analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrixMalEX=\"data_GG.csv\"\n",
    "matrixMal=\"data_GM.csv\"\n",
    "matrixIt=\"data_L.csv\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_qty=6 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
    "n_groups= 2\n",
    "repet = 10 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "time_constant = False # Cambiar segun el speedUp usado\n",
    "speedup = 0.66 # Porcentaje del speedup ideal\n",
    "\n",
    "p_value = 0.05\n",
    "values = [2, 10, 20, 40]\n",
    "#                      WORST          BEST\n",
    "dist_names = ['null', 'BalancedFit', 'CompactFit']\n",
    "\n",
    "processes = [1,10,20,40,80,120]\n",
    "\n",
    "labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],\n",
    "          ['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]\n",
    "labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',\n",
    "              '(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']\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)',\n",
    "            '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "            '(20,1)',  '(20,10)','(20,40)','(20,80)','(20,120)',\n",
    "            '(40,1)',  '(40,10)',  '(40,20)','(40,80)','(40,120)',\n",
    "            '(80,1)',  '(80,10)',  '(80,20)', '(80,40)','(80,120)',\n",
    "            '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
    "\n",
    "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
    "               '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "               '(20,40)','(20,80)','(20,120)',\n",
    "               '(40,80)','(40,120)',\n",
    "               '(80,120)']\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",
    "\n",
    "labelsExpandOrdered = ['(1,10)', '(1,20)', '(10,20)',\n",
    "                       '(1,40)','(10,40)','(20,40)',\n",
    "                       '(1,80)','(10,80)','(20,80)','(40,80)',\n",
    "                       '(1,120)', '(10,120)', '(20,120)','(40,120)','(80,120)']\n",
    "labelsShrinkOrdered = ['(10,1)', '(20,1)', '(40,1)', '(80,1)', '(120,1)',\n",
    "                '(20,10)',  '(40,10)',  '(80,10)',  '(120,10)', \n",
    "                '(40,20)', '(80,20)', '(120,20)',\n",
    "                '(80,40)','(120,40)',\n",
    "                '(120,80)']\n",
    "\n",
    "labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n",
    "labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n",
    "labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',\n",
    "               '(10,40)','(10,80)', '(10,160)',\n",
    "               '(20,40)','(20,80)', '(20,160)',\n",
    "               '(40,80)', '(40,160)',\n",
    "               '(80,160)']\n",
    "labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n",
    "               '(80,1)', '(80,10)', '(80,20)','(80,40)',\n",
    "               '(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']\n",
    "\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",
    "colors_spawn = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive']\n",
    "linestyle_spawn = ['-', '--', '-.', ':']\n",
    "markers_spawn = ['.','1','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": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfG = pd.read_csv( matrixMalEX )\n",
    "\n",
    "dfG = dfG.drop(columns=dfG.columns[0])\n",
    "dfG['S'] = dfG['N']\n",
    "dfG['N'] = dfG['S'] + dfG['%Async']\n",
    "dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
    "dfG['%Async'] = dfG['%Async'].fillna(0)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    group = dfG.groupby(['%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "else:        \n",
    "    group = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "\n",
    "grouped_aggG = group.agg(['median'])\n",
    "grouped_aggG.rename(columns={'median':'TE'}, inplace=True)\n",
    "\n",
    "grouped_aggG2 = group2.agg(['median'])\n",
    "grouped_aggG2.rename(columns={'median':'TE'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/2056908859.py:18: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n"
     ]
    }
   ],
   "source": [
    "dfM = pd.read_csv( matrixMal )\n",
    "dfM = dfM.drop(columns=dfM.columns[0])\n",
    "\n",
    "dfM['S'] = dfM['N']\n",
    "dfM['N'] = dfM['S'] + dfM['%Async']\n",
    "dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TH\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
    "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
    "\n",
    "dfM['%Async'] = dfM['%Async'].fillna(0)\n",
    "dfM['alpha'] = 1\n",
    "\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['median'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['alpha'] = \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['TC'] / \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux-1, css_aux, np_aux,ns_aux)]['TC']\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/1294489315.py:13: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "dfL['%Async'] = dfL['%Async'].fillna(0)\n",
    "dfL['omega'] = 1\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n",
    "\n",
    "#group\n",
    "grouped_aggL = groupL.agg(['median', 'count'])\n",
    "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
    "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2', 'omega', 'omega2'], axis='columns', inplace=True)\n",
    "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n",
    "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux), 'omega'] = \\\n",
    "                        grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti'] / \\\n",
    "                        grouped_aggL.loc[(0,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3419: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/tmp/ipykernel_2692/3028104048.py:17: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n"
     ]
    }
   ],
   "source": [
    "dfLT = pd.read_csv( matrixIt_Total )\n",
    "dfLT = dfLT.drop(columns=dfLT.columns[0])\n",
    "\n",
    "dfLT['%Async'] = dfLT['%Async'].fillna(0)\n",
    "dfLT['ItA']= dfLT.Ti.apply(lambda x: list(x.replace('(','').replace(')','').split(',')))\n",
    "dfLT['TiA']= dfLT.ItA.apply(lambda x: np.median(list(map(float,[y for y in x if y]))) )\n",
    "dfLT['TiA']= dfLT['TiA'].fillna(0)\n",
    "dfLT['ItA']= dfLT.ItA.apply(lambda x: len([y for y in x if y]))\n",
    "\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n",
    "else:\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n",
    "\n",
    "#group\n",
    "grouped_aggLT = groupLT.agg(['median'])\n",
    "grouped_aggLT.columns = grouped_aggLT.columns.get_level_values(0)\n",
    "grouped_aggLT.set_axis(['Sum','ItA'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/863462943.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfLT_aux = dfLT[dfLT[\"NP\"] == np_aux][dfLT[\"NS\"] == ns_aux][dfLT[\"Cst\"] == cst_aux][dfLT[\"Css\"] == css_aux]\n",
      "/tmp/ipykernel_2692/863462943.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfM_aux = dfM[dfM[\"NP\"] == np_aux][dfM[\"NS\"] == ns_aux][dfM[\"Css\"] == css_aux]\n"
     ]
    }
   ],
   "source": [
    "tc_list = []\n",
    "alpha_list = []\n",
    "omega_list = []\n",
    "ita_list = []\n",
    "dfLT['index'] = dfLT.index\n",
    "dfM['index'] = dfM.index\n",
    "for cst_aux in [0,1,2,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    dfLT_aux = dfLT[dfLT[\"NP\"] == np_aux][dfLT[\"NS\"] == ns_aux][dfLT[\"Cst\"] == cst_aux][dfLT[\"Css\"] == css_aux]\n",
    "                    dfM_aux = dfM[dfM[\"NP\"] == np_aux][dfM[\"NS\"] == ns_aux][dfM[\"Css\"] == css_aux]\n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        dfM_aux2= dfM_aux[dfM_aux[\"Cst\"] == cst_aux-1]\n",
    "                        dfM_aux2= dfM_aux2.sort_values(by=['TH'])\n",
    "                    dfM_aux = dfM_aux[dfM_aux[\"Cst\"] == cst_aux]\n",
    "                    dfM_aux= dfM_aux.sort_values(by=['TH'])\n",
    "                    index1_aux = dfM_aux.iloc[4][\"index\"]\n",
    "                    index2_aux = dfM_aux.iloc[5][\"index\"]\n",
    "                    \n",
    "                    # Comprobar que es un metodo asincrono\n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        value_aux1 = dfM_aux[dfM_aux[\"index\"] == index1_aux]['TC'].values\n",
    "                        value_aux2 = dfM_aux[dfM_aux[\"index\"] == index2_aux]['TC'].values\n",
    "                        valueS_aux1 = dfM_aux2.iloc[4]['TC']\n",
    "                        valueS_aux2 = dfM_aux2.iloc[5]['TC']\n",
    "                        value1_aux = (value_aux1 + value_aux2) / 2\n",
    "                        value2_aux = (value_aux1/valueS_aux1 + value_aux2/valueS_aux2) / 2\n",
    "                    else:\n",
    "                        value1_aux = dfM_aux['TC'].median()\n",
    "                        value2_aux = 1\n",
    "                    tc_list.append(float(value1_aux))\n",
    "                    alpha_list.append(float(value2_aux))\n",
    "                    \n",
    "                    value_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['ItA'].values\n",
    "                    value_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['ItA'].values\n",
    "                    value3_aux = (value_aux1 + value_aux2) / 2\n",
    "                    ita_list.append(int(value3_aux))\n",
    "                    \n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        iter_time_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['Time'].values\n",
    "                        iter_time_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['Time'].values\n",
    "                        if not time_constant:\n",
    "                            iter_time_aux1 = (iter_time_aux1 / np_aux) / speedup\n",
    "                            iter_time_aux2 = (iter_time_aux2 / np_aux) / speedup\n",
    "                        iter_Atime_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['TiA'].values\n",
    "                        iter_Atime_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['TiA'].values\n",
    "                        value4_aux = (iter_Atime_aux1/iter_time_aux1 + iter_Atime_aux1/iter_time_aux2) / 2\n",
    "                    else:\n",
    "                        value4_aux = 1\n",
    "                    omega_list.append(float(value4_aux))\n",
    "grouped_aggM['TC_A'] = tc_list\n",
    "grouped_aggM['ItA'] = ita_list\n",
    "grouped_aggM['Alpha_A'] = alpha_list\n",
    "grouped_aggM['Omega_A'] = omega_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "coherent_check_df = grouped_aggL.copy()\n",
    "# Añadir suma total de iteraciones\n",
    "coherent_check_df['Sum'] = 0\n",
    "coherent_check_df.loc[(1,slice(None)),'Sum'] = grouped_aggLT[(grouped_aggLT['Sum'] != 0)].loc[(slice(None)),'Sum'].values\n",
    "coherent_check_df = coherent_check_df[(coherent_check_df['Sum'] != 0)]\n",
    "# Añadir tiempos TE y TC\n",
    "coherent_check_df['TE'] = 0\n",
    "coherent_check_df['TEA'] = 0\n",
    "coherent_check_df['TR'] = 0\n",
    "coherent_check_df['TRA'] = 0\n",
    "for cst_aux in [1,3]:\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TE'] = grouped_aggG2.loc[('2,2',0,cst_aux-1,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TR'] = grouped_aggM.loc[('2,2',0,cst_aux-1,slice(None)),'TC'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TEA'] = grouped_aggG2.loc[('2,2',0,cst_aux,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TRA'] = grouped_aggM.loc[('2,2',0,cst_aux,slice(None)),'TC'].values\n",
    "# Calcular tiempos teoricos\n",
    "#coherent_check_df['Teorico-S'] = coherent_check_df['Ti'] * 3 + coherent_check_df['TR'] +  TIEMPOITERNS * 97\n",
    "#coherent_check_df['Rel-S'] = np.round(coherent_check_df['Teorico-S'] / coherent_check_df['TE'],2)\n",
    "#coherent_check_df['Teorico-A'] = coherent_check_df['Ti'] * 3 + coherent_check_df['Sum'] +  TIEMPOITERNS * (97 - coherent_check_df['Iters'])\n",
    "#coherent_check_df['Rel-A'] = np.round(coherent_check_df['Teorico-A'] / coherent_check_df['TEA'],2)\n",
    "coherent_check_df=coherent_check_df.droplevel('Tt').droplevel('%Async').droplevel('Dist')\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        aux_df = coherent_check_df.loc[(cst_aux, css_aux, slice(None))]\n",
    "        aux_df.to_excel(\"coherent\"+str(cst_aux)+\"_\"+str(css_aux)+\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_aggL.to_excel(\"resultL.xlsx\") \n",
    "grouped_aggLT.to_excel(\"resultLT.xlsx\")\n",
    "dfLT.to_excel(\"resultLT_all.xlsx\")\n",
    "grouped_aggM.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggG2.to_excel(\"resultG.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sum</th>\n",
       "      <th>ItA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2</th>\n",
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       "      <th>20</th>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>120</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.216691</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.271795</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.295311</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.204900</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.226526</td>\n",
       "      <td>3.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Sum  ItA\n",
       "Dist %Async Cst Css NP  NS                \n",
       "2    0.0    0   0   1   10   0.000000  0.0\n",
       "                        20   0.000000  0.0\n",
       "                        40   0.000000  0.0\n",
       "                        80   0.000000  0.0\n",
       "                        120  0.000000  0.0\n",
       "...                               ...  ...\n",
       "            3   1   120 1    0.216691  3.0\n",
       "                        10   0.271795  3.5\n",
       "                        20   0.295311  3.5\n",
       "                        40   0.204900  3.0\n",
       "                        80   0.226526  3.0\n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG\n",
    "dfM\n",
    "dfL\n",
    "dfLT\n",
    "grouped_aggG\n",
    "grouped_aggM\n",
    "grouped_aggL\n",
    "grouped_aggLT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>(0.069027, 0.098036, 0.125989, 0.112365, 0.109...</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>10</td>\n",
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       "</table>\n",
       "<p>2400 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  %Async   NP  N_par  NS  Dist  Compute_tam  Comm_tam  Cst  Css  Time  \\\n",
       "0     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "1     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "2     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "3     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "4     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "...  ..     ...  ...    ...  ..   ...          ...       ...  ...  ...   ...   \n",
       "2395  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2396  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2397  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2398  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2399  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "\n",
       "      Iters                                                 Ti       Sum  ItA  \\\n",
       "0         3                               (0.225269, 0.102594)  0.327863    2   \n",
       "1         3                               (0.197712, 0.111945)  0.309657    2   \n",
       "2         3                                        (0.172556,)  0.172556    1   \n",
       "3         3                               (0.208798, 0.101674)  0.310472    2   \n",
       "4         3                               (0.169751, 0.099995)  0.269746    2   \n",
       "...     ...                                                ...       ...  ...   \n",
       "2395      3           (0.061279, 0.086562, 0.102954, 0.064273)  0.315068    4   \n",
       "2396      3           (0.065522, 0.090022, 0.089555, 0.069523)  0.314622    4   \n",
       "2397      3  (0.065145, 0.10315, 0.110737, 0.085012, 0.093,...  1.037177   11   \n",
       "2398      3  (0.069027, 0.098036, 0.125989, 0.112365, 0.109...  0.998392   10   \n",
       "2399      3                     (0.065019, 0.091227, 0.065016)  0.221262    3   \n",
       "\n",
       "           TiA  \n",
       "0     0.163932  \n",
       "1     0.154829  \n",
       "2     0.172556  \n",
       "3     0.155236  \n",
       "4     0.134873  \n",
       "...        ...  \n",
       "2395  0.075417  \n",
       "2396  0.079539  \n",
       "2397  0.098615  \n",
       "2398  0.103513  \n",
       "2399  0.065019  \n",
       "\n",
       "[2400 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfLT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.716209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.716209</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.798951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.931771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.931771</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.992033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992033</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.992033</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">10</th>\n",
       "      <th>1</th>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.766185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.766185</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.766185</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.860920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.860920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.860920</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.890894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.890894</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890894</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20</th>\n",
       "      <th>1</th>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.790193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.790193</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.790193</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.864170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.864170</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.864170</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.088929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.088929</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.088929</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
       "      <th>1</th>\n",
       "      <td>0.029802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.029802</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.024519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.116366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116366</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.893692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.893692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893692</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.922904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.922904</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.922904</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">80</th>\n",
       "      <th>1</th>\n",
       "      <td>0.217198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.217198</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.180846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.180846</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.149038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.149038</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.148336</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148336</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148336</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.905912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.905912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.905912</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.231024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.231024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231024</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.148412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148412</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.177781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.177781</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.350567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.350567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.350567</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.156000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TC   TH   TS   TA        TR  alpha      TC_A  ItA  Alpha_A  \\\n",
       "NP  NS                                                                      \n",
       "1   10   0.283736  0.0  0.0  0.0  0.283736    1.0  0.283736    0      1.0   \n",
       "    20   0.716209  0.0  0.0  0.0  0.716209    1.0  0.716209    0      1.0   \n",
       "    40   0.798951  0.0  0.0  0.0  0.798951    1.0  0.798951    0      1.0   \n",
       "    80   0.931771  0.0  0.0  0.0  0.931771    1.0  0.931771    0      1.0   \n",
       "    120  0.992033  0.0  0.0  0.0  0.992033    1.0  0.992033    0      1.0   \n",
       "10  1    0.000982  0.0  0.0  0.0  0.000982    1.0  0.000982    0      1.0   \n",
       "    20   0.477040  0.0  0.0  0.0  0.477040    1.0  0.477040    0      1.0   \n",
       "    40   0.766185  0.0  0.0  0.0  0.766185    1.0  0.766185    0      1.0   \n",
       "    80   0.860920  0.0  0.0  0.0  0.860920    1.0  0.860920    0      1.0   \n",
       "    120  0.890894  0.0  0.0  0.0  0.890894    1.0  0.890894    0      1.0   \n",
       "20  1    0.001005  0.0  0.0  0.0  0.001005    1.0  0.001005    0      1.0   \n",
       "    10   0.001025  0.0  0.0  0.0  0.001025    1.0  0.001025    0      1.0   \n",
       "    40   0.790193  0.0  0.0  0.0  0.790193    1.0  0.790193    0      1.0   \n",
       "    80   0.864170  0.0  0.0  0.0  0.864170    1.0  0.864170    0      1.0   \n",
       "    120  1.088929  0.0  0.0  0.0  1.088929    1.0  1.088929    0      1.0   \n",
       "40  1    0.029802  0.0  0.0  0.0  0.029802    1.0  0.029802    0      1.0   \n",
       "    10   0.024519  0.0  0.0  0.0  0.024519    1.0  0.024519    0      1.0   \n",
       "    20   0.116366  0.0  0.0  0.0  0.116366    1.0  0.116366    0      1.0   \n",
       "    80   0.893692  0.0  0.0  0.0  0.893692    1.0  0.893692    0      1.0   \n",
       "    120  0.922904  0.0  0.0  0.0  0.922904    1.0  0.922904    0      1.0   \n",
       "80  1    0.217198  0.0  0.0  0.0  0.217198    1.0  0.217198    0      1.0   \n",
       "    10   0.180846  0.0  0.0  0.0  0.180846    1.0  0.180846    0      1.0   \n",
       "    20   0.149038  0.0  0.0  0.0  0.149038    1.0  0.149038    0      1.0   \n",
       "    40   0.148336  0.0  0.0  0.0  0.148336    1.0  0.148336    0      1.0   \n",
       "    120  0.905912  0.0  0.0  0.0  0.905912    1.0  0.905912    0      1.0   \n",
       "120 1    0.231024  0.0  0.0  0.0  0.231024    1.0  0.231024    0      1.0   \n",
       "    10   0.148412  0.0  0.0  0.0  0.148412    1.0  0.148412    0      1.0   \n",
       "    20   0.177781  0.0  0.0  0.0  0.177781    1.0  0.177781    0      1.0   \n",
       "    40   0.350567  0.0  0.0  0.0  0.350567    1.0  0.350567    0      1.0   \n",
       "    80   0.156000  0.0  0.0  0.0  0.156000    1.0  0.156000    0      1.0   \n",
       "\n",
       "         Omega_A  \n",
       "NP  NS            \n",
       "1   10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "10  1        1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "20  1        1.0  \n",
       "    10       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "40  1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "80  1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    120      1.0  \n",
       "120 1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM.loc[('2,2',0,2,0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Ti</th>\n",
       "      <th>Iters</th>\n",
       "      <th>To</th>\n",
       "      <th>Iters2</th>\n",
       "      <th>alpha</th>\n",
       "      <th>alpha2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tt</th>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>10</th>\n",
       "      <td>3.999165</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.999194</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.999186</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3.999236</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3.999194</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.070046</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.108073</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.075896</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.292376</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.090617</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.733503</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.069103</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.089061</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.068959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.083952</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Ti  Iters      To  Iters2     alpha  \\\n",
       "Tt  Dist %Async Cst Css NP  NS                                               \n",
       "0.0 2    0.0    0   0   1   10   3.999165    3.0  4485.0     3.0  1.000000   \n",
       "                            20   3.999194    3.0  4485.0     3.0  1.000000   \n",
       "                            40   3.999186    3.0  4485.0     3.0  1.000000   \n",
       "                            80   3.999236    3.0  4485.0     3.0  1.000000   \n",
       "                            120  3.999194    3.0  4485.0     3.0  1.000000   \n",
       "...                                   ...    ...     ...     ...       ...   \n",
       "1.0 2    0.0    3   1   120 1    0.070046    3.0    37.0     3.0  2.108073   \n",
       "                            10   0.075896    4.0    37.0     4.0  2.292376   \n",
       "                            20   0.090617    5.0    37.0     5.0  2.733503   \n",
       "                            40   0.069103    4.0    37.0     4.0  2.089061   \n",
       "                            80   0.068959    4.0    37.0     4.0  2.083952   \n",
       "\n",
       "                                 alpha2  \n",
       "Tt  Dist %Async Cst Css NP  NS           \n",
       "0.0 2    0.0    0   0   1   10       30  \n",
       "                            20       30  \n",
       "                            40       30  \n",
       "                            80       30  \n",
       "                            120      30  \n",
       "...                                 ...  \n",
       "1.0 2    0.0    3   1   120 1        30  \n",
       "                            10       40  \n",
       "                            20       54  \n",
       "                            40       37  \n",
       "                            80       39  \n",
       "\n",
       "[360 rows x 6 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.0\n"
     ]
    }
   ],
   "source": [
    "auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])\n",
    "auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')\n",
    "iters = auxIter['Iters1'].mean()\n",
    "print(iters)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547], [0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807]]\n",
      "[[2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703], [1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807]]\n",
      "[[0.22657399999999997, 0.22961033333333333, 0.37444533333333335, 1.0861523333333334, 0.18071299999999998, 0.2686593333333333, 0.48245, 1.8810366666666667, 0.22639533333333337, 0.31453400000000004, 0.564293, 2.4626886666666667, 0.4612826666666667, 1.0638560000000001, 1.5319243333333334, 2.1236686666666666], [0.21594133333333332, 0.36930899999999994, 1.1269756666666668, 1.1670603333333334, 0.22462733333333332, 0.47068400000000005, 1.5951943333333334, 1.693723, 0.7059706666666666, 1.368441, 1.8698483333333336, 2.2059883333333334, 0.4813296666666667, 1.3010543333333333, 1.8387883333333335, 2.1851773333333333]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:53: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "#Reserva de memoria para las estructuras\n",
    "TP_data=[0]*2\n",
    "TH_data=[0]*2\n",
    "TM_data=[0]*2\n",
    "\n",
    "TP_A_data=[0]*2\n",
    "TH_A_data=[0]*2\n",
    "TM_A_data=[0]*2\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    \n",
    "    TP_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "    TP_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "# Obtencion de los grupos del dataframe necesarios\n",
    "\n",
    "#ACTUALMENTE NO SE DIFERENCIAN LOS TIEMPOS DE ITERACIONES DE PADRES E HIJOS CUANDO COINCIDE EL NUMERO DE PROCESOS\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['NP'])['Ti']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['Dist', 'NP'])['Ti']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "grouped_aggL_aux = groupL_aux.agg(['mean'])\n",
    "grouped_aggL_aux.columns = grouped_aggL_aux.columns.get_level_values(0)\n",
    "grouped_aggL_aux.set_axis(['Ti'], axis='columns')\n",
    "\n",
    "#Calculo de los valores para las figuras\n",
    "#1=Best Fit\n",
    "#2=Worst Fit\n",
    "dist=1\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    i=0\n",
    "    r=0\n",
    "    for numP in values:\n",
    "        j=0\n",
    "        for numC in values:\n",
    "        \n",
    "            tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "            for tipo in [0]: #TODO Poner a 0,100\n",
    "                iters_aux=dfM[(dfM[\"NP\"] == numP)][(dfM[\"NS\"] == numC)][(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == tipo)]['Iters'].head(1).tolist()[0].split(',')\n",
    "                itersP_aux = int(iters_aux[0])\n",
    "                itersS_aux = int(iters_aux[1])\n",
    "                iters_mal_aux = 0\n",
    "                if tipo != 0:\n",
    "                    iters_mal_aux = grouped_aggL['Iters'].loc[(1,dist,tipo,numP,numC)]\n",
    "            \n",
    "                t_iterP_aux = grouped_aggL_aux['Ti'].loc[(dist,numP)]\n",
    "                t_iterS_aux = grouped_aggL_aux['Ti'].loc[(dist,numC)]\n",
    "            \n",
    "            \n",
    "                p1 = t_iterP_aux * itersP_aux\n",
    "                p2 = t_iterS_aux * max((itersS_aux - iters_mal_aux),0)\n",
    "                \n",
    "                array_aux = grouped_aggM[['TS', 'TA']].loc[(dist_v,tipo,numP,numC)].tolist()\n",
    "                p3 = tc_real + array_aux[0] + array_aux[1]\n",
    "                \n",
    "                #Guardar datos\n",
    "                if tipo == 0:\n",
    "                    TP_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_data[dist_index][i*len(values) + j] = p3\n",
    "                else:\n",
    "                    TP_A_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_A_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_A_data[dist_index][i*len(values) + j] = p3\n",
    "            j+=1\n",
    "        i+=1\n",
    "print(TP_data)\n",
    "print(TH_data)\n",
    "print(TM_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n",
    "#TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n",
    "#TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n",
    "\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "\n",
    "    width = 0.35\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index]).tolist()\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n",
    "    ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n",
    "    ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "    ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n",
    "    ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n",
    "    ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "    ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "    ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "    plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "    blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n",
    "    orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n",
    "    green_Spatch = mpatches.Patch(color='green', label='Children PR')\n",
    "    blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n",
    "    orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n",
    "    green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n",
    "\n",
    "\n",
    "    handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\n",
    "    \n",
    "    ax.axvline((3.5), color='black')\n",
    "    ax.axvline((7.5), color='black')\n",
    "    ax.axvline((11.5), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    plt.ylim((0, 25.5))\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_types_iker(checked_type='tc', used_direction='e', node_type=\"All\", normality='m'):\n",
    "    if checked_type=='te':\n",
    "        var_aux='TE'\n",
    "        tipo_fig=\"TE\"\n",
    "        grouped_aux=grouped_aggG2\n",
    "    elif checked_type=='tc':\n",
    "        var_aux='TC'\n",
    "        tipo_fig=\"Mall\"\n",
    "        grouped_aux=grouped_aggM\n",
    "    \n",
    "    if node_type=='Intra':\n",
    "        grouped_aux=grouped_aux.query('NP < 21 and NS < 21')\n",
    "    elif node_type=='Inter':\n",
    "        grouped_aux=grouped_aux.query('NP > 21 or NS > 21')\n",
    "\n",
    "    if used_direction=='s':\n",
    "        grouped_aux=grouped_aux.query('NP > NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsShrinkIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsShrinkInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsShrink\n",
    "        name_fig=\"Shrink\"\n",
    "        \n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "    elif used_direction=='e':\n",
    "        grouped_aux=grouped_aux.query('NP < NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsExpandIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsExpandInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsExpand\n",
    "        name_fig=\"Expand\"\n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "    title=tipo_fig+\"_Spawn_\"+node_type+\"_\"+name_fig+\"_\"+normality\n",
    "    return var_aux, grouped_aux, handles, used_labels, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_arrays_iker(grouped_aux, var_aux, used_direction='e', normality='m'):\n",
    "    vOrMult = list(grouped_aux.query('Cst == 0 and Css == 0')[var_aux])\n",
    "    vOrSingle = list(grouped_aux.query('Cst == 0 and Css == 1')[var_aux])\n",
    "    vMergeMult = list(grouped_aux.query('Cst == 2 and Css == 0')[var_aux])\n",
    "    vOrPthMult = list(grouped_aux.query('Cst == 1 and Css == 0')[var_aux])\n",
    "    vOrPthSingle = list(grouped_aux.query('Cst == 1 and Css == 1')[var_aux])\n",
    "    vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "    h_line = None\n",
    "    \n",
    "    if used_direction=='e':\n",
    "        vMergeSingle = list(grouped_aux.query('Cst == 2 and Css == 1')[var_aux])\n",
    "        vMergePthSingle = list(grouped_aux.query('Cst == 3 and Css == 1')[var_aux])\n",
    "    else:\n",
    "        #FIXME Que tenga en cuenta TH al realizar shrink merge\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "        vMergeSingle = None\n",
    "        vMergePthSingle = None\n",
    "    title_y = \"Total time(s)\"\n",
    "        \n",
    "    if normality == 'r':\n",
    "        vOrSingle = np.subtract(vOrMult, vOrSingle)\n",
    "        vOrPthMult = np.subtract(vOrMult, vOrPthMult)\n",
    "        vOrPthSingle = np.subtract(vOrMult, vOrPthSingle)\n",
    "        vMergeMult = np.subtract(vOrMult, vMergeMult)\n",
    "        vMergePthMult = np.subtract(vOrMult, vMergePthMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.subtract(vOrMult, vMergeSingle)\n",
    "            vMergePthSingle = np.subtract(vOrMult, vMergePthSingle)\n",
    "        vOrMult = None\n",
    "        h_line = 0\n",
    "        title_y = \"Saved time(s)\"\n",
    "    elif normality == 'n':\n",
    "        vOrSingle = np.divide(vOrSingle, vOrMult)\n",
    "        vOrPthMult = np.divide(vOrPthMult, vOrMult)\n",
    "        vOrPthSingle = np.divide(vOrPthSingle, vOrMult)\n",
    "        vMergeMult = np.divide(vMergeMult, vOrMult)\n",
    "        vMergePthMult = np.divide(vMergePthMult, vOrMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.divide(vMergeSingle, vOrMult)\n",
    "            vMergePthSingle = np.divide(vMergePthSingle, vOrMult)\n",
    "        vOrMult = np.divide(vOrMult, vOrMult)\n",
    "        h_line = 1\n",
    "        title_y = \"Relation Config time / Baseline Time\"\n",
    "    \n",
    "    data_array=[vOrMult,vOrSingle,vOrPthMult,vOrPthSingle,vMergeMult,vMergeSingle,vMergePthMult,vMergePthSingle]\n",
    "    v_lines=[]\n",
    "    value_aux = 0.4\n",
    "    if used_direction == 'e':\n",
    "        value_aux = 0.5\n",
    "    for i in range(0, len(vOrSingle)-1):\n",
    "        v_lines.append(value_aux + i)\n",
    "    return data_array, title_y, v_lines, h_line\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def legend_loc_iker(data_array, len_x, ylim_zero):\n",
    "    \n",
    "    max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    if(ylim_zero):\n",
    "        min_value = 0\n",
    "    middle_value = (max_value + min_value) / 2\n",
    "    offset = (max_value - min_value) * 0.1\n",
    "    \n",
    "    def array_check_loc(ini, end):\n",
    "        up = True\n",
    "        lower = True\n",
    "        for i in range(ini, end):\n",
    "            for j in range(len(data_array)):\n",
    "                if not (data_array[j] is None):\n",
    "                    if data_array[j][i] > (middle_value + offset):\n",
    "                        up = False\n",
    "                    elif data_array[j][i] < (middle_value - offset):\n",
    "                        lower = False\n",
    "                    if not up and not lower:\n",
    "                        break\n",
    "            else:\n",
    "                continue # Only executed if inner loop did NOT break\n",
    "            break # Only executed if inner loop did break\n",
    "        return up,lower\n",
    "    \n",
    "    up_left, lower_left = array_check_loc(0, math.floor(len_x/2))\n",
    "    up_right, lower_right = array_check_loc(0, math.floor(len_x/2))\n",
    "\n",
    "    legend_loc = 'best'\n",
    "    if up_left:\n",
    "        legend_loc = 'upper left'\n",
    "    elif up_right:\n",
    "        legend_loc = 'upper right'\n",
    "    elif lower_left:\n",
    "        legend_loc = 'lower left'\n",
    "    elif lower_right:\n",
    "        lower_right = 'lower right'\n",
    "\n",
    "    return legend_loc\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphic_iker(data_array, title=\"None\", title_y=\"None\", title_x=\"None\", handles=None, used_labels=None, v_lines=None, h_line=None, ylim_zero=True):\n",
    "    f=plt.figure(figsize=(30, 12))\n",
    "    ax=f.add_subplot(111)\n",
    "    x = np.arange(len(used_labels))\n",
    "    width = 0.45/4\n",
    "    \n",
    "    legend_loc = legend_loc_iker(data_array, len(used_labels), ylim_zero)\n",
    "\n",
    "    if not (data_array[0] is None):\n",
    "        ax.bar(x-width*3.5, data_array[0], width, color='green')\n",
    "    ax.bar(x-width*2.5, data_array[1], width, hatch=\"\", color='springgreen')\n",
    "    ax.bar(x-width*1.5, data_array[2], width, hatch=\"//\", color='blue')\n",
    "    ax.bar(x-width*0.5, data_array[3], width, hatch=\"\\\\\",color='darkblue')\n",
    "\n",
    "    ax.bar(x+width*0.5, data_array[4], width, hatch=\"||\", color='red')\n",
    "    if not (data_array[5] is None):\n",
    "        ax.bar(x+width*1.5, data_array[5], width, hatch=\"...\", color='darkred')\n",
    "        ax.bar(x+width*2.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    else:\n",
    "        ax.bar(x+width*1.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    if not (data_array[7] is None):\n",
    "        ax.bar(x+width*3.5, data_array[7], width, hatch=\"++\",color='olive')\n",
    "\n",
    "    ax.axhline((0), color='black', linestyle='dashed')\n",
    "    ax.set_ylabel(title_y, fontsize=30)\n",
    "    ax.set_xlabel(title_x, fontsize=28)\n",
    "    plt.xticks(x, used_labels, rotation=90)\n",
    "    plt.legend(handles=handles, loc=legend_loc, fontsize=26,ncol=2,framealpha=1)\n",
    "    \n",
    "    if not ylim_zero: # Modifica los limites del eje y. No es buena practica que no aparezca el 0\n",
    "        max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        max_value += max_value * 0.1\n",
    "        min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        min_value -= min_value * 0.1\n",
    "        if min_value < 0.1:\n",
    "            min_value = 0\n",
    "        plt.ylim((min_value, max_value))\n",
    "    \n",
    "    for line in v_lines:\n",
    "        ax.axvline((line), color='black')\n",
    "    if h_line != None:\n",
    "        ax.axhline((h_line), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/Spawn/\"+title+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checked_type='te' # Valores 'te' y 'tc'\n",
    "used_direction='e' # Valores 's' y 'e'\n",
    "node_type='All' # Valores 'Intra', 'Inter', 'All'\n",
    "normality='n'\n",
    "#Values 'n' (normalizar), 'l' (logaritmico), 'm' (sin modificaciones), 'r' (Comparar respecto al primero)\n",
    "\n",
    "ylim_zero = True\n",
    "\n",
    "var_aux, grouped_aux, handles, used_labels, title = get_types_iker(checked_type, used_direction, node_type, normality)\n",
    "array_aux, title_y, v_lines, h_line = obtain_arrays_iker(grouped_aux, var_aux, used_direction, normality)\n",
    "graphic_iker(array_aux, title, title_y, \"(NP, NC)\", handles, used_labels, v_lines, ylim_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    dfG_aux=dfG.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    dfG_aux=dfG.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "vOrMult = list(dfG_aux.query('Cst == 0 and Css == 0')['TE'])\n",
    "vOrSingle = list(dfG_aux.query('Cst == 0 and Css == 1')['TE'])\n",
    "vOrPthMult = list(dfG_aux.query('Cst == 1 and Css == 0')['TE'])\n",
    "vOrPthSingle = list(dfG_aux.query('Cst == 1 and Css == 1')['TE'])\n",
    "\n",
    "vMergeMult = list(dfG_aux.query('Cst == 2 and Css == 0')['TE'])\n",
    "vMergeSingle = list(dfG_aux.query('Cst == 2 and Css == 1')['TE'])\n",
    "vMergePthMult = list(dfG_aux.query('Cst == 3 and Css == 0')['TE'])\n",
    "vMergePthSingle = list(dfG_aux.query('Cst == 3 and Css == 1')['TE'])\n",
    "\n",
    "vOrMult2 = list(dfM_aux.query('Cst == 0 and Css == 0')['TC'])\n",
    "vOrSingle2 = list(dfM_aux.query('Cst == 0 and Css == 1')['TC'])\n",
    "vOrPthMult2 = list(dfM_aux.query('Cst == 1 and Css == 0')['TC'])\n",
    "vOrPthSingle2 = list(dfM_aux.query('Cst == 1 and Css == 1')['TC'])\n",
    "\n",
    "vMergeMult2 = list(dfM_aux.query('Cst == 2 and Css == 0')['TC'])\n",
    "vMergeSingle2 = list(dfM_aux.query('Cst == 2 and Css == 1')['TC'])\n",
    "vMergePthMult2 = list(dfM_aux.query('Cst == 3 and Css == 0')['TC'])\n",
    "vMergePthSingle2 = list(dfM_aux.query('Cst == 3 and Css == 1')['TC'])\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.scatter(vOrMult,vOrMult2, color='green')\n",
    "ax.scatter(vOrSingle,vOrSingle2,  color='springgreen')\n",
    "ax.scatter(vOrPthMult,vOrPthMult2, color='blue')\n",
    "ax.scatter(vOrPthSingle,vOrPthSingle2,color='darkblue')\n",
    "\n",
    "ax.scatter(vMergeMult,vMergeMult2, color='red')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergeSingle,vMergeSingle2,color='darkred')\n",
    "ax.scatter(vMergePthMult,vMergePthMult2, color='yellow')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergePthSingle,vMergePthSingle2,color='olive')\n",
    "\n",
    "ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"Time EX (s)\", fontsize=20)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=21,ncol=2)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Dispersion_Spawn_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "valores1 = [0]*10\n",
    "valores2 = [0.2]*10\n",
    "valores3 = [0.4]*10\n",
    "valores4 = [0.6]*10\n",
    "valores5 = [0.8]*10\n",
    "valores6 = [1]*10\n",
    "valores7 = [1.2]*10\n",
    "valores8 = [1.4]*10\n",
    "\n",
    "for np_aux in processes:\n",
    "    for ns_aux in processes:\n",
    "        if np_aux != ns_aux:\n",
    "            if np_aux > ns_aux:\n",
    "                used_labels=labelsShrink\n",
    "                name_fig=\"Shrink\"\n",
    "                handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "            else:\n",
    "                used_labels=labelsExpand\n",
    "                name_fig=\"Expand\"\n",
    "                handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "                \n",
    "            dfM_aux = dfM.query('NP == @np_aux and NS == @ns_aux')\n",
    "            vOrMult2 = list(dfM_aux.query('Cst == 0 and Css == 0')['TC'])\n",
    "            vOrSingle2 = list(dfM_aux.query('Cst == 0 and Css == 1')['TC'])\n",
    "            vOrPthMult2 = list(dfM_aux.query('Cst == 1 and Css == 0')['TC'])\n",
    "            vOrPthSingle2 = list(dfM_aux.query('Cst == 1 and Css == 1')['TC'])\n",
    "\n",
    "            vMergeMult2 = list(dfM_aux.query('Cst == 2 and Css == 0')['TC'])\n",
    "            vMergeSingle2 = list(dfM_aux.query('Cst == 2 and Css == 1')['TC'])\n",
    "            vMergePthMult2 = list(dfM_aux.query('Cst == 3 and Css == 0')['TC'])\n",
    "            vMergePthSingle2 = list(dfM_aux.query('Cst == 3 and Css == 1')['TC'])\n",
    "\n",
    "            f=plt.figure(figsize=(16, 8))\n",
    "            ax=f.add_subplot(111)\n",
    "\n",
    "            ax.scatter(vOrMult2,valores1, color='green')\n",
    "            ax.scatter(vOrSingle2,valores2,  color='springgreen')\n",
    "            ax.scatter(vOrPthMult2,valores3, color='blue')\n",
    "            ax.scatter(vOrPthSingle2,valores4,color='darkblue')\n",
    "\n",
    "            ax.scatter(vMergeMult2,valores5, color='red')\n",
    "            if np_aux < ns_aux:\n",
    "                ax.scatter(vMergeSingle2,valores6,color='darkred')\n",
    "            ax.scatter(vMergePthMult2,valores7, color='yellow')\n",
    "            if np_aux < ns_aux:\n",
    "                ax.scatter(vMergePthSingle2,valores8,color='olive')\n",
    "\n",
    "            ax.set_xlabel(\"Time TC(s)\", fontsize=16)\n",
    "            ax.set_ylabel(\"-\", fontsize=16)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "            plt.legend(handles=handles, loc='upper right', fontsize=14,ncol=2)\n",
    "    \n",
    "            ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "            ax.tick_params(axis='both', which='minor', labelsize=18)\n",
    "    \n",
    "            f.tight_layout()\n",
    "            f.savefig(\"Images/Spawn/Dispersion/Dispersion_Spawn_\"+name_fig+\"_\"+str(np_aux)+\"_\"+str(ns_aux)+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_direction='e'\n",
    "test_parameter='TA'\n",
    "#TS es merge y TA connect para tests solo spawn\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "dfM_aux = dfM_aux.copy()\n",
    "#dfM_aux['NTotal'] = dfM_aux['NP'] + dfM_aux['NS']\n",
    "dfM_aux['NTotal'] = dfM_aux['NS']\n",
    "\n",
    "#x = np.arange(len(used_labels))\n",
    "for cst_aux in [0,1,2,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        f=plt.figure(figsize=(20, 12))\n",
    "        ax=f.add_subplot(111)\n",
    "        \n",
    "        #sns.boxplot(y=test_parameter, x=\"NS\", hue=\"NP\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        sns.boxplot(y=test_parameter, x=\"NTotal\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        \n",
    "        # Anyade en el plot el numero de iteraciones - Por hacer TODO\n",
    "        # https://stackoverflow.com/questions/45475962/labeling-boxplot-with-median-values\n",
    "        #medians_aux = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median()\n",
    "        #m1 = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median().values\n",
    "        #mL1 = [str(np.ceil(s)) for s in m1]\n",
    "\n",
    "        #ind = 0\n",
    "        #for tick in range(len(ax.get_xticklabels())):\n",
    "        #    ax.text(tick-.2, m1[ind+1]+1, mL1[ind+1],  horizontalalignment='center',  color='w', weight='semibold')\n",
    "        #    ax.text(tick+.2, m1[ind]+1, mL1[ind], horizontalalignment='center', color='w', weight='semibold')\n",
    "        #    ind += 2 \n",
    "        \n",
    "        ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "        ax.set_xlabel(\"NC\", fontsize=20)\n",
    "        plt.legend(loc='upper left', fontsize=21, title = \"NP\")\n",
    "        ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "        ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "        \n",
    "        ax.axvline((.5), color='black')\n",
    "        ax.axvline((1.5), color='black')\n",
    "        ax.axvline((2.5), color='black')\n",
    "        ax.axvline((3.5), color='black')\n",
    "        ax.axvline((4.5), color='black')\n",
    "        \n",
    "        f.tight_layout()\n",
    "        f.savefig(\"Images/Spawn/Boxplot_\"+name_fig+\"_\"+test_parameter+\"_\"+labelsMethods[cst_aux*2 + css_aux]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C8Y5H3GHP2/MRrNZjD7/OAdbah6y1xdba4ry8vHaFQ9fx8strdeed7IOO9svqdYbSug11HQNww1qF5p9rADhUfHKe8gacr7ikLNdRAAAAgJgUlTMorLU/OPyYMcYj6RxJP5c0Ts1lwE8lZUu6o4O3rDnseWI7zm099vDrIMosWlSmefO2aN++RqWmxp/4BKDF7i0fKNBYq9x+57iOAnQ6ywwKAGHSWFepPZvfV0bPsUpI6eY6DgAAABBzonUGxRGstUFr7UxJEyQ92+pb3zTGTOng5fcd9jy5Hee2HhuK/TDQhf3wh2fqgw+upZxAu2348PdaM+eHrmMAjlg1f84AAEKrtnKdPnnhGu3dscR1FAAAACAmxdyrfWttQNItkipbHb6tg5ctP+x5j3ac23rs4ddBlImL8yo+3qu6uiY98MAnCgat60iIEIPPvU8jr3jSdQyg01nb8nOSJZ4AhEFq3hCdedN7yuo1znUUAAAAICbFXEEhSdbavZL+0+rQ+A5eb7ekslaHCttxeuuxqzqSA5Hj5ZfX6bbb3tLcuZtdR0GESMroo+TMItcxAAf2F7kUFABCzxefovTuI+RLSHcdBQAAAIhJMVlQtNjU6nFuCK63vNXj0W05wRhTqOY9MPZbEYIciADXXDNI8+dfr6lT+7iOgghRuekdbVr4kOsYQOdrmUHBEk8AwqGxrlKbFz2imsr1rqMAAAAAMSmWX+1ntHq8OwTXm9vqcV9jTO82nDP5sOdzQpADEcAYo+Li7pKktWt3q6HB7zgRurqda17Rmjk/dh0D6HTNG2SLJZ4AhEVjzU6teOMO7d3xiesoAAAAQEyK5YKidTkQio9MvXjY8y+24ZzWY+Zba1nvJ8Zs3VqtkSMf189//qHrKOjiPL5EBf31rmMAnc54fJryjXUqLL7VdRQAUcgYryTJBgOOkwAAAACxKSYLCmPMdZKGtDr0r45e01q7TNLHrQ59wxiTfazxxpiJks5udehvHc2AyNOzZ5ruv3+Kbr11pOso6OI8vkTZYBNvoCDmGGOUkNpdvoQ011EARCHj8UmSbJDZrAAAAIALEV9QGGPONMb82RgzoA1jjTHmFh1aBuyS9NdjjJ9ijLGtvn5yglt8r9XjfElPGmNSjnLdUyT9Qwd3/FwnCoqY9bWvjVRBQaqstWps5M1nHJ3XlyhJCjCLAjEmGGjS2nk/1+7N77uOAiAKHSgoLL+DAQAAAC74XAcIgQRJX5P0NWPMYjXv4/CppO2Sqlu+313SSElXSOrf6txGSTdaa0OxB4WstXOMMQ9I+nrLofMlLWk5tlRSoqSJkr6qg3tgNEi6yVrLx7ZimLVWN9zwHzU0BPT005fIsNY6DuNpKSiC/jop/ojeE4hawUCjSt77lXzxqcrqPd51HABRhhkUAAAAgFvRUFC0NrLlqy22SvqStfbNEGf4pqRsSZ9ved5f0m+PMbZW0heste+GOAMijDFGw4blyu+3spa9YHGkgwVFg+MkQOfyxafo/O/vcx0DQJQynuYJ5RQUAAAAgBvRUFCskHSvpPMkjZDkPcH4lZL+LukBa211qMPY5vnh1xpjXpP0I0mnHGVYQNIbku6w1q4NdQZEprvuGus6Arowry9JEks8AQAQSsYwgwIAAABwKeILCmvtTknfl/R9Y0yypGFqnrWQLylFzcs4VUnaLGmxtXZbO649Rwf3iWhvrn9I+ocxZrSkoZJ6SKpX88yNeS25gSN88ME2/eUvi/XooxfI6434bWIQIh5fgqSWJZ6AGBJoqtPKmXep++ArlNv3bNdxAESZg3tQBB0nAQAAAGJTxBcUrVlrayV91PLVJVhrF0la5DoHIse6dbs1d+4WbdlSrcLCjBOfgJiwfwYFSzwh1gQDDdq65DGl5g6moAAQcuxBAQAAALgVVQUFEA2uv/40XXnlqUpOjnMdBV1IVu8JmvCVhUpK7+M6CtCp9n+qmb15AISDNy5JZ970nhLSeriOAgAAAMQk1o8BuhhjjJKT4xQMWj300BLV1ja5joQuwJeQptScgfLGJbmOAnQua5v/NPzKAiD0jPEovfsIJaR0cx0FAAAAiEm82ge6qPnzd+irX52pGTNWuo6CLqCxdpc2fvwn1VSsdR0lJlSUztW8B4dpb9nSDo1BKOxfF54pFADCY/OiR7Rn2wLXMQAAAICYREEBdFHjxvXQRx9dp5tvHuY6CrqAxtpyrX7rbu0tW+I6StSrKJ2rRc9epbo9G7TwmcvUWFtxUmMQGrZlBoVhBgWAMFn55re1a+2rrmMAAAAAMYlX+0AXNnZsDxljVFZWo/LyWtdx4FBK9qk6+86tyh90uesoUW1/8RD010mSmur3aNHz0xVstXlqW8YghA4s8cQMCgDhMfm2Nep75rddxwAAAABiEgUF0MXV1/t1+un/0K23znIdBQ4Zj1dxiRnyeHyuo0Stw4sHSbKBRlWXfarVs+4+8Zi3vt/pmWMDMygAhFdCar588amuYwAAAAAxiXe6gC4uMdGne++dpBEj8lxHgUMBf73WzfuF8vqfp+zCSa7jRJ2jFQ/7Bf112rLkcRlvvDYvevjYYxY/poyCsSoYcnVnRI4Z1rIHBYDwKnn/fqV1G6q8ARe4jgIAAADEHD6OCESAa68drCFDciVJjY0Bx2ngysaPfq89Wz92HSMqLX/ttqMWD/sF/XXHLCdaj1nz9j3hiBfbWOIJQJht/OiPKi+Z6ToGAAAAEJMoKIAI8tOfvq/Jk59WUxMlRazxeBMkSUF/veMk0WnkFTMUn5wn440/5pjjlROS5PElafilj4Q6WsxLTO+p87+/T71H3uQ6CoAoZTxeWfYRAgAAAJygoAAiyGmn5WjUqG4KBKzrKOhkxhh5fIkKUFCERXr+cE24ZYHSu4+Ux5fU7vM9viSNmf4Cy28BQAQyHp+CQT78AQAAALhAQQFEkKuvHqg///lcJSayfUws8vgSmUERRvHJORp7/ZvqNeKGdpUUlBPh1VhbrmWv3abdWz50HQVAlGIGBQAAAOAOBQUQgdat261rrvm3qqsbXUdBJ/L6EhUMUFCEg7VW9dXbVL5+puKSc5SQ2kNt2ZTZ40vSoGn3Uk6EUaCpTuXr31BD9VbXUQBEKePxSZYZFAAAAIALfAwbiEDbt9forbc2acWKCo0b18N1HHQSjy9RgSYKilDZufZVGeNV3oALZINNmvfgUNnA/tLPSDrxUmpBf53WzfuF8gdervjknLDmjVVJGb015RtrXccAEMWM8SnIDAoAAADACQoKIAJNnNhLGzfeopSUY2/oi+jjYQZFuwWDftVWrNHesk+1d8diBf31Ou2C30uSSt7/jTy+BOUNuEAeb7yGXvwXNdVVaM3sH51wQ+zWmhqqtOj56Rp7/RvyePhnFQAiDUs8AQAAAO7wTgoQofaXE888s0oTJ/ZSQUGq40QIN68vUUFmUBxT0N+g6l0rtLdssap3LNHesiWq3rnsQNng8SUqo+B0WWtljNHIzz6h+OTcA+cnpOZr+Wtfb1c5IUk20Kjqsk+1etbdGnze/SH9O0Gq37tVy1+/XX3PuEPZfSa6jgMgClFQAAAAAO5QUAARbNu2fbrpptf19a+P1H33TXEdB2Hm8SYqwAyKA2oq16tiwyz1Hn2LjPFoxZvf0tYlj0uSfAnpSssfrt6jvqz0/BFK7z5CyTmnHjLDITG954HHFaVztejZq9pdTuwX9Ndpy5LHldFznAqGXN2xvxgO4W+sVvn6N1Qw9BrXUQBEKePxydqg6xgAAABATKKgACJYQUGq5s27RiNHdnMdBZ1gzOdekvHGuY7R6RrrKlVdtuTAMk0DJt6jlOwB2r3pHa1889vK6TtNKdn91WvEDcrte47Su49UUmaRjPG0+R7LX7vthOWEx5d03DFBf53WvH0PBUXI7d8L5MSblgPAySi+5l8yntj79xUAAADoCigogAhXXNxdklRT06jy8joVFmY4ToRw8SWkuY4Qdg37dmjvjsXaW7bkwJ/1VZsOfD8xvbcaqrcpJXuA8gddrtz+5ykhtXmj+MyeY6WeY0/qviOveFILn7lMTfV7Wm2UfZDHl6RB0+7Vunm/UFND1THHDLv04ZO6P47DNhcU7SmcAKA9Wi/3BwAAAKBzUVAAUcBaq/PPf0F1dX7Nn3+9PB4+aRyNti1/VvVVm9Rv/HdcRwmJoL9BO9f9RynZA5TWbaj2bJ2vj/4+9cD3k7NPUWbBWKWPvkXp+SOVlj/skDeR4hIzFZeYGZIs6fnDNOHm+Vr0/HRVl316yEwJjy9JY6a/oOzCScofeLkWPX+1qsuWHjGbIjVnkDILTg9JHhx0YNkVw881AOGxZcnjkox6jfii6ygAAABAzKGgAKKAMUY//OEZSkjwUk5EscqNc1S1Y1HEFRQ2GFBN5Vrt3bFEe8sWKyWrv3qPvlmS9Ok/b1TRuG8qrdtQpeadpkHTfq307iOV1m1Yp88YiU/O0djr39Dqt76vLYsfU9Bfd0g5cXDMm1o9625tWfL4gTF9xnxNGz/6nRY9d7VGXfWsfPEpnZo9FjCDAkC4bF/+jCRRUAAAAAAOGGvtiUehSykuLrYLFixwHQNdWGNjQPHxXtcxEIOCgUbt27VSe8sWa++OJaouW6LqnUsVaKqVJHm8Ceo5/HqddsEfJEnVu5YrOau/vL5El7GPsG35c1rz9j0afukjB8qJY40ZdunDyimcrG3LntbSV76irN4TNPrq5ykpQmRv2af64NHxGnnFk8ofeKnrOAAAAAAA4CQYYxZaa4sPP84MCiDK/P3vy/WLX3yojz++TpmZXetNX7RPRelcLX/tNo284kml5w876THhVL1zmeqqNqnbKRdJkj74fxO1b9dySZI3Pk3p+cPVc8SNSu8+UundRyglZ6A8noP/9KTlDen0zG1RMOTqE252ffiYgqHXSMajpf++WYue/axGT39RvvjUcEeNfuxBAQAAAABA1KKgAKLMoEHZGjYsV4EAs6MiWUXpXC169ioF/XVa+MxlmnDzfFWWztHONa9q+GWPHnNMfHJOWPI01e/R3rIlqi77VPt2rdSQix6QMUal8x/QrnX/Ud7tG2SMUb8zvy3j8Sotf4SSs/rF3JvKBUOmyxiPlv7ry/r05Rs1evrzriNFPPagABBupQseVFPdbg2Y+N+uowAAAAAxh4ICiDJjx/bQCy9c5joGOqB18SA1lwOLnp+unKIp2r7iWQ279G+q3DTvqGPGXv/GITMUTkbDvjLtLVuivTsWq7psifaWLVHdno0Hvp+QVqDG2p1KSMlXvwnfU/+J9xz4Xo8h0zt072jQ47SrZDw+JaYVuI4SFTzeOCWk9pDHl+Q6CoAoVbFxjuqrNlFQAAAAAA5QUABRateuWn3rW3P0y1+epT590l3HQRsdXk5Ikg00qrrs0wPPyzfM0uIXrj3qmNVvfV+Dz72vzfdrqtutyk3vKKvPWYpPylbpgr9o1cyDm3AnZ/VXevdR6jXyJqXnj1Ba/nAlpHQ7+P3MopP8m0a37oMuP/B427KnlTfgQsUlZrgLFMHSug3VlG+sdR0DQBQzHq9s0O86BgAAABCTKCiAKFVd3ajXX9+gyy7rT0ERIY5WTuwX9Ndp7/ZFknREOdF6zJbFjymjYOwR+yfYYEA1u9epekfzjIhup1ykrN4TtK98pRa/eK1GXfWcup1yoXIKJ2ngOb9SevcRSus2jDfVO6h29wYte/Vr6jf+OxrQaqYJAKDr8Hh8sjbgOgYAAAAQkygogCjVr1+mNmy4Ramp8a6joI2Wv3bbUYuH/WywSZKOOybor9Oat/9bqbkDm5dn2rFYe3csUfXOpQo01UiSjDdeiem9ldV7gtK7j9K4G+YordtQSVJq3mlKzTsthH+r2Jac1Vdjr39T6T1GuY4SsfZVrNbqt/5bAyb+QBn87wggDIzxyQYpKAAAAAAXKCiAKLa/nJgzZ5OyshI1YkS3E5wBl0ZeMUMLn75MTQ1VsoHGk7qGx5ekhtpd+uDR8ZIkb3yq0roNU88RX1R6/gildx+plJyB8njjmr8fl6TMguKQ/R1wpMyep0tq3ttj1azvafD5v1V8UrbjVJHDBprUWLPzpP+bAIATYYknAAAAwB0KCiDK1df7df31r2nMmHz985+fdR0Hx5GeP1wTblmgRc9freqypcedKXE0Hl+Sxkx/Qbu3fKDkrH5Kzx+p5Oz+MsYTpsRoj33lK7Vzzb9VU7lGxdf8W/HJOa4jRYS0bkN15k3vuI4BIIoZj4+CAgAAAHCEd62AKJeY6NNrr12pGTMudh0FbRCfnKOx17+pXiNukMeX1ObzPL5EjZn+grILJ6n/hO+px2lXKyXnFMqJLiSnaIpGXfWMairWaP5TF6uxdpfrSAAAtcygYA8KAAAAwAneuQJiwPDheUpNjZffH9Tatbtdx8EJeDw+DT7vfvUefUubSgqPL0mDpv1K2YWTOiEdOiK33zSNvuo51Vau0/wnL1ZDzU7Xkbq8qu2f6P1Hx6tq20LXUQBEqeY9KJhBAQDRqqJ0ruY9OEx7y5Z2aAwAIDwoKIAYcsstb2jy5KdVXc1a7l1dRelcbV70cJuWeQr667Ru3i/UWFvRCcnQUTl9p2r01c+rdvcGzX/yIjXUlLmO1KUFGqtVXfap/E37XEcBEKWMh1VvASBaVZTO1aJnr1Ldng1a+MxlR33N1JYxAIDwoaAAYsg3vzlGv/nNFKWlxbuOguPY/wtye/agaGqo0qLnpyvIJ0AjQk7RFI2Z/oLqqzZp/owL1bBvh+tIXZaVlSSWKwMQNoPPu19Tv7nRdQwAQIgd/rqqqX7PEa+Z2jIGABBevNoHYsjIkd30+c8PliQ1NbHWcld0MuWEJNlAo6rLPtXqWXeHKRlCLbtwksZ87iXV792qj2dcqMbacteRuiYbbHlgnMYAAABA5Dja66rDXzMdd8xb3+/0zAAQqygogBg0e/YmDRjwiNav3+M6Clo52XJiv6C/TluWPK5ty58LcTKES1bvCRpzzcvKKZykuMQs13G6puYJFDKGggJAeJSt/qeW/+c21zEAACFyvNdV+18zrXrrv48/ZvFjvK4CgE5CQQHEoP79MzVoUI7rGDjM8tduO2E5caJNs4P+Oq15+55QxkKYZfU6U6dd8AcZj1f1e7eqbu8W15G6FLt/BgVLPAEIk9rdJSrf8LbrGACAEDnR66qgv+6E+/3xugoAOg+v9oEY1KdPut544yr175/pOgpaGXnFk4pP6SbjPfoeIR5fkgZNu1fxyXnHHTPs0ofDGRNhYq3VJy9co0+e/9zBN+WhA1MoWOIJQJj0PeNOTb51hesYAIAQGXnFjOO+ZpLUpg+GDb/0kVBHAwAcBQUFEMNqa5t0222z9PHH211HgaT0/GGacPN8pXcfdcRMCY8vSWOmv6Deo76sCbcsUHr3kUcdM3r688opnNyZsREixhgNPv93Ou3837EhdCvWskk2AAAA2i49f/gxXzO1xf7XXtmFk8KQDgBwOF7tAzGssTGgV14p0TvvsKRMVxGfnKOx17+hXiNvPPDL9OG/IDePeVO9RtxwyBjKiciXWVCszJ5jJUmbFz2i2t0bHCfqAtgkG0CY7Vj1khY+81kF/Q2uowAAQuRor5nagnICADofBQUQwzIzE7V06Y369rdPdx0FrXg8Pg0+9z4NuejPSkgtOOovyB6PT4PPu//AGMqJ6NJYu0tr5/5M85+8ULW7S1zHccqXkKb07qPki09xHQVAlKqr2qTykpkKBptcRwEAhND+10y9R9/SppJi/5K6lBMA0LnM/qUTEDmKi4vtggULXMdAlFm+vFw7d9Zq6tQ+rqMAkLS37FMteOoSeXyJOv3a15SSPcB1JACIShs/fkCr3/qezr5zi+ISM13HAQCEUEXpXC169qoT7jmxX3xynibcskDxyTlhTgYAsccYs9BaW3z4cWZQAJC1Vrfc8qa+8Y23FAxSWgJdQXr+cJ1+7WsK+hs0f8aFqqlY4zoSAEQl42l+SWSDfsdJAACh1N5yQpKaGqq06PnpCvJvAgB0GgoKADLG6O9/v1CzZk2Xx8M670BXkdZtqE6/7jXZYJM+nnGh9lWsdh2p01WUztX7j46noAEQNsbjkyRZG3CcBAAQKidTTkiSDTSquuxTrZ51d5iSAQAOR0EBQJI0YECWundPkbVWq1dXuo4DoEVa3hCdfu1rkg1q/owLta98letIncrrTVRiWi8Zb7zrKACi1IGCIkhBAQDR4GTLif2C/jptWfK4ti1/LsTJAABHQ0EB4BC/+tXHGjXq7yop2eM6CoAWqXmn6fTr/iPJNJcUu1a4jtRpMnuN0+irn1VyZpHrKACilOdAQcFyHgAQDZa/dtsJywnjiTvu94P+Oq15+55QxgIAHAMFBYBD3HDDEP3yl2epsDDddRQAraTmDtLY6/6j+ORcBQONruMAQPQwXkkUFAAQLUZe8aTiU7odcwaux5ekvuO+2fzE+I45ZtilD4crIgCgFQoKAIfo0SNVd95ZLK/Xo6YmljoAupKUnFM1/uaPlN59pCSpoWan20CdYOeaVzT7j/1UU7HWdRQAUYolngAguqTnD9OEm+crvfsoeXxJh3zPeOM1ZvoLGjD5R/IlZCouMfOIMR5fkkZPf145hZM7MzYAxCwKCgBHtWJFuQYNelTvvLPlkOOBQFD33z9fubl/0m9+M1+BQNBRQiA2GdP8T3fpggf13kPFqt1d4jhReAX8dWqs2cnmtQDCxhefqsT0XpIxrqMAAEIkPjlHY69/Q71G3nhIATH80keVXThJxniU03eqPN549Rpxw4ExlBMA0PmOPpcNQMzr0yddAwZkKT7ee+DY2rW7NX36v7R27W7V1Pj14x+/pxkzVuqZZz6jU07JcpgWiD15/S9QfdVmJWUUuo4SXtZKOljMAECodTvlInU75SLXMQAAIebx+DT43PuUUTBWy179qhLTeqr7oMsPfL/HaVcpMa2nTpnyE2X0HKc1b9+jYZc+TDkBAJ2MggLAUaWmxuuNN66S1Dxr4ne/W6gf/eg9NTQEFAw2v2FYU+PXkiW7NGLE4/r5zyfozjuL5fHw6UOgMyRn9dXAc/5HklS/d6saanYqo8cox6lCz7YUFBI/WwAAANB+3QZcINmgug/67CHH8wdepvyBl0mSCoZcrYIhV7uIBwAxj48jAjiuVasq1Lv3X/WDH7yrujr/gXJiv2DQqq6ueTZFcfETWrt2t6OkQOxa9tqtWvDUxdqzdb7rKGHQ8jOHGRQAwqRq20ItfOaz2lex2nUUAEAYVJbOlQ36ldtv2hHfCwaa+PkPAI7xah/AUe3fa2L06Ce0fXuNGhqOv/5769kUv/nN/COKDADhM+TCPykuKUcLnr5Ue7Z85DpOSFnbvM+NYW14AGFig3411e2WDTS5jgIACIPykpnyxqcqs9cZR3xv1cy79NHjZysY9DtIBgCQKCgAHMXatbtVXPyEfvKT91VX1/Zf1JhNAbiRlNFbY697XQkp3bTgmcu0e8sHriOFjmUGBYDwyuw1TmfcOEdp3Ya6jgIACDFrrcpLZimncIo83vgjvt9zxBc15KIHDv7OCQDodLzaB3CEiROf0qeflqum5uQ+Sbh/NsXEiU+FOBmAY0lM76nTr/uPElJ7aOHTl2v35vdcRwqRlhkU7EEBAACA9rJB9T/r++o95itH/XZGj9HqPuhyebxxnRwMALAfBQWAI4wfX9BqY9qTY63V+PEFIUoEoC0S0wo09rr/KDGtQAufuUKVm95xHanDDvwsYoknAGGyt+xTvfe3M7R7y4euowAAQsx4vOo5/Hrl9j37mGP2la/UtuXPdmIqAEBrFBQAjnDHHWOUktKxT5CkpMTpzjuLQ5QIQFslpHbX6de9rsT0Xlr07JWqKJ3rOlKHJKb3Ut6Ai+SNS3IdBUCUCvrrtW/nMvkb9rqOAgAIsZ1rX1PdntLjjtn66T+07NX/UqCptpNSAQBao6AAcISJE3spMzOhQ9fIykrUWWf1DFEiAO2RkJqv06/7j5IyCrVz9b9cx+mQ3L5na/TVzyo+Oc91FABRynh8kiQbDDhOAgAIpYC/Xkte/qI2zv/TccflFE2VDTRq9+Yo2scNACIIBQWAIxhjdMcdY5Sc7Dup85OTfbrjjjEyLMkCOJOQ0k1jvzBTg869T5IUDLZ9w3sAiCUHCgrLz0kAiCYeb4LGf/lDFZ7+9eOOy+x1pownThUbZ3dSMgBAaxQUAI7qxhuHKhA4uX0oAgGrG24YEuJEANorLjFTxnhUV7VZ7z8yVuUls1xHaretS5/U7D/2U0NNmesoAKKU8XglSZYiFwCiijFGKdkDlJxZdNxxvvgUZfYaR0EBAI5QUAA4qpycJF10Ud9270trjHTRRX2Vk8N68UBX4Y1LVkJqD8Un57qO0m5JGX3U7dRL5PEmuo4CIEoZQ0EBANFo9Vv/3ebSIadoqqrLPlVjbXmYUwEADkdBAeCYTmaz7ORkNscGupr45BwVf/4VpXcfKUmqqVznNlA7ZPc5S0Mu+KPiEjNcRwEQpQ7uQRF0nAQAECp1VZu08eM/qnrn8jaNzymaKsmqsnReeIMBAI5AQQHgmE5ms+zsbDbHBrqi/XvCbP30Cb338OnaueYVx4naxlora4Oy9uSWnAOAEzlYUDCDAgCixf6lTXP7TWvT+PQeo+VLSGeZJwBwgIICwDG1d7NsNscGur5up35G6d1HavFL16ts9T9dxzmhTQse1Jv3pquprtJ1FABRik2yASD6lJfMVGJ6b6XkDGzTeI/Hp+w+k1SxcU54gwEAjkBBAeC4brxxqHy+tv2o8Pk8bI4NdHFxiZka87mXld59tJa89EXtWPWS60gn0DJzguITQJh4fQnK6FGsuKRs11EAACEQDDSpYuMc5fab1q4Pz2UXTVFT/W411u4KYzoAwOHa9rFoADErJydJVVW3H3fMJ5+UafToJ/SLX5zF5thABIhLzFDxNS9r4TNX6NOXb5QuC6r74Ctdxzoqe6Cf4DMVAMIjPjlPZ9w4x3UMAECI7Nn6kQKN1crtd267zus18kb1GX2LjMcbpmQAgKPh1T6ADhs1Kl9vvTVdt9460nUUAG3kS0jXmM+9pIyeY/XpP7+k7Suecx3pGPZvWssMCgAAAJxYecksGY9P2YWT23We15dIOQEADlBQAAiJs8/uI6/Xo5qaRgUCwROfAMA5X0KaxnzuJWX2OkOf/uvL2rbsGdeRjtQyhYIZFADCxd9Yo/f+doa2fjrDdRQAQAiUl8xUZs9xikvMaPe525Y9rY/+Pk3W8poWADoLr/YBhMzWrdU67bT/p4ce+tR1FABt5ItP1ejpLyq7z1la/dbd8jdUu450iAMvDtmDAkCYGI9XyZlF8iWmu44CAOighn1lqi5b0u7lnfbzeOPkS0hTU93uECcDABwLe1AACJmCglRdfHE/DR+e5zoKgHbwxado9NXPq27vZvkS0lzHOQwzKACEl9eXqFFXPu06BgAgBBprdymz15knXVB0H3xll92bDQCiFQUFgJAxxujPfz65XwQBuOWNS1ZqzkBZa7XunV8oKb23eo280XWsVtPrmUEBAACA40vrNlTjvjCzw9fxN+6TLz41BIkAACfCxxEBhFxjY0C//OWH+s9/SlxHAdBO1ga0d/siVW1f4DpKM/agANAJZv+hr0o++I3rGACADrA2KH/jvg5fp+SD32jOH/sr6G8IQSoAwInwah9AWMyYsUJvvlnqOgaAdvJ4fBp15dM67YI/SmrePNaltPwR6jXqS5LH6zQHgOjWVL9bgcautQcPAKB99m7/RG//rrfKN7zdoeuk5g5SoKlGe7Z+FKJkAIDjYYknACEXH+/VBx9cp4yMBNdRAJwEj6/5v9366u36+IlpKhp3u/qM+aqTLHn9z1Vef5aOAxBexngVDAZcxwAAdEBcUrYKx96m9PxhHbpOVu+zZIxXFRvnKLtwUojSAQCOhRkUAMJifzmxbds+rVlT6TgNgJMRn5yjtPxhWvnmt1U6/wEnGay1sjYo27LUEwCEg/H4JAoKAIhoyVl9NXDqzxWfnNeh68QlZiijoFgVG+eEJhgA4LgoKACETTBoNWXKM7rlljddRwFwEjzeeI24/Al1G3ipVs36njZ+/H+dnmH9u/+jN+9N7/T7AogtxuNTMOh3HQMAcJL8DXtVWTpPwUBjSK6XXTRFVdsXqKm+KiTXAwAcGwUFgLDxeIz+8pdz9eijF7iOAuAkebxxGnHZ48of9Fmtfuv72vDh7zv1/tmFk9T/rP+WMaZT7wsgthiPV5aCAgAiVnnJLM1/8iJVbV8UkuvlFE2RbFC7N70TkusBAI6NPSgAhNXZZ/c58DgQCMrrpRcFIo3HG6fhl/0/LTUerZn9A1kbUL8zv90p987uM1HZfSZ2yr0AxC5jvLKWggIAIlV5ySz5EjOVUVAckutlFoyVNy5ZFRvnqNupl4TkmgCAo6OgANApbr/9LW3btk/PP3+Z6ygAToLH49OwS/8mY7xaO+fHssGA+k/4btjv21RfpUBTjRLTCsJ+LwCxy3h8ssGg6xgAgJNgrVV5yUzlFE2VxxOat7k8vgRl9Z6gio2zQ3I9AMCx8VFmAJ2iT5909euXqUCAF/9ApPJ4fBr2mYfVY8g1WjfvZ9q1Pvz7y2z48Hea9+fTwn4fALGtuaBgBgUARKJ9u5arYd925fY7N6TXzSmaqpqK1aqv3hbS6wIADsUMCgCd4jvfOd11BAAhYDxeDbvkr8rte07IXwQenZUMn6cAEF65/aYpNXew6xgAgJNQvuEtSc0/y0Mpf+BlSkzvJV9CRkivCwA4FAUFgE61ePFOvffeVn3966NcRwFwkozHq4Jhn5ck1VSu1841/1bRuG+GZSNra4MyYoNsAOE15ML/cx0BAHCSyktmKjXvtJAvCZqUWaikzMKQXhMAcCQ+kgigUz300BL9z/98pH37Gl1HARAC25Y+oQ0f/laNNWXhuYG1UhiKDwAAAEQ+f+M+7d78fthm9tbuLlHpggdlrQ3L9QEAFBQAOtn//u8kLV16g1JT411HARACAyb9SGfe9K4SUrvLWhuGF29WhiWeAITZxzMu0NJ/3+I6BgCgnSpL35ENNIZ8eaeD15+nVTPvUm3lurBcHwDAEk8AOllGRoIkyVqrNWt2a+DAbMeJAHSEMR4lZfSRtVbr5v1cAX+dBp79PyFb7snaoMQSTwDCLLfvOYpLynEdAwDQTnu2fiRvXLKyeo0Py/XzB31Wuf3PC/nyUQCAg/hIIgAnfvzj91Rc/IS2bdvnOgqAEPE37FXpx/+nVbO+F7qZFJZNsgGEX7/xd6n3qC+5jgEAaKdTJv9YZ33lE3l8CWG5flxiBuUEAIQZMygAOHHTTUPVvXuK8vOTXUcBEALGGA069z7JeLRpwZ8lG9Sgc+/r8EwKa4Nh2XwbAFqzwYCsDcrjjXMdBQDQDsYYJab3DOs9KkvnafMnf9OwS/8mj4e30QAg1PhIIgAn+vbN1K23jpLXy48hIFoYYzRo2q9UOPYb2rTwL1r55rdalmjqCGZQAAi/j2dcoIXPXOY6BgCgHbYtfUpL/32LAv76sN6nsbZcO1a+oL3bFob1PgAQq3jFD8CpOXM26ayzntLevQ2uowAIAWOMBp79Pyoad4c2L3pYK964o0MlRW6/89T3jG+FMCEAHMl4fLLBgOsYAIB2aKzdpZrKdfL6EsN6n+zCSZKMKkrnhPU+ABCrmJsGwKnERJ+qqhq0fXuN0tPDs24ogM5ljNGpU38u4/Fqwwe/kQ0GNeTCP8qcxEyIvP7nKq//uWFICQAHeTw+BZrqXMcAALRD0bjbVTTu9rDfJz45V+ndR6hi42z1n/C9sN8PAGINBQUAp844o0BLltwgj4c15oFoYozRKZN/ImM8Knn/PsUlZmjg2b9s93Wa6nYrGGhQQmr3MKQEgGbNMyj8rmMAANooGGiUxxvfaffLLpqq0o//JH9jjXzxKZ12XwCIBSzxBMA5j8eosTGgv/xlsfz+jq5XD6CrMMZowKQf6dSpP1fP4def1DXWzv2J3v/bmSFOBgCHMsYraykoACBSrJ37U7370JhOW54vp2iqbLBJuze/3yn3A4BYQkEBoEuYOXOjvva1WXrttRLXUQCEkDFGfc+4U6m5g2Wt1fYVz7XrhWSPIZ/TwHP+N4wJAYA9KAAg0pSXzFRiWoGMx9sp98vqdaY83gRVbpzdKfcDgFjCEk8AuoSLLuqnDz+8TuPG9XAdBUCYVJbO1af/vEnBQJN6Dru2Tedk9R6vrN7jw5wMQKwzHi9LPAFAhKjfu1X7dq1QwbDrOu2e3rgkZfY6QxUUFAAQcsygANAlGGMOlBPl5bWy1jpOBCDUcoqmaMznXlbB0M+3+ZzaPRu1t2xpGFMBAHtQAEAkKd8wS5KU229ap943p2iqqncuVUPNzk69LwBEOwoKAF3K4sU71a/fI3rxxbWuowAIg9x+02SMUU3leq1889sKBpqOO77kvV9r0XNXdlI6ALHKGK+CLPEEABGhvGSmEtIKlJp7WqfeN7ffNOX0PUdN9bs79b4AEO2iZoknY0yipAmSpkoaLWmwpDxJcZKqJJVK+lDSs9bad8KUYY6kySdx6pnW2g9DHAeISEOH5uqGG4ZoxIg811EAhFFl6VxtWvhX1e/brhGXPSaPN/4YI62M4fMUAMIrp+85Ss7q5zoGAOAEgkG/KjbMVv6gy2WM6dR7p3cfqeJr/tmp9wSAWBDxr/iNMfnGmKck7ZI0S9I9ki6UVCQpRVK8mouKYkm3SZpnjPnAGDPYTWIAx+PzefR//3eOBgzIch0FQBj1HvUlDTznV9q5+l9a8tIXFQw0SpIqSudq3oPDDizrZG1Q0qEvPg8fAwAd1XP4dRow6QeuYwAATqBq23z5G6o6fXmn1prqdrMkMQCEUMQXFJJ6S7pGUuphx7dI+kjSbElrDvveGZLmG2POCmOu9ZLeaOPXnjDmACJSVVWDbrnlDX300XbXUQCESdHYr2vQufdr59pXtPjF61VeMkuLnr1KdXs2aOEzl6mxtkKyh86gqCide+QYAOggGwwcKEoBAF1XecksGeNVTtFUJ/ffvuI5vf37Pqrbs8HJ/QEgGkXNEk8t3pP0mKTXrbVbWn/DGNNX0i8l7d+ZM0XSP40xA6215WHI8g9r7U/CcF0gJhgjzZxZqpEjux3YPBtA9Cks/i8Zj1cr37hTu9a/LtmgJKmpfo8WPT9dSRmFzT8QdLCcCPrrDhkz9vo35PFE2680ADrTypnf0Y6VL+rsO0pdRwEAHEd5ySxlFJyuuMRMJ/fPKDhdAybeI29cspP7A0A0ioZX80FJL0v6qbV28bEGWWs3SLrWGLNd0rdaDmdL+r6kb4c5I4B2Sk9P0MqVNykpKc51FABhlpJzqownTjZ4cMNsG2hUddmnaqqrlOQ5opxoPWb1W9/X4HPvc5AcQLTIG3CRkrP6u44BADiBUyf/RFbulldKzixS/7PudnZ/AIhGJtbWzTPGxKt5+aVeLYc2WWsLQ3TtOTq4SfZPwzWDori42C5YsCAclwa6pMWLdyorK0GFhRmuowAIsaMVD4cwXvniUxUMNB5zjMeXpCEX/VkFQ64OY1IAAABA8jfu0+5N7ym3/7mHLEUKADg+Y8xCa23x4cdj7ieptbZR0n9aHepjjGFuHtBF7dvXqKlTn9E997zrOgqAMFj+2m3HLickyQbkb9h73DFBf53WvH1PGNIBiBVN9XtUV7XJdQwAwHHsWPmi9myd7zqGdq55VYueu1LVZZ+6jgIAUSHmCooWh++ome4kBYATSk2N1/PPX6r/+79zXEcBEAYjr5ih+OQ8GW/8cUYdf7anx5ek4Zc+EtpgAGLKhg9/r3f+MsJ1DADAMVhrteqtu7VpwYOuoyinaIokqWLjbLdBACBKxGpBUdTqcVBSODbJBhAi55xTqKysRFlr1dDgdx0HQAil5w/XhFsWKL37SHl8Se0+3+NL0pjpLyi7cFIY0gGIFcZ4ZIP8jgEAXZUxRhNunq9TpvzUdRQlpOYrNe80VWygoACAUIi5gsIYkyTpwlaH5ltrw/Fq5EJjzGxjzA5jTKMxZrcxZo0x5iljzM0sKwW0TyAQ1EUXvaA77uCXQCDaxCfnaOz1b6rXiBvaVVJQTgAIFePxSbKyNug6CgDgGOISM5SU0dt1DElSduEU7d7yvgL+etdRACDixVxBIel2Sa132n0iTPcZK2mKpHxJcZIyJZ0i6RpJD0vaZIz5rzDdG4g6Xq9Hp5/eXcOH57mOAiAMPB6fBp93v3qPvqVNJYXHl6RB0+6lnAAQEs0FhWSDAcdJAABHs3LmXdq69EnXMQ7IKZqqoL9ee7Z86DoKAES8mCoojDFDJf2k1aH1ai4LwqFB0jJJcyXNk7RGhy6inSPpQWPM48YYE6YMQFT52c/O0te+NtJ1DABhUlE6V5sXPXz8TbNbBP11WjfvF2qsPXxbKQBov4MFBcs8AUBX01Rfpc0LH1Jt5RrXUQ7I7nOWjPGqsnSO6ygAEPFipqAwxuRIeklSYsuhgKQbrbWNIbzNLkm/kTRRUqq1dpi1doq1drK1dqCkbpK+L2lfq3O+KOkXbcj/FWPMAmPMgl27doUwMhB5Xn11vR5++FPXMQCEUEXpXC169qo2lRP7NTVUadHz0xXkDUUAHWQ8XkkUFADQFVWWzpG1AeX2O9d1lAN8CWnK6DlWFRvmuI4CABEvJgqKln0n/ilpQKvD91hr3w3lfay1V1trv2Otffdo+1pYa8uttfdKGiNpe6tvfdcYc+oJrv2QtbbYWlucl8cSN4htjz66TA89tESBAOtEA9HgZMoJSbKBRlWXfarVs+4OUzIAscKYlhkUliWeAKCrKS+ZKW98mjIKxrqOcoicoimq2rFITXW7XUcBgIgW9QWFMSZe0ouSJrQ6/Cdr7a8cRZK1do2a96LYz6fmvTEAtMEjj5yv9967Vl5v1P8IA6LeyZYT+wX9ddqy5HFtW/5ciJMBiCXMoACArslaq/KSWcopmiKPN851nEPkFE2RbFB7ts13HQUAIlpUv7tnjImT9JykC1odflhdoAyw1s6T9E6rQxccayyAQ2VlJSo+3qvGxoCWLWPJMyCSLX/tthOWEyfaNDvor9Oat+8JZSwAMcbDJtkA0CXVVKxS/d4tXWp5p/0yCsZq0tdXKa//ea6jAEBEi9qCwjTP035K0qWtDj8q6avWWnv0szrd260e92spVAC00Ze+9LrOOec51dSEcisZAJ1p5BVPKj6lm4w3/qjf9/iSNGjavYpPzjvumGGXPhzOmACiXEZBsU6Z/GN545JdRwEAtFJeMkuSlNtvmuMkR/J445SU3st1DACIeFFZUBhjvJJmSLqy1eHHJN3ShcoJ6dB9KIykHFdBgEh0112n69FHz1dKytHftATQ9aXnD9OEm+crvfuoI2ZKeHxJGjP9BfUe9WVNuGWB0ruPPOqY0dOfV07h5M6MDSDKpHcfqX7j75IvIc11FABAK+XrZyolZ6CSMvq4jnJU1TuXafGL16lu7xbXUQAgYkVdQdFSTjwhaXqrw49L+rK1tqvtqHv4R7RObgFuIEaNGNFNF1/cX1Lz2qQAIlN8co7GXv+Geo288UABsb+cyC6c1GrMm+o14oZDxlBOAAgFf2ON6vaUKhhoch0FANAi0FSr3Zvf65LLO7VWtX2h6qs2uY4BABErqgqKlnLi75I+3+rw3yV9qQuWE5I0tNXjemttlbMkQASbMWOFJkx4So2NrBsNRCqPx6fB596nIRf9WQmpBYeUE4eMOe/+A2MoJwCEyq61r2reg0NUt2eD6ygAgBZN9XvU7dSL1e2Ui1xHOabUvCGadOtKZfUe7zoKAEQsn+sAoWKM8ah5GadrWx1+QtJNXbGcMMak6dD9Md51lQWIdBkZCUpNjVNVVYPy8lg7GohkBUOuVsGQqzs8BgDaI6PgdA29+C+KT+nmOgoAoEViWoFGXP531zGOyxgj6eCM/v3PAQBtFxUzKFrKiUclXd/q8D8k3dgVy4kW90nKbfX8eVdBgEh3ySX99cYbV1FOAACAk5Kc1Vc9h1+vuMRM11EAAC0iZV+Hyk3vaM7/DVBN+UrXUQAgIkV8QWGa6+m/Srqh1eEZkm7oaDlhjJlijLGtvn5ynLFPGmMuNcYcd1aKMSbZGPMXSV9tdXidmgsWACfJGKPKyjr99rcL2I8CAAC0S1Pdbu3Z8pH8jftcRwEASKrdvUHzHhikLUu69gwKSUrKKFJjTZkqNs52HQUAIlI0LPF0taSbWz23kvIlvdaOqXXftdZ+2sEc49W890WFMeY1SZ9IWi9pj5qLoB6txuS0Oq9K0lXWWnbkAzrouefW6LvfnaupU3tr1Kh813EAAECE2LP1Yy167kqNu2GOMguKXccBgJjnS0jToGm/ioj9xpIyeis5a4AqNs5R4elfdx0HACJONBQUh6/pYiRNa+c17g1RFqm5fPhCy9eJrJZ0rbV2SQjvD8Ssm28epkmTemnw4JwTDwYAAGhhPM0vi2zQ7zgJAECS4pNzI+rN/pyiKdq2/GkFA03yeONcxwGAiBLxSzx1IY9K+kBSfRvGrpT0TUmjrLWLwpoKiCFer+dAObFlS7XjNAAAIFIYj1cSBQUAdAXBQKO2LXtajXWVrqO0WU7fqQo07lPV9gWuowBAxIn4GRTW2sckPRama89R84yMtoz9maSfGWPiJQ2X1EvNsyly1FwEVUnaJulja+32cOQF0OzVV9fr8sv/qbfeulqTJvV2HQcAAHRxFBQA0HXs3vKBlv77Zo268ml1O/US13HaJLvPJElGFRtmK6vXma7jAEBEifiCoqux1jZKWtDyBcCBKVN661vfGqOhQ3NdRwEAABHgwBJPNug4CQCgouQtGY9P2RGw/8R+cUlZSu8xWhUbZ2vAxP92HQcAIgpLPAGIOikp8frVryYrOzvJdRQAABABjGEPCgDoKspLZiqz15nyJaS5jtIuOUVTVLVtvvyN+1xHAYCIQkEBIGpt2rRXl1zyotasiZy1SwEAQOdjk2wA6Brqq7ereudS5fY713WUdsspmiIb9Gv3pvdcRwGAiEJBASBqxcd7tWxZuVav3u06CgAA6MLYgwIAuoaKDW9JUkQWFJm9zlT/s76v5OwBrqMAQERhDwoAUat79xStXftlxcV5XUcBAABd2IGCwgYcJwGA2FZeMlPxKflK6zbUdZR28/oSNWDiPa5jAEDEYQYFgKi2v5x49dX1Ki+vdZwGAAB0RYlpPTX04geV3n2U6ygAELNsMKDyDW8rt9+5Msa4jnNSAk212rX+TTXWscwwALQVBQWAqLdp015dfvk/9bvfLXQdBQAAdEFxiZnqOfwLSs4sch0FAGJW1fZF8tfvVm6/aa6jnLR95au06NkrVFEyy3UUAIgYLPEEIOr16ZOumTOv0vjxPV1HAQAAXVDQ36C9OxYrKatICSn5ruMAQEyq3rVMxhOnnKKprqOctPT8ESq+5t/K7HWG6ygAEDGYQQEgJkyZ0kfx8V41NgbU0MAGmAAA4KCG2l366IlztGvd666jAEDM6j3yJp19xybFJ+e4jnLSjMernL5T5Y1Lch0FACIGBQWAmFFd3ahRo/6u//3fj1xHAQAAXUh8cq7GfO5l5faN3GVFACAa+BLSXEfosPq9W7V27k9Vu2ej6ygAEBEoKADEjLS0eF18cT+NG9fDdRQAANCFeH2Jyu03TYnpLAcJAC6Urf6n5j95sRpqylxH6bCAv14l79+n8pKZrqMAQESgoAAQU37968m68MJ+rmMAAIAuJBho0o5VL6umYq3rKAAQk2zQr2CgUfFJua6jdFhyVj8lZvRRxcbZrqMAQESgoAAQc4JBqwce+EQzZqxwHQUAAHQBQX+dlrx0vXat+4/rKAAQk7oPvlLjvjBTxuN1HaXDjDHKKZyiytJ5ssGA6zgA0OVRUACISU8/vUqvvFLiOgYAAOgCjMcnSbKWN5IAoLP5G/cpGGhyHSOkcoqmyF+/R3vLlriOAgBdHgUFgJjj8Ri98soVevLJi11HAQAAXcCBgiLod5wEAGLPpoUPafYfCtVUX+U6SshkF02WJJZ5AoA2oKAAEJMyMhJkjFFlZZ2WLt3lOg4AAHBof0ERpKAAgE5XXjJTSRmFikvMcB0lZBJS8pXabSgFBQC0AQUFgJj2mc+8pOnT/61g0LqOAgAAHDGm+WURMygAoHP5G6q1Z8sHyu13rusoIZdTOEV7Nn+gQFOd6ygA0KVRUACIafffP0VPP32JPB7jOgoAAHDIeHxsZgoAnayydK5s0K/cftNcRwm5nKKpCgYatGfrh66jAECX5nMdAABcOvPMggOPA4GgvF56WwAAYpHx+NgkGwA6WXnJTHnjU5XZ6wzXUUIuq88EJWUWqalut+soANCl8U4cAEj65S8/1LnnPsdSTwAAxKjmGRQs8QQAncVaq/KSWcounCyPN951nJDzxadq0teWqfvgK1xHAYAujYICACT17JmqAQOy1NDAGxMAAMQiY7wUFADQiWor16muqjQql3dqzVrLEoIAcBwUFAAg6cYbh+qhh85TUlKc6ygAAMCBkVf8Q71H3+w6BgDEjPKSmZKk3L7RW1DUVK7X3D+dqrI1/3YdBQC6LAoKAGhl3brduu++j13HAAAAnSynaKpScwa6jgEAMaO8ZJaSs09RclZf11HCJimjj7ILJyshpZvrKADQZbFJNgC0MmPGSv32twt0/fWnqUePVNdxAABAJ9m1/k3FJ+Uoo2CM6ygAEBMGnvM/aqgpcx0jrDzeOA2/9BHXMQCgSzPWsiFspCkuLrYLFixwHQOISg0NflVU1KuggHICAIBYMveBwcounKRhl/zVdRQAQJSp21MqX2KG4hIzXUcBAGeMMQuttcWHH2eJJwBoJSHBd6CcWLOm0nEaAADQWcZc87JOmfQj1zEAICZsX/Gcdqx62XWMTrGvYrXmPThEZav/5ToKAHRJFBQAcBR//vMnGjLkMa1YUe46CgAA6ASpOQOVmN7TdQwAiAmbFvxVWxb/P9cxOkVK9qmKT8lXxcbZrqMAQJfEHhQAcBTTpw/Uvn1NGjAgy3UUAADQCbYvf1a+hAzlDTjfdRQAiHpjr39DjXUVrmN0CmOMcoqmqGLDW7I2KGP4rDAAtMZPRQA4itzcZH33u2MVH+91HQUAAHSCkg9/qy1LHnMdAwBigvF4lZDSzXWMTpNTNFWNteXat2uF6ygA0OVQUADAcSxaVKYJE57Ujh01rqMAAIAw8nh8ssGA6xgAEPVWzvyu1r97r+sYnSq7aIokscwTABwFBQUAHEdqapwqKuq1ZUu16ygAACCcjFc26HedAgCiWjDQpG1L/6H66q2uo3SqpPReSsk+RRUb57iOAgBdDntQAMBxnHpqtlasuEkej3EdBQAAhFHzDAoKCgAIp6ptH8vfsFe5/c51HaXTZRdN0balTyoYaJTHG+86DgB0GcygAIAT8HiMAoGg/va3paqpaXQdBwAAhIHxeGUtBQUAhFN5yUwZj0/ZhZNdR+l0OUVTFWiqUdW2+a6jAECXQkEBAG2wcGGZbr75Dc2YsdJ1FAAAEAaGPSgAIOzKS2Yps+c4xSVmuI7S6bL7TJSMR+Ub3nYdBQC6FJZ4AoA2GDu2hz788DqNHdvddRQAABAGxngVDDJTEgDCpaGmTHt3LNYpk3/sOooTcUlZGnv9m0rrNsx1FADoUphBAQBtNG5cDxljtHt3vQKBoOs4AAAghAx7UABAWFWUNM8ciMX9J/bL6nWGfPEprmMAQJdCQQEA7VBaWqWBA/+mv/51iesoAAAghIzHxx4UABBG5SUzFZ+cp7T84a6jONNUX6W1836u3Vs+cB0FALoMlngCgHbo0yddX/jCaZowoafrKAAAIIQGnXsfe1AAQJjYYEDlG2Ypr//5MiZ2Pyvr8SWodP4DikvIUFavM13HAYAugYICANrBGKPf/Gaq6xgAACDEkjOLXEcAgKgVaKpVwdBrlVMU26+lvL5ETb29RN64ZNdRAKDLiN3aGgA6oK6uSXfdNUevvrredRQAABAC5SVvaevSJ13HAICo5EtI06Bp9ypvwPmuozhHOQEAh6KgAICT4PV69PrrG/XxxztcRwEAACGwbekMrX/3XtcxACAq7S37VMEg+/xIUmPtLs1/8mJtX/G86ygA0CWwxBMAnIT4eK8++ug6JSfHuY4CAABC4LQLfi9rg65jAEDUaarbrQ/+31nqP+F7GjDxHtdxnItLylF12VKVl8xUj9Ouch0HAJyjoACAk7S/nFi7drf8/qAGD85xnAgAAJwsX0K66wgAEJU8vkSNuPxxpeUNcR2lSzDGo+yiSarYOFvWWhljXEcCAKdY4gkAOsDvD+rcc5/T7be/7ToKAADogLI1/9b6937tOgYARB1vXJK6D/qsUnJOdR2ly8gpmqqG6m2qrVzrOgoAOEdBAQAd4PN59MQTF+nxxy90HQUAAHRARclbKp3/gOsYABBVrLUqnf+AaneXuI7SpeQUTZUkVWyc7TgJALhHQQEAHTRxYi8VFKRKkhoa2PgNAIBIZDw+9qAAgBDbV75Cq2Z9T5Wb3nEdpUtJyuyrpIxCCgoAEAUFAITMDTe8pmuvfdV1DAAAcBKMxycb5IMGABBK5SWzJEm5fac5TtK1GGOUXTRVlaXvKMi/PQBiHAUFAITIyJHdNGZMvoJB6zoKAABoJwoKAAi98pKZSs07TYnpPV1H6XJyiqbI31ClvTsWu44CAE75XAcAgGhx553FriMAAICTREEBAKHlb9yn3ZvfV2Hx11xH6ZJyiiZLkio3zlZmAa8lAcQuZlAAQIi9884W/fa3C1zHAAAA7WA8XgoKAAihytJ3ZAONyu3H8k5HE5+cp6Jx31Rat2GuowCAUxQUABBiTz65Ug888Ilqa5tcRwEAAG1kjFeSZaNsAAiR8pKZ8sYlK6vXeNdRuqyBZ/9SeQMucB0DAJyioACAEPv1ryfr009vUHJynOsoAACgjYynefVbZlEAQGiUl8xSduFkeXwJrqN0aXVVm1S/d6vrGADgDAUFAIRYWlq8UlLiFQgEtWTJTtdxAABAGxwsKAKOkwBA5KupXK+6PSUs73QCgaZavfOXESpd+BfXUQDAGQoKAAiTu++epwkTntKOHTWuowAAgBMoLP4vTb1jkzy+RNdRACDi1e5eL19ipnL7nes6SpfmjUvW8EsfUa/hX3QdBQCcMdZa1xnQTsXFxXbBAjbgBbq6jRur9N57W3XttYNljHEdBwAAAAA6TTDol6dldhoAAMaYhdba4sOPM4MCAMKkqChD1113mowxogwGAKBr27P1Y62Z/SP5G/e5jgIAEW3/ax/KibYJBhq1ffmzqtq20HUUAHCCggIAwuy110pUXPwP7d3b4DoKAAA4huqdy1Q6/wEFGlmaEQA6orJ0jt59aLT2la90HSUyGI9WvHGHtix5zHUSAHCCOhsAwiw3N0mJiV5VVtYrPT3BdRwAAHAUvUd9Sb1Hfcl1DACIeB5vvJIy+yoxvbfrKBHB4/Epq89EVWyc4zoKADhBQQEAYTZ2bA+9++7n2YcCAAAAQNTL6j1BY3pPcB0jouQUTdGuta+qds9GJWcWuY4DAJ2KJZ4AoBMYY1RT06j7758vvz/oOg4AADjM7i0faum/b1FDTZnrKAAQsfwN1Wqs3eU6RsTJKZoqSapkFgWAGERBAQCdZNasTbrrrrl6++1NrqMAAABJFaVzNe/BYdpbtlT1VZu0bdlT8tdXHXMMAOD4dqx6UbP/0E81letdR4koKTkDlZDaQxUbZ7uOAgCdjoICADrJpZf219KlN+i884pcRwEAIOZVlM7VomevUt2eDVr4zGUK+OskSTboP+aYxtoKV3EBICKUl8xUQloPJWf1cx0lohhjlFM0RRUb58haZtwDiC0UFADQSYwxGjo0T5K0dWu1rLWOEwEAEJv2Fw/BllKiqX6PNnz4e0k68MbQ0cYsen66gq0KDADAQcGgXxUbZiu337nsv3cSsoumqqmuQtU7l7mOAgCdioICADrZRx9tV//+j+jFF9e6jgIAQMw5vHiQJBtoVF1VafPjoP+YY6rLPtXqt77f6ZkBIBJUbZsvf0OVcvtNcx0lIuUUTZEklnkCEHMoKACgk40Zk6877hij8eMLXEcBACCmHK142M8GGiVJGz76v2OOCfrrtGXxY9q2/LmwZwWASFNeMkvGeA9s+Iz2SUwrUGruYNVXbXYdBQA6lWGJkchTXFxsFyxY4DoGAAAAEFHmPThMdXs2HHeMx5ugYKDhuGMSUgs05RtrQhkNACLeB49Nlscbr3FfmOk6SsQKBhrl8ca7jgEAYWGMWWitLT78ODMoAMCRXbtqNX36v/Thh9tcRwEAICaMvGKG4pPzZI7z5s+JygmPL0nDL30k1NEAIKI11u7S3u2LlNv3HNdRIhrlBIBYREEBAI4kJHi1ePEurVpV6ToKAAAxIT1/uCbcskDp3UfK40tq9/keX5LGTH9B2YWTwpAOACJX+YbZkqxy+53rOkpEszaohc9coZL373MdBQA6DQUFADiSnp6g5ctv1I03DnUdBQCAmBGfnKOx17+pXiNuaFdJQTkBAMdWUTJTcUk5Su8xynWUiGaMR/HJufLGp7qOAgCdxuc6AADEsrg4ryTpnXe2qFevVPXtm+k2EAAAMcDj8WnweffLeOO1edHDR90Q+5DxviQNmnYv5QQAHMPAc+5V71E3yxg+B9tRwz7zkOsIANCp+JcDAByrqmrQxRe/qJ///EPXUQAAiBkVpXPbVE5IUtBfpxVvfEsrZ35XVds/kbW2ExICQOSIT85RZq9xrmNEjWDQr6b6Pa5jAECnMPxyHXmKi4vtggULXMcAEELvvLNFo0d3U0oKm6IBABBuFaVztejZq9pUThxkJDW/dkrKKFT+oMuVP/ByZRQUyxgTlpwAEAm2r3heDfu2q/D02/h5GAI2GNCcP52q7oM+q8Hn3e86DgCEjDFmobW2+PDjLPEEAF3AxIm9JEmBQFANDQElJ8c5TgQAQHQ6uXJCkqw8viRl9hwnjzdepfP/rI0f/UGJ6b2UP/Ay5Q+8TJm9zmB5EwAxp7xkpvbtWq6isd9wHSUqGI9Xad2GqWLjbNdRAKBT8NszAHQRfn9QkyY9rW9/e47rKAAARKWTLyeaBf112rP1I/UY+nlN/eYGDbvkYaXlj9DmRY9owdOXKdDUfN36vVtlg4FQRgeALmvYJX/V6de94TpGVMnpO1U1FatVX73NdRQACDtmUABAF+HzeXThhX3Vv3+m6ygAAESl5a/d1qYNsY83Juiv05q371HBkKtVMOzzKhj2efkb9qp651L54lMkSYuen66ElHyN+dyLkiRrg8ysABDV9v/8Q2jkFE2VJFVsnKOew651nAYAwovfkgGgC/nBD87U5z8/2HUMAACi0sgrnlR8SjcZ79H3fPL4kjRo2r2KT8477phhlz58yDFfQrqyek+QJFlr1W/8d9R79C2SJH/DXs3900Ate/VW7Vr/poKBxhD+jQDArdVv36Olr/yX6xhRJ63bUMUl5aiSZZ4AxAAKCgDoYqy1mjFjhf78509cRwEAIKqk5w/ThJvnK737KHl8SYd8z+NL0pjpL6j3qC9rwi0LlN595FHHjJ7+vHIKJx/zHsYYdR/0WXU75UJJkr+hWtmFk1W2+mUtevYKzf5DXy399y3aufZVBfz1of9LAkAnsdZqx8oX5W+och0l6hjjUU7RFFVsnCNrres4ABBWFBQA0MUYY/Tii2v13HNrDvwyGggEdf/985Wb+yf95jfzFQgEHacEACAyxSfnaOz1b6jXyBsPFBD7y4nswkmtxrypXiNuOGTMicqJo0lM76nhlz6iqbdv0Oirn1e3Uz+jnete1yfPf06z/1CkJS/fqB2rXqasABBxaipWq37vZuX2O9d1lKiUUzRVDfu2q6ZitesoABBWhiY28hQXF9sFCxa4jgEgjPbubVBKSpy8Xo/Wrt2t6dP/pbVrd6umxq+UFJ9OPTVbzzzzGZ1ySpbrqAAARKxty5/Tmrfv0fBLHzlQThxrzLBLH253OXEswUCTKkvnqWz1Sypb/W811VVo8m1rlZjWQ3V7ShWXnCNffGpI7gUA4bLx4z9p9Vt3a9KtK5SU0cd1nKhTt6dU8x4cokHn3qfC4q+5jgMAHWaMWWitLT7iOAVF5KGgAGJDIBDUvfd+pJ///EM1NQUVDB78ee3xGCUkePXzn0/QnXcWy+MxDpMCAICTFQz6Vb1jiTIKxkiSFj57per2bNRZX1nY/P1AkzzeOJcRAeCoFjx9mer3bjnw8wqhN+/BYUrNO02jr3rGdRQA6LBjFRQ+F2EAAMe3f9bE0qXlCgSOLJKDQau6Or9+/OP3NGPGSmZTAAAQoTwe34FyQpL6jf+OmmorJDWXF/MeHKL0bsOVP+hy5Z1ykeKTsl1FBYADAk212r3pXfUefYvrKFFtyAV/VEJqvusYABBWFBQA0IUEAkH97ncL9aMfvaeGhsAhsyaOpqbGryVLdmnEiMeZTQEAQBTI6nXmgcfBplr1GHyVdqx+WbtefV3G41N24WTlD7pc+adeovjkPIdJAcSyyk3vKhhoYP+JMMvpO9V1BAAIO5Z4ikAs8QREp4N7TexRTU1Tu89nbwoAAKKTtVZ7d3yislUva8eql1W3p0QyHmX3maj8QZer++ArmVkBoFOtnHmXtiz+fzr7js3yxiW5jhPVdqx8Ud64ZOUNuMB1FADoEJZ4AoAubuLEp7RrV90JZ00cy/7ZFBMnPqUdO24NcToAAOCKMUYZPUYro8donTLlp6reuUxlq15S2aqXtPKNO5XZc5zik7JVu2ejPJ44Jab3dB0ZQJSrKJml7D4TKSc6wYYPf6v45G4UFACiFgUFAHQR48cX6OWX13XoGtZajR9fEKJEAACgqzHGKD1/mNLzh2nApB+qpmK1UnIGSpJK3vu1ytb8S1Nv3yCPN07+xhr54lMcJwYQbWwwoF4jb1JSVl/XUWLCqKueVXxKN9cxACBsKCgAoIu4444xmjmzVPv2tX95p/1SUuJ0551HzJYDAABRyBij1NxBB573PeNOdTvlInm8cbLW6sPHJsobn6b8gZep+6DLlZzVz2FaANHCeLwqGne76xgxIzGND6ABiG4e1wEAAM0mTuylzMyEDl0jKytRZ53Fsg4AAMSilJxT1O3USyRJ1gbUc/gXJElr5/xI7/xluN5/dLzWv/dr1VSscRkTQITbvfk9NdZVuo4RU1a//QNt/PhPrmMAQFhQUABAF2GM0R13jFFy8slNbktO9umOO8bIGBPiZAAAINJ4PD71PeNOnXnjXE26dYUGnnOvvL5krZv3M7370Gi998hYrXvnf1RXtcl1VAARJBho1MJnr9TauT9xHSWm7N2xSNuWPek6BgCEBQUFAHQhN944VIHAyW2SHQhY3XDDkBAnAgAAkS4po4+Kxt6mcV+cpcm3rdGgc+9TXFK21r/7v9pXvlqSVFe1WXt3LJG1J/d7CIDYYDw+FV/zLxUW3+o6SkzJKZqq6rJP1Vi7y3UUAAg5CgoA6EJycpJ00UV91d5JEMZIF13UVzk5SeEJBgAAokJiWoEKi7+msde9rinfWKecoimSpM2f/E0fPjZJTS3LtjTWVlBWADiCMR5l9hx7yP43CL/soqmSpIqNcx0nAYDQo6AAgC7mjjvGKCUlrl3nJCezOTYAAGifhNR8ebzNv3MUjf26Rl75tOKTcyRJi1+6XvP+fJpWzbpbu7d8KGuDLqMC6CJK3r9fe7Z+7DpGzMnoPkq+hAxVbJzjOgoAhBwFBQB0MSezWXZ2NptjAwCAkxefnKdup1x44HmvETcordswbVr0kD5+Yprm/mmgVr75HVVuelc2GOjQvSpK52reg8O0t2xph8YA6Fz11du1du5PVLnpXddRYo7xeJVdOFGVpXNcRwGAkKOgAIAupr2bZScn+zRkSI727WsKczIAABArCoZeo9FXP6uzv7lRwy59VBkFxdqy5DHNn3GB5vzfAK14/Zvau2NJu69bUTpXi569SnV7NmjhM5epsbbipMYA6HwVG96SJOX2O9dxktiUUzRVdXs2qnb3BtdRACCkKCgAoAu68cah8vna9iPaGKM339yol19eG+ZUAAAg1vgS0lUwZLpGXfmUpn5zo0Zc/riy+pylbcueUvXO5tkNDTVl2rV+poKB439YYn/xEPTXSZKa6vdo0fPTFQz62zUGgBvlJbMUn5KvtG5DXUeJSTkH9qGY7TgJAIRW2z6eCwDoVDk5Saqqur3N49esqdSpp2aHMREAAIh1vvhUdR98pboPvlKBplpJRpK0Y+WLWjXzLp31lUVKyTlVDfvKFJeYKY/v4JKVhxcPkmQDjaou+1SrZ92twefdf/wxb31fg8+9r9P+rgAOZYMBVWx4W3mnXChjjOs4MSk5+xQlpvVUxcbZ6j3qS67jAEDIUFAAQBTYX06sXl2pNWt26zOf6e84EQAAiGbeuOQDj3uNvEmpOYOUknOqJGnVrO+pvORN5Q24UPmDLpfHm6jFL157SPGwX9Bfpy1LHpfxxmvzooePPWbxY8ooGKuCIVeH7y8F4Jiqti9SU32lcvtNcx0lZhlj1HP4F1zHAICQo6AAgChy111ztWTJTp13XqESEvgRDwAAws/rS1RO36kHnvcaeYO88Snaufrf2r78GTXPtLDHPD/orztmOdF6zJq376GgABwpL5kpySin6GzXUWLagEk/cB0BAEIuat69MsYkSpogaaqk0ZIGS8qTFCepSlKppA8lPWutfacT8iRIukLSNZKGSCqQVCdpq6TZkp6w1i4Idw4AseVvfztfNTVNlBMAAMCZnKKpyimaquD5v9fuTe9q0yePaOfqf+lEJcXxeHxJGn7pIyFOCqCtyktmKaOgWPHJOa6jxLxg0K+mugolpOS7jgIAIRHx72AZY/Il/V7SJZJSjzEsr+WrWNJtxpgPJX3JWrsyTJlGSnpC0uE7RyVJypY0TNLtxpiHJH3LWlsTjhwAYk9eXrLy8pofP//8al1ySX8lJkb8j3oAABCBPN445fSdqpy+U9VQs0vzn7xQtRXrZG37Nrz2+JI0ZvoLyi6cFKakAI6nsbZCVdsXqP+E77mOAkkfP3GefPGpKv78v1xHAYCQ8LgOEAK91TxL4fByYoukj9Q8W2HNYd87Q9J8Y8xZoQ5jjBkhaZ4OLScqJL0naZGk+lbHvyLpn8aYuFDnABDbFi/eqauv/rf+8pclrqMAAAAoISVP47/8oXqPvlkeX1Kbz6OcANyr37tFKVn9ldvvXNdRIKnw9FvVe8wtrmMAQMgYa489zTYSGGOKJc1vefqepMckvW6t3XLYuL6Sfinp860OV0oaaK0tD1GWdEkrJPVsOdQo6Q5Jj1hrm1rG5Ej6haT/anXqH62132zrfYqLi+2CBawOBeD4Zs0q1dSpveX1RkMXDQAAosWqt/77hHtOSM3lxKBp96r3qC93UjIAx2OtlTHGdQwAQIQyxiy01hYffjwa3rUKSnpZ0ihr7VnW2kcOLyckyVq7wVp7raTftjqcLen7IczyXR0sJyTpemvtg/vLiZYcFdbar0n6S6txtxpjTg1hDgDQtGmF8no9qqpq0IcfbnMdBwAAQBWlc9tUTkjN+1Ksm/cLNdZWdEIyAEdjrVUw2LwsG+VE11G9c5l2b37fdQwACImILyistYustZ+11i5u4ynfV/PyT/tdFYocxpgUNc+W2O81a+1zxznlO5J2tTz2Sbo7FDkA4HBf+cqb+sxnXtK+fY2uowAAgBhWUTpXi569qk3lxH5NDVVa9Pz0A2+QAuhc1TuXafYfilRROtd1FLSy4vU7tPqt/3YdAwBCIuILivay1jZK+k+rQ32MMckhuPSFklJaPf/TCXLUqHk5qv0uN8awky2AkLvvvsl64YVLlZoa7zoKAACIUSdTTkiSDTSquuxTrZ7F57kAFzzeOOWfeqlSsln0oSvJKZqiqh2L1FS323UUAOiwmCsoWhw+Rzg9BNe8tNXjeklvteGcV1o9zpI0MQQ5AOAQffqka9Kk3pKkRYvK5PcHHScCAACx5GTLif2C/jptWfK4ti0/3gR1AOGQmjtIQy/+sxLTeriOglZy+k6VbFCVm95xHQUAOixWC4qiVo+DkkKxSfaoVo/nt8zUOJGPJTW1ej7qWAMBoKPWrKnUuHEz9Otff+w6CgAAiCHLX7utTRtiH0/QX6c1b98TylgATsDfWKPqnctkrXUdBYfJKDhd3rgUVWyc7ToKAHRYzBUUxpgkNS/HtN98a22HFjQ1xngltZ7vuLYt51lr63XofhindSQHABzPqadm68EHp+nrX6cLBQAAnWfkFU8qPqWbjPfoy016fEkaNO3eY35//5hhlz4crogAjqJy42y9/7cztHvzu66j4DAeb7yy+pylyo1zXEcBgA6LuYJC0u2SMlo9fyIE1yyQ1Pq36U3tOLe01eOiEGQBgGO6+ebhyshIUCAQ1Pr1e1zHAQAAMSA9f5gm3Dxf6d1HHTFTwuNL0pjpL6j3qC+r//jvHjh2+JjR059XTuHkTssMQCovmSVvXIoye45zHQVHkVM0VTWVa1W3d8uJBwNAFxZTBYUxZqikn7Q6tF5SKD6Gc/geFnvacW5Vq8dpxxpkjPmKMWaBMWbBrl272pMNAI5w++1va/z4J1VZeXJrQQMAALRHfHKOxl7/hnqNvPFAAbG/nMgunCRJyhtwgSQpq9f4Q8ZQTgCdz1qr8pKZyi6aIs9xZjfBnZyiKZLELAoAEc9pQWGMmdTyNbQT7pUj6SVJiS2HApJubONeESeSetjz9rzj13rsMQsKa+1D1tpia21xXl5eu8IBwOG+8Y1R+sUvzlJ29vHXewYAAAgVj8enwefepyEX/VkJqQWHlBOSlNZtmHyJmUrM6HVgDOUE4EZt5TrVVZUqt98011FwDKl5pyk+OY99KABEPN/JnmiMuUHSP621ezpw/zmSrKQ3JF3UgescV8u+E/+UNKDV4XustaFaSDHusOft2dOi9djDrwMAYTFoUI4GDcqRJO3YUaP8/GQZYxynAgAAsaBgyNUqGHL1EceNx6vsPmepsnSuhl70wFHHAOgc5SUzJUm5fSkouipjPMoumqKq7YtcRwGADjnpgkLS/5PUaIx5W9Jzkl621u5u68nGmNbTAIIdyHGi+8RLelHShFaH/2St/VUIb1Nz2PPEo446utZjD78OAITV5s17NXr0E/r2t4t1992sLQsAANw6ZfJP5I1Ldh0DiHnlJbOUnH2KkrP6uo6C4xh87n3yJWa6jgEAHdLRJZ7iJZ0v6RFJO4wxrxtjvmyMyT7eScaYDEkzWh3a3MEcx7pPnJrLkwtaHX5YzRtlh9K+w5635zfq1mOrQ5AFANqsV680/dd/jdAVV5ziOgoAAIBScwcpKaOP6xhATAs01aly0zss7xQB4pNz5fF05LPHAOBeRwqKn0laKcm0fMVJOlfSQ2ouK95s2dg51xiTYYyZbIz5pjHmcUnrJJ3T6lr/rwM5jsoY45P0lKRLWx1+VNJXrbU2xLcrP+x5j3ac23rs4dcBgLAyxujnPz9Lp57a3CtXVTU4TgQAAGLdtmXPaNPCh1zHAGLW7s3vK+ivU26/c11HQRuUfPBbrZx5l+sYAHDSTrqgsNb+xFo7RNJwSb+UtFYHywqfmguIByVtl1Qp6W1Jv5V0vaSclnGS9DNr7ccnm+NojDFeNc/QuLLV4cck3RKGckItS1uVtTpU2I7TW49dFZpEANB+9977kUaMeFzl5bWuowAAgBi2c+0r2rbsKdcxgJhVUTpHHm+Csvuc5ToK2qCxZqfqq7e6jgEAJy0U88DWSNomaaekU9S86XVr3qOcYyW9KulBa+1/QpDhgJZy4glJ01sdflzSl621YdvrQtJySfktj0e35QRjTKGk1sthrQh1KABoq2nTCrVjR40yMhJcRwEAADFs6MV/YR8KwKFTJv9YBUOv4b/DCDFo2r2uIwBAh3SooDDG9JP0b0mDWg5ZHZwZsV+FmmdM7P/+fgM7cu9j5PFK+rukz7c6/HdJXwpzOSFJcyWd3fK4rzGmt7X2RHtrTD7s+ZyQpwKANiou7q7i4u6SpPp6vxISvDLm8B/pAAAA4eWLT3EdAYhpHo9PaXlDXMdAOwWDfvajABCRTnqJJ2NMoqTXJQ3WwaWdGluO/UjSRZLyrbV5koZKuldSqQ4WGAMkvWKMCclCecYYj5qXcbq21eEnJN3UCeWEJL142PMvtuGc1mPmt6HQAICwKy+v1bhxM/TAA5+4jgIAAGLUqre+r5Uzv+s6BhBzylb/U6tm3a2Av951FLTD0le+qgVPXeI6BgCclI5skv1VNZcMVlKTpJ9IyrXWXmSt/YW19nVr7S5JstausNb+t6T+kj4rabEOlhr3GmP+qwM59pcTj6p5f4v9/iHpxk4qJ2StXSap9V4a3zDGZB9rvDFmog7OuJCkv4UrGwC0R3Z2kkaOzNMpp2S5jgIAAGJU474y7Vj5vMKwhSCA49i3a6V2rX9DHi/LvkaShNTu2rPlQ/kb97mOAgDt1pGC4rOtHn/dWvsza23N8U6wzf4p6QxJT7ccNpJuOdkQpnn9kb9KuqHV4RmSbuhoOWGMmWKMsa2+fnKCU77X6nG+pCeNMUfMTzbGnKLmAmX/bJJ1oqAA0EV4PEaPP36Rzj+/ryQpEOiUnhcAAOCA7KLJaqzZqZqKVa6jADGl/1l366xbFrDUa4TJKZwiG/Rr96b3XEcBgHbryOJ0+xck3GmtfaQ9J1prm4wxt0i6QFKmmmdinKyrJd3c+vJqLgdea8c/qN+11n7agQzNN7Z2jjHmAUlfbzl0vqQlLceWSkqUNFHNs08yWsY0qHkZKn9H7w8AofbMM6v061/P1+zZ05WezqeoAABA58juM0mSVFk6T6m5gx2nAWKDtVbGGBmP13UUtFNmrzPk8SaoonSO8gac7zoOALRLRwqKdDWXAatP5mRrbY0xZqma37BP6kCO5MOeG0nT2nmNeztw/8N9U1K2Dm7U3V/Sb48xtlbSF6y174bw/gAQMt26JSs3N0lNTcyiAAAAnScps0iJ6b1VWTpPfcZ81XUcICase+cXqtw4V2Ovf4OSIsJ445KU2ftMVWyc7ToKALRbR5Z4qmj5M7cD18hr+XNPB67RpVhrA9baayV9QdLaYwwLSHpN0khr7eGbawNAlzF1ah+9/vqVyslJYg1oAADQaYwxyi6cpMpN76iTthUEYl75+jdlPB7KiQiVUzRV+3YuU0NNmesoANAuHSkoVqp5tsJgY8zQ9p5sjDld0iA1z8LYcrIhrLWPWWtNB7/mHOPacw4b95N25PqHtfZUSWPUvD/G3ZLuUPOSVAXW2outtccqMACgyzDGqLa2SVdf/S/NmLHCdRwAABAjsgsnq6muUtU7l7uOAkS9hpqd2rvjE+X2O9d1FJyknKKpkqTKjXMdJwGA9unIEk8vS5ra8vhpY8zF1trStpxojCmQ9ESrQzM7kKNLs9YukrTIdQ4A6Ii4OI/27GlQZWW96ygAACBGZBce3IciPX+Y4zRAdKvY8LYkUVBEsPT8EfIlZqpi4xz1GDLddRwAaLOOzKB4RAdnPpwmabkx5jfGmGP+5miMyTPGfFPSYkmntBz2S3q0AzkAAGEWF+fVm29erW98Y7TrKAAAIEYkpfdSUmY/VW6a5zoKEPXKS2YqPjlXafnDXUfBSTIer7ILJ6li42yW5wUQUU56BoW1ts4Yc7OkVyR51bxZ9R2S7jDGlEtaLqmyZXiapH6S+qp5WSij5qWdrKT7rLUntdE2AKDzeDxGkvTxx9v1P//zkZ566mIlJcU5TgUAAKJZ79FfltiDAggra4MqL5ml3H7nypiOfI4VrvUZ81U11uxU89ttxnUcAGiTjizxJGvtm8aYGyT9TVJiy2Gj5s2vJx82fP9PxtY17m+ttT/oSAYAQOfavr1GS5fu0vbtNerXL9N1HAAAEMX6jvum6whA1Nu7Y7Ga6ipY3ikK5BQe/lYcAHR9Ha7GrbVPSRor6XUdWj6Yw75amytpirX2ro7eHwDQuS67bIBWrLiJcgIAAHQKf2ON6qo2u44BRK3ykpmSjHL7ne06CkJgX/kq7Vz7musYANBmHZpBsZ+1dpmki4wxvSVNkTRGUndJGWouJ6okbZf0iaQ5bd1MGwDQNSUk+BQMWv3sZ++ruLi7Lrmkv+tIAAAgSn3097OVmNZTYz73ousoQFQqL5ml9O6jFJ+c5zoKQmDjR39U2Zp/6+w7SlmyC0BECElBsZ+1drOkJ1q+AABRrKHBr3//u0QVFfUUFAAAIGwGTPqBfAnprmMAUauw+GsyHq/rGAiRfuPvUv+zvk85ASBiGGvtiUehSykuLrYLFixwHQMAtHdvg9LS4mUMG7ABAAAAAADg6IwxC621xYcfp04FAJy09PQEGWO0bds+3XnnbDU1BVxHAgAAUahy07vavfk91zGAqFNe8pb2Vax2HQMhVrb6n1r37v+6jgEAbUJBAQDosDlzNuvhhz/VsmXlrqMAAIAotPLNb2v9u/e6jgFEFWutlr92q9bN+7nrKAix3Zs/0Ib371egqc51FAA4IQoKAECHXXvtYK1bd7NGjcp3HQUAAESh7MJJ2r3lQwX9Da6jAFHDGKOxX5ilAZN+6DoKQiynaKqCgQbt2fqh6ygAcEIUFACAkOjePUWS9OKLa/TOO1scpwEAANEku3CSgv46VW1nLz4glJIyeis1Z6DrGAixrD4TZDw+VWyc4zoKAJwQBQUAIGQaGwO65553dd99811HAQAAUSS791mSjCo2znUdBYga6979X5Wt/pfrGAgDX3yqMgpOV8WG2a6jAMAJUVAAAEImPt6rN9+8Ss8++xnXUQAAQBSJS8pSevcRqtw0z3UUICr4G2tU8v592rOFJYCiVU7RVO3d8Yka6ypdRwGA46KgAACEVO/e6UpM9Km+3q8//WmRgkHrOhIAAIgC2X0mac/Wj9n0FQiB3ZvekQ00Krffua6jIExy+k6VZLV70zuuowDAcVFQAADC4vnn1+gb33ib/SgAAEBIZBdOkg00sukrEALlJTPljUtWZu8zXUdBmGT0KJY3PpVlngB0eT7XAQAA0em66wZr8OBsjRnT3XUUAAAQBbJ6j5cxXlWWzlNO0VTXcYCIVl4yS9l9JsnrS3QdBWHi8cYpu89ENsoG0OUxgwIAEBbGmAPlxJIlO7V48U7HiQAAQCTzJaQrvcdoVe9c5joKENFqKterdvd65fSb5joKwiyv//lKzOilQFOt6ygAcEzHnUFhjLm95eFGa+2/OiEPACDKBAJBXXPNK8rKStR7731exhjXkQAAQIQaM/1F+RIzXccAIlrFhlmSxP4TMaD36JvVe/TNrmMAwHGdaImn30uykt6QdEhBYYz5UcvDddbaJ0MfDQAQDbxej5577jPKyUminAAAAB0Sl5TlOgIQ8cpLZikps59Ssvu7joJOEvQ3yONLcB0DAI6qI0s8/UTSjyVdH5ooAIBoNXRonnr0SJW1Vv/5T4msta4jAQCACGSt1bLXbtOGD3/vOgoQkYL+BlWWzlUuyzvFjLXzfq55Dw6RtUHXUQDgqE5UUPAOEgAgZF54YY0uuuhFvfpqiesoAAAgAhlj1FRXIX/DHtdRgIjUULtLGQWnK2/ABa6joJNk9hynXiNuVNDf4DoKAByVOd6nWI0xeyWlSPrQWjvhsO8F1bL8k7X2orCmxCGKi4vtggULXMcAgHYLBq2ee261pk8fyHJPAAAAAAAAMcIYs9BaW3z48RPNoNgqyUgaboxJDUsyAEDM8HiMPve5QTLGaNeuWpWU7HEdCQAARKhg0O86AhBx/A17XUeAA/7GGlVtX+Q6BgAc1Yk2yf5Q0kBJyZLmGmP+KGmzpNa/CWYbYyZ1NIi1dl5HrwEAiAzWWl100QsKBKwWLPiCPB5mUwAAgLaxNqj3/3aGcorO1qBp97qOA0SM+r1bNe/Pp2nIxQ+q57BrXcdBJ1oz+4fatnSGzr5zszzeeNdxAOAQJyoo/ibphpbHIyU9etj3jaTTJc3uYA7bhiwAgChhjNFvfztViYleygkAANAuxngUn5yryk18xg1oD+Pxqe/47yiz4IjVNRDlcoqmaPOih1S1bb6yek848QkA0ImOu8STtfZdSb9WcxHR+iuUwnFNAEAXN3FiL51+eg9J0qpVFY7TAACASJJdOFnVZZ+qsZbfIYC2SkjN1ymTfqiUnFNdR0Eny+4zUTIeVWyc4zoKABzhRHtQyFp7t6RLJf1bUpmal3cyap71IB1ZXrT3CwAQw956q1RDhjyml15a6zoKAACIENmFzasM7978ruMkQGQIBv3atX6mAk21rqPAgbikLGV0H01BAaBLOmFBIUnW2lestZdZa3tYa+OttR4dLClet9Z6OvjlDevfEgDQZU2a1Es/+9kEnXdeoesoAAAgQmT0GCNvXLIqS1nmCWiLqm0LtOjZz2rXutddR4EjOX2nqGrrx2yUDqDLaVNBAQBAuMTFeXXPPWcoJSVejY0B7drFp7oAAMDxebzxyux1JgUF0EblJTMl41FO0VTXUeBIduEUWRtQ5ab3XEcBgEN0tKBgiSYAQMhceeU/ddFFL8jvD7qOAgAAurjswsnaV75SDTVlrqMAXV55ySxlFoxVXFKW6yhwJLPXGfL4ElWxcbbrKABwCF8Hzu3b8mddKIIAAPDVr47Q3r2N8vmY4AcAAI5v/z4UlaXvqMdpVzlOA3RdjbXl2rt9kQZMvMd1FDjk9SUqq9d4VZbOcR0FAA5x0u8AWWtLW752hjIQACB2XXJJf1177WBJ0p499Y7TAACAriy9+0h549NUuYllnoDjqdjwtiSr3H7nuo4Cx3L6TtW+XSvUUMNbeQC6jrB/RNUYwzJQAIB2WbSoTP36PaJXXlnvOgoAAOiiPB6fBk37lQqGfM51FKBLKy+ZpbikHKX3GOU6ChwrGHadJn1tuRJSurmOAgAHdGSJpyMYY86U9FlJZ0oaIClLUpwxplrSTkkLJM2V9KS1tjqU9wYARI/Bg7N1+eUDNHx4nusoAACgC+s14ouuIwBdmrVBlW+Ypdy+58gYllGNdRQTALqikPzrZIwZYYz5SNK7kr4tabykfEnxat5IO13NhcXnJP1Z0hZjzE+MMd5Q3B8AEF2SkuL06KMXqE+fdElSfb3fcSIAANAVWRvU7s3vqXrnMtdRgC6pumypGmt2srwTDqjYOFvL//MNWWtdRwEASSEoKIwxN0r6SFKxmssItfrziOEtf6ZJ+qGkd40xGR3NAACIXnfc8bYuvPCF/8/efYdHVW0NHP7tSU8oIQkQQknonVACSAlNQEEsKCIICtjxXhTQz16wV0S9NrCjooIFFSu9S+81tIROKgnpmdnfH5uE0JPMJCdlvc8zT+bMnLISwmTmrLPWIjvbbnUoQgghhCiF1v8wjOg171sdhhClUtLhfwEIbNDH4khEaZGWuI/YPX+RlRZrdShCCAE42eJJKTUQ+BhwA3JTr6nAPGAzEAtkYiooGmIqK8JzNwc6Ab8qpXprrR3OxCKEEKJ8iogIxtvbHZtNRhoJIYQQ4mxK2egw9Cd8AxpaHYoQpVK9DvdSo/EgvPxqWh2KKCVqh4+iTts7kJGxQojSQhW1pEsp5QXsBupikhOngEnAVK112iW2CwcmA7npew38V2v9YZECqYAiIiL02rVrrQ5DCCFKnN3uwM1NeucKIYQQQgghhBBClCVKqXVa64hzH3fmLM9IziQn4oFIrfWUSyUnALTWm7TWfYGPcmMDHnMiDiGEEBXAvn1JhId/yaJFMVaHIoQQQohSxJGTyb6Vk4nbN9/qUIQoVWL3/sOm2beTmXrC6lBEKXN481cs/7gj2iFtdIUQ1nMmQXFNvvsPaK03F3L7cUDuJLM6Sqk2TsQihBCinAsM9CEoyAd3d6mgEEIIIcQZys2T6NXvcWTrt1aHIkSpkpUaS8rxLXh4V7M6FFHK2Ny9ORW3g5PHNlgdihBCOJWgaHv6azwws7Aba63twCcX2J8QQghxnqpVvVi48Ba6d68DQFFbFAohhBCifFFKERAaSULMEnl/IEQ+tduMoPu9G7C5eVgdiihlAkJ7ApBwYKHFkQghhHMJihqY9k67nBhwvS3f/epOxCKEEKICyB3k9tFHGxk69DccDjkJIYQQQggIqNeDzJQjpCXutToUIUoFhz1LEnbiorz8alC5RmviJUEhhCgFnElQ5P6lU07sw5ltRTkXHAxKOX8LDrb6OxFCuFpWloP09BwyMnKsDkUIIYQQpUDe1cDRiy2ORIjSIWbdNBa/35TsjJNWhyJKqcCw3iQe+hd79iVHyQohRLFzJkFxApNgaK6UciviPlqfsz8h8hw/Xrr2I4QoPcaNa8evvw7G11fK1YUQQggBvgGN8KpUi4ToJVaHIkSpELdvLu6elfHwrmp1KKKUCgjrjbZnkXjoX6tDEUJUcM4kKHIn6fgDwwu7sVLKHbgr30MbnYhFCCFEBaKUwmZTJCVlMHjwbNatO2Z1SEIIIYSwkJlD0YOEmKXS1kZUePbsNBJjlhHUoK/VoYhSrFrdriibh7R5EkJYzpkExe+nvyrgbaVUeCG3fx9ojmkVFaO13uJELEIIISqgrCw7W7fGsWtXotWhCCGEEMJiAaE9yEo9QWr8TqtDEcJSCTHLcNgzCWrQz+pQRCnm7umHf53OMihbCGE5ZxIU3wDRmARDALBUKfWQUsrvUhsppdoppeZzdvXEq07EIYQQooKqUcOPrVtHc+utza0ORRSRzBsSQgjhKmfmUEibJ1Gxxe2bh83dm2p1u1kdiijlAsN6k3xsE1lpcVaHIoSowNyLuqHWOkspNRb4FXADKgGvA5OUUouBTUAskAVUBhoCXTFVE3BmQPYS4OOixiGEEKJi8/Iyf8oWLozh11/38tZbvVBKXWYrUVrIvCEhhBCu4usfRpXgdtiz060ORQhLxe+bS0C9SNw8fKwORZRyNRpfgyMnE60dVocihKjAipygANBa/6WUuhOTYMidVOoHDDh9uxCFqboA+Be4TssroRBCCCctWXKIv//eT2LiFQQEyIcxIYQQoiLqMmap1SEIYam0pAOkJkRRt/1dl19ZVHiVa7Sico1WVochhKjgnGnxBIDW+iugM7CKM1URKt/9/Mu5j6UAzwKRWusUZ2MQQgghnn66C6tXj5TkhBBCCCHkamBRYcXvmwcg8ydEgdmz06Q1nhDCUk4nKAC01pu01l2BjsArwCLgEJAG2IE4YDvwFWb2RG2t9Qtaa7srji+EEELYbIpKlTzJyXHw5JNL2bUrweqQhBBCCFHCcjKTWfpRW6LXfGh1KEJYIm7fPLyr1sM3oLHVoYgy4tDGL1gzYyBpSQesDkUIUUE51eLpXFrrdcA6V+5TCCGEKIwTJ9L4+OPNVK7syWOPdbY6HCGEEEKUIHevKlSr1x3famFWhyKEJepfMYHMtBMyk00UWM1mN+Ab0BivSsFWhyKEqKCU1vrya4lSJSIiQq9du9bqMIqdK99PaQ27d8M//8Dw4RAY6Lp9CyFKn+PHU6lZ08/qMEQBuPq1XgghhBBCCCGEEKWPUmqd1jri3Mdd0uJJiLJg0SIYNw7S0szytGnQvz9MnAiffw5r1px5TghRtuUmJ/bvT2LKlPKf0BVCCCHE2TJTT5CVFm91GEKUqNg9fxN/YJHVYYgy6FTcTvYuf13m9wghLCEJClFh3H03HD4MdeqYZZsNEhLgww/hjjugUyeoVAkaNYLBg+Hpp2HmTMjJsTZuIUTRffzxFl544V+OHUu1OhQhhBBClJCstDgWvduAw5u/sjoUIUrUnmUvs3f5q1aHIcqgk0fXs2fJ86Sc2Gp1KEKICkhaPJVB0uKp8C71a263w759sGULbN1qvm7ZAlFRJmGRlGRiee45SEyEt9822yUkQLVqro1TCOFaOTkODh9OITS0qtWhiIuQFk9CCCGKw7JpHfCpWo8Ot/xsdShClJicrFSyUk/gW62+1aGIMiYj5QiL32tC0z4vE9b5AavDEUKUUxdr8eTSIdlClEVubtC4sbndeOOZxzMyICbmzMmzxESTlMjVvbupyGjVClq3PvurzLgQonRwd7flJSe++WY7kZF1qFevisVRCSGEEKK4BYT24MiWGTjs2djcPKwOR4gS4e7ph7unJCdE4XlXDsEvsCnxBxZKgkIIUeKkxZMQF+HtDU2anFl++22YPv3M8sSJMHIkuLubVlDjxkGvXhAUBCEhZr7F1Kln1s/IKKnIhRDnio1N4/775/Hmm2usDkUIIYQQJSAgtAf27FSSj663OhQhSsSepS8RveYDq8MQZVhgWG8SDy7HkZNpdShCiApGKiiEKKK77jpzX2s4evTsNlFbt8KBA+b5zEyoWhVeegkefhhSU+H33021RZMmJskhhCg+1av7smzZcJo1C7A6FCGEEEKUgIB6kQAkxCzBv05ni6MRonhph52YtVOp3niA1aGIMiwwrBcx6z4i6cjqvNdQIYQoCXJaVAgXUMpUTYSEwFVXnf98VhY8+SR06WKWt2yBW24x9z09oXnz81tF1a1r3XyL4GA4ftz5/dSsCceOOb8fIVyhdevqAJw6lcVff+1nyJCmFkckhBBCiOLi6RtEpRqtiI9eTIOu/2d1OEIUq5NH15OdkUBQg75WhyLKsGr1IkHZiD+wSBIUQogSJS2eRKlVs2bp2o8zKleGZ56Bbt3Mcvv2sGEDfPUVjB9vEhtLlsBjj8GgQRAaCv7+8PffZv2jR2HRIkhPL5l4XZGccOV+hHCl115bzfDhv7N/f5LVoVR4DofVEQghhCjPAur1IOnQv9KuRJR7cfvmAorAsD5WhyLKMA/vqlStFUHCgUVWhyKEqGCkgkKUWuX5yntPT2jb1tzyS0qCbdvOtIhq2NA8/vvvcPfdsHcvNGgAv/wCixefqbZo0QL8/Er4mxCijHryySu46qow6tf3tzqUCm3DBhg92uoohBBClGeBoT2IWfsBSUfWEFCvu9XhCFFs4vbNo2pIBJ6+gVaHIsq4wLBe7F85meyMk3h4V7U6HCFEBSEJCiFKEX9/U2WRW2mRa/Bg0/IpLMwsb94MH354ZvC2UiZxkb9FVKtW0KwZ2KROSoizeHu70717HQBWrz5K/fpVqV7d1+KoKpbPP4exY6F6ddfuNynJvI4KIYQQANXqdgMUCdFLJEEhyq2stHhOHl1Lw26PWh2KKAcC6/fm8ObppCftxyO4rdXhCCEqCKW1tjoGUUgRERF67dq1VochLGa3m4qK/EO5t2yBqCjTNsXHB1JSwM3NnAxMS4P//Kdg+3bl7At5iRGl1cmTmYSGTuO66xoyffpAq8OpEDIy4IEH4OOP4cor4dtvoUYN1+0/PBz++svM0RFCCCEA4vcvpHJwOJ4+AVaHIkSxOLr9Bzb/MprOty/Av3Ynq8MRZZzWDkChrBqIKYQo15RS67TWEec+LhUUQpRRbm7QpIm53XjjmcczMmDHDjh82KwD8OuvkJh4JkHRsyfk5Jw/mDtQKoJFBVK1qhezZl1L+/alYFBNBRAdDTfdBOvWweOPwwsvmNeomjVdM6+mWjXYswe6d4d//jFVZUIIIURg/d5WhyBEsYrbNw9372pUrdXB6lBEOaCUacGgtZYkhRCixEjzFyHKGW9vaNfODNvO9fPP5oRdrogIcHeHWbNg3Djo3RuCgqBWLejfv+RjFsIq/fqFERjog8OhWbXqqNXhlFt//w3t25sKr9mz4eWXzyRQjx0zlVbO3hISYP58k4zt1s20whNCCCGyM5LY/+8Uko9ttDoUIVxOa038/nkE1e+NsrlZHY4oJ2L3zmXJB83JSDlidShCiAqiRBIUyhimlPpcKfWnUuobpdRdSinvkji+EMIM5s41ebIZsh0fbyot/voL3nwTrrrKnOQToqJ58801dOs2g23b4qwOpdx57TUYMABq14a1a+H664vvWJ07w9KlJgHbowcsW1Z8xxJCCFE2KOVG1OLnSIhZanUoQricPesUgWF9qNn0BqtDEeWId5UQqoZEYM9KtToUIUQFUeQZFEopH+BzTJIjFbhDX2BnSqmqwO9AlwvsZg8wSGsdVaQgKiiZQSGKm8ygEBVNcnImP/64m9GjW51Xymy3O5gyZR2vvrqKxx/vzPjxHXBzkwLEgvrkE5MQ/egj8PMrmWPGxJhqsOho+OUXqQwTQoiKLis9QWZQCCGEEEJY7GIzKJxJUNwEzAI0MFVrff9F1vseuPn0ogbOPfV5AGijtT5VpEAqIElQiOImCQpRkR0+nELlyp5UqeJFVFQiQ4f+SlRUIqmpOfj5udOkSQDff38tjRtXszrUUmvjRpMcyK2W0Nq1rysFERsLo0fD229D48Yle2whhBBCiJKQnnwI78q1ZVaAKBby+yWEcLWLJSicuQS0Z777sy5y0A6Y5ETuKcoE4FdgAeA4/Vgo8IQTcQghhBAukZaWTZcuM7j77n948801hId/yebNcaSm5gCQmprDpk2xhId/yeTJa3A4JAN3IY8+Co88Ajnmx1biyQmA6tXh999NckJrmDu35GMQQghROmQkH2b9D7cQf2CR1aEI4TI5mSks/bA1+5a/ZnUoohw6sm0mS95vRmrCbqtDEUJUAM4kKNqe/poNXKyh55357m8HWmqtb9Ba9wUGY5IUCrhDKSX9MoQQQljK19eDBx5oz+bNJ5g0aQXp6TnnJSEcDk16eg7PPruciIiviIpKtCja0iUz0wyoBvjyyzOzIEqDb74xbZ7mzbM6EiGEEFbw8KlG3N5/iNsnfwhEOaIUzftPpnrja6yORJRD/rU7ARC/f6HFkQghKgJnkgKhmMqIPVrrnIusc12++09prU/kLmitfwN+Ob1YnTMJDyGEEKLE2e0O3nxzDc88s5zdu5NITc2+5PpSTXFGTAxERsLQoaZaITgYatSwOqozhg83SYorr7Q6EiGEEFZw8/DFv3ZHEqKXWB2KEC7j7lmJuu3uoErN1laHIsohX/8wfPzrkyCVZ0KIEuBMgiLw9Ne4Cz2plGoEhJxeTAHmXGC13/Pdb+VELEKIUmrwYIiPtzoKIS4tKiqRiIivLlo1cTFSTWFaJ7VvD7t2wX/+Y007p8txc4NbbzWx7doF99xjKj6EEEJUHAGhPUk+vpHsjCSrQxHCaVprjmz9nsxTx6wORZRjgWG9SIhZgsNxsWuShRDCNZxJUHif/nqxj/hXnP6qgUUXqbLYl+9+kBOxCCFKqaNHwd/f3E9OtjQUIS4qMvLb07MmLl01cTG51RSRkd+6OLLSy+GAl16Cq66CWrVgzRq44Qaro7q8JUvg44/hmmsgJcXqaIQQQpSUgNAeoB0kHlxudShCOC0tYQ9bfruT47t+tToUUY4FhvUmJzOZ5KPrrQ5FCFHOOZOgSDv91f8iz0fmu3+xGRX5zwR5X2QdIUQZtnKluXo5JcUMqx05EnbLnC1RynTtGoLWzrVo0lrTtWvI5VcsB5KSTDLiqadM+6R//4UmTayOqmDuvtvMyFi0yLR8irtgHagQQojyxj+kEzZ3b2nzJMqFuH1zAQhq0M/iSER5FhDaA1DEH5A5FEKI4uVMguI4ZsB104sMuL463/2LXaZSNd/9tIusI4Qow3LbvWgNY8bAzz9D8+bm/r59l95WiJIyfnwH/Pw8nNqHn58HEyZEuCii0mvTJujQAf78E959F77+Gvz8rI6qcG6/3bwWbdkC3bubGRribLkzWYKC3mPy5DXY7Q6rQxJCCKfY3L3wr91ZEhSiXIjbNw/fao3wrVbf6lBEOebpG0SV4HBJUAghip0zCYqNp79WBq7N/4RS6kqg7unFU8Cai+wj/1/TExdZRwhRDlSpAq++apIS48fDd99B06amF3x0tNXRiYouMrIO/v5eTu2jWjVvunev7aKISqe1a+GKKyAjAxYvhnHjSufMiYK49lr4+2/Thq5bN9ixw+qISo8zM1mWEx+fwbPPLqdjx68r5IwVIUT5EhDak5QTW8hKkwFpouyy52SQELOUoAZ9rQ5FVAABob1IOrSKnKxUq0MRQpRjziQofsl3/0OlVB+llKdSqjPw8enHNfCL1tp+kX3kv9R0jxOxCCFcqGbN4ttPzZoweTLs3Qtjx5pWK40bw/33w+HDrjmuEIWllGL8+A74+roXaXtfX3fGj++AKqtn6wuobVt44AFYvx66drU6Guf16GESLdnZEBkJq1dbHZG1cqsmwsO/PD2TxYwPy52xEh7+JZMnrynwEHkhhChtTLsSSIi5WAdiIUq/xIMrcOSkS3snUSICw3qjHdkkHlxhdShCiHJMFbXntlLKC9gBhF5sFcABRGitN15gextwGKgJZAFVtdYXG7gt8omIiNBr1661OgwhXOLgQXj5Zfj0U3MV80KpHhUWiY9Pp3btj8jMvFhO/dIOHLib0NCql1+xjDl40CQlPvwQgoOtjqZ47N0L/frBiROwbRuEXuydTTkWFZXI0KG/EhWVdMlh8X5+7jRpEsD3319L48bVSjBCIYRwnsOezabZt1Gvw70EhvW2OhwhimTn/Mc5uG4avcfH4O5ZxvpsijLHnp1OQvQSqtXrLr9vQginKaXWaa3P641d5AqK08mEEUAqJhmReyPf11cvlJw47UpMckID6yQ5IUTFVLeuOfG5e7fpZw9w/Dg88ogMrxUlKzDQh4ED6xe6ZZFS0KRJtbzkxCuvrOLTT7cUQ4TWSEw0w+7Lcwukhg1h+XJ46aWKl5w4v2ri4skJkGoKIUTZZnPzoN1N30lyQpRpcfvmUq1uNzlZLEqEm4cP1RtdJb9vQohi5UyLJ7TWK4GOmHZPuQkGhWnXdI/W+qlLbD4h3/p/OBOHEKLsCwuD1q3N/fnz4Z13ICHB0pBEBVSUYdm+vh588slVAGit+euv/SxffqZf2aJFMWRlFa0qwyoOhxmCDdCmDezfD73L+bmcWrXgwQfN/Y0b4ZNPLA2nRJyZNbGC9PScAicbHA5NenoOzz67nIiIr2Q2hRCizMlKi5N+6qJMSj95kNS4nQQ1lPZOouSkn4xhz9KXyUqTKwiFEMXDqQQFgNZ6l9Z6MFAJCAGqaa2baK0v99H+VaD36duHzsYhhCg/br3VtJVp0sQs3347PPMMJCVZGpaoAIoyLDsg4MxwbKUUixcP48MPzdDCAwdO0rv3TN56y7Tl01pT1NaKJSUpCQYPhoEDYcEC85iPj6Uhlbh33oHnn4eUFKsjKV6Rkd8WqGriYnKrKSIjv3VxZEIIUXxSTmxl4TthxO6Ra+RE2ZNyYjPK5iEDskWJyjx1nL3LXuHk0Q1WhyKEKKecTlDk0lrbtdbHtNYnC7j+Eq314tM3uU5aCHGWGjXM1+xsyMiAF14wVRYvvADJyZaGJsqxwg7LvthwbC8vs32dOpX5448buf32lgAsXnyQFi0+Z8eOeNcG7iKbN0PHjvDHH+YkfXmvmriYadNg6VKoXNlUkzgcVkdUPLp2DXE6Yaa1pmvXEBdFJIQQxc8vqBmNez1HleB2VociRKHVaHwNfcbH4BfYzOpQRAVStVZ7eo+PprpU7gghionLEhRCCFEcPDxg5kzTcqVXL1NJUb8+vPoqnDpldXSiPBo9uhXu7gX78+jubmPUqJaXfH7AgAaEhFQCTAKkXr0qhIZWAeDvv/fz1VfbsNutPwP+9ddwxRWQmgqLFpnB2IWdx1FeeHiYWRRaw7hxMGqUSZaWN0VpaXYuPz8PJkw4b8aZEEKUWjabOw26PIRfQCOrQxGiSNy9Kp93cYwQxUnZ3PD0CbA6DCHKnNx5f0FB7zF58ppS8bm/tFKlvdWEOF9ERIReu3at1WEIYYl160yS4o8/ICgIHn0U7r8ffH2tjkyIwhs+fA7r1x9n5847UEoRHX2SunWrYLOV3IfOrCyYMAE++AB69oTvvoPg4BI7fKmmNbz8Mjz1FFxzjUmWlqfXGq019epN5dChomd769atTHT0PXKiRAhRpuRkpZIQvRj/2p3w9A2yOhwhCiTx0Ep2L3yGVgPfxy+widXhiAom+dgmdi98mhZXv4NvtfpWhyNEqRcVlcjQob8SFZVIamoOfn7uNGkSwPffX0vjxtWsDs8ySql1WuvzrnBzaQWFUqqXUupFpdRfSql1Sqk9Sqm9F1m3rlKqnlKqnitjEEKUbx06wO+/w8qV0L49/N//meHa5fHqZlH+zZhxDQsX3oJSCodD06PHd4we/WeJHf/QIejRwyQnHn4Y5s2T5ER+SsGTT8KHH5qk6FVXlY9ZOJmZOcyeHcWOHfGMH98Bb2+3Iu3nYi3OhBCitEtL2MOGH4YSt2+e1aEIUWCO7Ay0PRtPv5pWhyIqIDcPH+IPLCD+wEKrQxGiVMutmggP//L0vL8c4Mz8vvDwL5k8eQ0OhxQM5OeSBIVSqq9SajMwH3gc6Ae0BRoAYRfZ7AtgP7BPKdXFFXEIISqOK66Av/+GJUvg8cdNSxatYdYsyMy0OjohCkYpldf+yeHQvPZaT+64oxUAJ09m0qvXdyxZcrDYjn/rrbBtm/l/88Yb4F6w0RsVzn33mcqSVatMlcnRo1ZHVDhaa3btSmDz5lgAsrMdDB36G9Onb2f06FYUtZjWbteXbHEmhBClVeWarXH3rkZC9GKrQxGiwALr9+aK0Yvw8K5qdSiiAvINaIx35dqSoBDiEqKiEomI+IpJk1aQnp5zXhLC4dCkp+fw7LPLiYj4iqioRIsiLX2cTlAopR4H/gJaAuqc26VMybfeCGfjEEJUTJGRcNdd5v7KlTB0KHzzjbUxCVEU7u42hg1rRq9eprAwJiaZ+PgMPD3N1e0HDpzk11/3kJ1td+o4Wpu2TgBTp8KaNTBkiFO7rBCGDjXVW3v3Qvfu5mtplp6eza5dCXnLAwb8yNNPLwOgUiVPVq8eyQsvdCMw0IeBA+sXet6IUjBwYH0CA31cGbYQQpQIpWwE1OtOQvQSq0MRokDsORnYs9OtDkNUYEopAsJ6kXBgMVpLH30h8ju/auLSLT6kmuJ8TiUolFJjgJc4k2hIBD4DxgNbL7P5n6fXBxjgTBxCCAHQpYtpUTNypFn+/nv44gvIybE0LCGKpHXr6mzePIrOnWsBMH36NgYP/oW4OPPhNC0tm8LOkXI4TDLi3ntNoqJ5c2jWzOWhl1v9+sGCBabNU7dusGmT1RGdLTY2Le/+0KG/cf31swHzgfLLLwfwzjt98p5v27YGHh4m+VWUYdm+vh48+GAHDh9OcT5wIYSwQEBoD9JPRpOeFG11KEJc1vGds1nwdl1SE/ZYHYqowALDepOdkUDK8c1WhyJEqXG5qomLkWqKsxU5QaGUqgZMzvfQdCBUa32X1vpd4PClttda2zGVFwoIU0rVLWosQggB5oreK68ET0+zPGMGjBkDLVqYqgq7cxeeC1HilFJ5/f0ff7wzy5YNp1Yt0xLqrrv+plev7wuVpLDZoG1baNOmOKKtGDp1gmXLTFu5u++myO2RXCF/Nc2LL66kXr1ppKebq3Uefrgjb7/dO+/3IzKyDmFhF24JERlZB39/r0IdOyDAm82bT9C8+efs3p1w+Q2EEKKUCQjtAUBCjFRRiNIvbt9c3D0r4VutgdWhiAosMKwXAPEHFlkahxClSWTktwWqmriY3GqKyMhvXRxZ2eJMBcWdgD+ggV+01qO11qcKuY91+e63cCIWIYQ4z+zZ8PPP4ONjqipat4aZM81V5EKUNR4ebnTpEpK33K9fKIMHN85LYEyYsJC//tp/wW1nzIBFi8z9p5+GCRModEsfcUbz5rB8uXk9sern+Oef+wgMfD/vSpurrgrjlVciyckxCYmePety9dX1CzTAWinF+PEd8PUt2BCS3OHY113XiPHj29O4cTWAQlf0CCGElSoFtcDTN4j4AzKHQpRuWjuI2zePwPp9UcolY0SFKBKvSsFUCmoucyiEyKdr1xCnPwdprenaNeTyK5Zjzvx1uzrf/YeKuI/89YlhRQ9FCCHOpxTccANs2GCGACsFt9xiriD/+Wdrr3wWwlljxrRm/PgOACQlZfDTT7vZti0OgJwcB8uWHSIzUzNuHIwYAe+/b2W05U+9ehAWZhKed9wB06cX7/H270+iffvpzJljhl+0aBHI8OHN8hIkHTvWYvz4DlSu7Fmk/Y8e3Qp394K9LXR3tzFqVEtCQ6vy/PPdUUoRG5vGFVd8w8qVR4p0fCGEKGlKKQJCe5AQs0QSrKJUSz62kez0eIIa9LM6FCEICOtN4sEV2HMyrA5FiFKhKO1yz+Xn58GECREuiqhsKtilchfWDFM9sUtrfeFLNi8vKd/9C/cdEEIIJ9lspu/+4MFmLsVzz8GNN0K7dvDHHxAcbHWEQjjH39+b/fvvyWv5M29eNAMG/EjTpoPZtashEyZoXntNSiaKQ2YmxMSYmytlZdkZM+Yvevaswz33hBMSUonAQB88PEwSITS0KlOn9nfZ8QIDfTh58oEibx8Xl056eo7Tb86FEKIkBdTrwbEdP5GWuBe/gEZWhyPEBcXtmwsoghr0uey6QhS3wLBexKz9gKTDqwgM7Wl1OEJYLjKyDlWrenHqVNFaPAFUq+ZN9+61XRhV2eNMBUXQ6a/OXCqX//hy5kQIUazc3ODWW2HbNvjySwgNhRo1zHMHDkhFhSjbbDaFl5e57sDhqE2VKgM5eDCU77+HRo020rv3t5w8mWlxlOWPjw/8+Sc8+aRZjo4u+mvJ22+v48031wDg6enGsWOpJCWZfzMvL3fmzr2Zq66q74qwXa5580A2bhxFmzbVAfjoo43s2BFvcVRCCHFpuXMoTh5db3EkQlxc3L55VAluh6dvdatDEYKAet2p2fR63Nx9rQ5FCMs4HJq4uDTAfPZLTs7C3b1op7Vz2+cWpDVveeZMgiL19FcfJ/ZRM999+RQrhCgR7u5w++2mzZPNBomJpu3TE09YHZkQztEaXn8drr3Wk5CQFqxd687QoVC1qhe1avlRtaoZhDxjxg7++eeAtcGWIx4epoXcoUPmtWTsWLDbL7sZ8+dH8+qrq/KWV6w4zLJlh/M9P5RHHulUDBEXD5vNvKk+eTKTZ59dwbvvygk/IUTp5hvQmF7j9hDScqjVoQhxQdkZSZw8vJqgBn2tDkUIANy9qtD2xm/wr93R6lCEKDEOh+bIkTNjl3v2/I7hw38HzGegRx/tVOQEg92uGTWqpUviLMucSVCcwFQ9NHViH13z3T/kxH5QSlVRSvVSSj2klPpWKbVbKeVQSunTt0XO7L+AMSzKd7zC3K4o7tiEEBfn5wevvQbDh5vlfftgyRJrYxKisJKTTSuzRx81LcxWrzbDnAFGjGjBrFnXAWYA1yuvrOLDDzfmbRsTk2xBxOVP7dpw//0wdSoMG2baP+V34MBJ3nlnHQ6HKbGYPz+Gt95aS2ZmDgDffjuI2bNvKOGoXa9qVS82brydN94wZf8HDyZz4kTqZbYSQoiSp5TCq5L0+hSlV/yBRWhtJ6ihzJ8QpUtG8mHs2WlWhyFEsdBac+DAybzlO+/8iyuu+CZvZtXYsW25++7Wec8/+eQVDBrUgMLmKJSCgQPrExjozLX/5YMzCYrVp79WU0r1KuzGSikf4PTpQOzA8qIGopTahZlnsRB4ExgGNEbaRgkhCsDTE+69F9q0Mctvvgk9e0K/frBypbWxCVFQ69bBnDkweTLMnAmVK194PaUU69bdxgcfmCvx4uPTadToE157bdWFNxAFphS89BK89Rb88ANcfXU2M2fuJSnJDBFcsuQQ48cvZPt2M8z88cc7c+TI2LzWXG5uzrwtK11q1apEpUpmYPeYMX8RGfkdOTkOi6MSQojznYrbwYYfh3EqfpfVoQhxnrh983D3qkrVELlaXZQeSYfXsPj9psTtn291KEK4hNaaqKjEvAvJXnrpXxo1+oSUlCwARo5swfPPd8t7/tZbmzN0aLOz9lGUYdm+vjIcO5czQ7J/B247ff8VpVR3rXUBGhrkeQ0zx0IDS7XWKU7E0sSJbYvLXmBPAddNKsY4hBCFNHkyNGoEr74KXbvCgAFmsHZH+VwgSqEdO0ylRO/esHcv1Klz+W08Pd2oVatS3v0pU3rTp089ADZvjuXxx5fw9tt9aNy4WnGGXi7t3ZvEsGHuBAZWYsyYWBYt+plp0wZx993NGDy4MX361KNOHZM9qlzZ0+JoS8aUKb2Jjk7G3d0kYLKz7Xh4uFkclRBCGDZ3H1JObCUz5QiVAp1pDiCE6zXo+jA1m16PzebMqRshXKtKcDhNr3yVKjXDrQ5FiCLJrZAICvKlcmVPZs7cxbBhc9i48XbCw2tw/fWNCA72y6uIuPLK0MvuMzKyDv7+hRuWHRAgw7FzOXOp3o9A1On7nYCZSim/y22klPJQSr0F/Cffw685EUd+KcASYAowEtjgov0Wxdda66sLeNtpYZxCiHP4+MDEibB/v0lSrFoFnTrB9dfDxo1WRyfEGV9/Da1awYoVZrkgyYlzVa7syX/+047mzQMBOHQohR07EqhWzcyr2LDhOCtWHM4rZxVny8zM4fhx074oISGdxo0/Ydq0zdx+O/z4YzAeHjfz5puNiIkxP+vc5ERF0rp1dQYNagjAjz/upm3b6Rw65Mx1KUII4Tq+/mH0GLuVwLDeVocixHl8/cOoLu2dRCljc/MkrNN/8alaz+pQhCiwmJhkjh0zn9vWrz9Ogwaf8Mcf+wDo1asuH33Uj5AQcxFf69bVueuuNnkV4QWhlGL8+A74+hYsoSzDsc9W5ATF6WqJB4Dcev0bgN1KqWeUUt3INzz79HyICKXUE5ikxoOY9ksa+FFr/U9R4zhtBNAMqKq17qm1nqi1/gaQptpCiCLz8zP9/PfvhxdfNHMp2rUzff7377c6OiFg8GDTUsiV1T0DBzZg7967CAryBeDVV1dz442/YLebBEV6esGvCCmvcn8GWmtatvyCCRMWAhAQ4MOMGYPyhpzdcION+fNDOX7cnW7dTLVLRefv70XjxtWoWdPX6lCEEOIsWmtJxotSJXbPXxze/LX8XopSKSczmWM7fiQz9YTVoQhxQUeOnGLv3iQAkpIyCAubxrRpmwAID6/Be+9dSdeuIQDUrOnHvfeGU726c59RRo9ulVc1fjnu7jYZjp2PcvaPnVJqLPBe7iIm6UC+ZS7wmD79dR3QU2tdLJN1Tg/G7nl6cbHWuldxHOcix3tOaz2pOI4TERGh165dWxy7FkJcQlISvP02vP8+rFkDYWGQkwPuUnEtStDixSZhNnu2SaIVt5SULHbujKdjx1oAtG8/nS5dQnj//b7Ff/BSQmudd2XLmDF/smVLHGvXmi6X06dvIySkEn37Xrzsd9MmuOoqmDQJ7ruvJCIuG9LSspkwYSHPPNOF2rUrXmWJEKL0SIhZxqafb6PDsF+oUrON1eEIAcDGn28jNW4n3e5eY3UoQpwn5cRWVnx6Ba2umUrtNiOsDkcIjh9P5dixVMLDa6C1JiTkI/r0qcc331wDwNdfb6dz51rSxthiSql1WuvzBm84PY1Ra/0hMAiIyz0WZycm9DmP5a7zHdCjuJITQgjhav7+5gTjoUMmOQGm7dP48dbFJCoOrc0A9yuvhIMH4UQJXaxUubJnXnIiJ8fBjTc2JjLS9JLKyrJz991/s3Fj+b1yaurUTTRs+EnegOf+/cMYNqxZ3tWMt9/e8pLJCYDwcFM9kZucSJb6TgA2bDjBjBk72L493upQhBAVnK9/fbLSYkmIXmJ1KELkCb9hOhHD51gdhhAXVKl6Czx9qxN/YKHVoYgKKi4ujRUrDuctDx36G3fe+Tdg2i19/HF/HnusU97zI0e2kOREKeZ0ggJAa/0n0ACYCGzCtH1SnJ+YSAV+AbprrW/VWqe74vhCCFGSvExrfux2aNkSGjY8s3zggGVhiXIsORluvhn+7/9MUmz1aqhfv+TjcHe38dRTXRg2rBkAO3bE88MPuzl69BRgZjBs2xZ3qV0Uit3u4M031xAU9B6TJ6/BbndcfiMnLV9+mE6dvs6bkRAaWoVeveqSnJwJwPDhzXn44Y6F7hVa7fR74Q0bzL/dH3+4NOwyqVu32hw4cA/9+oUBsGBBDCkpWdYGJYQoFsHBoJTzt+Dg4onPu0ptfKs1lASFKFWUUnhVqml1GEJckFI2AkJ7En9gobQhEyUiMTGDv/8+02v7iSeWMWDAj3kXkr38ciQffnimyn/QoIa0bl29xOMUReOSBAWA1jpVa/221rodEAj0AAZjhlUPAiKAAK31YK31ClcdVwghrOLmBq+/DuPGmeVvv4XGjeHeeyEmxtrYRPmxbZsZ0j57NrzxBvzwA1SpYnVURnh4DY4eHUv//mEAfPHFNlq1+oJ9+5IAnPqwEhWVSETEV0yatJz4+AyefXY5HTt+TVRUogsiP+PIkVOMGvVH3tU3/v5euLvbiI01BZ5XX12fzz67moAAn0vtpsDCwuDqq6FtW5fsrswLDDQ/14SEdK677mceemiRpfEIIYrH8eOlaz8XEhDag8SDy3A4corvIEIU0N5lr7Jj7iNWhyHEJQXW701W6nFS42TQmnC9lJQs5szZmzf/7/PPt3L11T9y+LC5kOzBB9vz119DsNnMhWPdutXOq/wXZY/LEhT5aa1Paq2Xaa1/0VrP0Fr/obVer7WuSO/2BiilFiqljimlspRSiUqp3Uqpb5VSdymlZDqkEOVMnz4mOfHFFyZR8d//wuHDl91MiIv6/nvo3BkSE2H+fHj4YXMFaWni7e2Om5t5OzFyZHO+/nogDRr4AzBx4iKGDfutUImK3KqJ8PAv2bw5jtRU89YhNTWHTZtiCQ//ksmT1+BwFC35kZ1t59VXV/Hbb3sB08Jq7txoDhwwfZdatgxixYpbadeueK5YrFYNvvkGQkLMDJuZM037roouIMCHP/+8ieef7wbAqVNZeVdDCSFESQgI7UFOZjIpxzZZHYoQHNn6HemJ+6wOQ4hLCgzrDSBtnoRLpKdn89df+/Oq8xcvPsi11/7MypVHARg6tClLlgyjRg1zOrVlyyC6dAnJS1CIsq1YEhQCgE5AL6Am4AH4A42BYcDHQIxSSkZlClGOhITAe+9BVBSMHg1Tp5r2TxMmwLFjVkcnyhK73fzeDBtm5hds2AA9e1od1eXVqOHHiBEt8paDgnyoUcM3rx3S++9vYMOGi1/+eqZqYgXp6TnnJSEcDk16eg7PPruciIivClxN8fvve5k1axdg2lRNm7aZ+fOjAZOgOHz4Pm69tXmhvldXmD4dbrnF/Fs75Fw8kZF1CA72Q2vNmDF/0b//rCInooQQorAC6vUAICFG2jwJa6Ul7iMtcQ+BDfpefmUhLORTtR6+1RoSf2CR1aGIMignx8G8edFs325aBB8+fIoBA35k9uw9APTqVZcFC4bStWsIAHXqVCYysg4eHm6WxSyKjyQoik8msBVYDCwBdmMGhucKBD5USn2pCtDIWil1j1JqrVJqbWxsbLEELIRwjXr1THJi92649Vb43/+gQQN45BGQ/76iIGw2SEiABx6AhQtN8qssevLJK3j33SsBU6L72GNL+OGH3YBp/5Rbnnt+1UT2Jfd7uWqKvXuT+PnnqLzl//1vA6+/vhow/Zy3bBnF22/3yXu+sPMkXGX0aBg/Ht55B0aNguxLf9sVhlKKwYMbM3hwY7kiSghRYrwq1cQvsKnMoRCWi9s3D4CgBv0sjkSIywsM60VCzDIcdnkjKy5v0aIYli07BJjPgNde+zPTpm0GoGFDf+bPH8rtt5sL3ipV8qR373p4e7tbFq8oOZKgcK1YYDIQCVTSWrfWWvfSWvfUWjcFagCPA6fybXM78OLldqy1nqa1jtBaR1SvLkNehCgL6teHzz6DHTvgppvgzTdhzpyLr1/aB0iK4rdkifl9UQo+/9ycuPb0tDoq16hc2ZNDh+5j4sQIANauPUbdulOZOnXTJasmLiZ/NUX79tOZPn1b3nPvvLOOESN+JyPDtIf67LOrWb781rzn/fxKxw/VZoO33oKXXoKvv4bBgyEtzeqoSodbb23OuHHtATO4fMSI3zl5MtPiqIQQ5V1AaE8SD66QE23CUnH75uHj3wC/gIZWhyLEZQWE9cKelcLJo+usDkVcRO7FYEFB7zF58hrs9pIr3V616ii//ronb/n+++fx8surAPDycmfRolt44YXugLlIqU+feqXms5ooWaqoAyyVUre7MhCt9XRX7g9AKbUIyG2KsVhr3cvVxygKpVQTYBGQO70lB2iptd5dkO0jIiL02rVriyk6IURx2bkTGjUCd3eYNg2OHIGnnzbDtsG1swWkp33Zk5FhWoJFRMAvv1gdTfE7cuQUH3+8mQ8+2EhcXLpTrXyUMr/zu3bdQZMmAURHn0RrCAur6sKIi9fUqTB2LHTtCr/9ZmZVCGPq1E289dZa1qwZSZUqXlaHI4QogrLyHufYztls+nkknW6bR7U6VxTfgYS4CEdOJgverkdI61tpcdUUq8MR4rKy0uJZ9G4DmvV/k3rt77Y6HHGOqKhEhg79laioRFJTc/Dzc6dJkwC+//5aGjd2/QeOzZtjWb/+OKNHtwJg8ODZbNkSx549dwGwdWssdetWoWpVeU9fUSml1mmtI8573IkEhYOzWxY5RWvt8iZipTVBAaCU6oFp/5Trfa31fwuyrSQohCj7xo6FPXtg7lyznJMDHh6u278kKMqOU6fAx8ckqjZuNO3AqlSxOqqSc+ONs5k9e49Tv7NKQZcutZg//5YyXQI8axaMGAHNmsHff0OtWpffpqLIzMzBy8sdu93Bxx9v5o47WuPpKf1nhSgrykqCIistnoMbPiGk1XB8qtYrvgMJcRHxBxax9ttBtBsykxqNB1odjhAFkp1xEg/vsnNhUEVgtzuYMmUdzzyznMxM+1kXg9lsCi8vN154oRsTJkQ41VI1KiqROXP2Mn58B5RSPPbYEqZMWUdS0n/x8fFg374k/P29CAjwccW3JcqBiyUoXNHiSRXidrH1Kxyt9RJgab6HrrYqFiFEyfvwQ/j9d3M/JgZCQ62NR1hjxw7o2BGef94st21bsZITAOPHd8DPz7nsnJ+fB6++2rNMJycAbr4Z/vgD9u2Dbt3MV2F4eZl/23/+OcDYsfOYM2evxREJIcojT99AGnZ7VJITwjJx++ahbB4EhPawOhQhCkySE6VLVFTiJVvo5m+VGxHxFVFRiQXe9+HDKbz11lri49MBWLbsEBMnLmL3brOP8eM7cOjQvfj4mM93DRr4S3JCFIgzCYqY07foAtwOAamcSUbo07dDp5+PcSKOsmxBvvsNlFIuvH5aCFHa5c4WyMyE8HBrYxElb+ZMk5xISIBevayOxjqRkXXw93euxLdaNW+6d6/toois1bcvLFgAdepUvGRVQQwY0ICVK29l8ODGABw4cJKiVgMLIcSF5GSmELvnLxw5MvdGlLy4fXOpVrcr7p6VrA5FiAJLTz7Euu8HE79/odWhVGi5sybCw79k8+Y4UlMvPU8pNTWHTZtiCQ//ksmT11yw5W5iYgbvvLOO7dvjAIiOTuahhxaxatVRAG66qQlHj46ladMAAIKD/ahe3de135ioEIqcoNBah2mt6xfwVk9rXRloAPwfcPz0bnYAbbXW9V3wvZRFR/PdV0CgVYEIIazTuLG5alpUDNnZMHEi3HILtGkD69dD795WR2UdpRTjx3fA17do1Q++vu55JcXlRadOsHgxBAWZBOaGDVZHVLpccUUISiliY9Po2PFrHntsidUhCSHKkYSYJayfNYSkI6utDkVUMFo7qN5oALXb3GZ1KEIUiqdPIJmpx8nJSrE6lArrclUTF3NuNcW2bXG8//4GFi4015Hb7Q7Gj1/IvHlmuVOnWhw6dC8DBzYAoEoVL4KD/YrnmxIVSpFnUDh1UKWCgDlAR2Ad0E1rfenUXtGOs4hSOoMCQCk1AXgr30P+WuuTl9tOZlAIUT6Vlf7MouiOHjWJiaVLYdw4ePPNM5U0FVl8fDq1a39EZqa90Nt6eblx+PB9BAaWz9LhRx6B996D3btNVYU4w+HQ/O9/6+nfP4zmzQPRWperRJUQ5UlZeo+Tk5nCyaPr8K9zBW7u3sV7MCGEEMIFgoM/IDY2vcCJiQux2RTVq/vgcGiGDWvGu+9eCcCRI6cICZGqLuEaxTmDotC01nHAdUAy0AF40Yo4SoFW+e5nFCQ5IYQQomxauhTat4d16+Cbb+DddyU5kSsw0IeBA+sX+gSWUjBwYP1ym5wAk6CYOlWSExdisykefLADzZubAtSHHlrEgw8ukJZPQginuHtVJjCslyQnRIlLObFVWouJMk077DjsWVaHUSF17Rri9HtgrTVdu4awbdsY3nmnT97jkpwQJcGSBAWA1voE8CmmtdG9Sqnye3bhApRSlTFJmlzLrIpFCCFE8frhB9PGqXJlWLUKbr3V6ohKn6IMy/b19WDChPMuvihXgoLgttOdHpYsgbfftjScUktrnXdVtVRRCCGclRq/m92LJmHPTrM6FFFBOBw5rP7marb/85DVoQhRJGmJ+1jwdj2O75xtdSgVUlE+S53Lz898tqpe3VfeT4sSZ1mC4rSlp79WBvpcasVy6A0gKN/yD1YFIoQofyZNgunTzVX7hw9Lyyerde8OY8bAmjXQqtXl16+IijIsOyCg/AzHLogvv4QJE+CJJ+T/9LmUUkyZ0pu33zYDXXbtSmDKlLVOlbkLISqutKQD7F/5JkmHVlkdiqhAWg/6mLrt7rQ6DCGKxKdqKCgb8QcWWR1KhVSUz1LnqlatYn22EqWL1QmK+Hz3Qy2L4iKUUr2UUjrfbdIl1p2hlLpOKXXJKZ9KKV+l1EfAvfke3gN85pqohRACnn8eRo2CHj2gXj0zmBngo4/g0UfPrBcTA8nJ1sRY3u3cCWPHgt0OwcHw8cdQtarVUZVehR2WXR6HY1/OtGlw773wyivmq73wIzvKvdzfhy++2MqLL/5LbKxc/SyElex285pV1lSr0wWl3EiIWWx1KKKCsNncqdF4AFVrtbM6FCGKRNncCAiNJP7AQmm3aYGoqETuuadNgT9LnasifrYSpYvVCYrq+e5XLupOlFJPKaUyzr0BPfKt1uNC6yilPi5y9GfrCvwCHFNKTVdKTTidsOhxOtExXCn1PyCGs5MTJ4EhxTEkXAhRcaWnw65d8Ndf8NlnZ2YdbN9uWgzlGj7cnDQPDISOHWHoUHjsMdPzfu5c2LMHsqSNaJH8+y/8+CPs3Wt1JGXH6NGtcHcv2FsTd3cbo0a1LOaIShc3N/jwQ3jySZPwuuUWyJRW1Rf08suRrF07kpo1/dBas2rVUatDEqLCOXgQrrzSVH2VNe5elalSqwMJ0Usvv7IQLnBww2ekxG6zOgwhnBIY1puM5IOkJe6zOpRyz253kJxsPghERSXStOln+Pi4Y7cXLTlkt+sK99lKlC5FS625zuB892Od2I87cLlaJnWRdZxr0na+QOC207fL2QXcqrXe5OIYhBAVnJcXNGlibvm9++7Zy088Adu2wf79sG8fbNwIs2efqbgAuOIKWLnS3H/uOWjaFIYNM8txcSa5UdYvtAgOhuPHnd9PzZowZw5ERMDo0XD99VCtmvP7rSgCA304efIBq8Mo1ZSCF180sykmTIDERPN/tnKRL/Mon5RS1K/vD8Bvv+3l+utn89tvgxk0qKG1gQlRQfzwA9xzj7nI4fPPTZvDsiYwrAf7V04hJzMFdy95kRXFJystju1/PUjDyCeoXF1OEIqyKzDMtNpMOLAQvwB5z+VqWmuUUuTkOGjQ4GNuuqkJU6b0plEjfz799CquuiqMFSuOMHv2nkK1g1UKBg6sT2BghRoNLEoZyyoolFIjgRH5HvrXqlhc5DNgJZBRgHV3AA8C7bTW64s1KiGEuIRrroFHHjFXZf/9N+zebaovYmJg0SJzUmHixDPrz5xp5loA5OSYE/uVKpm5CtddB+PHwzvvwG+/wdatkJpqxXdVeK5ITuTup0cPOHLELEtyQhSX8ePNnJnFi6FPH4h15jKPcu7qq+vzwQd9GTCgPgBZWdIbS4jicuoU3HEH3HwzNG5sLnwYPdok8F3BVfspiIB6PdHaTuKhlSV3UFGuxUcvZsmHrUk+vuXsx/cvADRBDfpddB0hygLfag3xrlKX+AMLrQ6l3Bk7di5DhvwKmEryBx5oT//+plO+Uoo77mhN7dqVizQs29fXDMcWwkpFrqBQStUr5CYeQADQBrgFuBJT1aCBNVrr7UWNRWs9CZhU1O0vsd9FmBgLsu7zwPNKKU/M91gHU00RiEkEnQSOAKu11tJnQAhRarm5Qd265taz59nPbdsGDoe5n5MDU6aYyovcCoyFC83JifyefNJc8Z2RAS+8AEOGQLt2pi+11uBudS2fi02bBiEhVkchKoLbbjNJsJtvNv9XN2ww1VPibJ6ebowd2xaAU6ey6Njxax54oH3eY0II11i/3rSe27vX/O1/9lnwOH2O5Ngx5/adkQHe3s7HWBj+dTqjbB4kRC+hesP+JXtwUe7ERy9m/cwhOHLSWff99XS7aw2evoEAxO2bh4dPAPbMFNb/MPSC6wjXsdsdTJmyjldfXcXjj3dm/PgOuLlZ3f28fFBKERjWi+O756AddpTNzeqQyqzvv9/JzJm7+OGH605XBlelatUzb/QffrjjBbfLHZZ96lTBu8gHBMhwbGE9Z04LHcAkF4oq98R/GnC/E/spVbTWWcDa0zchhCh3bKffv3t7w7hxZz+ntWn7lJuw2L8fOnc2zx06BK+9Bs2bmwTF6tWm2iA0FOrXhwYNzC33fv36EBBQ9tpHjRxpdQSiIhk0yMyL2b1bkhMFYbdrOnSoSatWQVaHIkS5Y7ebixgWLTJ/311l4ULTWnLBAmhZgt1v3Dx88a/diYRoGZQtnJM/OQGQnZHE+h+G0mnk3yhlI27/PCrVaJ2XnDh3HZutnF3NY6GoqESGDv2VqKhEUlNzePbZ5XzzzQ6+//5aGjeW0mdXCAjrzeHNX5F8fBNVa7W3OpwyY8eOeD76aBMvv9wdPz9PTp7M5MiRU5w8mYm/vzePPNKpQPtRSjF+fAeeeWY5aWk5l11fhmOL0kLpwjQmy7+hUg5MgsKZ3+IY4DattUwfK4SIiAi9dq3kP4Qob1z5nqCIL+3FLifHnMDw8jIDuD/77OwKjLi4s9evUgVmzYL+/c36f/9tTlIEBprv0VU/s4rwsxcVw99/g7//mcSguLQPPthAlSpejBjRXD6YCVEEMTHw66/w3/+a5Zwc11dGxsZCs2bQooVpa2crwQud9yx9ib3LX6PP+Bg8vP1L7sCi3Dg3OZHL5u5DnfBR1G5zGys/74Zy80Tbs85fp+1omvd7oyRDLpdyqyaeeWY5mZl2HI4zb9htNoWXlxsvvNCNCRMisNnk/YAzMlOPs+jdhjTu9TwNuky8/AYVVGpqFr/8spdu3UIIDa3KggUxDBr0E/PnD6VLl5C8eRNFER+fToMGH5OcnHXZdatU8WTfvrtl/oQoMUqpdVrr83qKOZugKIoETHXBD8AMrXVaEfdTYUmCQojySU6SQ0rK2dUX+/bBAw+YPtaff276Wu/bZ6orpkyByZPPrrjI/7VWrYKfxJCfvSgP7HZTnVStmrmCWc63X5rDoenXbxZVqnjy00/XS4JCiCJ44gl47z3Ytcv83S0un30Gd94JH38Md91VfMc5V0LMUrb8dg/tbvqOKsHhJXdgUS5cLDmRy+buQ5XgtiRdYs6Jzd2HlgM/IKTlzcUVZrl3pmoiidTUi7e98fNzp0mTAKmmcIFDm76kWp2u+AU2tjqUUmXfviQcDk2jRtU4dCiFunWn8tZbvZgwIYKcHAdZWXZ8fQs3P0KIsqY4EhShhdwkC0jWWpeRkamllyQohCifgoNdM6y5Zk3nez2XRlqbn0/16mZOxp9/mqHduYmMQ4fOTg54eUFYGKxYYVpFrVplhlffcMP5J24lQSHKi6NHzf+PGjVcW2VUXtntDtLScqhc2ZMTJ1LZuTOBHj3qWh2WEKVaSgocPGgqGjIz4fBhc2FAURWkH7zW0KsXbNkCO3ea17iSkPtZWRKYoiiWfNia9KT9l15J2UBf+tpPr0oh9Bq324WRVQyXqpq4GKmmEK7kcGhiY9OoWdOP7Gw7QUHvM2RIEz799GoANm48QZs21eX3TFQoLk9QCOtIgkIIIc6XmWlaTezbd6YCIzoavv3WVFLcdRfMmXMmeXPnnbB5s6m4mDXLdXHIn1VRGmRnw003wYABMHas1dGUDfffP5cvv9xGdPQ9BAX5Wh2OEKXSqlUwYoT5W7dz55kh2EV1bj/4S13BvGMHhIebQdxffeXccQvLmVYbouJKPr6Zdd9dT3bmyfPaNxWUzd2HDkN/JCDUhYNdKoCCVk1cjFRTOMeek0Hsnj+pFNSMSkHNrQ6nRNntjrwke79+s8jMtLNkyTAAfv99L82bB9Kggb+FEQphrYslKEqwg6cQQghRfLy8TCuoq64yJ2Rffx2+//5Mm6fJk03v6lyNGpnKio0bLQlXiGKVc3om3v33w4svSuKsIF5/vSe//jo4LzmRkHDhlhxCVER2O7z0EnTrZhKgX3zhXHLCbnfw5ptrCA//ks2b40hNNS9aqak5bNoUS3j4l0yevOasK56bN4fHHoOvv4Z585z8hgrh+K5fWfxeY7LS4i6/shD5VKnZhm53r6VKcFts7oXv7y7JiaKLjPz29GtL4ZMTcOa1KDLyWxdHVjFoezabfxnD0W3fWx1KiXrzzTU0bvxp3t+ue+5pw9ixZ9oDXnNNQ0lOCHERkqAQQghRIVStCk2bnll+/HEzUHi3VMyLcsjHB378EW6/HZ5+GiZMAEdRp4dVEJUqeXLllaaD6YIFMYSGTmPp0kMWRyWE9WJioHdveOopuPlm2LQJIiOLvr+oqEQiIr5i0qQVpKfnnNd2xeHQpKfn8Oyzy4mI+IqoqMS85554wlxgMHYspJdQDtG7Sl0C6vXAniWdikXhefoG0mnkP9QJH1WoJIUkJ5zTtWsIznYL0VrTtWuIiyKqWNy9KtNlzDIaRj5ldSjFasmSg3TrNoP4ePMHqWnTAK65pkFeYuzmm5syfHjFqiARoqgkQSGEEEIIUQ55eJjh8hMmwDvvwKhR5spncXnNmgUwfHhzOnSoWSz7Dw4280GcvQUHF0t4QuT5/nto08ZUG06fDjNmgL9/0fZ1ftXEpV+QLlRN4e0NH30Ee/aYio6SULVWO9pc/xk+/oUdwSiEYbO507z/m9Rtf3eBkhQ2dx+a9X1VkhNOGD++A35+zvWg8/PzYMKE87qQiAKqXKMVNpu71WG41LFjqUycuJBNm04A4OPjTmamncOHTwFw7bUN+d//rqRyZU8rwxSiTJIEhRBCCCFEOWWzmfZmL79s2qIMHgxpaVZHVfqFhFRi2rT++Pp6kJ1tZ/Dg2cyde8Bl+z9+vHTtR4hzpaTA6NEwbJhprbRxI9x2m0mMFcXlqiYu5kLVFFdeaVrXDRlStFiKQmtN+smDJXdAUe7ERy/m4PqPceRcvvTHkZPOniUvkpUWXwKRlT9ZWXZq1PDF39/Lqf1Uq+ZN9+61+eGHXezdm+Sa4CqQ7Iwkds57lPgDC60OpchychzMnLmT5csPA+DhYWPq1E1s3GgSFB071mLt2tto06a6lWEKUS5cMp2plHqmpALRWj9fUscSQgghhKgolDItzQID4b77oH9/+O03qCYzHwvkxIk0oqISSUjIsDoUIUrMihUmqfnMM6ZNnLuTF8FGRn5LbGx6gRMT58rfD/7Ysft58knn4imsmHVT2Tn3YXqN24tXpeKprBLlV3z0YtbPHFKg5ESu7MyTrP9hKJ1G/l3urkJ3tYSEdFatOsqAAQ0AeOihRXzxxVaeeaYLkyatIC0tp9D79PJyY/z4DqSlZXPzzb/x/PPdePrpLmRm5nD99bMZN64d11zTELvdQXx8OtWr+6KKmsEtp9w8/Di06UscOZkEhvW2OpwCi4lJ5ujRVDp3roXNpnjwwYVcdVUY3brVJjDQh/j4/+LtLf8nhXC1y/2vmgSU1FhFSVAIIYQQQhSTe+4xg+FHjID/+z/45BOrIyobateuzIYNt+Ph4QbAn3/uo3btynK1nCh37HZYtQq6doWrrjIzmho0cM2+u3YNYfbsPU7t49x+8GlpcP/9Zh7GnXc6G+Gl+Yd0BCAhZgm1WtxcvAcT5UpRkhMA2p5FyvHN7Jr3GM37v1lM0ZVNR46cYs6cvdx6a3MqVfLk66938OCDCzh48F7q1KnMqFEt6dmzDt271+bpp5cX+TijRrXEx8eDHTvG5LXsiY/PIDY2nfR0k/TYt+8kTZp8yvTpA7jttpacOJHK11/vYMiQJtSrV8Ul329ZZXPzIKBud+IPLLI6lEvSWhMdnUxYWFUARo78g+TkTDZuHIXNpli6dFjec4AkJ4QoJgVp8aRK4CaEEEIIIYrZkCEwbx688YbMQSiM3OSE3e5g4sRFPPDAfIsjEsL1XnoJevY08x3AdckJKJ5+8D4+cPgwxMY6G93lVQ4Ox92rKgnRS4r/YKLcKGpyIpcjJ51Dm77kyLZZLo6sbDl69BTPP7+CqKhEADZvjuXee+eyZs0xAG66qTFLlgyjRg1fACIighkypCnBwZUYOLB+oVvTKQUDB9YnMNAHm03RrFkgtWtXBkwLyHXrbmPIkKYAVK3qyZQpvenatTYAmzbF8tBDizhw4CQAy5Ydon376Wzdal6o4uPT2b49jpwch1M/k7IiIKw3aYl7SD8ZY3UoZ8nKsufdnzhxEeHhX+Y9NnlyT777blDe840aVcPdXbrjC1HcLpf6W0LJVVAIIYQQQohiFhlpvsochMJzc7OxZMmwvCsnU1OzSE7OolatShZHJkTRnToFlSrBuHHQtCk0auT6Y0RG1sHf34tTpy49GPtSKlXypHLlM0kOpeDvv82sneJms7lTrW43SVCIQtn2x38vm5ywuftcch1HTjq7FzxJSMuKU7mTmJjBU08t44YbGtGvXxgZGTlMmrSCRo2q0bhxNXr0qMPu3XfSqJE/YCodcxMI5xo/vgNz50YX6rXH17fgw7Fr1PBj/PgOecv9+oURG3t/XsWFUooaNXwJCDDD0WfP3sNdd/3N3r130aCBP4sXH+Svv/bzxBNXULmyJ1rrctUqKrB+LwDiDyyiTvjtlsaSa/bsKG677Q+2bx9D3bpVGD68GeHh1fNaEHbsWMviCIWomC75dk5r3Utr3bskbiX1DQshhBBCCFFU1av75rVteOyxpYSHf0lSksynEGVPcjKMGgW9ekFWlplLc8stxXMspRTjx3fA17dorTF8fd2pXbsS1147O++xKVPW8uyzywCTqPj994wiz7goiIDQHqQl7iUj+XCxHUOUL21vnIGnXw2Um+cFn7e5+9Cs76t4+la/5Dqtr/u4OMO0jNbm/6vd7mDIkF947731AFSq5MGPP+7Oq5gIC6tKYuI4br21OWASCI0bVyvQifzc5GhhBASY4dhFFRTki5eXea3r1q02f/01hJAQcyFDv36hfPXVQEJDzfuIdeuO8+676/H2NpWaL7ywktDQqWRnm6v5N2w4zpIlB4sci9UqBbXA068GCRa2edq7N4kePb5j4UJTxdGyZRAjRrTAbje/f5061WL06FbSukkIi0mdkhBCCCGEEEXwn/+0ZdKkrvj7ewMU68lRIVzp33+hXTszCHvQoJKpQhg9ulXeCaHCsts1H3zQl6+/Hpj32I4dCWzceILsbFP9cfPNv9Cz5/d5z3/xxVb+/HOf03HnCgjtASBVFKLAqtRsTbe71lAluB02d5+znrO5+9Bh6I/UbXcn3e5eS5Xgthdcp/3QHwgM7VmSYRebtLQzVQyDB8/m7rv/AUx1YkaGncxMc1Lew8ONo0fHcv/97QCT4KxatXBJhlyFTY76+rozfnyHYqtiqFevCiNHtsDNzbzoTpwYwcmTD+S1kmzTpjqDBzfOW3777XXceuvvedu/8cZqHnlkcd5yUlLxJmadpZQiMLQX8QcWorXGbnfw5ptrCAp6j8mT12C3u77VVVaWnUcfXczMmTsBCA72JTMzJ6/6tXHjanz0Ub+z5koIIawnCQohhBAVXs2apWs/QoiyoVmzwLwTKFu2xNK27Zds2xZncVRCXFxODjz/PHTvDg4HLFkCkyaBewlcOBoY6EO9ehduw3Ipuf3gO3WqRc+edfMenzatP7/9diMeHvDhh5CeHk716m3znn/xxX/5+usdecvdu3/Ls8+eGZi7YEEMhw6lFDiOyjVa4eEdQEKMJChEwXn6BtJp5N/UaTsaZTMtymxuXnQY+mNe0sus8w91wkflJSnKenIiJ8fBvn1JecvDhv1Gr15nEoitWgXRrFlA3vKcOTfy0EMd85ZdmSAYPbpVgWcIuLvbGDWqpcuOXdBj5rrhhsa8/XafvOVXXunB7Nk35C3HxKTkVZYA3HTTr/TufebnOmPGDubPjy7egAspsH5vstJi2b5uJRERXzFp0nLi4zN49tnldOz49VnfT1HNmbOX774zCQkPDxu//baXjRvN3A8/P09WrRrJwIEuHKwkhHA5lVtWJ8qOiIgIvXbtWqvDEEIIIUQZ5sqLA8v620mHAzIyID397FvuY02aQK1acOIEzJkD/ftDnTqwbRvMmGHWOXDgCEuWLKBbt8Fo7XfW9u+9B1dcAb/8YlrqnDzputjL+s9elJwDB2DkSFi+HEaMgPffh6olcAHpkSOn8PJyIzDQh6++2sbdd/+Td6V0Qfj5efDnnzcRGVnnkuvdfjt89x1s3AgtWpiraE+dyiIgwAetNffdN5dOnWpx552tsdsd+Pq+w/jx7XnttZ44HJqhQ39l1KhWXHttQ7TWxMamUb2671knSjf8dCvJxzbS8/7tRf1xiApKO+wsfLcBOVkpRNwyOy85ca4j22axe8GTtL7u4zKVnEhKymD9+hP06VMPgPvvn8u33+4kPv6/2GyKGTN2EB+fzrhx7S2OtHz57rudaK0ZPty0v6pffxpduoQwY4YZ8jxgwA/06lWXRx/tDMDWrbGEhVWlUqULtxQrDqmJ0Sz7qCWf/TOAH5ZccVbFh82m8PJy44UXujFhQgQ2W8HenB49eooNG07kJR2uueZHjh9PY+3a2wCTIJPB1kKUTkqpdVrr8wb9SJM1IYQQQghRamh9drIgIwN8fU2CwOGAuXOhfn2TNEhJga++Oj+xcG6CYfhwGDYMjhwxyYXnnoObboLVq6FHD8jMvHRMn3wCd94J+/ebr7//bhIUUVHw+uvg4wM+PiH4+o5g1y6Ft7cmPn4Fdeq0ombNqnicnusbGmoSFO++W/w/RyHymzEDxo41/7++/tokKErCyZOZtGjxOSNGNOf99/sycmQLnnhiKYcOnSrwPgraD37yZPN/8957YfFi8PR0yxtMq5Ri6tT+Z62/aNEtBAWZ55OSMti5M4HY2DQAjh1LJSTkIz74oC9jx7YlKSmDDz7YSP92Hck4+StpSQfw9Q8r8PcgxImo38lOjyd88NcXTU4AhLS8uUwMxD52LJU//9zHsGHN8PHx4NNPt/Dww4s5dmwsNWv6cfvtLYmMrIPd7sBmc8ubHyFca9iwZmctb906Om8guNaaKlW88PMzb0Jychx06PA1Dz7YntdfN4nZhx9exE03NaFbt9p5M0FcWb0SFZXI0KGLuLl9E06l2c5rR+VwaNLTc3j22eV8880Ovv/+Who3rnbefrTWbN8eT4sWgSileOONNXzwwUbi4v5DpUqefPrp1QQGeuetL8kJIcoeqaAog6SCQgghhBDOcmUFxbffXjpJ0KwZPPCAWXf4cIiIgIceMidLGzc+f/1z3X03TJtm1rfZ4JlnTJLh6FEICTmznpubSRZ4e+cmDczt/vvhnnsgMdHs6777oG9fOHQI/ve/s9e90PbNm5sESUYGHD9u2rl5e5t4LvRz3Ls3iXbtpvP8890YP77Dec9L9YooSfPmQb9+0LWrSU7Ur1+8x9Nas2HDCdq3N30Pv/hiKz161KFBA38AJk9ewzPPLCctLeey+/L1deeFF7ozceJ5F9pd0GefmSRiblKxqBIS0pk+fTv9+oXSsmUQq1cfpXPnb5jzczeuaJvB/tgGDL3lH77+eiBdu9YmNjaNLVvi6Nw5GD+/krsy2Vl2u4MpU9bx6qurePzxzowf3yGvN75wrdVfX0V68kEi79uMzVb2rhM9evQUX365jVtuaUr9+v7MmbOXa6/9mSVLhhEZWYeYmGT27EmiW7eQvAHRonTJzrbz++/7qF+/KuHhNTh+PJWGDT9h8uRe3HtvOMeOpdKixed89FFfhg5tRmpqFosXH+KKK2rlJXsLKve15ZlnlpOZaS/QnIxzqylychwoZWaSfP75Fu6442+2bx9D8+aB7N+fRGpqNi1bBhXbvBAhRPG4WAWFyxIUyrwqRACdgDqAP1DQSUZaa+3EW8iKRRIUQgghhHBWcX+es9nOnODv3x+++cY8PngwdO4Mjz1mlkePBg+PSycImjaFTp3M+itXQr16ULs22O0QF3dmvdxKhdLg0KEUQkIqYbMptmyJpW7dynnDtCVBIUpCUhL4+5vfkW+/haFDS2bWxOTJa3j00SVs3TqaZs0Cz3s+Pj6dBg0+Jjk567L7qlLFk3377iYwsGAnx7SGXr1gyxbYuRNq1Chs9BeXkpKFh4cNb293Nm+O5eWX/+WllyJp2NCf77/fybBhc9i0aRRt2lRn0aIYpk7dzJQpvQkO9iM5ORObTZVoW5XLMVc2/0pUVCKpqTn4+bnTpEnARa9gFkWXfGwTKz/vRtM+LxPW+QGrwymQhIR0nn9+JYMHN6Znz7rs3p1A06afMWPGNQwf3pxTp7KIiUmmWbPAArflEaWPw2EGV3t4uHH4cAovvPAvd97Zio4da7FixWG6dfuW334bzKBBDdm+PY4XX/yXZ57pQrNmgWRm5qCUwtPT7ax9nnltMUmENg32M37wr7w042YOxQWRmX3+62DuOi98cwsnUupQt24VDh5M4ZtvruH66xtx9Ogpfv99H0OGNMl7LyWEKJuKNUGhlPov8BBQr6j70Fq7XX4tAZKgEEIIIYTzXHmSfPv286sQ3N2LPwlSFuTkOGjW7DPCwqowb95Q4HI/FwewDlgFdAY6ABe/onnQIBg3zlwhLz9vkeurr+DBB2HduuKvmAAzZyIry05YWFXi4tL45Ze9jB7d0pKr8XfsgPBwuOUW83NwtZQTWzkR9QcNuv5f3pW7iYkZrF9/nG7dauPt7c733+/ksceWsGnTKKpU8WLy5DU8/PBiEhP/i7+/N4sWxbBpUyz/+U+7Em9Fcqkrm4vaD15c2qn4Xexb/gbN+7+Jh7c/UPqqV3JyHIwc+Tt9+tTjnnvCyczMoVatj3jppe6MHdsWrTXx8ekEBflaFqMoWampWWzYcIJWrYLw9/dmwYIY7rjjL/788yaaNw9k1qxdDB8+h82bR9GiRRDbtsWxdu0xHnlkCXFx6TgcmjYN9vP87TPw8shGa/hhaVc++/vsdnv510k65cc9b/+H1Ew/vLzcWLZseF41nhClVXCwqbB2Vs2acOyY8/sp7YolQaGU8gR+Bq7Ofegym+Qe7Nz1tCQoCk4SFEIIIYRwllzFX3JWrTqKm5siIiIYu92Bu7sGLvTWNxH49fTXHMy4uADgWuDCVzTXqGGGdzdrZhIVt98OlSoVz/chyo7oaHj5ZTMjpbgHYWdn22nQ4BPatavBr78OLt6DFdDTT8P06WZgdjUXFwMc3PApO/6eSOTYrfhUrVugbdatO8bChQd5+OGOAEycuJBPP91CUtI4lFI89tgSFi06yL//jshb3+HQdOxYy6Wxn3tl88VINUXxsrJ6JTMzJ68F0003/UJISCX+978rAbjyypkMGFA/7/fUbndIyy9xUVu2xDJz5i6eeKIzPj4ep5NtS7nmmgb88cc+Wtc3iQdvT/NaY7crDsUFMvZ/Y3E4zHug3ORE7jrZOW5EHa7F/30yhuuua8pPP91g1bcnRIHJZ6rCKa4ExUfAPZjEgwIOYS736gKEnH58OlAZqAuEA56cSVT8AcQBaK3HFDmQCkYSFEIIIYRwlryZtsaLL67k6af3AkMxb4vhTNXEcsDOmbfKYN5iuwHdMN1Uz/6Hy8iAWbPgnXdg7VqoUgXuuAP+8x9o1KiYvxlRqsyYAXPmmHZqxV1No7Vm0aKD9O5tCujnzNlL8+aBNGzoX7wHLqCMDMjJKZ5kXU7WKdAad6/KRd6H1pqTJzPzWpV89tkWNm2K5Z13+gBw/fU/s2dPEtu2mY/Izz67HK01zz/fHTADigMDvfHwKNg1fq7oBy/VFEVzIupPfP1DqVS9RYlXrzgcmsOHU6hbtwoAw4b9xqFDp1i2bDgAjzyymKAgHx55pJPTxxIiO9vOgQPJHD16ikfuf5PHb56el3jIlZHlwV9r2/HRnIHnJSfyrzN/YwQ3T/ycyMg6JfktCFEk8pmqcFyeoFBKNQG2Y2rONfAE8LrWWiul/gSu4pzKCKWUH3A78BwQBMQAg7XWG4oURAUlCQohhBBCOEveTFvjp592c9NNMUDf04/kVk0kARe/ovli1RS5P3utYdUqePddk7Cw2+HRR+GVV1z+LYhS5uRJ+O9/zQDsrl3h99/N7Ini9PXX27nttj9YsGBoXpKiNMrIgBUroE8fqyMpnH37koiPT8+roBgz5k+0hi++GABA+/bTqVnTlz//HALAu++up1EjfwYObHDevgpaNXExUk1RdFo7WPpha/yCmlGl3SfFXr1y6lQWmzbF0q1bbQDuu28uP/20m+PH70cpxRdfbCUxMYMJEwo2dF6Ioog/sJjl02/Ay+PCv+eZ2e78trIjg65Ye15y4sw6HkTcOJWQVkOLM1QhXEI+UxVOcSQoXgEexSQnpmmtx+Z77oIJinzP1wTmAq2A40AbrXVskQKpgCRBIYQQQghnyZtp65ifvQNTMbH69KMF+SGeX01xoZ/9kSMwdSpERMC110JsLMycCaNGSfun8mbFChgxAg4ehGeegSeeKL5B2EePnuLEiTTCw2uQlWVn5sxdDB/erFS3gHnkEXj7bdi/H2rXdt1+T+yew+HNX9P2pm/z5lCUpJkzd+Lr68GgQQ0BCAn5kBtuaMQHH/QDoGnTT7ntthY89VQXgoM/IDY2DYej6Mez2RTVq/tw7Nj9rgi/QklPieXjj5bz2LMHXF69cuJEKnPnRjNkSBO8vNx55ZVVPPHEUmJj7ycoyJelSw+xb18SI0a0KPFZJ6LiWvJha9KS9l+y/3tGlsdFkxO5vCqF0GvcbtcGJ0QxkM9UhXOxBIUzf6Ui891/rTAbaq2PA4OANKAG8K4TcQghhBBCCFFmBAYmAl8BazGJiYJ+GtGY2RTLga9O7+d8ISHw3HMmOQEwe7a5wj4mxizb7UWPXZQOOTkwaRJERpoPxkuXmgRFcSUntNYMHPgTY8b8hdYaT083Ro5sUaqTEwD/93+m7ZUrkxMA2RmJnIiaw6m47a7dcQENHdosLzkBcOjQfbz+ek/ADDvu2zeUpk0DAOjYMdip5ASYf/+uXUOc20kFo7UmKiqRrj3+4InnoklPzylQcgJMa6b09ByefXY5ERFfERVlXuuPHUtl8uQ1HDyYDMCyZYcZOfIP1q8/AcAttzTlr79uolIl0z4wMrIOo0a1kuSEKFFtb/wGD+8gsnIu3oLucskJm7sPba77xNWhCSFKMWf+UuXWj0ZrrQ9cbCWl1AXfJmutY4AvMJeC3aCUquJELEIIIYQQQpQJ7u7fYrPFYeZNFEUONlss7u7fFmjtu++GbdugRQuzPGIEDBoE//yD0ycuRcnbvx969jRJqBEjzCDoLl1cfxytNXPm7CUnx4FSig8+6MvMmddaUjFQVNWrQ//+5n5qquv2G1CvBwAJ0Utct1Mn2Gwq76S0u7uN99/vy803NwVg4sQIfHycy1xpTV4v+HXrjtG69ResWnUUgA0bjjNgwA9s2xYHwObNsdx119/s358EwI4d8Tz33AqOHTP/AHv3JvHJJ5tJSsoA4NChFP76az9paeaEZXx8Olu2xJKdbV4f09OzSUzMKPDJ/dIi6dAK5r7XldhDu4rUWgsgNTWHjRtPcMUV3wDmZ/Pww4tZseIIAH37hrJx4+106hQMQIMG/lx1VX28vYspUylEAVSp2YbIe9eRkFGfjCyPQm+fbfekw9AfCQjtUQzRCSFKK2cSFNUwl3HFXOC5rHz3fS6xj/mnv3oCPZ2IRQghhBBCiDKha9cQitpmNVdhr2jOTU4AtGxpBmpfdZV5/P33ISXFqXBECdmwAdq2NQmnGTNg+nQzGL04LFgQw7XX/sx33+0EoEuXEBo1KpszCD77DBo0gBMnXLM/H/9QfKqGlpoExaX06lWXwEBvp/ZRqZIHV10VBoCXlxuNG1fDz8+cBM/MtBMfn4Hdbl7Tjh9P5c8/95OSYk7Kb98ez6RJK4iLSwPg33+PcPfd/3DihFmeO/cAAwb8mLf8ww+7adPmS2Jj0wH45JMtBAS8R0KCWX777XX4+r5NSoo55fDhhxtp2vRTsrJMQuPzz7fQp8/3ea+x33+/kzFj/sz7Xn77bS+TJi3PW160KIZPP92St7xu3TH+/nt/3vLevUls2XKmG3V8fDqxsWl5yxd7LT+w5n1qByUSn+xcXz2toW5dM5C9efNAjh4dyy23NAOgShUvwsNrlPpKJlHxePoGEtb/Z+ZviihUkiIjy4NK4R9LckKICsiZv2S5f4kvdDlA/o84tS6xj/xvEV1ceCuEEEIIIUTpM358B/z8Cn9VYX5+fh5FHnT69NMQHQ1ffWVObv/3v1CnDowfD3v2OBWWKGatWsFtt8GmTTB8uOv3f+xYKosWmevP+vSpx08/Xc/w4c1cf6AS1qULJCbCQw+5bp8BoT1IiFmG1qW7DEkpxfjxHfD1LdpV9b6+7jz3XDdatAgCoFWr6vz00/W0alUdgCuuCGH16pG0aWOW+/UL4/Dh+/KWb7qpCXb7Q7Rsaba/8cbGxMTcQ/36VQEYNKghK1feSq1afqe3D2XWrGsJCDBJlcjIOkyZ0pvKlU2FSLt2NfjPf9ri7W3ax9Sq5Uf79jXz2hg5HKZFUm6lT3R0MqtXH8v7fpYsOciHH27KW/7uu1088cTSvOX339/IXXf9k7f87LPLueGG2XnLY8fOpUeP7/KWBw/+hfbtp+ctjxz5OyOHTuPE7jlUCh2OXTuXHPLz8+B//7sSMJUywcF+Tu1PiJLSo0cYP6+5iTn/FixJkZHlwayV19Nz0I0lEJ0QorRxZkj2IUzyYa3WuvM5z00GJmCSGIO11r9eZB/XAr+cXu8prfUrRQqmgpEh2UIIIYRwVnAwHD/u/H5q1oRjxy6/njhDa029elM5dOhUkfdRt25loqPvcUm7nVWr4N13YdYsyM6GgQNhyhRo0sTpXQsXWL3anFifPRsCA4v3WNdc8yObNsWyf//deHhcvH94WfTMM/DCCzB3LvTt6/z+jmz9ji2/3UWXMcupEhzu/A6LUXx8OrVrf0RmZuHbynl5uXH48H0EBl6qMULZlZqaRXp6DkFBvoBpOXXyZGZeQmXjxhMkJmbQu3c9AObNiyYhIZ2hQ03ibsaMHZw8mcnYsW0BmDJlLQHp71PL9hORY7dRo86PnDpVtBZP4NrXeiFK2kdvTKNW2qOXnTkB4NDgUNXo9+BGPH2L+Y+dEC4kQ7ILpziGZEdh5kfUv8Bzm/Pdv+oS++iX736SE7EIIYQQQohCOHbMvAl29ibJicJzxRXN48d3cNkJq86d4ZtvTFXFs8/C1q1n2gbt2SPtn6zm7g5xccXzf01rzU8/7ebkyUwAJk/uxcKFt5S75ATAE09Ao0YwdixkZDi/v9wWJAkxpb/NU2CgDwMH1i/0SRSlYODA+uU2OQHg5+eZl5wAqFOncl5yAqBt2xp5yQkwcx9ykxMAt97aPC85ATDu/qbU8f6Hms0G41O1DpMmdS01r/VClKT46MU05MkCJScAbAo8bKdY/8NQHI6cYo5OCFHaOJOgWH/6a6BS6tz2TAs40wLqdqVU03M3Vkp1AO7K99BGJ2IRQgghhBCizBg9ulVez/bCsts1WmuSkzNdGlOtWjBpkhnCHGxmrnLXXdC9e8W4oqs02bcP3nrL3G/f3iSNWrZ0/XF27kxgyJBfmTrVtLxp1iyQxo3L5pyJy/H2ho8+Mkm3l192wf4qh+BbrVGZmEMBRWst5+tb9FZyFdXhLd+Qk3mS0I73A86/1o8aVQz/8YUoZvHRi1k/cwiOnPRCbacd2aQc38yueY8VU2RCiNLKmQTF4nz3r87/hNb6IDAPU2HhB6xUSj2jlBpw+vYKsBDwxiQyDgD/OhGLEEIIIYQQZYYzVzS3aVOdhx9ezLZt8QBkZdmdHrqdny3fJ4TXX4dXXjHHzcyEW2+Fv/4yfd6F62ltZoO0bQvPPw9Hj5rH3VxY0HD8eCo//bQbMEN3588fysSJFeMk9JVXmhker74KO3Y4v7+A0EgSDy4vE1f7RkbWwd/fq1DbBAR40727jIosKK0dxKz9iKohHfGv3QmQ6hVR8RQ1OZHLkZPOoU1fcmTbLBdHJoQozZxJUMwF0jBJiBEXeP4hIBOTgPAHngXmnL49AlTKt+7D2pWfqoQQQgghhCjlinpF85tv9mTDhtu54opaADz//EratZtOZqbrT5J26mRmUgDs2gULF8KAAdC8Ofzvf5Cc7PJDVlhJSTBiBNx+u0lQbNpkqlpc7emnlzNq1J95bZ16966XN2C4InjzTahUCe691/lEW/VGAwlq0J+cjJOuCa4YFba1nLQXKry4vf+Qlrgnr3oil1SviIpk2x//vWxywuZ+6cSbIyed3QuedGVYQrhUVhY8+ihMn251JOVHkd+Jaq3TMYOwJwNrlVK+5zy/FRiCSWKASWTkvwHYgfFa65+LGocQQgghhBBlUVGvaI6MrEPbtjXyThy2bh1E376heHmZE49Tp25ixYrDLo+3TRszp+Kbb6BaNXjgAahTx3zdvdvlh6tQli0zSYmZM80g54ULITTUNfvOnTOxf38SAM8/343162+natXC/e6VFzVqwBtvwNKl8MUXTu6r8QDCb/iizAx0HT26VYGTUe7uNmkvVEiHN3+FV+UQaja94azHpXpFVCRtb5yBp18NlJvnBZ+3ufvQrO+rePpWv+Q6ra/7uDjDFKJI0k6f4fbwMO8jtm61Np7yRF2ucEEpVV9rvb/IB1AqBJiIGZYdCngARzBzKt7WWm8r6r4rqoiICL127VqrwxBCCCGEEE6aPHkNzzyznLS0y1c/+Pq688IL3S/Zjicry069elO58cbGfPBBPwBOnswslpPRq1ebKorvv4fsbLj6apOsuPpqCt3OpKLKyTGtnF56CcLCYMYMM7TclU6cSKV+/Y+5995w3nqrt2t3XkY5HPDcc3DPPVDbBeeAM1NP4OVXw/kdiTLNnp1OWuJeKtdodd5zrn6tF6I0y0qLZ/0PQ0k5vvmsagqbuw8dhv5IQGiP0+vcTMqxTTjsmWet037oDwSG9rQidCEuKDoaXnvNvOfduROqVzdVFJ6ern3PWxF6Cyml1mmtz/sDV5AERQ5m3sSnwE9a64ziCVEUlCQohBBCCCHKh/j4dBo0+Jjk5KzLrluliif79t192Z7kqalZpKXlUL26Lzt3xhMePp3vvx/EDTc0dlXYZzl2DKZNMwOIQ0JgzRrzYS0721xhJi7s8GEYMgT+/RdGjTLJnsqVXbPv48dT+eWXPdxzTzgA69YdIzy8RoVq5VRQWjt3ciFq8SSi13xInwmHsLnJL3xFpbW+ZDus4nitF6I0czhy2DX/cQ5t/AJHTvpZyYmz1pn3GIc2fZm3jiQnRGmyZ4+ZxTZ9unmvMGaMubCkZs0z60iConCcSVA4MHMkAJKBGcDnWms5Q24RSVAIIYQQQoiCOHgwmbfeWsdjj3WiZk0/li07xJIlh3jggfZUqnTh1gpFlZUFR46YSoDERDOnYsoUGD7cpYcpN5KToXdveOQRuOUW1+77xRdX8vzzK4mKupPQ0Kqu3Xk5cvSoGfz+5JPQt2/R9pF0aBUnj22gTvjtuHn4Xn4DUe5kpcWxZsZAmvV9jcAwqVISIr8j22axe8GTtLnuk7OSE+eus2vB4wSF9qblNe9ju0jrJyFKyo4d8PLLprLV0xPuvtu8X6tT5/x1JUFROBdLUBTmEhoFVAXuA1YppTYppR5QSpWNhptCCCGEEEJUMHXrVmHKlN7UrOkHwPz5MUyZsg4PD/Mx4PDhFOx2JycFn+bpaZITAKmp0L8/tGhhlrdtgz//dH4ocVmXlASPPQYZGVCliqk2cUVyQmvNzz9HsXr1UQAmToxgy5bRkpy4jGrVTD/pxMSi78O/TmdCI+6T5EQFlpUWi7tXVbz8al5+ZSEqmJCWN9Nr3O6LJidy12k18EOObPuWfSsnl2B0Qpxt0yYYOhRatoSffoKJE2H/fnj33QsnJ+DsagpnuGo/ZVVBKigmAyOA3KaamjNDrjWQBfwKfAb8oy+3Q+E0qaAQQgghhBBFlZiYQbVq3gB06vQ1gYHe/PnnkGI95v33w4cfQuPG8N//wujR5gR9RTN3LgwcCL//bhI4rpKenk3jxp/Sq1ddvv76GtftuAJwtsUTQOapY6TEbieofh/XBCWEEBXQpl/GcHznbLreuYJKQc2tDkdUQNddB4sWwbhxMGECBAVZHVH5U+QKCq31Q0Bt4EZMIsKe+9Tpr17AEOAPIFop9ZxSqr5LohZCCCGEEEK4VG5yQmvN//1fR+67ry0AOTkObrvtD1auPOLyY779timTDwqCBx80g4nHjYNdu1x+qFInO9t82AXo1w/27nVNcuLEiVReeulfHA6Nj48HCxYM5YsvBji/4wpGKVPZM3WqaelQFPv/ncKGH4biyMm8/MqiXEk5sZXM1BNWhyFEudCs72u4e1Zm2x/j0LqCl1yKErFnD1xzjfkKZh5YdDS89JIkJ0pagVo8aa3tWuvZWusbgDrAI8AOzlRScPp+HeApIEoptUApdatSytvFMQshhBBCCCGcpJTi5pubcv31jQDYuzeJ+fOjOXEiDYDk5Ez2709yybE8Pc0sihUrYPVqGDzYDNZu1gyuvtpUFJTH9k9790JkpJlvsHeveaxePdfs+6+/DjBp0grWrz8OQJMmATIEu4gSEuDxx+Hee4v2exgQ2gNHTgZJR1a7PjhRammt2frHf1j73XVWhyJEueDlV4OmfV8l6fC/HFz/idXhiHJKazMHDKBSJdiyBXbvNsuhoab9oyh5hX4Hq7U+obV+U2vdCrgCmIYZnn3ufnsCXwFHlVLvK6XOK98QQgghhBBClA5NmwYQE3MvgwY1AODLL7fRsOEn7NnjRIP+C+jYEaZPh5gYeP552LwZBg2C11936WEspTV8+SW0bWuqRGbMgIYNnd2nZvbsKH77zWQ6Ro5swc6ddxAREex8wBVcUBC88QYsXQpffFH47avV7QbKRsKBxS6PTZReJw+vJvnoOuq2HWN1KEKUGyGthhMY1ofdi54lPfmQ1eGIckRr+Ptv6NHDVE1oDcHBZsbEwIFWRyecusRGa71aa30fUAu4DZh/zioyWFsIIYQQQogywt3dhpub+YgweHBjPvigL40amUvJXn75X558cimuGjlXsyY8/TQcOADffgsjR5rH//7bzKk4edIlhylxSUmmWmT0aGjf/szARWdpDS+++C/vvbcBAJtN0bChv/M7FgCMGWOqXR5+GE4UsmOPh7c/VWq2JSFmSfEEJ0ql6LUf4O5VlZDWI6wORYhyQylFiwHvgraz4+8JLnvPISoureHXX6FzZ1O1Gx0Nw4aZxwHc3KyNTxguqQHWWmdorb/RWvcD6gPPAQfOWU0BrYApwCGl1PdKqauUcnYkmRBCCCGEEMLV6tSpnDefAuDAgWT27TtJ7tv3JUsOkpGR4/RxPD3NB8U6dczytm3w55/g62uW9+0rO+2fliyB8HD48UfTv3jBAudaOsXGpvHoo4tJS8vGZlP88ssN/P77ja4LWOSx2eCjj+DUKZOkKKyA0B4kHV6DPTvN9cGJUic9+RDHd86mTtvRuHtWsjocIcoVX/8wGvV4mtg9f3J8509WhyPKKIcDZs2Cdu3g+ushLs60F92zB/7zH/N3X5QeLv/n0FrHaK2f01o3BK4EvgHSTz+dm4w4d7D2866OQwghhBBCCOE606b155tvrgHMgOYrr5zFc8+tcPlxJk6EnTvBw8MMmI6MhCZNzKDt0lpVkZ0NTz0FvXubhMvy5fDEE85flbdzZwJvvbWOJUtMm4vatSvLnIli1KIFPPoofPUVzD+3N8BlBIT2QDuySTq0qniCE6XKwXXT0GjqdbjX6lCEKJdCI+6naq0Iko9vtjoUUcbk5MA330CrVqaCNT3dtN3cvRvuvtu8TxOljyqJcimlVGVgODAaM7fiXFprLUU1BRQREaHXrl1rdRhCCCGEEKKCcjg0CxfG0KBBVerX92fTphOMHTuPTz7pT4sWQS47Tk6OqUZ4910zYNvPD0aNMi2gmjd32WGcdvQotG5trtB75x0zdLGofv11D8eOpXLPPeEAHD6cQu3alV0Uqbic9HRo08bc37IFvL0Ltl1OZgoL3q5LWOfxNOk1qdjiE9azZ6ex6L2mBIb2oO2N31gdjhDllj0nAzf3Ar4IC3FaSgqEhUFIiLl4ZMgQaeNUmiil1mmtz5tTXSKX32itU7TW07TWXYEWwJzcp0ri+EIIIYQQQgjXsdkUV14ZSv36/gDExaWTlpZNcLAfABs3nmDDhuNOH8fdHW65xVQkrF1rPmR+8om50r1/f5gzx7r2T1rD77+b49eqBVu3wqefOpecAJg+fRsff7wZh8N8VJLkRMny8YEPPzQtIF5+ueDbuXtVpmqtDiREyxyK8u7I1m/JyUgktON/LrlecDAo5fwtOLiEvjEhSpnc5MTJo+tJPLTS4mhEafbTT2a+hN0OlSvDv/+aGWC33CLJibKixOqDlVKVlVJ3AZ8B1yDJCSGEEEIIIcqFK68MZePGUQQE+ADw3HMruOaan8jJMdkDV1Rtd+gAX3wBBw/Ciy+aWRXXXguNG5sKhoJw5QnD+fNh0CAz4Dt330URF5fGAw/M58iRU4BppbVixa3YbDKqzyp9+5qh7QsXmpMdBRUQGknK8U3Ys9Mvv7Iok7TWRK/5gCrB7fCv0+WS6x53Pkfr0v0IURZp7WDLb3eze+EzVociSpn0dEhNNfdzciApCU6cMMuNG8uMibKm2Fs8KaV6AXcANwI+5z59+muM1jqsWAMpR6TFkxBCCCGEKM0SEzPYuTOBLl1C0FrTrdu3XH99Qx59tLPLjpGdDT//bAZqf/aZSRzMmmV6Dl+s/ZNy4Tl/h8O0nxo82Lmr8/buTaJNmy/45JOrGD68FPWtquBOnTLVFIX5t81Ki8Pm5om7V5XiC0xYKjsjia1/3E/NpjcQ0nLoJdd15etNCXTmFqLUOhW3A69KtfDw9rc6FFEKnDplKh0nTzYtP5966sxrpCtfd0XxuFiLp2JJUCil6gGjMDMnwnIfxlRN5P66ZAG/Ap8C/+iSGIZRTkiCQgghhBBClBVpadk88MACevSow+23tyQzM4fPP9/KsGHN8Pd3XW/p7GyoXRuuusoMOQaTRMh/BV1pOWH4++97WbPmGJMmdQMgISE9r/pElC5xcaZap2dPqyMRZU1peb0Rorxw2LPISo3Fu0ptq0MRFjh5Et57D6ZMgfh4U+04aRJ062Z1ZKIwin0GhVLKSyk1XCk1F9gHTALqcyYhwen7W4AJQG2t9VCt9d+SnBBCCCGEEKJ88vX14JNPruL221sCMH9+DGPHzmPVKtOXKTvb7pIWUB4e5kTySy+Z5U2bTIn/W2+Zsv/SZNGig8yatZv09GwASU6UYnfdZXpYZ2YWbP0jW79n+1/jizUmYY305EOkxkdZHYYQFdb6WUNZ/8PNOOzZVociSlBCAjzzDISGmmqJK66AlSth7lxJTpQnTicolFIdlVIfAEeBr4E+F9hvMjAV6KS1Dtdav6O1jnf22EIIIYQQQoiyZeDABqxffxv9+oUB8Pbb62jZ8nNSUrKc3nf16lCvnrmfmQkhIfDQQ6ayYuxYp3dfZElJGdx//9y8weHPPdeNjRtvx8fHw7qgRIG8/rqZN+LlVbD105L2cfLoWjmBVg7tXzmZFZ91IScz2epQhKiQ6ra7g5Tjmzmw+n9WhyJKwIkT8OijJjHxwgvQpw+sWwdz5pgkhShf3IuykVKqOnAbMAZokfvw6a/5L39ajGnh9IPWOqOoQQohhBBCCCHKj3btaubdb9y4Gr1716NyZU8AvvhiK/XrV6Vnz7pOHaNTJ1i6FDZsgP/9Dz7/3KndOe3nn/fQsmUQ7drVxNdXEhNlRZMmZ+6fPAlVq156/YbdHqNR98eLNyhhiYbdHyOo/pUyY0QIi9Rseh01ml7H3mUvU7PpdfgFNLI6JFGMtm+HN94wVYxPPmlmjInyq8AzKJRSbsA1mKTEQExyI39SIvf+YeBL4DOt9T6XRisAmUEhhBBCCCHKJ4dD06jRJ3TtGsLXX18DwMmTmVStWsDL1y8hNhZq1HB6N3ku9zHqn38OMGvWLqZN649SitTULPz8PF0XgChRjz0GP/0EmzeDdwFGp2itUTKts0KTGRRCuF5GylGWfxxB5Zpt6HjrH/I6W45oDRMngru7SUxoDdHREBZmdWTClYo8g0Ip1UIp9QZwCPgZuA7IveQn989kDvATJoERqrV+SpITQgghhBBCiMKw2RTbto1m8uReABw6lELNmh/wzTfbnd539epO76JQ9uxJZMmSQ8TGpgFIcqKM69sXoqLg5Zcvv27U4kms/qpf8QclSoTDkcPGn28jIWaZ1aEIUeF5V65Fkz4vkhizlMObvrQ6HOECR81IMpSCjAzIyjqzLMmJiqMgMyi2AhOBmpw/8Ho78BBm4PUQrfWfWmuH68MUQgghhBBCVAQ+Ph7UrOkHgLu7jXHj2tGlSwgA69cf54UXVnLyZAEnFpegU6ey+O9/5/Hrr3sAuOeecLZuHU2NGn4WRyZcoW9fGDkSXn0Vduy49LpuHn4kHf6XrLTYkglOFKsTu3/j+M6fyclIsjoUIUq94GBzYtnZW3DwxY9RJ3wU1ep1Z9eCJ8k8dazkvjnhUjt3wu23Q926sHq1eeyDD+Cdd6yNS1ijKEOyU4CPgSu01q211lO01nEujksIIYQQQghRwQUH+/HGG71o0MAfgAULYnjjjTXYbOa6qWPHUsnJcdX1UQ5gDfDe6a+F26+XlxtLlx5m27Z4wCRXPDzcXBSbKA0mT4ZKleC++y7dciegXg8AueK+nIhe8wE+/vWp3miA1aEIUeodP178+1HKRsur/4cjJ4Md/zzkmgOKErNli5kr0aIF/PgjPPgg1KtnnpOOXRVXQRMUClgKjAZqaa3v1VqvLraohBBCCCGEEOIcDz/ckQMH7s4bqD1y5O/07PmdC/acCHwFLAcyTn/9+vTjF7doUQyDBv1EVpYdDw831qwZyeOPd3ZBPKI0qlHD9MVesgS++OLi61Wp1R43Dz8SopeUWGyieJw8so6kQysJjbgPZbMu4XjLLTBrFqSmWhaCEKWKX2BjGnZ/nOO7fpFkcBmxbh3ccAO0aQN//mlmOx04YJL/l6qYERVDQRIUrwJNtNY9tdbTtdbpxR2UEEIIIYQQQlxIQIBP3v1x49rzwAPtATOU+M47/2Lx4oOF2Ftu1cSXQBxmtB6nv8aefnwNZ0bvnS09PYeoqERiYpIB8PSUionybswYiIyEhx82g9cvxObmQbW6XSVBUQ5Er/0AN8/K1G5zm6VxLFoEQ4eaJNnNN8PMmZKsECKs84O0vXEG1ep2szoUcQmrVsHAgRARAYsXw7PPmsTEyy+X/HwwUXpdNkGhtX5Ca72nJIIRQgghhBBCiIK6/vpG3HJLM8AM1P7nn+i8ZEFaWjZRUZeqgMitmliBSUicm4TQpx9ffnq9RMAOzAfWAzBgQAO2bRtDo0bVXPUtiVLOZoOPPoKUFJOkuJiA0J6kxu+S/uhlWEbKUY7t+InabW7D3auKpbEcOQILF8Lo0bB0qamoqF4dhgwxQ2WFqIhsbh7UbHodSimyZUZMqaL1mVaIv/wCa9aYhMSBAzBpEgQEWBmdKI2KMoNCCCGEEEIIIUqVunWrcODA3QwbZhIWs2btokmTT9mw4dxG1udWTWRfZs/5qynWYRIVp/KedXeXj1QVTYsW8MgjMH26OWl8IQGhp+dQSBVFmXVw/cdoRw6hEfdZHQpubtCrF7z/Phw+bCoq7rjDVFF4e5t13noLfv3VyiiFsEbiweUsfr+ZtHoqJQ4fhh494PffzXJuK6fHH4eqVS0NTZRi7lYHIIQQQgghhBCu4OZmw+10l6X+/cN4990+tG1bA4B33lkHnDh9S+JMO6eCyK2mWAFUA1q7LGZRNj35JPj4QMeOF36+Ss1w3L2qkhCzhFoth5ZscMJp9pwMDm74lBqNr8G3WoNCb599ubynE9zcoGdPc8vlcJjKniuvhOuuM1cu//ADXHUVVLG2+EOIYle5ZluCm9+ET9V6VodSYWkN0dEQFmZa0SkFmZnmOXkNEgUhCQohhBBCCCFEuVOrViXGjWuft3zgQDKwA5NsuPBMicvLwVRdfAvc72yIogzz8TFJCjAnZpQ6+3llc6Na3W4kRC8t+eCE045tn0V2ejz1Oo4t0vYeHi4O6DJsNti5E06dLu5as8bMrPDyMkmKm2+Ga6+Vq5dF+eTu6Uerge9bHUaF5HDAzz/Diy/CsWOwb5/5+7hEigdFIUk9shBCCCGEEKLcmzKlN9CAoicncmkgxPmARLmweTOEh8OOHec/V6ftaOp1uBetHSUfmHBKzWY30vraTwmo16NQ2+3fDxs2FFNQl2GznblSOSICli2D++6DdevgttvMVc3XXmtakyUlWROjEMUpI+Uo62fdTMqJrVaHUu7Z7TBjBrRubWbhpKbCK6+Au1wGL4pIEhRCCCGEEEKICqID4OylzR5AhAtiEeVBcLCZAXDy5PnP1Wg8kNCO96OUfOwua9w9/QhpdQvq3NKYS9i0Cbp2hVtvNSfvatZ0TSxF2Y/NBt26wdtvQ0wMrFgB//mPiXHUKJOsGDQI4uJcE6MQAOnprt1f27Zn7r//Pnz++Znlw4fPtBDKZXPz5OSRNWz94z9oh921wQjAtK/74gto3hxGjDDVgzNmmCT96NElXz0myg95pySEEEIIIYSoEGrUqAN4ObkXb2rUqO2KcEQ5UKMGrFoFV1xx4eczTx0j6fDqkg1KOGXnvEc5suXbQm2zcKEZCuvuDj/+aOZEHDtm2n85ezt2zLnvx2aDLl3MEO3oaPj3X3jgAYiPh4AAs87HH5uTjEIURkIC/PYbPPqoSc65uoXYiBFn7n/3nWkllKtTJ5McrlnTVAwNHgwPPRpIVPobJB9dx6JZH3H0qGvjKc2Cg02ywNlbcPCF92+3w9Sp0KQJjBkDfn7mtW7zZhg+nLz5X0IUlSQohBBCCCGEEBXC8eOKN9/sgK9v0XoQ+Pq6M3lyB44fL/hV1aL8UwrS0uDZZ+HEibOf2/HPQ2yaPQqtnW0tJkqCIyeTpMOrSU3cU+BtZs2Cq6+GOnVMpUKLFsUYoJOUgs6d4c03YeVKk7wA+PJLM1Q7108/mQSGEPnFxMDx4+b+ggUQGGiGsk+ZYn63Jk507fH+7//O3F+61Pxe5nr5ZXj+ebj+eqheHaKi4Kuv4N7Hh7Bq59Uk73yOpx6Jzlu/Xz+TiAPIyYGZM83/gUOHzHJZl/vv4ur9OE53KLTZYNo0k5T/7TdYvx5uvPHMa4gQzlLyRqnsiYiI0GvXrrU6DCGEEEIIIcqc+Ph0atf+iMzMwrd/8PJy4/Dh+wgM9CmGyERZtmOHmUUxbJjp8Z8r+dgmtHZQJbhtodoFCetordGOHGxul+9V8t57phqha1f49dczFQlljdaQnGyugI+OhrAwUw3Sp48ZsH3DDRAUZHWUoiQ5HLB9u2np066dqZYIDDSJgccfh8RE+PBD6N4dOnY0g5HBJCpcpSinK1NSYP+ugxyd3xF3/yvoc8/P2O2KgQPN4Pi77jrzO57LzQ1q1YK6dc++9e4NbdqcfZK+tCqOn/sff8CDD5qqq8DAM1VX8qdMOEMptU5rfV6vVElQlEGSoBBCCCGEEKLobrxxNrNn7ynUyQ+l4IYbGvHTTzcUW1yibHvqKXjpJZg3D6680upoRGHlZKXisGfi6XP5LIPW8PTT5t/7uutM+xmfcpK31NpcHT1rlrnt22dO4PbpY4bhyoqnDgAA6uZJREFUDh5srlgX5UtWFqxda4arL10Ky5ebJMQ118CcOWad6dNNu7DGjS++H6sTFLmi137EzrkP0/raTwhpNeys57KyYOdOUz1x8KC55b9/8CBkZMA775gE5M6dZhj099+bqoFdu+Czz0wSo06dMwmN6tWtO3nvyuMePgwhIbB1K0yYYBJRjRq5bv+iYpMERTkiCQohhBBCCCGKbsmSg1xzzU+cOpVd4G38/Dz488+biIysU4yRibIsPd2cxLLZTF9ub2/zeOzef8hKPUHtNiOtDVBcUvSa99m9aBLd792AT5WL/z/XGu6+Gz791FyN/eGHptqgPNIaNmww7Z9mzYI9e0yyolcv0y6nfn2rIxTOWLwY5s41SYlVq8xJeTBzBiIjTXVEjx7QoEHB91laEhRaO/h/9u47PIqqbQP4PZveG4FQQoBAgIROAKUkQUB6FVCRZgFRAbF8imLBil1fCyggCSACIqAiTekdEnpNQktCT08gdXfP98ey4ybZlmSTTbl/17VXdmbOnHlmd3Z3Ms+cc44s74d7afHoOfUo7J3Nz6oJoWkxYmuraVV07ZpmkO6JEzWDQ//1lyZZV1jsFMLB4b+ERaNGwOzZQEiIZjD669c169rbl32fjLHk6/7oo5qkK1FFYIKiBmGCgoiIiIio7IQQaNz4J1y7dtfsdfz93ZCQMJXd9JBR//4LPPww8M47wHvvaead/GMS0pMOIHx6HI+fKkqoVdj7Uwc4uPqh24R/TZafN0+TkHrvvdrT3YkQwMmTmkTFxo2aO+xdXDQXMjMygGefrT2vRXW1b59m7Ih33tFMDx+ueS87dvwvIdGzp2acgbKqKgkKALibch4Hfu4Ov9aj0G7Yz5YJ6j61GkhO1t/6Qju9di3QuTMQGQk89RRw6ZIm2RMZCfzyS8kupbSJDQ+P0r+Olnzdjx4FOnWyXH1EugwlKGponp+IiIiIiEg/SZIwa1ZnvPPOfuTkmB4d09nZFrNmdebFZTKpXz/giSc0F7Affxxo1QrwDgjHrfNrkZMWDxefIGuHSHokX9yM3IwrCOr9vsEyaWma7o5CQzV98Nc2kgR06KB5fPTRf/PXrAFu3ACmTdNM//235jXy87NGlARoLuxfvKjpqmnvXuDzzzVjiOzfD3z6KTBrFuDuDnz3HbBiBeDqau2IK4ZrndZoHvYWINQQQlj0N1yhAOrV0zxCS1xqLapfP00rpEY6DbNycjTdAd68+d8YF3Lcrppkxf79gJcXsGeP5rtn8mTNcqWyYlttMTlB1sAWFNUQW1AQEREREZVPamoumjVbhKysApNl3d3tcfnyFA6OTWa5c0eTmGjbFti1C8hJv4h9P3VAcP9v4N/pGWuHR3ocWTEQuRlX0eu501Ao9F/5GzlSM1js5cs1Z7wJSxACyMwEPD01AxTXqaPp+qZXL80A2488ohmAmCqOUqlp3bJ3r6aVxL59wO3bmmU+PppWEt26AffuaboYsjM9/nuZVaUWFNWBUqlJ8BVvhXH9OvDbb5pEyJQpwIYNwK1bmnVGjdK0hNFtebFwoeViqg2vO1kPu3iqQZigICIiIiIiqroWL9ZcVFqyBJg8WWD3Dy3h1egBtB+xzNqhUTFZt0/j4JIHEdT7QzR9YJbBcgkJmkdYWOXFVh2dPfvfANvnzmkuWPfsqemz/5FHgIYNrR1hzREbC8yYARw8CNy932Nhkyaa17tXL82jZUvNRe7K4uf3X3KkPOrV+++CvCXcjtuAu3fOIrDnbMtVWkkKCjTjWDRooJlevlwzZohul1LJyZbbHi8TU0VigqIGYYKCiIiIiIio6lKrgfBwzaCoCxcCp/56BqlXtiFi5hV2FVbFnNn4HG6dX4vwF2Jh5+RVZNm+fZoucH74oXIv8tYU5879l6w4e1Yzr0cPzevZvr11Y6tOtF365OYCDz2k6T5u5kxNIqBfv//Gj+jVq2g3QvSfc1tfRsb1w3hg4g4obB2sHY7FseUKVRccg4KIiIiIiIioEigUwJYtmkGEAcA7IAw3z67C3ZRzcPMNsW5wJMu/dwc3zq5Go3YTSyQn/vhDcyE4IABITQV8fa0TY3UWHAy8+67mcf68ph/+tWv/ey03bdK0AnjhBU3XQ6SRkKBJjmnHkGjVSvO6OTlpjkev+4dqvXrAqVPWjbW6aPnQh5AUdlDYVGD/VkRUZrwHgIiIiIiIiMjCtMmJ2FjgWmY4ACAtYY8VI6Liko7/DKEqQOMuzxWZv3Chpjui9u01F4qZnCi/1q2Bt98GTpz4r6uaTZs0AzVrx0T45x/NxfnaRK0GTp8GFiwAxo0DGjfWdNM0fjywcqUmIfHQQ/+VX7UKmDDBauFWWzZ2zlDY2KEwNx3JF7dYOxwiKoYtKIiIiIiIiIgqgBCaC90uLgH4bFIA0hJ2IyD0OdMrUqXIy7qGOoH94erTEoDm/frgA80d/wMHarom0iaayPK+/x7IyNB0T6NSaS7Qp6YCXbtqBtgePVpzsb60quo4CACQn6/p7qpTJ8302LGa1hGAJnGj211TmzaAjY1lt1/bxe+ei2unlqP70wflzz0RWR/HoKiGOAYFERERERFR9XD8uObCY3LM88jJuIyuT/Du3apErSqEwsYOKhUwfTrw44/ApEnAokX/3dlPlePiRU03UGvWAMeOaeZ16fJfsqJpU/PqqUr98WdlAQcOaMbecHMDPvxQ05IkLU3TVdPmzZoBjnv21Owfh6ipWPn37mD/wlC41GmJruO3QpJqRscyVemYJzKGg2TXIExQEBERERERVS8qpQoZmTbw8bF2JCSEQF7WNTh5+AMA8vI0d++vXw/Mng18/DEvFFvbpUv/JSuOHtXM69wZ+OorICzM+LrWvFh786Zm3AjtGBKnTmm6cdq4ERg0CIiPB86cAQYM0IwpQZXv+qkVOLPxWbTu/zUad5pi7XAsggkKqi4MJShqRqqQiIiIiIiIqAobMdIGo0bx4k9VkHHtAPbMD0bypX8AAC+9pBkU+3//A+bNY3KiKggMBF5/HYiJAS5fBj77TDP4vIeHZvn+/Zr36u5d68aZkQH8/DMwebIm5gYNgEcf1czz9ta0lti2DQjXDEODFi2AkSOZnLCmBm3HwadJb8TtfAd5WdetHQ4RgS0oqiW2oCAiIiIiIqpeFi8GTmyYhT4RmRj5UqS1w6nV8u/eRtLxxWj6wEuwsXPGrVvAwYOaC8dUPXzwgaY1xa1bgIMDsHs3UL8+EBRUsXeTq1TAt98CLVtqWkRcvw40agTUqfPf2BE9ewIdO7KLsKosJ/0K9i/uCp8mvdFx9GpI1TwryRYUVF2wi6cahAkKIiIiIiKi6kWtBuY8/QmyMu5h7sIP4Otr7Yhqt/PnNS0mvv8esLW1djRUFhkZgKen5nnr1sCFC0C7dppulSxl+3ZNd022tsCbb2rmNWmiGUR9wQLNdHw80Lw5W95UN1cPf4vYHW+i/Yhl8Gs9ytrhlAsTFFRdMEFRgzBBQUREREREVP2cOwd06AA8/jiwdKm1o6mdrp2IgoOrH/7YOQBvv60ZwLhZM2tHReWVlASsXasZs+LAAcvWLUnAww8DW+6Pb5+Z+V9XU1R9qdVKHF7aG3lZ19Bj6lHYO3lbO6Qy8/MDbt8ufz316mlaJRFVFI5BQURERERERGRFwcHAa6+p8cfvKdixw9rR1D7Kgru4sH0ObpxZiSlTNHfcMzlRM/j7A7NmacamsKSNG4G0tP+SEwCTEzWFQmGLkEE/oDAvAykXt1o7nHK5dUvT8qG8DyYnyFrYkJGIiIiIiIiokgxu0R8+k50xbdqfOHUKcHS0dkS1x9qFK+BZkIl8jxcA/Nc9EJEhgwZZOwKqSO712qHXtFNw8vC3dihEtRpbUBARERERERFVEs/67RDc+CCuXC7AvHnWjqZ2EAL4+GM17l7+ETezQhHcpau1QyKiKkKbnMi8eQyqwhwrR0NUOzFBQURERERERFRJvAPCAHUOpj95FPPmARcvWjuimk2lAmbOBP5Y+i8a+cajz2PPw9XV2lERUVWSk3EVh5f2xpVD31g7FKJaiV08EREREREREVUSL/+eACQ8OXoPWnR8EE2bWjuimis/H5gwQTNw8oq5P8DBtT4athlp7bCIqIpx9myCNkMXwTewv7VDIaqV2IKCiIiIiIiIqJLYO/vArV5b5KfuxvPPAzY2mrv8ybIyM4EBAzTJie8+Owcf+x1o3HkqFDZ21g6NiKqgBiFjYefoAbWqAGq10trhENUqTFAQERERERERVSLvxmHIuHYYKmUeNm8GgoOB5GRrR1Vz3LwJhIcD+/YBv/wC9G6zAApbRzTq8KS1QyOiKqwwNx0HI3si4ch31g6FqFZhgoKIiIiIiIioEnkHhEGtykfm9SMICAAaNADu3rV2VDXHvn3A5cvAxo3AmJGpuHFmJRq0eQz2znWsHRoRVWG2jp5w9mqGi3s/wr20S9YOh6jWYIKCiIiIiIiIqBJ5+fcAJAXSEvYgOBjYuRMci8ICsrM1f8eMAS5dAh5+GFDYOiIo4n0EdJlu3eCIqMqTJAmtH/4aCht7nNsyA0IIa4dEVCswQUFERERERERUiewcPeDu1xGpCbvlebduAS+/DOTlWTGwamz/fqBJE2DPHs20r6/mr629CwK6PA/XOq2sFhsRVR+ObvUR1PtDpCXswfVTy60dDlGtwAQFERERERERUSVr+dCHaNX3M3n69Gng66+BefOsGFQ11rq1psVEUNB/81Iub8O1k0s54C0RlUqjDpPh5d8DsTveRP7d29YOh6jGqzEJCkmS3CVJipAk6RVJklZKkhQnSZJakiRx/7GrkuNxkCTpcUmS/pQk6aIkSTmSJKVKknRKkqT/SZIUWpnxEBERERERUdXh3bgXPOp3lKf79QPGjdMkKC5csGJg1YgQwMqVmlYn3t6a535+/y2/eW4Nrhz8GpJUYy59kBnq1ata9VD1I0kKhAz8DurCXJz/91Vrh0NU40k1oT81SZJiAbQAIBkptlsIEVFJ8XQAsBxAGxNFFwJ4WQhxrzT1h4aGipiYmDJGR0RERERERFXBnfiNACTUbTEIAHD7NtCqFdCuHbBrFyAZ+w+3llOrgVdf1bQ6+d//gJkzS5YRQqDg3h04uPJKMxGV3uUDnyN+93vo+Mgq1A0aYu1wiKo9SZKOCiFK3LRfU24jCILx5ESlkSSpPYA9KJqcSAWwH8AxALo9ik4F8KckSXaVFyERERERERFVBVcOfo2rh/8nT9erB3z+uWYchago68VV1RUUAOPHa5ITM2cC0/WMf61W5kOSJCYniKjMmnSbBde6bXBu60tQFty1djhENVZNSVBoZUOTHPgawHgAxytz45IkuQPYCMDt/qwCAM8DqC+E6CmE6AygEYAfdVbrA+CLyoyTiIiIiIiIrK/9iKUIfXxDkXlPPQX07KlpHZCcbKXAqrDsbGDwYE13TvPmAd98AyiKXdnIy7qOnd81x+24DXrrICIyh8LGDm0GzUfQQx/Bxs7F2uEQ1Vg1JUHxBIBWADyEEOFCiJeFECsAZFVyHK8BaKgzPV4IsUAIUaidIYRIFUI8h6JJiuclSdIZyouIiIiIiIhqOkf3hlDY2BeZp1AAP/2kuRD/Krs+L+L2baB3b2DnTiAyEpg9W383WInHFkKZnwn3uu0qP0giqlE86ndCg5CxkCQJQqitHQ5RjVQjEhRCiF+FELHCigNqSJLkAmCWzqxNQog1RlZ5FYD2fhhbALMrKDQiIiIiIiKqouJ3z0VC9Pwi84KDgddeA5Yt01yMJ+DSJaBHD+D8eeCvv4DJk/WXUxXmIOn4EtQNGgInz4BKjZGIaq7rp1bgUFQYVMo804WJqFRqRIKiihgIQLe91/fGCt8fGDtKZ9YISZJsKyAuIiIiIiIiqqLSkw7ixplfS8yfMwdo2RI4XqkdF1dN588D3bsDGRnAjh3AoEGGy944swrKvHQ06fJCpcVHRDWfg6sf7F3qQVWQbe1QiGocJigsZ5jO8zwA281Y52+d514Aelk0IiIiIiIiIqrSvAPCkXXrJApy04rMd3ICTpwAXn7ZOnFVJY0aacbl2L8f6NbNcDkhBBKif4BbvfbwbNS98gIkohqvTrM+6Dx2Leydfa0dClGNwwSF5XTUeR4thCgwY50jAAp1pjsaKkhEREREREQ1j3dAGACB9KT9JZY5Omr+7twJxMZWblxVwaZNwL17gJsbsHatpkWJMalXduBeaiwCurwASd/gFERE5ZSTcRUXtr0OoVZZOxSiGoMJCguQJMkGgO4g1/HmrCeEyANwTWdWsCXjIiIiIiIioqrNs0EXKGydkJawR+/y7GzgkUeAjz6q5MCs7OpVYPhw4OOPzV8nIWY+7F3qon7rRyosLiKq3TKvH0FC9A9IPPqjtUMhqjE45oFlNABgrzOdWIp1EwA0vf+8iaUCIiIiIiIioqpPYesAr0YPGExQuLkBW7cCbdpUcmBW1qQJsHEjEBZmXvl7qXFIubQVgb3mQGHrUKGxEVHt5Rc8BjfOrkb87vdRt8UQOHkGWDskomqPLSgsw73YdEYp1s3Uee5mqJAkSVMlSYqRJCkmOTm5NLERERERERFRFeYdEIa7yWdRkKP/f70uXTRjUuTkaAaKrqkKC4GnnwY2bNBMP/zwf91cmZJyZTsUNg7w7/hUxQVIRLWeJEkI7v8NAODslhchhLBuQEQ1ABMUluFabDq3FOvqljWYoBBCLBRChAohQn19OSAPERERERFRTeEdEA4ASEvYa7BMQQHQuTMwa1YlBVXJ7t4Fhg0DliwBzp4t/foBoc8h7PlzcHCpZ/ngiIh0OHn4o0XEXKRe2YabZ3+zdjhE1R4TFJZhV2xaWYp1dcsWr4eIiIiIiIhqOHe/jrCxdzXYzRMA2NsDo0YBS5dqBs2uSZKTgYceAv75B1i4EJg9u3Trq5X5AAAHVyYniKhyNO40FR4NuuLCtv8z2PqNiMzDBIVl3Cs2bWYj1BJli9dDRERERERENZzCxg4NQh6Fg6uf0XJvvQU0awZMmwbk5VVScBXsyhWgRw/g9Glg3TpgypTSra9WK7FvURdc2v9pxQRIRKSHpLBBm0HfQ5mfjQvb3rB2OETVGhMUlnG32LRzKdbVLZttgViIiIiIiIiomgke8D8E9jTedMDJCViwAIiLAz75pJICq0AnTwLduwMpKcC2bcDw4aWvQ63MQ72WQ+Fer4PF4yMiMsbVNxjNHnwFN8+uQvKlf60dDlG1xQSFZaQUm65finV1yxavh4iIiIiIiGoJIdRQ5hu/b+3hh4Fx44B584ALFyopsAqwaxcQFgbY2gJ792paUZSFrb0rWj70EXyb97dofERE5mjW/f/gVq898rKSrB0KUbXFBIUFCCHSAdzWmRVQitV1y1bj00siIiIiIiIqKyEE9v7YAbE7THcV8tVXgLOzpqsnISohOAtLS9MMiN2wIXDgABASUrZ67qZcQMrlbRDV8UUgohpBYeuAByfvgX/Hp6wdClG1xQSF5ZzVed7JnBUkSQoA4K0z65xFIyIiIiIiIqJqQZIkBIQ+C9/mA02WrVcP+OwzYPduzaDZ1Y23N7BmDbBvH+DvX/Z6rhz8EifWj4cyP8tywRERlZKksIEQArcu/IGMGzHWDoeo2mGCwnJ26zxvKkmSOadZ4cWmd1kuHCIiIiIiIqpOArq8gLotBptV9umnNd0ivfIKkFUNrs8LAbz9NrBqlWa6f39NoqKs8u/exs1zv6Nh2/Gwc/SwTJBERGWkVuYidtvrSIyeb+1QiKodJigsZ12x6YlmrKNbJloIwQ7riIiIiIiIaikhBO6mXMDd1FiTZRUKYOFCTQsKd/dKCK6cCguBPXs0401YQtKxRRBqJRqHTrNMhURE5WBj54zQcRvRZshP1g6FqNphgsJChBBnABzRmTVDkiSD94NIktQLwEM6s36uqNiIiIiIiIioOhA48svDuHLwK7NKBwcDQ4ZoniuVFRhWOeTkABkZgL09sHkz8P335a9TpcxD0vHF8G0+EC7ezctfIRGRBbh4N4fCxg6FuenIzeQ9yETmYoLCCEmSIiRJEjqPuSZWeV3neT0Av0qS5KKn3hYAfgEg3Z91EUxQEBERERER1WqSpIB3415IS9hTqoGf588HOnUC8vIqMLgySE0F+vYFRowA1GrNwN6SZHI1k26dW4OCnBQEdHmu/JUREVmQEGoc/qUfTm94GkKorR0OUbVQIxIUkiS9JUlSXvEHgDCdYmH6ykiStMhScQghdgH4QWdWfwAnJUl6SZKkvpIkDZEk6VMA0QAa3y+TD+BJIUQVvd+FiIiIiIiIKot3QBjyspKQm3HV7HVatADatKlaCYqkJKBXL+DoUWDGDE2XVJYghEBC9Hy4+gbDOyDCMpUSEVmIJCnQtNsspCcdwLXjS6wdDlG1YGvtACzEFoCDiTKSgTJ2Fo7lRQDeAB6/Px0IwFD73BwAE4QQ+ywcAxEREREREVVD3gHhAIC0xD1w9mpq1jr9+mkeVcXZs5pBsLOzgX/+AcLDLVd3euJeZN85jZBBP0CyRHMMIiILa9D2Cdw4uwqxO9+Gb4tBcHRrYO2QiKq0GtGCoioRQqiEEOMATAAQb6CYCsAmAB2EEMUH1yYiIiIiIqJaysWnJexd6iHt6u5Sr3vmDPDSS0ApeoeyuH37gJ49NV067d1r2eQEACREz4edkw/qB4+1bMVERBYiSRJCBnwLoVbi3NaXStVlH1FtVCNaUAgh5gKYWwH17sJ/40SUdt1fAPwiSVInAG0A1AeQB+A6gD1CiDuWipOIiIiIiIhqBkmSNONQJGrGoShNK4GDB4FvvgHatwcmT66wEA3680/gsceAxo2BrVuBJk0sW78QAl6Ne8I7IAw2dk6WrZyIyIKcvZqhedhbiNsxB7dj/4Bfq5HWDomoypKYxat+QkNDRUxMjLXDICIiIiIiogqQdHwJzm2ZiZ5Tj8HFJ8js9dRqICwMuHBB86hTpwKDLGbRImDaNCA0FNi4sXK3TURUFanVShxeGoG87BvoOeUo7Jy8rB0SkVVJknRUCBFafD67eCIiIiIiIiKqQuRxKBL2lGo9hQL46ScgMxN49dWKiEw/tRr44w/NuBM7dlRMckKZn4Xrp3+FWplv+cqJiCqAQmGLkIE/oDAnFbE75lg7HKIqiwkKIiIiIiIioirE2asZHN0aIi1xX6nXDQkBXnsNWLoU2LmzAoLToVIB6emaxMiaNZounlxcKmZbty78gTN/T0X2nTMVswEiogrg7tceTbq9iOunluNeapy1wyGqktjFUzXELp6IiIiIiIhqtnup8XDyaAyFrUOp183NBdq0AWxtgZMnAUfHCggQwPjxwPnzmrEv7O0rZhtaQghkXj8Cz0bdKnZDREQWpirMRdbtk/Bq9IC1QyGyKnbxRERERERERFRNuPi0KFNyAgCcnIAFC4C4OOCTTywcmI7HHwcmTar45ASgGTycyQkiqo5s7Jzk5ERe9g0rR0NU9TBBQURERERERFTFqFWFiN3xFm6dX1em9R9+GBg3Dpg3D4iNtVxc168Dv/2meT54MDBzpuXqNuT4unG4cujrit8QEVEFunnud+xZ0AZZt05aOxSiKoUJCiIiIiIiIqIqRmFjh+SLm5B953SZ6/jqK8DPT9MNkyWcPw88+CDw7LNAWppl6jQl+84Z3In9q3I2RkRUgeo07YOALi/A2auptUMhqlJsrR0AEREREREREZXU45loSAqbMq9frx5w8SJgZ1f+WA4eBIYM0dS1Ywfg7V3+Os2RED0fNnbOaNh+UuVskIiogtg5eaFl7w8AaMbVkSTJyhERVQ1sQUFERERERERUBZUnOaFlZwcIASxbBqSklK2Ov/8G+vTRJCUOHAA6dix3WGYpyEnGzbOr0aDN47B3qqSMCBFRBctOPotDUeG4l3bJ2qEQVQlMUBARERERERFVQarCXBxe1gcJ0fPLtL6fHyBJgEKhGcza11czXdrH0KFAcDCwfz/QrJmFd9KIpONLoFblo3Hoc5W3USKiCmbn6IWc9Is4t2UmhBDWDofI6pigICIiIiIiIqqCbOycUJCbhtQrO8q0/u3blotl1y6gbl3L1WeKWlWAxKMLUadZP7jWaVV5GyYiqmCObg0QFPE+0hJ248bpX6wdDpHVMUFBREREREREVEX5BIQhLWk/1GqlVeNwda3c7d06vw4F924joMsLlbthIqJK0KjjU/Dy744L299E/l0LZpOJqiEmKIiIiIiIiIiqKO+AMKgKspF187i1Q6k0QggkRM+Hi08QfJr2sXY4REQWJ0kKhAz8HqrCe7iw7f+sHQ6RVTFBQURERERERFRFeTXuBQBIS9xj5UgqT/adM8i6dQwBoc9DkiRrh0NEVCFcfIIQ2GM2bp1fhzvxG60dDpHVMEFBREREREREVEU5uNSFa53WSEuoPQkK93pt8eBTB1C/zePWDoWIqEI1fWAWXH1DcG7rS1DmZ1k7HCKrYIKCiIiIiIiIqArzDghHxrWDUKsKrB1KpXGv1w629i7WDoOIqEIpbOwRMugH5GffRNyud60dDpFVMEFBREREREREVIV5B4RBVZiDzBsx1g6lwsXvfg+n/54GIYS1QyEiqhSeDUIR2OtNeDV60NqhEFmFrbUDICIiIiIiIiLDvBr3BCAhLWEPvPy7WzuciiUpIClsOPYEEdUqzXu+Ye0QiKyGCQoiIiIiIiKiKszeyRvNur8Kjwah1g6lwrUIe9vaIRARWYUQAgnR30OtzEez7q9aOxyiSsMEBREREREREVEV1yK8ZvdNLoQaGdcOwbPRg2w9QUS1kiRJyLp1EiplDoQQ/C6kWoNjUBARERERERFVcUII3EuNQ17WdWuHUiFSLm/DkV8eRnL8RmuHQkRkNW0G/YAOI1cwOUG1ChMURERERERERFWcMj8L+xaFIunEEmuHUiESon+Ag2t91Al82NqhEBFZjcLWAZIkISf9Mu7E/W3tcIgqBRMURERERERERFWcnaMH2o9YhkbtJlo7FIu7m3IeqVe2o3HnqVDY2Fs7HCIiq4vdMQen/noauZlJ1g6FqMIxQUFERERERERUDfi1GgEnzwBrh2FxCTELoLB1RKMOT1o7FCKiKqFV308BAOe2zoIQwsrREFUsJiiIiIiIiIiIqgFlwT1cO7kM2XfOWDsUiynITcON0ytRP+Qx2DvXsXY4RERVgpNHY7QIfxcpl7bi5rk11g6HqEIxQUFERERERERULQic2zKzRl2sunYiCmplLgK6PGftUIiIqpTGnZ+FR4MuuPDv/6EgJ8Xa4RBVGCYoiIiIiIiIiKoBW3tXeNQPRVrCHmuHYhFqVSGSjv4E7yYRcPMNsXY4RERViqSwQcig76HMz0Ts9jesHQ5RhWGCgoiIiIiIiKia8A7ohaybx6DMzzJZtl49y2zTUvUUdzv2T+RlX0dA6AsVswEiomrOzTcETR98BTfOrETK5W3WDoeoQjBBQURERERERFRNeAeEQwgV0pMOmCx76xYgRPkft25VzL4ItQpejXvCt3n/itkAEVENENj9Nbj4BOHslplQFty1djhEFscEBREREREREVE14dmwGyQb+xrRzVODNo+i6xNbIEm8NEFEZIjC1gEhA79HYU4KMm/EWDscIovjWQARERERERFRNWFj5wTPhl2Rlli9ExTp1w5BrSq0dhhERNWCl393hD1/Hj5NIqwdCpHFMUFBREREREREVI14Nw5D1q2TKMxNt3YoZZKXfRPRKwbg0r551g6FiKjasHf2gRACyRe3QK0qsHY4RBbDBAURERERERFRNeLTJByAQFrSPmuHUiYOrvXQ8ZFVaNThSWuHQkRUrWTeiMaxNaNx/dRya4dCZDG21g6AiIiIiIiIiMznUT8Utg4eyM1ItHYoZSJJCvg2H2DtMIiIqh3Phl3R4ZGV8G0+0NqhEFkMExRERERERERE1YjC1gG9X7wChY29tUMptZtnf0N28lk07/kmFLYO1g6HiKjaqRc0FABQmJcBWwd3SBI7yKHqjUcwERERERERUTVTHZMTQghcPvA5Ui7/C6kaxk9EVFXkZFzFvp864tqJKGuHQlRuTFAQERERERERVTN52TdxeHk/3I79y9qhmC3t6i7cTTmPgNDnIUmStcMhIqq2nDwC4OobjLidbyEv+6a1wyEqFyYoiIiIiIiIiKoZexdfSAoboBpd6E+ImQ97Z1/4BY+2dihERNWaJEkIHvAt1KoCnP/nZWuHQ1QuTFAQERERERERVTMKhS26PrFF7ou8qruXdhHJFzfDv9MzsLF1tHY4RETVnot3IJr3moM7cRtw68If1g6HAKQm7MaeBW2Rdft0ucrUNkxQEBEREREREVVTKmUeVMo8a4dhUmLMAkgKO/h3fMbaoRAR1RgBXWfArV57nP/nFRTmpls7nFotNWE3jv02GrkZV3B09XAU5KSWqUxtxAQFERERERERUTV0L+0idnzdqMqPQ1GYl4Hrp35B/eAxcHCtZ+1wiIhqDIXCFm0G/YDCnBTE7nzb2uHUWtrEg1qZC0Dzu3fs97FQq5WlKlNbMUFBREREREREVA05ezaFwtYRaQl7rB2KUddPLYeq8B4Cujxv7VCIiGocd78OCOg2E9dPRiE1Ybe1w6l1iiceAECoCpB9+xRit802XWb7G5Uec1XDBAURERERERFRNSQpbODt3xNpVfiClFCrkBjzI7z8e8Ddr4O1wyEiqpGa93wDTp7NcG7LLN6RX4n0JR601MpcXDu5FBe2v2m8zIko3Di7pjLCrbJsrR0AEREREREREZWNd0Av3In/G7mZSXDy8Ld2OCVIChu0H7EMQqitHQoRUY1lY+eMtkN/AqDp9okqx9lN0/UmHrTUylwkHVtkskzcjjloEDKmIkKsFtiCgoiIiIiIiKia8g4IB4Aq3c2TR4PO8GzYxdphEBHVaF6NHoRXoweRmrAbexa0Qdbt0wbLasq0NVqGTOswagXsnX0h2dgbLGMsOQEAClsntBu22NKhVStMUBARERERERFVU66+wbBz8qmS3Txl3TqJMxufR/7dW9YOhYioVkhN2I2jq4YjN+Mqjq4ahoKcVL1ljv02GrkZV3B09XC9Zcg87vXaoceUGLj7dYDC1qnU6ytsndB57Fp4B4RVQHTVBxMURERERERERNWUJCngHdALaYl7IISwdjhFZCefRfLFzVDYOlo7FCKiGk+beBD3x6AozMvAsd/HFhmToviYCfrKkGm63RbmZV9HuyGL0Kj9pFIlKZic+A87JSMiIiIiIiKqxrwbh+H2hT+Qm3EFzl7NrB2OrGHbcagfPBoKI11fEBFR+ekbrFmoC5F9+xRit81G64e/0F9GVaAps/0NtO73uTVCr1RCqKEquAu1qgD2znUAAGmJ+6CwcZC7Iry0/1MU5KRAmZ8NZX4mlAXZUOZlaf7mZ0KZn426LQaj/YgoAMCRX/qjUfuJaP3wF4CkQGLMfJNxKGyd0KrvJ0xO3McEBREREREREVE1pr3AkZawp8okKPKyb8DBtT6TE0REFUxf4kFLrczFtZNLIdnYGxysWa3MxbUTUfBo0LVKD9SsUuZBmZ8FZX4W1Mo8uNVtAwBIubwdyvxM+LUeBQC4uPdj3EuNvV9WJ8lwfxoQ8Kgfigcm7wIAnP/3VTh7NEHH0asAAEnHFkFVmANbB3fNw94d9s514OzV9P48D7j7tZfjaj9iKZw8GiM1YTeunYg0a1/Uylxc3PMh6rUcAXtnH8u9SNWUVNWagJJpoaGhIiYmxtphEBERERERURUghMD1U8vg07QPnNwbWTscqJR52PNDa9QPeRSt+n5i7XCIiGq0PQvaIjfjitEyClsnk4M1O7g2QMSMOEuGBgAQapUmOSAnCTSJgjqBD0OSJCRf+hfZd06h2YOvANC0YEi9uqtEgkGoCuQ67Z3roPeLVwEAx9c+hpz0y+jxzBEAwLHfRuNe2kVNMsHRHbb2brB18ICtgxtsHdxh5+ABR/dGckIjO/ksbOxc4OzZRBOvEJAkqVT7aCxJZIhkYw93v47oOn4rFIra0YZAkqSjQojQ4vNrx94TERERERER1VCSJKFR+0nWDkN269zvKMhJhm9gf2uHQkRU43UYtQJHVw1HYX5mkYv4ukxdOFfYOqHdsMVF5gkhoCrMgaogG4X3uzb6L8GQhXoth8PO0QOpV3bi5vnfEfzwV1DYOuDq4W9x7WSUXF5VeE/vNvu8cgu29q5IvboD108ukxMUqsIcQKjg6Foftj4t7ycWPO63XtA8t3P0lOsJGfgdINnI053G/m7OyyZz8w0pMl0ZyQlAp3ut+11w1WZMUBARERERERFVc4V5GbgTtxHeTcKt2opCCIGEmPlwrdMa3k0irBYHEVFt4V6vHXpMicGx38cg+/bpUl8oV9g4wK1uWzjdb0Fw7eRSxO54C6r8LAihMrieR/1OsHP0QE7mVaRc+gfKwnuwt3WAnZMPXH1DdLpIKtqCQftQ2DoCAIJ6f4CWD30s1xsU8V6p4rd39i1VeUsqa3JCS9sFl0fDblW6e62KxgQFERERERERUTVXmJuGMxufRfCAb+Hf8SmrxZGetA/Zt08hZOD3pb4LlYiIysbe2Qddx/+D2G2zce3kUrMvmCtsndAiYi5unP5V03IBgLNXIBqEjP2v5YJ90cSC9uHgWh8A4N/hSfh3eFKus2G7J9Cw3RNmx16duzc6u2m6Wa1TjJVRK3MRt2NOrU5QcAyKaohjUBAREREREZEuIQTupZyHS51WkCSF1eI4vvYxpCcdQPgLsbCxc7JaHEREtdWF7W8aHBBbl8LWCa36fgL/jk9XUmQ1T9bt0zi6ejgK8zL0dq+lfY0v7vnQYBdcClsndBr7O3wCwisjZKsyNAaF9c5aiIiIiIiIiMgiJEmCq2+wVZMTOelXcCduI/w7PsXkBBGRFaQm7DYrOQFo7ty/uOdDFOSkVkJkNZN7vbbo8Uw03P06QmFb9HdPYeuEzmPXwr/j0+gxJQbufh30lqktyQljmKAgIiIiIiIiqgFy0q/gzKbpuJsaa5XtJx79CZLCBv6dplpl+0REtVlZxkMozM/Esd/HQq1WVmBkNZume62taNRhspyA0CYnvAPCdMr8g0btJxUpw+SEBhMURERERERERDWApLDB9ZNRSL28vdK3rczPwrWTS1Gv1Ug4ujWo9O0TEdVmZR2sWagKkH37FGK3za6gyGoHhcIWrft9jpBB8+Hg2qBIcqJImYe/kMswOfGf6jsKCRERERERERHJnDwaw8mzCdIS9yCgy/OVuu27KbGwsXVEQJcXKnW7RES1XVmTE1pqZS6unVwKj4bdavVAzZbQIGSMydfQnDK1DVtQEBEREREREdUQ3gHhSEvcB6FWVep2PRt2Qfj0WHg2KDH2JRERVaCzm6abNSC2MWplLuJ2zLFkWERmY4KCiIiIiIiIqIbwbhwGZV4Gsu+crrRt5t+9DaFWQWFjX2nbJCIijQ6jfoW9S11IBr6DFbZOaNX3E9g7+xot03bYoooMk8ggJiiIiIiIiIiIaghtn9dpCXsqbZunNjyN6F8HVtr2iIjoP+712qLHM9Fw9+tYoqWEdrBm/45Po8eUGLj7ddBbhuMhkDUxQUFERERERERUQzi61YezdwukJuyutG027jQV/p2mVNr2iIioKHtnH3QdvxWNOkyWExDa5IQ2ca0p8w8atZ9UpAyTE2RtTFAQERERERER1SA+AWFITzoAtVpZKdur13IY6gdzwE8iImtSKGzRut/nCBk0Hw6uDYokJ4qUefgLuQyTE1QVMEFBREREREREVIN4B4RBVZCNrJvHK3Q7BTnJuLj3IxTkpFTodoiIyHwNQsYgYkZcieSEvjJMTlBVwAQFERERERERUQ3i1bgX3P06QVV4r0K3k3R8CS7tm4eCnOQK3Q4RERHVXLbWDoCIiIiIiIiILMfBpS4efLJiB8lWqwqQdGwRfJr2hWud1hW6LSIiIqq52IKCiIiIiIiIqAZSK/Mh1KoKqfvW+fXIv3sLAV1eqJD6iYiIqHZggoKIiIiIiIiohklPOoDtXzdCxvXDFq9bCIGE6B/g4t0CdZr1sXj9REREVHswQUFERERERERUw7jUaYVGHSbDztnH4nVnXD+MrFvH0LjL85AkXlYgIiKisuMYFEREREREREQ1jL2TN1r3+7xC6k6MmQ9bR080aDOuQuonIiKi2oO3OhARERERERHVQGq1EhnXj0ClzLNYnbmZSbh94U806vAkbO1dLFYvERER1U5MUBARERERERHVQKmXt+HwsocsOg5F4rGFAIDGnaZarE4iIiKqvdjFExEREREREVEN5OXfHZJkg7Sru+ETEG6ROusFDYODix+cPPwtUh8RERHVbkxQEBEREREREdVAtg7ucPfriLTEvRar07NhF3g27GKx+oiIiKh2YxdPRERERERERDWUd0AYMm/EQFlwr1z1CKFG/O73cS813kKRERERETFBQURERERERFRjeQeEQagLkXHtULnquZtyHlcP/w+ZN49ZKDIiIiIiJiiIiIiIiIiIaizPRg9CUtgiLWF3uepx8w1B+PQL8Gs90kKRERERETFBQURERERERFRj2dq7wKNBF6Ql7ClzHWpVAQDA3tkXCht7S4VGRERExAQFERERERERUU3m3bgXsm4dhzI/q0zrX/j3NUT/OhhCqC0cGREREdV2TFAQERERERER1WDeAWEQQoX0pIOlXrcgNw03zvwKJ48ASBIvIRAREZFl2Vo7ACIiIiIiIiKqOJ6NHsCDTx2EW92QUq97/eRSqApzENDl+QqIjIiIiGo7JiiIiIiIiIiIajAbW0e412tb6vXUaiUSj/4E74AwuNVtUwGRERERUW3H9plERERERERENVx28lmc3TwDBblpZq9zJ/Yv5GVdQ0CXFyowMiIiIqrNmKAgIiIiIiIiquGUeZm4dX4t7qXGmb1OQvR8OHk2g2/ggAqMjIiIiGozdvFEREREREREVMN5NuyG3rMSoVCYdxkg88ZRZFw/hFZ9P4WksKng6IiIiKi2YoKCiIiIiIiIqIaTFDaQSlE+IWY+bOzd0LDdhAqLiYiIiIhdPBERERERERHVAilXdmD/4q7Iv3fHaDlVYS5Sr+5Co/YTYevgXknRERERUW3EFhREREREREREtYCtvSvuJp9DeuJe+LV+xGA5GzsnhD13GmplfiVGR0RERLURW1AQERERERER1QLu9TvBxt4NaQl7DJZRq5UQQg0bO2fYOXlVYnRERERUGzFBQURERERERFQLKBS28PLvbjRBceP0Cuz7qSPy796uxMiIiIiotmKCgoiIiIiIiKiW8A4Iw720eORl39S73NGtITwbPQB7l7qVHBkRERHVRkxQEBEREREREdUSPgHhAGCwFUWdZn3RdshPkCSpMsMiIiKiWooJCiIiIiIiIqJawq1uW9g6eiItsWSC4ua531GQk2KFqIiIiKi2YoKCiIiIiIiIqBZITdiNvT91gFvddkhL2F1k2b20izj155OI3fYG9ixoi6zbp60UJREREdUmTFAQERERERER1XCpCbtx7LfRyM24gqxbx5CbcRW5mYny8sSYHwFJgVux65GbcQVHVw9HQU6qFSMmIiKi2oAJCiIiIiIiIqIaTJucUCtzAQBqZT7sXepBqJUAgMK8TFw7GXV/Wd79eRk49vtYqO+XISIiIqoINTJBIUlSY0mS5kiSdEiSpBuSJOVLkpQkSdJuSZJelCTJt4K2u0uSJFGGxwMVEQ8RERERERHVbsWTEwAg1IVQ5mchIXo+ACB+11xNYkKo/iujKkD27VOI3f5GpcdMREREtYettQOwNEmSZgD4FIBTsUWN7j/CALwjSdJzQojfKjs+IiIiIiIiosqgLzmhpVbm4tqJSEgKOyQdX6x3fU2ZKHg06IoGIWMqOlwiIiKqhWpUgkKSpA8AvFVsdjyAG9AkJwLvz/MGsFqSJBchRGQFhXMJwEUzy2ZUUAxERERERERUS53dNF1vckJLrcpH4tEfAQjDZZS5iNsxhwkKIiIiqhA1JkEhSdIoFE1OnAMwQQhxTKdMKIBlAFrfn7VQkqSzQogjFRDSL0KIuRVQLxEREREREZFJHUatwNFVw1GYnwmhKtBbRqgLjdahsHVCu2H6W1gQERERlVeNGINCkiQ7AJ/rzLoGoKducgIAhBAxAHoCuH5/li2ALyolSCIiIiIiIqJK5F6vHXpMiYG7XwcobIv3gmyawtYJnceuhXdAWAVER0RERFRDEhQAxgFopjP9shAiXV9BIUQagJd1ZvWSJIlnW0RERERERFTj2Dv7oOv4f9Co/aRSJSmYnCAiIqLKUFMSFLqdYd4AsN5E+XX3y+lbn4iIiIiIiKjGUChs0frhL+DfaYpZSQqFrRNa9f2EyQkiIiKqcNU+QSFJkhOAvjqztgghlMbWub98q86sYRURGxEREREREVFVkJqwG0nHFhkdNFtLrczFxT0foiAntRIiIyIiotqs2icoAAQDcNCZ3m/merrlGkuS5G25kIiIiIiIiIiqhtSE3Tj222izkhNahfmZOPb7WKjVRu//IyIiIiqXmpKg0BVv5nrFyxWvp7wGSpK0U5KkW5IkFUiSlC5JUpwkSSslSXpGkiRnC2+PiIiIiIiIqIiyJCcAQKgKkH37FGK3za6gyIiIiIhqRoKiSbHpRDPXSzBRT3l1BRABoB4AOwCeAFoAeAzAIgCJkiRNs/A2iYiIiIiIiACUPTmhpVbm4trJpbhxdo2FIyMiIiLSqAkJCvdi0xlmrpdZbNqt/KEUkQ/gDIDdAPYAiAMgdJb7AFggSdJSSZIkU5VJkjRVkqQYSZJikpOTLRwqERERERER1TRnN003mZwwNWi2WpmLuB1zLBkWERERkawmJChci02be2tI8XKWSFAkA/gSQC8ArkKItkKICCFEuBCiJYC6AN4AcFdnnYkAPjRVsRBioRAiVAgR6uvra4FQiYiIiIiIqCbrMOpX2LvUhWRjr3e5wtYJrfp+AntnX6Nl2g5bVJFhEhERUS1WExIUdsWmzR3Bq9BEPaUmhBgjhHhVCLFPCFEiDiFEihDiEwCdAdzUWfSaJElB5d0+ERERERERkZZ7vbbo8Uw03P06lmgpobB1Quexa+Hf8Wn0mBIDd78Oest0Gvs7fALCKzNsIiIiqkVqQoLiXrFpRzPXK96OtXg9FUYIEQfNWBRatgBmVtb2iYiIiIiIqHawd/ZB1/Fb0ajDZDkBoU1OeAeE6ZT5B43aTypShskJIiIiqmg1IUFxt9i0s5nrFS+XbYFYzCaE2ANgr86sAZW5fSIiIiIiIqodFApbtO73OUIGzYeDa4MiyYkiZR7+Qi7D5AQRERFVBltrB2ABxUeMrg8gxYz16hebNmcdS9sBzXgVANBMkiQ7IUTxrqeIiIiIiIiIyq1ByBg0CBlT7jJEREREllITWlBcKDYdYOZ6xcsVr6cy6I5DIQHwsUIMRERERERERERERESVriYkKM4Wm+5k5nq65QoAXLRMOKVSvJupXCvEQERERERERERERERU6ap9gkIIkQTgss4sczvJ1C23TwihslxUZmuj8zxPCJFphRiIiIiIiIiIiIiIiCpdtU9Q3Lde53mEJEmNjRW+v1w3QbG2QqIyHoMbgGE6s/ZVdgxERERERERERERERNZSUxIUkQDU958rALxtovw7+G/f7wL4rYLiMuZzAHV0pn+3QgxERERERERERERERFZha+0ALEEIcVaSpF8ATLw/6xlJkg4LIRYXLytJ0rMAntaZ9YUQIkVfvZIkRQDYqTPrPSHEXANlfwWwCsAmIYTSUKySJDkD+ArAszqzLwJYYmidiqBUKpGeno709HTk5ORApbJGD1dEREREREREVF3Z2NjA2dkZXl5e8PLygq1tjbjMRERElagm/XL8H4BeAJren14kSdJQaJIGNwA0BPA4gCE660RD05LBErrfrz9VkqRNAI4DuAQgA5rWGvV1yvjorJcJYLQQotBCcZiUl5eHuLg4uLq6wtfXF25ubrCxsYEkSZUVAhERERERERFVY0IIqFQqZGdnIz09HTdv3kRQUBAcHR2tHRoREVUjNSZBIYS4I0nSIABbAWjHoBiGouM86DoFYIgQIsfCofgAmHD/YUosgHFCiJMWjsEgpVKJuLg41K9fH76+vpW1WSIiIiIiIiKqQSRJgq2trdx6Ijk5GXFxcQgODmZLCiIiMltNGYMCACCEuACgLYD50IwtoU8qgA8BdBFC3LHg5pcAOAggz4yy5wG8CKCjEOKYBWMwKT09XW45QURERERERERkCb6+vnB1dUV6erq1QyEiomqkxqW0hRBZAF6QJOlVABEAAgB4AUiBpsulPcbGiChW1y4AZvV7JIR4H8D7kiTZA2gHoBE0rSl8oEkEZULT1dQRIcTNUuySRaWnpzM5QUREREREREQWp21JwesORERkrhqXoNASQuQC2GyF7RYAiLn/qHJycnLg5uZm7TCIiIiIiIiIqIZxc3NDQkKCtcMgIqJqpEZ18USmqVQq2NjYWDsMIiIiIiIiIqphbGxsoFKprB0GERFVI0xQ1EKSZFavVUREREREREREZuP1BiIiKi0mKIiIiIiIiIiIiIiIqNIxQUFERERERERERERERJWOCQoiIiIiIiIiIiIiIqp0TFAQEREREREREREREVGlY4KCiIiIiIiIiIiIiIgqHRMURFRlSZIESZIQERGhd/ncuXPlMrt27arU2Ij0iYqKko/JqKgoa4dDREREVGlMnbsTERER6cMEBZGVaE/g9T0UCgXc3d3RqlUrjB8/Hn///be1w6UaKjExEQqFQj72Jk+ebO2QiIiIKkXx868ZM2aYve6LL75YYn2qfvr06SO/f25ubrh79661QyIiIiKqdZigoErn5wdIUvkffn7W3pOKI4RAdnY2YmNjsWLFCgwdOhQRERFITU21dmhUw0RFRUEIIU///vvv/OeciKiWSU3YjT0L2iLr9ulylanuVq5ciYKCApPlCgsLsXLlykqIiCpSQkICdu7cKU/fvXsXa9assWJERERERLUTExRU6W7frlr1VAXr168v8li7di1++uknPPnkk3B0dAQA7N69G8OHDy9yMbm2mzt3LoQQEEKwKXkZCCGwdOnSIvPu3buH3377zUoRERFRZUtN2I1jv41GbsYVHF09HAU5JW+GMKdMdWZrawsASE1NxYYNG0yW//vvv5GcnFxkXap+it+kAQCRkZFWioaIiIio9mKCgqgKGDFiRJHHqFGjMHXqVCxZsgTR0dFwc3MDAOzfvx9btmyxcrRUU+zevRuXL18GADzxxBOws7MDwH/OiYhqC23iQa3MBQAU5mXg2O9joVYrS1WmugsMDERQUBAAmDV+kLZMUFAQAgMDKzAyqii6N2n4+vpi8ODBAIC9e/fi4sWL1gyNiIiIqNZhgoKoimvTpg2eeeYZeXr37t1WjIZqEt1ExMsvv4wBAwYAAPbt24f4+HhrhUVERJWgeOIBAISqANm3TyF222zTZba/UekxV6SJEycCALZs2YLbRprpJicnY/PmzQCASZMmVUpsZHm7du3ClStXAACPPfYYnnrqKXmZOUkqIiIiIrIcJiiIqoFWrVrJzzMzMw2Wu3DhAj7//HMMGzYMzZo1g7OzMxwcHFC/fn0MGDAA8+fPR15ensntqVQqLF++HEOHDoW/vz8cHR3h5OQEf39/dOrUCVOnTsW6deuQk5NjtJ6zZ8/i5ZdfRocOHeDt7Q0HBwc0bNgQw4YNw4oVK6BWq81/EfSYO3euPLDhrl27Siy/evVqicGfU1JSMHfuXLRt2xZubm5wc3NDp06dMG/ePJP7o1VQUICff/4Zw4YNk18fT09PtGvXDq+88gquXr1arv2qDNnZ2Vi7di0AICQkBJ06dcKECRPk5eb+c16WY2Xz5s3y+/Liiy+atZ0ZM2bI6xRvRdSkSRNIkoQmTZoAAJRKJRYuXIiePXuiTp06cHJyQosWLTB9+nRcu3bNrO0BwI4dOzB16lS0bt0anp6esLOzg6+vL3r16oW33noL586dM6uec+fO4dlnn0VgYCCcnJzg4+ODPn36YOXKlUa7bIuKipL3Wft+xMTE4JlnnkHz5s3h4uJi8Nj/559/MGHCBPl7wM3NDa1atcK0adNw9OhRo/FW1OcmKSkJs2fPRqdOnYp8HwwdOhRRUVFQqVRG19fGZE53buaUjY+PxyuvvILOnTvL76+Pjw9atmyJhx9+GJ999hnOnj1r1r4RVTf6Eg9aamUurp1cigvb3zRe5kQUbpytOf31T5w4EQqFAkqlEitWrDBY7pdffkFhYSEUCoWc1DBXSkoKPvroI/Tq1Qt+fn6wt7eXf1c+++wzZGdnG12/+O9dXl4evv32W/Ts2RP16tWDQqHQ+7136dIlPP/882jevDmcnJxQt25dhIWFYeHChfJ3b2m+Yw8ePIjnnnsOwcHB8PT0hKOjIxo3boxHH30UGzduLNVrYi26N2lMnDgRQ4YMgbe3NwBg6dKlZp+jHjt2DNOmTUPbtm3h7u4OOzs71K1bF8HBwRg6dCi+++47ORECaM6bGjVqBEmS4Ovra9aYJ8eOHZPfn8cee6zIMn3nwocOHcITTzyBgIAAODg4oG7duhgyZEipWmHfuHEDc+fORc+ePeVj1c3NDW3atMFTTz2FP/74A0ql6VZUOTk5+OKLLxAaGgovLy+4uLggJCQEb7zxBtLT042uW/yYTE9Px7x589ClSxfUqVOnyHmKrvKeb0RERJQY+H716tXo168f/Pz84ODggICAADz55JO4cOGCydcA0JybLl68GIMGDUKDBg3g4OAAHx8fhIaG4q233sLNmzeNrj958mQ5JlP/Z5hTNi8vD/Pnz0e/fv1Qv359ODg4wNXVFU2aNEHXrl3x4osvYvPmzSgsLDRr/4iIiMpN2387H9Xn0blzZ1FWMTExZV7XUgDLPaozAPLDlI8++kgu+/HHH+sts3Tp0iJ1GnoEBgaKc+fOGdxWcnKy6NKli1l1rV+/Xm8dhYWFYubMmUKhUBhdv2vXruLmzZsmX6Pw8HC9y9999125zM6dO0ssv3Llirx80qRJIjo6WjRs2NBgPB06dBCpqakG4xFCiOjoaNG0aVOj+2Vvby9+/PFHo/VY2+LFi+V4P/nkEyGEEHl5ecLT01MAEI0aNRIqlcpoHWU9VlQqlfwaenl5idzcXKPbyc3NFV5eXgKAaNy4cYm4AgICBAAREBAgkpOTRY8ePQzG4eXlZfJ78M6dO6Jv375m7VdxkZGR8rLIyEgRGRkpHBwcDK4/adIkg3EUr2vevHnCxsamRB26x352drYYOnSo0ZglSRIzZ840+P5WxOfmxx9/FE5OTkbjatu2rbhy5YrBOkx9H5Sm7KJFi4S9vb3J97d9+/Ymt0VUHe2e30Zs+djF6OOfz+qYLLPz2xbW3pVy0X7WW7ZsKYQQ8nd/27ZtDa7Tvn17AUD069dPCCFEy5YtDf4m6IqMjBRubm5Gv3Pq1asnDhw4YLAO3d+7y5cvi5CQkBJ1FP/e+/XXX41+/4aHh4uMjAyzvmPv3r0rHnvsMZPfnYMHDxZZWVlGXw9rysrKEs7OzgKAaNWqlTx/2rRp8j5s3brVZD3vvvuukCTJ5OsxfPjwIuu988478rLVq1eb3I5uXNu2bSsRg+75wEcffWT0/Pudd94xub1PPvlEODo6mtyvqKioEuvqHkeXLl0SwcHBBtcPCAgw+3f/6NGjwt/f3+R5lCXON8LDw+Wyubm5YsSIEQbrcnBwEJs2bTL6esbGxhb5ntD3cHFxEUuXLjVYx6RJk+SyxmI3p+zFixdF8+bNTb6/AMTx48eNbsuYqnDdgYiIqh4AMULPtW6O6kZUxeXl5RW5k69v3756y+Xk5ECSJHTu3BlhYWFo2bIlvLy8kJWVhYSEBKxevRpxcXG4dOkSBg4ciBMnTsDT07NEPVOmTEF0dDQAoHnz5nj88ccRFBQEJycnZGVlITY2Fnv27MHhw4f1xiGEwNixY7F+/XoAmn59H3/8cXTs2BEuLi5yLDExMThy5Aj69OmD6OhoODs7l/OVMi4pKQmDBw9GWloannjiCfTu3Ruurq44d+4cfvjhB6SmpuLEiROYNWsWli1bpreOgwcPom/fvvId43369MHAgQPh7++PvLw8HDx4EMuWLUNOTg6mTZsGBwcHvXd2VQXaOwcVCgWeeOIJAICDgwPGjh2LhQsX4tq1a/j333/Rv39/g3WU9VhRKBSYMmUK3nzzTaSnp2Pt2rVyDPr8/vvv8l12Tz/9NBQK/Y3/lEolHnnkEezfvx+9e/fGiBEjUL9+fVy/fh2LFy/G2bNnkZ6ejsceewxnz56Fvb19iTqSk5PRrVs3+W5HDw8PPP744+jSpQvc3d3l4+Tvv/822Rpjy5YtWLNmDTw8PPDCCy+gY8eOkCQJe/bsQWRkJAoLC7F06VKEhYUV6VpCn99++w2bN2+Gh4cHJk2ahM6dO8PGxgYnT56Eh4cHAM1dmQMHDsS+ffsAAJ6ennjqqafQqVMnKJVK7Nu3D8uWLUNBQQG+/fZb5ObmYuHChUa3a4nPzU8//YRp06bJ00OHDsXgwYPh6emJuLg4REZG4sqVKzh9+jR69uyJ48ePw9fX12hc5XH8+HE8++yzUKvVsLW1xSOPPIKwsDDUrVsXhYWFuHnzJo4fP45//vmnwmIgsrYOo1bg6KrhKMzPhFDpv4NbX8sJXQpbJ7QbtrgiwrOayZMnY9u2bTh9+jSOHTuGTp06FVl+/PhxnDx5Ui5rrv/973+YNWsWAM1v7SOPPIJevXrBx8cHaWlp2LJlC/7880/cvn0bffv2RXR0NIKDgw3Wl5+fj1GjRuHs2bPo2bMnHnnkETRo0ADJyclFuqfavn07JkyYIN8xHh4ejtGjR6Nu3bpITEzE8uXLsXv3bkyZMsXkPuTn56Nv3744dOgQAKBx48Z4/PHHERISAgcHB1y8eBHLli1DbGwsNm7ciBEjRuDff/81+JttTatXr5bP5XRbj06cOBE//vgjAM150sMPP2ywjj///BPvvfceAMDJyQmPP/44HnjgAXh7eyMvLw/Xrl1DTEwM/v333xLrTpkyBR999BFUKhUWLVqEsWPHGtxOTk4Ofv31VwBAs2bN8NBDDxksu3DhQqxcuRINGzbE5MmTERISgoKCAmzZsgWrV6+GEALvv/8+wsPDDdYzY8YMfP/99/J0//79MWDAADRo0AD5+fmIj4/Hjh07cODAAWiuMeiXlZWFwYMH48KFCxg2bBgGDhwIb29vXL58GQsWLEBiYiISEhIwceJE7Nmzx2A9gGbw+uHDh+PatWsYNGgQBg8ejDp16uD69etFWjlUxPmGtrVI586d8dhjj6Fx48ZISUnBihUrcODAAeTn52P8+PGIjY1FnTp1Sqx/7do19OzZE8nJyQA058uTJ09G8+bNkZ6ejr/++gubN2/GvXv3MHnyZNjY2Bg9Jy4vIQTGjBkjj7PSoUMHjB49Gs2aNYOdnR3S09Nx/vx57Ny5EydOnKiwOIiIiErQl7Xgo2o/2ILiv0d1Bp27U4pTqVQiOTlZbNiwQXTr1k0uN3nyZIP1nTlzRly+fNngcpVKJT7//HO5rrlz55Yoc/v2bfmuq9DQUHH37l2D9V29elVcvXq1xPxvvvlG3saIESNEZmam3vXffPNNudzrr7+ut4x2uSVaUAAQnp6e4tChQyXKXb58WW45YGNjI65fv16iTFZWlnznlouLi8G7peLj40Xjxo3lcsnJyXrLWVNcXJz8mvTp06fIsr1798rLHn30UYN1lPdYuXXrlrCzszP6/mqFhYXJ701SUlKJ5do7SrUPfa1XcnNzi3yWDN2xOHDgwCKvjaGWAWq1Wqxbt67EfN1WD4CmdcHt27dLlFu3bp1cpnXr1nq3UbyuVq1a6T02tT755BO5bMuWLfWWPXbsmPD29pbLbdiwoUQZS35urly5It/JaGNjo/d1z8nJEYMHD5a3N3r0aL37Z+r7wNyyL7zwgsnjQAghlEql2L9/v8ltEVVX+fdSxMGlvc1qKaGvdUXq1d3W3oVy0/3OFELzfeTu7i4AiBkzZpQoP3PmTAFAuLu7i5ycHCGE6RYUMTExwtbWVt5OXFyc3nJ///23/LvYtWtXvWWK/9599dVXBvetoKBANGvWTC47b968EmWUSqV4+umni9Rp6Dt21qxZcplp06aJ/Px8vducOHGiXG7BggUG47Om7t27C0DTojAhIaHIMu2d5Y6OjiI9Pd1gHdrfLRsbG6O/Fbm5ueLw4cMl5mtbO0qSZPT8fcmSJfLrqa8Vte65MKBp2aPvnOyrr76SywwcOFDvtlavXi2X8fLy0nturRUbGytOnz5dYr5uLPb29nrPM1JSUoq0Rtb3+hSvy8bGRvz2228G47Hk+YZuCwoAYs6cOUKtVhcpo1KpirSs+PTTT/XWNWDAgCLby8vLK1EmMjJSPqd2c3MTN27cKFHGUi0ooqOj5WVDhgwRSqXSYD1nz54VKSkpRrdlTFW47kBERFUPDLSgsPrFdj6qdoIiPNz04/PPi5aPjNQ8T07WX95U0qE0j/BwIf76S7O9Cxc009r/EfbvNy9+3fKVSffE19QjJCREfPnllyVOjstCe7E3MDCwxLKDBw+a9U+vIbm5uaJu3bryxVR9/7zq6tWrl/yPvr5ufiydoFi2bJnBWObMmWO03JdffmlWPUIIsX37drnsRx99ZLSsNbzxxhtyfPqa5wcGBgpA02w9LS1Nbx3lPVaEEGLMmDFyHYYu2MTGxsplBg8erLeM7gWbp556yuD2/v33X6PlDhw4IC9v3ry50aSLIbpJBTs7O3Hx4kWDZXW7okpMTDRalyRJ4sSJEwbrys/PF/Xq1RMAhK2trTh16pTBsmvWrJHr7dGjR4nllvzcvPTSS/Ly1157zWA9mZmZon79+vK+xsbGlihjqQRF//79BQDh4eFhke9Uoop2+Jf+Jh+XD31TpPy1k8uFEELk30s2ut6h5Q+Lnd82F1s/9TI/QTHPTexb3K1IPbfjNgohhLibEisO/9JfpCUdFEIIkZZ00Kz4dctXpuIJCiGEeOaZZwQA4ePjU+Q8pqCgQNSpU0cAEFOmTJHnm0pQaC9EOzg4iPj4eKPxvP3223Jd+i566/7ejRw50mhdut/1/fv3N1guPz9ftGjRwuj35o0bN+Ru8Yrf2FCcbmKkRYuq1wWY7nlFREREieXvvfeevHz+/PkG69G+7+3atStTHBs3bixyAdwQbTLF1tZWb7eouufCPj4+Bs/bVCqVfAONg4ODKCwsLLFc91g2p4srfXTPH95//32D5RYtWmSynG5dL730ktHtWvJ8QzdB8dBDDxmsKz4+3mi5kydPysubNGkiJzX10b15Qt/xYKkExcqVK+Vl+m60sSQmKIiISB9DCYqq1+aWiEqwt7eHi4sLNJ/l8unevTsAzYCJKSkpRZbpdrNUlsFht27dijt37gAAZs6cqbcLHV3jx48HoGkGru0yoKL4+vpi3LhxBpfrNnXXN/jx8uXLAQD169c32fT6oYceQoMGDQCgynUTo1Kp5K54nJ2dMWrUqBJltO9Lfn4+Vq5cqbee8h4rAIo0w1+8WH83Ibrzp06darJOY4Nuh4WFwdZW07Ohvvf4l19+kZ+/9dZbcHFxMbk9Y4YMGYLAwECDy00dc7p69eqF9u3bG1x+4MABuVuPgQMHom3btgbLjh49Gs2bNwcA7N+/X/7M6lPez826desAALa2tnjllVcM1uPu7o7nn38eACCEwB9//GGwbHlpj93s7GwkJiZW2HaIqgNJkuDsHQjvxmFQ2DqZsYICzp5NYefoWeGxWYu266bU1FT8/fff8vwNGzbI503mdu+Unp4uDxo9fPhw+bvXEO3vL2D6/GHGjBlGl//555/yc233UvrY29vjueeeM1rXb7/9Jg/mbOy7HADs7Ozw6KOPAgDi4+NNDuhb2ZYsWSI/1+3eSXeettsg3YG0i9P+lly7dg2ZmZmljmPAgAEICAiQt6Nv4OZz587hwIEDADTdFfn5+Rmtc+LEifDy8tK7TKFQIDw8HIDm/O7SpUtFlh89ehSxsbEANINEG+veyhw2NjaYPn26weWlOQcCTB/vFXW+Yey8snnz5vD39wdg/BwI0MTv5GT4O/a1116Tjzvd9SzNEufvREREFYFjUJBRu3aVvXydOvrX1+kqtNx062/Zsuh09+6li//+dXur0I7XoOvu3bu4cOECVq1ahePHj2PatGlYs2YN/vrrL6PjNWzbtg2rVq1CdHQ0EhMTkZ2drfefHgC4fv16kf5SQ0JC0KBBA9y4cQM///wzhBCYMmUKunbtalYfwnv37i0Sv6mT/uvXr8vPz58/j4iICJPbKKvQ0FDY2NgYXN6wYUP5uXa8A63MzEycOnUKgCZB8ddff5ncnqurKwDNflUl//zzj/y6jxw5Em5ubiXKTJgwQe5XecmSJfI/crrKe6wAQO/evREUFIS4uDhERUXhww8/hJ2dnbxcO0YDADRo0ACDBw82Wp+zs7PRC/P29vaoU6cObt26VeI9BiCP3SBJEoYOHWrWPhjzwAMPGF1u7JgrrlevXkaXHzlyRH5uzkWFfv36yf0PHz582OD+ludzc+fOHSQkJAAA2rdvj7p16xqN6eGHH8bbb78tx1RR+vXrh/Xr10OtVqN379548803MWLECL19RxNVBV2f2FLm8vbOdUyun5qwG8d+G21yzAkAgFBDmZ+FbhN3wN7Zp8RiF5+gItvzavRAqeL3amT8e7My9OjRAy1atEB8fDyWLl0qJ/KjoqIAAEFBQfLNHqbs378farUaAODo6GjyvKiwsFB+buz8wcbGBg8++KDRumJiYgBoLkyHhYUZLWvq/Ev3/O7OnTsm90P39+D8+fNo0qSJ0fKVRaVSyTecODk5YfTo0SXKNG3aFD169MC+ffsQHR2NM2fOoE2bNiXK9evXD8ePH0daWhrCw8Px+uuvY/DgwXB3dzcrFu14XG+99RZu3LiBjRs3YtiwYUXKLFq0SH5uzjgh5Tnv0J4DASgRR1kEBQUZTJaYikVf2aZNmxpcXpHnG+a8pklJSXr3oTTnZo0bN0arVq1w/vx5XLhwAVlZWWYfS6XRs2dPODk5ITc3F++99x7S09MxadIktGvXzuLbIiIiKg0mKIiqgBEjRhhc9s477+DJJ5/Er7/+iu3bt2PmzJl67zbPzMzE2LFjS3XHflZWVpFpGxsb/PTTT3jkkUdQUFCAJUuWYMmSJfD09MSDDz6Inj17on///ujcubPe+nTvknvttdfMjgMw/c9JeZm6+Ojg4CA/z8vLK7IsKSlJvsBw7NgxjBw50uztlmW/EhMTcezYMYPLW7VqhVatWpW6XqDo3YD67hwEgMDAQHTv3h0HDhzA0aNHcfr06RIX/st7rACaRMDUqVPx6quv4s6dO9iwYUORFh1//fWXfHf/k08+afRCOQD4+PgUGSxRH+37XPw9BiAPel23bl14e3sbrccc5TnmitP9R16fmzdvys+DgoJMxqZbRnfd4sqzDxUVU3k9/fTT+O2337Br1y5cuXIFU6ZMwdSpUxESEoLu3bsjIiICgwYNkgcfJ6rJSpWcuK8wPxPHfh+LruO3QqGomf9KTJo0CW+99RY2bdok/w5t3rxZXmYu3fOiZcuWyS0YzWHs/MHHxweOjo5G179x4wYAwM/Pz+iNLYBm8GVjdPejNIODA2U7DzKWAHF2di7z3f1bt26VX5fhw4cbvAA8ceJE+YJ9ZGQkvvzyyxJlZs+ejb///hvnzp3DyZMnMW7cONjY2KBDhw7o0aMHevfujf79+xu9a/7pp5/Ge++9h8LCQixevLhIYiA/P19Opvj7+6N///4m9688v9nacyAAaN26tcltVWQsxVnrHAgwfz/y8/ONxtWiRQuz4jp//jyEELh161aFJCi8vb3x9ddf47nnnoNSqcRXX32Fr776CnXr1kX37t3Rq1cvDBw40CLHABERUWmwiyeiKs7e3h4LFiyQT1KjoqKQlJRUotzo0aPl5ISbmxvGjRuHzz77DMuXL8fatWuxfv16rF+/Xm52D0Bvy4ohQ4bgyJEjGDFihHw3e0ZGBjZv3ow5c+YgNDQUbdu2xZYtJe+ILEsTdy1t1wEVxdy7+vUpz37p3g1prh07dmDkyJEGH6tWrSpTLGlpaXLrDz8/P/Tt29dg2YkTJ8rPDXVxUJ5jRWvy5MnyP3e6dwrqTkuShKefftrk/pXnPQb+S9hpW7+UV3nj0WXsAgeg6a5Iy5yuqXT3UXfd4sqzDxUVU3nZ29tj69at+Pzzz+W7eoUQOHPmDBYuXIhx48ahXr16eOGFF0okcYlqkrIkJwBAqAqQffsUYrfNrqDIrG/ixIlQKBRQKpVYsWIFfvnlFyiVSigUiiK/j6ZU1HmRqd8EALh37x4AmExOAKa/oyv7/M7YOZA53T0aYs5NGgAwduxYOQGkfe+L8/LywqFDhzBnzhzUq1cPgOa8+ujRo/j2228xcuRI1KtXD++8847B18DPz09OSmzatKlIy+L169cjNTUVgCaRYc7vcXl+s3V/7yxxHlQTzoEAy5wH2draFknIWCKu8nj22Wexc+dO9OnTR94/bcuoV155BcHBwejRo0eRFiBEREQVjQkKomrA3d1dbsqvUqmwY8eOIsv37NmDbdu2AdA0bb506RJWrFiB//u//8P48eMxatQojBgxAiNGjJD7SjWmffv28j9Gmzdvxttvv43w8HD5IvSZM2cwaNAgrFixosh6uifWV69eLdXg73Pnzi3PS1ShdPdr8uTJpR7Yvqr49ddf5Tu8bt26BVtbW0iSpPehOz7EL7/8YjDRUtZjRcvHx0fuYuGff/6RxwRITEzEv//+CwDo27ev0ab9lqJNAt69e7fCt2Vpul11aS9KGaO7j/q6+arOMWlbOxljb2+PV199FVeuXMHZs2excOFCTJo0CY0aNQKguRNy/vz5CAsLQ25u6S7eElUHZU1OaKmVubh2cilunF1j4ciqBn9/f7mP/KioKLm7wT59+sjfE+bQPX+Iiooq1bnDrtL2s1qM9kJtTk6OybKmvqO1+2Fra4vCwsJS7UdpW1xUFN2bNABg8ODBBs+BPD095bv679y5I48jUpybmxs+/PBD3LhxA8eOHcN3332HRx99VL7rPjs7Gx988AGGDRtm8HxQe76lUqmKJFC0N2koFAo89dRT5X8BTNC9W7+6nQdVxXMg3bqVSqVZibrKPA8KDw/Htm3bcOfOHaxfvx6vvfYaunXrJicsDhw4gJ49e5b7e4iIiMhcTFAQVRM+Pv/19axtnq6lTU4AwEcffQRfX1+D9Wj7aDWHm5sbBgwYgPfffx+7du3CzZs38dJLLwHQ3HH88ssvF2mFodsEuyYNvFbZ+2UqCVLWZI6xwR6NSU5ONvjPuVZpjxVd2n/O1Wq1PHjlzz//LP+DVZ67JUtDe9Hpzp07SEtLq5RtWkr9+vXl5/Hx8SbL65bRDuhe1WOyt7cHYPpuXO0gtuYKDg7GlClT5NZpO3bskFtWnDx5Ej///HOp6iOqDs5umm4yOWFq0Gy1MhdxO+ZYMqwqRXth/dSpU/I4VKW92G7N8yLt9+itW7dMJikuX75sdLl2P5RKJeLi4iwToBHGzoHKOuj2ihUrytxa19T5k0KhQMeOHTF9+nSsWrUKt2/fxvr16+XuIrdu3WrwPKpPnz7y4OlLliyBEAKXL1/Gzp07AQADBw406+ai8tJNvFW18dNMqYrnQEDZ45IkqcSA6LotMCx5HuTj44MRI0bg008/xaFDh5CYmIhx48YB0LQCf/XVV82ui4iIqDyYoCCqJrTNvIGSzZdv374tPw8MDDRYR0FBQbnuhPHx8cFXX32F0NBQAJoLubon3OHh4fJzfQN/V1d16tRBcHAwAODo0aN6u9iq6k6dOiWPaxEQEIB3333X5EN3QMbSJjdMHSu6evbsiZCQEACaf84LCwvlRIWvry+GDx9e6v0tC+1A1EIIbNiwoVK2aSldu3aVn2tbnhijW0Z3XUuqW7cuAgICAAAnTpxAcnKy0fK64+foi8nT0xNAyQRtceUdYLt37974/vvv5WndgUOJaooOo36FvUtdSDb2epcrbJ3Qqu8nsHf2NVqm7bBFepfVBKNGjSpyV7m7u3upxqACgLCwMHlspD///NOsO5stRfv7q1arsWfPHqNlTZ0b1oTzO93zmBdffNGs8yDtDT8bN26UxyIxh0KhwIgRI/D+++/L8wz9lmjH4wKAK1euYNu2bVi8eLHc4sKcwbEtQXsOBKBIS5PqwNLnG5ZSmnOzpKQkXLhwAYBmrLni409oz4EA4+dBKpUKMTExZYhWo2HDhli6dKmcIDl69ChbkhIRUaVggoKoGsjOzsbBgwfl6eIDl+n2L3zp0iWD9SxYsMDkSbs5tHcXAyjSL++gQYPkZu3Lli2rUa0otINiqtVqvPHGG1aOpvR0/zGfMmUK5s6da/Ixf/58+Z/zTZs2FUmEmcvQsVLcs88+C0DzD9qsWbPkwRonT54sdxdV0caPHy8//+ijj8zqJqCq6N69u/zP5MaNG3Hu3DmDZdetWycni3r27Im6detWWFyPPPIIAM17/8033xgsl52djfnz5wPQXKzRdxFQmyRMSEgwerfvt99+W46INcw9bomqK/d6bdHjmWi4+3Us0VJCYeuEzmPXwr/j0+gxJQbufh30luk09nf4BISjpnJycsKsWbPQrVs3dOvWDS+99JJZYz/oqlu3LgYMGAAAiIuLq9QWWbrJ/f/9738GyxUUFGDBggVG63rsscfkVmxff/01bt26ZZkgK8nJkydx/PhxAEDz5s3xzTffmHUe9MQTTwDQ/A788ssvpd6uub8lTz75pHyH/IIFCxAVFQVAcwf+4MGDS73dsujcuTNatWoFQJOw0r2IXx1Y8nzDUkaNGiU//+6774wOBv7555/LCUztvujSngMBKNHVr65Vq1aV+389W1vbIi1qeB5ERESVgQkKoiqusLAQzz33nDx4XYMGDYrcyQYAXbp0kZ+///778jgDujZs2IDZs40PaLl161b873//MzoY4sWLF+W7gFxdXYu02HBxccG7774LQPMP76BBg0zexRMdHY3XXnvNaJmq4IUXXpDvzlqxYgVeeuklo02ss7Ky8O233xbpfstaCgsL5TEgJEmS/+E2xdbWFo899hiAkv+cl/dYKW7ixIlyok37jyMAPPPMM2bFagkPPPAABg0aBEDTzH7EiBEGu3oSQlSpOwzt7e3lLrWUSiXGjBmDmzdvlih36tQpORkEwOR3QnnNmDFDvqD32WefYe3atSXK5OXlYfz48fIdgY888ghatGhRopz2Ih8AvP7663r7837nnXdMfuZeeeUVHDp0yGgZ3Yt17du3N1qWqLqyd/ZB1/Fb0ajDZDkBoU1OeAeE6ZT5B43aTypSpqYnJ7Tee+89HDp0CIcOHSpz94offvihnGifMWOGyQvdiYmJ+L//+79S3bGvz/Dhw+Xxm7Zs2YJPP/20RBmVSoXnn3/eZPcz/v7+mDFjBgBNi97+/fvj4sWLBssLIbB9+3Z89NFH5dgDy9G9SUP3ZgRTdAfSLt6SdOrUqThz5ozBdZVKpTyOBGD8t6ROnTryRen169fLv99PPvkkbG1tzY63PCRJwgcffCBPP/bYY0Zb1ly8eLFK3YhkyfMNS2nXrh0GDhwIQNON2pNPPqn3f4fly5fjhx9+AKDpMvX5558vUaZfv36wsbEBAPzwww96u+yNiYmRP6eGrFixApGRkUZbRRw6dEhO6DVr1qxCx+kgIiLSqpwzHiId9eoBZbgRW289NcUff/xRYt69e/dw4cIFrFq1Sv4nUKFQ4Pvvv5fvYtMaOXIkGjZsiOvXr+PIkSMIDg7G008/jWbNmiEjIwObNm3Chg0b4OzsjFGjRmHdunV647h58yZmzZqF1157Db1790a3bt3QrFkzODs7IyUlBdHR0fjtt9/kO8tnzZpV4m7C6dOnIzo6GsuWLUNiYiK6du2KAQMGyANLCiGQkpKC06dPY/v27bh06RICAwPx2WefWeCVrDguLi74448/EB4ejqysLHzzzTf47bffMHbsWLRr1w7u7u7Izs7GlStXcOTIEezcuRP5+flYvny5tUPH33//Ld9N1bNnzyJ39JkyYcIEfPfddwA0/5y/8sorACxzrOjy8PDAo48+WuQCQEREBIKCgkq7u+WydOlSdO3aVe5mITAwEI899hi6dOkCd3d3pKen49SpU9iwYQMSEhKq1CDor7zyCjZs2IB9+/bh3LlzCAkJwVNPPYVOnTpBqVRi//79WLp0qZzAnDJlSoXfmdmkSRN8/fXXmDZtGpRKJUaPHo3hw4dj0KBB8PT0RHx8PJYsWSK3iGjYsKH8T3pxTz31FD777DOkpaXh999/R69evfDEE0+gTp06SExMxKpVqxATE4PHHnsMq1atMhjT2rVr8dVXX6Fp06bo27cv2rVrh7p16yI/Px9JSUlYs2YNTpw4AUDTVVlljYFCZA0KhS1a9/scHg26Im7HHLQbtlhOThQp8/AX8GjYDXE75qDtsEW1IjlhKZ06dcKCBQswZcoU5OfnY8KECfjyyy8xfPhwNG/eHA4ODsjIyMCFCxewf/9+HDlyBEIIvPjii+Xarp2dHRYtWoT+/ftDpVJh9uzZ2Lx5M8aMGQNfX18kJiZi+fLlOHXqFEaPHo3ff/8dAORBcoubN28eTpw4ge3bt+PUqVMIDg7G8OHDERYWBj8/PxQWFuL27ds4efIk/v33X9y4cQN9+vTBnDnWHadE9yYNoHQJik6dOiE4OBjnzp3DmTNnEBMTI3edtWjRIixatAghISHo3bs32rRpA29vb9y7dw+XL1/GqlWr5MRPUFAQRo8ebXRbzz77LH799Vd5WpKkSr1JAwBGjx6N6dOn4/vvv0d6ejp69+6NAQMGoH///mjQoAEKCgpw6dIl7Ny5E3v37sXPP/8sd9FpbZY837CkhQsXolOnTkhOTsaqVatw7NgxTJo0Cc2bN0dGRgb++uuvIuOTLFiwoMjYFVoNGjTAuHHjsHz5cqSlpaFLly54/vnnERwcjLt372LXrl1YuXIlvLy88NBDDxlsZREfH4/33nsPM2bMQL9+/dClSxf4+/vDwcEBd+7cwd69e/HHH3/I48a9+eabFfPCEBERFWdsEDI+quajc+fOoqxiYmLKvC5ZFoBSPby9vcXq1asN1nfw4EHh5eVlcH1PT0+xceNG8e6778rzdu7cWaSOpUuXmhWLJEnixRdfFCqVSm8sarVafPDBB8LBwcGs+sLDw42+RoaWG9sXIYS4cuWKvHzSpEkGX7vSlL1w4YLo2LGjWfvl4OAgNm/ebHS7lWHo0KFyTD/99FOp12/ZsqW8/pEjR4QQljtWdB0+fLjIuitWrDArvoCAAAFABAQEWKTsrVu3RHh4uFn7VlxkZKS8PDIy0mgspsqWpi6t7OxsMWTIEJNxT58+3eB7UhGfmwULFghHR0ejcbVp00ZcuXLF6PY2bdpktJ4hQ4aIe/fuGf3uaNq0qVnHbkBAgDh69KjReIioetN+3lu2bFnmOnR/I43566+/RL169cz6/vHx8RHJyckl6ijN753Wr7/+avR7MywsTKSkpMjTw4YNM1hXfn6+mD59urCxsTFrPyZOnGh2nBVl7dq1cjwPPvhgqdefN2+evP7zzz8vz5ckyazXoF27diZ/27Rat24tr9evXz+z1jF1LlyWsu+//76wt7c3uW9Lly4tsa6x39/Sli1NXVqWON/QPQc0xZyysbGxRb4n9D2cnZ31vp66UlNTRYcOHQzWUb9+fXHo0CExadIkeV7x/XzvvffMOm7t7OzEJ598YnL/jeF1ByIi0gdAjNBzrZtdPBFVUU5OTmjYsCEGDBiAb775BvHx8Rg7dqzB8g888ABOnjyJ6dOnIzAwEPb29vDw8ECbNm3w+uuv4+TJk3L3NYZMmDABJ0+exFdffSXf2efi4gIbGxt4eHigQ4cOmD59Oo4ePYpvvvnG4F12kiThrbfewpUrV/D+++8jPDwcfn5+sLe3h6OjIxo1aoS+ffvi7bffxsGDB8s1cHdla9myJY4ePYo///wTkyZNQlBQENzd3WFjYwNPT0+0b98eEydORFRUFG7evFmkWxpruH37NjZv3gxA0w3QmDFjSl2HbhcH2sGrLXWs6OrSpQs8PDwAAN7e3kX67q1M9erVw65du7Bp0yZMmDABzZo1g4uLC+zs7FC3bl2Eh4dj7ty5iI2NtUp8xri6umLDhg3YsmULxo0bh4CAADg6OsLFxQVBQUGYOnUqoqOj8d1335n1nljKtGnTEBcXh9dffx0dOnSAp6cn7O3tUb9+fQwaNAiRkZE4ceKEydY9AwcOxIkTJ/Dkk0+icePGsLe3h6+vL3r37o3ly5fjr7/+KjImjz7Hjh3D+vXrMWPGDHTt2hV16tSBnZ0dHBwc0KhRIwwaNAg//vgjzp8/j06dOlnwVSCi2mzo0KG4cuUKfvzxRwwbNgz+/v5wcnKSv8cefPBBzJgxAxs2bMCNGzfkMb3K6/HHH8eZM2cwbdo0NG3aFA4ODqhTpw569uyJn376Cdu3by/Sx7y3t7fBuuzt7fHdd9/hwoULmD17Nrp16wZfX1/Y2trC2dkZTZs2xaBBg/Dxxx/j1KlTWLp0qUX2oTzK2r2T1hNPPCH/Xq5cuVIeR+DWrVtYuXIlpkyZgk6dOsHLyws2NjZwcnJCkyZNMHLkSKxYsQLHjh0zu+Vq37595eeVNTi2Pm+//Tbi4uLwxhtvoHPnzvD29oaNjQ3c3NzQtm1bPPPMM9i0aVOZXs+KZqnzDUsKCgrC6dOn5RZNfn5+sLOzg5eXFzp16oQ333wT8fHxmDhxotF6vL29ceDAAXzyySfo2LEjXF1d4eLiguDgYMyZMwcnT55Et27djNYxZ84cHDp0CB9//DEGDBiAJk2awMnJCba2tvDy8kLXrl3x+uuv49y5c3j99dct+TIQEREZJWmSF1SdhIaGClP9+hty9OhRdO7c2cIRERGV37Zt29CvXz8AwIsvvmh0kEMiIiKyjA0bNmDYsGEAgK+++koe04gqj1qtRpMmTZCUlARfX19cu3atRJeuRNUJrzsQEZE+kiQdFUKEFp/PFhRERFQl6A5MzH7/iYiIKsf3338vP4+IiLBeILXYxo0bkZSUBEAzODaTE0RERFSbMEFBRERWd+LECXmw+L59+yI4ONi6AREREdUAu3fvNrhMrVZj9uzZ+OeffwAAXbt2RceOHSsrNLpPpVLh/fffBwDY2triueees3JERERERJXL1toBEBFR7bRlyxao1WrExcXhs88+g1qtBgDMnTvXuoERERHVEH369EHTpk0xYMAAtG3bFt7e3sjLy8P58+exZs0axMfHA9CML/Hjjz9aOdra4/Tp07h+/TrS0tIQFRUFbfe9kydPrtTxEYiIiIiqAiYoiIjIKgYOHFhi3syZM9GjRw8rRENERFQzXbx4sUg3TsV5e3vjt99+Y+uJSvTll1+WGES8SZMm+PTTT60UEREREZH1MEFBRERW5erqiqCgIDz33HN46qmnrB0OERFRjbFjxw5s3rwZu3btws2bN5GamoqCggJ4e3sjODgYAwYMwLPPPgt3d3drh1or2djYoHHjxhgwYADeffddeHt7WzskIiIiokrHBAUREVmFEMLaIRAREdVoYWFhCAsLs3YYVExUVBSioqKsHQYRERFRlcBBsomIiIiIiIiIiIiIqNIxQUFERERERERERERERJWOCQoiIiIiIiIiIiIiIqp0TFAQEREREREREREREVGlY4KCiIiIiIiIiIiIiIgqHRMURERERERERERERERU6ZigICIiIiIiIiIiIiKiSscEBRERERERERERERERVTomKIiIiIiIiIiIiIiIqNIxQUFERERERERERERERJWOCQoiIiIiIiIiIiIiIqp0TFAQEREREREREREREVGlY4KCiIiIiIiIiIiIiIgqHRMURERERERERERERERU6ZigIKIqS5IkSJKEiIgIvcvnzp0rl9m1a1elxlbVRUVFya9NVFSUtcMplYiICDl2orKaPHmyfBxdvXrV2uEQERERERERkR5MUBBZifbCmb6HQqGAu7s7WrVqhfHjx+Pvv/+2drhEVMGWL19e5HuguiWWiIiIiIiIiIhKiwkKqlJUKjW++CIadep8jy+/jIZKpbZ2SFYhhEB2djZiY2OxYsUKDB06FBEREUhNTbV2aERUQSIjI41OExERERERERHVNLbWDoBIKz4+HWPH/oX4+HTcu6fEu+/ux4oV57F69VC0aOFl7fAq1Pr164tMq9VqpKSk4NChQ1i5ciXy8vKwe/duDB8+HHv37mXXN/fNnTsXc+fOtXYYVdLkyZMxefJka4dBZrp69WqJbsr27t2LS5cuITAw0DpBERERERERERFVMLagIKvTtppo334pTp1Kwb17SgDAvXtKnDyZjPbtl+LLL6OhVgsrR1pxRowYUeQxatQoTJ06FUuWLEF0dDTc3NwAAPv378eWLVusHC0RWVpUVBSE0HzHaRNLQgh280RERERERERENRoTFGRV8fHpCA1djrlzDyA3V1kiCaFWC+TmalpThIYuR3x8upUitZ42bdrgmWeekad3795txWiIyNKEEFi6dCkAwMfHB99//z18fHwAAEuXLoVaXTu7uiMiIiIiIiKimo8JCrKKkq0mCo2Wr02tKfRp1aqV/DwzM9NguQsXLuDzzz/HsGHD0KxZMzg7O8PBwQH169fHgAEDMH/+fOTl5ZncnkqlwvLlyzF06FD4+/vD0dERTk5O8Pf3R6dOnTB16lSsW7cOOTk5Rus5e/YsXn75ZXTo0AHe3t5wcHBAw4YNMWzYMKxYsaLcF17nzp0rDyhcvHscQNNtjna59q70lJQUzJ07F23btoWbmxvc3NzQqVMnzJs3z+T+aBUUFODnn3/GsGHD5NfH09MT7dq1wyuvvIKrV6+Wa790/fnnnxg7dqz8fjo6OqJhw4Zo3749JkyYgBUrViA9vWTiLioqyuRgy9rlERERAICcnBx88cUXCA0NhZeXF1xcXBASEoI33nhD7zb0OX78OCZPnoyAgAA4Ojqifv366N+/P1avXg1A/3tSHikpKfjoo4/Qq1cv+Pn5wd7eHr6+vujVqxc+++wzZGdnl3sbFW3nzp3yMfPYY4/BxcUFjz76KAAgKSkJ27dvN6uejIwMfPrppwgPD0fdunVhb28Pd3d3NGvWDN27d8fs2bOxa9cuuaUGALz++uvy+1G8qzlD2rVrB0mS4OTkVOS4qKjPm1KpxLJlyzBmzBg0adIELi4ucHBwgL+/PwYPHoxvvvkGd+7cMauurVu3YsSIEWjUqBEcHBzQoEEDjBkzBocPHza63uTJk+V9075X69atw7Bhw9C4cWPY29vr7XpPqVRi8eLFGDRoEBo0aAAHBwf4+PggNDQUb731Fm7evGl0u/o+x+fOncOzzz6LwMBAODk5wcfHB3369MHKlSuLvLfGHD58GFOnTkXLli3h5uYGFxcXBAYGYtKkSdixY4fRdXft2iXHZKqLPXPL7tq1C5MmTULLli3h6uoKe3t7+Pn5oU2bNhg9ejQWL16MW7dumbVvREREREREVM0IIfioZo/OnTuLsoqJiSnzupYSF5cmOnSIEi4u3wjg81I/XFy+Fh07LhVxcWnW3pVyASA/TPnoo4/ksh9//LHeMkuXLi1Sp6FHYGCgOHfunMFtJScniy5duphV1/r16/XWUVhYKGbOnCkUCoXR9bt27Spu3rxp8jUKDw/Xu/zdd9+Vy+zcubPE8itXrsjLJ02aJKKjo0XDhg0NxtOhQweRmppqMB4hhIiOjhZNmzY1ul/29vbixx9/NFqPKTk5OWLw4MFmvQ9ff/11ifUjIyPl5ZGRkXq3ofv6Xrp0SQQHBxvcRkBAgLhy5YrRmL/44gthY2NjsI7Ro0eLuLi4Iu+JPuHh4WZ9NiIjI4Wbm5vR16ZevXriwIEDRuuxtvHjx8vxHjp0SAghxKFDh+R5jz/+uMk6jhw5IurWrWvW8ZKeni6vd+nSJSFJkgAgBg4caHI7unGNHz++yLKK+rwFBgaa3KeIiIgS606aNElefunSJfHcc88ZXF+hUIjFixcbjEO3rgsXLoiRI0fqrUdXbGysaNmypdG4XVxcxNKlSw1ut/jnODIyUjg4OBisz9BnSquwsFBMmTLF5Os5ZswYkZOTo7eOnTt3yuXeffddo9szVValUolnnnnGrOP2xRdfNLotIiIiqjqqwnUHIiKqegDECD3XujlINlW6Xr1WIjk5t8ytILStKXr1Wolbt563cHRVT15eHlasWCFP9+3bV2+5nJwcSJKEzp07IywsDC1btoSXlxeysrKQkJCA1atXIy4uDpcuXcLAgQNx4sQJeHp6lqhnypQpiI6OBgA0b94cjz/+OIKCguDk5ISsrCzExsZiz549Bu84FkJg7Nix8t3Yvr6+ePzxx9GxY0e4uLjIscTExODIkSPo06cPoqOj4ezsXM5XyrikpCQMHjwYaWlpeOKJJ9C7d2+4urri3Llz+OGHH5CamooTJ05g1qxZWLZsmd46Dh48iL59+8p3fvfp0wcDBw6Ev78/8vLycPDgQSxbtgw5OTmYNm0aHBwcytxK4M0338TGjRsBAPXr18f48eMREhICV1dX3L17FxcvXsTBgwexZ8+eMtWvKysrC4MHD8aFCxcwbNgwDBw4EN7e3rh8+TIWLFiAxMREJCQkYOLEiQa3t3TpUrz66qvy9NChQzFkyBB4eHggPj4eS5Yswe+//26xAd7/97//YdasWQAABwcHPPLII+jVqxd8fHyQlpaGLVu24M8//8Tt27fRt29fREdHIzg42CLbtqSsrCysW7cOABAUFIRu3boBALp164agoCDExcVh/fr1yMjI0Pt5BTSf/ZEjR8qtCMLCwjBkyBA0btwYCoUCKSkpOHPmDLZv347Y2Ngi6zZr1gz9+vXDP//8g61btyIxMRGNGzc2GO+iRYvk51OmTDFYzhKft3379uHhhx9Gbm4uACAwMBBjx45F69at4eDggBs3buDw4cPYuHGjyZYDb731FlauXImgoCBMnDgRzZs3R3Z2NtatW4fNmzdDrVbj+eefR48ePYq0WNPnpZdewubNmxEYGIgJEyagZcuWyMnJKdL93rVr19CzZ08kJycD0HyXTp48Gc2bN0d6ejr++usvbN68Gffu3cPkyZNhY2ODJ554wuh2t2zZgjVr1sDDwwMvvPACOnbsCEmSsGfPHkRGRqKwsBBLly5FWFgYnnrqKb11TJw4EStXrgQAODo6YtKkSejevTtsbGwQExODn3/+GdnZ2VizZg0yMzOxZcsWi31m9fnuu++wePFiAICnpyfGjx+Pjh07wsPDAzk5Obh69SoOHz6MnTt3VlgMREREREREZGX6shZ8VO1HdW9BMXLkeiFJpW85ofuQpM/FyJHrrb0r5QIDd90KobmrNDk5WWzYsEF069ZNLjd58mSD9Z05c0ZcvnzZ4HKVSiU+//xzua65c+eWKHP79m251UNoaKi4e/euwfquXr0qrl69WmL+N998I29jxIgRIjMzU+/6b775plzu9ddf11tGu9wSLSgACE9PT/kOdV2XL18Wnp6eAoCwsbER169fL1EmKytL+Pv7y3c9b9q0SW9M8fHxonHjxnK55ORkveWMUSqVwsPDQ+B+y4Xbt28bLHvnzh1x/vz5EvNL04IC0LT62LBhQ4kyKSkpRVqMHD58WG8Z7eunUCjEihUrSpTJyckRAwYMMOtub1MtKGJiYoStra0AIFq2bCni4uL0lvv777+FnZ2dADStdaqihQsXyvv6wQcfFFn2wQcfyMsWLFhgsI41a9bI5Z577jmj2zt06JDIy8srMm/t2rVm3RGfnZ0tXF1dBQARFBRUYrklP28ZGRmifv36cl2vvfaaKCws1BvXvXv3xJYtW0rM1231AEBMnDhRbx0zZ840+foVr2vMmDEiPz9fb1khRJFjffTo0SVecyE0n1Ht962bm5u4ceOG3jK62+3QoYPe74N169bJZVq3bq03plWrVsll6tWrJ86ePVuizNWrV4t83r///vsSZSzZgiIkJEQAEB4eHiI2NtZgPZmZmeL48eNGt0VERERVR1W47kBERFUPDLSg4BgUZFRExCpERZ0BABQWqhARsQq//HIOAJCTU4iIiFVYvfoCACAzMx8REauwbl0cACAlJQcREauwYcMlAMCtW/cQEbEKDz7YAC4uduWKy8XFDkOGBCIiYhVOnNDcNRwdfRMREatw5ozmjtUDB64jImIVYmPTAAC7dychImIVLl/OAABs25aAiIhVSErKAgBs2XKlXDGVh7aPbu3DxsYGvr6+GDp0KA4fPoyQkBB8+eWXWLJkicE6QkJC0LRpU4PLFQoFXn31VYSFhQEAli9fXqLM5cuX5XEhxo0bBxcXF4P1BQQEICAgoMi8vLw8fPzxxwA042asXr0a7u7uetfXjhsAAAsWLDBrbIzy+vbbb+U71HU1bdoUL7zwAgDN+Bv6+vxftGgRkpKSAGjiHThwoN5tNG/eHJGRkQCAe/fuYeHChaWOMzk5WR5rZPjw4ahbt67Bsr6+vibv+DbHW2+9hSFDhpSY7+PjgzfffFOe3rp1a4kykZGRyMjIAABMnToV48aNK1HGyckJK1asgJeXV7ljfe+996BUKuHg4IC///4bLVq00Ftu8ODBmD17NgDgyJEjOHDgQLm3bWnaY0WSJIwfP77IsgkTJsh3r2vL6XPx4kX5ubFWDYCmZYaDg0ORecOGDUODBg3k7RgaG2blypW4e/euWdsByvd5++GHH+TxGR5//HF8+umnsLXV3+jT2dkZ/fv3NxpLq1atsGjRIr11fPjhh3BycgKg//gurlGjRoiMjIS9vb3e5adOncKWLVsAAE2aNMGyZctKvOaAZlyL5557DgCQnZ2NH374weh27ezs8Pvvv+v9Phg5ciR69OgBADh//rz8XaXr008/lZ9HRkbqbVEUEBCAVatWycfd559/DpVKZTSu8tAeu+Hh4QgKCjJYzt3dHR06dKiwOIiIiIiIiMh6mKCgStemTR14epa8WFMaXl6O6NjR8EXbmsbe3h4uLi4muzExR/fu3QEAly5dQkpKSpFlut0snT17ttR1b926Ve5mZubMmQYv4GlpL8hmZWXh0KFDpd5eafj6+uq9cK710EMPyc/PnTtXYrk2oVO/fn2TXbE89NBD8gXff/75p9Sxai+WGorF0mxsbDB9+nSDy029Nn/++af8/MUXXzRYj7e3NyZMmFDGKDXS09Plrq+GDx+O5s2bGy2ve9G/LO9FRbpw4QIOHjwIAOjZsyeaNGlSZHlAQICcUDxy5IjBz2R5P7e2trZ4+umnAQCJiYkGL9Jru3eyt7fHpEmTjNZZ3s+btls7hUKBDz/80PgOmOG5554z+H3k5uaG0NBQAMCVK1dMJkufeuopo8lbbZddADBjxowin+fiXnvtNTkZoLuePkOGDEFgYKDB5cZe06tXr+L48eMAgLZt2xpMsAJA165d5boSEhJw9OhRo3GVh/bYjY+PR2FhYYVth4iIiIiIiKoujkFBRu3a9Zj83M7Opsi0s7NdkWkPD4ci03XqOBeZ9vNzkadnzeqMd97Zj5wcZaljcna2xaxZndGxY70i9XfpUr/IdPfuDYtMh4f7F5nu2zcAffv+1wJgwADDrQ8qmna8Bl13797FhQsXsGrVKhw/fhzTpk3DmjVr8Ndffxkdr2Hbtm1YtWoVoqOjkZiYiOzsbIN3wF6/fh116tSRp0NCQtCgQQPcuHEDP//8M4QQmDJlCrp27QqFwnQ+c+/evUXi/+OPP4yWv379uvz8/PnziIiIMLmNsgoNDYWNjY3B5Q0bNpSfp6enF1mWmZmJU6dOAdAkKP766y+T23N1dQWg2a/S8vDwQNeuXXHkyBFs27YNI0eOxIwZM9CrVy/Y2ZWv9ZE+QUFBRls2GHtt1Go1jh07BgDw8/Mz2ZojIiIC3377bZlj3b9/v3yHv6Ojo8ljTPeiZ1nei4qk2ypi4sSJestMmDBBHtsgMjISX3zxRYkyffv2hSRJEEJg2rRpuHTpEsaNG2ewZYk+U6ZMwccffwyVSoVFixaVuIB96tQpeWyaESNGwNfX12h95fm8paWlyRfY27Rpg2bNmpm9H4Y88MADRpdr4xFCICMjA35+fgbLalt+GXLkyBH5+cMPP2y0bOPGjdGqVSucP38eFy5cQFZWlsFWZ+buA1DyNS1NTNoy2pYthw8fRteuXU2uUxb9+vXDb7/9hvPnz6Nv37549dVX0bdvX6NJHSIiIiIiIqpZmKAgq5g8uQ3mzNlXpnVVKoFJk0IsHJF1jRgxwuCyd955B08++SR+/fVXbN++HTNnzpQHFdWVmZmJsWPHluou8aysrCLTNjY2+Omnn/DII4+goKAAS5YswZIlS+Dp6YkHH3wQPXv2RP/+/dG5c2e99V29elV+/tprr5kdB1Dygpql6SZi9NHtgqX4HdRJSUnyRfFjx45h5MiRZm+3rPv1ww8/oE+fPsjKysIff/yBP/74Ay4uLujWrRt69uyJvn37okePHmYljkwpz2uTmZkpDxpuzoXk8l5s1j3Gli1bZnCAZX3K8l4cO3YMiYmJBpf37NnT5Ounj0qlklvlODo6YsyYMXrLjRkzBjNmzEBubi5++eUXfPLJJyW6KQoODsbs2bMxb9483Lt3D3PnzsXcuXPh7++P7t27IywsDIMHDy7RJZsuf39/DBw4EH///Tc2bNiA27dvo169evJycwfH1irPMaWbuGzdurXJbZmjPPEUp5sI0EfbNRUAs5JEQUFBOH/+PIQQuHXrlsEERXn2QTcmY10p6Suju66lffrpp9i3bx9u3LiBPXv2YM+ePXBwcEBoaCh69OiBhx56CA899FCFJGaJiIiIiIioamAXT2QVPj5OGDSoKe73bGE2SQIGDWoKH5/ac3elvb09FixYIF+0ioqK0tu/+OjRo+XkhJubG8aNG4fPPvsMy5cvx9q1a7F+/XqsX78ejz76qLyOvpYVQ4YMwZEjRzBixAj5olBGRgY2b96MOXPmIDQ0FG3btpX7WNelHTehLAoKCsq8rjnKcyG/PPtV1m5LQkNDceLECUycOFG+m/jevXvYsWMH3n//fYSFhSEwMBC//PJLmWPTKs9rc+/ePfm5sZY9Wsa6xjFHZR9j3377LUaOHGnwcebMmTLFsmXLFvnC79ChQ+Hh4aG3nLu7O4YPHw4AuH37NjZt2qS33Mcff4x169YVGfMhKSkJq1evxgsvvICmTZti0KBBiIuLMxjTs88+CwBQKpWIioqS5+fl5cnHWdOmTdGnTx+T+1eeY0o3captiVRelkjkaZm6uz87OxuApussfWNPFKe7j9p19SnPPujWa85n0NyYyqtJkyY4fvw4pk+fDk9PTwBAfn4+9u/fj88++wwDBgxAo0aN8M0331iki0MiIiIiIiKqepigIKuZNatzqQfLdna2w0svhVZQRFWXu7s7HnzwQQCapMKOHTuKLN+zZw+2bdsGAGjfvj0uXbqEFStW4P/+7/8wfvx4jBo1CiNGjMCIESPg7+9vcnvt27fH+vXrkZqais2bN+Ptt99GeHi4nLA4c+YMBg0aJPcTr6V7Uevq1asQQpj9mDt3bnleogqlu1+TJ08u1X6V56Ja06ZNsXTpUqSlpWHHjh348MMPMWDAAPkC6dWrVzFhwgR5YHJr0L3YqW1JYYxuQqMsdN+LqKioUr0Pu3btKte2LUm3e6c1a9ZAkiSDj1WrVuldr7iRI0fi0KFDuH79OlatWoWZM2eiXbt2ADRdF23evBldu3Y12NXVoEGD0LhxYwDA4sWL5WN3zZo18iDozzzzjDxmQkXRbUGgHZS7OnFzcwOgSfSYkxTT3UftuhUVE2DeZ9BSMRkacF1X3bp18d133+HOnTs4cOAAvvjiC4wYMUI+Du7cuYOXXnpJTqARERERERFRzcIEBVlNr16NSj1Ytre3I3r2NN69Rk3l4+MjP79x40aRZdrkBAB89NFHRvuHT0hIMHubbm5uGDBgAN5//33s2rULN2/exEsvvQRAc8Hz5ZdfLtIKQ7frk7IM1ltVWXu/HB0d0bt3b8yZMwebN2/GnTt38Omnn8oXit9//32kpqZWelyAZrwMbcuJy5cvmyxvThljKvu9MJUEKcu4KampqdiwYUOZ4tm4cSOSk5ONlmnQoAEeffRR/O9//8PJkycRFxeHvn37AtC0QHn77bf1rqdQKOTumy5evCiPfaHtUs7W1hZPPvlkmeIujYYNG8rHdlUbN8Qc9evXl5/Hx8ebLK8tI0mS0bEvrBEToDmedOm2CjGVgElJSTE3RNjZ2eHBBx/EK6+8gvXr1yM5ORlLliyRt7do0SKcPn3a7PqIiIiIiIioemCCgqxGkiTMmtUZzs7mDYWiHRy7ou/erap0L0AX76Lj9u3b8vPAwECDdRQUFJTrLnIfHx989dVXCA3VtGK5c+dOkQtZ4eHh8nN9A39XV3Xq1EFwcDAA4OjRo3q72KpMrq6ueO211/DII48A0HSJoh3AuLIpFAp06tQJAHDr1i1cuHDBaPnytmIICwuTvwP+/PNPs+7QrmpWrFghX9gNDw/Hu+++a/LRr18/AJouw0rbrVeLFi3w+++/y4NW79tnePyfp59+Wh7jYtGiRYiLi8OePXsAaLp/073QXVG8vb3/v737DpOkqvc//v7uLrCBtIBIXpIIqGQEJSMgoGJCEBEBMVz1CupF1B8qxquCKEYEFATuFTAAigqogHhFyRJESUqOu+SFZVl2v78/qmand5g83XNmut+v5+mnq6urT5/5zJkz3XWqTi38e/v73//OHXfc0fL3bKbGC0r//ve/73fbe+65Z+HfzPrrr9/n9SdGs07AItcy6nmB7K6pmOCFg+U9XXHFFYOs4QstvvjiHHzwwXz4wx9euO6yyy4bdnmSJEmSpLHJAQoVddBBL2fSpME1w0mTJrTdxbEH66mnnuKvf/3rwsc9LxzbOPf/v/71rz7LOf744wc8+now1lxzzYXLzz///MLlPffcc+GFXE877bS2OoviwAMPBKopSz71qU8Vrk2lr9/DaOu6RgLAt771rT63e/TRR0d8zYwVV1yR3XffHYBbb72VH/3oRyMqr4TGaZq+/OUvL7yodX+3Y445ptfXD9YyyyzD9OnTgf7bysorr8xee+0FwC9+8Qu+9rWvLXxuMBfHbpZ3vvOdQPX3duSRR47a+zbDW97yloXL3/nOd/q96PYxxxyzcJCta8CxFdZcc82FA4nXX3/9IgMQPV199dULpxGcMWMGm2+++SLPr7vuuiy++OJANeDY1zR2jz/++JAuYt9f3buU7OckSZIkSa3hAIWKWn75KTzxxKFkHj7g7YknDu2oi2N3mTdvHh/4wAcWXjh2lVVWWeRMBYAtt9xy4fIXvvAF5s6d+4JyzjvvPD75yU/2+14XXngh3/rWt/q9EPHtt9++8AjcJZdccpEzNqZNm8ZRRx0FVGdr7Lnnnlx99dX9vudVV13FEUcc0e82Y8GHPvQhZsyYAVRHwH/0ox/td3qTJ598km9/+9uLTL81WH/729/44he/uMiZMT3NmjWLn/3sZ0B1NlLXtQZKOOiggxYeVX3iiSfyk5/85AXbzJkzh/33359HH310xO/3pS99aeH1UD784Q8POOhx99138/GPf5yHH354xO89Utdddx3XXXcdUF1jZJttthnU6zbeeGNe8YpXAHDjjTdyzTXXLHzu29/+Nr/4xS/6vSD7z372s4XT7Wy88cb9vtd//Md/ANWZOSeffDIAq6+++sKBodHwgQ98YOHUQmeccQaf+MQn+tw5PWfOnH53uI+2jTbaiD322AOopjQ7+OCDe+0rTj/9dL73ve8B1XR6H/zgB1tar0984hMLlw866KBez3a6++67efvb375w0OTjH//4wjNvuiy22GLsvPPOQDVl4He/+90XlPP000+z33779TvF0wMPPMDhhx/e7xkyzzzzDKeeeurCxwO1XUmSJEnS+DO4uXUktdS55577gnVPP/00N998M2eeeSa33347UE2n893vfnfh0atd3vzmN7Pqqqty3333ceWVV7LhhhtyyCGHsPbaa/P444/z29/+lvPOO4+pU6fylre8hbPPPrvXejzwwAN85CMf4YgjjmCnnXZiq622Yu2112bq1KnMmjWLq666ip/+9KcLL7L6kY98ZOEFm7v853/+J1dddRWnnXYad999N6985SvZfffdec1rXsNqq61GZjJr1ixuvPFGLrroIv71r3+xzjrrcPTRRzchydaZNm0a5557LjvssANPPvkkxx13HD/96U/ZZ5992GijjVh66aV56qmnuOOOO7jyyiu55JJLmDt3LqeffvqQ3+uJJ57gs5/9LJ///OfZZpttePWrX816663HUkstxaOPPsqNN97IT37yk4U7+/fff/+FFzcuYYUVVuCb3/wmBx98MAsWLGD//ffnrLPO4nWvex3LLLMMt912G6eccgr//ve/edvb3rZwYGXChOGNkW+22WYcf/zxvPe972Xu3LkccMABHHvssbzxjW9k3XXXZYklluDxxx/n5ptv5rLLLuPKK68kMznssMOa+WMPS+PZD11nCQzWAQccsHAw75RTTll4ZPu1117LYYcdxvTp09ltt93YfPPNWXXVVZkwYQIPPvggv/vd77jwwguBajBroDOAdtllF9ZZZ51FzsY65JBDhv37Go5lllmGs846i1133ZVnn32Wo48+ml/84hfsu+++bLDBBiy++OI8+OCDXHXVVZx33nlssskm7LbbbqNWv4GceOKJbLbZZsycOZMzzzyTa6+9lgMPPJB1112Xxx9/nF/96lf85je/Wbj98ccf3/Lps/bZZx/OPfdczjjjDB544AE222wzDjroIF71qlcxceJErr76an70ox8tHAzfbbfd+hw0Ofzww7ngggsAOOyww7j88st57Wtfy+KLL85NN93Ej3/8Y+69917e/va3L3KR90Zz587l2GOP5dhjj2XLLbdku+22Y4MNNmDZZZfliSee4JZbbuEnP/kJ9913HwDbbbcd2267bQuSkSRJkiQV1d/FP72Nzdvmm2+ew3X11VcP+7VqLmBIt+WWWy7POuusPsv761//mtOnT+/z9csuu2z+5je/yaOOOmrhuksuuWSRMk499dRB1SUi8rDDDsv58+f3WpcFCxbkF7/4xVxiiSUGVd4OO+zQb0Z9Pd/fz5KZeccddyx8/sADD+wzu6Fse/PNN+emm246qJ9riSWWyPPPP7/f9+3NpZdeOuh2sc8+++QzzzzzgjJOOeWUhduccsopvb7PQPkOddtjjjkmJ06c2Gdd99577/zHP/6x8PGhhx7aazk77LDDwm3686tf/Spf/OIXDyqn5ZdfPmfOnDngz9lKc+fOzeWXX35hnW655ZYhvf6+++7LCRMmJJDTp0/PZ599NjMzDz744EFlMG3atDzttNMG9V5f+9rXFr5uwoQJeffddw/4mlb8vV1++eU5Y8aMAX+2nXba6QWvPfDAAxc+f8cdd/Rbn4G2HUpZXW655ZZ86Utf2m+9p06dmqeeemqfZQzm73go286bNy/f8573DJjn3nvv3Wu/0uizn/1sv/8jjjrqqLzkkksWrjvqqKMWef2dd9456H5up512ylmzZvVbH0mSNHa430GS1Bvg6uxlX7dnUEhj1JQpU1huueV4xStewe67784BBxzAcsst1+f2W2+9Nddffz1HH300559/Pvfccw9Tpkxh9dVX53Wvex0f/OAHWWONNbjyyiv7LOOAAw5gk0024aKLLuLSSy/lpptu4oEHHuDZZ59lySWXZK211mLbbbfl3e9+N5tuummf5UQEn/70pznkkEP44Q9/yEUXXcQtt9zCo48+yoQJE1hhhRVYf/31edWrXsWee+7J1ltvPaKsRtNLX/pSrrnmGs477zzOPvts/vrXv/Lggw/y9NNPs9RSSzFjxgw23nhjdt55Z/baa6+F8/4Pxfbbb88tt9zCxRdfzCWXXMINN9zAvffeyzPPPMPUqVNZY4012HrrrTnwwAPZfvvtW/BTDs/hhx/OzjvvzHHHHccll1zCww8/zPTp09loo4045JBD2HfffRe5aG5/7Xkw3vCGN3DHHXdw2mmn8dvf/pa//e1vzJo1i/nz57PMMsuw7rrrssUWW7Dbbrux2267veDMo9F23nnnLbzY/ZZbbsl66603pNevssoq7LzzzvzhD3/gscce49xzz2XfffflBz/4AQceeCAXX3wx//d//8ett966MIdll12W9ddfn1133ZX3vOc9C6dNGsguu+yycHn33Xdn9dVXH1Jdm2Wrrbbi1ltv5dRTT+WXv/zlwt9xRLDSSiux0UYb8drXvpb99tuvSP36s95663HjjTdy6qmn8vOf/5zrr7+eRx55ZGFfuvvuu/OhD31o0L+TZpg0aRInnXTSwr750ksv5YEHHmDBggWstNJKbLPNNhx88MELp3DqT9cZXt/5zne44ooreOKJJ1hxxRXZZptt+M///E+23XZb/vjHP/b5+hkzZnD33Xdz0UUXcckll/C3v/2Nu+++m9mzZ7PEEkuw6qqrssUWW/COd7yD17/+9U1MQZIkSZI0lkQ1eKHxZIsttsiB5vXvyzXXXPOCC15K0mj5zne+w6GHHgrA2WefzZvf/ObCNVJvPv3pT/PlL38ZgHPOOYc3velNZSskSZKkccP9DpKk3kTENZm5Rc/1XiRbkjQq5s2bxwknnABUF9od7AWiNbqee+65hRfHXnXVVT16XZIkSZIktYwDFJKkEZs9ezbXXHNNn8/PnTuXd7/73dx0001AdWH3FVdccbSqpyH4wQ9+wAMPPADABz7wASZNcjZISZIkSZLUGu51kCSN2OOPP84WW2zBRhttxK677sqGG27IMsssw+zZs7nhhhs466yzuO+++wCYPn06xx57bOEaq8ujjz7KlVdeydy5c7n88sv55je/CcDyyy/Phz/84cK1kyRJkiRJ7cwBCklS09xwww3ccMMNfT6/xhpr8Ktf/YrVVlttFGul/txwww3ssccei6yLCE444QSWXnrpQrWSJEmSJEmdwAEKSdKIrbzyypx//vlccMEF/PnPf+bhhx9m1qxZZCbLL788G2+8MW94wxs46KCDmDx5cunqqg8rrLACG2+8MZ/5zGfYYYcdSldHkiRJkiS1OQcoJEkjNnHiRHbffXd233330lXREO24445kZulqSJIkSZKkDuRFsiVJkiRJkiRJ0qhzgEKSJEmSJEmSJI06BygkSZIkSZIkSdKoc4BCkiRJkiRJkiSNOgcoOpAXQ5UkSZIkSc3m/gZJ0lA5QNFhJk6cyPz580tXQ5IkSZIktZn58+czceLE0tWQJI0jDlB0mKlTp/LUU0+VroYkSZIkSWozTz31FFOnTi1dDUnSOOIARYeZPn06jz32WOlqSJIkSZKkNvPYY48xffr00tWQJI0jDlB0mOnTpzN79mxmzpxZuiqSJEmSJKlNzJw5k9mzZztAIUkakkmlK6DRNWnSJNZbbz1uvfVWnnrqKaZPn85SSy3FxIkTiYjS1ZMkSZIkSeNAZjJ//nyeeuopHnvsMWbPns16663HpEnuapIkDZ7/NTrQ5MmT2XDDDXnssceYOXMmd911lxfOliRJkiRJQzJx4kSmTp3K9OnTWWONNRyckCQNmf85OtSkSZN40YtexIte9KLSVZEkSZIkSZIkdSCvQSFJkiRJkiRJkkadAxSSJEmSJEmSJGnUteUARUSsERFHRsTlEXF/RMyNiHsi4tKIOCwiWj6vUUQsERH7RcQvI+L2iHgmIh6JiBsi4lsRsUWr6yBJkiRJkiRJ0ljVdtegiIgPA18DpvR4arX6tj3w2Yj4QGb+tEV12AQ4HXh5j6emAMsBrwAOjYgTgY9l5tOtqIckSZIkSZIkSWNVWw1QRMQXgU/3WH0bcD/V4MQ69brlgLMiYlpmntLkOmwM/AlYqmH1I8DNVAMUGwKT6/XvA9aJiD0yc14z6yFJkiRJkiRJ0ljWNlM8RcRbWHRw4h/A5pm5XmbumJnrAlsC/2zY5sSIeGUT67A08Bu6ByeeAz4IrJyZ22bm5lQDJT9oeNlrgK83qw6SJEmSJEmSJI0HbTFAERGLAcc0rLoX2DYzr23cLjOvBrYF7qtXTaK5gwNHAKs2PH5nZh7feHZEZj6SmR9g0UGKD0bEek2shyRJkiRJkiRJY1pbDFAA7wDWbnj8scx8rLcNM/NR4GMNq7aLiO1HWoGImAZ8pGHVbzPzZ/285HBgZr08CfjkSOsgSZIkSZIkSdJ40S4DFG9rWL4fOGeA7c+ut+vt9cO1BzCt4fF3+9u4vjD2jxtWvSki2uqaIJIkSZIkSZIk9WXcD1BExBRgl4ZVF2Tm8/29pn7+woZVezWhKo1lPAtcNIjX/LpheTqwXRPqIUmSJEmSJEnSmDfuByiADYElGh5fNsjXNW63RkQsN8J6bNqwfFVmPjeI11wJzGt4vGlfG0qSJEmSJEmS1E7aZYCi0W2DfF3P7XqWM2gRMRFovMj1oOqQmc9SXdB7xHWQJEmSJEmSJGk8aYcBijV7PL57kK+7a4ByhmIVYPFh1KFnPUZSB0mSJEmSJEmSxo12uCjz0j0ePz7I1z3R4/FSBerQsx591iEi3ge8r344OyJuGcJ7tLMVgFmlK9GhzL4Mcy/H7Msx+3LMvhyzL8fsyzD3csy+HLMvx+zLMfsyzL0cs+82o7eV7TBAsWSPx3MG+bqe241kgGK4dei5bZ91yMwTgROHUqlOEBFXZ+YWpevRicy+DHMvx+zLMftyzL4csy/H7Msw93LMvhyzL8fsyzH7Msy9HLMfWDtM8bRYj8fPD/J183o87lnOaNSh57YjqYMkSZIkSZIkSeNGOwxQPN3j8eRBvm7KAOWMRh16bjuSOkiSJEmSJEmSNG60wwDF7B6Ppw7ydT23e6pAHXpuO5I6dCqnvSrH7Msw93LMvhyzL8fsyzH7csy+DHMvx+zLMftyzL4csy/D3Msx+wFEZpauw4hExEeAbzas2igzbxzE6zYCrm9Y9abM/OUw6zAdeLRh1Tcy878G+dprgU3rh+dm5puHUwdJkiRJkiRJksaTdjiD4uYej3u9Gngvem7Xs5xBy8zHgIeGUYee2w67DpIkSZIkSZIkjSftMEBxU4/Hmw3ydY3bPQfc3sR6DKoOETEDWK5h1T9GWAdJkiRJkiRJksaFcT9AkZn3AP9uWLXDIF/auN2fM3P+CKtyacPyWhGx+hDrAPDHEdZBkiRJkiRJkqRxYdwPUNTOaVjeMSLW6G/j+vnGwYFfNKEOZ/d4/K5BvKZxm6vqwRZJkiRJkiRJktpeuwxQnAIsqJcnAJ8ZYPvP0v2zzwZ+OtIKZObfgSsbVn04Ipbra/uI2A7YuWHVj0ZaB0mSJEmSJEmSxou2GKDIzJuA/2lY9Z6IeE9v20bE+4FDGlZ9PTNn9bHtjhGRDbfPDVCVTzQsvxj4SURM66Xcl9T1jXrV7ThAIUmSJEmSJEnqIJNKV6CJPg5sB6xVPz4pIt4AnAncD6wK7Ae8vuE1VwHHNKsCmfnHiPge8KF61WuB6+t1NwKT6zq+H1im3mYucHBmPt+sekiSJEmSJEmSNNa1zQBFZj4cEXsCFwJd16DYq7715gbg9Zn5TJOrchiwHNVgCMA6wDf62PYZ4IDM/HOT6yBJkiRJkiRJ0pjWFlM8dcnMm4FXAN+nurZEbx4BvgRsmZkPt6AO8zPzHcABwG19bDYf+C2wSWb2vLi2JEmSJEmSJEltLzKzdB1aIiKmADsCM4DpwCzgX8CfRnM6pYjYDHg5sDLwLHBfXYemD450ooiIbNdGPMaZfTlmX4a5S5IkSZIkNVfbDlCovUTEasDOwJZUAz5rAitSXdcDqsGfh4E7qa73cRVwSWbeO9p1bTdmX47Zl2Hu5UTE4sDWvDD7afUmT/PC7K/IzOdGu67txuzLMftyzL6ciFiDIeSemfeMfi3bj22+HLMvyz6nDNt9Wbb7Msx9eByg0JhV/1EfAOxD9Ye9yNN9vKxng74JOAv4n8y8q7k1bF9mX47Zl2Hu5dRnPL6FKvudgam9bVbf9/ah5RngEuCnwNktuLZU2zL7csy+HLMvJyK2ocr9dcBaQ3z5HVRT5P7U6/cNjW2+HLMvyz6nDNt9Wbb7Msx95Byg0JgTETsCHwP2pPsfV187CAeSDfcXAN/IzItHUr92ZvblmH0Z5l5ORKwNfAQ4EFiya/Uwi+vK/mngNOBbmdnXdaA6ntmXY/blmH0ZEbEU8F7gA8DajU9R5TjQ7yAbtu1yJ3A8cFJmPtG0yrYZ23w5Zl+OfU45tvtybPdlmHtzOUChMSMitgO+DGzTtarHJo8CNwC3A/cCj1GNrgcwBVgOWA1YF9iI6tojjboa+1+BIzPz0ib/COOW2Zdj9mWYezn1l5fPA28HJtD7B7c7GVz2M3p5bda3M4HPZebtzf0Jxi+zL8fsyzH7MiJiaeDjwKH0vaPqOeBu+s99DWCxHq9r3HH1beCYTvsS3x/bfDlmX459Tjm2+3Js92WYe2s4QKHi6qlVvgm8qWtVff8scCHVqU6XDPUfUUS8BNgJ2AN4Ld3zx3c1+l8CH+3kqVjMvhyzL8Pcy4mIJam+vPwnMIlFP8RdT509cGVmPjnIMpcGtqI7+40bnk7geeB7wFGZ+dRIf4bxyuzLMftyzL6MiJgAfAj4HLAsi+b+CPA7qtyvAP6Zmc8PUN5iwAZ0574rsHzDJgk8Ub/fdzNzQRN+jHHJNl+O2Zdjn1OO7b4c230Z5t5aDlCouIh4mmpnXtcf9xXASVTzr81u0ntMA95GdfrVq+rVCczJzCX7fGGbM/tyzL4Mcy8nIh6gukBYV/b3AacApzXrSKiIWIfqWiLvpjoqBarsH8rMVZrxHuOR2Zdj9uWYfRkRcT3VtZwaDwD4OXAq8MfMnD/C8icCOwLvpJrreUr9VAJ/z8yN+3hp27PNl2P25djnlGO7L8d2X4a5t5YDFCouIhZQ/cGdB3wlM69o8fu9EvgUsBdAZk5s5fuNZWZfjtmXYe7l1NlDdUTVV4GfteookProlr2BTwKbAGn2gNmPOrMvx+zLaMj9IeA44ITMfLxF77UM8H6qOc9XooNzB9t8SWZfjn1OObb7cmz3ZZh7azlAoeIi4mLgiMy8epTfd3Pg6Mx8zWi+71hi9uWYfRnmXk5E3AJ8KjPPHuX3fSvw5cxcfzTfdywx+3LMvhyzLyMiHqG6xtP3MnPuKL3nElRTjHwqM1cYjfcci2zz5Zh9OfY55djuy7Hdl2HureUAhSRJarmImDjS017H43uPBWZfjtmXY/ZlRMQyWehijiXfeyywzZdj9uXY55Rjuy/Hdl+GubeWAxSSJEmSJEmSJGnUTShdAUmSJEmSJEmS1HkcoJAkSZIkSZIkSaPOAQpJkiRJkiRJkjTqJpWugCRJksqJiCWADYANgTWBperbYsDTwFPALOBm4B+ZeV+ZmrYfsy/H7MuJiGUZXO63pxdMbBrbfDlmX5Z9Thm2+7Js92WY+/B5kWy1vYj4J7AEkJm5Tun6dBKzL8fsyzD3ciLiAmBxquxfU7o+Y11ETAT2A94G7ErVbgfrX8AvgFMz8+YWVK+tmX05Zl9ORGxLlftewBqDfNlzwJ+pcv9pZj7aouq1Ldt8OWZfln1OGbb7smz3ZZh7czhAobYXEXPo3mE4sXR9OonZl2P2ZZh7OWY/eBHxJuBooGsQLer7nh8Ko8e6aFhOYAFwKvDJzJzV/Jq2H7Mvx+zLiIgtgK8D23Wtani6ry+ivW0zGzgG+HpmPtvUSrYp23w5Zl+OfU45tvtybPdlmHtzOUChtudOq3LMvhyzL8PcyzH7wYmIo4DPNq7qscl8qg/Jc4DngcnA1PrWKOn+gvkvYM/MvL0VdW4XZl+O2ZcREQcCJ1JNK9wz86FozP1K4A3utOqfbb4csy/HPqcc2305tvsyzL35HKBQ23OnVTlmX47Zl2Hu5Zj9wCLiQ8B36P4gDHAV8GvgMuA24L7MXNDLa5cCZgCbATsAbwWWbijrTmCjzJzd2p9ifDL7csy+jIh4G3BW/bArr/uBC+nO/S7gCaodVvOAKVQ7q1Zm0dx3ACY0FH8d8MrMfL7VP8d4ZJsvx+zLsc8px3Zfju2+DHNvDQco1PbcaVWO2Zdj9mWYezlm37+IWJ3qgmyTqT5EXwe8PzOvGmZ5U4D/Aj5DdeQQwPcy89CR17a9mH05Zl9GRKxAlfty9ar7gI8BZ2fm/GGUtzpwFPBuuqdD+GJmfm7ktW0vtvlyzL4c+5xybPfl2O7LMPfWcYBCbc+dVuWYfTlmX4a5l2P2/YuI/wd8ieqD75+BXTPzuSaU+3rgHGAi8CTw4sycO9Jy24nZl2P2ZUTEocBxVLn/E9guMx9rQrnvB46vH84CVurtiNxOZpsvx+zLsc8px3Zfju2+DHNvHQcoNCZExJBHGof6FrjTqldmX47Zl2Hu5UTEv1tY/Jr1vdn3IiL+CmxFdeHBDTLztiaWfTqwP9UH9T0z88Jmld0OzL4csy8jIv4IbE+VzZaZeW0Ty/4V8Pq67Ndk5h+bVXY7sM2XY/bl2OeUY7svx3Zfhrm3zoSBN5FGRbTwpv6ZfTlmX4a5l7Mm1Zyba7bg5hEX/VuDKqObm/nlsXZuw/KMJpfdDsy+HLMvY+36/l/N/OJeO6NheZ0ml90ObPPlmH059jnl2O7Lsd2XYe4t4gCFxpLEHUylmH05Zl+GuZdl9qNv+fr+4RaUPatheXoLyh/vzL4csy9jRap+/t4WlH1fw/IKLSh/vLPNl2P25djnlGO7L8d2X4a5t8ikgTeRRsU8utvjqcCdTSz701RzF6p3Zl+O2Zdh7uXMp/vgiAuAh5pY9gF4Fkt/HgZWA9ZqQdlrNizP6mujDmb25Zh9GY8AKwOrtKDsxjJHPOdzG7LNl2P25djnlGO7L8d2X4a5t4gDFBorbgQ2oxqJ/EtmntSsgiPik7jDsD9mX47Zl2Hu5dwMvIwq+7My89RmFRwRb6e6SLZ692+qL5BrRMSOTZ7T9OAe76NFmX05Zl/G3VRf3l8SERtn5vVNLPvtDct3NbHcdmGbL8fsy7HPKcd2X47tvgxzbxGneNJYcU3D8hbFatGZzL4csy/D3Mu5umF582K16Ezn1vcBnBYRTZnLNyI+T3WhOKiOKPpTM8ptM+fW92Y/+s6t781+dJ3XsHx6RCzdjEIj4hBgr/rhbOCPzSi3zZxb39vmR9+59b3Zjz77nHLOre9t96PPdl+GubeIAxQaK9xpVY7Zl2P2ZZh7OY2DQ2Y/uv4HeJTq7JXVgBsi4mMRsexwCouIV0XEBVTTmlGX+4PMnN+MyrYZsy/H7Ms4HZhTL78cuDEi3hIRw/ruGRGrRcQJwIn1qgROy8y5I69q27HNl2P25djnlGO7L8d2X4a5t0hkep1KlRcRm9G90/A5YMnMfL5JZc8BFgfITKde6cHsyzH7Msy9nIjYGvhL/fAZYOnMXNCkss1+ABGxD3Am1QffqO+fpfqdXAbcRnXa8uNUH7yfByYDU6lOZZ5BNT3aDsDaXcXW9zcAW2bmvFH4UcYdsy/H7MuIiA8D32LR3O+nuv7QC3LPzOcjoq/cd6Q6sK4r97uAV2Tm7FH6ccYV23w5Zl+OfU45tvtybPdlmHtrOEChMSEiFgOeotq5lFT/hK5tUtlzqOYlT3davZDZl2P2ZZh7ORExBXiS7jM4N87MvzepbLMfhIh4N/ADuq+V0vWhekjF1PddH8qvAt6QmQ83pZJtyuzLMfsyIuKLwJF0ZwZDzx26f18B3AHsmZm3NKWSbco2X47Zl2OfU47tvhzbfRnm3nxO8aQxoR4R/wlwKdX8gis0sfjdgJ2AnZtYZtsw+3LMvgxzLycz51DNp3l3fVu3icW/l+pieu9uYpltJzNPBrajOqotejwdA9x6ehr4ArCjXx4HZvblmH0ZmfkZYG+qL9w9DZR7z+wTOA3YulO/uA+Fbb4csy/HPqcc2305tvsyzL35PINCkiSpA0XE9sDbqC7ItvogX/Yc1anLZwNnZeasFlWvrZl9OWY/+iJiErA/1Rf5XajOdhusfwPnAKdk5j9aUL22Z5svx+zLsM8py3Zfhu2+DHNvHgcoJEmSOlxETAdeBqwJLA0sCSxGdRTbbGAmcDNwuxcqbC6zL8fsR189B/OG9W1N+s/9psy8t0xN25NtvhyzL8M+pyzbfRm2+zLMfWQcoJAkSZIkSZIkSaPOa1BIkiRJkiRJkqRR5wCFJEmSJEmSJEkadZNKV0CSJEmSJElSZ4mINerF+Zl5X9HKSCrGa1BIkiRJkiRJGlURsQBIYFZmvrh0fTpJRHRdmNzsR0lE7ADsCKwEPAPcC1ySmdcVrNaY4BkUalsRcXHDw78Bx2Tmg6Xq00nMvhyzL8Pcy4mIkxseXgecmJnPFqpOR4qIfwJLAJmZ65SuTycx+3LMvoyIuABYnCr315Suz3gQETOADYClgYeBqzNz9jDKeQewLkBmfqGplWxTZl9OREwG1qTOPjPvHGY5rwFWBcjM05pVP71A1DeNruhxryGIiFcBhwLbAisAjwGXAl/PzGt6bLs+cDqwWR9lXQX8Z2Ze3dJKj2GeQaG21TAS32UucCJwdGbeX6ZWncHsyzH7Msy9nF6yfwg4GviBAxWjIyLm0L2jdmLp+nQSsy/H7Msw98GLiJ2o/h/23BnyHPBL4DOZedsQyjsfeC1mPyCzLyci1gO+DOwJTG546gHgf4CvZeZjQyjvfGA3quw9wLcFGj7LP5KZK5auTycx++GLiM8AR/HCwbUE5gOHZObp9bYvoxq4mF5v23NHfNfr5wBvycwLW1j1McsBCrWtXnZadXUEc4EfUn04cY7DFjD7csy+DHMvp5/sZ1LtHDg+M+eUqFuncIdhOWZfjtmXYe6DExGHAd/oetjLJgk8D3w6M48ZZJnuJB8Esy8nIt5KNQixOH1n/zjwH5n5s0GWafb9iIh3NaGYH1P9bmYDH+5tA89eeaGI2L4JxfyRKvsngb3o5e8mM//UhPdpKxFxCHBS/TDpvb95jmoqpyuozvJ/RcO2zwOzgKlUZ3k1lvMY8LJOnI3BAQq1rXqnVV8SmJuZU0erPp3E7Msx+zLMvZxBZD8zM1carfp0IncYlmP25Zh9GeY+sIh4PfCr+mFfO0661ifwW2DfzHxmgHLdUTsAsy8nIrYG/kQ1jXlv2Xft+OrK/njg0Mzs73Ok2Q+glwOFhlVMfd9nOWb/QqOUvWcO9RARSwN3AsvUqxK4ELiB6qytHYGN6/WXAN8DflE/vgs4Avh115n+9VlfRwDvpvv38L3MPLT1P83YYkNT28rMCV3LEbEisD2wQ317GdWXG7WA2Zdj9mWYe1FrNSy/mO7st6X64PiiEpWSJGm0RcQkqh2v0L2j46dU0wo9QjUn/97Aa+jeibsn8LuI2CMznxrVCrcRsy8nIgL4EYsOTlzJotm/FXhJw/MfAFaKiLdn5vMFqt1uBhxkGEG5HlXdv2ZcP6K3AT2vS9G7/YBlqTJ6DHh9Zl7euEFEHAocRzVY0XWQ4p3AVpk5q3HbzLwVeE9E3A78d736gIj4aGbOp4N4BoU6UkQsB2yfmeeWrkunMftyzL4Mcy+j/rK6CbBDZh5XtjbtzSOayzH7csy+DHPvX0TsA5xJteNkHvC2zDyvl+22pppWZT26d/5dBbw2M5/oo2yPJO+H2ZcTEXsAv6HKM4GPZOZ3e9luX+DbVBez7drx/Wtg78yc10fZZt+PHkfxt2qHttn3wuzLiIhfAG+myv6grutM9LLdT4C30/07emNm/nqAsq8BNq1fs2VmXtu0io8DnkGhjpSZjwLnlq5HJzL7csy+DHMvI6sjMP5W3zpeRHTUEThjidmXY/ZlRMS/W1i8ZyT2b8+G5S/1toMcIDMvj4jNgVOBt1DvDAH+EBG7ZubjLa9p+zH7ct7csPyd3gYnADLzrIj4E9V0K1vXq18PnBMRb8nM51pcz3Z3IdVUNU8O4TUB/Jvuo9E3b0G9OsGFwDFUF2cerAAupvsaFG9qfrXa0sb1/WzgJ/1s9wOqAYoAHh9ocKL2v1QDFAAvBxygkCRJUltp5Wnano7bP7Mvx+zLWJPWTQ/htBP927K+nwd8q78NM/NpYO+I+AbwEapsN6faUb6LO8qHzOzLeWV9v4DuKVJ6lZkPRMROVBfTfitV9nsA50bEmxykGLL3U+0YXxrYjWpq2w8OcmcsANVJzwAsyMy7ml7D9vUV4OPARKrsVwQOyczrBltAQ/bzMvPSZlewTb2Iqt+4aYApmK6r7xP45yDLvrFhefmhV218mzDwJpIkSWoDXVMfaPSZfTlmX465j76VqHL/+2CvaZCZHwM+R/eUN5tR7Sif3qpKtimzL2dVqvz+kZkzB9o4M+cC+wCn0D3g+VrglxGxeMtq2YYy8ySqQYkLqbJcjSrHMyLC68C1UGYeSXUm0N+pst8EuDIivhIRnm3YOlPq+9kDbNf4/DODLLtxuyl9btWmPINCkiSp/c2j+3PfqVQXamuWT1MdvaXemX05Zl/GfLoPhLsAeKiJZR+AZ1D0Z6n6/rGhvCgzv1Bf3+NrLLqjfJfMHFJZHczsy1m6vn94sC+opwI9JCKepbpgdlIdhf7L+kyKuc2vZnvKzPuAPSLiYOBYqgsI7wPsUl/o939K1q+dZea19ZRxnwE+CSxGNc3WmyPifZn5p6IVbE9PUbXxFQbYrvEMiBUHWXbjoN6gBrrbiQMUGjciYhWqU8aXqm+LAU9T/eHOAm71g0RrmH05Zl+GuZdTX9x6ZXrP/pHMHMq8tup2I9VOjwT+Uh/x1hQR8UncUdsfsy/H7Mu4meqI2gTOysxTm1VwRLwdr0PRn6epdtYO+Qj8zDwmIp6n2sGYVPNgXxQRr3FH+aCYfTnPAkvSPVAxaJn5oYh4DjiMRQcp3uhn/aHJzFMi4gLgROB1VDtoT42I/YH3Z+bdRSvYpjLzeeCoiDib6qygTYCXABdHxEnAEYM9q0uDch9VP79hRCzdz3fTber7ADaIiGUHMX3fdg3Lgx5wbRcOUGjMiog1qOaF3Iuqkx3oA8eCiLgT+CPVha/+UHfWGiKzL8fsyzD3ciJiMtXcv3tRfSFfj352PkXEg8A/qLPPzJtHoZrt4BqqHbUAWwBN21GrAZl9OWZfxtVUAxRQzavftAEKDeh+YBmqgyyGLDO/We8o/xbVztpNqHaU79KsCrYxsy/nIaqDWlYfzosz86MRMR/4GFX2uwK/ioi9mlfFzpCZDwBviIgDgG8Cy1EN+vw9Io7MzO8UrWAby8zrI2JL4P8BRwKLA++j+n18IDPPK1rB9nE11QWsF6O6htAXem5QH3B3eMOqiVSDoJ/vq9CIWA44sGHV35pQ13HFa1BozImI1SPix8C/gK8D21N92IuG28LNG24TgXWAdwO/AW6LiHeOXs3HP7Mvx+zLMPdyImJaRHyO6kvlz4F3Aa8AJrNo1j1vKwM7U30YvCki/hwR2476DzD+XN2wvHmxWnQmsy/H7Mu4pmHZ3EfXTfX99Ih4Wb9b9qHegfhhuq+LsDFwMcM4M6DDmH05/6jvXxwR6wyngMw8nOq7QNdn/12AX1N9LtUQZebpVAPVv6TKdEnguIi4LCI2KFq5NpaZ8zPzi1QHZVxDlf0qVBeBP9PrgjTF2Q3Ln4mIj0TEwjNq64GG/wVeTdWPn0T1e/h/EfHm3gqMiKWovg8vV6+6OzNvbUXlxzIHKDSm1EeI3EA1v2zXH3lv88z23HHY23MzqE4pPLs+Qlf9MPtyzL4Mcy8nIl5B9WXyM1RHvPU2INRvEQ33rwYujYjj6qNV1LuuHYYBvDwimn0WrRfD7ZvZl2P2ZXQNDAWwcUQ0+zunufftioblYR/9nZnfAz7UsGojYMvhltchzL6cqxqW9xxuIZl5BHA03Z8zd6Y6eEnDkJkPZeabgf2ppsgNqos6/y0iPtOC/8mqZebfga2ozqSYS5X924B/1me3aPh+SzWVJVT7EI4FZtaDb9dQnU23L9VnlWuofgdzqGYw+nlE/CoiDo6IXSLiDRHxBeBWYIe6zKSaJq3jRHVtIKm8iNgR+B3VH25SdaLPAn8FLgNuA+4CnqD6A3+e6oiGqVRH1M6gOo1/e2DthqITuCgzdxuFH2NcMvtyzL4Mcy8nIjYGLqWaRqsre4A7GFr2WwFTGspI4H8z812j9bOMJxGxGNV1PBanymrLzLy2SWXPoZqWKzPTOfl7MPtyzL6MiJgCPEn3wXAb1ztLmlG2ufcjIjajGiBK4PbMfOkIy3s/8P2uh/W92ffC7MuJiFcDf6bK/vrM3GyAlwxU3n9TXXC48XOq2Y9AfeT+8cBb6lVJddbRe4DL63WzMnOwFxPWIEXEhlTXpuga6Ezg98D7qb5/gdkPSURsTXV22xI09BFdT9f3zwDb1xcy/yLVQEVfO+C7vssGcAuwSSdeA8cBCo0JEbEk8HdgDao/zKeBLwI/HM6FwerpPo4CXlOvSuCjmfnt5tS4fZh9OWZfhrmXExGL0z1vZwILqI4Q+W5m/nOIZS1BdSTQJ4EN69UJvDMzz2hapdtIRJwMrFU//Epm/q5J5W5HvSMyMy9tRpntxuzLMfsyIuL3wLr1w49m5rlNKved1Gc9NvPi2+0kIu6hmtID4I2Z+esRlncIcALdZzq6o7YPZl9GPb3Kg3RPj7JDZv55hGV+Afg0DQfCmP3IRcTbgO8AK9L9XWACVcbuJG+R+kzG/6K6BsJkquyfAabVm5j9EEXEa4DTqA6g6+lJ4ICu637UZwudS3WGV8+BjMbHdwO7ZObtLar2mOYAhcaE+sPXSVR/nPdTfaj4dxPK/TLwqfrhPZk5Y6RlthuzL8fsyzD3ciLi7cBPqLJ/EtgzM/86wjIXp/p9dp2ufGtmrj+iikqSNE5FxLFUF0YFuDozd2pCmQcCP6LakeiO2j6YfTkR8UOqa8MBXJKZr+lv+0GW+Wm6L4Br9k0SEcsD3wP2qVd17ZR8xJ3krRURLwVOBl7FojvGHaAYhvrAx7dTTc+0ItVZu5cDp2bmzB7bTqA6sO6/eOF1hZ4Cfgx8ITMfaXG1xywHKDQmRMQFwG5UneSumXlxE8u+FNiuLnubzLx8gJd0FLMvx+zLMPdyIuIc4I1U+eybmT9vUrkTqc7M2Lgue/PMvK4ZZUuSJIiIlaimsyAz7ypcnY5i9gOrz6xdsn6Ymflok8rdkmqaUc+Wa7L6gsHfB15cr3In+Sior9n3UaoZBKbUq81+lNQH120JrE51fZD7gWsy8/miFRsDvEi2xoquOTrvaebOwtopvbyPupl9OWZfhrmX8/L6/qFmDU4AZOZ8qikQer6PJElqgsx8MDPvcgf56DP7gWXm3Mx8pL41ZXCiLveqzLzUwYnmy8xzMnPlzJxQ39xBPgqy8g1gNaopMNeiur6fRkFmPpeZl2XmmfXfwBUOTlQmla6AVFuJ6qjXOwbacBgay3xxn1t1LrMvx+zLMPdyVqHK/tYWlH1Lw3Jvc4FKkiRJUserr7045OsvSq3iGRQaK56s71doQdnLNyw/1YLyxzuzL8fsyzD3cubU98u0oOylGpafbUH5kiRJkiSpyRyg0FhxD9XFeTaIiHWaXPabGpbvbXLZ7cDsyzH7Msy9nPvozr7ZZzns2uN9JEmSJEnSGOcAhcaKC+v7AE6KiMWaUWhE7AHsVz+cC/yxGeW2GbMvx+zLMPdyLqnvFwOOa1ahEbEFcEj9cD7wp2aVLUmSJEmSWicys3QdJCLipcDf6R40uxZ4f2ZeO8zyJgMfBT4LLEE15/kZmfnOJlS3rZh9OWZfhrmXExGbA1dRZQRwHvCBzHxgBGXuB3wHWK4u97eZ+YaR1lUDi4jGi8z/DTgmMx8sVZ9OYvblmH0ZEXFyw8PrgBMz0+n8RoFtvhyzL8c+pxzbfTm2+zLMveIAhcaMiPgicCTVDqao768Efg1cBtwG3J+9NNqImAbMADYDdgDeSjXHedSbPAK8LDMfbvGPMS6ZfTlmX4a5lxMRJwDvpXuQYh5wDnX2mXnnAK+fBmxKlf0BwEvozv5pYJPM/Ffza66eImIB3b9HqM4cOhE4OjPvL1OrzmD25Zh9Gb3k/hBwNPCDTvwSP5ps8+WYfTn2OeXY7sux3Zdh7hUHKDRmRMRE4GSqHU6NOw0bzae68Owc4HlgMjAVmNazuIYyHgXemJmXtazy45zZl2P2ZZh7ORExlerMiZ3oPft5VNeQeJwXZr8y1ZkSC4trWJ4LvCMzz2lV3bWoXj5Md/0u5wI/BL6WmV4PpAXMvhyzL6Of3GdSfYk/PjPnlKhbu7PNl2P25djnlGO7L8d2X4a5Vxyg0JgTER8DPkN1RDJ078AarMbtLwXel5m3Na+G7cvsyzH7Msy9jIiYBHwD+A9gEr3n3vMDSm/Pd627HTjYgaHRVX+Y7ksCczNz6mjVp5OYfTlmX8Ygcp+ZmSuNVn06iW2+HLMvxz6nHNt9Obb7Msy94gCFxqSIWA74MLA38LIhvvxp4ALgR5l5QbPr1u7MvhyzL8Pcy4mI9YBPAXsB0/vYrL9Bo2uAHwE/zMznm19DDVZErAhsTzX11g7Uf0uZObFkvTqB2Zdj9qMnImY0PHwx3blvS3WQQZp769nmyzH70WWfMzbY7keX7b4Mc684QKExLyLWoppvfENgTWBpYElgMaqdg7OpTn26GbgJuLqT5mlrJbMvx+zLMPcy6jMqtmNo2V+amfeUqK8GVg/8bZ+Z55auS6cx+3LMfvRFRACbADtk5nFla9N5bPPlmH0Z9jll2e7LsN2X0Wm5O0AhSZIkSZIkSZJG3YTSFZAkSZIkSZIkSZ3HAQpJkiRJkiRJkjTqHKCQJEmSJEmSJEmjblLpCkiShi8i/l0vPpKZWxatTIeIiDWBHYCVgGeAe6ku2PxoyXpJIxURq1BdoHyp+tZ1gfKngFnArZk5t1gF25jZl2P2ZdQXflyZ3nN/JDOfLFi9tmabL8fsy7HPKcd2X47tvgxzHx4vkq22FxEnUHUImZmHlK7PeBERk4GlgVmZuWCYZbwcWA4gM//UxOqpFhFdv5tZmbli0cqMQxGxFvA+YFtgBeAx4FLge5l5b49tVwS+D7wJiB5FLQB+AXwiM+9qcbXVi4h4ETAFIDPvLlydcSEi1gDeCuwFbELV5/dnAXAn8Eeq9v6HzHy+dTVsX2ZfjtmXUX+u3IMq902B9YAl+nnJg8A/qHPPzJtbXcd2ZZsvx+zLsc8px3Zfju2+DHNvDgco1PYiYg6wOEBmTixcnTEtIpYDPgXsDaxRr14AXAWcBvxwKB8WIuJ8YDeqwSHP2GqBeoAiqUbiHaAYgog4kGrAYXLXKqosAWYDe2fm7+ttVwP+BMzosV3X66jXPQLsnpnXtrb2419ELEY1OLQ3sAHVl5eHgb8Ap2fm+UMsz/5mkCJideCLwP50T/fZc9At+1jf+NzdwGcy83+aXsk2ZfblmH0ZETEN+DjwUWDJxqcG8fLG/7V/BT6ZmX9uYvXamm2+HLMvxz6nHNt9Obb7Msy9uRygUNurByiWoNpp5QBFHyLi1cAvqc546OuDxO3AQZn510GWeT7wWsy+V/XRJSN1J9Xv5zGq0foX/DP0aPIXiog3AOfSPdjQmFvX4yeBrTPz5oj4I7B9L9vSy+vuAV7hqZt9i4iXAL8G1u1a1fB0V3/zJ+CQzPw3g2B/MzgRsQvwM6oBod7a/1Al1f+Od2TmsyOvYfsy+3LMvoyIeAVVX78aw8+78XeVwHeAj6ZfYvtlmy/H7MuxzynHdl+O7b4Mc28+ByjU9hygGFhErE91lsQ0ej+qobHjfB74f5n59UGU6w7DfjSc/TCiYur7vsrxaPIe6lMwbwdWobtt/xO4gepsilcDL6qf+xXwPeB39ePHga9QDW7cDUwFXkl15MRr6rdI4CuZ+enR+HnGm4hYFbgaeHG9qq8BIqgGid6bmT8bRLn2NwOIiB2p2vIkunN+luqoncuA24C7gCeAOVT9/WSqdr4y1RlEm1EN1q3dUHQCF2XmbqPwY4xLZl+O2ZcRERtTTZm4NIv263cwtNy3opq+r6uMBP43M981Wj/LeGObL8fsy7HPKcd2X47tvgxzb5HM9OatrW9UHcICYH7puozVG9WUKguA+fX93cAJwH8DP6E6Or/x+fnAcYMo93yz7zefBaNwM/sX5r5/Qzt+Gnhbj+cXB75Rb/Mc8Nt6+SHgJf2U+92G3B+gPgjA2wty+nWP/uRp4MK6r/kL1Qe4nv3NRwdRrv1N//ksSXXGVVemT1INrE0fZnnbAr9v7GuAQ0v/nGPxZvZm32m3+v/oDQ0ZzaMa7N9gGGUtAbwT+HuP3Pcr/XOOxZtt3uw78WafUzR723257G335t5Wt+IV8Oat1TccoBgon20bOsL5wNeBxXpsMxn4BPAMi+40PH6Ast1h2H8+jbk7QDF6uf+kIfvD+tnugh6/o3cOUO5E4JaG17ys9M861m7ARj0y/SmwXI9tVgOO7/G3MZ9qXs7+yra/6T+fQxqyvAdYu0nlfrmhv7mr9M85Fm9mb/addgPe3pD7Y8CrmlDm4sCpDbnfXPrnHIs327zZd+LNPqdo9rb7ctnb7s29rW5O8aQxISIubmHxO1CfLpVO+/ECEfFt4D+pTic7IzPf2c+2LwfOAdapVyXwo8x8Xx/bO+VKP3pM8fQ74MyhFgGcXJcxGzi0t40y89Th1rEdRcQ/gZdSDV4un33MaxoRu1OdPQHVUf7LZub8Acr+f8CXqH4nB2TmT5pW8TYQEUcDh1Pl8/vM3L2fbXcF/hdYnu5TXo/KzC/1sb39TT8i4gLqi4gDu2Zm0/7vRsSlwHZ12dtk5uXNKrsdmH05Zl9GRJwDvJEqm30z8+dNKnci1RSBG9dlb56Z1zWj7HZhmy/H7MuxzynHdl+O7b4Mc28d5yXXWLEjI5+LX8OzdcPykf1tmJl/j4itqOblfzXVTsNDImJCZr6nhXVsV2cA+1G1/d2ophP6YGbeN9gCIuLkenGuAxGDthJV5jf1NThRu6K+79q238GJ2rUNyy8aZv3a2asalv+rvw0z8/cRsTXVmRHrUvU3n6/7my+0sI7t6qX1/T3N/PJYO4XqC2TX+/gFclFmX47Zl/Hy+v6hZn1xB8jM+RFxAvD9hve5rlnltwnbfDlmX459Tjm2+3Js92WYe4tMKF0BqYdowU39m0G1A/bWzLxroI0z81FgF6o546HK+OCI+FHrqtieMnN/qtH3B6lyfD1wU0S8v2jF2t+S9f3jA2z3RMPyU4Msu3G7aYOtUAdZt76/MzNvGmjjzPw3sA1wfb0qgKMi4qgW1a+ddQ3M3dGCshvLfHGfW3Uusy/H7MtYhfqzZQvKvqVheeUWlD/e2ebLMfty7HPKsd2XY7svw9xbxDMoNFZkw/11VBdXapbtm1hWO1qmvh/0UfuZ+WxE7AX8HHgD1U7DgyIiMvPdLahj28rM8yLi/4BvAQcASwPfj4j9gPdk5u1FK9ieZlPlvMwA2zU+P32QZTdu9/RQKtUhlqXq5+8c7Asyc1ZE7EQ1KPpKqv7ms3V/87kW1LFdPQmsUN+abfmG5cEO5nUSsy/H7MuYQ3X9soH+zw7HUg3L/Z0F2als8+WYfTn2OeXY7sux3Zdh7i3iAIXGituBl1DtuPp6Zp7RrIIjYg6wRLPKa0PzgMWAqUN5UWbOi4i3Ul034S1UOw0PrHcaHtz8aravzHycKruzgBOAValOZ70hIj4PHJOZCwpWsd08SPWB4mURsURmzu1juy3r+6i3nZKZcwYoe6uG5UdGWM921NWOh9QnZ+YTEbEL1YXLu6aX+0zd33g2xeDcQzXt2AYRsU5m/quJZb+pYfneJpbbLsy+HLMv4z5gOarcV87MB5pY9q493keLss2XY/bl2OeUY7svx3Zfhrm3iFM8aay4pmF582K16EwzqXb2rTrUF2bm88C+wM8aVr8rIn7cnKp1lsz8LfAyqvk2g2pk/r+BqyJi05J1azNd14mYAvR37ZTGi44vPsC2RMQU4KCGVdcNo27t7hGqtr3SUF+YmbOpLoL9f/WqAD4dEV6PYnAap+U7KSIWa0ahEbEH1bV0AOYCf2xGuW3G7Msx+zIuqe8XA45rVqERsQVwSP1wPvCnZpXdRmzz5Zh9OfY55djuy7Hdl2HuLeIAhcaKqxuWHaAYXf+s71eLiFWG+uL6wsH7UZ1J0XXdjwMi4jRgYtNq2SEy88nMPATYg+qIlAA2Aa6IiK9GhGcDjdyvG5a/FhFvanwyIiZFxNeAPanO6jqX6vfw3/VFm18gIiYCP6Z7oG8mMOA1FjpQ17yaa0XE8v1u2YvMfJrqb6PrA1sAR0bEl5pUv3Z2GtWHXYAdgL9ExGbDLSwiJkfEp4Czqfr6BM7OTE/BfyGzL8fsyzi9YXnviDg3IkY0l3I99eUFVAdvJHBhZs4aSZltyjZfjtmXY59Tju2+HNt9GebeIpGZA28ltVhE7ED3SOSTmblsE8vumuIpM9Md5j1ExBeBI6k6wvdm5snDLGcCVWe9H4teU2QCZj8sEbEkcCzw3npVUk2H9t7M/FO9zYJ6/SOZuWKRio4z9SDPbVSDCUGV3z+AG6g+FLwaWLF+7maqo/b/RfUh+TngRKpBi3uozsLYEjgMeHlDeUdn5qdG62caLyLiaOBwqozekZlnDbOcKcBvgB3rVUl1dNVk7G/61KO/72qrV1IN2l1G9Xdxf/by4TAipgEzgM2ovoC+lWqqtKg3eQR4WWY+3OIfY1wy+3LMvoyIOIHq80tXrvOAc6hzz8w7B3j9NGBTqtwPoJoKtiv3p4FNmjyVSNuwzZdj9uXY55Rjuy/Hdl+GubeGAxQaEyJiKeDxhlXrZ+ZtTSrbAYp+RMTOwB+oOtfLMnPYFxWvBylOBfan+wMKmP2IRMRrgJOANen+J3gScATV340DFEMUEa8Dfkn3WT/QnW3X4+eB12fm7yLieOD9PbZbpEi62/z9wMvra4uoQUTsSfXBrevIkD1HUNYU4DxgZxb93dnf9KE+0+dkqg/CjV8iG82nuhDhHKq/gclU1yia1rO4hjIeBd6YmZe1rPLjnNmXY/ZlRMRUqj56J3rPfR7V/MqP88LcV6aa33lhcQ3Lc6kGuM9pVd3HO9t8OWZfjn1OObb7cmz3ZZh7azhAoTEjIm6hGjmE6o/yzCaVeyT1BeEz8/PNKLOd1EeTz6T7w8EmmXnjCMoLqmsovIuGztodhiNTj7IfDfwH3f8AHwC6puWa5QDF0ETEO4Ef0PsF4p8HPpyZJ9TbTqM6y2sLFh1869K17nFg98y8skXVHtfqs4JmUl3TYwHwkoGOMBmgvMlUA027Yn8zaBHxMeAzVEeoQe9tuj+N218KvK9ZBxW0O7Mvx+xHX0RMAr5B9dllEn3//1zkZb0837XuduBgd1YNjm2+HLMvwz6nLNt9Gbb7Msy9+RygkEREnEF1sWuAczLzrSMsL6imwem6yI87DJskInYEfgiszaJHjTtAMQwRsTrVmRE7UE3r9BRwOXB8Zt7UY9slqT6EHEQ96Nkgqc4MONwP0v2LiF8Cb6gfnlJfc2Uk5S1BdUrt7vUq+5tBiIjlgA8DewMvG+LLn6aaJ/VHmXlBs+vW7sy+HLMvIyLWAz4F7AVM72Oz/nZkXQP8CPhhZj7f/Bq2L9t8OWZfjn1OObb7cmz3ZZh78zhAIYmIWBFYvX64IDP/1qRy30Z9dHpmntqMMrVwapuvAR+i+x+dAxSjpL4I1s5UfzNzqaZ0+mNmPlS0YuNE/SHuFfXDeZn5qyaUuRjwEbr7G8+WG4KIWItqHtQNqaaSWxpYEliM6svibKozX26muvj71Zn5bJHKthmzL8fsR199tOF2DC33SzPznhL1bTe2+XLMvgz7nLJs92XY7ssw95FzgEKSxqmI2IDqqH+odvT+pWR9JEmSJEmSpKFwgEKSJEmSJEmSJI26CaUrIEmSJEmSJEmSOo8DFCqunqut4957LDD7csy+DHOXJEmSJEkaOxyg0FhwS0TsO9pvGhH7AbeM9vuOMWZfjtmXYe6Sxp2I+Hd9u6p0XTpJRKwZEQdGxCci4sMR8eaIWK50vaRWsr8pw/5Gnch2L6mL16BQcRGxAEjgn8BXgDMzc36L3msSsB9wBLAhQGZObMV7jQdmX47Zl2Hu7SsiTgAWAzIzDyldn/EiIiYDSwOzMnPBMMt4ObAcQGb+qYnVU63uu6D6Pa1YtDLjVESsBbwP2BZYAXgMuBT4Xmbe22PbFYHvA28CokdRC4BfAJ/IzLtaXG31EBEvAqYAZObdhavTluxvRs7+pn3Y5wye7b592O7L6PTcHaBQcRFxL7AK1U5DgIeAk4HTM7MpRxxHxAbAAcDBQNcH7QDuzcw1mvEe45HZl2P2ZZh7+4qIOcDi4EDQQOoj0z4F7A10tckFwFXAacAPM/P5IZR3PrAb1eCQU5m1QMPg6iPuMBy6iDiQakfI5K5VdP8fmA3snZm/r7ddDfgTMKPHdl2vo173CLB7Zl7b2tqPbxGxGNUOq72BDagGRB8G/kL1v/f8IZZnf9Ni9jcjY39Tln1OGbb7smz3ZZh7czlAoeIiYhpwFHAo1c6lxkZ5E/Bb4BLgysx8bJBlLg9sBewEvA54aePTwDzgW8DnM/Ppkf4M45XZl2P2ZZh7+6oHKJag+kDnAEUfIuLVwC+pznjoecRa19/D7cBBmfnXQZZ5PvBazL5XEdGMgck7qX4/jwGb8sLfXUceaTUYEfEG4Fy6d4I0Ztf1+Elg68y8OSL+CGzfy7b08rp7gFdk5pMtqfw4FxEvAX4NrNu1quHprv7mT8AhmfnvQZZpf9MP+5uy7G/Kss8pw3Zflu2+DHNvPgcoNGZExAzgC8A7gK4/xp4N9H6qHSf3AY8Cc6g6gilUO1tWo+ogVupZfH0/H/hf4HOZeWdzf4Lxy+zLMfsyzL39OEAxsIhYn+osiWl0t/fevkQCPA/8v8z8+iDK7egP0wNpOBp5RMXU932V05FHWg2knsLsdrrPnAuqKf5uoDrK89XAi+rnfgV8D/hd/fhxqqkAzwXuBqYCrwQ+DrymfosEvpKZnx6Nn2c8iYhVgauBF9er+tppBdWOq/dm5s8GUa79TT/sb8qxvynLPqcM231ZtvsyzL01HKDQmBMRawIfBQ6kOkWqp4EabW+j8E8BPwaOy8w7RlK/dmb25Zh9GebePhygGFhE/AXYmu4PzfcC51OdQr8msAewTMPzCXwnMz8yQLkd/WF6IA3zubeS2fciIvYHTqdqy89SnRn0s4bnFwe+CnyEalDuD8DuwExg28y8rY9yvwt8sH74ELBK+qVqERHxa2BPuvuTOcCf6e5vXglMYNH+5vDM/OYA5drf9MP+phz7m7Lsc8qw3Zdluy/D3FskM715G5M3qhH3/YCzqUYdFwzx9iRwDtUR0lNK/zzj6Wb2Zt9pN3Mf/zeqD4YLgPml6zIWb1QXLFxAdWbPfODrwGI9tpkMfAJ4pt6ma/vjByj7fLPvN5/G3Ifatwz2Zva9Z/+ThvwP62e7C3r8nt45QLkTgVsaXvOy0j/rWLoBG/XI86fAcj22WQ04vsffxnzgkwOUbX/Tfz72N+Wyt78pl719Trnsbfflsrfdm3tb3Tw1U2NWZj4LnAGcUV98Zsv6tiGwFtWpgtPqzZ+mGoW/g2oe+auAqzNz3mjXux2YfTlmX4a5j46IuLiFxS/ewrLbwT4Ny2dk5uE9N6j/Dr4WEb+hGnBbh+qon/dFxMTMfN/oVLWtXQicOcTXBHAy1dFXs6muoaPB2bS+fxY4oZ/tjqO6KGFQ9fFn9FdoZs6PiFOBL9WrNqb6f6DKOxuWf5+Z+/TcIDPvBT4QEWdTTYe4PFX+X46ISZn5pZ6v0ZDZ34wu+5ty7HPKsd2XY7svw9xbxAEKjQv1jr+/1DeNIrMvx+zLMPeW2pGRz42t4dm6YfnI/jbMzL9HxFZUcwW/muoD9SERMSEz39PCOrarM6jO0EqqL+fPAR/MzPsGW0BEnFwvzs3MU5tfxba1ElXuN9UDcH25or7v2nb+IMq+tmH5RcOsX7t6VcPyf/W3YWb+PiK2pjpqcF2q/ubzdX/zhRbWsV3Z35Rjf1OOfU45tvtybPdlmHuLTChdAUmS1HGiBTf1bwbVl8JbM/OugTbOzEeBXaiOwIUq44Mj4ketq2J7ysz9gTcCD1Ll+Hrgpoh4f9GKdYYl6/vHB9juiYblpwZZduN20/rcqjOtW9/fmZkDHvGamf8GtgGur1cFcFREHNWi+rUt+5ui7G/Ksc8px3Zfju2+DHNvEc+gkCRJoyUb7q+junZHs2zfxLLa0TL1/aCPos3MZyNiL+DnwBuoPlAfFBGRme9uQR3bVmaeFxH/B3wLOABYGvh+ROwHvCczby9awfY1myrrZQbYrvH56YMsu3G7p4dSqQ6wLFU/f+dgX5CZsyJiJ6pB0VdS9Tefrfubz7Wgjm3L/qYY+5tylsU+pxTbfTnLYrsvYVnMvSUcoJAkSaPlduAlVB/qvp6Z/c4/OxQRMQdYolnltaF5wGLA1KG8KDPnRcRbqeYxfwvVB+oD6w/UBze/mu0rMx+nyu4sqnmaVwW2A26IiM8Dx2TmgoJVbEcPUu0UeVlELJGZc/vYbsv6Puptp2TmnAHK3qph+ZER1rPddLXjIfXJmflEROxCdTHVrunlPlP3Nx5pOAT2N0XY35Rjn1OO7b4c230Z5t4iTvEkSZJGyzUNy5sXq0Vnmkn1QXjVob4wM58H9gV+1rD6XRHx4+ZUrbNk5m+BlwGnUP1OJgP/DVwVEZv291oNWdf81VOA/q6f0ngh4MUH2JaImAIc1LDqumHUrZ09QtW2VxrqCzNzNvBa4P/qVQF8OiKcq3kY7G9Glf1NOfY55djuy7Hdl2HuLeIAhSRJGi1XNyw7QDG6/lnfrxYRqwz1xfXFDPejOpOi67ofB0TEacDEptWyQ2Tmk5l5CLAHcA9VnpsAV0TEVyPCs4Ga49cNy1+LiDc1PhkRkyLia8CeVGd2nUv1u/jv+qKGLxARE4Ef0z3YNxMYcA7iDnNLfb9WRCw/1Bdn5tNUfxt/qlcFcGREfKlJ9eso9jejxv6mHPuccmz35djuyzD3FonMHHgrSZKkEYqIHYBL6odPZuayTSy7a4qnzEx3mPcQEV8EjqT6cvjezDx5mOVMAE6nGqxovKbIBMx+WCJiSeBY4L31qqSaDu29mfmnepsF9fpHMnPFIhUdh+odr7dR7eQIqgz/AdxAdST5q4EV6+dupjqq7V9Ug27PASdS7Uy5h+ro0C2Bw4CXN5R3dGZ+arR+pvEgIo4GDqfK5x2ZedYwy5kC/AbYsV6VwFyq3539zTDY37SO/U059jnl2O7Lsd2XYe6t4wCFJEkaFRGxFPB4w6r1M/O2JpXtAEU/ImJn4A9UH34vy8xhX1S8HqQ4Fdi/Li/qp8x+BCLiNcBJwJp0D/6cBBxB9XfjDsNhiIjXAb+k+8wf6M636/HzwOsz83cRcTzw/h7bLVIk3e3+fuDl9Xz/qkXEnlRH1SZwYWbuOYKypgDnATuz6O/N/mYE7G9aw/6mDPucsmz3ZdjuyzD31nGAQpIkjZqIuIXqQtlQHXVyZpPKPRKYBJCZn29Gme2kPsJtJjCtXrVJZt44gvKCak7zd9H9JbIjP0w3U0RMA44G/oPuL+gPAF3Tcs1yh+HQRcQ7gR/Q+0Xinwc+nJkn1NtOozrTawsWHYDr0rXucWD3zLyyRdUet+qj9GdSzTO+AHhJZt45gvImU+382hX7m6axv2kN+5vRZ59Tnu1+9NnuyzD31nGAQpIkqQNExBlUF7sGOCcz3zrC8oLq1PxD6lUd+WG6FSJiR+CHwNosekSVOwyHKSJWpzpicweq6SaeAi4Hjs/Mm3psuyTwDaoLdE7qUVRSHTl3eLPOAGtHEfFL4A31w1PqayCMpLwlgHOA3etV9jdNYn/TfPY3o88+pzzb/eiz3Zdh7q3hAIUkSVIHiIgVgdXrhwsy829NKvdt1EfMZeapzShTC0/7/hrwIbqPLnSH4SiKiJWpTrtfnWpe4PuBP2bmQ0UrNg5ExHrAK+qH8zLzV00oczHgI3T3N54t1yT2N+XZ34yMfc74ZLsfGdt9GebeGg5QSJIkSWNURGxAdSQiVF+C/lKyPpLal/2NJEkqwQEKSZIkSZIkSZI06iaUroAkSZIkSZIkSeo8DlBIkqSWi4ieF8DriPceC8y+HLMvx+zVaWzz5Zi9OpHtXlIzOUAhSZJGwy0Rse9ov2lE7AfcMtrvO8aYfTlmX47Zq9PY5ssxe3Ui272kpvEaFJIkqeUiYgGQwD+BrwBnZub8Fr3XJGA/4AhgQ4DMnNiK9xoPzL4csy/H7NtTRJwALAZkZh5Suj5jiW2+HLNvX/Y5fbPdty/bfRmdnrsDFJIkqeUi4l5gFaovMgAPAScDp2dmU46CiogNgAOAg4EVu1YD92bmGs14j/HI7Msx+3LMvj1FxBxgcXDnVE+2+XLMvn3Z5/TNdt++bPdldHruDlBIkqSWi4hpwFHAoVQfvBo/gNwE/Ba4BLgyMx8bZJnLA1sBOwGvA17a+DQwD/gW8PnMfHqkP8N4ZfblmH05Zt+e6i/vS1AdXdhxX977Y5svx+zbl31O32z37ct2X0an5+4AhSRJGjURMQP4AvAOoOuDV88PI/cDtwP3AY8Cc6i+lEwBlgNWA9YFVupZfH0/H/hf4HOZeWdzf4Lxy+zLMftyzL69dPqX98GwzZdj9u3HPmdgtvv2Y7svo9Nzd4BCkiSNuohYE/gocCCwdC+bDPQBJXpZ9xTwY+C4zLxjJPVrZ2ZfjtmXY/btodO/vA+Fbb4cs28f9jmDZ7tvH7b7Mjo9dwcoJElSMRExGXgz8DZgF2DJIRYxG7gI+BlwTmbOaW4N25fZl2P25Zj9+NbpX96HwzZfjtmPf/Y5Q2e7H/9s92V0eu4OUEiSpDEhIhYDtqxvGwJrAS8CptWbPA3MBO6gmtv2KuDqzJw3+rVtL2ZfjtmXY/atEREXt7D4HaiOsu3IL+8jZZsvx+xbxz5n7LLdt47tvgxzbx0HKCRJkiRJTRERCxh4Ko8RvQUd+uVd0gvZ56gT2e7LMPfWmVS6ApIkSZKkttPbfOIj5dF1kvpin6NOZLsvw9ybzAEKSZIkSVKzZMP9dcCTTSx7+yaWJak92OeoE9nuyzD3FnGKJ0mSJElSU0TELcBLqL68vzMzz2hi2R19AUlJL2Sfo05kuy/D3FtnQukKSJIkSZLaxjUNy5sXq4WkTmGfo05kuy/D3FvEAQpJkiRJUrNc3bDsl3dJrWafo05kuy/D3FvEAQpJkiRJUrN0HV0YwKYlKyKpI9jnqBPZ7ssw9xZxgEKSJEmS1CzXUs3NnMBSEfGSwvWR1N7sc9SJbPdlmHuLTCpdAUmSJElSe8jMpyLidqqLSEI1BcJtTSr+S/gdVlID+xx1Itt9GebeOpGZpesgSZIkSZIkSZI6jFM8SZIkSZIkSZKkUecAhSRJkiRJkiRJGnUOUEiSJEmSJEmSpFHnAIUkSZIkSZIkSRp1DlBIkiRJkkYsIiZ14ntLKsM+R53Idl+GubeWAxSSJEmSpGa4JSL2He03jYj9gFtG+30lFWefo05kuy/D3FsoMrN0HSRJkiRJ41xELAAS+CfwFeDMzJzfoveaBOwHHAFsCJCZE1vxXpLGJvscdSLbfRnm3loOUEiSJEmSRiwi7gVWofoCD/AQcDJwemY25ei/iNgAOAA4GFixazVwb2au0Yz3kDQ+2OeoE9nuyzD31nKAQpIkSZI0YhExDTgKOBRYnO4v8QA3Ab8FLgGuzMzHBlnm8sBWwE7A64CXNj4NzAO+BXw+M58e6c8gafywz1Enst2XYe6t5QCFJEmSJKlpImIG8AXgHUDXlAQ9v3jeD9wO3Ac8Csyh+jI+BVgOWA1YF1ipZ/H1/Xzgf4HPZeadzf0JJI0n9jnqRLb7Msy9NRygkCRJkiQ1XUSsCXwUOBBYupdNBvoyGr2sewr4MXBcZt4xkvpJai/2OepEtvsyzL25HKCQJEmSJLVMREwG3gy8DdgFWHKIRcwGLgJ+BpyTmXOaW0NJ7cQ+R53Idl+GuTeHAxSSJEmSpFEREYsBW9a3DYG1gBcB0+pNngZmAndQzel8FXB1Zs4b/dpKGu/sc9SJbPdlmPvwOUAhSZIkSZIkSZJG3YTSFZAkSZIkSZIkSZ3HAQpJkiRJkiRJkjTqHKCQJEmStIiIWDIi7o2IjIg7ImLx0nVSc0XEJfXv95mIWLN0fSRJktSZHKCQJEmS1NNngVXr5SMz87meG0TE5+od3I23Lw32DSJico/XHtTPtj/u5b0ab/Mj4rGIuDUifhoRH4iIpYf8U/df34N6ed//GWIZDza89nPDqMPKdT3+JyKuj4j7IuLZiJgdEfdExGURcVxEvCkilhiguCOABKYA3xxqXSRJkqRmcIBCkiRJ0kL10fSH1Q//CZwxhJd/NCJWbnqlBjYBWBZ4CfA24PvAfRHxoYiIFr7vOyJikxaWD0BEzIiIk4C7gVOA/YGNgFWAJYBpwGrAq6l+d+cAD9aDFSv2VmZmXgWcVz98U0Ts0NqfQpIkSXohBygkSZIkNfo80DWl01czM4fw2qnAUc2v0iKeBS7scfsDcAPwfMN2SwLfBY5pYV0C+GoLyyci9qEaKHoPMKnhqbnALcCfgf8DbqPKpsuyVIMV/4qItfso/isNy19uUpUlSZKkQXOAQpIkSRIAEbEW1dH5AA8xtLMnuhwSEes1r1Yv8FBm7t7jtmtmbgy8iGpH+4KG7f8rIl7Xwvq8NiJ2bkXBEfEJ4EyqaZi6nAe8Fpiemetn5naZuX1mrgdMB/agOsuia7BmSaDX6a4y83LgivrhNq36OSRJkqS+OEAhSZIkqctHgIn18imZOW+Qr3sCeLhengT8d5PrNSiZ+Xhmfhr4QI+nWnFWx60Ny19r9lRSEbEX1RkOXeU+DuyamXtl5u8yc07P12Tms5l5QWa+G1gfOHcQb3VSw/LHRlZrSZIkaWgcoJAkSZJEREwBDmpYddoQXv4s8MWGx2+NiK2aUa/hyMwTgesbVm3R17UYRuDIxvKprn3RFBGxClX+XYMTs4HtMvMPgy0jM/+VmW8GDgf6G2j6Kd1TQ+1ZX4NEkiRJGhUOUEiSJEkCeDPdUwHdlJn/HOLrTwD+1fD4a02p1fD9umE5qC4q3ezy/9Tw+MsRMamvjYfov4BlGh5/JDP/PpyCMvPYzLypn+eforqOB1Q5HTCc95EkSZKGwwEKSZIkSbDoGQC/HeqL6+mgPt2waoeI2HPEtRq+e3o8XqEF7/GJhuV1gfeNtMCIWLZHObcBJ4+03AE0/r73afF7SZIkSQs5QCFJkiR1uIhYHNilYdUlwyzqLOCahsdfiYhS3zkW6/H4uWa/QX2R6XMaVn02IqaNsNhdqS5s3eWEzMwRljmQxt/3yyNijRa/nyRJkgQ4QCFJkiQJtmTRneJXDqeQekd641kFGwHvHEG9RmKDHo8fatH7fAqYXy+/mGp6ppHYscfj34+wvAFl5m3Aow2rdm71e0qSJEngAIUkSZKkaoCiy/2Z+chwC8rMi1h0p/oXImKJYddsGCJiMvCWhlVzgb+14r0y8xYWnYLp8Ih40QiKbPxdPAP0ef2IJruhjzpIkiRJLeMAhSRJkqQNG5Zvb0J5nwC6piWaAXyoCWUOSj2l1A+AlRpW/zozn2nh234OmFMvLwV8ZgRlrdiwfH9mzu9zy+Zq/L2/fJTeU5IkSR3OAQpJkiRJazUs3zfSwjLzb1TXo+jy/yJimZGW25eImBgRL46ItwB/Bg5sePpZRjZgMKDMvB/4VsOq90fE2sMsbrmG5ceHXamhu7dhec1RfF9JkiR1MAcoJEmSJDVOSfRon1sNzZHAvHp5eRa9NsVIzIiIbLwBzwMPAr8AXtWw7Vxgv8z8Z5Peuz9fpTu7xYEvDbOcyQ3Lc0dUo6F5rGF5xT63kiRJkprIAQpJkiRJ0xqW5/S51RBk5r+BExpWHRYRKzej7MG8PfAHYPPMPHdU3jDzCeC/G1a9PSI2HUZRjQMFLTvrpBeNU2BNjoiJo/jekiRJ6lCTSldAkiRJ0pgSTSzrC1TTLS0FTKW6VsP7R1jms8ClPdbNB54EZlFdDPuSzLxjhO8zHN8FDgXWoMrxq8Brh1jGo3SfwbBcfxs2Wc/fe/a6lSRJktREDlBIkiRJerpheUqzCs3MmRFxLNXABMC7I+IbmXnLCIp9KDN3H3ntmi8z50bEZ4Ef16t2i4jXZOZFQyjmX8D69fIqEbF8Zj7SzHr2ofH3/mxmLhiF95QkSVKHc4onSZIkSTMblpt91P6xwEP18iQWnQapHZ0O3Njw+KsRMZSzUnqeHbL1yKs0KI2/94dH6T0lSZLU4RygkCRJktQ4HdJqzSw4M2cDX2xY9ZaI2KqZ7zGW1GcefKph1RbAPkMo4uIej98x4koNTuPv/c5Rek9JkiR1OAcoJEmSJN3UsLxuC8o/Ebi94fHRLXiPMSMzf8OiZ0J8KSIWG+RrrwGubli1d0Q0ddCoD42/9xv73EqSJElqIgcoJEmSJF3VsLxSRLyomYVn5jzg0w2rtgde18z3GIM+0bC8LvC+Ibz2qw3LiwOnDHGaqIUiYrmIWH4Qm27UsHxVn1tJkiRJTeQAhSRJkqSrgdkNj1sxBdNPWfTMgOLXooiINSMiG24/blbZmXkFcHbDqs8Akwf58rOBXzc83gX4QURMHEodImJL4Fpg9QG2Ww+Y3rDqkqG8jyRJkjRcDlBIkiRJHS4znwMuali1UwveI1n0rIL1mv0eY9D/A56vl18MLDOYF9VZHQD8u2H1+4A/RMSmA72+Hng5GfgrMGMQb9n4+74pM+8eTD0lSZKkkZpUugKSJEmSxoSfAW+sl18H/Fez3yAzL46I3wG7NbvssSgzb6kHCoYyvVPXax+PiB2BXwJdgxI7AtdExOXA74B/AjPr514MvATYA3glQzsYbY+G5Z8Ota6SJEnScDlAIUmSJAngHOApYCngpRHxssy8aYDXDMcngF2BYV1Tocle3OPxdS14j88B7wSmDvWFmXlPRGwLfAM4hOr7WwCvqm8Debp+7S19bRARSwKv7XpL4PSh1lOSJEkaLqd4kiRJkkRmPgP8uGHVAS16n+uAM1pR9jBs37D8EHBis98gMx8AjhvB65/JzP8AXgocDww0/VICfwMOB9bKzM9m5px+tt+b7mtjXJiZdwy3rpIkSdJQRTW9qSRJkqROFxFrA7cCE4GHgdXr61O0pYj4NdV0VgCHZ+axJeszWPVFrV8OrAAsT3Wdi8eAO4GrM/PxIZT1F7rPxtgtM3/f1MpKkiRJ/XCAQpIkSdJCEXEq8K764UGZeWrJ+rRKREwAHqW6cPVMYM36LJKOERGvBK6oH16emYOZNkqSJElqGqd4kiRJktToc8C8evmIiBgL14pohU2oBicAjum0wYnapxqWjyxWC0mSJHUsBygkSZIkLVRfg+Bb9cMNgf0KVqeVdqjvZwHfL1mREiJic+CN9cNfZebFJesjSZKkzuQUT5IkSZIWERFLAjcDq1Jd1+Cl7Xwtik4UERcDOwHPAht6cWxJkiSV4ACFJEmSJEmSJEkadU7xJEmSJEmSJEmSRp0DFJIkSZIkSZIkadQ5QCFJkiRJkiRJkkadAxSSJEmSJEmSJGnUOUAhSZIkSZIkSZJGnQMUkiRJkiRJkiRp1DlAIUmSJEmSJEmSRt3/B1V9UxVJaxpUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1584x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "test_parameter='alpha' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha_A'\n",
    "    name_legend = \"Values of α\"\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega_A'\n",
    "    name_legend = \"Values of ω\"\n",
    "df_aux=grouped_aggM\n",
    "if used_direction=='s':\n",
    "    df_aux=df_aux.query('NP > NS')\n",
    "    used_labels=labelsShrinkOrdered\n",
    "    name_fig= name_fig+\"Shrink\"\n",
    "elif used_direction=='e':\n",
    "    df_aux=df_aux.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig= name_fig+\"Expand\"\n",
    "elif used_direction=='a':\n",
    "    df_aux=df_aux\n",
    "    used_labels=labels\n",
    "    name_fig= name_fig+\"All\"    \n",
    "\n",
    "x = np.arange(len(used_labels))\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "plt.xticks(x, used_labels,rotation=90)\n",
    "ax.tick_params(axis='both', which='major', labelsize=36)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=36)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    df_aux2 = df_aux.query('Cst == @cst_aux')\n",
    "    for css_aux in [0,1]:\n",
    "        if cst_aux == 3 and css_aux == 1 and used_direction == 's':\n",
    "            continue\n",
    "        array_aux = df_aux2.query('Css == @css_aux')\n",
    "        if used_direction=='s':\n",
    "            array_aux = array_aux.sort_values(by=['NS'])\n",
    "        array_aux = array_aux[real_parameter].values\n",
    "        style_aux = cst_aux*2 + css_aux\n",
    "        ax.plot(x, array_aux, color=colors_spawn[style_aux], linestyle=linestyle_spawn[style_aux%4], \\\n",
    "                marker=markers_spawn[style_aux], markersize=18, label=labelsMethods[style_aux])\n",
    "        \n",
    "ax.set_ylim(0,3.2)\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=1)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "test_parameter='alpha' #Valores son \"alpha\" o \"omega\"\n",
    "    \n",
    "if used_direction=='s':\n",
    "    df_aux=grouped_aggM.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    np_aux = [10, 20,20, 40,40,40, 80,80,80,80, 120,120,120,120,120]\n",
    "    nc_aux = [1,  1,10,  1,10,20,  1,10,20,40,  1,10,20,40,80]\n",
    "elif used_direction=='e':\n",
    "    df_aux=grouped_aggM.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    np_aux = [1,1,1,1,1,        10,10,10,10,  20,20,20,  40,40,  80 ]\n",
    "    nc_aux = [10,20,40,80,120,  20,40,80,120, 40,80,120, 80,120, 120]\n",
    "elif used_direction=='a':\n",
    "    df_aux=grouped_aggM\n",
    "    used_labels=labels\n",
    "    name_fig=\"All\"\n",
    "    np_aux = [1,1,1,1,1,        10,10,10,10,10, 20,20,20,20,20, 40,40,40,40,40, 80,80,80,80,80, 120,120,120,120,120]\n",
    "    nc_aux = [10,20,40,80,120,  1,20,40,80,120, 1,10,40,80,120, 1,10,20,80,120, 1,10,20,40,120, 1,10,20,40,80]\n",
    "    \n",
    "x = np.arange(len(used_labels))\n",
    "handles = []\n",
    "\n",
    "f=plt.figure(figsize=(20, 12))\n",
    "#ax=f.add_subplot(111)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.azim = -60\n",
    "ax.dist = 10\n",
    "ax.elev = 10\n",
    "ax.set_xlabel(\"NP\", fontsize=20)\n",
    "ax.set_ylabel(\"NC\", fontsize=20)\n",
    "ax.set_zlabel(\"Alpha\", fontsize=20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    df_aux2 = df_aux.query('Cst == @cst_aux')\n",
    "    for css_aux in [0,1]:\n",
    "        array_aux = df_aux2.query('Css == @css_aux')['alpha'].values\n",
    "        ax.plot3D(np_aux, nc_aux, array_aux, colors_spawn[cst_aux*2 + css_aux])\n",
    "      \n",
    "        handles.append(handles_spawn[cst_aux*2 + css_aux])\n",
    "        \n",
    "#ax.set_zlim(0,4)\n",
    "plt.legend(handles=handles, loc='best', fontsize=20,ncol=2,framealpha=1)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/3dPlot_\"+name_fig+'_'+test_parameter+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df, tipo):\n",
    "    normality=[True] * (len(processes) * (len(processes)-1))\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",
    "                for cst_aux in [0,1,2,3]:\n",
    "                    for css_aux in [0,1]:\n",
    "                        df_aux = df.query('NP == @np_aux and NS == @ns_aux and Cst == @cst_aux and Css == @css_aux')\n",
    "                        dataList = list(df_aux[tipo])\n",
    "                        st,p = stats.shapiro(dataList) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                        if p < p_value:\n",
    "                            normality[i]=False\n",
    "                            total+=1\n",
    "            \n",
    "    \n",
    "    print(\"Se sigue una distribución guassiana: \" + str(normality) + \"\\nUn total de: \" + str(total) + \" no siguen una gaussiana\")\n",
    "    return normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_stat_difference(dataLists, parametric):\n",
    "    if parametric:\n",
    "        st,p=stats.f_oneway(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    else:\n",
    "        st,p=stats.kruskal(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    if p > p_value: # Si son iguales, no hay que hacer nada más\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_posthoc(dataLists, parametric):\n",
    "    data_stats=[]\n",
    "    ini=0\n",
    "    end=len(labelsMethods)\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",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medias diferentes\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",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medianas diferentes\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",
    "    return df_Res, data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aquellos grupos que tengán valores por encima del límite no se considerarán\n",
    "# Con sumar a si mismos su valor actual estarán fuera\n",
    "def check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary):\n",
    "    index_aux = 0\n",
    "    for cst_aux in [0,2]: # Primero los grupos síncronos\n",
    "        for css_aux in [0,1]:\n",
    "            if cst_aux == 2 and css_aux == 1 and np_aux > ns_aux: # Arreglo para coger bien el tiempo en Merge Single Shrink\n",
    "                index_aux = 1\n",
    "            tc_val = grouped_aggM.loc[('2,2',0, cst_aux, css_aux - index_aux, np_aux,ns_aux), 'TC_A']\n",
    "            if tc_val > tc_boundary:\n",
    "                data_stats[cst_aux*2 + css_aux]+=data_stats[cst_aux*2 + css_aux]\n",
    "    index_aux = 0\n",
    "    for cst_aux in [1,3]: # Segundo se comprueban los asíncronos\n",
    "        for css_aux in [0,1]:\n",
    "            if cst_aux == 3 and css_aux == 1 and np_aux > ns_aux: # Arreglo para coger bien el tiempo en Merge Single Shrink\n",
    "                index_aux = 1\n",
    "            tc_val = grouped_aggM.loc[('2,2',0, cst_aux, css_aux - index_aux, np_aux,ns_aux), 'TH']\n",
    "            if tc_val > tc_boundary:\n",
    "                data_stats[cst_aux*2 + css_aux]+=data_stats[cst_aux*2 + css_aux]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric):\n",
    "    best = 0\n",
    "    otherBest=[]\n",
    "    \n",
    "    # TODO Descomentar tras anyadir RMS perspective en results_with_st\n",
    "    #if rms_boundary != 0: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "    #    check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary) \n",
    "        \n",
    "    indexes = np.argsort(data_stats)\n",
    "    best = -1\n",
    "    i = 0\n",
    "    while best == -1:\n",
    "        index = indexes[i]\n",
    "        if not (shrink and (index == 5 or index == 7)): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            #if rms_boundary == 0 or data_stats[index] <= tc_boundary:\n",
    "            best = index\n",
    "        i+=1\n",
    "    otherBest=[]\n",
    "    for index in range(len(labelsMethods)): # Para cada metodo exceptuando best\n",
    "        if not (shrink and (index == 5 or index == 7)): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            if index != best and not df_Res.iat[best,index]: #Medias/Medianas iguales\n",
    "                #if data_stats[index] <= tc_boundary:\n",
    "                otherBest.append(index)\n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux) +\" \"+ str(parametric)+\" Mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    \n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perc_differences(dataLists, np_aux, ns_aux, shrink, rms_boundary, tc_boundary):\n",
    "    data_stats = []\n",
    "    ini=0\n",
    "    end=len(labelsMethods)\n",
    "    for i in range(ini,end):\n",
    "        data_stats.append(np.median(dataLists[i]))\n",
    "        \n",
    "    if rms_boundary != 0: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "        check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary) \n",
    "    indexes = np.argsort(data_stats)\n",
    "    \n",
    "    best = -1\n",
    "    bestMax = -1\n",
    "    otherBest=[]\n",
    "    for index in indexes: # Para cada metodo -- Empezando por el más bajo en media/mediana\n",
    "        if shrink and (index == 5 or index == 7): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            continue\n",
    "        \n",
    "        if best == -1:\n",
    "            best = index\n",
    "            bestMax = data_stats[best] * 0.05 + data_stats[best]\n",
    "        elif data_stats[index] <= bestMax: # Medias/Medianas diferentes && Media/Medianas i < Media/Mediana best\n",
    "            otherBest.append(index)\n",
    "                \n",
    "        \n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux)+\" Mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    \n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtiene \n",
    "def check_groups_stats(dataLists, np_aux, ns_aux, shrink, parametric):\n",
    "    global_difference=compute_global_stat_difference(dataLists, parametric)\n",
    "    if not global_difference:\n",
    "        print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene valores iguales\")\n",
    "        return\n",
    "    \n",
    "    df_Res,data_stats=compute_global_posthoc(dataLists,parametric)\n",
    "    return get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_with_st(tipo, data_aux):\n",
    "    normality=check_normality(data_aux,tipo)\n",
    "    if False in normality:\n",
    "        normality = False\n",
    "    else:\n",
    "        normality = True\n",
    "    \n",
    "    \n",
    "    results = []\n",
    "    shrink = False\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "                dataLists=[]\n",
    "                if np_aux > ns_aux:\n",
    "                    shrink = True\n",
    "                else:\n",
    "                    shrink = False\n",
    "                #normality=True\n",
    "                for cst_aux in [0,1,2,3]:\n",
    "                    for css_aux in [0,1]:\n",
    "                        dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                        lista_aux = list(dataSet_aux[tipo])\n",
    "                        dataLists.append(lista_aux)\n",
    "                        #Si permito el shaphiro, acabare comparando manzanas y naranjas\n",
    "                        # si hay distribuciones normales y no normales\n",
    "                        #st,p = stats.shapiro(lista_aux) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                        #if p < p_value:\n",
    "                        #normality=False\n",
    "\n",
    "                aux_data = check_groups_stats(dataLists, np_aux, ns_aux, shrink, normality)\n",
    "                results.append(aux_data)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_with_perc(tipo, data_aux, rms_boundary=0):\n",
    "    results = []\n",
    "    shrink = False\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "                dataLists=[]\n",
    "                if np_aux > ns_aux:\n",
    "                    shrink = True\n",
    "                else:\n",
    "                    shrink = False\n",
    "                \n",
    "                tc_boundary = dfM.query('NP == @np_aux and NS == @ns_aux')['TC'].max()\n",
    "                if rms_boundary == 0:\n",
    "                    for cst_aux in [0,1,2,3]:\n",
    "                        for css_aux in [0,1]:\n",
    "                            dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                            lista_aux = list(dataSet_aux[tipo])\n",
    "                            dataLists.append(lista_aux)\n",
    "                else:\n",
    "                    boundaries = []\n",
    "                    for cst_aux in [0,1,2,3]:\n",
    "                        for css_aux in [0,1]:\n",
    "                            dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                            lista_aux = list(dataSet_aux[tipo])\n",
    "                            dataLists.append(lista_aux)\n",
    "                            \n",
    "                            if cst_aux == 0 or cst_aux == 2:\n",
    "                                if cst_aux == 2 and css_aux == 1  and (np_aux > ns_aux):\n",
    "                                    new_boundary = tc_boundary\n",
    "                                else:\n",
    "                                    new_boundary = grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux), 'TC_A']\n",
    "                            else:\n",
    "                                if cst_aux == 3 and css_aux == 1 and (np_aux > ns_aux):\n",
    "                                    new_boundary = tc_boundary\n",
    "                                else:\n",
    "                                    new_boundary = grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux), 'TH']\n",
    "                            boundaries.append(new_boundary)\n",
    "                    tc_boundary = min(boundaries)\n",
    "                    tc_boundary = tc_boundary + tc_boundary*rms_boundary\n",
    "\n",
    "                aux_data = get_perc_differences(dataLists, np_aux, ns_aux, shrink, rms_boundary, tc_boundary)\n",
    "                results.append(aux_data)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Redimensión 1/10 Mejores: Merge single, Merge, \n",
      "Redimensión 1/20 Mejores: Merge single, Merge, \n",
      "Redimensión 1/40 Mejores: Baseline, Merge single, Merge, \n",
      "Redimensión 1/80 Mejores: Baseline, Merge, Merge single, \n",
      "Redimensión 1/120 Mejores: Baseline, Merge single, \n",
      "Redimensión 10/1 Mejores: Merge, \n",
      "Redimensión 10/20 Mejores: Merge, Merge single, \n",
      "Redimensión 10/40 Mejores: Merge, Merge single, \n",
      "Redimensión 10/80 Mejores: Merge, \n",
      "Redimensión 10/120 Mejores: Merge, Merge single, \n",
      "Redimensión 20/1 Mejores: Merge, \n",
      "Redimensión 20/10 Mejores: Merge, \n",
      "Redimensión 20/40 Mejores: Merge single, Merge, \n",
      "Redimensión 20/80 Mejores: Merge, \n",
      "Redimensión 20/120 Mejores: Merge, \n",
      "Redimensión 40/1 Mejores: Merge, \n",
      "Redimensión 40/10 Mejores: Merge, \n",
      "Redimensión 40/20 Mejores: Merge, \n",
      "Redimensión 40/80 Mejores: Merge, \n",
      "Redimensión 40/120 Mejores: Merge single, \n",
      "Redimensión 80/1 Mejores: Merge, \n",
      "Redimensión 80/10 Mejores: Merge, \n",
      "Redimensión 80/20 Mejores: Merge, \n",
      "Redimensión 80/40 Mejores: Merge, \n",
      "Redimensión 80/120 Mejores: Merge, Merge single, \n",
      "Redimensión 120/1 Mejores: Merge - Asynchronous, \n",
      "Redimensión 120/10 Mejores: Merge, \n",
      "Redimensión 120/20 Mejores: Merge, \n",
      "Redimensión 120/40 Mejores: Merge, \n",
      "Redimensión 120/80 Mejores: Merge, \n",
      "[[5, 4], [5, 4], [0, 5, 4], [0, 4, 5], [0, 5], [4], [4, 5], [4, 5], [4], [4, 5], [4], [4], [5, 4], [4], [4], [4], [4], [4], [4], [5], [4], [4], [4], [4], [4, 5], [6], [4], [4], [4], [4]]\n"
     ]
    }
   ],
   "source": [
    "checked_type='te'\n",
    "use_perc = True\n",
    "rms_boundary=0.1 # Poner a 0 para perspectiva de app. Valor >0 y <1 para perspectiva de RMS\n",
    "if checked_type=='te':\n",
    "    tipo=\"TE\"\n",
    "    data_aux=dfG\n",
    "elif checked_type=='tc':\n",
    "    tipo=\"TC\"\n",
    "    data_aux=dfM\n",
    "    \n",
    "if use_perc:\n",
    "    results = results_with_perc(tipo, data_aux, rms_boundary)\n",
    "else:\n",
    "    results = results_with_st(tipo, data_aux)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  5  5  0  0  0]\n",
      " [ 4 -1  4  4  4  4]\n",
      " [ 4  4 -1  5  4  4]\n",
      " [ 4  4  4 -1  4  5]\n",
      " [ 4  4  4  4 -1  4]\n",
      " [ 6  4  4  4  4  8]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor de los valores\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "unique, counts = np.unique(aux_array, return_counts=True)\n",
    "aux_dict = dict(zip(unique, counts))\n",
    "aux_keys=list(aux_dict.keys())\n",
    "aux_values=list(aux_dict.values())\n",
    "aux_ordered_index=list(reversed(list(np.argsort(aux_values))))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n",
    "\n",
    "if use_perc:\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",
    "                for index in aux_ordered_index:\n",
    "                    if aux_keys[index] in results[results_index]:\n",
    "                        heatmap[i][j]=aux_keys[index]\n",
    "                        break\n",
    "heatmap[len(processes)-1][len(processes)-1]=8\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adapta results a una cadena asegurando que cada cadena no se sale de su celda\n",
    "results_str = []\n",
    "max_counts = 1\n",
    "for i in range(len(results)):\n",
    "    results_str.append(list())\n",
    "    count = len(results[i])\n",
    "    new_data = str(results[i]).replace('[','').replace(']','')\n",
    "    remainder = count%3\n",
    "    \n",
    "    if count <= 3:\n",
    "        results_str[i].append(new_data)\n",
    "    else:\n",
    "        if remainder == 0:\n",
    "            results_str[i].append(new_data[0:8])\n",
    "            results_str[i].append(new_data[9:])\n",
    "        else:\n",
    "            index = 1 + (remainder -1)*3\n",
    "            results_str[i].append(new_data[0:index+1])\n",
    "            results_str[i].append(new_data[index+2:])\n",
    "        \n",
    "    if count > max_counts:\n",
    "        if count > 3:\n",
    "            aux_value = results_str[i].pop()[0:1]\n",
    "        results_str[i][0] = results_str[i][0][0:max_counts*3-2]\n",
    "        if remainder == 1 and max_counts > 1:\n",
    "            results_str[i][0] = results_str[i][0] + ' ' + aux_value\n",
    "#print(results_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/2593541567.py:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_2692/2593541567.py:37: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+processes, fontsize=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\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\"),colors.to_rgba(\"green\"), colors.to_rgba(\"springgreen\"),colors.to_rgba(\"blue\"),colors.to_rgba(\"darkblue\"),\n",
    "            colors.to_rgba(\"red\"),colors.to_rgba(\"darkred\"),colors.to_rgba(\"darkgoldenrod\"),colors.to_rgba(\"olive\"),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",
    "for i in range(len(processes)):\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            aux_color=\"white\"\n",
    "            if heatmap[i, j] == 1: # 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 (0)', 'Baseline single (1)','Baseline - Asynchronous (2)','Baseline single - Asynchronous (3)',\n",
    "                 'Merge (4)','Merge single (5)','Merge - Asynchronous (6)','Merge single - Asynchronous (7)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "colorbar.set_ticks([0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TE\n",
    "#colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TC\n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=32)\n",
    "#\n",
    "\n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Heatmap_\"+tipo+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "tipo=\"bcast\"\n",
    "if tipo == \"bcast\":\n",
    "    #bcast\n",
    "    x = [0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000001, 0.000001, 0.000000, 0.000001, 0.000001, 0.000001, 0.000001, 0.000000, 0.000001, 0.000001, 0.000001, 0.000001, 0.000000, 0.000000, 0.000001, 0.000001, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000001, 0.000001, 0.000000, 0.000000, 0.000000, 0.000000, 0.000001, 0.000001, 0.000000, 0.000000, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 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    "    slope = 4082094463.008053\n",
    "    intercept = 417444.446452\n",
    "elif tipo == \"allgather\":\n",
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    "    slope = 2687592726.595444\n",
    "    intercept = 580231.389302\n",
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    "    slope = 536190943.875754\n",
    "    intercept = 277159.974610\n",
    "elif tipo == \"allreduce\":\n",
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0.149312, 0.150316]\n",
    "    slope = 656145059.536116\n",
    "    intercept = 409351.405089\n",
    "y = [10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 10.000000, 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    "\n",
    "for i in range(len(x)):\n",
    "    if x[i] == 0:\n",
    "        x[i] = 0.0000009\n",
    "\n",
    "x = x[7*100+1:]\n",
    "y = y[7*100+1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfLinear_Reg = pd.DataFrame(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfLinear_Reg[tipo] = x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6968/2732890191.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  gruped_Linear = dfLinear_Reg.groupby([0])['bcast', 'allgather', 'reduce', 'allreduce'].agg(['mean','min','max'])\n"
     ]
    }
   ],
   "source": [
    "gruped_Linear = dfLinear_Reg.groupby([0])['bcast', 'allgather', 'reduce', 'allreduce'].agg(['mean','min','max'])\n",
    "gruped_Linear.to_excel(\"Linear_reg.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "x_values = []\n",
    "y_values = []\n",
    "for i in range(int(len(x)/100)):\n",
    "    sumX = 0\n",
    "    sumY = 0\n",
    "    for j in range(100):\n",
    "        sumX += x[i*100+j]\n",
    "        sumY += y[i*100+j]\n",
    "    x_values.append(sumX/100)\n",
    "    y_values.append(sumY/100)\n",
    "        \n",
    "x = np.array(x).reshape((-1, 1))\n",
    "y = np.array(y)\n",
    "\n",
    "x_values = np.array(x_values).reshape((-1, 1))\n",
    "y_values = np.array(y_values)\n",
    "\n",
    "xlog2 = np.log2(x)\n",
    "xlogn = np.log(x)\n",
    "\n",
    "xlog2_values = np.log2(x_values)\n",
    "xlogn_values = np.log(x_values)\n",
    "\n",
    "x_values2 = np.array(x_values) * 1000\n",
    "y_array = [y_values]\n",
    "aux_labels = [\"Expected\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9647563789648677\n",
      "1482714.0479379185\n",
      "[4.03454237e+09]\n",
      "[ 1929539.61523922  2545856.30743738 11908011.87333057]\n"
     ]
    }
   ],
   "source": [
    "reg = LinearRegression().fit(x, y)\n",
    "print(reg.score(x, y))\n",
    "print(reg.intercept_)\n",
    "print(reg.coef_)\n",
    "\n",
    "\n",
    "reg_array = reg.predict(x_values)\n",
    "print(reg_array)\n",
    "y_array.append(reg_array)\n",
    "aux_labels.append(\"LinearReg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9998389456572134\n",
      "-458670.07685885206\n",
      "[ 8.10773814e+09 -1.77597082e+12  1.67891878e+14 -5.43332539e+15\n",
      "  5.16648967e+16]\n",
      "[  417705.89089336  1557526.85830034 11293930.16471369]\n"
     ]
    }
   ],
   "source": [
    "degrees = 5\n",
    "poly = PolynomialFeatures(degree=degrees, include_bias=False)\n",
    "poly_features = poly.fit_transform(x)\n",
    "poly_reg = LinearRegression()\n",
    "poly_reg.fit(poly_features, y)\n",
    "\n",
    "print(poly_reg.score(poly_features, y))\n",
    "print(poly_reg.intercept_)\n",
    "print(poly_reg.coef_)\n",
    "\n",
    "poly = PolynomialFeatures(degree=degrees, include_bias=False)\n",
    "poly_features = poly.fit_transform(x_values)\n",
    "poly_array = poly_reg.predict(poly_features)\n",
    "print(poly_array)\n",
    "y_array.append(poly_array)\n",
    "aux_labels.append(\"PolyReg-\"+str(degrees))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure(figsize=(16, 8))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "for i in range(len(y_array)):\n",
    "    #ax.scatter(x_values2,y_array[i], color=colors_spawn[i])\n",
    "    ax.plot(y_array[i],x_values2, color=colors_spawn[i])\n",
    "ax.set_xlabel(\"Tamaño (Bytes)\", fontsize=36)\n",
    "ax.set_ylabel(\"Tiempo (ms)\", fontsize=36)\n",
    "#ax.axhline(7549560)\n",
    "plt.legend(labels=aux_labels, loc='best', fontsize=20,ncol=2,framealpha=1)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "f.savefig(\"Images/Regresion.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_diff = []\n",
    "aux_labels2 = aux_labels[1:]\n",
    "for i in range(len(y_array)-1):\n",
    "    y_diff.append(y_array[i+1] - y_array[0])\n",
    "    \n",
    "f=plt.figure(figsize=(16, 8))\n",
    "ax=f.add_subplot(111)\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "ax2.scatter([0]*len(y_array[0]),y_array[0],color=colors_spawn[i+1])\n",
    "for i in range(len(y_diff)):\n",
    "    ax.plot(y_diff[i],x_values2, color=colors_spawn[i+1])\n",
    "\n",
    "ax.set_xlabel(\"Tamaño (Bytes) - Error\", fontsize=36)\n",
    "ax2.set_ylabel(\"Tamaño (Bytes)\", fontsize=36)\n",
    "ax.set_ylabel(\"Tiempo (ms)\", fontsize=36)\n",
    "ax.axvline(0, color=\"black\", linestyle = 'dotted')\n",
    "ax.legend(labels=aux_labels2, loc='best', fontsize=20,ncol=2,framealpha=1)\n",
    "#plt.xscale(\"log\")\n",
    "#plt.yscale(\"log\")\n",
    "f.savefig(\"Images/Regresion_Error.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.986845, 3.995615, 3.995615, 3.995615, 3.995615, 3.995615]\n",
      "[0, 0.0009824999999999999, 0.4770405, 0.766185, 0.8609199999999999, 0.890894]\n",
      "[0, 79.98284, 3.99641, 1.99956, 0.9997100000000001, 0.6615499999999999]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux_labels = ['(10)', '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)']\n",
    "aux_values = [1, 20, 40, 80, 120]\n",
    "title_y=\"Execution time(s)\"\n",
    "title_x=\"(NP,NC)\"\n",
    "maxiters_aux = 30\n",
    "iters_aux1 = 10\n",
    "iters_aux2 = 20\n",
    "aggM_aux = grouped_aggM.query('NP == 10').query('Cst == 2').query('Css == 0')['TR'].values\n",
    "aux1 = grouped_aggL.query('NP == 10').query('Cst == 2').query('Css == 0').query('NS == 1')\n",
    "aux2 = grouped_aggL.query('NS == 10').query('Cst == 2').query('Css == 0')\n",
    "\n",
    "data_aux1 = [aux1['Ti'].values[0] * maxiters_aux]\n",
    "data_aux2 = [0]\n",
    "data_aux3 = [0]\n",
    "for i in range(len(aux_labels)-1):\n",
    "    data_aux1.append(aux1['Ti'].values[0] * iters_aux1)\n",
    "    data_aux2.append(aggM_aux[i])\n",
    "    aux_value = aux_values[i]\n",
    "    data_aux3.append(aux2.query('NP == @aux_value')['Ti'].values[0] * iters_aux2)\n",
    "print(data_aux1)\n",
    "print(data_aux2)\n",
    "print(data_aux3)\n",
    "sum_aux = np.add(data_aux1, data_aux2).tolist()\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(aux_labels))\n",
    "width = 0.5\n",
    "blue_Spatch = mpatches.Patch(facecolor='blue', label='Parents Time')\n",
    "orange_Spatch = mpatches.Patch(hatch='...',facecolor='orange', label='Resize Time')\n",
    "green_Spatch = mpatches.Patch(hatch='\\\\/',facecolor='green', label='Children Time')\n",
    "handles=[blue_Spatch,orange_Spatch,green_Spatch]\n",
    "\n",
    "ax.bar(x, data_aux1, width, color='blue')\n",
    "ax.bar(x, data_aux2, width, bottom=data_aux1,color='orange', hatch='...')\n",
    "ax.bar(x, data_aux3, width, bottom=sum_aux,color='green', hatch='\\\\/')\n",
    "\n",
    "ax.set_ylabel(title_y, fontsize=30)\n",
    "ax.set_xlabel(title_x, fontsize=28)\n",
    "plt.xticks(x, aux_labels, rotation=90)\n",
    "#plt.yscale(\"log\")\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "plt.legend(handles=handles, loc='upper left', fontsize=26)\n",
    "\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/malleabilityTest.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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