new_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",
    "\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\n",
    "\n",
    "from mpl_toolkits.mplot3d import axes3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllName=\"dataG.pkl\"\n",
    "ResizesName=\"dataM.pkl\"\n",
    "ItersName=\"dataL.pkl\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_cores=20\n",
    "repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "significance_value = 0.05\n",
    "processes = [2,10,20,40,80,120,160]\n",
    "\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)','(1,160)',\n",
    "            '(10,1)', '(10,20)',  '(10,40)', '(10,80)', '(10,120)','(10,160)',\n",
    "            '(20,1)', '(20,10)',  '(20,40)', '(20,80)', '(20,120)','(20,160)',\n",
    "            '(40,1)', '(40,10)',  '(40,20)', '(40,80)', '(40,120)','(40,160)',\n",
    "            '(80,1)', '(80,10)',  '(80,20)', '(80,40)', '(80,120)','(80,160)',\n",
    "            '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)','(120,160)',\n",
    "            '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n",
    "\n",
    "labelsExpand = ['(1,10)',   '(1,20)',   '(1,40)',  '(1,80)',  '(1,120)','(1,160)',\n",
    "            '(10,20)',  '(10,40)', '(10,80)', '(10,120)','(10,160)',\n",
    "            '(20,40)', '(20,80)', '(20,120)','(20,160)',\n",
    "            '(40,80)', '(40,120)','(40,160)',\n",
    "            '(80,120)','(80,160)',\n",
    "            '(120,160)']\n",
    "labelsShrink = ['(10,1)', \n",
    "            '(20,1)', '(20,10)',\n",
    "            '(40,1)', '(40,10)',  '(40,20)',\n",
    "            '(80,1)', '(80,10)',  '(80,20)', '(80,40)',\n",
    "            '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)',\n",
    "            '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n",
    "\n",
    "#                       WORST        BEST\n",
    "labels_dist = ['null', 'SpreadFit', 'CompactFit']\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",
    "    \n",
    "colors_m = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive','violet']\n",
    "linestyle_m = ['-', '--', '-.', ':']\n",
    "markers_m = ['.','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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfG = pd.read_pickle( AllName )\n",
    "\n",
    "dfG['ADR'] = round((dfG['ADR'] / dfG['DR']) * 100,1)\n",
    "dfG['SDR'] = round((dfG['SDR'] / dfG['DR']) * 100,1)\n",
    "       \n",
    "out_group = dfG.groupby(['Groups', 'ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_total']\n",
    "group = dfG.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','Groups'])['T_total']\n",
    "\n",
    "grouped_aggG = group.agg(['median'])\n",
    "grouped_aggG.rename(columns={'median':'T_total'}, inplace=True) \n",
    "\n",
    "out_grouped_G = out_group.agg(['median'])\n",
    "out_grouped_G.rename(columns={'median':'T_total'}, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19307/462116935.py:8: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  out_group = dfM.groupby(['NP','NC','ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','T_SR','T_AR']\n",
      "/tmp/ipykernel_19307/462116935.py:9: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  group = dfM.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','T_SR','T_AR']\n"
     ]
    }
   ],
   "source": [
    "dfM = pd.read_pickle( ResizesName )\n",
    "\n",
    "dfM['ADR'] = round((dfM['ADR'] / dfM['DR']) * 100,1)\n",
    "dfM['SDR'] = round((dfM['SDR'] / dfM['DR']) * 100,1)\n",
    "dfM['T_Redistribution'] = dfM['T_SR'] + dfM['T_AR']\n",
    "dfM['T_Malleability'] = dfM['T_spawn'] + dfM['T_Redistribution']\n",
    "       \n",
    "out_group = dfM.groupby(['NP','NC','ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','T_SR','T_AR']\n",
    "group = dfM.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_Malleability','T_Redistribution','T_spawn','T_spawn_real','T_SR','T_AR']\n",
    "\n",
    "grouped_aggM = group.agg(['median'])\n",
    "grouped_aggM.columns = grouped_aggM.columns.get_level_values(0)\n",
    "\n",
    "out_grouped_M = out_group.agg(['median'])\n",
    "out_grouped_M.columns = out_grouped_M.columns.get_level_values(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfL = pd.read_pickle( ItersName )\n",
    "\n",
    "dfL['ADR'] = round((dfL['ADR'] / dfL['DR']) * 100,1)\n",
    "dfL['SDR'] = round((dfL['SDR'] / dfL['DR']) * 100,1)\n",
    "dfL['ADR'].fillna(-1, inplace=True)\n",
    "dfL['SDR'].fillna(-1, inplace=True)\n",
    "dfL['DR'].fillna(-1, inplace=True)\n",
    "       \n",
    "aux_df = dfL[(dfL.Asynch_Iters == True)]\n",
    "group = aux_df.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_iter']\n",
    "grouped_aggLAsynch = group.agg(['median','count'])\n",
    "grouped_aggLAsynch.columns = grouped_aggLAsynch.columns.get_level_values(0)\n",
    "grouped_aggLAsynch['T_sum'] = grouped_aggLAsynch['count'] * grouped_aggLAsynch['median'] / repet\n",
    "grouped_aggLAsynch.rename(columns={'median':'T_iter'}, inplace=True) \n",
    "group = aux_df.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLAsynch['T_stages'] = aux_column\n",
    "\n",
    "aux_df = dfL[(dfL.Asynch_Iters == False)]\n",
    "group = aux_df.groupby('NP')['T_iter']\n",
    "grouped_aggLSynch = group.agg(['median'])\n",
    "grouped_aggLSynch.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLSynch['T_stages'] = aux_column\n",
    "\n",
    "aux_df2 = aux_df[(aux_df.Is_Dynamic == True)]\n",
    "group = aux_df2.groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_iter']\n",
    "grouped_aggLDyn = group.agg(['median'])\n",
    "grouped_aggLDyn.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df2.groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLDyn['T_stages'] = aux_column\n",
    "\n",
    "aux_df2 = aux_df[(aux_df.Is_Dynamic == False)]\n",
    "group = aux_df2.groupby('NP')['T_iter']\n",
    "grouped_aggLNDyn = group.agg(['median'])\n",
    "grouped_aggLNDyn.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df2.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLNDyn['T_stages'] = aux_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bt_scheme import PartialSolution, BacktrackingSolver\n",
    "def elegirConf(parameters):\n",
    "    class StatePS(PartialSolution):\n",
    "        def __init__(self, config):\n",
    "            self.config= config\n",
    "            self.n= len(config) #Indica el valor a añadir\n",
    "\n",
    "        def is_solution(self):\n",
    "            return self.n == len(parameters)\n",
    "\n",
    "        def get_solution(self):\n",
    "            return tuple(self.config)\n",
    "\n",
    "        def successors(self):\n",
    "            array = parameters[self.n]\n",
    "            for parameter_value in array: #Test all values of the next parameter\n",
    "                self.config.append(parameter_value)\n",
    "                yield StatePS(self.config)\n",
    "                self.config.pop()\n",
    "\n",
    "    initialPs= StatePS([])\n",
    "    return BacktrackingSolver().solve(initialPs)\n",
    "\n",
    "\n",
    "def obtenerConfs(parameters):\n",
    "    soluciones=[]\n",
    "    for solucion in elegirConf(parameters):\n",
    "        soluciones.append(solucion)\n",
    "    return soluciones\n",
    "\n",
    "def modifyToGlobal(parameters, len_parameters, configuration):\n",
    "    usable_configuration = []\n",
    "    for i in range(len(parameters)):\n",
    "        if len_parameters[i] > 1:\n",
    "            aux = (parameters[i][0], configuration[i])\n",
    "        else:\n",
    "            aux = (configuration[i])\n",
    "        usable_configuration.append(aux)\n",
    "        \n",
    "    return usable_configuration\n",
    "\n",
    "def modifyToLocalDynamic(parameters, len_parameters, configuration):\n",
    "    usable_configuration = []\n",
    "    for i in range(len(parameters)):\n",
    "        if len_parameters[i] > 1:\n",
    "            aux = (configuration[i], -1)\n",
    "        else:\n",
    "            aux = (-1)\n",
    "        usable_configuration.append(aux)\n",
    "        \n",
    "    return tuple(usable_configuration)\n",
    "\n",
    "def CheckConfExists(configuration, dataSet, type_conf='global'):\n",
    "    exists = False\n",
    "    config = list(configuration)\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                \n",
    "                if type_conf == 'global':\n",
    "                    config.append((np_aux, ns_aux))\n",
    "                elif type_conf == 'malleability':\n",
    "                    config.append(np_aux)\n",
    "                    config.append(ns_aux)\n",
    "                elif type_conf == 'local':\n",
    "                    config.append(np_aux)\n",
    "                    \n",
    "                if tuple(config) in dataSet.index:     \n",
    "                    exists = True # FIXME Return here true?\n",
    "                config.pop()\n",
    "                \n",
    "                if type_conf == 'malleability':\n",
    "                    config.pop()\n",
    "    return exists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0, 1], [0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 1, 1], [96.6, 0, 0, 1], [96.6, 0, 0, 2], [96.6, 0, 1, 1], [96.6, 0, 1, 2], [96.6, 1, 0, 1], [96.6, 1, 0, 2], [96.6, 1, 1, 1], [96.6, 1, 1, 2]]\n",
      "[[-1, (0, -1), (1, -1), (2, -1)], [-1, (0, -1), (0, -1), (2, -1)], [-1, (1, -1), (0, -1), (2, -1)], [-1, (1, -1), (1, -1), (1, -1)], [-1, (0, -1), (1, -1), (1, -1)], [-1, (0, -1), (0, -1), (1, -1)], [-1, (1, -1), (1, -1), (2, -1)], [-1, (1, -1), (0, -1), (1, -1)]]\n",
      "[[0, (0, 0), (0, 0), (1, 1)], [0, (0, 0), (0, 1), (1, 1)], [0, (0, 1), (0, 0), (1, 1)], [0, (0, 1), (0, 1), (1, 1)], [96.6, (0, 0), (0, 0), (1, 1)], [96.6, (0, 0), (0, 0), (1, 2)], [96.6, (0, 0), (0, 1), (1, 1)], [96.6, (0, 0), (0, 1), (1, 2)], [96.6, (0, 1), (0, 0), (1, 1)], [96.6, (0, 1), (0, 0), (1, 2)], [96.6, (0, 1), (0, 1), (1, 1)], [96.6, (0, 1), (0, 1), (1, 2)]]\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "adr = [0,96.6]\n",
    "sp_method = [0,1]\n",
    "rd_method = [0,1]\n",
    "rd_strat  = [1,2]\n",
    "parameters = [adr, sp_method, rd_method, rd_strat]\n",
    "parameters_names = ['ADR', 'Spawn_Method', 'Redistribution_Method', 'Redistribution_Strategy']\n",
    "len_parameters = [1,2,2,2]\n",
    "configurations_aux = obtenerConfs(parameters)\n",
    "configurations = []\n",
    "configurations_local_dynamic = set()\n",
    "configurations_local = set()\n",
    "configurations_simple = []\n",
    "for checked_conf in configurations_aux:\n",
    "    aux_conf = modifyToGlobal(parameters, len_parameters, checked_conf)\n",
    "    if CheckConfExists(aux_conf, grouped_aggG):\n",
    "        configurations.append(aux_conf)\n",
    "\n",
    "    if CheckConfExists(checked_conf, grouped_aggM, 'malleability'):\n",
    "        configurations_simple.append(list(checked_conf))\n",
    "        \n",
    "    aux_conf = modifyToLocalDynamic(parameters, len_parameters, checked_conf)\n",
    "    if CheckConfExists(aux_conf, grouped_aggLDyn, 'local'):\n",
    "        configurations_local_dynamic.add(aux_conf)\n",
    "\n",
    "configurations_local_dynamic = list(configurations_local_dynamic)\n",
    "for index in range(len(configurations_local_dynamic)):\n",
    "    configurations_local_dynamic[index] = list(configurations_local_dynamic[index])\n",
    "\n",
    "print(configurations_simple)\n",
    "print(configurations_local_dynamic)\n",
    "print(configurations)\n",
    "print(len(configurations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ALPHA COMPUTATION\n",
    "def compute_alpha(config_a, config_b):\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                config_a.append(np_aux)\n",
    "                config_a.append(ns_aux)\n",
    "                config_b.append(np_aux)\n",
    "                config_b.append(ns_aux)\n",
    "                grouped_aggM.loc[tuple(config_b),'Alpha'] = grouped_aggM.loc[tuple(config_b),'T_Malleability'] / grouped_aggM.loc[tuple(config_a),'T_Malleability']\n",
    "                config_a.pop()\n",
    "                config_a.pop()\n",
    "                config_b.pop()\n",
    "                config_b.pop()\n",
    "                \n",
    "                \n",
    "                config_a.insert(0,ns_aux)\n",
    "                config_a.insert(0,np_aux)\n",
    "                config_b.insert(0,ns_aux)\n",
    "                config_b.insert(0,np_aux)\n",
    "                out_grouped_M.loc[tuple(config_b),'Alpha'] = out_grouped_M.loc[tuple(config_b),'T_Malleability'] / out_grouped_M.loc[tuple(config_a),'T_Malleability']\n",
    "                config_a.pop(0)\n",
    "                config_a.pop(0)\n",
    "                config_b.pop(0)\n",
    "                config_b.pop(0)\n",
    "\n",
    "if not ('Alpha' in grouped_aggM.columns):\n",
    "    for config_a in configurations_simple:\n",
    "        for config_b in configurations_simple:\n",
    "            if config_a[1:-1] == config_b[1:-1] and config_a[0] == 0 and config_b[0] != 0:\n",
    "                compute_alpha(config_a, config_b)\n",
    "else:\n",
    "    print(\"ALPHA already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#OMEGA COMPUTATION\n",
    "def compute_omega(config):\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                if len(config) > len(parameters):\n",
    "                    config.pop()\n",
    "                    config.pop()\n",
    "                config.append(np_aux)\n",
    "                config.append(ns_aux)\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega'] = grouped_aggLAsynch.at[tuple(config),'T_iter'] / grouped_aggLSynch.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLAsynch.at[tuple(config),'T_stages'] / grouped_aggLSynch.at[np_aux,'T_stages']\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Omega' in grouped_aggLAsynch.columns):\n",
    "    for config in configurations:\n",
    "        if config[0] != 0:\n",
    "            compute_omega(config)\n",
    "else:\n",
    "    print(\"OMEGA already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#Dynamic Coherence COMPUTATION\n",
    "def compute_dyn_coherency(config):\n",
    "    for np_aux in processes:\n",
    "        for n_parents_aux in processes:\n",
    "            if np_aux != n_parents_aux:\n",
    "                config.append(np_aux)\n",
    "                config.append(n_parents_aux)\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency'] = grouped_aggLDyn.at[tuple(config),'T_iter'] / grouped_aggLNDyn.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLDyn.at[tuple(config),'T_stages'] / grouped_aggLNDyn.at[np_aux,'T_stages']\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Dyn_Coherency' in grouped_aggLDyn.columns):\n",
    "    for config in configurations_local_dynamic:\n",
    "        compute_dyn_coherency(config)\n",
    "else:\n",
    "    print(\"Dyn_Coherency already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_grouped_G.to_excel(\"resultG.xlsx\") \n",
    "out_grouped_M.to_excel(\"resultM.xlsx\") \n",
    "grouped_aggLAsynch.to_excel(\"AsynchIters.xlsx\")\n",
    "grouped_aggLDyn.to_excel(\"DynCoherence.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_iter</th>\n",
       "      <th>T_stages</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.633368</td>\n",
       "      <td>[0.62166, 3.2e-05, 2e-06, 0.011353]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.141878</td>\n",
       "      <td>[0.123063, 4.4e-05, 5e-06, 0.018725]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.100381</td>\n",
       "      <td>[0.071352, 9.8e-05, 6e-06, 0.028533]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.081748</td>\n",
       "      <td>[0.035681, 0.000212, 8.4e-05, 0.045265]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.070729</td>\n",
       "      <td>[0.018738, 0.000522, 0.000155, 0.05006]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.069799</td>\n",
       "      <td>[0.013387, 0.00101, 0.000148, 0.053359]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.069249</td>\n",
       "      <td>[0.010712, 0.001345, 0.000194, 0.054343]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       T_iter                                  T_stages\n",
       "NP                                                     \n",
       "2    0.633368       [0.62166, 3.2e-05, 2e-06, 0.011353]\n",
       "10   0.141878      [0.123063, 4.4e-05, 5e-06, 0.018725]\n",
       "20   0.100381      [0.071352, 9.8e-05, 6e-06, 0.028533]\n",
       "40   0.081748   [0.035681, 0.000212, 8.4e-05, 0.045265]\n",
       "80   0.070729   [0.018738, 0.000522, 0.000155, 0.05006]\n",
       "120  0.069799   [0.013387, 0.00101, 0.000148, 0.053359]\n",
       "160  0.069249  [0.010712, 0.001345, 0.000194, 0.054343]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggLSynch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6934/552606136.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test[\"Resize_Coherency\"] = test[\"T_Malleability\"] >= (test[\"T_spawn\"] + test[\"T_SR\"] + test[\"T_AR\"])\n",
      "/tmp/ipykernel_6934/552606136.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test[\"Resize_Coherency2\"] = test[\"T_Malleability\"] >= 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NP</th>\n",
       "      <th>NC</th>\n",
       "      <th>Total_Stages</th>\n",
       "      <th>Granularity</th>\n",
       "      <th>SDR</th>\n",
       "      <th>ADR</th>\n",
       "      <th>DR</th>\n",
       "      <th>Redistribution_Method</th>\n",
       "      <th>Redistribution_Strategy</th>\n",
       "      <th>Spawn_Method</th>\n",
       "      <th>...</th>\n",
       "      <th>T_iter</th>\n",
       "      <th>T_stages</th>\n",
       "      <th>T_spawn</th>\n",
       "      <th>T_spawn_real</th>\n",
       "      <th>T_SR</th>\n",
       "      <th>T_AR</th>\n",
       "      <th>T_Redistribution</th>\n",
       "      <th>T_Malleability</th>\n",
       "      <th>Resize_Coherency</th>\n",
       "      <th>Resize_Coherency2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>160</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.138975, 0.138623, 0.145609, 0.143285, 0.135...</td>\n",
       "      <td>((0.010725, 0.000534, 5e-05, 0.116726), (0.010...</td>\n",
       "      <td>0.699991</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.033815</td>\n",
       "      <td>1.107374</td>\n",
       "      <td>2.141189</td>\n",
       "      <td>2.841180</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>160</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.144654, 0.134697, 0.144184, 0.139219, 0.143...</td>\n",
       "      <td>((0.010724, 0.000413, 0.000297, 0.122341), (0....</td>\n",
       "      <td>0.289433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.024005</td>\n",
       "      <td>1.102639</td>\n",
       "      <td>2.126644</td>\n",
       "      <td>2.416077</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.137209, 0.149927, 0.148071, 0.153846, 0.161...</td>\n",
       "      <td>((0.013391, 0.000532, 0.000137, 0.113001), (0....</td>\n",
       "      <td>1.103932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.260267</td>\n",
       "      <td>2.895040</td>\n",
       "      <td>3.155307</td>\n",
       "      <td>4.259239</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.171813, 0.159961, 0.133724, 0.160519, 0.143...</td>\n",
       "      <td>((0.013379, 0.000184, 0.000238, 0.136431), (0....</td>\n",
       "      <td>1.231894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427662</td>\n",
       "      <td>3.007701</td>\n",
       "      <td>3.435363</td>\n",
       "      <td>4.667257</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>120</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.153822, 0.140212, 0.149297, 0.35497, 0.1534...</td>\n",
       "      <td>((0.013389, 0.000274, 0.000135, 0.132982), (0....</td>\n",
       "      <td>0.254933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.296134</td>\n",
       "      <td>2.913244</td>\n",
       "      <td>3.209378</td>\n",
       "      <td>3.464311</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>40</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.166288, 0.16779, 0.178504, 0.142442, 0.1775...</td>\n",
       "      <td>((0.035714, 0.000122, 7.6e-05, 0.127336), (0.0...</td>\n",
       "      <td>1.519905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111995</td>\n",
       "      <td>1.895083</td>\n",
       "      <td>2.007078</td>\n",
       "      <td>3.526983</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.149358, 0.153831, 0.153943, 0.1395, 0.14954...</td>\n",
       "      <td>((0.013389, 0.000142, 0.000235, 0.127162), (0....</td>\n",
       "      <td>1.678422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.449985</td>\n",
       "      <td>1.655973</td>\n",
       "      <td>5.105958</td>\n",
       "      <td>6.784380</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2414</th>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.110498, 0.098226, 0.097971, 0.09784, 0.0977...</td>\n",
       "      <td>((0.071338, 7.9e-05, 6e-06, 0.039075), (0.0713...</td>\n",
       "      <td>1.112722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.371996</td>\n",
       "      <td>2.023996</td>\n",
       "      <td>2.395992</td>\n",
       "      <td>3.508714</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>10</td>\n",
       "      <td>160</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.160399, 0.142582, 0.142452, 0.142528, 0.142...</td>\n",
       "      <td>((0.123088, 8e-05, 4e-06, 0.03722), (0.12307, ...</td>\n",
       "      <td>2.127073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.588013</td>\n",
       "      <td>3.185545</td>\n",
       "      <td>4.773558</td>\n",
       "      <td>6.900631</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>10</td>\n",
       "      <td>120</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>(0.157493, 0.1416, 0.141205, 0.141221, 0.14124...</td>\n",
       "      <td>((0.123101, 1.2e-05, 4e-06, 0.034375), (0.1230...</td>\n",
       "      <td>1.007496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.049457</td>\n",
       "      <td>3.269911</td>\n",
       "      <td>3.319368</td>\n",
       "      <td>4.326864</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NP   NC  Total_Stages  Granularity  SDR   ADR          DR  \\\n",
       "885   160   80             4       100000  3.4  96.6  3947883503   \n",
       "886   160   80             4       100000  3.4  96.6  3947883503   \n",
       "897   120   10             4       100000  3.4  96.6  3947883503   \n",
       "971   120   10             4       100000  3.4  96.6  3947883503   \n",
       "974   120   10             4       100000  3.4  96.6  3947883503   \n",
       "...   ...  ...           ...          ...  ...   ...         ...   \n",
       "2274   40  120             4       100000  3.4  96.6  3947883503   \n",
       "2400  120   80             4       100000  3.4  96.6  3947883503   \n",
       "2414   20   10             4       100000  3.4  96.6  3947883503   \n",
       "2427   10  160             4       100000  3.4  96.6  3947883503   \n",
       "2438   10  120             4       100000  3.4  96.6  3947883503   \n",
       "\n",
       "      Redistribution_Method  Redistribution_Strategy  Spawn_Method  ...  \\\n",
       "885                       1                        1             1  ...   \n",
       "886                       1                        1             1  ...   \n",
       "897                       1                        1             1  ...   \n",
       "971                       0                        1             1  ...   \n",
       "974                       0                        1             1  ...   \n",
       "...                     ...                      ...           ...  ...   \n",
       "2274                      1                        1             0  ...   \n",
       "2400                      0                        1             0  ...   \n",
       "2414                      0                        1             0  ...   \n",
       "2427                      0                        1             0  ...   \n",
       "2438                      1                        1             1  ...   \n",
       "\n",
       "                                                 T_iter  \\\n",
       "885   (0.138975, 0.138623, 0.145609, 0.143285, 0.135...   \n",
       "886   (0.144654, 0.134697, 0.144184, 0.139219, 0.143...   \n",
       "897   (0.137209, 0.149927, 0.148071, 0.153846, 0.161...   \n",
       "971   (0.171813, 0.159961, 0.133724, 0.160519, 0.143...   \n",
       "974   (0.153822, 0.140212, 0.149297, 0.35497, 0.1534...   \n",
       "...                                                 ...   \n",
       "2274  (0.166288, 0.16779, 0.178504, 0.142442, 0.1775...   \n",
       "2400  (0.149358, 0.153831, 0.153943, 0.1395, 0.14954...   \n",
       "2414  (0.110498, 0.098226, 0.097971, 0.09784, 0.0977...   \n",
       "2427  (0.160399, 0.142582, 0.142452, 0.142528, 0.142...   \n",
       "2438  (0.157493, 0.1416, 0.141205, 0.141221, 0.14124...   \n",
       "\n",
       "                                               T_stages   T_spawn  \\\n",
       "885   ((0.010725, 0.000534, 5e-05, 0.116726), (0.010...  0.699991   \n",
       "886   ((0.010724, 0.000413, 0.000297, 0.122341), (0....  0.289433   \n",
       "897   ((0.013391, 0.000532, 0.000137, 0.113001), (0....  1.103932   \n",
       "971   ((0.013379, 0.000184, 0.000238, 0.136431), (0....  1.231894   \n",
       "974   ((0.013389, 0.000274, 0.000135, 0.132982), (0....  0.254933   \n",
       "...                                                 ...       ...   \n",
       "2274  ((0.035714, 0.000122, 7.6e-05, 0.127336), (0.0...  1.519905   \n",
       "2400  ((0.013389, 0.000142, 0.000235, 0.127162), (0....  1.678422   \n",
       "2414  ((0.071338, 7.9e-05, 6e-06, 0.039075), (0.0713...  1.112722   \n",
       "2427  ((0.123088, 8e-05, 4e-06, 0.03722), (0.12307, ...  2.127073   \n",
       "2438  ((0.123101, 1.2e-05, 4e-06, 0.034375), (0.1230...  1.007496   \n",
       "\n",
       "     T_spawn_real      T_SR      T_AR  T_Redistribution  T_Malleability  \\\n",
       "885           0.0  1.033815  1.107374          2.141189        2.841180   \n",
       "886           0.0  1.024005  1.102639          2.126644        2.416077   \n",
       "897           0.0  0.260267  2.895040          3.155307        4.259239   \n",
       "971           0.0  0.427662  3.007701          3.435363        4.667257   \n",
       "974           0.0  0.296134  2.913244          3.209378        3.464311   \n",
       "...           ...       ...       ...               ...             ...   \n",
       "2274          0.0  0.111995  1.895083          2.007078        3.526983   \n",
       "2400          0.0  3.449985  1.655973          5.105958        6.784380   \n",
       "2414          0.0  0.371996  2.023996          2.395992        3.508714   \n",
       "2427          0.0  1.588013  3.185545          4.773558        6.900631   \n",
       "2438          0.0  0.049457  3.269911          3.319368        4.326864   \n",
       "\n",
       "     Resize_Coherency Resize_Coherency2  \n",
       "885             False              True  \n",
       "886             False              True  \n",
       "897             False              True  \n",
       "971             False              True  \n",
       "974             False              True  \n",
       "...               ...               ...  \n",
       "2274            False             False  \n",
       "2400            False             False  \n",
       "2414            False             False  \n",
       "2427            False              True  \n",
       "2438            False              True  \n",
       "\n",
       "[81 rows x 28 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=dfM[(dfM.Asynch_Iters > 0) & (dfM.Spawn_Strategy == 1)]\n",
    "\n",
    "for index in range(len(test)):\n",
    "    time_malleability_aux = test[\"T_Malleability\"].values[index]\n",
    "    \n",
    "    total_asynch_iters = int(test[\"Asynch_Iters\"].values[index])\n",
    "    asynch_iters = test[\"T_iter\"].values[index][-total_asynch_iters:]\n",
    "    time_iters_aux = 0\n",
    "    \n",
    "    if total_asynch_iters > 1:\n",
    "        time_iters_aux = np.sum(asynch_iters[:-1])\n",
    "    \n",
    "    if time_malleability_aux < time_iters_aux:\n",
    "        \n",
    "        print(test.iloc[index])\n",
    "        print(asynch_iters)\n",
    "        print(time_iters_aux)\n",
    "        print(time_malleability_aux)\n",
    "        print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_group_boundary(rms_boundary, np_aux, ns_aux):\n",
    "    tc_boundary = 0\n",
    "    boundaries = None\n",
    "    if rms_boundary != 0:\n",
    "        # El porcentaje de tc_boundary se tiene en cuenta para eliminar aquellos\n",
    "        # tiempos demasiado grandes en su malleability time respecto al más pequeño\n",
    "        boundaries = get_np_ns_data(\"T_Malleability\", grouped_aggM, configurations_simple, np_aux, ns_aux)\n",
    "        tc_boundary = min(boundaries)\n",
    "        tc_boundary = tc_boundary + tc_boundary*rms_boundary\n",
    "    return tc_boundary, boundaries\n",
    "\n",
    "# Aquellos grupos que tengán valores por encima del límite no se considerarán\n",
    "def check_groups_boundaries(dataLists, boundaries, tc_boundary):\n",
    "    for index in range(len(boundaries)):\n",
    "        if boundaries[index] > tc_boundary:\n",
    "            dataLists[index] = float('infinity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_perc_differences(dataLists, boundaries, tc_boundary):\n",
    "    perc = 1.05\n",
    "    if boundaries != None: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "        check_groups_boundaries(dataLists, boundaries, tc_boundary) \n",
    "    indexes = np.argsort(dataLists)\n",
    "    \n",
    "    best = -1\n",
    "    bestMax = -1\n",
    "    otherBest=[]\n",
    "    for index in indexes: # Para cada metodo -- Empezando por el tiempo más bajo en media/mediana\n",
    "        if best == -1:\n",
    "            best = index\n",
    "            bestMax = dataLists[best] * perc\n",
    "        elif dataLists[index] <= bestMax: # Media/Medianas i < Media/Mediana best\n",
    "            otherBest.append(index)\n",
    "                \n",
    "    otherBest.insert(0,best)\n",
    "    return otherBest\n",
    "\n",
    "def get_stat_differences(dataLists, df_Res, boundaries, tc_boundary):\n",
    "    if boundaries != None: # Si se usa perspectiva de RMS, se desconsideran valores muy altos\n",
    "        check_groups_boundaries(dataLists, boundaries, tc_boundary) \n",
    "    indexes = np.argsort(dataLists)\n",
    "    \n",
    "    best = -1\n",
    "    otherBest=[]  \n",
    "    for index in indexes: # Para cada metodo -- Empezando por el tiempo más bajo en mediana\n",
    "        if dataLists[index] != float('infinity'):\n",
    "            if best == -1:\n",
    "                best = index\n",
    "            elif not df_Res.iat[best,index]: # df_Res == False indicates 'index' and 'best' have the same mean/median\n",
    "                otherBest.append(index)\n",
    "                \n",
    "    otherBest.insert(0,best)\n",
    "    return otherBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_np = [\"T_total\"]\n",
    "separated_np = [\"T_Malleability\", \"T_Redistribution\", \"T_spawn\", \"T_SR\", \"T_AR\", \"Alpha\", \"Omega\"]\n",
    "\n",
    "def get_np_ns_data(tipo, data_aux, used_config, np_aux, ns_aux):\n",
    "    dataLists=[]\n",
    "    for config in used_config:\n",
    "        if tipo in grouped_np:\n",
    "            config.append((np_aux,ns_aux))\n",
    "        elif tipo in separated_np:\n",
    "            config.append(np_aux)\n",
    "            config.append(ns_aux)\n",
    "        \n",
    "        if tuple(config) in data_aux.index:\n",
    "            aux_value = data_aux.loc[tuple(config),tipo]\n",
    "            if isinstance(aux_value, pd.Series):\n",
    "                aux_value = aux_value.values[0]\n",
    "            if aux_value == 0: #Values of zero indicate it was not performed\n",
    "                aux_value = float('infinity')\n",
    "        else: # This configuration is not present in the dataset\n",
    "            aux_value = float('infinity')\n",
    "        dataLists.append(aux_value)\n",
    "        config.pop()\n",
    "        if tipo in separated_np:\n",
    "            config.pop()\n",
    "    return dataLists\n",
    "\n",
    "def get_config_data(tipo, data_aux, config):\n",
    "    dataLists=[]\n",
    "    for ns_aux in processes:\n",
    "        for np_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                \n",
    "                if tipo in grouped_np:\n",
    "                    config.append((np_aux,ns_aux))\n",
    "                elif tipo in separated_np:\n",
    "                    config.append(np_aux)\n",
    "                    config.append(ns_aux)\n",
    "                if tuple(config) in data_aux.index:\n",
    "                    aux_value = data_aux.loc[tuple(config),tipo]\n",
    "                    if isinstance(aux_value, pd.Series):\n",
    "                        aux_value = aux_value.values[0]\n",
    "                    if aux_value == 0: #Values of zero indicate it was not performed\n",
    "                        aux_value = float('infinity')\n",
    "                else: # This configuration is not present in the dataset\n",
    "                    aux_value = float('infinity')\n",
    "                dataLists.append(aux_value)\n",
    "                config.pop()\n",
    "                if tipo in separated_np:\n",
    "                    config.pop()\n",
    "    return dataLists\n",
    "\n",
    "def get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux):\n",
    "    dataLists=[]\n",
    "    if tipo in grouped_np:\n",
    "        tuple_data = (np_aux, ns_aux)\n",
    "        df_npns_aux = df_check.loc[(df_check['Groups']==tuple_data)]\n",
    "    elif tipo in separated_np:\n",
    "        df_npns_aux = df_check.loc[(df_check['NP']==np_aux)]\n",
    "        df_npns_aux = df_npns_aux.loc[(df_npns_aux['NC']==ns_aux)]\n",
    "        \n",
    "    for config in used_config:\n",
    "        df_config_aux = df_npns_aux\n",
    "        for index in range(len(config)):\n",
    "            aux_name = parameters_names[index]\n",
    "            aux_value = config[index]\n",
    "            df_config_aux = df_config_aux.loc[(df_config_aux[aux_name] == aux_value)]\n",
    "                \n",
    "        aux_value = list(df_config_aux[tipo])\n",
    "        if len(aux_value) > 0:\n",
    "            dataLists.append(aux_value)\n",
    "    return dataLists\n",
    "\n",
    "def get_df_config_data(df_check, tipo, config):\n",
    "    dataLists=[]\n",
    "    df_config_aux = df_check\n",
    "    for index in range(len(config)):\n",
    "        aux_name = parameters_names[index]\n",
    "        aux_value = config[index]\n",
    "        df_config_aux = df_config_aux.loc[(df_config_aux[aux_name] == aux_value)]\n",
    "        \n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                if tipo in grouped_np:\n",
    "                    tuple_data = (np_aux, ns_aux)\n",
    "                    df_aux = df_config_aux.loc[(df_config_aux['Groups']==tuple_data)]\n",
    "                elif tipo in separated_np:\n",
    "                    df_aux = df_config_aux.loc[(df_config_aux['NP']==np_aux)]\n",
    "                    df_aux = df_aux.loc[(df_aux['NC']==ns_aux)]\n",
    "                aux_value = list(df_aux[tipo])\n",
    "                if len(aux_value) > 0:\n",
    "                    dataLists.append(aux_value)\n",
    "    return dataLists\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df_check, tipo, used_config, fast=True):\n",
    "    normality_array=[True] * (len(processes) * (len(processes)-1) * len(used_config))\n",
    "    normality = True\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",
    "                dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n",
    "                for data_aux in dataLists:\n",
    "                    st,p = stats.shapiro(data_aux) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                    if p < significance_value: # Reject H0\n",
    "                        if fast:\n",
    "                            return False\n",
    "                        normality_array[i] = False\n",
    "                        normality = False\n",
    "                        total+=1\n",
    "    print(\"Se sigue una distribución guassiana: \" + str(normality) + \"\\nUn total de: \" + str(total) + \" no siguen una gaussiana\")\n",
    "    print(normality_array)\n",
    "    return normality\n",
    "\n",
    "def check_homoscedasticity(df_check, tipo, used_config, fast=True):\n",
    "    homoscedasticity_array=[True] * (len(processes) * (len(processes)-1))\n",
    "    homoscedasticity = True\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",
    "                dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n",
    "                st,p = stats.levene(*dataLists) # Tendrían que ser al menos 20 datos y menos de 50\n",
    "                if p < significance_value: # Reject H0\n",
    "                    if fast:\n",
    "                        return False\n",
    "                    homoscedasticity_array[i] = False\n",
    "                    homoscedasticity = False\n",
    "                    total+=1\n",
    "    print(\"Se sigue una distribución de datos Homocedastica: \" + str(homoscedasticity) + \"\\nUn total de: \" + str(total) + \" no siguen una homocedastica\")\n",
    "    print(homoscedasticity_array)\n",
    "    return homoscedasticity\n",
    "\n",
    "def compute_global_stat_difference(dataLists, parametric, np_aux, ns_aux):\n",
    "    if parametric:\n",
    "        st,p=stats.f_oneway(*dataLists)\n",
    "    else:\n",
    "        st,p=stats.kruskal(*dataLists)\n",
    "    if p > significance_value:\n",
    "        print(\"For NP \" + str(np_aux) + \" and \" + str(ns_aux) + \" is accepted H0\")\n",
    "        return True # Equal values || Accept H0\n",
    "    return False # Some groups are different || Reject H0\n",
    "\n",
    "def compute_global_posthoc(dataLists, parametric):\n",
    "    data_stats=[]\n",
    "    data_stats2=[]\n",
    "    ini=0\n",
    "    end=len(dataLists)\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",
    "            \n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < significance_value: # Different means || Reject H1\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",
    "            data_stats2.append(stats.iqr(dataLists[i],axis=0))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < significance_value: # Different medians || Reject H1\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",
    "    #print(data_stats2)\n",
    "    #aux_value = min(data_stats)\n",
    "    #print(data_stats.index(aux_value))\n",
    "    return df_Res, data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_with_perc(tipo, data_aux, used_config, rms_boundary=0):\n",
    "    results = []\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                tc_boundary, boundaries = create_group_boundary(rms_boundary, np_aux, ns_aux)\n",
    "                \n",
    "                #Get all values for particular config with these number of processes\n",
    "                dataLists = get_np_ns_data(tipo, data_aux, used_config, np_aux, ns_aux)\n",
    "\n",
    "                aux_data = get_perc_differences(dataLists, boundaries, tc_boundary)\n",
    "                results.append(aux_data)\n",
    "    return results\n",
    "\n",
    "def results_with_stats(tipo, df_check, used_config, rms_boundary=0):\n",
    "    results = []\n",
    "    use_parametric = check_normality(df_check, tipo, used_config)\n",
    "    if use_parametric:\n",
    "        use_parametric = check_homoscedasticity(df_check, tipo, used_config)\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                tc_boundary, boundaries = create_group_boundary(rms_boundary, np_aux, ns_aux)\n",
    "                \n",
    "                #Get all values for particular config with these number of processes\n",
    "                dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n",
    "                equal_set = compute_global_stat_difference(dataLists, use_parametric, np_aux, ns_aux)\n",
    "                if equal_set:\n",
    "                    aux_data = list(range(len(used_config))) # All data is equal\n",
    "                else:\n",
    "                    res_aux, times_aux = compute_global_posthoc(dataLists, use_parametric)\n",
    "                    aux_data = get_stat_differences(times_aux, res_aux, boundaries, tc_boundary)\n",
    "                \n",
    "                results.append(aux_data)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1], [2], [1, 3], [1], [1], [1], [2, 1], [2], [3, 1], [1], [1], [1], [2], [2], [3, 2], [1], [1], [1], [2, 3], [2, 3], [2], [2], [3, 1], [3, 1], [3], [1], [1], [3, 2, 1], [2], [2, 3], [3], [1], [1], [1, 3], [3, 2], [2], [3], [1], [1], [3, 1, 2], [3, 2], [2, 3]]\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "checked_type='T_Malleability'\n",
    "use_perc = False\n",
    "select_first_winner = False\n",
    "prefer_first_winner = False\n",
    "rms_boundary=0 # Poner a 0 para perspectiva de app. Valor >0 y <1 para perspectiva de RMS\n",
    "if checked_type=='T_total':\n",
    "    tipo=\"T_total\"\n",
    "    if use_perc:\n",
    "        data_aux = grouped_aggG\n",
    "    else:\n",
    "        data_aux = dfG\n",
    "    used_config = configurations\n",
    "elif checked_type=='T_Malleability' or checked_type=='T_spawn' or checked_type=='T_SR' or checked_type=='T_AR' or checked_type=='T_Redistribution':\n",
    "    tipo=checked_type\n",
    "    \n",
    "    if use_perc:\n",
    "        data_aux = grouped_aggM\n",
    "    else:\n",
    "        data_aux = dfM\n",
    "        if tipo == 'T_AR':\n",
    "            data_aux = data_aux[(data_aux.ADR > 0)]\n",
    "        elif tipo == 'T_SR':\n",
    "            data_aux = data_aux[(data_aux.ADR == 0)]\n",
    "        \n",
    "    used_config = configurations_simple\n",
    "    \n",
    "if use_perc:\n",
    "    results = results_with_perc(tipo, data_aux, used_config, rms_boundary)\n",
    "else:\n",
    "    results = results_with_stats(tipo, data_aux, used_config, rms_boundary)\n",
    "    \n",
    "if not use_perc and tipo == 'T_AR': #FIXME!!!! No tiene en cuenta total de configuraciones sincronos\n",
    "    for res_index in range(len(results)):\n",
    "        for inner_index in range(len(results[res_index])):\n",
    "            results[res_index][inner_index]+=4\n",
    "\n",
    "#Results is a 2 dimensional array. First dimension indicates lists of winners of a particulal number of processes (NP->NC). \n",
    "#Second dimension is an ordered preference of indexes in the array configurations.\n",
    "print(results)\n",
    "print(len(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1  1  2  1  1  1  1]\n",
      " [ 1 -1  2  1  1  1  1]\n",
      " [ 2  2 -1  2  1  1  1]\n",
      " [ 2  2  2 -1  2  1  1]\n",
      " [ 3  1  1  1 -1  2  2]\n",
      " [ 3  1  1  1  2 -1  2]\n",
      " [ 3  1  1  1  2  2  4]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor según el total de ocurrencias\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "aux_keys, aux_counts = np.unique(aux_array, return_counts=True)\n",
    "aux_ordered_index=list(reversed(np.argsort(aux_counts)))\n",
    "\n",
    "#Lista de indices de mayor a menor según el total de ocurrencias del primero de cada grupo\n",
    "aux_array = [0] * len(results)\n",
    "for index in range(len(results)):\n",
    "    aux_array[index] = results[index][0]\n",
    "aux_keys_best, aux_counts_best = np.unique(aux_array, return_counts = True)\n",
    "aux_ordered_best_index=list(reversed(np.argsort(aux_counts_best)))\n",
    "\n",
    "def heatmap_get_best(index, ordered_array, keys_array, counts_array, prefer_winner=False):\n",
    "    valid_candidates_indexes = []\n",
    "    prev_counts = -1\n",
    "    for tested_index in ordered_array:\n",
    "        if keys_array[tested_index] in results[index]:\n",
    "            if counts_array[tested_index] >= prev_counts:\n",
    "                prev_counts = counts_array[tested_index]\n",
    "                valid_candidates_indexes.append(tested_index)\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "    if prefer_winner: # Si esta activo, en caso de empate en ocurrencias se selecciona el menor tiempo\n",
    "        for tested_index in results[index]:\n",
    "            if tested_index in valid_candidates_indexes:\n",
    "                return tested_index\n",
    "    return min(valid_candidates_indexes) # En caso de empate se devuelve el que tiene menor valor (Suele ser la config más simple)\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n",
    "\n",
    "if select_first_winner:\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",
    "                index = heatmap_get_best(results_index, aux_ordered_index, aux_keys, aux_counts, prefer_first_winner)\n",
    "                heatmap[i][j]=aux_keys[index]\n",
    "                #index = heatmap_get_best(results_index, aux_ordered_best_index, aux_keys_best, aux_counts_best, prefer_first_winner)\n",
    "                #heatmap[i][j]=aux_keys_best[index]\n",
    "heatmap[-1][-1]=len(used_config)\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adapta results a una cadena asegurando que cada cadena no se sale de su celda\n",
    "def get_heatmap_multiple_strings(results): #FIXME Deprecated\n",
    "    results_str = []\n",
    "    max_counts = 1\n",
    "    max_per_line = 3\n",
    "    for i in range(len(results)):\n",
    "        results_str.append(list())\n",
    "        count = len(results[i])\n",
    "        results_aux = results[i]\n",
    "        if count > max_counts:\n",
    "            count = max_counts\n",
    "            results_aux = results[i][:count]\n",
    "        \n",
    "        remainder = count%max_per_line\n",
    "        if count <= max_per_line:\n",
    "            aux_str = str(results_aux).replace('[','').replace(']','')\n",
    "            results_str[i].append(aux_str)\n",
    "        else:\n",
    "            if remainder == 0:\n",
    "                index = count//2\n",
    "            else:\n",
    "                index = count - ((remainder-1)*max_per_line + 1)\n",
    "            aux_str = str(results_aux[:index]).replace('[','').replace(']','')\n",
    "            results_str[i].append(aux_str)\n",
    "            aux_str = str(results_aux[index:]).replace('[','').replace(']','')\n",
    "            results_str[i].append(aux_str)\n",
    "    return results_str\n",
    "\n",
    "def get_heatmap_strings(heatmap):\n",
    "    results_str = []\n",
    "    for i in range(len(processes)):\n",
    "        for j in range(len(processes)):\n",
    "            if i!=j:\n",
    "                results_str.append(list())\n",
    "                results_str[-1].append(heatmap[i][j])\n",
    "    return results_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2494/1414719895.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_2494/1414719895.py:50: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+processes, fontsize=36)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: Heatmap_T_Malleability.png\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\"), \n",
    "    colors.to_rgba(\"green\"), \n",
    "    colors.to_rgba(\"darkgreen\"),  # En lugar de \"darkgreen\"\n",
    "    colors.to_rgba(\"red\"), \n",
    "    colors.to_rgba(\"darkred\"),  # En lugar de \"darkred\"\n",
    "    colors.to_rgba(\"mediumseagreen\"),  # En lugar de \"mediumseagreen\"\n",
    "    colors.to_rgba(\"seagreen\"),  # En lugar de \"seagreen\"\n",
    "    colors.to_rgba(\"palegreen\"), \n",
    "    colors.to_rgba(\"springgreen\"), \n",
    "    colors.to_rgba(\"indianred\"), \n",
    "    colors.to_rgba(\"firebrick\"),\n",
    "    colors.to_rgba(\"darkgoldenrod\"),\n",
    "    colors.to_rgba(\"saddlebrown\"),\n",
    "    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",
    "results_str = get_heatmap_strings(heatmap)\n",
    "for i in range(len(processes)):\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            aux_color=\"white\"\n",
    "            if 0 <= heatmap[i, j] <= 1 or 4 <= heatmap[i, j] <= 7: # 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 - AllS (0)', 'Baseline - P2PS (1)',\n",
    "                    'Merge - AllS (2)','Merge - P2PS (3)',\n",
    "                    'Baseline - AllA (4)', 'Baseline - AllT (5)','Baseline - P2PA (6)','Baseline - P2PT (7)',\n",
    "                    'Merge - AllA (8)','Merge - AllT (9)','Merge - P2PA (10)','Merge - P2PT (11)']\n",
    "\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "tick_bar = []\n",
    "for i in range(len(used_config)):\n",
    "    tick_bar.append(0.37 + i*0.92) #Config de 12 valores\n",
    "colorbar.set_ticks(tick_bar) \n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=32)\n",
    "#\n",
    "\n",
    "f.tight_layout()\n",
    "print(\"Filename: Heatmap_\"+tipo+\".png\")\n",
    "f.savefig(\"Images/Heatmap_\"+tipo+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  6  8 10 11]\n",
      "[ 1 17 20  1  1 22 27  1]\n",
      "[ 2  3  8 10 11]\n",
      "[ 5  9 11 16  1]\n"
     ]
    }
   ],
   "source": [
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "aux_results, aux_counts = np.unique(aux_array, return_counts=True)\n",
    "print(aux_results)\n",
    "print(aux_counts)\n",
    "\n",
    "aux_array = [0] * len(results)\n",
    "for index in range(len(results)):\n",
    "    aux_array[index] = results[index][0]\n",
    "aux_results, aux_counts = np.unique(aux_array, return_counts = True)\n",
    "print(aux_results)\n",
    "print(aux_counts)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente código asume que para cada número de procesos padre/hijo existen valores en todas las configuraciones que se van a probar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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='a'\n",
    "test_parameter='T_total' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha'\n",
    "    name_legend = \"Values of α\"\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM[grouped_aggM[real_parameter] > 0]\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega'\n",
    "    name_legend = \"Values of ω\"\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggLAsynch[grouped_aggLAsynch[real_parameter] > 0]\n",
    "elif test_parameter == 'T_total':\n",
    "    name_fig=\"Ttotal\"\n",
    "    real_parameter='T_total'\n",
    "    name_legend = \"Time(s)\"\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggG\n",
    "    #data_aux = data_aux[data_aux.index.isin(df1.index)]\n",
    "    \n",
    "if used_direction=='s':\n",
    "    data_aux=data_aux.query('NP > NC')\n",
    "    name_fig= name_fig+\"Shrink\"\n",
    "elif used_direction=='e':\n",
    "    data_aux=data_aux.query('NP < NC')\n",
    "    name_fig= name_fig+\"Expand\"\n",
    "elif used_direction=='a':\n",
    "    name_fig= name_fig+\"All\"   \n",
    "\n",
    "plot_data = []\n",
    "for config in used_config:\n",
    "    if config[0] > 0:\n",
    "        dataLists = get_config_data(real_parameter, data_aux, config)\n",
    "        dataLists = list(filter(lambda x: x != float('infinity'), dataLists))\n",
    "        plot_data.append(dataLists)\n",
    "\n",
    "labels_aux = []\n",
    "for ns_aux in processes:\n",
    "    for np_aux in processes:\n",
    "        if used_direction=='s' and np_aux > ns_aux or used_direction=='e' and np_aux < ns_aux or used_direction=='a' and np_aux != ns_aux:\n",
    "            new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "            labels_aux.append(new_label)\n",
    "\n",
    "labelsMethods_aux = ['Baseline - AllA', 'Baseline - AllT','Baseline - P2PA','Baseline - P2PT',\n",
    "                    'Merge - AllA','Merge - AllT','Merge - P2PA','Merge - P2PT']\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(labels_aux))\n",
    "for index in range(len(plot_data)):\n",
    "    array_aux = plot_data[index]\n",
    "    ax.plot(x, array_aux, color=colors_m[index%len(colors_m)], linestyle=linestyle_m[index%len(linestyle_m)], \\\n",
    "        marker=markers_m[index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "plt.xticks(x, labels_aux,rotation=90)\n",
    "ax.tick_params(axis='both', which='major', labelsize=36)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=36)\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Gráfica de lineas para generar tiempos del grupo G."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "used_direction='e'\n",
    "test_parameter='T_total' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'T_total':\n",
    "    name_fig=\"Ttotal\"\n",
    "    real_parameter='T_total'\n",
    "    name_legend = \"Time(s)\"\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggG\n",
    "    #data_aux = data_aux[data_aux.index.isin(df1.index)]\n",
    "    \n",
    "if used_direction=='s':\n",
    "    data_aux_cmp=grouped_aggM.reset_index().query('NP > NC')\n",
    "    name_fig= name_fig+\"Shrink\"\n",
    "elif used_direction=='e':\n",
    "    data_aux_cmp=grouped_aggM.reset_index().query('NP < NC')\n",
    "    name_fig= name_fig+\"Expand\"\n",
    "elif used_direction=='a':\n",
    "    name_fig= name_fig+\"All\"   \n",
    "\n",
    "if used_direction!='a':\n",
    "    pruebaG = data_aux.reset_index()\n",
    "    pruebaG = pruebaG.loc[pruebaG.index.intersection(data_aux_cmp.index)]\n",
    "    data_aux = data_aux[(data_aux.T_total.isin(pruebaG.T_total))]\n",
    "\n",
    "plot_data = []\n",
    "for config in used_config:\n",
    "    if config[0] == 0:\n",
    "        dataLists = get_config_data(real_parameter, data_aux, config)\n",
    "        dataLists = list(filter(lambda x: x != float('infinity'), dataLists))\n",
    "        plot_data.append(dataLists)\n",
    "\n",
    "labels_aux = []\n",
    "for ns_aux in processes:\n",
    "    for np_aux in processes:\n",
    "        if used_direction=='s' and np_aux > ns_aux or used_direction=='e' and np_aux < ns_aux or used_direction=='a' and np_aux != ns_aux:\n",
    "            new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "            labels_aux.append(new_label)\n",
    "\n",
    "labelsMethods_aux = ['Baseline - AllA', 'Baseline - AllT','Baseline - P2PA','Baseline - P2PT',\n",
    "                    'Merge - AllA','Merge - AllT','Merge - P2PA','Merge - P2PT']\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(labels_aux))\n",
    "for index in range(len(plot_data)):\n",
    "    array_aux = plot_data[index]\n",
    "    ax.plot(x, array_aux, color=colors_m[index%len(colors_m)], linestyle=linestyle_m[index%len(linestyle_m)], \\\n",
    "        marker=markers_m[index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "\n",
    "ax.set_ylim(0,140)\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "plt.xticks(x, labels_aux,rotation=90)\n",
    "ax.tick_params(axis='both', which='major', labelsize=36)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=36)\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente generá una imagen en 3d de T_total para cada una de las diferentes configuraciones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_3d_image(config, name):\n",
    "    fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'}, figsize=(15, 15))\n",
    "\n",
    "    Z = [None] * len(processes)\n",
    "    X, Y = np.meshgrid(processes, processes)\n",
    "    for i in range(len(processes)):\n",
    "        np_aux = processes[i]\n",
    "        Z[i] = [0] * len(processes)\n",
    "        Z[i][i] = grouped_aggLSynch.loc[np_aux, 'T_iter'] * 1000\n",
    "        for j in range(len(processes)):\n",
    "            if i!=j:\n",
    "                ns_aux = processes[j]\n",
    "                config.append((np_aux,ns_aux))\n",
    "                aux = grouped_aggG.loc[tuple(config),'T_total']\n",
    "                config.pop()\n",
    "            \n",
    "                Z[i][j] = aux.values[0]\n",
    "                #Z[i][j] = Z[i][j] / Z[i][i]\n",
    "        #Z[i][i] = 1\n",
    "\n",
    "    Z = np.array(Z)\n",
    "\n",
    "    ax.plot_surface(X, Y, Z, rstride=1, cstride=1,\n",
    "                cmap='viridis', edgecolor='none')\n",
    "    ax.view_init(15, 25)\n",
    "    ax.set_xlabel(\"NC\", fontsize=16)\n",
    "    ax.set_ylabel(\"NP\", fontsize=16)\n",
    "    ax.set_zlabel(\"Normalized time\", fontsize=16)\n",
    "    ax.set_title(name, fontsize=10)\n",
    "    plt.show()\n",
    "    \n",
    "for index in range(len(configurations)):\n",
    "    used_config = configurations[index]\n",
    "    generate_3d_image(used_config,str(index))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente código es computar la coherencia de T_malleability respecto a los tiempos internos de maleabilidad (Coherency1)\n",
    "y por otro lado de T_malleability respecto a iteraciones asíncronas más los pasos síncronos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test=dfM[(dfM.Asynch_Iters > 0)]\n",
    "\n",
    "# El primer Coherency tendrá sentido cuando se recoga T_Malleability. Mas seguro con barriers en Malleability\n",
    "test[\"Resize_Coherency\"] = test[\"T_Malleability\"] >= (test[\"T_spawn\"] + test[\"T_SR\"] + test[\"T_AR\"])\n",
    "# El segundo Coherency tendrá sentido cuando se recoga T_Malleability. Mas seguro al usar Rigid para iteraciones\n",
    "test[\"Resize_Coherency2\"] = test[\"T_Malleability\"] >= 0\n",
    "\n",
    "for index in range(len(test)):\n",
    "    time_malleability_aux = test[\"T_Malleability\"].values[index]\n",
    "    time_synch_aux = test[\"T_SR\"].values[index]\n",
    "    time_spawn_aux = test[\"T_spawn\"].values[index]\n",
    "    is_asynch_spawn = (test[\"Spawn_Strategy\"].values[index] % 2 == 0)\n",
    "    \n",
    "    total_asynch_iters = int(test[\"Asynch_Iters\"].values[index])\n",
    "    asynch_iters = test[\"T_iter\"].values[index][-total_asynch_iters:]\n",
    "    time_iters_aux = np.sum(asynch_iters[:])\n",
    "    \n",
    "    sum_times = time_synch_aux + is_asynch_spawn * time_spawn_aux + time_iters_aux\n",
    "    \n",
    "    if time_malleability_aux < sum_times:\n",
    "        real_index = test.index.values[index]\n",
    "        test.at[real_index, \"Resize_Coherency2\"] = False\n",
    "test[(test.Resize_Coherency == False)]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente código es para utilizar Dask. Una versión que paraleliza una serie de tareas de Pandas.\n",
    "Tras llamar a compute se realizan todas las tareas que se han pedido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "ddf = dd.from_pandas(dfL[(dfL.Asynch_Iters == False)], npartitions=10)\n",
    "group = ddf.groupby('NP')['T_iter']\n",
    "grouped_aggLSynch = group.agg(['mean'])\n",
    "grouped_aggLSynch = grouped_aggLSynch.rename(columns={'mean':'T_iter'}) \n",
    "grouped_aggLSynch = grouped_aggLSynch.compute()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.75676344 0.03701228 3.76439349]\n",
      "[0.20630784 0.96375203 0.04733157]\n",
      "[2.17054203 1.7721764  0.83496214 0.27720765 0.59800783 0.42146685]\n",
      "[0.19532397 0.24843587 0.47871884 0.76709983 0.5796655  0.67410377]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[9.87, 9.03, 6.81],\n",
    "              [7.18, 8.35, 7.00],\n",
    "              [8.39, 7.58, 7.68],\n",
    "              [7.45, 6.33, 9.35],\n",
    "              [6.41, 7.10, 9.33],\n",
    "              [8.00, 8.24, 8.44]])\n",
    "b = np.array([[6.35, 7.30, 7.16],\n",
    "              [6.65, 6.68, 7.63],\n",
    "              [5.72, 7.73, 6.72],\n",
    "              [7.01, 9.19, 7.41],\n",
    "              [7.75, 7.87, 8.30],\n",
    "              [6.90, 7.97, 6.97]])\n",
    "c = np.array([[3.31, 8.77, 1.01],\n",
    "              [8.25, 3.24, 3.62],\n",
    "              [6.32, 8.81, 5.19],\n",
    "              [7.48, 8.83, 8.91],\n",
    "              [8.59, 6.01, 6.07],\n",
    "              [3.07, 9.72, 7.48]])\n",
    "my_daa_aux = [a,b,c]\n",
    "\n",
    "F1, p_aux = stats.f_oneway(a, b, c)\n",
    "F2, p_aux2 = stats.f_oneway(*my_daa_aux,axis=0)\n",
    "\n",
    "print(F1)\n",
    "print(p_aux)\n",
    "print(F2)\n",
    "print(p_aux2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>T_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADR</th>\n",
       "      <th>Spawn_Method</th>\n",
       "      <th>Redistribution_Method</th>\n",
       "      <th>Redistribution_Strategy</th>\n",
       "      <th>Groups</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\">(0, 0)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">(0, 0)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">(1, 1)</th>\n",
       "      <th>(10, 2)</th>\n",
       "      <td>396.244297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 2)</th>\n",
       "      <td>323.197388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 10)</th>\n",
       "      <td>122.029776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 2)</th>\n",
       "      <td>363.054878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40, 10)</th>\n",
       "      <td>115.773127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">96.6</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">(0, 1)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">(0, 1)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">(1, 2)</th>\n",
       "      <th>(160, 10)</th>\n",
       "      <td>110.560542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(160, 20)</th>\n",
       "      <td>91.163625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(160, 40)</th>\n",
       "      <td>85.075558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(160, 80)</th>\n",
       "      <td>73.307185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(160, 120)</th>\n",
       "      <td>69.804638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                               T_total\n",
       "ADR  Spawn_Method Redistribution_Method Redistribution_Strategy Groups                \n",
       "0.0  (0, 0)       (0, 0)                (1, 1)                  (10, 2)     396.244297\n",
       "                                                                (20, 2)     323.197388\n",
       "                                                                (20, 10)    122.029776\n",
       "                                                                (40, 2)     363.054878\n",
       "                                                                (40, 10)    115.773127\n",
       "...                                                                                ...\n",
       "96.6 (0, 1)       (0, 1)                (1, 2)                  (160, 10)   110.560542\n",
       "                                                                (160, 20)    91.163625\n",
       "                                                                (160, 40)    85.075558\n",
       "                                                                (160, 80)    73.307185\n",
       "                                                                (160, 120)   69.804638\n",
       "\n",
       "[252 rows x 1 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pruebaG = grouped_aggG.reset_index()\n",
    "pruebaM = grouped_aggM.reset_index().query('NP > NC') #Shrink\n",
    "pruebaG = pruebaG.loc[pruebaG.index.intersection(pruebaM.index)]\n",
    "prueba2 = grouped_aggG[(grouped_aggG.T_total.isin(pruebaG.T_total))]\n",
    "prueba2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}