new_analyser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "AllName=\"dataG.pkl\"\n",
    "ResizesName=\"dataM.pkl\"\n",
    "ItersName=\"dataL.pkl\"\n",
    "matrixIt_Total=\"data_L_Total.csv\"\n",
    "n_cores=20\n",
    "repet = 5 #CAMBIAR EL NUMERO SEGUN NUMERO DE EJECUCIONES POR CONFIG\n",
    "\n",
    "significance_value = 0.05\n",
    "processes = [2,10,20,40,80,120,160]\n",
    "\n",
    "positions = [321, 322, 323, 324, 325]\n",
    "positions_small = [221, 222, 223, 224]\n",
    "\n",
    "labels = ['(1,10)',   '(1,20)',   '(1,40)',  '(1,80)',  '(1,120)','(1,160)',\n",
    "            '(10,1)', '(10,20)',  '(10,40)', '(10,80)', '(10,120)','(10,160)',\n",
    "            '(20,1)', '(20,10)',  '(20,40)', '(20,80)', '(20,120)','(20,160)',\n",
    "            '(40,1)', '(40,10)',  '(40,20)', '(40,80)', '(40,120)','(40,160)',\n",
    "            '(80,1)', '(80,10)',  '(80,20)', '(80,40)', '(80,120)','(80,160)',\n",
    "            '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)','(120,160)',\n",
    "            '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n",
    "\n",
    "labelsExpand = ['(1,10)',   '(1,20)',   '(1,40)',  '(1,80)',  '(1,120)','(1,160)',\n",
    "            '(10,20)',  '(10,40)', '(10,80)', '(10,120)','(10,160)',\n",
    "            '(20,40)', '(20,80)', '(20,120)','(20,160)',\n",
    "            '(40,80)', '(40,120)','(40,160)',\n",
    "            '(80,120)','(80,160)',\n",
    "            '(120,160)']\n",
    "labelsShrink = ['(10,1)', \n",
    "            '(20,1)', '(20,10)',\n",
    "            '(40,1)', '(40,10)',  '(40,20)',\n",
    "            '(80,1)', '(80,10)',  '(80,20)', '(80,40)',\n",
    "            '(120,1)','(120,10)', '(120,20)','(120,40)','(120,80)',\n",
    "            '(160,1)','(160,10)', '(160,20)','(160,40)','(160,80)','(160,120)']\n",
    "\n",
    "#                       WORST        BEST\n",
    "labels_dist = ['null', 'SpreadFit', 'CompactFit']\n",
    "                  #0          #1                #2                        #3\n",
    "labelsMethods = ['Baseline', 'Baseline single','Baseline - Asynchronous','Baseline single - Asynchronous',\n",
    "                 'Merge','Merge single','Merge - Asynchronous','Merge single - Asynchronous']\n",
    "                  #4      #5             #6                     #7\n",
    "    \n",
    "colors_m = ['green','springgreen','blue','darkblue','red','darkred','darkgoldenrod','olive','violet']\n",
    "linestyle_m = ['-', '--', '-.', ':']\n",
    "markers_m = ['.','v','s','p', 'h','d','X','P','^']\n",
    "\n",
    "OrMult_patch = mpatches.Patch(hatch='', facecolor='green', label='Baseline')\n",
    "OrSing_patch = mpatches.Patch(hatch='', facecolor='springgreen', label='Baseline single')\n",
    "OrPthMult_patch = mpatches.Patch(hatch='//', facecolor='blue', label='Baseline - Asyncrhonous')\n",
    "OrPthSing_patch = mpatches.Patch(hatch='\\\\', facecolor='darkblue', label='Baseline single - Asyncrhonous')\n",
    "MergeMult_patch = mpatches.Patch(hatch='||', facecolor='red', label='Merge')\n",
    "MergeSing_patch = mpatches.Patch(hatch='...', facecolor='darkred', label='Merge single')\n",
    "MergePthMult_patch = mpatches.Patch(hatch='xx', facecolor='yellow', label='Merge - Asyncrhonous')\n",
    "MergePthSing_patch = mpatches.Patch(hatch='++', facecolor='olive', label='Merge single - Asyncrhonous')\n",
    "\n",
    "handles_spawn = [OrMult_patch,OrSing_patch,OrPthMult_patch,OrPthSing_patch,MergeMult_patch,MergeSing_patch,MergePthMult_patch,MergePthSing_patch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_16526/462116935.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_16526/462116935.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": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfL = pd.read_pickle( ItersName )\n",
    "\n",
    "dfL['ADR'] = round((dfL['ADR'] / dfL['DR']) * 100,1)\n",
    "dfL['SDR'] = round((dfL['SDR'] / dfL['DR']) * 100,1)\n",
    "dfL['ADR'].fillna(-1, inplace=True)\n",
    "dfL['SDR'].fillna(-1, inplace=True)\n",
    "dfL['DR'].fillna(-1, inplace=True)\n",
    "       \n",
    "aux_df = dfL[(dfL.Asynch_Iters == True)]\n",
    "group = aux_df.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_iter']\n",
    "grouped_aggLAsynch = group.agg(['median','count'])\n",
    "grouped_aggLAsynch.columns = grouped_aggLAsynch.columns.get_level_values(0)\n",
    "grouped_aggLAsynch['T_sum'] = grouped_aggLAsynch['count'] * grouped_aggLAsynch['median'] / repet\n",
    "grouped_aggLAsynch.rename(columns={'median':'T_iter'}, inplace=True) \n",
    "group = aux_df.groupby(['ADR','Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','NC'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLAsynch['T_stages'] = aux_column\n",
    "\n",
    "aux_df = dfL[(dfL.Asynch_Iters == False)]\n",
    "group = aux_df.groupby('NP')['T_iter']\n",
    "grouped_aggLSynch = group.agg(['median'])\n",
    "grouped_aggLSynch.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLSynch['T_stages'] = aux_column\n",
    "\n",
    "aux_df2 = aux_df[(aux_df.Is_Dynamic == True)]\n",
    "group = aux_df2.groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_iter']\n",
    "grouped_aggLDyn = group.agg(['median'])\n",
    "grouped_aggLDyn.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df2.groupby(['ADR', 'Spawn_Method','Redistribution_Method', 'Redistribution_Strategy','NP','N_Parents'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLDyn['T_stages'] = aux_column\n",
    "\n",
    "aux_df2 = aux_df[(aux_df.Is_Dynamic == False)]\n",
    "group = aux_df2.groupby('NP')['T_iter']\n",
    "grouped_aggLNDyn = group.agg(['median'])\n",
    "grouped_aggLNDyn.rename(columns={'median':'T_iter'}, inplace=True)\n",
    "group = aux_df2.groupby(['NP'])['T_stages']\n",
    "aux_column = group.apply(list).apply(lambda x: np.median(x,0))\n",
    "grouped_aggLNDyn['T_stages'] = aux_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 0, 0, 1], [0, 0, 1, 1], [0, 1, 0, 1], [0, 1, 1, 1], [96.6, 0, 0, 1], [96.6, 0, 0, 2], [96.6, 0, 1, 1], [96.6, 0, 1, 2], [96.6, 1, 0, 1], [96.6, 1, 0, 2], [96.6, 1, 1, 1], [96.6, 1, 1, 2]]\n",
      "[[-1, (0, -1), (1, -1), (2, -1)], [-1, (0, -1), (0, -1), (2, -1)], [-1, (1, -1), (0, -1), (2, -1)], [-1, (1, -1), (1, -1), (1, -1)], [-1, (0, -1), (1, -1), (1, -1)], [-1, (0, -1), (0, -1), (1, -1)], [-1, (1, -1), (1, -1), (2, -1)], [-1, (1, -1), (0, -1), (1, -1)]]\n",
      "[[0, (0, 0), (0, 0), (1, 1)], [0, (0, 0), (0, 1), (1, 1)], [0, (0, 1), (0, 0), (1, 1)], [0, (0, 1), (0, 1), (1, 1)], [96.6, (0, 0), (0, 0), (1, 1)], [96.6, (0, 0), (0, 0), (1, 2)], [96.6, (0, 0), (0, 1), (1, 1)], [96.6, (0, 0), (0, 1), (1, 2)], [96.6, (0, 1), (0, 0), (1, 1)], [96.6, (0, 1), (0, 0), (1, 2)], [96.6, (0, 1), (0, 1), (1, 1)], [96.6, (0, 1), (0, 1), (1, 2)]]\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "adr = [0,96.6]\n",
    "sp_method = [0,1]\n",
    "rd_method = [0,1]\n",
    "rd_strat  = [1,2]\n",
    "parameters = [adr, sp_method, rd_method, rd_strat]\n",
    "parameters_names = ['ADR', 'Spawn_Method', 'Redistribution_Method', 'Redistribution_Strategy']\n",
    "len_parameters = [1,2,2,2]\n",
    "configurations_aux = obtenerConfs(parameters)\n",
    "configurations = []\n",
    "configurations_local_dynamic = set()\n",
    "configurations_local = set()\n",
    "configurations_simple = []\n",
    "for checked_conf in configurations_aux:\n",
    "    aux_conf = modifyToGlobal(parameters, len_parameters, checked_conf)\n",
    "    if CheckConfExists(aux_conf, grouped_aggG):\n",
    "        configurations.append(aux_conf)\n",
    "\n",
    "    if CheckConfExists(checked_conf, grouped_aggM, 'malleability'):\n",
    "        configurations_simple.append(list(checked_conf))\n",
    "        \n",
    "    aux_conf = modifyToLocalDynamic(parameters, len_parameters, checked_conf)\n",
    "    if CheckConfExists(aux_conf, grouped_aggLDyn, 'local'):\n",
    "        configurations_local_dynamic.add(aux_conf)\n",
    "\n",
    "configurations_local_dynamic = list(configurations_local_dynamic)\n",
    "for index in range(len(configurations_local_dynamic)):\n",
    "    configurations_local_dynamic[index] = list(configurations_local_dynamic[index])\n",
    "\n",
    "print(configurations_simple)\n",
    "print(configurations_local_dynamic)\n",
    "print(configurations)\n",
    "print(len(configurations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ALPHA COMPUTATION\n",
    "def compute_alpha(config_a, config_b):\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                config_a.append(np_aux)\n",
    "                config_a.append(ns_aux)\n",
    "                config_b.append(np_aux)\n",
    "                config_b.append(ns_aux)\n",
    "                grouped_aggM.loc[tuple(config_b),'Alpha'] = grouped_aggM.loc[tuple(config_b),'T_Malleability'] / grouped_aggM.loc[tuple(config_a),'T_Malleability']\n",
    "                config_a.pop()\n",
    "                config_a.pop()\n",
    "                config_b.pop()\n",
    "                config_b.pop()\n",
    "                \n",
    "                \n",
    "                config_a.insert(0,ns_aux)\n",
    "                config_a.insert(0,np_aux)\n",
    "                config_b.insert(0,ns_aux)\n",
    "                config_b.insert(0,np_aux)\n",
    "                out_grouped_M.loc[tuple(config_b),'Alpha'] = out_grouped_M.loc[tuple(config_b),'T_Malleability'] / out_grouped_M.loc[tuple(config_a),'T_Malleability']\n",
    "                config_a.pop(0)\n",
    "                config_a.pop(0)\n",
    "                config_b.pop(0)\n",
    "                config_b.pop(0)\n",
    "\n",
    "if not ('Alpha' in grouped_aggM.columns):\n",
    "    for config_a in configurations_simple:\n",
    "        for config_b in configurations_simple:\n",
    "            if config_a[1:-1] == config_b[1:-1] and config_a[0] == 0 and config_b[0] != 0:\n",
    "                compute_alpha(config_a, config_b)\n",
    "else:\n",
    "    print(\"ALPHA already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#OMEGA COMPUTATION\n",
    "def compute_omega(config):\n",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                if len(config) > len(parameters):\n",
    "                    config.pop()\n",
    "                    config.pop()\n",
    "                config.append(np_aux)\n",
    "                config.append(ns_aux)\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega'] = grouped_aggLAsynch.at[tuple(config),'T_iter'] / grouped_aggLSynch.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLAsynch.at[tuple(config),'T_stages'] / grouped_aggLSynch.at[np_aux,'T_stages']\n",
    "                grouped_aggLAsynch.at[tuple(config),'Omega_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Omega' in grouped_aggLAsynch.columns):\n",
    "    for config in configurations:\n",
    "        if config[0] != 0:\n",
    "            compute_omega(config)\n",
    "else:\n",
    "    print(\"OMEGA already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n",
      "/home/usuario/miniconda3/lib/python3.9/site-packages/pandas/core/algorithms.py:1537: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  return arr.searchsorted(value, side=side, sorter=sorter)  # type: ignore[arg-type]\n"
     ]
    }
   ],
   "source": [
    "#Dynamic Coherence COMPUTATION\n",
    "def compute_dyn_coherency(config):\n",
    "    for np_aux in processes:\n",
    "        for n_parents_aux in processes:\n",
    "            if np_aux != n_parents_aux:\n",
    "                config.append(np_aux)\n",
    "                config.append(n_parents_aux)\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency'] = grouped_aggLDyn.at[tuple(config),'T_iter'] / grouped_aggLNDyn.at[np_aux,'T_iter']\n",
    "                value = grouped_aggLDyn.at[tuple(config),'T_stages'] / grouped_aggLNDyn.at[np_aux,'T_stages']\n",
    "                grouped_aggLDyn.at[tuple(config),'Dyn_Coherency_Stages'] = value.astype(object)\n",
    "                config.pop()\n",
    "                config.pop()\n",
    "\n",
    "if not ('Dyn_Coherency' in grouped_aggLDyn.columns):\n",
    "    for config in configurations_local_dynamic:\n",
    "        compute_dyn_coherency(config)\n",
    "else:\n",
    "    print(\"Dyn_Coherency already exists\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 148,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>T_Malleability</th>\n",
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       "      <th>T_spawn</th>\n",
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       "      <th>Alpha</th>\n",
       "      <th>Resize_Coherency</th>\n",
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       "    <tr>\n",
       "      <th>Spawn_Method</th>\n",
       "      <th>Redistribution_Method</th>\n",
       "      <th>Redistribution_Strategy</th>\n",
       "      <th>NP</th>\n",
       "      <th>NC</th>\n",
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       "      <td>0.369660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044619</td>\n",
       "      <td>1.020767</td>\n",
       "      <td>0.771066</td>\n",
       "      <td>1.212451</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.071983</td>\n",
       "      <td>1.104158</td>\n",
       "      <td>0.934080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.077780</td>\n",
       "      <td>1.001591</td>\n",
       "      <td>0.577800</td>\n",
       "      <td>1.933230</td>\n",
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       "      <th>40</th>\n",
       "      <td>1.843507</td>\n",
       "      <td>0.920655</td>\n",
       "      <td>0.918149</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.093907</td>\n",
       "      <td>0.818607</td>\n",
       "      <td>0.438732</td>\n",
       "      <td>7.390061</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.783597</td>\n",
       "      <td>0.869270</td>\n",
       "      <td>0.920874</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118352</td>\n",
       "      <td>0.750918</td>\n",
       "      <td>0.383346</td>\n",
       "      <td>11.721763</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1.738361</td>\n",
       "      <td>0.838482</td>\n",
       "      <td>0.945228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.127863</td>\n",
       "      <td>0.649751</td>\n",
       "      <td>0.333218</td>\n",
       "      <td>13.663289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">160</th>\n",
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       "      <td>3.503912</td>\n",
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       "      <td>0.336889</td>\n",
       "      <td>0.086018</td>\n",
       "      <td>0.374117</td>\n",
       "      <td>2.846176</td>\n",
       "      <td>1.135001</td>\n",
       "      <td>0.699213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.487746</td>\n",
       "      <td>3.075578</td>\n",
       "      <td>0.507027</td>\n",
       "      <td>0.308085</td>\n",
       "      <td>0.483989</td>\n",
       "      <td>2.632365</td>\n",
       "      <td>1.017463</td>\n",
       "      <td>0.745360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2.997133</td>\n",
       "      <td>1.817674</td>\n",
       "      <td>1.179459</td>\n",
       "      <td>1.134582</td>\n",
       "      <td>0.276230</td>\n",
       "      <td>1.497667</td>\n",
       "      <td>1.459617</td>\n",
       "      <td>0.251408</td>\n",
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       "      <td>0.749047</td>\n",
       "      <td>0.003917</td>\n",
       "      <td>0.860635</td>\n",
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       "      <td>0.507629</td>\n",
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       "      <td>0.548051</td>\n",
       "      <td>1.122762</td>\n",
       "      <td>0.842099</td>\n",
       "      <td>0.004101</td>\n",
       "      <td>0.543869</td>\n",
       "      <td>1.282174</td>\n",
       "      <td>0.425844</td>\n",
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      "text/plain": [
       "                                                                    T_Malleability  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                    \n",
       "0            0                     1                       2   10         1.450045   \n",
       "                                                               20         2.071983   \n",
       "                                                               40         1.843507   \n",
       "                                                               80         1.783597   \n",
       "                                                               120        1.738361   \n",
       "...                                                                            ...   \n",
       "1            1                     2                       160 10         3.503912   \n",
       "                                                               20         3.487746   \n",
       "                                                               40         2.997133   \n",
       "                                                               80         1.798157   \n",
       "                                                               120        1.665167   \n",
       "\n",
       "                                                                    T_Redistribution  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                      \n",
       "0            0                     1                       2   10           1.064642   \n",
       "                                                               20           1.104158   \n",
       "                                                               40           0.920655   \n",
       "                                                               80           0.869270   \n",
       "                                                               120          0.838482   \n",
       "...                                                                              ...   \n",
       "1            1                     2                       160 10           3.167023   \n",
       "                                                               20           3.075578   \n",
       "                                                               40           1.817674   \n",
       "                                                               80           0.864571   \n",
       "                                                               120          0.548051   \n",
       "\n",
       "                                                                     T_spawn  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC              \n",
       "0            0                     1                       2   10   0.369660   \n",
       "                                                               20   0.934080   \n",
       "                                                               40   0.918149   \n",
       "                                                               80   0.920874   \n",
       "                                                               120  0.945228   \n",
       "...                                                                      ...   \n",
       "1            1                     2                       160 10   0.336889   \n",
       "                                                               20   0.507027   \n",
       "                                                               40   1.179459   \n",
       "                                                               80   0.957581   \n",
       "                                                               120  1.122762   \n",
       "\n",
       "                                                                    T_spawn_real  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                  \n",
       "0            0                     1                       2   10       0.000000   \n",
       "                                                               20       0.000000   \n",
       "                                                               40       0.000000   \n",
       "                                                               80       0.000000   \n",
       "                                                               120      0.000000   \n",
       "...                                                                          ...   \n",
       "1            1                     2                       160 10       0.086018   \n",
       "                                                               20       0.308085   \n",
       "                                                               40       1.134582   \n",
       "                                                               80       0.749047   \n",
       "                                                               120      0.842099   \n",
       "\n",
       "                                                                        T_SR  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC              \n",
       "0            0                     1                       2   10   0.044619   \n",
       "                                                               20   0.077780   \n",
       "                                                               40   0.093907   \n",
       "                                                               80   0.118352   \n",
       "                                                               120  0.127863   \n",
       "...                                                                      ...   \n",
       "1            1                     2                       160 10   0.374117   \n",
       "                                                               20   0.483989   \n",
       "                                                               40   0.276230   \n",
       "                                                               80   0.003917   \n",
       "                                                               120  0.004101   \n",
       "\n",
       "                                                                        T_AR  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC              \n",
       "0            0                     1                       2   10   1.020767   \n",
       "                                                               20   1.001591   \n",
       "                                                               40   0.818607   \n",
       "                                                               80   0.750918   \n",
       "                                                               120  0.649751   \n",
       "...                                                                      ...   \n",
       "1            1                     2                       160 10   2.846176   \n",
       "                                                               20   2.632365   \n",
       "                                                               40   1.497667   \n",
       "                                                               80   0.860635   \n",
       "                                                               120  0.543869   \n",
       "\n",
       "                                                                       Alpha  \\\n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC              \n",
       "0            0                     1                       2   10   0.771066   \n",
       "                                                               20   0.577800   \n",
       "                                                               40   0.438732   \n",
       "                                                               80   0.383346   \n",
       "                                                               120  0.333218   \n",
       "...                                                                      ...   \n",
       "1            1                     2                       160 10   1.135001   \n",
       "                                                               20   1.017463   \n",
       "                                                               40   1.459617   \n",
       "                                                               80   1.319977   \n",
       "                                                               120  1.282174   \n",
       "\n",
       "                                                                    Resize_Coherency  \n",
       "Spawn_Method Redistribution_Method Redistribution_Strategy NP  NC                     \n",
       "0            0                     1                       2   10           1.212451  \n",
       "                                                               20           1.933230  \n",
       "                                                               40           7.390061  \n",
       "                                                               80          11.721763  \n",
       "                                                               120         13.663289  \n",
       "...                                                                              ...  \n",
       "1            1                     2                       160 10           0.699213  \n",
       "                                                               20           0.745360  \n",
       "                                                               40           0.251408  \n",
       "                                                               80           0.507629  \n",
       "                                                               120          0.425844  \n",
       "\n",
       "[336 rows x 8 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_aggM.loc[(96.6,slice(None))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>(0.098563, 0.068683, 0.090294, 0.083441, 0.086...</td>\n",
       "      <td>((0.010716, 0.000262, 0.00023, 0.086761), (0.0...</td>\n",
       "      <td>1.467106</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.592875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.592875</td>\n",
       "      <td>2.059981</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>2515</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.653634, 0.634598, 0.633836, 0.634582, 0.634...</td>\n",
       "      <td>((0.622565, 7e-06, 2e-06, 0.03106), (0.62308, ...</td>\n",
       "      <td>1.341950</td>\n",
       "      <td>1.074329</td>\n",
       "      <td>0.044368</td>\n",
       "      <td>1.800613</td>\n",
       "      <td>1.844981</td>\n",
       "      <td>3.186931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.653119, 0.633832, 0.633014, 0.633593, 0.633...</td>\n",
       "      <td>((0.621538, 0.000137, 3e-06, 0.03144), (0.6218...</td>\n",
       "      <td>1.382511</td>\n",
       "      <td>1.104917</td>\n",
       "      <td>0.044006</td>\n",
       "      <td>2.054798</td>\n",
       "      <td>2.098804</td>\n",
       "      <td>3.481315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2517</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.652854, 0.633725, 0.632971, 0.633643, 0.633...</td>\n",
       "      <td>((0.621539, 9.8e-05, 2e-06, 0.031214), (0.6218...</td>\n",
       "      <td>1.348554</td>\n",
       "      <td>0.975715</td>\n",
       "      <td>0.044106</td>\n",
       "      <td>1.975576</td>\n",
       "      <td>2.019682</td>\n",
       "      <td>3.368236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2518</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.652802, 0.633527, 0.633319, 0.633376, 0.633...</td>\n",
       "      <td>((0.621599, 0.000231, 2e-06, 0.030969), (0.622...</td>\n",
       "      <td>1.310184</td>\n",
       "      <td>1.022675</td>\n",
       "      <td>0.047264</td>\n",
       "      <td>1.617140</td>\n",
       "      <td>1.664404</td>\n",
       "      <td>2.974588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.653575, 0.634228, 0.634006, 0.63414, 0.6339...</td>\n",
       "      <td>((0.622518, 7e-06, 2e-06, 0.031046), (0.622878...</td>\n",
       "      <td>1.364008</td>\n",
       "      <td>1.040484</td>\n",
       "      <td>0.043897</td>\n",
       "      <td>1.749459</td>\n",
       "      <td>1.793356</td>\n",
       "      <td>3.157364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NP  NC  Total_Stages  Granularity    SDR   ADR          DR  \\\n",
       "0     160  40             4       100000  100.0   0.0  3947883504   \n",
       "1     160  40             4       100000  100.0   0.0  3947883504   \n",
       "2     160  40             4       100000  100.0   0.0  3947883504   \n",
       "3     160  40             4       100000  100.0   0.0  3947883504   \n",
       "4     160  40             4       100000  100.0   0.0  3947883504   \n",
       "...   ...  ..           ...          ...    ...   ...         ...   \n",
       "2515    2  40             4       100000    3.4  96.6  3947883503   \n",
       "2516    2  40             4       100000    3.4  96.6  3947883503   \n",
       "2517    2  40             4       100000    3.4  96.6  3947883503   \n",
       "2518    2  40             4       100000    3.4  96.6  3947883503   \n",
       "2519    2  40             4       100000    3.4  96.6  3947883503   \n",
       "\n",
       "      Redistribution_Method  Redistribution_Strategy  Spawn_Method  ...  \\\n",
       "0                         1                        1             0  ...   \n",
       "1                         1                        1             0  ...   \n",
       "2                         1                        1             0  ...   \n",
       "3                         1                        1             0  ...   \n",
       "4                         1                        1             0  ...   \n",
       "...                     ...                      ...           ...  ...   \n",
       "2515                      1                        2             1  ...   \n",
       "2516                      1                        2             1  ...   \n",
       "2517                      1                        2             1  ...   \n",
       "2518                      1                        2             1  ...   \n",
       "2519                      1                        2             1  ...   \n",
       "\n",
       "      Iters  Asynch_Iters                                             T_iter  \\\n",
       "0       500             0  (0.08095, 0.076509, 0.079877, 0.074691, 0.0760...   \n",
       "1       500             0  (0.083757, 0.069349, 0.068418, 0.065849, 0.061...   \n",
       "2       500             0  (0.096849, 0.072226, 0.075321, 0.065634, 0.075...   \n",
       "3       500             0  (0.07964, 0.070345, 0.073844, 0.086362, 0.0720...   \n",
       "4       500             0  (0.098563, 0.068683, 0.090294, 0.083441, 0.086...   \n",
       "...     ...           ...                                                ...   \n",
       "2515    500             4  (0.653634, 0.634598, 0.633836, 0.634582, 0.634...   \n",
       "2516    500             4  (0.653119, 0.633832, 0.633014, 0.633593, 0.633...   \n",
       "2517    500             4  (0.652854, 0.633725, 0.632971, 0.633643, 0.633...   \n",
       "2518    500             4  (0.652802, 0.633527, 0.633319, 0.633376, 0.633...   \n",
       "2519    500             4  (0.653575, 0.634228, 0.634006, 0.63414, 0.6339...   \n",
       "\n",
       "                                               T_stages   T_spawn  \\\n",
       "0     ((0.010705, 0.001643, 0.000203, 0.067232), (0....  1.347948   \n",
       "1     ((0.010718, 0.000521, 4.2e-05, 0.071717), (0.0...  1.408781   \n",
       "2     ((0.010704, 0.001999, 0.000233, 0.079332), (0....  1.336949   \n",
       "3     ((0.010704, 0.003768, 0.000384, 0.062777), (0....  1.444550   \n",
       "4     ((0.010716, 0.000262, 0.00023, 0.086761), (0.0...  1.467106   \n",
       "...                                                 ...       ...   \n",
       "2515  ((0.622565, 7e-06, 2e-06, 0.03106), (0.62308, ...  1.341950   \n",
       "2516  ((0.621538, 0.000137, 3e-06, 0.03144), (0.6218...  1.382511   \n",
       "2517  ((0.621539, 9.8e-05, 2e-06, 0.031214), (0.6218...  1.348554   \n",
       "2518  ((0.621599, 0.000231, 2e-06, 0.030969), (0.622...  1.310184   \n",
       "2519  ((0.622518, 7e-06, 2e-06, 0.031046), (0.622878...  1.364008   \n",
       "\n",
       "     T_spawn_real      T_SR      T_AR T_Redistribution T_Malleability  \n",
       "0        0.000000  0.752765  0.000000         0.752765       2.100713  \n",
       "1        0.000000  0.780452  0.000000         0.780452       2.189233  \n",
       "2        0.000000  0.526026  0.000000         0.526026       1.862975  \n",
       "3        0.000000  0.688739  0.000000         0.688739       2.133289  \n",
       "4        0.000000  0.592875  0.000000         0.592875       2.059981  \n",
       "...           ...       ...       ...              ...            ...  \n",
       "2515     1.074329  0.044368  1.800613         1.844981       3.186931  \n",
       "2516     1.104917  0.044006  2.054798         2.098804       3.481315  \n",
       "2517     0.975715  0.044106  1.975576         2.019682       3.368236  \n",
       "2518     1.022675  0.047264  1.617140         1.664404       2.974588  \n",
       "2519     1.040484  0.043897  1.749459         1.793356       3.157364  \n",
       "\n",
       "[2520 rows x 26 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index: 0 is coherent: False. Malleability=5.769278 Asynch=9.175561 Spawn=0\n",
      "Index: 1 is coherent: False. Malleability=3.682582 Asynch=7.481312 Spawn=0\n",
      "Index: 2 is coherent: False. Malleability=2.7591530000000004 Asynch=6.264269 Spawn=0\n",
      "Index: 3 is coherent: False. Malleability=4.966915999999999 Asynch=8.449237 Spawn=0\n",
      "Index: 4 is coherent: False. Malleability=1.866746 Asynch=6.59518 Spawn=0\n",
      " \n",
      "Index: 5 is coherent: False. Malleability=1.911482 Asynch=2.248515 Spawn=0\n",
      "Index: 6 is coherent: False. Malleability=1.9710299999999998 Asynch=2.760144 Spawn=0\n",
      "Index: 7 is coherent: False. Malleability=1.763885 Asynch=4.08746 Spawn=0\n",
      "Index: 8 is coherent: False. Malleability=1.906851 Asynch=4.300668999999999 Spawn=0\n",
      "Index: 9 is coherent: False. Malleability=1.855878 Asynch=2.45997 Spawn=0\n",
      " \n",
      "Index: 10 is coherent: True. Malleability=10.052676 Asynch=9.235494000000001 Spawn=0\n",
      "Index: 11 is coherent: False. Malleability=9.67812 Asynch=10.612835 Spawn=0\n",
      "Index: 12 is coherent: True. Malleability=8.962805000000001 Asynch=8.079384000000001 Spawn=0\n",
      "Index: 13 is coherent: True. Malleability=10.392319 Asynch=9.337685 Spawn=0\n",
      "Index: 14 is coherent: True. Malleability=8.995336 Asynch=7.994266 Spawn=0\n",
      " \n",
      "Index: 15 is coherent: False. Malleability=1.7968929999999999 Asynch=5.094866 Spawn=0\n",
      "Index: 16 is coherent: False. Malleability=1.9235609999999999 Asynch=4.126316 Spawn=0\n",
      "Index: 17 is coherent: False. Malleability=1.8718149999999998 Asynch=3.4169739999999997 Spawn=0\n",
      "Index: 18 is coherent: False. Malleability=1.87569 Asynch=2.93823 Spawn=0\n",
      "Index: 19 is coherent: False. Malleability=1.79898 Asynch=3.386877 Spawn=0\n",
      " \n",
      "Index: 20 is coherent: False. Malleability=10.042116 Asynch=12.982213 Spawn=0\n",
      "Index: 21 is coherent: False. Malleability=11.318541 Asynch=13.710560999999998 Spawn=0\n",
      "Index: 22 is coherent: False. Malleability=9.492362 Asynch=12.416377 Spawn=0\n",
      "Index: 23 is coherent: False. Malleability=10.821556000000001 Asynch=13.169641 Spawn=0\n",
      "Index: 24 is coherent: False. Malleability=11.011606 Asynch=13.887611 Spawn=0\n",
      " \n",
      "Index: 25 is coherent: True. Malleability=4.012426 Asynch=3.943674 Spawn=1\n",
      "Index: 26 is coherent: True. Malleability=4.008484 Asynch=3.9832379999999996 Spawn=1\n",
      "Index: 27 is coherent: True. Malleability=3.891018 Asynch=3.8657039999999996 Spawn=1\n",
      "Index: 28 is coherent: True. Malleability=4.078005 Asynch=3.916006 Spawn=1\n",
      "Index: 29 is coherent: True. Malleability=4.086148000000001 Asynch=4.062789 Spawn=1\n",
      " \n",
      "Index: 30 is coherent: True. Malleability=2.448576 Asynch=2.2079950000000004 Spawn=1\n",
      "Index: 31 is coherent: True. Malleability=2.645964 Asynch=2.6322539999999996 Spawn=1\n",
      "Index: 32 is coherent: True. Malleability=2.737607 Asynch=2.731388 Spawn=1\n",
      "Index: 33 is coherent: True. Malleability=2.701953 Asynch=2.693896 Spawn=1\n",
      "Index: 34 is coherent: True. Malleability=2.236782 Asynch=2.1637380000000004 Spawn=1\n",
      " \n",
      "Index: 35 is coherent: True. Malleability=2.9560090000000003 Asynch=2.0560650000000003 Spawn=1\n",
      "Index: 36 is coherent: True. Malleability=3.080058 Asynch=2.06241 Spawn=1\n",
      "Index: 37 is coherent: True. Malleability=2.9086489999999996 Asynch=2.04249 Spawn=1\n",
      "Index: 38 is coherent: True. Malleability=2.923109 Asynch=2.050565 Spawn=1\n",
      "Index: 39 is coherent: True. Malleability=3.072331 Asynch=2.060973 Spawn=1\n",
      " \n",
      "Index: 40 is coherent: True. Malleability=2.371952 Asynch=2.336518 Spawn=1\n",
      "Index: 41 is coherent: True. Malleability=2.912397 Asynch=2.876967 Spawn=1\n",
      "Index: 42 is coherent: True. Malleability=1.9813749999999999 Asynch=1.9483489999999999 Spawn=1\n",
      "Index: 43 is coherent: True. Malleability=2.630121 Asynch=2.592751 Spawn=1\n",
      "Index: 44 is coherent: True. Malleability=1.9863650000000002 Asynch=1.952095 Spawn=1\n",
      " \n",
      "Index: 45 is coherent: True. Malleability=2.233826 Asynch=1.032403 Spawn=1\n",
      "Index: 46 is coherent: True. Malleability=2.339574 Asynch=1.075062 Spawn=1\n",
      "Index: 47 is coherent: True. Malleability=1.3810799999999999 Asynch=1.0335960000000002 Spawn=1\n",
      "Index: 48 is coherent: True. Malleability=2.22958 Asynch=1.034224 Spawn=1\n",
      "Index: 49 is coherent: True. Malleability=1.525744 Asynch=1.1216970000000002 Spawn=1\n",
      " \n",
      "Index: 50 is coherent: True. Malleability=2.109603 Asynch=1.534721 Spawn=0\n",
      "Index: 51 is coherent: True. Malleability=2.207197 Asynch=2.207066 Spawn=0\n",
      "Index: 52 is coherent: False. Malleability=1.9944449999999998 Asynch=3.467385 Spawn=0\n",
      "Index: 53 is coherent: False. Malleability=1.967171 Asynch=4.047493 Spawn=0\n",
      "Index: 54 is coherent: False. Malleability=2.662519 Asynch=2.758863 Spawn=0\n",
      " \n",
      "Index: 55 is coherent: True. Malleability=3.5328549999999996 Asynch=2.944797 Spawn=1\n",
      "Index: 56 is coherent: True. Malleability=3.907324 Asynch=2.5966449999999996 Spawn=1\n",
      "Index: 57 is coherent: True. Malleability=2.978728 Asynch=2.612851 Spawn=1\n",
      "Index: 58 is coherent: True. Malleability=3.9025040000000004 Asynch=2.593594 Spawn=1\n",
      "Index: 59 is coherent: True. Malleability=3.6343900000000002 Asynch=2.9370700000000003 Spawn=1\n",
      " \n",
      "Index: 60 is coherent: False. Malleability=1.4569739999999998 Asynch=1.608166 Spawn=0\n",
      "Index: 61 is coherent: False. Malleability=1.4410089999999998 Asynch=2.284694 Spawn=0\n",
      "Index: 62 is coherent: False. Malleability=2.046685 Asynch=2.977262 Spawn=0\n",
      "Index: 63 is coherent: False. Malleability=1.450045 Asynch=2.8594399999999998 Spawn=0\n",
      "Index: 64 is coherent: False. Malleability=1.374937 Asynch=2.869937 Spawn=0\n",
      " \n",
      "Index: 65 is coherent: False. Malleability=1.949972 Asynch=3.931486 Spawn=0\n",
      "Index: 66 is coherent: False. Malleability=1.545868 Asynch=3.8275770000000002 Spawn=0\n",
      "Index: 67 is coherent: False. Malleability=1.350663 Asynch=4.169583 Spawn=0\n",
      "Index: 68 is coherent: False. Malleability=1.492829 Asynch=3.615311 Spawn=0\n",
      "Index: 69 is coherent: False. Malleability=1.865363 Asynch=4.123561 Spawn=0\n",
      " \n",
      "Index: 70 is coherent: True. Malleability=4.3013 Asynch=3.0139950000000004 Spawn=1\n",
      "Index: 71 is coherent: True. Malleability=3.689305 Asynch=2.241478 Spawn=1\n",
      "Index: 72 is coherent: True. Malleability=3.451848 Asynch=2.381691 Spawn=1\n",
      "Index: 73 is coherent: True. Malleability=3.9955299999999996 Asynch=3.102189 Spawn=1\n",
      "Index: 74 is coherent: True. Malleability=4.03238 Asynch=3.4184490000000003 Spawn=1\n",
      " \n",
      "Index: 75 is coherent: False. Malleability=2.443796 Asynch=2.988418 Spawn=0\n",
      "Index: 76 is coherent: False. Malleability=2.861472 Asynch=3.2014009999999997 Spawn=0\n",
      "Index: 77 is coherent: False. Malleability=2.7346250000000003 Asynch=3.587831 Spawn=0\n",
      "Index: 78 is coherent: False. Malleability=2.554404 Asynch=3.251627 Spawn=0\n",
      "Index: 79 is coherent: False. Malleability=2.7259510000000002 Asynch=3.514201 Spawn=0\n",
      " \n",
      "Index: 80 is coherent: False. Malleability=1.822506 Asynch=4.756646 Spawn=0\n",
      "Index: 81 is coherent: False. Malleability=1.8228989999999998 Asynch=4.363204 Spawn=0\n",
      "Index: 82 is coherent: False. Malleability=1.883979 Asynch=3.467364 Spawn=0\n",
      "Index: 83 is coherent: False. Malleability=1.910076 Asynch=3.307147 Spawn=0\n",
      "Index: 84 is coherent: False. Malleability=2.107411 Asynch=4.047352 Spawn=0\n",
      " \n",
      "Index: 85 is coherent: True. Malleability=2.94107 Asynch=1.790997 Spawn=1\n",
      "Index: 86 is coherent: True. Malleability=2.972085 Asynch=2.285608 Spawn=1\n",
      "Index: 87 is coherent: True. Malleability=3.455475 Asynch=2.175462 Spawn=1\n",
      "Index: 88 is coherent: True. Malleability=2.533761 Asynch=1.5561600000000002 Spawn=1\n",
      "Index: 89 is coherent: True. Malleability=3.587316 Asynch=2.524964 Spawn=1\n",
      " \n",
      "Index: 90 is coherent: True. Malleability=2.556413 Asynch=2.4362820000000003 Spawn=1\n",
      "Index: 91 is coherent: True. Malleability=2.58788 Asynch=2.452253 Spawn=1\n",
      "Index: 92 is coherent: True. Malleability=2.388922 Asynch=2.136937 Spawn=1\n",
      "Index: 93 is coherent: True. Malleability=2.435454 Asynch=2.16603 Spawn=1\n",
      "Index: 94 is coherent: True. Malleability=2.363419 Asynch=2.061846 Spawn=1\n",
      " \n",
      "Index: 95 is coherent: True. Malleability=3.7297409999999998 Asynch=3.62829 Spawn=1\n",
      "Index: 96 is coherent: True. Malleability=3.5584 Asynch=3.505967 Spawn=1\n",
      "Index: 97 is coherent: True. Malleability=3.624152 Asynch=3.1500340000000002 Spawn=1\n",
      "Index: 98 is coherent: True. Malleability=3.4594 Asynch=3.027023 Spawn=1\n",
      "Index: 99 is coherent: True. Malleability=3.627966 Asynch=3.093134 Spawn=1\n",
      " \n",
      "Index: 100 is coherent: False. Malleability=2.015423 Asynch=6.668033 Spawn=0\n",
      "Index: 101 is coherent: False. Malleability=1.683367 Asynch=6.792011 Spawn=0\n",
      "Index: 102 is coherent: False. Malleability=1.457789 Asynch=5.767256 Spawn=0\n",
      "Index: 103 is coherent: False. Malleability=1.842603 Asynch=6.276049 Spawn=0\n",
      "Index: 104 is coherent: False. Malleability=1.585757 Asynch=5.947028 Spawn=0\n",
      " \n",
      "Index: 105 is coherent: True. Malleability=6.515892 Asynch=6.126687 Spawn=0\n",
      "Index: 106 is coherent: True. Malleability=6.488033 Asynch=6.038800999999999 Spawn=0\n",
      "Index: 107 is coherent: True. Malleability=5.997654000000001 Asynch=5.8590029999999995 Spawn=0\n",
      "Index: 108 is coherent: True. Malleability=6.769225 Asynch=6.13349 Spawn=0\n",
      "Index: 109 is coherent: True. Malleability=7.061463999999999 Asynch=6.849137000000001 Spawn=0\n",
      " \n",
      "Index: 110 is coherent: True. Malleability=3.67381 Asynch=3.636483 Spawn=1\n",
      "Index: 111 is coherent: True. Malleability=3.410696 Asynch=3.370372 Spawn=1\n",
      "Index: 112 is coherent: True. Malleability=3.2298299999999998 Asynch=3.1931130000000003 Spawn=1\n",
      "Index: 113 is coherent: True. Malleability=3.22737 Asynch=3.188557 Spawn=1\n",
      "Index: 114 is coherent: True. Malleability=3.1241399999999997 Asynch=3.0902530000000006 Spawn=1\n",
      " \n",
      "Index: 115 is coherent: False. Malleability=12.49098 Asynch=14.326619999999998 Spawn=0\n",
      "Index: 116 is coherent: False. Malleability=11.413703000000002 Asynch=13.357718 Spawn=0\n",
      "Index: 117 is coherent: False. Malleability=11.240286000000001 Asynch=13.432268 Spawn=0\n",
      "Index: 118 is coherent: False. Malleability=11.546033999999999 Asynch=13.581966 Spawn=0\n",
      "Index: 119 is coherent: False. Malleability=11.176739999999999 Asynch=13.488712 Spawn=0\n",
      " \n",
      "Index: 120 is coherent: True. Malleability=2.090809 Asynch=1.349156 Spawn=1\n",
      "Index: 121 is coherent: True. Malleability=2.0938 Asynch=1.100987 Spawn=1\n",
      "Index: 122 is coherent: True. Malleability=2.9949440000000003 Asynch=1.757746 Spawn=1\n",
      "Index: 123 is coherent: True. Malleability=2.2078740000000003 Asynch=1.749873 Spawn=1\n",
      "Index: 124 is coherent: True. Malleability=2.6976440000000004 Asynch=1.467317 Spawn=1\n",
      " \n",
      "Index: 125 is coherent: False. Malleability=16.182844 Asynch=19.242428 Spawn=0\n",
      "Index: 126 is coherent: False. Malleability=17.328333 Asynch=19.887065999999997 Spawn=0\n",
      "Index: 127 is coherent: False. Malleability=15.667225000000002 Asynch=18.957495 Spawn=0\n",
      "Index: 128 is coherent: False. Malleability=10.899663 Asynch=14.420116 Spawn=0\n",
      "Index: 129 is coherent: True. Malleability=13.827452000000001 Asynch=11.83184 Spawn=0\n",
      " \n",
      "Index: 130 is coherent: True. Malleability=3.8882730000000003 Asynch=2.6697990000000003 Spawn=1\n",
      "Index: 131 is coherent: True. Malleability=4.1903179999999995 Asynch=2.8687159999999996 Spawn=1\n",
      "Index: 132 is coherent: True. Malleability=3.864618 Asynch=2.781953 Spawn=1\n",
      "Index: 133 is coherent: True. Malleability=3.9926559999999998 Asynch=2.757197 Spawn=1\n",
      "Index: 134 is coherent: True. Malleability=4.160841 Asynch=2.854034 Spawn=1\n",
      " \n",
      "Index: 135 is coherent: True. Malleability=2.194236 Asynch=1.990528 Spawn=1\n",
      "Index: 136 is coherent: True. Malleability=2.0850199999999997 Asynch=1.501039 Spawn=1\n",
      "Index: 137 is coherent: True. Malleability=2.085546 Asynch=1.4978799999999999 Spawn=1\n",
      "Index: 138 is coherent: True. Malleability=2.177474 Asynch=1.738177 Spawn=1\n",
      "Index: 139 is coherent: True. Malleability=1.92576 Asynch=1.4361979999999999 Spawn=1\n",
      " \n",
      "Index: 140 is coherent: True. Malleability=1.857966 Asynch=1.820726 Spawn=1\n",
      "Index: 141 is coherent: True. Malleability=1.8591220000000002 Asynch=1.8219539999999999 Spawn=1\n",
      "Index: 142 is coherent: True. Malleability=1.846884 Asynch=1.8039809999999998 Spawn=1\n",
      "Index: 143 is coherent: True. Malleability=1.8469130000000002 Asynch=1.8102079999999998 Spawn=1\n",
      "Index: 144 is coherent: True. Malleability=1.8664870000000002 Asynch=1.826771 Spawn=1\n",
      " \n",
      "Index: 145 is coherent: False. Malleability=4.281353 Asynch=8.274854 Spawn=0\n",
      "Index: 146 is coherent: False. Malleability=4.4434700000000005 Asynch=7.591950000000001 Spawn=0\n",
      "Index: 147 is coherent: False. Malleability=2.611434 Asynch=5.016627 Spawn=0\n",
      "Index: 148 is coherent: False. Malleability=2.400772 Asynch=3.934616 Spawn=0\n",
      "Index: 149 is coherent: False. Malleability=2.417455 Asynch=4.997183 Spawn=0\n",
      " \n",
      "Index: 150 is coherent: False. Malleability=4.076658 Asynch=4.674015000000001 Spawn=0\n",
      "Index: 151 is coherent: True. Malleability=4.569893 Asynch=3.8431089999999997 Spawn=0\n",
      "Index: 152 is coherent: False. Malleability=3.32954 Asynch=3.9137210000000002 Spawn=0\n",
      "Index: 153 is coherent: True. Malleability=3.881461 Asynch=3.6955080000000002 Spawn=0\n",
      "Index: 154 is coherent: True. Malleability=3.510513 Asynch=3.413406 Spawn=0\n",
      " \n",
      "Index: 155 is coherent: False. Malleability=2.111905 Asynch=6.355271 Spawn=0\n",
      "Index: 156 is coherent: False. Malleability=2.29635 Asynch=5.468001 Spawn=0\n",
      "Index: 157 is coherent: False. Malleability=2.0849919999999997 Asynch=5.852043 Spawn=0\n",
      "Index: 158 is coherent: False. Malleability=2.059084 Asynch=5.536004 Spawn=0\n",
      "Index: 159 is coherent: False. Malleability=2.468276 Asynch=4.996013 Spawn=0\n",
      " \n",
      "Index: 160 is coherent: True. Malleability=9.012761000000001 Asynch=8.30668 Spawn=0\n",
      "Index: 161 is coherent: True. Malleability=8.240565 Asynch=7.179946999999999 Spawn=0\n",
      "Index: 162 is coherent: True. Malleability=8.785789 Asynch=7.8430230000000005 Spawn=0\n",
      "Index: 163 is coherent: True. Malleability=8.881745 Asynch=7.882442 Spawn=0\n",
      "Index: 164 is coherent: False. Malleability=8.294376 Asynch=8.60674 Spawn=0\n",
      " \n",
      "Index: 165 is coherent: False. Malleability=5.95814 Asynch=7.02858 Spawn=0\n",
      "Index: 166 is coherent: False. Malleability=7.714995999999999 Asynch=7.986994 Spawn=0\n",
      "Index: 167 is coherent: True. Malleability=5.4204989999999995 Asynch=5.277449000000001 Spawn=0\n",
      "Index: 168 is coherent: False. Malleability=8.792103000000001 Asynch=11.696082 Spawn=0\n",
      "Index: 169 is coherent: False. Malleability=6.825086000000001 Asynch=7.596873 Spawn=0\n",
      " \n",
      "Index: 170 is coherent: False. Malleability=2.147852 Asynch=2.1927630000000002 Spawn=0\n",
      "Index: 171 is coherent: True. Malleability=2.394593 Asynch=2.357659 Spawn=0\n",
      "Index: 172 is coherent: False. Malleability=2.2607690000000003 Asynch=2.274459 Spawn=0\n",
      "Index: 173 is coherent: True. Malleability=2.328123 Asynch=2.296544 Spawn=0\n",
      "Index: 174 is coherent: True. Malleability=2.282977 Asynch=2.248748 Spawn=0\n",
      " \n",
      "Index: 175 is coherent: False. Malleability=1.708034 Asynch=3.139813 Spawn=0\n",
      "Index: 176 is coherent: False. Malleability=1.7204930000000003 Asynch=2.319997 Spawn=0\n",
      "Index: 177 is coherent: False. Malleability=1.6595529999999998 Asynch=3.711931 Spawn=0\n",
      "Index: 178 is coherent: True. Malleability=2.0540640000000003 Asynch=1.999996 Spawn=0\n",
      "Index: 179 is coherent: False. Malleability=1.718619 Asynch=4.491791 Spawn=0\n",
      " \n",
      "Index: 180 is coherent: False. Malleability=2.8406900000000004 Asynch=2.9331940000000003 Spawn=0\n",
      "Index: 181 is coherent: False. Malleability=2.607272 Asynch=2.729683 Spawn=0\n",
      "Index: 182 is coherent: False. Malleability=2.777976 Asynch=2.8096579999999998 Spawn=0\n",
      "Index: 183 is coherent: False. Malleability=3.262513 Asynch=3.8517949999999996 Spawn=0\n",
      "Index: 184 is coherent: False. Malleability=2.672649 Asynch=2.775615 Spawn=0\n",
      " \n",
      "Index: 185 is coherent: True. Malleability=4.106605999999999 Asynch=4.019533 Spawn=0\n",
      "Index: 186 is coherent: False. Malleability=4.7330369999999995 Asynch=5.981909999999999 Spawn=0\n",
      "Index: 187 is coherent: False. Malleability=5.419297 Asynch=6.325618 Spawn=0\n",
      "Index: 188 is coherent: False. Malleability=5.226915 Asynch=5.783595 Spawn=0\n",
      "Index: 189 is coherent: False. Malleability=5.006035 Asynch=5.280941 Spawn=0\n",
      " \n",
      "Index: 190 is coherent: True. Malleability=4.105327 Asynch=3.159016 Spawn=1\n",
      "Index: 191 is coherent: True. Malleability=4.118098 Asynch=3.0455579999999998 Spawn=1\n",
      "Index: 192 is coherent: True. Malleability=3.8862370000000004 Asynch=2.8559129999999997 Spawn=1\n",
      "Index: 193 is coherent: True. Malleability=3.928865 Asynch=3.013424 Spawn=1\n",
      "Index: 194 is coherent: True. Malleability=3.8903770000000004 Asynch=2.967021 Spawn=1\n",
      " \n",
      "Index: 195 is coherent: True. Malleability=2.988517 Asynch=2.737385 Spawn=1\n",
      "Index: 196 is coherent: True. Malleability=2.3649770000000006 Asynch=2.18347 Spawn=1\n",
      "Index: 197 is coherent: True. Malleability=2.273448 Asynch=2.1456500000000003 Spawn=1\n",
      "Index: 198 is coherent: True. Malleability=2.317771 Asynch=2.189419 Spawn=1\n",
      "Index: 199 is coherent: True. Malleability=2.626218 Asynch=2.372461 Spawn=1\n",
      " \n",
      "Index: 200 is coherent: True. Malleability=1.5279690000000001 Asynch=1.48143 Spawn=1\n",
      "Index: 201 is coherent: True. Malleability=1.492231 Asynch=1.446243 Spawn=1\n",
      "Index: 202 is coherent: True. Malleability=1.5032830000000001 Asynch=1.459439 Spawn=1\n",
      "Index: 203 is coherent: True. Malleability=1.5045680000000001 Asynch=1.4582890000000002 Spawn=1\n",
      "Index: 204 is coherent: True. Malleability=1.488097 Asynch=1.442583 Spawn=1\n",
      " \n",
      "Index: 205 is coherent: False. Malleability=12.014963 Asynch=14.083683 Spawn=0\n",
      "Index: 206 is coherent: False. Malleability=11.326519999999999 Asynch=13.642424 Spawn=0\n",
      "Index: 207 is coherent: False. Malleability=10.924031 Asynch=13.59371 Spawn=0\n",
      "Index: 208 is coherent: False. Malleability=11.963246999999999 Asynch=14.54354 Spawn=0\n",
      "Index: 209 is coherent: False. Malleability=12.652109 Asynch=14.996997 Spawn=0\n",
      " \n",
      "Index: 210 is coherent: False. Malleability=2.5019419999999997 Asynch=2.927353 Spawn=0\n",
      "Index: 211 is coherent: False. Malleability=2.258893 Asynch=3.308151 Spawn=0\n",
      "Index: 212 is coherent: False. Malleability=2.2652669999999997 Asynch=3.287375 Spawn=0\n",
      "Index: 213 is coherent: False. Malleability=2.411146 Asynch=3.271859 Spawn=0\n",
      "Index: 214 is coherent: False. Malleability=2.9366849999999998 Asynch=3.231583 Spawn=0\n",
      " \n",
      "Index: 215 is coherent: True. Malleability=2.630347 Asynch=1.797596 Spawn=1\n",
      "Index: 216 is coherent: True. Malleability=2.830556 Asynch=2.805963 Spawn=1\n",
      "Index: 217 is coherent: True. Malleability=2.626226 Asynch=2.6109039999999997 Spawn=1\n",
      "Index: 218 is coherent: True. Malleability=2.572405 Asynch=2.246672 Spawn=1\n",
      "Index: 219 is coherent: True. Malleability=2.602458 Asynch=2.567065 Spawn=1\n",
      " \n",
      "Index: 220 is coherent: True. Malleability=3.40974 Asynch=3.353882 Spawn=1\n",
      "Index: 221 is coherent: True. Malleability=2.7435650000000003 Asynch=2.6854329999999997 Spawn=1\n",
      "Index: 222 is coherent: True. Malleability=3.876054 Asynch=3.8203060000000004 Spawn=1\n",
      "Index: 223 is coherent: True. Malleability=3.6066770000000004 Asynch=3.54958 Spawn=1\n",
      "Index: 224 is coherent: True. Malleability=3.7899269999999996 Asynch=3.7350160000000003 Spawn=1\n",
      " \n",
      "Index: 225 is coherent: False. Malleability=1.794079 Asynch=2.305068 Spawn=0\n",
      "Index: 226 is coherent: False. Malleability=1.799173 Asynch=2.2159079999999998 Spawn=0\n",
      "Index: 227 is coherent: True. Malleability=1.6810049999999999 Asynch=1.600947 Spawn=0\n",
      "Index: 228 is coherent: True. Malleability=1.5879309999999998 Asynch=1.457358 Spawn=0\n",
      "Index: 229 is coherent: True. Malleability=1.719605 Asynch=1.587183 Spawn=0\n",
      " \n",
      "Index: 230 is coherent: False. Malleability=2.971054 Asynch=3.7291659999999998 Spawn=0\n",
      "Index: 231 is coherent: True. Malleability=1.943552 Asynch=1.938846 Spawn=0\n",
      "Index: 232 is coherent: True. Malleability=3.036492 Asynch=2.926794 Spawn=0\n",
      "Index: 233 is coherent: True. Malleability=2.180097 Asynch=1.751478 Spawn=0\n",
      "Index: 234 is coherent: False. Malleability=2.052086 Asynch=2.843474 Spawn=0\n",
      " \n",
      "Index: 235 is coherent: True. Malleability=2.302546 Asynch=2.282466 Spawn=1\n",
      "Index: 236 is coherent: True. Malleability=2.474872 Asynch=2.454467 Spawn=1\n",
      "Index: 237 is coherent: True. Malleability=2.145549 Asynch=2.133202 Spawn=1\n",
      "Index: 238 is coherent: True. Malleability=2.029998 Asynch=2.0097889999999996 Spawn=1\n",
      "Index: 239 is coherent: True. Malleability=1.705947 Asynch=1.6976390000000001 Spawn=1\n",
      " \n",
      "Index: 240 is coherent: False. Malleability=1.388678 Asynch=3.673837 Spawn=0\n",
      "Index: 241 is coherent: False. Malleability=1.6975630000000002 Asynch=3.800922 Spawn=0\n",
      "Index: 242 is coherent: False. Malleability=1.750086 Asynch=3.4438940000000002 Spawn=0\n",
      "Index: 243 is coherent: False. Malleability=1.794248 Asynch=3.131643 Spawn=0\n",
      "Index: 244 is coherent: False. Malleability=1.7121389999999999 Asynch=3.4037319999999998 Spawn=0\n",
      " \n",
      "Index: 245 is coherent: True. Malleability=1.345385 Asynch=0.184176 Spawn=1\n",
      "Index: 246 is coherent: True. Malleability=1.3961569999999999 Asynch=0.170367 Spawn=1\n",
      "Index: 247 is coherent: True. Malleability=1.388159 Asynch=0.155376 Spawn=1\n",
      "Index: 248 is coherent: True. Malleability=1.380494 Asynch=0.152741 Spawn=1\n",
      "Index: 249 is coherent: True. Malleability=1.385448 Asynch=0.185094 Spawn=1\n",
      " \n",
      "Index: 250 is coherent: False. Malleability=4.177512 Asynch=6.87701 Spawn=0\n",
      "Index: 251 is coherent: False. Malleability=4.000787 Asynch=7.311646 Spawn=0\n",
      "Index: 252 is coherent: False. Malleability=4.160771 Asynch=6.900589999999999 Spawn=0\n",
      "Index: 253 is coherent: False. Malleability=3.2311330000000003 Asynch=6.430838 Spawn=0\n",
      "Index: 254 is coherent: False. Malleability=4.2962560000000005 Asynch=7.018267 Spawn=0\n",
      " \n",
      "Index: 255 is coherent: True. Malleability=9.172131 Asynch=8.134045 Spawn=1\n",
      "Index: 256 is coherent: True. Malleability=9.031545 Asynch=8.13742 Spawn=1\n",
      "Index: 257 is coherent: True. Malleability=7.883055 Asynch=6.9437999999999995 Spawn=1\n",
      "Index: 258 is coherent: True. Malleability=8.659605 Asynch=7.641706999999999 Spawn=1\n",
      "Index: 259 is coherent: True. Malleability=7.927164 Asynch=6.93842 Spawn=1\n",
      " \n",
      "Index: 260 is coherent: False. Malleability=3.5562449999999997 Asynch=4.688192 Spawn=0\n",
      "Index: 261 is coherent: False. Malleability=3.5813960000000002 Asynch=4.63318 Spawn=0\n",
      "Index: 262 is coherent: False. Malleability=3.7239679999999997 Asynch=4.895821 Spawn=0\n",
      "Index: 263 is coherent: False. Malleability=3.400304 Asynch=4.542534 Spawn=0\n",
      "Index: 264 is coherent: True. Malleability=4.0352950000000005 Asynch=4.030516 Spawn=0\n",
      " \n",
      "Index: 265 is coherent: True. Malleability=1.8587639999999999 Asynch=0.584302 Spawn=1\n",
      "Index: 266 is coherent: True. Malleability=2.119407 Asynch=1.184332 Spawn=1\n",
      "Index: 267 is coherent: True. Malleability=2.029243 Asynch=1.0344389999999999 Spawn=1\n",
      "Index: 268 is coherent: True. Malleability=1.869309 Asynch=0.5863849999999999 Spawn=1\n",
      "Index: 269 is coherent: True. Malleability=2.143191 Asynch=1.157993 Spawn=1\n",
      " \n",
      "Index: 270 is coherent: False. Malleability=2.734725 Asynch=5.5874 Spawn=0\n",
      "Index: 271 is coherent: False. Malleability=2.231101 Asynch=5.928069 Spawn=0\n",
      "Index: 272 is coherent: False. Malleability=2.4820320000000002 Asynch=5.491424 Spawn=0\n",
      "Index: 273 is coherent: False. Malleability=2.205221 Asynch=5.992001 Spawn=0\n",
      "Index: 274 is coherent: False. Malleability=2.388459 Asynch=6.104013 Spawn=0\n",
      " \n",
      "Index: 275 is coherent: False. Malleability=6.821695 Asynch=7.0640979999999995 Spawn=0\n",
      "Index: 276 is coherent: False. Malleability=9.237345999999999 Asynch=9.412529 Spawn=0\n",
      "Index: 277 is coherent: True. Malleability=5.7281439999999995 Asynch=5.589914 Spawn=0\n",
      "Index: 278 is coherent: False. Malleability=8.269627 Asynch=8.283883 Spawn=0\n",
      "Index: 279 is coherent: False. Malleability=5.5542180000000005 Asynch=5.895376000000001 Spawn=0\n",
      " \n",
      "Index: 280 is coherent: True. Malleability=3.978714 Asynch=2.652774 Spawn=1\n",
      "Index: 281 is coherent: True. Malleability=4.090729 Asynch=2.799532 Spawn=1\n",
      "Index: 282 is coherent: True. Malleability=3.4608499999999998 Asynch=2.322457 Spawn=1\n",
      "Index: 283 is coherent: True. Malleability=3.313653 Asynch=2.257614 Spawn=1\n",
      "Index: 284 is coherent: True. Malleability=3.5450969999999997 Asynch=3.220605 Spawn=1\n",
      " \n",
      "Index: 285 is coherent: True. Malleability=1.755441 Asynch=1.7286860000000002 Spawn=1\n",
      "Index: 286 is coherent: True. Malleability=1.697178 Asynch=1.686163 Spawn=1\n",
      "Index: 287 is coherent: True. Malleability=1.691259 Asynch=1.6857419999999999 Spawn=1\n",
      "Index: 288 is coherent: True. Malleability=1.8045520000000002 Asynch=1.774446 Spawn=1\n",
      "Index: 289 is coherent: True. Malleability=1.754045 Asynch=1.729892 Spawn=1\n",
      " \n",
      "Index: 290 is coherent: False. Malleability=1.7254539999999998 Asynch=6.072003 Spawn=0\n",
      "Index: 291 is coherent: False. Malleability=1.729762 Asynch=5.392011 Spawn=0\n",
      "Index: 292 is coherent: False. Malleability=1.649195 Asynch=6.267309 Spawn=0\n",
      "Index: 293 is coherent: False. Malleability=1.682199 Asynch=5.224006 Spawn=0\n",
      "Index: 294 is coherent: False. Malleability=1.801317 Asynch=5.80321 Spawn=0\n",
      " \n",
      "Index: 295 is coherent: True. Malleability=1.90622 Asynch=1.899689 Spawn=1\n",
      "Index: 296 is coherent: True. Malleability=1.957233 Asynch=1.9552450000000001 Spawn=1\n",
      "Index: 297 is coherent: True. Malleability=1.685184 Asynch=1.669617 Spawn=1\n",
      "Index: 298 is coherent: True. Malleability=1.9863879999999998 Asynch=1.9727990000000002 Spawn=1\n",
      "Index: 299 is coherent: True. Malleability=2.148897 Asynch=2.14316 Spawn=1\n",
      " \n",
      "Index: 300 is coherent: True. Malleability=2.261305 Asynch=0.912774 Spawn=1\n",
      "Index: 301 is coherent: True. Malleability=3.082419 Asynch=2.0867069999999996 Spawn=1\n",
      "Index: 302 is coherent: True. Malleability=2.322349 Asynch=1.525425 Spawn=1\n",
      "Index: 303 is coherent: True. Malleability=2.554418 Asynch=1.3654169999999999 Spawn=1\n",
      "Index: 304 is coherent: True. Malleability=2.4085669999999997 Asynch=1.499601 Spawn=1\n",
      " \n",
      "Index: 305 is coherent: False. Malleability=4.473063 Asynch=5.488032 Spawn=0\n",
      "Index: 306 is coherent: False. Malleability=4.245232 Asynch=5.524589 Spawn=0\n",
      "Index: 307 is coherent: False. Malleability=5.165528 Asynch=5.498052 Spawn=0\n",
      "Index: 308 is coherent: False. Malleability=4.360845 Asynch=5.33357 Spawn=0\n",
      "Index: 309 is coherent: True. Malleability=5.253712 Asynch=4.899978 Spawn=0\n",
      " \n",
      "Index: 310 is coherent: False. Malleability=7.181476 Asynch=12.338814000000001 Spawn=0\n",
      "Index: 311 is coherent: False. Malleability=7.492731 Asynch=12.927340999999998 Spawn=0\n",
      "Index: 312 is coherent: False. Malleability=7.257468 Asynch=12.754297 Spawn=0\n",
      "Index: 313 is coherent: False. Malleability=7.325265 Asynch=12.897077 Spawn=0\n",
      "Index: 314 is coherent: False. Malleability=2.220536 Asynch=7.499311 Spawn=0\n",
      " \n",
      "Index: 315 is coherent: False. Malleability=2.483113 Asynch=4.210829 Spawn=0\n",
      "Index: 316 is coherent: False. Malleability=2.8883989999999997 Asynch=4.501709 Spawn=0\n",
      "Index: 317 is coherent: False. Malleability=2.9517619999999996 Asynch=4.391025 Spawn=0\n",
      "Index: 318 is coherent: False. Malleability=2.837731 Asynch=3.9354359999999997 Spawn=0\n",
      "Index: 319 is coherent: False. Malleability=2.994668 Asynch=4.724705 Spawn=0\n",
      " \n",
      "Index: 320 is coherent: False. Malleability=3.3732130000000002 Asynch=3.6399880000000002 Spawn=0\n",
      "Index: 321 is coherent: False. Malleability=2.094484 Asynch=3.384651 Spawn=0\n",
      "Index: 322 is coherent: False. Malleability=2.355092 Asynch=3.93074 Spawn=0\n",
      "Index: 323 is coherent: False. Malleability=2.044637 Asynch=3.7481649999999997 Spawn=0\n",
      "Index: 324 is coherent: False. Malleability=2.269584 Asynch=3.840873 Spawn=0\n",
      " \n",
      "Index: 325 is coherent: True. Malleability=2.128472 Asynch=1.122557 Spawn=1\n",
      "Index: 326 is coherent: True. Malleability=2.1204739999999997 Asynch=1.152479 Spawn=1\n",
      "Index: 327 is coherent: True. Malleability=2.108712 Asynch=1.176249 Spawn=1\n",
      "Index: 328 is coherent: True. Malleability=2.065903 Asynch=1.173438 Spawn=1\n",
      "Index: 329 is coherent: True. Malleability=2.053835 Asynch=1.179556 Spawn=1\n",
      " \n",
      "Index: 330 is coherent: False. Malleability=3.013863 Asynch=3.170998 Spawn=0\n",
      "Index: 331 is coherent: True. Malleability=2.748586 Asynch=2.604897 Spawn=0\n",
      "Index: 332 is coherent: False. Malleability=2.114745 Asynch=2.405883 Spawn=0\n",
      "Index: 333 is coherent: False. Malleability=2.345616 Asynch=2.479429 Spawn=0\n",
      "Index: 334 is coherent: False. Malleability=1.801336 Asynch=2.569546 Spawn=0\n",
      " \n",
      "Index: 335 is coherent: False. Malleability=1.815889 Asynch=6.119356 Spawn=0\n",
      "Index: 336 is coherent: False. Malleability=1.668352 Asynch=6.052027 Spawn=0\n",
      "Index: 337 is coherent: False. Malleability=1.606603 Asynch=5.579333 Spawn=0\n",
      "Index: 338 is coherent: False. Malleability=1.71103 Asynch=6.260001 Spawn=0\n",
      "Index: 339 is coherent: False. Malleability=1.9907620000000001 Asynch=5.988132 Spawn=0\n",
      " \n",
      "Index: 340 is coherent: True. Malleability=2.350689 Asynch=1.752999 Spawn=1\n",
      "Index: 341 is coherent: True. Malleability=2.92865 Asynch=1.766818 Spawn=1\n",
      "Index: 342 is coherent: True. Malleability=2.063442 Asynch=1.622299 Spawn=1\n",
      "Index: 343 is coherent: True. Malleability=3.3601659999999995 Asynch=2.212329 Spawn=1\n",
      "Index: 344 is coherent: True. Malleability=2.191996 Asynch=1.758037 Spawn=1\n",
      " \n",
      "Index: 345 is coherent: False. Malleability=7.337478 Asynch=13.094398 Spawn=0\n",
      "Index: 346 is coherent: False. Malleability=6.759184 Asynch=12.914737 Spawn=0\n",
      "Index: 347 is coherent: False. Malleability=6.857368999999999 Asynch=12.817863 Spawn=0\n",
      "Index: 348 is coherent: False. Malleability=6.662624999999999 Asynch=12.845068999999999 Spawn=0\n",
      "Index: 349 is coherent: False. Malleability=6.658784 Asynch=12.225394999999999 Spawn=0\n",
      " \n",
      "Index: 350 is coherent: False. Malleability=7.402098 Asynch=13.613745999999999 Spawn=0\n",
      "Index: 351 is coherent: False. Malleability=6.937353 Asynch=12.780757 Spawn=0\n",
      "Index: 352 is coherent: False. Malleability=7.193775 Asynch=13.766048999999999 Spawn=0\n",
      "Index: 353 is coherent: False. Malleability=2.63836 Asynch=8.846696 Spawn=0\n",
      "Index: 354 is coherent: False. Malleability=6.405861 Asynch=12.900469000000001 Spawn=0\n",
      " \n",
      "Index: 355 is coherent: True. Malleability=3.8378569999999996 Asynch=3.3691129999999996 Spawn=1\n",
      "Index: 356 is coherent: True. Malleability=3.386597 Asynch=2.9412890000000003 Spawn=1\n",
      "Index: 357 is coherent: True. Malleability=3.5893770000000003 Asynch=3.127187 Spawn=1\n",
      "Index: 358 is coherent: True. Malleability=3.789952 Asynch=3.24725 Spawn=1\n",
      "Index: 359 is coherent: True. Malleability=3.219831 Asynch=2.758327 Spawn=1\n",
      " \n",
      "Index: 360 is coherent: False. Malleability=6.150337 Asynch=13.141586 Spawn=0\n",
      "Index: 361 is coherent: False. Malleability=7.28923 Asynch=13.724228 Spawn=0\n",
      "Index: 362 is coherent: False. Malleability=6.9384689999999996 Asynch=13.806109 Spawn=0\n",
      "Index: 363 is coherent: False. Malleability=7.33076 Asynch=13.918391 Spawn=0\n",
      "Index: 364 is coherent: False. Malleability=6.9522889999999995 Asynch=13.603928 Spawn=0\n",
      " \n",
      "Index: 365 is coherent: True. Malleability=2.12774 Asynch=1.8540579999999998 Spawn=1\n",
      "Index: 366 is coherent: True. Malleability=2.274765 Asynch=2.029 Spawn=1\n",
      "Index: 367 is coherent: True. Malleability=2.2624079999999998 Asynch=2.010567 Spawn=1\n",
      "Index: 368 is coherent: True. Malleability=2.617397 Asynch=2.3332159999999993 Spawn=1\n",
      "Index: 369 is coherent: True. Malleability=1.908614 Asynch=1.548934 Spawn=1\n",
      " \n",
      "Index: 370 is coherent: True. Malleability=1.407792 Asynch=1.073022 Spawn=1\n",
      "Index: 371 is coherent: True. Malleability=2.02782 Asynch=1.668502 Spawn=1\n",
      "Index: 372 is coherent: True. Malleability=1.509573 Asynch=1.074273 Spawn=1\n",
      "Index: 373 is coherent: True. Malleability=2.431045 Asynch=1.136779 Spawn=1\n",
      "Index: 374 is coherent: True. Malleability=1.403009 Asynch=1.003958 Spawn=1\n",
      " \n",
      "Index: 375 is coherent: False. Malleability=1.922502 Asynch=3.567826 Spawn=0\n",
      "Index: 376 is coherent: False. Malleability=1.940127 Asynch=4.2440999999999995 Spawn=0\n",
      "Index: 377 is coherent: False. Malleability=1.8117839999999998 Asynch=3.680196 Spawn=0\n",
      "Index: 378 is coherent: False. Malleability=1.6907130000000001 Asynch=3.736777 Spawn=0\n",
      "Index: 379 is coherent: False. Malleability=2.0370470000000003 Asynch=4.31225 Spawn=0\n",
      " \n",
      "Index: 380 is coherent: True. Malleability=3.7383369999999996 Asynch=2.809546 Spawn=1\n",
      "Index: 381 is coherent: True. Malleability=3.857654 Asynch=2.851968 Spawn=1\n",
      "Index: 382 is coherent: True. Malleability=3.8034999999999997 Asynch=2.8909580000000004 Spawn=1\n",
      "Index: 383 is coherent: True. Malleability=3.750425 Asynch=2.8228709999999997 Spawn=1\n",
      "Index: 384 is coherent: True. Malleability=3.827522 Asynch=2.8839099999999998 Spawn=1\n",
      " \n",
      "Index: 385 is coherent: False. Malleability=5.208054 Asynch=9.800526000000001 Spawn=0\n",
      "Index: 386 is coherent: False. Malleability=2.8956660000000003 Asynch=7.393442 Spawn=0\n",
      "Index: 387 is coherent: False. Malleability=5.367484 Asynch=9.846604 Spawn=0\n",
      "Index: 388 is coherent: False. Malleability=4.303050000000001 Asynch=9.082498000000001 Spawn=0\n",
      "Index: 389 is coherent: False. Malleability=5.702392 Asynch=10.37125 Spawn=0\n",
      " \n",
      "Index: 390 is coherent: True. Malleability=1.784759 Asynch=0.883493 Spawn=1\n",
      "Index: 391 is coherent: True. Malleability=1.750003 Asynch=0.798257 Spawn=1\n",
      "Index: 392 is coherent: True. Malleability=1.76121 Asynch=0.817745 Spawn=1\n",
      "Index: 393 is coherent: True. Malleability=1.7684069999999998 Asynch=0.811976 Spawn=1\n",
      "Index: 394 is coherent: True. Malleability=1.634956 Asynch=0.803665 Spawn=1\n",
      " \n",
      "Index: 395 is coherent: True. Malleability=3.3672820000000003 Asynch=2.7385 Spawn=1\n",
      "Index: 396 is coherent: True. Malleability=3.896034 Asynch=3.259632 Spawn=1\n",
      "Index: 397 is coherent: True. Malleability=3.1901059999999997 Asynch=2.609512 Spawn=1\n",
      "Index: 398 is coherent: True. Malleability=3.5153860000000003 Asynch=2.7113340000000004 Spawn=1\n",
      "Index: 399 is coherent: True. Malleability=3.263026 Asynch=2.63701 Spawn=1\n",
      " \n",
      "Index: 400 is coherent: True. Malleability=5.228324000000001 Asynch=3.8936830000000007 Spawn=1\n",
      "Index: 401 is coherent: True. Malleability=3.760022 Asynch=3.189876 Spawn=1\n",
      "Index: 402 is coherent: True. Malleability=3.8007169999999997 Asynch=3.024133 Spawn=1\n",
      "Index: 403 is coherent: True. Malleability=3.685495 Asynch=3.086793 Spawn=1\n",
      "Index: 404 is coherent: True. Malleability=3.79607 Asynch=3.214483 Spawn=1\n",
      " \n",
      "Index: 405 is coherent: True. Malleability=3.726212 Asynch=3.6531469999999997 Spawn=1\n",
      "Index: 406 is coherent: True. Malleability=3.6447149999999997 Asynch=3.17061 Spawn=1\n",
      "Index: 407 is coherent: True. Malleability=3.488922 Asynch=3.2849969999999997 Spawn=1\n",
      "Index: 408 is coherent: True. Malleability=3.5289390000000003 Asynch=3.0651129999999998 Spawn=1\n",
      "Index: 409 is coherent: True. Malleability=3.550924 Asynch=3.4931879999999995 Spawn=1\n",
      " \n",
      "Index: 410 is coherent: True. Malleability=12.353520999999999 Asynch=11.025635 Spawn=1\n",
      "Index: 411 is coherent: True. Malleability=12.628646000000002 Asynch=11.234369000000001 Spawn=1\n",
      "Index: 412 is coherent: True. Malleability=11.442394 Asynch=10.118174 Spawn=1\n",
      "Index: 413 is coherent: True. Malleability=12.31866 Asynch=11.001404 Spawn=1\n",
      "Index: 414 is coherent: True. Malleability=13.430439999999999 Asynch=12.214668999999999 Spawn=1\n",
      " \n",
      "Index: 415 is coherent: True. Malleability=4.058953 Asynch=3.544188 Spawn=1\n",
      "Index: 416 is coherent: True. Malleability=4.360515 Asynch=3.7057740000000003 Spawn=1\n",
      "Index: 417 is coherent: True. Malleability=4.120233 Asynch=3.453035 Spawn=1\n",
      "Index: 418 is coherent: True. Malleability=4.2934079999999994 Asynch=3.723881 Spawn=1\n",
      "Index: 419 is coherent: True. Malleability=4.589867 Asynch=3.865189 Spawn=1\n",
      " \n",
      "Index: 420 is coherent: False. Malleability=2.642721 Asynch=3.776148 Spawn=0\n",
      "Index: 421 is coherent: False. Malleability=2.436431 Asynch=3.839381 Spawn=0\n",
      "Index: 422 is coherent: False. Malleability=2.576804 Asynch=3.6176950000000003 Spawn=0\n",
      "Index: 423 is coherent: False. Malleability=2.475974 Asynch=3.355653 Spawn=0\n",
      "Index: 424 is coherent: False. Malleability=2.571615 Asynch=3.465553 Spawn=0\n",
      " \n",
      "Index: 425 is coherent: True. Malleability=4.533553 Asynch=3.6667759999999996 Spawn=1\n",
      "Index: 426 is coherent: True. Malleability=4.009112 Asynch=3.250172 Spawn=1\n",
      "Index: 427 is coherent: True. Malleability=3.8059209999999997 Asynch=2.9269580000000004 Spawn=1\n",
      "Index: 428 is coherent: True. Malleability=4.4411 Asynch=3.816382 Spawn=1\n",
      "Index: 429 is coherent: True. Malleability=3.9454720000000005 Asynch=2.841014 Spawn=1\n",
      " \n",
      "Index: 430 is coherent: True. Malleability=3.792716 Asynch=3.7421999999999995 Spawn=1\n",
      "Index: 431 is coherent: True. Malleability=3.9789150000000006 Asynch=3.843688 Spawn=1\n",
      "Index: 432 is coherent: True. Malleability=3.932708 Asynch=3.750288 Spawn=1\n",
      "Index: 433 is coherent: True. Malleability=3.9890789999999994 Asynch=3.9018409999999997 Spawn=1\n",
      "Index: 434 is coherent: True. Malleability=3.8342400000000003 Asynch=3.681328 Spawn=1\n",
      " \n",
      "Index: 435 is coherent: False. Malleability=5.482176000000001 Asynch=5.633403 Spawn=0\n",
      "Index: 436 is coherent: True. Malleability=6.177931 Asynch=5.884485 Spawn=0\n",
      "Index: 437 is coherent: False. Malleability=5.133763 Asynch=5.310396000000001 Spawn=0\n",
      "Index: 438 is coherent: True. Malleability=5.743192 Asynch=5.489203 Spawn=0\n",
      "Index: 439 is coherent: True. Malleability=6.372109 Asynch=5.877038999999999 Spawn=0\n",
      " \n",
      "Index: 440 is coherent: True. Malleability=1.472962 Asynch=1.00722 Spawn=1\n",
      "Index: 441 is coherent: True. Malleability=1.41004 Asynch=0.864927 Spawn=1\n",
      "Index: 442 is coherent: True. Malleability=1.477004 Asynch=0.279158 Spawn=1\n",
      "Index: 443 is coherent: True. Malleability=1.467289 Asynch=0.550137 Spawn=1\n",
      "Index: 444 is coherent: True. Malleability=1.410008 Asynch=0.279864 Spawn=1\n",
      " \n",
      "Index: 445 is coherent: False. Malleability=1.7786529999999998 Asynch=2.804367 Spawn=0\n",
      "Index: 446 is coherent: False. Malleability=1.5067300000000001 Asynch=3.0893990000000002 Spawn=0\n",
      "Index: 447 is coherent: False. Malleability=1.679955 Asynch=3.430738 Spawn=0\n",
      "Index: 448 is coherent: False. Malleability=1.534625 Asynch=4.766983 Spawn=0\n",
      "Index: 449 is coherent: False. Malleability=1.5542880000000001 Asynch=3.458226 Spawn=0\n",
      " \n",
      "Index: 450 is coherent: False. Malleability=11.480577 Asynch=13.120664 Spawn=0\n",
      "Index: 451 is coherent: False. Malleability=12.129552 Asynch=13.521640999999999 Spawn=0\n",
      "Index: 452 is coherent: False. Malleability=11.055551000000001 Asynch=13.33581 Spawn=0\n",
      "Index: 453 is coherent: False. Malleability=11.707926 Asynch=13.731976 Spawn=0\n",
      "Index: 454 is coherent: False. Malleability=11.207563 Asynch=13.803515 Spawn=0\n",
      " \n",
      "Index: 455 is coherent: False. Malleability=1.766964 Asynch=23.185994 Spawn=0\n",
      "Index: 456 is coherent: False. Malleability=1.883608 Asynch=19.637856 Spawn=0\n",
      "Index: 457 is coherent: False. Malleability=1.879277 Asynch=21.235691000000003 Spawn=0\n",
      "Index: 458 is coherent: False. Malleability=1.731778 Asynch=25.720119 Spawn=0\n",
      "Index: 459 is coherent: False. Malleability=1.7835969999999999 Asynch=19.882422000000002 Spawn=0\n",
      " \n",
      "Index: 460 is coherent: False. Malleability=2.932091 Asynch=3.39202 Spawn=0\n",
      "Index: 461 is coherent: False. Malleability=2.767907 Asynch=3.424037 Spawn=0\n",
      "Index: 462 is coherent: False. Malleability=2.668603 Asynch=3.305183 Spawn=0\n",
      "Index: 463 is coherent: False. Malleability=2.670373 Asynch=3.147488 Spawn=0\n",
      "Index: 464 is coherent: False. Malleability=2.675787 Asynch=3.271762 Spawn=0\n",
      " \n",
      "Index: 465 is coherent: False. Malleability=9.656654 Asynch=10.405673 Spawn=0\n",
      "Index: 466 is coherent: True. Malleability=11.004984 Asynch=10.57504 Spawn=0\n",
      "Index: 467 is coherent: False. Malleability=9.557960999999999 Asynch=10.339279 Spawn=0\n",
      "Index: 468 is coherent: False. Malleability=8.212755999999999 Asynch=9.120199000000001 Spawn=0\n",
      "Index: 469 is coherent: False. Malleability=7.852722 Asynch=8.713103 Spawn=0\n",
      " \n",
      "Index: 470 is coherent: True. Malleability=0.571309 Asynch=0.237785 Spawn=1\n",
      "Index: 471 is coherent: True. Malleability=0.543131 Asynch=0.230149 Spawn=1\n",
      "Index: 472 is coherent: True. Malleability=0.561171 Asynch=0.225204 Spawn=1\n",
      "Index: 473 is coherent: True. Malleability=0.517842 Asynch=0.174514 Spawn=1\n",
      "Index: 474 is coherent: True. Malleability=0.533091 Asynch=0.241998 Spawn=1\n",
      " \n",
      "Index: 475 is coherent: True. Malleability=3.6771089999999997 Asynch=3.059002 Spawn=1\n",
      "Index: 476 is coherent: True. Malleability=3.9368950000000003 Asynch=3.171502 Spawn=1\n",
      "Index: 477 is coherent: True. Malleability=3.2808770000000003 Asynch=2.0059940000000003 Spawn=1\n",
      "Index: 478 is coherent: True. Malleability=3.551818 Asynch=2.341545 Spawn=1\n",
      "Index: 479 is coherent: True. Malleability=3.139614 Asynch=2.29989 Spawn=1\n",
      " \n",
      "Index: 480 is coherent: False. Malleability=1.6285340000000001 Asynch=4.024551 Spawn=0\n",
      "Index: 481 is coherent: False. Malleability=2.331022 Asynch=4.225405 Spawn=0\n",
      "Index: 482 is coherent: False. Malleability=1.8214919999999999 Asynch=3.261444 Spawn=0\n",
      "Index: 483 is coherent: False. Malleability=1.894027 Asynch=3.589035 Spawn=0\n",
      "Index: 484 is coherent: False. Malleability=1.8022300000000002 Asynch=3.888406 Spawn=0\n",
      " \n",
      "Index: 485 is coherent: False. Malleability=2.303006 Asynch=5.791431 Spawn=0\n",
      "Index: 486 is coherent: False. Malleability=2.1638960000000003 Asynch=5.447997 Spawn=0\n",
      "Index: 487 is coherent: False. Malleability=2.334085 Asynch=7.023258 Spawn=0\n",
      "Index: 488 is coherent: False. Malleability=2.050967 Asynch=6.36404 Spawn=0\n",
      "Index: 489 is coherent: False. Malleability=2.141041 Asynch=5.47202 Spawn=0\n",
      " \n",
      "Index: 490 is coherent: True. Malleability=2.945621 Asynch=2.0579389999999997 Spawn=1\n",
      "Index: 491 is coherent: True. Malleability=2.9257079999999998 Asynch=2.077178 Spawn=1\n",
      "Index: 492 is coherent: True. Malleability=2.744816 Asynch=1.950072 Spawn=1\n",
      "Index: 493 is coherent: True. Malleability=2.838794 Asynch=2.0258629999999997 Spawn=1\n",
      "Index: 494 is coherent: True. Malleability=2.929635 Asynch=2.118882 Spawn=1\n",
      " \n",
      "Index: 495 is coherent: True. Malleability=3.721561 Asynch=2.414231 Spawn=1\n",
      "Index: 496 is coherent: True. Malleability=2.986517 Asynch=2.4534430000000005 Spawn=1\n",
      "Index: 497 is coherent: True. Malleability=3.063849 Asynch=2.342782 Spawn=1\n",
      "Index: 498 is coherent: True. Malleability=3.039489 Asynch=2.454852 Spawn=1\n",
      "Index: 499 is coherent: True. Malleability=4.073056 Asynch=2.7294579999999997 Spawn=1\n",
      " \n",
      "Index: 500 is coherent: False. Malleability=1.579832 Asynch=5.295336000000001 Spawn=0\n",
      "Index: 501 is coherent: False. Malleability=1.75937 Asynch=3.47127 Spawn=0\n",
      "Index: 502 is coherent: False. Malleability=2.085404 Asynch=3.379263 Spawn=0\n",
      "Index: 503 is coherent: False. Malleability=1.75146 Asynch=3.292016 Spawn=0\n",
      "Index: 504 is coherent: False. Malleability=1.5100730000000002 Asynch=2.2054739999999997 Spawn=0\n",
      " \n",
      "Index: 505 is coherent: True. Malleability=3.783322 Asynch=3.1804679999999994 Spawn=1\n",
      "Index: 506 is coherent: True. Malleability=3.372682 Asynch=2.765579 Spawn=1\n",
      "Index: 507 is coherent: True. Malleability=3.743395 Asynch=2.897314 Spawn=1\n",
      "Index: 508 is coherent: True. Malleability=3.4247210000000003 Asynch=2.881143 Spawn=1\n",
      "Index: 509 is coherent: True. Malleability=4.213547999999999 Asynch=2.9260759999999997 Spawn=1\n",
      " \n",
      "Index: 510 is coherent: True. Malleability=2.148 Asynch=1.062746 Spawn=1\n",
      "Index: 511 is coherent: True. Malleability=1.3844049999999999 Asynch=1.048967 Spawn=1\n",
      "Index: 512 is coherent: True. Malleability=2.1570869999999998 Asynch=1.084281 Spawn=1\n",
      "Index: 513 is coherent: True. Malleability=1.0997190000000001 Asynch=0.732067 Spawn=1\n",
      "Index: 514 is coherent: True. Malleability=1.1323699999999999 Asynch=0.76465 Spawn=1\n",
      " \n",
      "Index: 515 is coherent: True. Malleability=2.539777 Asynch=1.548148 Spawn=1\n",
      "Index: 516 is coherent: True. Malleability=2.5272769999999998 Asynch=1.561331 Spawn=1\n",
      "Index: 517 is coherent: True. Malleability=2.39501 Asynch=1.444257 Spawn=1\n",
      "Index: 518 is coherent: True. Malleability=2.5251349999999997 Asynch=1.569406 Spawn=1\n",
      "Index: 519 is coherent: True. Malleability=2.590746 Asynch=1.524108 Spawn=1\n",
      " \n",
      "Index: 520 is coherent: True. Malleability=1.743185 Asynch=1.724908 Spawn=1\n",
      "Index: 521 is coherent: True. Malleability=1.796519 Asynch=1.769091 Spawn=1\n",
      "Index: 522 is coherent: True. Malleability=1.72695 Asynch=1.710426 Spawn=1\n",
      "Index: 523 is coherent: True. Malleability=1.733495 Asynch=1.714877 Spawn=1\n",
      "Index: 524 is coherent: True. Malleability=1.6372499999999999 Asynch=1.630507 Spawn=1\n",
      " \n",
      "Index: 525 is coherent: False. Malleability=5.243297999999999 Asynch=6.6053820000000005 Spawn=0\n",
      "Index: 526 is coherent: False. Malleability=5.582517 Asynch=7.372706 Spawn=0\n",
      "Index: 527 is coherent: True. Malleability=4.454136 Asynch=4.447566 Spawn=0\n",
      "Index: 528 is coherent: True. Malleability=5.040718 Asynch=4.6116589999999995 Spawn=0\n",
      "Index: 529 is coherent: False. Malleability=5.073158 Asynch=6.668364 Spawn=0\n",
      " \n",
      "Index: 530 is coherent: True. Malleability=2.741424 Asynch=2.690823 Spawn=1\n",
      "Index: 531 is coherent: True. Malleability=2.6831039999999997 Asynch=2.61149 Spawn=1\n",
      "Index: 532 is coherent: True. Malleability=2.8171 Asynch=2.650466 Spawn=1\n",
      "Index: 533 is coherent: True. Malleability=2.666808 Asynch=2.6493320000000002 Spawn=1\n",
      "Index: 534 is coherent: True. Malleability=2.786194 Asynch=2.6477749999999998 Spawn=1\n",
      " \n",
      "Index: 535 is coherent: True. Malleability=2.6778690000000003 Asynch=1.690436 Spawn=1\n",
      "Index: 536 is coherent: True. Malleability=2.7322870000000004 Asynch=1.7146210000000002 Spawn=1\n",
      "Index: 537 is coherent: True. Malleability=2.649954 Asynch=1.688226 Spawn=1\n",
      "Index: 538 is coherent: True. Malleability=2.692402 Asynch=1.679195 Spawn=1\n",
      "Index: 539 is coherent: True. Malleability=2.700157 Asynch=1.707036 Spawn=1\n",
      " \n",
      "Index: 540 is coherent: True. Malleability=16.608955 Asynch=15.344953 Spawn=0\n",
      "Index: 541 is coherent: False. Malleability=17.689051 Asynch=22.406922 Spawn=0\n",
      "Index: 542 is coherent: True. Malleability=12.39654 Asynch=12.078810999999998 Spawn=0\n",
      "Index: 543 is coherent: True. Malleability=17.018791 Asynch=16.305329 Spawn=0\n",
      "Index: 544 is coherent: True. Malleability=15.316167 Asynch=14.994268 Spawn=0\n",
      " \n",
      "Index: 545 is coherent: True. Malleability=1.037036 Asynch=0.195647 Spawn=1\n",
      "Index: 546 is coherent: True. Malleability=1.000491 Asynch=0.227121 Spawn=1\n",
      "Index: 547 is coherent: True. Malleability=0.992163 Asynch=0.260474 Spawn=1\n",
      "Index: 548 is coherent: True. Malleability=0.9655420000000001 Asynch=0.212064 Spawn=1\n",
      "Index: 549 is coherent: True. Malleability=1.039406 Asynch=0.229072 Spawn=1\n",
      " \n",
      "Index: 550 is coherent: True. Malleability=2.524815 Asynch=2.289235 Spawn=0\n",
      "Index: 551 is coherent: True. Malleability=2.454721 Asynch=2.194545 Spawn=0\n",
      "Index: 552 is coherent: True. Malleability=2.543196 Asynch=2.268555 Spawn=0\n",
      "Index: 553 is coherent: True. Malleability=2.5677440000000002 Asynch=2.315266 Spawn=0\n",
      "Index: 554 is coherent: True. Malleability=2.326399 Asynch=2.222671 Spawn=0\n",
      " \n",
      "Index: 555 is coherent: False. Malleability=4.748696000000001 Asynch=7.3428070000000005 Spawn=0\n",
      "Index: 556 is coherent: False. Malleability=5.975569999999999 Asynch=7.963158 Spawn=0\n",
      "Index: 557 is coherent: False. Malleability=6.82177 Asynch=8.020105000000001 Spawn=0\n",
      "Index: 558 is coherent: False. Malleability=7.109024 Asynch=8.858338 Spawn=0\n",
      "Index: 559 is coherent: False. Malleability=8.004615999999999 Asynch=9.126377 Spawn=0\n",
      " \n",
      "Index: 560 is coherent: True. Malleability=1.624866 Asynch=1.599838 Spawn=1\n",
      "Index: 561 is coherent: True. Malleability=1.744208 Asynch=1.7208860000000001 Spawn=1\n",
      "Index: 562 is coherent: True. Malleability=1.6438449999999998 Asynch=1.627996 Spawn=1\n",
      "Index: 563 is coherent: True. Malleability=1.5506250000000001 Asynch=1.5444680000000002 Spawn=1\n",
      "Index: 564 is coherent: True. Malleability=1.909617 Asynch=1.883614 Spawn=1\n",
      " \n",
      "Index: 565 is coherent: True. Malleability=1.7151239999999999 Asynch=0.84586 Spawn=1\n",
      "Index: 566 is coherent: True. Malleability=1.777083 Asynch=0.814891 Spawn=1\n",
      "Index: 567 is coherent: True. Malleability=1.8109709999999999 Asynch=0.839519 Spawn=1\n",
      "Index: 568 is coherent: True. Malleability=1.75672 Asynch=0.816932 Spawn=1\n",
      "Index: 569 is coherent: True. Malleability=1.74498 Asynch=0.82273 Spawn=1\n",
      " \n",
      "Index: 570 is coherent: False. Malleability=1.8416299999999999 Asynch=6.948586 Spawn=0\n",
      "Index: 571 is coherent: False. Malleability=1.834191 Asynch=6.663998 Spawn=0\n",
      "Index: 572 is coherent: False. Malleability=1.849099 Asynch=6.41312 Spawn=0\n",
      "Index: 573 is coherent: False. Malleability=1.932625 Asynch=6.669303 Spawn=0\n",
      "Index: 574 is coherent: False. Malleability=1.7590750000000002 Asynch=6.787996 Spawn=0\n",
      " \n",
      "Index: 575 is coherent: True. Malleability=4.836391999999999 Asynch=3.90464 Spawn=1\n",
      "Index: 576 is coherent: True. Malleability=4.9366900000000005 Asynch=4.156175 Spawn=1\n",
      "Index: 577 is coherent: True. Malleability=4.912997 Asynch=3.932624 Spawn=1\n",
      "Index: 578 is coherent: True. Malleability=4.799063 Asynch=4.052207 Spawn=1\n",
      "Index: 579 is coherent: True. Malleability=5.058343 Asynch=4.046381 Spawn=1\n",
      " \n",
      "Index: 580 is coherent: True. Malleability=1.3226879999999999 Asynch=1.015332 Spawn=1\n",
      "Index: 581 is coherent: True. Malleability=1.357029 Asynch=1.120315 Spawn=1\n",
      "Index: 582 is coherent: True. Malleability=1.354962 Asynch=1.073954 Spawn=1\n",
      "Index: 583 is coherent: True. Malleability=1.33975 Asynch=1.058222 Spawn=1\n",
      "Index: 584 is coherent: True. Malleability=1.359659 Asynch=1.014776 Spawn=1\n",
      " \n",
      "Index: 585 is coherent: True. Malleability=3.634346 Asynch=2.510265 Spawn=1\n",
      "Index: 586 is coherent: True. Malleability=3.3467160000000002 Asynch=2.8085299999999997 Spawn=1\n",
      "Index: 587 is coherent: True. Malleability=3.4877459999999996 Asynch=2.394219 Spawn=1\n",
      "Index: 588 is coherent: True. Malleability=4.277406 Asynch=3.229215 Spawn=1\n",
      "Index: 589 is coherent: True. Malleability=3.455352 Asynch=2.862895 Spawn=1\n",
      " \n",
      "Index: 590 is coherent: False. Malleability=4.832 Asynch=10.109783 Spawn=0\n",
      "Index: 591 is coherent: False. Malleability=6.757515 Asynch=12.641901 Spawn=0\n",
      "Index: 592 is coherent: False. Malleability=7.756724 Asynch=13.616745 Spawn=0\n",
      "Index: 593 is coherent: False. Malleability=6.983988 Asynch=13.712831 Spawn=0\n",
      "Index: 594 is coherent: False. Malleability=7.1284719999999995 Asynch=13.584984 Spawn=0\n",
      " \n",
      "Index: 595 is coherent: False. Malleability=11.681225 Asynch=14.581894 Spawn=0\n",
      "Index: 596 is coherent: False. Malleability=11.157636 Asynch=13.45425 Spawn=0\n",
      "Index: 597 is coherent: False. Malleability=11.743032 Asynch=14.05914 Spawn=0\n",
      "Index: 598 is coherent: False. Malleability=11.740889 Asynch=13.788783 Spawn=0\n",
      "Index: 599 is coherent: False. Malleability=7.900912 Asynch=10.578062 Spawn=0\n",
      " \n",
      "Index: 600 is coherent: True. Malleability=3.517806 Asynch=3.3355300000000003 Spawn=1\n",
      "Index: 601 is coherent: True. Malleability=3.5915600000000003 Asynch=3.262485 Spawn=1\n",
      "Index: 602 is coherent: True. Malleability=3.4638269999999998 Asynch=3.3726749999999996 Spawn=1\n",
      "Index: 603 is coherent: True. Malleability=3.5393960000000004 Asynch=3.1326519999999993 Spawn=1\n",
      "Index: 604 is coherent: True. Malleability=3.484654 Asynch=3.1358430000000004 Spawn=1\n",
      " \n",
      "Index: 605 is coherent: True. Malleability=1.903301 Asynch=1.4136520000000001 Spawn=1\n",
      "Index: 606 is coherent: True. Malleability=1.895041 Asynch=0.972086 Spawn=1\n",
      "Index: 607 is coherent: True. Malleability=1.8385159999999998 Asynch=0.879952 Spawn=1\n",
      "Index: 608 is coherent: True. Malleability=2.090423 Asynch=1.0816970000000001 Spawn=1\n",
      "Index: 609 is coherent: True. Malleability=2.037502 Asynch=1.011596 Spawn=1\n",
      " \n",
      "Index: 610 is coherent: False. Malleability=6.819903 Asynch=12.723316 Spawn=0\n",
      "Index: 611 is coherent: False. Malleability=3.928556 Asynch=10.029029 Spawn=0\n",
      "Index: 612 is coherent: False. Malleability=4.908351 Asynch=10.357847 Spawn=0\n",
      "Index: 613 is coherent: False. Malleability=6.35445 Asynch=12.154912 Spawn=0\n",
      "Index: 614 is coherent: False. Malleability=6.699184000000001 Asynch=12.168513 Spawn=0\n",
      " \n",
      "Index: 615 is coherent: False. Malleability=3.2972590000000004 Asynch=6.527698 Spawn=0\n",
      "Index: 616 is coherent: False. Malleability=3.130185 Asynch=6.179726 Spawn=0\n",
      "Index: 617 is coherent: False. Malleability=3.174817 Asynch=6.676475 Spawn=0\n",
      "Index: 618 is coherent: False. Malleability=3.239617 Asynch=6.891357 Spawn=0\n",
      "Index: 619 is coherent: False. Malleability=3.206528 Asynch=6.524542 Spawn=0\n",
      " \n",
      "Index: 620 is coherent: True. Malleability=8.747409 Asynch=7.828624 Spawn=0\n",
      "Index: 621 is coherent: False. Malleability=6.713533 Asynch=6.722764 Spawn=0\n",
      "Index: 622 is coherent: True. Malleability=7.049771 Asynch=6.369288 Spawn=0\n",
      "Index: 623 is coherent: True. Malleability=6.958076 Asynch=6.147653 Spawn=0\n",
      "Index: 624 is coherent: False. Malleability=6.604157 Asynch=6.847343 Spawn=0\n",
      " \n",
      "Index: 625 is coherent: False. Malleability=3.199356 Asynch=3.952497 Spawn=0\n",
      "Index: 626 is coherent: False. Malleability=3.271833 Asynch=3.679805 Spawn=0\n",
      "Index: 627 is coherent: False. Malleability=2.842041 Asynch=3.359268 Spawn=0\n",
      "Index: 628 is coherent: False. Malleability=2.585958 Asynch=3.160698 Spawn=0\n",
      "Index: 629 is coherent: False. Malleability=2.1104709999999995 Asynch=2.570112 Spawn=0\n",
      " \n",
      "Index: 630 is coherent: True. Malleability=2.513143 Asynch=0.747722 Spawn=1\n",
      "Index: 631 is coherent: True. Malleability=2.518656 Asynch=0.876031 Spawn=1\n",
      "Index: 632 is coherent: True. Malleability=2.5179150000000003 Asynch=0.837279 Spawn=1\n",
      "Index: 633 is coherent: True. Malleability=2.492329 Asynch=0.859736 Spawn=1\n",
      "Index: 634 is coherent: True. Malleability=2.443446 Asynch=0.876311 Spawn=1\n",
      " \n",
      "Index: 635 is coherent: False. Malleability=2.2828980000000003 Asynch=6.775149000000001 Spawn=0\n",
      "Index: 636 is coherent: False. Malleability=2.3592500000000003 Asynch=9.379483999999998 Spawn=0\n",
      "Index: 637 is coherent: False. Malleability=2.308756 Asynch=5.827966999999999 Spawn=0\n",
      "Index: 638 is coherent: False. Malleability=2.236244 Asynch=9.240333 Spawn=0\n",
      "Index: 639 is coherent: False. Malleability=2.654663 Asynch=9.961871 Spawn=0\n",
      " \n",
      "Index: 640 is coherent: False. Malleability=1.51817 Asynch=1.549591 Spawn=0\n",
      "Index: 641 is coherent: False. Malleability=1.48594 Asynch=1.5551840000000001 Spawn=0\n",
      "Index: 642 is coherent: False. Malleability=1.4706380000000001 Asynch=1.547623 Spawn=0\n",
      "Index: 643 is coherent: False. Malleability=1.577215 Asynch=1.6271909999999998 Spawn=0\n",
      "Index: 644 is coherent: False. Malleability=1.5126659999999998 Asynch=1.5526849999999999 Spawn=0\n",
      " \n",
      "Index: 645 is coherent: False. Malleability=1.8224339999999999 Asynch=2.047318 Spawn=0\n",
      "Index: 646 is coherent: False. Malleability=2.071786 Asynch=3.479512 Spawn=0\n",
      "Index: 647 is coherent: False. Malleability=1.8175979999999998 Asynch=2.00001 Spawn=0\n",
      "Index: 648 is coherent: True. Malleability=2.764836 Asynch=2.371978 Spawn=0\n",
      "Index: 649 is coherent: False. Malleability=1.75928 Asynch=2.400075 Spawn=0\n",
      " \n",
      "Index: 650 is coherent: True. Malleability=4.343668999999999 Asynch=4.034045 Spawn=0\n",
      "Index: 651 is coherent: False. Malleability=4.905945 Asynch=5.095810999999999 Spawn=0\n",
      "Index: 652 is coherent: False. Malleability=4.6208860000000005 Asynch=5.065007 Spawn=0\n",
      "Index: 653 is coherent: False. Malleability=5.052339 Asynch=5.372807 Spawn=0\n",
      "Index: 654 is coherent: True. Malleability=5.570426 Asynch=5.31406 Spawn=0\n",
      " \n",
      "Index: 655 is coherent: True. Malleability=8.359894 Asynch=7.783379 Spawn=0\n",
      "Index: 656 is coherent: True. Malleability=9.267147 Asynch=8.798772 Spawn=0\n",
      "Index: 657 is coherent: True. Malleability=9.503123 Asynch=8.981832 Spawn=0\n",
      "Index: 658 is coherent: True. Malleability=7.3981189999999994 Asynch=6.359163 Spawn=0\n",
      "Index: 659 is coherent: True. Malleability=8.34984 Asynch=7.790355 Spawn=0\n",
      " \n",
      "Index: 660 is coherent: False. Malleability=1.676903 Asynch=3.023762 Spawn=0\n",
      "Index: 661 is coherent: False. Malleability=2.005693 Asynch=3.676057 Spawn=0\n",
      "Index: 662 is coherent: False. Malleability=1.9512520000000002 Asynch=3.327823 Spawn=0\n",
      "Index: 663 is coherent: False. Malleability=1.711796 Asynch=3.588012 Spawn=0\n",
      "Index: 664 is coherent: False. Malleability=2.045151 Asynch=3.607915 Spawn=0\n",
      " \n",
      "Index: 665 is coherent: True. Malleability=16.165894 Asynch=15.179038999999998 Spawn=1\n",
      "Index: 666 is coherent: True. Malleability=16.972047999999997 Asynch=15.977993999999999 Spawn=1\n",
      "Index: 667 is coherent: True. Malleability=16.656813 Asynch=15.742743999999998 Spawn=1\n",
      "Index: 668 is coherent: True. Malleability=15.006084 Asynch=14.017777999999998 Spawn=1\n",
      "Index: 669 is coherent: True. Malleability=14.446112 Asynch=13.485359999999998 Spawn=1\n",
      " \n",
      "Index: 670 is coherent: False. Malleability=6.190093 Asynch=7.111567 Spawn=0\n",
      "Index: 671 is coherent: False. Malleability=4.605984 Asynch=5.673800999999999 Spawn=0\n",
      "Index: 672 is coherent: False. Malleability=6.4166620000000005 Asynch=6.836621 Spawn=0\n",
      "Index: 673 is coherent: False. Malleability=6.368479 Asynch=6.632918 Spawn=0\n",
      "Index: 674 is coherent: False. Malleability=6.0364 Asynch=7.04425 Spawn=0\n",
      " \n",
      "Index: 675 is coherent: False. Malleability=1.935047 Asynch=5.075936 Spawn=0\n",
      "Index: 676 is coherent: False. Malleability=1.883227 Asynch=4.675661 Spawn=0\n",
      "Index: 677 is coherent: False. Malleability=2.009874 Asynch=4.087979 Spawn=0\n",
      "Index: 678 is coherent: False. Malleability=1.960578 Asynch=4.403239 Spawn=0\n",
      "Index: 679 is coherent: False. Malleability=1.971673 Asynch=4.741474 Spawn=0\n",
      " \n",
      "Index: 680 is coherent: True. Malleability=5.734635 Asynch=4.089412 Spawn=1\n",
      "Index: 681 is coherent: True. Malleability=5.904592000000001 Asynch=4.03251 Spawn=1\n",
      "Index: 682 is coherent: True. Malleability=5.70328 Asynch=3.836313 Spawn=1\n",
      "Index: 683 is coherent: True. Malleability=5.020869 Asynch=3.4444060000000003 Spawn=1\n",
      "Index: 684 is coherent: True. Malleability=4.888942 Asynch=3.403623 Spawn=1\n",
      " \n",
      "Index: 685 is coherent: False. Malleability=8.374424000000001 Asynch=14.098932999999999 Spawn=0\n",
      "Index: 686 is coherent: False. Malleability=4.931126 Asynch=11.544664999999998 Spawn=0\n",
      "Index: 687 is coherent: False. Malleability=7.048494000000001 Asynch=13.477408 Spawn=0\n",
      "Index: 688 is coherent: False. Malleability=6.7023589999999995 Asynch=13.821509 Spawn=0\n",
      "Index: 689 is coherent: False. Malleability=6.894037 Asynch=13.651637000000001 Spawn=0\n",
      " \n",
      "Index: 690 is coherent: True. Malleability=4.803108 Asynch=3.733533 Spawn=1\n",
      "Index: 691 is coherent: True. Malleability=4.680747 Asynch=3.2541510000000002 Spawn=1\n",
      "Index: 692 is coherent: True. Malleability=4.842725 Asynch=4.029100000000001 Spawn=1\n",
      "Index: 693 is coherent: True. Malleability=4.426382 Asynch=2.925914 Spawn=1\n",
      "Index: 694 is coherent: True. Malleability=4.652315 Asynch=2.9281099999999998 Spawn=1\n",
      " \n",
      "Index: 695 is coherent: True. Malleability=1.8749609999999999 Asynch=0.898752 Spawn=1\n",
      "Index: 696 is coherent: True. Malleability=1.7981570000000002 Asynch=1.058583 Spawn=1\n",
      "Index: 697 is coherent: True. Malleability=1.43321 Asynch=1.17112 Spawn=1\n",
      "Index: 698 is coherent: True. Malleability=1.785851 Asynch=1.116917 Spawn=1\n",
      "Index: 699 is coherent: True. Malleability=2.043032 Asynch=1.088903 Spawn=1\n",
      " \n",
      "Index: 700 is coherent: True. Malleability=1.5350989999999998 Asynch=1.489056 Spawn=1\n",
      "Index: 701 is coherent: True. Malleability=1.5360390000000002 Asynch=1.483508 Spawn=1\n",
      "Index: 702 is coherent: True. Malleability=1.5076490000000002 Asynch=1.465007 Spawn=1\n",
      "Index: 703 is coherent: True. Malleability=1.48498 Asynch=1.441737 Spawn=1\n",
      "Index: 704 is coherent: True. Malleability=1.47885 Asynch=1.435677 Spawn=1\n",
      " \n",
      "Index: 705 is coherent: True. Malleability=3.48714 Asynch=2.097042 Spawn=1\n",
      "Index: 706 is coherent: True. Malleability=3.267104 Asynch=2.7772300000000003 Spawn=1\n",
      "Index: 707 is coherent: True. Malleability=3.714228 Asynch=2.5564669999999996 Spawn=1\n",
      "Index: 708 is coherent: True. Malleability=3.531705 Asynch=2.6304220000000003 Spawn=1\n",
      "Index: 709 is coherent: True. Malleability=3.629948 Asynch=2.56913 Spawn=1\n",
      " \n",
      "Index: 710 is coherent: False. Malleability=1.613384 Asynch=3.203233 Spawn=0\n",
      "Index: 711 is coherent: True. Malleability=1.91035 Asynch=1.621274 Spawn=0\n",
      "Index: 712 is coherent: False. Malleability=1.820954 Asynch=4.4812639999999995 Spawn=0\n",
      "Index: 713 is coherent: True. Malleability=1.676882 Asynch=1.164594 Spawn=0\n",
      "Index: 714 is coherent: False. Malleability=1.628789 Asynch=3.855189 Spawn=0\n",
      " \n",
      "Index: 715 is coherent: True. Malleability=2.9233580000000003 Asynch=1.766055 Spawn=1\n",
      "Index: 716 is coherent: True. Malleability=2.378141 Asynch=1.985146 Spawn=1\n",
      "Index: 717 is coherent: True. Malleability=2.059801 Asynch=1.603779 Spawn=1\n",
      "Index: 718 is coherent: True. Malleability=2.065448 Asynch=1.624873 Spawn=1\n",
      "Index: 719 is coherent: True. Malleability=2.054768 Asynch=1.663955 Spawn=1\n",
      " \n",
      "Index: 720 is coherent: True. Malleability=4.014368 Asynch=2.730943 Spawn=1\n",
      "Index: 721 is coherent: True. Malleability=3.946369 Asynch=2.651164 Spawn=1\n",
      "Index: 722 is coherent: True. Malleability=4.08471 Asynch=2.769337 Spawn=1\n",
      "Index: 723 is coherent: True. Malleability=3.800394 Asynch=2.6625929999999998 Spawn=1\n",
      "Index: 724 is coherent: True. Malleability=3.94489 Asynch=2.815816 Spawn=1\n",
      " \n",
      "Index: 725 is coherent: False. Malleability=2.280073 Asynch=6.251785 Spawn=0\n",
      "Index: 726 is coherent: False. Malleability=2.135725 Asynch=5.78 Spawn=0\n",
      "Index: 727 is coherent: False. Malleability=2.3232999999999997 Asynch=5.520031 Spawn=0\n",
      "Index: 728 is coherent: False. Malleability=2.064587 Asynch=5.816019 Spawn=0\n",
      "Index: 729 is coherent: False. Malleability=3.0620450000000003 Asynch=6.335395 Spawn=0\n",
      " \n",
      "Index: 730 is coherent: False. Malleability=2.702254 Asynch=6.172002 Spawn=0\n",
      "Index: 731 is coherent: False. Malleability=2.6340209999999997 Asynch=5.719993 Spawn=0\n",
      "Index: 732 is coherent: False. Malleability=2.373771 Asynch=6.539489 Spawn=0\n",
      "Index: 733 is coherent: False. Malleability=2.523917 Asynch=6.419989 Spawn=0\n",
      "Index: 734 is coherent: False. Malleability=2.604782 Asynch=6.339929 Spawn=0\n",
      " \n",
      "Index: 735 is coherent: True. Malleability=1.063457 Asynch=0.281033 Spawn=1\n",
      "Index: 736 is coherent: True. Malleability=1.006652 Asynch=0.278925 Spawn=1\n",
      "Index: 737 is coherent: True. Malleability=0.984491 Asynch=0.261042 Spawn=1\n",
      "Index: 738 is coherent: True. Malleability=1.0411 Asynch=0.274017 Spawn=1\n",
      "Index: 739 is coherent: True. Malleability=1.009269 Asynch=0.221617 Spawn=1\n",
      " \n",
      "Index: 740 is coherent: True. Malleability=4.059323 Asynch=3.2450489999999994 Spawn=1\n",
      "Index: 741 is coherent: True. Malleability=3.943061 Asynch=3.55254 Spawn=1\n",
      "Index: 742 is coherent: True. Malleability=4.162676 Asynch=3.563491 Spawn=1\n",
      "Index: 743 is coherent: True. Malleability=4.0122990000000005 Asynch=3.607198000000001 Spawn=1\n",
      "Index: 744 is coherent: True. Malleability=4.008679 Asynch=3.475448 Spawn=1\n",
      " \n",
      "Index: 745 is coherent: False. Malleability=3.529284 Asynch=4.358528000000001 Spawn=0\n",
      "Index: 746 is coherent: False. Malleability=3.8000740000000004 Asynch=4.244076 Spawn=0\n",
      "Index: 747 is coherent: False. Malleability=3.605531 Asynch=4.784196000000001 Spawn=0\n",
      "Index: 748 is coherent: False. Malleability=3.7770149999999996 Asynch=3.914617 Spawn=0\n",
      "Index: 749 is coherent: False. Malleability=3.571649 Asynch=4.265245 Spawn=0\n",
      " \n",
      "Index: 750 is coherent: True. Malleability=2.107576 Asynch=0.961006 Spawn=0\n",
      "Index: 751 is coherent: False. Malleability=1.8377219999999999 Asynch=4.067048 Spawn=0\n",
      "Index: 752 is coherent: False. Malleability=2.0638579999999997 Asynch=3.776014 Spawn=0\n",
      "Index: 753 is coherent: False. Malleability=2.038955 Asynch=3.819607 Spawn=0\n",
      "Index: 754 is coherent: False. Malleability=2.103815 Asynch=3.631343 Spawn=0\n",
      " \n",
      "Index: 755 is coherent: False. Malleability=2.492594 Asynch=3.920476 Spawn=0\n",
      "Index: 756 is coherent: True. Malleability=3.2852490000000003 Asynch=3.1573010000000004 Spawn=0\n",
      "Index: 757 is coherent: False. Malleability=2.58042 Asynch=4.260408999999999 Spawn=0\n",
      "Index: 758 is coherent: False. Malleability=2.456211 Asynch=4.287034 Spawn=0\n",
      "Index: 759 is coherent: False. Malleability=2.39858 Asynch=3.2007570000000003 Spawn=0\n",
      " \n",
      "Index: 760 is coherent: True. Malleability=3.497503 Asynch=2.4793480000000003 Spawn=1\n",
      "Index: 761 is coherent: True. Malleability=3.509692 Asynch=2.566091 Spawn=1\n",
      "Index: 762 is coherent: True. Malleability=3.439254 Asynch=2.572478 Spawn=1\n",
      "Index: 763 is coherent: True. Malleability=3.489127 Asynch=2.5271969999999997 Spawn=1\n",
      "Index: 764 is coherent: True. Malleability=3.476173 Asynch=2.5551530000000002 Spawn=1\n",
      " \n",
      "Index: 765 is coherent: True. Malleability=3.6881369999999998 Asynch=3.1659800000000002 Spawn=1\n",
      "Index: 766 is coherent: True. Malleability=3.4996829999999997 Asynch=2.9359509999999993 Spawn=1\n",
      "Index: 767 is coherent: True. Malleability=3.5830789999999997 Asynch=3.0147990000000005 Spawn=1\n",
      "Index: 768 is coherent: True. Malleability=3.550782 Asynch=3.1012570000000004 Spawn=1\n",
      "Index: 769 is coherent: True. Malleability=3.379791 Asynch=2.850914 Spawn=1\n",
      " \n",
      "Index: 770 is coherent: True. Malleability=7.965285999999999 Asynch=7.294652999999999 Spawn=0\n",
      "Index: 771 is coherent: True. Malleability=6.619817 Asynch=6.363785 Spawn=0\n",
      "Index: 772 is coherent: True. Malleability=7.318974000000001 Asynch=6.939315000000001 Spawn=0\n",
      "Index: 773 is coherent: True. Malleability=6.3300399999999994 Asynch=6.249067999999999 Spawn=0\n",
      "Index: 774 is coherent: True. Malleability=7.704093 Asynch=7.19467 Spawn=0\n",
      " \n",
      "Index: 775 is coherent: True. Malleability=2.117143 Asynch=2.0667910000000003 Spawn=1\n",
      "Index: 776 is coherent: True. Malleability=2.0215829999999997 Asynch=1.7110249999999998 Spawn=1\n",
      "Index: 777 is coherent: True. Malleability=2.063146 Asynch=1.8734419999999998 Spawn=1\n",
      "Index: 778 is coherent: True. Malleability=1.984205 Asynch=1.7358030000000002 Spawn=1\n",
      "Index: 779 is coherent: True. Malleability=2.0144219999999997 Asynch=1.728139 Spawn=1\n",
      " \n",
      "Index: 780 is coherent: False. Malleability=4.134908 Asynch=7.783842999999999 Spawn=0\n",
      "Index: 781 is coherent: False. Malleability=4.982978 Asynch=8.150884999999999 Spawn=0\n",
      "Index: 782 is coherent: False. Malleability=2.036577 Asynch=5.555016999999999 Spawn=0\n",
      "Index: 783 is coherent: False. Malleability=2.2166889999999997 Asynch=5.3056529999999995 Spawn=0\n",
      "Index: 784 is coherent: False. Malleability=5.194171 Asynch=8.444826 Spawn=0\n",
      " \n",
      "Index: 785 is coherent: True. Malleability=13.597395000000002 Asynch=12.259022000000002 Spawn=1\n",
      "Index: 786 is coherent: True. Malleability=13.58093 Asynch=12.20538 Spawn=1\n",
      "Index: 787 is coherent: True. Malleability=13.02971 Asynch=11.679117999999999 Spawn=1\n",
      "Index: 788 is coherent: True. Malleability=14.101556 Asynch=12.785543999999998 Spawn=1\n",
      "Index: 789 is coherent: True. Malleability=13.482831 Asynch=12.16063 Spawn=1\n",
      " \n",
      "Index: 790 is coherent: True. Malleability=3.3764390000000004 Asynch=2.2565009999999996 Spawn=1\n",
      "Index: 791 is coherent: True. Malleability=3.446453 Asynch=2.790544 Spawn=1\n",
      "Index: 792 is coherent: True. Malleability=3.5039119999999997 Asynch=2.5525990000000003 Spawn=1\n",
      "Index: 793 is coherent: True. Malleability=3.5717779999999997 Asynch=2.287963 Spawn=1\n",
      "Index: 794 is coherent: True. Malleability=3.519044 Asynch=2.622343 Spawn=1\n",
      " \n",
      "Index: 795 is coherent: True. Malleability=9.845023999999999 Asynch=7.958805 Spawn=1\n",
      "Index: 796 is coherent: True. Malleability=8.48274 Asynch=6.676411 Spawn=1\n",
      "Index: 797 is coherent: True. Malleability=8.443833 Asynch=6.69757 Spawn=1\n",
      "Index: 798 is coherent: True. Malleability=8.459493 Asynch=6.690405 Spawn=1\n",
      "Index: 799 is coherent: True. Malleability=6.676744 Asynch=4.708852 Spawn=1\n",
      " \n",
      "Index: 800 is coherent: True. Malleability=12.375799 Asynch=11.245458 Spawn=0\n",
      "Index: 801 is coherent: True. Malleability=13.026607 Asynch=11.742227 Spawn=0\n",
      "Index: 802 is coherent: True. Malleability=13.244163 Asynch=12.227657 Spawn=0\n",
      "Index: 803 is coherent: True. Malleability=12.505782 Asynch=11.181324 Spawn=0\n",
      "Index: 804 is coherent: True. Malleability=12.399996 Asynch=11.639221 Spawn=0\n",
      " \n",
      "Index: 805 is coherent: True. Malleability=1.9653090000000002 Asynch=1.759892 Spawn=1\n",
      "Index: 806 is coherent: True. Malleability=1.854142 Asynch=1.5570350000000002 Spawn=1\n",
      "Index: 807 is coherent: True. Malleability=1.786375 Asynch=1.550433 Spawn=1\n",
      "Index: 808 is coherent: True. Malleability=1.7335550000000002 Asynch=1.538203 Spawn=1\n",
      "Index: 809 is coherent: True. Malleability=1.8654279999999999 Asynch=1.543452 Spawn=1\n",
      " \n",
      "Index: 810 is coherent: True. Malleability=4.072447 Asynch=3.584079 Spawn=0\n",
      "Index: 811 is coherent: True. Malleability=4.484779 Asynch=4.176932 Spawn=0\n",
      "Index: 812 is coherent: True. Malleability=5.206995999999999 Asynch=4.944978 Spawn=0\n",
      "Index: 813 is coherent: False. Malleability=3.900809 Asynch=3.9445499999999996 Spawn=0\n",
      "Index: 814 is coherent: False. Malleability=3.724983 Asynch=4.9242230000000005 Spawn=0\n",
      " \n",
      "Index: 815 is coherent: True. Malleability=2.938474 Asynch=2.353361 Spawn=0\n",
      "Index: 816 is coherent: True. Malleability=3.065346 Asynch=2.619305 Spawn=0\n",
      "Index: 817 is coherent: True. Malleability=3.0119550000000004 Asynch=2.277831 Spawn=0\n",
      "Index: 818 is coherent: True. Malleability=3.2041310000000003 Asynch=2.424037 Spawn=0\n",
      "Index: 819 is coherent: True. Malleability=2.783793 Asynch=2.423999 Spawn=0\n",
      " \n",
      "Index: 820 is coherent: True. Malleability=3.0942999999999996 Asynch=2.6089979999999997 Spawn=1\n",
      "Index: 821 is coherent: True. Malleability=2.997133 Asynch=2.113446 Spawn=1\n",
      "Index: 822 is coherent: True. Malleability=2.835635 Asynch=1.184924 Spawn=1\n",
      "Index: 823 is coherent: True. Malleability=3.1002030000000005 Asynch=1.9299979999999997 Spawn=1\n",
      "Index: 824 is coherent: True. Malleability=2.382091 Asynch=1.3117670000000001 Spawn=1\n",
      " \n",
      "Index: 825 is coherent: False. Malleability=13.408707 Asynch=14.486075 Spawn=0\n",
      "Index: 826 is coherent: False. Malleability=10.985544 Asynch=13.033966 Spawn=0\n",
      "Index: 827 is coherent: False. Malleability=8.374776 Asynch=10.173422 Spawn=0\n",
      "Index: 828 is coherent: False. Malleability=11.210747999999999 Asynch=13.85261 Spawn=0\n",
      "Index: 829 is coherent: False. Malleability=11.929214 Asynch=14.292 Spawn=0\n",
      " \n",
      "Index: 830 is coherent: True. Malleability=6.885634 Asynch=6.561191000000001 Spawn=0\n",
      "Index: 831 is coherent: True. Malleability=7.053806999999999 Asynch=5.906476 Spawn=0\n",
      "Index: 832 is coherent: True. Malleability=6.676578 Asynch=5.826954000000001 Spawn=0\n",
      "Index: 833 is coherent: True. Malleability=6.431115 Asynch=5.958116 Spawn=0\n",
      "Index: 834 is coherent: True. Malleability=6.118622 Asynch=5.758164 Spawn=0\n",
      " \n",
      "Index: 835 is coherent: False. Malleability=8.536745 Asynch=8.600558 Spawn=0\n",
      "Index: 836 is coherent: True. Malleability=8.318248 Asynch=8.170258 Spawn=0\n",
      "Index: 837 is coherent: False. Malleability=7.45904 Asynch=7.534761 Spawn=0\n",
      "Index: 838 is coherent: True. Malleability=9.326963 Asynch=9.194861 Spawn=0\n",
      "Index: 839 is coherent: True. Malleability=6.63694 Asynch=6.453939999999999 Spawn=0\n",
      " \n",
      "Index: 840 is coherent: False. Malleability=2.6849730000000003 Asynch=2.843705 Spawn=0\n",
      "Index: 841 is coherent: True. Malleability=2.41128 Asynch=2.204458 Spawn=0\n",
      "Index: 842 is coherent: True. Malleability=2.9560020000000002 Asynch=2.159258 Spawn=0\n",
      "Index: 843 is coherent: False. Malleability=2.586881 Asynch=3.2268550000000005 Spawn=0\n",
      "Index: 844 is coherent: True. Malleability=2.675581 Asynch=1.929278 Spawn=0\n",
      " \n",
      "Index: 845 is coherent: True. Malleability=2.123401 Asynch=1.67873 Spawn=1\n",
      "Index: 846 is coherent: True. Malleability=3.6160699999999997 Asynch=3.218418 Spawn=1\n",
      "Index: 847 is coherent: True. Malleability=2.1306 Asynch=1.7078300000000002 Spawn=1\n",
      "Index: 848 is coherent: True. Malleability=2.536522 Asynch=2.120906 Spawn=1\n",
      "Index: 849 is coherent: True. Malleability=2.247284 Asynch=1.86608 Spawn=1\n",
      " \n",
      "Index: 850 is coherent: True. Malleability=3.762193 Asynch=2.8694210000000004 Spawn=1\n",
      "Index: 851 is coherent: True. Malleability=3.916459 Asynch=3.0522039999999997 Spawn=1\n",
      "Index: 852 is coherent: True. Malleability=3.6256 Asynch=2.684411 Spawn=1\n",
      "Index: 853 is coherent: True. Malleability=3.836378 Asynch=2.898275999999999 Spawn=1\n",
      "Index: 854 is coherent: True. Malleability=3.996529 Asynch=3.082087 Spawn=1\n",
      " \n",
      "Index: 855 is coherent: True. Malleability=1.237594 Asynch=0.891944 Spawn=1\n",
      "Index: 856 is coherent: True. Malleability=1.3010590000000002 Asynch=1.132284 Spawn=1\n",
      "Index: 857 is coherent: True. Malleability=1.154561 Asynch=1.123284 Spawn=1\n",
      "Index: 858 is coherent: True. Malleability=1.239659 Asynch=0.8789480000000001 Spawn=1\n",
      "Index: 859 is coherent: True. Malleability=1.213003 Asynch=1.031928 Spawn=1\n",
      " \n",
      "Index: 860 is coherent: True. Malleability=1.920704 Asynch=0.087965 Spawn=1\n",
      "Index: 861 is coherent: True. Malleability=2.250603 Asynch=0.9624370000000001 Spawn=1\n",
      "Index: 862 is coherent: True. Malleability=2.0596660000000004 Asynch=0.075331 Spawn=1\n",
      "Index: 863 is coherent: True. Malleability=2.003603 Asynch=0.78352 Spawn=1\n",
      "Index: 864 is coherent: True. Malleability=1.856146 Asynch=0.680974 Spawn=1\n",
      " \n",
      "Index: 865 is coherent: False. Malleability=6.116812 Asynch=6.202229 Spawn=0\n",
      "Index: 866 is coherent: True. Malleability=6.8468230000000005 Asynch=6.4705710000000005 Spawn=0\n",
      "Index: 867 is coherent: True. Malleability=6.195866 Asynch=5.834598 Spawn=0\n",
      "Index: 868 is coherent: True. Malleability=7.034719 Asynch=6.533245000000001 Spawn=0\n",
      "Index: 869 is coherent: True. Malleability=7.052043 Asynch=6.765982000000001 Spawn=0\n",
      " \n",
      "Index: 870 is coherent: True. Malleability=4.007175 Asynch=3.2337339999999997 Spawn=1\n",
      "Index: 871 is coherent: True. Malleability=3.9866210000000004 Asynch=3.777262 Spawn=1\n",
      "Index: 872 is coherent: True. Malleability=4.051562 Asynch=3.4296990000000003 Spawn=1\n",
      "Index: 873 is coherent: True. Malleability=3.9885900000000003 Asynch=3.2967630000000003 Spawn=1\n",
      "Index: 874 is coherent: True. Malleability=3.869452 Asynch=3.323617 Spawn=1\n",
      " \n",
      "Index: 875 is coherent: True. Malleability=1.7879990000000001 Asynch=1.473496 Spawn=1\n",
      "Index: 876 is coherent: True. Malleability=2.0351470000000003 Asynch=1.480346 Spawn=1\n",
      "Index: 877 is coherent: True. Malleability=1.9852659999999998 Asynch=1.442321 Spawn=1\n",
      "Index: 878 is coherent: True. Malleability=2.033825 Asynch=1.6376080000000002 Spawn=1\n",
      "Index: 879 is coherent: True. Malleability=1.75553 Asynch=1.404952 Spawn=1\n",
      " \n",
      "Index: 880 is coherent: False. Malleability=1.9987620000000001 Asynch=6.204034 Spawn=0\n",
      "Index: 881 is coherent: False. Malleability=1.627416 Asynch=6.696 Spawn=0\n",
      "Index: 882 is coherent: False. Malleability=1.744414 Asynch=6.309056 Spawn=0\n",
      "Index: 883 is coherent: False. Malleability=2.3950940000000003 Asynch=6.180169 Spawn=0\n",
      "Index: 884 is coherent: False. Malleability=1.585301 Asynch=6.752058 Spawn=0\n",
      " \n",
      "Index: 885 is coherent: True. Malleability=3.827565 Asynch=2.5052139999999996 Spawn=1\n",
      "Index: 886 is coherent: True. Malleability=3.777098 Asynch=3.09979 Spawn=1\n",
      "Index: 887 is coherent: True. Malleability=3.210598 Asynch=2.067959 Spawn=1\n",
      "Index: 888 is coherent: True. Malleability=4.032414 Asynch=3.01876 Spawn=1\n",
      "Index: 889 is coherent: True. Malleability=3.8625749999999996 Asynch=3.3997569999999997 Spawn=1\n",
      " \n",
      "Index: 890 is coherent: True. Malleability=2.7470359999999996 Asynch=2.115481 Spawn=1\n",
      "Index: 891 is coherent: True. Malleability=2.8110999999999997 Asynch=2.142094 Spawn=1\n",
      "Index: 892 is coherent: True. Malleability=4.226844 Asynch=3.5637270000000005 Spawn=1\n",
      "Index: 893 is coherent: True. Malleability=2.099875 Asynch=1.438459 Spawn=1\n",
      "Index: 894 is coherent: True. Malleability=2.070277 Asynch=1.433726 Spawn=1\n",
      " \n",
      "Index: 895 is coherent: False. Malleability=3.524476 Asynch=6.881264 Spawn=0\n",
      "Index: 896 is coherent: False. Malleability=4.003524 Asynch=7.155308 Spawn=0\n",
      "Index: 897 is coherent: False. Malleability=2.9515309999999997 Asynch=6.327153000000001 Spawn=0\n",
      "Index: 898 is coherent: False. Malleability=2.71356 Asynch=6.3733439999999995 Spawn=0\n",
      "Index: 899 is coherent: False. Malleability=2.3006729999999997 Asynch=5.53945 Spawn=0\n",
      " \n",
      "Index: 900 is coherent: False. Malleability=2.64803 Asynch=4.251377 Spawn=0\n",
      "Index: 901 is coherent: False. Malleability=2.704408 Asynch=4.200626 Spawn=0\n",
      "Index: 902 is coherent: False. Malleability=2.6396770000000003 Asynch=3.514194 Spawn=0\n",
      "Index: 903 is coherent: False. Malleability=2.527472 Asynch=3.1388510000000003 Spawn=0\n",
      "Index: 904 is coherent: False. Malleability=2.655508 Asynch=3.582098 Spawn=0\n",
      " \n",
      "Index: 905 is coherent: True. Malleability=3.916544 Asynch=2.7468360000000005 Spawn=1\n",
      "Index: 906 is coherent: True. Malleability=3.930352 Asynch=2.838305 Spawn=1\n",
      "Index: 907 is coherent: True. Malleability=3.91505 Asynch=2.8296870000000003 Spawn=1\n",
      "Index: 908 is coherent: True. Malleability=3.822992 Asynch=2.737069 Spawn=1\n",
      "Index: 909 is coherent: True. Malleability=3.891324 Asynch=2.840946 Spawn=1\n",
      " \n",
      "Index: 910 is coherent: False. Malleability=2.464831 Asynch=6.619437 Spawn=0\n",
      "Index: 911 is coherent: False. Malleability=2.667625 Asynch=6.483928 Spawn=0\n",
      "Index: 912 is coherent: False. Malleability=2.722761 Asynch=6.475367 Spawn=0\n",
      "Index: 913 is coherent: False. Malleability=3.37943 Asynch=6.208009 Spawn=0\n",
      "Index: 914 is coherent: False. Malleability=2.592018 Asynch=5.966896 Spawn=0\n",
      " \n",
      "Index: 915 is coherent: False. Malleability=2.097969 Asynch=4.154693 Spawn=0\n",
      "Index: 916 is coherent: False. Malleability=1.686569 Asynch=3.962085 Spawn=0\n",
      "Index: 917 is coherent: False. Malleability=2.016713 Asynch=4.036447 Spawn=0\n",
      "Index: 918 is coherent: False. Malleability=2.095236 Asynch=3.753926 Spawn=0\n",
      "Index: 919 is coherent: False. Malleability=1.881201 Asynch=3.795981 Spawn=0\n",
      " \n",
      "Index: 920 is coherent: True. Malleability=4.324164 Asynch=4.286251 Spawn=1\n",
      "Index: 921 is coherent: True. Malleability=4.694414 Asynch=4.659053 Spawn=1\n",
      "Index: 922 is coherent: True. Malleability=4.430639 Asynch=4.397940999999999 Spawn=1\n",
      "Index: 923 is coherent: True. Malleability=4.887157 Asynch=4.848641000000001 Spawn=1\n",
      "Index: 924 is coherent: True. Malleability=4.197628 Asynch=4.164473 Spawn=1\n",
      " \n",
      "Index: 925 is coherent: False. Malleability=1.309661 Asynch=4.603956 Spawn=0\n",
      "Index: 926 is coherent: False. Malleability=1.3233869999999999 Asynch=4.155415 Spawn=0\n",
      "Index: 927 is coherent: False. Malleability=1.607059 Asynch=3.252124 Spawn=0\n",
      "Index: 928 is coherent: False. Malleability=1.829924 Asynch=3.143814 Spawn=0\n",
      "Index: 929 is coherent: False. Malleability=2.188539 Asynch=3.679936 Spawn=0\n",
      " \n",
      "Index: 930 is coherent: False. Malleability=5.074457 Asynch=5.237646 Spawn=0\n",
      "Index: 931 is coherent: False. Malleability=5.317748999999999 Asynch=5.6049679999999995 Spawn=0\n",
      "Index: 932 is coherent: False. Malleability=5.066411 Asynch=5.479542 Spawn=0\n",
      "Index: 933 is coherent: False. Malleability=5.508665 Asynch=5.889785 Spawn=0\n",
      "Index: 934 is coherent: False. Malleability=5.977062999999999 Asynch=6.439153000000001 Spawn=0\n",
      " \n",
      "Index: 935 is coherent: True. Malleability=2.641797 Asynch=1.607994 Spawn=1\n",
      "Index: 936 is coherent: True. Malleability=2.7154290000000003 Asynch=1.7432679999999998 Spawn=1\n",
      "Index: 937 is coherent: True. Malleability=2.5540339999999997 Asynch=1.59253 Spawn=1\n",
      "Index: 938 is coherent: True. Malleability=2.509445 Asynch=1.579601 Spawn=1\n",
      "Index: 939 is coherent: True. Malleability=2.805677 Asynch=1.743766 Spawn=1\n",
      " \n",
      "Index: 940 is coherent: True. Malleability=1.954103 Asynch=1.9124720000000002 Spawn=1\n",
      "Index: 941 is coherent: True. Malleability=2.11391 Asynch=2.0583500000000003 Spawn=1\n",
      "Index: 942 is coherent: True. Malleability=1.8855279999999999 Asynch=1.83644 Spawn=1\n",
      "Index: 943 is coherent: True. Malleability=2.087043 Asynch=2.047003 Spawn=1\n",
      "Index: 944 is coherent: True. Malleability=1.990686 Asynch=1.953961 Spawn=1\n",
      " \n",
      "Index: 945 is coherent: False. Malleability=5.284396 Asynch=7.592408000000001 Spawn=0\n",
      "Index: 946 is coherent: False. Malleability=1.8533299999999997 Asynch=4.417337 Spawn=0\n",
      "Index: 947 is coherent: False. Malleability=3.258467 Asynch=7.112031999999999 Spawn=0\n",
      "Index: 948 is coherent: False. Malleability=1.731099 Asynch=4.882602 Spawn=0\n",
      "Index: 949 is coherent: False. Malleability=3.684079 Asynch=5.8662980000000005 Spawn=0\n",
      " \n",
      "Index: 950 is coherent: True. Malleability=2.3248640000000003 Asynch=2.248205 Spawn=1\n",
      "Index: 951 is coherent: True. Malleability=2.439179 Asynch=2.3488830000000003 Spawn=1\n",
      "Index: 952 is coherent: True. Malleability=2.134525 Asynch=1.694172 Spawn=1\n",
      "Index: 953 is coherent: True. Malleability=2.6094150000000003 Asynch=2.5338929999999995 Spawn=1\n",
      "Index: 954 is coherent: True. Malleability=2.249604 Asynch=2.157585 Spawn=1\n",
      " \n",
      "Index: 955 is coherent: True. Malleability=1.515896 Asynch=0.555582 Spawn=1\n",
      "Index: 956 is coherent: True. Malleability=1.353005 Asynch=0.542028 Spawn=1\n",
      "Index: 957 is coherent: True. Malleability=1.556235 Asynch=0.6460570000000001 Spawn=1\n",
      "Index: 958 is coherent: True. Malleability=1.694669 Asynch=0.789353 Spawn=1\n",
      "Index: 959 is coherent: True. Malleability=1.633587 Asynch=0.705432 Spawn=1\n",
      " \n",
      "Index: 960 is coherent: True. Malleability=8.239199 Asynch=7.5941 Spawn=0\n",
      "Index: 961 is coherent: False. Malleability=7.053029 Asynch=7.166764 Spawn=0\n",
      "Index: 962 is coherent: False. Malleability=6.47702 Asynch=6.662781 Spawn=0\n",
      "Index: 963 is coherent: False. Malleability=7.285503 Asynch=7.312344 Spawn=0\n",
      "Index: 964 is coherent: True. Malleability=6.820189999999999 Asynch=6.008597 Spawn=0\n",
      " \n",
      "Index: 965 is coherent: True. Malleability=4.313454 Asynch=3.272129 Spawn=1\n",
      "Index: 966 is coherent: True. Malleability=4.180943 Asynch=3.6281949999999994 Spawn=1\n",
      "Index: 967 is coherent: True. Malleability=4.558085 Asynch=3.325217 Spawn=1\n",
      "Index: 968 is coherent: True. Malleability=4.41964 Asynch=2.913671 Spawn=1\n",
      "Index: 969 is coherent: True. Malleability=4.065751 Asynch=3.0461510000000005 Spawn=1\n",
      " \n",
      "Index: 970 is coherent: False. Malleability=2.439488 Asynch=2.568462 Spawn=0\n",
      "Index: 971 is coherent: True. Malleability=2.8240239999999996 Asynch=2.526355 Spawn=0\n",
      "Index: 972 is coherent: False. Malleability=2.225784 Asynch=2.609634 Spawn=0\n",
      "Index: 973 is coherent: False. Malleability=2.464086 Asynch=2.539008 Spawn=0\n",
      "Index: 974 is coherent: False. Malleability=1.909614 Asynch=2.192161 Spawn=0\n",
      " \n",
      "Index: 975 is coherent: True. Malleability=1.750537 Asynch=1.51286 Spawn=1\n",
      "Index: 976 is coherent: True. Malleability=1.8081170000000002 Asynch=1.478698 Spawn=1\n",
      "Index: 977 is coherent: True. Malleability=1.7209789999999998 Asynch=1.432375 Spawn=1\n",
      "Index: 978 is coherent: True. Malleability=1.826307 Asynch=1.48576 Spawn=1\n",
      "Index: 979 is coherent: True. Malleability=1.752858 Asynch=1.506875 Spawn=1\n",
      " \n",
      "Index: 980 is coherent: False. Malleability=4.817914999999999 Asynch=8.684021 Spawn=0\n",
      "Index: 981 is coherent: False. Malleability=4.461505 Asynch=6.954464999999999 Spawn=0\n",
      "Index: 982 is coherent: False. Malleability=3.642689 Asynch=6.513119 Spawn=0\n",
      "Index: 983 is coherent: False. Malleability=2.914282 Asynch=5.655723 Spawn=0\n",
      "Index: 984 is coherent: False. Malleability=1.52676 Asynch=3.736737 Spawn=0\n",
      " \n",
      "Index: 985 is coherent: True. Malleability=4.2416789999999995 Asynch=4.204449 Spawn=1\n",
      "Index: 986 is coherent: True. Malleability=4.257829 Asynch=4.2082489999999995 Spawn=1\n",
      "Index: 987 is coherent: True. Malleability=4.5705919999999995 Asynch=4.531346 Spawn=1\n",
      "Index: 988 is coherent: True. Malleability=4.305941000000001 Asynch=4.268186999999999 Spawn=1\n",
      "Index: 989 is coherent: True. Malleability=4.494441 Asynch=4.450828 Spawn=1\n",
      " \n",
      "Index: 990 is coherent: True. Malleability=5.274399000000001 Asynch=4.085926 Spawn=0\n",
      "Index: 991 is coherent: True. Malleability=4.551812 Asynch=3.855321 Spawn=0\n",
      "Index: 992 is coherent: True. Malleability=4.721206 Asynch=3.787692 Spawn=0\n",
      "Index: 993 is coherent: True. Malleability=5.046507999999999 Asynch=3.863324 Spawn=0\n",
      "Index: 994 is coherent: True. Malleability=5.0759799999999995 Asynch=3.838765 Spawn=0\n",
      " \n",
      "Index: 995 is coherent: False. Malleability=1.993409 Asynch=4.162768 Spawn=0\n",
      "Index: 996 is coherent: False. Malleability=2.978343 Asynch=5.36281 Spawn=0\n",
      "Index: 997 is coherent: False. Malleability=2.925701 Asynch=5.373489 Spawn=0\n",
      "Index: 998 is coherent: False. Malleability=3.68116 Asynch=6.062389 Spawn=0\n",
      "Index: 999 is coherent: False. Malleability=3.879558 Asynch=6.247171 Spawn=0\n",
      " \n",
      "Index: 1000 is coherent: False. Malleability=1.9121320000000002 Asynch=9.752477 Spawn=0\n",
      "Index: 1001 is coherent: False. Malleability=1.8724530000000001 Asynch=7.652798000000001 Spawn=0\n",
      "Index: 1002 is coherent: False. Malleability=1.921336 Asynch=9.280282 Spawn=0\n",
      "Index: 1003 is coherent: False. Malleability=1.758299 Asynch=14.575389 Spawn=0\n",
      "Index: 1004 is coherent: False. Malleability=1.851029 Asynch=9.523809 Spawn=0\n",
      " \n",
      "Index: 1005 is coherent: False. Malleability=4.640234 Asynch=8.265966 Spawn=0\n",
      "Index: 1006 is coherent: False. Malleability=3.8385510000000003 Asynch=7.619337 Spawn=0\n",
      "Index: 1007 is coherent: False. Malleability=3.8733679999999997 Asynch=7.790791 Spawn=0\n",
      "Index: 1008 is coherent: False. Malleability=3.927971 Asynch=7.871171 Spawn=0\n",
      "Index: 1009 is coherent: False. Malleability=3.138557 Asynch=6.904773 Spawn=0\n",
      " \n",
      "Index: 1010 is coherent: False. Malleability=1.9717730000000002 Asynch=6.128355 Spawn=0\n",
      "Index: 1011 is coherent: False. Malleability=2.077254 Asynch=6.52694 Spawn=0\n",
      "Index: 1012 is coherent: False. Malleability=1.9431280000000002 Asynch=6.620429 Spawn=0\n",
      "Index: 1013 is coherent: False. Malleability=1.878934 Asynch=6.671295 Spawn=0\n",
      "Index: 1014 is coherent: False. Malleability=1.864137 Asynch=6.524018 Spawn=0\n",
      " \n",
      "Index: 1015 is coherent: True. Malleability=1.936902 Asynch=1.076845 Spawn=1\n",
      "Index: 1016 is coherent: True. Malleability=3.193603 Asynch=1.910418 Spawn=1\n",
      "Index: 1017 is coherent: True. Malleability=2.3688390000000004 Asynch=1.32005 Spawn=1\n",
      "Index: 1018 is coherent: True. Malleability=2.084646 Asynch=0.8246169999999999 Spawn=1\n",
      "Index: 1019 is coherent: True. Malleability=2.8662199999999998 Asynch=1.683994 Spawn=1\n",
      " \n",
      "Index: 1020 is coherent: False. Malleability=4.918575000000001 Asynch=7.488823 Spawn=0\n",
      "Index: 1021 is coherent: False. Malleability=3.2211220000000003 Asynch=5.620172 Spawn=0\n",
      "Index: 1022 is coherent: False. Malleability=4.597031 Asynch=7.195546 Spawn=0\n",
      "Index: 1023 is coherent: False. Malleability=1.814965 Asynch=5.206787 Spawn=0\n",
      "Index: 1024 is coherent: False. Malleability=4.073728 Asynch=7.15226 Spawn=0\n",
      " \n",
      "Index: 1025 is coherent: True. Malleability=3.957784 Asynch=3.3708970000000003 Spawn=1\n",
      "Index: 1026 is coherent: True. Malleability=3.897158 Asynch=3.5716179999999995 Spawn=1\n",
      "Index: 1027 is coherent: True. Malleability=3.900892 Asynch=3.345905 Spawn=1\n",
      "Index: 1028 is coherent: True. Malleability=3.8343220000000002 Asynch=3.437264 Spawn=1\n",
      "Index: 1029 is coherent: True. Malleability=3.882834 Asynch=3.387161 Spawn=1\n",
      " \n",
      "Index: 1030 is coherent: True. Malleability=3.258058 Asynch=2.372316 Spawn=1\n",
      "Index: 1031 is coherent: True. Malleability=3.0925890000000003 Asynch=2.438209 Spawn=1\n",
      "Index: 1032 is coherent: True. Malleability=3.641209 Asynch=2.79326 Spawn=1\n",
      "Index: 1033 is coherent: True. Malleability=3.215859 Asynch=2.49991 Spawn=1\n",
      "Index: 1034 is coherent: True. Malleability=3.4739940000000002 Asynch=2.761303 Spawn=1\n",
      " \n",
      "Index: 1035 is coherent: True. Malleability=4.007413 Asynch=2.8349979999999997 Spawn=1\n",
      "Index: 1036 is coherent: True. Malleability=3.933642 Asynch=2.563578 Spawn=1\n",
      "Index: 1037 is coherent: True. Malleability=3.0723380000000002 Asynch=2.46741 Spawn=1\n",
      "Index: 1038 is coherent: True. Malleability=3.0848950000000004 Asynch=2.457347 Spawn=1\n",
      "Index: 1039 is coherent: True. Malleability=3.046738 Asynch=2.538948 Spawn=1\n",
      " \n",
      "Index: 1040 is coherent: True. Malleability=4.120705999999999 Asynch=3.188515 Spawn=1\n",
      "Index: 1041 is coherent: True. Malleability=4.018683 Asynch=3.1180349999999994 Spawn=1\n",
      "Index: 1042 is coherent: True. Malleability=4.095382 Asynch=3.135396 Spawn=1\n",
      "Index: 1043 is coherent: True. Malleability=4.001094 Asynch=3.06999 Spawn=1\n",
      "Index: 1044 is coherent: True. Malleability=4.1874009999999995 Asynch=2.9931099999999997 Spawn=1\n",
      " \n",
      "Index: 1045 is coherent: True. Malleability=3.5438709999999998 Asynch=2.9680569999999995 Spawn=1\n",
      "Index: 1046 is coherent: True. Malleability=3.490423 Asynch=2.8741030000000003 Spawn=1\n",
      "Index: 1047 is coherent: True. Malleability=4.595972 Asynch=3.2389319999999997 Spawn=1\n",
      "Index: 1048 is coherent: True. Malleability=3.298037 Asynch=2.764441 Spawn=1\n",
      "Index: 1049 is coherent: True. Malleability=3.6104920000000003 Asynch=2.835559 Spawn=1\n",
      " \n",
      "Index: 1050 is coherent: False. Malleability=3.272759 Asynch=5.2232520000000005 Spawn=0\n",
      "Index: 1051 is coherent: False. Malleability=3.162509 Asynch=5.209884 Spawn=0\n",
      "Index: 1052 is coherent: False. Malleability=2.393417 Asynch=4.2800780000000005 Spawn=0\n",
      "Index: 1053 is coherent: False. Malleability=3.099143 Asynch=5.069144 Spawn=0\n",
      "Index: 1054 is coherent: False. Malleability=2.286728 Asynch=4.372154999999999 Spawn=0\n",
      " \n",
      "Index: 1055 is coherent: True. Malleability=4.1507249999999996 Asynch=3.205256 Spawn=1\n",
      "Index: 1056 is coherent: True. Malleability=4.207019 Asynch=3.29404 Spawn=1\n",
      "Index: 1057 is coherent: True. Malleability=4.169681 Asynch=3.1998159999999998 Spawn=1\n",
      "Index: 1058 is coherent: True. Malleability=4.138858 Asynch=3.216773 Spawn=1\n",
      "Index: 1059 is coherent: True. Malleability=4.043562 Asynch=3.1045670000000003 Spawn=1\n",
      " \n",
      "Index: 1060 is coherent: True. Malleability=1.278858 Asynch=1.265742 Spawn=1\n",
      "Index: 1061 is coherent: True. Malleability=1.226632 Asynch=1.2148919999999999 Spawn=1\n",
      "Index: 1062 is coherent: True. Malleability=1.280459 Asynch=1.268122 Spawn=1\n",
      "Index: 1063 is coherent: True. Malleability=1.524422 Asynch=1.5114990000000001 Spawn=1\n",
      "Index: 1064 is coherent: True. Malleability=1.256891 Asynch=1.245128 Spawn=1\n",
      " \n",
      "Index: 1065 is coherent: False. Malleability=2.691353 Asynch=6.002962 Spawn=0\n",
      "Index: 1066 is coherent: False. Malleability=3.272526 Asynch=6.596006 Spawn=0\n",
      "Index: 1067 is coherent: False. Malleability=2.5737189999999996 Asynch=6.635991 Spawn=0\n",
      "Index: 1068 is coherent: False. Malleability=3.0387370000000002 Asynch=5.904007 Spawn=0\n",
      "Index: 1069 is coherent: False. Malleability=3.295478 Asynch=6.724007 Spawn=0\n",
      " \n",
      "Index: 1070 is coherent: True. Malleability=2.8540990000000006 Asynch=1.8994080000000002 Spawn=1\n",
      "Index: 1071 is coherent: True. Malleability=2.7463189999999997 Asynch=1.881201 Spawn=1\n",
      "Index: 1072 is coherent: True. Malleability=2.139634 Asynch=1.198869 Spawn=1\n",
      "Index: 1073 is coherent: True. Malleability=4.957901000000001 Asynch=3.985598 Spawn=1\n",
      "Index: 1074 is coherent: True. Malleability=2.1932549999999997 Asynch=1.163535 Spawn=1\n",
      " \n",
      "Index: 1075 is coherent: False. Malleability=1.815791 Asynch=22.275489 Spawn=0\n",
      "Index: 1076 is coherent: False. Malleability=1.738361 Asynch=46.560947000000006 Spawn=0\n",
      "Index: 1077 is coherent: False. Malleability=1.734518 Asynch=13.854247 Spawn=0\n",
      "Index: 1078 is coherent: False. Malleability=1.71311 Asynch=20.987909000000002 Spawn=0\n",
      "Index: 1079 is coherent: False. Malleability=1.805898 Asynch=20.803651000000002 Spawn=0\n",
      " \n",
      "Index: 1080 is coherent: True. Malleability=4.844867000000001 Asynch=4.3538559999999995 Spawn=0\n",
      "Index: 1081 is coherent: True. Malleability=4.483273 Asynch=4.044586 Spawn=0\n",
      "Index: 1082 is coherent: True. Malleability=5.589265999999999 Asynch=5.407768 Spawn=0\n",
      "Index: 1083 is coherent: False. Malleability=3.804967 Asynch=5.778922 Spawn=0\n",
      "Index: 1084 is coherent: True. Malleability=6.166912 Asynch=5.4346179999999995 Spawn=0\n",
      " \n",
      "Index: 1085 is coherent: True. Malleability=2.1487849999999997 Asynch=1.9462259999999998 Spawn=1\n",
      "Index: 1086 is coherent: True. Malleability=1.850331 Asynch=1.692917 Spawn=1\n",
      "Index: 1087 is coherent: True. Malleability=1.8764440000000002 Asynch=1.712833 Spawn=1\n",
      "Index: 1088 is coherent: True. Malleability=1.8507600000000002 Asynch=1.706351 Spawn=1\n",
      "Index: 1089 is coherent: True. Malleability=1.859719 Asynch=1.674771 Spawn=1\n",
      " \n",
      "Index: 1090 is coherent: True. Malleability=4.198998 Asynch=4.1512709999999995 Spawn=1\n",
      "Index: 1091 is coherent: True. Malleability=4.6705 Asynch=4.623438 Spawn=1\n",
      "Index: 1092 is coherent: True. Malleability=4.473607 Asynch=4.437931000000001 Spawn=1\n",
      "Index: 1093 is coherent: True. Malleability=4.36708 Asynch=4.322891 Spawn=1\n",
      "Index: 1094 is coherent: True. Malleability=4.2351220000000005 Asynch=4.168946 Spawn=1\n",
      " \n",
      "Index: 1095 is coherent: True. Malleability=1.8053489999999999 Asynch=1.570463 Spawn=1\n",
      "Index: 1096 is coherent: True. Malleability=1.985825 Asynch=1.8027510000000002 Spawn=1\n",
      "Index: 1097 is coherent: True. Malleability=2.554123 Asynch=2.5494390000000005 Spawn=1\n",
      "Index: 1098 is coherent: True. Malleability=1.824088 Asynch=1.809415 Spawn=1\n",
      "Index: 1099 is coherent: True. Malleability=1.930779 Asynch=1.463085 Spawn=1\n",
      " \n",
      "Index: 1100 is coherent: True. Malleability=1.7149569999999998 Asynch=1.328813 Spawn=1\n",
      "Index: 1101 is coherent: True. Malleability=1.519668 Asynch=1.116353 Spawn=1\n",
      "Index: 1102 is coherent: True. Malleability=1.5529959999999998 Asynch=1.1687360000000002 Spawn=1\n",
      "Index: 1103 is coherent: True. Malleability=1.398907 Asynch=0.9721460000000001 Spawn=1\n",
      "Index: 1104 is coherent: True. Malleability=1.536156 Asynch=1.142939 Spawn=1\n",
      " \n",
      "Index: 1105 is coherent: True. Malleability=2.3698 Asynch=1.970618 Spawn=1\n",
      "Index: 1106 is coherent: True. Malleability=2.223052 Asynch=1.7846540000000002 Spawn=1\n",
      "Index: 1107 is coherent: True. Malleability=3.1651249999999997 Asynch=1.882689 Spawn=1\n",
      "Index: 1108 is coherent: True. Malleability=2.205278 Asynch=1.814277 Spawn=1\n",
      "Index: 1109 is coherent: True. Malleability=3.069149 Asynch=2.024578 Spawn=1\n",
      " \n",
      "Index: 1110 is coherent: True. Malleability=3.644441 Asynch=2.6238550000000003 Spawn=1\n",
      "Index: 1111 is coherent: True. Malleability=3.711502 Asynch=2.661739 Spawn=1\n",
      "Index: 1112 is coherent: True. Malleability=3.5993239999999997 Asynch=2.5316620000000007 Spawn=1\n",
      "Index: 1113 is coherent: True. Malleability=3.62211 Asynch=2.515615 Spawn=1\n",
      "Index: 1114 is coherent: True. Malleability=3.585094 Asynch=2.562233 Spawn=1\n",
      " \n",
      "Index: 1115 is coherent: False. Malleability=7.482478 Asynch=14.020036999999999 Spawn=0\n",
      "Index: 1116 is coherent: False. Malleability=6.10846 Asynch=12.153912 Spawn=0\n",
      "Index: 1117 is coherent: False. Malleability=7.2278579999999994 Asynch=13.927679 Spawn=0\n",
      "Index: 1118 is coherent: False. Malleability=8.10403 Asynch=14.345331000000002 Spawn=0\n",
      "Index: 1119 is coherent: False. Malleability=4.429137 Asynch=10.834454000000001 Spawn=0\n",
      " \n",
      "Index: 1120 is coherent: True. Malleability=2.599563 Asynch=1.715193 Spawn=1\n",
      "Index: 1121 is coherent: True. Malleability=2.644342 Asynch=1.714988 Spawn=1\n",
      "Index: 1122 is coherent: True. Malleability=2.66933 Asynch=1.710251 Spawn=1\n",
      "Index: 1123 is coherent: True. Malleability=2.6609030000000002 Asynch=1.743489 Spawn=1\n",
      "Index: 1124 is coherent: True. Malleability=2.757089 Asynch=1.770463 Spawn=1\n",
      " \n",
      "Index: 1125 is coherent: True. Malleability=0.53355 Asynch=0.176518 Spawn=1\n",
      "Index: 1126 is coherent: True. Malleability=0.5552919999999999 Asynch=0.187113 Spawn=1\n",
      "Index: 1127 is coherent: True. Malleability=0.509889 Asynch=0.152339 Spawn=1\n",
      "Index: 1128 is coherent: True. Malleability=0.574114 Asynch=0.218311 Spawn=1\n",
      "Index: 1129 is coherent: True. Malleability=0.593872 Asynch=0.212528 Spawn=1\n",
      " \n",
      "Index: 1130 is coherent: False. Malleability=1.872236 Asynch=3.907484 Spawn=0\n",
      "Index: 1131 is coherent: False. Malleability=1.9198439999999999 Asynch=4.012822 Spawn=0\n",
      "Index: 1132 is coherent: False. Malleability=1.746931 Asynch=3.611696 Spawn=0\n",
      "Index: 1133 is coherent: False. Malleability=1.8906079999999998 Asynch=3.884529 Spawn=0\n",
      "Index: 1134 is coherent: False. Malleability=2.02843 Asynch=3.68364 Spawn=0\n",
      " \n",
      "Index: 1135 is coherent: False. Malleability=2.031548 Asynch=4.491209 Spawn=0\n",
      "Index: 1136 is coherent: False. Malleability=1.980572 Asynch=4.212649 Spawn=0\n",
      "Index: 1137 is coherent: False. Malleability=1.805917 Asynch=3.668251 Spawn=0\n",
      "Index: 1138 is coherent: False. Malleability=1.8718369999999998 Asynch=3.511946 Spawn=0\n",
      "Index: 1139 is coherent: False. Malleability=1.8746390000000002 Asynch=4.427446 Spawn=0\n",
      " \n",
      "Index: 1140 is coherent: True. Malleability=2.210744 Asynch=0.961881 Spawn=1\n",
      "Index: 1141 is coherent: True. Malleability=2.230293 Asynch=1.28842 Spawn=1\n",
      "Index: 1142 is coherent: True. Malleability=2.280584 Asynch=1.899292 Spawn=1\n",
      "Index: 1143 is coherent: True. Malleability=2.217712 Asynch=1.482613 Spawn=1\n",
      "Index: 1144 is coherent: True. Malleability=2.842664 Asynch=1.276661 Spawn=1\n",
      " \n",
      "Index: 1145 is coherent: True. Malleability=6.274902000000001 Asynch=5.609818000000001 Spawn=0\n",
      "Index: 1146 is coherent: True. Malleability=6.634528 Asynch=6.019977 Spawn=0\n",
      "Index: 1147 is coherent: True. Malleability=6.278214999999999 Asynch=5.680230000000001 Spawn=0\n",
      "Index: 1148 is coherent: True. Malleability=6.557734 Asynch=6.279008 Spawn=0\n",
      "Index: 1149 is coherent: True. Malleability=6.19407 Asynch=5.514883000000001 Spawn=0\n",
      " \n",
      "Index: 1150 is coherent: True. Malleability=2.3069740000000003 Asynch=1.133969 Spawn=1\n",
      "Index: 1151 is coherent: True. Malleability=1.603859 Asynch=1.1967139999999998 Spawn=1\n",
      "Index: 1152 is coherent: True. Malleability=1.615132 Asynch=1.190258 Spawn=1\n",
      "Index: 1153 is coherent: True. Malleability=2.2032480000000003 Asynch=1.051022 Spawn=1\n",
      "Index: 1154 is coherent: True. Malleability=3.1713880000000003 Asynch=1.074122 Spawn=1\n",
      " \n",
      "Index: 1155 is coherent: False. Malleability=2.584006 Asynch=3.4379980000000003 Spawn=0\n",
      "Index: 1156 is coherent: False. Malleability=2.5574689999999998 Asynch=4.659798 Spawn=0\n",
      "Index: 1157 is coherent: False. Malleability=2.9557909999999996 Asynch=3.93241 Spawn=0\n",
      "Index: 1158 is coherent: False. Malleability=2.5913 Asynch=4.009842 Spawn=0\n",
      "Index: 1159 is coherent: False. Malleability=2.6727190000000003 Asynch=3.8031249999999996 Spawn=0\n",
      " \n",
      "Index: 1160 is coherent: False. Malleability=1.798562 Asynch=4.746722 Spawn=0\n",
      "Index: 1161 is coherent: False. Malleability=1.853182 Asynch=3.403224 Spawn=0\n",
      "Index: 1162 is coherent: False. Malleability=1.884103 Asynch=3.863336 Spawn=0\n",
      "Index: 1163 is coherent: False. Malleability=1.74719 Asynch=5.867284 Spawn=0\n",
      "Index: 1164 is coherent: False. Malleability=1.841162 Asynch=5.211543 Spawn=0\n",
      " \n",
      "Index: 1165 is coherent: True. Malleability=4.025034 Asynch=2.874907 Spawn=1\n",
      "Index: 1166 is coherent: True. Malleability=3.873738 Asynch=2.9260659999999996 Spawn=1\n",
      "Index: 1167 is coherent: True. Malleability=3.9875499999999997 Asynch=3.0682859999999996 Spawn=1\n",
      "Index: 1168 is coherent: True. Malleability=3.8818580000000003 Asynch=2.858277 Spawn=1\n",
      "Index: 1169 is coherent: True. Malleability=4.2030840000000005 Asynch=3.121277 Spawn=1\n",
      " \n",
      "Index: 1170 is coherent: True. Malleability=3.063482 Asynch=2.975645 Spawn=1\n",
      "Index: 1171 is coherent: True. Malleability=2.995527 Asynch=2.917425 Spawn=1\n",
      "Index: 1172 is coherent: True. Malleability=3.047487 Asynch=2.9620840000000004 Spawn=1\n",
      "Index: 1173 is coherent: True. Malleability=3.0105459999999997 Asynch=2.924329 Spawn=1\n",
      "Index: 1174 is coherent: True. Malleability=2.99556 Asynch=2.920394 Spawn=1\n",
      " \n",
      "Index: 1175 is coherent: True. Malleability=1.886689 Asynch=0.9810700000000001 Spawn=1\n",
      "Index: 1176 is coherent: True. Malleability=2.112323 Asynch=1.231605 Spawn=1\n",
      "Index: 1177 is coherent: True. Malleability=1.900914 Asynch=0.85041 Spawn=1\n",
      "Index: 1178 is coherent: True. Malleability=1.9160469999999998 Asynch=1.055101 Spawn=1\n",
      "Index: 1179 is coherent: True. Malleability=2.036947 Asynch=1.1091419999999999 Spawn=1\n",
      " \n",
      "Index: 1180 is coherent: True. Malleability=14.76229 Asynch=13.779329 Spawn=1\n",
      "Index: 1181 is coherent: True. Malleability=13.126505 Asynch=11.977681999999998 Spawn=1\n",
      "Index: 1182 is coherent: True. Malleability=13.466198 Asynch=12.550792999999999 Spawn=1\n",
      "Index: 1183 is coherent: True. Malleability=12.913516 Asynch=11.961029 Spawn=1\n",
      "Index: 1184 is coherent: True. Malleability=14.799847 Asynch=13.866758 Spawn=1\n",
      " \n",
      "Index: 1185 is coherent: True. Malleability=4.882433 Asynch=4.828518 Spawn=1\n",
      "Index: 1186 is coherent: True. Malleability=4.625657 Asynch=4.5442 Spawn=1\n",
      "Index: 1187 is coherent: True. Malleability=4.852633 Asynch=4.818486999999999 Spawn=1\n",
      "Index: 1188 is coherent: True. Malleability=4.646518 Asynch=4.607162 Spawn=1\n",
      "Index: 1189 is coherent: True. Malleability=4.344825 Asynch=4.2112359999999995 Spawn=1\n",
      " \n",
      "Index: 1190 is coherent: True. Malleability=5.134576999999999 Asynch=4.533399 Spawn=0\n",
      "Index: 1191 is coherent: True. Malleability=4.696287 Asynch=4.324778 Spawn=0\n",
      "Index: 1192 is coherent: True. Malleability=5.112285 Asynch=4.324181 Spawn=0\n",
      "Index: 1193 is coherent: True. Malleability=5.885578000000001 Asynch=5.155286 Spawn=0\n",
      "Index: 1194 is coherent: False. Malleability=4.618071 Asynch=4.984879000000001 Spawn=0\n",
      " \n",
      "Index: 1195 is coherent: True. Malleability=3.9475569999999998 Asynch=3.746912 Spawn=1\n",
      "Index: 1196 is coherent: True. Malleability=3.8259870000000005 Asynch=3.460784 Spawn=1\n",
      "Index: 1197 is coherent: True. Malleability=4.047351 Asynch=3.8975 Spawn=1\n",
      "Index: 1198 is coherent: True. Malleability=3.8364309999999997 Asynch=3.71301 Spawn=1\n",
      "Index: 1199 is coherent: True. Malleability=3.8563229999999997 Asynch=3.369735 Spawn=1\n",
      " \n",
      "Index: 1200 is coherent: True. Malleability=1.824838 Asynch=0.745709 Spawn=1\n",
      "Index: 1201 is coherent: True. Malleability=1.7590029999999999 Asynch=0.849059 Spawn=1\n",
      "Index: 1202 is coherent: True. Malleability=1.9516079999999998 Asynch=1.110161 Spawn=1\n",
      "Index: 1203 is coherent: True. Malleability=1.224343 Asynch=0.8813519999999999 Spawn=1\n",
      "Index: 1204 is coherent: True. Malleability=2.318741 Asynch=1.6451879999999999 Spawn=1\n",
      " \n",
      "Index: 1205 is coherent: False. Malleability=4.171205 Asynch=4.228542 Spawn=0\n",
      "Index: 1206 is coherent: True. Malleability=3.962687 Asynch=3.8890670000000007 Spawn=0\n",
      "Index: 1207 is coherent: True. Malleability=4.193897 Asynch=3.878953 Spawn=0\n",
      "Index: 1208 is coherent: True. Malleability=6.106694 Asynch=5.334588 Spawn=0\n",
      "Index: 1209 is coherent: False. Malleability=4.25294 Asynch=4.276834999999999 Spawn=0\n",
      " \n",
      "Index: 1210 is coherent: False. Malleability=11.728652 Asynch=15.104264 Spawn=0\n",
      "Index: 1211 is coherent: False. Malleability=11.35784 Asynch=14.309898 Spawn=0\n",
      "Index: 1212 is coherent: False. Malleability=10.733425 Asynch=13.740932 Spawn=0\n",
      "Index: 1213 is coherent: False. Malleability=8.264557 Asynch=11.213455 Spawn=0\n",
      "Index: 1214 is coherent: False. Malleability=10.843741000000001 Asynch=13.324076 Spawn=0\n",
      " \n",
      "Index: 1215 is coherent: False. Malleability=3.2467230000000002 Asynch=4.326873 Spawn=0\n",
      "Index: 1216 is coherent: False. Malleability=2.985223 Asynch=3.7426010000000005 Spawn=0\n",
      "Index: 1217 is coherent: False. Malleability=3.265659 Asynch=4.293518 Spawn=0\n",
      "Index: 1218 is coherent: False. Malleability=3.449331 Asynch=4.313027 Spawn=0\n",
      "Index: 1219 is coherent: False. Malleability=3.434762 Asynch=3.837545 Spawn=0\n",
      " \n",
      "Index: 1220 is coherent: False. Malleability=1.75506 Asynch=11.263776000000002 Spawn=0\n",
      "Index: 1221 is coherent: False. Malleability=1.723258 Asynch=9.326497999999999 Spawn=0\n",
      "Index: 1222 is coherent: False. Malleability=1.8435069999999998 Asynch=7.868948999999999 Spawn=0\n",
      "Index: 1223 is coherent: False. Malleability=1.844379 Asynch=8.753541 Spawn=0\n",
      "Index: 1224 is coherent: False. Malleability=1.874646 Asynch=38.04828599999999 Spawn=0\n",
      " \n",
      "Index: 1225 is coherent: False. Malleability=2.502538 Asynch=4.32339 Spawn=0\n",
      "Index: 1226 is coherent: False. Malleability=3.31462 Asynch=4.326939 Spawn=0\n",
      "Index: 1227 is coherent: False. Malleability=1.841452 Asynch=3.636515 Spawn=0\n",
      "Index: 1228 is coherent: False. Malleability=2.544102 Asynch=3.527811 Spawn=0\n",
      "Index: 1229 is coherent: False. Malleability=2.104919 Asynch=3.829242 Spawn=0\n",
      " \n",
      "Index: 1230 is coherent: True. Malleability=2.6168750000000003 Asynch=1.7261110000000002 Spawn=1\n",
      "Index: 1231 is coherent: True. Malleability=2.653277 Asynch=1.673684 Spawn=1\n",
      "Index: 1232 is coherent: True. Malleability=2.707102 Asynch=1.73066 Spawn=1\n",
      "Index: 1233 is coherent: True. Malleability=2.688976 Asynch=1.737284 Spawn=1\n",
      "Index: 1234 is coherent: True. Malleability=2.660209 Asynch=1.694966 Spawn=1\n",
      " \n",
      "Index: 1235 is coherent: True. Malleability=2.790541 Asynch=2.779773 Spawn=1\n",
      "Index: 1236 is coherent: True. Malleability=2.896932 Asynch=2.244025 Spawn=1\n",
      "Index: 1237 is coherent: True. Malleability=2.9044230000000004 Asynch=2.478058 Spawn=1\n",
      "Index: 1238 is coherent: True. Malleability=2.989803 Asynch=2.338545 Spawn=1\n",
      "Index: 1239 is coherent: True. Malleability=3.208701 Asynch=3.199675 Spawn=1\n",
      " \n",
      "Index: 1240 is coherent: False. Malleability=3.529243 Asynch=3.974426 Spawn=0\n",
      "Index: 1241 is coherent: False. Malleability=3.416165 Asynch=3.445131 Spawn=0\n",
      "Index: 1242 is coherent: False. Malleability=3.541199 Asynch=3.770185 Spawn=0\n",
      "Index: 1243 is coherent: True. Malleability=3.4363770000000002 Asynch=3.380535 Spawn=0\n",
      "Index: 1244 is coherent: False. Malleability=3.403695 Asynch=3.778876 Spawn=0\n",
      " \n",
      "Index: 1245 is coherent: True. Malleability=12.897609 Asynch=11.397006999999999 Spawn=1\n",
      "Index: 1246 is coherent: True. Malleability=11.133469 Asynch=9.632038999999999 Spawn=1\n",
      "Index: 1247 is coherent: True. Malleability=12.316714999999999 Asynch=10.872015000000001 Spawn=1\n",
      "Index: 1248 is coherent: True. Malleability=12.23226 Asynch=10.771895 Spawn=1\n",
      "Index: 1249 is coherent: True. Malleability=11.793211 Asynch=10.285566 Spawn=1\n",
      " \n",
      "Index: 1250 is coherent: True. Malleability=10.793839 Asynch=9.811077000000001 Spawn=0\n",
      "Index: 1251 is coherent: True. Malleability=10.556692 Asynch=9.661622000000001 Spawn=0\n",
      "Index: 1252 is coherent: True. Malleability=11.237448 Asynch=10.685171 Spawn=0\n",
      "Index: 1253 is coherent: True. Malleability=10.043517 Asynch=9.485961 Spawn=0\n",
      "Index: 1254 is coherent: False. Malleability=11.013221999999999 Asynch=12.946526 Spawn=0\n",
      " \n",
      "Index: 1255 is coherent: True. Malleability=4.502705 Asynch=3.465694 Spawn=1\n",
      "Index: 1256 is coherent: True. Malleability=4.756632 Asynch=3.6270159999999994 Spawn=1\n",
      "Index: 1257 is coherent: True. Malleability=4.256621 Asynch=3.1658049999999998 Spawn=1\n",
      "Index: 1258 is coherent: True. Malleability=4.79013 Asynch=3.6984359999999996 Spawn=1\n",
      "Index: 1259 is coherent: True. Malleability=4.34288 Asynch=3.2603180000000003 Spawn=1\n",
      " \n",
      "Index: 1260 is coherent: False. Malleability=2.568535 Asynch=2.64902 Spawn=0\n",
      "Index: 1261 is coherent: True. Malleability=3.829472 Asynch=2.371875 Spawn=0\n",
      "Index: 1262 is coherent: False. Malleability=3.780057 Asynch=3.9857050000000003 Spawn=0\n",
      "Index: 1263 is coherent: False. Malleability=3.010485 Asynch=4.084235 Spawn=0\n",
      "Index: 1264 is coherent: False. Malleability=3.3064409999999995 Asynch=4.177067 Spawn=0\n",
      " \n",
      "Index: 1265 is coherent: False. Malleability=2.071983 Asynch=4.82333 Spawn=0\n",
      "Index: 1266 is coherent: False. Malleability=2.550854 Asynch=8.542449 Spawn=0\n",
      "Index: 1267 is coherent: False. Malleability=2.023353 Asynch=3.0269320000000004 Spawn=0\n",
      "Index: 1268 is coherent: False. Malleability=1.911212 Asynch=3.555075 Spawn=0\n",
      "Index: 1269 is coherent: False. Malleability=2.1043570000000003 Asynch=2.841709 Spawn=0\n",
      " \n",
      "Index: 1270 is coherent: True. Malleability=1.4738930000000001 Asynch=0.281149 Spawn=1\n",
      "Index: 1271 is coherent: True. Malleability=1.428882 Asynch=0.30471 Spawn=1\n",
      "Index: 1272 is coherent: True. Malleability=1.468575 Asynch=0.279479 Spawn=1\n",
      "Index: 1273 is coherent: True. Malleability=1.504953 Asynch=0.304542 Spawn=1\n",
      "Index: 1274 is coherent: True. Malleability=1.4708719999999997 Asynch=0.282401 Spawn=1\n",
      " \n",
      "Index: 1275 is coherent: True. Malleability=2.6280710000000003 Asynch=1.691704 Spawn=1\n",
      "Index: 1276 is coherent: True. Malleability=2.453498 Asynch=1.56736 Spawn=1\n",
      "Index: 1277 is coherent: True. Malleability=2.572407 Asynch=1.6415330000000001 Spawn=1\n",
      "Index: 1278 is coherent: True. Malleability=2.637328 Asynch=1.6733609999999999 Spawn=1\n",
      "Index: 1279 is coherent: True. Malleability=2.527745 Asynch=1.608119 Spawn=1\n",
      " \n",
      "Index: 1280 is coherent: True. Malleability=2.729374 Asynch=2.4692800000000004 Spawn=0\n",
      "Index: 1281 is coherent: True. Malleability=2.814746 Asynch=2.536798 Spawn=0\n",
      "Index: 1282 is coherent: True. Malleability=2.653454 Asynch=2.397168 Spawn=0\n",
      "Index: 1283 is coherent: True. Malleability=2.637082 Asynch=2.3695429999999997 Spawn=0\n",
      "Index: 1284 is coherent: True. Malleability=2.7272990000000004 Asynch=2.4501540000000004 Spawn=0\n",
      " \n",
      "Index: 1285 is coherent: True. Malleability=1.670813 Asynch=0.8843810000000001 Spawn=1\n",
      "Index: 1286 is coherent: True. Malleability=1.370245 Asynch=0.646721 Spawn=1\n",
      "Index: 1287 is coherent: True. Malleability=1.6651670000000003 Asynch=0.668708 Spawn=1\n",
      "Index: 1288 is coherent: True. Malleability=2.277563 Asynch=1.4532960000000001 Spawn=1\n",
      "Index: 1289 is coherent: True. Malleability=1.177557 Asynch=0.919397 Spawn=1\n",
      " \n",
      "Index: 1290 is coherent: True. Malleability=2.81478 Asynch=2.369199 Spawn=1\n",
      "Index: 1291 is coherent: True. Malleability=2.8004620000000005 Asynch=2.392062 Spawn=1\n",
      "Index: 1292 is coherent: True. Malleability=3.008447 Asynch=2.5313049999999997 Spawn=1\n",
      "Index: 1293 is coherent: True. Malleability=3.626419 Asynch=2.2373200000000004 Spawn=1\n",
      "Index: 1294 is coherent: True. Malleability=3.0124419999999996 Asynch=2.442684 Spawn=1\n",
      " \n",
      "Index: 1295 is coherent: True. Malleability=1.121601 Asynch=0.692362 Spawn=1\n",
      "Index: 1296 is coherent: True. Malleability=1.420058 Asynch=1.105745 Spawn=1\n",
      "Index: 1297 is coherent: True. Malleability=1.057053 Asynch=0.663313 Spawn=1\n",
      "Index: 1298 is coherent: True. Malleability=1.500982 Asynch=1.064239 Spawn=1\n",
      "Index: 1299 is coherent: True. Malleability=1.3864779999999999 Asynch=1.0486309999999999 Spawn=1\n",
      " \n",
      "Index: 1300 is coherent: True. Malleability=1.991854 Asynch=1.9614260000000001 Spawn=0\n",
      "Index: 1301 is coherent: True. Malleability=2.038693 Asynch=1.9919609999999999 Spawn=0\n",
      "Index: 1302 is coherent: True. Malleability=1.9988240000000002 Asynch=1.93198 Spawn=0\n",
      "Index: 1303 is coherent: True. Malleability=2.045337 Asynch=1.993675 Spawn=0\n",
      "Index: 1304 is coherent: True. Malleability=1.9439229999999998 Asynch=1.9132029999999998 Spawn=0\n",
      " \n",
      "Index: 1305 is coherent: False. Malleability=2.692915 Asynch=3.363747 Spawn=0\n",
      "Index: 1306 is coherent: False. Malleability=2.6828089999999998 Asynch=2.757451 Spawn=0\n",
      "Index: 1307 is coherent: True. Malleability=3.077602 Asynch=2.988776 Spawn=0\n",
      "Index: 1308 is coherent: False. Malleability=2.524093 Asynch=2.7236059999999997 Spawn=0\n",
      "Index: 1309 is coherent: True. Malleability=3.1594490000000004 Asynch=2.932636 Spawn=0\n",
      " \n",
      "Index: 1310 is coherent: True. Malleability=3.03534 Asynch=2.287766 Spawn=1\n",
      "Index: 1311 is coherent: True. Malleability=3.6134329999999997 Asynch=2.7011339999999997 Spawn=1\n",
      "Index: 1312 is coherent: True. Malleability=3.891852 Asynch=3.390178 Spawn=1\n",
      "Index: 1313 is coherent: True. Malleability=2.8755479999999998 Asynch=2.4385950000000003 Spawn=1\n",
      "Index: 1314 is coherent: True. Malleability=3.309606 Asynch=2.723296 Spawn=1\n",
      " \n",
      "Index: 1315 is coherent: False. Malleability=1.971368 Asynch=10.39432 Spawn=0\n",
      "Index: 1316 is coherent: False. Malleability=2.005742 Asynch=9.041139 Spawn=0\n",
      "Index: 1317 is coherent: False. Malleability=2.051235 Asynch=7.768702000000001 Spawn=0\n",
      "Index: 1318 is coherent: False. Malleability=1.806718 Asynch=9.816962000000002 Spawn=0\n",
      "Index: 1319 is coherent: False. Malleability=2.001134 Asynch=8.65063 Spawn=0\n",
      " \n",
      "Index: 1320 is coherent: True. Malleability=3.059146 Asynch=2.701598 Spawn=1\n",
      "Index: 1321 is coherent: True. Malleability=3.560224 Asynch=3.022928 Spawn=1\n",
      "Index: 1322 is coherent: True. Malleability=3.420548 Asynch=2.806839 Spawn=1\n",
      "Index: 1323 is coherent: True. Malleability=3.288391 Asynch=2.8920919999999994 Spawn=1\n",
      "Index: 1324 is coherent: True. Malleability=3.2974609999999998 Asynch=2.745314 Spawn=1\n",
      " \n",
      "Index: 1325 is coherent: True. Malleability=2.067503 Asynch=1.606452 Spawn=1\n",
      "Index: 1326 is coherent: True. Malleability=1.931136 Asynch=1.5588669999999998 Spawn=1\n",
      "Index: 1327 is coherent: True. Malleability=2.615324 Asynch=2.244621 Spawn=1\n",
      "Index: 1328 is coherent: True. Malleability=2.034299 Asynch=1.6762329999999999 Spawn=1\n",
      "Index: 1329 is coherent: True. Malleability=2.3774029999999997 Asynch=1.9904059999999997 Spawn=1\n",
      " \n",
      "Index: 1330 is coherent: True. Malleability=3.786009 Asynch=2.736196 Spawn=1\n",
      "Index: 1331 is coherent: True. Malleability=3.7266010000000005 Asynch=3.3291910000000002 Spawn=1\n",
      "Index: 1332 is coherent: True. Malleability=3.8127780000000002 Asynch=3.026275 Spawn=1\n",
      "Index: 1333 is coherent: True. Malleability=3.8148779999999998 Asynch=3.075816 Spawn=1\n",
      "Index: 1334 is coherent: True. Malleability=3.820869 Asynch=3.320107 Spawn=1\n",
      " \n",
      "Index: 1335 is coherent: True. Malleability=3.0381280000000004 Asynch=2.201381 Spawn=1\n",
      "Index: 1336 is coherent: True. Malleability=3.8054749999999995 Asynch=2.6840549999999994 Spawn=1\n",
      "Index: 1337 is coherent: True. Malleability=3.334205 Asynch=2.4503099999999995 Spawn=1\n",
      "Index: 1338 is coherent: True. Malleability=3.2469400000000004 Asynch=2.7112540000000003 Spawn=1\n",
      "Index: 1339 is coherent: True. Malleability=4.242609 Asynch=2.93948 Spawn=1\n",
      " \n",
      "Index: 1340 is coherent: True. Malleability=2.644519 Asynch=2.631139 Spawn=1\n",
      "Index: 1341 is coherent: True. Malleability=2.787578 Asynch=2.77727 Spawn=1\n",
      "Index: 1342 is coherent: True. Malleability=2.608122 Asynch=2.414077 Spawn=1\n",
      "Index: 1343 is coherent: True. Malleability=2.985974 Asynch=2.96889 Spawn=1\n",
      "Index: 1344 is coherent: True. Malleability=2.934415 Asynch=2.927118 Spawn=1\n",
      " \n",
      "Index: 1345 is coherent: True. Malleability=2.083005 Asynch=1.722702 Spawn=1\n",
      "Index: 1346 is coherent: True. Malleability=2.187076 Asynch=1.8200820000000002 Spawn=1\n",
      "Index: 1347 is coherent: True. Malleability=3.0759119999999998 Asynch=1.837957 Spawn=1\n",
      "Index: 1348 is coherent: True. Malleability=2.147583 Asynch=1.714268 Spawn=1\n",
      "Index: 1349 is coherent: True. Malleability=2.075988 Asynch=1.678223 Spawn=1\n",
      " \n",
      "Index: 1350 is coherent: True. Malleability=0.7559 Asynch=0.733486 Spawn=1\n",
      "Index: 1351 is coherent: True. Malleability=0.782586 Asynch=0.765704 Spawn=1\n",
      "Index: 1352 is coherent: True. Malleability=0.645714 Asynch=0.62327 Spawn=1\n",
      "Index: 1353 is coherent: True. Malleability=0.648058 Asynch=0.627206 Spawn=1\n",
      "Index: 1354 is coherent: True. Malleability=0.7453830000000001 Asynch=0.7184820000000001 Spawn=1\n",
      " \n",
      "Index: 1355 is coherent: False. Malleability=5.009339 Asynch=6.484009 Spawn=0\n",
      "Index: 1356 is coherent: False. Malleability=3.9621690000000003 Asynch=7.231996 Spawn=0\n",
      "Index: 1357 is coherent: False. Malleability=3.980485 Asynch=6.604365 Spawn=0\n",
      "Index: 1358 is coherent: False. Malleability=3.930714 Asynch=6.839987 Spawn=0\n",
      "Index: 1359 is coherent: False. Malleability=4.19759 Asynch=7.1184 Spawn=0\n",
      " \n",
      "Index: 1360 is coherent: False. Malleability=1.7456809999999998 Asynch=25.770936000000003 Spawn=0\n",
      "Index: 1361 is coherent: False. Malleability=1.659054 Asynch=19.048436000000002 Spawn=0\n",
      "Index: 1362 is coherent: False. Malleability=2.054586 Asynch=28.675625000000007 Spawn=0\n",
      "Index: 1363 is coherent: False. Malleability=1.692947 Asynch=28.665271999999998 Spawn=0\n",
      "Index: 1364 is coherent: False. Malleability=1.790025 Asynch=20.899115 Spawn=0\n",
      " \n",
      "Index: 1365 is coherent: True. Malleability=2.064645 Asynch=1.085534 Spawn=1\n",
      "Index: 1366 is coherent: True. Malleability=2.570688 Asynch=0.976032 Spawn=1\n",
      "Index: 1367 is coherent: True. Malleability=1.8450380000000002 Asynch=1.210654 Spawn=1\n",
      "Index: 1368 is coherent: True. Malleability=2.069026 Asynch=1.017549 Spawn=1\n",
      "Index: 1369 is coherent: True. Malleability=1.6576719999999998 Asynch=1.2174070000000001 Spawn=1\n",
      " \n",
      "Index: 1370 is coherent: True. Malleability=5.087378 Asynch=3.8455470000000003 Spawn=0\n",
      "Index: 1371 is coherent: True. Malleability=5.113683 Asynch=3.830082 Spawn=0\n",
      "Index: 1372 is coherent: True. Malleability=5.020023 Asynch=3.995811 Spawn=0\n",
      "Index: 1373 is coherent: True. Malleability=4.666746 Asynch=4.075972999999999 Spawn=0\n",
      "Index: 1374 is coherent: True. Malleability=4.824116999999999 Asynch=3.7453469999999998 Spawn=0\n",
      " \n",
      "Index: 1375 is coherent: True. Malleability=5.780769 Asynch=5.310898 Spawn=0\n",
      "Index: 1376 is coherent: True. Malleability=6.135841 Asynch=5.404885 Spawn=0\n",
      "Index: 1377 is coherent: True. Malleability=5.180259 Asynch=5.092110999999999 Spawn=0\n",
      "Index: 1378 is coherent: True. Malleability=5.830954999999999 Asynch=5.356885 Spawn=0\n",
      "Index: 1379 is coherent: True. Malleability=5.76166 Asynch=5.614400000000001 Spawn=0\n",
      " \n",
      "Index: 1380 is coherent: True. Malleability=2.59209 Asynch=1.949114 Spawn=1\n",
      "Index: 1381 is coherent: True. Malleability=2.27456 Asynch=0.875593 Spawn=1\n",
      "Index: 1382 is coherent: True. Malleability=2.419697 Asynch=0.9625900000000001 Spawn=1\n",
      "Index: 1383 is coherent: True. Malleability=1.6406010000000002 Asynch=0.7224569999999999 Spawn=1\n",
      "Index: 1384 is coherent: True. Malleability=1.7728929999999998 Asynch=1.140055 Spawn=1\n",
      " \n",
      "Index: 1385 is coherent: True. Malleability=4.38152 Asynch=3.0410920000000004 Spawn=1\n",
      "Index: 1386 is coherent: True. Malleability=4.564756 Asynch=3.209948 Spawn=1\n",
      "Index: 1387 is coherent: True. Malleability=3.6524080000000003 Asynch=3.0911790000000003 Spawn=1\n",
      "Index: 1388 is coherent: True. Malleability=4.093783 Asynch=3.030201999999999 Spawn=1\n",
      "Index: 1389 is coherent: True. Malleability=4.484274 Asynch=2.895226 Spawn=1\n",
      " \n",
      "Index: 1390 is coherent: True. Malleability=2.601173 Asynch=2.282601 Spawn=1\n",
      "Index: 1391 is coherent: True. Malleability=2.5652009999999996 Asynch=2.237625 Spawn=1\n",
      "Index: 1392 is coherent: True. Malleability=2.568597 Asynch=2.245449 Spawn=1\n",
      "Index: 1393 is coherent: True. Malleability=2.546786 Asynch=2.238514 Spawn=1\n",
      "Index: 1394 is coherent: True. Malleability=2.5657240000000003 Asynch=2.239714 Spawn=1\n",
      " \n",
      "Index: 1395 is coherent: False. Malleability=2.098172 Asynch=2.632327 Spawn=0\n",
      "Index: 1396 is coherent: False. Malleability=3.058369 Asynch=3.293519 Spawn=0\n",
      "Index: 1397 is coherent: False. Malleability=1.991674 Asynch=2.581924 Spawn=0\n",
      "Index: 1398 is coherent: True. Malleability=2.478871 Asynch=2.372186 Spawn=0\n",
      "Index: 1399 is coherent: False. Malleability=2.479602 Asynch=2.8735540000000004 Spawn=0\n",
      " \n",
      "Index: 1400 is coherent: True. Malleability=4.64302 Asynch=3.661662 Spawn=0\n",
      "Index: 1401 is coherent: True. Malleability=5.257786 Asynch=4.443383 Spawn=0\n",
      "Index: 1402 is coherent: True. Malleability=4.6763580000000005 Asynch=3.462357000000001 Spawn=0\n",
      "Index: 1403 is coherent: True. Malleability=4.281878 Asynch=3.3007750000000002 Spawn=0\n",
      "Index: 1404 is coherent: True. Malleability=4.90527 Asynch=3.404687 Spawn=0\n",
      " \n",
      "Index: 1405 is coherent: False. Malleability=2.139344 Asynch=6.416474 Spawn=0\n",
      "Index: 1406 is coherent: False. Malleability=1.657732 Asynch=6.131961 Spawn=0\n",
      "Index: 1407 is coherent: False. Malleability=1.804119 Asynch=5.871991 Spawn=0\n",
      "Index: 1408 is coherent: False. Malleability=1.697304 Asynch=5.917158 Spawn=0\n",
      "Index: 1409 is coherent: False. Malleability=1.713008 Asynch=6.019999 Spawn=0\n",
      " \n",
      "Index: 1410 is coherent: True. Malleability=4.310288 Asynch=3.3729020000000003 Spawn=1\n",
      "Index: 1411 is coherent: True. Malleability=5.2756229999999995 Asynch=3.3169750000000002 Spawn=1\n",
      "Index: 1412 is coherent: True. Malleability=4.01269 Asynch=3.4474589999999994 Spawn=1\n",
      "Index: 1413 is coherent: True. Malleability=5.115755999999999 Asynch=3.167379 Spawn=1\n",
      "Index: 1414 is coherent: True. Malleability=4.757269 Asynch=3.242746 Spawn=1\n",
      " \n",
      "Index: 1415 is coherent: True. Malleability=1.6587990000000001 Asynch=1.6434199999999999 Spawn=1\n",
      "Index: 1416 is coherent: True. Malleability=1.5576919999999999 Asynch=1.528348 Spawn=1\n",
      "Index: 1417 is coherent: True. Malleability=1.5328819999999999 Asynch=1.486151 Spawn=1\n",
      "Index: 1418 is coherent: True. Malleability=1.724798 Asynch=1.7122259999999998 Spawn=1\n",
      "Index: 1419 is coherent: True. Malleability=1.76173 Asynch=1.731255 Spawn=1\n",
      " \n",
      "Index: 1420 is coherent: False. Malleability=2.0545210000000003 Asynch=4.41723 Spawn=0\n",
      "Index: 1421 is coherent: False. Malleability=3.5456770000000004 Asynch=3.736777 Spawn=0\n",
      "Index: 1422 is coherent: False. Malleability=2.5389619999999997 Asynch=3.252003 Spawn=0\n",
      "Index: 1423 is coherent: False. Malleability=2.622251 Asynch=3.309057 Spawn=0\n",
      "Index: 1424 is coherent: True. Malleability=3.4880499999999994 Asynch=3.3549619999999996 Spawn=0\n",
      " \n",
      "Index: 1425 is coherent: False. Malleability=13.002215 Asynch=13.714804 Spawn=0\n",
      "Index: 1426 is coherent: False. Malleability=14.224005000000002 Asynch=14.746882 Spawn=0\n",
      "Index: 1427 is coherent: False. Malleability=14.923791000000001 Asynch=15.93385 Spawn=0\n",
      "Index: 1428 is coherent: False. Malleability=13.530581999999999 Asynch=13.701254 Spawn=0\n",
      "Index: 1429 is coherent: False. Malleability=13.494729 Asynch=14.584277 Spawn=0\n",
      " \n",
      "Index: 1430 is coherent: False. Malleability=1.924969 Asynch=6.171407 Spawn=0\n",
      "Index: 1431 is coherent: False. Malleability=2.062704 Asynch=6.276511 Spawn=0\n",
      "Index: 1432 is coherent: False. Malleability=1.812228 Asynch=6.656015 Spawn=0\n",
      "Index: 1433 is coherent: False. Malleability=1.7350619999999999 Asynch=6.371358 Spawn=0\n",
      "Index: 1434 is coherent: False. Malleability=1.7716999999999998 Asynch=6.559424 Spawn=0\n",
      " \n",
      "Index: 1435 is coherent: True. Malleability=3.9741420000000005 Asynch=3.3284699999999994 Spawn=1\n",
      "Index: 1436 is coherent: True. Malleability=3.9364739999999996 Asynch=3.586826 Spawn=1\n",
      "Index: 1437 is coherent: True. Malleability=4.071376 Asynch=3.2203370000000002 Spawn=1\n",
      "Index: 1438 is coherent: True. Malleability=4.072704 Asynch=3.7355220000000005 Spawn=1\n",
      "Index: 1439 is coherent: True. Malleability=3.9663579999999996 Asynch=3.487985 Spawn=1\n",
      " \n",
      "Index: 1440 is coherent: True. Malleability=4.257361 Asynch=4.179881 Spawn=1\n",
      "Index: 1441 is coherent: True. Malleability=4.159182 Asynch=4.09604 Spawn=1\n",
      "Index: 1442 is coherent: True. Malleability=3.9892329999999996 Asynch=3.9239110000000004 Spawn=1\n",
      "Index: 1443 is coherent: True. Malleability=3.969474 Asynch=3.8947610000000004 Spawn=1\n",
      "Index: 1444 is coherent: True. Malleability=4.359381 Asynch=4.296301000000001 Spawn=1\n",
      " \n",
      "Index: 1445 is coherent: False. Malleability=2.434085 Asynch=8.992958 Spawn=0\n",
      "Index: 1446 is coherent: False. Malleability=6.7834579999999995 Asynch=13.622578 Spawn=0\n",
      "Index: 1447 is coherent: False. Malleability=2.891505 Asynch=8.94306 Spawn=0\n",
      "Index: 1448 is coherent: False. Malleability=7.02526 Asynch=13.300531 Spawn=0\n",
      "Index: 1449 is coherent: False. Malleability=4.093484 Asynch=10.166702 Spawn=0\n",
      " \n",
      "Index: 1450 is coherent: False. Malleability=4.743166 Asynch=10.682329 Spawn=0\n",
      "Index: 1451 is coherent: False. Malleability=3.4392709999999997 Asynch=9.482989 Spawn=0\n",
      "Index: 1452 is coherent: False. Malleability=3.8216339999999995 Asynch=9.07809 Spawn=0\n",
      "Index: 1453 is coherent: False. Malleability=7.696567 Asynch=13.204322000000001 Spawn=0\n",
      "Index: 1454 is coherent: False. Malleability=5.523294 Asynch=11.022566999999999 Spawn=0\n",
      " \n",
      "Index: 1455 is coherent: False. Malleability=2.282357 Asynch=5.464645 Spawn=0\n",
      "Index: 1456 is coherent: False. Malleability=2.070965 Asynch=5.39598 Spawn=0\n",
      "Index: 1457 is coherent: False. Malleability=2.085289 Asynch=5.262825 Spawn=0\n",
      "Index: 1458 is coherent: False. Malleability=2.254059 Asynch=5.198031 Spawn=0\n",
      "Index: 1459 is coherent: False. Malleability=2.078168 Asynch=5.518619 Spawn=0\n",
      " \n",
      "Index: 1460 is coherent: True. Malleability=9.027958 Asynch=8.488586000000002 Spawn=0\n",
      "Index: 1461 is coherent: True. Malleability=9.144954 Asynch=9.07761 Spawn=0\n",
      "Index: 1462 is coherent: True. Malleability=8.447587 Asynch=7.803838 Spawn=0\n",
      "Index: 1463 is coherent: False. Malleability=7.967725 Asynch=8.090774 Spawn=0\n",
      "Index: 1464 is coherent: True. Malleability=8.335607999999999 Asynch=7.99981 Spawn=0\n",
      " \n",
      "Index: 1465 is coherent: True. Malleability=11.990065000000001 Asynch=10.918570999999998 Spawn=1\n",
      "Index: 1466 is coherent: True. Malleability=15.288960999999999 Asynch=14.366521 Spawn=1\n",
      "Index: 1467 is coherent: True. Malleability=13.405452 Asynch=12.473375 Spawn=1\n",
      "Index: 1468 is coherent: True. Malleability=12.868735999999998 Asynch=11.936541 Spawn=1\n",
      "Index: 1469 is coherent: True. Malleability=13.458421000000001 Asynch=12.532001 Spawn=1\n",
      " \n",
      "Index: 1470 is coherent: True. Malleability=2.099091 Asynch=2.079145 Spawn=1\n",
      "Index: 1471 is coherent: True. Malleability=1.940753 Asynch=1.9336440000000001 Spawn=1\n",
      "Index: 1472 is coherent: True. Malleability=1.891543 Asynch=1.887779 Spawn=1\n",
      "Index: 1473 is coherent: True. Malleability=1.8823189999999999 Asynch=1.86554 Spawn=1\n",
      "Index: 1474 is coherent: True. Malleability=1.880007 Asynch=1.874651 Spawn=1\n",
      " \n",
      "Index: 1475 is coherent: True. Malleability=5.467351000000001 Asynch=4.683885999999999 Spawn=0\n",
      "Index: 1476 is coherent: True. Malleability=5.285416 Asynch=4.378515 Spawn=0\n",
      "Index: 1477 is coherent: True. Malleability=4.257212 Asynch=3.9304660000000005 Spawn=0\n",
      "Index: 1478 is coherent: True. Malleability=5.034283 Asynch=4.396652999999999 Spawn=0\n",
      "Index: 1479 is coherent: True. Malleability=5.069388 Asynch=4.554616 Spawn=0\n",
      " \n",
      "Index: 1480 is coherent: True. Malleability=1.639451 Asynch=1.379367 Spawn=0\n",
      "Index: 1481 is coherent: True. Malleability=1.5602070000000001 Asynch=1.542877 Spawn=0\n",
      "Index: 1482 is coherent: True. Malleability=1.690958 Asynch=1.287492 Spawn=0\n",
      "Index: 1483 is coherent: True. Malleability=1.6636749999999998 Asynch=1.364323 Spawn=0\n",
      "Index: 1484 is coherent: True. Malleability=1.522464 Asynch=1.182525 Spawn=0\n",
      " \n",
      "Index: 1485 is coherent: False. Malleability=1.616053 Asynch=3.413321 Spawn=0\n",
      "Index: 1486 is coherent: False. Malleability=1.716143 Asynch=3.5401770000000004 Spawn=0\n",
      "Index: 1487 is coherent: False. Malleability=1.811636 Asynch=4.075836 Spawn=0\n",
      "Index: 1488 is coherent: False. Malleability=1.62487 Asynch=3.848017 Spawn=0\n",
      "Index: 1489 is coherent: False. Malleability=1.679512 Asynch=3.3742270000000003 Spawn=0\n",
      " \n",
      "Index: 1490 is coherent: False. Malleability=1.756438 Asynch=3.745174 Spawn=0\n",
      "Index: 1491 is coherent: False. Malleability=1.965544 Asynch=3.637768 Spawn=0\n",
      "Index: 1492 is coherent: False. Malleability=1.623961 Asynch=2.989929 Spawn=0\n",
      "Index: 1493 is coherent: False. Malleability=1.588367 Asynch=3.954981 Spawn=0\n",
      "Index: 1494 is coherent: False. Malleability=1.625492 Asynch=3.558298 Spawn=0\n",
      " \n",
      "Index: 1495 is coherent: False. Malleability=1.777317 Asynch=6.609628000000001 Spawn=0\n",
      "Index: 1496 is coherent: False. Malleability=1.5216669999999999 Asynch=6.058744 Spawn=0\n",
      "Index: 1497 is coherent: False. Malleability=1.616749 Asynch=3.0793909999999998 Spawn=0\n",
      "Index: 1498 is coherent: False. Malleability=1.485084 Asynch=3.364134 Spawn=0\n",
      "Index: 1499 is coherent: False. Malleability=1.506012 Asynch=6.430758 Spawn=0\n",
      " \n",
      "Index: 1500 is coherent: False. Malleability=3.153735 Asynch=4.271679 Spawn=0\n",
      "Index: 1501 is coherent: False. Malleability=2.064383 Asynch=2.511548 Spawn=0\n",
      "Index: 1502 is coherent: False. Malleability=1.86143 Asynch=3.039048 Spawn=0\n",
      "Index: 1503 is coherent: False. Malleability=2.09038 Asynch=2.5269530000000002 Spawn=0\n",
      "Index: 1504 is coherent: True. Malleability=2.189452 Asynch=2.138445 Spawn=0\n",
      " \n",
      "Index: 1505 is coherent: True. Malleability=4.695713 Asynch=4.6607389999999995 Spawn=1\n",
      "Index: 1506 is coherent: True. Malleability=4.929341 Asynch=4.895115 Spawn=1\n",
      "Index: 1507 is coherent: True. Malleability=4.342475 Asynch=4.304724 Spawn=1\n",
      "Index: 1508 is coherent: True. Malleability=4.714097 Asynch=4.6772469999999995 Spawn=1\n",
      "Index: 1509 is coherent: True. Malleability=4.9530639999999995 Asynch=4.9178630000000005 Spawn=1\n",
      " \n",
      "Index: 1510 is coherent: True. Malleability=7.142918999999999 Asynch=6.387824 Spawn=0\n",
      "Index: 1511 is coherent: True. Malleability=6.213164000000001 Asynch=5.634322 Spawn=0\n",
      "Index: 1512 is coherent: True. Malleability=7.656589 Asynch=7.47236 Spawn=0\n",
      "Index: 1513 is coherent: False. Malleability=6.125906 Asynch=6.36979 Spawn=0\n",
      "Index: 1514 is coherent: True. Malleability=7.219200000000001 Asynch=7.210824 Spawn=0\n",
      " \n",
      "Index: 1515 is coherent: True. Malleability=6.362598 Asynch=4.602277 Spawn=1\n",
      "Index: 1516 is coherent: True. Malleability=5.628806999999999 Asynch=4.0989379999999995 Spawn=1\n",
      "Index: 1517 is coherent: True. Malleability=5.7691930000000005 Asynch=3.8980460000000003 Spawn=1\n",
      "Index: 1518 is coherent: True. Malleability=6.496480999999999 Asynch=4.874434 Spawn=1\n",
      "Index: 1519 is coherent: True. Malleability=6.389775 Asynch=4.314972999999999 Spawn=1\n",
      " \n",
      "Index: 1520 is coherent: True. Malleability=3.784918 Asynch=2.6841420000000005 Spawn=1\n",
      "Index: 1521 is coherent: True. Malleability=3.8626899999999997 Asynch=2.763584 Spawn=1\n",
      "Index: 1522 is coherent: True. Malleability=3.824876 Asynch=2.704962 Spawn=1\n",
      "Index: 1523 is coherent: True. Malleability=3.7546559999999998 Asynch=2.688066 Spawn=1\n",
      "Index: 1524 is coherent: True. Malleability=3.6846099999999997 Asynch=2.549061 Spawn=1\n",
      " \n",
      "Index: 1525 is coherent: False. Malleability=9.869146 Asynch=12.636999 Spawn=0\n",
      "Index: 1526 is coherent: False. Malleability=10.392614 Asynch=13.604579999999999 Spawn=0\n",
      "Index: 1527 is coherent: False. Malleability=11.459404 Asynch=14.955276999999999 Spawn=0\n",
      "Index: 1528 is coherent: False. Malleability=10.539105 Asynch=13.383143 Spawn=0\n",
      "Index: 1529 is coherent: False. Malleability=10.426212 Asynch=14.150242 Spawn=0\n",
      " \n",
      "Index: 1530 is coherent: False. Malleability=2.5505869999999997 Asynch=2.644125 Spawn=0\n",
      "Index: 1531 is coherent: False. Malleability=2.396663 Asynch=2.783616 Spawn=0\n",
      "Index: 1532 is coherent: False. Malleability=3.880425 Asynch=4.132021999999999 Spawn=0\n",
      "Index: 1533 is coherent: False. Malleability=3.596413 Asynch=3.8644879999999997 Spawn=0\n",
      "Index: 1534 is coherent: False. Malleability=3.098419 Asynch=4.063484 Spawn=0\n",
      " \n",
      "Index: 1535 is coherent: False. Malleability=2.1481310000000002 Asynch=8.554770000000001 Spawn=0\n",
      "Index: 1536 is coherent: False. Malleability=2.0882620000000003 Asynch=7.427757999999999 Spawn=0\n",
      "Index: 1537 is coherent: False. Malleability=1.9482279999999998 Asynch=9.16992 Spawn=0\n",
      "Index: 1538 is coherent: False. Malleability=2.086138 Asynch=9.813041000000002 Spawn=0\n",
      "Index: 1539 is coherent: False. Malleability=2.032725 Asynch=7.562829000000001 Spawn=0\n",
      " \n",
      "Index: 1540 is coherent: False. Malleability=7.290236 Asynch=13.221622 Spawn=0\n",
      "Index: 1541 is coherent: False. Malleability=5.826319 Asynch=12.246718999999999 Spawn=0\n",
      "Index: 1542 is coherent: False. Malleability=6.769932 Asynch=13.494377 Spawn=0\n",
      "Index: 1543 is coherent: False. Malleability=6.513899 Asynch=12.904474 Spawn=0\n",
      "Index: 1544 is coherent: False. Malleability=7.04775 Asynch=13.196337 Spawn=0\n",
      " \n",
      "Index: 1545 is coherent: False. Malleability=2.007603 Asynch=2.792871 Spawn=0\n",
      "Index: 1546 is coherent: False. Malleability=1.8646759999999998 Asynch=2.53658 Spawn=0\n",
      "Index: 1547 is coherent: False. Malleability=1.802395 Asynch=4.649179999999999 Spawn=0\n",
      "Index: 1548 is coherent: False. Malleability=1.9382329999999999 Asynch=2.643583 Spawn=0\n",
      "Index: 1549 is coherent: False. Malleability=2.046486 Asynch=2.395124 Spawn=0\n",
      " \n",
      "Index: 1550 is coherent: True. Malleability=8.386335 Asynch=7.5672749999999995 Spawn=0\n",
      "Index: 1551 is coherent: True. Malleability=7.3243860000000005 Asynch=6.809246 Spawn=0\n",
      "Index: 1552 is coherent: True. Malleability=6.949281999999999 Asynch=6.303586 Spawn=0\n",
      "Index: 1553 is coherent: True. Malleability=6.838628999999999 Asynch=6.098894 Spawn=0\n",
      "Index: 1554 is coherent: True. Malleability=7.166263 Asynch=6.551526999999999 Spawn=0\n",
      " \n",
      "Index: 1555 is coherent: True. Malleability=3.178027 Asynch=2.121356 Spawn=1\n",
      "Index: 1556 is coherent: True. Malleability=3.095497 Asynch=2.0914 Spawn=1\n",
      "Index: 1557 is coherent: True. Malleability=3.1369849999999997 Asynch=2.151122 Spawn=1\n",
      "Index: 1558 is coherent: True. Malleability=3.043356 Asynch=2.121763 Spawn=1\n",
      "Index: 1559 is coherent: True. Malleability=3.124237 Asynch=2.0810889999999995 Spawn=1\n",
      " \n",
      "Index: 1560 is coherent: False. Malleability=4.384333 Asynch=4.928423 Spawn=0\n",
      "Index: 1561 is coherent: False. Malleability=3.491809 Asynch=5.013826 Spawn=0\n",
      "Index: 1562 is coherent: False. Malleability=3.473288 Asynch=4.719905 Spawn=0\n",
      "Index: 1563 is coherent: False. Malleability=4.284873999999999 Asynch=4.526435 Spawn=0\n",
      "Index: 1564 is coherent: False. Malleability=3.9118329999999997 Asynch=4.671984 Spawn=0\n",
      " \n",
      "Index: 1565 is coherent: False. Malleability=5.513432 Asynch=6.0172159999999995 Spawn=0\n",
      "Index: 1566 is coherent: False. Malleability=5.214695 Asynch=5.910665 Spawn=0\n",
      "Index: 1567 is coherent: False. Malleability=8.40674 Asynch=9.926801000000001 Spawn=0\n",
      "Index: 1568 is coherent: False. Malleability=7.132312000000001 Asynch=7.956271000000001 Spawn=0\n",
      "Index: 1569 is coherent: False. Malleability=9.258478 Asynch=9.6464 Spawn=0\n",
      " \n",
      "Index: 1570 is coherent: False. Malleability=1.537506 Asynch=3.067386 Spawn=0\n",
      "Index: 1571 is coherent: False. Malleability=1.8525040000000002 Asynch=3.383427 Spawn=0\n",
      "Index: 1572 is coherent: True. Malleability=3.1738519999999997 Asynch=2.89953 Spawn=0\n",
      "Index: 1573 is coherent: False. Malleability=1.904988 Asynch=4.035998 Spawn=0\n",
      "Index: 1574 is coherent: False. Malleability=2.00393 Asynch=4.291486 Spawn=0\n",
      " \n",
      "Index: 1575 is coherent: True. Malleability=1.749072 Asynch=0.8841089999999999 Spawn=1\n",
      "Index: 1576 is coherent: True. Malleability=1.6744309999999998 Asynch=0.87253 Spawn=1\n",
      "Index: 1577 is coherent: True. Malleability=1.796864 Asynch=0.8763 Spawn=1\n",
      "Index: 1578 is coherent: True. Malleability=1.68465 Asynch=0.875474 Spawn=1\n",
      "Index: 1579 is coherent: True. Malleability=1.9556179999999999 Asynch=0.9897589999999999 Spawn=1\n",
      " \n",
      "Index: 1580 is coherent: False. Malleability=3.476145 Asynch=6.448043 Spawn=0\n",
      "Index: 1581 is coherent: False. Malleability=3.688714 Asynch=6.467988 Spawn=0\n",
      "Index: 1582 is coherent: False. Malleability=3.1133189999999997 Asynch=6.19727 Spawn=0\n",
      "Index: 1583 is coherent: False. Malleability=4.3661010000000005 Asynch=6.847986 Spawn=0\n",
      "Index: 1584 is coherent: False. Malleability=3.230851 Asynch=6.424841 Spawn=0\n",
      " \n",
      "Index: 1585 is coherent: False. Malleability=1.729289 Asynch=3.073685 Spawn=0\n",
      "Index: 1586 is coherent: False. Malleability=1.963202 Asynch=3.45176 Spawn=0\n",
      "Index: 1587 is coherent: False. Malleability=1.683734 Asynch=5.338994 Spawn=0\n",
      "Index: 1588 is coherent: False. Malleability=1.972815 Asynch=3.979184 Spawn=0\n",
      "Index: 1589 is coherent: False. Malleability=1.724706 Asynch=3.651833 Spawn=0\n",
      " \n",
      "Index: 1590 is coherent: True. Malleability=3.3512370000000002 Asynch=3.047339 Spawn=1\n",
      "Index: 1591 is coherent: True. Malleability=3.475911 Asynch=2.964457 Spawn=1\n",
      "Index: 1592 is coherent: True. Malleability=3.4724139999999997 Asynch=2.661818 Spawn=1\n",
      "Index: 1593 is coherent: True. Malleability=3.390257 Asynch=3.024463 Spawn=1\n",
      "Index: 1594 is coherent: True. Malleability=3.434139 Asynch=2.8778740000000003 Spawn=1\n",
      " \n",
      "Index: 1595 is coherent: True. Malleability=4.063648000000001 Asynch=2.90338 Spawn=1\n",
      "Index: 1596 is coherent: True. Malleability=4.067425 Asynch=2.925955 Spawn=1\n",
      "Index: 1597 is coherent: True. Malleability=3.9641740000000003 Asynch=2.846374 Spawn=1\n",
      "Index: 1598 is coherent: True. Malleability=4.104002 Asynch=2.99079 Spawn=1\n",
      "Index: 1599 is coherent: True. Malleability=4.23859 Asynch=3.106642 Spawn=1\n",
      " \n",
      "Index: 1600 is coherent: False. Malleability=6.702343 Asynch=6.79036 Spawn=0\n",
      "Index: 1601 is coherent: False. Malleability=4.41458 Asynch=5.602152 Spawn=0\n",
      "Index: 1602 is coherent: False. Malleability=6.423714 Asynch=7.883781 Spawn=0\n",
      "Index: 1603 is coherent: False. Malleability=4.510467 Asynch=5.874295 Spawn=0\n",
      "Index: 1604 is coherent: False. Malleability=6.6039069999999995 Asynch=8.451801 Spawn=0\n",
      " \n",
      "Index: 1605 is coherent: True. Malleability=2.262179 Asynch=1.868676 Spawn=1\n",
      "Index: 1606 is coherent: True. Malleability=3.0112550000000002 Asynch=1.91297 Spawn=1\n",
      "Index: 1607 is coherent: True. Malleability=2.516387 Asynch=1.8869240000000003 Spawn=1\n",
      "Index: 1608 is coherent: True. Malleability=2.5385469999999994 Asynch=1.9935100000000001 Spawn=1\n",
      "Index: 1609 is coherent: True. Malleability=2.368354 Asynch=1.917896 Spawn=1\n",
      " \n",
      "Index: 1610 is coherent: True. Malleability=4.302139 Asynch=3.2186600000000003 Spawn=1\n",
      "Index: 1611 is coherent: True. Malleability=4.283345000000001 Asynch=3.2142290000000004 Spawn=1\n",
      "Index: 1612 is coherent: True. Malleability=4.444826 Asynch=3.140667 Spawn=1\n",
      "Index: 1613 is coherent: True. Malleability=4.59038 Asynch=3.259385 Spawn=1\n",
      "Index: 1614 is coherent: True. Malleability=4.084968 Asynch=2.991473 Spawn=1\n",
      " \n",
      "Index: 1615 is coherent: True. Malleability=1.89209 Asynch=1.094583 Spawn=1\n",
      "Index: 1616 is coherent: True. Malleability=2.0755109999999997 Asynch=1.062634 Spawn=1\n",
      "Index: 1617 is coherent: True. Malleability=1.995675 Asynch=1.078886 Spawn=1\n",
      "Index: 1618 is coherent: True. Malleability=1.9096479999999998 Asynch=0.968244 Spawn=1\n",
      "Index: 1619 is coherent: True. Malleability=1.837723 Asynch=0.97243 Spawn=1\n",
      " \n",
      "Index: 1620 is coherent: False. Malleability=2.538924 Asynch=4.370228 Spawn=0\n",
      "Index: 1621 is coherent: False. Malleability=2.916216 Asynch=4.818579 Spawn=0\n",
      "Index: 1622 is coherent: False. Malleability=2.359109 Asynch=3.795357 Spawn=0\n",
      "Index: 1623 is coherent: False. Malleability=3.52334 Asynch=4.81856 Spawn=0\n",
      "Index: 1624 is coherent: False. Malleability=2.922164 Asynch=3.653764 Spawn=0\n",
      " \n",
      "Index: 1625 is coherent: True. Malleability=1.540114 Asynch=0.7154309999999999 Spawn=1\n",
      "Index: 1626 is coherent: True. Malleability=1.763166 Asynch=0.802786 Spawn=1\n",
      "Index: 1627 is coherent: True. Malleability=1.62408 Asynch=0.696769 Spawn=1\n",
      "Index: 1628 is coherent: True. Malleability=1.645255 Asynch=0.716688 Spawn=1\n",
      "Index: 1629 is coherent: True. Malleability=2.025736 Asynch=1.125662 Spawn=1\n",
      " \n",
      "Index: 1630 is coherent: False. Malleability=2.283263 Asynch=3.035638 Spawn=0\n",
      "Index: 1631 is coherent: False. Malleability=2.404269 Asynch=3.4184609999999997 Spawn=0\n",
      "Index: 1632 is coherent: True. Malleability=3.448027 Asynch=3.3989450000000003 Spawn=0\n",
      "Index: 1633 is coherent: False. Malleability=3.308598 Asynch=3.494518 Spawn=0\n",
      "Index: 1634 is coherent: False. Malleability=2.3181540000000003 Asynch=2.803335 Spawn=0\n",
      " \n",
      "Index: 1635 is coherent: True. Malleability=3.9464189999999997 Asynch=2.8120049999999996 Spawn=1\n",
      "Index: 1636 is coherent: True. Malleability=3.937976 Asynch=2.842751 Spawn=1\n",
      "Index: 1637 is coherent: True. Malleability=4.289765 Asynch=3.10404 Spawn=1\n",
      "Index: 1638 is coherent: True. Malleability=4.21735 Asynch=3.0832550000000003 Spawn=1\n",
      "Index: 1639 is coherent: True. Malleability=4.064209 Asynch=2.942727 Spawn=1\n",
      " \n",
      "Index: 1640 is coherent: True. Malleability=2.845876 Asynch=1.70094 Spawn=1\n",
      "Index: 1641 is coherent: True. Malleability=2.57076 Asynch=1.607373 Spawn=1\n",
      "Index: 1642 is coherent: True. Malleability=2.588778 Asynch=1.6853690000000001 Spawn=1\n",
      "Index: 1643 is coherent: True. Malleability=2.6292560000000003 Asynch=1.653027 Spawn=1\n",
      "Index: 1644 is coherent: True. Malleability=2.73781 Asynch=1.7191279999999998 Spawn=1\n",
      " \n",
      "Index: 1645 is coherent: True. Malleability=5.117718 Asynch=4.25915 Spawn=1\n",
      "Index: 1646 is coherent: True. Malleability=4.74117 Asynch=3.9213620000000002 Spawn=1\n",
      "Index: 1647 is coherent: True. Malleability=5.280582 Asynch=4.42888 Spawn=1\n",
      "Index: 1648 is coherent: True. Malleability=5.235174000000001 Asynch=4.330061000000001 Spawn=1\n",
      "Index: 1649 is coherent: True. Malleability=5.032131 Asynch=3.9513879999999997 Spawn=1\n",
      " \n",
      "Index: 1650 is coherent: True. Malleability=1.484439 Asynch=0.759309 Spawn=1\n",
      "Index: 1651 is coherent: True. Malleability=2.370422 Asynch=0.624502 Spawn=1\n",
      "Index: 1652 is coherent: True. Malleability=2.429891 Asynch=0.869991 Spawn=1\n",
      "Index: 1653 is coherent: True. Malleability=1.490864 Asynch=0.651597 Spawn=1\n",
      "Index: 1654 is coherent: True. Malleability=1.490072 Asynch=0.733581 Spawn=1\n",
      " \n",
      "Index: 1655 is coherent: False. Malleability=1.9952509999999999 Asynch=5.771406 Spawn=0\n",
      "Index: 1656 is coherent: False. Malleability=1.943299 Asynch=6.320076 Spawn=0\n",
      "Index: 1657 is coherent: False. Malleability=1.890302 Asynch=6.504025 Spawn=0\n",
      "Index: 1658 is coherent: False. Malleability=1.964078 Asynch=5.776004 Spawn=0\n",
      "Index: 1659 is coherent: False. Malleability=1.923498 Asynch=5.668022 Spawn=0\n",
      " \n",
      "Index: 1660 is coherent: False. Malleability=1.8168380000000002 Asynch=6.496023 Spawn=0\n",
      "Index: 1661 is coherent: False. Malleability=1.964057 Asynch=6.287969 Spawn=0\n",
      "Index: 1662 is coherent: False. Malleability=1.811903 Asynch=6.575951 Spawn=0\n",
      "Index: 1663 is coherent: False. Malleability=1.8514270000000002 Asynch=6.255169 Spawn=0\n",
      "Index: 1664 is coherent: False. Malleability=1.900158 Asynch=6.742603 Spawn=0\n",
      " \n",
      "Index: 1665 is coherent: True. Malleability=4.899266 Asynch=3.9785479999999995 Spawn=1\n",
      "Index: 1666 is coherent: True. Malleability=4.577003 Asynch=3.579083 Spawn=1\n",
      "Index: 1667 is coherent: True. Malleability=4.715743 Asynch=3.852379 Spawn=1\n",
      "Index: 1668 is coherent: True. Malleability=4.5496490000000005 Asynch=3.72489 Spawn=1\n",
      "Index: 1669 is coherent: True. Malleability=4.629158 Asynch=3.711102 Spawn=1\n",
      " \n",
      "Index: 1670 is coherent: True. Malleability=3.837922 Asynch=2.356389 Spawn=1\n",
      "Index: 1671 is coherent: True. Malleability=4.539524 Asynch=2.589285 Spawn=1\n",
      "Index: 1672 is coherent: True. Malleability=4.697198 Asynch=2.6870459999999996 Spawn=1\n",
      "Index: 1673 is coherent: True. Malleability=4.7957719999999995 Asynch=2.8783650000000005 Spawn=1\n",
      "Index: 1674 is coherent: True. Malleability=3.834918 Asynch=2.687336 Spawn=1\n",
      " \n",
      "Index: 1675 is coherent: True. Malleability=3.186931 Asynch=3.142331 Spawn=1\n",
      "Index: 1676 is coherent: True. Malleability=3.481315 Asynch=3.4371099999999997 Spawn=1\n",
      "Index: 1677 is coherent: True. Malleability=3.368236 Asynch=3.323944 Spawn=1\n",
      "Index: 1678 is coherent: True. Malleability=2.974588 Asynch=2.9266880000000004 Spawn=1\n",
      "Index: 1679 is coherent: True. Malleability=3.1573640000000003 Asynch=3.11325 Spawn=1\n",
      " \n"
     ]
    }
   ],
   "source": [
    "#FIXME Reescribir a comprobar suma de iters async contra T_Malleability\n",
    "#for index in range(len(configurations)):\n",
    "    #if configurations[index][0] > 0:\n",
    "        #configM = configurations_simple[index]\n",
    "        #configL = configurations[index]\n",
    "        #grouped_aggM.loc[(*configM,slice(None)),'Resize_Coherency'] = (grouped_aggLAsynch.loc[(*configL,slice(None))]['T_sum'] / grouped_aggM.loc[(*configM,slice(None))]['T_Malleability']).values\n",
    "test=dfM[(dfM.Asynch_Iters > 0)]\n",
    "for index in range(len(test)):\n",
    "    aux_value = test[\"T_iter\"].values[index][-test[\"Asynch_Iters\"].values[index]:]\n",
    "    aux_value = np.sum(aux_value)\n",
    "    coherent = test[\"T_Malleability\"].values[index] > aux_value\n",
    "    print(\"Index: \" +str(index) + \" is coherent: \" + str(coherent) + \". Malleability=\" + str(test[\"T_Malleability\"].values[index]) + \" Asynch=\"+ str(aux_value) + \\\n",
    "          \" Spawn=\"+str(test[\"Spawn_Method\"].values[index]))\n",
    "    if index%5==4:\n",
    "        print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_np = [\"T_total\"]\n",
    "separated_np = [\"T_Malleability\", \"T_Redistribution\", \"T_spawn\", \"T_SR\", \"T_AR\", \"Alpha\", \"Omega\"]\n",
    "\n",
    "def get_np_ns_data(tipo, data_aux, used_config, np_aux, ns_aux):\n",
    "    dataLists=[]\n",
    "    for config in used_config:\n",
    "        if tipo in grouped_np:\n",
    "            config.append((np_aux,ns_aux))\n",
    "        elif tipo in separated_np:\n",
    "            config.append(np_aux)\n",
    "            config.append(ns_aux)\n",
    "        \n",
    "        if tuple(config) in data_aux.index:\n",
    "            aux_value = data_aux.loc[tuple(config),tipo]\n",
    "            if isinstance(aux_value, pd.Series):\n",
    "                aux_value = aux_value.values[0]\n",
    "            if aux_value == 0: #Values of zero indicate it was not performed\n",
    "                aux_value = float('infinity')\n",
    "        else: # This configuration is not present in the dataset\n",
    "            aux_value = float('infinity')\n",
    "        dataLists.append(aux_value)\n",
    "        config.pop()\n",
    "        if tipo in separated_np:\n",
    "            config.pop()\n",
    "    return dataLists\n",
    "\n",
    "def get_config_data(tipo, data_aux, config):\n",
    "    dataLists=[]\n",
    "    for ns_aux in processes:\n",
    "        for np_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                \n",
    "                if tipo in grouped_np:\n",
    "                    config.append((np_aux,ns_aux))\n",
    "                elif tipo in separated_np:\n",
    "                    config.append(np_aux)\n",
    "                    config.append(ns_aux)\n",
    "                if tuple(config) in data_aux.index:\n",
    "                    aux_value = data_aux.loc[tuple(config),tipo]\n",
    "                    if isinstance(aux_value, pd.Series):\n",
    "                        aux_value = aux_value.values[0]\n",
    "                    if aux_value == 0: #Values of zero indicate it was not performed\n",
    "                        aux_value = float('infinity')\n",
    "                else: # This configuration is not present in the dataset\n",
    "                    aux_value = float('infinity')\n",
    "                dataLists.append(aux_value)\n",
    "                config.pop()\n",
    "                if tipo in separated_np:\n",
    "                    config.pop()\n",
    "    return dataLists\n",
    "\n",
    "def get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux):\n",
    "    dataLists=[]\n",
    "    if tipo in grouped_np:\n",
    "        tuple_data = (np_aux, ns_aux)\n",
    "        df_npns_aux = df_check.loc[(df_check['Groups']==tuple_data)]\n",
    "    elif tipo in separated_np:\n",
    "        df_npns_aux = df_check.loc[(df_check['NP']==np_aux)]\n",
    "        df_npns_aux = df_npns_aux.loc[(df_npns_aux['NC']==ns_aux)]\n",
    "        \n",
    "    for config in used_config:\n",
    "        df_config_aux = df_npns_aux\n",
    "        for index in range(len(config)):\n",
    "            aux_name = parameters_names[index]\n",
    "            aux_value = config[index]\n",
    "            df_config_aux = df_config_aux.loc[(df_config_aux[aux_name] == aux_value)]\n",
    "                \n",
    "        aux_value = list(df_config_aux[tipo])\n",
    "        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",
    "                dataLists.append(aux_value)\n",
    "    return dataLists\n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_normality(df_check, tipo, used_config, fast=True):\n",
    "    normality_array=[True] * (len(processes) * (len(processes)-1))\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",
    "                st,p = stats.shapiro(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",
    "                    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 compute_global_stat_difference(dataLists, parametric, np_aux, ns_aux):\n",
    "    if parametric:\n",
    "        st,p=stats.f_oneway(*dataLists)\n",
    "    else:\n",
    "        st,p=stats.kruskal(*dataLists)\n",
    "    if p > significance_value:\n",
    "        print(\"For NP \" + str(np_aux) + \" and \" + str(ns_aux) + \" is accepted H0\")\n",
    "        return True # Equal values || Accept H0\n",
    "    return False # Some groups are different || Reject H0\n",
    "\n",
    "def compute_global_posthoc(dataLists, parametric):\n",
    "    data_stats=[]\n",
    "    data_stats2=[]\n",
    "    ini=0\n",
    "    end=len(dataLists)\n",
    "    if parametric:\n",
    "        df_aux = sp.posthoc_ttest(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.mean(dataLists[i]))\n",
    "            \n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < significance_value: # Different means || Reject H1\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    else:\n",
    "        df_aux = sp.posthoc_conover(dataLists)\n",
    "        df_Res = df_aux.copy()\n",
    "        for i in range(ini,end):\n",
    "            data_stats.append(np.median(dataLists[i]))\n",
    "            data_stats2.append(stats.iqr(dataLists[i],axis=0))\n",
    "            for j in range(ini,end):\n",
    "                if df_Res.iat[i,j] < significance_value: # Different medians || Reject H1\n",
    "                    df_Res.iat[i, j] = True\n",
    "                else:\n",
    "                    df_Res.iat[i, j] = False\n",
    "    #print(df_Res)\n",
    "    #print(df_aux)\n",
    "    #print(data_stats)\n",
    "    #print(data_stats2)\n",
    "    #aux_value = min(data_stats)\n",
    "    #print(data_stats.index(aux_value))\n",
    "    return df_Res, data_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "    for np_aux in processes:\n",
    "        for ns_aux in processes:\n",
    "            if np_aux != ns_aux:\n",
    "                tc_boundary, boundaries = create_group_boundary(rms_boundary, np_aux, ns_aux)\n",
    "                \n",
    "                #Get all values for particular config with these number of processes\n",
    "                dataLists = get_df_np_ns_data(df_check, tipo, used_config, np_aux, ns_aux)\n",
    "                equal_set = compute_global_stat_difference(dataLists, use_parametric, np_aux, ns_aux)\n",
    "                if equal_set:\n",
    "                    aux_data = list(range(len(used_config))) # All data is equal\n",
    "                else:\n",
    "                    res_aux, times_aux = compute_global_posthoc(dataLists, use_parametric)\n",
    "                    aux_data = get_stat_differences(times_aux, res_aux, boundaries, tc_boundary)\n",
    "                \n",
    "                results.append(aux_data)\n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "checked_type='T_spawn'\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",
    "    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",
    "#Results is a 2 dimensional array. First dimension indicates lists of winners of a particulal number of processes (NP->NC). \n",
    "#Second dimension is an ordered preference of indexes in the array configurations.\n",
    "print(results)\n",
    "print(len(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1 10 10 10 10 10 10]\n",
      " [10 -1 10 10 10 10 10]\n",
      " [ 3 10 -1 10 10 10 10]\n",
      " [10 10 10 -1 10 10 10]\n",
      " [10  3 10 10 -1 10 10]\n",
      " [10 10 10 10 10 -1 10]\n",
      " [10 10 10 10 10 10 12]]\n"
     ]
    }
   ],
   "source": [
    "#Lista de indices de mayor a menor según el total de ocurrencias\n",
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "aux_keys, aux_counts = np.unique(aux_array, return_counts=True)\n",
    "aux_ordered_index=list(reversed(np.argsort(aux_counts)))\n",
    "\n",
    "#Lista de indices de mayor a menor según el total de ocurrencias del primero de cada grupo\n",
    "aux_array = [0] * len(results)\n",
    "for index in range(len(results)):\n",
    "    aux_array[index] = results[index][0]\n",
    "aux_keys_best, aux_counts_best = np.unique(aux_array, return_counts = True)\n",
    "aux_ordered_best_index=list(reversed(np.argsort(aux_counts_best)))\n",
    "\n",
    "def heatmap_get_best(index, ordered_array, keys_array, counts_array, prefer_winner=False):\n",
    "    valid_candidates_indexes = []\n",
    "    prev_counts = -1\n",
    "    for tested_index in ordered_array:\n",
    "        if keys_array[tested_index] in results[index]:\n",
    "            if counts_array[tested_index] >= prev_counts:\n",
    "                prev_counts = counts_array[tested_index]\n",
    "                valid_candidates_indexes.append(tested_index)\n",
    "            else:\n",
    "                break\n",
    "                \n",
    "    if prefer_winner: # Si esta activo, en caso de empate en ocurrencias se selecciona el menor tiempo\n",
    "        for tested_index in results[index]:\n",
    "            if tested_index in valid_candidates_indexes:\n",
    "                return tested_index\n",
    "    return min(valid_candidates_indexes) # En caso de empate se devuelve el que tiene menor valor (Suele ser la config más simple)\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "used_aux=0\n",
    "heatmap=np.zeros((len(processes),len(processes))).astype(int)\n",
    "\n",
    "if select_first_winner:\n",
    "    for i in range(len(processes)):\n",
    "        for j in range(len(processes)):\n",
    "            if i==j:\n",
    "                heatmap[i][j]=-1\n",
    "                used_aux+=1\n",
    "            else:\n",
    "                results_index = i*len(processes) + j - used_aux\n",
    "                heatmap[i][j] = results[results_index][0]\n",
    "else:\n",
    "    for i in range(len(processes)):\n",
    "        for j in range(len(processes)):\n",
    "            if i==j:\n",
    "                heatmap[i][j]=-1\n",
    "                used_aux+=1\n",
    "            else:\n",
    "                results_index = i*len(processes) + j - used_aux\n",
    "                index = heatmap_get_best(results_index, aux_ordered_index, aux_keys, aux_counts, prefer_first_winner)\n",
    "                heatmap[i][j]=aux_keys[index]\n",
    "                #index = heatmap_get_best(results_index, aux_ordered_best_index, aux_keys_best, aux_counts_best, prefer_first_winner)\n",
    "                #heatmap[i][j]=aux_keys_best[index]\n",
    "heatmap[-1][-1]=len(used_config)\n",
    "print(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5287/1944789397.py:49: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(['']+processes, fontsize=36)\n",
      "/tmp/ipykernel_5287/1944789397.py:50: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['']+processes, fontsize=36)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: Heatmap_T_spawn.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea un heatmap teniendo en cuenta los colores anteriores\n",
    "f=plt.figure(figsize=(24, 12))\n",
    "ax=f.add_subplot(111)\n",
    "\n",
    "myColors = (colors.to_rgba(\"white\"), \n",
    "    colors.to_rgba(\"green\"), \n",
    "    colors.to_rgba(\"darkgreen\"),  # En lugar de \"darkgreen\"\n",
    "    colors.to_rgba(\"red\"), \n",
    "    colors.to_rgba(\"darkred\"),  # En lugar de \"darkred\"\n",
    "    colors.to_rgba(\"mediumseagreen\"),  # En lugar de \"mediumseagreen\"\n",
    "    colors.to_rgba(\"seagreen\"),  # En lugar de \"seagreen\"\n",
    "    colors.to_rgba(\"palegreen\"), \n",
    "    colors.to_rgba(\"springgreen\"), \n",
    "    colors.to_rgba(\"indianred\"), \n",
    "    colors.to_rgba(\"firebrick\"),\n",
    "    colors.to_rgba(\"darkgoldenrod\"),\n",
    "    colors.to_rgba(\"saddlebrown\"),\n",
    "    colors.to_rgba(\"white\"))\n",
    "cmap = LinearSegmentedColormap.from_list('Custom', myColors, len(myColors))\n",
    "\n",
    "im = ax.imshow(heatmap,cmap=cmap,interpolation='nearest')\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "used_aux=0\n",
    "results_str = get_heatmap_strings(heatmap)\n",
    "for i in range(len(processes)):\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            aux_color=\"white\"\n",
    "            if 0 <= heatmap[i, j] <= 1 or 4 <= heatmap[i, j] <= 7: # El 1 puede necesitar texto en negro\n",
    "                aux_color=\"black\"\n",
    "            results_index = i*len(processes) +j-used_aux\n",
    "            if len(results_str[results_index]) == 1:\n",
    "                text = results_str[results_index][0]\n",
    "                ax.text(j, i, text, ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n",
    "            else:\n",
    "                add_aux = 0.33\n",
    "                for line in range(len(results_str[results_index])):\n",
    "                    i_range = i - 0.5 + add_aux\n",
    "                    ax.text(j, i_range, results_str[results_index][line],\n",
    "                            ha=\"center\", va=\"center\", color=aux_color, fontsize=36)\n",
    "                    add_aux+=0.33\n",
    "        else:\n",
    "            used_aux+=1\n",
    "\n",
    "ax.set_ylabel(\"NP\", fontsize=36)\n",
    "ax.set_xlabel(\"NC\", fontsize=36)\n",
    "\n",
    "ax.set_xticklabels(['']+processes, fontsize=36)\n",
    "ax.set_yticklabels(['']+processes, fontsize=36)\n",
    "\n",
    "\n",
    "labelsMethods_aux = ['Baseline - AllS (0)', 'Baseline - P2PS (1)',\n",
    "                    'Merge - AllS (2)','Merge - P2PS (3)',\n",
    "                    'Baseline - AllA (4)', 'Baseline - AllT (5)','Baseline - P2PA (6)','Baseline - P2PT (7)',\n",
    "                    'Merge - AllA (8)','Merge - AllT (9)','Merge - P2PA (10)','Merge - P2PT (11)']\n",
    "colorbar=f.colorbar(im, ax=ax)\n",
    "tick_bar = []\n",
    "for i in range(len(used_config)):\n",
    "    tick_bar.append(0.37 + i*0.92) #Config de 12 valores\n",
    "colorbar.set_ticks(tick_bar) \n",
    "colorbar.set_ticklabels(labelsMethods_aux)\n",
    "colorbar.ax.tick_params(labelsize=32)\n",
    "#\n",
    "\n",
    "f.tight_layout()\n",
    "print(\"Filename: Heatmap_\"+tipo+\".png\")\n",
    "f.savefig(\"Images/Heatmap_\"+tipo+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  6  8 10 11]\n",
      "[ 1 17 20  1  1 22 27  1]\n",
      "[ 2  3  8 10 11]\n",
      "[ 5  9 11 16  1]\n"
     ]
    }
   ],
   "source": [
    "aux_array = []\n",
    "for data in results:\n",
    "    aux_array+=data\n",
    "aux_results, aux_counts = np.unique(aux_array, return_counts=True)\n",
    "print(aux_results)\n",
    "print(aux_counts)\n",
    "\n",
    "aux_array = [0] * len(results)\n",
    "for index in range(len(results)):\n",
    "    aux_array[index] = results[index][0]\n",
    "aux_results, aux_counts = np.unique(aux_array, return_counts = True)\n",
    "print(aux_results)\n",
    "print(aux_counts)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente código asume que para cada número de procesos padre/hijo existen valores en todas las configuraciones que se van a probar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "used_direction='e'\n",
    "test_parameter='omega' #Valores son \"alpha\" o \"omega\"\n",
    "\n",
    "if test_parameter == 'alpha':\n",
    "    name_fig=\"Alpha_\"\n",
    "    real_parameter='Alpha'\n",
    "    name_legend = \"Values of α\"\n",
    "    used_config = configurations_simple\n",
    "    data_aux = grouped_aggM[grouped_aggM[real_parameter] > 0]\n",
    "elif test_parameter == 'omega':\n",
    "    name_fig=\"Omega_\"\n",
    "    real_parameter='Omega'\n",
    "    name_legend = \"Values of ω\"\n",
    "    used_config = configurations\n",
    "    data_aux = grouped_aggLAsynch[grouped_aggLAsynch[real_parameter] > 0]\n",
    "if used_direction=='s':\n",
    "    data_aux=data_aux.query('NP > NC')\n",
    "    name_fig= name_fig+\"Shrink\"\n",
    "elif used_direction=='e':\n",
    "    data_aux=data_aux.query('NP < NC')\n",
    "    name_fig= name_fig+\"Expand\"\n",
    "elif used_direction=='a':\n",
    "    name_fig= name_fig+\"All\"    \n",
    "\n",
    "plot_data = []\n",
    "for config in used_config:\n",
    "    if config[0] > 0:\n",
    "        dataLists = get_config_data(real_parameter, data_aux, config)\n",
    "        dataLists = list(filter(lambda x: x != float('infinity'), dataLists))\n",
    "        plot_data.append(dataLists)\n",
    "\n",
    "labels_aux = []\n",
    "for ns_aux in processes:\n",
    "    for np_aux in processes:\n",
    "        if used_direction=='s' and np_aux > ns_aux or used_direction=='e' and np_aux < ns_aux or used_direction=='a' and np_aux != ns_aux:\n",
    "            new_label = \"(\" + str(np_aux) + \",\" + str(ns_aux) + \")\"\n",
    "            labels_aux.append(new_label)\n",
    "#print(data_aux[real_parameter])\n",
    "#print(plot_data)\n",
    "#print(len(plot_data))\n",
    "#print(labels_aux)\n",
    "#print(len(labels_aux))\n",
    "labelsMethods_aux = ['Baseline - AllA', 'Baseline - AllT','Baseline - P2PA','Baseline - P2PT',\n",
    "                    'Merge - AllA','Merge - AllT','Merge - P2PA','Merge - P2PT']\n",
    "\n",
    "f=plt.figure(figsize=(22, 14))\n",
    "ax=f.add_subplot(111)\n",
    "x = np.arange(len(labels_aux))\n",
    "for index in range(len(plot_data)):\n",
    "    array_aux = plot_data[index]\n",
    "    ax.plot(x, array_aux, color=colors_m[index%len(colors_m)], linestyle=linestyle_m[index%len(linestyle_m)], \\\n",
    "        marker=markers_m[index%len(markers_m)], markersize=18, label=labelsMethods_aux[index])\n",
    "\n",
    "ax.set_xlabel(\"(NP,NC)\", fontsize=36)\n",
    "ax.set_ylabel(name_legend, fontsize=36)\n",
    "plt.xticks(x, labels_aux,rotation=90)\n",
    "ax.tick_params(axis='both', which='major', labelsize=36)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=36)\n",
    "plt.legend(loc='best', fontsize=30,ncol=2,framealpha=0.8)\n",
    "        \n",
    "f.tight_layout()\n",
    "f.savefig(\"Images/LinePlot_\"+name_fig+\".png\", format=\"png\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, subplot_kw={'projection': '3d'})\n",
    "\n",
    "# Get the test data\n",
    "#X, Y, Z = axes3d.get_test_data(0.05)\n",
    "\n",
    "aux = grouped_aggG.loc[u_sols[0],'T_total']\n",
    "Z = [None] * len(processes)\n",
    "X, Y = np.meshgrid(processes, processes)\n",
    "removed_index = 0\n",
    "for i in range(len(processes)):\n",
    "    Z[i] = [0] * len(processes)\n",
    "    for j in range(len(processes)):\n",
    "        if i!=j:\n",
    "            real_i = i - removed_index\n",
    "            real_j = j - removed_index\n",
    "            Z[i][j] = aux.values[real_i*len(processes)+real_j]\n",
    "        else:\n",
    "            Z[i][j] = 0\n",
    "            removed_index += 1  \n",
    "Z = np.array(Z)\n",
    "\n",
    "ax.plot_wireframe(X, Y, Z, rstride=20, cstride=10)\n",
    "ax.set_proj_type('ortho')  # FOV = 0 deg\n",
    "ax.set_title(\"'ortho'\\nfocal_length = ∞\", fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "El siguiente código es para utilizar Dask. Una versión que paraleliza una serie de tareas de Pandas.\n",
    "Tras llamar a compute se realizan todas las tareas que se han pedido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd\n",
    "ddf = dd.from_pandas(dfL[(dfL.Asynch_Iters == False)], npartitions=10)\n",
    "group = ddf.groupby('NP')['T_iter']\n",
    "grouped_aggLSynch = group.agg(['mean'])\n",
    "grouped_aggLSynch = grouped_aggLSynch.rename(columns={'mean':'T_iter'}) \n",
    "grouped_aggLSynch = grouped_aggLSynch.compute()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.75676344 0.03701228 3.76439349]\n",
      "[0.20630784 0.96375203 0.04733157]\n",
      "[2.17054203 1.7721764  0.83496214 0.27720765 0.59800783 0.42146685]\n",
      "[0.19532397 0.24843587 0.47871884 0.76709983 0.5796655  0.67410377]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[9.87, 9.03, 6.81],\n",
    "              [7.18, 8.35, 7.00],\n",
    "              [8.39, 7.58, 7.68],\n",
    "              [7.45, 6.33, 9.35],\n",
    "              [6.41, 7.10, 9.33],\n",
    "              [8.00, 8.24, 8.44]])\n",
    "b = np.array([[6.35, 7.30, 7.16],\n",
    "              [6.65, 6.68, 7.63],\n",
    "              [5.72, 7.73, 6.72],\n",
    "              [7.01, 9.19, 7.41],\n",
    "              [7.75, 7.87, 8.30],\n",
    "              [6.90, 7.97, 6.97]])\n",
    "c = np.array([[3.31, 8.77, 1.01],\n",
    "              [8.25, 3.24, 3.62],\n",
    "              [6.32, 8.81, 5.19],\n",
    "              [7.48, 8.83, 8.91],\n",
    "              [8.59, 6.01, 6.07],\n",
    "              [3.07, 9.72, 7.48]])\n",
    "my_daa_aux = [a,b,c]\n",
    "\n",
    "F1, p_aux = stats.f_oneway(a, b, c)\n",
    "F2, p_aux2 = stats.f_oneway(*my_daa_aux,axis=0)\n",
    "\n",
    "print(F1)\n",
    "print(p_aux)\n",
    "print(F2)\n",
    "print(p_aux2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Stage_Bytes</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_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.08095, 0.076509, 0.079877, 0.074691, 0.076...</td>\n",
       "      <td>(((0.010705, 0.001643, 0.000203, 0.067232), (0...</td>\n",
       "      <td>(1.347948,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.752765,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>80.055689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.083757, 0.069349, 0.068418, 0.065849, 0.06...</td>\n",
       "      <td>(((0.010718, 0.000521, 4.2e-05, 0.071717), (0....</td>\n",
       "      <td>(1.408781,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.780452,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>78.698831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.096849, 0.072226, 0.075321, 0.065634, 0.07...</td>\n",
       "      <td>(((0.010704, 0.001999, 0.000233, 0.079332), (0...</td>\n",
       "      <td>(1.336949,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.526026,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>78.275454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.07964, 0.070345, 0.073844, 0.086362, 0.072...</td>\n",
       "      <td>(((0.010704, 0.003768, 0.000384, 0.062777), (0...</td>\n",
       "      <td>(1.44455,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.688739,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>77.281291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3947883504</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>((0.098563, 0.068683, 0.090294, 0.083441, 0.08...</td>\n",
       "      <td>(((0.010716, 0.000262, 0.00023, 0.086761), (0....</td>\n",
       "      <td>(1.467106,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>(0.592875,)</td>\n",
       "      <td>(0,)</td>\n",
       "      <td>79.911654</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>1675</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653634, 0.634598, 0.633836, 0.634582, 0.63...</td>\n",
       "      <td>(((0.622565, 7e-06, 2e-06, 0.03106), (0.62308,...</td>\n",
       "      <td>(1.34195,)</td>\n",
       "      <td>(1.074329,)</td>\n",
       "      <td>(0.044368,)</td>\n",
       "      <td>(1.800613,)</td>\n",
       "      <td>363.805409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653119, 0.633832, 0.633014, 0.633593, 0.63...</td>\n",
       "      <td>(((0.621538, 0.000137, 3e-06, 0.03144), (0.621...</td>\n",
       "      <td>(1.382511,)</td>\n",
       "      <td>(1.104917,)</td>\n",
       "      <td>(0.044006,)</td>\n",
       "      <td>(2.054798,)</td>\n",
       "      <td>360.893782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.652854, 0.633725, 0.632971, 0.633643, 0.63...</td>\n",
       "      <td>(((0.621539, 9.8e-05, 2e-06, 0.031214), (0.621...</td>\n",
       "      <td>(1.348554,)</td>\n",
       "      <td>(0.975715,)</td>\n",
       "      <td>(0.044106,)</td>\n",
       "      <td>(1.975576,)</td>\n",
       "      <td>359.383625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.652802, 0.633527, 0.633319, 0.633376, 0.63...</td>\n",
       "      <td>(((0.621599, 0.000231, 2e-06, 0.030969), (0.62...</td>\n",
       "      <td>(1.310184,)</td>\n",
       "      <td>(1.022675,)</td>\n",
       "      <td>(0.047264,)</td>\n",
       "      <td>(1.61714,)</td>\n",
       "      <td>362.372775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>100000</td>\n",
       "      <td>3.4</td>\n",
       "      <td>96.6</td>\n",
       "      <td>3947883503</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>...</td>\n",
       "      <td>(0, 8, 8, 33176880)</td>\n",
       "      <td>(500, 500)</td>\n",
       "      <td>(4, 0)</td>\n",
       "      <td>((0.653575, 0.634228, 0.634006, 0.63414, 0.633...</td>\n",
       "      <td>(((0.622518, 7e-06, 2e-06, 0.031046), (0.62287...</td>\n",
       "      <td>(1.364008,)</td>\n",
       "      <td>(1.040484,)</td>\n",
       "      <td>(0.043897,)</td>\n",
       "      <td>(1.749459,)</td>\n",
       "      <td>363.027286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_Resizes  Total_Groups  Total_Stages  Granularity    SDR   ADR  \\\n",
       "0                 1             2             4       100000  100.0   0.0   \n",
       "1                 1             2             4       100000  100.0   0.0   \n",
       "2                 1             2             4       100000  100.0   0.0   \n",
       "3                 1             2             4       100000  100.0   0.0   \n",
       "4                 1             2             4       100000  100.0   0.0   \n",
       "...             ...           ...           ...          ...    ...   ...   \n",
       "1675              1             2             4       100000    3.4  96.6   \n",
       "1676              1             2             4       100000    3.4  96.6   \n",
       "1677              1             2             4       100000    3.4  96.6   \n",
       "1678              1             2             4       100000    3.4  96.6   \n",
       "1679              1             2             4       100000    3.4  96.6   \n",
       "\n",
       "              DR Redistribution_Method Redistribution_Strategy Spawn_Method  \\\n",
       "0     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "1     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "2     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "3     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "4     3947883504                (0, 1)                  (1, 1)       (0, 0)   \n",
       "...          ...                   ...                     ...          ...   \n",
       "1675  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1676  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1677  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1678  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "1679  3947883503                (0, 1)                  (1, 2)       (0, 1)   \n",
       "\n",
       "      ...          Stage_Bytes       Iters Asynch_Iters  \\\n",
       "0     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "1     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "2     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "3     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "4     ...  (0, 8, 8, 33176880)  (500, 500)       (0, 0)   \n",
       "...   ...                  ...         ...          ...   \n",
       "1675  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1676  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1677  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1678  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "1679  ...  (0, 8, 8, 33176880)  (500, 500)       (4, 0)   \n",
       "\n",
       "                                                 T_iter  \\\n",
       "0     ((0.08095, 0.076509, 0.079877, 0.074691, 0.076...   \n",
       "1     ((0.083757, 0.069349, 0.068418, 0.065849, 0.06...   \n",
       "2     ((0.096849, 0.072226, 0.075321, 0.065634, 0.07...   \n",
       "3     ((0.07964, 0.070345, 0.073844, 0.086362, 0.072...   \n",
       "4     ((0.098563, 0.068683, 0.090294, 0.083441, 0.08...   \n",
       "...                                                 ...   \n",
       "1675  ((0.653634, 0.634598, 0.633836, 0.634582, 0.63...   \n",
       "1676  ((0.653119, 0.633832, 0.633014, 0.633593, 0.63...   \n",
       "1677  ((0.652854, 0.633725, 0.632971, 0.633643, 0.63...   \n",
       "1678  ((0.652802, 0.633527, 0.633319, 0.633376, 0.63...   \n",
       "1679  ((0.653575, 0.634228, 0.634006, 0.63414, 0.633...   \n",
       "\n",
       "                                               T_stages      T_spawn  \\\n",
       "0     (((0.010705, 0.001643, 0.000203, 0.067232), (0...  (1.347948,)   \n",
       "1     (((0.010718, 0.000521, 4.2e-05, 0.071717), (0....  (1.408781,)   \n",
       "2     (((0.010704, 0.001999, 0.000233, 0.079332), (0...  (1.336949,)   \n",
       "3     (((0.010704, 0.003768, 0.000384, 0.062777), (0...   (1.44455,)   \n",
       "4     (((0.010716, 0.000262, 0.00023, 0.086761), (0....  (1.467106,)   \n",
       "...                                                 ...          ...   \n",
       "1675  (((0.622565, 7e-06, 2e-06, 0.03106), (0.62308,...   (1.34195,)   \n",
       "1676  (((0.621538, 0.000137, 3e-06, 0.03144), (0.621...  (1.382511,)   \n",
       "1677  (((0.621539, 9.8e-05, 2e-06, 0.031214), (0.621...  (1.348554,)   \n",
       "1678  (((0.621599, 0.000231, 2e-06, 0.030969), (0.62...  (1.310184,)   \n",
       "1679  (((0.622518, 7e-06, 2e-06, 0.031046), (0.62287...  (1.364008,)   \n",
       "\n",
       "     T_spawn_real         T_SR         T_AR     T_total  \n",
       "0            (0,)  (0.752765,)         (0,)   80.055689  \n",
       "1            (0,)  (0.780452,)         (0,)   78.698831  \n",
       "2            (0,)  (0.526026,)         (0,)   78.275454  \n",
       "3            (0,)  (0.688739,)         (0,)   77.281291  \n",
       "4            (0,)  (0.592875,)         (0,)   79.911654  \n",
       "...           ...          ...          ...         ...  \n",
       "1675  (1.074329,)  (0.044368,)  (1.800613,)  363.805409  \n",
       "1676  (1.104917,)  (0.044006,)  (2.054798,)  360.893782  \n",
       "1677  (0.975715,)  (0.044106,)  (1.975576,)  359.383625  \n",
       "1678  (1.022675,)  (0.047264,)   (1.61714,)  362.372775  \n",
       "1679  (1.040484,)  (0.043897,)  (1.749459,)  363.027286  \n",
       "\n",
       "[2520 rows x 26 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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