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 seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n",
    "from scipy import stats\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrixMalEX=\"data_GG.csv\"\n",
    "matrixMal=\"data_GM.csv\"\n",
    "matrixIt=\"data_L.csv\"\n",
    "n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
    "n_groups= 2\n",
    "repet = 3 #CAMBIAR EL PRIMER NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "p_value = 0.05\n",
    "values = [2, 10, 20, 40]\n",
    "#                      WORST          BEST\n",
    "dist_names = ['null', 'BalancedFit', 'CompactFit']\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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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', 'Groups'])['TE']\n",
    "else:        \n",
    "    group = dfG.groupby(['Dist', '%Async', 'Groups'])['TE']\n",
    "\n",
    "#group\n",
    "grouped_aggG = group.agg(['mean'])\n",
    "grouped_aggG.rename(columns={'mean':'TE',}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "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",
    "\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async','NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async','NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['mean'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: 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",
      "  \n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "dfL['%Async'] = dfL['%Async'].fillna(0)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'NP', 'NS'])['Ti', 'To']\n",
    "\n",
    "#group\n",
    "grouped_aggL = groupL.agg(['mean', 'count'])\n",
    "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
    "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], axis='columns')\n",
    "\n",
    "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n",
    "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_aggL.to_excel(\"resultL.xlsx\") \n",
    "grouped_aggM.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggG.to_excel(\"resultG.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Groups</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
       "      <th>CommTam</th>\n",
       "      <th>Time</th>\n",
       "      <th>Iters</th>\n",
       "      <th>TE</th>\n",
       "      <th>S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.387869</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.423120</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.472982</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.144715</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.152502</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.953515</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2,10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.952264</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.561208</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.498228</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.564727</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    N  %Async Groups Dist  Matrix  CommTam  Time Iters        TE  S\n",
       "0   0     0.0  20,10  1,1  100000        0   0.4  1,10  0.387869  0\n",
       "1   0     0.0  20,10  1,1  100000        0   0.4  1,10  0.423120  0\n",
       "2   0     0.0  20,10  1,1  100000        0   0.4  1,10  0.472982  0\n",
       "3   0     0.0  40,10  1,1  100000        0   0.4  1,10  1.144715  0\n",
       "4   0     0.0  40,10  1,1  100000        0   0.4  1,10  1.152502  0\n",
       ".. ..     ...    ...  ...     ...      ...   ...   ...       ... ..\n",
       "91  0     0.0   2,10  1,1  100000        0   0.4  1,10  0.953515  0\n",
       "92  0     0.0   2,10  1,1  100000        0   0.4  1,10  0.952264  0\n",
       "93  0     0.0   40,2  1,1  100000        0   0.4  1,10  0.561208  0\n",
       "94  0     0.0   40,2  1,1  100000        0   0.4  1,10  0.498228  0\n",
       "95  0     0.0   40,2  1,1  100000        0   0.4  1,10  0.564727  0\n",
       "\n",
       "[96 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"16\" valign=\"top\">1,1</td>\n",
       "      <td rowspan=\"16\" valign=\"top\">0.0</td>\n",
       "      <td>10,10</td>\n",
       "      <td>0.508879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,2</td>\n",
       "      <td>0.995088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,20</td>\n",
       "      <td>0.669420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,40</td>\n",
       "      <td>1.458222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,10</td>\n",
       "      <td>0.951549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,2</td>\n",
       "      <td>2.219202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,20</td>\n",
       "      <td>0.909392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,40</td>\n",
       "      <td>1.355253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,10</td>\n",
       "      <td>0.427990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,2</td>\n",
       "      <td>0.421782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,20</td>\n",
       "      <td>0.763020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,40</td>\n",
       "      <td>1.546889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,10</td>\n",
       "      <td>1.101985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,2</td>\n",
       "      <td>0.541388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,20</td>\n",
       "      <td>1.294718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,40</td>\n",
       "      <td>1.755568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"16\" valign=\"top\">2,2</td>\n",
       "      <td rowspan=\"16\" valign=\"top\">0.0</td>\n",
       "      <td>10,10</td>\n",
       "      <td>0.745033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,2</td>\n",
       "      <td>0.662288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,20</td>\n",
       "      <td>1.523874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10,40</td>\n",
       "      <td>1.443683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,10</td>\n",
       "      <td>1.109833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,2</td>\n",
       "      <td>2.217484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,20</td>\n",
       "      <td>1.599292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2,40</td>\n",
       "      <td>1.529876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,10</td>\n",
       "      <td>1.279719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,2</td>\n",
       "      <td>0.897674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,20</td>\n",
       "      <td>1.671315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20,40</td>\n",
       "      <td>1.728683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,10</td>\n",
       "      <td>1.308367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,2</td>\n",
       "      <td>0.771345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,20</td>\n",
       "      <td>1.640378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40,40</td>\n",
       "      <td>1.744211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          TE\n",
       "Dist %Async Groups          \n",
       "1,1  0.0    10,10   0.508879\n",
       "            10,2    0.995088\n",
       "            10,20   0.669420\n",
       "            10,40   1.458222\n",
       "            2,10    0.951549\n",
       "            2,2     2.219202\n",
       "            2,20    0.909392\n",
       "            2,40    1.355253\n",
       "            20,10   0.427990\n",
       "            20,2    0.421782\n",
       "            20,20   0.763020\n",
       "            20,40   1.546889\n",
       "            40,10   1.101985\n",
       "            40,2    0.541388\n",
       "            40,20   1.294718\n",
       "            40,40   1.755568\n",
       "2,2  0.0    10,10   0.745033\n",
       "            10,2    0.662288\n",
       "            10,20   1.523874\n",
       "            10,40   1.443683\n",
       "            2,10    1.109833\n",
       "            2,2     2.217484\n",
       "            2,20    1.599292\n",
       "            2,40    1.529876\n",
       "            20,10   1.279719\n",
       "            20,2    0.897674\n",
       "            20,20   1.671315\n",
       "            20,40   1.728683\n",
       "            40,10   1.308367\n",
       "            40,2    0.771345\n",
       "            40,20   1.640378\n",
       "            40,40   1.744211"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
       "      <th>CommTam</th>\n",
       "      <th>Time</th>\n",
       "      <th>Iters</th>\n",
       "      <th>TC</th>\n",
       "      <th>TH</th>\n",
       "      <th>TS</th>\n",
       "      <th>TA</th>\n",
       "      <th>S</th>\n",
       "      <th>TR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.367837</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.367904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.403131</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.403192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.452980</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.453039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.099791</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.099867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>1.125973</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.126036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.423576</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.423622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.423226</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.423301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.549139</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.549201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>94</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.488046</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.488109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>1,1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1,10</td>\n",
       "      <td>0.542324</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.542392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    N  %Async  NP  NS Dist  Matrix  CommTam  Time Iters        TC        TH  \\\n",
       "0   0     0.0  20  10  1,1  100000        0   0.4  1,10  0.367837  0.000067   \n",
       "1   0     0.0  20  10  1,1  100000        0   0.4  1,10  0.403131  0.000061   \n",
       "2   0     0.0  20  10  1,1  100000        0   0.4  1,10  0.452980  0.000059   \n",
       "3   0     0.0  40  10  1,1  100000        0   0.4  1,10  1.099791  0.000076   \n",
       "4   0     0.0  40  10  1,1  100000        0   0.4  1,10  1.125973  0.000063   \n",
       ".. ..     ...  ..  ..  ...     ...      ...   ...   ...       ...       ...   \n",
       "91  0     0.0   2  10  1,1  100000        0   0.4  1,10  0.423576  0.000046   \n",
       "92  0     0.0   2  10  1,1  100000        0   0.4  1,10  0.423226  0.000075   \n",
       "93  0     0.0  40   2  1,1  100000        0   0.4  1,10  0.549139  0.000062   \n",
       "94  0     0.0  40   2  1,1  100000        0   0.4  1,10  0.488046  0.000063   \n",
       "95  0     0.0  40   2  1,1  100000        0   0.4  1,10  0.542324  0.000068   \n",
       "\n",
       "     TS   TA  S        TR  \n",
       "0   0.0  0.0  0  0.367904  \n",
       "1   0.0  0.0  0  0.403192  \n",
       "2   0.0  0.0  0  0.453039  \n",
       "3   0.0  0.0  0  1.099867  \n",
       "4   0.0  0.0  0  1.126036  \n",
       "..  ...  ... ..       ...  \n",
       "91  0.0  0.0  0  0.423622  \n",
       "92  0.0  0.0  0  0.423301  \n",
       "93  0.0  0.0  0  0.549201  \n",
       "94  0.0  0.0  0  0.488109  \n",
       "95  0.0  0.0  0  0.542392  \n",
       "\n",
       "[96 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>TC</th>\n",
       "      <th>TH</th>\n",
       "      <th>TS</th>\n",
       "      <th>TA</th>\n",
       "      <th>TR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"16\" valign=\"top\">1,1</td>\n",
       "      <td rowspan=\"16\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"4\" valign=\"top\">2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.354418</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.421456</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.421514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.549015</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.549065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.095739</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.095784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.356249</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.438358</td>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.438410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.629802</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.629853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.395133</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.395191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.401695</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.401754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.407983</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.408045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.742614</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.742671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.474785</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.474850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.526503</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.526567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.066937</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.067003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.262803</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.262866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.707607</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.707672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"16\" valign=\"top\">2,2</td>\n",
       "      <td rowspan=\"16\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"4\" valign=\"top\">2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.421873</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.421931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.619649</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.619712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.308732</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.308786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.268303</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.268354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.413054</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.413116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.705488</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.457638</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.457691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.369427</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.369507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">20</td>\n",
       "      <td>2</td>\n",
       "      <td>0.876231</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.876311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.243403</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.243476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.614983</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.615053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.666040</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.666127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.755518</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.755582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.236325</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.236400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.575315</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.575377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.682241</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.682313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         TC        TH   TS   TA        TR\n",
       "Dist %Async NP NS                                        \n",
       "1,1  0.0    2  2   0.354418  0.000053  0.0  0.0  0.354472\n",
       "               10  0.421456  0.000058  0.0  0.0  0.421514\n",
       "               20  0.549015  0.000050  0.0  0.0  0.549065\n",
       "               40  1.095739  0.000045  0.0  0.0  1.095784\n",
       "            10 2   0.356249  0.000062  0.0  0.0  0.356311\n",
       "               10  0.438358  0.000052  0.0  0.0  0.438410\n",
       "               20  0.629802  0.000051  0.0  0.0  0.629853\n",
       "               40  1.395133  0.000058  0.0  0.0  1.395191\n",
       "            20 2   0.401695  0.000059  0.0  0.0  0.401754\n",
       "               10  0.407983  0.000062  0.0  0.0  0.408045\n",
       "               20  0.742614  0.000058  0.0  0.0  0.742671\n",
       "               40  1.474785  0.000065  0.0  0.0  1.474850\n",
       "            40 2   0.526503  0.000064  0.0  0.0  0.526567\n",
       "               10  1.066937  0.000066  0.0  0.0  1.067003\n",
       "               20  1.262803  0.000063  0.0  0.0  1.262866\n",
       "               40  1.707607  0.000065  0.0  0.0  1.707672\n",
       "2,2  0.0    2  2   0.421873  0.000057  0.0  0.0  0.421931\n",
       "               10  0.619649  0.000064  0.0  0.0  0.619712\n",
       "               20  1.308732  0.000055  0.0  0.0  1.308786\n",
       "               40  1.268303  0.000051  0.0  0.0  1.268354\n",
       "            10 2   0.413054  0.000062  0.0  0.0  0.413116\n",
       "               10  0.705488  0.000056  0.0  0.0  0.705544\n",
       "               20  1.457638  0.000053  0.0  0.0  1.457691\n",
       "               40  1.369427  0.000080  0.0  0.0  1.369507\n",
       "            20 2   0.876231  0.000080  0.0  0.0  0.876311\n",
       "               10  1.243403  0.000073  0.0  0.0  1.243476\n",
       "               20  1.614983  0.000070  0.0  0.0  1.615053\n",
       "               40  1.666040  0.000087  0.0  0.0  1.666127\n",
       "            40 2   0.755518  0.000064  0.0  0.0  0.755582\n",
       "               10  1.236325  0.000075  0.0  0.0  1.236400\n",
       "               20  1.575315  0.000062  0.0  0.0  1.575377\n",
       "               40  1.682241  0.000072  0.0  0.0  1.682313"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>N_par</th>\n",
       "      <th>NS</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Compute_tam</th>\n",
       "      <th>Comm_tam</th>\n",
       "      <th>Time</th>\n",
       "      <th>Iters</th>\n",
       "      <th>Ti</th>\n",
       "      <th>Tt</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019623</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019644</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1812</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009814</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1813</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009810</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1814</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009828</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1815</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009808</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1816</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009819</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1817 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  %Async  NP  N_par  NS  Dist  Compute_tam  Comm_tam  Time  Iters  \\\n",
       "0     0     0.0  20      0  10     1       100000         0   0.4      1   \n",
       "1     0     0.0  20      0  10     1       100000         0   0.4      1   \n",
       "2     0     0.0  20      0  10     1       100000         0   0.4      1   \n",
       "3     0     0.0  20      0  10     1       100000         0   0.4      1   \n",
       "4     0     0.0  20      0  10     1       100000         0   0.4      1   \n",
       "...  ..     ...  ..    ...  ..   ...          ...       ...   ...    ...   \n",
       "1812  0     0.0  40      0   2     1       100000         0   0.4      1   \n",
       "1813  0     0.0  40      0   2     1       100000         0   0.4      1   \n",
       "1814  0     0.0  40      0   2     1       100000         0   0.4      1   \n",
       "1815  0     0.0  40      0   2     1       100000         0   0.4      1   \n",
       "1816  0     0.0  40      0   2     1       100000         0   0.4      1   \n",
       "\n",
       "            Ti   Tt    To  \n",
       "0     0.019619  0.0  22.0  \n",
       "1     0.019645  1.0  22.0  \n",
       "2     0.019623  1.0  22.0  \n",
       "3     0.019624  1.0  22.0  \n",
       "4     0.019644  1.0  22.0  \n",
       "...        ...  ...   ...  \n",
       "1812  0.009814  1.0  11.0  \n",
       "1813  0.009810  1.0  11.0  \n",
       "1814  0.009828  1.0  11.0  \n",
       "1815  0.009808  1.0  11.0  \n",
       "1816  0.009819  1.0  11.0  \n",
       "\n",
       "[1817 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tt</th>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"4\" valign=\"top\">2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.199694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.199705</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.199701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.199761</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>0.039242</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">1.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">2</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>0.031814</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"4\" valign=\"top\">40</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010084</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.010247</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.010257</td>\n",
       "      <td>34.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.011543</td>\n",
       "      <td>35.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Ti  Iters     To  Iters2\n",
       "Tt  Dist %Async NP NS                                \n",
       "0.0 1    0.0    2  2   0.199694    1.0  224.0     1.0\n",
       "                   10  0.199705    1.0  224.0     1.0\n",
       "                   20  0.199701    1.0  224.0     1.0\n",
       "                   40  0.199761    1.0  224.0     1.0\n",
       "                10 2   0.039242    1.0   44.0     1.0\n",
       "...                         ...    ...    ...     ...\n",
       "1.0 2    0.0    20 40  0.031814   22.0   22.0    22.0\n",
       "                40 2   0.010084   23.0   11.0    23.0\n",
       "                   10  0.010247   30.0   11.0    30.0\n",
       "                   20  0.010257   34.0   11.0    34.0\n",
       "                   40  0.011543   35.0   11.0    35.0\n",
       "\n",
       "[64 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO EJECUCCION\n",
      "Distribución BalancedFit -------------------------\n",
      "Para  2  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.312 Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 1.188 Síncrono\n",
      "EX numC= 20 p = 0.0 Diff = 1.289 Síncrono\n",
      "EX numC= 40 p = 0.0 Diff = 1.478 Síncrono\n",
      "Para  10  padres\n",
      "EX numC= 2 p = 0.0 Diff = 3.621 Asíncrono\n",
      "EX numC= 10 p = 0.023 Diff = 0.055 Asíncrono\n",
      "EX numC= 20 p = 0.021 Diff = 0.13 Síncrono\n",
      "EX numC= 40 p = 0.046 Diff = 0.708 Síncrono\n",
      "Para  20  padres\n",
      "EX numC= 2 p = 0.0 Diff = 5.84 Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 0.481 Asíncrono\n",
      "EX numC= 20 p = 0.001 Diff = 0.159 Síncrono\n",
      "EX numC= 40 p = 0.004 Diff = 0.278 Síncrono\n",
      "Para  40  padres\n",
      "EX numC= 2 p = 0.0 Diff = 7.587 Asíncrono\n",
      "EX numC= 10 p = 0.004 Diff = 0.829 Asíncrono\n",
      "EX numC= 40 p = 0.016 Diff = 0.18 Síncrono\n",
      "Distribución CompactFit -------------------------\n",
      "Para  2  padres\n",
      "EX numC= 2 p = 0.0 Diff = 0.25 Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 1.177 Síncrono\n",
      "EX numC= 20 p = 0.002 Diff = 2.108 Síncrono\n",
      "EX numC= 40 p = 0.001 Diff = 1.252 Síncrono\n",
      "Para  10  padres\n",
      "EX numC= 2 p = 0.0 Diff = 3.563 Asíncrono\n",
      "EX numC= 10 p = 0.025 Diff = 0.104 Síncrono\n",
      "EX numC= 40 p = 0.042 Diff = 0.716 Síncrono\n",
      "Para  20  padres\n",
      "EX numC= 2 p = 0.0 Diff = 5.352"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if sys.path[0] == '':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 0.666 Asíncrono\n",
      "EX numC= 20 p = 0.017 Diff = 0.655 Síncrono\n",
      "EX numC= 40 p = 0.008 Diff = 0.549 Síncrono\n",
      "Para  40  padres\n",
      "EX numC= 2 p = 0.0 Diff = 10.203 Asíncrono\n",
      "EX numC= 10 p = 0.0 Diff = 0.977 Asíncrono\n",
      "EX numC= 40 p = 0.002 Diff = 0.371 Síncrono\n",
      "SINC: 18 || ASINC: 14\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO EJECUCCION\")\n",
    "sinc = 0\n",
    "asinc = 0\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                group = str(numP) + \",\" + str(numC)\n",
    "                v1 = dfG[(dfG[\"%Async\"] == 0.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
    "                v2 = dfG[(dfG[\"%Async\"] == 100.0)][(dfG.Groups == group)][(dfG[\"Dist\"] == dist_v)]['TE']\n",
    "                res = stats.ttest_ind(v1, v2)\n",
    "                diff = grouped_aggG['TE'].loc[(dist_v, 0.0, group)] - grouped_aggG['TE'].loc[(dist_v, 100.0, group)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                    asinc+=1\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                    sinc+=1\n",
    "                    \n",
    "                if res[1] < p_value:\n",
    "                    print(\"EX numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)\n",
    "print(\"SINC: \" + str(sinc) + \" || ASINC: \" + str(asinc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO MALLEABILITY\n",
      "Distribución BalancedFit -------------------------\n",
      "Para  2  padres\n",
      "TR numC= 2 p = 0.0 Diff = 1.671 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 1.577 Síncrono\n",
      "TR numC= 20 p = 0.0 Diff = 1.51 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 1.534 Síncrono\n",
      "Para  10  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.393 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.343 Síncrono\n",
      "TR numC= 20 p = 0.0 Diff = 0.302 Síncrono\n",
      "TR numC= 40 p = 0.044 Diff = 0.893 Síncrono\n",
      "Para  20  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.172 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.378 Síncrono\n",
      "TR numC= 20 p = 0.004 Diff = 0.386 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 0.479 Síncrono\n",
      "Para  40  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.444 Síncrono\n",
      "TR numC= 10 p = 0.036 Diff = 0.361 Síncrono\n",
      "TR numC= 20 p = 0.02 Diff = 0.578 Síncrono\n",
      "TR numC= 40 p = 0.001 Diff = 0.543 Síncrono\n",
      "Distribución CompactFit -------------------------\n",
      "Para  2  padres\n",
      "TR numC= 2 p = 0.0 Diff = 1.699 Síncrono\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if __name__ == '__main__':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TR numC= 10 p = 0.0 Diff = 1.559 Síncrono\n",
      "TR numC= 20 p = 0.002 Diff = 2.282 Síncrono\n",
      "TR numC= 40 p = 0.001 Diff = 1.363 Síncrono\n",
      "Para  10  padres\n",
      "TR numC= 2 p = 0.0 Diff = 0.43 Síncrono\n",
      "TR numC= 10 p = 0.0 Diff = 0.533 Síncrono\n",
      "Para  20  padres\n",
      "TR numC= 2 p = 0.001 Diff = 0.586 Síncrono\n",
      "TR numC= 10 p = 0.009 Diff = 0.442 Síncrono\n",
      "TR numC= 20 p = 0.001 Diff = 1.036 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 0.803 Síncrono\n",
      "Para  40  padres\n",
      "TR numC= 2 p = 0.04 Diff = 0.443 Síncrono\n",
      "TR numC= 10 p = 0.016 Diff = 0.38 Síncrono\n",
      "TR numC= 20 p = 0.016 Diff = 0.534 Síncrono\n",
      "TR numC= 40 p = 0.0 Diff = 0.633 Síncrono\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO MALLEABILITY\")\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                v1 = dfM[(dfM[\"%Async\"] == 0.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TS']\n",
    "                v2 = dfM[(dfM[\"%Async\"] == 100.0)][(dfM.NP == numP)][(dfM.NS == numC)][(dfM[\"Dist\"] == dist_v)]['TA']\n",
    "                res = stats.ttest_ind(v1, v2)\n",
    "                diff = grouped_aggM['TS'].loc[(dist_v, 0.0, numP, numC)] - grouped_aggM['TA'].loc[(dist_v, 100.0, numP, numC)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                if res[1] < p_value:\n",
    "                    print(\"TR numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,3)), mejor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TIEMPO Iters\n",
      "Distribución BalancedFit -------------------------\n",
      "Para  2  padres\n",
      "Ti numC= 10 p = 0.007 Diff = 0.0142 Síncrono\n",
      "Para  10  padres\n",
      "Ti numC= 2 p = 0.022 Diff = 0.0083 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.4113 Síncrono\n",
      "Para  20  padres\n",
      "Ti numC= 2 p = 0.001 Diff = 0.0288 Síncrono\n",
      "Ti numC= 10 p = 0.024 Diff = 0.0497 Síncrono\n",
      "Ti numC= 20 p = 0.021 Diff = 0.1218 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.3469 Síncrono\n",
      "Para  40  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.1678 Síncrono\n",
      "Ti numC= 10 p = 0.0 Diff = 0.2696 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.3707 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.5085 Síncrono\n",
      "Distribución CompactFit -------------------------\n",
      "Para  2  padres\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  if sys.path[0] == '':\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  del sys.path[0]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ti numC= 40 p = 0.021 Diff = 0.399 Síncrono\n",
      "Para  10  padres\n",
      "Ti numC= 2 p = 0.012 Diff = 0.0321 Síncrono\n",
      "Ti numC= 10 p = 0.001 Diff = 0.2175 Síncrono\n",
      "Ti numC= 20 p = 0.003 Diff = 0.5792 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.5931 Síncrono\n",
      "Para  20  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.2775 Síncrono\n",
      "Ti numC= 10 p = 0.0 Diff = 0.3815 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.561 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.6162 Síncrono\n",
      "Para  40  padres\n",
      "Ti numC= 2 p = 0.0 Diff = 0.1849 Síncrono\n",
      "Ti numC= 10 p = 0.0 Diff = 0.2827 Síncrono\n",
      "Ti numC= 20 p = 0.0 Diff = 0.3832 Síncrono\n",
      "Ti numC= 40 p = 0.0 Diff = 0.5335 Síncrono\n"
     ]
    }
   ],
   "source": [
    "print(\"TIEMPO Iters\")\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                #exp = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == 1)][(dfL[\"%Async\"] == 0.0)][(dfL.NP == numP)][(dfL.NS == numC)]\n",
    "                #TimeOp = exp['Ti'] \n",
    "                #print(TimeOp)\n",
    "                v1 = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
    "                v2 = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == 100.0)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti']\n",
    "                res = stats.ttest_ind(v1, v2, equal_var = False)\n",
    "                diff = grouped_aggL['Ti'].loc[(0, dist, 100.0, numP, numC)] - grouped_aggL['Ti'].loc[(1, dist, 100.0, numP, numC)]\n",
    "                if diff > 0:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                if res[1] < p_value:\n",
    "                    #and abs(diff) > grouped_aggL['Ti'].loc[(0, dist, 0.0, numP, numC)]\n",
    "                    print(\"Ti numC=\", numC, \"p =\", round(res[1],3), \"Diff =\", abs(round(diff,4)), mejor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\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": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribución BalancedFit -------------------------\n",
      "Para  2  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.334\n",
      "NC=10 Es mejor Síncrono con una diff de 1.174\n",
      "NC=20 Es mejor Síncrono con una diff de 1.306\n",
      "NC=40 Es mejor Síncrono con una diff de 1.429\n",
      "Para  10  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 3.619\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.059\n",
      "NC=20 Es mejor Síncrono con una diff de 0.098\n",
      "NC=40 Es mejor Síncrono con una diff de 0.716\n",
      "Para  20  padres\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:16: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NC=2 Es mejor Asíncrono con una diff de 5.845\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.427\n",
      "NC=20 Es mejor Síncrono con una diff de 0.114\n",
      "NC=40 Es mejor Síncrono con una diff de 0.268\n",
      "Para  40  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 7.579\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.847\n",
      "NC=20 Es mejor Síncrono con una diff de 0.103\n",
      "NC=40 Es mejor Síncrono con una diff de 0.331\n",
      "Distribución CompactFit -------------------------\n",
      "Para  2  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 0.307\n",
      "NC=10 Es mejor Síncrono con una diff de 1.156\n",
      "NC=20 Es mejor Síncrono con una diff de 2.077\n",
      "NC=40 Es mejor Síncrono con una diff de 1.257\n",
      "Para  10  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 3.581\n",
      "NC=10 Es mejor Síncrono con una diff de 0.131\n",
      "NC=20 Es mejor Síncrono con una diff de 0.358\n",
      "NC=40 Es mejor Síncrono con una diff de 0.694\n",
      "Para  20  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 5.43\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.363\n",
      "NC=20 Es mejor Síncrono con una diff de 0.626\n",
      "NC=40 Es mejor Síncrono con una diff de 0.591\n",
      "Para  40  padres\n",
      "NC=2 Es mejor Asíncrono con una diff de 10.253\n",
      "NC=10 Es mejor Asíncrono con una diff de 0.961\n",
      "NC=20 Es mejor Asíncrono con una diff de 0.082\n",
      "NC=40 Es mejor Síncrono con una diff de 0.422\n"
     ]
    }
   ],
   "source": [
    "#iters = dfM['Iters'].mean()\n",
    "resultados = [0,0]\n",
    "for dist in [1,2]:\n",
    "    print(\"Distribución \" + dist_names[dist] + \" -------------------------\")\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    for numP in values:\n",
    "        print(\"Para \", numP, \" padres\")\n",
    "        for numC in values:\n",
    "            #if numP != numC:\n",
    "                Titer = dfL[(dfL[\"Tt\"] == 0)][(dfL[\"Dist\"] == dist)][(dfL.NP == numC)]['Ti'].mean() #Tiempo por iteracion\n",
    "                i=0\n",
    "                for adr in [0.0, 100.0]:\n",
    "                \n",
    "                    auxExp = dfM[(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == adr)][(dfM.NP == numP)][(dfM.NS == numC)]\n",
    "                    Tr = auxExp['TS'].mean() + auxExp['TA'].mean() #Tiempo de redistribucion\n",
    "                    M_it = dfL[(dfL[\"Tt\"] == 1)][(dfL[\"Dist\"] == dist)][(dfL[\"%Async\"] == adr)][(dfL.NP == numP)][(dfL.NS == numC)]['Ti'].count()/3 #Iteraciones asincronas\n",
    "                    #No se presupone una diferencia temporal entre iteraciones normales y asincronas\n",
    "                    if(M_it > iters):\n",
    "                        M_it = iters\n",
    "                    resultados[i] = (iters - M_it) * Titer + Tr\n",
    "                    i+=1\n",
    "\n",
    "                if resultados[0] > resultados[1]:\n",
    "                    mejor = \"Asíncrono\"\n",
    "                else:\n",
    "                    mejor = \"Síncrono\"\n",
    "                diff = abs(round(resultados[0] - resultados[1], 3))\n",
    "                print(\"NC=\"+ str(numC) + \" Es mejor \" + mejor + \" con una diff de \"+  str(diff))\n",
    "                #TODO Comprobar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.1997398478260869, 0.1997398478260869, 0.1997398478260869, 0.1997398478260869, 0.03965863157894737, 0.03965863157894737, 0.03965863157894737, 0.03965863157894737, 0.02013464705882353, 0.02013464705882353, 0.02013464705882353, 0.02013464705882353, 0.00981611111111111, 0.00981611111111111, 0.00981611111111111, 0.00981611111111111], [0.1994873076923077, 0.1994873076923077, 0.1994873076923077, 0.1994873076923077, 0.03969724242424243, 0.03969724242424243, 0.03969724242424243, 0.03969724242424243, 0.0196615, 0.0196615, 0.0196615, 0.0196615, 0.009970239999999998, 0.009970239999999998, 0.009970239999999998, 0.009970239999999998]]\n",
      "[[1.5979187826086951, 0.31726905263157895, 0.14094252941176472, 0.04908055555555555, 0.5992195434782607, 0.03965863157894737, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.5958984615384617, 0.27788069696969697, 0.078646, 0.03988095999999999, 0.1994873076923077, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]\n",
      "[[0.3544183333333333, 0.4214563333333334, 0.5490153333333333, 1.0957393333333332, 0.35624900000000004, 0.43835799999999997, 0.6298016666666667, 1.3951330000000002, 0.4016946666666667, 0.40798266666666666, 0.7426136666666666, 1.474785, 0.526503, 1.066937, 1.2628026666666667, 1.7076066666666667], [0.4218733333333334, 0.6196486666666666, 1.3087316666666666, 1.2683030000000002, 0.41305433333333336, 0.705488, 1.4576376666666666, 1.3694266666666668, 0.8762309999999999, 1.2434033333333334, 1.6149826666666665, 1.6660403333333331, 0.7555176666666666, 1.2363253333333333, 1.5753153333333334, 1.682241]]\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n",
    "TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n",
    "TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n",
    "\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "\n",
    "    width = 0.35\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index]).tolist()\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n",
    "    ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n",
    "    ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "    ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n",
    "    ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n",
    "    ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "    ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "    ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "    plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "    blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n",
    "    orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n",
    "    green_Spatch = mpatches.Patch(color='green', label='Children PR')\n",
    "    blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n",
    "    orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n",
    "    green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n",
    "\n",
    "\n",
    "    handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\n",
    "    \n",
    "    ax.axvline((3.5), color='black')\n",
    "    ax.axvline((7.5), color='black')\n",
    "    ax.axvline((11.5), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    plt.ylim((0, 3.5))\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": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-b31e6e75557d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mtipo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m                 \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgrouped_aggM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist_v\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtipo\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TS'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'TA'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m                 \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    808\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m         \u001b[0;31m# no multi-index, so validate all of the indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m         \u001b[0;31m# ugly hack for GH #836\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    233\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Too many indexers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m                 raise ValueError(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1724\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1725\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1727\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m    272\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0;31m# a scalar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m   3136\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"loc\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3137\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3138\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"label\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3140\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[0;34m(self, form, key)\u001b[0m\n\u001b[1;32m   3338\u001b[0m             \u001b[0;34m\"cannot do {form} indexing on {klass} with these \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3339\u001b[0m             \"indexers [{key}] of {kind}\".format(\n\u001b[0;32m-> 3340\u001b[0;31m                 \u001b[0mform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3341\u001b[0m             )\n\u001b[1;32m   3342\u001b[0m         )\n",
      "\u001b[0;31mTypeError\u001b[0m: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>"
     ]
    }
   ],
   "source": [
    "#Reserva de memoria para las estructuras\n",
    "TC_data=[0]*2\n",
    "TS_data=[0]*2\n",
    "TA_data=[0]*2\n",
    "\n",
    "TC_A_data=[0]*2\n",
    "TS_A_data=[0]*2\n",
    "TA_A_data=[0]*2\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "\n",
    "    TC_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TS_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TA_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "    TC_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TS_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TA_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\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",
    "            test_tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "            \n",
    "            for tipo in [0, 100]:\n",
    "            \n",
    "                test=grouped_aggM.loc[(dist_v,tipo,numP,numC)][['TS', 'TA']]\n",
    "                test=test.tolist()\n",
    "                    \n",
    "                if tipo == 0:\n",
    "                    TC_data[dist_index][i*len(values) + j] = test_tc_real\n",
    "                    TS_data[dist_index][i*len(values) + j] = test[0] \n",
    "                    TA_data[dist_index][i*len(values) + j] = 0\n",
    "                else:\n",
    "                    TC_A_data[dist_index][i*len(values) + j] = test_tc_real\n",
    "                    TS_A_data[dist_index][i*len(values) + j] = test[0]\n",
    "                    TA_A_data[dist_index][i*len(values) + j] = test[1]\n",
    "            j+=1\n",
    "        i+=1\n",
    "                    \n",
    "                    \n",
    "##########################\n",
    "\n",
    "print(TC_data)\n",
    "#print(TA_A_data[1])\n",
    "#print(TS_data)\n",
    "#print(TA_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "    width = 0.35\n",
    "    sumaTC_TS_A = np.add(TC_A_data[dist_index], TS_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TC_data[dist_index], width, color='chocolate')\n",
    "    ax.bar(x+width/2, TS_data[dist_index], width, bottom=TC_data[dist_index], color='purple')\n",
    "\n",
    "    ax.bar(x-width/2, TC_A_data[dist_index], width, hatch=\"\", color='chocolate')\n",
    "    ax.bar(x-width/2, TS_A_data[dist_index], width, bottom=TC_A_data[dist_index], hatch=\"\\\\/...\", color='purple')\n",
    "    ax.bar(x-width/2, TA_A_data[dist_index], width, bottom=sumaTC_TS_A, hatch=\"\", color='red')\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",
    "    labels = ['Spawn', 'Synchronous', 'Asynchronous'] # Necesario para subdividir\n",
    "    brown_Spatch = mpatches.Patch(color='chocolate', label='Spawn')\n",
    "    purple_Spatch = mpatches.Patch(color='purple', label='Synchronous')\n",
    "    red_Apatch = mpatches.Patch(facecolor='red', label='Asynchronous')\n",
    "\n",
    "    #handles=[(brown_Spatch, brown_Apatch),purple_Spatch,red_Apatch] Dos colores para misma leyenda\n",
    "    handles=[brown_Spatch,purple_Spatch,red_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, labels=labels, loc='upper left', fontsize=24, ncol=1, handler_map={tuple: HandlerTuple(ndivide=None)})\n",
    "#bbox_to_anchor=(1, 0.5) --> Para sacar fuera de la grafica la leyenda\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",
    "    #ax.axvline(4)\n",
    "    plt.ylim((0, 8))\n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/Malt_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAANYCAYAAABJlYhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeZhfVX0/8PdJQkJWCCQCCZIANYRFtBAREUgoSDG1EmyLWEGhID+tFkEoT39uEETcUKnLj4q2CoKCtIJiC8piAOsCKSJCIBYtKFtJCJIFwpKc3x+ZGWdCZpK5mcnMZF6v57nPXc65537u1yfzx5vjuaXWGgAAAAAA6K4hfV0AAAAAAAADk4AZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQzr6wIGmgkTJtSpU6f2dRkAA87ChQuTJLvttlsfVwIwMPk7CtCcv6EAG2fhwoVZvnz54lrrxLXbBMzdNHXq1MyfP7+vywAYcGbNmpUkmTdvXp/WATBQ+TsK0Jy/oQAbZ9asWbn55psfXFebJTIAAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARob1dQGD0apVq7J06dIsW7YszzzzTFavXt3XJQGdGDJkSEaOHJmxY8dm3LhxGTp0aF+XBAAAANBvCJg3seeeey4PPvhgRo0ala233jqTJ0/OkCFDUkrp69KAtdRas3r16qxYsSLLli3L4sWLM2XKlAwfPryvSwMAAADoFwTMm9CqVavy4IMPZsKECRk/fnxflwOsRyklQ4cOzbhx4zJu3Lg8+eSTefDBB7PLLruYyQwAAAAQazBvUkuXLs2oUaOEyzBAjR8/PqNGjcrSpUv7uhQAAACAfkHAvAktW7YsY8eO7esygI0wduzYLFu2rK/LAAAAAOgXBMyb0DPPPJPRo0f3dRnARhg9enSeeeaZvi4DAAAAoF8QMG9Cq1evzpAhfnIYyIYMGZLVq1f3dRkAAAAA/YK0cxMrpfR1CcBG8G8YAAAA4A8EzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZmDAOuOMM1JKSSkl8+fP7+tyAAAAAAYdAXM/Vsrg2jaFqVOntgWSDz/8cJd9V61ala233rqt/wknnLDe8S+44IK2/qeddlpPld2nlixZkrPPPjtnn312vvvd7/bYuMcee2zbb7Uh24QJE7r9jMcff7yt9v/4j//osdoBAAAAWEPAzKAyc+bMtuObb765y7533nlnnnrqqQ3uv3afWbNmdb/AfmjJkiWZO3du5s6d26MB86bw+OOPt9UuYAYAAADoecP6ugDYlGbNmpVLLrkkyZow+K//+q877bt2oPw///M/+d3vfpeXvvSl6+xfa82tt96aJBkyZEgOPvjgHqp683fqqad2CP/XZcSIES+6dv755+f888/vrbIAAAAAWA8BM4NKd2Ywt7Yfcsgh+fGPf5xnn302N998c4499th19r/77rvzxBNPJEn23nvvjB8/voeq3vztu+++mTNnTl+XAQAAAEA3WSKDQWWXXXbJTjvtlCRZuHBhHnvssXX2W716ddts5MMPPzz77bdfkmTevHmdjt2+bXNZHgMAAAAAuiJgZtDZkFnMd911V5588sm2/q3LXXQ163lD11+utebyyy/Pm970prz0pS/NlltumfHjx+cVr3hFTj/99Pz617/usv4bbrih7cN35557bpLkvvvuyymnnJLp06dn3LhxKaXk0ksv7XDfwoULc9ppp2WfffbJ1ltvnS222CITJkzIbrvtlsMPPzyf+tSncu+997b1v//++1NKycte9rK2a//8z/+8zg/wPfTQQ13W3FvOOOOMthrmz5/fdv3uu+9OKSUvf/nL26598YtfXGftixcv7ovSAQAAADYLlshg0Jk5c2a+/vWvJ1kTCr/5zW9+UZ/WsHjUqFGZMWNGli1blo9+9KO5//7788gjj2TSpEkvuueWW25J0vX6y48++mjmzJmT2267rcP1Z599Nr///e9z11135fOf/3w++tGP5u///u836H2++tWv5m//9m+zcuXKTvv80z/9U0455ZQ8//zzHa4/8cQTeeKJJ/KrX/0q119/fa644ooOQS0AAMD6lLmlx8esZ9UeHxNgY5We/3OXnN0bg27av6MCZgad9rOLO5uR3Hp9//33zxZbbJEDDjggw4YNywsvvJCbb745b3nLWzr0X7BgQRYtWpSk8/WXly5dmoMOOqhthvLkyZNzwgknZM8998yKFSvy/e9/P//6r/+a559/PmeeeWZqrTnzzDO7fJdbbrklN910U7bYYou84x3vyAEHHJARI0bk3nvvzXbbbZckuf322/Pud787q1evzrBhw/KXf/mXOfjggzNx4sQ8//zzefTRR3PHHXfkBz/4QYexd9hhh1x11VV57LHH8q53vStJcthhh+Xd7373i+qYMGFCl3VualOmTMlVV12V3/72t3nve9+bJJk9e3be8Y53vKjvuHHjNnV5AADQZ3onHOmFMQEYMATMDDq77rprdtxxxzz00ENZsGBBHn/88bzkJS9pa6+1ts1Gbl1OY8yYMXnlK1+Z+fPnZ968eS8KmDdk/eX2y1/MnDkz3/3udzuEmyeeeGKuvfbaHHXUUXn22WfzwQ9+MLNnz85ee+3V6btcf/31mTRpUm688cZMnz59nX3++Z//OatXr06SXHHFFXnTm960zn4vvPBCh5nVo0ePzpw5c3L//fe3XZsyZcqA+Bjf2LFjM2fOnNx9991t13beeecBUTsAAADAQGINZgal9uswt4bJre6+++488cQTSdJhqYuu1mFe3/rLjz32WC655JIkydZbb51vfetb65w5+/rXvz5nn312kuT555/P+eefv953+fKXv9xpuJykLSDeZpttOg2Xk2TYsGE54IAD1vu83nDcccetc33k1m3YMP8tDAAAAKA/EjAzKHW1TEbr+YgRI7L//vu3XW8NmBcuXJjHHnuswz3rW3/5e9/7Xp577rkkyQknnNBhxvTa3vOe92T06NFJkquvvjq1dr5mzq677prZs2d32p6sWUc6SZ566qk++xgfAAAAAJsn0wIZlNrPYO4sYH7Vq16VLbfcsu36QQcdlFJK2xIaRx99dJKOgXNn6y+3X3ri8MMP77K2MWPG5IADDsj111+fp556Kvfdd1923333dfY98MADuxwrSV73utflmmuuyapVqzJz5sy8//3vz5w5c7Ltttuu995N5dRTT+3wv8nahgzx38IAAAAA+iMBM4PSy172skyePDkPP/xw25IYrYHr2usvt9pmm22yxx575J577sm8efPaAuYNWX/50UcfbTueNm3aeuubNm1arr/++rZ7OwuYJ0+evN6xTj755Fx55ZW59dZb85vf/CYnnXRS3vGOd2SvvfbKAQcckFmzZmX27Nl9+rG7fffd1/rIAAAAAAOQaYEMWq0BcvuP+rV+9C/JOpe6WNc6zOtbfzlJli1b1nbcuvxFV8aMGbPOe9c2cuTI9Y41YsSI3HDDDfnEJz6RKVOmJFnzzr/85S/zpS99KW95y1uy3Xbb5ZRTTunyWQAAAACwNgEzg9a6lslo3Xf2wbvWgHnBggVZtGhRh3s6W385ScaOHdt2vGLFivXWtnz58nXe29Tw4cNz5pln5oEHHsjdd9+dL33pS3nb297WNgN65cqV+fznP59Zs2Zl5cqVG/08AAAAAAYHATOD1ro+9Ne632effTrMIm7VPkC+5ZZbcv/99+eRRx5J0vn6y0myww47tB3/93//93pra99n0qRJ6+3fHXvuuWdOPvnkXHzxxXnooYdyww03ZKeddkqS3HHHHfna177Wo88DAAAAYPMlYGbQmjZtWlvwe9ddd+XJJ59sC5g7m4k8adKk7LrrrknWrL28IesvJ8l+++3Xdty6tnJnVqxYkR//+MdJkq222iq77bbbet9lYxx66KH53Oc+13b+ox/9qEN7+w/s1Vp7tZaeNpBrBwAAABgIBMwMaq3LZKxevTpf/vKX89hjj3W4vi4HHXRQkjWznTdk/eUkecMb3pDhw4cnSb761a9m8eLFnfb94he/2LZExlFHHZVSyoa9zEaYOnVq2/ELL7zQoa39TO4NWd6jPxnItQMAAAAMBAJmBrX2ofCnP/3pJGtmvR544IGd3tM6u/nuu+/Odddd13ZPZ7Oek2T77bfP29/+9iTJkiVL8uY3v3mdH9T7wQ9+kLPOOivJmnWgTz/99O690Dqcdtpp+dnPftZlnwsvvLDt+BWveEWHtokTJ7YFtT//+c83up5NadKkSRkxYkSSgVc7AAAAwEAwrK8LgL7UPmB+/PHHk6xZS3nrrbfu9J7WILnW2jYTuav1l1udf/75uemmm/LrX/86N910U/bYY4/8zd/8TXbfffc8/fTT+cEPfpBvfetbbUs5fPSjH81ee+21Ma+XJLnyyitzwQUXZJdddslhhx2WvffeOxMnTsyzzz6b3/72t7nyyivzi1/8IsmaMPmkk07qcH8pJYccckiuueaa/OpXv8oxxxyTOXPmdPiNDjnkkLYgtz8ZNmxYDj744Fx//fW56667ctxxx+UNb3hDttpqq7Y+hx56aLbYYos+rBIAAABg4BIwM6jttttu2X777duWxkg6X3+51a677prJkyfn4YcfbrvW1fIYrcaNG5dbb701Rx55ZG6//fY89NBDOeecc17Ub4sttsi5556bM888c8NfpAut6xD/5je/yUUXXdRpv5133jlXXXVVJk6c+KK2s846K9dff31WrlyZK664IldccUWH9t/97nfZcccde6TenjZ37tzcfPPNee6553LppZfm0ksv7dC+aNGiTJgwoY+qAwAAABjYLJHRj9U6uLa+svZ6y+sLmJM/rMPcakMC5iTZYYcd8rOf/Szf/OY3M2fOnEyePDnDhw/PuHHjstdee+W0007LggULeixcTpJf/OIX+fa3v533vOc92W+//TJhwoQMGzYsI0aMyI477pjZs2fnoosuyoIFC160PEarfffdN/Pnz8+JJ56YadOmZdSoUT1WX297zWtek9tvvz3HH398/uiP/mhA1Q4AAADQ35Xal8neADRjxow6f/78Rvfee++92X333Xu4ImBT82+5mdb/EDNv3rw+rQNgoPJ3FOgJvfIN8bN7ftB6Vs9mFf6GAj1hoPwNTXrn7+jNN9/8X7XWGWu3mcEMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARvpFwFxKGVtKeWMp5SOllGtLKYtLKbVlm97DzxpaSpnfbvyze3J8AAAAAIDBYlhfF9Di0CRXbaJn/V2SfTfRswAAAAAANlv9JWBOkseTzE9ye5KHk1zU0w8opeyY5CNJHkyyZZLtevoZAAAMLGVu6ZVx61m1V8YFAID+pL8EzNfUWq9uPSmlTO2l53w+yZgkb03yuV56BgAAAADAoNAv1mCuta7q7WeUUt6YZE6S79Vav9vbzwMAAAAA2Nz1i4C5t5VSRif5QpJnkpzSx+UAAAAAAGwW+ssSGb3tI0lemuRDtdb/6etiAAA2Rm+sGWy9YAAAoInNfgZzKeWPs2bW8q+SfLKPywEAAAAA2Gxs1gFzKWVIki8lGZrkPbXW5xqOc3IpZX4pZf6iRYt6tEYAAAAAgIFqsw6Yk7w7yauSfKvWen3TQWqtF9VaZ9RaZ0ycOLHnqgMAAAAAGMA224C5lDIpyblJlid5Xx+XAwAAAACw2dmcP/L3sSTjknwoyVOllDFrtbd+HWd4a1utdfkmrA8AAAAAYEDbbGcwJ5nSsv9IkmXr2HZqaf+/7a4BAAAAALCBNueAGQAAAACAXrTZBsy11lm11tLZluTBlq5z210DAAAAAGAD9ZuAuZQyoXVLMr5d09bt20opQ9a6r7ZsZ2/SggEAAAAABrl+EzAnWdRuu6Pd9Z+s1bbTi28F+rvtt98+pZRMnz69r0sBAAAAoIcM6+sC6FyZO7hW7ahn1U32rFI6/21LKRkzZkwmTZqUGTNm5Jhjjskb3vCGTVbbxpg3b17mzZuXJDn++OMzderUHhm3q99rXd773vfmggsu6NY9N9xwQ370ox8lSU466aTsuOOO3bofAAAAgE2v3wTMTddA3oj7pja5j81frTXLli3LwoULs3Dhwlx22WWZOXNm/u3f/i3bbrttX5fXpXnz5mXu3LlJklmzZvVYwLwp3HDDDfnEJz6RJDniiCMEzAAAAAADQL8JmKGvXHXVVR3OV69encWLF+enP/1pvvnNb2blypW5+eabc+SRR+bWW2/t9mzezc3av9e67Lrrri+69thjj/VGOQAAAAD0IQEzg96cOXPWef3kk0/O+973vhxwwAFZtmxZ/vM//zPXXXddXv/612/iCvuXzn4vAAAAAAaf/vSRP+h39tprr5x00klt5zfffHMfVgMAAAAA/YuAGdZj+vTpbcdPPfXUevvfc889ed/73pdXvvKV2WabbTJixIhMnjw5b3zjG3PZZZdl9erV6x3jO9/5To4++ujssssuGTVqVLbccstMnjw5r3jFK3Lcccflsssuy5NPPtnW/+yzz04ppW395SQ55JBDUkrpsM2aNat7L9+Dtt9++5RSOvyeSfIP//APKaW0rb+cJK95zWteVPsRRxyxqUsGAAAAYD0skQHrsXjx4rbjnXbaqdN+L7zwQk4//fR84QtfeFGI/Mgjj+SRRx7JNddck8997nP5zne+k+233/5FYzzzzDP5q7/6q/z7v//7i9pax7jrrrty6aWX5rOf/WxOPfXUjXgzAAAAANg4AmbowsqVK3PZZZe1nR922GHr7FdrzdFHH932AbyJEyfmLW95S/74j/84o0ePzoMPPpgrrrgi8+fPz2233ZZDDz00t99+e0aNGtVhnPe///1t4fIOO+yQY489NnvuuWfGjBmT5cuX5/77789PfvKT3HLLLR3uO+aYY/LKV74yl19+ea644ookyUc+8pHstddeHfpNmDBh436QXvC2t70t+++/f77+9a/n29/+dpLkE5/4RKZNm9ah33bbbdcX5QEAAADQBQEzrGX16tVZsmRJfvrTn+bcc8/NggULkiTHH398XvWqV63zns997nNt4fKcOXNy8cUXZ9y4cR36nHHGGfnABz6Q8847LwsWLMg555yTj3/8423tq1atyle/+tUkyZQpU3LbbbflJS95yTqft2jRojzxxBNt59OnT8/06dNz5513tl078MAD+3RJjA21xx57ZI899shPf/rTtmsHH3xw9t9//z6sCgAAAIANYQ1mBr211/odOnRoJk6cmD//8z/Pz372s+y555759Kc/nX/5l39Z5/0rV67Meeedl2RN0HvFFVe8KFxu9dGPfjQHHXRQkuTCCy/MypUr29oWLVrUtsbzkUce2Wm4nKyZIb32Wsabytq/19rb8ccf3yd1AQAAALDpCZhhPYYPH57Ro0en1rrO9u9///t5/PHHkySnnHJKhg8f3uV4xx57bJJk6dKlHWbtjhw5su24ddY0AAAAAPRnlshg0Gtd2qK95cuX57777svll1+en//853nnO9+ZK6+8Mt/97ndftG7yrbfe2uG+q6++usvnPfzww23H9957b9syFltttVX222+/3Hbbbbnhhhty1FFH5e/+7u9y0EEHZYstttiIN+xZ6/q92uvqQ4gAAAAAbF4EzAx6c+bM6bTtwx/+cE444YR84xvfyI033phTTjklX/nKVzr0eeCBB9qOzzzzzG49+8knn+xw/sUvfjGHHnpoli5dmquvvjpXX311Ro8enVe/+tU58MADc9hhh+W1r31thgzpu//zQVe/FwAAAACDiyUyoAvDhw/PhRde2Lam8te+9rX87ne/69Cndd3kJp577rkO5zNmzMidd96Zt73tbW1LZqxYsSI33XRTzjnnnBx88MHZddddc+mllzZ+JgAAAAD0FAEzrMe4cePymte8JkmyatWq3HTTTR3ax4wZ03b8wAMPpNa6wdvZZ5/9ouftvPPOufjii7NkyZLcdNNNOffcc3PEEUe0Bc4PPPBAjjvuuLYPCwIAAABAXxEwwwbYdttt244feeSRDm2TJ09uO77nnnt67JlbbrllDjnkkHzgAx/Itddem8cffzyf+MQnUkpJkpxzzjl54okneux5AAAAANBdAmbYAO2D3NGjR3domzlzZtvx+j6AtzHGjBmTM888M3/xF3+RJHn22Wdz++23d+jTfm3mWmuv1dIbBnLtAAAAAIOVgBnWY9myZfnJT37Sdr777rt3aJ89e3YmTJiQJLnkkkt6dBbzukydOrXt+IUXXujQ1n65jhUrVvRqHT1tINcOAAAAMFgJmKELzz//fN71rndl6dKlSZJJkyZ1mLGcrJnRfNZZZyVZ89G+2bNnZ/78+V2Oe/vtt+fMM8/scO3nP/95PvKRj+R///d/O71v8eLFufLKK5MkpZTsvffeHdp33nnntuM77rhjPW/Xvwzk2gEAAAAGq2F9XQD0tauvvvpF11asWJH77rsvl19+ee6///4ka5Zw+MIXvpDhw4e/qP973vOe3H777bnkkkvy29/+Nvvtt1+OOOKIHHroodlxxx1Ta83ixYvzy1/+MjfeeGN+/etfZ9ddd80nP/nJtjGeeuqpfPjDH87cuXPz2te+NgcccECmTZuWsWPHZsmSJfnlL3+Zb3zjG1myZEmS5K1vfWt22mmnDnUcdNBB2WKLLfL888/nU5/6VFsIPWLEiCTJNttsk/3226/HfrueNHPmzAwdOjSrVq3KeeedlxdeeCF77bVX2+89ceLE7Lvvvn1cJQAAAADtCZgZ9I466qj19tlmm21y4YUXdtn3a1/7Wl72spfl3HPPzbPPPptrr7021157baf9d9xxxw7nrWsQr1q1KrfccktuueWWTu89+uijc9FFF73o+oQJE3LGGWfkYx/7WJYvX54Pf/jDHdpnzpyZefPmdTpuX5o0aVJOOeWUfPazn81TTz2VD3zgAx3a//RP/zTXXXddH1UHAAAAwLoImPuxepYPnfWVkSNHZptttsnLX/7yHHHEETnuuOOyzTbbdHlPKSUf/OAHc+KJJ+YrX/lKbrzxxixcuDBLlizJkCFDMmHChEyfPj2vec1rMnv27Oy///4d7j/44IOzcOHC3HTTTfnhD3+Yu+66Kw899FCefvrpjBo1KjvttFP233//vP3tb8/BBx/caR3nnXde9t5771x88cW58847s2TJkjz33HM98rv0ts985jPZZ5998vWvfz2/+MUvsmTJkjz//PN9XRYAAAAAnRAwMyjV2nvh/Q477JAPfehD+dCHPtTte6dNm5Zp06blne9850bVcMwxx+SYY47ZqDHa64nf67HHHtugfscee2yOPfbYjX4eAAAAAL3PR/4AAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiI/8AQAAMKiUuaXHx6xn9d6HxAH6E39DWZsZzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMC5k2sVl/FhIHMv2EAAACAPxAwb0JDhgzJ6tWr+7oMYCOsXr06Q4b40wkAAACQCJg3qZEjR2bFihV9XQawEVasWJGRI0f2dRkAAAAA/YKAeRMaO3Zsli1b1tdlABth2bJlGTt2bF+XAQAAANAvCJg3oXHjxuXpp5/Ok08+2delAA08+eSTefrppzNu3Li+LgUAAACgXxjW1wUMJkOHDs2UKVPy4IMP5umnn87YsWMzevToDBkyJKWUvi4PWEutNatXr86KFSuybNmyPP3005kyZUqGDh3a16UBAAAA9AsC5k1s+PDh2WWXXbJ06dL8/ve/z6OPPurDf9CPDRkyJCNHjszYsWOz/fbbC5cBAAAA2hEw94GhQ4dm/PjxGT9+fF+XAgAAAADQmDWYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAIABo5Se3wAAgOYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoZ1tcFJEkpZWySQ5K8KsmMlv22Lc2711rvazjuxCRvSnJYkn2STE6yKslvk9yY5IJa6/0bVz0AAAAAwODULwLmJIcmuaoXxn0kHd9xeZLhSaa3bCeWUv6m1vrNXng2AAAAAMBmrT8tkfF4kv9IMjfJyT005rAktyR5e5Idaq1jk4xKcmCSO5NsmeSSUsrePfQ8AAAAAIBBo7/MYL6m1np160kpZWoPjTuz1npL+wu11lVJ/rOUcniSu5O8JMlpSU7ooWcCAAAAAAwK/WIGc0vo2xvj3tJF26KsmTGdJPv2xvMBAAAAADZn/SJg7kNPtOyH9mkVAAAAAAAD0GAPmGe27O/u0yoAAAAAAAagQRswl1KOTDKj5fSrfVkLAAAAAMBANCgD5lLK5CQXtZx+t9Z63Xr6n1xKmV9Kmb9o0aLeLxAAAAAAYAAYdAFzKWVMkquTvCTJg0lOXN89tdaLaq0zaq0zJk6c2NslAgAAAAAMCIMqYC6lbJnkO1mzNMaiJH9aa13ct1UBAAAAAAxMgyZgLqUMT/KvSf4kye+THF5rXdi3VQEAAAAADFyDImAupQxL8s0kf5ZkeZLZtdY7+7YqAAAAAICBbbMPmEspQ5JcnORNSZ5J8sZa60/6tioAAAAAgAFtPHsAACAASURBVIFvsw6YSyklyUVJ/jrJc0neVGv9Yd9WBQAAAACweeg3AXMpZULrlmR8u6at27e1zEhuf19t2c5ex7CfSXJikheSHF1rva7XXgAAAAAAYJAZ1tcFtLOok+trL2exc5IH1jdYKWWnJKe2nNYkXyqlfKmz/rXW7TegRgAAAAAAWvSngLmntZ/pvEWS7fqqEAAAAACAzVG/CZhrraUn76u1PpCk0ZgAAAAAAKxfv1mDGQAAAACAgUXADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANDKsrwsAAACgc2Vu6fEx61m1x8cEAAYnM5gBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAA2AyV0vMbrE3ADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaGdbXBQCDU5lbenzMelbt8TEHCr8nAAAA0BfMYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0Miwvi4gSUopY5MckuRVSWa07Ldtad691nrfRo4/LsmZSf4iyZQkzyS5M8mFtdZ/3ZixAQAAAOi/ytzS42PWs2qPjwkDVb8ImJMcmuSq3hi4lLJjkluS7NxyaXmScUn+JMmflFL+qdb6rt54NgAAAADA5qw/LZHxeJL/SDI3yck9MWAppST516wJlx9I8tpa69gkY7NmRvPqJO8spbyjJ54HAAAAADCY9JcZzNfUWq9uPSmlTO2hcY9M8uqsCZKPqrXemSS11pVJPlVKmZTk1CTnlFIurrU+10PPBQAAAADY7PWLGcy11lW9NPRbW/Y3tIbLazk/SU2yfdYsmQEAAAAAwAbqFwFzL5rVsv/+uhprrQ8nuaflVMAMAAAAANANm23AXEp5SZIJLaf3dNF1Qct+j96tCAAAAABg87LZBsxJdmh3/EgX/VrbduisQynl5FLK/FLK/EWLFvVIcQAAAAAAA93mHDCPbnf8TBf9nm7Zj+msQ631olrrjFrrjIkTJ/ZIcQAAAAAAA93mHDCXdse1z6oAAAAAANhMbc4B8/J2x6O66NfatryLPgAAAAAArGVzDpjbr7s8qYt+rW2P9mItAG1K6fkNAAAAoC9stgFzrXVRksUtp3t20XWPlv2C3q0IAAAAAGDzstkGzC1+2LJ/3boaSymT84fw+cZNUhEAAAAAwGZicw+Yv9GyP7yU8op1tL8vaz4G+Gj+EEYDAAAAALAB+k3AXEqZ0LolGd+uaev2baWUIWvdV1u2s9cx7HeS/Cxr3vOqUsr+LfeMKKWcnuTUln5n1Vqf6/GXAgAAAADYjA3r6wLaWdTJ9Z+sdb5zkgc2ZMBaay2l/GWSW1ru+0kpZXmSLfOHd/+nWuuXu18uAAAAAMDg1m9mMPeWWutDSV6Z5Lwk92VNsLwsa5bEOLrW+q4+LA8AAAAAYMDqNzOYa62lt+6rtS5N8oGWDQAAAACAHrDZz2AGAAAAAKB3CJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoRMAMAAAAA0IiAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAA+oVSen4DepeAGQAAAACARgTMAAAAAAA0ImAGAAAAAKARATMAAAAAAI0ImAEAAAAAaETADAAAAABAIwJmAAAAAAAaETADAAAAANCIgBkAAAAAgEYEzAAAAAAANCJgBgAAAACgEQEzAAAAAACNCJgBAAAAAGhEwAwAAAAAQCMCZgAAAAAAGhEwAwAAAADQiIAZAAAAAIBGBMwAAAAAADQiYAYAAAAAoBEBMwAAAAAAjQiYAQAAAABoRMAMAAAAAEAjAmYAAAAAABoZ1t0bSiljk7w2yU5JJiR5JsnjSe6std7Ts+UBAAAAANBfbVDAXEoZmeQtSU5Msl/+MPO5tOxrS7/FSf4tyYW11l/2bKkAAAAAAPQnXQbMpZRhSU5J8oEk45OsTPLTJLcneSzJkiQjk2ybZHqS/ZO8M8n/KaXckOT0WuvdvVY9AAAAAAB9Zn0zmO9LsnOS65JcnOQ7tdZnu7qhlDItyfFJ3pbk56WUE2utl/RArQAAAAAA9CPrC5gXJPmLWusvNnTAWuuvkry/lHJ2kr/NmhnOAAAAAABsZroMmGutb2w6cK31uSQXNL0fAAAAAID+bcj6uwAAAAAAwIutb4mMDVJKmZ7k9UmeTnJ5rfWpnhgXAAAAAID+q1szmEspHy6lPFpK2abdtcOS/DzJ+Un+X5I7Sinb9myZAAAAAAD0N91dIuP1Se6rtS5pd+1jSWqSs5JcmGTnJO/tmfIAAAAAAOivuhswT01yb+tJKWVykn2T/L9a67m11vckuSnJnB6rEAAAAACAfqm7AfP4JO1nL782a2Yvf6/dtf9KstNG1gUAAAAAQD/X3YB5UZLJ7c4PSfJ8kp+1uza8wbgAAAAAAAwww7rZ/84kbyyl7JVkZZI3J/lRrfWZdn2mJnm0Z8oDAAAAAKC/6u5M408m2SrJL5IsbDn+dGtjKWXLJLOSzO+h+gAAAAAA6Ke6FTDXWm9N8oYkVye5Kslf1lqvbdflgCQPtLR1Wyll+1LKP5ZSfl1KWVlK+d9SyjWllEObjNdu3KNKKd8rpTxaSnm+lLKslHJnKeXjpZTtNmZsAAAAAIDBqrtLZKTWel2S6zppuynJHzcppJSyd5KbkmzbcmlpkglZE2j/WSnl/bXWj3dzzCFJLkny1naXlyUZleQVLds7SilH1Fpvb1I3AAAAAMBg1S8+xldKGZnku1kTLv88yV611q2SjM+aJThKko+VUg7v5tDvyB/C5X9Msl2tdVySLZMckeS3SbZJ8s2WMBoAAAAAgA3UZahaSpmxMYOXUrYspey+AV3/T5IpSZYn+fNa6z1JUmtdWms9I2uW5EiSj3WzhL9u2f+w1npqrfXxlnFfqLV+P8nbW9p3TbJ3N8cGAAAAABjU1jdr97ZSylWllFd3Z9BSylallPcm+U2Sv9qAW1pnGX+j1vrwOto/1bLfp5QyvRultK6vfEcn7f/V7nh0N8YFAAAAABj01hcwn5RkvyQ/LqXcV0o5u5TyulLK+PadSilDSyl7lFL+ppRyRZJHk3w2ya1JvtrVA0opY5Ps23L6/U66/TTJUy3Hf7Kemtt7oGXf2brQrc99NsmCbowLAAAAADDodRkw11r/Jcm0JB9KMjLJh7PmA3+LSykrSymPllKeTPJckl8m+UqSN7X0eW2t9c211t+tp4bds2aN5SS5p5M6VidZ2HK6x3rf6g++3LL/k1LKZ0spL0mSUsqwUsqfJrm4pf2cWuuT3RgXAAAAAGDQW++H7WqtK2qt5yXZOcnrk3wmyW1Jnsiaj/Alyf1Jvp3k75JMrbW+qdb6kw2sYYd2x4900a+1bYcu+qxd+78l+UCSVUlOTfK/pZSlSVZmTQi+LMkJLe8HAAAAAEA3DNvQji2ziL+fzpexaKr92sfPdNHv6Zb9mG6O/7EkDyb5Usuzxq717AmllCEt77dOpZSTk5ycJDvttFM3Hw8AAAAAsHla7wzmTaCsv0vDgdes73xNkkuT/DDJq7MmYJ6a5J1JxmXNBwQv62qcWutFtdYZtdYZEydO7K1yAQAAAAAGlP4QMC9vdzyyi36j1tF/fT6T5M+S3FBr/fNa62211uW11gdrrV9KclSSmuSYUsrru1U1AAAAAMAg1x8C5vbrLk/qol9r26MbMmgpZVySE1pOL1hXn1rrLUnuaDk9ckPGBQAAAABgjf4QMN+XNbOIk2TPdXUopQxJslvL6YINHPdlSYa2HP9PF/1+07KfuoHjAgAAAACQfhAw11qXJZnfcvq6Trq9OslWLcc3buDQ7T/a19WX+aa07Jdt4LgAAAAAAKQfBMwtvtGyf2spZYd1tJ/Rsv+vWuvCDRzzviQrW45PXleHUso+SfZpOf3ZBo4LAAAAAED6T8D8pSQPJhmb5HullD2SpJQytpTyySRvaun3/rVvLKXUlu3s9tdrrc8kuaTl9KhSypdLKS9tuWfLUsqRSa5OMizJ0iRf6/G3AgAAAADYjA3r6wKSNWFwS+B7Y9bMKL6nlLI0yZisCcFrkvfXWn/QzaFPT7JHkgOTnJTkpFLKiiQj84dwfVmSv6q1Lt74NwEAAAAAGDwazWAupexdSvl4KeU7pZQb2l2fWko5upQyvrtj1lp/kWSvJJ/Lmg/vjUjyRJJ/T/K6WuvHG4y5PMmsJH+T5AdJFrWM+0ySu5N8NsnLGwTXAAAAAACDXrdnMJdSzsmapSpaw+narnlIkm8mOTXJ57s7dq31sSTvbdk29J6ynvZVSb7asgEAAAAA0EO6NYO5lHJMkg8muT7JK5N8rH17rfU3SeYneWNPFQgAAAAAQP/U3SUyTklyf5Ija613JXluHX3uTfKyjS0MAAAAAID+rbsB88uTfL/Wuq5gudUjSbZrXhIAAAAAAANBdwPmkmT1evpsl2Rls3IAAAAAABgouhsw/3eSAzprLKUMTXJgkns2pigAAAAAAPq/7gbM30qyTynl9E7a/2+SP0ryjY2qCgAAAACAfm9YN/tfkOSvknyylHJ0kpokpZTzkxyUZEaSnya5qCeLBAAAAACg/+lWwFxrfaaUckiSf0zy/9m793BZzrpO9N9fsrnkyi3BXOQmKhAiaIBBBxgQDHq4GASfEY06HlHwgoKAMxpHLqKCcETBa1AZUAmjIBIERu5HFEUMAkK4OMAYnwMBE9CEQAIk+3f+qFrulZ21116rdq3d3Xt9Ps9TT3eveutdb/929dtrf7u66twkR4+rnpTh3Mx/mOTx3X3trKMEAAAAAGDpbPcI5nT3FUm+r6qelOReSW6V5Iok7+zuy2YeHwAAAAAAS2rbAfOa7v5MktfPOBYAAAAAAFbIdi/yBwAAAAAASSYcwVxVxyZ5TJKvTfLlSW60QbPu7gcd4tgAAAAAAFhi2wqYq+puSd6Q5OQktUnTPpRBAQAAAACw/LZ7ioxfzRAuPy3J7ZPcqLuP2mA5eu6BAgAAAACwXLZ7ioyvT/In3f3zOzEYAAAAAABWx3aPYL4qySU7MRAAAAAAAFbLdgPmtyS5904MBAAAAACA1bLdgPm8JHepqp+qqs0u8gcAAAAAwBFuW+dg7u6PVdV9k/x1kh+sqvckuWLjpv2YOQYIAAAAAMBy2lbAXFVfnuTCJLcYlzscoGknETADAAAAABzBthUwJ/nVJF+d5EVJXpLkE0munXtQAAAAAAAsv+0GzA9M8vru/oGdGAwAAAAAAKtjuxf5OyrJ+3ZiIAAAAAAArJbtBszvSHLmTgwEAAAAAIDVst2A+WeSPKCqHr0TgwEAAAAAYHVs9xzMD03yliQvraofSvKuJFds0K67+5mHOjgAAAAAAJbXdgPmp6+7/5/GZSOdRMAMAAAAAHAE227A/I07MgoAAAAAAFbOtgLm7v6LnRoIAAAAAACrZbsX+QMAAAAAgCQCZgAAAAAAJtr0FBlVtTfJ3iRndPc/jo97C/12d2/3/M4AAAAAAKyQg4XAb8sQKH9+v8cAAAAAAOxymwbM3f2AzR4DAAAAALB7HfQczFX1vVV1t8MxGAAAAAAAVsdWLvL34iSP2OFxAAAAAACwYrYSMAMAAAAAwA0ImAEAAAAAmETADAAAAADAJHu22O7mVXXb7XTc3f88YTwAAAAAAKyIrQbMTxiXrept9A0AAAAAwAraagh8ZZJ/28mBAAAAAACwWrYaMP9Kd//cjo4EAAAAAICV4iJ/AAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmOSgF/nrbiE0AAAAAAA3IDwGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASfYsegAAAMCRpZ5Rs/fZT+vZ+wQA4NA5ghkAAIClVLUzCwAwHwEzAAAAAACTCJgBAAAAAJhkqQLmqjqlqp5fVR+tqmuq6lNV9WdV9aAZ+r51Vf1CVb23qq6oqquq6n9X1cuq6pw5xg8AAAAAsJsszUX+qupuSd6S5Fbjj65MclKShyV5aFWd193Pntj3Q5L8YZJbjD+6Osl1Sb5yXE5OcuH00QMAAAAA7D5LETBX1TFJXp0hXH53ku/p7our6sQkT03y5CTPqqq/7+43bLPv+yX50yQ3TvJHSX6+u98/rrtlkvsnucNsTwYAAABgonrG/Fei7Kf17H0CrFmKgDnJ45LcLslVSR7e3R9Pku6+MslTquqOSR6R5FlJthwwV9VNk/yPDOHyC7v7cevXd/dnMoTPAAAAAABs07Kcg/nc8faCtXB5P88db8+qqjtvo9/vSHLHJP+a4ShoAAAAAABmsvCAuapOSHKP8eHrD9DsHUmuGO8/cBvdrwXXr+juqyYMDwAAAACAA1h4wJzkLknWTjB08UYNuntvkg+PD8/YSqdVVUnuPT78q6o6q6peWVWXVdU1VfWRqvqNqrrdoQweAAAAAGC3WoaA+dR19z+xSbu1dadu0ma9L0ty4nj/rhmOgv62JMcm+VKGU2f8SJL3jhcCPKCqemxVXVRVF1122WVb/PUAAAAAAEe2ZQiYj1t3/+pN2n1+vD1+i/3efN39n0zyqSRnJzm+u09Icp8k/5jkZkleUVW3OFBH3f3C7r5nd9/z5JNP3uKvBwAAAAA4si1DwFwHbzLJ+udWSb67u9/U3Z0k3f3XSb49yd4kt07yAzs0DgAAAACAI9IyBMzrL753zCbtjt2g/Vb7fW93/8X+Dbr7fUneND78pi32CwAAAABAliNgXn/e5dM2abe27tIt9vupJNeN9z+8Sbu1dbfZYr8AAAAAACTZs+gBJPlQks5wGou7ZoMwuKqOSnKn8eEHttJpd3+hqj6a5KvH/g+6yZZGCwBsWz1j/jNi9dO8dQMAACzawo9g7u7PJrlofHj2AZrdO8PF+JLkzdvofq3tnTdps7bukm30CwAAAACw6y08YB5dMN6eW1WnbrD+KePtu7p7s9Nd7O8Pxtu7V9UD9l9ZVV+T5EHjw9dto18AAAAAgF1vWQLm8zMcQXxCktdU1RlJUlUnVNVzkjxybHfe/htWVY/L0/df191/k+RPx4d/UFUPqqoat/uGJK/IUINLkrxo3qcEAAAAAHBkW4ZzMKe7r66qczKc0uKsJBdX1ZVJjs8QAHeS87r7DRO6/74MF/C7Z5I3Jfl8VV2XIcxOhosGntPdnz+0ZwEAAAAAsLssyxHM6e73JjkzyQuSfCzJTZJ8Oslrk5zd3c+e2O+VSf5jkicneVeS6zIE6x9I8uwkdx9/NwAAAAAA27AURzCv6e5PJnnCuGx1m4Nelr67v5TkeeMCAAAAAMAMluYIZgAAAAAAVouAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAO6hq/gUAAGBZCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAwQdX8C8CqETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGTioqvkXAAAAAFafgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACT7Fn0AGAV1DPmvypdP61n7xMAAAAADidHMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhkqQLmqjqlqp5fVR+tqmuq6lNV9WdV9aAZf8fRVXVRVfW4PH2uvgEAAAAAdpOlCZir6m5J3p/kx5N8RZIvJDkpycOSvLGqfmqmX/VjSe4xU18AAAAAALvWUgTMVXVMklcnuVWSdyc5s7tvluQWSX45SSV5VlU9+BB/z5cneWaSS5J86pAGDQAAAACwyy1FwJzkcUlul+SqJA/v7ouTpLuv7O6nJHnV2O5Zh/h7fi3J8RmOkr7mEPsCAAAAANjVliVgPne8vaC7P77B+ueOt2dV1Z2n/IKq+tYkj0jymu5+9ZQ+AAAAAADYZ+EBc1WdkH3nRH79AZq9I8kV4/0HTvgdxyX59SRXZzh6GQAAAACAQ7TwgDnJXTKcYzlJLt6oQXfvTfLh8eEZE37HM5PcJskvdvf/mbA9AAAAAAD7WYaA+dR19z+xSbu1dadu0uYGqurrMhy1/I9JnrO9oQEAAGxd1fwLAMAyW4aA+bh196/epN3nx9vjt9pxVR2V5PwkRyd5fHd/cfvDS6rqsVV1UVVddNlll03pAgAAAADgiLMMAfNOfib/o0nuleSPu/uNUzvp7hd29z27+54nn3zyfKMDAAAAAFhhyxAwX7Xu/jGbtDt2g/YHVFWnJfn5sf2Tpg0NAAAAAIADWYaAef15l0/bpN3auku32O+zkpyY5JeSXFFVx69fsu/I6Ruv+xkAAAAAAFu0DAHzh5L0eP+uGzUYz6V8p/HhB7bY7+3G22cm+ewGy23H9T+97mcAAAAAAGzRwgPm7v5skovGh2cfoNm9k9xsvP/mHR8UAAAAAAAHtfCAeXTBeHtuVZ26wfqnjLfv6u4Pb6XD7n5Ad9eBliSXjE2fse5nAAAAAABs0bIEzOdnCHxPSPKaqjojSarqhKp6TpJHju3O23/DqupxefrhGiwAAAAAAMmeRQ8gSbr76qo6J8PpL85KcnFVXZnk+AwheCc5r7vfsMBhAgAAAACwzrIcwZzufm+SM5O8IMnHktwkyaeTvDbJ2d397AUODwAAAACA/SzFEcxruvuTSZ4wLlvdZtK5k7v79lO2AwAAAABgsDRHMAMAAAAAsFoEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZALieqvkXAAAAjkwCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAu1jV/AsAALvHnkUPAACWTT1j/nSkn9az9wkAAACL5ghmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwMwRp2r+BQAAAAC4IQEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAhTebdAAAIABJREFUAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAKy0qvkXAAAAYGsEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYJI9ix4AO6OeUbP32U/r2fsEAAAAAFaXI5gBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMC8BKrmXwAAAAAAdpqAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMsVcBcVadU1fOr6qNVdU1Vfaqq/qyqHjSxv5Or6nFV9fJ1fX6uqj5YVb9eVV8593MAAAAAANgt9ix6AGuq6m5J3pLkVuOPrkxyUpKHJXloVZ3X3c/eZrefyPWf41VJbpzkzuPymKr6/u5+2SENHgAAAABgF1qKI5ir6pgkr84QLr87yZndfbMkt0jyy0kqybOq6sHb7HpPkrcl+S9JTu3uE5Icm+S+Sd6T5KZJfn8MtwEAAAAA2IalCJiTPC7J7TIcYfzw7r44Sbr7yu5+SpJXje2etc1+79/d9+/u3+/uT459Xtfdb0/y4CT/kiGE/ok5ngQAAAAAwG6yLAHzuePtBd398Q3WP3e8Pauq7rzVTrv7bZusuyzJ68aH99hqnwAAAAAADBYeMFfVCdkX8L7+AM3ekeSK8f4DZ/z1nx5vj56xTwAAAACAXWHhAXOSu2Q4x3KSXLxRg+7em+TD48MzZvzd9x9v3z9jnwAAAAAAu8IyBMynrrv/iU3ara07dZM2W1ZV5yS55/jwf8zRJwAAAADAbrIMAfNx6+5fvUm7z4+3xx/qL6yq05O8cHz46u7+84O0f2xVXVRVF1122WWH+usBAAAAAI4IyxAw18GbzPjLqo5P8qokt05ySZLHHGyb7n5hd9+zu+958skn7/QQAQAAAABWwjIEzFetu3/MJu2O3aD9tlTVTZNcmOHUGJcl+ebuvnxqfwAAAAAAu9kyBMzrz7t82ibt1tZdOuWXVNWNk7wiyQOT/FuSB3f3hzffCgAAAACAA1mGgPlDSXq8f9eNGlTVUUnuND78wHZ/QVXtSfKyJA/NcAT0Q7r7PdsfKgAAAAAAaxYeMHf3Z5NcND48+wDN7p3kZuP9N2+n/zGcfkmSR2a4iOC3dvffTBgqAAAAAADrLDxgHl0w3p5bVadusP4p4+27tnNai6qqJC9M8l1Jvpjkkd391kMaKQAAAAAASZYnYD4/ySVJTkjymqo6I0mq6oSqek6Go4+T5Lz9N6yqHpenb9Dv85I8Jsm1Sf5zd//5TgweAAAAAGA32rPoASRJd19dVedkOP3FWUkurqorkxyfIQTvJOd19xu22mdV3TbJE9d+RZLzq+r8TcZwytTxAwAAAADsRksRMCdJd7+3qs5M8tNJHpbk9CSfTvLOJL/S3ds693Kuf3T2jZJ82SwDBQAAAAAgyRIFzEnS3Z9M8oRx2eo2dYCf/1OSDdcBAAAAAHDoluUczAAAAAAArBgBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYRMAMAAAAAMAkAmYAAAAAACYRMAMAAAAAMImAGQAAAACASQTMAAAAAABMImAGAAAAAGASATMAAAAAAJMImAEAAAAAmETADAAAAADAJAJmAAAAAAAmETADAAAAADCJgBkAAAAAgEkEzAAAAAAATCJgBgAAAABgEgEzAAAAAACTCJgBAAAAAJhEwAwAAAAAwCQCZgAAAAAAJhEwAwAAAAAwiYAZAAAAAIBJBMwAAAAAAEwiYAYAAAAAYBIBMwAAAAAAkwiYAQAAAACYZKkC5qo6paqeX1UfraprqupTVfVnVfWgQ+z3xKr6+ar6YFV9vqo+XVVvrqpvn2vsAAAAAAC7zdIEzFV1tyTvT/LjSb4iyReSnJTkYUneWFU/NbHfL0/yniQ/k+TOSa5LcmKSByZ5eVX91qGPHgAAAABg91mKgLmqjkny6iS3SvLuJGd2982S3CLJLyepJM+qqgdvs99K8ookd0jyT0nu090nJDkhyX9NsjfJD1XVD870VAAAAAAAdo2lCJiTPC7J7ZJcleTh3X1xknT3ld39lCSvGts9a5v9npPk3hmC5G/r7r8e+72mu5+b5AVju5+rqhsf4nMAAAAAANhVliVgPne8vaC7P77B+ueOt2dV1Z0n9Pum7n7PBuv/nySd5JQMp8wAAAAAAGCLFh4wV9UJSe4xPnz9AZq9I8kV4/3tBMEP2KzfMcy+eEK/AAAAAAC73sID5iR3yXCO5WRf2Hs93b03yYfHh2dspdOqunWGiwQesN/RB7bTLwAAAAAAg+ruxQ6g6pzsO8fyid392QO0+9Mkj0jyyu5+1Bb6vXuStdNi3K2733eAdr+S5IlJ/r6773GANo9N8tjx4Z2yL+xeZicluXzRgziCqOf81HRe6jkv9Zyfms5LPeelnvNT03mp5/zUdF7qOS/1nJ+azks957VK9bxdd5+8/w/3LGIk+zlu3f2rN2n3+fH2+MPdb3e/MMkLt/h7l0JVXdTd91z0OI4U6jk/NZ2Xes5LPeenpvNSz3mp5/zUdF7qOT81nZd6zks956em81LPeR0J9VyGU2TUwZsccr+LPUwbAAAAAOAItAwB81Xr7h+zSbtjN2i/1X6PPWCr7fcLAAAAAECWI2D+xLr7p23Sbm3dpQvud1Ws1Ck9VoB6zk9N56We81LP+anpvNRzXuo5PzWdl3rOT03npZ7zUs/5qem81HNeK1/PZbjI3wlJrshwSotHdfcrN2hzVJLPJLlZkh/t7t/cYt+XZThR9pO7+3kHaPO+JGcmeW53/9dpzwIAAAAAYPdZ+BHM3f3ZJBeND88+QLN7ZwiXk+TN2+j+rZv1W1WnJ7nrhH4BAAAAAHa9hQfMowvG23Or6tQN1j9lvH1Xd394Qr8Prqq7b7D+SRmOnL40+8JoAAAAAAC2YFkC5vOTXJLkhCSvqaozkuH0GVX1nCSPHNudt/+GVdXj8vQN+r0wyd9meJ5/WlVfP25zk6p6cpInju2e1t1fnPMJAQAAAAAc6fYsegBJ0t1XV9U5GU5TcVaSi6vqyiTHZwiHO8l53f2GbfbbVfXtSd6W5A5J/qaqrkpy0+x77r/d3b8z01MBAAAAANg1luUI5nT3ezNcbO8FST6W5CZJPp3ktUnO7u5nT+z3/0vytUl+McmHMgTLn81wSoz/3N0/fOijBwDYWVV1dFXduqpuV1XHL3o8q049WXb2UYDpzKFweFV3L3oMHKKqOi7JlyU5KckxGYL5y7v7kwsd2AqqqlsnuW+GC0uemv1qmuFDir9M8u7u3ruoca6Kqjoqwwc8m9Xzr7r78oUNcoWo5/y85neG96V5jNePeHCS+2XYR0/ar8kXk3w4wz76l0le292fO6yDXCHqOT/vS/Oyj86rqk5K8o3ZV8+1ffTGSf4t13+f/8vufueChroS1HNnmEfnYw6dn9f9vI70egqYV1BVHZ3kYdk3eZ6R4WKF+7siydsz7Jwv7+7/c9gGuUKq6pZJvjvJ/53kbutX7dd0/Yvlc0lekeRF3f1XOzvC1TL+kfSwDPV8YIZT3VyvSa5fy2R4o/+fSV7c3f+844NcIeo5P6/5+Xlfms94hM1/SfKYJGsXKN6oluut7aufS/JHSX63u/92Z0a4WtRzft6X5mUfnVdVVZKHZKjnQ7PvtIib1XStnpck+b0M++nHd2yQK0Q9d4Z5dD7m0Pl53c9rN9VTwLxCqup2SR6f5HuSnJyDT5xrelzemuR3uvuPd2aEq6WqTkvytCTfm+ETo7V6/nOS92b49Ohfk1yT5BbjcvsMnzDfdGzbGT5henp3v/xwjX0ZVdUxGS6c+WMZjlxcq+faJ8UHqucpY7u1/fQNGS68+XeHa+zLSD3n5zU/P+9L86mqG2eo5U8nuWWGWn46w8WKL8rm++i9xuVrsu8/pX+e5Ke7+x8O5/NYFuo5P+9L87KPzq+qHpnk55PcKUNd9mZ4z95KPe+R5OYZanltkt9N8szd/M0b9ZyfeXQ+5tCd4XU/r11Xz+62LPmS4T/tL8iw0+0dl3cn+fUk35fk65LcJsMnn3vG9l+d4Uiyn0nyqgxHje1Ncl2Sf0jy8EU/rwXX9JcyfGK5N8M5uV+a5JwkJ21h26MzBE7/Pcn719X175LcY9HPbUH1/OEkn1i3f749yROS3DPJjQ6y7elJHpFhwrx8XT3/JMkdF/3c1PPIWLzmZ6+n96X5a3rJWIsrkrwoydlJjtpmH6cneUqSd421vTbJYxb93NRz9RfvSztSU/vovPV8x1jPa5O8JckPJrnlNvu4z/g+dulYz6uSPGrRz009j4zFPDp7Pc2h89fU6149D+05L3oAli38Iw1hyN4kH03y1CRfNaGPmyR51Pgm9KVxR3/Sop/bAmu6N8k/Jvn+JMccYl9nJnlJhk+en7ro57bAen4myc8lue0h9LMnycOT/MXYp3qq55w19Zqfr57el+av6afHWt5spv6+Mcmbkvzsop+beq7+4n1pR2pqH523nl/IEDJ95Qx9HZ3h1AUf2q37qHruSE3No/PW0xw6f0297tXzkBanyFgBVfXBJM9K8tLuvm6G/r4yyU8l+Uh3P/tQ+1tFVfVdSf5nz3jRrqq6fZLTu/vtc/W5KqrqvCS/1t2fnbHP+yS5eXe/dq4+V4V6zs9rfl7el+ZXVcf1DlxoZqf6XXbqOS/vS/Ozj86rqm7bM5+Xdjxv5mm9Aue9nJt6zs88Oi9z6Py87ue1G+spYF4BVVW9A/9QO9UvAEc270sAAACsETADAAAAADDJnkUPAACAA6uqWyY5NcNFE5PhAh+XdvdnFjeq1aWeLDv76Pyq6risq+du/Qr8XNSTZWYO3Rle9/M6EuvpCGZIUlV7kjxwXM7IBm9IST6Q5M1J3trd1y5inKukqr46W6hnd//vxYxwtajn4lXV6UmOnvtcWkeSqjo2yZ2z8T76we6+elFjWzVV9dAk52Z43Z98gGaXZXhfeml3v+5wjW0VqSfLzj46r6o6M8l3ZajnXbLvPWnNVUk+mKGeL+vu9x/eEa4W9WTZmUPn53U/r91QTwHzCqqq+yW5R4YrSb4/Q6i0aeBZVb+S5MTufsxhGOJKqarHZLia7ylrPzpA07UXy6UZrtz5op0e2yqqqm/KcPGvs9b/eL9m6yeei5Kc191v3umxrSL1nF9VfUuSn8z159EXJfm9zS4CWFWXJjm5u337Z52qulGSx2b4o/4/ZPM59G+T/GGS3+3uLx2eEa6WqjolyR8nuc/ajw6yydrr/6+SfEd3f3KnxraK1HN+43+QfiL7zaHd/caDbGcO3YB9dF7jB53nJ/nODLXcSj07yQVJfvhIOIJsTuq5M8yj8zGHzs/rfl67qZ4C5hVSVacm+ZMk995v1SVJfqK7L9xk20uT3Lq7j97BIa6cqvqNJD+U4UXeSS7OcCToJ5J8fmx2bJLTMhw5etd1bX+rux9/uMe8zKrqvyX5xeybNK9I8uFsXM87JbnZ+LNO8lPd/dzDN9rlp57zq6onJVmry/o3907yriSP7u6PHWBb8+h+quqMJBcm+Yoc/I+lNZ3ko0nO6e4P7tTYVlFVHZ/kPUnuMP7oTUlen83flx6c5JuSHJXkI0m+bpX+EN1J6jm/qnp0kpdkOM3e2mt+7T8TFyb5gQN9BdkcekP20XmNH3j+ZZJ7Zdg//zHJG7J5Pc/O8DdUJ3lnkvv5puJAPXeGeXQ+5tD5ed3Pa9fVs7stK7AkuUmGTzavS7I3yTUZvuKxd1yuS/L8jB8abLD9pUmuW/TzWKYlyaPG2l2b5HlJTtvCNqcl+eVxm+uSPHLRz2NZlgxf9VjbP1+Z5BsOtD+O7SvJ12f40GTt3+EBi34ey7Ko547U9GuTfGmsz8VJnprkRzMc9bD288uTfMMBtjePXr8et07yqbFuV2b4ZP6RGU6RcWKG/zgdPd6/87jutzN8ULI3ySczHIWz8OeyLEuSZ461+ViSr93Gdncft7kuyTMW/TyWZVHP2et5xwz/EVp7/b4owwd271z3t+hHk3zlAbY3h96wJvbReev5lLGelyX51m1s9/Bxm+uSPHnRz2NZFvXckZqaR+etpzl0/pp63avn9Oe76AFYtvgPlfzYuv/Ef2+SPePPz0jyp+vekF6+tm6/7b0Z3bAmb5r6gk3y5LHmb1z081iWJcmrx3o+Z8K2zxnreeGin8eyLOq5IzV90ViXNyW56X7rzspwzqu9ST6b5EEbbG8evX49fnWs17uSnL6N7U4bt7kuyfMW/TyWaclwNMN1Sb5+wrbfMP57XLzo57Esi3rOXs9fW/eaP2m/deeMYcneca78mg22N4fesCb20Xnr+fdjPR8yYduHrO3fi34ey7Ko547U1Dw6bz3NofPX1OtePScvTpGxIqrqbRnOK/Rj3f2bG6z/wSQvSHLjDF8LeWR3X7Nuva/T7KeqLk9yQpITuvuL29z2xhlOwn5ld5+0E+NbNVX1ySS3SnKL7r5qm9sen+Tfklze3accrP1uoJ7zq6qPZPgK3d17g4smjHV7WZKHJvlChvOyvXrdevPoOuvqeWZv81QX46k13p/ko939VTsxvlVUVZ9Lcm133+ygjTfe/soMF6I8bt6RrSb1nFdVfSDDVzbv291/s8H605O8KsM5RT+T4T9T71y33hy6H/vovMZ6HNXd+184aavbX5Vkb3efOO/IVpN6zs88Oi9z6Py87ue12+p51KIHwJbddbx9yUYru/t3knxLhiPvvjnJ/6oqE+XmjklyzXbD5SQZt7lm7IPBzZJctd0wNEnGba7K8FV6Buo5v9OSXL1RuJz8e90ekeGCCjdJ8vKq+o7DOL5Vc3qGD9m2fR7l7v5Ahm/knD77qFbb1UluOp6vbVvGDz5vMvbBQD3nddskX0zyjo1WdvfHkzwgyVuT3DLJG6vqPx220a0m++i8rktydFVt9ZoA/66qjspwWqfrZh/V6lLP+ZlH52UOnZ/X/bx2VT0FzKvjhCT/1pucgL67/yLDCev/Ncl/SvKmqhIwHdglSY6vqv0vmnhQ4zbHJ/mnuQe1wj6e5MSquvN2N6yqu2QIQz8++6hWl3rOb+2KvAdu0H1dku9J8jtJbpTkD6vq+3Z+aCvpygxz6LY/zByPFj8uw4ei7PMPGc5dPeUCso/PsM++Z9YRrTb1nNfRSb7Ym3z9cfw79SFJXpfhb9fXVdXZh2l8q8g+Oq8PZvg257kTtv2uDGHTB2Yd0WpTz/mZR+dlDp2f1/28dlU9Bcyr418zhE2bfjrX3Rcl+cYMF6r6D0neWlW3OgzjW0WvzHBhtJdW1ddsdaOx7QUZgqpX7tDYVtFrM9TzD6pqy6dlqKovS/IHGer5mh0a2ypSz/n9c5Jjq+o2mzXqweMynCfv6CS/W1U/fDgGuGLemeHviKdO2PZnM9T2b2cd0er7rQyv++dU1XOr6tSDbVBVp1TVc5L8UobX/W/v8BhXiXrO6+MZPlS69WaNuvsLSb4twzVCjk1yYVU9/DCMbxXZR+f14gz1PL+qfnQ8QnFTVXXjqvqRDHXssQ8GL456zs08Oi9z6PxeHK/7Ob04u6iezsG8IqrqrRmOSn7geKTywdrfOcmbk5yS4ROP22Q417DzNY2q6oQk701y+wxfO3hThvNXX5zkExm+LtMZ3tRPy3CakrOTPDhDMLJ2tdptn8LgSDQGmxcnuUWG0zO8NMkbcv16JsNpRdbX89wMR9tenuFcrv9yeEe+nNRzflX1+xnq8yPdff4Wt3lWkv+WfUc/l3l0UFXflGGf7Az/AXpud28aGFfVvZL8ZJJHjT86u7vfsqMDXTFV9cIkP5B9+9z7s/n70l0zBP2V5IXd/UMLGPbSUs/5VNUfZ3jtfm93v3QL7Y9O8vtJvjPJl8Yf7zGHXp99dF5V9dok/1eGul2R5G3ZvJ73S3LzDPV8bXcL8dZRz3mZR+dnDp2f1/28dlM9BcwroqqekeGIr/O7e0tH0lXVHTOEzLfJsHO2N6PrGy+k8CcZjvZODvL1+Qx1TIaj7r59PE8Wo6q6W5JXZzi/2FYnl8pwupJzuvsfdmpsq0g951VV353hj/R3dfe9trHdzyZ5RsZ/A/PoPlX1M0memX375xUZvgq20R9Md8lwbvFk2E9/trt/4bAOeEWMR8w/LcnaEU4Hev2vvSf9S5Knd7ejcDagnvOoqsdmOJrm/+3uB25xm8pwyqHvH3/kb9EN2EfnU1V7kvxCkh/P8NXi5OD1/EKS5yf579197c6OcLWo57zMozvDHDovr/t57aZ6CphXRFWdleSiJJ9L8hXdfdkWt7tNhiNzvyrejA6oqs7JcGTjA5KcdIBml2e44MIF3X3hYRrayqmqmyb5oQznDLpH9k2S++sM+/QFGT44uebwjHC1qOd8xm8t/H2Gc7V9d3e/fRvbPjHJ82IevYGqekiSX0xyt/1Wrf2Bsf8++94k53X3/9rpsa2y8St035ThtFdnJDk1w3mrK8O3Gj6R4RtKb03y5ikXrN1N1PPQVdVJSS7NcOTX/bv7r7ax7a8keULMoQdkH53X+E2wR+WG9UyG/0+tr+cru/tTixjnqlDPeZhHd445dH5e9/PaDfUUMK+QqvquDCeif3t3f2Qb252cIaA6qrufsVPjO1KM9brBG1J3X77Qga2g8UJed8nGb/AfcnqR7VFPlllVnZEt/FHf3R9c2CCBhRkPejiquy9Z9FgAVpF5FFhmAmYAAAAAACY5atEDAACARauq21bVbRc9DgAAWDWOYAZYceM5x347wznZHrPo8QCLMV649uju/udFj2XVVNVxST6bZG9371n0eOBgxosG/cck6e63LXg47HLmUFaNOZRlcyTMowJmmKCqbp7k3Rle/Hdc9HiWTVXdL8MF6Y5O8v4MF1LY9Oqn44UrThSQbt+6NyMX/WAljB+KPDpJuvv3FzycpVNV35LkJ3P9efRFSX6vu/dust2lSU5e1T9KF8k8yqqpqlsluSwr/B/RnVJVZyb5iew3h3b3Gw+ynTl0InMoq8Ycujnz6OF3JMyjAmaYYN0b0sq++HdCVZ2a5E+S3Hu/VZck+YnuvnCTbS9Ncmv1HGzza9rHZriAWie5fYYLqyVJHMnIMvJH/YFV1ZOSPHft4bpVneRdSR7d3R87wLbm0XWq6kXbaL4nyXdnqPNL1v3cN0NYSv4W3VhVPTrDa3hP9s2ha//hvTDJD3T3Zw6wrTl0nap66jaa3zjJeRlqfb2Lynf3z805LpiDOfTAzKPz2W3zqIAZJvCGdENVdZMM4cddMrwRfTHDJ3C3Gpt0kl9P8sTeYOLxZnR9VbU3+97Ip2rhHcvIHLqxqvraJH+X4UiRDyb5oySfTnL/JN82/vwzSR7e3X+zwfbm0XUmzKP7/yeqYh9lSZlHb6iq7pjkfUlumuRfkrwu++bQe2Z4bf9Tkm/u7o9ssL05dJ0Z5tDhgXqyhMyhGzOPzmu3zaOCB2Auj01yRpKrkjw+yQXdfW1VnZHkF5KcM/78tKr6zoOdMoN/VwdvAhwhfjxDiPyWJA/r7mvGn/9GVZ2V5KVJ7pTkDVX1iO5+84LGuWo+lOE/SZs5Osl9M/xB71yMHBZV9eBD2PzE2QZy5HhihlDk3RnCj8vXVlTVOUnOT3KHJH9ZVQ/u7vctZpgr51+SXHOQNpXkthnmUN+e47Awh+4I8+jO2BXzqCOY2bWq6hOHsnmSL4tPPP9dVb0tyX2S/Fh3/+YG638wyQsyfPXj9UkeuS488Wnnfqrq2gz72d8meWaSz2/S/Ngkr83wZvTA9Su6+y92aozsblX12EPY/Lgkvxxz6PVU1Ucy/NF+9+5+/wbrj0/ysiQPTfKFJN/R3a9et948uk5VvSrJtya5IsnPJvmNjb5BM7Y9PsmVsU9yGM3wbSVH2a9TVR/I8CHcfQ/wLY/Tk7wqwzlF///27jxYsrLM8/j3KShwqgpkl0IBYcYNWkE2Q9CGsmmHAESxZ1pUsDEEFAIVEdqtERUFgXYBpBVFXEZaYYhQcEa7VdDGolE2N2QZ2dQAVDZZCpDtmT/ec6nk1l3zvnkzT+b3E5Fxq26eN334eeo9J588+Z67gT0z87KO551DO0TEFcB2lKsV35GZ/2eKbZ1DNe+cQ+tzHq1r1OZRG8waWR0HpLlcIdraf/y1RcRdwDqUG/WtmGSbXSnrNq1FuUJs77FtPRg9VURsT/mEeDvKJ5hHZOa3Jtm29TcEUPt4Ul9fRDxIWZd6yRTbrEZZF+8NwKPAmzLznOY559FxmqttTgWeRbka57DON0J3iLuLAAAeAUlEQVQd2zmPat55LlpXRDxA+TbCoik+TFoMXAAso/ybf1VmXtw85xzaISIWUL5Z8xHKB8PfpjRIVrmyzjlU/eAcWp/zaF2jNo/aYNbIiogVlK9/fJJyV9TZWAKcRov/8dcWEY8A92fm+tNstwPwb8C6wGWUr97c58FoVc0B6e2UA9IS4LuUK8RvHrdd6w9Gap+Ok/o/Uq6mnY0FwKa4zz5Fc1x6IjPXmma7AD4HHAw8DhycmV92Hp1YM0d+lLJMUwBfBN6bmfeM28Z5VPMqIm4FNgb+R2Z+c5ZjN6B85dZ9thERDwGPZObTp9luTcpNqfekfENs38z8vnPoxJorFj9DWe7uQcrSdyd3LnfnHKp+cA6tz3m0N0ZlHrXBrJEVEcuBlwJHZeanZjnWmwKMExF/BNajfNr56DTbvhD4AbAB8HPglZQmvwejCYw7ID0MnACcmJmPNM+3/mA06CLir5s/XtO5Ftkoi4ibgM2BN4xdQTuLsZ7UTyAirgWeCzw7M38/g+1PoXwI9UTz84M4j06qWcf6DMrXOu8E3pOZX2qecx7toYjYrPnj7dOdI4ySZhmXVwEnZOY/zXKs56LjdCwztDQzp1x3PSIWAt+g3ED1YeB1wOdxDp1UROxDOR99JnA9cHhmXtQ85xzaY86jq3IOrc95tLeGfR5d0O8C1HsRcVHz+HhEbNjvegbI5ZQrmXbsdyFD4hrKnLLzdBtmuRnArsAfgG2BHwH/pZfFtVlm3pqZ+wKvoZwIfQj41RxvbKHZ+RHwQ+DmiDjRuRQocyh0N4f66fbExjLdcyYbZ+Y7gRMpc+9nAPfLKWTmVcBOlBvYrAmcGRHLI+JF/a1sJNzcPG6MiEMjYo1+FzQgxs5Fd+p3IUPiqubn3063YdOg+3vKuvZPA86jXCihSWRZ8//5wCnAc4DvR8S/RsTG/a1sZDiPrso5tD7n0R4a9nnUBvNo2I3SzDua0hw5KSI26m9JA2HsjfwOfa1ieFxMOcDvN5ONM/M64K+B3wNb4Z18p9UckF5AOSBtCXw3Is6lrC2q3gvK2lljc+k/97mefruCksn2/S5kiHyPkulBMx2Qme8Djm3GzWUNwpGQxamUufRblA9Fr6DcdFK9M7Z/PovyYciNEXF4f0saCGPnot3Mo49T7tHw23rltN4PKPvZW2aycWY+DuwPnAUsbB6aQmY+mJlHUhp6V1LO+68D3tnXwkaD8+iqnEPrcx7tsWGeR10iYwRExI8oV4stpXz1FuDBqW4iNAqaZQeOomTz7skWsZ9k7OrALgCZ+R+9qbBdmq8eXwGsALbMzDtmOG5TyoHsObT46yDzLSK2pXwFaQfgIcoV4ObXI80NKqHMo7tSPrh77ijnHRG7ARcBD2TmrD4gioi1KTcHycxc1oPyWiki1qJcObI6sH9mXjKLsUdQ7ingPDALEbE3cDplTXAwv56IiH9o/jg2h+4MLBn1rJsrEF8AkJm/6HM5rdcsv3Q75SKqXTNz+SzGfory5t45YIaa+wEcDhxHuYG3N+/tIefRVTmH1uc8Or+GbR61wTxiIuIZlMbIyzNz1D/xVGUR8QbKp5aXZOYNsxi3IfA2YEFmfrhX9Q2bcQektWnxwaiNImKDUV6Pudn/1gbIzHv7XI7UtYhYBLwH2AwgM9/c34qGX3MT2+0y84p+1yKNaS56WJCZXtE4CxGxFPg4K+dQPzieB86jGkTOo90ZlnnUBrMktVxzNei6AB7MJUmSJEnSfLLBLEmSJEmSJEnqyur9LkCSpH5pvhr/fMp6dmPr0j9AWXvs2sx8qF+1SVIbRMR6TDCHZubd/atKktrDeVTSMPAK5hHR3NButcz8Xb9rkaR+ioiFwCHAGyl3741JNk3gp8DXgDMz89H5qVCSBltE7EWZQ18BbDjJZncAFwJnZ+Z35qs2SWoD51FJw8YGc8tExB7A0cD2wGrA1cBZwBcz84kpxt0ObJiZXrUuaWRFxFbA+cCWTN5YHi+BG4FXZ+a1vapNkgZdRGwMnAvsMvaraYaMvdFYDrwuM//Qq9okqQ2cRyUNKxvMLRIRRwInj/2146kErgT2y8ybJhl7O7BRZq7W2yolaTBFxEbAryhXiTwAfB34d+Aa4DbgQcp8uhjYBNgKeCXwemAt4E/ACzPzjnkvXpL6LCKWAD8Htmh+9QNWnUMBFvHUOXR3YAFwA/DizFwxj2VL0sBwHpU0zGwwt0REbAtcTrlq+VrgHOAuYFdg3+b3dwOvysxLJxhvg1nSSIuITwPvAH4G7JOZt85w3CbAt4FtgVMy88jeVSlJgykijgM+ANwCvDYzfz7DcdsA3wQ2Bz6amcf2rEhJGmDOo5KGmQ3mloiIs4ADgYuAvTPz4Y7ntgPOBp4HrABek5kXjhtvg1nSSIuIGyhXjPzVbJe6aJbWuBq4MTOf04v6JGmQRcQ1lHPNXTLzJ7Mc+1LgEsrNU7fuRX2SNOicRyUNMxvMLdHRGNkmM6+e4PkllK977wX8hbI+0wUdz9tgljTSIuIh4OHMXLfL8X8G1sjMRXUrk6TBFxErgMcy8+ldjr+PcsPpxXUrk6R2cB6VNMwW9LsAzdgmwEMTNZcBMvMB4DXAvwJrAv87Il43j/VJ0qC7D1gSEbM+KW8+xFsM3F+9Kklqh4eAp0XEwtkOjIg1KOenD1WvSpLaw3lU0tCywdweyco7yE68QebjwAHAF4CFwNci4sDelyZJrXAZ5bj3wS7GHkNZ6/6nVSuSKomIv24eG/S7lmFgnhP6JbA6cHgXYw+nnJvOaL1RTS8iNmses25UaVXmWZd5Tsp5dEC4j9ZnpnW1MU8bzO3xO2BRRGw61UZZvBU4jdIMOTMiDp2PAkdJRFzUPD4eERv2u562M8+6zHNSpwABHBUR50XES6YbEBE7RsS5wFGUD/k+3eMaR4LNu574EfBD4OaIONF/+3P2I8xzvM9S5tCTIuLkiFg63YCI2DgiTgJOpMyhn+txjaPk5uZxY0Qc2lzdqO6ZZ13mOTHn0cHhPlqfmdbVujxdg7klIuKrwBuBwzLzjBmOOQF4Dyuvfg7XYK4jIp5g5RXlDwH/AvxzZv6pf1W1l3nWZZ6Ti4gPAMexMp97gWuB2yhZJbCIsizRC4CxNfICOCYzPzavBQ+pjn30QVbun3f0t6p2azLt9CDwucw8qh/1tJ15TiwiPg8cxMpzy6uBXzPxHLp181hAmUM/n5lv60PZQ2ncPpqU/w9OzMzP9KmkVjPPusxzcs6jg8F9tD4zrauNedpgbomI2B/4KnBlZu44i3HHAB+maabYYK4jIn5EyXQp8Nzm1w9m5pK+FdVi5lmXeU4tIvYEjgdeNO6psQNijPv9L4D3Z+Z3e13bqLB5V19E7Nr8cSmwK7Ab8FyP+90xz8k134w7Ftio+dVkbybG5tI/AR/KTK+6qygi/qH549g+ujOwxH20O+ZZl3lOzXm0/9xH6zPTutqYpw3mloiItYCrKGs27Z+Zl8xi7BHAJykraAzszthWEfEMyhvPl2dmN+tpqYN51mWek4uIrYBlwFaUA/diyon8A5RPiK8BfpiZ1/atyCFl825+RMQGmXlnv+sYFua5UvM1zd2ZwRwKXJiZj/Sp1JEREQuA7TLzin7XMgzMsy7zXJXz6GBxH63PTOtqQ542mCVJUt/ZvJMkSZKkdrLBLEmSJEmSJEnqyur9LkCSJEkTi4hFwPMpX50dW0f9AeB24NrMfKhftbWReaoNImI9JthHM/Pu/lXVXuZZl3nOn4jYDCAzf9fvWtrEfbQ+M61rWPP0CuYWiIgtM/Omyq+5AHiWB6vuRcQzgdXMsA7zrMs862rWydsPIDO/2udyBpbNuzoiYiFwCPBGYCdWvfHkmAR+CnwNODMzH52fCtvFPAeDx6WpRcRelH30FcCGk2x2B3AhcHZmfme+amsj86zLPOdfRCwG7geeyEwvDJyG+2h9ZlrXKORpg7kFIuIR4OvA8Zl5/RxfayHwZuA9wFcy8yMVSmy1iNgDOBrYHlgNuBo4C/hiZj4xxbjbgQ094D+VedZlnoMhItanHPA9yR/H5l1dzc0nzwe2ZPIsx0vgRuDV3pTyqcyzPo9LdUXExsC5wC5jv5pmyNibt+XA6zLzD72qrY3Msy7z7J+OBnN6E+TJuY/WZ6Z1jVKeNphbICKWAzsDTwA/Br4BnJeZd81wfAC7Ua6+ey2wHrACOCAzz+9FzW0REUcCJ4/9teOpBK4E9pvs6vHmjdJGHvBXMs+6zHNwdDSYPcnvYPOurojYCPgV5aqGBygfLv875S7ytwEPUvJbDGxCuev8K4HXA2sBfwJemJl3zHvxA8g86/O4VFdELAF+DmzR/OoHrLqPAiziqfvo7sAC4AbgxZm5Yh7LHljmWZd51hcRZ81i89WB/Snz61c6fp+Z+ZaqhbWU+2h9ZlrXqOVpg7klImIf4HjKDpfN4zeUk/lfAncC9wCPAOsA61J24h2AF1PePAXwKHAGcNyov2GKiG2ByylX31wLnAPcBewK7Nv8/m7gVZl56QTjfaPUwTzrMs/BYoN5VTbv6ouITwPvAH4G7JOZt85w3CbAt4FtgVMy88jeVdke5lmXx6X6IuI44APALcBrM/PnMxy3DfBNYHPgo5l5bM+KbBHzrMs864uIJ1h5deKMhjQ/s+Pvnos23EfrM9O6Ri1PG8wt0lyJvAdwELA3sLB5aqr/E8cOSjdRvr74pcy8vWdFtkjzCfKBwEXA3pn5cMdz2wFnA8+jXO39msy8cNx43yh1MM+6zLO+iDhkDsMXA5/Ak/on2byrLyJuoHw4/Fezvbq7uZr8auDGzHxOL+prG/Osy+NSfRFxDSWzXTLzJ7Mc+1LgEsr69lv3or62Mc+6zLO+jgbzdZQP2qeyGvCyZvuLO5/IzGU9KbBl3EfrM9O6Ri1PG8wt1dx1chllHZedKDdU2gBYk3L1yJ3A9ZQdcnlmXtGnUgdWxxvPbTLz6gmeX0K5Im8v4C+U9W8u6HjeN0odzLMu86yvi6tGVnkJbDA/yeZdfRHxEPBwZq7b5fg/A2tk5qK6lbWTedblcam+iFgBPJaZT+9y/H2UGycurltZO5lnXeZZX0R8C9gHuBc4Bjg9J2nINHPqfXjuOSn30frMtK5Ry9MGs0ZWRDxIuWHXkim2WY2y5tUbKMuLvCkzz2me841SB/Osyzzr62gw/5HS/JiNBcCmeJL/JJt39UXEHyn3SVhntmutNW9E7wHuzsxn9KK+tjHPujwu1RcRd1KWDFqSs7zxaUSsQbkB2P2ZuUEv6msb86zLPHsjIl4NnAo8i/ItsMMy87IJtvMmf9NwH63PTOsatTwX9LsAqY/G1rKefIPMx4EDgC9QliT5WkQc2PvSWsk86zLP+m5pfr4rM7eYzQPYvo91D6r7gCXNG6BZaZp3Y2+ctNJllHOzD3Yx9hjK12l/WrWidjPPujwu1fdLyo28Du9i7OGUjGe0nuOIMM+6zLMHMvN8yn0pTgW2Af4zIs6IiK4+sB9x7qP1mWldI5WnDWaNst8BiyJi06k2yuKtwGmUN5tnRsSh81Fgy5hnXeZZ3+XNzx27GOvXfVZl866+UyhLsRwVEedFxEumGxARO0bEucBRlP300z2usU3Msy6PS/V9lrKPnhQRJ0fE0ukGRMTGEXEScCJlH/1cj2tsE/Osyzx7JDNXZOa7gJdQrmI+GLg+It7c38pax320PjOta6TydIkMjayI+CrwRsrXks6Y4ZgTgPew8iqe8CtLhXnWZZ71RcTRlAP1f8z25igRsT5wB35N8UkRsTvwPcq+9k3g5MycsmEcETsCRwN/1/zqbzPzop4W2jIR8QHgOFZ+qHEvcC1wG/BQ8/tFwCbAC4CxNd0COCYzPzavBQ8486zH41JvRMTnKTfwHsvoauDXTLyPbt08FlD20c9n5tv6UPbAMs+6zLP3IiKAt1OOVUuAS4HDgBtxiYxpuY/WZ6Z1jVKeNpg1siJif+CrwJWZOeMrGiPiGODDNG9WPeAX5lmXedYXEbsBFwEPZObasxy7NnAB5STfO3c3bN71RkTsCRwPvGjcU2M5x7jf/wJ4f2Z+t9e1tZF51uFxqXeaK7yPBTZqfjXZG7SxffVPwIcyszVXNc0n86zLPOdHRGxC+ebHvsBjwFnAIdhgnpb7aH1mWteo5GmDWSMrItYCrqKsibN/Zl4yi7FHAJ/EA/6TzLMu86yvuUJkbYDMvLfP5QwNm3e9ExFbAcsoazUupaxbHcADlCb+NcAPM/PavhXZIuY5Nx6Xequ5mc/uzGAfBS7MzEf6VGormGdd5jl/ImJv4HTKzaXBeXNG3EfrM9O6RiFPG8ySJGnObN5JkiTNXUQsoiwvtBlAZro2s6SBZ4NZkiRJkiRJktSVBf0uQJIkSZIkSeqViNgsIjbrdx3DxEzranueNpg1kiJiyx685oI2TwZzYZ51mWd9ZqphFxFrRMSbIuJN/a5lGJjnUzmHDqaIeKYZ1mOedZnnUzmP9ldELAZuAW7qcylDw0zrGoY8bTBrVF0XEV+JiOfN9YUiYmFEHAL8BjhwzpW1k3nWZZ71mekAsXnXE2sBX6bcdV5zZ55P5RzaQxGxR0RcGBF/joj7I+LSiDg4IqZ7r3YFLX4j2ivmWZd5VuM8OhjG33xac2emdbU2T9dg1kiKiOXAzsATwI+BbwDnZeZdMxwfwG7AfsBrgfWAFcABmXl+L2oeZOZZl3nWZ6aDJSLWB+4AnsjM1ftdzzDoyNS7zVdgnk/lHNo7EXEkcPLYXzueSuBKYL/MnLBJFxG3Axu5j65knnWZZz3Oo/VFxGw+BF4d2J+y736l4/eZmW+pWliLmWldo5anDWaNrIjYBzge2IryjzgpnwJfCfwSuBO4B3gEWAdYF9gC2AF4MbCYcqL1KHAGcFxm3jG//xWDwzzrMs/6zHRw2Lyrz0zrMs9VOYfWFxHbApcDqwHXAucAdwG7Avs2v78beFVmXjrBeBt4HcyzLvOsz3m0roh4gpLhjIc0P7Pj7x7nO5hpXaOWpw1mjbTmk+A9gIOAvYGFzVNT/cMY+0d/E+Wrs1/KzNt7VmSLmGdd5lmfmQ4Gm3cTa77u2q3FwCcw0yeZZ33OoXU1VzYdCFwE7J2ZD3c8tx1wNvA8ylWKr8nMC8eNt4HXwTzrMs/ecB6tp6N5dx3wp2k2Xw14WbP9xZ1PZOaynhTYQmZa16jlaYNZakTEesAyYBdgJ2ApsAGwJuXT+TuB64FLgOWZeUWfSm0F86zLPOsz07mxeVdfF1c5rPISmOmTzLO3nEPnLiJuoFyduE1mXj3B80uArwN7AX8BXpeZF3Q8bwOvg3nWZZ695zw6NxHxLWAf4F7gGOD0nKTB1eyv9+FxfUpmWteo5WmDWZIkzZrNu/o6Mv0j5c36bCwANsVMn2SeGnQR8SBlLfolU2yzGmUtxjdQvhb/psw8p3nOBl4H86zLPNUGEfFq4FTgWcDPgMMy87IJtlsM3I/H9WmZaV2jlKcNZkmSNGs27+qLiJuAzYE3jL1Bn8XYDShfvTPThnlq0EXECkoDb61ptgvgc8DBwOPAwZn5ZRt4T2WedZmn2qJpzH0UOJxyAcMXgfdm5j3jtml1824+mWldo5Lngn4XIEmSWumW5ue7MnOL2TyA7ftY9yC7vPm5YxdjvWJgVeapQfc7YFFEbDrVRlm8FTiNskbjmRFx6HwU2DLmWZd5qhUyc0Vmvgt4CeUK0YOB6yPizf2trL3MtK5RydMGsyRJ6obNu/quoFzVYAO+DvPUoBubR/ecycaZ+U7gRMp7uM8AG/aorrYyz7rMU62SmVdR1rI+grKO9ZkRsTwiXtTfytrLTOsa9jxtMEuSpG7YvKtv7M18N5k+Srnj9MXTbThCzFOD7nuUefSgmQ7IzPcBxzbjokd1tZV51mWeap3mivpTgRcA3wJ2ppyzfqKvhbWYmdY1zHm6BrMkSZq1iNgNuAh4IDPXnuXYtYELKOdYy3pQXis161iuDZCZ9/a5nNYzTw26iFgLuApYHdg/My+ZxdgjgE/S4rUaazPPusxTwyAi9gZOp9z7A9wn58xM6xqmPG0wS5KkWbN5J0mSpEEXEYuA9wCbAWTmUK172w9mWtew5GmDWZIkSZIkSZLUFddgliRJkiRJkiR1xQazJElSn0XElj14zQURsVnt120D89Sgcx+tyzzrMk8NOvfR+sy0rlHM0wazJEmalVE8YZoH10XEVyLieXN9oYhYGBGHAL8BDpxzZe1knhp07qN1mWdd5qlB5z5an5nWNXJ52mCWJEmzNXInTPPgMuAA4NcR8cOIeGtErD/TwVEsi4gzgNuAzwIbAr/oTbkDzzw16NxH6zLPusxTg859tD4zrWvk8vQmf5IkaVYiYjmwM/AE8GPgG8B5mXnXDMcHsBuwH/BaYD1gBXBAZp7fi5rbICL2AY4HtgKyefwGuBL4JXAncA/wCLAOsC6wBbAD8GJgMRDAo8AZwHGZecf8/lcMDvPUoHMfrcs86zJPDTr30frMtK5Ry9MGsyRJmrVRO2GaL03zfQ/gIGBvYGHz1FQnbNH8vAk4C/hSZt7esyJbxDw16NxH6zLPusxTg859tD4zrWuU8rTBLEmSujJKJ0z9EBHrAcuAXYCdgKXABsCawN2UJv71wCXA8sy8ok+ltoJ5atC5j9ZlnnWZpwad+2h9ZlrXsOdpg1mSJM3ZsJ8wSZIkSZImZoNZkiRJkiRJktSVBf0uQJIkSZIkSZLUTjaYJUmSJEmSJEldscEsSZIkSZIkSeqKDWZJkiRJkiRJUldsMEuSJEmSJEmSumKDWZIkSZqhiMjm8duIeNok29zSbLP6JGPHHo9HxJ0RcVFEvLFCbbt1vPa5k2zz7Ob55RM8t05EfCQifh4RD0TEXyLi1oj4SUR8IiJePNcaJUmSNHxWn34TSZIkSeNsBhwBfLyLsR9ufi4Enge8BlgWEdtn5pGV6vufEfHSzLx0JhtHxCbAJcCzgZuAs4G7gWcCz6f8tz4E/KxSfZIkSRoSkZn9rkGSJElqhYhI4B4gKRdr/NfMvHPcNrcAmwMLM/OxcWPJzBi3/d8A32/+umVm3tJlbbsBPwRuAP4b8J+Zucu4bZ4N3Axckpkv6/j9mcBbgLOAg3Lcm4SIWAoszcyruqlNkiRJw8slMiRJkqTZeRA4DlgbOHauL5aZFwLXAQHsONfXA34KnA/sHBF/N8MxOzc/TxvfXG5qvN3msiRJkiZig1mSJEmavdOBG4G3RsRzK7ze2FXNtb5e+I/AY8DHI2LhDLa/q/lZ479FkiRJI8QGsyRJkjRLmfko8F7KOsrdrMP8pIjYnbIWcwKXz706yMz/B5xBWSrj0BkMOaf5eWZEnBQRu0fE+jVqkSRJ0nCzwSxJkiR1ITPPAy4F9o2Il023/ZiI+FDz+FhEnAf8G+UK5k9n5m8rlvhh4D7ggxHx9Gm2PR04gdIwP5qyJvSdEXFzRHwhIrapWJckSZKGiA1mSZIkqXvvbn5+IiJiyi1XOrZ5vA94BfBj4IDMPLJmYZl5B+Xq6vWBD0yzbWbm+4GlwH7Ap4GLm78fBFwZEQfXrE+SJEnDwQazJEmS1KXMvBQ4D9gJ+PsZjonmsSAz18vMZZn5tR6V+Cng98A7ImLzGdT258w8JzPflZm7UprTHwVWA06LiGf0qE5JkiS1lA1mSZIkaW7eCzwKnBARa/S7mE6Z+TDwT8CawPFdjF+RmccAy5vX2KVuhZIkSWo7G8ySJEnSHGTmjcC/AFsAb+9zORP5X8DPgNcDO3T5Gvc3P2e6DIgkSZJGhA1mSZIkae4+AvyZstbxkhovGBFfjoiMiAPn8jqZmcBRlObwCZP8bx0dEVtP8tzLgGXAY5SbGkqSJElPWr3fBUiSJEltl5l3R8TxwEkVX3bsYpDH5vpCmXlRRHwH2HOSTd4InBQR1wE/AW4HFgNbU25EGMC7M/O2udYiSZKk4WKDWZIkSarjVOAw4NmVXu+FlKUp/m+l1zsa+O+UG/aN92ZgL0ozeTdgY0pT+Vbg68BnM3N5pTokSZI0RKJ8Y06SJEnSoIiIdYC7gE9k5j/2ux5JkiRpMq7BLEmSJA2elwOPAp/sdyGSJEnSVLyCWZIkSZIkSZLUFa9gliRJkiRJkiR1xQazJEmSJEmSJKkrNpglSZIkSZIkSV2xwSxJkiRJkiRJ6ooNZkmSJEmSJElSV2wwS5IkSZIkSZK6YoNZkiRJkiRJktSV/w9cVvDU631cqgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "j = 0\n",
    "f=plt.figure(figsize=(20, 12))\n",
    "    \n",
    "ax=f.add_subplot(111)\n",
    "grouped_aggM_aux.unstack().plot(kind='bar', color=['b', 'g'], ax=ax) \n",
    "ax.set_ylabel(\"Time (s)\", fontsize=20)\n",
    "ax.set_xlabel(\"NP, NS\", fontsize=20)\n",
    "ax.legend([\"Worst Fit\", \"Best Fit\"], fontsize=30);\n",
    "\n",
    "ax.axvline((3.5), color='black')\n",
    "ax.axvline((7.5), color='black')\n",
    "ax.axvline((11.5), color='black')\n",
    "ax.axvline((15.5), color='black')\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/TCR_Tiempo_Barras.png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para Tipo = 1\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "100.0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2896\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2897\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 100.0",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-9993a3bce204>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpositions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mt_par\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped_aggL\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ti'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m         \u001b[0mgrouped_aggL\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ti'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnumP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m                     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    803\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    807\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;31m# we may have a nested tuples indexer here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_nested_tuple_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_nested_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;31m# we maybe be using a tuple to represent multiple dimensions here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_nested_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    978\u001b[0m             \u001b[0;31m# selectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 980\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         \u001b[0;31m# handle the multi-axis by taking sections and reducing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1841\u001b[0m             \u001b[0;31m# nested tuple slicing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_nested_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1843\u001b[0;31m                 \u001b[0mlocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1844\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1845\u001b[0m                 \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlocs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_locs\u001b[0;34m(self, seq)\u001b[0m\n\u001b[1;32m   3071\u001b[0m                 indexer = _update_indexer(\n\u001b[1;32m   3072\u001b[0m                     _convert_to_indexer(\n\u001b[0;32m-> 3073\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3074\u001b[0m                     ),\n\u001b[1;32m   3075\u001b[0m                     \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36mget_loc_level\u001b[0;34m(self, key, level, drop_level)\u001b[0m\n\u001b[1;32m   2854\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0milevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2855\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2856\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2857\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaybe_droplevels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2858\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[0;34m(self, key, level, indexer)\u001b[0m\n\u001b[1;32m   2937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2938\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2939\u001b[0;31m             \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2941\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexsort_depth\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/numeric.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m    477\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2898\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2899\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Float64HashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 100.0"
     ]
    },
    {
     "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": [
    "for i in range(1,3):\n",
    "    print(\"Para Tipo = \" + str(i))\n",
    "    \n",
    "    j = 0\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "    numC =2 \n",
    "    for numP in values:\n",
    "\n",
    "        ax=f.add_subplot(positions[j])\n",
    "        \n",
    "        t_par = grouped_aggL['Ti'].loc[(0,i,100,numP,slice(None))].mean() \n",
    "        grouped_aggL['Ti'].loc[(1,i,100,numP,slice(None))].plot(kind='bar',color='green', ax=ax) \n",
    "        \n",
    "        ax.axhline(y=t_par, xmin=0, xmax=1, color='purple')\n",
    "        ax.set_ylabel(\"Time (s)\", fontsize=20)\n",
    "        ax.set_xlabel(\"NP,NC\", fontsize=20)\n",
    "        ax.tick_params(axis='both', which='major', labelsize=18)\n",
    "        ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "        \n",
    "        locs, labels_aux = plt.xticks()\n",
    "        plt.xticks(locs, labels=labelsP[j], rotation=90)\n",
    "        \n",
    "        \n",
    "        blue_patch = mpatches.Patch(color='green', label='Malleable iteration')\n",
    "        handles=[blue_patch]\n",
    "        plt.legend(handles=handles, loc='upper left', fontsize=12)\n",
    "        \n",
    "        f.tight_layout()\n",
    "        f.savefig(\"Images/Iter_type=\"+dist_names[i]+\"_Perc_type=\"+str(100)+\".png\", format=\"png\")\n",
    "        j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['N', '%Async', 'Groups', 'Dist', 'Matrix', 'CommTam', 'Time', 'Iters',\n",
       "       'TE', 'S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure(figsize=(18, 10))\n",
    "#for numP in values:\n",
    "\n",
    "x = np.arange(len(labelsP_J))\n",
    "\n",
    "width = 0.4\n",
    "middle = 0\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index])\n",
    "    bar_res = np.add(sumaTP_TM, TH_data[dist_index])\n",
    "\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index])\n",
    "    sumaTP_TM_A = np.add(sumaTP_TM_A, TH_A_data[dist_index])\n",
    "    \n",
    "    bar_res = np.divide(bar_res, sumaTP_TM_A)\n",
    "    bar_res = (bar_res-1)*100\n",
    "\n",
    "    supper = np.ma.masked_where(bar_res < middle, bar_res)\n",
    "    slower = np.ma.masked_where(bar_res > middle, bar_res)\n",
    "\n",
    "    plt.ylim(min(bar_res)-20, max(bar_res)+150) #FIXME Error cuando el max o min de dist=1 es mayor que el de dist=2\n",
    "\n",
    "    offset = -width/2 # Best Fit\n",
    "    patch = \"\"\n",
    "    if dist == 2:\n",
    "        offset = (width/2) # Worst Fit\n",
    "        patch = \"\\\\/...\"\n",
    "        \n",
    "    ax.bar(x+offset, supper, width, color=\"orange\", hatch=patch)\n",
    "    ax.bar(x+offset, slower, width, color=\"cyan\", hatch=patch)\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"Porcentual difference(%)\", fontsize=20)\n",
    "ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "\n",
    "orange_Bf_patch = mpatches.Patch(facecolor='orange', label='Best Fit AC Improvement')\n",
    "blue_Bf_patch = mpatches.Patch(facecolor='cyan', label='Best Fit SC Improvement')\n",
    "orange_Wf_patch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Worst Fit AC Improvement')\n",
    "blue_Wf_patch = mpatches.Patch(hatch='\\\\/...', facecolor='cyan', label='Worst Fit SC Improvement')\n",
    "handles=[orange_Bf_patch, blue_Bf_patch, orange_Wf_patch, blue_Wf_patch]\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=24)\n",
    "\n",
    "ax.axhline((middle), color='black')\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",
    "    #ax.axvline(4)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/EX_Difference.png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 1, 2, 10,  20,  40]\n",
    "y = [0, 4, 2, 0.4, 0.2, 0.1]\n",
    "z = [0,]\n",
    "for i in range(len(x)-1):\n",
    "    z.append(y[1]/y[i+1])\n",
    "f=plt.figure(figsize=(10, 8))\n",
    "ax=f.add_subplot(111)\n",
    "ax.plot(x,z)\n",
    "ax.set_ylabel(\"SpeedUp\", fontsize=22)\n",
    "ax.set_xlabel(\"Processes\", fontsize=22)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=18)\n",
    "f.tight_layout()\n",
    "f.savefig(\"Test.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAANYCAYAAABJlYhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzde1iVZb7/8c8NCqgLz4hnUTRP7dItpSkknoomU8r8NU3jVidqLLWaZNKa2qntNqPpjJlkNjFB4zhepZmalDYKRuQhPFukpaGFlic0FVOB+/cHsDbIQVwcFuD7dV3rgvXch+fLcuRqPt19H2OtFQAAAAAAAAAA18rD3QUAAAAAAAAAAGomAmYAAAAAAAAAgEsImAEAAAAAAAAALiFgBgAAAAAAAAC4hIAZAAAAAAAAAOCSOu4u4HrSvHlzGxAQ4O4yAADF2LdvnySpa9eubq4EAK4dv8MA1HT8HgOA6m/btm0nrLV+V14nYK5CAQEBSklJcXcZAIBihIaGSpISExPdWgcAuILfYQBqOn6PAUD1Z4w5VNx1WmQAAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxSx90FoGQXL17UqVOndPbsWWVnZ7u7HKBG8PDwkI+PjxwOh5o0aSIPD/49GgAAAAAAQGUhYK6mLl68qMOHD6tJkyYKCAhQ3bp1ZYxxd1lAtWatVU5OjjIzM3X69Gn9/PPPateunerU4VcdAAAAAABAZeBoXzV16tQpNWnSRM2bN5eXlxfhMlAGxhh5enrK19dXbdu2lbe3t06dOuXusgAAAAAAAGotAuZq6uzZs2rYsKG7ywBqLGOMmjVrpjNnzri7FAAAAAAAgFqLgLmays7OVt26dd1dBlCjeXl5KSsry91lAAAAAAAA1FoEzNUYbTGA8uHvEAAAAAAAQOUiYAYAAAAAAAAAuISAGQAAAAAAAADgEgJmAAAAAAAAAIBLCJhRYxhjZIyRr6+vjh8/Xuyc2NhYGWMUERFRxdW5z7hx42SMUWJiYpXd88KFC5o/f76GDRumVq1aycvLS40aNVKfPn0UGRmp1NTUImvOnTuniRMnqm3btqpTp46MMZo3b16V1QwAAAAAAICKV8fdBcA1Lee01E/nf3J3GVfl38BfP0b+WKF7njt3TrNnz9Yrr7xSofuibLZu3ar77rtP6enp8vHxUd++fdW6dWudPXtWO3bs0Ny5c/XXv/5Vf//73zV27FjnumnTpun1119X586d9cADD6hu3brq0aOHG38SAAAAAAAAlBcBcw1VE8JlqeLrNMbI29tb0dHRmjJlilq2bFmh+6N0e/bs0aBBg5SZmamnn35a06dPl6+vr3PcWqv169crMjJS3333XaG1K1euVL169bRjxw45HI6qLh0AAAAAAACVoFq0yDDG+BpjRhhjXjLGfGSMOWGMsXmvbuXYN63APld7jS1mfVnW3V++nx7XwsPDQxMmTNCFCxcUFRXl7nKuK9Za/fa3v1VmZqaef/55zZ07t1C4LOX+C4ChQ4dqy5YtGj58eKGx9PR0tWjRgnAZAAAAAACgFqkWAbOkIZJWSnpeUpikZhW073FJP5XyOldg7vZS9jlRyh6/VFCtKKNp06apfv36WrRokX744Ycyr8vKytKCBQsUFBQkh8Mhh8OhW265RdHR0crKyioyPzQ0VMYYpaWlacmSJerXr598fX3VuHFjSVJaWpqMMQoNDVVmZqamTp2qgIAA1atXTzfeeKMWL17s3CsxMVFDhgxRo0aN1KhRI91///3F1p6enq7Zs2dr0KBBateunby9veXn56fhw4crISHBhU+r4qxdu1a7d+9WmzZt9N///d+lzvX29lZQUJAkKSAgQMYYWWt16NAhZy/tgICAKqgaAAAAAAAAlak6tcg4JilF0heS0iW9Wd4NrbW3lDZujFkpaYSk7dbaPaVMvcVam1beelAx/P39NWnSJM2ePVsvv/yyFi5ceNU12dnZGjlypOLj4+Xr66uhQ4dKkjZs2KBJkyZp7dq1+uCDD+ThUfTfuURFRemtt97SgAEDNHz4cH3//feFxi9duqShQ4dq//79Cg0N1ZkzZ7Rx40aNGTNGOTk5ql+/vh588EEFBQUpLCxMKSkpWr58ufbs2aNdu3bJx8fHudeKFSs0depUdenSRT179tSAAQOUlpam+Ph4xcfHKzY2Vv/1X/9Vzk/QNWvWrJEkjR49WnXr1i3zuvvvv18nTpxQXFycGjRooPvvzz3037x580qpEwAAAAAAAFWnugTMq621H+S/McYEVPYNjTF+ku7KextX2fdDxXrmmWe0cOFCxcTEaNq0aerQoUOp8+fNm6f4+Hj17NlT69evl7+/vyTp6NGjGjRokFavXq358+frqaeeKrL2nXfe0YYNGzRw4MBi9960aZMGDhyo7777ztky4pNPPtEdd9yhZ599Vr/88ovee+89hYeHS5IuXryosLAwJSYmaunSpRo3bpxzr5CQEO3Zs0c33nhjoXukpKRo6NCheuKJJzRq1Cg1aNCgzJ9VRdm5c6ckqU+fPte0bs6cOZKkuLg4NW/eXLGxsRVdGgAAAAAAANykWrTIsNZmu+G2v5FUV9JlSUvccH+UQ7NmzfTkk0/q8uXLeumll646f/78+ZKkv/zlL85wWZJatWqluXPnSpJeffXVYtc+/PDDJYbLUm5f6EWLFhXqRzxs2DD16tVLR44c0V133eUMl6Xc9hH5QfbGjRsL7XXzzTcXCZclKSgoSBMnTtSZM2e0YcOGq/24leLEiROSJD8/P7fcHwAAAAAAANVPdTnB7A75D/VbY6094dZK4JIpU6ZowYIFiouL07PPPqvAwMBi5x0+fFiHDx+Wn5+f7rjjjiLjd999t5o1a6a0tDT98MMPatu2baHxESNGlFpHhw4d1LVr1yLXAwMDtXPnzmLvmV/rkSNHioxdunRJa9eu1datW3Xs2DFdvHhRkvTNN98U+goAAAAAAAC423UZMBtj/kNS77y3ZWmP8a4xpouk+sp9cOAWSX+31q6ppBJRBo0bN9aUKVP0wgsvaMaMGXrnnXeKnZcf4pbWRiMgIEAnT55Uenp6kYC5ffv2pdZx5fx8DoejxPH8sfzwON+XX36pESNG6ODBgyXe7+zZs6XWU5oTJ04oMjKyyPWIiAgFBweXuja/Z/Lx48ddvj8AAAAAAABql2rRIsMNxuV9PSGpLCHxLZI8ldtOo42k+yR9aIx51xjjVSkVokyefPJJNWvWTEuWLNHXX39d6lxjzFX3K25OwYfwFae4BwNey3hBo0eP1sGDB/XII49o+/btOnPmjLKzs2Wt1aJFiyRJ1toy73elc+fOKS4ursjr22+/veraXr16SZK2bdvm8v0BAAAAAABQu1x3AbMxxlPSQ3lvl1hrL5cyPU5SmKQm1tqG1lqHpO6S3s4bHy1pwVXu96gxJsUYk8LJz4rn6+urZ555RtnZ2Zo+fXqxc1q3bi1JSktLK3Gf/LH8ue6Qmpqq1NRU9enTR2+++aZ69+6thg0bOgPqsoTAVxMQECBrbZFXwQcNluTuu++WJL333nvKysoqdy0AAAAAAACo+a67gFm5gXH+U95KbY9hrR1nrV1rrT1d4NrX1trfSXol71KEMaZbKXu8aa0NstYG8XC0yjFp0iT5+/vr3Xff1d69e4uMt2/fXu3bt9fx48e1bt26IuMfffSRTp48qYCAgBLbXVSFjIwMSVK7du2KjF26dEnvv/9+VZdUyJ133qmbbrpJ6enpmjlzZqlzL168qJSUlCqqDAAAAAAAAO5yPQbM+Q/322Ot3V6OfWZIuiDJSLq73FXBZfXr19e0adNkrdXChQuLnTN58mRJuQ8GPHbsmPP6jz/+qClTpkjKbbfhTp07d5aHh4c2bNhQqN3H5cuX9dRTT+nAgQNurC63fcg//vEP1a9fXy+99JIiIyOL7QedmJio/v3768MPP3RDlQAAAAAAAKhK11XAbIxpLGlE3tuyPNyvRNba85Lyj8t2Ks9eKL8JEyaoTZs2yszMLHb8D3/4g371q19p79696tKli+677z7de++9uuGGG5Samqp77rnHGUK7S4sWLfTII4/o559/Vq9evfSrX/1KDzzwgAIDAxUXF6dJkya5tT5Juummm7Rhwwa1adNGc+fOlb+/vwYPHqyHHnpII0aMUPv27TVo0CDt2rVLgYGB7i4XAAAAAAAAlayOuwuoYg9K8paULemfFbBf/hPhXH/qmov8G/jrp/M/VfVtr5l/A/+rT6oAPj4+eu655zRx4sRixz09PbVy5UotXLhQsbGxWrt2rSSpW7duGj9+vCZMmCBPT88qqbU00dHR6tGjh9566y0lJibK4XAoJCREM2fO1BdffOHu8iRJffv21f79+/XWW29p1apV2rNnj5KSklS/fn116dJFDz74oCIiItSlSxd3lwoAAAAAAGqbJebqc67mN1Ue5dVqxtrq94EaYwIkfZf3tru19uuSZ1/Tvpsl9ZW0xlo7vJx7NZB0XFI9SX+01s652pqgoCBb1r60qamp6t69e3lKBCD+LqHsQkNDJeW2eQGAmobfYQBqOn6PASgzAma3McZss9YGXXn9ummRYYzpqtxwWSpDewxjzNX+1/qCcsNlKym+fNUBAAAAAAAAQM1TbQJmY0zz/JekJgWGGhccM8Z4XLHO5r2mX+UW+Q/3y5C0qgwlvWuMedkYE2SM8Spwv67GmL9Jmpp3Kc5a+1UZ9gMAAAAAAACAWqU69WA+XsL1TVe87ygp7Vo2zgulf5v3dqm19mIZlvlJul/Sc5KyjTFnlNu/uUGBOcskTbiWWgAAAAAAAACgtqhOAXNlGiypXd73V22Pked/Je2W1E9SW0lNJeUotzf0Zkmx1tp1FVwnAAAAAAAAANQY1SZgtta61KG7LOustf+WdE3754XHBMgAAAAAAAAAUIJq04MZAAAAAAAAAFCzEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDDXUC1bSsZU/1fLlhX3MxtjZIyRr6+vjh8/Xuyc2NhYGWMUERFRcTeu5saNGydjjBITEyv9Xvl/BgVfdevWVevWrTVq1Ch9/vnnJa795ptvNHLkSDVr1kweHh4yxmjnzp2VXjMAAAAAAAAqTx13FwDX/PSTuysom8qo89y5c5o9e7ZeeeWVit8cZTJq1Cg5HA5J0vnz57V79269//77WrFihRYvXqzf/OY3hebn5OTo/vvv1+7du3Xbbbepc+fO8vDwUNOmTd1RPgAAAAAAACoIATNqFGOMvL29FR0drSlTpqhlRR6RRpnNmTNHAQEBzvfWWj3//PP63//9Xz3xxBMaPXq06tat6xz/7rvvtHv3boWEhOjTTz91Q8UAAAAAAACoDLTIQI3i4eGhCRMm6MKFC4qKinJ3OchjjNGLL76oOnXq6OTJk/ryyy8Ljaenp0uSOnXq5I7yAAAAAAAAUEkImFHjTJs2TfXr19eiRYv0ww8/lHldVlaWFixYoKCgIDkcDjkcDt1yyy2Kjo5WVlZWkfmhoaEyxigtLU1LlixRv3795Ovrq8aNG0uS0tLSZIxRaGioMjMzNXXqVAUEBKhevXq68cYbtXjxYudeiYmJGjJkiBo1aqRGjRrp/vvvL7b29PR0zZ49W4MGDVK7du3k7e0tPz8/DR8+XAkJCS58WlXHy8vL+dnkf575n9HAgQMlSXFxcc7ezePGjXNXqQAAAAAAAKggBMyocfz9/TVp0iRdvHhRL7/8cpnWZGdna+TIkZo8ebL279+voUOHaujQodq3b58mTZqk++67Tzk5OcWujYqK0pgxY+Tl5aXhw4frxhtvLDR+6dIlDR06VDExMQoKClJwcLD279+vMWPG6J133tGyZcs0bNgwZWZmKiwsTM2bN9fy5cs1ZMgQ/fLLL4X2WrFihaZOnar09HT17NlT9957rwIDAxUfH68hQ4bonXfece1DqwKHDh3SiRMnVLduXXXu3FmS5HA4NHbsWN15552SpMDAQI0dO1Zjx45VcHCwO8sFAAAAAABABaAHM2qkZ555RgsXLlRMTIymTZumDh06lDp/3rx5io+PV8+ePbV+/Xr5+/tLko4ePapBgwZp9erVmj9/vp566qkia9955x1t2LDBeQr3Sps2bdLAgQP13XffydfXV5L0ySef6I477tCzzz6rX375Re+9957Cw8MlSRcvXlRYWJgSExO1dOnSQid5Q0JCtGfPniIhdkpKioYOHaonnnhCo0aNUoMGDcr8WVW2/If85X92jz32mPMkc/PmzRUbG6vExEStXbtWwcHBio2NdWO1AAAAAAAAqEicYEaN1KxZMz355JO6fPmyXnrppavOnz9/viTpL3/5izNclqRWrVpp7ty5kqRXX3212LUPP/xwieGylNsXetGiRc5wWZKGDRumXr166ciRI7rrrruc4bIkeXt7O8PYjRs3Ftrr5ptvLhIuS1JQUJAmTpyoM2fOaMOGDVf7cStdx44dna0uHA6H+vfvr6+++krz58/XvHnz3F0eAAAAAAAAqggnmFFjTZkyRQsWLFBcXJyeffZZBQYGFjvv8OHDOnz4sPz8/HTHHXcUGb/77rvVrFkzpaWl6YcfflDbtm0LjY8YMaLUOjp06KCuXbsWuR4YGKidO3cWe8/8Wo8cOVJk7NKlS1q7dq22bt2qY8eO6eLFi5Kkb775ptBXdxo1apQcDoek3H7LP/zwgzZt2qTp06erRYsWeuCBB9xcIQAAAAAAAKoCATNqrMaNG2vKlCl64YUXNGPGjBL7E+eHuKW10QgICNDJkyeVnp5eJGBu3759qXVcOT9ffgBb3Hj+WH54nO/LL7/UiBEjdPDgwRLvd/bs2VLrKc2JEycUGRlZ5HpERMQ19USeM2eOAgICCl1LS0vT7bffrgcffFCtW7dWSEiIy3UCAAAAAACgZqBFBmq0J598Us2aNdOSJUv09ddflzrXGHPV/Yqb4+PjU+oaD4/S/xpdbbyg0aNH6+DBg3rkkUe0fft2nTlzRtnZ2bLWatGiRZIka22Z97vSuXPnFBcXV+T17bffurxnvoCAAE2dOlXWWv31r38t934AAAAAAACo/giYUaP5+vrqmWeeUXZ2tqZPn17snNatW0vKPWFbkvyx/LnukJqaqtTUVPXp00dvvvmmevfurYYNGzoD6ooKga21RV4FHzRYHh07dpQk7du3r0L2AwAAAAAAQPVGwIwab9KkSfL399e7776rvXv3Fhlv37692rdvr+PHj2vdunVFxj/66COdPHlSAQEBJba7qAoZGRmSpHbt2hUZu3Tpkt5///2qLuma5bf2yG8BAgAAAAAAUO0YU/4XnAiYUePVr19f06ZNk7VWCxcuLHbO5MmTJeU+GPDYsWPO6z/++KOmTJkiKbfdhjt17txZHh4e2rBhQ6F2H5cvX9ZTTz2lAwcOuLG6q0tLS9Ps2bMl5T44EQAAAAAAALUfD/lDrTBhwgTNmTNH6enpxY7/4Q9/UEJCguLj49WlSxcNGTJE1lqtX79eZ8+e1T333OMMod2lRYsWeuSRR7Ro0SL16tVLgwcPlq+vrzZt2qSTJ09q0qRJWrBggVtrzBcZGek8pZyVlaUffvhBmzZt0qVLl9SvXz9naA8AAAAAAIDajYC5hvL3l376yd1VXJ2/f9Xcx8fHR88995wmTpxY7Linp6dWrlyphQsXKjY2VmvXrpUkdevWTePHj9eECRPk6elZNcWWIjo6Wj169NBbb72lxMREORwOhYSEaObMmfriiy/cXZ7T8uXLnd8bY9SoUSMFBQXpgQce0IQJE+Tl5eXG6gAAAAAAAFBVjLXW3TVcN4KCgmxKSkqZ5qampqp79+6VXBFQ+/F3CWUVGhoqSUpMTHRrHQDgCn6HAajp+D0GoMyWVED/44fKv4Wuw0zVGLPNWht05XV6MAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJfUcXcBAAAAAACghjGm/HtYW/49AABuxwlm1BjGmCKvRo0aqW/fvpo3b54uX758TfslJibKGKNx48ZVTsFuZoxRQECAu8soZPr06cX+OTocDvXq1Uv/8z//o8zMzBLX//Wvf1X37t3l7e0tY4zCw8OrsHoAAAAAAABciRPMNdX7LaVffnJ3FVfn4y/d92OFbjlq1Cg5HA5Za5WWlqZNmzZp69atWrVqlT7++GN5eXlJkmJjYzV+/Hi9+OKLmj59eoXWgPIJDAxUcHCwJCknJ0dHjhxRcnKyXnjhBa1YsUJJSUmqX79+oTXLli3T008/raZNm2rkyJGqX7++/vM//9Md5QMAAAAAACAPAXNNVRPCZalS6pwzZ06hk7k7d+5UaGioEhIS9Le//U0TJ06s8HuiYgUHBys2NrbQtUOHDqlfv37avn273njjDT399NOFxleuXClJeu+99zR48OCqKhUAAAAAAACloEUGarxevXo5w8gPPvjAzdXAVR06dNCjjz4qSfr000+LjKenp0uSOnXqVKV1AQAAAAAAoGQEzKgVevfuLUk6fPiwJCk0NFTjx4+XJM2YMaNQv98rT85KUkZGhiZOnKg2bdrI29tbXbt21dy5c2WLeehEfm/jS5cuaebMmerWrZu8vb2L9APesmWLRo8erVatWsnLy0tt27ZVRESEs8Yr779gwQKFhYUpICBAPj4+atKkiQYPHqzly5eX+HOfOnVKkyZNUuvWreXj46MePXro1VdfLbbufGvXrlVYWJjatm0rb29vtWrVSv369dNzzz1Xav/jqtCiRQtJUlZWlvPauHHjZIxRQkKCJKljx47OP8u0tDR3lAkAAAAAAIA8tMhArXD27FlJkre3tyQpLCxMWVlZSk5O1s0336xevXo553bu3LnQ2tOnT+u2227T6dOnFRISotOnTyspKUmRkZE6e/Zssf2bc3JyFB4erk8//VQDBw7UTTfdpGbNmjnHX3/9dU2ePFmSFBQUpJCQEO3bt08xMTFatWqVNm7cqO7duzvnJycna/LkyWrfvr26dOmi2267TUeOHFFSUpISEhL00ksv6fnnny9UQ0ZGhoKDg5WamqqWLVtq5MiRysjIUGRkpL799ttiP6eFCxfq8ccfl6enp/r376+QkBBlZGRo//79ioqK0uOPP16k93FVSklJkaRCn01+r+aPP/5YP/30k7MHtyTnVwAAAAAAALgHATNqhdWrV0uSbrrpJknStGnT1LJlSyUnJys8PLzUh/ytXLlS9957r5YsWSIfHx9JuaePBwwYoDlz5igyMrJIkPn999/L29tb+/btU5s2bQqNbd68WU888YT8/f21atUqBQUFOcdiYmIUERGh8ePHa/Pmzc7r3bt316ZNm9SvX79Cex04cECDBw/W9OnTNWbMGHXo0ME59txzzyk1NVVhYWFavny5Mxj+4osvSuxRPGvWLBlj9Pnnn+vWW28tNLZlyxY1adKkxM+pslhrdeTIEf3rX//SO++8o8aNG+vxxx93jkdERCgiIkKhoaH66aefivTgBgAAAAAAgPvQIgM1lrVWhw4d0rRp07R06VIZY/T73//+mvdxOBxatGiRM1yWpL59+yosLEznz5/Xtm3bil0XFRVVJFyWpD//+c/Kzs7WG2+8UShclqSHH35YI0aM0JYtW7Rjxw7n9cDAwCLhcv71559/XtnZ2Vq1apXz+vnz5xUXFycPDw+99tprhU4d33LLLSU+6PD48eNq1KhRkXA5/2du0KBBsesqWlxcnLPNhYeHh9q2bas//vGPGjJkiDZt2qSOHTtWSR0AAAAAAAAoH04wo8YpLnz08vLSvHnzFBIScs37BQUFyc/Pr8j1rl27as2aNTpy5EiRMWOM7rnnniLXc3JytH79etWvX1933XVXsfcLCQnRqlWrtHXrVmfv6Py1CQkJSk5O1tGjR/XLL7/IWqujR49Kkr755hvn3G3btunChQu69dZbi7T8kKQHH3xQs2bNKnK9T58+SkpKUkREhJ5++mn16NGj2BorW2BgoLP1hZTb7mPPnj365JNPFBkZqX/84x9uOU0NAAAAAACAa0PAjBonvwevMUYOh0PdunXTvffeq9atW7u0X7t27Yq97uvrK0m6ePFikbEWLVo4+z0XdOLECZ07d05SbuhdmhMnTji/T09P14gRI7R9+/YS5+f3mZbkDL0LtswoqKQWEq+//rrCw8MVExOjmJgYtWjRQgMGDFB4eLh+/etfX7VmSXrrrbf02WefFbrWvHlzzZkz56pr8wUHBxd52GJOTo5mzpypGTNmaPTo0fr3v/9d5v0AAACAcjGm/HuU8qBtAABqMwJm1DgV3YPXw+PaO8UUbKdRUE5OjqTcthujRo0qdY+ePXs6v4+IiND27dsVHh6uqVOnqmvXrmrYsKE8PT21bt063XnnnbIF/oHVXuUfXk0J/4B844036ssvv9S6dev00UcfaePGjVqxYoVWrFihWbNmKTk5WY0bNy51788++0xxcXGFrnXo0OGaAubieHh46MUXX9TChQu1fv167d6929lTGwAAAAAAANUTATNQgZo3by4fHx95eHjo7bffLjHoLej8+fNat26d/P39tWzZMnl6ehYa//bbb4usyT+tfejQoWL3TEtLK/F+3t7euueee5wtPg4ePKhx48YpKSlJr7zyil5++eVS642NjS1y+riiGGPUoUMHHTt2TPv27SNgBgAAAAAAqOZ4yB9qrfx2D1lZWVV2zzp16ig0NFQ///yz1q9fX6Y1Z86cUU5Ojlq1alUkXIbQjFMAACAASURBVJakpUuXFrnWp08f1atXT9u2bdPBgwfLtKYknTp1UmRkpCRp7969ZV5XGXJycpzhuMPhcGstAAAAAAAAuDoCZtRa+ad89+3bV6X3/dOf/iQPDw+NHz9eCQkJRcZPnjyp6OhoXbhwQZLk7++vxo0ba+/evUpKSnLOs9YqKiqq0LV8DodDY8aMUXZ2tiZPnuzcS8p9AOCCBQuKrMnMzNT8+fOVkZFRZCw+Pl5Syf2oq0JOTo5mzJih48ePq2HDhoUeAggAAAAAAIDqiRYZqLX69eunFi1aaNmyZQoNDVWnTp3k4eGh3/3ud+rfv3+l3Tc4OFjR0dGaNGmSBg8erJ49e+qGG25QVlaWDh8+rK+++kqXL1/WQw89pHr16snT01NTp07Vs88+q0GDBik0NFR+fn7avn27Dhw4oClTpmju3LlF7hMVFaWNGzcqPj5egYGBuv3225WRkaGEhAQ9+uijio6OLjT/0qVLevLJJxUZGanevXurU6dOysrK0o4dO3TgwAH5+flpypQplfa5FPTZZ59p3LhxzvenT5/W7t279d1336lOnTp68803nQ9ZBAAAAAAAQPXFCeaaysff3RWUjRvr9PHx0Zo1azRs2DDt3LlTsbGxiomJ0f79+yv93hMmTFBKSorGjh2rc+fOac2aNUpKSlJWVpbGjBmjDz/8UI0aNXLOnzZtmhYvXqxevXpp8+bNWrt2rTp27KjExEQNHz682Hs0bdpUycnJeuyxx2St1QcffKDDhw8rKipKr732WpH5DodD0dHRGjFihE6dOqXVq1dr3bp1qlevnqZOnapdu3apY8eOlfaZFHTgwAHFxcU5Xx999JGstRo7dqxSUlL0wAMPVEkdAAAAAAAAKB9jrXV3DdeNoKAgm5KSUqa5qamp6t69eyVXBNR+/F1CWYWGhkqSEhMT3VoHALiC32FAOZXh4dxXdb39f+sK/sz4PQagzJZUwO+fh8q/xXX3e1+SMWabtTboyuucYAYAAAAAAAAAuISAGQAAAAAAAADgEgJmAAAAAAAAAIBLCJgBAAAAAAAAAC4hYAYAAAAAAAAAuISAGQAAAAAAAADgEgJmAAAAAAAAAIBLCJgBAAAAAAAAAC4hYAYAAAAAAAAAuISAGQAAAAAAAADgEgJmAAAAAAAAAIBLCJgBAAAAAAAAAC4hYAYAAAAAAAAAuISAGQAAAAAAAADgEgLmmsqYmvGq0B/ZFHk1atRIffv21bx583T58uVr2i8xMVHGGI0bN65C66wujDEKCAhwdxkl2rZtm37/+9+rW7duatiwoby9vdWuXTuNHDlS//jHP3Tp0qUia9auXav+/fvL4XDIGKPGjRu7oXIAAAAAAADkq+PuAoBrNWrUKDkcDllrlZaWpk2bNmnr1q1atWqVPv74Y3l5eUmSYmNjNX78eL344ouaPn26e4uGU1ZWliZPnqw33nhDkhQYGKjBgwfLx8dH33//veLj47Vq1SrNmDFD3377rXPdoUOHdN999+ny5csaNmyY/Pz8VL9+fXf9GAAAAAAAABABM2qgOXPmFDqZu3PnToWGhiohIUF/+9vfNHHiRPcVh6saP368Fi9erMDAQP3973/X7bffXmj81KlTeuWVVzR37txC1//9738rMzNTL7zwgmbOnFmVJQMAAAAAAKAEtMhAjderVy89/fTTkqQPPvjAzdWgNO+//74WL14sPz8/JSUlFQmXJalp06aKiorShg0bCl1PT0+XJHXq1KlKagUAAAAAAMDVETCjVujdu7ck6fDhw5Kk0NBQjR8/XpI0Y8aMQn2bY2Nji6zPyMjQxIkT1aZNG3l7e6tr166aO3eurLVF5ub3Nr506ZJmzpypbt26ydvbW+Hh4YXmbdmyRaNHj1arVq3k5eWltm3bKiIiwlnjlfdfsGCBwsLCFBAQIB8fHzVp0kSDBw/W8uXLS/y5T506pUmTJql169by8fFRjx499OqrrxZbd761a9cqLCxMbdu2lbe3t1q1aqV+/frpueeeU2ZmZonrKsLs2bMl5f6ZtGrVqtS5wcHBknJbnRhj9OKLL0rKPQFd2p8lAAAAAAAAqg4tMlArnD17VpLk7e0tSQoLC1NWVpaSk5N18803q1evXs65nTt3LrT29OnTuu2223T69GmFhITo9OnTSkpKUmRkpM6ePVts/+acnByFh4fr008/1cCBA3XTTTepWbNmzvHXX39dkydPliQFBQUpJCRE+/btU0xMjFatWqWNGzeqe/fuzvnJycmaPHmy2rdvry5duui2227TkSNHlJSUpISEBL300kt6/vnnC9WQkZGh4OBgpaamqmXLlho5cqQyMjIUGRlZqHdxQQsXLtTjjz8uT09P9e/fXyEhIcrIyND+/fsVFRWlxx9/vNL6Gp84cUJbt26VMUa//vWvy7yuc+fOGjt2rHbu3Kldu3ZpwIABzj/DK/8sAQAAAAAAULUImFErrF69WpJ00003SZKmTZumli1bKjk5WeHh4aU+5G/lypW69957tWTJEvn4+EjKPX08YMAAzZkzR5GRkXI4HIXWfP/99/L29ta+ffvUpk2bQmObN2/WE088IX9/f61atUpBQUHOsZiYGEVERGj8+PHavHmz83r37t21adMm9evXr9BeBw4c0ODBgzV9+nSNGTNGHTp0cI4999xzSk1NVVhYmJYvX+4Mhr/44gsNHjy42J911qxZMsbo888/16233lpobMuWLWrSpEmJn1N57dq1S9ZaBQYGXtN9goODFRwcrOnTp2vXrl2KiIjQuHHjKq1OAAAAAAAAlB0tMlBjWWt16NAhTZs2TUuXLpUxRr///e+veR+Hw6FFixY5w2VJ6tu3r8LCwnT+/Hlt27at2HVRUVFFwmVJ+vOf/6zs7Gy98cYbhcJlSXr44Yc1YsQIbdmyRTt27HBeDwwMLBIu519//vnnlZ2drVWrVjmvnz9/XnFxcfLw8NBrr71W6NTxLbfcUuKDDo8fP65GjRoVCZfzf+YGDRoUu64inDhxQpLk5+dXafcAAAAAAABA1eIEM2qcjh07Frnm5eWlefPmKSQk5Jr3CwoKKjb07Nq1q9asWaMjR44UGTPG6J577ilyPScnR+vXr1f9+vV11113FXu/kJAQrVq1Slu3bnX2js5fm5CQoOTkZB09elS//PKLrLU6evSoJOmbb75xzt22bZsuXLigW2+9tdg2EQ8++KBmzZpV5HqfPn2UlJSkiIgIPf300+rRo0exNQIAAAAAAABlQcCMGmfUqFFyOBwyxsjhcKhbt26699571bp1a5f2a9euXbHXfX19JUkXL14sMtaiRQtnv+eCTpw4oXPnzknKDb1Lk3+iV5LS09M1YsQIbd++vcT5+X2mJTlD74ItMwoKCAgo9vrrr7+u8PBwxcTEKCYmRi1atNCAAQMUHh6uX//611etWZLeeustffbZZ4WuNW/eXHPmzCl1XfPmzSXlnqIGAAAAAABA7VAtAmZjjK+kQZJukRSU9zX/iWndrbVfu7hvqKSEMkz1s9aeKGnQGDNa0mOSbpZUT9IhScslzbLWni1pHSrHnDlzSgxQXeHhce2dYgq20ygoJydHUm7bjVGjRpW6R8+ePZ3fR0REaPv27QoPD9fUqVPVtWtXNWzYUJ6enlq3bp3uvPNOWWud8wt+XxxjTLHXb7zxRn355Zdat26dPvroI23cuFErVqzQihUrNGvWLCUnJ6tx48al7v3ZZ58pLi6u0LUOHTpcNWC++eabZYzRwYMHdfr06aveBwAAAAAAANVftQiYJQ2RtKIS98+RVNqxyZySBowxb0p6JO9tlqRfJHWT9CdJDxpjQqy1RXso4LrUvHlz+fj4yMPDQ2+//XaJQW9B58+f17p16+Tv769ly5bJ09Oz0Pi3335bZE3+ae1Dhw4Vu2daWlqJ9/P29tY999zjbPFx8OBBjRs3TklJSXrllVf08ssvl1pvbGysYmNjS51TnObNm+vWW2/Vli1b9K9//UuPPfbYNe8BAAAAAACA6qU6PeTvmKR4STMkPVrBe39vrW1ZyutUcYuMMY8pN1zOkfRHSQ5rra+kAco9xdxJ0rsVXCsqSH67h6ysrCq7Z506dRQaGqqff/5Z69evL9OaM2fOKCcnR61atSoSLkvS0qVLi1zr06eP6tWrp23btungwYNlWlOSTp06KTIyUpK0d+/eMq9zxTPPPCNJmj59un788cdS5yYnJ1dqLQAAAAAAACi/6hIwr7bW+ltr77bWTpf0ibsLMsZ4S5qe9/ZVa+0ca+1FSbLWfi7pXklW0gBjTNGnvcHt8k/57tu3r0rv+6c//UkeHh4aP368EhKKdmg5efKkoqOjdeHCBUmSv7+/GjdurL179yopKck5z1qrqKioQtfyORwOjRkzRtnZ2Zo8ebJzLyn3AYALFiwosiYzM1Pz589XRkZGkbH4+HhJJfejrij33Xeffvvb3+rYsWMKCQkp9mc7ffq0XnjhBQ0aNKhSawEAAABqGmPK/wIAoKJVixYZ1tpsd9dQjKGSWig3RJ575aC1docx5t+Shkl6SNLqqi0PV9OvXz+1aNFCy5YtU2hoqDp16iQPDw/97ne/U//+/SvtvsHBwYqOjtakSZM0ePBg9ezZUzfccIOysrJ0+PBhffXVV7p8+bIeeugh1atXT56enpo6daqeffZZDRo0SKGhofLz89P27dt14MABTZkyRXPnFvmfoKKiorRx40bFx8crMDBQt99+uzIyMpSQkKBHH31U0dHRheZfunRJTz75pCIjI9W7d2916tRJWVlZ2rFjhw4cOCA/Pz9NmTKl0j6XfG+//bYaNGigRYsW6fbbb1fnzp31H//xH/L29tb333+vrVu36vLly7rhhhsqvRYAAAAAAFA1Kupfctl/Vsw+qDjV5QRzdZR/fHKvtTa9hDlr874OroJ6CrO2ZrzcyMfHR2vWrNGwYcO0c+dOxcbGKiYmRvv376/0e0+YMEEpKSkaO3aszp07pzVr1igpKUlZWVkaM2aMPvzwQzVq1Mg5f9q0aVq8eLF69eqlzZs3a+3aterYsaMSExM1fPjwYu/RtGlTJScn67HHHpO1Vh988IEOHz6sqKgovfbaa0XmOxwORUdHa8SIETp16pRWr16tdevWqV69epo6dap27dqljh07Vtpnkq9OnTp644039MUXX+jRRx+Vh4eHPvnkE73//vs6dOiQfvWrX+mf//ynvvzyy0qvBQAAAAAAAOVjrJtDwOIYYwIkfZf3tru19msX9wmVlCApU9I+SV3zhtIlJUp6zVq7p4S18ZLukrTUWvtgCXPuUm7faEnys9aeKK2eoKAgm5KSUqbaU1NT1b179zLNBVAy/i6hrEJDQyVJiYmJbq0DAFzB7zCgnCriWF0V/H/ralVmBRfD7zGg9qu4E8wVsNFD5d/C3Qcr3cEYs81aG3Tl9evlBHN9Sb0lXVRuW5Auyn143w5jTGQJa1rlfT1Syr4Fx1qVOAsAAAAAAAAAaqHaHjCflvSKpCBJ9ay1TZUbNg+U9LkkT0mvGGN+U8zaBnlfLxQzli+zwPeO4iYYYx41xqQYY1KOHz9+rfUDAAAAAAAAQLVVqwNma+1Oa+0z1tpt1tpf8q5lW2s/VW6P5eS8qbOMMVd+Fvnn7ct13t1a+6a1NshaG+Tn51eerQAAAAAAAACgWqnVAXNprLWXJL2Q97atcltoFHQu72v9UrYpOHauxFkAAAAAAAAAUAtdtwFzni0Fvu90xVh+f+XWpawvOHa0QioCAAAAAAAAgBrieg+YC7qyFcZXeV97lrKmR97X49baExVfEgAAAAAAAABUX9d7wNy3wPdpV4wl5H3taYwp6RTzHXlf11dkUfmsLVf7Z+C6x98hAAAAAACAylWrA2ZjjCllrK6kmXlvj0rafsWU9ZKOKfczerqY9TdLGpr39p/lLvYKnp6eunz5ckVvC1xXLl26pDp16ri7DAAAAAAAgFqr2gTMxpjm+S9JTQoMNS44ZozxuGKdzXtNL2bbvcaYycaYLvlhszHG0xgTrNwAOThv3rPW2pyCC621FyXl7/kHY8wUY4x33h63SVqh3M8v2Vr7Ybl++GL4+vrq559/ruhtgeuGtVYnT55Uo0aN3F0KAAAAAABArVWdjvYdL+H6pived1TRdhYl6SFpft73F40xZyU1lOSVdy1b0p+stXHFLbbWLjTG9Jb0iKQ5kqKMMRclOfKmHJT0/8pYyzVp2rSpDh8+LElq2LCh6tatq1IOZANQbqick5OjzMxMnT59WllZWWrRooW7ywIAAAAAAKi1qlPAXBl+L2mApD6SWij3ZPQFSfskbZS00Fr7VcnLJWvto8aYf0uaIKmXpHqSvpa0XNIsa+3Zyijc29tb7du316lTp5SWlqbs7OzKuA1Q63h4eKhevXpq0KCBmjRpIg+PavMfagAAAAAAANQ61SZgtta6dDy3tHXW2jclvelyUf+3z7uS3i3vPtfK29tbrVq1UqtWrar61gAAAAAAAABwVRztAwAAAAAAAAC4pNqcYAYAAAAAAJVsCc/2AQBULE4wAwAAAAAAAABcQsAMAAAAAAAAAHAJATMAAAAAAAAAwCUEzAAAAAAAAAAAlxAwAwAAAAAAAABcQsAMAAAAAAAAAHBJHXcXAAAAAAAAAKD6MjNMBexiK2APVEcEzAAAAAAAVDLCGQBAbUWLDAAAAAAAAACASzjBDAAAAAAAgOtKRfxXBfZF/qsCQOIEMwAAAAAAAADARQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxSx90FAAAAAAAAAHCRMRWzj7UVsw+uO5xgBgAAAAAAAAC4hIAZAAAAAAAAAOASAmYAAAAAAAAAgEsImAEAAAAAAAAALiFgBgAAAAAAAAC4hIAZAAAAAAAAAOCSOu4uAAAAAKhRlpjy7/EbW/49AAAAgGqAE8wAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCT2YAQAAUCOYGeXvfWxfpPcxAAAAUJEImAEAAFB+pgIefGcJfwEAAICahhYZAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxCwAwAAAAAAAAAcAkBMwAAAAAAAADAJQTMAAAAAAAAAACXEDADAAAAAAAAAFxSx90FAADgEmPKv4e15d8DAAAAAIDrGCeYAQAAAAAAAAAuIWAGAAAAAAAAALiEgBkAAAAAAAAA4BJ6MAMAAABVrSL6yEv0kgcAAIDbcYIZAAAAAAAAAOASTjADAAAAAAAA7rCkgv6rJsCNOMEMAAAAAAAAAHAJATMAAAAAAAAAwCUEzAAAAAAAAAAAlxAwAwAAAAAAAABcwkP+AAAAAKC2MRXw0Chry78HAACo9TjBDAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJfQgxkAqgt6JQIAAAAAgBqGE8wAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJcQMAMAAAAAAAAAXELADAAAAAAAAABwCQEzAAAAAAAAAMAlBMwAAAAAAAAAAJcQMAMAAAAAAAAAXFLH3QVIkjHGV9IgSbdICsr72ixvuLu19msX9/WTdJ+koZL+U1IbSdmSDktaL2metfbbUtbbMtxmtLV2mSv1AQCA65uZYcq9h32xLP+4AgAAAACVo1oEzJKGSFpRCfseUeGf8ZwkL0nd8l4PG2N+Z63911X2OaHcYLo4v5S7SgAAAACoYfiXZAAAQKo+AbMkHZOUIukLSemS3qyAPetI+lRSjKR11tofjTGekvpJWiCpl6R3jDFfWmt3l7LPLdbatAqoBwAAAAAAAABqjeoSMK+21n6Q/8YYE1BB+w601n5a8IK1NltSsjHmDkl7JbWQ9AdJ4yvongAAAAAAAABwXagWD/nLC30rY99PSxk7Lik+722fyrg/AAAAAAAAANRm1SJgdqOTeV893VoFAAAAAAAAANRA13vAPDDv696rzHvXGJNhjLlojPnBGLPcGHN3ZRcHAAAAAAAAANXZdRswG2NGSgrKe/v2VabfotxTzpcltZF0n6QPjTHvGmO8Kq9KAAAAVCRjyv8CAAAA8H+uy4DZGNNG0pt5b1dZaz8uYWqcpDBJTay1Da21Dknd9X+B9GhJC65yr0fN/2fvzoNtvco7Mf9euCA0XLBAV0hAY0PZQYjBgIWhA8QgBrvN1FYEaaMkpowZXJUONuAmiLglubFlBg90uwojV9LIFS5daigmNUFMjkkx2BEgYwSou41F2gjMFQYJoQGQ3vyxvxMdLmfa6+yrs8+5z1P11Tettb533yqE9NPSWlWXV9Xlhw4dWkD1AAAAAADL4agLmKvqhCTvSnJyki8necF6bbv7+d19WXd/a9WzL3b3Lyd5/fToV6rqtA3GuKi7z+juMw4cOLCYHwEAAAAAsASOqoC5qu6W5N2ZLY1xKMnPdve1g8NdkOSmJJXEeswAAAAAwFHnqAmYp7WS357kzCTfSvK07r5qdLzu/k5u3xzwgduvEAAAAABgdzkqAuaq2pfkbZnNNL4hyc939xWLGHo69wLGAgAAAADYVfZ8wFxVd8pss76zMlvS4lnd/YkFjHt8kodMt1dvdzwAAAAAgN1m304XcCRVVSW5KMnzknw3yVnd/Wdb7dvdG81M/s0kx2Y2e/l9260VAAAAgCRVm7fZzIaRDrBISzODuapOWjmSnLjq1Y+sfjfNSF7dr6fj/DWG/f0kL0jy/STP7e73z1HSJVX121V1xrR+88r3HlRVf5LkldOji7v783OMCwAAAACwJyzTDOZD6zw/fDmLB2QLS1JU1f2T/Np020neXFVvXq99d59y2KMDSc5Ocm6SW6vquiTHJDl+VZu3J3nJZrUAAAAAAOxFyxQwL9rqmc53SXLvOfv/TpLPJnlskvsluWeS25L8bZJPJnlLd39gAXUCAAAAAOxKSxMwd/fQAjvr9evuq5MML9ozhccCZAAAAACAdSzNGswAAAAAAOwuAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgyL6dLgAAAACAPeJg7XQFwB3MDGYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhAmYAAAAAmFPV9g/YCwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQ/btdAEAHIUO1k5XAAAAACyAGcwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAzZt9MFAIyqC2oh4/R5vZBxAAAAAI42ZjADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMWYqAuar2V9WzqupfVdX/WVXXVlVPx2kLGP/uVfWaqvpCVd1YVd+oqg9X1dlb7P+cqvrI1O/GaZzXVNX+7dYGAAAAALBb7dvpAiZPTvLOIzFwVd0vyUeTPGB6dEOSuyc5M8mZVfXH3f2rG/S/KMkLp9vvJ7k5yWlJXp3kF6vqCd19zZGoHQAAAABgmS3FDObJ15O8L8kFSV60iAGrqpK8PbNw+eokj+vu/Un2J/kXSW5L8pKqeuE6/X81s3D5tiS/keSEqf/jknw5yQOTXLKIWgEAAAAAdptlCZjf29337u6nd/f5ST64oHGfneQxmQXEv9DdH0+S7r65u1+f5F9P7X6rqu66umNVHZPk/On2jd39hu6+Zer/8SS/kKSTPK6qnrmgegEAAAAAdo2lWCKju289QkOfM50/1N1XrPH+DUlemuSUzJbMeP+qd09JcnJmIfLvHd6xuz9TVR9K8tTpO+9dYN0AAAAAW1YX1LbH6PN6AZUAR5tlmcF8pDxxOl+21svu/kqSK6fbMw97/aTp/Lmp3VpWxj28LwAAAADAnjccMFfVXavqPlV14iILWpSqOjnJSdPtlRs0/fx0Pv2w5yv3W+l7oKpO2qAdAAAAAMCes+WAuar2V9ULq+qSqvpakpuS/Jck11bVLVX1/1TVa6vqMUes2vmcuur6mg3arbw79bDnO6WvgQAAIABJREFUpx72fqO+a/VPklTVi6rq8qq6/NChQxsMBQAAAACwu2waMFfVfavqjzMLU9+c5Owkd01yVZJPJLkiyVeTPCLJbyT5eFV9qqp+8YhVvTXHr7q+aYN2N07nE9bpv5W+a/VPknT3Rd19RnefceDAgQ2GAgAAAADYXTbc5K+qLkjy8iTHJPlgkn+X5GPd/TdrtD0uyU8n+dnMNr17a1W9NMmLuvuziy58C1avbj+ySv1KfyvcA3uGjT8AAACARdpsBvNvJLkoyf27++e7+0/XCpeTpLtv7O7/q7tfleRHkzw7yV2S/NOFVrx1N6y6Pm6Ddivvbjjs+Q2Hvd+o71r9AQAAAAD2tA1nMCf58e7eaA3iNXV3J3lvkvdW1SlDlW3f6rrvk+Sv12l3n+n81TX6P2LV+436rtUfAAAAAGBP23AG80i4vMYYX9vuGIPfPZTk2un2IRs0PX06f/6w5yv3W+l7qLuv3aAdwJ5Rtf0DAAAA2Bs23eRvq6rqxKo6fvOWd6g/m85PXetlVd03twfIH16n70Oqar1ZzE9bpy8AAAAAwJ43V8BcVU+uqtdV1Ymrnp1cVX+e2Wzhf6iq3190kdtwcDo/rap+co33L8tsM7+v5vZAecWHk3w9sz+jlx3ecRrvKdPtWxdSLQAAAADALjLvDOZ/nuSs7v7mqmdvSPKEJP85yTeSvLSqnjtvIVV10sqR5MRVr35k9buqutNh/Xo6zl9j2Hcn+YvMfuc7q+qxU59jqurlSX5tanded393dcfuviXJypi/XlUvr6pjpv7/OMk7p3E/1t2Xzvt7AQAAAAB2u802+TvcTyb585Wbqjo2ydlJPtjdP1tV+zPbTO8lSS6Zc+xD6zz/xGH3D0hy9VYG7O6uqrOTfHTq94mquiHJ3XL7b//j7v6Tdfq/qaoemeSFmQXpF1bVLUlOmJp8KcncYToAAAAAwF4wb8B8cpLVG/89JrOw9i1J0t3frqpLk/zCQqpbgO7+u6p6RJJXJjkryY8l+XaSK5K8qbv//Sb9X1RVH8osNH9EkmOTfDHJO5K8tru/fQTLBwAA7gB1wWJ2oe3zeiHjAADsFvMGzLdkFrCueEKSzmyG8Irrk9xz3kK6e+jv6LbSr7uvT/Lq6Rj5xiWZf0Y2AAAAAMCeNu8azH+b5MxV9/9tkv/U3V9Z9ewfZbbhHwAAAAAAe9i8AfPFSR5WVX9RVf93koclOXhYm0cluWoRxQEAAAAAsLzmXSLjTUkem+S/S1JJ3pvktSsvq+qnkzw4ydsWVSAAAAAAAMtproC5u7+X5HlV9ZLZ7Q9tcPelJI9McvViygMAAAAAYFnNO4M5yf+/ad5az6+N9ZcBAAAAAI4K867BDAAAAAAASTYJmKvqr6rq2SMDV9XJVfXGqnrlWGkAAAAAACyzzWYwfzPJO6vqC1X1yqp6wEaNq+qYqvonVfW2zNZh/qUkVy6mVAAAAAAAlsmGazB39xOr6uwkr0lyYZLfqaqvJbk8yVczC6DvluReSU5L8vAkd0nyvSRvSfIvu/vrR6x6AAAAAAB2zKab/HX325O8vaqemuQFSZ6U5JlrNL01yRVJ3pHkf+vuQ4ssFAAAAACA5bJpwLyiuz+Y5INJUlUPSnL/zGYu35Tk60mu7O7rj0SRAAAAAAAsny0HzKt191VJrlpwLQAAAAAA7CKbbfIHAAAAAABrGprBXFUPT/K8JA9Ocnx3P2V6/mNJfjrJB7v7mwuqEQAAAACAJTR3wFxVv5Xk3Nw++7lXvb5Tkrcl+bUk/2bb1QEAAAAAsLTmWiKjqv5Zkv81s83+HpHkwtXvu/tLSS5P8qxFFQgAAAAAwHKadw3m/znJf07y7O7+bJLvrtHmC0l+YruFAQAAAACw3OYNmB+W5LLuXitYXnFNknuPlwQAAAAAwG4wb8BcSW7bpM29k9w8Vg4AAAAAALvFvAHzf0ryX6/3sqrunOTxSa7cTlEAAAAAACy/eQPmS5I8qqpevs77VyX58SQHt1UVAAAAAABLb9+c7f8wyXOSvK6qnpukk6Sq3pDkCUnOSPLJJBctskgAAAAAAJbPXAFzd99UVU9K8sYk5yS58/TqZZmtzfx/JPmfuvv7C60SAAAAAIClM+8M5nT3dUmeX1UvS/LoJPdKcl2Sv+zuQwuuDwAAAACAJTV3wLyiu/8hyWULrAUAAAAAgF1k3k3+AAAAAAAgycAM5qo6LskLkjwiyf2S3GWNZt3dT95mbQAAAAAALLG5AuaqeniSDyQ5kKQ2aNrbKQoAAAAAgOU37xIZf5hZuHxekh9LcpfuvtMax50XXSgAAAAAR07V9g/g6DPvEhmPTfKO7n7NkSgGAAAAAIDdY94ZzDck+fKRKAQAAAAAgN1l3oD5I0kecyQKAQAAAABgd5k3YD43yYOr6n+psrIOAAAAAMDRbK41mLv7S1X1+CQfT/LCqroiyXVrN+0XLKJAAAAAAACW01wBc1XdL8m7k5w4HQ9Yp2knETADAAAAAOxhcwXMSf4wyX+V5H9PcnGSa5J8f9FFAQAAAACw/OYNmM9Mcll3/8qRKAYAAAAAgN1j3k3+7pTkr49EIQAAAAAA7C7zBsyfTPLQI1EIAAAAAAC7y7wB86uTPLGq/tmRKAYAAAAAgN1j3jWYn57kI0neWlUvSfKpJNet0a67+19ttzgAAAAAAJbXvAHz+auu/5vpWEsnETADAAAAAOxh8wbMTzoiVQAAAAAAsOvMFTB3958fqUIAAAAAANhd5t3kDwAAAAAAkgiYAQAAAAAYtOESGVV1W5Lbkpze3f9xuu8tjNvdPe/6zgAAAAAA7CKbhcAfzSxQvvGwewAAAAAAjnIbBszd/cSN7gEAAAAAOHptugZzVf2PVfXwO6IYAAAAAAB2j61s8veWJP/0CNcBAAAAAMAus5WAGQAAAAAAfoiAGQAAAACAIQJmAAAAAACG7Ntiux+pqvvPM3B3/78D9QAAAAAAsEtsNWB+6XRsVc8xNgAAwMaqtj9G9/bHAADgB2w1BL4+ybeOZCEAAAAAAOwuWw2Y/6C7f+uIVgIAAAAAwK5ikz8AAAAAAIYImAEAAAAAGCJgBgAAAABgiIAZAAAAAIAhm27y191CaAAAAAAAfojwGAAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYMi+nS4AAAAAhh2sna4AAI5qAmYAAACAI6kW8C9Curc/BsARYIkMAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYMhSBcxVdUpVvbGq/qaqbq6qv6+q91bVkwfHu7qqeovHL63Rfyv9zt7+LwcAlk7V9g8AAIA9bt9OF7Ciqh6e5CNJ7jU9uj7JSUmekeTpVXVud//unMMeSnK3Dd4fn+SE6frTG7S7Nsmt67y7ec6aAAAAAAD2hKUImKvq2CTvySxc/kyS/6G7r6yquyf5l0lenuTCqvp0d39gq+N296M3+e67kzwryae7+683aPro7r56q98FAAAAADgaLMsSGS9O8qNJbkjyzO6+Mkm6+/rufkWSd03tLlzUB6vqQJJ/Mt1evKhxAQAAAACOFssSMJ8znQ9291fWeP/66fyoqjptQd98XpK7JPlekoMLGhMAAAAA4Kix4wFzVe1P8lPT7WXrNPtkkuum6zMX9OmVTf3+Q3dfu6AxAQAAAACOGjseMCd5cJKVbdavXKtBd9+W5Krp9vTtfrCqHpbkkdPtVpbHuKSqvllVt1TV31XVO6rq6dutAwAAAABgN1uGgPnUVdfXbNBu5d2pG7TZqudP52uT/IcttH90kjtntpzGfZOcleTSqrqkqu66UceqelFVXV5Vlx86dGgbJQMAAAAALJdlCJiPX3V90wbtbpzOJ2znY1V15/zgms/f26D5xUl+LsmJ3X337j4hsxnX/3Z6/5wkf7TR97r7ou4+o7vPOHDgwHZKBwAAAABYKssQMNfmTRbq55Lce7recHmM7n5+d1/W3d9a9eyL3f3LuX3jwV9Z4MaDAAAAAAC7xjIEzDesuj52g3bHrdF+xMrmfn/d3Z/exjgXZDbjupJYjxkAAAAAOOosQ8C8et3l+2zQbuXdV0c/VFU/kuRZ0+1WNvdbV3d/J8nnptsHbmcsAAAAAIDdaBkC5i8m6en6IWs1qKo7JXnQdPv5bXzrF5Mck+TWJG/dxjgrVpb36A1bAQAA8EOqtn8AADtrxwPm7v52ksun26eu0+wxSe4xXX94G59bWR7j/d39tW2Mk6o6PrcH4ldvZywAAAAAgN1oxwPmycHpfE5VnbrG+1dM509191UjH6iqB2UWVCdbWB6jatN/F/6bma0Z3UneN1ITAAAAAMButiwB85uTfDnJ/iSXVtXpSVJV+6vqdUnOmtqde3jHqurpOH+Tb6zMXv5mkvdsoaZLquq3q+qMqrrrqu89qKr+JMkrp0cXd/d2lu0AAAAAANiV9u10AUnS3TdV1bMzW/7iUUmurKrrk5yQWQjeSc7t7g+MjD+t4fzfT7f/rrtv2UK3A0nOzizUvrWqrsts/ebjV7V5e5KXjNQEAAAAALDbLUXAnCTd/VdV9dAkr0ryjCT3TfKNJH+Z5A+6eztrL5+Z5B9N15sujzH5nSSfTfLYJPdLcs8ktyX52ySfTPKW0cAbAAAAAGAvWJqAOUmmjfdeOh1b7bPpvsHd/aEkc+0vPIXHAmQAAAAAgHUsyxrMAAAAAADsMgJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIft2ugAAYG+oC2rbY/R5vYBKAAAAuKOYwQwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBEwAwAAAAAwBABMwAAAAAAQwTMAAAAAAAMETADAAAAADBk304XAADADjtYO10BAACwS5nBDAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMGTfThcAsCccrJ2uAAAAAOAOJ2AGjpxaQOjavf0xAAAAADgiLJEBAAAAAMAQATMAAAAAAEMEzAAAAAAADBEwAwAAAAAwRMAMAAAAAMAQATMAAAAAAEMEzAAAAAAADBEwAwAAAAAwRMAMAAAAAMAQATMAAAAAAEMEzAAAAAAADBEwAwAAAAAwRMAMAAAAAMAQATMAAAAAAEMEzAAAAAAADBEwAwAAAAAwRMAMAAAAAMAQATMAAAAAAEMEzAAAAAAADBEwAwAAAAAwZN9OFwAAAACwtA7WTlcAsNTMYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIUsVMFfVKVX1xqr6m6q6uar+vqreW1VPHhzviVXVWzhO2mSc51TVR6rqG1V1Y1V9oapeU1X7x34pAAAAAMDut2+nC1hRVQ9P8pEk95oeXZ/kpCTPSPL0qjq3u393cPjbkhza5P16dV2U5IXT7feT3JzktCSvTvKLVfWE7r5msC4AAAAAgF1rKWYwV9WxSd6TWbj8mSQP7e57JDkxye8lqSQXVtXTBj/xX7r7lA2Of1inrl/NLFy+LclvJDmhu/cneVySLyd5YJJLBmsCAAAAANjVliJgTvLiJD+a5IYkz+zuK5Oku6/v7lckedfU7sI7qqCqOibJ+dPtG7v7Dd19y1TXx5P8QpJO8riqeuYdVRcAAAAAwLJYloD5nOl8sLu/ssb710/nR1XVaXdQTU9JcnJmIfLvHf6yuz+T5EPT7TmHvwcAAAAA2Ot2PGCeNsr7qen2snWafTLJddP1mUe8qJknTefPrRN6J7fXe0fVBAAAAACwNHY8YE7y4MzWWE6SK9dq0N23Jblquj194BsHqurTVfWd6fiPVXVRVT1sgz4r31mzpsnnV41/0kBdAAAAAAC71jIEzKeuur5mg3Yr707doM16jkvyyCS3JNmX5Ccy27zvM1X1ik3q2kpNo3UBAAAAR0jV9g8ANrYMAfPxq65v2qDdjdP5hDnG/lZm6zefkeTY7r5nZmHzzyT5eJI7J3l9VT1vg7q2UtO6dVXVi6rq8qq6/NChQ3OUDgCwOf/gDAAA7KRlCJiP2D/WdPcV3f0vuvtT3X3z9OzW7v5oZmssf2xq+tqqOvzPYqWu3mYNF3X3Gd19xoEDB7YzFAAAAADAUlmGgPmGVdfHbtDuuDXaD+vu7yb5zen2fpktobFWXcdlfavfLaQuAAAAAIDdYt9OF5AfXMf4Prl9M7/D3Wc6f3WB3/6LVdcPTPKpw+p6xKrvblTTousCAACAo1ZdsKj/2Hlb/1EyAFuwDAHzFzP7K34leUjWCJin5SseNN1+/gjVcfj/63w+yc9PNa3n9Ol8qLuvPSJVAQAAR5eDFkcHAHaPHV8io7u/neTy6fap6zR7TJJ7TNcfXuDnH7Pq+urD3v3ZdH5IVa03i/lpR6AmAAAAAIBdYccD5snB6XxOVZ26xvtXTOdPdfd6S2j8kKr190Wvqrsk+a3p9qtJPn1Ykw8n+Xpmf0YvW6P/TyZ5ynT71q3WBAAAAACwVyxLwPzmJF9Osj/JpVV1epJU1f6qel2Ss6Z25x7esap6Os5fY9zPVdU/r6qfWAmbq+rOVfX4zALkx0/tXtXdt63u2N23JFkZ89er6uVVdcw0xj9O8s7M/vw+1t2Xjv5wAAAAAIDdahnWYE5331RVz84s9H1Ukiur6vokJ2QW4naSc7v7A3MOfXqSfz1d31JV305y9yR3nZ7dmuTV3X3xOnW9qaoemeSFSd6Q5MKqumWqK0m+lOS5c9YEAAAAALAnLEXAnCTd/VdV9dAkr0ryjCT3TfKNJH+Z5A+6e2Sd4xcneVySn0pycpITk9yU2UaCf57kTd294aaB3f2iqvpQkpckeUSSYzPbmPAdSV47rSENAABLpS7Y/kZxfd7h+2ADAMAPWpqAOUm6+2tJXjodW+2z7t85d/dFSS5aQF2XJLlku+MAAAAAAOwly7IGMwAAAAAAu4yAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYMi+nS4AWEIHa6crAAAAAGAXMIMZAAAAAIAhAmYAAAAAAIYImAEAAAAAGGINZgA4GtSC1lbvXsw4AAAA7AlmMAMAAAAAMMQMZtiqRcz+M/MPAAAAgD3EDGYAAAAAAIYImIGjXtX2DwAAAICjkYAZAAAAAIAhAmYAAAAAAIbY5A+WSF2w/bUW+jwbCQIAAABwxzCDGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIhN/gCAvefg9jdNBQAAYHNmMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABD9u10AQAAwB52sHa6AgAAjiAzmAEAAAAAGCJgBgAAWJCq7R8AALuJgBkAAAAAgCECZgAAAAAAhgiYAQAAAAAYImAGAAAAAGDIvp0uAO4QB+2WAgAAAACLZgYzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAzZt9MFAACbOFg7XQEAAACsyQxmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAlkbVYg5gMfzvEQCAzQiYAQAAAAAYImAGAAAAAGCIgBkAAAAAgCECZgAAAAAAhgiYAQAAAAAYImAGAAAAAGCIgBkAAAAAgCECZgAAAAAAhgiYAQAAAAAYImAGAAAAAGCIgBkAAAAAgCH7droAAFhWdUFte4w+rxdQCQAAACwnM5gBAAAAABiyVAFzVZ1SVW+sqr+pqpur6u+r6r1V9eTB8Q5U1Yur6t+vGvM7VfWFqvqjqvrxTfr3Fo6zx34tAAAAAMDutjRLZFTVw5N8JMm9pkfXJzkpyTOSPL2qzu3u351z2Gvyg7/xhiR3TXLadLygqn65u9+2yTjXJrl1nXc3z1kTHFG1/f+iP/3W7Y8BAAAAwN63FDOYq+rYJO/JLFz+TJKHdvc9kpyY5PeSVJILq+ppcw69L8lHk/xSklO7e3+S45I8PskVSe6W5E+ncHsjj+7uU9Y5Lp2zJgAAAACAPWEpAuYkL07yo5nNMH5md1+ZJN19fXe/Ism7pnYXzjnuz3T3z3T3n3b316Yxb+3ujyV5WpKvZxZC//oifgQAAAAAwNFkWQLmc6bzwe7+yhrvXz+dH1VVp2110O7+6AbvDiV533T7U1sdEwAAAACAmR0PmKtqf24PeC9bp9knk1w3XZ+5wM9/YzrfeYFjAgAAAAAcFXY8YE7y4MzWWE6SK9dq0N23Jblquj19gd/+men8uU3aXVJV36yqW6rq76rqHVX19AXWAQAAAACw6yxDwHzqqutrNmi38u7UDdpsWVU9O8kZ0+2/3aT5ozOb5fy9JPdNclaSS6vqkqq66yLqAQAAAADYbZYhYD5+1fVNG7S7cTqfsN0PVtV9k1w03b6nu9+/TtOLk/xckhO7++7dfUJmM65XAunnJPmjTb71oqq6vKouP3To0HZLBwAAAABYGssQMNfmTRb4saoTkrwryclJvpzkBeu17e7nd/dl3f2tVc++2N2/nNs3HvyVjTYe7O6LuvuM7j7jwIEDi/kRAAAAAABLYBkC5htWXR+7Qbvj1mg/l6q6W5J3Z7Y0xqEkP9vd1w4Od0FmM64rifWYAQAAAICjzjIEzKvXXb7PBu1W3n115CPTWslvT3Jmkm8leVp3X7Vxr/V193dy++aADxwdB4C9rWr7BwAAACyrZQiYv5ikp+uHrNWgqu6U5EHT7efn/UBV7UvytsxmGt+Q5Oe7+4r5S/3hoadzb9gKAAAAAGAP2vGAubu/neTy6fap6zR7TJJ7TNcfnmf8KZy+OMlZmS1p8azu/sRAqYePe3xuD8Sv3u54AAAAAAC7zY4HzJOD0/mcqjp1jfevmM6fmmdZi6qqJBcleV6S7yY5q7v/bI6+G/nNzNaM7iTv22pNAAAAAAB7xbIEzG9O8uUk+5NcWlWnJ0lV7a+q12U2+zhJzj28Y1X1dJy/xri/n+QFSb6f5Lnd/f45arqkqn67qs6Y1m9e+d6DqupPkrxyenRxd8+9bAcAAAAAwG63b6cLSJLuvqmqnp3Z8hePSnJlVV2f5ITMQvBOcm53f2CrY1bV/ZP82sonkry5qt68QQ2nHPboQJKzMwu1b62q65Ick+T4VW3enuQlW60JAAAAAGAvWYqAOUm6+6+q6qFJXpXkGUnum+QbSf4yyR9091xrL+cHZ2ffJcm95+z/O0k+m+SxSe6X5J5Jbkvyt0k+meQt8wTeAAAAAAB7zdIEzEnS3V9L8tLp2GqfNddK7u6rk2y2jvJG434giQAZAAAAAGAdy7IGMwAAAAAAu4yAGQAAAACAIQJmAAAAAACGCJgBAAAAABgiYAYAAAAAYIiAmaVXtf0DAAAAAFi8fTtdAHtbXbCIdLcXMAYAAAAAsGhmMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAAMETADAAAAADAEAEzAAAAAABDBMwAAAAAAAwRMAMAAAAA8P+1d+dhkpXV4ce/Z2YAgQHZZVEUjSK4oCAuIAqKRBHEJbghRqOgGIJIUFyiqCAoiAaUCIhEjLggbiSauIAbiCKIC4IYRDQ/QNmXYR1mzu+P97ZTNL3UW93VVdX3+3meenq67n3vnDrPrdO3Tt373p7YYJYkSZIkSZIk9cQGsyRJkiRJkiSpJzaYJUmSJEmSJEk9scEsSZIkSZIkSeqJDWZJkiRJkiRJUk9sMEuSJEmSJEmSemKDWZIkSZIkSZLUExvMkiRJkiRJkqSe2GCWJElQMtEfAAAgAElEQVSSJEmSJPXEBrMkSZIkSZIkqSc2mCVJkiRJkiRJPbHBLEmSJEmSJEnqiQ1mSZIkSZIkSVJPbDBLkiRJkiRJknpig1mSJEmSJEmS1BMbzJIkSZIkSZKknthgliRJkiRJkiT1xAazJEmSJEmSJKknNpglSZIkSZIkST2xwSxJkiRJkiRJ6slQNZgjYsOIODYifh8Rd0XEXyLiPyPi2TPc7poRcXhEXBoRd0TEDRFxVkT8XZfj94yIs5txdzTbOTwi1phJXJIkSZIkSZI0yoamwRwRjwcuBg4AHg7cDawH7AZ8JyLe3uN2Hwz8AngX8GhgGbAm8CzgSxHxiWnGnwScDuzUjFvWbOddwC8iYuNe4pIkSZIkSZKkUTcUDeaIWBU4E1gXuAh4bGY+EFgbOAYI4MiI2KVyuwGcAWwGXAlsn5lrAGsAbwOWA2+MiH0mGb8fsE+z3luBxc347YE/Uhrhp1e9WEmSJEmSJEmaJ4aiwQy8AXgosATYPTN/A5CZt2bmwcDXmvWOrNzuHsBTKA3iF2Xmj5vt3pWZRwPHNeu9PyJW7hwYEasA721+PTYzP5yZdzfjfwy8CEhg+4jYvTIuSZIkSZIkSRp5w9Jg3qv5+bnMvGqC5Uc3P7eOiEf3sN3vZuYvJlj+YUqTeEPKlBmddgY2aJYfM35gZl4EfHfc/yNJkiRJkiRJrTHwBnNzo7xtml+/NclqPwFuaf49vhE8lR2n2m7TzP7NJNvdqfl58SRN787t1sQkSZIkSZIkSfPCwBvMwBaUOZZhRbP3PjJzOXBZ8+uW3Ww0Ijag3CRw0u02Lplku2O/dzN2/YhYb4r1JEmSJEmSJGneGYYG80Yd/756ivXGlm00xTqzud2Nxi2famxNXJIkSZIkSZI0L0RmDjaAiFcCpzW/rpSZ906y3mnAK4FvZ+bfdrHd7YBzm18fmZmXT7LeB4B3Ar/LzM07nv8d8EjgA5n5L5OMfSTwu+bX7TLzvAnW2RfYt/l1c1acia25tR5w/aCDGCHmq545q2O+6piveuasjvmqY77qmK965qyO+apjvuqZszrmq475qmfOBuehmbn++CcXDSKScWL6VWa83V666GPjZ9SBz8yTgJNmsg3NXERckJlPGnQco8J81TNndcxXHfNVz5zVMV91zFcd81XPnNUxX3XMVz1zVsd81TFf9czZ8BmGKTKWdPx71SnWW22C9bvd7mqTrjX5dpeMWz7V2Jq4JEmSJEmSJGleGIYGc+c8xhtPsd7YsmvmaLtXj1s+1diauCRJkiRJkiRpXhiGBvNvWTENxWMmWiEiFlDmLwa4pJuNZuZ1rJiPZcLtNracZLtjv3cz9rrMdO6X4eY0JXXMVz1zVsd81TFf9cxZHfNVx3zVMV/1zFkd81XHfNUzZ3XMVx3zVc+cDZmB3+QPICLOB7YFTsjM/SZY/jTgx82vj87Mrm6UFxGnA3sC/5OZz5tg+SbA/1HmW35uZn6rY9muwDeA5cBDMvPqCcZ/C9gF+EJmvqKbmCRJkiRJkiRpvhiGM5gBPtf83CsiNppg+cHNzwu7bS6P2+4uEbHVBMsPojSXrwG+N27ZWcC1lBwdNH5gs72dm19Pq4hJkiRJkiRJkuaFYWkwnwj8EVgD+K+I2BIgItaIiKOAFzfrvXP8wIjI5vHeCbb7deCnlNf51Yh4ajNmlYj4Z+DAZr1DM/OezoGZeTcwts23RMQ/R8QqzfinAV9ttntuZv5Xby9bkiRJkiRJkkbXUEyRAX89I/gsYN3mqVuBxZQmbgLvzMwPTjBu7AW8LzPfO8HyBwM/BDZrnloCPABY1Pw+4bQcHeNPAvZpfl0K3N3EBXAFsMNE02dIkiRJkiRJ0nw3LGcwk5m/BB4LHEdp3K4C3ECZB/k5EzWXu9zu/wOeABxBuaHgIuA2ypQYL52qudyM3xd4WbP+kmb8b4EPAE+wuSxJkiRJkiSprYbmDGZJkiRJkiRJ0mgZmjOYJUmSJEmSJEmjZdH0q0hqk4hYSJkLfVXghsxcMuCQpFbzPal+iIh1gScDGwHr0exfwPWUqcB+mV7mdh/mTP3k/tWbiNiCiXN2WWbeNMjYhpH5Ur9Yw3rje7KO+RpuTpGheSkiFlDm3n4Kk/+ROyczrx9YkEOiucHmLsAOlHytN26Ve4DLgB81j29k5u1zGuQQiYj1gJ1Yka+x/Wtl4GZW7F8/An6UmecPKNShYL7q+Z6sZ83vTkQ8HngtZf969DSr3wb8GPgC8KXMvLPP4Q0lc1bPut899696zf61FyVn2wFrTrJqApdS9rMvZuYP5ibC4WK+6lnDumcNq+d7so75Gi02mDVvNA2G3Sh/5J4FLB6/CqXwdLqM8kfu05n5p74HOSQiYjHw98DrgK3Gnp5m2Fjubge+CJycmT/tT4TDJSIC2JWSr+ez4uqPqXI2lq8/Ap+i7GNX9S3IIWK+6vmerGfN715EvAI4CNh67Knm562UJvxNwF3A2s1jfWBhs05SbnJ8GnBkZv7fHIU9UOasjnW/jvtXvYjYDngLsDuwEvfdt5YBt7AiZ6uOG56Um8h/Eji+DV/Kmq861rA61rB6vifrmK/RZINZIy8iVgUOBP4JeBAris/YWX7Xc/8/cg8DNmzWy+bxbeDQzPzZXMU+1yJiZWB/4B3AOpRc3QD8FLgA+CWT52vb5vE4VjRu/gd4R2b+ai5fx1yKiBcDhwObU173cspZC93kaxtgLUqu7gVOBg7LzD/P6YuYQ+arju/Jetb87kXE84AjgMdT8nQVcAZwLnBBZl45ybhVKR8at6U08Z9J+WB4F/AJ4IjMvKHf8Q+COatn3e+e+1e9iHgMcCSl6ReU1/wtmpxRLru/adyYlbnv38ldgb+h7GfXAR8ATsjMpXPzKuaO+apnDeueNaye78k65mvEZaYPHyP7APYDrqYcCCynFJ43A08CVppm7CbACykHAtc345cBXwYeMejX1qd8/ZEV3/idAjwHWFC5jU2Ag4ELm5zdC7xu0K+tT/n6SZOve4GzgX2AdSq3sT3wceCaJl9LgJcM+rWZr+F4+J6szpc1vy5fy4GlwGeAZ9CcWNDDdtanNPQvb3L2nkG/NnM2HA/rvvvXHOTs3iZvPwBeDazR43a2AY6hNAqXAe8a9GszX4N/WMOq82UNq3+tvifNV2sensGskRYRyylzYX2ccnl4T5c8R8Qi4HmUJs0OwHsz8/2zFuiQiIgbgGOBYzPzllnY3k7Au4AfZOZhM93esImIuymXbx2RmZfPcFsLKX8kDwE+N0/3L/NVyfdkHWt+nYg4iXL56R9maXsLgFcCmZmnzcY2h405q2Pdr+P+VS8ivg0cnpk/nKXtrQkcANyUmcfPxjaHifmqYw2rYw2r53uyjvkabTaYNdIi4p3AxzLztlnc5vbAWpn5jdna5rCIiNWzD3MQ9Wu7gxYRm/bawJpimwFsnPNwjjbzVc/3ZB1rvjRcrPuSRpk1TJJmjw1mSZIkSZIkSVJPFgw6AEmSJEmSJEnSaFo06AAkDYeIWAfYCFjcPLUEuCYzbxxcVMMtIlanI1/zcUqC2WS+pMGLiMXA04EtmaDmA5cA52TmksFEOHzMmfrJ/as3EfFopshZZv52ULENI/OlfrGG9cb3ZB3zNRqcIkNqsYh4PrAX8CzK3Xwnch1wFnBaZn5zrmIbRhHxWMqNKJ4FbMGKP25jlgCXUvL1+cy8eG4jHC7mq15EPIRy1+OFwMWZeVkXYw4CFs/Hm8l0IyIeRdnHpvpgc1Zm/u9gIhwOEbEF8H5gN2DlaVa/BziTcvPDS/sd27AyZ/UiYhVKvV8I/K6b+dIjYk9g1cz8TL/jGybuX/UiYl3g7cArKPV+KtdQbt52VGbe0O/YhpH5qmcN6541rJ7vyTrma/TYYNa80TSz3kJHcwY4JTO/M824a4D1M7M1Z/RHxIbA6cD2Y09NM2SsUJwDvCwz/9yv2IZRRKwGnEj54xZ0l68EPgfs17Yzdc1Xvebsj5OBPcctOg94c2ZeOMXYa4ANMnNhH0McOhGxM3AksHXn0+NW6zzIuQB4Z2ae1e/Yhk1E7EXZv1ZmRY6uBa4G7mh+Xw3YGNigY+jdwD9k5ufnKNShYc7qRMRC4APAP1LyArAU+DLwjqluotXS4zD3r0oRsSNlf1qL+9b6m7lvztbqWJbATcBLMvMHcxDm0DBfdaxhdaxh9XxP1jFfo8kGs+aFiHg5cCpl2pexAjS2c38deP1kUz20rTnTNLJ+AWzWPPVd4FuUs/wmOijYEtgF2Jkyb/vlwBPb0gSMiJWAHwHbUvat3wHfZup8PQfYnLIPng/skJn3zm3kg2G+6jV3G/8+5fLCiZrxS4G3Z+ZHJxnfqhoGEBGHAEewIl+3AJcx8T62OfDA5rmk5PLouYt2sCJia+AnlL+PFwIfAb492dkdzdkiuwAHUt7HS4GnZuZFcxPx4JmzehFxOvASJv6S51bKcdiXJxnbqhrm/lUvIh5BOXZdHfg/4ASaY9fMvGvcug9gxbHrG4FNKVe0PCEzr5jLuAfFfNWzhnXPGlbP92Qd8zXCMtOHj5F+AI+gNBSWA38GTgGOpjSqlgPLgN8DfzPJ+GuAZYN+HXOYr8OavFxBKbzdjtuqGbMMeN+gX8cc5uvgJl/XAS+oGLd7M2YZ8M+Dfh3ma3gfwN83ObsHeA/lwGh14PmUs27H6thHJhnfthr2rCYfy4GvAE+j+cJ8kvUDeCrlLIjlwL3AjoN+HXOYr883r/szwIKKcdGMWQ58btCvw5wN7wPYo6NOnQI8A3gM5UzAP3W87w6YZHzbapj7V33OPtm87v8BVq8YtxqlKbEcOGnQr8N8DefDGladL2tYfc58T5qvVjw8g1kjLyI+RjkAuAj428y8vmPZHpRL9TcA/gLskpm/Hje+bd86X0I5o2/7zPxJ5dinAecCl2bmY/oR37CJiJ9Tmuu7Z+Uc1BGxK/BfwEWZuU0/4hs25qteRHwbeDbli5v3j1sWwOGU+cegfPDZNzv+eLewhp1Jab4fk5lvqxx7FOVLkP/MzD36Ed+wiYirgAcBG3b+fexy7HqUv51/zsxN+hHfMDJndSLi65Q5OI/PzAPGLVudchz2SsqZgIdm5uHj1mlbDXP/qhQRfwQeDDwsM/+vcuymwJXAnzLzYbMf3fAxX3WsYXWsYfV8T9YxX6PLBrNGXkfD9OmZed4EyzcBvkaZm/lGYNfMPL9jedsOCm4H7s3MB0678sTjbwUWZubqsxvZcGpe74LMHH+Dum7HLwGWZ+aasxvZcDJf9SLiL8B6wLqZefMk67wC+DTlcsQvAntn5rJmWdtq2J+BdYG1s/KO5M0UQTcD12fmhv2Ib9hExF3AHZm5To/jbwIekJmrzm5kw8uc1YmIqynNhk1ykns0RMTbKdPaJPDhzDykY1nbapj7V6WIuBO405x1x3zVsYbVsYbV8z1Zx3yNrgWDDkCaBZtSLi2f8GzczLwK2BH4HrAO8J2IeMacRTd87gQe0MyVWyUiVgZWabbRFsuAhc2ZpFUiYgHlhpPLZj2q4WW+6q0N3DJZcxkgy81QXky5OcrLgDN6eQ/PEw8EltQ2lwGaMUuA1nyBQTlT6IER8eDagRHxEEq+r531qIabOauzLnDbZI0ZgMz8ILAfpTlzcER8fK6CG0LuX/VuBNZs5nKt0oxZs9lGW5ivOtawOtawer4n65ivEWWDWfPBQuCezkvGx8tyQ7pdgW8CawDfjIjnzFF8w+ZXlLMg9+9h7P7ASpRJ99viUsodkvfqYewrKQ35S2Y1ouFmvurdBixuGuyTysxvUOaqvhN4AXBmc2OLtrmKctD56NqBEbEF5aDzqlmPanh9lzLv4ScjYrVuB0XEqpQ58BL4Tp9iG1bmrM6dwLS1KDNPBF5LmRtxv4j4VL8DG1LuX/V+RMnZR3r4Avsjzc8fzm5IQ8181bGG1bGG1fM9Wcd8japBTwLtw8dMH8DllDMeN+hi3ZVYcaOnOyjNmrbdmGHP5vUvpdwMcaMuxmwIHNWMWQb83aBfxxzma98mX7dT5vpeuYsxKwNvopwpuQzYZ9Cvw3wN7wP4cfO6n9zl+k+nTPOwjHJlxg0tq2HHNvvYzyjz/3U77kGUmyYuAz466Ncxh/n6m4731pXA24AnTPTebN6LTwDeCvyhGXMb8IhBvw5zNryP5r24DHh8l+vvSbkaYxlwGuVMtjbVMPev+pxtQ7lacRnlisWXUqZJmmz9tZv97LxmzN3A1oN+HeZrOB/WsOp8WcPqc+Z70ny14uEczBp5EXE68BLg1Zl5WhfrL6TcwfYVlIYpwKJsybxZABFxEvB6yjfICVwM/Aa4mvItflLuwrox5S7Kj6Fc8RCUO7K+cQBhD0xEfAN4HiUvt1C+EZ0qXzsAa1Hy9Y3M3H0AYQ+M+arT3Hjun4GPZOZbuxzzZOC/WZG3bEsNi4gHUfantSkfcE4Dvs199zGAVVmxjz2Hclb9msD1wGMzszWXZzZX7HyJ8vo7D/xu5L7vyc657gK4lfKF4nfnKNShYc66FxHHUb5QfH9mvq/LMbsDp1OaD62qYeD+1YuI+HvgJMrJImM5+zMTH1uMzbEflGP9fTLzM3Ma8ICZr+5Zw+pZw+r5nqxjvkaTDWaNvIjYFzgB+H5mPqvLMUG5ROcfmqdadVAAEBH7AYcCGzRPTVYMxi5LuRZ4b2ae0O/Yhk1ELAI+ABxAmcIBps/X3ZQzLf8lM+/tb4TDxXzViYgdgB9QzkTeLLucWzgitgK+RXkPt6qGRcTjgTMpc/B3eyATwB+BPTLzV/2KbVg1jfl3UObwftA0q/8F+ALwwcz8S79jG1bmrDsR8VzKFGRXAQ/PzKXTDBkbtzPwVWB1WlbDwP2rFxHxOOAwypfY092HYCllvzy0jTUfzFe3rGG9sYbV8z1Zx3yNHhvMGnkRsR5lmosFwDMz85yKsR8F3kwLDwrgrzft2xnYCdgS2IhykBSUMwOvpsyH+z3grMy8Z0ChDoXmQOol3D9fUKaE6MzXV9p8AAXmq0ZEvJNy4PSVzPx1xbhHAYcACzLztf2Kbxg180+/kTJ39zas+LJivKRMjfE54MTMvGtuIhxOzResWzJ1zb80PUD8K3M2tebKsBMo93c4PjMvqBi7PeXDY2TmTn0Kcai5f9WLiAdSpouaKmfnZOatAwtyiJivqVnDZsYaVs/3ZB3zNTpsMKv1mrvZLsjMPw46FklSvYhYDGzBxAedv+32rHBJkiRJUj0bzJIkSZIkSZKkniwadACSJEnSKIqIlShzUGZmHjboeCSpRjM9xF4A3hRL0qixhg0Xz2CW1LXm5m3bAWTmDwccjkZcRKwO3AYsz0y/8OxBRDyj+eclmXn9QIPR0GumhNoGWAhcnJmXdTHmIGBxZr6/3/GNoo461sp7OcxURGza/POabm+s1WbN/vYxyv72ukHHMwgRsQplSqSFwO8y87YuxuwJrGrz4f48FpsZa1gda5g1bLZZw4aLDWa1WkSc3fzzfOCYzLxukPEMu4hYF7iOFhbwiHgs8BY6mjPAKZn5nWnGXQOs37Z8dcPGzMxFxHLKTezuAP4N+LB1bHIRsSbwNco+9+xBxzNXmjmqTwb2HLfoPODNmXnhFGOvATbwPTox69jMRMSy5p9XAUcCn2r7DYWn0nEc1rr9rTlL7QPAPwKrNU8vBb4MvCMz/zTFWI/FJmENmxlrWB1rmDVstlnDhosNZrVaR3MG4E5WNGiuHVxUw6utBwUR8XLgVMq0QtE8PbbffB14fWbeOMnYVjVnIuI9FauvDLyTksv3dS7wbMnuNDWs0x3ACZl58CDiGXZtrGHN3d2/T7n7dkywylLg7Zn50UnGt6qGwX0aBjORfhCc3rgalpQbc34oMz8+oJCGWhtr2JiIOB14CfevYwncSjkW+/IkY1tVxyLiiprVgYdS8th5w/PMzEfMamDzkDWsjjXMGtYNa9jossGsVouI71OK0UbAo5qn78jMxQMLaoi18aAgIh4B/Bp4AHAt8E3gBuCZwJMo+8+VwN9m5uUTjG/bAUHnlzZdDWl+3mdMW/I1UxHxzOafG1H2yR2BR5m/ibW0hv098O/AvcDhwKcpNWxHyhc7W1Pef8dm5kETjG9VDYMJv7jpRWv2sZlo9k9YUcO2o0zJYu4m0MYaBhARewBfpdSqU7lvHTsEeDCwHDgoM4+bYHyr6ljHsdhEXyp2q1X7WK+sYXWsYdawbljDRpdnVqjVMnPHsX9HxIMoRX6HQcUzFyJilxkMX3PWAhkdB1KayxdRmsh/nee2OVg4EdgM+FFE7JKZvx5MmEPnWuCuadYJYFPKAcSkl4Vpcpn5g45fvwAQEesNKBwNp70o77HDx10Z8I2I+Cal6fx24M0RsQawb3r2AZSc/RQ4iam/NFsFOKFZ5x/mIK55JTNP7fj1gxGxgPKlx7wVEZ+bwfCVZy2Q0fIPlPfY8Zl5QMfzv4mIT1OOxV4JfDQi1szMwwcQ4zD6CfCtadZZmeZGpYBXj1WyhlWzhlnDaljDRoxnMEst08MZpvfbBC36RjAiLgE2B56emedNsHwTypyu2wA3Artm5vkdy9v2jfMFlAPrK4EDMvO/plh3MeWSsNbsT5q5iPjxDIYvornyoC37XET8BVgPWDczb55knVdQzqZZBHwR2DszlzXLWlXD4K9fHh5HOaPoAuBNk81T7dx/quWZWfUi4mrgQcAmmfnnSdZ5O3AEJbcfzsxDOpa1qo5FxIGURsvqwFeAt2Tm/5tkXWuYqljD6lnD6ljDRpcNZqllPCioExFLKDf1W22ys/qaP2xnAjtR/sDtnpk/bJa17YBgAXAAKw4K/pPSaL7fGcoeEKgX1rA6EXEPsCQz15lmvecDX6KckXsm8NLMXNq2GjamqU+HA/s3T50IvCszb5lgPeuYuhYRS4EFwLeBCRsNU1gFeDkt298i4m7gzsxca5r13gAcT/n78InM3L95vnV1rDkB4uPAHsASynHZR8e+POxYzxqmKtawetawetaw0WSDWWqZiLgK2BD4u8z8auXY9ShTH7SmgEfEncA9mfnAadZbhXIX4F0pN1p7UWZ+p40HBHC/g4I7KHdNPjoz7+1YxwOCSUTEasCjKXP6jc0JvwS4Brg0M+8cVGyDFhF3ASsBnwVqbgIC5a7db6VF+1xE3ACsATwgM6ecWzgink25cemqlA+OLwL+QAtr2JiI2JrSXN4G+Avwtsz8j47l1rFJRMQ6TFDDcpKb4rZFRPwSeCzwhsw8uXJsW+cvvZlSwx7Qxbp7A6dQGmCfzszXtfVYDCAiXkA5HtsEuAT4x7GTIJrl1rBJWMMmZg2rZw3rnTVstNhgVus1jbCFE51hOR9FxNeA3YEjM/NfKse27qAgIi6nzLG8UWZeO826K1HmwX0RZf7hl1Hm72zlAQHc76DgMmD/zDy7WeYBQYdm/9mXMmfuk5n8DN2xuWE/C5ycmUvnJsLhEBHnU5p9B2Tm8ZVj21jDfgw8BXha5/Q9U6z/dOC/KE3pHwKPB9ZqS74mEhEB/BNwGKXRcA6wX2ZeYh27r+ZM+L2AZwHrT7LadcBZwGmZ+c25im1YRMTJwGsp9fsNlWNbV8MAIuJnlOm3npiZv+pi/T0pfyMXUY7LnkOZJqg1OevU1KnDKHVsASU3b83Ma61h92UNm541rJ41bGasYaNjwaADkGZLRDw3Is6KiJsj4raIOC8i9mku2Z/KBdSfBTfKfkZpXD150IGMiJ83P58z3YpNo++lwOcpNwY8A5jysvT5LjPPpJyJeyzwSOA7EfG5iNhwsJENl4jYkvKt/HHAUyl/n2OSxwLgaZTG/W8iYotBxDxAYzXsSYMOZESc0/zcs5uVM/McYBfgZuAZwNp9imtkZHEcsAXlDO8dgIsi4ihWnNnWahGxYUT8kDK9ysuADZi8hm1AuUT6PyPiBy38e2ANqzd2D4wXdbNyZn4J+DvgHsq+1uqb32bm7Zl5EOXY/0Jgb+C3EfEm7AcA1rBK1rB61rAZsIaNDs9g1rwQEQcBR4/92rEoKUXo5Zk5YRO5bZecRMQuwP8AN2XmupVj1wJ+ASzPzIf3I75hExH7AicA38/MZ3U5JoBPUu4YDH6jCkBEPJFyqfmTKDf3O4oyx2mr8xMRGwC/ppwps4TyBcW3KA3nqylTjCRlTuuNgS0pDcBXUM4yvRZ4XGZeN+fBD0BEvBb4FHBJZj62cmzrzpyJiB2AHwA3AJtl5pIux21F2Q83oEX56kZE7EaZI/HBlPffg2hxjqLcsPUXlKt9AL7L/WsYlClqOmvYzpQPhpdTzuq6fQ7DHpiIeBzwH8DdwFOz4sNYRKwKvA0gM9/XnwiHT0Q8F/gmcBXw8G6v3ImInYGvUv5+tvY92qk5Rt2fcjbgGsBvKNMdtDY/1rA61rB61rDZYw0bbjaYNfIi4gmUb1IXApcCX6R8kH4m5VvChcCNlBuvnTfB+LY1mFemnIVFZv5ywOEMvWbe6WsoB5DPbM7u63bsR4E34x+8v5rgoCBoeX4i4l8pN0a8CHhBZl7V5biNKTdRfAJwbPPN/rwXEX8DfBi4F9iz8oPNypTGPJl5an8iHD4R8U7KvNVfycxfV4x7FHAIsCAzX9uv+EZRM0/6YZT37kJaXMci4jDgXcCVwIsz8xddjtuK8sH5ocDhmXlo34LUSIuIhZQv+xcBx2fmBRVjt6e8VyMzd+pTiCMnIjaiXAk1dkalNcwapj6xhs0+a9hwssGskRcRpwCvAc4GdsvMuzqWbQ2cBmwO3A68MDPPGje+VQ1mza2IeAilOfPHQccyTJqDgg8CmwK0+YCpY57vx2bmpZVjtwQuBn6fmY/sR3ySJhcRj6CcyUxm/mDA4QxERFxCOc7aPjN/Ujn2acC5lJuXPqYf8UmaXEQ8E3gYtOuL107WMGl0WSl1LH0AABa/SURBVMOGiw1mjbyO5sxWmXnxBMsXUy45fz7lUp6XNfPCji23wSxpYCLiTuCuzOxprtvmztQrZ+ZqsxuZJE0vIm4H7s3MB/Y4/lbKzZZXn93IJGl61jBJmh1OiK35YGPgzomaywDNfJMvBD4HrAJ8KSJeNofxSdJUbgUWN3dBrtJ8gTZ292RJGoQ7gQdExEq1A5tpa1ZptiFJg2ANk6RZYINZ80E2j8lXyFxGudvoJynzUH42Il7T/9AkaVrnU/4ev6eHse+mzP/601mNSAIi4hnNo9V3L6/R0pz9ijKv5P49jN2fclzW1ZynbRcRmzaP6kZYW5mzOi3NlzVsjrR0/5oRc1bHfA2WDWbNB38CVmvmup1UFm8APkZpyJwcEfvNRYDzRUSc3Tw+GBHrDzqeYWe+6rQ4X8dSbnZ4cEScERFPmW5ARGwbEacDB1O+YPvXPsc4L7S0+TcT3we+B/whIj7Usvdlr75P+3L2CUoNOyoijm7m2J9SRGwYEUcBH6LUsBP6HON88Yfm8fuI2K85e1JTM2d12pgva9jcaeP+NVPmrI75GiDnYNbIi4jPAHsBb8rME7sccyRwCCvOfg7nYJ5eRCxnxdnidwL/Bnw4M68dXFTDy3zVaXO+IuJdlDtEj73+W4BLgaspuUhgNcqUQFsAY/MEBvDuzPzAnAY8ojr2sTtYsX9dN9iohleTr053ACdk5sGDiGcUtDVnEXES8HpWHFddDPyGiWvYY5rHAkoNOykz3ziAsEfOuP0rKfn9UGZ+fEAhDT1zVqet+bKGzY227l8zYc7qmK/BssGskRcRrwI+A1yYmdtWjHs38D6aho4N5ulFxPcp+doIeFTz9B2ZuXhgQQ0x81Wn7fmKiF2BI4DHj1s09oc6xj3/S+Cdmfnf/Y5tvmhr869XzZ25obwnnwnsCDzKv5eTa3POmqvCDgU2aJ6a7EPGWC27FnhvZnrmX5ci4u+bf47tX9sBi9uwf/XKnNVpc76sYf3X5v2rV+asjvkaLBvMGnkRsQbwc8rcWa/KzHMrxh4IfIQyg4ZFp0JEPIjywXmHzOxlzrJWMV912pyviNgS2AnYknJwtDrlw8wSyrfwlwDfy8xLBxbkiGpz82+2RMR6mXn9oOMYJW3KWXMp6s50UcOAszLzngGFOi9ExAJg68y8YNCxjApzVqdt+bKGza227V+zwZzVMV9zywazJElqrTY1/yRJkiSpH2wwS5IkSZIkSZJ6smjQAUiSJGnuRMRqwKMpl/6OzXG+BLgGuDQz7xxUbMPKnKnfImIdJti/MvPGwUU13MxZHfM1OyJiU4DM/NOgYxkm7l/1zFkd8zX8PINZIy0iHp6ZV8zyNhcAD/agYWIRsQmw0Px0x3zVMV91mrkCXw6QmZ8ZcDgDY/NvehGxErAvsBfwZO5/08gxCfwU+CxwcmYunZsIh485mxttrvsR8XzK/vUsYP1JVrsOOAs4LTO/OVexDStzVsd8za6IWB24DViema0/Wc/9q545q2O+RosNZo20iLgH+DxwRGZeNsNtrQS8FjgEODUz3z8LIQ6tiHgu8FZgG2AhcDFwCvCpzFw+xbhrgPXbdlBlvuqYr7kREetSDqpa90HH5l/3mhtHfh14OJPnabwEfg/s0cYbSpqzetb97kXEhsDpwPZjT00zZOwD2znAyzLzz/2KbViZszrmqz86GsytvkG8+1c9c1bHfI0mG8waaRFxDrAdsBz4EfAF4IzMvKHL8QHsSDkD8MXAOsDtwN6Z+fV+xDwMIuIg4OixXzsWJXAh8PLJzgxvPghu0KaDKvNVx3zNnY4Gc6s+6Nj8615EbAD8mnLWxxLKl7LfAi4BrgbuoORmdWBjYEtgF+AVwBrAtcDjMvO6OQ9+QMxZPet+9yJiMfALYLPmqe9y//0LYDXuu3/tDCwALgeemJm3z2HYA2XO6pivOhFxSsXqi4BXUWrbqR3PZ2a+blYDG1LuX/XMWR3zNbpsMGvkRcQLgCMohSWbx/9SPtD8CrgeuAm4B1gLWJtSrJ4EPJHyATGApcCJwGHz+UNhRDwB+Bnl7KJLgS8CNwDPBF7UPH8jsHtmnjfB+LZ9EDRfFczX3Gpjg9nmX52I+FfgAOAi4AWZeVWX4zYG/hN4AnBsZh7UvyiHizmrY92vExGHAe8CrgRenJm/6HLcVsBXgYcCh2fmoX0LcsiYszrmq05ELGfF2Y9dDWl+ZsfvbToOc/+qZM7qmK/RZYNZ80JzJvJzgdcDuwErNYum2sHHDg6uoFzC+e+ZeU3fghwSzbf0rwHOBnbLzLs6lm0NnAZsTjmT+4WZeda48W37IGi+KpivehGx7wyGrw4cQ7s+2Nj8qxARl1O+VH1s7ZnbzZniFwO/z8xH9iO+YWTO6lj360TEJZR8bJ+ZP6kc+zTgXMrc8o/pR3zDyJzVMV91OhrMv6V8CT2VhcDTm/V/2LkgM3fqS4BDxv2rnjmrY75Glw1mzTvN3UV3oszX82TKTZ/WA1ahnEFzPXAZpfCck5kXDCjUgej44LxVZl48wfLFlDMCnw/cTZnD6MyO5W37IGi+Kpivej2cOXO/TdCuBrPNvwoRcSdwV2au3eP4m4GVM3O12Y1seJmzOtb9OhFxO3BvZj6wx/G3Um6KuPrsRja8zFkd81UnIr4GvAC4BXg3cHxO0iRp6tmttOi4azz3r3rmrI75Gl02mKWWiYg7KDcEWzzFOgsp84q9kjJ1yKsz84vNsrZ9EDRfFcxXvY4G818ozZcaC4CH0KIPOjb/6kTEXyj3F1irdi665oP0TcCNmfmgfsQ3jMxZHet+nYi4njJdz+KsvOloRKxMucHYbZm5Xj/iG0bmrI75qhcRewDHAQ+mXCH1psw8f4L1Wn+TP/eveuasjvkaXQsGHYCkOTc2T/XkK2QuA/YGPkmZbuSzEfGa/oc2lMxXHfNV78rm51syc7OaB7DNAOMelFuBxc2HvCpN82/sw2FbnE853ntPD2PfTbkc+KezGtHwM2d1rPt1fkW5Udj+PYzdn5K/ruajnEfMWR3zVSnLzd23pDSZtwJ+HBEnRkRPX2bPc+5f9cxZHfM1omwwS+3zJ2C1iHjIVCtl8QbgY5QPyydHxH5zEeCQMV91zFe9nzU/t+1hbBsvQ7L5V+dYyjQqB0fEGRHxlOkGRMS2EXE6cDBlH/vXPsc4bMxZHet+nU9Q9q+jIuLoiNhougERsWFEHAV8iLJ/ndDnGIeNOatjvnqQmbdn5luAp1DOYt4HuCwiXjvYyIaO+1c9c1bHfI0op8iQWiYiPgPsRbn068QuxxwJHMKKs5SiLZeFma865qteRLyVcjD0g9obxETEusB1tOhSzYjYGfg2ZV/5KnB0Zk7ZMI6IbYG3Ai9pnnpOZp7d10CHSES8CziMFV9I3AJcClwN3Nk8vxqwMbAFMDbnXQDvzswPzGnAQ8Ccdc+6Xy8iTqLcmHrs9V8M/IaJ96/HNI8FlP3rpMx84wDCHihzVsd8zUxEBPBPlL8Di4HzgDcBv6flU2SA+1cvzFkd8zWabDBLLRMRrwI+A1yYmV2fMRkR7wbeR/Nhuy0HVearjvmqFxE7AmcDSzJzzcqxawJnUj7otOLu5WDzrxcRsStwBPD4cYvGchjjnv8l8M7M/O9+xzaszFl3rPu9ac7ePhTYoHlqsg9lY/vZtcB7M7O1Z2WZszrma+YiYmPKVRcvAu4FTgH2peUNZnD/6oU5q2O+Ro8NZqllImIN4OeUeY1elZnnVow9EPgILTqoMl91zFe95iyZNQEy85YBhzMybP71JiK2BHaizDW5EWVO6gCWUBr0lwDfy8xLBxbkkDFnU7Pu9665GdHOdLF/AWdl5j0DCnVomLM65mt2RMRuwPGUGytDS2vWeO5f9cxZHfM1WmwwS5KkkWXzT5Ik9VtErEaZ2mdTgMx0bmZJ6mCDWZIkSZIkSZLUkwWDDkCSJEmSJEnzS0RsGhGbDjqOUWLO6piv4WGDWWqRiHh4H7a5YL4WdPNVx3zVM2caZhGxckS8OiJePehYRkXbcmYNm3sRsYn5qWPO6rQpX9aw/oqI1YErgSsGHMrIMGd1zNdwscEstctvI+LUiNh8phuKiJUiYl/gf4HXzDiy4WS+6piveuZsDrWt+TcL1gA+DZwy4DhGSdtyZg3rUUQ8NyLOioibI+K2iDgvIvaJiOk+n11ASz9Im7M65qsr1rC5Mf7Gy5qeOatjvoaAczBLLRIR5wDbAcuBHwFfAM7IzBu6HB/AjsDLgRcD6wC3A3tn5tf7EfMgma865queOZtbEbEucB2wPDMXDTqeYdeRr8zMhYOOZxS0LWfWsN5ExEHA0WO/dixK4ELg5Zk5YYMvIq4BNmjD/tXJnNUxX92xhtWLiJovUBcBr6Lsd6d2PJ+Z+bpZDWyImbM65mt02WCWWiYiXgAcAWxJKcRJ+ab9QuBXwPXATcA9wFrA2sBmwJOAJwKrUw5UlwInAodl5nVz+yrmjvmqY77qmbO507bm30yZr3ptzJk1rE5EPAH4GbAQuBT4InAD8EzgRc3zNwK7Z+Z5E4xvTfNvjDmrY77qWMPqRMRySo66HtL8zI7fW/M3EsxZLfM1umwwSy3UfNv+XOD1wG7ASs2iqQrCWOG+gnLp779n5jV9C3KImK865queOZsbLW3+7TuD4asDx9CifIE564U1rHvNmVmvAc4GdsvMuzqWbQ2cBmxOOQvyhZl51rjxrWr+gTmrZb7qWcO619H8+y1w7TSrLwSe3qz/w84FmblTXwIcQuasjvkaXTaYpZaLiHWAnYDtgScDGwHrAatQzm64HrgMOBc4JzMvGFCoQ8F81TFf9czZ1Gz+1enhLJD7bYIW5QvM2UxZw6YWEZdTzn7cKjMvnmD5YuDzwPOBu4GXZeaZHcvb2PwzZxXM18xYw6YWEV8DXgDcArwbOD4naSo1+9qttPhvIpizWuZrdNlgliRJI8PmX52OfP2F0kiosQB4CC3KF5gz9VdE3EGZB37xFOsspMwl+UrKZfevzswvNsta1/wzZ3XMl/otIvYAjgMeDFwEvCkzz59gvdWB2/BvojmrZL5Gkw1mSZI0Mmz+1YmIK4CHAq8cax5UjF2Pcmlia/IF5kz9FRG3U5p/a0yzXgAnAPsAy4B9MvPTbWz+mbM65ktzoWnsHQ7sT/ny/lPA2zPzpnHr2PxrmLM65mv0LBh0AJIkSRWubH6+JTM3q3kA2www7kH5WfNz2x7GtvUsBHOmfvoTsFpEPGSqlbJ4A/AxyhyTJ0fEfnMR4BAyZ3XMl/ouM2/PzLcAT6GcYboPcFlEvHawkQ0vc1bHfI0eG8ySJGmU2PyrcwHlrI82Ntd7Zc7UT2M1bNduVs7MNwMfonxu+ziwfp/iGmbmrI750pzJzJ9T5qo+kDJP9ckRcU5EPH6wkQ0vc1bHfI0OG8ySJGmU2PyrM9Zo6CVfSyl35P7hdCvOM+ZM/fRtSg17fbcDMvMdwKHNuOhTXMPMnNUxX5pTzdnwxwFbAF8DtqMcrx0z0MCGmDmrY75Gg3MwS5KkkREROwJnA0syc83KsWsCZ1KOU3fqQ3hDp5ljc02AzLxlwOGMBHOmfoqINYCfA4uAV2XmuRVjDwQ+QsvmmjRndcyXBi0idgOOp9z3AtyfpmXO6piv4WSDWZIkjQybf5IkScMtIlYDDgE2BchM582dhjmrY76Gjw1mSZIkSZIkSVJPnINZkiRJkiRJktQTG8ySJEnzUEQ8vA/bXBARm872doeFOVM/uX/VM2d1zJf6yf2rnjmrY75Gmw1mSZI0EjzorPbbiDg1Ijaf6YYiYqWI2Bf4X+A1M45seJkz9ZP7Vz1zVsd8qZ/cv+qZszrma4TZYJYkSaPCg8465wN7A7+JiO9FxBsiYt1uB0exU0ScCFwNfAJYH/hlf8IdCuZM/eT+Vc+c1TFf6if3r3rmrI75GmHe5E+SJI2EiDgH2A5YDvwI+AJwRmbe0OX4AHYEXg68GFgHuB3YOzO/3o+YBy0iXgAcAWwJZPP4X+BC4FfA9cBNwD3AWsDawGbAk4AnAqsDASwFTgQOy8zr5vZVzC1zpn5y/6pnzuqYL/WT+1c9c1bHfI0uG8ySJGlkeNBZr2msPxd4PbAbsFKzaKqDwGh+XgGcAvx7Zl7TtyCHjDlTP7l/1TNndcyX+sn9q545q2O+RpMNZkmSNFI86OxdRKwD7ARsDzwZ2AhYD1gFuJHSoL8MOBc4JzMvGFCoQ8OcqZ/cv+qZszrmS/3k/lXPnNUxX6PDBrMkSRpZHnRKkiRJ0mDZYJYkSZIkSZIk9WTBoAOQJEmSJEmSJI0mG8ySJEmSJEmSpJ7YYJYkSZIkSZIk9cQGsyRJkjQLIiKbxx8j4gGTrHNls86iScaOPZZFxPURcXZE7DULse3Yse3TJ1nnYc3yc6bYznMi4rSI+ENE3BERd0bE5RHxHxHxvJnGKUmSpNGzaPpVJEmSJFXYFDgQ+GAPY9/X/FwJ2Bx4IbBTRGyTmQfNUnx7RsTTMvO8bgdExBrAZ5p47gLOBr4CLAU2A3YFXhURx2TmwbMUpyRJkkZAZOagY5AkSZJGXkQkcBOQlBM5HpGZ149b50rgocBKmXnvuLFkZoxb/9nAd5pfH56ZV/YY247A94DLgb8BfpyZ249b52HAH4BzM/PpHc8vAL4J/G2zjVdl5tXjxq4CvBF4VGb+Yy8xSpIkaTQ5RYYkSZI0e+4ADgPWBA6d6cYy8yzgt0AA2850e8BPga8D20XES7oc8wpKc/lyYPfxzeUmzrsz81hgts6yliRJ0oiwwSxJkiTNruOB3wNviIhHzcL2xs5qnq1LD98G3At8MCJW6mL9fZufH87M26daMTPvnmlwkiRJGi02mCVJkqRZlJlLgbdT5lHuZR7mv4qInSlzMSfws5lHB5n5O+BEylQZ+03z/y8Cntr8etZs/P+SJEmaX7zJnyRJkjTLMvOMiDgPeFFEPD0zz+lmXES8t/ln503+AvhoZv5xFkN8H7A38J6IODUzb5lkvXWAlZt//79Z/P8lSZI0T3gGsyRJktQf/9z8PCYiYso1Vzi0ebwDeBbwI2DvzJzVuY0z8zrK2dXrAu+aYtVu45YkSVJL2WCWJEmS+iAzzwPOAJ4MvLTLMdE8FmTmOpm5U2Z+tk8hfhT4P+CAiHjoJOvcANzT/HuTPsUhSZKkEWaDWZIkSeqftwNLgSMjYuXpVp5LmXkX8C/AKsARk6xzL/CT5tdnz1FokiRJGiE2mCVJkqQ+yczfA/8GbAb804DDmch/ABcBrwCeNMk6JzU/D46I1abaWESsMouxSZIkaQTYYJYkSZL66/3AzZS5jhfPxgYj4tMRkRHxmplsJzMTOJgy1/KRk6z2eeBbwCOBr0fERhPEs3JE/CNwzEzikSRJ0uhZNOgAJEmSpPksM2+MiCOAo2Zxs2Mnitw70w1l5tkR8U1g10mWL4+IPSlnO+8BXBERZwGXAsuAh1Kmz1gf+PBM45EkSdJo8QxmSZIkqf+OA66cxe09DrgN+MYsbe+tlGbxhDLztsx8IfC3wFeBxwD7AwcCTwG+CzwvM986S/FIkiRpRES5Kk6SJEnSKIiItYAbgGMy822DjkeSJEnt5hnMkiRJ0mjZAVgKfGTQgUiSJEmewSxJkiRJkiRJ6olnMEuSJEmSJEmSemKDWZIkSZIkSZLUExvMkiRJkiRJkqSe2GCWJEmSJEmSJPXEBrMkSZIkSZIkqSc2mCVJkiRJkiRJPbHBLEmSJEmSJEnqyf8HpfyMOwDfnWoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "#mpict=[73.51697466666666, 65.51325966666666, 52.19395466666668, 34.024326, \n",
    "#       60.883046666666665, 6.740908999999999, 4.857252666666667, 8.431013333333334, \n",
    "#       8.392006666666667, 9.646620333333333, 8.497321000000001, 12.982642333333333, \n",
    "#       8.007896666666667, 9.762626333333332, 6.662559000000001, 5.316057333333333]\n",
    "#mpivar=[75.53585966666667, 69.56025666666666, 18.082787666666665, 154.39648000000003,\n",
    "#        11.998184, 1.27766, 2.194493, 2.2691966666666668,\n",
    "#        1.8346349999999998, 1.7718876666666665, 1.6892053333333334, 1.5759526666666668,\n",
    "#        1.9237193333333333, 1.1625573333333332, 1.2879283333333333, 0.850577]\n",
    "#threadct=[2.0040453333333335, 2.00311, 2.6234766666666665, 2.699171666666667,\n",
    "#          2.0707343333333337, 2.2307106666666665, 4.222584666666666, 4.246569666666667,\n",
    "#          4.020083, 4.503111666666666, 5.467942333333333, 5.048009,\n",
    "#          3.8683916666666662, 4.705335000000001, 5.640752666666667, 5.811503999999999]\n",
    "#threadvar=[1.9998449999999999, 2.000283666666667, 2.957152666666667, 2.401677,\n",
    "#           0.8674843333333334, 0.6187566666666666, 1.3398349999999999, 1.735828,\n",
    "#           1.4827233333333334, 1.245435, 1.7185836666666665, 1.7706360000000003,\n",
    "#           1.645278, 1.414596666666667, 1.6812523333333333, 1.5372543333333333]\n",
    "\n",
    "threadbf = [0.3616343333333334, 0.4390483333333333, 0.6118543333333334, 1.116497, \n",
    "            0.34279300000000007, 0.39713933333333334, 0.6350386666666668, 1.4056896666666667, \n",
    "            0.37821333333333335, 0.488335, 0.7908086666666666, 1.4691523333333334, \n",
    "            0.5579729999999999, 1.1923566666666667, 1.3307016666666667, 1.7391976666666666]\n",
    "\n",
    "threadcf = [0.42423133333333335, 0.6090126666666666, 1.3089426666666668, 1.3461553333333331,\n",
    "            0.427392, 0.7682310000000001, 1.3941153333333334, 1.3357656666666664, \n",
    "            0.8323596666666666, 1.318649, 1.5996213333333333, 1.6247436666666666, \n",
    "            0.7896679999999999, 1.2355183333333333, 1.4249120000000002, 1.6693683333333331]\n",
    "\n",
    "normalbf = [0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335,\n",
    "            0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, \n",
    "            0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, \n",
    "            0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665]\n",
    "\n",
    "normalcf = [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, \n",
    "            0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, \n",
    "            0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, \n",
    "            0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]\n",
    "\n",
    "x = np.arange(len(labelsP_J))\n",
    "\n",
    "width = 0.45/2\n",
    "\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.bar(x-width/2, normalbf, width, hatch=\"\", color='blue')\n",
    "\n",
    "ax.bar(x-width*1.5, normalcf, width, hatch=\"\",color='green')\n",
    "\n",
    "ax.bar(x+width/2, threadbf, width, hatch=\"\", color='orange')\n",
    "\n",
    "ax.bar(x+width*1.5, threadcf, width, hatch=\"\", color='red')\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",
    "normalbf_patch = mpatches.Patch(color='blue', label='Normal - Bf')\n",
    "normalcf_patch = mpatches.Patch(color='green', label='Normal - Cf')\n",
    "threadbf_patch = mpatches.Patch(color='orange', label='Pthreads - Bf')\n",
    "threadcf_patch = mpatches.Patch(hatch='', facecolor='red', label='Pthreads - Cf')\n",
    "\n",
    "\n",
    "handles=[normalcf_patch,normalbf_patch,threadbf_patch,threadcf_patch]\n",
    "\n",
    "plt.legend(handles=handles, loc='upper left', fontsize=21)\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",
    "    #ax.axvline(4)\n",
    "#plt.ylim((0, 15))\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Mall_AR.png\", format=\"png\")\n",
    "j = (j+1)%5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}