analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from scipy import stats\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrixMalEX=\"data_GG.csv\"\n",
    "matrixMal=\"data_GM.csv\"\n",
    "matrixIt=\"data_L.csv\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_qty=2 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
    "n_groups= 2\n",
    "repet = 10 #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",
    "processes = [1,10,20,40,80,160]\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfG = pd.read_csv( matrixMalEX )\n",
    "\n",
    "dfG = dfG.drop(columns=dfG.columns[0])\n",
    "dfG['S'] = dfG['N']\n",
    "dfG['N'] = dfG['S'] + dfG['%Async']\n",
    "dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
    "dfG['%Async'] = dfG['%Async'].fillna(0)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    group = dfG.groupby(['%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "else:        \n",
    "    group = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "\n",
    "grouped_aggG = group.agg(['median'])\n",
    "grouped_aggG.rename(columns={'median':'TE'}, inplace=True)\n",
    "\n",
    "grouped_aggG2 = group2.agg(['median'])\n",
    "grouped_aggG2.rename(columns={'median':'TE'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['median'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: 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"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "dfL['%Async'] = dfL['%Async'].fillna(0)\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To']\n",
    "\n",
    "#group\n",
    "grouped_aggL = groupL.agg(['median', 'count'])\n",
    "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
    "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2'], 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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:17: 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"
     ]
    }
   ],
   "source": [
    "dfLT = pd.read_csv( matrixIt_Total )\n",
    "dfLT = dfLT.drop(columns=dfLT.columns[0])\n",
    "\n",
    "dfLT['%Async'] = dfLT['%Async'].fillna(0)\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n",
    "else:\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum']\n",
    "\n",
    "#group\n",
    "grouped_aggLT = groupLT.agg(['median'])\n",
    "grouped_aggLT.columns = grouped_aggLT.columns.get_level_values(0)\n",
    "grouped_aggLT.set_axis(['Sum'], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_aggL.to_excel(\"resultL.xlsx\") \n",
    "grouped_aggLT.to_excel(\"resultLT.xlsx\")\n",
    "dfLT.to_excel(\"resultLT_all.xlsx\")\n",
    "grouped_aggM.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggG2.to_excel(\"resultG.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
       "      <th>CommTam</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</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>20,80</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>6.634278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,80</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>6.671452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,80</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>6.528489</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,80</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>6.598294</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20,80</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>6.580460</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2395</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,80</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>7.632454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,80</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>7.279475</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2397</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,80</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>7.590942</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2398</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,80</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>7.298454</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2399</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40,80</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>7.356583</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  %Async Groups  NP  NS Dist  Matrix  CommTam  Cst  Css  Time Iters  \\\n",
       "0     0     0.0  20,80  20  80  2,2  100000        0    3    0   4.0  3,97   \n",
       "1     0     0.0  20,80  20  80  2,2  100000        0    3    0   4.0  3,97   \n",
       "2     0     0.0  20,80  20  80  2,2  100000        0    3    0   4.0  3,97   \n",
       "3     0     0.0  20,80  20  80  2,2  100000        0    3    0   4.0  3,97   \n",
       "4     0     0.0  20,80  20  80  2,2  100000        0    3    0   4.0  3,97   \n",
       "...  ..     ...    ...  ..  ..  ...     ...      ...  ...  ...   ...   ...   \n",
       "2395  0     0.0  40,80  40  80  2,2  100000        0    0    1   4.0  3,97   \n",
       "2396  0     0.0  40,80  40  80  2,2  100000        0    0    1   4.0  3,97   \n",
       "2397  0     0.0  40,80  40  80  2,2  100000        0    0    1   4.0  3,97   \n",
       "2398  0     0.0  40,80  40  80  2,2  100000        0    0    1   4.0  3,97   \n",
       "2399  0     0.0  40,80  40  80  2,2  100000        0    0    1   4.0  3,97   \n",
       "\n",
       "            TE  S  \n",
       "0     6.634278  0  \n",
       "1     6.671452  0  \n",
       "2     6.528489  0  \n",
       "3     6.598294  0  \n",
       "4     6.580460  0  \n",
       "...        ... ..  \n",
       "2395  7.632454  0  \n",
       "2396  7.279475  0  \n",
       "2397  7.590942  0  \n",
       "2398  7.298454  0  \n",
       "2399  7.356583  0  \n",
       "\n",
       "[2400 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>TE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>Groups</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"11\" valign=\"top\">2,2</td>\n",
       "      <td rowspan=\"11\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td>1,10</td>\n",
       "      <td>51.150761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1,160</td>\n",
       "      <td>15.706663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1,20</td>\n",
       "      <td>32.814834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1,40</td>\n",
       "      <td>22.719029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1,80</td>\n",
       "      <td>17.936359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">3</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td>80,1</td>\n",
       "      <td>388.108210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80,10</td>\n",
       "      <td>39.045234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80,160</td>\n",
       "      <td>3.600383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80,20</td>\n",
       "      <td>19.728576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80,40</td>\n",
       "      <td>10.049131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    TE\n",
       "Dist %Async Cst Css Groups            \n",
       "2,2  0.0    0   0   1,10     51.150761\n",
       "                    1,160    15.706663\n",
       "                    1,20     32.814834\n",
       "                    1,40     22.719029\n",
       "                    1,80     17.936359\n",
       "...                                ...\n",
       "            3   1   80,1    388.108210\n",
       "                    80,10    39.045234\n",
       "                    80,160    3.600383\n",
       "                    80,20    19.728576\n",
       "                    80,40    10.049131\n",
       "\n",
       "[240 rows x 1 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggG"
   ]
  },
  {
   "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>NS</th>\n",
       "      <th>Dist</th>\n",
       "      <th>Matrix</th>\n",
       "      <th>CommTam</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</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>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.213137</td>\n",
       "      <td>0.975649</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.188786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.308836</td>\n",
       "      <td>1.046099</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.354935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.135379</td>\n",
       "      <td>1.060276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.195655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.217985</td>\n",
       "      <td>0.957856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.175841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.192607</td>\n",
       "      <td>0.933632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.126239</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2395</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>2.351927</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.351927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2396</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>2.027680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.027680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2397</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>2.312305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.312305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2398</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>1.998197</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.998197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2399</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>2,2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3,97</td>\n",
       "      <td>2.088411</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.088411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  %Async  NP  NS Dist  Matrix  CommTam  Cst  Css  Time Iters        TC  \\\n",
       "0     0     0.0  20  80  2,2  100000        0    3    0   4.0  3,97  1.213137   \n",
       "1     0     0.0  20  80  2,2  100000        0    3    0   4.0  3,97  1.308836   \n",
       "2     0     0.0  20  80  2,2  100000        0    3    0   4.0  3,97  1.135379   \n",
       "3     0     0.0  20  80  2,2  100000        0    3    0   4.0  3,97  1.217985   \n",
       "4     0     0.0  20  80  2,2  100000        0    3    0   4.0  3,97  1.192607   \n",
       "...  ..     ...  ..  ..  ...     ...      ...  ...  ...   ...   ...       ...   \n",
       "2395  0     0.0  40  80  2,2  100000        0    0    1   4.0  3,97  2.351927   \n",
       "2396  0     0.0  40  80  2,2  100000        0    0    1   4.0  3,97  2.027680   \n",
       "2397  0     0.0  40  80  2,2  100000        0    0    1   4.0  3,97  2.312305   \n",
       "2398  0     0.0  40  80  2,2  100000        0    0    1   4.0  3,97  1.998197   \n",
       "2399  0     0.0  40  80  2,2  100000        0    0    1   4.0  3,97  2.088411   \n",
       "\n",
       "            TH   TS   TA  S        TR  \n",
       "0     0.975649  0.0  0.0  0  2.188786  \n",
       "1     1.046099  0.0  0.0  0  2.354935  \n",
       "2     1.060276  0.0  0.0  0  2.195655  \n",
       "3     0.957856  0.0  0.0  0  2.175841  \n",
       "4     0.933632  0.0  0.0  0  2.126239  \n",
       "...        ...  ...  ... ..       ...  \n",
       "2395  0.000000  0.0  0.0  0  2.351927  \n",
       "2396  0.000000  0.0  0.0  0  2.027680  \n",
       "2397  0.000000  0.0  0.0  0  2.312305  \n",
       "2398  0.000000  0.0  0.0  0  1.998197  \n",
       "2399  0.000000  0.0  0.0  0  2.088411  \n",
       "\n",
       "[2400 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM"
   ]
  },
  {
   "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></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>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"11\" valign=\"top\">2,2</td>\n",
       "      <td rowspan=\"11\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.330893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.330893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.245451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.245451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.890816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.890816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.919644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.919644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>1.176074</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.176074</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\">3</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">160</td>\n",
       "      <td>1</td>\n",
       "      <td>0.040586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.043647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.043647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.040643</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.040643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.109038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.109038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>0.131250</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.131250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   TC   TH   TS   TA        TR\n",
       "Dist %Async Cst Css NP  NS                                    \n",
       "2,2  0.0    0   0   1   10   0.330893  0.0  0.0  0.0  0.330893\n",
       "                        20   1.245451  0.0  0.0  0.0  1.245451\n",
       "                        40   0.890816  0.0  0.0  0.0  0.890816\n",
       "                        80   0.919644  0.0  0.0  0.0  0.919644\n",
       "                        160  1.176074  0.0  0.0  0.0  1.176074\n",
       "...                               ...  ...  ...  ...       ...\n",
       "            3   1   160 1    0.040586  0.0  0.0  0.0  0.040586\n",
       "                        10   0.043647  0.0  0.0  0.0  0.043647\n",
       "                        20   0.040643  0.0  0.0  0.0  0.040643\n",
       "                        40   0.109038  0.0  0.0  0.0  0.109038\n",
       "                        80   0.131250  0.0  0.0  0.0  0.131250\n",
       "\n",
       "[240 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Cst</th>\n",
       "      <th>Css</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>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0</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>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0</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>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>224.0</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>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.424027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</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>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.532020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>224.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239995</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.099933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.099903</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239997</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.099926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239998</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.100183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239999</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>100000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.099943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        N  %Async  NP  N_par  NS  Dist  Compute_tam  Comm_tam  Cst  Css  Time  \\\n",
       "0       0     0.0  20      0  80     2       100000         0    3    0   4.0   \n",
       "1       0     0.0  20      0  80     2       100000         0    3    0   4.0   \n",
       "2       0     0.0  20      0  80     2       100000         0    3    0   4.0   \n",
       "3       0     0.0  20      0  80     2       100000         0    3    0   4.0   \n",
       "4       0     0.0  20      0  80     2       100000         0    3    0   4.0   \n",
       "...    ..     ...  ..    ...  ..   ...          ...       ...  ...  ...   ...   \n",
       "239995  0     0.0  40      0  80     2       100000         0    0    1   4.0   \n",
       "239996  0     0.0  40      0  80     2       100000         0    0    1   4.0   \n",
       "239997  0     0.0  40      0  80     2       100000         0    0    1   4.0   \n",
       "239998  0     0.0  40      0  80     2       100000         0    0    1   4.0   \n",
       "239999  0     0.0  40      0  80     2       100000         0    0    1   4.0   \n",
       "\n",
       "        Iters        Ti   Tt     To  \n",
       "0           3  0.199811  0.0  224.0  \n",
       "1           3  0.199813  0.0  224.0  \n",
       "2           3  0.199895  0.0  224.0  \n",
       "3           3  0.424027  1.0  224.0  \n",
       "4           3  0.532020  1.0  224.0  \n",
       "...       ...       ...  ...    ...  \n",
       "239995      3  0.099933  0.0  112.0  \n",
       "239996      3  0.099903  0.0  112.0  \n",
       "239997      3  0.099926  0.0  112.0  \n",
       "239998      3  0.100183  0.0  112.0  \n",
       "239999      3  0.099943  0.0  112.0  \n",
       "\n",
       "[240000 rows x 15 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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></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>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">2</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0.0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">0</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td>10</td>\n",
       "      <td>1.999120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.999183</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>1.999150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>1.999131</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>1.999115</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2242.0</td>\n",
       "      <td>3.0</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 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 rowspan=\"5\" valign=\"top\">1</td>\n",
       "      <td rowspan=\"5\" valign=\"top\">3</td>\n",
       "      <td rowspan=\"2\" valign=\"top\">20</td>\n",
       "      <td>80</td>\n",
       "      <td>0.105980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.105645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">40</td>\n",
       "      <td>80</td>\n",
       "      <td>0.068359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>0.052386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>160</td>\n",
       "      <td>0.026651</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>330 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Ti  Iters      To  Iters2\n",
       "Tt  Dist %Async Cst Css NP NS                                  \n",
       "0.0 2    0.0    0   0   1  10   1.999120    3.0  2242.0     3.0\n",
       "                           20   1.999183    3.0  2242.0     3.0\n",
       "                           40   1.999150    3.0  2242.0     3.0\n",
       "                           80   1.999131    3.0  2242.0     3.0\n",
       "                           160  1.999115    3.0  2242.0     3.0\n",
       "...                                  ...    ...     ...     ...\n",
       "1.0 2    0.0    1   3   20 80   0.105980    1.0   112.0     1.0\n",
       "                           160  0.105645    1.0   112.0     1.0\n",
       "                        40 80   0.068359    1.0    56.0     1.0\n",
       "                           160  0.052386    1.0    56.0     1.0\n",
       "                        80 160  0.026651    2.0    28.0     2.0\n",
       "\n",
       "[330 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547], [0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807]]\n",
      "[[2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703], [1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807]]\n",
      "[[0.22657399999999997, 0.22961033333333333, 0.37444533333333335, 1.0861523333333334, 0.18071299999999998, 0.2686593333333333, 0.48245, 1.8810366666666667, 0.22639533333333337, 0.31453400000000004, 0.564293, 2.4626886666666667, 0.4612826666666667, 1.0638560000000001, 1.5319243333333334, 2.1236686666666666], [0.21594133333333332, 0.36930899999999994, 1.1269756666666668, 1.1670603333333334, 0.22462733333333332, 0.47068400000000005, 1.5951943333333334, 1.693723, 0.7059706666666666, 1.368441, 1.8698483333333336, 2.2059883333333334, 0.4813296666666667, 1.3010543333333333, 1.8387883333333335, 2.1851773333333333]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:53: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "#Reserva de memoria para las estructuras\n",
    "TP_data=[0]*2\n",
    "TH_data=[0]*2\n",
    "TM_data=[0]*2\n",
    "\n",
    "TP_A_data=[0]*2\n",
    "TH_A_data=[0]*2\n",
    "TM_A_data=[0]*2\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    \n",
    "    TP_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "    TP_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "# Obtencion de los grupos del dataframe necesarios\n",
    "\n",
    "#ACTUALMENTE NO SE DIFERENCIAN LOS TIEMPOS DE ITERACIONES DE PADRES E HIJOS CUANDO COINCIDE EL NUMERO DE PROCESOS\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['NP'])['Ti']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['Dist', 'NP'])['Ti']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "grouped_aggL_aux = groupL_aux.agg(['mean'])\n",
    "grouped_aggL_aux.columns = grouped_aggL_aux.columns.get_level_values(0)\n",
    "grouped_aggL_aux.set_axis(['Ti'], axis='columns')\n",
    "\n",
    "#Calculo de los valores para las figuras\n",
    "#1=Best Fit\n",
    "#2=Worst Fit\n",
    "dist=1\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    i=0\n",
    "    r=0\n",
    "    for numP in values:\n",
    "        j=0\n",
    "        for numC in values:\n",
    "        \n",
    "            tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "            for tipo in [0]: #TODO Poner a 0,100\n",
    "                iters_aux=dfM[(dfM[\"NP\"] == numP)][(dfM[\"NS\"] == numC)][(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == tipo)]['Iters'].head(1).tolist()[0].split(',')\n",
    "                itersP_aux = int(iters_aux[0])\n",
    "                itersS_aux = int(iters_aux[1])\n",
    "                iters_mal_aux = 0\n",
    "                if tipo != 0:\n",
    "                    iters_mal_aux = grouped_aggL['Iters'].loc[(1,dist,tipo,numP,numC)]\n",
    "            \n",
    "                t_iterP_aux = grouped_aggL_aux['Ti'].loc[(dist,numP)]\n",
    "                t_iterS_aux = grouped_aggL_aux['Ti'].loc[(dist,numC)]\n",
    "            \n",
    "            \n",
    "                p1 = t_iterP_aux * itersP_aux\n",
    "                p2 = t_iterS_aux * max((itersS_aux - iters_mal_aux),0)\n",
    "                \n",
    "                array_aux = grouped_aggM[['TS', 'TA']].loc[(dist_v,tipo,numP,numC)].tolist()\n",
    "                p3 = tc_real + array_aux[0] + array_aux[1]\n",
    "                \n",
    "                #Guardar datos\n",
    "                if tipo == 0:\n",
    "                    TP_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_data[dist_index][i*len(values) + j] = p3\n",
    "                else:\n",
    "                    TP_A_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_A_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_A_data[dist_index][i*len(values) + j] = p3\n",
    "            j+=1\n",
    "        i+=1\n",
    "print(TP_data)\n",
    "print(TH_data)\n",
    "print(TM_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAANYCAYAAABJlYhKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOzdeXhV1b3/8c9KAhkgzBDmoYAhYikRKMhkIqRMllEmQVOpxSrPVX4IwhWZAldFQFGsVaEFZxQuIIgMCkSQoRCmakVALpPKKDKPSdbvj+SEzMPOSU6S8349z3mSvffaa3/PwW9oP+ysbay1AgAAAAAAAAAgr3w8XQAAAAAAAAAAoHgiYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBE/TxdQ1FSpUsXWr1/f02UAALKxf/9+SVJoaKiHKwGAgsPPOgAlHT/nAKB42blz51lrbdX0+wmY06lfv77i4uI8XQYAIBsRERGSpNjYWI/WAQAFiZ91AEo6fs4BQPFijDma2X6WyAAAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAI0UiYDbG1DXGjDTGrDDGHDPG3DDGXDLG7DXGvGiMqZHFefWNMTYXr5aF/Z4AAAAAAAAAoKTz83QBxpg6ko5IMql2X5RURlKz5NdwY0w/a+2GbKY6lc2xW/mtEwAAAAAAAACQlscDZkm+yV9XSlogaZ219ldjTGlJnST9TVIDScuMMaHW2pOZTWKtrV4YxQIAAAAAAAAAkhSFJTJ+lRRurb3fWrvYWvurJFlrb1prV0nqLum6pHKSHvNgnQAAAAAAAACAVDx+B7O19oKkvdkc/94Ys01ShKQWhVVXbiQmJurXX3/V5cuXdf36dSUmJnq6JADwCpMmTZIk7du3z8OVeJaPj48CAgJUtmxZVaxYUT4+ReHfjQEAAAAA3sTjAXMu/ZL81TfbUYUoPj5ex48fl5+fnypVqqSgoCD5+PjIGJPzyQCAfHEFqaGhoR6uxHOstUpMTNTVq1d1/vx5Xbx4UXXq1JGfX3H5qx0AAAAAUBIU+VudjDF+ktolb36bzbitxpiLxphrxpjDxpj3jTHtC6quc+fOyd/fX7Vr11ZwcLB8fX0JlwEAhcYYI19fXwUHB6t27dry9/fXuXPnPF0WAAAAAMDLFPmAWdIISdUlJUp6N5txbZLHSFJ9SUMkbTLGzDYFkPxeuHBBlStXJlQGAHicMUaVK1fWhQsXPF0KAAAAAMDLFOmA2RjTTNLzyZuvW2v/k27IdUlvSOooKdhaW0FSkJLWal6RPOYpSf+dw3WGG2PijDFxZ86cyVVt8fHxKl26dO7eCAAABax06dKKj4/3dBkAAAAAAC9TZANmY0wNScuUFBjvlDQ2/Rhr7Ulr7Qhr7SZr7eXkfdZau8ta21PSouShzxpjKmR1LWvt29baltballWrVs1LjXl4RwAAFBz+TgIAAAAAeEKRDJiNMZUkrZXUQNJBST2stdcdTOUKpctI6uSm8gAAAAAAAAAAKoIBszGmvKQ1ku6SdExSZ2vtKSdzWWsPS3KtefEb91QIAAAAAAAAAJCKWMBsjCkj6XNJLSWdVFK4fCy/0yZ/tfmcBwAAAAAAAACQSpEJmI0xgUp6MF9bSb8oKVw+mM85G0iqkrx5JF8FIleMMRle5cuXV+vWrTV79mzdunXL0yUWS7Vr187wuQYHBys8PFxTp07VlStX0oz/8ssvM4z39fVVlSpV1KlTJ33wwQceeicoCPRdwXD1nZ+fn77//vtMx7h6rWvXrpnupwcBAAAAACWdn6cLkCRjTGlJSyRFSjov6Q/W2v/k4jxjrc3uzuTnk79ek7Q+34XmUfXq0ilHi3sUrpAQ6eRJ987Zr18/lS1bVtZaHTlyRFu3btX27du1fPlyrV69WqVLl3bvBT2kffv22rx5s44fP67atWsX+PW6deumatWqSZJ+/PFHbd26VRMnTtSiRYv09ddfq1y5cmnGBwcHq2/fvpKkW7du6cCBA1q/fr3Wr1+vjRs36q233irwmgubMdUltVHSM0IlqbekbZISlPTcz9nJ+1Mfz832SEn9Jfm6ZX5rXdvu4219Fxsbq9DQ0AK/XkJCgqZMmaKPPvooz+d6Yw8CAAAAALyLxwNmY4yvpA8ldZV0SVI3a+2uXJ4ea4xZraQ7n/dZaxOMMUZSc0kTdTvZmW6tPefm0nNUHMJlqWDqnDlzpurXr5+yvWfPHkVERGjDhg2aO3euRowY4f6LeoFnn31W7du3T9k+dOiQ7rvvPn3zzTd64YUX9MILL6QZX61aNS1YsCDNvsWLF6t///56++23NWzYMLVu3bowSi9EbbLYlz4gzottyeemDpDdOb970HcFIzAwUB9//LHGjx+vu+66K0/nemcPAgAAAAC8SVFYIqOdpH7J35eStMwYczKL145059ZT0l3K30i6Zow5K+mKpF26HS6/Limm4N8GstO8eXONGjVKkrRsmfvv3PRWDRs21KRJkyTl/nN94IEH1LlzZ0nS6tWrC6w2z1mm23cLp96OUFJAnKCkMDin8S4jdfvu5IgCmL/g0HfuMWLECFlrU3otv0p+DwIAAAAAvElRCJhT1xAgKSSbV9V0546RNFfSXknnJJWTlChpv6R/Smpjrf2vHJbRQCEJDw+XJB07dvu5jefOndOcOXPUtWtX1a9fXwEBAapUqZI6deqkpUuXZjrP0KFDZYzR119/rc8//1wRERGqUKGCjDG6fPlyyrizZ89q7NixCgsLU2BgoCpUqKBOnTpp1apVGeb84YcfZIxR586dde3aNY0bN07169eXv7+/GjRooAkTJqRZx9Y1fvPmzZKkOnXqpKyz6ud3+xcDbt68qb///e/6/e9/rypVqigwMFD16tVTly5d9Pbbb+fvA02W2eeak6ZNm0qSTp8+7ZYaio8IJYXA/SXF5mJ8rG7fuRxRAPMXvJLcdxEREYXSd0888YRq1KihpUuXavfu3Xk+PzPe24MAAAAAgJLG40tkWGtjJRmH5y5SUpqDYuDSpUuSJH9//5R9Gzdu1JNPPql69eqpcePGuueee/TTTz/pq6++0vr16/XCCy9o3Lhxmc733nvvae7cuWrVqpW6d++ugwcPKmmFFOn7779XVFSUfvzxR9WvX19du3bVxYsXtXXrVnXv3l2vvPKKRo4cmWHOGzduqHPnztq3b5/uvfdeNW3aVBs3btS0adN04sQJzZs3T5JUrlw5RUdHa9WqVTp9+rT69++voKAgSZKvr2/KfIMHD9aSJUtUtmxZdejQQeXLl9eJEycUFxeno0ePavjw4QXyueb2HNd6ziVTdmsrL1LSku8hkk5mMb66pFOSNigpOM7tWs25nb9wlOS+69q1q0JCQiQVbN8FBgbqv//7v/Xkk09q4sSJWrFiRZ7Oz4x39CAAAAAAwCtYa3mlerVo0cLmxnfffZfjGKn4vNxFkpVkDx8+nOHYoEGDrCQ7ZMiQlH0HDx6027ZtyzD2wIEDtnbt2tbPz88eP348zbEhQ4ZYSdYYYxctWpTh3Pj4eHvnnXdaSXbGjBk2ISEh5dj+/fttvXr1rJ+fX5o/w4MHD6bU3r59e3v+/Pk0tQQHB1tjjD169Giaa7Vr185KylBj6jkbNGhgf/nllzTHbt68aTdu3JjhnKzUqlXLSrKbNm3KcGz06NFWkr333ntT9n3xxRdWkm3YsGGG8Tdu3LANGjSwkjL97Iu72/9d90p+ZbUdkvxnviGT4xuSj4XkYb68ze/e9+ydfRcbG5uhjoLouxMnTtjr16/bOnXqWEn2X//6V8oYV6916dIlzbme6sHc/N0EoPi499570/z9DgAlDT/nAKB4kRRnM8lTi8ISGSjBrLU6evSoxo0bp4ULF8oYo8ceeyzleKNGjTJ9wFXjxo317LPPKj4+Psu7BXv27KkHHnggw/6lS5fqu+++U//+/TV69Gj5+Nz+z/yOO+7QjBkzFB8fn3JXZGq+vr6aN2+eypcvn6aWBx98UNZabdq0Kdfv/cyZM5KkFi1aqFKlSmmOlSpVSh06dMj1XJn56aef9NJLL+nVV1+VJD3++OPZjr9165a+/fZbDRgwQIcPH9bIkSNL+MPFclr7+KSS7k52LWfhOh6bvG+Dbt99nJv58jp/waHv3N93/v7+Gj9+vCRp4sSJjubwvh4EAAAAAHgDjy+RgZKpQYMGGfaVLl1as2fPzhDwJCQkaMOGDdq8ebNOnDihGzduyFqrn3/+WZJ08ODBTK/Rs2fPTPevXbtWktSvX79Mj7uuv3379kzrDg0NzbDftc9VU26EhYUpKChIn376qV555RUNHjxY1atXz/X5mcksHPPx8dGECRM0cODADMcOHTqUsnxBai+99JLGjBmTr1pKhgjdXjPZtdpOXtZczs/87kffFUzfuQwbNkzTp0/XmjVrtHnzZrVr1y7Hc+hBAAAAAEBJR8CMAtGvXz+VLVtWxhiVLVtWTZo0UZ8+fVSzZs00444fP66ePXtqz549Wc7lWqs0vbp162a6/8iRI5KkQYMGadCgQVnOe/bs2Qz76tSpk+nY4OBgSUlrxeZWhQoVNG/ePA0fPlyjRo3SqFGj1KhRI0VERGjw4MG67777cj2XS7du3VStWjUZYxQYGKjGjRurZ8+eatiwYZZ19+3bV5J0+fJlbd++XcePH9dzzz2nli1bKjIyMs81FH15WStZkmZL8lXSmslS0rrJs3U7YM7rfHmd333ou4LpO5dSpUpp4sSJeuSRRzRhwgStX78+x3O8swcBAAAAAN6EgBkFYubMmapfv36O44YNG6Y9e/aoX79+GjNmjEJDQxUcHCxfX199/vnn6tGjh5KWeMkoICAg0/2JiYmSboexWXE9HCy11L/W7w6DBw9WVFSUli9fri+++EJfffWV5s2bp3nz5unhhx/WO++8k6f5nn32WbVv3z7X46tVq6YFCxakbMfHx+u//uu/9Oabb+qhhx7S/v37VaZMmTzVgKKLvkvi7r5L7aGHHtILL7ygDRs2aMOGDTmOpwcBAAAAACUdATM85uLFi1q3bp1q1qypTz75JEPI9MMPPziat3bt2pKkxx57TL169cp3nflVpUoVDRs2TMOGDZMkbdy4Uf3799e7776rP/3pT4V6B6Ofn59ee+01xcbG6vvvv9err76qZ599ttCuXziW5XF7pG6vuazk70fmY768zl+46Lv89Z2vr68mTZqkIUOGaMKECZo8eXKezveOHgQAAAAAeBMe8gePOX/+vKy1qlmzZqZ3MH788ceO5o2KipKU9NCxgla6dGlJSXcl5lbHjh1T1kv+9ttvC6Su7JQqVUpTp06VJL388su6cuVKoddQdMQq7ZrLEbq9ZnJsAc/vGfRd/vtu0KBBatq0qTZv3qw1a9bk+Xx6EAAAAABQkhAww2Nq1Kih4OBg7d27V5s3b07Zb63V1KlTtWXLFkfzDhgwQKGhoXr33Xf1/PPPZ1i/NSEhQatWrUpzTadca9vu378/w7GdO3dq6dKlunnzZpr9ly9f1saNGyVlvfZsQevXr5+aNWumX375RW+++aZHaih4vXV7PeTMtqsraV1kV/jrOh6RvC8yeUxu58vr/J5Rkvru8OHDGY4VRt/5+Pik3Ln8xhtvOJrDO3oQAAAAAOANCJjhMaVKldIzzzyjW7du6d5771Xnzp01ePBg3XHHHYqJidHTTz/teN5ly5apbt26Gj9+vOrVq6eoqCgNHDhQ7dq1U0hIiLp3767du3fn+z307NlTkjRw4EANGDBAjz76qB577DFJSeFX3759VbVqVXXq1ElDhgxRz549VbduXe3du1ft27fX/fffn+8anDDGaNKkSZKkWbNm5ekhaiVDrKRTSnrgXkQmxyOSj52SszuZczO/Z5Skvhs1apTH+q5fv35q3ry5rl696uh8ehAAAAAAUFKwBnMBCgmRTp3ydBU5y+SZW4XmueeeU7169TR79mxt27ZN/v7+at26td555x1dvXpVs2bNcjRvkyZNtGfPHs2ZM0dLly7Vtm3bFB8frxo1aqhVq1bq1auX+vfP/zIFAwYM0I8//qh58+bp008/1c2bN+Xr66u33npL7dq107Rp07RhwwYdOHBAmzdvVvny5XXHHXfo4Ycf1rBhw+Tn57kW7NOnj5o3b649e/boH//4h5544gmP1eJOt59Nl93ayBGS0j/ELv34kzkcz+/8nlMS+m7Xrl1avHixx/rOGKMpU6bka73pktqDAAAAAADvYqxNH4J4t5YtW9q4uLgcx+3bt09hYWGFUBEAID3XsjShoaEerqRo4e8moGSJiIiQJMXGxnq0DgAoKPycA4DixRiz01rbMv1+lsgAAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABH/DxdQIm2pLp0/ZSnq8hZQIjU96RbpjLGZNgXHBysxo0bq0+fPho1apSCgoLccq28GDp0qD744ANt2rRJ7du3L/Trp/fll18qKioqzT4fHx9VrlxZLVq00BNPPKE//vGPaY673kNq/v7+qlmzpjp27KgxY8aoadOmBV57kfdhxv8GsxL7ndT/NWnRk1LEnVnvcyq7+c9ctPmbPBX6LndS9127du309ddfZzrOVfdHH32kQYMGZdifGj0IAAAAAPB2BMwFqTiEy1KB1NmvXz+VLVtW1lodP35cW7du1a5du7R48WJ9/fXXKlu2rNuvWRwFBwerb9++kqSbN2/qu+++0+rVq7V69WpNnDhRU6ZMyXBOeHi4mjVrJkk6f/684uLi9M4772jhwoVatWqVIiMjC/U9FGcRdyaFv64QWHJfuJzT/AWBvsu9zZs3a82aNerSpUuez6UHAQAAAAC4jYAZBWLmzJmqX79+yvbBgwfVvn177d27V6+++qrGjx9fqPXMmDFDzz33nOrVq1eo181JtWrVtGDBgjT75s2bp7/85S+aNm2aBg0apLCwsDTH+/btq+eeey5l+/r163rooYe0ePFiPfHEE9q3b19hlF5k9X456euyUbnbnr1a8vWRIv8naTukfNI+V8Cc1/lyPf+0/L3PzNB3uRMYGKhr165pwoQJjgJmehAAAAAAgNtYgxmFonHjxho1KilxW716daFfv0aNGmrSpIkCAwML/dp59eijj6pDhw5KTEzU8uXLcxwfEBCg2bNnS5K+//57HT16tKBLRDFB32WuQ4cOatasmXbs2KEVK1bkez56EAAAAADgzQiYUWhca5OePn060+PWWn3wwQeKjIxUxYoVFRAQoLCwMMXExOjatWsZxl++fFkvvviimjdvrgoVKqhMmTL6zW9+o169emnx4sVpxg4dOlTGmDRrrrr2Zfd6//3308xz5coVPf/882revLnKlCmj4OBg3XPPPXrvvffy+/GkER4eLkk6duxYrsbXqlVL5cuXl5T15+stlo26ffdwbrZHdpUSEqUN45NeCYlJ+5zOl9f5C1pJ7buBAwfq008/dfSZGGNSlp+ZOHGirM3/etj0IAAAAADAW7FEBgrNpUuXJCUtC5FeYmKiBg8erE8++URly5ZVq1atVKFCBcXFxWnSpElatWqVNmzYoICAAElSfHy8OnXqpO3bt6ty5crq2LGjgoKC9OOPP2rDhg26ceOGHnjggWzr6dixo/z8Mm+Bjz/+WNevX5evr2/KvpMnTyoqKkrffvutatSoocjISCUkJGjr1q16+OGHtWvXLr3yyitOP540XJ+Vv79/rsYnJCTo6tWrkjL/fJG5zB7Cl3rN5IJ4yJ9r/jMFsERGZkpq323evFljx47ViRMnHPVd79691aJFC+3cuVNLlixRv3798jxHavQgAAAAAMBbETCj0Lh+Rb9r14y3b06fPl2ffPKJIiMj9eGHH6p69eqSpBs3buivf/2rFixYoGnTpmnatKRULjY2Vtu3b1fr1q0VGxubEoBJ0tWrV/Xvf/87x3qGDx+u4cOHZ9g/ZcoUXb9+XW3atFGfPn1S9kdHR+vbb7/VyJEj9eKLL6aEvydOnND999+v2bNnq3v37oqKisrDp5LRtWvXtG7dOklKeZBYTtavX69bt24pLCxMdevWzdf1S4qc1kau/oR06kLSXcURd6Y9vujJpDWTQ8pLJ9/I3Xx5nb+wlNS+27Rpk/7617/mq+9iYmLUo0cPTZo0SX369JGPj/Nf6qEHAQAAAADeiiUyUKCstTp27JgmT56s9957T/fcc4+eeuqpNGNu3rypmTNnqkyZMlq4cGFKyCUl3cH7t7/9TVWrVtVbb72V8qvsZ86ckZS0lmrqkEuSgoKC1KZNG0f1Llq0SFOmTFHdunW1bNmylLnj4uK0du1a/f73v9esWbPS3Flco0YNvfnmm5KU8tWJW7duae/everbt6+OHTumqlWr5nhX5fnz57V8+XINGzZMZcuW1dy5c2WMcVyDt4j9Lin8DSmf+V3KEXcmHTt1IWlsQcxfkLyh76pVq5ayzIXTvuvevbvatGmj//znP/rkk08czUEPAgAAAAC8HXcwo0A0aNAgw74ePXpo6dKlKlWqVJr9cXFxOnfunLp27Zrpr5YHBQXp7rvv1po1a/R///d/atiwocLDw+Xj46O5c+cqLCxMffr0UcWKFfNVc1xcnKKjoxUUFKTly5crJCQk5djatWslSX379s30LseWLVsqMDBQ27dvz9M1Dx06lGkYVaNGDS1ZskTBwcEZjk2YMEETJkxIs69KlSravn27wsLC8nT9kiz1Osipt13LVrjuLM5q/Mk30i5xkdV8TucvCN7Wd3fddZejvktt6tSpioqK0uTJk9W/f/80y3NkhR4EAAAAAOA27mBGgejXr5+io6M1ePDglAfWrVy5Us8//3yGsUeOHJGU9Kv8WT30a82aNZKks2fPSpKaNGmiGTNm6OrVq/rzn/+sKlWq6Le//a2efPJJ7dixI8/1/vTTT+rVq5euX7+u999/X7/73e8yrXHcuHGZ1ufj46Nr166l1JdbwcHBio6OVnR0tIYNG6ann35aH374oX744Ycs7wYNDw9XdHS0Hn74YXXp0kX+/v46e/asBg8enLIGLDKX2ZrI2Ym48/aaybm5kzmv87ubN/VdkyZNFBYW5qjvUuvcubPuvfde7d+/Xx988EGuzqEHAQAAAAC4jTuYUSBmzpyp+vXrp2x/9NFHGjJkiGJiYtS9e3e1atUq5VhiYqIkqXHjxmrbtm2281aqVCnl+1GjRmnAgAH69NNP9eWXX2rTpk2aM2eO5syZowkTJigmJiZXtV69elW9evXSzz//rBdeeEG9e/fOMMZVY4cOHfSb3/wmy7lyc/djatWqVdOCBQvydE7fvn313HPPpWwfO3ZMkZGR2rt3r8aPH++2Bw0WV1mtjTyya1L46+sjzV59OwDOaS3l2auTznEFx7NXu2n+B93zflPzpr67cOGCJKl8+fJ57rv0pk6dqo4dOyomJkYPPpjzHww9CAAAAADAbQTMKBSDBw9WbGys3n77bY0dO1br169POVa7dm1JUtOmTfMcttauXVsjRozQiBEjlJiYqOXLl+vBBx/UtGnTFB0drYYNG2Z7vrVW0dHR2rlzpx566CGNGzcuy+tISXeIpl/L1tPq1q2rBQsWqGPHjnrjjTf01FNPpQkZvc22H6Q2jTLuSx8Q50WbRmkDZHfPX1BKct/t379fkhQaGpqn2jPToUMHRUVF6YsvvsjzZyHRgwAAAAAA78YSGSg0kydPVmBgoDZs2KDNmzen7G/durWCg4O1YcMGnT9/3vH8Pj4+6t27tzp37ixrrb77Luc1DSZNmqTFixerbdu2mjt3bpbjoqKiJElLly51XF9B6tChg3r06KGbN29q+vTpni7HoxISk8Jgl5Fdk/a5lq1YNirtesi53XYtl+Hu+QsafZc7U6dOTfl68+bNPJ9PDwIAAAAAvBUBMwpNjRo19Nhjj0mSpk2blrI/MDBQo0eP1oULF9S3b18dPnw4w7kHDhzQ/PnzU7bXrVunL7/8MuVX6F1OnTqlnTt3SpLq1KmTbT0LFy7U1KlTVa9ePS1dulT+/v5Zjm3Xrp0iIyP11Vdf6cknn9SlS5fSHLfWatOmTVq1alW21yxIU6ZMkSTNnz9fP//8s8fq8LTUaya7e03k9Gsye3rN5dyg73KndevW6tGjh44dO6aVK1c6moMeBAAAAAB4I5bIQKEaN26c3nrrLa1evVq7du3S3XffLUkaP3689u/frw8//FBNmjRReHi46tevr3PnzunIkSM6ePCgWrRooUceeUSStHv3bo0ZM0ZVqlRRixYtVKVKFZ07d04bN27UlStXNHDgQDVv3jzbWsaOHStJqlWrlp555plMxzz22GO65557JCWtZ9ulSxfNmTMn5YFkISEhOnHihA4ePKgTJ07o6aefVrdu3dz1ceVJixYt9Mc//lErVqzQzJkz9fLLL3ukDk9zhcCR/5O0vWF80r6c1lrOy7Y75i9MJbHvDh06pKNHj+rMmTNu67uYmBitXLnS8YP66EEAAAAAgDciYC5IASHS9VOeriJnASGFdqmQkBA9/vjjevnllzVt2jQtWbJEUtLD8T744AP1799fc+fO1Y4dO7Rr1y5VqlRJderU0bhx4zRw4MCUeXr16qXz58/rq6++0jfffKOzZ8+qcuXKatmypf7yl79o0KBBOdaSkJAgSdqyZYu2bNmS6ZjOnTunBF0hISHatm2b3nrrLX388cfavXu3rl27ppCQEIWGhurpp5/O1XUL0uTJk7VixQq99dZbevbZZ1WlShWP1uMRD1pFSLLT0u5O/1C9/Gy7Y/7CVBL7rnLlymrQoIHGjh3rtr67++671adPn3wtyUEPAgAAAAC8jbHWerqGIqVly5Y2Li4ux3H79moPu9sAACAASURBVO1TWFhYIVQEAEjPnQ/5K0n4uwkoWSIiIiRJsbGxHq0DAAoKP+cAoHgxxuy01rZMv581mAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMKxLVr1/Taa68pKipKNWrUUOnSpVW+fHm1aNFCo0eP1r59+9KMnzx5sowxWrBgQa6vceTIERljFBERkWZ/bGysjDH605/+lKea69evL2NMns4pKiIiImSMSfMKCgrSnXfeqdGjR+v06dNpxrs+u9QvHx8fVaxYUW3bttWcOXMUHx/voXcDp+i7wpW671avXp3pGNfn1ahRo0z304MAAAAAgOLOz9MFlGTVZ1bXqSunPF1GjkLKhOjk6JNum2/79u3q27evfvrpJwUEBKh169aqWbOmLl26pN27d2vWrFl65ZVX9M9//lPR0dFuuy6kdu3apQRZp0+f1rZt2zRr1ix99NFH2rJli+rVq5fhHNefQUJCgo4cOaItW7Zo69at+uyzz/T555/L19e3UN9DfpkpyWHlR8k7Bivj9mFJ70gqI2lMJsclaYakK5KiJTXIYT4H89tJNk/vKyf0nWdNnDhRXbt2dXRuSetBAAAAAIB3IWAuQMUhXJbcW+c333yjyMhIXb16VaNGjdLkyZMVHBycctxaq3Xr1mn06NE6fPhwvq5Vq1Yt7du3T0FBQfktu8R49NFH09xBeubMGXXv3l1xcXEaPXq0Fi1alOGc9Hev/utf/1JERITWrl2rhQsXasiQIQVcdQEZnMX2YUmLdDs4zmr8mFRj+2czn9P53Yi+86zAwEDt2LFDy5cvV8+ePfN8fontQQAAAACAV2CJDLiNtVZDhw7V1atX9dxzz2nWrFlpQi5JMsaoc+fO+te//qX7778/X9crVaqUmjRporp16+ZrnpKsatWqmjVrliRp5cqVunXrVo7ntG7dOiWkzurX/out1IFxgxzGKnlM/+RzcpPL5nV+N6DvPG/EiBGSku5itjb/d6aX6B4EAAAAAJQ4BMxwmzVr1ujf//63atWqpYkTJ2Y71t/fXy1btsz02LfffqvevXurUqVKCgoKUps2bbRq1aoM47JaCzY7165d0/jx49WgQQMFBASoYcOGmjRpkm7evJnp+NTryp48eVKPPvqoateuLT8/P82ePTtlnLVWH330ke677z5VrFhRAQEBCgsL0+TJk3X16tUM87rWbj1y5Ig+/fRTtW3bVmXKlFGFChXUu3dv7d+/P9fvKSfh4eEp7/3s2bO5Oqdp06aSlGHt5mLhI91ejiL1tiv8NZK25WK8y7bkc1whs7vnzyf6zvN916tXL7Vs2VJ79+7V//7v/zqaI71i3YMAAAAAAK9CwAy3WblypSSpf//+KlWqlKM5du7cqdatW+vAgQP6wx/+oN/+9rcpd12uW7cuX/XdvHlTXbp00fPPP68LFy6oR48eCgsL04wZM/TAAw9ke+fhmTNn1KpVK61cuVL33HOPunXrlrJEQGJiooYOHaoHH3xQO3bsUPPmzdW9e3dduXJFU6ZMSVm6IDN///vf1bdvX/n6+qpHjx6qUqWKPv30U3Xo0MFtwdKlS5dSvvf398/TOdWqVXNLDYXqxyz2ue4sru1gztq6fSdzQcyfD97cd2PGjCkyfRcTEyMp6cGJiYmJjuZIrVj3IAAAAADAq7AGM9xmz549kqQWLVo4nuP111/X9OnT9cwzz6TsmzlzpsaMGaOpU6eqU6dOjud+5ZVXtGnTJoWHh+uLL75Q5cqVJUmHDh1Sx44d9fPPP2d57ueff64+ffroww8/VEBAQJpjs2bN0ocffqiOHTtq4cKFqlGjhqSkYO2JJ57QP/7xD8XExOjFF1/MMO9rr72m9evX695775Uk3bp1SwMGDNCyZcv0xhtvaPLkyY7fr8uKFSskJa2dW6lSpVyd4/q1fKcPLfMoK6lNqu02SrtsRfqlK3K7trJ0O2R25/z55K19N3/+fH322WdFpu+6deume+65R1u3btXChQv14IMP5nmO1Ip1DwIAAAAAvAp3MMNtXMsvVK1a1fEcrVu3ThNySdJTTz2lihUrasuWLblaQzgrb7zxhqSkQNgVcklSw4YNNWHChGzP9ff315w5czKEXPHx8XrppZcUFBSUJuSSpNKlS2vOnDmqXr263n777UzvanzqqadSQi4paX3b8ePHS5K++uqrvL/JVM6cOaP58+enfJ6PP/54tuMTExN16NAhPf7449q4caN69+6tgQMH5qsGj0i9ZrK710ROvyazB9ZcTs9b+27evHkKDAwsUn03depUSdKUKVOUkJCQ5/NLTA8CAAAAALwKATOKlG7dumXYV6pUKTVo0EC3bt3K9RrC6R07dkzHjh1TtWrVFBkZmeF4Tncb3n333apVq1aG/bt27dLZs2fVtm3bNCGXS2BgoFq0aKFff/1VBw8ezHA8s/cbGhoqSdne2ZmVRx55RMYYGWNUrVo1DRs2TBcvXlR0dLTGjRuX6Tmu8b6+vmrUqJHefPNN/fWvf9WSJUvk51cMf8nBFQK/k/xyhb85rYWc2213zV+EFMe++/XXXxUeHl4k+s6lU6dOioiI0IEDB/T+++/n+rwS14MAAAAAAK/C/3OF21SpUkVS0p2zTtWpUyfT/cHBwZKkGzduOJrXFRrVq1cv0+PlypVThQoVdP78+UyP161bN9P9R44ckSR9+eWXMsZkW8PZs2dTQiyXzN5vft5ru3bt1KhRI0lSQECA6tWrp27duql58+ZZnhMdHS1Jun79uvbs2aP9+/frzTffVJs2bVKOoejy5r7bsmVLkei71KZOnaoOHTooJiZGQ4YMydU59CAAAAAAoDgjYIbbNG/eXF9//bV27typoUOHOprDx6dgbqrP7kFiuZH+V/RdXL9+36hRI7Vr1y7bOVIvD+Di7vf76KOP6k9/+lOezlmwYEGa7Zdeekljx47ViBEjFBkZmWXIV2S5lq1w5XKuJSzystZydtvumt9NvLnv6tWrp4iIiGznKIy+S619+/b6wx/+oLVr12r+/PmKiorK8ZwS14MAAAAAAK9CwAy36dGjh15//XUtWrRIM2bMKFK/2l2zZk1J0tGjRzM9fvHixSzvosxO7dq1JUlNmjTJEBIVV88884zWrVuntWvXavLkyfrnP//p6ZLyJv2ayP0z2edUZmsuu3N+B7y57xo0aFAk+27q1Klau3atpk2blmat59wq9j0IAAAAAPAqrMEMt+nSpYuaNWumn376STExMdmOvXHjhuLi4gqpsqQ7HevUqaPTp09n+hCvhQsXOpq3VatWKl++vL766iudO3cuv2UWGdOnT5cxRu+9917KcgTFhpG0LdX2tuR9rgfzOV172RUuu3v+fPLWvgsODtaOHTuKZN/9/ve/1/33369jx45p7ty5juYo1j0IAAAAAPAqBMxwG1cYEhQUpKlTp2r06NG6dOlShnGxsbFq27atPvvss0Kt7/HHH5ckPf3002lCqcOHD+cYzGXF399fzzzzjC5duqQ+ffro0KFDGcYcPHiw2N2B2Lx5c/Xq1Uvx8fGaPn26p8vJm9pZ7HPdafyjgzl/1O27lAti/nzw1r7785//rCtXrhTZvouJiZExRm+88Yaj84t1DwIAAAAAvAoBM9yqWbNmWr9+vWrVqqVZs2YpJCRE9913n4YMGaKePXuqbt26ioyM1N69e9WwYcNCre3pp59Wu3bttHPnTjVq1EgPPPCAevbsqbvuukvh4eGO1zkdN26cHnroIW3cuFFhYWFq3bq1Bg4cqC5duig0NFR33HGHXnvtNTe/m4I3efJkGWM0f/78lIe1FQuDlXa9Y9d2AyWFwFZSm1yMd2mTfI5rCQx3z+8G3th3w4cPV69evYps34WHh6tPnz66evWq4zmKbQ8CAAAAALxK0VmsswQKKROiU1dOebqMHIWUCXHrfK1bt9aBAwc0b948LV++XN988402bdqkoKAgNW7cWIMHD9ajjz6qxo0bu/W6OSldunTKuqgffPCBVqxYoZo1a2rkyJGaNGmS7rjjDkfz+vj46N1331W/fv00d+5c7dixQ7t371bFihVVp04dPfPMMxo4cKCb303B+93vfqc+ffpoyZIlmjlzpl5++WVPl5QrdlIOD5Zb4GDSvJzjZH438Ma+mz59uh555JEi23dTpkzRsmXLUh5KmFfFtQcBAAAAAN7FWJtDGONlWrZsaXOzRum+ffsUFhZWCBUBANLbv3+/JCk0NNTDlRQt/N0ElCwRERGSkpY5AoCSiJ9zAFC8GGN2Wmtbpt/PEhkAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAFzPlhrPV0CAACS+DsJAAAAAOAZBMwO+fr6KiEhwdNlAAAgSUpISJCvr6+nywAAAAAAeBkCZoeCgoJ0+fJlT5cBAIAk6fLlywoKCvJ0GQAAAAAAL0PA7FC5cuV07tw57mIGAHhcQkKCzp07p3Llynm6FAAAAACAl/HzdAHFVXBwsK5du6ajR4+qUqVKKlu2rHx9fWWM8XRpRV7cz3GFer2WNVsW6vUAoDBYa5WQkKDLly/r3LlzKlOmjIKDgz1dFgAAAADAyxAwO2SMUbVq1XTp0iVdvHhRp0+f5m7mXDp7/myhXm/fhX2Fej0ABe/kyZOSpMTERA9X4lm+vr4KCgpSlSpVFBwczD9yAgAAAAAKHQFzPhhjVK5cOX4lOY/unHJnoV7PTrKFej0ABe/xxx+XJMXGxnq2EAAAAAAAvBxrMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARP08XAAAAAKD4MVNMoV7PTrKFej0AAADkTpEImI0xdSX1ldRJ0u8khUi6Ken/JK2S9Kq19kQ255eWNFLSg5IaSYqXtE/SfElzrbX8r1EAAAAAAJAnhfmPafxDGoDiyuMBszGmjqQjklL/1L4oqYykZsmv4caYftbaDZmcX07SekktknddlRQoqU3y64/GmD7W2vgCexMAAAAAAAAA4IU8HjBL8k3+ulLSAknrrLW/Jt+V3EnS3yQ1kLTMGBNqrT2Z7vy5SgqXz0mKTp7HR9JQSW9Kul/SFEnjC/h9AACQgrtdAAAAAADeoCg85O9XSeHW2vuttYuttb9KkrX2prV2laTukq5LKifpsdQnGmPCJQ1I3nzEWvuZTZJgrX1H0rjkY//PGFOtUN4NAAAAAAAAAHgJjwfM1toL1tq92Rz/XtK25M0W6Q4/mPx1v7V2eSanvy3pgpKWzOib31oBAAAAAAAAALd5PGDOpV+Sv/qm2x+Z/HVtZidZa69J2pS8eV8B1AUAAAAAAAAAXqvIB8zGGD9J7ZI3v02130hqkrz5n2ym+C75653urw4AAAAAAAAAvFeRD5gljZBUXVKipHdT7S8nqUzy9z9nc77rWA33lwYAAAAAAAAA3qtIB8zGmGaSnk/efN1am/pO5TKpvr+WzTRXk7+WzeY6w40xccaYuDNnzjgrFgAAAAAAAAC8TJENmI0xNSQtkxQkaaeksemHpPre5uda1tq3rbUtrbUtq1atmp+pAAAAAAAAAMBrFMmA2RhTSUkP7msg6aCkHtba6+mGXU71fVA207mOXc5mDAAAAAAAAAAgj4pcwGyMKS9pjaS7JB2T1NlaeyqToRclXUn+vmY2U7qOnXBbkQAAAAAAAACAohUwG2PKSPpcUktJJ5UULh/LbKy11kral7zZNJtp70z++p276gQAAAAAAAAAFKGA2RgTKGmFpLaSflFSuHwwh9M2JH+NymLOAEkdkjfXuaNOAAAAAAAAAECSIhEwG2NKS1oiKVLSeUl/sNb+JxenfpT8tYkx5v5Mjv9FUnlJ1yQtdUetAAAAAAAAAIAkHg+YjTG+kj6U1FXSJUndrLW7cnOutXa3pE+SNxcYY7q75jTGPCxpevKxV6y1p91bOQAAAAAAAAB4Nz9PFyCpnaR+yd+XkrTMGJPV2OPW2lbp9v1FUkNJLSStNMZcleQryT/5+GeSJrm1YgAAAAAAAABAkQiYU99FHZD8ysr19DustReNMW0l/T9JgyU1knRD0m5J8yXNTX4gIAAAAAAAAADAjTweMFtrYyVlectyLue4qaTlMKbnNBYAAAAAAAAA4B4eX4MZAAAAAAAAAFA8ETADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAAAAAAAADAEQJmAAAAAAAAAIAjBMwAAAAAAAAAAEcImAEAAAAAAAAAjhAwAwAAAAAAAAAcIWAGAAAAAAAAADhCwAwAAAAAAAAAcISAGQAAAAAAAADgCAEzAAAAAAAAAMARAmYAAAAAAAAAgCMEzAAAAAAAAAAARwiYAQAAAAAAAACOEDADAAAAAAAAABwhYAYAAAAAAAAAOELADAAAAAAAAABwhIAZAAAAAAAAAOAIATMAAAAAAAAAwBECZgAAAAAAAACAIwTMAAAAAAAAAABHCJgBAAAAAAAAAI4QMAMAAAAAAAAAHCFgBgAAAAAAAAA4QsAMAAAAAAAAAHCEgBkAAAAAAAAA4AgBMwAA+P/s3X2wbWldH/jv092o0LxI6LZbvaN2CsKLio5psCel1m0cX0aDdKmdEagMVHQIQ2VQUHyDAK2iiVjYJjMmYiWxrUBP2QpYWIaX0nviTPRq2orDSPtGbJ1pCW0TtIEWtIVn/th7c/bdvc9+ec5ea6+9zudTdeqcddbaz3nu767122v/9lq/DQAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmgygwl1IeVUr5ulLK95dS/l0p5f2llDr9etKax9YNvr6xr38LAAAAAMBZccW+JzD15UnefMox3p/kYyes++gpxwYAAAAAYMFQCsxJ8qdJ7kzyH5P8SZLXb/n4p9Va/2jXkwIAAAAAYLmhFJjfWmt9y2yhlPI5+5sKAAAAAACbGEQP5lrrSa0tAAAAAAAYqEEUmAEAAAAAODxjKjD/TCnlz0opf1lKuaeU8nOllK/d96QAAAAAAMZqTAXmpyW5PMmDST4zydcn+YVSys+UUj5przMDAAAAABihMRSYb0vy1UkeW2t9dK31kUmenOTfTNffnOR/WzVAKeUFpZQ7Syl33nfffd3OFgAAAABgJA6+wFxrfX6t9e211j+f+93v1lr/QZLXTn/1LaWUJ60Y4/W11utrrddfffXVXU8ZAAAAAGAUDr7AvMYtST6SpCTRjxkAAAAAYIdGXWCutT6Q5Leni39zn3MBAAAAABibUReYp8r0e93rLAAAAAAARmbUBeZSypVJPne6+Ed7nAoAAAAAwOgcdIG5lFLWbPKPkzw8k6uXf7H7GQEAAAAAnB1X7HsCM6WUq+YWHzv386curPtArfXj059/ppTy+0nenORdtda/mo71xCTfkeRbptvdVmu9q6OpAwAAAACcSYMpMCe574Tf/9rC8nU5bndxdZJvTPK9ST5WSrk/yScnuXJu+59N8sLdTRMAAAAAgGRYBeYWP5jkXUluSHIuyd9I8vEkdye5mOSnaq3v2N/0AAAAAADGazAF5lrrun7Kyx7zjiQKyAAAAAAAe3DQH/IHAAAAAMD+KDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJo0F5hLKZ9USvmMUspjdzkhAAAAAAAOwxWbblhKeVSSb0ryFUm+LMnVc+v+Osm7kvxykjfVWn99x/MEAAAAAGBg1haYSymfmeQfJ3lukiunv/7zJL+X5ANJHp7kcUm+MMnfTvIdpZTfSvIjtdbbu5g0AAAAAAD7t7LAXEq5Jcm3J/nkJO9M8n8k+Q+11v+8ZNtHJHl6kq/KpBj9hlLKtyZ5Qa31XbueOAAAAAAA+7WuB/PLkrw+yWfVWr+m1vrTy4rLSVJr/Yta61Gt9XuSfHaSZyV5WJKbdjpjAAAAAAAGYV2LjMfXWt+77aC11prkrUneWkq5tmlmAAAAAAAM2sormFuKy0vGeN9pxwAAAAAAYHjWtcjYWCnlsaWUK9dvCQAAAADAGGxVYC6lfHkp5YdLKY+d+92nlVL+fZL3J/lAKeV1u54kAAAAAADDs+0VzP9rkq+vtf7Z3O9+JMmXJnlPkv+a5FtLKX9vR/MDAAAAAGCgti0wf0GS/2u2UEp5eJJvTPLOWusTkzwxyf+X5IU7myEAAAAAAIO0bYH505LMf/DfFyf5lCQ/lSS11g8l+YVMCs0AAAAAAIzYtgXmv0zy8LnlL01Sk/zK3O8+mORvnHJeAAAAAAAM3LYF5ruTPGNu+RuS/EGt9U/mfvffZPKBfwAAAAAAjNi2Bebbknx+KeXXSyn/Z5LPT/LGhW2+KMnv7WJyAAAAAAAM1xVbbv8vktyQ5H9MUpK8Nck/na0spTw9yZOT3L6rCQIAAAAAMExbFZhrrQ8meU4p5YWTxfqhhU3+MMl/m+SPdjM9AAAAAACGatsrmJMktdYPnvD790f/ZQAAAACAM2HbHswAAAAAAJBkTYG5lPJ/l1Ke1TJwKeXTSik/Vkr5rrapAQAAAAAwZOuuYP6zJG8upfxOKeW7SinXrdq4lPLJpZT/oZRyeyZ9mJ+X5N27mSoAAAAAAEOysgdzrfV8KeUbk/xAkh9K8oOllPcluTPJf8mkAP0pSR6X5ElJnprkYUkeTPJTSV5Za/3TzmYPAAAAAMDerP2Qv1rrzyb52VLKVyT55iQ3Jnnmkk0/luS3kvxckn9Va71vlxMFAAAAAGBY1haYZ2qt70zyziQppTwxyWdlcuXyR5L8aZJ311o/2MUkAQAAcTLEugAAIABJREFUAAAYno0LzPNqrb+X5Pd2PBcAAAAAAA7Iug/5AwAAAACApZquYC6lPDXJc5I8OcmVtdb/fvr7z0ny9CTvrLX+2Y7mCAAAAADAAG1dYC6lfF+S783x1c91bvVlSW5P8m1J/vmpZwcAAAAAwGBt1SKjlPJNSV6RyYf9fWGSH5pfX2v9wyR3Jvm6XU0QAAAAAIBh2rYH84uTvCfJs2qt70ryV0u2+Z0kTzjtxAAAAAAAGLZtC8yfn+TttdZlheWZ9ya5pn1KAAAAAAAcgm0LzCXJx9dsc02Sj7ZNBwAAAACAQ7FtgfkPkvydk1aWUi5P8iVJ3n2aSQEAAAAAMHzbFph/JskXlVK+/YT135Pk8UneeKpZAQAAAAAweFdsuf2tSW5O8sOllL+XpCZJKeVHknxpkuuTXEzy+l1OEgAAAACA4dmqwFxr/Ugp5cYkP5bkuUkun656aSa9mf9tkn9Ua/3rnc4SAAAAAIDB2fYK5tRa70/y/FLKS5M8Lcnjktyf5DdqrffteH4AAAAAAAzU1gXmmVrrB5K8fYdzAQAAAADggGz7IX8AAAAAAJCk4QrmUsojknxzki9Mci7Jw5ZsVmutX37KuQEAAAAAMGBbFZhLKU9N8o4kVycpKzatp5kUAAAAAADDt22LjFszKS6/KsnnJHlYrfWyJV+X73qiAAAAAAAMy7YtMm5I8nO11h/oYjIAAAAAAByOba9g/nCSP+5iIgAAAAAAHJZtC8y/nOSLu5gIAAAAAACHZdsC8/cmeXIp5btLKas+5A8AAAAAgJHbqgdzrfUPSylfkuRXk/zPpZTfSnL/8k3rN+9iggAAAAAADNNWBeZSyrkkP5/ksdOv607YtCZRYAYAAAAAGLGtCsxJbk3yt5L86yS3JXlvkr/e9aQAAAAAABi+bQvMz0jy9lrrt3QxGQAAAAAADse2H/J3WZL/p4uJAAAAAABwWLYtMF9M8nldTAQAAAAAgMOybYH55UnOl1K+qYvJAAAAAABwOLbtwfy1SX45yRtKKS9M8ptJ7l+yXa21fv9pJwcAAAAAwHBtW2B+9dzPXzb9WqYmUWAGAAAAABixbQvMN3YyCwAAAAAADs5WBeZa67/vaiIAAAAAAByWbT/kDwAAAAAAkigwAwAAAADQaGWLjFLKx5N8PMlTaq2/P12uG4xba63b9ncGAAAAAOCArCsC/0omBeW/WFgGAAAAAOCMW1lgrrWeX7UMAAAAAMDZtbYHcynlfyqlPLWPyQAAAAAAcDg2+ZC/n0pyU8fzAAAAAADgwGxSYAYAAAAAgIdQYAYAAAAAoIkCMwAAAAAATa7YcLtPLaV81jYD11r/34b5AAAAAABwIDYtMH/r9GtTdYuxAQAAAAA4QJsWgT+Y5M+7nAgAAAAAAIdl0wLzj9Zav6/TmQAAAAAAcFB8yB8AAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACarP2Qv1qrIjQAAAAAAA+heAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoMkgCsyllEeVUr6ulPL9pZR/V0p5fymlTr+etMHjSynlBaWUXyul/Hkp5UOllP9USnlZKeWT+vg3AAAAAACcNVfsewJTX57kzS0PLKU8LMlbknzN9Fd/leRjSb5w+nVzKeUZtdYP72KiAAAAAABMDOIK5qk/TfKLSW5J8oItHvcDmRSXP5rk+UkekeTKJM9M8oEkT0vyE7ucKAAAAAAAwykwv7XWek2t9Wtrra9O8s5NHlRKuTbJt04Xv6vWelut9WN14heS/IPpumeXUp66+2kDAAAAAJxdgygw11o/1vjQb0jyyUnuT/L6JeP+fJLfT1KSPKd5ggAAAAAAPMQgCsyncOP0+6/UWj96wjbvmH5/Rg/zAQAAAAA4Mw69wPyU6fd3r9jmrun3J5dSSsfzAQAAAAA4Mw69wPzp0+/vXbHNbN0jp18AAAAAAOzAoReYr5x+/8iKbf5i7uelBeZSygtKKXeWUu687777djY5AAAAAIAxO/QC80w91YNrfX2t9fpa6/VXX331ruYEAAAAADBqh15gfmD6/RErtplf9+EO5wIAAAAAcKYceoF51l/5M1ZsM1v34SgwAwAAAADszKEXmO+afv/cFds8Zfr9d2qtp2qlAQAAAADAsUMvMF+Yfv/SUsqnnLDNV0y//1IP8wEAAAAAODMOvcD8piR/meRTk3zL4spSyjOTPDGTDwG8vd+pAQAAAACM22AKzKWUq2ZfSR47t+pT59eVUj4x51rr+5L82HTxh0spf7+Ucvl0vK9J8m+m626vtb6rj38HAAAAAMBZccW+JzDnvhN+/2sLy9cl+aO55Vck+bwkX5Pkp5P8ZCnlY0keMV3/H5O8cHfTBAAAAAAgGdAVzK1qrQ8meWYmReSLmbTMqEl+K8l3JfmSWuuH9jdDAAAAAIBxGswVzLXWcorHfjzJT0y/AAAAAADowcFfwQwAAAAAwH4oMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABocsW+JwBsrtxSevtb9VW1t78FAAAAwGFyBTMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAk4MvMJdSnl9KqWu+PrzveQIAAAAAjM0V+57ADj2Y5AMnrHugz4kAAAAAAJwFYyow/2qt9fy+JwEAAAAAcFYcfIsMAAAAAAD2Q4EZAAAAAIAmY2qRAQAAMHjlltLb36qvqr39LQDgbBrTFcyfW0p5dynlI6WUD5VSfruU8qOllOv2PTEAAAAAgDEa0xXMVyV5XJI/S/LoJJ87/fqHpZRvqbW+cZ+TAwAAADgL+rxTI3G3BuzbGK5gfm+SVyX5vCSfUmt9XJJHJvnaJHcleXiSny6lfNlJA5RSXlBKubOUcud9993Xx5wBAAAAAA7ewReYa63vqLV+X6313bXWv5r+7i9rrb+Y5O8keU+Sy5P8kxVjvL7Wen2t9fqrr766n4kDAAAAABy4MbXIeIha6/2llB9M8q+T3FBKubrW6hJlOGPcngUAAADQjYO/gnkDvz79XpJ8zh7nAQAAAAAwKqO+gnlq/tJFlxUCwIC54wAAAOCwnIUrmJ8+9/Mf720WAAAAAAAjc9AF5lLKysucSimPTvLd08Xf0H8ZAAAAAGB3DrrAnOSzSykXSynfXEr5rNkvSymfVEr56iT/IcnfSvLxJN+zr0kCAAAAAIzRGHowf/H0K6WUjyZ5IMmjkzxsuv4vkryw1vrL+5keAAAAAMA4HXqB+d4kL07yJUm+IMnVSR6TSZH5D5L8UpJ/UWvVexkAAAAAYMcOusBca/1Ikn8+/QIAAAAAoEeH3oMZAAAAAIA9UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmV+x7AgAMV7ml9Pr36qtqr38PAAAAOB1XMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCZX7HsCAACcXrml9Pa36qtqb38LAAAYNgVmAAAABq/PN9ISb6YBwKa0yAAAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJpcse8JAAAAAABJuaX09rfqq2pvf4txcwUzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAECTK/Y9AQAAAKA75ZbS29+qr6q9/S0AhsEVzAAAAAAANHEFMwAnqk/Y9wwAAACAIVNgBkZPkRQAAACgG1pkAAAAAADQxBXMAACcWX1+8FXiw68AABgfVzADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNrtj3BAAAAAAAWpVbSq9/r76q9vr3hs4VzAAAAAAANFFgBgAAAACgiRYZAAAwEp+4PfT26S+enfble5I8MF2+Msm5hfWznwF6VJ40zXNd5rf57QFYS4EZAADGZrEwsu3yDUnuSPK86fId09+dtD1AX+Q3gMHRIgMAAMbi7h2NcUeSm5NcN/26efq7XYwPsC/yG0AnXMFMkuntlPO3At2d5LZMbhd62fT329569NpMbjt6XiZP3G41AgDoVpfnbzcvGR9gX7rOb16/AmzMFcwsd10mT64PpO2d3Lunj71yOhYAAN3r8vzttOeHALsgvwEMjiuYObb4zuzLcuktRJv2tpo9ZvbO8Enbs7X6hH3PAA7XJz74ambxFslNtDwGoE9dn7/Nj/+dO5gvwLb6yG/LxgfgRK5gZrVte1IpvgCHQn4Dxqrr/DYbH2Af+shvejIDbEWBmYnbF36eX76YpOT4SXZx/Wx59uRdpo85aTyAfek6v8l3wFB0nd/mtwXoUx/5bX58ANZSYGYz53L8Tu49S9bfk+N3hs/1OC+A05LfgLGS34Cx6jq/zY8PwFp6MDPx7BN+XlyePcneMPe7G3LpbUeLtx7pXQUMRdf5Tb4DhqLr/CbfAfvSZ37TDghgI65gZjuLPan0JAXGQn4Dxkp+A8aq6/wmRwJsxBXMHJv1nXr2Bss3J7ltujz7NN4NH19fuasJA2zh9nSe3y5ZdnUfsA995Lf5ZYB96Tq/yXcAG3MFMwAAAAAATVzBzLFNe1HNbjt63nR5dguSXn3AkG3Sa35X+U2+A/ZFfgPOiq7zm3wHsDFXMLOdxZ5Wiz2vAA6V/AaMlfwGjFXX+U2OBNiIK5iZ2KQ36ezTeEuSizn+wIOL09/NntgvnvD46XJ5bt3p1Nepz+n1z3Wqz9iJWzuxa9d57DrOb3r1AYPRdX7Tax7Ylz7y2/z437nDuQOMlCuY2cw9OX6CPrdk/bkcv1N8T4/zAjgt+Q0YK/kNGKuu89v8+ACs5QpmJlb1Jp29czt/29FJj509id+wYjyAfek6v8l3wFB0nd9uCMB+9JHf5scHYC0FZlZb7Gm1znzPK0/IwJDJb8BYdZ3fZuO7bRwORn3CvmewQ33kN+d6AFtRYObYYu+p1yZ5IJNP471uyfpVyzcnuS3JlUletrAeYB/m+4V2ld/mx5+tA+hTH/ltNj7APsyKv13mt2XjA3AiPZhZ7u5MnlyvTNs7t9dNH/tAfPIuMCxd57fZ+AD70Ed+ax0fYBfkN4DBcQUzE3fn+J3Z2W1Bs3duZ9b1qlpcflkuvcVotv7Vp5wrQItnp/v8Nj8+wD70kd8UX4Ah6Dq/uXIZYGMKzEzMf0LuLntOLfa8AtiXXffUW5bf9OyDgzOqvqSJ/AawivwG0AkFZiZKJj2pksltQRdz/AS7TW+rZcsXF8YH2IdZ370u89vi+AD70nV+m+9rD9CnPvLb/PYArKXADAAwAqO7EhcAYADKk8qlb1jck+PPXLkyybm0v+GxbNmbGxwgBWYmao57ht6R5Ia5ddv2tlpcviGX9iR1JTOwD89L9/ltcXyAfek6v3nxyx54I40k8hv9m98ntt1/WvZPOEAKzEzM95ya70l12tu8d93zFKDVYs+9LvLbLscHaHF35Dc4AOX55fhY2uVrpmVj3Z3UrzzluGMhv3EaXe8/s/G/85TjDEh5fuk8vzl+h0GBmYnrcumtGTfnuF/py6a/3/bWjtdmctvI7NN4Z+sB9mF2u1mX+W1+/FfvdPZwppRSlh9fd2fnx+/orojsI7/Njw+06fP111ivyO06v409fmzntPvPNvvnmMwu8vH6a/QUmFnuukwO/gfSdiXM3dPHXtnwWIAudZ3fZuNz5pVbyuSHbQuk6woEJyzXV+5u7nu37vhyfnKyPvJb6/jAyeS3zclv9K3r/Wfsx6/XX2dCqbXuew6Dcv3119c777xz39Po3SdeBC9qud1g3WNe3e8+N6ZdvJzw39SFruO2k31u023tc8363OeSzWN3/vz5JMnR0dHGYz9kn+siv+1JfdV4droTc0NHuozdqfa5hn2t1ytxn9PtPrd2P9jh8dv7FcxbxK4513Wd3/Z0C69c16bruG29z51i/6w/2TzNNl3mujcu3weO7kpu/mfJHS9Ozj9ls6FaHtOprvPclaX7/Lancz15rl2n53Sb7nMH+Pqi831uLteNIr/N6/h8eKhKKb9Za71+8feX7WMyHJD5nqV3b7D9QIsvHIht959t90+YJ7/Rp67z28hyYH3Cmq+vTC68JLnqTcmFB9dvf+HB6bYvmTx2ft0odZ3fZuND0k9+m41/Bpx/yqSQcvM/mxRW1hl08aUrfeQ3ry+Ys+z8ocvzk7GS38ZNiwwmZr1JZz9nbvlikpLjJ+aLC+tn288+TbVMt7luYb3eVSyz7f7Tun9C0n1+k++Y11d+m40/sg9wuul1k+9veeny5Vvfllx+2fELj1vftnz7b/vqyTaXXzbZZvYCZXG80ek6v81vOwLlSdMrrFbF59yS9auW70lSszz+Y9JHfpuNP6I8d9Pr+stv839rFPrIb/PjjynX3VK6z28jPB9ed3zNL+/k+H1mp/+cXvWR3+aX2R8FZjZzLg99App3Tx76BA+bOu3+s27/hFXkN7rUdX6bH/8MuuHxl74AueHxl66/+J6HvoA5U+S3zd2T3cfnrJyf9JHfzuD+Kb+t0fX+Mz9+z+2AetHH+ckZdtrjd8xXMSfy21jpwbxAD+Y1lt1itO1tR/rhNhtlD+Zdth04af+8zT7XatQ9mBftIr/tiX597Trt1/f80n1+m/vdmHown9Sb9CTLbqEc7G2VffRgXnTA+W1er/01uz4/6VEvPZhPY4v9c8g907e2YZ47qPw202ee6zq/9fyBf4Prmb7OqviPqU//luclMwdx/O7xnO4g4rOKHsyXcAUz25nvSTW7WurAXpwwELOeZrvcf07aP0fmVC/kFvsYro3/GXrSlN/YsavelNzxkh2dHD8hOTp3fMKdJDfvcvwDN9/T7xPxOZQXJ32Q39brMj7L4j+iuJ+66Cu/rSS/rdF1fhvRsZpk9294rYo/jt81xGdcFJg5tkmvpdnyzUlumy4/L5Mnlk0fD8kn9p8LL588eazrpbTN8tG55MbXHI8/+3lUtjleF5e3OX7HZFWv+V3lt/nlEfWdo83s5HiX+e2OF1+a35aNPwarepOeNj5jjNcl+shv88tjMSuS7Do+J8X/1buY9DC0Hq+Ly2clv83rOr+NPX6d57cx5rvb0n1+m40/Ml3nt7Efr13nt9H1mj9Al+17AgAAAAAAHCY9mBfowbyBrW+xX6AHc7Mx9WA+esXkH7PrW2DmezbNxn//h0a2zzX2AEuWx2dV/Mtzhxm7TnowJ6fPb3uiB3O7LmN39aNL5/ltb7cQDqwHczKw+Kyyjx7MycHmt3m99UxPdh+fxfiPqKfrac5LZgZ7/A6gB3My4PicpO88N4L8NtN5D+Zdfj7EzEDiP8QezMkBHL97PqcbfHxW0YP5Eq5gZjuLPZvmey7dveJxsOD8Uy7tuXR01+nHXPxAgNn4TJwUn13F/+DJb+xYH/nN8TshPmvIb+t1GZ8l8a9P6O9r6By/q4nPGl3nt7HlyB7y2yfGx/G7hviMiwIzE7cv/LxsefbkUZJcnFt/cfq72ZPUSY+HJW59W3L5ZcdPIje97rifUrL58uzJ6fLLJmPOjz82u47PqviPTtf5Tb5jTh/5bX78sek6v52ZfNdVfhtbrusiPqviPyJ95LcxHq995rexxa6X/DY//th0nd9m449In/ltbMdrH/ltbDE7VD7kj83ck+N3JpedHJ9LckOOn2DO9Tc1Dt8Nj0++7auPn2BuePx2j7/4nuN3PsdYUD6tdfE5bfwPnvy2VrmyXBqfxQ93WYzPNh8Oc0+SmuPxx/RBOuk+v82Pf9+/3M2cD4n8tob8trll//bTxmdd/Eeij/x2Fo9f8Vmj6/w2P/4YdZ3fzh3GHRRdcfyuJj7jpAfzAj2Yl1i87WWVTbbVg7nZmHowL+vFtHiLzCY2ecxQ+wi3OnpF2Wl8Vm071NjtrC/prvPbnvTSr6+L+Awgpn316+sqv81v22uvugH0YD5tftubvnqTdp3f7t5i2x3ptAfzrs6FN3xMr4WXjo/Xo1eUzvPb3o7fPfVgPpj4nKTrPLdNH+EDOz/p/JxuVf/qHcen9wJzh8drn6+/etfzOd3BxWcVPZgvoUUGq237hHPGevqV55eUK8vk+y0bfm3zmIVtx27bnkuDf8LpSFfxOXM9r+S3zXURnwEUl/vUdX47Szkwkd/W6jq/jfW28ZPIbyv1kd9m458F8tsG+shvIz1+H9Kn/SuTCy9JrnpTcuHB9T3dLzw43fYlk8cecv/3bXj9tRviM25aZHBs8dbl1yZ5IMnzMnly3eRW59nyzUluS3JlkpctrB+T27L7+KyL/0jM+iS95aUPXb7jxcmNr0mueUzyvh9fvv21L0ruvT+58PLJE8+q8cami/icFP9RuT3d57f58WfrxqCv/Day9hgzXee3Mea7a1/UfX6bjT86feS32fgjMiuGrNp/js7t7vjNiI7Xm17XT34b4wc3d53fxvj8kOS4+Ov8ZGuz43X2c+L11yb6fP011vOTrvPb/Lkj++EKZpa7O5Mn1yvT9s7tddPHPpBxX+nXVXxOG/8Dd/4pkyePe+9f/k7l0V2Tddc85uxdtZd0H5/58Uep6/w2G/+skt9Wkt8200d+G+2VMH3kN8ev43eJPs5Pxkx+24L8tnPy22p9vv4a4/HbdXxm47NfejAvOLM9mOf7a+7ytqBlY42oB/Op21Ysi8+q+PcYu330YF607BaalrYYQ+0j3Gwau13F5yRHdyU3vmaYsWvuS9p1ftvTbZW99+vbxIDis0pfPZgXdX38dm4PPV13nd8Ooadrc2/SEeW3efs6Xhcd3PE7gP6ag47PKj31dO06vw25T//OPldjUwPNbzNdn9Oty3UHffz20DO9j9dfvX9w8wD79J841tD2Tz2YL6FFBhPzn5C7yyfX+Z5XY/0E3tNYFp+BnNwMwXzPpdltkQdxctOTruMzuhjv+sWD43e1xviMrWffSeS31frIb7Pxe30R1wf5rXOO39XEZzN95Lf58UeX61rJbys5flfr6/xkTPrIb/bPYVBgZqJk0pMqmdwWdDHHT7Db9LZatnxxYfyx2XV81sV/ZNb1Xrr1bcnll016LiWTW2NufdvxE8imvZvGZL532q7icxZ6p335AexTAAAgAElEQVSi716X+W1x/LHpOr/dnuSVO5zvnvWV30Z5vKb7/DYbf7T6OD8Z0blJ6/7Tun+OTdf5bYzx6yO/zY8/Kl5/nYrzk+31+fprfqwxuPyy7vPb2GJ2qMZ8Wg0AAAAAQIf0YF5wZnswX1m6u0Vo/rb0JLltmD1dW5y6B3Py0Pisiv8Z68GcXNpTKWm7BWasPZiT3cRnlaHGrrkvadf5bU+3WA6yB3PSFJ/eW2T00K/vJF0fv53qoadr1/HZS5/DZNi9SecNJL/NG0oP5uTAjt+eezAnBxafVTqM3dWPLp3nt721yBh6nhtgfpvZdw/m5ICP357O6fo4Pxlqz/QWs1zXZX7b2/6pB/MlXMHMxOwJdb4n1bJP193WYs/TATxpD8qy+Owy/gdusWH/fM+lMX667ra6js/oYtxHfnP8HhOfleS31frIb/MvTkZFfuuc43c18dlMH/ltfnym5LeVHL+r9XV+MiZ95Df75zDowczEdbm019TNOe5X+rLp77ftbfXaJA8ked7C+GPy2uw+PuviPxLreitd+6Lk3vuTCy+fPHHMr7/jxZOeS9c8Jnnfj2823tjsOj4nxX9UZv1Cu8xv8+O/eqez36tnvXXyfdv9Z9n+eXRu/f45Nl3ntzHG78bXdJ/fZuOPTh/5bTr+GD+U0/nJ9uZ7k8pvm+sjv405fkn6e/01ol7M88er/Ladvl5/XXh59/+WPs2KwF3mt9n49Q3d/3s4mSuYWe66TJ5cH0jbO7l3Tx97ZcZ91XJX8Tlt/A/c0V2TJ49rHrP8xf/5p0zW3Xv/2Xynsuv4zI8/Sl3nt9n4Z5TjdzXx2Uwf+W2UxeWkn/zm/MTxu0Qf5ydjJr9toef8Vp/Q39e+yG+r9fn6a4zHb9fxmY3PfunBvODM9mA+qY/V4i1Em1j3mB77CCcd92Bu6em6TUwXtz0DPZgXb3tZZdNth9pHuNWmfay2ieVJjxlq7HbSr6+L/Dan1xcJe+ivuUwXx2/neuzBfJDxOcme97ld5Le96aM3acf5bbZt/cmNp7UbA+mZfnDHb88907s8Pxlyz/St7eBc+DSP6VTXeW72+UF9vP7K+M/pDjq/zeuhZ3ofr7961+M+d5DxWUUP5ku4gpnVtu1J1fKC5oDVr0wuvCS56k3JhQfXvyN94cHpti+ZPHbtu9gL44/dtk8eZ7XnUlfxGfSTdxfkt411sf+cteO36/ichRjOk9/W6Dq/zcY/I+S31frIb6Ptmb6E/LaBPvLbGenJLL9txuuv3RCfcVNgZuL2hZ/nly8mKTl+kl1cP1uePXmX6WNOGm9kbn1bcvllx0nyptcd9wNKjpdnyfHyyyaPWVx/0vL8+GO0y/icFP+x2XV8VsV/dLrObyPNd33kt9n4Y9JHfpsff2y6zm+L249O1/ltftsR6CO/jXV/6yO/jfH8pM/8Nrp9r4/8Nj/+yPR1fjImfea3scWuj/w2tpgdKgVmNnMux+/k3rNk/T05fmf4XI/zGogbHn/8TtzF9zx0/cX3HL/zdsPj28cfq13F56T4n3Vd758HT35bq4/8Ntbjt+v4zI9/Fslva8hvG5Pf2vWR387i8Ss+a3Sd3+bHH6E+zk/OMsfvauIzTnowL9CDeY1ltxhte9vRiHowb9Jfc5e3dfTZD7evHsy7jM9J8b/xNSPa55KN++Euatk/R92DedEu8tucsfXr6zq/jbG/5tErSuf5bW+3DQ6k7/fM4OKzSse9STftrznY+KzSY3/Nrs9PejWw43XRoPfPPfb9nhl0fE7SY57r4/yk1xgPrGf6OqviP6Zzuj5ff/Vuj88RBxGfVfRgvsQV+5gMB2y+J9Ws994Z7Um6zHxPodnVZAeTHHs267m0y/icFH8m7J9ryG8rdb3/zMYfkz7ym+N3QnxWE5/1+shv8+OL+zH752ris1of5ydjsuvi3ar44/hdR3zGRYGZY7O+U8/eYPnmJLdNl5+XSfFl08ePyE2vS97y0uOfk8nyHS9ObnzNZPnCyyfJcX794vbbLI9FV/FZFv/Zz2Oyy/isiv+o3J7u89v88it3MutB6Cu/zZbHYnZy3GV+Wzb+GJz0/LpuuWX/HJtdx+esxG/2Irev85P6hm7+HfvQerwuLp+V/Dav6/x2luLn/GQzN76mn9dfYywO9vX6a0z727w+zk/GGrtDocBMksmt3Dc9cvLzW6a3da9aPnowuXH62AvnkvNbPP7nO/kXAAxLry1tntPbn+pc721ZxK6JuLUbU+wAACDRg/khzmoP5m16Cs3fVpNsfwvDUHu6NlnT5zDZ7S0eY+rBfPSKSex2fQvMsvi//0Mj2ueSU/U63Hb/HOrx2kkP5uTSnsuZ+7mlRUaP/eb30TM96eb4HVPP9MWerqe1Lv5jeo5oyXOt++dQ81yyux7MSffHby966pmedH9+Mraerqc12P1zID1dBxufk/Tca/7g4rNKx8frLj8f4hNjDiX+AzleFw0mPifZ83PE4OOzih7Ml7hsH5PhcC32bJrvmTPrqXuWic/muojPSfFn4qzvn/UJq78uPJhc9abkwkuS+pWTrwsvmf7uwfWPX/wam673n9n4Y9JHfjsrx+864rOa+KzXR36bH59j9s/VxGe1Ps5PxqSv119jO6dr5fhdTXzGZTQF5lLKtaWUHyul/OdSykdLKfeWUt5aSvnyfc/tEMz3W73pdcuXZwf/5Zclt77teP2tb5v8bpYETnr8WO06PqviP0a7jM9J8R+bXcdnVfzHpuv8Ntb49ZHfZuOPSR/5bX78sek6v431eJ3p4/xkTPrIb2M8J0n6yW9jPF77zG9jjF1fr7/GWCjt6/xkTPrMb2M9XheXvf4an1Ec9qWUpyb57SQvTvI3k/xlkquS/N0k7yylfPcepzcKF99z/M7SDY9/6PobHn/8TtPF9/Q/v33rOj6z8cdqV/E5Kf5nneN3NfFZr4/8Ntbjt+v4zI9/Fjl+VxOfzclv7frIb2dx/xSf1fp6/TXWuyH7OD85yxy/q4nPOB18D+ZSysOT/E6Sz07yn5L8/Vrru0spj07yyiTfPt30q2qt71g33lntwVxWtsU5SnJzJs1Iz68ZaZtt+9HlLj6J21G6jU/LY06v69SwfJ87yqHEZ5XuY3eUbuKzzbbd6LIH87qe6etuWd5m22Rc/XCPXlF2Hp+THjPkfrhbm9vndhWfVdv22b96CD2Yd3X8DnmfO02u6zK/zR4zpp7pq/a5Lo7fsT1HdJ3fZtva59q3Heo+15Lnttnnun7+3bme++EeXHxW6bhPf9f5bW+xHPA+N4j4rKIH8yXGcAXzP8ykuPzhJM+stb47SWqtH6y1fkeSt0y3+6E9ze/AHWW7gtP56bY3Tx87dkfpNj7bjn/ozkd8NtFVfM7nLB2/256wnOWeYF3EZ/AnjDvWdXzOQgznOX5X6zo+Y71t/CTy22p95LezdKeG/LZeH/nN8bvcWYvPTB/57Swcv+IzblfsewI78Nzp9zfWWv9kyfrXJrkpyReVUp5Ua/3d/qZ2aG6afp/V5K9Ncm+SC5kUnhbXr1q+I8mNSa5J8r4Tth+DG9NdfJbFf0yx6yI+J403Nl3EZ138D99Nr0ve8tLJz9e+KLn3/uTCyycnLrO+XbP1q5YnV1Al1zwmed+PL9/+2hd1+2/pWxfxWRX/sdl1fE5aHpNrX7T7+JwU/7HpI7/Nxr/xNd3/e/rWdX4b4/E6e37tMr/Njz8mXee3sT6/zopTzk+2N38+3PX5yZj0ld+cn5xu/xxr7A7FQReYSymPSvK3p4tvP2Gzi0nuT/KYJM9IosC8kaNMilPXpO3K0PPTx947HatljEPQVXyOcrr4H7rzEZ9Vzqfb+MyPPz5Hd01Obq55TNuVF+efMnnsvfdPxlocYzb+WbVpfFrjf+jEZzNdxWdd/Megj/x2VvdP8Vmt6/iMPaby2+b6zm+9thd5Tm9/6hJdHL+9t6DqMHZ95Lf58Xtte9bDPtfX668x7XOH6KB7MJdSnp7k16eLT6q1/t4J2/16kqcn+d9rrf9o1ZhntwfzUY4LUUfZXduBXY7VpvsezKdxlIfGZ9nv+refHsyLjjLU+KzSX+yO0m18jnYwxna67sG8y9v6lo21rz6RQ+iHu2hdfE6K/5D74W5ty/6ap90/h9pfs8Wy/ppdHb9D7una2pu06/w29p7p6+zi+B3T8bpJf81dHb9j2ufmj9U+zk+Gel6yq8/V2FTL/jnm43XRLo/fMR2vqz7foIvj9/0fGs8+19Kn/8SxBvT6K+nheB2ok3owH3qB+Vk5vr/70bXWD52w3Zszuf/7TbXWb1g15tktMF+dSUEq2X3x7mhuzOxw3M0Mu8CcPDQ+wyieDqPAnAw1Pqv0G7ujHFp8VumywDx5I63r/Laf+A/neF10lCHEZ5X9PkccZejxOUk/+9xRuo3PbPz7djTeZrouvOzm3CQZ4v45rHO6owwtPifZ/wc3J0OOzyrd7nN9vv7qN9cNucCcXFqQSob15ve+C8zJ9vE5yRgLzMnu4nOSIb8h1OLqR5edx+ek+I9qnxuwsRaYn5PkDdPFh9Va//qE7d6Q5DlJ3lFr/aol61+Q5AXTxScmWXolNDtzVZL373sSB0rs2ohbO7FrJ3ZtxK2d2LURt3Zi107s2ohbO7FrJ3ZtxK2d2LURt358dq316sVfHnQP5iQ7uUaj1vr6JK/fxVisV0q5c9m7Hawndm3ErZ3YtRO7NuLWTuzaiFs7sWsndm3ErZ3YtRO7NuLWTuzaiNt+XbbvCZzSh+d+fviK7R6xZHsAAAAAAE7h0AvM7537+TNWbDdb9186nAsAAAAAwJly6AXm300yayL9ucs2KKVclklf5SS5q49JsZZ2JO3Ero24tRO7dmLXRtzaiV0bcWsndu3Ero24tRO7dmLXRtzaiV0bcdujg/6QvyQppfxGkqcl+Ze11v9lyfr/LsmvThefVGv1AX4AAAAAADtw6FcwJ8kbp9+fW0r59CXrv2P6/TcVlwEAAAAAdmcMBeafSPLHSR6V5BdKKU9JklLKo0opP5zk66fbfe+e5gcAAAAAMEoH3yIjSUopX5Dkl5I8bvqrDyZ5ZCYF9Jrke2ut/2RP0wMAAAAAGKVRFJiTpJRybZLvSfJ3k3xmJkXm30jyo7XWX9rn3AAAAAAAxmg0BWYAAAAAAPo1hh7MAAAAAADswRX7ngCwXinl8kx6jD88yX+ttX54z1MCTuB4pS+llMcleXqST09yVab73P/f3r3HXTbX/R9/va85YGacz5SSIhRyqhxiJJUcklsIRUUpSW6i3JKIIkS5QxJKRTrQnftOkcNIOaQkQyH0Y8I4zxjM4fP747uuZrtch72/+7DWda338/HYj8vsvb7bZ78fa33X2t+91ncBM4G7gD+HL1UblLOzXvM61x5JazN4dndHxJNl1lZlzs16zX1dPm+v+ZxdNXiKDOs6SX3ABsCbGXpHMy0iZpZWZMUUN67cDtiSlNtyAxZ5EbgbuL54/DIiZve0yIqStBwwlYXZ9a9zE4GnWLjOXQ9cHxE3lVRq5Ti7PN5e2+N9RGskrQfsR1rnXj/C4s8CvwN+BPw4IuZ0ubxKc3bt8T6idV7n8hXr216k7DYDlhhi0QCmk9a7iyPi2t5UWE3OrT3u5/K4r8vj7TWfs6smDzBbVxQDBjuQdjTbAFMGLkLa2BvdTdrRnB8RD3a9yIqRNAX4EPARYP3+p0do1p/hbOBi4NyI+EN3KqwuSQK2J2X3HhZenTFcfv3ZPQB8h7TePdS1IivK2eXx9toe7yNaJ2lP4FBgw/6nir/PkAbjnwSeB5YuHssD44plApgFXAScGBH/7FHZleDs8nkfkcfrXD5JmwGfAXYEJvDSdW0+8DQLs1tsQPMA7gO+DZxZpx9znVs+93P53Nfl8faaz9lVmweYraMkLQYcAnwKWJGFG3z/GXwzefmO5tXASsVyUTyuBI6JiJt7VXtZJE0EDgI+ByxDyuxx4A/ALcCfGTq3TYrHG1k4IPN/wOci4vZefo6ySHofcDywFimDBaQzC5rJbiNgKVJu84BzgeMi4l89/RAlcXat8/baHu8jWifp3cAJwHqkvB4CLgVuAG6JiPuHaLcY6QvfJqTB/K1IX+qeB74FnBARj3e7/jI5u/Z4H9E6r3P5JK0LnEga4BPps/+KIjvSZfVPDmgzkZfuX7cHXkta7x4DvgycFRFze/Mpes+5tcf9XB73dXm8veZzdqNERPjhR0cewIHAw6Qd8wLSxv5pYGNgwghtVwXeS9oxzyzazwd+AqxR9mfrcm4PsPDXtvOAdwB9Lb7HqsBhwK1FdvOAj5T92XqQ3e+L7OYBVwP7A8u0+B6bA98EZhTZzQJ2LfuzObtqPry9tpWd9xF5uS0A5gIXAm+jODkg432WJw3s31Nk94WyP5uzq+7D+4js3LzO5Wc3r8jvWuCDwOKZ77MRcAppUHA+cFTZn825VfPhfq6t7NzX5X1eb6/Obkw/fAazdYykBaS5qb5JuvQ76xJmSeOBd5MGYLYEvhgRX+pYoRUj6XHgdOD0iHi6A+83FTgKuDYijmv3/apM0guky6pOiIh72nyvcaSd1RHAD8byOgfOLpe313zeR+SRdA7p0tF/dOj9+oAPABERF3XiPavK2eXzPiKP17l8kq4Ejo+I6zr0fksABwNPRsSZnXjPKnJu+dzP5XNfl8fbaz5nNzp4gNk6RtLngW9ExLMdfM/NgaUi4pedes+qkTQ5ujD/T7fet0okrZY7SDXMewpYJcb4PGrOLo+313zeR5iNHt5HmNlY537OzKyzPMBsZmZmZmZmZmZmZln6yi7AzMzMzMzMzMzMzEan8WUXYGZDk7QMsDIwpXhqFjAjIp4or6rRQ9JkGrIb61MQdJKzM6suSVOALYB1GGQfAdwJTIuIWeVUWF3OznrN61x7JL2eYbKLiLvKqq3KnJv1mvu6fN5e8zm7avEUGWYVI+k9wF7ANqQ76w7mMeAq4KKIuKJXtVWdpDeQbhCxDbA2C3cy/WYB00nZ/TAi7uhthdXl7PJIeiXpbsTjgDsi4u4m2hwKTBnrN4BphqQ1SevccF9GroqIv5dTYbVIWhv4ErADMHGExV8ELifdBHF6t2urOmfXHkmLkPYN44C/NTOXuqTdgMUi4sJu11dFXufySVoWOBLYk7RvGM4M0o3aToqIx7tdW5U5t/a4n8vjvi6Pt9d8zq66PMBsXVEMVn2GhoEX4LyI+PUI7WYAy0dE7c6ul7QScAmwef9TIzTp33inAbtHxL+6VVvVSZoEnE3ayYjmsgvgB8CBdT4719nlKc7SOBfYbcBLNwKfjohbh2k7A1ghIsZ1scRKk7QtcCKwYePTAxZrPEC5Bfh8RFzV7dqqStJepHVuIguzehR4GHiu+PckYBVghYamLwAfjogf9qjUynF2+SSNA74MfJKUEcBc4CfA54a7QVbNj+m8zmWStDVp/VqKl+4XnuKl2S3V8FoATwK7RsS1PSizcpxbPvdz+dzX5fH2ms/ZVZsHmK3jJO0BXECagqV/o+9f0S4DPjrUFA91HXgpBqv+BKxePPUb4Feks/cG20GvA2wHbEuaS/0e4E11HOyTNAG4HtiEtL79DbiS4bN7B7AWab28CdgyIub1tvLyObs8xR3CryFdBjjYgPxc4MiIOG2I9rXs5/pJOgI4gYXZPQ3czeDr3FrAksVzQcr15N5VWw2SNgR+T9qv3gqcClw51JkYxZkd2wGHkLbvucBbIuK23lRcHc6uPZIuAXZl8B+AniEd0/1kiLa17Ou8zuWTtAbpeHgy8E/gLIrj4Yh4fsCyi7LwePjjwGqkq182iIj7ell32Zxbe9zP5XFfl8fbaz5nNwpEhB9+dOwBrEEaIFgA/As4DziZNBC1AJgP3Au8doj2M4D5ZX+OEnI7rsjnPlKn12y79Ys284Fjy/4cJWV3WJHdY8BOLbTbsWgzH/jPsj+Hsxs9D+BDRW4vAl8gHbBMBt5DOtO2v687dYj2teznis++TZHNAuCnwFspfuweYnkBbyGdqbAAmAdsXfbnKCG3Hxaf/0Kgr4V2KtosAH5Q9udwdqPrAezc0J+dB7wNWJd0lt+DDdvkwUO0r2Vf53Wurey+XXz+/wMmt9BuEmmQYQFwTtmfw7mNnof7ubayc1+Xl5u3V2c3Zh8+g9k6StI3SDvk24B3RsTMhtd2Jl2KvwLwCLBdRPxlQPta/gos6U7SmXqbR8TvW2z7VuAGYHpErNuN+qpM0h9JA+07RovzUUvaHvgf4LaI2Kgb9VWZs8sj6Urg7aQfdb404DUBx5PmBYP0ZeWAaNjZ1rWfA5B0OWkg/pSI+GyLbU8i/Sjyi4jYuRv1VZWkh4AVgZUa96tNtl2OtM/9V0Ss2o36qszZ5ZN0GWlOzTMj4uABr00mHdN9gHSW3zERcfyAZWrZ13mdyyfpAeAVwKsj4p8ttl0NuB94MCJe3fnqqsu55XM/l899XR5vr/mcXfV5gNk6qmGgdIuIuHGQ11cFfk6am/kJYPuIuKnh9VrupCXNBuZFxJIjLjx4+2eAcRExubOVVV/x2fsiYuBN6ZptPwtYEBFLdLay6nN2eSQ9AiwHLBsRTw2xzJ7A+aTLBi8G9omI+cVrteznACT9C1gWWDpavIt4MZXQU8DMiFipG/VVlaTngeciYpnM9k8Ci0bEYp2trPqcXT5JD5MGD1aNIe7zIOlI0pQ3AXwtIo5oeK2WfZ3XuXyS5gBznF1rnFs+93P53Nfl8faaz9lVX1/ZBdiYsxrpsvFBz8KNiIeArYHfAssAv5b0tp5VV11zgEWLOXFbImkisEjxHnU0HxhXnDnaEkl9pJtQzu94VaODs8uzNPD0UIPLAJFuWvI+0k1Mdgcuzdm+x6AlgVmtDi4DFG1mAbX6QaPwCLCkpFe02lDSK0m5P9rxqkYHZ5dvWeDZoQZdACLiK8CBpIGXwyR9s1fFVZjXuXxPAEsUc7W2pGizRPEedePc8rmfy+e+Lo+313zOruI8wGydNg54sfFy8IEi3Yhue+AKYHHgCknv6FF9VXU76UzHgzLaHgRMIE14X0fTSXcu3iuj7QdIg/N3drSi0cPZ5XkWmFIMsg8pIn5Jmq96DrATcHlxw4k6e4h0YPj6VhtKWpt0YPhQx6uqvt+Q5iz8tqRJIy3cT9JipPnqAvh1l2qrOmeXbw4wYp8VEWcD+5HmNjxQ0ne6XVjFeZ3Ldz0pu1Mzfvw+tfh7XWdLGhWcWz73c/nc1+Xx9prP2VVd2ZNA+zG2HsA9pDMaV2hi2QksvHHTc6SBmFreKAHYrchhLummiCs30WYl4KSizXzgP8r+HCVld0CR3WzS/N8Tm2gzEfgE6WzI+cD+ZX8OZzd6HsDvis++aZPLb0Ga2mE+6eqNx+vYzxVZnF6sczeT5uxrtt2KpBsozgdOK/tzlJDbaxu2ufuBzwIbDLbNFtvoBsDhwD+KNs8Ca5T9OZzd6HoU2+l8YL0ml9+NdNXGfOAi0plptevrvM61ld1GpCsh55Ouhnw/aUqloZZfuljvbizavABsWPbncG6j5+F+rq3s3Nfl5ebt1dmN2YfnYLaOknQJsCvwwYi4qInlx5HuIrsnaaAUYHzUcx6rc4CPkn7NDeAO4K/Aw6Rf14N0B9RVSHc3Xpd0FYJId0P9eAllV4KkXwLvJmX0NOmXyeGy2xJYipTdLyNixxLKrgRn17riZnP/CZwaEYc32WZT4H9ZmF3UtJ9bkbR+LU36UnIRcCUvXecAFmPhOvcO0ln2SwAzgTdERO0uqSyu9PkxKYfGg7cneOm22jgvnYBnSD9A/qZHpVaOs8sj6QzSj49fiohjm2yzI3AJaTChzn2d17lMkj4EnEM6EaU/u38x+HFJ/3z8In2P2D8iLuxpwRXh3PK4n2uP+7o83l7zObtq8wCzdZSkA4CzgGsiYpsm24h0mcyHi6fqvJM+EDgGWKF4aqgNtP+SkEeBL0bEWd2urcokjQe+DBxMmrYBRs7uBdLZlP8VEfO6W2F1ObvWSdoSuJZ0JvLq0eR8wpLWB35F2r7r3M+tB1xOmrO/2YMQAQ8AO0fE7d2qreqKAfrPkeb1XnGExR8BfgR8JSIe6XZtVefsWifpXaTpzB4CXhMRc0do0t9uW+BnwGTq3dd5ncsk6Y3AcaQfwEe6f8Fc0np6TJ33D+Dccrifa5/7ujzeXvM5u+ryALN1lKTlSNNc9AFbRcS0FtqeBnwa76QnAtsCU4F1gJVJBy8infH3MGne298CV0XEiyWVWjnFAc6uvDw7SNNANGb307of2DRydq2R9HnSAc1PI+IvLbRbEzgC6IuI/bpVX9UVc1F/nDSX90Ys/PFioCBNjfED4OyIeL43FVZb8cPsOgy/j5gePsh7GWfXvOIqs7NI94g4MyJuaaHt5qQvf4qIqV0qcVTwOpdP0pKkaaaGy25aRDxTWpEV5Nya536uc9zX5fH2ms/ZVY8HmK1SijvK9kXEA2XXYmZm3SdpCrA2gx8Y3tXsGeJmZmZmZmZWDg8wm5mZmZmZmZmZmVmW8WUXYGZmZmZWNkkTSPNIRkQcV3Y9ZmadVkwJsReAb3ZlZmOV+7py+Axms1GuuEnbZgARcV3J5dgYJWky8CywICL842QmSW8r/vPOiJhZajE2KhVTSW0EjAPuiIi7m2hzKDAlIr7U7fpGs4Z+rtb3gugESasV/zmj2Ztm2b/XwW+Q1sGPlF1PmSQtQpo+aRzwt4h4tok2uwGLeTBhaD6e6xz3c/nc1yXu57rHfV05PMBslSHp6uI/bwJOiYjHyqxntJC0LPAYNe88Jb0B+AwNAy/AeRHx6xHazQCWr3N2zfDAS2dIWkC6cd1zwH8DX3Nf1xxJSwA/J62Dby+7nl4r5qo+F9htwEs3Ap+OiFuHaTsDWMHb7vDcz3WOpPnFfxdORl4AACAASURBVD4EnAh8xzclHlnDMV1t18HirLMvA58EJhVPzwV+AnwuIh4cpq2P6Ubgfq5z3M/lq3tf536u+9zXlcMDzFYZDQMvAHNYOPjyaHlVVV/dd9AAkvYALiBN+6Pi6f516TLgoxHxxBBtazvwIukLLSw+Efg8KddjG1/wWZHNK/q5Rs8BZ0XEYWXUM5rUua8r7sx+DelO2RpkkbnAkRFx2hDt69zPzR95qRGFv8i1ZkBfF6Sbdn41Ir5ZUkmjQp37uX6SLgF25eV9XQDPkI7pfjJE21r2dZLua2Vx4FWkPBtvqh4RsUZHCxvj3M/lq3tf534uj/u66vMAs1WGpGtIHcDKwJrF089FxJTSihoFvIPWGsBfgEWBR4ErgMeBrYCNSevU/cA7I+KeQdrXeSfd+KNOU02Kvy9pU8fscknaqvjPlUnr6NbAms5wZHXu6yR9CPguMA84Hjif1M9tTfrBZ0PSdnl6RBw6SPu693Ptqt06165inYWFfd1mpGlanOMw6tzPAUjaGfgZqT+7gJf2dUcArwAWAIdGxBmDtK9lX9dwPDfYD5DNquU61w73c/nq3Ne5n8vnvq76fDaGVUZEbN3/35JWJHWyW5ZVTy9J2q6N5kt0rJDR6RDS4PJtpEHkf89rW+zAzwZWB66XtF1E/KWcMivtUeD5EZYRsBpppz7kZVs2vIi4tuGfPwKQtFxJ5djosRdp2zt+wBUDv5R0BWnQ+Ujg05IWBw4In0HQKIA/AOcw/I9qiwBnFct8uAd1jVkRcUHDP78iqY/0Q8iYJ+kHbTSf2LFCRqcPk7a/MyPi4Ibn/yrpfNIx3QeA0yQtERHHl1Bjlf0e+NUIy0ykuJkp4CvQ2lDnfg7c17XB/Vz73NdVlM9gNquAjDNJX/YW1PTXOEl3AmsBW0TEjYO8vipp3taNgCeA7SPipobX6/wr8C2kA+H7gYMj4n+GWXYK6ZKtWq5n1hmSftdG8/EUVyXUbR2U9AiwHLBsRDw1xDJ7ks6CGQ9cDOwTEfOL1+rcz+0MnEE6I+gW4BNDzVft+fqsE3yGVT5JDwMrAqtGxL+GWOZI4ARSxl+LiCMaXqtlXyfpENIAymTgp8BnIuL/DbGs+znrCPd1edzP5XNfV30eYDarAO+g80maRbqp36ShztgrdjCXA1NJO5odI+K64rU676T7gINZuKP+BWmg+WVnKHsnbZ3gvi6PpBeBWRGxzAjLvQf4MelM3MuB90fE3Dr3c/Dv/ut44KDiqbOBoyLi6UGWcz9nbZE0F+gDrgQGHTwYxiLAHtR0HZT0AjAnIpYaYbmPAWeS9iXfioiDiudr29cVJ1R8E9gZmEU6tjut/4fGhuXcz1lHuK/L436uPe7rqs0DzGYVIOkhYCXgPyLiZy22XY40xUEtO09Jc4AXI2LJEZZbhHRn3u1JN1bbJSJ+XfedNLxsR/0c6a7GJ0fEvIZlvJMehqRJwOtJ8/D1zxs/C5gBTI+IOWXVViWSngcmAN8HWrlRB6S7bB9ODddBSY8DiwOLRsSwcwpLejvp5qaLkb707QL8g5r3cwCSNiQNLm8EPAJ8NiK+1/C6+7kmSFqGQfq6GOJmunUj6c/AG4CPRcS5Lbat7bykAJKeIvVzizax7D7AeaQBrvMj4iM+pgNJO5GO6VYF7gQ+2X9SRfG6+7kmuJ8bmfu6PO7nOsN9XTV5gNkqpRjoGjfYGZRjmaSfAzsCJ0bEf7XYtrY7aABJ95DmWF45Ih4dYdkJpHlvdyHNObw7aU7O2u+k4WU76ruBgyLi6uI176QHKNanA0jz427K0Gfl9s//+n3g3IiY25sKq0fSTaTBvYMj4swW29a2ryumFnkz8NbGKX6GWX4L4H9Ig9LXAesBS9Utt8FIEvAp4DjSwME04MCIuNP93NCKs+P3ArYBlh9isceAq4CLIuKKXtVWNZLOBfYj9fcfa7Ftbfs5AEk3k6buelNE3N7E8ruR9q3jScd37yBNJVS77BoVfdlxpL6uj5TR4RHxqPu5obmfa437ujzu5zrHfV319JVdgI1Nkt4l6SpJT0l6VtKNkvYvLskfzi20flbbWHAzaXBq07ILGYX+WPx9x0gLFgN77wd+SLox4KXAsJec10lEXE46C/d04HXAryX9QNJK5VZWPZLWIf1afgbwFtL+VEM8+oC3kgbv/ypp7TJqroj+vm7jsgsZZaYVf3drZuGImAZsBzwFvA1Yukt1jTqRnAGsTTrTe0vgNkknsfBMNStIWknSdaQpV3YHVmDovm4F0iXPv5B0bY33He7n8vXfS2OXZhaOiB8D/wG8SFr3fNNcICJmR8ShpO8VtwL7AHdJ+gT+/v8y7ueyua/L436uQ9zXVY/PYLaOk3QocHL/PxteCtKGv0dEDDqIXNdLPiRtB/wf8GRELNti26WAPwELIuI13aivyiQdAJwFXBMR2zTZRsC3SXfxBf+y+TKS3kS6lHxj0s39TiLNYVr7rCStAPyFdHbLLNIPFr8iDTg/TJpmJEjzWq8CrEMa7NuTdEbpo8AbI+KxnhdfMkn7Ad8B7oyIN7TYts5nu2wJXAs8DqweEbOabLc+ad1cgRrm1gxJO5DmOHwFadtcEWcF0H9z1z+RrhIC+A0v7+sgTV/T2NdtS/pidw/pDK3ZPSy7dJLeCHwPeAF4S7TwZUvSYsBnASLi2O5UWF2S3gVcATwEvKbZK34kbQv8jLTf9fbboDjmPYh0lt/iwF9J0xo4J9zPtcN9XR73c93hvq4aPMBsHSVpA9KvmeOA6aQ72T8ObEX6lW4c8ATpJms3DtK+rgPME0lnUxERfy65nFGlmIN6Bukgb6vizL1m254GfBrveAY1yI5aOCskfZ10c8TbgJ0i4qEm261CupHiBsDpxS/utSLptcDXgHnAbi1+GZlIGqQnIi7oToXVJenzpPmrfxoRf2mh3ZrAEUBfROzXrfpGs2IO9eNI2/U43M8BIOk44CjgfuB9EfGnJtutT/oS/Crg+Ig4pmtF2pgiaRzppIHxwJkRcUsLbTcnbceKiKldKnHUkrQy6Uqq/rMm3c/hfs56z/1cd7mvK5cHmK2jJJ0H7AtcDewQEc83vLYhcBGwFjAbeG9EXDWgfS0HmK08kl5JGnh5oOxaqqrYUX8FWA2g7gc0DfN+vyEiprfYdh3gDuDeiHhdN+ozszyS1iCdyUxEXFtyOaWTdCfpmG3ziPh9i23fCtxAusnput2oz8xaJ2kr4NVQzx9rB3I/ZzY2ua8rhweYraMaBl7Wj4g7Bnl9Culy8veQLqfZvZj3tf91DzCbWaVJmgM8HxFZ89oWd4+eGBGTOluZmVnnSJoNzIuIJTPbP0O6cfPkzlZmZtYZ7ufMzDrHE19bp60CzBlscBmgmDfyvcAPgEWAH0vavYf1mZm16xlgSnF34pYUP7L139XYzKzK5gCLSprQasNiSptFivcwM6sq93NmZh3iAWbrtCgeQy8QMZ90h89vk+aT/L6kfbtfmplZR9xE2n9+IaPt0aQ5Xv/Q0YrMhiDpbcXDdx1vkbPjdtIckQdltD2IdIzX1HymlkharXi0PNhVd84uj3NzP1cGr3d5nFs+Z9cbHmC2TnsQmFTMazukSD4GfIM02HKupAN7UeBYI+nq4vEVScuXXc9o4uzy1Ty700k3PDxM0qWS3jxSA0mbSLoEOIz0I9zXu1zjmOPBvmzXAL8F/iHpqzXcXttxDfXO7lukvu4kSScX8/EPS9JKkk4Cvkrq687qco1jzT+Kx72SDizOkLTmOLs8dc/N/Vw56r7e5XJu+ZxdD3gOZusoSRcCewGfiIizm2xzIukO9/1nP8tzMDdP0gIWnjU+B/hv4GsR8Wh5VY0Ozi5f3bOTdBTpLs79GTwNTAceJuURwCTStEFrA/1z+wk4OiK+3NOCx4CGde45Fq5vj5VbVfUVuTV6DjgrIg4ro57RxNmBpHOAj7LwGO0O4K8M3tetWzz6SH3dORHx8RLKHrUGrHNByvmrEfHNkkoaNZxdHufmfq4MXu/yOLd8zq43PMBsHSVpb+BC4NaI2KSFdkcDx1IM1niAuXmSriHltjKwZvH0cxExpbSiRglnl8/ZgaTtgROA9Qa81L9j1YDn/wx8PiL+t9u1jUUe7MtT3EUb0ra6FbA1sKb3syNzdklxhdkxwArFU0N9eejv8x4FvhgRPquvRZI+VPxn/zq3GTClbutcDmeXx7kl7ud6y+tdHueWz9n1hgeYraMkLQ78kTSX1d4RcUMLbQ8BTiXNoOENPYOkFUlfgLeMiJy5xGrL2eWre3aS1gGmAuuQDlomk76AzCL9On4n8NuImF5akWOAB/s6R9JyETGz7DpGo7pmV1xKui1N9HXAVRHxYkmljimS+oANI+KWsmsZbZxdnjrn5n6uPHVe79rh3PI5u+7wALOZmZmNSnUd7DMzMzMzM6sSDzCbmZmZmZmZmZmZWZbxZRdgZmZmZq2RNAl4PekS3v65z2cBM4DpETGnrNqqztlZGSQtwyDrXEQ8UV5Vo4Ozy+PcOk/SagAR8WDZtVSV17s8zi2fs6sOn8FsHSPpNRFxX4ffsw94hXfiw5O0KjDOObXO2eVzdq0r5vfbAyAiLiy5nErwYF/zJE0ADgD2Ajbl5TeS7BfAH4DvA+dGxNzeVFhdzq73vI8ASe8hrXPbAMsPsdhjwFXARRFxRa9qqzpnl8e5dY+kycCzwIKI8Il6Dbze5XFu+ZxdNXmA2TpG0ovAD4ETIuLuNt9rArAfcARwQUR8qQMlVp6kdwGHAxsB44A7gPOA70TEgmHazQCWr/PBjrPL5+x6R9KypIOdWn858WBf64qbSV4GvIah8xoogHuBnet8k0ln1x7vI1onaSXgEmDz/qdGaNL/hWwasHtE/KtbtVWds8vj3LqvYYDZN6QveL3L49zyObtq8wCzdYykacBmwALgeuBHwKUR8XiT7QVsTTq7733AMsBsYJ+IuKwbNVeJpEOBk/v/2fBSALcCewx1hnjxJW6Fuh7sOLt8zq63GgaYa/vlxIN9rZO0AvAX0hkas0g/5v6KdDf7h4HnSBlNBlYB1gG2A/YEFgceBd4YEY/1vPiSObv2eB/ROklTgD8BqxdP/YaXr3MAk3jpOrct0AfcA7wpImb3sOxKcHZ5nFs+See1sPh4YG9S/3dBw/MRER/paGGjgNe7PM4tn7OrPg8wW0dJ2gk4gbQxR/H4O+lLyO3ATOBJ4EVgKWBpUgexMfAm0hc8AXOBs4Hj6vClTtIGwM2kM4OmAxcDjwNbAbsUzz8B7BgRNw7SvpZf4sDZtcPZ9V7dB5g92JdH0teBg4HbgJ0i4qEm260C/ALYADg9Ig7tXpXV5OzyeR+RR9JxwFHA/cD7IuJPTbZbH/gZ8Crg+Ig4pmtFVpSzy+Pc8klawMIzHJtqUvyNhn/X9ZjO610G55bP2VWfB5it44ozkd8FfBTYAZhQvDTcyta/s76PdNnldyNiRteKrJji1/N9gauBHSLi+YbXNgQuAtYindH93oi4akD7Wn6JA2fXDmeXR9IBbTSfDJxCfb+MeLAvg6R7SD/GvqHVM7iLM8bvAO6NiNd1o74qc3b5vI/II+lOUi6bR8TvW2z7VuAG0vzz63ajvipzdnmcW76GAea7SD9iD2ccsEWx/HWNL0TE1K4UWGFe7/I4t3zOrvo8wGxdVdzRcyppjpxNSTdxWg5YhHTWy0zgbtLGPi0ibimp1FI1fAFePyLuGOT1KaQz/d4DvECaP+jyhtdr+SUOnF07nF2ejLNdXvYW1HeA2YN9GSTNAZ6PiKUz2z8FTIyISZ2trPqcXT7vI/JImg3Mi4glM9s/Q7o54uTOVlZ9zi6Pc8sn6efATsDTwNHAmTHEAEnR5z1DTY/hBvJ6l8e55XN21ecBZrMKkPQc6aZfU4ZZZhxpvq8PkKYQ+WBEXFy8VssvceDs2uHs8jQMMD9CGlRpRR/wSmr65cSDfXkkPUK6L8FSrc4bV3whfhJ4IiJW7EZ9Vebs8nkfkUfSTNKUPlOixRuTSppIuonYsxGxXDfqqzJnl8e5tUfSzsAZwCtIV1h9IiJuGmQ53+Svgde7PM4tn7Orvr6yCzAzYOF81UMvEDEf2Af4Nmnake9L2rf7pVWes8vn7PLcX/z9TESs3soD2KjEuqvgGWBK8SWtJcVgX/+Xu7q5iXTM9oWMtkeTLuv9Q0crGj2cXT7vI/LcTroZ2EEZbQ8i5djUvJJjkLPL49zaEOlm8uuQBpnXB34n6WxJWT+G14jXuzzOLZ+zqzgPMJtVw4PAJEmvHG6hSD4GfIP0pfdcSQf2osAKc3b5nF2em4u/m2S0rftlQx7sy3M6aWqVwyRdKunNIzWQtImkS4DDSOvd17tcY1U5u3zeR+T5FmmdO0nSyZJWHqmBpJUknQR8lbTOndXlGqvK2eVxbm2KiNkR8RngzaSzmPcH7pa0X7mVVZrXuzzOLZ+zqzhPkWFWAZIuBPYiXZJ1dpNtTgSOYOEZRqrj5VrOLp+zyyPpcNJByrWt3tRF0rLAY9T08kpJ2wJXktadnwEnR8SwA8aSNgEOB3YtnnpHRFzd1UIrSNJRwHEs/JHiaWA68DAwp3h+ErAKsDbQPz+dgKMj4ss9LbhCnF0e7yPySTqHdLPr/hzuAP7K4OvcusWjj7TOnRMRHy+h7EpwdnmcW+dIEvAp0n5jCnAj8AngXjxFxkt4vcvj3PI5u2rzALNZBUjaG7gQuDUimj4rUtLRwLEUX5rreLDj7PI5uzyStgauBmZFxBIttl0CuJz05aR2dxwHD/a1Q9L2wAnAegNe6s9SA57/M/D5iPjfbtdWdc6udd5HtKc4i/sYYIXiqaG+dPWve48CX4yI2p9d5ezyOLfOkrQK6cqMXYB5wHnAAXiA+SW83uVxbvmcXXV5gNmsAiQtDvyRNKfQ3hFxQwttDwFOpaYHO84un7PLU5zZsgRARDxdcjmjkgf72iNpHWAqac7IlUlzUwuYRRqovxP4bURML63IinJ2zfM+on3FTYW2pYl1DrgqIl4sqdTKcXZ5nFvnSdoBOJN0k2aoeb82GK93eZxbPmdXTR5gNjMzs1J4sM/MzMyqTtIk0vQ/qwFEhOdmNjMbwAPMZmZmZmZmZmZmZpalr+wCzMzMzMzMzMxsbJK0mqTVyq5jtHFu+Zxd73mA2axkkl7Thffsq0Nn6uzyObs8zs1GG0kTJX1Q0gfLrmW0qXN27uvKI2lV55TH2eWpa27u53pH0mTgfuC+kksZVZxbPmdXDg8wm5XvLkkXSFqr3TeSNEHSAcDfgX3brqz6nF0+Z5fHuZWkzoN9bVocOB84r+Q6RqM6Z+e+rk2S3iXpKklPSXpW0o2S9pc00vevW6j5F2Jnl8e5tcz9XO8NvImzNce55XN2PeQ5mM1KJmkasBmwALge+BFwaUQ83mR7AVsDewDvA5YBZgP7RMRl3ai5KpxdPmeXx7mVR9KywGPAgogYX3Y9o0VDbr7rfYvqnJ37uvZIOhQ4uf+fDS8FcCuwR0QMOqAnaQawQt3WuX7OLo9za537ufZIauXH1/HA3qT18YKG5yMiPtLRwirOueVzdtXnAWazCpC0E3ACsA6pEwzSL+C3ArcDM4EngReBpYClgdWBjYE3AZNJB5NzgbOB4yLisd5+inI4u3zOLo9zK0edB/va4dzy1T0793V5JG0A3AyMA6YDFwOPA1sBuxTPPwHsGBE3DtK+loN94OxyObd87ufySVpAyqvpJsXfaPh37favzi2fs6s+DzCbVUTxK/i7gI8COwATipeG20j7O837SJfwfjciZnStyIpydvmcXR7n1nt1HuwrLrvNNRk4hRrmBs6uXe7rWlecYbUvcDWwQ0Q83/DahsBFwFqkMx3fGxFXDWhf58E+Z5fBubXH/VyehsG+u4BHR1h8HLBFsfx1jS9ExNSuFFhRzi2fs6s+DzCbVZCkZYCpwObApsDKwHLAIqQzEGYCdwM3ANMi4paSSq0cZ5fP2eVxbs3zYF+ejDM2XvYW1DA3cHad5L6uOZLuIZ3huH5E3DHI61OAHwLvAV4Ado+Iyxter+1gn7PL49w6x/1c8yT9HNgJeBo4GjgzhhhcKtbBZ/D+1Lm1wdlVnweYzczMrCc82JenIbdHSIMDregDXkkNcwNnZ70n6TnSXPFThllmHGlOyA+QLq3/YERcXLxW28E+Z5fHuVlZJO0MnAG8ArgN+ERE3DTIcpOBZ/H+FHBu7XB21eYBZjMzM+sJD/blkXQf8CrgA/0DAi20XY50GWHtcgNnZ70naTZpsG/xEZYTcBawPzAf2D8izq/zYJ+zy+PcrEzFQN7xwEGkEwG+AxwZEU8OWMaDfQ2cWz5nV119ZRdgZmZmtXF/8fczEbF6Kw9goxLrLtvNxd9NMtrW/UwCZ2e99iAwSdIrh1soko8B3yDNFXmupAN7UWCFObs8zs1KExGzI+IzwJtJZ5TuD9wtab9yK6s255bP2VWXB5jNzMysVzzYl+cW0hkadR5kz+XsrNf6+7ntm1k4Ij4NfJX0veybwPJdqms0cHZ5nJuVLiL+SJq3+hDSnNXnSpomab1yK6s255bP2VWPB5jNzMysVzzYl6d/8CAnt7mku2dfN9KCY5Szs167ktTPfbTZBhHxOeCYop26VNdo4OzyODerhOIs+TOAtYGfA5uRjv1OKbWwinNu+ZxdtXgOZjMzM+sJSVsDVwOzImKJFtsuAVxOOpac2oXyKquYN3MJgIh4uuRyRhVnZ70maXHgj8B4YO+IuKGFtocAp1LTOSOdXR7nZlUlaQfgTNI9NMDrWVOcWz5nVy4PMJuZmVlPeLDPzMzMrD4kTQKOAFYDiAjPk9sE55bP2ZXHA8xmZmZmZmZmZmZmlsVzMJuZmZmZmZmZmZlZFg8wm5mZmVWUpNd04T37JK3W6fetGmdnveZ1Lp+zy+PcrAxe7/I4t3zObnTwALOZmZl1nQ8Ms90l6QJJa7X7RpImSDoA+Duwb9uVVZ+zs17zOpfP2eVxblYGr3d5nFs+ZzcKeIDZzMzMesEHhnluAvYB/irpt5I+JmnZZhsrmSrpbOBh4FvA8sCfu1NupTg76zWvc/mcXR7nZmXwepfHueVzdqOAb/JnZmZmXSdpGrAZsAC4HvgRcGlEPN5kewFbA3sA7wOWAWYD+0TEZd2ouSok7QScAKwDRPH4O3ArcDswE3gSeBFYClgaWB3YGHgTMBkQMBc4GzguIh7r7acoh7OzXvM6l8/Z5XFuVgavd3mcWz5nV30eYDYzM7Oe8IFhvmKA/V3AR4EdgAnFS8MdyKn4ex9wHvDdiJjRtSIrytlZr3mdy+fs8jg3K4PXuzzOLZ+zqzYPMJuZmVnP+MCwfZKWAaYCmwObAisDywGLAE+QBurvBm4ApkXELSWVWjnOznrN61w+Z5fHuVkZvN7lcW75nF31eIDZzMzMSuEDQzMzMzMzs9HPA8xmZmZmZmZmZmZmlqWv7ALMzMzMzMzMzMzMbHTyALOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmdkgJEXxeEDSokMsc3+xzPgh2vY/5kuaKelqSXt1oLatG977kiGWeXXx+rRh3ucdki6S9A9Jz0maI+keSd+T9O526zQzMzOzsW/8yIuYmZmZmdXaasAhwFcy2h5b/J0ArAW8F5gqaaOIOLRD9e0m6a0RcWOzDSQtDlxY1PM8cDXwU2AusDqwPbC3pFMi4rAO1WlmZmZmY5AiouwazMzMzMwqR1IATwJBOjFjjYiYOWCZ+4FXARMiYt6AtkSEBiz/duDXxT9fExH3Z9a2NfBb4B7gtcDvImLzAcu8GvgHcENEbNHwfB9wBfDO4j32joiHB7RdBPg4sGZEfDKnRjMzMzOrB0+RYWZmZmY2tOeA44AlgGPafbOIuAq4CxCwSbvvB/wBuAzYTNKuTbbZkzS4fA+w48DB5aLOFyLidKBTZ1mbmZmZ2RjlAWYzMzMzs+GdCdwLfEzSmh14v/6zmjt1KeFngXnAVyRNaGL5A4q/X4uI2cMtGBEvtFucmZmZmY1tHmA2MzMzMxtGRMwFjiTNo5wzD/O/SdqWNBdzADe3Xx1ExN+As0lTZRw4wv9/PPCW4p9XdeL/b2ZmZmb15pv8mZmZmZmNICIulXQjsIukLSJiWjPtJH2x+M/Gm/wJOC0iHuhgiccC+wBfkHRBRDw9xHLLABOL//5/Hfz/m5mZmVlN+QxmMzMzM7Pm/Gfx9xRJGnbJhY4pHp8DtgGuB/aJiI7ObRwRj5HOrl4WOGqYRZut28zMzMysKR5gNjMzMzNrQkTcCFwKbAq8v8k2Kh59EbFMREyNiO93qcTTgH8CB0t61RDLPA68WPz3ql2qw8zMzMxqxAPMZmZmZmbNOxKYC5woaeJIC/dSRDwP/BewCHDCEMvMA35f/PPtPSrNzMzMzMYwDzCbmZmZmTUpIu4F/htYHfhUyeUM5nvAbcCewMZDLHNO8fcwSZOGezNJi3SwNjMzMzMbgzzAbGZmZmbWmi8BT5HmOp7SiTeUdL6kkLRvO+8TEQEcRppr+cQhFvsh8CvgdcBlklYepJ6Jkj4JnNJOPWZmZmY29o0vuwAzMzMzs9EkIp6QdAJwUgfftv/Ej3ntvlFEXC3pCmD7IV5fIGk30tnOOwP3SboKmA7MB15Fmj5jeeBr7dZjZmZmZmObz2A2MzMzM2vdGcD9HXy/NwLPAr/s0PsdThosHlREPBsR7wXeCfwMWBc4CDgEeDPwG+DdEXF4h+oxMzMzszFK6So6MzMzMzMrg6SlgMeBUyLis2XXY2ZmZmbWCp/BbGZmZmZWri2BucCpZRdiZmZmZtYqn8FsZmZmZmZmZmZmZll8BrOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZBTL7wgAAADdJREFUmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZmZmZmZmZZfn/nf2eLxNNQjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n",
    "#TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n",
    "#TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n",
    "\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "\n",
    "    width = 0.35\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index]).tolist()\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n",
    "    ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n",
    "    ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "    ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n",
    "    ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n",
    "    ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "    ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "    ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "    plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "    blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n",
    "    orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n",
    "    green_Spatch = mpatches.Patch(color='green', label='Children PR')\n",
    "    blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n",
    "    orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n",
    "    green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n",
    "\n",
    "\n",
    "    handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\n",
    "    \n",
    "    ax.axvline((3.5), color='black')\n",
    "    ax.axvline((7.5), color='black')\n",
    "    ax.axvline((11.5), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    plt.ylim((0, 25.5))\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/numpy/lib/stride_tricks.py:262: UserWarning: Warning: converting a masked element to nan.\n",
      "  args = [np.array(_m, copy=False, subok=subok) for _m in args]\n"
     ]
    },
    {
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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 right', 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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,160)',\n",
    "            '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,160)',\n",
    "            '(20,1)',  '(20,10)','(20,40)','(20,80)','(20,160)',\n",
    "            '(40,1)',  '(40,10)',  '(40,20)','(40,80)','(40,160)',\n",
    "            '(80,1)',  '(80,10)',  '(80,20)', '(80,40)','(80,160)',\n",
    "            '(160,1)', '(160,10)', '(160,20)','(160,40)','(160,80)']\n",
    "\n",
    "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,160)',\n",
    "               '(10,20)', '(10,40)','(10,80)','(10,160)',\n",
    "               '(20,40)','(20,80)','(20,160)',\n",
    "               '(40,80)','(40,160)',\n",
    "               '(80,160)']\n",
    "labelsShrink = ['(10,1)', \n",
    "               '(20,1)',  '(20,10)', \n",
    "               '(40,1)',  '(40,10)',  '(40,20)',\n",
    "               '(80,1)',  '(80,10)',  '(80,20)', '(80,40)',\n",
    "               '(160,1)', '(160,10)', '(160,20)','(160,40)','(160,80)']\n",
    "\n",
    "labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n",
    "labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n",
    "labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',\n",
    "               '(10,40)','(10,80)', '(10,160)',\n",
    "               '(20,40)','(20,80)', '(20,160)',\n",
    "               '(40,80)', '(40,160)',\n",
    "               '(80,160)']\n",
    "labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n",
    "               '(80,1)', '(80,10)', '(80,20)','(80,40)',\n",
    "               '(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']\n",
    "\n",
    "                #0          #1                 #2                     #3\n",
    "labelsMethods = ['Baseline', 'Baseline single','Baseline - Pthreads','Baseline single - Pthreads',\n",
    "                 'Merge','Merge single','Merge - Pthreads','Merge single - Pthreads']\n",
    "                 #4      #5             #6                 #7\n",
    "\n",
    "OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n",
    "OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n",
    "OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Pthreads')\n",
    "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Pthreads')\n",
    "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
    "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
    "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Pthreads')\n",
    "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Pthreads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_types_iker(checked_type='tc', used_direction='e', node_type=\"All\", normality='m'):\n",
    "    if checked_type=='te':\n",
    "        var_aux='TE'\n",
    "        tipo_fig=\"TE\"\n",
    "        grouped_aux=grouped_aggG2\n",
    "    elif checked_type=='tc':\n",
    "        var_aux='TC'\n",
    "        tipo_fig=\"Mall\"\n",
    "        grouped_aux=grouped_aggM\n",
    "    \n",
    "    if node_type=='Intra':\n",
    "        grouped_aux=grouped_aux.query('NP < 21 and NS < 21')\n",
    "    elif node_type=='Inter':\n",
    "        grouped_aux=grouped_aux.query('NP > 21 or NS > 21')\n",
    "    \n",
    "    if used_direction=='s':\n",
    "        grouped_aux=grouped_aux.query('NP > NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsShrinkIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsShrinkInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsShrink\n",
    "        name_fig=\"Shrink\"\n",
    "        \n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "    elif used_direction=='e':\n",
    "        grouped_aux=grouped_aux.query('NP < NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsExpandIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsExpandInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsExpand\n",
    "        name_fig=\"Expand\"\n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "\n",
    "    title=tipo_fig+\"_Spawn_\"+node_type+\"_\"+name_fig+\"_\"+normality\n",
    "    return var_aux, grouped_aux, handles, used_labels, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_arrays_iker(grouped_aux, var_aux, used_direction='e', normality='m'):\n",
    "    vOrMult = list(grouped_aux.query('Cst == \"0\" and Css == \"0\"')[var_aux])\n",
    "    vOrSingle = list(grouped_aux.query('Cst == \"0\" and Css == \"1\"')[var_aux])\n",
    "    vMergeMult = list(grouped_aux.query('Cst == \"2\" and Css == \"0\"')[var_aux])\n",
    "    \n",
    "    if var_aux == 'TC': # Para el tiempo TC se utiliza el que no tiene en cuenta la app para los hilos\n",
    "        vOrPthMult = list(grouped_aux.query('Cst == \"1\" and Css == \"0\"')['TH'])\n",
    "        vOrPthSingle = list(grouped_aux.query('Cst == \"1\" and Css == \"1\"')['TH'])\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')['TH'])\n",
    "    else:\n",
    "        vOrPthMult = list(grouped_aux.query('Cst == \"1\" and Css == \"0\"')[var_aux])\n",
    "        vOrPthSingle = list(grouped_aux.query('Cst == \"1\" and Css == \"1\"')[var_aux])\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')[var_aux])\n",
    "\n",
    "    \n",
    "    if used_direction=='e':\n",
    "        vMergeSingle = list(grouped_aux.query('Cst == \"2\" and Css == \"1\"')[var_aux])\n",
    "        if var_aux == 'TC': # Para el tiempo TC se utiliza el que no tiene en cuenta la app para los hilos\n",
    "            vMergePthSingle = list(grouped_aux.query('Cst == \"3\" and Css == \"1\"')['TH'])\n",
    "        else:\n",
    "            vMergePthSingle = list(grouped_aux.query('Cst == \"3\" and Css == \"1\"')[var_aux])\n",
    "    else:\n",
    "        #FIXME Que tenga en cuenta TH al realizar shrink merge\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == \"3\" and Css == \"0\"')[var_aux])\n",
    "        vMergeSingle = None\n",
    "        vMergePthSingle = None\n",
    "    title_y = \"Total time(s)\"\n",
    "        \n",
    "    if normality == 'r':\n",
    "        vOrSingle = np.subtract(vOrMult, vOrSingle)\n",
    "        vOrPthMult = np.subtract(vOrMult, vOrPthMult)\n",
    "        vOrPthSingle = np.subtract(vOrMult, vOrPthSingle)\n",
    "        vMergeMult = np.subtract(vOrMult, vMergeMult)\n",
    "        vMergePthMult = np.subtract(vOrMult, vMergePthMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.subtract(vOrMult, vMergeSingle)\n",
    "            vMergePthSingle = np.subtract(vOrMult, vMergePthSingle)\n",
    "        vOrMult = None\n",
    "        title_y = \"Saved time(s)\"\n",
    "    elif normality == 'n':\n",
    "        vOrSingle = np.divide(vOrSingle, vOrMult)\n",
    "        vOrPthMult = np.divide(vOrPthMult, vOrMult)\n",
    "        vOrPthSingle = np.divide(vOrPthSingle, vOrMult)\n",
    "        vMergeMult = np.divide(vMergeMult, vOrMult)\n",
    "        vMergePthMult = np.divide(vMergePthMult, vOrMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.divide(vMergeSingle, vOrMult)\n",
    "            vMergePthSingle = np.divide(vMergePthSingle, vOrMult)\n",
    "        vOrMult = np.divide(vOrMult, vOrMult)\n",
    "        title_y = \"Relation Config time / Baseline Time\"\n",
    "    \n",
    "    data_array=[vOrMult,vOrSingle,vOrPthMult,vOrPthSingle,vMergeMult,vMergeSingle,vMergePthMult,vMergePthSingle]\n",
    "    return data_array, title_y\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def legend_loc_iker(data_array, len_x):\n",
    "    max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    if(min_value < 0):\n",
    "        min_value = 0\n",
    "    middle_value = (max_value + min_value) / 2\n",
    "    offset = (max_value - min_value) * 0.1\n",
    "    \n",
    "    def array_check_loc(ini, end):\n",
    "        up = True\n",
    "        lower = True\n",
    "        for i in range(ini, end):\n",
    "            for j in range(len(data_array)):\n",
    "                if not (data_array[j] is None):\n",
    "                    if data_array[j][i] > (middle_value + offset):\n",
    "                        up = False\n",
    "                    elif data_array[j][i] < (middle_value - offset):\n",
    "                        lower = False\n",
    "                    if not up and not lower:\n",
    "                        break\n",
    "            else:\n",
    "                continue # Only executed if inner loop did NOT break\n",
    "            break # Only executed if inner loop did break\n",
    "        return up,lower\n",
    "    \n",
    "    up_left, lower_left = array_check_loc(0, math.floor(len_x/2))\n",
    "    up_right, lower_right = array_check_loc(0, math.floor(len_x/2))\n",
    "\n",
    "    legend_loc = 'best'\n",
    "    if up_left:\n",
    "        legend_loc = 'upper left'\n",
    "    elif up_right:\n",
    "        legend_loc = 'upper right'\n",
    "    elif lower_left:\n",
    "        legend_loc = 'lower left'\n",
    "    elif lower_right:\n",
    "        lower_right = 'lower right'\n",
    "\n",
    "    return legend_loc\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphic_iker(data_array, title=\"None\", title_y=\"None\", title_x=\"None\", handles=None, used_labels=None, ylim_zero=True):\n",
    "    f=plt.figure(figsize=(30, 12))\n",
    "    ax=f.add_subplot(111)\n",
    "    x = np.arange(len(used_labels))\n",
    "    width = 0.45/4\n",
    "    \n",
    "    legend_loc = legend_loc_iker(data_array, len(used_labels))\n",
    "\n",
    "    if not (data_array[0] is None):\n",
    "        ax.bar(x-width*3.5, data_array[0], width, color='green')\n",
    "    ax.bar(x-width*2.5, data_array[1], width, hatch=\"\", color='springgreen')\n",
    "    ax.bar(x-width*1.5, data_array[2], width, hatch=\"//\", color='blue')\n",
    "    ax.bar(x-width*0.5, data_array[3], width, hatch=\"\\\\\",color='darkblue')\n",
    "\n",
    "    ax.bar(x+width*0.5, data_array[4], width, hatch=\"||\", color='red')\n",
    "    if not (data_array[5] is None):\n",
    "        ax.bar(x+width*1.5, data_array[5], width, hatch=\"...\", color='darkred')\n",
    "        ax.bar(x+width*2.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    else:\n",
    "        ax.bar(x+width*1.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    if not (data_array[7] is None):\n",
    "        ax.bar(x+width*3.5, data_array[7], width, hatch=\"++\",color='olive')\n",
    "\n",
    "    ax.axhline((0), color='black', linestyle='dashed')\n",
    "    ax.set_ylabel(title_y, fontsize=20)\n",
    "    ax.set_xlabel(title_x, fontsize=20)\n",
    "    plt.xticks(x, used_labels, rotation=90)\n",
    "    plt.legend(handles=handles, loc=legend_loc, fontsize=21,ncol=2)\n",
    "    \n",
    "    if not ylim_zero: # Modifica los limites del eje y. No es buena practica que no aparezca el 0\n",
    "        max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        max_value += max_value * 0.1\n",
    "        min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        min_value -= min_value * 0.1\n",
    "        if min_value < 0.1:\n",
    "            min_value = 0\n",
    "        plt.ylim((min_value, max_value))\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/Spawn/\"+title+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checked_type='tc' # Valores 'te' y 'tc'\n",
    "used_direction='s' # Valores 's' y 'e'\n",
    "node_type='All' # Valores 'Intra', 'Inter', 'All'\n",
    "normality='m'\n",
    "#Values 'n' (normalizar), 'l' (logaritmico), 'm' (sin modificaciones), 'r' (Comparar respecto al primero)\n",
    "\n",
    "ylim_zero = True\n",
    "\n",
    "var_aux, grouped_aux, handles, used_labels, title = get_types_iker(checked_type, used_direction, node_type, normality)\n",
    "array_aux, title_y = obtain_arrays_iker(grouped_aux, var_aux, used_direction, normality)\n",
    "graphic_iker(array_aux, title, title_y, \"Parents, Children\", handles, used_labels, ylim_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    dfG_aux=dfG.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    dfG_aux=dfG.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "vOrMult = list(dfG_aux.query('Cst == \"0\" and Css == \"0\"')['TE'])\n",
    "vOrSingle = list(dfG_aux.query('Cst == \"0\" and Css == \"1\"')['TE'])\n",
    "vOrPthMult = list(dfG_aux.query('Cst == \"1\" and Css == \"0\"')['TE'])\n",
    "vOrPthSingle = list(dfG_aux.query('Cst == \"1\" and Css == \"1\"')['TE'])\n",
    "\n",
    "vMergeMult = list(dfG_aux.query('Cst == \"2\" and Css == \"0\"')['TE'])\n",
    "vMergeSingle = list(dfG_aux.query('Cst == \"2\" and Css == \"1\"')['TE'])\n",
    "vMergePthMult = list(dfG_aux.query('Cst == \"3\" and Css == \"0\"')['TE'])\n",
    "vMergePthSingle = list(dfG_aux.query('Cst == \"3\" and Css == \"1\"')['TE'])\n",
    "\n",
    "vOrMult2 = list(dfM_aux.query('Cst == \"0\" and Css == \"0\"')['TC'])\n",
    "vOrSingle2 = list(dfM_aux.query('Cst == \"0\" and Css == \"1\"')['TC'])\n",
    "vOrPthMult2 = list(dfM_aux.query('Cst == \"1\" and Css == \"0\"')['TC'])\n",
    "vOrPthSingle2 = list(dfM_aux.query('Cst == \"1\" and Css == \"1\"')['TC'])\n",
    "\n",
    "vMergeMult2 = list(dfM_aux.query('Cst == \"2\" and Css == \"0\"')['TC'])\n",
    "vMergeSingle2 = list(dfM_aux.query('Cst == \"2\" and Css == \"1\"')['TC'])\n",
    "vMergePthMult2 = list(dfM_aux.query('Cst == \"3\" and Css == \"0\"')['TC'])\n",
    "vMergePthSingle2 = list(dfM_aux.query('Cst == \"3\" and Css == \"1\"')['TC'])\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.scatter(vOrMult,vOrMult2, color='green')\n",
    "ax.scatter(vOrSingle,vOrSingle2,  color='springgreen')\n",
    "ax.scatter(vOrPthMult,vOrPthMult2, color='blue')\n",
    "ax.scatter(vOrPthSingle,vOrPthSingle2,color='darkblue')\n",
    "\n",
    "ax.scatter(vMergeMult,vMergeMult2, color='red')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergeSingle,vMergeSingle2,color='darkred')\n",
    "ax.scatter(vMergePthMult,vMergePthMult2, color='yellow')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergePthSingle,vMergePthSingle2,color='olive')\n",
    "\n",
    "ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"Time EX (s)\", fontsize=20)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=21,ncol=2)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Dispersion_Spawn_\"+name_fig+\"_Diff.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se sigue una distribución guassiana: False\n",
      "Han fallado un total de: 46\n"
     ]
    }
   ],
   "source": [
    "normality=True\n",
    "total=0\n",
    "#Comprobar para cada configuración si se sigue una distribución normal/gaussiana\n",
    "for np_aux in processes:\n",
    "    for ns_aux in processes:\n",
    "        if np_aux != ns_aux:\n",
    "            for cst_aux in ['0','1','2','3']:\n",
    "                for css_aux in ['0','1']:\n",
    "                    dataList = list(dfG.query('NP == @np_aux and NS == @ns_aux and Cst == @cst_aux and Css == @css_aux')['TE'])\n",
    "                    st,p = stats.shapiro(dataList) # Tendrían que ser al menos 20 datos\n",
    "                    if p < p_value:\n",
    "                        normality=False\n",
    "                        total+=1\n",
    "                        #print(\"Se renuncia a H0\")   \n",
    "                        #print(np_aux, ns_aux, cst_aux, css_aux, st, p)\n",
    "                    #else:\n",
    "                        #print(\"Se acepta H0\") #H0 es aceptar una distribución Gaussiana\n",
    "                        #print(np_aux, ns_aux, cst_aux, css_aux, st, p)\n",
    "                        \n",
    "    \n",
    "print(\"Se sigue una distribución guassiana: \" + str(normality)+ \"\\nHan fallado un total de: \" + str(total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Función para comprobar medias de distribuciones no normales\n",
    "def check_means(dataLists, np_aux, ns_aux, shrink):\n",
    "    st,p=stats.kruskal(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    if p > p_value: # Si son iguales, no hay que hacer nada más\n",
    "        print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene medias iguales\")\n",
    "        return\n",
    "    #else: # Si son diferentes, hay que comprobar cuales lo son\n",
    "        #print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene medias diferentes ---------------------------\")\n",
    "    \n",
    "    best = 0\n",
    "    otherBest=[]\n",
    "    for i in range(1,8):\n",
    "        st,p=stats.mannwhitneyu(dataLists[best],dataLists[i])\n",
    "        if p < p_value: # Medianas diferentes\n",
    "            st,p=stats.mannwhitneyu(dataLists[best],dataLists[i], alternative='greater')\n",
    "            if p < p_value: # Mediana i < Mediana best -- Modificar best\n",
    "                best=i\n",
    "                for j in otherBest:\n",
    "                    st,p=stats.mannwhitneyu(dataLists[best],dataLists[j], alternative='less')\n",
    "                   \n",
    "                    if p < p_value: # Media newBest < Media otherBest[j] -- Eliminar otherBest[j]\n",
    "                        otherBest.remove(j)\n",
    "        else: #Medias iguales\n",
    "            otherBest.append(i)\n",
    "    \n",
    "    if shrink: # Las opciones Merge single(7) y Merge single - Pthreads(8) no se utilizan al reducir\n",
    "        if 5 in otherBest:\n",
    "            otherBest.remove(5)\n",
    "        if 7 in otherBest:\n",
    "            otherBest.remove(7)\n",
    "            \n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux) + \": tiene los siguientes mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Redimensión 1/10: tiene los siguientes mejores: Baseline, Baseline single, Merge, Merge single, \n",
      "Redimensión 1/20: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 1/40: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 1/80: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 1/160: tiene los siguientes mejores: Baseline, Merge, Merge single, \n",
      "Redimensión 10/1: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 10/20: tiene los siguientes mejores: Merge, \n",
      "Redimensión 10/40: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 10/80: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 10/160: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 20/1: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 20/10: tiene los siguientes mejores: Baseline single - Pthreads, Merge, Merge - Pthreads, \n",
      "Redimensión 20/40: tiene los siguientes mejores: Merge, Merge single, Merge - Pthreads, Merge single - Pthreads, \n",
      "Redimensión 20/80: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 20/160: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 40/1: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 40/10: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 40/20: tiene los siguientes mejores: Merge, Merge - Pthreads, \n",
      "Redimensión 40/80: tiene los siguientes mejores: Merge, Merge single, Merge - Pthreads, \n",
      "Redimensión 40/160: tiene los siguientes mejores: Merge, Merge single, \n",
      "Redimensión 80/1: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 80/10: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 80/20: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 80/40: tiene los siguientes mejores: Merge, Merge - Pthreads, \n",
      "Redimensión 80/160: tiene los siguientes mejores: Merge - Pthreads, Merge single - Pthreads, \n",
      "Redimensión 160/1: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 160/10: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 160/20: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 160/40: tiene los siguientes mejores: Baseline single - Pthreads, \n",
      "Redimensión 160/80: tiene los siguientes mejores: Merge, Merge - Pthreads, \n"
     ]
    }
   ],
   "source": [
    "checked_type='te'\n",
    "if checked_type=='te':\n",
    "    tipo=\"TE\"\n",
    "    data_aux=dfG\n",
    "elif checked_type=='tc':\n",
    "    tipo=\"TC\"\n",
    "    data_aux=dfM\n",
    "    \n",
    "results = []\n",
    "shrink = False\n",
    "for np_aux in processes:\n",
    "    for ns_aux in processes:\n",
    "        if np_aux != ns_aux:\n",
    "            dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "            dataLists=[]\n",
    "            if np_aux > ns_aux:\n",
    "                shrink = True\n",
    "            else:\n",
    "                shrink = False\n",
    "            \n",
    "            for cst_aux in ['0','1','2','3']:\n",
    "                for css_aux in ['0','1']:\n",
    "                    if (cst_aux == 1 or cst_aux == 3) and checked_type == 'tc': # Usar tiempo sin app para TC con hilos\n",
    "                        lista_aux = list(dataSet.query('Cst == @cst_aux and Css == @css_aux')['TH'])\n",
    "                    else:\n",
    "                        lista_aux = list(dataSet.query('Cst == @cst_aux and Css == @css_aux')[tipo])\n",
    "                    dataLists.append(lista_aux)\n",
    "            aux_data = check_means(dataLists, np_aux, ns_aux, shrink)\n",
    "            results.append(aux_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  4  4  4  4  4]\n",
      " [ 3 -1  4  4  4  4]\n",
      " [ 3  4 -1  4  4  4]\n",
      " [ 3  3  4 -1  4  4]\n",
      " [ 3  3  3  4 -1  6]\n",
      " [ 3  3  3  3  4  8]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor de los valores\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "unique, counts = np.unique(aux_array, return_counts=True)\n",
    "aux_dict = dict(zip(unique, counts))\n",
    "aux_keys=list(aux_dict.keys())\n",
    "aux_values=list(aux_dict.values())\n",
    "aux_ordered_index=list(reversed(list(np.argsort(aux_values))))\n",
    "\n",
    "labels_aux = [1, 10, 20, 40, 80, 160]\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(labels_aux),len(labels_aux))).astype(int)\n",
    "for i in range(len(labels_aux)):\n",
    "    for j in range(len(labels_aux)):\n",
    "        if i==j:\n",
    "            heatmap[i][j]=-1\n",
    "            used_aux+=1\n",
    "        else:\n",
    "            results_index = i*len(labels_aux) +j-used_aux\n",
    "            for index in aux_ordered_index:\n",
    "                if aux_keys[index] in results[results_index]:\n",
    "                    heatmap[i][j]=aux_keys[index]\n",
    "                    break\n",
    "heatmap[len(labels_aux)-1][len(labels_aux)-1]=8\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea un heatmap teniendo en cuenta los colores anteriores\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "myColors = (colors.to_rgba(\"white\"),colors.to_rgba(\"green\"), colors.to_rgba(\"springgreen\"),colors.to_rgba(\"blue\"),colors.to_rgba(\"darkblue\"),\n",
    "            colors.to_rgba(\"red\"),colors.to_rgba(\"darkred\"),colors.to_rgba(\"yellow\"),colors.to_rgba(\"olive\"),colors.to_rgba(\"white\"))\n",
    "cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))\n",
    "\n",
    "im = ax.imshow(heatmap,cmap=cmap,interpolation='nearest')\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(len(labels_aux)):\n",
    "    for j in range(len(labels_aux)):\n",
    "        if i!=j:\n",
    "            if heatmap[i, j] == 2 or heatmap[i, j] == 3:\n",
    "                text = ax.text(j, i, heatmap[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"white\", fontsize=25)\n",
    "            else:\n",
    "                text = ax.text(j, i, heatmap[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"black\", fontsize=25)\n",
    "\n",
    "ax.set_ylabel(\"Parents\", fontsize=30)\n",
    "ax.set_xlabel(\"Children\", fontsize=30)\n",
    "\n",
    "ax.set_xticklabels(['']+labels_aux, fontsize=30)\n",
    "ax.set_yticklabels(['']+labels_aux, fontsize=30)\n",
    "\n",
    "#\n",
    "labelsMethods_aux = ['Invalid (-1)','Baseline (0)', 'Baseline single (1)','Baseline - Pthreads (2)','Baseline single - Pthreads (3)',\n",
    "                 'Merge (4)','Merge single (5)','Merge - Pthreads (6)','Merge single - Pthreads (7)','Invalid (8)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TE\n",
    "#colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TC\n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=20)\n",
    "#\n",
    "\n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Heatmap_\"+tipo+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 1798.4039776258546\n",
      "Coefficients: \n",
      " [ 345.54008701 -250.14657137]\n",
      "Predicted Stock Index Price: \n",
      " [1422.86238865]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:      Stock_Index_Price   R-squared:                       0.898\n",
      "Model:                            OLS   Adj. R-squared:                  0.888\n",
      "Method:                 Least Squares   F-statistic:                     92.07\n",
      "Date:                Tue, 15 Feb 2022   Prob (F-statistic):           4.04e-11\n",
      "Time:                        16:10:06   Log-Likelihood:                -134.61\n",
      "No. Observations:                  24   AIC:                             275.2\n",
      "Df Residuals:                      21   BIC:                             278.8\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const              1798.4040    899.248      2.000      0.059     -71.685    3668.493\n",
      "Interest_Rate       345.5401    111.367      3.103      0.005     113.940     577.140\n",
      "Unemployment_Rate  -250.1466    117.950     -2.121      0.046    -495.437      -4.856\n",
      "==============================================================================\n",
      "Omnibus:                        2.691   Durbin-Watson:                   0.530\n",
      "Prob(Omnibus):                  0.260   Jarque-Bera (JB):                1.551\n",
      "Skew:                          -0.612   Prob(JB):                        0.461\n",
      "Kurtosis:                       3.226   Cond. No.                         394.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "\n",
    "Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],\n",
    "                'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],\n",
    "                'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],\n",
    "                'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],\n",
    "                'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719]        \n",
    "                }\n",
    "\n",
    "df = pd.DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price'])\n",
    "\n",
    "X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets\n",
    "Y = df['Stock_Index_Price']\n",
    " \n",
    "# with sklearn\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, Y)\n",
    "\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "\n",
    "# prediction with sklearn\n",
    "New_Interest_Rate = 2.75\n",
    "New_Unemployment_Rate = 5.3\n",
    "print ('Predicted Stock Index Price: \\n', regr.predict([[New_Interest_Rate ,New_Unemployment_Rate]]))\n",
    "\n",
    "# with statsmodels\n",
    "X = sm.add_constant(X) # adding a constant\n",
    " \n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    " \n",
    "print_model = model.summary()\n",
    "print(print_model)"
   ]
  },
  {
   "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
}