analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import math\n",
    "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": 2,
   "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 = True # Cambiar segun el speedUp usado\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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9826/2056908859.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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9826/1294489315.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": 6,
   "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_9826/3028104048.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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9826/2150887515.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_9826/2150887515.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\n",
    "                            iter_time_aux2 = iter_time_aux2 / np_aux\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": 8,
   "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": 9,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
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      "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    0.603147  2.0\n",
       "                        10   0.576215  2.0\n",
       "                        20   0.584821  2.0\n",
       "                        40   0.611443  2.0\n",
       "                        80   0.604689  2.0\n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "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": 30,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "              TE\n",
       "Groups          \n",
       "1,10    6.369967\n",
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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    "grouped_aggG.loc[('2,2',0,2,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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": [
    {
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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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AAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABocsW+JwBsrtxSevtb9VW1t78FAAAAwGFyBTMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAk4MvMJdSnl9KqWu+PrzveQIAAAAAjM0V+57ADj2Y5AMnrHugz4kAAAAAAJwFYyow/2qt9fy+JwEAAAAAcFYcfIsMAAAAAAD2Q4EZAAAAAIAmY2qRAQAAMHjlltLb36qvqr39LQDgbBrTFcyfW0p5dynlI6WUD5VSfruU8qOllOv2PTEAAAAAgDEa0xXMVyV5XJI/S/LoJJ87/fqHpZRvqbW+cZ+TAwAAADgL+rxTI3G3BuzbGK5gfm+SVyX5vCSfUmt9XJJHJvnaJHcleXiSny6lfNlJA5RSXlBKubOUcud9993Xx5wBAAAAAA7ewReYa63vqLV+X6313bXWv5r+7i9rrb+Y5O8keU+Sy5P8kxVjvL7Wen2t9fqrr766n4kDAAAAABy4MbXIeIha6/2llB9M8q+T3FBKubrW6hJlOGPcngUAAADQjYO/gnkDvz79XpJ8zh7nAQAAAAAwKqO+gnlq/tJFlxUCwIC54wAAAOCwnIUrmJ8+9/Mf720WAAAAAAAjc9AF5lLKysucSimPTvLd08Xf0H8ZAAAAAGB3DrrAnOSzSykXSynfXEr5rNkvSymfVEr56iT/IcnfSvLxJN+zr0kCAAAAAIzRGHowf/H0K6WUjyZ5IMmjkzxsuv4vkryw1vrL+5keAAAAAMA4HXqB+d4kL07yJUm+IMnVSR6TSZH5D5L8UpJ/UWvVexkAAAAAYMcOusBca/1Ikn8+/QIAAAAAoEeH3oMZAAAAAIA9UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmV+x7AgAMV7ml9Pr36qtqr38PAAAAOB1XMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCZX7HsCAACcXrml9Pa36qtqb38LAAAYNgVmAAAABq/PN9ISb6YBwKa0yAAAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJpcse8JAAAAAABJuaX09rfqq2pvf4txcwUzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAECTK/Y9AQAAAKA75ZbS29+qr6q9/S0AhsEVzAAAAAAANHEFMwAnqk/Y9wwAAACAIVNgBkZPkRQAAACgG1pkAAAAAADQxBXMAACcWX1+8FXiw68AABgfVzADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNrtj3BAAAAAAAWpVbSq9/r76q9vr3hs4VzAAAAAAANFFgBgAAAACgiRYZAAAwEp+4PfT26S+enfble5I8MF2+Msm5hfWznwF6VJ40zXNd5rf57QFYS4EZAADGZrEwsu3yDUnuSPK86fId09+dtD1AX+Q3gMHRIgMAAMbi7h2NcUeSm5NcN/26efq7XYwPsC/yG0AnXMFMkuntlPO3At2d5LZMbhd62fT329569NpMbjt6XiZP3G41AgDoVpfnbzcvGR9gX7rOb16/AmzMFcwsd10mT64PpO2d3Lunj71yOhYAAN3r8vzttOeHALsgvwEMjiuYObb4zuzLcuktRJv2tpo9ZvbO8Enbs7X6hH3PAA7XJz74ambxFslNtDwGoE9dn7/Nj/+dO5gvwLb6yG/LxgfgRK5gZrVte1IpvgCHQn4Dxqrr/DYbH2Af+shvejIDbEWBmYnbF36eX76YpOT4SXZx/Wx59uRdpo85aTyAfek6v8l3wFB0nd/mtwXoUx/5bX58ANZSYGYz53L8Tu49S9bfk+N3hs/1OC+A05LfgLGS34Cx6jq/zY8PwFp6MDPx7BN+XlyePcneMPe7G3LpbUeLtx7pXQUMRdf5Tb4DhqLr/CbfAfvSZ37TDghgI65gZjuLPan0JAXGQn4Dxkp+A8aq6/wmRwJsxBXMHJv1nXr2Bss3J7ltujz7NN4NH19fuasJA2zh9nSe3y5ZdnUfsA995Lf5ZYB96Tq/yXcAG3MFMwAAAAAATVzBzLFNe1HNbjt63nR5dguSXn3AkG3Sa35X+U2+A/ZFfgPOiq7zm3wHsDFXMLOdxZ5Wiz2vAA6V/AaMlfwGjFXX+U2OBNiIK5iZ2KQ36ezTeEuSizn+wIOL09/NntgvnvD46XJ5bt3p1Nepz+n1z3Wqz9iJWzuxa9d57DrOb3r1AYPRdX7Tax7Ylz7y2/z437nDuQOMlCuY2cw9OX6CPrdk/bkcv1N8T4/zAjgt+Q0YK/kNGKuu89v8+ACs5QpmJlb1Jp29czt/29FJj509id+wYjyAfek6v8l3wFB0nd9uCMB+9JHf5scHYC0FZlZb7Gm1znzPK0/IwJDJb8BYdZ3fZuO7bRwORn3CvmewQ33kN+d6AFtRYObYYu+p1yZ5IJNP471uyfpVyzcnuS3JlUletrAeYB/m+4V2ld/mx5+tA+hTH/ltNj7APsyKv13mt2XjA3AiPZhZ7u5MnlyvTNs7t9dNH/tAfPIuMCxd57fZ+AD70Ed+ax0fYBfkN4DBcQUzE3fn+J3Z2W1Bs3duZ9b1qlpcflkuvcVotv7Vp5wrQItnp/v8Nj8+wD70kd8UX4Ah6Dq/uXIZYGMKzEzMf0LuLntOLfa8AtiXXffUW5bf9OyDgzOqvqSJ/AawivwG0AkFZiZKJj2pksltQRdz/AS7TW+rZcsXF8YH2IdZ370u89vi+AD70nV+m+9rD9CnPvLb/PYArKXADAAwAqO7EhcAYADKk8qlb1jck+PPXLkyybm0v+GxbNmbGxwgBWYmao57ht6R5Ia5ddv2tlpcviGX9iR1JTOwD89L9/ltcXyAfek6v3nxyx54I40k8hv9m98ntt1/WvZPOEAKzEzM95ya70l12tu8d93zFKDVYs+9LvLbLscHaHF35Dc4AOX55fhY2uVrpmVj3Z3UrzzluGMhv3EaXe8/s/G/85TjDEh5fuk8vzl+h0GBmYnrcumtGTfnuF/py6a/3/bWjtdmctvI7NN4Z+sB9mF2u1mX+W1+/FfvdPZwppRSlh9fd2fnx+/orojsI7/Njw+06fP111ivyO06v409fmzntPvPNvvnmMwu8vH6a/QUmFnuukwO/gfSdiXM3dPHXtnwWIAudZ3fZuNz5pVbyuSHbQuk6woEJyzXV+5u7nu37vhyfnKyPvJb6/jAyeS3zclv9K3r/Wfsx6/XX2dCqbXuew6Dcv3119c777xz39Po3SdeBC9qud1g3WNe3e8+N6ZdvJzw39SFruO2k31u023tc8363OeSzWN3/vz5JMnR0dHGYz9kn+siv+1JfdV4droTc0NHuozdqfa5hn2t1ytxn9PtPrd2P9jh8dv7FcxbxK4513Wd3/Z0C69c16bruG29z51i/6w/2TzNNl3mujcu3weO7kpu/mfJHS9Ozj9ls6FaHtOprvPclaX7/Lancz15rl2n53Sb7nMH+Pqi831uLteNIr/N6/h8eKhKKb9Za71+8feX7WMyHJD5nqV3b7D9QIsvHIht959t90+YJ7/Rp67z28hyYH3Cmq+vTC68JLnqTcmFB9dvf+HB6bYvmTx2ft0odZ3fZuND0k9+m41/Bpx/yqSQcvM/mxRW1hl08aUrfeQ3ry+Ys+z8ocvzk7GS38ZNiwwmZr1JZz9nbvlikpLjJ+aLC+tn288+TbVMt7luYb3eVSyz7f7Tun9C0n1+k++Y11d+m40/sg9wuul1k+9veeny5Vvfllx+2fELj1vftnz7b/vqyTaXXzbZZvYCZXG80ek6v81vOwLlSdMrrFbF59yS9auW70lSszz+Y9JHfpuNP6I8d9Pr+stv839rFPrIb/PjjynX3VK6z28jPB9ed3zNL+/k+H1mp/+cXvWR3+aX2R8FZjZzLg99App3Tx76BA+bOu3+s27/hFXkN7rUdX6bH/8MuuHxl74AueHxl66/+J6HvoA5U+S3zd2T3cfnrJyf9JHfzuD+Kb+t0fX+Mz9+z+2AetHH+ckZdtrjd8xXMSfy21jpwbxAD+Y1lt1itO1tR/rhNhtlD+Zdth04af+8zT7XatQ9mBftIr/tiX597Trt1/f80n1+m/vdmHown9Sb9CTLbqEc7G2VffRgXnTA+W1er/01uz4/6VEvPZhPY4v9c8g907e2YZ47qPw202ee6zq/9fyBf4Prmb7OqviPqU//luclMwdx/O7xnO4g4rOKHsyXcAUz25nvSTW7WurAXpwwELOeZrvcf07aP0fmVC/kFvsYro3/GXrSlN/YsavelNzxkh2dHD8hOTp3fMKdJDfvcvwDN9/T7xPxOZQXJ32Q39brMj7L4j+iuJ+66Cu/rSS/rdF1fhvRsZpk9294rYo/jt81xGdcFJg5tkmvpdnyzUlumy4/L5Mnlk0fD8kn9p8LL588eazrpbTN8tG55MbXHI8/+3lUtjleF5e3OX7HZFWv+V3lt/nlEfWdo83s5HiX+e2OF1+a35aNPwarepOeNj5jjNcl+shv88tjMSuS7Do+J8X/1buY9DC0Hq+Ly2clv83rOr+NPX6d57cx5rvb0n1+m40/Ml3nt7Efr13nt9H1mj9Al+17AgAAAAAAHCY9mBfowbyBrW+xX6AHc7Mx9WA+esXkH7PrW2DmezbNxn//h0a2zzX2AEuWx2dV/Mtzhxm7TnowJ6fPb3uiB3O7LmN39aNL5/ltb7cQDqwHczKw+Kyyjx7MycHmt3m99UxPdh+fxfiPqKfrac5LZgZ7/A6gB3My4PicpO88N4L8NtN5D+Zdfj7EzEDiP8QezMkBHL97PqcbfHxW0YP5Eq5gZjuLPZvmey7dveJxsOD8Uy7tuXR01+nHXPxAgNn4TJwUn13F/+DJb+xYH/nN8TshPmvIb+t1GZ8l8a9P6O9r6By/q4nPGl3nt7HlyB7y2yfGx/G7hviMiwIzE7cv/LxsefbkUZJcnFt/cfq72ZPUSY+HJW59W3L5ZcdPIje97rifUrL58uzJ6fLLJmPOjz82u47PqviPTtf5Tb5jTh/5bX78sek6v52ZfNdVfhtbrusiPqviPyJ95LcxHq995rexxa6X/DY//th0nd9m449In/ltbMdrH/ltbDE7VD7kj83ck+N3JpedHJ9LckOOn2DO9Tc1Dt8Nj0++7auPn2BuePx2j7/4nuN3PsdYUD6tdfE5bfwPnvy2VrmyXBqfxQ93WYzPNh8Oc0+SmuPxx/RBOuk+v82Pf9+/3M2cD4n8tob8trll//bTxmdd/Eeij/x2Fo9f8Vmj6/w2P/4YdZ3fzh3GHRRdcfyuJj7jpAfzAj2Yl1i87WWVTbbVg7nZmHowL+vFtHiLzCY2ecxQ+wi3OnpF2Wl8Vm071NjtrC/prvPbnvTSr6+L+Awgpn316+sqv81v22uvugH0YD5tftubvnqTdp3f7t5i2x3ptAfzrs6FN3xMr4WXjo/Xo1eUzvPb3o7fPfVgPpj4nKTrPLdNH+EDOz/p/JxuVf/qHcen9wJzh8drn6+/etfzOd3BxWcVPZgvoUUGq237hHPGevqV55eUK8vk+y0bfm3zmIVtx27bnkuDf8LpSFfxOXM9r+S3zXURnwEUl/vUdX47Szkwkd/W6jq/jfW28ZPIbyv1kd9m458F8tsG+shvIz1+H9Kn/SuTCy9JrnpTcuHB9T3dLzw43fYlk8cecv/3bXj9tRviM25aZHBs8dbl1yZ5IMnzMnly3eRW59nyzUluS3JlkpctrB+T27L7+KyL/0jM+iS95aUPXb7jxcmNr0mueUzyvh9fvv21L0ruvT+58PLJE8+q8cami/icFP9RuT3d57f58WfrxqCv/Day9hgzXee3Mea7a1/UfX6bjT86feS32fgjMiuGrNp/js7t7vjNiI7Xm17XT34b4wc3d53fxvj8kOS4+Ov8ZGuz43X2c+L11yb6fP011vOTrvPb/Lkj++EKZpa7O5Mn1yvT9s7tddPHPpBxX+nXVXxOG/8Dd/4pkyePe+9f/k7l0V2Tddc85uxdtZd0H5/58Uep6/w2G/+skt9Wkt8200d+G+2VMH3kN8ev43eJPs5Pxkx+24L8tnPy22p9vv4a4/HbdXxm47NfejAvOLM9mOf7a+7ytqBlY42oB/Op21Ysi8+q+PcYu330YF607BaalrYYQ+0j3Gwau13F5yRHdyU3vmaYsWvuS9p1ftvTbZW99+vbxIDis0pfPZgXdX38dm4PPV13nd8Ooadrc2/SEeW3efs6Xhcd3PE7gP6ag47PKj31dO06vw25T//OPldjUwPNbzNdn9Oty3UHffz20DO9j9dfvX9w8wD79J841tD2Tz2YL6FFBhPzn5C7yyfX+Z5XY/0E3tNYFp+BnNwMwXzPpdltkQdxctOTruMzuhjv+sWD43e1xviMrWffSeS31frIb7Pxe30R1wf5rXOO39XEZzN95Lf58UeX61rJbys5flfr6/xkTPrIb/bPYVBgZqJk0pMqmdwWdDHHT7Db9LZatnxxYfyx2XV81sV/ZNb1Xrr1bcnll016LiWTW2NufdvxE8imvZvGZL532q7icxZ6p335AexTAAAgAElEQVSi716X+W1x/LHpOr/dnuSVO5zvnvWV30Z5vKb7/DYbf7T6OD8Z0blJ6/7Tun+OTdf5bYzx6yO/zY8/Kl5/nYrzk+31+fprfqwxuPyy7vPb2GJ2qMZ8Wg0AAAAAQIf0YF5wZnswX1m6u0Vo/rb0JLltmD1dW5y6B3Py0Pisiv8Z68GcXNpTKWm7BWasPZiT3cRnlaHGrrkvadf5bU+3WA6yB3PSFJ/eW2T00K/vJF0fv53qoadr1/HZS5/DZNi9SecNJL/NG0oP5uTAjt+eezAnBxafVTqM3dWPLp3nt721yBh6nhtgfpvZdw/m5ICP357O6fo4Pxlqz/QWs1zXZX7b2/6pB/MlXMHMxOwJdb4n1bJP193WYs/TATxpD8qy+Owy/gdusWH/fM+lMX667ra6js/oYtxHfnP8HhOfleS31frIb/MvTkZFfuuc43c18dlMH/ltfnym5LeVHL+r9XV+MiZ95Df75zDowczEdbm019TNOe5X+rLp77ftbfXaJA8ked7C+GPy2uw+PuviPxLreitd+6Lk3vuTCy+fPHHMr7/jxZOeS9c8Jnnfj2823tjsOj4nxX9UZv1Cu8xv8+O/eqez36tnvXXyfdv9Z9n+eXRu/f45Nl3ntzHG78bXdJ/fZuOPTh/5bTr+GD+U0/nJ9uZ7k8pvm+sjv405fkn6e/01ol7M88er/Ladvl5/XXh59/+WPs2KwF3mt9n49Q3d/3s4mSuYWe66TJ5cH0jbO7l3Tx97ZcZ91XJX8Tlt/A/c0V2TJ49rHrP8xf/5p0zW3Xv/2Xynsuv4zI8/Sl3nt9n4Z5TjdzXx2Uwf+W2UxeWkn/zm/MTxu0Qf5ydjJr9toef8Vp/Q39e+yG+r9fn6a4zHb9fxmY3PfunBvODM9mA+qY/V4i1Em1j3mB77CCcd92Bu6em6TUwXtz0DPZgXb3tZZdNth9pHuNWmfay2ieVJjxlq7HbSr6+L/Dan1xcJe+ivuUwXx2/neuzBfJDxOcme97ld5Le96aM3acf5bbZt/cmNp7UbA+mZfnDHb88907s8Pxlyz/St7eBc+DSP6VTXeW72+UF9vP7K+M/pDjq/zeuhZ3ofr7961+M+d5DxWUUP5ku4gpnVtu1J1fKC5oDVr0wuvCS56k3JhQfXvyN94cHpti+ZPHbtu9gL44/dtk8eZ7XnUlfxGfSTdxfkt411sf+cteO36/ichRjOk9/W6Dq/zcY/I+S31frIb6Ptmb6E/LaBPvLbGenJLL9txuuv3RCfcVNgZuL2hZ/nly8mKTl+kl1cP1uePXmX6WNOGm9kbn1bcvllx0nyptcd9wNKjpdnyfHyyyaPWVx/0vL8+GO0y/icFP+x2XV8VsV/dLrObyPNd33kt9n4Y9JHfpsff2y6zm+L249O1/ltftsR6CO/jXV/6yO/jfH8pM/8Nrp9r4/8Nj/+yPR1fjImfea3scWuj/w2tpgdKgVmNnMux+/k3rNk/T05fmf4XI/zGogbHn/8TtzF9zx0/cX3HL/zdsPj28cfq13F56T4n3Vd758HT35bq4/8Ntbjt+v4zI9/Fslva8hvG5Pf2vWR387i8Ss+a3Sd3+bHH6E+zk/OMsfvauIzTnowL9CDeY1ltxhte9vRiHowb9Jfc5e3dfTZD7evHsy7jM9J8b/xNSPa55KN++Euatk/R92DedEu8tucsfXr6zq/jbG/5tErSuf5bW+3DQ6k7/fM4OKzSse9STftrznY+KzSY3/Nrs9PejWw43XRoPfPPfb9nhl0fE7SY57r4/yk1xgPrGf6OqviP6Zzuj5ff/Vuj88RBxGfVfRgvsQV+5gMB2y+J9Ws994Z7Um6zHxPodnVZAeTHHs267m0y/icFH8m7J9ryG8rdb3/zMYfkz7ym+N3QnxWE5/1+shv8+OL+zH752ris1of5ydjsuvi3ar44/hdR3zGRYGZY7O+U8/eYPnmJLdNl5+XSfFl08ePyE2vS97y0uOfk8nyHS9ObnzNZPnCyyfJcX794vbbLI9FV/FZFv/Zz2Oyy/isiv+o3J7u89v88it3MutB6Cu/zZbHYnZy3GV+Wzb+GJz0/LpuuWX/HJtdx+esxG/2Irev85P6hm7+HfvQerwuLp+V/Dav6/x2luLn/GQzN76mn9dfYywO9vX6a0z727w+zk/GGrtDocBMksmt3Dc9cvLzW6a3da9aPnowuXH62AvnkvNbPP7nO/kXAAxLry1tntPbn+pc721ZxK6JuLUbU+wAACDRg/khzmoP5m16Cs3fVpNsfwvDUHu6NlnT5zDZ7S0eY+rBfPSKSex2fQvMsvi//0Mj2ueSU/U63Hb/HOrx2kkP5uTSnsuZ+7mlRUaP/eb30TM96eb4HVPP9MWerqe1Lv5jeo5oyXOt++dQ81yyux7MSffHby966pmedH9+Mraerqc12P1zID1dBxufk/Tca/7g4rNKx8frLj8f4hNjDiX+AzleFw0mPifZ83PE4OOzih7Ml7hsH5PhcC32bJrvmTPrqXuWic/muojPSfFn4qzvn/UJq78uPJhc9abkwkuS+pWTrwsvmf7uwfWPX/wam673n9n4Y9JHfjsrx+864rOa+KzXR36bH59j9s/VxGe1Ps5PxqSv119jO6dr5fhdTXzGZTQF5lLKtaWUHyul/OdSykdLKfeWUt5aSvnyfc/tEMz3W73pdcuXZwf/5Zclt77teP2tb5v8bpYETnr8WO06PqviP0a7jM9J8R+bXcdnVfzHpuv8Ntb49ZHfZuOPSR/5bX78sek6v431eJ3p4/xkTPrIb2M8J0n6yW9jPF77zG9jjF1fr7/GWCjt6/xkTPrMb2M9XheXvf4an1Ec9qWUpyb57SQvTvI3k/xlkquS/N0k7yylfPcepzcKF99z/M7SDY9/6PobHn/8TtPF9/Q/v33rOj6z8cdqV/E5Kf5nneN3NfFZr4/8Ntbjt+v4zI9/Fjl+VxOfzclv7frIb2dx/xSf1fp6/TXWuyH7OD85yxy/q4nPOB18D+ZSysOT/E6Sz07yn5L8/Vrru0spj07yyiTfPt30q2qt71g33lntwVxWtsU5SnJzJs1Iz68ZaZtt+9HlLj6J21G6jU/LY06v69SwfJ87yqHEZ5XuY3eUbuKzzbbd6LIH87qe6etuWd5m22Rc/XCPXlF2Hp+THjPkfrhbm9vndhWfVdv22b96CD2Yd3X8DnmfO02u6zK/zR4zpp7pq/a5Lo7fsT1HdJ3fZtva59q3Heo+15Lnttnnun7+3bme++EeXHxW6bhPf9f5bW+xHPA+N4j4rKIH8yXGcAXzP8ykuPzhJM+stb47SWqtH6y1fkeSt0y3+6E9ze/AHWW7gtP56bY3Tx87dkfpNj7bjn/ozkd8NtFVfM7nLB2/256wnOWeYF3EZ/AnjDvWdXzOQgznOX5X6zo+Y71t/CTy22p95LezdKeG/LZeH/nN8bvcWYvPTB/57Swcv+IzblfsewI78Nzp9zfWWv9kyfrXJrkpyReVUp5Ua/3d/qZ2aG6afp/V5K9Ncm+SC5kUnhbXr1q+I8mNSa5J8r4Tth+DG9NdfJbFf0yx6yI+J403Nl3EZ138D99Nr0ve8tLJz9e+KLn3/uTCyycnLrO+XbP1q5YnV1Al1zwmed+PL9/+2hd1+2/pWxfxWRX/sdl1fE5aHpNrX7T7+JwU/7HpI7/Nxr/xNd3/e/rWdX4b4/E6e37tMr/Njz8mXee3sT6/zopTzk+2N38+3PX5yZj0ld+cn5xu/xxr7A7FQReYSymPSvK3p4tvP2Gzi0nuT/KYJM9IosC8kaNMilPXpO3K0PPTx947HatljEPQVXyOcrr4H7rzEZ9Vzqfb+MyPPz5Hd01Obq55TNuVF+efMnnsvfdPxlocYzb+WbVpfFrjf+jEZzNdxWdd/Megj/x2VvdP8Vmt6/iMPaby2+b6zm+9thd5Tm9/6hJdHL+9t6DqMHZ95Lf58Xtte9bDPtfX668x7XOH6KB7MJdSnp7k16eLT6q1/t4J2/16kqcn+d9rrf9o1ZhntwfzUY4LUUfZXduBXY7VpvsezKdxlIfGZ9nv+refHsyLjjLU+KzSX+yO0m18jnYwxna67sG8y9v6lo21rz6RQ+iHu2hdfE6K/5D74W5ty/6ap90/h9pfs8Wy/ppdHb9D7una2pu06/w29p7p6+zi+B3T8bpJf81dHb9j2ufmj9U+zk+Gel6yq8/V2FTL/jnm43XRLo/fMR2vqz7foIvj9/0fGs8+19Kn/8SxBvT6K+nheB2ok3owH3qB+Vk5vr/70bXWD52w3Zszuf/7TbXWb1g15tktMF+dSUEq2X3x7mhuzOxw3M0Mu8CcPDQ+wyieDqPAnAw1Pqv0G7ujHFp8VumywDx5I63r/Laf+A/neF10lCHEZ5X9PkccZejxOUk/+9xRuo3PbPz7djTeZrouvOzm3CQZ4v45rHO6owwtPifZ/wc3J0OOzyrd7nN9vv7qN9cNucCcXFqQSob15ve+C8zJ9vE5yRgLzMnu4nOSIb8h1OLqR5edx+ek+I9qnxuwsRaYn5PkDdPFh9Va//qE7d6Q5DlJ3lFr/aol61+Q5AXTxScmWXolNDtzVZL373sSB0rs2ohbO7FrJ3ZtxK2d2LURt3Zi107s2ohbO7FrJ3ZtxK2d2LURt358dq316sVfHnQP5iQ7uUaj1vr6JK/fxVisV0q5c9m7Hawndm3ErZ3YtRO7NuLWTuzaiFs7sWsndm3ErZ3YtRO7NuLWTuzaiNt+XbbvCZzSh+d+fviK7R6xZHsAAAAAAE7h0AvM7537+TNWbDdb9186nAsAAAAAwJly6AXm300yayL9ucs2KKVclklf5SS5q49JsZZ2JO3Ero24tRO7dmLXRtzaiV0bcWsndu3Ero24tRO7dmLXRtzaiV0bcdujg/6QvyQppfxGkqcl+Ze11v9lyfr/LsmvThefVGv1AX4AAAAAADtw6FcwJ8kbp9+fW0r59CXrv2P6/TcVlwEAAAAAdmcMBeafSPLHSR6V5BdKKU9JklLKo0opP5zk66fbfe+e5gcAAAAAMEoH3yIjSUopX5Dkl5I8bvqrDyZ5ZCYF9Jrke2ut/2RP0wMAAAAAGKVRFJiTpJRybZLvSfJ3k3xmJkXm30jyo7XWX9rn3AAAAAAAxmg0BWYAAAAAAPo1hh7MAAAAAADswRX7ngCwXinl8kx6jD88yX+ttX54z1MCTuB4pS+llMcleXqST09yVab73P/f3r3HXTbX/R9/va85YGacz5SSIhRyqhxiJJUcklsIRUUpSW6i3JKIIkS5QxJKRTrQnftOkcNIOaQkQyH0Y8I4zxjM4fP747uuZrtch72/+7DWda338/HYj8vsvb7bZ78fa33X2t+91ncBM4G7gD+HL1UblLOzXvM61x5JazN4dndHxJNl1lZlzs16zX1dPm+v+ZxdNXiKDOs6SX3ABsCbGXpHMy0iZpZWZMUUN67cDtiSlNtyAxZ5EbgbuL54/DIiZve0yIqStBwwlYXZ9a9zE4GnWLjOXQ9cHxE3lVRq5Ti7PN5e2+N9RGskrQfsR1rnXj/C4s8CvwN+BPw4IuZ0ubxKc3bt8T6idV7n8hXr216k7DYDlhhi0QCmk9a7iyPi2t5UWE3OrT3u5/K4r8vj7TWfs6smDzBbVxQDBjuQdjTbAFMGLkLa2BvdTdrRnB8RD3a9yIqRNAX4EPARYP3+p0do1p/hbOBi4NyI+EN3KqwuSQK2J2X3HhZenTFcfv3ZPQB8h7TePdS1IivK2eXx9toe7yNaJ2lP4FBgw/6nir/PkAbjnwSeB5YuHssD44plApgFXAScGBH/7FHZleDs8nkfkcfrXD5JmwGfAXYEJvDSdW0+8DQLs1tsQPMA7gO+DZxZpx9znVs+93P53Nfl8faaz9lVmweYraMkLQYcAnwKWJGFG3z/GXwzefmO5tXASsVyUTyuBI6JiJt7VXtZJE0EDgI+ByxDyuxx4A/ALcCfGTq3TYrHG1k4IPN/wOci4vZefo6ySHofcDywFimDBaQzC5rJbiNgKVJu84BzgeMi4l89/RAlcXat8/baHu8jWifp3cAJwHqkvB4CLgVuAG6JiPuHaLcY6QvfJqTB/K1IX+qeB74FnBARj3e7/jI5u/Z4H9E6r3P5JK0LnEga4BPps/+KIjvSZfVPDmgzkZfuX7cHXkta7x4DvgycFRFze/Mpes+5tcf9XB73dXm8veZzdqNERPjhR0cewIHAw6Qd8wLSxv5pYGNgwghtVwXeS9oxzyzazwd+AqxR9mfrcm4PsPDXtvOAdwB9Lb7HqsBhwK1FdvOAj5T92XqQ3e+L7OYBVwP7A8u0+B6bA98EZhTZzQJ2LfuzObtqPry9tpWd9xF5uS0A5gIXAm+jODkg432WJw3s31Nk94WyP5uzq+7D+4js3LzO5Wc3r8jvWuCDwOKZ77MRcAppUHA+cFTZn825VfPhfq6t7NzX5X1eb6/Obkw/fAazdYykBaS5qb5JuvQ76xJmSeOBd5MGYLYEvhgRX+pYoRUj6XHgdOD0iHi6A+83FTgKuDYijmv3/apM0guky6pOiIh72nyvcaSd1RHAD8byOgfOLpe313zeR+SRdA7p0tF/dOj9+oAPABERF3XiPavK2eXzPiKP17l8kq4Ejo+I6zr0fksABwNPRsSZnXjPKnJu+dzP5XNfl8fbaz5nNzp4gNk6RtLngW9ExLMdfM/NgaUi4pedes+qkTQ5ujD/T7fet0okrZY7SDXMewpYJcb4PGrOLo+313zeR5iNHt5HmNlY537OzKyzPMBsZmZmZmZmZmZmZln6yi7AzMzMzMzMzMzMzEan8WUXYGZDk7QMsDIwpXhqFjAjIp4or6rRQ9JkGrIb61MQdJKzM6suSVOALYB1GGQfAdwJTIuIWeVUWF3OznrN61x7JL2eYbKLiLvKqq3KnJv1mvu6fN5e8zm7avEUGWYVI+k9wF7ANqQ76w7mMeAq4KKIuKJXtVWdpDeQbhCxDbA2C3cy/WYB00nZ/TAi7uhthdXl7PJIeiXpbsTjgDsi4u4m2hwKTBnrN4BphqQ1SevccF9GroqIv5dTYbVIWhv4ErADMHGExV8ELifdBHF6t2urOmfXHkmLkPYN44C/NTOXuqTdgMUi4sJu11dFXufySVoWOBLYk7RvGM4M0o3aToqIx7tdW5U5t/a4n8vjvi6Pt9d8zq66PMBsXVEMVn2GhoEX4LyI+PUI7WYAy0dE7c6ul7QScAmwef9TIzTp33inAbtHxL+6VVvVSZoEnE3ayYjmsgvgB8CBdT4719nlKc7SOBfYbcBLNwKfjohbh2k7A1ghIsZ1scRKk7QtcCKwYePTAxZrPEC5Bfh8RFzV7dqqStJepHVuIguzehR4GHiu+PckYBVghYamLwAfjogf9qjUynF2+SSNA74MfJKUEcBc4CfA54a7QVbNj+m8zmWStDVp/VqKl+4XnuKl2S3V8FoATwK7RsS1PSizcpxbPvdz+dzX5fH2ms/ZVZsHmK3jJO0BXECagqV/o+9f0S4DPjrUFA91HXgpBqv+BKxePPUb4Feks/cG20GvA2wHbEuaS/0e4E11HOyTNAG4HtiEtL79DbiS4bN7B7AWab28CdgyIub1tvLyObs8xR3CryFdBjjYgPxc4MiIOG2I9rXs5/pJOgI4gYXZPQ3czeDr3FrAksVzQcr15N5VWw2SNgR+T9qv3gqcClw51JkYxZkd2wGHkLbvucBbIuK23lRcHc6uPZIuAXZl8B+AniEd0/1kiLa17Ou8zuWTtAbpeHgy8E/gLIrj4Yh4fsCyi7LwePjjwGqkq182iIj7ell32Zxbe9zP5XFfl8fbaz5nNwpEhB9+dOwBrEEaIFgA/As4DziZNBC1AJgP3Au8doj2M4D5ZX+OEnI7rsjnPlKn12y79Ys284Fjy/4cJWV3WJHdY8BOLbTbsWgzH/jPsj+Hsxs9D+BDRW4vAl8gHbBMBt5DOtO2v687dYj2teznis++TZHNAuCnwFspfuweYnkBbyGdqbAAmAdsXfbnKCG3Hxaf/0Kgr4V2KtosAH5Q9udwdqPrAezc0J+dB7wNWJd0lt+DDdvkwUO0r2Vf53Wurey+XXz+/wMmt9BuEmmQYQFwTtmfw7mNnof7ubayc1+Xl5u3V2c3Zh8+g9k6StI3SDvk24B3RsTMhtd2Jl2KvwLwCLBdRPxlQPta/gos6U7SmXqbR8TvW2z7VuAGYHpErNuN+qpM0h9JA+07RovzUUvaHvgf4LaI2Kgb9VWZs8sj6Urg7aQfdb404DUBx5PmBYP0ZeWAaNjZ1rWfA5B0OWkg/pSI+GyLbU8i/Sjyi4jYuRv1VZWkh4AVgZUa96tNtl2OtM/9V0Ss2o36qszZ5ZN0GWlOzTMj4uABr00mHdN9gHSW3zERcfyAZWrZ13mdyyfpAeAVwKsj4p8ttl0NuB94MCJe3fnqqsu55XM/l899XR5vr/mcXfV5gNk6qmGgdIuIuHGQ11cFfk6am/kJYPuIuKnh9VrupCXNBuZFxJIjLjx4+2eAcRExubOVVV/x2fsiYuBN6ZptPwtYEBFLdLay6nN2eSQ9AiwHLBsRTw2xzJ7A+aTLBi8G9omI+cVrteznACT9C1gWWDpavIt4MZXQU8DMiFipG/VVlaTngeciYpnM9k8Ci0bEYp2trPqcXT5JD5MGD1aNIe7zIOlI0pQ3AXwtIo5oeK2WfZ3XuXyS5gBznF1rnFs+93P53Nfl8faaz9lVX1/ZBdiYsxrpsvFBz8KNiIeArYHfAssAv5b0tp5VV11zgEWLOXFbImkisEjxHnU0HxhXnDnaEkl9pJtQzu94VaODs8uzNPD0UIPLAJFuWvI+0k1Mdgcuzdm+x6AlgVmtDi4DFG1mAbX6QaPwCLCkpFe02lDSK0m5P9rxqkYHZ5dvWeDZoQZdACLiK8CBpIGXwyR9s1fFVZjXuXxPAEsUc7W2pGizRPEedePc8rmfy+e+Lo+313zOruI8wGydNg54sfFy8IEi3Yhue+AKYHHgCknv6FF9VXU76UzHgzLaHgRMIE14X0fTSXcu3iuj7QdIg/N3drSi0cPZ5XkWmFIMsg8pIn5Jmq96DrATcHlxw4k6e4h0YPj6VhtKWpt0YPhQx6uqvt+Q5iz8tqRJIy3cT9JipPnqAvh1l2qrOmeXbw4wYp8VEWcD+5HmNjxQ0ne6XVjFeZ3Ldz0pu1Mzfvw+tfh7XWdLGhWcWz73c/nc1+Xx9prP2VVd2ZNA+zG2HsA9pDMaV2hi2QksvHHTc6SBmFreKAHYrchhLummiCs30WYl4KSizXzgP8r+HCVld0CR3WzS/N8Tm2gzEfgE6WzI+cD+ZX8OZzd6HsDvis++aZPLb0Ga2mE+6eqNx+vYzxVZnF6sczeT5uxrtt2KpBsozgdOK/tzlJDbaxu2ufuBzwIbDLbNFtvoBsDhwD+KNs8Ca5T9OZzd6HoU2+l8YL0ml9+NdNXGfOAi0plptevrvM61ld1GpCsh55Ouhnw/aUqloZZfuljvbizavABsWPbncG6j5+F+rq3s3Nfl5ebt1dmN2YfnYLaOknQJsCvwwYi4qInlx5HuIrsnaaAUYHzUcx6rc4CPkn7NDeAO4K/Aw6Rf14N0B9RVSHc3Xpd0FYJId0P9eAllV4KkXwLvJmX0NOmXyeGy2xJYipTdLyNixxLKrgRn17riZnP/CZwaEYc32WZT4H9ZmF3UtJ9bkbR+LU36UnIRcCUvXecAFmPhOvcO0ln2SwAzgTdERO0uqSyu9PkxKYfGg7cneOm22jgvnYBnSD9A/qZHpVaOs8sj6QzSj49fiohjm2yzI3AJaTChzn2d17lMkj4EnEM6EaU/u38x+HFJ/3z8In2P2D8iLuxpwRXh3PK4n2uP+7o83l7zObtq8wCzdZSkA4CzgGsiYpsm24h0mcyHi6fqvJM+EDgGWKF4aqgNtP+SkEeBL0bEWd2urcokjQe+DBxMmrYBRs7uBdLZlP8VEfO6W2F1ObvWSdoSuJZ0JvLq0eR8wpLWB35F2r7r3M+tB1xOmrO/2YMQAQ8AO0fE7d2qreqKAfrPkeb1XnGExR8BfgR8JSIe6XZtVefsWifpXaTpzB4CXhMRc0do0t9uW+BnwGTq3dd5ncsk6Y3AcaQfwEe6f8Fc0np6TJ33D+Dccrifa5/7ujzeXvM5u+ryALN1lKTlSNNc9AFbRcS0FtqeBnwa76QnAtsCU4F1gJVJBy8infH3MGne298CV0XEiyWVWjnFAc6uvDw7SNNANGb307of2DRydq2R9HnSAc1PI+IvLbRbEzgC6IuI/bpVX9UVc1F/nDSX90Ys/PFioCBNjfED4OyIeL43FVZb8cPsOgy/j5gePsh7GWfXvOIqs7NI94g4MyJuaaHt5qQvf4qIqV0qcVTwOpdP0pKkaaaGy25aRDxTWpEV5Nya536uc9zX5fH2ms/ZVY8HmK1SijvK9kXEA2XXYmZm3SdpCrA2gx8Y3tXsGeJmZmZmZmZWDg8wm5mZmZmZmZmZmVmW8WUXYGZmZmZWNkkTSPNIRkQcV3Y9ZmadVkwJsReAb3ZlZmOV+7py+Axms1GuuEnbZgARcV3J5dgYJWky8CywICL842QmSW8r/vPOiJhZajE2KhVTSW0EjAPuiIi7m2hzKDAlIr7U7fpGs4Z+rtb3gugESasV/zmj2Ztm2b/XwW+Q1sGPlF1PmSQtQpo+aRzwt4h4tok2uwGLeTBhaD6e6xz3c/nc1yXu57rHfV05PMBslSHp6uI/bwJOiYjHyqxntJC0LPAYNe88Jb0B+AwNAy/AeRHx6xHazQCWr3N2zfDAS2dIWkC6cd1zwH8DX3Nf1xxJSwA/J62Dby+7nl4r5qo+F9htwEs3Ap+OiFuHaTsDWMHb7vDcz3WOpPnFfxdORl4AACAASURBVD4EnAh8xzclHlnDMV1t18HirLMvA58EJhVPzwV+AnwuIh4cpq2P6Ubgfq5z3M/lq3tf536u+9zXlcMDzFYZDQMvAHNYOPjyaHlVVV/dd9AAkvYALiBN+6Pi6f516TLgoxHxxBBtazvwIukLLSw+Efg8KddjG1/wWZHNK/q5Rs8BZ0XEYWXUM5rUua8r7sx+DelO2RpkkbnAkRFx2hDt69zPzR95qRGFv8i1ZkBfF6Sbdn41Ir5ZUkmjQp37uX6SLgF25eV9XQDPkI7pfjJE21r2dZLua2Vx4FWkPBtvqh4RsUZHCxvj3M/lq3tf534uj/u66vMAs1WGpGtIHcDKwJrF089FxJTSihoFvIPWGsBfgEWBR4ErgMeBrYCNSevU/cA7I+KeQdrXeSfd+KNOU02Kvy9pU8fscknaqvjPlUnr6NbAms5wZHXu6yR9CPguMA84Hjif1M9tTfrBZ0PSdnl6RBw6SPu693Ptqt06165inYWFfd1mpGlanOMw6tzPAUjaGfgZqT+7gJf2dUcArwAWAIdGxBmDtK9lX9dwPDfYD5DNquU61w73c/nq3Ne5n8vnvq76fDaGVUZEbN3/35JWJHWyW5ZVTy9J2q6N5kt0rJDR6RDS4PJtpEHkf89rW+zAzwZWB66XtF1E/KWcMivtUeD5EZYRsBpppz7kZVs2vIi4tuGfPwKQtFxJ5djosRdp2zt+wBUDv5R0BWnQ+Ujg05IWBw4In0HQKIA/AOcw/I9qiwBnFct8uAd1jVkRcUHDP78iqY/0Q8iYJ+kHbTSf2LFCRqcPk7a/MyPi4Ibn/yrpfNIx3QeA0yQtERHHl1Bjlf0e+NUIy0ykuJkp4CvQ2lDnfg7c17XB/Vz73NdVlM9gNquAjDNJX/YW1PTXOEl3AmsBW0TEjYO8vipp3taNgCeA7SPipobX6/wr8C2kA+H7gYMj4n+GWXYK6ZKtWq5n1hmSftdG8/EUVyXUbR2U9AiwHLBsRDw1xDJ7ks6CGQ9cDOwTEfOL1+rcz+0MnEE6I+gW4BNDzVft+fqsE3yGVT5JDwMrAqtGxL+GWOZI4ARSxl+LiCMaXqtlXyfpENIAymTgp8BnIuL/DbGs+znrCPd1edzP5XNfV30eYDarAO+g80maRbqp36ShztgrdjCXA1NJO5odI+K64rU676T7gINZuKP+BWmg+WVnKHsnbZ3gvi6PpBeBWRGxzAjLvQf4MelM3MuB90fE3Dr3c/Dv/ut44KDiqbOBoyLi6UGWcz9nbZE0F+gDrgQGHTwYxiLAHtR0HZT0AjAnIpYaYbmPAWeS9iXfioiDiudr29cVJ1R8E9gZmEU6tjut/4fGhuXcz1lHuK/L436uPe7rqs0DzGYVIOkhYCXgPyLiZy22XY40xUEtO09Jc4AXI2LJEZZbhHRn3u1JN1bbJSJ+XfedNLxsR/0c6a7GJ0fEvIZlvJMehqRJwOtJ8/D1zxs/C5gBTI+IOWXVViWSngcmAN8HWrlRB6S7bB9ODddBSY8DiwOLRsSwcwpLejvp5qaLkb707QL8g5r3cwCSNiQNLm8EPAJ8NiK+1/C6+7kmSFqGQfq6GOJmunUj6c/AG4CPRcS5Lbat7bykAJKeIvVzizax7D7AeaQBrvMj4iM+pgNJO5GO6VYF7gQ+2X9SRfG6+7kmuJ8bmfu6PO7nOsN9XTV5gNkqpRjoGjfYGZRjmaSfAzsCJ0bEf7XYtrY7aABJ95DmWF45Ih4dYdkJpHlvdyHNObw7aU7O2u+k4WU76ruBgyLi6uI176QHKNanA0jz427K0Gfl9s//+n3g3IiY25sKq0fSTaTBvYMj4swW29a2ryumFnkz8NbGKX6GWX4L4H9Ig9LXAesBS9Utt8FIEvAp4DjSwME04MCIuNP93NCKs+P3ArYBlh9isceAq4CLIuKKXtVWNZLOBfYj9fcfa7Ftbfs5AEk3k6buelNE3N7E8ruR9q3jScd37yBNJVS77BoVfdlxpL6uj5TR4RHxqPu5obmfa437ujzu5zrHfV319JVdgI1Nkt4l6SpJT0l6VtKNkvYvLskfzi20flbbWHAzaXBq07ILGYX+WPx9x0gLFgN77wd+SLox4KXAsJec10lEXE46C/d04HXAryX9QNJK5VZWPZLWIf1afgbwFtL+VEM8+oC3kgbv/ypp7TJqroj+vm7jsgsZZaYVf3drZuGImAZsBzwFvA1Yukt1jTqRnAGsTTrTe0vgNkknsfBMNStIWknSdaQpV3YHVmDovm4F0iXPv5B0bY33He7n8vXfS2OXZhaOiB8D/wG8SFr3fNNcICJmR8ShpO8VtwL7AHdJ+gT+/v8y7ueyua/L436uQ9zXVY/PYLaOk3QocHL/PxteCtKGv0dEDDqIXNdLPiRtB/wf8GRELNti26WAPwELIuI13aivyiQdAJwFXBMR2zTZRsC3SXfxBf+y+TKS3kS6lHxj0s39TiLNYVr7rCStAPyFdHbLLNIPFr8iDTg/TJpmJEjzWq8CrEMa7NuTdEbpo8AbI+KxnhdfMkn7Ad8B7oyIN7TYts5nu2wJXAs8DqweEbOabLc+ad1cgRrm1gxJO5DmOHwFadtcEWcF0H9z1z+RrhIC+A0v7+sgTV/T2NdtS/pidw/pDK3ZPSy7dJLeCHwPeAF4S7TwZUvSYsBnASLi2O5UWF2S3gVcATwEvKbZK34kbQv8jLTf9fbboDjmPYh0lt/iwF9J0xo4J9zPtcN9XR73c93hvq4aPMBsHSVpA9KvmeOA6aQ72T8ObEX6lW4c8ATpJms3DtK+rgPME0lnUxERfy65nFGlmIN6Bukgb6vizL1m254GfBrveAY1yI5aOCskfZ10c8TbgJ0i4qEm261CupHiBsDpxS/utSLptcDXgHnAbi1+GZlIGqQnIi7oToXVJenzpPmrfxoRf2mh3ZrAEUBfROzXrfpGs2IO9eNI2/U43M8BIOk44CjgfuB9EfGnJtutT/oS/Crg+Ig4pmtF2pgiaRzppIHxwJkRcUsLbTcnbceKiKldKnHUkrQy6Uqq/rMm3c/hfs56z/1cd7mvK5cHmK2jJJ0H7AtcDewQEc83vLYhcBGwFjAbeG9EXDWgfS0HmK08kl5JGnh5oOxaqqrYUX8FWA2g7gc0DfN+vyEiprfYdh3gDuDeiHhdN+ozszyS1iCdyUxEXFtyOaWTdCfpmG3ziPh9i23fCtxAusnput2oz8xaJ2kr4NVQzx9rB3I/ZzY2ua8rhweYraMaBl7Wj4g7Bnl9Culy8veQLqfZvZj3tf91DzCbWaVJmgM8HxFZ89oWd4+eGBGTOluZmVnnSJoNzIuIJTPbP0O6cfPkzlZmZtYZ7ufMzDrHE19bp60CzBlscBmgmDfyvcAPgEWAH0vavYf1mZm16xlgSnF34pYUP7L139XYzKzK5gCLSprQasNiSptFivcwM6sq93NmZh3iAWbrtCgeQy8QMZ90h89vk+aT/L6kfbtfmplZR9xE2n9+IaPt0aQ5Xv/Q0YrMhiDpbcXDdx1vkbPjdtIckQdltD2IdIzX1HymlkharXi0PNhVd84uj3NzP1cGr3d5nFs+Z9cbHmC2TnsQmFTMazukSD4GfIM02HKupAN7UeBYI+nq4vEVScuXXc9o4uzy1Ty700k3PDxM0qWS3jxSA0mbSLoEOIz0I9zXu1zjmOPBvmzXAL8F/iHpqzXcXttxDfXO7lukvu4kSScX8/EPS9JKkk4Cvkrq687qco1jzT+Kx72SDizOkLTmOLs8dc/N/Vw56r7e5XJu+ZxdD3gOZusoSRcCewGfiIizm2xzIukO9/1nP8tzMDdP0gIWnjU+B/hv4GsR8Wh5VY0Ozi5f3bOTdBTpLs79GTwNTAceJuURwCTStEFrA/1z+wk4OiK+3NOCx4CGde45Fq5vj5VbVfUVuTV6DjgrIg4ro57RxNmBpHOAj7LwGO0O4K8M3tetWzz6SH3dORHx8RLKHrUGrHNByvmrEfHNkkoaNZxdHufmfq4MXu/yOLd8zq43PMBsHSVpb+BC4NaI2KSFdkcDx1IM1niAuXmSriHltjKwZvH0cxExpbSiRglnl8/ZgaTtgROA9Qa81L9j1YDn/wx8PiL+t9u1jUUe7MtT3EUb0ra6FbA1sKb3syNzdklxhdkxwArFU0N9eejv8x4FvhgRPquvRZI+VPxn/zq3GTClbutcDmeXx7kl7ud6y+tdHueWz9n1hgeYraMkLQ78kTSX1d4RcUMLbQ8BTiXNoOENPYOkFUlfgLeMiJy5xGrL2eWre3aS1gGmAuuQDlomk76AzCL9On4n8NuImF5akWOAB/s6R9JyETGz7DpGo7pmV1xKui1N9HXAVRHxYkmljimS+oANI+KWsmsZbZxdnjrn5n6uPHVe79rh3PI5u+7wALOZmZmNSnUd7DMzMzMzM6sSDzCbmZmZmZmZmZmZWZbxZRdgZmZmZq2RNAl4PekS3v65z2cBM4DpETGnrNqqztlZGSQtwyDrXEQ8UV5Vo4Ozy+PcOk/SagAR8WDZtVSV17s8zi2fs6sOn8FsHSPpNRFxX4ffsw94hXfiw5O0KjDOObXO2eVzdq0r5vfbAyAiLiy5nErwYF/zJE0ADgD2Ajbl5TeS7BfAH4DvA+dGxNzeVFhdzq73vI8ASe8hrXPbAMsPsdhjwFXARRFxRa9qqzpnl8e5dY+kycCzwIKI8Il6Dbze5XFu+ZxdNXmA2TpG0ovAD4ETIuLuNt9rArAfcARwQUR8qQMlVp6kdwGHAxsB44A7gPOA70TEgmHazQCWr/PBjrPL5+x6R9KypIOdWn858WBf64qbSV4GvIah8xoogHuBnet8k0ln1x7vI1onaSXgEmDz/qdGaNL/hWwasHtE/KtbtVWds8vj3LqvYYDZN6QveL3L49zyObtq8wCzdYykacBmwALgeuBHwKUR8XiT7QVsTTq7733AMsBsYJ+IuKwbNVeJpEOBk/v/2fBSALcCewx1hnjxJW6Fuh7sOLt8zq63GgaYa/vlxIN9rZO0AvAX0hkas0g/5v6KdDf7h4HnSBlNBlYB1gG2A/YEFgceBd4YEY/1vPiSObv2eB/ROklTgD8BqxdP/YaXr3MAk3jpOrct0AfcA7wpImb3sOxKcHZ5nFs+See1sPh4YG9S/3dBw/MRER/paGGjgNe7PM4tn7OrPg8wW0dJ2gk4gbQxR/H4O+lLyO3ATOBJ4EVgKWBpUgexMfAm0hc8AXOBs4Hj6vClTtIGwM2kM4OmAxcDjwNbAbsUzz8B7BgRNw7SvpZf4sDZtcPZ9V7dB5g92JdH0teBg4HbgJ0i4qEm260C/ALYADg9Ig7tXpXV5OzyeR+RR9JxwFHA/cD7IuJPTbZbH/gZ8Crg+Ig4pmtFVpSzy+Pc8klawMIzHJtqUvyNhn/X9ZjO610G55bP2VWfB5it44ozkd8FfBTYAZhQvDTcyta/s76PdNnldyNiRteKrJji1/N9gauBHSLi+YbXNgQuAtYindH93oi4akD7Wn6JA2fXDmeXR9IBbTSfDJxCfb+MeLAvg6R7SD/GvqHVM7iLM8bvAO6NiNd1o74qc3b5vI/II+lOUi6bR8TvW2z7VuAG0vzz63ajvipzdnmcW76GAea7SD9iD2ccsEWx/HWNL0TE1K4UWGFe7/I4t3zOrvo8wGxdVdzRcyppjpxNSTdxWg5YhHTWy0zgbtLGPi0ibimp1FI1fAFePyLuGOT1KaQz/d4DvECaP+jyhtdr+SUOnF07nF2ejLNdXvYW1HeA2YN9GSTNAZ6PiKUz2z8FTIyISZ2trPqcXT7vI/JImg3Mi4glM9s/Q7o54uTOVlZ9zi6Pc8sn6efATsDTwNHAmTHEAEnR5z1DTY/hBvJ6l8e55XN21ecBZrMKkPQc6aZfU4ZZZhxpvq8PkKYQ+WBEXFy8VssvceDs2uHs8jQMMD9CGlRpRR/wSmr65cSDfXkkPUK6L8FSrc4bV3whfhJ4IiJW7EZ9Vebs8nkfkUfSTNKUPlOixRuTSppIuonYsxGxXDfqqzJnl8e5tUfSzsAZwCtIV1h9IiJuGmQ53+Svgde7PM4tn7Orvr6yCzAzYOF81UMvEDEf2Af4Nmnake9L2rf7pVWes8vn7PLcX/z9TESs3soD2KjEuqvgGWBK8SWtJcVgX/+Xu7q5iXTM9oWMtkeTLuv9Q0crGj2cXT7vI/LcTroZ2EEZbQ8i5djUvJJjkLPL49zaEOlm8uuQBpnXB34n6WxJWT+G14jXuzzOLZ+zqzgPMJtVw4PAJEmvHG6hSD4GfIP0pfdcSQf2osAKc3b5nF2em4u/m2S0rftlQx7sy3M6aWqVwyRdKunNIzWQtImkS4DDSOvd17tcY1U5u3zeR+T5FmmdO0nSyZJWHqmBpJUknQR8lbTOndXlGqvK2eVxbm2KiNkR8RngzaSzmPcH7pa0X7mVVZrXuzzOLZ+zqzhPkWFWAZIuBPYiXZJ1dpNtTgSOYOEZRqrj5VrOLp+zyyPpcNJByrWt3tRF0rLAY9T08kpJ2wJXktadnwEnR8SwA8aSNgEOB3YtnnpHRFzd1UIrSNJRwHEs/JHiaWA68DAwp3h+ErAKsDbQPz+dgKMj4ss9LbhCnF0e7yPySTqHdLPr/hzuAP7K4OvcusWjj7TOnRMRHy+h7EpwdnmcW+dIEvAp0n5jCnAj8AngXjxFxkt4vcvj3PI5u2rzALNZBUjaG7gQuDUimj4rUtLRwLEUX5rreLDj7PI5uzyStgauBmZFxBIttl0CuJz05aR2dxwHD/a1Q9L2wAnAegNe6s9SA57/M/D5iPjfbtdWdc6udd5HtKc4i/sYYIXiqaG+dPWve48CX4yI2p9d5ezyOLfOkrQK6cqMXYB5wHnAAXiA+SW83uVxbvmcXXV5gNmsAiQtDvyRNKfQ3hFxQwttDwFOpaYHO84un7PLU5zZsgRARDxdcjmjkgf72iNpHWAqac7IlUlzUwuYRRqovxP4bURML63IinJ2zfM+on3FTYW2pYl1DrgqIl4sqdTKcXZ5nFvnSdoBOJN0k2aoeb82GK93eZxbPmdXTR5gNjMzs1J4sM/MzMyqTtIk0vQ/qwFEhOdmNjMbwAPMZmZmZmZmZmZmZpalr+wCzMzMzMzMzMxsbJK0mqTVyq5jtHFu+Zxd73mA2axkkl7Thffsq0Nn6uzyObs8zs1GG0kTJX1Q0gfLrmW0qXN27uvKI2lV55TH2eWpa27u53pH0mTgfuC+kksZVZxbPmdXDg8wm5XvLkkXSFqr3TeSNEHSAcDfgX3brqz6nF0+Z5fHuZWkzoN9bVocOB84r+Q6RqM6Z+e+rk2S3iXpKklPSXpW0o2S9pc00vevW6j5F2Jnl8e5tcz9XO8NvImzNce55XN2PeQ5mM1KJmkasBmwALge+BFwaUQ83mR7AVsDewDvA5YBZgP7RMRl3ai5KpxdPmeXx7mVR9KywGPAgogYX3Y9o0VDbr7rfYvqnJ37uvZIOhQ4uf+fDS8FcCuwR0QMOqAnaQawQt3WuX7OLo9za537ufZIauXH1/HA3qT18YKG5yMiPtLRwirOueVzdtXnAWazCpC0E3ACsA6pEwzSL+C3ArcDM4EngReBpYClgdWBjYE3AZNJB5NzgbOB4yLisd5+inI4u3zOLo9zK0edB/va4dzy1T0793V5JG0A3AyMA6YDFwOPA1sBuxTPPwHsGBE3DtK+loN94OxyObd87ufySVpAyqvpJsXfaPh37favzi2fs6s+DzCbVUTxK/i7gI8COwATipeG20j7O837SJfwfjciZnStyIpydvmcXR7n1nt1HuwrLrvNNRk4hRrmBs6uXe7rWlecYbUvcDWwQ0Q83/DahsBFwFqkMx3fGxFXDWhf58E+Z5fBubXH/VyehsG+u4BHR1h8HLBFsfx1jS9ExNSuFFhRzi2fs6s+DzCbVZCkZYCpwObApsDKwHLAIqQzEGYCdwM3ANMi4paSSq0cZ5fP2eVxbs3zYF+ejDM2XvYW1DA3cHad5L6uOZLuIZ3huH5E3DHI61OAHwLvAV4Ado+Iyxter+1gn7PL49w6x/1c8yT9HNgJeBo4GjgzhhhcKtbBZ/D+1Lm1wdlVnweYzczMrCc82JenIbdHSIMDregDXkkNcwNnZ70n6TnSXPFThllmHGlOyA+QLq3/YERcXLxW28E+Z5fHuVlZJO0MnAG8ArgN+ERE3DTIcpOBZ/H+FHBu7XB21eYBZjMzM+sJD/blkXQf8CrgA/0DAi20XY50GWHtcgNnZ70naTZpsG/xEZYTcBawPzAf2D8izq/zYJ+zy+PcrEzFQN7xwEGkEwG+AxwZEU8OWMaDfQ2cWz5nV119ZRdgZmZmtXF/8fczEbF6Kw9goxLrLtvNxd9NMtrW/UwCZ2e99iAwSdIrh1soko8B3yDNFXmupAN7UWCFObs8zs1KExGzI+IzwJtJZ5TuD9wtab9yK6s255bP2VWXB5jNzMysVzzYl+cW0hkadR5kz+XsrNf6+7ntm1k4Ij4NfJX0veybwPJdqms0cHZ5nJuVLiL+SJq3+hDSnNXnSpomab1yK6s255bP2VWPB5jNzMysVzzYl6d/8CAnt7mku2dfN9KCY5Szs167ktTPfbTZBhHxOeCYop26VNdo4OzyODerhOIs+TOAtYGfA5uRjv1OKbWwinNu+ZxdtXgOZjMzM+sJSVsDVwOzImKJFtsuAVxOOpac2oXyKquYN3MJgIh4uuRyRhVnZ70maXHgj8B4YO+IuKGFtocAp1LTOSOdXR7nZlUlaQfgTNI9NMDrWVOcWz5nVy4PMJuZmVlPeLDPzMzMrD4kTQKOAFYDiAjPk9sE55bP2ZXHA8xmZmZmZmZmZmZmlsVzMJuZmZmZmZmZmZlZFg8wm5mZmVWUpNd04T37JK3W6fetGmdnveZ1Lp+zy+PcrAxe7/I4t3zObnTwALOZmZl1nQ8Ms90l6QJJa7X7RpImSDoA+Duwb9uVVZ+zs17zOpfP2eVxblYGr3d5nFs+ZzcKeIDZzMzMesEHhnluAvYB/irpt5I+JmnZZhsrmSrpbOBh4FvA8sCfu1NupTg76zWvc/mcXR7nZmXwepfHueVzdqOAb/JnZmZmXSdpGrAZsAC4HvgRcGlEPN5kewFbA3sA7wOWAWYD+0TEZd2ouSok7QScAKwDRPH4O3ArcDswE3gSeBFYClgaWB3YGHgTMBkQMBc4GzguIh7r7acoh7OzXvM6l8/Z5XFuVgavd3mcWz5nV30eYDYzM7Oe8IFhvmKA/V3AR4EdgAnFS8MdyKn4ex9wHvDdiJjRtSIrytlZr3mdy+fs8jg3K4PXuzzOLZ+zqzYPMJuZmVnP+MCwfZKWAaYCmwObAisDywGLAE+QBurvBm4ApkXELSWVWjnOznrN61w+Z5fHuVkZvN7lcW75nF31eIDZzMzMSuEDQzMzMzMzs9HPA8xmZmZmZmZmZmZmlqWv7ALMzMzMzMzMzMzMbHTyALOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmdkgJEXxeEDSokMsc3+xzPgh2vY/5kuaKelqSXt1oLatG977kiGWeXXx+rRh3ucdki6S9A9Jz0maI+keSd+T9O526zQzMzOzsW/8yIuYmZmZmdXaasAhwFcy2h5b/J0ArAW8F5gqaaOIOLRD9e0m6a0RcWOzDSQtDlxY1PM8cDXwU2AusDqwPbC3pFMi4rAO1WlmZmZmY5AiouwazMzMzMwqR1IATwJBOjFjjYiYOWCZ+4FXARMiYt6AtkSEBiz/duDXxT9fExH3Z9a2NfBb4B7gtcDvImLzAcu8GvgHcENEbNHwfB9wBfDO4j32joiHB7RdBPg4sGZEfDKnRjMzMzOrB0+RYWZmZmY2tOeA44AlgGPafbOIuAq4CxCwSbvvB/wBuAzYTNKuTbbZkzS4fA+w48DB5aLOFyLidKBTZ1mbmZmZ2RjlAWYzMzMzs+GdCdwLfEzSmh14v/6zmjt1KeFngXnAVyRNaGL5A4q/X4uI2cMtGBEvtFucmZmZmY1tHmA2MzMzMxtGRMwFjiTNo5wzD/O/SdqWNBdzADe3Xx1ExN+As0lTZRw4wv9/PPCW4p9XdeL/b2ZmZmb15pv8mZmZmZmNICIulXQjsIukLSJiWjPtJH2x+M/Gm/wJOC0iHuhgiccC+wBfkHRBRDw9xHLLABOL//5/Hfz/m5mZmVlN+QxmMzMzM7Pm/Gfx9xRJGnbJhY4pHp8DtgGuB/aJiI7ObRwRj5HOrl4WOGqYRZut28zMzMysKR5gNjMzMzNrQkTcCFwKbAq8v8k2Kh59EbFMREyNiO93qcTTgH8CB0t61RDLPA68WPz3ql2qw8zMzMxqxAPMZmZmZmbNOxKYC5woaeJIC/dSRDwP/BewCHDCEMvMA35f/PPtPSrNzMzMzMYwDzCbmZmZmTUpIu4F/htYHfhUyeUM5nvAbcCewMZDLHNO8fcwSZOGezNJi3SwNjMzMzMbgzzAbGZmZmbWmi8BT5HmOp7SiTeUdL6kkLRvO+8TEQEcRppr+cQhFvsh8CvgdcBlklYepJ6Jkj4JnNJOPWZmZmY29o0vuwAzMzMzs9EkIp6QdAJwUgfftv/Ej3ntvlFEXC3pCmD7IV5fIGk30tnOOwP3SboKmA7MB15Fmj5jeeBr7dZjZmZmZmObz2A2MzMzM2vdGcD9HXy/NwLPAr/s0PsdThosHlREPBsR7wXeCfwMWBc4CDgEeDPwG+DdEXF4h+oxMzMzszFK6So6MzMzMzMrg6SlgMeBUyLis2XXY2ZmZmbWCp/BbGZmZmZWri2BucCpZRdiZmZmZtYqn8FsZmZmZmZmZmZmZll8BrOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZBTL7wgAAADdJREFUmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZmZmZmZmZZfn/nf2eLxNNQjcAAAAASUVORK5CYII=\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": 26,
   "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": 27,
   "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": 28,
   "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": 29,
   "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": 16,
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\n",
    "test_parameter='alpha' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha_A'\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega_A'\n",
    "    \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(test_parameter.capitalize(), 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": 10,
   "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": 11,
   "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": 19,
   "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": 13,
   "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": 14,
   "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": 15,
   "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": 16,
   "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": 17,
   "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": 18,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se sigue una distribución guassiana: [False, True, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]\n",
      "Un total de: 107 no siguen una gaussiana\n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False  False   True   True   True   True   True  False\n",
      "2  False  False   True   True   True   True   True  False\n",
      "3   True   True  False  False   True   True   True   True\n",
      "4   True   True  False  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True   True  False  False  False\n",
      "8  False  False   True   True   True   True  False  False\n",
      "Redimensión 1/10 False Mejores: Merge single, Merge, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 1/20 False Mejores: Merge single, Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True  False  False  False   True\n",
      "2   True  False   True  False   True   True   True  False\n",
      "3  False   True  False   True   True  False  False  False\n",
      "4   True  False   True  False   True   True   True  False\n",
      "5  False   True   True   True  False  False  False   True\n",
      "6  False   True  False   True  False  False  False   True\n",
      "7  False   True  False   True  False  False  False   True\n",
      "8   True  False  False  False   True   True   True  False\n",
      "Redimensión 1/40 False Mejores: Merge, Baseline, Merge single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True  False  False  False   True\n",
      "2   True  False   True   True   True   True   True  False\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True  False\n",
      "5  False   True   True   True  False  False  False   True\n",
      "6  False   True   True   True  False  False  False   True\n",
      "7  False   True   True   True  False  False  False   True\n",
      "8   True  False   True  False   True   True   True  False\n",
      "Redimensión 1/80 False Mejores: Baseline, Merge, Merge single, Merge - Asynchronous, \n",
      "Configuración: 1/120 tiene valores iguales\n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False  False   True   True   True   True   True  False\n",
      "2  False  False   True   True   True   True   True  False\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8  False  False   True   True   True   True   True  False\n",
      "Redimensión 10/1 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 10/20 False Mejores: Merge single, Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False   True   True   True\n",
      "6   True   True   True   True   True  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 10/40 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True  False\n",
      "2   True  False  False   True   True   True   True   True\n",
      "3  False  False  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False   True  False   True\n",
      "6   True   True   True   True   True  False  False   True\n",
      "7   True   True   True   True  False  False  False   True\n",
      "8  False   True   True   True   True   True   True  False\n",
      "Redimensión 10/80 False Mejores: Merge single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True  False\n",
      "3  False   True  False   True   True   True   True  False\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True  False  False   True   True   True   True  False\n",
      "Redimensión 10/120 False Mejores: Merge, Merge single, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 20/1 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False   True   True   True\n",
      "6   True   True   True   True   True  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 20/10 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True  False\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True  False\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True  False  False  False   True\n",
      "8  False   True  False   True   True   True   True  False\n",
      "Redimensión 20/40 False Mejores: Merge, Merge single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True  False\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True  False\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True  False  False  False   True\n",
      "8  False   True  False   True   True   True   True  False\n",
      "Redimensión 20/80 False Mejores: Merge, Merge single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True  False\n",
      "2   True  False   True   True   True   True   True  False\n",
      "3  False   True  False   True   True   True   True  False\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True  False  False  False   True\n",
      "8  False  False  False   True   True   True   True  False\n",
      "Redimensión 20/120 False Mejores: Merge, Merge single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 40/1 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 40/10 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 40/20 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True  False   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 40/80 False Mejores: Merge single, Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True  False\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False   True   True   True\n",
      "6   True   True   True   True   True  False  False   True\n",
      "7   True   True   True   True   True  False  False   True\n",
      "8  False   True   True   True   True   True   True  False\n",
      "Redimensión 40/120 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False  False   True   True   True   True  False   True\n",
      "2  False  False   True   True   True   True   True  False\n",
      "3   True   True  False  False   True   True   True  False\n",
      "4   True   True  False  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7  False   True   True   True  False  False  False   True\n",
      "8   True  False  False   True   True   True   True  False\n",
      "Redimensión 80/1 False Mejores: Merge, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True  False\n",
      "2   True  False  False   True   True   True   True   True\n",
      "3   True  False  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True  False  False  False  False\n",
      "8  False   True   True   True   True   True  False  False\n",
      "Redimensión 80/10 False Mejores: Merge, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False   True   True\n",
      "7   True   True   True   True   True   True  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 80/20 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True  False   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True  False  False  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 80/40 False Mejores: Merge, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True   True  False  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 80/120 False Mejores: Merge, Merge single, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False  False   True   True  False  False  False   True\n",
      "2  False  False  False   True  False  False  False   True\n",
      "3   True  False  False  False   True   True   True  False\n",
      "4   True   True  False  False   True   True   True  False\n",
      "5  False  False   True   True  False  False  False   True\n",
      "6  False  False   True   True  False  False  False   True\n",
      "7  False  False   True   True  False  False  False   True\n",
      "8   True   True  False  False   True   True   True  False\n",
      "Redimensión 120/1 False Mejores: Merge, Baseline, Baseline single, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True  False\n",
      "2   True  False  False   True   True   True   True   True\n",
      "3   True  False  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False  False   True\n",
      "6   True   True   True   True  False  False  False  False\n",
      "7   True   True   True   True  False  False  False  False\n",
      "8  False   True   True   True   True  False  False  False\n",
      "Redimensión 120/10 False Mejores: Merge, Merge - Asynchronous, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True  False\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True   True  False  False   True\n",
      "8  False   True   True   True   True   True   True  False\n",
      "Redimensión 120/20 False Mejores: Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False   True   True   True   True   True   True   True\n",
      "2   True  False   True   True   True   True   True   True\n",
      "3   True   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False   True  False  False\n",
      "6   True   True   True   True   True  False   True   True\n",
      "7   True   True   True   True  False   True  False  False\n",
      "8   True   True   True   True  False   True  False  False\n",
      "Redimensión 120/40 False Mejores: Merge - Asynchronous, Merge, \n",
      "       1      2      3      4      5      6      7      8\n",
      "1  False  False  False   True   True   True   True   True\n",
      "2  False  False   True   True   True   True   True   True\n",
      "3  False   True  False   True   True   True   True   True\n",
      "4   True   True   True  False   True   True   True   True\n",
      "5   True   True   True   True  False  False   True   True\n",
      "6   True   True   True   True  False  False  False   True\n",
      "7   True   True   True   True   True  False  False   True\n",
      "8   True   True   True   True   True   True   True  False\n",
      "Redimensión 120/80 False Mejores: Merge, \n",
      "[[5, 4, 6], [5, 4], [4, 0, 5, 6], [0, 4, 5, 6], None, [4], [5, 4], [4], [5, 6], [4, 5], [4], [4], [4, 5, 6], [4, 5, 6], [4, 5, 6], [4], [4], [4], [5, 4], [4], [4, 6], [4, 6], [4], [4, 6], [4, 5], [4, 0, 1, 6], [4, 6], [4], [6, 4], [4]]\n"
     ]
    }
   ],
   "source": [
    "checked_type='tc'\n",
    "use_perc = False\n",
    "rms_boundary=0 # 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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  2  7  7  6  2]\n",
      " [ 2 -1  7  7  6  6]\n",
      " [ 4  6 -1  6  6  6]\n",
      " [ 4  4  6 -1  6  6]\n",
      " [ 6  6  6  6 -1  6]\n",
      " [ 6  6  6  6  6  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": 43,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_8791/2593541567.py:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_8791/2593541567.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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JkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJkiRJTRlIkCRJkiRJGmERMTMisnr8qdPtGaiI+FPtdczsdHs0ttU+a7d1ui1askTEqdVnZ3ZETB6B660fEU9X13zvcF9vJBhIkCRJkiSpgyLittrgWEbEoxGxTD/O/1CP8zMi3j2cbZbUXUT8d4/v4Fc73SZJRURsD+xb7X48M+f3UubEXn6Xtvs4oGd9mflv4PvV7uciYsawvcARYiBBkiRJkqTRZXlgz36UP3C4GiKpbQf32H/7SNz1LKkt/1f9+w/g58NQ/x1Njn8ZeBqYDnxyGK47oiZ1ugGSJEmSJGmRBIISHDipVeGI+C9g82p3Id4wKI24iNgK2KbafQqYAswAXgf8skPNkgRExBuAF1a7X8nMbFL0J8D1bVb7GmCHavs24I+9FcrMeyPiJOAQ4N0R8dXMvLPNa4w6BhIkSZIkSRo9/gC8Enh5RDwnM//VovxB1b8LgT8BOw5j2zSEMvM2StBIS776bISPAt+tHTeQIHXW/1b/PgT8tFmhzDwPOK+dCnukMjqhj+AElPRGhwBLAR8EPtLONUYj71SQJEmSJGn0+GH1bwAH9FUwIqYA+1S7vwOW2LscpSVVRCwN7Fft3k0ZNPxrtb9zRKzVkYZJIiJmAS+pdk/LzKeGoM4XAZtVuwuBE/oqn5lXAVdXu+/ozxpIo42BBEmSJEmSRo8rgWuq7f0joq+/2/eg5F2GFgMZvYmIqRHxnog4JyLuiIh5EfFYRNwYEcdUaZNa1bFooejasVdHxCkRMTsinmy2EGVErBMRX42If1TlHo6IqyLi0xGxalXm8L4Ws+ylzjWr8y+OiHsj4pmIeDAiLo+IL0TE2v17l1peb8OI+HJE/C0iHoqI+dW/N0XERRHxpYjYMSKW6uXcmbXX9qcm9R9QK3N4dWz5iPhIRPy9em3zIuLWiDguIjbqR9uH/P1v87rPi4gvRsRlEXF/1Uf3V332iYhYaSiuM4J2B1astk/NzIU00pJNpEVAsK76Tr4rIs6NiLuqvp0bEbdXn+GTIuJtEbFaj/NOin4utB4RK0bEU9U5T0TEcj2e7+27vV313f5Xde6DEXFh1ea2s57UXucvI+Lf1fWfjoh7IuL31WewP5/l10bEGVF+jj1dfZ7Oi4i92zj3T7X3bmZ17EVRfgbeFOVn4qLvX49zl6u+i7+v2v509Z5cWf1c2GCA198iIo6OiFuq/n+k+hlzaERM7cf7sn71Xbs8IuZU7bs3Iv5Y1bVCi/Nb/ozqUX6xn1dNyq1Ye9/urdr1RPW5+lv13u8ZEdPbfa19qK8hNFRrI7yjtn1+m6mKflH9uzzlZ8YSydRGkiRJkiSNLicA3wTWBXYCftukXNcAycPAGcAu7V4gIl4DHAes2eOpKcBG1eOdEXE88J7MnN9GnUsBPwDe1kbZPSmvc1rt8DLAVtXjPVWZtkXEYcCngJ4DbStVjxcAH4qID2fm9/tTd5PrfRD4CtBzQd0Vq8fzgJdR0mq8DvjVEFxzS+B0oOcA5XOqx9sjYt/MPL1FPUP+/rdSDTT/H/BeFh+PmlE9tgUOjYgDMvPMobz+MKqnNeoKIPwE+AawNHBQRHyhReoTImIzymdkZi9Pr1s9XkD5fp1O9wXZj6XxvTsYOLqNdu9XtQ/gZ5n5eB9tC8pn/SN0T8e1NLB99XhLROyWmU/0ddGI2AM4Clitl6fXqB47AkdExLp9DdJGmQ1yPI0ZIV1mADtTZoS8CXhrZj7bV7tqdX6Fkp6qz7RjEfFayndoRo+nun7ebE35eXNkZh7RzrWrej9EWaC3/nNlKiXH/wspAeadMvM/Ler5BPBpSjqdutWrxyuAj0XEwZl5drvtG6yIeDllUH2VHk8tBaxfPV4IvBP4GqUvBnqtCcBe1e4jwMUDratW57JAPUB1fJunngN8vtreGzhlsG3pBAMJkiRJkiSNLqdQBu2WogQLFgskRMR6NNZDOC0zny5jfa1FxNsoA2ATq0OzKamR7qKME2wJvLa6/sGUQfE92qj6W5TBzKeAc2ksWrk58HTt+rsAP6YxJvEAcCZwe3WtXShpI86qjrfzmo6l5KDucmn1mAMsB2xHGdSfAnwvIiZn5rfbqbvJ9V5PGSjucjXwZ6BrcG+V6jW8lO6D9YOxFiV/9+qU9/YCynu3FqV/Vqf02ckRcXVm3tqk7UP+/rcSEROBs4Fdq0Nda3pcRhngW5myNsjWwArA6RGxx2gPJkTE+jS+h1dm5g0AmflwRPyK0i/PoQza9roYa1XPNOA3wDrVoTmUvr4VmEf5DG8AvIgy0NpNZl4cEf8ENgFmRcQWmXlti+bXAyCtBkM/SxnQnQv8GrihOv4SyoA9wMuBr1MGgHsVER+gfG+6flg9Svn5dhPldc6gLFr9Usrnc2Iv1dQdRwkiPEIZqL2ZEtzYsaoDyqDttcAXWtQFJej3bmBB1a4rgPmUwOqiAElEvJEyGN7Vvgco35fbKN+hXYFNKcGAwyNiemZ+qI3rHwx8EniWElS6srr+VsAbq+ttBvyIxvu+mIj4Kt3z8N9E+Zk8B1gPeAMlkLMK8MuI2LtV8HEoRJkRdg7l8wzld85vKe/bfMp3/3mUz9UaQ3DJWTQCPX+uZgsN1ptptP8Bys+1dlwHPEj5WbdjRCydmU+3OGfUMZAgSZIkSdIokpkPRsQ5lEHIN0bEipn5cI9iB9IYjGs7rVFEbEG5e3kiZVDwEODHPe+WrgZIz6AEFXaPiIMy84c96+vh3ZSBrzdl5h1Nrr8cZdZC13jEWcDbM/OxWplDgUMpd+UetFgli9d5CI0gwmxg38y8rJdyr6SktlgR+GpE/DYzb2xVfxP1u2QPyMwfNWnbUpTZCLcP8Dp176AMML4zM4/rcZ3DKAPPL6Xcvfwx4F29tGfI3/82fZZGEOEyyh3iN/fSvn0og6RLASdExEWZ+dAQtWE4HETje3hSj+dOohGAO5g+AgmUwcmuIMKvgTdn5tzeClbf4c16eeo4ykB+V7s+2OxiEbE1ZXAa4MbMvKSPtkEZ3L4U2DMz7+lR157Azyjvw8ERcURm3t3LNXeg3GHe9X4dC3wsMx/tpeyKlO/0vD7atB4lcHkW5Tv4SO25T0XEh6vrQbnz/huZ2Vd9UH6G3Qa8LjOv761ARKxOWcumK4hwOnBQL9+hj1ICwgAfjIjfZ2arWUmfpARD3tDzZ1NEbEcZdJ8KvDoiXpKZl/bSvp3pHkQ4DPhKfRA9Ij5CCR69uXodP4iIv/bWb0PsnTQG4Y8D/ru3mSLVDJgXUwbdB+Plte3FficMUD2t0UntzNYDyMyMiMspAaBlKEGOVt+7Ucc1EiRJkiRJGn26Bu2nAG+pP1ENsuxf7V6bmVf0o94vVnUCvCUzT+st5Upm/ht4DY27cA+L1lMeHgJ2bRZEqBxAuYMeyl2ye9cH4KprZ2Z+BTiG1ulFpgJHVruPAjv2FkSo6v09jfdtMmWwfKC2rv69vlkQobrmM5l5emZeN4hr1R3WM4hQXedxut9h3iwH9wEM4fvfjohYk0bg5TZgp96CCNW1f1IrOx34n8Fef7hUaVMOqHafpczyqPsN5Y5lgD1a5Hvfurb9mWZBBIDMvDYze14LSuCi6w7nt0Yv63LU9Gc2ApQ7x1/TM4hQtecXlEAClHHGNzap4/9oDL4fm5nv6i2IUNX5cGZ+JTPva9GuqyhBl0d6qePrwN+q3RVozBzpyzPAa5sFESofoLEmxlXAPk2+Q/9H91lLh7dx/SeAXXoLcGbmxUB9FlWz73j9Ot/JzC/1vBO/Sj+1H43B9enAh9to32DVP+eHNUs3Vb1/l7YReGllVm170D+DI2JjGjNdoARl+6M+S6jlGkSjkYEESZIkSZKGxypRFrnsejRN+dGL84GuQbued4W/kkYe9f7MRphJ467wv7TKi10NGnYNWD6XkqqjL9/PzPtblHlrbfvIFqkdjqCkGOnLm2mkrjiq1aKXmXkOcEu1+7oWdfela0C07YVPh8ADdB9I7KYafOxKObNK9L6w9FC//+04hEae9i/0HHTtxTE0AlivH4LrD5edga73+Lyen/3qTuWfVLtTgH37qKuewmdAn6nMfJAyiwjKndxv6K1cta5AV1vms/hMit58rbfB+ppf1La37vlkRLyIsr4DlIDfR3qWGaDPZuYzA21XL36emf9oUebtte2Pt1h74XDKzC+AF0TE81vUfXwVxG2m1fv8PMqd/FBSzH2mWUVVuz9eO/T2NoLFg1X/nC8zzNeCkiapy1DMCqsH4C4ZwIy2ehs2HIL2jDgDCZIkSZIkDY85mTmr9ji23RMzcwGNAb4XVOlMunQFFp6hfws27kjjDvPz2zzn6tr2rGaFKr/u68mImEJj8CtpkVs6M++ldTqKV9a2+/uaZlRrTQzENdW/G0TEVyJiJAbFft9i0BRKWpQuq9afGKb3vx396qPqNf6z2t26Wl9hNOptkeWe6rNVDm5SBhqfJ4BvR8RABxnrP2OaXW93GnfUn9NG8A/K7Iq+NP3cVeqfgV9kiwWZ27SAsk5IX1q1q6dWP8PWp7FA/WOtrl8Fzeqf+e1aXH+w7/O2te3f95ISr6ffU/L2Q1kvYaMW5Qer/jk/vpqtNJzqwdR7B1NRREymexCp3UWW6+ptWKdpqVHMQIIkSZIkSaNTfU2CAwGq9Chvqo6dk5lz+lHflrXtIyIiWz2Ao2rnzKBvre7OnElJKQRwZ7O0Jj20SkdRf00Xtvma9qqd0+o1NfOV2vahwH8i4hcR8f6IeFE16DTU2rmj9vHads9Fnmcy9O9/O+p9dHubfdSV9mMijUHvtkXESyPio308DmldS5/1z6AxW+IRygKyi6nSjnXNEtkmIrZqUuVplPRBUII9N0XEXyPiyIjYLSLazRX/J8o6IQCviojeBivrM5zaTc3S6rPX1+cOus9m+nub12xlTmY+2aJMq3b11OpnWP0O92urgG8rV9a2WwWIBvs+19t3VYu6qNLaXV07NNx3yR9NCcAAvIry8+APEfGpiHhVtYbLUFq+tt00XVibXkcjePM4jXRe/VH/vA71ax0RBhIkSZIkSRqFMvMWGosxvrUanH4LjTUO2k5rVBnswpXLtni+1cD09Nr2g80K9dCq3HC/pl5l5hmURTe7XvNylIV1vwX8FXg4Ik6PiF2bVDEQT7XTtNp2zzGf6bXtoXr/+xQRk+g+mDcQA+mjV1Ny8jd7fGKQbXobjaDMzzOzr745ubbd6yyBao2LnWgMOgfwIkrqmXOAByLiioj4SF+DrdXAcFdwoL6GQ6m0pDfrmh1wF23O4mnx+qDvzx10/562MwOiHYP9PvSm1c+welCr3SBuvdxKLcr2+Zp6rGfT2+sZ7vYNSmbeTkkJdmt1aBKwA2Ux9t9Sfm7+OSIOabHGR7sm1bb7SkHVjvp39ydtBLF6U2/DcASbh92k1kUkSZIkSVKH/JCSrmIVyh2RXXcT3wuc18+66mMAp9L9TtR2/KWvJ3su6NmLgeTfbnVO/TV9lu537LbjX/0sv0hmHh8RpwP7ALvQ6Ccog9+7A7tHxG8oC8IORTqXwRiO97+Vev8sAP7fAOpolZ6lE+qDihtFxIl9lF2ltr1fRBza28B8Zt4UEbMoAYXdge2BTSh9EMA21eOjEbFXtfhub04APkcZqDwwIj5fG4A+kEafntDGd3Y4LLa4+yjSn/djIK9jJF/7qGxfZv61WrT4dZRZPdsDz6menkhJ/7QdcGhEvCEz/9l7TW2ZS2PmxhQGOCshItaiBEC69HeR5S719U8GEojoOAMJkiRJkiSNXj+jLLC7LGVwsCtFyEltptWoq99dfmVmfn0I2tcf9QHhdmcStLpD9kFgtWr7rMy8sq/CQ61agPZo4OhqodKNgVdQZid03fm9K2UB4f1Gsm29GI73v0+Z+VREzKUsrDoR+GFmPjSYOtu87uGUhW6HXES8hO6peravHu1YkZKa7Me9PVkN+F9QPahSGr0MeA0lYLUcsDpwTkQ8LzMf6KWO+yPibMpncH3KHd9/iIgJwP5dxej/jKbBqP/sWa1pqdGv/tldpWmp7urlhjsoNtTtqwcW2gkqtrVWTLXQ8xnVg2qthO0pwYU9gKUpaZZ+FRGbtlgUvi8P0AgkrET396c/DqSxUPR1mTnQ9Fz1n6eLfXeXBKY2kiRJkiRplKruYv95tVsfvBzIIGA9//e2TUsNn9uA+dX2OhGxQhvnbN7i+U6/pkWy+Gdmfj8zdwLeTGMgbp+IGNa0IW24jaF//9sxavpoiPS1aPKQnp+ZD2bmmZn5TsrAatcMmunAvn2c2tuiyzsBXYuL/z4z/91uO4bADbXtF47gdYfaLbXtLargTCtb17ZvblpqaNTbt3XTUpUq+Flfw6Rn++qzqNpZY2LdNsosJjPvycyfZOZ+VXu6BvyfQ5ntNVD1z/jaTUv1oXqPDqwdGsgiy13Wqm3fNoh6OsZAgiRJkiRJo1vPoMElmXnTAOq5oLa9a0SsPog29VuVzqVrAdCgsVhtryJiDRqL7jZTf00HVYM+o0Jm/hzo6qcJDP9Cpn0apve/Hd36aAjq65iIWBbYu3Zoo8yMVg/Kndpdabd2jIj1+3vtzLyP7gGCjfsofgGNgcrdq0Xa6+/9YAZDB+L3te09I6KdQelRpwq+3FPtrkBZMLipaj2LekqcS5qVHSL1+ndqI1i4A40ZCXNYPJDwCNA1G+C5bQROXt1OI/tS/W6rL2Tc1+e8lWtr2xsNsI4daKReeprua570V/21XDOIejrGQIIkSZIkSaNYZl5EWSD2qOpxxADruRH4XbU7FfheuwPvQzhAf2pt++MRsXQfZT9NI51EX/V13b26FfD+dhvSgaDDvBG+Xm+G+v1vx9E0ZkK8MSLe2O6JoykwVNmbxp3Zl2dmW3eYZ+Y84JfVbs87nAeq6eepSpHUFSyYArwXeGO1/xBVSpmRUqWCuaLaXQH46khef4idVNv+XLWgeDOfprFY+BWZef3wNQuqz+Nfq90p9JHeKyImAl+oHfpRj8WcqdLndQ3GL08jXVtv9e1OG7MgBmAwPzf/VtvesmmpvtVnEJ05yNRsW9W2B5oeqaMMJEiSJEmSNMpl5scy873V44LWZzR1KNC10OubgNOrO897FRFrR8T/0v+FnZs5kbJQNJS7M38aEcv3uGZExKHAu2mx+GdmPg58onbo6xFxeERMbXZORGwZEd8BvjSA9hMR60XExRGxd3WHem9lIiLeT+MO1DnAYBYNHSonMoTvfzsy8zagvh7HTyLif5oNwFbXf2lEnEQ/AkMjpD6oeGrTUr07rbZ9QP3u7oj4SUR8uq+ZChGxKd3fj4taXO+HlAWuAT5DyTsPcMogcs4PxqE0FjN+V0Qc3eyO+YhYKSI+FhGjcT2Fb9EIXv4XcGrPGRbVZ/hDwEdqhz8zQu07vLb9wYg4tGdArvq5dTLwourQI8A3mtT3y9r2tyNi1Z4FIuK1lJ8tfYqIP0TEh/qaDRcR2wFvrR1q9Tnvy+9ofOa26+/JEbEiZeHzLgNdZJlqVtDzq92bMvP2gdbVSS62LEmSJEnSOJGZV0fEAZRBpMmUYMJrIuKPwNXAo5Q7aNcCtgG2oNxBfUNv9Q3g+o9FxDuAsyl3u78BmB0RZwC3Uxaj3RXYjMad012Dt70Oamfm0RGxCWWQdQJlwO69EXEBJWf4XMpd0BtQ8rN35Yk/aoAvIyi5/rcF5kXE34DrgPur669BSWdSHxT+VGbO71nRSBuO979Nn6CkFnkjZUD7u5QZERdQ8pg/Q8n7vxFlcLNroPHanhV1SkRsDLy02l0I/LSfVfwe+A/lta1DSQPTFaBbnTLb4YiIuAG4HLiDcjf2KpS7qXegcUPwpcA5fV0sM++JiF9TUljVx/8GPBg6GJn5xypA9bXq0Lsoa4ecT1lH4ylgVcpd7dtS2vyz3urqpMz8T0QcDPyC8h16M/CKiDiLkk6q/h3q8s3M/PUIte/8iPgajSDGV4ADI+I3lEWv16V877u+YwuAd2Tm3U2qPBr4QFV+Y+DGiPg55bWuALwceDHwJCUY8aE+mvccSlDxqxFxFSXV2t2U7/+qlO/+i2vlf5aZV7f1wnuRmXMi4hLKguXPj4jVqhRh7dqPMrMDyuv9ffOiLe1I4/t79iDq6SgDCZIkSZIkjSOZ+dOIuIuy9sKGlIHdXeh7UcshS8mRmedGxH6U1CvLAjOAd/Yodj+wB93zi/eVyuUD1QDslygDeSsD+/TRjKcZ+MKnCygDyRMoKaJeUT168xTw8cw8eoDXGnLD8f63cc0FEbEHJaDw/yhrBqwJ7N/HaU8wuhYkrc9G+ENm3tu0ZC+q9+CnlEHZrvq6AgnP1IpuRvdB6J7OA/at0s60chzd18K4LDOva7PJQy4zvx4R9wLfoXxHV6AMxPdaHHh2pNrWH5l5ZkS8gXIX/iqUQfBDeik6H/hCZh4+cq2DzPxoRDwCfApYCtikevT0IHBQZjYd2M7MR6q0RedSgn0rsvjPizmUn7frtGha1+d8AvCC6tHMSb1cZyBOpgQSghI478/P4vp3/oc9Uz/1U31mw2DWWegoAwnjwBVXwKjLKqgRMagfcVriHTGgzLmSpCXVeV9YItct1CC976jRkHJdS6LMvKS6w3p3YDfKXaCrActR7iy9hzIL4SLgV5n5ryG+/k8j4lLgg8BrKANQ8yl3xZ8FfK+687c+yPhoizqPjYgfA2+nLIK6FWWQb2ngMcqg9LWUu0p/nZkPD7Dtd1bpoHahDFBtCcykDLIlJU3IjdV1TsjMOwdyneE0HO9/G9dcSMkpfzRljYBXAptSBpQnVvX/izIz5gLgvMx8cjDXHCoRMZnyuepyWrOyLZxKI5Dw+ohYJTPnAK8FtqfctfxC4LmU7+NSlIDKHZR87z/OzD/043q/odzxvVa135HZCHWZ+eOI+BXlM7ArZdbTytXTXSnA/gj8JDPv6kwrW8vMX0fEBpQAwm6UgfqVKP11O+UzfGxmzu5Q+z4fEadRBuNfTZmJtRxlptGNwK+r9rX8XmfmpVVqrY9Sfl6sSwmodv28OCoz761mu/VlK8r3/hWUIMIGlJ/RkyiLkf8L+AtwUmZe3o+X25efUNYYWgHYlzYDCRGxDY01DRZSAu8DUqWS6gro/bWTwbzBisEFU7QkiJiVZVacxhu/3uPbEUcYQZTGmxcv1WuaZklj2PuOmsfNdy0Y97/011wz8l3v6nQrFnf44VyRmbM63Y4lWZX6Zqdqd+PMvKmT7RlvfP+XbBGxDCWd0nKUFF9rZOZjnW2VNLIi4v8oQRCAzTLzHyN8/YNpBPH2ysxfjOT1h5KLLUuSJEmSpFGnWpyyKyf9E5T1DjRCfP/HhD0pQQQo+eYNImg8+iolkAZ9r+Ew5KqFrj9Y7V5P98WrlzgGEiRJkiRJ0mj0CUoufYCzqtQ4Gjm+/0u+/65tH9OxVkgdVC2w/K1q9+0RsVZf5YfYbsDzq+1PLOk/Rw0kSJIkSZKkERMRq0TEtyJivSbPLxURnwY+Uh1KyuKsGgK+/+NDROwFvKja/Xtm/rWT7ZE67AuU9UKWAg4fiQtGxATgyGr3t30tar2kcLFlSZIkSZI0kiYB7wfeFxF/By4D7q2Orw/sDKxRK/+1zPzbiLdy7PL9H4Oqu6zfBEwGNgfeWnv6kx1plDRKZOYTEbEfsAPwbERMzsz5w3zZtSmpjH4JnDzM1xoRBhIkSZIkSVInBOWO6Rc1eX4B8GUcBB0uvv9jy4b0PnPk6My8YKQbI402mXkhcOEIXu8ORmj2w0gxkCBJkiRJkkbSfZTB650pd4euBcwApgGPALcBfwR+kJku8Dv0fP/HvkeAm4GjGCN3QkvqPAMJkiRJkiRpxGRmAn+vHp/rcHPGHd//sSkz/0SZZSJJw8LFliVJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlMGEiRJkiRJkiRJUlOTOt0ASZIkSZIGY9qECbx4qamdbkYvnux0AyRJkoaEMxIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTBhIkSZIkSZIkSVJTkzrdAGkoLL00bLst7LADbLMNbLIJzJgBkyfDo4/C7bfDX/8KP/sZXHxxp1sraTA+85kc1PmPPHIb3/rW+kPUGo00+1+SJEmSpJFnIEFLtFVXhW9+E3bbDZZbrnmZVVeF//oveN/74NJL4aCD4MYbR7SpkkaJuXPndLoJ6iD7f6wJVlpve1bb6PVMX+vFLD1tdSZPmc78px7m6SfuY+7Dt/LQHRfz8B0X88Scf3S6sRpy9r8kSZI0UgwkaIm2zjrwlrcsfvzOO+Huu2HuXFhrLdhoo8ZzL3kJXHYZ7LILXHLJyLVV0tCYPfu8fpWfPn0mq6yy8aL9a689ZaibpBFk/6vL8qtvzaY7f4MV1py12HNLT1udpaetzvKrb8nqm+wOwG+/tAKZC0a6mRom9r8kSZI0sgwkaMy4+GI48UQ477wSRKibOROOPBL23bfsT5sGZ51VAgwPPjjSLZU0GKeeumu/yu+995mLBpKfffZprr325OFolkaI/S+ANZ6/D89/7dFMmND4r+yC+XOZ+9CtPDPvQSZMmsLUFdZjynJrdLCVGi72vyRJkjTyDCS0ISKWB7YBXgDMqv59LhBVkQsz8xWDvMbGwNuBnYF1gOWB/wA3AT8HfpqZjw/mGmPRwoVwxhlwxBFwzTXNy912G+y3H9x7L3zkI+XYyivDYYfBRz86Ik2V1AHLLrsaG274mkX7N954JvPmPdTBFmkk2f9j0xqb7sXmrz2GmDARgCcfms0tF36WB2afy8Jnn+pWdulpazDjubuw9pb7kwxufQ2NDva/JGk4RcRtwHoAmRl9l5YaIuJI4OPAI8AGmTmsf3hUY5X/AlYGvpaZjm5p2E3odANGu4i4ifJD4I/AV4F9gA1pBBEGW/+kiPgccD1wGCVgMQNYmvLL69XAccD1EbHDUFxzLLnqKth9976DCHWHHVbSHnXZc8/haZek0WHLLd/OxImTF+1fddXxHWyNRpr9P/ZMWX4dNt3l24sGkR/89x/5y/Ev4b4bf7nYIDLA00/cy11Xn8Bff/QKyIUj21gNOftfY1lE3BYRWXs8GhHL9OP8D/U4PyPi3cPZZo2siHhNj/79RafbJKmIiOcC1W2rfLHdIEJErBURH42IP0XEHRHxVEQ8GBE3RMTpEfHeiNigt3Mz8zHg89Xu+yNi08G/EqlvBhJaex5DFDRo4njgk8DEaj+BfwAXAbUhb9YFfhsRrx7Gtox58+fDb37T2F9vPZg6tXPtkTS8tt76oEXbjzxyO//61+862BqNNPt/7Nl0l28yaenlAJj36B1cdfo+LHx2XodbpZFi/2ucWR7oz21PBw5XQzRqHNxj//URMaMjLZHU0xcoNwQ/CHy3VeGImBgRHwduBv4PeDklO8nSwErApsDuwHeAT/VR1dHA/cBk4IuDaL/UFgMJ7XucMrj/DeCtwFWDrTAiPkxJZ9TlImDjzNwsM1+emesCrwLuqZ6fBPw8ItYb7LXHs55rIiy/fGfaIWl4rbPOtt0W2b366hPA1Bbjhv0/9iy78kbM2GDnRfs3//HTLJj/ZAdbpJFk/2uc6fqF1VZwICL+C9i82nX6zRhUBQxeV+12TcGaDLytMy2S1CUitqIR+P1OZs5tUX4ScCpwJNA18+wqSgDik5SgxAnAX4AFfdWVmU8B36p2Xx8RLxrAS5DaZiChtf2AjYEVqsH9D2fmqcBjg6k0IlYGPl07dBXw6sy8uV4uM38HbA88UR1aHvjcYK493s2c2dhesADmzOlYUyQNo/rd6JkLq4FkjRf2/9izztaNGzGffvIB7rvpzM41RiPO/tc484fq35dHxHPaKN/1S28h8KdhaZE67e2UwAGUgcau6Vg9ZylIGnkfo2QyeZaSmryVbwJ7V9s3AS/NzG0y832ZeWRmfiIzD8rMbYFVgK+1qO94YH6tLdKwMZDQQmaelpk3ZeZQ38b4XmCF2v67MvPpJm24le7Bg/0iYuYQt2dcmDIFdt21sX/ZZSWYIGlsmTx5WTbb7M2L9v/1r9/x6KN3dLBFGkn2/9i0ynN2WrQ9518XkAuf7WBrNNLsf40zP6z+DeCAvgpGxBTKOn4Av6N7elyNHV3BoqeAHwBnVfubRsSLO9MkSRGxBrBXtXteZt7TovwOwH9Xu/+iBBEubVY+Mx/JzOv6qjMz7wN+Xe2+0SwmGk4GEjpnr9r23zPzshblf0BjCuMEYI9hadUY9/73w/Tpjf2TT+5YUyQNo+c/fx+WWmraon0X2R1f7P+xZ9KU6Syz0nMX7T9y998AWHraGjxn2//lxQdcxA4fvJ2dDp3Dy997My/Y52xmvugDTJ6yYqearCFk/2scuhK4ptrePyL6+rt9D2B6td3v6XcRMTUi3hMR51QLfc6LiMci4saIOKZKm9SqjkULRdeOvToiTomI2RHxZPX8Ab2cu05EfDUi/lGVezgiroqIT0fEqlWZw2sLDC9WRy91rlmdf3FE3BsRz1SLl14eEV+IiLX79y51VkS8hJIvHeDszHwUOKlWpO1ZCVVe9v0i4pcR8e/qPX8qIu6KiCsj4mcRcUjPgciI+GytD77Uj2vdU52zsOeCsdXisl11zqyObRERR0fELRExNyIeiYi/RcShEdH26oYRMal6nadVn8HHqs/B/RHx5+pz8IJ+1Ldd9Xn+V20x3Asj4l1Vmpq+zj2x9jpfUR3bNCK+HhHXR8RD1XMn9nLu0hHxzoj4ddVHT1Xfkesj4tsRsXUbbe/t+s+pfe+eqN6fayLicxHR9i/PiFg1Ij4VEZdExH219/jS6nu7eht1dLXttjbKvqJW/sQ+yk2t+ubc6n2bV32ebq9+DpwUEW+LiNXafa19eCslDTnAz9sofziNdVjf1e6izG3oWnx9AqY80zDq8weehkeU6amb1Q79qtU5mflQRFwK7FAdej2tpzepZrPN4PDDG/uzZ8Nx7Uw6k7TEqae1mTv3QW688czONUYjzv4fe5ZbdXPq42hzH7yFtbZ4Gxvv9GUmLd19saOJy63JlOXWZJX1d+Q5L/0YN//xU9x19Q97VqkliP2vceoESvqLdYGdgN82Kde1jsLDwBnALu1eICJeQ0nDsWaPp6YAG1WPd0bE8cB7MnM+LUTEUpSb4FoOZEXEnpTXOa12eBlgq+rxnqpM2yLiMMrCpD0HnVeqHi8APhQRH87M7/en7g6qBwq6Agi/Bf4DrA7sHREfzMw+F46JiLUoYw9b9fL0WtVja8pNj1cAs2rP/wD4BGWQcv+I+GRmtpoatiuwRrX9pyrTQl/t+xDwZRopnKD04wurx/4RsVNm/qdFPS8DfgSs38vTM6rHdsBhEbF9Zv65j7oC+ArwERqDv1AWxN2+erwlInbLzCd6qaK3Oj9Q1blUi3IvBH4KzOzx1NKU4OFmwHsj4hjgfW30R1e9+1C+99N6PLVF9Tiwep9vbFHPQZSfUcv1eKrrPX4x8NHqu3ZsO20bChGxGeVzPrOXp9etHi+g/Iw6nf4tat+brhRFCZzbom2bUj4zADdUqcyHyrmU9HYTqjZ9fgjrlhYxkNAZPaPGl7R53iU0AglbDVlrxoGVVoIzzoCp1X8nn30WDjgA5rf8r7CkJc3KK2/EOuu8dNH+ddedwoIFz3SwRRpJ9v/YtNTUlbvtz3juLsx80fsX7T/9xH08+dBsYsIEpq28EZOnrgTA5CkrsNmu32bK8usw+6IjRrTNGjr2v8apU2gMNh5IL4GEKHeN71jtnpaZT5dxz9Yi4m2UQfyJ1aHZlNRId1HGCbYEXltd/2BgRdqbFf8tygDdU5SBreur45sDi1L5RsQuwI9pjEk8AJwJ3F5daxfKQOlZ1fF2XtOxwCG1Q5dWjzmUwc7tgJdRAiXfi4jJmfntdurulIiYRmOg8n7gfIDMXBARp1IGuJcD3kwfM1KiRGPPpDGO8DhwHnAj8CSwLGXgdRawSc/zM/OOiPgtpV9WB14DnN2i+fUASKvpoQdT1n54ljIIfCUl5/tWwBspn9PNKAGCnZtVEhF7UD5XXcGIeZTP9XWUdSdXogyWv4wSpJjYSzV1nwU+CsylpI65oTr+klo7Xg58HXhni7qg9NN7qu0LKZ/PJynv/SO11/Ei4PeUfoHSX2dR8ukvS/nev5AS3Hg3sHpE7N5GSu5XU3LoT6CsxfLX6rVtRAkgTaEElH4REVs3Cx5GxHuB79QO3Un5PNxD+Xy8rnpNywLHRMQymfnNFm0btOr78htgnerQHMrn/FbKZ2E5YAPgRfQeaOrv9VYDtql2r8/MVitw1gO9v6nq2J7Sh9tS3rvHKD+Pzwe+l5n3t9OWzHw4Iq6u2vP8iFgnM011pyFnIKEzNu2xf0ub59XLLR8Ra2fmXUPUpjFryhQ46yzYcMPGsU98Ai5pN3wjaYmyzTbdZ3dfeaVpbcYT+39smjRlhW77XYPI8x69k3+c/0Hm3Hr+ouciJrL6pnuyyau/xuQp0wHYYNtDeew/V3L/zeeMWJs1dOx/jUeZ+WBEnEMZvH9jRKyYmQ/3KHYgjbuk205rFBFbAMdSBlHnUgbff9xzEDIi1qfMctgS2D0iDsrMVlN83k0ZBH5TZva6QFFELEe5w71rPOIs4O2Z+VitzKHAoZQ71A9arJLF6zyERhBhNrBvb+mDI+KVlPQjKwJfjYjftrrzusP2pnHn+Gk97jr/ESWQAGUgvq/PwCtozDC4HNglMx/srWCVQWHHXp46lsZA6EH0EUiIkpbqtdXuI5Q7v/vySeBm4A09+yMitqME0qYCr46Il/SWUz4iNqG8J11BhLOAd/c2gyEilgX2oww0t2rXpcCePXPfV7Nlfkb5Dh4cEUdk5t0t6nsP8BCwe2Ze2FuBKCmcTqMRRPgzsFeVB79ebj9Kn0+mBFv+GziqxfUPA+6jfD+7vYcR8QXgIspsgs0od+r/uJf2bU737BjfBT6Smc/UynwE+D/gA9Whr0TEhZl5VYv2DdabaQQRfg28OTPn9law+jm4WW/P9cP2NH4Gt0pXDmWWRpdrIuIoGusldFmlerwY+EhEvDMzF+uHJi6jEdjYHji1zfOktrlGQmfMrG0voERt23F7H/WoF5Mnwy9/Cdtt1zj2ne/AV77SuTZJGj4TJkxiiy0aM+nvvvsy7r+/z7WpNIbY/2PXxElTFjv2zNwH+fspr+o2iAyQuYB7b/gpl//k9Sx8dtHNrzxvh89Cn2nGNVrZ/xrHugbtpwBvqT9RpVzZv9q9NjOv6Ee9X6zqBHhLZp7W253Mmflvyp3nXSlbDovWUx4eAnZtFkSoHEC56xnKHdZ714MI1bUzM78CHEP3lDKLqQZej6x2HwV2bLYGYWb+nsb7NpkSrBjNektrBEC1AGvXWhrbRsTGfdRTz4rwpWZBhKref2XmD3p56hxKOiWA17bIL/92GgP6p2bmU32UhfIZ26W3oE5mXgzUZ47s3qSOz9MYfP8NZbC+1zRImflkZh6bmdf39nzNXcBreltANzN/QQkkQBlbe2OLurrs0yyIUHk78Jxq+25gt55BhOr6p1JmS3T5RJVarC8LKcGaxQIx1Xv/6dqhZu/zx2mkZTonM99XDyJUdc3PzA9SApFQPgufbNG2oVD/nH+mWRABIDOv7ccAfTP19F/t/NFRu72V/6ERRLiRMqvlE5SfeV39PQ04LSK6/fzvw7W17Zbr20gD4f+mO6OezPXxzFzQ5nmP9tjvmYtukSgL8lweEZeXWaLjz6RJ8POfw667No4de2xZcFnS2PS85+3GtGmNNb2uuqq3v4E0Vtn/Y9ezzyye9nn2RZ/jqceaT8x87N4rueOKYxbtL7vShqy07vZNy2v0sv+1hFul6++y6tFO+pMu59O46aznXfmvpHFjWX9mI8yk5K4H+Etm9pmephpA7Rpsey6Lz67v6fttpOJ4a237yMx8umlJOIJy811f3ky5ixrgqFbpPDLzHBqz/V/Xou6Oqe6wf0m1e32Tu7nrwYW+Zm7UU/i0vWhxXTUb4sRqdxJlwLuZA2vb7fyH7PgqcNXML2rbiy0wHBFrAG+qdhcA/52ZC9u4bitfy8xHBtquXlySmRe0KLN/bfvzPYNsPXwX6ArarUFJXdSXczLzb3083+p9XpbuKc7+t8X16s+/ISKmtyg/WIP+nPfT82rbPW/87U19Ieuu2QmfATbLzI9k5hcy892UgMMZtbLHtrkwdL0NGzYtJQ2CgYTOqC9qM68f5/Us2zSQUEXXZ2XmrMb/qcaPiRPhxz+GN7yhcez44+Fd7+pcmyQNv622avz99MwzT3LddYO9yURLEvt/7FrwTPf1CxcufJZ7bvhpy/PuvvaUbvsrredA8pLI/tcSbk7X32XVo+1FR6sbzroGil9QpeLo0vVL7xnKegrt2pHGHf7n91Ww5ura9qxmhSq/7uvJiJhCY4AyaZFnPzPvpXXKkFfWtvv7mmZUa02MRk1nI9ScSllXAODtEdEsffU1te3PR8QLBtim4yj9Bk0CFxHxEhoBpysz8+o26v1Ni+dvrm2v2svzO9D4XP8pM29r45rtGGy7emr1/ViashBwl180KwtQBUvqaaO2a1a20ufrqXL8d6VQ6+31vJDGTJMbMvOfLeq7BegKgE2ke2qf4VD/nH87IoZ7MH3t2va9bZTvucD1aZn52Z5Br8x8HNiXxudrGvC+Nuqvt2GdpqWkQTCQ0BmTa9vPNi21uJ5lJ/daapybMAFOPRX23LNx7IQT4JBDmp8jack3bdrqbLhhYwrSP/7xC5555vEOtkgjyf4f256Z2z2F8dyHbmVBG/37xJx/sGB+4z6MZVYc9Lp66gD7X+NcfU2CAwGqu3q77r4+p40FPuu2rG0fERHZ6kH3vOut7lJrtd7ATBp/x96ZmT1n3femVcqQ+mu6sM3XtFftnH7feRcRm0XER/t69LfOHvVPpixaDSUdTa+5zquUN10Lca8G7NakygtoDOiuB1weEddExFcjYveIWLOddmXmvyiL9AJsHBEv7aVYPcDQ7vTQVndz13/o9xyMhe4zZf7e5jXbMdh29dTO96MrbdBdbX63r6xttxo4b+eu+a7X1Nvrqd+B3+56B/1p32CdRklHBSVgeVNE/DUijoyI3SJi5SG+Xj3bSNM0SjU9bw7+UrOCVTqwr9cONftu19WncDa98VgaDBdb7oz6l3vxpK/N9Sy7+DzvcW7CBDj5ZNh778axE0+Egw+GxbN+ShpLttrqACZMaPxau+oqF9kdT+z/se2JOd3/7p7/1ENtnzv/qYeZOLnMbp88ZcUWpTUa2f8azzLzloi4BNgWeGtEfIyyXkLX34ZtpzWqDHYgbdkWz7cKDEyvbTfN099Dq3LD/Zp681+UxWT78tUB1NvldTTuCP99bzn6a06irGUBZRbDmT0LZObCiHgNcDKwU3V4i+rxEYCIuBH4KSU9VF/5kY+jMQvkYOAvXU9UqW+6/hqfRxnYbUefayhkZtaW5+jthtj6Z6BVaq22tbG2Q32UoZ0bdVt9P+q/qNoNENbLrdSibKvXA43X1NvaJMPdvkHJzMcjYifK524bymt4UfUAyIi4qnr+2OrO/8Goj6m2c5Nw/XpzqnVO+vKH2vbmETGpx4LrPdWf88ZjDQsDCZ1Rn5+9TD/O61nWWy1rJkyAk06CffdtHPvRj+CggwwiSOPBVls1UrE++OAt3HHHnzvYGo00+39smz9vDk8/eT9LL1vGVCZMXLrtcydMapRd8Gx/MkpqtLD/JX5ICSSsQhlg7rrj+17gvH7WVR8DOJXuaYva8Ze+nmwjL32rxZoHck79NX2W/v+d/K9+lh8J9bRGK0fEiX2Urd9wuGtErNlkceD/AK+qUg+9GXg5JZDQlVd+Y0q+9g9HxP6ZeUbPOipnUAaHVwHeHBEfyMyuMY4307gT+hdtzjgZaqP5r//+rNswkNcxkq99VLYvM2+KiFmUgNnuwPbAJpSfI0EJMGwDfDQi9qoW8x6o+iyEdm4S/k/VFmjMnOhLvcwESiCmr0BZfV0IbzzWsDCQ0Bn16P6yEbFcm5HQNXrs92cK65gWUWYe7Ldf49hJJ8GBBxpEkMaDddd9GSuv3Jhp693o44v9Pz48dPuFrLFpyUQxdXp76awnTZne7S70Z54YspsUNcLsf41zPwO+Tblz/nM00ricVK2j0B/1u/uvzMyvNy05PB6ubbc7k6DVXcwPUtL6AJyVmVf2VXgoZOaJNBYeHlIRsRawc+1Q18BnOyZSFuv9YrMCmXkpcGl1reWBlwK7UHKyz6AEAn4aEVtn5g29nP9MRPyIMpNhGiV40JWCq57WaCT/Q1b/XLezKO1oVZ9yt0qb59TLPdy01NAYzva1E2Rs60bczExKOq8LAKqURi+jzNzZh/IZXx04JyKe12IGTl/q57Uz2+J6ynoe7eo5mtVqdKvehoG+JqlPrpHQGT3z4rW7uFO93EK6L+wzbkXAD38Ib3tb49jJJ8MBBxhEkMaLrbdu/M2ycOGzXHPNjzrYGo00+398uO/GMxdtLzV1ZZZfo/WYyirr70RE47+7j9z91+FomkaA/a/xrLrb++fVbj0XfH/TGkH3v0W3HXCjBu42YH61vU5ErNDGOZu3eL7Tr2moHUhjlsBA9LoIcm8y87HMPC8zPwg8B/hb9dRk4B19nHpcbftggIh4Ho3Ffmdn5oVtt3jw6gGPF47gdYfa7ZQF1KF8P9oZrN+6tj3cY0S3NLluX1q1r2s2SztrTKzb5jW7ycwHM/PMzHwnZZ2GrllI0ykBtIH6d2177aalGq6oba/VRvl6nQvoHsjpTb3O29qoX+o3Awmd0TOq3+7dBfVyt2Wm87OBY44pQYMup5wC++9vEEEaL5ZaahqbbtpYL++WW87liSf+08EWaSTZ/+PHA7PP46nH7l60v8G2/9tn+ZgwifVf8uFF+wvmz+WBWy8YtvZpeNn/0mJBg0sy86YB1FP/IuwaEasPok39VuWc71qkNYDX91U+ItagrEfQl/prOihqyfSXNFXbD6wd2jkzo50HjUHe50bEy/t77Spg9Y3aoY37KHsTcFG1+9KI2JjOzUYA+CONtEGviIh2b9YcVTLzaboPNu/RV/ko0fJ6mUuGo101f6cRCHx+RGzUV+GI2IBGIGEBjUBV3X3Vvyu1ETjZucXzLVULlB9bO9T0c96Ga2vbfb4XlXNorGMwIyKe36L8jrXtK9uYgVZ/Lde00R6p3wwkdMbldM9X1u4v+e1r238astYswfbaCw45pLG/cCGsthqcey785jftPTZvdX+LpFHt+c9/C0st1Vgjz7Q244v9P34sXPA0t1x0xKL9VTd8Lc/d/lO9lo0Jk9jsNd9j+dW2WHTsjiuPY/48s0Iuqex/jXeZeRFlcd+jqscRfZ/RtJ4bgd9Vu1OB77U78D6EA/Sn1rY/HhF9LXzyaVrfnX8qjTt1twLe325DRmHQYQfKzAAoA6y/78e59YWND25aqn2tblysz0o4BHh7tf0sw5T2qZlq/YeuNR0mAt+P+pS0JUt9au3HI2K5piXhPTQyV9wLnD9srQIy80ng9Nqhpim0Kl+ikbLozMx8pJcy9VRkb2pWUUT8F2WNmKE2mBt064GRLVsVzsyHgLNrhw5rVjYipgAfrh36ZRvt2apJ26Qh4xoJHZCZ8yLiPBqR4z0i4n2ZObfZORGxHY3/UED3H97j1jI9MuRNmACvelX/6vjSl4auPZJGXj2tzRNP/Iebb/51B1ujkWb/jy/3XHcaMzbYldU3KX9nbrDt/7LK+q/k7mtP4ckHb4YJE1huxvNZZ+uDWLa2bsaj917F7Is+16lma4jY/xrvMvNjQ1TVoZQc+VMoA3enR8T/ZOa9vRWOiLWB/Sh3xw76jmDKIPP/o6wBuDElH//bM/Ox2jUD+Cjwbkpe8KYD/pn5eER8Avh+dejrEbEi8OVms/gjYktK6p65QN9TnEZWPQDwk36ugXEqZbFkgD2rMYZHASLim5QB/hN6W/egKrMW8MnaoYt6K1fzC8raHStSgjdd40vnVgP7I+1TwK6UPPq7Uj7X7+mtLRExjfKZviQzrx/ZZrZ0EuU7ugEllc/ZEbF3ZnZb6Cci9gHqa5wcmZnzGX5foCxivBTwpuqz9bHM7ErJRERMpgQR9qwOzQc+36S+XwJd04s/FxF/ysx6CiUi4kWUQFGfQcWI+AnwD+DkzPx3kzKb0j3Y2Opz3pfLKOs+rAi8JCImtLHg/McpAZHJwL4RcSOl7xadV30+T6Ixy+EB4Ht9VVr9zOxKLfY41Too0lAzkNA5x9MIJKwAfAg4so/yn6lt30HjLhJJGrdWWWUT1l77xYv2r7nmR/R/zUEtqez/8enacw5mwsTJrPq83QBYYc1ZrLDmrKblH77zL1z9y7ey8NmnRqqJGkb2vzR4mXl1RBwAnEwZzHoT8JqI+CNwNfAoZWHntSjpdbegDOT3OgA9gOs/FhHvoNyZOxF4AzA7Is6g5IhfkTIQvBllpsEZNAbYe01gm5lHR8QmlAHCCZS/n98bERdQUv7MpfzdvQElh37XXdxHDcVrGgoRMZ0yQNvltCZFe5WZt0TE5cAsymyTtwBHV09PpyzC/JGIuJWSoubflAHHlSgBnV0onweAW4EftLjeUxFxMt2DCLQ6b7hk5j8j4kBKQGUS8EZg5+ozcB0lF//KlM/z9pRAWn8Wvh0R1Y2n+wF/oARFXgHcEhFnATdRvps7Ai+qnXYmLQaah7B910XER4DvVIc+ALwxIs6hzIpYjTJQvn7ttI9l5tVNqjyd0j+bV+deExE/p7zWZYAXU17vQsoMiKZ38VMWUN4bOCIibqBkA7mDMutgFcqsgR1oZGe5lJJuaEAyc0FE/Ap4G+U7Novy3errnJsi4gM0+uuzlIDCrymLhq9L+ZnctWj4fGCfeqC1ia1pLLZ83ggFlTQOGUjokMz8TURcSCOt0acj4qrMPLdn2Yg4EtipdujT9WjvePajH5WHpPFpzpx/csQRo21GukaK/T8+5YJnuOr0fVhz831Z/yUfZVrtzvO6eY/dxW1/+zZ3XnksufDZXstoyWP/S0MjM38aEXdR1l7YEFiaMpC8Sx+nDdmd25l5bjVYejxlYHQG8M4exe6n3HxXnwXRNA1JZn6gGjz8EiUYsTKwTx/NeJrhX5y2P/ajDG5DWay4zwHJJk6lDGZCCb50BRLq4wcbVI9mLgP2rNZMaOU4ut/dfS+w2JjGSMnMn0XEg5TP9TqUgMrrab4Wx6gcbM3Mv0XEDsDPKEGv5SmD1YsVpeT7f2/myK0SmZnfjYh5wDcpiySvB7y3l6JPAh/OzGN7ea6rrvkRsSflZtmuPnt7j2JzKemz7qHvQEL9c75Z9WjmPGDffs766c3JNPpmd1oEEgAy8/sRsQD4GuX925je12q4jxJE+FMb7agHIU9uo7w0IAYSWoiIT9J9el+XpWrb20dEb7c5nZyZh/RyvMs7KRHQlar6zo6IH1OiyQ9SIrgHAi+rnXM2/lCQJEnj3D3XncY9153GtBmbsdyqm7H0sqtDTOCZuXN4/L6refz+0ZapQEPJ/pcGLzMvqRbJ3R3YjXLn72rAcpQBwHsosxAuAn6Vmf8a4uv/NCIuBT4IvIYyiDifMivhLOB7mfmfiHhz7bRHW9R5bPU39duBV1Fyhq9CCZQ8BtxGWSD198CvM/PhIXxJg1VPa9Sv2Qg1PwG+SpnpMSsitsjMaykpok4EXknp540ofb0MZZD2Lsoivz8Hzm53UDozr4+Iv1Z1Apw4BAOzg5KZv4+I5wJvpdwZvw0lUDWJMsOla6Hon2XmdR1raAuZ+fdqMeMDKLN2tqR8lucBd1MWmD4+M69qWsnwtu/4ahbCuykByOdS7sp/lDKj5Tzg6HbSXGXmzRGxOWVNgDdQAl0TgDurer6bmbMj4hUtqnotZbbJjpSZR8+lfM6XosxIuYOydsCPM/MP/Xi5ffk9MLu61j4R8fE20ht1/az6DWWR8t2AmZRZUw9Rgra/Ao6r1qXoU5XWqCtoeifwmwG8DqktMYJByyVSRBxO97RC/fGjzDygRf3bUf6TtFJf5Sp/AF7X11oKvV9jVpYZXRpv/HqPb96pLY0/L64tPC1pfHjfUfO4+a4F4/6X/vPWnpjf+Z+pnW7GYnb5+JNXZGbz/FtqqUpN0zVDf+PMvKmT7VF3ETGbMvCbwPMyc3aHmySNqIj4H+C71e5rMnNEB/Ij4pU00p8fmplfHcnra3xZUlexHzMy82LKdKsfU6ZW9uYuSmT2Vf0NIkiSJEmStCSq1g14abX7BGW9A40SEbE9jTRJFxpE0Dh1PGWWCJSxu5HWdc0HaKQzk4aFqY1ayMzDgcOH+Rr/oSyusgJlIZ21KdNJ76NMu7t0JPPdSZIkSZI0CnyCkn4H4Kx2UoZoRP1PbfuYjrVC6qBq4fHPUr4DO0XErMwckbQgEbEFJTUcwOfbXNtEGjADCaNIZj5KSXMkSZIkSdKYFBGrAJ8Cvp6Zt/fy/FLA/wM+Uh1K4Dsj10K1EhEvBvasdu8CftnB5kid9gPKehFbA1+krNEyEr5Y/XsD8L0RuqbGMQMJkiRJkiRpJE0C3g+8LyL+DlwG3FsdXx/YGVijVv5rmfm3EW+lFomI5YD9KQs5b0hZBLgrXfZnM/OZDjVN6rjMXBgRBwJvAoiIlTLzoeG8ZkQsD3T9/DwnM58dzutJYCBBkiRJkiR1RgAvqh69WQB8GfjkiLVIzaxM77NCzqXcjS2Na5l5DXDNCF7vMeCIkbqeBAYSJEmSJEnSyLqPEjzYGdgBWAuYAUwDHgFuA/4I/CAzXWB59HkCuBU4Afi+azpK0vhgIEGSJEmSJI2YauD579Xjcx1ujtqQmbdRZpBIksapCa2LSJIkSZIkSZKk8cpAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJaspAgiRJkiRJkiRJampSpxsgSZIkSdJgzLt7Idd9/MlON0OSJGnMckaCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqykCCJEmSJEmSJElqalKnG6Dh9wKu4HKi081QB5z/xWU73QR10Gc+k51ugjrk/C9O63QTJEmSJEnSGOKMBEmSJEmSJEmS1JSBBEmSJEmSpBEWETMjIqvHnzrdnoGKiD/VXsfMTrenmYiYEhGzq3b+aISuuV91vWcjYsuRuKYkDRcDCZIkSZIkdVBE3FYbiM2IeDQilunH+R/qcX5GxLuHs83SEuhQYAPgaeBTzQr18l1q9XhvH9c8DbgKmAgcNaSvRpJGmIEESZIkSZJGl+WBPftR/sDhaog0FkTEqsDHqt0fZuYdI3HdzEzg89XuthHxppG4riQNBxdbliRJkiRp9EggKMGBk1oVjoj/AjavdhfiDYNSbw4DplG+I19t85yHgS+0Ue7iFs+fAdwEbAR8PiLOrAIMkrREMZAgSZIkSdLo8QfglcDLI+I5mfmvFuUPqv5dCPwJ2HEY26YhlJm3UYJGGkYRsSJwSLV7XhvfqS6PZWa7QYemMjMj4ljga8CmwG7AOYOtV5JGmncqSJIkSZI0evyw+jeAA/oqGBFTgH2q3d8Bdw5fs6Ql1sHAstX2D/sqOIxOAp6ttj/QoTZI0qAYSJAkSZIkafS4Erim2t4/Ivr6u30PYHq1fUJ/LxQRUyPiPRFxTkTcERHzIuKxiLgxIo6p0ia1qmPRQtG1Y6+OiFMiYnZEPFk9f0Av564TEV+NiH9U5R6OiKsi4tNVTnsi4vDaoraL1dFLnWtW518cEfdGxDMR8WBEXB4RX4iItfv3LrW83oYR8eWI+FtEPBQR86t/b4qIiyLiSxGxY0Qs1cu5M2uv7U9N6j+gVubw6tjyEfGRiPh79drmRcStEXFcRGzUj7YP+fvf5nWfFxFfjIjLIuL+qo/ur/rsExGx0lBcp+aA6t8ngXOHuO62ZOYcyowhgB0jYp1OtEOSBsPURpIkSZIkjS4nAN8E1gV2An7bpFzXIssPU/Kw79LuBSLiNcBxwJo9nppCyeW+EfDOiDgeeE9mzm+jzqWAHwBva6PsnpTXOa12eBlgq+rxnqpM2yLiMOBTwNQeT61UPV4AfCgiPpyZ3+9P3U2u90HgK8DkHk+tWD2eB7wM+F/gdcCvhuCaWwKnAxv0eOo51ePtEbFvZp7eop4hf/9biYhJwP8B72Xx8agZ1WNb4NCIOCAzzxyCaz4f2Kza/UNmzhtsnYNwDuX7HMCbKamOJGmJYSBBkiRJkqTR5RTKAPVSlGDBYoGEiFiPxnoIp2Xm0xHtpduPiLdRBpEnVodmU1Ij3UUZJ9gSeG11/YMpg+J7tFH1tyhBhKcod35fXx3fHHi6dv1dgB/TGJN4ADgTuL261i6Uwd+zquPtvKZjaeTBB7i0eswBlgO2owzqTwG+FxGTM/Pb7dTd5HqvB75RO3Q18GfgP9X+KtVreCndB+sHYy3gPGB1ynt7AeW9W4vSP6tT+uzkiLg6M29t0vYhf/9biYiJwNnArtWhrjU9LgMeAVamrA2yNbACcHpE7DEEwYTX1Lb/1M9zV4qIXwPbVO17ktK/fwFOz8z+zm74Y492GUiQtEQxkCBJkiRJ0iiSmQ9GxDmUweE3RsSKmflwj2IH0liot+20RhGxBXAsJYgwlzL4/uPMzB7l1qfMctgS2D0iDsrMVvnl301JzfSmzLyjyfWXo8xa6BqPOAt4e2Y+VitzKHAo8GUai0n39ZoOoRFEmA3sm5mX9VLulcDPKYPlX42I32bmja3qb+Kjte0DMvNHTdq2FGU2wu0DvE7dOyh59t+Zmcf1uM5hlCDDSykzMj4GvKuX9gz5+9+mz9IIIlwGvDUzb+6lffsAP6IERE6IiIsy86FBXPflte3FPhMtLEf3QMT06rExcFBEXE75rN3SZn03UL5zywAvjYhJmflsi3MkadRwjQRJkiRJkkafrkH7KcBb6k9EmXqwf7V7bWZe0Y96v1jVCfCWzDytZxABIDP/TRlEfaI6dFi0nvLwELBrsyBC5QDKHfQANwF71wexq2tnZn4FOIZGsKRXETEVOLLafRTYsbcgQlXv72m8b5Mpg+UDtXX17/XNggjVNZ/JzNMz87pBXKvusJ5BhOo6j1Nmj3TZvcn5BzCE7387ImJNGoGX24CdegsiVNf+Sa3sdOB/Bnn5WbXt/vTBQuBvwNHAp4FPAt8H/tmj7suq9EktZeZCSjABynewrfMkabQwkCBJkiRJ0uhzPnBPtd3zrvBXAjOr7f7MRphJ467wv2Tm2X2Vz8x7KClwAJ4LbNriEt/PzPtblHlrbfvIzHy6aUk4AljQor43U3LrAxyVmXf2VTgzzwG67iB/XYu6+9KVFqrnegzD6QGgaTqmanZF10D1Kk0Wlh7q978dh1BmGAB8oWfgohfH0AhgvX6gF42I5YFVq93HMvORNk/9f8AamfnizHxPZn4uM4/MzP/OzE2BN1D6AkoaprMjYkrT2rqrz0zZsM1zJGlUMJAgSZIkSdLwWCUiLq893tnuiZm5ADip2n1BlZKoS1dg4RnKegrt2pHGHebnt3nO1bXtWc0KVX7d15PVYGvXnfxJyZnfVGbeS+t0NK+sbff3Nc2o1poYiGuqfzeIiK9ExDIDrKc/fp+Zz7QoU7/Tf9X6E8P0/rejX31UvcauO/+3rtZXGIh1atv3tntSZn65r4BYFYDbibJmAsD69JJGqol6O9ZpWkqSRiEDCZIkSZIkDY85mTmr9ji2n+fX1yQ4ECAipgNvqo6dk5lz+lHflrXtIyIiWz2Ao2rnzKBvrdYbmElJKQRwZ2Y+2kabW6Wjqb+mC9t8TXvVzmn1mpr5Sm37UOA/EfGLiHh/RLwoIiY3O3EQ2lln4fHads9Fnmcy9O9/O+p9dHubffRfVfmJlDUtBmL52vbcAdbRq8y8lrK4eJc3t3nqk7Xt5YauRZI0/AwkSJIkSZI0ClWLuF5S7b61Gpx+C401DtpOa1RZeZBNWrbF860GpqfXth9s85qtyg33a+pVZp5BWfy46zUvR1kc+1vAX4GHI+L0iNi1SRUD8VQ7Tatt9xzzmV7bHqr3v08RMYnuA/oDMaA+orGgNJRFqofaz2rbL4yIdsbY6u0YjmCTJA2bSa2LSJIkSZKkDvkhsC2wCiWnf1dao3uB8/pZV30M4FS6py1qx1/6erJaTLYvA1m4t9U59df0Wbrfkd+Of/Wz/CKZeXxEnA7sA+xCo5+gDH7vDuweEb8B3pyZT/Re04gZjve/lXr/LKCsP9BfDw/w2vVZCO2uYdAfs2vbkyhBrQealO1SX1PjyaalJGkUMpAgSZIkSdLo9TPKArvLAp+jseDxSdU6Cv1Rv7v8ysz8+hC0rz/qA8LtziRYqcXzDwKrVdtnZeaV/W7VIFQL+B4NHB0RAWwMvIIyO6FrbYBdKQsI7zeSbevFcLz/fcrMpyJiLrAMJU3RDzPzocHU2Q/1Qf1BvY4mssV+b+rtaBV0kKRRxdRGkiRJkiSNUtVd7D+vdjetPdXftEbQfQ2DbQfcqIG7DZhfba8TESu0cc7mLZ7v9GtaJIt/Zub3M3MnSt78rsHlfSJiOAaz++M2hv79b0en+uhuGq939UEs2tzMc2vbC4B2AiRr1bZvG9LWSNIwM5AgSZIkSdLo1jNocElm3jSAei6obe8aEasPok39lplPAVdVuwG8vq/yEbEGjUV3m6m/poOqWQGjQmb+HOjqpwnAhh1sznC9/+3o1kdDUF9bqhk7N1S7E+k+8D8U6ot2X9ZGai8oM1a6XDvE7ZGkYWUgQZIkSZKkUSwzLwL+DziqehwxwHpuBH5X7U4FvtfuwPsQDtCfWtv+eEQs3UfZT1MGgFvV13Un+FbA+9ttSAeCDvNG+Hq9Ger3vx1H05gZ8MaIeGO7Jw5BH/2ttr3lIOtaJCI2Az5YO/TzJkXr56wIrFvt/jsz7x+q9kjSSDCQIEmSJEnSKJeZH8vM91aPC1qf0dShwFPV9puA06s7z3sVEWtHxP/S/4WdmzmRslA0lLuzfxoRy/e4ZkTEocC7aZF3PjMfBz5RO/T1iDg8IqY2OycitoyI7wBfGkD7iYj1IuLiiNg7IpZtUiYi4v007kCfA/xzINcbYicyhO9/OzLzNqC+HsdPIuJ/IqLXdTur6780Ik6iH4GhJn5b296uVeGIuDwi9oqIposzR8RrgT8A06pDdwDfb6Mt2zdplyQtEVxsWZIkSZKkcSIzr46IA4CTgcmUYMJrIuKPwNXAo5SFndcCtgG2oKTBuaG3+gZw/cci4h3A2ZS73d8AzI6IM4DbgRUpixNvRplpcAZwcNfpTeo8OiI2oQw6TwA+A7w3Ii4AbgHmAisAGwAvBNarTj1qgC8jKLn+twXmRcTfgOuA+6vrrwHsDKxfO+dTmTm/Z0UjbTje/zZ9AtgIeCOwNPBdyoyIC4B/A88A06syLwK60m4NNv3PbykzQaYCO7VR/gWUBc6fiIi/AtdTFvReCKwJ7ED3tUoeBV6fme3MNnllbfvsNspL0qhiIEGSJEmSpHEkM38aEXdR1l7YkDKwu0v1aOb6Ibz+uRGxH3A8JWgxA3hnj2L3A3tQBuS7NB2szcwPRMQNlFkGKwIrA/v00YyngZv733qgLKy7kBI0mAq8onr05ing45l59ACvNeSG4/1v45oLImIPSkDh/wHLUAbm9+/jtCcY5ILEmflEFSTZF9gkIjauUny1Mo0SeOgr+PAXYP/MnN2qsipF05uq3ftxRoKkJZCBBEmSJEmSxpnMvCQiNgZ2B3YDXgysBiwHPAncQ5mFcBHwq8z81xBf/6cRcSklz/xrgHUoefRvB84CvpeZ/4mIN9dOe7RFncdGxI+BtwOvoqyZsAolUPIYZVD6WuD3wK8z8+EBtv3OKh3ULsDLKLn3Z1LuqE/gEeDG6jonZOadA7nOcBqO97+Nay4EPhcRRwMHUu7Q35QS9JlY1f8vysyYC4DzMvPJwVyzcgwlkED176f7KLs15bvw4qptM2h8hh6hvD9/BX6WmX/uRxteBqxdbf8wM5/tx7mSNCpE5qDT3WmUmxWRl3e6EeqI87/Qa7pOjRM7H/ZEp5ugDjn/i9NaF5IkjQnvO2oeN9+1YKQXjB111onID3S6Eb04FK7IzFmdbseSrEp903VX+MaZeVMn2zPejJX3PyIuA2ZRAmTrjfRAfkScDLyVEqyZmZn3jOT1JWkouNiyJEmSJEkadSJiOvDSavcJynoHGiFj7P3/bPXvmsDeI3nhiFgL6JrZ8QODCJKWVAYSJEmSJEnSaPQJSi59gLOq1DgaOWPm/c/McyhrGgD8b7VmwUj5MLAUZdHvz43gdSVpSBlIkCRJkiRJIyYiVomIb0XEek2eXyoiPg18pDqUwHdGrIFj3Dh+/99PWSR7cxprJgyriFgH+O9q9/OZee9IXFeShoOLLUuSJEmSpJE0iTKo+76I+DtwGXBvdXx9YGdgjVr5r2Xm30a8lWPXuHz/M/OKiDiI8hqXHqHLrg98GXgW+NoIXVOShoWBBEmSJEmS1AkBvKh69GYBZRD2kyPWovFl3L3/mfmjEb7eRcBFI3lNSRouBhIkSZIkSdJIuo8yeL0zsAOwFjADmAY8AtwG/JGyMO2SvMDvaOX7L0nqNwMJkiRJkiRpxGRmAn+vHi4+O8J8/yVJA+Fiy5IkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqSkDCZIkSZIkSZIkqalJnW6ANCJOOQX226/7sZkz4fbbO9IcjZzNX388a262d7djF35vU5569I4OtUiSJEmSJElashhI0Ni3226LBxE0Lsx47q6LBREkjUXBSuttz2obvZ7pa72YpaetzuQp05n/1MM8/cR9zH34Vh6642IevuNinpjzj043VkPKvh/f7H9JkiRppBhI0Ng2fTocc0ynW6EOmDRlOpvu8u1ON0PSMFt+9a3ZdOdvsMKasxZ7bulpq7P0tNVZfvUtWX2T3QH47ZdWIHPBSDdTw8C+H9/sf0mSJGlkGUjQ2PbNb8Kaa5bt88+HnXfuaHM0cjbe6ctMWW4NAOb863es8pydOtwiSUNtjefvw/NfezQTJjT+O7Ng/lzmPnQrz8x7kAmTpjB1hfUW/SzQ2GHfj2/2v3qzGvDRTjeiF4d2ugGSJElDxEBCGyJiCrAtsAOwDbAJMAOYDDwK3A78FfhZZv55gNfYGHg7sDOwDrA88B/gJuDnwE8z8/HBvZJxZtddYf/9y/avfgW/+IWBhHFilQ1ezVqbl3RW98/+DffdeKaBBGmMWWPTvdj8tccQEyYC8ORDs7nlws/ywOxzWfjsU93KLj1tDWY8dxfW3nJ/kuxEczWE7Pvxzf6XJEmSOsNAQh8iYjXgm8BuwLQmxWZUj1nAeyPir8BBmfnPNq8xCfgMcBgwscfT61WPVwOfiogDMvOP/X0d49Lyy8Oxx5btxx6D97wHXvnKzrZJI2LS0suz2S7fAeDZpx/jn+d9kJVmvqKzjZI0pKYsvw6b7vLtRQOJD/77j1z5izez8Nl5vZZ/+ol7uevqE7jr6hNGspkaBvb9+Gb/S5IkSZ0zodMNGOXWAfZh8SDCXcDfgD8CN/d47sXAZRGxXZvXOB74JI0gQgL/AC4C7qyVWxf4bUS8uu3Wj2df/zqsvXbZ/n//D+66q7Pt0YjZ6JVfZMryawFw858+w1OP393hFkkaapvu8k0mLb0cAPMevYOrTt+n6UCixhb7fnyz/yVJkqTOMZDQvkuAQ4B1MnOdzHxxZu6YmRsBzwF+XCu7LHBWRKzSV4UR8WFKOqMuFwEbZ+ZmmfnyzFwXeBVwT/X8JODnEbHeEL2msenVr4aDDy7bf/4zfP/7nW2PRszK67+Stbcs6awevvMv3HnlcR1ukaShtuzKGzFjg0aaupv/+GkWzH+ygy3SSLHvxzf7X5IkSeosAwl9WwicCWydmdtl5g8yc7Fb2zPz35m5L/D12uGVKOmKehURKwOfrh26Cnh1Znab4ZCZvwO2B56oDi0PfG4Ar2V8mDYNjqsGj596Ct7xjs62RyNm4lLT2GzX7wKw4NmnuP7c/+lwiyQNh3W2PnjR9tNPPsB9N53ZucZoRNn345v9L0mSJHWWgYQ+ZOaVmfmmzLy6zVMOo6Q96rJnH2XfC6xQ239XZj7dpB230j14sF9EzGyzTePLV78K665btj/7Wbi5Z+YpjVUb7fgFpq6wDgC3Xvwl5j50S4dbJGk41BdOn/OvC8iFz3awNRpJ9v34Zv9LkiRJneViy0MoM5+JiN9QUiABrBsRy2Tm3F6K71Xb/ntmXtai+h8ARwBTKAGgPYCvDbbNY8qOO8K73lW2r74avvKVjjZHI2el9V7BOlsfBMBj913LbX/9RodbJGk4TJoynWVWeu6i/Ufu/hsAS09bg7W2fDurbvhapk5fj4mTl2X+vId4Ys6NPPjv33P3NScx/6mHO9VsDQH7fnyz/yVJkqTOM5Aw9B7ssb880C2QEBHPATarHfpVq0oz86GIuBTYoTr0egwkNCy7LPzgB2X72WdLSqMFCzrbJo2IiZOXZbPXlJRGCxc+yw3n/g+Z9r00Fi236uZENCZTzn3wFtba4m1svNOXmbT08t3KTlxuTaYstyarrL8jz3npx7j5j5/irqt/ONJN1hCx78c3+1+SJEnqPAMJQ29mbXshMKeXMlv32L+kzbovoRFI2KpfrRrrvvxlWH/9sv2Nb8AVV3S2PRoxz9vhcywzfSYAt//9uzz2n6s62yBJw2apqSt325/x3F2Y+aL3L9p/+on7ePKh2cSECUxbeSMmT10JgMlTVmCzXb/NlOXXYfZFR4xomzU07Pvxzf6XJEmSOs9AwhCKiKnArrVDl2VmbwlcN+2x324y93q55SNi7d4Wfx53Xv5yeM97yvbs2fDpT/ddXmPGiuu+jHW2KQtqz334Vmb/+fMdbpGk4TRpygrd9rsGEuc9eif/OP+DzLn1/EXPRUxk9U33ZJNXf43JU6YDsMG2h/LYf67k/pvPGbE2a2jY9+Ob/S9JkiR1nostD633030B5ZOblJtZ214A3NNm/bf3Uc/4NHUqHH88TKg+yoccAk891dk2aURMmDSV57/mqEWpDm44930sfNa+l8ayiZOmLHbsmbkP8vdTXtVtIBEgcwH33vBTLv/J61n47NOLjj9vh89C+N+fJY19P77Z/5IkSVLn+b/pIRIRzwcOrx26FTiuSfF6MtfHs/2E7o/22F+uj/a8MyIuj4jLH2iz8iXSl74EG2xQto87Dv70p442RyPneTt8lmVWfA4Ad119Ag/dcVGHWyRpuD37zJOLHZt90ed46rHmk/Meu/dK7rjimEX7y660ISutu/2wtE/Dx74f3+x/SZIkqfMMJAyBiFgZOAPoul1qAXBAZj7T5JRpte15/bhUz7JNAwmZeWxmzsrMWTP6cYElyiabwHvfW7bvuQcOPbSz7dGIWXbljVn3Be8C4KnH7+WmP3yywy2SNBIWPPNEt/2FC5/lnht+2vK8u689pdv+Sus5mLikse/HN/tfkiRJ6jzXSBikal2Es4Dn1g5/IjMv7uO0ybXt3tZQaKZn2cm9lhovVl21kdJozTXhkUf6d/5tt3Xf7lqsWaPeUsvOWJTSaMpya/DKD9/dr/Nf/t//WLQ975Hbuej7mw1p+yQNj2fmzum2P/ehW1nwzOMtz3tizj9YMH8eEydPBWCZFf15v6Sx78c3+1+SJEnqPGckDEJELAX8Eti2dvi7mfnlFqfW52cvnvS1uZ5lF5/nLUnSGPXEnBu77c9/6qG2z53/1MOLtidPWXHI2qSRYd+Pb/a/JEmS1HnOSBigiJgM/BzYpXb4OMqCy63U52cv04/L9izb+lassWz+fJgzp3W5LksvDcvVskE99BAsXNjY1hIjF8znmbkPtl1+wsSlmLR0o+/nz3uIzCzbtQEGSaPb/HlzePrJ+1l62VUBmDBx6bbPnTCpUXbBs/3JKqjRwL4f3+x/SZIkqfMMJAxAREwCfgy8vnb4h8C7smt0sm/19Y+XjYjlMrOdoMAaPfb7MYo+Bv3lLzCjHytA7L8/nHhiY3+bbeD224e8WRp+j9z9V/74rfXaLr/m5vux+W6NBRf/csJ2PPXoHcPRNEnD7KHbL2SNTfcCYOr09n4OTJoyvdudyM88cf+wtE3Dy74f3+x/SZIkqbNMbdRPETEROBXYo3b4ROCQNoMIADf22G93RLRebiFwc5vnSZI0Jtx345mLtpeaujLLr7FNy3NWWX+nReuqQAlGaslj349v9r8kSZLUWQYS+qEKIpwMvLl2+EfAwZm5sB9V3dBjv/VfQouXuy0znZ8tSRpXHph9Hk891lhgfYNt/7fP8jFhEuu/5MOL9hfMn8sDt14wbO3T8LHvxzf7X5IkSeosAwltqoIIJwFvqR0+CTion0EEgMvpvlDyy9s8b/va9p/6eU1JkpZ4Cxc8zS0XHbFof9UNX8tzt/9Ur2VjwiQ2e833WH61LRYdu+PK45g/b3xnBlxS2ffjm/0vSZIkdZZrJLQhypzoE4F9a4dPBg4cQBCBzJwXEefRSI+0R0S8LzPn9tGG7YDn1A6d3t/rSpI0Ftxz3WnM2GBXVt/kTUC5M3mV9V/J3deewpMP3gwTJrDcjOezztYHsezKz1t03qP3XsXsiz7XqWZrCNj345v9L0mSJHWOgYQWqiDCD4G31g6fAhwwkCBCzfE0AgkrAB8Cjuyj/Gdq23cAvxvEtSVJWqJde87BTJg4mVWftxsAK6w5ixXWnNW0/MN3/oWrf/lWFj771Eg1UcPEvh/f7H9JkiSpM6L99YHHn4gI4FjgHbXDpwJvH2QQoav+P9FIa/QM8KbMPLeXckcCH68dOiAzf9TudWZF5OWDaaiWWOd/YdlON0EdtPNhT3S6CeqQ8784rdNNGDFrbr4v67/ko0yr3X1cN++xu7jtb9/mziuPJRc+O8Kt03Cy78c3+7/hfUfN4+a7FkSn29Fpo/VvnoArMrN5tEuSJGkJ4YyEvu1F9yBCAqsB55YYQ1s+lpnXNnnuncClwErAUsDZEfFj4EzgQWB94EDgZbVzzqakVZIkady757rTuOe605g2YzOWW3Uzll52dYgJPDN3Do/fdzWP3399p5uoYWLfj2/2vyRJkjSyDCT0bZke+wHs1M86vtTsicy8OSLeAJxFCSZMpKRQemuTU/4AvGUoZkNIkjSWPPHADTzxwA2dboY6wL4f3+x/SZIkaWRM6HQDxrvMvBjYDPgx8HSTYncBHwZe1deCzJIkSZIkSZIkDTVnJPQhM08EThyB6/wH2DciVgBeAawNLAfcB9wEXJouZiFJkiRJ0qgVEX8GtgMuysyXtyo/BNfbFri42n1dZv5quK8pSRq/nJEwimTmo5l5VmYelZlfyswTMvMvBhEkSZIkaeyKiNsiImuPRyOiZ6rdvs7/UI/zMyLePZxtVncR8TZKEAHgYwOs48s9+vBPfZXPzEsoqZIBvhURSw/kupIktcNAgiRJkiRJo8vywJ79KH/gcDVErVUD+EdWu7/JzL8NoI4XAh8ZwOU/W/37HOC/B3C+JEltMZAgSZIkSdLo0TUjva3gQET8F7B5tbtwWFqkVt4NrFNtf7m/J1eBiBOAicAz/Tk3M68ELqh2D4uIZft7fUmS2mEgQZIkSZKk0eMP1b8vj4jntFH+oOrfhcCfhqVFaioiJgEfrnb/kZkXDqCaw4FNgaeArw3g/KOrf2fQ+DxIkjSkDCRIkiRJkjR6/LD6N4AD+ioYEVOAfard3wF3Dl+z1MQbgXWr7R/2Ua5XETELOLTa/Sxw8wDacDYwp9p+/wDOlySpJQMJkiRJkiSNHlcC11Tb+0dEX3+37wFMr7ZP6O+FImJqRLwnIs6JiDsiYl5EPBYRN0bEMVXapFZ1LFoounbs1RFxSkTMjognq+cP6OXcdSLiqxHxj6rcwxFxVUR8OiJWrcocXlt8eLE6eqlzzer8iyPi3oh4JiIejIjLI+ILEbF2/96lluopqH7RnxMjYikaKY2uBv5vIA3IzGeBM6vd50bEdn0UlyRpQAwkSJIkSZI0unQFBdYFduqjXNcg9sPAGf25QES8BpgNfA/YjZLjfwqwHLAR8E7g7xHxg4iY3GadS0XEScD5wH7ABsAyTcruCfyDssDwJlW56cBWwBHANRGxbT9f02HVazoC2BZYHZgMrAS8ADgMuCUi3tOfevu43nTg1dXudZl5ez+r+DTwfOBZ4OAqIDBQ59S29x5EPZIk9WpSpxsgSZIkSZK6OQX4CrAUJVjw254FImI9YMdq97TMfDoi2qo8It5G4054KIPvvwPuoowTbAm8trr+wcCKlNkPrXwLeBsl1/+5wPXV8c2Bp2vX3wX4MY0xiQcod9TfXl1rF2Az4Cwad9q3ek3HAofUDl1aPeZQgiPbAS+jBEu+FxGTM/Pb7dTdh1fXXsOf+nNiRGwD/G+1+7Vq0eTBuIiyTsYE4DXA+wZZnyRJ3RhIkCRJkiRpFMnMByPiHMrg/RsjYsXMfLhHsQMp6yhAP9IaRcQWwLGUIMJcyuD7jzMze5RbnzLLYUtg94g4KDNbrQHwbkpqpjdl5h1Nrr8c8AMa4xFnAW/PzMdqZQ6lrBvwZdpYPDgiDqERRJgN7JuZl/VS7pXAzynBiq9GxG8z88ZW9ffh5bXtxa7XR3snU/psEnALZbHlQcnMRyLiFspskudExFqZefdg65UkqYupjSRJkiRJGn26Bu2nAG+pPxFl6sH+1e61mXlFP+r9YlUnwFsy87SeQQSAzPw35c72J6pDh0XrKQ8PAbs2CyJUDgDWqrZvAvauBxGqa2dmfgU4hkawpFcRMRU4stp9FNixtyBCVe/vabxvk2kscjxQs2rb1/XjvE8BWwAJHJKZTw2yHV2urW23XN9CkqT+MJAgSZIkSdLwWKVa5Lfr8c5+nHs+cE+13fOu/FcCM6vt/sxGmAnsWu3+JTPP7qt8Zt5DSUEE8Fxg0xaX+H5m3t+izFtr20dm5tNNS5a1Dha0qO/NwIxq+6jMvLOvwpl5DmUWAMDrWtTdyvNq222tjxARWwH/r9o9NjMvHGQb6upt2HAI65UkyUCCJEmSJEnDZE5mzqo9jm33xMxcAJxU7b6gSknUpSuw8AxlPYV27UjjDv/z2zzn6tr2rGaFKr/u68mImAJsXe0m0CqQcS+tUwa9srbd39c0o1prot8iYlnK4tAAT/eSeqq3cyYDJ1JmQ9wNfGwg1+7DvbXtdYa4bknSOGcgQZIkSZKk0am+JsGBABExHXhTdeyczJzTj/q2rG0fERHZ6gEcVTtnBn1rtd7ATMogOsCdmfloG21ulTKo/poubPM17VU7p9Vramb52vbcNs/5eK297+mZ0mkIPFnbXm6I65YkjXMGEiRJkiRJGoUy8xbgkmr3rdUd7W+hscZB22mNKisPsknLtni+VWBgem37wTav2arccL+mZibVtp9tVbiaUfKJavcnVYqloVZvx+SmpSRJGoBJrYtIkiRJkqQO+SGwLbAKJad/V1qje4Hz+llXfQzgVLqnLWrHX/p6MjMXtji/1WLNAzmn/po+Czzez/r/1c/yXeqzEKY0LdVwFGVw/0HgAwO8ZitTa9tPNi0lSdIAGEiQJEmSJGn0+hnwbcqd85+jseDxSdU6Cv1Rv7v/ysz8+hC0rz/q6wi0O5NgpRbPPwisVm2flZlX9rtVA/MIMJ8SHJgWEUtl5jN9lO9as2Bl4L6ItmIqL69SMXVZPzNv66N8/b16oJ0LSJLULlMbSZIkSZI0SmXmE8DPq91Na0/1N60RdF/DYNsBN2rgbqMMvgOsExErtHHO5i2e78hrqoI4d1a7Aaw5Utfuw1q17ds61QhJ0tjkjARJkiRJkka3E4ADavuXZOZNA6jngtr2rhGxemb+Z1At64fMfCoirgJeSBl8fz1wcrPyEbEG8F8tqr0A2L3aPigivpuZ2dcJQ+ha4DnV9kb0PXj/C0p6qlaeSyMgch/d01c90eLcjWvb17RxLUmS2mYgQZIkSZKkUSwzL4qI/wOWqQ6dNcB6boyI3wE7UfLpfy8i9mhn4D0iYogG6E+lBBIAPh4RP8vMp5uU/TQwsY36jqSk9dkKeD/wrXYaMgSv6W/AG6vtLYHzmxXMzI+22aYDaAQSbszMA9o8L4Atqt2nKEEOSZKGjKmNJEmSJEka5TLzY5n53upxQeszmjqUMtAM8Cbg9OrO/15FxNoR8b/0f2HnZk6kLBQN5Q76n0bE8j2uGRFxKPBuoM+B/sx8HPhE7dDXI+LwiJja7JyI2DIivgN8aQDtr/ttbXu7QdY1WFsA06vtC/sIzkiSNCDOSJAkSZIkaZzIzKuru95PpiwU/CbgNRHxR+Bq4FHKws5rAdtQBqgDuGGIrv9YRLwDOJsy2+ANwOyIOAO4HVgR2BXYDHgIOAM4uOv0JnUeHRGbUGYjTAA+A7w3Ii4AbgHmAisAG1BmQ6xXnXrUIF/OVcAdwLrA9hExOTPntzhnuLyytn12h9ogSRrDDCRIkiRJkjSOZOZPI+IuytoLGwJLA7tUj2auH8LrnxsR+wHHU4IWM4B39ih2P7AHsHPt2Lw+6vxARNxAmWWwIrAysE8fzXgauLn/re92zYyIU4CPUwIVr2ToZm70V9c6EfOBn3aoDZKkMczURpIkSZIkjTOZeQkltdBewI+Am4BHgAXAY8CNwOnAB4ANMrOvQfmBXP+nwKbAN6prz6XMhrgW+BywZWZeTBmg7/JoizqPpcw2eC9lHYnbgSeBZymzG66kpFZ6G7BGZn57CF7KDyjvGcC+Q1Bfv0XEesBLq93TM/PBTrRDkjS2OSNBkiRJkqQOysyZQ1TPAcAB/Si/EPhF9RjoNWcO4tw7gA9Xj2Y2qW3f1kadj1NSFg02bVFbMvPfVVqmPYG9IuKDmfnQIOo7kRLs6I93UNJPAXxtoNeWJKkvzkiQJEmSJEmjTkRMp3Gn/ROU9Q5Go89R1m+YArxnJC9cLSr9rmr3N5l5+UheX5I0fhhIkCRJkiRJo9EngGWq7bOqGRSjTmZeC/yk2v1ARCzTV/kh9g7KGhMLKe+XJEnDwkCCJOn/s3ffYZJV1cLG3zXMwJBzzkGiqCgqKiJJQDAjwsUAomJOiAGvIIIiejFc/QyAGFBBTBdFEUQQMBAkiYKIgOQ45IEZJq3vj3166nRNVXV1rO7p9/c89cypU7v22VWnqmZmr7PXkiRJksZMRKwWEf9b5fZv9fiSEXEU8OFqVwJfG7MBDs1HKfUYVgc+NBYHjIjlaAQPvp2ZV4/FcSVJk5M1EiRJkiRJ0liaCrwfeF9EXA78Fbin2r8xsCewdq39FzPzsjEf5SBk5p0R8QZgO0rh6LGwCfCtanu8B1okSROcgQRJkiRJktQLATy/urUyH/g88MkxG9EwZOYvgV+O4fGuBa4dq+NJkiY3AwmSJEmSJGks3UcJHuwJ7AKsS0kJtBzwCHAr8AdKup7xWmBZkqRJZdiBhIhYDXgGsBowh/IPgqszc/Zw+5YkSZIkSYuXzEzg8up2bI+HI0mSujDkQEJEvAz4FLA9ZTli3VMRcRZwVGb+axjjkyRJkiRJkiRJPTRlKE+KiC8AvwaeW/URTbfpwOuAqyLiFSMzVEmSJEmSJEmSNNYGHUiIiHcDh9NYhZAtmvXtWxo4IyK2HNrwJEmSJEmSJElSLw0qtVFELA8cTyNQEMDfKKsT7gCmAVsBrwXWqtpNB74C7DUiI5YkSZIkSZIkSWNmsDUS3gAsRwkQLADenZknNzeKiA8DJwJvrnbtHhEbZuZtwxmsJEmSJEmSJEkaW4NNbbRLbftzrYIIAJn5FPAW4JJqVzQ9V5IkSZIkSZIkTQCDDSQ8q/pzHnBCp4aZmU1tthvksSRJkiRJkiRJUo8NNpCwOiWt0fWZ+VgX7f9c215tkMeSJEmSJEmSJEk9NthAwgrVn/d32X5Gi+dKkiRJkiRJkqQJYrCBhL7287tpnJkLaneXGOSxJEmSJEmSJElSjw02kCBJkiRJkiRJkiYRAwmSJEmSJEmSJKktAwmSJEmSJEmSJKmtqUN83ksi4pZRek5m5qZDGZQkSZIkSZIkSRpZQw0kTAc26rJtdvmcBKLWXpIkSZIkSZIk9dhQAgkxSu0H268kSZIkSZIkSRplgw0kfH9URiFJkiRJkiRJksalQQUSMvMtozUQSSNvz0880eshqIfOZbleD0E9sucRM3s9BPXIuZ/zey9pcrqS9Qg+0OthtPCRXg9AkiRpREzp9QAkSZIkSZIkSdL4ZSBBkiRJkiRJkiS1ZSBBkiRJkiRJkiS1ZSBBkiRJkiRJkiS1NahiyxHx5tEaSF1mnjoWx5EkSZIkSZIkSZ0NKpAAfA/IURhHMwMJkiRJkiRJkiSNA4MNJIy0aLFvLAIVkiRJkiRJkiSpC0MJJLSa/B+uvuDBaPQtSZIkSZIkSZKGaLCBhKVH+Pj7AMcBm+NKBEmSJEmSJEmSxp1BBRIy86mROGhEPBf4ArBTX9eU1QgJnDYSx5AkSZIkSZIkScM3ZSwPFhFPi4ifApdSgghRu/0OeHZmvmksxyRJkiRJkiRJktobk2LLEbEmcDRwSHXMei2EK4CPZeYfxmIskiRJkiRJkiSpe6MaSIiI5YGPAR8AlqGRvgjgJuCTmfmT0RyDJEmSJEmSJEkaulEJJETENOA9wCeAVekfQLgfOBY4KTPnjcbxJUmSJEmSJEnSyBjxQEJEvBE4BtiQ/gGEmcAXgS9m5hMjfVxJkiRJkiRJkjTyRiyQEBF7AZ8DnkH/AMI84ETgmMycMVLHkyRJkiRJkiRJo2/YgYSIeC7weeAlLR7+MfDfmfmf4R5HkiRJkiRJkiSNvSEHEiJiM+A4YN++XbWHzwM+lpnXDH1okiRJkiRJkiSp1wYdSIiINYFPAW+tnl8PIFxJCSBcMDLDkyRJkiRJkiRJvTSoQEJEHAN8CFiG/gGEm4FPZuYZIzg2SZIkSZIkSZLUY4NdkfBJShHlvmLK9wPHAidm5vwRHpskSZIkSZIkSeqxodZIyOrPpSjBhU9GRIfmg+s7M9cdqc4kSZIkSZIkSdLQDbnYcmXF6jZiUQQaQQpJkiRJkiRJktRjQwkkjGTQQJIkSZIkSZIkjWODDSR8elRGIUmSJEmSJEmSxqVBBRIy00CCJEmSJEmSJEmTyJReD0CSJEmSJGmwIiKr261tHt+o1ubCsR3dxBMRB9fer6N7PZ6hGuhzIY2UiNi59nn7Xq/Ho4kjIqZHxE3VZ+f7Y3TMN1THmxcRzxxKHwYSJEmSJEnqoYi4tTYZ1eo2JyIeiIhLIuKLQ50AkDR5RMRPmn5HXt7rMUla6CPApsBTwJGtGkTEMlWw6iMRcUZEXBURt0fEkxExKyLuiYjzI+LIiNiwi2OeBlwNLAF8fSiDNpAgSZIkSdL4Ng1YDdgBOAy4OiJOjIhpvR2WpPEoIlYFXtW0+629GIuk/iJiDeCj1d3vZObtbZq+FvgD8AXg9cB2wPrA0sB0YC1gV+AY4MaIOCYiot1xMzOBz1R3XxQRrxns2AdbbFmSJEmSJI2ebwE3N+2bTpk82APYCAjgUGAp4OAxHJukieFNwJLV9mzKb8jLI2LNzLyvd8OSBBwBLAcsAE7oov1TwN8o/za4v7pNBTYGXgqsS/m+HwmsCbyjQ1//B/wL2AL4TEScWQUYumIgQZIkSZKk8eOMzLyw1QMRsQRwNPDJatdBEfHVzLxqjMY2oWTmrZSgi7qQmd8DvtfjYWhk9K0+mAUcBfwPZQ7wzdW2pB6IiJWBt1d3z8nMWzo0v4Ky4uDPmTmnTX9TgQ8Dx1e7Do2IH2Xmxa3aZ2ZGxEnAF4GtgZcDZ3U7flMbSZIkSZI0AWTm/Mw8EriytnufXo1H0vgTEc8Dnl7d/SVwMmVVApjeSOq1twLLVtvf6dQwM2/IzD+0CyJUbeZl5ueBn9V2HzDAGE4F5lXbHxigbT8GEiRJkiRJmlgurG2v265RFC+IiE9HxHkRcUdVoHF2RNwdEedGxAcjYrluDhoRS0fEOyLi7Ii4s+rryYi4LSKuiIhTI+JNEbFmF32tExFHRcSfqoKRcyLiwaqf4yJivW7GNMAxNqoVmr2wTZuDa22OrvatEBEfjojLqzHNioibI+LkiNhikGN4TkR8JSL+FhEzIuKp6vX+PiI+EBHLDPd1Nh3vJRFxSkRcFxGPRcTciLgvIv4REb+NiI9FxHZtnrvIe9GizfdqbXau9m0SESdExPURMbM67t8i4tjq6ttux75D9Rm6tfqM3hsRF0XE2yNiyarNhbXjbzTY96fNcXeJiG9V43+4Okd3RcRZEXFIdcXvRFIPFpyamY8Cv6rubxERL+q2o9r39I9RCr7PiYhHI+KmiPhLRPxvROwTEcvWnjMlIv5TO09bdXmsvWvPuajpsUW+y1EcGOV37K7ad+v/ImKPbl9j7XV+MiL+UPU1O8pv2y0RcWZEvCciVuuyryWj/E5eXH33Zkf57T0tIp7bxfP7Xuettdf5uup1/afqb+H3r+m5G0fE56L8jtZ/b/4QpWDvioM9frVvn+r4t1d93h8R50TE/t28J7V+doyIkyLihupzNCvK3x+/iIg3Rll11+n5A/5GNbVf5PeqTbunRcTnI+KyiHgoyu/mQxHxr+o8Hh8Ru0b1OzRMB1d/PgGcPQL99bmgtr1xp4aZOYPGvyN2jYj1uz3IRPsxlCRJkiRJDQ92eOxiYMc2j61d3fYAjoiI/dqlQgCIiG2AX1NqNDTboLo9h5Kb/efA6zr0dQQll/PSTQ+tUt2eA3woIg7LzG+262c0RMQzKePftOmhTarbmyPiwMz8+QD9LAucBBzY4uG1qttuwMci4nWZ+ZdhjntJytWtb2jx8BrVbRtgL+D4iFg+M2cO55jVcQ+gXPHeHIx6RnV7S0Tsnpk3DNDP8ZTio/VUVGtWt52At8UQCoMOcMw1gB9Scow3W6e6vRz4aES8eqDXMB5ECUz1XY18L/C7avtUSrFWKIGGP3fR1/7At1n03E4DVqB8R14AvB94H/D/ADJzQUScAhxbtT8E+EgXwz+ktn3KAGNbBfgxi567tYBXA6+OiC9l5ocH6GcK8CnKZ296iyYbV7dXUQrdN/8uNPe3IfAL4NlND60H/BdwQER8MDO/2qmfWn8rUV7nnl20/W9KGqvmye6+35udKZ/lt2bmr+hCRCxFORfNvyurV2Pas/pevjEz5zU/v9bPssB3gf1aPNz398drKL+H+2bmjd2MbyRExAcpxYynNT20cnXbHHgx8DHgFZS/B4d6rKdTfocBLsjMWUPtq4VVattPdtH+LGB3ym/u6ympjgZkIEGSJEmSpInlxbXtTpObq1d/3gVcBvwbeJQyYbIpZVK5b5L5txHx3My8vrmTKCsWfksp+AwwAziHUvhxFrB81d/zGeBKyCi5md9e23VJdZtR9bNj9fqmA9+IiGndTrqNgHUpr2st4B/AecAD1f59q/1LAj+IiGsys7koNrDw/bqIxmTinKqvvwEzq372okxQrQ1cEBE7Zeblwxj78TQm++ZTrk69GniIUpR7LeBZwPOAjlf9DsIelAnYKdXxLqVMYG1BmTCcTnnvfhYR22Xm3FadRMSxlEm6PjdQPm8PUiYYX1WN+6dA10VBO4mIdYE/0QiMzaSc++spaYA2oKQNW796PX+OiOdUdTfGs9dTJvkBTsvM+dX2ucB9lMDM6yPiA5n5eLtOIuLZwI9ofFZupJzjOykFYlcGtgJeVG03+w6lnssSlODbEQNMNK9GmaSF8hv1s3ZtKXOZPwN2oQRLzgJuo/x+7A1sW7U7LCKuzMzT2hwzgDPoH/S8nRJ8uZ0ywbousAMlKDbQ92Z5yhXmWwP/qbbvBlalBDc2qfr8SkRclZl/GqC/oJyDPSnvyW8oRXKXBLanfM/7XssJlDz5ff5VHX8GsCHlO7QmsBrwi4jYf6BgaOVkyu/KI5T3+UbK78muwAurNvsD1wLHtXwRJch5HiXo1OciyvdvDmVi/RWUwPLTKd+1F2TmTV2Mb1gi4pXAl2u7rgH+SPlcQXm/tqG81q5W7g1g79r2hSPQH7AwsPa2Qfb9h9r23hhIkCRJkiRp8VFdPfsJyqQqlEmqn3Z4yhnAb9pNUFcTPMdSJoOXAb5GuUq+2etpBBF+A7w+M1te8RgRz6BxxWXzY2+nEUS4CTgwM//aot1ulNe1MnBCRPxujK4Gfxslb/ShmXly05iOoEw0v5Ay4fVR4B1t+jmRRhDhHOCQzLynqb8APkiZvFkKOD0itmw32d5JdbXvO6u7jwA7Z+bf2rRdGXgjMOjjtHAEZXL6NZl5SdNxjqOsiFmd8nl4HXB6i/E8l/KZ7nMkcFxmLqi1+TDlaubXMQKBhOp7dDqNIMKpwPurFED1dtOAz1EmaFcBfkD/IN541C+tUd9GZs6LiNOAD1Hys/etNmjngzQmzo8CPpOZi7z3VSqaXWnUYOg73t0R8RvglZRA5cuBMzsc7000rqQ/vd3vS6UvNdO3gMPqV3VX39Mv08j7fhTQMpBA+az1BRHmUt6bb9WCLwtFxGY0VnS089raMT9XD5xU4zq9ahPVsQdaZdB3pf65lCv+Z7RqFBF70j+IcATwhRbfoVOq17AE8O2IuDQz7+pw/A0p5+WXwMGZ+UjtsSMj4jAak88fjYgvt7nC/tM0gggzgddl5rlNr2ETSvqtbSiT9z+KiB1afeZG2OG17YMz8/utGlV/V76CErAajpfUthf5u28wImI65e/l3YCPU84XlGBPx9oLlesogd9lgBdGxNROwb4+1kiQJEmSJGn82D8iDm+6fSIivkmZIOhLF3IvsG9mzm7XUWZ+qtNV7pk5JzM/RiN/+q7VhFmzek79T3Wa5MvMazOz1YTx0sBnq7uPAru2CiJUfZwPHFTdnUZ3aVFGyhHNQYRqTI/Tf5L2tc1tACJiexrpjC4DXtkcRKj6y8z8Mo2rYTdh4AKZ7WxJI03UD9sFEarjPpyZX8vMp4Z4rLoFwKuagwjVcW6gTKj2afl+USbA+uamvpeZn6lPgFZ9zaS8p/+gf+qjodqXRkDgF5l5UHMQoTru3Mw8nJKqBmDHiBi3gYSI2JxGKrNrW3wOTq1tD1R0ue87/yglsNNyQrcqAH9eZv6xxcP179FAx+s6rVHlN5n5ruaJ62qcHwHuqHZtUaVl66dKa/Xx2q6DMvPrrYIIVb83ZWbLK+6bfD0zj22ekK2K5b6dsoILYPeIWL6L/m6iBOpaBhEqR9e2v5aZx7f5Dr2BxuT1SpRUTQO5mhI4fqT5gcz8EuU3DmBFSkCpnypw+f7aroOagwhVX7dQVmn1rZJ5HiX4NNr6Puf/aBdEgIV/V/48M/8+zONtX9seVF8RMb1W7yEpn6UbgW/SCCJcCOyYmU8M1F/1GbmuujudRoH2jgwkSJIkSZI0OlaLUvSy73ZoF895J/A/TbfPVvs3pVw5+wVg68y8dITGWZ/4bzVRWk/p0VzXoFuvp5Fq6euZeUenxpl5FiUVEzRSnoy2B4C2aZSqyfG+iZfVonVB6PfUto/sYoVBPZ3EK7sa5aJG4vwMxVmZeVmHx+vpaRYp8FxNpPa95qT/hGg/1ft4bLvHB+ndte2Pt23VMBLnaCy0XI3QJzOvoaSgAdghIrbu0FffZ2pJhp4K67eUVEgAL4uItVs1iojn0ZjE/FtmXtFF359s90D1WanXAGhVXPwtNL4r57cKfg7BXOCYDuN6iEZB3CnAM7vo8/Od8uhXwaMdqruzKfUe2h1/Hv1X/7y5WhnVyTFVEKSdjt9xSoqzvoLyl2fmL1q06RvfnfT//T2oXdsR1PfZHvXfzYhYgbI6B+CxVsGZYbgf2C8zd8nMBwbxvPoKi6d18wQDCZIkSZIkjY4Zmbl97XbSCPQ5jZJW51dVYeCuRMS6EfGyiHhrRHywvuKB/ukWtmjx9PqVzV+NiK4mHJrUUyYtckVqG9dUf65eFTEdbecPMGkG5QrQPmu0eLzvdc6hizzVmXk3JYAB/a9WHYx/0khV9KaIOKBK3zPaftvpweoq6oeru63eq+fSSLl9TWYOlDbk19Tywg9FtTKmL7f7fzLz353a942ttj3UczSqImIq8Obq7nxKbv1Wul2V0PedXxo4pbqyfFCqq/v7UqwsQfuJ4fo4ulmNcG8VFOmk2+8pwPe6OGY3rszM+wdoM9C4mv1mgMdfVNs+PzMfbtuyakOpPQIlhVCr3/s+8ym1DToZ6PXUx9cpDV+fn7R57mjp+5xvGhFfqIqVj5b1a9uLrFLrwlzKapu+2zGUQtwPU977H0fE9yNi1UH0WR/H+m1b1VgjQZIkSZKk8WOXzLywvqPKQ74ypVju2yj5zXcELomIvZvbNz13f8qkw3O6PP5KLfadRrkCeD3KVaf/iojLKZNSlwCXZOaDLZ5XVw96XDTwhbCLWJ3h56ceSDf91wvU9iu+WU229k3GLAnMGeTrXH3gJovKzMcj4huUvPBLUlaYnBAR51AKml6Wmf8cSt8D6Pb9WpnWhUo3r20PmOYjM5+MiFvo8srZNrakkYt/4ypFyGAM6RxV38NOE3X/yMxzhtJ3ZR9KQW2A32fmvW3a/Qj4PGVi/00R8fE2q2a+RKkfMJVSU+N1EXEBpUjupcBfO10pX3MK5bdjCmUVwPH1B6uJ276UXk8BP+yiz2F9Tyv11RjDKXJeNxLjqnusVVq0JvXv0NUDHTwzMyKuoRFIeRqluHkrM7pIkTPQ6xnU+CgrvuZQvqNrRcTy2aEo+Aj4Ao3UZR8B3hkRv6PUd7kMuKqLVWXdWqG23akGSEtVYO6E5v1VrYRPUy4weDPwvIh4UbUCZiD189tNqi1XJEiSJEmSNJ5VechnZObvM/MASjFNKFcLn94q13YU36ZcsdhtEAFKruTm4z8O7A5c1dc98HxKmoyzgAci4sqI+HCHvN+DuUqylWWH+fxutK03UVOfeG6eU+nla/wIpVh23xX761Ku9P4ucH1E3BsR32yVL34YBvN+tYqorFTbHigQNdh27fTqHL2LRVOW1W9DrY/Rp2Naoz5VgKHvKvPVaZOqqapf8hpKLRYovwt7U4IQFwEPR8Q5EbF/p9UvmXk78Lvq7uYtaky8jsYE6y+6uKIehv89hf6fg4FWEXRrJMZVt0jdjhbqK0U61VGoq7dbpUO7kXg9gxpfNVle/wx0Gt+wZeb/UYLzfe/18pQaKv9LCZg9HBE/j4iXjcDh6hfzD1jUuFuZObuqdfT/ql1b0iLg0EZ9HNO6eYIrEiRJkiRJmlj+h1LAcm3KVchvpBRcrHsrjcnFuZQUI2dR0uDcD8zqKywaEbvQyN3dUmb+qyokvDulcO5OwFaUCeIAnl3dDo+I/TLzT01d1OcfjqH/lazduGWQ7Xuh/hofoFztOiaqq2bfHxFfphRV3ZWSO70v9/ealDob74iIz2Xmf4/V2DoYSuHk4RZbrp+jfwHfHuTzu5ncHVNV7YG9a7teFRF7dHhKfVXFW4Gft2qUmb+OiE0ok/37UFZBrVs9vBSwZ3X7cES8qsPV8ydRCun2Ha9emLkeABnsuRgpg12VMlYWDNykn6G8jrF87eNyfJl5SkT8nBLM24uSUmm16uFlKX/fvTYifkspPD1ziIeqr0JYJGA/Aj5HqdETwBsi4r2ZOdDKh3ptiAELNIOBBEmSJEmSJpTMnF+lFnpVtWtHFg0kvL+2vV9m/rJDlyt2edykXM18HkCVi/nFlEnMAyhXc64FnBURmzcVfXyQMpkN8MvMvIrFT/1q+aUys9urQkdMZv4H+AzwmYiYRlmNsjtwII3Azyci4tbMPHmsx9ekfuVxtysFhnuFcv0czRmrc5SZO49i9wfTvyDy6wfx3D0jYr2q0O0iqvRFP6huRMTGwM7AqynBhSUotS5+SvkdauUsysqGtSgpkt5XpePajEZx91uAPwxi3MP1II2gyJqMwwBRl+rpa1Zr26q/ertuVoAMx6DGV0vj16d5fPXAQjdBxa5qHlSFj78FfKsqQL0l5XO+L400UC8DTqQEaoei/vfhiK+0yMy7I2IGJVC4JOX3/soBnlYfR1dFmk1tJEmSJEnSxFO/0rBf3vYq7/i21d1bBggiADx9KAPIzAcz88zMPJSSa7tv1cBKlInrunoe7rEootkLM2ik71ghIrbt1Hi0ZebczLw0Mz8DbENJfdTn3T0aVl290PGA71X1ud5kBI7Zl/5p66EUER6HDhnGc6dQAhFdycz/ZOZ3M/NVlCLtT1UPvahd8ffMnEdJsQXlCu++NE6H0JgM/k4VqBwr19W2nzeGxx1p9e/QdgM1ribJ6+fpxnZtR8igxkepXdFXw+TeFvUR6qsBBqoxAbBBF236yeKfmfnNzNydEpjr+2weEBFDDQLcRVkdCKX+wxKdGg9RfcFAN/2vW9u+tZsDGEiQJEmSJGniqU+QNF+1WZ8c7abg4r7DHUxm3kdJYdJny6Ym59W2D4khVFse76qJ0PNru4YzwTuiqrF9trar+fz0wl9p5Oh+VkQMNOn3crqbHGuruvL4iuruEpTipBNWRLwE2Ky6exewRGbGQDf6rx4Y0vcxM/9M/1UEnT5T36YxGXtINYna997PB7432OMPU/17evAYH3sk/bm2vXtEDLS6bBcaKwNmMPqBhPr4XtdF+/3aPLfPfbXtzVs8vlBErE53wYuOMvOnlDRoUObRh1TsvUol2BfAWoLG93ZERMSm9P+7/64unlb/zl7bzXEMJEiSJEmSNIFUecufX9vVnCboYRqTdltFxNK0EREHA88ayfFVZjXd/xGNoMaz6J96qaMJFnSoX/X/rojo+mrnMX6dzednzGXmY5S0N1CuTD+6XdsqTdMnR+jQ9XN0VPV96so4/CzWawycnpnd5tX/C40rkDemTDAPV9vPVGbeQqMOyw7AYTSuhj4nM7uZ9BxJ36Ux3t0iYrjFrnsiM2+kFAWGknf/6HZtq+DNcbVd3x+DVSA/pbF6boeIeFW7hhGxDv3/Xvhei2Z/o1E7YteIWKnDsY+ksbphJA3nt/Oy2nbLFTzD8Ona9nUDfaeq1Vh9wdv/ZGZXRccNJEiSJEmSNEFExEaU4qh9KQxmAafX21QFFvuuul4WODEilmrR10GUnM8dJ5Mi4scRcVSVH71dm63pPwl0cdOYHgfqBX6/FBFHDxDkeGZEfA04vtP4xpPqKu0zqrtLAedGxOvbTUBHxNSIeGlEnAm8ZijHjIg3VOdo54hoOc9Tvc9fru26uFW7HjiexsTgWyLiv5tfQ0QsRwlEbcvIFF89nTKRDiVH+EURsXu7xhExPSJeHRF/oBQUHxeqq8/rV3n/qNvnVhPI9d+NekCCiLg2It7WKfVTROwLvLS6Ow+4ZIDD1lcs1Se0Txl4xCOrqt9S/105NSLe3S7dTEQ8LSI+MTajG7Sja9sfjIiPNP/eRMSylDoXfQHoR+j/ezAqMvNh4H9ru05t9V2r/l77LY16PZcDv2nR3+M0VrctQ/m7bVpTX0tU5+p9ncYWERtGxJ8iYv/q/WnVJiLi/TSu3J8B/LNTvwP4XW27XU2RvmOvGBG/j4i9IqJtjeOIWC0ivkv/2g3d/J25U5txdWSxZUmSJEmSxo/9I2L7pn1TKHUHtqMUfqxPnByembe36Od4SsAB4E3AThFxNnAnJbXFHpS8+fMpk3r/3aKPPmsB+wOfjojrKEGK2ylBjNUoV1buQuNixUtoXGm+UGZ+KyK2ogQcpgCfAt4bEedRcmk/SZlI2pSSt3zD6qlf7zC28egQyth3oJy3M4DjIuIC4A7KxPnKlHzgz6dR8PKHQzzeNMr52R94ICIupdSkeJgSSNoAeEU1FoA5lPe+5zLz8oj4AvDxatdngDdGxG8oK1g2oBT2XZMy+Z80amwMKaiQmQsi4rXAHylpStYDzouIv1MCLPdQPp+rUuqHPJ9GPvbxtCLhv4C+QNw/M/OaQT7/R8AR1fZrI2KlKvUTlKDNycA3IuKvlCvB76V8dtekFKKt11b5YlNx9VbOpBR0XZ3GfOR9tPitGCOfofymvpryHfo68LGIOJfy+xaUz8YOwDOA2+gfABkXMvPciPgi8OFq1xcoQbnfUopKbwC8ivI7DuU3/21juArkaMrn5QXACpTv2oWU1EVPUf4eeiWNz/IM4A0dVkscQwlgTaHUL3h2FYh9CFgH2JtSS+V6yu/ga9v0E5TfkhcBsyLiMuDvwP1V32sDe1JW7PQ5MjPnNnc0CL+j/L25NNA2eFkb327V7aFqfP+kvM4FlN+nbSnvbX3lxcmZ2c3fJbvVtn/VzeDBQIImix/+EN7QVFh9o43gttt6MhyNIc/9pLXtK09hnW3277fvom9szexHW/0/W9LEFKyy4U6sucUrWWndHVhqubWYNn0l5s5+mKdm3seTD9/MQ7f/iYdv/xMzZ1zf68FqxHn+tdh6Z5ftHgY+mJmntnowM38REZ+iTOIEZWL7XU3NngDeQcml3CmQMKe2vU11a+cc4MAqH3SrcX2gCkYcT5lMX5VG8dVWnmL083iPqMx8MiJ2Bk6gnM+plODIph2e9iBlAnso6udndUrQ4BVt2t4DvDEz/zbEY424zDyiuqr4MMpndUsWzbd/BSV/+hm1fUNOMZKZ90XEc4FvUQIwQZmU61T0+S4WrUfSS/VVBF2vRuiTmddFxN8ogcDplCua+4J2cymT69OAF1a3VuYDXwIGvFo/M+dExKk0JrwBTq2KMY+5KqD0OkrtkMMor3UD4O1tnjKcCeRRlZmHR8QjNNL5bFXdmj0IHJKZXU8cj8DY5kTESympivpW0Oxc3ZpdB7w2M2/q0N9fIuJ9wP+jfG83Aw5vanYNJUD0adqbT5mQn0KZ2G83JoDZwCcy81sd+htQZs6MiP8DDqSkHdwyM29o17y2vQrwsurWzqOUQMfXOrQBFqZo61sBdz+uSJBqXv7yRSeSNTl47iet1Td72SJBBEmLlxXW2o6t9/wyK67TfNEuLLXcWiy13FqssNYzWWurchHS745fkTZzWpqAPP+ahJIy6f8ApSDiOcCPa1cPt35S5jERcT5lBcCLgDWAxymrEs6mXLl4SzXp3ck+lDQIu1JWCmxGuSp5SWAm5erdyyg52i9o10ltXCdFxOmUYqsvpdRMWI2SCugxSu72aykFUX9TpceYUDLzKeB9EXECpZjrLpTioH2rDx4GbgKupEzi/D4z57ToqptjnRYRVwJ7USZ8t6ZcSb08JcjwAOVK218DP8jMJ4b4skZNNRH6C+DdlM/ampT0KzcCpwHfycynmorJPjrMYz4K/FdEfIayaucllKuPV6Gk6nmoOv7lwLnARYOoQTCqIuIZQP0vwdOG2NWPaORqfyuNQMLqlFVLL6Gkc+p7X6ZQ3vd/AxcB3+swEdrKyfQPJIx5WqO6KuD58Yj4FmUl0UspAb+VKd+duym/RecCP+nVOLuRmZ+JiNOAQynnbkPKb8BDlCvzfwOcVH3ux3psTwD7RcSLKb/7O1Gu+F+SMpF9JfAL4LR2Qeim/r5RXaH/IRq/F49TiiKfTvm77alOJU0y846IWJvyu/liyvdgI8rKraT8/txA+Xvou5l5x2BfdxsnUgIJVH8e1WZ8j0bEupRz+UJKkHNjymdzCuX13kNZLfQ74OdV6qduvJjydwSU39aug3kx+nU11GvbR+QVAzdbPK20Elx3HayzzqKPeVX64s1zz7nHtUzzt9ibOn0lXvS2vzJ9+bUXeWyyrEjY84iZvR6CeuTczy03cKPFwNpPP4Cn7/MtpkxpXBMzf+6TPPnQzcyZ9SBTpk5n6RU37Pc74ETy4sPz39/7vj6LG++cP55SbfRExPoJH+j1MFr4yJWZuWjES9KgVXnCH6NcPXxvZi76D36Na9VEcl99jj9l5ot7OR6pF6p0YdtTAlUbjvWqnIj4AfBGyiqbjTLz7m6f64oELd6+8pXGRPK558Kee/Z0OBpDnvtJa8vdP79w8mjGLb9ntU0GSj0oaSJZe+v92HafE4kppRbeEw/dxL8vOoYHbjqbBfNm92u71HJrs/pme7HeMw8iR6Q2o3rN8y9Jk9quNPKoX9nLgWjI3lLb/nbPRiH11jGUugTrUFKrDTo12VBVqxxeX9399mCCCNAohKRhiIgfRUQ23TYaZB9bRsRxEXFlRNwfEbMj4taIODci3hYRy4/S8BdfL3sZHHRQ2f71r+H003s7Ho0dz/2ktdqme7DutiWd1f03/ZZ7rv9pj0ckaSRNX2F9tt7rqwsnkR/8zx/4yykv4L4bfrHIJDLAUzPv4c5rvsul398ZxkcmAg2D51+SJq+IWIL++c7P7NFQNEQRsSpl0hRK2phxnSpIGi2ZeRaleDyUAt9jubL0MEpKqSeBYwf7ZAMJwxQRr6CR22ooz58aEccC/wCOoOSeW52SG3JDSi6sk4F/RMQuwx/xJLHCCnDSSWX7scfgXc01xbTY8txPWlOXWoFt9ip1heY99Rj/POeDvR2QpBG39V5fYepS5dqKWY/eztU/P4AF84ZcZ1ETjOdfkhZPEfHSiPhQRKzQ5vHVgJ8CO1S77gN+PFbj04g5Blim2j4pM/1LXJPZ+ynFnrdlGPPKgxER61Pq0AB8JjPvGWwfpjYahohYmVIkYzhOoRQa6ZPAP4EZlCIa61f7NwB+FxH7ZGbX1bQnrS99Cdar6oZ8/ONw5529HY/Gjud+0tpit88xfYV1Abjxwk8x+/G7ejwiSSNp2VW3YPVNG2nqbvzDUcyfO+5qRWqUeP4labG2KvAl4LiIuJhSPPQhYFlgG8oFln0F4BYAh2amRcHGuYh4HqU4+7KUYuN9f5E/ApzQo2FJ40JmXhkRh1Dmfpcao8NuDHyeUkz+i0PpwEDC8HyFUmUcSoXsPQbz5Ig4jP5BhIuBt2fmjbU2uwPfp+TNmgr8NCKekZmLf6XYodpjD3jrW8v2H/8I3/xmb8ejseO5n7RW3Xg31ntmSWf18B1/4Y6rTu7xiCSNtPW3e+vC7aeeeID7/nVm7wajMef5l6RJYTplXqXd3MqjwCGZ+auxG5KGYW/gU037Enh3Zj7Qg/FI40pmfn+Mj3cxjWLnQ2JqoyGKiL1pBAF+AwwqCXuVG+6o2q6rgT3qQQSAzPw9sBPQF21fgSHksJo0llsOTq4mEGfPhre9rbfj0djx3E9aSyy5HNu87P8BMH/ebP5x9nt6PCJJo6FeOH3GLeeRC+b1cDQaa55/SVqsnQm8jHKx5qXAf4AngKeAe4DfAx8FNsrMX/RmiBqme4FzgBdnpkUMpQnKFQlDEBErAlUSdh4H3gXsNshu3gusWLv/jsx8qlXDzLy5qqPw+WrXGyLiqMy8dZDHXPydcAJssEHZPuYYuPHGzu21+PDcT1pb7HocS69YssDd/KfjefKhf/d4RJJG2tTpK7HMKpstvP/IXZcBsNRya7PuM9/MGk/bh6VX2pAlpi3L3FkPMXPGDTz4n/O562+nMnf2w70atkaI51+SFm+ZOZsyyXxOr8eikZOZRwNH93gYkkaQgYSh+RKwbrX98cy8YwgFtverbV+emX8doP23gU9TlvpNAfZliPmsFlu77grveEfZvuYa+MIXejocjSHP/aS1yoY7s/52hwDw2H3XcuulX+7xiCSNhuXX2JaIxkLaJx/8N+s+401sufvnmbpU/7qMSyy/DtOXX4fVNt6VTV74UW78w5Hcec13xnrIGkGef0mSJKn3TG00SBGxJ3BIdfdPwKCTsEfEJpSCQX1+PdBzMvMh4JLarlcO9riLtWWXhW9/u2zPm1fS2syf39sxaWx47ietJaYtyzZ7l5RGCxbM47qz30Om515aHC259Kr97q++2V48fZ9vLpxEfmrmfTx0+595+M5LmDvroYXtpk1fkW1e9lU226k5Pa8mEs+/JEmS1HuuSBiEiFge6Kvg+RTwtszMIXS1XdP9P3f5vD9TKt0DPGsIx118ff7zsPHGZfvLX4Yrr+zteDR2PPeT1ua7HMsyK20EwG2X/z8eu/fq3g5I0qiZOn3Ffvc3ev77AZj16B1cf+4HmXHzuQsfi1iCtbZ+HVvt8UWmTV8JgE1f9BEeu/cq7r/xrDEbs0aO51+SJEnqPVckDM4JwPrV9jGZ+a8h9rN10/1uE3rX260QEesN8fiLl5e8BN71rrJ9001w1FGd22vx4bmftFbe4MWs/+xSUPvJh2/mpj9+pscjkjSalpg6fZF9c558kMt/+NJ+k8gAmfO557ozuOLHr2TBvEb5qc13OQbCf/pORJ5/SZIkqff813SXImI34NDq7t+A4SRh36i2PR+4u8vn3dahn8lp6aXhlFNgSvVRfvvbYfbs3o5JY8NzP2lNmbo0T9/76wvzZV939vtYMM9zLy3O5s15YpF9N118LLMfu7Ptcx675ypuv/LEhfeXXeVprLLBTqMyPo0uz78kSZLUewYSuhARy1GKHUOZ+H9bZs4bRpf1qnCPZ/dJvR9tur98u4YRcWhEXBERVzww6OFNIMcfD5tuWrZPPhkuvLCnw9EY8txPWpvvcgzLrLwJAHde810euv3iHo9I0mibP2dmv/sLFszj7uvOGPB5d137w373V9nQieSJyPMvSZIk9Z6BhO58nsbV/1/OzCuG2d9yte1Zg3hec9u2gYTMPCkzt8/M7Vcf1NAmkK22gve+t2zffTd85CO9HY/Gjud+0lp21S3Z4DnvAGD24/fwrws+2eMRSRoLc56c0e/+kw/dzPw5jw/4vJkzrmf+3MY/n5ZZeeMRH5tGn+dfkiRJ6j2LLQ8gInYGqiTs3AyMRBL2abXtwaxsaG47rWWryWKNNRppbdZZBx55ZHDPv/XW/tsb+5/LCcNzP2ktuezqC1MaTV9+bXY77K5BPf8l775+4fasR27j4m9uM6LjkzQ6Zs64od/9ubMf6vq5c2c/zBLTlgZg2vSVR3RcGhuef0mSJKn3XJHQQUQsA5wCRLXr7Zk5mBUE7dQTvS5aPa695raLJoyVJElazMydNYOnnrh/4f0pSyzV9XOnTG20nT9vJP4Zp7Hm+ZckSZJ6zxUJnR0PbFJtfzsz/zBC/dYTvS4ziOc1tx14TffibO5cmDFj4HZ9lloKlq9lg3roIViwoLGticNzP2nl/LnMefLBrttPWWJJpi7VOPdzZz1EZpbt2Q+P+PgkjZ6HbruItbfeD4ClV9qwq+dMnb5Sv6vQ58y8v0NrjWeef0mSJKm3DCS0ERFbA1USdu4BRjIJe73+8bIRsXxmdhMUWLvp/iBmUhdDf/kLrD6IChAHHQTf+17j/rOfDbfdNuLD0hjw3E9aj9x1KX/43+4mkADW2fYNbPvyExfe/8t3d2T2o7ePxtAkjbL7bjhz4UTykkuvygprP5vH7rmq43NW23j3henQoPyGaGLy/EuSJEm9ZWqj9tagkdJobeDhiMh2N+C7Tc//T+3xW5seu6HpfrezYvV2C4Abu3yeJEnShPbATecw+7FGXZRNX/Sxju1jylQ2fsFhC+/Pn/skD9x83qiNT6PL8y9JkiT1loGE3riu6f6zu3xevd2tI1SvQZIkadxbMP8p/n3xpxfeX+Np+7DZTke2bBtTprLN3t9ghTWfsXDf7VedzNxZk3sx50Tm+ZckSZJ6y9RG7c0Fuk/EDUsBy9XuP0xZNQDQnIT9Ckqh5GWr+y8BTu3iGDvVti8cxNgkSZImvLv/fhqrb/oy1trqNUC5Kn21jXfjrmt/yBMP3ghTprD86k9n/e0OYdlVN1/4vEfvuZqbLj62V8PWCPH8S5IkSb1jIKGNzPwzsFq37SPiYPqnN3p2Zt7apu9ZEXEOsG+1a9+IeF9mPtmh/x1pFH4G+Hm3Y5MkSVpcXHvWW5myxDTW2PzlAKy4zvasuM72bds/fMdfuOYXb2TBvNljNUSNIs+/JEmS1BumNuqdU2rbKwIfGqD9p2rbtwO/H/ERSZIkjXM5fw5X//wA/v7rQ5n5YPtyUbMeu5N/nvdR/nra3sx58v4xHKFGk+dfkiRJ6o3IzF6PYbHQYkXCxu1WJNSecyElrRHAHOA1mXl2i3afBT5R23VwZn6/27FtH5FXdNtY0mLj3OOWHbiRFkt7HjGz10NQj5z7ueUGbrSYWW71bVh+jW1Yatm1IKYw58kZPH7fNTx+/z96PTSNAc8/vO/rs7jxzvnR63H0WsT6CR/o9TBa+MiVmdl+2YwkSdIEYWqj3joUuARYBVgS+FVEnA6cSanPsDHwFuDFtef8CvjB2A5TkiRpfJr5wHXMfOC6Xg9DPeL5V8OawOG9HkQLH+n1ACRJkkaEgYQeyswbI+JVwC8pwYQlgDdWt1YuAP4rMxe0eVySJEmSJEmSpBFljYQey8w/AdsApwNPtWl2J3AY8NJOBZklSZIkSZIkSRpprkgYIZn5PeB7Q3zuvcCBEbEisDOwHrA8cB/wL+CStJiFJEmSJEmSJKkHDCSMI5n5KCXNkSRJkiRJkiRJ44KpjSRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJkiRJUlsGEiRJkiRJ0oQTEVndbm3z+Ea1NheO7eik4fHzq+GIiD9Wn52Lxuh4L6p9Xl8+FsfU2DOQIEmSJElSD0XErbUJmFa3ORHxQERcEhFfjIhn9nrMmtwi4gtNn9H39npMkoqIeBOwY3X3ox3arRAR+0bE1yPiL9XfM3Mi4rGI+HdEnB4R+0XEtIGOmZl/Bn5Z3f3fiFhq+K9E442BBEmSJEmSxrdpwGrADsBhwNURcWI3kzvSSIuIqcCbm3a/tRdjkdRfNYH/2erubzPzsjbtTgbuB34GvBt4AeXvmWnA8sBmwAHAT4BrI+K5XRz+mOrPTao+tZiZ2usBSJIkSZKkhb4F3Ny0bzqwPrAHsBEQwKHAUsDBYzg2CeDlwJrV9mzK5/NZEfHszLyqd8OSBLyT8vcFwOc7tHsx5e8QgDnAFcCVwH3V/ucAewJLAFsCf4iI3TPz0nYdZuZVEXEe8FLgiIg4KTOfGM6L0fhiIEGSJEmSpPHjjMy8sNUDEbEEcDTwyWrXQRHxVSdvW8vMWylBF42s+uqDw4H/V9vvZ1HqkWq10GHV3eszc6D6CP8Avg78ODMfadHfVsD/AVsAywKnRsTTM3NOhz6/RQkkrA4cAnxtUC9C45qpjSRJkiRJmgAyc35mHkm5arTPPr0ajyafiFgHeFl191Lgm8Bd1f0DI2LpngxMEsCrgQ2q7e8M0PYdwDMy81utgggAmflPYC9gVrXraZRVCp38CphRbb9/gLaaYAwkSJIkSZI0sVxY2163XaMoXhARn46I8yLijoiYFRGzI+LuiDg3Ij4YEct1c9CIWDoi3hERZ0fEnVVfT0bEbRFxRUScGhFviog1u+hrnYg4KiL+FBH3VAU+H6z6OS4i1utmTAMcY6NaMeAL27Q5uNbm6GrfChHx4Yi4vBrTrIi4OSJOjogtBjmG50TEVyLibxExIyKeql7v7yPiAxGxzHBf5xg7iJLqBODUzFwA/Ki6vxLw2m47ioiVq/f5/Oo9eSoiZkbELRFxWVUH5HURsVLT8y6unbO9ujzWVrXn3BoR0fT4wsdq+/aJiP+LiNursd0fEedExP7dvsba6zwsIn7b9B28vfoufSQi1h+4p4Xf6QOr7+5dtc/T/0XEHl08f2Fh99q+PSLihxFxU0Q8UT1+cIvnrhERR0bEnyPivuo7e3+UIvBHR8RaQzz+jtXxb6nelwcj4qLqt6brTCoR8Yzqu/b3iHi46uvO6n1/V0RMH+D5O9c+B9/r4nhH19of3KFd32/dH6NRzPjR6v3+S0T8b/VZW7bb19rBW2rbP+vUMDMvyszs1KZqdyslONDnBQO0nwecWd3dLCJ27NBcE4ypjSRJkiRJmrge7PDYxUC7SZy1q9selFzW+2Xmxe06iohtgF9TajQ026C6PQd4E/Bz4HUd+joCOBJovnp9ler2HOBDEXFYZn6zXT+jISKeSRn/pk0PbVLd3hwRB2bmzwfoZ1ngJODAFg+vVd12Az4WEa/LzL8Me/Bj45DqzznAGdX294GPVttvpRFYaCsiXkKZ6Fyt6aElgY2r2/MotUC+SEmh1OckSn73vuOd08W46+mYvtNpAjVKsdpTgDc0PbQ65WrsPSPiNcAbq0nTtiLi3cDngBVaPLx+dXsZcDyNAE27vlYBfkxJG1O3FuVK9FdHxJcy88Od+qn1tyTwbcp3dqC2hwBfoRThrVu9uu0AHF59Z0/q8vgBfAH4MP1TkC0F7FTd/isiXp6ZMzv0MxX4MqW4b/MF0+tWt72Aj0fE/p1y/I+0Kuj0baA5WDuN8pnYlDIx/37gfTTShA3lWCtRfs8B/p6Ztw21rxb+U9tetYv2ZwFvq7b3B/40gmNRDxlIkCRJkiRpYnlxbfuGDu1Wr/68C7gM+DfwKGUSa1PK5Noa1e23EfHczLy+uZMoKxZ+S6OA5wzK5O3NlJQXy1f9PZ8yAdxWRJwEvL2265LqNqPqZ8fq9U0HvhER0zLzq536HEHrUl7XWpTc4ecBD1T79632Lwn8ICKuyczmotjAwvfrIuDZ1a45VV9/A2ZW/ewFbE4J5lwQETtl5uWj9LpGRETsDGxW3f11Zj4EkJnXR8SVlADQzhGxabv3pupnPcpEY9+k9J3A74BbgbnAipT35gWU96fZz4CvAisDr4yI1TJzRot2fcebRmOyfAHw3QFe6smUIMIj1ThvpExu7wq8sGqzP3AtcFyH434J+FBt1/3AucAtlNe5NvDc6jZQxpCplNe9C3BvNa7bKO/h3sC2VbvDIuLKzDxtgP4A/pfyvswGzqZ85qn6eqr2Ot5L/zz3d1CuUL+b8ll+BSXAuCxwYkQsk5lf6eL4x1ACRE8CvwGuq/a/gEb6nJcAX6IElNo5nf6ByyuB84HHKal4XkX5TG0AnB8RLx2LwF1EPJsSVOsLEN0IXED5vC+gfH63Al5UbQ/XHjTmeS8cgf7q6ivfHuii/cWU1ziF8vl83wiPRz1iIEGSJEmSpAkgIqYAn6BcqQ1lIu+nHZ5yBvCbdhPU1RXJx1KuJl+GMlm4W4umr6cRRPgN8PrMfLJNn88Atmnz2NtpBBFuAg7MzL+2aLcb5XWtDJwQEb/LzE4Bk5HyNmAecGhmntw0piMoQYYXUlZSfJSSY7yVE2kEEc4BDsnMe5r6C+CDlKvtlwJOj4gtM3PuyLyUUVG/qv/UpsdOpQQSgpJe5ZO0dyiNIMLJwLtbXdlfvUc70HQFdGbOjogfUK7iXhJ4I+Vq+XZeTgmWAfwuM+/o0HZDyuT6L4GDm3LHHxkRh1HOGcBHI+LLmTmrqQ8i4s00gggJfAY4LjNnt2i7DvCeDmOCMtkMpZDtYfVjVp/NLwMfqHYdBXQTSHgnpTj2azLz9lYNImJbGq8XyhXzH64X242IDwP/Uzv+FyLiosy8eoDjf5ISRHxdZt7ddNzXAT+hfJ7eGhGfzsy7mjuIiHfQCCLMA96Wmd9varNG1ddLKL9zP4pSMPiJAcY3XB+kEUQ4CvhMq5UwEbEEJUi1yGdjkF5S217kd3WootQ9eXlt1x8Hek5mPhIR/6YUad4kItZtdf408VgjQZIkSZKk8WP/iDi86faJiPgm5YrWY6t29wL7tpqY7JOZn+p0lXtmzsnMj9HIf71rRGzWoul2te1PtQsiVH1em5mnN++vJqM+W919FNi1VRCh6uN8Si5+KKsnPtLueKPgiOYgQjWmx+k/kd6yFkBEbE8jndFlwCubgwhVf5mZX6ZMAENJm3TAcAY+miJiRcqqDCirR85uanIa5Sp7gIOrydF26p+nI9qlB6reo0sy89ctHq6fo7e2eJw2j58yQFuAqynBskdajOlLlPMK5Sr3XZvbVKmRPlfb9d+ZeVS772pm3p2Z/93FuH6Tme9qDlxUk9MfoawUANiiSkU2kIeAl7ULIlQ+QQnWAJyVme+rBxGq48/NzA8C/1ftmkbnQFKfO4G9m4MIVZ8/o0z+Q5m7fHVzmyqlUf19+1hzEKHq637Kqom+17kRjRRdo6nvc/4oJYjUMp1WZs7PzPMyc8AJ+gFsX9v++zD7qjuSxoqJmyirKrpxbW37uSM4HvWQgQRJkiRJksaPd1Ku7q3fPlvt35QyWfsFYOsRzPVdn/h/cYvH65PCzXUNuvV6GqmWvj7AVeFk5lmUVExQJgHHwgOUlDktVasi+tKvrBatC0LXryw/sosVBvWrvV/Z1Sh740Aa5/7Hza+rSi3UV6tgXRqpaVqpf56GVGw6M/8B9H3+nx4RLScqq6v9+woyP0BZaTCQY5ony5vUi9hu1+LxVwPrVNv/Bj7fxTG70XZyvjof9YK4rcbV7JvVJHtLVZ2PfWu7PjZAf/XHXxVNRbJb+GKrYE3NQO/zrjRWSt1NSdXUUhUIPLa266B2bUdQ3+d8SQaofzFCNq9tj0h9hCqdWT2Qe/ggVk3Vx/C0kRiPes9AgiRJkiRJo2O1iLiiduuU57tb0yhpdX5VFQbuSkSsGxEvi4i3RsQH6yse6J8SY4sWT/9bbfurETGUSaF6yqRzu3zONdWfq0fEhkM45mCdP8AEMpRVIX3WaPF43+ucQxd5yqursftyjm/fqW2PdUpr1Kd+NXinVQL1z9Mp1WT/UNSL+rY73kE0JnF/0MUk6HxKPYtOuv0MAJyamQsG6K8b92bmNQO0GWhczX4zwOPPo/zeAFyXmf/s1Dgz/01ZzQHlPd9hgP5/O8DjA72eF9W2/y8z5w/Q308paaYAnlUFSkZT3+d8acrnfCTqILRUvZaVqrtPZebDI9DnZpRgTl9a/G9lZjeBuD71lVjrt22lCcUaCZIkSZIkjY4ZmTnYyeFdMvPC+o4qTczKwLMoefz3pxQlviQi9m5u3/Tc/SlXlD6ny+Ov1GLfaZSrodejXBn8r4i4nFLQ9BLgksx8cIB+60GPi0r6+0FZnRG6yraDbvp/vLa9XP2BaqKwb8JsSWDOIF/n6gM3WVRE7AU8vUOTOzLzjKH0XfX/TBqfnxvapaSiFAB+mPJZfUVErNHmivdvAe8GVgBeCtwWEX8E/kBZZXBpdQX5QM6g1EZYATggIj7Uol7BW2rb3+6izxld5M5v+xmobF3bHqkC2sP6bLYxUN2R+hXuA9U76HMVjdUDT6OxSqWVgV7TQK9nUOPLzEcj4hbKyq4lKOnERjIFULMvUeo3TKXU8XhdRFxAKcR+KfDXVvU1hmiF2nbb1HPdioj1KQG1vvokF1BqPgxG/Xu0fNtWmlAMJEiSJEmSNI5VV9rOAH4P/D4irqHkYF+aUqR38+aJ16pQ7ckMnD++2fQWx388InanBBSeTSmA+vzqBpARcXX1+EltJoFXbbFvMEb76mHorthpPc95c5aHXr3GA+icquUiyqT7UNU/Qz9o1ygz50TEGZQ0XNMoRYu/2KLdbRGxJ/BDyqTuVGCX6gYwPyIuoax8+H67VSKZ+WREnFYdb0XKpO3C8UXETjRSqlwy0BX1leF+BqD/56Bt6qBBGolxNXt0gMfrV9DP6KK/5nardGrYqb5LX5PadqvXM9TxbVptdxzfcGXmXyPiNZTf4bUov617VzeApyLiQuC7wE+HuXKlPr/bsuZIt6oVQhdQakkAXAy8IjOfGmRX9XFMa9tKE4qBBEmSJEmSJpb/Ad4PrE2ZoHoj8M2mNm+lMQE8F/gO5Yrxf1ImN2f1pQKJiF0YoIBmZv6rKiS8O6XQ8E7AVpSgQlACDM8GDo+I/TLzT01d1OcfjqH/1cbduGWQ7Xuh/hofoNSymNCqwsFvqO16UUR8r8NTNqptv5UWgQSAzLw0Irak1L94JeXztEn18BKUFTc7Ah+JiFd1CAKcRAkk9B2vHuioB0C6WY0wGloW2B0PBjlxPZTXMZavfVyOLzN/HRGbUIJc+1A+0+tWDy9FqSWyJ/Dh6nO+SGH2LtVXISwSDO5WFUT4A7BZtetPlILYQ1nlUK+nM9AqH00QBhIkSZIkSZpAMnN+lVroVdWuHVk0kPD+2vZ+A+S2XrHL4yYl3cV5ABGxKqU4896Uq+KXpwQ2zqpWSTxQe/qDwJrV9i8z86pujjnB1NM7LZWZJ4zFQTPzYODgUer+tfS/cnvvdg1b2CoiXpCZl7R6MDPnAf9X3fomMXeiBBf2pUy0Pg34dURs3eqK6My8OiKupKRe2ikiNs3MmyNiecrkLcBM4CeDGPdw1T8Ha7ZtNf49VNtercvn1NsNO0//AEZ6fPXAQjc5yboqFF6lL/pBdSMiNgZ2phTl3ocSOHsupYbDjt302cIjlIDxNGC5iFiyi3ov/dSCCH0po/4CvKyLVF/t1H83HmjbShOKxZYlSZIkSZp46leI9sutHxHLANtWd2/pokBmp/z6bWXmg5l5ZmYeSpnw7Vs1sBJwYFPzej72F7F4mkEjxcoKEbFtp8YTxGBTYw35+Zl5d2b+ODPfQKmp0TdRvAmwV4en9hVdDuCQavu/aEz0/jgzZ3Y/5GG7rrb9vDE87kj7d217u7at+qu3u7Ftq5ExqPFFxIo0Vr3MZ9FVTvXPSDc1Jjboos0iMvM/mfndzHwVpdB9X4DsRVU9kqH0OR+4o7obwKAKmFdBhAtpBBEuAfYa5vdm3dr2rcPoR+OIgQRJkiRJkiae+iRW85W19dzhDzGwfYc7mMy8j8aELsCWTU3Oq20fEkOotjzeVSs2zq/tOqRd24kgIjYCdq3uzgZWzMwY6Eb5bPZd3b1/RHQzKdtPZv6L/qsImj9PdafTSJ1yUFWcvP7enzLY4w9T/TPw5oiYqHNvl1Oucgd4ekRs0alxRGxKY0J/PnDZKI4N4M+17VdX572TfWmsNLimxZX299W2N6eDiFiSRk2PIcvMP1NWAfTp9DkfyLW17Y7nqi4i1qUEEfrqiVxKCSIMNv1cs/pr+dsw+9I4MVF/zCRJkiRJmpSqnNvPr+1qThP0MI2J3K0iYmnaiIiDgWeN5Pgqs5ru/4hGUONZ9E+91NEECzp8rbb9rojo+or0cfg6D6Ex8frrzHysmydl5h2UAq1Qrux+/QiMpfnzVD/e48CPq7vrAh+i8f24LjMvHYHjD8aZwN3V9tOAj47x8UdENdH+89quzw3wlONpfF7OzMxHRmNcNRfQuAp/PeC97RpGxLLAkbVd32tuk5l30kjB8/SI6BRMeC+wxmAG26W2n/Mu1AM3Xa1sqIIIf6ARRLgM2LPb73qHfgN4RnV3Nv2DHJrADCRIkiRJkjRBVFeJ/5xGzcNZlCuyF6oKY15R3V0WOLEqmtvc10HAiQxQdDQifhwRR1W5vdu12Zr+wYGL649Xk73/Xdv1pYg4eoAgxzMj4muUCcoJobrC+Izq7lLAuRHx+nZBgoiYGhEvjYgzgdeM0TAHVF1Ff3Bt148G2cVpte1+6Y0i4oKI+FBErNXh+DtSioj3ubhd20p9NUx9wnusVyNQ5aY/orbruOqz3rIIbkSsGxGfGZvRDdpxQF+u/ddExFeqq/EXiohpEfFFGjUp5gKj/nqqGhv14/xPRLyhuV1ErAb8ikYh8FuB77bp9he17W+3Wk0TEYfQxW9SRFwbEW+LiJU7tNkXeGl1dx4lpdBQ/a62PWCthTZBhD2GG0SoPIOS4g7golb1TTQxWWxZkiRJkqTxY/+I2L5p3xTKpMx2wG6Ugpp9Ds/M21v0czyNq4nfRClEezZwJ6Xg6B7ANpQUJMfRf5K/2VrA/sCnI+I6SpDidkoQYzXK1a+70LhY8RLgrOZOMvNbEbEVJeAwBfgU8N6IOI+S7/xJSuHnTSm55Tesnvr1DmMbjw6hjH0Hynk7gzKZ3HcF9QJK+qmtKVfO9xUl/eGYj7S9PYD1q+1HgLMH+fyfUlZnLAm8MCK2zMy+OhmbAF8CToiIq4GrgbsoE9ZrUN6THWp9/SQzr+l0sMy8PCKupUxg9s11zaEqcDvWMvPUiHgO5bMelM/6uyLiHEpu/nnA2sD2lM96AJ/sxVg7ycy/R8SHaay0+QAljdBZwD2UYtKvAOpBxo8OdL5GcHwnRcRLKUGMacAPI+KDlPRSjwObUYoar1Q9ZRbwhg4FhL9A+b1chlJI/l8R8TPgXhq/m0+v7v8aeFuH4W0LnAx8IyL+Sknvcy/l+78mpeByvT7NF5sK1A/W1ZTf5Q0ov/fTMnNuh/b1IMJcSiDi0C4WRj2amScP0Ga32vavBupQE4eBBEmSJEmSxo93dtnuYeCDmXlqqwcz8xcR8SngaMok5YbAu5qaPQG8gzKJ2ymQMKe2vU11a+cc4MCq+GercX2gCkYcT5lMXxU4oEN/TzH6RVtHVGY+GRE7AydQzudUSnBk0w5Pe5AyMTte1FcR/Ky6yr5rmflwRPwWeFWtv49U2319TQGeU93aORU4tMvDnkz/1FK/zMwZ7RqPtuqzfitwLGVl0BrAm9s0nzdW4xqszPx/ETEL+AolVdWGtE4j9ARwWGae1OKx0fRflPoG76J8pravbs3uAPbPzLZX/WfmLdWqhh9TVhStw6Jp2P4DvJYSoOhkLiW4MQ14YXVrZT4lsPaJAfrrKDMzIn5Y9bMiZTL/nA5PeVptexr9Uz91chvlu9bJa6s/59JYoaXFgKmNJEmSJEka3xKYSZnA+iVlwmyTdkGEhU/KPIZyVe1PKMGCuZQ6BddSJvKfkZndpKzZB9idsnLh95TUILMoE2CPAn8Hvg3slpkvy8zm4s/N4zqJxmTkLykTU09QJlMfotR8+B7lyuC1M/OrXYxxXMnMpzLzfZQroo8GLqIECp6qbvcCfwL+l/L+rlOlReq5KhXMK2u7TmvXdgD1z9abI6JvJc2zqv6/RHlf7qTkUZ9HCZBdSQkIPDczDxpEWpQfVv30+fYQxz1iMvPLlKv1P0F5rfdSvoezKZ/731BqOqzfro/xIDNPoQTCPkVZcfQA5XXMoKTE+TSwWQ+CCGTmvMx8L2XF1teA6yi/S3Mo37lzgfcAm3cKItT6O5OymuAkyuqR2ZRVOVdRUlZt1+WKi9Up9UG+TnnP7q3GNI8SOLwU+Dzw9Mz8aGYu6OoFd/Ztyu8ywIEj0N+gRcSGNIImP8/MB3sxDo2OyOyYClGLgc3XWyK/9p62aSclSdJiYs8jZvZ6COqhcz+3SBpfTQLv+/osbrxz/ngr0DrmIrbPRkmA8SSuzMxWV8ZKGmERsQElyBWUSfpNRmhyVpowIuKnlFRPs4F1M/OhAZ4y0sc/lkaarudm5nj8y1lD5IoESZIkSZIkTXQHU4IIAN81iKBJ6ljKKrbpLJrOblRFxNKUdHkAvzWIsPgxkCBJkiRJkqQJq0qb9Pbq7nzglB4OR+qZzLyWUuMB4AMRscwYHv5tlJROC+hcd0cTlIEESZIkSZIkTWQfBNartn+emXf2cCxSr32UUndmdUoNjlEXEcvRCB58OzOvHovjamxN7fUAJEmSJEmSpG5FxJaUAuDTgedRcsJDKQD86V6NSxoPMvPOiHgDpQD1k2N02E2Ab1XbXxujY2qMGUiQJEmSJEnSRLIDrScrj8zM68d6MNJ4k5m/BH45hse7Frh2rI6n3jCQIEmSJEmSpInqQeA64ITMPKvXg5GkxZWBBEmSJEmSJE0Ymfk94Hs9HoYkTSoWW5YkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW0ZSJAkSZIkSZIkSW1N7fUAJEmSJEkalufcDVcc3etRLCp6PQBJkqSR4YoESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLUloEESZIkSZIkSZLU1tReD0AaC9u+8hTW2Wb/fvsu+sbWzH709h6NSGPFcz95ee4nL8+9NFkEq2y4E2tu8UpWWncHllpuLaZNX4m5sx/mqZn38eTDN/PQ7X/i4dv/xMwZ1/d6sJIkSdKEZiBBi73VN3vZIhNKmhw895OX537y8txLk8MKa23H1nt+mRXX2X6Rx5Zabi2WWm4tVljrmay11WsB+N3xK5I5f6yHKUmSJC02DCRosTZ1+kpsvddXez0M9YDnfvLy3E9enntpclj76Qfw9H2+xZQpjf/KzJ/7JE8+dDNzZj3IlKnTWXrFDZm+/No9HKUkSZK0eDGQoMXalrt/fuF/Imfc8ntW22T3Ho9IY8VzP3l57icvz720+Ft76/3Ydp8TiSlLAPDEQzfx74uO4YGbzmbBvNn92i613NqsvtlerPfMg0iyF8OVJEmSFhsWWx6EiJgeEQdGxGkRcUNEPBIR86o/b4iI0yPijRGx9BD63jIijouIKyPi/oiYHRG3RsS5EfG2iFh+NF7T4my1Tfdg3W3fAMD9N/2We67/aY9HpLHiuZ+8PPeTl+deWvxNX2F9tt7rqwuDCA/+5w/85ZQXcN8Nv1gkiADw1Mx7uPOa73Lp93eGXDC2g5U0JiIiq9utbR7fqNbmwrEd3cQTEQfX3q+jez2eoRroczFeRMTWETGnGutbxuiYJ1fHuy0ilhmLY0pafBhI6FJE7AP8G/gR8F/AFsCKwBLVn1sABwA/AG6KiFd22e/UiDgW+AdwBPBsYHVgKWBDYA/gZOAfEbHLSL6mxdnUpVZgm72+BsC8px7jn+d8sLcD0pjx3E9envvJy3MvTQ5b7/UVpi5Vrq2Z9ejtXP3zA1gwb1aPRyWNjOoisuxwmxMRD0TEJRHxxYh4Zq/HLGnYvgZMo8wHfb9do4hYJiJeGBHvi4jvR8Q/qotahxL0+RQwC9gA+O/hDF7S5GMgoQsR8UbgV8B6td2zgCuA84ErgfplUOsAZ3YZUT4F+CQlIAGQwPXAxcAdtXYbAL+LiD2G8hommy12+xzTV1gXgBsv/BSzH7+rxyPSWPHcT16e+8nLcy8t/pZddQtW33TPhfdv/MNRzJ/7RA9HJI25acBqwA7AYcDVEXFiREzr7bAkDUVE7A3sWt09NrP10rmI2AZ4DPgz8FXgzcA2NOaQBiUz7wa+Xd39UESsM5R+JE1O1kgYQERsAJxII+gyC/g4cHJmzqq1WwZ4B3AcMB0I4BsRcXFm3tym78Mofwn0uRh4e2beWGuzOyUyvQ7lfP00Ip6RmbeN0Etc7Ky68W6s98yDAHj4jr9wx1Un93hEGiue+8nLcz95ee6lyWH97d66cPupJx7gvn+d2bvBSKPvW0Dz/yGnA+tTVqxvRPn/5qGUlewHj+HYJI2Mz1R/3gz8rEO7aSwaNJgLPAUsN8RjnwC8G1iacmHru4fYj6RJxkDCwN4B1PPGvSkzf97cKDOfBL4cEXcBZ1S7p1P+cfex5vYRsSpwVG3X1cAemflUU7+/j4idgGsof0msABxL/wCEKkssuRzbvOz/ATB/3mz+cfZ7ejwijRXP/eTluZ+8PPfS5FEvnj7jlvPIBfN6OBpp1J2RmRe2eiAilgCOpkz+ARwUEV/NzKvGaGwTSmbeSgm6qAuZ+T3gez0exmKvyjSxXXX3xHarESrzgKsomTD6btcCJwEHDeX4mXl7RJwNvAJ4S0R8KjMfGEpfkiYXAwkDe0lt+/pWQYS6zPxJlZ9uq2rXjm2avpdSW6HPO5qDCLU+b67qKHy+2vWGiDiq+keRarbY9TiWXnF9AG7+0/E8+dC/ezwijRXP/eTluZ+8PPfS5DB1+koss8pmC+8/ctdlACy13Nqs+8w3s8bT9mHplTZkiWnLMnfWQ8yccQMP/ud87vrbqcyd/XCvhi2NisycDxwZES8DnlPt3ocy0ShpYnh/9ed8OtRGAMjMf9D4ri8UMez42CmUQMJ0ygW0n+ncXJKskdCNNWrb13b5nHq71dq02a+2fXlm/nWAPr9Now7DFGDfLscyaayy4c6sv90hADx237XceumXezwijRXP/eTluZ+8PPfS5LH8GtsS0fhvy5MP/pt1n/Emdjz0Sp6205GsuPazWXLpVVli6nSmL78Oq228K1vs+lle/K5/sN6zDunhyKVRdWFte912jaJ4QUR8OiLOi4g7ImJWRMyOiLsj4tyI+GBEdJUiJSKWjoh3RMTZEXFn1deTEXFbRFwREadGxJsiYs0u+lonIo6KiD9FxD1VQekHq36Oi4j1Buqji2NsVCtIe2GbNgc3F62NiBUi4sMRcXk1plkRcXNEnBwRWwxyDM+JiK9ExN8iYkZEPFW93t9HxAeqNMkjJiJeEhGnRMR1EfFYRMyNiPuqAr2/jYiPRcR2bZ67yHvRos33am12rvZtEhEnRMT1ETGzOu7fIuLYiFh5EGPfofoM3Vp9Ru+NiIsi4u0RsWTV5sLa8Tca7PvT5ri7RMS3qvE/XJ2juyLirIg4JCJG7CLciFgL2Ku6e3Fm3j9SfQ/SucDMantIKxskTT6uSBjY47Xt6V0+p95ukcugImITSnGcPr8eqMPMfCgiLgF2qXa9Evhil+NZ7C0xbVm22bukt1iwYB7Xnf0eysU6Wtx57icvz/3k5bmXJpcll1613/3VN9uLjZ7//oX3n5p5H088dBMxZQrLrboF05ZeBYBp01dkm5d9lekrrM9NF396TMcsjbEHOzx2Me1Xya9d3fYAjoiI/TLz4nYdRSn6+mtKjYZmG1S35wBvAn4OvK5DX0cAR1JytNetUt2eQykEe1hmfrNdP6MhIp5JGf+mTQ9tUt3eHBEHDpStICKWpaSfObDFw2tVt92Aj0XE6zLzL8Mc95LAd4A3tHh4jeq2DWUS+/iIWD4zZ7ZoO9jjHgCczKL5+p9R3d4SEbtn5g0D9HM88FH6p6Jas7rtBLwtIl4z3PE2HXMN4IfAS1s8vE51eznw0Yh49UCvoUuvo1HzYMC5oNGSmbMj4jzgNcBmEfGczLyyV+ORNDEYSBjYpcCzq+0XRsSSmTmnXeOIWAp4YW1Xq3+INUf//9zlWP5MI5DwrC6fMylsvsuxLLPSRgDcdvn/47F7r+7tgDRmPPeTl+d+8vLcS5PL1Okr9rvfF0SY9egdXH/uB5lx87kLH4tYgrW2fh1b7fFFpk1fCYBNX/QRHrv3Ku6/8awxG7M0Bl5c2+40ubl69eddwGXAv4FHKQVcN6VMKvdNMv82Ip6bmdc3d1KtWPgtpeAzwAzgHEqh2FnA8lV/zwc27jTwiDgJeHtt1yXVbUbVz47V65sOfCMipmXmVzv1OYLWpbyutYB/AOcBD1T79632Lwn8ICKuyczmotjAwvfrIhpzCXOqvv5GuQq876r0zSnBnAsiYqfMvHwYYz+eRhBhPnABpRbjQ5Si3GtR5hGex6LFe4dqD8rk/5TqeJcCTwJbULIwTKe8dz+LiO0yc26rTqKkcq7XlryB8nl7kBKgelU17p8CORIDj4h1gT/RCIzNpJz76ynZIDagpA1bv3o9f64m228d5qH3rm1fOMy+husPlEAClHEZSJDUkYGEgX2TUjB5KuUfV58FPtKh/edo/GNtJvD/WrTZuul+t0md6+1WiIj1MvPOLp+72Fp5gxez/rPfBsCTD9/MTX80td9k4bmfvDz3k5fnXpp8lpi66KLgOU8+yOU/fCmzH+v/T+HM+dxz3Rk88dC/ef4bz2PK1KUA2HyXY7j/37+BjvUspfEvSp6vT1AmVQHupkyutnMG8Jt2E9TVVezHUiaDlwG+RrlKvtnraQQRfgO8PjOfbNPnM+i/Ar/+2NtpBBFuAg5sleY3InajvK6VgRMi4ncjdDX4QN5GKW57aGae3DSmIygTzS+krKT4KCW3fCsn0gginAMckpn3NPUXwAcpmQaWAk6PiC3bTbZ3Uq1+eGd19xFg58z8W5u2KwNvBAZ9nBaOAO4DXpOZlzQd5zjKhZWrUz4PrwNObzGe51I+032OBI6rFyCOiA8D3636GHYgofoenU4jiHAq8P7MfLSp3TTKHM+HKStlfkD/IN5Qjtu3Qugpuk+fPVrq372dejYKSROGNRIGUBW2eR8log9weET8JiL2jIhVImKJ6s+XRcQ5wIeqdo8D+2Xm7S263ai2PZ/yj79u3Nahn0lpytSlefreX1+YN/e6s9/HgnmzB3iWFgee+8nLcz95ee6lyWnenCcW2XfTxccuEkSoe+yeq7j9yhMX3l92laexygbOkWjC2D8iDm+6fSIivgncSJn4B7gX2Dcz2/5lmJmf6nSVe2bOycyPAb+qdu0aEZu1aFpfVf+pdkGEqs9rM7PVhPHSlAvzoKyK2LVdrcDMPJ9G3vZpdL6Yb6Qd0RxEqMb0OPDW2q7XtnpyRGxPI53RZcArm4MIVX+ZmV8G+go9bQIcMMQxb0kjTdQP2wURquM+nJlfy8ynhnisugXAq5qDCNVxbgCOqu1q+X4BH6cxN/W9zPxMPYhQ9TWT8p7+g/6pj4ZqXxoBgV9k5kHNQYTquHMz83DgF9WuHSNiyIEEysqG5avtGzJz3jD6Ggl/r21v37NRSJowDCR0ITO/RcmL969q196UqwoepFyt8CBwNrAnJTBwFvCCzDynTZcr1LYfz+4TOzf/xbZ8y1ZARBwapUjVFY8+MSIr/8alzXc5hmVW3gSAO6/5Lg/d3jalpxYznvvJy3M/eXnupclp/pz+KbwXLJjH3dedMeDz7rr2h/3ur7KhgQT1xGp9/y+rbod28Zx3Av/TdPtstX9TypXkXwC2zsxLR2ic9Yn/VhOl9VQ4zXUNuvV6Gqv3v56Zd3RqnJln0ViV/4ohHnOwHgDaplGqJsevq+6uFq0LQr+ntn1kFysM6rUPX9nVKBc1EudnKM7KzMs6PP6z2vYiBZ4jYnkarzmBo9t1VL2Px7Z7fJDeXdv+eBftR+IcQUll1af5QtExl5lPUFKKAawUEat3ai9JpjbqUmaeExF7AP8LvLpD098D/y8zr+vQpl6EaNYghtHctm0gITNPohR2YvP1llgsIwnLrrolGzynrCSd/fg9/OuCT/Z4RBornvvJy3M/eXnupclrzpMz+t1/8qGbmT/n8QGfN3PG9cyfO4slppU5tWVW7pi2XRotMzJzpK/0nUZJq/PCiHhvp6vP66qc8M+gFJBdnv7zAfXCwlu0eHr9GF+NiP0zs9sUvX3qKZPObduqv2uApwGrR8SGmTnak6/nd6qJWLmRRuqmNYDm5VF9r3MOXeTAz8y7I+IBSpBlqJ+Vf1ICTNOAN0XE74GfNF/ZPwp+2+nBzJwREQ9TUlSt0aLJc2l8Dq/p4vz+mnLx5pBrPFQrY/rqWv6ny8/xNbXt4Xyf64GnRVap9Mg9wGrV9vqUYJoktWQgoQtVoaT/oeRL7HvP5lCuRHiY8o+wbSg5JfcE9oyIi4E3trnKYlptezBL2ZrbTmvZapJYctnVF6a3mL782ux22F2Dev5L3t2oITbrkdu4+Jst03hqHPLcT16e+8nLcy9NXjNn9E+LPnf2Q10/d+7shxcGEqZNX3lExyWNol0y88L6johYgjIZ+yzK/0v3p+RavyQi9m5u3/Tc/SmpgZ7T5fFXarHvNOCTlInQ7YB/RcTlwPlUxZIz88EB+n1mbfuiUiJgUFZn9K/i7qb/eiSzfpFgX/2BvloSSwJzBvk6h3RFeGY+HhHfAD5QHfd0Sm2JcygFhS/LzH8Ope8BdPt+rUzTe1WpX6H/9xaP95OZT0bELZTg0lBtSXmPADaOiMFeeDmcq/br2SnapgcbY/X8gW0vVpUkMLXRgKqldhdQlpFOpfzYfwhYJTOfnZm7ZebzKP/YOojGsrCdgD9GxFotuq3/UC9aPa695raLJoyVJEmSFiNzZ83gqSfuX3h/yhJLdf3cvmLLAPPnDWYhsDS+ZOb8zJyRmb/PzAMoRW6hpLE5vfp/az9RfBv4Md0HEaDF/1Gr+gC7A1f1dQ88n1Ik9yzggYi4MiI+3GoslVUHMYZWlh3m87vRTfGl+sRz85xKL1/jRyjFsvtSJ69LqenwXeD6iLg3Ir4ZESN5NcVg3q9WEZWVatsDBaIG266dXp6j+sW8va6P0Kc+jkl9saqkgbkiYWBfpCy3g7IKYY/M/HNzoypf36kRcSlwKSXiviHwDRYtKlRP9LrMIMbS3HbgNd2LsZw/lzlPdv9viClLLMnUpRr/pp076yEyy79p5s5+eMTHp9HjuZ+8PPeTl+demtweuu0i1t56PwCWXmnDrp4zdfpK/VYhzJl5f4fW0oTzP8D7gbWBtYA3At9savNWGsWB5wLfoUz6/xO4H5jVV68vInahXEDXVmb+qyokvDvl/7g7AVtRJogDeHZ1Ozwi9svMPzV1UZ9/OIbB/3/2lkG274X6a3yAUstiTFRzEu+PiC8DbwB2BXagUTNhTcoFku+IiM9l5n+P1dg6GErh5OEWW66fo38B3x7k8xcpyjwI9VUIg7modDTVa2p4saqkjgwkdBAR6wBvqe06uVUQoS4zb4yIz9H4B8OrI2KTzKz/o6eec27ZiFi+usJjIGs33Z/RstUk8chdl/KH/+3uP5IA62z7BrZ9+YkL7//luzsy+9HbR2NoGmWe+8nLcz95ee6lye2+G85cGEhYculVWWHtZ/PYPVd1fM5qG+++MCUalN8RaXGRmfOr1EKvqnbtyKKBhPfXtvfLzF926HLFLo+bwHnVjYhYlVKceW/gAEpqlLWAsyJi88ys/9/3QcpkNsAvM7Pzl3hiql/1sFRmnjDWA8jM/wCfAT4TEdMoq1F2Bw6kEfj5RETcmpknj/X4mtSv7uh2pcAqwzxm/RzNGeNzVP8+DPd1jJT6OKyPIKkjUxt1tiv9gy3/1+XzzqxtB/CSpsdvaLrf7cxIvd0CSpEnSZIkabH2wE3nMPuxRm2UTV/0sY7tY8pUNn7BYQvvz5/7JA/cfN6ojU/qkfrVzf3ytkfEMsC21d1bBggiADx9KAPIzAcz88zMPJSSt77vArqVKBPXdfX/B79oKMebAGbQuOBvhYjYtlPj0ZaZczPz0sz8DKWu49dqD7+7R8Oqqxc6HvC9qj7Xm4zAMfvSP21d1bUYK/+pba/XttUYiVLAY53q7gKgVY1PSVrIQEJn6zfd7/ZHtfmSx+Y6Cdc13X92l/3W292amSZ6lSRJ0mJvwfyn+PfFn154f42n7cNmOx3Zsm1Mmco2e3+DFdZ8xsJ9t191MnNnTerFvFo8bVDbbs7bV58c7aZC+b7DHUxm3gecVNu1ZVOTejTvkBhCteXxrlqxcX5t1yG9Gkuzamyfre1qPj+98FcaOfqfFREbdGoMvBxYYjgHzMxHgCuqu0sAbx5Of4P0dxo1I7YYw+O2sxHQV0zohsx8qodjkTQBGEjorPlHdOmWrRbVXMvgyab7V9A/91zzioV2dqptX9jlcyRJkqQJ7+6/n8a9/2wsEN70RR9jh4MuZP3t3sYqG+zEKhvtzIbPfS8vetvlrLtt40LoR++5mpsuPrYXQ5ZGTURsQil23Kc5TdDDNCYst4qItv+XjYiDgWeN5PgqzRe+/YhGUONZ9E+91NEECzrUr/p/V0Q8r9snjvHr7PmFiZn5GKVuB5RsDke3a1ulafrkCB26fo6Oqr5PXRnOOaqCGH2ZJdaKiDWG2tcIeVZt+7JeDULSxGEgobO7m+4/t2WrRTX/Q+HO+p1qJcE5tV37Vkv02oqIHem/hO/nXY5FkiRJWixce9Zbuf/GXy+8v+I627P1Xl/huW84m+f+16/ZcvfjWXbVzRc+/vAdf+Gqn+zLgnmzezFcaVRExEaU/w/2peGdBZxeb5OZT9K46npZ4MSIWIomEXEQcCKNoEO7Y/44Io6KiI07tNma/sGBi5vG9DhQL/D7pYg4eoAgxzMj4mvA8Z3GN55UdRXPqO4uBZwbEa9vNwEdEVMj4qURcSbwmqEcMyLeUJ2jnaNeHKZ/m6WBL9d2XdyqXQ8cT0mrA/CWiPjv5tcQEctRAlHbMsBntUunA3+ptlcBLoqI3ds1jojpEfHqiPgD3WeUaOd3te0dh9nXcNUvVv1d21aSVLHYcmcXUv6S6vsL/wMR8f3MnNvuCdU/Dg6v7VoAXNSi6Sk0lo+uCHyI/ssMm32qtn078PuOI5ckSZIWMzl/Dlf//ADW2fZANn7B4SxXCxrUzXrsTm697KvccdVJ5IJ5LdtI49j+EbF9074plLoD2wG7AdNqjx2emc3pdaFM0PZdgPYmYKeIOJtyodtqwB6UvPnzgePoP8nfbC1gf+DTEXEdJUhxOyWIsRrwTGAXGhcrXkLjSvOFMvNbEbEVJeAwhfL/3PdGxHmU3PVPUv5/vCnlAr2+OoFf7zC28egQyth3oJy3M4DjIuICSsrkBZT0U1tTVpb0Fbz94RCPN41yfvYHHoiISyk1KR6mBJI2AF5RjQVgDv3nGHomMy+PiC8AH692fQZ4Y0T8hrKCZQPg1ZRC3X+hzNH01dgYUlAhMxdExGuBP1Jqe6wHnBcRf6cEWO6hfD5XpdQPeT6wXPX04a4aORN4X7W9O/CLgZ4QEZ+mfFfr6r8Rr4+I5jon12XmQOd4t+rPOcC5A41DkgwkdJCZ90bEryl/4UL5C+RHEXFwdYVHPxExFfgK5S+DPr/IzEUSsmbmbyPiIhppjY6KiKsz8+wW/X62qc+jMnPOkF7UJHb333/E3X//Ua+HoR7w3E9envvJy3MvLd7u/vtp3P3301hu9W1Yfo1tWGrZtSCmMOfJGTx+3zU8fv8/ej1EaTje2WW7h4EPZuaprR7MzF9ExKco6WKCMrH9rqZmTwDvAO6icyCh/v/PbVh0UrPuHODAzJzf6sHM/EAVjDieMpm+KnBAh/6eopEOZkLIzCcjYmfgBMr5nEoJjmza4WkPUiawh6J+flanzGG8ok3be4A3ZubfhnisEZeZR1Spiw6jfFa3ZNEaDlcA+9FY7QHDSM+UmfdFxHOBb1ECMEFZ8dCp6PNdLFqPZLAupAST1gdeHRHvqepXdPISOqfE3qq61a3WqcOI2IzGa/11Zg73dUmaBAwkDOxw4MU0Ivf7AS+MiO9Rcsg9DCxPWd52ECWa3edB4KMd+j6UcqXGKsCSwK8i4nRKhPpBYGPgLdXx+/wK+MEwXo8kSZK0WJj5wHXMfOC6Xg9DGgtJmfR/ALiWMln/4yrnevsnZR4TEedTVgC8CFgDeJyyKuFs4OTMvKWa9O5kH0oalF0pKwU2o1whviQwk7I64TLg9My8YMAXk3lS9X/fNwMvpeRqX42SCugx4NbqdZ4P/GYiTnJWhWvfFxEnAAdTVmxsTmP1wcPATcCVlLQyvx/qBYOZeVpEXAnsBbyQstJhPcpcxRzK5+bvwK+BH2TmE+366pXMPDwifgG8m/JZWxN4hBJEOg34TmY+FREr1p726DCP+SjwXxHxGcqqnZdQ5mFWoRSBfqg6/uWUK/YvyswFbbrr9pgLIuLbwKeBtSmfiwG/M6PgwNr2SW1bSVJNDBz4VETsQFlutvYgnnYX8JrM/OsAfe8I/JLGPyY6uQB4RavVEJ1svt4S+bX3dFsnWpIkTVR7HjGz10NQD537ueUGbqTFzvu+Posb75w/kQrRjorYfp3kikN7PYxFxaevzMzmNEWShqDKAvEYsDRwb2YOZo5m3IiI1SjBt6WBn2Tm/mN8/CnAfyhpo67LzOa0SJLUksWWu5CZl1LSGn2BEsnv5H7KEs2nDxREqPr+E2VZ6OmUJZut3ElZ4vfSwQYRJEmSJEmSFgO7UibfoazkmJCq9NffrO6+NiI27NR+FOxLCSIAHDPGx5Y0gZnaqEuZ+RDwsYj4BGXi/1mUXI7L0lhi+jfg+na5IDv0fS9wYLVEb2caSxDvA/4FXNJFzjxJkiRJkqTFTkQsQUkH1OfMHg1lpHwOeBuwAvAR4L1jeOy+wtZXAT8dw+NKmuAMJAxSFSS4trqNdN+PUtIcSZIkSZIkLfYi4qWULBCnZOZjLR5fjZLHf4dq133Aj8duhCMvM2dExNHAl4C3R8QXM/M/o33ciNiPUuMzgfd50aqkwTCQIEmSJEmSpF5ZlTKhflxEXEzJ9vAQJQPENsAe1TbAAuDQzFwcCkN9jZKqaUlKkedRDyRUx/s0cEdm/mUMjidpMWIgQZIkSZIkSb02nRI02KPN448Ch2Tmr8ZuSKMnM+cBx43xMU8dy+NJWrwYSJAkSZIkSVKvnAm8DNiTkr5oTWANypzVQ8B1wO+AkzPzkd4MUZJkIEGSJEmSJEk9kZmzgXOqmyRpnJrS6wFIkiRJkiRJkqTxy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqy0CCJEmSJEmSJElqa2qvByBJkiRJ0rDcfQ98+tO9HoUkSdJiyxUJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpLQMJkiRJkiRJkiSpram9HoAkSZJGxrmfW67XQ1AP7XnEzF4PQT2wws+37/UQJEmSNAm4IkGSJEmSJEmSJLVlIEGSJEmSJEmSJLVlIEGSJEmSJE04EZHV7dY2j29Ua3Ph2I6utyJi9Yh4uHrtnx6jY/53dbzHImLtsTimJGnsGEiQJEmSJKmHIuLW2oR3q9uciHggIi6JiC9GxDN7PWaNe8cDKwH3A/8zUOOIWCoi3h4Rv4uIOyPiqYi4JyIujogPRsQKXRzzy8A9wPLACcMZvCRp/DGQIEmSJEnS+DYNWA3YATgMuDoiToyIab0dlsajiHg6cHB194uZObOL9lcCJwEvBdYFlgTWAl5MCRD8PSJ27NRPZj5JI4DwXxHx7KG+BknS+DO11wOQJEmSJEkLfQu4uWnfdGB9YA9gIyCAQ4GlaEwYS32OpVw4+hjl89RWRGwEnEcJGgA8AvwcuBVYG3ht9dgGwNkR8ZLMvLpDlycBR1JWQ3wG2HtoL0GSNN4YSJAkSZIkafw4IzMvbPVARCwBHA18stp1UER8NTOvGqOxTSiZeSsl6DJpRMQWwKuquz/MzMcGeMopNIIIfwZelZkP1vo7HDgNeDUlZdEPI+IZmTm/VWeZOTMifgC8D3hZRGybmX8f8guSJI0bpjaSJEmSJGkCyMz5mXkkJQ1Nn316NR6NS++jETz5TqeGEfFSYNfq7kPAvvUgAkBmzgLeANxU7doaOGiAMZxS2/5AF2OWJE0ABhIkSZIkSZpYLqxtr9uuURQviIhPR8R5EXFHRMyKiNkRcXdEnFsV0l2um4NGxNIR8Y6IOLsqyDsrIp6MiNsi4oqIODUi3hQRa3bR1zoRcVRE/Kkq6jsnIh6s+jkuItbrZkwDHGOjWsHqC9u0ObjW5uhq3woR8eGIuLwa06yIuDkiTq6u+B/MGJ4TEV+JiL9FxIxaEePfR8QHImKZ4b7O2rGWBP6ruvufzLyyU3vgzbXtEzPzvlaNmmofND+vVfu/0Qg8vD4ilh5gHJKkCcDURpIkSZIkTVwPdnjsYqBdgdy1q9sewBERsV9mXtyuo4jYBvg1pUZDsw2q23OAN1Fy7L+uQ19HUPLoN08wr1LdngN8KCIOy8xvtutnNETEMynj37TpoU2q25sj4sDM/PkA/SxLqRdwYIuH16puuwEfi4jXZeZfhj34ci5XqbZ/PcD4pgCvqO36yQB9/wz4JmW1w4sjYrXMnNGh/VnAhyjpkPamvKeSpAnMQIIkSZIkSRPLi2vbN3Rot3r1513AZcC/gUeBaZSJ8r2ANarbbyPiuZl5fXMn1YqF31IKPgPMAM6hFIWeRZks3hR4PrBxp4FHxEnA22u7LqluM6p+dqxe33TgGxExLTO/2qnPEbQu5XWtBfyDUoT4gWr/vtX+JYEfRMQ1mdlcFBtY+H5dBDy72jWn6utvwMyqn72AzSnBnAsiYqfMvHyY468XNr5wgLabACtW248D13ZqnJkPRsQ/KamNpgDPAC7o8JQ/UAIJfeMykCBJE5yBBEmSJEmSJoDqKvJPAM+rdt0N/LTDU84AftNugrpKhXMs8FFgGeBrlKvkm72eRhDhN8Drq3Q3rfp8BrBNm8feTiOIcBNwYGb+tUW73Siva2XghIj4XWZ2CpiMlLcB84BDM/PkpjEdQQkyvJCykuKjwDva9HMijSDCOcAhmXlPU38BfBD4IrAUcHpEbJmZc4cx/pfUthd5X5vUz9HNmbmgi/5vogQS+p7fKZBQP/5OXfQtSRrnDCRIkiRJkjQ6VouIK2r3T8rMkwZ4zv4RsX3TviUpE/kvpZFy515KcdzZ7TrKzE91OlBmzqGk1tkSeCWwa0Rslpk3NTXdrrb9qXZBhKrPa2lxdXuVJ/+z1d1HgV0z8442fZwfEQcBv6KsnvgI8NZOr2UEHdEcRKjG9HhEvBX4Z7XrtbQIJFTnri+d0WXAK1sFBzIzgS9XtSAOo6wQOAD4wVAGXaVS2rK6+2i797Zmrdr2nV0ept7nWm1bAZl5b0Q8QFkVs1lErJyZD3d5HEnSOGQgQZIkSZKk0TEjM5uDAgN55wCPzwW+DBw/ghOzp1MCCVDSCjUHEpaobQ+1cO7raaRa+vpAE92ZeVZE/Bt4Gv1z+Y+mB4C2aZQy84aIuI5yNf5qEbFeZjZPwr+ntn1kFysMvkgJJEA5B0MKJACbUVIOAdzWRfvla9tPdHmMegBp+batGm6jcc43Y+BVEpKkccxAgiRJkiRJE8c0SlqdF0bEezPzb908KSLWpeS1X4cyCVyfD6gXFt6ixdPrx/hqROyfmf8e3LD7pUw6t8vnXEMJJKweERtmZjcT5MNxfrVKo5MbaaQFWoNFr+bve51zGLhOAZl5d+3K/cEGnerWq23f07ZVQz0gNNBr7lNf/bJMF+3r41gfAwmSNKEZSJAkSZIkafzYJTMvrO+IiCUo9QKeRcnjvz+lKPElEbF3c/um5+5PSQ30nC6Pv1KLfacBn6RMVm8H/CsiLgfOpyqWnJkPDtDvM2vbF5USAYOyOt1daT8c3fT/eG17ufoDEbEyjVoSSwJzBvk6Vx+4SVsr1Lbbpp6qmVXbXrLLY0wf5DHqKx26WcEgSRrHpgzcRJIkSZIk9Upmzs/MGZn5+8w8ADiiemhpSpHeRSZpo/g28GO6DyJA/8nivuM/DuwOXNXXPfB8SuHns4AHIuLKiPhwq7FUVh3EGFpZdpjP70bbehM1WdtunlPp5WusXyg6r4v29YBIt8etr0J4vG2r1uOY1uUxJEnjlCsSJEmSJEmaWP4HeD+wNqXo7RuBbza1eSuNAsVzge9QJv3/CdwPzMrM+QARsQtwQacDZua/qkLCu1MKDe8EbEUJKgTw7Op2eETsl5l/auqiPv9wDN1NRNfdMsj2vVB/jQ8AXxjDY9dXCCwSDGrh3tr2ul0eo54+6b4u2tfTJ3Vbh0GSNE4ZSJAkSZIkaQLJzPlVaqFXVbt2ZNFAwvtr2/tl5i87dLlil8dN4LzqRkSsSinOvDdwACV9zVrAWRGxeWY+UHv6g8Ca1fYvM/MqFj/19E5LZeYJY3js+nu9Shftr69tbxoRUzJzwQDP2ay2fV0Xx6iP44G2rSRJE4KpjSRJkiRJmnjqV6D3y60fEcsA21Z3bxkgiADw9KEMIDMfzMwzM/NQSlHkvlUDKwEHNjW/obb9oqEcbwKYUd0AVoiIbTs1HmH/qW2v17ZVw83Ao9X2CjQ+Ly1FxCrA1tXdBfQvwN1OfaXDrV20lySNYwYSJEmSJEmaeDaobT/c9NjKte2Huuhr3+EOJjPvA06q7dqyqcl5te1DYgjVlse7asXG+bVdh4zhse+g8TlYPyKWHqD9AuDXtV37DXCIfSkprAD+lJkzOjWOiCWBjau7M5kYqakkSR0YSJAkSZIkaQKJiE0oxY77NKcJephGUeCtOk0qR8TBwLNGcnyVWU33f0QjqPEs+qde6miCBR2+Vtt+V0Q8r9snjsDrvLz6cwoDrDCo/KC2/c6IWKPNuJYGPlLbdWoXfW9Do8DyX7tImyRJGucMJEiSJEmSNEFExEbAz2nUPJwFnF5vk5lPAldUd5cFToyIpVr0dRBwIo2gQ7tj/jgijoqIjTu02Zr+wYGLm8b0OPDftV1fioijBwhyPDMivgYc32l840lm/hk4o7q7FHBuRLy+XZAgIqZGxEsj4kzgNcM8/O9q2zt2MdZzgQuru6sCP69SGNXHtzTwQ0rqKigpqr7fxVh2ajMuSdIEZbFlSZIkSZLGj/0jYvumfVModQe2A3ajcaU3wOGZeXuLfo6nBBwA3gTsFBFnA3cCqwF7UK4anw8cR/9J/mZrAfsDn46I6yhBitspQYzVgGcCu9C4WPES4KzmTjLzWxGxFSXgMAX4FPDeiDgP+Del7sOKwKbA84ANq6d+vcPYxqNDKGPfgXLezgCOi4gLgDsoNQZWptQceD6NosQ/HOZxfwl8sdreHfhSF895K/AXSiHsHYGbIuJnwG2U874vsHbVdibwxsyc10W/u9W2f9VFe0nSOGcgQZIkSZKk8eOdXbZ7GPhgZrZMM5OZv4iITwFHU3Lbbwi8q6nZE8A7gLvoHEiYU9veprq1cw5wYGbObzOuD1TBiOMpk+mrAgd06O8p4MYOj487mflkROwMnEA5n1MpwZFNOzztQeCeYR735oj4C/BCYLeIWDEzHx3gObdExB7Aj4GtKOfk7S2a3gm8ITOvHGgcEbEc8NLq7lWZef1gXockaXwykCBJkiRJ0viWlEn/B4BrKZP1P87MRzo+KfOYiDifsgLgRcAawOOUSeGzgZOrieSdBzj+PpRUNbtSVgpsRrmCfUnKVeq3A5cBp2fmBQO+mMyTIuJ04M2UCednUVY2LAU8Btxavc7zgd9kZnMx6XEvM58C3hcRJwAHU1ZsbE5j9cHDwE3AlZTUP7/PzDktuhqsEymBhCUpqwm+08VYr42I7YCDKEWXt6acj74x/gL4dmY+1uUYXgtMr7ZP6tRQkjRxRGbHVIhaDGy+3hL5tfe0TTspSZKkxcCeR8zs9RDUA9tvvz1XXHHFRCpEOypinUje0etRtHA0V2Zmc5oiadRExDRKIGYd4PLMfH7nZ4zKGP5ISZM0A9ggM5sLb0uSJiCLLUuSJEmSJC0GMnMujeLUz4uIF47l8av6Hn2Fnv/HIIIkLT4MJEiSJEmSJC0+TqSsSgD4xBgf+4jqz7uBr43xsSVJo8hAgiRJkiRJ0mKiqrVwWHV3n4h40VgcNyKeC7ymunu4qxEkafFisWVJkiRJkqTFSGb+X0R8EFiZUhh7LKwNHAM8lpmnj9ExJUljxECCJEmSJEnSYiYz/3eMj/cr4FdjeUxJ0tgxtZEkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWrLQIIkSZIkSZIkSWpraq8HII2k6SusxzpP/y9W3+xlTF9hfZZcehWeevIBZj1yK/f961fcc91PmDtrRq+HqTG07StPYZ1t9u+376JvbM3sR2/v0Yg0Vjz3k5fnfnLz/EuSJEnSyDOQoMXGBs95J5vvcgxLTFum3/6lV1iPpVdYj1U22JFNd/wY1///9u473rKqPPj475nCFKYwAwyiw1AdkCJt1PcV6aHqawmQIhLAhsYkRl+NYlAJtuArgUSMghoLCrGAYGgGRKRHRmwUAZE61IEBLkxhyvP+cc7M7HO4+95zbjv33P37QLbfDQAAIlBJREFUfj73w17rrLX2ujx7OMx+9l7r8g/w2O8v6NAsNZI23e6wF91MUjUY++oy9tVm/CVJkiRpeJhI0Jiw3T4fZ9u9PtJQ9/xTd7Oi51Emz3gpU2dtC8AGUzZmt7d8m99dPJWHf/edTkxVI2TC5I3Y8dB/6/Q01AHGvrqMfbUZf6na9nzpniz85MJOT+NF4uTo9BQkSZKGhHskqOvNmf/GhiTCc0/cwQ3/sRfXnbU7N597GNd+ZVdu/MbePLf49+va7HTYF5m5+Z6dmK5GyA5/ciqTp28OwOI/Xtnh2WgkGfvqMvbVZvwlSZIkafiMmURCRMyIiP0i4v9GxHkRcVdErImIrP9cPcBxp0fE6yPi/0XElRHxYEQsjYjlEfFoRFxX/2zXQc5/QUT8a0T8NiKerJ/jDxFxYUT8ZURMGsz4Y1WMm8D2B3x6XXnZsw/xP985iJ7HftPQ7tlHf8UvzjmI5c8uAmDc+InMP+CzIzpXjZxNtj2Yl+1yNACP/+EyHrn9Bx2ekUaKsa8uY19txl+SJEmShteYSCRExJ3A08DPgC8AfwG8HBjwe6QRsX1EXAQ8AVwMfAg4EJgLTAEmAZsBe9U/+3VEXBIRc9s8z4YRcRZwM/B3wC7A7Po5tgXeBJwLLIyI3Qb6+4xVm+/0Z0ydtc268p0/PZFVy5/ute3K5Uv4/U9PXFeePW8vZm2x13BPUSNswqQZ7HToFwFYteJZ7rj87zs7IY0YY19dxr7ajL8kSZIkDb8xkUgA5jOIpEGJXYA3UksYFN0L3AhcDdzX9Nnh1G74b9/KCSJiIvBj4N2F6pXAb4FrgccK9TsD1wz2zYexZrMd3rLueHnPwzx+54/7bP/4nRexvOfhXvtrbNj+wM8xecbLALjr6k+yvGdRh2ekkWLsq8vYV5vxlyRJkqThN1YSCWv1ANcApwNvA341BGOuAX4CvBWYk5nbZOZrM3P/zNwaeCVwRaH9ZsDFLS5FdBpwQKF8PrB1Zu6amfsALwX+HHi2/vn0+tjTB/crjQ3jJkxm4632X1de/McryFzdZ5/M1Q3rJs95+eHDNj+NvI23PpC5ux4LwJIHb+DBW77a4RlppBj76jL21Wb8JUmSJGlkjJVEwtHADsDMzNw3Mz+Ymd9l/Q34gVgJfBOYn5mHZuZ5mflEc6PM/B1wKPDDQvV2NL5l8CL1txbeW6i6GDgqM9c9RpeZazLz+8BhwNo75HOBD7f/64w90zbZgfETJq8rP/3QTS31K7abMnMeEyfPGvK5aeSN32AaOx12JgCrVy3n1kvf1+EZaaQY++oy9tVm/CVJkiRp5IyJREJmnpuZd2ZmDuGYF2Xm8Zl5Twtt1wDvAZYVqo/sp9tHgQn145XAe8rmn5k3AGcXqj4QEVP7m9dYt+EmOzSUn3+q31ABsHRJY7vmcdSdtj/gs0yZuQUA91z3zyx96u4Oz0gjxdhXl7GvNuMvSZIkSSNnTCQSRoPMfBK4rlD1irK29b0R3lSo+lHxTYQSZxaOp1F7S6HSpszcsqG8/NkHW+q37JkHmsaZN2RzUmfM3nI/ttj97QA8+9hvue+m0zs8I40UY19dxr7ajL8kSZIkjSwTCUPrycLxjD7a7Q0U19O5uL+BM/N2ahs9r/XG9qY29kzYoHGriFUrnmmp36oVjSteNY+j7jJ+4obsdHgtz7ZmzSpuu/R9/e6VobHB2FeXsa824y9JkiRJI89EwtDaqnD8eB/tdm8qX9/i+MV2zWNUzvgNpjWUV69cVtKy0epVje2ax1F3mb//p5i60VYA3P+LM3n20aHYY13dwNhXl7GvNuMvSZIkSSPPRMIQiYgtgFcXqm7oo/mOheOVwH0tnqa4+O/8iBjfYr8xady4CQ3lXLOqpX65emXjOOMnDtmcNLJmzdubLfZ4J1Db++IP1366wzPSSDH21WXsq834S5IkSVJnmEgYOh+j8d/nOX203apwvKi+WXMr7i8cTwI2b7HfmLR65dKG8rgJk1vqN37ilKZxnh+yOWnkjJswhZ0P/xIRtT92t136t6xZtbzDs9JIMPbVZeyrzfhLkiRJUueYSBgCEXEQcEKh6trMvKSPLsX9E55u41TNmwCULu4fEe+OiIURsfCZ57ONU3SPVS80JgDGT5zaUr9xExrbrVrx3JDNSSNn/v6nMHXWNgA89Otv8NQD13R4Rhopxr66jH21GX9JkiRJ6hwTCYMUEdsA5wJRr+oB3tFPt+Ki/K0t7N9729JEQmaenZkLMnPBzA2jrFlXW7nsiYbypGkvaanfpGmbNZRfWPZkSUuNVhtuvAPz9qzl7pb3PMKdV53U4RlppBj76jL21Wb8JUmSJKmzJvTfRGUiYlPgMmCTQvW7M/Puki5rFRflb21h/97bVnpx/+efvKuhPGXmFjz3xG399psyc16f42j022DDTdctbTF5+uYc+MFFbfXf969vX3e87On7uebLOw3p/DR8jH11GftqM/6SJEmS1Fm+kTBAEbER8BNgfqH6w5n5ny10L67J09rC/r23rfTi/s89cUdDecZmu7XUb8ZL1rdbs2oFS5fcM4SzkiRJkiRJkqSxxTcSBiAipgOXA7sXqk/KzC+0OERxUf7WFvbvvW1PG33HnOU9i1i65I/r1kueNe91cH3//WZt8bp1x0seuhFa3utao0WuXskLS1tfkmrc+A2YMGn9SmArlz1FZm3vkJXLlwz5/DR8jH11GftqM/6SJEmS1FkmEtoUEdOoLWf0mkL1yZn5mTaGKS7uv3kb/ZrbVn5x/8fu+i+2fs37AZg9b28mz5jL8mcfKm0/ecZcZs9bn0h47M6Lhn2OGnpPL7qJn/3rli23f+kuR7PLG85aV77hG69j+TMPDMfUNMyMfXUZ+2oz/pIkSZLUWS5t1IaI2BC4BNirUH1KZv5Tm0P9vnA8u/6GQyuKf4N+NDOfbvO8Y86i336HXLMagBg3nm33+kif7bfd66PEuPEArFrRw6N3XDDsc5QkSZIkSZKkbmYioUURMZVaEmGfQvWnMvOTAxiueUfg3Xtt9WJ7FI5vL21VIc8vvoOHb1u/LcXc3Y7nZbse22vbubu9nbm7HbeufN8v/o2Vyyr/UockSZIkSZIk9cmljVpQTyJcDOxbqP50Zn5igEP+vKm8L3BNP3OYTONySlcP8Nxjzl1XncSsua9l6qytAdj58C8xZ7vDeOSO81nR8wiTp2/OS3Y8ijkvP3xdn2ceXsi9N53RoRlLkiRJkiRJUvcwkdCPiJgC/BjYv1D9mcz8+EDHzMwHIuKXwJ71qmMi4tO5dhfA3h0JTCmUzx/o+ceaF5Y+wS3fP4I9/+JCpsycB8Cc+W9gzvw39Nq+57HfccsPjmLNqmUjOU1JkiRJkiRJ6koubdSH+lsAFwIHFqo/m5knDcHwXy8cvxx4ax/zmAScWKi6KTNd2qjg+afu4vqvvYYHfnk2q1b09NrmhaVPcs/1p3LjN/fhhaVP9NpGkiRJkiRJktQo+n4IvrtFxNWsX47o55m5Xxt9N6CWRDisUP25zPzYEM1tIrV9DrarVy0BDsnMm5vaTaCWdPirQvV+mdm8PFKp+XPH5xffN6X/hmPEuAmTmT1vbybPnMfEyRuxcumTLH36XpY8cB2Zqzs9PUmSpGFxyInPdXoK6oAFCxawcOHC6PQ8Om3BggW5cOHCTk/jRSLil5m5oNPzkCRJGqwxsbRRRJwE9PaWwAaF430iYnkvbc7JzHf1Uv/3NCYRVgB7RMTlbUzt2Mx8rLcPMnNlRBwPXAlMAmYB10TE14ErgB5ge+AEYNdC1zPbSSJU0ZpVy1n8xys6PQ1JkiRJkiRJGhPGRCKB2u8xqZ82UdJmYkn7qU3lScAhbc6rz9cAMvO6iDga+Hb9fJOB99V/enMutQSHJEmSJEmSJEkjwj0SOiwzzwd2Ay4FytbduRs4JjOPTtfmkSRJkiRJkiSNoDHxRkJmngycPNrH7ONcdwOvj4g5wD7Ay6i9nfAIcGtm3jIS85AkSZIkSZIkqdmYSCSMFZn5OPDDTs9DkiRJkiRJkqS1XNpIkiRJkiRJkiSVMpEgSZIkSZIkSZJKmUiQJEmSJEmSJEmlTCRIkiRJkiRJkqRSJhIkSZIkSZIkSVIpEwmSJEmSJEmSJKmUiQRJkiRJkiRJklTKRIIkSZIkSZIkSSplIkGSJEmSJEmSJJUykSBJkiRJkiRJkkqZSJAkSZIkSZIkSaVMJEiSJEmSJEmSpFImEiRJkiRJkiRJUikTCZIkSZIkSZIkqZSJBEmSJEmSJEmSVMpEgiRJkiRJkiRJKmUiQZIkSZIkSZIklTKRIEmSJEmSJEmSSplIkCRJkiRJkiRJpUwkSJIkSZIkSZKkUiYSJEmSJEmSJElSKRMJkiRJkiRJkiSplIkESZIkSZIkSZJUykSCJEmSJEmSJEkqZSJBkiRJkiRJkiSVMpEgSZIkSZIkSZJKmUiQJEmSJEmSJEmlTCRIkiRJkiRJkqRSJhIkSZIkSZIkSVIpEwmSJEmSJEmSJKmUiQRJkiRJkiRJklTKRIIkSZIkSZIkSSplIkGSJEmSJEmSJJUykSBJkiRJkiRJkkqZSJAkSZIkSZIkSaVMJEiSJEmSJEmSpFImEiRJkiRJkiRJUikTCZIkSZIkSZIkqZSJBEmSJEmSJEmSVMpEgiRJkiRJkiRJKmUiQZIkSZIkSZIklTKRIEmSJEmSJEmSSplIkCRJkiRJkiRJpUwkSJIkSZIkSZKkUiYSJEmSJEmSJElSKRMJkiRJkiRJkiSplIkESZIkSZIkSZJUKjKz03PQMIuIJ4D7Oz2PDtkEWNzpSahjjH91GfvqMvbVZeyrrcrx3zIzN+30JDotIi6ndh2MNosz89BOT0KSJGmwTCRoTIuIhZm5oNPzUGcY/+oy9tVl7KvL2Feb8ZckSZKGl0sbSZIkSZIkSZKkUiYSJEmSJEmSJElSKRMJGuvO7vQE1FHGv7qMfXUZ++oy9tVm/CVJkqRh5B4JkiRJkiRJkiSplG8kSJIkSZIkSZKkUiYSJEmSJEmSJElSKRMJkiRJkiRJkiSp1IROT0AaChExA9gD2BNYUP/ndkDUm/w8M/frzOzUrpGIZ0TsAPwVcAiwBTADeBS4E/gB8L3M7BnMOdS+iJgM7AXsT+0aeAWwKTAReAa4H7gJ+H5mXjvAcxj7LhQR3wXe2lS9dWbe18YYxn4Uq//5/1PgDdT+/L8EmAY8Ry1OvwIuAc7PzGVtjm3sR8hwfYdHxHRgH2A/YHdge2Bjag9GPQ38AbgR+E5m/mYQ818AHEPte+hlwBTgYeBW4HvABZm5YqDjS5IkSd3KzZbV9SLiTuDlrP8Lam9MJHSJ4Y5nREwAPgmcCIzvo+kDwHGZ+bOBnEftiYjNgDOo3UCc1mK3m4C3Z+YdLZ7D2HepiPg/wI97+ailRIKxH/0i4vXAV4C5LTR/GHhvZvZ2TTSPa+xH0HB8h0fE9sDnqSWBJrXY7VLghMx8qI3zbAj8C/DufpreChyTmb9udWxJkiRpLDCRoK4XEa1cxCYSusRwxzMivkXtqdS1ErgDWAxsTe1J1bVWAa/PzP8eyLnUuvoToDf38tFDwCJgKbUnQ+c3ff48cGhmXtfCOYx9F4qIWcBtwOa9fNxqIsHYj2IR8TbgWzQuubmMWtyfATYCdgImFz5P4B2Z+Y1+xjb2I2g4vsMj4khqb400u5faWyUrgK3qP0WPAftm5p0tnGMicDlwQKF6JbVr5Rlq3z2bFT7rAfYezJsPkiRJUrdxjwSNJT3ANcDpwNuoLYGg7jXk8YyID9J4Q+kaYIfM3Ckz983MecBB1J52hdrybz+IiC0He2615XrgXcAWmblFZv6vzDwgM7cHtgHOK7TdELgoIjbpa0Bj39XOYH0Soe0bvMZ+dIuIecBZrP9/0mXA+4GNM/NVmfknmbmA2hI2HwSWr+0K/HtEbNvH2Ma+c4bj/8nWAD+htsTZnMzcJjNfm5n7Z+bWwCuBKwrtNwMujohW3mI4jcYkwvnUEpW7ZuY+wEuBPweerX8+vT729MH9SpIkSVL3MJGgseBoYAdgZv3GwAcz87us/8ueusuwxDMiNgY+Uaj6FXBwZt5VbJeZV1Jbg/m5etUM4FODObdasga4ENg9M1+XmV/rbUmKzLw3M99KbfmJtWZTW7akV8a+e0XE4ay/EXwJjUmkVvob+9HvBGBqoXxMZv5b8x4Imbk0M08Hji1UT6ZkGRpj3zHD8R2+EvgmMD8zD83M8zLzieZGmfk74FDgh4Xq7ehnqaL60knvLVRdDByVmYsKY6/JzO8DhwGr69VzgQ+3/+tIkiRJ3clEgrpeZp6bmXem63SNCcMYz78BZhbKJ5RtlpiZ99B4I+noiNhqiOejgsy8JTPf0saa0ydSW/ZorSP7aGvsu1BEzATOrhd7aLzR1ypjP/rtWzi+PTPP76tx/WZucV+U15U0NfYdMBzf4Zl5UWYeX49Tf23XAO+h9mbLWn19PwB8lNrbKFBLWrynbP6ZeQPr/7sE8IGImNpbW0mSJGmsMZEgqSqOKhz/IjN7W4+/6GusX0JjHHDEsMxKA5KZLwCXFarm9XEzx9h3p3+hti8GwEcz88EBjGHsR785hePfttin2K5sWTNjX1GZ+SRQ3DfnFWVt63sjvKlQ9aPimwglziwcT6P2loIkSZI05plIkDTmRcQ21DbqXOvi/vpk5lPAjYWqNw71vDRoTzaVZzQ3MPbdKSIOAd5eL14HfHkAYxj77tBTOJ5c2qpRsd2S5g+NvWj8fnjRd0PB3sCsQrmVa+V2ahs9r+W1IkmSpEowkSCpCnZvKl/fYr9iu92GZioaQlsVjtcAi3tpY+y7TH3z0q/WiyuAdw5wmRRj3x1uKhy/NiI26KtxfePc1xaqrumlmbHXVoXjx/toNxTXSvMYkiRJ0phkIkFSFezYVL67xX7FdjMiYu4QzUeDFBFTaFxO4ubMXNVLU2Pffb4AbFE/PiUz7xzgOMa+O3wZWPtndw7wmX7afw7YtH78HI3LzKxl7CssIrYAXl2ouqGP5sVrZSVwX4unKV4r8yNifIv9JEmSpK5lIkFSFWxVOF4NPNxiv/v7GEed9Xc0bqR6Tkm7rQrHxn6Ui4gDgXfXi78BPj+I4bYqHBv7USozbwX+llqMAD4UEZdExCERMTsixtf/eVhEXA58oN6uBzgqMx/oZditCsfGvno+RuPfccq+H6AxxovqmzW3onitTAI2b7GfJEmS1LUmdHoCkjQCiusj92Tm6tKWjZ5pKk8fovloECJiZ+DkQtU9rF8Kp5mx7xIRMY3ahrdQu/n7zpK3TFpl7LtEZn4lIu4DzgC2Bw6v//RmNXApcGJm3lbSxthXVEQcBJxQqLo2My/po0vxWnm6jVN5rUiSJKlyfCNBUhVMKxwva6Nfc1tvFHRYRGwM/Ij1m62uBo7LzBdKuhj77nEq658OPj0zFw5yPGPfRTLzcuBg4MJ+ml4JnNlHEgGMfSXVN9k+F4h6VQ/wjn66ea1IkiRJLTKRIKkKJhaO23nCubntxF5baUTU90W4CNiuUP2PmXldH92MfReIiP2A99aL9wCfGIJhjX2XiIhpEfFlarF/c736BeBXwFXAzcDSev0hwE8i4uf1tfB7Y+wrJiI2BS4DNilUvzsz+9sfw2tFkiRJapGJBElV8HzheHJpqxdrbvt8r6007CJiA+ACYK9C9ZmZeWo/XY39KBcRU4Gvs/4p4ndlZjtPBpcx9l0gIqZTSxa8h9qSm0up7YMwOzP3yMwDM/PVwEbAscDietd9gGsj4iW9DGvsKyQiNgJ+AswvVH84M/+zhe5eK5IkSVKLTCRIqoLnCsdT2+jX3LZnCOaiNkXEROAHwKGF6q9S23C5P8Z+9PtnYJv68dcy82dDNK6x7w6nAa+qH78AHJyZZ2Rmw43ZzFyZmd+mlkxcUq/eEvj3XsY09hVRT0RdDuxeqD4pM7/Q4hBeK5IkSVKLTCRIqoInCscb1m88tGLzpvLiXltp2ETEBOA84I2F6v8ATsjMbGEIYz+KRcSOwN/Ui48AHx7C4Y39KBcRLwWOL1R9NTOv76tPZt4FfK5Q9eb62vhFxr4C6hu0Xwa8plB9cmZ+po1hitdKc/z70tz2yTb6SpIkSV3JRIKkKvh9U3nLFvsV260B7hqa6agVETEe+C5wRKH6m9SWvmkliQDGfrSbw/oljTYHlkRElv0A32jqf2/h8/uaPjP2o98B1JYzWutHLfa7sHAcwL5Nnxv7MS4iNgQuoXG5u1My85/aHKp4rcxuI+lUvFYezcyn2zyvJEmS1HVMJEiqgtuaynu02K/Y7r4hWrddLagnEc4B/qxQ/S3gHZm5po2hjH11GfvRr3mz5Adb7PdAU7l5nwRjP4bV91W5hNo+GWt9KjM/OYDhmq+V3Xtt9WLFa+X2AZxXkiRJ6jomEiRVwUIaN0Jsfnq1TPEmxdVDNhv1qZ5E+Dbwl4XqbwNvbzOJAMZ+tFtJbUmQVn+ea+q/pPDZU02fGfvRb0VTeUqL/ZrXp1/aVDb2Y1Q9iXAxjTH9dGZ+YoBD/ryp3O+1EhGTaVxO6eoBnluSJEnqKiYSJI159adKLy9UHVG/GVEqIl7H+g1gAc4fjrmpUUSMo7Z80VsL1ecAxw8giWDsR7nMvD4zN2n1B/jbpiH2KHy+R9PYxn70e7ip/KpeW73Yq5vKDxULxn5siogpwI+B/QvVn8nMjw90zMx8APhloeqYiIiy9nVH0pj08lqRJElSJZhIkFQVXy8czwQ+0E/74hIJDwBXDvmM1KCeRPgP4G2F6u8Axw0kiVBg7KvL2I9uVwPF/U7eHxET++pQv8n7oULVGl78VDkY+zGl/hbAhcCBherPZuZJQzB88Vp5OY2J7OZ5TAJOLFTdlJkubSRJkqRKMJEgqRIy8zIabzZ9IiIO761tRHwG+JNi28x8YTjnV3X1m4NnAccWqr8LHDvIJIKxrzBjP7pl5qPUlqlZa2fgu2VvD0TEBOCLNMbpgsxc3MvYxn6MiIgNgAuAgwvVn8vMfxyiU3wN+EOh/MWIeNHbMfXr72xgx0L1R4doDpIkSdKoF5nZfytpFIuIk4DenkjbAFj7enoCvd0UOCcz3zVcc1P7hjOeETEfuBGYXa9aDZxH7SnHJ4GtgeOBvQvdfgy8ZbA3s9W3iPgz4HuFqgR+Si1GrfqHzPxtyfjGfgyIiOOAbxSqts7M+/rpY+xHsXp8/gfYqFC9iNoSZ/9DbR+M6dQ2tz2W2hPjaz0JvCoz7+1jbGM/gobjOzwi/gE4tVC1gvb3JTg2Mx8r+7C+tNWVwKR61XJqbypcAfQA2wMnALsWup2Zmc3LrUmSJElj1oROT0AaAhNY/xe/MlHSps8lFNQRwxbPzLwrIt4EXETtxtJ4asvovK2ky1XAX3pDaUQ0P4EcND4h3Ip/LvvA2FeXsR/d6vE5jNoT55vXq18G9Pe0+SJqN/x7TSIUxjb2I2s4vsObvx8mAYe0Oa8+N/LOzOsi4mjg2/XzTQbeV//pzbnA37c5B0mSJKmrubSRpErJzOuAnag9lbqipNlDwAeBgzJz6UjNTcPL2FeXsR/dMvMmassafR54op/mj1NLGu6cmTe3MLaxV0sy83xgN+BSyt+Guxs4JjOPzsx23piTJEmSup5LG0mqrIiYCewHzKW2dMZjwJ3Ajel/HMc0Y19dxn50i4jx1G787wZsDGwIPE8twfAb4PaB3sA19mpVRMwB9qH2dsxk4BHg1sy8paMTkyRJkjrIRIIkSZIkSZIkSSrl0kaSJEmSJEmSJKmUiQRJkiRJkiRJklTKRIIkSZIkSZIkSSplIkGSJEmSJEmSJJUykSBJkiRJkiRJkkqZSJAkSZIkSZIkSaVMJEiSJEmSJEmSpFImEiRJkiRJkiRJUikTCZIkSZIkSZIkqZSJBEmSJEmSJEmSVMpEgiRJHRARJ0dENv18uo3+k5v6Htfm+beJiPdGxA8j4ncR8UhEvBARz0bE/RFxVUScGhEHR8T4tn9BSZIkSZI0Zkzo9AQkSdI6H4iIL2XmI8N1gojYCTgZOAKIXppMBKYD84D9gX8AHouIrwCnZWbPcM1NkiRJkiSNTr6RIEnS6DEV+ORwDR4R7wd+AxxJYxLheeAO4BrgRuCPwMrC55vV53VPRGw4XPOTJEmSJEmjk4kESZJGl3dExPyhHjQivgScAaxdpmgN8C1gX2BWZu6Ymftm5mszc1tgY2pvLZwPZL3PptTeWJAkSZIkSRViIkGSpM57Bni8fjwB+OxQDh4Rfwf8daHqQWDPzDwuM6/JzJXNfTKzJzMvyMwjgd2Ba4dyTpIkSZIkqXuYSJAkqfOWA58qlI+IiNcMxcARsRvwhULVIuB/Z+avWx0jM38DHACcxvq3EyRJkiRJUkWYSJAkaXQ4C7inUD51iMb9OOuXI0rguMxc1O4gmbkqMz+Umc8M0bwkSZIkSVKXMJEgSdIoUF9e6KRC1b4RcfhgxqzvtfDmQtVPM/PKwYwpSZIkSZKqx0SCJEmjx/eAXxbKn4uIwXxXv4nG7/qvDGIsSZIkSZJUUSYSJEkaJTIzgY8Uql4JvG0QQ+5XHB746SDGkiRJkiRJFWUiQZKkUSQzfwpcUag6JSImDXC4VxWO78rMpwc8MUmSJEmSVFkmEiRJGn0+Qu0NAoAtgfe1O0B9SaSNC1UPDsG8JEmSJElSBZlIkCRplMnMX1HbL2Gtj0XEzDaH2YjG7/mnBzktSZIkSZJUUSYSJEkanf4RWFk/3pjGvRNaMbmpvGLQM5IkSZIkSZVkIkGSpFEoM/8InFWoen9EbN7GEEuayu2+0SBJkiRJkgSYSJAkaTQ7BeipH08FTm61Y2YuA5YXqmYP3bQkSZIkSVKVmEiQJGmUyswngNMKVW+PiO3bGOKewvHOERFDMzNJkiRJklQlJhIkSRrdTgMeqx9PAD7bRt+fF45nADsO1aQkSZIkSVJ1mEiQJGkUy8zngE8Vqv40Il7TYvermspvHZpZSZIkSZKkKjGRIEnS6Hc28IdC+fMt9vsvYFGh/M6ImDZks5IkSZIkSZVgIkGSpFEuM1cCJxWq9gFe30K/F4DTC1VzgDMGOo+I2DIiJg20vyRJkiRJ6k4mEiRJ6g7fBxYWyq3ulXAmcEuh/I6I+Hi7J4+IQ+vjTGm3ryRJkiRJ6m4mEiRJ6gKZmcBHClXzW+y3AjgCeKpQfUpE/DAituuvf0TsEhE/Ai4DZrcxZUmSJEmSNEZM6PQEJElSazLzqoj4b+DgNvvdFxH7UtszYat69RHAmyLiGuAKanswLAYmApsBOwGHA68cmtlLkiRJkqRuZSJBkqTu8hHgICDa6ZSZt0bEq4AvU0siBLX/Dzig/tOfxdSWU3q2rdlKkiRJkqSu59JGkiR1kcz8NXDeAPsuzsyjgN2Ac4DH++myGrgOeA+wTWaenplrBnJuSZIkSZLUvaK25LIkSaqaiAhgF2B7YBNgFrACWEJtqaNfZubznZuhJEmSJEkaDUwkSJIkSZIkSZKkUi5tJEmSJEmSJEmSSplIkCRJkiRJkiRJpUwkSJIkSZIkSZKkUiYSJEmSJEmSJElSKRMJkiRJkiRJkiSplIkESZIkSZIkSZJUykSCJEmSJEmSJEkqZSJBkiRJkiRJkiSVMpEgSZIkSZIkSZJKmUiQJEmSJEmSJEmlTCRIkiRJkiRJkqRS/x9pO6WVQ+8D7AAAAABJRU5ErkJggg==\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": 2,
   "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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0.000206, 0.000205, 0.005969, 0.002010, 0.002004, 0.002009, 0.002005, 0.002008, 0.002003, 0.002006, 0.002007, 0.002007, 0.002003, 0.002008, 0.002003, 0.002009, 0.002004, 0.002009, 0.002003, 0.002008, 0.002005, 0.002007, 0.002003, 0.002008, 0.002005, 0.002008, 0.002005, 0.002005, 0.002003, 0.002007, 0.002002, 0.002007, 0.002003, 0.002007, 0.002004, 0.002006, 0.002002, 0.002007, 0.002003, 0.002006, 0.002004, 0.002005, 0.002002, 0.002004, 0.002002, 0.002005, 0.002002, 0.002005, 0.002002, 0.002007, 0.002003, 0.002007, 0.002002, 0.002009, 0.002002, 0.002005, 0.002004, 0.002008, 0.002003, 0.002007, 0.002003, 0.002003, 0.002003, 0.002010, 0.002003, 0.002005, 0.002000, 0.002007, 0.002002, 0.002009, 0.002001, 0.002006, 0.002002, 0.002006, 0.002001, 0.001998, 0.002001, 0.002010, 0.002002, 0.002003, 0.002000, 0.002007, 0.002004, 0.002008, 0.002001, 0.002004, 0.002005, 0.002006, 0.002001, 0.002007, 0.002002, 0.002009, 0.002001, 0.002005, 0.002003, 0.002008, 0.001999, 0.002004, 0.002002, 0.002005, 0.002003, 0.002006, \\\n",
    "     0.059939, 0.024504, 0.022647, 0.022617, 0.021730, 0.024531, 0.024927, 0.024392, 0.024493, 0.024490, 0.024438, 0.021964, 0.021842, 0.024483, 0.021804, 0.024507, 0.024354, 0.024353, 0.024456, 0.022079, 0.024322, 0.024579, 0.022457, 0.024401, 0.024604, 0.024532, 0.023252, 0.023547, 0.023316, 0.021491, 0.024583, 0.024424, 0.024541, 0.022293, 0.024417, 0.023698, 0.024408, 0.021841, 0.023707, 0.023017, 0.024536, 0.024420, 0.021870, 0.021531, 0.022964, 0.023716, 0.024605, 0.024343, 0.024588, 0.022115, 0.021860, 0.023939, 0.021542, 0.024609, 0.024339, 0.022058, 0.024504, 0.024357, 0.024535, 0.021893, 0.024482, 0.022914, 0.022199, 0.024370, 0.024429, 0.024554, 0.023071, 0.023255, 0.023273, 0.024601, 0.022383, 0.024586, 0.022459, 0.024462, 0.022297, 0.024515, 0.021501, 0.023307, 0.023315, 0.021560, 0.024507, 0.023080, 0.022861, 0.022383, 0.024513, 0.022016, 0.022958, 0.022520, 0.021587, 0.024413, 0.022460, 0.023133, 0.021825, 0.023516, 0.022206, 0.023532, 0.024548, 0.022841, 0.024491, 0.023761]\n",
    "    slope = 4082094463.008053\n",
    "    intercept = 417444.446452\n",
    "elif tipo == \"allgather\":\n",
    "    x = [0.000005, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000002, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 0.000001, 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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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  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Tiempo')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#f, (ax1, ax2) = plt.subplots(nrows=2, sharex=True, gridspec_kw={'height_ratios': (1,3)}, figsize=(10,4))\n",
    "f=plt.figure(figsize=(16, 8))\n",
    "ax=f.add_subplot(111)\n",
    "    \n",
    "thresh = 100000000 - 1\n",
    "\n",
    "x_low = []\n",
    "x_high = []\n",
    "\n",
    "y_low = []\n",
    "y_high = []\n",
    "\n",
    "y2_low = []\n",
    "y2_high = []\n",
    "\n",
    "#Bucle de bajas prestaciones...\n",
    "for i in range(len(x)):\n",
    "    if y[i] < thresh:\n",
    "        x_low.append(x[i])\n",
    "        y_low.append(y[i])\n",
    "        y2_low.append(intercept + slope*x[i])\n",
    "    else:\n",
    "        x_high.append(x[i])\n",
    "        y_high.append(y[i])\n",
    "        y2_high.append(intercept + slope*x[i])\n",
    "\n",
    "ax1.scatter(x_low,y_low, color='red')\n",
    "ax1.plot(x_low,y2_low)\n",
    "\n",
    "ax2.scatter(x_high,y_low, color='red')\n",
    "ax2.plot(x_high,y2_low)\n",
    "\n",
    "ax1.set_ylabel(\"Tamaño\", fontsize=36)\n",
    "ax1.set_xlabel(\"Tiempo\", fontsize=36)\n",
    "#ax.set_ylim(0,110000000)"
   ]
  },
  {
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9962787972985218\n",
      "409360.65712023154\n",
      "[6.56144796e+08]\n",
      "[  413953.67068972   411329.09150716   410672.94671151 ...\n",
      " 99880255.53157426 98379652.38394217 99038421.75876622]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "x = np.array(x).reshape((-1, 1))\n",
    "y = np.array(y)\n",
    "\n",
    "reg = LinearRegression().fit(x, y)\n",
    "print(reg.score(x, y))\n",
    "print(reg.intercept_)\n",
    "print(reg.coef_)\n",
    "\n",
    "print(reg.predict(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}