analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from scipy import stats\n",
    "import scikit_posthocs as sp\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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 = [1,10,20,40,80,120]\n",
    "\n",
    "labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],\n",
    "          ['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]\n",
    "labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',\n",
    "              '(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']\n",
    "positions = [321, 322, 323, 324, 325]\n",
    "positions_small = [221, 222, 223, 224]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3862/2056908859.py:18: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n"
     ]
    }
   ],
   "source": [
    "dfM = pd.read_csv( matrixMal )\n",
    "dfM = dfM.drop(columns=dfM.columns[0])\n",
    "\n",
    "dfM['S'] = dfM['N']\n",
    "dfM['N'] = dfM['S'] + dfM['%Async']\n",
    "dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TH\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
    "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
    "\n",
    "dfM['%Async'] = dfM['%Async'].fillna(0)\n",
    "dfM['alpha'] = 1\n",
    "\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['median'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['alpha'] = \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['TC'] / \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux-1, css_aux, np_aux,ns_aux)]['TC']\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3862/3029782824.py:13: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'alpha']\n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "dfL['%Async'] = dfL['%Async'].fillna(0)\n",
    "dfL['alpha'] = 1\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'alpha']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'alpha']\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', 'alpha', 'alpha2'], axis='columns', inplace=True)\n",
    "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n",
    "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux), 'alpha'] = \\\n",
    "                        grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti'] / \\\n",
    "                        grouped_aggL.loc[(0,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "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', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "coherent_check_df = grouped_aggL.copy()\n",
    "# Añadir suma total de iteraciones\n",
    "coherent_check_df['Sum'] = 0\n",
    "coherent_check_df.loc[(1,slice(None)),'Sum'] = grouped_aggLT[(grouped_aggLT['Sum'] != 0)].loc[(slice(None)),'Sum'].values\n",
    "coherent_check_df = coherent_check_df[(coherent_check_df['Sum'] != 0)]\n",
    "# Añadir tiempos TE y TC\n",
    "coherent_check_df['TE'] = 0\n",
    "coherent_check_df['TEA'] = 0\n",
    "coherent_check_df['TR'] = 0\n",
    "coherent_check_df['TRA'] = 0\n",
    "for cst_aux in [1,3]:\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TE'] = grouped_aggG2.loc[('2,2',0,cst_aux-1,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TR'] = grouped_aggM.loc[('2,2',0,cst_aux-1,slice(None)),'TC'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TEA'] = grouped_aggG2.loc[('2,2',0,cst_aux,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TRA'] = grouped_aggM.loc[('2,2',0,cst_aux,slice(None)),'TC'].values\n",
    "# Calcular tiempos teoricos\n",
    "#coherent_check_df['Teorico-S'] = coherent_check_df['Ti'] * 3 + coherent_check_df['TR'] +  TIEMPOITERNS * 97\n",
    "#coherent_check_df['Rel-S'] = np.round(coherent_check_df['Teorico-S'] / coherent_check_df['TE'],2)\n",
    "#coherent_check_df['Teorico-A'] = coherent_check_df['Ti'] * 3 + coherent_check_df['Sum'] +  TIEMPOITERNS * (97 - coherent_check_df['Iters'])\n",
    "#coherent_check_df['Rel-A'] = np.round(coherent_check_df['Teorico-A'] / coherent_check_df['TEA'],2)\n",
    "coherent_check_df=coherent_check_df.droplevel('Tt').droplevel('%Async').droplevel('Dist')\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        aux_df = coherent_check_df.loc[(cst_aux, css_aux, slice(None))]\n",
    "        aux_df.to_excel(\"coherent\"+str(cst_aux)+\"_\"+str(css_aux)+\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "<p>2400 rows × 15 columns</p>\n",
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       "      Unnamed: 0  N  %Async  Groups   NP  NS Dist  Matrix  CommTam  Cst  Css  \\\n",
       "0              0  0     0.0   40,10   40  10  2,2  100000        0    3    0   \n",
       "1              1  0     0.0   40,10   40  10  2,2  100000        0    3    0   \n",
       "2              2  0     0.0   40,10   40  10  2,2  100000        0    3    0   \n",
       "3              3  0     0.0   40,10   40  10  2,2  100000        0    3    0   \n",
       "4              4  0     0.0   40,10   40  10  2,2  100000        0    3    0   \n",
       "...          ... ..     ...     ...  ...  ..  ...     ...      ...  ...  ...   \n",
       "2395         795  0     0.0  120,10  120  10  2,2  100000        0    3    0   \n",
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       "2399         799  0     0.0  120,10  120  10  2,2  100000        0    3    0   \n",
       "\n",
       "      Time Iters         TE  S  \n",
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       "2395   4.0  3,97  38.333576  0  \n",
       "2396   4.0  3,97  38.551992  0  \n",
       "2397   4.0  3,97  38.096905  0  \n",
       "2398   4.0  3,97  38.306287  0  \n",
       "2399   4.0  3,97  35.718237  0  \n",
       "\n",
       "[2400 rows x 15 columns]"
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     "execution_count": 91,
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       "                                    TE\n",
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       "      Unnamed: 0  N  %Async   NP  NS Dist  Matrix  CommTam  Cst  Css  Time  \\\n",
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       "2399         799  0     0.0  120  10  2,2  100000        0    3    0   4.0   \n",
       "\n",
       "     Iters        TC   TH   TS   TA  S        TR  alpha  \n",
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       "2397  3,97  0.333766  0.0  0.0  0.0  0  0.333766      1  \n",
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       "\n",
       "[2400 rows x 19 columns]"
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     },
     "execution_count": 93,
     "metadata": {},
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>10</th>\n",
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       "      <td>0.315527</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.861425</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.861425</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.988951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.988951</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.911823</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.911823</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.360395</td>\n",
       "      <td>1.550456</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.428876</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.428876</td>\n",
       "      <td>2.692918</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.463684</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.463684</td>\n",
       "      <td>2.264129</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.265142</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.265142</td>\n",
       "      <td>0.864532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.402624</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.402624</td>\n",
       "      <td>0.987730</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   TC   TH   TS   TA        TR     alpha\n",
       "Dist %Async Cst Css NP  NS                                              \n",
       "2,2  0.0    0   0   1   10   0.315527  0.0  0.0  0.0  0.315527  1.000000\n",
       "                        20   0.860505  0.0  0.0  0.0  0.860505  1.000000\n",
       "                        40   0.861425  0.0  0.0  0.0  0.861425  1.000000\n",
       "                        80   0.988951  0.0  0.0  0.0  0.988951  1.000000\n",
       "                        120  0.911823  0.0  0.0  0.0  0.911823  1.000000\n",
       "...                               ...  ...  ...  ...       ...       ...\n",
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       "                        10   0.428876  0.0  0.0  0.0  0.428876  2.692918\n",
       "                        20   0.463684  0.0  0.0  0.0  0.463684  2.264129\n",
       "                        40   0.265142  0.0  0.0  0.0  0.265142  0.864532\n",
       "                        80   0.402624  0.0  0.0  0.0  0.402624  0.987730\n",
       "\n",
       "[240 rows x 6 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "        Unnamed: 0  N  %Async   NP  N_par  NS  Dist  Compute_tam  Comm_tam  \\\n",
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       "\n",
       "[240000 rows x 17 columns]"
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     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfL"
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  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3.999236</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3.999194</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.070046</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.108073</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.075896</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.292376</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.090617</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.733503</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.069103</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.089061</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.068959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.083952</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Ti  Iters      To  Iters2     alpha  \\\n",
       "Tt  Dist %Async Cst Css NP  NS                                               \n",
       "0.0 2    0.0    0   0   1   10   3.999165    3.0  4485.0     3.0  1.000000   \n",
       "                            20   3.999194    3.0  4485.0     3.0  1.000000   \n",
       "                            40   3.999186    3.0  4485.0     3.0  1.000000   \n",
       "                            80   3.999236    3.0  4485.0     3.0  1.000000   \n",
       "                            120  3.999194    3.0  4485.0     3.0  1.000000   \n",
       "...                                   ...    ...     ...     ...       ...   \n",
       "1.0 2    0.0    3   1   120 1    0.070046    3.0    37.0     3.0  2.108073   \n",
       "                            10   0.075896    4.0    37.0     4.0  2.292376   \n",
       "                            20   0.090617    5.0    37.0     5.0  2.733503   \n",
       "                            40   0.069103    4.0    37.0     4.0  2.089061   \n",
       "                            80   0.068959    4.0    37.0     4.0  2.083952   \n",
       "\n",
       "                                 alpha2  \n",
       "Tt  Dist %Async Cst Css NP  NS           \n",
       "0.0 2    0.0    0   0   1   10       30  \n",
       "                            20       30  \n",
       "                            40       30  \n",
       "                            80       30  \n",
       "                            120      30  \n",
       "...                                 ...  \n",
       "1.0 2    0.0    3   1   120 1        30  \n",
       "                            10       40  \n",
       "                            20       54  \n",
       "                            40       37  \n",
       "                            80       39  \n",
       "\n",
       "[360 rows x 6 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Sum</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"11\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>40</th>\n",
       "      <th>80</th>\n",
       "      <td>1.427236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">80</th>\n",
       "      <th>1</th>\n",
       "      <td>0.173856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.207770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.157496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.184899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Sum\n",
       "Dist %Async Cst Css NP NS          \n",
       "2    0.0    0   0   1  10  0.000000\n",
       "                       20  0.000000\n",
       "                       40  0.000000\n",
       "                       80  0.000000\n",
       "                    10 1   0.000000\n",
       "...                             ...\n",
       "            3   1   40 80  1.427236\n",
       "                    80 1   0.173856\n",
       "                       10  0.207770\n",
       "                       20  0.157496\n",
       "                       40  0.184899\n",
       "\n",
       "[160 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggLT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.0\n"
     ]
    }
   ],
   "source": [
    "auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])\n",
    "auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')\n",
    "iters = auxIter['Iters1'].mean()\n",
    "print(iters)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547], [0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807]]\n",
      "[[2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703], [1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807]]\n",
      "[[0.22657399999999997, 0.22961033333333333, 0.37444533333333335, 1.0861523333333334, 0.18071299999999998, 0.2686593333333333, 0.48245, 1.8810366666666667, 0.22639533333333337, 0.31453400000000004, 0.564293, 2.4626886666666667, 0.4612826666666667, 1.0638560000000001, 1.5319243333333334, 2.1236686666666666], [0.21594133333333332, 0.36930899999999994, 1.1269756666666668, 1.1670603333333334, 0.22462733333333332, 0.47068400000000005, 1.5951943333333334, 1.693723, 0.7059706666666666, 1.368441, 1.8698483333333336, 2.2059883333333334, 0.4813296666666667, 1.3010543333333333, 1.8387883333333335, 2.1851773333333333]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:53: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "#Reserva de memoria para las estructuras\n",
    "TP_data=[0]*2\n",
    "TH_data=[0]*2\n",
    "TM_data=[0]*2\n",
    "\n",
    "TP_A_data=[0]*2\n",
    "TH_A_data=[0]*2\n",
    "TM_A_data=[0]*2\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    \n",
    "    TP_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "    TP_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "# Obtencion de los grupos del dataframe necesarios\n",
    "\n",
    "#ACTUALMENTE NO SE DIFERENCIAN LOS TIEMPOS DE ITERACIONES DE PADRES E HIJOS CUANDO COINCIDE EL NUMERO DE PROCESOS\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['NP'])['Ti']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['Dist', 'NP'])['Ti']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "grouped_aggL_aux = groupL_aux.agg(['mean'])\n",
    "grouped_aggL_aux.columns = grouped_aggL_aux.columns.get_level_values(0)\n",
    "grouped_aggL_aux.set_axis(['Ti'], axis='columns')\n",
    "\n",
    "#Calculo de los valores para las figuras\n",
    "#1=Best Fit\n",
    "#2=Worst Fit\n",
    "dist=1\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    i=0\n",
    "    r=0\n",
    "    for numP in values:\n",
    "        j=0\n",
    "        for numC in values:\n",
    "        \n",
    "            tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "            for tipo in [0]: #TODO Poner a 0,100\n",
    "                iters_aux=dfM[(dfM[\"NP\"] == numP)][(dfM[\"NS\"] == numC)][(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == tipo)]['Iters'].head(1).tolist()[0].split(',')\n",
    "                itersP_aux = int(iters_aux[0])\n",
    "                itersS_aux = int(iters_aux[1])\n",
    "                iters_mal_aux = 0\n",
    "                if tipo != 0:\n",
    "                    iters_mal_aux = grouped_aggL['Iters'].loc[(1,dist,tipo,numP,numC)]\n",
    "            \n",
    "                t_iterP_aux = grouped_aggL_aux['Ti'].loc[(dist,numP)]\n",
    "                t_iterS_aux = grouped_aggL_aux['Ti'].loc[(dist,numC)]\n",
    "            \n",
    "            \n",
    "                p1 = t_iterP_aux * itersP_aux\n",
    "                p2 = t_iterS_aux * max((itersS_aux - iters_mal_aux),0)\n",
    "                \n",
    "                array_aux = grouped_aggM[['TS', 'TA']].loc[(dist_v,tipo,numP,numC)].tolist()\n",
    "                p3 = tc_real + array_aux[0] + array_aux[1]\n",
    "                \n",
    "                #Guardar datos\n",
    "                if tipo == 0:\n",
    "                    TP_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_data[dist_index][i*len(values) + j] = p3\n",
    "                else:\n",
    "                    TP_A_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_A_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_A_data[dist_index][i*len(values) + j] = p3\n",
    "            j+=1\n",
    "        i+=1\n",
    "print(TP_data)\n",
    "print(TH_data)\n",
    "print(TM_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABocsW+JwBsrtxSevtb9VW1t78FAAAAwGFyBTMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJooMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAk4MvMJdSnl9KqWu+PrzveQIAAAAAjM0V+57ADj2Y5AMnrHugz4kAAAAAAJwFYyow/2qt9fy+JwEAAAAAcFYcfIsMAAAAAAD2Q4EZAAAAAIAmY2qRAQAAMHjlltLb36qvqr39LQDgbBrTFcyfW0p5dynlI6WUD5VSfruU8qOllOv2PTEAAAAAgDEa0xXMVyV5XJI/S/LoJJ87/fqHpZRvqbW+cZ+TAwAAADgL+rxTI3G3BuzbGK5gfm+SVyX5vCSfUmt9XJJHJvnaJHcleXiSny6lfNlJA5RSXlBKubOUcud9993Xx5wBAAAAAA7ewReYa63vqLV+X6313bXWv5r+7i9rrb+Y5O8keU+Sy5P8kxVjvL7Wen2t9fqrr766n4kDAAAAABy4MbXIeIha6/2llB9M8q+T3FBKubrW6hJlOGPcngUAAADQjYO/gnkDvz79XpJ8zh7nAQAAAAAwKqO+gnlq/tJFlxUCwIC54wAAAOCwnIUrmJ8+9/Mf720WAAAAAAAjc9AF5lLKysucSimPTvLd08Xf0H8ZAAAAAGB3DrrAnOSzSykXSynfXEr5rNkvSymfVEr56iT/IcnfSvLxJN+zr0kCAAAAAIzRGHowf/H0K6WUjyZ5IMmjkzxsuv4vkryw1vrL+5keAAAAAMA4HXqB+d4kL07yJUm+IMnVSR6TSZH5D5L8UpJ/UWvVexkAAAAAYMcOusBca/1Ikn8+/QIAAAAAoEeH3oMZAAAAAIA9UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmV+x7AgAMV7ml9Pr36qtqr38PAAAAOB1XMAMAAAAA0ESBGQAAAACAJgrMAAAAAAA0UWAGAAAAAKCJAjMAAAAAAE0UmAEAAAAAaKLADAAAAABAEwVmAAAAAACaKDADAAAAANBEgRkAAAAAgCZX7HsCAACcXrml9Pa36qtqb38LAAAYNgVmAAAABq/PN9ISb6YBwKa0yAAAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAEATBWYAAAAAAJpcse8JAAAAAABJuaX09rfqq2pvf4txcwUzAAAAAABNFJgBAAAAAGiiwAwAAAAAQBMFZgAAAAAAmigwAwAAAADQRIEZAAAAAIAmCswAAAAAADRRYAYAAAAAoIkCMwAAAAAATRSYAQAAAABoosAMAAAAAECTK/Y9AQAAAKA75ZbS29+qr6q9/S0AhsEVzAAAAAAANHEFMwAnqk/Y9wwAAACAIVNgBkZPkRQAAACgG1pkAAAAAADQxBXMAACcWX1+8FXiw68AABgfVzADAAAAANBEgRkAAAAAgCYKzAAAAAAANFFgBgAAAACgiQIzAAAAAABNrtj3BAAAAAAAWpVbSq9/r76q9vr3hs4VzAAAAAAANFFgBgAAAACgiRYZAAAwEp+4PfT26S+enfble5I8MF2+Msm5hfWznwF6VJ40zXNd5rf57QFYS4EZAADGZrEwsu3yDUnuSPK86fId09+dtD1AX+Q3gMHRIgMAAMbi7h2NcUeSm5NcN/26efq7XYwPsC/yG0AnXMFMkuntlPO3At2d5LZMbhd62fT329569NpMbjt6XiZP3G41AgDoVpfnbzcvGR9gX7rOb16/AmzMFcwsd10mT64PpO2d3Lunj71yOhYAAN3r8vzttOeHALsgvwEMjiuYObb4zuzLcuktRJv2tpo9ZvbO8Enbs7X6hH3PAA7XJz74ambxFslNtDwGoE9dn7/Nj/+dO5gvwLb6yG/LxgfgRK5gZrVte1IpvgCHQn4Dxqrr/DYbH2Af+shvejIDbEWBmYnbF36eX76YpOT4SXZx/Wx59uRdpo85aTyAfek6v8l3wFB0nd/mtwXoUx/5bX58ANZSYGYz53L8Tu49S9bfk+N3hs/1OC+A05LfgLGS34Cx6jq/zY8PwFp6MDPx7BN+XlyePcneMPe7G3LpbUeLtx7pXQUMRdf5Tb4DhqLr/CbfAfvSZ37TDghgI65gZjuLPan0JAXGQn4Dxkp+A8aq6/wmRwJsxBXMHJv1nXr2Bss3J7ltujz7NN4NH19fuasJA2zh9nSe3y5ZdnUfsA995Lf5ZYB96Tq/yXcAG3MFMwAAAAAATVzBzLFNe1HNbjt63nR5dguSXn3AkG3Sa35X+U2+A/ZFfgPOiq7zm3wHsDFXMLOdxZ5Wiz2vAA6V/AaMlfwGjFXX+U2OBNiIK5iZ2KQ36ezTeEuSizn+wIOL09/NntgvnvD46XJ5bt3p1Nepz+n1z3Wqz9iJWzuxa9d57DrOb3r1AYPRdX7Tax7Ylz7y2/z437nDuQOMlCuY2cw9OX6CPrdk/bkcv1N8T4/zAjgt+Q0YK/kNGKuu89v8+ACs5QpmJlb1Jp29czt/29FJj509id+wYjyAfek6v8l3wFB0nd9uCMB+9JHf5scHYC0FZlZb7Gm1znzPK0/IwJDJb8BYdZ3fZuO7bRwORn3CvmewQ33kN+d6AFtRYObYYu+p1yZ5IJNP471uyfpVyzcnuS3JlUletrAeYB/m+4V2ld/mx5+tA+hTH/ltNj7APsyKv13mt2XjA3AiPZhZ7u5MnlyvTNs7t9dNH/tAfPIuMCxd57fZ+AD70Ed+ax0fYBfkN4DBcQUzE3fn+J3Z2W1Bs3duZ9b1qlpcflkuvcVotv7Vp5wrQItnp/v8Nj8+wD70kd8UX4Ah6Dq/uXIZYGMKzEzMf0LuLntOLfa8AtiXXffUW5bf9OyDgzOqvqSJ/AawivwG0AkFZiZKJj2pksltQRdz/AS7TW+rZcsXF8YH2IdZ370u89vi+AD70nV+m+9rD9CnPvLb/PYArKXADAAwAqO7EhcAYADKk8qlb1jck+PPXLkyybm0v+GxbNmbGxwgBWYmao57ht6R5Ia5ddv2tlpcviGX9iR1JTOwD89L9/ltcXyAfek6v3nxyx54I40k8hv9m98ntt1/WvZPOEAKzEzM95ya70l12tu8d93zFKDVYs+9LvLbLscHaHF35Dc4AOX55fhY2uVrpmVj3Z3UrzzluGMhv3EaXe8/s/G/85TjDEh5fuk8vzl+h0GBmYnrcumtGTfnuF/py6a/3/bWjtdmctvI7NN4Z+sB9mF2u1mX+W1+/FfvdPZwppRSlh9fd2fnx+/orojsI7/Njw+06fP111ivyO06v409fmzntPvPNvvnmMwu8vH6a/QUmFnuukwO/gfSdiXM3dPHXtnwWIAudZ3fZuNz5pVbyuSHbQuk6woEJyzXV+5u7nu37vhyfnKyPvJb6/jAyeS3zclv9K3r/Wfsx6/XX2dCqbXuew6Dcv3119c777xz39Po3SdeBC9qud1g3WNe3e8+N6ZdvJzw39SFruO2k31u023tc8363OeSzWN3/vz5JMnR0dHGYz9kn+siv+1JfdV4droTc0NHuozdqfa5hn2t1ytxn9PtPrd2P9jh8dv7FcxbxK4513Wd3/Z0C69c16bruG29z51i/6w/2TzNNl3mujcu3weO7kpu/mfJHS9Ozj9ls6FaHtOprvPclaX7/Lancz15rl2n53Sb7nMH+Pqi831uLteNIr/N6/h8eKhKKb9Za71+8feX7WMyHJD5nqV3b7D9QIsvHIht959t90+YJ7/Rp67z28hyYH3Cmq+vTC68JLnqTcmFB9dvf+HB6bYvmTx2ft0odZ3fZuND0k9+m41/Bpx/yqSQcvM/mxRW1hl08aUrfeQ3ry+Ys+z8ocvzk7GS38ZNiwwmZr1JZz9nbvlikpLjJ+aLC+tn288+TbVMt7luYb3eVSyz7f7Tun9C0n1+k++Y11d+m40/sg9wuul1k+9veeny5Vvfllx+2fELj1vftnz7b/vqyTaXXzbZZvYCZXG80ek6v81vOwLlSdMrrFbF59yS9auW70lSszz+Y9JHfpuNP6I8d9Pr+stv839rFPrIb/PjjynX3VK6z28jPB9ed3zNL+/k+H1mp/+cXvWR3+aX2R8FZjZzLg99App3Tx76BA+bOu3+s27/hFXkN7rUdX6bH/8MuuHxl74AueHxl66/+J6HvoA5U+S3zd2T3cfnrJyf9JHfzuD+Kb+t0fX+Mz9+z+2AetHH+ckZdtrjd8xXMSfy21jpwbxAD+Y1lt1itO1tR/rhNhtlD+Zdth04af+8zT7XatQ9mBftIr/tiX597Trt1/f80n1+m/vdmHown9Sb9CTLbqEc7G2VffRgXnTA+W1er/01uz4/6VEvPZhPY4v9c8g907e2YZ47qPw202ee6zq/9fyBf4Prmb7OqviPqU//luclMwdx/O7xnO4g4rOKHsyXcAUz25nvSTW7WurAXpwwELOeZrvcf07aP0fmVC/kFvsYro3/GXrSlN/YsavelNzxkh2dHD8hOTp3fMKdJDfvcvwDN9/T7xPxOZQXJ32Q39brMj7L4j+iuJ+66Cu/rSS/rdF1fhvRsZpk9294rYo/jt81xGdcFJg5tkmvpdnyzUlumy4/L5Mnlk0fD8kn9p8LL588eazrpbTN8tG55MbXHI8/+3lUtjleF5e3OX7HZFWv+V3lt/nlEfWdo83s5HiX+e2OF1+a35aNPwarepOeNj5jjNcl+shv88tjMSuS7Do+J8X/1buY9DC0Hq+Ly2clv83rOr+NPX6d57cx5rvb0n1+m40/Ml3nt7Efr13nt9H1mj9Al+17AgAAAAAAHCY9mBfowbyBrW+xX6AHc7Mx9WA+esXkH7PrW2DmezbNxn//h0a2zzX2AEuWx2dV/Mtzhxm7TnowJ6fPb3uiB3O7LmN39aNL5/ltb7cQDqwHczKw+Kyyjx7MycHmt3m99UxPdh+fxfiPqKfrac5LZgZ7/A6gB3My4PicpO88N4L8NtN5D+Zdfj7EzEDiP8QezMkBHL97PqcbfHxW0YP5Eq5gZjuLPZvmey7dveJxsOD8Uy7tuXR01+nHXPxAgNn4TJwUn13F/+DJb+xYH/nN8TshPmvIb+t1GZ8l8a9P6O9r6By/q4nPGl3nt7HlyB7y2yfGx/G7hviMiwIzE7cv/LxsefbkUZJcnFt/cfq72ZPUSY+HJW59W3L5ZcdPIje97rifUrL58uzJ6fLLJmPOjz82u47PqviPTtf5Tb5jTh/5bX78sek6v52ZfNdVfhtbrusiPqviPyJ95LcxHq995rexxa6X/DY//th0nd9m449In/ltbMdrH/ltbDE7VD7kj83ck+N3JpedHJ9LckOOn2DO9Tc1Dt8Nj0++7auPn2BuePx2j7/4nuN3PsdYUD6tdfE5bfwPnvy2VrmyXBqfxQ93WYzPNh8Oc0+SmuPxx/RBOuk+v82Pf9+/3M2cD4n8tob8trll//bTxmdd/Eeij/x2Fo9f8Vmj6/w2P/4YdZ3fzh3GHRRdcfyuJj7jpAfzAj2Yl1i87WWVTbbVg7nZmHowL+vFtHiLzCY2ecxQ+wi3OnpF2Wl8Vm071NjtrC/prvPbnvTSr6+L+Awgpn316+sqv81v22uvugH0YD5tftubvnqTdp3f7t5i2x3ptAfzrs6FN3xMr4WXjo/Xo1eUzvPb3o7fPfVgPpj4nKTrPLdNH+EDOz/p/JxuVf/qHcen9wJzh8drn6+/etfzOd3BxWcVPZgvoUUGq237hHPGevqV55eUK8vk+y0bfm3zmIVtx27bnkuDf8LpSFfxOXM9r+S3zXURnwEUl/vUdX47Szkwkd/W6jq/jfW28ZPIbyv1kd9m458F8tsG+shvIz1+H9Kn/SuTCy9JrnpTcuHB9T3dLzw43fYlk8cecv/3bXj9tRviM25aZHBs8dbl1yZ5IMnzMnly3eRW59nyzUluS3JlkpctrB+T27L7+KyL/0jM+iS95aUPXb7jxcmNr0mueUzyvh9fvv21L0ruvT+58PLJE8+q8cami/icFP9RuT3d57f58WfrxqCv/Day9hgzXee3Mea7a1/UfX6bjT86feS32fgjMiuGrNp/js7t7vjNiI7Xm17XT34b4wc3d53fxvj8kOS4+Ov8ZGuz43X2c+L11yb6fP011vOTrvPb/Lkj++EKZpa7O5Mn1yvT9s7tddPHPpBxX+nXVXxOG/8Dd/4pkyePe+9f/k7l0V2Tddc85uxdtZd0H5/58Uep6/w2G/+skt9Wkt8200d+G+2VMH3kN8ev43eJPs5Pxkx+24L8tnPy22p9vv4a4/HbdXxm47NfejAvOLM9mOf7a+7ytqBlY42oB/Op21Ysi8+q+PcYu330YF607BaalrYYQ+0j3Gwau13F5yRHdyU3vmaYsWvuS9p1ftvTbZW99+vbxIDis0pfPZgXdX38dm4PPV13nd8Ooadrc2/SEeW3efs6Xhcd3PE7gP6ag47PKj31dO06vw25T//OPldjUwPNbzNdn9Oty3UHffz20DO9j9dfvX9w8wD79J841tD2Tz2YL6FFBhPzn5C7yyfX+Z5XY/0E3tNYFp+BnNwMwXzPpdltkQdxctOTruMzuhjv+sWD43e1xviMrWffSeS31frIb7Pxe30R1wf5rXOO39XEZzN95Lf58UeX61rJbys5flfr6/xkTPrIb/bPYVBgZqJk0pMqmdwWdDHHT7Db9LZatnxxYfyx2XV81sV/ZNb1Xrr1bcnll016LiWTW2NufdvxE8imvZvGZL532q7icxZ6p335AexTAAAgAElEQVSi716X+W1x/LHpOr/dnuSVO5zvnvWV30Z5vKb7/DYbf7T6OD8Z0blJ6/7Tun+OTdf5bYzx6yO/zY8/Kl5/nYrzk+31+fprfqwxuPyy7vPb2GJ2qMZ8Wg0AAAAAQIf0YF5wZnswX1m6u0Vo/rb0JLltmD1dW5y6B3Py0Pisiv8Z68GcXNpTKWm7BWasPZiT3cRnlaHGrrkvadf5bU+3WA6yB3PSFJ/eW2T00K/vJF0fv53qoadr1/HZS5/DZNi9SecNJL/NG0oP5uTAjt+eezAnBxafVTqM3dWPLp3nt721yBh6nhtgfpvZdw/m5ICP357O6fo4Pxlqz/QWs1zXZX7b2/6pB/MlXMHMxOwJdb4n1bJP193WYs/TATxpD8qy+Owy/gdusWH/fM+lMX667ra6js/oYtxHfnP8HhOfleS31frIb/MvTkZFfuuc43c18dlMH/ltfnym5LeVHL+r9XV+MiZ95Df75zDowczEdbm019TNOe5X+rLp77ftbfXaJA8ked7C+GPy2uw+PuviPxLreitd+6Lk3vuTCy+fPHHMr7/jxZOeS9c8Jnnfj2823tjsOj4nxX9UZv1Cu8xv8+O/eqez36tnvXXyfdv9Z9n+eXRu/f45Nl3ntzHG78bXdJ/fZuOPTh/5bTr+GD+U0/nJ9uZ7k8pvm+sjv405fkn6e/01ol7M88er/Ladvl5/XXh59/+WPs2KwF3mt9n49Q3d/3s4mSuYWe66TJ5cH0jbO7l3Tx97ZcZ91XJX8Tlt/A/c0V2TJ49rHrP8xf/5p0zW3Xv/2Xynsuv4zI8/Sl3nt9n4Z5TjdzXx2Uwf+W2UxeWkn/zm/MTxu0Qf5ydjJr9toef8Vp/Q39e+yG+r9fn6a4zHb9fxmY3PfunBvODM9mA+qY/V4i1Em1j3mB77CCcd92Bu6em6TUwXtz0DPZgXb3tZZdNth9pHuNWmfay2ieVJjxlq7HbSr6+L/Dan1xcJe+ivuUwXx2/neuzBfJDxOcme97ld5Le96aM3acf5bbZt/cmNp7UbA+mZfnDHb88907s8Pxlyz/St7eBc+DSP6VTXeW72+UF9vP7K+M/pDjq/zeuhZ3ofr7961+M+d5DxWUUP5ku4gpnVtu1J1fKC5oDVr0wuvCS56k3JhQfXvyN94cHpti+ZPHbtu9gL44/dtk8eZ7XnUlfxGfSTdxfkt411sf+cteO36/ichRjOk9/W6Dq/zcY/I+S31frIb6Ptmb6E/LaBPvLbGenJLL9txuuv3RCfcVNgZuL2hZ/nly8mKTl+kl1cP1uePXmX6WNOGm9kbn1bcvllx0nyptcd9wNKjpdnyfHyyyaPWVx/0vL8+GO0y/icFP+x2XV8VsV/dLrObyPNd33kt9n4Y9JHfpsff2y6zm+L249O1/ltftsR6CO/jXV/6yO/jfH8pM/8Nrp9r4/8Nj/+yPR1fjImfea3scWuj/w2tpgdKgVmNnMux+/k3rNk/T05fmf4XI/zGogbHn/8TtzF9zx0/cX3HL/zdsPj28cfq13F56T4n3Vd758HT35bq4/8Ntbjt+v4zI9/Fslva8hvG5Pf2vWR387i8Ss+a3Sd3+bHH6E+zk/OMsfvauIzTnowL9CDeY1ltxhte9vRiHowb9Jfc5e3dfTZD7evHsy7jM9J8b/xNSPa55KN++Euatk/R92DedEu8tucsfXr6zq/jbG/5tErSuf5bW+3DQ6k7/fM4OKzSse9STftrznY+KzSY3/Nrs9PejWw43XRoPfPPfb9nhl0fE7SY57r4/yk1xgPrGf6OqviP6Zzuj5ff/Vuj88RBxGfVfRgvsQV+5gMB2y+J9Ws994Z7Um6zHxPodnVZAeTHHs267m0y/icFH8m7J9ryG8rdb3/zMYfkz7ym+N3QnxWE5/1+shv8+OL+zH752ris1of5ydjsuvi3ar44/hdR3zGRYGZY7O+U8/eYPnmJLdNl5+XSfFl08ePyE2vS97y0uOfk8nyHS9ObnzNZPnCyyfJcX794vbbLI9FV/FZFv/Zz2Oyy/isiv+o3J7u89v88it3MutB6Cu/zZbHYnZy3GV+Wzb+GJz0/LpuuWX/HJtdx+esxG/2Irev85P6hm7+HfvQerwuLp+V/Dav6/x2luLn/GQzN76mn9dfYywO9vX6a0z727w+zk/GGrtDocBMksmt3Dc9cvLzW6a3da9aPnowuXH62AvnkvNbPP7nO/kXAAxLry1tntPbn+pc721ZxK6JuLUbU+wAACDRg/khzmoP5m16Cs3fVpNsfwvDUHu6NlnT5zDZ7S0eY+rBfPSKSex2fQvMsvi//0Mj2ueSU/U63Hb/HOrx2kkP5uTSnsuZ+7mlRUaP/eb30TM96eb4HVPP9MWerqe1Lv5jeo5oyXOt++dQ81yyux7MSffHby966pmedH9+Mraerqc12P1zID1dBxufk/Tca/7g4rNKx8frLj8f4hNjDiX+AzleFw0mPifZ83PE4OOzih7Ml7hsH5PhcC32bJrvmTPrqXuWic/muojPSfFn4qzvn/UJq78uPJhc9abkwkuS+pWTrwsvmf7uwfWPX/wam673n9n4Y9JHfjsrx+864rOa+KzXR36bH59j9s/VxGe1Ps5PxqSv119jO6dr5fhdTXzGZTQF5lLKtaWUHyul/OdSykdLKfeWUt5aSvnyfc/tEMz3W73pdcuXZwf/5Zclt77teP2tb5v8bpYETnr8WO06PqviP0a7jM9J8R+bXcdnVfzHpuv8Ntb49ZHfZuOPSR/5bX78sek6v431eJ3p4/xkTPrIb2M8J0n6yW9jPF77zG9jjF1fr7/GWCjt6/xkTPrMb2M9XheXvf4an1Ec9qWUpyb57SQvTvI3k/xlkquS/N0k7yylfPcepzcKF99z/M7SDY9/6PobHn/8TtPF9/Q/v33rOj6z8cdqV/E5Kf5nneN3NfFZr4/8Ntbjt+v4zI9/Fjl+VxOfzclv7frIb2dx/xSf1fp6/TXWuyH7OD85yxy/q4nPOB18D+ZSysOT/E6Sz07yn5L8/Vrru0spj07yyiTfPt30q2qt71g33lntwVxWtsU5SnJzJs1Iz68ZaZtt+9HlLj6J21G6jU/LY06v69SwfJ87yqHEZ5XuY3eUbuKzzbbd6LIH87qe6etuWd5m22Rc/XCPXlF2Hp+THjPkfrhbm9vndhWfVdv22b96CD2Yd3X8DnmfO02u6zK/zR4zpp7pq/a5Lo7fsT1HdJ3fZtva59q3Heo+15Lnttnnun7+3bme++EeXHxW6bhPf9f5bW+xHPA+N4j4rKIH8yXGcAXzP8ykuPzhJM+stb47SWqtH6y1fkeSt0y3+6E9ze/AHWW7gtP56bY3Tx87dkfpNj7bjn/ozkd8NtFVfM7nLB2/256wnOWeYF3EZ/AnjDvWdXzOQgznOX5X6zo+Y71t/CTy22p95LezdKeG/LZeH/nN8bvcWYvPTB/57Swcv+IzblfsewI78Nzp9zfWWv9kyfrXJrkpyReVUp5Ua/3d/qZ2aG6afp/V5K9Ncm+SC5kUnhbXr1q+I8mNSa5J8r4Tth+DG9NdfJbFf0yx6yI+J403Nl3EZ138D99Nr0ve8tLJz9e+KLn3/uTCyycnLrO+XbP1q5YnV1Al1zwmed+PL9/+2hd1+2/pWxfxWRX/sdl1fE5aHpNrX7T7+JwU/7HpI7/Nxr/xNd3/e/rWdX4b4/E6e37tMr/Njz8mXee3sT6/zopTzk+2N38+3PX5yZj0ld+cn5xu/xxr7A7FQReYSymPSvK3p4tvP2Gzi0nuT/KYJM9IosC8kaNMilPXpO3K0PPTx947HatljEPQVXyOcrr4H7rzEZ9Vzqfb+MyPPz5Hd01Obq55TNuVF+efMnnsvfdPxlocYzb+WbVpfFrjf+jEZzNdxWdd/Megj/x2VvdP8Vmt6/iMPaby2+b6zm+9thd5Tm9/6hJdHL+9t6DqMHZ95Lf58Xtte9bDPtfX668x7XOH6KB7MJdSnp7k16eLT6q1/t4J2/16kqcn+d9rrf9o1ZhntwfzUY4LUUfZXduBXY7VpvsezKdxlIfGZ9nv+refHsyLjjLU+KzSX+yO0m18jnYwxna67sG8y9v6lo21rz6RQ+iHu2hdfE6K/5D74W5ty/6ap90/h9pfs8Wy/ppdHb9D7una2pu06/w29p7p6+zi+B3T8bpJf81dHb9j2ufmj9U+zk+Gel6yq8/V2FTL/jnm43XRLo/fMR2vqz7foIvj9/0fGs8+19Kn/8SxBvT6K+nheB2ok3owH3qB+Vk5vr/70bXWD52w3Zszuf/7TbXWb1g15tktMF+dSUEq2X3x7mhuzOxw3M0Mu8CcPDQ+wyieDqPAnAw1Pqv0G7ujHFp8VumywDx5I63r/Laf+A/neF10lCHEZ5X9PkccZejxOUk/+9xRuo3PbPz7djTeZrouvOzm3CQZ4v45rHO6owwtPifZ/wc3J0OOzyrd7nN9vv7qN9cNucCcXFqQSob15ve+C8zJ9vE5yRgLzMnu4nOSIb8h1OLqR5edx+ek+I9qnxuwsRaYn5PkDdPFh9Va//qE7d6Q5DlJ3lFr/aol61+Q5AXTxScmWXolNDtzVZL373sSB0rs2ohbO7FrJ3ZtxK2d2LURt3Zi107s2ohbO7FrJ3ZtxK2d2LURt358dq316sVfHnQP5iQ7uUaj1vr6JK/fxVisV0q5c9m7Hawndm3ErZ3YtRO7NuLWTuzaiFs7sWsndm3ErZ3YtRO7NuLWTuzaiNt+XbbvCZzSh+d+fviK7R6xZHsAAAAAAE7h0AvM7537+TNWbDdb9186nAsAAAAAwJly6AXm300yayL9ucs2KKVclklf5SS5q49JsZZ2JO3Ero24tRO7dmLXRtzaiV0bcWsndu3Ero24tRO7dmLXRtzaiV0bcdujg/6QvyQppfxGkqcl+Ze11v9lyfr/LsmvThefVGv1AX4AAAAAADtw6FcwJ8kbp9+fW0r59CXrv2P6/TcVlwEAAAAAdmcMBeafSPLHSR6V5BdKKU9JklLKo0opP5zk66fbfe+e5gcAAAAAMEoH3yIjSUopX5Dkl5I8bvqrDyZ5ZCYF9Jrke2ut/2RP0wMAAAAAGKVRFJiTpJRybZLvSfJ3k3xmJkXm30jyo7XWX9rn3AAAAAAAxmg0BWYAAAAAAPo1hh7MAAAAAADswRX7ngCwXinl8kx6jD88yX+ttX54z1MCTuB4pS+llMcleXqST09yVab73P/f3r3HXTbX/R9/va85YGacz5SSIhRyqhxiJJUcklsIRUUpSW6i3JKIIkS5QxJKRTrQnftOkcNIOaQkQyH0Y8I4zxjM4fP747uuZrtch72/+7DWda338/HYj8vsvb7bZ78fa33X2t+91ncBM4G7gD+HL1UblLOzXvM61x5JazN4dndHxJNl1lZlzs16zX1dPm+v+ZxdNXiKDOs6SX3ABsCbGXpHMy0iZpZWZMUUN67cDtiSlNtyAxZ5EbgbuL54/DIiZve0yIqStBwwlYXZ9a9zE4GnWLjOXQ9cHxE3lVRq5Ti7PN5e2+N9RGskrQfsR1rnXj/C4s8CvwN+BPw4IuZ0ubxKc3bt8T6idV7n8hXr216k7DYDlhhi0QCmk9a7iyPi2t5UWE3OrT3u5/K4r8vj7TWfs6smDzBbVxQDBjuQdjTbAFMGLkLa2BvdTdrRnB8RD3a9yIqRNAX4EPARYP3+p0do1p/hbOBi4NyI+EN3KqwuSQK2J2X3HhZenTFcfv3ZPQB8h7TePdS1IivK2eXx9toe7yNaJ2lP4FBgw/6nir/PkAbjnwSeB5YuHssD44plApgFXAScGBH/7FHZleDs8nkfkcfrXD5JmwGfAXYEJvDSdW0+8DQLs1tsQPMA7gO+DZxZpx9znVs+93P53Nfl8faaz9lVmweYraMkLQYcAnwKWJGFG3z/GXwzefmO5tXASsVyUTyuBI6JiJt7VXtZJE0EDgI+ByxDyuxx4A/ALcCfGTq3TYrHG1k4IPN/wOci4vZefo6ySHofcDywFimDBaQzC5rJbiNgKVJu84BzgeMi4l89/RAlcXat8/baHu8jWifp3cAJwHqkvB4CLgVuAG6JiPuHaLcY6QvfJqTB/K1IX+qeB74FnBARj3e7/jI5u/Z4H9E6r3P5JK0LnEga4BPps/+KIjvSZfVPDmgzkZfuX7cHXkta7x4DvgycFRFze/Mpes+5tcf9XB73dXm8veZzdqNERPjhR0cewIHAw6Qd8wLSxv5pYGNgwghtVwXeS9oxzyzazwd+AqxR9mfrcm4PsPDXtvOAdwB9Lb7HqsBhwK1FdvOAj5T92XqQ3e+L7OYBVwP7A8u0+B6bA98EZhTZzQJ2LfuzObtqPry9tpWd9xF5uS0A5gIXAm+jODkg432WJw3s31Nk94WyP5uzq+7D+4js3LzO5Wc3r8jvWuCDwOKZ77MRcAppUHA+cFTZn825VfPhfq6t7NzX5X1eb6/Obkw/fAazdYykBaS5qb5JuvQ76xJmSeOBd5MGYLYEvhgRX+pYoRUj6XHgdOD0iHi6A+83FTgKuDYijmv3/apM0guky6pOiIh72nyvcaSd1RHAD8byOgfOLpe313zeR+SRdA7p0tF/dOj9+oAPABERF3XiPavK2eXzPiKP17l8kq4Ejo+I6zr0fksABwNPRsSZnXjPKnJu+dzP5XNfl8fbaz5nNzp4gNk6RtLngW9ExLMdfM/NgaUi4pedes+qkTQ5ujD/T7fet0okrZY7SDXMewpYJcb4PGrOLo+313zeR5iNHt5HmNlY537OzKyzPMBsZmZmZmZmZmZmZln6yi7AzMzMzMzMzMzMzEan8WUXYGZDk7QMsDIwpXhqFjAjIp4or6rRQ9JkGrIb61MQdJKzM6suSVOALYB1GGQfAdwJTIuIWeVUWF3OznrN61x7JL2eYbKLiLvKqq3KnJv1mvu6fN5e8zm7avEUGWYVI+k9wF7ANqQ76w7mMeAq4KKIuKJXtVWdpDeQbhCxDbA2C3cy/WYB00nZ/TAi7uhthdXl7PJIeiXpbsTjgDsi4u4m2hwKTBnrN4BphqQ1SevccF9GroqIv5dTYbVIWhv4ErADMHGExV8ELifdBHF6t2urOmfXHkmLkPYN44C/NTOXuqTdgMUi4sJu11dFXufySVoWOBLYk7RvGM4M0o3aToqIx7tdW5U5t/a4n8vjvi6Pt9d8zq66PMBsXVEMVn2GhoEX4LyI+PUI7WYAy0dE7c6ul7QScAmwef9TIzTp33inAbtHxL+6VVvVSZoEnE3ayYjmsgvgB8CBdT4719nlKc7SOBfYbcBLNwKfjohbh2k7A1ghIsZ1scRKk7QtcCKwYePTAxZrPEC5Bfh8RFzV7dqqStJepHVuIguzehR4GHiu+PckYBVghYamLwAfjogf9qjUynF2+SSNA74MfJKUEcBc4CfA54a7QVbNj+m8zmWStDVp/VqKl+4XnuKl2S3V8FoATwK7RsS1PSizcpxbPvdz+dzX5fH2ms/ZVZsHmK3jJO0BXECagqV/o+9f0S4DPjrUFA91HXgpBqv+BKxePPUb4Feks/cG20GvA2wHbEuaS/0e4E11HOyTNAG4HtiEtL79DbiS4bN7B7AWab28CdgyIub1tvLyObs8xR3CryFdBjjYgPxc4MiIOG2I9rXs5/pJOgI4gYXZPQ3czeDr3FrAksVzQcr15N5VWw2SNgR+T9qv3gqcClw51JkYxZkd2wGHkLbvucBbIuK23lRcHc6uPZIuAXZl8B+AniEd0/1kiLa17Ou8zuWTtAbpeHgy8E/gLIrj4Yh4fsCyi7LwePjjwGqkq182iIj7ell32Zxbe9zP5XFfl8fbaz5nNwpEhB9+dOwBrEEaIFgA/As4DziZNBC1AJgP3Au8doj2M4D5ZX+OEnI7rsjnPlKn12y79Ys284Fjy/4cJWV3WJHdY8BOLbTbsWgzH/jPsj+Hsxs9D+BDRW4vAl8gHbBMBt5DOtO2v687dYj2teznis++TZHNAuCnwFspfuweYnkBbyGdqbAAmAdsXfbnKCG3Hxaf/0Kgr4V2KtosAH5Q9udwdqPrAezc0J+dB7wNWJd0lt+DDdvkwUO0r2Vf53Wurey+XXz+/wMmt9BuEmmQYQFwTtmfw7mNnof7ubayc1+Xl5u3V2c3Zh8+g9k6StI3SDvk24B3RsTMhtd2Jl2KvwLwCLBdRPxlQPta/gos6U7SmXqbR8TvW2z7VuAGYHpErNuN+qpM0h9JA+07RovzUUvaHvgf4LaI2Kgb9VWZs8sj6Urg7aQfdb404DUBx5PmBYP0ZeWAaNjZ1rWfA5B0OWkg/pSI+GyLbU8i/Sjyi4jYuRv1VZWkh4AVgZUa96tNtl2OtM/9V0Ss2o36qszZ5ZN0GWlOzTMj4uABr00mHdN9gHSW3zERcfyAZWrZ13mdyyfpAeAVwKsj4p8ttl0NuB94MCJe3fnqqsu55XM/l899XR5vr/mcXfV5gNk6qmGgdIuIuHGQ11cFfk6am/kJYPuIuKnh9VrupCXNBuZFxJIjLjx4+2eAcRExubOVVV/x2fsiYuBN6ZptPwtYEBFLdLay6nN2eSQ9AiwHLBsRTw2xzJ7A+aTLBi8G9omI+cVrteznACT9C1gWWDpavIt4MZXQU8DMiFipG/VVlaTngeciYpnM9k8Ci0bEYp2trPqcXT5JD5MGD1aNIe7zIOlI0pQ3AXwtIo5oeK2WfZ3XuXyS5gBznF1rnFs+93P53Nfl8faaz9lVX1/ZBdiYsxrpsvFBz8KNiIeArYHfAssAv5b0tp5VV11zgEWLOXFbImkisEjxHnU0HxhXnDnaEkl9pJtQzu94VaODs8uzNPD0UIPLAJFuWvI+0k1Mdgcuzdm+x6AlgVmtDi4DFG1mAbX6QaPwCLCkpFe02lDSK0m5P9rxqkYHZ5dvWeDZoQZdACLiK8CBpIGXwyR9s1fFVZjXuXxPAEsUc7W2pGizRPEedePc8rmfy+e+Lo+313zOruI8wGydNg54sfFy8IEi3Yhue+AKYHHgCknv6FF9VXU76UzHgzLaHgRMIE14X0fTSXcu3iuj7QdIg/N3drSi0cPZ5XkWmFIMsg8pIn5Jmq96DrATcHlxw4k6e4h0YPj6VhtKWpt0YPhQx6uqvt+Q5iz8tqRJIy3cT9JipPnqAvh1l2qrOmeXbw4wYp8VEWcD+5HmNjxQ0ne6XVjFeZ3Ldz0pu1Mzfvw+tfh7XWdLGhWcWz73c/nc1+Xx9prP2VVd2ZNA+zG2HsA9pDMaV2hi2QksvHHTc6SBmFreKAHYrchhLummiCs30WYl4KSizXzgP8r+HCVld0CR3WzS/N8Tm2gzEfgE6WzI+cD+ZX8OZzd6HsDvis++aZPLb0Ga2mE+6eqNx+vYzxVZnF6sczeT5uxrtt2KpBsozgdOK/tzlJDbaxu2ufuBzwIbDLbNFtvoBsDhwD+KNs8Ca5T9OZzd6HoU2+l8YL0ml9+NdNXGfOAi0plptevrvM61ld1GpCsh55Ouhnw/aUqloZZfuljvbizavABsWPbncG6j5+F+rq3s3Nfl5ebt1dmN2YfnYLaOknQJsCvwwYi4qInlx5HuIrsnaaAUYHzUcx6rc4CPkn7NDeAO4K/Aw6Rf14N0B9RVSHc3Xpd0FYJId0P9eAllV4KkXwLvJmX0NOmXyeGy2xJYipTdLyNixxLKrgRn17riZnP/CZwaEYc32WZT4H9ZmF3UtJ9bkbR+LU36UnIRcCUvXecAFmPhOvcO0ln2SwAzgTdERO0uqSyu9PkxKYfGg7cneOm22jgvnYBnSD9A/qZHpVaOs8sj6QzSj49fiohjm2yzI3AJaTChzn2d17lMkj4EnEM6EaU/u38x+HFJ/3z8In2P2D8iLuxpwRXh3PK4n2uP+7o83l7zObtq8wCzdZSkA4CzgGsiYpsm24h0mcyHi6fqvJM+EDgGWKF4aqgNtP+SkEeBL0bEWd2urcokjQe+DBxMmrYBRs7uBdLZlP8VEfO6W2F1ObvWSdoSuJZ0JvLq0eR8wpLWB35F2r7r3M+tB1xOmrO/2YMQAQ8AO0fE7d2qreqKAfrPkeb1XnGExR8BfgR8JSIe6XZtVefsWifpXaTpzB4CXhMRc0do0t9uW+BnwGTq3dd5ncsk6Y3AcaQfwEe6f8Fc0np6TJ33D+Dccrifa5/7ujzeXvM5u+ryALN1lKTlSNNc9AFbRcS0FtqeBnwa76QnAtsCU4F1gJVJBy8infH3MGne298CV0XEiyWVWjnFAc6uvDw7SNNANGb307of2DRydq2R9HnSAc1PI+IvLbRbEzgC6IuI/bpVX9UVc1F/nDSX90Ys/PFioCBNjfED4OyIeL43FVZb8cPsOgy/j5gePsh7GWfXvOIqs7NI94g4MyJuaaHt5qQvf4qIqV0qcVTwOpdP0pKkaaaGy25aRDxTWpEV5Nya536uc9zX5fH2ms/ZVY8HmK1SijvK9kXEA2XXYmZm3SdpCrA2gx8Y3tXsGeJmZmZmZmZWDg8wm5mZmZmZmZmZmVmW8WUXYGZmZmZWNkkTSPNIRkQcV3Y9ZmadVkwJsReAb3ZlZmOV+7py+Axms1GuuEnbZgARcV3J5dgYJWky8CywICL842QmSW8r/vPOiJhZajE2KhVTSW0EjAPuiIi7m2hzKDAlIr7U7fpGs4Z+rtb3gugESasV/zmj2Ztm2b/XwW+Q1sGPlF1PmSQtQpo+aRzwt4h4tok2uwGLeTBhaD6e6xz3c/nc1yXu57rHfV05PMBslSHp6uI/bwJOiYjHyqxntJC0LPAYNe88Jb0B+AwNAy/AeRHx6xHazQCWr3N2zfDAS2dIWkC6cd1zwH8DX3Nf1xxJSwA/J62Dby+7nl4r5qo+F9htwEs3Ap+OiFuHaTsDWMHb7vDcz3WOpPnFfxdORl4AACAASURBVD4EnAh8xzclHlnDMV1t18HirLMvA58EJhVPzwV+AnwuIh4cpq2P6Ubgfq5z3M/lq3tf536u+9zXlcMDzFYZDQMvAHNYOPjyaHlVVV/dd9AAkvYALiBN+6Pi6f516TLgoxHxxBBtazvwIukLLSw+Efg8KddjG1/wWZHNK/q5Rs8BZ0XEYWXUM5rUua8r7sx+DelO2RpkkbnAkRFx2hDt69zPzR95qRGFv8i1ZkBfF6Sbdn41Ir5ZUkmjQp37uX6SLgF25eV9XQDPkI7pfjJE21r2dZLua2Vx4FWkPBtvqh4RsUZHCxvj3M/lq3tf534uj/u66vMAs1WGpGtIHcDKwJrF089FxJTSihoFvIPWGsBfgEWBR4ErgMeBrYCNSevU/cA7I+KeQdrXeSfd+KNOU02Kvy9pU8fscknaqvjPlUnr6NbAms5wZHXu6yR9CPguMA84Hjif1M9tTfrBZ0PSdnl6RBw6SPu693Ptqt06165inYWFfd1mpGlanOMw6tzPAUjaGfgZqT+7gJf2dUcArwAWAIdGxBmDtK9lX9dwPDfYD5DNquU61w73c/nq3Ne5n8vnvq76fDaGVUZEbN3/35JWJHWyW5ZVTy9J2q6N5kt0rJDR6RDS4PJtpEHkf89rW+zAzwZWB66XtF1E/KWcMivtUeD5EZYRsBpppz7kZVs2vIi4tuGfPwKQtFxJ5djosRdp2zt+wBUDv5R0BWnQ+Ujg05IWBw4In0HQKIA/AOcw/I9qiwBnFct8uAd1jVkRcUHDP78iqY/0Q8iYJ+kHbTSf2LFCRqcPk7a/MyPi4Ibn/yrpfNIx3QeA0yQtERHHl1Bjlf0e+NUIy0ykuJkp4CvQ2lDnfg7c17XB/Vz73NdVlM9gNquAjDNJX/YW1PTXOEl3AmsBW0TEjYO8vipp3taNgCeA7SPipobX6/wr8C2kA+H7gYMj4n+GWXYK6ZKtWq5n1hmSftdG8/EUVyXUbR2U9AiwHLBsRDw1xDJ7ks6CGQ9cDOwTEfOL1+rcz+0MnEE6I+gW4BNDzVft+fqsE3yGVT5JDwMrAqtGxL+GWOZI4ARSxl+LiCMaXqtlXyfpENIAymTgp8BnIuL/DbGs+znrCPd1edzP5XNfV30eYDarAO+g80maRbqp36ShztgrdjCXA1NJO5odI+K64rU676T7gINZuKP+BWmg+WVnKHsnbZ3gvi6PpBeBWRGxzAjLvQf4MelM3MuB90fE3Dr3c/Dv/ut44KDiqbOBoyLi6UGWcz9nbZE0F+gDrgQGHTwYxiLAHtR0HZT0AjAnIpYaYbmPAWeS9iXfioiDiudr29cVJ1R8E9gZmEU6tjut/4fGhuXcz1lHuK/L436uPe7rqs0DzGYVIOkhYCXgPyLiZy22XY40xUEtO09Jc4AXI2LJEZZbhHRn3u1JN1bbJSJ+XfedNLxsR/0c6a7GJ0fEvIZlvJMehqRJwOtJ8/D1zxs/C5gBTI+IOWXVViWSngcmAN8HWrlRB6S7bB9ODddBSY8DiwOLRsSwcwpLejvp5qaLkb707QL8g5r3cwCSNiQNLm8EPAJ8NiK+1/C6+7kmSFqGQfq6GOJmunUj6c/AG4CPRcS5Lbat7bykAJKeIvVzizax7D7AeaQBrvMj4iM+pgNJO5GO6VYF7gQ+2X9SRfG6+7kmuJ8bmfu6PO7nOsN9XTV5gNkqpRjoGjfYGZRjmaSfAzsCJ0bEf7XYtrY7aABJ95DmWF45Ih4dYdkJpHlvdyHNObw7aU7O2u+k4WU76ruBgyLi6uI176QHKNanA0jz427K0Gfl9s//+n3g3IiY25sKq0fSTaTBvYMj4swW29a2ryumFnkz8NbGKX6GWX4L4H9Ig9LXAesBS9Utt8FIEvAp4DjSwME04MCIuNP93NCKs+P3ArYBlh9isceAq4CLIuKKXtVWNZLOBfYj9fcfa7Ftbfs5AEk3k6buelNE3N7E8ruR9q3jScd37yBNJVS77BoVfdlxpL6uj5TR4RHxqPu5obmfa437ujzu5zrHfV319JVdgI1Nkt4l6SpJT0l6VtKNkvYvLskfzi20flbbWHAzaXBq07ILGYX+WPx9x0gLFgN77wd+SLox4KXAsJec10lEXE46C/d04HXAryX9QNJK5VZWPZLWIf1afgbwFtL+VEM8+oC3kgbv/ypp7TJqroj+vm7jsgsZZaYVf3drZuGImAZsBzwFvA1Yukt1jTqRnAGsTTrTe0vgNkknsfBMNStIWknSdaQpV3YHVmDovm4F0iXPv5B0bY33He7n8vXfS2OXZhaOiB8D/wG8SFr3fNNcICJmR8ShpO8VtwL7AHdJ+gT+/v8y7ueyua/L436uQ9zXVY/PYLaOk3QocHL/PxteCtKGv0dEDDqIXNdLPiRtB/wf8GRELNti26WAPwELIuI13aivyiQdAJwFXBMR2zTZRsC3SXfxBf+y+TKS3kS6lHxj0s39TiLNYVr7rCStAPyFdHbLLNIPFr8iDTg/TJpmJEjzWq8CrEMa7NuTdEbpo8AbI+KxnhdfMkn7Ad8B7oyIN7TYts5nu2wJXAs8DqweEbOabLc+ad1cgRrm1gxJO5DmOHwFadtcEWcF0H9z1z+RrhIC+A0v7+sgTV/T2NdtS/pidw/pDK3ZPSy7dJLeCHwPeAF4S7TwZUvSYsBnASLi2O5UWF2S3gVcATwEvKbZK34kbQv8jLTf9fbboDjmPYh0lt/iwF9J0xo4J9zPtcN9XR73c93hvq4aPMBsHSVpA9KvmeOA6aQ72T8ObEX6lW4c8ATpJms3DtK+rgPME0lnUxERfy65nFGlmIN6Bukgb6vizL1m254GfBrveAY1yI5aOCskfZ10c8TbgJ0i4qEm261CupHiBsDpxS/utSLptcDXgHnAbi1+GZlIGqQnIi7oToXVJenzpPmrfxoRf2mh3ZrAEUBfROzXrfpGs2IO9eNI2/U43M8BIOk44CjgfuB9EfGnJtutT/oS/Crg+Ig4pmtF2pgiaRzppIHxwJkRcUsLbTcnbceKiKldKnHUkrQy6Uqq/rMm3c/hfs56z/1cd7mvK5cHmK2jJJ0H7AtcDewQEc83vLYhcBGwFjAbeG9EXDWgfS0HmK08kl5JGnh5oOxaqqrYUX8FWA2g7gc0DfN+vyEiprfYdh3gDuDeiHhdN+ozszyS1iCdyUxEXFtyOaWTdCfpmG3ziPh9i23fCtxAusnput2oz8xaJ2kr4NVQzx9rB3I/ZzY2ua8rhweYraMaBl7Wj4g7Bnl9Culy8veQLqfZvZj3tf91DzCbWaVJmgM8HxFZ89oWd4+eGBGTOluZmVnnSJoNzIuIJTPbP0O6cfPkzlZmZtYZ7ufMzDrHE19bp60CzBlscBmgmDfyvcAPgEWAH0vavYf1mZm16xlgSnF34pYUP7L139XYzKzK5gCLSprQasNiSptFivcwM6sq93NmZh3iAWbrtCgeQy8QMZ90h89vk+aT/L6kfbtfmplZR9xE2n9+IaPt0aQ5Xv/Q0YrMhiDpbcXDdx1vkbPjdtIckQdltD2IdIzX1HymlkharXi0PNhVd84uj3NzP1cGr3d5nFs+Z9cbHmC2TnsQmFTMazukSD4GfIM02HKupAN7UeBYI+nq4vEVScuXXc9o4uzy1Ty700k3PDxM0qWS3jxSA0mbSLoEOIz0I9zXu1zjmOPBvmzXAL8F/iHpqzXcXttxDfXO7lukvu4kSScX8/EPS9JKkk4Cvkrq687qco1jzT+Kx72SDizOkLTmOLs8dc/N/Vw56r7e5XJu+ZxdD3gOZusoSRcCewGfiIizm2xzIukO9/1nP8tzMDdP0gIWnjU+B/hv4GsR8Wh5VY0Ozi5f3bOTdBTpLs79GTwNTAceJuURwCTStEFrA/1z+wk4OiK+3NOCx4CGde45Fq5vj5VbVfUVuTV6DjgrIg4ro57RxNmBpHOAj7LwGO0O4K8M3tetWzz6SH3dORHx8RLKHrUGrHNByvmrEfHNkkoaNZxdHufmfq4MXu/yOLd8zq43PMBsHSVpb+BC4NaI2KSFdkcDx1IM1niAuXmSriHltjKwZvH0cxExpbSiRglnl8/ZgaTtgROA9Qa81L9j1YDn/wx8PiL+t9u1jUUe7MtT3EUb0ra6FbA1sKb3syNzdklxhdkxwArFU0N9eejv8x4FvhgRPquvRZI+VPxn/zq3GTClbutcDmeXx7kl7ud6y+tdHueWz9n1hgeYraMkLQ78kTSX1d4RcUMLbQ8BTiXNoOENPYOkFUlfgLeMiJy5xGrL2eWre3aS1gGmAuuQDlomk76AzCL9On4n8NuImF5akWOAB/s6R9JyETGz7DpGo7pmV1xKui1N9HXAVRHxYkmljimS+oANI+KWsmsZbZxdnjrn5n6uPHVe79rh3PI5u+7wALOZmZmNSnUd7DMzMzMzM6sSDzCbmZmZmZmZmZmZWZbxZRdgZmZmZq2RNAl4PekS3v65z2cBM4DpETGnrNqqztlZGSQtwyDrXEQ8UV5Vo4Ozy+PcOk/SagAR8WDZtVSV17s8zi2fs6sOn8FsHSPpNRFxX4ffsw94hXfiw5O0KjDOObXO2eVzdq0r5vfbAyAiLiy5nErwYF/zJE0ADgD2Ajbl5TeS7BfAH4DvA+dGxNzeVFhdzq73vI8ASe8hrXPbAMsPsdhjwFXARRFxRa9qqzpnl8e5dY+kycCzwIKI8Il6Dbze5XFu+ZxdNXmA2TpG0ovAD4ETIuLuNt9rArAfcARwQUR8qQMlVp6kdwGHAxsB44A7gPOA70TEgmHazQCWr/PBjrPL5+x6R9KypIOdWn858WBf64qbSV4GvIah8xoogHuBnet8k0ln1x7vI1onaSXgEmDz/qdGaNL/hWwasHtE/KtbtVWds8vj3LqvYYDZN6QveL3L49zyObtq8wCzdYykacBmwALgeuBHwKUR8XiT7QVsTTq7733AMsBsYJ+IuKwbNVeJpEOBk/v/2fBSALcCewx1hnjxJW6Fuh7sOLt8zq63GgaYa/vlxIN9rZO0AvAX0hkas0g/5v6KdDf7h4HnSBlNBlYB1gG2A/YEFgceBd4YEY/1vPiSObv2eB/ROklTgD8BqxdP/YaXr3MAk3jpOrct0AfcA7wpImb3sOxKcHZ5nFs+See1sPh4YG9S/3dBw/MRER/paGGjgNe7PM4tn7OrPg8wW0dJ2gk4gbQxR/H4O+lLyO3ATOBJ4EVgKWBpUgexMfAm0hc8AXOBs4Hj6vClTtIGwM2kM4OmAxcDjwNbAbsUzz8B7BgRNw7SvpZf4sDZtcPZ9V7dB5g92JdH0teBg4HbgJ0i4qEm260C/ALYADg9Ig7tXpXV5OzyeR+RR9JxwFHA/cD7IuJPTbZbH/gZ8Crg+Ig4pmtFVpSzy+Pc8klawMIzHJtqUvyNhn/X9ZjO610G55bP2VWfB5it44ozkd8FfBTYAZhQvDTcyta/s76PdNnldyNiRteKrJji1/N9gauBHSLi+YbXNgQuAtYindH93oi4akD7Wn6JA2fXDmeXR9IBbTSfDJxCfb+MeLAvg6R7SD/GvqHVM7iLM8bvAO6NiNd1o74qc3b5vI/II+lOUi6bR8TvW2z7VuAG0vzz63ajvipzdnmcW76GAea7SD9iD2ccsEWx/HWNL0TE1K4UWGFe7/I4t3zOrvo8wGxdVdzRcyppjpxNSTdxWg5YhHTWy0zgbtLGPi0ibimp1FI1fAFePyLuGOT1KaQz/d4DvECaP+jyhtdr+SUOnF07nF2ejLNdXvYW1HeA2YN9GSTNAZ6PiKUz2z8FTIyISZ2trPqcXT7vI/JImg3Mi4glM9s/Q7o54uTOVlZ9zi6Pc8sn6efATsDTwNHAmTHEAEnR5z1DTY/hBvJ6l8e55XN21ecBZrMKkPQc6aZfU4ZZZhxpvq8PkKYQ+WBEXFy8VssvceDs2uHs8jQMMD9CGlRpRR/wSmr65cSDfXkkPUK6L8FSrc4bV3whfhJ4IiJW7EZ9Vebs8nkfkUfSTNKUPlOixRuTSppIuonYsxGxXDfqqzJnl8e5tUfSzsAZwCtIV1h9IiJuGmQ53+Svgde7PM4tn7Orvr6yCzAzYOF81UMvEDEf2Af4Nmnake9L2rf7pVWes8vn7PLcX/z9TESs3soD2KjEuqvgGWBK8SWtJcVgX/+Xu7q5iXTM9oWMtkeTLuv9Q0crGj2cXT7vI/LcTroZ2EEZbQ8i5djUvJJjkLPL49zaEOlm8uuQBpnXB34n6WxJWT+G14jXuzzOLZ+zqzgPMJtVw4PAJEmvHG6hSD4GfIP0pfdcSQf2osAKc3b5nF2em4u/m2S0rftlQx7sy3M6aWqVwyRdKunNIzWQtImkS4DDSOvd17tcY1U5u3zeR+T5FmmdO0nSyZJWHqmBpJUknQR8lbTOndXlGqvK2eVxbm2KiNkR8RngzaSzmPcH7pa0X7mVVZrXuzzOLZ+zqzhPkWFWAZIuBPYiXZJ1dpNtTgSOYOEZRqrj5VrOLp+zyyPpcNJByrWt3tRF0rLAY9T08kpJ2wJXktadnwEnR8SwA8aSNgEOB3YtnnpHRFzd1UIrSNJRwHEs/JHiaWA68DAwp3h+ErAKsDbQPz+dgKMj4ss9LbhCnF0e7yPySTqHdLPr/hzuAP7K4OvcusWjj7TOnRMRHy+h7EpwdnmcW+dIEvAp0n5jCnAj8AngXjxFxkt4vcvj3PI5u2rzALNZBUjaG7gQuDUimj4rUtLRwLEUX5rreLDj7PI5uzyStgauBmZFxBIttl0CuJz05aR2dxwHD/a1Q9L2wAnAegNe6s9SA57/M/D5iPjfbtdWdc6udd5HtKc4i/sYYIXiqaG+dPWve48CX4yI2p9d5ezyOLfOkrQK6cqMXYB5wHnAAXiA+SW83uVxbvmcXXV5gNmsAiQtDvyRNKfQ3hFxQwttDwFOpaYHO84un7PLU5zZsgRARDxdcjmjkgf72iNpHWAqac7IlUlzUwuYRRqovxP4bURML63IinJ2zfM+on3FTYW2pYl1DrgqIl4sqdTKcXZ5nFvnSdoBOJN0k2aoeb82GK93eZxbPmdXTR5gNjMzs1J4sM/MzMyqTtIk0vQ/qwFEhOdmNjMbwAPMZmZmZmZmZmZmZpalr+wCzMzMzMzMzMxsbJK0mqTVyq5jtHFu+Zxd73mA2axkkl7Thffsq0Nn6uzyObs8zs1GG0kTJX1Q0gfLrmW0qXN27uvKI2lV55TH2eWpa27u53pH0mTgfuC+kksZVZxbPmdXDg8wm5XvLkkXSFqr3TeSNEHSAcDfgX3brqz6nF0+Z5fHuZWkzoN9bVocOB84r+Q6RqM6Z+e+rk2S3iXpKklPSXpW0o2S9pc00vevW6j5F2Jnl8e5tcz9XO8NvImzNce55XN2PeQ5mM1KJmkasBmwALge+BFwaUQ83mR7AVsDewDvA5YBZgP7RMRl3ai5KpxdPmeXx7mVR9KywGPAgogYX3Y9o0VDbr7rfYvqnJ37uvZIOhQ4uf+fDS8FcCuwR0QMOqAnaQawQt3WuX7OLo9za537ufZIauXH1/HA3qT18YKG5yMiPtLRwirOueVzdtXnAWazCpC0E3ACsA6pEwzSL+C3ArcDM4EngReBpYClgdWBjYE3AZNJB5NzgbOB4yLisd5+inI4u3zOLo9zK0edB/va4dzy1T0793V5JG0A3AyMA6YDFwOPA1sBuxTPPwHsGBE3DtK+loN94OxyObd87ufySVpAyqvpJsXfaPh37favzi2fs6s+DzCbVUTxK/i7gI8COwATipeG20j7O837SJfwfjciZnStyIpydvmcXR7n1nt1HuwrLrvNNRk4hRrmBs6uXe7rWlecYbUvcDWwQ0Q83/DahsBFwFqkMx3fGxFXDWhf58E+Z5fBubXH/VyehsG+u4BHR1h8HLBFsfx1jS9ExNSuFFhRzi2fs6s+DzCbVZCkZYCpwObApsDKwHLAIqQzEGYCdwM3ANMi4paSSq0cZ5fP2eVxbs3zYF+ejDM2XvYW1DA3cHad5L6uOZLuIZ3huH5E3DHI61OAHwLvAV4Ado+Iyxter+1gn7PL49w6x/1c8yT9HNgJeBo4GjgzhhhcKtbBZ/D+1Lm1wdlVnweYzczMrCc82JenIbdHSIMDregDXkkNcwNnZ70n6TnSXPFThllmHGlOyA+QLq3/YERcXLxW28E+Z5fHuVlZJO0MnAG8ArgN+ERE3DTIcpOBZ/H+FHBu7XB21eYBZjMzM+sJD/blkXQf8CrgA/0DAi20XY50GWHtcgNnZ70naTZpsG/xEZYTcBawPzAf2D8izq/zYJ+zy+PcrEzFQN7xwEGkEwG+AxwZEU8OWMaDfQ2cWz5nV119ZRdgZmZmtXF/8fczEbF6Kw9goxLrLtvNxd9NMtrW/UwCZ2e99iAwSdIrh1soko8B3yDNFXmupAN7UWCFObs8zs1KExGzI+IzwJtJZ5TuD9wtab9yK6s255bP2VWXB5jNzMysVzzYl+cW0hkadR5kz+XsrNf6+7ntm1k4Ij4NfJX0veybwPJdqms0cHZ5nJuVLiL+SJq3+hDSnNXnSpomab1yK6s255bP2VWPB5jNzMysVzzYl6d/8CAnt7mku2dfN9KCY5Szs167ktTPfbTZBhHxOeCYop26VNdo4OzyODerhOIs+TOAtYGfA5uRjv1OKbWwinNu+ZxdtXgOZjMzM+sJSVsDVwOzImKJFtsuAVxOOpac2oXyKquYN3MJgIh4uuRyRhVnZ70maXHgj8B4YO+IuKGFtocAp1LTOSOdXR7nZlUlaQfgTNI9NMDrWVOcWz5nVy4PMJuZmVlPeLDPzMzMrD4kTQKOAFYDiAjPk9sE55bP2ZXHA8xmZmZmZmZmZmZmlsVzMJuZmZmZmZmZmZlZFg8wm5mZmVWUpNd04T37JK3W6fetGmdnveZ1Lp+zy+PcrAxe7/I4t3zObnTwALOZmZl1nQ8Ms90l6QJJa7X7RpImSDoA+Duwb9uVVZ+zs17zOpfP2eVxblYGr3d5nFs+ZzcKeIDZzMzMesEHhnluAvYB/irpt5I+JmnZZhsrmSrpbOBh4FvA8sCfu1NupTg76zWvc/mcXR7nZmXwepfHueVzdqOAb/JnZmZmXSdpGrAZsAC4HvgRcGlEPN5kewFbA3sA7wOWAWYD+0TEZd2ouSok7QScAKwDRPH4O3ArcDswE3gSeBFYClgaWB3YGHgTMBkQMBc4GzguIh7r7acoh7OzXvM6l8/Z5XFuVgavd3mcWz5nV30eYDYzM7Oe8IFhvmKA/V3AR4EdgAnFS8MdyKn4ex9wHvDdiJjRtSIrytlZr3mdy+fs8jg3K4PXuzzOLZ+zqzYPMJuZmVnP+MCwfZKWAaYCmwObAisDywGLAE+QBurvBm4ApkXELSWVWjnOznrN61w+Z5fHuVkZvN7lcW75nF31eIDZzMzMSuEDQzMzMzMzs9HPA8xmZmZmZmZmZmZmlqWv7ALMzMzMzMzMzMzMbHTyALOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmdkgJEXxeEDSokMsc3+xzPgh2vY/5kuaKelqSXt1oLatG977kiGWeXXx+rRh3ucdki6S9A9Jz0maI+keSd+T9O526zQzMzOzsW/8yIuYmZmZmdXaasAhwFcy2h5b/J0ArAW8F5gqaaOIOLRD9e0m6a0RcWOzDSQtDlxY1PM8cDXwU2AusDqwPbC3pFMi4rAO1WlmZmZmY5AiouwazMzMzMwqR1IATwJBOjFjjYiYOWCZ+4FXARMiYt6AtkSEBiz/duDXxT9fExH3Z9a2NfBb4B7gtcDvImLzAcu8GvgHcENEbNHwfB9wBfDO4j32joiHB7RdBPg4sGZEfDKnRjMzMzOrB0+RYWZmZmY2tOeA44AlgGPafbOIuAq4CxCwSbvvB/wBuAzYTNKuTbbZkzS4fA+w48DB5aLOFyLidKBTZ1mbmZmZ2RjlAWYzMzMzs+GdCdwLfEzSmh14v/6zmjt1KeFngXnAVyRNaGL5A4q/X4uI2cMtGBEvtFucmZmZmY1tHmA2MzMzMxtGRMwFjiTNo5wzD/O/SdqWNBdzADe3Xx1ExN+As0lTZRw4wv9/PPCW4p9XdeL/b2ZmZmb15pv8mZmZmZmNICIulXQjsIukLSJiWjPtJH2x+M/Gm/wJOC0iHuhgiccC+wBfkHRBRDw9xHLLABOL//5/Hfz/m5mZmVlN+QxmMzMzM7Pm/Gfx9xRJGnbJhY4pHp8DtgGuB/aJiI7ObRwRj5HOrl4WOGqYRZut28zMzMysKR5gNjMzMzNrQkTcCFwKbAq8v8k2Kh59EbFMREyNiO93qcTTgH8CB0t61RDLPA68WPz3ql2qw8zMzMxqxAPMZmZmZmbNOxKYC5woaeJIC/dSRDwP/BewCHDCEMvMA35f/PPtPSrNzMzMzMYwDzCbmZmZmTUpIu4F/htYHfhUyeUM5nvAbcCewMZDLHNO8fcwSZOGezNJi3SwNjMzMzMbgzzAbGZmZmbWmi8BT5HmOp7SiTeUdL6kkLRvO+8TEQEcRppr+cQhFvsh8CvgdcBlklYepJ6Jkj4JnNJOPWZmZmY29o0vuwAzMzMzs9EkIp6QdAJwUgfftv/Ej3ntvlFEXC3pCmD7IV5fIGk30tnOOwP3SboKmA7MB15Fmj5jeeBr7dZjZmZmZmObz2A2MzMzM2vdGcD9HXy/NwLPAr/s0PsdThosHlREPBsR7wXeCfwMWBc4CDgEeDPwG+DdEXF4h+oxMzMzszFK6So6MzMzMzMrg6SlgMeBUyLis2XXY2ZmZmbWCp/BbGZmZmZWri2BucCpZRdiZmZmZtYqn8FsZmZmZmZmZmZmZll8BrOZmZmZmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZBTL7wgAAADdJREFUmZmZmZmZZfEAs5mZmZmZmZmZmZll8QCzmZmZmZmZmZmZmWXxALOZmZmZmZmZmZmZZfn/nf2eLxNNQjcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n",
    "#TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n",
    "#TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n",
    "\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "\n",
    "    width = 0.35\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index]).tolist()\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n",
    "    ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n",
    "    ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "    ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n",
    "    ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n",
    "    ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "    ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "    ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "    plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "    blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n",
    "    orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n",
    "    green_Spatch = mpatches.Patch(color='green', label='Children PR')\n",
    "    blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n",
    "    orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n",
    "    green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n",
    "\n",
    "\n",
    "    handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\n",
    "    \n",
    "    ax.axvline((3.5), color='black')\n",
    "    ax.axvline((7.5), color='black')\n",
    "    ax.axvline((11.5), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    plt.ylim((0, 25.5))\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
    "            '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "            '(20,1)',  '(20,10)','(20,40)','(20,80)','(20,120)',\n",
    "            '(40,1)',  '(40,10)',  '(40,20)','(40,80)','(40,120)',\n",
    "            '(80,1)',  '(80,10)',  '(80,20)', '(80,40)','(80,120)',\n",
    "            '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
    "\n",
    "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
    "               '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "               '(20,40)','(20,80)','(20,120)',\n",
    "               '(40,80)','(40,120)',\n",
    "               '(80,120)']\n",
    "labelsShrink = ['(10,1)', \n",
    "               '(20,1)',  '(20,10)', \n",
    "               '(40,1)',  '(40,10)',  '(40,20)',\n",
    "               '(80,1)',  '(80,10)',  '(80,20)', '(80,40)',\n",
    "               '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
    "\n",
    "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 - Asyncrhonous','Baseline single - Asyncrhonous',\n",
    "                 'Merge','Merge single','Merge - Asyncrhonous','Merge single - Asyncrhonous']\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 - Asyncrhonous')\n",
    "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n",
    "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
    "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
    "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n",
    "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)',\n",
    "            '(10,1)', '(10,20)', '(10,40)','(10,80)',\n",
    "            '(20,1)',  '(20,10)','(20,40)','(20,80)',\n",
    "            '(40,1)',  '(40,10)',  '(40,20)','(40,80)',\n",
    "            '(80,1)',  '(80,10)',  '(80,20)', '(80,40)']\n",
    "\n",
    "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)',\n",
    "               '(10,20)', '(10,40)','(10,80)',\n",
    "               '(20,40)','(20,80)',\n",
    "               '(40,80)']\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",
    "\n",
    "labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n",
    "labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n",
    "labelsExpandInter = ['(1,40)','(1,80)',\n",
    "               '(10,40)','(10,80)',\n",
    "               '(20,40)','(20,80)',\n",
    "               '(40,80)']\n",
    "labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n",
    "               '(80,1)', '(80,10)', '(80,20)','(80,40)']\n",
    "\n",
    "                #0          #1                 #2                     #3\n",
    "labelsMethods = ['Baseline', 'Baseline single','Baseline - Asyncrhonous','Baseline single - Asyncrhonous',\n",
    "                 'Merge','Merge single','Merge - Asyncrhonous','Merge single - Asyncrhonous']\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 - Asyncrhonous')\n",
    "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n",
    "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
    "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
    "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n",
    "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_types_iker(checked_type='tc', used_direction='e', node_type=\"All\", normality='m'):\n",
    "    if checked_type=='te':\n",
    "        var_aux='TE'\n",
    "        tipo_fig=\"TE\"\n",
    "        grouped_aux=grouped_aggG2\n",
    "    elif checked_type=='tc':\n",
    "        var_aux='TC'\n",
    "        tipo_fig=\"Mall\"\n",
    "        grouped_aux=grouped_aggM\n",
    "    \n",
    "    if node_type=='Intra':\n",
    "        grouped_aux=grouped_aux.query('NP < 21 and NS < 21')\n",
    "    elif node_type=='Inter':\n",
    "        grouped_aux=grouped_aux.query('NP > 21 or NS > 21')\n",
    "\n",
    "    if used_direction=='s':\n",
    "        grouped_aux=grouped_aux.query('NP > NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsShrinkIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsShrinkInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsShrink\n",
    "        name_fig=\"Shrink\"\n",
    "        \n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "    elif used_direction=='e':\n",
    "        grouped_aux=grouped_aux.query('NP < NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsExpandIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsExpandInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsExpand\n",
    "        name_fig=\"Expand\"\n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "    title=tipo_fig+\"_Spawn_\"+node_type+\"_\"+name_fig+\"_\"+normality\n",
    "    return var_aux, grouped_aux, handles, used_labels, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_arrays_iker(grouped_aux, var_aux, used_direction='e', normality='m'):\n",
    "    vOrMult = list(grouped_aux.query('Cst == 0 and Css == 0')[var_aux])\n",
    "    vOrSingle = list(grouped_aux.query('Cst == 0 and Css == 1')[var_aux])\n",
    "    vMergeMult = list(grouped_aux.query('Cst == 2 and Css == 0')[var_aux])\n",
    "    vOrPthMult = list(grouped_aux.query('Cst == 1 and Css == 0')[var_aux])\n",
    "    vOrPthSingle = list(grouped_aux.query('Cst == 1 and Css == 1')[var_aux])\n",
    "    vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "    h_line = None\n",
    "    \n",
    "    if used_direction=='e':\n",
    "        vMergeSingle = list(grouped_aux.query('Cst == 2 and Css == 1')[var_aux])\n",
    "        vMergePthSingle = list(grouped_aux.query('Cst == 3 and Css == 1')[var_aux])\n",
    "    else:\n",
    "        #FIXME Que tenga en cuenta TH al realizar shrink merge\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "        vMergeSingle = None\n",
    "        vMergePthSingle = None\n",
    "    title_y = \"Total time(s)\"\n",
    "        \n",
    "    if normality == 'r':\n",
    "        vOrSingle = np.subtract(vOrMult, vOrSingle)\n",
    "        vOrPthMult = np.subtract(vOrMult, vOrPthMult)\n",
    "        vOrPthSingle = np.subtract(vOrMult, vOrPthSingle)\n",
    "        vMergeMult = np.subtract(vOrMult, vMergeMult)\n",
    "        vMergePthMult = np.subtract(vOrMult, vMergePthMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.subtract(vOrMult, vMergeSingle)\n",
    "            vMergePthSingle = np.subtract(vOrMult, vMergePthSingle)\n",
    "        vOrMult = None\n",
    "        h_line = 0\n",
    "        title_y = \"Saved time(s)\"\n",
    "    elif normality == 'n':\n",
    "        vOrSingle = np.divide(vOrSingle, vOrMult)\n",
    "        vOrPthMult = np.divide(vOrPthMult, vOrMult)\n",
    "        vOrPthSingle = np.divide(vOrPthSingle, vOrMult)\n",
    "        vMergeMult = np.divide(vMergeMult, vOrMult)\n",
    "        vMergePthMult = np.divide(vMergePthMult, vOrMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.divide(vMergeSingle, vOrMult)\n",
    "            vMergePthSingle = np.divide(vMergePthSingle, vOrMult)\n",
    "        vOrMult = np.divide(vOrMult, vOrMult)\n",
    "        h_line = 1\n",
    "        title_y = \"Relation Config time / Baseline Time\"\n",
    "    \n",
    "    data_array=[vOrMult,vOrSingle,vOrPthMult,vOrPthSingle,vMergeMult,vMergeSingle,vMergePthMult,vMergePthSingle]\n",
    "    v_lines=[]\n",
    "    value_aux = 0.4\n",
    "    if used_direction == 'e':\n",
    "        value_aux = 0.5\n",
    "    for i in range(0, len(vOrSingle)-1):\n",
    "        v_lines.append(value_aux + i)\n",
    "    return data_array, title_y, v_lines, h_line\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def legend_loc_iker(data_array, len_x, ylim_zero):\n",
    "    \n",
    "    max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    if(ylim_zero):\n",
    "        min_value = 0\n",
    "    middle_value = (max_value + min_value) / 2\n",
    "    offset = (max_value - min_value) * 0.1\n",
    "    \n",
    "    def array_check_loc(ini, end):\n",
    "        up = True\n",
    "        lower = True\n",
    "        for i in range(ini, end):\n",
    "            for j in range(len(data_array)):\n",
    "                if not (data_array[j] is None):\n",
    "                    if data_array[j][i] > (middle_value + offset):\n",
    "                        up = False\n",
    "                    elif data_array[j][i] < (middle_value - offset):\n",
    "                        lower = False\n",
    "                    if not up and not lower:\n",
    "                        break\n",
    "            else:\n",
    "                continue # Only executed if inner loop did NOT break\n",
    "            break # Only executed if inner loop did break\n",
    "        return up,lower\n",
    "    \n",
    "    up_left, lower_left = array_check_loc(0, math.floor(len_x/2))\n",
    "    up_right, lower_right = array_check_loc(0, math.floor(len_x/2))\n",
    "\n",
    "    legend_loc = 'best'\n",
    "    if up_left:\n",
    "        legend_loc = 'upper left'\n",
    "    elif up_right:\n",
    "        legend_loc = 'upper right'\n",
    "    elif lower_left:\n",
    "        legend_loc = 'lower left'\n",
    "    elif lower_right:\n",
    "        lower_right = 'lower right'\n",
    "\n",
    "    return legend_loc\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphic_iker(data_array, title=\"None\", title_y=\"None\", title_x=\"None\", handles=None, used_labels=None, v_lines=None, h_line=None, ylim_zero=True):\n",
    "    f=plt.figure(figsize=(30, 12))\n",
    "    ax=f.add_subplot(111)\n",
    "    x = np.arange(len(used_labels))\n",
    "    width = 0.45/4\n",
    "    \n",
    "    legend_loc = legend_loc_iker(data_array, len(used_labels), ylim_zero)\n",
    "\n",
    "    if not (data_array[0] is None):\n",
    "        ax.bar(x-width*3.5, data_array[0], width, color='green')\n",
    "    ax.bar(x-width*2.5, data_array[1], width, hatch=\"\", color='springgreen')\n",
    "    ax.bar(x-width*1.5, data_array[2], width, hatch=\"//\", color='blue')\n",
    "    ax.bar(x-width*0.5, data_array[3], width, hatch=\"\\\\\",color='darkblue')\n",
    "\n",
    "    ax.bar(x+width*0.5, data_array[4], width, hatch=\"||\", color='red')\n",
    "    if not (data_array[5] is None):\n",
    "        ax.bar(x+width*1.5, data_array[5], width, hatch=\"...\", color='darkred')\n",
    "        ax.bar(x+width*2.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    else:\n",
    "        ax.bar(x+width*1.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    if not (data_array[7] is None):\n",
    "        ax.bar(x+width*3.5, data_array[7], width, hatch=\"++\",color='olive')\n",
    "\n",
    "    ax.axhline((0), color='black', linestyle='dashed')\n",
    "    ax.set_ylabel(title_y, fontsize=30)\n",
    "    ax.set_xlabel(title_x, fontsize=28)\n",
    "    plt.xticks(x, used_labels, rotation=90)\n",
    "    plt.legend(handles=handles, loc=legend_loc, fontsize=26,ncol=2,framealpha=1)\n",
    "    \n",
    "    if not ylim_zero: # Modifica los limites del eje y. No es buena practica que no aparezca el 0\n",
    "        max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        max_value += max_value * 0.1\n",
    "        min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        min_value -= min_value * 0.1\n",
    "        if min_value < 0.1:\n",
    "            min_value = 0\n",
    "        plt.ylim((min_value, max_value))\n",
    "    \n",
    "    for line in v_lines:\n",
    "        ax.axvline((line), color='black')\n",
    "    if h_line != None:\n",
    "        ax.axhline((h_line), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/Spawn/\"+title+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checked_type='te' # Valores 'te' y 'tc'\n",
    "used_direction='e' # Valores 's' y 'e'\n",
    "node_type='All' # Valores 'Intra', 'Inter', 'All'\n",
    "normality='n'\n",
    "#Values 'n' (normalizar), 'l' (logaritmico), 'm' (sin modificaciones), 'r' (Comparar respecto al primero)\n",
    "\n",
    "ylim_zero = True\n",
    "\n",
    "var_aux, grouped_aux, handles, used_labels, title = get_types_iker(checked_type, used_direction, node_type, normality)\n",
    "array_aux, title_y, v_lines, h_line = obtain_arrays_iker(grouped_aux, var_aux, used_direction, normality)\n",
    "graphic_iker(array_aux, title, title_y, \"(NP, NC)\", handles, used_labels, v_lines, ylim_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    dfG_aux=dfG.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    dfG_aux=dfG.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "vOrMult = list(dfG_aux.query('Cst == 0 and Css == 0')['TE'])\n",
    "vOrSingle = list(dfG_aux.query('Cst == 0 and Css == 1')['TE'])\n",
    "vOrPthMult = list(dfG_aux.query('Cst == 1 and Css == 0')['TE'])\n",
    "vOrPthSingle = list(dfG_aux.query('Cst == 1 and Css == 1')['TE'])\n",
    "\n",
    "vMergeMult = list(dfG_aux.query('Cst == 2 and Css == 0')['TE'])\n",
    "vMergeSingle = list(dfG_aux.query('Cst == 2 and Css == 1')['TE'])\n",
    "vMergePthMult = list(dfG_aux.query('Cst == 3 and Css == 0')['TE'])\n",
    "vMergePthSingle = list(dfG_aux.query('Cst == 3 and Css == 1')['TE'])\n",
    "\n",
    "vOrMult2 = list(dfM_aux.query('Cst == 0 and Css == 0')['TC'])\n",
    "vOrSingle2 = list(dfM_aux.query('Cst == 0 and Css == 1')['TC'])\n",
    "vOrPthMult2 = list(dfM_aux.query('Cst == 1 and Css == 0')['TC'])\n",
    "vOrPthSingle2 = list(dfM_aux.query('Cst == 1 and Css == 1')['TC'])\n",
    "\n",
    "vMergeMult2 = list(dfM_aux.query('Cst == 2 and Css == 0')['TC'])\n",
    "vMergeSingle2 = list(dfM_aux.query('Cst == 2 and Css == 1')['TC'])\n",
    "vMergePthMult2 = list(dfM_aux.query('Cst == 3 and Css == 0')['TC'])\n",
    "vMergePthSingle2 = list(dfM_aux.query('Cst == 3 and Css == 1')['TC'])\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.scatter(vOrMult,vOrMult2, color='green')\n",
    "ax.scatter(vOrSingle,vOrSingle2,  color='springgreen')\n",
    "ax.scatter(vOrPthMult,vOrPthMult2, color='blue')\n",
    "ax.scatter(vOrPthSingle,vOrPthSingle2,color='darkblue')\n",
    "\n",
    "ax.scatter(vMergeMult,vMergeMult2, color='red')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergeSingle,vMergeSingle2,color='darkred')\n",
    "ax.scatter(vMergePthMult,vMergePthMult2, color='yellow')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergePthSingle,vMergePthSingle2,color='olive')\n",
    "\n",
    "ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"Time EX (s)\", fontsize=20)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=21,ncol=2)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Dispersion_Spawn_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_direction='e'\n",
    "test_parameter='TA'\n",
    "#TS es merge y TA connect para tests solo spawn\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "dfM_aux = dfM_aux.copy()\n",
    "#dfM_aux['NTotal'] = dfM_aux['NP'] + dfM_aux['NS']\n",
    "dfM_aux['NTotal'] = dfM_aux['NS']\n",
    "\n",
    "#x = np.arange(len(used_labels))\n",
    "for cst_aux in [0,1,2,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        f=plt.figure(figsize=(20, 12))\n",
    "        ax=f.add_subplot(111)\n",
    "        \n",
    "        #sns.boxplot(y=test_parameter, x=\"NS\", hue=\"NP\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        sns.boxplot(y=test_parameter, x=\"NTotal\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        \n",
    "        # Anyade en el plot el numero de iteraciones - Por hacer TODO\n",
    "        # https://stackoverflow.com/questions/45475962/labeling-boxplot-with-median-values\n",
    "        #medians_aux = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median()\n",
    "        #m1 = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median().values\n",
    "        #mL1 = [str(np.ceil(s)) for s in m1]\n",
    "\n",
    "        #ind = 0\n",
    "        #for tick in range(len(ax.get_xticklabels())):\n",
    "        #    ax.text(tick-.2, m1[ind+1]+1, mL1[ind+1],  horizontalalignment='center',  color='w', weight='semibold')\n",
    "        #    ax.text(tick+.2, m1[ind]+1, mL1[ind], horizontalalignment='center', color='w', weight='semibold')\n",
    "        #    ind += 2 \n",
    "        \n",
    "        ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "        ax.set_xlabel(\"NC\", fontsize=20)\n",
    "        plt.legend(loc='upper left', fontsize=21, title = \"NP\")\n",
    "        ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "        ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "        \n",
    "        ax.axvline((.5), color='black')\n",
    "        ax.axvline((1.5), color='black')\n",
    "        ax.axvline((2.5), color='black')\n",
    "        ax.axvline((3.5), color='black')\n",
    "        ax.axvline((4.5), color='black')\n",
    "        \n",
    "        f.tight_layout()\n",
    "        f.savefig(\"Images/Spawn/Boxplot_\"+name_fig+\"_\"+test_parameter+\"_\"+labelsMethods[cst_aux*2 + css_aux]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df, tipo):\n",
    "    normality=[True] * (len(processes) * (len(processes)-1))\n",
    "    total=0\n",
    "    i=-1\n",
    "    #Comprobar para cada configuración si se sigue una distribución normal/gaussiana\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                i+=1\n",
    "                for cst_aux in [0,1,2,3]:\n",
    "                    for css_aux in [0,1]:\n",
    "                        df_aux = df.query('NP == @np_aux and NS == @ns_aux and Cst == @cst_aux and Css == @css_aux')\n",
    "                        dataList = list(df_aux[tipo])\n",
    "                        st,p = stats.shapiro(dataList) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                        if p < p_value:\n",
    "                            normality[i]=False\n",
    "                            total+=1\n",
    "            \n",
    "    \n",
    "    print(\"Se sigue una distribución guassiana: \" + str(normality) + \"\\nUn total de: \" + str(total) + \" no siguen una gaussiana\")\n",
    "    return normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_stat_difference(dataLists, parametric):\n",
    "    if parametric:\n",
    "        st,p=stats.f_oneway(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    else:\n",
    "        st,p=stats.kruskal(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    if p > p_value: # Si son iguales, no hay que hacer nada más\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_posthoc(dataLists, parametric):\n",
    "    data_stats=[]\n",
    "    ini=0\n",
    "    end=len(labelsMethods)\n",
    "    if parametric:\n",
    "        df_aux = sp.posthoc_ttest(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.mean(dataLists[i]))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medias diferentes\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    else:\n",
    "        df_aux = sp.posthoc_conover(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.median(dataLists[i]))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medianas diferentes\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    #print(df_Res)\n",
    "    #print(df_aux)\n",
    "    #print(data_medians)\n",
    "    return df_Res, data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric):\n",
    "    best = 0\n",
    "    otherBest=[]\n",
    "    for i in [1,4,5]:\n",
    "    #for i in range(1,len(labelsMethods)):\n",
    "        #Descomentar para que sea perspectiva de RMS\n",
    "        #if (i == 3 or i == 2): \n",
    "        #    continue\n",
    "        if df_Res.iat[best,i] and data_stats[i] < data_stats[best]: # Medias/Medianas diferentes && Media/Medianas i < Media/Mediana best\n",
    "            best=i\n",
    "            for j in otherBest:\n",
    "                index = otherBest.index(j)\n",
    "                if data_stats[best] < data_stats[index]: # Media/Mediana newBest < Media/Mediana j -- Eliminar j\n",
    "                    otherBest.remove(j)          \n",
    "        elif not df_Res[best+1][i+1]: #Medias/Medianas iguales\n",
    "            otherBest.append(i)\n",
    "    \n",
    "    if shrink: # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "        if best == 5:\n",
    "            best=4\n",
    "        elif best ==7:\n",
    "            best=6\n",
    "        if 5 in otherBest:\n",
    "            otherBest.remove(5)\n",
    "        if 7 in otherBest:\n",
    "            otherBest.remove(7)\n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux) +\" \"+ str(parametric)+\" Mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    \n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtiene \n",
    "def check_groups_stats(dataLists, np_aux, ns_aux, shrink, parametric):\n",
    "    global_difference=compute_global_stat_difference(dataLists, parametric)\n",
    "    if not global_difference:\n",
    "        print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene valores iguales\")\n",
    "        return\n",
    "    \n",
    "    df_Res,data_stats=compute_global_posthoc(dataLists,parametric)\n",
    "    return get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se sigue una distribución guassiana: [False, True, True, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]\n",
      "Un total de: 115 no siguen una gaussiana\n",
      "Redimensión 1/10 False Mejores: Merge, Merge single, \n",
      "Redimensión 1/20 False Mejores: Merge, Merge single, \n",
      "Redimensión 1/40 False Mejores: Merge, Merge single, \n",
      "Redimensión 1/80 False Mejores: Baseline, Baseline single, Merge, Merge single, \n",
      "Redimensión 1/120 False Mejores: Baseline, Baseline single, Merge, Merge single, \n",
      "Redimensión 10/1 False Mejores: Merge, \n",
      "Redimensión 10/20 False Mejores: Merge, Merge single, \n",
      "Redimensión 10/40 False Mejores: Merge, Merge single, \n",
      "Redimensión 10/80 False Mejores: Merge, \n",
      "Redimensión 10/120 False Mejores: Merge, Merge single, \n",
      "Redimensión 20/1 False Mejores: Merge, \n",
      "Redimensión 20/10 False Mejores: Merge, \n",
      "Redimensión 20/40 False Mejores: Merge, \n",
      "Redimensión 20/80 False Mejores: Merge, \n",
      "Redimensión 20/120 False Mejores: Merge single, \n",
      "Redimensión 40/1 False Mejores: Merge, \n",
      "Redimensión 40/10 False Mejores: Merge, \n",
      "Redimensión 40/20 False Mejores: Merge, \n",
      "Redimensión 40/80 False Mejores: Merge, Merge single, \n",
      "Redimensión 40/120 False Mejores: Merge, Merge single, \n",
      "Redimensión 80/1 False Mejores: Baseline, Baseline single, Merge, \n",
      "Redimensión 80/10 False Mejores: Merge, \n",
      "Redimensión 80/20 False Mejores: Merge, \n",
      "Redimensión 80/40 False Mejores: Merge, \n",
      "Redimensión 80/120 False Mejores: Merge, Merge single, \n",
      "Redimensión 120/1 False Mejores: Baseline, Baseline single, Merge, \n",
      "Redimensión 120/10 False Mejores: Merge, \n",
      "Redimensión 120/20 False Mejores: Merge, \n",
      "Redimensión 120/40 False Mejores: Merge, \n",
      "Redimensión 120/80 False Mejores: Merge, \n"
     ]
    }
   ],
   "source": [
    "checked_type='tc'\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",
    "normality=check_normality(data_aux,tipo)\n",
    "if False in normality:\n",
    "    normality = False\n",
    "else:\n",
    "    normality = True\n",
    "    \n",
    "results = []\n",
    "shrink = False\n",
    "for np_aux in processes:\n",
    "    for ns_aux in processes:\n",
    "        if np_aux != ns_aux:\n",
    "            dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "            dataLists=[]\n",
    "            if np_aux > ns_aux:\n",
    "                shrink = True\n",
    "            else:\n",
    "                shrink = False\n",
    "            #normality=True\n",
    "            for cst_aux in [0,1,2,3]:\n",
    "                for css_aux in [0,1]:\n",
    "                    dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                    lista_aux = list(dataSet_aux[tipo])\n",
    "                    dataLists.append(lista_aux)\n",
    "                    #Si permito el shaphiro, acabare comparando manzanas y naranjas\n",
    "                    # si hay distribuciones normales y no normales\n",
    "                    #st,p = stats.shapiro(lista_aux) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                    #if p < p_value:\n",
    "                    #normality=False\n",
    "\n",
    "            aux_data = check_groups_stats(dataLists, np_aux, ns_aux, shrink, normality)\n",
    "            results.append(aux_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  6  6  6  6  6]\n",
      " [ 3 -1  6  6  6  6]\n",
      " [ 3  6 -1  6  6  6]\n",
      " [ 3  3  6 -1  6  6]\n",
      " [ 3  3  6  6 -1  6]\n",
      " [ 3  3  3  6  6  8]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor de los valores\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "unique, counts = np.unique(aux_array, return_counts=True)\n",
    "aux_dict = dict(zip(unique, counts))\n",
    "aux_keys=list(aux_dict.keys())\n",
    "aux_values=list(aux_dict.values())\n",
    "aux_ordered_index=list(reversed(list(np.argsort(aux_values))))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n",
    "for i in range(len(processes)):\n",
    "    for j in range(len(processes)):\n",
    "        if i==j:\n",
    "            heatmap[i][j]=-1\n",
    "            used_aux+=1\n",
    "        else:\n",
    "            results_index = i*len(processes) +j-used_aux\n",
    "            for index in aux_ordered_index:\n",
    "                if aux_keys[index] in results[results_index]:\n",
    "                    heatmap[i][j]=aux_keys[index]\n",
    "                    break\n",
    "heatmap[len(processes)-1][len(processes)-1]=8\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3862/1658605003.py:25: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=30)\n",
      "/tmp/ipykernel_3862/1658605003.py:26: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+processes, fontsize=30)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 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=(20, 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(processes)):\n",
    "    for j in range(len(processes)):\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=28)\n",
    "            else:\n",
    "                text = ax.text(j, i, heatmap[i, j],\n",
    "                       ha=\"center\", va=\"center\", color=\"black\", fontsize=28)\n",
    "\n",
    "ax.set_ylabel(\"NP\", fontsize=30)\n",
    "ax.set_xlabel(\"NC\", fontsize=30)\n",
    "\n",
    "ax.set_xticklabels(['']+processes, fontsize=30)\n",
    "ax.set_yticklabels(['']+processes, fontsize=30)\n",
    "\n",
    "#\n",
    "labelsMethods_aux = ['Baseline (0)', 'Baseline single (1)','Baseline - Asynchronous (2)','Baseline single - Asynchronous (3)',\n",
    "                 'Merge (4)','Merge single (5)','Merge - Asynchronous (6)','Merge single - Asynchronous (7)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "colorbar.set_ticks([0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TE\n",
    "#colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TC\n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=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)"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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