test_7_test_hipotesis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Librerias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import json\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "from scipy import stats\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leer_json_y_convertir_a_np(path):\n",
    "    dict_total = {}\n",
    "    for carpeta in os.listdir(path):\n",
    "        carpeta_path = os.path.join(path, carpeta)\n",
    "        datos_np = []\n",
    "        if os.path.isdir(carpeta_path):\n",
    "            for archivo in os.listdir(carpeta_path):\n",
    "                archivo_path = os.path.join(carpeta_path, archivo)\n",
    "                if archivo.endswith('.json'):\n",
    "                    with open(archivo_path, 'r') as f:\n",
    "                        datos_json = json.load(f)\n",
    "                        # Convertir lista de listas a arreglo numpy\n",
    "                        datos_np.append(np.array(datos_json))\n",
    "        #print(carpeta)\n",
    "        dict_total[carpeta] = datos_np\n",
    "    return dict_total\n",
    "\n",
    "def promedio_y_desviacion(lista_de_arreglos):\n",
    "    # Convertir la lista de arreglos en un solo arreglo numpy tridimensional\n",
    "    arreglo_tridimensional = np.array(lista_de_arreglos)\n",
    "\n",
    "    # Calcular el promedio y la desviación estándar elemento por elemento\n",
    "    promedio = np.mean(arreglo_tridimensional, axis=0)\n",
    "    desviacion_estandar = np.std(arreglo_tridimensional, axis=0)\n",
    "\n",
    "    return promedio, desviacion_estandar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_de_hipotesis(paths, nick):\n",
    "  data = []\n",
    "  for path_i in paths:\n",
    "    dict_datos = leer_json_y_convertir_a_np(path_i)\n",
    "    list_to_tuple_level = []\n",
    "    for key in dict_datos:\n",
    "      array_list = dict_datos[key]\n",
    "      auc_mean, _ = promedio_y_desviacion(array_list)\n",
    "      flat_arr = auc_mean.flatten()\n",
    "      list_to_tuple_level.append(flat_arr)\n",
    "    # create a matrix stack:\n",
    "    satack_matrix = np.column_stack(tuple(list_to_tuple_level))\n",
    "    # test:\n",
    "    satack_matrix_t = satack_matrix.T\n",
    "    s,p = stats.friedmanchisquare(*satack_matrix_t)\n",
    "    data.append((path_i, s, p))\n",
    "  # Convert the list of tuples to a DataFrame\n",
    "  df = pd.DataFrame(data, columns=['path', 'statistical', 'p-value'])\n",
    "  df.to_excel(f'test_hipotesis_data/{nick}_data.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1Q:\n",
    "paths = ['resultados_1q_kaggle/3Layers/', \n",
    "         'resultados_1q_kaggle/5Layers/',\n",
    "        'resultados_1q_kaggle/10Layers/']\n",
    "test_de_hipotesis(paths, \"1q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2Q:\n",
    "paths = ['resultados_2q_kaggle/3Layers/', \n",
    "         'resultados_2q_kaggle/5Layers/',\n",
    "        'resultados_2q_kaggle/10Layers/' ]\n",
    "\n",
    "test_de_hipotesis(paths, \"2q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2Q-e:\n",
    "paths = ['resultados_2q_kaggle_entenglament/3Layers/', \n",
    "         'resultados_2q_kaggle_entenglament/5Layers/',\n",
    "         'resultados_2q_kaggle_entenglament/10Layers/']\n",
    "\n",
    "test_de_hipotesis(paths, \"2qe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4Q:\n",
    "paths = ['resultados_4q_kaggle/3Layers/' ,\n",
    "         'resultados_4q_kaggle/5Layers/' ,\n",
    "         'resultados_4q_kaggle/10Layers/' ]\n",
    "\n",
    "test_de_hipotesis(paths, \"4q\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4Q-e:\n",
    "paths = ['resultados_4q_kaggle_entenglament/3Layers/' ,\n",
    "         'resultados_4q_kaggle_entenglament/5Layers/' ,\n",
    "         'resultados_4q_kaggle_entenglament/10Layers/' ]\n",
    "\n",
    "test_de_hipotesis(paths, \"4qe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mean ranking and HT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def json_to_array(path):\n",
    "    \"\"\"\n",
    "    Lee archivos JSON desde una ruta especificada y convierte el contenido\n",
    "    de listas de listas a un array de numpy.\n",
    "    \n",
    "    Args:\n",
    "        path (str): La ruta a la carpeta que contiene los archivos JSON.\n",
    "    \n",
    "    Returns:\n",
    "        np.ndarray: Un array de numpy con el contenido de los archivos JSON.\n",
    "    \"\"\"\n",
    "    all_data = []\n",
    "\n",
    "    filepath = os.path.join(path)\n",
    "    with open(filepath, 'r') as file:\n",
    "        data = json.load(file)\n",
    "        all_data.extend(data)  # Suponiendo que cada archivo contiene una lista de listas\n",
    "\n",
    "    # Convertir la lista de listas a un array de numpy\n",
    "    array_data = np.array(all_data)\n",
    "    \n",
    "    return array_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def leer_json_y_convertir_a_np(path):\n",
    "    \n",
    "    dict_total = {}\n",
    "    for carpeta in os.listdir(path):\n",
    "        carpeta_path = os.path.join(path, carpeta)\n",
    "        datos_np = []\n",
    "        if os.path.isdir(carpeta_path):\n",
    "            for archivo in os.listdir(carpeta_path):\n",
    "                archivo_path = os.path.join(carpeta_path, archivo)\n",
    "                if archivo.endswith('.json'):\n",
    "                    with open(archivo_path, 'r') as f:\n",
    "                        datos_json = json.load(f)\n",
    "                        # Convertir lista de listas a arreglo numpy\n",
    "                        datos_np.append(np.array(datos_json))\n",
    "        #print(carpeta)\n",
    "        dict_total[carpeta] = datos_np\n",
    "    return dict_total\n",
    "\n",
    "def promedio_y_desviacion(lista_de_arreglos):\n",
    "    # Convertir la lista de arreglos en un solo arreglo numpy tridimensional\n",
    "    arreglo_tridimensional = np.array(lista_de_arreglos)\n",
    "\n",
    "    # Calcular el promedio y la desviación estándar elemento por elemento\n",
    "    promedio = np.mean(arreglo_tridimensional, axis=0)\n",
    "    desviacion_estandar = np.std(arreglo_tridimensional, axis=0)\n",
    "\n",
    "    return promedio, desviacion_estandar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5       , 0.5       , 0.41751387, 0.40069482, 0.72842464,\n",
       "        0.76184395, 0.7727521 , 0.76756691, 0.76333986, 0.76950989],\n",
       "       [0.5       , 0.5       , 0.42183984, 0.41610198, 0.73360982,\n",
       "        0.76757063, 0.76382808, 0.77337353, 0.75710249, 0.76863398],\n",
       "       [0.5       , 0.5       , 0.41370004, 0.43073691, 0.73442583,\n",
       "        0.73931028, 0.75637207, 0.75465911, 0.77300462, 0.76650725],\n",
       "       [0.5       , 0.5       , 0.42395701, 0.41878468, 0.69112211,\n",
       "        0.76491216, 0.77818879, 0.76742051, 0.76244919, 0.78148187],\n",
       "       [0.5       , 0.5       , 0.42850473, 0.41711598, 0.70242246,\n",
       "        0.74791909, 0.76519393, 0.75607327, 0.75256653, 0.75759724],\n",
       "       [0.5       , 0.5       , 0.42204946, 0.47421473, 0.64228344,\n",
       "        0.69118808, 0.72684192, 0.71225854, 0.73077506, 0.74830392],\n",
       "       [0.5       , 0.5       , 0.43828524, 0.49305706, 0.60163615,\n",
       "        0.60599523, 0.63335914, 0.63910224, 0.64266634, 0.65987341],\n",
       "       [0.5       , 0.50270931, 0.46998968, 0.44932432, 0.47815761,\n",
       "        0.56681979, 0.49364424, 0.50419717, 0.55423771, 0.47377299],\n",
       "       [0.50422705, 0.48586701, 0.47493752, 0.43750151, 0.49315437,\n",
       "        0.49692244, 0.53301357, 0.54258917, 0.48145759, 0.50724332],\n",
       "       [0.5       , 0.50015175, 0.44137501, 0.51821067, 0.52945949,\n",
       "        0.45992256, 0.48372858, 0.5217289 , 0.51256714, 0.50131775]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pa = \"results_of_test/test_5/results_of_benchmarks/resultados_1q_kaggle/3Layers\"\n",
    "f = leer_json_y_convertir_a_np(pa)\n",
    "arr = f['FIDELITY']\n",
    "A,B = promedio_y_desviacion(arr)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIDELITY (10, 10) 3.63 1.230081298126266\n",
      "RENYI_0_5 (10, 10) 2.31 1.460787458872782\n",
      "RENYI_2 (10, 10) 3.49 1.0535179163165664\n",
      "TRACE (10, 10) 3.66 1.3358143583597237\n",
      "VN (10, 10) 1.91 0.8496469855181034\n",
      "FIDELITY (10, 10) 3.16 1.4402777509911067\n",
      "RENYI_0_5 (10, 10) 2.91 1.3122118731363468\n",
      "RENYI_2 (10, 10) 3.69 1.3014991356124674\n",
      "TRACE (10, 10) 3.01 1.117989266495882\n",
      "VN (10, 10) 2.23 1.4687069142616573\n",
      "FIDELITY (10, 10) 2.68 1.4062716664997554\n",
      "RENYI_0_5 (10, 10) 2.36 1.345511055324333\n",
      "RENYI_2 (10, 10) 3.95 1.2835497652993435\n",
      "TRACE (10, 10) 3.29 1.0610843510296437\n",
      "VN (10, 10) 2.72 1.364404632064843\n",
      "FIDELITY (10, 10) 3.35 1.306713434537198\n",
      "RENYI_0_5 (10, 10) 2.28 1.42884568795934\n",
      "RENYI_2 (10, 10) 3.78 1.2536347155371854\n",
      "TRACE (10, 10) 3.25 1.1521718621802912\n",
      "VN (10, 10) 2.34 1.266649122685521\n",
      "FIDELITY (10, 10) 3.04 1.392264342716569\n",
      "RENYI_0_5 (10, 10) 2.58 1.4154857823376399\n",
      "RENYI_2 (10, 10) 3.94 1.2068139873236472\n",
      "TRACE (10, 10) 3.16 1.1976643937263896\n",
      "VN (10, 10) 2.28 1.2496399481450646\n",
      "FIDELITY (10, 10) 2.76 1.6193825983997732\n",
      "RENYI_0_5 (10, 10) 3.03 1.2284543133547945\n",
      "RENYI_2 (10, 10) 3.92 1.0552724766618335\n",
      "TRACE (10, 10) 3.18 1.219672087079146\n",
      "VN (10, 10) 2.11 1.2401209618420295\n",
      "FIDELITY (10, 10) 3.35 1.4168627315304756\n",
      "RENYI_0_5 (10, 10) 2.61 1.4414922823241196\n",
      "RENYI_2 (10, 10) 3.81 1.2222520198387894\n",
      "TRACE (10, 10) 3.05 1.1169153951844337\n",
      "VN (10, 10) 2.18 1.2520383380711633\n",
      "FIDELITY (10, 10) 3.49 1.3747363383572868\n",
      "RENYI_0_5 (10, 10) 2.64 1.4595889832415152\n",
      "RENYI_2 (10, 10) 3.75 1.2359207094308275\n",
      "TRACE (10, 10) 2.94 1.1473447607410774\n",
      "VN (10, 10) 2.18 1.2359611644384303\n",
      "FIDELITY (10, 10) 3.5 1.5329709716755893\n",
      "RENYI_0_5 (10, 10) 2.67 1.456399670420177\n",
      "RENYI_2 (10, 10) 3.54 1.033634364753804\n",
      "TRACE (10, 10) 3.07 1.2430205147140572\n",
      "VN (10, 10) 2.22 1.2851459061133872\n",
      "FIDELITY (10, 10) 3.25 1.2678722333105965\n",
      "RENYI_0_5 (10, 10) 2.02 1.4420818284688288\n",
      "RENYI_2 (10, 10) 4.13 1.1194194924156002\n",
      "TRACE (10, 10) 3.23 1.0849423947841654\n",
      "VN (10, 10) 2.37 1.0644716999526103\n",
      "FIDELITY (10, 10) 3.24 1.4291256067959877\n",
      "RENYI_0_5 (10, 10) 2.41 1.517201370945861\n",
      "RENYI_2 (10, 10) 3.89 1.1304423912787418\n",
      "TRACE (10, 10) 3.21 1.1427598172844546\n",
      "VN (10, 10) 2.25 1.1258330249197703\n",
      "FIDELITY (10, 10) 3.32 1.370255450636851\n",
      "RENYI_0_5 (10, 10) 3.15 1.299038105676658\n",
      "RENYI_2 (10, 10) 3.64 1.3676256797823008\n",
      "TRACE (10, 10) 2.57 1.2349493916756267\n",
      "VN (10, 10) 2.32 1.3629380029920657\n",
      "FIDELITY (10, 10) 2.82 1.5644807445283564\n",
      "RENYI_0_5 (10, 10) 3.41 0.8728688332160796\n",
      "RENYI_2 (10, 10) 3.98 1.1998333217576516\n",
      "TRACE (10, 10) 2.84 1.238709005376162\n",
      "VN (10, 10) 1.95 1.2359207094308275\n",
      "FIDELITY (10, 10) 3.45 1.3738631664034084\n",
      "RENYI_0_5 (10, 10) 2.3 1.4730919862656235\n",
      "RENYI_2 (10, 10) 3.61 1.173839852790831\n",
      "TRACE (10, 10) 3.06 1.2395160345876934\n",
      "VN (10, 10) 2.58 1.335514881983724\n",
      "FIDELITY (10, 10) 3.11 1.4959612294441322\n",
      "RENYI_0_5 (10, 10) 3.12 1.409113196304683\n",
      "RENYI_2 (10, 10) 3.54 1.2362847568420472\n",
      "TRACE (10, 10) 2.84 1.1551623262554922\n",
      "VN (10, 10) 2.39 1.4825316185498372\n",
      "   Case  N_qubits  Nl  Entanglement     F_loss  Promedio  Desviacion_Estandar\n",
      "0    c1         1   3         False   FIDELITY      3.63             1.230081\n",
      "1    c1         1   3         False  RENYI_0_5      2.31             1.460787\n",
      "2    c1         1   3         False    RENYI_2      3.49             1.053518\n",
      "3    c1         1   3         False      TRACE      3.66             1.335814\n",
      "4    c1         1   3         False         VN      1.91             0.849647\n",
      "..  ...       ...  ..           ...        ...       ...                  ...\n",
      "70  c15         4  10          True   FIDELITY      3.11             1.495961\n",
      "71  c15         4  10          True  RENYI_0_5      3.12             1.409113\n",
      "72  c15         4  10          True    RENYI_2      3.54             1.236285\n",
      "73  c15         4  10          True      TRACE      2.84             1.155162\n",
      "74  c15         4  10          True         VN      2.39             1.482532\n",
      "\n",
      "[75 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "cases = {\n",
    "    \"c1\":  [1, 3,  False],\n",
    "    \"c2\":  [1, 5,  False],\n",
    "    \"c3\":  [1, 10, False],\n",
    "    \"c4\":  [2, 3,  False],\n",
    "    \"c5\":  [2, 5,  False],\n",
    "    \"c6\":  [2, 10, False],\n",
    "    \"c7\":  [2, 3,  True],\n",
    "    \"c8\":  [2, 5,  True],\n",
    "    \"c9\":  [2, 10, True],\n",
    "    \"c10\": [4, 3,  False],\n",
    "    \"c11\": [4, 5,  False],\n",
    "    \"c12\": [4, 10, False],\n",
    "    \"c13\": [4, 3,  True],\n",
    "    \"c14\": [4, 5,  True],\n",
    "    \"c15\": [4, 10, True]\n",
    "}\n",
    "\n",
    "# Lista para almacenar DataFrames temporales\n",
    "dataframes = []\n",
    "\n",
    "for key in cases:\n",
    "    N_qubits, Nl, enta  = cases[key]\n",
    "\n",
    "    label_enta = \"\" if not enta else \"e\"\n",
    "    F_loss = [\"FIDELITY\", \"RENYI_0_5\", \"RENYI_2\", \"TRACE\", \"VN\"]\n",
    "\n",
    "    for f in F_loss:\n",
    "        file_name = f\"{N_qubits}q{label_enta}_NL_{Nl}_loss_{f}.json\"\n",
    "        folder = \"results_of_test/test_6/results_rankin/\"\n",
    "        filepath = os.path.join(folder, file_name)\n",
    "        \n",
    "        try:\n",
    "            AR = json_to_array(filepath)\n",
    "            promedio = np.mean(AR)\n",
    "            desviacion_estandar = np.std(AR)\n",
    "            print(f, AR.shape, promedio, desviacion_estandar)\n",
    "\n",
    "            # Crear un DataFrame temporal y añadirlo a la lista\n",
    "            df_temp = pd.DataFrame([{\n",
    "                \"Case\": key,\n",
    "                \"N_qubits\": N_qubits,\n",
    "                \"Nl\": Nl,\n",
    "                \"Entanglement\": enta,\n",
    "                \"F_loss\": f,\n",
    "                \"Promedio\": promedio,\n",
    "                \"Desviacion_Estandar\": desviacion_estandar\n",
    "            }])\n",
    "            dataframes.append(df_temp)\n",
    "        \n",
    "        except Exception as e:\n",
    "            print(f\"Error procesando el archivo {file_name}: {e}\")\n",
    "\n",
    "# Concatenar todos los DataFrames temporales en uno solo\n",
    "df = pd.concat(dataframes, ignore_index=True)\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "print(df)\n",
    "\n",
    "# Guardar el DataFrame a un archivo CSV (opcional)\n",
    "df.to_csv(\"resultados.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIDELITY (10, 10) 3.63 1.230081298126266\n",
      "RENYI_0_5 (10, 10) 2.31 1.460787458872782\n",
      "RENYI_2 (10, 10) 3.49 1.0535179163165664\n",
      "TRACE (10, 10) 3.66 1.3358143583597237\n",
      "VN (10, 10) 1.91 0.8496469855181034\n",
      "FIDELITY (10, 10) 3.16 1.4402777509911067\n",
      "RENYI_0_5 (10, 10) 2.91 1.3122118731363468\n",
      "RENYI_2 (10, 10) 3.69 1.3014991356124674\n",
      "TRACE (10, 10) 3.01 1.117989266495882\n",
      "VN (10, 10) 2.23 1.4687069142616573\n",
      "FIDELITY (10, 10) 2.68 1.4062716664997554\n",
      "RENYI_0_5 (10, 10) 2.36 1.345511055324333\n",
      "RENYI_2 (10, 10) 3.95 1.2835497652993435\n",
      "TRACE (10, 10) 3.29 1.0610843510296437\n",
      "VN (10, 10) 2.72 1.364404632064843\n",
      "FIDELITY (10, 10) 3.35 1.306713434537198\n",
      "RENYI_0_5 (10, 10) 2.28 1.42884568795934\n",
      "RENYI_2 (10, 10) 3.78 1.2536347155371854\n",
      "TRACE (10, 10) 3.25 1.1521718621802912\n",
      "VN (10, 10) 2.34 1.266649122685521\n",
      "FIDELITY (10, 10) 3.04 1.392264342716569\n",
      "RENYI_0_5 (10, 10) 2.58 1.4154857823376399\n",
      "RENYI_2 (10, 10) 3.94 1.2068139873236472\n",
      "TRACE (10, 10) 3.16 1.1976643937263896\n",
      "VN (10, 10) 2.28 1.2496399481450646\n",
      "FIDELITY (10, 10) 2.76 1.6193825983997732\n",
      "RENYI_0_5 (10, 10) 3.03 1.2284543133547945\n",
      "RENYI_2 (10, 10) 3.92 1.0552724766618335\n",
      "TRACE (10, 10) 3.18 1.219672087079146\n",
      "VN (10, 10) 2.11 1.2401209618420295\n",
      "FIDELITY (10, 10) 3.35 1.4168627315304756\n",
      "RENYI_0_5 (10, 10) 2.61 1.4414922823241196\n",
      "RENYI_2 (10, 10) 3.81 1.2222520198387894\n",
      "TRACE (10, 10) 3.05 1.1169153951844337\n",
      "VN (10, 10) 2.18 1.2520383380711633\n",
      "FIDELITY (10, 10) 3.49 1.3747363383572868\n",
      "RENYI_0_5 (10, 10) 2.64 1.4595889832415152\n",
      "RENYI_2 (10, 10) 3.75 1.2359207094308275\n",
      "TRACE (10, 10) 2.94 1.1473447607410774\n",
      "VN (10, 10) 2.18 1.2359611644384303\n",
      "FIDELITY (10, 10) 3.5 1.5329709716755893\n",
      "RENYI_0_5 (10, 10) 2.67 1.456399670420177\n",
      "RENYI_2 (10, 10) 3.54 1.033634364753804\n",
      "TRACE (10, 10) 3.07 1.2430205147140572\n",
      "VN (10, 10) 2.22 1.2851459061133872\n",
      "FIDELITY (10, 10) 3.25 1.2678722333105965\n",
      "RENYI_0_5 (10, 10) 2.02 1.4420818284688288\n",
      "RENYI_2 (10, 10) 4.13 1.1194194924156002\n",
      "TRACE (10, 10) 3.23 1.0849423947841654\n",
      "VN (10, 10) 2.37 1.0644716999526103\n",
      "FIDELITY (10, 10) 3.24 1.4291256067959877\n",
      "RENYI_0_5 (10, 10) 2.41 1.517201370945861\n",
      "RENYI_2 (10, 10) 3.89 1.1304423912787418\n",
      "TRACE (10, 10) 3.21 1.1427598172844546\n",
      "VN (10, 10) 2.25 1.1258330249197703\n",
      "FIDELITY (10, 10) 3.32 1.370255450636851\n",
      "RENYI_0_5 (10, 10) 3.15 1.299038105676658\n",
      "RENYI_2 (10, 10) 3.64 1.3676256797823008\n",
      "TRACE (10, 10) 2.57 1.2349493916756267\n",
      "VN (10, 10) 2.32 1.3629380029920657\n",
      "FIDELITY (10, 10) 2.82 1.5644807445283564\n",
      "RENYI_0_5 (10, 10) 3.41 0.8728688332160796\n",
      "RENYI_2 (10, 10) 3.98 1.1998333217576516\n",
      "TRACE (10, 10) 2.84 1.238709005376162\n",
      "VN (10, 10) 1.95 1.2359207094308275\n",
      "FIDELITY (10, 10) 3.45 1.3738631664034084\n",
      "RENYI_0_5 (10, 10) 2.3 1.4730919862656235\n",
      "RENYI_2 (10, 10) 3.61 1.173839852790831\n",
      "TRACE (10, 10) 3.06 1.2395160345876934\n",
      "VN (10, 10) 2.58 1.335514881983724\n",
      "FIDELITY (10, 10) 3.11 1.4959612294441322\n",
      "RENYI_0_5 (10, 10) 3.12 1.409113196304683\n",
      "RENYI_2 (10, 10) 3.54 1.2362847568420472\n",
      "TRACE (10, 10) 2.84 1.1551623262554922\n",
      "VN (10, 10) 2.39 1.4825316185498372\n"
     ]
    }
   ],
   "source": [
    "cases = {\n",
    "    \"c1\":  [1, 3,  False],\n",
    "    \"c2\":  [1, 5,  False],\n",
    "    \"c3\":  [1, 10, False],\n",
    "    \"c4\":  [2, 3,  False],\n",
    "    \"c5\":  [2, 5,  False],\n",
    "    \"c6\":  [2, 10, False],\n",
    "    \"c7\":  [2, 3,  True],\n",
    "    \"c8\":  [2, 5,  True],\n",
    "    \"c9\":  [2, 10, True],\n",
    "    \"c10\": [4, 3,  False],\n",
    "    \"c11\": [4, 5,  False],\n",
    "    \"c12\": [4, 10, False],\n",
    "    \"c13\": [4, 3,  True],\n",
    "    \"c14\": [4, 5,  True],\n",
    "    \"c15\": [4, 10, True]\n",
    "}\n",
    "\n",
    "# Lista para almacenar los resultados\n",
    "results = []\n",
    "\n",
    "for key in cases:\n",
    "    N_qubits, Nl, enta  = cases[key]\n",
    "\n",
    "    label_enta = \"\" if not enta else \"e\"\n",
    "    F_loss = [\"FIDELITY\", \"RENYI_0_5\", \"RENYI_2\", \"TRACE\", \"VN\"]\n",
    "\n",
    "    case_results = []\n",
    "\n",
    "    for f in F_loss:\n",
    "        file_name = f\"{N_qubits}q{label_enta}_NL_{Nl}_loss_{f}.json\"\n",
    "        folder = \"results_of_test/test_6/results_rankin/\"\n",
    "        filepath = os.path.join(folder, file_name)\n",
    "        \n",
    "        try:\n",
    "            AR = json_to_array(filepath)\n",
    "            promedio = np.mean(AR)\n",
    "            desviacion_estandar = np.std(AR)\n",
    "            print(f, AR.shape, promedio, desviacion_estandar)\n",
    "\n",
    "            # Guardar el resultado en formato string\n",
    "            result_string = f\"{f} ({promedio:.2f})\"\n",
    "            case_results.append((result_string, promedio))\n",
    "        \n",
    "        except Exception as e:\n",
    "            print(f\"Error procesando el archivo {file_name}: {e}\")\n",
    "\n",
    "    # Ordenar los resultados por el promedio de mayor a menor\n",
    "    case_results.sort(key=lambda x: x[1], reverse=True)\n",
    "    \n",
    "    # Agregar los resultados ordenados al diccionario de resultados\n",
    "    \n",
    "    results.append({\n",
    "        \"Case\": key,\n",
    "        \"Quinto Lugar\": case_results[0][0],\n",
    "        \"Cuarto Lugar\": case_results[1][0],\n",
    "        \"Tercer Lugar\": case_results[2][0],\n",
    "        \"Segundo Lugar\": case_results[3][0],\n",
    "        \"Primer Lugar\": case_results[4][0]\n",
    "    })\n",
    "\n",
    "# Crear el DataFrame final\n",
    "df_final = pd.DataFrame(results)\n",
    "\n",
    "\n",
    "\n",
    "# Guardar el DataFrame a un archivo CSV (opcional)\n",
    "df_final.to_csv(\"resultados_ordenados.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Case</th>\n",
       "      <th>Quinto Lugar</th>\n",
       "      <th>Cuarto Lugar</th>\n",
       "      <th>Tercer Lugar</th>\n",
       "      <th>Segundo Lugar</th>\n",
       "      <th>Primer Lugar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c1</td>\n",
       "      <td>TRACE (3.66)</td>\n",
       "      <td>FIDELITY (3.63)</td>\n",
       "      <td>RENYI_2 (3.49)</td>\n",
       "      <td>RENYI_0_5 (2.31)</td>\n",
       "      <td>VN (1.91)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c2</td>\n",
       "      <td>RENYI_2 (3.69)</td>\n",
       "      <td>FIDELITY (3.16)</td>\n",
       "      <td>TRACE (3.01)</td>\n",
       "      <td>RENYI_0_5 (2.91)</td>\n",
       "      <td>VN (2.23)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c3</td>\n",
       "      <td>RENYI_2 (3.95)</td>\n",
       "      <td>TRACE (3.29)</td>\n",
       "      <td>VN (2.72)</td>\n",
       "      <td>FIDELITY (2.68)</td>\n",
       "      <td>RENYI_0_5 (2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c4</td>\n",
       "      <td>RENYI_2 (3.78)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>TRACE (3.25)</td>\n",
       "      <td>VN (2.34)</td>\n",
       "      <td>RENYI_0_5 (2.28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c5</td>\n",
       "      <td>RENYI_2 (3.94)</td>\n",
       "      <td>TRACE (3.16)</td>\n",
       "      <td>FIDELITY (3.04)</td>\n",
       "      <td>RENYI_0_5 (2.58)</td>\n",
       "      <td>VN (2.28)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c6</td>\n",
       "      <td>RENYI_2 (3.92)</td>\n",
       "      <td>TRACE (3.18)</td>\n",
       "      <td>RENYI_0_5 (3.03)</td>\n",
       "      <td>FIDELITY (2.76)</td>\n",
       "      <td>VN (2.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c7</td>\n",
       "      <td>RENYI_2 (3.81)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>TRACE (3.05)</td>\n",
       "      <td>RENYI_0_5 (2.61)</td>\n",
       "      <td>VN (2.18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c8</td>\n",
       "      <td>RENYI_2 (3.75)</td>\n",
       "      <td>FIDELITY (3.49)</td>\n",
       "      <td>TRACE (2.94)</td>\n",
       "      <td>RENYI_0_5 (2.64)</td>\n",
       "      <td>VN (2.18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c9</td>\n",
       "      <td>RENYI_2 (3.54)</td>\n",
       "      <td>FIDELITY (3.50)</td>\n",
       "      <td>TRACE (3.07)</td>\n",
       "      <td>RENYI_0_5 (2.67)</td>\n",
       "      <td>VN (2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>c10</td>\n",
       "      <td>RENYI_2 (4.13)</td>\n",
       "      <td>FIDELITY (3.25)</td>\n",
       "      <td>TRACE (3.23)</td>\n",
       "      <td>VN (2.37)</td>\n",
       "      <td>RENYI_0_5 (2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c11</td>\n",
       "      <td>RENYI_2 (3.89)</td>\n",
       "      <td>FIDELITY (3.24)</td>\n",
       "      <td>TRACE (3.21)</td>\n",
       "      <td>RENYI_0_5 (2.41)</td>\n",
       "      <td>VN (2.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c12</td>\n",
       "      <td>RENYI_2 (3.64)</td>\n",
       "      <td>FIDELITY (3.32)</td>\n",
       "      <td>RENYI_0_5 (3.15)</td>\n",
       "      <td>TRACE (2.57)</td>\n",
       "      <td>VN (2.32)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c13</td>\n",
       "      <td>RENYI_2 (3.98)</td>\n",
       "      <td>RENYI_0_5 (3.41)</td>\n",
       "      <td>TRACE (2.84)</td>\n",
       "      <td>FIDELITY (2.82)</td>\n",
       "      <td>VN (1.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>c14</td>\n",
       "      <td>RENYI_2 (3.61)</td>\n",
       "      <td>FIDELITY (3.45)</td>\n",
       "      <td>TRACE (3.06)</td>\n",
       "      <td>VN (2.58)</td>\n",
       "      <td>RENYI_0_5 (2.30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c15</td>\n",
       "      <td>RENYI_2 (3.54)</td>\n",
       "      <td>RENYI_0_5 (3.12)</td>\n",
       "      <td>FIDELITY (3.11)</td>\n",
       "      <td>TRACE (2.84)</td>\n",
       "      <td>VN (2.39)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Case    Quinto Lugar      Cuarto Lugar      Tercer Lugar     Segundo Lugar  \\\n",
       "0    c1    TRACE (3.66)   FIDELITY (3.63)    RENYI_2 (3.49)  RENYI_0_5 (2.31)   \n",
       "1    c2  RENYI_2 (3.69)   FIDELITY (3.16)      TRACE (3.01)  RENYI_0_5 (2.91)   \n",
       "2    c3  RENYI_2 (3.95)      TRACE (3.29)         VN (2.72)   FIDELITY (2.68)   \n",
       "3    c4  RENYI_2 (3.78)   FIDELITY (3.35)      TRACE (3.25)         VN (2.34)   \n",
       "4    c5  RENYI_2 (3.94)      TRACE (3.16)   FIDELITY (3.04)  RENYI_0_5 (2.58)   \n",
       "5    c6  RENYI_2 (3.92)      TRACE (3.18)  RENYI_0_5 (3.03)   FIDELITY (2.76)   \n",
       "6    c7  RENYI_2 (3.81)   FIDELITY (3.35)      TRACE (3.05)  RENYI_0_5 (2.61)   \n",
       "7    c8  RENYI_2 (3.75)   FIDELITY (3.49)      TRACE (2.94)  RENYI_0_5 (2.64)   \n",
       "8    c9  RENYI_2 (3.54)   FIDELITY (3.50)      TRACE (3.07)  RENYI_0_5 (2.67)   \n",
       "9   c10  RENYI_2 (4.13)   FIDELITY (3.25)      TRACE (3.23)         VN (2.37)   \n",
       "10  c11  RENYI_2 (3.89)   FIDELITY (3.24)      TRACE (3.21)  RENYI_0_5 (2.41)   \n",
       "11  c12  RENYI_2 (3.64)   FIDELITY (3.32)  RENYI_0_5 (3.15)      TRACE (2.57)   \n",
       "12  c13  RENYI_2 (3.98)  RENYI_0_5 (3.41)      TRACE (2.84)   FIDELITY (2.82)   \n",
       "13  c14  RENYI_2 (3.61)   FIDELITY (3.45)      TRACE (3.06)         VN (2.58)   \n",
       "14  c15  RENYI_2 (3.54)  RENYI_0_5 (3.12)   FIDELITY (3.11)      TRACE (2.84)   \n",
       "\n",
       "        Primer Lugar  \n",
       "0          VN (1.91)  \n",
       "1          VN (2.23)  \n",
       "2   RENYI_0_5 (2.36)  \n",
       "3   RENYI_0_5 (2.28)  \n",
       "4          VN (2.28)  \n",
       "5          VN (2.11)  \n",
       "6          VN (2.18)  \n",
       "7          VN (2.18)  \n",
       "8          VN (2.22)  \n",
       "9   RENYI_0_5 (2.02)  \n",
       "10         VN (2.25)  \n",
       "11         VN (2.32)  \n",
       "12         VN (1.95)  \n",
       "13  RENYI_0_5 (2.30)  \n",
       "14         VN (2.39)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Case</th>\n",
       "      <th>Quinto Lugar</th>\n",
       "      <th>Cuarto Lugar</th>\n",
       "      <th>Tercer Lugar</th>\n",
       "      <th>Segundo Lugar</th>\n",
       "      <th>Primer Lugar</th>\n",
       "      <th>statistical</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c1</td>\n",
       "      <td>TRACE (3.66)</td>\n",
       "      <td>FIDELITY (3.63)</td>\n",
       "      <td>RENYI_2 (3.49)</td>\n",
       "      <td>RENYI_0_5 (2.31)</td>\n",
       "      <td>VN (1.91)</td>\n",
       "      <td>112.695755</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c2</td>\n",
       "      <td>RENYI_2 (3.69)</td>\n",
       "      <td>FIDELITY (3.16)</td>\n",
       "      <td>TRACE (3.01)</td>\n",
       "      <td>RENYI_0_5 (2.91)</td>\n",
       "      <td>VN (2.23)</td>\n",
       "      <td>38.543892</td>\n",
       "      <td>8.653577e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c3</td>\n",
       "      <td>RENYI_2 (3.95)</td>\n",
       "      <td>TRACE (3.29)</td>\n",
       "      <td>VN (2.72)</td>\n",
       "      <td>FIDELITY (2.68)</td>\n",
       "      <td>RENYI_0_5 (2.36)</td>\n",
       "      <td>106.785114</td>\n",
       "      <td>3.527334e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c4</td>\n",
       "      <td>RENYI_2 (3.78)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>TRACE (3.25)</td>\n",
       "      <td>VN (2.34)</td>\n",
       "      <td>RENYI_0_5 (2.28)</td>\n",
       "      <td>77.470752</td>\n",
       "      <td>5.978844e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c5</td>\n",
       "      <td>RENYI_2 (3.94)</td>\n",
       "      <td>TRACE (3.16)</td>\n",
       "      <td>FIDELITY (3.04)</td>\n",
       "      <td>RENYI_0_5 (2.58)</td>\n",
       "      <td>VN (2.28)</td>\n",
       "      <td>70.833083</td>\n",
       "      <td>1.513876e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>c6</td>\n",
       "      <td>RENYI_2 (3.92)</td>\n",
       "      <td>TRACE (3.18)</td>\n",
       "      <td>RENYI_0_5 (3.03)</td>\n",
       "      <td>FIDELITY (2.76)</td>\n",
       "      <td>VN (2.11)</td>\n",
       "      <td>58.359223</td>\n",
       "      <td>6.414596e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c7</td>\n",
       "      <td>RENYI_2 (3.81)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>TRACE (3.05)</td>\n",
       "      <td>RENYI_0_5 (2.61)</td>\n",
       "      <td>VN (2.18)</td>\n",
       "      <td>83.028643</td>\n",
       "      <td>3.972781e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c8</td>\n",
       "      <td>RENYI_2 (3.75)</td>\n",
       "      <td>FIDELITY (3.49)</td>\n",
       "      <td>TRACE (2.94)</td>\n",
       "      <td>RENYI_0_5 (2.64)</td>\n",
       "      <td>VN (2.18)</td>\n",
       "      <td>87.465484</td>\n",
       "      <td>4.547125e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c9</td>\n",
       "      <td>RENYI_2 (3.54)</td>\n",
       "      <td>FIDELITY (3.50)</td>\n",
       "      <td>TRACE (3.07)</td>\n",
       "      <td>RENYI_0_5 (2.67)</td>\n",
       "      <td>VN (2.22)</td>\n",
       "      <td>90.129702</td>\n",
       "      <td>1.235815e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>c10</td>\n",
       "      <td>RENYI_2 (4.13)</td>\n",
       "      <td>FIDELITY (3.25)</td>\n",
       "      <td>TRACE (3.23)</td>\n",
       "      <td>VN (2.37)</td>\n",
       "      <td>RENYI_0_5 (2.02)</td>\n",
       "      <td>86.767280</td>\n",
       "      <td>6.396576e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c11</td>\n",
       "      <td>RENYI_2 (3.89)</td>\n",
       "      <td>FIDELITY (3.24)</td>\n",
       "      <td>TRACE (3.21)</td>\n",
       "      <td>RENYI_0_5 (2.41)</td>\n",
       "      <td>VN (2.25)</td>\n",
       "      <td>74.050682</td>\n",
       "      <td>3.163529e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c12</td>\n",
       "      <td>RENYI_2 (3.64)</td>\n",
       "      <td>FIDELITY (3.32)</td>\n",
       "      <td>RENYI_0_5 (3.15)</td>\n",
       "      <td>TRACE (2.57)</td>\n",
       "      <td>VN (2.32)</td>\n",
       "      <td>21.292149</td>\n",
       "      <td>2.771074e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c13</td>\n",
       "      <td>RENYI_2 (3.98)</td>\n",
       "      <td>RENYI_0_5 (3.41)</td>\n",
       "      <td>TRACE (2.84)</td>\n",
       "      <td>FIDELITY (2.82)</td>\n",
       "      <td>VN (1.95)</td>\n",
       "      <td>144.825078</td>\n",
       "      <td>2.614593e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>c14</td>\n",
       "      <td>RENYI_2 (3.61)</td>\n",
       "      <td>FIDELITY (3.45)</td>\n",
       "      <td>TRACE (3.06)</td>\n",
       "      <td>VN (2.58)</td>\n",
       "      <td>RENYI_0_5 (2.30)</td>\n",
       "      <td>86.915989</td>\n",
       "      <td>5.948160e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c15</td>\n",
       "      <td>RENYI_2 (3.54)</td>\n",
       "      <td>RENYI_0_5 (3.12)</td>\n",
       "      <td>FIDELITY (3.11)</td>\n",
       "      <td>TRACE (2.84)</td>\n",
       "      <td>VN (2.39)</td>\n",
       "      <td>7.411090</td>\n",
       "      <td>1.156944e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Case    Quinto Lugar      Cuarto Lugar      Tercer Lugar     Segundo Lugar  \\\n",
       "0    c1    TRACE (3.66)   FIDELITY (3.63)    RENYI_2 (3.49)  RENYI_0_5 (2.31)   \n",
       "1    c2  RENYI_2 (3.69)   FIDELITY (3.16)      TRACE (3.01)  RENYI_0_5 (2.91)   \n",
       "2    c3  RENYI_2 (3.95)      TRACE (3.29)         VN (2.72)   FIDELITY (2.68)   \n",
       "3    c4  RENYI_2 (3.78)   FIDELITY (3.35)      TRACE (3.25)         VN (2.34)   \n",
       "4    c5  RENYI_2 (3.94)      TRACE (3.16)   FIDELITY (3.04)  RENYI_0_5 (2.58)   \n",
       "5    c6  RENYI_2 (3.92)      TRACE (3.18)  RENYI_0_5 (3.03)   FIDELITY (2.76)   \n",
       "6    c7  RENYI_2 (3.81)   FIDELITY (3.35)      TRACE (3.05)  RENYI_0_5 (2.61)   \n",
       "7    c8  RENYI_2 (3.75)   FIDELITY (3.49)      TRACE (2.94)  RENYI_0_5 (2.64)   \n",
       "8    c9  RENYI_2 (3.54)   FIDELITY (3.50)      TRACE (3.07)  RENYI_0_5 (2.67)   \n",
       "9   c10  RENYI_2 (4.13)   FIDELITY (3.25)      TRACE (3.23)         VN (2.37)   \n",
       "10  c11  RENYI_2 (3.89)   FIDELITY (3.24)      TRACE (3.21)  RENYI_0_5 (2.41)   \n",
       "11  c12  RENYI_2 (3.64)   FIDELITY (3.32)  RENYI_0_5 (3.15)      TRACE (2.57)   \n",
       "12  c13  RENYI_2 (3.98)  RENYI_0_5 (3.41)      TRACE (2.84)   FIDELITY (2.82)   \n",
       "13  c14  RENYI_2 (3.61)   FIDELITY (3.45)      TRACE (3.06)         VN (2.58)   \n",
       "14  c15  RENYI_2 (3.54)  RENYI_0_5 (3.12)   FIDELITY (3.11)      TRACE (2.84)   \n",
       "\n",
       "        Primer Lugar  statistical       p-value  \n",
       "0          VN (1.91)   112.695755  0.000000e+00  \n",
       "1          VN (2.23)    38.543892  8.653577e-08  \n",
       "2   RENYI_0_5 (2.36)   106.785114  3.527334e-22  \n",
       "3   RENYI_0_5 (2.28)    77.470752  5.978844e-16  \n",
       "4          VN (2.28)    70.833083  1.513876e-14  \n",
       "5          VN (2.11)    58.359223  6.414596e-12  \n",
       "6          VN (2.18)    83.028643  3.972781e-17  \n",
       "7          VN (2.18)    87.465484  4.547125e-18  \n",
       "8          VN (2.22)    90.129702  1.235815e-18  \n",
       "9   RENYI_0_5 (2.02)    86.767280  6.396576e-18  \n",
       "10         VN (2.25)    74.050682  3.163529e-15  \n",
       "11         VN (2.32)    21.292149  2.771074e-04  \n",
       "12         VN (1.95)   144.825078  2.614593e-30  \n",
       "13  RENYI_0_5 (2.30)    86.915989  5.948160e-18  \n",
       "14         VN (2.39)     7.411090  1.156944e-01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "# Directorio donde están los archivos Excel\n",
    "path = 'results_of_test/test_6/results_test_hipotesis'\n",
    "\n",
    "# Listar todos los archivos en el directorio\n",
    "files = [f for f in os.listdir(path) if f.endswith('.xlsx')]\n",
    "\n",
    "# Crear una lista para almacenar los DataFrames\n",
    "dfs = []\n",
    "\n",
    "# Leer cada archivo Excel y seleccionar las dos columnas\n",
    "for file in files:\n",
    "    df = pd.read_excel(os.path.join(path, file), usecols=['statistical', 'p-value'])  # Reemplaza 'Columna1' y 'Columna2' con los nombres de tus columnas\n",
    "    dfs.append(df)\n",
    "\n",
    "# Concatenar todos los DataFrames en uno solo\n",
    "combined_df = pd.concat(dfs, ignore_index=True)\n",
    "\n",
    "\n",
    "\n",
    "# Concatenar el DataFrame existente con el nuevo DataFrame\n",
    "final_df = pd.concat([df_final, combined_df], axis=1)\n",
    "\n",
    "# Mostrar el DataFrame final\n",
    "final_df.head(15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = final_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Case</th>\n",
       "      <th>First Place</th>\n",
       "      <th>Second Place</th>\n",
       "      <th>Third Place</th>\n",
       "      <th>Fourth Place</th>\n",
       "      <th>Fifth Place</th>\n",
       "      <th>Statistical</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>$N_{q}$ = 1, $N_{L}$ = 3, Ent= False</td>\n",
       "      <td>VN (1.91)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.31)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.49)</td>\n",
       "      <td>FIDELITY (3.63)</td>\n",
       "      <td>TRACE (3.66)</td>\n",
       "      <td>112.7</td>\n",
       "      <td>0.00e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>$N_{q}$ = 1, $N_{L}$ = 5, Ent= False</td>\n",
       "      <td>VN (2.23)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.91)</td>\n",
       "      <td>TRACE (3.01)</td>\n",
       "      <td>FIDELITY (3.16)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.69)</td>\n",
       "      <td>38.54</td>\n",
       "      <td>8.65e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>$N_{q}$ = 1, $N_{L}$ = 10, Ent= False</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.36)</td>\n",
       "      <td>FIDELITY (2.68)</td>\n",
       "      <td>VN (2.72)</td>\n",
       "      <td>TRACE (3.29)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.95)</td>\n",
       "      <td>106.8</td>\n",
       "      <td>3.53e-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 3, Ent= False</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.28)</td>\n",
       "      <td>VN (2.34)</td>\n",
       "      <td>TRACE (3.25)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.78)</td>\n",
       "      <td>77.47</td>\n",
       "      <td>5.98e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 5, Ent= False</td>\n",
       "      <td>VN (2.28)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.58)</td>\n",
       "      <td>FIDELITY (3.04)</td>\n",
       "      <td>TRACE (3.16)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.94)</td>\n",
       "      <td>70.83</td>\n",
       "      <td>1.51e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 10, Ent= False</td>\n",
       "      <td>VN (2.11)</td>\n",
       "      <td>FIDELITY (2.76)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (3.03)</td>\n",
       "      <td>TRACE (3.18)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.92)</td>\n",
       "      <td>58.36</td>\n",
       "      <td>6.41e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 3, Ent= True</td>\n",
       "      <td>VN (2.18)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.61)</td>\n",
       "      <td>TRACE (3.05)</td>\n",
       "      <td>FIDELITY (3.35)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.81)</td>\n",
       "      <td>83.03</td>\n",
       "      <td>3.97e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 5, Ent= True</td>\n",
       "      <td>VN (2.18)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.64)</td>\n",
       "      <td>TRACE (2.94)</td>\n",
       "      <td>FIDELITY (3.49)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.75)</td>\n",
       "      <td>87.47</td>\n",
       "      <td>4.55e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>$N_{q}$ = 2, $N_{L}$ = 10, Ent= True</td>\n",
       "      <td>VN (2.22)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.67)</td>\n",
       "      <td>TRACE (3.07)</td>\n",
       "      <td>FIDELITY (3.50)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.54)</td>\n",
       "      <td>90.13</td>\n",
       "      <td>1.24e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>$N_{q}$ = 4, $N_{L}$ = 3, Ent= False</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.02)</td>\n",
       "      <td>VN (2.37)</td>\n",
       "      <td>TRACE (3.23)</td>\n",
       "      <td>FIDELITY (3.25)</td>\n",
       "      <td>RENYI $\\alpha=2$ (4.13)</td>\n",
       "      <td>86.77</td>\n",
       "      <td>6.40e-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>$N_{q}$ = 4, $N_{L}$ = 5, Ent= False</td>\n",
       "      <td>VN (2.25)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.41)</td>\n",
       "      <td>TRACE (3.21)</td>\n",
       "      <td>FIDELITY (3.24)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.89)</td>\n",
       "      <td>74.05</td>\n",
       "      <td>3.16e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>$N_{q}$ = 4, $N_{L}$ = 10, Ent= False</td>\n",
       "      <td>VN (2.32)</td>\n",
       "      <td>TRACE (2.57)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (3.15)</td>\n",
       "      <td>FIDELITY (3.32)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.64)</td>\n",
       "      <td>21.29</td>\n",
       "      <td>2.77e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>$N_{q}$ = 4, $N_{L}$ = 3, Ent= True</td>\n",
       "      <td>VN (1.95)</td>\n",
       "      <td>FIDELITY (2.82)</td>\n",
       "      <td>TRACE (2.84)</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (3.41)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.98)</td>\n",
       "      <td>144.8</td>\n",
       "      <td>2.61e-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>$N_{q}$ = 4, $N_{L}$ = 5, Ent= True</td>\n",
       "      <td>RENYI $\\alpha=0.5$ (2.30)</td>\n",
       "      <td>VN (2.58)</td>\n",
       "      <td>TRACE (3.06)</td>\n",
       "      <td>FIDELITY (3.45)</td>\n",
       "      <td>RENYI $\\alpha=2$ (3.61)</td>\n",
       "      <td>86.92</td>\n",
       "      <td>5.95e-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Case                First Place  \\\n",
       "0    $N_{q}$ = 1, $N_{L}$ = 3, Ent= False                  VN (1.91)   \n",
       "1    $N_{q}$ = 1, $N_{L}$ = 5, Ent= False                  VN (2.23)   \n",
       "2   $N_{q}$ = 1, $N_{L}$ = 10, Ent= False  RENYI $\\alpha=0.5$ (2.36)   \n",
       "3    $N_{q}$ = 2, $N_{L}$ = 3, Ent= False  RENYI $\\alpha=0.5$ (2.28)   \n",
       "4    $N_{q}$ = 2, $N_{L}$ = 5, Ent= False                  VN (2.28)   \n",
       "5   $N_{q}$ = 2, $N_{L}$ = 10, Ent= False                  VN (2.11)   \n",
       "6     $N_{q}$ = 2, $N_{L}$ = 3, Ent= True                  VN (2.18)   \n",
       "7     $N_{q}$ = 2, $N_{L}$ = 5, Ent= True                  VN (2.18)   \n",
       "8    $N_{q}$ = 2, $N_{L}$ = 10, Ent= True                  VN (2.22)   \n",
       "9    $N_{q}$ = 4, $N_{L}$ = 3, Ent= False  RENYI $\\alpha=0.5$ (2.02)   \n",
       "10   $N_{q}$ = 4, $N_{L}$ = 5, Ent= False                  VN (2.25)   \n",
       "11  $N_{q}$ = 4, $N_{L}$ = 10, Ent= False                  VN (2.32)   \n",
       "12    $N_{q}$ = 4, $N_{L}$ = 3, Ent= True                  VN (1.95)   \n",
       "13    $N_{q}$ = 4, $N_{L}$ = 5, Ent= True  RENYI $\\alpha=0.5$ (2.30)   \n",
       "\n",
       "                 Second Place                Third Place  \\\n",
       "0   RENYI $\\alpha=0.5$ (2.31)    RENYI $\\alpha=2$ (3.49)   \n",
       "1   RENYI $\\alpha=0.5$ (2.91)               TRACE (3.01)   \n",
       "2             FIDELITY (2.68)                  VN (2.72)   \n",
       "3                   VN (2.34)               TRACE (3.25)   \n",
       "4   RENYI $\\alpha=0.5$ (2.58)            FIDELITY (3.04)   \n",
       "5             FIDELITY (2.76)  RENYI $\\alpha=0.5$ (3.03)   \n",
       "6   RENYI $\\alpha=0.5$ (2.61)               TRACE (3.05)   \n",
       "7   RENYI $\\alpha=0.5$ (2.64)               TRACE (2.94)   \n",
       "8   RENYI $\\alpha=0.5$ (2.67)               TRACE (3.07)   \n",
       "9                   VN (2.37)               TRACE (3.23)   \n",
       "10  RENYI $\\alpha=0.5$ (2.41)               TRACE (3.21)   \n",
       "11               TRACE (2.57)  RENYI $\\alpha=0.5$ (3.15)   \n",
       "12            FIDELITY (2.82)               TRACE (2.84)   \n",
       "13                  VN (2.58)               TRACE (3.06)   \n",
       "\n",
       "                 Fourth Place              Fifth Place Statistical   p-value  \n",
       "0             FIDELITY (3.63)             TRACE (3.66)       112.7  0.00e+00  \n",
       "1             FIDELITY (3.16)  RENYI $\\alpha=2$ (3.69)       38.54  8.65e-08  \n",
       "2                TRACE (3.29)  RENYI $\\alpha=2$ (3.95)       106.8  3.53e-22  \n",
       "3             FIDELITY (3.35)  RENYI $\\alpha=2$ (3.78)       77.47  5.98e-16  \n",
       "4                TRACE (3.16)  RENYI $\\alpha=2$ (3.94)       70.83  1.51e-14  \n",
       "5                TRACE (3.18)  RENYI $\\alpha=2$ (3.92)       58.36  6.41e-12  \n",
       "6             FIDELITY (3.35)  RENYI $\\alpha=2$ (3.81)       83.03  3.97e-17  \n",
       "7             FIDELITY (3.49)  RENYI $\\alpha=2$ (3.75)       87.47  4.55e-18  \n",
       "8             FIDELITY (3.50)  RENYI $\\alpha=2$ (3.54)       90.13  1.24e-18  \n",
       "9             FIDELITY (3.25)  RENYI $\\alpha=2$ (4.13)       86.77  6.40e-18  \n",
       "10            FIDELITY (3.24)  RENYI $\\alpha=2$ (3.89)       74.05  3.16e-15  \n",
       "11            FIDELITY (3.32)  RENYI $\\alpha=2$ (3.64)       21.29  2.77e-04  \n",
       "12  RENYI $\\alpha=0.5$ (3.41)  RENYI $\\alpha=2$ (3.98)       144.8  2.61e-30  \n",
       "13            FIDELITY (3.45)  RENYI $\\alpha=2$ (3.61)       86.92  5.95e-18  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Supongamos que df_final es tu DataFrame cargado\n",
    "\n",
    "# Lista personalizada para la columna 'Case'\n",
    "custom_case_list = ['$N_{q}$ = 1, $N_{L}$ = 3, Ent= False',\n",
    "                    '$N_{q}$ = 1, $N_{L}$ = 5, Ent= False',\n",
    "                    '$N_{q}$ = 1, $N_{L}$ = 10, Ent= False',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 3, Ent= False',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 5, Ent= False',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 10, Ent= False',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 3, Ent= True',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 5, Ent= True',\n",
    "                    '$N_{q}$ = 2, $N_{L}$ = 10, Ent= True',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 3, Ent= False',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 5, Ent= False',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 10, Ent= False',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 3, Ent= True',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 5, Ent= True',\n",
    "                    '$N_{q}$ = 4, $N_{L}$ = 10, Ent= True']\n",
    "\n",
    "# Cambiar el contenido de la columna 'Case'\n",
    "df_final['Case'] = custom_case_list\n",
    "\n",
    "# Renombrar columnas\n",
    "column_translation = {\n",
    "    'Case': 'Case',\n",
    "    'Primer Lugar': 'First Place',\n",
    "    'Segundo Lugar': 'Second Place',\n",
    "    'Tercer Lugar': 'Third Place',\n",
    "    'Cuarto Lugar': 'Fourth Place',\n",
    "    'Quinto Lugar': 'Fifth Place',\n",
    "    'statistical': 'Statistical',\n",
    "    'p-value': 'p-value'\n",
    "}\n",
    "\n",
    "df_final = df_final.rename(columns=column_translation)\n",
    "\n",
    "# Función para mapear valores específicos\n",
    "def map_renyi(value):\n",
    "    value = value.replace('RENYI_2', r'RENYI $\\alpha=2$')\n",
    "    value = value.replace('RENYI_0_5', r'RENYI $\\alpha=0.5$')\n",
    "    return value\n",
    "\n",
    "# Aplicar la función de mapeo a las columnas relevantes\n",
    "for col in ['First Place', 'Second Place', 'Third Place', 'Fourth Place', 'Fifth Place']:\n",
    "    df_final[col] = df_final[col].apply(map_renyi)\n",
    "\n",
    "# Convertir 'p-value' a notación científica como string\n",
    "df_final['p-value'] = df_final['p-value'].apply(lambda x: '{:.2e}'.format(x))\n",
    "\n",
    "# Redondear 'Statistical' a 4 cifras significativas\n",
    "df_final['Statistical'] = df_final['Statistical'].apply(lambda x: '{:.4g}'.format(x))\n",
    "\n",
    "# Reordenar columnas\n",
    "column_order = ['Case', 'First Place', 'Second Place', 'Third Place', 'Fourth Place', 'Fifth Place', 'Statistical', 'p-value']\n",
    "df_final = df_final[column_order]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Mostrar el DataFrame resultante\n",
    "df_final.head(14)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La tabla en formato LaTeX ha sido exportada y guardada en 'tabla.tex'.\n"
     ]
    }
   ],
   "source": [
    "# Convertir el DataFrame a una cadena en formato LaTeX\n",
    "latex_str = df_final.to_latex(index=False)  # index=False para no incluir el índice\n",
    "\n",
    "# Guardar la cadena LaTeX en un archivo .tex\n",
    "with open('tabla.tex', 'w') as file:\n",
    "    file.write(latex_str)\n",
    "\n",
    "print(\"La tabla en formato LaTeX ha sido exportada y guardada en 'tabla.tex'.\")"
   ]
  }
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