outliers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fbcc6a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'voluntario_upv_1-_1': ['ResultsJNDsNoiseFlatBG20211129T112353.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211129T120005.mat'],\n",
       " 'voluntario_upv_2-_2': ['ResultsJNDsNoiseFlatBG20211129T133717.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211129T140322.mat'],\n",
       " 'voluntario_upv_3-_3': ['ResultsJNDsNoiseFlatBG20211129T163621.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211129T171934.mat'],\n",
       " 'voluntario_upv_4-_4': ['ResultsJNDsNoiseFlatBG20211130T105700.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220111T121738.mat'],\n",
       " 'voluntario_upv_5-_5': ['ResultsJNDsNoiseFlatBG20211201T114113.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211210T120626.mat'],\n",
       " 'voluntario_upv_6-_6': ['ResultsJNDsNoiseFlatBG20211202T140213.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211202T142641.mat'],\n",
       " 'voluntario_upv_7-_7': ['ResultsJNDsNoiseFlatBG20211203T102719.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220124T090423.mat'],\n",
       " 'voluntario_upv_8-_8': ['ResultsJNDsNoiseFlatBG20211210T101730.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211210T112932.mat'],\n",
       " 'voluntario_upv_9-_9': ['ResultsJNDsNoiseFlatBG20211210T105955.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220127T121914.mat'],\n",
       " 'voluntario_upv_11-_11': ['ResultsJNDsNoiseFlatBG20211210T145230.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220124T123446.mat'],\n",
       " 'voluntario_upv_12-_12': ['ResultsJNDsNoiseFlatBG20211210T160923.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211210T163932.mat'],\n",
       " 'voluntario_upv_13-_13': ['ResultsJNDsNoiseFlatBG20211217T141916.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211222T112032.mat'],\n",
       " 'voluntario_upv_14-_14': ['ResultsJNDsNoiseFlatBG20211217T171615.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20211220T112329.mat'],\n",
       " 'voluntario_upv_16-_16': ['ResultsJNDsNoiseFlatBG20220111T140857.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220113T142427.mat'],\n",
       " 'voluntario_upv_17-_17': ['ResultsJNDsNoiseFlatBG20220117T125215.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220117T131752.mat'],\n",
       " 'voluntario_upv_18-_18': ['ResultsJNDsNoiseFlatBG20220117T161356.mat'],\n",
       " 'voluntario_upv_19-_19': ['ResultsJNDsNoiseFlatBG20220118T094314.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220118T100419.mat'],\n",
       " 'voluntario_upv_20-_20': ['ResultsJNDsNoiseFlatBG20220118T104104.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220118T110520.mat'],\n",
       " 'voluntario_upv_21-_21': ['ResultsJNDsNoiseFlatBG20220120T182621.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220120T185212.mat'],\n",
       " 'voluntario_upv_22-_22': ['ResultsJNDsNoiseFlatBG20220121T112005.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220121T114403.mat'],\n",
       " 'voluntario_upv_23-_23': ['ResultsJNDsNoiseFlatBG20220121T122915.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220121T130458.mat'],\n",
       " 'voluntario_upv_26-_26': ['ResultsJNDsNoiseFlatBG20220125T111912.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220125T114644.mat'],\n",
       " 'voluntario_upv_28-_28': ['ResultsJNDsNoiseFlatBG20220127T124659.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220128T123931.mat'],\n",
       " 'voluntario_upv_29-_29': ['ResultsJNDsNoiseFlatBG20220127T133215.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220131T132610.mat'],\n",
       " 'voluntario_upv_30-_30': ['ResultsJNDsNoiseFlatBG20220131T144132.mat'],\n",
       " 'voluntario_upv_31-_31': ['ResultsJNDsNoiseFlatBG20220202T131819.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T143731.mat'],\n",
       " 'voluntario_upv_32-_32': ['ResultsJNDsNoiseFlatBG20220202T133856.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T145451.mat'],\n",
       " 'voluntario_upv_33-_33': ['ResultsJNDsNoiseFlatBG20220202T135933.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T151326.mat'],\n",
       " 'voluntario_upv_34-_34': ['ResultsJNDsNoiseFlatBG20220203T123900.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220204T113646.mat'],\n",
       " 'voluntario_upv_35-_35': ['ResultsJNDsNoiseFlatBG20220204T105347.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220210T105658.mat'],\n",
       " 'voluntario_upv_36-_36': ['ResultsJNDsNoiseFlatBG20220204T122529.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220204T122529.mat']}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from unidecode import unidecode\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "from scipy.optimize import minimize\n",
    "from scipy.stats import norm\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "PATH_UPV = \"./DatosExperimento1/UPV\"\n",
    "\n",
    "# Leer el archivo Excel en un DataFrame de pandas\n",
    "df = pd.read_excel(PATH_UPV+'/Exp1_Observadores_UPV.xlsx')\n",
    "    \n",
    "if not os.path.exists('Resultados_UPV'):\n",
    "    os.makedirs('Resultados_UPV')\n",
    "\n",
    "# Crear un diccionario vacío para almacenar los resultados\n",
    "voluntarios_upv = {}\n",
    "\n",
    "# Iterar sobre cada fila del DataFrame\n",
    "for idx, row in df.iterrows():\n",
    "    # Tratamiento de nombre y apellidos para evitar que una misma persona se trate como diferente:\n",
    "    # Quito espacios, mayusculas, minuscula s, etc\n",
    "    nombre = unidecode(row['Nombre'].lower())\n",
    "    apellidos = unidecode(row['Apellidos'].lower())\n",
    "    fichero = row['Fichero']\n",
    "    # Crear la clave del diccionario usando el nombre y apellidos\n",
    "    clave = f\"{nombre}-{apellidos}\".replace(\" \", \"\")\n",
    "    \n",
    "    # Agregar el valor de la columna \"Fichero\" a la lista correspondiente en el diccionario\n",
    "    if clave in voluntarios_upv:\n",
    "        voluntarios_upv[clave].append(fichero)\n",
    "    else:\n",
    "        voluntarios_upv[clave] = [fichero]\n",
    "voluntarios_upv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75ad490b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normcdf2(x, mu, sigma):\n",
    "    '''\n",
    "    Utilizo la función norm.cdf de la librería scipy.stats para calcular la función de distribución acumulada (CDF) \n",
    "    de la distribución normal, y luego dividimos el resultado por dos y sumamos 0.5 para ajustar el rango a [0,1].\n",
    "    '''\n",
    "    y = (norm.cdf(x, mu, sigma) + 1) / 2\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "840be161",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normcdf2_minimizer(X, params):\n",
    "    # X incluye mu y sigma\n",
    "    mu = X[0]\n",
    "    sigma = X[1]\n",
    "\n",
    "    # x/y valores para ajustar\n",
    "    x = params['x']\n",
    "    y = params['y']\n",
    "\n",
    "    # los valores ajustados con mu y sigma dados\n",
    "    yfit = normcdf2(x, mu, sigma)\n",
    "\n",
    "    # cálculo del error\n",
    "    err = np.sum((yfit - y)**2)\n",
    "\n",
    "    return err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90814468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El voluntario voluntario_upv_1-_1 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   4   5   6   7   8   9  10  12  13  14  15  16  17  18  19  22\n",
      "  24  25  28  29  30  33  34  35  39  40  44  46  47  49  50  51  54  56\n",
      "  57  60  64  65  66  67  68  69  72  73  74  75  76  77  79  83  85  86\n",
      "  87  88  90  91  93  94  95  96  97  98 100 102 103 104 105 106 107 108\n",
      " 112 116 119 123 124 127 128 132 134 139 141 145 147 148 149 150 151 153\n",
      " 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_2-_2 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   7   8   9  10  11  12  13  14  15  16  17  18  19\n",
      "  20  21  22  23  24  25  27  28  29  30  31  32  33  34  36  37  38  39\n",
      "  41  43  44  46  47  49  50  51  52  53  54  55  56  57  58  59  61  62\n",
      "  63  64  65  66  67  68  69  71  72  73  74  75  76  77  78  79  80  81\n",
      "  82  83  84  85  86  87  88  89  91  92  93  95  96  97  98 100 101 102\n",
      " 104 105 107 108 109 110 111 112 115 116 117 121 124 125 126 127 128 129\n",
      " 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148\n",
      " 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_3-_3 es un outlier.\n",
      "Sus experimentos outliers son: [  6  19  20  46  51  53  55  66  79  84  99 117 118].\n",
      "El voluntario voluntario_upv_4-_4 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   4   7   8   9  10  11  13  14  15  16  17  18  19  21  23  24\n",
      "  27  28  29  31  32  33  37  38  40  41  42  44  45  46  47  48  49  51\n",
      "  53  57  58  59  60  62  64  68  73  74  76  77  79  82  87  89  91  96\n",
      "  97  98 100 101 102 104 107 108 111 114 115 118 119 126 130 135 136 144\n",
      " 146 148 151 152 153 155 156 157 159].\n",
      "El voluntario voluntario_upv_5-_5 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   7   8   9  11  13  14  15  17  18  19  21  24  25  26  28\n",
      "  32  33  34  37  38  39  50  51  52  55  58  60  62  64  67  68  69  70\n",
      "  72  73  74  76  78  79  85  94  95 102 103 106 107 108 110 112 115 116\n",
      " 117 118 121 128 135 136 139 142 147 149 150 153 154].\n",
      "El voluntario voluntario_upv_6-_6 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   9  11  12  14  16  21  22  30  32  34  40\n",
      "  41  42  43  45  47  50  51  52  53  54  56  60  61  62  64  66  68  70\n",
      "  71  72  81  82  83  85  88  90  92  94 101 103 107 112 120 121 123 125\n",
      " 126 127 128 130 132 135 136 137 138 141 143 145 147 148 152 155 156 159].\n",
      "El voluntario voluntario_upv_7-_7 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   9  12  16  19  20  23  26  28  34  41\n",
      "  42  43  44  45  46  47  48  49  50  53  54  55  58  59  61  65  69  76\n",
      "  77  78  79  80  82  83  84  86  87  88  89  90  91  92  94  95  96  97\n",
      "  98  99 101 102 103 104 105 106 107 108 109 110 111 115 116 117 118 119\n",
      " 120 121 123 125 127 128 129 130 132 133 134 135 136 137 138 139 140 141\n",
      " 143 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_8-_8 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
      "  73  74  75  76  77  78  79  80  82  83  84  85  87  88  89  90  91  92\n",
      "  93  94  95  96  97  98 100 101 102 104 105 106 107 108 109 110 111 112\n",
      " 113 114 115 116 118 119 120 121 123 125 126 127 129 130 132 133 134 135\n",
      " 136 137 138 139 140 141 142 144 145 146 147 148 149 150 152 153 154 155\n",
      " 156 157 158 159].\n",
      "El voluntario voluntario_upv_9-_9 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  12  14  15  16  17  18  19  22\n",
      "  23  24  25  27  28  29  30  31  32  34  35  39  40  41  43  44  45  46\n",
      "  47  48  49  51  52  53  54  56  57  58  59  60  61  62  63  64  65  67\n",
      "  68  69  73  74  75  76  79  81  82  83  87  89  90  91  92  93  94  95\n",
      "  96  97  98 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114\n",
      " 115 116 117 118 119 120 123 124 125 126 129 132 133 134 135 136 137 139\n",
      " 140 141 142 143 145 146 147 148 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_11-_11 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   3   4   6  10  14  15  21  22  25  26  27  29  34  40  42  44\n",
      "  53  54  55  56  57  58  60  61  67  69  72  75  77  78  83  84  87  89\n",
      "  91  92  93  94  98  99 101 103 105 108 113 121 122 123 125 126 127 128\n",
      " 129 130 131 134 135 137 138 139 140 141 143 147 148 149 151 152 153].\n",
      "El voluntario voluntario_upv_12-_12 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   4   5   6   7   8   9  11  14  15  16  17  18  19  22  23\n",
      "  24  25  26  29  33  34  35  36  37  39  43  44  45  46  48  51  52  55\n",
      "  58  59  60  63  67  68  69  71  73  74  75  76  77  78  79  81  82  83\n",
      "  88  90  91  92  93  94  96  97 100 101 102 104 105 106 107 108 109 110\n",
      " 111 112 113 114 115 116 118 120 125 127 132 135 137 138 139 141 142 143\n",
      " 145 146 147 148 150 151 153 155 156 158].\n",
      "El voluntario voluntario_upv_13-_13 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  33  34  35  36  37  38  39\n",
      "  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  57  58  59\n",
      "  60  63  64  67  68  69  73  74  75  76  77  78  79  80  82  83  87  88\n",
      "  89  91  92  93  94  95  96  97  98 100 102 103 104 105 106 107 108 109\n",
      " 110 111 113 114 115 116 117 118 119 120 121 123 125 126 127 128 129 130\n",
      " 131 132 133 134 135 136 137 138 141 143 144 145 146 147 148 149 150 151\n",
      " 152 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_14-_14 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   7  10  12  21  24  27  30  33  35  37  38  39  42  43  45  46\n",
      "  47  49  51  53  56  60  66  68  69  72  73  75  78  80  82  84  86  91\n",
      "  93  98 105 112 113 115 117 124 125 133 134 136 137 144 146 148 149 157\n",
      " 159].\n",
      "El voluntario voluntario_upv_16-_16 es un outlier.\n",
      "Sus experimentos outliers son: [  7  12  14  25  31  41  48  51  59  63  65  66  75  85  90  92 100 101\n",
      " 106 109 112 120 122 123 126 128 131 150].\n",
      "El voluntario voluntario_upv_17-_17 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   3   4   5   6   7   8  10  11  12  13  14  15  16  17  18  19\n",
      "  22  23  24  25  26  27  28  29  30  32  33  34  35  36  37  38  39  40\n",
      "  41  42  43  44  45  46  47  49  50  51  52  54  55  56  57  58  59  62\n",
      "  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80\n",
      "  81  82  84  85  86  87  88  89  90  92  93  94  95  96  98 100 101 102\n",
      " 103 104 105 106 107 108 110 111 112 113 114 115 116 117 118 119 120 121\n",
      " 122 123 124 125 126 127 128 129 130 132 133 135 136 137 138 139 140 141\n",
      " 143 145 146 147 148 149 150 151 152 153 154 155 156 157 159].\n",
      "El voluntario voluntario_upv_18-_18 es un outlier.\n",
      "Sus experimentos outliers son: [  3   4   8   9  12  15  17  18  23  24  25  26  27  29  30  32  38  40\n",
      "  43  44  45  46  47  48  52  53  54  56  57  59  60  61  67  70  71  72\n",
      "  73  77  78  79  81  82  83  85  88  90  92  93  94  95  97 100 103 104\n",
      " 107 112 113 114 115 116 117 119 120 123 124 125 126 128 129 130 132 133\n",
      " 134 135 137 139 141 142 143 144 145 146 147 148 150 151 155 156 158 159].\n",
      "El voluntario voluntario_upv_19-_19 es un outlier.\n",
      "Sus experimentos outliers son: [  1   4   9  20  22  26  34  37  41  42  43  51  52  53  56  59  62  66\n",
      "  68  72  85  86  89  90  93  94  95  97 109 113 114 117 121 122 123 125\n",
      " 126 128 131 134 136 137 138 139 140 141 143 144 146 148 151 153 154 158\n",
      " 159].\n",
      "El voluntario voluntario_upv_20-_20 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  51  52  54  55\n",
      "  56  57  58  59  60  61  62  63  64  65  67  68  69  70  71  72  73  74\n",
      "  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92\n",
      "  93  94  95  96  97  98 100 101 102 103 104 105 106 107 108 109 110 111\n",
      " 112 113 114 115 116 118 119 121 123 124 125 126 127 128 129 130 131 132\n",
      " 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150\n",
      " 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_21-_21 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   6  10  11  12  13  14  16  17  18  21  22  25  26  27\n",
      "  28  31  34  35  38  40  42  43  44  45  46  47  52  53  57  58  59  63\n",
      "  65  66  67  68  69  73  74  76  77  79  80  81  83  84  85  86  87  89\n",
      "  90  91  92  94  97  99 101 106 107 108 114 116 118 119 120 121 122 123\n",
      " 126 127 128 130 132 133 134 135 136 137 138 139 140 141 142 144 145 146\n",
      " 147 148 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_22-_22 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   4   5   7   8   9  11  12  13  14  16  17  21  24  26  27  28\n",
      "  29  30  32  33  35  37  38  45  49  52  55  56  58  60  63  68  71  72\n",
      "  73  74  75  78  81  84  86  87  88  90  91  93  94  96  98  99 100 104\n",
      " 105 106 107 108 111 112 113 115 117 118 123 128 129 152 154 157].\n",
      "El voluntario voluntario_upv_23-_23 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
      "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90\n",
      "  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108\n",
      " 109 110 111 112 113 114 115 116 117 118 119 121 122 123 128 129 130 131\n",
      " 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149\n",
      " 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_26-_26 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2  12  16  22  25  27  28  29  31  32  33  34  36  41  46  47  53\n",
      "  56  61  66  71  73  74  75  81  83  86  88  92  93  94  99 100 101 102\n",
      " 111 117 123 126 129 131 136 138 141 145 146 150 156 157 158].\n",
      "El voluntario voluntario_upv_28-_28 es un outlier.\n",
      "Sus experimentos outliers son: [  2   3   7   9  11  12  13  14  15  16  17  18  20  21  22  23  24  25\n",
      "  26  27  28  29  30  32  35  36  37  38  39  40  41  45  47  52  53  54\n",
      "  59  60  61  62  64  65  66  68  69  71  72  73  74  75  76  78  79  81\n",
      "  82  83  84  85  87  89  90  91  96  98  99 101 103 104 106 110 112 113\n",
      " 115 117 118 119 121 122 123 124 125 126 128 129 130 131 132 133 134 135\n",
      " 136 137 138 140 141 142 143 145 148 150 151 152 153 154 155 156 159].\n",
      "El voluntario voluntario_upv_29-_29 es un outlier.\n",
      "Sus experimentos outliers son: [  7   8   9  10  11  12  13  18  20  21  22  23  24  26  27  29  30  32\n",
      "  34  35  37  40  41  42  43  44  45  46  50  51  52  53  54  55  59  60\n",
      "  61  62  63  64  65  66  68  69  71  75  77  78  79  80  81  82  83  84\n",
      "  85  86  87  88  89  90  91  92  93  95  96  98 100 101 103 106 108 110\n",
      " 111 112 113 115 117 120 122 123 125 126 127 128 129 130 131 132 133 134\n",
      " 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152\n",
      " 153 154 155 156 158 159].\n",
      "El voluntario voluntario_upv_30-_30 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   6   7   8   9  11  12  13  14  15  16  17  19  20\n",
      "  21  23  24  25  26  27  28  29  31  34  35  36  37  38  39  40  41  43\n",
      "  44  45  46  49  50  51  52  54  56  57  58  59  61  63  64  65  67  68\n",
      "  69  70  72  73  74  75  76  77  78  79  80  81  83  84  87  89  90  91\n",
      "  95  97  98  99 101 102 103 105 109 111 112 113 117 119 120 121 122 123\n",
      " 125 127 128 129 130 131 132 134 135 137 139 141 142 144 145 146 147 151\n",
      " 152 156].\n",
      "El voluntario voluntario_upv_31-_31 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   7   8   9  10  11  12  13  14  15  16  18  19\n",
      "  20  21  23  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41\n",
      "  43  44  45  46  47  48  49  51  52  53  55  56  57  58  59  60  61  62\n",
      "  63  64  65  67  68  69  71  72  73  74  75  76  77  78  79  80  81  83\n",
      "  84  85  87  88  89  90  91  92  93  95  96  97  98  99 100 101 103 106\n",
      " 107 108 109 110 112 113 115 116 117 118 119 121 122 123 124 128 129 130\n",
      " 131 133 134 135 136 139 140 141 143 144 145 146 147 148 149 150 152 153\n",
      " 154 155 156 157 158 159].\n",
      "El voluntario voluntario_upv_32-_32 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   8   9  12  13  15  16  17  18  19  20  21  22  23  24  27\n",
      "  29  30  31  32  33  34  36  38  39  41  42  46  50  52  57  58  60  62\n",
      "  64  65  66  70  73  75  78  81  82  83  85  86  89  90  91  94  95  96\n",
      "  98 102 104 106 108 110 111 112 113 114 116 117 118 119 124 125 126 129\n",
      " 130 133 134 135 138 139 140 141 144 147 152 153 154 157 158].\n",
      "El voluntario voluntario_upv_33-_33 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   9  10  11  14  15  16  18  20  21  22\n",
      "  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  42\n",
      "  43  44  45  46  47  48  49  50  53  54  55  56  57  58  59  60  61  62\n",
      "  63  65  67  68  69  70  71  72  73  74  75  77  79  80  81  82  83  85\n",
      "  86  87  89  90  91  92  93  95  96  97 100 102 103 104 105 107 108 109\n",
      " 110 111 112 113 115 117 118 120 121 124 125 126 127 130 131 132 135 136\n",
      " 138 140 142 144 145 146 147 148 150 151 153 154 156 157 158 159].\n",
      "El voluntario voluntario_upv_34-_34 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   3   4   5   6   7   8   9  10  11  13  16  18  20  22  24  25\n",
      "  26  27  28  29  31  32  35  37  38  39  40  41  42  43  44  45  46  47\n",
      "  52  54  56  58  59  61  62  63  64  67  68  70  71  72  73  74  75  80\n",
      "  83  85  87  88  89  90  91  92  93  94  95  96  97  98 100 101 102 103\n",
      " 104 105 106 107 108 109 111 113 115 116 118 121 123 125 126 129 130 131\n",
      " 134 135 136 138 139 140 141 145 146 147 148 149 150 151 156].\n",
      "El voluntario voluntario_upv_35-_35 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   5   6   7   9  10  11  12  14  15  16  17  18  19  20  21\n",
      "  24  25  26  27  28  29  30  31  32  33  34  37  38  39  40  41  42  44\n",
      "  46  47  48  51  52  53  54  62  65  66  67  68  69  71  72  73  75  76\n",
      "  77  78  79  80  81  84  86  88  89  90  92  93  94  95  96  97  98 100\n",
      " 101 102 103 104 105 106 107 108 109 111 112 114 116 117 118 120 121 123\n",
      " 124 127 130 132 134 137 138 140 141 147 149 152 154 155 156 158 159].\n",
      "El voluntario voluntario_upv_36-_36 es un outlier.\n",
      "Sus experimentos outliers son: [  2  13  20  24  30  36  37  40  42  53  61  66  70  84  91  99 111 117\n",
      " 119 122 123 124 129 132 143 144 149 153 155].\n"
     ]
    }
   ],
   "source": [
    "# Variables generales\n",
    "ruidos = scipy.io.loadmat('ruidosExp1.mat')\n",
    "ruidos = np.array(ruidos['ruidos'])\n",
    "minX = np.min(ruidos[:, 2])\n",
    "maxX = np.max(ruidos[:, 2])\n",
    "LoopNumber = 16\n",
    "numberObservations = 10\n",
    "    \n",
    "for voluntario, ficheros in voluntarios_upv.items():    \n",
    "    mean_general_sin_voluntario = None\n",
    "    num_ficheros = 0\n",
    "    \n",
    "    # Calculo de la media general sin el voluntario actual\n",
    "    for vol, fichs in voluntarios_upv.items():\n",
    "        if vol!=voluntario:\n",
    "            for fic in fichs:\n",
    "                data_file = PATH_UPV+'/'+fic\n",
    "                result = scipy.io.loadmat(data_file)\n",
    "                result = np.array(result['Results'])\n",
    "                if mean_general_sin_voluntario is None:\n",
    "                    mean_general_sin_voluntario = result\n",
    "                else:\n",
    "                    mean_general_sin_voluntario = mean_general_sin_voluntario + result \n",
    "                    # Suma de los resultados\n",
    "                    # Promediar la probabilidad y Promediar el tiempo\n",
    "                num_ficheros+=len(fichs)\n",
    "    mean_general_sin_voluntario = mean_general_sin_voluntario/num_ficheros\n",
    "    \n",
    "    \n",
    "    # Calculo de la media del voluntario\n",
    "    mean_voluntario = None\n",
    "    for fichero in ficheros:\n",
    "        data_file = PATH_UPV+'/'+fichero\n",
    "        result = scipy.io.loadmat(data_file)\n",
    "        result = np.array(result['Results'])\n",
    "        if mean_voluntario is None:\n",
    "            mean_voluntario = result\n",
    "        else:\n",
    "            mean_voluntario = mean_voluntario + result \n",
    "            # Suma de los resultados\n",
    "            # Promediar la probabilidad y Promediar el tiempo\n",
    "    mean_voluntario = mean_voluntario/len(ficheros) # Media de los resultados para un mismo voluntario\n",
    "    \n",
    "    # Calculo el rango esperado\n",
    "    std_general = [np.std(mean_general_sin_voluntario[:,0]), np.std(mean_general_sin_voluntario[:,1])]\n",
    "    lower_bound = mean_general_sin_voluntario - 2 * np.array(std_general).reshape((1, 2))\n",
    "    upper_bound = mean_general_sin_voluntario + 2 * np.array(std_general).reshape((1, 2))\n",
    "    \n",
    "    # Comparar la media del voluntario actual con la media general y detectar outliers\n",
    "    # Realiza los gráficos si es outlier\n",
    "    experimento_outlier = (mean_voluntario < lower_bound) | (mean_voluntario > upper_bound)\n",
    "    # print(\"Ambas columnas son outliers\\n\")\n",
    "    ambas = np.where(experimento_outlier.all(axis=1))[0]\n",
    "    # print(ambas) # Ambos son Outlier\n",
    "    # print(\"Al menos una son outliers\\n\")\n",
    "    al_menos_una = np.where(experimento_outlier)[0]\n",
    "    # print(al_menos_una) # Al menos un es Outlier\n",
    "\n",
    "    if (len(ambas)>3):\n",
    "        print(f\"El voluntario {voluntario} es un outlier.\")\n",
    "        print(f\"Sus experimentos outliers son: {ambas}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d4e0ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'voluntario_ugr_1-_1': ['ResultsJNDsNoiseFlatBG20220401T131441.mat'],\n",
       " 'voluntario_ugr_2-_2': ['ResultsJNDsNoiseFlatBG20220317T113653.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220407T113228.mat'],\n",
       " 'voluntario_ugr_3-_3': ['ResultsJNDsNoiseFlatBG20220201T134054.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T124958.mat'],\n",
       " 'voluntario_ugr_4-_4': ['ResultsJNDsNoiseFlatBG20220427T114635.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220408T103353.mat'],\n",
       " 'voluntario_ugr_5-_5': ['ResultsJNDsNoiseFlatBG20220401T121203.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220323T132705.mat'],\n",
       " 'voluntario_ugr_6-_6': ['ResultsJNDsNoiseFlatBG20220303T125835.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220314T113216.mat'],\n",
       " 'voluntario_ugr_7-_7': ['ResultsJNDsNoiseFlatBG20220318T133520.mat'],\n",
       " 'voluntario_ugr_8-_8': ['ResultsJNDsNoiseFlatBG20220411T193944.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220420T172846.mat'],\n",
       " 'voluntario_ugr_9-_9': ['ResultsJNDsNoiseFlatBG20220223T112641.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220223T161544.mat'],\n",
       " 'voluntario_ugr_10-_10': ['ResultsJNDsNoiseFlatBG20220209T132743.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220204T134543.mat'],\n",
       " 'voluntario_ugr_11-_11': ['ResultsJNDsNoiseFlatBG20220202T113037.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220208T122326.mat'],\n",
       " 'voluntario_ugr_12-_12': ['ResultsJNDsNoiseFlatBG20220204T112945.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220426T124527.mat'],\n",
       " 'voluntario_ugr_13-_13': ['ResultsJNDsNoiseFlatBG20220121T133331.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220204T123031.mat'],\n",
       " 'voluntario_ugr_14-_14': ['ResultsJNDsNoiseFlatBG20220114T110151.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220121T104058.mat'],\n",
       " 'voluntario_ugr_15-_15': ['ResultsJNDsNoiseFlatBG20220406T123850.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220421T122349.mat'],\n",
       " 'voluntario_ugr_16-_16': ['ResultsJNDsNoiseFlatBG20211217T115223.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220114T114049.mat'],\n",
       " 'voluntario_ugr_17-_17': ['ResultsJNDsNoiseFlatBG20220411T201459.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220421T180502.mat'],\n",
       " 'voluntario_ugr_18-_18': ['ResultsJNDsNoiseFlatBG20220422T111834.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220426T093703.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220405T100824.mat'],\n",
       " 'voluntario_ugr_19-_19': ['ResultsJNDsNoiseFlatBG20220331T123440.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220419T122341.mat'],\n",
       " 'voluntario_ugr_20-_20': ['ResultsJNDsNoiseFlatBG20220302T112937.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220304T105123.mat'],\n",
       " 'voluntario_ugr_21-_21': ['ResultsJNDsNoiseFlatBG20220114T120209.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220128T092751.mat'],\n",
       " 'voluntario_ugr_22-_22': ['ResultsJNDsNoiseFlatBG20220328T140217.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220330T140514.mat'],\n",
       " 'voluntario_ugr_23-_23': ['ResultsJNDsNoiseFlatBG20220127T145837.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220121T122318.mat'],\n",
       " 'voluntario_ugr_24-_24': ['ResultsJNDsNoiseFlatBG20220202T174903.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220207T134041.mat'],\n",
       " 'voluntario_ugr_25-_25': ['ResultsJNDsNoiseFlatBG20220121T112811.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220127T141004.mat'],\n",
       " 'voluntario_ugr_26-_26': ['ResultsJNDsNoiseFlatBG20220425T172713.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220422T124814.mat'],\n",
       " 'voluntario_ugr_27-_27': ['ResultsJNDsNoiseFlatBG20220201T111133.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T134220.mat'],\n",
       " 'voluntario_ugr_28-_28': ['ResultsJNDsNoiseFlatBG20220405T132104.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220401T134721.mat'],\n",
       " 'voluntario_ugr_29-_29': ['ResultsJNDsNoiseFlatBG20220216T114246.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220223T104302.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220301T101810.mat'],\n",
       " 'voluntario_ugr_30-_30': ['ResultsJNDsNoiseFlatBG20220317T132259.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220407T125413.mat'],\n",
       " 'voluntario_ugr_31-_31': ['ResultsJNDsNoiseFlatBG20220420T175405.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220427T184121.mat'],\n",
       " 'voluntario_ugr_32-_32': ['ResultsJNDsNoiseFlatBG20220114T124227.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220202T105219.mat'],\n",
       " 'voluntario_ugr_33-_33': ['ResultsJNDsNoiseFlatBG20220211T125927.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220405T122450.mat'],\n",
       " 'voluntario_ugr_34-_34': ['ResultsJNDsNoiseFlatBG20220114T122007.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220128T130104.mat'],\n",
       " 'voluntario_ugr_35-_35': ['ResultsJNDsNoiseFlatBG20220308T111929.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220209T104348.mat'],\n",
       " 'voluntario_ugr_36-_36': ['ResultsJNDsNoiseFlatBG20220428T132532.mat'],\n",
       " 'voluntario_ugr_37-_37': ['ResultsJNDsNoiseFlatBG20220407T102908.mat',\n",
       "  'ResultsJNDsNoiseFlatBG20220317T095724.mat']}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PATH_UGR = \"./DatosExperimento1/UGR\"\n",
    "\n",
    "# Leer el archivo Excel en un DataFrame de pandas\n",
    "df = pd.read_excel(PATH_UGR+'/Exp1_Observadores_UGR.xlsx')\n",
    "    \n",
    "if not os.path.exists('Resultados_UGR'):\n",
    "    os.makedirs('Resultados_UGR')\n",
    "\n",
    "# Crear un diccionario vacío para almacenar los resultados\n",
    "voluntarios_ugr = {}\n",
    "\n",
    "# Iterar sobre cada fila del DataFrame\n",
    "for idx, row in df.iterrows():\n",
    "    # Tratamiento de nombre y apellidos para evitar que una misma persona se trate como diferente:\n",
    "    # Quito espacios, mayusculas, minuscula s, etc\n",
    "    nombre = unidecode(row['Nombre'].lower())\n",
    "    apellidos = unidecode(row['Apellidos'].lower())\n",
    "    fichero = row['Fichero']\n",
    "    # Crear la clave del diccionario usando el nombre y apellidos\n",
    "    clave = f\"{nombre}-{apellidos}\".replace(\" \", \"\")\n",
    "    \n",
    "    # Agregar el valor de la columna \"Fichero\" a la lista correspondiente en el diccionario\n",
    "    if clave in voluntarios_ugr:\n",
    "        voluntarios_ugr[clave].append(fichero)\n",
    "    else:\n",
    "        voluntarios_ugr[clave] = [fichero]\n",
    "voluntarios_ugr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a0c87b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El voluntario voluntario_ugr_1-_1 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   5   8  11  13  15  21  22  23  27  28  30  36  37  40  41  42\n",
      "  45  46  50  53  54  57  59  63  66  70  71  75  79  81  91  92 100 102\n",
      " 104 110 111 112 118 120 125 127 130 136 151 153].\n",
      "El voluntario voluntario_ugr_2-_2 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   8   9  10  11  12  13  14  15  16  17  18\n",
      "  20  21  22  23  24  25  26  27  28  29  31  33  34  35  36  37  38  39\n",
      "  40  41  42  43  45  46  47  48  49  50  51  52  53  55  56  58  60  61\n",
      "  62  64  65  68  71  72  73  74  75  76  78  79  80  81  84  85  86  87\n",
      "  88  89  90  92  93  94  95  96  98  99 100 101 102 103 106 108 109 110\n",
      " 112 113 114 117 118 119 121 124 125 126 127 128 129 131 132 133 134 135\n",
      " 136 137 138 139 141 142 143 144 145 146 147 148 150 151 152 153 154 155\n",
      " 156 157 158 159].\n",
      "El voluntario voluntario_ugr_3-_3 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   4   5   7  12  13  15  18  24  25  27  28  30  32  34  39\n",
      "  40  41  42  43  44  48  50  51  52  53  55  61  62  63  64  65  72  78\n",
      "  79  81  82  83  84  85  86  89  90  92  93  94  96  97 100 101 102 103\n",
      " 104 107 110 111 113 116 120 122 123 124 125 126 127 128 129 131 132 133\n",
      " 134 135 136 137 138 140 141 142 144 145 146 148 149 150 151 152 153 156\n",
      " 158 159].\n",
      "El voluntario voluntario_ugr_4-_4 es un outlier.\n",
      "Sus experimentos outliers son: [  2   8  14  15  29  31  34  37  39  41  43  44  50  54  56  58  65  66\n",
      "  69  73  77  80  81  82  83  85  87  88  91  92  93  98 101 102 104 105\n",
      " 106 111 112 116 120 122 123 126 127 128 129 130 133 134 135 136 137 138\n",
      " 139 140 141 142 143 144 145 146 147 148 151 152 153 154 157 158].\n",
      "El voluntario voluntario_ugr_5-_5 es un outlier.\n",
      "Sus experimentos outliers son: [ 12  21  81  82  84  85  88 120 122 126 134 135].\n",
      "El voluntario voluntario_ugr_6-_6 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_7-_7 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  32  33  35  36  37  39\n",
      "  40  41  42  43  44  45  46  47  48  49  51  52  53  54  55  56  57  58\n",
      "  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76\n",
      "  77  78  79  81  82  83  84  85  86  87  88  90  91  92  93  94  95  96\n",
      "  97  98  99 101 102 103 104 105 106 107 108 109 110 111 112 113 115 117\n",
      " 118 119 121 122 124 126 129 130 131 132 133 134 136 137 138 139 140 141\n",
      " 142 143 144 145 146 147 148 149 150 152 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_8-_8 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   5   7   8   9  10  11  12  13  14  16  17  20  21  23  26  27\n",
      "  30  32  33  34  35  36  37  39  40  41  44  45  46  47  48  49  50  51\n",
      "  52  53  54  55  56  57  59  60  61  63  64  65  66  67  68  69  70  71\n",
      "  72  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 101 102 103 104 105 106 107 108 109 110\n",
      " 111 113 114 115 116 117 118 119 122 123 124 125 126 127 129 130 131 133\n",
      " 134 135 136 137 138 139 141 142 143 144 145 146 147 148 149 150 151 152\n",
      " 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_9-_9 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  13  14  16  17  18  19  20\n",
      "  22  23  25  26  27  28  29  31  32  34  35  36  37  38  39  40  41  42\n",
      "  43  44  45  46  47  48  49  50  52  53  54  55  56  57  58  59  60  61\n",
      "  62  64  65  66  67  68  69  72  73  74  75  76  77  78  79  80  81  82\n",
      "  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100\n",
      " 101 102 103 104 105 106 107 108 109 111 113 114 115 116 117 118 119 120\n",
      " 122 123 124 125 126 127 128 129 131 132 133 134 135 136 138 139 140 141\n",
      " 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_10-_10 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
      "  19  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37\n",
      "  38  39  40  41  42  43  44  45  46  47  48  49  50  52  53  54  55  56\n",
      "  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74\n",
      "  75  76  77  78  79  81  82  83  84  85  86  87  88  89  90  91  92  93\n",
      "  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111\n",
      " 112 113 114 115 116 117 118 119 122 123 124 125 126 127 128 129 131 132\n",
      " 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150\n",
      " 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_11-_11 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   3  10  23  27  36  41  43  46  49  50  63  64  87 104 105 117\n",
      " 120 122 123 128 137].\n",
      "El voluntario voluntario_ugr_12-_12 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  41  42  43  44  45  46  47  48  49  50  52  53  54  55  56\n",
      "  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74\n",
      "  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92\n",
      "  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110\n",
      " 111 112 113 114 115 116 117 118 119 120 121 122 123 125 126 127 128 129\n",
      " 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148\n",
      " 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_13-_13 es un outlier.\n",
      "Sus experimentos outliers son: [  0   2   7   8   9  10  11  14  20  23  25  26  32  35  36  40  42  44\n",
      "  46  47  48  49  54  56  59  61  67  68  69  72  82  83  84  85  86  88\n",
      "  92  93  95  96  97  98  99 100 103 105 106 107 108 109 111 112 113 114\n",
      " 115 116 118 119 121 122 123 124 125 126 127 129 130 134 135 137 138 141\n",
      " 143 146 147 148 149 150 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_14-_14 es un outlier.\n",
      "Sus experimentos outliers son: [  0   6  11  18  25  27  29  31  32  33  35  36  37  38  65  66  74  77\n",
      "  88  90 103 110 114 122 125 132 136 138 141 142 150 153].\n",
      "El voluntario voluntario_ugr_15-_15 es un outlier.\n",
      "Sus experimentos outliers son: [  1   4   5   7   9  14  15  21  25  29  31  34  47  48  63  69  74  76\n",
      "  77  79  84  86  90  91  95  98 106 110 111 112 116 118 120 121 124 126\n",
      " 133 141 143 147].\n",
      "El voluntario voluntario_ugr_16-_16 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18\n",
      "  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72\n",
      "  73  74  75  76  77  78  79  80  81  82  83  84  85  86  88  89  90  91\n",
      "  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109\n",
      " 110 111 112 113 114 115 116 117 118 119 120 121 122 127 128 129 130 131\n",
      " 132 133 134 135 136 137 138 139 140 141 143 144 145 147 148 149 150 151\n",
      " 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_17-_17 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   5   6   7   8   9  10  11  12  13  14  15  16  18  19  20\n",
      "  21  22  23  24  25  26  28  29  30  31  33  34  35  36  37  39  40  41\n",
      "  42  43  44  45  46  47  48  49  50  51  52  54  55  56  57  58  59  60\n",
      "  61  62  63  64  65  66  67  68  69  70  71  72  73  75  76  77  78  79\n",
      "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
      "  98  99 101 102 103 104 105 106 107 108 110 111 112 113 114 115 116 117\n",
      " 118 119 120 121 122 123 124 125 126 127 129 131 132 133 134 135 136 137\n",
      " 138 139 140 141 142 143 145 146 147 148 149 151 152 153 154 155 156 157\n",
      " 158 159].\n",
      "El voluntario voluntario_ugr_18-_18 es un outlier.\n",
      "Sus experimentos outliers son: [  2  10  28  30  33  34  39  43  44  48  50  54  59  67  72  77  85  88\n",
      "  90  91  95  98 104 111 112 114 118 120 122 126 132 133 135 145 146 147\n",
      " 148 149 159].\n",
      "El voluntario voluntario_ugr_19-_19 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   8   9  13  15  19  20  21  22  23  27  30  31\n",
      "  34  38  39  40  41  42  43  46  47  48  49  51  53  56  64  66  67  68\n",
      "  70  72  73  74  75  76  77  80  81  82  84  85  86  87  88  90  91  93\n",
      "  94  95  97  98  99 100 101 102 105 106 111 114 115 117 118 119 120 122\n",
      " 123 124 125 126 127 130 131 133 134 135 137 139 140 141 143 144 145 146\n",
      " 147 148 149 150 151 152 153 154 155 156 158].\n",
      "El voluntario voluntario_ugr_20-_20 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   4   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20\n",
      "  21  22  23  25  26  27  29  30  31  32  33  34  35  36  37  38  39  40\n",
      "  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58\n",
      "  59  60  61  62  63  64  65  66  67  68  69  70  71  72  74  75  76  77\n",
      "  79  80  82  83  84  85  86  87  88  89  90  91  92  94  95  96  97  98\n",
      "  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116\n",
      " 117 118 119 124 125 128 129 130 131 132 133 134 136 137 138 139 140 141\n",
      " 142 144 145 146 147 148 149 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_21-_21 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   4   9  14  15  21  29  30  32  34  36  38  49  54  57  67  69\n",
      "  71  72  78  79  92 102 108 109 111 114 118 122 137 157 159].\n",
      "El voluntario voluntario_ugr_22-_22 es un outlier.\n",
      "Sus experimentos outliers son: [ 12  13  20  21  23  26  30  32  33  40  41  42  52  54  55  71  82  90\n",
      "  92  95 100 104 110 111 118 122 123 124 125 127 128 129 133 134 150].\n",
      "El voluntario voluntario_ugr_23-_23 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  15  16  17  18\n",
      "  19  20  25  28  29  32  33  34  35  36  38  40  41  42  43  44  45  46\n",
      "  47  48  49  51  52  53  55  56  57  58  59  60  62  64  65  66  67  68\n",
      "  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86\n",
      "  87  88  89  90  91  92  94  95  96  97  98  99 100 101 102 103 104 105\n",
      " 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 123 124 125\n",
      " 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144\n",
      " 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_24-_24 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   4  10  11  12  13  14  19  20  21  22  23  24  26  28  30\n",
      "  31  33  34  35  36  37  39  41  50  51  52  53  54  60  61  62  63  64\n",
      "  70  72  80  81  82  83  84  90  91 100 101 102 104 110 111 120 122 124\n",
      " 126 127 128 129 130 131 133 140 143 150 151 153].\n",
      "El voluntario voluntario_ugr_25-_25 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  26  27  28  29  30  31  32  33  34  35  36\n",
      "  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  72  73\n",
      "  74  75  76  77  78  79  80  81  83  84  85  86  87  88  89  90  91  92\n",
      "  93  94  95  96  98  99 100 101 102 103 104 105 107 108 109 110 111 112\n",
      " 113 114 115 116 117 118 119 120 122 124 125 126 127 128 132 133 134 135\n",
      " 136 137 138 139 140 141 142 143 144 145 146 147 148 149 151 152 153 154\n",
      " 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_26-_26 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   5   6   7   8   9  10  11  12  13  14  15  17  18  19  21\n",
      "  22  23  24  26  29  32  35  36  38  39  40  41  42  43  44  45  46  47\n",
      "  48  49  50  51  52  53  54  55  56  58  59  61  62  65  67  70  71  72\n",
      "  73  74  76  77  78  79  80  81  84  85  86  87  88  89  92  93  94  95\n",
      "  96  97  98  99 100 101 103 105 106 107 108 109 110 112 114 115 116 117\n",
      " 118 119 122 123 124 125 126 127 128 129 133 134 135 136 137 138 139 142\n",
      " 143 145 146 147 148 149 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_27-_27 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   3   4   6   7  29  39  43  45  54  71  72  80  81  83  93  94\n",
      "  97 102 106 110 112 117 120 121 124 125 126 128 129 130 137 138 144 153\n",
      " 154 159].\n",
      "El voluntario voluntario_ugr_28-_28 es un outlier.\n",
      "Sus experimentos outliers son: [  1   4   8  21  43  46  48  51  56  58  70  81  85  93  95  98  99 100\n",
      " 110 115 122 129 130 131 132 139 144 152].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El voluntario voluntario_ugr_29-_29 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  48  49  50  51  52  53  54\n",
      "  55  56  57  58  59  61  62  63  64  65  66  67  68  69  70  71  72  73\n",
      "  74  75  76  77  78  79  81  82  83  84  85  86  87  88  89  90  91  92\n",
      "  93  94  95  97  98  99 100 101 102 103 104 105 106 107 109 110 111 112\n",
      " 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130\n",
      " 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149\n",
      " 150 151 152 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_30-_30 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  21  22  23  24  25  26  27  28  29  30  31  33  34  35  36  37\n",
      "  38  39  40  41  42  44  45  46  47  48  49  50  51  52  53  54  55  56\n",
      "  57  58  59  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75\n",
      "  76  77  78  79  80  81  82  83  84  85  86  87  88  89  91  92  93  94\n",
      "  95  96  97  98  99 100 102 103 104 105 106 107 108 109 110 111 112 113\n",
      " 114 115 116 117 118 119 122 124 125 126 127 128 129 130 131 132 133 134\n",
      " 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152\n",
      " 153 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_31-_31 es un outlier.\n",
      "Sus experimentos outliers son: [  2   6   8  12  21  24  29  33  42  45  47  49  61  64  85  94  99 113\n",
      " 116 122 126 148 154 155 158].\n",
      "El voluntario voluntario_ugr_32-_32 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   4  10  11  13  22  23  24  29  30  32  34  42  43  46  48  53\n",
      "  54  55  60  61  72  82  88  94  96  99 100 105 107 110 113 114 135 139\n",
      " 140 143 144 147 148 149 152 154 157].\n",
      "El voluntario voluntario_ugr_33-_33 es un outlier.\n",
      "Sus experimentos outliers son: [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  25  27  28  29  31  32  33  34  35  36  37  38\n",
      "  39  40  41  43  44  45  48  49  50  51  52  54  55  56  57  58  59  60\n",
      "  61  62  63  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79\n",
      "  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97\n",
      "  98  99 100 102 103 104 105 106 107 108 109 111 112 113 114 115 116 117\n",
      " 118 119 121 122 123 125 126 127 128 129 130 131 132 133 134 135 136 137\n",
      " 138 139 141 142 143 144 145 146 147 148 149 150 151 153 154 155 156 157\n",
      " 158 159].\n",
      "El voluntario voluntario_ugr_34-_34 es un outlier.\n",
      "Sus experimentos outliers son: [  0   4   6   7  10  11  12  13  15  17  20  21  27  30  31  32  33  34\n",
      "  35  37  40  41  44  48  49  51  52  56  57  66  74  86  89  90  95  97\n",
      "  99 101 110 112 113 119 131 137 141 145 146 148 152 153 156 158].\n",
      "El voluntario voluntario_ugr_35-_35 es un outlier.\n",
      "Sus experimentos outliers son: [  1   2   3   4   5   6   7   8   9  11  12  13  14  15  16  17  18  19\n",
      "  21  22  23  24  25  26  27  28  29  30  31  32  34  35  36  37  38  39\n",
      "  40  41  43  45  46  47  48  49  51  52  53  55  56  57  58  59  61  62\n",
      "  63  64  65  66  67  68  69  70  72  73  75  76  77  78  79  80  82  83\n",
      "  85  87  88  89  90  91  92  93  95  96  97  99 100 101 102 103 104 105\n",
      " 106 107 108 109 110 112 113 114 115 116 117 118 119 120 124 126 127 128\n",
      " 130 131 132 133 134 135 137 138 139 140 141 143 144 145 146 147 148 149\n",
      " 152 154 156 157 158 159].\n",
      "El voluntario voluntario_ugr_36-_36 es un outlier.\n",
      "Sus experimentos outliers son: [  2   3   4   5   6   7   8   9  10  12  14  15  16  17  18  19  20  21\n",
      "  22  23  24  25  26  27  28  29  30  31  32  34  35  37  38  39  40  41\n",
      "  42  43  44  45  46  47  48  49  50  52  53  54  55  56  57  58  59  60\n",
      "  61  62  63  64  65  66  67  68  69  71  72  73  75  76  77  78  79  80\n",
      "  81  82  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100\n",
      " 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119\n",
      " 123 124 125 126 127 129 130 131 132 133 134 135 136 137 138 141 142 143\n",
      " 144 145 146 147 148 149 150 152 154 155 156 157 158 159].\n",
      "El voluntario voluntario_ugr_37-_37 es un outlier.\n",
      "Sus experimentos outliers son: [ 15  29  31  37  40  67  69 111 114 116 121 127 143].\n"
     ]
    }
   ],
   "source": [
    "# Variables generales\n",
    "ruidos = scipy.io.loadmat('ruidosExp1.mat')\n",
    "ruidos = np.array(ruidos['ruidos'])\n",
    "minX = np.min(ruidos[:, 2])\n",
    "maxX = np.max(ruidos[:, 2])\n",
    "LoopNumber = 16\n",
    "numberObservations = 10\n",
    "    \n",
    "for voluntario, ficheros in voluntarios_ugr.items():    \n",
    "    mean_general_sin_voluntario = None\n",
    "    num_ficheros = 0\n",
    "    \n",
    "    # Calculo de la media general sin el voluntario actual\n",
    "    for vol, fichs in voluntarios_ugr.items():\n",
    "        if vol!=voluntario:\n",
    "            for fic in fichs:\n",
    "                data_file = PATH_UGR+'/'+fic\n",
    "                result = scipy.io.loadmat(data_file)\n",
    "                result = np.array(result['Results'])\n",
    "                if mean_general_sin_voluntario is None:\n",
    "                    mean_general_sin_voluntario = result\n",
    "                else:\n",
    "                    mean_general_sin_voluntario = mean_general_sin_voluntario + result \n",
    "                    # Suma de los resultados\n",
    "                    # Promediar la probabilidad y Promediar el tiempo\n",
    "                num_ficheros+=len(fichs)\n",
    "    mean_general_sin_voluntario = mean_general_sin_voluntario/num_ficheros\n",
    "    \n",
    "    \n",
    "    # Calculo de la media del voluntario\n",
    "    mean_voluntario = None\n",
    "    for fichero in ficheros:\n",
    "        data_file = PATH_UGR+'/'+fichero\n",
    "        result = scipy.io.loadmat(data_file)\n",
    "        result = np.array(result['Results'])\n",
    "        if mean_voluntario is None:\n",
    "            mean_voluntario = result\n",
    "        else:\n",
    "            mean_voluntario = mean_voluntario + result \n",
    "            # Suma de los resultados\n",
    "            # Promediar la probabilidad y Promediar el tiempo\n",
    "    mean_voluntario = mean_voluntario/len(ficheros) # Media de los resultados para un mismo voluntario\n",
    "    \n",
    "    # Calculo el rango esperado\n",
    "    std_general = [np.std(mean_general_sin_voluntario[:,0]), np.std(mean_general_sin_voluntario[:,1])]\n",
    "    lower_bound = mean_general_sin_voluntario - 2 * np.array(std_general).reshape((1, 2))\n",
    "    upper_bound = mean_general_sin_voluntario + 2 * np.array(std_general).reshape((1, 2))\n",
    "    \n",
    "    # Comparar la media del voluntario actual con la media general y detectar outliers\n",
    "    # Realiza los gráficos si es outlier\n",
    "    experimento_outlier = (mean_voluntario < lower_bound) | (mean_voluntario > upper_bound)\n",
    "    # print(\"Ambas columnas son outliers\\n\")\n",
    "    ambas = np.where(experimento_outlier.all(axis=1))[0]\n",
    "    # print(ambas) # Ambos son Outlier\n",
    "    # print(\"Al menos una son outliers\\n\")\n",
    "    al_menos_una = np.where(experimento_outlier)[0]\n",
    "    # print(al_menos_una) # Al menos un es Outlier\n",
    "    if (len(ambas)>3):\n",
    "        print(f\"El voluntario {voluntario} es un outlier.\")\n",
    "        print(f\"Sus experimentos outliers son: {ambas}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee802375",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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