wwag7V/P8NzxNzrdEGMGTMGLVq0wAcffICcnBzUqlULM2bMcPkcgb+/PyZMmIA2bdpgzJgx2LBhA44cOYIpU6bkm0Dz8vJySR9GIlPumUUjl+jRowd0Oh3+/PNPjBo1SjT5l5979+7hk08+Mdb0tLfGLwC8+eabKFmyJJKTk9GnTx8sXLjQ+Eb8ueeeQ+/evXHkyBEMGjQIFy5cMB6Xnp6O3377DV27dkVsbCz8/f3Rv39/u+MwLGE7efIk3n77bcTGxoq2Z2dnY+rUqcallqb1r9PS0rBkyRL88MMPEAQB7du3tzsOA8Pklq0f3nx8fIwf3pQo72QLw5sFNX54s0SJD28BAQGYNWsWvL29ce7cOaxdu9bucymlbt262LhxI3r16oXk5GSMHj0a//vf//DgwQN3hwZAn3SaPHkyDhw4gMWLF2PgwIFo0KAB/P39jXeEOHpXiOHOqy1btth9DiUmxgwl68z1lzFMin311VdOnxQD9BNjOp3OeKegKcNdP/n9vJSaGAOQb+8BZ06MtW7dGjqdDjNmzDDevWZN165dMXToUOh0Oly/fh19+/a16biCePnll/HBBx/g559/xpEjR9C9e3djLwlrH35OnDiBWbNmOZxsb9u2LXQ6HebNm4dNmzYhLy8PWq0WGzduxLx58+Dt7Q1/f398++23SExMlB0fGxuLOXPmQBAEtGzZ0u44DAxJ3SeeeAIZGRn45JNPkJeX5/B57WVI6q5cuRKVKlUyJnUNpfzcyZDU3bRpExYvXoyuXbsiODgYOp0OeXl5iI6OxoYNGzB16lR88MEH6N27t93X6tSpE3Q6HX744QesXbu2wK/Vq1atwk8//QRBEBxOhrdq1Qo6nQ7Tp083viYYaDQaPP300+jevbvFUpnR0dHG3x1H7iw2JC537dqFTz75xHgTBqDvG1CiRAl8//33Zvud5eXlYc6cOfjtt98gCAK6dOlidxyGn8e8efMs9laLiYnB999/D0EQZP1sUlNTMWPGDGOzU0dUqlQJwKO/X9YYErsAjIldw802SjKX2AWAY8eO5XusUoldQJzENZAmdi1RKrGrptdXNb+2Aq59fVXLe1dAPe9f+d7VMkN1CkO1CWsmT56MBg0aQKfTYe3atViwYIEiMVhjuHnUlrkIw9yTIyuHDEkpc+/PTd8jGFbxmGMomW0pkVYQbdu2Nf69jYqKwubNm1GuXDm7/nPUs88+i02bNuG5557DzZs38cYbb+Cbb75xKMFJ5AxMOhRi7du3xwsvvACdToctW7agY8eOOHz4cL7HDRs2DM899xx+//13APpmUvk1vLQmKCgICxcuRLFixZCWloYZM2bg6aefRt++ffH5558jICAApUqVQmRkJLp27YqmTZvimWeeQZMmTfD555/j7t270Gg0mDRpkt0Nig3fh+HncfDgQbRr1w69evXCyJEj8fbbb6Nly5bGVQ6GHgWAPjvfvHlzTJ06FdnZ2ahatSrefPNNu+Mw4Ic3OTV9eKtatSr69u0LnU6HOXPmWH0D7ir+/v748ssvsWDBApQoUQLbt29Hp06dHGqoqjQvLy+0aNECo0ePxqpVq3D8+HHs2LEDy5cvx+zZsx06t1omxtQyKQZwYsxU3759ERoaiqSkJPTo0QOfffYZ1qxZY/XN97Bhw/Dqq69Cp9Ph2rVr6Nevn13XNken0yEmJgZ//vknvv32W7zxxht4/fXXAeg/iL3xxhuyCaHNmzfjo48+Qt++fZGWloagoCCH/t68+eabKFu2LDIyMvDxxx+jUaNGaNCgAT799FNkZmaic+fO6Ny5M27cuIFu3bph7dq1uHTpEqKiorBo0SJ069YNCQkJCAwMVOxnw6RuwTk7qdu7d280btwYOTk5+OKLL9CuXTtMnDgRv/32GyIjI3HhwgVcu3YN169fx4ULF3D06FFs2rQJU6dOxUsvvYQJEyZAq9WiTp06xue4vQYMGICiRYsiPj4ePXv2xIcffmh1MsHgyJEjmDBhArp3747ExEQUK1YMb7zxht1xNGvWDL169YJOp8Pvv/+OF154Ad9//z0OHz6MjIwMzJ49G0WLFsWgQYPw3nvvYenSpVizZg2+/fZbvPjii8a/d82aNcPLL79sdxyDBg1C0aJFkZWVhcGDB2PcuHHYv38/rl69ijNnzmDhwoXo0aMH7t+/D29vbwwePBgAkJmZienTp6NTp044deoUNBqNQzfuAEzsmqO2xK7aXl/V/toKOP/1VS3vXQH1vH/le1fLDP0jFy1alO++vr6+mDt3LipWrAidTmes5qCkLVu2iPpcdu7cGTqdDr/++mu+xxpe2xypmFG/fn3j92b6fj47Oxvff/+98bEhCWXOsmXLIAiCYiXnnnvuObz77rvG544tCSJnKV68OObOnYuJEyfCz88PixcvRvfu3dkcmlRF0LGbSKGWk5ODqVOnYvXq1dBqtdi9e3e+S1v79OljnGTu3LkzJk6c6PByTkC/emLy5MnYvn27sV6jdJLb3NMxPDwcEydONE6AOyIrKwtjx47Fpk2bjGOGGAzXbtGiBWbMmGHMhut0OtSqVQt5eXl49tln8fXXX1tsNlUQs2bNwty5cxESEoJffvnFav1Mgx9++MH4B7xy5cqYMmUKXn31VQiC4NAflxo1akAQBISHh6Nu3bqoU6cO6tSpA19fX/Tp0weVKlXC+vXrLdYAPHHiBN566y2kp6dj4MCBGDVqlF1xGL6/gIAAjB8/Hh07doROp8PmzZsxfvx45OTkwMfHB6VKlcLatWtldyzExsaie/fuePDgAd566y18+OGHdsVhkJycjGXLlgEAunXr5pLSALZ68OABxo4da2xQ17VrV+Mb8OPHjytWr9Hw3FDynPbKzc1F//79cfToUQiCgHLlyqF169aoVasWypUrh+DgYPj5+UEQBGRmZiI1NRW3b9/G+fPnsXv3bly/fh06nQ5169bFqlWr7F5eGhcXh44dOyI1NRV+fn5o27YtRowYkW+i68iRI/jzzz/x22+/ITMzE8HBwdi6dWu+ze3z8+WXX2L16tUQBAGhoaF47bXX0KxZM0RERODOnTsYNmwY4uPj8eyzz6JZs2bw9/fHjRs38Ndff+HWrVvQ6XRo3ry5Melqj/v37+Oll15CamoqvLy80K1bN7zwwgsIDw9HWloaDh06hMWLF+PBgwfw8fHBxo0bUaVKFWRmZmLu3LnYvHkz7ty5A41Gg5UrVxo/dBXU0aNH8e677yIlJQWCIEAQBJw+fTrf5pSTJ0/G0qVLjU3ZHH1NTUhIQFRUFM6dO4eoqChERUUZ6+AaBAUFyRog1q9fH1lZWdDpdPDz88MPP/zg0IQYoJ8kePfdd3Hjxg3ReO3atbFkyRIkJyfj1Vdfxf37983+Tfb29saMGTOsNgy0x9SpU7F48WKUKlUKu3btckoDUXvs2bMHn3/+Oe7fv4+SJUti4sSJeO6555zyWujoOQ2JrdjYWCQnJzt0g0h2djZGjx6Nbdu2AbC9mazhvVObNm0wZcoURWqhHzp0CMOGDUNqaioEQcBvv/2GmjVrWj2mY8eOiI6Ohk6ng7+/P+bNmyebICoow+Tc4sWLkZuba/ZnYni9kI4B+htG5s2bh6CgIIfiOHDgAN5//31jc15zMXh5eWHSpEnGCbiMjAxjaUxBEPD555+jb9++DsWRkJCAl19+GYmJifDz80OHDh1Qr149dO3a1ernhHHjxhlrT/v6+iIrK0uR962mDD8XQRCg0Wig1WpRu3ZtrFmzRlT6YvPmzdizZw/+/PNPaLVaFC1aFFu3bs23lIklycnJeOWVV3Dnzh0IggB/f39otVrjCuFu3brBx8cHq1evRnh4OP6/vXuPqynd/wD+WbuoJOQ6CrmlGnczOC4pnMYQQmLmuA+G4zLHHJcxR8wYfhnGoBoajPtIrkMuYaLhlLspl3IpUolsdJVq72r9/nD2mrbdvU0rfd6vV6/Xbq+1nv0pu2Xt57ue55k6dSo6dOgAtVqNCxcu4JdffkFSUhJMTU0REBBQ6hyvk+P59W2eWwH5nF/lcu0KyOv6ldeu+Ttz5gymTJkCQRAwePBgzJo1q8g1LR88eIBPP/0USUlJ0owMAQEBZTrPav5+NKpVq4bWrVvDzs4Ovr6+yMnJwcqVK/OdKjQ2NhZLlizBf//7XwiCAC8vr1JfQ54/fx4TJkyAIAho2bIlBg0aBLVajYCAANy7dw9NmjSBqakpbt26hdmzZ2PixIlSbrVajdWrV2Pz5s1lzvE6URTh4uKCyMhIODk5wcvLSy/tlkVcXBzmzp2LsLAwGBoaYvr06ZgyZQoUCoWsPs9T5cOiQyWRkJCAU6dOFbqQl8aGDRugVCoxaNAgvfzn+brnz5/j+PHjuHHjBu7evYunT58iPT0dmZmZMDY2hqmpKRo1agQ7Ozs4ODigZ8+eep97LiwsDIcOHUJ4eDhSUlJgamoKa2tr9O/fP9+Fmw4cOID27duXeVhrXvzwpkuuH97kbP/+/fDw8MDLly+lzg85dYrpm1w6xuTSKQawY+x1T548gY+PjzQ66ty5c8U67sSJE/Dw8MCTJ0/KfE7NT2pqqlSACA8Ph0qlwtq1a7X26dOnDx4/fiyNFnr93FxaKpUKx48fx/Xr16FQKNCuXTv0799f+r81KioKixYtwp9//ql1nLW1NRYuXIguXbroJUdeLOrK7/wKADdu3MCmTZsQEhKCtLS0Qvc1NzeHg4MDXFxc9HIuy+vx48fw8fHBqVOnEBQUVOTNL1OnTsXZs2fRvXt3fPXVV3qdyz4qKgq+vr44fvx4sUaZtm3bFiNGjMDw4cOL/X9UUe7cuYMVK1bg3LlzOjfotG7dGl999ZXO32nfvn3Rrl07TJgwQW93eLKwq0uOhV25nl/f1rkVkNf5VS7XroB8rl957VqwOXPm4MiRI1KeunXr4vjx44WuX3H79m1MnDgRz58/18t59ttvv8WtW7dw584dZGZmSs/nvVnT0NBQuq7U+Pjjj6XFsEVRRJ8+fXTWRSqpFStWSIUDDc2/i7e3N168eIF58+ZBEAQ0aNAA7dq1g0qlQmhoKFJTUyGKIuzt7bFx48Yy5XhdcHAwJk2aBIVCgYCAAGnGhvKUm5srrUWVk5ODdu3a4fvvv0f//v1lcz6kyodFB6JyxA9vuuT44U3u4uLi8NVXX+HPP//U+wWF5n2gj7kn9UkOHWNy6hQD2DGWn+Tk5BLN4aq5e+r69etwd3fXa5biCAsLg6WlZbEW6HsT7t27hwcPHkCtVqN58+Zo1apVueSQi8pW1H1dXFwcHj16hKSkJGRlZcHAwADVqlWDmZkZmjZtWm7v0/zExsbCzMxMb/NqF+Thw4eIjIyUbpjJyMiAiYmJdMOMra2tXkbDFiQxMRE3b95EamoqqlWrBmtr6zJPJ1lSLOzqkmNhV87e9LkVkOf5VQ7XroC8rl957apLM4pgx44dyM7OhomJCUJDQ4s8LiYmBl9++SUiIiIAQC/n2dzcXERFRWn1Edy+fRsvX76EhYUFTp8+rbV/t27dpOnTnJ2d4eHhASMjozJlAIC9e/di48aNUv+AlZUV5syZI33eX7p0qTTF0uuzWPTo0QOenp5lLlBVJDdv3sTcuXMRHR0NY2NjZGZmyu58SJUHiw5E5Ywf3nTxw1vJiaIoLeoltwLBm1ZROsbeVqcYwI6x4tL8zcjhLlC5ZJFLDrl4+PAh5s2bV6mKukTFxcJuybCw+5c3ecMMIP/za0W5dgVY1C0vSqUSgYGBUCqVmDVrVrGOUavV2L17N7Zv3464uLg3Mre/KIq4f/8+lEqlTkFs9uzZMDMzw+DBg9GpUye9v3ZKSgoMDAzyLSCcO3cOe/fu1TrHOjs7o1+/fnrPURFkZWVh+fLl8PX1BQAWHajcsOhAJCP88FYyb+rDm1KpxIYNG/DHH39AqVSiRo0a6NSpE0aNGoWuXbsWeuzLly/RqVMnKBQK6U6TdyGLXHKURHJyMh4+fAhjY2M0bdq03OY0lksOOWWRSw5bW1soFApcvXq13C/C5ZKlvHOkpaXh5s2bSExMhImJCZo2bYrmzZu/9RyvZ0lMTESPHj3KJYucfifAq5sSMjMzUa9ePb3daVrRs1SGHJcvX4apqSnef/99vbZbkbPIJUdJZWVlITY2Fmq1Gk2aNCm3O4CzsrIQExOD7OxsNG7cGGZmZuWSQ5Plbf5O0tPT8eTJE7x48UJas87U1BT16tXTyzRKFTELc+hXSfsV6N0UHByMo0ePAgC+++47VKlSpZwTUWXDogMRUR5hYWGYOnUqUlJStIbYaj68Ozs7Y8mSJQV2xmk62PUx+kQuWeSSI6+srCzcv38fCoUCLVu21Fr35fr161i+fLnWSBhTU1MMHToUM2fO1OsHBrnkkFMWueQoDjlNvyCXLG8qx86dOwEAn3zySb7rNMXHx2PFihUIDAxEbm6u1jZLS0tMnDgRn3766TuVRS45NF6+fIlDhw7hxo0b8PDw0Nr29OlTrF+/HidOnMCzZ88AAAYGBmjTpg1cXV31OtWFnLIwx18054bPPvsMs2fP1prH+22TSxa55Mjr8ePHuH79OgwMDNC2bVs0aNBA2paYmIhVq1bh4MGDyMnJAfDqveLo6Ii5c+fqdU5yueSQU5bY2Fhs3boVZ8+e1ZnGNi9zc3O0b98eTk5OGDhwYJHTH1XkLMxBRPRuY9GBiOh/EhMTMWjQIDx//hyGhobo3bs3mjRpgpiYGJw5cwZqtRqCIMDGxgabNm1CnTp1dNrQVwe7XLLIJUde69evx6ZNm6T5cBs2bIjFixfD3t4eYWFhmDBhAjIzM3XmZRUEAVZWVti8ebNepo6RSw45ZSnPHD/99FOpjhEEAZ9//rnOnT8zZswoVQ45ZZFLDqDwERTXrl3D559/Li34lx9BENC1a1esW7cO1apVK3UOOWWRSw7g1VzjM2bMgFKphIGBAW7evCltCwsLw7Rp05CUlJRvFkEQ0L59e3h6emp15lX0LMyhTTOFpiAI6NixI5YvX15uU4/IJYtccgCvCv7ffvstDh48KD1naGiIqVOnYvr06UhMTMTYsWOlBYHzEgQBZmZm2LBhAzp06PBO5JBbFh8fH/z8889QqVQFntNff30AaNCgARYvXgwHB4cyZ5BbFuYontu3b0vrLWpGsykUClStWhXm5uawtLSEra2t3qY5Zg4i0icWHYiI/sfT0xM+Pj4wMTHBtm3btBYGe/z4MRYuXIjg4GAIgoCWLVti+/btOvOb6quDXS5Z5JJD45tvvsGePXt0PhRUrVoVO3bswOLFixEREQEjIyMMHjwYrVu3RmZmJv773/8iJCQEgiDAzs4Oe/fuzffO4oqWQ05ZyjuH5o7TktBkze+4srxX5ZJFLjnyZnl9BEVycjIGDBggLSDp6OiIvn37wsLCAmq1GlFRUTh69Chu3boFQRDQvXt3bNq0qdQ55JRFLjmUSiUGDRokFTiaNWuGQ4cOoWrVqlAqlRg8eDCSk5OhUCjg4OCAzp07w8zMDM+ePUNISAiuXLki/f3u2rWrTItGyiULc+jSvF8FQUBubi5MTEwwffp0jBs37q1P1yCXLHLJIYoiJk6ciPPnz+fbef79998jNDQUfn5+MDY2xsiRI9G5c2cIgoArV67Az88PGRkZqFevHo4dO1bqaY7kkkNuWXx8fODl5QVRFNGoUSN0794d5ubmSE1NxeXLlxEVFYX33nsPCxYsQHZ2Nm7fvo2QkBCpwGhgYIBly5Zh8ODBpc4gtyzMUbjMzExs27YNe/bskdbaKkq9evUwfvx4fPLJJ2W+EYE5iEhfWHQgIvqfYcOG4datW/j3v/+NyZMn62wXRRH/93//h19//RWCIKBNmzbYvn27VmeRvjrY5ZJFLjkA4MKFCxg/fjwEQUDv3r2lC/ydO3fi8uXLsLCwwKNHj1CrVi3s2LED1tbWWscfPXoU8+bNQ25uLpYuXQpXV9cKnUNOWeSQo1+/foiJiYEgCMW6U60ot2/fLvWxcskilxxAwR3sP/74IzZu3AhDQ0OsXr0aTk5O+R6/YcMGrFq1CoIg4Mcff8SAAQMqfBa55Fi2bBm2bduG6tWrY+XKlXB0dJS2ffvtt/Dz84OpqSnWr1+PDz/8UOf448ePY+7cucjOzoa7uztGjRpVqhxyysIcujTv1/Xr12PhwoV48uQJBEFA48aNMWvWLPTv3/+trWchlyxyyXHw4EHMnz8fgiDA2dkZn3zyCapWrYoDBw7Az88PFhYWePbsGYyMjLBjxw6dO4Dv3LmDMWPGIC0tDbNmzcKUKVMqdA45Zbl+/TpGjBgBQRAwe/ZsTJw4Uec9sW/fPnzzzTdo164dfH19pe23b9/GokWLcP36dRgbG+PIkSNo1KhR6X4hMsrCHIWLiorC1KlTER8fr3XtZmBgABMTExgbG0MURWRlZSEjI0OaFgyAdP7ZtGlTmUddMYe2vP//lpUgCAgKCtJbe0SyJhIRkSiKotipUyfR1tZWvHv3bqH7LV26VLSxsRFtbW3Fzz//XMzJyZG2paenS9vehSxyySGKovjll1+KNjY24qxZs7SeV6vV4uDBg6XX2LdvX4FtLF++XLSxsREnTJhQ4XPIKYsccmRkZIgLFy6UXuvrr78WU1NTxezs7Hy/1Gq1tG9aWprO9rKQSxa55BBFUWr35cuXWs/3799ftLW1FVetWlVkG+7u7qKNjY04adKkdyKLXHJ89NFHoq2trbhr1y6dbT179hRtbW1FX1/fQtvw8fERbWxsxJEjR5Y6h5yyMIeuvO/XpKQkcfr06aKNjY30vJOTk7h7924xKyurTK9TkbLIJce4ceNEW1tbcc6cOTrbFi1aJOX59ddfC2xj69atoo2Njejm5lbhc8gpy+zZs0UbGxtx6dKlhe63cuVK0dbWVty9e7fW85mZmaKLi4toa2tbZBsVJQtzFCw5OVm0t7cXbWxsxNatW4vz588Xf//9dzE+Pj7f67Ds7GwxPj5e/P3338X58+eLrVu3Fm1sbMSePXuKz58/Zw495RBFUezYsaN0fi/rV1k/ExNVJBzpQFRO9u3bp9f2hg8fXupj5ZKlvHO0bdsW2dnZuHDhAmrWrFnovrNnz8bRo0chCAJGjBiBxYsXA9DfXf1yySKXHADQu3dvJCQkYM+ePWjbtq3Wtt9++w1ff/01BEHAuXPndKZ40oiKisLAgQNhbm6O8+fPV+gccsoilxwA8Mcff8Dd3R3Pnz9Ho0aN8P333+ODDz7Id983vXizXLLIIUdB7bZv3x4qlQqHDx9Gy5YtC23j9u3bGDJkCOrWrYvg4OAKn0UuOdq1awe1Wo2zZ8+iXr160vPZ2dlo06ZNkX+7APDo0SP06dMHNWvWxMWLF0uVQ05ZmENXfu/X4OBgeHh44P79+wBe3b1Zp04dODs7Y8CAAWjfvn2pX68iZJFLjq5duyI1NRX79+/H+++/r7Xtzp07cHFxgSAIOu+jvB4+fIi///3vqFGjBi5dulShc8gpi4ODA5RKJY4cOYIWLVoUuN/9+/cxYMAAdOjQAX5+flrbTp8+jWnTpqFp06Y4fvx4qXLIKQtzFGzNmjX4+eefUbNmTWzatAlt2rQp0fHXr1/H5MmTkZqaigkTJmDevHnMoYccwKtphefMmYOrV69CEAQ0b95ca9rhklq2bFmpjuOIC6poDMs7AFFl5e7urrch14IglKnoIJcs5Z2jdu3aUCqViI6OLnLRuGXLluHRo0cIDQ3Fnj17YGFhUaZh4HLNIpccAPDs2TMAgKWlpc62Tp06SY+rV69eYBuaha41Cx1X5BxyyiKXHMCri3F/f3+4u7vj9OnTGDt2LMaNG4dZs2ahatWqZWq7omaRS478VKlSBSqVqsBOn7zq168PAEhJSXmns7ztHEZGRlCr1cjOztZ63tDQENWqVUNGRgZMTU0LbcPQ8NVHCpVKVeoccsrCHMXTs2dPHD58GH5+fti6dSvi4uLw7NkzbN++Hdu3b4eFhQX69u2Ljh07okOHDmjYsKHeM8gtS3nkSE9PBwC89957OtvynkcKK05p3kcZGRkVPoecsjx//hwAULdu3UL302y/d++ezjZNoaq4c9nLPQtzFCwwMBCCIGDOnDkl7mAHXhWq58yZg4ULF+L06dOl7mRnDl0NGzbE5s2bMW3aNISEhODx48dYs2aNzpSxb1pqaipevnypl7be1pSIVLkpyjsAUWU1evRo6UQvimKZv96FLOWdQ3PhWJwFOatWrYp169ahSZMmEEURa9aswY4dO0r8mnLPIpccwF8f/h4+fKizrX79+ujQoQPee+89xMfHF9hGdHQ0ABQ5aqMi5JBTFrnk0KhduzbWrVuHJUuWwMjICFu2bIGrq2uZFzKvyFnkkuN1mnm0i/OBXXPncI0aNd7pLG87h2ae47Nnz+ps69ixIwAgPDy80DY0o5PK2oEqlyzMUXwGBgYYNWoUTp48CU9PT3Tq1Em6DouPj8eOHTvw73//G3369MEHH3yAgQMHYtKkSe90lredo1atWgBejRh8XWRkpPQ4Li6uwDY0HaqatipyDjll0Zybi1oT6cGDBwCgNR+9hqaAYmBgUOoccsrCHAXTXCf37t271G1oji1LIYQ58mdkZISffvoJzZs3R0ZGBubPn4/c3Nwyt1sSR48elUYrC4KAFi1aYOjQoaX6GjJkyFvNTpUTiw5E5cTd3R3e3t6oUqWKtADv77//jlOnTpX4KzAw8J3IUt45XF1dIYoiAgMD8dVXX+Hx48eF7m9ubo4NGzbA3NwcoijCw8MDCxYsKO2PL8sscskBQLrDZevWrTrbTExM4Ofnh6CgIDRt2rTANjZu3AhBEHSmAKqIOeSURS45Xufm5oZDhw6hffv2iIyMhJubG3x8fN76BwQ5ZSnvHEeOHNG6W9DFxQWiKGL//v1FHrt27Vq9vkfkkqW8c/Tt2xeiKGLVqlU6nS+jR4+WtuXX4QK8ult0zZo1EAQBvXr1KnUOOWVhjpITBAH9+vWDr68vTp8+jfnz5+ODDz6QFrIXRRHp6emIiopCSEhIpcjytnJ06NBButkj74gWlUoFT09P6ftff/21wDa2b98OQRDKNF2IXHLIKUvbtm2lHK+PWMpLc/2U313TJ06cAAA0a9as1DnklIU5ilaWkWmakTn6GMnKHLpMTEzg5eUFQ0NDREREYM+ePXppt7g0Iy569OgBURTx+PFjfPbZZ1i2bFmpvojeNBYdiMpR37594enpCUEQEB4ejsOHD8PS0rJUX+9KlvLM4eDggIEDB0IURfj7+6NPnz6wt7eX7mLJT9OmTbFlyxbUqVMHoijqZT5POWWRSw7g1RodoigiICAAc+fOle7wLY6nT59i/vz50ryVbm5uFT6HnLLIJUd+GjduDF9fX3zxxRcAAC8vL3z66afSyIq3SS5ZyjPHokWLMHDgQHzwwQcYO3YsoqKiUKVKFfj6+uLYsWP5HhMbG4vJkydLd2y7urq+U1nKO8e4ceNQt25dpKamYtSoUdiwYYM0dL937974xz/+gcuXL2PixIlaHd8vX77EgQMHMHToUDx+/BjGxsYYP358qXPIKQtzlI2FhQXGjx+PnTt34tKlS9i8eTNmz56NIUOGoEuXLtJonsqU5U3mGDVqFAAgNDQUrq6u2LBhA9auXYthw4YhNDQUVlZWsLOzg6+vL3755Ret0cBqtRorVqzAyZMnAaBMd77KJYecsmimeg0LC8OUKVN0bt5RqVRYvnw5jh07BkEQ4OLiIm1LT0/H1q1b4e3tDUEQ8PHHH5c6h5yyMEfBNOs4HTlypNRtaNZILGydCuYom5YtW0qF/7Vr1xZatHoT5DDigqi4uJA0kQx4e3tj7dq1qFKlCvz9/fV+t0RFzFJeOXJycrBy5Urs2LED2dnZMDExQWhoaJHHxcTE4Msvv0RERAQAlHnRZDllkUsOAPjiiy9w8uRJCIIAQRCwefNm/O1vfyv0mJkzZyIoKAg5OTkQRREfffQRvLy83okccsoilxyFuXnzJubOnYvo6GgYGxsjMzPzjS4kXRGyvK0c3377LW7duoU7d+4gMzNTej7vlHqGhoa4fv06FIq/7on5+OOPERMTI+3Tp08frFu37p3IIpccABAREYEJEyYgJSUFgiDAyMgIbdq0gZWVFWrVqgV/f39p7RYzMzNUrVoVSUlJyM3NhSiKUCgUWLlyJQYMGFCmHHLKwhza3uRi9xU1i1xyAMCKFSuwefNmrTm6RVGEgYEBvL298eLFC8ybNw+CIKBBgwZo164dVCoVQkNDkZqaClEUYW9vj40bN74TOeSUJe/1kYGBAdq0aQMLCwukp6cjLCxMeq2OHTvi119/hYGBAVJSUtCzZ09kZ2dDFEVYW1tj3759MDIyeieyMEf+tm/fDg8PD1SpUgULFy6Em5tbiebd37VrF5YuXYrc3FwsWLAAo0ePZg495MhPamoqtm/fDgAYNmwYLCws9NZ2cUVFRWHo0KHIzs7GN998g08++eStZyAqCosORDIgiiJcXFwQGRkJJyenN9rpVlGylHcOpVKJwMBAKJVKzJo1q1jHqNVq7N69G9u3b0dcXJze5kqXSxY55FCr1Vi+fDn8/PyQk5ODP/74Aw0aNCj0mFGjRuHq1asAXk1bsmTJkjIPsZVLDjllkUuOomRlZWH58uXw9fUFgHLtMJJLlreZIzc3F1FRUYiIiEB4eDjCw8Nx+/ZtvHz5EhYWFjh9+rTW/t26dUNSUhIAwNnZGR4eHnr5YC+nLHLJ8fTpUyxbtgwnTpyQput5/cN9fh8bLCwssGTJEvTo0aPMGeSWhTn+IqcOdrlkkUsOjb1792Ljxo2IjY0FAFhZWWHOnDlwcnICACxdulSaTihvcRMAevToAU9PT1SvXv2dySGXLFlZWVi4cCH8/f2l515/rW7dumH16tXS+hGiKKJ169bIzc2Fo6MjPDw8ULt27TLlkFMW5shfdnY2xo8fjytXrkAQBFhaWsLBwQGtW7eGpaUlatasCSMjIwiCgMzMTLx48QKPHj3CrVu38Mcff+DBgwcQRRHt2rXDrl27Sr3WBHNUHMuXL8eWLVtQr149BAUFwdDQsLwjEWlh0YFIJoKDgzFp0iQoFAoEBATAysqq0meRS47SSE5OLvMiePoilyz6zJGQkIBTp05Jw+cLs2HDBiiVSgwaNEhaGFtf5JJDTlnkkqMowcHBOHr0KADgu+++Q5UqVd7q68sxS3nlEEUR9+/fh1KpRLdu3bS2zZ49G2ZmZhg8eDA6depUabKUZ47nz5/j+PHjuHHjBu7evYunT58iPT0dmZmZMDY2hqmpKRo1agQ7Ozs4ODigZ8+eb+yDvFyyMAcwf/58CIJQ7udLOWWRS47XpaSkwMDAIN/O8nPnzmHv3r148OAB1Go1mjdvDmdnZ/Tr1++dzSGXLGFhYTh06BDCw8ORkpICU1NTWFtbo3///nB0dNTZ/8CBA2jfvr1ep4WRWxbm0KVSqTBv3jxpetri3tmv6dbr06cPvv/+e2mhbObQTw65ksOIC6LCsOhAREREREREREQkAzdu3MCmTZsQEhKCtLS0Qvc1NzeHg4MDXFxcdG5UYA795iCikmHRgYiIiIiIiIiISGbi4uLw6NEjJCUlISsrCwYGBqhWrRrMzMzQtGlT1KtXjznKIQcRFY1FByIiIiIiIiIiIiIi0guuMkJERERERERERET0mn379um1veHDh+u1PSK54kgHIiIiIiIiIiIiotfY2toWexHrogiCgIiICL20RSR3HOlARERERERERERE9JrRo0fD19cXoiiiPO/b5ogLqmg40oGIiIiIiIiIiIgoH6dOncKXX34JtVqN1q1bY/Xq1VAoFKVqy9LSslTHccQFVTQsOhAREREREREREZUTudzFzhwFCwoKwvTp0yGKImbOnIlp06bpIVnxLV26VG8jLgRBwK1bt/SUjCh/LDoQERERERERERGVE7ncxc4chfP29sbatWtRpUoV+Pv7o1mzZnppt7jkMOKCqLhK984kIiIiIiIiIiKiMhs9erTUya65k70sX8yhnxyvmzFjBlq1aoXs7GysXr1ab+0WV9++feHp6QlBEBAeHo7Dhw/D0tKyVF9EbxpHOhAREREREREREZUjudzFzhyFCw4OxqRJk6BQKBAQEAArKyu9tV1c5T3igqg4WHQgIiIiIiIiIiIqZ+W9bgBzVAyiKMLFxQWRkZFwcnKCl5dXeUci0sHplYiIiIiIiIiIiMpZ79698c9//hOiKMLHxwfR0dHMIYMcciMIAubNmwdRFBEYGIiYmJjyjkSkgyMdiIiIiIiIiIiIZEAud7EzBxGVBUc6EBERERERERERyYBc7mJnDiIqC450ICIiIiIiIiIiIiIiveBIByIiIiIiIiIiIiIi0gsWHYiIiIiIiIiIiIiISC9YdCAiIiIiIiIiIiIiIr1g0YGIiIiIiIiIiIiIiPSCRQciIiIiqhS++OIL2NjYwMbGBosWLSpVG2FhYVIbTk5OEEVRzykL5u3tLb323r1738hrzJ8/X3qNc+fOlbodTRu9evXSYzoiIiIiIqoIWHQgIiIiokph+PDh0uPjx49DpVKVuI2DBw9Kj11dXSEIgj6iERERERERvTNYdCAiIiKiSqFnz5547733AAApKSk4e/ZsiY5XqVQICAgAABgYGGDo0KF6z0hERERERFTRsehARERERJWCQqHQKhQcPny4RMcHBQUhOTkZANCrVy80aNBAn/GKNHPmTNy5cwd37tyBm5vbW31tIiIiIiKi4mLRgYiIiIgqjbxTIgUFBeHFixfFPva3336THuedqomIiIiIiIj+wqIDEREREVUajRs3RpcuXQAAWVlZOHnyZLGOS0xMRHBwMACgXr16cHR0fFMRiYiIiIiIKjTD8g5ARERERPQ2DR8+HBcvXgTwaoqlYcOGFXnM4cOHoVarAQBDhgyBoeFfl9EJCQnYv38/Ll26hOjoaCQnJ0MQBNSoUQOtWrWCvb093NzcYGpqqtPuxYsXMXbsWADAqlWr0L59e3h4eODChQsQRREWFhYYOnQoJk2aBG9vb/z0008AgKVLlxY4xdL58+dx4sQJ/Pnnn1AqlUhLS4OJiQnq1KmDDh06YNCgQejZs2exflcvXrzApk2bcOLECcTHx8PY2BjW1tbo378/3NzcULVq1WK1U5CrV69i//79uHLlCpRKJQRBQIMGDdClSxd8+umnsLOzK/T4pKQk7N69G2fOnMG9e/eQnp4OMzMzWFpaolu3bhg5ciQaN25cpoxERERERFQyLDoQERERUaXSr18/LFmyBKmpqbhw4QKUSiXq169f6DEHDx6UHuedWsnHxwdr166VChJ5ZWZmQqlUIjg4GFu2bMGmTZvQsmXLAl8jISEBy5Ytw9OnT6XnoqKiYGRkVKyfKzExEf/6179w6dIlnW1paWlIS0vDgwcPcPDgQfTv3x8rV67UKp68Li4uDu7u7oiPj9f6mS5fvozLly9j27ZtWL9+PZo1a1asfHllZWXB3d0d/v7+Otuio6MRHR2NPXv2YPTo0fj6669hYGCgs19oaCimTp0qrbOhkZSUhKSkJNy8eRNbtmzB7Nmz8dlnn5U4IxERERERlQ6LDkRERERUqRgZGcHZ2Rm7du1Cbm4ujh07hvHjxxe4/927dxEREQEA6NKlC5o2bQoA2Lx5M9asWQMAqFatGlxcXNC2bVtUr14dqampuHbtGo4cOYKMjAwkJCRg7ty5WutCvM7T0xNZWVlwdHRE//79kZaWhhMnTmDw4MFF/kw5OTmYOHGilLNFixZwdnZG06ZNoVAo8OTJE5w6dUoqSAQEBKBjx44YN25cgW0uWbIEarUajRs3hpubGxo3boy4uDjs2bMHDx8+RExMDMaMGYPDhw/D3Ny8yIx5s06ePFkabVKnTh0MHToUtra2yM3NRUREBA4cOIDU1FTs2LEDqampWLFihVYbycnJmD59ujSq5KOPPkL37t1Rq1YtJCYm4sKFCzh58iSys7OxfPlyWFtbw97evtgZiYiIiIio9Fh0ICIiIqJKZ/jw4di1axcAwN/fv9CiQ95CgaurK4BXIwe8vb0BvCo4+Pn5wcbGRus4Nzc3jB49Gm5ublCpVIiIiMCdO3d09tPIysrCwIED8eOPP0rPjRkzplg/z4EDB6SCQ/fu3bF+/XqdqY/Gjx+PVatWYf369QCAQ4cOFVp0UKvV6N27N9asWQNjY2Pp+bFjx2LGjBkIDg7G06dP8cMPP8DDw6NYOYFXo0M0BQd7e3usWbMG1atXl7a7uLhgypQpmDx5Mm7evIlDhw6hV69eGDhwoLSPv78/nj9/DgCYOXMmpk+frvUa//jHP7B37164u7sDALZu3cqiAxERERHRW8KFpImIiIio0mnTpg1sbW0BAOHh4bh//36+++Xk5ODw4cMAgBo1auDjjz8GAAQFBeHly5cAXnVwF1RIsLW1xd///nfp+3v37hWa64svvijZD/I/x44dkx4vWLCgwLUW/vnPf0IQhGJladiwIX788UetggMAmJiYYOXKlahVqxYA7QJAUdLT07Ft2zYAQIMGDeDl5aVVcNCoXbs2Vq9eLf0cPj4+WtvzZs/7+83Lzc0N77//Ptq0aVPk9FlERERERKQ/LDoQERERUaWkGbUAQCosvC4kJERaY2HgwIFSB/yAAQMQGBiILVu2FDpaAIDWQsaZmZkF7le/fn1YWVkVO39e3t7eOHToEDZu3FjouhEmJiaoW7eulEUUxQL3HTVqVL6LXwOAubk5XFxcALwaEXHmzJli5Tx79ixSU1MBAIMGDUK1atUK3LdJkybo0aMHgFdrW8TExEjbateuLT3evHkzVCpVvm389ttv2L9/P5YtW1asfEREREREVHacXomIiIiIKqXBgwfjhx9+gEqlwpEjR/Cvf/1LZ5+CFpA2NDRE48aNtQoKeaWnpyMyMhJhYWEICgqSns/JySkwT4sWLUrxU7xSvXp12NraSqM3XvfkyRNERETgypUrSEtLk57Pzc3Nd5Fm4NU0TYX58MMPpVELYWFhGDZsWJE5Q0NDpccvXrxAYGBgofvnXUT72rVrUlFmwIAB+Pnnn5Gbm4uDBw8iJCQEffr0gb29Pbp27YoaNWoUmYWIiIiIiN4MFh2IiIiIqFKqVasW+vbti4CAAMTGxiIsLAwdOnSQtr948QKnTp0CALz//vto3bp1vu1ERkbi7NmziIyMRGxsLGJjY/Hs2bNCRxHkRx8d5Wq1GsHBwQgNDUV0dDRiY2MRFxeH9PT0fPcvLGNBBRUNS0tL6bFmNEhREhISpMd+fn7w8/Mr1nEAkJiYKD22trbGf/7zH3h4eCA3NxdPnz7F7t27sXv3bhgYGKBt27ZwcHBAv379ylTMISIiIiKikmPRgYiIiIgqreHDhyMgIADAqymW8hYdAgICpOmQ8o5y0EhISMCCBQsQHBycb9vGxsbo1KkTsrOzcenSpSKzFLQOQ3EFBgZiyZIlWh37edWvXx89e/bE6dOnkZycXGR7r6/l8DoTExPpcVZWVrEy5h1lUVIvXrzQ+n7MmDFo3749fvnlF5w5c0b6t8rJyUFYWBjCwsLg6emJ3r17Y/HixWjQoEGpX5uIiIiIiIqPRQciIiIiqrS6d+8OCwsLPHr0CMeOHcPXX38NQ8NXl8iaqZWMjY0xaNAgreMSExMxcuRIqYPf1NQUXbt2hZ2dHZo1awZra2u0aNECVapUwZo1a4pVdCiLw4cPY+7cudLIBSsrK3z44YewtrZG8+bN0apVKzRs2BAA4ODgUKw2s7KyCi2EaBbSBgAzM7NitZm3kLF37160a9euWMcVpF27dvDy8kJGRgYuXryIc+fO4cKFC7h79670uwgKCsKDBw9w8ODBIgspRERERERUdiw6EBEREVGlpVAoMHToUKxduxaJiYkICQmBg4MD4uLicPXqVQDARx99pDP1kaenp1Rw6NWrF1atWlVgx7tm4eQ3JTMzE0uWLJE62b/77juMHDmywP2LmychIaHQYkJsbKz02MLColht1qtXT3p8//79MhcdNExMTODo6AhHR0cAgFKpxJEjR+Dp6YnMzExER0fj0KFDhf5eiIiIiIhIPxTlHYCIiIiIqDwNGzYMgiAAgDTVUkBAgNSJ7+bmpnPM6dOnpceLFi0qtHM+IiJCelzSdR6K4+rVq0hJSQEAdO7cudCO9ZiYGK0RCoXluXbtWqGve+HCBelx+/bti5U1b5Eh7++wID4+Ppg/fz68vLwQHR0N4NXi1wEBAVi3bh3WrFmT73H169fHZ599hjlz5kjP5f13ICIiIiKiN4cjHYiIiIioUmvUqBH+9re/4fz58wgMDIRKpcKJEycAvJqmqHPnzjrHJCUlSY+rV69eYNtXrlzR6rxXq9V6TF6yLACwadMmre+zs7NRpUqVfPf19fXF0KFDYWBgoLMtISEBhw8fBvBqaiV7e/tiZXV0dETVqlWhUqkQGBiIW7duwc7OLt99Hz9+jLVr10KtVkOhUEjraigUCvzwww+Ij4+HQqHAqFGjtEZQ5GVubi49NjU1LVZGIiIiIiIqG450ICIiIqJKT9OhnZaWhoMHD+LmzZvS85pREHk1btxYeuzr65tvm5cvX8asWbOQm5srPadSqfQZWyfLxYsXce/ePZ19cnJy4Onpid27d2s9X9gC0OHh4Vi6dKlWfgBITk7GjBkzpBETY8aMKXaHft26daWRIzk5OZg+fXq+eVNTUzFt2jSpSOPs7Kw1hZOzszOAV6Me5s+fn+/vNSMjA5s3b5a+79q1a7EyEhERERFR2XCkAxERERFVek5OTqhZsyZSUlKwcuVKAIChoSGGDBmS7/6urq744YcfAABeXl64ceMG7O3tUatWLSiVSpw5cwYXLlzQmb4oLS1N79nbtm2LVq1a4e7du3j58iVGjBiBESNGwMbGBqIoIjo6GkeOHEF8fLzOsWlpaahVq1a+7dauXRu+vr64du0ahgwZgrp16+LevXvw8/PDs2fPAAAdOnTAtGnTSpR37ty5uHLlCu7cuYP4+HgMGTIEgwYNQufOnaFQKBAZGYm9e/ciOTkZANCwYUP85z//0Wpj4sSJ+O233/D06VMEBwfDyckJQ4cOhZWVFYBX00jt378fSqUSANClS5diL6BNRERERERlw6IDEREREVV6RkZGGDhwIHbu3Cmtj9CrVy/Ur18/3/3Hjx+Pa9eu4eTJkwCAoKAgBAUF6exna2uLGTNmYMaMGQCAW7du6T27QqHA6tWrMX78eDx9+hQvXrzQusM/r9GjR8PAwADbtm0DANy+fVtrpERe3t7emDlzJsLDwxEeHq6z3d7eHmvWrClweqaCmJiYYMeOHZg7dy7OnDkDlUqF/fv3Y//+/Tr72tnZ4aeffkLt2rW1nq9VqxZ++eUXTJkyBQkJCUhISICPj0++r9ejRw94enqWKCMREREREZUeiw5ERERERHg1ldLOnTul7/NbQFrD0NAQXl5eOHLkCA4ePIiIiAikpKTAyMgIdevWha2tLQYMGAAnJycYGBjA0tIS8fHxCAkJwfPnz1GnTh29Zm/ZsiX8/f2xbds2nDlzBjExMVCpVDA1NUWjRo3QsWNHuLm5wdbWFhcvXpSKDv7+/nBycsq3TTs7O/j7+2P9+vUICgrCkydPYGZmhrZt28LV1RX9+vUrdd6aNWtiw4YNOH/+PPz9/fHnn39CqVRCrVajVq1aaNOmDfr37w9nZ2cYGub/kcXW1hbHjh3D/v37ERQUhMjISCQnJ8PQ0BB169ZFx44d4ezsDEdHx1LnJCIiIiKikhPE18d8ExERERERERERERERlQIXkiYiIiIiIiIiIiIiIr1g0YGIiIiIiIiIiIiIiPSCRQciIiIiIiIiIiIiItILFh2IiIiIiIiIiIiIiEgvWHQgIiIiIiIiIiIiIiK9YNGBiIiIiIiIiIiIiIj0gkUHIiIiIiIiIiIiIiLSCxYdiIiIiIiIiIiIiIhIL1h0ICIiIiIiIiIiIiIivWDRgYiIiIiIiIiIiIiI9IJFByIiIiIiIiIiIiIi0gsWHYiIiIiIiIiIiIiISC9YdCAiIiIiIiIiIiIiIr1g0YGIiIiIiIiIiIiIiPTi/wHINz0WpvV4YwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1700x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of the sequential forward selection process for the gradient boosting model.\n",
    "# The optimal model fit is shown by the vertical dotted lines\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "y1 = sfs_gb_df.loc[:,'avg_score']\n",
    "y2 = sfs_gb_df.loc[:,'std_err']\n",
    "x = range(1,117, 1) \n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(17, 10))\n",
    "\n",
    "plt.ylim([0.85, 0.99])\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xticks(np.arange(0, 118, step=3), rotation='vertical', fontsize=20)\n",
    "plt.xlim([1, 116])\n",
    "\n",
    "#plt.title('Gradient Boosting', fontsize=22)\n",
    "ax2 = ax1.twinx()\n",
    "plt.yticks(fontsize=20)\n",
    "line1, = ax1.plot(x, y1, 'b-', linewidth=3, label='Accuracy Score')\n",
    "line2, = ax2.plot(x, y2, 'r-', linewidth=3, label='Std Error')\n",
    "plt.axvline(list(sfs_gb_df_opt[\"idx_len\"]), color = 'darkgreen', linestyle='dashed', linewidth=2)\n",
    "\n",
    "ax2.legend(handles=[line1, line2], loc='right', fontsize=23)\n",
    "\n",
    "ax1.set_xlabel('Variables', fontsize=23)\n",
    "ax1.set_ylabel('Accuracy Score', fontsize=23)\n",
    "ax2.set_ylabel('Std Error', fontsize=23)\n",
    "\n",
    "#plt.savefig('GradientboostingForwardSelectionFNLRA.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "#sfs_gb_df_maxmin.to_csv('sfs_gb_df_maxminRA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>feature_idx</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>std_err</th>\n",
       "      <th>idx_len</th>\n",
       "      <th>avg_score_rank</th>\n",
       "      <th>std_err_rank</th>\n",
       "      <th>total_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(69,)</td>\n",
       "      <td>0.89793</td>\n",
       "      <td>0.002265</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 69)</td>\n",
       "      <td>0.957313</td>\n",
       "      <td>0.002026</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 1, 69)</td>\n",
       "      <td>0.968015</td>\n",
       "      <td>0.001966</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 1, 69, 74)</td>\n",
       "      <td>0.9726</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(0, 1, 10, 69, 74)</td>\n",
       "      <td>0.973306</td>\n",
       "      <td>0.001289</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(0, 1, 10, 63, 69, 74)</td>\n",
       "      <td>0.974129</td>\n",
       "      <td>0.001324</td>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(0, 1, 10, 11, 63, 69, 74)</td>\n",
       "      <td>0.974364</td>\n",
       "      <td>0.001345</td>\n",
       "      <td>7</td>\n",
       "      <td>9.5</td>\n",
       "      <td>34.0</td>\n",
       "      <td>50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 1, 10, 11, 19, 63, 69, 74)</td>\n",
       "      <td>0.974952</td>\n",
       "      <td>0.001608</td>\n",
       "      <td>8</td>\n",
       "      <td>18.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 10, 11, 19, 63, 67, 69, 74)</td>\n",
       "      <td>0.97507</td>\n",
       "      <td>0.001527</td>\n",
       "      <td>9</td>\n",
       "      <td>21.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(0, 1, 10, 11, 19, 63, 67, 69, 74, 104)</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>10</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(0, 1, 3, 10, 11, 19, 63, 67, 69, 74, 104)</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>11</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>162.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(0, 1, 3, 4, 10, 11, 19, 63, 67, 69, 74, 104)</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>12</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(0, 1, 3, 4, 5, 10, 11, 19, 63, 67, 69, 74, 104)</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>13</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 19, 63, 67, 69, 74,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>14</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 19, 63, 67, 69,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>15</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 19, 63, 67,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>167.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 63,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>17</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>18</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>19</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...</td>\n",
       "      <td>0.975658</td>\n",
       "      <td>0.001617</td>\n",
       "      <td>20</td>\n",
       "      <td>65.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>171.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx avg_score   std_err  \\\n",
       "1                                               (69,)   0.89793  0.002265   \n",
       "2                                             (0, 69)  0.957313  0.002026   \n",
       "3                                          (0, 1, 69)  0.968015  0.001966   \n",
       "4                                      (0, 1, 69, 74)    0.9726  0.001468   \n",
       "5                                  (0, 1, 10, 69, 74)  0.973306  0.001289   \n",
       "6                              (0, 1, 10, 63, 69, 74)  0.974129  0.001324   \n",
       "7                          (0, 1, 10, 11, 63, 69, 74)  0.974364  0.001345   \n",
       "8                      (0, 1, 10, 11, 19, 63, 69, 74)  0.974952  0.001608   \n",
       "9                  (0, 1, 10, 11, 19, 63, 67, 69, 74)   0.97507  0.001527   \n",
       "10            (0, 1, 10, 11, 19, 63, 67, 69, 74, 104)  0.975658  0.001617   \n",
       "11         (0, 1, 3, 10, 11, 19, 63, 67, 69, 74, 104)  0.975658  0.001617   \n",
       "12      (0, 1, 3, 4, 10, 11, 19, 63, 67, 69, 74, 104)  0.975658  0.001617   \n",
       "13   (0, 1, 3, 4, 5, 10, 11, 19, 63, 67, 69, 74, 104)  0.975658  0.001617   \n",
       "14  (0, 1, 3, 4, 5, 7, 10, 11, 19, 63, 67, 69, 74,...  0.975658  0.001617   \n",
       "15  (0, 1, 3, 4, 5, 7, 10, 11, 13, 19, 63, 67, 69,...  0.975658  0.001617   \n",
       "16  (0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 19, 63, 67,...  0.975658  0.001617   \n",
       "17  (0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 63,...  0.975658  0.001617   \n",
       "18  (0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...  0.975658  0.001617   \n",
       "19  (0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...  0.975658  0.001617   \n",
       "20  (0, 1, 3, 4, 5, 7, 10, 11, 13, 14, 18, 19, 21,...  0.975658  0.001617   \n",
       "\n",
       "    idx_len  avg_score_rank  std_err_rank  total_score  \n",
       "1         1             1.0         116.0        118.0  \n",
       "2         2             2.0         115.0        119.0  \n",
       "3         3             3.0         114.0        120.0  \n",
       "4         4             4.0          47.0         55.0  \n",
       "5         5             5.0          22.0         32.0  \n",
       "6         6             6.0          29.0         41.0  \n",
       "7         7             9.5          34.0         50.5  \n",
       "8         8            18.0          57.0         83.0  \n",
       "9         9            21.0          52.0         82.0  \n",
       "10       10            65.0          86.0        161.0  \n",
       "11       11            65.0          86.0        162.0  \n",
       "12       12            65.0          86.0        163.0  \n",
       "13       13            65.0          86.0        164.0  \n",
       "14       14            65.0          86.0        165.0  \n",
       "15       15            65.0          86.0        166.0  \n",
       "16       16            65.0          86.0        167.0  \n",
       "17       17            65.0          86.0        168.0  \n",
       "18       18            65.0          86.0        169.0  \n",
       "19       19            65.0          86.0        170.0  \n",
       "20       20            65.0          86.0        171.0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs_gb_df_maxmin.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model to training data\n",
    "gbc.fit(x_train_gb, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
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NGqRGjRpZaaWVSsdygw02+MbjutFGG+Xss88uff3888/nnnvuSZL897//zdixY9O7d+8cd9xxpX356aeffu397ZJkxowZ+eyzz5a4z77OtGnTUrdu3QwaNKh0GV1ZWVmmTJlS+vk6ffr0vPbaaznssMNK35etWrXKyJEjS2eUPfHEE+nUqVN69uxZ2saKK65YOv5f1KRJk0r7bNNNN11snUcffTQPP/xwLr744uyyyy5JPj9+n376aS644ILsuuuupbMHV1999QwePLjSGYMAP3aiFAAsozp16qR79+65++67S1HqzjvvzC677LLYzZifeOKJ1KhRo/RLxyK77757hg8fnscffzzVqlXL9OnT06NHj0rr7LzzzpWi1N///vdssskmWWONNUq/tFevXj1dunRZpnupfPkyn+T/7nny6aef5rnnnsvBBx+cioqK0uutvfbaad68eR555JH07t07e+65Z/bcc8989tlneeutt/Lmm2/mhRdeyIIFC76zT03baKONKn392GOPpaysLHXq1CmNa6WVVkr79u3z6KOPVnn7XzyLoXHjxkk+jzCLVKtWLaussko++eSTSs9bFBkX2XHHHXP11Vfn2WefLV0W9MVYlHx+f5kTTjghjz/+eOmX3zp16pSCxdepyn7+cvhr0qRJ6RPfnn322cybNy/bb799pXUWnd21tMe9rKwsF154YX71q19l++23T5cuXbL//vt/43v4si9fNrfIm2++mR122KHSY2uuuWYeeOCB0tdfnhNLs3+eeuqp1KpVK126dCk9b9HluE8++eRXjuWxxx5LRUVFunfvXimUde/ePWPGjMnTTz9dCjkbbLBBpSi3xhprJPl8n1bVUUcdVQpn8+fPz7vvvpurr746++23X/7whz+kZcuWeeKJJ7LmmmsuFsJ33333TJo0Kc8991zpuC9pHpaVlWXEiBF5+eWX07Vr13Tp0qXKcTf56rm36P0/++yzmTNnzmJnNe26667fGKUWhZgFCxZUeTzJ58fh6quvTvL55c9vvvlmXnvttfzjH/8ozY3VVlstG2ywQU455ZQ8+uij6dKlSzp37ly65DL5PEJdf/31ef/997Ptttuma9eulc6Uq6q///3vqVatWrp27brY3Jo8eXL+/e9/Z5NNNkmSNG/eXJAC/ueIUgDwLey8886le5LUq1cvf//730tn23zRzJkz07Bhw8Xul7MofnzyySeZOXNmksVvgrtonUU+/vjjvPnmm1/7CVNV/eV38803/9pls2bNysKFCzN27NiMHTt2seUrrLBCks8vITrjjDNy2223Zf78+VlrrbXStm3b1KxZs9I9ir6N1VZbrdLXH3/8ce66667F7r2SLNuNhL/q07C+7jKhL/ryJV+LXnvWrFmlY/rlY1izZs00bNiwUuBaddVVl/jJclXZz18ee/Xq1UvrLLr58tftp6U97r/97W9Tr1693HTTTTn33HNzzjnnZKONNsqJJ56Yrbba6hvfyyINGzbMiiuuWAonizRt2rRSMB01alReeeWVSut8eU4szf6ZOXNmGjRosNgv918+Rl/0xZtVf5X333+/9O+v2u/J18e3b7LmmmtW+v5s27Ztunbtmm7dumXEiBG57LLLMnPmzMX2Q/J/+6Yq8/Diiy/OZZddlrvvvjv33HNPqlevnk6dOmXo0KFZe+21l3rc3zT3Ft2T7Mtz76vewxc1aNAg9erV+8p77C3y3//+N3Pnzk2DBg2+cvnkyZNz0UUX5d13302DBg2y8cYbV7oMrlq1ahk/fnzGjBmT+++/P7fccktq1aqV7bbbLkOHDk2DBg1y0kknpUmTJpk8eXJOO+20JJ8flyFDhnzlmVBL8vHHH6eioiLt2rX7yuXTpk0rRakl7SOAHyNRCgC+hS5duqR+/fq59957U79+/ay11lrZbLPNFltvlVVWyYwZMzJ//vxKYWratGlJPv/FfNHlH9OnT6/03EW/EC9Sv379dOzYsXTZ1ZfVrl3727ylSurVq5dq1arlwAMP/MpfyBf98jls2LDce++9ueSSS9KpU6esuOKKSbLEMLEowixYsKB0WdYXb8b9TerXr59OnTot9umHSb7yZtnfly8fn0XHb9VVV82sWbOSJB988EGly47mzZuXGTNmfOUlP99kWffzl6288spJPg8E66+/funxd999N2+++WY222yzpTru1atXT+/evdO7d+9Mnz49f/3rX3PZZZfl6KOPzqOPPrrUc7FHjx554IEHUl5eXoqDtWvXrhRkvi40fNHS7J+GDRtmxowZleZcsvhx/KJF++uqq65KvXr1Flu+6J5yRahXr17WWWedvPnmm0k+/9my6N9f9MEHHyRJpei0pHlYv379HH/88Tn++OPzn//8J3/+858zevTonHbaabnyyiu/k/E3adIkyeJz78sfnPBVOnfunMcffzyfffZZKYx+0c0335xhw4bl2muvXez+TU899VQGDRqU/fffPwcffHBpHOedd17pktXk8zOqhg4dmlNPPTUvv/xy7rnnnowdOzarrLJKTjvttNSuXTuHH354Dj/88Lzzzjt58MEHM3r06AwYMOArP/hiSerXr58VV1yxdBbXl33xcl6A/0XO/wSAb6F27drp0aNH7rvvvtx9991feyZFx44ds2DBgsXO6ll0ud0WW2yR9dZbL02bNi3de2WRBx98cLFtvf766/nZz36WzTffvPRn8uTJ+eMf/1jpF+1va6WVVsqmm26a//znP5Vea8MNN8zIkSNLn5T19NNPp6ysLNttt10pBPzzn//MRx99VOkMkS+fnbIoQLz77rulx/7xj38s1dg6duyYV199NZtssklpXJtttll+//vf5/777/9W77sqvng5WfL5ja7r1q1b+sS6JLn99tsrrXPnnXdmwYIFi11y9WVf3l9Lu5+XpFWrVqlVq1b+/Oc/V3r8qquuyjHHHJM6deos1XHfb7/9cuaZZyb5PML16tUrvXv3zieffPK1n4D2Vfr27ZsFCxbkxBNP/Mqbos+ZM2epPlVxafbPVlttlfnz5+dPf/pT6Xlz587NI4888rXb7dChQ5LP72v0xf3x8ccf55JLLvnGoPVl3/byq/Ly8rz22mulWNGhQ4dMnTq1UlhJPv/ZUqtWrbRq1Wqp5uHUqVPTtWvX0s+f9ddfP4ccckg6deqU9957L0m+k58tG2+8cerXr5/77ruv0uP33nvvEp970EEH5eOPP87FF1+82LLp06fnyiuvzLrrrvuV96x75plnsnDhwvTr168UpBYsWFC61HfhwoV55pln0qlTpzz//POpVq1aNtlkk/Tv3z8bbbRR3nvvvcyZMyc77rhj6dP2mjVrlt69e6dnz56lfVRVHTt2zH//+99UVFRUmlv//ve/M2rUqK/8IAqA/yXOlAKAb2mXXXZJ3759U7169dINyr+sS5cuKSsry6mnnppp06Zl0003zRNPPJGxY8dmr732Kt0M97jjjsuAAQNy8sknZ6eddsqzzz6b6667rtK2DjzwwNx222058MADc9BBB6Vhw4a56667cuONN1a698l3ZdHNnQcMGJDdd989CxYsyPjx4/Pcc8+VbprcqlWr3H333bnuuuvSvHnzvPzyyxkzZkyqVatW6XLClVdeOc8880yefPLJtG/fPl27ds3ZZ5+dU045JYccckjee++9jBw58ivPRvmyI444Ivvtt1/69u2bX/7yl1lhhRVyww035E9/+lOVb/b+bdxzzz1ZbbXVSjeKnjhxYvr3758VV1wxG2ywQfbaa6+MHDkyc+bMSVlZWV566aWMHDkyZWVl2Wabbb5x2yuvvHJefPHFPPHEE2nVqtVS7+cladSoUX7961/nqquuSu3atbPlllvm//2//5drrrkmxx57bGrWrLlUx71Dhw4ZP358VltttbRt2zbvv/9+JkyYkI4dO5Yuz3r11Vczd+7cb7y0acMNN8yFF16YQYMGZc8998w+++yTFi1aZP78+XnmmWcyadKkfPjhh/ntb3/7je9rafbPVlttlc6dO+fkk0/O9OnTs+aaa+bqq6/ORx999LWf/rfRRhtl9913zymnnJKpU6eWPv3x4osvzlprrfWVN2n/OovOurr//vvTpUuXb/y0zLfeeivPPvts6esPP/wwV155ZebOnVvaF7169cq1116bo446Kv369cvaa6+dBx54IDfddFOOOuqorLzyyll55ZWXOA+rV6+eJk2a5Mwzz0x5eXnWWWed/POf/8xf//rX9O3bN8nnZ/UkyV/+8pesssoq2XjjjZf6fS+y0kor5be//W0uvfTS1K1bNx07dswTTzxR+jn3TdGuTZs2OeaYY3LJJZfktddey1577ZWGDRvm3//+d8aPH5/Zs2fniiuu+MrLYFu1apUkOf3007P33ntn1qxZueaaa/Lyyy8n+fzSv0033TR16tTJwIEDc/TRR2e11VbLo48+mpdeeim//vWvU6dOnbRs2TIjR45MrVq10qJFi7z++uu55ZZbsuOOO1Z5XySffyBBhw4dcsQRR+SII45I8+bN8/zzz2fEiBHp3LnzMl2KDPBjIkoBwLfUqVOnrLzyymnatOnX/oK56FO6Lr300tIvwGuttVb69+9f6fKzXXfdNdWrV8/o0aNz2223ZaONNsrpp5+eY489trTOGmuskeuvvz4XXnhhhg4dms8++yzrrbdehg0bVunT6b4rnTt3zrhx4zJy5Mj069cvtWrVSsuWLTNhwoTSGQmDBw/OvHnzcskll2Tu3LlZa621cvjhh+fVV1/NAw88ULpU6rDDDsvo0aNzyCGH5K677srPfvaznHvuuRkzZkwOPfTQNG/ePGeccUbOOOOMJY5r4403zsSJE3PxxRdn4MCBqaioyEYbbZRRo0YtdrP479MxxxyTJ554IjfccEOaNm2aIUOG5Je//GVp+bBhw7Luuuvmpptuyrhx47L66qunT58+OfLII5d41sxBBx2Us846KwcffHAmTJiwVPt5aR1//PFZbbXVct1112X8+PFZa621cuKJJ+ZXv/pVkqU77sccc0xq166dm266KaNGjUr9+vXTvXv3DBgwoPQ6p512WqZOnbrYGWVftt1222Xy5Mm57rrrMmnSpEydOjUVFRVZe+21s8suu2S//fZbYvxZ2nk4cuTIXHDBBbn00kvz2WefZZdddsk+++yz2JljX3T22Wfn8ssvz/XXX5/33nsvq666anbZZZf87ne/q9IZRGVlZenUqVMuvPDC/P3vf88VV1zxteuOGTMmY8aMSfJ5rKlfv35atmyZcePGlT5Brm7duvnDH/6QCy+8MJdeemnKy8uz/vrrL/bzYGnm4ciRI3PRRRdl+PDhmTFjRpo2bZqjjjqq9ImDG264YXbddddMnDgxDz/8cO64446lft9f1Ldv3yxcuDA33HBDxo0bl9atW+e4447L2WefXTrD7escfvjh2XTTTTNx4sScffbZ+fjjj9OkSZN06dIlhx122NdeSllWVpYhQ4ZkwoQJpZBcVlaWkSNH5sgjj8zTTz+drl27Zvz48bnwwgszbNiwzJo1K+utt15OP/309OrVK8nnUeuSSy7J+PHj88EHH2TVVVfNz3/+8xxzzDHLtC+qV6+eK664IsOHD8/ll1+e6dOnZ4011siBBx74rW6gDvBjUa3iu7r7KADAT8iUKVPSo0ePnH322aVfWIFvNn/+/Nxxxx0pKytL06ZNS49PnDgxZ555Zh5//PHS2WQA/O9zphQAAFCImjVrZuzYsbnqqqty+OGHp2HDhnn55ZczfPjw7LnnnoIUwE+MKAUAABTmsssuy0UXXZShQ4dm1qxZadasWQ488MDSvasA+Olw+R4AAAAAhft2n0kLAAAAAMtAlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACldzeQ8AvqiioiIffTQ7CxdWLO+h8CNQvXq1NGpUz5yhSswbqsqcoarMGZaFeUNVmTNUVdFzpnHj+kse0/c+CqiCatWqpXr1ast7GPxIVK9ezZyhyswbqsqcoarMGZaFeUNVmTNU1Q9xzohSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFq7m8BwBftNuA25b3EAAAAGC5Gz+4+/IewvfOmVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSPyAzZ87M0KFD07Vr17Rp0yadO3fOoEGD8t577y3xuVOmTEmLFi0yZcqUr1x+8803p3v37ks1joqKigwYMCBt2rRJ9+7dM2bMmPz2t79d4vNGjBiRPn36LNVrAAAAAD9tNZf3APg//fv3T/369TNp0qQ0btw4H374YYYNG5bf/OY3uf3221OzZjGHa9q0abnjjjty8803p2XLloW8JgAAAPDT4kypH5Cnn34622+/fRo3bpwkWW211XLiiSemdevWmTVrVl577bX07ds33bp1S6tWrbLLLrvkwQcfrLSNW2+9Ndttt106deqUk08+OeXl5V/5Wi+88EL69OmTDh06ZIcddsjvf//7VFRU5MUXX8yOO+6YJOndu3cuvfTSSmdAlZeXp3///ikrK8vWW2+dgw8+OK+99lppu7Nnz87JJ5+czp07p6ysLBdffPH3sasAAACAHzlR6gekZ8+eOfXUUzN06NDcddddmTp1aho3bpxzzjknjRo1ytFHH52NNtoo999/f5566ql07tw5Q4cOrbSNp556KjfeeGMmT56cV155JWedddZir/P+++/ngAMOyE477ZRHH300o0ePzrXXXpsbbrghm266ae64444kyR133JF+/fpVeu748eNTXl6ev/71r3nwwQfTuHHjXHDBBaXlL774Yjp06JCHH344w4cPz+WXX55nnnnmu99ZAAAAwI+ay/d+QM4888yUlZXlrrvuypAhQ/LJJ59knXXWydFHH53dd989l19+edZYY41UVFRk6tSpWXnllfP+++9X2sbgwYPTqFGjJEm/fv1y+OGH58wzz6y0zuTJk9O8efP07t07SbLBBhvk4IMPzjXXXJP99tvvG8dYp06dvPzyy7n11luz9dZb56yzzkr16v/XNjfccMPsscceSZItt9wyq622Wt566620bdv2W+8fAAAA+KmoWfO7PY+oRo3qlf7+IRClfkCqV6+ePfbYI3vssUcqKiry2muv5bbbbsvAgQPTuHHjlJeX54gjjsgHH3yQ5s2bp1GjRqmoqKi0jbXWWqv076ZNm2bu3Ln5+OOPK60zderUvPDCC2nfvn3psYULF6ZGjRpLHOMhhxyS2rVrZ9KkSTn99NOz9tprZ8CAAdlhhx2SJA0aNKi0fu3atbNgwYIq7gkAAAD4aWvYsN73st2VV677vWx3WYhSPxAPP/xw+vXrlwcffDANGjRItWrVssEGG2TAgAF55JFH8vTTT2f06NEZOXJk6VP07r333tx3332VtvP+++9npZVWSvL5J/KtuOKKpTOnFmnSpEnKysoybty40mMzZszI7NmzlzjOf/3rX+nevXsOPPDAfPLJJ7n22mvTv3//PPbYY992FwAAAAD/vxkzlvw7elXUqFE9K69cN7NmfZoFCxZ+p9v+KksT1X4452z9xHXo0CGrrrpqTjjhhPzrX//KvHnzUl5ensmTJ+eNN97IdtttlwULFqRu3c+L5quvvppRo0YlSebOnVvazvnnn5+ZM2fmvffey/Dhw7Pvvvsu9lq77bZbnn322UyePDnz58/PtGnTcthhh+Wcc85Z4jj/+Mc/ZuDAgZk+fXpWWmmlrLTSSllxxRVTu3bt72hPAAAAAPPnL/xO/ywKUQsWfLfb/bo/S8OZUj8QderUybXXXpuRI0fm8MMPz/Tp01OrVq20adMmEyZMyMYbb5yBAwfm+OOPz6effpomTZpkn332yfnnn59XXnmldNlc27Zts9NOO6V69erZdddd079//8Vea80118yVV16ZCy64IGeeeWZq1KiRbt265aSTTlriOI899ticfvrp6dmzZz777LOsv/76GT16dFZYYYXvepcAAAAA/8OqVXz5pkSwHO024LblPQQAAABY7sYP7v6dbq9mzepp2LBeZsyYvdRnMn0bjRvXX+I6Lt8DAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFK5aRUVFxfIeBHzRjBmzM3/+wuU9DH4EatasnoYN65kzVIl5Q1WZM1SVOcOyMG+oKnOGqip6zjRuXH+J6zhTCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABSu5vIeAHzRbgNuW95DgO/E+MHdl/cQAAAAftCcKQUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAo3I8iSr3xxhvLewgAAAAAfIeqFKVmzpyZoUOHpmvXrmnTpk06d+6cQYMG5b333iut071799x8883f2QAnTpyYU0455VtvZ/LkyenZs+d3MKLKhgwZkrZt26Zt27bZfPPNs/HGG5e+btu2bZ566qkMHjw4gwcP/s5fe1n06dMnI0aMKOz1vuv5AAAAAPxvqFKU6t+/f2bMmJFJkybl2Wefza233pq5c+fmN7/5TebPn/+9DPCjjz76Traz++6758477/xOtvVFp59+ep555pk888wzOe2009KsWbPS188880zat2//nb8mAAAAwI9dlaLU008/ne233z6NGzdOkqy22mo58cQT07p168yaNau03gsvvJD99tsv7dq1S8+ePfPEE0+Ulv3rX//KIYccko4dO6ZLly4ZOnRoPvnkkyTJzTffnF69euWggw5K+/btc8stt+Tyyy/PU089VYo79957b3r27JktttgiO++8c0aPHl3adosWLXLllVemW7duadu2bfr27Zv333+/tO3u3bsnSR5//PF07949Y8aMyTbbbJOOHTvm6KOPTnl5eWlbd955Z3bbbbdsscUW6dWrV/72t79Vacd+2fTp09OvX7+UlZWlc+fOueaaa0rLZsyYkVNOOSWdO3dOWVlZ+vbtW7pkccqUKWnRokXOOeecdOjQIaeddlpGjBiRfv365bjjjkv79u3TpUuXXHjhhUmSZ599Nptsskmls9f+3//7f2nTpk3p/b355ps56KCD0qFDh/To0SP33HNPad0PP/wwxx13XLbeeut07tw5Q4YMqbRfJk2alF69eqWsrKy0jxeFw4qKilx22WXp3Llz2rdvn3PPPTcLFiz4VvsNAAAA+N9Usyor9+zZM6eeemqeeuqpdOzYMa1bt86aa66Zc845p9J6f/vb3zJ27Ng0a9YsQ4cOzSmnnJJ77703M2bMyK9//ev06tUrI0aMyCeffJLjjjsuAwcOzJgxY5J8HrTOOeecXHbZZVm4cGGmTJmSJ554In/4wx8yZ86cHH/88Rk7dmzKysry4osvpnfv3uncuXNatWqVJLn11lvzhz/8IQ0aNMjAgQPTv3//XHvttYu9l6lTp+b999/P/fffn/fffz+9e/fOtddem0MPPTR//etfc+qpp2bMmDFp165dHnrooRx99NG58cYbs+GGGy7Tjn7sscdy+eWXZ/jw4bn11ltzwgknZPvtt88aa6yRfv36pXr16rnllltSv379DB8+PAceeGDuuOOO0vNnz56dRx55JHPmzMlVV12V++67L+ecc07OPffc/O1vf0vfvn3To0ePtGnTJuuvv34mT56cQw89tLRPdtxxx6y00kpJkkceeSRXXnllNtlkk4wZMyYnnHBCevTokRo1auSII47Ieuutl3vvvTfz5s3LCSeckCFDhuSiiy7K888/nzPPPDNXX311WrVqlffeey8HHHBArr766vzud7/LTTfdlKuuuipXXnllNtxww4wcObJSHIOfkpo1fxS37PtJqlGjeqW/YUnMGarKnGFZmDdUlTlDVf0Q50yVotSZZ56ZsrKy3HXXXRkyZEg++eSTrLPOOjn66KOz++67l9bbd999s8466yRJdtppp9I9hf785z+nVq1aOe6441KjRo3UqVMnp5xySnr27JkPPvggSVKrVq3sscceqV79q3dSnTp1MmnSpCxcuDDt2rXL008/XWndY445JmuvvXaSZODAgdlpp50yZcqUr9zWkUcemTp16mTddddNWVlZXn/99STJNddck1/+8pfp0KFDkmTbbbdN9+7dc/311y/z/a223nrrdOrUKcnncW/w4MF5++23M3fu3DzxxBO58847S2egHXfccbn99tvz17/+Na1bt06S7Lnnnqldu3Zq166dJFlvvfWy5557Jkm6du2axo0b54033kibNm3Sq1ev3HLLLTn00EMzb9683HHHHZXuI7XLLrukZcuWpX9feumlmT59eqZNm5YXXnghEyZMSL169ZIkgwYNyk477ZRTTjklG220Ue64446stdZamTlzZqZNm5ZGjRqVzka77bbbss8++5S2fcwxx+TGG29cpv0FP3YNG9Zb3kNgCVZeue7yHgI/MuYMVWXOsCzMG6rKnKGqfkhzpkpRqnr16tljjz2yxx57pKKiIq+99lpuu+22DBw4MI0bN85WW22VJGnQoEHpObVq1SpdwjV9+vQ0a9YsNWrUKC1fa621knx+5lKSNG7c+BuD1HXXXZfRo0dnwIABKS8vz4477piTTz45q6yySpJk3XXXLa3frFmzJCkFry9bFIEWjbOioqI0lieeeCLXXXddafmCBQuy5ZZbLsVe+mpf3CeLwtKCBQvy4YcfJkkppCVJjRo10rRp00ydOrUUpVZfffWvHfui8S9cuDBJsscee+Siiy7Kiy++mClTpqR+/fqlwPblsdSqVStJMn/+/EyZMiULFixI165dK227du3aefvtt7Pxxhvn6quvzu23354VV1wxLVq0SHl5eWm/TZs2LU2bNq30PhYdA/ipmTFj9vIeAl+jRo3qWXnlupk169MsWLBweQ+HHwFzhqoyZ1gW5g1VZc5QVUXPmaX5P+qXOko9/PDD6devXx588ME0aNAg1apVywYbbJABAwbkkUceyYsvvliKUl9nzTXXzDvvvJMFCxaUwtRbb72V5PPI8p///CfVqlX72ueXl5dn2rRppfsnvfTSSzn22GNz2WWXZdCgQUmS999/PxtttFGSlM6QatasWeksqKXRpEmT7LnnnqXL35LknXfeSZ06dZZ6G0trzTXXTPL5flh0aeCCBQvyzjvvVApP37Rfvmy11VZLly5dcuedd2bKlCnp1avXUj2/SZMmqVOnTh5//PHS8Zk7d27efvvtrLvuuhk/fnweeeSR3H777VlttdWSJIcddlil57/99tulrysqKjJt2rSlHjf8L5k/3/8w+KFbsGCh40SVmDNUlTnDsjBvqCpzhqr6Ic2Zpb6QsEOHDll11VVzwgkn5F//+lfmzZuX8vLyTJ48OW+88Ua6deu2xG0sOgPnggsuyJw5c/LBBx9k2LBh2XLLLUtx5stWWGGF0tk4s2fPziGHHJLbb789FRUVWX311VO9evU0bNiwtP6oUaPywQcfZNasWTn33HOzzTbbZI011ljat5kk2WeffXL11Vfn+eefT/L5jcJ79epV6R5P35XVV189Xbt2zZlnnpkPPvggc+bMyQUXXJAFCxZk2223Xebt7r333rn//vvz6KOPZq+99lqq57Rq1SrrrrtuzjnnnMyePTtz5szJWWedlQMPPDALFixIeXl5atasmVq1amX+/Pm57bbb8vDDD2fevHlJkl/84he58cYb88wzz2TevHkZM2bM156lBgAAAPy0LXWUqlOnTq699to0btw4hx9+eNq3b59u3bpl8uTJmTBhQpo3b77EbdSvXz8TJkzIK6+8kq5du2bXXXfNmmuumeHDh3/tc7bddtt8/PHH2WKLLVK3bt1ceumlGTt2bNq1a5ddd901W265ZQ488MDS+i1btsyvfvWrdO/ePSuvvHIuuOCCpX2LJTvttFOOPfbYnHjiiWnXrl2OOeaYHHjggenTp0+Vt7U0zjvvvKy99trZa6+90qlTp/zrX//KVVddVekyu6rq1q1bZs+enVatWlW6pO6b1KxZM5dffnk+/PDD7LDDDuncuXPeeuutTJgwISussEIOOuigNG3aNNtuu2222WabTJ48Ob/61a/yyiuvJEl23XXX9OvXL/3790/Hjh3z9ttvp0WLFsv8HgAAAID/XdUqFt0Q6H9AixYtcvXVV6esrGx5D+UHYa+99sohhxySXXbZZXkPZantNuC25T0E+E6MH9x9eQ+Br1GzZvU0bFgvM2bM/sGctswPmzlDVZkzLAvzhqoyZ6iqoudM48b1lzym730UFO7111/P448/ng8++CDbbbfd8h4OAAAAwGJEqf9Bp5xySl577bWcc845pU/6AwAAAPgh+Z+KUv/617+W9xB+EK655prlPQQAAACAb7TUNzoHAAAAgO+KKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFC4mst7APBFt1+4R2bMmJ358xcu76HwI1CzZvU0bFjPnAEAAPgRcqYUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhai7vAcAX7TbgtuU9BH6Erj55u+U9BAAAAKrImVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFC4H02UWrBgQd5+++3lPQy+xHEBAAAAlsX3FqW6d++ezTffPG3btk3btm3Tpk2b7LHHHvnjH/9Yab22bdvmqaeeWuL2+vfvn1tvvfV7Gu2ye/nll/Ob3/wmHTt2zNZbb52BAwfmo48+Ki1/7rnn8otf/CJt27ZN9+7dK73/ioqKjBo1Kt27d0+7du2y22675Z577vlOxvX444+nRYsWpf3funXrdOzYMYcddlj+/e9/fyevkVQ+LlOmTEmLFi0yZcqU72z7AAAAwP+m7/VMqdNOOy3PPPNMnnnmmTzxxBM58sgjc8455+SKK64orfPMM8+kffv2S9zWjBkzvs+hLpM5c+bkt7/9bdq2bZu//e1vueOOO/Lxxx/nxBNPTJLMnDkzhx56aPbcc888+eSTGTZsWM4+++w8//zzSZKrrroqN998c8aOHZunn346/fv3z8CBA0vLvwuL9v9zzz2Xu+++O82aNcsvf/nL/Oc///lOtv9DPC4AAADAD19hl+/Vrl07O+ywQwYNGpSRI0emvLw8SdKiRYs8/vjjSZJ77703PXv2zBZbbJGdd945o0ePTpKcdNJJeeqpp3L55ZfnsMMOS5I88MAD2W+//bLVVluldevW2X///fPGG28kSW6++eb88pe/zJlnnpktt9wyW221VU466aTMmzcvSTJ//vwMHz48Xbt2Tbt27dK7d++8/PLLSZK5c+dm+PDh6dGjRzp27JhDDjkkb7755le+p3feeScbb7xxjjzyyNSuXTsNGzbMvvvumyeffDJJct9996VBgwbp3bt3atasma222iq77bZbJk6cmCSZNWtWjjzyyDRv3jzVqlVL9+7d07x58/zjH/9I8vnZZhMmTMjuu++e1q1b55e//GVeeOGFHHLIIWnbtm122WWXKgWsVVddNUOGDMkGG2yQUaNGlR5/9NFH8/Of/zzt27dPz549M3ny5NKywYMH58QTT8yvf/3rtGnTJjvvvHP+9Kc/fe1xSZLbb789O++8c9q0aZMDDzww77///lKPEQAAAPhpqFn0C3br1i2nnHJK/vGPf6RLly6lx+fMmZPjjz8+Y8eOTVlZWV588cX07t07nTt3zrBhw/LWW2+lY8eOOfroo/Pee+/lmGOOyfDhw9O9e/fMmDEjRx11VEaNGpXzzz8/SUrbf/jhh/PSSy/lgAMOSKdOndKzZ8+MGTMmd9xxR8aNG5ef/exnGTlyZPr27ZsHHnggF198cR577LH8/ve/z+qrr56xY8fmoIMOyl133ZUVVlih0ntZf/31c+WVV1Z67N57703Lli2TJP/+97+z0UYbVVq+wQYbZNKkSUmSfv36VVr22muv5d///nfp+Unyxz/+MePHj89KK62UvffeO3369Mm4cePSsmXLDBw4MBdccEGuvvrqKh2DbbfdtvScl19+OYcffnjOP//89OjRI88991yOOOKINGzYMNtss02S5JZbbsmFF16Y8ePH54477sjvfve73HbbbYsdl0WX7b3wwgu58cYbs3Dhwhx44IEZNWpUTj/99CqNEaqiRo0fze3x+AFYNF/MG5aWOUNVmTMsC/OGqjJnqKof4pwpPEo1bNgwSfLxxx8vtqxOnTqZNGlSFi5cmHbt2uXpp59O9eqL76xGjRrlzjvvzDrrrJPy8vK89957adiwYaUzcurUqZPDDjss1apVS6tWrdKiRYu8/vrrST6PLH379s0GG2yQJDn88MPTtWvXLFy4MNdff30uvfTSrL322kmSI488MjfeeGP+8pe/ZMcdd/za91VRUZFLLrkkDz74YK655pokyezZs1O3bt3F3uN///vfxZ7/+uuv55BDDsnuu++eDh06lB7fe++906RJkyRJq1atUl5enrZt2yZJOnfunDFjxnztmL5Ow4YNS/v/+uuvT48ePbLDDjskSdq1a5d99tknEydOLEWpbt26ZZdddkmS7Lnnnrn++utz11135eijj/7K7R922GGpX79+kmSbbbb5Ti9HhK+y8sp1l7wSfIl5Q1WZM1SVOcOyMG+oKnOGqvohzZnCo9Sim4CvuuqqlR6vU6dOrrvuuowePToDBgxIeXl5dtxxx5x88slZZZVVKq1bq1at3HHHHbn++utTrVq1bLTRRikvL0/Nmv/3dlZdddVUq1at0nMqKiqSJB988EGaNWtWWla7du20adMm06dPz3//+98cc8wxlWLYvHnzMnXq1K99T+Xl5TnhhBPywgsv5JprrkmLFi2SJHXr1s0nn3xSad05c+akXr16lR574IEHMnjw4PTq1SuDBg2qtKxBgwalf9eoUaPSvqhevXrpPVXF9OnT06hRoyTJ1KlT89hjj1W6r9eCBQuyzjrrlL5eb731Kj2/adOm+eCDD752+18cc61atbJgwYIqjxGqYtasT7NgwcLlPQx+JGrUqJ6VV65r3rDUzBmqypxhWZg3VJU5Q1UVPWcaNqy3xHUKj1IPPPBAVlxxxbRu3brS4+Xl5Zk2bVouvPDCJMlLL72UY489Npdddtlioebuu+/ONddck+uuuy7rrrtukuSMM87IK6+8slRjaNq0ad59993S1/Pmzcv555+fgw8+OCussELGjx+fNm3alJb/5z//yRprrPGV23rrrbdyyCGHpFmzZpk0aVIp9iTJRhttlEceeaTS+q+++mo23HDD0tejRo3KlVdemdNPPz277bbbYtv/Ylj7rjz44IPp1KlTkqRJkybZa6+9Kl1eN23atEqx68v3hJoyZUq6d+/+nY8LltWCBQszf77/EFM15g1VZc5QVeYMy8K8oarMGarqhzRnCruQcO7cubnrrrty0UUXpX///llppZUqLZ89e3YOOeSQ3H777amoqMjqq6+e6tWrly73q127dumso08++STVq1dPnTp1UlFRkYceeii33npr6UbmS9KrV6+MGzcur7/+eubPn5/LL788f/rTn9KoUaP8/Oc/z4UXXpj33nsvCxcuzC233JJdd931K292PnPmzBxwwAFp165dxo0bVylIJcn222+fDz/8ML///e8zb968PPbYY7n99tuz9957J0kmTJiQCRMmZOLEiV8ZpL5rH3zwQYYOHZq33norRx11VJLk5z//ee6444787W9/y8KFC/PGG29k//33z/jx40vPu//++/Poo49m/vz5mTRpUl555ZXsuuuuSSofFwAAAICl9b2eKXXqqafmjDPOSJKssMIKWX/99XPaaaeV7k/0RWussUYuvfTSXHLJJRkyZEjq1KmTXXbZJQceeGCSz+9lNHTo0Pzzn//M73//+zz99NPp2bNnatSokfXXXz8HHHBAJk6cmLlz5y5xXL/97W8zf/78HHzwwZk5c2Y233zzjB07NrVq1cqgQYMyYsSI/OpXv8rHH3+ctddeO5deemk23XTTxbZz880355133sndd9+de+65p9KyZ555Jg0bNsz48eMzbNiwXHrppWnUqFFOPvnkbLnllqmoqMioUaPy6aefpnfv3pWe27dv30qfZvdtLLr/VLVq1VK/fv107NgxN9xwQ+meWa1bt85FF12Uiy66KMccc0zq1q2bXXfdNccee2xpG+3bt8/YsWNz1FFHZb311ssVV1xRev4Xj8t55533nYwZAAAA+N9XrWJZbkrET8bgwYOTJOecc04hr7fbgNsKeR3+t1x98nY/mNNP+eGrWbN6GjaslxkzZps3LBVzhqoyZ1gW5g1VZc5QVUXPmcaN6y9xnR/O5wACAAAA8JMhSgEAAABQuMI/fY8fl6Iu2wMAAAB+WpwpBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFK7m8h4AfNHtF+6RGTNmZ/78hct7KPwI1KxZPQ0b1suMGbOX91AAAACoImdKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMLVXN4DgC/abcBty3sIP1rjB3df3kMAAACApeZMKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwo9T2YNm1a/vvf/y7vYQAAAAD8YIlSX2HIkCFp27Zt2rZtm8033zwbb7xx6eu2bdvmqaee+trnfvjhh9lxxx3z0UcfLdVr9enTJyNGjPjKZSNGjMgmm2xSet1WrVqlS5cuOfXUU/Pxxx8v9fvp2bNnJk+evMTX+yaDBw/O4MGDKz329ttvZ4cddsjBBx+c8vLyKm8TAAAA+OmqubwH8EN0+umn5/TTT0+S3HzzzRk5cmQeeOCBpXrunDlzvtOzpNq3b58//OEPpa9fe+21DB06NAcccED++Mc/pnbt2kvcxp133vmdjWeRl156KYcccki6dOmS008/PTVrmkoAAADA0nOm1DL417/+lUMOOSQdO3ZMly5dMnTo0HzyySdZsGBBdt111yTJrrvumrvuuitz587Nueeem5133jlt27bNVlttlTPOOCMVFRXL9NrNmzfPyJEj89Zbb+WWW25JkpSXl+fkk0/ODjvskDZt2mSbbbbJZZddVnpO9+7dc/PNN1fazvvvv59NN900//jHP0qPffjhh2nZsmXeeuutbxzDY489lj59+mS//fbLWWedVQpSCxYsyCWXXJKtt946nTp1yqmnnpr99ttvsdcGAAAAcHpLFc2YMSO//vWv06tXr4wYMSKffPJJjjvuuAwcODBjxozJHXfckR49euSOO+7IWmutlbFjx+bhhx/OVVddldVXXz3PPPNM9t9//2y33XbZaqutlmkMq6yyStq1a5fHHnss++67by644IJMmTIlkyZNSv369XPfffelX79+2XnnnbPuuut+5TbWWGONbL311rntttvSrl27JMnkyZPTtm3brLPOOl/72nfffXcGDhyYo446Kn379q20bNy4cZk8eXKuuuqqrLPOOhkxYkSeeeaZ7LPPPsv0PqmamjV/eo25Ro3qlf6GpWHeUFXmDFVlzrAszBuqypyhqn6Ic0aUqqI///nPqVWrVo477rjUqFEjderUySmnnJKePXvmgw8+WGz9ffbZJ3vttVdWXXXVTJs2LXPmzEm9evXy/vvvf6txNGjQoHTfqqOPPjo1atTISiutlPfeey8rrLBCks9vuP51USpJ9t5775x66qk56aSTUrt27dxyyy056KCDvnb9v//973nwwQfTqlWr3H777endu3dWWmml0vJJkybl0EMPzQYbbJAk+d3vflc6m4vvX8OG9Zb3EJablVeuu7yHwI+QeUNVmTNUlTnDsjBvqCpzhqr6Ic0ZUaqKpk+fnmbNmqVGjRqlx9Zaa60kydSpU7PaaqtVWv/TTz/N6aefnieffDJNmjTJpptumoqKiixcuPBbjeOjjz5Ko0aNSmMaNmxYXnzxxay11lrZbLPNkmSJr9G9e/eceuqp+etf/5pmzZpl6tSp2XHHHb92/YqKilxzzTVp2rRp9t577wwcODCjRo1KtWrVkiTvvvtu1lxzzdL6NWrUSLNmzb7V+2TpzZgxe3kPoXA1alTPyivXzaxZn2bBgm/3PcVPh3lDVZkzVJU5w7Iwb6gqc4aqKnrOLM2JE6JUFa255pp55513smDBglKYWnQPpsaNGy92r6iTTz45q6yySv72t79lhRVWyMKFC9OhQ4dvNYYZM2bkmWeeySmnnJIkOeaYY9K9e/eMGzcuNWvWzIwZM3LjjTcucTu1a9fObrvtljvvvDPNmjXLzjvvnBVXXPFr1+/UqVM23HDDJMmll16afffdNyNGjEi/fv2SJM2aNcs777xTWr+ioiLvvvvut3mrVMH8+T/d/xAtWLDwJ/3+WTbmDVVlzlBV5gzLwryhqswZquqHNGd+OBcS/kh07do1SXLBBRdkzpw5+eCDDzJs2LBsueWWWXPNNUuXzpWXl5f+XmGFFVK9evWUl5fnvPPOS3l5eebNm7dMr/+vf/0r/fr1y0YbbZTdd989SfLJJ5+kTp06qVGjRj766KOceeaZSbJUr/Hzn/88Dz/8cO6///706tVrqcfRokWLDB06NKNHj87999+fJNl3330zfvz4vP7665k7d25GjRqVadOmLcO7BAAAAP7XiVJVVL9+/UyYMCGvvPJKunbtml133TVrrrlmhg8fniRZbbXVsv3222fffffNddddl5NPPjkvv/xyOnbsmJ122inl5eXZZptt8sorryzV6z311FNp27Zt2rZtmy222CL9+vVLq1atMmHChNSqVStJcvbZZ+euu+5Ku3bt0qtXr6yxxhrZdNNNl+o1Nt5446yzzjqpXr16tthiiyrtiz333DP77rtvBg4cmFdeeSUHHHBAunfvnv322y/dunXLxx9/nCZNmpTGCQAAALBItYovX2/GT85RRx2VVq1a5dBDD/1W23nuueey5pprlu6rVVFRkS233DIXXXRRtt5666Xaxm4DbvtWY/gpGz+4+/IeQuFq1qyehg3rZcaM2T+Y00/54TNvqCpzhqoyZ1gW5g1VZc5QVUXPmcaN6y9xHWdK/YS9/fbbuf/++/Poo49W6dK9r3P77bdn4MCB+eSTTzJ//vxMmDAhSdKmTZtvvW0AAADgf4sbnf+EjRw5Mn/+859z4oknLvapgcvid7/7XU4//fRsv/32mTt3blq2bJlx48alXr0l33EfAAAA+GkRpX7Czj333O90eyuttFLOO++873SbAAAAwP8ml+8BAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFq7m8BwBfdPuFe2TGjNmZP3/h8h4KAAAA8D1yphQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAoXM3lPQD4ot0G3La8h7BMxg/uvryHAAAAAD8qzpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFE6UAgAAAKBwohQAAAAAhROlAAAAACicKAUAAABA4UQpAAAAAAonSgEAAABQOFEKAAAAgMKJUgAAAAAUTpQCAAAAoHCiFAAAAACFE6UAAAAAKJwoBQAAAEDhRCkAAAAACidKAQAAAFA4UQoAAACAwolSAAAAABROlAIAAACgcKIUAAAAAIUTpQAAAAAonCgFAAAAQOFEKQAAAAAKJ0oBAAAAUDhRCgAAAIDCiVIAAAAAFK7m8h7A8taiRYussMIKqVGjRioqKlKrVq20b98+Q4YMSdOmTZMk3bt3z1FHHZVevXot8+tMmTIlPXr0yJ///OestdZaS7Vu3bp1U61atSTJwoULU7du3Wy55ZYZOnRoGjRosMxjSZLBgwcnSc4555zFlk2bNi1HHHFEXn311Wy33Xb5+OOP0759+xx22GHfuM0+ffqkY8eOOfroo7/V2AAAAID/fT/5KJUkY8eOTVlZWZKkvLw8xx13XI4//vhcc801y3Vcd9xxR6WA9e9//zuHH354hg0blvPPP/97e93HHnssU6dOzRNPPJHatWt/b68DAAAA/HS5fO9LVlpppeyzzz755z//+ZXL33///fzud79L9+7d07p16/To0SOTJk0qLX/77bdz2GGHZYsttshWW22VoUOHZu7cuYttZ+TIkdlmm23y6quvLvXYNtxww2y//fZ56aWXSo899dRT6d27d9q3b5/u3bvnkksuqfR6V111Vbbffvu0bds2vXr1yt///vfFtjt16tT06NEjZ511Vq666qqcdNJJmTFjRsrKyvLoo4+mT58+GTFiRJLPw1jv3r3ToUOHbLvtthk0aFDKy8tL23rzzTdz0EEHpUOHDunRo0fuueeepX5/AAAAwE+HM6W+ZObMmbnzzjuzww47fOXyk08+OQ0aNMidd96Z2rVr5+qrr84ZZ5yRnXfeOSussEIOPvjglJWV5aGHHsqcOXNy8MEHZ8SIEdl3331L2xg+fHgmT56ca6+9NmuvvfZSjauioiIvvPBC7rnnnuy8885Jkv/85z/5zW9+k+OOOy4TJkzIu+++m6OPPjrl5eU5+eSTc/PNN2f06NG57LLL0rp169x00005/PDD85e//KW03bfffjsHHHBA9txzz/Tr1y9JUr9+/YwcOTIPPPBAkmTMmDGl9U877bRstdVWueaaazJjxowccMAB+eMf/5jf/OY3SZJHHnkkV155ZTbZZJOMGTMmJ5xwQnr06JFatWot/UH4EapZU99dHmrUqF7pb1ga5g1VZc5QVeYMy8K8oarMGarqhzhnRKkkhx12WGrUqJGFCxdm9uzZqV+/fi6//PKvXPfMM89MvXr1UqtWrbzzzjupV69e5syZk5kzZ2bKlCmZOnVqTjzxxNStWzf16tXLyJEjs3DhwtLzhw8fnnvuuSf33Xdf6Z5VX2f33XdP9erVM2/evMydOzebbbZZDjjggOy///5Jkttvvz0tWrTIAQcckCRZd911M2DAgPTr1y8nnnhibrnlluy7775p27ZtkuQXv/hFmjdvnjp16iT5/AypPn36pFu3bqUgtSQrrLBCHn744TRv3jxbbbVVbrvttlSv/n8TepdddknLli1L/7700kszffr0NGnSZKm2/2PVsGG95T2En7SVV667vIfAj5B5Q1WZM1SVOcOyMG+oKnOGqvohzRlRKslll11WuqfUnDlzMnHixBxwwAG54YYbSoFlkbfffjvnnXde3njjjay33npZd911k3x+I/IPPvggDRs2TN26/3eAF90TasqUKUk+v/ytQYMGuf3223PooYd+47gmT56ctdZaKx999FHOOOOMvPTSS9l5551Ts+bnh2369OmLnWm11lprZc6cOZk+fXo++OCDNGvWrNLydu3alf791FNPZeutt86f//zn9O/fP6usssoS99Ull1ySESNG5OKLL86xxx6bdu3aZejQodlwww2TpNIN2BedHTV//vwlbvfHbsaM2ct7CD9JNWpUz8or182sWZ9mwYKFS34CxLyh6swZqsqcYVmYN1SVOUNVFT1nlubkDVHqS+rUqZODDz44V1xxRR599NFKUWrevHnp27dvjj322PzqV79KtWrV8s9//jOTJ09OkjRp0iQzZszIp59+WgpTTz31VP75z39mu+22S5JcfPHFeeONN9KvX7907do1LVq0WOKYGjVqlPPOOy8HHnhgDjrooNxwww1ZaaWVsuaaa+a+++6rtO5bb72V2rVrZ5VVVknTpk3z7rvvVlp+8cUXZ/fdd0/y+ZlM5513Xn75y1/mtNNOy0UXXfSN41i4cGFefPHFHH300TnxxBPz7rvv5uyzz87gwYNz0003LfF9/C+bP99/BJanBQsWOgZUmXlDVZkzVJU5w7Iwb6gqc4aq+iHNmR/OhYQ/EPPnz89NN92UWbNmZYsttqi0bN68eZkzZ07q1KmTatWq5Z133il9Ct68efPSqlWrrLfeejn33HPz6aef5sMPP8zZZ5+djz76qLSNWrVqZdttt80uu+ySgQMHfuVN0L9KrVq1ctFFF+XDDz/MsGHDkiQ9e/bMa6+9lquuuipz587NW2+9lYsuuii77bZbateunV69euWGG27I888/n4ULF+amm27KxIkT07Bhw9I2a9SokbPPPjt/+tOfctddd33jGKpXr54zzzwzl1xyST777LM0atQoK6zw/7V3ryFW1vsewH+VWiqlOzIlJBJzEvG2ctS8hNYE5k4dX2hFSVZahmmhEZ5SxAuJlallmqacF5KYNEhlaiLahV7kjGQpdFGnvEBkZTd10mmc57zY6N5z3OwzS/C/FsfPB+bF/HkYv8KX9az5+sx4+dmvBwAAANBYRqmIeOSRRyKXy0Uul4s+ffrEmjVrYuHChQ1+1C0iokWLFjFv3rxYunRp5HK5eOCBB2LAgAFxzTXXxN69e6Np06axfPnyOHLkSAwePDjKy8ujd+/e//b3NU2fPj1++eWXs/+rXWO0bds25syZE+vXr4/NmzdH+/btY9WqVbFly5bo379/3HfffTFgwICYOXNmREQMHz48Jk+eHE8//XSUlpbGunXrYuXKlXH11Vc3+LodO3aMyZMnx+zZs+PIkSP/McPixYujuro6Bg4cGP37949jx47F3LlzG/13AAAAAIiIuCTLsqzQIeCM4U+9U+gI5+W//+v2Qke4KDVpcmn87W8t49dfTxTN46cUP70hXzpDvnSG86E35EtnyFfqzrRpc+X/eY0npQAAAABIzigFAAAAQHJGKQAAAACSM0oBAAAAkJxRCgAAAIDkjFIAAAAAJGeUAgAAACA5oxQAAAAAyRmlAAAAAEjOKAUAAABAckYpAAAAAJIzSgEAAACQnFEKAAAAgOSMUgAAAAAkZ5QCAAAAIDmjFAAAAADJGaUAAAAASM4oBQAAAEByRikAAAAAkjNKAQAAAJCcUQoAAACA5IxSAAAAACRnlAIAAAAgOaMUAAAAAMkZpQAAAABIzigFAAAAQHJGKQAAAACSM0oBAAAAkJxRCgAAAIDkjFIAAAAAJGeUAgAAACA5oxQAAAAAyRmlAAAAAEjOKAUAAABAckYpAAAAAJIzSgEAAACQnFEKAAAAgOSMUgAAAAAkZ5QCAAAAIDmjFAAAAADJNSl0APhXG14qj19/PRF1dfWFjgIAAABcQJ6UAgAAACA5oxQAAAAAyRmlAAAAAEjOKAUAAABAckYpAAAAAJIzSgEAAACQnFEKAAAAgOSMUgAAAAAkZ5QCAAAAIDmjFAAAAADJGaUAAAAASM4oBQAAAEByRikAAAAAkjNKAQAAAJCcUQoAAACA5IxSAAAAACRnlAIAAAAgOaMUAAAAAMkZpQAAAABIzigFAAAAQHJGKQAAAACSM0oBAAAAkJxRCgAAAIDkjFIAAAAAJGeUAgAAACA5oxQAAAAAyRmlAAAAAEjOKAUAAABAckYpAAAAAJIzSgEAAACQnFEKAAAAgOSMUgAAAAAkZ5QCAAAAIDmjFAAAAADJGaUAAAAASM4oBQAAAEByRikAAAAAkjNKAQAAAJCcUQoAAACA5IxSAAAAACRnlAIAAAAgOaMUAAAAAMkZpQAAAABIzigFAAAAQHKXZFmWFToEAAAAABcXT0oBAAAAkJxRCgAAAIDkjFIAAAAAJGeUAgAAACA5oxQAAAAAyRmlAAAAAEjOKAUAAABAckYpAAAAAJIzSgEAAACQnFGKpI4ePRoTJ06M0tLS6Nu3bzz33HNRV1f3b6/96KOPYvjw4dGzZ88YOnRofPDBB4nTUgzy6cwZW7ZsibKyskQJKUb59Gbt2rUxZMiQyOVyMWTIkFizZk3itBSDxnamvr4+lixZEoMGDYpcLhfDhw+PTZs2FSAxhXY+96e9e/dGjx49YseOHYlSUmzy6c348eOjW7dukcvlzn58/PHHiRNTaPl0prKyMkaPHh25XC4GDRoUK1asSJyWYtDYzowfP77B60sul4ubbropZs6cmTZwBgmNGTMme+qpp7Kamprs0KFD2V133ZWtXLnynOu+++67rFu3btnWrVuzv/76K9u4cWPWvXv37IcffihAagqpsZ3Jsiyrra3NXn/99axLly7ZbbfdljgpxaSxvdm6dWtWWlqa7dq1K6uvr88+++yzrLS0NHv//fcLkJpCamxnVq9end1+++3ZwYMHsyzLsu3bt2edO3c++zkXj3zuT1mWZTU1NdmwYcOykpKS7NNPP02YlGKST2/69u2b7dixI3FCik1jO7N///6sR48e2fr167P6+vrsq6++yvr06ZNt3ry5AKkppHzvT2e89dZb2aBBg7IjR44kSPlPRimSOXDgQFZSUtJgWNq4cWM2ePDgc65duHBh9tBDDzU4GzduXPbyyy9f8JwUj3w6k2X/eAEeN25ctmjRIqPURSyf3rzxxhvZihUrGpw9/vjj2dy5cy94TopHPp05ffp0duLEiSzLsuzUqVNZRUVFlsvlkr+Bo7DyvT9lWZZNmzYtW7x4sVHqIpZPbw4dOpR17tw5O3bsWMqIFJl8OjNnzpxs6tSpDc6+/fbb7Mcff7zgOSke53N/yrIsq66uzrp3755VVVVd6Ijn8ON7JLNv375o3bp1tG3b9uxZx44d4/vvv48//vijwbX79++PkpKSBmc33nhjfP3110myUhzy6UxExIsvvhirVq2K66+/PmVMikw+vbn//vvj0UcfPfv50aNHo6qqKrp27ZosL4WXT2cuvfTSaNGiRXzyySfRo0ePmD59ejz55JNx7bXXpo5NAeV7f3r77bfj4MGDMWnSpJQxKTL59GbPnj3RsmXLmDJlStxyyy0xbNiwqKioSB2ZAsunM7t374727dvH1KlTo2/fvjF06NCorKyMNm3apI5NAeV7fzpj9uzZMXLkyCgtLU0Rs4Emyf9ELlonTpyI5s2bNzg783lNTU1cddVV//HaK664Impqai58UIpGPp2JiGjXrl2ybBSvfHtzxk8//RQTJkyIrl27xrBhwy54TorH+XSmT58+sWfPnqiqqoqJEydGmzZt4u9//3uSvBRePp2prq6ORYsWxdq1a+Oyyy5LmpPikk9vamtro2fPnjFlypTo1KlT7NixIyZPnhwtW7aMoUOHJs1N4eTTmd9//z1Wr14dixYtihdeeCF27doVEyZMiFatWsWdd96ZNDeFcz7vaXbu3BlffPFFLFiwIEnG/82TUiTTokWL+PPPPxucnfm8ZcuWDc6bN28eJ0+ebHB28uTJc67j/7d8OgNnnE9vPv/88xg1alR06NAhXnvttWjSxL/ZXEzOpzPNmjWLJk2aRL9+/aK8vDw2bNhwwXNSPBrbmVOnTsWUKVPi2Wefjeuuuy5pRopPPq81I0eOjFWrVkWXLl2iadOmMXDgwBg5cmRs3rw5WV4KL5/ONGvWLMrKymLw4MHRpEmT6N27d5SXl+vMReZ83tOsW7cuhg4dWrCn6oxSJNOpU6f47bff4ueffz57Vl1dHe3atYsrr7yywbUlJSWxb9++Bmf79++PTp06JclKccinM3BGvr2pqKiIBx98MMaOHRsvvfRSNGvWLGVcikA+nZk/f37Mnz+/wVltbW20bt06RVSKRGM7s2fPnjhw4EBMnz49SktLz/5YxGOPPRazZs1KHZsCy+e1pqKi4pwxoba2Ni6//PIkWSkO+XSmY8eOUVtb2+Ds9OnTkWVZkqwUh3zfB9fV1cW2bdtixIgRKWM2YJQimRtuuCF69eoV8+bNi+PHj8fhw4dj2bJlMWrUqHOuHTFiRFRWVsamTZuirq4uNm3aFJWVlVFeXl6A5BRKPp2BM/LpzZYtW2LWrFmxZMmSePjhhwuQlmKQT2dKS0vjzTffjKqqqqivr4/t27fHpk2bYvTo0QVITqE0tjOlpaWxe/fu2Llz59mPiIjly5cbpS5C+bzWHD9+PObOnRtffvll1NfXx4cffhjvvfde3HPPPQVITqHk05l77703tm3bFu+8805kWRZVVVWxYcMG3z9dZPL9/umbb76JU6dOxc0335w46T8ZpUjqlVdeibq6uigrK4u77747br311pg4cWJERORyuXj33Xcj4h9L/9KlS2PFihXRu3fvWLZsWSxZsiQ6dOhQyPgUQGM7A/+qsb159dVX4/Tp0/HEE09ELpc7+zFz5sxCxqcAGtuZO+64I2bMmBEzZsyI3r17x9KlS2PJkiUFfTNHYbg/cT4a25uxY8fGmDFjYtKkSZHL5WLBggXx/PPPF+SXEFNYje1Mv379YtmyZbF69ero1atXPPPMMzFt2rQoKysrZHwKIJ/70+HDh6NVq1YFfQrzkszzfAAAAAAk5kkpAAAAAJIzSgEAAACQnFEKAAAAgOSMUgAAAAAkZ5QCAAAAIDmjFAAAAADJGaUAAAAASM4oBQAAAEByRikAAAAAkjNKAQAAAJCcUQoAAACA5IxSAAAAACT3P3IaoElgxqzPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.figure()\n",
    "fig = plt.figure(figsize=(12,16)) # define plot area\n",
    "ax = fig.gca() # define axis\n",
    "#ax.set_xlim([0,1.0])\n",
    "plt.title('Model Feature Importances: Gradient Boosting Classifier\\n', fontsize=12)\n",
    "feature_importances_gb = pd.Series(gbc.feature_importances_, index=x_train_gb.columns)\n",
    "feature_importances_gb.sort_values(inplace=True)\n",
    "feature_importances_gb.plot(kind=\"barh\", figsize=(12,10))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe of the variables and their importance value\n",
    "feat_import_df_gb = pd.DataFrame(feature_importances_gb.sort_values(ascending=False))\n",
    "feat_import_df_gb = feat_import_df_gb.reset_index()\n",
    "feat_import_df_gb.columns = ['Variable', 'Importance_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the top variables that contribute to >98% of importance score\n",
    "def important_variables(numbers):\n",
    "    total = 0\n",
    "    for number in numbers:\n",
    "        if total > 0.98:\n",
    "            break\n",
    "        total += number\n",
    "    return number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting the top variables only\n",
    "x_train_top_variables_gb = feat_import_df_gb.loc[feat_import_df_gb['Importance_value'] >= important_variables(feat_import_df_gb.iloc[:,1]), ['Variable','Importance_value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving copy of file\n",
    "#x_train_top_variables_gb.to_csv('x_train_top_variables_gbRA.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Variable</th>\n",
       "      <th>Importance_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sablefish</td>\n",
       "      <td>0.684606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shortspine Thornyhead</td>\n",
       "      <td>0.126712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Distance 202m Depth</td>\n",
       "      <td>0.100655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total Daily Kg</td>\n",
       "      <td>0.055493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Black Rockfish</td>\n",
       "      <td>0.032534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable  Importance_value\n",
       "0              Sablefish          0.684606\n",
       "1  Shortspine Thornyhead          0.126712\n",
       "2    Distance 202m Depth          0.100655\n",
       "3         Total Daily Kg          0.055493\n",
       "4         Black Rockfish          0.032534"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top variables that contributed to 98% or more of the importance score\n",
    "x_train_top_variables_gb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top variables that are important, which are used in a grid search for best hyperparameters\n",
    "x_train_gb_grid = x_train.loc[:,list(x_train_top_variables_gb.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import uniform as sp_randFloat\n",
    "from scipy.stats import randint as sp_randInt \n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "gbm_clf = GradientBoostingClassifier()\n",
    "\n",
    "parameters = {'learning_rate': sp_randFloat(),\n",
    "              'subsample'    : sp_randFloat(),\n",
    "              'max_depth'    : sp_randInt(2, 10),\n",
    "              'n_estimators' : sp_randInt(50, 1000),\n",
    "              'min_samples_split' : sp_randInt(2, 100),\n",
    "              'min_samples_leaf'  : sp_randInt(1, 10),\n",
    "             }\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x00000196837C32E0&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683835F10&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructur...ete_frozen object at 0x0000019685A29160&gt;,\n",
       "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683648820&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019685A241F0&gt;,\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019685A240D0&gt;},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x00000196837C32E0&gt;,\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683835F10&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructur...ete_frozen object at 0x0000019685A29160&gt;,\n",
       "                                        &#x27;min_samples_split&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683648820&gt;,\n",
       "                                        &#x27;n_estimators&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019685A241F0&gt;,\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019685A240D0&gt;},\n",
       "                   scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=10, estimator=GradientBoostingClassifier(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'learning_rate': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x00000196837C32E0>,\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683835F10>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructur...ete_frozen object at 0x0000019685A29160>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019683648820>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019685A241F0>,\n",
       "                                        'subsample': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019685A240D0>},\n",
       "                   scoring='accuracy')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_randm = RandomizedSearchCV(estimator=gbm_clf, param_distributions = parameters, \n",
    "                           cv = 10, n_iter = 100, n_jobs=-1, scoring='accuracy')\n",
    "gb_randm.fit(x_train_gb_grid, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# new model using the best parameters from gridsearch\n",
    "gb_best_model = GradientBoostingClassifier(**gb_randm.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier(learning_rate=0.007966977595900926, max_depth=4,\n",
       "                           min_samples_leaf=4, min_samples_split=93,\n",
       "                           n_estimators=951, subsample=0.5523841440289768)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(learning_rate=0.007966977595900926, max_depth=4,\n",
       "                           min_samples_leaf=4, min_samples_split=93,\n",
       "                           n_estimators=951, subsample=0.5523841440289768)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier(learning_rate=0.007966977595900926, max_depth=4,\n",
       "                           min_samples_leaf=4, min_samples_split=93,\n",
       "                           n_estimators=951, subsample=0.5523841440289768)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model with best para and top variables to the training data\n",
    "gb_best_model.fit(x_train_gb_grid, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modify test data to only include top variables \n",
    "x_test_gb_grid = x_test.loc[:,list(x_train_top_variables_gb.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model prediction on test data\n",
    "y_pred_gb = gb_best_model.predict(x_test_gb_grid)\n",
    "\n",
    "probas_gb = gb_best_model.predict_proba(x_test_gb_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\n",
      "Random Forest Classifier Performance Matrix:\n"
     ]
    },
    {
     "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>Predicted True</th>\n",
       "      <th>Predicted False</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual True</th>\n",
       "      <td>1100</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual False</th>\n",
       "      <td>31</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Predicted True  Predicted False\n",
       "Actual True             1100               31\n",
       "Actual False              31              964"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import *\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print (\"\\033[1m\\nRandom Forest Classifier Performance Matrix:\")\n",
    "pd.DataFrame(\n",
    "    confusion_matrix(y_test, y_pred_gb, labels=[1,0]),\n",
    "    columns=['Predicted True', 'Predicted False'],\n",
    "    index=['Actual True', 'Actual False']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ROC analysis\n",
    "LW_r = 1.5 # line width for plots\n",
    "LL_r = \"lower right\" # legend location\n",
    "LC_r = 'darkgreen' # Line Color\n",
    "\n",
    "fpr_r, tpr_r, th_r = roc_curve(y_test, probas_gb[:,1]) # False Positive Rate, True Posisive Rate, probability thresholds\n",
    "AUC = auc(fpr_r, tpr_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x550 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the ROC curve \n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.figure()\n",
    "fig = plt.figure(figsize=(10,10)) # define plot area\n",
    "ax = fig.gca() # define axis\n",
    "plt.title('ROC curve: Gradient Boosting Classifier', fontsize=16)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.plot(fpr_r, tpr_r, color=LC_r,lw=LW_r, label='ROC curve (area = %0.2f)' % AUC)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=LW_r, linestyle='--') # reference line for random classifier\n",
    "plt.legend(loc=LL_r)\n",
    "#plt.savefig('GradientBoosting_ROC_CurveFNLRA.png', dpi=800)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance Matrix for Gradient Boosting Model\n",
      "\n",
      "Random Gradient Boosting Accuracy rate: 0.971\n",
      "\n",
      "Random Gradient Boosting Error rate: 0.029\n",
      "\n",
      "Random Gradient Boosting Precision: 0.973\n",
      "\n",
      "Random Gradient Boosting Recall: 0.973\n",
      "\n",
      "Random Gradient Boosting F1 score: 0.973\n"
     ]
    }
   ],
   "source": [
    "print('Performance Matrix for Gradient Boosting Model')\n",
    "\n",
    "AR = accuracy_score(y_test, y_pred_gb)\n",
    "print (\"\\nRandom Gradient Boosting Accuracy rate:\", np.round(AR, 3))\n",
    "ER = 1.0 - AR\n",
    "print (\"\\nRandom Gradient Boosting Error rate:\", np.round(ER, 3))\n",
    "P = precision_score(y_test, y_pred_gb)\n",
    "print (\"\\nRandom Gradient Boosting Precision:\", np.round(P, 3))\n",
    "R = recall_score(y_test, y_pred_gb)\n",
    "print (\"\\nRandom Gradient Boosting Recall:\", np.round(R, 3))\n",
    "F1 = f1_score(y_test, y_pred_gb)\n",
    "print (\"\\nRandom Gradient Boosting F1 score:\", np.round(F1, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combined ROC curve plot for the two models. \n",
    "\n",
    "LW_r = 1.5 # line width for plots\n",
    "LL_r = \"lower right\" # legend location\n",
    "LC_r = 'darkgreen' # Line Color\n",
    "\n",
    "fpr_r, tpr_r, th_r = roc_curve(y_test, probas_rf[:,1]) # False Positive Rate, True Posisive Rate, probability thresholds\n",
    "AUC = auc(fpr_r, tpr_r)\n",
    "\n",
    "\n",
    "LW_g = 1.5 # line width for plots\n",
    "LL_g = \"lower right\" # legend location\n",
    "LC_g = 'darkblue' # Line Color\n",
    "\n",
    "fpr_g, tpr_g, th_g = roc_curve(y_test, probas_gb[:,1]) # False Positive Rate, True Posisive Rate, probability thresholds\n",
    "AUC = auc(fpr_g, tpr_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-whitegrid') #plt.style.use('ggplot')\n",
    "fig = plt.figure(figsize=(10, 10)).gca()\n",
    "plt.title('ROC curve:\\nRandom Forest and Gradient Boosting Models', fontsize=12)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.plot(fpr_r, tpr_r, color=LC_r,lw=LW_r, label='Random Forest ROC curve (area = %0.2f)' % AUC)\n",
    "plt.plot(fpr_g, tpr_g, color=LC_g,lw=LW_g, label='Gradient Boosting ROC curve (area = %0.2f)' % AUC)\n",
    "plt.plot([0, 1], [0, 1], color='darkred', lw=LW_r, linestyle='--') # reference line for random classifier\n",
    "plt.legend(loc=LL_r) \n",
    "plt.tight_layout()\n",
    "#plt.savefig('GBandRF_ROC_CurveFNLRA.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combining the variables of importance for the two models\n",
    "x_train_top_variables_gb.rename(columns={\"Importance_value\": \"GB_Importance_value\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_top_variables.rename(columns={\"Importance_value\": \"RF_Importance_value\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "varimport_df = pd.merge(x_train_top_variables, x_train_top_variables_gb, on='Variable', how='outer') #x_train_top_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Variable</th>\n",
       "      <th>RF_Importance_value</th>\n",
       "      <th>GB_Importance_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sablefish</td>\n",
       "      <td>0.387732</td>\n",
       "      <td>0.684606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total Daily Kg</td>\n",
       "      <td>0.182826</td>\n",
       "      <td>0.055493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black Rockfish</td>\n",
       "      <td>0.180296</td>\n",
       "      <td>0.032534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Distance 202m Depth</td>\n",
       "      <td>0.119692</td>\n",
       "      <td>0.100655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shortspine Thornyhead</td>\n",
       "      <td>0.079255</td>\n",
       "      <td>0.126712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Cabezon</td>\n",
       "      <td>0.028866</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable  RF_Importance_value  GB_Importance_value\n",
       "0              Sablefish             0.387732             0.684606\n",
       "1         Total Daily Kg             0.182826             0.055493\n",
       "2         Black Rockfish             0.180296             0.032534\n",
       "3    Distance 202m Depth             0.119692             0.100655\n",
       "4  Shortspine Thornyhead             0.079255             0.126712\n",
       "5                Cabezon             0.028866                  NaN"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the combined variables of importanc for the two models\n",
    "varimport_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "varimport_df.set_index('Variable', inplace=True) #rf_feat_import_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ploting the variables of importance for the two models\n",
    "font_color = '#000000'\n",
    "hfont = {'fontname': 'Times'} #'Calibri'}\n",
    "facecolor = '#FFFFFF' #'#eaeaf2'\n",
    "color_red = '#5E2423'\n",
    "color_blue = '#0B5394'\n",
    "index = varimport_df.index\n",
    "column0 = varimport_df['RF_Importance_value']\n",
    "column1 = varimport_df['GB_Importance_value']\n",
    "title0 = 'Random Forest'\n",
    "title1 = 'Gradient Boosting'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-whitegrid')\n",
    "fig, axes = plt.subplots(figsize=(10,5), ncols=2, facecolor=facecolor, sharey=True) #, sharex=True)\n",
    "\n",
    "axes[0].barh(index, column0, align='center', color=color_blue, zorder=10)\n",
    "axes[0].set_title(title0, fontsize=13) #pad=15,\n",
    "axes[1].barh(index, column1, align='center', color=color_blue, zorder=10)\n",
    "axes[1].set_title(title1, fontsize=13)\n",
    "axes[0].set_ylabel(\"Variables\", fontsize=14)\n",
    "axes[0].set_yticklabels(index,fontsize=12)\n",
    "axes[0].set_xticks([0.0,0.1,0.2,0.3,0.4,0.5, 0.6,0.7]) \n",
    "axes[0].set_xticklabels([0.0,0.1,0.2,0.3,0.4,0.5, 0.6,0.7],fontsize=12)\n",
    "axes[1].set_xticks([0.0,0.1,0.2,0.3,0.4,0.5, 0.6,0.7]) \n",
    "axes[1].set_xticklabels([0.0,0.1,0.2,0.3,0.4,0.5, 0.6,0.7],fontsize=12)\n",
    "# If you have positive numbers and want to invert the x-axis of the left plot\n",
    "#axes[0].invert_xaxis() \n",
    "\n",
    "\n",
    "\n",
    "# To show data from highest to lowest\n",
    "plt.gca().invert_yaxis()\n",
    "fig.text(0.58, 0.1, 'Variable of Importances', ha='center', va='top',fontsize=14)\n",
    "\n",
    "\n",
    "axes[0].set(yticks=varimport_df.index)\n",
    "plt.subplots_adjust(wspace=0.09, top=0.93, bottom=0.17, left=0.21, right=0.95)\n",
    "#plt.subplots_adjust(wspace=0.09, top=0.85, bottom=0.1, left=0.18, right=0.95)\n",
    "\n",
    "\n",
    "#fig.tight_layout()\n",
    "\n",
    "#plt.savefig('VariablesImportanceBidirectionalRA5.png', dpi=600)\n",
    "#filename = 'VariablesImportanceBidirectionalRA3'\n",
    "#plt.savefig(filename+'.png', facecolor=facecolor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.97, 0.5, 'Std Error')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting the sequential forward selection outputs for the two models\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "y1 = sfs_rf_df.loc[:,'avg_score']\n",
    "y2 = sfs_rf_df.loc[:,'std_err']\n",
    "y3 = sfs_gb_df.loc[:,'avg_score']\n",
    "y4 = sfs_gb_df.loc[:,'std_err']\n",
    "x = range(1,117, 1) \n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(2, 1, sharex='col', sharey='row', figsize=(15, 10))\n",
    "\n",
    "# first plot\n",
    "ax2=ax[0].twinx()\n",
    "ax[0].set_ylim([0.88, 1.00])\n",
    "#plt.xticks(np.arange(0, 118, step=4))\n",
    "plt.ylim([0.0006, 0.0021])\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "line1, = ax[0].plot(x, y1, 'b-', linewidth=2, label='Accuracy Score')\n",
    "line2, = plt.plot(x, y2, 'r-', linewidth=2, label='Std Error')\n",
    "plt.axvline(list(sfs_rf_df_opt[\"idx_len\"]), color = 'darkgreen', linestyle='dashed', linewidth=1.5)\n",
    "\n",
    "\n",
    "plt.legend(handles=[line1, line2], fontsize=14, loc='lower right')\n",
    "\n",
    "#second plot\n",
    "ax2=ax[1].twinx()\n",
    "ax[1].set_ylim([0.88, 1.00])\n",
    "plt.ylim([0.0006, 0.0021])\n",
    "plt.yticks(fontsize=16)\n",
    "\n",
    "# x-axis ticks\n",
    "ax[1].set_xticks(np.arange(0, 118, step=5))\n",
    "\n",
    "line3, = ax[1].plot(x, y3, 'b-', linewidth=2, label='Accuracy Score')\n",
    "line4, = plt.plot(x, y4, 'r-', linewidth=2, label='Std Error')\n",
    "plt.axvline(list(sfs_gb_df_opt[\"idx_len\"]), color = 'darkgreen', linestyle='dashed', linewidth=1.5)\n",
    "\n",
    "ax[0].tick_params(axis='both', which='major', labelsize=16)\n",
    "ax[1].tick_params(axis='both', which='major', labelsize=16)\n",
    "\n",
    "# Plot titles\n",
    "ax[0].set_title('Random Forest',fontsize=18)\n",
    "ax[1].set_title('Gradient Boosting',fontsize=18)\n",
    "\n",
    "plt.legend(handles=[line3, line4], fontsize=14, loc='lower right')\n",
    "\n",
    "#common labels\n",
    "fig.text(0.5, 0.06, 'Variables', ha='center', va='top',fontsize=20)\n",
    "fig.text(0.075, 0.5, 'Accuracy Score', ha='center', va='center', rotation='vertical',fontsize=20)\n",
    "fig.text(0.97, 0.5, 'Std Error', ha='center', va='center', rotation='vertical',fontsize=20)\n",
    "#plt.subplots_adjust(right=1.0)\n",
    "#plt.tight_layout()\n",
    "#plt.savefig('RFGBForwardSelectionFNLRA2.png', dpi=600)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_cols = list(x_train_top_variables.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Random Forest Metrics for Year - 2019\n",
      "Computing Random Forest Metrics for Year - 2018\n",
      "Computing Random Forest Metrics for Year - 2017\n",
      "Computing Random Forest Metrics for Year - 2016\n",
      "Computing Random Forest Metrics for Year - 2015\n",
      "Computing Random Forest Metrics for Year - 2014\n",
      "Computing Random Forest Metrics for Year - 2013\n",
      "Computing Random Forest Metrics for Year - 2012\n",
      "Computing Random Forest Metrics for Year - 2011\n",
      "Computing Random Forest Metrics for Year - 2010\n",
      "Computing Random Forest Metrics for Year - 2009\n",
      "Computing Random Forest Metrics for Year - 2008\n",
      "Computing Random Forest Metrics for Year - 2007\n",
      "Computing Random Forest Metrics for Year - 2006\n",
      "Computing Random Forest Metrics for Year - 2005\n",
      "Computing Random Forest Metrics for Year - 2004\n",
      "Computing Random Forest Metrics for Year - 2003\n",
      "Computing Random Forest Metrics for Year - 2002\n"
     ]
    }
   ],
   "source": [
    "# Selected subset of variables were also used to fit the models whose \n",
    "#training data iterated through all but one year, and were tested on the remaining year.\n",
    "\n",
    "# this was to evaluate the persistence of model efficacy despite inter-annual variability\n",
    "Randon_Forest_results_df = pd.DataFrame(columns=['Year', 'Accuracy Rate','Error Rate','Precision','Recall','F1-Score'])\n",
    "\n",
    "for year in range(2019, 2001, -1):\n",
    "    print('Computing Random Forest Metrics for Year - {}'.format(year))\n",
    "\n",
    "    # training data is for all years except the current year\n",
    "    train_subset = wcomp_pivot.loc[wcomp_pivot['Year'] != year]\n",
    "    X_train, y_train = train_subset[rf_cols], train_subset['Label']\n",
    "\n",
    "    # testing data is all rows for current year\n",
    "    test_subset = wcomp_pivot.loc[wcomp_pivot['Year'] == year]\n",
    "    X_test, y_test = test_subset[rf_cols], test_subset['Label']\n",
    "\n",
    "    pipe = make_pipeline(MinMaxScaler(), rfc_clf)\n",
    "    pipe.fit(X_train, y_train)  # apply scaling on training data\n",
    "    y_pred = pipe.predict(X_test) # getting predictions for test year data\n",
    "\n",
    "    # computing accuracy metrics\n",
    "    AR = accuracy_score(y_test, y_pred)\n",
    "    ER = 1.0 - AR\n",
    "    P = precision_score(y_test, y_pred)\n",
    "    R = recall_score(y_test, y_pred)\n",
    "    F1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    # adding accuracy metries to our results dataframe\n",
    "    Randon_Forest_results_df.loc[-1] = [year, AR, ER, P, R, F1]  # adding a row\n",
    "    Randon_Forest_results_df.index = Randon_Forest_results_df.index + 1  # shifting index\n",
    "    Randon_Forest_results_df = Randon_Forest_results_df.sort_index()  # sorting by index\n",
    "\n",
    "Randon_Forest_results_df.loc['mean'] = Randon_Forest_results_df.mean()\n",
    "Randon_Forest_results_df.Year = Randon_Forest_results_df.Year.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\n",
      " Randon Forest Metrics by Test Year\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>Accuracy Rate</th>\n",
       "      <th>Error Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.962617</td>\n",
       "      <td>0.037383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.961905</td>\n",
       "      <td>0.980583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.942943</td>\n",
       "      <td>0.057057</td>\n",
       "      <td>0.933852</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.961924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.953982</td>\n",
       "      <td>0.046018</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.956835</td>\n",
       "      <td>0.953405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.964775</td>\n",
       "      <td>0.035225</td>\n",
       "      <td>0.970696</td>\n",
       "      <td>0.963636</td>\n",
       "      <td>0.967153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>0.957529</td>\n",
       "      <td>0.942966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.961652</td>\n",
       "      <td>0.038348</td>\n",
       "      <td>0.977961</td>\n",
       "      <td>0.951743</td>\n",
       "      <td>0.964674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.962433</td>\n",
       "      <td>0.037567</td>\n",
       "      <td>0.961078</td>\n",
       "      <td>0.975684</td>\n",
       "      <td>0.968326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.968978</td>\n",
       "      <td>0.031022</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.967105</td>\n",
       "      <td>0.971901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.968709</td>\n",
       "      <td>0.031291</td>\n",
       "      <td>0.970930</td>\n",
       "      <td>0.982353</td>\n",
       "      <td>0.976608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.976010</td>\n",
       "      <td>0.023990</td>\n",
       "      <td>0.979296</td>\n",
       "      <td>0.981328</td>\n",
       "      <td>0.980311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.976119</td>\n",
       "      <td>0.023881</td>\n",
       "      <td>0.987692</td>\n",
       "      <td>0.963964</td>\n",
       "      <td>0.975684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.964883</td>\n",
       "      <td>0.035117</td>\n",
       "      <td>0.965278</td>\n",
       "      <td>0.961938</td>\n",
       "      <td>0.963605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.959930</td>\n",
       "      <td>0.040070</td>\n",
       "      <td>0.960714</td>\n",
       "      <td>0.957295</td>\n",
       "      <td>0.959002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.983333</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.982196</td>\n",
       "      <td>0.982196</td>\n",
       "      <td>0.982196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.983895</td>\n",
       "      <td>0.016105</td>\n",
       "      <td>0.993921</td>\n",
       "      <td>0.973214</td>\n",
       "      <td>0.983459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.982783</td>\n",
       "      <td>0.017217</td>\n",
       "      <td>0.979472</td>\n",
       "      <td>0.985251</td>\n",
       "      <td>0.982353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.970724</td>\n",
       "      <td>0.029276</td>\n",
       "      <td>0.959627</td>\n",
       "      <td>0.980952</td>\n",
       "      <td>0.970173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.953368</td>\n",
       "      <td>0.046632</td>\n",
       "      <td>0.958175</td>\n",
       "      <td>0.940299</td>\n",
       "      <td>0.949153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.965952</td>\n",
       "      <td>0.034048</td>\n",
       "      <td>0.968693</td>\n",
       "      <td>0.968609</td>\n",
       "      <td>0.968526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Accuracy Rate  Error Rate  Precision    Recall  F1-Score\n",
       "0     2002       0.962617    0.037383   1.000000  0.961905  0.980583\n",
       "1     2003       0.942943    0.057057   0.933852  0.991736  0.961924\n",
       "2     2004       0.953982    0.046018   0.950000  0.956835  0.953405\n",
       "3     2005       0.964775    0.035225   0.970696  0.963636  0.967153\n",
       "4     2006       0.950000    0.050000   0.928839  0.957529  0.942966\n",
       "5     2007       0.961652    0.038348   0.977961  0.951743  0.964674\n",
       "6     2008       0.962433    0.037567   0.961078  0.975684  0.968326\n",
       "7     2009       0.968978    0.031022   0.976744  0.967105  0.971901\n",
       "8     2010       0.968709    0.031291   0.970930  0.982353  0.976608\n",
       "9     2011       0.976010    0.023990   0.979296  0.981328  0.980311\n",
       "10    2012       0.976119    0.023881   0.987692  0.963964  0.975684\n",
       "11    2013       0.964883    0.035117   0.965278  0.961938  0.963605\n",
       "12    2014       0.959930    0.040070   0.960714  0.957295  0.959002\n",
       "13    2015       0.983333    0.016667   0.982196  0.982196  0.982196\n",
       "14    2016       0.983895    0.016105   0.993921  0.973214  0.983459\n",
       "15    2017       0.982783    0.017217   0.979472  0.985251  0.982353\n",
       "16    2018       0.970724    0.029276   0.959627  0.980952  0.970173\n",
       "17    2019       0.953368    0.046632   0.958175  0.940299  0.949153\n",
       "mean  2010       0.965952    0.034048   0.968693  0.968609  0.968526"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\033[1m\\n Randon Forest Metrics by Test Year\")\n",
    "Randon_Forest_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gradient boosting best model parameters\n",
    "gbm_clf = GradientBoostingClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_cols = list(x_train_top_variables_gb.iloc[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing Gradient Boosting Metrics for Year - 2019\n",
      "Computing Gradient Boosting Metrics for Year - 2018\n",
      "Computing Gradient Boosting Metrics for Year - 2017\n",
      "Computing Gradient Boosting Metrics for Year - 2016\n",
      "Computing Gradient Boosting Metrics for Year - 2015\n",
      "Computing Gradient Boosting Metrics for Year - 2014\n",
      "Computing Gradient Boosting Metrics for Year - 2013\n",
      "Computing Gradient Boosting Metrics for Year - 2012\n",
      "Computing Gradient Boosting Metrics for Year - 2011\n",
      "Computing Gradient Boosting Metrics for Year - 2010\n",
      "Computing Gradient Boosting Metrics for Year - 2009\n",
      "Computing Gradient Boosting Metrics for Year - 2008\n",
      "Computing Gradient Boosting Metrics for Year - 2007\n",
      "Computing Gradient Boosting Metrics for Year - 2006\n",
      "Computing Gradient Boosting Metrics for Year - 2005\n",
      "Computing Gradient Boosting Metrics for Year - 2004\n",
      "Computing Gradient Boosting Metrics for Year - 2003\n",
      "Computing Gradient Boosting Metrics for Year - 2002\n"
     ]
    }
   ],
   "source": [
    "# Selected subset of variables were also used to fit the models whose \n",
    "#training data iterated through all but one year, and were tested on the remaining year.\n",
    "\n",
    "# this was to evaluate the persistence of model efficacy despite inter-annual variability\n",
    "\n",
    "Gradient_Boosting_results_df = pd.DataFrame(columns=['Year', 'Accuracy Rate','Error Rate','Precision','Recall','F1-Score'])\n",
    "\n",
    "for year in range(2019, 2001, -1):\n",
    "    print('Computing Gradient Boosting Metrics for Year - {}'.format(year))\n",
    "\n",
    "    # training data is for all years except the current year\n",
    "    train_subset = wcomp_pivot.loc[wcomp_pivot['Year'] != year]\n",
    "    X_train, y_train = train_subset[gb_cols], train_subset['Label']\n",
    "\n",
    "    # testing data is all rows for current year\n",
    "    test_subset = wcomp_pivot.loc[wcomp_pivot['Year'] == year]\n",
    "    X_test, y_test = test_subset[gb_cols], test_subset['Label']\n",
    "\n",
    "    pipe = make_pipeline(MinMaxScaler(), gbm_clf)\n",
    "    pipe.fit(X_train, y_train)  # apply scaling on training data\n",
    "    y_pred = pipe.predict(X_test) # getting predictions for test year data\n",
    "\n",
    "    # computing accuracy metrics\n",
    "    AR = accuracy_score(y_test, y_pred)\n",
    "    ER = 1.0 - AR\n",
    "    P = precision_score(y_test, y_pred)\n",
    "    R = recall_score(y_test, y_pred)\n",
    "    F1 = f1_score(y_test, y_pred)\n",
    "\n",
    "    # adding accuracy metries to our results dataframe\n",
    "    Gradient_Boosting_results_df.loc[-1] = [year, AR, ER, P, R, F1]  # adding a row\n",
    "    Gradient_Boosting_results_df.index = Gradient_Boosting_results_df.index + 1  # shifting index\n",
    "    Gradient_Boosting_results_df = Gradient_Boosting_results_df.sort_index()  # sorting by index\n",
    "\n",
    "Gradient_Boosting_results_df.loc['mean'] = Gradient_Boosting_results_df.mean()\n",
    "Gradient_Boosting_results_df.Year = Gradient_Boosting_results_df.Year.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m\n",
      " Gradient Boosting Metrics by Test Year\n"
     ]
    },
    {
     "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>Year</th>\n",
       "      <th>Accuracy Rate</th>\n",
       "      <th>Error Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.971963</td>\n",
       "      <td>0.028037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.985507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.927928</td>\n",
       "      <td>0.072072</td>\n",
       "      <td>0.916031</td>\n",
       "      <td>0.991736</td>\n",
       "      <td>0.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.968142</td>\n",
       "      <td>0.031858</td>\n",
       "      <td>0.974453</td>\n",
       "      <td>0.960432</td>\n",
       "      <td>0.967391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.966732</td>\n",
       "      <td>0.033268</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.968807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.955000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>0.932836</td>\n",
       "      <td>0.965251</td>\n",
       "      <td>0.948767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.960177</td>\n",
       "      <td>0.039823</td>\n",
       "      <td>0.977901</td>\n",
       "      <td>0.949062</td>\n",
       "      <td>0.963265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.962433</td>\n",
       "      <td>0.037567</td>\n",
       "      <td>0.961078</td>\n",
       "      <td>0.975684</td>\n",
       "      <td>0.968326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.979927</td>\n",
       "      <td>0.020073</td>\n",
       "      <td>0.983498</td>\n",
       "      <td>0.980263</td>\n",
       "      <td>0.981878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.971317</td>\n",
       "      <td>0.028683</td>\n",
       "      <td>0.971042</td>\n",
       "      <td>0.986275</td>\n",
       "      <td>0.978599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.983586</td>\n",
       "      <td>0.016414</td>\n",
       "      <td>0.989562</td>\n",
       "      <td>0.983402</td>\n",
       "      <td>0.986472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.980597</td>\n",
       "      <td>0.019403</td>\n",
       "      <td>0.996894</td>\n",
       "      <td>0.963964</td>\n",
       "      <td>0.980153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.964883</td>\n",
       "      <td>0.035117</td>\n",
       "      <td>0.968531</td>\n",
       "      <td>0.958478</td>\n",
       "      <td>0.963478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.970383</td>\n",
       "      <td>0.029617</td>\n",
       "      <td>0.978261</td>\n",
       "      <td>0.960854</td>\n",
       "      <td>0.969479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.987500</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.996970</td>\n",
       "      <td>0.976261</td>\n",
       "      <td>0.986507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.986823</td>\n",
       "      <td>0.013177</td>\n",
       "      <td>0.996960</td>\n",
       "      <td>0.976190</td>\n",
       "      <td>0.986466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.979914</td>\n",
       "      <td>0.020086</td>\n",
       "      <td>0.982196</td>\n",
       "      <td>0.976401</td>\n",
       "      <td>0.979290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.976888</td>\n",
       "      <td>0.023112</td>\n",
       "      <td>0.974684</td>\n",
       "      <td>0.977778</td>\n",
       "      <td>0.976228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.965458</td>\n",
       "      <td>0.034542</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.940299</td>\n",
       "      <td>0.961832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.969980</td>\n",
       "      <td>0.030020</td>\n",
       "      <td>0.975725</td>\n",
       "      <td>0.969653</td>\n",
       "      <td>0.972490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Accuracy Rate  Error Rate  Precision    Recall  F1-Score\n",
       "0     2002       0.971963    0.028037   1.000000  0.971429  0.985507\n",
       "1     2003       0.927928    0.072072   0.916031  0.991736  0.952381\n",
       "2     2004       0.968142    0.031858   0.974453  0.960432  0.967391\n",
       "3     2005       0.966732    0.033268   0.977778  0.960000  0.968807\n",
       "4     2006       0.955000    0.045000   0.932836  0.965251  0.948767\n",
       "5     2007       0.960177    0.039823   0.977901  0.949062  0.963265\n",
       "6     2008       0.962433    0.037567   0.961078  0.975684  0.968326\n",
       "7     2009       0.979927    0.020073   0.983498  0.980263  0.981878\n",
       "8     2010       0.971317    0.028683   0.971042  0.986275  0.978599\n",
       "9     2011       0.983586    0.016414   0.989562  0.983402  0.986472\n",
       "10    2012       0.980597    0.019403   0.996894  0.963964  0.980153\n",
       "11    2013       0.964883    0.035117   0.968531  0.958478  0.963478\n",
       "12    2014       0.970383    0.029617   0.978261  0.960854  0.969479\n",
       "13    2015       0.987500    0.012500   0.996970  0.976261  0.986507\n",
       "14    2016       0.986823    0.013177   0.996960  0.976190  0.986466\n",
       "15    2017       0.979914    0.020086   0.982196  0.976401  0.979290\n",
       "16    2018       0.976888    0.023112   0.974684  0.977778  0.976228\n",
       "17    2019       0.965458    0.034542   0.984375  0.940299  0.961832\n",
       "mean  2010       0.969980    0.030020   0.975725  0.969653  0.972490"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"\\033[1m\\n Gradient Boosting Metrics by Test Year\")\n",
    "Gradient_Boosting_results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28411.854374170303"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#duration in secs for the process to finish\n",
    "end = time.time()\n",
    "end - start"
   ]
  }
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