new_analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 66,
   "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 = 10 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "significance_value = 0.05\n",
    "processes = [2,20,40,80,120,160]\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','darkgreen','red','darkred','mediumseagreen','seagreen','palegreen','springgreen','indianred','firebrick','darkgoldenrod','saddlebrown']\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": 48,
   "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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5684/462116935.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_5684/462116935.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": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfL = pd.read_pickle( ItersName )\n",
    "\n",
    "#Fixme comprobar si hay iters asincronas antes de esto\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": 35,
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0, 1], [0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 1, 1], [0, 2, 0, 1], [0, 2, 1, 1], [100, 0, 0, 1], [100, 0, 1, 1], [100, 1, 0, 1], [100, 1, 1, 1], [100, 2, 0, 1], [100, 2, 1, 1]]\n",
      "[[-1, (1, -1), (1, -1), (1, -1)], [-1, (0, -1), (1, -1), (1, -1)], [-1, (2, -1), (1, -1), (1, -1)], [-1, (0, -1), (0, -1), (1, -1)], [-1, (2, -1), (0, -1), (1, -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)], [0, (0, 2), (0, 0), (1, 1)], [0, (0, 2), (0, 1), (1, 1)], [100, (0, 0), (0, 0), (1, 1)], [100, (0, 0), (0, 1), (1, 1)], [100, (0, 1), (0, 0), (1, 1)], [100, (0, 1), (0, 1), (1, 1)], [100, (0, 2), (0, 0), (1, 1)], [100, (0, 2), (0, 1), (1, 1)]]\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "adr = [0,100]\n",
    "sp_method = [0,1,2]\n",
    "rd_method = [0,1]\n",
    "rd_strat  = [1]\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": 51,
   "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",
    "                #grouped_aggM.loc[tuple(config_b),'Alpha'] = grouped_aggM.loc[tuple(config_b),'T_Redistribution'] / grouped_aggM.loc[tuple(config_a),'T_Redistribution']\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",
    "                #out_grouped_M.loc[tuple(config_b),'Alpha'] = out_grouped_M.loc[tuple(config_b),'T_Redistribution'] / out_grouped_M.loc[tuple(config_a),'T_Redistribution']\n",
    "                \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": null,
   "metadata": {},
   "outputs": [],
   "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": 52,
   "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": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Malleability Coherence COMPUTATION\n",
    "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 = np.sum(asynch_iters)\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": [
    "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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>T_Malleability</th>\n",
       "      <th>T_Redistribution</th>\n",
       "      <th>T_spawn</th>\n",
       "      <th>T_spawn_real</th>\n",
       "      <th>T_SR</th>\n",
       "      <th>T_AR</th>\n",
       "      <th>Alpha</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>NP</th>\n",
       "      <th>NC</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>3.752015</td>\n",
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       "      <td>7.051643</td>\n",
       "      <td>5.734007</td>\n",
       "      <td>1.287871</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.734007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <th>1</th>\n",
       "      <th>160</th>\n",
       "      <th>20</th>\n",
       "      <td>6.385955</td>\n",
       "      <td>5.147990</td>\n",
       "      <td>1.253753</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>5.336817</td>\n",
       "      <td>4.104014</td>\n",
       "      <td>1.222010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>5.458008</td>\n",
       "      <td>1.191391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.458008</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                         T_Malleability  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                   \n",
       "0.0   0            0                     1                       160 20        8.499887   \n",
       "                   1                     1                       160 20        5.182835   \n",
       "      1            0                     1                       160 20        4.037880   \n",
       "                   1                     1                       160 20        3.927668   \n",
       "      2            0                     1                       160 20        7.051643   \n",
       "                   1                     1                       160 20        6.385955   \n",
       "100.0 0            0                     1                       160 20        5.255706   \n",
       "                   1                     1                       160 20        5.336817   \n",
       "      1            0                     1                       160 20        4.015236   \n",
       "                   1                     1                       160 20        4.092398   \n",
       "      2            0                     1                       160 20        6.720818   \n",
       "                   1                     1                       160 20        6.632618   \n",
       "\n",
       "                                                                         T_Redistribution  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                     \n",
       "0.0   0            0                     1                       160 20          7.168089   \n",
       "                   1                     1                       160 20          3.976021   \n",
       "      1            0                     1                       160 20          3.875850   \n",
       "                   1                     1                       160 20          3.752015   \n",
       "      2            0                     1                       160 20          5.734007   \n",
       "                   1                     1                       160 20          5.147990   \n",
       "100.0 0            0                     1                       160 20          4.034014   \n",
       "                   1                     1                       160 20          4.104014   \n",
       "      1            0                     1                       160 20          3.916052   \n",
       "                   1                     1                       160 20          3.909810   \n",
       "      2            0                     1                       160 20          5.463979   \n",
       "                   1                     1                       160 20          5.458008   \n",
       "\n",
       "                                                                          T_spawn  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC             \n",
       "0.0   0            0                     1                       160 20  1.285624   \n",
       "                   1                     1                       160 20  1.219105   \n",
       "      1            0                     1                       160 20  0.133642   \n",
       "                   1                     1                       160 20  0.144682   \n",
       "      2            0                     1                       160 20  1.287871   \n",
       "                   1                     1                       160 20  1.253753   \n",
       "100.0 0            0                     1                       160 20  1.267632   \n",
       "                   1                     1                       160 20  1.222010   \n",
       "      1            0                     1                       160 20  0.110316   \n",
       "                   1                     1                       160 20  0.123278   \n",
       "      2            0                     1                       160 20  1.287527   \n",
       "                   1                     1                       160 20  1.191391   \n",
       "\n",
       "                                                                         T_spawn_real  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                 \n",
       "0.0   0            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "      1            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "      2            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "100.0 0            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "      1            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "      2            0                     1                       160 20           0.0   \n",
       "                   1                     1                       160 20           0.0   \n",
       "\n",
       "                                                                             T_SR  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC             \n",
       "0.0   0            0                     1                       160 20  7.168089   \n",
       "                   1                     1                       160 20  3.976021   \n",
       "      1            0                     1                       160 20  3.875850   \n",
       "                   1                     1                       160 20  3.752015   \n",
       "      2            0                     1                       160 20  5.734007   \n",
       "                   1                     1                       160 20  5.147990   \n",
       "100.0 0            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "      1            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "      2            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "\n",
       "                                                                             T_AR  \\\n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC             \n",
       "0.0   0            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "      1            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "      2            0                     1                       160 20  0.000000   \n",
       "                   1                     1                       160 20  0.000000   \n",
       "100.0 0            0                     1                       160 20  4.034014   \n",
       "                   1                     1                       160 20  4.104014   \n",
       "      1            0                     1                       160 20  3.916052   \n",
       "                   1                     1                       160 20  3.909810   \n",
       "      2            0                     1                       160 20  5.463979   \n",
       "                   1                     1                       160 20  5.458008   \n",
       "\n",
       "                                                                            Alpha  \n",
       "ADR   Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC            \n",
       "0.0   0            0                     1                       160 20       NaN  \n",
       "                   1                     1                       160 20       NaN  \n",
       "      1            0                     1                       160 20       NaN  \n",
       "                   1                     1                       160 20       NaN  \n",
       "      2            0                     1                       160 20       NaN  \n",
       "                   1                     1                       160 20       NaN  \n",
       "100.0 0            0                     1                       160 20  0.618327  \n",
       "                   1                     1                       160 20  1.029710  \n",
       "      1            0                     1                       160 20  0.994392  \n",
       "                   1                     1                       160 20  1.041941  \n",
       "      2            0                     1                       160 20  0.953085  \n",
       "                   1                     1                       160 20  1.038626  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM.query('NP==160 and NC==20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADR    Spawn_Method  Redistribution_Method  Redistribution_Strategy  NP   NC \n",
      "100.0  1             1                      1                        2    20     0.828853\n",
      "                                                                          40     0.899854\n",
      "                                                                          80     0.912384\n",
      "                                                                          120    0.935752\n",
      "                                                                          160    0.976895\n",
      "                                                                     20   40     1.013810\n",
      "                                                                          80     1.014453\n",
      "                                                                          120    0.987039\n",
      "                                                                          160    0.993694\n",
      "                                                                     40   80     1.000736\n",
      "                                                                          120    0.978872\n",
      "                                                                          160    1.011090\n",
      "                                                                     80   120    0.972535\n",
      "                                                                          160    0.999404\n",
      "                                                                     120  160    0.917490\n",
      "Name: Alpha, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "valores = grouped_aggM.query('ADR == 100 and Redistribution_Method==1 and Spawn_Method==1 and NP<NC')['Alpha']\n",
    "print(valores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_np = [\"T_total\"]\n",
    "separated_np = [\"T_Malleability\", \"T_Redistribution\", \"T_spawn\", \"T_SR\", \"T_AR\", \"Alpha\", \"Omega\", \"count\"]\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",
    "    procsLists=[]\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",
    "                    procsLists.append((np_aux,ns_aux))\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, procsLists\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": 79,
   "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): #TODO Comprobar CDF de los grupos\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 H0\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 H0\n",
    "                    df_Res.iat[i, j] = True # Not equal medians\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False # Equal medians\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": 75,
   "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",
    "    print(\"Se usan tests parametricos: \"+str(use_parametric))\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",
    "                    if np_aux == 2 and ns_aux == 40:\n",
    "                        print(res_aux)\n",
    "                        print(times_aux)\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": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se usan tests parametricos: False\n",
      "       1      2      3      4      5      6      7      8      9      10  \\\n",
      "1   False   True   True   True   True   True   True   True   True   True   \n",
      "2    True  False   True  False  False  False   True  False  False   True   \n",
      "3    True   True  False  False   True   True  False   True  False   True   \n",
      "4    True  False  False  False  False   True   True  False  False   True   \n",
      "5    True  False   True  False  False  False   True  False  False  False   \n",
      "6    True  False   True   True  False  False   True  False   True  False   \n",
      "7    True   True  False   True   True   True  False   True   True   True   \n",
      "8    True  False   True  False  False  False   True  False   True  False   \n",
      "9    True  False  False  False  False   True   True   True  False   True   \n",
      "10   True   True   True   True  False  False   True  False   True  False   \n",
      "11   True  False  False  False  False   True   True  False  False   True   \n",
      "12   True  False  False  False   True   True  False   True  False   True   \n",
      "\n",
      "       11     12  \n",
      "1    True   True  \n",
      "2   False  False  \n",
      "3   False  False  \n",
      "4   False  False  \n",
      "5   False   True  \n",
      "6    True   True  \n",
      "7    True  False  \n",
      "8   False   True  \n",
      "9   False  False  \n",
      "10   True   True  \n",
      "11  False  False  \n",
      "12  False  False  \n",
      "[3.8480600000000003, 2.1302405, 2.1476735, 2.133754, 2.128617, 2.126146, 2.187595, 2.127134, 2.133725, 2.1264885, 2.1387609999999997, 2.138086]\n",
      "[[2, 8, 9, 7], [5, 9, 7, 4, 1], [11, 7, 9, 8, 1, 5, 2], [9, 8, 3, 7, 5, 11, 4], [10, 9, 8, 2, 11, 7, 3, 1], [8, 9, 2], [3, 9], [3, 9], [2, 3, 9, 8], [3, 9, 8, 2, 4], [8, 2, 9], [2, 3, 8, 9], [3, 9], [3, 5, 4, 9], [4, 5, 3, 8, 9], [9, 8], [3, 8, 2], [2, 3, 9, 8], [9, 3, 8], [3, 9], [9, 8], [3, 8], [3, 9, 8], [9, 8, 3], [3, 9], [9, 8], [3], [3, 9], [3, 9, 8], [2, 8, 9, 3]]\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "checked_type='T_Redistribution'\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": "raw",
   "metadata": {},
   "source": [
    "Se usan tests parametricos: False\n",
    "       1      2      3      4      5      6      7      8\n",
    "1  False   True   True   True   True   True   True   True\n",
    "2   True  False   True  False   True  False  False   True\n",
    "3   True   True  False  False  False   True  False   True\n",
    "4   True  False  False  False   True   True  False   True\n",
    "5   True   True  False   True  False   True   True   True\n",
    "6   True  False   True   True   True  False   True  False\n",
    "7   True  False  False  False   True   True  False   True\n",
    "8   True   True   True   True   True  False   True  False\n",
    "[3.8480600000000003, 2.1302405, 2.1476735, 2.133754, 2.187595, 2.127134, 2.133725, 2.1264885]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 5 6 7]\n",
      "[ 1 10 21  3 15 20]\n",
      "[[-1  7  7  7  3  7]\n",
      " [ 7 -1  3  3  3  3]\n",
      " [ 7  3 -1  3  3  3]\n",
      " [ 7  3  3 -1  3  3]\n",
      " [ 7  3  3  3 -1  3]\n",
      " [ 7  3  3  3  3  8]]\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(aux_keys)\n",
    "print(aux_counts)\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5684/3507456282.py:53: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_5684/3507456282.py:54: 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_Redistribution.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\"), #BAllS\n",
    "    colors.to_rgba(\"darkgreen\"), #BP2PS\n",
    "    colors.to_rgba(\"red\"), #MAllS\n",
    "    colors.to_rgba(\"darkred\"),  #MP2PS\n",
    "    #colors.to_rgba(\"blue\"),  #BIntraAllS\n",
    "    #colors.to_rgba(\"royalblue\"), #BIntraP2PS\n",
    "    colors.to_rgba(\"mediumseagreen\"), #BAllA\n",
    "    #colors.to_rgba(\"seagreen\"),  #BAllT\n",
    "    colors.to_rgba(\"palegreen\"), #BP2PA\n",
    "    #colors.to_rgba(\"springgreen\"), #BP2PT\n",
    "    colors.to_rgba(\"indianred\"), #MAllA \n",
    "    #colors.to_rgba(\"firebrick\"), #MAllT\n",
    "    colors.to_rgba(\"darkgoldenrod\"), #MP2PA\n",
    "    #colors.to_rgba(\"saddlebrown\"), #MP2PT\n",
    "    #colors.to_rgba(\"mediumblue\"),  #BIntraAllA\n",
    "    #colors.to_rgba(\"mediumslateblue\"), #BIntraP2PA\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(\"NH\", 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",
    "labelsMethods_aux = ['Baseline - AllS (0)', 'Baseline - P2PS (1)',\n",
    "                    'Merge - AllS (2)','Merge - P2PS (3)',\n",
    "                    'Baseline - AllA (4)', 'Baseline - P2PA (6)',\n",
    "                    'Merge - AllA (8)','Merge - P2PA (10)']\n",
    "#labelsMethods_aux = ['Baseline - AllS (0)', 'Baseline - P2PS (1)',\n",
    "#                    'Merge - AllS (2)','Merge - P2PS (3)',\n",
    "#                    'BaselineIntra - AllS (4)', 'BaselineIntra - P2PS (5)',\n",
    "#                    'Baseline - AllA (6)', 'Baseline - P2PA (7)',\n",
    "#                    'Merge - AllA (8)','Merge - P2PA (9)',\n",
    "#                    'BaselineIntra - AllA (10)', 'BaselineIntra - P2PA (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",
    "    tick_bar.append(0.35 + i*0.89) #Config de 8 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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 5 6 7]\n",
      "[ 1 10 21  3 15 20]\n",
      "[2 3 5 6 7]\n",
      "[ 5 14  1  2  8]\n"
     ]
    }
   ],
   "source": [
    "aux_array = [] #Counts all\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) # Counts ganador celda\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_arrays(arrays, norm_array):\n",
    "    new_arrays = arrays.copy()\n",
    "    for index in range(len(new_arrays)):\n",
    "        new_arrays[index] = np.divide(norm_array, new_arrays[index])\n",
    "    return new_arrays\n",
    "\n",
    "def create_labels_lineplot(used_direction, user_condition=lambda a, b: True):\n",
    "    labels_aux = []\n",
    "    if used_direction == 's':\n",
    "        for ns_aux in processes:\n",
    "            for np_aux in processes:\n",
    "                if used_direction=='s' and np_aux > ns_aux and np_aux != ns_aux and user_condition(np_aux, ns_aux):\n",
    "                    new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "                    labels_aux.append(new_label)\n",
    "    else:\n",
    "        for np_aux in processes:\n",
    "            for ns_aux in processes:\n",
    "                if ((used_direction=='e' and np_aux < ns_aux) or used_direction=='a') and np_aux != ns_aux and user_condition(np_aux, ns_aux):\n",
    "                    new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "                    labels_aux.append(new_label)\n",
    "    return labels_aux\n",
    "\n",
    "def reorder_data(plot_data, actual_order, expected_order):\n",
    "    ordered_indexes = []\n",
    "    len_order = len(actual_order)\n",
    "    for index in range(len_order):\n",
    "        actual_order[index] = str(actual_order[index]).replace(\" \", \"\")\n",
    "    for index in range(len_order):\n",
    "        ordered_indexes.append(actual_order.index(expected_order[index]))\n",
    "\n",
    "    for index in range(len(plot_data)):\n",
    "        old_array = plot_data[index]\n",
    "        new_array = []\n",
    "        for i in ordered_indexes:\n",
    "            new_array.append(old_array[i])\n",
    "        plot_data[index] = new_array\n",
    "\n",
    "    return plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.1230700000000002, 3.730729, 4.6479675, 5.26466, 5.6192905, 3.0939765, 2.863269, 2.647779, 2.6379925, 3.2440125, 3.038315, 2.7640025, 5.1167615, 3.5700545, 7.090923]\n",
      "[1.4319549999999999, 1.394601, 1.220685, 1.298375, 1.3671995, 0.9400275, 0.6719925, 0.6960415, 0.7100595000000001, 0.68602, 0.534454, 0.5140184999999999, 0.5239955, 0.510054, 0.5126065]\n",
      "[1.357688, 1.2037054999999999, 1.1534455000000001, 1.130536, 1.092661, 0.5921194999999999, 0.56791, 0.6282909999999999, 0.656953, 0.4608915, 0.4152825, 0.39157, 0.32342899999999997, 0.27383749999999996, 0.333684]\n",
      "[2.0162865, 1.6124580000000002, 1.3236625, 1.223817, 1.1527145, 0.5932595, 0.5585425, 0.6140625, 0.648736, 0.4475355, 0.4096425, 0.382405, 0.31442749999999997, 0.27645600000000004, 0.3180485]\n",
      "[1.4769519999999998, 1.323029, 1.501624, 1.458258, 1.448923, 0.8399605, 0.6505395, 0.703739, 0.7473245, 0.7015480000000001, 0.53992, 0.4693765, 0.480945, 0.4350995, 0.444936]\n",
      "[1.3755335, 1.239099, 1.3777045, 1.458161, 1.239231, 0.8138375, 0.6265985000000001, 0.6751545, 0.694267, 0.6432665, 0.529161, 0.43877900000000003, 0.4668525, 0.479574, 0.39924899999999997]\n",
      "[1.493053, 1.8106175, 1.9629, 2.0201770000000003, 2.0660965, 0.90201, 0.751945, 0.799928, 0.792003, 0.77396, 0.6174645000000001, 0.6120245, 0.6260335, 0.547955, 0.5444735]\n",
      "[1.3314165, 1.3399135, 1.2740145, 1.2413235, 1.380285, 0.9260120000000001, 0.6979385, 0.70788, 0.7500135, 0.6700465, 0.6044959999999999, 0.5380275, 0.583941, 0.489979, 0.56203]\n",
      "[1.435041, 1.210299, 1.1321245, 1.0854905000000001, 1.0665645000000001, 0.66032, 0.581952, 0.63175, 0.6605585, 0.47579099999999996, 0.40801350000000003, 0.38675899999999996, 0.3030835, 0.271762, 0.2407405]\n",
      "[1.4948575, 1.2083599999999999, 1.1219510000000001, 1.0785845, 1.071128, 0.5237855, 0.5626085000000001, 0.616053, 0.6464595, 0.4435395, 0.390105, 0.37999, 0.27031550000000004, 0.321728, 0.22490749999999998]\n",
      "[1.4368455, 1.187773, 1.3968129999999999, 1.4161044999999999, 1.3485585, 0.9199809999999999, 0.6431825, 0.722252, 0.7410805, 0.714459, 0.5510455, 0.5539400000000001, 0.613928, 0.465757, 0.490037]\n",
      "[1.313856, 1.3673935, 1.34475, 1.495737, 1.2663950000000002, 1.0199085, 0.6539535000000001, 0.7104845, 0.7598875, 0.6176865, 0.5516115, 0.493641, 0.541794, 0.49077899999999997, 0.4776335]\n"
     ]
    },
    {
     "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='e'\n",
    "test_parameter='T_Redistribution' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha'\n",
    "    name_legend = \"Valores de α\"\n",
    "    normalize = True\n",
    "    allow_all = False\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM[grouped_aggM[real_parameter] > 0]\n",
    "elif test_parameter == 'T_Malleability':\n",
    "    name_fig=\"Malleability_\"\n",
    "    real_parameter='T_Malleability'\n",
    "    name_legend = \"Time(s)\"\n",
    "    normalize = False\n",
    "    allow_all = True\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM\n",
    "elif test_parameter == 'T_Redistribution':\n",
    "    name_fig=\"Redistribution_\"\n",
    "    real_parameter='T_Redistribution'\n",
    "    name_legend = \"Tiempo(s)\"\n",
    "    normalize = False\n",
    "    allow_all = True\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega'\n",
    "    name_legend = \"Values of ω\"\n",
    "    normalize = True\n",
    "    allow_all = False\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggLAsynch[grouped_aggLAsynch[real_parameter] > 0]\n",
    "elif test_parameter == 'iters_count':\n",
    "    name_fig=\"Iters_\"\n",
    "    real_parameter='count'\n",
    "    name_legend = \"Asynchronous iterations\"\n",
    "    normalize = False\n",
    "    allow_all = True\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggLAsynch[grouped_aggLAsynch[real_parameter] > 0]\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",
    "#data_aux=data_aux.query('NP == 160 or NC == 160')\n",
    "\n",
    "plot_data = []\n",
    "for config in used_config:\n",
    "    if config[0] > 0 or allow_all:\n",
    "        dataLists,procsLists = 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 = create_labels_lineplot(used_direction, lambda a, b: a == 160 or b == 160)\n",
    "labels_aux = create_labels_lineplot(used_direction)\n",
    "plot_data = reorder_data(plot_data, procsLists, labels_aux)\n",
    "    \n",
    "#labelsMethods_aux = ['Baseline - AllA', 'Baseline - P2PA',\n",
    "#                    'Merge - AllA','Merge - P2PA',\n",
    "#                    'BaselineIntra - AllA', 'BaselineIntra - P2PA']\n",
    "labelsMethods_aux = ['Baseline - AllS', 'Baseline - P2PS',\n",
    "                    'Merge - AllS','Merge - P2PS',\n",
    "                    'BaselineIntra - AllS', 'BaselineIntra - P2PS',\n",
    "                    'Baseline - AllA', 'Baseline - P2PA',\n",
    "                    'Merge - AllA','Merge - P2PA',\n",
    "                    'BaselineIntra - AllA', 'BaselineIntra - P2PA']\n",
    "colors_m = ['green','darkgreen','red','darkred', 'blue', 'royalblue', 'mediumseagreen','palegreen','indianred','darkgoldenrod','mediumblue', 'midnightblue']\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",
    "    plot_index = index\n",
    "    if real_parameter == 'Alpha' or real_parameter == 'Omega' or real_parameter == 'count': #FIXME This line is a lie...\n",
    "        plot_index = 6 + index #FIXME This line is a lie...\n",
    "    #if real_parameter != 'Alpha' and index > 3: #FIXME This line is a lie...\n",
    "    #    plot_index = 4 + (index-4)*2 #FIXME This line is a lie...\n",
    "    print(array_aux)\n",
    "    ax.plot(x, array_aux, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n",
    "        marker=markers_m[plot_index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "\n",
    "ax.set_xlabel(\"(NP,NH)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "if normalize:\n",
    "    ax.axhline(y=1, color='black', linestyle='--')\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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='s'\n",
    "test_parameter='T_total' #Valores son \"alpha\" o \"omega\"\n",
    "intranode=False\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",
    "    data_aux_cmp=grouped_aggM.reset_index()\n",
    "    name_fig= name_fig+\"All\"\n",
    "    \n",
    "if intranode:\n",
    "    data_aux_cmp = data_aux_cmp.query('NP <= 20 and NC <= 20')\n",
    "else:\n",
    "    #data_aux_cmp = data_aux_cmp.query('NP > 20 and NC > 20')\n",
    "    data_aux_cmp = data_aux_cmp.query('NP == 160 or NC == 160')\n",
    "\n",
    "if used_direction!='a' or True:\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,procsLists = 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",
    "plot_data_normalized = normalize_arrays(plot_data[1:], plot_data[0])\n",
    "name_legend=\"SpeedUp over Baseline\"\n",
    "\n",
    "labels_aux = create_labels_lineplot(used_direction, lambda a, b: a == 160 or b == 160)\n",
    "#labels_aux = create_labels_lineplot(used_direction)\n",
    "#labelsMethods_aux = ['Baseline - All', 'Baseline - P2P','Merge - All','Merge - P2P']\n",
    "labelsMethods_aux = ['Baseline - AllS', 'Baseline - P2PS',\n",
    "                    'Merge - AllS','Merge - P2PS',\n",
    "                    '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",
    "ax2 = ax.twinx()\n",
    "x = np.arange(len(labels_aux))\n",
    "for index in range(len(plot_data_normalized)):\n",
    "    array_aux = plot_data_normalized[index]\n",
    "    index= index+1\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",
    "ax2.plot(x, plot_data[0], color='black', linestyle=linestyle_m[0], \\\n",
    "        marker=markers_m[0], markersize=18, label=labelsMethods_aux[0])\n",
    "ax.axhline(y=1, color='black', linestyle='--')\n",
    "\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "ax.tick_params(axis='both', which='both', labelsize=36)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels_aux, rotation=90)\n",
    "#ax.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "\n",
    "ax2.set_ylabel('Baseline Time(s)', fontsize=36)\n",
    "ax2.tick_params(axis='y', which='both', labelsize=36)\n",
    "#ax2.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "\n",
    "f.legend(bbox_to_anchor=(0.5, 0.98), fontsize=26,ncol=2,framealpha=0.8)\n",
    "\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": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_11912/3519964640.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  dfG['Redistribution_Method'].iloc[where_true] = [(0, 2)] * len(where_true)\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>Total_Resizes</th>\n",
       "      <th>Total_Groups</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>Iters</th>\n",
       "      <th>Asynch_Iters</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_Malleability</th>\n",
       "      <th>T_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100074, 0.100068, 0.100163, 0.100258, 0.10...</td>\n",
       "      <td>(((0.100073,), (0.100068,), (0.100077,), (0.10...</td>\n",
       "      <td>(0.663998,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.300401,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>[1.966417]</td>\n",
       "      <td>3.111253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100299, 0.100173, 0.100076, 0.100077, 0.10...</td>\n",
       "      <td>(((0.100299,), (0.100166,), (0.100076,), (0.10...</td>\n",
       "      <td>(0.747897,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.225241,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>[1.975229]</td>\n",
       "      <td>3.103462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100071, 0.101263, 0.100182, 0.100076, 0.10...</td>\n",
       "      <td>(((0.100071,), (0.10035,), (0.100076,), (0.100...</td>\n",
       "      <td>(0.662863,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.3332,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>[1.998003]</td>\n",
       "      <td>3.125520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100171, 0.100067, 0.100545, 0.100076, 0.10...</td>\n",
       "      <td>(((0.100064,), (0.100066,), (0.100545,), (0.10...</td>\n",
       "      <td>(0.620327,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.144137,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>[1.765891]</td>\n",
       "      <td>2.886963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 2)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100296, 0.100076, 0.100311, 0.100164, 0.10...</td>\n",
       "      <td>(((0.100166,), (0.100071,), (0.100291,), (0.10...</td>\n",
       "      <td>(0.661238,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.303134,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>[1.965799]</td>\n",
       "      <td>3.101193</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>1195</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100926, 0.10083, 0.101691, 0.101116, 0.100...</td>\n",
       "      <td>(((0.100808,), (0.100772,), (0.100783,), (0.10...</td>\n",
       "      <td>(0.126827,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.269128,)</td>\n",
       "      <td>[1.397743]</td>\n",
       "      <td>2.614686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.105365, 0.101082, 0.104509, 0.100901, 0.10...</td>\n",
       "      <td>(((0.100806,), (0.100772,), (0.100781,), (0.10...</td>\n",
       "      <td>(0.129203,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.361718,)</td>\n",
       "      <td>[1.496593]</td>\n",
       "      <td>2.612697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100876, 0.100847, 0.101514, 0.100818, 0.10...</td>\n",
       "      <td>(((0.100782,), (0.100781,), (0.100799,), (0.10...</td>\n",
       "      <td>(0.105669,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.297036,)</td>\n",
       "      <td>[1.404399]</td>\n",
       "      <td>2.447727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.100859, 0.100237, 0.100222, 0.100204, 0.10...</td>\n",
       "      <td>(((0.099892,), (0.099905,), (0.099889,), (0.09...</td>\n",
       "      <td>(0.113739,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.346015,)</td>\n",
       "      <td>[1.464036]</td>\n",
       "      <td>2.557442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5000000000</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(5, 5)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.104668, 0.100913, 0.100822, 0.100855, 0.10...</td>\n",
       "      <td>(((0.100783,), (0.100784,), (0.100772,), (0.10...</td>\n",
       "      <td>(0.137782,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(1.269511,)</td>\n",
       "      <td>[1.412476]</td>\n",
       "      <td>2.594925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2400 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_Resizes  Total_Groups  Total_Stages  Granularity    SDR    ADR  \\\n",
       "0                 1             2             1       100000  100.0    0.0   \n",
       "1                 1             2             1       100000  100.0    0.0   \n",
       "2                 1             2             1       100000  100.0    0.0   \n",
       "3                 1             2             1       100000  100.0    0.0   \n",
       "4                 1             2             1       100000  100.0    0.0   \n",
       "...             ...           ...           ...          ...    ...    ...   \n",
       "1195              1             2             1       100000    0.0  100.0   \n",
       "1196              1             2             1       100000    0.0  100.0   \n",
       "1197              1             2             1       100000    0.0  100.0   \n",
       "1198              1             2             1       100000    0.0  100.0   \n",
       "1199              1             2             1       100000    0.0  100.0   \n",
       "\n",
       "              DR Redistribution_Method Redistribution_Strategy Spawn_Method  \\\n",
       "0     5000000000                (0, 2)                  (1, 1)       (0, 1)   \n",
       "1     5000000000                (0, 2)                  (1, 1)       (0, 1)   \n",
       "2     5000000000                (0, 2)                  (1, 1)       (0, 1)   \n",
       "3     5000000000                (0, 2)                  (1, 1)       (0, 1)   \n",
       "4     5000000000                (0, 2)                  (1, 1)       (0, 1)   \n",
       "...          ...                   ...                     ...          ...   \n",
       "1195  5000000000                (0, 1)                  (1, 1)       (0, 1)   \n",
       "1196  5000000000                (0, 1)                  (1, 1)       (0, 1)   \n",
       "1197  5000000000                (0, 1)                  (1, 1)       (0, 1)   \n",
       "1198  5000000000                (0, 1)                  (1, 1)       (0, 1)   \n",
       "1199  5000000000                (0, 1)                  (1, 1)       (0, 1)   \n",
       "\n",
       "      ...   Iters Asynch_Iters  \\\n",
       "0     ...  (5, 5)       (0, 0)   \n",
       "1     ...  (5, 5)       (0, 0)   \n",
       "2     ...  (5, 5)       (0, 0)   \n",
       "3     ...  (5, 5)       (0, 0)   \n",
       "4     ...  (5, 5)       (0, 0)   \n",
       "...   ...     ...          ...   \n",
       "1195  ...  (5, 5)       (0, 0)   \n",
       "1196  ...  (5, 5)       (0, 0)   \n",
       "1197  ...  (5, 5)       (0, 0)   \n",
       "1198  ...  (5, 5)       (0, 0)   \n",
       "1199  ...  (5, 5)       (0, 0)   \n",
       "\n",
       "                                                 T_iter  \\\n",
       "0     ((0.100074, 0.100068, 0.100163, 0.100258, 0.10...   \n",
       "1     ((0.100299, 0.100173, 0.100076, 0.100077, 0.10...   \n",
       "2     ((0.100071, 0.101263, 0.100182, 0.100076, 0.10...   \n",
       "3     ((0.100171, 0.100067, 0.100545, 0.100076, 0.10...   \n",
       "4     ((0.100296, 0.100076, 0.100311, 0.100164, 0.10...   \n",
       "...                                                 ...   \n",
       "1195  ((0.100926, 0.10083, 0.101691, 0.101116, 0.100...   \n",
       "1196  ((0.105365, 0.101082, 0.104509, 0.100901, 0.10...   \n",
       "1197  ((0.100876, 0.100847, 0.101514, 0.100818, 0.10...   \n",
       "1198  ((0.100859, 0.100237, 0.100222, 0.100204, 0.10...   \n",
       "1199  ((0.104668, 0.100913, 0.100822, 0.100855, 0.10...   \n",
       "\n",
       "                                               T_stages      T_spawn  \\\n",
       "0     (((0.100073,), (0.100068,), (0.100077,), (0.10...  (0.663998,)   \n",
       "1     (((0.100299,), (0.100166,), (0.100076,), (0.10...  (0.747897,)   \n",
       "2     (((0.100071,), (0.10035,), (0.100076,), (0.100...  (0.662863,)   \n",
       "3     (((0.100064,), (0.100066,), (0.100545,), (0.10...  (0.620327,)   \n",
       "4     (((0.100166,), (0.100071,), (0.100291,), (0.10...  (0.661238,)   \n",
       "...                                                 ...          ...   \n",
       "1195  (((0.100808,), (0.100772,), (0.100783,), (0.10...  (0.126827,)   \n",
       "1196  (((0.100806,), (0.100772,), (0.100781,), (0.10...  (0.129203,)   \n",
       "1197  (((0.100782,), (0.100781,), (0.100799,), (0.10...  (0.105669,)   \n",
       "1198  (((0.099892,), (0.099905,), (0.099889,), (0.09...  (0.113739,)   \n",
       "1199  (((0.100783,), (0.100784,), (0.100772,), (0.10...  (0.137782,)   \n",
       "\n",
       "     T_spawn_real         T_SR         T_AR T_Malleability   T_total  \n",
       "0            (0,)  (1.300401,)         (0,)     [1.966417]  3.111253  \n",
       "1            (0,)  (1.225241,)         (0,)     [1.975229]  3.103462  \n",
       "2            (0,)    (1.3332,)         (0,)     [1.998003]  3.125520  \n",
       "3            (0,)  (1.144137,)         (0,)     [1.765891]  2.886963  \n",
       "4            (0,)  (1.303134,)         (0,)     [1.965799]  3.101193  \n",
       "...           ...          ...          ...            ...       ...  \n",
       "1195         (0,)         (0,)  (1.269128,)     [1.397743]  2.614686  \n",
       "1196         (0,)         (0,)  (1.361718,)     [1.496593]  2.612697  \n",
       "1197         (0,)         (0,)  (1.297036,)     [1.404399]  2.447727  \n",
       "1198         (0,)         (0,)  (1.346015,)     [1.464036]  2.557442  \n",
       "1199         (0,)         (0,)  (1.269511,)     [1.412476]  2.594925  \n",
       "\n",
       "[2400 rows x 27 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "where_true = np.where(dfG.Redistribution_Method==(0,0))[0]\n",
    "dfG['Redistribution_Method'].iloc[where_true] = [(0, 2)] * len(where_true)\n",
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ethernet\n",
    "b1_aux = [82.747868, 83.191989, 95.520019, 87.435987, 90.843995, 150.356100]\n",
    "b2_aux = [75.238174, 74.380054, 74.755995, 42.656109, 21.588040, 17.843997]\n",
    "m1_aux = [74.654167, 74.357901, 74.351350, 43.599915, 21.637509, 15.128712]\n",
    "m2_aux = [74.353249, 74.359214, 74.356160, 43.874266, 21.511082, 14.969010]\n",
    "\n",
    "b3_aux = [105.128014, 110.004008, 126.552019, 116.312400, 95.752019, 151.023994]\n",
    "b4_aux = [83.021885, 77.632630, 75.396010, 43.076039, 24.028075, 19.556047]\n",
    "m3_aux = [118.275992, 83.027866, 81.008479, 46.432212, 24.247949, 17.725083]\n",
    "m4_aux = [119.286457, 84.205993, 80.741585, 47.144632, 24.206617, 17.738496]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Infiniband\n",
    "b1_aux = [64.669525, 35.171971, 38.916010, 47.456630, 56.288048, 119.428020]\n",
    "b2_aux = [36.538361, 15.536046, 13.396083, 9.652013, 5.772058, 5.615009]\n",
    "m1_aux = [61.664380, 18.400559, 19.112526, 22.155880, 11.712381, 30.775627]\n",
    "m2_aux = [33.428639, 13.905561, 14.691367, 7.363081, 6.629037, 12.150872]\n",
    "\n",
    "b3_aux = [91.368664, 60.648074, 53.663981, 49.152031, 64.752057, 118.243807]\n",
    "b4_aux = [84.941260, 34.039990, 26.008021, 12.298989, 7.916004, 5.736054]\n",
    "m3_aux = [105.839726, 26.822071, 23.834452, 12.876862, 9.063136, 7.007535]\n",
    "m4_aux = [75.412319, 25.566336, 22.129483, 12.491161, 7.903744, 6.534291]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82.747868, 83.191989, 95.520019, 87.435987, 90.843995, 150.3561]\n",
      "[74.654167, 74.357901, 74.35135, 43.599915, 21.637509, 15.128712]\n",
      "[75.238174, 74.380054, 74.755995, 42.656109, 21.58804, 17.843997]\n",
      "[74.353249, 74.359214, 74.35616, 43.874266, 21.511082, 14.96901]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plot_data_e = [b1_aux, m1_aux, b2_aux, m2_aux] #Expand\n",
    "plot_data_s = [b3_aux, m3_aux, b4_aux, m4_aux] #Shrink\n",
    "\n",
    "labels_aux_e = create_labels_lineplot('e', lambda a, b: a == 160 or b == 160)\n",
    "labels_aux_s = create_labels_lineplot('s', lambda a, b: a == 160 or b == 160)\n",
    "#labels_aux = create_labels_lineplot(used_direction)\n",
    "\n",
    "labelsMethods_aux = ['Baseline - AllS', 'Merge - AllS',\n",
    "                     'Baseline - AllA','Merge - AllA']\n",
    "\n",
    "#labelsMethods_aux = ['Baseline - All', 'Baseline - P2P','Merge - All','Merge - P2P']\n",
    "\n",
    "f, (axe, axs)=plt.subplots(1,2,figsize=(22, 14))\n",
    "x = np.arange(len(labels_aux_e))\n",
    "for index in range(len(plot_data_e)):\n",
    "    array_aux_e = plot_data_e[index]\n",
    "    array_aux_s = plot_data_s[index]\n",
    "    plot_index = index\n",
    "    if index > 0:\n",
    "        plot_index = 2**plot_index\n",
    "    print(array_aux_e)\n",
    "    axe.plot(x, array_aux_e, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n",
    "        marker=markers_m[plot_index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "    axs.plot(x, array_aux_s, color=colors_m[plot_index%len(colors_m)], linestyle=linestyle_m[plot_index%len(linestyle_m)], \\\n",
    "        marker=markers_m[plot_index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "\n",
    "axe.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "axe.set_ylabel(\"Time(s)\", fontsize=36)\n",
    "axe.set_xticks(x)\n",
    "axe.set_xticklabels(labels_aux_e, rotation=90)\n",
    "axe.tick_params(axis='both', which='major', labelsize=36)\n",
    "axe.tick_params(axis='both', which='minor', labelsize=36)\n",
    "\n",
    "axs.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "axs.set_ylabel(\"Time(s)\", fontsize=36)\n",
    "axs.set_xticks(x)\n",
    "axs.set_xticklabels(labels_aux_s, rotation=90)\n",
    "axs.tick_params(axis='both', which='major', labelsize=36)\n",
    "axs.tick_params(axis='both', which='minor', labelsize=36)\n",
    "\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/LinePlot_100Gb.png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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