{ "cells": [ { "cell_type": "code", "execution_count": 262, "metadata": { "tags": [] }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "from pandas import DataFrame, Series\n", "import numpy as np\n", "import math\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import matplotlib.colors as colors\n", "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n", "from matplotlib.colors import LinearSegmentedColormap\n", "from scipy import stats\n", "import scikit_posthocs as sp\n", "import sys\n", "\n", "from mpl_toolkits.mplot3d import axes3d" ] }, { "cell_type": "code", "execution_count": 263, "metadata": { "tags": [] }, "outputs": [], "source": [ "AllName=\"dataG.pkl\"\n", "ResizesName=\"dataM.pkl\"\n", "NameExtension=\".pkl\"\n", "n_cores=20\n", "repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n", "\n", "significance_value = 0.05\n", "processes = [2,20,40,80,160]\n", "\n", "positions = [321, 322, 323, 324, 325]\n", "positions_small = [221, 222, 223, 224]\n", "\n", "labels = ['(1,10)', '(1,20)', '(1,40)', '(1,80)', '(1,120)','(1,160)',\n", " '(10,1)', '(10,20)', '(10,40)', '(10,80)', '(10,120)','(10,160)',\n", " '(20,1)', '(20,10)', '(20,40)', '(20,80)', '(20,120)','(20,160)',\n", " '(40,1)', '(40,10)', '(40,20)', '(40,80)', '(40,120)','(40,160)',\n", " '(80,1)', '(80,10)', '(80,20)', '(80,40)', '(80,120)','(80,160)',\n", " '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)','(120,160)',\n", " '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n", "\n", "labelsExpand = ['(1,10)', '(1,20)', '(1,40)', '(1,80)', '(1,120)','(1,160)',\n", " '(10,20)', '(10,40)', '(10,80)', '(10,120)','(10,160)',\n", " '(20,40)', '(20,80)', '(20,120)','(20,160)',\n", " '(40,80)', '(40,120)','(40,160)',\n", " '(80,120)','(80,160)',\n", " '(120,160)']\n", "labelsShrink = ['(10,1)', \n", " '(20,1)', '(20,10)',\n", " '(40,1)', '(40,10)', '(40,20)',\n", " '(80,1)', '(80,10)', '(80,20)', '(80,40)',\n", " '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)',\n", " '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n", "\n", "# WORST BEST\n", "labels_dist = ['null', 'SpreadFit', 'CompactFit']\n", " #0 #1 #2 #3\n", "labelsMethods = ['Baseline', 'Baseline single','Baseline - Asynchronous','Baseline single - Asynchronous',\n", " 'Merge','Merge single','Merge - Asynchronous','Merge single - Asynchronous']\n", " #4 #5 #6 #7\n", " \n", "colors_m = ['green','darkgreen','red','darkred','mediumseagreen','seagreen','palegreen','springgreen','indianred','firebrick','darkgoldenrod','saddlebrown']\n", "linestyle_m = ['-', '--', '-.', ':']\n", "markers_m = ['.','v','s','p', 'h','d','X','P','^']\n", "\n", "OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n", "OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n", "OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Asyncrhonous')\n", "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n", "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n", "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n", "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n", "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')\n", "\n", "handles_spawn = [OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]" ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "tags": [] }, "outputs": [], "source": [ "dfG = pd.read_pickle( AllName )\n", "\n", "dfG['ADR'] = round((dfG['ADR'] / dfG['DR']) * 100,1)\n", "dfG['SDR'] = round((dfG['SDR'] / dfG['DR']) * 100,1)\n", " \n", "out_group = dfG.groupby(['Groups', 'ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_total']\n", "group = dfG.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','Groups'])['T_total']\n", "\n", "grouped_aggG = group.agg(['median'])\n", "grouped_aggG.rename(columns={'median':'T_total'}, inplace=True) \n", "\n", "out_grouped_G = out_group.agg(['median'])\n", "out_grouped_G.rename(columns={'median':'T_total'}, inplace=True) " ] }, { "cell_type": "code", "execution_count": 265, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_711407/1991315790.py:9: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n", " out_group = dfM.groupby(['NP','NC','ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_Malleability','T_Redistribution','T_spawn','T_SR','T_AR']\n", "/tmp/ipykernel_711407/1991315790.py:10: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n", " group = dfM.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_Malleability','T_Redistribution','T_spawn','T_SR','T_AR']\n" ] } ], "source": [ "dfM = pd.read_pickle( ResizesName )\n", "\n", "dfM['ADR'] = round((dfM['ADR'] / dfM['DR']) * 100,1)\n", "dfM['SDR'] = round((dfM['SDR'] / dfM['DR']) * 100,1)\n", "dfM['T_Redistribution'] = dfM['T_SR'] + dfM['T_AR']\n", "dfM.loc[dfM['T_Malleability']==0,'T_Malleability'] = dfM['T_spawn'] + dfM['T_Redistribution']\n", "#dfM['T_Malleability'] = dfM['T_spawn'] + dfM['T_Redistribution'] #TODO Borrar esta linea\n", " \n", "out_group = dfM.groupby(['NP','NC','ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_Malleability','T_Redistribution','T_spawn','T_SR','T_AR']\n", "group = dfM.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_Malleability','T_Redistribution','T_spawn','T_SR','T_AR']\n", "\n", "grouped_aggM = group.agg(['median'])\n", "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)\n", "\n", "out_grouped_M = out_group.agg(['median'])\n", "out_grouped_M.columns = out_grouped_M.columns.get_level_values(0)" ] }, { "cell_type": "code", "execution_count": 266, "metadata": { "tags": [] }, "outputs": [], "source": [ "from bt_scheme import PartialSolution, BacktrackingSolver\n", "def elegirConf(parameters):\n", " class StatePS(PartialSolution):\n", " def __init__(self, config):\n", " self.config= config\n", " self.n= len(config) #Indica el valor a añadir\n", "\n", " def is_solution(self):\n", " return self.n == len(parameters)\n", "\n", " def get_solution(self):\n", " return tuple(self.config)\n", "\n", " def successors(self):\n", " array = parameters[self.n]\n", " for parameter_value in array: #Test all values of the next parameter\n", " self.config.append(parameter_value)\n", " yield StatePS(self.config)\n", " self.config.pop()\n", "\n", " initialPs= StatePS([])\n", " return BacktrackingSolver().solve(initialPs)\n", "\n", "\n", "def obtenerConfs(parameters):\n", " soluciones=[]\n", " for solucion in elegirConf(parameters):\n", " soluciones.append(solucion)\n", " return soluciones\n", "\n", "def modifyToGlobal(parameters, len_parameters, configuration):\n", " usable_configuration = []\n", " for i in range(len(parameters)):\n", " if len_parameters[i] > 1:\n", " aux = (parameters[i][0], configuration[i])\n", " else:\n", " aux = (configuration[i])\n", " usable_configuration.append(aux)\n", " \n", " return usable_configuration\n", "\n", "def modifyToLocalDynamic(parameters, len_parameters, configuration):\n", " usable_configuration = []\n", " for i in range(len(parameters)):\n", " if len_parameters[i] > 1:\n", " aux = (configuration[i], -1)\n", " else:\n", " aux = (-1)\n", " usable_configuration.append(aux)\n", " \n", " return tuple(usable_configuration)\n", "\n", "def CheckConfExists(configuration, dataSet, type_conf='global'):\n", " exists = False\n", " config = list(configuration)\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " \n", " if type_conf == 'global':\n", " config.append((np_aux, ns_aux))\n", " elif type_conf == 'malleability':\n", " config.append(np_aux)\n", " config.append(ns_aux)\n", " elif type_conf == 'local':\n", " config.append(np_aux)\n", " \n", " if tuple(config) in dataSet.index: \n", " exists = True # FIXME Return here true?\n", " config.pop()\n", " \n", " if type_conf == 'malleability':\n", " config.pop()\n", " return exists" ] }, { "cell_type": "code", "execution_count": 267, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0, 1, 0, 0], [99.8, 1, 0, 0]]\n", "[[0, (0, 1), (0, 0), (0, 0)], [99.8, (0, 1), (0, 0), (0, 0)]]\n", "2\n" ] } ], "source": [ "adr = [0,99.8]\n", "sp_method = [0,1]\n", "rd_method = [0,1]\n", "rd_strat = [0,1]\n", "parameters = [adr, sp_method, rd_method, rd_strat]\n", "parameters_names = ['ADR', 'Spawn_Method', 'Redistribution_Method', 'Redistribution_Strategy']\n", "len_parameters = [1,2,2,2]\n", "configurations_aux = obtenerConfs(parameters)\n", "configurations = []\n", "configurations_simple = []\n", "for checked_conf in configurations_aux:\n", " aux_conf = modifyToGlobal(parameters, len_parameters, checked_conf)\n", " if CheckConfExists(aux_conf, grouped_aggG):\n", " configurations.append(aux_conf)\n", "\n", " if CheckConfExists(checked_conf, grouped_aggM, 'malleability'):\n", " configurations_simple.append(list(checked_conf))\n", "\n", "print(configurations_simple)\n", "print(configurations)\n", "print(len(configurations))" ] }, { "cell_type": "code", "execution_count": 272, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ALPHA already exists\n" ] } ], "source": [ "#ALPHA COMPUTATION\n", "def compute_alpha(config_a, config_b):\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " config_a.append(np_aux)\n", " config_a.append(ns_aux)\n", " config_b.append(np_aux)\n", " config_b.append(ns_aux)\n", " grouped_aggM.loc[tuple(config_b),'Alpha'] = grouped_aggM.loc[tuple(config_b),'T_Malleability'] / grouped_aggM.loc[tuple(config_a),'T_Malleability']\n", " #grouped_aggM.loc[tuple(config_b),'Alpha'] = grouped_aggM.loc[tuple(config_b),'T_Redistribution'] / grouped_aggM.loc[tuple(config_a),'T_Redistribution']\n", " config_a.pop()\n", " config_a.pop()\n", " config_b.pop()\n", " config_b.pop()\n", " \n", " \n", " config_a.insert(0,ns_aux)\n", " config_a.insert(0,np_aux)\n", " config_b.insert(0,ns_aux)\n", " config_b.insert(0,np_aux)\n", " out_grouped_M.loc[tuple(config_b),'Alpha'] = out_grouped_M.loc[tuple(config_b),'T_Malleability'] / out_grouped_M.loc[tuple(config_a),'T_Malleability']\n", " #out_grouped_M.loc[tuple(config_b),'Alpha'] = out_grouped_M.loc[tuple(config_b),'T_Redistribution'] / out_grouped_M.loc[tuple(config_a),'T_Redistribution']\n", " \n", " config_a.pop(0)\n", " config_a.pop(0)\n", " config_b.pop(0)\n", " config_b.pop(0)\n", "\n", "if not ('Alpha' in grouped_aggM.columns):\n", " for config_a in configurations_simple:\n", " for config_b in configurations_simple:\n", " if config_a[1:-1] == config_b[1:-1] and config_a[0] == 0 and config_b[0] != 0:\n", " compute_alpha(config_a, config_b)\n", "else:\n", " print(\"ALPHA already exists\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Las dos " ] }, { "cell_type": "code", "execution_count": 269, "metadata": { "tags": [] }, "outputs": [], "source": [ "#Malleability Coherence COMPUTATION\n", "test=dfM[(dfM.ADR > 0)]\n", "coherence_boundary = 1.01 # Allows a 1% error\n", "# Coherence of inner times\n", "aux_df = test[\"T_Malleability\"] * coherence_boundary < (test[\"T_spawn\"] + test[\"T_Redistribution\"])\n", "if not test[aux_df].empty:\n", " print(\"Coherence fails for inner times of malleability in \" + str(len(test[aux_df])) + \" cases\")\n", " display(test[aux_df])" ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "tags": [] }, "outputs": [], "source": [ "out_grouped_G.to_excel(\"resultG.xlsx\") \n", "#out_grouped_M.to_excel(\"resultM.xlsx\")" ] }, { "cell_type": "code", "execution_count": 273, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
T_MalleabilityT_RedistributionT_spawnT_SRT_ARAlpha
ADRSpawn_MethodRedistribution_MethodRedistribution_StrategyNPNC
0.01002201.3868150.0312030.6467060.0312030.000000NaN
401.3525180.0340940.6376260.0340940.000000NaN
801.5523630.0363930.7914900.0363930.000000NaN
1601.5453320.0368720.8743270.0368720.000000NaN
2021.1870190.0618560.0001090.0618560.000000NaN
401.2008330.3256290.7034150.3256290.000000NaN
801.2781440.1614470.7750600.1614470.000000NaN
1601.4463890.0518480.9237990.0518480.000000NaN
4023.0178691.5489730.0018131.5489730.000000NaN
200.7442470.4549770.0030830.4549770.000000NaN
801.3507730.2758890.9223040.2758890.000000NaN
1601.2331660.1179390.8754460.1179390.000000NaN
8022.5521380.7034790.0022190.7034790.000000NaN
200.8410910.4786620.0015140.4786620.000000NaN
400.5537220.3543070.0015800.3543070.000000NaN
1601.0737130.1240820.8084620.1240820.000000NaN
16022.6210920.3598600.0021110.3598600.000000NaN
200.9064100.1692820.0017900.1692820.000000NaN
400.5504380.2586250.0016350.2586250.000000NaN
800.3741570.2842540.0028230.2842540.000000NaN
99.81002204.2661671.4517580.6677360.0161361.4342923.076234
404.1375841.4568060.5997610.0183151.4365123.059171
804.4086371.5015670.8837830.0172991.4844302.839952
1607.2021434.3374071.0696660.0209724.3184924.660580
2021.6153900.2518080.0001210.0279360.2238721.360880
401.5802830.2186220.6838960.0081700.2098651.315989
801.6844430.2239380.8382150.0088360.2151791.317882
1601.7848950.2262460.8752250.0104750.2160701.234035
4023.4279250.3265450.0017330.0603130.2643841.135876
200.9052000.1912180.0070470.0123860.1819051.216263
801.4810950.2052170.7695450.0063520.1989781.096480
1601.5926550.1917860.9187610.0067240.1847501.291517
8022.9431960.2958630.0018310.0575370.2318761.153228
201.0312600.1798970.0016710.0155070.1657271.226098
400.7770160.1794970.0037950.0097220.1681961.403260
1601.4902900.2607140.8877160.0018640.2561011.387978
16022.9959850.3071020.0017290.0590270.2431021.143029
201.3044140.2122350.0036420.0218760.1675251.439099
400.8511720.2002430.0017750.0114220.1856431.546354
800.7302410.2216390.0100930.0014860.2202501.951697
\n", "
" ], "text/plain": [ " T_Malleability \\\n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 1.386815 \n", " 40 1.352518 \n", " 80 1.552363 \n", " 160 1.545332 \n", " 20 2 1.187019 \n", " 40 1.200833 \n", " 80 1.278144 \n", " 160 1.446389 \n", " 40 2 3.017869 \n", " 20 0.744247 \n", " 80 1.350773 \n", " 160 1.233166 \n", " 80 2 2.552138 \n", " 20 0.841091 \n", " 40 0.553722 \n", " 160 1.073713 \n", " 160 2 2.621092 \n", " 20 0.906410 \n", " 40 0.550438 \n", " 80 0.374157 \n", "99.8 1 0 0 2 20 4.266167 \n", " 40 4.137584 \n", " 80 4.408637 \n", " 160 7.202143 \n", " 20 2 1.615390 \n", " 40 1.580283 \n", " 80 1.684443 \n", " 160 1.784895 \n", " 40 2 3.427925 \n", " 20 0.905200 \n", " 80 1.481095 \n", " 160 1.592655 \n", " 80 2 2.943196 \n", " 20 1.031260 \n", " 40 0.777016 \n", " 160 1.490290 \n", " 160 2 2.995985 \n", " 20 1.304414 \n", " 40 0.851172 \n", " 80 0.730241 \n", "\n", " T_Redistribution \\\n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 0.031203 \n", " 40 0.034094 \n", " 80 0.036393 \n", " 160 0.036872 \n", " 20 2 0.061856 \n", " 40 0.325629 \n", " 80 0.161447 \n", " 160 0.051848 \n", " 40 2 1.548973 \n", " 20 0.454977 \n", " 80 0.275889 \n", " 160 0.117939 \n", " 80 2 0.703479 \n", " 20 0.478662 \n", " 40 0.354307 \n", " 160 0.124082 \n", " 160 2 0.359860 \n", " 20 0.169282 \n", " 40 0.258625 \n", " 80 0.284254 \n", "99.8 1 0 0 2 20 1.451758 \n", " 40 1.456806 \n", " 80 1.501567 \n", " 160 4.337407 \n", " 20 2 0.251808 \n", " 40 0.218622 \n", " 80 0.223938 \n", " 160 0.226246 \n", " 40 2 0.326545 \n", " 20 0.191218 \n", " 80 0.205217 \n", " 160 0.191786 \n", " 80 2 0.295863 \n", " 20 0.179897 \n", " 40 0.179497 \n", " 160 0.260714 \n", " 160 2 0.307102 \n", " 20 0.212235 \n", " 40 0.200243 \n", " 80 0.221639 \n", "\n", " T_spawn \\\n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 0.646706 \n", " 40 0.637626 \n", " 80 0.791490 \n", " 160 0.874327 \n", " 20 2 0.000109 \n", " 40 0.703415 \n", " 80 0.775060 \n", " 160 0.923799 \n", " 40 2 0.001813 \n", " 20 0.003083 \n", " 80 0.922304 \n", " 160 0.875446 \n", " 80 2 0.002219 \n", " 20 0.001514 \n", " 40 0.001580 \n", " 160 0.808462 \n", " 160 2 0.002111 \n", " 20 0.001790 \n", " 40 0.001635 \n", " 80 0.002823 \n", "99.8 1 0 0 2 20 0.667736 \n", " 40 0.599761 \n", " 80 0.883783 \n", " 160 1.069666 \n", " 20 2 0.000121 \n", " 40 0.683896 \n", " 80 0.838215 \n", " 160 0.875225 \n", " 40 2 0.001733 \n", " 20 0.007047 \n", " 80 0.769545 \n", " 160 0.918761 \n", " 80 2 0.001831 \n", " 20 0.001671 \n", " 40 0.003795 \n", " 160 0.887716 \n", " 160 2 0.001729 \n", " 20 0.003642 \n", " 40 0.001775 \n", " 80 0.010093 \n", "\n", " T_SR \\\n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 0.031203 \n", " 40 0.034094 \n", " 80 0.036393 \n", " 160 0.036872 \n", " 20 2 0.061856 \n", " 40 0.325629 \n", " 80 0.161447 \n", " 160 0.051848 \n", " 40 2 1.548973 \n", " 20 0.454977 \n", " 80 0.275889 \n", " 160 0.117939 \n", " 80 2 0.703479 \n", " 20 0.478662 \n", " 40 0.354307 \n", " 160 0.124082 \n", " 160 2 0.359860 \n", " 20 0.169282 \n", " 40 0.258625 \n", " 80 0.284254 \n", "99.8 1 0 0 2 20 0.016136 \n", " 40 0.018315 \n", " 80 0.017299 \n", " 160 0.020972 \n", " 20 2 0.027936 \n", " 40 0.008170 \n", " 80 0.008836 \n", " 160 0.010475 \n", " 40 2 0.060313 \n", " 20 0.012386 \n", " 80 0.006352 \n", " 160 0.006724 \n", " 80 2 0.057537 \n", " 20 0.015507 \n", " 40 0.009722 \n", " 160 0.001864 \n", " 160 2 0.059027 \n", " 20 0.021876 \n", " 40 0.011422 \n", " 80 0.001486 \n", "\n", " T_AR \\\n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 0.000000 \n", " 40 0.000000 \n", " 80 0.000000 \n", " 160 0.000000 \n", " 20 2 0.000000 \n", " 40 0.000000 \n", " 80 0.000000 \n", " 160 0.000000 \n", " 40 2 0.000000 \n", " 20 0.000000 \n", " 80 0.000000 \n", " 160 0.000000 \n", " 80 2 0.000000 \n", " 20 0.000000 \n", " 40 0.000000 \n", " 160 0.000000 \n", " 160 2 0.000000 \n", " 20 0.000000 \n", " 40 0.000000 \n", " 80 0.000000 \n", "99.8 1 0 0 2 20 1.434292 \n", " 40 1.436512 \n", " 80 1.484430 \n", " 160 4.318492 \n", " 20 2 0.223872 \n", " 40 0.209865 \n", " 80 0.215179 \n", " 160 0.216070 \n", " 40 2 0.264384 \n", " 20 0.181905 \n", " 80 0.198978 \n", " 160 0.184750 \n", " 80 2 0.231876 \n", " 20 0.165727 \n", " 40 0.168196 \n", " 160 0.256101 \n", " 160 2 0.243102 \n", " 20 0.167525 \n", " 40 0.185643 \n", " 80 0.220250 \n", "\n", " Alpha \n", "ADR Spawn_Method Redistribution_Method Redistribution_Strategy NP NC \n", "0.0 1 0 0 2 20 NaN \n", " 40 NaN \n", " 80 NaN \n", " 160 NaN \n", " 20 2 NaN \n", " 40 NaN \n", " 80 NaN \n", " 160 NaN \n", " 40 2 NaN \n", " 20 NaN \n", " 80 NaN \n", " 160 NaN \n", " 80 2 NaN \n", " 20 NaN \n", " 40 NaN \n", " 160 NaN \n", " 160 2 NaN \n", " 20 NaN \n", " 40 NaN \n", " 80 NaN \n", "99.8 1 0 0 2 20 3.076234 \n", " 40 3.059171 \n", " 80 2.839952 \n", " 160 4.660580 \n", " 20 2 1.360880 \n", " 40 1.315989 \n", " 80 1.317882 \n", " 160 1.234035 \n", " 40 2 1.135876 \n", " 20 1.216263 \n", " 80 1.096480 \n", " 160 1.291517 \n", " 80 2 1.153228 \n", " 20 1.226098 \n", " 40 1.403260 \n", " 160 1.387978 \n", " 160 2 1.143029 \n", " 20 1.439099 \n", " 40 1.546354 \n", " 80 1.951697 " ] }, "execution_count": 273, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_aggM" ] }, { "cell_type": "code", "execution_count": 274, "metadata": { "tags": [] }, "outputs": [], "source": [ "def create_group_boundary(rms_boundary, np_aux, ns_aux):\n", " tc_boundary = 0\n", " boundaries = None\n", " if rms_boundary != 0:\n", " # El porcentaje de tc_boundary se tiene en cuenta para eliminar aquellos\n", " # tiempos demasiado grandes en su malleability time respecto al más pequeño\n", " boundaries = get_np_ns_data(\"T_Malleability\", grouped_aggM, configurations_simple, np_aux, ns_aux)\n", " tc_boundary = min(boundaries)\n", " tc_boundary = tc_boundary + tc_boundary*rms_boundary\n", " return tc_boundary, boundaries\n", "\n", "# Aquellos grupos que tengán valores por encima del límite no se considerarán\n", "def check_groups_boundaries(dataLists, boundaries, tc_boundary):\n", " for index in range(len(boundaries)):\n", " if boundaries[index] > tc_boundary:\n", " dataLists[index] = float('infinity')\n" ] }, { "cell_type": "code", "execution_count": 275, "metadata": { "tags": [] }, "outputs": [], "source": [ "def get_perc_differences(dataLists, boundaries, tc_boundary):\n", " perc = 1.05\n", " if boundaries != None: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n", " check_groups_boundaries(dataLists, boundaries, tc_boundary) \n", " indexes = np.argsort(dataLists)\n", " \n", " best = -1\n", " bestMax = -1\n", " otherBest=[]\n", " for index in indexes: # Para cada metodo -- Empezando por el tiempo más bajo en media/mediana\n", " if best == -1:\n", " best = index\n", " bestMax = dataLists[best] * perc\n", " elif dataLists[index] <= bestMax: # Media/Medianas i < Media/Mediana best\n", " otherBest.append(index)\n", " \n", " otherBest.insert(0,best)\n", " return otherBest\n", "\n", "def get_stat_differences(dataLists, df_Res, boundaries, tc_boundary):\n", " if boundaries != None: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n", " check_groups_boundaries(dataLists, boundaries, tc_boundary) \n", " indexes = np.argsort(dataLists)\n", " \n", " best = -1\n", " otherBest=[] \n", " for index in indexes: # Para cada metodo -- Empezando por el tiempo más bajo en mediana\n", " if dataLists[index] != float('infinity'):\n", " if best == -1:\n", " best = index\n", " elif not df_Res.iat[best,index]: # df_Res == False indicates 'index' and 'best' have the same mean/median\n", " otherBest.append(index)\n", " \n", " otherBest.insert(0,best)\n", " return otherBest" ] }, { "cell_type": "code", "execution_count": 276, "metadata": { "tags": [] }, "outputs": [], "source": [ "grouped_np = [\"T_total\"]\n", "separated_np = [\"T_Malleability\", \"T_Redistribution\", \"T_spawn\", \"T_SR\", \"T_AR\", \"Alpha\"]\n", "\n", "def get_np_ns_data(tipo, data_aux, used_config, np_aux, ns_aux):\n", " dataLists=[]\n", " for config in used_config:\n", " if tipo in grouped_np:\n", " config.append((np_aux,ns_aux))\n", " elif tipo in separated_np:\n", " config.append(np_aux)\n", " config.append(ns_aux)\n", " \n", " if tuple(config) in data_aux.index:\n", " aux_value = data_aux.loc[tuple(config),tipo]\n", " if isinstance(aux_value, pd.Series):\n", " aux_value = aux_value.values[0]\n", " if aux_value == 0: #Values of zero indicate it was not performed\n", " aux_value = float('infinity')\n", " else: # This configuration is not present in the dataset\n", " aux_value = float('infinity')\n", " dataLists.append(aux_value)\n", " config.pop()\n", " if tipo in separated_np:\n", " config.pop()\n", " return dataLists\n", "\n", "def get_config_data(tipo, data_aux, config):\n", " dataLists=[]\n", " procsLists=[]\n", " for ns_aux in processes:\n", " for np_aux in processes:\n", " if np_aux != ns_aux:\n", " \n", " if tipo in grouped_np:\n", " config.append((np_aux,ns_aux))\n", " elif tipo in separated_np:\n", " config.append(np_aux)\n", " config.append(ns_aux)\n", " if tuple(config) in data_aux.index:\n", " procsLists.append((np_aux,ns_aux))\n", " aux_value = data_aux.loc[tuple(config),tipo]\n", " if isinstance(aux_value, pd.Series):\n", " aux_value = aux_value.values[0]\n", " if aux_value == 0: #Values of zero indicate it was not performed\n", " aux_value = float('infinity')\n", " else: # This configuration is not present in the dataset\n", " aux_value = float('infinity')\n", " dataLists.append(aux_value)\n", " config.pop()\n", " if tipo in separated_np:\n", " config.pop()\n", " return dataLists, procsLists\n", "\n", "def get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux):\n", " dataLists=[]\n", " if tipo in grouped_np:\n", " tuple_data = (np_aux, ns_aux)\n", " df_npns_aux = df_check.loc[(df_check['Groups']==tuple_data)]\n", " elif tipo in separated_np:\n", " df_npns_aux = df_check.loc[(df_check['NP']==np_aux)]\n", " df_npns_aux = df_npns_aux.loc[(df_npns_aux['NC']==ns_aux)]\n", " \n", " for config in used_config:\n", " df_config_aux = df_npns_aux\n", " for index in range(len(config)):\n", " aux_name = parameters_names[index]\n", " aux_value = config[index]\n", " df_config_aux = df_config_aux.loc[(df_config_aux[aux_name] == aux_value)]\n", " \n", " aux_value = list(df_config_aux[tipo])\n", " if len(aux_value) > 0:\n", " dataLists.append(aux_value)\n", " return dataLists\n", "\n", "def get_df_config_data(df_check, tipo, config):\n", " dataLists=[]\n", " df_config_aux = df_check\n", " for index in range(len(config)):\n", " aux_name = parameters_names[index]\n", " aux_value = config[index]\n", " df_config_aux = df_config_aux.loc[(df_config_aux[aux_name] == aux_value)]\n", " \n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " if tipo in grouped_np:\n", " tuple_data = (np_aux, ns_aux)\n", " df_aux = df_config_aux.loc[(df_config_aux['Groups']==tuple_data)]\n", " elif tipo in separated_np:\n", " df_aux = df_config_aux.loc[(df_config_aux['NP']==np_aux)]\n", " df_aux = df_aux.loc[(df_aux['NC']==ns_aux)]\n", " aux_value = list(df_aux[tipo])\n", " if len(aux_value) > 0:\n", " dataLists.append(aux_value)\n", " return dataLists\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 277, "metadata": { "tags": [] }, "outputs": [], "source": [ "def check_normality(df_check, tipo, used_config, fast=True):\n", " normality_array=[True] * (len(processes) * (len(processes)-1) * len(used_config))\n", " normality = True\n", " total=0\n", " i=-1\n", " #Comprobar para cada configuración si se sigue una distribución normal/gaussiana\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " i+=1\n", " dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n", " for data_aux in dataLists:\n", " st,p = stats.shapiro(data_aux) # Tendrían que ser al menos 20 datos y menos de 50\n", " if p < significance_value: # Reject H0\n", " if fast:\n", " return False\n", " normality_array[i] = False\n", " normality = False\n", " total+=1\n", " print(\"Se sigue una distribución guassiana: \" + str(normality) + \"\\nUn total de: \" + str(total) + \" no siguen una gaussiana\")\n", " print(normality_array)\n", " return normality\n", "\n", "def check_homoscedasticity(df_check, tipo, used_config, fast=True):\n", " homoscedasticity_array=[True] * (len(processes) * (len(processes)-1))\n", " homoscedasticity = True\n", " total=0\n", " i=-1\n", " #Comprobar para cada configuración es homoestatica\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " i+=1\n", " dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n", " st,p = stats.levene(*dataLists) # Tendrían que ser al menos 20 datos y menos de 50\n", " if p < significance_value: # Reject H0\n", " if fast:\n", " return False\n", " homoscedasticity_array[i] = False\n", " homoscedasticity = False\n", " total+=1\n", " print(\"Se sigue una distribución de datos Homocedastica: \" + str(homoscedasticity) + \"\\nUn total de: \" + str(total) + \" no siguen una homocedastica\")\n", " print(homoscedasticity_array)\n", " return homoscedasticity\n", "\n", "def compute_global_stat_difference(dataLists, parametric, np_aux, ns_aux):\n", " if parametric:\n", " st,p=stats.f_oneway(*dataLists)\n", " else:\n", " st,p=stats.kruskal(*dataLists)\n", " if p > significance_value:\n", " print(\"For NP \" + str(np_aux) + \" and \" + str(ns_aux) + \" is accepted H0\")\n", " return True # Equal values || Accept H0\n", " return False # Some groups are different || Reject H0\n", "\n", "def compute_global_posthoc(dataLists, parametric): #TODO Comprobar CDF de los grupos\n", " data_stats=[]\n", " data_stats2=[]\n", " ini=0\n", " end=len(dataLists)\n", " if parametric:\n", " df_aux = sp.posthoc_ttest(dataLists)\n", " df_Res = df_aux.copy()\n", " for i in range(ini,end):\n", " data_stats.append(np.mean(dataLists[i]))\n", " \n", " for j in range(ini,end):\n", " if df_Res.iat[i,j] < significance_value: # Different means || Reject H0\n", " df_Res.iat[i, j] = True\n", " else:\n", " df_Res.iat[i, j] = False\n", " else:\n", " df_aux = sp.posthoc_conover(dataLists)\n", " df_Res = df_aux.copy()\n", " for i in range(ini,end):\n", " data_stats.append(np.median(dataLists[i]))\n", " #data_stats2.append(stats.iqr(dataLists[i],axis=0))\n", " for j in range(ini,end):\n", " if df_Res.iat[i,j] < significance_value: # Different medians || Reject H0\n", " df_Res.iat[i, j] = True # Not equal medians\n", " else:\n", " df_Res.iat[i, j] = False # Equal medians\n", " #print(df_Res)\n", " #print(df_aux)\n", " #print(data_stats)\n", " #print(data_stats2)\n", " #aux_value = min(data_stats)\n", " #print(data_stats.index(aux_value))\n", " return df_Res, data_stats" ] }, { "cell_type": "code", "execution_count": 278, "metadata": { "tags": [] }, "outputs": [], "source": [ "def results_with_perc(tipo, data_aux, used_config, rms_boundary=0):\n", " results = []\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " tc_boundary, boundaries = create_group_boundary(rms_boundary, np_aux, ns_aux)\n", " \n", " #Get all values for particular config with these number of processes\n", " dataLists = get_np_ns_data(tipo, data_aux, used_config, np_aux, ns_aux)\n", "\n", " aux_data = get_perc_differences(dataLists, boundaries, tc_boundary)\n", " results.append(aux_data)\n", " return results\n", "\n", "def results_with_stats(tipo, df_check, used_config, rms_boundary=0):\n", " results = []\n", " use_parametric = check_normality(df_check, tipo, used_config)\n", " if use_parametric:\n", " use_parametric = check_homoscedasticity(df_check, tipo, used_config)\n", " print(\"Se usan tests parametricos: \"+str(use_parametric))\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux:\n", " tc_boundary, boundaries = create_group_boundary(rms_boundary, np_aux, ns_aux)\n", " \n", " #Get all values for particular config with these number of processes\n", " dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n", " equal_set = compute_global_stat_difference(dataLists, use_parametric, np_aux, ns_aux)\n", " if equal_set:\n", " aux_data = list(range(len(used_config))) # All data is equal\n", " else:\n", " res_aux, times_aux = compute_global_posthoc(dataLists, use_parametric)\n", " aux_data = get_stat_differences(times_aux, res_aux, boundaries, tc_boundary)\n", " \n", " results.append(aux_data)\n", " \n", " return results" ] }, { "cell_type": "code", "execution_count": 281, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[0, 1], [1, 0], [0, 1], [0, 1], [0, 1], [1, 0], [0, 1], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [1, 0], [1, 0], [1, 0], [0, 1], [1, 0], [1, 0], [0, 1], [1, 0]]\n", "20\n" ] } ], "source": [ "checked_type='T_total'\n", "use_perc = True\n", "select_first_winner = False\n", "prefer_first_winner = False\n", "rms_boundary=0 # Poner a 0 para perspectiva de app. Valor >0 y <1 para perspectiva de RMS\n", "if checked_type=='T_total':\n", " tipo=\"T_total\"\n", " if use_perc:\n", " data_aux = grouped_aggG\n", " else:\n", " data_aux = dfG\n", " used_config = configurations\n", "elif checked_type=='T_Malleability' or checked_type=='T_spawn' or checked_type=='T_SR' or checked_type=='T_AR' or checked_type=='T_Redistribution':\n", " tipo=checked_type\n", " \n", " if use_perc:\n", " data_aux = grouped_aggM\n", " else:\n", " data_aux = dfM\n", " if tipo == 'T_AR':\n", " data_aux = data_aux[(data_aux.ADR > 0)]\n", " elif tipo == 'T_SR':\n", " data_aux = data_aux[(data_aux.ADR == 0)]\n", " \n", " used_config = configurations_simple\n", " \n", "if use_perc:\n", " results = results_with_perc(tipo, data_aux, used_config, rms_boundary)\n", "else:\n", " results = results_with_stats(tipo, data_aux, used_config, rms_boundary)\n", " \n", "if not use_perc and tipo == 'T_AR': #FIXME!!!! No tiene en cuenta total de configuraciones sincronos\n", " for res_index in range(len(results)):\n", " for inner_index in range(len(results[res_index])):\n", " results[res_index][inner_index]+=4\n", "\n", "#Results is a 2 dimensional array. First dimension indicates lists of winners of a particulal number of processes (NP->NC). \n", "#Second dimension is an ordered preference of indexes in the array configurations.\n", "print(results)\n", "print(len(results))" ] }, { "cell_type": "code", "execution_count": 282, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0 1]\n", "[20 20]\n", "[[-1 0 0 0 0]\n", " [ 0 -1 0 0 0]\n", " [ 0 0 -1 0 0]\n", " [ 0 0 0 -1 0]\n", " [ 0 0 0 0 2]]\n" ] } ], "source": [ "#Lista de indices de mayor a menor según el total de ocurrencias\n", "aux_array = []\n", "for data in results:\n", " aux_array+=data\n", "aux_keys, aux_counts = np.unique(aux_array, return_counts=True)\n", "aux_ordered_index=list(reversed(np.argsort(aux_counts)))\n", "\n", "#Lista de indices de mayor a menor según el total de ocurrencias del primero de cada grupo\n", "aux_array = [0] * len(results)\n", "for index in range(len(results)):\n", " aux_array[index] = results[index][0]\n", "aux_keys_best, aux_counts_best = np.unique(aux_array, return_counts = True)\n", "aux_ordered_best_index=list(reversed(np.argsort(aux_counts_best)))\n", "\n", "def heatmap_get_best(index, ordered_array, keys_array, counts_array, prefer_winner=False):\n", " valid_candidates_indexes = []\n", " prev_counts = -1\n", " for tested_index in ordered_array:\n", " if keys_array[tested_index] in results[index]:\n", " if counts_array[tested_index] >= prev_counts:\n", " prev_counts = counts_array[tested_index]\n", " valid_candidates_indexes.append(tested_index)\n", " else:\n", " break\n", " \n", " if prefer_winner: # Si esta activo, en caso de empate en ocurrencias se selecciona el menor tiempo\n", " for tested_index in results[index]:\n", " if tested_index in valid_candidates_indexes:\n", " return tested_index\n", " return min(valid_candidates_indexes) # En caso de empate se devuelve el que tiene menor valor (Suele ser la config más simple)\n", "\n", "i=0\n", "j=0\n", "used_aux=0\n", "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n", "\n", "if select_first_winner:\n", " for i in range(len(processes)):\n", " for j in range(len(processes)):\n", " if i==j:\n", " heatmap[i][j]=-1\n", " used_aux+=1\n", " else:\n", " results_index = i*len(processes) + j - used_aux\n", " heatmap[i][j] = results[results_index][0]\n", "else:\n", " for i in range(len(processes)):\n", " for j in range(len(processes)):\n", " if i==j:\n", " heatmap[i][j]=-1\n", " used_aux+=1\n", " else:\n", " results_index = i*len(processes) + j - used_aux\n", " index = heatmap_get_best(results_index, aux_ordered_index, aux_keys, aux_counts, prefer_first_winner)\n", " heatmap[i][j]=aux_keys[index]\n", " #index = heatmap_get_best(results_index, aux_ordered_best_index, aux_keys_best, aux_counts_best, prefer_first_winner)\n", " #heatmap[i][j]=aux_keys_best[index]\n", "heatmap[-1][-1]=len(used_config)\n", "print(aux_keys)\n", "print(aux_counts)\n", "print(heatmap)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "tags": [] }, "outputs": [], "source": [ "#Adapta results a una cadena asegurando que cada cadena no se sale de su celda\n", "def get_heatmap_multiple_strings(results): #FIXME Deprecated\n", " results_str = []\n", " max_counts = 1\n", " max_per_line = 3\n", " for i in range(len(results)):\n", " results_str.append(list())\n", " count = len(results[i])\n", " results_aux = results[i]\n", " if count > max_counts:\n", " count = max_counts\n", " results_aux = results[i][:count]\n", " \n", " remainder = count%max_per_line\n", " if count <= max_per_line:\n", " aux_str = str(results_aux).replace('[','').replace(']','')\n", " results_str[i].append(aux_str)\n", " else:\n", " if remainder == 0:\n", " index = count//2\n", " else:\n", " index = count - ((remainder-1)*max_per_line + 1)\n", " aux_str = str(results_aux[:index]).replace('[','').replace(']','')\n", " results_str[i].append(aux_str)\n", " aux_str = str(results_aux[index:]).replace('[','').replace(']','')\n", " results_str[i].append(aux_str)\n", " return results_str\n", "\n", "def get_heatmap_strings(heatmap):\n", " results_str = []\n", " for i in range(len(processes)):\n", " for j in range(len(processes)):\n", " if i!=j:\n", " results_str.append(list())\n", " results_str[-1].append(heatmap[i][j])\n", " return results_str" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_4495/84511707.py:65: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax.set_xticklabels(['']+processes, fontsize=36)\n", "/tmp/ipykernel_4495/84511707.py:66: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax.set_yticklabels(['']+processes, fontsize=36)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Filename: Heatmap_T_total.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Crea un heatmap teniendo en cuenta los colores anteriores\n", "f=plt.figure(figsize=(24, 12))\n", "ax=f.add_subplot(111)\n", "\n", "myColors = (colors.to_rgba(\"white\"), \n", " colors.to_rgba(\"green\"), #BCOLS\n", " colors.to_rgba(\"darkgreen\"), #BP2PS\n", " #colors.to_rgba(\"blue\"), #BRMA1S\n", " #colors.to_rgba(\"royalblue\"), #BRMA2S\n", " colors.to_rgba(\"red\"), #MCOLS\n", " colors.to_rgba(\"darkred\"), #MP2PS\n", " #colors.to_rgba(\"mediumblue\"), #MRMA1S\n", " #colors.to_rgba(\"mediumslateblue\"), #MRMA2S\n", " #colors.to_rgba(\"blue\"), #BIntraCOLS\n", " #colors.to_rgba(\"royalblue\"), #BIntraP2PS\n", " colors.to_rgba(\"mediumseagreen\"), #BCOLA\n", " colors.to_rgba(\"seagreen\"), #BCOLT\n", " colors.to_rgba(\"palegreen\"), #BP2PA\n", " colors.to_rgba(\"springgreen\"), #BP2PT\n", " #colors.to_rgba(\"purple\"), #BRMA1A\n", " #colors.to_rgba(\"darkviolet\"), #BRMA1T\n", " #colors.to_rgba(\"indigo\"), #BRMA2A\n", " #colors.to_rgba(\"rebeccapurple\"), #BRMA2T\n", " colors.to_rgba(\"indianred\"), #MCOLA \n", " colors.to_rgba(\"firebrick\"), #MCOLT\n", " colors.to_rgba(\"darkgoldenrod\"), #MP2PA\n", " colors.to_rgba(\"saddlebrown\"), #MP2PT\n", " #colors.to_rgba(\"magenta\"), #MRMA1A\n", " #colors.to_rgba(\"violet\"), #MRMA1T\n", " #colors.to_rgba(\"deeppink\"), #MRMA2A\n", " #colors.to_rgba(\"mediumvioletred\"), #MRMA2T\n", " #colors.to_rgba(\"mediumblue\"), #BIntraCOLA\n", " #colors.to_rgba(\"mediumslateblue\"), #BIntraP2PA\n", " colors.to_rgba(\"white\"))\n", "cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))\n", "\n", "im = ax.imshow(heatmap,cmap=cmap,interpolation='nearest')\n", "\n", "# Loop over data dimensions and create text annotations.\n", "used_aux=0\n", "results_str = get_heatmap_strings(heatmap)\n", "for i in range(len(processes)):\n", " for j in range(len(processes)):\n", " if i!=j:\n", " aux_color=\"white\"\n", " if 0 <= heatmap[i, j] <= 1 or 4 <= heatmap[i, j] <= 7: # El 1 puede necesitar texto en negro\n", " aux_color=\"black\"\n", " results_index = i*len(processes) +j-used_aux\n", " if len(results_str[results_index]) == 1:\n", " text = results_str[results_index][0]\n", " ax.text(j, i, text, ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n", " else:\n", " add_aux = 0.33\n", " for line in range(len(results_str[results_index])):\n", " i_range = i - 0.5 + add_aux\n", " ax.text(j, i_range, results_str[results_index][line],\n", " ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n", " add_aux+=0.33\n", " else:\n", " used_aux+=1\n", "\n", "ax.set_ylabel(\"NS\", fontsize=36)\n", "ax.set_xlabel(\"NT\", fontsize=36)\n", "\n", "ax.set_xticklabels(['']+processes, fontsize=36)\n", "ax.set_yticklabels(['']+processes, fontsize=36)\n", "\n", "\n", "labelsMethods_aux = ['Baseline - COLS (0)', 'Baseline - P2PS (1)',\n", " 'Merge - COLS (2)','Merge - P2PS (3)',\n", " 'Baseline - COLA (4)', 'Baseline - COLT (5)','Baseline - P2PA (6)','Baseline - P2PT (7)',\n", " 'Merge - COLA (8)','Merge - COLT (9)','Merge - P2PA (10)','Merge - P2PT (11)']\n", "\n", "#labelsMethods_aux = ['Baseline - COLS (0)', 'Baseline - P2PS (1)',\n", "# 'Baseline - RMA1S (2)', 'Baseline - RMA2S (3)',\n", "# 'Merge - COLS (4)','Merge - P2PS (5)',\n", "# 'Merge - RMA1S (6)', 'Merge - RMA2S (7)',\n", "# 'Baseline - COLA (8)', 'Baseline - COLT (9)','Baseline - P2PA (10)','Baseline - P2PT (11)',\n", "# 'Baseline - RMA1A (12)', 'Baseline - RMA1T (13)','Baseline - RMA2A (14)','Baseline - RMA2T (15)',\n", "# 'Merge - COLA (16)','Merge - COLT (17)','Merge - P2PA (18)','Merge - P2PT (19)',\n", "# 'Merge - RMA1A (20)','Merge - RMA1T (21)','Merge - RMA2A (22)','Merge - RMA2T (23)']\n", "\n", "colorbar=f.colorbar(im, ax=ax)\n", "tick_bar = []\n", "for i in range(len(used_config)):\n", " #tick_bar.append(0.41 + i*0.96) #Config de 24 valores\n", " tick_bar.append(0.37 + i*0.92) #Config de 12 valores\n", " #tick_bar.append(0.35 + i*0.89) #Config de 8 valores\n", "colorbar.set_ticks(tick_bar) \n", "colorbar.set_ticklabels(labelsMethods_aux)\n", "colorbar.ax.tick_params(labelsize=32)\n", "#\n", "\n", "f.tight_layout()\n", "print(\"Filename: Heatmap_\"+tipo+\".png\")\n", "f.savefig(\"Images/Heatmap_\"+tipo+\".jpeg\", format=\"jpeg\", dpi=300)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11]\n", "[25 18 33 29 10 12 14 8 35 33 38 25]\n", "[ 0 1 2 3 6 7 8 9 10 11]\n", "[ 8 2 7 2 2 1 6 2 10 2]\n" ] } ], "source": [ "aux_array = [] #Counts all\n", "for data in results:\n", " aux_array+=data\n", "aux_results, aux_counts = np.unique(aux_array, return_counts=True)\n", "print(aux_results)\n", "print(aux_counts)\n", "\n", "aux_array = [0] * len(results) # Counts ganador celda\n", "for index in range(len(results)):\n", " aux_array[index] = results[index][0]\n", "aux_results, aux_counts = np.unique(aux_array, return_counts = True)\n", "print(aux_results)\n", "print(aux_counts)\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "El siguiente código asume que para cada número de procesos padre/hijo existen valores en todas las configuraciones que se van a probar" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "tags": [] }, "outputs": [], "source": [ "def normalize_arrays(arrays, norm_array):\n", " new_arrays = arrays.copy()\n", " for index in range(len(new_arrays)):\n", " new_arrays[index] = np.divide(norm_array, new_arrays[index])\n", " return new_arrays\n", "\n", "def create_labels_lineplot(used_direction, user_condition=lambda a, b: True):\n", " labels_aux = []\n", " if used_direction == 's':\n", " for ns_aux in processes:\n", " for np_aux in processes:\n", " if used_direction=='s' and np_aux > ns_aux and np_aux != ns_aux and user_condition(np_aux, ns_aux):\n", " new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n", " labels_aux.append(new_label)\n", " else:\n", " for np_aux in processes:\n", " for ns_aux in processes:\n", " if ((used_direction=='e' and np_aux < ns_aux) or used_direction=='a') and np_aux != ns_aux and user_condition(np_aux, ns_aux):\n", " new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n", " labels_aux.append(new_label)\n", " return labels_aux\n", "\n", "def reorder_data(plot_data, actual_order, expected_order):\n", " ordered_indexes = []\n", " len_order = len(actual_order)\n", " for index in range(len_order):\n", " actual_order[index] = str(actual_order[index]).replace(\" \", \"\")\n", " for index in range(len_order):\n", " ordered_indexes.append(actual_order.index(expected_order[index]))\n", "\n", " for index in range(len(plot_data)):\n", " old_array = plot_data[index]\n", " new_array = []\n", " for i in ordered_indexes:\n", " new_array.append(old_array[i])\n", " plot_data[index] = new_array\n", "\n", " return plot_data" ] }, { "cell_type": "code", "execution_count": 251, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Total_ResizesTotal_GroupsSDRADRDRRedistribution_MethodRedistribution_StrategySpawn_MethodSpawn_StrategyGroupsItersT_spawnT_SRT_ART_MalleabilityT_total
012100.00.03550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 20)(500, 500)(0.00179,)(0.303186,)(0,)(0.90641,)170.946754
112100.00.03550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 20)(500, 500)(0.002427,)(0.169282,)(0,)(0.970759,)176.884340
212100.00.03550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 20)(500, 500)(0.0018,)(0.333988,)(0,)(1.006021,)172.216504
312100.00.03550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 20)(500, 500)(0.001542,)(0.119925,)(0,)(0.879846,)182.188719
412100.00.03550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 20)(500, 500)(0.001487,)(0.162517,)(0,)(0.781511,)257.321413
...................................................
95120.299.83550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 40)(500, 500)(0.001867,)(0.012053,)(0.198771,)(0.851172,)157.064828
96120.299.83550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 40)(500, 500)(0.001594,)(0.011036,)(0.170305,)(0.771764,)162.476163
97120.299.83550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 40)(500, 500)(0.001827,)(0.01112,)(0.169132,)(0.802863,)164.167608
98120.299.83550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 40)(500, 500)(0.001688,)(0.011422,)(0.487583,)(1.068855,)167.163320
99120.299.83550186388(0, 0)(0, 0)(0, 1)(0, 0)(160, 40)(500, 500)(0.001775,)(0.0146,)(0.185643,)(0.89982,)229.011648
\n", "

200 rows × 16 columns

\n", "
" ], "text/plain": [ " Total_Resizes Total_Groups SDR ADR DR \\\n", "0 1 2 100.0 0.0 3550186388 \n", "1 1 2 100.0 0.0 3550186388 \n", "2 1 2 100.0 0.0 3550186388 \n", "3 1 2 100.0 0.0 3550186388 \n", "4 1 2 100.0 0.0 3550186388 \n", ".. ... ... ... ... ... \n", "95 1 2 0.2 99.8 3550186388 \n", "96 1 2 0.2 99.8 3550186388 \n", "97 1 2 0.2 99.8 3550186388 \n", "98 1 2 0.2 99.8 3550186388 \n", "99 1 2 0.2 99.8 3550186388 \n", "\n", " Redistribution_Method Redistribution_Strategy Spawn_Method Spawn_Strategy \\\n", "0 (0, 0) (0, 0) (0, 1) (0, 0) \n", "1 (0, 0) (0, 0) (0, 1) (0, 0) \n", "2 (0, 0) (0, 0) (0, 1) (0, 0) \n", "3 (0, 0) (0, 0) (0, 1) (0, 0) \n", "4 (0, 0) (0, 0) (0, 1) (0, 0) \n", ".. ... ... ... ... \n", "95 (0, 0) (0, 0) (0, 1) (0, 0) \n", "96 (0, 0) (0, 0) (0, 1) (0, 0) \n", "97 (0, 0) (0, 0) (0, 1) (0, 0) \n", "98 (0, 0) (0, 0) (0, 1) (0, 0) \n", "99 (0, 0) (0, 0) (0, 1) (0, 0) \n", "\n", " Groups Iters T_spawn T_SR T_AR \\\n", "0 (160, 20) (500, 500) (0.00179,) (0.303186,) (0,) \n", "1 (160, 20) (500, 500) (0.002427,) (0.169282,) (0,) \n", "2 (160, 20) (500, 500) (0.0018,) (0.333988,) (0,) \n", "3 (160, 20) (500, 500) (0.001542,) (0.119925,) (0,) \n", "4 (160, 20) (500, 500) (0.001487,) (0.162517,) (0,) \n", ".. ... ... ... ... ... \n", "95 (160, 40) (500, 500) (0.001867,) (0.012053,) (0.198771,) \n", "96 (160, 40) (500, 500) (0.001594,) (0.011036,) (0.170305,) \n", "97 (160, 40) (500, 500) (0.001827,) (0.01112,) (0.169132,) \n", "98 (160, 40) (500, 500) (0.001688,) (0.011422,) (0.487583,) \n", "99 (160, 40) (500, 500) (0.001775,) (0.0146,) (0.185643,) \n", "\n", " T_Malleability T_total \n", "0 (0.90641,) 170.946754 \n", "1 (0.970759,) 176.884340 \n", "2 (1.006021,) 172.216504 \n", "3 (0.879846,) 182.188719 \n", "4 (0.781511,) 257.321413 \n", ".. ... ... \n", "95 (0.851172,) 157.064828 \n", "96 (0.771764,) 162.476163 \n", "97 (0.802863,) 164.167608 \n", "98 (1.068855,) 167.163320 \n", "99 (0.89982,) 229.011648 \n", "\n", "[200 rows x 16 columns]" ] }, "execution_count": 251, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfG" ] }, { "cell_type": "code", "execution_count": 256, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['(2,20)', '(2,40)', '(2,80)', '(2,160)', '(20,40)', '(20,80)', '(20,160)', '(40,80)', '(40,160)', '(80,160)']\n", "[0.031203, 0.034094, 0.036393, 0.036872, 0.325629, 0.161447, 0.051848, 0.275889, 0.117939, 0.124082]\n", "[0.016136, 0.018315, 0.017299, 0.020972, 0.00817, 0.008836, 0.010475, 0.006352, 0.006724, 0.001864]\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAACI0AAAVuCAYAAADydV5rAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xUVf7/8fekh5AESOgdaQGRLpCAUkQEUQEB20pZJaisuurqri6Irn5XXfcn6CpKCWADRdeCdERESKiKDVQ6pNAREgKBlPn9cXdCJhNgMpmZOzN5PR+PPM69J/ee+2YW94bczz3HYrVarQIAAAAAAAAAAAAAAEClEmR2AAAAAAAAAAAAAAAAAHgfRSMAAAAAAAAAAAAAAACVEEUjAAAAAAAAAAAAAAAAlRBFIwAAAAAAAAAAAAAAAJUQRSMAAAAAAAAAAAAAAACVEEUjAAAAAAAAAAAAAAAAlRBFIwAAAAAAAAAAAAAAAJUQRSMAAAAAAAAAAAAAAACVUIjZAQBPKioqUlZWlqKjo2WxWMyOAwAAAAAAAAAAAACAx1mtVuXk5KhevXoKCrr4fCIUjSCgZWVlqWHDhmbHAAAAAAAAAAAAAADA69LT09WgQYOLfp+iEQS06OhoScZ/CDExMSanAQAAAAAAAAAAAADA87Kzs9WwYcPiZ+YXQ9EIApptSZqYmBiKRgAAAAAAAAAAAAAAlYrtmfnFXHzhGgAAAAAAAAAAAAAAAAQsikYAAAAAAAAAAAAAAAAqIYpGAAAAAAAAAAAAAAAAKiGKRgAAAAAAAAAAAAAAACohikYAAAAAAAAAAAAAAAAqIYpGAAAAAAAAAAAAAAAAKiGKRgAAAAAAAAAAAAAAACohikYAAAAAAAAAAAAAAAAqIYpGAAAAAAAAAAAAAAAAKiGKRgAAAAAAAAAAAAAAACohikYAAAAAAAAAAAAAAAAqIYpGAAAAAAAAAAAAAAAAKiGKRgAAAAAAAAAAAAAAACohikYAAAAAAAAAAAAAAAAqIYpGAAAAAAAAAAAAAAAAKiGKRgAAAAAAAAAAAAAAACohikYAAAAAAAAAAAAAAAAqoRCzAwAAAAAAAACBqKioSAUFBSoqKjI7CgAAAADABwQFBSkkJERBQb4zvwdFIwAAAAAAAICbFBQUKCcnRzk5OcrNzTU7DgAAAADAB0VFRSk6OlrR0dEKCTG3bIOiEQAAAAAAAMANcnNzlZ6eLqvVqqioKNWpU0dhYWEKCgqSxWIxOx4AAAAAwERWq1VFRUU6f/68cnJydOjQIR0+fFgNGzZUVFSUabkoGgEAAAAAAAAqyFYwEhUVpbp165r+phgAAAAAwDdFRUWpevXqKigo0MGDB5Wenm5q4YjvLJQDAAAAAAAA+KGCgoLigpH69etTMAIAAAAAuKyQkBDVr19fUVFRSk9PV0FBgSk5KBoBAAAAAAAAKiAnJ0dWq1V169ZVUBC/bgMAAAAAOCcoKEh169aV1WpVTk6OORlMuSoAAAAAAAAQIHJychQVFcUMIwAAAACAcgsJCVFUVBRFIwAAAAAAAIC/KSoqUm5urqKjo82OAgAAAADwU9HR0crNzVVRUZHXr03RCAAAAAAAAOAi25rTYWFhJicBAAAAAPgr278pbf/G9CaKRgAAAAAAAAAX2d4CCwri12wAAAAAANfY/k3JTCMAAAAAAACAH7JYLGZHAAAAAAD4KTP/TUnRCAAAAAAAAAAAAAAAQCVE0QgAAAAAAAAAAAAAAEAlRNEIAAAAAAAAAAAAAABAJUTRCAAAAAAAAAAAAAAAQCVE0QgAAAAAAAAAAAAAAEAlRNEIAAAAAAAAAAAAAABAJUTRCAAAAAAAAAAAAAAAQCUUYnYAAAAAAAAAAADgH3Jzc7V161bt3btXR44c0dmzZxUZGanq1aurdu3a6tSpk+rWrevWa2ZmZur777/Xvn37lJ2dLavVqqpVq6pRo0a68sor1bx5c7dezxuOHDmib7/9VhkZGTp58qTOnTunqKgoxcbGqnHjxmrWrJmaNGkii8VidlQAgI/hXgx3o2gEAAAAAAAAADyoSZMm2r9/v0P/m2++qfvuu8+lMWfPnq177rnHob9x48bat2+fS2MCF5OTk6O5c+fqww8/1IYNG1RYWHjJ4+vXr6+bbrpJd911l5KSklwqfDh8+LDefPNNzZ8/Xzt27Ljs9UaOHKk//elPatasWbmvNXfuXI0dO7Z4/9prr9XXX39d7nEu59SpU0pJSVFKSoq2b99+2eOrVaumzp07q0+fPrrhhhvUuXNnt2cCKgvuxfB33Is966mnntILL7xg1/f3v/9dzz//vNcymInlaQAAAAAAAADABHPmzHH53NmzZ7sxCVC2wsJCvfLKK2rYsKEeeughpaamXvYhlWS8jfzWW2+pV69e6tChgxYvXuz0Nc+fP69//OMfatKkiZ599tnLPqSyXW/KlClq2bKlHnzwQeXk5Dh9PW/54osvlJCQoMcee8ypghFJOnnypFatWqWJEyeqS5cuOnnypGdDwuvOnzihs5mZOn/smApyclR07pysRUVmx6pUuBfD13Ev9rzCwkK98847Dv1z58516rMOBMw0AgAAAAAAAAAm2LRpk7Zt26a2bduW67wdO3YoNTXVQ6kAw6FDhzRixAitW7euzO+3aNFC9evXV82aNVVYWKhDhw4pIyNDBw4csDvuxx9/1ODBgzV//nzdfvvtl73msGHDtH79eofvRUVFqW3btqpXr55CQkKUlZWl3377TcePHy8+prCwUK+//rpWrlyphQsXqmXLli78yd1v7ty5uueee1RUqhggIiJCbdq0UaNGjRQVFaXTp0/r2LFj+vnnn3Xq1CmT0sJbis6f16+TJqkgO9vhe5bQUAWFhsoSFqagsDAFhYcrODxcQRERxldYmLEfFibL/9oy98PDFRoXp/D4eBP+hP6BezF8Gfdi71i2bJkyMzMd+jMzM7V8+XINGjTIhFTeRdEIAAAAAAAAAHhRaGio8vPzJRlvOP/73/8u1/kl32wuORbgLhkZGerdu7d2795t19+kSRM98cQTGjx4sBo2bFjmuTt27NDixYv11ltv2b2ZnJeXd8lrHj58WL1799Zvv/1m19+hQwdNmjRJAwcOVGRkpN33CgoK9PXXX+vll1/WihUrivt/++03XXPNNVqzZo1atWrl1J/ZU7777jvde++9dgUjzZo107PPPqthw4apSpUqDudYrdbiz/HTTz+96MNC+DdLaKhCYmPLLBqx5uerMD9fOnOm7JODgiSLRbbFJqySVFQkWa0Oh4bExOjKqVMVFBrqtuyBgHsxfB33Yu9JSUm55PcqQ9EIy9MAAAAAAAD/d+CANGeO9NprRlvqzSoA8CWDBw8u3n7vvfdUUFDg9Lmlp8++6aab3JoNyM/P14gRIxweUj3zzDP69ddfdf/991/0IZUktWzZUo888oi2bdummTNnqm7dupe9ZlFRke666y6Hh1T/+Mc/tGXLFg0bNszhIZUkhYSE6LrrrtPy5cs1d+5chZZ4KH748GHdeuutOnv27GWv70mPPPKI3dT2SUlJ2rp1q/7whz+UWTAiSRaLRa1atdKjjz6qtWvXavv27brvvvsUHBzsrdjwAovFojo33+zayUVFUmGhrP/7UmFhmQUjslgUXquWLCG8Q14a92L4Mu7F3nPkyBEtWrSoeL9ly5Zq0aJF8f4XX3yho0ePmhHNqygaAQAAAAAA/mvTJummm6QmTaQ//lF65BGjbdJEuvlmafNmsxMCgIM//vGPxduHDx8u1xrzS5cu1cGDB8scC3CHv/zlL9qwYUPxflBQkObOnavJkycrPDzc6XFCQkJ07733auvWrerVq9clj33hhRe0atUqu75XXnlFkyZNcrpQYvTo0frggw8UUuLh+LZt2/TnP//Z6czulpGRoW+++aZ4PyQkRO+//75iYmLKNU5CQoLefPNNRUdHuzsiTFa9a1eF1qjhuQtYrap3222yWCyXP7aS4V4MX8a92Hveeecdu5mCRo8erdGjRxfv5+fn69133zUjmldRNAIAAAAAAPzTJ59ISUnS0qUX3qy0Tf1utUpLlkiJicZxAOBDunfvroSEhOL9OXPmOH1uyenwExIS1K1bN7dmQ+W2bds2/ec//7Hre/TRR+0enpRX7dq19dVXX6lv375lfv/gwYP6xz/+Ydd3++2365FHHin3tYYNG6a//vWvdn0zZszQ1q1byz2WO6xdu9Zuv0ePHmrcuLEpWeCbLMHBqlNixgu3CgpSdNu2im7d2jPj+znuxfBV3Iu9q+R/z0FBQRo1apRGjRqloKALZRSXWr4mUDAfFQAAAAAA8D+bNkm33Xbxqbgl43sWi3FcWprUtat3MwLAJYwdO1ZPPPGEJGnx4sU6cuSIatWqdclzjh49ajd9trvfbN6+fbt++uknHT16VNnZ2apRo4bq1q2rnj17Ki4uzq3XOnv2rNatW6fMzEwdPnxYwcHBuvrqq3XNNddc9Jz8/HytXbtWe/bs0dGjRxUXF6f69esrMTFR1atXd2s+STpx4oTS0tJ06NAhHTt2TBEREapZs6Y6dOigtm3buv16vuDFF1+UtcR9tUWLFnr++ecrPG5ISIgaNWpU5vdee+01nT9/vng/NjZWU6dOdflaEydO1AcffGA3pf+///1vvf/++y6P6aqsrCy7/QYNGng9A3xf3DXXKOvjj1V45ox7By4qUr2RI907ZoDhXsy92BdxL/aetLQ0/fLLL8X7/fr1K75X9+vXTytXrpRk/He5YcMGde/e3ZSc3kDRCAAAAAAA8D/PP28Ui1ysYMTGdszzz0uff+6dbECgOXBAWrVKysmRoqOlfv2ki/zCGc4bNWqUnnrqKRUUFKigoEDvvvuuHnvssUueU3L67JCQEN19990VznH8+HG9/PLLeu+995SZmVnmMUFBQUpMTNTkyZN13XXXOTXumDFj9Pbbbxfv7927V02aNFF6erqeeuopffbZZzp9+rTdObfcckuZD6pycnL07LPPKiUlRSdPnnT4fnh4uG6++Wa99NJLatq0qfbt26emTZsWf3/06NGaO3euU7mtVqs+/PBDTZ06VZs3b1aRbQarUurXr68HH3xQDz30kCIjI50a29cdPHhQH3zwgV3fgw8+WK5p8MsrLy9P06dPt+sbPXq0ateu7fKYERER+tOf/mT3dvSCBQv08ssvq169ei6P64qCggK7/RMnTnj1+vAPQeHhqjVggA5+9tnlf7Z1etAgxXbooKhmzdwzXoDiXsy92NdwL/au0jOIjBkzxm7bVjRiOzaQi0ZYngYAAAAAAPiXAwekRYuMmUScUVgoffGFcR4A523aJN10k9SkifTHP0qPPGK0TZpIN98sbd5sdkK/Vrt2bQ0aNKh435lp8UseM2jQoAr9Ml8yHnw1a9ZML7300kUfUklSUVGR1q1bp/79++vuu++2exO1PD777DNdddVVeu+99xweUl3Mtm3blJCQoP/3//5fmQ+pJOncuXP66KOP1LFjR7u3v8trz5496ty5s+644w5t3Ljxog+pJCkzM1N/+9vf1KZNG23bts3la/qSL7/80q7IISIiQqNGjfLoNTdu3Kjff//dru/ee++t8Lh33323wsLCivcLCgr05ZdfVnjc8io9Y8G6det0/Phxr+eA76vZv78swcHuG7CoSPWGD3ffeAGKezH3Yl/Dvdh7Tp8+rQULFhTvx8TEaOjQocX7Q4cOVWxsbPH+hx9+qNzcXK9m9CaKRgAAAAAAgH9Ztar8b2FardJXX3kmDxCIPvlESkqSli698N+b7Zf2Vqu0ZImUmGgcB5eVnNJ+27Zt2nyJQpyNGzfaPRCp6HT4Tz/9tEaPHq3s7OziPovFotatW+umm27SnXfeqYEDB6pmzZp257333nsaNGiQwwwKl7Nx40bdfvvtxQ+bqlWrpv79++uOO+7QwIED1bhxY4dzfvvtN/Xu3dvhIVqNGjV0/fXX64477tD111+vGjVqSJJOnTqlkSNH6scffyxXNknatGmTunXrpq1bt9r1x8XF6brrrtPtt9+uoUOH6sorr7T7/r59+5SUlKTvv/++3Nf0NWvWrLHb79Spk93DEk/45ptv7Pbr1Kmjdu3aVXjcuLg4derUya5v7dq1FR63vHr06GG3n5ubqzvvvNPh4RwQEh2t+D59pCA3PLYLClL1Hj0U2bBhxceqBLgXcy/2JdyLvefDDz+0K5waOXKk3Yw1kZGRGlliia+cnBx99NFHXs3oTRSNAAAAAAAA/5KTU/5fqAcFSSV+GQvgEjZtkm67zZil52Iz+ti+d9ttzDhSATfeeKPdG8qzZ8++6LElv1erVi27N6PL6+2339Zzzz1XvB8UFKQHH3xQ+/bt0y+//KKFCxfq/fff15IlS3T48GF9+umnalRiSaJVq1Zp0qRJ5brmuHHjdO7cOTVo0EAffPCBjh07phUrVmjevHlasmSJ9u7dq6lTpxYfX1hYqLvvvlvHjh0r7qtZs6beffddHT58WMuXL9e8efO0fPlyHT58WO+8847i4+N19uxZ3XPPPeXKdujQIQ0ZMsTuWt26ddOKFSt09OhRrVy5UvPnz9cnn3yin376STt37tSQIUOKj7U9IMvJySnXdX1Namqq3X7Xrl09fs20tDS7/S5durht7M6dO1/yWt7QunVrhz/TihUr1Lx5cz3++ONKS0sr90NfBK5aAwe6Z3kaq1V1b7214uNUEtyLuRf7Eu7F3nOppWku1lf6nEBC0QgAAAAAAPAv0dEXZjxwVlGRFBPjmTxAoHn+eeOh1eUeXNmOef557+QKQCEhIfrDH/5QvP/BBx8oLy/P4bizZ8/qww8/LN6/++67FRoa6tI19+/fr/vvv794Pzw8XIsXL9Zrr71m9zDKxmKxaMiQIdq8ebOaN29e3P/yyy9r7969Tl83JydHzZo10/r163XbbbcpuNQyDBaLRU2aNCnenzlzpt3b3jVr1tTatWv1hz/8QSEhIXbnhoSE6O6779aaNWsUHx9v98DJGffcc48OHjxot5+amqr+/fvLYrE4HN+8eXN9+umneuihh4r7du7cqSlTppTrur6m9FvkCQkJHr9mRkaG3X7btm3dNnbpN9FLX8tbXnnlFYe/sydOnNC///1vJSUlKSYmRr169dKjjz6q+fPna//+/abkhPnCa9ZU9W7dKjbbSFCQ4q65RhEVXDKlMuFebH8d7sXm4l7sHb/88ovWr19fvN+iRQslJSU5HJeYmKiWLVsW769bt06//fabVzJ6G0UjAAAAAADAv/TrJ5Xxi8NLslikvn09kwdwldUq5eb61tevv0qLFl18hpHSCgulL74wzjM7e8kvd7yp7SUlp7Y/efKkPv30U4djPv74Y506dap4f+zYsS5f7+WXX9bZs2eL96dMmaIbbrjhsufVqlVL8+bNK94vLCws94OZuXPnqkGDBk4d+/rrrzvst2rV6pLntGnTRq+++mq5Mm3ZskVLliwp3u/Ro4dmzJjh8CCtLK+88oquuuoqu4znzp0r1/V9RX5+vsPb2dWqVfP4dU+cOGG3X716dbeNXTp/dna2KbN69OrVS3PnzlV4eHiZ3z979qzWrVunKVOm6M4771STJk3UuHFjTZgwQRs3bvRy2sqtMC/vol9F58975dia119f/uLoUmoNGqSiUv9fVHT+/CVzuPtYf8O9uGzci72Le7H3lJ4xZPTo0Rc9tvRsI5eajcifhVz+EAAAAAAAAB/SqJE0eLC0ZIlzD7aDg6UbbzTOA3zJmTNS1apmp6g4q1XywluQ5XL6tBQVZXYKp7Rp00bdunUrfjg8e/Zs3XHHHXbHlPzl9NVXX+3yG6C5ubl2YzVr1kzjx493+vyuXbuqV69exevRL1y4UK+99ppT5yYlJalXr15OHfvjjz9q27ZtxfstW7a0W1P+Uu68805NnjxZu3btcur40vn/+c9/KsjJt/yDg4P18MMPF0/Bf/ToUa1fv169e/d26nxfUvqBkWTOgyp3XrOssU6cOKFatWq57RrOuuuuu3TllVfqscce06pVqy57/IEDBzRt2jRNmzZNffr00ZQpU9S+fXsvJK3cfhg37qLfi2nfXs3/8pfi/Z8mTHAo+LCp2rq1Wv7978X72x59VAUXWTKjStOmav2PfxTv75s2rbyx7RUV6Ze//lVh8fG6skQxwY7nn9eZi8xIERIdratKXHfXyy/r9K+/lnlsUFiYOpR42LrntdeU/cMPDsd1evddV/8EpuBe7Ih7sfdxL/aO/Px8vVvi/6OCgoI0atSoix4/atQoTZw4UUX/K+h755139H//938Os+34O2YaAQAAAAAA/mfSJGP2kMvNOGI7ZuJE7+QCABeUfMP5q6++0oEDB4r39+zZozVr1hTvV+TN5nXr1tm92Tx8+HCnH8rY9OnTp3h7//79dlkvZciQIU5fo/R69yNGjHD63PIe/+WXXxZv16lTR9dee225rlXy85BU/BAP5itrOQMztW/fXl9++aW2bNmihx9+WE2bNnXqvNWrV6t79+567733PJwQqNy4F9vjXgx38LV7sSR98cUXOnLkSPF+37591bBhw4seX79+fV133XXF+4cOHbKbGSdQBFYJDAAAAAAAqBy6dpU+/FC67TbpYtPbBgcbBSMLFhjHA76mShVjRgxf8u670v33l/+8t96S/vAH9+dxVZUqZicol9tvv11//vOfdfbsWRUVFentt9/WpEmTJBlvNlv/t9xOZGSkw5vP5bFu3Tq7/Xr16mnfvn3lGiMsLMxuf8+ePWrkxExOHTt2dPoaP5R6a71Lly5OnysZb2E7Y/fu3Tp48GDxfvPmzbV///5yXet8qZkGdu/eXa7zL+bQoUPKc3KJhzp16igiIqJC16tRo4ZDX8llGDylRo0aysrK8sg1T548Web1zNa5c2d17txZU6dOVUZGhtavX6/vvvtO3333nTZu3FjmZ5CXl6cxY8aoVq1auv76601IXTm0nznzot+zlHqo3+6NN5w+tu0rrzh9bJsXX1RRUZF2PPus8rKynF9uzWJRrRtuUN1hw/63a/+gtuXEibI6uexN88cfd/rYZg895PSxvo57sT3uxdyL3cEX78Wll5cpvfxMWcaMGaMVK1YU76ekpOjmm292dzRTUTQCAAAAAAD807Bh0urV0jXXXPiFelDQhXXgW7SQ3nmHghH4LovF95ZQGTTIyOXsQyrJOH7gQN/7s/iRmJgY3XrrrcUzCcydO1cTJ06U1WrVO++8U3zcsGHDFBsb6/J10tPT7fb//Oc/689//rPL40llT6VelvJMQ37s2DG7/caNG5crkzMPziTHz2PdunVOz/5wMc5+Hpdz++23273VfimrV6+u8DT8oaGhio6OVk6JJTTKetDjbqUfVP3+++9uG7v0WDExMT43lXyDBg00YsSI4jfyi4qKtGnTJr377ruaPXu23cPKwsJCjRs3Tjt37nR4YAz3CC7HA19PHRsUHq4gSXWGDNG+SxSmOJwXFqY6t9xy0WsFlePvjKeO9XXci+1xL+Ze7A6+di/OysrSsmXL7PIM+1+x3aUMHTpUsbGxxQU1S5Ys0aFDh1SnTh2PZfU2lqcBAAAAAAD+y/Zwu2ZNafZsacoU6d57je/l5UmdO5ubD/A3jRpJgwcbM/U4IzhYuukm4zxUSMlp8W3T4K9YscLuYUpFpsOXpOPHj1fo/LKUfLBxKdHR0U6PWfoBSXnOlYwHAM4w8/PwRfXq1bPb/+WXXzx+zfr169vtb9u2zW1jlx6r9LV8UVBQkLp376433nhD27ZtU7t27ey+f+DAAX300UcmpYM3Ve/aVaHOvo1vsaj24MEKoXizwrgXX8C92Bzciz1r7ty5KiwsLN4fOXKkIiMjL3teRESEbr/99uL9goICvf322x7JaBaKRgAAAAAAgP9KTTXaXr2ksWOlhx6SXntNqlZN2rdPKrE+NgAnTZpkFGRdbg1y2zETJ3onV4Dr3bu33Zu1c+bMsZs+u3Hjxurbt2+FrlF6+nZ3sJZnVhonhYeH2+2XN7ezx/vL5+EtSUlJdvubNm3y+DUTExPt9jdv3uy2sbds2XLJa/m6Zs2aacmSJYoqVQhQcnp8BC5LcLDqDB7s1LHBkZGqNWCAhxNVDtyLL+BebA7uxZ5jtVodlqaZNWuWLBaLU1/Tp0+3O7f0WP6OohEAAAAAAOC/0tKMtuQvnyIjpbvvNrZL/WIHgBO6dpU+/NCYReRiM47YvrdgAUtAuYnFYrFbU/3jjz/WwoULi/fHjBkjy+UKeS4jPj7ebj8tLU1Wq7VCX86sA19e1atXt9sv7zTpzk5LX/rzSE5OrvDn8fXXX5cr68V8/fXXTl+zotPh21x77bV2+999913xNOye0qtXL7v9Q4cO6aeffqrwuMePH9d33313yWv5gwYNGui2226z6/v1119NSgNvi7vmGgVXqXLZ4+oMGaJgJ96Ux+VxL76AezH34orytXvxmjVrtHv3breNt2PHDq1bt85t45mNohEAAAAAAOCfrNayi0YkKTnZaBculA4e9G4uIBAMG2b89zVo0IUZR4L+96tEi0W68Ubj+0OHmpcxAI0ZM0ZB//ucz5w5o3PnzklyfIjlqtq1a9vt79ixo8JjekLjxo3t9sv74MLZ4/3l8/CW6667TiEhIcX7eXl5evfddz16ze7du6tatWp2fSkpKRUe97333rN7ez0kJETXXXddhcc1Q4cOHez2y/vgFv4rKDzcmEHkEkUKIbGxqtmvnxdTBT7uxQbuxebgXuw57vgzeWNMs1A0AgAAAAAA/NPu3dLRo1J4uNSpk/33rrzSKCQpKJDmzDEnH+DvunY1Cq/27TP+O5oyxWj37ZM+/5wZRjygUaNG6lfGw78+ffqoSZMmFR6/9JTgvrrMRffu3e32V69eXa7znT2+bdu2io2NLd5fv369cnJyynWtQFKvXj2HWS3+85//eGTpAJvIyEgl2wo9/+ftt9/WkSNHXB4zLy9P//nPf+z6hg8frvr167s8ppmCS834VPLvLAJfzf79ZbnYrF+S6g4bpqCwMC8mCnzciw3ci83BvdgzTp06pf/+97/F+2FhYdq2bZv27t1brq8dO3bYLRv30UcfBczfV4pGAAAAAACAf0pNNdouXYzCkdLGjzfamTOloiLv5QICTaNG0pgx0kMPGW2jRmYnCmh//OMfnepzRb9+/eweQC9cuLBCDwQ8pWfPnoqIiCje//zzz53OeejQIbulBC4lODjY7sHguXPnPP42r6/761//are/Y8cOPf300xUet6CgQAcOHCjzew8//LBCQ0OL90+ePKmHH37Y5Ws9//zzDtPP/+Uvf3F5PLNt377dbt9fi1/gmpDoaMX36XNhti8bi0WhcXGKv+Yac4IFOO7F3IvNxL3Y/ebNm6ezZ88W7w8cOFBt2rRRkyZNyvXVokUL3XLLLcXj5Obm6oMPPjDjj+R2FI0AAAAAAAD/dLGlaWxGjJCqVTNmRVi50lupAKBChg0bprVr19p9DR8+3C1jV69eXXfddVfx/unTp33yYXq1atXs3rI9d+6c/vznPzt17kMPPVSut3EffPBBu/1nn31WByvxsmbt2rXTAw88YNf38ssv67333nN5zMOHD6tfv3766quvyvx+vXr1NGnSJLu+Dz74QFOmTCn3tT777DO99NJLdn333HOPOnfuXO6x3GHv3r06efKky+efPHlSCxYssOvr27dvBVPB39QaONBYlrEkq1X1hg+XpcQyFnAf7sXci83Evdj9Si8jc+edd7o8VulzA2WJGopGAAAAAACAf7pc0UhkpDRqlLE9Y4Z3MgFABYWFhalnz552X+FlzabkomeeecZuvHfffVd//etfVVhYWK5xtm/frm+++cZtuUp74oknFFZiyYX58+fr0UcfVUFBQZnH5+fn6+GHH9ZHH31Uruv07t1b/fv3L94/cuSIBg0apIyMjHKNk5OTo3nz5pXrHF81ZcoUdS2x/FRRUZFGjRql559/vlwPAQsLC5WSkqKOHTte9u/K3//+d/Xu3duu79FHH9X//d//Of13891339Vtt91m93ckISFBr732mtOZ3W3NmjVq0qSJnn766XLPJHDmzBndcccdOnr0aHFfeHi4hg0b5u6Y8HHhNWuqerduF2YbsVgUXqeOalzsZ2BUGPdiA/di83Avdp8ffvhB3377bfF+dHS0brrpJpfHGzBggOLj44v3N27cqG3btlUooy+gaAQAAAAAAPifkycl2y9mLvULc9vazAsXSpX4bTUAsGnatKlmlCqk+9e//qWePXvqiy++uOiDIEnat2+f3njjDfXt21dt27a96Nuq7tCmTRs9++yzdn1TpkxR+/btNXXqVG3cuFE7d+7Uxo0bNXXqVLVv3774gcTtt99ermu9/fbbatCgQfH+999/r6uuukr/+te/dOzYsYuel5OTo4ULF+qee+5R/fr19dRTT5Xrur4qLCxMH3/8sZo0aVLcZ7VaNWnSJLVu3VrTp09XZmbmRc/fuXOnpkyZojZt2ujee+916m3xoKAgzZs3T82bN7frnzhxorp27apPP/1UeXl5DucVFhZq1apVGjBggEaNGmX3IK1mzZr6+OOPVaVKFSf+1Pby8vK0b98+l75Kzyxy6tQpPffcc6pfv74GDx6sefPmXfJBaG5urt577z21b99ey5Yts/ve3/72N7u/q6g8at9444XlFq1W1Rs5UpbSS9bAb3AvdsS92B73Yvfdi0vPBDJkyBBFRkaWO49NSEiIw8xDs2fPdnk8X8G8VQAAAAAAwP9s2GBM0928uVSr1sWPa9tWSkqSUlOlOXOkAPklIgBUxKhRo3To0CE9+eSTKvrfQ8gNGzbo5ptvVpUqVdSxY0fVrl1bkZGRysnJ0bFjx7R9+/YKLbPhir/+9a86cOCA3nzzzeK+7du365FHHrnoOX379tXzzz9vt768xWK55HXq1q2rRYsWadCgQcrKypIk/f777/rrX/+qv/3tb0pISFCzZs0UGxurc+fO6eTJk9q9e7f27dsna4klI2rUqOHqH9XnNGrUSKmpqRo+fLjWr19f3L93717dd999kqSWLVuqQYMGio+PV2FhoQ4dOqT09HQdOHCgzDEv98Cobt26WrNmjYYOHapNmzYV92/dulXDhg1T1apVdeWVV6pu3boKCQnRwYMH9csvv+j48eMOYzVv3lwLFy5UQkKCK398bdy4UU2bNnXp3MmTJ+uZZ55x6C8oKNDixYu1ePFiSVL9+vV1xRVXKC4uTlFRUcrJydH+/fu1ffv2Mt8iHzlypCZOnOhSJvi/Kk2aKLpNG+Vs367Ihg1VrUsXsyOhgrgX2+Ne7Ih7ccXvxefOndP7779v972KLE1Tcoy33nqreP/dd9/Viy++qNDQ0AqPbRaKRgAAAAAAgP+53NI0JSUnG0UjM2dKf/vbham9AaASe+KJJ3TVVVdp7NixOnToUHH/mTNnlJqa6tQY1atX91Q8ScYDpmnTpql58+aaPHmyTp8+fcnj//SnP+n//b//p927d9v1R0dHX/Za7du319atW3X33XdrxYoVxf1Wq1Xbt2/X9u3bLzuGpz8Pb6tXr56++eYbTZkyRc8//7yys7Ptvr9jxw7t2LHjsuN06dJFL774ovr16+fUNdeuXav/+7//07/+9S+7N5pPnz6tDRs2XPL84OBgJScn68UXX1RMTMxlr+dpdevWVWxsrE6dOuXwvczMzEu+JW4TGRmpJ598Uk899ZSCg4M9ERN+os6QIco/dUr177jjsg/g4R+4F9vjXuyIe3HFfPrppzpx4kTxfs2aNXXddddVeNyePXuqUaNGxcU5R48e1cKFC3XrrbdWeGyz8FsSAAAAAADgf8pTNDJihFStmrRvn7RypSdTAYBfueGGG7R371698cYb6tChw2UfQoaGhioxMVHPPPOMduzYoYcfftgrOR999FHt2LFDL730knr27Km6desqLCxMderUUefOnfX444/r559/1n/+8x+FhYU5vIUdGxvr1HVq1aql5cuX65tvvtGQIUMUFRV12XOaNm2qe++9V0uXLtWWLVtc+eP5tJCQED3++ONKT0/Xq6++qh49eijIieLLhg0basKECVq/fr02b97s1EMqm7CwMD377LPau3evJk2a5DBNflnq1aunhx9+WL/99pumTZtm+kMqmwEDBujo0aNasWKFHn30UXXr1s3pt5Bbtmypp59+Wr/++qsmTZpEwQgUnZCgNi++qJh27cyOAjfiXmyPe7Ej7sWuK700zciRIxUSUvE5NSwWi8MSTKWv5W8s1pJz9gABJjs7u7iS2xf+zwkAAAAA4AYFBVL16tLp09KPP0rO/OL84Yel116Thg2T/vtfz2dEpZGXl6e9e/eqadOmioiIMDsOUCEnTpzQhg0bdPDgQZ04cUL5+fmqWrWqatWqpZYtW6p169YurUnvbSkpKbr33nuL919//XVNmDCh3OMUFBRoy5Yt2rVrl44fP66cnBxVqVJFsbGxatasmRISElSnTh13RvcLp0+f1tatW7Vnzx4dPXpUZ8+eVZUqVVStWjXVrVtXnTt3Vu3atd16zfT0dH3//ffav3+/srOzZbVaFRUVpUaNGqldu3Zq0aKFW6/nSefOndOuXbu0c+dOHTx4UDk5OcX/rcXExKhx48Zq37694uLizI4KwATci+1xLy4b9+LA5Il/Wzr7rJyiEQQ0ikYAAAAAIABt3Sp16iTFxEi//+7ccjPbtklXXikFB0vp6VLdup7PiUqBohHA94wePVrvvPNO8f769evVvXt3ExMBAFC5cC8Gys/MohGWpwEAAAAAAP7FtjRNjx7OFYxIUtu2UlKSVFgozZnjuWwAAFMdP35cH330UfF+RESEOnToYF4gAAAqGe7FgP+haAQAAAAAAPgXW9FIYmL5zktONtqZM6WiIvdmAgD4hAceeEBnz54t3h85ciSzAAEA4EXciwH/Q9EIAAAAAADwL64WjYwYIVWrJu3bJ61c6e5UAAAPGDhwoDZs2HDZ43JycjRq1CgtWLCguC8oKEgPPfSQJ+MBABDwuBcDgS/E7AAAAAAAAABOy8oyij6CgqRu3cp3bmSkNGqU9Npr0owZ0oABHokIAHCfZcuWadmyZWrbtq2GDBmirl27qnHjxqpatapycnJ04MABff3113rnnXd04sQJu3Mff/xxde7c2aTkAAAEBu7FQOCjaAQAAAAAAPgP2ywjV10lRUeX//zkZKNo5PPPpYMHpbp13ZsPAOAR27Zt07Zt25w+/s4779Rzzz3nwUQAAFQu3IuBwMXyNAAAAAAAwH+4ujSNTdu2UlKSVFgozZnjvlwAAI+Ii4sr1/G1atXSq6++qvfff1+hoaEeSgUAQOXBvRgIfMw0AgAAAAAA/EdFi0YkY7aR1FRp5kzpb38zlroBAPikQ4cOae3atVqzZo22bNmi3bt369ChQ8rNzZXFYlH16tVVq1Ytde3aVX369NGtt96qyMhIs2MDABAwuBcDgc9itVqtZocAPCU7O1uxsbE6deqUYmJizI4DAAAAAKiIs2el2FgpP1/au1dq0sT1cerVk06elJYtkwYMcGdKVDJ5eXnau3evmjZtqoiICLPjAAAAAAD8kCf+benss3JepQEAAAAAAP5hyxajYKRuXalxY9fHiYyURo0ytmfMcE82AAAAAAAAP0TRCAAAAAAA8A8ll6axWCo2VnKy0X7+uXTwYMXGAgAAAAAA8FMUjQAAAAAAAP9Qsmikotq2lZKSpMJCac6cio8HAAAAAADghygaAQAAAAAAvs9qvVA0kpTknjFts43MnCkVFblnTAAAAAAAAD9C0QgAAAAAAPB9O3dKx45J4eFSx47uGXPECKlaNWnfPmnlSveMCQAAAAAA4EcoGgEAAAAAAL7PNstI165SWJh7xoyMlEaNMranT3fPmAAAAAAAAH6EohEAAAAAAOD7bEUjiYnuHde2RM3ChdLBg+4dGwAAAAAAwMdRNAIAAAAAAHyfrWgkKcm947Zta4xZWCjNmePesVGpWK1WsyMAAAAAAPyUmf+mpGgEAAAAAAD4tt9/l7ZtM7Z79HD/+OPHG+3MmVJRkfvHR0ALCjJ+vVbE3x0AAAAAgIts/6a0/RvTmygaAQAAAAAAvm3DBqNt0UKqWdP94w8fLlWrJu3bJ61c6f7xEdBCQkIkSefPnzc5CQAAAADAX9n+TWn7N6Y3UTQCAAAAAAB8m21pmsREz4wfGSmNGmVsT5/umWsgYAUFBSkqKko5OTlmRwEAAAAA+KmcnBxFRUUx0wgAAAAAAIADW9FIUpLnrpGcbLQLF0oHD3ruOghI0dHRys3NVUFBgdlRAAAAAAB+pqCgQLm5uYqOjjbl+hSNAAAAAAAA31VQIG3caGx7aqYRSWrb1ihKKSyUZs/23HUQkKKjo2WxWHTw4MHidagBAAAAALicoqIiHTx4UBaLhaIRAAAAAAAABz/+KOXmSrGxUkKCZ681frzRzpwp8eAf5RASEqKGDRsqNzdXmZmZzDgCAAAAALisgoICZWZmKjc3Vw0bNlRISIgpOcy5KgAAAAAAgDNsS9P06CF5el3f4cOlhx+W9u+XVqyQbrjBs9dDQImKilLDhg2Vnp6unTt3KioqStHR0QoLC1NQUJAsFovZEQEAAAAAJrJarSoqKtL58+eVk5Oj3NxcWSwWNWzYUFFRUablomgEAAAAAAD4LlvRiCeXprGJjJRGjZJefVWaMYOiEZRbVFSUmjdvrpycHOXk5OjQoUNmRwIAAAAA+KCoqCjVqVNH0dHRps0wYmOxWq1WUxMAHpSdna3Y2FidOnVKMTExZscBAAAAAJRX48bSgQPSqlVS376ev962bdKVV0rBwVJ6ulS3rueviYBVVFSkgoICFbHcEQAAAABAUlBQkEJCQhTk6dlU5fyzcmYaAQAAAAAAvikjwygYCQqSrr7aO9ds21ZKSpJSU6XZs6W//90710VACgoKUlhYmNkxAAAAAAC4KM+XrwAAAAAAALhi/Xqjbd9eqlrVe9cdP95oZ86UmCECAAAAAAAEMIpGAAAAAACAb0pLM9rERO9ed/hwqXp1af9+acUK714bAAAAAADAiygaAQAAAAAAvik11WiTkrx73chIadQoY3vGDO9eGwAAAAAAwIsoGgEAAAAAAL7nzBlp61Zj29szjUhScrLRLlwoZWV5//oAAAAAAABeQNEIAAAAAADwPVu2SAUFUr16UqNG3r9+mzZSz55SYaE0Z473rw8AAAAAAOAFFI0AAAAAAADfk5ZmtImJksViTgbbbCMzZ0pFReZkAAAAAAAA8CCKRgAAAAAAgO9JTTXapCTzMgwfLlWvLu3fL61YYV4OAAAAAAAAD6FoBAAAAAAA+Bar1X6mEbNERkqjRhnbM2aYlwMAAAAAAMBDKBoBAAAAAAC+ZccO6cQJKSJC6tDB3Cy2JWoWLpSysszNAgAAAAAA4GYUjQAAAAAAAN9im2Wka1cpLMzcLG3aSD17SoWF0pw55mYBAAAAAABwM4pGAAAAAACAb7EVjSQlmZvDxjbbyMyZRvEIAAAAAABAgKBoBAAAAAAA+JbUVKNNTDQ3h83w4VL16tL+/dLKlWanAQAAAAAAcBuKRgAAAAAAgO84cUL65Rdju0cPc7PYREZKo0YZ2zNmmJsFAAAAAADAjULMDuBvjh49qs2bN2v37t3Kzs5WaGio4uLi1KZNG3Xp0kWhoaFmR7Rz/Phx7d69W+np6Tp8+LByc3N17tw5Va1aVbGxsWrQoIE6deqkuLg4j2f5+eef9dNPPykrK0tnz55VVFSUGjZsqA4dOqh58+Yevz4AAAAAwA9s2GC0LVtK8fHmZikpOVl69VVp4UIpK0uqV8/sRAAAAAAAABVG0YiTPv74Y7366qtKTU2V1Wot85jo6GiNHDlSTzzxhFq2bOnlhFJubq7S0tKUmpqqDRs26Mcff9TBgwedOrdFixa66667NGbMGDVu3NhtmX7//XdNnTpVKSkpyszMvOT177vvPt1///2KjIx02/UBAAAAAH4mLc1ok5LMzVFamzZSz57SunXSnDnS3/9udiIAAAAAAIAKs1gvVgEBSVJmZqbuuusurVmzxulzwsLCNHHiRE2cOFEWi8WD6ey99dZbuv/++ys0RmhoqP7yl79o4sSJqlKlSoXG+uyzzzRu3DgdO3bM6XOaNGmiefPmqYebpiDOzs5WbGysTp06pZiYGLeMCQAAAADwoD59pK+/lmbOlO691+w09t5911impnFjafduKTjY7EQAAAAAAABlcvZZeZAXM/mdHTt2qGvXruUqGJGk8+fP6+mnn9Zdd92lwsJCD6XzjPz8fL3wwgvq1atXuYo9Snvttdc0bNiwco+xb98+9e7dW59//rnL1wYAAAAA+Kn8fGnTJmM7MdHcLGUZPlyqXl3av19audLsNAAAAAAAABXG8jQXcfz4cfXv37/M5V06d+6sW265RU2bNtXZs2e1Y8cOzZs3T1lZWXbHzZ8/X7Vq1dLUqVO9lNpeeHi4OnTooNatW6tFixaqXbu2oqOjZbFYlJ2drd27d2vTpk1as2aNQ3HLd999p379+mnTpk0KDw8v13U/+ugjPfzwww79oaGhGj58uLp27aq6devq4MGD2rRpk/773/8qPz+/+Ljz589r5MiR+uabb9StWzfX/vAAAAAAAP/z44/SmTNStWpS69Zmp3EUGWnMNPLqq9L06dINN5idCAAAAAAAoEJYnuYibr31Vn3yySd2fdHR0Xr33Xd1yy23OByfn5+vf/7zn3rmmWccvrdkyRINHDjQU1GLzZo1S7Nnz9bAgQPVv39/derUSWFhYZc97+DBg/r73/+uOXPmOHzvySef1D//+U+nM2RmZiohIUE5OTl2/T169NBHH32k+vXrO5yTkZGhESNGaMOGDXb9TZo00bZt2yq0TA7L0wAAAACAH/nPf6SHHpIGDZIWLzY7Tdm2b5fatjWWpjlwQKpXz+xEAAAAAAAADliepgJWrlzpUDASFhamr776qsyCEcmYRWPy5Mllziry4IMPqqCgwBNR7dx7771KS0vTpEmT1L17d6cKRiSpbt26mj17tl588UWH77322ms6c+aM0xmeeOIJh4KRpKQkrVq1qsyCEUlq0KCBvvrqKyWWmnp43759eumll5y+NgAAAADAz6WmGq0vLk1j06aN1LOnVFgolfHyBQAAAAAAgD+haKQMzz33nEPf5MmT1aVLl8ue+/DDD6t///52fbt379a8efPcls9T/vrXv6p79+52fbm5uVqyZIlT5+/atUsffPCBXV9kZKTmzp2ryMjIS557seNeffVVZWdnO3V9AAAAAICfS0szWl8uGpGk8eONduZMo3gEAAAAAADAT1E0Usr27du1du1au774+Hg99thjTo/xwgsvOPS9+eabFc7mDXfddZdD3/bt2506d/r06SoqKrLru/fee9W8eXOnzm/RooXuueceu75Tp05p/vz5Tp0PAAAAAPBj6enGV3CwdPXVZqe5tFtvlapXl/bvl1auNDsNAAAAAACAyygaKaWsAoWxY8cqPDzc6TE6d+6szp072/Vt2LBBe/furXA+T7viiisc+g4ePOjUuaVnGZGk8ba3r5xU1vEUjQAAAABAJbB+vdF26CBFRZka5bIiI6VRo4zt6dPNzQIAAAAAAFABFI2UsmzZMoe+4cOHl3ucss4pa2xfk5eX59AXFhZ22fO2bdumjIwMu76EhAS1bdu2XNe/8sor1apVK7u+1NRU5eTklGscAAAAAICfSU01Wl9fmsYmOdlov/hCysoyNwsAAAAAAICLKBopITc3V999951dX5UqVdSxY8dyj9WrVy+HvtLL3viizZs3O/SVNftIaWX92Xr27OlShtKfXUFBgdbb3jgDAAAAAASmtDSj9ZeikTZtpJ49pcJCafZss9MAAAAAAAC4hKKREr7//nsVFRXZ9XXp0kWhoaHlHqtr164O53377bcVyudpJ06cUEpKikP/9ddff9lzy/qz9ejRw6UciWX8gtDXPzsAAAAAQAXk5kpbtxrb/lI0Ikm2JVZnzTKKRwAAAAAAAPwMRSMl/Prrrw59zZs3d2mssLAwNWjQwK5v9+7dKigocGk8Tzt69KhuuukmHTlyxK5/4MCBat269WXPd+dnV9bMJr/99ptLYwEAAAAA/MCWLUbRRf36UsOGZqdx3q23StWrS/v3SytXmp0GAAAAAACg3CgaKWHfvn0OfY0bN3Z5vEaNGtntFxYW6sCBAy6P524FBQX64YcfNGnSJLVs2VJptqmA/6d69ep68803nRrLnZ9d6c9Nkvbs2ePSWAAAAAAAP5CaarRJSZLFYm6W8oiMlEaPNranTzc3CwAAAAAAgAtCzA7gSw4dOuTQ17ACbziVde7hw4fVrFkzl8csr+3bt+vOO++06yssLNTp06eVlZWl8+fPl3le7dq1tWTJEqcLPw4fPmy3b7FYVL9+fZcyN2jQQBaLRVar9aLjAwAAAAACiO0lBn9amsZm3Dhp6lTpiy+krCypXj2zEwEAAAAAADiNopESTpw44dBXtWpVl8cr69zjx4+7PJ4rzpw5ox9++MHp40NDQ3X33XfrxRdfVM2aNZ06JycnR/n5+XZ9kZGRCg4OLldWm5CQEIWHhysvL6+4z9ufGwAAAADAS4qKpPXrjW1/LBpp00bq2VNat06aPVuaONHsRAAAAAAAAE5jeZoScnNzHfoiIyNdHq+sc8+cOePyeJ42dOhQ7dixQykpKU4XjEju/9zKOt/Zz+3cuXPKzs62+wIAAAAA+LAdO6QTJ4ylXjp0MDuNa8aPN9pZs6TCQnOzAAAAAAAAlANFIyWUni1DkiIiIlwer6zCiYstB+MLPv30U7Vv316PPfaYjhw54vR57v7cJMfPztnP7YUXXlBsbGzxV0WWFwIAAAAAeEFqqtFefbUUGmpuFlfdeqtUvbq0f7+0YoXZaQAAAAAAAJxG0chlWCwWt55rtVorEqfcunTpIqvVaveVl5enQ4cOae3atfrXv/6ldu3aFR+fnZ2tV155RW3bttXixYtdvm5FPreyznf2c3vyySd16tSp4q/09PQK5QAAAAAAeFhamtH649I0NpGR0ujRxvaMGeZmAQAAAAAAKAeKRkoILeONprNnz7o8XlnnhoWFuTyeu4SHh6t27drq2bOnHn/8cf34449asGCB4uPji485duyYhgwZok8++eSy47n7cyvrfGc/t/DwcMXExNh9AQAAAAB8WCAUjUhScrLRfvGFlJVlbhYAAAAAAAAnUTRSQpUqVRz63F00EhUV5fJ4njRixAh98803doUjBQUFGjNmjPbs2XPJc939uZV1vq9+bgAAAACACjh+XPr1V2O7Rw9zs1RUQoLUq5dUWCjNnm12GgAAAAAAAKdQNFJCXFycQ9/p06ddHq+sc8u6hq9ISEjQG2+8YdeXk5Ojp59++pLnxcTEOMw2kpeXp8LCQpdyFBQUKC8vz67Plz83AAAAAICLNmww2tatpUD4d59ttpFZs4ziEQAAAAAAAB9H0UgJtWvXdujLyMhwebz09HSnruFLRo4cqZYtW9r1ffjhhzp58uQlz6tVq5bdflFRkbJcnI43MzNTVqvVrs/XPzcAAAAAgAtSU43W35emsbn1Vql6dWn/fmnFCrPTAAAAAAAAXBZFIyU0bdrUoW///v0uj3fgwAG7/eDgYDVq1Mjl8bxl4MCBdvsFBQVKtf0i7yLc+dmV/twuNj4AAAAAwM+lpRltoBSNREZKo0cb2zNmmJsFAAAAAADACRSNlNCqVSuHvl27drk01vnz5x1mGrniiisUEhLi0nje1KRJE4e+PXv2XPIcd352u3fvduhr3bq1S2MBAAAAAHxUfr60aZOxHShFI9KFJWq++EJycQZOAAAAAAAAb6FopISOHTsqKMj+I9myZYsKCgrKPdaWLVuUn59v19epU6cK5fOWiIgIh76cnJxLntO5c2eHvvXr17t0/TTbm2Yl+MtnBwAAAABw0g8/SGfPSjVqSGW8iOC3EhKkXr2kwkJp9myz0wAAAAAAAFwSRSMlREVFqWPHjnZ9ubm52rp1a7nHWrdunUPfNddc43I2bzpy5IhDX3x8/CXP6dWrl0NfWZ+BM0qfFxISoh49erg0FgAAAADAR9mWQe3RQwoKsF9P2GYbmTnTKB4BAAAAAADwUQH2W5mKu+GGGxz6/vvf/5Z7nI8//tipsX3Rhg0bHPrq1KlzyXOuvPJK1a9f365v+/bt+uWXX8p17bLOSUxMVExMTLnGAQAAAAD4ONssk4G0NI3N8OFS9erSgQPSihVmpwEAAAAAALgoikZKueOOOxz65syZo/Pnzzs9xtatW7V582a7vm7duqlp06YVzudphw4d0urVq+36goKC1K1bt8uee/vttzv0TZ8+vVzXL+v4sv43AQAAAAD4uUAuGomIkEaPNrZnzDA3CwAAAAAAwCVQNFJK27Zt1bNnT7u+I0eOaMqUKU6P8dRTTzn03X///RXO5g2PPfaY8vLy7Pp69Oih2rVrX/bc5ORkBZWaUnjmzJnas2ePU9fevXu3Zs2aZdcXGxtL0QgAAAAABJr0dCkjQwoOlq6+2uw0nmFbouaLL6SsLHOzAAAAAAAAXARFI2WYOHGiQ9/kyZP13XffXfbc119/XcuWLbPra9q0qe68806nrt27d29ZLBa7r7lz5172vPfff18fffSRrFarU9cpraioSI899pjmzZvn8L2HHnrIqTFatmypkSNH2vWdOXNGY8aMcShEKS0vL0+jR4/WmTNn7PoffPBBxcbGOnV9AAAAAICfSE012o4dpSpVzM3iKQkJUq9eUmGhNHu22WkAAAAAAADKRNFIGQYMGKBbbrnFru/cuXPq06ePvvjiizLPyc/P13PPPVdmgcVrr72m0NBQj2S12blzp0aOHKl27drp5Zdf1v79+506r6ioSCtXrlS3bt30yiuvOHz/+uuvdygEuZR//etfqlq1ql3f2rVrdd111ynrIm9WZWZmqm/fvkq1/dLwfxo3bqwnn3zS6WsDAAAAAPxEIC9NU5JttpGZM43iEQAAAAAAAB8TYnYAXzVr1ix9++23ysjIKO7Lzs7WzTffrC5duuiWW25R06ZNdfbsWe3cuVPvv/++MjMzHcb505/+pMGDB3st97Zt2/TEE0/oiSeeUPPmzdWpUyddeeWVio+PV7Vq1RQSEqKcnBwdPnxYP/zwg9atW1dmbkm68sor9e6775br+g0bNtTMmTMdlpRJTU1V06ZNNWLECHXt2lV16tTRwYMHtWnTJn388cfKz8+3Oz40NFTz589XlUB94wwAAAAAKrPKUjQyfLj00EPSgQPSihXSwIFmJwIAAAAAALBD0chFxMfHa8WKFerbt68OHTpk970tW7Zoy5Ytlx1j5MiRmjp1qocSXt6uXbu0a9cuLViwoNznJiUl6bPPPlN8fHy5z7399tt18OBBPfroo3b958+f1/vvv6/333//kueHhobqgw8+UI8ePcp9bQAAAACAj8vNlb7/3thOSjI1isdFREijR0tTp0rTp1M0AgAAAAAAfA7L01xCQkKCNm/erF69epXrvNDQUD3zzDOaP3++goODPZTOXlRUlFvGiYuL01tvvaW1a9e6VDBi88gjj+i///2v4uLiynVe48aNtXr1ag0bNszlawMAAAAAfNimTcZSLQ0bSg0amJ3G82xL1CxaJF1kpk8AAAAAAACzUDRyGQ0aNNCaNWv04YcfKjExURaL5aLHVq1aVWPHjtVPP/2kyZMnKyjIex/v448/rj179uiNN97QiBEj1KhRI6fPrVGjhm666SYtWLBAmZmZGj9+/CX/nM4aNmyYduzYoUmTJqlevXqXPLZ58+Z6+eWX9csvvygp0N80AwAAAIDKrLIsTWOTkCD16mUUysyZY3YaAAAAAAAAOxar1Wo1O4Q/OXLkiDZt2qQ9e/YoOztbISEhio+PV0JCgrp27aqwsDCzIxY7duyYdu3apX379uno0aPKzc1VQUGBYmJiFBMTo/j4eLVr106NGzf2eBar1aqff/5ZP/74o7KyspSXl6cqVaqoYcOG6tixo1q0aOGR62ZnZys2NlanTp1STEyMR64BAAAAACiHG2+UliyRXn1Veughs9N4x3vvSXffLTVqJO3ZI3lpVlIAAAAAAFB5OfusnKIRBDSKRgAAAADAhxQVSfHx0u+/S5s3S126mJ3IO/LypHr1jD/3kiXSwIFmJwIAAAAAAAHO2WflLE8DAAAAAAC849dfjcKJKlWk9u3NTuM9ERHS6NHG9vTp5mYBAAAAAAAogaIRAAAAAADgHWlpRnv11VJoqLlZvC052WgXLZIyM83NAgAAAAAA8D8UjQAAAAAAAO+wFY0kJpqbwwwJCVKvXlJhoTR7ttlpAAAAAAAAJFE0AgAAAAAAvKUyF41I0vjxRjtrllE8AgAAAAAAYDKKRgAAAAAAgOcdOyb99pux3aOHuVnMcuutUo0a0oED0ooVZqcBAAAAAACgaAQAAAAAAHjB+vVGm5BgFE5URhER0ujRxvb06eZmAQAAAAAAEEUjAAAAAADAGyr70jQ248YZ7aJFUmamuVkAAAAAAEClR9EIAAAAAADwPIpGDAkJUq9eUmGhNHu22WkAAAAAAEAlR9EIAAAAAADwrPPnpU2bjO2kJHOz+ILx44121iyjeAQAAAAAAMAkFI0AAAAAAADP+v57KS9PqlFDatnS7DTmu/VW47M4cEBavtzsNAAAAAAAoBKjaAQAAAAAAHhWyaVpLBZzs/iCiAhp9Ghje8YMc7MAAAAAAIBKjaIRAAAAAADgWSWLRmAYN85oFy2SMjPNzQIAAAAAACotikYAAAAAAIDnWK1SaqqxnZRkbhZfkpAgXXONVFgozZ5tdhoAAAAAAFBJUTQCAAAAAAA858ABKStLCgmRunQxO41vSU422lmzjOIRAAAAAAAAL6NoBAAAAAAAeI5taZqOHaUqVczN4mtuvVWqUcMorFm+3Ow0AAAAAACgEqJoBAAAAAAAeI6taCQx0dwcvigiQho92tieMcPcLAAAAAAAoFKiaAQAAAAAAHiOrWgkKcncHL5q3DijXbRIysw0NwsAAAAAAKh0KBoBAAAAAACecfq09MMPxnaPHuZm8VUJCdI110iFhdLs2WanAQAAAAAAlQxFIwAAAAAAwDM2bTKKIRo1kho0MDuN70pONtpZs4zPCwAAAAAAwEsoGgEAAAAAAJ5hW5omMdHcHL7u1lulGjWkAwek5cvNTgMAAAAAACoRikYAAAAAAIBn2IpGkpLMzeHrIiKk0aON7RkzzM0CAAAAAAAqFYpGAAAAAACA+xUVSevXG9vMNHJ548YZ7aJFUmamuVkAAAAAAEClQdEIAAAAAABwv19+kU6elKpUka66yuw0vi8hQbrmGqmwUJo92+w0AAAAAACgkqBoBAAAAAAAuJ9taZpu3aSQEHOz+IvkZKOdOdMoHgEAAAAAAPAwikYAAAAAAID72YpGkpLMzeFPbr1VqlFDSk+Xli83Ow0AAAAAAKgEKBoBAAAAAADul5pqtImJ5ubwJxER0ujRxvaMGeZmAQAAAAAAlQJFIwAAAAAAwL2OHpV27jS2u3c3N4u/sS1Rs2iRlJlpbhYAAAAAABDwKBoBAAAAAADutX690bZpI1Wvbm4Wf9O6tXTNNVJhoTR7ttlpAAAAAABAgKNoBAAAAAAAuFdamtGyNI1rbLONzJxpFI8AAAAAAAB4CEUjAAAAAADAvVJTjTYpydwc/urWW6UaNaT0dGn5crPTAAAAAACAAEbRCAAAAAAAcJ/z56XNm41tZhpxTUSENHq0sT19urlZAAAAAABAQKNoBAAAAAAAuM/WrdK5c1JcnNSihdlp/JdtiZpFi6TMTHOzAAAAAACAgEXRCAAAAAAAcJ+0NKNNTJQsFnOz+LPWraVrrpGKiqSUFLPTAAAAAACAAEXRCAAAAAAAcJ/UVKNNSjI3RyAYP95oZ82SCgvNzQIAAAAAAAISRSMAAAAAAMA9rNYLRSOJieZmCQTDhkk1akjp6dLy5WanAQAAAAAAAYiiEQAAAAAA4B7790uHDkkhIVKXLman8X8REdLo0cb29OnmZgEAAAAAAAGJohEAAAAAAOAeaWlG26mTFBlpbpZAkZxstIsWSZmZ5mYBAAAAAAABh6IRAAAAAADgHralaZKSzM0RSFq3lq65RioqklJSzE4DAAAAAAACDEUjAAAAAADAPWwzjSQmmpsj0Iwfb7SzZkmFheZmAQAAAAAAAYWiEQAAAAAAUHE5OdKPPxrbFI2417BhUlyclJ4uLVtmdhoAAAAAABBAKBoBAAAAAAAVt2mTsYRK48ZSvXpmpwksERHS6NHG9owZ5mYBAAAAAAABhaIRAAAAAABQcbalaZKSzM0RqMaNM9pFi6TMTHOzAAAAAACAgEHRCAAAAAAAqLjUVKNlaRrPaN1auvZaYzaXlBSz0wAAAAAAgABB0QgAAAAAAKiYoiJp/Xpjm6IRz0lONtpZs6TCQnOzAAAAAACAgEDRCAAAAAAAqJjt26XsbCkqSmrXzuw0gWvYMCkuTkpPl5YtMzsNAAAAAAAIABSNAAAAAACAiklLM9ru3aWQEHOzBLKICGn0aGN7xgxzswAAAAAAgIDAb3IAAAAAAEDFpKYaLUvTeN64cdIrr0iLFkkZGVKDBmYnAgAAlcWBA9KqVVJOjhQdLfXrJzVqZHYqAABQQcw0AgAAAAAAKsY20whFI57XurV07bVSUZE0e7bZaQAAQGWwaZN0001SkybSH/8oPfKI0TZpIt18s7R5s9kJAQBABVA0AgAAAAAAXHfkiLRrl2SxGMvTwPOSk4121iypsNDcLAAAILB98omUlCQtXSpZrUZfUZHRWq3SkiVG4fAnn5iXEQAAVAhFIwAAAAAAwHXr1xtt27ZStWqmRqk0hg2T4uKk9HRp2TKz0wAAgEC1aZN0221GkerFClVt37vtNmYcAQDAT1E0AgAAAAAAXJeaarQsTeM9ERHS6NHG9owZ5mYBAACB6/nnjdlEbDOMXIztmOef904uAADgVhSNAAAAAAAA16WlGS1FI941bpzRLlokZWSYmwUAAASeAweMnzOcXQqvsFD64gvjPAAA4FcoGgEAAAAAAK45d07assXYpmjEu1q3lq69VioqkmbPNjsNAAAINKtWXX6GkdKsVumrrzyTBwAAeAxFIwAAAAAAwDVbtxqFIzVrSs2bm52m8klONtpZs5x/CxgAAMAZOTlSUDkfIQUFSdnZnskDAAA8hqIRAAAAAADgmtRUo01MlCwWc7NURsOGSXFxUnq6tGyZ2WkAAEAgiY42ZjQrj6IiKSbGM3kAAIDHUDQCAAAAAABck5ZmtCxNY46ICGn0aGN7xgxzswAAgMDSr1/5i4ItFqlvX8/kAQAAHkPRCAAAAAAAKD+rlaIRX2BbombRIikjw9wsAAAgcDRqJA0eLAUHO3d8cLB0003GeQAAwK9QNAIAAAAAAMpv3z7p0CEpNFTq3NnsNJVXq1bStdca08HPnm12GgAAEEgmTTJmD7ncjCO2YyZO9E4uAADgVhSNAAAAAACA8ktNNdrOnaXISHOzVHa22UZmzZIKC83NAgAAAkfXrtKHHxqziARd5HGSxWJ8f8EC43gAAOB3KBoBAAAAAADlx9I0vmPYMCkuTkpPl5YtMzsNAAAIJMOGGT/31ax5oa9kAUlIiPTVV9LQod7PBgAA3IKiEQAAAAAAUH4UjfiOiAhp9Ghje/p0c7MAAIDAc9VV0unTxvbkydKUKVJKilSvnpSfL+3ZY24+AABQIRar1Wo1OwTgKdnZ2YqNjdWpU6cUExNjdhwAAAAACAzZ2VL16lJRkZSVJdWta3Yi/Pab1Lq18ebv/v1SgwZmJwIAAIFixQppwADjZ77MTGNJGkl64QXpqaeMZWk2bTI3IwAAcODss3JmGgEAAAAAAOWzcaNRMNK0KQUjvqJVK+naa43/XWbPNjsNAAAIJIsXG+2gQRcKRiTpnnuksDBp82bjCwAA+CWKRgAAAAAAQPmwNI1vGj/eaGfNkgoLzc0CAAACg9V6oWjkxhvtv1erljRihLE9bZp3cwEAALehaAQAAAAAAJQPRSO+aehQKS5OSk+Xli0zOw0AAAgEO3ZIu3dLoaHSddc5fn/CBKP94APp+HHvZgMAAG5B0QgAAAAAAHBeYaG0YYOxTdGIb4mIkEaPNranTzc3CwAACAy2WUauvVaKjnb8fvfuUocOUl6eNGeOV6MBAAD3oGgEAAAAAAA4b9s2KTtbqlpVatfO7DQoLTnZaBcvljIyzM0CAAD838WWprGxWKQHHjC233xTKiryTi4AAOA2FI0AAAAAAADn2Zam6d5dCg42NwsctWplvAlcVCSlpJidBgAA+LPsbOmbb4ztwYMvftydd0qxsdKePdLy5d7JBgAA3IaiEQAAAAAA4Dxb0QhL0/iu8eONdtYsYzkhAAAAV6xcKRUUSC1bSs2bX/y4qChpzBhje9o0r0QDAADuQ9EIAAAAAABwHkUjvm/YMCkuzlieZtkys9MAAAB/dbmlaUq6//4L5+zd67lMAADA7SgaAQAAAAAAzjl8WNq921i7vnt3s9PgYsLDL7ztO326qVEAAICfKiqSliwxtp0pGmnVSrruOslq5ecPAAD8DEUjAAAAAADAObZZRq680li3Hr5r3DijXbzYmHEEAACgPL77zigYjo6WevVy7pwJE4w2JUXKy/NcNgAA4FYUjQAAAAAAAOewNI3/aNVKuvZa4y3hlBSz0wAAAH9jW5qmf38pLMy5cwYPlho2lI4dkz76yHPZAACAW1E0AgAAAAAAnEPRiH8ZP95oZ82SCgvNzQIAAPyLrWjEmaVpbEJCLvz8MW2a+zMBAACPoGgEAAAAAABc3rlz0pYtxnZSkrlZ4Jxhw6S4OGN5mqVLzU4DAAD8xeHD0ubNxvagQeU79957pdBQacMGY4kbAADg8ygaAQAAAAAAl/ftt9L581KtWlKzZmangTPCw6UxY4ztGTNMjQIAAPzIkiVG27mzVKdO+c6tXVsaPtzYZrYRAAD8AkUjAAAAAADg8kouTWOxmJsFzhs3zmgXL5bS083NAgAA/IMrS9OU9MADRjtvnvT77+7JBAAAPIaiEQAAAAAAcHkli0bgP1q1knr3loqKpNmzzU4DAAB83fnz0ooVxrarRSNJSdJVV0lnz0pz57otGgAA8AyKRgAAAAAAwKVZrReKRpKSzM2C8ktONtpZs6TCQnOzAAAA37ZunZSTYyxJ2KWLa2NYLBdmG5k2zSheBQAAPouiEQAAAAAAcGl79kiHD0thYVKnTmanQXkNGybFxUkZGdLSpWanAQAAvsy2NM3AgVJQBR4h3XWXFBMj7dolffmle7IBAACPoGgEAAAAAABcmm2Wkc6dpYgIc7Og/MLDpTFjjO0ZM0yNAgAAfJytaMTVpWlsqlaVRo82tt94o2JjAQAAj6JoBAAAAAAAXJqtaCQx0dwccN24cUa7eLGUnm5uFgAA4Jt275Z++00KCZGuv77i49mWqFm0SNq/v+LjAQAAj6BoBAAAAAAAXBpFI/6vVSupd2+pqEiaPdvsNAAAwBfZZhnp2VOKja34eK1bS337Gj9/TJ9e8fEAAIBHUDQCAAAAAAAu7tQp6aefjG2KRvxbcrLRzpolFRSYmwUAAPgeW9HI4MHuG3PCBKOdNUs6d8594wIAALehaAQAAAAAAFzcxo2S1So1aybVqWN2GlTEsGFSXJyUkSEtW2Z2GgAA4EtOn5a+/trYvvFG9417881SvXrS0aPSxx+7b1wAAOA2FI0AAAAAAICLY2mawBEeLo0ZY2zPmGFqFAAA4GNWrZLOnzcKhVu1ct+4ISHS+PHG9rRp7hsXAAC4DUUjAAAAAADg4igaCSy2JWoWL5bS083NAgAAfIdtaZobb5QsFveOPW6cUTySliZ9/717xwYAABVG0QgAAAAAAChbYaG0YYOxnZRkbha4R8uWUu/eUlGRNHu22WkAAIAvsFqlJUuMbXcuTWNTt66xTJ7EbCMAAPggikYAAAAAAEDZfv5ZysmRoqOltm3NTgN3sc02MmuWVFBgbhYAAGC+H36QMjOlKlWka6/1zDUmTDDa99+XTp70zDUAAIBLKBoBAAAAAABlsy1N0727FBxsbha4z7BhUlyclJEhLVtmdhoAAGA229I0110nRUR45hq9ehlFyGfOSG+/7ZlrAAAAl1A0AgAAAAAAymYrGklMNDcH3Cs8XBozxtiePt3UKAAAwAfYikY8sTSNjcVyYbaRadOMJXEAAIBPoGgEAAAAAACULTXVaJOSzM0B97MtUbNkiZSebm4WAABgnqNHpQ0bjO1Bgzx7rT/8wVj2cMcOadUqz14LAAA4jaIRAAAAAADg6OBBae9e463Qbt3MTgN3a9lS6t1bKiqSZs82Ow0AADDLsmXGrB/t20sNGnj2WtHR0qhRxva0aZ69FgAAcBpFIwAAAAAAwNH69Ubbrp0UE2NuFnjG+PFGO2uWVFBgbhYAAGAObyxNU9L99xvt558z2xkAAD6CohEAAAAAAOAoLc1oExPNzQHPGTpUiouTMjKMt4wBAEDlUlAgLV9ubHuraKRt2wuznc2Y4Z1rAgCAS6JoBAAAAAAAOLIVjSQlmZsDnhMeLo0ZY2xPn25qFAAAYIK0NOnkSaOI1JvLET7wgNHOnCmdP++96wIAgDJRNAIAAAAAAOzl5UnffmtsM9NIYEtONtolS5giHgCAysa2NM0NN0jBwd677pAhUt260uHD0iefeO+6AACgTBSNAAAAAAAAe99+a7z1Wbu21LSp2WngSS1bXpgiPiXF7DQAAMCbbEUj3lqaxiY09ELh6htvePfaAADAAUUjAAAAAADAnm1pmsREyWIxNws8b/x4o01JkQoKzM0CAAC8Y/9+ads2KShIGjDA+9dPTjZmN1m3TvrxR+9fHwAAFKNoBAAAAAAA2LMVjSQlmZsD3jF0qBQfL2VkSMuWmZ0GAAB4g22WkaQkqUYN71+/Xj3jZxBJmjbN+9cHAADFKBoBAAAAAAAXWK1SaqqxnZhobhZ4R3i4NGaMsT19uqlRAACAl5i1NE1JEyYY7XvvSadOmZcDAIBKjqIRAAAAAABwwe7d0tGjUliY1KmT2WngLePGGe2SJVJ6urlZAACAZ505I331lbFtZtHItddKbdpIubnSO++YlwMAgEqOohEAAAAAAHCBbWmaLl2MGShQObRsKfXuLRUVSSkpZqcBAACetHq1lJcnNWoktW1rXg6LRXrgAWN72jRjxjsAAOB1FI0AAAAAAIALbEUjSUnm5oD3jR9vtCkpUkGBuVkAAIDnlFyaxmIxN8vdd0tRUdKvvxrFLAAAwOsoGgEAAAAAABekphptYqK5OeB9Q4dK8fFSRoa0dKnZaQAAgCdYrfZFI2aLiTEKRyRjthEAAOB1FI0AAAAAAADDyZPStm3Gdo8epkaBCcLDpTFjjO0ZM0yNAgAAPGTbNunAASkiQurTx+w0BtsSNZ99ZhSvAgAAr6JoBAAAAAAAGDZuNN4+veIKqXZts9PADOPGGe2SJVJ6urlZAACA+y1aZLR9+0pVqpibxaZdO6lXL6mwUJo50+w0AABUOhSNAAAAAAAAQ1qa0bI0TeXVsqXx1nFRkZSSYnYaAADgbr60NE1JEyYY7YwZUn6+uVkAAKhkKBoBAAAAAACG1FSjTUoyNwfMlZxstCkpUkGBuVkAAID7nDhxoUjY14pGhg41Zro7dEj69FOz0wAAUKlQNAIAAAAAAIzigI0bjW1mGqnchg6V4uOljAxp6VKz0wAAAHdZvtyYTaxtW6lxY7PT2AsLu1C4Om2auVkAAKhkKBoBAAAAAADSzz9Lp09LMTFSmzZmp4GZwsOlMWOM7RkzTI0CAADcyFeXprFJTpaCg6U1a4yfTQEAgFdQNAIAAAAAAC5MVd69u/HLelRu48YZ7ZIlUnq6uVkAAEDFFRZKy5YZ275aNNKggXTLLcb2m2+amwUAgEqEohEAAAAAACClphptUpK5OeAbWraU+vQxprBPSTE7DQAAqKiNG6Xjx6Vq1Xx7KcIHHjDad96RsrPNzQIAQCVB0QgAAAAAALgw04gvP0SAdyUnG21KilRQYG4WAABQMbalaQYMkEJCzM1yKX37Sq1aGcsmvvee2WkAAKgUfPgnA9909OhRbd68Wbt371Z2drZCQ0MVFxenNm3aqEuXLgoNDTU7op3CwkLt3LlT27dv15EjR3Ty5EkFBQWpevXqiouLU4cOHdSsWTOzYwIAAAAAzJSVJe3bJwUFSVdfbXYa+IqhQ6X4eCkjQ1q6VLrpJrMTAQAAV9mKRgYPNjfH5VgsxmwjDz8svfGGdP/9Rh8AAPAYikac9PHHH+vVV19VamqqrFZrmcdER0dr5MiReuKJJ9SyZUsvJzQUFRUpLS1NX375pVavXq2NGzfq3LlzlzynZs2auvnmmzVhwgR17NixQte3uPGHt/nz5+v2229323gAAAAAgItYv95o27WTYmLMzQLfER4ujRkj/fvf0owZFI0AAOCvMjKkH34wii9uuMHsNJc3erT05JPS9u3SN99I115rdiIAAAIay9NcRmZmpnr37q0RI0Zo3bp1Fy0YkaScnBylpKSoXbt2eu655y55rLvt3btXDz74oBo0aKBevXrp2Wef1TfffHPZghHJmD0lJSVFnTp10pAhQ3To0CEvJAYAAAAA+IzUVKNNSjI3B3zPuHFGu2SJlJ5ubhYAAOCaJUuMtnt3YxYxXxcbK/3hD8b2G2+YmwUAgEqAopFL2LFjh7p27ao1a9aU67zz58/r6aef1l133aXCwkIPpbO3Zs0avf766zp48GCFxvn888/Vtm1bpdnWsgYAAAAABD7bvwETE83NAd/TsqXUp49UVCSlpJidBgAAuMK2NM2NN5qbozweeMBoP/3UWEoRAAB4DMvTXMTx48fVv3//MoswOnfurFtuuUVNmzbV2bNntWPHDs2bN09ZpX5wmT9/vmrVqqWpU6d6KbWj4OBgdezYUT179lTDhg1Vq1YtBQcH6/Dhw9q4caMWL16snJwcu3NOnDihgQMHatWqVerSpUuFrl+7dm3VqVPHpXOrV69eoWsDAAAAAJxw9qz03XfGNkUjKEtysrR6tTRrljRxohTCr5MAAPAbeXnSl18a2/5UNNK+vTELXmqqNHOmNHmy2YkAAAhY/Cv/IpKTk3XgwAG7vujoaL377ru65ZZbHI7/5z//qX/+85965pln7PpfffVVDRgwQAMHDvRkXDsWi0V9+vTRmDFjNGTIEEVHR1/02JMnT+of//iHpk6darecTnZ2tu666y799NNPCgsLcznLfffd5/CZAAAAAAB8yLffSvn5Up06UpMmZqeBLxo61JjKPjNTWrpUuukmsxMBAABnrVkjnTkj1a9vFGL4kwkTjKKRGTOkp56SQkPNTgQAQEBieZoyrFy5Up988oldX1hYmL766qsyC0YkKTQ0VJMnTy5zVpEHH3xQBQUFnohqJyQkRGPHjtXOnTu1atUq3X333ZcsGJGkatWq6ZVXXtGcOXNksVjsvrdjxw795z//8WRkAAAAAIDZUlONNilJKvXvQkCSFB4ujRljbE+fbmoUAABQTralaQYN8r+f9YYNk2rVMpan+fxzs9MAABCwKBopw3PPPefQN3nyZKeWann44YfVv39/u77du3dr3rx5bstXls6dO+vXX3/V7NmzdcUVV5T7/NGjR2vChAkO/e+884474gEAAAAAfFVamtGyNA0uZdw4o126VCo1MysAAPBRVqu0aJGx7U9L09iEh1/4GWTaNHOzAAAQwCgaKWX79u1au3atXV98fLwee+wxp8d44YUXHPrefPPNCme7lHbt2rlULFLS008/reDgYLu+H3/8Uenp6RUaFwAAAADgo6xWikbgnJYtpT59pKIiafZss9MAAABn/PqrtHevFBYm9etndhrXJCdLQUHS6tXS9u1mpwEAICBRNFLK/PnzHfrGjh2r8PBwp8fo3LmzOnfubNe3YcMG7d27t8L5PKlmzZplzqayZ88eE9IAAAAAADxu1y7p2DHjLc6OHc1OA1+XnGy0s2ZJXliGFwAAVJBtaZrevaWqVU2N4rJGjaSbbjK2PfxyLgAAlRVFI6UsW7bMoW/48OHlHqesc8oa29c0atTIoe/QoUMmJAEAAAAAeJxtlpGuXY3CEeBShg6V4uOlzExjmRoAAODbbEUj/rg0TUkTJhjt229Lp0+bmwUAgABE0UgJubm5+u677+z6qlSpoo4uvG3Vq1cvh77Sy974ojNnzjj0RUZGmpAEAAAAAOBxqalGy9I0cEZ4uDRmjLE9fbqpUQAAwGWcOiWtW2ds+3vRSL9+UosWUk6O9N57ZqcBACDgUDRSwvfff6+ioiK7vi5duig0NLTcY3Xt2tXhvG+//bZC+bxh165dDn1169Y1IQkAAAAAwONsM41QNAJnjRtntEuXSgcOmJsFAABc3IoVxnJyrVpJV1xhdpqKCQqSHnjA2J42TbJazc0DAECAoWikhF9//dWhr3nz5i6NFRYWpgYNGtj17d69WwU+vObvtm3b9Ntvv9n1RUZGqm3bti6PuXXrVj3yyCPq3r276tevr4iICMXExKhJkybq2rWr7rvvPs2bN09Hjx6taHwAAAAAQHmcPClt22Zs9+hhahT4kZYtpT59pKIiKSXF7DQAAOBiAmVpGpvRo6XISOmnny7MoAIAANyCopES9u3b59DXuHFjl8dr1KiR3X5hYaEO+PBbONPLmFq2f//+qlKlistjLly4UFOnTtXGjRuVlZWlc+fOKScnR/v379eWLVs0ffp03XXXXWrUqJHGjx+v3bt3V+SPAAAAAABw1oYNRtuihVSrlrlZ4F/GjzfalBTjDWYAAOBbioqMWcGkwCkaqV5duusuY3vaNHOzAAAQYCgaKeHQoUMOfQ0bNnR5vLLOPXz4sMvjedJvv/1WZtHIONu0sx6Wl5enGTNmqF27dkrhTSUAAAAA8LzUVKNlaRqU15AhUny8lJl54YEUAADwHVu2SEeOSNHRUs+eZqdxH9sSNf/9r1TG8xwAAOAaikZKOHHihENf1apVXR6vrHOPHz/u8niekp+fr7vvvlvnz5+36+/Zs6cGDx5c4fFDQkJUt25dtW7dWi1atFBcXNxFjz179qzuvfdePfjggxW+LgAAAADgEtLSjJaiEZRXeLg0dqyxXcYLKAAAwGS2pWkGDJDCwszN4k4dOxrLKubnS7NmmZ0GAICAQdFICbm5uQ59kZGRLo9X1rlnzpxxeTxPeeyxx7R582a7voiIiDJnHnFGeHi4brzxRr3++uv64YcflJubq6ysLP3yyy/asWOHjh07psOHD+ujjz5S//79yxzj9ddf10svveTS9QEAAAAAl1FQIG3caGxTNAJX3Huv0S5dKvnwUrwAAFRKtqKRQFmapiTbbCPTp7NMHgAAbkLRSAn5+fkOfRERES6PV1bRSOnZPMz21ltv6T//+Y9D/8svv6w2bdqUe7yXX35ZGRkZWrRokSZMmKCrrrpKYWVUMteqVUvDhw/XihUrtHr1atWrV8/hmCeffFJbtmwp1/XPnTun7Oxsuy8AAAAAQCk//STl5kqxsZIL//YD1LKl1KePVFQkscwsAAC+4+BB6dtvje2BA83N4gkjRhjL5GVkSF98YXYaAAACAkUjl2GxWNx6rtVqrUgct1q4cKH+9Kc/OfTfddddZfY74y9/+Yvi4+PLdU7v3r21adMmNWjQwK7farXqb3/7W7nGeuGFFxQbG1v81bBhw3KdDwAAAACVQmqq0fboIQXxqwG4aPx4o01J4U1fAAB8xdKlRtu1q1S7trlZPCE8/MKMZ2+8YW4WAAACBL8ZKiE0NNSh7+zZsy6PV9a5Zc26YYavvvpKt912mwoLC+36r7vuOqWY8IZQ/fr19dlnnymo1C8rV61ape+//97pcZ588kmdOnWq+Cs9Pd3NSQEAAAAgAKSlGS1L06Aihgwx3vTNzJSWLDE7DQAAkAJ7aRqb++6TLBZp1Srp11/NTgMAgN+jaKSEKlWqOPS5u2gkKirK5fHcJS0tTbfccovy8vLs+nv27KnPPvtM4eHhpuTq3Lmz7rjjDof+pbbKaCeEh4crJibG7gsAAAAAUApFI3CH8HBp7Fhje8YMc7MAAADp/Hlp5UpjO5CLRho3lgYPNrbffNPcLAAABACKRkqIi4tz6Dt9+rTL45V1blnX8KbNmzdr4MCBDtmuvvpqLV682PSiljvvvNOhb9WqVSYkAQAAAIAAlZkp7d9vLEtz9dVmp4G/GzfOaJculQ4cMDcLAACV3dq1Uk6OsSxNp05mp/GsCROMdu5cKTfX1CgAAPg7ikZKqF3G+n4ZGRkuj1fW0ihlXcNbtm7dqgEDBig7O9uuv1OnTlq+fLlPzMpx7bXXOvQd4JdOAAAAAOA+tllG2reXoqPNzQL/16KF1LevVFQkmbDcLQAAKMG2NM2gQUaBcCDr319q3lzKzpbef9/sNAAA+LUA/6mhfJo2berQt3//fpfHK13sEBwcrEaNGrk8XkX8+OOPuu666/T777/b9bdv314rV65UtWrVTMlVWlRUlEPxytGjR01KAwAAAAABiKVp4G7JyUabkiIVFJibBQCAymzRIqMN5KVpbIKCpPvvN7bfeEOyWs3NAwCAH6NopIRWrVo59O3atculsc6fP+8w08gVV1yhkJAQl8ariJ9//ln9+vXTiRMn7PrbtWunL7/8UjVq1PB6pkspvUTO2bNnTUoCAAAAAAGIohG425AhUny8sfTRkiVmpwEAoHLaudP4Cg01ZuGoDMaMkSIipB9/vPAzLgAAKDeKRkro2LGjgkpN2bZlyxYVuPCWzJYtW5Sfn2/X18mENQS3b9+ufv366dixY3b9bdu21apVqxQfH+/1TJdTOqsvZgQAAAAAv3T2rPTdd8Y2RSNwl/BwaexYY3vGDHOzAABQWdmWpunVS/KBpei9okYN6c47je1p08zNAgCAH6NopISoqCh17NjRri83N1dbt24t91jr1q1z6LvmmmtczuaKX3/9VX379tWRI0fs+lu3bq1Vq1apZs2aXs3jjF27djkU2/hiTgAAAADwS5s3G8uH1KsnNW5sdhoEknHjjHbpUqnUcr0AAMALbEUjlWFpmpIeeMBoP/pIOnzY3CwAAPgpikZKueGGGxz6/vvf/5Z7nI8//tipsT1lx44d6tu3rw6X+iGpVatWWr16tWrXru21LOWxpIxpbNu3b29CEgAAAAAIQCWXprFYzM2CwNKihdS3r1RUJKWkmJ0GAIDKJSdHWrPG2K5sRSOdO0tXXy3l5/MzCAAALqJopJQ77rjDoW/OnDk6f/6802Ns3bpVmzdvtuvr1q2bmjZtWuF8zti1a5f69OmjgwcP2vW3aNFCX331lerUqeOVHOV1/vx5TZkyxaF/4MCBJqQBAAAAgABUsmgEcLfkZKNNSTFmtAEAAN7x5ZdG0cQVV0gtW5qdxvsmTDDat96SCgvNzQIAgB+iaKSUtm3bqmfPnnZ9R44cKbOY4WKeeuoph77777+/wtmcsXfvXvXt21dZWVl2/VdccYVWr16tevXqeSWHK/7+979r3759dn1Vq1bVgAEDzAkEAAAAAIHEaqVoBJ41dKhUs6aUmSmVMZMoAADwkJJL01TG2eRGjpTi4qT0dGnRIrPTAADgdygaKcPEiRMd+iZPnqzvvvvusue+/vrrWrZsmV1f06ZNdeeddzp17d69e8tisdh9zZ0716lz09PT1bdvX6Wnp9v1N2vWTKtXr1b9+vWdGscVCxYs0IYNG1w612q16vnnn9e///1vh+89/vjjqlatWgXTAQAAAAC0Y4d0/LgUESF17Gh2GgSisDBpzBhje8YMU6MAAFBpWK0XijUr29I0NhER0j33GNvTppmbBQAAP0TRSBkGDBigW265xa7v3Llz6tOnj7744osyz8nPz9dzzz2nhx56yOF7r732mkJDQz2S1SYrK0t9+/Z1mKmjSZMmWr16tRo2bOjR66elpalHjx7q3bu3UlJSdOzYMafO27Bhg/r3769JkyY5fO+KK67QY4895u6oAAAAAFA52WYZ6drVeLgPeMK4cUa7dKl04IC5WQAAqAy2bpUOHpSioqRrrzU7jXnuu8+YZWXFCqNYGgAAOC3E7AC+atasWfr222+VkZFR3Jedna2bb75ZXbp00S233KKmTZvq7Nmz2rlzp95//31lZmY6jPOnP/1JgwcP9njeyZMna9euXQ79BQUFuvnmmys09n333af77rvPqWPXrFmjNWvWaPz48WrXrp3at2+vhIQE1ahRQ7GxsSosLNSJEyf0yy+/aM2aNfr555/LHKdWrVpavny5oqKiKpQdAAAAAPA/LE0Db2jRQurbV/rqKyklRXr2WbMTAQAQ2GxL0/TvL4WHm5vFTE2bSoMGGZ/HW29Jr7xidiIAAPwGRSMXER8frxUrVqhv3746dOiQ3fe2bNmiLVu2XHaMkSNHaurUqR5KaC8/P7/M/oyMDLvCF1eU/vM7o7CwUN9//72+//77cp+bkJCgBQsW6Iorrij3uQAAAACAi6BoBN6SnGwUjcyaJU2aJIXw6ycAADzGVjRSWZemKemBB4zPY84c6fnnpSpVzE4EAIBfYHmaS0hISNDmzZvVq1evcp0XGhqqZ555RvPnz1dwcLCH0gWeiIgIPfLII/ruu+905ZVXmh0HAAAAAALHiRPS9u3GNkUj8LShQ6WaNaWsLGnJErPTAAAQuI4elTZtMrYHDTI3iy+44QZjxpGTJ6X5881OAwCA36Bo5DIaNGigNWvW6MMPP1RiYqIsFstFj61atarGjh2rn376SZMnT1ZQUOX5eCdNmqQFCxbogQceUOfOnRUREeHUeaGhoeratateeuklZWZm6pVXXnH6XAAAAACAkzZsMNqWLaX4eHOzIPCFhUljxhjbM2aYGgUAgIC2dKlktUodO0r16pmdxnxBQdL99xvbb7xhfDYAAOCyLFYrd83yOHLkiDZt2qQ9e/YoOztbISEhio+PV0JCgrp27aqwsDCzI/qEgoIC7d69W/v371dGRoZOnTqlM2fOKDg4WLGxsapevboaNWqkTp06ebRIJDs7W7GxsTp16pRiYmI8dh0AAAAA8GkTJ0r/93/Gg/w5c8xOg8pg506jSCkoSNq7V2rUyOxEAAAEnttukxYsMH7We+45s9P4huPHpfr1pXPnpPXrpe7dzU4EAIBpnH1WzqKy5VSrVi0NHjzY7Bg+LyQkRK1atVKrVq3MjgIAAAAASEszWpamgbe0aCH17St99ZWUkiI9+6zZiQAACCz5+dLy5cb2jTeam8WXxMVJt98uvf22MdsIRSMAAFxW5Vk/BQAAAACAyqigQNq40dhOSjI3CyqX5GSjnTXL+HsIAADcJy1NOnXKWHqwa1ez0/iWCROMdsEC6ehRc7MAAOAHKBoBAAAAACCQ/fCDdOaMVK2a1Lq12WlQmQwdKtWsKWVlSUuWmJ0GAIDAsmiR0Q4cKAUHm5vF13TtKnXpIp0/b8x4BgAALomiEQAAAAAAApltaZoePaQgfg0ALwoLk8aMMbanTzc1CgAAAWfxYqNlaZqy2WYbeestqbDQ3CwAAPg4flsEAAAAAEAgsxWNJCaamwOV07hxRrt0qXTggLlZAAAIFHv3Sr/8YswwMmCA2Wl80223STVqSPv3M+MZAACXQdEIAAAAAACBzFY0kpRkbg5UTi1aSH37SlYr08MDAOAutllGkpKMJQjhKDJS+uMfje1p08zNAgCAj6NoBAAAAACAQJWRYczuEBxsrO0OmGH8eKOdNUsqKDA3CwAAgYClaZxz332SxSItWybt2mV2GgAAfBZFIwAAAAAABCrbLCPt20tVq5qbBZXXkCFSzZpSVhbTwwMAUFG5udLq1cY2RSOXdsUV0g03GNtvvWVuFgAAfBhFIwAAAAAABCpb0Uhiork5ULmFhUljxhjb06ebGgUAAL/31VfSuXNS48ZSmzZmp/F9DzxgtLNnS2fPmpsFAAAfRdEIAAAAAACBiqIR+Ipx44x26VJjySQAAOAa29I0gwcbS6/g0gYONApsfv9d+uADs9MAAOCTKBoBAAAAACAQnTkjbd1qbCclmZsFaNFC6ttXslqlWbPMTgMAgH+yWi8UjbA0jXOCg6X77ze2p00zNwsAAD6KohEAAAAAAALR5s1SQYFUv77UsKHZaQBp/HijTUkx/m4CAIDy+eknKSNDioyUevc2O43/+OMfjeXytmyRNm0yOw0AAD6HohEAAAAAAAJRyaVpmLocvmDIEKlmTSkr68Jb0gAAwHm2+2e/fkbhCJxTs6Z0223GNrONAADggKIRAAAAAAACUcmiEcAXhIVJY8ca2zNmmJsFAAB/xNI0rnvgAaP94APp2DFzswAA4GMoGgEAAAAAINAUFV0oGklKMjcLUNK4cUa7dKl04IC5WQAA8CfHj0vr1xvbgwaZm8UfdesmdeoknTsnzZljdhoAAHwKRSMAAAAAAASaHTukEyeMacs7dDA7DXBB8+bGlPpWqzRrltlpAADwH8uXG4XB7dpJjRqZncb/WCwXZht5802psNDcPAAA+BCKRgAAAAAACDS2WUa6dpVCQ83NApSWnGy0KSlSQYG5WQAA8BcsTVNxd9whVasm7d1rFOEAAABJFI0AAAAAABB4bEUjiYnm5gDKMmSIVLOmlJV14QEYAAC4uIICY2k3iaKRiqhSRRo71th+4w1zswAA4EMoGgEAAAAAINCkphptUpK5OYCyhIVdeGAzY4a5WQAA8AcbNki//y5Vry517252Gv92//1Gu3SptGePuVkAAPARFI0AAAAAABBIjh+Xfv3V2OahAnzVuHFGu3SptH+/uVkAAPB1tpm5brhBCgkxN4u/a9FCuv56yWqV3nrL7DQAAPgEikYAAAAAAAgkGzYYbatWUny8uVmAi2neXOrXz3hgk5JidhoAAHybrWiEpWncY8IEo01Jkc6eNTcLAAA+gKIRAAAAAAACSVqa0SYmmpsDuJzkZKNNSZEKCszNAgCArzpwQPrpJykoyJhpBBV3441So0bSiRPSggVmpwEAwHQUjQAAAAAAEEhSU402KcncHMDlDBki1awpZWVdeIMaAADYW7LEaLt3l+LizM0SKIKDpfvuM7anTTM3CwAAPoCiEQAAAAAAAkV+vrRpk7HNTCPwdWFh0tixxvaMGeZmAQDAV7E0jWfcc4/xs8imTdKWLWanAQDAVBSNAAAAAAAQKH74wViXvXp1qVUrs9MAlzdunNEuXSrt329uFgAAfM3Zs9KqVcY2RSPuVauWNGKEsc1sIwCASo6iEQAAAAAAAkVamtH26GGsew/4uubNpX79JKtVSkkxOw0AAL7l66+NwpEGDaSrrjI7TeB54AGjnT9fOn7c3CwAAJiI3yABAAAAABAoUlONNinJ3BxAeSQnG21KilRQYG4WAAB8ScmlaSwWc7MEoh49pA4dpLw8ae5cs9MAAGAaikYAAAAAAAgUtplGEhPNzQGUx5AhUs2aUlbWhYdjAABUdlarfdEI3M9iuTDbyJtvSkVF5uYBAMAkFI0AAAAAABAI0tOljAwpOFjq2tXsNIDzwsKksWON7RkzzM0CAICv+OUXad8+KTxc6tvX7DSB6847pdhYafduacUKs9MAAGAKikYAAAAAAAgEtllGOnSQoqJMjQKU27hxRrt0qbR/v7lZAADwBbZZRvr04Wc7T4qKksaMMbanTTM1CgAAZqFoBAAAAACAQGArGklKMjcH4IrmzaV+/Yyp+FNSzE4DAID5WJrGe+6/32gXLTJmdwEAoJKhaAQAAAAAgECQmmq0iYnm5gBclZxstCkpUkGBuVkAADDTyZPSunXGNkUjnteqlXTddUbx6vTpZqcBAMDrKBoBAAAAAMDf5eZK339vbFM0An81ZIhUs6aUlXXh7WoAACqjFSukwkIpIUFq2tTsNJXDAw8Y7axZUl6euVkAAPAyikYAAAAAAPB3mzcbDxYaNJAaNjQ7DeCasDBp7Fhjm7d8AQCV2aJFRsssI95z003Gz9LHjkkff2x2GgAAvIqiEQAAAAD4/+zdd3iUVeL28XvS6IQaegkdEaUKJKiI0tsq9k4RkOiubV1ddXHX/u66lp+EDjbsjap0QROQLiDSa2ihSAKhpM37x9kJhARIeSZnZvL9XNdc58yTmTO32RVD5p5zAH8XH29GdhmBv3voITP+8IO0e7fdLAAA2JCRIX3/vZlTGik6ISHS8OFmPnq03SwAABQxSiMAAAAAAPi7uDgzRkfbzQEUVqNG0o03Sm63NGmS7TQAABS9FSvMbhfh4fxsV9SGDpVCQ6Vly6TVq22nAQCgyFAaAQAAAADAn2VmSkuXmjk7jSAQeD7lO2mSlJ5uNwsAAEVt1iwzdu9uCgwoOtWrSwMHmnlsrN0sAAAUIUojAAAAAAD4s82bpT/+kEqVkq6+2nYaoPAGDJCqVpX27z/3xhkAAMWF5799HE1jR0yMGT/5xPyMDQBAMUBpBAAAAAAAfxYfb8ZrruHTqAgMYWHSoEFmPm6c3SwAABSl/fulNWskl0vq1ct2muIpOlpq2VI6fVp6/33baQAAKBKURgAAAAAA8GdxcWbkzHsEkoceMuMPP0i7d9vNAgBAUZk924zt20sREXazFFcu17ndRsaMMUdBAgAQ4CiNAAAAAADgzzw7jURF2c0BOKlRI+nGGyW3W5o40XYaAACKBkfT+IZ77pHKl5e2bpXmz7edBgAAr6M0AgAAAACAvzpyRNq82cw7drSbBXDa8OFmnDRJSk+3mwUAAG87e1aaN8/M+/a1m6W4K1tWeuABM4+NtZsFAIAiQGkEAAAAAAB/tWyZGZs1kypXtpsFcNqAAWZr/gMHzn3yGgCAQLVkiZSSItWoIbVubTsNHn7YjDNmSHv22M0CAICXURoBAAAAAMBfxcWZMTrabg7AG8LCpEGDzHzcOLtZAADwNk9BsndvyeWymwVS8+ZS165SZiY/hwAAAh6lEQAAAAAA/FV8vBmjouzmALxl6FAz/vCDtHu33SwAAHiTpzTSp4/dHDhn5EgzTpxojg8CACBAURoBAAAAAMAfpaVJy5ebOaURBKpGjaQbb5TcbvOGDQAAgWjLFmnbNik0VLrpJttp4DFggFSzppSYKH39te00AAB4DaURAAAAAAD80dq10pkzUqVKUpMmttMA3jN8uBknTZLS0+1mAQDAGzy7jFx/vVSunN0sOCck5NzPIbGxdrMAAOBFlEYAAAAAAPBHcXFmjIqSgvjrPQLYgAFSRIR04IA0c6btNAAAOI+jaXzXQw+Z8khcnPTrr7bTAADgFfxWCQAAAAAAfxQfb0aOpkGgCwuTBg0y8/Hj7WYBAMBpycnSkiVmTmnE99SoId1yi5mz2wgAIEBRGgEAAAAAwN+43dl3GgEC3dChZvzhB2n3brtZAABw0rx5Ulqa1LixucH3jBxpxo8/lo4ftxoFAABvoDQCAAAAAIC/2btX2r/fbJXdvr3tNID3NWok3XSTKUxNnGg7DQAAzuFoGt933XVSixbSqVPShx/aTgMAgOMojQAAAAAA4G88u4y0bi2VLm03C1BUhg0z46RJUnq63SwAADghM1OaPdvMKY34Lpfr3G4jsbGmxAoAQAChNAIAAAAAgL+JjzcjR9OgOBkwQIqIkA4ckGbOtJ0GAIDCW71aOnRIKlvW7GYB33XffeZ/p82bpYULbacBAMBRlEYAAAAAAPA3lEZQHIWFSYMGmfn48XazAADgBM/RNN26mf/OwXeVKyfdf7+Zjx5tNwsAAA6jNAIAAAAAgD85eVL69VczpzSC4mboUDP+8IO0a5fVKAAAFJqnNMLRNP7Bc0TNtGlSQoLdLAAAOIjSCAAAAAAA/mT5cikjQ6pbV6pd23YaoGg1aiTddJPkdkuTJtlOAwBAwR06JK1YYea9e9vNgrxp0UK6/nopM1MaN852GgAAHENpBAAAAAAAf8LRNCjuhg0z46RJUlqa3SwAABTU99+bsU0bqUYNu1mQdzExZpwwQUpNtZsFAACHUBoBAAAAAMCfUBpBcTdggBQRIR04cG5bfwAA/I3nv2F9+9rNgfz5059MyefQIembb2ynAQDAEZRGAAAAAADwF5mZ0tKlZk5pBMVVWJg0aJCZjx9vNwsAAAWRlibNnWvmffrYzYL8CQ09t+tZbKzdLAAAOITSCAAAAAAA/mLTJun4cal0aenqq22nAex56CEz/vCDtGuX1SgAAOTbzz9Lyclm56x27WynQX499JAUHCz99JO0fr3tNAAAFBqlEQAAAAAA/EVcnBk7dJBCQuxmAWxq2FC66SbJ7ZYmTbKdBgCA/PEcTdOrlxTE2zR+p1Yt6eabzZzdRgAAAYCfRgAAAAAA8Bfx8WbkaBrg3NbwkyaZbf4BAPAXntIIR9P4r5EjzfjRR1JSkt0sAAAUEqURAAAAAAD8BaUR4JwBA8y2/gcOnHvzDQAAX7djhzlyMCRE6t7ddhoUVJcuUvPmUkqKKY4AAODHKI0AAAAAAOAPjhyRtmwx844d7WYBfEFYmDRokJmPG2c3CwAAeeUpOnbuLIWH282CgnO5zu02EhtrjswDAMBPURoBAAAAAMAfeHYZueIKqVIlu1kAX/HQQ2acM0fatctqFAAA8oSjaQLHffdJZcpIv/8u/fij7TQAABQYpREAAAAAAPwBR9MAOTVsKN10k/l076RJttMAAHBpJ09KixaZOaUR/xceboojktltBAAAP0VpBAAAAAAAf0BpBMjd8OFmnDRJSkuzmwUAgEtZsEBKTZUiI6VmzWyngRM8R9R8+620b5/dLAAAFBClEQAAAAAAfF1qqrRihZlTGgGy699fioiQDhw4t+U/AAC+6PyjaVwuu1ngjJYtpWuvlTIypAkTbKcBAKBAKI0AAAAAAODr1qyRzpyRKleWmjSxnQbwLWFh0qBBZj5unN0sAABcjNstzZ5t5hxNE1g8u42MH8+uZwAAv0RpBAAAAAAAX3f+0TR8KhXI6aGHzDhnjrRrl9UoAADk6tdfzfElpUtLXbrYTgMn3XKLVK2a2fXsu+9spwEAIN8ojQAAAAAA4OvOL40AyKlhQ+mmm8ynuCdOtJ0GAICcPEfT3HijVLKk3SxwVljYuQLr6NF2swAAUACURgAAAAAA8GVuN6URIC+GDzfj5MlsDQ8A8D2e0ghH0wSm4cOl4GBp8WLpt99spwEAIF8ojQAAAAAA4Mt275b275dCQqT27W2nAXxX//5SRITZGt7zxhwAAL7gyBFp2TIzpzQSmGrXNj+LSFJsrN0sAADkE6URAAAAAAB8mWeXkTZtpFKl7GYBfFlYmDR4sJmPG2c3CwAA5/vhB7N73NVXm3IBAlNMjBk//FA6ccJuFgAA8oHSCAAAAAAAvoyjaYC8GzrUjHPmSLt2WY0CAEAWjqYpHrp2lZo2lU6elD76yHYaAADyjNIIAAAAAAC+jNIIkHcNG0o33WQ+zT1xou00AABI6elmpxGJ0kigc7mkkSPNPDbW/DwCAIAfoDQCAAAAAICvOnFC+vVXM4+OtpsF8BfDh5tx8mQpLc1uFgAAli6Vjh+XKleWOnSwnQbedv/9UunS0m+/SUuW2E4DAECeUBoBAAAAAMBXLV8uZWZK9epJNWvaTgP4h/79pYgI6cABaeZM22kAAMWd52ianj2l4GC7WeB9FSpI995r5rGxVqMAAJBXlEYAAAAAAPBVHE0D5F9YmDR4sJmPH283CwAAntIIR9MUH54jar75xpRYAQDwcZRGAAAAAADwVZRGgIIZOtSMc+ZIu3ZZjQIAKMZ275Y2bJCCgqQePWynQVG5+mpztGR6ujRhgu00AABcFqURAAAAAAB8UWamtHSpmUdH280C+JuGDaVu3SS3W5o40XYaAEBx5dllJCpKqlTJbhYULc9uI+PGSWlpdrMAAHAZlEYAAAAAAPBFGzdKSUlSmTJSy5a20wD+Z9gwM06ezJs1AAA7OJqm+Bo4UIqIkPbvl6ZPt50GAIBLojQCAAAAAIAv8hxN06GDFBJiNwvgj/r3N2/WHDggzZxpOw0AoLg5dUpauNDMKY0UPyVKnDsuLzbWbhYAAC6D0ggAAAAAAL7IUxqJirKbA/BXYWHS4MFmPn683SwAgOJn0SLpzBmpTh3pyittp4ENw4dLQUGmPPT777bTAABwUZRGAAAAAADwRZ7SSHS03RyAP/N8wnfOHGnXLqtRAADFzPlH07hcdrPAjrp1pX79zHzMGLtZAAC4BEojAAAAAAD4msREaetWM+/Y0W4WwJ81bCh16ya53dLEibbTAACKC7c7e2kExdfIkWb84APp5Em7WQAAuAhKIwAAAAAA+JqlS83YooVUoYLVKIDfGzbMjJMnS2lpdrMAAIqH336T9uyRSpaUuna1nQY23XST1LixlJwsTZ1qOw0AALmiNAIAAAAAgK/xHE0TFWU3BxAIBgyQqlWTDhyQZs60nQYAUBx4dhm54QapdGm7WWBXUJD08MNmPnq02YUGAAAfQ2kEAAAAAABfQ2kEcE5oqDRokJmPH283CwCgePCURvr2tZsDvuHBB6VSpaT166W4ONtpAADIgdIIAAAAAAC+5OxZacUKM4+OtpsFCBRDh5pxzhxp1y6rUQAAAe6PP84VgPv0sZsFvqFiRenuu8189Gi7WQAAyAWlEQAAAAAAfMmaNaY4UqWK1KiR7TRAYGjYUOrWzWwJP3Gi7TQAgEA2Z46UkSG1aCHVq2c7DXzFyJFm/Ppr6eBBu1kAALgApREAAAAAAHzJ+UfTuFx2swCBZNgwM06aJKWl2c0CAAhcnqNp2GUE52vTRurY0fwMQoEVAOBjKI0AAAAAAOBLzi+NAHDOgAFStWrm070zZ9pOAwAIRBkZ0vffmzmlEVwoJsaM48ZJ6el2swAAcB5KIwAAAAAA+Aq3W4qLM/PoaLtZgEATGioNGmTm48fbzQIACEzLl0tHj0oVKlAARk633mqOoExIkGbMsJ0GAIAslEYAAAAAAPAVu3aZXRBCQ6W2bW2nAQLPQw+Zcc4c8+8bAABO8hxN06OHFBJiNwt8T8mS0tChZh4bazcLAADnoTQCAAAAAICv8BxN06aNVKqU3SxAIGrQQOrWzezqM3Gi7TQAgEDjKY1wNA0uZvhwyeWS5s+XNm+2nQYAAEmURgAAAAAA8B2e0gjbmQPeM2yYGSdNktLS7GYBAASOhARp7VpTCOjZ03Ya+Kr69aW+fc18zBirUQAA8KA0AgAAAACAr4iLM2N0tN0cQCAbMECqVs0cBTVzpu00AIBAMXu2GTt0kKpWtZsFvm3kSDO+/76UkmI1CgAAEqURAAAAAAB8Q3KytH69mXfqZDcLEMhCQ6VBg8x83Di7WQAAgYOjaZBX3btLDRtKSUnSJ5/YTgMAAKURAAAAAAB8wvLlUmam2bK6Zk3baYDA9tBDZpw7V9q5024WAID/O3NGmj/fzCmN4HKCgqSHHzbz2FjJ7babBwBQ7FEaAQAAAADAF8THmzEqym4OoDho0EDq1s28STNxou00AAB/t3ixdOqUKf62amU7DfzBoEFSyZLS2rXS0qW20wAAijlKIwAAAAAA+IK4ODNGR9vNARQXw4ebcfJkKS3NbhYAgH/zHE3Tu7fkctnNAv9QqZJ0111mHhtrNwsAoNijNAIAAAAAgG0ZGdKyZWbOTiNA0ejfX6pWTTp4UJo503YaAIC/crvPlUY4mgb5MXKkGb/8UkpMtJsFAFCsURoBAAAAAMC2jRul5GSpbFnpyittpwGKh9BQafBgMx83zm4WAID/2rxZ2rFDCguTbrrJdhr4k3btpGuukVJTpUmTbKcBABRjlEYAAAAAALAtPt6MHTpIISF2swDFydChZpw7V9q5024WAIB/8uwy0qWLKQAD+eHZbWTsWLP7IAAAFlAaAQAAAADAtrg4M0ZH280BFDcNGkjdupmjBSZOtJ0GAOCPOJoGhXHHHVKlStKePef+vwQAQBGjNAIAAAAAgG2enUaiouzmAIqj4cPNOHmylJZmNwsAwL8kJUk//WTmlEZQECVLSkOGmPno0XazAACKLUojAAAAAADYdOiQtH275HKZ42kAFK3+/aVq1aSDB6UZM2ynAQD4k3nzpPR0qWlTqWFD22ngr0aMMH8XmDtX2rrVdhoAQDFEaQQAAAAAAJuWLjVjixZShQpWowDFUmioNHiwmY8fbzcLAMC/cDQNnNCggdSrl5mPGWM3CwCgWKI0AgAAAACATXFxZoyOtpsDKM6GDjXj3LnSzp12swAA/ENmpjR7tplTGkFhxcSYccoU6dQpu1kAAMUOpREAAAAAAGyKjzdjVJTdHEBx1qCB1L275HZLEyfaTgMA8AerVkmJiVK5clLnzrbTwN/16CFFRkrHj0uffmo7DQCgmKE0AgAAAACALWfPSitXmjmlEcCuYcPMOHmylJZmNwsAwPd5jqbp3l0KC7ObBf4vOFh6+GEzHz3aFFkBACgilEYAAAAAALBl9WopNVWqWlVq2NB2GqB4699fqlZNOnhQmjHDdhoAgK+bOdOMHE0DpwwaJJUoIa1ZI/3yi+00AIBihNIIAAAAAAC2eI6miY6WXC67WYDiLjRUGjzYzMePt5sFAODbDhwwx9NIUq9edrMgcFSpIt15p5nHxtrNAgAoViiNAAAAAABgS1ycGTmaBvANQ4eace5caedOu1kAAL7r++/N2K6dVL263SwILCNHmvHzz6XDh+1mAQAUG5RGAAAAAACwwe0+t9MIpRHANzRoIHXvbv79nDjRdhoAgK+aNcuMHE0Dp11zjSkjpaZKkyfbTgMAKCZcbrfbbTuEPzl8+LBWrFih7du3Kzk5WaGhoapcubKuuOIKtWvXTqGhobYjZpORkaGtW7dq48aNSkxM1PHjxxUUFKSKFSuqcuXKatWqlRo0aFAkWTZs2KD169dr//79On36tMqUKaM6deqoVatWatSokVdeMzk5WeHh4UpKSlL58uW98hoAAAAAUCA7dkgNG5ojMZKTpZIlbScCIElffy3deqv55PiePebfUQAAPFJTzTEiJ05Iy5dL7dvbToRAM2WKOTKvfn1p2zYpONh2IgCAn8rre+UhRZjJr3311Vd65513FBcXp4v1bMqVK6fbb79dTz/9tJo0aVLECY3MzEzFx8dr/vz5WrRokX755RedPXv2ks+pWrWq+vfvr5iYGLVu3drRPH/88YfefvttTZo0Sfv27bvo4xo3bqwRI0bo4YcfVqlSpRzNAAAAAAA+ybPLSNu2FEYAX9K/v1StmnTwoDRjhnTLLbYTAQB8yU8/mcJItWrm5zjAaXfeKT35pLRrlzkKqW9f24kAAAGO42kuY9++ferSpYtuu+02/fzzzxctjEjSiRMnNGnSJLVs2VIvvfTSJR/rtJ07d+rRRx9V7dq1de211+qf//ynlixZctnCiGR2T5k0aZLatGmjP/3pTzp48KAjmb777js1adJE//rXvy5ZGJGkrVu36sknn9QVV1yhpUuXOvL6AAAAAODT4uLMGB1tNweA7EJDzad7JWn8eLtZAAC+x3M0Ta9eUhBvscALSpU697NIbKzdLACAYoGfaC5hy5Ytat++vRYvXpyv56Wmpuof//iH7rnnHmVkZHgpXXaLFy/We++9pwMHDhRqnWnTpqlFixaK93zirYDeffdd3XLLLTpy5Ei+nrdr1y516dJF06ZNK9TrAwAAAIDP8/y9KyrKbg4AOQ0dasa5c6WdO+1mAQD4Fk9ppE8fuzkQ2B5+2Iw//CBt3243CwAg4HE8zUUcPXpU3bp1y7WE0bZtWw0YMECRkZE6ffq0tmzZok8++UT79+/P9rhPP/1UERERevvtt4sodU7BwcFq3bq1OnfurDp16igiIkLBwcE6dOiQfvnlF82aNUsnTpzI9pxjx46pV69eWrBggdq1a5fv1/zyyy/1l7/8Jcf10NBQ3XrrrWrfvr1q1KihAwcOaPny5fr666+VlpaW9bjU1FTdfvvtWrJkiTp06JD/f2gAAAAA8HXJydL69WbeqZPdLAByatBA6t7dlEYmTpReecV2IgCAL9i2TdqyRQoJMf+dALylYUOpZ09TGhk7Vvr3v20nAgAEMJe7KM9Q8SMDBw7UN998k+1auXLl9NFHH2nAgAE5Hp+WlqZXX31VL774Yo6vzZ49W7169fJWVEnS+++/r0GDBkmSXC6XbrjhBj344IP605/+pHLlyl30ecePH9e//vUvvf322zmO02nSpInWr1+vsLCwPOfYt2+fmjdvnqOI0qlTJ3355ZeqVatWjuckJCTotttu07Jly7Jdr1+/vn777TeVLl06z69/oeTkZIWHhyspKUnly5cv8DoAAAAA4Kh588wbDZGR0o4dttMAyM3XX0u33ipVry7t2WOOrQEAFG/vvCM99pjUtau0YIHtNAh0M2ZI/ftLlSpJCQnm2BoAAPIhr++VczxNLubNm5ejMBIWFqaFCxfmWhiRzC4ao0aNynVXkUcffVTp6eneiJpNSEiIBg0apK1bt2rBggW67777LlkYkaQKFSrov//9r6ZMmSKXy5Xta1u2bNH//d//5SvD008/naMwEh0drQULFuRaGJGk2rVra+HChYq6YEvmXbt26Y033sjX6wMAAACAX4iLM2N0tN0cAC6uf3+pWjXp4EHzpg0AABxNg6LUu7dUr5507Jj0+ee20wAAAhilkVy89NJLOa6NGjUqT0e1/OUvf1G3bt2yXdu+fbs++eQTx/Llpm3bttq0aZMmT56shg0b5vv5DzzwgGJiYnJc//DDD/O8xrZt2/TZZ59lu1aqVCm9//77KnWZBuzFHvfOO+8oOTk5zxkAAAAAwC/Ex5vxgvI8AB8SGioNHmzm48fbzQIAsO/kSWnxYjOnNIKiEBwsjRhh5qNH280CAAholEYusHHjRv3000/ZrlWpUkVPPvlkntd47bXXclwbM2ZMobNdSsuWLQtUFjnfP/7xDwUHB2e7tm7dOu3duzdPzx83bpwyMzOzXRs6dKgaNWqUp+c3btxYQ4YMyXYtKSlJn376aZ6eDwAAAAB+ISND8hzPSWkE8G0PPWTGuXOlnTvtZgEA2DV/vpSaKjVsKDVpYjsNioshQ6SwMGnlSmnFCttpAAABKsTbL3D27FmtXbtWW7Zs0a5du7R3716dOHFCKSkpSk9PV+nSpVWmTBlFRESoXr16ioyM1NVXX63atWt7O1qucisoDBo0SCVKlMjzGm3btlXbtm21atWqrGvLli3Tzp07FRkZ6UhOb6hataratWunX375Jdv1HTt2qE6dOpd9/oW7jEjS8OHD85Vh+PDheu+997Jd+/TTT/O9DgAAAAD4rN9+k06ckMqVk6680nYaAJcSGSl1725KIxMnSq+8YjsRAMCW84+mueCod8BrqlaVbr9d+vhjs9vI++/bTgQACECOl0bS0tK0ePFiff/99/rxxx+1YcMGpaen53udiIgIdejQQT169FDv3r1Vr149p6Pm6ocffshx7dZbb833Orfeemu20ohn7YcffrjA2YpC3bp1c5RGDh48eNnn/fbbb0pISMh2rXnz5mrRokW+Xv/KK69U06ZNtXnz5qxrcXFxOnHihMqVK5evtQAAAADAJ8XFmbFjR7PlNADfNmyYKY1Mniy9+KI5tgYAULy43dLs2WbO0TQoajExpjTy2WfSm29KlSvbTgQACDCOHU+zaNEiDRo0SFWqVFGPHj309ttva82aNUpLS5Pb7c737dChQ5oxY4YeeeQRNWjQQK1atdJbb72lQ4cOORU5h5SUFK1evTrbtdKlS6t169b5Xuvaa6/Nce3CY2980alTp3JcK1Wq1GWfl9s/W+fOnQuU4cLvXXp6upYuXVqgtQAAAADA58THm5GjaQD/0L+/VK2adPCgNGOG7TQAABvWrpX275fKlJGuv952GhQ3HTpIrVtLZ8+aEisAAA4rVGnk5MmTeuutt9SgQQPddNNN+vDDD3XixIms4ofL5SrU7fwSybp16/TUU0+pTp06uu222xTv+SWbg9auXavMzMxs19q1a6fQAnyCpH379jmed+HOI75o27ZtOa7VqFHjss/L7Z+tU6dOBcoQlcsvTv3hewcAAAAAeUJpBPAvoaHS4MFmPm6c3SwAADtmzjTjTTdJ+TjKHnCEy2V2G5GkMWOkC97HAgCgsApUGvnjjz/0zDPPqHbt2nrqqae0a9euXIsiBdlh5PxbbiWS9PR0ffPNN7r22mvVqVOnXI+TKahNmzbluNaoUaMCrRUWFqbatWtnu7Z9+/YCHdVTVH777bdsx8JIZpeRvBwx4+T3rmHDhjmuXZgLAAAAAPzSwYPSjh3mF78dOthOAyCvHnrIjHPnSjt32s0CACh6s2aZkaNpYMtdd0kVKpifQxx8XwwAAEkKyc+DU1JS9Oabb+q///1v1o4ikuRyuSQpq+whSSVLllTLli119dVXq2XLlqpXr55q166tGjVqqHTp0ipVqpRCQkJ0+vRpnT59WseOHVNCQoL27dunTZs26ddff9W6deuUkJCQ9fqe1/G81i+//KI+ffooOjpar732mqKjowv1zdi1a1eOa/Xq1SvwenXr1tXO836RkJGRoT179qhBgwYFXtObxuXyaZlu3bqpdOnSl32uk9+7unXr5ri2Y8eOAq0FAAAAAD7Fs8tIy5ZSeLjdLADyLjJS6t7dlEYmTpReecV2IgBAUTl8WFq+3Mx797abBcVX6dLSoEHSW29JsbH8fxEA4Kg8l0Y++eQTPf300zpw4ECuZRFJat26tfr166cbbrhBnTp1UlhY2GXXLVOmjMqUKaMqVaqoSZMmOb6+a9cuLVq0SPPnz9esWbOUnJyc47V//vlnXXfddbrjjjv0n//8RzVr1szrP1Y2Bw8ezHGtTp06BVrrYs89dOiQT5ZGNm/enGtp5CHPJ2ku49ChQ9nuu1wu1apVq0BZateunbWzzMXWBwAAAAC/xNE0gP8aPtyURiZPll580RxbAwAIfN9/L7ndUqtWUgF/5w044uGHTWlk9myz40hkpO1EAIAAkafjabp06aL77rtP+/fvzzo2RjKFjSZNmuj111/X9u3btWrVKr344ou6/vrr81QYyYv69etr0KBBmjp1qhITEzVjxgzdddddCgsLy3aEjdvt1ueff65mzZppxowZBXqtY8eO5bhWtmzZAmfP7blHjx4t8HrekpaWpvvuu0+pqanZrnfu3Fl9+/a97PNPnDihtLS0bNdKlSql4ODgAuUJCQlRiQvOhfTF7xsAAAAA5BulEcB/9esnVatmjpkq4O+eAAB+iKNp4CsaNzY7n7nd0tixttMAAAJInkojS5YskXRud4+goCDdcccd+vHHH/X777/r6aefVmQRNBrDwsLUp08fTZ06Vfv27dObb76p+vXrZ5VH3G63UlJStGbNmgKtn5KSkuNaqVKlCpw3t+eeOnWqwOt5y5NPPqkVK1Zku1ayZMlcdx7JjdPft9yen9fv29mzZ5WcnJztBgAAAAA+4cwZadUqM6c0Avif0FBp8GAzz+PvTAAAfi4tTZozx8wpjcAXjBxpxkmTzN8vAABwQJ5KI5LZVSQsLEzDhw/X5s2b9emnn+q6667zZrZLqlSpkh5//HFt3bpVH330ka688spCr3nhbhmSKU8UVG7FiQt387Bt7Nix+r//+78c1//973/riiuuyNMaTn/fpJzfu7x+31577TWFh4dn3QpzvBAAAAAAOGrVKik11exU4IPHlgLIA88xvnPnmm3hAQCBLT5eSkqSqlSRrrnGdhpA6ttXqltXOnpU+uIL22kAAAEiT6WRoKAgPfDAA9qyZYvGjBmjBj70y62goCDdc889+vXXX/XRRx+pfv36jq7v2V3Fqee63e7CxHHU9OnT9cgjj+S4fs899+R6PT8K833L7fl5/b49++yzSkpKyrrt3bu3UDkAAAAAwDHnH01TyL8zAbAkMtJsCy9JEybYzQIA8D7P0TS9ekkFPI4dcFRwsDR8uJnHxtrNAgAIGHkqjaxdu1ZTpkzx+V0b7rnnHm3atEl33XVXgZ4fGhqa49rp06cLnCe354aFhRV4PSctXLhQd9xxhzIyMrJdv+mmmzRp0qR8reX09y235+f1+1aiRAmVL18+2w0AAAAAfML5pREA/svzRs3kyebYAgBA4PKURjiaBr5k6FBzbN4vv5w7/hIAgELIU2nEiaNfikpoaKgaN25coOeWLl06xzWnSyNlypQp8HpOiY+P14ABA3TmgvPuOnfurO+++04lSpTI13pOf99ye74vfN8AAAAAoMDcbkojQKDo10+qXl06dEiaMcN2GgCAt+zaJW3caHZ26NHDdhrgnIgI6bbbzJzdRgAADshTaaS4qFy5co5rJ0+eLPB6uT03t9coSitWrFCvXr1yZLvmmms0a9asApUzypcvn2O3kTNnzuTYxSSv0tPTcxRabH/fAAAAAKBQtm+XEhOlsDCpbVvbaQAURmioNHiwmY8bZzcLAMB7PLuMREdLFSpYjQLkEBNjxk8+kY4ds5sFAOD3KI2cp1q1ajmuJSQkFHi9vXv35uk1isqaNWvUo0cPJScnZ7vepk0bzZkzp1BHuURERGS7n5mZqf379xdorX379sntdme7ZvP7BgAAAACF5tllpF07KZ+7OwLwQUOHmnHuXGnnTrtZAADewdE08GWdOklXXy2dOSO9/77tNAAAP0dp5DyRkZE5ru3evbvA6+3Zsyfb/eDgYNWtW7fA6xXGunXrdNNNN+mPP/7Idv3qq6/WvHnzVKGQTWknv3cXft8utj4AAAAA+A2OpgECS2Sk1L27mU+YYDcLAMB5p05JixaZOaUR+CKXSxo50sxjY6XMTLt5AAB+jdLIeZo2bZrj2rZt2wq0Vmpqao6dRho2bKiQkJACrVcYGzZs0I033qhjF2xR1rJlS82fP1+VKlUq9Gs4+b3bvn17jmvNmjUr0FoAAAAA4BMojQCBZ/hwM06eLKWl2c0CAHDWwoVmB4d69aQrrrCdBsjdPfdI5cubozDnzbOdBgDgxyiNnKd169YKCsr+LVm5cqXS09PzvdbKlSuVdsEvDNq0aVOofAWxceNG3XjjjTpy5Ei26y1atNCCBQtUpUoVR16nbS5nci9durRAa8V7fpl6HhvfOwAAAABwRFKStGGDmVMaAQJHv35S9erSoUPS9Om20wAAnHT+0TQul90swMWUKSM9+KCZjx5tNQoAwL/5VGkkPT1d48aNU/fu3VW9enWVKlVKdevWVb9+/fTFF194/fXLlCmj1q1bZ7uWkpKiNWvW5Hutn3/+Oce16667rsDZCmLTpk3q2rWrEhMTs11v1qyZFixYoKpVqzr2Wtdee22Oa7l9D/LiwueFhISoU6dOBVoLAAAAAKxbtkxyu6WGDaVq1WynAeCU0FBp8GAzHz/ebhYAgHPcbmnmTDPnaBr4Os8RNTNnSrt2WY0CAPBfjpdGTpw4ob///e9Zt+eee05nzpy57PP27t2rNm3aaOTIkVqwYIESExN19uxZJSQkaPbs2brrrrt0ww036ODBg05HzqZnz545rn399df5Xuerr77K09resmXLFnXt2lWHDh3Kdr1p06ZatGiRqjn8i8orr7xStWrVynZt48aN+v333/O1Tm7PiYqKUvny5QudEQAAAACs4GgaIHANHWo+gT53rrRjh+00AAAnrF8vJSRIpUpJN9xgOw1waU2bSjfeaMpO48bZTgMA8FOOl0ZmzJih119/XW+88YbeeOMNxcfHq2TJkpd8zqlTp9StWzdt2LBBbrdbbrdbLpcr6+a5tmTJEvXs2VMnTpxwOnaWu+66K8e1KVOmKDU1Nc9rrFmzRitWrMh2rUOHDoqMjCx0vrzYtm2bbrjhBh04cCDb9caNG2vhwoWqXr26V173zjvvzHFtXD5/SMnt8bn9bwIAAAAAfoPSCBC4IiOl7t3NfOJEu1kAAM7wHE3TtaspjgC+LibGjBMnSmfP2s0CAPBLjpdGpk2bJklyu92SpOHDh1/2OS+++KK2bNmSa1Hk/AKJ2+3W+vXr9dhjjzkdO0uLFi3UuXPnbNcSExP11ltv5XmNv//97zmuPfzww4XOlhc7d+5U165dtX///mzXGzZsqEWLFqlmzZpee+1hw4YpKCj7/6UmTJigHXn8pM327ds18YJfsISHh1MaAQAAAOC/MjLM8TQSpREgUA0bZsbJk6W0NLtZAACF5ymNcDQN/EW/flLt2tKRI9KXX9pOAwDwQ46XRuLi4uRyuSRJwcHB6tWr1yUff/ToUcXGxmY9RzKFk1atWunOO+/U9ddfn/U1T3Hkgw8+0Lp165yOnuX555/PcW3UqFFavXr1ZZ/73nvv6Ycffsh2LTIyUnfffXeeXrtLly7ZyjMul0vvv/9+np67d+9ede3aVXv37s12vUGDBlq0aFGO42Oc1qRJE91+++3Zrp06dUoPPvjgZY8oOnPmjB544AGdOnUq2/VHH31U4eHhjmcFAAAAgCKxfr108qRUvrzUooXtNAC8oV8/qXp16dAhafp022kAAIVx9Ki0dKmZUxqBvwgJkTwf4I6NtZsFAOCXHC2N7N+/P2uHC5fLpXbt2l32Df/PPvssqyjgdrsVHBysqVOnavXq1frkk0+0aNEiLV++XFWrVs16jtvtzrEjhZN69OihAQMGZLt29uxZ3XDDDZoxY0auz0lLS9NLL72kP//5zzm+9u677yo0NNQrWT3279+vrl27ateuXdmu169fX4sWLVKdOnW8+voe/+///T+VLVs227WffvpJN910U47dTzz27dunrl27Ki4uLtv1evXq6dlnn/VaVgAAAADwOs/RNB07SsHBdrMA8I7QUGnwYDMfP95uFgBA4cyZI2VmSldeKdWtazsNkHdDh5qfSZYuldassZ0GAOBnQpxcbPv27dnuX3XVVZd9zueffy5JWcfQDBs2LMdxJG3atNHEiRPVv3//rN1GvvzyS7377rvOhb/AxIkTtWrVKiUkJGRdS05OVv/+/dWuXTsNGDBAkZGROn36tLZu3aqpU6dq3759OdZ55JFH1LdvX6/l9Bg1apS2bduW43p6err69+9fqLVHjBihESNG5OmxderU0YQJE3L8bxgXF6fIyEjddtttat++vapXr64DBw5o+fLl+uqrr5R2wfatoaGh+vTTT1W6dOlCZQcAAAAAqzylEY6mAQLb0KHSa69Jc+dKO3ZIDRrYTgQAKAiOpoG/ql5dGjhQ+uwzs9vIhAm2EwEA/IijpZHdu3dLOlcAadas2SUfn5KSomXLlmUVQSTpsccey/Wxffv2VbNmzbR582ZJUmJiovbs2aO6Xmr7VqlSRXPnzlXXrl118ODBbF9buXKlVq5cedk1br/9dr399tteyXehC0sXHgkJCdmKLwVx4T//5dx55506cOCAnnjiiWzXU1NTNXXqVE2dOvWSzw8NDdVnn32mTp065TsrAAAAAPgUSiNA8RAZKXXvbj6hPnGi9OqrthMBAPIrI0PyHD1PaQT+aORIUxqZOlX6f/9PqljRdiIAgJ9w9HiaP/74I9v9ipf5D1J8fLzS09Oz7rds2VKNGjW66OO7deuWVS6RpHXr1hUwad40b95cK1as0LXXXpuv54WGhurFF1/Up59+quBiuv3w448/rq+//lqVK1fO1/Pq1aunRYsW6ZZbbvFSMgAAAAAoIgcOSDt3SkFBUocOttMA8LZhw8w4ebJ0kQ/3AAB82LJl0rFj5o12PtAIf9S5s9SypXT6tPTBB7bTAAD8iKOlkVOnTmW7X65cuUs+ftmyZZLO7UzSu3fvSz6+efPm2e4fOHCgACnzp3bt2lq8eLE+//xzRUVFyeVyXfSxZcuW1aBBg7R+/XqNGjVKQUGOfnv9zi233KItW7bohRdeUM2aNS/52EaNGunf//63fv/9d0VHRxdRQgAAAADwIs8uIy1bSuXL280CwPv69TNbwx86JE2fbjsNACC/PEfT9OwphTi6STtQNFwus9uIZI6oycy0mwcA4Dcc/cknIyMj2/2LHZni8csvv2S7f7kdPSpVqiRJWcWN5OTk/EYsEJfLpdtvv1233367EhMTtXz5cu3YsUPJyckKCQlRlSpV1Lx5c7Vv315hYWGFeq0ff/yxQM97//339f777xfqtb2hUqVK+te//qV//vOf2rBhg9atW6f9+/frzJkzKl26tOrUqaPWrVurcePGtqMCAAAAgLM4mgYoXkJDpcGDzdE048dLAwfaTgQAyA9PaYSjaeDP7r1XevppaetWacECqVs324kAAH7A0dLIhTuLHD9+/KKPdbvdio+Pl8vlytppJOoyv0i78KiXs2fPFjhrQUVERKhv375F/rr+zuVyqWXLlmrZsqXtKAAAAABQNCiNAMXP0KHSa69Jc+dKO3ZIDRrYTgQAyIu9e6V168yxgj172k4DFFzZstIDD0jvvWd2G6E0AgDIA0fPT6lYsaKkczuBbNmy5aKPXbFiRbZSSdOmTRUeHn7J9T2Pd7vdkqTSpUsXIi0AAAAAAF5y+rS0apWZcwQnUHxERkrdu5v5xIl2swAA8m72bDN27ChVrmw3C1BYniNqpk+X9uyxmwUA4BccLY00b948a+52uy951Mo333yTNXe5XOrcufNl1z969Gi2++U5ExoAAAAA4ItWrZLS0qTq1aX69W2nAVCUhg0z4+TJ5s8BAIDv42gaBJLmzaUbbpAyM82ReQAAXIajpZEWLVooNDQ06/7atWu1bNmyHI87ffq03n///ayjaSTphhtuuOz6GzduzHa/Xr16hUwMAAAAAIAXnH80zf924wRQTPTrZwpjhw6ZT/gCAHzbmTPSggVmTmkEgSImxowTJkhnz9rNAgDweY6WRkqWLKkePXrI7XZnFULuu+8+7dy5M+sxmZmZiomJUWJiYrbn9cnDD2Nr1qzJOvpGkho2bOhkfAAAAAAAnHF+aQRA8RIaKg0ebObjxtnNAgC4vB9/lE6dkmrXlq66ynYawBn9+0s1a0qJidJ5O/8DAJAbR0sjkjRo0KCsucvl0vbt29W8eXP17NlT99xzj5o0aaIPPvggq1Ticrk0cOBAlStX7pLrHj58WL/99lvW/fLly6s+W/wCAAAAAHyN232uNBIdbTcLADuGDjW7DM2bJ+3YYTsNAOBSZs40Y+/e7BCHwBEaeu7IvNGj7WYBAPg8x0sjN998s6699tqsY2ckKTU1VfPmzdNnn32mHTt2ZPtaSEiIXnjhhcuuO23aNGVmZkoyZZQOHTo4HR0AAAAAgMLbtk06fFgqUUJq3dp2GgA2REZK3bub+cSJdrMAAC7O7ZZmzTJzjqZBoHnoISkkRIqLk3791XYaAIAPc7w0Ikkff/yxateunbWTiOdImQvvS9Jrr72mxo0bX3bNL774ImsNSbruuuu8kBwAAAAAgELy7DLSrp0pjgAonoYPN+PkyVJamt0sAIDc/f67tGuX+ZntxhttpwGcVbOmdPPNZh4bazcLAMCneaU0UqdOHf3000/q0qWL3G531k1S1rxkyZJ666239MQTT1x2vd9//13z58/PVjbp3bu3N6IDAAAAAFA4ntJIVJTdHADs6ttXql5dOnRImj7ddhoAQG48u4x06SKVKWM1CuAVMTFm/PhjKSnJbhYAgM8K8dbC9erV08KFCxUXF6dp06Zpx44dOnHihCpXrqwOHTrozjvvVLVq1fK01qRJkxQeHp51v3r16mrVqpWXkgMAAAAAUAhxcWaMjrabA4BdoaHS4MHSq69K48ZJAwfaTgQAuBBH0yDQXXed1KKF9Ntv0gcfSH/+s+1EAAAf5HJ7tgABAlBycrLCw8OVlJSk8uXL244DAAAAINAdPy5VrGjmhw5JERFW4wCwbOdOqWFDye2Wtm+XGjSwnQgA4HH8uFSlipSRwZ/RCGyxsWbHkaZNzZFM5+3qDwAIbHl9r9wrx9MAAAAAAFAsLVtmxkaNKIwAkCIjpe7dzXzCBLtZAADZzZ1rCiPNmlEYQWC7916pbFlp82Zp4ULbaQAAPojSCAAAAAAATomPN2NUlN0cAHzH8OFmnDxZSk21mwUAcA5H06C4KF9euv9+M4+NtZsFAOCTKI0AAAAAAOCUuDgzRkfbzQHAd/TtK1WvLiUmSjNm2E4DAJCkzEzp++/NvG9fu1mAojBypBmnTZMSEuxmAQD4HEojAAAAAAA4IT1d+uUXM2enEQAeoaHSkCFmPm6c3SwAAGPFCunwYSk8nLIviocWLaTrrzdHMo0fbzsNAMDHUBoBAAAAAMAJ69dLKSlm++crrrCdBoAvGTJEcrmkefOkHTtspwEAeI6m6d7dlPuA4sCz28j48RyZBwDIJk+lkY0bN3o7h2PS09O1bds22zEAAAAAAMVNfLwZO3WSgviMBoDzREaaNyYlacIEu1kAAOdKI3362M0BFKWbbzZH5h06JH37re00AAAfkqffYl199dUaPny4Dh486O08hfLFF1+oefPm+uSTT2xHAQAAAAAUN57SCEfTAMjN8OFmnDyZT/cCgE0HDkirV5sdoHr1sp0GKDqhodKwYWYeG2s3CwDAp+SpNJKRkaGJEyeqUaNGeuKJJ7Rv3z5v58qXr776Sm3bttVdd92lHWzxCQAAAACwIS7OjNHRdnMA8E19+5pP9yYmStOn204DAMXX7NlmbN9eioiwmwUoasOGScHB0pIl5nhNAACUx9KIx6lTp/TOO++oYcOGeuihh7R69Wpv5bqsEydOKDY2Vk2bNtUdd9yhtWvXyu12W8sDAAAAACjG9u2Tdu82x9Jcc43tNAB8UWioNGSImY8fbzcLABRnHE2D4qxWLelPfzLzMWOsRgEA+I48lUaioqLkdrvlcrkkSampqZo8ebLat2+v9u3ba9y4cTp8+LBXg0pSZmamFi5cqCFDhqhmzZp69NFHtXXr1mxlkdKlS6t169ZezwIAAAAAQJalS8141VVSuXJ2swDwXUOHmuMQ5s2T2C0XAIre2bPmz2CJ0giKr5gYM370kZScbDcLAMAn5Kk08vPPP2vKlCmKiIjIVh5xu91atWqVRo4cqZo1a+qGG27Qm2++qVWrVjm268fBgwf12WefaejQoapevbq6deum999/XykpKTmyDBw4UL///rv69evnyGsDAAAAAJAn8fFmjIqymwOAb6tfX+rRw8wnTLAaBQCKpSVLpJMnzXFhfPgUxVWXLlLz5ubfhY8+sp0GAOADXO58tDuSk5P1+uuv6//+7/+UkpKSrbAhKeu+JJUvX16tWrXSVVddpSuvvFL16tVTrVq1VL16dZUpU0YlS5aUJKWnp+v06dM6duyYEhISlJCQoM2bN2v9+vX69ddftX379qw1z4/qcrmy7nfo0EGvvfaaunTpUvDvBAJScnKywsPDlZSUpPLly9uOAwAAACBQdeggLV8uTZ0q3X237TQAfNm330q33CJFREh790phYbYTAUDx8dhj0jvvSIMHS5Mm2U4D2PPee9Kjj5ryyG+/mZ3QAAABJ6/vleerNOKRmJioV155RZMmTdKpU6eylUUuLHZcyvnFj9xc+LULSypt2rTRP/7xD/Xv3z+//wgoJiiNAAAAAPC606el8uWl9HRp506zkwAAXExamlS3rnTwoPTll9Ktt9pOBADFR+PG0rZt0tdfmwIfUFwlJUm1akkpKdKiRWb3EQBAwMnre+V5Op7mQhEREXrnnXe0d+9evfTSS6pWrZrcbnfWcTGem6Ss67ndMjMzL/n189c6v2DSt29fLVy4UCtXrqQwAgAAAACwa+VKUxipUUOqV892GgC+LjRUGjLEzMePt5sFAIqTLVtMYSQ0VOrWzXYawK7wcOnee8189Gi7WQAA1hWoNOJRsWJFPffcc9q7d6+mT5+uW2+9VSVKlMgqfUjKUfzIz+38AkmzZs30yiuvaPfu3Zo+fTpH0QAAAAAAfEN8vBmjotjWGUDeDB1q/ryYN08672hmAIAXzZplxuuuk8qVs5sF8AUjR5rx22+l/fvtZgEAWFWo0ohHcHCw+vbtqy+++EJHjhzRtGnTNGLECDVr1ixH+SOvt4oVK6pXr1566623tHnzZv3222969tlnVbt2bSciAwAAAADgjLg4M0ZH280BwH/Ury/16GHmEydajQIAxYanNNKnj90cgK+46iqpc2cpI4PdzwCgmHO5PVuCeMnJkye1evVqbdmyRbt27VJCQoKSk5N16tQpZWRkqFSpUipTpowiIiJUr149NWjQQFdddZUaNGjgzVgoJvJ6ThMAAAAAFIjbLVWtKh09Ki1bJnXoYDsRAH/x7bfSLbdIERHS3r1SWJjtRAAQuE6ckCpXltLSpM2bpSZNbCcCfMNnn0l33WWO2ty92xzfBAAIGHl9r9zrpRHAJkojAAAAALxqyxapaVOpRAkpOZk3fQHkXVqaVK+edOCA9OWX0q232k4EAIHrm2+kgQOlRo2krVttpwF8R2qqVLeudOiQ9MUX0m232U4EAHBQXt8rd+R4GgAAAAAAiqX4eDO2b09hBED+hIZKgwebOVvCA4B3eY6m6dvXbg7A14SFSQ89ZOaxsXazAACsoTQCAAAAAEBBxcWZMTrabg4A/mnoUMnlkubNk7Zvt50GAAJTZqY0e7aZ9+ljNwvgi4YNk4KCpB9/lH77zXYaAIAFlEYAAAAAACgoz04jUVF2cwDwT/XrSz16mPnEiVajAEDAWrNGOnhQKltWuu4622kA31OnjjRggJmPGWM3CwDACkojAAAAAAAUxB9/SBs3mnmnTnazAPBfw4aZcfJkKTXVbhYACESeo2m6deM4QeBiRo4044cfSidO2M0CAChylEYAAAAAACiIZcvM2LixVLWq3SwA/FffvlKNGlJiojR9uu00ABB4PKURjqYBLu7GG6WmTU1h5OOPbacBABQxa6WRtLQ0JSYmatOmTVqxYoU2bdqkxMREpaen24oEAAAAAEDexcWZMTrabg4A/i00VBo82MzHjbObBQACTWKitGKFmffubTcL4MtcLunhh808NlZyu+3mAQAUqSIrjbjdbn333XeKiYlRq1atVLp0adWoUUMtWrRQx44d1aJFC9WoUUOlSpVS69atFRMTo2nTpsnNf5gAAAAAAL4oPt6MUVF2cwDwf0OHmjdr5s+Xtm+3nQYAAsf335s3v9u0Mbs6Abi4Bx6QSpeWNmyQfvrJdhoAQBHyemkkIyNDb731lho0aKCBAwdq7NixWrdunTIyMuR2u3PcMjIy9Ouvv2rs2LG65ZZb1LBhQ7377rvKzMz0dlQAAAAAAPImPV365RczpzQCoLDq15d69DDziROtRgGAgMLRNEDeVagg3XOPmcfGWo0CAChaLrcXt/LYsWOH7r77bq1YsSLHjiEul+uiz8vtsR06dNDUqVMVGRnplawITMnJyQoPD1dSUpLKly9vOw4AAACAQLF6tdS2rfnF6tGjUpC1018BBIrvvpNuvlmKiJD27pXCwmwnAgD/lpYmVakiJSdLy5ZJHTrYTgT4vrVrpdatpZAQac8edugBAD+X1/fKvfZbrfXr16tdu3ZZhRGXy5XtltsuI55bbo9dtmyZ2rVrpw0bNngrMgAAAAAAeRMXZ8ZOnSiMAHBGnz7mjZnERGn6dNtpAMD//fyzKYxUrSq1b287DeAfWrUyOymmp7P7GQAUIyHeWHTv3r3q1auXjh8/nlX8kM7tIFKiRAldddVVuuKKK1SxYkWVKVNGKSkpOn78uDZu3Kh169bpzJkzkpStOPLHH3+od+/eio+PV+3atb0RHQAAAACAy4uPNyNH0wBwSmioNHiw9Mor0rhx0q232k4EAP7NczRNr16UfIH8iIkxf98ZN0569lmz6wgAIKB55Xiavn37avbs2dmOoHG73erSpYtGjhypAQMGKDQ09KLPT0tL0/Tp0zVmzBgtXLgwW+nE5XKpV69emjlzptOxEYA4ngYAAACAV9SrZ7ZrXrBA6trVdhoAgWLXLqlBA8ntlrZtkxo2tJ0IAPxX8+bSpk3S559Lt99uOw3gP86elerUkQ4flr7+WrrlFtuJAAAFZO14msWLF2crjLjdbpUrV06fffaZFi5cqFtvvfWShRFJCg0N1cCBAzV//nx98cUXWf8Anh1Hvv/+ey1ZssTp6AAAAAAAXF5CgimMBAVJ11xjOw2AQFK/vtSjh5lPmGA1CgD4tR07TGEkOFjq3t12GsC/lCghPfSQmY8ebTcLAKBIOF4aeeedd7LmbrdbFStW1MKFC3V7AZu8t956qxYuXKgKFSpc9HUAAAAAACgyS5ea8eqrpbJl7WYBEHiGDzfjlClSaqrdLADgrzxH03TuLF3w3gKAPBg2zJTkFy6Ufv/ddhoAgJc5WhpJT0/XggULsnYEcblc+u9//6s2bdoUat3WrVvrrbfeylrT7XZr/vz5Sk9Pdyg5AAAAAAB5FBdnxuhouzkABKY+faQaNaTERGn6dNtpAMA/eUojffrYzQH4q3r1pL59zXzMGLtZAABe52hpZPny5Tpx4kTW/UaNGumBBx5wZO37779fjRs3zrp/8uRJLV++3JG1AQAAAADIs/h4M0ZF2c0BIDCFhkpDhpj5uHF2swCAP0pJkX780cw9b3oDyL+YGDN+8IF08qTdLAAAr3K0NLJ3796sucvl0s033+zk8rrlllvkdruz7u/Zs8fR9QEAAAAAuKRTp6Q1a8yc0ggAbxkyRHK5pPnzpe3bbacBAP+yYIF09qwUGSk1a2Y7DeC/brpJatRISk6Wpk61nQYA4EWOlkYSExMlKavYcf7OIE5o1KhRtvuHDx92dH0AAAAAAC5p5UopPV2qWVOqW9d2GgCBqn59qUcPM58wwWoUAPA75x9N43LZzQL4s6AgaeRIM4+Nlc77UDcAILA4WhpJSUnJdr98+fJOLp+1nut/P+hd+HoAAAAAAHhVXJwZo6N5EwKAdw0fbsYpU6TUVLtZAMBfuN3S7Nlm3qeP3SxAIHjwQalUKWndunN/FwIABBxHSyOVK1fOdv/gwYNOLq9Dhw5JOreTyYWvBwAAAACAV8XHm5GjaQB4W58+Uo0aUmKiNG2a7TQA4B/WrZMSEqTSpaUuXWynAfxfxYrS3XebeWys3SwAAK9xtDQSEREh6dxOIMuXL3dyea1YsSLb/apVqzq6PgAAAAAAF+V2UxoBUHRCQ6UhQ8x8/Hi7WQDAX3iOprnxRqlkSbtZgEDhOaLmq6+k/324GwAQWBwtjVxxxRVZc7fbrRkzZujEiROOrH3ixAlNnz49q5AiSS1atHBkbQAAAAAALmvLFunYMfMGRKtWttMAKA6GDjVHYc2fL23fbjsNAPg+T2mEo2kA57RpI3XsKKWlSRMn2k4DAPACR0sjjRs3VmRkZNb9EydO6Omnn3Zk7WeffVZJSUlZ9+vXr6/GjRs7sjYAAAAAAJflOcP7mmuksDC7WQAUD/XqST17mvmECXazAICvO3pUWrbMzHv3tpsFCDSe3UbGjpXS0+1mAQA4ztHSiCT169dPbrdbLpdLbrdb48eP1+uvv16oNf/zn/8oNjY2a02Xy6X+/fs7lBgAAAAAgDzgaBoANgwbZsYpU6TUVLtZAMCX/fCDlJkpXXWVVKeO7TRAYLntNqlKFSkhQZo503YaAIDDHC+N/O1vf1Pp0qUlKavk8dxzz+n222/XgQMH8rXWwYMHdeedd+pvf/tbtuulSpVybAcTAAAAAADyhNIIABv69JFq1JASE6Vp02ynAQDf5Xkjm6NpAOeVLCkNGWLmsbF2swAAHOd4aaRGjRp64okn5Ha7JZ0rjnz99ddq0KCB7rjjDn355ZfauXNnrs/fuXOnvvzyS91xxx1q0KCBvvzyy2w7l7hcLj355JOqUaOG09EBAAAAAMjdsWPS77+beadOdrMAKF5CQ8+9STN+vN0sAOCr0tPNTiMSpRHAW0aMkFwuad48acsW22kAAA5yuT3tDgdlZmbqT3/6k2bOnCmXyyVJ2UokHmFhYQoPD1eZMmWUkpKipKQkpZ63zWZuz+nbt6++++67bNeAi0lOTlZ4eLiSkpJUvnx523EAAAAA+KtZs6S+faWmTaVNm2ynAVDc7N4tRUZKbre0davUqJHtRADgW376SbruOqlSJbMzU3Cw7URAYOrXz+zq89hj0ltv2U4DALiMvL5X7vhOI5IUFBSkzz//XDfeeGO24odntxDP7ezZs0pMTNTOnTuVmJios2fPZvu65zmSKZDceOON+vzzzymMAAAAAACKFkfTALCpXj2pZ08znzjRbhYA8EWzZpmxZ08KI4A3xcSYccoUKSXFbhYAgGO8UhqRpFKlSmnu3Ll69dVXFRISkqM8kpebZMoiISEheuONNzRnzhyVLFnSW5EBAAAAAMgdpREAtg0bZsYpU6TzduoFAOhcaYSjaQDv6t5dathQSkqSPv3UdhoAgEO8VhqRTEHkmWee0ZYtW/TEE08oPDw8204inpukXK+Hh4frqaee0tatW/XXv/6VHUYAAAAAAEUvLU1avtzMKY0AsKVvX6lGDXPswrRpttMAgO/Ys0fasEEKCjq3KxMA7wgKkh5+2MxHjzZH5wEA/J7L7S66P9HPnj2rFStWKC4uTqtXr9aRI0f0xx9/6MSJEypXrpwqVqyoqlWrqk2bNoqKitI111yjsLCwooqHAJTXc5oAAAAA4KJWrpTat5cqVpSOHDG/KAUAG154QXr5Zemmm6R582ynAQDfMGaMNHKkFB0t/fyz7TRA4Dt2TKpVSzpzxuzI2KmT7UQAgIvI63vlIUWYSSVKlFDnzp3VuXPnonxZAAAAAAAKznM0TadOFEYA2DV0qPTKK9L8+dK2bVKjRrYTAYB9nqNp+va1mwMoLipVku66yxyZN3o0pREACAD8tgsAAAAAgEvxlEY4mgaAbfXqnTt6YeJEu1kAwBecPi0tXGjmffrYzQIUJyNHmvHLL83ReQAAv0ZpBAAAAACAS6E0AsCXDBtmxilTpNRUu1kAwLZFi0xxpE4d6corbacBio927cwRnqmp0qRJttMAAAqJ0ggAAAAAABezd6+5BQdL11xjOw0AmOMXatQwn+qdNs12GgCwy3M0TZ8+kstlNwtQ3MTEmHHsWCkjw24WAEChUBoBAAAAAOBiPLuMtGollSljNQoASJJCQqQhQ8x8/Hi7WQDAJrc7e2kEQNG6/XapUiVpz55z/y4CAPxSSFG/YEZGhtauXatVq1YpMTFRx48f14kTJ1SuXDlVqFBBERERatu2rVq1aqXg4OCijgcAAAAAwDkcTQPAFw0dKr3yijR/vrRtm9Soke1EAFD0Nm6Udu+WSpaUuna1nQYofkqVMkXWf/9bio2V+ve3nQgAUEBFVhqZPXu2xo4dqwULFujMmTOXfXzJkiV144036uGHH1avXr2KICEAAAAAABegNALAF9WrJ/XsKX3/vTRxovT667YTAUDR8+xscMMNUunSdrMAxdWIEdJ//iPNmSNt3So1bmw7EQCgALx+PM2SJUvUrFkz9evXT7NmzdLp06fldrsvezt9+rRmzZqlvn37qlmzZlqyZIm3owIAAAAAcE5KirRmjZlHR9vNAgAXGjbMjFOmSKmpdrMAgA0cTQPY16CB5Png99ixdrMAAArMq6WRxx9/XF27dtWWLVuyyiAulyvPN89ztmzZoq5du+qJJ57wZlwAAAAAAM5ZsULKyJBq15bq1LGdBgCy69tXqlFDSkyUpk2znQYAitYff0hxcWZOaQSwa+RIM06eLJ06ZTcLAKBAvFIacbvdevDBB/Xuu+8qMzMzWxHE8/XL3SRle15mZqbeeecdPfjgg1lfBwAAAADAaziaBoAvCwmRhgwx83Hj7GYBgKI2Z44p915xhVS/vu00QPHWs6cUGSkdPy599pntNACAAvBKaeT555/Xhx9+KElZRRHJlEVq1aqlmJgYTZo0SXFxcdqwYYN27typDRs2KC4uTpMnT1ZMTIxq1aqVrRzi2Xnko48+0vPPP++N2AAAAAAAnENpBICvGzpUcrmkBQukbdtspwGAosPRNIDvCA6WRoww89GjJT74DQB+x+V2eNuO9evXq23btsrIyMi65na71aRJE7355pvq3bt3tiLJxbjdbs2ePVt//etftWnTpmy7lISGhmrlypVq2bKlk9ERgJKTkxUeHq6kpCSVL1/edhwAAAAA/iIzU6paVTp2TFq+XGrf3nYiAMhd797S999Lf/ub9PrrttMAgPdlZEjVqklHj0o//ihdf73tRACOHDHHep49Ky1bJnXoYDsRAEB5f6/c8Z1G/vWvfyk9PV2SsnYKufvuu7V+/Xr16dMnT4URyews0qdPH61bt0733ntvtl1H0tPT9dJLLzkdHQAAAAAAY/NmUxgpVUpq1cp2GgC4uOHDzThlipSaajcLABSF5ctNYSQ8nB3hAF9RpYp0xx1mHhtrNwsAIN8cLY2kpKRo9uzZWUfJuFwu9e3bVx9//LFCQ0MLtGZISIg+/PBD9e/fP2tNt9utWbNmKSUlxcn4AAAAAAAYnqNprrlGKuDfZwGgSPTpI9WsKSUmStOm2U4DAN7nOZqmRw9+TgN8SUyMGT//3Ow8AgDwG46WRuLi4nT69Oms+yVLltS4ceMcWXvs2LEqVapU1v0zZ84oLi7OkbUBAAAAAMjGUxrh06sAfF1IiDRkiJk79Hs4APBpntJInz52cwDIrn17qW1bc0TN5Mm20wAA8sHR0khCQkLW3OVyqXfv3qpevboja1evXl19+vTJdkzN+a8HAAAAAIBjKI0A8CdDhkgul7RggbRtm+00AOA9+/ZJa9eaP/N69bKdBsD5XK5zu42MGSNlZNjNAwDIM0dLI4mJiZKUVey49tprnVxenTt3zvX1AAAAAABwzNGj0qZNZt6pk90sAJAX9epJPXua+YQJdrMAgDfNnm3GDh2kqlXtZgGQ0x13SBUrSrt2ST/8YDsNACCPHC2NhIWFZbvv1C4jF67ncrkkSaGcVwgAAAAAcNrSpWZs1kyqXNluFgDIq+HDzThlipSaajcLAHgLR9MAvq10aWnwYDMfPdpuFgBAnjlaGqlRo0a2+ydPnnRyeaWkpEg6t5NJzZo1HV0fAAAAAACOpgHgl/r0kWrWlA4flqZNs50GAJx39qw0f76ZUxoBfNeIEWb84Qdp+3a7WQAAeeJoaaRVq1aSzu0Est3h/xhsu+BM1quvvtrR9QEAAAAAoDQCwC+FhEhDhpj5uHF2swCANyxeLKWkmILc/96LAOCDGjUyx+a53dLYsbbTAADywNHSSPPmzRUZGSnJ7AbyzTffOLm8vv3226xCSt26dXXFFVc4uj4AAAAAoJhLS5OWLzfz6Gi7WQAgv4YMkVwuacEC6YIPXwGA3/McTdO7t/mzDoDvGjnSjJMnS6dP280CALgsR0sjkvTII49kHR+zZcsWffzxx46sO3XqVG3atEmS2cnkkUcecWRdAAAAAACyrF1rfqlZqZLUpIntNACQP/XqSb16mfmECXazAICT3O5zpRGOpgF8X+/e5ueSY8ekzz+3nQYAcBleKY1cccUVcrlccrvd+stf/qLVq1cXas01a9boz3/+c9YuI82bN9ef//xnJ+ICAAAAAHCO52iaTp2kIMf/ygwA3jdsmBmnTJFSU+1mAQCnbNkibd8uhYVJN91kOw2AywkOlkaMMPPYWLtZAACX5fhvwEJDQzVt2jRVrVpVkvTHH3+oa9euBd5xZOrUqeratauOHz8ut9utiIgIfffddwoNDXUyNgAAAAAA50ojUVF2cwBAQfXpI9WsKR0+LH33ne00AOAMzy4j118vlS1rNwuAvBkyxBS9VqwwNwCAz/LKx6YaNmyoZcuWqVWrVpKk5ORkPfDAA+rQoYMmTpyow4cPX/L5R44c0cSJE9WxY0fdf//9SkpKktvtVqtWrbRs2TI1atTIG7EBAAAAAMWZ2y3FxZl5dLTdLABQUCEh5k0aSRo/3m4WAHDKzJlm5GgawH9UrSrdfruZs9sIAPg0l9vtdju54ODBg7Pmqamp+uabb3T27Fl5XsZzxEytWrXUtGlThYeHq0yZMkpJSVFSUpK2bNmihIQESdL50UqVKqVbbrkl3zuMuFwuTZo0qbD/WPBTycnJCg8PV1JSksqXL287DgAAAABftmePOXc7OFhKTpZKl7adCAAKZvduKTLSlOG2bpX4ABYAf5aUJFWpIqWn82ca4G+WLjW7OJYsKSUkSJUr204EAMVKXt8rd7w0EhQUlFUMudCFL5Xb4/LymLxyu91yuVzKyMgo8Brwb5RGAAAAAOTZZ59Jd90ltWvH9skA/F+fPtLs2dLTT0tvvGE7DQAU3FdfSbfdJjVpIm3ebDsNgPxwu6W2baU1a6R//1t66inbiQCgWMnre+VeOZ5GMoWN3Aog5988jzn/duFjclszLzcAAAAAAPIlPt6MUVF2cwCAE4YNM+OUKVJqqt0sAFAYs2aZkaNpAP/jckkjR5r5mDFSZqbdPACAXHmtNJJb6eNij7lYSSQvj7/YDQAAAACAfImLM2N0tN0cAOCEPn2kmjWlw4el776znQYACiYz0+yaJFEaAfzV3XdL4eHSjh3SnDm20wAAchHi9IJ169altAEAAAAA8C8nT0q//mrm7DQCIBCEhEhDhkgvvSSNHy/dfrvtRACQf6tWSYmJUrly0rXX2k4DoCBKl5YGDZLefluKjZV69bKdCABwAcdLI7t27XJ6SQAAAAAAvGvFCikjQ6pTR6pd23YaAHDGkCHSyy9LCxZI27ZJjRrZTgQA+eM5mqZbNykszG4WAAX38MOmNDJrlrRzpxQZaTsRAOA8XjueBgAAAAAAvxEfb0Z2GQEQSOrVO/dp3jfflKZMkd5914x79tjNBgB54SmNcDQN4N+aNDHlL7dbGjfOdhoAwAUojQAAAAAAEBdnxuhouzkAwGlduphx7Fhp8GDp8cfNWL++1L+/2WkJAHzRwYPSypVm3ru33SwACi8mxowTJ0pnztjNAgDIhtIIAAAAAKB4y8yUli41c3YaARBIvvlG+vvfs1/LzDSj2y3Nnm3+3Pvmm6LPBgCX8/33ZmzXTqpe3W4WAIXXp485DvToUenLL22nAQCch9IIAAAAAKB427RJOn5cKl1auuoq22kAwBnLl0t33CFlZFz8MRkZ5nbHHew4AsD3cDQNEFhCQqQRI8x89Gi7WQAA2VAaAQAAAAAUb/HxZrzmGik01G4WAHDKyy+b3UTc7ks/zvOYl18umlwAkBepqdLcuWZOaQQIHEOGmL9z/fKLtGqV7TQAgP+hNAIAAAAAKN7i4szI0TQAAsWePdLMmZfeZeR8GRnSjBnmeQDgC37+WTpxQqpWTWrb1nYaAE6pVk267TYzj421mwUAkIXSCAAAAACgePPsNBIdbTcHADhlwYLL7zByIbdbWrjQO3kAIL88R9P06iUF8TYGEFBGjjTjJ59If/xhNwsAQJIUUpQvdvToUW3evFnHjx9XUlKS0tLSCrXe/fff71AyAAAAAECxdOSItGWLmXfsaDcLADjlxAnzJmtmZt6fExQkJSd7LxMA5IenNMLRNEDgiYqSrr5a+vVXacoU6YknbCcCgGLP66WRX375RVOmTNH333+vhIQER9emNAIAAAAAKJSlS83YvLlUqZLdLADglHLl8lcYkczjy5f3Th4AyI9t26TNm6WQEKlbN9tpADjN5TK7jQwfLo0ZIz32GDsKAYBlXvtTeM+ePerXr5+ioqI0YcIE7d27V26327EbAAAAAACF5jmaJirKbg4AcNKNN5o3ZPLD5ZK6dvVOHgDID88uI9deK4WH280CwDvuvtuUVbdtk+bNs50GAIo9r5RG1q9fr3bt2mn27NlZJQ+Xy+XYDQAAAAAAR8TFmTE62m4OAHBS3bpS375ScHDeHh8cLPXrZ54HALZxNA0Q+MqWlR580MxjY61GAQBILrfD23YcO3ZMzZs31+HDh80LnFfycPKlXC6XMjIyHFsPgSk5OVnh4eFKSkpSebZYBQAAAHC+1FTz6dUzZ6RNm6SmTW0nAgDnrFhhdlHKyJAu9Ts5l8uURuLjpfbtiy4fAOTm5EmpcmXzc9rvv0vNmtlOBMBbNm0yx4QGBUk7dkj16tlOBAABJ6/vlYc4/cLPPfecDh8+nKMs0rx5cw0cOFCtW7dW/fr1Va5cOYWGhjr98gAAAAAA5M3ataYwUqmS1KSJ7TQA4Kz27aXPP5fuuMOURi724SuXS/riCwojAHzD/PmmMNKgAYVeINA1a2aO1FuwQBo3Tnr1VduJAKDYcrQ0cvbsWX388cdZhRG3263SpUtr/Pjxuvvuu518KQAAAAAACic+3oxRUeZNUwAINLfcYv6se+klaeZMUx4JCpIyM82fe263FBYmXXON7aQAYJx/NA0/nwGBb+RIUxqZOFEaNUoqUcJ2IgAoloKcXGzx4sVKSUmRZAojLpdL33zzDYURAAAAAIDviYszY3S03RwA4E3t20vTp0u7dklTpkhvvWXGHTukTp3MjktPPWU7JQCYItvs2Wbep4/dLACKRv/+Uq1a0uHD0ldf2U4DAMWWo6WR3bt3Z81dLpduuukmde/e3cmXAAAAAACg8Nzu7DuNAECgq1tXevBB6c9/NmP9+tLo0Wbnkc8+kxYtshwQQLG3dq20f79UurR0/fW20wAoCiEh0vDhZh4bazcLABRjjpZGjhw5IsnsMiJJvXr1cnJ5AAAAAACcsWePeVMiJERq1852GgCwo3Vr6eGHzTwmRkpLs5sHQPHmOZqmWzepZEm7WQAUnYceMn8vi4835TEAQJFztDRSqlSpbPdr1arl5PIAAAAAADjDs8tI69bm06wAUFy99JJUtar0++/SO+/YTgOgOPOURjiaBiheqleXBg40c3YbAQArHC2N1K5dO9v906dPO7k8AAAAAADOiIszY3S03RwAYFvFitIbb5j5P/8p7dtnNw+A4unwYemXX8y8d2+7WQAUvZgYM06dKh0/bjUKABRHjpZG2v1vS1+XyyVJ2rt3r5PLAwAAAADgDM9OI1FRdnMAgC944AGpUyfp5EnpqadspwFQHP3wg+R2S61aSexgDhQ/nTtLV14pnTolffCB7TQAUOw4WhqpX7++rr766qz78+bNc3J5AAAAAAAK7+RJ6ddfzbxTJ7tZAMAXBAVJo0eb8bPPpEWLbCcCUNxwNA1QvLlc53YbiY2VMjPt5gGAYsbR0ogkPf7443K73XK73fr555+1du1ap18CAAAAAICCW77c/BKybl3pgmNWAaDYat1aGjHCzB95REpLs5sHQPGRni7NmWPmlEaA4uuee6Ry5aQtW6SFC22nAYBixfHSyH333adrr71WkuR2uzVs2DCdPn3a6ZcBAAAAAKBg4uLMGB1tNwcA+JqXX5aqVJE2bpTefdd2GgDFRXy8dPy4+fPnmmtspwFgS7ly5sg8yeyABgAoMo6XRlwulz777DPVr19fbrdbq1atUr9+/XT48GGnXwoAAAAAgPyLjzdjVJTdHADgaypWlN54w8xffFHav99qHADFhOdomp49peBgu1kA2PXww2acPl3au9duFgAoRlxut9vtjYX37Nmjfv36af369XK5XKpSpYqeeOIJDRo0SBEREd54ySJx+PBhrVixQtu3b1dycrJCQ0NVuXJlXXHFFWrXrp1CQ0NtR8R5kpOTFR4erqSkJJUvX952HAAAAAC2ZWZKlSpJSUnSqlVSmza2EwGAb8nMNDsxLVsm3XWX9MknthMBCHRXXin99pv06afSnXfaTgPAtq5dpUWLpOeeM7ugAQAKLK/vlXutNCJJp0+f1jPPPKPRo0crMzNTLpdLktS4cWM1atRIFStWLHDJwuVyadKkSU7GvaSvvvpK77zzjuLi4nSxb1m5cuV0++236+mnn1aTJk2KLNul7N69WytXrsy6rVq1Sn/88Ue2x1x//fX68ccfHXk9z//GTvj00091ZyH/kkBpBAAAAEA2v/1m3pgoU8Zsgx4SYjsRAPie1auldu0kt9u8adOli+1EAALVrl1SZKTZYeTwYbPjEYDi7auvpNtukyIizG4jYWG2EwGA38rre+Ve/e1YZmamatSooYoVK+rYsWNZZYstW7Zo69atBV7X7XYXWWlk3759uueee7R48eLLPvbEiROaNGmSPvroIz3//PN6/vnnHS1R5MW8efO0ZMmSrJLIkSNHivT1AQAAAMCnxcWZsUMHCiMAcDFt2pjt4WNjpZgYae1aid11AXiD52iaqCgKIwCMAQOkmjXNMXlff212PgMAeFWQtxaeM2eOmjRpoueee07Hjh2TZHah8NzcbneBbkVpy5Ytat++fZ4KI+dLTU3VP/7xD91zzz3KyMjwUrrc/fWvf9XLL7+sH374gcIIAAAAAFwoPt6MUVF2cwCAr3v5ZalKFWnjRun//s92GgCBylMa6dPHbg4AviM0VBo2zMxjY+1mAYBiwisfq/rss890//33Kz09PevahTtuFPUOHPl19OhRdevWTQcOHMjxtbZt22rAgAGKjIzU6dOntWXLFn3yySfav39/tsd9+umnioiI0Ntvv11EqX1PtWrVVL169QI9tyLNcgAAAABOozQCAHlTsaL0xhvSkCHSqFHSnXeaT/0CgFNOnTJHYEmURgBk99BDpsD688/SunXSVVfZTgQAAc3x0simTZs0aNAgpaenZyuGnL9LSLly5VSuXDmF+vC2lsOGDdOePXuyXStXrpw++ugjDRgwIMfjX331Vb366qt68cUXs11/55131KNHD/Xq1cubcS+rfPnyat26tRo2bKjJkycX2euOGDEix/cEAAAAAKw4fFjyHJXasaPdLADgDx58UBo/XvrlF+mvf5WmTrWdCEAgWbhQOnNGqltXatHCdhoAvqRmTenmm6UvvzS7jYwdazsRAAQ0x0sjzzzzjM6ePZtVGHG73SpZsqQGDx6sgQMHqnXr1qpQoYLTL+uoefPm6Ztvvsl2LSwsTAsXLlS7du1yfU5oaKhGjRqlChUq6LHHHsv2tUcffVSbNm1SSBGdl12mTBm1atVK7dq1y7o1bdpULpdLu3btKtLSCAAAAAD4DM8uIy1amE/QAwAuLShIGj1aat9e+uQT86nfLl1spwIQKM4/msbHdyYHYMHIkaY08vHHZvez8HDbiQAgYDnaYjh06JBmzZoll8slt9stl8ulZs2aafbs2apfv76TL+VVL730Uo5ro0aNumhh5Hx/+ctfNGvWLM2bNy/r2vbt2/XJJ5/o/vvvdzRnbqZNm6batWsrODjY668FAAAAAH6Fo2kAIP/atpVGjJDGjJEeeURas0by4d2DAfgJtzt7aQQALnT99dIVV0gbN0offig9+qjtRAAQsIKcXCw+Pl4ZGRlZ90uUKKFZs2b5VWFk48aN+umnn7Jdq1Klip588sk8r/Haa6/luDZmzJhCZ8uLevXqURgBAAAAgNxQGgGAgnn5ZalyZem336T33rOdBkAg2LBB2rtXKlVK6trVdhoAvsjlMruNSOaIGrfbbh4ACGCOlkZ2796dNXe5XBo4cKAiIyOdfAmv+/TTT3NcGzRokEqUKJHnNdq2bau2bdtmu7Zs2TLt3Lmz0PkAAAAAAAWQmiqtWGHmlEYAIH8qVTLbwkvSqFHSgQN28wDwf55dRrp2NcURAMjNffdJZctKmzZJixbZTgMAAcvR0sipU6ckSe7/tf06d+7s5PJF4ocffshx7dZbb833Ork9J7e1AQAAAABFYPVq6exZqUoVqXFj22kAwP8MGiR16CCdOCH99a+20wDwdxxNAyAvypc3xRHJ7DYCAPAKR0sj5cqVy3a/cuXKTi7vdSkpKVq9enW2a6VLl1br1q3zvda1116b49qFx94AAAAAAIrI+UfTuFx2swCAPwoKkkaPNn+GTp0qLV5sOxEAf3Xs2LmfzSiNALgczxE1330nJSRYjQIAgcrR0siFR9EkJSU5ubzXrV27VpmZmdmutWvXTqGhofleq3379jmet2rVqkLlAwAAAAAU0PmlEQBAwbRtKw0fbuaPPCKlpdnNA8A/zZkjZWZKV14p1a1rOw0AX3flldJ110kZGdKECbbTAEBAcrQ00qFDBwUFBcn1v09tbd261cnlvW7Tpk05rjVq1KhAa4WFhal27drZrm3fvl3p6ekFWs9frVmzRo8//rg6duyoWrVqqWTJkipfvrzq16+v9u3ba8SIEfrkk090+PBh21EBAAAABCq3W4qLM3NKIwBQOK+8IlWuLG3YIL33nu00APwRR9MAyK+YGDOOHy+lptrNAgAByNHSSNWqVXXdddfJ7XbL7XZr5syZTi7vdbt27cpxrV69egVer+4FLemMjAzt2bOnwOv5o+nTp+vtt9/WL7/8ov379+vs2bM6ceKEdu/erZUrV2rcuHG65557VLduXQ0fPlzbt2+3HRkAAABAoNm9Wzp4UAoNldq1s50GAPxbpUrS66+b+ahR0oEDdvMA8C8ZGdIPP5g5pREAefWnP0nVq5u/1333ne00ABBwHC2NSNKzzz4rSXK5XPr999/19ddfO/0SXnPw4MEc1+rUqVPg9XJ77qFDhwq8XiA7c+aMxo8fr5YtW2rSpEm24wAAAAAIJJ5dRtq0kUqVspsFAALB4MHSNddIJ05If/2r7TQA/Mkvv0hHj0oVK0qdOtlOA8BfhIVJw4aZ+ejRdrMAQAByvDTSrVs3DR48WG63W5I0cuRIbdu2zemX8Ypjx47luFa2bNkCr5fbc48ePVrg9fxVSEiIatSooWbNmqlx48aqXLnyRR97+vRpDR06VI8++mgRJgQAAAAQ0OLjzcjRNADgjKAg84aNyyVNnSotWWI7EQB/4dmdvEcPKSTEbhYA/mXYMCk42PzcsWGD7TQAEFAcL41I0pgxY9SrVy+53W4dPnxY119/vRYsWOCNl3JUSkpKjmulCvEptNyee+rUqQKv5y9KlCihPn366L333tOvv/6qlJQU7d+/X7///ru2bNmiI0eO6NChQ/ryyy/VrVu3XNd477339MYbb+T7tc+ePavk5ORsNwAAAADFHKURAHBeu3bnPvEbEyOlpdnNA8A/zJplRo6mAZBftWqZY2okKTbWahQACDReKY2EhoZq+vTpeuyxx+RyuXTgwAF1795dPXv21BdffJHrMTC+IC2Xv9yWLFmywOvlVhpJTU0t8Hr+4N///rcSEhI0c+ZMxcTE6KqrrlJYWFiOx0VEROjWW2/V3LlztWjRItWsWTPHY5599lmtXLkyX6//2muvKTw8POtWmOOFAAAAAASAEyekdevMnNIIADjrlVekypXNp33ZKh7A5ezda34uc7mknj1tpwHgj0aONONHH0l8aBgAHOP4/m8NGjTIdj80NFRpaWlyu92aN2+e5s2bJ8kUKipWrKjQ0NACvY7L5dL27dsLnTcvr+Pkcz3H9gSqp556Kt/P6dKli5YvX66OHTsqISEh67rb7dYzzzyj+fPn53mtZ599Vk888UTW/eTkZIojAAAAQHH2yy9SZqZUv76US1kdAFAIlStLr71mdhwZNUq64w6pRg3bqQD4qtmzzdixo1Slit0sAPzTDTdIzZpJmzaZ4khMjO1EABAQHC+N7Nq1Sy6XS263O1tpwnPN49SpU4U6qqUwZY6Lya3Acvr06QKvl9tzc9t1A1KtWrX03Xff6ZprrlFmZmbW9QULFmjt2rVq1apVntYpUaKESpQo4aWUAAAAAPwOR9MAgHcNGSJNnCgtXy49/bR5AwcAcsPRNAAKy+Uyu438+c/miJqRI801AECheOV4Gin3UofL5XLk5i2lS5fOcc3p0kiZMmUKvF6ga9u2re66664c17///nsLaQAAAAAEBEojAOBdQUHmaBqXS/r4Y2nJEtuJAPiiM2ekBQvMnNIIgMK4/36pTBlp40Zp8WLbaQAgIHilNOJ2u71685bKlSvnuHby5MkCr5fbc3N7DZxz991357i2wPOXCQAAAADIj8xMaelSM6c0AgDe066dOaJGkh55REpPt5sHgO/58Ufp1CmpVi3p6qttpwHgz8LDpXvvNfPYWLtZACBAOH48zQMPPOD0kkWmWrVqOa4lJCQUeL29e/fm6TVwzvXXX5/j2p49eywkAQAAAOD3fvtNSk6WypaVWra0nQYAAtsrr0hffimtX292HvnLX2wnAuBLPEfT9O7NURIACm/kSGncOOnbb6X9+6WaNW0nAgC/5nhpZMqUKU4vWWQiIyNzXNu9e3eB17uw7BAcHKy6desWeL3ioEyZMipfvrySk5Ozrh0+fNhiIgAAAAB+y3M0TYcOUojjf/0FAJyvcmXptdek4cOlf/xDuuMOqXp126kA+AK3+1xppG9fu1kABIarrpI6d5Z+/lmaMEEaNcp2IgDwa145nsZfNW3aNMe1bdu2FWit1NTUHDuNNGzYUCH8ovKyypQpk+3+6dOnLSUBAAAA4Nc8pRGOpgGAojFkiNS+vdnl6emnbacB4Cs2bZJ27pRKlJBuvNF2GgCBYuRIM44bJ6Wl2c0CAH6O0sh5WrduraCg7N+SlStXKr0A57CuXLlSaRf8R6pNmzaFyldcHDlyJNv9KlWqWEoCAAAAwK9RGgGAohUcbI6mcbmkjz6SfvrJdiIAvsCzy0iXLtIFHxgEgAIbOFCKiJAOHJCmTbOdBgD8GqWR85QpU0atW7fOdi0lJUVr1qzJ91o///xzjmvXXXddgbMVF9u2bctRtqlataqlNAAAAAD81qFD0rZt5o3Ljh1tpwGA4qN9e+mhh8w8JkYqwIexAAQYT2mkTx+7OQAElrCwcz9zxMbazQIAfo7SyAV69uyZ49rXX3+d73W++uqrPK2N7GbPnp3j2tVXX20hCQAAAAC/tnSpGVu0kCpUsBoFAIqdV1+VKlWS1q/nTRyguEtKkjwfsKQ0AsBpw4dLQUHSokXSxo220wCA36I0coG77rorx7UpU6YoNTU1z2usWbNGK1asyHatQ4cOioyMLHS+QJaamqq33norx/VevXpZSAMAAADAr3E0DQDYU7my9NprZv7CC9LBg3bzALBn7lyz41CzZlKDBrbTAAg0depI/fub+ZgxdrMAgB+zUhpJTk7Wjh07tGLFCi1ZskRLliyxESNXLVq0UOfOnbNdS0xMzLXMcDF///vfc1x7+OGHC50t0D333HPatWtXtmtly5ZVjx497AQCAAAA4L8ojQCAXUOGSO3aScnJ0t/+ZjsNAFs4mgaAt8XEmPGDD6QTJ+xmAQA/VWSlkRkzZmjw4MFq2LChKlasqMaNG6tjx4664YYb1LVr10s+d+PGjVq9enXW7ciRI17N+vzzz+e4NmrUKK1evfqyz33vvff0ww8/ZLsWGRmpu+++O0+v3aVLF7lcrmy3999/P0/PtemLL77QsmXLCvRct9utl19+Wf/5z39yfO2vf/2rKrCVNAAAAID8OHtWWrnSzKOj7WYBgOIqONgcTeNySR9+eO54CgDFR2am5DmOnNIIAG/p2lVq0sQURqZOtZ0GAPyS10sjM2fOVMuWLfWnP/1JH3zwgXbu3Cm3253jdiljx45V+/bts26PPfaYVzP36NFDAwYMyHbt7NmzuuGGGzRjxoxcn5OWlqaXXnpJf/7zn3N87d1331VoaKhXsvqK+Ph4derUSV26dNGkSZPyXOxZtmyZunXrphdeeCHH1xo2bKgnn3zS6agAAAAAAt3q1aY4UrWq1LCh7TQAUHy1by8NHWrmMTHmiAoAxceKFdLhw1L58tIFu3sDgGOCgqSRI8189GjpMu85AgByCvHWwpmZmXrmmWf05ptvSlJWMcTlcmV73OUKI5L05JNPauzYsUr/318sv/32W504cULlypVzOPU5EydO1KpVq5SQkJB1LTk5Wf3791e7du00YMAARUZG6vTp09q6daumTp2qffv25VjnkUceUd++fb2W80L79+9X7969L/r11NTUHNdWrlypVq1aXfQ5NWvW1GxPI/wyFi9erMWLF2v48OFq2bKlrr76ajVv3lyVKlVSeHi4MjIydOzYMf3+++9avHixNmzYkOs6ERERmjNnjsqUKZOn1wUAAACALOcfTXPB30EBAEXs1Velr7+W1q0zO4/k8oErAAHKczRN9+5SgH+oEoBlDzwg/f3v0oYNZneza6+1nQgA/IrXSiPDhw/X5MmT5Xa7s45ZuXBXkQsLJBdTr1499evXT99++60k6cyZM5o2bZruvfder2SXpCpVqmju3Lnq2rWrDh48mO1rK1eu1ErPVseXcPvtt+vtt9/2UsLcpaam6tdff83Xc1JSUi75nOPHj+c7R0ZGhtauXau1a9fm+7nNmzfXF198oYZ8IhAAAABAQZxfGgEA2FWliimOjBghvfCCdMcdUrVqtlMBKAqe0ghH0wDwtgoVpHvukSZMMLuNUBoBgHzxyvE07777riZNmiRJ2coiN954o/773//q22+/zXch4I477shaT5LmzZvnbOhcNG/eXCtWrNC1+fyPS2hoqF588UV9+umnCg4O9lK6wFOyZEk9/vjjWr16ta688krbcQAAAAD4I7dbioszc0ojAOAbhg6V2raVkpOlp5+2nQZAUThwwBwZKEm9etnNAqB48BxR8/XX0gUfBgcAXJrjpZGjR4/qhRdeyCp3uN1u1a5dWz///LPmzZunxx57TAMGDFDZsmXztW7fvn1VokSJrDUXLFjgdPRc1a5dW4sXL9bnn3+uqKioS+6OUrZsWQ0aNEjr16/XqFGjFBTklU6OT3rhhRf0xRdfaOTIkWrbtq1KliyZp+eFhoaqffv2euONN7Rv3z7997//zfNzAQAAACCHnTulQ4fMFujt2tlOAwCQpOBgczSNyyV9+OG5ch+AwOU57rx9e3YXAlA0WrUyHxxITzc7jgAA8szlPv+8GAc8++yzeuONN7J2GKlWrZpWrlypWrVqZXtc69attW7duqzjazIyMi67docOHbRy5cqs5xw6dEhVqlRxMv5lJSYmavny5dqxY4eSk5MVEhKiKlWqqHnz5mrfvr3CwsKKNI+vSk9P1/bt27V7924lJCQoKSlJp06dUnBwsMLDw1WxYkXVrVtXbdq08WpJJDk5WeHh4UpKSlL58uW99joAAAAAfMTHH0v33Sd17CgtXWo7DQDgfA89JE2cKF19tbRypRTitZOzAdh2yy3St99KL74ojRplOw2A4mLqVOnee6VataRdu/hZA0Cxl9f3yh3/0/KLL77IKoy4XC6NHTs2R2GkoNq1a6cVK1Zk3d+0aZM6d+7syNp5FRERob59+xbpa/qjkJAQNW3aVE2bNrUdBQAAAEBxEh9vRo6mAQDf89prZsv4X3+VxoyRHn3UdiIA3nD2rOQ5Xr5PH7tZABQvt94qPf64tG+fNH26KbABAC7L0fNTduzYoZ07d2bdb9q0qQYMGODY+o0aNcp2//zXAgAAAACA0ggA+LAqVaRXXzXzF14wx4kBCDw//SSdPClVry61aWM7DYDipEQJaehQM4+NtZsFAPyIo6WRX3/9NWvucrnUs2dPJ5dXhQoVst1PSkpydH0AAAAAgB9LTpbWrzdzSiMA4Jseekhq21ZKSpL+9jfbaQB4w6xZZuzdWwpy9C0IALi84cPNnz0LFkibNtlOAwB+wdGf2A4fPixJcrvdkqQWLVo4uXzWOTsul0uSdPLkSUfXBwAAAAD4sV9+kTIzpchIqUYN22kAALkJDpZGjzbzDz6Q4uLs5gHgPE9phKNpANhQr57Ut6+ZjxljNwsA+AlHSyNHjx7Ndv/CnUEKKyUlRdK5UkpYWJij6wMAAAAA/BhH0wCAf+jQQRoyxMxjYqT0dLt5ADhn61ZzCw2VunWznQZAcTVypBnff98clwUAuCRHSyNly5bNdv/UqVNOLp+1k4lH5cqVHV0fAAAAAODHKI0AgP947TWpYkXp11+lsWNtpwHgFM8uI9ddJ5UrZzcLgOKrWzepUSNzhOknn9hOAwA+z9HSSEREhKRzx8ckJiY6ubxWrlyZ7T6lEQAAAACAJCkjQ1q61Myjo+1mAQBcXtWq0iuvmPnzz0uHDtnNA8AZHE0DwBcEBUkPP2zmo0dL/zvBAACQO0dLIzUuODN61apVjq2dkZGhH3/8MauQIklXXHGFY+sDAAAAAPzYb79JJ05IZctKV15pOw0AIC+GDZPatJGSkqRnnrGdBkBhnTghLV5s5pRGANg2aJBUqpS0bt25XSkBALlytDRyzTXXqGTJkpIkt9utefPmKTU11ZG1P/vsMx067xMHtWvXVoMGDRxZGwAAAADg5zy/BOzYUQoOtpsFAJA3wcHm07+S9P77vKED+Lv586W0NHMkRJMmttMAKO4qVpTuusvMY2PtZgEAH+doaaRkyZK69tpr5f7fNk/Hjh3TRx99VOh1jx8/rn/84x9yuVxyu91yuVzq2rVrodcFAAAAAAQIzxuNUVF2cwAA8qdjR2nIEDOPiTHHjQHwTxxNA8DXxMSY8csvOQoPAC7B0dKIJN17772SlFXw+Nvf/qYDBw4UeL2zZ8/qrrvu0s6dO7NdHzlyZKFyAgAAAAACSFycGaOj7eYAAOTfa69JFSpIa9dKY8faTgOgIDIzKY0A8D1t2kgdOphdkCZNsp0GAHyWV0ojzZs3l2SKI8eOHVPXrl21e/fufK+1detWde7cWXPnzs2xy0j79u2djg4AAAAA8EcHD0o7dkgul/mFIADAv1StKr36qpk/95yUmGg3D4D8W7PG/ExWpox03XW20wDAOZ7dRsaOldLT7WYBAB/leGnE5XLpzTfflMvlyrq/efNmXXnllfrnP/+phISESz4/LS1N8+bN0wMPPKAWLVpo9erVWcfdSOYInDfffNPp2AAAAAAAf7V0qRmvvFIKD7ebBQBQMMOGmU8DJyVJzzxjOw2A/PLsMtKtm1SihN0sAHC+226TqlSR9u4992cVACAbx0sjktSzZ0+9/vrrWWUPl8ullJQU/etf/1K9evVUt25dbdu2LVsZZODAgWrTpo0qVqyonj176uOPP1Z6enrW7iKecfTo0brqqqu8ERsAAAAA4I/i480YFWU3BwCg4IKDpffeM/MpU84VAgH4B46mAeCrSpaUhgwx89Gj7WYBAB/lldKIJD311FN6+umnsxVH3G633G63EhISdOrUqazHut1ufffdd1q7dq1OnTqV9TiXy5Vtx5J//etfevDBB70VGQAAAADgj+LizBgdbTcHAKBwOnWSBg8285EjpYwMu3kA5E1iorRihZn37m03CwDkZvhwc5zpvHnSli220wCAz/FaaUSSXn/9dX399dcqX758thLI+UUQz/zCr59/vXTp0vryyy/13HPPeTMuAAAAAMDfnDkjrVpl5uw0AgD+7/XXpQoVpLVrpbFjbacBkBfffy+53VLr1lLNmrbTAEBOkZHndkIaM8ZuFgDwQV4tjUjSzTffrC1btujZZ59VeHh41i4i5x9Nc35JRFLW10uUKKE///nP2rZtm2655RZvRwUAAAAA+JvVq6XUVCkiQmrQwHYaAEBhVa0qvfKKmT//vHT4sN08AC6Po2kA+IORI804ZYqUkmI3CwD4GK+XRiSpatWqeuWVV7R37159++23euyxx9S+fXtVr15dYWFhWbuMVKxYUc2aNdMDDzygjz/+WHv37tXbb7+tatWqFUVMAAAAAIC/iY83Y1SU2W4YAOD/hg83OxYcPy4984ztNAAuJS1NmjPHzPv2tZsFAC6lRw/zQYOkJOnTT22nAQCf4nKfv+WHJadPn1bJkiWz7TYCOCE5OVnh4eFKSkpS+fLlbccBAAAA4LSbb5a++07697+lp56ynQYA4JSlS88dOxYfL3XqZDcPgNz9+KN0ww1ml6CDB6WgIvmcKgAUzH/+I/31r1KrVmbXSt6XBBDg8vpeuU/8BFeqVCkKIwAAAACA/HG7s+80AgAIHJ06SYMGmXlMjJSRYTcPgNx5jqbp1YvCCADfN2iQVLKktHattGyZ7TQA4DP4KQ4AAAAA4J927JASE6WwMKlNG9tpAABOe/11qUIFac0aadw422kA5MZTGunTx24OAMiLypWlO+8089hYu1kAwIdQGgEAAAAA+CfPLiNt25pPiwEAAktEhPTyy2b+3HPS4cN28wDIbudO6fffpeBgqXt322kAIG9GjjTjF1+YDyEAACiNAAAAAAD8VFycGaOj7eYAAHjPiBFSq1bS8ePSM8/YTgPgfJ5dRjp3NrsCAYA/aN/e3FJTpcmTbacBAJ9AaQQAAAAA4J88O41ERdnNAQDwnuBgafRoM588WVq2zG4eAOdwNA0Af+XZbWTsWCkjw24WAPABlEYAAAAAAP4nKUnasMHMO3WymwUA4F1RUdKDD5p5TAxv7gC+ICVFWrTIzCmNAPA3d9whVaok7d4tzZ5tOw0AWBeSlwd9+OGHuV6///778/xYb8jt9QEAAAAAxcAvv0hut9SggVS9uu00AABve+MN6dtvpdWrpfHjpYcftp0IKN4WLpTOnpXq15eaN7edBgDyp1QpafBg6T//kWJjpX79bCcCAKtcbrfbfbkHBQUFyeVy5biekUur/2KP9YbcXh84X3JyssLDw5WUlKTy5cvbjgMAAADAKaNGSf/6l3TvvdJHH9lOAwAoCu+9Jz36qFShgrRli1S1qu1EQPE1fLgpcMXEmH83AcDfbN8uNW5sPoywdavUqJHtRADguLy+V56v42ncbnfWLT+P9cYNAAAAAFCMxcebMTrabg4AQNEZMUJq1Uo6flx69lnbaYDiy+0+d5wDR9MA8FcNG0o9e5r52LF2swCAZfkqjbhcrjzvIuJ5rDduAAAAAIBiLCNDWrbMzKOi7GYBABSdkBBp9GgznzTp3H8LABStdeukhARzvEOXLrbTAEDBxcSYcfJk6dQpu1kAwKI8l0bYZQQAAAAA4BM2bJBOnpTKlZNatLCdBgBQlKKipAcfNPOY/8/enYdHVR7sH7/PTCYbJGENewABEREFISKJKIIUEdxw1/ZXfe2mgIhb3ShaeG2tGyrWWqvdXrVatSoW3FBpTUDAfUMUgbDKToDsM/P742FChmwzk0yemcn3c11znSdnzjlzZxQInHueZ6opEgJoWf/+t9mOG2eKIwAQr04/XerTR9q9W3r2WdtpAMCapFAOWrt2bcgXDOdYAAAAAADCFlia5sQTJbfbbhYAQMu7+27pX/+SPvxQevxxs2wNgJYTKI2wNA2AeOd2S1ddJf3yl2Y2s8svl1jxAEAr5PiZugMJrLi4WFlZWdq7d68yMzNtxwEAAADQHH74Q+mpp6Q77pBmz7adBgBgw8MPS9dcI7VvL61eLXXqZDsR0Drs3CllZ0s+n7R+vZSTYzsRADTNjh1Sz55Sebn0/vvSCSfYTgQAzSbUe+UhL08jSWPHjq1+/OxnP2tySAAAAAAAwhaYaSQvz24OAIA9V10lHXecmU7+lltspwFaj9deM4WRIUMojABIDJ06SRddZMaPPGI3CwBYElZp5N1339WSJUu0ZMkSrVy5MlqZAAAAAACo25Yt0tq1ZsrgkSNtpwEA2JKUdOjGzp/+ZD4ZDCD6AkvTTJ5sNwcANKerrzbbZ581M48AQCsTVmkkgBVtAAAAAABWLF1qtkOGSCxBCQCtW36+9OMfm/HUqZLXazcPkOiqqsxMI5I0aZLdLADQnE44QRo+3CxR8+STttMAQIuLqDQCAAAAAIAVBQVmm59vNwcAIDbcfbeUlSV98IH0+OO20wCJbdkysyRUhw7SiSfaTgMAzcdxDs028oc/UEQF0OpQGgEAAAAAxI/CQrPNy7ObAwAQG7p0kebMMeNbb2VKeSCaAkvTnH665HbbzQIAze3ii6X27c1yqIFZlQCglaA0AgAAAACID2Vl5pPkEqURAMAhV10lHXusmQHh1lttpwESV6A0wtI0ABJRerp0xRVm/Pvf280CAC2M0ggAAAAAID588IFUWWk+Vd63r+00AIBYkZQkPfKIGf/pT9Ly5XbzAImoqEj67DPJ5TIzjQBAIrrqKrNdtEj67ju7WQCgBVEaAQAAAADEh4ICs83PN2tOAwAQcNJJ0v/7f5LfL02dKnm9thMBiWXhQrMdNUrq0MFuFgCIlv79pQkTzM8Tf/iD7TQA0GIojQAAAAAA4kNhodmyNA0AoC6/+52UmSmtXGlmHAHQfFiaBkBrcfXVZvvEE1Jpqd0sANBCKI0AAAAAAGKf309pBADQsC5dpDlzzPjWW6UdO+zmARJFaam0eLEZUxoBkOgmTZJycqRdu6TnnrOdBgBaBKURAAAAAEDsW7NG2r5dSkmRjj/edhoAQKy6+mrp2GPNjZ5bb7WdBkgM77xjiiM9e0pDhthOAwDR5XZLv/iFGT/yiN0sANBCKI0AAAAAAGJfQYHZjhhhiiMAANQlKUmaP9+M//Qnaflyu3mARFBzaRrHsZsFAFrClVdKycnSihXmAQAJjtIIAAAAACD2sTQNACBUo0dLP/qRWdps6lTJ67WdCIhffn9waQQAWoPsbOmCC8z40UftZgGAFuD4/X5/qAe7XC45jiO/36+2bdtqxIgR0czWIMdxtDiwjiJQj+LiYmVlZWnv3r3KzMy0HQcAAABApIYMkT7/XPrXv6RzzrGdBgAQ67ZulQYOlIqLpccek372M9uJgPj0xRfSMceYmd527pTatLGdCABaxtKl5kMLqanSxo1Sx462EwFA2EK9V54U6QscOHBAS5YsifT0JvH7/XKYBg8AAAAAWoc9e8wNC0kaNcpqFABAnOjaVfr1r6Vrr5VuuUU67zxu9gCRCMwycuqpFEYAtC4nnigNHSp9/LH0l79I119vORAARE/Ey9P4/X4rDwAAAABAK7NsmZkavX9/qUsX22kAAPFi6lQzU9WuXdKtt9pOA8QnlqYB0Fo5jvlZQjJL1Ph8dvMAQBRFXBpxHMfKAwAAAADQyhQWmm1ent0cAID4kpQkPfKIGT/+uLRihd08QLzZvVsqKDBjSiMAWqNLLpGysqQ1a6Q33rCdBgCiJu5mGmG2EQAAAABoZSiNAAAiNXq09MMfmhmrpk7lU8JAON54Q/J6paOPlvr2tZ0GAFpemzbSFVeYcaCICgAJKCnSE/v376/HH3+8ObMAAAAAABCsqkp6/30zpjQCAIjEPfdIr7xiZhp54gnppz+1nQiIDyxNAwDSVVdJ8+aZ3xPXrZP69LEcCACaX8SlkbZt2+qUU05pziwAAAAAAAT77DNp/34pM9N8yhUAgHB17Srdeac0c6Z0883SlClSx462UwGxzeuVFi0yY0ojAFqzI4+Uxo+X3nxT+sMfpN/+1nYiAGh2ES9Pf3mBcAABAABJREFUAwAAAABA1AWWphk1SnK77WYBAMSvadOkIUOkXbuk226znQaIfStWSDt2SFlZzPYGAFdfbbZPPCGVldnNAgBRQGkEAAAAABC7AqURblYAAJoiKUmaP9+M//hHaeVKu3mAWBdYmmbCBMnjsZsFAGybPFnq1cuU6f75T9tpgPhWVCT9+c/SQw+ZbVGR7UQQpREAAAAAQCyjNAIAaC4nnyz98IeS3y9NnSr5fLYTAbErUBphaRoAMOXTn//cjH//e7tZgHi1fLl05plSnz7S//yPWTryf/7HfH3WWWaWM1hDaQQAAAAAEJs2b5bWrZNcLmnkSNtpAACJ4He/kzIyzD9aP/mk7TRAbNq8WfroI8lxpIkTbacBgNjwk5+YmZeWLZM+/NB2GiC+vPiilJ8vLVpkCtzSoQK33y8tXGg+LPTii/YytnKURgAAAAAAsSkwy8ixx5obfAAANFW3btKvf23GN98s7dplNw8QixYuNNsTTpA6d7abBQBiRZcu0vnnmzGzjQChW75cuugiyes1j7oEnrvoImYcsYTSCAAAAAAgNrE0DQAgGqZNk445Rtq5U7rtNttpgNjz6qtmy9I0ABBs6lSzffppafduu1mAeDF3rplNJDDDSH0Cx8yd2zK5EITSCAAAAAAgNlEaAQBEQ1KS9MgjZvzYY9LKlXbzALGkvFx66y0zpjQCAMHy8sxMmKWl0l/+YjsNEPuKikwZtb4ZRg7n9UoLFpjz0KIojQAAAAAAYk9p6aF1oimNAACa28knS5ddZj7NOHXqoTXVgdZuyRLpwAGzlNOwYbbTAEBscRzp6qvN+Pe/5+cHoCa/X9q3T/rmG+m//5X++U/pllsan2Gkruu8/XZ0MqJeSbYDAAAAAABQy8qVUmWluWHRp4/tNACARHTPPdIrr5h11p98UvrJT2wnAuz797/N9owzzM1RAECwyy6TbrpJ+vZbMzPTD35gOxEQXeXl0vffS1u31v8IPF9S0vTXc7mk4uKmXwdhiag04vDDIgAAAAAgmmouTcPfQQEA0dCtm3TnndJ110k33yxNmSJ16GA7FWCP33+oNMLSNABQt7ZtpR//WHr4YTPbCKURxCOvV9qxI7jwUd9j9+7wrp2RIXXtah6lpeEvBenzSZmZ4Z2DJgu7NOIPdwoZAAAAAADCVbM0AgBAtEybJj3xhPTFF9Jtt0mPPmo7EWDP6tXSmjWSxyOddprtNAAQu66+2pRGFiyQioqknBzbiQBT/iwubrgAEnhs2xbe8koez6EiSEOPLl2kNm0OnVdUZGaPDadf4DjS2LGhH49mEVZpZO3atdXj5OTkZg8DAAAAAID8fkojAICW4fFIjzwijRkjPfaYWaJm+HDbqQA7ArOMnHKK+ZQwAKBuRx1lbmq//bb5+eF//9d2IiSy0tKGZwOp+VxZWejXdRypc+eGCyCBcfv2kc0Cm5MjTZ4sLVxoZjdpjNttZjujiNXiHD9ThyCBFRcXKysrS3v37lUmUxkBAAAA8WH1amngQCklxXxKhg8tAACi7bLLpKeflkaONMVFl8t2IqDljRtnboDOmyfNmGE7DQDEthdflM47z9x037DB/P0VCFVVlbR9e2jLw+zdG961s7Jqlz7qenTuLCWFvShJ+FasMB8I8nobnnHEcUxppLBQys2Nfq5WItR75S3wfwIAAAAAAGEIzDKSm0thBADQMu65x0wx//770p//LF15pe1EQMsqLpb+8x8znjTJbhYAiAdnnSX16CFt2iS98IJ06aW2E8E2v1/asye05WG2bw9vyZaUlNCXh0lLi9q3GJHcXOnZZ6WLLjLfc10zjrjdpjTy3HMURiyhNAIAAAAAiC0sTQMAaGndu0t33CFdf710883SuedKHTrYTgW0nDffNJ96PvJIqX9/22kAIPYlJUk//7n0q1+Zpe4ojSSukpKGCyA1ZwqpqAj9ui6XlJ3deAmka1cze0gky8PEiilTzL/1zJkjvfqqKY+4XJLPZ76vSZOk22+nMGIRpREAAAAAQGyhNAIAsGH6dOnJJ6UvvjD/aP3739tOBLScf//bbJllBABC95OfSL/+tfk77McfS0OH2k6EUFVWStu2Nb40zNat0r594V27ffvapY+6Hp06mRk2WovcXOmVV6SiIrMcXnGxlJkpjR0r5eTYTtfqOX5/OHPfAPEl1HWaAAAAAMSI3bsPfbJ72zazxi4AAC3l3XelU081n3hcsUIaPtx2IiD6fD4z287330tvvSWNG2c7EQDEj4svNktv/PSn0h//aDtN6+b3S7t2hbY8zI4d4V07LS205WGys6XU1Oh8f0AEQr1XTmkECY3SCAAAABBnFi2SzjhDGjBAWr3adhoAQGt06aXSM89II0eaTw67XLYTAdG1cqX59G9GhrmJlpxsOxEAxI///Ec65RQpPV3atElq1852osSzf39oS8N8/72ZQSRUbnfDM4HUnCkkIyO+l4dBqxXqvXKWpwEAAAAAxA6WpgEA2HbvvdKCBdL770t/+Yv0P/9jOxEQXYGlacaPpzACAOEaPVo65hjp88+lv/5VmjHDdqL4UFFxqPDR2BIxBw6Ed+2OHUNbHqZjR8rBwEGURgAAAAAAsYPSCADAtu7dpTvukG64QfrlL6Vzzjm0dBqQiAKlkUmT7OYAgHjkONLVV5vH738vXXNN652RwueTdu4MbXmYXbvCu3Z6utStW2jLw1CABMLG8jRIaCxPAwAAAMSRqiozle+BA+ZTWoMH204EAGitKiuloUOlL780N4EeecR2IiA6vv/e3GSTpC1bDo0BAKHbt0/q0cNsn3pKKi8344wMadw4KSfHdsLI+f3mewl1eRivN/RrJyU1XgIJzBTStm30vkcggbE8DQAAAAAgvnz6qSmMZGVJgwbZTgMAaM08HlMUOfVU6dFHpSuvlI4/3nYqoPktXGi2w4dTGAGASGVkSBMmSM8/L112mdnncpmZNxxHmjxZmjVLys21m7OmsjJT8mhsaZitW6XS0vCu3blzaMvDtG/P8jBAjKA0AgAAAACIDYGlaUaN4h+OAAD2jRkjXXKJ9Mwz0tSpUkEBfz4h8bA0DQA03YsvSi+9FLzP5zNbv98U9BYtkp59VpoyJXo5vF5px47QlofZsye8a2dkND4rSNeupjDi8UTl2wMQPZRGAAAAAACxIVAaycuzmwMAgIB775UWLJCWLZP++lfpiitsJwKaT0WF9MYbZkxpBAAis3y5dNFFDS/L4vWaGUcuusj8vTecGUf8fmnv3tCWh9m27VBZJRTJyaEVQbp0kdLTQ78ugLhDaQQAAAAAEBsKCsyW0ggAIFZ07y7Nni3deKN0003SOeeYqdSBRPDee9K+fVJ2tjRihO00ABCf5s41xQ6/v+HjAsfMnSu9/LJZ8uXw0kd9j/Ly0PM4jvl9/fDSR11lkHbtzPEAWj1KIwAAAAAA+zZulIqKzLT/I0faTgMAwCEzZkh//rP05ZfSrFnS/Pm2EwHNI7A0zcSJLL0EAJEoKpJefbXxwkiA1yu98opZ6mX//vBeKysrtFlBOnWSkrj9CyA8/K4BAAAAALBv6VKzPe44qW1bu1kAAKjJ4zFFkbFjpUcfla68Uho2zHYqoOkCpRGWpgGAyCxeHHphpKZAYSQ1NbSlYbp0kdLSmjc7ANRAaQQAAAAAYF9hodmyNA0AIBadeqp08cXSP/4hTZ1qlvVgZgbEszVrpK+/Np9G/8EPbKcBgPi0b5/5ecDnC/0cx5FuucUse5eZyfIwAGICf7MBAAAAANhXUGC2lEYAALHq3nvNbFhLl0p//avtNEDTBGYZGT3aLHkAAAhfRkZ4hRHJzEwyYID5vZfCCIAYQWkEAAAAAGBXSYn00UdmnJ9vNwsAAPXp0UOaPduMf/lLafduu3mApmBpGgBounHjwi9+OI5Z8g4AYgilEQAAAACAXStXSlVVUvfuUk6O7TQAANRvxgxp0CBp+3Zp1izbaYDI7N8vvfuuGVMaAYDI5eRIkydLbndox7vd0pln8vdeADGH0ggAAAAAwK7CQrPNy2N6XgBAbPN4pPnzzfjRR6WPP7YaB4jI4sVSRYV0xBHSwIG20wBAfJs1y/w9trG/ywaOuf32lskFAGGgNAIAAAAAsKugwGzz8uzmAAAgFGPHShddJPl80tSpZgvEk5pL01DYBYCmyc2Vnn3WzCJS34wjgeeee84cDwAxhtIIAAAAAMAev//QTCP5+XazAAAQqnvvldq0MX+G/e1vttMAofP7pYULzZilaQCgeUyZYn4mOOOMQ2U818FbsI5jfr8tLJTOPddeRgBogOP3+/22QwDRUlxcrKysLO3du1eZmZm24wAAAAA43NdfS0cdJaWmSnv3SsnJthMBABCae+6RbrpJ6txZWr1aatfOdiKgcR9/LA0bJqWnSzt3mp/BAADNp6hIevttqbhYysw0M5Tl5NhOBaCVCvVeeVILZgIAAAAAIFhglpHcXAojAID4MmOG9OST0qpV0qxZ0sMP204ENC6wNM1pp1EYAYBoyMmRLr/cdgoACAvL0wAAAAAA7AmURvLy7OYAACBcycnS/Plm/PvfmxkcgFgXKI2wNA0AAAAOojQCAAAAALCnoMBs8/Pt5gAAIBLjxkkXXij5fNLUqWYLxKrt26Vly8z4jDPsZgEAAEDMoDQCAAAAALBj1y7pq6/MeNQou1kAAIjUffdJbdqY2bP+/nfbaYD6vfaa5PdLxx0n9expOw0AAABiBKURAAAAAIAdgU+6Hnmk1KmT3SwAAESqZ0/pV78y4xtvlPbssRoHqBdL0wAAAKAOlEYAAAAAAHYUFpptXp7dHAAANNW110pHHWWW/wgUSIBYUlUlvf66GVMaAQAAQA2URgAAAAAAdhQUmG1+vt0cAAA0VXKy9PDDZvzII9Inn9jNAxyusNDMgtOxozRypO00AAAAiCGURgAAAAAALa+yUlq+3IyZaQQAkAhOO0264ALJ55OmTpX8ftuJgEMCS9OcfrrkdtvNAgAAgJhCaQQAAAAA0PI+/VQqKZHatTPT+QMAkAjuv19q08bMpvX3v9tOAxwSKI2wNA0AAAAOQ2kEAAAAANDyCgvNdtQoycVfTQEACaJnT2nWLDO+8UazHAhg2/r10hdfmBlGJkywnQYAAAAxhn+ZAwAAAAC0vIICs83Pt5sDAIDmNnOmNHCgtG2bNHu27TTAoVlG8vKkDh3sZgEAAEDMoTQCAAAAAGh5gZlG8vLs5gAAoLklJ0vz55vx/PnSJ5/YzQOwNA0AAAAaQGkEAAAAANCyNmwwD7dbys21nQYAgOZ32mnSBRdIPp80dark99tOhNaqpER6+20zpjQCAACAOlAaAQAAAAC0rKVLzfa446S2be1mAQAgWu67T0pPN0uy/f3vttOgtXrnHamsTMrJkQYPtp0GAAAAMYjSCAAAAACgZRUUmG1+vt0cAABEU69e0qxZZnzjjdKePVbjoJWquTSN49jNAgAAgJhEaQQAAAAA0LIKC802L89uDgAAou2666SBA6Vt26TZs22nQWvj9weXRgAAAIA6UBoBAAAAALScAwekjz4yY0ojAIBEl5wsPfywGc+fL336qd08aF2++EIqKpJSU6VTT7WdBgAAADGK0ggAAAAAoOWsXCl5vVKPHmbafgAAEt348dL550s+nzR1qpn9AWgJr75qtmPHSunpdrMAAAAgZlEaAQAAAAC0nIICs83LkxzHbhYAAFrK/febm/bvvSf93//ZToPWgqVpAAAAEAJKIwAAAACAllNYaLb5+XZzAADQknr1kmbNMuMbb5T27rWbB4lv165DP3dRGgEAAEADKI0AAAAAAFqGzyctXWrGeXl2swAA0NKuu0468kjp+++l2bNtp0Gie/1187PX4MFS79620wAAACCGURoBAAAAALSM1avNp17T0qShQ22nAQCgZSUnSw8/bMbz50uffmo3DxIbS9MAAAAgRJRGAAAAAAAto6DAbHNzJY/HbhYAAGz4wQ+k886TvF5p6lTJ77edCInI65Vee82MKY0AAACgEZRGAAAAAAAto7DQbPPz7eYAAMCm+++X0tOl996TnnrKdhokovffl3bulNq1Y0lAAAAANIrSCAAAAACgZQRKI9y8AAC0Zjk50u23m/ENN0h799rNg8QTWJpmwgQpKcluFgAAAMQ8SiMAAAAAgOjbuVNatcqMTzzRbhYAAGy77jppwADp+++lO+6wnQaJJlAamTzZbg4AAADEBUojAAAAAIDoW7bMbAcOlDp1spsFAADbUlKkhx8244cflj77zG4eJI6NG6VPPpEcRzr9dNtpAAAAEAcojQAAAAAAoq+gwGzz8+3mAAAgVkyYIE2ZInm90tSpkt9vOxESwcKFZnviiRR1AQAAEBIWNAzT9u3btWLFCq1Zs0bFxcXyeDzq2LGjjj76aI0YMUIej8d2xJj1+eef67PPPtPmzZtVWlqqNm3aqFevXho6dKj69+9vOx4AAACAaCosNNu8PLs5AACIJQ88IC1aJP33v9LTT0uXXWY7EeJdYGmaSZPs5gAAAEDccPx+KuyheP755/Xggw+qoKBA9b1lGRkZuvDCC3XTTTfpyCOPbOGEdVu/fr1WrlxZ/fjggw+0e/fuoGNOOeUUvfvuu1F5/d27d2vevHl64okntGnTpnqPGzBggH7xi1/oqquuUlpaWrO9fnFxsbKysrR3715lZmY223UBAAAAhKGyUsrKkkpLpS+/lAYNsp0IAIDYcddd0m23SV27Sl9/LfFvWIhUWZnUsaNUUiJ99JE0dKjtRAAAALAo1HvllEYasWnTJl122WVasmRJyOckJyfr9ttv1+233y7HcaKYrrY333xT//nPf6pLIjt27Gj0nGiVRl566SX99Kc/DSlDQJ8+ffT0009r1KhRzZKB0ggAAAAQA1aulHJzpfbtpR07JBcrpQIAUK28XBoyRPrmG2nmTOn++20nQrx6/XXp9NOlHj2kDRukFv63aQAAAMSWUO+V8y91DVi9erVyc3PDKoxIUkVFhX71q1/psssuk9frjVK6ut14442aO3euXnvttbDKGs3toYce0pQpU8LOsG7dOo0ZM0Yvv/xylJIBAAAAaHEFBWabl0dhBACAw6WkSA8/bMYPPSR99pndPIhfgaVpzjiDwggAAABClmQ7QKzauXOnxo8fry1bttR6bvjw4Tr77LPVt29flZaWavXq1Xr66ae1efPmoOOeeeYZZWdna968eS2UOjb885//1IwZM2rt93g8Ov/885Wbm6tu3bppy5YtWr58uV544QVVVlZWH1dRUaELL7xQ//nPfzRy5MiWjA4AAAAgGgoLzTYvz24OAABi1YQJ0rnnSv/6lzRtmvTuu9z0R3j8funVV8140iS7WQAAABBXWJ6mHuedd55efPHFoH0ZGRn6+9//rrPPPrvW8ZWVlbrrrrt0xx131Hpu4cKFmjhxYrSiBhk6dKg++eSTWvszMzM1bNgw9evXT08++WTQc825PM2mTZs0aNAg7du3L2j/qFGj9M9//lM9evSodc7GjRt1wQUXaNmyZUH7+/Tpoy+++ELp6ekR52F5GgAAACAG9OolbdwovfOONGaM7TQAAMSm9eulQYOk0lLp//5Puuwy24kQT776Sjr6aCk5Wdq5U2rb1nYiAAAAWMbyNE3w5ptv1iqMJCcn6+23366zMCKZWTRmz55d56wi06dPV1VVVTSi1qlNmzbKz8/XjBkz9Pe//11fffWV9uzZo3fffVezZs2K6mvfdNNNtQoj+fn5Wrx4cZ2FEUnq2bOn3n77beUd9qnDdevW6e67745aVgAAAAAtYMMGUxhxu6XcXNtpAACIXb17S7fdZsY33CAVF9vNg/gSWJpmzBgKIwAAAAgLpZE6zJkzp9a+2bNna8SIEY2eO2PGDI0fPz5o35o1a/T00083W76GvPzyy9q7d6/ee+89zZs3Tz/84Q911FFHyWmB6Sy//fZb/eMf/wjal5aWpr/85S9KS0tr8Nz6jnvwwQdVzF+QAQAAgPhVUGC2w4ZJbdrYzQIAQKy74Qapf39p61bpzjttp0E8CZRGWJoGAAAAYaI0cpgvv/xS//3vf4P2derUSddff33I1/jNb35Ta9+jjz7a5Gyh6N27t9xud4u81uEee+wx+Xy+oH0/+clP1L9//5DOHzBggK688sqgfXv37tUzzzzTbBkBAAAAtLDCQrM9bGZBAABQh5QU6eGHzfjBB6XPP7ebB/Fh717pvffMmNIIAAAAwkRp5DB1FRSuuOIKpaSkhHyN4cOHa/jw4UH7li1bprVr1zY5Xyw7fJYRSfr5z38e1jXqOp7SCAAAABDHKI0AABCe00+Xzj1X8nqladMkv992IsS6N96QqqqkgQOlfv1spwEAAECcoTRymNdee63WvvPPPz/s69R1Tl3XThRffPGFNm7cGLRv0KBBGjx4cFjXOeaYYzRw4MCgfQUFBdq3b1+TMwIAAABoYQcOSB9/bMaURgAACN0DD0hpadKSJRIfqEJjWJoGAAAATUBppIYDBw7oww8/DNqXnp6uYcOGhX2t0aNH19p3+LI3iaSu7+2kk06K6FqHv3dVVVVaunRpRNcCAAAAYNHy5eZT0r16mQcAAAhN797SbbeZ8Q03SMXFdvMgdvl80qJFZkxpBAAAABGgNFLDxx9/LJ/PF7RvxIgR8ng8YV8rNze31nkffPBBk/LFsrq+t1GjRkV0rbw6PoGYyO8dAAAAkLBYmgYAgMjdcIPUv7+0ZYt055220yBWrVwpbdsmZWZKEX6IDwAAAK0bpZEaVq1aVWtf//79I7pWcnKyevbsGbRvzZo1qqqqiuh6sa4537t+day7+fXXX0d0LQAAAAAWURoBACByKSnSQw+Z8YMPSp9/bjcPYlNgaZof/EBKTrabBQAAAHGJ0kgN69atq7Wvd+/eEV8vJycn6Guv16uioqKIrxfLmvO9O/x9k6TvvvsuomsBAAAAsMTnkwLLTFIaAQAgMhMnSuecY5Z7mzZN8vttJ0KsCZRGWJoGAAAAEaI0UsPWrVtr7evVhHW36zr3+++/j/h6sezw78txHPXo0SOia/Xs2VOO4zR4fQAAAAAxbtUqafduKT1dOu4422kAAIhfDzwgpaZKS5ZI//iH7TSIJVu2SIFlvSdOtJsFAAAAcYvSSA27du2qta9t27YRX6+uc3fu3Bnx9WLVvn37VFlZGbQvLS1Nbrc7ouslJSUpJSUlaF8ivm8AAABAQgssTXPCCZLHYzcLAADxrE8f6bbbzPj666V9+6zGQQxZtMhsc3OlLl3sZgEAAEDcojRSw4EDB2rtS0tLi/h6dZ1bUlIS8fViVXO/b3Wdn4jvGwAAAJDQAqURlqYBAKDpbrhB6tfPzCxx55220yBWsDQNAAAAmgGlkRoOny1DklJTUyO+Xl3FiYqKioivF6ua+32Tar93ob5v5eXlKi4uDnoAAAAAsIDSCAAAzSc1VXr4YTN+8EHpiy/s5oF9FRXSm2+aMaURAAAANAGlkUY4jtOs5/r9/qbEiRtNed/qOj/U9+03v/mNsrKyqh+9evVqUg4AAAAAEdixQ/r6azMeNcpuFgAAEsXEidLZZ0tVVdK0aVIr+XdG1OO//zVLFXXpIh1/vO00AAAAiGOURmrw1LHOdmlpacTXq+vc5OTkiK8Xq5r7favr/FDft1tuuUV79+6tfmzYsKFJOQAAAABEYOlSsx00SOrQwW4WAAASybx5ZtaRd9+Vnn3WdhrYFFia5owzJBf/zA8AAIDI8dNkDenp6bX2NXdppE2bNhFfL1Y19/tW1/mhvm8pKSnKzMwMegAAAABoYSxNAwBAdPTpI916qxlff72ZaQKt06uvmi1L0wAAAKCJKI3U0LFjx1r79u/fH/H16jq3rteId5mZmbVmGykrK5PX643oelVVVSorKwval4jvGwAAAJCwKI0AABA9N94o9esnbd4s/frXttPAhm++MQ+PRxo/3nYaAAAAxDlKIzV06dKl1r6NGzdGfL26lkap6zUSQXZ2dtDXPp9PmzdvjuhamzZtkv+wNVkT9X0DAAAAEk5FhbR8uRlTGgEAoPmlpkoPPmjG8+ZJX35pNQ4sCCxNM3q0xEzLAAAAaCJKIzX07du31r7169dHfL2ioqKgr91ut3JyciK+Xixrzvfu8PetvusDAAAAiEEffyyVlUkdOkgDB9pOAwBAYpo0STrrLKmqSpo2TTrsA1hIcIHSCEvTAAAAoBlQGqlhYB3/oPntt99GdK2KiopaM43069dPSUlJEV0v1jXne7dmzZpa+4466qiIrgUAAACghdVcmsZx7GYBACCRzZtnZh155x3p2Wdtp0FL2bdPWrLEjCmNAAAAoBlQGqlh2LBhcrmC35KVK1eqqqoq7GutXLlSlZWVQfuOP/74JuWLZcOHD6+1b+nSpRFdqzDwj8w1JPJ7BwAAACSUmqURAAAQPX37SrfcYsbXX2/KBEh8b70lVVZK/fpJRx5pOw0AAAASAKWRGtq0aaNhw4YF7Ttw4IA++uijsK/13nvv1dp38sknR5wt1o0ePbrWvrreg1Acfl5SUpJGjRoV0bUAAAAAtCC/XyooMGNKIwAARN9NN0lHHCFt3izNmWM7DVpCzaVpmNUNAAAAzYDSyGFOP/30WvteeOGFsK/z/PPPh3TtRHHMMceoR48eQfu+/PJLffXVV2Fdp65z8vLylJmZ2eSMAAAAAKKsqMjctEpKknJzbacBACDxpaZKDz1kxg88IH35pd08iC6/X1q40IwnT7abBQAAAAmD0shhLrnkklr7/vznP6uioiLka3z00UdasWJF0L6RI0eqb9++Tc4Xyy6++OJa+x577LGwrlHX8XX9NwEAAAAQgwJL0wwbJqWn280CAEBrMWmSdNZZUlWVNH26KRYgMX30kbRli9SmjZTAs1oDAACgZVEaOczgwYN10kknBe3btm2bHnjggZCvceutt9bad9VVVzU5W6z72c9+Jpcr+H+pxx9/XN99911I569Zs0Z/+tOfgvZlZWVRGgEAAADiRaA0wtI0AAC0rHnzzKwjb78tPfec7TSIlsDSNOPHSykpdrMAAAAgYVAaqcPtt99ea9/s2bP14YcfNnru/Pnz9dprrwXt69u3ry699NKQXnvMmDFyHCfo8Ze//CWkc2078sgjdeGFFwbtKykp0eWXX66ysrIGzy0rK9OPf/xjlZSUBO2fPn26srKymj0rAAAAgCigNAIAgB19+0o332zG110n7dtnNw+iI1AamTTJbg4AAAAkFEojdZgwYYLOPvvsoH3l5eU69dRTtWDBgjrPqays1Jw5c3TNNdfUeu6hhx6Sx+OJStZY87vf/U5t27YN2vff//5Xp512mjZv3lznOZs2bdLYsWNVUFAQtL9379665ZZbopYVAAAAQDPav1/65BMzpjQCAEDLu+km6YgjpM2bpTlzbKdBc9u+XVq+3IzPOMNuFgAAACSUJNsBYtWf/vQnffDBB9q4cWP1vuLiYp111lkaMWKEzj77bPXt21elpaX65ptv9NRTT2nTpk21rjNt2jRNnjy5xXJv3rxZZzTwl4aKiopa+1auXKmhQ4fWe0737t21cOHCkF6/V69eevzxx2stKVNQUKC+ffvqggsuUG5urrp27aotW7Zo+fLlev7551VZWRl0vMfj0TPPPKN01kEHAAAA4sPy5ZLXK+XkSD172k4DAEDrk5YmPfigdOaZ0gMPSFdcIQ0aZDsVmsuiRZLfLw0bJnXvbjsNAAAAEgilkXp06tRJb7zxhsaOHautW7cGPbdy5UqtXLmy0WtceOGFmjdvXpQS1q2iokKfBD7dF6IDBw40eM6ePXvCut7FF1+sLVu26LrrrquV7amnntJTTz3V4Pkej0f/+Mc/NGrUqLBeFwAAAIBFLE0DAIB9kyeb0siCBdK0adJbb0mOYzsVmgNL0wAAACBKWJ6mAYMGDdKKFSs0evTosM7zeDy644479Mwzz8jtdkcpXWybOXOmXnjhBXXs2DGs83r37q133nlHU6ZMiVIyAAAAAFFBaQQAgNgwb56UkiK9/bb0z3/aToPmUFkpvf66GVMaAQAAQDOjNNKInj17asmSJXr22WeVl5cnp4Fmftu2bXXFFVfos88+0+zZs+Vyte63d8qUKVq9erVmzZql7o1Mmdi/f3/dc889+uqrr5Sfn99CCQEAAAA0C59PWrrUjPl5HgAAu444QrrlFjO+7jpp/367edB0hYXS3r1Sp05Sbq7tNAAAAEgwjt/v99sOEU+2bdum5cuX67vvvlNxcbGSkpLUqVMnDRo0SLm5uUpOTrYdMSb5/X59/vnn+vTTT7V582aVlZUpPT1dvXr10rBhwzRgwICovG5xcbGysrK0d+9eZWZmRuU1AAAAgFbviy+kY46R0tPNDY0kVkIFAMCq0lJp8GBp7Vrpppuku++2nQhNceON0r33Sj/6kfS3v9lOAwAAgDgR6r1y/iUvTNnZ2Zo8ebLtGHHHcRwNGTJEQ4YMsR0FAAAAQHMLLE0zciSFEQAAYkFamvTQQ9KZZ0r33y9dfrk0aJDtVIjUv/9ttixNAwAAgCho3eunAAAAAACaLlAaycuzmwMAABwyebJ5VFVJ06dLTDgdn9aulb76SnK7pQkTbKcBAABAAqI0AgAAAABomoICs83Pt5sDAAAEe/BBKSVFWrxYev5522kQicAsI/n5Urt2VqMAAAAgMVEaAQAAAABEbvt26ZtvzPjEE+1mAQAAwY44Qrr5ZjOeOVPav99uHoSPpWkAAAAQZZRGAAAAAACRW7rUbI8+Wmrf3m4WAABQ2y9/KfXtK23aJM2dazsNwnHggPTOO2ZMaQQAAABRQmkEAAAAABC5wkKzzcuzmwMAANQtLc0sUyNJ990nrVplNw9C9/bbUnm51Lu3KegCAAAAUUBpBAAAAAAQuYICs6U0AgBA7DrzTDNTRVWVNH265PfbToRQBJammTxZchy7WQAAAJCwKI0AAAAAACJTUSGtWGHG+fl2swAAgIY9+KCUkiK99Zb0wgu206Axfv+h0ghL0wAAACCKKI0AAAAAACLz0UdmyvSOHaUBA2ynAQAADenXT/rlL8145kxp/367edCwzz6TNm40ywuNGWM7DQAAABIYpREAAAAAQGQKC802L48p0wEAiAc33yz16WPKCP/7v7bToCGBWUbGjTPFEQAAACBKKI0AAAAAACJTUGC2eXl2cwAAgNCkpZllaiTpvvukr7+2mwf1Y2kaAAAAtBBKIwAAAACA8Pn9h0oj+fl2swAAgNCdeaZ0xhlSZaU0fbr5Mx2xZedOaelSMz7jDLtZAAAAkPAojQAAAAAAwrd+vbR1q5SUJI0YYTsNAAAIleNIDz0kpaRIb74pvfCC7UQ43OuvSz6fNGSIlJNjOw0AAAASHKURAAAAAED4CgvN9vjjzVT3AAAgfvTrJ910kxnPnCkdOGA3D4KxNA0AAABaEKURAAAAAED4AkvT5OXZzQEAACJz881Snz7Sxo3S3Lm20yCgqkpatMiMKY0AAACgBVAaAQAAAACELzDTSH6+3RwAACAy6enSvHlmfN990tdfW42Dg5Ytk3bvltq3l0480XYaAAAAtAKURgAAAAAA4dm3T/r0UzNmphEAAOLXWWdJZ5whVVZK11wj+f22EyGwNM3pp0tJSXazAAAAoFWgNAIAAAAACM/y5ZLPJ/XuLXXvbjsNAACIlONIDz4oJSdLb7whvfii7UQIlEZYmgYAAAAthNIIAAAAACA8gaVpmGUEAID417+/9MtfmvHMmdKBA3bztGZFRdJnn0kul5lpBAAAAGgBlEYAAAAAAOEpKDDb/Hy7OQAAQPO4+WYzg9iGDdL//q/tNK3XwoVme+KJUseOdrMAAACg1aA0AgAAAAAInc8nLV1qxsw0AgBAYkhPl+bNM+N775VWr7Yap9ViaRoAAABYQGkEAAAAABC6L7+UioulNm2kIUNspwEAAM3l7LOliROlykpp+nTJ77edqHUpLZUWLzZjSiMAAABoQZRGAAAAAAChKyw025EjpaQku1kAAEDzcRzpoYek5GTpjTekf/3LdqLW5d13TXGkZ0/p2GNtpwEAAEArQmkEAAAAABC6ggKzzc+3mwMAADS//v2lm24y42uvlQ4csBqnVam5NI3j2M0CAACAVoXSCAAAAAAgdIGZRvLy7OYAAADRccstUu/e0oYN0l132U7TOvj9waURAAAAoAVRGgEAAAAAhGbbNunbb834xBPtZgEAANGRni7Nm2fG99wjrV5tNU6r8NVX0rp1UkqKNHas7TQAAABoZSiNAAAAAABCs3Sp2Q4eLLVrZzUKAACIorPPlk4/XaqslK65xsyEgegJzDJy6qlSmzZ2swAAAKDVoTQCAAAAAAhNQYHZ5ufbzQEAAKLLcaSHHpKSk6XXX5deesl2osTG0jQAAACwiNIIAAAAACA0hYVmm5dnNwcAAIi+AQOkG28042uvlUpKrMZJWHv2SO+9Z8aURgAAAGABpREAAAAAQOPKy6WVK82Y0ggAAK3DrbdKOTlSUZF011220ySmN96QvF5p0CCpb1/baQAAANAKURoBAAAAADTuo49McaRTJ6l/f9tpAABAS0hPl+bNM+N77pG++cZqnIT06qtmyywjAAAAsITSCAAAAACgcQUFZpuXJzmO3SwAAKDlnHOONGGCVFEhTZ8u+f22EyUOr1datMiMKY0AAADAEkojAAAAAIDGFRaabX6+3RwAAKBlOY708MNScrL0+uvSSy/ZTpQ4VqyQduyQsrL4GQsAAADWUBoBAAAAADTM7z9UGsnLs5sFAAC0vAEDpBtuMONrr5VKSqzGSRj//rfZ/uAHksdjNwsAAABaLUojAAAAAICGrVsnbd1qbmYMH247DQAAsOHWW6WcHKmoSLrrLttpEkOgNMLSNAAAALCI0ggAAAAAoGEFBWZ7/PFSWprdLAAAwI42baQHHjDje+6RvvnGbp54t3mz9NFHZvmfiRNtpwEAAEArRmkEAAAAANCwwNI0+fl2cwAAALvOPVeaMEGqqJBmzDBL2CEyCxeabW6ulJ1tNwsAAABaNUojAAAAAICGBUojeXl2cwAAALscR3roIbNk3aJF0ssv204UvwJL00yebDcHAAAAWj1KIwAAAACA+hUXS599ZsajRtnNAgAA7DvySOmGG8z42mulkhKrceJSebn05ptmPGmS3SwAAABo9SiNAAAAAADq9/77ks8n9ekjde9uOw0AAIgFt90m9eolrV8v/eY3ttPEn//8RzpwQOrWTRo2zHYaAAAAtHKURgAAAAAA9QssTZOfbzcHAACIHW3aSA88YMa/+5307bd288SbwNI0Z5xhlvwBAAAALKI0AgAAAACoX6A0kpdnNwcAAIgtU6ZIP/iBVFEhXXON5PfbThQ/AqURlqYBAABADKA0AgAAAACom9crLVtmxpRGAABATY4jPfyw5PFIixZJr7xiO1F8WL3azMzi8UinnWY7DQAAAEBpBAAAAABQjy++kIqLpbZtpWOOsZ0GAADEmiOPlG64wYxnzJBKSuzmiQeBWUZOOUXKyLCbBQAAABClEQAAAABAfQJL05x4opSUZDcLAACITbfdJvXqJa1fL/32t7bTxD6WpgEAAECMoTQCAAAAAKhboDTC0jQAAKA+bdpIDzxgxr/7nVl6BXUrLpb+8x8zpjQCAACAGEFpBAAAAABQN0ojAAAgFFOmSOPHS+XlZpkav992otj05ptSZaU0YIB5AAAAADGA0ggAAAAAoLbvv5fWrJEcxyxPAwAAUB/HkR5+WPJ4pIULpQULbCeKTSxNAwAAgBhEaQQAAAAAUFtglpFjjpGysuxmAQAAsW/gQOn66814xgyptNRunljj85lCjURpBAAAADGF0ggAAAAAoDaWpgEAAOG6/XapZ09p3Trpt7+1nSa2fPihmcmtbVvp5JNtpwEAAACqURoBAAAAANRGaQQAAISrTRvpgQfM+O67zVJ3MAJL04wfLyUn280CAAAA1EBpBAAAAAAQrLxcWrnSjCmNAACAcJx3nnTaaebniWuukfx+24liQ6A0wtI0AAAAiDGURgAAAAAAwT74QKqokLKzpX79bKcBAADxxHGkhx+WPB5p4UJpwQLbiez7/ntpxQozPuMMu1kAAACAw1AaAQAAAAAEq7k0jePYzQIAAOLPUUdJ111nxjNmSKWldvPYtmiR2Q4fLnXrZjcLAAAAcBhKIwAAAACAYDVLIwAAAJG4/XapZ09p3Trp7rttp7GLpWkAAAAQwyiNAAAAAAAO8fspjQAAgKZr21a6/34z/u1vpTVr7OaxpbJSeuMNM6Y0AgAAgBhEaQQAAAAAcMh330nffy8lJ5sp1AEAACJ1/vnSuHFSebl07bW209jx3ntScbGUnS2NGGE7DQAAAFALpREAAAAAwCGBWUaGD5dSU+1mAQAA8c1xpPnzJY9HevVVacEC24laXmBpmokTJRf/HA8AAIDYw0+pAAAAAIBDWJoGAAA0p6OOkmbONOMZM6TSUrt5WlqgNMLSNAAAAIhRlEYAAAAAAIdQGgEAAM1t1iypRw9p7Vrp7rttp2k5330nrVolJSVJP/iB7TQAAABAnSiNAAAAAACMvXulzz4zY0ojAACgubRtK91/vxn/9remTNEaBGYZOekkKSvLbhYAAACgHpRGAAAAAADG++9Lfr90xBFS16620wAAgERywQXSuHFSeblZpqY1YGkaAAAAxAFKIwAAAAAAg6VpAABAtDiO9PDDZqmWV1+VFiywnSi69u+X3nnHjCmNAAAAIIZRGgEAAAAAGJRGAABANA0aJM2cacYzZkilpXbzRNPixVJFhdS3r3TUUbbTAAAAAPWiNAIAAAAAkLxeadkyM6Y0AgAAomXWLKlHD2ntWul3v7OdJnpqLk3jOHazAAAAAA2gNAIAAAAAkD7/XNq3T8rIkI45xnYaAACQqDIypPvuM+Pf/lb67ju7eaLB75cWLjRjlqYBAABAjKM0AgAAAAA4tDTNiSdKbrfdLAAAILFdeKE0dqxUViZde63tNM3vk0+kTZuk9HRpzBjbaQAAAIAGURoBAAAAABwqjbA0DQAAiDbHkebPl5KSpAULpFdftZ2oeQWWphk3TkpNtZsFAAAAaASlEQAAAACAVFBgtpRGAABASxg0SJo504xnzDCzjiSKQGmEpWkAAAAQByiNAAAAAEBrt2WLtHat+dTviSfaTgMAAFqLWbOk7t2l776Tfvc722max44d0rJlZkxpBAAAAHGA0ggAAAAAtHZLl5rtkCFSZqbdLAAAoPXIyJDuv9+Mf/MbU2KNd6+9Jvn90nHHST172k4DAAAANIrSCAAAAAC0doWFZsvSNAAAoKVdeKE0dqxZnubaa22naTqWpgEAAECcoTQCAAAAAK0dpREAAGCL40gPPywlJUmvvHKodBGPqqrMTCMSpREAAADEDUojAAAAANCalZVJH3xgxvn5drMAAIDW6eijD80ycs015ueTeLR0qbRnj9SxozRypO00AAAAQEgojQAAAABAa/bBB1JFhdSli9S3r+00AACgtfrVr6Tu3aXvvpPuucd2msgEZkk5/XTJ7babBQAAAAgRpREAAAAAaM1qLk3jOHazAACA1isjQ7rvPjO+6y5p7Vq7eSIRKI2wNA0AAADiCKURAAAAAGjNapZGAAAAbLroIunUU83yNDNn2k4TnvXrpc8/l1wuacIE22kAAACAkFEaAQAAAIDWyu+XCgrMOD/fbhYAAADHkR5+WEpKkl5+WVq40Hai0AVmGcnLkzp0sJsFAAAACAOlEQAAAABordaskbZvl5KTpeOPt50GAABAGjxYmjHDjK+5xsw6Eg9YmgYAAABxitIIAAAAALRWgaVpRoyQUlLsZgEAAAiYPVvq3t0UXO+5x3aaxpWUSG+/bcaURgAAABBnKI0AAAAAQGsVKI3k5dnNAQAAUFNGhnTvvWZ8113SunVW4zTqnXfMjCi9eknHHGM7DQAAABAWSiMAAAAA0FoVFJhtfr7dHAAAAIe7+GLplFNMGePaa22naVjNpWkcx24WAAAAIEyURgAAAACgNdqzR/riCzMeNcpqFAAAgFocR3rkEcntll5+WVq0yHaiuvn9waURAAAAIM5QGgEAAACA1uj9981Njn79pC5dbKcBAACobfBgacYMM54+3cw6Emu++EIqKpJSU6WxY22nAQAAAMJGaQQAAAAAWqPCQrPNy7ObAwAAoCGzZ0vduklr1kj33ms7TW2BWUbGjpXS0+1mAQAAACJAaQQAAAAAWqOCArOlNAIAAGJZZuahsshdd0nr1lmNUwtL0wAAACDOURoBAAAAgNamqsosTyNJ+fl2swAAADTmkkukU06RSkulmTNtpzlk9+5Ds7dRGgEAAECcojQCAAAAAK3N559L+/ebT+4efbTtNAAAAA1zHGn+fMntll56SVq0yHYi4/XXJa9XGjxY6t3bdhoAAAAgIpRGAAAAAKC1CXwi9sQTzc0XAACAWHfMMdKMGWZ8zTVSebndPBJL0wAAACAhUBoBAAAAgNamoMBs8/Ls5gAAAAjH7NlS167St99K995rN4vXe2jGE0ojAAAAiGOURgAAAACgtQnMNJKfbzcHAABAODIzD5VF/vd/pfXr7WVZvlzauVNq144iLgAAAOIapREAAAAAaE02b5bWrZNcLumEE2ynAQAACM+ll0onnyyVlkozZ9rLEViaZsIEKSnJXg4AAACgiSiNAAAAAEBrsnSp2Q4ZYj6tCwAAEE8cR3rkEcntlv71L+m11+zkCJRGWJoGAAAAcY7SCAAAAAC0JgUFZss06gAAIF4dc4x0zTVmPH26VF7esq+/caP08cemwHL66S372gAAAEAzozQCAAAAAK1JYaHZ5ufbzQEAANAUd9whde0qffutdN99LfvaCxea7ciRUufOLfvaAAAAQDOjNAIAAAAArUVpqfThh2bMTCMAACCeZWZK995rxnPnSuvXt9xrszQNAAAAEgilEQAAAABoLT74QKqsNJ/K7dPHdhoAAICmufRS6eSTTTH2uuta5jXLyqS33jJjSiMAAABIAJRGAAAAAKC1KCgw27w8yXHsZgEAAGgqx5Hmz5fcbunFF6XXX4/+ay5ZIpWUSN27S0OHRv/1AAAAgCijNAIAAAAArUVhodnm59vNAQAA0FyGDJGmTzfj6dOl8vLovl5gaZozzqCECwAAgIRAaQQAAAAAWgO//1BpJC/PbhYAAIDmdMcdUpcu0jffSPfdF73X8fsPlUZYmgYAAAAJgtIIAAAAALQG334r7dghpaRIw4bZTgMAANB8srKke+8147lzpaKi6LzO119L330nJSdLp50WndcAAAAAWhilEQAAAABoDQKzjIwYYYojAAAAieSyy6TRo6XSUmnmzOi8RmCWkTFjpLZto/MaAAAAQAujNAIAAAAArUFBgdnm59vNAQAAEA2OI82fL7nd0osvSm+80fyvwdI0AAAASECURgAAAACgNQjMNJKXZzcHAABAtBx7rDRtmhlPny6Vlzfftffulf77XzOmNAIAAIAEQmkEAAAAABLdnj3SF1+Y8ahRVqMAAABE1Z13Sl26SKtXS/ff33zXffNNqapKGjhQ6tev+a4LAAAAWEZpBAAAAAAS3bJlZtu/v5SdbTcLAABANGVlSffcY8Zz50pFRc1zXZamAQAAQIKiNAIAAAAAia6gwGzz8+3mAAAAaAk//KF00klSSYl03XVNv57PJy1caMaURgAAAJBgKI0AAAAAQKIrLDTbvDy7OQAAAFqC40iPPCK53dILL0hvvNG0633wgbRtm5SRYcooAAAAQAKhNAIAAAAAiayqSnr/fTOmNAIAAFqLY4+Vpk414+nTpfLyyK8VWJrmBz+QkpObng0AAACIIZRGAAAAACCRffaZdOCAlJUlHX207TQAAAAt5847pS5dpNWrpQceiPw6r75qtixNAwAAgAREaQQAAAAAEllBgdmOGiW5+CsgAABoRdq1k373OzOeM0cqKgr/Glu2mOVpJGnixGaLBgAAAMQK/sUQAAAAABJZYaHZsjQNAABojX70I+mkk6SSEun668M/f9Eisx0xQuratXmzAQAAADGA0ggAAAAAJDJKIwAAoDVzHGn+fDPj2vPPS2++Gd75//632bI0DQAAABIUpREAAAAASFSbNknr15ubJCecYDsNAACAHccdJ02bZsbTp0sVFaGdV1FxqGRCaQQAAAAJitIIAAAAACSqwCwjxx4rZWTYzQIAAGDTnXdK2dnS119LDzwQ2jn//a+0b5/UpYs0fHh08wEAAACWUBoBAAAAgEQVKI3k59vNAQAAYFu7dtI995jxr38tbdjQ+DmBpWkmTjQztwEAAAAJKMl2gHizfft2rVixQmvWrFFxcbE8Ho86duyoo48+WiNGjJDH47EdsV7ffvutPv74Y23YsEEHDhxQWlqaunfvrmOPPVaDBw+2HQ8AAABAcwuURvLy7OYAAACIBT/6kfTHP0oFBdL110vPPdfw8YHSyOTJ0c8GAAAAWOL4/X6/7RDx4Pnnn9eDDz6ogoIC1feWZWRk6MILL9RNN92kI488soUT1q20tFSPPvqo/vCHP+ibb76p97iePXvqyiuv1LXXXqt27dpF/HqO40R87uGeeeYZXXzxxU26RnFxsbKysrR3715lZmY2UzIAAAAgDpSWSpmZUlWVtHat1KeP7UQAAAD2ffKJdPzxks8nvfmmdNppdR/37bfSgAGSxyPt2GF+rgIAAADiSKj3yplTrxGbNm3SmDFjdMEFF+i9996rtzAiSfv27dMTTzyhIUOGaM6cOQ0e2xIKCgo0aNAgXX/99Q0WRiRp48aNuvPOO3XkkUfqlVdeaaGEAAAAAKJmxQpTGOnWTerd23YaAACA2HDccdLUqWY8bZpUUVH3cYFZRkaPpjACAACAhEZppAGrV69Wbm6ulixZEtZ5FRUV+tWvfqXLLrtMXq83Suka9uKLL+rUU0/V+vXrwzpv+/btOuecc/T73/8+SskAAAAAtIjA0jT5+VIzzggIAAAQ9379ayk7W/r6a+mBB+o+JlAamTSp5XIBAAAAFiTZDhCrdu7cqfHjx2vLli21nhs+fLjOPvts9e3bV6WlpVq9erWefvppbd68Oei4Z555RtnZ2Zo3b14LpTYKCwt18cUXq7KyMmi/y+XS5MmTddJJJ6lXr17avn27PvnkEz3zzDMqKSmpPs7v92vatGnq1q2bzj333CZl6dKli7p27RrRue3bt2/SawMAAACtWqA0kpdnNwcAAECsaddO+t3vpMsvl+bMkS67TOrZ89Dz+/dLgQ8SUhoBAABAgnP8ttdQiVHnnXeeXnzxxaB9GRkZ+vvf/66zzz671vGVlZW66667dMcdd9R6buHChZo4cWK0ogY5cOCAjj76aBUVFQXtHzhwoP71r39p0KBBtc7ZtWuXrrjiilrL0mRlZWnVqlVhlT6cwz7BOHv27Drfk5YS6jpNAAAAQELx+6XOnaWdO6Vly6SRI20nAgAAiC0+n1l6prBQuvBC6dlnDz330kvSuedK/fpJ33zDrG0AAACIS6HeK2d5mjq8+eabtQojycnJevvtt+ssjEiSx+PR7Nmz65xVZPr06aqqqopG1FruuuuuWoWRAQMGqLCwsM7CiCR16NBBL774os4777yg/Xv37tUvf/nLqGUFAAAAECWrV5vCSGqqNGyY7TQAAACxx+WSHnnEbJ97TnrrLamoSPrzn6W77zbHnHwyhREAAAAkPEojdZgzZ06tfbNnz9aIESMaPXfGjBkaP3580L41a9bo6aefbrZ89dmzZ4/mz58ftM/lcunJJ59Uhw4dGjzX7XbrscceU5cuXYL2P/XUU/ruu++aPSsAAACAKAosTZObKyUn280CAAAQq4YOla6+2ozPPVfq00f6n/8xM7VJ0l/+Ip11lrRihaWAAAAAQPRRGjnMl19+qf/+979B+zp16qTrr78+5Gv85je/qbXv0UcfbXK2xjz11FMqLi4O2jdp0iSddNJJIZ3fsWNH3XjjjUH7vF6vHn/88WbLCAAAAKAFBEojeXl2cwAAAMS6E04w2/37zRJ/Nfn90sKF5meqw2amBgAAABIFpZHDPPPMM7X2XXHFFUpJSQn5GsOHD9fw4cOD9i1btkxr165tcr6G/OMf/6i17xe/+EVY17j88strfa91vScAAAAAYhilEQAAgMYtX25mFmmI12seF13EjCMAAABISJRGDvPaa6/V2nf++eeHfZ26zqnr2s1l7969Wrp0adC+tm3basKECWFdp2PHjhozZkzQvvXr1+urr75qakQAAAAALWHXLunLL8141Ci7WQAAAGLZ3Lm1Zxepi99vHnPnRj8TAAAA0MIojdRw4MABffjhh0H70tPTNWzYsLCvNXr06Fr7Dl/2pjktXbpUXq83aN+oUaPkdrvDvlZLZwcAAADQjJYtM9sjj5Q6d7abBQAAIFYVFUmvvmpmEQmF1ystWGDOAwAAABIIpZEaPv74Y/l8vqB9I0aMkMfjCftaubm5tc774IMPmpSvIXVde1SEnyrMq2MK62hmBwAAANCMWJoGAACgcYsXhzbLSE1+v/T229HJAwAAAFiSZDtALFm1alWtff3794/oWsnJyerZs6fWrl1bvW/NmjWqqqpSUlLzv+3Nmb1fv3619n399dcRXeujjz7SzJkztXTpUm3YsEE7d+5UcnKyOnTooM6dO2v48OE6+eSTNX78eHXmU5AAAABA01EaAQAAaNy+fZLLJR32IcIGuVxScXH0MgEAAAAWUBqpYd26dbX29e7dO+Lr5eTkBJVGvF6vioqKdMQRR0R8zfo0Z/YePXrI7XYHLXfz3XffRXStV155pda+8vJy7du3T+vXr9fKlSv12GOPKTU1Vf/v//0/3XTTTXWWVgAAAACEoKpKev99M6Y0AgAAUL+MjPAKI5I5PjMzOnkAAAAAS1iepoatW7fW2terV6+Ir1fXud9//33E12tIc2Z3u93q1q1b0L5o5Q4oKyvTH//4Rw0ZMkRPPPFEVF8LAAAASFiffCKVlEjt2kmDBtlOAwAAELvGjZMcJ7xzHEcaOzY6eQAAAABLKI3UsGvXrlr72rZtG/H16jp3586dEV+vIdHOXlFRof3790d0raSkJHXr1k1HHXWUBgwYoI4dO9Z7bGlpqX7yk59o+vTpEb0WAAAA0KoFlqYZNcpMnw4AAIC65eRIkydLbndox7vd0plnmvMAAACABMK/ItZw4MCBWvvS0tIivl5d55aUlER8vYbEUvaUlBRNmjRJ8+fP1yeffKIDBw5o8+bN+uqrr7R69Wrt2LFD33//vf75z39q/PjxdV5j/vz5uvvuu8POXV5eruLi4qAHAAAA0GoESiMsTQMAANC4WbPM7CGNzTgSOOb221smFwAAANCCKI3UUFlZWWtfampqxNerq3hRUVER8fUaEivZ77nnHm3cuFGvvvqqpk6dqmOPPVbJycm1jsvOztb555+vN954Q++88466d+9e65hbbrlFK1euDCv3b37zG2VlZVU/mrK8EAAAABB3KI0AAACELjdXevZZM4tIfTOOBJ577jlzPAAAAJBgKI00wgl3XctGzvX7/U2J0+TXb8q5oWS/4YYb1KlTp7Bea8yYMVq+fLl69uxZ6/VuvvnmsK51yy23aO/evdWPDRs2hHU+AAAAELc2bpSKisxNjRNOsJ0GAAAgPkyZYoq3Z5xxaMaRwDJ/jiNNmmSeP/dcexkBAACAKEqyHSCWeDyeWvtKS0sjvl5d59Y160Zz8Hg8Ki8vr/X6bdu2jeh6LZldknr06KGXXnpJJ5xwgnw+X/X+xYsX6+OPP9bQoUNDuk5KSopSUlKilBIAAACIYYFZRo47Torw7wEAAACtUm6u9MorpoD79ttScbGUmSmNHSvl5NhOBwAAAEQVM43UkJ6eXmtfc5dG2rRpE/H1GhLP2QOGDx+uSy65pNb+RYsWRfV1AQAAgITA0jQAAABNk5MjXX65dM01ZkthBAAAAK0ApZEaOnbsWGvf/v37I75eXefW9RrNIdrZk5OTI561JByXXnpprX2LFy+O+usCAAAAcY/SCAAAAAAAAIAwURqpoUuXLrX2bdy4MeLrbdiwIaTXaA7Nmd3r9WrLli2NXj8aTjnllFr7ioqKWuS1AQAAgLhVUiJ99JEZUxoBAAAAAAAAECJKIzX07du31r7169dHfL3Dyw5ut1s5UZrSsDmzb968WVVVVY1ePxratGmjzMzMoH3bt29vkdcGAAAA4taKFVJVldSjB9OoAwAAAAAAAAgZpZEaBg4cWGvft99+G9G1Kioqas000q9fPyUlJUV0vcY0Z/Y1a9bU2nfUUUdFdK1ItGnTJujr0tLSFnttAAAAIC7VXJrGcexmAQAAAAAAABA3KI3UMGzYMLlcwW/JypUra826EYqVK1eqsrIyaN/xxx/fpHwNGT58eK19S5cujehahYF/cK4hmtkPt2PHjqCvO3Xq1GKvDQAAAMSlmqURAAAAAAAAAAgRpZEa2rRpo2HDhgXtO3DggD4KrA0ehvfee6/WvpNPPjnibI3Jy8uT2+0O2rd06VJ5vd6wr9XS2Wv69ttva5VtOnfu3CKvDQAAAMQln4/SCAAAAAAAAICIUBo5zOmnn15r3wsvvBD2dZ5//vmQrt1csrKydOKJJwbt27dvn958882wrrNr1y698847QftycnI0aNCgJmcMxcKFC2vtO+6441rktQEAAIC4tHq1tGuXlJYmHVaCBwAAAAAAAICGUBo5zCWXXFJr35///GdVVFSEfI2PPvpIK1asCNo3cuRI9e3bt8n5GnLxxRfX2veHP/whrGv89a9/VVlZWdC+ut6TaKioqNADDzxQa//EiRNb5PUBAACAuBSYZSQ3V/J47GYBAAAAAAAAEFcojRxm8ODBOumkk4L2bdu2rc4yQ31uvfXWWvuuuuqqJmdrzA9/+ENlZGQE7VuwYIEKA/+I3Ijdu3frnnvuCdrndrv105/+tNkyNuS2227TunXrgva1bdtWEyZMaJHXBwAAAOISS9MAAAAAAAAAiBClkTrcfvvttfbNnj1bH374YaPnzp8/X6+99lrQvr59++rSSy8N6bXHjBkjx3GCHn/5y19COrddu3aaOnVq0D6fz6crrrhCu3fvbvBcn8+nn//859qyZUvQ/ksuuUT9+vVr9LWfe+45LVu2LKSch/P7/Zo7d67uvffeWs/deOONateuXUTXBQAAAFqFggKzpTQCAAAAAAAAIEyURuowYcIEnX322UH7ysvLdeqpp2rBggV1nlNZWak5c+bommuuqfXcQw89JE8LTRN92223qWfPnkH7Vq9erby8PK1atarOc3bv3q0pU6bon//8Z9D+zMxM3X333SG9bmFhoUaNGqUxY8boiSee0I4dO0I6b9myZRo/frxmzZpV67l+/frp+uuvD+k6AAAAQKu0c6cU+Dl/1Ci7WQAAAAAAAADEnSTbAWLVn/70J33wwQfauHFj9b7i4mKdddZZGjFihM4++2z17dtXpaWl+uabb/TUU09p06ZNta4zbdo0TZ48ucVyt23bVk8//bTGjh2rqqqq6v2rVq3S4MGDdeaZZ2r06NHq0aOHduzYoU8++UTPPPOMDhw4UOtaTzzxhLp37x7W6y9ZskRLlizRz3/+cw0ZMkTHHXecBg0apA4dOigrK0ter1e7du3SV199pSVLlujzzz+v8zrZ2dl6/fXX1aZNm/DeAAAAAKA1Ccz2N3Cg1KmT3SwAAAAAAAAA4g6lkXp06tRJb7zxhsaOHautW7cGPbdy5UqtXLmy0WtceOGFmjdvXpQS1m/06NF6+umndemllwYVR3w+n15++WW9/PLLDZ7vOI7mzZun888/P+IMXq9XH3/8sT7++OOwzx00aJCee+65kJbFAQAAAFq1wkKzZWkaAAAAAAAAABFgeZoGDBo0SCtWrNDo0aPDOs/j8eiOO+7QM888I7fbHaV0Dbvgggv09ttvKycnJ6zzOnXqpBdffLHOZXaiLTU1VTNnztSHH36oY445psVfHwAAAIg7BQVmS2kEAAAAAAAAQAQojTSiZ8+eWrJkiZ599lnl5eXJcZx6j23btq2uuOIKffbZZ5o9e7ZcLrtv7+jRo/XVV1/pnnvuUf/+/Rs8tkePHpo1a5ZWr16tc845J+zXmjVrlp577jldffXVGj58uFJTU0M6z+PxKDc3V3fffbc2bdqk+++/P+RzAQAAgFatslJavtyM8/PtZgEAAAAAAAAQlxy/3++3HSKebNu2TcuXL9d3332n4uJiJSUlqVOnTho0aJByc3OVnJxsO2K9vvnmG3300UfasGGDSkpKlJqaqu7du+vYY4/VkCFDmvW1qqqqtGbNGq1fv14bN27U3r17VVJSIrfbraysLLVv3145OTk6/vjjo1oSKS4uVlZWlvbu3avMzMyovQ4AAADQ4laulHJzpfbtpR07JMuldQAAAAAAAACxI9R75UktmCkhZGdna/LkybZjRGTAgAEaMGBAi7xWUlKSBg4cqIEDB7bI6wEAAACtTmGh2Y4aRWEEAAAAAAAAQET4l0UAAAAAiEcFBWabl2c3BwAAAAAAAIC4RWkEAAAAAOJRYKaR/Hy7OQAAAAAAAADELUojAAAAABBvNmyQNm6U3G4pN9d2GgAAAAAAAABxitIIAAAAAMSbwCwjQ4dKbdpYjQIAAAAAAAAgflEaAQAAAIB4EyiN5OXZzQEAAAAAAAAgrlEaAQAAAIB4U1Bgtvn5dnMAAAAAAAAAiGuURgAAAAAgnhw4IH38sRkz0wgAAAAAAACAJqA0AgAAAADxZMUKyeuVevaUevWynQYAAAAAAABAHKM0AgAAAADxpLDQbJllBAAAAAAAAEATURoBAAAAgHhSUGC2+fl2cwAAAAAAAACIe5RGAAAAACBe+HzS0qVmzEwjAAAAAAAAAJqI0ggAAAAAxIuvv5Z275bS0qTjjrOdBgAAAAAAAECcozQCAAAAAPGisNBsTzhB8njsZgEAAAAAAAAQ95JsBwAAAAAANKKoSFq8WHr8cfP14MF28wAAAAAAAABICJRGAAAAYF/ghvi+fVJGhjRunJSTYzsVYN/y5dKcOdK//y35/Yf2P/qotGGDNGuWlJtrLx8AAAAAAACAuEZpBAAAAPYcfkPc5ZJ8PslxpMmTuSGO1u3FF6WLLjK/NmoWRiTz9cKF0qJF0rPPSlOm2MkIAAAAAAAAIK65bAcAAABAK/Xii1J+vrnpHbgh7vOZbeCGeF6eOQ5obZYvN4URr9c86hJ47qKLpBUrWjYfAAAAAAAAgIRAaQQAAAAtjxviQMPmzq17hpHDBY6ZO7dlcgEAAAAAAABIKCxPAwAAgJYXyQ3xl19umWyIDX6/mXmm5qOufaE+35Rzo3ntup7btUt69dXGf30EeL3SggVSUZGUkxPd/y4AAAAAAAAAEgqlEQAAALSsoqLIboivWCF17574hYFYuHYsfE8Ij98vvf22dPnltpMAAAAAAAAAiCOURgAAABDM75cqKqTS0tAeZWWhH1taKm3ZEnphpGamE06IzveLxOA4kst16HH414c/Gno+0uea69xVq8wSTuFwuaTi4ui8twAAAAAAAAASFqURAACirahIWrxY2rdPysiQxo1j+QCEx+sNr5TRlEJH4BFuqaMlOI6UlBR7N/hbw7mxmKnm845jHoniz38OvzTi80mZmdHJAwAAAAAAACBhURoBACBali+X5syR/v1vcwPe5TI39RxHmjxZmjVLys21nRLh8vul8vLISxyRFDoqK+19v44jpaU17+Pdd6V77gk/y5NPsvQGWodx48yvvXDKW44jjR0bvUwAAAAAAAAAEpLj98fix0iB5lFcXKysrCzt3btXmXzyEkBLevFF6aKLzA0/r7f28263ucH37LPSlCktny+RVFU1T4Ej1FJHWZndWThSUkzxIjW1+cscdT08nuafwaGoSOrTJ/wb4uvWMUsPWo+zzpIWLqz7z5DDud3SpEnSyy9HPxcAAAAAAACAuBDqvXJKI0holEYAWLF8uZSfb270NfTHrOOYG32FhYkz44jP13yzcIRa5qiqsvf9ut3BBYtoFzlSU82MNYmAG+JAw1askPLyWuefJQAAAAAAAACaLNR75SxPAwBAc5s719zga6yXGThm7tzo3Az3+82yJpEshxLpo7y8+b+PcNRX2ohWmcPjsfv9xrNZs6RFixpfgsNxzOP221suGxALcnPNbFShzFr13HMURgAAAAAAAABEhJlGkNCYaQRAi4t02Y2nn5ayspq/xOHzRe1bbVRSUmgzZzRXgSMlJXFm4WgtQl3G6bnnpHPPbfl8QCxYsUKaM0d69VXza8XlMr+3O4505pmmUEVhBAAAAAAAAMBhWJ4GEKURABHw+UzZ4sABaf9+sw08Gvo6MF61Svr0U9vfRd3CXQalqSWOJCY0Qwi4IQ6EpqhIevttqbhYysyUxo6VcnJspwIAAAAAAAAQoyiNAKI0AiQsv98sgxJusSOU4kdJiZ3vqUsXqUeP6BU5UlLMTXggVnFDHAAAAAAAAACAZhPqvXI+AgwAiJ7KyoZn5WjK1y2x7Ep6utSmjXm0bVv3+PCvP/pI+stfwn+t3/5Wuvzy5v4OgPiRk8OvAQAAAAAAAAAAWhilEQBNU1QkLV4s7dsnZWRI48bxyfB44/Wa2TUiXY6loa8rK6OfPyUltDJHOMWPNm1MYcTlCj9PUZH017+a2VBC5ThmVgUAAAAAAAAAAACgBVEaARCZ5culOXOkf//b3Bx3uczMD44jTZ4szZol5ebaTpk4/H6ptLR5ll85/LmysujnT0qKrNjRWNGjTRtz7ViSk2N+DSxcaAo5jXG7pUmTKFsBAAAAAAAAAACgxTl+fzgfhQbiS6jrNCFML74oXXSRKTLUdVPc7TblkWeflaZMafl8tvj9UkVFdJZjOXAgvJkrIuE4kc/K0djXycnm+q3FihVSXp759dHQfzfHMb9eCgspWQEAAAAAAAAAAKDZhHqvPMY+ng0g5i1fbgojDd0M93rNzfCLLorNm+FVVc23/MrhX4cys0RTpaVFZzmWtLTWVeyIptxcU5oKpVz13HOx92sEAAAAAAAAAAAArQKlEQDhmTvX3ARvbNaLwDFz50ovvxz+6/h8UklJdJZjqaiI7HsPR3Jy883SUfPr9HRTNkDsmzLFlKbmzJFefbX2Mk6TJkm3305hBAAAAAAAAAAAANawPA0SGsvTNLOiIqlPn/CWSXEc6Ve/MiWKcIoepaVR+zaqud3NN0vH4V97PNHPj/hRVCS9/bZUXCxlZkpjx0o5ObZTAQAAAAAAAAAAIEGxPA2A5rd4cXiFEckcf+edTXvd5pql4/DnUlJYjgUtIydHuvxy2ykAAAAAAAAAAACAIJRGAIRu375Dy2uEY9gw84ik6JGWZl4TAAAAAAAAAAAAANCsKI0ACF1GRviFEUm65hpmWQAAAAAAAAAAAACAGMPH9wGEbty48JdzcRxp7Njo5AEAAAAAAAAAAAAARIzSCIDQ5eRIkydLbndox7vd0plnmvMAAAAAAAAAAAAAADGF0giA8MyaZWYPaWzGkcAxt9/eMrkAAAAAAAAAAAAAAGGhNAIgPLm50rPPmllE6ptxJPDcc8+Z4wEAAAAAAAAAAAAAMYfSCIDwTZkiFRZKZ5xxaMYR18HfThxHmjTJPH/uufYyAgAAAAAAAAAAAAAalGQ7AIA4lZsrvfKKVFQkvf22VFwsZWZKY8dKOTm20wEAAAAAAAAAAAAAGkFpBEDT5ORIl19uOwUAAAAAAAAAAAAAIEwsTwMAAAAAAAAAAAAAANAKURoBAAAAAAAAAAAAAABohSiNAAAAAAAAAAAAAAAAtEKURgAAAAAAAAAAAAAAAFohSiMAAAAAAAAAAAAAAACtEKURAAAAAAAAAAAAAACAVojSCAAAAAAAAAAAAAAAQCtEaQQAAAAAAAAAAAAAAKAVSrIdAAAAAABaq4pdu+QtLZU7JUWulBS5kpPleDxyXPT7AQAAAAAAAEQfpREAAAAAsMBXUaFVs2apqri41nOOxyOXxyMnOVmu5GS5UlJMsSQ11TySk83XyclyDm7r/DolRZ6OHZXSqZOF7xAAAAAAAABArKM0AgAAAAAWOB6PkrKy6iyN+Csr5a2slEpK6j7Z5ZIcR07geEny+SS/v9ahSZmZOmbePLk8nmbLDgAAAAAAACAxMOcxAAAAAFjgOI66nnVWZCf7fJLXK//Bh7zeOgsjchylZGfLSeLzAgAAAAAAAABqozQCAAAAAJa0z82Vp0OH6L2A36/uF10kx3EaPxYAAAAAAABAq0NpBAAAAAAscdxudZ08OToXd7mUMXiwMo46KjrXBwAAAAAAABD3KI0AAAAAgEUdTz5Z7vT05r+wz6fuF17Y/NcFAAAAAAAAkDAojQAAAACARa6UFGVPmCA15xIyLpeyjj9ebY44ovmuCQAAAAAAACDhUBoBACBCFbt2qXTTJlXs2KGqffvkKy+X3+ezHQsAEIc6jx8vx+1uvgv6fOp+/vnNdz0AAAAAAAAACSnJdgAAAOKRr6JCq2bNUlVxca3nHI9HLo9HTnKyXMnJcqWkyJ2SIldqqnkkJ5uvk5PlHNzW+XVKijwdOyqlUycL3yEAoCUlZWSo06mnavvixVJTC4gul9qPHKm0Xr2aJxwAAAAAAACAhEVpBACACDgej5KysuosjfgrK+WtrJRKSuo+2eWSHEeBRQj8krlB6PfXOjQpM1PHzJsnl8fTbNkBALEpe+JEbX/rraZfyO9Xt/POa/p1AAAAAAAAACQ8lqcBACACjuOo61lnRXayzyd5vfIffMjrrbMwIsdRSna2nCQ6ngDQGqR07qz2I0eacmGkXC51PPlkpXbp0nzBAAAAAAAAACQsSiMAAESofW6uPB06RO8F/H51v+giOY7T+LFAjKrYtUulmzapYscOVe3bJ195ufxNXXoDSGBdJk1q2vI0jqNu557bfIEAAAAAAAAAJDQ+ugwAQIQct1tdJ0/Whr/9rfkv7nIpY9AgZRx1VPNfG2ghvooKrZo1q85lnByPRy6PR05yslzJyXKlpMidkiJXaqp5JCebr5OT5Rzc1vl1Soo8HTsqpVMnC98hEDm/3y9/VVXQ8mMla9eqYtcupXTvrvItW+qehaohLpc6n3aakjt2bOa0AAAAAAAAABIVpREAAJqg48kna/Pzz8tbUtK8F/b51P3CC5v3mkALczweJWVl1Vka8VdWyltZKdX3a8flkhxHgXl2/JKZfaGOm+hJmZk6Zt68oJvvQDT5qqrkLSmRr7RU3sMe8vvV8aSTqo/d/MILKi0qkrekpPoYX2mpvCUlcqWm6rg//KH62E3PPqt9X3wRcS7HcdTxpJPk9/uZpQoAAAAAAABASCiNAKhTxa5d8paWVn+K25WcLMfjkeNiVSugJldKirInTNCWl14K/xPh9V7UpayhQ9XmiCOa53qAJY7jqOtZZ2ndI4+Ef/LB5Tka/VXlOErJzpaTxI+1aJy/qkresjJ5S0rk93qV2q1b9XO7li5Vxc6dQaWOQMnDnZamfjNnVh+7+te/VsnatXW+hrtt26DSyP6vv9b+r76q81hvaWlQwSO1Z0/5ysvlpKaqZM0a+UpLw/v+vF6tmjVLyZ06afD991dft2rfPrnbtqVIAgAAAAAAAKAW/nUdQC0sJwCEp/P48dq6YIH8VVXNc0GfT93PP795rgVY1j43V5s6dFDlrl3ReQG/X90vuoib4a1I6aZN8u7fHzxrx8GHKzVVXSdPrj52zYMPqmzjxurn/RUV1c8lZ2frmPvuq/76+4ULVbpuXZ2v6W7bNuhrV2qq2aakyJ2WJldamtwHH0mHHZs9YYI6jBold3q6OS411Rybni53WlrQsb1++MPq8a5ly8IrXLlcSu3WTWVbtsjTrl3Qr4nVd92lyl27lNa7t9L79FF6795K69NHqd26UQgGAAAAAAAAWjlKIwBqYTkBIDxJGRnqdOqp2r54cfXsCBFzudR+5Eil9erVPOEAyxy3W10nT9aGv/2t+S/ucilj0CBlHHVU818bzcLv89UqdnhLS+XyeJRx9NHVx21+/nlV7t4dNLtH4LyULl105O23Vx+75v77VbFtW52vl5KdHVQaqdi+XeVbt9Y6zpWcXOvnj6yhQ5XWs2dQoSNQCElq0ybo2H7XXWdKtG53o+9Bu+HDGz2mLmEVrhxH3c49V93OOUe+ioqgn+F8VVWq2LZNvooK7f/qq6BZT1zJyWqXm6s+v/hF9T6/z0eRBAAAAAAAAGhFKI0AqIXlBIDwZU+cqO1vvdX0C/n96nbeeU2/DhBDOp58sjY//7y89RUOI+XzqfuFFzbvNSHJFAd85eXye71BM2fsXrFC3gMHDpU6apQ8UrKz1ePii6uP/XzmTFXs2FHn9dP79tVRv/519de7CgtVsX17nce6UlKCvk7p3FmOy2UKHampQQUPT7t2Qcf2+vGPJZ8vaCYQd1panWWP7mH83us+ONNINIVTuHKnpSl7wgRJpgiSXGMmN1dSko577DGVbtqk0vXrVbJunUrWr1fp+vXmv3GNYq/f59OnU6cqpXPn4FlJcnLkSk5u/m8SAAAAAAAAgHXcrQVQJ5YTAMKT0rmz2o8cqd3Ll0c+24jLpY6jRyu1S5fmDQdY5kpJUfaECdry0kt1zjwV2UVdyho6VG2OOKJ5rpdA/H6/KvfsMbN7BEodZWXylpTIV1oqT/v2aj9yZPXx3953X9ByL97SUvnKyiS/XxmDB2vAzTdXH1v05JPy7t9f5+um9+0bvKPGn/GO211d7nClpSmlW7egQ7MnTJCvoiJodo/qksdhs3zUzNOYtgMGhHxsLAq1cNX1nHNqLXVTk5OUpPTevZXeu7c6nnyyJFMQOXwWlrItW+Tdv18l+/erZO1a7Qw84XIptXt3dRozprqcAgAAAAAAACAxUBoBUCeWEwDC12XSJO1etizyCxxcXgBIRJ3Hj9fWBQvkr6pqngv6fOp+/vnNc60Y4Pf75Ssvl6+szBQ3SkrkSk1VWo8e5nmvV1sXLDi01Mthy7i0PfJI5VxxRfX1Pp8xo96CTsbgwUGlkQOrV9dbSvCVl9c611deXmsJF3damjwdOgQde+Stt8rxeOROT290KTqKCHULpXCVlJWlzuPGhX1t52ARpKbU7t01+L77VHJwRpLSdetUsnatqvbtU9nGjfIeOFB9bMWuXfrmN78xs5H06aO0g7OS1JyZBgAAAAAAAEDsozQCoF4sJwAYfr8/aFac8m3b5KuslL+iQr7KSvkqKuSrqJC/slJpOTkq3bgx/NlGDpap9qxcKcflktxuOS6XnINbd3q6soYNqz58/zffyFdREXSM43ZLLpdcHk/QjcDK4mLJ56t+/vDjmfEHLSEpI0OdTj1V2xcvjnw2ngCXS+1HjlRar17NE64J/H6/5PebX7eSfFVVOvD110GFjkAJxFdWpvTevdVp7FhzbHm5vrz55upjDn9f2uXm6ohrrjFfuFza8uKL9RYHPFlZ1WPHcZTUtq38Pl/wzB0HSx6Hv285V15ZvdyLOz09aJYP57CyxxHTpoX83tRcIgWRa6xw1W3KlGZbOsY5uHxgSna22ufmSjo4c83u3SpZt06pXbtWH1uybp3Kt25V+datQYXJ5E6dlNanjzqPHavMIUOaJRcAAAAAAACA6KE0AqBeLCeAWOP3+4OKGv5AYaOyUv7KSrnT06tvhvp9Pu14993axx8cp/XsGfTJ9tVz58pbXl7n8RlHHaX+N91UfexXt91mlm6oQ2r37hHdEHfcbpV//702/t//1fl8SnZ2UGlkw1//qtL16+s8NikrS8fOn1/99XcPPqgDq1fXeawrNVVDH3886Nh9q1YFFUsCJRZXUpIG3XVX9bGbnntOB1avNuWTOo7v+4tfyEkyP2rsWLJEJWvXmucDx9Q4vsukSdU3Pfd9+aXKNm+ut+SSddxx1ceWbd2qyj17Dr32Yeckd+4s18EM3rIy+auqgjI6Llf1zX5EX/bEidr+1ltNv5Dfr27nndfky/gqKw/N3HFYwSMlO1ttjzxSklR14IA2/O1v1cu7BJZ6CZzbYfRo9b7yShOtslLf/Pa39b5mu9zc6tKI4/GoYufO4D9jHafOZVkcx1Hn8ePlJCUFze4RKHh42rULep0hjzwSciGs/QknhHQc7Ki3cOU48nTooE4Hl5uJFsdxlNyhg5IPm0km8Gdjybp11bOSlG/bpoodO1SxY4fajRhRfeyBb7/VlpdeCpqVJLljR0qLAAAAAAAAQAygNAKgQSwngLr4fb5aJQx3Wlr1J9195eUq/vzzWscExul9+qjd8OGSzM3Yoj/9qXax4+BMHu1GjFDPyy4z1y0r0yc/+1m9udqdcIKOmD69+usNf/5zvcdmHntsUGmkZO1a+Soq6jz28P1JGRnyJiXJlZwsl8cj5+DW5fEopUcPye1W2caNoZetHEfZp58uf2WlKvfskd/rld/nk9/rlXw++X2+WjeEU7p0kd/nk2ocG/g6KTMztNeVKavU5C0pkXf//rqPTQr+saFs40bt//rr+i9+1VXVw32ff97g0j3ZEyZIB4sguwoLtXPJknqPHfLww9Wlke1vvqntb7xR77FH33NP9Sfjt77yir5fsKD2QY4jx+XSwDvvVHrv3pKk7xct0vf//ndwYaXGuPdPf1p97O4VK7Rj8eJ6Sy5dJ0+uLjPtX71au5cvr1WwCXzdLje3Om/Z1q3av2pVrYJL4LrpffpU38St2rdP5d9/b16/5iwyB89LysyUOzVVkuSvqpKvsjK4vNNCxZmUzp3VfuRI7V6+PPLZRlwudTjpJCWlp6t82za5UlKqf+/xlpRoV2Fhrdk9AuOs449Xl4kTJUkVO3fq82uvrfdlOo4ZU10acRxHuwsL6z225oxcrpQUpfboUecMH4fP8uG4XBp4xx1ypaQcmuUjJaXeG+m9fvSjkN8mbsYnljoLV36/up9/fq3fm1uKOz1dmUOGBM0m4i0pqV7aJmPgwOr9+7/5RsWffKLiTz45dH7btkrv3Vvpffqo45gxQbOYAAAAAAAAAGg5lEYANChRlxNIFIHyhuNyVd9E95aWqmzTpqDiRWAmDl9FhdKPOKJ6ppeKnTv1/auvHlpi5bDjO+Tnq/Npp0mSyrZs0apf/Ur+ykpTZjhM9umnV5c7qvbv13fz5tWbu+OYMdWlEUnas3JlvcdW7t1bPXbVXCbB7a4uagRKG0HLM7hcapebKycpyRzj8ciVnGy2Ho9Su3ULep2+06aZpV0CRZCDx7s8HrkO3mwPOOb+++vNK0m7li3TukceafCYmlzJyeoyaZKSaswq0Jia5ZjGDJw1S5L5/6XOokkNvX/2M3nLykxZpY7ySk1dJk9Wh/z84GO83kPXrVFEaHfCCUrp1q3Okovf5wu66Znep4+8JSW1jgmcV3O5jKSMjODrHpalZimmrv9vzRN+c2yNvL7SUlXV+H+v1imVldXjim3btO+LL+o9tuYsAKVFRdr++uv1Hpvao0f1jdMD336roieeqPfYPldfrQ6jRkmS9q1apbUPPVTvsTlXXqlOY8ZIkoq//FJr7rkn+ADHqS6R9Lj0UnUeN646w3fz59deBunguPP48ep40kmSpLLvv9fGv/2t7plnXC61O/54tRsxQl0mTWqwQNQon0+7/vMf7frPfyRJ2WecoZ6XXCLJ3LDe8Ne/1ntqSo2b0u60tOqxKzW1Vsmj5hJPrtRU9bj00qAZPoKWcUlPP/RWulw6uoGZRg7HzFsIRa3CleMopUsXdcjLsx0tiDs9XRmDBilj0KCg/VlDh8qVlGRmJVm/XqUbN8q7f7/2ffGF9n3xRdCsJHs//ljFn39uZiXp3Vup3bvXKjgCAAAAAAAAaD6URgA0KtaWE4hlvooKVRYXVxc0ai6f4quoUHrfvkrp1EmSmUVg97JltWfiOHh8p7FjlXXssZLMJ3TX//GPQaUOX2WldPAmeI9LL63+9Hzphg1aPWdOvRm7nXde9U1Kb0lJg/9t29b4lLDjdte5JIvjdstJTg4qCLhSUtSmf//qgkbNmThcyclqc/DT+5LkTklRrx//OKjQUXOcVLMIkpSk4/74R/N8CDeQjrjmmkaPCai59EtTtc/N1aYOHVS5a1fjBzuOukyeHFZhJFLVS7E08Kn05I4dQ75e2xr/HRvTPjdX7XNzQzq282mnVZeVGtPtnHPU7ZxzQjq2x8UXq8dFF1UXWw4vxnhqzNDSadw4ZQ0fXuuYQIklpUbpKHPoUHnatz9UWDns+JpFhfQ+fdTlzDPrfH35fEHvv6d9e2Udf3xweabG8UkZGdXHujweJWdn1y7lBMozNf+b11We8fvlr6qS/+A4wFdRocqdO+t9T2sWa7z796v400/rPTa5Uye1GzHC3Aju108la9bUe2yoAmW5AHebNmo3YkRwoSM1tfrrmjMZuNLSdNxjj8mVmtroTCuOy1X9eyxgS1Dhyu9X9wsvjJvltVK7dQsqa/oqK1W2cWP10jY1C8V7P/5YOxYvrv7a8XiUlpNTPStJ+5Ejg4paAAAAAAAAAJrG8ftDnTsfiD/FxcXKysrS3r17lRnGcg2obe0jjzR5OYGOo0er909+0rzBavAfnC3AX6OkUbOEkdq9e/WN+bItW7T/66+DSxgVFdXjTuPGKT0nR5JU/Omn2vrKK/XO3NH75z+vvhm++/33tXb+/Hoz9v7pT9Xx4KwDez/+WGvuu6/eY3v+6EfK/sEPJEn7v/5aq+fOrffY7hdcoK5nnSXJlEbW3H9/raJGYOaM9iNHqv3IkZKkyuJibX/zzUMzawSOPXhuSteuSuvRw7y/VVUq37Gj1kwc8XLDqqVtf/NNbfjb3xo9zp2ermPmzQua9QCIJr/PZwoihxVLqssobdpU35D1lpaqbMuW+ssz3bsrtUsXSeb3k+JPPqk9Q0xVlfw+n9oeeWR1EW33smVaG8ZsPAFOUpKOnDVLKV26yJ2ayuwDaHW++c1vtO/LL5XWq5eO+t//TchliPZ++qn2ffpp9awkhxdWhzzySHXJb/eKFaras0dpffooPSdHrpQUG5EBAAAAAACAmBTqvXJmGgEQkiYvJ+A46nbuuZLMEgblW7bUWg4lUPLodOqp8rRrJ0na88EH2rV0aa2iRuC8vtOmKb1vX0nSttde06ann643Qv+bblLmkCGSpP2rVqnoySfrPTZzyJDq0kjVgQPa//XX9R7rKy+vHtc5W0aNrbtt2+pjkzt3VscxY2odG5iZo+2AAdXHpvXqpSNvv73OmTgC45rHHvPAA/XmrcmTmanuIc7+4iQlBX1KHw3rePLJ2vz88/KWlDR4XNdzzqEwghbluFxmdqAQuNPSQl4+xZOZqY6jR4d0bLuRI5X6yisq27gxaGaTBjmOsidOZDkXtGpdzzlHlXv3qscllyRkYUSSso49tnqmNb/Pp/Jt21Sybp1K161TxY4dQbNC7XjnHe377DPzheMotXt3pffpo7SDs5K0HTiQcisAAAAAAADQCEojAEKS3qePMo4+WvtWrYpotpH2J55YvezCrvfe09aXXqr32MzjjqsujZRv3ao9779f77E1b8i7ahQnJB2aaeNgqaLmJ9KTO3dW1rBhtYoagZk5ai490XbAAPWdNq3eQkhSjZsXWcOGaVgDZZSa0nr0UO8rrwzpWHd6etBSMYh9rpQUZU+YoC0vvVTvTfGkrCx1HjeuZYMBMcBxHHU96yytC2O2EVdysrpMmhTFVEDsyxg0SEf/9re2Y7QYx+VSateuprR64om1ns8cPFiOy6WSdetUtXevyjZtUtmmTVJBgdzp6Tr2D3+oPnbPypVypaQorXfvoOIJAAAAAAAA0NpRGgEQsi5nnql9X34Z0bmBJVkkKbljR6X37Vt7Jo5ACePgEjKSlDF4sHr+6Ed1FzY8HqX17Bn0Gu1HjapeOqWhT+BmHnOMMo85JqTsyZ06KblTpwi+a7R2nceP19YFC+Svqqrz+W5TpsgV4owPQKJpn5urTR06qHLXrsYPdhx1mTw56M8HAOgyaVJ1maxyzx6zpM3BZW1cyclBPwtueuYZlW/bJknydOyo9IOzkaT36aO0Pn2U3L69le8BAAAAAAAAsI3SCICQZQwerNRevcJeTqDL5MnKPPro6l2dxoxRpzFjQjo98I/5oXAlJ3MDHjElKSNDnU49VdsXLw6eocdx5OnQQZ1qlKmA1sZxu9V18mRt+NvfGj3WnZam7AkTWiAVgHjladdOWUOHKmvo0FrP+X0+pR9xhORyqXzrVlXu3Km9O3dq74cfSpLS+/bVUb/+dfXxxZ99ppQuXZTcuXPCLgMEAAAAAAAABFAaARAylhMAwpc9caK2v/VW8E6/X93PP19OEn8Mo3XrePLJ2vz880FLjdWl6znnyJ2W1kKpACQax+VS36lTJUne0lKVFhUdmpVk3TpTKDnIV1mpb++7T/J65U5PV1qNGUnS+/RRSteuclwuW98KAAAAAAAA0Oy4WwUgLCwnAIQnpXNntR85UruXLzezjTiOUrp0UYe8PNvRAOtcKSnKnjBBW156qd4ZrJKystR53LiWDQYgYbnT0tR24EC1HTiwep+/xu8/VcXFSs/JUemGDfKWlGj/V19p/1dfVT/fPi9Pfa+6ypzn86l040alde9OERQAAAAAAABxi3/ZAhAWlhMAwtdl0iTtXrbMfOH3q/uFF/IpZeCgzuPHa+uCBfJXVdX5fLcpU1h6DEBU1VyCJrljRx3161/LV1Wlsk2bVLJunUoDs5IUFSmtV6/qY8u3b9eq226T4/EorVcvpffurbSDM5Kk9ezJ710AAMSQil275C0tlTslRa6UFLmSk+V4PPzdHAAAABClEQARYDkBIDzpffoo4+ijte/LL5XWq5fajRhhOxIQM5IyMtTp1FO1ffFiMxtPgOPI06GDOp18sr1wAFotV1KS0nv3Vnrv3tIpp0gyM4v4Kyurj6ncsUOu/8/efYdHVeV/HP/cSSY9BFIglNA7qATCIgiIKAKCUkQQdVdcC2t37Sj23wq2Reyurq5lRRcb9ooFBRSQIl2kdwhIepmZ+/sj5DKTTGYmySQTkvfrefIw9+bcc86E+c65d+53zomOlis/X3mbNytv82a3CmxqdcEFVgK1q7hYpsPBuTEAACHgKirS+rvukiMrq9zvDLtdNrtdRkSEbBERskVGliSWREWV/ERElGxHRMg4+q/X7chI2ZOSFJmcHIJnCAAAAFQPSSMAKo3lBIDKSx07VsVHjqjl5Mke32gGIDUdOVIHvvrKc6dpqsWECSz5AKDOMGw2GZGR1nZ8jx466bnnVHTgQMlMJFu3Km/bNuVv3SpHdrbsiYlW2ey1a/X7Y48pMjVVMW3bliSktG2r6LZtWcoRAIAaZtjtCk9I8Jo0YhYXy1lcLFX0xSibTTIMlV7Fm1JJsruXz8PCGzVSz8cfl81uD1rfAQAAgNrAp/AAqoTlBIDKie/WTd1nzgx1N4A6KTIlRU369dPhn38u+QDWMBTZrJkSBwwIddcAwCfDZlNks2aKbNZMTfr1kySZpqniQ4cU5pYMUrB7t2SaKtyzR4V79ujwokXW7yJSUtTm0ksV36NHyfEuF1PlAwAQRIZhKPWcc7T16acrf/DR2RC9f2XKoxFFNm1K0jsAAACOS5zFAqgSlhMAAARTs1GjdHjx4pIN01SLiRO5aQrguGQYhiKSkjz2NRs5UokDBlgzkZTOSlK0f7+KDhxQWHy8VfbAV19p30cfKfrobCSlM5PYk5KYrQwAgCpq0revdiUmqvjQoZppwDTVYtIkxmoAAAAcl0gaAVBlLCcAAAiWmLZtFd+9u7LXrlV0WpoaZ2SEuksAEFT2hAQlnHiiEk480drnyM1V/rZtim7RwtqXt3Wrig8fVvHhw8pascLaHxYXp5i2bdX60ksVmZxcm10HUMcVHTokZ36+wiIjZYuMlC0iQobdTgIu4MYIC1Pq6NHa8eqrwa/cZlN8t26K79o1+HUDAAAAtYC7ugCqjOUEAADBlDp2rIqPHFHLyZP5hh6ABiE8Nlbx3bt77Ev7y1+UPGSIx6wk+bt2yZmTo+w1axTutuzN7rffVs6GDYpp21bRR2cliWrenBvFQAPiKirS+rvukiMrq9zvDLtdNrtdRkSEbBERskVGliSWREWV/ERElGxHRMg4+q/X7chI2ZOSSFjDcS9p8GDtfvttOfPygluxy6UWEycGt04AAACgFpE0AqBaWE4AABAs8d26qfvMmaHuBgCEVFhUlOI6d1Zc587WPldRkfJ37lTh3r0Ki4629uesX6+cDRuUs369tc+IiFBM69aKbtNGrS66SLZ6MAMgsygAFTPsdoUnJHhNGjGLi+UsLpYqukFus0mGodJUXVMq+UKIaZYrGt6okXo+/rhsdnvQ+g7UNltkpJoOH64977/v9XVetUptSujVS7Ht2wenPgAAACAEjv9PjwCEFMsJAAAAADXLFhGh2Pbty92QSpsyRXlbtiivdEaSbdvkKixU7qZNKjxwQK2nTLHK7nz9dTkLChRzdEaS6LQ02SIja/mZVB6zKAC+GYah1HPO0dann678wS6XpKPJIr4bUWTTpixDi3ohZdgw7f3wQ5kOR3AqdLnUYsKE4NQFAAAAhAhXewCqjeUEAAAAgNoX3aqVolu1UtKgQZIk0+VS4b59ytu6Va7CQo+yhxYvluPIEWV+913JDsNQVIsWimnbVnGdOyt56NDa7n5AmEUB8K9J377alZio4kOHaqYB01SLSZO43ke9EB4fr+TTTtOBr7+2EqeqzGZTk379FJ2WFpzOAQAAACHCXK4Aqq10OYFGJ5wQ6q4AAAAADZZhsymqeXMl9u+v5CFDrP2my6W0iy9W6jnnqNGJJyq8USPJNFWwa5cO/fijDi1c6FHPjtdf196PPlLWr7/KkZ1dy8/CU+ksClXicklOp8yjP3I6vS9HwCwKOM4ZYWFKHT26Ziq32RTfo4fiu3atmfqBEGg6cmRwlqcxTTU/99zq1wMAAACEGJ+IAAAAAABQjxk2m5r07asmfftKkkzTVPEffyh/61blbdsme5MmVllnfr4OfPGFx820iKQkRbdtq5g2bRTfvbviunSp1f4ziwLgX9Lgwdr99ttyVjTzTlW5XGoxcWJw6wRCLDIlRU369dPhn3+u+mwjNpuSBg1SVLNmwe0cAKDOKjp0SM78fGuJS1tEhAy7XYaN7+cDOP6RNAIAAAAAQANiGIYimjRRRJMmSkhP9/idefQGcWlCSeHevSrKzFRRZqaOLFumwgMHrKQR0+HQnvffV3SbNopp21YRyck1knhROovCjldfDXrdstkU360bsyjguGeLjFTT4cO15/33gzODgiTZbEro1Uux7dsHpz6gBpimWTKjVHGxTIdDptMpV+ljh0P2xESFx8ZKkor/+EO5mzfLdDoV1aqVtHhx1Rs2DDUfNy5IzwIIPW6GA765ioq0/q67vC6badjtstntMiIiZIuIkC0ysiSWoqJKfiIiSrYjImQc/dfrdmSk7ElJikxODsEzBNDQkTQCAAAAAAAkSeGxsR7LXDjz8pS3fXtJEsnWrR5LUubv2qW98+ZZ22GxsYpp29ZKIonr3FkRSUlB6RezKAD+pQwbpr0ffijT4QhOhS6XWkyYEJy6cNwyTdNKwDAdDoVFR1vLeRUfOaKizEyP37vcHsf36CF7QoIkKW/LFmX9+qvH761jiovV7KyzFJ2WJkk6smKF9n38cbn6Ssu2uewyJfTqJUk6vGiRtj77bIX9b/O3vynplFMkSbm//67Njz8elL9LbPv21hiXv2uXdr7+usJiYqyf8NhY63FM27aKatGi5O/pcsl0OEpuxjPDFeoIboYD/hl2u8ITErzGiVlcLGdxsVTRtYrNJhmGSt/1Talkpisvib7hjRqp5+OPy2a3B63vABAIkkYAAAAAAIBXYTExiu/a1etMHDa7XUmnnqq8rVtVsHOnnLm5yl6zRtlr1kiSWpx3nlLPOUdSyY3FrFWrrBtnRlhYpfrBLAqAf+Hx8Uo+7TQd+Prrqi+5UcpmU5N+/ayb+Kgdzvx8OfPzvSZgmA6HYjt3lu1owkbu77+rYNeuY2WKi0tm2Tj6uNno0dYMG4cWLdKRX37xrK+0fHGx2l93nSKbNpUk7f3oo5KEDbeZO9x1ue8+630zc8EC7X7rrQqfT6c77rCSRnI3bdLuuXMrLOv+enNkZSln/foKy7oKC63HpQksFptNRni4bOHhMsLDPWZJCI+PV0z79tbvnfn5ytuypcJ2fEno08d6XPzHH8pevbrCsi0mTVLq0aSR/O3btf6uu2SEh3skmYQdTTJJPPlkNc7IkFSSuHlk5UqFxcYq3L1sTIxsERFV6jfgDTfDAf8Mw1DqOedo69NPV/7go+dlfq9iDEORTZuWH9sAoBbwzgMAAAAAACotqkULtbnsMkmSq7hYBbt2Ke/ojCT527Ypxi0ZI2fjRm37178kldyYiE5LU0zbttbMJNFpaX5vIDCLAuBf05EjdeCrr6pfkWmq+bnnVr+eOqB0ZodySRgul6JSU61yeVu3qjgrq3xShcMhuVxKOeMMq2zm998rf8eOkmVQnE6rvOvo4w5//7uVrLDrzTd1ZMUKawmVsokgJz73nMKioiRJO157TYcWLKjwuZzw1FOyHU3COPTjjzrw5ZcVlk0eMsRKGsnfvl2HfSzF4szPP/b3cjjkzMmp+O/p9h4cHhMje2LisQQNu11GWFjJrATh4QqLjrbKRrVqpaRTT/VM6HD7iWze3Cob17Wr2l1zjYywsJI6yxwT4TZTQUJ6uk587jmrTV9LacR17qyu99137LmYptbdeacKdu4MPCHRMNR0xAg1HTbM2hXdsqXaTJ0qZ17esZ/cXOtxZLNmVllnbq71d3RkZZW7SR/Ttq31uHD/fm195hnv3bDblXrOOWo+dqykksSVna+/biWfhLnNdBIeE6PI1FQrMQgoi5vhQGCa9O2rXYmJKj50qGYaME21mDSJmagAhAQjNAAAAAAAqBab3W4lgVT0+7guXZS3bZtcBQXK27xZeZs3W79ve/XVSjz5ZElS0cGDKjp0SNGtW1s3UiVmUQACEZmSoib9+unwzz9XPU5sNiUNGqQotxvdvlhJGS6XR8wW7NsnV0GB16QKW0SEGvXsaZXNXLBAjqys8kkYDofCY2PV4rzzrLI7XnlFBXv2eJ2JIyw21iMpYMMDDyh340av/Q6LidFJzz9vbe96801rpqTyhcM8kkb++OUXHVm2rOK/idNpJS8UHT6sgl27Ki5bXCwd/bvZ7HbJZvNMwnBLmHBPbIhq1UqNTjrJaxKGLTxcNrf/i4RevWRv3Nh7Eobd7pFMkHzaaWrct++xJIyy9bslZSQPHarkoUMrfG7u4rt1U3y3bgGVjWzaNOAEB9vRpTOqoio3ym0REUodM8Zjpg9748ZKGjgwoOPjunfXSf/6l5VQ4nBLLnHm5iq2U6dj/QsLU1y3bp7JKHl5kmnKLC72uPle/McfOvzTTxW222z0aLWcNElSSTLK2ttvt5JLwsvMdtKoZ09rthNXcbFy1q8vNyuKjRv/9Q43wwH/jLAwpY4erR2vvhr8ym22krHSywyPAFAbOLsDAAAAAAA1KqFXLyX06iXT5VLhgQPKPzojSemPe7LJocWLS5Y7MAxFNm+umDZtrISUpFNPZRYFhJR5dMp9938Nm826gWy6XHJkZZUkUrhckssl0zRL/nW5FB4bK3vjxiVlHQ7lbd9u/c6q9+i2vXFjK7HJdDr1xy+/lKuv9JiI5GQ16tFDktRs1Cifs0r45XKpYM8ebfy//1N069ZK+8tfrF+tveMOOY4c8UjYKE1OienQQV3vvdcq+9uDD1Z48zGqZUt1nznT2t770Ucq3L3ba9mI5GSPpJHc33+vcEmR8Ph4j22vs06EhZUkVURGeuyOTE2VIyenfJKE3S5bWJhM07RudjbOyFBU8+ZekypsdrvHTdHU0aOVPHiwlazhPhOHER6usKOzgUhS2pQpan3JJV6fW1kpQ4cqJcCEjbguXRTXpUtAZe0JCdaSMg1BpW6UG4bHsj9VYRiGwqKjS2ZhSUryWTY6LU2d77jDY5/pcslVUCBHXp7C3F7D9saN1eqii6wZThxlEk3cZ2dx5uXJLC6W48gROY4cUaE8hUVHW0kjxYcPa9PDD5d/HhERCo+JUdJpp6nF+PEl9ebna9dbb3ldTicsNlYRiYnW+x/qHm6GA4FJGjxYu99+uySJL5hcLrWYODG4dQJAJZA0AgAAAAAAaoVhsymqWTNFNWumJv36SSpZHqAse+PGKv7jDxXu3q3C3bt1eNEi63fxJ5xQMhtALc2iUBeYplkuocAWEWHdEHcWFJTM6FA2SeHovxHJyVZSQ/Eff6jo0KEKExVi2rVTeFycpJJvo+dv3+5RrvSxXC7Fd+9u3Ygs2L1bWb/+6jWpQi6XGmdkWAkQ+Tt2KPOHH6x6ytafNHCgdYM7b9s27fvoI6+JEnK5lHz66Wrcu7dVdserr3p/bqappsOHK3nIEKsPv//zn571uv3Nmp51lpqPGVNSdtcurZs2rcLlK5qOHKlWF1wgSSo6dEhr/v73Cv8vk4cOtZICnPn52nDPPRWWTTzlFLX9299KXgMOh7Y88USFZRtnZFhJIxXN+FMZpbNzlI1Pb8tplCq7dJQ9IUGm01mSUHF02ZLSpIqIlBTP/qenq7h9e6+zZpS+Hks1Hz9eztzcY0kYbse4z/4gSe2vu06maZYkaRxtv6LlS1pPmeL371Iq0FklJFVqRiO+gV/7KnOjPCw6Wk2HD6+FXlXMsNmsRAx39saNA+5bdKtW6jFr1rEldNxmO3Hk5Smuc2errOlyKbp1a8+ZTiSZRUUqLioqmSnnKEd2tg5+/XWF7bq//zlyc7Vu2rRyiSWlySaxHTsqIT3d6kPe1q3Hfh8dzRInNYSb4YB/tshINR0+XHvefz/wpc38VmpTQq9einVb3hMAahtnVwAAAAAAIGTK3iRNHT1aqaNHq/iPP0pmItm2zZqZpPjwYTUfP17Zv/5a9QaPJmDsePVVj+SGtClTrJvJB778Ujm//WYlE5RNWGh/7bXWLAV7P/pIR5Yv95ooYZqmOk+bZs1+sOfdd5W5YEGFiQpdH3jAWpJh19y52vfxxyXJMV4+kO76j38opnVrSdL+zz7TnnfeqfApd7nnHsV27ChJOrRwoXbNmVNh2Y63324lH2T9+qt2/Oc/FZZt//e/W0kjuZs3a+frr1dYNjI11bp5XrB3r/Z/8kmFZWM7dLCSRoqPHPE5a0ajE0+0HrsKCipcikSSR7KD6XSq6ODBCsuaRUXWY8Nm831TwC2BybDZJMOQbDYZNlvJ6/voYxmGZ1KDzVby9zMM6/eGzVZS3jBkd5uFwAgLU2znzp71udUf066dR5fiunVTzrp1Ffe5ImFhanX++Qpv1EhGeHi5GSc63nJLyfMoTcKw2z2SPNx1vf/+gJttef75AZdN6NUr4LJlZx4BvAn0Rnnq2LElM4Qc54zwcEUmJ0tus49UJCo1Vd3+8Q9r23S55MzPtxJNwtySumxRUUodO9ZzOR23hBT39xNnbq6KDx9W8eHDXttNHjrUShrxlmBni4y0Ek0aZ2SoxdHZw0yXS3veecdaaqf039JklPD4+HIJNziGm+FAYFKGDdPeDz8slzBbZS6XWkyYEJy6AKCKSBoBAAAAAAB1jr1xY2tZm1LO/HyFRUcrvnt3Za9fX7XZRkxTmd9/X2532l/+Ih1NGsn57TeP2U3KcjkcVtJI4b59PhMVXO7fws7L852o4P58TFNyOiss63EzxzB8JirILTEnLCZGEUlJHgkK7skH7kkN9saNPRMVyvxrb9TIKhuZklIye4xbu1Zig81mJcNIJTchm44c6TVRQjabx0wZUS1aqNVFF3m273ZcjNtNqKgWLdTuuuu8llOZPkSmpqrLvfd6Jnm4Hec+u0Vk06Y64cknvfbVMAwZYWFW2YjERPUOcGr/8NhY9Zw1K6CyRni4utx1V0BlJanTtGlad+edKti5M/Abf4ahZmedpaYjRlRYJKZNm4D7ABwvArlRHp6QoJTTT6/djtVBhs2m8NhYr0v02Bs1spI3/LE3aaKuDzwgZ25uuaV0nLm5inWb7cRVWCh7YqKceXlyFRRY+1yFhSo+fFgOt7LO/Hzt/eCDCttt3K+f2l9zjaSSMXfd9OklywXFxCi8TKJJdMuWHomJhfv3W7OiVDRjUX3AzXDAv/D4eCWfdpoOfP111Wc/LGWzqUm/fpWamQwAaoJhepsHFqgnsrKylJCQoCNHjqiR24dZAAAAAIDjV9bq1dr00EOVP9BmU/Jpp5V8U7xMUkHq2WdbN/+PrFihwr17K0xUaNK/v2x2uyQpb8sWFR48WGGiQlynTlYiRuGBA3IcOVJhYkVESopsR2drcOblyVlY6L3eozNW1OebVqi+Q4sXa+vTTwdc3hYZqZ6zZ3u9GQzUd47sbP163XUV3ihPu+QSpQwdWsu9Qlmm0+mRYOLIy5O9USPrZqsjN1d73n7bMxHFbbaTxAED1Pqvf7XKrjq6BJg3ZRNMlk+ZYiUV2aKiPGYxieveXS3Gj7eOPfDllx6zoXgswxMdXefH7x2vvhrUm+HtrroqOB0D6pDCAwe05qabqj8rj2Go+yOPHFdLZwI4vgR6r5yZRgAAAAAAwHElvkcPRaWlVX4WhVGj1HLiRL9FK7P8RUy7duWWBqlIZEqKIlNSAipbenMJqKomfftqV2Kiig8d8l/YMNRs9GgSRtBgVfitccOQPTFRyYMHh65zsBhhYQqPj69w6anw2FilXXxxhce7f3/WFhGhjrfd5jW5xJGXZy3rJpUsf2az2+U6unSZq6BAroICFWdmlrTbuPGxNlwu7XjttQrPT+JPOEGdbr3V2t706KMywsK8LqcTkZKi+G7drLLO/HzZIiNrPOmk6ciROvDVV9WvyDTVPMDZZ4DjTekse4d//rnqCVY2m5IGDSJhBECdQNIIAAAAAAA4rhiGodRzzqncLAoREWo2alQN9gqoW4ywMKWOHq0dASyXExYdrabDh9dCr4C6y+uNctNUiwkTZITzMXp9YLgt12az29WoZ8+AjguLiVGvf/9bLoej3FI6zrw82d2TRhwONTn5ZI8klNJEFLOoyCMh1HS5lLVqlc8EE/ekkdU33GAt1Vd2FpOYtm3VfOxYq+zhn3+WER5u/T78aPlAkk64GQ4EptmoUTq8eHHVKzAMNR83LngdAoBq4GwXAAAAAAAcd5hFAfAvafBg7X77bTnz8nyWSx07tmTZJqABK3ej3DAU2ayZEgcMCHXXUEfYwsNla9RIdh9Tu9siIipcjsVVXCzT6Ty2wzTV7uqr5czN9VxS52hCivtMZqbLJWd+vmSaVhkdPHis7sJCj7a2//vf3t/7DUNxXbuq8x13HCv70ksynU6PZXSiWras3vI0VbwZbppmSRLN0Z/S2WFKlwWUJGdBwbG/o3u5o2XtCQlW2eIjR0pmiDn6O4/6JUU1b26VLdy/3/obe9R59HFsx45W4lH+rl1yZGWVK1e63ahnT2vZw7ytW1WUmem1TpmmGvfpYy1lmLtpkwr27Cn5fenf3+WSefTfxFNOscbrnA0blLd1q2e50gQkl0tJQ4ZYr9XsdeuUs26dx+/d+5IybJgikpJKyq5dqyO//OK1rzJNNR050vq7Za9Zo8wff/RaTqapZqNHK6ZtW6vs/i+/rLjs2WdbSVLZ69Zpz3vvWa/Bsn1pPmaMEtLTrb/Djtde8/g7uR+TOmaMkk45xfr7bjmadG6a5rGyR/9tPmaMUoYNs/7ffpsxw+P37n/j1HPOUfOxYxXfvbuy162r/DI1hqH4bt2Ut22bijIzFR4Xp7C4OIXHxlqvHQCoTSSNAAAAAACA4w6zKAD+2SIj1XT4cO15//0Kb2aEJyQo5fTTa7djQB3l8a1x01SLiRNrfCkQNBw2u11yS34wwsLUpF+/gI41bDb1evFFOfPzraQSh/tsJ26JEqUJDg732U5yc2U6HJJplrshffinn/wmF1aKzaaUM87Q77NmqejAAa9JFVHNm6vbP/5hHbJ22rSSZQe9sCcl6YTHH7e2f5sxQ3mbN3stGxYXp5Oefdba3vL008pZt85rWcNuV/pLL1nbO159VVkrV1b4tNJfeUU6mjSy59139cfPP1dY9qQXXlDY0b/z/i++0KEFCyose8JTT1lJI5k//KCDX39dYdlGJ55oJY0cWb5c+z7+uMKyCenpVtJITmkSRgUaZ2RYSSN5W7Zo/+efV1i2ycknW0kjBXv2+HxuiaecIh1NGik6dEhHli2ruOygQdZjR3Z2hf9vklSclWU9dhYUKH/btgrLOnNzrceu4mIVuSVblSvrlnxlulw+46I0canZ2Wcre+3aCstVXIGp7NWrlb16tcfu5KFD1fqSS0r6k5enLU8/XZJM4vZTuh3ZrJkimzatfNsA4AVJIwAAAAAA4LjELAqAfynDhmnvhx+W3Cz0ovn48dbNKqChi2nbtuRb42vXKjotTY0zMkLdJcBii4iQLSLCI0HEG8Mw1PGWW8rtdxUVyZmXd2zWhKNaTJokZ06Ox1I6zrw8FR04oMK9eyvdTyMsTKlnn60Nv/xS4Tmaq7jYc4evWRoqMYOD+xJEUkmijhERIUOSjiaAlSaCGW4JPJIUHhdXstSQYVg/Vn1l6o1ITFRUixbly5YpJ0mRzZoptmPH8uWOlnVP4olu1UqNTjzRe1nD8Bivo9u0UZP+/T2ek/txYW4z7MW0b6/kowmihpc+h7u9pmI7dlSzs88u+b373+1o2YjkZI+yLSZNOvZ3KvM3jmrZ0qNs2iWXeG1fhqHYDh2Ole3QQW2vvrrCsjGtWx8r2759yeu9gr+Ze1JFTNu26nLPPeX6WVrWPbaiW7VS94cfPlZv2b/v0aWm4nv0UFSrVirYtatSr9Woli0VkZQkR06OHDk5VgyGxcVZZYqzs0uWsKqAe4KJIzdX626/3SPBxP1xTLt2iu/eXVJJYpkjK0vhcXHMaoKgKjp0qGQZtchI2SIjZYuIkGG3k4B7nDBMs7JzJjVsBw4c0JIlS/T7778rKytLdrtdSUlJ6t69uzIyMmQvc6JRl2zatEkrVqzQjh07lJubq+joaLVo0UInnniievToUePtr169Wr/++qt2796t/Px8xcbGKi0tTb169VLHjh1rpM2srCwlJCToyJEjauRj2kAAAAAAwPFpz7vv+p1Foec//8lNcTRoO159VQe+/tpzqQHDkD0xUT0ffVRGON8rA0plr1unHa+8olYXXqhGJ5wQ6u4AIWOaptbdeWfJDCCB3kY6uiRgy4kTVbh/f8lsDDabxw13GYZs4eElCRpHObKzZbpcXpMfZLMpLCrKKus6mgTpntDgkeAB1LJDixdr69FlbwJhi4xUz9mzyy2daTqdMp1O67rFkZurI8uWyZGd7ZFcUvo4aeBANRs1SpJUsHev1npJFiuVfPrpaj1lSkm92dladXQZrbCYmHKJJo169lTS0ZlfTJdL2WvWKDw+XmGxsQqPi5MtKop4QzmuoiKt/vvfS5buKsOw260kPltEhGyRkSWJJVFRJT8RESXbEREyjv7rdTsyUvakJEW6Ja/Bv0DvlXNFGKC3335bs2fP1o8//qiK8mzi4+M1ceJE3XrrrercuXMt99C7/Px8Pfvss3ruuef022+/VViuVatWuvTSS3XDDTeosdvJWnUdPnxYjz/+uP79739r165dFZbr1KmT/va3v+nKK69UNN/+AgAAAAAEiFkUAP+ajhypA1995bnTNNViwgQSRoAy4rt1U/eZM0PdDSDkDMNQ6jnnVO5meESEdRO7MstmhMfHB94G4xbqmCZ9+2pXYqKKDx3yX/hoYlXZhBGpZNYZ95k/wmNjlTR4cEB9iEhMVNcHHrASTJw5OXLk5lqPY92+uO1wmwGodIahov37rX1h0dFW0ogzN1ebHn7Ys5/h4VYCSZM//UnNx4+XVJJgsv/TT63fhcfHH0tIiY3lnLOeM+x2hSckeE0aMYuL5SwuliqaIdRmOzajjyRTKkl293I/PrxRI/V8/PGSJd8QVMw04seuXbt04YUX6rvvvgv4mIiICE2fPl3Tp08Pabbdjz/+qAsvvFDbfKznVlZKSopefPFFnXPOOdVu//3339fll1+ugz7WiCurbdu2euONN9T/6LRq1cVMIwAAAABQ/zGLAuDflqef1uGffy6JE8NQZLNm6v7QQ0wXDQCokOl0avWNNwZ8M7z5+PFqPnZsjfcLqGsOfPmldrz6qt9yYTEx6vn44yFfOtN0OuXIzfWYucSZkyNHdraiW7e2ZtoqyszUpkcflfNoAopZZmmpimYw8SZx0CC1veKKkvZdLm19/nmFH00wCYuPPzbbSWysIhITPWYjwvGhsrPuVNrRpaw63303s91UAjONBMHGjRs1ZMgQ7dmzp1LHFRUV6e6779a6dev02muvKSwEa4K9++67Ov/881Vcdm1APw4cOKCxY8fqqaee0lU+3tz9eeKJJ3TDDTdUOCtLRbZu3aohQ4bof//7n8aMGVPl9gEAAAAADQezKAD+NRs1SocXLy7ZME21mDiRhBEAgE9GWJhSR48O7GZ4dLSaDh9eC70C6p6kwYO1++235axoJoWjUseODXnCiFQS2/ZGjWT382XriKQkdZ8xQ1LJklWuwkKPRBN7QoJV1jRNJZ5ySrmldJy5uZLkMfujIydHhxcurLDdJiefrHZXX11Sr9OpdXfeaS2f476UTnhcnKJatFCc2+oPptPpMWMLak+lZt2pCtNUi0mTSBipIXxyUoHMzEwNGzbMa8JInz59NGbMGLVr1075+fnauHGj3njjDe3evduj3Jw5c9S0aVM9/vjjtdTrEgsXLvSaMGKz2TR69GgNHDhQaWlpOnDggFauXKk5c+Yoz20gM01T11xzjZo3b65x48ZVuv25c+fq+uuvL7ffbrdrwoQJ6tu3r5o3b649e/bo559/1jvvvOPR16KiIk2cOFHff/+9+vXrV+n2AQAAAAANS2RKipr061duFoXEAQNC3TWgzohp21bx3bsre+1aRaelqXFGRqi7BAA4DhxvN8OBULBFRqrp8OHa8/77XpfUkKTwhASlnH567XYsiAzDUFhUlMKiohSRnFzu9/ZGjdT2b38rt990uazEkVI2u10tJ0/2SC5xf2xv0sQq68jNVcGuXRX2q8nJJ1tJI6bTqeWXXCJbVJSVXOKeYBLboYMSTznFOjZv61aFxcQoPD5etqgokhGqqTKJhpVmsym+WzfFd+0a/LohieVpKnTuuefq3Xff9dgXHx+v1157zesMGMXFxXrwwQd17733lvvdJ598opEjR9ZUVz3k5uaqe/fu2r59u8f+Ll266L333lO3bt3KHXPo0CFdcskl+uCDDzz2JyQkaP369UpNTQ24/V27dqlbt27Kzs722N+/f3/NnTtXLVu2LHfMzp07dd5552lx6bddjmrbtq3WrFmjmJiYgNsvi+VpAAAAAKBhyNu6VevvusvabnfddWrSt28IewTUPdnr1mnHK6+o1YUXWtOOAwDgz5533/V7M7znP//pMZMA0NA4srP163XXyXQ4vP4+7ZJLlDJ0aC336vjnKipSzm+/WcvnlE00ie/eXc3OOkuSVHzkiH695poK62rSv7/aHV1lwXQ6tfzo0jpSScKD+0wm8T16qLnbF+szFyxQWHS0R5nwuDhmtizDVVioX6+7zm+iYVV0ue8+xbZvH/R66zuWp6mGL7/8slzCSEREhObPn6+MCr6FYbfbdc8996hx48a64YYbPH537bXXav369QqvhTeOBx98sFzCSKdOnbRw4UIlJiZ6PSYxMVHvvvuuJk2apHfeecfaf+TIEd1222165ZVXAm7/1ltvLZcwcsopp+jLL79UdAVZxq1atdL8+fN1xhlnaKHbdFRbt27VQw89pPvuuy/g9gEAAAAADROzKAD+xXfrpu4zZ4a6GwCA40zKsGHa++GHFd4Mbz5+PAkjaPDC4+OVfNppOvD11yWzH5YyDNkTE5U8eHDoOnccs0VEqFGPHgGVDY+P14nPPFNu5pLSn+i0NKuss6BA9iZN5MjJkVlcLNPplOPIETmOHJEk2d3uqZoOh7b961/e+xcVpcYZGWo7daq1b+ecObJFRnqd7SQ8Pr5ez8oUyKw7la/UpoRevUgYqWHMNOLF4MGDtWDBAo99//jHP3THHXcEdPyZZ56pL7/80mPfK6+8or/85S9B66M3f/zxh9q0aaOsrCxrn81m03fffaeBAwf6PT4zM1M9evTQvn37rH1hYWHauHGj2gcQiJs2bVKXLl3kchsMo6OjtWrVKnXs2NHv8b/99ptOOukk5efnW/sSEhK0ffv2Ks8SwkwjAAAAANBwMIsCAABAzdjx6qsV3gzv+eijfNsekFR44IDW3HRTuZvlbaZOVVIA9+kQGq7CQo/kEmdOjuyNGyuuSxdJkjM/X1ueecZzKZ3cXOv/2WMGE4dDyy+5pMK2Gp14ojrecou1/dvDD8tmtx9LLImPtxJNIpKTFdO2bc098Rrib9adquj24IMeST8IHDONVNHatWvLJYwkJyfrpptuCriOGTNmlEsaefbZZ2s8aeS///2vR8KIJI0aNSqghBFJSkpK0i233KKbb77Z2ud0OvXCCy9oxowZfo9//vnnPRJGJOmyyy4LKGFEKpkR5dJLL9VTTz1l7Tty5IjmzJmjqW4ZegAAAAAAeMMsCgAAADWj6ciROvDVV547TVMtJkwgYQQ4KjIlRU369dPhn38uSbAyDEU2a6bEAQNC3TX4YIuMVERkpCKSkrz+Piw6Wh3L3Cc2XS45c3PlyMnxeA80XS6lnnNOhbOdhMXFWWVdDoeyf/21wn6VTTD59brrZISFlcxWEhvrMYtJdKtWatKvn1W26OBBhcXEyBYdLcMwKv03qY4KZ92pCptNTfr1I2GkFjCSlzFnzpxy+y655BJFRkYGXEefPn3Up08fLVu2zNq3ePFibdmyRe3atQtKP7158803y+3729/+Vqk6pkyZojvvvFOFhYXWvjlz5gSUNOKt/come0ydOtUjaaS0fZJGAAAAAAAAAAAIDW6GA4FpNmqUDi9eXLJhmmoxcaIMmy20nULQGTZbyawg8fEe+20REWpx3nkVHmc6ncfqkNTummu8Jpg4c3IU1aqVVdblcKj48GFJJQkhZTU68USPpJG106bJVVAghYUp/GiCSekSObEdOij1nHOsskdWrpQtIuLY8jlxcbJVMxnQa6JhVZimmp97bvXrgV8kjZTx2Weflds3YcKEStczYcIEj6SR0rqvvPLKKvfNlyNHjmjRokUe++Li4jR8+PBK1ZOUlKQhQ4bo888/t/Zt27ZN69atU7du3So8bs2aNdq5c6fHvm7duqlHgOuMlerZs6e6dOmiDRs2WPt+/PFHZWdnK77MGy8AAAAAAAAAAKgd3AwH/Itp21bx3bsre+1aRaelqXFGRqi7hDrECAs79jg83CPRw+dxNpu6P/ywNbuJIydHjuxsK9HEPcHEdDiOzfDhdMqRlSWH20oV7okrkrTlySflcptMQJJsUVEKj4tTXNeuauv2xf59n3wiIyzMSkBxn+0kLCbGmtWkXKJhVdhsSho0SFHNmlXteFQKSSNucnNz9csvv3jsi4mJUXp6eqXrGjRoULl9CxYsqLGkkUWLFslZJsj79++vMLc3n0ANGjTII2lEKum7r6SRskv6SAp4WRxv7bsnjTgcDi1atEhnnnlmleoDAAAAAAAAAADVw81wIDCpY8eq+MgRtZw8udaXBkH9ZNhsimrePLCy4eHq9e9/y1VU5HUWE3vjxlZZ0+VSdFrasXK5uZJpylVQoKKCAjmysz3q3vPuu+USTErFduqkLnffbW27ioqqtzyNYaj5uHFVPx6VQtKImxUrVshV5sWbkZEhu91e6br69u0ru92u4uJia1/ZmUeCyVvd/fv3r1JdA7xMJ+ev78Fu/8UXXyxXP0kjAAAAAAAAAACEDjfDAf/iu3VT95kzQ90NNHC2iAhFJCYqIjGxwjKGzaYu99xjbZsul5x5eXLk5sqZnS3D7R656XIpceDA8kvpZGfLVVSksNhYj7qzV6+uRudtSjnjDEUkJVW9DlQKSSNu1q9fX25fx44dq1RXRESEWrVqpS1btlj7fv/9dzkcDoVXcx0ob4LZ9w4dOpTb5z7zR11sHwAAAAAAAAAA1CxuhgNA/WXYbNaSMyqzLIxhs6n1lClej3MVFcnlNpGCaZpqeeGFyvv9d2V+/33l+xEWptSzz670cag6Fptzs3Xr1nL72rRpU+X6Wrdu7bHtdDq1ffv2KtfnSzD73rJly3LL2mzevLnW2i/7dwukfQAAAAAAAAAAAABA7bJFRCjcbaYRwzCUMnSoWl92maLS0qTKzExlGGo6YoTsCQk10FNUhKQRN3v37i23Ly0trcr1eTt23759Va7Pl2D2PSwsTM3LrIvlr99lf28Yhlq2bFml9lu1alVuWrua+rsBAAAAAAAAAAAAAILLMAylnnOOZJoBH2OLiFCzUaNqsFfwhqQRN4cOHSq3Ly4ursr1eTs2MzOzyvX5UtN9LyoqUk5Ojtey2dnZKnabckiSoqOjy81WEqjw8HBFRkZ67KupvxsAAAAAAAAAAAAAIPia9O0re2JiYIUNQ81Gj/aYtQS1g6QRN7m5ueX2RUdHV7k+b8fm5eVVuT5fQtn3YLft7fia+rsBAAAAAAAAAAAAAILPCAtT6ujRAZUNi45W0+HDa7hH8IakETdlZ8uQpKioqCrX5y1xoqioqMr1+RLKvge7bW/tB/p3KywsVFZWlscPAAAAAAAAAAAAAKD2JQ0erLCYGL/lUseOVVg1JyZA1ZA04odhGEE91qzEmk3VFcq+V6dtb8cH2vaMGTOUkJBg/aSlpVWrHwAAAAAAAAAAAACAqrFFRpbMIOLj/nF4QoJSTj+9FnsFdySNuLHb7eX25efnV7k+b8dGRERUuT5fQtn3YLft7fhA/27Tpk3TkSNHrJ8dO3ZUqx8AAAAAAAAAAAAAgKpLGTZMRlhYhb9vPn68bDV0Hx3+kTTiJsbLtDjBTryIjY2tcn2+hLLvwW7b2/GB/t0iIyPVqFEjjx8AAAAAAAAAAAAAQGiEx8cr+bTTJFuZ9ATDkD0pScmDB4emY5BE0oiHpKSkcvtycnKqXJ+3Y721EQw13feIiAjFxcV5LduoUaNys40UFBTI6XRWqW2Hw6GCggKPfTX1dwMAAAAAAAAAAAAA1KymI0dKpum50zTVYsIEGeHhoekUJJE04qFZs2bl9u3cubPK9XlbGsVbG8EQzL47nU7t2bPHb/3umjZt6rHtcrm0e/fuKrW/a9cumWXeMGrq7wYAAAAAAAAAAAAAqFmRKSlq0q/fsdlGDEORqalKHDAgtB0DSSPu2rVrV27ftm3bqlzf9u3bPbbDwsLUunXrKtfnSzD7vnv3bjkcDr/111T7Zf9ugbQPAAAAAAAAAAAAAKi7mo0aJblcJRumqRYTJ8oou2QNah3/A266dOlSbt+mTZuqVFdRUVG5mUY6dOig8BqaWieYff/999/L7evatWudbh8AAAAAAAAAAAAAUHfFtG2r+O7dJUnRaWlqnJER4h5BImnEQ3p6umxlMpmWLl1abtaNQCxdulTFxcUe+3r37l2t/vnSp0+fcvsWLVpUpboWLlxYbp+/voe6fQAAAAAAAAAAAABA3ZY6dqyiWrZUy8mTZRhGqLsDkTTiITY2Vunp6R77cnNztXz58krX9cMPP5TbN3jw4Cr3zZ8BAwYoLCzMY9+iRYvkdDorXVdV+j5o0KCA6qlK++Hh4erfv3+V6gIAAAAAAAAAAAAA1A3x3bqp+8yZanTCCaHuCo4iaaSMESNGlNv3zjvvVLqet99+O6C6gyUhIUEnn3yyx77s7Gx9+eWXlarn0KFD+uabbzz2tW7dWt26dfN5XM+ePdWyZUuPfWvXrtW6desq1b63YwYMGKBGjRpVqh4AAAAAAAAAAAAAAOAbSSNlTJ48udy+l19+WUVFRQHXsXz5ci1ZssRjX79+/dSuXbtq98+X888/v9y+5557rlJ1vPLKKyooKPDY5+1vEmj7zz//fKXa91Y+0PYBAAAAAAAAAAAAAEDgSBopo0ePHho4cKDHvv3792vWrFkB13HHHXeU23fllVdWu2/+XHTRRYqPj/fY9+GHH2rhwoUBHX/48GE98sgjHvvCwsJ0+eWXB3T8FVdcIZvN8yX1wgsvaPPmzQEd//vvv+vFF1/02JeQkEDSCAAAAAAAAAAAAAAANYCkES+mT59ebt8999yjX375xe+xTz31lD777DOPfe3atdMFF1wQUNtDhgyRYRgeP//5z38COrZx48a6+uqrPfa5XC5dcsklOnz4sM9jXS6Xpk6dqj179njsnzx5sjp06BBQ+507d9bEiRM99uXl5WnKlCnlZi8pq6CgQBdffLHy8vI89l977bVKSEgIqH0AAAAAAAAAAAAAABA4kka8GD58uMaMGeOxr7CwUKeddpo+/PBDr8cUFxfrgQce0HXXXVfud0888YTsdnuN9LWsO++8U61atfLYt3HjRg0YMEDr16/3eszhw4c1fvx4zZ0712N/o0aN9NBDD1Wq/YcfflhxcXEe+xYsWKAzzjhDu3fv9nrMrl27NHToUP34448e+9u0aaNp06ZVqn0AAAAAAAAAAAAAABCY8FB3oK568cUXtWzZMu3cudPal5WVpXPOOUcZGRkaM2aM2rVrp/z8fP3222/673//q127dpWr55prrtHo0aNrrd9xcXF64403NHToUDkcDmv/+vXr1aNHD5199tkaNGiQWrZsqYMHD2rlypWaM2eOcnNzy9X173//Wy1atKhU+2lpaXrhhRfKLSnz448/ql27djrvvPPUt29fpaamas+ePfr555/19ttvq7i42KO83W7XnDlzFBMTU6n2AQAAAAAAAAAAAABAYAzTNM1Qd6KuWrdunYYOHaq9e/dW6fiJEyfqjTfeUFhYWMDHDBkyRN99953HvpdffllTpkypVNtz587VBRdc4JE4EijDMPT44497nTUlULNmzdKNN95YpWPtdrvefPNNjR8/vsrtl8rKylJCQoKOHDmiRo0aVbs+AAAAAAAAAAAAAADqukDvlbM8jQ/dunXTkiVLNGjQoEodZ7fbde+992rOnDmVShgJpvPOO0/z589X69atK3VccnKy3n333WoljEjS3//+d73zzjtKSkqq1HFt2rTRN998E5SEEQAAAAAAAAAAAAAAUDGSRvxo1aqVvvvuO7311lsaMGCADMOosGxcXJwuueQS/frrr7rnnntks4X2zzto0CCtW7dOjzzyiDp27OizbMuWLXXXXXdp48aNGjt2bFDaHz9+vDZu3Ki77rrL7zI3HTt21COPPKJ169bplFNOCUr7AAAAAAAAAAAAAACgYixPU0n79+/Xzz//rM2bNysrK0vh4eFKTk5Wt27d1LdvX0VERIS6ixX67bfftHz5cu3YsUN5eXmKiopSixYtdOKJJ+qEE06o0bZN09Tq1au1atUq7d69WwUFBYqJiVFaWprS09PVqVOnGmmX5WkAAAAAAAAAAAAAAA1NoPfKSRpBvUbSCAAAAAAAAAAAAACgoQn0XjnL0wAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QOGh7gBQk0zTlCRlZWWFuCcAAAAAAAAAAAAAANSO0nvkpffMK0LSCOq17OxsSVJaWlqIewIAAAAAAAAAAAAAQO3Kzs5WQkJChb83TH9pJcBxzOVyaffu3YqPj5dhGKHuTr2VlZWltLQ07dixQ40aNQp1d4A6iTgB/CNOAN+IEcA/4gTwjRgB/CNOAN+IEcA/4gTwjRipPaZpKjs7Wy1atJDNZquwHDONoF6z2Wxq1apVqLvRYDRq1Ig3d8AP4gTwjzgBfCNGAP+IE8A3YgTwjzgBfCNGAP+IE8A3YqR2+JphpFTF6SQAAAAAAAAAAAAAAACot0gaAQAAAAAAAAAAAAAAaIBIGgFQbZGRkbrnnnsUGRkZ6q4AdRZxAvhHnAC+ESOAf8QJ4BsxAvhHnAC+ESOAf8QJ4BsxUvcYpmmaoe4EAAAAAAAAAAAAAAAAahczjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEAkjQAAAAAAAAAAAAAAADRAJI0AAAAAAAAAAAAAAAA0QCSNAAAAAAAAAAAAAAAANEDhoe4AgPqhsLBQubm5cjgciomJUWxsrAzDCHW3gDqFOAF8I0YAAAAA1KbNmzdryZIl2rhxo7Zu3aodO3YoOzu73HVJ06ZN1aZNG7Vr104nnXSSevfurZiYmFB3HwBQRzCeAL4RI3UfSSMAKuXXX3/VkiVLPN7cd+7cKYfDUa5sYmKix5t737591bdvXyUmJoag50DtIU4A34gRwL+srCwtXbq0ShfUffv2VZcuXUL9FIAaRYwA/hEnQHm7d+/WJ598ok8//VTfffedDh8+7LWcaZrWY2+J7DabTT169NDw4cN11llnaeDAgQoLC6uxfgOhwlgCeMd4AvhGjBx/DNP9fwMAysjLy9O8efP08ccf64svvlBmZqbH7wN5C3F/ozcMQ3369NFZZ52lMWPGqFevXsHuMlDriBPAN2IECMyiRYv08ccf69NPP9XKlSvLxYavWCl7YZ2QkKBhw4bprLPO0qhRo5ScnFwjfQZqEzEC+EecAOXl5ORo7ty5evXVV7VgwQIrDgKNh0DKJSUlafLkyfrzn/+sjIyMIPUcCA3GEsA7xhPAN2Lk+EbSCACvvv32W7388st67733lJubK8n7G3Ygywb4Oq5Hjx66+OKLdfHFF3PRgOMOcQL4RowA/m3evFmvvPKKXnvtNW3btk1SxRfJvmLFV4yEh4dr+PDhuvjiizVmzBiFhzPhJI4fxAjgH3ECeLdlyxY9/vjjevnll71ejwRjKUxv9aWnp+umm27SxIkT+SYsjhuMJUDFGE8A34iR+oGkEQCW4uJivfHGG5o1a5Z+/fVXSYG9sVcmu9zbMYZhKDIyUn/+8591ww03qFu3blXpPlAriBPAN2IECMx3332nf/7zn/r4449lmmbAF9NlYyXQsqXlUlNTdc0112jq1Kks84Q6jRgB/CNOAO82bNige+65R++8845cLpf1Oi77Wvd2DWK32xUTE6Po6GiFh4crPz9f+fn5ysvL89pWRd+ONQxDLVu21B133KHLLruMm+OosxhLgIoxngC+ESP1C0kjAORyufTSSy/pgQce0M6dOyu8OCj7dtG4cWO1atVKzZs39/rmfujQIe3cuVN79+6Vw+HwONZbvYZhyDAMTZgwQffff786d+5cE08XqBLiBPCNGAEC89VXX2n69OlasmSJJHm9oPZ2iRYXF+czTvbv3y+Xy+VxTEUX6YZhKDo6Wtdcc41uu+02NWnSJKjPEagOYgTwjzgBvNu5c6fuvvtuvf7663I6neViwz0u2rZtq759++qkk07SCSecoDZt2qhVq1YV3rwuKirSzp07tWvXLq1fv14rV67UypUrtXTpUhUWFnq0496WYRhq27at7r//fl144YU18ryBqmAsASrGeAL4RozUTySNAA3ce++9p9tvv12bNm3y+cbepk0bnXbaaR5v7vHx8QG1YZqmNm3aZL25L1iwQD/99FO5N3j39sPCwnTxxRfrH//4h5o2bRq05wtUBXEC+EaMAP4tW7ZMt9xyi7777jtJJa9Vbxe5kZGR6t+/f7kL6kaNGvms3+l0avfu3R4X1AsWLND27dutMt7ipFGjRrr55pt18803KzIyMqjPGagMYgTwjzgBvCsuLtYjjzyiGTNmKC8vzyM2Sl+r0dHRGjFihM4++2yddtppatOmTVDaLiws1MKFC/XVV1/p3Xff1YYNGyR5j5WTTz5ZTz/9tHr16hWUtoGqYCwBKsZ4AvhGjNRzJoAGaf369eaZZ55p2mw20zAM0zAM02azWdthYWHm4MGDzSeeeMLcsmVL0NvPz883v/jiC3Pq1Klms2bNvPbBZrOZjRs3Np944gnT6XQGvQ+AP8QJ4BsxAviXmZlpXn755WZYWJjH69I9btq0aWPeeOON5jfffGMWFBQEtf3Nmzeb//rXv8zhw4ebERERHnHi/rhdu3bmBx98ENS2gUAQI4B/xAngW+fOnb3Ghs1mM88880zzrbfeMnNzc2ulL6tXrzanTZtmNm/e3GushIeHm6+++mqt9AVwx1gC+Md4AvhGjNRvJI0ADVRERITHG3rpG2mHDh3MBx980NyzZ0+t9cXlcplffPGFee6551oXDWUHnBkzZtRaf4BSxAngGzEC+JeYmOj1gjo+Pt684oorzJ9++qnW+nL48GHz+eefNzMyMir8kPaJJ56otf4ApkmMAIEgTgDfysZG48aNzdtvv93cunVryPrkcDjMd9991xw8eHC55Pb77rsvZP1Cw8VYAvjHeAL4RozUb7ZQz3QCIDSKi4utx6ZpasiQIfr000+1adMmTZs2TampqbXWF8MwNGzYML399tvasWOHpk+frsaNG3tMbVVUVFRr/QFKESeAb8QI4N/hw4cllbxGTdNU+/bt9eyzz2rPnj16/vnn9ac//anW+tK4cWNdccUVWrJkiX755RdddNFFCgsLs/rn3l+gthAjgH/ECeCfaZpKSUnRjBkztH37ds2YMSNo06FXRVhYmMaNG6fvvvtOCxYs0FlnneWxdCdQ2xhLgMAwngC+ESP1F0kjQANmmqZGjhypRYsWaf78+Ro+fHiou6SmTZvq/vvv1/bt2/Xwww8rKSkp1F1CA0ecAL4RI4B/pmmqR48emjNnjjZs2KCpU6cqNjY2pH3q1auXXn31VW3atElXXXWV7HZ7SPuDho0YAfwjToCKxcXF6Z577tHvv/+u2267TfHx8aHukodTTjlFH330kX744QcNGDAg1N1BA8ZYAvjGeAL4RozUb4ZJug3QIPXr108PP/ywTj311FB3xafs7Gw99NBDatKkiW666aZQdwcNDHEC+EaMAP61adNG999/v/7yl79Y35qri7Zs2aK77rpLXbt21fTp00PdHTQgxAjgH3EC+LZ//341bdo01N0I2IEDB5SSkhLqbqCBYSwB/GM8AXwjRuo3kkYAAAAAoIYUFhYqMjIy1N0I2PHWXxz/jrfX3PHWX9QPx9vr7njrLwA0BMfbe/Px1l8AAI53JI0AAAAAAAAAAAAAAAA0QLZQdwAAAAAAAAAAAAAAAAC1j6QRAAAAAAAAAAAAAACABoikEQAAAAAAAAAAAAAAgAaIpBEAAAAAAAAAAAAAAIAGKDzUHQAAAAAAAAAAIFTy8vKUl5enoqIimaapiIgIxcbGKiYmJtRdAwAcRxhPAN+IkbqLpBEAleJyubR9+3bt27dPmZmZXt/ck5KS1KxZM7Vu3Vo2GxMaoeEhTgDfiBEgME6nUwcPHvQZJ8nJyQoLCwt1V4GQIEYA/4gTwNOGDRu0YMECLVu2TBs2bNDmzZu1b98+FRUVeS0fERGhZs2aqV27duratat69+6twYMHq0uXLrXccyB0GEuA8hhPAN+IkeOPYZqmGepOAKi7tm3bps8++8x6c//999/ldDoDOjYsLEwdOnSw3txHjBihNm3a1HCPgdpHnAC+ESOAfwUFBfruu+88Lqh37tzpN1ZsNptatWrlcUF96qmnKioqqpZ6DtQOYgTwjzgBvNu6dateeOEFzZkzR9u2bfP4XaAfjRuG4bHdunVrXXDBBbrsssvUrl27oPUVCDXGEqBijCeAb8TI8Y2kEQDluFwuvfHGG3r22We1ePFia39V3y7c3+T79eunq666SpMnTyb7HMc14gTwjRgBAvPDDz/o2Wef1bx585Sfn2/tr2ysuMdIVFSUxo4dq7/97W8aNGhQ0PoKhAIxAvhHnADe7d+/X7fddpv++9//yul0eo2JsjcmKlLRsWFhYbrgggs0c+ZMpaamVrvPQKgwlgAVYzwBfCNG6geSRgB4+Pzzz3Xttdfq999/l+T5Bh3om3pZZd9mDMNQ+/bt9cQTT2jkyJFV7ywQIsQJ4BsxAvi3evVqXXvttfr+++8leX+NV0ZFxw8aNEhPPvmkTjjhhGr0Fqh9xAjgH3ECVGzevHm65JJLdOTIEeu17S8mKhtD7vU2atRIL730ksaNG1eNXgO1j7EE8I3xBPCNGKk/SBoBIKnkTfemm27S7Nmz/d7cq+o0UmWPNQxD1157rWbNmlXlm4hAbSJOAN+IESAwTzzxhG699VYVFxf7vaD2FyuBHGe32/XQQw/phhtuqFqHgVpGjAD+ESdAxZ599llde+21crlckjxf42XjISwsTM2bN1dcXJyio6MVHR0twzCUn5+v/Px85eTkaM+ePXI4HB7HeavTMAw98cQTuvrqq2vqqQFBxVgC+MZ4AvhGjNQvJI0AkCRddNFFmjNnjkzTrPCN3W63q1WrVmrdurXS0tJ8vrnv2LFD27dv144dO1RcXGzVUbZuwzA0adIkvfHGG7XzRIFqIE4A34gRwL9p06bp4Ycf9vqhrHushIeHq2XLlgHFya5du3zGSOm+m2++WQ899FBNP0WgWogRwD/iBKjYhx9+qLFjx3q9JomMjNSQIUM0aNAgDRgwQB06dFDLli1ls9l81ulyubR79279/vvvWrhwoX744Qd98803KigoKNeGzWbTe++9p7PPPrvGniMQDIwlgG+MJ4BvxEj9Q9IIAP3f//2f7r77bq8n8n369NGECRM0aNAg9e3bV3a7vVJ1FxcXa+nSpfrhhx/09ttva8mSJZKOXTSUDij33Xefpk+fHqRnBAQfcQL4RowA/r344ou64oorJHm+fiUpNTVVY8aMsS6oW7du7fdiupRpmtq+fbt1QT1v3jzt3r3ba4LV888/r8suuyzIzwwIDmIE8I84ASp25MgRde7cWQcOHPCIj2bNmumWW27RlClTlJiYGJS2Dh8+rP/85z969NFHtWfPHo/2UlJStHHjRiUkJASlLSDYGEsA3xhPAN+IkXrKBNCgbdy40YyMjDRtNpv1YxiGeeaZZ5rLli0Lenu//PKLOWLECNMwDI/2IiMjzY0bNwa9PSAYiBPAN2IE8G/Xrl1mfHx8uTjp2bOn+c4775gOhyNobTkcDvPdd981TzzxxHJxEh8fb+7evTtobQHBQowA/hEngG/33nuv9Xo1DMM0DMOcMGGCmZmZWWNtHj582DzvvPM82rXZbOa9995bY20C1cFYAvjHeAL4RozUTySNAA3cZZddVu5Ndvbs2TXe7pNPPulxoWCz2czLLrusxtsFqoI4AXwjRgD/brzxxnJxcuONN5pFRUU11mZRUZF50003eW0XqGuIEcA/4gTwLS0tzeN1OnbsWNPlctV4uy6XyxwzZoxHnKSlpdV4u0BVMJYA/jGeAL4RI/UTy9MADZjD4VBSUpJycnKsqQHvvfde3XXXXbXS/gMPPKB77rlHhmHINE3Fx8crMzNT4eHhtdI+EAjiBPCNGAH8M01TTZs21aFDh6w4mTp1qp555plaaf/KK6/U888/b8VJcnKy9u/fXyttA4EgRgD/iBPAt7Vr16pnz57WazQ2NlY7duxQ48aNa6X9P/74Q2lpacrLy7NidNWqVerRo0ettA8EgrEE8I/xBPCNGKm/AluMDkC9tHTpUmVnZ1vbLVq00B133FFr7U+bNk0tW7a0tnNycrRkyZJaax8IBHEC+EaMAP6tWLFCmZmZ1naTJk302GOP1Vr7//znPz3Wks3MzNQvv/xSa+0D/hAjgH/ECeDbqlWrrMeGYWjy5Mm1dvNCkho3bqzJkyfL/fuZ7n0C6gLGEsA/xhPAN2Kk/iJpBGjA1q5daz02DEMXXHCBwsLCaq398PBwXXjhhR5v7uvWrau19oFAECeAb8QI4N/q1autx4Zh6MILL1R0dHSttR8dHa2LLrrII07c+wSEGjEC+EecAL7t3r1bkqzX6EknnVTrfSjbZmmfgLqCsQTwj/EE8I0Yqb9IGgEasNLM8tI3986dO9d6Hzp16uSxffDgwVrvA+ALcQL4RowA/u3bt0/SsTjp3r17rfehW7duXvsE1AXECOAfcQL4lp+f77GdkpJS631ITk6WVHIzXpIKCgpqvQ+AL4wlgH+MJ4BvxEj9RdII0IAVFxd7bNdmZnnZNkvf3B0OR633AfCFOAF8I0YA/woLCz22ExISar0PpW2WxknZ2AVCiRgB/CNOAN9iY2M9tnfs2FHrfdi1a5ekYzfkY2Jiar0PgC+MJYB/jCeAb8RI/UXSCNCANWrUyGN7y5Yttd6H0jZL39zL9gkINeIE8I0YAfyLi4vz2N62bVut92H79u2SjsVJ2Yt8IJSIEcA/4gTwLS0tTdKxG9E//PBDrfdhwYIFXvsE1BWMJYB/jCeAb8RI/UXSCNCAdejQQVLJm7tpmpo3b16t96Fsm+3bt6/1PgC+ECeAb8QI4F+7du0kHbug/vLLL2u9D1988YXHdtu2bWu9D0BFiBHAP+IE8K1Xr17WY9M09dFHH2njxo211v6GDRv04YcfWjEqSenp6bXWPhAIxhLAP8YTwDdipP4iaQRowPr27evxxrps2TJ99NFHtdb+Rx99pKVLl1p9MAxDf/rTn2qtfSAQxAngGzEC+NenTx/rsWma+uabb/TTTz/VWvuLFy/W/PnzPWLVvU9AqBEjgH/ECeBbhw4d1KlTJ0kl1wROp1Pjxo3TkSNHarztP/74Q+PGjZPL5bL2dezY0UqwB+oKxhLAP8YTwDdipP4iaQRowJKTk3XKKafINE3rG+IXXXSRli9fXuNtr1ixQhdddJHVrmEY6t+/v5KTk2u8baAyiBPAN2IE8K9ly5bWNzFKX6/jx4+vlXVft2/frnPPPdfjg9mTTjpJrVq1qvG2gUARI4B/xAng39SpU60lLwzD0Lp163TyySdr0aJFNdbm4sWL1b9/f61fv97jumTq1Kk11iZQVYwlQGAYTwDfiJH6iaQRoIG77rrrrMeGYSgrK0sDBgzQQw89pOLi4qC353A49Mgjj2jAgAHKysry+N31118f9PaAYCBOAN+IEcC/q666yuOCes+ePerdu7feeuutGmtz7ty56tu3r/bs2SNJ1gX11VdfXWNtAlVFjAD+ESeAb1dddZVat25tbRuGoQ0bNmjw4MEaN26cPv/8cyuGqsM0TX3xxRcaP368Bg0apA0bNni02bp1a1111VXVbgeoCYwlgH+MJ4BvxEj9ZJjB+F8DcFwbMmSIvv/+eyvTu/TEPTk5WX/961917rnnKj09XWFhYVWq3+Vyafny5XrnnXf08ssva//+/R7fSDcMQ4MGDdK3334bxGcFBBdxAvhGjAC+uVwu9erVS2vWrLH2lb52TzjhBF155ZUaP368UlJSqtXOwYMH9e677+q5557TypUrrTZK9ezZU8uXL5fNxvcHULcQI4B/xAng348//qihQ4fK4XBIksfNcUmKi4tT//791b9/f3Xs2FFt2rRRWlqa4uLiFB0draioKBmGofz8fOXn5ysnJ0c7d+7Utm3btGnTJi1atEiLFi1SdnZ2ufpN05Tdbtf8+fN1yimnhODZA/4xlgCBYTwBfCNG6h+SRgBo9+7dysjI0L59+6x9Zd/gY2JilJGRUaU396VLlyo3N9drvaZpqnnz5lqyZIlatGhRm08bqBTiBPCNGAH8K52uMycnx9pX9vXcqVOnal1Qb9y40Wu9pmkqPj5eixcvVrdu3WrzaQMBI0YA/4gTwL93331XkydPtm5iSMdez5I8blxXhbe6TNNUeHi43njjDU2YMKFa9QM1jbEECAzjCeAbMVK/kDQCQJK0ceNGDRs2TDt27PB4Iw/GG3xFdZimqVatWumLL75Q165dq1Q3UJuIE8A3YgTwb+HChRo1apTH0kplL8mCESfu9ZimqUaNGumjjz7SwIEDq1Q3UFuIEcA/4gTwb+HChZo8eXK5axOp/Ou8srzV16pVK73xxhvEB44bjCVAYBhPAN+IkfqDub8ASJI6d+6s5cuXa9y4cTJN0yMLvPSndH9lf9zrkGTtHzNmjJYvX85NPhw3iBPAN2IE8G/AgAFasmSJ+vTp4zVGghUn7rHSu3dvLVmyhAtqHBeIEcA/4gTwb8CAAVq3bp2mTZum+Pj4Cq9PqvIjHbseiYuL0+23365169YRHziuMJYAgWE8AXwjRuoPZhoBUM6XX36pBx54QD/88IO1L5jTSA0cOFDTp0/XmWeeWa06gVAiTgDfiBHAN9M09eKLL2rmzJnasmWLpOrHiHvdktS2bVvdfvvtuuyyy1grHMcdYgTwjzgBAnPkyBG9/vrreuutt7Ro0SI5nU6P3/uLm7Ifn4eFhenkk0/W+eefr4suukgJCQlB7zNQWxhLgMAxngC+ESPHN5JGAFRo3bp1mjNnjj777DP98ssvcrlcVarHZrMpPT1dI0aM0OTJk9W9e/cg9xQIHeIE8I0YAXxzuVz67LPP9Oabb+qLL77Q/v37vZYLdIrPlJQUnXnmmTr//PM1YsQIhYWFBb3PQG0iRgD/iBMgcFlZWVq0aJGWLVum9evXa+vWrdq7d68OHTqk3NxcFRcXS5LsdrtiYmKUlJSkZs2aqV27durSpYv69OmjAQMGqFGjRiF+JkBwMZYAlcN4AvhGjBx/SBoBEJC8vDytWLGiSm/u6enpiomJCfEzAGoecQL4RowA/m3YsKFaF9Qs1YT6jhgB/CNOAADVxVgCAEDDQtIIAAAAAAAAAAAAAABAA8QCcgAAAAAAAAAAAAAAAA0QSSMAAAAAAAAAAAAAAAANEEkjAAAAAAAAAAAAAAAADRBJIwAAAAAAAAAAAAAAAA1QeKg7AABAQ7Z3714VFRVZ261btw5hbwAAAAAAgCTl5eVp8eLF2r17tzIzM1VQUKCkpCSlpKQoPT2d63cAQEAYTwDfiJG6wTBN0wx1JwAAaKjS09O1atUqSZJhGHI4HCHuEVB3OJ1OLV26VKtWrdKhQ4cUExOjlJQUde7cWb179w5194CQ2rhxo3744YcKL6j79+8vu90e6m4CdQLnW4B/e/fu1a+//upxztWxY0clJyeHumtArXI6nXr99df1r3/9S0uXLvU5ZrRv317nnXeebrzxRmIFEGMJ4I7xBPCNGKl7SBoBACCE0tPTtXLlSkklNzGcTmeIewQER1ZWlj755BNt2LBB+/btU1RUlDp06KDTTz9dXbt29XlsQUGBZs6cqWeeeUaZmZleyyQnJ2vSpEmaPn26mjZtWhNPAahz/vjjD82ePVv/+te/tHfvXp9lo6Ojdc4552j69Onq3r17LfUQqJs430J9t3jx4nLnXAMHDlRCQoLfY//zn//o8ccf16+//lrud4ZhKD09XZMnT9bVV1+tyMjImug+EDQul6vcOVKLFi0CPv7bb7/VJZdcou3bt0uSAvnY3DAMxcTEaPr06brtttsq12GgDmEsAY5hPAF8I0bqJ5JGAHi1cuVKffTRR9q8ebP27t2r6OhoNW/eXH379tXo0aOVmJgYlHZGjRqldevWSSp50//999+DUi9wvCj95qtpmtzEQL1w5MgR3X777Xr55ZdVXFzstcwZZ5yhp59+Wh07diz3u40bN2rMmDHauHGj3wsGwzAUGxurJ598UhdffHFQ+g/UlPz8fC1fvtzaNgxD/fv3D/j41157Tddcc41ycnICupgubcMwDF1++eV64oknmHkEDRbnW6iPnE6nHn30Uc2ePVv79u0r9/uIiAj95S9/0YMPPqikpKRyv9+/f78mTZqk77//XpLvD2oNw1CrVq300ksv6fTTTw/ekwCC7O2339akSZOs7R49elgzTfkza9Ys3XbbbR7fcjUMw+9xpbFjGIbGjh2r1157TTExMZXsORAajCWAd4wngG/ESP1E0ggAD99//72uvfZarV69usIyNptN48eP1/33368uXbpUqz2+9YeGjpsYqE9+++03nX766dq1a5ffm9qxsbGaN2+ehg4dau3bunWrBgwYYGWq+7tgcL9YeOyxx3TDDTdU7wkANei1117TlClTrO1+/fpp4cKFAR17/fXX66mnnvJ4zQfCvfyf/vQnffDBB0pJSalcx4F6gPMt1DcHDx7UmWeeqZUrV/q9QZeamqrPP/9cPXv2tPZnZmbq1FNP1bp16/yOLe71R0RE6LXXXtN5550XpGcCBNdf//pX/ec//5FU8pp+4okndPXVV/s97pVXXtEll1xiHeeuohjzVs4wDJ1zzjl67733qtB7oHYxlgAVYzwBfCNG6ieSRgBY7rrrLj344IOS/E8HZRiG7Ha7pk2bprvuuks2m61KbfIBLho6YgD1xd69e5WRkaHdu3dL8n1Tu3SMiY2N1bJly9S5c2eZpqnBgwfrxx9/9DjW23jk7WLBZrPp22+/1cCBA4PxdICgmzx5st566y1JJa/hl156KaAZcmbMmKE777zTOs6dvw933cuVzmzyzTffMOMIGhzOt1CfZGVlacCAAVq7dq2kwM65mjZtqhUrVig1NVWSNG7cOM2bN8/vuOLt95GRkVq6dKl69OhR7ecCBFuHDh20detW6/1+586dat68uc9jVq9erYyMjHKzJJZeY/Ts2VPp6elKSkpSVFSU/vjjD23dulWLFy/WoUOHJB2LldJ277vvPk2fPr1mniQQBIwlgG+MJ4BvxEj9RNIIAEnSHXfcoZkzZ1rblfl2d//+/fW///2vUmuWleIDXBwP2rdvX2N17969W8XFxVYMtGnTxmd5lnFCXTVhwgS9++67fhM+JM8T/FNPPVXffPON5s6dq0mTJnn8LioqSqeffrpOPvlkpaSkKC8vT5s3b9Znn32mTZs2lWurQ4cOWr9+vcLCwmrwmQJV06pVK+3Zs0emaSosLEz79+9XkyZNfB7z448/avDgweX2m6apJk2aaNCgQV4vqBcuXKgVK1ZIKn9BffXVV+uJJ54I+vMDqst95qlgW7JkifLy8qw4OPXUU32WNwxDX3/9dY31B6iOK6+8Us8//3xAiYSGYVj7x44dq3fffVdffPGFRowY4TE+SCVTSpc95/r222+Vk5NT7pyrd+/eWrp0aU09RaBKDh48qKZNm1qv+169eumXX37xe1zZG9+lN7RvueUWXXnllRXeADFNUx999JEeeOABLV261OP4mJgY/f7772rWrFnwniAQRIwlQMUYTwDfiJF6zATQ4H3xxRemYRimzWbz+DEMw+uPtzItWrQwV65cWem2e/XqZdVjs9lq4NkB1Vf6+qwoJmrzhzhBXbRw4UKv48NJJ51kzpw505w3b5752WefmS+88II5btw4jzI2m8388ccfzVNOOcUj1saPH2/u3LmzwjZff/11MyEhoVxd77zzTi0+cyAwu3bt8oiR/v37B3Tcqaee6nGcYRhmSkqK+e9//9ssKCjweeyqVavMcePGlTs+IiLC/P3334PxtICg8nY9Esyfiq5nvJXjfAt11a+//ur1NZuammr+7W9/M2fPnm0+99xz5p133mn27t3b49zKZrOZq1atMocPH+6x/+STTzaXLl3qtb28vDzz//7v/8yIiIhyMfL555/X8rMHfPvuu+883uMvu+wyv8esXbu23LlSmzZtzDVr1gTcrsPhMG+88cZy8XbzzTdX5+kANYaxBPCN8QTwjRipv0gaARq4oqIis3Xr1uXesG02m3nOOeeYzz33nPnJJ5+Yb7/9tjlz5kwzIyOj3IetpdtNmjQxFy9eXKn2SRrB8YCbGIBvl1xySbkT9oceesh0uVxey//4449mSkqK9dqeNGmSGRYWZh176aWXBtTuihUrzPj4eI92R4wYEcynBgTF119/7fEef9VVV/k9ZsmSJeXi6qSTTjL37dtXqbZnz55drp6pU6dW9akANaYuJOlyvoW67tprry33Wr3yyivN3Nxcr+XnzJljxsTEWOPP3/72N9Nut1vbo0ePNgsLC/22+8UXX1g3+0rbPffcc4P99IBqefnllz3ezx9//HG/xzz66KMeMRUXF2du2LChSu1PmTLFo662bdtWqR6gpjGWAL4xngC+ESP1ly3UM50ACK133nlHO3bs8JjSqXXr1vr55581b948TZ06VSNHjtS5556r2267TUuWLNHSpUs1ePBgjyVqDMPQH3/8oWHDhmnBggWhfEoAgFpWuiyNeXTa/5tuukm33nprhUudDRgwQO+99571+7lz58rlckmSOnbsqGeeeSagdk866SQ9+uijVrumaWrBggVWXUBdsWXLFknHpm3u1q2b32M+//xzj+0mTZroiy++UNOmTSvV9nXXXadbb73VI04++OCDStUB1KbSa4tg/wTSBlDXvfXWWx7nXBdddJGeeeYZxcTEeC1//vnn67///a81/rz44otyOBwyTVPNmzfX66+/roiICL/tDhs2TPfee6/HWPLVV18F9bkB1VW61n2ppKQkv8eULkVW+tq+8cYb1blz5yq1/9hjjykhIcHa3r59uzZt2lSluoCaxFgC+MZ4AvhGjNRfJI0ADdy//vUv67FpmmrWrJkWLlyoPn36VHhM79699e2332rWrFmy2+3WfsMwlJOTo5EjR2r+/Pk12m8gFMySGbqC/hNoG0BdtHHjRmVlZVnbjRo10n333ef3uFNOOUXjx4/3eG2XXjQE8oFTqb/+9a9q0aKFtZ2fn69ff/014OOB2vDHH394bCcmJvo9pvRcqvSC+s4776x0wkipe+65x+PYffv2afXq1VWqC6hpnG8B3m3btk0HDhywtqOiovT444/7PW7s2LEaPny4TNO0EmsNw9D111/v8WGrPzfccIPH+JWdna3169cH/gSAGpafn++xHR0d7feYzZs3eyQNXnzxxVVuPzExUaNHj/YYS1atWlXl+oCawFgC+Md4AvhGjNRfJI0ADVhxcbEWLVrkkV3+7LPPetx88+X666/Xt99+63ETwjAM5eXlafTo0friiy9qqutAyBiGoZNPPllr166Vy+Wq9s9JJ51knTAZhuG3vNPpDPFfAPDkflJuGIbGjBkT0MWCJF1wwQXl9p199tmVaj88PFwjR470uFDgQyfUNQUFBR7bgSRGbd++3WN8OP/886vcfnR0tMaMGeMRJ+vWratyfUBNKz3f+vnnn7Vly5Zq/WzevFndunXziKdAjgHqmuXLl1uPDcPQ6NGj1aRJk4CO9fah7Lnnnlup9qOjo8udc5GAiLokKirKY/vgwYN+j3G/eZ6cnKz27dtXqw/9+/evsH6gLmAsAfxjPAF8I0bqL5JGgAZs+fLlKiwstLa7dOmisWPHVqqOk08+WYsXL1aXLl08lqspKCjQmDFj9MknnwSzy0BIXHTRRVZilST99NNPSk9P14wZM1gGAw1eZmampGPLbmRkZAR8bNmySUlJAScuuuvdu7fHdtlZHYBQKzvV8/79+/0e437R3bRpUzVv3rxafSg7i1wgF/VAbSpd/tL9fGvkyJFauHCh2rRpU+Wftm3blkvUCuQ4oK4p/SC09Jzr5JNPDvjYfv36eWw3bty4Sh/U/ulPf/LYPnz4cKXrAGpK6dTopePInj17/B6TnZ1tPU5JSal2H0rrKO1D2enbgVBjLAH8YzwBfCNG6i+SRoAGzH2dL8MwNGLEiCrV06ZNG/3www/q06ePR+JIYWGhxo8frw8//DAo/QVC5dVXX9W8efPUrFkz6zVeWFio6dOnq2/fvkx/hgatbIJGZU78y5ZNTk6uUh/KXqwcOXKkSvUANaV0VrbS1+jOnTv9HpObm2s9DmR9WH9Kp4Eu7QPJVahrvv32Wz3++OOKjo62zrcyMzN10UUXacyYMdq9e3eIewiEVtn37dTU1ICPLVu2qudcZT+cZSxBXdKpUyeP7W+++cbvMfHx8dZj95sZVZWTkyPp2A358PDwatcJBBNjCeAf4wngGzFSf5E0AjRgpZncpW+sPXr0qHJdiYmJmj9/vk455RSPxJGioiKdd955mjdvXvU7DITQ2WefrTVr1uj888+3vgVrmqaWL1+uvn376u6771ZxcXGouwnUOvf1KL1tV+bYqs7cU/Y496lugbqgc+fO1mPTNPXll1/6PcZ9muhgfJBamkxVGh+BLJED1LbrrrtOK1assK4pSs+3PvroI/Xo0UMvvfRSqLsIhEzZZSrtdnvAx5YtW9UPVW02z48RWToTdUnv3r2t8xvTNLVo0SK/M6t16tTJOjfau3evR9JuVWzcuNFjOxjfpAWCibEE8I/xBPCNGKm/SBoBGrCsrCyP7caNG1ervvj4eH3++ec67bTTyiWOTJw4Ue+880616gdCrUmTJnrjjTf0zjvveHwzori4WP/4xz/Uu3dv/fzzzyHuJVC7EhISPLYrs4Zk2QuKqi6XUXaK3eqOZ0Cw9erVy2OJmuXLl2vbtm0+j+natavHBXV1p20uu1Z4Zb5VCNSmjh076vvvv9ejjz6qqKgoj1mkLr/8cp155pnavn17iHsJ1L6y51ylSwQGoux0zVU95yq7LGHZPgGhFB0drVNPPdV6fTqdTt19990+jzn11FOtxw6HQ59++mm1+vDBBx94JMZ369atWvUBwcZYAvjHeAL4RozUXySNAA1YVFSUx3YwpvOPiYnRxx9/rDPPPNMjcaS4uFiTJ0/W3Llzq90GEGrjxo3TmjVrNGHCBI9vwa5Zs0annHKKbrnlFhUUFIS6m0CtKLs0zLJlywI+tmzZw4cPa/PmzZXuw9KlSz22SRpBXWO3261zo9Ix49Zbb/V5zNChQ63HLpdL77//fpXbdzqdev/99z0uqKszwxxQ0wzD0I033qjly5erX79+HrHz1VdfqWfPnnr66adD3U2gVpVdZmzlypUBH7tixQqP7YMHDwa09ri/ejjnQl0zZcoUSbLGjBdeeEHffvttheUvvPBCj/L33XdflWc/fOONN7Ru3TprOy4uThkZGVWqC6gpjCVAYBhPAN+IkfqJpBGgASs9KS+9UKjMt8N9iYqK0gcffKCRI0d6JI44HA5deOGFevPNN4PSDhBKSUlJ+t///qe33nrL46a50+nUP//5T5100klasGBBiHsJ1LyTTjrJemyapj744IOAk6a8jQfvvfdepdovLCzUp59+6nEzvGvXrpWqA6gNl156qfXYNE29/fbbmjNnToXl//znP8tms3lcUOfl5VWp7aeeespjZpPExET16tWrSnUBtalz58768ccfNXPmTEVGRsowDBmGoZycHF133XUaPHiwNm3aFOpuArXCPdmv9Jwr0Cn9vZ1fffjhh5Vq3+l06rPPPvM45+rQoUOl6gBq2qRJk6xlAUuvz88++2x9//33XsufeOKJOu+886zkxLVr13qcswVq2bJluvLKK63zNsMwNH78eIWFhVXr+QDBxlgCBIbxBPCNGKmfSBoBGrA2bdp4bK9ZsyZodUdEROj999/X2WefXS5x5M9//rPeeOONoLUFhNJ5552nNWvWaNy4cR7fgv3tt9902mmn6eqrr672Gn1AXdapUyeP6WQPHz6sf/zjH36PW7JkiebOnetxkm+apmbNmlWpma+eeuopj2lxo6OjdcIJJ1TuSQC1YNSoUdY3H0pf7xdffHGF50Tt27fX1KlTrfjYsWOHxo8fr6Kiokq1+/HHH+vWW2/1iLXJkydX+/kAtcUwDN16661atmyZMjIyPMaMH374QSeddJIeeeQR65oDqK+6d+/usdTZnj179Mwzz/g97rffftOrr75a7pzrscceq9SY8vrrr2vHjh3Wtt1uV3p6euWeBFDDbDabnnzySWvbMAzl5ubqjDPO0G233eY1uf2ZZ55R06ZNJZXcRH/11Vd15pln+l1KUCqZDe65557TaaedpuzsbI9+3HTTTUF4RkBwMZYAgWE8AXwjRuopE0CDtW/fPtMwDNNms5mGYZht27YNehvFxcXm2LFjrXZK2woPDzdfeeUVs1evXqZhGNbvgePZG2+8YSYnJ3vElc1mM9u0aWN+/vnnXo/p1auXR1ngeHTJJZd4vO7DwsLM2bNnV1h+6dKlZosWLaxjxo8fb8bGxlrbZ599tllUVOS33e+++86MioryiKEzzzwzmE8NCKqlS5eaERERHudENpvNnDhxorlr165y5XNzc80OHTp4nEf17NnTXLBggd+2Dh8+bN52221meHi4R3xGR0ebW7durYmnB9Q4p9NpPvjgg2ZkZGS5OPrTn/5krl692utxnG+hvjjvvPPKvafPmzevwvLbt283u3btah1z+umnm5GRkdb25ZdfHlC7a9euNRMSEjziaODAgcF6WkDQ3XrrreU+h7LZbGZKSop50003mb/++qtH+UWLFpnJyckeZcPDw80xY8aYTz/9tLlw4UJzw4YN5tatW82VK1ea77//vnnrrbeaaWlpHseU/nvdddeF6JkD/jGWAIFjPAF8I0bqF8M0+ToO0JA1b95c+/fvt7LEf/vtN7Vv3z6obTgcDk2aNEnvvfeeNf2gaZoKCwtTVFSU8vLyrPYDnRIRqKv279+vK664Qh988IH1zQupJNv24osv1qxZszxmZUhPT9eqVauIARzXFi9erAEDBni8xxuGofT0dJ1//vnq1q2bIiIitGPHDn322Wd677335HK5rHLvv/++/vOf/3iME/3799dTTz3ldQmNoqIiPfnkk7rrrrtUUFDg8W2nN998U+edd15tPn2gUmbPnq2///3v5eIlPDxcZ599tqZMmaJTTz1V8fHxkqRNmzZpxIgR2rx5s1WHYRg64YQTdOaZZyo9PV1JSUmKjIzUkSNHtGXLFi1cuFAff/yx8vPzPb4JaBiGHnjgAd1xxx0hee5AsKxZs0YXX3yxfvnlF4/zrYiICN1xxx264447FB4ebpXnfAv1xZdffqnhw4eXG0PGjRvn9ZzrueeeU25urlXu9ddf1yuvvKIvvvhCUsl4cu6552r27Nlq3ry51zbfeecdTZ06VYcOHfIYT1544QX99a9/rbXnDlTWX/7yF73++use8SIdW6I5KSlJ/fv3V3p6ulq2bKns7GxNnz5dhYWF5cpWpGw50zR1yimnaP78+bLb7TXyvIDqYiwBKofxBPCNGKk/SBoBGrjzzz9f//vf/ySVvOHec889uvvuu4PejtPp1AUXXGAtRSAduyhxf8wHuKgvXnvtNd1www06fPiwxwVxamqqnnnmGY0ZM0YSNzFQf0ycOFFvv/12hRcI7tzf/0844QStWLFC33//vYYMGVIu2apnz546+eSTlZKSovz8fG3ZskXffPONsrKyyo0jbdu21caNGz1uFAJ10fTp0/Xggw9WGC82m03du3e3LqjDw8P1yCOPqLCw0Gt5b7xdUI8fP15vv/12zTwpoJY5nU49+OCD+r//+z85HA5Jx8aXE044QS+99JJ69+4tifMt1C9Dhw7Vt99+6/W6uiz3xMF27drpt99+08cff6wxY8Z4XKNERUXpjDPOKHfO9emnn2rjxo3lzrlSUlK0detWRUdH19rzBqrilltu0WOPPeYRI+4fhfu7VvH3sXnZeocMGaJ58+ZZyb9AXcVYAlQO4wngGzFSP5A0AjRw//rXv/S3v/3NOslv27atxzdZg8nlcumiiy7Sm2++We6ihA9wUR/t3btXl112mT755JNyr/mJEyfqySef1LBhw7iJgXph//796t27t/bs2WPtq+g0s/R9PyIiQgsXLrRu6o0bN07z5s3zSBwpLe/O281wm82mr776SkOGDAnm0wJqzNNPP62bbrpJxcXF1r6yMePttV/VC+qLLrpI//73v/kGBuqdVatW6eKLL9bKlSs9xo/w8HDddNNNuu+++9SvXz/Ot1BvbNq0SRkZGR5refs757LZbPr00081bNgwSdKgQYP0448/lkvWLcvbOZdhGHrrrbc0YcKEoD4voKZ8+OGHuuqqq7Rr164Kryu88feNV/c6IiIidPvtt+uuu+5SWFhYtfoL1AbGEqDyGE8A34iR4x9JI0ADt337drVt29YjcePDDz/UWWedVSPtmaapKVOm6LXXXit3M4MPcFFfvfzyy7rxxht15MgRj1hLSkqSaZrKzMyUJGIAx71NmzbptNNO83px4K70JH/OnDkaN26ctf/gwYM6+eSTtWXLFo+yZXm78Jg5c6ZuvfXWIDwLoPYsW7ZMl19+uVasWFGpC2opsItq0zSVmJioRx99VFOmTKlOV4E6zeFw6IEHHtCMGTOsc6nS861OnTopOzvbSmrkfAv1wU8//aThw4crKysroFmnnnjiCV1zzTXW/s2bN+vkk0+2rkPcy7rzNjZdddVVeuqpp6r7FIBalZeXp0cffVRPPfWUDh48KCnwGxTeuC+LNnnyZE2fPl0dOnQISl+B2sJYAlQe4wngGzFyfCNpBID69++vn3/+2dru06ePx3awmaapyy+/XC+99BLL06DB2LVrly677DJ9/vnnXr+FQQygvjhy5Ihuv/12vfzyyyoqKvJaZsCAAXrmmWd04oknlvvdjh07dPbZZ2vVqlUBrWcZHR2tWbNm6YorrghK/4HaZpqmXnvtNT3yyCNas2aNtb8qF9Xul3apqamaOnWqrr/+ejVu3DgYXQXqvOXLl+viiy/W6tWrOd9CvbdlyxZdddVV+vzzzyss06ZNGz3zzDMaOXJkud+tWrVKZ511lnbv3h3QOZdhGLrtttv04IMPVrvvQKgUFBRo3rx5mjt3rubPn68//vij0nUkJCRo8ODBGjVqlM477zw1adIk+B0FagljCVA1jCeAb8TI8YmkEQD65z//qZtvvtlj37x583T22WfXaLtXX321nn32WWubD3DRELzwwgu65ZZbyn2Tg5sYqG+ysrL08ccfa/369dq/f78iIiLUrl07DR061GuyiLvi4mI98sgjevLJJ7Vv3z6vZRITEzVhwgTdfffdatGiRU08BaDW/fTTT5o7d66+/PJLrV692u9sI+7CwsLUvXt3DRkyRKNGjdLpp5/OVJ1okIqLi3XvvffqkUceKXdexfkW6qP169frvffe83rONWrUKJ9jweHDh3XbbbfptddeU2FhodcyNptNZ5xxhh588EFrSUGgPjBNU2vXrtWvv/6qzZs3a8+ePTp8+LAKCwtlmqbsdrtiY2OVlJSk5s2bq3379urWrZs6deoU6q4DQcdYAlQd4wngGzFy/CBpBEBILVy4UMXFxdb2qaeeGsLeALVj+/btuvTSS/X111977OcmBuDJ6XRq6dKlWrVqlTIzMxUVFaWUlBR16tRJffv2rdb0hkBdl5OTo9WrVwd8Qd2pUydFR0eHuttAnbF06VJNmTJFa9eu9djP+RZQXmZmpr766iuv51xnnHGGEhMTQ91FAEAdx1gCAMDxjaQRAABCZO7cudq9e7fHvuuvvz5EvQEAAKhfioqKNHv27HLnW7NmzQpRjwAAAAAAAOoekkYAAAAAAAAAAAAAAAAaIFuoOwAAAAAAAAAAAAAAAIDaR9IIAAAAAAAAAAAAAABAA0TSCAAAAAAAAAAAAAAAQANE0ggAAAAAAAAAAAAAAEADFB7qDgAAAAAAAAAAcDzbu3evioqKrO3WrVuHsDcAgOMV4wngGzFSMwzTNM1QdwJAw/P99997bHfq1EnNmzcPUW+Auok4AXwjRoDA/fLLL8rJybG2Bw8eHMLeAHXH9u3bPbabNWumyMjIEPUGqJuIEyAw6enpWrVqlSTJMAw5HI4Q9wioOxhLgMAxngC+ESM1g6QRACFhs9lkGIa1HRERoSuuuELTpk1TampqCHsG1B3ECeAbMQIEjgtqwLuyY0lKSopuu+02XXnllYqKigphz4C6gzgBApOenq6VK1dKKjnfcjqdIe4RUHcwlgCBYzwBfCNGaoYt1B0A0LCZpinTNFVYWKinnnpKHTp00M0336wDBw6EumtAnUGcAL4RI0BgSmOF7w0AntxjY//+/br55pvVoUMHPfXUUx5T3gINGXECBMb9pjgAT4wlQOAYTwDfiJHgI2kEQEgZhmH9mKap/Px8zZo1S+3bt9e0adN06NChUHcRCDniBPCNGAECwwU14J37OFI6luzZs0fXX3+9OnbsqOeff57ZedDgEScAgOpiLAEAoO4iaQRAneF+wZCbm6uHH35Y7dq109133x3qrgF1BnEC+EaMAACqy30s2blzp6666ip16tRJL730Uqi7BtQZxAkAoLoYSwAAqDsMk7mJAYRA27ZtPb7teujQIWVnZ1vbpb8zTZM1ydBgESeAb8QIELj09HStWrWKeADKsNkq/i6N+xhD7KAhI06AwHC+BVSMsQQIHOMJ4BsxUjPCQ90BAA3T1q1bPbZdLpeWLVum+fPn65tvvtGPP/6o3Nzc0HQOqCOIE8A3YgQAUF0ul8tj++DBg/rmm2+ssWTjxo0h6hlQdxAnAIDqYiwBAKBuY6YRAHWSw+HQTz/9pK+//lrffvut5s+fH+ouAXUOcQL4RowAx/AtDKBqdu/erW+++cYaSzZv3hzqLgF1DnEClOB8C6g6xhLgGMYTwDdipGaQNAIAAAAANWz79u0hbX/kyJFav349F9QAAKDeCgsLC3UXJLG8BgAc7xhPAN+IkfqJpBEAAAAAqGE2m81jre5Q4YIaAADUVzabLdRdkGEYnG8BwHGO8QTwjRipn8JD3QEAAAAAaAhCna9fF5JWAAAAahLnOwCAYGA8AXwjRuofkkYAAAAAoBZwQQ0AAFDzShN1OfcCAFQH4wngGzFSv5A0AgAAAAC1JJSzjXARDwAAGgrDMJSQkKCEhIRaa3P37t1yOBy11h4AoOYxngC+ESP1B0kjAAAAAFDD3BM2DMNQmzZt1KZNm1prf+nSpcrLy6u19gAAAGpb69attWPHDmt70qRJevbZZ2ut/fT0dK1atarW2gMA1AzGE8A3YqR+ImkEAAAAAGpYhw4d9Pvvv1vbQ4YM0UsvvVRr7XNBDQAA6rs+ffpo+/btMgxDpmlq6dKloe4SAOA4xHgC+EaM1E8kjQAAAABADcvIyNCmTZu4oAaCYN++fdqwYYM2b96sffv2KTMzU3l5eSoqKpJpmoqIiFBsbKySkpLUrFkztWvXTl27dlWzZs1C3XWg1hAnaIgyMjL03nvvWdurV6+Ww+FQeDgfgQNVwViChorxBPCNGKmf+N8DEFJ79+5VUVGRtd26desQ9gaom4gTwDdiBMeDjIwMvfnmm9b2+vXrVVBQoKioqBD2Cjg+ZGdn67333tOnn36q77//Xnv37q1SPampqRo8eLBGjBihcePGqVGjRkHuKRA6xAlQcr7lrqioSCtXrlSfPn1C1CPg+MJYApRgPAF8I0bqJ8M0TTPUnQBQt+zevVtz587VRx99pM2bN2vv3r2Kjo5W8+bN1bdvX40ZM0ZnnXWW7HZ7tdtynyrdMAw5HI5q1wnUBuIE8I0YATx9//33GjJkiDXTiGEYWrBggQYMGFAr7ZfGSWnbTqezVtoFqmPTpk2aOXOm5syZo4KCAklSdT/CMAxDkhQZGakLLrhAt912mzp16lTtvgKhQpwAxxw+fFhJSUke51vPPPOMpk6dWivtc76F4xVjCeCJ8QTwjRipn2yh7gCAuqO4uFj333+/OnXqpBtvvFHz58/Xli1blJ+fr0OHDmnNmjV65ZVXNH78eLVv317/+c9/gtKuaZrWD1DXESeAb8QI4F16err1wWkplqgBvHM4HJo2bZp69uypl19+Wfn5+dZ7vGEY1fopraegoEAvv/yyevbsqdtvv13FxcWhftpApRAnQHlNmjRRu3btPPZxvgVUjLEE8I7xBPCNGKmfSBoBIEnKy8vTWWedpfvuu6/CCwTp2E25Xbt26dJLL9Xpp5+unTt3VqvtsjdQgLqKOAF8I0aAisXHx6tz584e+7igBsrLzMzUoEGD9PDDD6uoqMjnOFLZH0nlbmYUFxfrkUce0cCBA3Xw4MFQPnUgYMQJULGMjAwrJiTOt4CKMJYAvjGeAL4RI/VPeKg7ACD0TNPUmDFj9PXXX0uq+MZb2f2maeqbb75Renq63njjDQ0bNqzG+wqECnEC+EaMAP5lZGRow4YNIbugZiYe1HXZ2dkaMmSI1qxZI8lzzCh9/cbExCgjI0MdOnRQ69atlZaWpri4OEVHRys6OlqGYSg/P1/5+fnKycnRjh07tH37dv3+++9asmSJ8vLyrLrdb4gsWbJEp512mhYuXKj4+PhafuZA4IgTwLc+ffpo7ty5VjysXbtWhYWFioyMrPG2mzdvrj/++KPG2wGqi7EE8I/xBPCNGKl/SBoBoNmzZ+vrr7/2eiPPm9JypZnimZmZGjVqlP71r39pypQpNd1dICSIE8A3YgTwLyMjQ//973+tuNi4caNyc3MVGxtbK203bty4xtsBquOyyy7TmjVryt24SEpK0pQpUzRhwgT16dNH4eFV+yjD6XTql19+0dy5c/XKK6/owIEDHt+AXbt2rS677DK99dZbwXpKQNARJ4Bvo0aN0p49ezz2/fHHH2rWrFmNt/3JJ5/UeBtAMDCWAP4xngC+ESP1j2HydTOgQTt8+LDS0tKUn59v7TNNU82bN9fNN9+ss846S61bt1ZeXp42bdqkuXPn6vXXX9f+/fvLXVjYbDbNnj1bV199dcDtp6ena9WqVdY0Vk6nM6jPDwgG4gTwjRgBArNt2za9//77HvsuuugiJSUlhaZDQB0yf/58nXHGGR7fRI2IiNDdd9+tm2++WREREUFtr7i4WI899pjuu+8+FRUVWW0ahqEvv/xSQ4cODWp7QDAQJwCA6mIsAQAA3pA0AjRwjz32mG655RYr09swDA0fPlxz586t8FuvBQUFmjFjhmbOnCmHw2HtLz3+oYce0s033xxQ+9zow/GAOAF8I0YAANU1atQoffrpp9ZYEh8fr6+//loZGRk12u6yZcs0dOhQ5eTkWOPIiBEj9PHHH9dou0BVECcAgOpiLAEAAN6QNAI0cCeddJJWr15tbffp00cLFy4MaPrBpUuX6rzzztO2bds8stMNw9ADDzygO+64w28d3OjD8YA4AXwjRgAA1ZGVlaXk5GQ5nU7rvfy9997TOeecUyvtf/DBBxo7dqx188Rut+vAgQNq1KhRrbQPBII4AQBUF2MJAACoiC3UHQAQOllZWdZNvtL8sRdffDHg9SozMjK0bNkyDRo0yDq+9KT/rrvu0n333VczHQdqEXEC+EaMAACq6+eff/aYdSo9Pb3Wbl5I0jnnnKPevXtb2w6HQz/99FOttQ8EgjgBAFQXYwkAAKgISSNAA7ZkyRKPG3QDBw7UiSeeWKk6EhMT9dVXX2nixInlbvbdf//9mj59etD7DdQm4gTwjRgBAFTXb7/9Zj02DENjx46t9T6MHTtW7hOxbtq0qdb7APhCnAAAqouxBAAAVISkEaAB27Vrl8f20KFDq1SP3W7XnDlzdMUVV5S72TdjxgxNmzat2n0FQoU4AXwjRgAA1XXkyBFJx2asateuXa33obTN0qXSSvsE1BXECQCguhhLAABARUgaARqww4cPSwrOhYJhGHruuef097//vdzNvocffli33HJL9TsMhABxAvhGjAAAqqv0pkGpwsLCWu9DKNoEKoM4AQBUF2MJAACoCEkjQAOWm5vrsR0TE1PtOh977DHdfvvt5W72/fOf/9SNN95Y7fqB2kacAL4RIwCA6mrSpImkYzcy3KdOry2lbZaOPYmJibXeB8AX4gQAUF2MJQAAoCIkjQANWHx8vMf2wYMHg1Lvgw8+qLvvvrvczb7Zs2fr+uuvD0obQG0hTgDfiBEAQHV16dLFemyapv773//K6XTWWvtOp1P//e9/Pb59694noC4gTgAA1cVYAgAAKkLSCNCAlc0u379/f9Dqvvfee3X//feXu9n31FNP6ZprrglaO0BNI04A34gRAEB1/elPf1JUVJS1vWvXLj300EO11v7MmTO1c+dOazsyMlL9+vWrtfaBQBAnQN21fft2jx+W30BdxVgC1G2MJ4BvxEjNImkEaMA6derksb106dKg1j99+nTNmDGj3M2+Z599VldeeWVQ2wJqCnEC+EaMAHXb999/7/GzZ8+eUHcJKCc6OlqjR4+WaZrW+/xdd92lZ555psbbfuaZZ3T33Xdb7RqGodGjR3vcUAHqAuIEqLvatm2rdu3aWT9t2rTRrFmzVFBQEOquAR4YS4C6jfEE8I0YqVmGWfoJPIAGJycnR40aNbJO1ps0aaLMzMygt/Poo4/q1ltvtb6FXnphcOmll+rnn3/WqlWrJJXcCKzNKRGBQBAngG/ECFC32Ww2j+mfIyIidMUVV2jatGlKTU0NYc8AT0uXLvX4pmnp+/yZZ56pmTNn6qSTTgpqe6tWrdK0adP02Wefedw4sdlsWrx4sTIyMoLaHhAMxAlQN9ls5b+XaRiGUlNTNW3aNF1xxRWKiIgIQc+A8hhLgLqL8QTwjRipWSSNAA1c165d9dtvv1kn7QsXLqyRaQFnzZqlm266qdzNvlKl29zoQ11EnAC+ESNA3VWaNOJ+2WcYhqKionTllVfqtttuU0pKSgh7CBxz1VVX6bnnnvP6Pt+3b19NmDBBAwcOVEZGhsLDwytVt8Ph0LJly/TDDz/onXfe0U8//eTRRum/U6dOrZVv2wJVRZwAdU/ZJF1JHjMltmzZUnfeeacuvfTSSsclUBMYS4C6ifEE8I0YqVkkjQAN3JVXXqnnn3/eeqOtyRP2J554QjfccIPHBYkkjwsGbvShLiJOAN+IEaDu8ndBHRMTo2uuuUa33HKLEhMTQ9FFwFJUVKRhw4ZpwYIF5d7nJVn77Ha70tLS1KZNG6WlpSkuLk7R0dGKioqSYRjKz89Xfn6+cnJytHPnTm3btk07duxQUVGRVZd7HJRuDxw4UF999RXfTEKdRpwAdY+3861S7nHUunVr3XXXXfrrX/9am90DymEsAeomxhPAN2KkZpE0AjRw//vf/3T++edbN9saN26sPXv2KDIyskbae/bZZ3XNNdeU28+NPtRlxAngGzEC1F2BXlDHxcXp+uuv1/3331+b3QPKycvL06RJk/Txxx+Xm02qrIpe22X5O9Y0TY0cOVL/+9//FBsbW4VeA7WLOAHqFm9TpZdiZkTUVYwlQN3DeAL4RozUrIr/ugAahBEjRnhkdR85ckQvvvhijbV35ZVX6rnnnqux+oGaQJwAvhEjQN3VunVrj5+4uDiZplnuG3/Z2dn6xz/+EcquApKkmJgYffjhh5o9e7bi4+M9Xqtlf0pfy/5+vB0rlXyQFBcXp1mzZunjjz/m5gWOG8QJULe4XC6Pn/379+utt97S1KlT1alTJ49zL6CuYCwB6h7GE8A3YqRmMdMIAJ1zzjn66KOPrO0WLVpo8+bNNTpF4CuvvKJLL73UegMn8w91HXEC+EaMAMcHl8ulZcuWaf78+frmm2/0448/Kjc3V5KIH9Q5mZmZmj17tl588UXt3bvX2h/ot13Lcv/4IzU1VZdddpmuu+46JScnV7uvQKgQJ0Ddt3v3bn3zzTf6+uuv9e2332rz5s2h7hLggbEEOD4wngC+ESPVQ9IIAH344Yf65z//6bFv2rRpOvPMM2u03TfffFN/+ctf5HA4JHGjAnUbcQL4RowAxyeHw6GffvrJuqCeP39+qLsElONyufTtt9/qs88+04IFC7Ry5UoVFBRUqo6oqCideOKJGjRokEaMGKHTTjvN59S2wPGGOAEAVBdjCQAADRdJIwAAAAAA4LjhdDq1bds2bd26VXv37tWhQ4eUm5ur4uJiSZLdbldMTIySkpLUrFkztWvXTm3atFFYWFiIew7UHuIEAFBdjCUAADQcJI0AAAAAAAAAAAAAAAA0QMwLBgAAAAAAAAAAAAAA0ACRNAIAAAAAAAAAAAAAANAAkTQCAAAAAAAAAAAAAADQAJE0AgAAAAAAAAAAAAAA0ACFh7oDAEJj7dq16t69e6i7ERCHw6GtW7eqY8eOoe4KGhjiBPCNGAEAAABwPNq3b582bNigzZs3a9++fcrMzFReXp6KiopkmqYiIiIUGxurpKQkNWvWTO3atVPXrl3VrFmzUHcdqLM++ugjHTp0yNr+y1/+EsLeALWD8QTwjRg5fhimaZqh7gSA2me32/XXv/5V9913n1JTU0PdnQr973//05133qk///nPuvvuu0PdHTQwxAngGzECAAAA4HiQnZ2t9957T59++qm+//577d27t0r1pKamavDgwRoxYoTGjRunRo0aBbmnwPErPT1dq1atsradTmcIewPUDMYTwDdi5PhF0gjQQNlsNhmGoejoaF1xxRW66aab1LJly1B3y/L2229rxowZWrFihSTpnnvu4UYfah1xAvhGjADHj71796qoqMjabt26dQh7A9Q9xAjgH3GC49GmTZs0c+ZMzZkzRwUFBZKk6n4cbhiGJCkyMlIXXHCBbrvtNnXq1KnafQWOd+np6Vq5cqWkkjghaQT1CeMJ4BsxcvyzhboDAEIrLy9Ps2fPVocOHXT55Zfrl19+CVlfsrOz9cwzz6hLly6aNGmSVqxYUe1BBQgG4gTwjRgBqmb37t2aPXu2hg0bpg4dOig2NlbJyck64YQT9Ne//lXz5s1TcXFxUNoaOXKk2rVrp3bt2ql9+/ZBqROoacQI4B9xAnjncDg0bdo09ezZUy+//LLy8/NlmqZM05RhGNX6Ka2noKBAL7/8snr27Knbb789aLEGHM9Kb/AB9QXjCeAbMVJ/MNMI0EANHDhQCxcutE7kS9/AJal379667LLLNH78eKWkpNRoP1wul7799lv997//1f/+9z/l5eVZN/dKB4XY2Fi98cYbOvvss2u0L0BZxAngGzECVE1xcbFmzJihhx56qMJvX5TGUosWLfTAAw9oypQp1WqTb/3heEKMAP4RJ0DFMjMzNXr0aP38888e1wXuqvqReEX1GIahjIwMffzxx0pOTq5S3cDxrnR5mtLPBhgncLxjPAF8I0bqF5JGgAbslVde0e233659+/Z53PCTSt54bTabBg4cqNGjR2vIkCHq3bt3ULLF9+7dq2+//VZfffWVPvjgA2VmZpZru/Txueeeq1mzZqlVq1bVbheoCuIE8I0YASonLy9PY8aM0fz58z0unN3jwttNvyFDhuiVV16p8uuYD3BxvCBGAP+IE6Bi2dnZGjBggNasWSPJe1zExMQoIyNDHTp0UOvWrZWWlqa4uDhF/3979x4XdZn////5RkQQMcVjGZKYKathoka7uYWWmofStvzYtmqZpXawAx5S85SpSGWKlVpqm+5qSda2ZpElHlvbbyIqHVZUzAMWaqIiDAgM8/vDH5NvBxkQhgHmcb/duC3XBe/reo07ry5mrtdcbz8/+fn5yTAM5eTkKCcnR1lZWTp27JiOHj2q1NRU7dy5UxaL5Ypjt2/fXjt27FBAQEBlPWSgTI4ePeqysfv06aN9+/bZ14nDhw873SzkVmeoqlhPgJKRIzUPRSOAh8vMzNTcuXP15ptvKjs7u9gNvyL169fXLbfcorCwMHXo0EHBwcFq0aKFmjdvLn9/f/n6+kq6eBxVTk6OMjIylJaWprS0NKWkpOj777/X3r17lZqaah/z8je4itoRERGKjo5WZGSkq/8JAKfIE6Bk5AhQOjabTb169VJCQoKk0h/dXPScbtSokVavXq2ePXuWeW42+lAdkCOAc+QJULLBgwfro48+cthcaNSokR599FE9+OCD6ty5s7y9va9qfKvVqqSkJH300UdasWKFTp065XDy4oMPPqg1a9ZUyOMBKpqXl5dLbyFzpWLG4hiGoYKCApfFApQH6wlQMnKk5qFoBIAk6eTJk5o9e7aWL18ui8VyxU8oleaP/ZL+s3Kl43KL+sPDwzVt2jTdd999ZX4MgKuRJ0DJyBGgZAsWLFBUVFSpj+osLoe8vb317rvvlvkWA2z0oTogRwDnyBPgyjZt2qS7777b9PrAx8dH06ZN07hx4+Tj41Oh8+Xn52vevHl6+eWXlZeXZ5/TMAx9/fXX6tGjR4XOB1QELy8vd4dgx1qCqor1BCgZOVIzUTQCwOTMmTNatGiR3n77baWnp0uquHuQXWkswzDUr18/RUVF8WlwVAvkCVAycgRwdObMGQUFBSknJ8feZ7PZdO2112rcuHHq27evWrZsKYvFooMHD+qjjz7SP//5T508edJhw8/Ly0uxsbF6+umnSz0/G32o6sgRwDnyBChZv379FB8fby9CDwgIUEJCgrp06eLSeXft2qUePXooKyvLnh/33HOPPv/8c5fOC1yNqnLSCGsJqjLWE6Bk5EjNRNEIgGJZrVbFx8dr5cqVWr9+vXJzcyWV/ujbK7n0PzmhoaEaMmSIhg4detX3VAbciTwBSkaOAL+bN2+exo8fb39BbRiGevfurY8++kj+/v7FXpObm6vo6GjNnTvXdGxz0fUxMTEaN25cqeZnow9VHTkCOEeeAFeWmZmpxo0by2q12p+j//rXvyrt9MF169Zp4MCB9vysXbu2Tp06pfr161fK/EBpFRWNVIVtIdYSVEWsJ0DJyKCk9BsAADKHSURBVJGai6IRAE5ZLBYlJCQoPj5eW7duVUpKigoLC8s8TmBgoCIiItSrVy/17dtXbdq0cUG0gHuQJ0DJyBF4uo4dO+qHH36wtzt37qwdO3aU6t6uiYmJGjRokI4cOeJw/9ZXXnlFkydPdjoGG32o6sgRwDnyBLiyjRs3qlevXvYNhPDwcCUmJlZqDF26dNHu3bvtOfLll1+qZ8+elRoD4MylRSOGYaht27Zq1qxZhYydmJgoi8ViH/uOO+5wes3mzZsrZG6gorCeACUjR2ou568qAXi8unXr6t5779W9994rScrKylJSUpL279+vw4cPKy0tTZmZmbJYLLJarfLz85O/v7+aNm2q4OBghYSEKCwsTCEhIW5+JIDrkCdAycgReLLMzEz7Jl/RC9ply5aVapNPuvhieNeuXbr//vu1fft2GYZhf3E+depU5efna/r06a58CIBLkSOAc+QJULIDBw7YvzcMQwMHDqz0GAYOHKikpCR7++DBg2xgoMoqWgPS09M1fvx4DR8+vNxjFhUXFqEgBNUR6wlQMnKk5qJoBECZ1atXT3fccUepqsUBT0WeACUjR+BJdu7cad/gMwxD3bp1U1hYWJnGCAwM1MaNGzV06FDFxcWZNvtmzpyp/Px8zZo1y0WPAHAtcgRwjjwBSnbu3DlJvxdVtWrVqtJjKJqz6DSfopiAqmTZsmUaO3aszp07J8MwdPbsWT3++ONas2aNli5dqqCgIHeHCLgV6wlQMnKk5vJydwAAAAAAUJMdP37c1O7Ro8dVjVO7dm198MEHGjlypP0e5EWbfdHR0Zo0aVK5YwXcgRwBnCNPgJIVbRoUuXDhQqXH4I45gbJ67LHH9MMPP6hnz572DT+bzaavvvpK7du316JFi9wdIuBWrCdAyciRmouiEQAAAABwoTNnzkiSfXOuPJ/CMAxDS5Ys0QsvvOCw2ffqq69q/Pjx5Q8YqGTkCOAceQKUrGHDhpJ+38i49Oj0ylI0Z1FeBQYGVnoMQGm0aNFCGzZs0JIlS1SvXj1JF3MnKytLY8aMUWRkpFJTU90cJeAerCdAyciRmouiEQAAAABwoezsbFO7bt265R5z3rx5mjhxosNm3xtvvKGoqKhyjw9UJnIEcI48AUrWtm1b+/c2m02rVq2S1WqttPmtVqtWrVpl+vTtpTEBVdHIkSOVnJysHj16mE4d2bZtm8LCwjRv3jz7GgF4CtYToGTkSM1F0QgAAAAAuFBAQICp/dtvv1XIuHPmzNG0adMcNvtiY2P13HPPVcgcQGUgRwDnyBOgZLfeeqt8fX3t7ePHjysmJqbS5p87d67S0tLs7Tp16igiIqLS5geuVnBwsDZu3Ki33npL/v7+ki6uBTk5OZowYYL++Mc/6qeffnJzlEDlYT0BSkaO1FwUjQAAAACAC11+dOfJkycrbOwZM2Zo5syZDpt9b731lp555pkKmwdwJXIEcI48AUrm5+en/v37m05LmDp1qhYtWuTyuRctWqRp06bZ5zUMQ/379zdtqABV3VNPPaW9e/fqzjvvNOXRd999p/DwcM2aNatSP0kOuAvrCVAycqTmomgEAAAAAFyoTZs2pnZiYmKFjj9lyhRFR0c7bPYtXrxYTz75ZIXOBbgCOQI4R54Azk2YMMFeWFX0HB4zZoz69OmjvXv3Vvh8ycnJ6tevn8aMGWO6hYdhGJowYUKFzwe4WqtWrbR582YtWLBAfn5+MgxDhmEoLy9P06dPV5cuXbR79253hwm4HOsJUDJypGYybNyUDgAAAABcJisrS/Xr17e/kG7YsKFOnz5d4fO8/vrrphfuRZ+6GDFihL777jslJydLuviimk8JoiohRwDnyBOgdJ566iktWbLE4TksSV27dtWDDz6obt26qUuXLvL29i7T2AUFBdq1a5e++eYbffzxx/p//+//meYo+t9Ro0ZVyqdtAVdKTU3V8OHD9c0339if35Lk7e2t8ePHa/r06fLx8TFd06lTJyUnJ9tzgXUC1RnrCVAycqTmoWgEAAAAAFysXbt2OnDggP2F7Y4dO1xyz9X58+dr7Nixxb5ov7TNG7ioasgRwDnyBHAuLy9PPXv21Pbt203P4SJFfbVr11ZQUJCCg4MVFBSkevXqyc/PT76+vjIMQzk5OcrJyVFWVpbS0tJ05MgRHTt2THl5efaxLj2Zp6jdrVs3bdy40WEzHaiObDabFixYoClTpig3N9feZxiG2rVrp+XLl+u2226z/z5FI6hJWE+AkpEjNQ9FIwAAAADgYk8++aTeeecd+wtcV34aYuHChXr++ecdXrRf+mkM3sBFVUOOAM6RJ0DpWCwWDR48WJ9//rlDwdPlLv15SZxda7PZ1KdPH8XFxcnf3/8qogaqrv3792v48OH69ttvTaeOeHl5acyYMZozZ478/PwoGkGNw3oClIwcqVm83B0AAAAAANR03bt3t39vs9n04Ycf6sKFCy6Z69lnn9Xbb79tbxfdixyoysgRwDnyBCidunXr6rPPPlNsbKwCAgJMRU+Xf9lstlJ9FXetdDEX69Wrp/nz5+vzzz9n8wI10k033aRvvvlGMTExqlOnjj0HCgsLtXDhQoWFhWnz5s2Sit/sA6or1hOgZORIzcJJIwAAAADgYpmZmWratKny8/PtL4IXLlyop59+2mVzLl26VKNHjzb18ak/VFXkCOAceQKU3enTpxUbG6tly5YpPT3d3n+1RVCXvpXevHlzPf7443r22WfVuHHjcscKVAf79u3TI488op07d5pOHTEMQz4+PsrLy2OdQI3EegKUjByp/igaAQAAAIBKcN9992n9+vX29nXXXadDhw659P6rK1as0IgRI+wvtnkDF1UZOQI4R54AV6ewsFBbtmzRl19+qe3bt2vv3r3Kzc0t0xi+vr4KCwvTn//8Z91zzz3q3r27vLw4yBuep7CwUDExMZo5c6by8vIkcRszeA7WE6Bk5Ej1RdEIAAAAAFSCzz77TG+88Yapb9KkSerVq5dL5/3www81bNgwFRQUSBJv4KLKIkcA58gToGJYrVYdOXJEhw8fVnp6ujIyMpSdna38/HxJUu3atVW3bl01atRIzZo1U6tWrRQcHKxatWq5OXKg6vjxxx/1yCOPKCkpyfRJcopG4ElYT4CSkSPVB0UjAAAAAAAAAAAAKBOr1aro6GjNmjXLfuqIRHEhAADVDUUjAAAAAAAAAAAAuCrHjx/Xb7/9Zurr2LGjm6IBAABlRdEIAAAAAAAAAAAAAACAB/JydwAAAAAAAAAAAAAAAACofBSNAAAAAAAAAAAAAAAAeCBvdwcAAAAAAAAAAAAAADXZ+vXrlZGRYW8PGzbMjdEAwO8Mm81mc3cQAAAAAFAT/fTTT/rDH/7g7jBKpaCgQIcPH9aNN97o7lDgQcgRwDnyBABQXqwlQNXQqVMnJScn29tWq9WN0QBVy549e7RhwwYlJycrIyNDdevWVZMmTXTTTTepd+/eat++vbtDrNG4PQ0AAAAAuEjHjh01atQopaenuzuUEsXFxSk0NFSrV692dyjwMOQI4Bx5AlQP6enpOnr0qP0LqEpYS4Cqw2azic/zoyaxWq2Kj4/X6NGj1b17d/3hD39QeHi4Bg0apCVLlig7O7vE6//3v/8pMjJSnTt31uTJk/Xhhx/qq6++0qeffqqlS5dq/PjxCgsLU1hYmL744otKelSeh6IRAAAAAHARq9WqZcuW6cYbb1RUVJSOHz/u7pBM1q5dq86dO+uvf/2rDh065O5w4IHIEcA58gS4er/88otiY2PVs2dPtW7dWv7+/mrcuLFuvvlmPfbYY/r3v/+t/Pz8CpmrT58+atWqlVq1aqWQkJAKGROoKKwlQNVhGIa7QwAqzLfffqtOnTqpf//+Wrp0qbZt26Z9+/Zpz549+uSTT/T000+rZcuWVywG/OSTT9S5c2dt377dXlB1pa8ffvhB9957r0aMGKHCwsJKfqQ1H0UjAAAAAOBiFotFsbGxat26tZ544gklJSW5LZbz589r0aJFatu2rQYPHqw9e/bwKSe4HTkCOEeeAKWXn5+vmTNnqk2bNoqKitKmTZv0888/KycnRxkZGfrxxx+1YsUK/eUvf1FISIjef//9Cpn30o0NoCpiLQEAVJRPP/1U3bt3148//mj6G6ioMKqofebMGQ0dOlQxMTGm6zdv3qyHHnpIubm59uucfdlsNr3//vt66KGH3PGQazTDxioMAAAAAC7RrVs37dixw/SCuej78PBwPf744/rLX/6iJk2auDSOwsJCbdmyRatWrVJcXJwsFov9DdmiF93+/v5avXq17r33XpfGAlyKHAGcI0+AsrFYLBowYIA2bdpk2oC+9JPdl78lbhiGIiMjtWLFCl1//fVXNW+nTp2UnJxsz1Gr1Xp1DwBwAdYSoPRceYuxPn36aN++ffYcPHz4sNNiqZYtW7osHuBqJSYm6vbbb7ef2ObsBJ2i5/z69evVp08fZWdnKzQ0VGlpaaa16Uou/zvOMAwtXrxYI0eOrIBHA4miEQAAAABwqRUrVmjixIk6ceKEwwthwzDk5eWlbt26qX///oqMjFR4eHiFHFebnp6uLVu2aOPGjVq3bp1Onz7tMHfR9w888IDmz59/1ZskQHmQI4Bz5AlQOjabTb169VJCQoKk0t8CoOh53KhRI61evVo9e/Ys89wUjaCqYy0BSsfLy8ult5C5UkFjcQzDUEFBgctiAa6G1WpV586dlZycXGJRbpFL15zWrVsrJSVF8+fP1/jx400/CwsL09ChQ3XbbbepSZMmslgsOnTokD7//HOtWrXKdEtBm82mgIAAHTt2TPXr13fho/UcFI0AAAAAgItlZmZq7ty5evPNN5WdnV3sm7RF6tevr1tuuUVhYWHq0KGDgoOD1aJFCzVv3lz+/v7y9fWVJBUUFNiPWE9LS1NaWppSUlL0/fffa+/evUpNTbWPefmbUkXtiIgIRUdHKzIy0tX/BECJyBHAOfIEcG7BggWKiopy2IRztolx6e94e3vr3Xff1aOPPlqmuSkaQXXAWgI45+Xl5e4Q7FhPUBV9/PHHGjRokGkNqV27tkaNGqW//e1vCg0NVZ06dXTs2DF9/vnnevXVV/Xrr79KuvicjouL06RJk3Tw4EFJF3Nu3rx5evbZZ69YSPXzzz/r/vvvtxeqFP29FRsbq2eeeaZyHngNR9EIAAAAAFSSkydPavbs2Vq+fLksFssVP5FRmk8blfRSrrgj1y/tDw8P17Rp03TfffeV+TEArkSOAM6RJ0Dxzpw5o6CgIOXk5Nj7bDabrr32Wo0bN059+/ZVy5YtZbFYdPDgQX300Uf65z//qZMnTzrkkZeXl2JjY/X000+Xen6KRlCdsJYAV1ZVThphPUFV1adPH23YsMG+BtSvX1/r169Xt27div39jIwM9enTRzt37pRhGOrcubMSExMlXcyBRYsWadSoUU7nPXfunLp06aJDhw5Jupgj4eHh9rFQPhSNAAAAAEAlO3PmjBYtWqS3335b6enpkhzfLCrPS7XixjIMQ/369VNUVBSf4EOVR44AzpEngNm8efPsx5wXPV979+6tjz76SP7+/sVek5ubq+joaM2dO9d0/H/R9TExMRo3blyp5qdoBNURawngqKhopCpsn7KeoKrJz89XQECA8vPz7f9NX758udMT2tLS0tShQwedP39e0u/rwR133KHNmzeXev5Nmzbp7rvvtueot7e3zp07Jz8/v/I8LIiiEQAAAABwG6vVqvj4eK1cuVLr169Xbm6uJOef6HPm0pd5oaGhGjJkiIYOHcq9wVHtkCOAc+QJcFHHjh31ww8/2NudO3fWjh075O3t7fTaxMREDRo0SEeOHDGdhGAYhl555RVNnjzZ6RgUjaA6Yy0Bfndp0YhhGGrbtq2aNWtWIWMnJibKYrGYNsydKcuGOuBqu3btUteuXe050qpVK9NtyErywgsvKDY21pRfn3zyiQYMGFCmGIr+5isaY+vWrVc85QSlR9EIAAAAAFQBFotFCQkJio+P19atW5WSkqLCwsIyjxMYGKiIiAj16tVLffv2VZs2bVwQLVD5yBHAOfIEniozM1MNGzaU9Huxx+7duxUWFlbqMTIyMnT//fdr+/btDoUj06ZN0/Tp00u8nqIR1BSsJfB0l96exmazqUGDBpo3b56GDx9e7rFZK1DdrVq1SkOHDrXnyNNPP62FCxeW6trNmzfrrrvusheN1KpVSxkZGQoICChTDOPGjdMbb7wh6WJx48qVK/W3v/2tbA8EDpyXWQMAAAAAXK5u3bq69957de+990qSsrKylJSUpP379+vw4cNKS0tTZmamLBaLrFar/Pz85O/vr6ZNmyo4OFghISEKCwtTSEiImx8J4BrkCOAceQJPtXPnTvsGnGEY6tatW5kKRqSLG9wbN27U0KFDFRcXZx/LZrNp5syZys/P16xZs1z0CICqg7UEnm7ZsmUaO3aszp07J8MwdPbsWT3++ONas2aNli5dqqCgIHeHCLjN2bNnJf1eWFuWv7duvvlmU7t58+ZlLhiRLp5cVVxMKB+KRgAAAACgCqpXr57uuOOOUh1XC3gicgRwjjyBpzh+/Lip3aNHj6sap3bt2vrggw/UoEEDvfvuu6bCkejoaFmtVkVHR1dEyEC1wVoCT/PYY4+pd+/eeuyxx/T111/b14GvvvpK7du319y5c/XUU0+5O0zALc6dO2dqF530VhqX/26DBg2uKoai64pOO8nMzLyqcWDm5e4AAAAAAAAAAAC4WmfOnJF08VOvktSqVaurHsswDC1ZskQvvPCCfbyiDcNXX31V48ePL3/AAIAqrUWLFtqwYYOWLFmievXqSbq4FmRlZWnMmDGKjIxUamqqm6MEKl+dOnVM7QsXLpT62st/12KxXFUMOTk5praPj89VjQMzikYAAAAAAAAAANVWdna2qV23bt1yjzlv3jxNnDjRoXDkjTfeUFRUVLnHBwBUfSNHjlRycrJ69Ohhvx2HzWbTtm3bFBYWpnnz5tnXCcATXH46yOWnvZXkl19+MbVPnjx5VflTNE7RtVd7YgnMKBoBAAAAAAAAAFRbAQEBpvZvv/1WIePOmTNH06ZNcygciY2N1XPPPVchcwAAqrbg4GBt3LhRb731lvz9/SVdXA9ycnI0YcIE/fGPf9RPP/3k5iiBynHttddK+v3WMDt27Cj1tf/5z39MbYvFoj179pQ5hsvnbNSoUZnHgCOKRgAAAAAAAAAA1VbDhg0l/b6BcfLkyQobe8aMGZo5c6ZD4chbb72lZ555psLmAQBUbU899ZT27t2rO++803TqyHfffafw8HDNmjVLVqvV3WECLhUeHm7/3mazKT4+Xunp6aW69r333nPoW7FiRZnmP3nypDZs2GD/m0+SwsLCyjQGikfRCAAAAAAAAACg2mrTpo2pnZiYWKHjT5kyRdHR0Q6FI4sXL9aTTz5ZoXMBAKquVq1aafPmzVqwYIH8/PxkGIYMw1BeXp6mT5+uLl26aPfu3e4OE3CZ5s2bKzg42N7Oz8/X6NGjnV7397//Xdu3b5dhGPL19ZV0sehk2bJl2rdvX6nnnzRpki5cuGBvBwYGKiQkpAyPAFdC0QgAAAAAAAAAoNpq3769/XubzeZw/HlFePHFF/Xqq686FI68++67GjlypGw2m/1nAICa7dlnn9XevXt1++23m04d2bt3ryIiIvTSSy8pLy/P3WECLvHwww+bnvefffaZHnzwwWJPerNarZo/f75GjRpl7xsxYoSaNm0q6eItagYOHKijR486nTcmJkZ///vf7fMahqE+ffpU3APzcIaNv2QBAAAAAAAAANVYu3btdODAAfsmwo4dOxQREVHh88yfP19jx461H4teNF+Roja3KACAms9ms2nBggWaMmWKcnNz7X2GYahdu3Zavny5brvtNvvvd+rUScnJyawVqNYOHz6sdu3aKT8/X9Lvz3lfX191795doaGh8vHx0bFjx5SQkKD09HRT0e2uXbu0dOlSLV682F4A0qhRI02ZMkVDhgxRo0aNTPP997//1ezZs/XFF1+YilUMw9C2bdt0++23V/q/QU1E0QgAAAAAAAAAoFp78skn9c4779gLOEaNGqVFixa5ZK6FCxfq+eefNxWOSDJtYrARCACeY//+/Ro+fLi+/fZb+1ogSV5eXhozZozmzJkjPz8/ikZQY0yfPl2vvPJKsX8LXery/oEDB+rjjz9Wamqq2rVrp8LCQtPvGIah1q1bq0mTJsrJydGRI0d05swZ+1iX/q115513atOmTZXyeD0BRSMAAAAAAAAAgGotLi5ODz30kH0zoUGDBvr1119Vp04dl8y3ePFiPfPMMw79bAQCgGey2Wx6/fXXNX36dF24cMHeZxiGQkJC9O677yoqKkp79+6VJNYKVGv5+fnq3bu3tmzZ4nDi2qUuLSpp0aKFkpKS1KRJE0nS1KlTNXv2bIfCk6LrShorICBAycnJCg4OrvgH56EoGgEAAAAAAAAAVGuZmZlq2rSp8vPz7Zt0Cxcu1NNPP+2yOZcuXarRo0eb+igaAQDPtm/fPj3yyCPauXOnaePbMAz5+PgoLy+PtQI1Qk5OjgYNGqQvvvjC4YSRS9lsNoWEhCg+Pl5t2rSx9xcUFOgvf/mL1q9f7/SEkkv769Wrp08//VQ9evSowEcDL3cHAAAAAAAAAABAedSvX1+9evWybzLYbDZFR0crLy/PZXM+8cQTeu+990rcKAEAeJZ27drp22+/1ezZs+Xj42O/5YbNZrOfQALUBH5+flq/fr0++OADde3aVdLFv78u/QoMDNTkyZO1d+9eU8GIJHl7e+vjjz/WqFGj7L9fpChvihT9/JZbbtGWLVsoGHEBThoBAAAAAAAAAFR7n332md544w1T36RJk9SrVy+Xzvvhhx9q2LBhKigokMQtBwAAF/3444965JFHlJSU5LABzlqBmuaXX35RSkqKTp48KR8fH7Vq1UphYWHy8nJ+hsWOHTs0b948ff3118rKyjL9rF69errrrrs0ePBgDR48mGJdF6FoBAAAAAAAAAAAAAAqmNVqVXR0tGbNmmU6/YqiEcCR1WrVoUOHdPr0afn6+qpJkyZq3ry5atWq5e7QajyKRgAAAAAAAAAAAADARY4fP67ffvvN1NexY0c3RQMAZhSNAAAAAAAAAAAAAAAAeCDnNxECAAAAAAAAAAAAAABAjUPRCAAAAAAAAAAAAAAAgAeiaAQAAAAAAAAAAAAAAMADUTQCAAAAAAAAAKiWfvrpJ3eHUGoFBQU6ePCgu8MAABSD9QQoGTlSs1E0AgAAAAAAAAColjp27KhRo0YpPT3d3aGUKC4uTqGhoVq9erW7QwEAFIP1BCgZOVKzUTQCAAAAAAAAAKiWrFarli1bphtvvFFRUVE6fvy4u0MyWbt2rTp37qy//vWvOnTokLvDAQBcAesJUDJypGajaAQAAAAAAAAAUK1ZLBbFxsaqdevWeuKJJ5SUlOS2WM6fP69Fixapbdu2Gjx4sPbs2SObzea2eAAApcd6ApSMHKmZKBoBAAAAAAAAAFRLf/rTn2Sz2WQYhiQpLy9P7733nrp27aquXbvqnXfe0alTp1weR2FhoTZt2qQRI0bouuuu05gxY3TgwAHTxkXdunXVqVMnl8cCACg71hOgZORIzWbYKLcBAAAAAAAAAFRTK1as0MSJE3XixAn7RkbR296GYcjLy0vdunVT//79FRkZqfDwcPvvlUd6erq2bNmijRs3at26dTp9+rTD3EXfP/DAA5o/f76uv/76cs8LAHAN1hOgZORIzUXRCAAAAAAAAACgWsvMzNTcuXP15ptvKjs7u9iNjCL169fXLbfcorCwMHXo0EHBwcFq0aKFmjdvLn9/f/n6+kqSCgoKlJOTo4yMDKWlpSktLU0pKSn6/vvvtXfvXqWmptrHvPRt9ks3LiIiIhQdHa3IyEhX/xMAACoA6wlQMnKkZqJoBAAAAAAAAABQI5w8eVKzZ8/W8uXLZbFYTBsXl28ylOTSTYjiXP6zyzdMwsPDNW3aNN13331lfgwAAPdjPQFKRo7ULBSNAAAAAAAAAABqlDNnzmjRokV6++23lZ6eLslx06I8b40XN5ZhGOrXr5+ioqL4lCsA1BCsJ0DJyJGagaIRAAAAAAAAAECNZLVaFR8fr5UrV2r9+vXKzc2V5PxTr85c+rZ6aGiohgwZoqFDh+r6668v17gAgKqJ9QQoGTlSvVE0AgAAAAAAAACo8SwWixISEhQfH6+tW7cqJSVFhYWFZR4nMDBQERER6tWrl/r27as2bdq4IFoAQFXFegKUjBypfigaAQAAAAAAAAB4nKysLCUlJWn//v06fPiw0tLSlJmZKYvFIqvVKj8/P/n7+6tp06YKDg5WSEiIwsLCFBIS4u7QAQBVCOsJUDJypOqjaAQAAAAAAAAAAAAAAMADebk7AAAAAAAAAAAAAAAAAFQ+ikYAAAAAAAAAAAAAAAA8EEUjAAAAAAAAAAAAAAAAHoiiEQAAAAAAAAAAAAAAAA9E0QgAAAAAAAAAAAAAAIAHomgEAAAAAABAks1mU7du3WQYhgzDUK1atfTDDz+4Oyx4sLffftv+fDQMQ1OnTnV3SAAAAACAGsaw2Ww2dwcBAAAAAADgbitWrNCjjz5qb48YMULLli1zX0AuYLFYtHv3bv388886d+6czp07p1q1asnf31/XXHONWrZsqRtuuEFBQUHy8uKzRu6Wn5+vP/zhDzp48KAkqU6dOvrxxx/VunVrN0cGAAAAAKgpKBoBAAAAAAAe79y5c2rbtq1OnDghSfLz89PBgwd13XXXlXhdZGSktm7d6tDv7e2tn376SW3atLnqmG688Ualpqba28HBwTp8+HCZxzl9+rRWrlypf/zjH0pOTpbVanV6Tb169RQeHq5bb71Vd911lyIjI+Xr61vmua/GDTfcoCNHjjj0N2jQQIcOHVLDhg2vemxvb2/T47/zzju1ZcsW0+/MmDFDL7/88lXPUR5///vfTYVLkhQXF6fBgwfb2/3799dnn31WyZEBAAAAAGoqPjICAAAAAAA8XkxMjL1gRJKeeOIJpwUjJSkoKNDkyZMrIrRyxRATE6OgoCBFRUVp9+7dpSoYkaSsrCxt27ZNr7/+uvr06aPAwEB98MEHLo64ZGfPntWcOXPcGoM7DBo0SKGhofb2+vXrHQpdAAAAAAC4WhSNAAAAAAAAj3bixAktXLjQ3q5du7bGjRtX7nHXrl2r7777rtzjXI2zZ8/qz3/+syZOnKicnJxyj5eTk2MqqnGXt956S8eOHXN3GJXKMAy9+OKLpr6XXnrJTdEAAAAAAGoab3cHAAAAAAAA4E5z5sxRdna2vT1kyBAFBQVVyNgTJkyo9FMhzp8/r549eyoxMbHYn4eHh+uPf/yj2rVrpwYNGsjX11dnzpxRRkaGfvjhB+3atUspKSkqLCys1LhLIzc3V1OnTtX777/vsjmaN2+ujh07lumao0eP6syZM6a+oKAgBQYGlmmcK/3+ww8/rClTpigtLU2StGPHDn3xxRfq27dvmcYHAAAAAOByFI0AAAAAAACPlZGRoWXLlpn6nnrqqQobf+vWrZW+uT916tRiC0YGDRqkV155RW3btnU6xokTJ/Tpp5/qk08+0caNG6tUAck//vEPjR07VjfffLNLxh89erRGjx5dpmseffRRrVixwtQ3c+ZMPfrooxUSU+3atfX4449rxowZ9r6YmBiKRgAAAAAA5cbtaQAAAAAAgMdavHixLBaLvR0eHq4uXbpU6BwTJ06stKKLgwcP6q233nLof+211xQXF1eqghFJatasmUaNGqUNGzbo4MGDGjduXJlPzXCVwsJCTZw40d1hVLoRI0aoVq1a9va2bdu0a9cuN0YEAAAAAKgJKBoBAAAAAAAeqaCgwKHAYsSIEeUas1atWhowYICp7/vvv9c//vGPco1bWmvWrJHVajX13XfffRo3btxVj9mqVSu99tprGjZsWHnDuyohISHq1KmTqe+LL77Q1q1b3RKPu1x//fXq2bOnqS82NtZN0QAAAAAAagqKRgAAAAAAgEeKj49Xenq6ve3l5aUHHnig3ONGR0ebToSQpGnTpunChQvlHtuZ+Ph4h74xY8a4fF5XMgxDc+fOdeifMGGCG6Jxr0GDBpnaH3/8sc6fP++maAAAAAAANQFFIwAAAAAAwCOtXLnS1O7WrZuaNWtW7nFDQ0M1fPhwU9/Ro0f15ptvlntsZ44cOeLQ17lzZ5fP62q9evXS3Xffber77rvvtHbtWjdF5B4DBw6Ut7e3vW2xWDzu3wAAAAAAULEoGgEAAAAAAB4nOztb69evN/X179+/wsZ/+eWX5efnZ+qLjo7W2bNnK2yO4pw8edKhz9/f36VzVpaYmBgZhmHqe+mll1RQUOCmiCpfYGCg/vSnP5n6Vq9e7aZoAAAAAAA1AUUjAAAAAADA4yQkJCg3N9fUFxkZWWHjX3fddXr++edNfRkZGYqOjq6wOYrj6+vr0Ffc6SPVUXh4uB566CFT3/79+7V06VI3ReQe3bt3N7W3bdumrKwsN0UDAAAAAKjuKBoBAAAAAAAeJz4+3tSuX7++wsPDK3SOF198UY0aNTL1LVy4UGlpaRU6z6WaN2/u0BcXF+ey+Srb7Nmz5ePjY+qbOXOmsrOz3RRR5bu8aCQvL08JCQluigYAAAAAUN1RNAIAAAAAADzO1q1bTe1bbrlFtWrVqtA5rrnmGr300kumvtzcXE2bNq1C57nU5bcukS7eFufbb7912ZyVqVWrVho9erSpLz09XfPmzXNTRJWvS5cuDrfp2bJli3uCAQAAAABUexSNAAAAAAAAj3L+/HmlpKSY+sLCwlwy11NPPaXg4GBT38qVK/Xjjz+6ZL4HH3zQoS87O1t33nmnnn/+eR08eNAl81amqVOnKiAgwNT3+uuv69SpU26KqHL5+/urdevWpr6dO3e6KRoAAAAAQHVH0QgAAAAAAPAoSUlJKiwsNPV16NDBJXPVqVNHr7zyiqnParVq4sSJLpmvX79+uvXWWx368/PzFRsbqzZt2igsLEwvvPCC1qxZo0OHDrkkDldq3Lixxo8fb+o7f/68Zs6c6aaIKt/NN99sau/evVtWq9VN0QAAAAAAqjOKRgAAAAAAgEfZv3+/Q9/lp4FUpL/97W/q2LGjqW/9+vXavn27S+ZbvXq1GjdufMWff//991qwYIEeeughtW7dWoGBgbr77rs1depUff3118rOznZJXBUpKipKzZs3N/W988471bII5mpc/ny1WCxKS0tzUzQAAAAAgOqMohEAAAAAAOBRDh8+7NDXokULl83n5eWluXPnOvS/+OKLLpmvdevW2rJli9q1a1eq3z9z5owSEhI0a9Ys9erVS40bN9bgwYP15ZdfuiS+iuDv76/p06eb+vLz8/XSSy+5KaLKdf311zv0Ffe8BgAAAADAGYpGAAAAAACARynuRIbLT62oaPfcc4969Ohh6vv222/1ySefuGS+9u3bKzExUTNmzNA111xTpmtzc3MVFxenPn366Pbbb9d3333nkhjL6/HHH9dNN91k6luzZo127drlpogqz7XXXuvQd+zYMTdEAgAAAACo7igaAQAAAAAAHiUzM9Ohz9/f3+XzxsTEyDAMU9/kyZNVUFDgkvmKTuM4evSoli1bprvuuku1a9cu0xg7duzQ7bffrrffftslMZaHt7e35syZY+qz2WwuO8GlKqlbt65D3/nz590QCQAAAACguqNoBAAAAAAAeBSLxeLQ5+vr6/J5u3Tpov/7v/8z9aWkpGj58uUunbd+/foaMWKENm7cqLNnzyohIUEzZ87UgAED1LJlS6fXFxQU6JlnnqmShSMPPPCAbrvtNlNfQkKCNmzY4KaIKoefn59DX3Z2thsiAQAAAABUdxSNAAAAAAAAj2K1Wk1twzDk5VU5b5HMnj3b4bSPl19+udhCFleoW7euevTooalTp+rTTz/VkSNHdOrUKX3yySd68skn1aRJkyte+/zzzyspKalS4iyLmJgYh76JEyfKZrO5IZrK4e3t7dDnqhNrAAAAAAA1G0UjAAAAAADAo1x+SoPNZlNeXl6lzN26dWuNHDnS1Pfrr7/qjTfeqJT5i9O4cWPdf//9WrRokY4fP64lS5aoYcOGDr9XUFCgadOmuSHCkt1xxx3q16+fqW/Pnj1atWqVmyJyvZycHIe+4m5ZAwAAAACAMxSNAAAAAAAAj+Lv7+/QV9wmvKtMmzZNAQEBpr7XXntNv/32W6XFcCW1a9fWqFGjtGfPHgUHBzv8/PPPP1d6erobIivZ3LlzHU6LmTp1aqUVA1W24p6vxT2vAQAAAABwhqIRAAAAAADgUZo2berQl5GRUanzjx071tSXmZmpWbNmVVoMzrRs2VKrV68u9mcJCQmVHI1zHTp00LBhw0x9hw8f1qJFi9wUkWsV93wt7nkNAAAAAIAzFI0AAAAAAACPUtwJGmlpaZUaw9ixY9WsWTNT3+LFi/Xzzz9Xahwl+dOf/qQuXbo49KekpLghGudmzpwpX19fU9+sWbOUmZnppohcp7jna3HPawAAAAAAnKFoBAAAAAAAeJSQkBCHvsouGqlXr56mTZtm6svLy9OUKVMqNQ5nbr31Voe+qnAbneIEBQVpzJgxpr7Tp08rJibGTRG5zvHjxx36brjhhsoPBAAAAABQ7VE0AgAAAAAAPEpYWJhDnztOzxg5cqTatGlj6vvggw+0e/fuSo/lSq655hqHPm9vbzdEUjqTJk1Sw4YNTX0LFizQL7/84qaIXGPfvn2mdqtWrVS/fn03RQMAAAAAqM4oGgEAAAAAAB6ldevWCgwMNPV9//33lR6Ht7e3Zs+ebeqz2Wx68cUXKz2WKzlx4oRD3+W31alKGjZsqEmTJpn6LBaLZsyY4Z6AXMBmszk8X7t27eqmaAAAAAAA1R1FIwAAAAAAwONERESY2nv37nVLHIMGDXK4BczXX3+tr7/+2i3xXG7z5s0Ofa1bt3ZDJKU3ZswYBQUFmfree+89h9M5qqvU1FRlZWWZ+i5/PgMAAAAAUFoUjQAAAAAAAI/Tq1cvUzs1NdVttzCJiYlx6Js4caJsNluZx3r//feVmZlZEWHpgw8+0M8//2zq8/b2Vu/evStkfFfx9fXVzJkzTX1Wq9XhBJLqavv27Q59Vf3/EwAAAABA1UXRCAAAAAAA8Dh9+/Z16NuyZUvlByIpMjJSffr0MfUlJSXp0KFDZR5r7ty5Cg4O1ssvv6zTp09fdUzffPONnnzySYf+e+65Rw0bNnR6fWRkpAzDMH29//77Vx1PWQ0bNkwdOnQw9X366aeyWq2VFoOrXH76S8uWLdW+fXs3RQMAAAAAqO4oGgEAAAAAAB7npptuUmhoqKlv48aNborm4mkjXl4V8zbN2bNnNWPGDF177bUaMGCA1q5d63A7kys5ceKEJkyYoLvuukvnzp0z/czHx0fz5s2rkBhdzcvLS3PnznV3GBXOZrMpISHB1DdgwAA3RQMAAAAAqAm83R0AAAAAAACAOwwZMkQvvfSSvb1u3ToVFBTI27vy3y65+eabNWTIEK1cubLCxszPz9e6deu0bt061apVS+3bt1dERIRCQkLUqFEjNWzYUHl5eTp37pwOHDigxMRE/ec//1FhYWGx482fP1833XRThcXnav369dOdd96prVu3ujuUCvPf//7X4TZKw4YNc1M0AAAAAICagKIRAAAAAADgkYYOHaopU6bIZrNJkk6fPq0tW7bo7rvvdks8r7zyitasWaMLFy5U+NhWq1XJyclKTk4u87Xe3t568803NXr06AqPy9ViYmJ02223uTuMCvPxxx+b2qGhoerSpYubogEAAAAA1ATcngYAAAAAAHikoKAg9evXz9S3atUqN0UjtWzZUs8880y5xpgyZYp69+4tHx+fCokpMjJSu3fvLnPByKlTpxz62rdvXyExlUVERIQeeOCBSp/XFaxWq9asWWPqq46FPAAAAACAqoWiEQAAAAAA4LGioqJM7bi4OJ07d85N0UiTJ09WgwYNrvr6IUOG6Msvv9SpU6e0du1aPffcc+rcuXOpb7ljGIZuvPFGRUVFKTExUZs3b1aHDh3KFMPJkyf1008/mfr69eunrl27lmmcijJnzhy33HKoosXHxystLc3evuaaa/TYY4+5MSIAAAAAQE1g2IrOYAUAAAAAAPBAnTt3VlJSkr395ptvlvvEj6qmoKBAP//8sw4cOKBff/1VmZmZys7OVp06dRQQEKD69evrhhtu0M0336yAgIByzfXhhx/qr3/9q6lv165dCg8PL9e4nm7AgAFat26dvT1+/Hi9+uqrbowIAAAAAFATUDQCAAAAAAA82r///W8NHDjQ3r7xxhuVkpIiLy8OaL0ao0aN0rvvvmtvDxw4UP/617/cGFH1d+DAAbVr106FhYWSJH9/f6WmpqpZs2ZujgwAAAAAUN3x7gcAAAAAAPBoAwYMUEREhL198OBBrV271o0RVW+bNm2yf28Yhl5++WU3RlMzvPrqq/aCEUl67rnnKBgBAAAAAFQIThoBAAAAAAAeb/PmzerRo4e9fcsttygpKUmGYbgxquonLS1NQUFB9vagQYMUFxfnxoiqv+PHjyskJER5eXmSpMDAQKWmpqpBgwbuDQwAAAAAUCNw0ggAAAAAAPB43bt31wMPPGBv79mzR2vWrHFjRNXTpaeMeHl5acaMGe4LpoaYMWOGvWBEkmbNmkXBCAAAAACgwnDSCAAAAAAAgKRjx44pNDRU2dnZkqSQkBD973//k4+Pj5sjqz6GDx+u999/X5L08MMPa9WqVe4NqJrbt2+fOnToIKvVKknq1KmTEhMT5eXF58AAAAAAABWDohEAAAAAAID/37///W/t3r3b3n744Yd10003uTEieLKvvvpKO3bssLfvv/9+dezY0Y0RAQAAAABqGopGAAAAAAAAAAAAAAAAPBBnWQIAAAAAAAAAAAAAAHggikYAAAAAAAAAAAAAAAA8EEUjAAAAAAAAAAAAAAAAHoiiEQAAAAAAAAAAAAAAAA9E0QgAAAAAAAAAAAAAAIAHomgEAAAAAAAAAAAAAADAA1E0AgAAAAAAAAAAAAAA4IEoGgEAAAAAAAAAAAAAAPBAFI0AAAAAAAAAAAAAAAB4IIpGAAAAAAAAAAAAAAAAPBBFIwAAAAAAAAAAAAAAAB7o/wMWUefMSTza4gAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "used_direction='e'\n", "test_parameter='T_SR'\n", "\n", "if test_parameter == 'alpha':\n", " name_fig=\"Alpha_\"\n", " real_parameter='Alpha'\n", " name_legend = \"Values of α\"\n", " normalize = True\n", " allow_all = False\n", " used_config = configurations_simple\n", " data_aux = grouped_aggM[grouped_aggM[real_parameter] > 0]\n", "elif test_parameter == 'T_Malleability':\n", " name_fig=\"Malleability_\"\n", " real_parameter='T_Malleability'\n", " name_legend = \"Time(s)\"\n", " normalize = False\n", " allow_all = True\n", " used_config = configurations_simple\n", " data_aux = grouped_aggM\n", "elif test_parameter == 'T_Redistribution':\n", " name_fig=\"Redistribution_\"\n", " real_parameter='T_Redistribution'\n", " name_legend = \"Tiempo(s)\"\n", " normalize = False\n", " allow_all = True\n", " used_config = configurations_simple\n", " data_aux = grouped_aggM\n", "elif test_parameter == 'T_spawn':\n", " name_fig=\"Spawn_\"\n", " real_parameter='T_spawn'\n", " name_legend = \"Tiempo(s)\"\n", " normalize = False\n", " allow_all = True\n", " used_config = configurations_simple\n", " data_aux = grouped_aggM\n", "elif test_parameter == 'T_SR':\n", " name_fig=\"SR_\"\n", " real_parameter='T_SR'\n", " name_legend = \"Tiempo(s)\"\n", " normalize = False\n", " allow_all = True\n", " used_config = configurations_simple\n", " data_aux = grouped_aggM\n", " \n", "if used_direction=='s':\n", " data_aux=data_aux.query('NP > NC')\n", " name_fig= name_fig+\"Shrink\"\n", "elif used_direction=='e':\n", " data_aux=data_aux.query('NP < NC')\n", " name_fig= name_fig+\"Expand\"\n", "elif used_direction=='a':\n", " name_fig= name_fig+\"All\" \n", "#data_aux=data_aux.query('NP == 160 or NC == 160')\n", "\n", "plot_data = []\n", "for config in used_config:\n", " if config[0] > 0 or allow_all:\n", " dataLists,procsLists = get_config_data(real_parameter, data_aux, config)\n", " dataLists = list(filter(lambda x: x != float('infinity'), dataLists))\n", " plot_data.append(dataLists)\n", " \n", "#labels_aux = create_labels_lineplot(used_direction, lambda a, b: a == 160 or b == 160)\n", "labels_aux = create_labels_lineplot(used_direction)\n", "plot_data = reorder_data(plot_data, procsLists, labels_aux)\n", "\n", "labelsMethods_aux = ['Merge - COLS',\n", " 'Merge - COLA',]\n", "colors_m = ( \n", " #colors.to_rgba(\"green\"), #BCOLS\n", " #colors.to_rgba(\"darkgreen\"), #BP2PS\n", " #colors.to_rgba(\"blue\"), #BRMA1S\n", " #colors.to_rgba(\"royalblue\"), #BRMA2S\n", " colors.to_rgba(\"red\"), #MCOLS\n", " #colors.to_rgba(\"darkred\"), #MP2PS\n", " #colors.to_rgba(\"mediumblue\"), #MRMA1S\n", " #colors.to_rgba(\"mediumslateblue\"), #MRMA2S\n", " #colors.to_rgba(\"blue\"), #BIntraCOLS\n", " #colors.to_rgba(\"royalblue\"), #BIntraP2PS\n", " #colors.to_rgba(\"mediumseagreen\"), #BCOLA\n", " #colors.to_rgba(\"seagreen\"), #BCOLT\n", " #colors.to_rgba(\"palegreen\"), #BP2PA\n", " #colors.to_rgba(\"springgreen\"), #BP2PT\n", " #colors.to_rgba(\"purple\"), #BRMA1A\n", " #colors.to_rgba(\"darkviolet\"), #BRMA1T\n", " #colors.to_rgba(\"indigo\"), #BRMA2A\n", " #colors.to_rgba(\"rebeccapurple\"), #BRMA2T\n", " colors.to_rgba(\"indianred\"), #MCOLA \n", " #colors.to_rgba(\"firebrick\"), #MCOLT\n", " #colors.to_rgba(\"darkgoldenrod\"), #MP2PA\n", " #colors.to_rgba(\"saddlebrown\"), #MP2PT\n", " #colors.to_rgba(\"magenta\"), #MRMA1A\n", " #colors.to_rgba(\"violet\"), #MRMA1T\n", " #colors.to_rgba(\"deeppink\"), #MRMA2A\n", " #colors.to_rgba(\"mediumvioletred\"), #MRMA2T\n", " #colors.to_rgba(\"mediumblue\"), #BIntraCOLA\n", " #colors.to_rgba(\"mediumslateblue\"), #BIntraP2PA\n", " )\n", "\n", "f=plt.figure(figsize=(22, 14))\n", "ax=f.add_subplot(111)\n", "print(labels_aux)\n", "x = np.arange(len(labels_aux))\n", "for index in range(len(plot_data)):\n", " array_aux = plot_data[index]\n", " plot_index = index\n", " print(array_aux)\n", " ax.plot(x, array_aux, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n", " marker=markers_m[plot_index%len(markers_m)], markersize=18, label=labelsMethods_aux[plot_index])\n", "\n", "ax.set_xlabel(\"(NS,NT)\", fontsize=36)\n", "ax.set_ylabel(name_legend, fontsize=36)\n", "if normalize:\n", " ax.axhline(y=1, color='black', linestyle='--')\n", "plt.xticks(x, labels_aux,rotation=90)\n", "ax.tick_params(axis='both', which='major', labelsize=36)\n", "ax.tick_params(axis='both', which='minor', labelsize=36)\n", "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n", " \n", "#plt.ylim([0,8])\n", "f.tight_layout()\n", "f.savefig(\"Images/LinePlot_\"+name_fig+\".eps\", format=\"eps\", dpi=300)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Gráfica de lineas para generar tiempos del grupo G." ] }, { "cell_type": "code", "execution_count": 247, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[814.67876291, 808.5604651, 795.9308548, 795.54596305, 190.34062719, 177.72450495, 176.88434005, 163.42062998, 161.76110101, 147.07955885]\n", "[816.94620895, 807.90405488, 792.34561992, 789.45519304, 183.67259693, 176.26401806, 173.38925004, 159.47613788, 164.16760778, 146.63839984]\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "used_direction='s'\n", "test_parameter='T_total' #Valores son \"alpha\" o \"omega\"\n", "intranode=False\n", "\n", "if test_parameter == 'T_total':\n", " name_fig=\"Ttotal\"\n", " real_parameter='T_total'\n", " name_legend = \"Time(s)\"\n", " used_config = configurations\n", " data_aux = grouped_aggG\n", " #data_aux = data_aux[data_aux.index.isin(df1.index)]\n", " \n", "if used_direction=='s':\n", " data_aux_cmp=grouped_aggM.reset_index().query('NP > NC')\n", " name_fig= name_fig+\"Shrink\"\n", "elif used_direction=='e':\n", " data_aux_cmp=grouped_aggM.reset_index().query('NP < NC')\n", " name_fig= name_fig+\"Expand\"\n", "elif used_direction=='a':\n", " data_aux_cmp=grouped_aggM.reset_index()\n", " name_fig= name_fig+\"All\"\n", " \n", "if intranode:\n", " data_aux_cmp = data_aux_cmp.query('NP <= 20 and NC <= 20')\n", "#else:\n", " #data_aux_cmp = data_aux_cmp.query('NP > 20 and NC > 20')\n", "#data_aux_cmp = data_aux_cmp.query('NP == 160 or NC == 160')\n", "\n", "if used_direction!='a' or True:\n", " pruebaG = data_aux.reset_index()\n", " pruebaG = pruebaG.loc[pruebaG.index.intersection(data_aux_cmp.index)]\n", " data_aux = data_aux[(data_aux.T_total.isin(pruebaG.T_total))]\n", "\n", "plot_data = []\n", "for config in used_config:\n", " #if config[0] == 0:\n", " dataLists,procsLists = get_config_data(real_parameter, data_aux, config)\n", " dataLists = list(filter(lambda x: x != float('infinity'), dataLists))\n", " plot_data.append(dataLists)\n", "\n", " \n", " \n", "#labels_aux = create_labels_lineplot(used_direction, lambda a, b: a == 160 or b == 160)\n", "labels_aux = create_labels_lineplot(used_direction)\n", "plot_data = reorder_data(plot_data, procsLists, labels_aux)\n", "#plot_data_normalized = normalize_arrays(plot_data[1:], plot_data[0])\n", "#name_legend=\"SpeedUp over Baseline COLS\"\n", "\n", "for array_aux in plot_data:\n", " print(array_aux)\n", "labelsMethods_aux = ['Merge - COLS',\n", " 'Merge - COLA',]\n", "colors_m = ( \n", " #colors.to_rgba(\"green\"), #BCOLS\n", " #colors.to_rgba(\"darkgreen\"), #BP2PS\n", " #colors.to_rgba(\"blue\"), #BRMA1S\n", " #colors.to_rgba(\"royalblue\"), #BRMA2S\n", " colors.to_rgba(\"red\"), #MCOLS\n", " #colors.to_rgba(\"darkred\"), #MP2PS\n", " #colors.to_rgba(\"mediumblue\"), #MRMA1S\n", " #colors.to_rgba(\"mediumslateblue\"), #MRMA2S\n", " #colors.to_rgba(\"blue\"), #BIntraCOLS\n", " #colors.to_rgba(\"royalblue\"), #BIntraP2PS\n", " #colors.to_rgba(\"mediumseagreen\"), #BCOLA\n", " #colors.to_rgba(\"seagreen\"), #BCOLT\n", " #colors.to_rgba(\"palegreen\"), #BP2PA\n", " #colors.to_rgba(\"springgreen\"), #BP2PT\n", " #colors.to_rgba(\"purple\"), #BRMA1A\n", " #colors.to_rgba(\"darkviolet\"), #BRMA1T\n", " #colors.to_rgba(\"indigo\"), #BRMA2A\n", " #colors.to_rgba(\"rebeccapurple\"), #BRMA2T\n", " colors.to_rgba(\"indianred\"), #MCOLA \n", " #colors.to_rgba(\"firebrick\"), #MCOLT\n", " #colors.to_rgba(\"darkgoldenrod\"), #MP2PA\n", " #colors.to_rgba(\"saddlebrown\"), #MP2PT\n", " #colors.to_rgba(\"magenta\"), #MRMA1A\n", " #colors.to_rgba(\"violet\"), #MRMA1T\n", " #colors.to_rgba(\"deeppink\"), #MRMA2A\n", " #colors.to_rgba(\"mediumvioletred\"), #MRMA2T\n", " #colors.to_rgba(\"mediumblue\"), #BIntraCOLA\n", " #colors.to_rgba(\"mediumslateblue\"), #BIntraP2PA\n", " )\n", "\n", "\n", "f=plt.figure(figsize=(22, 14))\n", "ax=f.add_subplot(111)\n", "#ax2 = ax.twinx()\n", "x = np.arange(len(labels_aux))\n", "\n", "for index in range(len(plot_data)):\n", "#for index in range(len(plot_data_normalized)):\n", " #array_aux = plot_data_normalized[index]\n", " #index = index + 1\n", " array_aux = plot_data[index]\n", " ax.plot(x, array_aux, color=colors_m[index%len(colors_m)], linestyle=linestyle_m[index%len(linestyle_m)], \\\n", " marker=markers_m[index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n", "#ax2.plot(x, plot_data[0], color='black', linestyle=linestyle_m[0], \\\n", "# marker=markers_m[0], markersize=18, label=labelsMethods_aux[0])\n", "#ax.axhline(y=1, color='black', linestyle='--')\n", "\n", "ax.set_xlabel(\"(NS,NT)\", fontsize=36)\n", "ax.set_ylabel(name_legend, fontsize=36)\n", "ax.tick_params(axis='both', which='both', labelsize=36)\n", "ax.set_xticks(x)\n", "ax.set_xticklabels(labels_aux, rotation=90)\n", "ax.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n", "\n", "#ax2.set_ylabel('Baseline Time(s)', fontsize=36)\n", "#ax2.tick_params(axis='y', which='both', labelsize=36)\n", "#ax2.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n", "\n", "#f.legend(bbox_to_anchor=(0.5, 0.98), fontsize=26,ncol=2,framealpha=0.8)\n", "\n", "plt.ylim([0,850])\n", "f.tight_layout()\n", "f.savefig(\"Images/LinePlot_\"+name_fig+\".eps\", format=\"eps\", dpi=300)" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "El siguiente generá una imagen en 3d de T_total para cada una de las diferentes configuraciones." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def generate_3d_image(config, name):\n", " fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'}, figsize=(15, 15))\n", "\n", " Z = [None] * len(processes)\n", " X, Y = np.meshgrid(processes, processes)\n", " for i in range(len(processes)):\n", " np_aux = processes[i]\n", " Z[i] = [0] * len(processes)\n", " Z[i][i] = grouped_aggLSynch.loc[np_aux, 'T_iter'] * 1000\n", " for j in range(len(processes)):\n", " if i!=j:\n", " ns_aux = processes[j]\n", " config.append((np_aux,ns_aux))\n", " aux = grouped_aggG.loc[tuple(config),'T_total']\n", " config.pop()\n", " \n", " Z[i][j] = aux.values[0]\n", " #Z[i][j] = Z[i][j] / Z[i][i]\n", " #Z[i][i] = 1\n", "\n", " Z = np.array(Z)\n", "\n", " ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n", " cmap='viridis', edgecolor='none')\n", " ax.view_init(15, 25)\n", " ax.set_xlabel(\"NC\", fontsize=16)\n", " ax.set_ylabel(\"NP\", fontsize=16)\n", " ax.set_zlabel(\"Normalized time\", fontsize=16)\n", " ax.set_title(name, fontsize=10)\n", " plt.show()\n", " \n", "for index in range(len(configurations)):\n", " used_config = configurations[index]\n", " generate_3d_image(used_config,str(index))" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "El siguiente código es computar la coherencia de T_malleability respecto a los tiempos internos de maleabilidad (Coherency1)\n", "y por otro lado de T_malleability respecto a iteraciones asíncronas más los pasos síncronos." ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_5572/389841414.py:4: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " test[\"Resize_Coherency\"] = test[\"T_Malleability\"] >= (test[\"T_spawn\"] + test[\"T_SR\"] + test[\"T_AR\"])\n", "/tmp/ipykernel_5572/389841414.py:6: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " test[\"Resize_Coherency2\"] = test[\"T_Malleability\"] >= 0\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
NPNCTotal_StagesGranularitySDRADRDRRedistribution_MethodRedistribution_StrategySpawn_Method...T_iterT_stagesT_spawnT_spawn_realT_SRT_ART_MalleabilityT_RedistributionResize_CoherencyResize_Coherency2
33651604041000003.496.63947883503310...(0.080089, 0.068342, 0.07505, 0.072798, 0.0699...((0.010719, 0.000228, 0.000233, 0.066149), (0....1.3619930.00.6559436.7333548.7379037.389297FalseTrue
33691604041000003.496.63947883503310...(0.086699, 0.066044, 0.075696, 0.06642, 0.0639...((0.010715, 0.000236, 0.000177, 0.075494), (0....1.3865560.00.5719066.3519658.3025906.923871FalseTrue
3377108041000003.496.63947883503210...(0.161248, 0.144384, 0.144557, 0.14431, 0.1442...((0.12486, 2.3e-05, 4e-06, 0.036361), (0.12485...1.3058790.00.3358676.9758508.6018657.311717FalseTrue
3378108041000003.496.63947883503210...(0.161471, 0.14405, 0.14408, 0.144116, 0.14397...((0.124837, 5.1e-05, 4e-06, 0.036579), (0.1248...1.2580650.00.3879887.2796568.9218227.667644FalseTrue
3379108041000003.496.63947883503210...(0.159191, 0.142719, 0.142697, 0.142688, 0.142...((0.12483, 6.2e-05, 5e-06, 0.034293), (0.12483...1.2815820.00.3240054.4755776.0534414.799582FalseTrue
..................................................................
49428012041000003.496.63947883503210...(0.084336, 0.068905, 0.069441, 0.069713, 0.068...((0.01874, 0.000139, 0.000249, 0.065207), (0.0...1.5626540.00.5666019.52206011.64251810.088661FalseTrue
49461016041000003.496.63947883503210...(0.158281, 0.141393, 0.14133, 0.141432, 0.1413...((0.124845, 7.9e-05, 6e-06, 0.033346), (0.1248...1.2072220.00.3318894.6514296.1667394.983318FalseTrue
5015204041000003.496.63947883503310...(0.11029, 0.096805, 0.096761, 0.09668, 0.09663...((0.071351, 4.6e-05, 4e-06, 0.038875), (0.0713...1.5720050.00.3839558.76784910.6919479.151804FalseTrue
5019204041000003.496.63947883503310...(0.112086, 0.100412, 0.100434, 0.100389, 0.100...((0.071344, 6e-05, 5e-06, 0.040672), (0.071354...1.4222740.00.39773510.34793712.15830310.745672FalseTrue
50248016041000003.496.63947883503310...(0.075681, 0.067709, 0.077475, 0.072077, 0.061...((0.018734, 0.000245, 0.000277, 0.056425), (0....1.4615190.00.4023689.19997811.0535839.602346FalseTrue
\n", "

112 rows × 28 columns

\n", "
" ], "text/plain": [ " NP NC Total_Stages Granularity SDR ADR DR \\\n", "3365 160 40 4 100000 3.4 96.6 3947883503 \n", "3369 160 40 4 100000 3.4 96.6 3947883503 \n", "3377 10 80 4 100000 3.4 96.6 3947883503 \n", "3378 10 80 4 100000 3.4 96.6 3947883503 \n", "3379 10 80 4 100000 3.4 96.6 3947883503 \n", "... ... ... ... ... ... ... ... \n", "4942 80 120 4 100000 3.4 96.6 3947883503 \n", "4946 10 160 4 100000 3.4 96.6 3947883503 \n", "5015 20 40 4 100000 3.4 96.6 3947883503 \n", "5019 20 40 4 100000 3.4 96.6 3947883503 \n", "5024 80 160 4 100000 3.4 96.6 3947883503 \n", "\n", " Redistribution_Method Redistribution_Strategy Spawn_Method ... \\\n", "3365 3 1 0 ... \n", "3369 3 1 0 ... \n", "3377 2 1 0 ... \n", "3378 2 1 0 ... \n", "3379 2 1 0 ... \n", "... ... ... ... ... \n", "4942 2 1 0 ... \n", "4946 2 1 0 ... \n", "5015 3 1 0 ... \n", "5019 3 1 0 ... \n", "5024 3 1 0 ... \n", "\n", " T_iter \\\n", "3365 (0.080089, 0.068342, 0.07505, 0.072798, 0.0699... \n", "3369 (0.086699, 0.066044, 0.075696, 0.06642, 0.0639... \n", "3377 (0.161248, 0.144384, 0.144557, 0.14431, 0.1442... \n", "3378 (0.161471, 0.14405, 0.14408, 0.144116, 0.14397... \n", "3379 (0.159191, 0.142719, 0.142697, 0.142688, 0.142... \n", "... ... \n", "4942 (0.084336, 0.068905, 0.069441, 0.069713, 0.068... \n", "4946 (0.158281, 0.141393, 0.14133, 0.141432, 0.1413... \n", "5015 (0.11029, 0.096805, 0.096761, 0.09668, 0.09663... \n", "5019 (0.112086, 0.100412, 0.100434, 0.100389, 0.100... \n", "5024 (0.075681, 0.067709, 0.077475, 0.072077, 0.061... \n", "\n", " T_stages T_spawn \\\n", "3365 ((0.010719, 0.000228, 0.000233, 0.066149), (0.... 1.361993 \n", "3369 ((0.010715, 0.000236, 0.000177, 0.075494), (0.... 1.386556 \n", "3377 ((0.12486, 2.3e-05, 4e-06, 0.036361), (0.12485... 1.305879 \n", "3378 ((0.124837, 5.1e-05, 4e-06, 0.036579), (0.1248... 1.258065 \n", "3379 ((0.12483, 6.2e-05, 5e-06, 0.034293), (0.12483... 1.281582 \n", "... ... ... \n", "4942 ((0.01874, 0.000139, 0.000249, 0.065207), (0.0... 1.562654 \n", "4946 ((0.124845, 7.9e-05, 6e-06, 0.033346), (0.1248... 1.207222 \n", "5015 ((0.071351, 4.6e-05, 4e-06, 0.038875), (0.0713... 1.572005 \n", "5019 ((0.071344, 6e-05, 5e-06, 0.040672), (0.071354... 1.422274 \n", "5024 ((0.018734, 0.000245, 0.000277, 0.056425), (0.... 1.461519 \n", "\n", " T_spawn_real T_SR T_AR T_Malleability T_Redistribution \\\n", "3365 0.0 0.655943 6.733354 8.737903 7.389297 \n", "3369 0.0 0.571906 6.351965 8.302590 6.923871 \n", "3377 0.0 0.335867 6.975850 8.601865 7.311717 \n", "3378 0.0 0.387988 7.279656 8.921822 7.667644 \n", "3379 0.0 0.324005 4.475577 6.053441 4.799582 \n", "... ... ... ... ... ... \n", "4942 0.0 0.566601 9.522060 11.642518 10.088661 \n", "4946 0.0 0.331889 4.651429 6.166739 4.983318 \n", "5015 0.0 0.383955 8.767849 10.691947 9.151804 \n", "5019 0.0 0.397735 10.347937 12.158303 10.745672 \n", "5024 0.0 0.402368 9.199978 11.053583 9.602346 \n", "\n", " Resize_Coherency Resize_Coherency2 \n", "3365 False True \n", "3369 False True \n", "3377 False True \n", "3378 False True \n", "3379 False True \n", "... ... ... \n", "4942 False True \n", "4946 False True \n", "5015 False True \n", "5019 False True \n", "5024 False True \n", "\n", "[112 rows x 28 columns]" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test=dfM[(dfM.Asynch_Iters > 0)]\n", "\n", "# Seguro con barriers en Malleability\n", "test[\"Resize_Coherency\"] = test[\"T_Malleability\"] >= (test[\"T_spawn\"] + test[\"T_SR\"] + test[\"T_AR\"])\n", "# Seguro al usar Rigid para iteraciones\n", "test[\"Resize_Coherency2\"] = test[\"T_Malleability\"] >= 0\n", "\n", "for index in range(len(test)):\n", " time_malleability_aux = test[\"T_Malleability\"].values[index]\n", " time_synch_aux = test[\"T_SR\"].values[index]\n", " time_spawn_aux = test[\"T_spawn\"].values[index]\n", " is_asynch_spawn = (test[\"Spawn_Strategy\"].values[index] % 2 == 0)\n", " \n", " total_asynch_iters = int(test[\"Asynch_Iters\"].values[index])\n", " asynch_iters = test[\"T_iter\"].values[index][-total_asynch_iters:]\n", " time_iters_aux = np.sum(asynch_iters[:])\n", " \n", " sum_times = time_synch_aux + is_asynch_spawn * time_spawn_aux + time_iters_aux\n", " \n", " if time_malleability_aux < sum_times:\n", " real_index = test.index.values[index]\n", " test.at[real_index, \"Resize_Coherency2\"] = False\n", "test[(test.Resize_Coherency == False)].query('Redistribution_Method > 1')" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "El siguiente código es para utilizar Dask. Una versión que paraleliza una serie de tareas de Pandas.\n", "Tras llamar a compute se realizan todas las tareas que se han pedido." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import dask.dataframe as dd\n", "ddf = dd.from_pandas(dfL[(dfL.Asynch_Iters == False)], npartitions=10)\n", "group = ddf.groupby('NP')['T_iter']\n", "grouped_aggLSynch = group.agg(['mean'])\n", "grouped_aggLSynch = grouped_aggLSynch.rename(columns={'mean':'T_iter'}) \n", "grouped_aggLSynch = grouped_aggLSynch.compute()" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "Las siguientes dos celdas de código son para comprobar los Empirical Comulative distribution functions(ecdf).\n", "Si dos ecdf crean una intersección el test de Conover-Iman no es fiable.\n", "#TODO No esta terminado. Falta obtener cual ha sido el metodo ganador antes de llamar a \"check_specific_ecdf\"" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "def test_ecdf(data1, data2):\n", " diff_aux = data1[0] - data2[0]\n", " cmp_f = (lambda a, b: a-b < 0) # Se esperan siempre diferencias positivas, si una es negativa se cruzan los ecdf's\n", " if diff_aux < 0:\n", " cmp_f = (lambda a, b: a-b > 0) # Se esperan siempre diferencias negativas, si una es positivas se cruzan los ecdf's\n", " \n", " for index_value in range(1,len(data1)):\n", " if cmp_f(data1[index_value], data2[index_value]):\n", " return False\n", " return True\n", "\n", "def plot_ecdf(data1, data2, ecdf1, ecdf2, title):\n", " f=plt.figure(figsize=(22, 14))\n", " ax=f.add_subplot(111)\n", "\n", " ax.plot(data1, ecdf1, color=colors_m[0], linestyle=linestyle_m[0], \\\n", " marker=markers_m[0], markersize=18)\n", " ax.plot(data2, ecdf2, color=colors_m[2], linestyle=linestyle_m[2], \\\n", " marker=markers_m[2], markersize=18)\n", "\n", " ax.set_xlabel(\"Values\", fontsize=36)\n", " ax.set_ylabel(\"Bins\", fontsize=36)\n", " ax.tick_params(axis='both', which='major', labelsize=36)\n", " ax.tick_params(axis='both', which='minor', labelsize=36)\n", " ax.set_title(title, fontsize=40)\n", " \n", " f.tight_layout()\n", "\n", "def check_specific_ecdf(np_aux, ns_aux, configuration_index):\n", " df_check = dfM\n", " tipo = 'T_Redistribution'\n", " used_config = configurations_simple\n", " dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n", " ecdfLists = []\n", " for index_i in range(len(dataLists)):\n", " dataLists[index_i].sort()\n", " ecdfLists.append( np.arange(1, len(dataLists[index_i])+1)/float(len(dataLists[index_i])) )\n", "\n", " index_i = configuration_index\n", " data_i = dataLists[index_i]\n", " ecdf_i = ecdfLists[index_i]\n", " for index_j in range(len(dataLists)):\n", " if index_i != index_j:\n", " data_j = dataLists[index_j]\n", " ecdf_j = ecdfLists[index_j]\n", " res_aux = test_ecdf(data_i, data_j)\n", " if not res_aux:\n", " aux_title = \"NP=\"+str(np_aux)+\"/NC=\"+str(ns_aux)+\" -- Comparing C\"+str(index_i) + \" vs C\"+str(index_j)\n", " plot_ecdf(data_i, data_j, ecdf_i, ecdf_j, aux_title)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for np_aux in processes:\n", " for ns_aux in processes:\n", " if np_aux != ns_aux and not (np_aux==2 and ns_aux==20):\n", " check_winner_ecdf(np_aux, ns_aux, 9)\n", "\n", " \n", " " ] }, { "cell_type": "raw", "metadata": {}, "source": [ "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "a = 5\n", "b = 3\n", "c = False\n", "d = a + b * c\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Ethernet\n", "b1_aux = [82.747868, 83.191989, 95.520019, 87.435987, 90.843995, 150.356100]\n", "b2_aux = [75.238174, 74.380054, 74.755995, 42.656109, 21.588040, 17.843997]\n", "m1_aux = [74.654167, 74.357901, 74.351350, 43.599915, 21.637509, 15.128712]\n", "m2_aux = [74.353249, 74.359214, 74.356160, 43.874266, 21.511082, 14.969010]\n", "\n", "b3_aux = [105.128014, 110.004008, 126.552019, 116.312400, 95.752019, 151.023994]\n", "b4_aux = [83.021885, 77.632630, 75.396010, 43.076039, 24.028075, 19.556047]\n", "m3_aux = [118.275992, 83.027866, 81.008479, 46.432212, 24.247949, 17.725083]\n", "m4_aux = [119.286457, 84.205993, 80.741585, 47.144632, 24.206617, 17.738496]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Infiniband\n", "b1_aux = [64.669525, 35.171971, 38.916010, 47.456630, 56.288048, 119.428020]\n", "b2_aux = [36.538361, 15.536046, 13.396083, 9.652013, 5.772058, 5.615009]\n", "m1_aux = [61.664380, 18.400559, 19.112526, 22.155880, 11.712381, 30.775627]\n", "m2_aux = [33.428639, 13.905561, 14.691367, 7.363081, 6.629037, 12.150872]\n", "\n", "b3_aux = [91.368664, 60.648074, 53.663981, 49.152031, 64.752057, 118.243807]\n", "b4_aux = [84.941260, 34.039990, 26.008021, 12.298989, 7.916004, 5.736054]\n", "m3_aux = [105.839726, 26.822071, 23.834452, 12.876862, 9.063136, 7.007535]\n", "m4_aux = [75.412319, 25.566336, 22.129483, 12.491161, 7.903744, 6.534291]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[64.669525, 35.171971, 38.91601, 47.45663, 56.288048, 119.42802]\n", "[61.66438, 18.400559, 19.112526, 22.15588, 11.712381, 30.775627]\n", "[36.538361, 15.536046, 13.396083, 9.652013, 5.772058, 5.615009]\n", "[33.428639, 13.905561, 14.691367, 7.363081, 6.629037, 12.150872]\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "plot_data_e = [b1_aux, m1_aux, b2_aux, m2_aux] #Expand\n", "plot_data_s = [b3_aux, m3_aux, b4_aux, m4_aux] #Shrink\n", "\n", "labels_aux_e = create_labels_lineplot('e', lambda a, b: a == 160 or b == 160)\n", "labels_aux_s = create_labels_lineplot('s', lambda a, b: a == 160 or b == 160)\n", "#labels_aux = create_labels_lineplot(used_direction)\n", "\n", "labelsMethods_aux = ['Baseline - AllS', 'Merge - AllS',\n", " 'Baseline - AllA','Merge - AllA']\n", "\n", "#labelsMethods_aux = ['Baseline - All', 'Baseline - P2P','Merge - All','Merge - P2P']\n", "\n", "f, (axe, axs)=plt.subplots(1,2,figsize=(12, 7))\n", "x = np.arange(len(labels_aux_e))\n", "for index in range(len(plot_data_e)):\n", " array_aux_e = plot_data_e[index]\n", " array_aux_s = plot_data_s[index]\n", " plot_index = index\n", " if index > 0:\n", " plot_index = 2**plot_index\n", " print(array_aux_e)\n", " axe.plot(x, array_aux_e, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n", " marker=markers_m[plot_index%len(markers_m)], markersize=9, label=labelsMethods_aux[index])\n", " axs.plot(x, array_aux_s, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n", " marker=markers_m[plot_index%len(markers_m)], markersize=9, label=labelsMethods_aux[index])\n", "\n", "axe.set_xlabel(\"(NP,NC)\", fontsize=18)\n", "axe.set_ylabel(\"Time(s)\", fontsize=18)\n", "axe.set_xticks(x)\n", "axe.set_xticklabels(labels_aux_e, rotation=90)\n", "axe.tick_params(axis='both', which='major', labelsize=18)\n", "axe.tick_params(axis='both', which='minor', labelsize=18)\n", "\n", "axs.set_xlabel(\"(NP,NC)\", fontsize=18)\n", "axs.set_ylabel(\"Time(s)\", fontsize=18)\n", "axs.set_xticks(x)\n", "axs.set_xticklabels(labels_aux_s, rotation=90)\n", "axs.tick_params(axis='both', which='major', labelsize=18)\n", "axs.tick_params(axis='both', which='minor', labelsize=18)\n", "\n", "plt.legend(loc='best', fontsize=15,ncol=2,framealpha=0.8)\n", " \n", "f.tight_layout()\n", "f.savefig(\"Images/LinePlot_100Gb.png\", format=\"png\")" ] }, { "cell_type": "code", "execution_count": 258, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2 = pd.read_pickle( 'dataG-NOMAM.pkl' )\n", "df2.reset_index(drop=True, inplace = True)\n", "df2\n", "\n", "group = df2.groupby(['Groups'])['T_total']\n", "\n", "grouped_aggG2 = group.agg(['median'])\n", "grouped_aggG2.rename(columns={'median':'T_total'}, inplace=True) \n", "\n", "values_tmp = []\n", "values_speedUP = [1]\n", "values_eff = [1]\n", "for value in grouped_aggG2.values:\n", " values_tmp.append(value[0])\n", "for index in range(1, len(values_tmp)):\n", " value_aux = values_tmp[0] / values_tmp[index]\n", " values_speedUP.append(value_aux)\n", " value_aux = (values_tmp[0] * processes[0] / (values_tmp[index] * processes[index])) * 100\n", " values_eff.append(value_aux)\n", "values_eff[0] = 100" ] }, { "cell_type": "code", "execution_count": 259, "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 20, 40, 80, 160]\n", "[1429.511096, 195.93417907, 174.12488914, 149.39460301, 157.55367208]\n", "[1, 7.295874067430005, 8.20968847739305, 9.568693026376014, 9.073169016804295]\n", "[100, 72.95874067430005, 41.04844238696524, 23.921732565940033, 11.341461271005368]\n" ] } ], "source": [ "print(processes)\n", "print(values_tmp)\n", "print(values_speedUP)\n", "print(values_eff)" ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.arange(len(processes))\n", "f=plt.figure()\n", "ax1=f.add_subplot(111)\n", "ax2 = ax1.twinx()\n", "\n", "#ax1.plot(x, values_tmp, color=colors_m[0], linestyle=linestyle_m[0], \\\n", "# marker=markers_m[0], markersize=9, label=\"Time(s)\")\n", "# Tseq * NPseq / T(NP) * NP\n", "ax2.plot(x, values_eff, color=colors_m[2], linestyle=linestyle_m[1], \\\n", " marker=markers_m[1], markersize=9, label=\"Efficiency(%)\")\n", "ax1.plot(x, values_speedUP, color=colors_m[6], linestyle=linestyle_m[2], \\\n", " marker=markers_m[2], markersize=9, label=\"SpeedUP\")\n", "\n", "\n", "ax1.set_xlabel(\"NP\", fontsize=18)\n", "ax1.set_ylabel(\"SpeedUP\", fontsize=18)\n", "ax1.set_xticks(x)\n", "ax1.set_xticklabels(processes, rotation=90)\n", "ax1.tick_params(axis='both', which='major', labelsize=18)\n", "ax1.tick_params(axis='both', which='minor', labelsize=18)\n", "#ax1.set_yscale(\"log\")\n", "\n", "\n", "ax2.set_ylabel(\"Efficiency(%)\", fontsize=18)\n", "ax2.set_xticks(x)\n", "ax2.set_xticklabels(processes, rotation=90)\n", "ax2.tick_params(axis='both', which='major', labelsize=18)\n", "ax2.tick_params(axis='both', which='minor', labelsize=18)\n", "\n", "\n", "f.legend(loc = 'upper center', bbox_to_anchor=(0.65, 0.67), fontsize=15,framealpha=0.8)\n", " \n", "f.tight_layout()\n", "f.savefig(\"Images/Escalability.eps\", format=\"eps\", dpi=300)" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "tags": [] }, "outputs": [], "source": [ "group = dfM.groupby(['ADR','NP','NC'])['T_Malleability']\n", "grouped_aggTM = group.agg(['median', 'max', 'min', 'mean', 'std'])\n", "grouped_aggTM['diff'] = grouped_aggTM['max'] - grouped_aggTM['min'] \n", "\n", "group = dfG.groupby(['ADR','Groups'])['T_total']\n", "grouped_aggTG = group.agg(['median', 'max', 'min', 'mean', 'std'])\n", "grouped_aggTG['diff'] = grouped_aggTG['max'] - grouped_aggTG['min'] " ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "36.831434125500266" ] }, "execution_count": 292, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_aggTG['std'].max()" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
medianmaxminmeanstddiff
ADRNPNC
0.02201.3868151.5002131.2856641.3801030.0891200.214549
401.3525181.4051071.3099341.3527430.0386790.095173
801.5523631.7175851.5129631.5883850.0865090.204622
1601.5453321.6204911.3831131.5378940.0932600.237378
2021.1870191.2286581.1636971.1974700.0268490.064961
401.2008331.5689211.1022791.2665910.1821030.466642
801.2781441.4953691.1336071.2820790.1358770.361762
1601.4463891.5262511.3341211.4394920.0688250.192130
4023.0178693.3117312.9415173.0737910.1420910.370214
200.7442471.2617220.5886350.8226880.2591260.673087
801.3507731.4837741.0738591.2847860.1796260.409915
1601.2331661.6132021.1682811.3052660.1853520.444921
8022.5521382.6255322.5192062.5681190.0446020.106326
200.8410911.0685130.7722040.8965870.1396680.296309
400.5537220.5798550.4420840.5309220.0534760.137771
1601.0737131.0923751.0001131.0648540.0373750.092262
16022.6210923.1928832.4499582.6983210.2858130.742925
200.9064101.0060210.7815110.9089090.0870880.224510
400.5504380.5892020.4486330.5399100.0551800.140569
800.3741570.4687810.3329850.3899920.0508690.135796
99.82204.2661674.3483024.1609524.2546320.0834170.187350
404.1375844.5735724.0702704.2351350.2031960.503302
804.4086375.9594174.1744334.7065810.7157161.784984
1607.2021438.8583855.3966047.0978511.6735123.461781
2021.6153901.6336551.5893691.6132990.0158430.044286
401.5802832.0317601.5339121.6629590.2086330.497848
801.6844431.8944221.6460581.7130610.1035290.248364
1601.7848952.0991911.6367891.8504110.1952320.462402
4023.4279253.5702542.9594133.3600430.2369970.610841
200.9052001.2779390.8704771.0318890.1990460.407462
801.4810951.6744681.4259721.5363500.1097180.248496
1601.5926551.7551781.5246531.6054530.0905080.230525
8022.9431963.1925822.8350682.9590960.1389150.357514
201.0312602.0553460.9198611.2297940.4703401.135485
400.7770160.8742510.7457370.7922530.0487230.128514
1601.4902901.6365101.4155061.5177260.1037800.221004
16022.9959853.1983342.7162262.9683270.2089200.482108
201.3044141.3778261.0228681.2487930.1427330.354958
400.8511721.0688550.7717640.8788950.1167790.297091
800.7302410.9676270.6324650.7574420.1287870.335162
\n", "
" ], "text/plain": [ " median max min mean std diff\n", "ADR NP NC \n", "0.0 2 20 1.386815 1.500213 1.285664 1.380103 0.089120 0.214549\n", " 40 1.352518 1.405107 1.309934 1.352743 0.038679 0.095173\n", " 80 1.552363 1.717585 1.512963 1.588385 0.086509 0.204622\n", " 160 1.545332 1.620491 1.383113 1.537894 0.093260 0.237378\n", " 20 2 1.187019 1.228658 1.163697 1.197470 0.026849 0.064961\n", " 40 1.200833 1.568921 1.102279 1.266591 0.182103 0.466642\n", " 80 1.278144 1.495369 1.133607 1.282079 0.135877 0.361762\n", " 160 1.446389 1.526251 1.334121 1.439492 0.068825 0.192130\n", " 40 2 3.017869 3.311731 2.941517 3.073791 0.142091 0.370214\n", " 20 0.744247 1.261722 0.588635 0.822688 0.259126 0.673087\n", " 80 1.350773 1.483774 1.073859 1.284786 0.179626 0.409915\n", " 160 1.233166 1.613202 1.168281 1.305266 0.185352 0.444921\n", " 80 2 2.552138 2.625532 2.519206 2.568119 0.044602 0.106326\n", " 20 0.841091 1.068513 0.772204 0.896587 0.139668 0.296309\n", " 40 0.553722 0.579855 0.442084 0.530922 0.053476 0.137771\n", " 160 1.073713 1.092375 1.000113 1.064854 0.037375 0.092262\n", " 160 2 2.621092 3.192883 2.449958 2.698321 0.285813 0.742925\n", " 20 0.906410 1.006021 0.781511 0.908909 0.087088 0.224510\n", " 40 0.550438 0.589202 0.448633 0.539910 0.055180 0.140569\n", " 80 0.374157 0.468781 0.332985 0.389992 0.050869 0.135796\n", "99.8 2 20 4.266167 4.348302 4.160952 4.254632 0.083417 0.187350\n", " 40 4.137584 4.573572 4.070270 4.235135 0.203196 0.503302\n", " 80 4.408637 5.959417 4.174433 4.706581 0.715716 1.784984\n", " 160 7.202143 8.858385 5.396604 7.097851 1.673512 3.461781\n", " 20 2 1.615390 1.633655 1.589369 1.613299 0.015843 0.044286\n", " 40 1.580283 2.031760 1.533912 1.662959 0.208633 0.497848\n", " 80 1.684443 1.894422 1.646058 1.713061 0.103529 0.248364\n", " 160 1.784895 2.099191 1.636789 1.850411 0.195232 0.462402\n", " 40 2 3.427925 3.570254 2.959413 3.360043 0.236997 0.610841\n", " 20 0.905200 1.277939 0.870477 1.031889 0.199046 0.407462\n", " 80 1.481095 1.674468 1.425972 1.536350 0.109718 0.248496\n", " 160 1.592655 1.755178 1.524653 1.605453 0.090508 0.230525\n", " 80 2 2.943196 3.192582 2.835068 2.959096 0.138915 0.357514\n", " 20 1.031260 2.055346 0.919861 1.229794 0.470340 1.135485\n", " 40 0.777016 0.874251 0.745737 0.792253 0.048723 0.128514\n", " 160 1.490290 1.636510 1.415506 1.517726 0.103780 0.221004\n", " 160 2 2.995985 3.198334 2.716226 2.968327 0.208920 0.482108\n", " 20 1.304414 1.377826 1.022868 1.248793 0.142733 0.354958\n", " 40 0.851172 1.068855 0.771764 0.878895 0.116779 0.297091\n", " 80 0.730241 0.967627 0.632465 0.757442 0.128787 0.335162" ] }, "execution_count": 288, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grouped_aggTM" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 4 }