analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.legend_handler import HandlerLine2D, HandlerTuple\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from scipy import stats\n",
    "import scikit_posthocs as sp\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrixMalEX=\"data_GG.csv\"\n",
    "matrixMal=\"data_GM.csv\"\n",
    "matrixIt=\"data_L.csv\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_qty=6 #CAMBIAR SEGUN LA CANTIDAD DE NODOS USADOS\n",
    "n_groups= 2\n",
    "repet = 10 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "time_constant = False # Cambiar segun el speedUp usado\n",
    "speedup = 0.66 # Porcentaje del speedup ideal\n",
    "\n",
    "p_value = 0.05\n",
    "values = [2, 10, 20, 40]\n",
    "#                      WORST          BEST\n",
    "dist_names = ['null', 'BalancedFit', 'CompactFit']\n",
    "\n",
    "processes = [1,10,20,40,80,120]\n",
    "\n",
    "labelsP = [['(2,2)', '(2,10)', '(2,20)', '(2,40)'],['(10,2)', '(10,10)', '(10,20)', '(10,40)'],\n",
    "          ['(20,2)', '(20,10)', '(20,20)', '(20,40)'],['(40,2)', '(40,10)', '(40,20)', '(40,40)']]\n",
    "labelsP_J = ['(2,2)', '(2,10)', '(2,20)', '(2,40)','(10,2)', '(10,10)', '(10,20)', '(10,40)',\n",
    "              '(20,2)', '(20,10)', '(20,20)', '(20,40)','(40,2)', '(40,10)', '(40,20)', '(40,40)']\n",
    "positions = [321, 322, 323, 324, 325]\n",
    "positions_small = [221, 222, 223, 224]\n",
    "\n",
    "labels = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
    "            '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "            '(20,1)',  '(20,10)','(20,40)','(20,80)','(20,120)',\n",
    "            '(40,1)',  '(40,10)',  '(40,20)','(40,80)','(40,120)',\n",
    "            '(80,1)',  '(80,10)',  '(80,20)', '(80,40)','(80,120)',\n",
    "            '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
    "\n",
    "labelsExpand = ['(1,10)', '(1,20)', '(1,40)','(1,80)','(1,120)',\n",
    "               '(10,20)', '(10,40)','(10,80)','(10,120)',\n",
    "               '(20,40)','(20,80)','(20,120)',\n",
    "               '(40,80)','(40,120)',\n",
    "               '(80,120)']\n",
    "labelsShrink = ['(10,1)', \n",
    "               '(20,1)',  '(20,10)', \n",
    "               '(40,1)',  '(40,10)',  '(40,20)',\n",
    "               '(80,1)',  '(80,10)',  '(80,20)', '(80,40)',\n",
    "               '(120,1)', '(120,10)', '(120,20)','(120,40)','(120,80)']\n",
    "\n",
    "labelsExpandOrdered = ['(1,10)', '(1,20)', '(10,20)',\n",
    "                       '(1,40)','(10,40)','(20,40)',\n",
    "                       '(1,80)','(10,80)','(20,80)','(40,80)',\n",
    "                       '(1,120)', '(10,120)', '(20,120)','(40,120)','(80,120)']\n",
    "labelsShrinkOrdered = ['(10,1)', '(20,1)', '(40,1)', '(80,1)', '(120,1)',\n",
    "                '(20,10)',  '(40,10)',  '(80,10)',  '(120,10)', \n",
    "                '(40,20)', '(80,20)', '(120,20)',\n",
    "                '(80,40)','(120,40)',\n",
    "                '(120,80)']\n",
    "\n",
    "labelsExpandIntra = ['(1,10)', '(1,20)','(10,20)']\n",
    "labelsShrinkIntra = ['(10,1)', '(20,1)', '(20,10)']\n",
    "labelsExpandInter = ['(1,40)','(1,80)', '(1,160)',\n",
    "               '(10,40)','(10,80)', '(10,160)',\n",
    "               '(20,40)','(20,80)', '(20,160)',\n",
    "               '(40,80)', '(40,160)',\n",
    "               '(80,160)']\n",
    "labelsShrinkInter = ['(40,1)', '(40,10)', '(40,20)',\n",
    "               '(80,1)', '(80,10)', '(80,20)','(80,40)',\n",
    "               '(160,1)', '(160,10)', '(160,20)','(160,40)', '(160,80)']\n",
    "\n",
    "                #0          #1                 #2                     #3\n",
    "labelsMethods = ['Baseline', 'Baseline single','Baseline - Asynchronous','Baseline single - Asynchronous',\n",
    "                 'Merge','Merge single','Merge - Asynchronous','Merge single - Asynchronous']\n",
    "                 #4      #5             #6                 #7\n",
    "colors_spawn = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive']\n",
    "linestyle_spawn = ['-', '--', '-.', ':']\n",
    "markers_spawn = ['.','v','s','p', 'h','d','X','P']\n",
    "\n",
    "OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n",
    "OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n",
    "OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Asyncrhonous')\n",
    "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n",
    "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
    "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
    "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n",
    "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')\n",
    "\n",
    "handles_spawn = [OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfG = pd.read_csv( matrixMalEX )\n",
    "\n",
    "dfG = dfG.drop(columns=dfG.columns[0])\n",
    "dfG['S'] = dfG['N']\n",
    "dfG['N'] = dfG['S'] + dfG['%Async']\n",
    "dfG['%Async'] = (dfG['%Async'] / dfG['N']) * 100\n",
    "dfG['%Async'] = dfG['%Async'].fillna(0)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    group = dfG.groupby(['%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "else:        \n",
    "    group = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'Groups'])['TE']\n",
    "    group2 = dfG.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP','NS'])['TE']\n",
    "\n",
    "grouped_aggG = group.agg(['median'])\n",
    "grouped_aggG.rename(columns={'median':'TE'}, inplace=True)\n",
    "\n",
    "grouped_aggG2 = group2.agg(['median'])\n",
    "grouped_aggG2.rename(columns={'median':'TE'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/2056908859.py:18: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n"
     ]
    }
   ],
   "source": [
    "dfM = pd.read_csv( matrixMal )\n",
    "dfM = dfM.drop(columns=dfM.columns[0])\n",
    "\n",
    "dfM['S'] = dfM['N']\n",
    "dfM['N'] = dfM['S'] + dfM['%Async']\n",
    "dfM[\"TR\"] = dfM[\"TC\"] + dfM[\"TH\"] + dfM[\"TS\"] + dfM[\"TA\"]\n",
    "dfM['%Async'] = (dfM['%Async'] / dfM['N']) * 100\n",
    "\n",
    "dfM['%Async'] = dfM['%Async'].fillna(0)\n",
    "dfM['alpha'] = 1\n",
    "\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 3) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "#dfM = dfM.drop(dfM.loc[(dfM[\"Cst\"] == 2) & (dfM[\"Css\"] == 1) & (dfM[\"NP\"] > dfM[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupM = dfM.groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "else:\n",
    "    groupM = dfM.groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['TC', 'TH', 'TS', 'TA', 'TR', 'alpha']\n",
    "\n",
    "#group\n",
    "grouped_aggM = groupM.agg(['median'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['alpha'] = \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux)]['TC'] / \\\n",
    "                        grouped_aggM.loc[('2,2',0, cst_aux-1, css_aux, np_aux,ns_aux)]['TC']\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/1294489315.py:13: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n"
     ]
    }
   ],
   "source": [
    "dfL = pd.read_csv( matrixIt )\n",
    "dfL = dfL.drop(columns=dfL.columns[0])\n",
    "\n",
    "dfL['%Async'] = dfL['%Async'].fillna(0)\n",
    "dfL['omega'] = 1\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n",
    "else:\n",
    "    groupL = dfL[dfL['NS'] != 0].groupby(['Tt', 'Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Ti', 'To', 'omega']\n",
    "\n",
    "#group\n",
    "grouped_aggL = groupL.agg(['median', 'count'])\n",
    "grouped_aggL.columns = grouped_aggL.columns.get_level_values(0)\n",
    "grouped_aggL.set_axis(['Ti', 'Iters', 'To', 'Iters2', 'omega', 'omega2'], axis='columns', inplace=True)\n",
    "grouped_aggL['Iters'] = np.round(grouped_aggL['Iters']/repet)\n",
    "grouped_aggL['Iters2'] = np.round(grouped_aggL['Iters2']/repet)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux), 'omega'] = \\\n",
    "                        grouped_aggL.loc[(1,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti'] / \\\n",
    "                        grouped_aggL.loc[(0,2,0, cst_aux, css_aux, np_aux,ns_aux)]['Ti']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3419: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/tmp/ipykernel_2692/3028104048.py:17: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n"
     ]
    }
   ],
   "source": [
    "dfLT = pd.read_csv( matrixIt_Total )\n",
    "dfLT = dfLT.drop(columns=dfLT.columns[0])\n",
    "\n",
    "dfLT['%Async'] = dfLT['%Async'].fillna(0)\n",
    "dfLT['ItA']= dfLT.Ti.apply(lambda x: list(x.replace('(','').replace(')','').split(',')))\n",
    "dfLT['TiA']= dfLT.ItA.apply(lambda x: np.median(list(map(float,[y for y in x if y]))) )\n",
    "dfLT['TiA']= dfLT['TiA'].fillna(0)\n",
    "dfLT['ItA']= dfLT.ItA.apply(lambda x: len([y for y in x if y]))\n",
    "\n",
    "\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 3) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "#dfL = dfL.drop(dfL.loc[(dfL[\"Cst\"] == 2) & (dfL[\"Css\"] == 1) & (dfL[\"NP\"] > dfL[\"NS\"])].index)\n",
    "\n",
    "if(n_qty == 1):\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n",
    "else:\n",
    "    groupLT = dfLT[dfLT['NS'] != 0].groupby(['Dist', '%Async', 'Cst', 'Css', 'NP', 'NS'])['Sum', 'ItA']\n",
    "\n",
    "#group\n",
    "grouped_aggLT = groupLT.agg(['median'])\n",
    "grouped_aggLT.columns = grouped_aggLT.columns.get_level_values(0)\n",
    "grouped_aggLT.set_axis(['Sum','ItA'], axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/863462943.py:12: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfLT_aux = dfLT[dfLT[\"NP\"] == np_aux][dfLT[\"NS\"] == ns_aux][dfLT[\"Cst\"] == cst_aux][dfLT[\"Css\"] == css_aux]\n",
      "/tmp/ipykernel_2692/863462943.py:13: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  dfM_aux = dfM[dfM[\"NP\"] == np_aux][dfM[\"NS\"] == ns_aux][dfM[\"Css\"] == css_aux]\n"
     ]
    }
   ],
   "source": [
    "tc_list = []\n",
    "alpha_list = []\n",
    "omega_list = []\n",
    "ita_list = []\n",
    "dfLT['index'] = dfLT.index\n",
    "dfM['index'] = dfM.index\n",
    "for cst_aux in [0,1,2,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if np_aux != ns_aux:\n",
    "                    dfLT_aux = dfLT[dfLT[\"NP\"] == np_aux][dfLT[\"NS\"] == ns_aux][dfLT[\"Cst\"] == cst_aux][dfLT[\"Css\"] == css_aux]\n",
    "                    dfM_aux = dfM[dfM[\"NP\"] == np_aux][dfM[\"NS\"] == ns_aux][dfM[\"Css\"] == css_aux]\n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        dfM_aux2= dfM_aux[dfM_aux[\"Cst\"] == cst_aux-1]\n",
    "                        dfM_aux2= dfM_aux2.sort_values(by=['TH'])\n",
    "                    dfM_aux = dfM_aux[dfM_aux[\"Cst\"] == cst_aux]\n",
    "                    dfM_aux= dfM_aux.sort_values(by=['TH'])\n",
    "                    index1_aux = dfM_aux.iloc[4][\"index\"]\n",
    "                    index2_aux = dfM_aux.iloc[5][\"index\"]\n",
    "                    \n",
    "                    # Comprobar que es un metodo asincrono\n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        value_aux1 = dfM_aux[dfM_aux[\"index\"] == index1_aux]['TC'].values\n",
    "                        value_aux2 = dfM_aux[dfM_aux[\"index\"] == index2_aux]['TC'].values\n",
    "                        valueS_aux1 = dfM_aux2.iloc[4]['TC']\n",
    "                        valueS_aux2 = dfM_aux2.iloc[5]['TC']\n",
    "                        value1_aux = (value_aux1 + value_aux2) / 2\n",
    "                        value2_aux = (value_aux1/valueS_aux1 + value_aux2/valueS_aux2) / 2\n",
    "                    else:\n",
    "                        value1_aux = dfM_aux['TC'].median()\n",
    "                        value2_aux = 1\n",
    "                    tc_list.append(float(value1_aux))\n",
    "                    alpha_list.append(float(value2_aux))\n",
    "                    \n",
    "                    value_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['ItA'].values\n",
    "                    value_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['ItA'].values\n",
    "                    value3_aux = (value_aux1 + value_aux2) / 2\n",
    "                    ita_list.append(int(value3_aux))\n",
    "                    \n",
    "                    if cst_aux == 1 or cst_aux == 3:\n",
    "                        iter_time_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['Time'].values\n",
    "                        iter_time_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['Time'].values\n",
    "                        if not time_constant:\n",
    "                            iter_time_aux1 = (iter_time_aux1 / np_aux) / speedup\n",
    "                            iter_time_aux2 = (iter_time_aux2 / np_aux) / speedup\n",
    "                        iter_Atime_aux1 = dfLT_aux[dfLT_aux[\"index\"] == index1_aux]['TiA'].values\n",
    "                        iter_Atime_aux2 = dfLT_aux[dfLT_aux[\"index\"] == index2_aux]['TiA'].values\n",
    "                        value4_aux = (iter_Atime_aux1/iter_time_aux1 + iter_Atime_aux1/iter_time_aux2) / 2\n",
    "                    else:\n",
    "                        value4_aux = 1\n",
    "                    omega_list.append(float(value4_aux))\n",
    "grouped_aggM['TC_A'] = tc_list\n",
    "grouped_aggM['ItA'] = ita_list\n",
    "grouped_aggM['Alpha_A'] = alpha_list\n",
    "grouped_aggM['Omega_A'] = omega_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "coherent_check_df = grouped_aggL.copy()\n",
    "# Añadir suma total de iteraciones\n",
    "coherent_check_df['Sum'] = 0\n",
    "coherent_check_df.loc[(1,slice(None)),'Sum'] = grouped_aggLT[(grouped_aggLT['Sum'] != 0)].loc[(slice(None)),'Sum'].values\n",
    "coherent_check_df = coherent_check_df[(coherent_check_df['Sum'] != 0)]\n",
    "# Añadir tiempos TE y TC\n",
    "coherent_check_df['TE'] = 0\n",
    "coherent_check_df['TEA'] = 0\n",
    "coherent_check_df['TR'] = 0\n",
    "coherent_check_df['TRA'] = 0\n",
    "for cst_aux in [1,3]:\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TE'] = grouped_aggG2.loc[('2,2',0,cst_aux-1,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TR'] = grouped_aggM.loc[('2,2',0,cst_aux-1,slice(None)),'TC'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TEA'] = grouped_aggG2.loc[('2,2',0,cst_aux,slice(None)),'TE'].values\n",
    "    coherent_check_df.loc[(1,2,0,cst_aux,slice(None)),'TRA'] = grouped_aggM.loc[('2,2',0,cst_aux,slice(None)),'TC'].values\n",
    "# Calcular tiempos teoricos\n",
    "#coherent_check_df['Teorico-S'] = coherent_check_df['Ti'] * 3 + coherent_check_df['TR'] +  TIEMPOITERNS * 97\n",
    "#coherent_check_df['Rel-S'] = np.round(coherent_check_df['Teorico-S'] / coherent_check_df['TE'],2)\n",
    "#coherent_check_df['Teorico-A'] = coherent_check_df['Ti'] * 3 + coherent_check_df['Sum'] +  TIEMPOITERNS * (97 - coherent_check_df['Iters'])\n",
    "#coherent_check_df['Rel-A'] = np.round(coherent_check_df['Teorico-A'] / coherent_check_df['TEA'],2)\n",
    "coherent_check_df=coherent_check_df.droplevel('Tt').droplevel('%Async').droplevel('Dist')\n",
    "for cst_aux in [1,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        aux_df = coherent_check_df.loc[(cst_aux, css_aux, slice(None))]\n",
    "        aux_df.to_excel(\"coherent\"+str(cst_aux)+\"_\"+str(css_aux)+\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_aggL.to_excel(\"resultL.xlsx\") \n",
    "grouped_aggLT.to_excel(\"resultLT.xlsx\")\n",
    "dfLT.to_excel(\"resultLT_all.xlsx\")\n",
    "grouped_aggM.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggG2.to_excel(\"resultG.xlsx\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Sum</th>\n",
       "      <th>ItA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">2</th>\n",
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       "      <th>20</th>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.000000</td>\n",
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       "      <th>120</th>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.216691</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.271795</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.295311</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.204900</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.226526</td>\n",
       "      <td>3.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Sum  ItA\n",
       "Dist %Async Cst Css NP  NS                \n",
       "2    0.0    0   0   1   10   0.000000  0.0\n",
       "                        20   0.000000  0.0\n",
       "                        40   0.000000  0.0\n",
       "                        80   0.000000  0.0\n",
       "                        120  0.000000  0.0\n",
       "...                               ...  ...\n",
       "            3   1   120 1    0.216691  3.0\n",
       "                        10   0.271795  3.5\n",
       "                        20   0.295311  3.5\n",
       "                        40   0.204900  3.0\n",
       "                        80   0.226526  3.0\n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG\n",
    "dfM\n",
    "dfL\n",
    "dfLT\n",
    "grouped_aggG\n",
    "grouped_aggM\n",
    "grouped_aggL\n",
    "grouped_aggLT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>(0.069027, 0.098036, 0.125989, 0.112365, 0.109...</td>\n",
       "      <td>0.998392</td>\n",
       "      <td>10</td>\n",
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       "</table>\n",
       "<p>2400 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      N  %Async   NP  N_par  NS  Dist  Compute_tam  Comm_tam  Cst  Css  Time  \\\n",
       "0     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "1     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "2     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "3     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "4     0     0.0   40      0  10     2       100000         0    3    0   4.0   \n",
       "...  ..     ...  ...    ...  ..   ...          ...       ...  ...  ...   ...   \n",
       "2395  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2396  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2397  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2398  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "2399  0     0.0  120      0  10     2       100000         0    3    0   4.0   \n",
       "\n",
       "      Iters                                                 Ti       Sum  ItA  \\\n",
       "0         3                               (0.225269, 0.102594)  0.327863    2   \n",
       "1         3                               (0.197712, 0.111945)  0.309657    2   \n",
       "2         3                                        (0.172556,)  0.172556    1   \n",
       "3         3                               (0.208798, 0.101674)  0.310472    2   \n",
       "4         3                               (0.169751, 0.099995)  0.269746    2   \n",
       "...     ...                                                ...       ...  ...   \n",
       "2395      3           (0.061279, 0.086562, 0.102954, 0.064273)  0.315068    4   \n",
       "2396      3           (0.065522, 0.090022, 0.089555, 0.069523)  0.314622    4   \n",
       "2397      3  (0.065145, 0.10315, 0.110737, 0.085012, 0.093,...  1.037177   11   \n",
       "2398      3  (0.069027, 0.098036, 0.125989, 0.112365, 0.109...  0.998392   10   \n",
       "2399      3                     (0.065019, 0.091227, 0.065016)  0.221262    3   \n",
       "\n",
       "           TiA  \n",
       "0     0.163932  \n",
       "1     0.154829  \n",
       "2     0.172556  \n",
       "3     0.155236  \n",
       "4     0.134873  \n",
       "...        ...  \n",
       "2395  0.075417  \n",
       "2396  0.079539  \n",
       "2397  0.098615  \n",
       "2398  0.103513  \n",
       "2399  0.065019  \n",
       "\n",
       "[2400 rows x 16 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfLT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.716209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.716209</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.798951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.798951</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.931771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.931771</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.931771</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.992033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992033</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.992033</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">10</th>\n",
       "      <th>1</th>\n",
       "      <td>0.000982</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000982</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.477040</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.477040</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.766185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.766185</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.766185</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.860920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.860920</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.860920</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.890894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.890894</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.890894</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20</th>\n",
       "      <th>1</th>\n",
       "      <td>0.001005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.001025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001025</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.790193</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.790193</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.790193</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.864170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.864170</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.864170</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.088929</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.088929</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.088929</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
       "      <th>1</th>\n",
       "      <td>0.029802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.029802</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.024519</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.024519</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.116366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116366</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.893692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.893692</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.893692</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.922904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.922904</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.922904</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">80</th>\n",
       "      <th>1</th>\n",
       "      <td>0.217198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.217198</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.180846</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180846</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.180846</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.149038</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149038</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.149038</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.148336</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148336</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148336</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.905912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.905912</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.905912</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.231024</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.231024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231024</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.148412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.148412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.148412</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.177781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.177781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.177781</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.350567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.350567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.350567</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.156000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TC   TH   TS   TA        TR  alpha      TC_A  ItA  Alpha_A  \\\n",
       "NP  NS                                                                      \n",
       "1   10   0.283736  0.0  0.0  0.0  0.283736    1.0  0.283736    0      1.0   \n",
       "    20   0.716209  0.0  0.0  0.0  0.716209    1.0  0.716209    0      1.0   \n",
       "    40   0.798951  0.0  0.0  0.0  0.798951    1.0  0.798951    0      1.0   \n",
       "    80   0.931771  0.0  0.0  0.0  0.931771    1.0  0.931771    0      1.0   \n",
       "    120  0.992033  0.0  0.0  0.0  0.992033    1.0  0.992033    0      1.0   \n",
       "10  1    0.000982  0.0  0.0  0.0  0.000982    1.0  0.000982    0      1.0   \n",
       "    20   0.477040  0.0  0.0  0.0  0.477040    1.0  0.477040    0      1.0   \n",
       "    40   0.766185  0.0  0.0  0.0  0.766185    1.0  0.766185    0      1.0   \n",
       "    80   0.860920  0.0  0.0  0.0  0.860920    1.0  0.860920    0      1.0   \n",
       "    120  0.890894  0.0  0.0  0.0  0.890894    1.0  0.890894    0      1.0   \n",
       "20  1    0.001005  0.0  0.0  0.0  0.001005    1.0  0.001005    0      1.0   \n",
       "    10   0.001025  0.0  0.0  0.0  0.001025    1.0  0.001025    0      1.0   \n",
       "    40   0.790193  0.0  0.0  0.0  0.790193    1.0  0.790193    0      1.0   \n",
       "    80   0.864170  0.0  0.0  0.0  0.864170    1.0  0.864170    0      1.0   \n",
       "    120  1.088929  0.0  0.0  0.0  1.088929    1.0  1.088929    0      1.0   \n",
       "40  1    0.029802  0.0  0.0  0.0  0.029802    1.0  0.029802    0      1.0   \n",
       "    10   0.024519  0.0  0.0  0.0  0.024519    1.0  0.024519    0      1.0   \n",
       "    20   0.116366  0.0  0.0  0.0  0.116366    1.0  0.116366    0      1.0   \n",
       "    80   0.893692  0.0  0.0  0.0  0.893692    1.0  0.893692    0      1.0   \n",
       "    120  0.922904  0.0  0.0  0.0  0.922904    1.0  0.922904    0      1.0   \n",
       "80  1    0.217198  0.0  0.0  0.0  0.217198    1.0  0.217198    0      1.0   \n",
       "    10   0.180846  0.0  0.0  0.0  0.180846    1.0  0.180846    0      1.0   \n",
       "    20   0.149038  0.0  0.0  0.0  0.149038    1.0  0.149038    0      1.0   \n",
       "    40   0.148336  0.0  0.0  0.0  0.148336    1.0  0.148336    0      1.0   \n",
       "    120  0.905912  0.0  0.0  0.0  0.905912    1.0  0.905912    0      1.0   \n",
       "120 1    0.231024  0.0  0.0  0.0  0.231024    1.0  0.231024    0      1.0   \n",
       "    10   0.148412  0.0  0.0  0.0  0.148412    1.0  0.148412    0      1.0   \n",
       "    20   0.177781  0.0  0.0  0.0  0.177781    1.0  0.177781    0      1.0   \n",
       "    40   0.350567  0.0  0.0  0.0  0.350567    1.0  0.350567    0      1.0   \n",
       "    80   0.156000  0.0  0.0  0.0  0.156000    1.0  0.156000    0      1.0   \n",
       "\n",
       "         Omega_A  \n",
       "NP  NS            \n",
       "1   10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "10  1        1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "20  1        1.0  \n",
       "    10       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "40  1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    80       1.0  \n",
       "    120      1.0  \n",
       "80  1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    120      1.0  \n",
       "120 1        1.0  \n",
       "    10       1.0  \n",
       "    20       1.0  \n",
       "    40       1.0  \n",
       "    80       1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM.loc[('2,2',0,2,0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Ti</th>\n",
       "      <th>Iters</th>\n",
       "      <th>To</th>\n",
       "      <th>Iters2</th>\n",
       "      <th>alpha</th>\n",
       "      <th>alpha2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tt</th>\n",
       "      <th>Dist</th>\n",
       "      <th>%Async</th>\n",
       "      <th>Cst</th>\n",
       "      <th>Css</th>\n",
       "      <th>NP</th>\n",
       "      <th>NS</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>10</th>\n",
       "      <td>3.999165</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.999194</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>3.999186</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>3.999236</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>3.999194</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">0.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">120</th>\n",
       "      <th>1</th>\n",
       "      <td>0.070046</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.108073</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.075896</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.292376</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.090617</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.733503</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.069103</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.089061</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.068959</td>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.083952</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Ti  Iters      To  Iters2     alpha  \\\n",
       "Tt  Dist %Async Cst Css NP  NS                                               \n",
       "0.0 2    0.0    0   0   1   10   3.999165    3.0  4485.0     3.0  1.000000   \n",
       "                            20   3.999194    3.0  4485.0     3.0  1.000000   \n",
       "                            40   3.999186    3.0  4485.0     3.0  1.000000   \n",
       "                            80   3.999236    3.0  4485.0     3.0  1.000000   \n",
       "                            120  3.999194    3.0  4485.0     3.0  1.000000   \n",
       "...                                   ...    ...     ...     ...       ...   \n",
       "1.0 2    0.0    3   1   120 1    0.070046    3.0    37.0     3.0  2.108073   \n",
       "                            10   0.075896    4.0    37.0     4.0  2.292376   \n",
       "                            20   0.090617    5.0    37.0     5.0  2.733503   \n",
       "                            40   0.069103    4.0    37.0     4.0  2.089061   \n",
       "                            80   0.068959    4.0    37.0     4.0  2.083952   \n",
       "\n",
       "                                 alpha2  \n",
       "Tt  Dist %Async Cst Css NP  NS           \n",
       "0.0 2    0.0    0   0   1   10       30  \n",
       "                            20       30  \n",
       "                            40       30  \n",
       "                            80       30  \n",
       "                            120      30  \n",
       "...                                 ...  \n",
       "1.0 2    0.0    3   1   120 1        30  \n",
       "                            10       40  \n",
       "                            20       54  \n",
       "                            40       37  \n",
       "                            80       39  \n",
       "\n",
       "[360 rows x 6 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97.0\n"
     ]
    }
   ],
   "source": [
    "auxIter = pd.DataFrame(dfM['Iters'].str.split(',',1).tolist(),columns = ['Iters0','Iters1'])\n",
    "auxIter['Iters1'] = pd.to_numeric(auxIter['Iters1'], errors='coerce')\n",
    "iters = auxIter['Iters1'].mean()\n",
    "print(iters)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de aquí se muestran gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.21241231578947362, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.04632109565217393, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.025296672413793103, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547, 0.0355868547008547], [0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.1981199732142857, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.06233977876106192, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.026912142857142853, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807, 0.0343439649122807]]\n",
      "[[2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703, 2.1241231578947364, 0.4632109565217393, 0.25296672413793103, 0.35586854700854703], [1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807, 1.9811997321428572, 0.6233977876106191, 0.2691214285714285, 0.343439649122807]]\n",
      "[[0.22657399999999997, 0.22961033333333333, 0.37444533333333335, 1.0861523333333334, 0.18071299999999998, 0.2686593333333333, 0.48245, 1.8810366666666667, 0.22639533333333337, 0.31453400000000004, 0.564293, 2.4626886666666667, 0.4612826666666667, 1.0638560000000001, 1.5319243333333334, 2.1236686666666666], [0.21594133333333332, 0.36930899999999994, 1.1269756666666668, 1.1670603333333334, 0.22462733333333332, 0.47068400000000005, 1.5951943333333334, 1.693723, 0.7059706666666666, 1.368441, 1.8698483333333336, 2.2059883333333334, 0.4813296666666667, 1.3010543333333333, 1.8387883333333335, 2.1851773333333333]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:36: FutureWarning: set_axis currently defaults to operating inplace.\n",
      "This will change in a future version of pandas, use inplace=True to avoid this warning.\n",
      "/home/usuario/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:53: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "#Reserva de memoria para las estructuras\n",
    "TP_data=[0]*2\n",
    "TH_data=[0]*2\n",
    "TM_data=[0]*2\n",
    "\n",
    "TP_A_data=[0]*2\n",
    "TH_A_data=[0]*2\n",
    "TM_A_data=[0]*2\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    \n",
    "    TP_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "    TP_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TH_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "    TM_A_data[dist_index]=[0]*len(values)*(len(values))\n",
    "\n",
    "# Obtencion de los grupos del dataframe necesarios\n",
    "\n",
    "#ACTUALMENTE NO SE DIFERENCIAN LOS TIEMPOS DE ITERACIONES DE PADRES E HIJOS CUANDO COINCIDE EL NUMERO DE PROCESOS\n",
    "if(n_qty == 1):\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['NP'])['Ti']\n",
    "else:\n",
    "    groupM_aux = dfM.groupby(['NP', 'NS', 'Dist'])['TC']\n",
    "    groupL_aux = dfL[dfL['Tt'] == 0].groupby(['Dist', 'NP'])['Ti']\n",
    "\n",
    "grouped_aggM_aux = groupM_aux.agg(['mean'])\n",
    "grouped_aggM_aux.columns = grouped_aggM_aux.columns.get_level_values(0)\n",
    "\n",
    "grouped_aggL_aux = groupL_aux.agg(['mean'])\n",
    "grouped_aggL_aux.columns = grouped_aggL_aux.columns.get_level_values(0)\n",
    "grouped_aggL_aux.set_axis(['Ti'], axis='columns')\n",
    "\n",
    "#Calculo de los valores para las figuras\n",
    "#1=Best Fit\n",
    "#2=Worst Fit\n",
    "dist=1\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    dist_v = str(dist)+\",\"+str(dist)\n",
    "    i=0\n",
    "    r=0\n",
    "    for numP in values:\n",
    "        j=0\n",
    "        for numC in values:\n",
    "        \n",
    "            tc_real = grouped_aggM_aux.loc[(numP,numC,dist_v)]['mean']\n",
    "            for tipo in [0]: #TODO Poner a 0,100\n",
    "                iters_aux=dfM[(dfM[\"NP\"] == numP)][(dfM[\"NS\"] == numC)][(dfM[\"Dist\"] == dist_v)][(dfM[\"%Async\"] == tipo)]['Iters'].head(1).tolist()[0].split(',')\n",
    "                itersP_aux = int(iters_aux[0])\n",
    "                itersS_aux = int(iters_aux[1])\n",
    "                iters_mal_aux = 0\n",
    "                if tipo != 0:\n",
    "                    iters_mal_aux = grouped_aggL['Iters'].loc[(1,dist,tipo,numP,numC)]\n",
    "            \n",
    "                t_iterP_aux = grouped_aggL_aux['Ti'].loc[(dist,numP)]\n",
    "                t_iterS_aux = grouped_aggL_aux['Ti'].loc[(dist,numC)]\n",
    "            \n",
    "            \n",
    "                p1 = t_iterP_aux * itersP_aux\n",
    "                p2 = t_iterS_aux * max((itersS_aux - iters_mal_aux),0)\n",
    "                \n",
    "                array_aux = grouped_aggM[['TS', 'TA']].loc[(dist_v,tipo,numP,numC)].tolist()\n",
    "                p3 = tc_real + array_aux[0] + array_aux[1]\n",
    "                \n",
    "                #Guardar datos\n",
    "                if tipo == 0:\n",
    "                    TP_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_data[dist_index][i*len(values) + j] = p3\n",
    "                else:\n",
    "                    TP_A_data[dist_index][i*len(values) + j] = p1\n",
    "                    TH_A_data[dist_index][i*len(values) + j] = p2\n",
    "                    TM_A_data[dist_index][i*len(values) + j] = p3\n",
    "            j+=1\n",
    "        i+=1\n",
    "print(TP_data)\n",
    "print(TH_data)\n",
    "print(TM_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TP_A_data=[[0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.1997793257575758, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.040469166666666695, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.019951386363636366, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729, 0.010227022727272729], [0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.20020575000000002, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.039894712121212116, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.020662818181818185, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332, 0.010635333333333332]]\n",
    "#TH_A_data=[[1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729, 1.9977932575757578, 0.40469166666666695, 0.19951386363636364, 0.10227022727272729], [2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331, 2.0020575000000003, 0.39894712121212117, 0.20662818181818185, 0.10635333333333331]]\n",
    "#TM_A_data=[[0.2083043333333333, 0.2661843333333333, 0.41778833333333326, 0.9868953333333335, 0.242685, 0.3060793333333333, 0.4986676666666667, 1.2530743333333334, 0.305179, 0.373607, 0.7375183333333334, 1.5113886666666667, 0.501651, 0.8987069999999999, 1.138518666666667, 1.5091376666666665], [0.205789, 0.4116923333333334, 1.0607546666666667, 0.9947066666666666, 0.27494700000000005, 0.669121, 1.2705783333333334, 1.3951336666666665, 0.4765406666666667, 0.9758123333333333, 1.267633, 1.4479673333333334, 0.4905743333333333, 1.0088953333333333, 1.4447113333333332, 1.4516683333333333]]\n",
    "\n",
    "\n",
    "for dist in [1,2]:\n",
    "    dist_index=dist-1\n",
    "    f=plt.figure(figsize=(20, 12))\n",
    "#for numP in values:\n",
    "\n",
    "    x = np.arange(len(labelsP_J))\n",
    "\n",
    "    width = 0.35\n",
    "    sumaTP_TM = np.add(TP_data[dist_index], TM_data[dist_index]).tolist()\n",
    "    sumaTP_TM_A = np.add(TP_A_data[dist_index], TM_A_data[dist_index]).tolist()\n",
    "\n",
    "    ax=f.add_subplot(111)\n",
    "\n",
    "    ax.bar(x+width/2, TP_data[dist_index], width, color='blue')\n",
    "    ax.bar(x+width/2, TM_data[dist_index], width, bottom=TP_data[dist_index],color='orange')\n",
    "    ax.bar(x+width/2, TH_data[dist_index], width, bottom=sumaTP_TM, color='green')\n",
    "\n",
    "    ax.bar(x-width/2, TP_A_data[dist_index], width, hatch=\"\\\\/...\", color='blue')\n",
    "    ax.bar(x-width/2, TM_A_data[dist_index], width, bottom=TP_A_data[dist_index], hatch=\"\\\\/...\", color='orange')\n",
    "    ax.bar(x-width/2, TH_A_data[dist_index], width, bottom=sumaTP_TM_A, hatch=\"\\\\/...\", color='green')\n",
    "\n",
    "    ax.set_ylabel(\"Time(s)\", fontsize=20)\n",
    "    ax.set_xlabel(\"NP, NC\", fontsize=20)\n",
    "    plt.xticks(x, labelsP_J, rotation=90)\n",
    "\n",
    "    blue_Spatch = mpatches.Patch(color='blue', label='Parents PR')\n",
    "    orange_Spatch = mpatches.Patch(color='orange', label='Resize PR')\n",
    "    green_Spatch = mpatches.Patch(color='green', label='Children PR')\n",
    "    blue_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='blue', label='Parents NR')\n",
    "    orange_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='orange', label='Resize NR')\n",
    "    green_Apatch = mpatches.Patch(hatch='\\\\/...', facecolor='green', label='Children NR')\n",
    "\n",
    "\n",
    "    handles=[blue_Spatch,orange_Spatch,green_Spatch,blue_Apatch,orange_Apatch,green_Apatch]\n",
    "\n",
    "    plt.legend(handles=handles, loc='upper left', fontsize=21,ncol=2)\n",
    "    \n",
    "    ax.axvline((3.5), color='black')\n",
    "    ax.axvline((7.5), color='black')\n",
    "    ax.axvline((11.5), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    plt.ylim((0, 25.5))\n",
    "    #ax.axvline(4)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/EX_Partitions_\"+dist_names[dist]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_types_iker(checked_type='tc', used_direction='e', node_type=\"All\", normality='m'):\n",
    "    if checked_type=='te':\n",
    "        var_aux='TE'\n",
    "        tipo_fig=\"TE\"\n",
    "        grouped_aux=grouped_aggG2\n",
    "    elif checked_type=='tc':\n",
    "        var_aux='TC'\n",
    "        tipo_fig=\"Mall\"\n",
    "        grouped_aux=grouped_aggM\n",
    "    \n",
    "    if node_type=='Intra':\n",
    "        grouped_aux=grouped_aux.query('NP < 21 and NS < 21')\n",
    "    elif node_type=='Inter':\n",
    "        grouped_aux=grouped_aux.query('NP > 21 or NS > 21')\n",
    "\n",
    "    if used_direction=='s':\n",
    "        grouped_aux=grouped_aux.query('NP > NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsShrinkIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsShrinkInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsShrink\n",
    "        name_fig=\"Shrink\"\n",
    "        \n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "    elif used_direction=='e':\n",
    "        grouped_aux=grouped_aux.query('NP < NS')\n",
    "        if node_type=='Intra':\n",
    "            used_labels=labelsExpandIntra\n",
    "        elif node_type=='Inter':\n",
    "            used_labels=labelsExpandInter\n",
    "        elif node_type=='All':\n",
    "            used_labels=labelsExpand\n",
    "        name_fig=\"Expand\"\n",
    "        if normality=='r':\n",
    "            handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "        else:\n",
    "            handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "    title=tipo_fig+\"_Spawn_\"+node_type+\"_\"+name_fig+\"_\"+normality\n",
    "    return var_aux, grouped_aux, handles, used_labels, title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def obtain_arrays_iker(grouped_aux, var_aux, used_direction='e', normality='m'):\n",
    "    vOrMult = list(grouped_aux.query('Cst == 0 and Css == 0')[var_aux])\n",
    "    vOrSingle = list(grouped_aux.query('Cst == 0 and Css == 1')[var_aux])\n",
    "    vMergeMult = list(grouped_aux.query('Cst == 2 and Css == 0')[var_aux])\n",
    "    vOrPthMult = list(grouped_aux.query('Cst == 1 and Css == 0')[var_aux])\n",
    "    vOrPthSingle = list(grouped_aux.query('Cst == 1 and Css == 1')[var_aux])\n",
    "    vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "    h_line = None\n",
    "    \n",
    "    if used_direction=='e':\n",
    "        vMergeSingle = list(grouped_aux.query('Cst == 2 and Css == 1')[var_aux])\n",
    "        vMergePthSingle = list(grouped_aux.query('Cst == 3 and Css == 1')[var_aux])\n",
    "    else:\n",
    "        #FIXME Que tenga en cuenta TH al realizar shrink merge\n",
    "        vMergePthMult = list(grouped_aux.query('Cst == 3 and Css == 0')[var_aux])\n",
    "        vMergeSingle = None\n",
    "        vMergePthSingle = None\n",
    "    title_y = \"Total time(s)\"\n",
    "        \n",
    "    if normality == 'r':\n",
    "        vOrSingle = np.subtract(vOrMult, vOrSingle)\n",
    "        vOrPthMult = np.subtract(vOrMult, vOrPthMult)\n",
    "        vOrPthSingle = np.subtract(vOrMult, vOrPthSingle)\n",
    "        vMergeMult = np.subtract(vOrMult, vMergeMult)\n",
    "        vMergePthMult = np.subtract(vOrMult, vMergePthMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.subtract(vOrMult, vMergeSingle)\n",
    "            vMergePthSingle = np.subtract(vOrMult, vMergePthSingle)\n",
    "        vOrMult = None\n",
    "        h_line = 0\n",
    "        title_y = \"Saved time(s)\"\n",
    "    elif normality == 'n':\n",
    "        vOrSingle = np.divide(vOrSingle, vOrMult)\n",
    "        vOrPthMult = np.divide(vOrPthMult, vOrMult)\n",
    "        vOrPthSingle = np.divide(vOrPthSingle, vOrMult)\n",
    "        vMergeMult = np.divide(vMergeMult, vOrMult)\n",
    "        vMergePthMult = np.divide(vMergePthMult, vOrMult)\n",
    "        if used_direction=='e':\n",
    "            vMergeSingle = np.divide(vMergeSingle, vOrMult)\n",
    "            vMergePthSingle = np.divide(vMergePthSingle, vOrMult)\n",
    "        vOrMult = np.divide(vOrMult, vOrMult)\n",
    "        h_line = 1\n",
    "        title_y = \"Relation Config time / Baseline Time\"\n",
    "    \n",
    "    data_array=[vOrMult,vOrSingle,vOrPthMult,vOrPthSingle,vMergeMult,vMergeSingle,vMergePthMult,vMergePthSingle]\n",
    "    v_lines=[]\n",
    "    value_aux = 0.4\n",
    "    if used_direction == 'e':\n",
    "        value_aux = 0.5\n",
    "    for i in range(0, len(vOrSingle)-1):\n",
    "        v_lines.append(value_aux + i)\n",
    "    return data_array, title_y, v_lines, h_line\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def legend_loc_iker(data_array, len_x, ylim_zero):\n",
    "    \n",
    "    max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "    if(ylim_zero):\n",
    "        min_value = 0\n",
    "    middle_value = (max_value + min_value) / 2\n",
    "    offset = (max_value - min_value) * 0.1\n",
    "    \n",
    "    def array_check_loc(ini, end):\n",
    "        up = True\n",
    "        lower = True\n",
    "        for i in range(ini, end):\n",
    "            for j in range(len(data_array)):\n",
    "                if not (data_array[j] is None):\n",
    "                    if data_array[j][i] > (middle_value + offset):\n",
    "                        up = False\n",
    "                    elif data_array[j][i] < (middle_value - offset):\n",
    "                        lower = False\n",
    "                    if not up and not lower:\n",
    "                        break\n",
    "            else:\n",
    "                continue # Only executed if inner loop did NOT break\n",
    "            break # Only executed if inner loop did break\n",
    "        return up,lower\n",
    "    \n",
    "    up_left, lower_left = array_check_loc(0, math.floor(len_x/2))\n",
    "    up_right, lower_right = array_check_loc(0, math.floor(len_x/2))\n",
    "\n",
    "    legend_loc = 'best'\n",
    "    if up_left:\n",
    "        legend_loc = 'upper left'\n",
    "    elif up_right:\n",
    "        legend_loc = 'upper right'\n",
    "    elif lower_left:\n",
    "        legend_loc = 'lower left'\n",
    "    elif lower_right:\n",
    "        lower_right = 'lower right'\n",
    "\n",
    "    return legend_loc\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphic_iker(data_array, title=\"None\", title_y=\"None\", title_x=\"None\", handles=None, used_labels=None, v_lines=None, h_line=None, ylim_zero=True):\n",
    "    f=plt.figure(figsize=(30, 12))\n",
    "    ax=f.add_subplot(111)\n",
    "    x = np.arange(len(used_labels))\n",
    "    width = 0.45/4\n",
    "    \n",
    "    legend_loc = legend_loc_iker(data_array, len(used_labels), ylim_zero)\n",
    "\n",
    "    if not (data_array[0] is None):\n",
    "        ax.bar(x-width*3.5, data_array[0], width, color='green')\n",
    "    ax.bar(x-width*2.5, data_array[1], width, hatch=\"\", color='springgreen')\n",
    "    ax.bar(x-width*1.5, data_array[2], width, hatch=\"//\", color='blue')\n",
    "    ax.bar(x-width*0.5, data_array[3], width, hatch=\"\\\\\",color='darkblue')\n",
    "\n",
    "    ax.bar(x+width*0.5, data_array[4], width, hatch=\"||\", color='red')\n",
    "    if not (data_array[5] is None):\n",
    "        ax.bar(x+width*1.5, data_array[5], width, hatch=\"...\", color='darkred')\n",
    "        ax.bar(x+width*2.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    else:\n",
    "        ax.bar(x+width*1.5, data_array[6], width, hatch=\"xx\", color='yellow')\n",
    "    if not (data_array[7] is None):\n",
    "        ax.bar(x+width*3.5, data_array[7], width, hatch=\"++\",color='olive')\n",
    "\n",
    "    ax.axhline((0), color='black', linestyle='dashed')\n",
    "    ax.set_ylabel(title_y, fontsize=30)\n",
    "    ax.set_xlabel(title_x, fontsize=28)\n",
    "    plt.xticks(x, used_labels, rotation=90)\n",
    "    plt.legend(handles=handles, loc=legend_loc, fontsize=26,ncol=2,framealpha=1)\n",
    "    \n",
    "    if not ylim_zero: # Modifica los limites del eje y. No es buena practica que no aparezca el 0\n",
    "        max_value = np.nanmax([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        max_value += max_value * 0.1\n",
    "        min_value = np.nanmin([x for x in data_array if x is not None]) # Los valores None es necesario evitarlos\n",
    "        min_value -= min_value * 0.1\n",
    "        if min_value < 0.1:\n",
    "            min_value = 0\n",
    "        plt.ylim((min_value, max_value))\n",
    "    \n",
    "    for line in v_lines:\n",
    "        ax.axvline((line), color='black')\n",
    "    if h_line != None:\n",
    "        ax.axhline((h_line), color='black')\n",
    "    \n",
    "    ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "    ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "    \n",
    "    f.tight_layout()\n",
    "    f.savefig(\"Images/Spawn/\"+title+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checked_type='te' # Valores 'te' y 'tc'\n",
    "used_direction='e' # Valores 's' y 'e'\n",
    "node_type='All' # Valores 'Intra', 'Inter', 'All'\n",
    "normality='n'\n",
    "#Values 'n' (normalizar), 'l' (logaritmico), 'm' (sin modificaciones), 'r' (Comparar respecto al primero)\n",
    "\n",
    "ylim_zero = True\n",
    "\n",
    "var_aux, grouped_aux, handles, used_labels, title = get_types_iker(checked_type, used_direction, node_type, normality)\n",
    "array_aux, title_y, v_lines, h_line = obtain_arrays_iker(grouped_aux, var_aux, used_direction, normality)\n",
    "graphic_iker(array_aux, title, title_y, \"(NP, NC)\", handles, used_labels, v_lines, ylim_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    dfG_aux=dfG.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    dfG_aux=dfG.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "vOrMult = list(dfG_aux.query('Cst == 0 and Css == 0')['TE'])\n",
    "vOrSingle = list(dfG_aux.query('Cst == 0 and Css == 1')['TE'])\n",
    "vOrPthMult = list(dfG_aux.query('Cst == 1 and Css == 0')['TE'])\n",
    "vOrPthSingle = list(dfG_aux.query('Cst == 1 and Css == 1')['TE'])\n",
    "\n",
    "vMergeMult = list(dfG_aux.query('Cst == 2 and Css == 0')['TE'])\n",
    "vMergeSingle = list(dfG_aux.query('Cst == 2 and Css == 1')['TE'])\n",
    "vMergePthMult = list(dfG_aux.query('Cst == 3 and Css == 0')['TE'])\n",
    "vMergePthSingle = list(dfG_aux.query('Cst == 3 and Css == 1')['TE'])\n",
    "\n",
    "vOrMult2 = list(dfM_aux.query('Cst == 0 and Css == 0')['TC'])\n",
    "vOrSingle2 = list(dfM_aux.query('Cst == 0 and Css == 1')['TC'])\n",
    "vOrPthMult2 = list(dfM_aux.query('Cst == 1 and Css == 0')['TC'])\n",
    "vOrPthSingle2 = list(dfM_aux.query('Cst == 1 and Css == 1')['TC'])\n",
    "\n",
    "vMergeMult2 = list(dfM_aux.query('Cst == 2 and Css == 0')['TC'])\n",
    "vMergeSingle2 = list(dfM_aux.query('Cst == 2 and Css == 1')['TC'])\n",
    "vMergePthMult2 = list(dfM_aux.query('Cst == 3 and Css == 0')['TC'])\n",
    "vMergePthSingle2 = list(dfM_aux.query('Cst == 3 and Css == 1')['TC'])\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "ax.scatter(vOrMult,vOrMult2, color='green')\n",
    "ax.scatter(vOrSingle,vOrSingle2,  color='springgreen')\n",
    "ax.scatter(vOrPthMult,vOrPthMult2, color='blue')\n",
    "ax.scatter(vOrPthSingle,vOrPthSingle2,color='darkblue')\n",
    "\n",
    "ax.scatter(vMergeMult,vMergeMult2, color='red')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergeSingle,vMergeSingle2,color='darkred')\n",
    "ax.scatter(vMergePthMult,vMergePthMult2, color='yellow')\n",
    "if used_direction=='e':\n",
    "    ax.scatter(vMergePthSingle,vMergePthSingle2,color='olive')\n",
    "\n",
    "ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "ax.set_xlabel(\"Time EX (s)\", fontsize=20)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "plt.legend(handles=handles, loc='upper right', fontsize=21,ncol=2)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Dispersion_Spawn_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "valores1 = [0]*10\n",
    "valores2 = [0.2]*10\n",
    "valores3 = [0.4]*10\n",
    "valores4 = [0.6]*10\n",
    "valores5 = [0.8]*10\n",
    "valores6 = [1]*10\n",
    "valores7 = [1.2]*10\n",
    "valores8 = [1.4]*10\n",
    "\n",
    "for np_aux in processes:\n",
    "    for ns_aux in processes:\n",
    "        if np_aux != ns_aux:\n",
    "            if np_aux > ns_aux:\n",
    "                used_labels=labelsShrink\n",
    "                name_fig=\"Shrink\"\n",
    "                handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "            else:\n",
    "                used_labels=labelsExpand\n",
    "                name_fig=\"Expand\"\n",
    "                handles=[OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "                \n",
    "            dfM_aux = dfM.query('NP == @np_aux and NS == @ns_aux')\n",
    "            vOrMult2 = list(dfM_aux.query('Cst == 0 and Css == 0')['TC'])\n",
    "            vOrSingle2 = list(dfM_aux.query('Cst == 0 and Css == 1')['TC'])\n",
    "            vOrPthMult2 = list(dfM_aux.query('Cst == 1 and Css == 0')['TC'])\n",
    "            vOrPthSingle2 = list(dfM_aux.query('Cst == 1 and Css == 1')['TC'])\n",
    "\n",
    "            vMergeMult2 = list(dfM_aux.query('Cst == 2 and Css == 0')['TC'])\n",
    "            vMergeSingle2 = list(dfM_aux.query('Cst == 2 and Css == 1')['TC'])\n",
    "            vMergePthMult2 = list(dfM_aux.query('Cst == 3 and Css == 0')['TC'])\n",
    "            vMergePthSingle2 = list(dfM_aux.query('Cst == 3 and Css == 1')['TC'])\n",
    "\n",
    "            f=plt.figure(figsize=(16, 8))\n",
    "            ax=f.add_subplot(111)\n",
    "\n",
    "            ax.scatter(vOrMult2,valores1, color='green')\n",
    "            ax.scatter(vOrSingle2,valores2,  color='springgreen')\n",
    "            ax.scatter(vOrPthMult2,valores3, color='blue')\n",
    "            ax.scatter(vOrPthSingle2,valores4,color='darkblue')\n",
    "\n",
    "            ax.scatter(vMergeMult2,valores5, color='red')\n",
    "            if np_aux < ns_aux:\n",
    "                ax.scatter(vMergeSingle2,valores6,color='darkred')\n",
    "            ax.scatter(vMergePthMult2,valores7, color='yellow')\n",
    "            if np_aux < ns_aux:\n",
    "                ax.scatter(vMergePthSingle2,valores8,color='olive')\n",
    "\n",
    "            ax.set_xlabel(\"Time TC(s)\", fontsize=16)\n",
    "            ax.set_ylabel(\"-\", fontsize=16)\n",
    "#plt.xticks(x, used_labels, rotation=90)\n",
    "            plt.legend(handles=handles, loc='upper right', fontsize=14,ncol=2)\n",
    "    \n",
    "            ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "            ax.tick_params(axis='both', which='minor', labelsize=18)\n",
    "    \n",
    "            f.tight_layout()\n",
    "            f.savefig(\"Images/Spawn/Dispersion/Dispersion_Spawn_\"+name_fig+\"_\"+str(np_aux)+\"_\"+str(ns_aux)+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_direction='e'\n",
    "test_parameter='TA'\n",
    "#TS es merge y TA connect para tests solo spawn\n",
    "    \n",
    "if used_direction=='s':\n",
    "    dfM_aux=dfM.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergePthMult_patch]\n",
    "elif used_direction=='e':\n",
    "    dfM_aux=dfM.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    handles=[OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]\n",
    "# < Expand\n",
    "# > Shrink\n",
    "\n",
    "dfM_aux = dfM_aux.copy()\n",
    "#dfM_aux['NTotal'] = dfM_aux['NP'] + dfM_aux['NS']\n",
    "dfM_aux['NTotal'] = dfM_aux['NS']\n",
    "\n",
    "#x = np.arange(len(used_labels))\n",
    "for cst_aux in [0,1,2,3]:\n",
    "    for css_aux in [0,1]:\n",
    "        f=plt.figure(figsize=(20, 12))\n",
    "        ax=f.add_subplot(111)\n",
    "        \n",
    "        #sns.boxplot(y=test_parameter, x=\"NS\", hue=\"NP\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        sns.boxplot(y=test_parameter, x=\"NTotal\", data = dfM_aux[(dfM_aux.Cst == cst_aux)][(dfM_aux.Css == css_aux)], orient=\"v\", ax=ax)\n",
    "        \n",
    "        # Anyade en el plot el numero de iteraciones - Por hacer TODO\n",
    "        # https://stackoverflow.com/questions/45475962/labeling-boxplot-with-median-values\n",
    "        #medians_aux = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median()\n",
    "        #m1 = dfM[(dfM.Cst == cst_aux)][(dfM.Css == css_aux)].groupby(['NS','NP'])['TC'].median().values\n",
    "        #mL1 = [str(np.ceil(s)) for s in m1]\n",
    "\n",
    "        #ind = 0\n",
    "        #for tick in range(len(ax.get_xticklabels())):\n",
    "        #    ax.text(tick-.2, m1[ind+1]+1, mL1[ind+1],  horizontalalignment='center',  color='w', weight='semibold')\n",
    "        #    ax.text(tick+.2, m1[ind]+1, mL1[ind], horizontalalignment='center', color='w', weight='semibold')\n",
    "        #    ind += 2 \n",
    "        \n",
    "        ax.set_ylabel(\"Time TC(s)\", fontsize=20)\n",
    "        ax.set_xlabel(\"NC\", fontsize=20)\n",
    "        plt.legend(loc='upper left', fontsize=21, title = \"NP\")\n",
    "        ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "        ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "        \n",
    "        ax.axvline((.5), color='black')\n",
    "        ax.axvline((1.5), color='black')\n",
    "        ax.axvline((2.5), color='black')\n",
    "        ax.axvline((3.5), color='black')\n",
    "        ax.axvline((4.5), color='black')\n",
    "        \n",
    "        f.tight_layout()\n",
    "        f.savefig(\"Images/Spawn/Boxplot_\"+name_fig+\"_\"+test_parameter+\"_\"+labelsMethods[cst_aux*2 + css_aux]+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "test_parameter='alpha' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha_A'\n",
    "    name_legend = \"Values of α\"\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega_A'\n",
    "    name_legend = \"Values of ω\"\n",
    "df_aux=grouped_aggM\n",
    "if used_direction=='s':\n",
    "    df_aux=df_aux.query('NP > NS')\n",
    "    used_labels=labelsShrinkOrdered\n",
    "    name_fig= name_fig+\"Shrink\"\n",
    "elif used_direction=='e':\n",
    "    df_aux=df_aux.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig= name_fig+\"Expand\"\n",
    "elif used_direction=='a':\n",
    "    df_aux=df_aux\n",
    "    used_labels=labels\n",
    "    name_fig= name_fig+\"All\"    \n",
    "\n",
    "x = np.arange(len(used_labels))\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "plt.xticks(x, used_labels,rotation=90)\n",
    "ax.tick_params(axis='both', which='major', labelsize=36)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=36)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    df_aux2 = df_aux.query('Cst == @cst_aux')\n",
    "    for css_aux in [0,1]:\n",
    "        if cst_aux == 3 and css_aux == 1 and used_direction == 's':\n",
    "            continue\n",
    "        array_aux = df_aux2.query('Css == @css_aux')\n",
    "        if used_direction=='s':\n",
    "            array_aux = array_aux.sort_values(by=['NS'])\n",
    "        array_aux = array_aux[real_parameter].values\n",
    "        style_aux = cst_aux*2 + css_aux\n",
    "        ax.plot(x, array_aux, color=colors_spawn[style_aux], linestyle=linestyle_spawn[style_aux%4], \\\n",
    "                marker=markers_spawn[style_aux], markersize=18, label=labelsMethods[style_aux])\n",
    "        \n",
    "ax.set_ylim(0,3.2)\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=1)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "test_parameter='alpha' #Valores son \"alpha\" o \"omega\"\n",
    "    \n",
    "if used_direction=='s':\n",
    "    df_aux=grouped_aggM.query('NP > NS')\n",
    "    used_labels=labelsShrink\n",
    "    name_fig=\"Shrink\"\n",
    "    np_aux = [10, 20,20, 40,40,40, 80,80,80,80, 120,120,120,120,120]\n",
    "    nc_aux = [1,  1,10,  1,10,20,  1,10,20,40,  1,10,20,40,80]\n",
    "elif used_direction=='e':\n",
    "    df_aux=grouped_aggM.query('NP < NS')\n",
    "    used_labels=labelsExpand\n",
    "    name_fig=\"Expand\"\n",
    "    np_aux = [1,1,1,1,1,        10,10,10,10,  20,20,20,  40,40,  80 ]\n",
    "    nc_aux = [10,20,40,80,120,  20,40,80,120, 40,80,120, 80,120, 120]\n",
    "elif used_direction=='a':\n",
    "    df_aux=grouped_aggM\n",
    "    used_labels=labels\n",
    "    name_fig=\"All\"\n",
    "    np_aux = [1,1,1,1,1,        10,10,10,10,10, 20,20,20,20,20, 40,40,40,40,40, 80,80,80,80,80, 120,120,120,120,120]\n",
    "    nc_aux = [10,20,40,80,120,  1,20,40,80,120, 1,10,40,80,120, 1,10,20,80,120, 1,10,20,40,120, 1,10,20,40,80]\n",
    "    \n",
    "x = np.arange(len(used_labels))\n",
    "handles = []\n",
    "\n",
    "f=plt.figure(figsize=(20, 12))\n",
    "#ax=f.add_subplot(111)\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.azim = -60\n",
    "ax.dist = 10\n",
    "ax.elev = 10\n",
    "ax.set_xlabel(\"NP\", fontsize=20)\n",
    "ax.set_ylabel(\"NC\", fontsize=20)\n",
    "ax.set_zlabel(\"Alpha\", fontsize=20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=22)\n",
    "\n",
    "for cst_aux in [1,3]:\n",
    "    df_aux2 = df_aux.query('Cst == @cst_aux')\n",
    "    for css_aux in [0,1]:\n",
    "        array_aux = df_aux2.query('Css == @css_aux')['alpha'].values\n",
    "        ax.plot3D(np_aux, nc_aux, array_aux, colors_spawn[cst_aux*2 + css_aux])\n",
    "      \n",
    "        handles.append(handles_spawn[cst_aux*2 + css_aux])\n",
    "        \n",
    "#ax.set_zlim(0,4)\n",
    "plt.legend(handles=handles, loc='best', fontsize=20,ncol=2,framealpha=1)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/3dPlot_\"+name_fig+'_'+test_parameter+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df, tipo):\n",
    "    normality=[True] * (len(processes) * (len(processes)-1))\n",
    "    total=0\n",
    "    i=-1\n",
    "    #Comprobar para cada configuración si se sigue una distribución normal/gaussiana\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                i+=1\n",
    "                for cst_aux in [0,1,2,3]:\n",
    "                    for css_aux in [0,1]:\n",
    "                        df_aux = df.query('NP == @np_aux and NS == @ns_aux and Cst == @cst_aux and Css == @css_aux')\n",
    "                        dataList = list(df_aux[tipo])\n",
    "                        st,p = stats.shapiro(dataList) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                        if p < p_value:\n",
    "                            normality[i]=False\n",
    "                            total+=1\n",
    "            \n",
    "    \n",
    "    print(\"Se sigue una distribución guassiana: \" + str(normality) + \"\\nUn total de: \" + str(total) + \" no siguen una gaussiana\")\n",
    "    return normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_stat_difference(dataLists, parametric):\n",
    "    if parametric:\n",
    "        st,p=stats.f_oneway(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    else:\n",
    "        st,p=stats.kruskal(dataLists[0],dataLists[1],dataLists[2],dataLists[3],dataLists[4],dataLists[5],dataLists[6],dataLists[7])\n",
    "    if p > p_value: # Si son iguales, no hay que hacer nada más\n",
    "        return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_global_posthoc(dataLists, parametric):\n",
    "    data_stats=[]\n",
    "    ini=0\n",
    "    end=len(labelsMethods)\n",
    "    if parametric:\n",
    "        df_aux = sp.posthoc_ttest(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.mean(dataLists[i]))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medias diferentes\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    else:\n",
    "        df_aux = sp.posthoc_conover(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.median(dataLists[i]))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < p_value: # Medianas diferentes\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    #print(df_Res)\n",
    "    #print(df_aux)\n",
    "    #print(data_stats)\n",
    "    return df_Res, data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aquellos grupos que tengán valores por encima del límite no se considerarán\n",
    "# Con sumar a si mismos su valor actual estarán fuera\n",
    "def check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary):\n",
    "    index_aux = 0\n",
    "    for cst_aux in [0,2]: # Primero los grupos síncronos\n",
    "        for css_aux in [0,1]:\n",
    "            if cst_aux == 2 and css_aux == 1 and np_aux > ns_aux: # Arreglo para coger bien el tiempo en Merge Single Shrink\n",
    "                index_aux = 1\n",
    "            tc_val = grouped_aggM.loc[('2,2',0, cst_aux, css_aux - index_aux, np_aux,ns_aux), 'TC_A']\n",
    "            if tc_val > tc_boundary:\n",
    "                data_stats[cst_aux*2 + css_aux]+=data_stats[cst_aux*2 + css_aux]\n",
    "    index_aux = 0\n",
    "    for cst_aux in [1,3]: # Segundo se comprueban los asíncronos\n",
    "        for css_aux in [0,1]:\n",
    "            if cst_aux == 3 and css_aux == 1 and np_aux > ns_aux: # Arreglo para coger bien el tiempo en Merge Single Shrink\n",
    "                index_aux = 1\n",
    "            tc_val = grouped_aggM.loc[('2,2',0, cst_aux, css_aux - index_aux, np_aux,ns_aux), 'TH']\n",
    "            if tc_val > tc_boundary:\n",
    "                data_stats[cst_aux*2 + css_aux]+=data_stats[cst_aux*2 + css_aux]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric):\n",
    "    best = 0\n",
    "    otherBest=[]\n",
    "    \n",
    "    # TODO Descomentar tras anyadir RMS perspective en results_with_st\n",
    "    #if rms_boundary != 0: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "    #    check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary) \n",
    "        \n",
    "    indexes = np.argsort(data_stats)\n",
    "    best = -1\n",
    "    i = 0\n",
    "    while best == -1:\n",
    "        index = indexes[i]\n",
    "        if not (shrink and (index == 5 or index == 7)): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            #if rms_boundary == 0 or data_stats[index] <= tc_boundary:\n",
    "            best = index\n",
    "        i+=1\n",
    "    otherBest=[]\n",
    "    for index in range(len(labelsMethods)): # Para cada metodo exceptuando best\n",
    "        if not (shrink and (index == 5 or index == 7)): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            if index != best and not df_Res.iat[best,index]: #Medias/Medianas iguales\n",
    "                #if data_stats[index] <= tc_boundary:\n",
    "                otherBest.append(index)\n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux) +\" \"+ str(parametric)+\" Mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    \n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perc_differences(dataLists, np_aux, ns_aux, shrink, rms_boundary, tc_boundary):\n",
    "    data_stats = []\n",
    "    ini=0\n",
    "    end=len(labelsMethods)\n",
    "    for i in range(ini,end):\n",
    "        data_stats.append(np.median(dataLists[i]))\n",
    "        \n",
    "    if rms_boundary != 0: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "        check_groups_boundaries(data_stats, np_aux, ns_aux, tc_boundary) \n",
    "    indexes = np.argsort(data_stats)\n",
    "    \n",
    "    best = -1\n",
    "    bestMax = -1\n",
    "    otherBest=[]\n",
    "    for index in indexes: # Para cada metodo -- Empezando por el más bajo en media/mediana\n",
    "        if shrink and (index == 5 or index == 7): # Las opciones Merge single(5) y Merge single - Pthreads(7) no se utilizan al reducir\n",
    "            continue\n",
    "        \n",
    "        if best == -1:\n",
    "            best = index\n",
    "            bestMax = data_stats[best] * 0.05 + data_stats[best]\n",
    "        elif data_stats[index] <= bestMax: # Medias/Medianas diferentes && Media/Medianas i < Media/Mediana best\n",
    "            otherBest.append(index)\n",
    "                \n",
    "        \n",
    "    stringV=\"\"\n",
    "    for i in otherBest:\n",
    "        stringV+=labelsMethods[i]+\", \"\n",
    "    print(\"Redimensión \" + str(np_aux) + \"/\" + str(ns_aux)+\" Mejores: \" + labelsMethods[best]+\", \" + stringV)\n",
    "    otherBest.insert(0,best)\n",
    "    \n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Obtiene \n",
    "def check_groups_stats(dataLists, np_aux, ns_aux, shrink, parametric):\n",
    "    global_difference=compute_global_stat_difference(dataLists, parametric)\n",
    "    if not global_difference:\n",
    "        print(\"Configuración: \" + str(np_aux) + \"/\" + str(ns_aux) + \" tiene valores iguales\")\n",
    "        return\n",
    "    \n",
    "    df_Res,data_stats=compute_global_posthoc(dataLists,parametric)\n",
    "    return get_stat_differences(df_Res, data_stats, np_aux, ns_aux, shrink, parametric)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_with_st(tipo, data_aux):\n",
    "    normality=check_normality(data_aux,tipo)\n",
    "    if False in normality:\n",
    "        normality = False\n",
    "    else:\n",
    "        normality = True\n",
    "    \n",
    "    \n",
    "    results = []\n",
    "    shrink = False\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "                dataLists=[]\n",
    "                if np_aux > ns_aux:\n",
    "                    shrink = True\n",
    "                else:\n",
    "                    shrink = False\n",
    "                #normality=True\n",
    "                for cst_aux in [0,1,2,3]:\n",
    "                    for css_aux in [0,1]:\n",
    "                        dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                        lista_aux = list(dataSet_aux[tipo])\n",
    "                        dataLists.append(lista_aux)\n",
    "                        #Si permito el shaphiro, acabare comparando manzanas y naranjas\n",
    "                        # si hay distribuciones normales y no normales\n",
    "                        #st,p = stats.shapiro(lista_aux) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                        #if p < p_value:\n",
    "                        #normality=False\n",
    "\n",
    "                aux_data = check_groups_stats(dataLists, np_aux, ns_aux, shrink, normality)\n",
    "                results.append(aux_data)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_with_perc(tipo, data_aux, rms_boundary=0):\n",
    "    results = []\n",
    "    shrink = False\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                dataSet = data_aux.query('NP == @np_aux and NS == @ns_aux')\n",
    "                dataLists=[]\n",
    "                if np_aux > ns_aux:\n",
    "                    shrink = True\n",
    "                else:\n",
    "                    shrink = False\n",
    "                \n",
    "                tc_boundary = dfM.query('NP == @np_aux and NS == @ns_aux')['TC'].max()\n",
    "                if rms_boundary == 0:\n",
    "                    for cst_aux in [0,1,2,3]:\n",
    "                        for css_aux in [0,1]:\n",
    "                            dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                            lista_aux = list(dataSet_aux[tipo])\n",
    "                            dataLists.append(lista_aux)\n",
    "                else:\n",
    "                    boundaries = []\n",
    "                    for cst_aux in [0,1,2,3]:\n",
    "                        for css_aux in [0,1]:\n",
    "                            dataSet_aux = dataSet.query('Cst == @cst_aux and Css == @css_aux')\n",
    "                            lista_aux = list(dataSet_aux[tipo])\n",
    "                            dataLists.append(lista_aux)\n",
    "                            \n",
    "                            if cst_aux == 0 or cst_aux == 2:\n",
    "                                if cst_aux == 2 and css_aux == 1  and (np_aux > ns_aux):\n",
    "                                    new_boundary = tc_boundary\n",
    "                                else:\n",
    "                                    new_boundary = grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux), 'TC_A']\n",
    "                            else:\n",
    "                                if cst_aux == 3 and css_aux == 1 and (np_aux > ns_aux):\n",
    "                                    new_boundary = tc_boundary\n",
    "                                else:\n",
    "                                    new_boundary = grouped_aggM.loc[('2,2',0, cst_aux, css_aux, np_aux,ns_aux), 'TH']\n",
    "                            boundaries.append(new_boundary)\n",
    "                    tc_boundary = min(boundaries)\n",
    "                    tc_boundary = tc_boundary + tc_boundary*rms_boundary\n",
    "\n",
    "                aux_data = get_perc_differences(dataLists, np_aux, ns_aux, shrink, rms_boundary, tc_boundary)\n",
    "                results.append(aux_data)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Redimensión 1/10 Mejores: Merge single, Merge, \n",
      "Redimensión 1/20 Mejores: Merge single, Merge, \n",
      "Redimensión 1/40 Mejores: Baseline, Merge single, Merge, \n",
      "Redimensión 1/80 Mejores: Baseline, Merge, Merge single, \n",
      "Redimensión 1/120 Mejores: Baseline, Merge single, \n",
      "Redimensión 10/1 Mejores: Merge, \n",
      "Redimensión 10/20 Mejores: Merge, Merge single, \n",
      "Redimensión 10/40 Mejores: Merge, Merge single, \n",
      "Redimensión 10/80 Mejores: Merge, \n",
      "Redimensión 10/120 Mejores: Merge, Merge single, \n",
      "Redimensión 20/1 Mejores: Merge, \n",
      "Redimensión 20/10 Mejores: Merge, \n",
      "Redimensión 20/40 Mejores: Merge single, Merge, \n",
      "Redimensión 20/80 Mejores: Merge, \n",
      "Redimensión 20/120 Mejores: Merge, \n",
      "Redimensión 40/1 Mejores: Merge, \n",
      "Redimensión 40/10 Mejores: Merge, \n",
      "Redimensión 40/20 Mejores: Merge, \n",
      "Redimensión 40/80 Mejores: Merge, \n",
      "Redimensión 40/120 Mejores: Merge single, \n",
      "Redimensión 80/1 Mejores: Merge, \n",
      "Redimensión 80/10 Mejores: Merge, \n",
      "Redimensión 80/20 Mejores: Merge, \n",
      "Redimensión 80/40 Mejores: Merge, \n",
      "Redimensión 80/120 Mejores: Merge, Merge single, \n",
      "Redimensión 120/1 Mejores: Merge - Asynchronous, \n",
      "Redimensión 120/10 Mejores: Merge, \n",
      "Redimensión 120/20 Mejores: Merge, \n",
      "Redimensión 120/40 Mejores: Merge, \n",
      "Redimensión 120/80 Mejores: Merge, \n",
      "[[5, 4], [5, 4], [0, 5, 4], [0, 4, 5], [0, 5], [4], [4, 5], [4, 5], [4], [4, 5], [4], [4], [5, 4], [4], [4], [4], [4], [4], [4], [5], [4], [4], [4], [4], [4, 5], [6], [4], [4], [4], [4]]\n"
     ]
    }
   ],
   "source": [
    "checked_type='te'\n",
    "use_perc = True\n",
    "rms_boundary=0.1 # Poner a 0 para perspectiva de app. Valor >0 y <1 para perspectiva de RMS\n",
    "if checked_type=='te':\n",
    "    tipo=\"TE\"\n",
    "    data_aux=dfG\n",
    "elif checked_type=='tc':\n",
    "    tipo=\"TC\"\n",
    "    data_aux=dfM\n",
    "    \n",
    "if use_perc:\n",
    "    results = results_with_perc(tipo, data_aux, rms_boundary)\n",
    "else:\n",
    "    results = results_with_st(tipo, data_aux)\n",
    "\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  5  5  0  0  0]\n",
      " [ 4 -1  4  4  4  4]\n",
      " [ 4  4 -1  5  4  4]\n",
      " [ 4  4  4 -1  4  5]\n",
      " [ 4  4  4  4 -1  4]\n",
      " [ 6  4  4  4  4  8]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor de los valores\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "unique, counts = np.unique(aux_array, return_counts=True)\n",
    "aux_dict = dict(zip(unique, counts))\n",
    "aux_keys=list(aux_dict.keys())\n",
    "aux_values=list(aux_dict.values())\n",
    "aux_ordered_index=list(reversed(list(np.argsort(aux_values))))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n",
    "\n",
    "if use_perc:\n",
    "    for i in range(len(processes)):\n",
    "        for j in range(len(processes)):\n",
    "            if i==j:\n",
    "                heatmap[i][j]=-1\n",
    "                used_aux+=1\n",
    "            else:\n",
    "                results_index = i*len(processes) +j-used_aux\n",
    "                heatmap[i][j] = results[results_index][0]\n",
    "else:\n",
    "    for i in range(len(processes)):\n",
    "        for j in range(len(processes)):\n",
    "            if i==j:\n",
    "                heatmap[i][j]=-1\n",
    "                used_aux+=1\n",
    "            else:  \n",
    "                results_index = i*len(processes) +j-used_aux\n",
    "                for index in aux_ordered_index:\n",
    "                    if aux_keys[index] in results[results_index]:\n",
    "                        heatmap[i][j]=aux_keys[index]\n",
    "                        break\n",
    "heatmap[len(processes)-1][len(processes)-1]=8\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adapta results a una cadena asegurando que cada cadena no se sale de su celda\n",
    "results_str = []\n",
    "max_counts = 1\n",
    "for i in range(len(results)):\n",
    "    results_str.append(list())\n",
    "    count = len(results[i])\n",
    "    new_data = str(results[i]).replace('[','').replace(']','')\n",
    "    remainder = count%3\n",
    "    \n",
    "    if count <= 3:\n",
    "        results_str[i].append(new_data)\n",
    "    else:\n",
    "        if remainder == 0:\n",
    "            results_str[i].append(new_data[0:8])\n",
    "            results_str[i].append(new_data[9:])\n",
    "        else:\n",
    "            index = 1 + (remainder -1)*3\n",
    "            results_str[i].append(new_data[0:index+1])\n",
    "            results_str[i].append(new_data[index+2:])\n",
    "        \n",
    "    if count > max_counts:\n",
    "        if count > 3:\n",
    "            aux_value = results_str[i].pop()[0:1]\n",
    "        results_str[i][0] = results_str[i][0][0:max_counts*3-2]\n",
    "        if remainder == 1 and max_counts > 1:\n",
    "            results_str[i][0] = results_str[i][0] + ' ' + aux_value\n",
    "#print(results_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2692/2593541567.py:36: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_2692/2593541567.py:37: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+processes, fontsize=36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea un heatmap teniendo en cuenta los colores anteriores\n",
    "f=plt.figure(figsize=(24, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "myColors = (colors.to_rgba(\"white\"),colors.to_rgba(\"green\"), colors.to_rgba(\"springgreen\"),colors.to_rgba(\"blue\"),colors.to_rgba(\"darkblue\"),\n",
    "            colors.to_rgba(\"red\"),colors.to_rgba(\"darkred\"),colors.to_rgba(\"darkgoldenrod\"),colors.to_rgba(\"olive\"),colors.to_rgba(\"white\"))\n",
    "cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))\n",
    "\n",
    "im = ax.imshow(heatmap,cmap=cmap,interpolation='nearest')\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "used_aux=0\n",
    "for i in range(len(processes)):\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            aux_color=\"white\"\n",
    "            if heatmap[i, j] == 1: # El 1 puede necesitar texto en negro\n",
    "                aux_color=\"black\"\n",
    "            results_index = i*len(processes) +j-used_aux\n",
    "            if len(results_str[results_index]) == 1:\n",
    "                text = results_str[results_index][0]\n",
    "                ax.text(j, i, text, ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n",
    "            else:\n",
    "                add_aux = 0.33\n",
    "                for line in range(len(results_str[results_index])):\n",
    "                    i_range = i - 0.5 + add_aux\n",
    "                    ax.text(j, i_range, results_str[results_index][line],\n",
    "                            ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n",
    "                    add_aux+=0.33\n",
    "        else:\n",
    "            used_aux+=1\n",
    "\n",
    "ax.set_ylabel(\"NP\", fontsize=36)\n",
    "ax.set_xlabel(\"NC\", fontsize=36)\n",
    "\n",
    "ax.set_xticklabels(['']+processes, fontsize=36)\n",
    "ax.set_yticklabels(['']+processes, fontsize=36)\n",
    "\n",
    "#\n",
    "labelsMethods_aux = ['Baseline (0)', 'Baseline single (1)','Baseline - Asynchronous (2)','Baseline single - Asynchronous (3)',\n",
    "                 'Merge (4)','Merge single (5)','Merge - Asynchronous (6)','Merge single - Asynchronous (7)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "colorbar.set_ticks([0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TE\n",
    "#colorbar.set_ticks([-2.55, 0.35, 1.25, 2.15, 3.05, 3.95, 4.85, 5.75, 6.65]) #TC\n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=32)\n",
    "#\n",
    "\n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/Spawn/Heatmap_\"+tipo+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.986845, 3.995615, 3.995615, 3.995615, 3.995615, 3.995615]\n",
      "[0, 0.0009824999999999999, 0.4770405, 0.766185, 0.8609199999999999, 0.890894]\n",
      "[0, 79.98284, 3.99641, 1.99956, 0.9997100000000001, 0.6615499999999999]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux_labels = ['(10)', '(10,1)', '(10,20)', '(10,40)','(10,80)','(10,120)']\n",
    "aux_values = [1, 20, 40, 80, 120]\n",
    "title_y=\"Execution time(s)\"\n",
    "title_x=\"(NP,NC)\"\n",
    "maxiters_aux = 30\n",
    "iters_aux1 = 10\n",
    "iters_aux2 = 20\n",
    "aggM_aux = grouped_aggM.query('NP == 10').query('Cst == 2').query('Css == 0')['TR'].values\n",
    "aux1 = grouped_aggL.query('NP == 10').query('Cst == 2').query('Css == 0').query('NS == 1')\n",
    "aux2 = grouped_aggL.query('NS == 10').query('Cst == 2').query('Css == 0')\n",
    "\n",
    "data_aux1 = [aux1['Ti'].values[0] * maxiters_aux]\n",
    "data_aux2 = [0]\n",
    "data_aux3 = [0]\n",
    "for i in range(len(aux_labels)-1):\n",
    "    data_aux1.append(aux1['Ti'].values[0] * iters_aux1)\n",
    "    data_aux2.append(aggM_aux[i])\n",
    "    aux_value = aux_values[i]\n",
    "    data_aux3.append(aux2.query('NP == @aux_value')['Ti'].values[0] * iters_aux2)\n",
    "print(data_aux1)\n",
    "print(data_aux2)\n",
    "print(data_aux3)\n",
    "sum_aux = np.add(data_aux1, data_aux2).tolist()\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(aux_labels))\n",
    "width = 0.5\n",
    "blue_Spatch = mpatches.Patch(facecolor='blue', label='Parents Time')\n",
    "orange_Spatch = mpatches.Patch(hatch='...',facecolor='orange', label='Resize Time')\n",
    "green_Spatch = mpatches.Patch(hatch='\\\\/',facecolor='green', label='Children Time')\n",
    "handles=[blue_Spatch,orange_Spatch,green_Spatch]\n",
    "\n",
    "ax.bar(x, data_aux1, width, color='blue')\n",
    "ax.bar(x, data_aux2, width, bottom=data_aux1,color='orange', hatch='...')\n",
    "ax.bar(x, data_aux3, width, bottom=sum_aux,color='green', hatch='\\\\/')\n",
    "\n",
    "ax.set_ylabel(title_y, fontsize=30)\n",
    "ax.set_xlabel(title_x, fontsize=28)\n",
    "plt.xticks(x, aux_labels, rotation=90)\n",
    "#plt.yscale(\"log\")\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "plt.legend(handles=handles, loc='upper left', fontsize=26)\n",
    "\n",
    "    \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/malleabilityTest.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "processes = [2,10,20,40,80,120,160,200]\n",
    "removed= []\n",
    "\n",
    "#Audik\n",
    "aux_real = [[171.737683, 171.721757, 171.712188, 172.025576, 171.824876, 172.195573, 172.085584, 172.184726, 171.713528, 172.086622, 172.066681, 172.964532, 172.229674, 171.916479, 171.984405, 172.127481, 171.669226, 171.739088, 171.974926, 171.751413], [68.331026, 68.713697, 68.206408, 67.885963, 68.693203, 68.568758, 68.54315, 68.511886, 68.390384, 68.5941, 68.464007, 68.369401, 68.422746, 68.535845, 68.503719, 68.404653, 68.792502, 68.477037, 68.512661, 68.559632], [40.694064, 40.989289, 40.742276, 41.025095, 40.863411, 41.005171, 40.705705, 42.076044, 40.49322, 40.933576, 40.632797, 41.346974, 42.414697, 39.96742, 40.927754, 39.910709, 39.778547, 40.712781, 41.478955, 40.403166], [30.04606, 29.085335, 27.345307, 30.238709, 34.168857, 29.744953, 27.631665, 28.170737, 27.113985, 27.96026, 29.121938, 29.229577, 27.341234, 27.349849, 29.133614, 27.205169, 30.273867, 28.97881, 30.29552, 29.045342], [23.08803, 23.585224, 23.425999, 22.946976, 25.210113, 23.013397, 23.421095, 22.66769, 24.621629, 23.740202, 24.069326, 24.87756, 24.931077, 24.022584, 23.596376, 22.101887, 22.086059, 23.988373, 22.905784, 22.300686], [20.578533, 21.146572, 23.113501, 22.796446, 27.166076, 21.821828, 21.016317, 23.468519, 19.655022, 23.826594, 21.723859, 24.282099, 24.850651, 26.058704, 21.709948, 20.57436, 21.496566, 22.860724, 25.819541, 23.12921], [25.043008, 24.306459, 22.952676, 23.921501, 23.581935, 24.324931, 27.242627, 22.308218, 24.807042, 26.235116, 22.731008, 21.579133, 21.154274, 23.548367, 27.395588, 23.154962, 23.773857, 26.417468, 23.717991, 25.39742], [24.616342, 24.039981, 24.206909, 24.654299, 28.106219, 26.687135, 23.869267, 24.097884, 25.629433, 27.700148, 25.425307, 26.413679, 25.972044, 25.791893, 25.866064, 24.27758, 25.591149, 26.614081, 26.93407, 29.430697]]\n",
    "aux_sint = [[171.134606, 171.096637, 171.138184, 171.13844, 171.098454, 171.162362, 171.082225, 171.079004, 171.114536, 171.227906, 171.071966, 171.130859, 171.104273, 171.07788, 171.072194, 171.069075, 171.140295, 171.174013, 171.25217, 171.117231], [67.571535, 67.520025, 68.079664, 66.738064, 67.532297, 68.069914, 68.139622, 68.190914, 68.243213, 67.492165, 68.112115, 68.059658, 68.160322, 68.085698, 67.525525, 68.187291, 68.02039, 68.176564, 67.980121, 68.312952], [42.473247, 45.53225, 43.409906, 42.42814, 42.118484, 41.443236, 42.156082, 42.256023, 42.111314, 41.603221, 42.184052, 40.928944, 42.07551, 40.908719, 44.262672, 43.524809, 43.537822, 39.367229, 42.1689, 42.298479], [], [26.457291, 24.824646, 33.71007, 25.375645, 25.186218, 26.786703, 26.456784, 23.513595, 30.993635, 24.907237, 24.459753, 30.953045, 25.399975, 27.167862, 27.861115, 27.324158, 26.498797, 29.3722, 26.955239, 20.790296], [26.147293, 27.675856, 25.173467, 25.416053, 25.663571, 25.885352, 26.732089, 26.314055, 28.788113, 23.810305, 26.504224, 25.912384, 27.372798, 29.319178, 28.562837, 23.406136, 27.48343, 28.364868, 28.199635, 26.765826], [30.542833, 36.02513, 29.552827, 29.877948, 35.743363, 29.331332, 31.847175, 34.500058, 26.578159, 32.713294, 34.322071, 29.951176, 32.658056, 34.987492, 28.456334, 34.068832, 30.607325, 29.413311, 30.532202, 30.318787], [32.60606, 25.885468, 33.127615, 33.713661, 31.726036, 31.823991, 32.649191, 30.652817, 30.046183, 37.428624, 29.665286, 38.247098, 33.328042, 35.135827, 34.260634, 28.044284, 36.702605, 33.427365, 32.09166, 28.789384]]\n",
    "\n",
    "#Queen\n",
    "#aux_real = [[640.313059, 643.368851, 644.812491, 642.399734, 642.226814, 642.35475, 643.609192, 643.139744, 643.61705, 643.132647, 643.070286, 641.406328, 643.896836, 643.121044, 643.905589, 643.581402, 643.184715, 642.431289, 643.143977, 641.346561], [152.345899, 151.53339, 149.128109, 151.998913, 149.090488, 151.955341, 151.659499, 149.203061, 150.377392, 152.248175, 151.157503, 150.235083, 151.598016, 148.938792, 150.996531, 152.080666, 152.054726, 152.084692, 149.608602, 152.129981], [101.902944, 104.00788, 111.700969, 104.470693, 107.641521, 109.437217, 105.494903, 105.986737, 112.172214, 107.554958, 105.88158, 103.885249, 104.048641, 109.519779, 104.72926, 106.655908, 112.435562, 109.019183, 109.924859, 106.986846], [85.844223, 90.53081, 92.977514, 86.023776, 89.785998, 91.638115, 88.630006, 95.970387, 88.875131, 86.355962, 90.481832, 91.073597, 97.963737, 84.313078, 95.048194, 95.72992, 89.552759, 89.888328, 91.38958, 87.145446], [83.436137, 83.105543, 85.827104, 87.823647, 78.886258, 76.341861, 80.18113, 79.869432, 79.663487, 84.819399, 81.543829, 76.885674, 78.460587, 78.630554, 87.104227, 77.991342, 77.524938, 86.127078, 74.564246, 81.977689], [85.21399, 78.378531, 84.526313, 84.21782, 84.658752, 77.547238, 81.649375, 82.462926, 81.282893, 88.414904, 86.387445, 78.367787, 85.032026, 75.91001, 81.139097, 84.108863, 82.793723, 80.279569, 81.509798, 74.677693], [85.845828, 93.088063, 85.805088, 85.299568, 87.980509, 83.421145, 77.985249, 81.953544, 80.678587, 89.825371, 89.70287, 87.75817, 78.578479, 81.567104, 86.481628, 81.899106, 87.496995, 81.568352, 87.55542, 79.566851], [96.258907, 82.779222, 94.729087, 87.904027, 82.401901, 84.358796, 81.886275, 84.079098, 100.73735, 89.685091, 86.394575, 80.389717, 81.828049, 86.734577, 82.226097, 85.730868, 92.968317, 84.245582, 91.952588, 86.288488]]\n",
    "aux_sint = [[641.085136, 641.141923, 640.777817, 641.301842, 640.976289, 641.020807, 640.8895, 641.161191, 641.071841, 641.100684, 641.182133, 641.304331, 641.05437, 641.227479, 641.203772, 641.015224, 640.991178, 641.13879, 641.230584, 640.926491], [147.558438, 151.115339, 150.933246, 151.395058, 151.546527, 150.531321, 150.06193, 150.472433, 150.003948, 149.91831, 150.517089, 150.229956, 150.597576, 150.082463, 150.86667, 150.090303, 150.422634, 150.671741, 150.672547, 150.238191], [108.25061, 111.097101, 104.213767, 110.52693, 112.378777, 119.460641, 112.216745, 104.715758, 108.586798, 118.500656, 111.702574, 107.967457, 110.703181, 110.870681, 113.971439, 114.412381, 108.385472, 108.404177, 107.221267, 107.396806], [96.21288, 88.36866, 85.354348, 87.616739, 89.807877, 90.15373, 88.666607, 91.701534, 89.501369, 95.262825, 87.00914, 96.041417, 94.04346, 84.264348, 86.212998, 86.943123, 90.104903, 89.290074, 104.344068, 88.445732], [79.012331, 76.963467, 93.635579, 79.515014, 82.320548, 74.996362, 78.712431, 72.23961, 77.006344, 90.117262, 79.983322, 85.569436, 88.489679, 75.017607, 84.355796, 80.785074, 83.582964, 75.955045, 74.408056, 76.856599], [86.036661, 90.114675, 81.199959, 92.82014, 91.197787, 84.41333, 82.947796, 77.121997, 86.190024, 78.995126, 81.440768, 84.931682, 84.696228, 88.9819, 67.965288, 90.338966, 79.17985, 82.231772, 84.687085, 75.87027], [94.179702, 98.994605, 93.155944, 102.084059, 90.722862, 102.024703, 97.968444, 101.484367, 85.187726, 87.207102, 86.122057, 76.514077, 95.983665, 100.865989, 98.479677, 105.413981, 106.241153, 106.36522, 83.973521, 80.865762], [98.877075, 86.054865, 109.090461, 100.612676, 90.638634, 83.918211, 100.890697, 98.085722, 95.14564, 100.055308, 93.566746, 111.359513, 107.407847, 101.431541, 112.982436, 100.120412, 95.134338, 96.016258, 91.694929, 93.917124]]\n",
    "\n",
    "\n",
    "#TEST Codigo sin captura de tiempos\n",
    "#Codigo sin captura\n",
    "#aux_sint = [[172.04165, 166.565937, 174.073495, 175.127555, 171.268598, 179.163764, 173.093602, 173.310969, 173.688159, 176.989948], [68.426733, 68.518644, 67.934562, 67.770617, 68.418071, 68.441444, 68.489923, 68.576862, 68.357891, 68.507931], [42.795763, 40.645969, 43.651983, 44.816286, 41.884911, 40.704261, 41.76031, 43.314722, 43.712624, 41.83065], [34.192324, 34.384576, 34.783733, 30.904571, 33.712345, 29.96747, 34.664818, 34.72399, 28.920618, 29.126176], [33.248177, 33.448689, 33.0446, 29.792059, 30.750121, 29.591303, 29.085716, 30.602103, 31.814472, 30.39379], [35.421251, 28.404227, 30.464738, 31.917447, 33.279842, 30.914243, 28.864047, 31.015124, 31.052509, 27.903691], [37.979388, 33.597368, 38.465519, 37.411409, 42.863337, 36.253646, 39.904695, 46.736134, 37.887813, 39.185104], []]\n",
    "#Codigo con captura\n",
    "#aux_real = [[172.23061, 172.274031, 172.224975, 172.199288, 172.142126, 172.133047, 172.262196, 172.206232, 172.197641, 172.201656], [67.729525, 68.280772, 68.348186, 68.354578, 68.405148, 68.548252, 68.404705, 68.461352, 68.347126, 68.37433], [48.270716, 43.616036, 42.091586, 41.417283, 42.089665, 41.92953, 42.563296, 41.799318, 40.910303, 45.403695], [34.600393, 31.826466, 32.053285, 30.025669, 30.139128, 32.118884, 35.913205, 33.58797, 31.433738, 30.869531], [36.761911, 27.334207, 32.128742, 27.013322, 24.008163, 29.872332, 28.812638, 31.073714, 30.33739, 30.352381], [29.629487, 25.729118, 29.081673, 29.723521, 29.247765, 35.299698, 31.711029, 31.346167, 30.085182, 27.99439], [42.031709, 41.964477, 33.98214, 37.872788, 34.108217, 37.903346, 41.666486, 37.516745, 39.800734, 35.537407], []]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para 2 los grupos son ==True== Normal-->True<-- Varianza-->False<--\n",
      "Para 10 los grupos son ==True== Normal-->False<-- Varianza-->True<--\n",
      "Para 20 los grupos son ==True== Normal-->False<-- Varianza-->True<--\n",
      "Para 40 los grupos son ==True== Normal-->False<-- Varianza-->True<--\n",
      "Para 80 los grupos son ==True== Normal-->True<-- Varianza-->False<--\n",
      "Para 120 los grupos son ==True== Normal-->True<-- Varianza-->True<--\n",
      "Para 160 los grupos son ==True== Normal-->True<-- Varianza-->True<--\n"
     ]
    }
   ],
   "source": [
    "#TODO Guardar funcion como comparacion de dos datos\n",
    "normality = [True]*len(processes)\n",
    "variance = [True]*len(processes)\n",
    "results = [True]*len(processes)\n",
    "# Check for assumption of normality\n",
    "for i in range(len(processes)):\n",
    "    if(processes[i] in removed):\n",
    "        continue\n",
    "        \n",
    "    st,p = stats.shapiro(aux_real[i])\n",
    "    if p < p_value:\n",
    "        normality[i]=False\n",
    "        continue\n",
    "    st,p = stats.shapiro(aux_sint[i])\n",
    "    if p < p_value:\n",
    "        normality[i]=False\n",
    "\n",
    "# Check for assumption of homoestacity\n",
    "if True in normality: # Only needed for normal distributions\n",
    "    for i in range(len(processes)):\n",
    "        if(processes[i] in removed):\n",
    "            continue\n",
    "        st,p = stats.bartlett(aux_real[i],aux_sint[i])\n",
    "        if p < p_value:\n",
    "            variance[i] = False\n",
    "    \n",
    "# Check if groups are equal (True) or not (False)\n",
    "for i in range(len(processes)):\n",
    "    if(processes[i] in removed):\n",
    "        continue\n",
    "        \n",
    "    if normality[i]:\n",
    "        st,p = stats.ttest_ind(aux_real[i], aux_sint[i], equal_var=variance[i])\n",
    "    else:\n",
    "        st,p = stats.mannwhitneyu(aux_real[i], aux_sint[i])\n",
    "    if p < p_value:\n",
    "        results[i]=False\n",
    "\n",
    "for i in range(len(processes)):\n",
    "    if(processes[i] in removed):\n",
    "        continue\n",
    "    \n",
    "    print(\"Para \"+str(processes[i])+\" los grupos son ==\"+str(results[i])+\"== Normal-->\"+str(normality[i])+\"<-- Varianza-->\"+str(variance[i])+\"<--\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": [
    "processes = [2,10,20,40,80,120,160]\n",
    "aux_cores = 20\n",
    "title_y=\"Porcentual difference(%)\"\n",
    "title_x=\"Number of Nodes\"\n",
    "aux_labels = ['System_1 - Audik', 'System_1 - Queen', 'System_2 - Audik', 'System_2 - Queen', 'System_3 - Audik', 'System_3 - Queen']\n",
    "#aux_labels = ['System_1', 'System_2', 'System_3']\n",
    "data1 = [100.22, 100.82, 99.51, 101.39, 101.10, 100.20, 102.80]\n",
    "data2 = [99.88, 100.78, 99.18, 100.54, 102.30, 103.14, 101.42]\n",
    "data3 = [100.14, 100.37, 100.78, 102.55, 108.96, 110.18, 102.91]\n",
    "data4 = [100.06, 100.16, 101.13, 101.99, 102.44, 104.88, 102.65]\n",
    "data5 = [100.38, 101.55, 101.46, 103.42, 102.44, 101.02, 114.71]\n",
    "data6 = [100.03, 100.97, 101.01, 100.57, 101.28, 102.05, 101.09]\n",
    "\n",
    "\n",
    "data_x = [data1, data2, data3, data4, data5, data6]\n",
    "#data_x = [data2, data4, data6]\n",
    "style_aux=0\n",
    "\n",
    "nodes = []\n",
    "for i in range(len(processes)):\n",
    "    nodes.append(processes[i]/aux_cores)\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "for i in range(len(data_x)):\n",
    "    ax.plot(nodes, data_x[i], color=colors_spawn[i], linestyle=linestyle_spawn[i%4], \\\n",
    "                marker=markers_spawn[i], markersize=18, label=aux_labels[i])\n",
    "ax.axhline((100), color='black')\n",
    "\n",
    "ax.set_ylabel(title_y, fontsize=30)\n",
    "ax.set_xlabel(title_x, fontsize=28)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "plt.legend(loc='best', fontsize=26)\n",
    "\n",
    "    \n",
    "f.tight_layout()\n",
    "ax.set_ylim(90,120)\n",
    "f.savefig(\"Images/malleabilityTest.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": [
    "processes = [2,10,20,40,80,120,160]\n",
    "aux_cores = 20\n",
    "title_y=\"Porcentual difference(%)\"\n",
    "title_x=\"Number of Nodes\"\n",
    "aux_labels = ['System_1 - Audik', 'System_1 - Queen', 'System_2 - Audik', 'System_2 - Queen', 'System_3 - Audik', 'System_3 - Queen']\n",
    "data1 = [99.60, 100.38, 104.60, 111.05, 132.55, 131.33, 150.13]\n",
    "data2 = [99.57, 99.63, 101.22, 106.77, 112.37, 114.37, 125.83]\n",
    "data3 = [100.22, 100.47, 101.59, 105.68, 115.49, 118.51, 110.06]\n",
    "data4 = [100.32, 100.49, 100.67, 104.42, 112.88, 116.31, 116.04]\n",
    "data5 = [100.31, 102.19, 101.88, 108.02, 106.44, 104.87, 111.85]\n",
    "data5 = [99.85, 98.70, 97.80, 110.26, 109.10, 111.28, 107.41]\n",
    "\n",
    "\n",
    "\n",
    "data_x = [data1, data2, data3, data4, data5, data6]\n",
    "\n",
    "style_aux=0\n",
    "\n",
    "nodes = []\n",
    "for i in range(len(processes)):\n",
    "    nodes.append(processes[i]/aux_cores)\n",
    "\n",
    "f=plt.figure(figsize=(30, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "for i in range(len(data_x)):\n",
    "    ax.plot(nodes, data_x[i], color=colors_spawn[i], linestyle=linestyle_spawn[i%4], \\\n",
    "                marker=markers_spawn[i], markersize=18, label=aux_labels[i])\n",
    "ax.axhline((100), color='black')\n",
    "\n",
    "ax.set_ylabel(title_y, fontsize=30)\n",
    "ax.set_xlabel(title_x, fontsize=28)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=30)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=28)\n",
    "plt.legend(loc='best', fontsize=26)\n",
    "\n",
    "    \n",
    "f.tight_layout()\n",
    "ax.set_ylim(50,160)\n",
    "f.savefig(\"Images/malleabilityTest.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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