new_analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllName=\"dataG.pkl\"\n",
    "ResizesName=\"dataM.pkl\"\n",
    "ItersName=\"dataL.pkl\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_cores=20\n",
    "repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "significance_value = 0.05\n",
    "processes = [2,10,20,40,80,120,160]\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','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive','violet']\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": 3,
   "metadata": {},
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4027/462116935.py:8: 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_spawn_real','T_SR','T_AR']\n",
      "/tmp/ipykernel_4027/462116935.py:9: 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_spawn_real','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['T_Malleability'] = dfM['T_spawn'] + dfM['T_Redistribution']\n",
    "       \n",
    "out_group = dfM.groupby(['NP','NC','ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','T_SR','T_AR']\n",
    "group = dfM.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4027/3865383421.py:26: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  group = aux_df[(dfL.Is_Dynamic == True)].groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_iter']\n",
      "/tmp/ipykernel_4027/3865383421.py:33: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  group = aux_df[(dfL.Is_Dynamic == False)].groupby('NP')['T_iter']\n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_pickle( ItersName )\n",
    "\n",
    "dfL['ADR'] = round((dfL['ADR'] / dfL['DR']) * 100,1)\n",
    "dfL['SDR'] = round((dfL['SDR'] / dfL['DR']) * 100,1)\n",
    "dfL['ADR'].fillna(-1, inplace=True)\n",
    "dfL['SDR'].fillna(-1, inplace=True)\n",
    "dfL['DR'].fillna(-1, inplace=True)\n",
    "       \n",
    "group = dfL.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_iter']\n",
    "grouped_aggLAsynch = group.agg(['mean'])\n",
    "grouped_aggLAsynch.columns = grouped_aggLAsynch.columns.get_level_values(0)\n",
    "grouped_aggLAsynch.rename(columns={'mean':'T_iter'}, inplace=True) \n",
    "group = dfL.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.mean(x,0))\n",
    "grouped_aggLAsynch['T_stages'] = aux_column\n",
    "#grouped_aggLAsynch.set_axis(['T_iter', 'Omega'], axis='columns', inplace=True)\n",
    "\n",
    "group = dfL.groupby('NP')['T_iter']\n",
    "grouped_aggLSynch = group.agg(['mean'])\n",
    "grouped_aggLSynch.rename(columns={'mean':'T_iter'}, inplace=True)\n",
    "group = dfL.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.mean(x,0))\n",
    "grouped_aggLSynch['T_stages'] = aux_column\n",
    "\n",
    "aux_df = dfL[(dfL.Asynch_Iters == False)]\n",
    "group = aux_df[(dfL.Is_Dynamic == True)].groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_iter']\n",
    "grouped_aggLDyn = group.agg(['mean'])\n",
    "grouped_aggLDyn.rename(columns={'mean':'T_iter'}, inplace=True)\n",
    "group = aux_df.groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.mean(x,0))\n",
    "grouped_aggLDyn['T_stages'] = aux_column\n",
    "\n",
    "group = aux_df[(dfL.Is_Dynamic == False)].groupby('NP')['T_iter']\n",
    "grouped_aggLNDyn = group.agg(['mean'])\n",
    "grouped_aggLNDyn.rename(columns={'mean':'T_iter'}, inplace=True)\n",
    "group = aux_df.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.mean(x,0))\n",
    "grouped_aggLNDyn['T_stages'] = aux_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0, 1], [0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 1, 1], [96.6, 0, 0, 1], [96.6, 0, 0, 2], [96.6, 0, 1, 1], [96.6, 0, 1, 2], [96.6, 1, 0, 1], [96.6, 1, 0, 2], [96.6, 1, 1, 1], [96.6, 1, 1, 2]]\n",
      "[[-1, (0, -1), (1, -1), (2, -1)], [-1, (0, -1), (0, -1), (2, -1)], [-1, (1, -1), (0, -1), (2, -1)], [-1, (1, -1), (1, -1), (1, -1)], [-1, (0, -1), (1, -1), (1, -1)], [-1, (0, -1), (0, -1), (1, -1)], [-1, (1, -1), (1, -1), (2, -1)], [-1, (1, -1), (0, -1), (1, -1)]]\n",
      "[[0, (0, 0), (0, 0), (1, 1)], [0, (0, 0), (0, 1), (1, 1)], [0, (0, 1), (0, 0), (1, 1)], [0, (0, 1), (0, 1), (1, 1)], [96.6, (0, 0), (0, 0), (1, 1)], [96.6, (0, 0), (0, 0), (1, 2)], [96.6, (0, 0), (0, 1), (1, 1)], [96.6, (0, 0), (0, 1), (1, 2)], [96.6, (0, 1), (0, 0), (1, 1)], [96.6, (0, 1), (0, 0), (1, 2)], [96.6, (0, 1), (0, 1), (1, 1)], [96.6, (0, 1), (0, 1), (1, 2)]]\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "adr = [0,96.6]\n",
    "sp_method = [0,1]\n",
    "rd_method = [0,1]\n",
    "rd_strat  = [1,2]\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_local_dynamic = set()\n",
    "configurations_local = set()\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",
    "    aux_conf = modifyToLocalDynamic(parameters, len_parameters, checked_conf)\n",
    "    if CheckConfExists(aux_conf, grouped_aggLDyn, 'local'):\n",
    "        configurations_local_dynamic.add(aux_conf)\n",
    "\n",
    "configurations_local_dynamic = list(configurations_local_dynamic)\n",
    "for index in range(len(configurations_local_dynamic)):\n",
    "    configurations_local_dynamic[index] = list(configurations_local_dynamic[index])\n",
    "\n",
    "print(configurations_simple)\n",
    "print(configurations_local_dynamic)\n",
    "print(configurations)\n",
    "print(len(configurations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "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",
    "                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",
    "                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": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#OMEGA COMPUTATION\n",
    "def compute_omega(config):\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                if len(config) > len(parameters):\n",
    "                    config.pop()\n",
    "                    config.pop()\n",
    "                config.append(np_aux)\n",
    "                config.append(ns_aux)\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega'] = grouped_aggLAsynch.at[tuple(config),'T_iter'] / grouped_aggLSynch.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLAsynch.at[tuple(config),'T_stages'] / grouped_aggLSynch.at[np_aux,'T_stages']\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Omega' in grouped_aggLAsynch.columns):\n",
    "    for config in configurations:\n",
    "        if config[0] != 0:\n",
    "            compute_omega(config)\n",
    "else:\n",
    "    print(\"OMEGA already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#Dynamic Coherence COMPUTATION\n",
    "def compute_dyn_coherency(config):\n",
    "    for np_aux in processes:\n",
    "        for n_parents_aux in processes:\n",
    "            if np_aux != n_parents_aux:\n",
    "                config.append(np_aux)\n",
    "                config.append(n_parents_aux)\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency'] = grouped_aggLDyn.at[tuple(config),'T_iter'] / grouped_aggLNDyn.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLDyn.at[tuple(config),'T_stages'] / grouped_aggLNDyn.at[np_aux,'T_stages']\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Dyn_Coherency' in grouped_aggLDyn.columns):\n",
    "    for config in configurations_local_dynamic:\n",
    "        compute_dyn_coherency(config)\n",
    "else:\n",
    "    print(\"Dyn_Coherency already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_grouped_G.to_excel(\"resultG.xlsx\") \n",
    "out_grouped_M.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggLAsynch.to_excel(\"AsynchIters.xlsx\")\n",
    "grouped_aggLDyn.to_excel(\"DynCoherence.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3620\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3621\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4027/1421572016.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0maux_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAsynch_Iters\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'T_iter'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0maux_data2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAsynch_Iters\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Asynch_Iters'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0maux_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maux_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maux_data2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux_data\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3621\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3623\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3624\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3625\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "#aux_data = dfM[(dfM.Asynch_Iters>0) & (dfM.NP==80) & (dfM.NC==10) & (dfM.Redistribution_Method==1) & (dfM.Redistribution_Strategy==1) & (dfM.Spawn_Method==0)]['T_iter']\n",
    "aux_data = dfM[(dfM.Asynch_Iters>0)]['T_iter']\n",
    "aux_data2 = dfM[(dfM.Asynch_Iters>0)]['Asynch_Iters']\n",
    "aux_data = aux_data[0][aux_data2[0]:]\n",
    "print(aux_data)\n",
    "print(np.median(aux_data) * 3)\n",
    "print(np.sum(aux_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_np = [\"T_total\"]\n",
    "separated_np = [\"T_Malleability\", \"T_Redistribution\", \"T_spawn\", \"T_SR\", \"T_AR\", \"Alpha\", \"Omega\"]\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",
    "    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",
    "                    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_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",
    "        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",
    "                dataLists.append(aux_value)\n",
    "    return dataLists\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df_check, tipo, used_config, fast=True):\n",
    "    normality_array=[True] * (len(processes) * (len(processes)-1))\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",
    "                st,p = stats.shapiro(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",
    "                    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 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):\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 H1\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 H1\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\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": null,
   "metadata": {},
   "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",
    "    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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_type='T_spawn'\n",
    "use_perc = False\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",
    "    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",
    "#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": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1 10 10 10 10 10 10]\n",
      " [10 -1 10 10 10 10 10]\n",
      " [ 3 10 -1 10 10 10 10]\n",
      " [10 10 10 -1 10 10 10]\n",
      " [10  3 10 10 -1 10 10]\n",
      " [10 10 10 10 10 -1 10]\n",
      " [10 10 10 10 10 10 12]]\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(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5287/1944789397.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_5287/1944789397.py:50: 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_spawn.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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kpJmZmZmZmVmxBSBSorFuUlLS1sDG2WEHnkU50BxTdjwSWAfYGxiXnfsMKbH20bK6a9E1IfkCcDPwFPAGKXG4FzARGJzday1J+0dENBDbk7w9UTcG2JyUkBwCLAP8r6QhEfFT4D8VnqncD3Pl7wKv1ah7WwNxViRpJeCo7PDCiHi+RvURFc69kZ0f2o3bD+LtCcnFwBxgbDfaAzgLOIH0ufgOqW9bxklJMzMzMzMzs2L7G7ArsJOktSPiyTr1D89+dgA3Abv0YWzWYhFRcSpuNtLufFJyEmBfSR+JiMvKqi4gJbfPjoh/VmhnMHA8cHJ2aj9gMvC7BsJ7rkZ86wFXAJ1rNJ4m6ZKIeI4604sl5ZOSv46IpxuIpTuOJiV5AX5Rp+4c4O/APbnX46QE75rduHcH8FBZe/cDxwIndqM9ImKOpPOBLwB7Sto4Iv7Vnba6w/8aYmZmZmZmZlZsZ2c/RUoOVSVpBHBgdng98FzfhWX9SUTMBg4AnsmdPrSs2t3AOhFxZKWEZNbO4oj4NvCr3OmjKtVtMr7HSSM3O7JTI0kJz35B0nDgyOzwoYi4q1b9iPhNROwcEV+NiN9HxGMNjiat1t7siNg4IiZHxM8j4raImNvd9nJ+myt/sRfaa5iTkmZmZmZmZmbFdi/wQFb+lKRaf9f/GKUpvI2MbOtC0khJ/yXpCknPSpqX7d77qKQzs6nh9dpYsklP7twHJF0gaaqkN7P3J1e4dnVJp0t6JKv3mqT7JH1L0juyOiflNv54WxsV2lwlu/4WSdOyjUNelXS3pO9KWq2531L/la0heVHu1DZl70+tMyU576e58hZZ0q6n8T0M3FktvjbbG1g+K1/SzkB6U0Q8AEzNDveXNLJW/d7kpKSZmZmZmZlZ8XUmGNcAdqtRr3ODm9eAPzdzA0l7kZIXvwA+BKxOWh9vNLA+aRTZnZJ+I6mhNfOy3YTPA64BPkFaz7DiLuKS9gMeAb4KbJjVGwdsRppK/ICk7Zt8puOyZzoZ2B5YibTe33LAlqRNTZ6Q9F/NtNvPPZUrr9BL7UD6nfWG3oqvtx2QK1/Ztij6xhXZz9G0cF1JrylpZmZmZmZmVnwXAD8gbYRxGHBteQVJa1JaP/L3EbFAUkONSzqElPgcnJ2aSpr+/Twpt7Ap8MHs/kcA40mjMuv5KXAIMB+4irRmHqSNeBbk7r8HaYRfZx5jBnAZaSryeNImKRsBU7LzjTzTWaQNXzrdnr1eISVndgDeS0q8/kLS0Ij4WSNt93PL5so9mf67aq4cwKs9aCuvt+LrNZKGAO/PDl8nreU4kNwIfDkr7wVc2oqbOilpZmZmZmZmVnAR8aqkK0iJwI9IGh8R5TsQH0ZadxKamLotaRPSLr2DSUmizwAXla+PJ2kt0ujLTYGPSjo8Is4ub6/M50jTz/eNiGer3H808BtKOYwpwKHZGomddY4h7dD8fUob+dR6ps9QSkhOBQ6utEagpF1JU3XHA6dLujYiHq3Xfj+3c65cb1OkWvbPle+IiIU9aAtYsm7je3KnehJfb9qctEs4wL0R0VGrcgHlP/s7tuqmnr5tZmZmZmZmNjB0JgBHAAfl31AaEvmp7PDBiLiniXZPy9oEOCjbtONtG3ZExFOkUVZzslPHqf5QzJnAntUSkpnJlEblPQYckE9IZveOiPgBcCalxGtF2Zp5p2aHs4Bdqm1aEhE3UPq9DSUlPgtL0u6kqfedrupmOyuSdn3udGZP4so5EZiQO+5WfH1gq1y5ZbtTt0pETCeNPgZYR9L4VtzXSUkzMzMzMzOz/mNCtsFK5+vI+pcscQ3wYlYuHy24KzAxKzczSnIisGd2eFtEXF6rfkS8SGkjlXWAd9a5xS8j4uU6dT6ZK58aEQuq1kxrQy6u097+lNYqPCMiau5AHhFXAE9khx+u03a/I2mEpHdJ+i5wOaWk7Wt03aym0fYGA+eQRo8C3AOc14P4Rkt6r6SLSWt4dro5Swr3B+vlys9UrVVs+edapxU39PRtMzMzMzMzs/7jlYjYqn61t4uIxdmmMV8HtpS0SUQ8mL3dmaRcSFp/slG7UEpiXdPgNffnylsBD9eo+5daDUkaQZo6C2ndwnpJ0WmS7gK2rVFt11y5mWdaF1hB0poR0W8TU/ldzWuYDewTEa904xY/Jq3hCfAGcGAT05l3ajC+R4GPdyO2vpLfgX1a26LoW/nnWp2uU7r7hJOSZmZmZmZmZgPH2aSkJKQ1JL8saRywb3buiiYTUZvmyidLOrnJeOrtnlxvfcaJpGnTAM9FxKwG7vkvaicl88/090Y3+8lZgSZHy0naiNKI04oi4vRmA+mGuaQ1Mk+oN0K0EknfBI7ODt8iJSSn9mJ804FfA6dFxLxebLenxuTK/WLznT7wZq48uhU3dFLSzMzMzMzMbICIiCck3QpsD3xS0rGk9SU714RseOp2ZvkehjSqzvv1kozjcuVGd3euV6+vn6mSrYEf1qnTW0nJ8nUvF5B+z48DD3Q32Zd9lk7JDheR1vZsds3HJ4Ff5o6DlOSbSRpR+++IqDf9vh3y+bNFbYuib+Wfa2jVWr3ISUkzMzMzMzOzgeVsUlJyAmkNxM6p29OAvzbZVj5vcCFdp2Y34rZabzYw7bfpYYwNXJN/pm+TpiA3o7/sCF1RX4y4lPTfpJ3NISWvDoyIP3ejqedaNCK0t+VHR46oWqvYRubKb1at1YuclDQzMzMzMzMbWP4I/Iw0ou8USpvNnNeNUWj5UYf3RsSPeyG+ZryWKzc6wnG5Ou+/CqyYladExL1NR9WkiDiHtDlM4WQJyc5RnouBgyPi0jaG1A4zcuV6n6+iyj/XjKq1epF33zYzMzMzMzMbQCJiDmndQOi6+3WzU7eh65qP23c7qO57mrR2IcDqksY2cM3Gdd5v9zMVhqRj6JqQ/GREXFLjkoHqqVx5taq1im3VXPnpVtzQSUkzMzMzMzOzgac8AXlrRDzWjXauy5X3lLRSD2JqWkTMB+7LDgXsXau+pJVJ6zfWkn+mw9WNnW6WBtkakj/IDhcDh0TEH9oYUjs9mCuv37Yo+oikYcBa2eEcWrREgZOSZmZmZmZmZgNMRPyDNMLtjOzV7K7Zne08ClyfHY4EftFoEq8Xk30X5srfkDS8Rt1vAYMbaG9mVt6M0m7SdS0tCcwsIdm5hmRnQvKiNobUbnfkyptWrVVcG1Ha3OauBtZ67RVOSpqZmZmZmZkNQBFxbEQclb2uq39FVccA87PyvsCl2YjEiiStJulrNL+pTjXnkDbpAdgAuFjSmLJ7Kptq/DnSjs5VRcQbwPG5Uz+WdJKkkdWukbSppJ8D3+tG/IWS/R7zCclDl/KEJBExHfhXdriBpJ7u4N7f7JgrX9uqm3qjGzMzMzMzMzOrKiLulzQZOJ80mmpfYC9JN5J2455F2lRnVWALYBPSVOuHe+n+syV9GricNApyH2CqpD8DzwDjgT1Jo71mAn8Gjui8vEqbv5K0IWmU5CDgROAoSdcBT5B2Wx4LTAK2AdbMLj2jN56pv5J0MKUp2wC3A6tkm93Uc3VE9Eqf91OXkdYrFbAraUOpmiT9qcLpFXLlkyWVbyrzx4io2LakzwPvKzudXzd25wr3fDki/l+dUHfNlS+vU7fXOClpZmZmZmZmZjVFxMWSnietVbkuMBzYI3tV81Av3v8qSZ8AfktKgK4AHFlW7WXgY8DuuXPzarT5RUkPk0Y/jift7n1gjTAWAI83H32hrFd2vEP2asQr9FIiup+6ADghK3+UBpKSpM9jLTtWOFfre7N1nTbXpJRA7/RMrQAkLQu8Pzu8NyIeqVW/N3n6tpmZmZmZmZnVFRG3kqZPfxw4F3gMeJ00xXc2aVfrS4EvApMiolaCrzv3v5g0Kuwn2b3nkkZpPgicAmwaEbeQRjh2mlWnzbNISZyjgCmkBM6bwCLSqMt7SdPHDwFWjoif9d4TWZFExOPAjdnhh8qXECiwjwIjsvJZrbyxImoutWADwFZbbRV33313u8OwJlxz2rLtDsHMzMzMCuALZ8zj8ecXLxUbb/Sm1ccMiS9uO67dYVR0zHWv3hMRW7U7jiLLpmDvlh1u0M1dx83eRtIHgSuzw/8XEb9sZzy9QdLNpNGwrwBrRETV0cW9zSMlzczMzMzMzGxAkDQO2C47nENaH9KsV0TEX4DOUV9flFTovJqkrShNz/9hKxOS4KSkmZmZmZmZmQ0cxwPLZOUpEdHRzmBsQOrcuX190tTnIjsu+/ki8PNW39xJSTMzMzMzMzPr1yRNkPRTSeWbeHS+P0zSt4CvZqeCNiRZbOCLiGsp7VB9iqTB7YynuyRtDeybHf53q0dJgnffNjMzMzMzM7P+bwhwNPAFSXcCdwHTsvNrkXbcXjlX/0cRcUfLo7SlxdHAfVl5TeDJNsbSXSsD3wZmR8RF7QjASUkzMzMzMzMzKwoB785elSwGvg98s2UR2VInIp4BTmp3HD0REZdTGvHZFk5KmpmZmZmZmVl/9xIpEbk78D5gVWAFYFngdeBp4EbgNxHhzW3MCsBJyaXA7On3cc1py7Y7DGvC7sfNaXcI1iR/x8zMzMyKY+SYYOP3zW93GJVd1+4A+qeICODO7HVKm8Mxs17gjW7MzMzMzMzMzMyspZyUNDMzMzMzMzMzs5ZyUtLMzMzMzMzMzMxayklJMzMzMzMzMzMzayknJc3MzMzMzMzMzKylnJQ0MzMzMzMzMzOzlnJS0szMzMzMzMzMzFrKSUkzMzMzMzMzMzNrKSclzczMzMzMzMzMrKWclDQzMzMzMzMzM7OWclLSzMzMzMzMzMzMWspJSTMzMzMzMzMzM2spJyXNzMzMzMzMzMyspZyUNDMzMzMzMzMzs5ZyUtLMzMzMzMzMzMxayklJMzMzMzMzMzMzayknJc3MzMzMzMzMzKylnJQ0MzMzMzMzMzOzlnJS0szMzMzMzMzMzFrKSUkzMzMzMzMzMzNrKSclzczMzMzMzMzMrKWclDQzMzMzMzMzM7OWclLSzMzMzMzMzMysQZJWkPSapJB0crvj6Q5Ja0lakD3DUe2IwUlJMzMzMzMzs4KR9HSWTOh8zZK0TBPXf7ns+pD0ub6M2fqGpIkV+jL/mi/pJUk3S/qBpI0aaHNTSf8t6TJJT0iaI2lhrp1TJU1qML7JdeKbI+k5SVdLOlbSik08+zvL2rq70Wt76HvAOOBl4Ic14hsmaUtJn5V0lqR7st9jZ7zndDcASTtI+o2kx7Pf4SxJD0v6H0mb1Ls+Ip4CfpkdniJphe7G0l1OSpqZmZmZmZkV3xhgvybqH9ZXgVi/Mxx4B7ADcAzwUJYgG1FeUdK7JU0F7icl2/YB1gFGAUNz7XwDeCxLcg7tYXyjgNWAPYDvA09K+nyD1x5RdrylpE17GE9Nkt4FTM4OfxQRc6rUGwO8AdwN/Ar4DLAF6ffYk/sPk/Rr4GbS869L+h2OAd4JfBG4p8ERnN8HFpASrN/sSVzdMaTVNzQzMzMzMzOzXhWASInG8+pVlrQ1sHF22IEHLA00x5QdjyQlFvcmJZ8gJcgmAB8tq7sWkB8B+QIp+fUUKcG2GrAXMBEYnN1rLUn7R0Q0ENuTlEbndRoDbE5KSg4BlgH+V9KQiPhptYayZOgh2eF8oDPJegRwdAOxdNcppO/MbFKysZpBwLCyc4uBOcDY7txYkoBzgIOyUwFcC9xJSnbuCGxH+j1+S9LgiKiabIyIaZLOI30ePifp9Ih4rjuxdYeTkmZmZmZmZmbF9jdgV2AnSWtHxJN16h+e/ewAbgJ26cPYrMUi4vRK57ORe+eTkpMA+0r6SERcVlZ1ASm5fXZE/LNCO4OB44HOkXj7kUYO/q6B8J6rEd96wBXAetmp0yRdEhEvVmlrb6BzyvH/AJ8AVgc+KemYiFjQQDxNkbQ+afQowAURMbtG9Q7gIeCe3Ot+4FjgxG6G8ElKCck5wL4RcX1ZjJ8kJS4HA8dL+ktE3F6jzV+SkpLDgC8BX+1mbE3zv4aYmZmZmZmZFdvZ2U9RmlZaUTZl98Ds8HqgZaOirL2yBNoBwDO504eWVbsbWCcijqyUkMzaWRwR36brKMEeb5QSEY+TRm52ZKdGUntJgvzU7XOAC7PyeGDfnsZTxRdI3zMofe8qiojZEbFxREyOiJ9HxG0RMbe7N86Swd/JnfpKeUIyu+8FwA9yp75fJ877SMlSgE83szZtTzkpaWZmZmZmZlZs9wIPZOVPSar1d/2PUZrC28jIti4kjZT0X5KukPSspHmSZkt6VNKZ2dTwem0s2aQnd+4Dki6QNFXSm9n7kytcu7qk0yU9ktV7TdJ9kr4l6R1ZnZNyG4m8rY0Kba6SXX+LpGnZRiSvSrpb0nclrdbcb6n/ioj5wEW5U9uUvT81Ip5vsLn81OotJA3vhfgeJk1Frhhfp6xPds8O74yIx4Bzc1XK15rsMUnDKI1SfCoi7unte9SxM7BGVn6O2knRHwKdCdD3SlqrTtt/yn6O4e1T+vuMk5JmZmZmZmZmxdeZYFwD2K1Gvc4Nbl4D/tzMDSTtBUwFfgF8iDRVdgQwGlgfOBK4M9sRuKHNPLJNO84DriFNv51EWlOwUt39gEdI00s3zOqNAzYjTSV+QNL2TT7TcdkznQxsD6xEWptvOWBL4DjgCUn/1Uy7/dxTuXJPdlx+qux4uR60Va3davFNppTTOg8gIh4ljfQE2FXSxF6Kp9MHKD3jlb3cdiP2yZX/LyIWV6sYEa8BN+ROfaRO21fkygc0H1r3eE1JMzMzMzMzs+LrnLI5jJR4vLa8gqQ1Ka0f+fuIWJD2zahP0iGkxOfg7NRU0vTv50m5hU2BD2b3P4I0hfZjDTT9U9JmJfOBq0hr8EHaiGfJmoCS9iCN8OvMY8wALiNNRR5P2iRlI2BKdr6RZzqLtJZep9uz1yukROsOwHtJiddfSBoaET9rpO1+btlcudvTiYFVc+UAXu1BW3k148s2e+lcF3Uh8Ifc2+cCW1Ha+Km7azdWsleufFMvttuoLXLlWxqofzPw4ay8eZ26/yL13/LALpKG98WanOWclDQzMzMzMzMruIh4VdIVpETgRySNz0ZL5R1GaT28hqduS9oEOIuUkJxLSuRdVL7bcjZF9M+kBOVHJR0eETXX3QM+R5p+vm9EPFvl/qOB31DKYUwBDs1vMiLpGNJO0N+nlLCq9UyfoZSQnAocHBF3Vai3K3AJKfF5uqRrsxF5RbZzrlxvU6Ra9s+V74iIhT1oC4BsCvh7cqcqxbcLaZdwgKsiIp8MvQj4MWm062GSTo6IjvIGummnXPltn5UWeGeu/EQD9afmyhvVqhgRIelu0pT4ZUiJ3VubjrBJnr5tZmZmZmZmNjB0JgBHUFr7DlgyuuxT2eGDTa6Hd1rWJsBBEfH78oQkQEQ8RRpNNic7dZzqD8WcCexZLSGZmUxpVN5jwAHlux5H8gPgTEqJ14okjQROzQ5nAbtUSkhm7d5A6fc2lJT4LCxJu5Om3ne6qpvtrEjaRbrTmT2JK+dEYELuuFJ8+fUiz8u/kSUoO69ZHXh/bwQlaRSwQXY4KyJaukFUlqwdnzvVyLqf+RhXaqD+g7ly3bVhe4OTkmZmZmZmZmb9x4Rsg5XO15FNXHsN8GJWLh8tuCswMSs3M0pyIrBndnhbRFxeq35EvEhpI5V16Dq6q5JfRsTLdep8Mlc+tc600pOBqmvtZfantFbhGfUSTBFxBaWRaR+uVbc/kjRC0rskfRe4nFLS9jW6blbTaHuDSbtddybJ7qEsOdhke6MlvVfSxaQ1PDvdnCWF83XzO2vPBP5Socl8LL214c06lHJoz9Sq2EdGlx2/2cA1+anv5ddXkn+udRuo32Oevm1mZmZmZmbWf7wSEVt158KIWJxtGvN1YEtJm0RE5+in/Bp8FzTR7C6UkljXNHjN/bnyVsDDNepWSiotIWkEpfXwgpRUqyoipkm6C9i2RrVdc+VmnmldYAVJa0ZEOxJTDcnval7DbGCfiHilG7f4MWkNT4A3gAObmCK9U4PxPQp8vML5T1AatfuHKlPGr6S0PuI+kiZ08znz8juwT+thW90xsuy4kany83PliptHlck/1+oN1O8xJyXNzMzMzMzMBo6zSUlJSGtIflnSOEqjy65oMkGzaa58sqSTm4yn3u7O9dZnnEiaNg3wXETMauCe/6J2UjL/TH9vdLOfnBVocrScpI0ojTitKCJObzaQbphLWiPzhO5MQZb0TeDo7PAtUkJyao1LmjUd+DVwWkTMq/B+fuTj+ZUaiIiF2ajL/0faeOmTwP/0MK4xuXJPNgfqrvLfxTC6Jh0rGZErNxJzfvRlIyMre8xJSTMzMzMzM7MBIiKekHQrsD3wSUnHktaX7ExQNDx1O7N8D0MaVef9eknGcblyo7s716vX189UydbAD+vU6a2kZPm6lwtIv+fHgQeqJPvqyj5Lp2SHi0hreza7JuWTwC9zx0FKmM0kjaj9d0RUnH4vaQtgs+zw8Yj4Z437nEdKSkJKZP5Pk3GWy+fPFvWwre54o+x4FPWTkvnRkeXXV5J/rqFVa/UiJyXNzMzMzMzMBpazSUnJCaQ1EDunbk8D/tpkW/m8wYV0nZrdiNtqvdnAtN+mhzE2cE3+mb5NYwmbvJ7sWN3n+mLEpaT/Ju1sDil5dWBE/LkbTT3Xg/i6rA8p6Zw69ReR+vpdkraJiDu7eV/oOtJwRNVafSQiFkh6nVKSflXqJ9/zU85fauA2+SnijaxZ2WNOSpqZmZmZmZkNLH8EfkYaTXUKpc1mzqs2Cq2GfOLj3oj4cS/E14zXcuVGRzguV+f9V4EVs/KUiLi36aiaFBHnkDaHKZwsIdk5ynMxcHBEXNriGEYCB+dOrZe9GnUE0JOk5Ixcud7nq688AmyXldej627ZlayTK9da17VT/rlmVK3Vi5yUtJYbPGw0Y1bajDErbcbYlbdgzEqbscz4SUhpI6uZz9zMXb+vudRGXaOWW49VNj6I5dfajRFjVmPI8NEsnPMSb858gumP/pnp/76UxQvn9MbjmPVb/q4Vj/useNxnxeM+Kx73mVnzImKOpEuAyXTd/brZqdvQdc3H7UmbnLTS06S1C4cCq0sa28C6khvXef9RSr+X7YE+T0oWlaRjgB9kh4uBT0bEJW0I5WN0ncrfrAMlfTkiurse5FO58mpVa/WteyklJbcH/lSn/ntz5fsaaH/VXPnpxsPqPiclraV2OPJelllunSX/E9nbpMFMeu83WOs9X2XQoK4f75Hj1mTkuDWZsPZuTNr+azx05eeY+ew/+iQOs3bzd6143GfF4z4rHvdZ8bjPzHrkd6SkZKdbI+KxbrRzXa68p6SVImJ6jyJrQkTMl3QfsA1pWvbeVNngBEDSyqT1G2u5DvhoVj5c0v9GRCM7Qi9VsjUkO6dsLwYOiYg/tCmc/NTtz0bEWY1cJOk6YDfSRjUfB87tzs0j4jlJrwHjScnxkd1dm7MHpgBHZeWPSvrvGutvjiM9d6fLGmh/g1z5ge4E2Ky++a+7WRWjll+vz/6nEmCjD/6CSdt/bcn/VEZ0MGfGv5n57C3Mm1XaWGzk2DXY8sApLL/WLn0Wi1k7+btWPO6z4nGfFY/7rHjcZ2bdFxH/IE25PSN7Nbtrdmc7jwLXZ4cjgV+owe2qG63XgAtz5W9IGl6j7reAwQ20NzMrb0ZpN+m6evGZ+rUqCcmL2hTLJGCn7HAh9UcI5uU/O0dUrdWYzunfg6g/Grcv3AR0/sdpDbr+o0O5/6a00c0tEdHIOqib5co9mereMI+UtLZYtGA2s196kNnT72P29PuYuM3RjFlpsx61uebWR7Hqxp9Ycjzz2Vt4+OqjmDtz6pJzy03cmY0/dBYjRq/CoMFD2fQj53Pbb7dl/uznKjVpVnj+rhWP+6x43GfF4z4rHveZWfdExLG91NQxwO2kDT72BS6V9PmImFapsqTVgE8AuwC798L9zwG+DqxMGs11saRDI2J27p4iJWI+R9rRuWryMCLekHQ8pV2gfyxpPPD9aqPfJG0KfJq04cnXevxE/Vg2ZTufkDy0XQnJzOGU+vPqiJhZq3KZ/yP18wjgvZLWi4jHuxnHtZQ+zzvQosRdp4hYJOkESmuT/kTSUxHxt3w9SQeTvi+d8uWKspGV78oOH4uIZ3oecX1OSjZA0hhgC2BLYKvs5zqUvhR/j4ide3iPDYBDSR/w1UlDi6cDjwGXABdHRLM7gvU7D045jFnT72fuzCe6nF9t08N61O7QkcsxaYfS92z29Pu5+w97E4sXdqk38+mbuPOC3dnu8NsYMnw0Q0eMZZ0dT+ChK4/s0f3N+ht/14rHfVY87rPicZ8Vj/vMrH+IiPslTSZNmx5KSkzuJelG0m7cs0ib6qxK+rvzJqS/LzeyuUYj958t6dPA5aRRkPsAUyX9GXiGNKV2T2Aj0gjIP1MaFVdxWnZE/ErShqRRkoOAE4Gjsum+T5CSj2OBSaSp42tml57RG8/UX2UJrR/kTt0OrJJtdlPP1RHRK32ei2cw8Kncqd83c3322bkS2C87dTgNJOmqmAL8KCvvRgPrq0r6PPC+stP5dV53llQ+8vPliPh/VZo8D9gL2B8YDVwv6RpSgnQIsCMpYdrpexFxa704Sf+A0Dk14fIG6vcKJyXrkPQYsC41/pWlh+0PIf3hdxxvH2K+Zvb6AHCCpMkRcWNfxNEq0x7pm/Vw19jyswwdMW7J8cN/Pfpt/1PZad7rT/GfW7/P+rt8B4BVNjqAqTd/h/mznu2T2Mzawd+14nGfFY/7rHjcZ8XjPjPrPyLiYknPk9aqXBcYDuyRvap5qBfvf5WkTwC/JSVAVwDK/4XgZdKGKPnRmVXX/YuIL0p6GPgeKbG5PHBgjTAWAN0dZVcU5Tta70DXJFctr9BLieicPShtwPIGcEU32riQUlLyU5K+GRGLmm0kIv4j6TbSZjO7Nrjp0takz2Q1nXmfvKqjFCMiJB0KvAkcRspVVfoeLiZ9rk+oE1+nj+bKVdds7W1eU7K+9eijhGTmt8A3KSUkg7TN+z8orRUAab2AayV9oA9jKawVN9h3Sfn1F+9i9rTam6e98MC5LH4r/bdJgwaz4vr79Gl8ZgOFv2vF4z4rHvdZ8bjPisd9ZtY92YirDShtGPIY8DopATKbtKv1pcAXgUkRUSvB1537X0waZfaT7N5zSaM0HwROATaNiFtIIxw71UwaZRumrEnaQGQKKSH0JrCINOryXtJ02UOAlSPiZ733RNaA/DqQf+7m5jJXAa9l5ZVIIw2768zs5zBqJxv7TEQsiIjDSaMifwdMJX0X3gD+Dfwc2DIivtnIBk6SRpE2kAL4Z0T8q28ifzsnJRv3BilR+BPgkzS2nXpNkr5CmrLd6R/ABhGxUUTsFBFrAO8HXszeHwJcIqk8i75UGzluIqNXKI1+njH1r3WveWv+a7z+Qmn5h3es+8E+ic1sIPF3rXjcZ8XjPise91nxuM9soIiIiRGh7PVoD9qZnGvnVw3U74iIP2XXbRAR4yNiSESMjYgNI2K/iPhZrY018rF3I95nI+Ir2b1HRcS4iNg0Ir6V2xV8w9wlTzfQ5hsRcUZEfCSLbdmIGBoRy0fElhFxWERcEBGv1WurHSLi6Vwf9mhQVUSclG+rydc5Vdo8J1dn5ybj+Wju2k/Vv6JiGwsjYrlcOz2ZnnwRpRzNZxu49+Ru/B4nNvhcN0fE4RGxbvZdGBMR74yIoyOimd2zDyRNBYfS9PSWcFKyvk+Q/iVobJYo/EpEXEj6V6Buk7Q8aVewTvcBH4iyBVcj4npS9ntOdmoM6V+ALDNmxU27HL/+/D8buu71F0r1Rr+jHRtnmRWLv2vF4z4rHvdZ8bjPisd9ZjawZRt2bJcdziGtD2nWKyLiLdK0aIBtJG1Xq35/l20Q9aXs8CHSxkAt46RkHRHx+4h4rJEhr006iq5Dyj8bEQuqxPAfuiYiPyFpYi/HU1ijJmzQ5Ti/Y2It+XpDR4xl+OhVejUus4HG37XicZ8Vj/useNxnxeM+MxvwjgeWycpTIqKjncHYgHQmpRG432hjHL3hQ5R23T6+1d8XJyXb5+O58p0RcVed+r8B5mflQbRp7YL+aOTYNZaUOzoWMX/OtIaumzfruS7HI8d6VrxZLf6uFY/7rHjcZ8XjPise95lZMUmaIOmn1ZYzkzRM0reAr2angrS2nlmvioiFwFeyww9K2r6d8XSXpEHAqdnhtT2c1t4t3n27DSStDWyUO3VlvWsiYqak2yltJb83LZ7r318NGTZmSXnxgjegwcT+ogVd1zseMmzZXo3LbKDxd6143GfF4z4rHvdZ8bjPzAprCHA08AVJdwJ3AdOy82uRdtxeOVf/RxFxR8ujtKVCRPxZ0pdIu7av2OZwums10nTt/6OFO27nOSnZHpuXHd/a4HW3UkpKbtZr0RTc4GGjlpQXL5pfo2ZX5XUHDxtdpaaZgb9rReQ+Kx73WfG4z4rHfWZWeALenb0qWQx8H/hmyyKypVJE/LTdMfRERDwLnNTOGJyUbI93lh03uvBuvt4YSatFxPO9FFNhDRo0dEk5OhY1fF153UGD/XUwq8XfteJxnxWP+6x43GfF4z4zK6yXSInI3UmDdVYFVgCWBV4nrfF3I/CbiPDmNmYF4P+StsfEXHkxpe3k63mmQjtLfVJy8VtvLikPGjKi4evK6y5eOLfXYjIbiPxdKx73WfG4z4rHfVY87jOzYso2n70ze51Sp7qZFYCTku0xJld+IyIWN3jdrLLjqnNGJB0JHAnwjnFqLrqCWZT7H8vBQ0c2fF153UUL5/RaTGYDkb9rxeM+Kx73WfG4z4rHfWYAMWMIC3/TX5eBe7LdAZiZtYR3326P/KrY85q4rrxu1aRkRJwVEVtFxFZjRw3spORbc19ZUh4ybFkGN7jo+PBRK3VtZ94rVWqaGfi7VkTus+JxnxWP+6x43GdmZmb9g5OS7TE0V258IZu31x1asdZSZs6rj3c5HjlmjYauGzm2VC86FvPmzKm9GpfZQOPvWvG4z4rHfVY87rPicZ+ZmZn1D05KtsebuXLjC9m8ve6bFWstZebM+HeX4zErbdrQdfl682Y9Q0cTuy+aLY38XSse91nxuM+Kx31WPO4zMzOz/sFJyfbIL0CzTBPXldd9oxdiKbzZ0+/tsqbP+DV2aOi68atvv6Q889mbez0us4HG37XicZ8Vj/useNxnxeM+MzMz6x+clGyPGbnyKElV14Yss3LZsReyAToWzeeVJ69fcrzi+vswaEjtRcvHrfYelhm/9pLjlx6b0mfxmQ0U/q4Vj/useNxnxeM+Kx73mZmZWf/gpGR7PFp2vGaD1+XrdQCPV6u4tHnhgXOXlIeOGMfEbY6qWX/SDsctKc+b9SyvPnVjn8VmNpD4u1Y87rPicZ8Vj/useNxnZmZm7eekZHs8XHa8RYPX5es9HRHN7Nw9oL3y5HXMfKY0jWbS9l9nwqQPVKy7zo4nMmGtXZYcT735O0THW30eo9lA4O9a8bjPisd9Vjzus+Jxn5mZmbWfIqLdMRSSpJuAnbLDv0fEzk1cO5I0hXtUdursiDiigev+A3TOG2noGoD1VhscP/987SkprbL2dsey9vbHvu38oMHDkFKOPKKDjsUL31Zn2kMX8fDVX6ja9jLLrcO7D72BYSOXB6CjYxHTH7mElx+/koXzZjJy7JqsuskhLLdGaT2glx+/kvsuPQjoX9+D3Y+bU7+S9SvXnLZsu0Powt+14nGfFY/7rHjcZ8XjPqvvC2fM4/HnF6vdcRTNusOHx49XW63dYVS095NP3hMRW7U7DjOzvjak3QEsjSJinqS/Ah/LTn1M0hciYm61ayTtQCkhCXBpX8bYVzRoCIOH1N5wXBpUsY4GDa153dyZU7nvTwey+X5/YNjI5Rk0aAirvOsgVnnXQRXrv/r0TTww5TD60/9UmvUWf9eKx31WPO6z4nGfFY/7zMzMbODy9O32+W2uPBb4cp36J+bKzwLXV6u4NHv9+du59ddbM+3hP7J40fyKdebNfp5Hr/8ad1/0YToWeQa8WXf4u1Y87rPicZ8Vj/useNxnZmZm7ePp293Uk+nbVdpYCOwbEVdVqHcq8I3cqckRcW55vWr60/TtVhoyfAzj13gvI0avypBhy7LgzZeZO/MJXn/hjnaHVpenbxdPf5u+3UpF/q4trdxnxeM+Kx73WfEUtc88fbt7PH3bzKz9PH27DknfBL5Z4a1hufKOkir90+r5EfGZGs0fCdwOLJe1d7mki4DLgFeBtYDDgPfmrrkcOL/hB1iKLVowmxlP/KXdYZgNeP6uFY/7rHjcZ8XjPise95mZmVlrOSlZ3xBgeJ06qlKn5kI2EfG4pH2AKaTE5GDgk9mrkr8BB0VER514zMzMzMzMzMzM+i2vKdlmEXELsBFwEbCgSrXnga8A76+1GY6ZmZmZmZmZmVkReKRkHRFxEnBSH99jOnCwpLHAzsBqwGjgJeAx4Pbw4p9mZmZmZmZmZjZAOCnZj0TELNJUbjMzMzMzMzMzswHL07fNzMzMzMzMzMyspZyUNDMzMzMzMzMzs5ZyUtLMzMzMzMzMzMxayklJMzMzMzMzMzMzayknJc3MzMzMzMzMzKylnJQ0MzMzMzMzM7MuJE2WFNnrpCp1TsrVmdzaCBsnaYSkqVmc57bonp/I7rdI0qatuGfROClpZmZmZmZmVjCSns4lg0LSLEnLNHH9l8uuD0mf68uYrbUk7VXWv39q4b1vqvD56varF0I6BpgELABOaCD+d0s6U9LDkl6XNF/SM5L+IukwScMauOfvgfuAwcAZPYp+gHJS0szMzMzMzKz4xgD7NVH/sL4KxPqNI8qO95a0QlsiaSNJ7wCOzQ7Pjohna9QdI+li4J/AkcA7gbHAcGANYC/gbOBeSZvUum9EBPCd7HB7Sfv26EEGoCHtDsDMzMzMzMzMeiQAkRKN59WrLGlrYOPssAMPWBpwsuTjh7PD+cAIYChwCPDjFoTwS+DKGu9/AHh/Vr4buLgPYzkOWJb0WT+9WiVJI4FrgXfnTt8E3Aa8CUwE9gZWBDYCrpe0Q0Q8XuPefwYeA9YHviPpsixZaTgpaWZmZmZmZlZ0fwN2BXaStHZEPFmn/uHZzw5S0mWXPozN2uNQUhIS4JvAKcBI0ujJPk9KRkTNJKOkZSklJR+OiKrJwp6QNB74THb41zrfjZMpJSRfAz4WETeWtfcl4NfAwcAKwPmStq2WaIyIkHQW8CPSqMsPAVd083EGHP9riJmZmZmZmVmxnZ39FDC5VkVJI4ADs8Prgef6Lixro87E83zgN8CU7PidkrZtT0htcQQwKiufXa2SpNHAF3KnPl2ekASIiLnAp0hrRQJsAxxQJ4bzgEVZ+YsNxLzUcFLSzMzMzMzMrNjuBR7Iyp+SVOvv+h8DxmXl3zV7I0kjJf2XpCskPStpnqTZkh7NNgbZuoE2lmzSkzv3AUkXZDskv1ltN2dJq0s6XdIjWb3XJN0n6VvZ2oFN7wgtaZXs+lskTZO0UNKrku6W9F1JqzX3W2ovSe8hjcoDuDwiZtF1Wn/5WpMD2eTs55vAVTXq7Uya4g7wTET8X7WKEbEI+HnuVM31WSPiFdKIZIBdJK1eq/7SxElJMzMzMzMzs+LrTDCuAexWo15nAuU10np3DZO0FzAV+AVpGurqpETOaNKaeUcCd0r6jaShVRvq2uYwSecB1wCfIO2QXHEXcUn7AY8AXwU2zOqNAzYjTb19QNL2TT7TcdkznQxsD6xEmva8HLAlaT3CJyT9VzPttlk+6diZjLwWmJ6VD5A0igFO0rtIaz8C/C0i5tWoPilXvreB5u/JlXfJponX0jllW8D+DbS/VPCakmZmZmZmZmbFdwHwA2AYKfF4bXkFSWtSWj/y9xGxQFJDjUs6hJT4HJydmkqa/v08KbewKfDB7P5HAONJozLr+Slp85X5pJFsD2XnNwYW5O6/B3ARpTzGDOAy4JnsXnuQElBTsvONPNNZlNYbBLg9e71CSrTuALyXlHj9haShEfGzRtpul2ytxs7pxC+Tkr1ExGJJF5ISuqNJibGmR8oWzF658k116o7MlWslLzvNz5U7P/+17pGfCr4XaY3JpZ6TkmZmZmZmZmYFFxGvSrqClAj8iKTxEfFaWbXDSCO1oImElKRNgLNICcm5pETeReWbe0haizT6clPgo5IOj4iq6/hlPkcambZvRDxb5f6jSesiduYwpgCHRsTsXJ1jgGOA71NaT7HWM32GUkJyKnBwRNxVod6uwCWkxOfpkq6NiEfrtd9GB5B2moaUeF6Ue+9cUlISUuJ4oCcld8qV39a3ZV7NlddooO3yKdjvonZS8mHSd2cZYDtJQ8r6Zqnk6dtmZmZmZmZmA0NnAnAEcFD+DaUhkZ/KDh+MiPz003pOo7Te3kER8ftKuw1HxFOkUWBzslPHqf5QzJnAntUSkpnJwKpZ+THggHxCMrt3RMQPgDMpJV4rkjQSODU7nAXsUikhmbV7A6Xf21BS4rM/qzR1G4CI+BeltUe3l7RBy6Jqj61y5X/VqXt3rryNpJXr1P9I2fFKtSpHRAcpMQnpu/SuOu0vFZyUNDMzMzMzM+s/JmQbrHS+jmzi2muAF7Ny+WjBXYGJWbmZUZITgT2zw9si4vJa9SPiRdI0a4B1KG24Us0vI+LlOnU+mSufGhELqtZMa0MurtPe/sAKWfmMiKi5A3lEXAE8kR1+uE7bbSNpQ+A92eFDEXFfhWr5RGXdEaVFJWkM8I7scHZEvF7nkvtICW9ISxD8UlLF2cXZuqXl38sxDYT1TK68bgP1BzwnJc3MzMzMzMz6j1ciYqvc66xGL4yIxZSSTltm0647dSagFpLWn2zULpRGHl7T4DX358pbVauU+UutNyWNADbPDgOolxSdRv2purvmys0+0wrZ2pz9UdVRkjkXAp3Thg+tlngbAPLTq6fVq5yN/D2W9BkD2Ae4SdKeksZKGippXUknANeREpf55HjFzZnK5OPwDtx4TUkzMzMzMzOzgeRs4OtZ+TDgy5LGAftm566IiFeaaG/TXPlkSSc3Gc8Kdd6vtz7jRNK0aYDnImJWA/f8F7Btjffzz/T3Rjf7yVmBrqPe6pK0EaURpxVFxOnNBpJrfyhpwyCADlLysdI9XpJ0LWma/YqkXdQv6+59+7H8yMW5jVwQEZdna5P+gDSIb3vS5kuV3EDaSKhzmYTZVerlvZkrj24kpoHOSUkzMzMzMzOzASIinpB0Kymh8klJx5ISJ51rQja7ucnyPQxpVJ336yUZx+XKr1arVKZevb5+pkq2Bn5Yp063k5KkaeWd05VvyKbRV3MepZ2pj2BgJiXz+a6GN5SJiB9Juhc4hfQdKvcGacf4b9N1lO/rDTSfj2No1VpLESclzczMzMzMzAaWs0kJlQmkZFXn1O1pwF+bbCufN7iQrlOzG3FbrTezDUBqaXoYYwPX5J/p26REUzOebLJ+K+Snbi8v6ZwadUfkyntKWqVOErOI8qMjR1StVUFE3AjsIGk14N2kEaUdpNGxN0XEPABJ+c1qnnhbQ283Mld+s2qtpYiTkmZmZmZmZmYDyx+Bn5FG9J1CabOZ87J1J5uRH3V4b0T8uBfia8ZruXKjIxyXq/P+q6REE8CUiLi36aiaFBHnAOf0RduSVgV2z53aIns1YjBpd/HTejuuNpuRK9f7PFQUEc8Dz1d6L9sAKr9D9+0NNJmPY0bVWksRb3RjZmZmZmZmNoBExBzgkuwwv/t1s1O3oeuaj5Wms/a1p4G3svLqksY2cM3Gdd5v9zP1tsNIycXuGoi7cL9A6XOzkqSe/H4qOTBXfiwinm7gmlVz5UbqD3hOSpqZmZmZmZkNPOUJyFsj4rFutHNdrrynpJV6EFPTImI+cF92KGDvWvUlrUxav7GW/DMdrm7sdNNfZLEflju1e0SokRelKcfrSNqp9dH3nWxE8MPZ4WBgnd5qW9IywP/LnfpFg5dukCs/2FvxFJmTkmZmZmZmZmYDTET8g7SxyhnZq9ldszvbeRS4PjscCfyi0SReLyb78jtJf0PS8Bp1v0X9UYMXAjOz8mbA0Y0G0g8TmO8D1s7KL5F2hW7U73PlI6rWKq47cuVNq9Zq3o+B1bPyM6Q1XGuSNB5YIzt8KiJe7sV4CstJSTMzMzMzM7MBKCKOjYijstd19a+o6hhgflbeF7g0G5FYkaTVJH2N5jfVqeYc0iY9kEabXSxpTNk9JekY4HNA1GosIt4Ajs+d+rGkkySNrHaNpE0l/Rz4Xjfi70v5ZOIfmlwzNJ/s3a/BqfFFcm2uvEO9ypIGSTpGUsW1SyWNk/Q74LPZqQ7g8Gy5hHp2rBLXUs0b3ZiZmZmZmZlZVRFxv6TJwPnAUFJici9JN5J2455F2lRnVdIGK5uQplo/XKm9btx/tqRPA5eTRkHuA0yV9GfSSLXxwJ7ARqQRkH+mlKyrmKCMiF9J2pA0SnIQcCJwlKTrSNOa5wJjgUnANsCa2aVn9MYz9QZJ44CP5k79vkrViiLiCUl3A1uRRsEeBPyq1wJsv2uBeaRn262B+oOAHwDflXQradmAl4FlScnw3Umfc4DFwOSI+FuDseyaK1/e4DUDnpOSZmZmZmZmZlZTRFws6XnSWpXrAsOBPbJXNQ/14v2vkvQJ4LekxNAKwJFl1V4GPkbXnajn1Wjzi5IeJo1+HE/a3fvAavWBBcDjzUffZz4BjMjKUyPizm60cSEpKQkpkTtgkpIRMSdLXB8MbChpg2w5gnqGADtlr0oeBz4fEddXeb+LbMr/vtnhy3ik5BKevm1mZmZmZmZmdUXEraQRYx8HzgUeA14njRqbTdrV+lLgi8CkiKiV4OvO/S8m7Sb+k+zec0mjNB8ETgE2jYhbSCMcO82q0+ZZpFGQRwFTSCMv3wQWkUZd3kuaPn4IsHJE/Kz3nqjH8lO3mxolmfMHUv8BbCVpk56F1O+cmSsfXKtiRCwiJbRPA24m7ZA9D5gDTAX+mLWxcaMJycx7gdWy8tnZfQxQRM2lFmwAWG+1wfHzz1ddGsP6od2Pa2RJCutPrjlt2XaHYGZmZkuhL5wxj8efX9zfNt7o99YdPjx+vNpq9Su2wd5PPnlPRGxVv6ZVk03B7pyuu0E3dx23AULSXaTRoC8Ca7Y6KSjpfOCTwFvAxIh4sZX37888UtLMzMzMzMzMBoRsncXtssM5pPUhben27eznKsABrbyxpFWB/bPD3zgh2ZWTkmZmZmZmZmY2UBwPLJOVp0RERzuDsfaLiCuA27LDr2VrPLbKV4BhpKUGTmnhfQvBSUkzMzMzMzMz69ckTZD0U0lrVnl/mKRvAV/NTgXw85YFaP3d0UAHsDF11pbsLZJWB/5fdvidiJjWivsWiXffNjMzMzMzM7P+bggpsfQFSXcCdwHTsvNrkTYoWTlX/0cRcUfLo7R+KSLukXQ46bMyvEW3XQv4PmnTpB+16J6F4qSkmZmZmZmZmRWFgHdnr0oWkxJB32xZRFYIEXFui+/3D+Afrbxn0TgpaWZmZmZmZmb93UukROTuwPuAVYEVgGWB14GngRtJm4l4cxuzAnBS0szMzMzMzMz6tYgI4M7s5Q1DzAYAJyWXAvNe6OBf33iz3WFYU5ZtdwDWpN2Pm9PuEKxJ15zm75mZmZmZmVm7OClpZmZmZmZmS5Whyy7Lqttt1+4wKnvyyXZHYGbWEoPaHYCZmZmZmZmZmZktXZyUNDMzMzMzMzMzs5ZyUtLMzMzMzMzMzMxayklJMzMzMzMzMzMzayknJc3MzMzMzMzMzKylnJQ0MzMzMzMzMzOzlnJS0szMzMzMzMzMzFrKSUkzMzMzMzMzMzNrKSclzczMzMzMzMzMrKWclDQzMzMzMzMzM7OWclLSzMzMzMzMzMzMWspJSTMzMzMzMzMzM2spJyXNzMzMzMzMzMyspZyUNDMzMzMzMzMzs5ZyUtLMzMzMzMzMzMxayklJMzMzMzMzMzMzayknJc3MzMzMzMzMzKylnJQ0MzMzMzMzMzOzlnJS0szMzMzMzMzMzFrKSUkzMzMzMzMzMzNrKSclzczMzMzMzMysC0mTJUX2OqlKnZNydSa3NsLukXRqFu9rkpZrwf3GSHolu+fpfX2/InFS0szMzMzMzKxgJD2dSwaFpFmSlmni+i+XXR+SPteXMVtrSdqrrH//1O6Y8iT9sSy+D7XgnusAX80OT4uImXXqryTpa5Kuk/SipHmS5kuaLunGLCm7Vq02ImI28J3s8GhJ7+z5kwwMTkqamZmZmZmZFd8YYL8m6h/WV4FYv3FE2fHeklZoSyRlJC0P7FN2ujzevvBdYDjwKvC/tSpmSfqpwPeA3YCVgRHZ9SsCOwMnAo9JOlmSajT3K+BlYChwWs8eYeBwUtLMzMzMzMys2CL72VCiUdLWwMbZYUefRGRtlSUfP5wdzs9+DgUOaU9Eb3MIMCwrd8b3IUkr9tUNJW1GKXH/84iYW6Pu/wN+CYzKTr0GnA+cShr1eAEwK3tvKPAtaiQbI2I+8NPscG9J7+7eUwwsTkqamZmZmZmZFdvfsp87SVq7gfqHZz87gJv6JCJrt0NJyTKAbwLzsnIrRiM2ojOOecAJWXkIKe6+ciwgYBHw62qVJI0DfpA7dRawRkQcGhHfjIgTIuIQYA3gd7l6x0has8b9fwu8lYtlqeekpJmZmZmZmVmxnZ39FDC5VkVJI4ADs8Prgef6Lixro87E83zgN8CU7PidkrZtT0iJpG2Ad2WHU0gJws7Rkn2SNJW0MvDx7PCvEfFijep7UBoh+RTw/yJiTnmlbK3II4Gns1ODgA9UazQiXgL+kh1+pE4Cc6ngpKSZmZmZmZlZsd0LPJCVPyWp1t/1PwaMy8q/q1GvIkkjJf2XpCskPZtt/DFb0qOSzsymhtdrY8kmPblzH5B0gaSpkt6stpuzpNUlnS7pkazea5Luk/QtSe/I6jS1I7SkVbLrb5E0TdJCSa9KulvSdyWt1txvqb0kvQfo3Ezl8oiYBZyXq9Lu0ZL5+5+XxXd5dry+pO374J6fJI3EBLikTt2JufIdEbG4WsWIWAT8M3dqQp22OzcbGkT/mUrfNk5KmpmZmZmZmRVfZ4JxDdKmHNV0rjv5GvDnZm4gaS/Sxh+/AD4ErE7a+GM0sD5p1Nidkn4jaWjVhrq2OUzSecA1wCeASUDFXcQl7Qc8Qto9ecOs3jhgM+Bk4IFmE1qSjsue6WRge2Al0rTn5YAtgeOAJyT9VzPttlmXpF/281pgelY+QNIo2iDbIb5zpO70LC7o+6TpAdnPAK6qU3derlwvyQiQ3zzomTp1r6K0jusBtSouDYbUr2JmZmZmZmZm/dwFpHXwhpESj9eWV8imi+6SHf4+IhbU3jC4y7WHkBKfg7NTU0nTv58n5RY2BT6Y3f8IYDxpVGY9PyWNGJtPStg8lJ3fGFiQu/8ewEWU8hgzgMtISaDxpCm3G5GmA1/W4DOdBXwmd+r27PUKKdG6A/BeUuL1F5KGRsTPGmm7XSQtSynZ9TIp2UtELJZ0ISmhOxrYn26MlO0F+5N2iof0GewchXgN8BJpV+v9JX0xIt7ojRtmm+dskR0+FBGv1Lnk5lx5Z0lbRsQ9Vdp+N7BTdjiT0vTsiiLiNUn3Z/G8S9LqEbHULqHgpKSZmZmZmZlZwUXEq5KuICUCPyJpfES8VlbtMNK6k9BEQkrSJqTNPgYDc0mJvIsiIsrqrUUafbkp8FFJh0fE2eXtlfkcafr5vhHxbJX7jyati9iZw5gCHJqt6ddZ5xjgGOD7lNZTrPVMn6GUkJwKHBwRd1Wotytpuu944HRJ10bEo/Xab6MDgGWz8u+z6cWdziUlJSEljtuRlKw0ipOIWCTp98CXSes5HkDq896wI6XP/dv6uFxE3Jt9lz5M+sz9TdLPgYtJSXABawIHAUdldd4gfYZmVW61i7soJUl3BC5s/FEGFk/fNjMzMzMzMxsYOhOAI0gJkyWUhkR+Kjt8sNrIrypOy9oEOCgifl+ekASIiKeAvYDOTUGOU/2hmDOBPaslJDOTgVWz8mPAAfmEZHbviIgfAGdSSkBVJGkkcGp2OAvYpVJCMmv3Bkq/t6GkxGd/VjHpBxAR/6K09uj2kjZoWVSApPVIo08hfQYfKKvSV1O4t8qV/9XgNQcD/5eVxwDHAw+SPi+vk36PXycl6v8IbBUR1zTY9oO5ct01WAcyJyXNzMzMzMzM+o8J2QYrna8jm7j2GqBzV+Hy0YK7UtrAo5lRkhOBPbPD2yLi8hrVyXY1vig7XIfShivV/DIiXq5T55O58qkRsaBqzbQ2ZNWNSTL7U1oH8Ix602cj4grgiezww3XabhtJGwLvyQ4fioj7KlTLJ/7qjijtZVUTpgARcT+lhN22kup9dhq1Xq5cb83HzljmAPuRlgW4s0bVqcAtNLeLfT6GdZu4bsBxUtLMzMzMzMys/3glIrbKvc5q9MJsfb7OZM+W2bTrTp0JqIWk9ScbtQulkYeNjgS7P1feqlqlTM01+CSNADbPDoPSLs0VRcQ06k/R3TVXbvaZVsjW5uyPaib9MhcCnVO6D5XUkmX9svscmh0upvqU5b4YLZnfPX1aE9ftSRpRuw1pRO95wInAt7LyTNLapz8D7pO0ToPt5mNYvYl4BhyvKWlmZmZmZmY2cJxNmlYKaQ3JL0saB+ybnbuigY0+8jbNlU+WdHKT8axQ5/166zNOJE2bBniuwTX7/gVsW+P9/DP9vdHNfnJWoMERd50kbURpxGlFEXF6s4Hk2h9K2jAI0u7OFZN+EfGSpGtJ0+xXJO2ifll379uED5J2Nge4PiKmV6l3IWld0MHAIZK+HhFv9fDeY3LluY1cIOl44DvZ4QXA5yLizbI6ywK/JI3kXR+4TtImDWzQk29ndCPxDFROSpqZmZmZmZkNEBHxhKRbge2BT0o6lrS+ZOeakM1ubrJ8D0MaVef9eknGcbnyqw3es169vn6mSrYGflinTreTkqRp5e/Iyjdk0+irOY+UlIQ0GvGyHty3UY2M4iQipku6jjRtegVgb+DSHt47n/taVLVWRtL7KCUkbwcm53YJz8c6R9Jk0jIF25IS6F8hLSFQSz6GoVVrLQWclDQzMzMzMzMbWM4mJSUnkJJVnVO3pwF/bbKtfN7gQrpOzW7EbbXejIiOOtc3PYyxgWvyz/Rt0s7JzXiyyfqtkE/6LS/pnBp1R+TKe0papU4Ss0ckrUwpCQqwj6QP1LgkP7r2CHqelMyPjhxRtVbJV3Pln1ZKSHaKiMWSfkppZO5HqZ+UHJkrv1m11lLASUkzMzMzMzOzgeWPpHXuRgGnUNps5rxaCZYq8qMO742IH/dCfM14LVdudITjcnXef5U0dRlgSkTc23RUTYqIc4Bz+qJtSasCu+dObZG9GjGYtLv4ab0dV87k7D6d9m/i2t0lrRYRz/fg/jNy5XqfDShtFgSN7dad3027kY1r8jHMqFprKeCNbszMzMzMzMwGkGzn4Euyw/wOxs1O3Yauaz5u3+2guu9poHNNwdUljW3gmo3rvN/uZ+pth9E16desvt6FuyftDyIlNXviqVx5taq1SvLrPEYD9fMjc+uN/AVYNVd+uoH6A5ZHSlr/JrHCxhuzynbbscImm7D8hhsyZs01GTlhAkNGjuStuXOZP3MmMx58kOf//nceufBC5r70UrujHpAGDxvNmJU2Y8xKmzF25S0Ys9JmLDN+ElL6t42Zz9zMXb+vuW5zXaOWW49VNj6I5dfajRFjVmPI8NEsnPMSb858gumP/pnp/76UxQvn9MbjmPVL/p4Vj/useNxnxeM+M+u239E1mXNrRDzWjXauy5X3lLRSjU1Kel1EzJd0H2kHZJHWGDy/Wv1sqvDWdZq9jjTNFuBwSf8bEY0kn/odpV16Dsud2j0irm3w2sdJI/vWkbRTRPy9D+LbibTmIsALwBoNTNlH0vbALdnh4ZJO7UEf5Ucyrt9A/VeAlbPyJsC/69TP73LfyO7eG+TKDzRQf8ByUtL6ta2+/GV2/tGPqr4/fMwYho8Zw9iJE1ln773Z4bvf5a4f/pDbv/1tOt7q6QZd1mmHI+9lmeXWWfI//71NGsyk936Dtd7zVQYN6vrH0shxazJy3JpMWHs3Jm3/NR668nPMfPYffRKHWTv5e1Y87rPicZ8Vj/vMrPsi4h+Sfggsk52a0s12HpV0PbAbaS28X0j6WCMJIknqpWTfhaSkJMA3JP0xIhZUqfst6o8avBA4lTSNdjPgaOCnjQTSi8/UW94HrJ2VXwJuaOLa3wMnZuUjgF5PStJ1rcuLGklIZm4jjSKcCKxFes6/dTOGO3LlTavW6lr/I1n5C5IuqRa3pMHAF3OnGvkdblYltqWOp29b/6au6xMvXriQmY89xvM338wz11/P9LvuYsHs2UveHzJ8OO/55jfZd8oUBg8b1upoB6xRy6/XZ38ZANjog79g0vZfW/KXgYgO5sz4NzOfvYV5s55bUm/k2DXY8sApLL/WLn0Wi1m7+HtWPO6z4nGfFY/7zKxnIuLYiDgqe11X/4qqjgHmZ+V9gUuzEYkVSVpN0tdoflOdas6hNAJtA+BiSWPK7ilJxwCfo86U24h4Azg+d+rHkk6SNLLaNZI2lfRz4HvdiL8v5ZN+f2hyzdALc+X9Gpwa37Csvf2q3K+mLPF7Ue7UEdXqNuAuSmuTvkf1/8Nydq68PfBbScuUV5K0LGnU7ruzUwH8qlbD2cjWHbLDN0i7ey+1PFLS+rWOt97imRtu4D9XXMELN9/Myw88QCzu+mesBg1irT33ZKcf/pDlN9wQgLX23JNtTziBW084oR1hD1iLFsxm9ksPMnv6fcyefh8TtzmaMStt1qM219z6KFbd+BNLjmc+ewsPX30Uc2dOXXJuuYk7s/GHzmLE6FUYNHgom37kfG777bbMn/1cpSbNCs3fs+JxnxWP+6x43Gdm7RUR90uaTErADCUlJveSdCNpN+5ZpE11ViVtsLIJaar1w710/9mSPg1cThoFuQ8wVdKfgWeA8cCewEbATODPlJJYFROUEfErSRuSRkkOIo0YPErSdcATpB2bxwKTSKM018wuPaM3nqk3SBpHaRo6pJGPDYuIJyTdDWxFGgV7EHWSak06iNJO0/+OiPubvP5C4Lis/FFJ4yLi9WaDyHbIvhI4BBhHet47a9S/QtLFwAHZqcnAh7M2Ov8jsS5pZ/vxuUt/GBF31wlnc0ob3fw1IpbqKZ5OSjZA0ghSdvx9pD9gNyRtUT+U9IfvM8A/gT9GxM3dvMcGwKGkHbNWB8YA04HHSAsUX5z9a85S5d6f/Yx7f/azmnWio4Mn//IXXrjlFj5xxx0st35aImLLL36RO049lUXz59e83up7cMphzJp+P3NnPtHl/GqbHlblisYMHbkck3b4+pLj2dPv5+4/7E0sXtil3synb+LOC3Znu8NvY8jw0QwdMZZ1djyBh648skf3N+tP/D0rHvdZ8bjPisd9ZtZ/RMTFkp4nrVW5LjAc2CN7VfNQL97/KkmfAH5LSoCuAJR/GV8GPkbXnajn1Wjzi5IeJo1+HE/a3fvAGmEsAB5vPvo+8wlgRFaeGhFVE201XEhK0kFK5PZmUjI/urHhUZKdIuJhSQ+QplyPID1vd5PC55OSkpASufV+V4eQpsMfRUpaL0/apbySt4CTaGwH83wSueraqEsLT9+uQdKKki4ibdF+PWl4956kNQ1GAcNIfxBuRfqg/kPS7dm/tjR6jyGSTiH9YX0cKem5AukP+DWBDwC/Bh6S9L5eerQBacGsWfzz1FOXHA8bPZqV3/3uGldYo6Y9csnb/jLQG9bY8rMMHTFuyfHDfz36bX8Z6DTv9af4z63fX3K8ykYHMGLsGr0ek1m7+HtWPO6z4nGfFY/7zKx/iYhbSdOnPw6cSxpE8zqwGJhN2tX6UtIae5MiolaCrzv3v5i0m/hPsnvPJQ0UehA4Bdg0Im4hjXDsNKtOm2eR/u59FGndzWeAN4FFpFGX95Kmjx8CrBwRtUfNtFY+6dfUKMmcP5D6D2ArSZvUqtyorJ2tcqe6G18+mdmTKdw3UBrleGC9KdwR8VZEfJE0KO27wM2kpPdCUnJ6OnAjaYTt2hHx3XprjWZTtzu/E88BV3fzWQYMJyVrW530gVm27PzzpMVIb+Tt/0qyLXCXpB1ozG+Bb1JaiDeAR4B/kD6kndYArpX0gYajXwq9dHfXkdKjVq66zIn1AytusO+S8usv3sXsaffWrP/CA+ey+K30D50aNJgV19+nT+MzGwj8PSse91nxuM+Kx31mA0FETIwIZa9He9DO5Fw7dUfJRURHRPwpu26DiBgfEUMiYmxEbBgR+0XEzyLiyUZi70a8z0bEV7J7j4qIcRGxaUR8K7creH6g0NMNtPlGRJwRER/JYls2IoZGxPIRsWVEHBYRF0TEa/XaaqWI2CLXdyfWv6JiG9Oz/uts58Hce+fkzp9U5fqTcnXOyZ1/MHdeEfFUN+P7Ya6NLbrTRtZOB/A/2eGadB1NW+u6xyPi+IjYMSJWjIjhETEiIlaOiF0i4tsR8XyDYexCWg4A4GcRsaiphxiAnJRs3K3AZ4DVI2L1iNg2+wCuT9rpKr8A6yhgiqQJtRqU9BXSlO1O/wA2iIiNImKniFgDeD/wYvb+EOASSWtiFQ0aOrTL8cLcJjjWv4wcN5HRK7xzyfGMqfXXwH5r/mu8/kJplP071v1gn8RmNlD4e1Y87rPicZ8Vj/vMbGDL1lncLjucQ1of0gzSoLAXsvJX2nD/znvOoHenyReWk5K1dQCXAZtHxA4R8ZtKGfCIeCoiDgZ+nDu9HKUFWd9G0vLAt3Kn7gM+EBFdRl5GxPXAjqQ/TCGtNXlKN55lqbD6+0oz3Be/9RbT7uzOkhrWCmNW3LTL8evP/7Oh615/oVRv9Ds27tWYzAYaf8+Kx31WPO6z4nGfmQ14xwOdOyVPyUbImRER84FvZ4e7SdqqVv3elE1n3ys7/E5EzKlVf2nhpGQNEXFvROzbxA5Rx5Gmdnfar0bdo+i6zsVnI2JBlTj+Q9dE5CckTWwwpqXG+PXWY9vjj19y/PA55zDvlVfaGJHVMmrCBl2O8ztd1pKvN3TEWIaPXqVX4zIbSPw9Kx73WfG4z4rHfWZWTJImSPpptZmDkoZJ+hbw1exUAD9vWYBWFL8hDQqDxjam6S2d93oY+EUL79uvefftXhQRCyVdTZrmDbCGpGUiYm6F6h/Ple+MiLvqNP8b4GTSjlODSDuK/ainMRfdsNGjWW799VnnIx9hi6OPZtjo0QC8cNtt3PiVdozGtkaNzC0Q39GxiPlzpjV03bxZz3U5Hjl2TRa88WKV2mZLN3/Pisd9Vjzus+Jxn5kV1hDgaOALku4E7gKmZefXIq0RmN9U4EcRcUfLo7R+LSI6JB0G7AsgabmImNmX95Q0hrTb913AFV5LssRJyd73atnxGNKOYEtIWhvYKHfqynqNRsRMSbcDnfOT92YpTEp+7OqrWWuPPaq+P3fGDO796U+58wc/oOOtt1oYmTVryLAxS8qLF7wBDc6qWLSg6+Z5Q4aV70NlZp38PSse91nxuM+Kx31mVngC3p29KlkMfJ+0oazZ20TEA8ADLbzfbNIgMyvjpGTvm5grdwCV5g9vXnZ8a4Nt30opKblZU1EtBRbMns0DZ57Jw+ee64RkAQweNmpJefGi+Q1fV1538LDRvRaT2UDj71nxuM+Kx31WPO4zs8J6iZSI3J309+JVgRWAZYHXSbts3wj8JiK8uY1ZATgp2YskjQT2zJ26q8qw3HeWHTf6B2a+3hhJqzWx9fyAMP3uu5eUNXgwI8aNY7kNNmDY6NEMHzOG93zzm2xz7LH88zvf4fZTvB9QfzZoUGmn9OhofPR6ed1Bg/3HmFk1/p4Vj/useNxnxeM+MyumiAjSFNg78eavZgOC/0vau46m6+Y151epNzFXXgw0uhjNMxXaWaqSkreecMLbT0qsueuubH/KKayy7bYMHjaM7b/9bUYstxw3fvnLrQ/SGrL4rTeXlAcNGdHwdeV1Fy+stGSrmYG/Z0XkPise91nxuM/MzMz6Bycle4mkdwEn5U79B/h1lepjcuU3ImJxg7eZVXZcdc6IpCOBIwHGNdh4YUXwzPXX8+zf/sYHf/97NjjgAAC2/NKXmHrZZTz397+3OUCrZFHuLwSDh45s+LryuosWzum1mMwGGn/Pisd9Vjzus+JxnxnAv9cazpbnT2p3GJVd0O4AzMxaY1C7AxgIJC0P/Jm0Mzak0Y+TI2JhlUvyq2LPa+JW5XWrJiUj4qyI2CoitlpaluCOjg6u/cxnmPdqaa+hzY86qo0RWS1vzS0ttzpk2LIMbnCx+OGjVurazrxKy7aaGfh7VkTus+JxnxWP+8zMzKx/cFKyh7J1JKcA6+ROHx8Rt9S4bGiu3MxW8OV1h1astRRb+MYbPHX11UuOV9luuzZGY7XMefXxLscjx6zR0HUjx5bqRcdi3pw5tVfjMhtI/D0rHvdZ8bjPisd9ZmZm1j84KdkDkoYB/wdsnzv9vxHx/TqXvpkrN76Qzdvrvlmx1lJu9rPPLimPnDChjZFYLXNm/LvL8ZiVNm3ouny9ebOeoaOJXTPNljb+nhWP+6x43GfF4z4zMzPrH5yU7CZJQ4FLgD1yp39N2uymnvwCNMs0cdvyum80ce1SY/jY0l5D8197rY2RWC2zp9/bZS2m8Wvs0NB141cv/RvAzGdv7vW4zAYSf8+Kx31WPO6z4nGfmZmZ9Q9OSnaDpCHARcDeudNnA5+NiGigiRm58ihJVdeGLLNy2bEXsqlg9Z12WlJ+/T//aWMkVkvHovm88uT1S45XXH8fBg2pvdj8uNXewzLj115y/NJjU/osPrOBwN+z4nGfFY/7rHjcZ2ZmZv2Dk5JNkjQYuBD4WO70OcBnGkxIAjxadrxmg9fl63UAj1eruLTa8OCDmfCudy05/s/ll7cxGqvnhQfOXVIeOmIcE7epvTHRpB2OW1KeN+tZXn3qxj6LzWyg8PeseNxnxeM+Kx73mZmZWfs5KdmELCF5PrB/7vS5wBER0dFEUw+XHW/R4HX5ek9HRDM7dxfOyttuy25nnMG4SZMaqr/xpz/N7r/97ZLjuS+/zANnntlX4VkveOXJ65j5TGn606Ttv86ESR+oWHedHU9kwlq7LDmeevN3iI63+jxGs6Lz96x43GfF4z4rHveZmZlZ+6nxwX1LtywheR5wcO70ecBhTSYkO3fsngGMyk6dHRFHNHDdf4DOeSMNXQOwuhRfbCbAfmL1nXbigJtuAuCl++7juZtu4pUHH2TOtGksfOMNBg8fzqiVVuIdm23Guh/9KOPXKW2AvmjBAqbsu2+XnbiLZOPvjqpfqYXW3u5Y1t7+2LedHzR4GFL6t42IDjoWL3xbnWkPXcTDV3+hatvLLLcO7z70BoaNXB6Ajo5FTH/kEl5+/EoWzpvJyLFrsuomh7DcGqV1nF5+/Eruu/QgoP/8+bX7cXPqV7J+5ZrTlm13CF34e1Y87rPicZ8Vj/usvi+cMY/Hn1+sdsdRNNpqleDuI9sdRmU6+Z6I2KrdYZiZ9bUh7Q6gCJT+j+ccuiYkz6cbCUmAiJgn6a+UpoB/TNIXImJujRh2oJSQBLi02fsW2Yqbb86Km2/eUN03nn+evx5+OM9cd10fR7X00KAhDB5Se6N4aVDFOho0tOZ1c2dO5b4/Hcjm+/2BYSOXZ9CgIazyroNY5V0HVaz/6tM38cCUw+hPfxkw6w3+nhWP+6x43GfF4z4zMzMbuDx9u44sIXk28Mnc6QuAyd1JSOb8NlceC3y5Tv0Tc+VngeurVRwoXn3kEe447TSm33MPHYsWNVT/H1//OmdvuKETkgXz+vO3c+uvt2baw39k8aL5FevMm/08j17/Ne6+6MN0LBrQKxeY9Ql/z4rHfVY87rPicZ+ZmZm1j6dv1yBJwFnAp3OnLwQO7WFCsrP9m4DOraIXAvtGxFUV6p0KfCN3anJEnFter5qiTt/OGzJyJBM23phxkyYxasUVGTpqFIsXLmTBrFm88dxzvHz//bw5bVq7w+w1/W36disNGT6G8Wu8lxGjV2XIsGVZ8ObLzJ35BK+/cEe7Q6vJ07eLp79N326lon7Plmbus+JxnxVPUfvM07e7x9O3zczaz9O3a/s4XROSAawIXJXylQ05NiIerPLekcDtwHLAMOBySRcBlwGvAmsBhwHvzV1zOWnq+FJl0bx5TL/zTqbfeWe7Q7E+tmjBbGY88Zd2h2E2oPl7Vjzus+JxnxWP+8zMzKy1nJSsbZmyYwG7NdnG96q9ERGPS9oHmEJKTA4mTRP/ZJVL/gYc1BujNM3MzMzMzMzMzNrFa0q2WUTcAmwEXAQsqFLteeArwPtrbYZjZmZmZmZmZmZWBB4pWUNEnEPadbuv7zMdOFjSWGBnYDVgNPAS8Bhwe3jxTzMzMzMzMzMzGyCclOxHImIWaSq3mZmZmZmZmZnZgOXp22ZmZmZmZmZmZtZSTkqamZmZmZmZmZlZSzkpaWZmZmZmZmZmZi3lpKSZmZmZmZmZmZm1lJOSZmZmZmZmZrZUkBTZ6+kq70/M1bmptdGZdY+kC7PP7FRJQ1twv7UkLcjueVR323FS0szMzMzMzKxgJD2dS55Vei2UNEPS7ZJ+JGnTdsdsSwdJk+t8NudIek7S1ZKOlbRinfaGSNpF0qmSrpP0vKT5kuZm7fxF0hcljWswvnNqxNYh6XVJT0j6o6RDJI0su/6kOs/X7Gvnbv+yUzw7Agdnh9+IiLeafOZ6r8nl7UXEU8Avs8NTJK3QndidlDQzMzMzMzMbeIYCE4Btga8A90k6sxWjqMzqGAWsBuwBfB94UtLnK1WUdCQwHbgB+AawG7AqMBwYmbWzF/A/wFOSDulhbALGAusAHwfOAx7taeKwj/0w+/kIcEkftP9slfPfBxYA44BvdqfhId0MyMzMzMzMzMz6h18B/yk7NwJYHfgAMJGUbDmSlMyZ3MLYbOn2JKURdZ3GAJuTkpJDgGWA/5U0JCJ+WlZ3C2D5rBzAv4HbgBeAxcB6wN5Zm+OA8ySNi4ifNxjfdcC1ueNBpGT+e0kJfYA1gKsk7RIR/8zqz6nR5iTgc1n5NeC7dWIo/+42TNI+wDbZ4Q8iIqpU/QPwUIPN7gW8Lys/DdxYqVJETJN0HvAZ4HOSTo+I5xq8B+CkpJmZmZmZmVnRXRwRN1V6Q9Jg4CRKI5k+JelnEXFvi2IrlIh4mpTAtd7xXEScXukNSesBV5ASiwCnSbokIl4sqzoD+AVwbjZtuLydccA5wD7ZqdMlXRsRjzUQ32014tuLNPJwGdKozF8Cm0fEbaTEaEXZqMrOpOTsau33kq9lP2cCF1erFBF/Bf7aSINl07V/VyPRCel38hlgGPAl4KuN3KOTp2+bmZmZmZmZDVARsTgiTgDuyZ3+YLviMesUEY8DHwU6slMjgf3Kqv0WmBgRJ1VKSGbtvE6aav1gdmoYaVRwT+O7iq5Jts0kbdLTdnuLpK2A92SHv4+I+b3Q5ruBjbLDDuB3tepHxH3A/dnhpyUt08z9nJQ0MzMzMzMzG/huypVXrVZJyXsknZxtKvKcpHnZxiIvSrpG0pckLdvITSWNlPRZSVdlG5TMyzYoeUbS3ZLOyzYTqbnZSdbWKpK+JekWSdOUNvN5NWvnu5JWaySmOveou/u2um7kclJ2boykr0q6M4tpnqT/SPq1pPWbjGFLSf8j6QFJryjtcjxN0vVKG7o0lfjpzyLiYeDO3Kltyt6/KyLmNtDOW3SdJv6eanWbdAGwKHe8TbWKbXBYrtxba0l+Ole+psHp2H/Kfo4hJZkb5unbZmZmZmZmZkuXV2u89w9ghyrvrZy9PgAcJ+njEfGPag1J2gi4krSmZbk1steWwCHApbx9lFy+reOAE0ij6fKWy15bAl+W9JWIKF/DsE8p7Wx+KWktwby1s9ehkg6OiEvrtDMKOIvSTsp5K2WvXYGvSdovm0Y8EDxFaf3Gbu3inGun0/JVazUhIuZImkH63EPP4us1kgaRRocCvA7c0gttjgIOyJ36bYOXXgF8JysfQErkNsRJSTMzMzMzM7OB77258qM16nUmXV4A7gCeAGaRdvOeRNqc5B3Z62pJW0fEI+WNZCMpryZttgPwCmlNu/8A84DRWXvvBtaqFbiks0jr1nW6PXu9krWzQ/Z8I4BfSBoaET+r1WYvWpX0XCuRNhK5jrQG4qrAx7Lzw4DzJd0fERU3Ncl+X38nbewCsDBr6wHSpiorkX7365ESZH+TtGNE3FmhuaLJj7qtOyqyhvwI4Bk9aKfcqFy5J/H1pq0ofVdvjoiOWpUbtD/p+wTp93d5g9f9i/QPHcsDu0gaHhELGrnQSUkzMzMzMzOzASobUfUNStNOX6T2VM+Lgb9US3ZJGgacAhxL2gDk56TRe+X2p5SQ/Auwf7VpuNk6fRtVee8zlBKSU4GDI+KuCvV2JT3XeEobndRKvvaWT5Om9x4ZEb8ui+k4UsJyO9IIz2OBz1Zp50xKCcm/AodHxLSy9kTaTORHpF3UL5K0QTZ1uZAkDafrVOsne9Dc/rnyzT1oZwlJW5CmJXfqSXy9aadc+W3fh27KT90+r9HPVUSEpLuB3Ul/JmwF3NrItU5KmpmZmZmZmRXbAdmmF3nDSEnB91OaVjwd+FitDTEi4sRaN4qIhaTpwxsAe5NGRq0TEVPLqm6eK59Ya13AiHiQ0iYlS0gaCZyaHc4Cdqm2xl1E3CDpU6TRXUOBY4Ajaj1LLzquPCGZxfSGpCOAf2enPkqFpGTWd51Ttu8A9q6UEMp2Qf5JtnbmV0hTww8Ezu+Vp2iPE4EJueOrutOIpPeTkmKQksRn9zCuzgT8j3Kn5tF1bdZ2yn/f/9XTxrLv83a5U79psokHKf3+t8ZJSTMzMzMzM7PCmZCNOup0VkScVeeaz9V5/y3gJ8D3IuK1HkVXchEpKQlp6nR5UnJwrly+DmSj9qc0RfWMeptuRMQVkp4A1gU+3M17NmsGUHWqeEQ8Kulh0kjQCZJWi4jny6p9Plc+oYERaj8iJSUh9UGhkpKSRgObAUdRNroxIm7oRnsr0TUJ+fOIeKKbsQ0iTUPeATietFZpp9Mj4o3utNsH1suVn+mF9vIJ/Fu7Mco4H8O6jV7kpKSZmZmZmZlZ//FKRJSPeuypoaSpw9tJOioiHmjkIkmrApsAq5DWmsvnEPKbulTaXTp/j59JOqAbiaL8tPBrGrzmflJSZAVJa0ZEbyRsarkhGz1ay+OUpqe/AyhPSnY+50IaGIkXES9mm6+sQNcRc/3RTpKigXqPUtq4pWHZaNrLgM6d1+8HjmuiiRMl1RwdnPkTcHJTwfWt/E7z06rWaoCkocChuVONbnCTl49h9aq1yjgpaWZmZmZmZlZs74uIm/InJA0mra+4GWmtuANIo79ul7RXef2yaw8gTX/eslqdMuMqnPs98E1S8mRz4DFJdwI3kG1UExG1dgEH2DRX/ntaUrEpK9A7o8hqaaT9/Oi6/KYuSBpPKYkzDFjY5HN2azdoSXsA76pR5bmIuLg7bTdpOvBr4LSImNfMhdn06j+RNkuClOzdt9FNVhr0L+DHEXFOL7bZG/LrXPZ0850Pk5LlkD6rf+xGG2/myqOr1irjpKSZmZmZmZnZABMRi0m7U18PXC/pfuA00lTqiyStVz4VNdtI5dc0vxbjiAr3f0PSbqTk5BaASMmjzgRSSLove/+sKtNil28yjnKj6lfpsarrc+bkRwoOKnuvXc94IPCpGu//nbTpUU89CfwydxykJNpM4GHg39lntSnZ6L4/Antlp6YBu0bE0002dR1wbe64g7Tb+UvA/S0Yadtd+Xzeoh62lf++/yEi3qxas7p8DEMbvchJSTMzMzMzM7OB74fA0cDKwErAJ+maLIKUnOhMULxFWqfvCtJGLS8D8zoTSJLeB/yt1g0j4rFsE5fdSJu87AhsSEpQipSs3AL4b0kfj4hbyprI5yy+TdcRh43oLzsl15J/xhnAD9oVSB95LiJO780Gs4TkxcA+2anppNHCj3ejudt6O74WmUtp1O0IujlaMluiYffcqWY3uOmUXze24aSmk5JmZmZmZmZmA1xELM6mT3cmcnbg7UnJo3Plj0fElBpNjm3wvkEajXYdgKTlSRvj7EUarTealCS9Ihu9OSN3+avAill5SkTc28g9CyY/hX14qxJkETEZmNyKe/Wm3AjJj2SnXiLtyv5Y24JqjxmUkpLLkUaedsdhlDal+ldE3NnNdpYri60h5cOGzczMzMzMzGxgyo+m6rIWoaRlgI2zwyfrJCSh9nqEVUXEqxFxWUQcSdqQpnM04zjg4LLq+R2At+/O/QrglewFMEbSxrUqL80qJCRfJiUk/922oNrnqVx5taq1asiWazgsd6o7G9x0WjVXfrrRi5yUNDMzMzMzM1s6rJErv1b23vhcuZFRVx/raTAR8RJwVu7UBmVVrsuVD1c3drrp77KRpDfkTh3erlj6swoJyRmkNSQfaVtQ7fVgrrx+N9t4H7B2Vl4AnN+DePLf3QcavchJSTMzMzMzM7MBTtLalDaZASifCv0apQ1ZNpQ0kiokTSbt6t3byndfvpBSgnQzuk4vr6lgCcyf58r/JWmbRi8s2HN2S5aQvISuCcldIuKhtgXVfnfkyptWrVVbfoObyyKiu1PAoeufBw1PAXdS0szMzMzMzGwAkzQRuJTSvhLzgIvydSJiLnB3djgKOFPS8AptfQo4k647Sle65x8kfUvSWjXqvJOuicZ/lMX0BnB87tSPJZ1UJ2G6qaSfA9+rFV9/EhG3UtrpejhwjaT9qyUcJQ2R9H5JlwH7tijMtsglJDvXQnVCMrmetFM4pPVhmyJpPGnzqU7d3eAGSeMoLefwWDM7lnujGzMzMzMzM7NiOyDb5TpvEGmdxs2BXYGhuff+OyKerdDO90jJS4BDgB0lXQU8D0wAPgBsBCwGvkvXhGG5lYADgJMlPUxKeD5LSohOII3ueh+lwVK3k3b67iIifiVpQ1LychBwInCUpOuAJ0jrZI4FJgHbAGtml55RI7b+6HBS7NuS+u1i4LuS/gY8R0pAjQfeSRrx2rmxyAUtj7S1zqCUkAS4CthD0h4NXHtWRMzum7DaKyJekXQradOod0laMVsOoVGfIO3aDWkNyBuqV61rF0rf48ubudBJSTMzMzMzM7Ni+1yD9V4DvhQR51V6MyL+T9KJwEmASEmy/yqr9ibwWeAFaiclF+bKG2Wvav4KHBwRi6vE9cUssfk9UmJuedLO3dUsAB6v8X6/ExFzJe0MnE7qzyGkROukGpe9Ckzr8+Daa72y4081ce2fgAGZlMycT0pKijRi9ldNXJufun12trZpd+VHXDa1LqWTkmZmZmZmZmYDT5ASiDNIm2L8FfhDRLxe86KIb0u6gTQycXvgHcAbpNGSVwG/jognswRaLR8EdiSNotoGWAdYERgGzCGNmrwDuCgi/lb3YSLOknQRcCjwftIadhNI051nk0Z7PUga8fWXiCjfyKffi4gFwBcknQ5MJo0kXY/SqMjXgKnAPcC1wPURsbBCU7Z0+APwQ9JI4YNpMCkpaQtKa0B2AL/rbgCSRgF7Z4f/jIh/NXV9z5KhVgSrS/HFdgdhTdn4u6PaHYI1affj5rQ7BGvSNact2+4QzMzMeuwLZ8zj8ecXD/iNLnqbtloluPvIdodRmU6+JyLKp2Kbmb2NpB8C/50dbtTq3cglHUFpPcqPR8SfmrneG92YmZmZmZmZmZkVz+mkdVUBvtzKG2cbMX0pO3wI+L9m23BS0szMzMzMzMzMrGCyzW1+mh0eKmnVFt7+Q5R23T4+IjpqVa7ESUkzMzMzMzMzM7Ni+i5p46lhpE2q+pykQcCp2eG1EdHUrtudvNGNmZmZmZmZmZlZAUXEHEmfIG2MtEjS0Ih4q49vuxppuvb/0eSO23lOSpqZmZmZmZmZmRVURPwd+HsL7/csvTAq09O3zczMzMzMzMzMrKWclDQzMzMzMzMzM7OWclLSzMzMzMzMzMzMWsprSi4FRq46iI0/P7LdYZgNaNectmy7Q7Am7X7cnHaHYE3y98zMzMzMbOBwUtLMzMzMzMyWKmvEUL6xaOV2h1HR59odgJlZi3j6tpmZmZmZmZmZmbWUk5JmZmZmZmZmZmbWUk5KmpmZmZmZmZmZWUs5KWlmZmZmZmZmZmYt5aSkmZmZmZmZmZmZtZSTkmZmZmZmZmZmZtZSTkqamZmZmZmZmZlZSzkpaWZmZmZmZmZmZi3lpKSZmZmZmZmZmZm1lJOSZmZmZmZmZmZm1lJOSpqZmZmZmZmZmVlLOSlpZmZmZmZmZmZmLeWkpJmZmZmZmZmZmbWUk5JmZmZmZmZmZmbWUk5KmpmZmZmZmZmZWUs5KWlmZmZmZmZmZmYt5aSkmZmZmZmZmZmZtZSTkmZmZmZmZmZmZtZSTkqamZmZmZmZmZlZSzkpaWZmZmZmZmZLBUmRvZ6u8v7EXJ2bWhudWfdIen/uc/u+Ft3zuux+d0jqVn7RSUkzMzMzMzOzgpH0dC4JUem1UNIMSbdL+pGkTdsdsy0dJE2u89mcI+k5SVdLOlbSinXaGyJpF0mnZomw5yXNlzQ3a+cvkr4oaVyD8Z1TI7YOSa9LekLSHyUdImlkE8/+/8raO73Ra7tL0lDg59nh1RFxY4PXLZP11Z8lTc365Y3s2a+TdIKkbWs0cSwQwDbAEd2J3UlJMzMzMzMzs4FnKDAB2Bb4CnCfpDOzBIZZO40CVgP2AL4PPCnp85UqSjoSmA7cAHwD2A1YFRgOjMza2Qv4H+ApSYf0MDYBY4F1gI8D5wGPStq5wevLk3OHtuA79xlg/ax8ciMXSNoXeBT4HfARYBKpX5YlPftuwLeBP1RrIyLuA67MDr8taZlmAx/S7AVmZmZmZmZm1q/8CvhP2bkRwOrAB4CJpGTLkaRkzuQWxmZLtyeBX5adGwNsTkpKDgGWAf5X0pCI+GlZ3S2A5bNyAP8GbgNeABYD6wF7Z22OA86TNC4ifk5jrgOuzR0PIiXz30tK6AOsAVwlaZeI+Ge1hiRtlsULMJ/0HVwB+DDwfw3G0xRJI4BvZoc3RcQdDVxzNCmJq+zUM8DfgGeBDmBlYE1gxwZC+B7p+VYCjgJ+0ET4TkqamZmZmZmZFdzFEXFTpTckDQZOopS4+JSkn0XEvS2KrVAi4mlKyRrrueciouIUZknrAVeQEosAp0m6JCJeLKs6A/gFcG5EPFWhnXHAOcA+2anTJV0bEY81EN9tNeLbC7iElDQdSUqubl6jrfwoyf8G/jd3vk+SksChpCQipN9RTZL2AzoTv28AnwcujIiOCnWHUUqyVhQRt0l6ENgE+JKk/4mIhY0G7+nbZmZmZmZmZgNURCyOiBOAe3KnP9iueMw6RcTjwEdJo/MgJf72K6v2W2BiRJxUKSGZtfM6aar1g9mpYaRRwT2N7yrgq7lTm0napFJdScOBT2SHL5ASmJ2jKneXtGpP46ni6OznTOCyWhUlrUApcbkQ2C0izq+UkASIiIW1RobmnJ39XBnYv4H6SzgpaWZmZmZmZjbw3ZQrV02QKHmPpJOzzS6ekzQv21jkRUnXSPqSpGUbuamkkZI+K+mqbIOSedkGJc9IulvSedlmIjU3O8naWkXStyTdImlatpnPq1k735W0WiMx1blH3d23yzZyOSk7N0bSVyXdmcU0T9J/JP1a0vqV2qkRw5aS/kfSA5JekbQge97rsw1dml67r7+KiIeBO3Ontil7/66ImNtAO2/RdZr4e3onQi4AFuWOt6lS76PA+KzcOfLwvOx4MH2wZIKkrYGNssMp2e+glqNI08kBfhgRd9aq3IRLSVProcnn9PRtMzMzMzMzs6XLqzXe+wewQ5X3Vs5eHwCOk/TxiPhHtYYkbUTaCGNihbfXyF5bAoeQEhvlo+TybR0HnEAaTZe3XPbaEviypK9ERPkahn1KaWfzS0mbheStnb0OlXRwRFxap51RwFnAwRXeXil77Qp8TdJ+EXFbj4PvH56itH7jCrUqNtBOp+Wr1mpCRMyRNIPSFOlq8eWnbncmI/8A/IS0juvhkr4bEfG2K7vvgFz5yqq1WLKMw6ezww5Ku3X3WEQ8L+l+0tT290l6R0S83Mi1TkqamZmZmZmZDXzvzZUfrVGvM+nyAnAH8AQwi7Sb9yTS5iTvyF5XS9o6Ih4pbyQbSXk1abMdgFeAv5I25JkHjM7aezewVq3AJZ1F2mG40+3Z65WsnR2y5xsB/ELS0Ij4Wa02e9GqpOdaCXiItHHKjOz8x7Lzw4DzJd0fEeUbEgFLfl9/p7SG38KsrQeAOVk7e5DWX1wZ+JukHXtxtFs75Ufd1h0VWUN+BPCMHrRTblSu/Lb4JK0F7JId3puN/iQiXpN0JelzsDawM3BjL8a1V/YzSJ+dWjYFVsnF+JKkSaQ1JfcifU8XAc9nMZ4ZEf9qIpYbSUnJQcDuwPmNXOSkpJmZmZmZmdkAJWkQ8A1K005fJG3eUc3FwF+qJbuyzS9OAY4lbQDyc9LovXL7U0pI/gXYv9o03Gydvo2qvPcZSgnJqcDBEXFXhXq7kp5rPKWNTmolX3vLp0nJnCMj4tdlMR1HSlhuRxrheSzw2SrtnEkpIflX4PCImFbWnoAvAT8ijb67SNIGDUzb7beytRjzU62f7EFz+fUMb+5BO0tI2oK0s3enSvEdTmlzpPPK3juPlJSENJqyV5KS2fqQG2aHT0VErdHPUBqJCnCfpMNIG/GULwXwzuz1X5J+Ahxbbc3JMvnv5I44KWlmZmZmZma2VDhA0lZl54aRkoLvpzSteDrwsYiYX62hiDix1o2ynXW/JmkDYG9gF0nrRMTUsqr5XYpPrLUuYEQ8SGmTkiUkjQROzQ5nAbtExHNV2rhB0qeAy0mjOo+h65TavnRceUIyi+kNSUcA/85OfZQKScms7zqnbN8B7F0p0ZhN/f1JtnbmV0ij7w6kwQRQP3UiMCF3fFV3GpH0ftIIPUhJ4rNrVG+0zWGkBHCneXRdm7Uz6T85d9+Lypq5mjRqcwXgY5KOyjbm6an8972REY3r5sqbkr4bg7LYLgOeJi2DsCcpKTmItMnPaKon0vPy39+tG6gPOClpZmZmZmZm1p9MkHR37visiDirzjWfq/P+W6S17b4XEa/1KLqSi0hJSUhTp8uTkoNz5fJ1IBu1P6Xp5GdUS0h2iogrJD1BSsB8uJv3bNYMoOpU8Yh4VNLDpJGgEyStFhHPl1X7fK58QgMjH39ESkpC6oNCJSUljQY2I2280mV0Y0Tc0I32VqJrEvLnEfFEN2MbRFqPcgfgeNJapZ1Oj4g3yi7ZHejcYOmv5WspRsRbkv4AfIG0vMDBlHbA7on1cuVnGqg/PlfuHDV9BfDJiJjd+YakY4GvU/rHgCMlXR4Rf6nTfj6GdavWKuOkpJmZmZmZmVn/8UpElI967KmhpKnD22UjtR5o5CJJqwKb/H/27jtMsqJ8+/j33rwsm2CBJS9Bck5KjoKgYkCikqOvCKiAIipBBQNmUQH1hyhJREGQDAISJEgSkJxhFzYAC5vD8/5RZ7bP9Hac6e6Znr0/19XX1Dldp071VB9cn6mqh7QX3XA6xxDySV1KZZfO3+PnkvbtQqAovyz8xhqveYQUFFlK0soRUUvApjtuzWaPVvIMheXpS5P27cvr+JyzKZqJV0pEvJElX1mKzjPmeqPtJdWS3OUpYO96G89m015FITD4CHBKHU2cJqni7ODMX4AzSpwvleCm2B9IQcmO+o0ISuYzzY8vW6tg8aLj54F9I2JG/mS2VPusLGP8QdnpU0hbMJQVEdMkvUf678RikpaIiCnVOuWgpJmZmZmZmVl72zEibs+fyLLtjibNSDuClKl3G+BeSXsU1y+6dl/S8udNy9UpMqrEuUuAb5CCJxsDT0u6H7iVLFFNDfvgbZgr35G2VKzLUtQ2i6w7amk/P7uuU3BI0mgKe28OAmbX+Tm7lK1a0keA9SpUeTUiLu9K23WaAFwAnF0cIKsmW179F1KyJEjB3k9FxKwG9u+/wI8j4sIS91+Kwmzhd0gzDxcSEf/JzZbdRNJGEfFIN/uV3+eyluRAxb/bn1X5fZ9FISi5laTRNcyynkYKSpL9dFDSzMzMzMzMbFETEfNI2alvAW6R9AhwNmkp9aWS1iheipolUrmA+vdiHFLi/u9J2oUUnNyElAjkgxQCSCHp4ez980ssi4W0jLY7hlWv0m1l9+fMyc8U7Ff0Xk99xv2Agyu8fwcp6VF3vQD8OnccpCDaFOAJ4H/Zd7UukgYCf6aQgXo8sHNEvFRnUzcDN+WO55Oynb8JPFJlpu2BpFnIAFdU2quVtMT+e1n5cAozJ7sqH8+bW0P94ufrtkqVI+JpSW+QZkmL9IeFitcU9WNg2Vo5DkqamZmZmZmZ9X0/BI4DlgXGAp+jc7AIUrCkIyA5h7RP3zWkRC1vATM6AkiSdqS2wMZmwC6kJC/bkTIGK3ttkr1OlLR3RNxV1EQ+ZnEmCwdWqulOJudWyX/GicAPeqojTfJqRJzTyAazgOTlwCeyUxNIs4Wf6UJz93Sjf/ng/ZqSLqxQN5/M57OSTqoSxKwmPztyoT8KlDCh6Lh4C4FSXiMFJaFz/8vJ7x07rYb63Q9KShpD2mNiDGn/gzeBh7v5yzUzMzMzMzOzBomIedny6Y5AzjYsHJQ8LlfeOyKurtDkyBrvG6TZaDcDSFqSlBhnD9JsveGkIOk12ezNibnLJwPLZOWrI+KhWu7ZZvJL2Ac3OoBXTkQcQiFrdNvIzZD8ZHbqTVJW9qdb3I8tSVmqO2yXvWoxGvgUC2fqrkf+OVmihvqPd+EeUaa8kCxB0Mhc3WpbMwALTxuumaTdJf2bFG29mfTLvBK4C5gi6fJsY0wzMzMzMzMz63n52VWd9iKUtBiwfnb4QpWAJFTej7CsiJgcEVdFxFGkhDQdsxlHkTIT5z2VK2/dlfu1gUnZC2CEpPUrVV6UlQhIvkUKSP6vB7pT7xYHjb7+xVx5hbK1Cv5TdLx8Ddfk251YtlayDIWJj2/UkPwJ6GJQUtIPgGuBzbM2VPQaAnwGeEjSx7tyDzMzMzMzMzNrqJVy5eKkFaNz5aoJKoC9utuZiHgTOD93aq2iKjfnyoepC5luertsJumtuVOH9VRferMSAcmJpD0kn+yBvgwjJY7qsGZEqNoLWIzCFgQ7SVqlG914LH//apWzvTEfzp3aqVJ9SWtQCFzOA6rNUs4/u49W60+HupdvS/p/wInZYWSv4v8wdEzrHApcLmmTiHgKM6D/oOGMGLsRI8ZuxMhlN2HE2I1YbPRqpNm+MOXlf/HAJbt36x7DlliD5dbfnyVX2YUhI1ZgwODhzH7/TaZNeZYJT/2NCf+7knmz32/Ex1lkeNzaj8fMrPn8nLUfj1n78ZiZNYakVSkkmYGFgwxvU/j/92tLGlouO6+kQ0hZvRut+H4XA98lLU/diLS8/Ge1NCRJWcCvHfyCQpDr85IujYj7a7mwzT5nl2QBySsobD0wkTRDsitLkhthXwpZ1B+sdS/LiJgh6a+kBEMCDgW+1cU+PEHat3EYsJ6kfhExv8o1/0dKWANwvKTfVcjAfWqufEtETK3S9ka58n1V6i5QV1BS0nBStqCOL7xIEdBrgVdJ2XXWJm1gOzarNwT4KfCReu5lfdM2Rz3EYkusvuAfkY0m9We1bb/OKlt+hX79On+9h45amaGjVmbMqruw2tZf5fFrj2HKK3c2pR99jcet/XjMzJrPz1n78Zi1H4+ZWWNIGkfabq3jiz6Dov3sImK6pAdJKyKHAedJOjIiZhW1dTBwHqUnKOXrXQY8CfwxIl4sU2cdOu9j2ekhyzJ4n0ph78sfSxoNfL9CwHRD4AjSUvWvlutfbxIRd0u6nBTsGgzcKOloUkbnhQKOkgYAOwJfAC4C/trK/rZSLwxIQuel1xfXee0lFLKeHyLp9BqCiQuJiLmSbgM+TgqQbkT12YznkZ631bPXZZI+F1HIfJ/NRv4acFB2aj4pyVQ1+f00bypbq0i9MyU/S/qwQerY/4uIC4orSfoK6cN2fIhdJK1cJZW6LQKGLblGU9tf96O/Yvn1P7vgOGI+0yY9zewZkxk6cmWGjlwRgKEjV2LT/a7moSv2YvKL1bLam8et/XjMzJrPz1n78Zi1H4+ZWc32zbJc5/Uj7dO4MbAzaRJRhxMj4pUS7XyPFLwEOBDYTtJ1pCy8Y4BdgXVJyznPovNsqmJjSUG2MyQ9ATwIvEIKiI4BNiQF1jr+6nAvKdN3JxHxG0lrk4Ip/YDTgGMl3Qw8Swo+jgRWA7YAVs4uPbdC33qjw0h9/xBp3C4HzsoCT6+SYjCjSclVPkghucmfWt7T1jqXQkAS4DrgI5Jqmfh2fg0z/OoiaS1gq+xwPmmc6nErKTfLWGBF0jN1Qxe7cxUpKAkpw33FoGREzJZ0AHAbKba3J/C8pL8BL5G+U7uTnvEOp0XEPZXazYLk22eHE4CaZvlC/UHJHXPls0sFJAEiYpakQ0mb1m5J+uvJjsCFdd7P+qi5s6Yy9c3HmDrhYaZOeJhxWxzHiLEbdavNlTc/ttM/Kqe8chdPXH8s06c8t+DcEuN2YP2Pnc+Q4cvRr/9ANvzkH7nndx9i5tRXu3XvRYXHrf14zMyaz89Z+/GYtR+PmVlVx9RY723ghIi4qNSbEfFXSacBp5P+f/zKwOeLqk0DjgZep3JQMp/oYl06BzqK3QAcEBHzyvTr+Cyw+T1SYG5JUubucmYBNS2p7S2ymao7AOeQxnMAKdC6WoXLJgPjm965nlX816mDS9Yq7S9AQ4OSdJ4leVtE1PX7j4h52azY43PtdTUo+Rfgl6StEz8N/KCG+z+QBXT/CKxCSnh1VImqs4CvRcRPa+jHDhT2pL2knpmf9QYlN8p+ziU9KGVFREg6h8JfWTamjwYlJV3MwlnCVomIl+poYy3SzNLdSNHyEaQI89OkqcqX56fUtqvHrj6Udyc8wvQpz3Y6v8KGh3ar3YFDl2C1bb624HjqhEd48LI9iXmdEz5Neel27v/Tbmx12D0MGDycgUNGsvp23+Txa0s9g9bB49Z+PGZmzefnrP14zNqPx8ysW4IUQJxISopxA3BZRLxT8aKIMyXdSpqZuDWwNCk5x2ukWWoXRMQLWQCtko+SlnTuRJrBuDopQ+8g4H3SrMn7gEsjouoU5Ig4X9KlpP/f/GFSfGIMabnzVNJMr8dIM9H+ERHFiXx6vWyp/BezWMohpMlda1CYFfk28Bwpk/JNpL3+aspybN2XLSU/KHfqki42dTGFoOSeksZExKRKF5QSEVOzbRIOBbaQtHpEPFfDdXdLWo/0Hfs0KUnN0qTn8gVSgqlfRUStf4HriIcFUHLyYjn1bs6yVHaTJ2ucAnt3rjymznu1hSy7eHFAsp7rB0j6NvA4cAqwCen3PJj0V6ldSYP6uKQdyzbUJsY/ecVC/6hshJU2PZqBQ0YtOH7ihuMW+kdlhxnvvMjzd39/wfFy6+7LkJErlaxricet/XjMzJrPz1n78Zi1H4+ZWXkRMa5Ktt9+ETE8IlaNiE9GxG+qBSRzbd8dEftGxAoRMSgiloyIDSPilIh4Iatze+5eh5RoY05E3BoRp0bEhyNilYhYLCIGRMSoiNggIo6sJSCZa/O9iDg3+zzjImLxiBiY9W/TiDg0Iv5UKSCZ6/O4Mu+/lKuzQ5k6F+bqnF5Dvw/J1b+9hvovR8QZEbFDRCwXEUOy17IRsW1EnBAR1/XWgGTR72eHbra1Q5XveaXXS2XazI/H6XX0ZU5ELJO79v+6+JkeyLUxuCsByZwfZz8FHFlHH6ZHxK8iYpfcc75ERGyWPec1BSQljaCQpOm6qDPJdb1ByRHZz7dqrJ//xY4oW6tNZRvsntfNZn4HfAPonx0HaTPgO0n7RnRYCbhJ0q7dvF+ftMxan1pQfueNB5g6vvL+rq8/+gfmzUn7Iqtff5ZZ8xMV61tzeNzaj8fMrPn8nLUfj1n78ZiZmVlfECnhT0eioyMlLV6pfhMcDSyWlWtJiNNJvUHJjvol93koFp3XkfcvW7F9/RRYNivXnF2og6Qv03nq753AWhGxbkRsHxErkaalv5G9PwC4QtLK2AJDR41j+FLrLDie+Fz17RjmzHybd14v7L269Ac+2pS+WXket/bjMTNrPj9n7cdj1n48ZmZm1sd8gxSnG03t+8t2m6TBwAnZ4d8iouYENx3qDUpaRtIeFAKK/wAurfP6JYFv5U49DOwaEZ02442IW0j7cLyfnRoBfLsrfe6rRiyzYafjd177d03XvfN6od7wpddvaJ+sOo9b+/GYmTWfn7P24zFrPx4zMzPrSyLifxSyzX9N0sgW3fo4YDlgJnBiVxpwULILsgE+Pzt8j4WzkdXiWCD/RTk60qa2C4mI5+kciPyspHFduGefNGzMWp2O8xkTK8nXGzhkJIOHL9fQflllHrf24zEzaz4/Z+3HY9Z+PGZmZtYHfQs4nZSNu1LG9kaaBZwBfK5jr9l6OSjZNT8Gls/KX6t1A9Aie+fK90fEA1Xq/5YUfYY0bnt14Z590tDcRuPz589l5vvja7puxrudh23oSK+KbyWPW/vxmJk1n5+z9uMxaz8eMzMz62si4t0sOdLpEVF5o+TG3fPn2f2u7GobA7p43faS6o2C1npNRESrorp1k7QbcFh2eBfw6y60sSqwbu7UtdWuiYgpku4FOjJw7wn8qN5790UDBhVyKM2b9R502sq0vLmz3i1qp9X7wS7aPG7tx2Nm1nx+ztqPx6z9eMzMzMx6h64GJYcA42qsGzVeE6QU5lGhTo+SNBy4IDucBRwREV3p78ZFx3fXeN3dFIKSG3Xhvn1S/0HDFpTnzZ1ZoWZnxXX7DxresD5ZdR639uMxM2s+P2ftx2PWfjxmZmZmvUNXlm8re9Vbv9o19bTZU84BVszKZ0bE011sZ52i42drvC5fb4SkFbp4/z6lX7+BC8oxf27N1xXX7de/qzF66wqPW/vxmJk1n5+z9uMxaz8eMzMzs96h3v8l/UNTetEGJO0MHJUdPgr8oBvNjcuV5wFv1HjdyyXaea0b/egT5s2ZtqDcb8CQmq8rrjtv9vSG9cmq87i1H4+ZWfP5OWs/HrP24zEzMzPrHeoKSkbEoc3qSG8maXFSohlIQcQjIqL2P6subESu/F5EzKvxuneLjsuuGZF0FFkQdelR7TAJtevm5v5h2X/g0JqvK647d/b7DeuTVedxaz8eM7Pm83PWfjxm7cdjZgAT33qPX/30nz3dDTOzRZqzb9fm+xRmN/4kIh7sZnv5XbFn1HFdcd2yQcmIOD8iNouIzUYO69tByTnTJy0oDxi0OP1r3HR88LCxnduZMalMTWsGj1v78ZiZNZ+fs/bjMWs/HjMzM7PewUHJKiTtAHw+O3we+FYDmh2YK9cz47K47sCStRYx709+ptPx0BEr1XTd0JGFejF/HtOmPNfQflllHrf24zEzaz4/Z+3HY9Z+PGZmZma9g4OSFUhaDPgdhSQ8R0ZEPTMby5mWK9e+kc3CdaeVrLWIeX/i/zodjxi7YU3X5evNePdl5teRfdG6z+PWfjxmZs3n56z9eMzaj8fMzMysd3BQsrLvAatm5d9GRKM2HclvQLNYHdcV132vAX1pe1MnPNRpT5/RK21T03WjV9x6QXnKK/9qeL+sMo9b+/GYmTWfn7P24zFrPx4zMzOz3qGuRDeSDmpWR/Ii4qJW3KcSSesAx2aH44GTGtj8xFx5mKThEVFLgHHZomNvZAPMnzuTSS/cwti1PgnAMmt+gv/ddCLz55af1DpqhS1ZbPSqC47ffPrqZnfTinjc2o/HzKz5/Jy1H49Z+/GYmZmZ9Q51BSWBC4FoQj+K9XhQEliawrLtZYG3pboSxryYq/9yRIzLvfdUUd2VgcdraHPlXHk+8Ey5ioua1x/9w4J/WA4cMopxWxzLC/f8sGz91bY5ZUF5xruvMPlFZ97rCR639uMxM2s+P2ftx2PWfjxmZmZmPa+nl2+rxGtR8ETR8SY1Xpev91KD9rfsEya9cDNTXi4so1lt668xZrVdS9ZdfbvTGLPKTguOn/vXd4j5c5reR1uYx639eMzMms/PWfvxmLUfj5mZmVnPU0TtEx8lzW9SPzo6ISAion+T7lMzSVsD9azLGAwsnjt+mzSbEeCViFgQUJQ0lLSEe1h26vcRcXgNfXqewh6XNV0DsMYK/eMXXxhaS9WmW3Wrk1l165MXOt+v/yCkFCOPmM/8ebMXqjP+8Ut54vovlm17sSVW54MH3cqgoUsCMH/+XCY8eQVvPXMts2dMYejIlVl+gwNZYqXCfkBvPXMtD1+5P62ZANy+PG7tx2NW3W6nvF+9kvUqN569ePVKLeTnrP14zNqPx6y6L547g2dem7eoTO5omMVWXCI+cHzpQHRPe+yky/8TEZv1dD/MzJqt3uXbjY5sfRQ4C1iD3vS/7EBE3A2MqbW+pEOA/8ud2iQiXirT9gxJNwB7Zaf2kvTFiJheof1tKAQkAa6stW+9ifoNoP+AygnHpX4l66jfwIrXTZ/yHA//ZT82/sxlDBq6JP36DWC59fZnufX2L1l/8ku38+jVh9LLvnq9kset/XjMzJrPz1n78Zi1H4+ZmZlZ31XX8u2ImNWIF7ABcANwBfAB0v+yK/t5SaM/ZC/1u1x5JPClKvVPy5VfAW5peI/6gHdeu5e7L9ic8U/8mXlzZ5asM2Pqazx1y1d58NKPV9zQ3FrH49Z+PGZmzefnrP14zNqPx8zMzKzn1LV8u9s3kz5Amhn56Y5TubdvAr4aEY+2rEMNVGKm5CrlZkrmrrkd2D47nA18KiKuK1Hvu8DXc6cOiYg/1Nq33rR8u5UGDB7B6JW2Zcjw5RkwaHFmTXuL6VOe5Z3X7+vprlkFHrf2065j5uXb7ae3Ld9upXZ9zhZlHrP2065j5uXbXePl22ZmPa/e5dtdImkZ4HTgsOye+f/RfJAUjFwUU9gdBdwLLAEMAv4u6VLgKmAysApwKLBt7pq/A39sbTfb09xZU5n47D96uhtWJ49b+/GYmTWfn7P24zFrPx4zMzOz1mpqUFLScOCrwPHAYhSWaAM8B3wjIv7czD70ZhHxjKRPkBLqLAH0Bz6XvUq5Ddg/IpqVcMjMzMzMzMzMzKzp6tpTslaSBko6AXgeOIVClmmAt4BjgXUW5YBkh4i4C1gXuBSYVabaa8CXgQ9XSoZjZmZmZmZmZmbWDho+U1LS54AzgZXpPDPyfeBHwI8iYlqj79vTIuJC4MIuXjsBOEDSSGAHYAVgOPAm8DRwb7Ry808zMzMzMzMzM7MmalhQUtJHgLNJmbXzwci5wHnAmRExqVH364si4l3SUm4zMzMzMzMzM7M+q9tBSUmbA9+nkEU67zLg1Ih4sbv3MTMzMzMzMzMzs76hy0FJSasDZwF7dZzKvX0zKaP2I13vmpmZmZmZmZmZmfVFdQclJS0DnAYcnl2fD0b+hxSMvK0x3TMzMzMzMzMzM7O+pq6gpKQzgS8Bi9E5GPk88I2IuLyBfTMzMzMzMzMzM7M+qF+d9b9BISAZpOzQxwJrOSBpZmZmZmZmZr2ZpMheL5V5f1yuzu2t7Z1VIunC3NjsUKbO7bk641rawTpIWkfS7Kyfh7bonhdk93tZ0mKtuGc19QYlO3Rk1h5MClS+KumNBr1eb8xHMzMzMzMzM+ubJL2UC76Ues2WNFHSvZJ+JGnDnu6zLdok/aDoO3psC+9d6Vmp93V7A7r0C2Ag8DjwhzJ93qELfVuvwj1PA2YAKwGnNuAzdFt3s2+PzF6qVrEOUb2KmZmZmZmZmVUwEBiTvT4EfEnSBcCxETGnR3tmixxJA4CDik4fDvyyB7rToyTtAeyUHX47Iua34r4R8Yak3wJfJP334NyIeKMV9y6nK0HJRgYgzczMzMzMzKx7fkPK9ZA3BFgR2BUYR/r/8keRVjwe0sK+mQF8DFgmK88kfT83krRJRDzUgvufVOX9zwOrZuXLgQcr1H21m335TvbzeeAvNV7zIKlf1Yyv8v45wP8DhpJWPv+/Gu/fFPUGJc9oSi/MzMzMzMzMrKsuj4jbS70hqT9wOikAAXCwpJ+3KBDUdiLiJTwZqxkOz5VPpDBD8nCg6d/FiDin0vuSPkYhKHlDRFzYjH5I2hXYODs8r45Zkk9U+wy1iIhXJF0HfBw4VNJpETGxu+12VV1ByYhwUNLMzMzMzMysTUTEPOCbknYHNs1Of5QWBILMACQtB+yeHf4b+DVwCrA8cICkEyNiRk/1r8WOy37Oo8xeki3wO1JQcghwNIWZmy3X1UQ3ZmZmZmZmZtY+bs+Vly9XScmWks6QdLOkVyXNkDQzS057o6QTJC1ey00lDZV0tKTrJL2WtTU9ywD8oKSLJB0oaZka2lpO0rck3SVpfJbMZ3LWzlmSVqilT1XuUTX7tqRDcnVOz86NkPQVSfdnfZoh6XmljMdr1tmHTSX9VNKjkiZJmpV93lskHa9ekjm5DgcD/bPyRdnswIuz41HAp3uiU60maSzwkezwzoh4q4e6ciPwflY+uIf6AHQ/0Y2ZmZmZmZmZtZfJFd67E9imzHvLZq9dgVMk7R0Rd5ZrSNK6wLWkPS2LrZS9NgUOBK4EPlOhrVOAb5L2wstbInttSkre8eWI+HW5dpohy2x+JbBa0VurZq+DJB0QEVdWaWcYcD5wQIm3x2avnYGvSvpMRNzT7c63xmHZz9kU9kX8A3ByVj6cQpCyL/sMheDstT3ViYiYKelm4FPA6pI2jYj/9ERfHJQ0MzMzMzMz6/u2zZWfqlBvqezn68B9wLPAu6Rs3quRZnotnb2ul7R5RDxZ3Eg2k/J6UrIdgEnADaTkHjOA4Vl7HwRWqdRxSecDR+ZO3Zu9JmXtbJN9viHAryQNjIifV2qzgZYnfa6xwOPAzcDE7Pxe2flBwB8lPRIRxQmJgAW/rzuATbJTs7O2HiXNauuYZbcGKTB8m6TtIuL+Jn2uhpC0A7B6dnhtREwBiIgnJf2HFEzeQdJq5X43fcgeufLtdV67laS7gHWAxYF3gJdJf0S4MCL+W2d7/yQFJTv65aCkmZmZmZmZmTWOpH7A14EtslNvAFdUuORy4B/lgl2SBgHfJs1yWwz4BWn2XrF9KAQk/wHsExHTy7S5AbBumfeOpBCQfA44ICIeKFFvZ9LnGg2cI+mmiKgUfG2UI4C5wFERcUFRn04hBSy3Is3wPJm0h18p51EISN4AHBYRnTIpSxJwAvAjUhb1SyWtFRFzGvNRmiKf4OaiovcuIgUlBRxKIRlTn5M9hx0zkGcBj9XZxAeyV4elstdmwJclXQx8PiLeq7G9/DO0XZ19aRgHJc3MzMzMzMx6jzGSHswdnx8R51e5Zl9JmxWdG0QKCn6YwrLiCcBeETGzXEMRcVqlG0XEbNLy4bWAPYGdJK0eEc8VVd04Vz6tXEAya/MxSgRpJA0FvpsdvgvsFBGvlmnjVkkHA38nzeo8ic4BsWY6pTggmfXpPUmHA//LTn2aEkHJbOw6lmzfB+xZKtAYEQH8JNs788ukpeH7AX9syKdoMEkjSbNFIc1qva6oyiXAOaTxOiTLBD2vhV1spTVJs3oBnoqIuXVcOx24G/gv6fc4mDT2uwIde7F+FlhP0rY1BibzMyuL/9vRMg5KmpmZmZmZmfUekyKi3iDBMVXenwP8BPheRLzdtW4t5FJSUBLS0unioGT/XLl4H8ha7UNhOfm55QKSHSLiGknPkmaUfbyL96zXRKDsUvGIeErSE6SZoGMkrRARrxVV+0Ku/M0aZj7+iBSUhDQGvTIoSQq0doz9ZcWfKyImSbqBNFbLA7uxcOCyr1gjV365xmvGk/Zb/UupPyRIGkyaOfsdUnxvQ9KM21J7knYSEdMkTQLGAKMkLRURE2vsV8M4+7aZmZmZmZlZ3zaQtHT471lSlppIWl7S7pIOzzJun9jxArbPVS2VXfrRXPnnkj5Qok41+WXhN9Z4zSPZz6UkrdyFe9br1mz2aCXP5MpLl3i/43POpoa9BiPiDVIwFHpwllsNKi3d7vCHMvX7mnxm+PFla+VExNMR8adyM5sjYlZEfJ/Os2/3k7RRjX3K92PFsrWayDMlzczMzMzMzNrbjhFxe/6EpP6k/RU3Iu17uC9pT7t7Je1RXL/o2n1Jy583rfH+o0qcu4S0R+AKpKXcT0u6H7iVLFFNRFTKAg5p5leHO9KWinVZitpnpXVVLe3nl9Munn9D0mgKAaFBwOw6P+dS1assTNJHgPUqVHk1Ii6v8H619jek8P15qtQ+oJlrgLdJ39WPS1o6It7q6n17sRG5ctmtDLoiIn6fbROwFWl/zr0pBOcrmZYrDy9bq4kclDQzMzMzMzPrY7K9+SYBtwC3SHoEOJu0nPZSSWsU7z2XJVK5gPpnrA0pcf/3JO1CCk5uQgqWfDB7AYSkh7P3zy+zD96Sdfaj2LBuXl+Lsvtz5kSuXLxitac+437AwRXev4OU9Kir8t+hssvLI2K2pMtJWxAMJC1X/lE37ttb5eNv9ewnWas/k4KSAFvWeE2+HwMb253aOChpZmZmZmZm1vf9EDgOWBYYC3wO+HVRncMpBJPmAL8nzWT7H/AWMKMjEYmkHYHbKt0wIp7OkrjsQkrysh2wNilAKVKwchPgREl7R8RdRU3kYxZn0nnGYS1eqLN+T8h/xonAD3qqI42S7XX42dyprSVdWOGScbny4fTNoGR+duRCQfwGyO/pukzZWp3l93qdVrZWEzkoaWZmZmZmZtbHRcS8bPn0J7JT27BwUPK4XHnviLi6QpMja7xvADdnLyQtSUqMswdptt5wUpD0mmz2Zj7ZxmQKAZarI+KhWu7ZZvJL2AdHxDmtuGlEHAIc0qTmPw0skTveo45r15a0ZUTc2+A+9bT893qJsrW6LsqUK8n3o+VJbsCJbszMzMzMzMwWFfnZWp32IpS0GLB+dvhClYAkVN6PsKyImBwRV0XEUaQs2R2zGUexcNbgp3LlrbtyvzYwKXsBjJC0fqXKbaK7CWv6YsKbF3PlFcrW6rrVc+Wqe3JmWzUslx3OBypmtm8WByXNzMzMzMzMFg0r5cpvF703OleeUkNbe3W3MxHxJnB+7tRaRVVuzpUPUxcy3fR22UzSW3OnDuupvjSCpHHATtnhTGBkRKjai/Td7Jjht6+kxUs0387+S+HzlcpW312fyZXvq6H+OGBwVn4qImY1vEc1cFDSzMzMzMzMrI+TtCqFJDMAxUuh36YQNFlb0lDKkHQIKat3o80oOr6YQoB0IzovL6+ozQKYv8iVPy9pi1ov7IWf8zDSfqEA10bE1FouiohXgTuzw8WBfZrQtx4TEe8Az2SHYyUt3ai2JR1M2hKhw19quGyjXLmWIGZTOChpZmZmZmZm1odls9eupJBXYgZwab5OREwHHswOhwHnZQlLits6GDiPKvvWSbpM0rckrVKhzjp0DjTemX8/y8h9au7UjyWdXiVguqGkXwDfq9S/3iQi7qaQ6XowcKOkfcoFHCUNkPRhSVcBn2pRN6uS1I/O+1ReXGcTl+TKfXEJ90258jaVKkraTtKVkrap8D0YJOkrwAW501dExH9q6Mt2ZfrVUk50Y2ZmZmZmZtbe9s2yXOf1I+3TuDGwMzAw996JEfFKiXa+RwpeAhwIbCfpOuA1YAywK7AuMA84i84Bw2JjgX2BMyQ9QQp4vkIKiI4BNgR2pDBZ6l5Spu9OIuI3ktYmBS/7AacBx0q6GXiWtE/mSGA1YAtg5ezScyv0rTc6jNT3D5HG7XLgLEm3kfb7m09aYr8OacZrR5KSP7W8p+XtCqyYld8Brqvz+itIs0YHAVtJWisinqpyTTu5CvhiVt4F+GuFuv1ICYM+DYyXdB9ppuU7pN/PqqTf99jcNY8BR9TYl52zn7OBG2u8puEclDQzMzMzMzNrb8fUWO9t4ISIuKjUmxHxV0mnAaeTluCuDHy+qNo04GjgdSoHJWfnyutmr3JuAA6IiHll+nV8Ftj8HikwtyQpc3c5sygslW0LETFd0g7AOaTxHEAKtK5W4bLJwPimd652+dmNf4mI2WVrlhARb0u6nkKG+MOBkxrVuV7gdlKAeUXgk5K+kO0pWs2ywCcrvB/AhcDx2eziiiStTiGp1bURUby/bMs4KGlmZmZmZmbW9wQpgDiRNIPqBuCybG+78hdFnCnpVtLMxK2BpYH3SLMlrwMuiIgXsgBaJR8lLRHdiTSDcXVgGdIsr/dJsybvAy6NiNuqfpiI8yVdChwEfJi0J94Y0nLnqcBL2ee8FfhHTwZauipLNvJFSeeQlkHvCKxBYVbk28BzwH9IS25vqTfw1yySxgB75k5dUq5uFRdTCEoeJOnrETGnW53rJSJivqTfAmeQAo07AuW++/cA25Nmzn6QFJweQwrIQ/ouPAXcBfwhIp6voyv5LPfnl63VAqotKGvtbI0V+scvvlB2yw0zs0XSbqe839NdsDrdeHZfS8JoZtZ9Xzx3Bs+8Nq+3Jbro9RZbcYn4wPG79nQ3SnrspMv/ExHFS7HNrA/IgrevAEOBP0fEvi2+fz/gRVK28yciYr1W3r+YE92YmZmZmZmZmZk1WURMAn6dHX5a0sqV6jfBXqSAJMCZLb73QhyUNDMzMzMzMzMza42zSVsODKD1e2Z+Lfv5ECmxUI9yUNLMzMzMzMzMzKwFstmSp2eHR0papRX3lbQ3sAlpv9kv1phkp6mc6MbMzMzMzMzMzKx1fkHaV3IQsAppn8dmG0pKsvNqRNzTgvtV5aCkmZmZmZmZmZlZi0TEXOCsFt/zolberxZevm1mZmZmZmZmZmYt5aCkmZmZmZmZmZmZtZSDkmZmZmZmZmZmZtZS3lPSrBf679en9XQXrE7rnzWsp7tgdbrx7MV7ugtWp91Oeb+nu2Bd4GfNzMzMzEpxUNLMzMzMzMwWKf2HDGTEWsv1dDfMzBZpXr5tZmZmZmZmZmZmLeWgpJmZmZmZmZmZmbWUg5JmZmZmZmZmZmbWUg5KmpmZmZmZmZmZWUs5KGlmZmZmZmZmZmYt5aCkmZmZmZmZmZmZtZSDkmZmZmZmZmZmZtZSDkqamZmZmZmZmZlZSzkoaWZmZmZmZmZmZi3loKSZmZmZmZmZmZm1lIOSZmZmZmZmZmZm1lIOSpqZmZmZmZmZmVlLOShpZmZmZmZmZmZmLeWgpJmZmZmZmZmZmbWUg5JmZmZmZmZmZmbWUg5KmpmZmZmZmZmZWUs5KGlmZmZmZmZmZmYt5aCkmZmZmZmZmZmZtZSDkmZmZmZmZmZmZtZSDkqamZmZmZmZ2SJBUmSvl8q8Py5X5/bW9s4qkXRhbmx2KFPn9lydcS3tYBdJ+lfW3ztadL+tc7+jj7XinuU4KGlmZmZmZmbWZiS9lAsslHrNljRR0r2SfiRpw57usy3aJP2g6Dt6bE/3qYOk/pLeyPVtlqQlW3DfA4FtssOTu9jG94t+r7dXqh8RdwNXZ4c/kzS4K/dtBAclzczMzMzMzPqegcAY4EPAl4GHJZ0naWDPdssWRZIGAAcVnT68J/pSxh7AsrnjQcDnmnnDLBj43ezw+oi4rwttbAF8pQu3PzP7uSrw/7pwfUMM6Kkbm5mZmZmZmVlD/AZ4vujcEGBFYFdgHCDgKGAwcEgL+2YG8DFgmaw8k/T93EjSJhHxUM91a4F8gHQW6Tk5HPhZE+95DOkZBfh+vRdnQc3/A/oDs0mB1JpExEOSbgY+DJwi6fyImFZvH7rLMyXNzMzMzMzM2tvlEXFO0es7EXE0sDrwnVzdgyVt0kP97PUi4qWIUPbaoaf704fkg34nljnfIyQtA3w0O7wHuDIrry9p8ybdcwBpBjPAkxHRlf0kTwfWIQV5f9SF63+T/VwKOKwL13ebg5JmZmZmZmZmfVREzIuIbwL/yZ3+aLn6Zo0maTlg9+zw38Cvgdez4wMkDe2RjhUcTGEl8UXZq0OzgqafBFbKyr+v92JJmwEnZYdnAs90oQ9/ByZl5eO6cH23OShpZmZmZmZm1vfdnisvX66Ski0lnSHpZkmvSpohaWaWCORGSSdIWryWm0oaKuloSddJei1ra7qklyU9KOkiSQdms9WqtbWcpG9JukvS+CyZz+SsnbMkrVBLn6rco2r2bUmH5Oqcnp0bIekrku7P+jRD0vOSLpC0Zp192FTSTyU9KmlSlnRlvKRbJB0vabHufs4WO5i0xBjgooiYD1ycHY8CPt0TncrpmCU4C7gcuAV4Izu3f5N+34fmyn+p50JJgygs234E+GFXOhARc4GrssPVJW1ToXpTOChpZmZmZmZmtmiZXOG9O0lLWL8F7AKsQNr/bzApEciuwE+A5yVtV+kmktYFniQtE92dFAwdAgwlzRLbFDiQNDPt3CptnQI8B5wBbA2MJSXzWSJr5xTgWUmfr9ROM2SZzR8CzgE2z/o0hJRE5AjgMUl71dDOMEkXAw8CxwMbAEuS9gocC+wM/BR4TtJWjf8kTdMR9JtNCvoB/CH3fo8t4c4CcR1B42si4p2ImAdckp0bAXymwfccRXqOAP4bES/X2cS3gPWAucDhWXCxq67JlfftRjtd4kQ3ZmZmZmZmZn3ftrnyUxXqLZX9fB24D3gWeJcUAFwN+AiwdPa6XtLmEfFkcSPZTMrrKSTymATcQErIMwMYnrX3QWCVSh2XdD5wZO7UvdlrUtbONtnnGwL8StLAiPh5pTYbaHnS5xoLPA7cDEzMzu+VnR8E/FHSIxFRnJAIWPD7ugPo2O9zdtbWo8D7WTsfAdYgBYdvk7RdRNzfpM/VEJJ2IO1rCnBtREwBiIgnJf2HFFDeQdJq5X43TXZErpxftv0HCntfHl70XnftSiEed3s9F2b7wX41O/xRA5IE3QnMJ01a3AP4Yjfbq4uDkmZmZmZmZmZ9lKR+wNeBLbJTbwBXVLjkcuAf5YJd2dLRbwMnA4sBvyDN4Cu2D4WA5D+AfSJiepk2NwDWLfPekRQCks8BB0TEAyXq7Uz6XKOBcyTdFBGVgq+NcgRpxtpREXFBUZ9OIQUstyLNDj0ZOLpMO+dRCEjeABwWEeOL2hNwAimpyWDgUklrRcScxnyUpsjPgiwO7F1ECkqKtJz5G63qFICk4cDe2eFEUhAdgIh4XNLDwMbAdpI+EBHPNujW2+fKC32Xy5E0kLRsewDpjwWnd7cjEfGOpGdJs0VXlbR8RLxe7bpG8fJtMzMzMzMzs95jTLZHYsfrqBqu2VfSiUWvr0v6NSkBxrezehOAvSJiZrmGIuK0SrPvImJ2RHyVlCQDYCdJq5eounGufFq5gGTW5mMRcWnx+SwBynezw3eBnUoFJLM2biXtXQhpVudJpeo1ySnFAcmsT+/ROShXcu/ELGnJAdnhfcCexQHJrL2IiJ+Qls9DWh6+X3c63kySRpJmi0Ka1XpdUZVLgI6A6iGS+tNa+5MC6wCXllgGnQ+iNjI79Wa58n/ruO6bpCX9ARxZ6Tmu02O5clOyjZfjmZJmZmZmZmZmvcekiNiserVOjqny/hxSIOt7EfF217q1kEuBPbPytqRZjHn5AFNXsyvvQ2E5+bkR8WqlyhFxTTbr6wPAx7t4z3pNBMouFY+IpyQ9QZoJOkbSChHxWlG1L+TK36xh5uOPgC9n5T2BP9bZ51Y5gMLYX1b8uSJikqQbSGO1PLAbCwcum6nSLE5IQdMfkmJnB0v6RrbfZHetkSvXtJ+kpI2Ar2WH50fEHQ3oR6k+fKCB7VblmZJmZmZmZmZmfdtA0tLhv2dJWWoiaXlJu0s6PMu4vWAmJp2XoJbKLv1orvxzSV0JduSXhd9Y4zWPZD+XkrRyF+5Zr1sjYnaVOs/kykuXeL/jc86mhj0GI+INUjAUOs+6622qBf2ghxLeSFqPwpYGT0bEf4rrRMRbpKX0kPbx3KMB9x1GyjgOMKuWPxJky7YvJD3Hr5Oe5UbKz8pdsWytJvBMSTMzMzMzM7P2tmNE3J4/kS2FHQ1sRNr3cF9SQph7Je1RXL/o2n1Jy583rfH+o0qcu4S0R+AKpKXcT0u6H7iVLFFNRFTKAg6QD6DekbZUrMtS1DgTrRtqaf+9XHnx/BuSRlMIBA0CZtf5OZeqXmVhkj5CyuBczqsRcXmF96u1vyGF789T5Zbdk7I/v036rn5c0tJZMLDZ8gHQSjNNLwI+lrvmmgp1azEiVy67pUGRr1N4Fj4fEVO72Ydi03Ll4Q1uuyIHJc3MzMzMzMz6mGyZ6STgFuAWSY8AZ5OW014qaY1sz8MFskQqF1D/jLUhJe7/nqRdSMHJTUjJTD6YvQAiSyRyCWk56nvFbQBL1tmPYsO6eX0tatnXL3Ll4hWrPfUZ96OwB2cpd5CSHnVVTUG/iJgt6XLSFgQDgQNJy9ObJkvW9LnscD7wpwrV/w68Qwq8f1TS2IiY0I3b5+NwxXtYLiRLAnVqdnhZRHQ3KFpKvh8Dm9B+WQ5KmpmZmZmZmfV9PwSOIy1DHUsKyvy6qM7hFIJJc4Dfk2aG/Q94C5jRsaeepB2B2yrdMCKezpK47EJK8rIdsDYpQClSsHIT4ERJe0fEXUVN5GMWZ9J5xmEtXqizfk/If8aJwA96qiONImkw8Nncqa0lXVjhknG58uE0OSgJfAIYk5XfB75TZXbqNFJQcgBwEN0bo/zsyIWC+SWcSwoUTgaO78Z9K8nv+TqtbK0mcFDSzMzMzMzMrI+LiHnZ8ulPZKe2YeGg5HG58t4RcXWFJkfWeN8Abs5eSFqSlBhnD9JsveGkIOk12ezNibnLJwPLZOWrI+KhWu7ZZvJL2AdHxDmtuGlEHAIc0qTmPw0skTuuZy/GtSVtGRH3NrhPeUfkyiOoPGO02GF0Lyj5DingPxBYXNKgKnuSdiztXxJ4s8al/dtLys/OXSUiXqpQPz9WE8vWagInujEzMzMzMzNbNORnaXXai1DSYsD62eELVQKSUHk/wrIiYnJEXBURR5Ey/XbMZhxFytac91SuvHVX7tcGJmUvgBGS1q9UuU10N2FN0xLeSFqJNHO3q9aUtE1XL85mGndkkRewXDf60ijL58ovtfLGnilpZmZmZmZmtmhYKVcuzvo7OleeUkNbe3W3MxHxpqTzge9lp9YqqnIzadYdwGGSfpnNvOwzIiIk3UpKRARpJt6XerBL3SJpHLBTdjgTWKaWxCySViQlDRKwr6QTIuL9JnTxUAoT9C6NiOJAeLn+nQp8Jzs8HCjeaqAejwGrZuU1qRwI/AuFpeaVrE4hcP8mhazhkJaoV5J/7h6t4V4N46CktVz/QcMZMXYjRozdiJHLbsKIsRux2OjVkNJ/F6a8/C8euGT3bt1j2BJrsNz6+7PkKrswZMQKDBg8nNnvv8m0Kc8y4am/MeF/VzJvdjP++2ZILLX++iy31VYstcEGLLn22oxYeWWGjhnDgKFDmTN9OjOnTGHiY4/x2h138OTFFzP9zTd7utd9kp+19uMxM2s+P2ftx2Nm1hiSVqWQZAageCn026SELCItoR0aETPKtHUIKat3oxXf72Lgu6TlpRuRlpf/rJaGJKmNApi/oBCU/LykSyPi/lou7IWf8zDSdwjg2lozRUfEq5LuBLYnZSjfh7SnacNkiZwOzZ26uI7LL6EQlNxb0nFlkjPV4j7gk1l5Q+DGchUj4sRaGsyeyY6g5FPZ8vxarhOwQXY4kxQwbRkHJa2ltjnqIRZbYvUF/4hsNKk/q237dVbZ8iv069f56z101MoMHbUyY1bdhdW2/iqPX3sMU165syn9WJRt9qUvscOPyu9LPHjECAaPGMHIceNYfc892eass3jghz/k3jPPZP6cOS3sad/mZ639eMzMms/PWfvxmJk1RjZ77UoKMYAZwKX5OhExXdKDwOakjM7nSToyImYVtXUwcB6FAGa5e14GPAn8MSJeLFNnHTrvY9npIcsyeJ9KYe/LH0saDXy/QsB0Q9KegdOBr5brX28SEXdnGaj3BQYDN0o6GriiVMBR0gBgR+ALwEXAX1vZ33KU/mN9SO5UPUE/SIG/7bPy4TQ4KElatr1yVp4E3FTrhRHxoqR7gS1Jz8d+pEz1XXETcHZW3oaeTW60AWnrBIA7ip/3ZnNQ0lpq2JJrNLX9dT/6K5Zfv5DkK2I+0yY9zewZkxk6cmWGjkx7xA4duRKb7nc1D12xF5NfrJgwzupVtPHuvNmzeffFF5n+1lvMmzWLwSNHMnrNNRk8YgQAAwYPZstvfIOxm27KVZ/8JPNmV9rj12rlZ639eMzMms/PWfvxmJnVbN8sy3VeP1KwYWNgZ1JijQ4nRsQrJdr5Hil4CXAgsJ2k64DXSEtIdwXWBeYBZwGnVujTWFKQ7QxJTwAPAq+QAqJjSDPEdqSwlPZeUqbvTiLiN5LWJgUv+wGnAcdKuhl4lhR8HAmsBmxBIeh0boW+9UaHkfr+IdK4XQ6cJek20h6E80lL7NchzXjtSE7yp5b3tLxdKSRmeQe4rs7rryDNGh0EbCVprYh4qso19cjvVXlFRNQ7K+ZiUlCyo62uBiUfJj0LK5GesYFd6Euj7Jwr/73VN3dQ0nrE3FlTmfrmY0yd8DBTJzzMuC2OY8TYjbrV5sqbH9vpH5VTXrmLJ64/lulTnltwbolxO7D+x85nyPDl6Nd/IBt+8o/c87sPMXPqq6WatC6YP2cOL996K89fcw2v/+tfvPXoo8S8eZ3qqF8/Vtl9d7b/4Q9Zcu21AVhl99350De/yd3f/GZPdLvP8rPWfjxmZs3n56z9eMzMqjqmxnpvAydExEWl3oyIv0o6DTidNAtyZeDzRdWmAUcDr1M5KJmfbbBu9irnBuCALAlIqX4dnwU2v0cKzC1JmqlWzizgmQrv9zrZTNUdgHNI4zmAFGhdrcJlk4HxTe9c7fJBv79UySq9kIh4W9L1FDLEHw6c1IiOSVqCwpJpqH8WJ8CfgZ+SxuaDktaNiCfqbSTbR/RPwNdJAfWd6bwHZCt17Nk6hxQIbykHJesgaQhpwD4GbEL6y8/ipE1DJ5Ci3f8Ariw3lbxC22sBBwG7kf6yMCJr82nSXwsu78Z+Bb3GY1cfyrsTHmH6lGc7nV9hw0PLXFGbgUOXYLVtvrbgeOqER3jwsj2JeZ3/Gzjlpdu5/0+7sdVh9zBg8HAGDhnJ6tt9k8evPapb97eCh37+cx76+c8r1on583nhH//g9bvu4rP33ccSa64JwKbHH8993/0uc2fObEVX+zQ/a+3HY2bWfH7O2o/HzKxbghRAnEjaJ+4G4LKIeKfiRRFnZolXjiPtUbc08B5ptuR1wAUR8UIWQKvko8B2pKQnW5AScSxDmgX3Pmmm2H2kZCNVpyBHxPmSLiX9/+YPk/aYHENa7jyVlCzkMeBW4B8RUZzIp9fLls5+UdI5pGXQOwJrUJgV+TbwHPAf0hLgW+oN/DWLpDHAnrlTl3SxqYspBCUPkvT1Bs0i/BzpuwIpoc499TYQERMl3QTskZ06HPhyF/vzW9L2Av1JWedbHpSUtDKwVXZ4ZURMbnUfmrM5Sx8k6aOkqeEXA/uTMiSNJH2BRmbH+wF/BJ6TtGeZporbHSDp28DjwCmkYOdSpIdlZdL05wuAxyXt2MjP1BPGP3nFQv+obISVNj2agUNGLTh+4objFvpHZYcZ77zI83d/f8Hxcuvuy5CRK5Wsa8016913+fd3v7vgeNDw4Sz7wQ9WuMJq5Wet/XjMzJrPz1n78ZiZlRcR4yJCFV79ImJ4RKwaEZ+MiN9UC0jm2r47IvaNiBUiYlBELBkRG0bEKRHxQlbn9ty9DinRxpyIuDUiTo2ID0fEKhGxWEQMiIhREbFBRBxZS0Ay1+Z7EXFu9nnGRcTiETEw69+mEXFoRPypUkAy1+dxZd5/KVdnhzJ1LszVOb2Gfh+Sq397DfVfjogzImKHiFguIoZkr2UjYtuIOCEirustAUmAiJgUEYNzn/OfXWznilwby+QDkrX8HrPfWUedl3Lnf54f+4iuJQeKiI/m2ulqQJJI+6z+LTvcO5vJ2WVF38kdarzsCAr7wpZPDNFEDkrWQNLnSGvrV8idnkHaE+NW0l8p8lO7lgOuklTLn3B/B3yDFNyE9NesJ0kb/ObXhawE3CRp1658hr5umbU+taD8zhsPMHV8cSK5zl5/9A/Mm5Mms6pff5ZZ8xMV61vzvPngg52Ohy27bA/1xGrhZ639eMzMms/PWfvxmJmZWS/wbVIMaAgLb5PQVJKGkrZhALg+Ih6sVL9ZHJSsQtJKpMxiHb+rGcDxwJIRsXlE7BIRm5H2tPgyheCkgF9JKrv/g6Qvk6aed7gTWCsi1o2I7SNiJdK09Dey9wcAV2RTbC0zdNQ4hi+1zoLjic9Vn/U8Z+bbvPP6/QuOl/7AR5vSN6uu38CBnY5nT53aQz2xavystR+PmVnz+TlrPx4zMzPrDSLiMeCy7PB4SYu18PZHkFbpzqfy3rBN5aBkdUcD+S/Ggdm03057RkbE9Ij4CXBw7vQQoOSGM5KWBL6VO/UwsGtEdNqMNyJuIe3D8X52agQpmm6ZEcts2On4ndf+XdN177xeqDd86fUb2ier3Yo7FnYlmDdnDuPvv79CbetJftbaj8fMrPn8nLUfj5mZmfUiJ5P2fl0K+FIrbihpcQqByN9GxMOtuG8pDkpWt32u/GREXFmpckT8Gfhf7tQ2ZaoeS9qLssPRkTa1LdXm83QORH5W0rhK/ViUDBuzVqfjfMbESvL1Bg4ZyeDhyzW0X1bd6DXW4EOnFv4o88SFFzJj0qQe7JFV4met/XjMzJrPz1n78ZiZmVlvERGvAZ8FzgCmt+i2qwK/ye759RbdsyQHJatbOld+rMZr8vXGlKmzd658f0Q8UKXN31JYGt4P2KvGvvR5Q3Mbjc+fP5eZ74+v6boZ777a6XjoSK+Kb4VBw4czdrPN2OY73+HABx9ksaWWAuD1e+7hn1/u8j7B1gJ+1tqPx8ys+fyctR+PmZmZ9SYRcXVEnJ6tvm3F/R7L7nd69EDG7bwBPXnzNvFerjykxmvy9RbK+iVpVWDd3KlrqzUYEVMk3Qt0rHXdkx7KjtTbDBg0YkF53qz3IObXdN3cWe8WtbN4Q/tlyV7XX88qH/lI2fenT5zIQz/7Gff/4AfMnzOnbD3reX7W2o/HzKz5/Jy1H4+ZmZlZ7+CZktXlN5nZStKgSpUlDQa2yp26s0S1jYuO766xL/l6G9V4TZ/Xf9CwBeV5c2dWqNlZcd3+g4Y3rE9Wm1lTp/LoeefxxB/+4IBkG/Cz1n48ZmbN5+es/XjMzMzMegcHJav7NTA3Ky8NfLdK/bNJG5RCSk7zyxJ11ik6frbGvuTrjZC0Qo3X9Wn9+hWyN8f8uRVqdlZct19/TxxuhgkPPsiLN9zAizfcwEs338yEBx5g9ntpAvLgESPY8hvf4Ijnn2fLb36zh3tq1fhZaz8eM7Pm83PWfjxmZmZmvYP/l7SKiHhc0hdJwcX+wImS1gF+DjwAvEtKWPNB4Hhgt+zS94B9IuKVEs2Oy5XnAW/U2J2XS7TzWo3X9lnz5kxbUO43oNYV9gvXnTe7VXvKLlruLhVslFh5553Z+tvfZrkPfYj+gwax9ZlnMmSJJfjnl1qScMy6wM9a+/GYmTWfn7P24zEzMzPrHRyUrEFE/EbSS8BPgTWBPbJXKfOA64BTIuKJMnVG5MrvRcS8GrvybtFx2TUjko4CjgJYepRqbL49zc39w7L/wKE1X1dcd+7s9xvWJ6sigpdvuYVXbruNj15yCWvtuy8Am55wAs9ddRWv3nFHD3fQSvGz1n48ZmbN5+es/XjMDGDUc2/xiY+3JKdE3e7q6Q6YmbWIl2/XKCJuAHYFrqpS9RbglxUCkgD5XbFn1NGN4rplg5IRcX5EbBYRm40c1reDknOmT1pQHjBocfrXuOn44GFjO7czY1KZmtYsMX8+Nx15JDMmFxJ+bXzssT3YI6vEz1r78ZiZNZ+fs/bjMTMzM+sdHJSsgaTFJf0aeB74ZHZ6NvAwcBtpGXfH+o3dgBsl3SFpxTJNDsyVa9/IZuG6A0vWWsS8P/mZTsdDR6xU03VDRxbqxfx5TJvyXEP7ZbWZ/d57vHj99QuOl9tqqwq1rSf5WWs/HjOz5vNz1n48ZmZmZr2Dg5JVSBpOCjweQ1ruPh34ErBERGwSETtHxBbAKOBgoONPptsB/5I0duFWmZYr176RzcJ1p5WstYh5f+L/Oh2PGLthTdfl681492Xm15F90Rpr6iuFrVeHjhnTgz2xSvystR+PmVnz+TlrPx4zMzOz3sFByep+BGyelWcDu0bETyOiU0AwIuZExEXA1sDb2emVgV+VaDO/Ac1idfSluO57dVzbZ02d8FCnPX1Gr7RNTdeNXnHrBeUpr/yr4f2y2g0eOXJBeebbb1eoaT3Jz1r78ZiZNZ+fs/bjMTMzM+sdHJSsQNJywKG5UxdExN2VromIZ4Czc6c+KWnVomoTc+Vh2WzMWixbdOyNbID5c2cy6YVbFhwvs+Yn6Deg8qblo1bYksVGF4blzaevblr/rLoVt99+Qfmd55/vwZ5YJX7W2o/HzKz5/Jy1H4+ZmZlZ7+CgZGU70TlD+d9qvO6qXFnA9kXvP1V0vHKN7ebrzQeeKVdxUfP6o39YUB44ZBTjtqicLGW1bU5ZUJ7x7itMfvGfTeubVbb2AQcwZr31Fhw///e/92BvrBo/a+3HY2bWfH7O2o/HzMzMrOc5KFlZcaKaV2u87pWi4+J9JYszc29SY7v5ei9FRD2Zu/u0SS/czJSXC8toVtv6a4xZbdeSdVff7jTGrLLTguPn/vUdYv6cpvdxUbDshz7ELueey6jVVqup/vpHHMFuv/vdguPpb73Fo+ed16zuWQP4WWs/HjOz5vNz1n48ZmZmZj1PEdHTfei1JH2ZtKdkh40i4tEarhsNTMmdOiEifpZ7fyhpCfew7NTvI+LwGtp9HuhYN1LTNQBrrNA/fvGFyktSWmXVrU5m1a1PXuh8v/6DkFKMPGI+8+fNXqjO+Mcv5Ynrv1i27cWWWJ0PHnQrg4YuCcD8+XOZ8OQVvPXMtcyeMYWhI1dm+Q0OZImVCvsBvfXMtTx85f5A73oO/vv19sxhtOL227Pv7bcD8ObDD/Pq7bcz6bHHeH/8eGa/9x79Bw9m2NixLL3RRnzg059m9OqrL7h27qxZXP2pT3XKxN1O1j9rWPVKLeRnrf14zKrb7ZT3q1eyXufGsxfv6S4s4Oes/XjMqvviuTN45rV56ul+tJsVpTi+pztRxknwn4jYrKf7YWbWbAOqV1mkvVF0vDlQNSgJbFF0/Fr+ICJmSLoB2Cs7tZekL0bE9HINStqGQkAS4Moa+tHrqN8A+g+onHBc6leyjvoNrHjd9CnP8fBf9mPjz1zGoKFL0q/fAJZbb3+WW2//kvUnv3Q7j159KL3pH5V9yTIbb8wyG29cU933XnuNGw47jJdvvrnJvVp0+FlrPx4zs+bzc9Z+PGZmZmZ9l5dvV3Y7nf/Vcbykiv+6kSTgxNyp+cAdJar+LlceCXypSl9Oy5VfAW4pV3FR9s5r93L3BZsz/ok/M2/uzJJ1Zkx9jadu+SoPXvpx5s/1CvhGmvzkk9x39tlM+M9/mD93bk317/za1/j92ms7INlm/Ky1H4+ZWfP5OWs/HjMzM7Oe4+XbVUj6O/Dx3KkrgENKzWqUNAD4KfCF3Om/RMTeZdq+nUISnNnApyLiuhL1vgt8PXfqkIj4Q3G9cnrT8u1WGjB4BKNX2pYhw5dnwKDFmTXtLaZPeZZ3Xr+vp7tWVbsu384bMHQoY9Zfn1GrrcawZZZh4LBhzJs9m1nvvst7r77KW488wrTx43u6mw3T25Zvt1I7P2uLqnYdMy/fbk+9afl2K7Xrc7Yoa9cx8/LtrvHybTOznufl29WdCGwLjMqO9wa2knQhcB/wNjCclITmYOADuWsnAwtvglNwFHAvsAQwCPi7pEtJ2bsnA6sAh2b37/B34I/d+DyLjLmzpjLx2X/0dDcWWXNnzGDC/fcz4f77e7or1mR+1tqPx8ys+fyctR+PmZmZWWs5KFlFRDwjaXfgr8Cy2enlgVOrXPo6aebji1Xa/gRwNSkw2R/4XPYq5TZg/4iYX8dHMDMzMzMzMzMz61W8p2QNIuLfwHrAD0hZsyt5C/gesF5EPFBD23cB6wKXArPKVHsN+DLw4UrJcMzMzMzMzMzMzNqBZ0rWKCKmAF+V9HVSEHEjYElgGDCNFKx8FHgyIubV2fYE4ABJI4EdgBVIS8LfBJ4G7g1v/mlmZmZmZmZmZn2Eg5J1ygKOj2WvRrf9Lmkpt5mZmZmZmZmZWZ/l5dtmZmZmZmZmZmbWUg5KmpmZmZmZmZmZWUs5KGlmZmZmZmZmZlYHSRdLCknPSRrYgvutImlWds9jm32/VnBQ0szMzMzMzKzNSHopC050vN6VtFgd13+p6PqQdEwz+2zNIWlcibHMv2ZKelPSvyT9QNK6NbS5oaQTJV0l6VlJ70uanWvnu5JWq7F/h1Tp3/uSXpV0vaSTJS1TY7srSPqapBskvSZpmqQ5kt6R9KSkv0s6XdKHJQ2ppc1aSdoOOCA7/HpEzClRp5+kTSUdI+m3ku6V9Hz2rM6WNEnS/ZJ+JmmLaveMiBeBX2eH35a0VOM+Uc9wohszMzMzMzOz9jcC+AxwUY31D21iX6x3GQwsnb22AU6SdAFwXETMzFeU9EHgYqBcwDHfzlcl/Rg4tVRQrg7DstcKwEeA0ySdHBHnlqosqR9wOvBVYFCJKiOz19rAx7Nz75GekUb5YfbzSeCKMnWWAx6s0MaS2Wtz4DhJfwUOj4h3KlzzfeAYYBTwDeD42rvc+zgoaWZmZmZmZtbeAhAp0Fg1KClpc2D97HA+XkXZ15xUdDwUWB3YkxTMAjgSGAN8uqjuKnQOSL4O/At4kRTYWwHYAxgH9M/utYqkfSIiaujbCxRm+3UYAWxMCkgOABYDfilpQET8rEQbFwIH5o4nArcAzwHTsvbGAZsBa2R1ap5FXI2kTwAdMxt/UMPnng88AzwBvJm95gHLkoK7G2b1Pg2sJmnLiJhRqqGIGC/pItL4HSPpnIh4tVsfqAc5KGlmZmZmZmbW3m4Ddga2l7RqRLxQpf5h2c/5wO3ATk3sm7VYRJxT6rykEcAfScFJgE9J+mREXFVUdRYpuP37iPh3iXb6A6cCZ2SnPgMcAvxfDd17tUL/1gCuoRBIPFvSFRHxRq7OXhQCkvOBrwE/LTdTU9KqWf8OK/V+F301+zkFuLxCvXeBvYDbKs1+lLQHcCkpmLohKdB7ZoV2f00KSg4CTgC+UmO/ex3/NcTMzMzMzMysvf0++ylScKisbG+9/bLDW4C2nWVl9YmIqcC+wMu50wcVVXsQWD0ijioVkMzamRcRZwK/yZ3uduKViHiGNFtwfnZqKCmgmHdUrvzjiPhhpaXjEfFCRPyAtJS72yRtBmyZHV5SvPy96N7vRcRfqyzHJiKuA76eO7VfubpZ/YeBR7LDI+rZS7a3cVDSzMzMzMzMrL09BDyalQ/O9twrZy8KS3hrmdnWiaShkj4v6RpJr0iaIWmqpKcknZctDa/WxoIkPblzu0r6U5bJeFr2/iElrl1R0jlZIpNpkt6W9LCkb0laOqtzei6JykJtlGhzuez6uySNz5KQTJb0oKSzJK1Q32+p98qCaJfmTm1R9P5zEfFajc3ll1ZvImlwA/r3BHB/uf4Bm+bKl1KjGpeW1yK/F2u5vSS74rZceZUa6v8l+zmChZfgtw0HJc3MzMzMzMzaX0eAcSVglwr1OoIqbwN/q+cG2TLT54BfAR8DVgSGAMOBNUmz2O7PMg0PrLHNQdkeeTcCnyXtZ1hy5pekz5ASi3yFNPNtMVKAdSPSUuJHJW1d52c6JftMZwBbA2OBgcASpADYKcCzkj5fT7u93Iu5cncyOL9YdLxEN9oq125x//LJahoVaKxJFuzfOzt8B7irgc3nf3fTa6h/Ta68bwP70VLeU9LMzMzMzMys/f0J+AFpn7lDgZuKK0hamcL+kZdExCxJNTUu6UBS4LN/duo50vLv10ixhQ2Bj2b3PxwYTZqVWc3PSHsEzgSuAx7Pzq9P2tuw4/4fIc2M64hjTASuIi1FHk1KkrIucHV2vpbPdD5pb74O92avSaRA6zbAtqTA668kDYyIn9fSdi+3eK5cSwCsnOVz5QAmd6OtvEr9m0AKhkMa84cbdM9abEYhSPqviJhfqXKtsmDniblTt9dw2X9Jv+8lgZ0kDY6IWVWu6XUclDQzMzMzMzNrcxExWdI1pEDgJyWNjoi3i6odStp3EupYui1pA+B8UkByOimQd2nxklhJq5BmX24IfFrSYRHx++L2ihxDWn7+qYh4pcz9hwO/pRDDuBo4KNsjsaPOSaQEId+nhqQmko6kEJB8DjggIh4oUW9n0jLd0cA5km6KiKeqtd/L7ZArV0uKVMk+ufJ9ETG7G20BkC0B3zJ3qrh/N5GC3gCnS5oCXNiigNz2ufJC35V6SBoALA18CPgSKQAOMAM4vdr1ERGSHgR2I80Y3gy4uzt96glevm1mZmZmZmbWN3QEAIcA++ffUJoSeXB2+FhE/KeOds/O2gTYPyIuKbVHX0S8COwBvJ+dOkXVp2JOAXYvF5DMHEJhVt7TwL75gGR278gSmpxHIfBakqShwHezw3eBnUoFJLN2b6XwextICny2LUm7kZbed7iui+0sA5ycO3Ved/qVcxowJndc3L+zgI6xH0RKtvOWpKslnSrpo5KWbVBfim2WK/+33osl/Tu3l+oc4HXgSgoByVeAbSOi1rYfy5Wr7uXaGzkoaWZmZmZmZtZ7jMkSrHS8jqp+yQI3Am9k5eLZgjsD47JyPbMkxwG7Z4f3RMTfK9WPiDcoJCBZHVinyi1+HRFvVanzuVz5u1VmxZ0BzKvS3j4UluGeGxEVM5BHxDXAs9nhx6u03etIGiJpPUlnAX+nELR9m87Jamptrz9wIWn2KMB/gIu60b/hkraVdDlpD88O/8qCwgtExAuk7+PrudMjgD2B7wDXAm9IelHSbyTlZ1121xq58stla9VvDvAt4AN1/rEg34cPNLA/LePl22ZmZmZmZma9x6SI2Kx6tYVFxLwsaczXgE0lbRARHbOpOoKUs0n7T9ZqJwpBrBtrvOaRXHkz4IkKdf9RqSFJQ4CNs8MgBdXKiojxkh4gLYstZ+dcuZ7P9AFgKUkrR0Qjg1INlc9qXsFU4BMRMakLt/gxaT9HgPeA/erYX3H7Gvv3FIWkMp1ExD2S1gSOJs1i3aBEtXHZ+0dLugE4LCLG19jHcvJZ2LvS1s+B5bLyUNLemDuRkjudCewm6fN1zJTM92HFsrV6MQclzczMzMzMzPqO35OCkpD2kPySpFHAp7Jz19QZiNowVz5D0hl19qdadudq+zOOIy2bBng1It6t4Z7/pXJQMv+Z7qg12U/OUtQ5U07SuhRmnJYUEefU25EumE7aI/Ob1WaIliLpG8Bx2eEcUkDyuQb2bwJwAXB2RMwoVykippGCoz+WtBywFSlb+kaksR+Vq/4R4D5JH+xmYDKf+bvuBEERcUnxuWx7gwNIe7ZuDdwjaZeIuK+GJqflysPr7U9v4KCkmZmZmZmZWR8REc9KupsU4PicpJNJ+0t27AlZ89LtzJLd7NKwKu9XCzKOypVrze5crV6zP1MpmwM/rFKnUUHJ4n0vZ5F+z88Aj1YK9lWSfZe+nR3OJe3tWe+elC8Av84dBynAN4U0o/Z/EVFt+X0n2ZYBf8leHdmstwe+DuySVVsR+CW1ZYQvJx9Dm9uNdhbI9ma9WNJMUv8XB/4kaa0afg/5PgwsW6sXc1DSzMzMzMzMrG/5PSkoOYa0B2LH0u3xwA11tpWPG1xM56XZtbin0ps1LPutexpjDdfkP9OZpCXI9ehOxuqma8aMS0knkjKbQwqG7RcRf+tCU682e0Zo9p36J/BPSRcAR2RvfUrS0jXsYVrOdFLQEFKQv+7ZkuVExJWSngbWJO3FuiNwS5XLhubK08rW6sUclDQzMzMzMzPrW/5M2r9uGGlmW0eymYvqnYVG51mHD0XEjxvQv3q8nSvXOsNxiSrvTwaWycpXR8RDdfeqThFxISk5TNvJApIdszznAQdExJU92KV6fI20jUF/UrB6M7qYcRyYSCEouQRpdmcjPUYKSkLaYqBaUDL/PZ/Y4L60hLNvm5mZmZmZmfUhEfE+ad9A6Jz9ut6l29B5z8etu9yprnuJtHchwIqSRtZwzfpV3u/pz9Q2JJ1E54Dk5yLiigqX9CoRMRnIz4ys5ftTzou58gpla3VdfuJg/xrqL58rv9TYrrSGg5JmZmZmZmZmfU9xAPLuiHi6C+3cnCvvLmlsN/pUt4iYCTycHQrYs1J9ScuS9m+sJP+ZDlMXMt0sCrI9JH+QHc4DDoyIy3qwS3WTNAgYnTtV676kpTyWK69ZtlYXZN/BzXKnXqvhsrVy5Ucb2Z9WcVDSzMzMzMzMrI+JiDtJM9zOzV71Zs3uaOcpCstIhwK/qjWI18Bg38W58tclDa5Q91tUn2V2MYWltxtRyCZd1aISwMwCkh17SHYEJC/twS4BIOnDdY7B5ygkeZoL/Kcbt89nxN6wbK2uOYKUjAdgPnBrDddslCvf3+D+tISDkmZmZmZmZmZ9UEScHBHHZq+bq19R1knAzKz8KeDKbEZiSZJWkPRV6k+qU86FpCQ9kGaHXS5pRNE9lS01PoaU0bmsiHgPODV36seSTpc0tNw1kjaU9Avge13of1vJfo/5gORBvSEgmfkj8KSkEyQtV66SpH6SDicF5DtcmS3n7qpbSAFDgG2qVZZ0uaTDir+rRXUGSfoy8Kvc6f+LiDertD0KWC87fDoiXq7Wn97IiW7MzMzMzMzMrKyIeETSIaSA0EBSYHIPSf8kZeN+l5RUZ3lgE2AD0lLrJxp0/6mSjgD+TpoF+QngOUl/A14mLc/dHViXNAPyb8DhHZeXafM3ktYmzZLsB5wGHCvpZuBZUmblkcBqwBbAytml55Zors+QdACFJdsA9wLLZcluqrk+Ihoy5lWsBfwE+JGkp4AHgNeBqcBiwKrATkA+aPki8KXu3DQiJkm6G9gWWE/SMlWCh2sC+5BmF98P/Je0v+VMYASwRtbPfMKaB4Cv1NCdnShMNPx7XR+kF3FQ0szMzMzMzMwqiojLJb1G2qvyA8Bg4CPZq5zHG3j/6yR9FvgdKQC6FHBUUbW3gL2A3XLnZlRo83hJT5BmP44mZffer0I3ZgHP1N/7trJG0fE21DArMDOJBgWiK/gnKSg+mBSUW4fOyZxKuQY4JiLGV6lXiz+SgpLK+vGbCnU7ZlUOzq7Ztkrd3wBfy2byVvPpoj61JQclzczMzMzMzKyqiLhb0lqkgMjHgA8BywDDgWnAG6Sg1J3AtRHxQoPvf7mke4ETgD1Ie/DNIc2WvBr4VURMkLRP7rJ3q7R5vqRLgYOAD5P26RtDCiRNJWU1foy0x98/IuLtBn4kq1NE7C9pOLA9KVi6MSlIvhRpluR04G3gadIekFdERCOTwFxG2qt1JHAAlYOS25NmNG5LmkG8WtbPQaTnZTIpcH8XcGlEvFpLByQNo5Dw6d8R8d/6P0bv4KCkmZmZmZmZWZuJiHENaucQ4JA66s8H/pK9unrPcd249hXgy9mrnLVz5ZdqaPM9CgmB2k5EvESaudeItk4HTm9EW7k2LyTtC9qo9t4Drs1eLRUR70m6ADgR2FbSOhHxZLm6pGD51Q3uxn6kPwQA/KjBbbeUE92YmZmZmZmZWZ+QJQDZKjt8n7Q/pFkjnUOakQnd3KeyXlnm8ROyw8eBv7by/o3moKSZmZmZmZmZ9RWnkpbxAlydzew0a5gsuc3PssODJC3fwtt/jELW7VPb/fvtoKSZmZmZmZmZ9WqSxkj6maSVy7w/SNK3KGQuDuAXLeugLWrOImX8HkSDl7uXI6kf8N3s8KaIaNus2x28p6SZmZmZmZmZ9XYDgOOAL0q6H3gAGJ+dX4WUcXvZXP0fRcR9Le+lLRIi4v0sG/yOwFxJAyNiTpNvuwJpufZfaeOM23kOSpqZmZmZmZlZuxDwwexVyjzg+8A3WtYjWyRFxB3AHS283yu0aFZmqzgoaWZmZmZmZma93ZukQORupNlpywNLAYsD75CybP8T+G1EOLmNWRtwUNLMzMzMzMzMerWICOD+7PXtHu6OmTWAE92YmZmZmZmZmZlZSzkoaWZmZmZmZmZmZi3loKSZmZmZmZmZmZm1lIOSZmZmZmZmZmZm1lJOdGPWC61/1rCe7oKZWa9z49mL93QXrAt2O+X9nu6C1cnPmi0KlgFO7OlOlHFST3fAzKxFPFPSzMzMzMzMzMzMWspBSTMzMzMzMzMzM2spByXNzMzMzMzMzMyspRyUNDMzMzMzMzMzs5ZyUNLMzMzMzMzMzMxaykFJMzMzMzMzMzMzaykHJc3MzMzMzMzMzKylHJQ0MzMzMzMzMzOzlnJQ0szMzMzMzMzMzFrKQUkzMzMzMzMzMzNrKQclzczMzMzMzMzMrKUclDQzMzMzMzMzM7OWclDSzMzMzMzMzMzMWspBSTMzMzMzMzMzM2spByXNzMzMzMzMzMyspRyUNDMzMzMzMzMzs5ZyUNLMzMzMzMzMzMxaykFJMzMzMzMzMzPrRNIhkiJ7nV6mzum5Ooe0tof1kbSOpNlZXw9t0T0vyO73sqTFWnHPduKgpJmZmZmZmVmbkfRSLhgUkt6tJ+gh6UtF14ekY5rZZ2stSXsUje9ferg//SXtLun3kh6VNFnSHEnTJb0u6W5J50s6UtKqTejCL4CBwOPAHyr0cw1Jx0r6s6QnJU3N+jlR0v2SfiJpgxrveRowA1gJOLXbn6CPcVDSzMzMzMzMrP2NAD5TR/2WzBSzHnV40fGekpbqiY5I2hB4ELiO9N3bAFgCGAAMBZYDtgKOBM4Hnpf05Qbefw9gp+zw2xExv0SdVSU9CjxNCmDuDawNDM/6OQbYHDgBeFTShZKGV7pvRLwB/DY7/JKk5RrwcfqMAT3dATMzMzMzMzPrlgBECvZcVK2ypM2B9bPD+XjCUp+TBR8/nh3OBIaQZgkeCPy4xX3ZGPgnMDI7NR/4NylI+Rbpu7sUKVC5OTAsq9fI5c7fyX4+D5SbMbp01ocOE4G7gGeAd4FlgA8D62TvHwysLmmXiJhZ4d7nAP+PFHz9RlY2HJQ0MzMzMzMza3e3ATsD20taNSJeqFL/sOznfOB2CjPIrO84iBSEhBQI+zYpKHY4LQxKShJwIYWA5MPAZyPif2XqDyZ9Hw8FZjeoD7sCG2eH55WaJZkznxS0vAC4rcyMyiOAX5NialsDp5CWaZcUEa9Iuo4UJD5U0mkRMbFLH6aP8V9DzMzMzMzMzNrb77OfAg6pVFHSEGC/7PAW4NXmdct6UEfgeSZp+fDV2fE6kj7Uwn58iMLsw/eB3csFJAEiYlZEXB8R+wA/bFAfjst+zqPCXpLAy8A6EbFvRNxSLngZEb8lBXo7fF5Stfja77KfQ4Cja+jzIsFBSTMzMzMzM7P29hDwaFY+uEqAZC9gVFb+v3pvJGmopM9LukbSK5JmZIlAnpJ0XrY0vFobC5L05M7tKulPkp6TNK1cNmdJK0o6J0tAMk3S25IelvQtSUtnderKCC1puez6uySNzzI0T5b0oKSzJK1Q32+pZ0naksIS479HxLt0XtZfvNdkM22aK/8zIt6s9cKIiOq1KpM0FvhIdnhnRLxV4X7jI+LpGpv+FTA3Ky8FrF6l/o2koCykZd+Gg5JmZmZmZmZmfUFHgHElYJcK9ToS3LwN/K2eG2TJQp4jBWQ+BqxImvk1HFgTOAq4X9JvJQ0s21DnNgdJuogUtPkssBpl9hKU9BngSeArpAQki5ECrBsBZ5CSj2xd52c6JftMZ5CW4o4lLXteghRQOwV4VtLn62m3h+WDjh3ByJuACVl5X0nDaI0RuXK3g4xd8Bmgf1a+tlGNRsR7wKTcqSWr1J8J3Jwdri5p00r1FxXeU9LMzMzMzMys/f0J+AEwiBR4vKm4gqSVKewfeUlEzEpb/lUn6UBS4LMjwPMcafn3a6TYwobAR7P7Hw6MJs3KrOZnpOQrM0mZmR/Pzq8PzMrd/yPApRTiGBOBq0hLbkeTZsOtS1qmfFWNn+l8UrbnDvdmr0mkQOs2wLakwOuvJA2MiJ/X0nZPkbQ4sG92+BYp2EtEzJN0MSmgOxzYhy7MlO2CCbnyhySNzGZutsoeufLtjWo02/tyTO5ULXtE/hP4VFbeA/hPo/rTrhyUNDMzMzMzM2tzETFZ0jWkQOAnJY2OiLeLqh1K2ncS6ghISdoAOJ8UkJxOCuRdWry8VtIqpNmXGwKflnRYRPy+uL0ix5CWn38qIl4pc//hpH0RO2IYVwMHRcTUXJ2TgJOA71PYT7HSZzqSQkDyOeCAiHigRL2dgStIgc9zJN0UEU9Va78H7QssnpUviYi5uff+QApKQgoctyIoeStpL8f+pOzWf5P0hUr7SjZKto3BNtnhLOCxBjb/KQrfx/GkrN7V5L9f2zWwL23Ly7fNzMzMzMzM+oaOAOAQYP/8G1kW5I697B6LiHpmaZ2dtQmwf0RcUmq/v4h4kTQDrGPvvFNUfSrmFFLyk5IBycwhwPJZ+Wlg33xAMrt3RMQPgPMoBF5LkjQU+G52+C6wU6mAZNburRR+bwNJgc/erNTSbQAi4r8U9h7dWtJaze5MRLwM/CZ3akfgSUmPSTpX0uGSNpU0qAm3X5M0KxTgqaIAbZdl35/v5E6dX+P+l//NlTdrRF/anYOSZmZmZmZmZr3HmCzBSsfrqDquvRF4IysXzxbcGRiXleuZJTkO2D07vCci/l6pfkS8QVpmDSn5xzoVqgP8ulLykczncuXvRsSssjXT3pDzqrS3Dyk5CcC5EVExA3lEXAM8mx1+vErbPUbS2sCW2eHjEfFwiWr5QGXVGaUNcgJwQdG59YH/R5oB+yDwjqR/SvqCpJENuu8aufLLDWoT4OekvU8hZa8/p5aLImIahX0oR0laqlL9RYGDkmZmZmZmZma9x6SI2Cz3Or/WCyNiHoWg06bZsusOHQGo2aT9J2u1E4WZhzfWeM0juXK1GWH/qPSmpCHAxtlhANWCouPpvEy2lJ1z5Xo/01LZ3py9UdlZkjkXU8gafZCkpm/rFxFzI+Io4IPAJRRm0uYNBXYAfgm8IKkRGcLzWdPHN6A9JH0JOCI7nEuaOVzq85ST78eKjehTO/OekmZmZmZmZmZ9x++Br2XlQ4EvSRpFIcHGNRExqdSFZWyYK58h6Yw6+1NtNli1/RnHkZZNA7xaY5KU/wIfqvB+/jPdUWuyn5ylqHPmnaR1Kcw4LSkiappxV6b9gaSEQQDzScHHUvd4U9JNpGX2y5CyqF/V1fvWIyLuBz6bLdXeBNiCFHDejJSkqGMglgB+K2nZiPhOycZqk8/8Pb0b7QAg6SDgR7lT/y8i7q6zmWm58vCytRYRDkqamZmZmZmZ9RER8ayku4Gtgc9JOpm0v2THnpD1JjdZsptdGlbl/WpBxlG58uQa71mtXrM/UymbAz+sUqfLQUnSsvKls/Kt2TL6ci6ikJX6cFoUlOwQEbOBf2cvACSNBQ4CTqUQTDxT0jUR8ejCrdQkH/Pq1n6SkvYnBfw7AqdfjIjiJem1yPdjYNlaiwgHJc3MzMzMzMz6lt+TgpJjSMGqjqXb44Eb6mwrHze4mM5Ls2txT6U3I2J+levrnsZYwzX5z3Qm8F6d7b9QZ/1WyC93XlLShRXqDsmVd5e0XJUgZtNFxATgB1kG+ftIswgFHAV8oYvN5mdHDilbqwpJ+wF/JGUQBzghIn7ZxeaG5srTytZaRDgoaWZmZmZmZta3/JmUjGMY8G0KyWYuyvadrEd+1uFDEfHjBvSvHm/nyrXOcFyiyvuTSUuXAa6OiIfq7lWdIuJC4MJmtC1peWC33KlNslct+pOyi5/d6H51RUT8T9LvSMlxIM0w7aqJuXK170RJkvYl7cHaEZD8SkT8rBt9yvdjYtlaiwgnujEzMzMzMzPrQ7LEG1dkh/ns1/Uu3YbOez5u3eVOdd1LwJysvGKNmZnXr/J+T3+mRjuUQtCsK1qVhbtW/8uVu5OJ+8VceYWytcrIZkheTOF3e1J3gvJKm5culx3OJ2XuXqQ5KGlmZmZmZmbW9xQHIO+OiKe70M7NufLu2d5/LRMRM4GHs0MBe1aqL2lZqs+uy3+mw9SFTDe9Rdb3Q3OndosI1fICns2uWV3S9q3vfVnL5Mq17iNayn9JGdsB1qznwmwPyfwMya92JxFRZhwwOCs/FRGzutle23NQ0szMzMzMzKyPiYg7SYlVzs1e9WbN7mjnKeCW7HAo8Ktag3gNDPblM0l/XdLgsjXhW1SfNXgxMCUrbwQcV2tHemEAc0dg1az8JnBrHddekisfXrZWN0haN1teXmv9IRSyiEPaX7JLIuId4JnscKykpStUz/dhfzrvIfnViPhBV/uRs1Gu3OXP1Zc4KGlmZmZmZmbWB0XEyRFxbPa6ufoVZZ0EzMzKnwKuzGYkliRpBUlfpf6kOuVcSErSA7AWcLmkEfkKSk4CjqEwO66kiHiPlOW5w48lnS5paLlrJG0o6RfA97rQ/2bKBxMvq3PP0Hyw9zM1Lo2v17bAC5IulLSzpLIBY0njgGuBD2Sn5pKSNnXHTbnyNtUqlwhIfq1BAUmA7cr0a5HlRDdmZmZmZmZmVlZEPCLpEFKwZiApMLmHpH+SsnG/S0qqszwpwcoGpKXWTzTo/lMlHQH8nRQs+gTwnKS/AS8Do4HdgXVJMyD/RiFYVzJAGRG/kbQ2aZZkP+A04FhJN5OWNU8n7We4GrAFsHJ26bmN+EyNIGkU8OncqUvKVC0pIp6V9CCwGWkW7P7AbxrWwYJBpGQ6BwPvSHqA9N2YTNpbcQzpe7MNnWe5fjMi/tvNe18FfDEr7wL8tVxFSdvSOSD5JDBP0ok13OeeiKiYaR7YOfs5G7ixhjb7PAclreX6DxrOiLEbMWLsRoxcdhNGjN2IxUavhpQm7k55+V88cMnu3brHsCXWYLn192fJVXZhyIgVGDB4OLPff5NpU55lwlN/Y8L/rmTe7Pcb8XEWGR639uMxaz8es/bjMTNrPj9nZr1DRFwu6TXSXpUfIO2N95HsVc7jDbz/dZI+C/yOFABdCjiqqNpbwF50zkQ9o0Kbx0t6gjT7cTQpu/d+Fboxi8Jy4N7gs8CQrPxcRNzfhTYuJgUlIQVyGx2UfAZ4nhTcBRgFfDh7lTMJODkiupKYqdjtpIQyKwKflPSFiCg3k3Y1OgdF1yFtgVCLM4CyQUlJq1NIwHRtRLxdru6ixEFJa6ltjnqIxZZYfcE/IhtN6s9q236dVbb8Cv36df56Dx21MkNHrcyYVXdhta2/yuPXHsOUV+5sSj/6Go9b+/GYtR+PWfvxmJk1n58zs94lIu6WtBZpdt7HgA+RkpIMB6YBb5BmwN1JCry80OD7Xy7pXuAEYA9SoGkOabbk1cCvImKCpH1yl71bpc3zJV0KHEQKlG1Emrk3GJhKyv79GGmvxn/0smBSful2XbMkcy4DziEF4zaTtEFEPNbtnmUi4jZSIp11gR1I35m1STNPO5bgvw+8Rvo93wBcnWWQb8T950v6LSlouCxpD87bGtF2nQ7Ilc/vgfv3Sg5KWksNW3KNpra/7kd/xfLrf3bBccR8pk16mtkzJjN05MoMHbkiAENHrsSm+13NQ1fsxeQXe+K/R+3F49Z+PGbtx2PWfjxmZs3n58ysvIgY16B2DgEOqaP+fOAv2aur9xzXjWtfAb6cvcpZO1d+qYY236OQEKhtRMQmDWhjAmViQxFxIWk/z0rXnw6cXsN9niAFrHvid/wr4GukJepHUyYoWcvn7Qqlv6x1BJCfiAgv3c70maBktsntJsCmpKnHmwKrk/axALgjInboQrvDSZuR7gBsTEojvyRpz4l3gOeAe4E/RcSj3ej/ZqQMUzuS9uEYSvor0+PA5cBf+1K6+LmzpjL1zceYOuFhpk54mHFbHMeIsRt1q82VNz+20z8qp7xyF09cfyzTpzy34NwS43Zg/Y+dz5Dhy9Gv/0A2/OQfued3H2Lm1Fe7de9Fhcet/XjM2o/HrP14zMyaz8+ZmdUq22dxq+zwfdL+kLYIi4hJkn5NCmR/WtLKEfFyC7uwF7BSVj6zhfft9fpEUFLS06Q9LVStbh1trgn8gLQXxeAy1ZbJXlsDJ0q6Djg6Il6r4z7DgB+z8F4YkPYzWI20ie/jkg6MiEdq/hC90GNXH8q7Ex5h+pTO/7uwwoaHdqvdgUOXYLVtvrbgeOqER3jwsj2JebM71Zvy0u3c/6fd2OqwexgweDgDh4xk9e2+yePXlvr1WwePW/vxmLUfj1n78ZiZNZ+fMzPrglOBxbLy1dnMTrOzgSNIS8ZPAo5t4b07/gfnIeCKFt6312vO5iyttwYNDEhm1gf2ZOGA5IukmZG3s/A08D2AB7OAZlWSBpKyh+X/VTOHtI/Cv4A3c+fXA+6UtGFt3e+dxj95xUL/qGyElTY9moFDRi04fuKG4xb6R2WHGe+8yPN3f3/B8XLr7suQkSuVrGuJx639eMzaj8es/XjMzJrPz5mZdZA0RtLPJK1c5v1Bkr4FfCU7FcAvWtZB69UiYhKFZeZHSlqlFfeVtDdpVW8AX6yQZGeR1FeCkh3eI22o+xPgc8DDDWhzPilV+wHA0hGxakRsFRE7RsQqwAbAzbn6ywDXSio3uzLvR8BOueMrgVUiYsOI2A5YDtiXtLkupM2Dr82WlFvOMmt9akH5nTceYOr4hyrWf/3RPzBvTkrCpn79WWbNTzS1f1aax639eMzaj8es/XjMzJrPz5lZWxoAHAe8KOnfkn4h6euSviXp/0iThs6gMGHpRxFxXw/11XqnX5Bm0p4NtCQoSdqa7wzgyIgom517UdVXgpKfBdYCRkbE9hHx5Yi4mEIwryvmkDY4XSMiPhIRl0bExOJKEfFf4CN03uR3dUovx14gm035+dypa4G9I+L1XNvzI+LPwO7AvOz0CqSpxpYZOmocw5daZ8HxxOduqHrNnJlv887r9y84XvoDH21K36w8j1v78Zi1H49Z+/GYmTWfnzOztifgg6Tlt98lBXwOIWVWhvT/nc8CTu6JzlnvFRFzI+KsiDg9ywreintelN3vd624X7vpE0HJiLgkIp5u5DTYiLg6Ig6NiOdrqDsfOAaYkTv9mSqXfY3Cnp5zgGPK9T+LpudTxn9J0mKl6i6KRizTeUX7O6/9u6br3nm9UG/40us3tE9Wncet/XjM2o/HrP14zMyaz8+ZWdt6kxSM/BbwT+AZ4G3S/5+eCDxAyguxdkSc6mWyZr1fnwhK9gYRMRm4K3dq7XJ1s70k82s+/pafIVnGL3PlxUmzJw0YNmatTsf5jImV5OsNHDKSwcOXa2i/rDKPW/vxmLUfj1n78ZiZNZ+fM7P2FMn9EfHtiNgpItaMiCUiYlBELB0RW0TEVyPC2bbN2oSDko01OVceUaHetsDo3PG11RqOiCdJSXY67Flf1/quobmNxufPn8vM98fXdN2Md18taqfkfsnWJB639uMxaz8es/bjMTNrPj9nZmZmvYODko01Lld+q0K9jYuO766x/Xy94jYWWQMGFeK/82a9BzG/puvmznq3qJ3FG9ovq8zj1n48Zu3HY9Z+PGZmzefnzMzMrHdwULJBJK0IbJE7VSmr0jq58hxSlrBa5KehryGpf43X9Wn9Bw1bUJ43d2bN1xXX7T/ISc1byePWfjxm7cdj1n48ZmbN5+fMzMysd3BQsnG+Tuff5x8r1B2XK7+eJcqpxcu58mAK2cUWaf36DVxQjvlza76uuG6//gPK1LRm8Li1H49Z+/GYtR+PmVnz+TkzMzPrHfy/pA0g6cPA0blT/4qIf1S4JL/f5Dt13OrdouOyf56VdBRwFMDSo1THLdrPvDnTFpT7DRhS83XFdefNnt6wPll1Hrf24zFrPx6z9uMxM2s+P2cG8J8xoE/2dC/K+G1Pd8DMrDU8U7KbJK0KXAJ0RP7eAw6vcll+A5oZddyuuG7ZoGREnB8Rm0XEZiOH9e2g5NzcPyz7Dxxa83XFdefOfr9hfbLqPG7tx2PWfjxm7cdjZtZ8fs7MzMx6Bwclu0HSUsD1wJjc6aMi4tkyl3QYmCvXvmZk4boDS9ZaxMyZPmlBecCgxelf46bjg4eN7dzOjEllalozeNzaj8es/XjM2o/HzKz5/JyZmZn1Dg5KdpGkUcCNwBq50ydFxGU1XD4tV659zcjCdaeVrLWIeX/yM52Oh45Yqabrho4s1Iv585g25bmG9ssq87i1H49Z+/GYtR+PmVnz+TkzMzPrHRyU7AJJw4EbgI1zp78REefU2ER+rcdiddy6uO57dVzbZ70/8X+djkeM3bCm6/L1Zrz7MvPryL5o3edxaz8es/bjMWs/HjOz5vNzZmZm1js4KFknSYuTlmx/MHf69Ij4bh3NTMyV68mgXVx3ch3X9llTJzzUaU+f0SttU9N1o1fcekF5yiv/ani/rDKPW/vxmLUfj1n78ZiZNZ+fMzMzs97BQck6SBoG/APYOnf6zIg4o86mnsqVl8hmXtZi5Vx5QkS8U+d9+6T5c2cy6YVbFhwvs+Yn6Deg8qblo1bYksVGr7rg+M2nr25a/6w0j1v78Zi1H49Z+/GYmTWfnzMzM7PewUHJGklajBSQ3C53+tsRcVoXmnui6HjjkrUWtkmu/GQX7ttnvf7oHxaUBw4Zxbgtjq1Yf7VtTllQnvHuK0x+8Z9N65uV53FrPx6z9uMxaz8eM7Pm83NmZmbW8xyUrEEWkLwW2D53+jsR8a0uNnlH0fH2JWt17sMQOi8Zv72L9+6TJr1wM1NeLiyjWW3rrzFmtV1L1l19u9MYs8pOC46f+9d3iPlzmt5HW5jHrf14zNqPx6z9eMzMms/PmZmZWc9TRPR0H5pG0u0UAn53RMQOXWhjKHANsHPu9Hcj4hvd7NuDwKbZ4bPAmlFhMCR9Dvhj7tS6EVHTbMk1Vugfv/hC5SUprbLqViez6tYnL3S+X/9BSClGHjGf+fNmL1Rn/OOX8sT1Xyzb9mJLrM4HD7qVQUOXBGD+/LlMePIK3nrmWmbPmMLQkSuz/AYHssRKhdX3bz1zLQ9fuT/Qd5+DRvC4tR+PWfvxmLUfj1ltdjvl/eqVrFe58ezFe7oLC/g5q+6L587gmdfmqaf70W60lIJP9nQvyvgt/4mIzXq6G2ZmzTagpzvQm2WzE6+ic0DyrO4GJDO/oxCU/ABwAHBxmX4MBk7Jnfp3rQHJ3kb9BtB/wJDKddSvZB31G1jxuulTnuPhv+zHxp+5jEFDl6RfvwEst97+LLfe/iXrT37pdh69+lB60z8qeyuPW/vxmLUfj1n78ZiZNZ+fMzMzs77Ly7fLkDQI+CuQX8dxdkSc2qBb/BZ4Lnf8C0mbl+jHAOB8YJ3c6a81qA99zjuv3cvdF2zO+Cf+zLy5M0vWmTH1NZ665as8eOnHmT93Rot7aKV43NqPx6z9eMzaj8fMrPn8nJmZmfWcPrF8W9I3gFKzFwcBHUsZAlh4XQf8MSKOLNHmycD3c6dmUf8+jgdHxJvl3pS0DXALMDg7NZM0g/Jm4D1gTeBoYMPcZb+MiPLrUEroTcu3W2nA4BGMXmlbhgxfngGDFmfWtLeYPuVZ3nn9vp7umlXgcWs/HrP24zFrP+08Zl6+3X560/LtVmrX58zLt7vGy7fNzHpeX1m+PYBCYK8clalTbl3HYkXHg4Hd6uxXxUhgRNwl6bPARdn9hgBfyF6lXAKcUGcfFllzZ01l4rP/6OluWJ08bu3HY9Z+PGbtx2Nm1nx+zszMzFrLy7d7WERcCWwEXAfMK1PtWeDAiPhsRJSrY2ZmZmZmZmZm1hb6xEzJiDgdOL23t1nhXs8CH5W0NLAdsDxp1uR44PGIeKgV/TAzMzMzMzMzM2uFPhGU7Csi4i3gLz3dDzMzMzMzMzMzs2by8m0zMzMzMzMzMzNrKQclzczMzMzMzMzMrKUclDQzMzMzMzOzRYKkyF4vlXl/XK7O7a3tnVnXSBoi6bnse/uHFt3zs9n95krasCttOChpZmZmZmZm1mYkvZQLnpV6zZY0UdK9kn7U1aCBWb0kHVLlu/m+pFclXS/pZEnLVGlvgKSdJH1X0s2SXpM0U9L0rJ1/SDpe0qga+3dhhb7Nl/SOpGcl/VnSgZKG1tjuxpLOkXS3pLckzcpeEyX9R9Ilkr4iaTNJqqXNOpwErAbMAr5ZoY/9Ja2fjdEvs/8+TO9iIP4S4GGgP3BuVzrtRDdmZmZmZmZmfc9AYEz2+hDwJUkXAMdGxJwe7Zkt6oZlrxWAjwCnSTo5IhYKbEk6CjgLWLJMWytkrz2A0yUdFxF/7EbfBIzMXqsDewPfkXRwRNxe8gJpSeA3wGfKtNnxHG4C7J+duwbYsxv9zN9/aeDk7PD3EfFKheqvAxWDwLWKiJD0HeBKYGtJn4qIv9XThoOSZmZmZmZmZu3tN8DzReeGACsCuwLjSMGWo4DBwCEt7Jst2l4Afl10bgSwMSkgOQBYDPilpAER8bOiuptQCEgG8D/gHlJwbR6wBim4NwIYBVwkaVRE/KLG/t0M3JQ77kcKIG5LCuYDrARcJ2mniPh3/mJJI4HbgfVyp58G/gW8CswBRgNrAVtQCAguVmP/anEKsDgwHzinSt0hRccBvEPqY1f8jfR51yQFb6+KiKj1YgclzczMzMzMzNrb5RVmcfUHTge+kZ06WNLPI+KhFvWtrUTES6QArjXGqxFRMlAmaQ3SjME1slNnS7oiIt4oqjoR+BXwh4h4sUQ7o4ALgU9kp86RdFNEPF1D/+6p0L89gCtIAcShpODqxkXVvkMhIDkZODgi/lGmPQGbAZ8Flquhb1VJGg0cmR3eEBEvVLnkGdIfMP6Te30a+L+u3D+bLXk+8CNgHeBjpDGtifeUNDMzMzMzM+ujImJeRHyTFHzo8NGe6o9Zh4h4hhQQm5+dGsrCS6B/B4yLiNNLBSSzdt4hLbN+LDs1iDQruLv9uw74Su7URpI26DiQNBg4OPd+2YBk1l5ExAMRcQKwb3f7lzmctBQe4PfVKkfEFhGxf0ScExH/jIipDejDRcDcrHx8PRc6KGlmZmZmZmbW992eKy9frpKSLSWdkSUVeVXSjCyxyBuSbpR0gqTFa7mppKGSjpZ0XZagZEaWWONlSQ9KuihLJlJ1nztJy0n6lqS7JI3PkvlMzto5S9IKtfSpyj2qZt8uSuRyenZuRJbE5P6sTzMkPS/pAklr1tmHTSX9VNKjkiZlyVLGS7olS+jSyKW/PSoingDuz53aouj9ByJieg3tzKHzMvEtG9ND/kQh4Aad+/cBYHhWngpcV2uj9SxxruKQ7Oe0eu7fSBExicJ/X3aStGKt13r5tpmZmZmZmdmiZXKF9+4Etinz3rLZa1fgFEl7R8Sd5RqStC5wLWlPy2IrZa9NgQNJyTLKJQpB0imkrMLFmZCXyF6bkpL5fDkiivcwbCqlzOZXkrIf562avQ6SdEBEXFmlnWHA+cABJd4em712Br4q6TMRcU+3O987vEhh/8alutlOh3KJceoSEe9Lmkj63kPn/o0oqtuoQGNNJK0HrJsd3hYRM1p5/yLXALuQtj7Yh7ScuyoHJc3MzMzMzMz6vm1z5acq1OsIurwO3Ac8C7xLyua9Gik5ydLZ63pJm0fEk8WNZDMprycl2wGYBNxA2s9uBmmG2WrAB4FVKnU827PuyNype7PXpKydbbLPNwT4laSBEfHzSm020PKkzzUWeJyUOGVidn6v7Pwg4I+SHomI4oREwILf1x2kxC4As7O2HgXez9r5CGn/xWWB2yRtFxH3l2iu3eRn3VadFVlBfgbwxG60U2xYrpzv34RceYSkLSPi3gbet5o9cuXbW3jfUv6ZK++Bg5JmZmZmZmZmizZJ/YCvU1h2+gYpeUc5lwP/KBfskjQI+DZwMikByC9Is/eK7UMhIPkPYJ9yy3CzffrWLfPekRQCks8BB0TEAyXq7Uz6XKMpJDqpFHxtlCNIy3uPiogLivp0CilguRVphufJwNFl2jmPQkDyBuCwiBhf1J6AE0gBn8HApZLWypYut6VsX8b8UutqiVoq2SdX/lc32llA0iZ0nhG5oH8R8YKk54DVs1OXSToiIm5uxL1rsH2uvNAz0WJPkAK2iwFbZZnU51a5xkFJMzMzMzMzs15kjKQHc8fnR8T5Va7ZV9JmRecGkYKCH6awrHgCsFdEzCzXUEScVulGETGbtHx4LWBP0h5yq0fEc0VV81mKT6u0L2BEPEYhSckCkoYC380O3wV2iohXy7Rxq6SDgb+TZnWeREoC0gqnFAcksz69J+lw4H/ZqU9TayjprAAAQgVJREFUIiiZjV3Hku37gD1LBRqz5cE/yfbO/DJpafh+wB8b8il6xmnAmNxxl/ZFlPRhYLfscC41JH2poc1BdJ7xN4OFZySeQiHIvxJwk6TXsnoPAo8ADzcooUyx/DP/3ya0X7OImC/pCWBz0ozl9UifvSIHJc3MzMzMzMx6j0kRURxgrOaYKu/PAX4CfC8i3u5atxZyKSkoCWnpdHFQsn+uXLwPZK32obCc/NxyAckOEXGNpGdJCUg+3sV71msiUHapeEQ8lQVr1iUFnFeIiNeKqn0hV/5mDTMff0QKSkIag7YKSkoaDmwEHEvR7MaIuLUL7Y2lcxDyFxHxbBf71o+0H+U2wKmkvUo7nBMR7+XrR8RfJB0D/JQUjANYAfhc9gKYL+m/pID5/5XLIl5nP0eQtlAAmJplIO9pL5OCkpCewUeqXeCgpJmZmZmZmVnfNpC0dHgrScdGxKO1XCRpeWADYDnS3o35GEI+qUup7NL5e/xc0r5dCBTll4XfWOM1j5ACIktJWjkiXq7znvW6NZs9WskzFJanLw0UByU7PudsatgbMCLeyJKvLEXn2XK90faSakkA8xSwd72NZ7NpryIFAiGN/yl1NHGapIqzgzN/Ac4o9UZEnCfpOtLS+v1Iz0teP2DD7PU1SecCX63he1NJPsP1+LK1Wivfj5oycDsoaWZmZmZmZtbedoyI2/MnJPUn7a+4EWnfw31Js7/ulbRHcf2ia/clLX/etFydIqNKnLsE+AYpWLQx8LSk+4FbyRLVRESlLOCQgjgd7khbKtZlKdLsrWaqpf387Lp8UhckjaYQwBkEzK7zc3YpW7Wkj5CW2JbzakRc3pW26zQBuAA4u97s0dny6r+QkiVBCvZ+KiJmNbB//wV+HBEXVqqUzeL9iqQTgbVJ2cQ3Ju0TuilpD1BIfyA4AVhb0kcjYl4X+5Xf57I7yYEaaVquPLyWCxyUNDMzMzMzM+tjsmDHJOAW4BZJjwBnk5ZSXyppjeKlqFkilQuofy/GIcUnsv0UdyEFJzcBRAoedQSQQtLD2fvnF/cls2Sd/Sg2rHqVbiu7P2dOfqZgv6L3euoz7gccXOH9O0hJj7rrBeDXueMgBdGmkJKj/K8rgTlJA4E/U8hAPR7YOSJeqrOpm4GbcsfzSdnO3wQeqXembbbv55PZq6OviwGfBE4nzeKFtP/l/yMliuqKfDyvakKZFsn3Y2AtFzgoaWZmZmZmZtb3/RA4DlgWGEva7+7XRXUOpxCQnEPap+8aUqKWt4AZHQEkSTsCt1W6YUQ8nSVx2YWU5GU70iwyZa9NsteJkvaOiLuKmsjHLM6k84zDWnQnk3Or5D/jROAHPdWRJnk1Is5pZINZQPJy4BPZqQmk2cLPdKG5exrdv2JZkqdLJP2dlBV8o+ytY+h6UDI/O3KhPwr0kPzesdPK1spxUNLMzMzMzMysj4uIedny6Y5AzjYsHJQ8LlfeOyKurtDkyBrvG6TZaDcDSFqSlBhnD9JsveGkIOk12ezNibnLJwPLZOWrI+KhWu7ZZvJL2Ac3O0DWISIOAQ5pxb0aKTdD8pPZqTdJWdmf7rFO1Sgi3pd0OmkPTIB1JA2LiJoCeEXyz8kS3e1bg+T7MbFsrZziacNmZmZmZmZm1jflZ1d12oswW2K6fnb4QpWAJFTej7CsiJgcEVdFxFGkpawdsxlHAQcUVX8qV966K/drA5OyF8AISetXqrwoKxGQfIsUkPxfj3WqfsV9HVGyVnWvk2YzA4zN9pDtacvnyi/VcoGDkmZmZmZmZmaLhpVy5beL3hudK0+poa29utuZiHgTOD93aq2iKjfnyoepC5luertsJumtuVOH9VRferMSAcmJpD0knyx7Ue+0TNFxtWRPJWXbKDyRHfYHVu9Opxok//w+VssFDkqamZmZmZmZ9XGSVqWQZAageCn02xQSsqwtaShlSDqEwr54jVScffliCgHSjei8vLyiNgtg5vcV/LykLWq9sM0+Z5dkAckr6ByQ3CkiHu+xTpG2Isj2TK3HkbnyIxExuxtduC9X3rBsrRbIssh3/NHjxYh4q5brHJQ0MzMzMzMz68MkjQOupJBXYgZwab5OlozjwexwGHCepMEl2joYOI/OGaVL3fMySd+StEqFOuvQOdB4Z1Gf3gNOzZ36saTTqwRMN5T0C+B7lfrXm0TE3RQyXQ8GbpS0T7mAo6QBkj4s6SrgUy3qZo/IBSQ79kLtFQHJzFLAA5JulrS/pLKZ0CUtLuknwIG50+d18/75rOHbdLOt7touV76pbK0iTnRjZmZmZmZm1t72LTFjqx9pn8aNgZ2Bgbn3ToyIV0q08z1S8BJS8GQ7SdcBrwFjgF2BdYF5wFn8//buO06yqkz8/+eZwMwQZkgCEocgIKhkZCWKCoY1orBiQlgBVwTTKrgGxBXDV9SfYgLXXUFABFQUFCUsIIoIkhZRokRBcg4zzDy/P+5t+nZNVVd1d1V13+7P+/Wq19x76txzT/XTNd391AlDE4aNVgP2Aj4TEX+mSHjeRpEQXZliZNdLGRwsdTHFTt9DZOZ3IuL5FMnLacCngYMi4mzgBop1MucB6wPbAuuUl35zmL5NRPtS9H07iridDBwZEecBtwOLKabYb0Ix4nVgU5Ef9r2n/fVNBhOSAL8EXhkRr+zg2mMy85HedGuIl5ePpyPiCuAKiuTpExSx3ATYFVi2cs0vGbp0wWj8huL9NKe8f1sRsSewZ0PxOpXjTSPi1MbrMvPNbZp+WeX45530BUxKSpIkSZJUdwd2WO9B4AOZeVyzJzPzJxHxaeBwICiSFe9tqPY4cADFRhvDJSWr01I3LR+tnAXsXa6T16xfh5SJzS9QJOZWoti5u5WngeuHeX7CycwnImIX4MsU8ZxBkWhdf5jL7gfu6nnnxteGDefvGsG1pwK9TEo+BPwR2Ibi/TKLIqm83TDXLAS+Dnw8MxeP5eblbt4/pdgg6vkRsXFm/rXNZZsw/HqwK7d5fgnliN6BEbv34EhJSZIkSZKmtKRIIN5LsenEWcCPMvOhYS/KPCIizqUYmbg9sArwKMVoyV8Cx2bmzWUCbTivoZjSuSvFCMYNKDb5WAp4jGLU5CXASZl5XtsXk3lMRJwEvBN4BcUakytTJIIeodjt92qKTWPOzMzGjXwmvMx8Gnh/RHwZ2IdiJOmGDI6KfBC4EfgTReLnnDGuSagxyMy7gRdHxOoU3+fbU+xKvx5FzGZSfK/fQ7EpzQXAKZl5Zxe78V0Gd63fG/hUF9vu1I7AmuXx9zPzmU4vjGKjJ01mG645Pb/xvpZLbkiSJPXM7oc9Nt5d0Aj9+vPLtq+kCeP933yS6+9YNOk3uui2eE7ks1tmTDTf40+ZOdLNMyRNURFxKbA18HdgnZEkBbt0/+OBt1OMAp2fmX/v9Fo3upEkSZIkSZLq6Yjy39Up1nHtm4hYg8E1Kr83koQkmJSUJEmSJEmSaikzfwH8vjz9WKtd23vkQxRLMjwBfHakF5uUlCRJkiRJkurrYIod2l/I4BqTPRURawH/Vp7+Z2aOeNMlN7qRJEmSJEmSaioz/xQR+wLrUmz+1A/rAl8EngGOGk0DJiUlSZIkSZKkGsvMH/T5fhcCF46lDadvS5IkSZIkSeork5KSJEmSJEmS+sqkpCRJkiRJkqS+MikpSZIkSZIkqa9MSkqSJEmSJEnqK5OSkiRJkiRJkvpqxnh3QJIkSZPXrz+/7Hh3QSO0+2GPjXcXNAJzT9t6vLtQTzOBNce7E5I0tTlSUpIkSZIkSVJfmZSUJEmSJEmS1FcmJSVJkiRJkiT1lUlJSZIkSZIkSX1lUlKSJEmSJElSX5mUlCRJkiRJktRXJiUlSZIkSZIk9ZVJSUmSJEmSJEl9ZVJSkiRJkiRJUl+ZlJQkSZIkSZLUVyYlJUmSJEmSJPWVSUlJkiRJkiRJfWVSUpIkSZIkSVJfmZSUJEmSJEmS1FcmJSVJkiRJkiT1lUlJSZIkSZIkSX1lUlKSJEmSJElSX5mUlCRJkiRJU0JEZPm4pcXz8yt1zu9v7zSciPifSmx2aVHn/Eqd+X3t4AhFxHvKfi6MiA37cL/pEfGX8p6n9vp+nTApKUmSJElSzUTELZXkS7PHgoi4NyIujoijImKz8e6zpraI+FLD9+hB49yfZSPi3RFxakTcGBEPR8QzEfFoRNwcEeeU7509I2LFLt97BeDI8vTYzLy+g2vWi4gjI+LKiHggIp6IiJsi4sSIeHW76zNzEXBYebpHRLxi9K+gO2aMdwckSZIkSVLXzQRWLh/bAR+MiGOBgzJz4bj2TFNORMwA3tlQvB9w9Dh0h4h4S3nvVZo8vWz5WBd4WVm2OCK2y8xLu9SFwyjemwsZTE62FBH/BnwZmNPw1Hrl460R8TPgXZn5SKt2MvNnEXE18CLgSxGxZWbm6F7C2JmUlCRJkiSp3r4D3NRQNhtYC9gNmA8EsD8wC9inj32TAP4ZWLU8fori+3PzMil2eT87EhH7AcdSvCcAngTOA/4MPEiR+HsusEX5mE4x03hWl+6/GjAwSvSEzLyjTf0DgW9Wiq4GzgKeADYDXkuR33sD8NOIeFVmLhimyS8CJwCbA28BfjzyV9EdJiUlSZIkSaq3kzPz/GZPRMR04HDgE2XRuyLi6/1OBNVFZt7CYLJK3bNf5fgjDI6Q3A/o2/diRKwFfJ3BGJ8AHJKZ97eovyJF0u89QLdGFB7M4IjHbw1XMSLWB/6/StFhmfmFhjpbAL+iSPruCnwY+PwwzZ5atrky8FHGMSnpmpKSJEmSJE1SmbkoMz8J/KlS/Jrx6o+mnohYHXhVefoH4NvAneX53hHROCW5l94OLF0e/xF4Z6uEJEBmPpCZP8jMHYDfj/XmETGLYsQywDUdTAc/AliqPD6xMSFZ9vEKhk6NPzQilm/VYDmK8ofl6VYRsUMnfe8Fk5KSJEmSJE1+51eO12hVKQr/FBGfiYizI+L2iHgyIp6KiL9HxK8j4gMRsWwnN42IORFxQET8MiLuKNt6IiJujYjLIuK4iHhHRKzaQVurR8SnIuKiiLir3Mzn/rKdIyNizU761OYebXffjoh9KnUOL8vmRsSHI+KPZZ+eLDchOTYiNhphH7aKiK9FxFURcV9EPF2+3nMi4pCIWLp9KxPKuyimQAMcl5mLKUYoAiwPvKmPfdmqcnxy2ZeOdGntxdcBK5XHpwxXsXyPDXxtkiJB2apvv6FI+ALMpZjKPZzq7tv7tKnbMyYlJUmSJEmaWlqODAMupBgR9ing5cCaFOv/zaJYZ2834KvATRGx03A3iYhNgWsp1rx8FUUydDbF1NW1KRJE7wCOY+iaec3aOgy4EfgMsD2wGsVmPiuW7RwG3BAR7x2unV4odza/nGIjkm3KPs2m2IDkX4GrI2KPDtpZJiJOAC4DDqHYjGQlipFyq1FsuvI14MaIeEn3X0nP7Fv+uwA4uTz+QeX5/eifuZXj8djgZa/K8Rlt6u5G8X0EcHVmXtemfjXJ+cY2dX/P4P8De5QbEfWda0pKkiRJkjT57Vg5/usw9Z5T/nsncAlwA/AwRQJwfeCVFDsWrwL8KiK2ycxrGxspR3n9imKzHYD7KDbnuIliY5HlyvZeTLHLcUsRcQzFmn4DLi4f95Xt7FC+vtnAtyJiZmZ+fbg2u2gNite1GnANcDZwb1m+R1m+FHB8RFyZmY0bEgHPfr0uALYsixaUbV0FPFa280pgQ4rk8HkRsVNm/rFHr6srImIXYIPy9IzMfAAgM6+NiD9RJJR3iYj1W31tuuzuyvErKRLsfVEm/l5Rnj4EXNnmki0rxxd1cIvfVo63GK5iZmZEXEAxEnN54J8aru8Lk5KSJEmSJE1SETEN+DiwbVn0d4afNnoycGarZFdELAV8lmKDjKWBb1CM4Gu0J4MJyTOBPTPziRZtvgjYtMVz72EwIXkjsHezdfgi4mUUr2sF4MsR8ZvMHC752i3/CjwD7J+Zxzb06TCKhOVLKEaHfhQ4oEU732UwCXUWsG9m3tXQXgAfAI6iGLl6UkRsnJkLu/NSeqI6CvK4hueOo0hKBvBuBjdj6qXfUIzOBdgtIr4BHJGZ9/bh3lswOFLz8g6mjlffEzd00P6NleO1ImJuZj4yTP1LGZwevhPjkJR0+rYkSZIkSRPHyuUaiQOP/dtfwl4R8ZGGx8cj4tvA9RRJRChGie2RmU+1aigzPz3c6LvMXJCZHwN+XhbtGhEbNKlaHan16VYJybLNqzPzpMbycgOUz5WnDwO7ttoYJDPPpVi7EIpRnf/e6n49cFhjQrLs06MMTco1XTsxIrYG9i5PLwFe15iQLNvLzPwqg6P71gP+ZSwd76WImEcxWhSKUa2/bKhyIjCQUN2n3Cm+137E0BGKBwF3RsT5EfGFiNgrIjYsE8DdtnXl+P86qL9a5fiOdpUz80Hg8UpRu3Var64cb9NBf7rOkZKSJEmSJE0c92Xm1u2rDXFgm+cXUiSyvlAmLrrhJIpNO6CYOn1jw/PVBNNod1fek8Hp5N/MzNuHq5yZv4iIG4DnAa8d5T1H6l6g5VTxzPxrRPyZYtTbyhGxZmY2JpjeVzn+ZAcjH48CPlQevw44foR97pe9GYz9jxpfV2beFxFnUcRqDWB3lkxcdlVmPhMRr6bY6GVgXc6ZwM7lY8D95UZHPwJ+mpmLunD7DSvHt3ZQf7nK8eMtaw31BLBMk+ubqfbheR2231WOlJQkSZIkaXKbSTF1+OflpiwdiYg1IuJVEbFfueP2syMxGZrAaba79FWV469HxGiSHtVp4b/u8Jory3+fExHrjOKeI3VuZi5oU+f6yvEqTZ4feJ0LGLpLelOZ+XeKZCgMHX030Qw3dXtA3ze8KUeh7kiR9D4XaJZwXIlilOcpwDURsV0Xbl3dHX6JkbBNVJP57b7HBlRHQbfbpb3ah7Va1uohR0pKkiRJklRvL83M86sF5VTYFYDNKdY93ItiQ5iLI+LVjfUbrt2LYvrzVh3ef/kmZSdSrBG4JsVU7usi4o8USaCLgYszc7hdwAGqCdQLRjGj9jl0NiJtLDpp/9HK8bLVJyJiBQYTQksBC0b4Op/TvsqSIuKVwAuGqXJ7Zp48zPPt2t+Mwe+fv7aadg/8AniQ4nv1tRGxSmbeM9r7dqpcz/EU4JSImAtsRzGFeXOKzZeqSbqNKb7/XpWZ543httWdv1suZ1DxZOV4qQ7vMbty3O4e1dGXy0ZEZGZfdyQ3KSlJkiRJ0iRTTje9DzgHOCcirgQ+TzH66qSI2LBc8/BZ5Tp6xzLyEWuzGwsy89GIeDlFcnJLis1MXlw+ADIiriifP6axL6WVRtiPRsu0rzJmLdfnrKgmehpnrI7Xa/wXBtfgbOYCik2PRqv6PdRyenlmLoiIkymWIJhJsQnNUWO474iVm8H8pnwAEBGbAO8H9qeI2VLAcRGxwXBrsrZRzcE900H96nui0zhXR0c2e09VVfsQFEsudNKvrjEpKUmSJEnS5Pf/gIOB51JsoPF24NsNdfZjMJm0EPg+xUi2vwD3AE8OrK0XES8Fhh01lpnXlZu4vJxik5edgOdTJECCIlm5JfCRiHhLZl7U0EQ1Z3EE7ZMsjW4eYf3xUH2N9wJfGq+OdEtEzALeVinaPiL+Z5hL5leO96PPSclmMvNa4L0RcRHww7J4DYr1L4fbvX441ZGLSyTym7i7crxGu8rlxkLV5GW7EadDpodnZl8TkmBSUpIkSZKkSS8zF5XTp19fFu3AkknJgyvHb8nM04dpcl6H903g7PJBRKxEsZ7fqylG6y1HkST9RTl6897K5fczuIPw6Zl5eSf3rJnqFPZZmfnlftw0M/cB9ulR828CVqycv3oE1z4/Iv4pMy/ucp9GJTNPKNdQ3bws2obRJyWr39srtqw16FrgDeXxhsPUG1Bdt/WOzHy4Tf1qH+5tWauH3OhGkiRJkqSpoTpSa8hahBGxNPDC8vTmNglJGH49wpYy8/7M/Flm7k+RRBkYzbg8xW7NVX+tHG8/mvvVwH3lA2BuRLxwuMo1MdYNa/qy4c0I/KVy3FEyvoW/VY7XbFlrUDUJ38n3/46V4ys6qF8dfXlLB/W7zqSkJEmSJElTw9qV4wcbnluhcvxAB23tMdbOZOY/gGMqRRs3VDm7crxvjGKnm4muHEl6bqVo3/HqSzdExHxg1/L0KWBeZka7B8X35sDam3tFxLJNmh8vq1aO223ONJyrK8fNdqxv9GsG1yzdLCLajZZ8c+X4px20X32/XdVB/a4zKSlJkiRJ0iQXEesxuMkMDB2FBUWSciAp9PyImEMLEbEPg9NZu+nJhvMTGEyQbs7Q6eXDqlkC8xuV4/dGxLadXjgBX+e+FOuFApxRbiLTVmbeDlxYni4L7NmDvhERO0REx5sDRcRGDB2BeMkYbl+9drOWtUqZ+Rjws4GuAJ9sVbfcVOol5emjleuGs3mLvvWNSUlJkiRJkiaxcvTaaQzuK/EkcFK1TmY+AVxWni4DfLfcsKSxrXcB32XojtLN7vmjiPhURKw7TJ1NGJpovLD6fLkj939Uir4SEYe3SZhuFhHfAL4wXP8mksz8HYM7Xc8Cfh0Re7ZKOEbEjIh4RUT8DHhjn7rZVkRMY+g6lSeMsIkTK8e9msJ9EHBrRHy+3VT5iNiBYrTizLLo1vJ8VDLzbuD/ytONy/VV2/kkxaZTAG+PiH9v0s/NgOMqRV/MzMaR0M3sVDk+u2WtHnKjG0mSJEmS6m2vcpfrqmkU6zRuAbyMwcQKwEcy87Ym7XyBInkJ8A5gp4j4JXAHsDKwG7ApsAg4kqEJw0arAXsBn4mIP1MkPG+jSIiuTDFS7KUMDpa6mGKn7yEy8zsR8XyK5OU04NPAQRFxNnADxTqZ84D1gW2BdcpLvzlM3yaifSn6vh1F3E4GjoyI84DbgcUUU+w3oRjxOrBJyQ+XaGn87AasVR4/BPxyhNefQjFqdCngJRGxcWb+tc01o7EScChwaETcDvyRYm3TBynyZGtQjDqsJi2fBN6dmU8xNj8r2w2K9+WPh6ucmTdGxAeBo8uiL0XE24CzKL73N6PYEXzg/X0B0HazpIhYGXhReXppZt41spfRHSYl1XfTl1qOuattztzVNmfec7dk7mqbs/QK61N8qAIP3PpbLj3xVWO6xzIrbsjqL3wrK637cmbPXZMZs5ZjwWP/4PEHbuDuv/6Uu/9yGosWPNaNlzNlGLf6MWb1Y8zqx5jVjzGTNEkd2GG9B4EPZOZxzZ7MzJ9ExKeBwymSJusA722o9jhwAHAnwyclF1SONy0frZwF7J2Zi1r065AysfkFisTcShQ7d7fyNHD9MM9POJn5RETsQpFQOpAiX7N++WjlfmBckkktVEc3npqZC1rWbCIzH4yIXzG4Q/x+wBIjA8foj8ArGEzqrsVgIrWV/wMO6NKO4D9kcBr2m2iTlATIzG+Wo1C/BMymSEQ2m/79C+Admfl0B/14A4MfCBzfQf2eMCmpvtph/8tZesUNnv3Fv9siprP+jh9n3X/6MNOmDf32nrP8OsxZfh1WXu/lrL/9x7jmjAN54LYLW7SkKuNWP8asfoxZ/Riz+jFmkqaYpEgg3kuxwcZZwI8y86FhL8o8IiLOpRiZuD2wCsUadXdQjHw7NjNvLhNow3kNxfTQXSlGMG5AsWHIUsBjFKMmLwFOyszz2r6YzGMi4iTgnRRJpc0pRlzOAh6h2D34aopNY87scPrqhFImk94fEV+mmAb9UmBDBhNoDwI3An8CfgOcM9LEX6+UI+9eVyk6sVXdNk5gMCn5zoj4eGYuHO6CkcjMr0TE1ylGpO4EbEOx6cxzgeUokumPUIycvAI4HTi3VcJ8FPe/PiL+lyK2/xwRcztZdzMzv1GOWn4P8CqKjYFmA3dTvI+Oz8wzR9CVgZ3un2AcR9ualFRfLbNSu82ixmbT13yLNV74tmfPMxfz+H3XseDJ+5kzbx3mzCs+AJkzb222+pfTufyUPbj/b21//k15xq1+jFn9GLP6MWb1Y8wkTSaZOb+Hbf8O+F2bOuczuKFJs+cXUiQIz21VZxT9epRiWvaop2aXOz0P9/wtDPO6yjr/A/zPCO65D0PXWmxX/1bgM+WjFjLzPooE8VjbOYUWX/9Ovo6ZuUsH93gGuKh8jIejKJKSywBvA77dyUWZeRPltPOx3DwiNgB2KU//ezwT+JMmKRkRc4Etga2Arct/N2Dwm/mCTr45O7jPUsDuFIvJbk2RTZ9L8enT3cC1FHP4zy+/YTptd2uKNTteSrF+wRzg78A1FGtJ/KTDIbi18MzTj/DIP67mkbuv4JG7r2D+tgczd7XNx9TmOtscNOQPgQduu4g//+ognnjgxmfLVpy/Cy/852OYvdzqTJs+k83ecDy//6/teOqR28d076nCuNWPMasfY1Y/xqx+jJkkSRovmXlmRFxGkVM6JCK+m5mL+9iFD1Lkyp4GvtjH+y5hUiQlI+I64Hm0+TSjC/d5BcXios0+Zl+jfGxFkVy8E1izgzaXAb4C7N/k6YH1I14PXBMR78jMK0fV+Qni6tPfzcN3X8kTD9wwpHzNzd49pnZnzlmR9XcY/LDgkbuv5LIfvY5cNHQk+wO3nM8ff7g7L9n398yYtRwzZ89jg50+yTVnNPvya4Bxqx9jVj/GrH6MWf0YM0mSNEH8B8VO3htRrC15aj9uGhGrAgO/+HwrM8f1k9HeLKjTfxvS+4TkxynWbKgmJB8BrgTOo5jDf/8I25wJ/JyhCcmFFOtg/Bb4R6X8BcCF5VbvtXXXtacs8YdAN6y91QHMnL38s+d/PuvgJf4QGPDkQ3/jpt8Nfhiw+qZ7MXve2l3v02Ri3OrHmNWPMasfY1Y/xkySJE0EmfkbinwQwGcjYnqfbv0pipm59wBH9OmeLU2WpOSAR4ELga8Cb6dYlHTMIuJQ4HOVosspFhZdOTO3yMyXZeZ2mbkyxYjND1FM427nKIpFfwecBqybmZtl5k7A6sBeFMlPKBZdPSMilhvbK5p8Vt34jc8eP/T3S3nkrsuHrX/nVT9g0cInAYhp01l1o9cPW1+9Ydzqx5jVjzGrH2NWP8ZMkiSNwsEU64aeTLHTfU+Vic87y3vu3W7Tq36YLEnJtwEbA/Myc+fM/FBmnsBgMm/UImJb4D8rRT8Ets3Ms5rtAJWZN2bmVzNztzbtbgS8t1J0BvCWzLyz0tbizPwxRQJ0YKenNYF/H92rmZzmLD+f5Z6zybPn9954VttrFj71IA/d+cdnz1d53mt60je1Ztzqx5jVjzGrH2NWP8ZMkiSNRmbempmHl4+b+3C/RZl5ZHm/rm1ANRaTIimZmSdm5nWZmd1sNyKmAd8HBobRXgrs06Wt4A9lcE3PhcCBrfqfmb8HjqkUfTAilu5CHyaFuasOndH+0B1/6Oi6h+4crLfcKi/sap/UnnGrH2NWP8asfoxZ/RgzSZKk0ZkUScke2h3YtHJ+cDcSkuVaktV5Oj+tjpBs4ejK8bIUoycFLLPyxkPOq7tcDqdab+bsecxabvWu9kvDM271Y8zqx5jVjzGrH2MmSZI0OiYlh3dg5fiqzOzso+/2dgRWqJyf0e6CzLwW+Ful6HVd6kvtzaksDr948TM89dhdHV335MNDN5maM6/nSziowrjVjzGrH2NWP8asfoyZJEnS6JiUbKFcAPTllaKft6o7Cls0nP+uw+uq9RrbmLJmLDX32eNFTz8Kubij6555+uGGdpbtar80PONWP8asfoxZ/Riz+jFmkiRJo2NSsrUXAtV1G38PEBHPi4gvRMTVEfFQRDwREbdFxM8j4t86XOtxk8rxQuCWDvt0Q+V4wz5uGT+hTV9qmWePFz3zVMfXNdadvpSbmveTcasfY1Y/xqx+jFn9GDNJkqTRMSnZ2uYN59dHxMeBa4CPUSQt5wFzgLWA1wLfBP4WEW9o0/b8yvGdmR1+pA63Vo5nAc/t8LpJbdq0mc8e5+JnOr6use606TNa1FQvGLf6MWb1Y8zqx5jVjzGTJEkaHX/7aW3lhvODgA9Wzm8HbgZmAy8ABj4mXwX4SUQcmJnH0NzcyvFDI+jTww3nLT9Sj4j9gf0BVlk+RnCL+lm08PFnj6fNmN3xdY11Fy14omt9UnvGrX6MWf0Ys/oxZvVjzKR62mr1rbjs05eNdzeaisMn999vkjTAkZKtLd9wPpCQvAbYITPXzsxdMnM7YKXy+QVlnQCOjohtW7RdXTToyRH0qbFuy6RkZh6TmVtn5tbzlpncP9SeqfwxMH3mnI6va6z7zILHutYntWfc6seY1Y8xqx9jVj/GTJIkaXRMSrbW7KPuG4AdM3PIxjSZ+XRmfg3Ys1I8E/h8i7ZnVo47n+ezZN2ZTWtNMQufuO/Z4xlLLcv0DheKn7XMakPbefK+FjXVC8atfoxZ/Riz+jFm9WPMJEmSRsekZGuPNyk7JDMfanVBZp4O/KRStGtErN+m7c7n+SxZt1kfp5zH7r9+yPmcuWt3dN2ceYP1cvEiHn/gxq72S8MzbvVjzOrHmNWPMasfYyZJkjQ6JiVbe7Th/B7grA6u+++G85c2qVOdn9PJbt2t6jb2cUp67N6/DDmfu9pmHV1Xrffkw7eyeAQ7ZmrsjFv9GLP6MWb1Y8zqx5hJkiSNjknJ1u5tOL88M7OD6/7UcN5spGS17ZHsoN1Y9/4RXDtpPXL35UPWYVph7R06um6FtbZ/9viB237b9X5peMatfoxZ/Riz+jFm9WPMJEmSRsekZGvXNpw/0OF1jQsCrdikzl+rz0dEyw1rGqxTOb57uKnkU8niZ57ivpvPefZ81Y1ez7QZwy80v/ya/8TSK6z37Pk/rju9Z/1Tc8atfoxZ/Riz+jFm9WPMJEmSRsekZGvXAtWRkbM6vK5x3cdmu2v/ueF8iw7b3rJy3Jg0ndLuvOoHzx7PnL0887c9aNj66+9w2LPHTz58G/f/7X971je1Ztzqx5jVjzGrH2NWP8ZMkiRp5ExKtpCZjwOXVIrWa1W3QeN07bub1Lmg4Xzndo1GxGzgxZWi8zvsz5Rw381n88Ctg1Of1t/+UFZef7emdTfY6dOsvO6uz57f+Nv/JBcv7HkftSTjVj/GrH6MWf0Ys/oxZpIkSSMXnS2TWE8RcT6DCb8LMnOXEV7/YeDL5ekiYK3MvKvNNYcCn68U7ZyZFzapdxmwVXl6A7DRcGtWRsTbgeMrRZtmZkejJTdcc3p+433DTyPql/Ve8lHW2/6jS5RPm74UEUWOPHMxixctWKLOXdecxJ9/9f6WbS+94ga8+J3nstSclQBYvPgZ7r72FO65/gwWPPkAc+atwxovegcrrj24htM915/BFae9laGDYtXIuNWPMasfY1Y/xqx+jFlndj/ssfaVNGFsvfXWXHbZZTHe/aibrbfeOi+77LLx7kZTEfGnzNx6vPshSb02Y7w7MMGdAHwGWAaYDhwGHNyqckQsD1R/W70LuLhF9f9iMCn5PGDv8n7N2p1V3nvAHzpNSE40MW0G02c0znBvqBPTmtaJaTOHve6JB27kilP/hS3e/COWmrMS06bNYPUXvJXVX/DWpvXvv+V8rjr93Uy0PwQmIuNWP8asfoxZ/Riz+jFmkiRJE4fTt4eRmXcD/69SdFBE7N+sbkQsC5wCrF4pPjIzW83H+R5wY+X8GxGxTZN2ZwDHAJtUig/toPtT0kN3XMzvjt2Gu/78YxY981TTOk8+cgd/PedjXHbSa1n8TLMlP9Vvxq1+jFn9GLP6MWb1Y8wkSZI6Nymmb0fEJ4BPNHlqKWBgKkMCS87FgeMz8z3DtD0bOAfYvlJ8LnAicBPFxjbbAAcCa1TqnAm8LjMXD9P2DmXbA5voPEUxgvJs4FFgI+AAYLPKZUdnZuu5Q01MpOnb/TRj1lxWWHtHZi+3BjOWWpanH7+HJx64gYfuvKT9xRo3xq1+jFn9GLP6MWb1U+eYOX27Xpy+PTpO35ak8TdZpm/PoP3u2NGizrBzcTLzqYh4LfBLYLuy+GXlo5WfAe8YLiFZtn1RRLwNOA5YmiLB+b7y0cyJwAeGa1ODnnn6Ee694czx7oZGyLjVjzGrH2NWP8asfoyZJEnS8Jy+3YHMfBDYAfgwcPswVa8D3gW8KTM7+og5M08DNqdIei5qUe0GiiTn2zKzVR1JkiRJkiSpFibFSMnMPBw4vMf3WAR8JSK+SjFde2NgNYpE4j3AJZl5/SjbvgF4TUSsAuxEMQ18NsVGOddk5uVdeAmSJEmSJEnShDApkpL9lMUinH8sH91u+x7g1G63K0mSJEmSJE0kTt+WJEmSJEmS1FcmJSVJkiRJkiT1lUlJSZIkSZIkSX1lUlKSJEmSJElSX5mUlCRJkiRJktRXJiUlSZIkSZIk9ZVJSUmSJEmSJEl9ZVJSkiRJkiRJUl+ZlJQkSZIkSZLUVyYlJUmSJEmSJPWVSUlJkiRJkiRJfWVSUpIkSZIkSVJfmZSUJEmSJEmS1FcmJSVJkiRJkiT1lUlJSZIkSZIkSX1lUlKSJEmSJElSX5mUlCRJkiRJktRXJiUlSZIkSZIk9ZVJSUmSJEmSJEl9ZVJSkiRJkiRJUl+ZlJQkSZIkSZLUVyYlJUmSJEmSJPWVSUlJkiRJkiRJfWVSUpIkSZIkSVJfmZSUJEmSJEmS1FcmJSVJkiRJkiT1lUlJSZIkSZIkSX1lUlKSJEmSJElSX5mUlCRJkiRJktRXJiUlSZIkSZIk9ZVJSUmSJEmSJEl9ZVJSkiRJkiRJUl+ZlJQkSZIkSZLUVyYlJUmSJEmSJPWVSUlJkiRJkiRJfWVSUpIkSZIkSVJfmZSUJEmSJEmS1FcmJSVJkiRJkiT1VWTmePdBPRYR9wK3jnc/emBl4L7x7oRGxJjVjzGrJ+NWP8asfoxZ/UzWmK2Tmc8Z707UTUScRfE9MRHdl5mvHO9OSFKvmZRUbUXEZZm59Xj3Q50zZvVjzOrJuNWPMasfY1Y/xkySpInF6duSJEmSJEmS+sqkpCRJkiRJkqS+MimpOjtmvDugETNm9WPM6sm41Y8xqx9jVj/GTJKkCcQ1JSVJkiRJkiT1lSMlJUmSJEmSJPWVSUlJkiRJkiRJfWVSUpIkSZIkSVJfzRjvDkjtRMRsYHvgpcCWwPOB5wAzgYeBW4E/AD/OzN+OVz8nu4iYS/H13wrYuvx3AyDKKhdk5i5jvMfGwDuB3YG1gLnA3cB1wCnAyZn56FjuMVX0431jvPorIk4A9m4oXjczbxlBG8asB8r325uAf6Z4v60GLAs8RvH1vQI4EzgtM58cYdvGrI1e/XyKiOWAnYBdgC2AjYCVKD7Ufwi4EbgY+GFmXjWG/m8NvIPi/+s1gDnA34FrgJOBn2Tm06NtfyLqx+8U5X2WonjvvLG8z3Mp3kP3UryPrgUuAM7PzJtG0O6Ui5kkSb3gRjeasCJiVeBrFH/kLdvhZX8A9s3Mv/SqX1NRRFwHPI/BPxaaGfUfEBExA/g0cBgwfZiqtwH7ZOb/juY+U0E/3jfGq/8i4rXAz5s81VFS0pj1TkS8BvgOsGYH1f8OvDczm8WysV1j1oFe/HyKiI2AL1Eks2Z1eNkvgQMy844R3GcZ4CvA/m2qXgO8IzOv7LTtiazXv1NU7vMK4Ghgww6q35mZbd/DUzVmkiT1iklJTVjlp9CXNnnqDuBO4AmKT6cbf9l8HHhlZl7U2x5OHRHRyX8UY0lK/oBiJNCABP4C3AesSzE6aMAzwGsy8zejuddk14/3jfHqr4hYAfgzxQifRp0mJY1ZD0TE24EfMHQ5nCcp4vUwsDywKTC78nwC+2Xmf7dp25h1oBc/nyLizRSjUBv9jWJ03dPA/PJR9Q9g58y8roN7zATOAnatFC+kiPHDFP9Hr1p57lFgx7GMyJwoev07RXmPjwOfayh+BLgZeABYhmJk5krlc22TklM5ZpIk9YprSqoufge8B1grM9fKzO0yc9fM3AhYDzipUncZ4PSIWHk8OjrJPQpcCHwVeDvFlMQxiYgPMfQP7wuBjTNz08zcOTPXBl5BMcIIimUnTomIdcZ67ymg6+8b4zUuvsZgQnLESSdj1hsRsTbwXQZ/l3oSOARYKTO3ycyXZ+bWFEmPDwFPDVwKfCsi1h+mbWM2cl3/+QQsBn5NsWzCKpm5Xma+JDNfmpnrAi8Czq7UXxU4IyI6GV15FEOTW6dRfMiwWWbuBKwO7EWRSANYrmx7ubG9pAmlFzEjIg5laELycuBVwMqZuUVmvqz8ebgyxYjND1FM427HmEmS1G2Z6cPHhHxQrDX0U2DzDusfRTGSZOBx1Hi/hsnyoPiDbCPK0dWV8vMrX+/zR9HuShTrcg20cTkwq0Xd9Sn+gBmoe9x4f10m4qOX7xvjNS7xfHXla3gGsE9DvOa3ud6Y9S42n2uIxR5t6u/ZUP+Lxqwrcej6zyfg9cB/A+t3UHcaxajKamzf3+aajShG2A3U/0Vj/yt1X0IxEnag7hHj/TWfiDGrtLFtw9freGB6F/o8pWPmw4cPHz589OrhSElNWJl5eWa+MTtfj+cwiimqA97c/V5NTZl5YmZel5ndXu/hIGBe5fyAbLEwfBYL0H+2UvS2iJjf5f7UXo/fN8arjyJiHnBMefoo8N5RNGPMemfnyvG1mXnacJUz88cU0zwH7NCiqjEbgV78fMrM0zPz3dnBxieZuRg4kGKk7IB2v38cyuBmkwuBA1v1PzN/z+D/AwAfjIil2/VrIuvV7xQRMQ34PoNrsF5Ksd7qoi40P6VjJklSr5iU1KSRmQuAX1WK1vaXwAnvLZXjP2Zms7UQq77H4BTIacAePenVFDLC943x6q+vUKz/CXBoZt4+ijaMWe+sUjm+usNrqvVaLZVgzGomM+8HquvxPr9V3XJdwtdXin6amXe2ucXRleNlKaYia0m7U6zhOuDgbiQkjZkkSb1jUlKTzf0N53PHpRdqKyLWY+gfD2e0uyYzHwAurhS9rtv9mqLavm+MV39FxO7AvuXpRcC3R9GGMeutRyvHs1vWGqpa78HGJ41ZrVX/Hx3ud48dgRUq553E+FqKTXYGGOPmDqwcX5WZf+hSu8ZMkqQeMSmpyWZ+5XgxxS6lmpi2aDj/XYfXVett3p2uTHnzK8et3jfGq0/KTRGOLU+fBv51lNMcjVlvVRMeL4mIpYarXG5+8pJK0YVNqhmz+ppfOb5nmHrdiHFjG1NeREwHXl4p+nkXmzdmkiT1iElJTRoRMYeh02Muzcxnxqs/amuThvMbOryuWm9uRKzZpf5MSSN43xiv/vkysFZ5fERmXjfKdoxZb32bYjMLKKZyf26YugCfB55THj/G0OmdA4xZDUXEWhQbrAz4/TDVqzFeCNzS4W2qMd6wTMJp0AuB6tIjvweIiOdFxBci4uqIeCginoiI2yLi5xHxbx0u82PMJEnqEZOSmkwOZujmAMePV0fUkfmV40XA3zu87tZh2tHIdfq+mV85Nl49EhEvA/YvT68CvjSG5uZXjo1Zl2XmNcD7Kb62AB+JiDMjYveIWDEippf/vioizgI+WNZ7FHhLZt7WpNn5lWNjVh8fZ+jv1MP9/jG/cnxnuVFOJ6oxngU8t8PrporNG86vj4iPA9cAH6NIWs4D5lB86PNa4JvA3yLiDW3anl85NmaSJHXRjPZVpIkvIl4AHF4puonB6Y+amKprbj06gsXoH244X65L/ZlyRvi+MV49FhHLUmxaAkVC6l/HONrbmPVYZn4nIm4BvgZsBLy6fDSzCPglcFhm/rlFHWNWMxHxCuCAStFvM/PMYS6pxvihEdzKGA+vceOogxj8IADgduBminVdXwAsU5avAvwkIg7MzGNozphJktQjjpRU7UXESsBPGdxAYBGwT7mrsCauZSvHT47gusa6/pI/CqN43xiv3vsigyNyvpqZl42xPWPWB5l5FrAb8LM2Vc8Bjh4mIQnGrFbKjYlOBKIsehTYr81lxrg3lm84H0hIXgPskJlrZ+YumbkdsFL5/MDPuwCOjohtac6YSZLUIyYlVWvleninAxtUiv8jMy8apy6pczMrxyMZDdZYd2bTWmpplO8b49VDEbEL8N7y9CbgU11o1pj1WEQsGxHfpojZG8riBcAVwHnApcATZfnuwK8j4oJyDcJmjFlNRMRzgF8xdITe/pnZbh1QY9wbs5uU3QDsmJlDNqbJzKcz82vAnpXimRTrvjZjzCRJ6hGTkqqtcqfTnwDbV4qPzswvjlOXNDKPV46b/THRSmPdx5vWUlNjeN8Yrx4pN1r4LwZHW70nM0cyGqcVY9ZD5S7p5wEHUiyH8wTF6KsVM3PLzHxZZm5LMYLrXQzuar8T8NuIWK1Js8asBiJieeDXwIaV4n/PzB91cLkx7o1mX49DMvOhVhdk5ukUPw8H7BoR67dp25hJktRFJiVVSxExEzgFeGWl+FiKTTtUD49VjjvZ/bJV3Ue70JcpYYzvG+PVO18A1iuPv5eZ/9uldo1Zbx0FbFMeLwB2y8yvZeaQxENmLszM4yg+CHiwLF4H+FaTNo3ZBFcmo88CtqgUfyIzv9xhE8a4Nxq/HvdQxKmd/244f2mTOsZMkqQeMSmp2omIGcBJwOsqxd8HDsjMHJ9eaRTurRwvU/6h14nG3Svva1pLQ3ThfWO8eiAiNqHYkAHgLuDfu9i8MeuRiFgdeHel6NjGKaKNMvN6hk4PfUO5JmGVMZvAys2ofgW8uFJ8eGZ+bgTNVGM8kt2YG+veP4Jrp4J7G84v7/Bn258azpuNlDRmkiT1iElJ1UpETAdOAPaoFP8PxXRHE5L18teG83U6vK5abzFwfXe6M3l16X1jvHpjFQanbT8XeDAistWDJUf1/K3y/C0Nzxmz3tmVYsr2gJ92eN3PKscB7NzwvDGboCJiGeBMhi59cURmfmaETVVjvOIIEs/VGN893LTkKerahvMHOryuMYG/YpM6xkySpB4xKanaKBMrxzN0YfIfAPtl5uLx6ZXGoHEH2i07vK5a75Yurb03aXXxfWO86seY9U7jRjW3d3jdbQ3njetKGrMJqFz39UyK9UAHfDYzPz2K5hpjvEXTWkuqxrgxAafia1L9kG1Wh9c1rvvY7L1jzCRJ6hGTkqqFMrFyHPDWSvFxwL4mJGvrMoYu+t44YqiV6h+F53etN5NQl983xqs3FlJM6ev08VjD9Q9WnmscGWTMeufphvM5HV7XuMbcEw3nxmyCKROSZzA0Fv+ZmZ8aZZMXNJy3jXFEzGbolPHzR3nvSatcy/WSSlHj0gitNE7XvrtJHWMmSVKPmJTUhBcR0yimmu5dKT4eeLcJyfoqR/JUF6Hfo/zjr6WI2IGhf2ic1ou+TQbdft8Yr97IzN9l5sqdPoD3NzSxZeX5LRvaNma98/eG822a1lrStg3nd1RPjNnEEhFzgJ8zdPOTz2XmJ0fbZmbextB1DN8REdGqfunNDE18G+PmTq0cvygiOln/8ZUN579vrGDMJEnqHZOSmtDKxMr3gbdXin8I7GNCclL4r8rxPOCDbepXp8rdBpzT9R5NAj183xiv+jFmvXE+Q6eKHlLubt9SmcT4SKVoMUuOwAJjNiGUI91+BrysUnxkZn6iC81XY/w8hn541NiPWcBhlaI/ZKZTgZs7gcGRxtMZ+nVbQkQsz9APeu4CLm5R3ZhJktQDJiU1YZV/wH0XeFel+ATgXSYkJ4fM/BVD/yj/VES8ulndiPgc8PJq3cxc0Mv+1VEv3zfGq36MWW9k5t0UU3oHvAA4odWoxoiYAXyDoV/fn2TmErtkG7PxFxFLAT8BdqsUfz4z/6NLt/gecGPl/BsRscRo2/L75hhgk0rxoV3qw6RTvi//X6XooIjYv1ndcif1U4DVK8VHZubCFs0bM0mSeiDcsFgTVUTsCZxcKUrgXGDRCJr5aGZe3dWOTUER8Qmg2eiQpRjcOTiBZn8MH5+Z7xmm7Q0pRiYM7Hi5CDiJYoTK/cC6wLuBHSuX/Rx4o8npJfX6fWO8xldE7MPQHbjXzcxb2lxjzHqg/LpeAixfKb6TYtmESyjW+1yOYrOLd1GMrhpwP7BNZv5tmLaNWQd68fMpIj4KfLFS9DQjXxPwXZn5j1ZPltPuz2FwQ5anKEbjnQ08CmwEHABsVrns6MxsXMKhdnr8O8Vsiq9rdZf0c4ETgZsoNrbZBjgQWKNS50zgdcO9f6ZyzCRJ6pUZ490BaRiNI06CoSNCOvGFLvVlqptB+50so0WdYac0Zub1EfF64HSKP8CnU0w7fnuLS84D3jrV/vAegZ6+b4xX/Riz3ii/rq+iGFE3sHbdGkC70XR3UiQPmyYkK20bs8704udT4/+js4DdR9ivYTc/ysyLIuJtFJuPLU2RLHtf+WjmROADI+zDRNXL3ymeiojXAr8EtiuLX8bQafiNfga8o937Z4rHTJKknnD6tqRxl5kXAZtSjARq3NV2wB3Ah4BXZGbjjrXqI+NVP8asNzLzDxRTt78E3Num+j0UCf8XZOalHbRtzCa5zDwN2JwigdZqNPsNFAmzt2XmSEa8T1mZ+SCwA/Bh4PZhql5HMYr5TZn5WIdtGzNJkrrI6duSJpSImAfsAqxJMfXxHxR/OFyc/oc14Riv+jFmvRER0ymSiJsDKwHLUGy6cS9wFXDtaBMUxmzyi4hVgJ0oRtvOpth05ZrMvHxcO1Zz5TrL2wAbA6tRJBLvAS7JzOvH2LYxkyRpjExKSpIkSZIkSeorp29LkiRJkiRJ6iuTkpIkSZIkSZL6yqSkJEmSJEmSpL4yKSlJkiRJkiSpr0xKSpIkSZIkSeork5KSJEmSJEmS+sqkpCRJkiRJkqS+MikpSZIkSZIkqa9MSkqSJEmSJEnqK5OSkiRJkiRJkvrKpKQkSeMgIg6PiGx4/OcIrp/dcO0+I7z/ehHx3og4NSL+LyLuiogFEfFIRNwaEedFxBcjYreImD7iFyhJkiRJw5gx3h2QJEnP+mBEfDMz7+rVDSJiU+BwYA8gmlSZCSwHrA28FPgo8I+I+A5wVGY+2qu+SZIkSZo6HCkpSdLEsTTw6V41HhGHAFcBb2ZoQvJx4C/AhcDFwM3Awsrzq5b9uikilulV/yRJkiRNHSYlJUmaWPaLiA273WhEfBP4GjAwFXsx8ANgZ2CFzNwkM3fOzJdk5vrAShSjKU8DsrzmORQjKSVJkiRpTExKSpI0/h4G7imPZwBHdrPxiDgY+LdK0e3AVpm5T2ZemJkLG6/JzEcz8yeZ+WZgC+C33eyTJEmSpKnNpKQkSePvKeCzlfM9IuLF3Wg4IjYHvlwpuhP4p8y8stM2MvMqYFfgKAZHTUqSJEnSqJmUlCRpYvgucFPl/ItdaveTDE65TmCfzLxzpI1k5jOZ+ZHMfLhL/ZIkSZI0hZmUlCRpAiinUH+iUrRzRLx6LG2Wa1O+oVJ0bmaeM5Y2JUmSJKkbTEpKkjRxnAz8qXL++YgYy8/q1zP0Z/13xtCWJEmSJHWNSUlJkiaIzEzgY5WiFwFvH0OTu1SbB84dQ1uSJEmS1DUmJSVJmkAy81zg7ErRERExa5TNbVM5vj4zHxp1xyRJkiSpi0xKSpI08XyMwV2u1wHeN9IGymnfK1WKbu9CvyRJkiSpK0xKSpI0wWTmFRTrSw74eETMG2EzyzP05/xDY+yWJEmSJHWNSUlJkiam/wAWlscrMXStyU7Mbjh/esw9kiRJkqQuMSkpSdIElJk3A9+tFB0SEc8dQRMPNpyPdKSlJEmSJPWMSUlJkiauI4BHy+OlgcM7vTAznwSeqhSt2L1uSZIkSdLYmJSUJGmCysx7gaMqRftGxEYjaOKmyvELIiK60zNJkiRJGhuTkpIkTWxHAf8oj2cAR47g2gsqx3OBTbrVKUmSJEkaC5OSkiRNYJn5GPDZStGbIuLFHV5+XsP53t3plSRJkiSNjUlJSZImvmOAGyvnX+rwul8Ad1bO/zUilu1aryRJkiRplExKSpI0wWXmQuATlaKdgNd0cN0C4KuVolWAr422HxGxTkTMGu31kiRJkjTApKQkSfXwY+Cyynmna0seDVxeOd8vIj450ptHxCvLduaM9FpJkiRJamRSUpKkGsjMBD5WKdqww+ueBvYAHqgUHxERp0bEBu2uj4gXRsRPgV8BK46gy5IkSZLU0ozx7oAkSepMZp4XEb8BdhvhdbdExM4Ua0zOL4v3AF4fERcCZ1OsWXkfMBNYFdgUeDXwou70XpIkSZIGmZSUJKlePga8AoiRXJSZ10TENsC3KRKSQfF7wK7lo537KKaMPzKi3kqSJElSE07fliSpRjLzSuCkUV57X2a+BdgcOB64p80li4CLgAOB9TLzq5m5eDT3liRJkqSqKJaokiRJU01EBPBCYCNgZWAF4GngQYrp3H/KzMfHr4eSJEmSJiuTkpIkSZIkSZL6yunbkiRJkiRJkvrKpKQkSZIkSZKkvjIpKUmSJEmSJKmvTEpKkiRJkiRJ6iuTkpIkSZIkSZL6yqSkJEmSJEmSpL4yKSlJkiRJkiSpr0xKSpIkSZIkSeork5KSJEmSJEmS+sqkpCRJkiRJkqS+MikpSZIkSZIkqa/+f013gjwCt9pkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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\"), \n",
    "    colors.to_rgba(\"darkgreen\"),  # En lugar de \"darkgreen\"\n",
    "    colors.to_rgba(\"red\"), \n",
    "    colors.to_rgba(\"darkred\"),  # En lugar de \"darkred\"\n",
    "    colors.to_rgba(\"mediumseagreen\"),  # En lugar de \"mediumseagreen\"\n",
    "    colors.to_rgba(\"seagreen\"),  # En lugar de \"seagreen\"\n",
    "    colors.to_rgba(\"palegreen\"), \n",
    "    colors.to_rgba(\"springgreen\"), \n",
    "    colors.to_rgba(\"indianred\"), \n",
    "    colors.to_rgba(\"firebrick\"),\n",
    "    colors.to_rgba(\"darkgoldenrod\"),\n",
    "    colors.to_rgba(\"saddlebrown\"),\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(\"NP\", fontsize=36)\n",
    "ax.set_xlabel(\"NC\", fontsize=36)\n",
    "\n",
    "ax.set_xticklabels(['']+processes, fontsize=36)\n",
    "ax.set_yticklabels(['']+processes, fontsize=36)\n",
    "\n",
    "\n",
    "labelsMethods_aux = ['Baseline - AllS (0)', 'Baseline - P2PS (1)',\n",
    "                    'Merge - AllS (2)','Merge - P2PS (3)',\n",
    "                    'Baseline - AllA (4)', 'Baseline - AllT (5)','Baseline - P2PA (6)','Baseline - P2PT (7)',\n",
    "                    'Merge - AllA (8)','Merge - AllT (9)','Merge - P2PA (10)','Merge - P2PT (11)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "tick_bar = []\n",
    "for i in range(len(used_config)):\n",
    "    tick_bar.append(0.37 + i*0.92) #Config de 12 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+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  6  8 10 11]\n",
      "[ 1 17 20  1  1 22 27  1]\n",
      "[ 2  3  8 10 11]\n",
      "[ 5  9 11 16  1]\n"
     ]
    }
   ],
   "source": [
    "aux_array = []\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)\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\n",
    "test_parameter='omega' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha'\n",
    "    name_legend = \"Values of α\"\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM[grouped_aggM[real_parameter] > 0]\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega'\n",
    "    name_legend = \"Values of ω\"\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggLAsynch[grouped_aggLAsynch[real_parameter] > 0]\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",
    "\n",
    "plot_data = []\n",
    "for config in used_config:\n",
    "    if config[0] > 0:\n",
    "        dataLists = 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 = []\n",
    "for ns_aux in processes:\n",
    "    for np_aux in processes:\n",
    "        if used_direction=='s' and np_aux > ns_aux or used_direction=='e' and np_aux < ns_aux or used_direction=='a' and np_aux != ns_aux:\n",
    "            new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "            labels_aux.append(new_label)\n",
    "#print(data_aux[real_parameter])\n",
    "#print(plot_data)\n",
    "#print(len(plot_data))\n",
    "#print(labels_aux)\n",
    "#print(len(labels_aux))\n",
    "labelsMethods_aux = ['Baseline - AllA', 'Baseline - AllT','Baseline - P2PA','Baseline - P2PT',\n",
    "                    'Merge - AllA','Merge - AllT','Merge - P2PA','Merge - P2PT']\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(labels_aux))\n",
    "for index in range(len(plot_data)):\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",
    "\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\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",
    "f.tight_layout()\n",
    "f.savefig(\"Images/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'})\n",
    "\n",
    "# Get the test data\n",
    "#X, Y, Z = axes3d.get_test_data(0.05)\n",
    "\n",
    "aux = grouped_aggG.loc[u_sols[0],'T_total']\n",
    "Z = [None] * len(processes)\n",
    "X, Y = np.meshgrid(processes, processes)\n",
    "removed_index = 0\n",
    "for i in range(len(processes)):\n",
    "    Z[i] = [0] * len(processes)\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            real_i = i - removed_index\n",
    "            real_j = j - removed_index\n",
    "            Z[i][j] = aux.values[real_i*len(processes)+real_j]\n",
    "        else:\n",
    "            Z[i][j] = 0\n",
    "            removed_index += 1  \n",
    "Z = np.array(Z)\n",
    "\n",
    "ax.plot_wireframe(X, Y, Z, rstride=20, cstride=10)\n",
    "ax.set_proj_type('ortho')  # FOV = 0 deg\n",
    "ax.set_title(\"'ortho'\\nfocal_length = ∞\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.75676344 0.03701228 3.76439349]\n",
      "[0.20630784 0.96375203 0.04733157]\n",
      "[2.17054203 1.7721764  0.83496214 0.27720765 0.59800783 0.42146685]\n",
      "[0.19532397 0.24843587 0.47871884 0.76709983 0.5796655  0.67410377]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[9.87, 9.03, 6.81],\n",
    "              [7.18, 8.35, 7.00],\n",
    "              [8.39, 7.58, 7.68],\n",
    "              [7.45, 6.33, 9.35],\n",
    "              [6.41, 7.10, 9.33],\n",
    "              [8.00, 8.24, 8.44]])\n",
    "b = np.array([[6.35, 7.30, 7.16],\n",
    "              [6.65, 6.68, 7.63],\n",
    "              [5.72, 7.73, 6.72],\n",
    "              [7.01, 9.19, 7.41],\n",
    "              [7.75, 7.87, 8.30],\n",
    "              [6.90, 7.97, 6.97]])\n",
    "c = np.array([[3.31, 8.77, 1.01],\n",
    "              [8.25, 3.24, 3.62],\n",
    "              [6.32, 8.81, 5.19],\n",
    "              [7.48, 8.83, 8.91],\n",
    "              [8.59, 6.01, 6.07],\n",
    "              [3.07, 9.72, 7.48]])\n",
    "my_daa_aux = [a,b,c]\n",
    "\n",
    "F1, p_aux = stats.f_oneway(a, b, c)\n",
    "F2, p_aux2 = stats.f_oneway(*my_daa_aux,axis=0)\n",
    "\n",
    "print(F1)\n",
    "print(p_aux)\n",
    "print(F2)\n",
    "print(p_aux2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Resizes</th>\n",
       "      <th>Total_Groups</th>\n",
       "      <th>Total_Stages</th>\n",
       "      <th>Granularity</th>\n",
       "      <th>SDR</th>\n",
       "      <th>ADR</th>\n",
       "      <th>DR</th>\n",
       "      <th>Redistribution_Method</th>\n",
       "      <th>Redistribution_Strategy</th>\n",
       "      <th>Spawn_Method</th>\n",
       "      <th>...</th>\n",
       "      <th>Stage_Bytes</th>\n",
       "      <th>Iters</th>\n",
       "      <th>Asynch_Iters</th>\n",
       "      <th>T_iter</th>\n",
       "      <th>T_stages</th>\n",
       "      <th>T_spawn</th>\n",
       "      <th>T_spawn_real</th>\n",
       "      <th>T_SR</th>\n",
       "      <th>T_AR</th>\n",
       "      <th>T_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.08095, 0.076509, 0.079877, 0.074691, 0.076...</td>\n",
       "      <td>(((0.010705, 0.001643, 0.000203, 0.067232), (0...</td>\n",
       "      <td>(1.347948,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.752765,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>80.055689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.083757, 0.069349, 0.068418, 0.065849, 0.06...</td>\n",
       "      <td>(((0.010718, 0.000521, 4.2e-05, 0.071717), (0....</td>\n",
       "      <td>(1.408781,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.780452,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>78.698831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.096849, 0.072226, 0.075321, 0.065634, 0.07...</td>\n",
       "      <td>(((0.010704, 0.001999, 0.000233, 0.079332), (0...</td>\n",
       "      <td>(1.336949,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.526026,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>78.275454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.07964, 0.070345, 0.073844, 0.086362, 0.072...</td>\n",
       "      <td>(((0.010704, 0.003768, 0.000384, 0.062777), (0...</td>\n",
       "      <td>(1.44455,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.688739,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>77.281291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.098563, 0.068683, 0.090294, 0.083441, 0.08...</td>\n",
       "      <td>(((0.010716, 0.000262, 0.00023, 0.086761), (0....</td>\n",
       "      <td>(1.467106,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.592875,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>79.911654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653634, 0.634598, 0.633836, 0.634582, 0.63...</td>\n",
       "      <td>(((0.622565, 7e-06, 2e-06, 0.03106), (0.62308,...</td>\n",
       "      <td>(1.34195,)</td>\n",
       "      <td>(1.074329,)</td>\n",
       "      <td>(0.044368,)</td>\n",
       "      <td>(1.800613,)</td>\n",
       "      <td>363.805409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653119, 0.633832, 0.633014, 0.633593, 0.63...</td>\n",
       "      <td>(((0.621538, 0.000137, 3e-06, 0.03144), (0.621...</td>\n",
       "      <td>(1.382511,)</td>\n",
       "      <td>(1.104917,)</td>\n",
       "      <td>(0.044006,)</td>\n",
       "      <td>(2.054798,)</td>\n",
       "      <td>360.893782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.652854, 0.633725, 0.632971, 0.633643, 0.63...</td>\n",
       "      <td>(((0.621539, 9.8e-05, 2e-06, 0.031214), (0.621...</td>\n",
       "      <td>(1.348554,)</td>\n",
       "      <td>(0.975715,)</td>\n",
       "      <td>(0.044106,)</td>\n",
       "      <td>(1.975576,)</td>\n",
       "      <td>359.383625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.652802, 0.633527, 0.633319, 0.633376, 0.63...</td>\n",
       "      <td>(((0.621599, 0.000231, 2e-06, 0.030969), (0.62...</td>\n",
       "      <td>(1.310184,)</td>\n",
       "      <td>(1.022675,)</td>\n",
       "      <td>(0.047264,)</td>\n",
       "      <td>(1.61714,)</td>\n",
       "      <td>362.372775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653575, 0.634228, 0.634006, 0.63414, 0.633...</td>\n",
       "      <td>(((0.622518, 7e-06, 2e-06, 0.031046), (0.62287...</td>\n",
       "      <td>(1.364008,)</td>\n",
       "      <td>(1.040484,)</td>\n",
       "      <td>(0.043897,)</td>\n",
       "      <td>(1.749459,)</td>\n",
       "      <td>363.027286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_Resizes  Total_Groups  Total_Stages  Granularity    SDR   ADR  \\\n",
       "0                 1             2             4       100000  100.0   0.0   \n",
       "1                 1             2             4       100000  100.0   0.0   \n",
       "2                 1             2             4       100000  100.0   0.0   \n",
       "3                 1             2             4       100000  100.0   0.0   \n",
       "4                 1             2             4       100000  100.0   0.0   \n",
       "...             ...           ...           ...          ...    ...   ...   \n",
       "1675              1             2             4       100000    3.4  96.6   \n",
       "1676              1             2             4       100000    3.4  96.6   \n",
       "1677              1             2             4       100000    3.4  96.6   \n",
       "1678              1             2             4       100000    3.4  96.6   \n",
       "1679              1             2             4       100000    3.4  96.6   \n",
       "\n",
       "              DR Redistribution_Method Redistribution_Strategy Spawn_Method  \\\n",
       "0     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "1     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "2     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "3     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "4     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "...          ...                   ...                     ...          ...   \n",
       "1675  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1676  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1677  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1678  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1679  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "\n",
       "      ...          Stage_Bytes       Iters Asynch_Iters  \\\n",
       "0     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "1     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "2     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "3     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "4     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "...   ...                  ...         ...          ...   \n",
       "1675  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1676  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1677  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1678  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1679  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "\n",
       "                                                 T_iter  \\\n",
       "0     ((0.08095, 0.076509, 0.079877, 0.074691, 0.076...   \n",
       "1     ((0.083757, 0.069349, 0.068418, 0.065849, 0.06...   \n",
       "2     ((0.096849, 0.072226, 0.075321, 0.065634, 0.07...   \n",
       "3     ((0.07964, 0.070345, 0.073844, 0.086362, 0.072...   \n",
       "4     ((0.098563, 0.068683, 0.090294, 0.083441, 0.08...   \n",
       "...                                                 ...   \n",
       "1675  ((0.653634, 0.634598, 0.633836, 0.634582, 0.63...   \n",
       "1676  ((0.653119, 0.633832, 0.633014, 0.633593, 0.63...   \n",
       "1677  ((0.652854, 0.633725, 0.632971, 0.633643, 0.63...   \n",
       "1678  ((0.652802, 0.633527, 0.633319, 0.633376, 0.63...   \n",
       "1679  ((0.653575, 0.634228, 0.634006, 0.63414, 0.633...   \n",
       "\n",
       "                                               T_stages      T_spawn  \\\n",
       "0     (((0.010705, 0.001643, 0.000203, 0.067232), (0...  (1.347948,)   \n",
       "1     (((0.010718, 0.000521, 4.2e-05, 0.071717), (0....  (1.408781,)   \n",
       "2     (((0.010704, 0.001999, 0.000233, 0.079332), (0...  (1.336949,)   \n",
       "3     (((0.010704, 0.003768, 0.000384, 0.062777), (0...   (1.44455,)   \n",
       "4     (((0.010716, 0.000262, 0.00023, 0.086761), (0....  (1.467106,)   \n",
       "...                                                 ...          ...   \n",
       "1675  (((0.622565, 7e-06, 2e-06, 0.03106), (0.62308,...   (1.34195,)   \n",
       "1676  (((0.621538, 0.000137, 3e-06, 0.03144), (0.621...  (1.382511,)   \n",
       "1677  (((0.621539, 9.8e-05, 2e-06, 0.031214), (0.621...  (1.348554,)   \n",
       "1678  (((0.621599, 0.000231, 2e-06, 0.030969), (0.62...  (1.310184,)   \n",
       "1679  (((0.622518, 7e-06, 2e-06, 0.031046), (0.62287...  (1.364008,)   \n",
       "\n",
       "     T_spawn_real         T_SR         T_AR     T_total  \n",
       "0            (0,)  (0.752765,)         (0,)   80.055689  \n",
       "1            (0,)  (0.780452,)         (0,)   78.698831  \n",
       "2            (0,)  (0.526026,)         (0,)   78.275454  \n",
       "3            (0,)  (0.688739,)         (0,)   77.281291  \n",
       "4            (0,)  (0.592875,)         (0,)   79.911654  \n",
       "...           ...          ...          ...         ...  \n",
       "1675  (1.074329,)  (0.044368,)  (1.800613,)  363.805409  \n",
       "1676  (1.104917,)  (0.044006,)  (2.054798,)  360.893782  \n",
       "1677  (0.975715,)  (0.044106,)  (1.975576,)  359.383625  \n",
       "1678  (1.022675,)  (0.047264,)   (1.61714,)  362.372775  \n",
       "1679  (1.040484,)  (0.043897,)  (1.749459,)  363.027286  \n",
       "\n",
       "[2520 rows x 26 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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