Detección series de armónicos - Espectro importado - Short.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cec51fc8",
   "metadata": {},
   "source": [
    "# ALGORITMO PARA IDENTIFICACIÓN SERIES DE ARMÓNICOS EN UN ESPECTRO DADO\n",
    "\n",
    "El objetivo de este programa es identificar las series de armónicos más importantes de un espectro de frecuencias dado.\n",
    "\n",
    "El esquema principal de este script es:\n",
    "* Importar el fichero de datos de un espectro de frecuencias (en formato csv)\n",
    "    * Modificar el espectro si éste tiene ski-slope\n",
    "* Detectar picos e incertidumbres de los mismos:\n",
    "    * Detección de picos con la función *encontrar_picos*\n",
    "    * Funciones auxiliares creadas: *_peak_prominences*, *_peak_widths*\n",
    "    * Cálculo de las incertidumbres\n",
    "* Interpolación del espectro para estimar y corregir la localización de los picos\n",
    "    * Función utilizada: *peak_estimator* con tipo de ventana: 'hann' o 'rect'\n",
    "* Recalcular la incertidumbre de los picos\n",
    "* Guardar todos los datos de picos e incertidumbres en un DataFrame\n",
    "* Algoritmo principal de detección de series de armónicos\n",
    "    * Introducción al algoritmo\n",
    "    * Funciones auxiliares\n",
    "    * Detección de series de armónicos\n",
    "    * Criterios de clasificación de las series encontradas\n",
    "    * Resultados obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e04c6e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import encontrar_picos, _peak_prominences, _peak_widths, peak_estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a566b0",
   "metadata": {},
   "source": [
    "## Importar los datos de un espectro de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8b55dee5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.125000e+00, 6.970920e-01],\n",
       "       [4.687500e+00, 6.532500e-01],\n",
       "       [6.250000e+00, 7.628560e-01],\n",
       "       ...,\n",
       "       [4.995313e+03, 1.439000e-03],\n",
       "       [4.996875e+03, 8.610000e-04],\n",
       "       [4.998438e+03, 1.147000e-03]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### CARGAR DOCUMENTO CSV ####\n",
    "\n",
    "datos = 'ejemploscsv/Machine 2 - C1V.csv'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = np.loadtxt(datos, delimiter=',', skiprows=1) #separador decimales: .\n",
    "                                                    #separador columnas: ,\n",
    "                                                    #skiprows: cuantas lineas iniciales me salto\n",
    "# Mostramos los datos\n",
    "display(data)\n",
    "frecuencias = data[:,0]\n",
    "amplitudes = data[:,1]\n",
    "\n",
    "# Mostramos la gráfica\n",
    "plt.plot(frecuencias, amplitudes)\n",
    "plt.xlabel('Frecuencia (Hz)')\n",
    "plt.ylabel('Amplitud')\n",
    "plt.title('Espectro de frecuencias del conjunto de datos\\n'+datos, fontsize='14')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2466725",
   "metadata": {},
   "source": [
    "### Modificar el espectro si éste tiene ski-slope\n",
    "\n",
    "Modificamos la señal transformando los primeros *nceros* valores a 0.\n",
    "\n",
    "*Nota: para modificar el espectro también existe otra técnica que es integrar la señal.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a2def9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nnceros = 100\\namplitudes[0:100] = [0]*nceros\\n\\nplt.plot(frecuencias, amplitudes)\\nplt.xlabel('Frecuencia (Hz)')\\nplt.ylabel('Amplitud')\\nplt.title('Espectro de frecuencias del conjunto de datos\\n'+datos, fontsize='14')\\n\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modificar la variable nceros para eliminar el \"ski-slope\"\n",
    "\"\"\"\n",
    "nceros = 100\n",
    "amplitudes[0:100] = [0]*nceros\n",
    "\n",
    "plt.plot(frecuencias, amplitudes)\n",
    "plt.xlabel('Frecuencia (Hz)')\n",
    "plt.ylabel('Amplitud')\n",
    "plt.title('Espectro de frecuencias del conjunto de datos\\n'+datos, fontsize='14')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f4ec78e",
   "metadata": {},
   "source": [
    "## Detección de picos e incertidumbre de los mismos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "feb4a155",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = np.std(amplitudes) #calculo la desviacion tipica de las amplitudes\n",
    "\n",
    "#### MODIFICAR LOS SIGUIENTES PARÁMETROS PARA LA DETECCION DE PICOS E INCERTIDUMBRES\n",
    "\n",
    "altura_pico = s          # Minima altura para detectar un pico en la gráfica\n",
    "altura_relativa = 0.75   # Altura relativa a la cual se mide la anchura del pico (en porcentaje con respecto a su prominencia)\n",
    "                         # 1 significa que se mide desde la base del pico.\n",
    "prominencia = None       # Prominencia: Desnivel mínimo que hay que descender desde la cima de un pico para ascender a otro, cualquiera que sea, más alto.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e71248a",
   "metadata": {},
   "source": [
    "###### Detección de los picos\n",
    "\n",
    "Artículo de Medium \"Algorithmic Thinking and Peak Finding\": \n",
    "https://rabingaire.medium.com/algorithmic-thinking-peak-finding-ad6f7415d154"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3573eb61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 55 picos. Con los siguientes indices: [2, 8, 15, 19, 22, 29, 36, 39, 50, 60, 111, 122, 126, 156, 184, 198, 246, 266, 308, 370, 376, 379, 398, 403, 407, 415, 431, 446, 494, 566, 646, 799, 803, 846, 848, 927, 951, 958, 982, 985, 989, 999, 1006, 1015, 1030, 1032, 1040, 1046, 1051, 1069, 1082, 1199, 1237, 1247, 1599]\n"
     ]
    }
   ],
   "source": [
    "peaks = encontrar_picos(amplitudes, altura_pico)\n",
    "print(\"Se han detectado\", len(peaks), \"picos. Con los siguientes indices:\", peaks) #cuantos picos hay detectados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c9415a1",
   "metadata": {},
   "source": [
    "###### Funciones  de la librería scipy.signal para encontrar las incertidumbres\n",
    "\n",
    "Para encontrar los intervalos de incertidumbre alrededor de todos los picos detectados, se van a utilizar algunas de las funciones ya creadas de la librería *scipy*. La idea es imitar el funcionamiento de la función [*peak_widths*](https://github.com/scipy/scipy/blob/v1.8.0/scipy/signal/_peak_finding.py#L462-L587).\n",
    "\n",
    "Para ello, se han transcrito y modificado ligeramente las funciones [*_peak_prominences*  y *_peak_widths*](https://github.com/scipy/scipy/blob/main/scipy/signal/_peak_finding_utils.pyx)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7dc36ef",
   "metadata": {},
   "source": [
    "##### Cálculo de las incertidumbres\n",
    "Calculamos las prominencias, anchuras y extremos izquierdo y derecho de las incertidumbres con la ayuda de las funciones auxiliares anteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eafc90a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "prominences, left_bases, right_bases = _peak_prominences(amplitudes, peaks, 1)\n",
    "widths, width_heights, left_ips, right_ips = _peak_widths(amplitudes, peaks, altura_relativa, prominences, left_bases, right_bases)\n",
    "\n",
    "# Calculamos los valores de frecuencia (Hz) de los extremos izquierdos y derechos de las incertidumbres de cada pico\n",
    "left_cotas = []\n",
    "right_cotas = []\n",
    "\n",
    "for p in range(len(peaks)):\n",
    "    entera_l = int(left_ips[p])\n",
    "    decimal_l = left_ips[p] - entera_l\n",
    "    entera_r = int(right_ips[p])\n",
    "    decimal_r = right_ips[p] - entera_r\n",
    "    \n",
    "    left_cotas.append((frecuencias[entera_l+1]-frecuencias[entera_l])*decimal_l + frecuencias[entera_l])\n",
    "    right_cotas.append((frecuencias[entera_r+1]-frecuencias[entera_r])*decimal_r + frecuencias[entera_r])\n",
    "    \n",
    "# Calculamos los valores de incertidumbre en términos de valores de frecuencia (Hz)\n",
    "widths_new = np.array(right_cotas) - np.array(left_cotas)\n",
    "widths = widths_new.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "04c94294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 55 picos con los siguientes indices: [2, 8, 15, 19, 22, 29, 36, 39, 50, 60, 111, 122, 126, 156, 184, 198, 246, 266, 308, 370, 376, 379, 398, 403, 407, 415, 431, 446, 494, 566, 646, 799, 803, 846, 848, 927, 951, 958, 982, 985, 989, 999, 1006, 1015, 1030, 1032, 1040, 1046, 1051, 1069, 1082, 1199, 1237, 1247, 1599]\n",
      "\n",
      "Los picos corresponden a las siguientes frecuencias:  [   6.25     15.625    26.5625   32.8125   37.5      48.4375   59.375\n",
      "   64.0625   81.25     96.875   176.5625  193.75    200.      246.875\n",
      "  290.625   312.5     387.5     418.75    484.375   581.25    590.625\n",
      "  595.3125  625.      632.8125  639.0625  651.5625  676.5625  700.\n",
      "  775.      887.5    1012.5    1251.563  1257.813  1325.     1328.125\n",
      " 1451.563  1489.063  1500.     1537.5    1542.188  1548.438  1564.063\n",
      " 1575.     1589.063  1612.5    1615.625  1628.125  1637.5    1645.313\n",
      " 1673.438  1693.75   1876.563  1935.938  1951.563  2501.563 ]\n",
      "\n",
      "A estos picos se les asocia los siguientes 55 valores de incertidumbre: [ 1.94284595  4.06369963  3.15739108  2.89713767  2.34375     4.44905548\n",
      "  2.37345057  4.72679992  4.88713964  4.71587862 12.44683624 10.39798738\n",
      "  2.1713385   5.69589046  5.0192767   4.68542597  4.71070625 11.30074441\n",
      "  4.54030635  4.91319252  3.28382701 10.84099423  4.75385248  2.35544989\n",
      "  4.0375643  11.75179772  5.97298452 10.21891927  4.37708658  5.02744824\n",
      "  5.0372818   4.08335229  5.40416785  1.769861    8.52172394  5.23518251\n",
      "  5.74694963  4.0358598   2.79182725  2.57899659  5.2056535   5.53132781\n",
      "  4.61789827  4.44642311  8.27656557  1.4971382   7.25950572  4.5751711\n",
      "  4.98260606  5.16316993  5.4264246   5.83819241  5.20652919  5.35903513\n",
      "  7.85147247]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resumen y gráfica\n",
    "print(\"Se han detectado\", len(peaks), \"picos con los siguientes indices:\", peaks)\n",
    "print(\"\\nLos picos corresponden a las siguientes frecuencias: \", frecuencias[peaks])\n",
    "print(\"\\nA estos picos se les asocia los siguientes\", len(widths), \"valores de incertidumbre:\", widths)\n",
    "\n",
    "## Graficar las incertidumbres de cada pico\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(frecuencias, amplitudes)\n",
    "# añadimos las anchuras\n",
    "for p in range(len(peaks)):\n",
    "    ax.add_patch(Rectangle((left_cotas[p], 0), widths[p], amplitudes[peaks][p], color=\"red\", alpha=0.5))\n",
    "    x1 = left_cotas[p]\n",
    "    x2 = right_cotas[p]\n",
    "    y1 = y2 = width_heights[p]\n",
    "    ax.plot([x1, x2], [y1, y2], color=\"black\")\n",
    "\n",
    "# mostramos el gráfico\n",
    "plt.title('Incertidumbres asociadas a cada pico')\n",
    "plt.legend(['Espectro', 'Anchuras calculadas'])\n",
    "plt.xlim([0,100])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab026c1",
   "metadata": {},
   "source": [
    "## Interpolación del espectro para estimar y corregir la localización de los picos\n",
    "\n",
    "Función utilizada: *peak_estimator* con tipo de ventana: 'hann' o 'rect'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d08e2300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interpolación del espectro para corregir la localización de los picos\n",
    "peaks_x = []\n",
    "peaks_y = []\n",
    "\n",
    "for p in peaks:\n",
    "    try:\n",
    "        x, y = peak_estimator(frecuencias[p-1:p+2], amplitudes[p-1:p+2], win='hann')\n",
    "        peaks_x.append(x)\n",
    "        peaks_y.append(y)\n",
    "        \n",
    "    except ValueError:\n",
    "        print(\"Error en pico\", p, \". Intervalo:\", (frecuencias[p-1:p+2], amplitudes[p-1:p+2]))\n",
    "        \n",
    "\n",
    "# mostrar los resultados\n",
    "plt.plot(frecuencias, amplitudes)\n",
    "plt.plot(frecuencias[peaks], amplitudes[peaks], 'bo')\n",
    "plt.plot(peaks_x, peaks_y, 'ro')\n",
    "plt.title('Picos del espectro')\n",
    "plt.legend(['Espectro', 'Picos encontrados', 'Picos encontrados corregidos por interpolación'])\n",
    "plt.xlim([0,30])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "27562717",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cambiamos los valores del espectro por los estimados\n",
    "frecuencias_new = frecuencias.copy()\n",
    "frecuencias_new[peaks] = peaks_x\n",
    "amplitudes_new = amplitudes.copy()\n",
    "amplitudes_new[peaks] = peaks_y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61426a7d",
   "metadata": {},
   "source": [
    "## Recalcular la incertidumbre de los picos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad3acba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Recalcular la anchura o incertidumbre de los nuevos picos\n",
    "prominences_new, left_bases_new, right_bases_new = _peak_prominences(amplitudes_new, peaks, 1)\n",
    "widths_new, width_heights_new, left_ips_new, right_ips_new = _peak_widths(amplitudes_new, peaks, altura_relativa, prominences_new, left_bases_new, right_bases_new)\n",
    "\n",
    "# Calculamos los valores de frecuencia (Hz) de los extremos izquierdos y derechos de las incertidumbres de cada pico\n",
    "left_cotas_new = []\n",
    "right_cotas_new = []\n",
    "\n",
    "for p in range(len(peaks)):\n",
    "    entera_l = int(left_ips_new[p])\n",
    "    decimal_l = left_ips_new[p] - entera_l\n",
    "    entera_r = int(right_ips_new[p])\n",
    "    decimal_r = right_ips_new[p] - entera_r\n",
    "    \n",
    "    left_cotas_new.append((frecuencias[entera_l+1]-frecuencias[entera_l])*decimal_l + frecuencias[entera_l])\n",
    "    right_cotas_new.append((frecuencias[entera_r+1]-frecuencias[entera_r])*decimal_r + frecuencias[entera_r])\n",
    "    \n",
    "# Calculamos los valores de incertidumbre en términos de valores de frecuencia (Hz)\n",
    "widths_new = np.array(right_cotas_new) - np.array(left_cotas_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bfb6bc9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 55 picos con los siguientes indices: [2, 8, 15, 19, 22, 29, 36, 39, 50, 60, 111, 122, 126, 156, 184, 198, 246, 266, 308, 370, 376, 379, 398, 403, 407, 415, 431, 446, 494, 566, 646, 799, 803, 846, 848, 927, 951, 958, 982, 985, 989, 999, 1006, 1015, 1030, 1032, 1040, 1046, 1051, 1069, 1082, 1199, 1237, 1247, 1599]\n",
      "\n",
      "Los picos corresponden a las siguientes frecuencias:  [   5.65015525   15.32145688   25.82047839   33.43750282   37.5\n",
      "   49.20990562   59.04934942   64.51454372   81.44440462   96.75560545\n",
      "  175.84898487  193.48176107  199.77758148  246.25501367  290.33203019\n",
      "  312.77269648  387.06803479  419.43681009  483.87111808  580.64253673\n",
      "  590.21464869  595.81368026  625.49019554  632.34049397  639.68485546\n",
      "  651.84712038  677.34375     699.90266421  774.23245855  887.89142999\n",
      " 1012.58280676 1250.99379777 1258.04821986 1325.42898368 1328.56616963\n",
      " 1451.37448241 1489.53795744 1500.7815     1537.5        1541.64799394\n",
      " 1548.20584489 1563.73982753 1574.56563212 1589.80105519 1612.98535068\n",
      " 1614.91205541 1628.74313596 1638.21851359 1645.06636875 1672.8515984\n",
      " 1693.17386234 1876.74804443 1935.42350374 1950.87776377 2502.21106313]\n",
      "\n",
      "A estos picos se les asocia los siguientes 55 valores de incertidumbre: [ 2.06938078  4.0007136   2.72950934  2.72697497  2.34375     4.31283293\n",
      "  2.36162387  4.5906176   4.85757547  4.7088159  11.40383761 10.36113077\n",
      "  2.17490428  5.4131824   4.97490876  4.64581758  4.61456416 10.93998241\n",
      "  4.39953152  4.63891742  3.21635338 10.6007707   4.60338806  2.33329944\n",
      "  3.94160039 11.67384738  4.14971632 10.20922899  4.24402279  4.93179628\n",
      "  5.03372263  3.98902329  5.37249819  1.82090575  8.36357979  5.21682217\n",
      "  5.55490687  3.91823905  2.75359967  2.44286348  5.18048396  5.47593833\n",
      "  4.40907985  4.2950514   8.05374623  1.84933464  5.97642362  4.40150067\n",
      "  4.94989346  4.72408259  5.24638785  5.81795487  5.06308166  4.86996523\n",
      "  7.34588806]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resumen y gráfica\n",
    "print(\"Se han detectado\", len(peaks), \"picos con los siguientes indices:\", peaks)\n",
    "print(\"\\nLos picos corresponden a las siguientes frecuencias: \", frecuencias_new[peaks])\n",
    "print(\"\\nA estos picos se les asocia los siguientes\", len(widths_new), \"valores de incertidumbre:\", widths_new)\n",
    "\n",
    "## Graficar las incertidumbres de cada pico\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(frecuencias_new, amplitudes_new)\n",
    "# añadimos las anchuras\n",
    "for p in range(len(peaks)):\n",
    "    ax.add_patch(Rectangle((left_cotas_new[p], 0), widths_new[p], amplitudes_new[peaks][p], color=\"red\", alpha=0.5))\n",
    "    x1 = left_cotas_new[p]\n",
    "    x2 = right_cotas_new[p]\n",
    "    y1 = y2 = width_heights_new[p]\n",
    "    ax.plot([x1, x2], [y1, y2], color=\"black\")\n",
    "\n",
    "# mostramos el gráfico\n",
    "plt.title('Incertidumbres asociadas a cada pico')\n",
    "plt.legend(['Espectro', 'Anchuras calculadas'])\n",
    "plt.xlim([40,60])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e959ad",
   "metadata": {},
   "source": [
    "## Guardar en un DataFrame los datos obtenidos de picos e incertidumbres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49e1dd6d",
   "metadata": {
    "scrolled": true
   },
   "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>Indice pico</th>\n",
       "      <th>Frecuencia dato (Hz)</th>\n",
       "      <th>Frecuencia estimada (Hz)</th>\n",
       "      <th>Anchura dato (Hz)</th>\n",
       "      <th>Anchura estimada (Hz)</th>\n",
       "      <th>Altura dato</th>\n",
       "      <th>Altura estimada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>6.2500</td>\n",
       "      <td>5.650155</td>\n",
       "      <td>1.942846</td>\n",
       "      <td>2.069381</td>\n",
       "      <td>0.762856</td>\n",
       "      <td>0.839690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>15.6250</td>\n",
       "      <td>15.321457</td>\n",
       "      <td>4.063700</td>\n",
       "      <td>4.000714</td>\n",
       "      <td>1.657238</td>\n",
       "      <td>1.698170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>26.5625</td>\n",
       "      <td>25.820478</td>\n",
       "      <td>3.157391</td>\n",
       "      <td>2.729509</td>\n",
       "      <td>0.266342</td>\n",
       "      <td>0.308707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>32.8125</td>\n",
       "      <td>33.437503</td>\n",
       "      <td>2.897138</td>\n",
       "      <td>2.726975</td>\n",
       "      <td>0.236748</td>\n",
       "      <td>0.262766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>37.5000</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>2.343750</td>\n",
       "      <td>2.343750</td>\n",
       "      <td>0.230172</td>\n",
       "      <td>0.231649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29</td>\n",
       "      <td>48.4375</td>\n",
       "      <td>49.209906</td>\n",
       "      <td>4.449055</td>\n",
       "      <td>4.312833</td>\n",
       "      <td>4.664819</td>\n",
       "      <td>5.475036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>36</td>\n",
       "      <td>59.3750</td>\n",
       "      <td>59.049349</td>\n",
       "      <td>2.373451</td>\n",
       "      <td>2.361624</td>\n",
       "      <td>0.163313</td>\n",
       "      <td>0.167966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "      <td>64.0625</td>\n",
       "      <td>64.514544</td>\n",
       "      <td>4.726800</td>\n",
       "      <td>4.590618</td>\n",
       "      <td>0.298128</td>\n",
       "      <td>0.314755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>81.2500</td>\n",
       "      <td>81.444405</td>\n",
       "      <td>4.887140</td>\n",
       "      <td>4.857575</td>\n",
       "      <td>0.220307</td>\n",
       "      <td>0.222520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>96.8750</td>\n",
       "      <td>96.755605</td>\n",
       "      <td>4.715879</td>\n",
       "      <td>4.708816</td>\n",
       "      <td>0.846156</td>\n",
       "      <td>0.849350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>111</td>\n",
       "      <td>176.5625</td>\n",
       "      <td>175.848985</td>\n",
       "      <td>12.446836</td>\n",
       "      <td>11.403838</td>\n",
       "      <td>0.195098</td>\n",
       "      <td>0.223594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>122</td>\n",
       "      <td>193.7500</td>\n",
       "      <td>193.481761</td>\n",
       "      <td>10.397987</td>\n",
       "      <td>10.361131</td>\n",
       "      <td>0.311280</td>\n",
       "      <td>0.317265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>126</td>\n",
       "      <td>200.0000</td>\n",
       "      <td>199.777581</td>\n",
       "      <td>2.171339</td>\n",
       "      <td>2.174904</td>\n",
       "      <td>0.190714</td>\n",
       "      <td>0.193226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>156</td>\n",
       "      <td>246.8750</td>\n",
       "      <td>246.255014</td>\n",
       "      <td>5.695890</td>\n",
       "      <td>5.413182</td>\n",
       "      <td>0.255381</td>\n",
       "      <td>0.282970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>184</td>\n",
       "      <td>290.6250</td>\n",
       "      <td>290.332030</td>\n",
       "      <td>5.019277</td>\n",
       "      <td>4.974909</td>\n",
       "      <td>0.222500</td>\n",
       "      <td>0.227614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>198</td>\n",
       "      <td>312.5000</td>\n",
       "      <td>312.772696</td>\n",
       "      <td>4.685426</td>\n",
       "      <td>4.645818</td>\n",
       "      <td>0.282783</td>\n",
       "      <td>0.288404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>246</td>\n",
       "      <td>387.5000</td>\n",
       "      <td>387.068035</td>\n",
       "      <td>4.710706</td>\n",
       "      <td>4.614564</td>\n",
       "      <td>1.166205</td>\n",
       "      <td>1.225427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>266</td>\n",
       "      <td>418.7500</td>\n",
       "      <td>419.436810</td>\n",
       "      <td>11.300744</td>\n",
       "      <td>10.939982</td>\n",
       "      <td>0.155640</td>\n",
       "      <td>0.176573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>308</td>\n",
       "      <td>484.3750</td>\n",
       "      <td>483.871118</td>\n",
       "      <td>4.540306</td>\n",
       "      <td>4.399532</td>\n",
       "      <td>0.414310</td>\n",
       "      <td>0.443251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>370</td>\n",
       "      <td>581.2500</td>\n",
       "      <td>580.642537</td>\n",
       "      <td>4.913193</td>\n",
       "      <td>4.638917</td>\n",
       "      <td>0.587487</td>\n",
       "      <td>0.648262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Indice pico  Frecuencia dato (Hz)  Frecuencia estimada (Hz)  \\\n",
       "0             2                6.2500                  5.650155   \n",
       "1             8               15.6250                 15.321457   \n",
       "2            15               26.5625                 25.820478   \n",
       "3            19               32.8125                 33.437503   \n",
       "4            22               37.5000                 37.500000   \n",
       "5            29               48.4375                 49.209906   \n",
       "6            36               59.3750                 59.049349   \n",
       "7            39               64.0625                 64.514544   \n",
       "8            50               81.2500                 81.444405   \n",
       "9            60               96.8750                 96.755605   \n",
       "10          111              176.5625                175.848985   \n",
       "11          122              193.7500                193.481761   \n",
       "12          126              200.0000                199.777581   \n",
       "13          156              246.8750                246.255014   \n",
       "14          184              290.6250                290.332030   \n",
       "15          198              312.5000                312.772696   \n",
       "16          246              387.5000                387.068035   \n",
       "17          266              418.7500                419.436810   \n",
       "18          308              484.3750                483.871118   \n",
       "19          370              581.2500                580.642537   \n",
       "\n",
       "    Anchura dato (Hz)  Anchura estimada (Hz)  Altura dato  Altura estimada  \n",
       "0            1.942846               2.069381     0.762856         0.839690  \n",
       "1            4.063700               4.000714     1.657238         1.698170  \n",
       "2            3.157391               2.729509     0.266342         0.308707  \n",
       "3            2.897138               2.726975     0.236748         0.262766  \n",
       "4            2.343750               2.343750     0.230172         0.231649  \n",
       "5            4.449055               4.312833     4.664819         5.475036  \n",
       "6            2.373451               2.361624     0.163313         0.167966  \n",
       "7            4.726800               4.590618     0.298128         0.314755  \n",
       "8            4.887140               4.857575     0.220307         0.222520  \n",
       "9            4.715879               4.708816     0.846156         0.849350  \n",
       "10          12.446836              11.403838     0.195098         0.223594  \n",
       "11          10.397987              10.361131     0.311280         0.317265  \n",
       "12           2.171339               2.174904     0.190714         0.193226  \n",
       "13           5.695890               5.413182     0.255381         0.282970  \n",
       "14           5.019277               4.974909     0.222500         0.227614  \n",
       "15           4.685426               4.645818     0.282783         0.288404  \n",
       "16           4.710706               4.614564     1.166205         1.225427  \n",
       "17          11.300744              10.939982     0.155640         0.176573  \n",
       "18           4.540306               4.399532     0.414310         0.443251  \n",
       "19           4.913193               4.638917     0.587487         0.648262  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creamos el DataFrame con los datos de los picos obtenidos\n",
    "import pandas as pd\n",
    "\n",
    "datos_picos = {'Indice pico': peaks, \n",
    "               'Frecuencia dato (Hz)': frecuencias[peaks],\n",
    "               'Frecuencia estimada (Hz)': frecuencias_new[peaks],\n",
    "               'Anchura dato (Hz)': widths,\n",
    "               'Anchura estimada (Hz)': widths_new,\n",
    "               'Altura dato': amplitudes[peaks],\n",
    "              'Altura estimada': amplitudes_new[peaks]}\n",
    "df_picos = pd.DataFrame(datos_picos)\n",
    "df_picos[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd495c30",
   "metadata": {},
   "source": [
    "## Algoritmo principal de detección de series de armónicos\n",
    "\n",
    "El algoritmo propuesto en este script ha sido obtenido del artículo científico: [*Identification of harmonics and sidebands in a finite set of spectral components*](https://hal.archives-ouvertes.fr/hal-00845120v2).\n",
    "\n",
    "Timothée Gerber, Nadine Martin, Corinne Mailhes. Identification of harmonics and sidebands in a finite set of spectral components. The Tenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM & MFPT 2013), Jun 2013, Krakow, Poland.\n",
    "hal-00845120v2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c12608a",
   "metadata": {},
   "source": [
    "### Introducción al algoritmo\n",
    "\n",
    "\n",
    "*The context of our work takes place at the output of a spectral analysis tool which provides a set S of spectral components. Within this set, each component C<sub>i</sub> is characterized by at least its central frequency ν<sub>i</sub>, the uncertainty ∆ν<sub>i</sub> directly linked to the spectral resolution and its amplitude A<sub>i</sub>.\\\n",
    "S = {C<sub>1</sub> (v<sub>1</sub>, ∆ν<sub>1</sub>, A<sub>1</sub>), ..., C<sub>F</sub> (v<sub>F</sub>, ∆ν<sub>F</sub>, A<sub>F</sub>)} where F is the total number of spectral components detected.*\n",
    "\n",
    "*The purpose of this study consists then in identifying the harmonic series and sidebands in the set S of detected spectral components.*\\\n",
    "\n",
    "*Harmonic series identification from estimated components is a nontrivial problem because of estimation errors. In fact, estimation errors do not preserve the accuracy of the relation between an estimated frequency and its harmonics. So, in order to find the harmonic frequency of order r of an estimated frequency ν<sub>i</sub>, looking for a detected component at a frequency exactly equal to r × ν<sub>i</sub> will not be sufficient.\\\n",
    "\\\n",
    "In this paper, we propose to use the uncertainty ∆ν<sub>i</sub> of each detected component to bypass the drawbacks of the non-exact frequency estimation. Each estimated frequency ν<sub>i</sub> is thus represented by a confidence interval of width ∆ν<sub>i</sub> centred on ν<sub>i</sub>. The harmonic detection is then completed by intersection of these intervals, as detailed in what follows.*\n",
    "\n",
    "##################################################\n",
    "\n",
    "Traducción al español:\\\n",
    "El contexto de nuestro trabajo tiene lugar en un conjunto S de componentes espectrales calculadas anteriormente.\n",
    "Dentro de este conjunto, cada componente C<sub>i</sub> se caracteriza por su frecuencia central ν<sub>i</sub>, la incertidumbre ∆ν<sub>i</sub> que está directamente ligada con la resolución espectral y su amplitud A<sub>i</sub>.\\\n",
    "Es decir S = {C<sub>1</sub> (v<sub>1</sub>, ∆ν<sub>1</sub>, A<sub>1</sub>), ..., C<sub>F</sub> (v<sub>F</sub>, ∆ν<sub>F</sub>, A<sub>F</sub>)} donde F es el número total de componentes espectrales detectadas.\n",
    "\n",
    "El propósito de este estudio consiste en identificar la series armónicas y las bandas laterales del conjunto S de componentes espectrales detectadas.\n",
    "\n",
    "La identificación de series armónicas de componentes espectrales estimadas es un problema no trivial a causa de los errores de esstimación. De hecho, los errores de estimación no preservan la precisión de la relación entre la frecuencia estimada y sus armónicos. Así que para encontrar la frecuencia del armónico de orden r de una frecuencia estimada ν<sub>i</sub>, no basta con buscar la componente detectada de la frecuencia exacta r × ν<sub>i</sub>.\\\n",
    "\n",
    "En este paper, se propone usar la incertidumbre ∆ν<sub>i</sub> de cada componente con el objetivo de evitar los inconvenientes de la estimación \"no exacta\" de la frecuencia. Entonces, cada frecuncia estimada ν<sub>i</sub> estará representada por un intervalo de confianza de anchura ∆ν<sub>i</sub> centrada en ν<sub>i</sub>. Posteriormente la detección de armónicos se ejecutará intersecando estos intervalos. Más detalles posteriormente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42ed4d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tenemos 55 componentes espectrales estimadas.\n",
      "\n",
      "Estas componentes corresponden a las siguientes frecuencias: \n",
      " [   5.65015525   15.32145688   25.82047839   33.43750282   37.5\n",
      "   49.20990562   59.04934942   64.51454372   81.44440462   96.75560545\n",
      "  175.84898487  193.48176107  199.77758148  246.25501367  290.33203019\n",
      "  312.77269648  387.06803479  419.43681009  483.87111808  580.64253673\n",
      "  590.21464869  595.81368026  625.49019554  632.34049397  639.68485546\n",
      "  651.84712038  677.34375     699.90266421  774.23245855  887.89142999\n",
      " 1012.58280676 1250.99379777 1258.04821986 1325.42898368 1328.56616963\n",
      " 1451.37448241 1489.53795744 1500.7815     1537.5        1541.64799394\n",
      " 1548.20584489 1563.73982753 1574.56563212 1589.80105519 1612.98535068\n",
      " 1614.91205541 1628.74313596 1638.21851359 1645.06636875 1672.8515984\n",
      " 1693.17386234 1876.74804443 1935.42350374 1950.87776377 2502.21106313]\n"
     ]
    }
   ],
   "source": [
    "F = len(peaks)                     # F: numero de componentes detectadas en el espectro\n",
    "v = frecuencias_new[peaks].copy()  # v: frecuencias de los picos\n",
    "Dv = widths.copy()                 # Dv: incertidumbres asociadas a cada pico de frecuencia\n",
    "A = amplitudes_new[peaks].copy()   # A: amplitudes asociadas a cada pico de frecuencia\n",
    "\n",
    "print(\"Tenemos\", F, \"componentes espectrales estimadas.\")\n",
    "print(\"\\nEstas componentes corresponden a las siguientes frecuencias: \\n\", v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "711e792b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### PARAMETROS A MODIFICAR\n",
    "orden_maximo = 10*F        # Maximo orden con el cual se calcula una frecuencia de la serie\n",
    "\n",
    "criterio_parada = 5        # Numero maximo de veces que no se encuentra armonico, tras el cual\n",
    "                           # el algoritmo deja de iterar (deja de buscar armonicos en la serie)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326e2380",
   "metadata": {},
   "source": [
    "#### Import the main functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81030b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from functions import interseccion, intervalosarmonicos, encontrar_armonico, encontrar_seriearmonica"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7861bb",
   "metadata": {},
   "source": [
    "#### Programa principal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5ecee357",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Calcular los intervalos de cada componente i\n",
    "\n",
    "intervalo_componente = []\n",
    "\n",
    "for j in range(F): \n",
    "    j_liminf = v[j] - (Dv[j]/2)\n",
    "    j_limsup = v[j] + (Dv[j]/2)\n",
    "    intervalo_componente.append( [j_liminf, j_limsup] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e5bbce5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "La serie armonica encontrada para la componente 0 es: [5.403747893901415, 0, 15.321456881663607, 0, 25.820478393441686, 33.43750281593051, 37.5, 0, 49.209905616605575, 0, 59.04934941875985, 64.51454372441758, 0, 0, 81.44440461629709, 0, 0, 96.75560545082685] y se compone de 10 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 1 es: [16.24805974064178, 33.43750281593051, 49.209905616605575, 64.51454372441758, 81.44440461629709, 96.75560545082685, 0, 0, 0, 0, 175.848984866943, 193.48176107462967, 0, 0, 246.25501366972114, 0, 0, 290.33203018931454] y se compone de 10 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 2 es: [24.372089610962668, 49.209905616605575, 0, 96.75560545082685, 0, 0, 175.848984866943, 193.48176107462967, 0, 246.25501366972114, 0, 290.33203018931454, 0, 0, 0, 0, 419.43681008812825] y se compone de 8 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 3 es: [32.324718076371475, 64.51454372441758, 96.75560545082685, 0, 0, 193.48176107462967, 0, 0, 290.33203018931454, 0, 0, 387.0680347916496, 419.43681008812825, 0, 483.8711180805334, 0, 0, 580.642536727567, 0, 651.847120377562, 677.34375, 0, 0, 774.2324585535872] y se compone de 12 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 4 es: [38.62324253128723, 0, 0, 0, 193.48176107462967, 0, 0, 0, 0, 387.0680347916496, 419.43681008812825, 0, 0, 0, 580.642536727567, 0, 651.847120377562, 699.9026642102681, 0, 774.2324585535872, 0, 0, 887.8914299898835] y se compone de 9 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 5 es: [48.74417922192534, 96.75560545082685, 0, 193.48176107462967, 246.25501366972114, 290.33203018931454] y se compone de 5 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 6 es: [58.139778163333574, 0, 175.848984866943, 0, 290.33203018931454, 0, 0, 0, 0, 580.642536727567, 639.6848554574467, 699.9026642102681] y se compone de 6 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 7 es: [62.77381732006802, 0, 193.48176107462967, 0, 312.7726964751424, 0, 0, 0, 0, 625.4901955417666] y se compone de 4 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 8 es: [82.89580360512898, 0, 246.25501366972114, 0, 419.43681008812825, 0, 580.642536727567] y se compone de 4 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 9 es: [96.77117518690437, 193.48176107462967, 290.33203018931454, 387.0680347916496, 483.8711180805334, 580.642536727567, 677.34375, 774.2324585535872, 0, 0, 0, 0, 1258.0482198649922, 0, 1451.3744824055918, 1548.2058448860416, 1645.066368750025, 0, 0, 1935.4235037380874] y se compone de 13 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 10 es: [174.95173690186468, 0, 0, 699.9026642102681, 0, 0, 0, 0, 1574.5656321167821] y se compone de 3 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 11 es: [193.54235037380874, 387.0680347916496, 580.642536727567, 774.2324585535872, 0, 0, 0, 1548.2058448860416, 0, 1935.4235037380874] y se compone de 6 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 12 es: [198.8474727854393, 0, 595.8136802602813, 0, 0, 0, 0, 1589.801055187638] y se compone de 3 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 13 es: [248.25632624055973, 0, 0, 0, 0, 1489.5379574433584] y se compone de 2 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 14 es: [290.2748964811184, 580.642536727567, 0, 0, 1451.3744824055918] y se compone de 3 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 15 es: [312.7818465910811, 625.4901955417666, 0, 1250.9937977701263, 1563.7398275332525, 1876.748044426766, 0, 2502.2110631308883] y se compone de 6 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 16 es: [387.0847007476175, 774.2324585535872, 0, 1548.2058448860416, 1935.4235037380874] y se compone de 4 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 17 es: [418.65350391091374, 0, 1258.0482198649922, 1672.8515984032156] y se compone de 3 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 18 es: [483.85587593452186, 0, 1451.3744824055918, 1935.4235037380874] y se compone de 3 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 22 es: [625.5636931821622, 1250.9937977701263, 1876.748044426766, 2502.2110631308883] y se compone de 4 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 25 es: [650.2925879226922, 0, 1950.8777637680764] y se compone de 2 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 28 es: [774.1029224430208, 1548.2058448860416] y se compone de 2 armonicos.\n",
      "\n",
      "La serie armonica encontrada para la componente 31 es: [1251.0890686820035, 2502.2110631308883] y se compone de 2 armonicos.\n"
     ]
    }
   ],
   "source": [
    "lista_series = []\n",
    "lista_numero_armonicos = []\n",
    "\n",
    "for i in range(F):\n",
    "    output, indices, numero = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    \n",
    "    lista_series.append(output)\n",
    "    lista_numero_armonicos.append(numero)\n",
    "    \n",
    "    if numero > 1:\n",
    "        print(\"\\nLa serie armonica encontrada para la componente\", i, \"es:\", output, \"y se compone de\", numero, \"armonicos.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad51d6b3",
   "metadata": {},
   "source": [
    "### Criterio de caracterización de las series\n",
    "\n",
    "*The proposed method is exhaustive and identifies every harmonic series present in the spectrum. As a consequence, the number of series detected is large and some of them are not always relevant. Nevertheless, in the literature, there is no precise definition of a harmonic series (apart from a mathematical point of view). Moreover, the relevancy of a series depends on the application and the physical context of the studied signals. That is the reason why keeping all the series detected is necessary. Rather than eliminating the “false” series, the proposed method classifies the detected series thanks to the following three characterisation criteria.\\\n",
    "\\\n",
    "These criteria have been chosen as a comparison of each detected series to the corresponding “perfect” one. A “perfect” series is defined as a spectral comb going till the end of the spectrum with no harmonic missing.*\n",
    "\n",
    "#########################################################################\n",
    "\n",
    "Traducción al español:\\\n",
    "El método propuesto es exhaustivo e identifica todas las series armónicas presentes en el espectro. Como consecuencia, el número de series detectadas es muy grande y algunas de las series no son relevantes. Sin embargo, en la literatura, no hay una definición precisa de lo que es una serie armónica (aparte de la definición matemática). Además, la relevancia de cada serie depende de la aplicación o contexto físico de las señales estudiadas. Por esta razón, vamos a tener en cuenta todas las series detectadas. En vez de eliminar las series \"incorrectas\", el método propuesto clasifica las series detectadas gracias a unos criterios de caracterización.\\\n",
    "\n",
    "Estos criterios se han elegido como comparación de cada serie detectada a la serie \"perfecta\" correspondiente. Una serie \"perfecta\" se define como el peine de frecuencias que llega hasta el final del espectro y sin que le falte ningún armónico.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9ba0a4",
   "metadata": {},
   "source": [
    "#### First criterion: density (D)\n",
    "\n",
    "The first criterion denoted $D_i$, highlights the density of the series, in order to differentiate series with several harmonics missing from series with almost all harmonics present.\n",
    "\n",
    "$D_i = \\frac{\\text{card}(H_i)}{r_i^{max}}$, with $r_i^{max}$ the rank of the last harmonic in the series $H_i$.\n",
    "\n",
    "A series in which lots of harmonics are missing will have a small density whereas a \"full\" series including all harmonics will have a density equal to 1.\n",
    "\n",
    "#### Primer criterio: densidad (D)\n",
    "\n",
    "El primer criterio denotado como $D_i$ destaca la densidad de la serie, con el objetivo de diferenciar series a las cuales les faltan un número de armónicos con las series que están casi completas o completas.\n",
    "\n",
    "$D_i = \\frac{\\text{card}(H_i)}{r_i^{max}}$, siendo $r_i^{max}$ el orden del último armónico encontrado en la serie $H_i$.\n",
    "\n",
    "Una serie a la cual le faltan muchos armónicos tendrá una densidad pequeña mientras que una serie \"completa\" que incluya todos sus armónicos tendrá una densidad igual a 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cfb79044",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_D = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    if numero_armonicos > 1:\n",
    "        rmax = len(serie)\n",
    "        H = 0\n",
    "        for r in range(len(serie)):\n",
    "            if (serie[r] != 0):\n",
    "                H = H + 1\n",
    "        D = H/rmax\n",
    "    else:\n",
    "        D = 0.0\n",
    "\n",
    "    lista_D.append(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6527a7b4",
   "metadata": {},
   "source": [
    "#### Second criterion: richness (R)\n",
    "\n",
    "The second criterion is based on $N_i^{max}$ which is the maximum size of the series based on the frequency of its fundamental $ν_i$ and of the highest frequency $ν_F$ in the set S. This has to be compared to $r_i^{max}$ to define the second criterion, the richness $R_i$ of the series.\n",
    "\n",
    "$R_i= \\frac{r_i^{max}}{N_i^{max}}$ with $N_i^{max} = \\text{int}(\\frac{ν_F}{ν_i})$ with int( ) providing the integer part.\n",
    "\n",
    "This will help to consider in a different way two series with the same cardinal and the same harmonic orders.\n",
    "\n",
    "#### Segundo criterio: riqueza (R)\n",
    "\n",
    "El segundo criterio se vasa en $N_i^{max}$ que es el tamaño máximo de la serie basado en su frecuencia fundamental $ν_i$ y en la frecuencia más alta $ν_F$ del conjunto S. Esto se compara con $r_i^{max}$ para definir este segundo criterio, la riqueza $R_i$ de la serie.\n",
    "\n",
    "$R_i= \\frac{r_i^{max}}{N_i^{max}}$ con $N_i^{max} = \\text{int}(\\frac{ν_F}{ν_i})$ siendo int( ) la parte entera.\n",
    "\n",
    "Este criterio ayuda a considerar de manera diferente dos series con el mismo cardinal y los armónicos de los mismos órdenes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9587e55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_R = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    if numero_armonicos > 1:\n",
    "        rmax = len(serie)\n",
    "        Nmax = int(v[F-1]/serie[0])\n",
    "    \n",
    "        try:\n",
    "            R = rmax/Nmax\n",
    "        except ZeroDivisionError:\n",
    "            R = 0.0   \n",
    "    else: \n",
    "        R = 0.0\n",
    "\n",
    "    lista_R.append(R)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41bf92a7",
   "metadata": {},
   "source": [
    "##### Third criterion: Total Harmonic Distortion (THD)\n",
    "\n",
    "$TDH = \\frac{\\sqrt{\\sum \\text{Power of harmonics}^2}}{\\text{Power of fundamental frequency}} = \\frac{\\sqrt{\\sum A_1^2 + ... + A_{N-1}^2}}{A_0}$, where $A_i$ is the amplitude or power of each harmonic detected $i=0,...N-1$ in the series, where $N$ is the total number of harmonics of the series.\n",
    "\n",
    "This criterion will be helpful in applications where amplitude behaviour in harmonic series is known a priori and awaited.\n",
    "\n",
    "\n",
    "##### Tercer criterio: Distorsión armónica total (THD)\n",
    "\n",
    "$TDH = \\frac{\\sqrt{\\sum \\text{Potencia de los armónicos}^2}}{\\text{Potencia de la frecuencia fundamental}} = \\frac{\\sqrt{\\sum A_1^2 + ... + A_{N-1}^2}}{A_0}$, donde $A_i$ es la amplitud o potencia de cada componente $i=0,...N-1$ de la serie armónica, siendo $N$ el número de armónicos de dicha serie.\n",
    "\n",
    "Este criterio es muy útil en aplicaciones donde se sabe la amplitud de la serie armónica a priori."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e64836e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lista_THD = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    if numero_armonicos > 1:\n",
    "        N = len(indices)\n",
    "        THD = np.sqrt( sum(np.power(A[indices[1:N]], [2]*(N-1))) / (A[indices[0]]**2) )\n",
    "    else:\n",
    "        THD = 0.0\n",
    "\n",
    "    lista_THD.append(THD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661b47e4",
   "metadata": {},
   "source": [
    "#### Otro criterio que puede ser útil: número de armónicos encontrados de la serie\n",
    "\n",
    "A desarrollar.\n",
    "\n",
    "Este criterio también puede ser útil. El coeficiente NAS sería más alto cuántos más armónicos haya en la serie detectada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35bf3c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_NAS = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    NAS = numero_armonicos\n",
    "    lista_NAS.append(NAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ad3355",
   "metadata": {},
   "source": [
    "#### Graficar las puntuaciones de los criterios para cada serie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0ad51737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f9e387e3ac0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DEs1uHAJGt3lfBlT1537JJBCKYImEMNjsrRE9qkG4QqFIYfol9EKItqsNlgFbExz2ITBeCDFWCGEBLgZe6s/9kok/FMEaCWGw2xAWCxGjUfWNVSgUKU2PdfRCiKeBU4B8IcQh4BbgFCHELPRUzD7gW7FjS4GHpZRnSSnDQojrgFcBI/ColHLbYDxEXwgENHKiYSJ2GwBhqx1DUAm9QqFIXXoUeinlJQk2P9LFsVXAWW3erwQ6lV4eTTSfblFsigl9xGrHFAwgpUSIRNMKCoVCMbwZcRYIAa8evZvtdgCkzYYtFCQYjh7NYSkUKc1QtSlubGxk1qxZzJo1i+LiYkaNGtX6XtO0VnO0OI899hjXXXcdALfeemvr8ePHj+crX/kK27dv79d4B5sRJ/SaT6+wMaXHhN6ehj2iukwpFIPFULYpzsvLY/PmzWzevJlrrrmG73//+63vLRZLj+fHj9+9ezcXXXQRS5YsYTDLw/vLiBP6UCyit8Yrbuxp2MKa6jKlUAwSw82muL9cdNFFLF26tN/fQgaTEWdqFvLpQm9J03P0hrQ00sI1eFRErxgB1Nx+O8EdybUptk6eRPFPf9rl/uFkU9yRuKtlnKamJs4999wuj589ezaVlcn9+yaDkSf0fn0y1pKuR/SG9HRs4SA+TQm9QnE06ItN8ccff9zqhOl0Otm9ezcWi6XPNsXdiXVb4q6WcR577DG6s2fprVvm582IE/pIbHGUNSb0xvR07OGgiugVI4LuIu/BYjjYFCeLjz76iDlzEtrNHFVGXI4+GovoDbHySnNGGvZwEJ+mcvQKxWAwnGyKB8Jzzz3HqlWruOSSRBXpR5eRF9EHdKEXVisAlswMiIbx+IJHc1gKRcoStym+/vrrueOOO7DZbFRUVPCnP/2p296sbW2Kr7zySr73ve+xb98+Zs+ejZSSgoICXnjhhW7vvWzZMtatW8fMmTMRQvTKprgv3H333Tz55JN4vV6mTZvGm2++yWB6dfWXXtkUf94Mpk3xgz+9h5NX/JVjVr2KZcwYDj74MJ6772LDPf/HZadNG5R7KhRHE2VTnHp8njbFw5JoUI/chU1P3dgcsb6xbtVlSqFQpCYjTuiJ+doYYkJvifWN1ZTQKxSKFGUECn37iN6QFmsQroRekcIMxRSton/0599yRAq9RCDMZoDW5iNhr2o+okhNbDYbjY2NSuxTACkljY2N2GKBam8ZcVU3QtMImS2tTpWtDcJVO0FFilJWVsahQ4eGpAeLou/YbDbKysr6dM6IE3qDFiBiPmJWZEhXQq9Ibcxmc69WnipSlxGXujFoWgehj62oU+0EFQpFijLihN4Y6iD0rX1jVY5eoVCkJj0KvRDiUSFEnRBia5ttfxBCVAohPhZCPC+EyO7i3H1CiE+EEJuFEIOzAqqPGEMaUYu19b0h1oDEEFBCr1AoUpPeRPSPAWd02PYaME1KOQPYBfykm/MXSylndbVi6/PGHA4i2wi9MJkImy0YYtYICoVCkWr0KPRSyrVAU4dtq6SUcbvH94G+TQEfJcKRKOZwqJ3QA0SsNoxBJfQKhSI1SUaO/pvAK13sk8AqIcRGIcTVSbjXgAiEo1giIbB2FHo7Vi1AOKL6xioUitRjQOWVQoifAWHgqS4OWSilrBJCFAKvCSEqY98QEl3rauBqgDFjxgxkWF0SCEWwRkOtzpVxpD0NWziIV4vgsI+4+WmFQpHi9FvVhBBXAGcDl8oultxJKativ+uA54EujaCllA9JKedIKecMls1nIBTBGg612h+0Yrdjj2iqQbhCoUhJ+iX0QogzgB8D50opE5arCCHShRCZ8dfAUmBromM/LwKhCJZoZ6EX9jTs4YBqJ6hQKFKS3pRXPg2sAyYKIQ4JIa4C7gUy0dMxm4UQD8SOLRVCrIydWgS8I4TYAqwHXpZS/ndQnqKXBEJRrJEQxo5Cn56GPazhCaouUwqFIvXoMUcvpUzUF+uRLo6tAs6Kvd4LzBzQ6JJMIBTBGgmhdRB6Y6xBuErdKBSKVGREzTwG/BomGcWYZm+33ZSRTpoSeoVCkaKMLKGPWRGb7O0jenNGBrawhlfl6BUKRQoyotwrtZjQm+3tI3pLRjoRGcHnVYumFApF6jGiInrNpztUmjukbmxZet/YgNP9uY9JoVAoBpsRJfSheESf3l7orZm6VXHQozzpFQpF6jGyhN6vp2YsHYTemKH6xioUitRlRAl92KcLvS3WVSpOa99YjxJ6hUKReowooY8EEufo481HVINwhUKRiowsoY9F9B0tEOJCH1U5+na0+DRWbDp0tIehUCgGyIgS+mjMc97QUehjqZuoT0X0bVmx6TA/eHYLtS5VdqpQDGdGltDHukgJa+KIHtVOsB0NniAAzT7tKI9EoVAMhBEl9DKoC5fB1t6PPi70IlZnr9Bp9OgC7/SFjvJIFArFQBhRQk88ore1n4wVdjtSCERACX1bGr0xofcroVcohjMjS+i7iOiFwUDYYsUUVELflkav/vdSQq9QDG9GltBrQSIGI8LU2eInYrVjCgboolnWiKRJRfQKRUowooReaEHCJnPCfVGbHVs4iD+kmo/EiefoXUroFYphzYgSeqMWJGK2JNwn7Xbs4SAe5UkP6E1a4n8LFdErFMObESX0Bk0jYrYm3Kf3jQ3iU+0EgSNpG1BCr1AMd3rTM/ZRIUSdEGJrm225QojXhBC7Y79zujj3DCHETiHEHiHETckceH8whjWilsQRvUhLwxbRVEQfQwm9QpE69Caifww4o8O2m4A3pJTjgTdi79shhDAC9wFnAlOAS4QQUwY02gFiDGlEu4joDenpekSvqYgejiyWspuNSugVimFOj0IvpVwLNHXYfB7weOz148CXE5w6F9gjpdwrpdSAZ2LnHRWklJhDQaQlsdAbY0Kv+sbqxCP6sfnpSugVimFOf3P0RVLKaoDY78IEx4wCDrZ5fyi2LSFCiKuFEBuEEBvq6+v7Oayu0SJRLJEw2BILvTkjHbvqG9tKvOJmXEE6Tr/6mygUw5nBnIwVCbZ1WaQupXxISjlHSjmnoKAg6YMJhKJYIqEuI3pzRjq2cBBvQEWvoK+KNRsFo3LsOP2aWl+gUAxj+iv0tUKIEoDY77oExxwCRrd5XwZU9fN+AyYQimCNhjoZmsWxZmViROJzK2MzgEZPkNx0C9l2C6GIVOsLFIphTH+F/iXgitjrK4AXExzzITBeCDFWCGEBLo6dd1QIhCJYIiFEF6mb1r6xqp0goOfo89KtOOz6AjOVp1cohi+9Ka98GlgHTBRCHBJCXAXcAZwmhNgNnBZ7jxCiVAixEkBKGQauA14FdgDPSim3Dc5j9EwgFMUa0TB0MDSLY87IAJTQx2nwauRlWJTQKxQpQGfTlw5IKS/pYtepCY6tAs5q834lsLLfo0sigVAEayRE2N5VeWWsnaBbdZkCaPIGGZuXdkTolVWxQjFsGTErY/1aGEskjLGLiN6QFmsQ7lVCD3rVTa5K3SgUKcGIEfqAP4ABidGeeDI2HtFHlNDj1yL4tAhFBo2M91cDSugViuHMiBF6zaN7zZvSuo/oo15VdRP3oT/mo7UEfn4TOQGXEnqFYhgzcoQ+1vjb3JXQxyJ66VdCH18Vm+ltAcAR8iqrYoViGDNyhN6rR/RdC70e0aOEvnVVbJrHCUARmoroFYphzIgR+lCs8be1y9RNrEG4X7UTjPeKtbiaASgkqIReoRjGjBihD8cE3JKRlnC/sFiIGgwYVN9YGmPOlcZm3csuX6qIXqEYzowYoQ/5AkDXEb0QgrDVjikw/IT+B89uZtW2mqRdr8mrYTEZiDY1AJAb8SuhVyiGMSNG6CMxAe+qvBIgarVjDQXRwtHPa1gDRgtHWbHpMG/tTGQ31D8aPBr5dhORJj11kx0O0KKEXqEYtowYoY8G9HSEwda10Eu73iB8OHnSN/v0fHq9O5i0azZ5g5QbgxDVP/Ayw35VdaNQDGNGjtDHcvRduVcCyFjf2OHkSR8X+GQKfaNXY5Q8ksJK13w4/SFlVaxQDFNGjtDHI/puUjfCbsce0fAOowbh8QqZpAq9R6M0ElshbDSSFvAqq2KFYhgzYoReBvXJWNFN6saQNvwi+niFTL0nmLSIu9EbpCCku3haysuxBnTRVxOyCsXwZMQIPcFYRG9N7F4JYMgYfn1j4028QxGZFCH2aWECoSi5QV3orceMw+JzA0roFYrhyogRetGLiN6Uno4tPMxSN7FVrJCc9E38ell+F4aMDEyFRRg9MaFXVsUKxbBkxAg9WpCw0YwwdP3I5syMYRjRJ1noYzn/dG8Lpvx8jA4HwufFIKMqolcohikjRugNmkbYbO72GEtGOvaIhtObvInNwabBEyTDqvePqfckI6LXr2F1xYU+CyElaaGAEnqFYpjSb6EXQkwUQmxu8+MSQlzf4ZhThBDONsfcPOAR9xODphExWbo9xu7IBOCv/93KW5XJW4A0mDR6g0ws1sedzIje2NKEsSAfg8MBQGasxFKhUAw/+i30UsqdUspZUspZwPGAD3g+waFvx4+TUv6yv/cbKMZQkIi564lYONI39thMA998/EMeXPPpkK8db3BrjM1Px2IyJDVHT3MTpvwCjHGhD/vUoimFYpiSrNTNqcCnUsr9SbpenwlHotz60jb+u7U64X5jSCNq7T6ij3vSP/jVyZw1rYTfvlLJD5/dQmCI1o9LKWn0BsnPsFKQYU2K0Dd5g2QZwki3uzVHD8qqWKEYziRL6C8Gnu5i33whxBYhxCtCiKlJul8nTEYD/95SxZpd9Qn3G0Ma0tJ9RB+3KraEgtz7teP4wWkTWPHRYS5+6H3qXIGkj3mguAJhQhFJfoaFgkxrcnL0Xo0KoUf1bYW+AE353SgUw5QBC70QwgKcC/xfgt2bgHIp5UzgHuCFbq5ztRBigxBiQ319YrHuibH56eyt79zzNRqVmMO9EPpY8xHp8yGE4LunjueBr89mZ42bZX99j2B4aEX28Rr6/AyrLvRJSt2MEXrzFVPBEaHPj6rJWIViuJKMiP5MYJOUsrbjDimlS0rpib1eCZiFEPmJLiKlfEhKOUdKOaegoKBfAxmbn85nDZ2FPhCOYI2EoJcRfdsG4WdMK+Hmc6ZwuMVPVcvQiurj+fSyV55lcvOBVuEfCE1ejZJITOjz8zFmZQGQE1FCr1AMV5Ih9JfQRdpGCFEshBCx13Nj92tMwj0TMrYgnTp3EHegvSAFQlEs0RB0s1gKjgi99LVvJ1iarXvYNyZBSJNJoyeIIRoh7R9/Y+onb9Po1QhHBmax3OgJUhjWP+iM+fkIiwWRloZDedIrFMOWAQm9ECINOA1Y0WbbNUKIa2Jvzwe2CiG2AH8BLpaDWMYyLl9PvexraC/UgZAe0Ytu7A/gSOqmbUQPkJeuT+K2XZw0FGjwBHFoXoSUZHlbkPJIY+/+oE/uauQF3SAEptxcAIwOB5masipWKIYrAxJ6KaVPSpknpXS22faAlPKB2Ot7pZRTpZQzpZTzpJTvDXTAXY4lEqFi7ydUOKvZ2+Bpty8QimCJhLq1P4AjQu9bt45o8Ej0np+hf0A0DrGFVA0ejdygbk9gj/V3rRtAnt6rRQiGozgCLoy5uQiTvhDLmJVFuuZVVsUKxTAldVbGGgxwy00sPbC+U57eH4voDbbuI3pjVha5V30T18pX2HfhRQR37wYgNxbRNw6xiL7RG2SM1L+9WJx6f9eBVN40xZ4vw+vElH9kKsXocGAP+JRVsUIxTEkZoRdCYBkzhmOCzZ2EPhCK6kJvT9wvti1FN9xA2QP3E25o4LOvnk/TE//AbBRk2kwDSosMBg1ujbKoLvSiuQmDjA6o8qYh9o3F5u4s9Fa//i1J5ekViuFHygg9gKWiglHehk5CHwyGsETDmHpI3cTJPOUUxr34Aunz51N7++0cvPpbjDMEklLVkkwavUGK4g1CIhEcQc+AhD4e0ZudTe2E3uDIwuxTQq9QDFdSS+jLy8lx1rO/1tUulxyItRE09SKij2PKz6fsgfspvuVmfOvX85OXfoez2dPziZ8jjR6ttUEIwOiod0BC3+gNgpSI5iZMBe0jeqNXWRUrFMMV09EeQDKxlJfr5YbN9TR4NAoy9Zx80B0T+rTeRfRxhBDkXHIJwmxG/vwXROtqkj7mgVDvCZIdcLe+Lxf+AeXoG70a6aEAaBrGdqmbbISmYYmEVESvUAxDUiuiH1sBwChvPXvrj0S6Wqwu3tyHiL4tpqJiACJNzQMbYBIJhiO4A2EyvU7MY8YAUBYZYETv0SiN6qkgU/6RRWvxRVPKwVKhGJ6kltCXlwMwytM+Tx+KpW4sGWn9uq4pT68nN7haiESHRnlhvAIozdOCbdIkAIpDbhoGkqP3aowm9u2nbUSfrdsgZISU0CsUw5GUEnpjbi6GjAxG+zoIvU+3LrD0M6I3xhYOOYIeWnxDo/KmsXXitBlzSTHG3Fzygu6BVd14gpTJIz43ceJ+N1lK6BWKYUlKCb0QAktFBeOCzextI/RhXyyiTx+40DcOkRLLBm8QWziIIeDHmJ+PqbAQh8+JOxjGr/Wv1r3Jq1EUiqdu2lTdxFI3hcqqWKEYlqSU0IOevil117WL6CP+gQm9wWJB2tNwBL1DpsSywR0kJ7Yq1pRfgKmwgHS3PofQ3zE2ejTyNTfCbG4Vd9AnYwEKpBJ6hWI4kpJCn+Vq4nCdszWfHvHrqRtjP1M3ACInB4fm6dfq2EhU8o/393P7yh1JsxBo9GrkxCpuTAUFmAoKsLToq2P7Y4MgpaTJq5EdcGMsyCfmRQccydHnKatihWJYklLllaBX3ggZJd/dwOFmP2Py0ggHdKEX1r6VV7bFlJuLo9bbZwfLjfub+MUL29he7QLgguPLGF+U2e9xxGn0BCmKuUyaCvTUjcHZ3O/Vse5gGC0SJdPnaldxAzEPIKORbCX0CsWwJCUjeoBST0OruZmMRfQ9ed10hzU/D4fm6bUNQp07wA+e3cxX719Hs0/jtnP15lqrd/avqUpHGjwaZdEj+XRzYSEiGtVXx/YjdRNfFWv3tLTLz4M+92HMysIRUlbFCsVwJGWFvsxT35qnl8FYRN9LC4REmPJyydG8NPRC6J9Yt48ld67h31uq+PYpx/DGDxdxxYIKJhRl8NbOun6PoS0NniDFER8YjRizszEVFgJQEHD1K6KPO3Oanc2dhB70WvoMTTUIVyiGIymXujE6HBhzcigPNLa2FZQBXcQMAxH63Fyygh4a3d13mdrX4OXmF7cxf1wev1k2jXEFGa37Fk8s5NF3P8MTDJNhHdifPm5/YMrNRRiNmGJducrx9U/oPRqGaASDq6X1Wm0xZDtID/harYrb5vBHOk1ejZw0s/qbKIYsKRfRgx7Vj2vrYqnpwjeQiN6Yk4spGsHT5Oz2uP1Neh36D5dOaCfyAIsmFhCKSN7b09DvccRpiNkfGGP17vGIvqyffjeNXq21iUnbGvo4RocDW8CrrIo7UOcKcOLtryftm5pCMRikrNAXuY6UWIpgAIlAWCz9vqYxNweAUGNTt8cdbtZLOePtB9sypzyXdIuR1bsGlqePRvUKmUyfszX6NuXlgRCUhPuZo/dq5MareBKmbhxYlYNlJ/Y16j79u2uHluGdQtGW1BT6sRVkuJpoaHASCEUQWpCwaWBfreNt9cLN3Qt9VYsfo0FQmNl+4rfluRU03f5rFh6Tx+rKugGVWTr9IcJR2W7iVJjNGPPyyA/2zwahwROkJNp5sVQco8OBSQl9J+piqbyBdPZSKAabgfaM3SeE+EQIsVkIsSHBfiGE+IsQYo8Q4mMhxOyB3K+3xCdkS7wN7Gv0IjSNsLn/0TzoqRsAs9uFFu66AXdVi5/iLBsmo/6nleEwNb/+DdU/+xnN/3yaM7ICVDkD7K7rfwTY6A0iZBSLq6VdKaSpoIAcv5N6T7DPHyRNXo2yqP5txJjfOUdvdDgw+DwYZFRZFbchniYbiPWEQjHYJCOiXyylnCWlnJNg35nA+NjP1cD9Sbhfj7SrvKn3YtCCRAYo9KZY6sYR7L7E8lCLn1GxtE3E6eTg1VfT/OSTOM47D4DZ9Xp7wtUDyOk2eDSyNB8iEmkXfZsKC8jwtKCFo7gC4T5ds9GjURyJR/R5nfYbHVkIKUkLBWjpIqIPRbr+AExV4pF8XQ+T9ArF0WSwUzfnAU9InfeBbCFEySDf80gtvbeBvQ1eTCGNqLn/NfTQxu9G694GoarFT2m2jeDevey78CK8H26g5De/pvR3d2AuH4Nx80YmFmUOqJ6+wRNssyr2iNCbCwuxxZqE9zXCbPTqVTyGzMyE1UmGmLFZZhfGZlUtfmbcuoo3K2v7dN/hTp0rLvQqolcMXQYq9BJYJYTYKIS4OsH+UcDBNu8PxbZ1QghxtRBigxBiQ339wCYrDenpmAoKWvvHGkMa0QFMxAIY7HakzU52N8ZmkaikxhlgVs0O9l10MRGPh/LHHyP7q18FIH3efHwffMDiY3P5cF8TnmDfou44jR6tjc9Nm4i+oBCjsxlDNNJ3oY99eCTKz8MRB8vMLmrp36yswx+K8NLmqj7dd7gTn/hWqRvFUGagQr9QSjkbPUVzrRDi5A77E81+JkweSykfklLOkVLOKUhQx91XLOXlVPh1u2JTWCNqGVhED2DIydEdLLuI6OvcATJ9Tub+7XbMo0Yx9tnlpM0+Mi2RPn8+Ua+XJaKRUETybj/LLBs9QXITCX1hAUJKcvq4Ojbuc5Ppd/Ys9F2sjl0TqyRas6t+yHj2fx7UufSUjTsQJqDKThVDlAEJvZSyKva7DngemNvhkEPA6Dbvy4DPJeSzjK2gwFXPrlo35kgIrAMXelNuLg7N26WxWVWLnzJPPSISoejGGzCPav/lJe3EuSAE5fu2kmE19Tt9U+/RGBWN+8a3mYyN1dLn9nF1bL0nSDgqSfc4E9bQwxGhL0hgVayFo7y3p4HiLBvNvhCbD7b05XGGNfXuIFaT/r9RPI2jUAw1+i30Qoh0IURm/DWwFNja4bCXgMtj1TfzAKeUsrrfo+0DlvJy7B4nEbcba1iDJET0lrxcsjVvl6mbwy0B8v0tAJhKOk9FmHJysE2ZQuD991l4bB5rdvavzLLRE6Qk7EGkpemGY/HrF+hCX6T1rQHJ9irdcM3mbmnXK7YtrUIvOxubbTrQjFeL8MOlEzCIgU00DydCkSiNXo3JJbqls5qQVQxVBhLRFwHvCCG2AOuBl6WU/xVCXCOEuCZ2zEpgL7AH+Bvw7QGNtg+Y27QVtEZDMABDszimXN3vpqvUzeFmP4W+Fv3+xcUJj0lfMB/fli0sqciiyhlgV4KFNpGoZMvBli4/BBq9GvkhT6c0i6lQj+7HyL7ZIGyrcmENawift5NzZZx439jcBA6Wa3bVYzIIzpxewvHlObxZOTKEPv7Nbtoo/W+j8vSKoUq/hV5KuVdKOTP2M1VK+ZvY9geklA/EXksp5bVSymOklNOllJ1q7QcLa0UFoFfeWCKhAVkUxzHG/W66EPqqFj+jQi6M2dkYuvC+T5s3D0Ih5nsPAJ2j3wZPkMsf/YDz7nuX13ckFsyGLiZO46tjSyN9y9Fvq3IyNU2fGO4qRy8sFkRaGtnhBEK/s545FTlkWE0snlTItioXta7Uj27jEfzUUkfsvRJ6xdAkJVfGApjHjAEhKPM2YImEB2RoFseUm4M5EsLd7Eq4Py70idI2cdKOPx5hsWDdsolJxe3LLD/c18SX/vI2G/Y1YzMbuixVbPRoZPhcne2ETSaM+XkUaJ4+RZdbD7uYla5PJHaVowc9feMIt5+MrXMF2F7t4uQJ+jeBxRP19NFISN/Ec/ITijIxGoRK3SiGLCkr9AarFVNJMRO0ZqyRUFKE3pirLyTSuvC7OdziJ9/fgrkboTfYbNhnz8a7bh2LJhawYX8TrkCIB9d8ysUPvY/dbOT5by/klAmFrNlZ3yl9EwhF8ATDpCXwjQcwFxSSG3D2Wuid/hAHmnxMtOji3VVED4mtitfu1iuHFsWEflJxJiUO24hI38Qj+GKHjfwMi5qMVQxZUlboQU/flPkasEY0jPZkCL2+Ojbahd/N4RY/DndTl/n5OOnz5xOsrGRxoZlQRPLVv77Hb1+pZOmUIl76zheYUprFookFVDkD7OlgldDgCWKOhDH7PK05+baYCgrI8rbQ5A32qswxPhFbIXT7g26F3uHAHjxiVQx6fr4g08qU2ISkEILFkwp5Z3cDwXBqlxvGP0wLMqwUZtr6ZSanUHwepLTQm8vLKWiuwSSjSRH6uLGZ3efGp7Vf7OQKhAh5vFj9XkwlPQj9gvkATKjeSabVxGcNXm4+ewp/vXQ2WTYzQGsqZE0Hp0t9sZQuzolE2VRYiN3VTFQeaSbSHduqdNvlorAXDIbWFcCJMDoc2AOeVqviSFTy9u56Th5f0M4wbvHEQrxahA37mnu8/3Cmzh0gJ82MxWSgMNOqInrFkCWlhd5SXo45EKs3t6cN+HqtNgjBzk3Cq1r8FMRKK83F3bs82KZMwZCVRfCD93n0Gyfw4nUL+eYXxrYTy1HZdsYXZnQS+rb2B4lKIU2FhZjcToy9XB27rcpFUZYVq6sFY6yJSVcYHFmY/bofjtMf4uNDLbT4Qiya2P6bxcJj87CYDCmfvqlzBymIuZQWZFrVZKxiyJLaQh+rvAEozM8a8PVMOTFjswR+N+2EvoeIXhiNpJ84F++77zGnPKe1aqMjiyYU8MHepnbfHtrbHyRI3RQWxlbH9q6WfluVk2mlDsINDd2mbSBmVezR793iC7F2VwNCwEnHtj8vzWJi3ri8lG/GUe8OUphhpemppyg1hXudLlMoPm9SW+hjtfQAlvTE5Y59QaSlIS2WhBH94ZYA+fEa+m4mY+OkzZ9PqKqK0MGDXR6zaGIBWiTKB3uPzAk0eBMbmsWJr5TtzepYvxZhT52HqaVZvRT6bAwhDUskhNMfYs2uOmaWZZOT3tlHaPHEAvbWe9nf6O32msOZeneQSd4aan/1ayZsW6eny1SeXjEESW2hLyuDWCoiGXX0QoiY3423U/77cLOf4qAThGi1IuiO9Pl6nt773roujzmhIheb2dAufdPg1igKe/T7JMint7NB6EF0KmtcRCVMKXUQbqjvWehji6YyNB8HmnxsPtjSWm3TkXiZZaqmb6SU1LuDjPY3ApDdrD+nSt8ohiIpLfTCbMZcpvvNGJIwGQtgzssjW/PQkCBHPybsxlRQgDCbe7yOpaICU0kJ3vff7/IYm9nI/HF57YS+0RukOOLDmJOT8D7xSpzScM+19FtjFTdTSzOJ1Dd0W0MPYMw+YlX88sfVRCWd8vNxKvLTGZefzlsDsGMeyrT4QmiRKIVu/fkyGnRnD1VLrxiKpLTQw5H0TTIietCFPkfzdmo+UtXipyTo7LHiJo4QgvR58/CtW4eMdt2wY9GEAj5rOJICafRoFGhd2wmb8vLAYKAs0nOT8O1VThx2M0WaGxkKYSrqfuzxiD5T8/HOngYcdjMzy7IBiGoaNb+5nVDVEc+6xZMKeX9vY6cKpVQg/m0pp0UXenOt/tzKBkExFBkBQl8BgCEJXjegr47NDnX2u6lq8ZPra+mx4qYt6QvmE3E6CezY0eUxi2IpkHhU35X9QRxhNGLKy6Mo1PNk7NbDLqaNyiLwyScA2KdN7fZ4Qxur4khU8oXx+RgNeqWQb906mv/xD5qXP9t6/JJJhWjhKO/uaez2usOReClleiySl1WHQUpVYqkYkqS+0FckN6I35uSSFWjffCQUiVLj9JPhauzVRGyc9HnzAHC9vLLLqL4iL40xuWms2RkXeo1Mn6vbNIupsJDcoLvbHH0oEmVnjZuppQ4CW7YgzGasU6Z0O16jIxuAAvTrts3Px+caPGvWtG47oSKXdIsxJatv4ikaU00VGAzIQIAK/CpHrxiSpLzQZ5x0EukLFmAZW5GU6xlzc7GENZzN7tZtta4A6ZoPkxbssbSyLaaCAtIXLKDp0UfZe+ZZND/zDFG/v90xQghOmVjAe582EghFaPIEsHu6thMGXegd3u5tEHbXetAiUaaWZuHf8jHWyZMx9NCFy+jQUzf50URC/x4AwcpKQrW6R4/FZOAL4/N5q7J/dsxDmXp3UO9zUF+HfeZMACZFe289oVB8nqS80FvGjGHMo49gzMhIyvXiTcJDbfxuqloCFMRKK019SN0AjH7oQUbd/UcMWVnU3HobexYvof4vfyHccKT71KIJBfhDEVZtr8WuBTCGQ13aCYMu9Onu5m67HsVXxE4pTMe/bRv2GTN6HKshIwOMRvJkkEnFmRRl6d+SQnV1BHfvxnHeuUD7qP7EsXlUOwMpZw9Q5w5SrjlBStK/sBCAcVqzmoxVDElSXuiTTXx1rGxpbo1SD7f4er1YqiPCZCLrzDOpeHY55U/+A/vxx9Nw/wPs+eJpBCorAZg3Lg+L0cCKTYeOLJbqpt2iqaAAi8eJKRruMsLcVuUizWJkVEs10udrjUq7HasQGLOyWFRi5a4LjxzvW6enbXIuvxxTaQmeNWtb900oygRgTwLf/aFAVYufUKTryfCuqHMHmRRtASD9xBPBaGSUr1GlbhRDEiX0fSReu57pd+Py69UkVW07S/VgaNYVQgjS5sxh9H33Mu7ll0EImp9+BoB0q4kTxuawdlc9uYGufW5axxgrscwOeFix6XDCY7ZVOZlckkUwPhE7s+eIHvTKm5yIv91qXu9772HMycE2eTIZJ5+Md906opo+hzGhSP8mtavWnfB6/UVKyel3r+Wxdz/r9zVcgRBL7lrNPz840Odz61wBxgZbALCMG4d51CgK3Q3UuYMpl6ZSDH+U0PeRVr8bzUNDbNHU4VgNPWZzj4uOeoN13Fiylp6Ga+VKogE9FbBoQgFRSZuIvvscPcA5ZSYeWvtpJ7uGaFSyvcrFtNIs/B9vwZiTg3n06ESX6oQh20HEecSPX0qJ9711pM+fhzAYyFi0COnz4fvwQ0D3gMmymdhdl9yIvsoZYGetm7d396/BOkBltZtAKMpHB/puvlbvCTLK34ghPR1jTg6W0aNxNNehhaO4An0rJ5VSsnZXPVFln6AYJAbSM3a0EOItIcQOIcQ2IcT3EhxzihDCKYTYHPu5eWDDPfocMTY70iT8cLOf0SEX5qIihCE5n52OZcuIut2433gDgEUTdPFutT/oLqKPpXW+dkwagXCUe97Y3W7/vkYvXi3C1FIH/i1bsM2Y3s5QrTuMDgcRp7P1vbZnD+H6etIXLAD0SiJhseBdq6dvhBBMKMpkd5JTN5XV+odNZU3/vylU1vT/GvWuIIWueszlYxBCYCkfQ1qs1LK+j3n6dXsbufzR9SlZnaQYGgxElcLAD6WUk4F5wLVCiET1eW9LKWfFfn45gPsNCQzp6UizGYd2pKVgVYufgoCzRx/6vpA2dy7m0lKcK54H9BRIcZaNPM2NMJsxZHVt0maORfR5ARcXnzCapz440M5zZlt8RWy2Ee3Tvb3Kz8cxZrUX+ni1TVzoDXY7aSeeiGf1kQnZ8UUZ7KpzJzWlERfnwy3+Tq0Ne8uOav0ae+o8aOHe5+n9WgR3MIyjuQ7L6DEAmEePwej1kKH5+lxL//EhZ7vfCkWyGUjP2Gop5abYazewAxiVrIENVYQQGGN+Nw1eDSklVS1+cjzN3bYQ7PN9DAYcX/4y3vfeI1RTgxCCM6YVM1r6MBbkdxuBG3NzwWgkXF/P904dj9lo4M5Vu1r3b61yYjYKRtXuAymxz+iD0DscRFxHUjee997T7aBLS1u3ZSxahLZ/P9q+fQCML8ykxRfqZBsxEHZUHxnDzn5G9ZU1LoSAcFTyaX3vv3HUuQMYohHsjbVYxuhCbxmjp75KvQ19npCNf/DGfysUySYpeQYhRAVwHPBBgt3zhRBbhBCvCCG6XHophLhaCLFBCLGhvn5o+6OYcnNxBD00eTSc/hC+YAi7q+fOUn3F8eXzQEqcL74EwE/OmsTJ+cZuK24gtjo2P59wXT2FWTb+56Sx/HtLFZ/EIsbtVS4mFmcS3voxAPYZ03s9JqPDQdTlQkajSE3D9+EG0hcuaHdMxqKTAfDE0jfjYxOyu+uSNyG7o9rFjDJH6+u+Eo1Kdta4WXiMngKLp3F6Q707SIG/BRGJYI4JfFzwS7yNfa6lj5e6bq9SEb1icBiw0AshMoDngOullB3/b9kElEspZwL3AC90dR0p5UNSyjlSyjkFPQjZ0cacm0tu2EejN8jhFj/ZQQ+GSBhzafIietDFI23OHJzPP4+UEqvJCI0N3dbQxzEVFBCu03O+V588jpw0M7/7byVSSrYedjK1xIF/y8dYxo1r9bDpDUZHFkhJ1O3Gt3kz0udrTdu0jnv0aCzjxrWmb+IllsnK0wdCET5r8HLKhAKy08x9Euk4B5t9+LQIZ04vxmI0UFnd+w+hOneQUq9u62AZo6+8jk9mj/E19qmW3hsM81mDl+w0M1XOAM3e5H3rUSjiDEjohRBmdJF/Skq5ouN+KaVLSumJvV4JmIUQAy9LOcoY8/LI0XRP+qqWAIU+vWqjv6WV3eFYtgxt3z78mzcD9Mo3HvTKm7jQZ9rMfGfJeN7Z08CzGw7S7AsxrTQT/8cf92qhVFvifjcRp1PPzxuNpJ14YqfjMhYtwvfhh0S9XgozrWTaTEmL6HfXeohKmFySxeTirNZce1+InzO11MGxhRns6EP6p84VoMSrV/vEUzYGmw1TUREVWnOfUjeVNS6khC/P0rOeKn2jGAwGUnUjgEeAHVLKP3ZxTHHsOIQQc2P3G/YOV6bcHDIDHho8QQ43t10sldyIHiDz9NMRdjvO519AhkJEmpt7KfQFhNukwC6dN4bRuXZueWkbAFONPiKNjb2un49jbCf067BPn44xM7PTcRmLFiFDIbzvv99aebMrSRH9jlgEP6kki0klmeyscfe5NDGen59QlMGkkszWKp7eUO8JUuprQlgsmIqKWrdbRo+m1NfUp9RNXNjPP74MgO3VKn2jSD4DiegXApcBS9qUT54lhLhGCHFN7Jjzga1CiC3AX4CLZQqsJjHm5GINBXE6vVQ5AxTHmnUnO0cPYMxIJ2vpUlwrV+oWwFL26BsPekQfaW5uXbhkNRn50dKJBEJRhIDRNXsBsPUxoo8LfejgQQJbt3ZK28RJm30chvT0NumbDPYkqZa+stqN3WxkTG4ak4uz8IciHGjy9fkaFXnppFlMTC7Oos4d7GQ93RV1riAVgSbMo0e3K6c1l4+hwFnXp4h+e5WLnDQzU0uzKHHYVESvGBQGUnXzjpRSSClntCmfXCmlfEBK+UDsmHullFOllDOllPOklO8lb+hHD2Or300jh1v8VEQ9CLu9Na2RbBzLlhH1eGh+ZjnQfQ19nPiEbbjuSFR/zoxSppZmMbEok+j2rQibDduECX0aS1zoXateg2i000RsHGGxkL5wIZ61a5FScmxhJk1erdPirf6wo9rFhOJMjAbBpJLM1m19obLGxaRi/dz4NXqb669zByn1NWLpsMjMMnoM6V4nzqbeR+XbqlxMLXUghGBqaZYSesWgoFbG9oO4DYJwtXCwyUdp0Im5pKTXi476StrcEzCPGkXL8pjQ92Ky2j5tGgAtzzzdus1gEDzxzbk8euUJ+DdvwTZ1aq+6YbUlLvSe1asxpKV1m+PPWLSIcG0twZ07W60QBjohK6WkssbF5JhITyjKxCDoU47dGwyzv8nHpGJ9Ejr+u7cTsnWuAHmueizlY9ptj+fr0xtruzSTa8sRq2j9/lNKHeyt9+DXej5XoegLSuj7QXx1bFbAS2WNm3x/86CkbeLEa+qjPj090ZuI3jZ5Mo6vfIXGxx4nuGdP6/a8DCslaUYCO3b0eSIWjnSZkoEAaXPndvtBkXHySQB41qxlfGGs8maAE7J17iDNvhCTS/Rx2MxGxuan9ynHvqvWjZRHIvmCTCv5GZZeR/RaXT2WkIZ5dHuhN8cqcEp7WWK5p063ip4SE/qppVlE5ZE5CIUiWSih7wfGHD11kx3UV1Rmupp73UKwvzi+fN6R+/fST6fwRz/EkJ5OzS9/1W5VamDnTqSm9WlFbBxhsSDS0gC6zM/HMRUUYJs6Fc+aNRRlxSpvBhjRx1M08bQL6JOyfbExiB87ufhIWemk4t5dIxyJYq/X2wZ2FdGX9HLRVOsK5TZCD3reXqFIJkro+4EpLw/Qjc1M0TBWd3OfWgj2B8vo0aSdcAIGhwODtXdtEU25uRR+/3p869fj+s/Lrdv9m7cAvXes7Eg8fdNVfr4tGYsW4d+8mUhzM+MLMwbsYhkX40ltRHpycSYHmnx4gr0zE9tZ4ybdYqQsx966bVKxXr0T6aF6p9GrUeyJ1dB3yNEbs7KQWY7Yoqmea+m3VTmxm42MzdfTWqOy7TjsZpWnVyQdJfT9wJCZCSYTjqCXXL8LIWWffej7Q/Gtt1B6x2/7dE72BRdgmz6d2t//johbF0n/xx9jKijod92/MSsLU1ERlnHjejw287QvQjSK5803mVCUOeDKmx3VLkodNhxpR1JGcdHf2cuUx45qfWWwwXBkTmVSSRbBcJR9bTyBElHnClLqbUAaDO1sH+KYR4+m1Ns7X/ptVS4mlWS29t0VQjClJEutkFUkHSX0/UAIgSE7B4fmodAfWyw1CDX0HbEecwyZixf36RxhNFJ8881EGhqpv+ceAPwfb8E2c0a/J4+zL7yA/Gu/3avzrZMmYS4rw7VqFccWZtDo1To1Vu8LldXu1vx8nMmxlEdvFk7pk7luJnW4RjwV1NOEbL0nQIm3EVlYjEjQetFeUU6Jt6HHHL2Ukh1VrtZ0TZyppXoKKdyPZigKRVcooe8n5rxcsoNeCvx69DUYi6WShX36NLIvvojmJ5/C+/77hPYf6Fd+Pk7upZeSc+GFvTpWCEHm0qV4173PxAz9g6G/C6eC4Qif1nuYVJJJJBrhyv9eyb92/YtSh41Mm6lXk6k1rgBOf4jJxZlIKWl85BG0Awc4tjADo0H0eI06V5ASb2Orx01HrOVjKPS30NDU/TeDg01+3MEwU0ral+ROHaV/s/i0vvvzFYq+oIS+nxhzc8gL+6iI6hHgYFbdJIPC66/HmJ3N4e9dD9Anx8qBknnaFyEUYszujwDY08/Km0/rvISjkknFWbxb9S4bazfy8CcPI5FMLs7qVXlk/JhJJVn4PlhP3R/upP5Pf8ZmNjIuP73HbwW6z00DaRXlCfebR4/BICXBw4e6vU7cyKxzRO9ot1+hSAZK6PuJKSeXbM3L6LBHnyCNVaIMVYwOB4U/+pHuJW8wYJ/WpZFoK8srl3PuC+fywp4XiMr+pxLsM2diKizE8PZqMq2mfkf08Wh7ckkm/7fr/zAIA4c9h3m/6n3dxqCmZ8/7eOnixOJMmp/RWzW6XnuNcENDrHqn+4jeWdtAZsiPvTyx0McrcUR1T0LvwmgQTCxubx8xLj8dq8mgKm8USUUJfT8x5uaSG/IwwxIY8tF8HMeXzyPthBOwT5+OIT2922Mf3/Y4v/7g17QEWvjFu7/g6yu/zsf1H/frvsJgIPOLX8T79ttMzjX3u5Z+R7ULi8mA3eZh7aG1fL3si+RYHPzfrv9jUnEWnmCYQ83+bq9RWe1mVLYdu6sZ9+uvE15wHIRCtDy3gknFmRxq9uMKdN3IJHTwINCmtFJK2LsaQnqVTbwSx1pb3e04tlU5ObYgA5vZSCSs8d6GvxIOBTAZDUwqzlSVN4qkooS+nxhzczD6feS01A3p/HxbhMHA6If/xphHH+n2uIc/eZg7N9zJaeWn8foFr3P7F26nxlvDpSsv5adv/5Q6X99b3mUuPQ0ZCLCo5dN+19JX1riZWJTJS5/p3zAuXvc452mw+uBqSnL1yc+erBAqa1xMLsnE+dxzEA7zg+kfUz0pn5bly5lcqH8r29VNPb2o1putty6W2v4CPHEevPkrQF/jELbYyGyq7bZUc1ubidjlr3+fb227n3+8ei2gr5DdVuVUTcYVScN0tAcwXInbIGh795I25/ikXjsqo2yu28yxOceSZenaKz4qo2yq3cQbB94gz57HkjFLGOfovuTRYLVCF3X4Ukoe2PIAf93yV84aexa/+cJvMBlMnHPMOSwZvZi/fXQvT+xczhv7V7EkbwbZ9gIy0wvJTCsg05pFpjkTo8HY6bpjMscwds4cjNnZTNu7icaSUho9QfIyerceIM6OajenTMhjxe4VLBAZjA6F+OrBHTw2upQdnjcRoozKGjdLpyb+hqVP5npZOqmA5t8/y94JmdTk+nl6cjM/eD7CMfu2AvoHypyK3ITXsNbGFkuNLoNICN7QBZ71f4MTr0Fkj0YrKqHE00CjN0hhpq3TNerdQercQaaUZuHz1PFg9Rocfng4+gFfdR5kamkWT68/wKFmP6Nzh3ZKUDE8UELfH0J+jJUxDxkpMR/8Nxw8G0afMOBLSyn5w4d/4MkdT2IURo4rPI5FZYs4uexkxjrGIoRgT/Me/rP3P6z8bCXV3mosBgtaVOPPm/5MRVYFS8YsYcmYJUzPn45B9O5Lm5SSv3z0Fx7+5GHOG/slbitejPHVn8HB9eCpJd1Tx/XREF8xmfhTbjYbg+twGwx4etEM3Qg8dcY/yDl1CfmvvIqp6Ax213n6JPT17iANniA2xx5qamq4sbYeTvohFR8/y9yIiZf2Ps+Y3Bu6zbHvqfMQiUpmV28nXF3D8/MMXD/7BzxmfBjvGz7SX1pBVsmyLq8hpSSrqRZ/Vo4+J7PxMWj6FM78Paz6Oay5A867D1kyipLtu6h3Jxb67bFvHVNKs/jHG99n6nbJd/4d5cEzDPw96/ucfMLfAD3qV0KvSAZK6PuKqxqeuQRT3TZAtyIwW/zwyGlwwlVw6s1g66OLZTQCLQeg8VPu3/lPnmz8kK8a88ktnc0azz7u2ngXd228i1EZo0g3p7OreRdGYWR+6Xy+O/u7LMmfhUtGWF29jjcPvMkT257g0a2PkmvLpSKrgtKMUkozShmVMYrSjFLSTen4w358YR++kA9f2MeWQ+/wwsE3OJ8sfrHmUQyR+8Fkg9FzoXAyZBRCRhFjMgr5Y7refBxPLRF3DR73YdyeGty+OqLR9pO2YRnmB5FD/Pz1a/n7F3+F87kVzKrfze66Wcwbl9frP1G8L+y+4OvkSQOniAz8eWdhmVPK+e//ghuNYaYUHaKyuvM3ijjxipuSNStxZZqoO66UyzeugFEn8t9pq/jK2rXM+8aXuqzecfnDFHoa0IpKQfPB6js4WDabnzW+y9UzzuMLm/8J87+DpXwMJRvWUdfia62iaUu8oqbM3sgv92/ijtckCME335D8uHwHX5l/EIPQPxDOmDY85n8UQ5vUEvqnvwaRBAtVDGbIHQcFEyB/IhRMhLQOX82jEQi6QfPqomZMYNZ1eCM8cykE3Ri/8nt48/cAmC57AFyvwPoHYcd/4IzfwpTzIEEao5X6XbDjJaj8D9Rug4jGE1mZ3J+Xw5e9QW72HMCwZxPfnbqM6qWP87ZrD2sPrcWtublp7k2cXnE6+T4nrP0DfHwZabZsLj7+Ci6e+3Ncadm8feht1lWt47DnMJtqN7Hys5U9Vs583eniRoMNccL/wLGnQvkCMNu7PccIOGI/XXHLc1/nWs8WntT+w2np6ZxSu43dtWd0e92O7Kh2IUxOPmlexzedLQQyLuHQ167AeuwxLF5cRk40gD/tbT5rzMenhUmzdP5Pu7LGRVmwGbn+fV5dANd6g5j3v8/XDn3I1088lmXrmjnts3XcVrgIKWWnBWF17oDeQnDaBFj/ED5PDd8tL2dP/UfcYErnaXsmFW/+ioyxZxCMRmjZXwVTOs/fbKtyUZZjZ/k7N3DZq1Gs0syYvz/EgW//P656JcDjRTdyTMFP1ApZRdJILaEPtEA4gcdIOKhXRoTbVGSk5YE9FzQPBFwQarNAxWSD4hlQetyRn9qt8OK1+ofAVaswWkoBXejN5cdA2R0w40L49/fgX98Akx2KpkDx9NjPDP3Do/Jl2PFvqK/U71V2Apx4DSsMfv5w+FVOG3UStyz+M4ZoBN77C7x9FyW7XuXCk2/gwkV3gckKjZ/Cf38BHy8HowXmXg2uw/Dun+HdP5M18Sy+NPdqvrTwVxATq1A0RJ2vjipPFb6QjzR3LWlblmPfu5p0Uzrpx3+TjHO/ATmJywYHwsnnPMi5jy/g4epVLDlxDvPe28of++gfv6PGRU7hR4SQnFefxeGXV2GpqCC49zPq0sbx5bmbecKwHgxfZFeth1mjsztdo7LGzSW164kKycE52fx8/0dwxu+wr7uPi8NONh4rmL5hFYHFJybMj9fXt5AXcBEYVYh854/cPHYKe3013DL/Fv686c9cXzaGp3a+TO6ML1IN+PbvBzrP32yvcnF8QTVV7+/h7E+h6Gc3kj5vHsU/+Sn84mbWrzvA7DmfsbYq8aIshaKvpJbQf2Nl1/uiUXAehIZdUL8TGnbqAm/LAmsWWDPBmkkDEaobKmls2EHTzv+jcefTNBqNSGBZ6TQmXbQc0vMxRqNgNEI0irkwlsoYNRv+9y3Y8SIc2gg1H8O2F/RcbhxhgPKFMOcqmPQlcIzi1X2vctvaG1lYupA7Fv8Jk9GsfygsuhFmXASv/hTeuA0+elK/x9YVusDP+3+w4LuQGWtn13IQNv5dv1/lfyCnQv8mk5aHOS2PUWl5jLLnwL63YfuL+nMv+KF+HXvOoPyTAGBJ58alf+X91/+Xp7O38LVAAMPWzUDPpmhxdlQ7sWatZXGtH+31HEw5WZQ/8TieNWuo/tnPOTuax9/P0rBlf0Bl9YKEQr/7UBPfrXybjccKvun8FMO8a2HeNVA+ny8/cjo3HVfICc96WVD1MZU18zoJfcun+8gBimQlf7dGeVW6uX729Zw/4XxGZ47mW699i58Vl/K7/fr8TfjAgU5j8MSagc8z/pXz35AYZ04i59KvAZB9/vk0/nsFX1u9mWfKH6Da+QuavBq56Z2tFhSKvpBaQt8dBoMereaUw/jTWjf7Qj421G5gXdU63qtaxV7n3tjxQK7uKphhsBKWEf4p65j37k+4cuqVLChdgDE7GwwCYbEgpWRX8y7eOvgW62vWEyWKvWw09ooJ2KMSu+YlTYI9fyJ2ex52kx17w0Y8VWv4w4Y/MKtgFncvvhuLscP/1DnlcPFTsOd1eOXHemqoo8DHyR6tzxGcfCNse15PDXnqoOkz8DVBMJYKsGbpx8z7f51TWIOEo3wht5SdwQ+iq7jQJJi296Nei1goEmW/90MybF6ueNGKjAhGP/Qgpvx8sr/6VbQDB2l88EGuzYzw+Px32FH9zU7XqHcHmbjnfTJ8AQ5MjXBFyYlw2i/1nSUzMZ97D0tf+Q612TmcffB1Kqsv5rQp7f++/n37ATjgXcOfK3JYWr6Ub07T73ViyYn84Pgf8IcNf+AJzw7mG7Iw1BzuNI4d1S4qLFuYuaoJixRU3PmX1naEQgjKf383O844lRNedbJpwYdsq5rLSeN7bjSjUHTHgIReCHEG8Gf0VO3DUso7OuwXsf1nAT7gSinlpoHcs69IKWkONtPgb6DB30Cjv7H19Y6mHXxU9xHhaBir0crxRcez7NhlVDgqyLPlkWfPI9eWi81kwxl08q9d/+KpHU9xzevXMD5nPDdnmDDa7Pzm/d+w5tAaqr3VCAST8yZjN9lpDjRTFa7CH/a3/gTr1nUa4+Tcydx76r3YTd3kw4/9Ily7Xi/pM3eu5GiH2QazLtF/2hLWwN8M1gywdL9gajA4+bQ/cPoT7/HRWCcnHdrE7monJx7bs4jtrfcyOuNlvrUigrnFQNkjf8F67LGt+wu+911Chw6x6OWX2ZTv45PIGmB6u2vsrHFz3sFXqcmGZcVmOP/vYGzzn/+MCzjt8Cbu3b2C096pY8vHn8Cp49tdIxxbLPWLinSOySznVwt/1S6Pf9mUy9jW8An3yP9yQmYEe6wUsy3bq1x8qX45Mz+DrB/9v05Wx+biYgpv/CHmX/6BM6qfY3vVZUroFQOm30IvhDAC9wGnAYeAD4UQL0kpt7c57ExgfOznROD+2O+kE5VRtjZsZb9rP/td+zngOsA+1z4OuA/gDXU2iLIZbVQ4KrhsymXML5nP7KLZWI1dl/s5rA6umn4Vl0+5nJWfreSxbY/x5MR6NBOs2/MC80vnc83Mazi57GTy7V03BolEIwQiAV34Q378ET9js8ZiTjT52xGDsfsJ3p4wWTp/C/g8MRi58Ut/5/ZPvsoJu31sX/ceJx57Xo+nbdi7lfPeqWXyfij57a9Jnzev3X5hMFDy29vR9u/muv/s4pFlTyLlte1EeNcHa5lf4+SDL0RYfMHTkN7538iw9FecsO99QutqqHjvfvj+V9rtTztcic8q8WXYePSL95Nmbp/aEUJw68Jfsrf2I3bmHWZU8x46Uv3hoyx9V6P52EwmffPahM9bdMk32Puvh1nyXjMrJ/wVFt3V499IoegO0d/Vd0KI+cCtUsrTY+9/AiCl/G2bYx4EVkspn4693wmcIqXsdn34nDlz5IYNG/o0nqiMMvepuQQjQQzCQEl6CRVZFYzJGsPozNEUphWSb89v/UkzpQ2ox6uUkg9qPkCLaMwtnovN1EOUrWhl9arbyLv+Gbx28PfizyaiUNQMxgtPZcIv7+3yuHBzMxvPOgmLJ0JLh3Vm9oD+k/nn/2HcF3/Y5TWkp4Hnvn4S43dDU3b7fQ43HM6D3L/9gYXHnt3lNQ67DrDi/53B4k2S+g6ZsTQ/WEJwzIrlZI3vuvGL98Aedpx7DlEBXlVKP2II2ARnvrG95wMTIITYKKWck2jfQFI3o4CDbd4fonO0nuiYUUAnoRdCXA1cDTBmzJiOu3vEIAzcd+p9FNgLKMss65zrTjJCCOaVzOv5QEUnTll6C8+c+i62fd37wbSlZkYBF912T7fHmHJyyLrzD2y76yeYOjTndgLh8RWc343IA4iMfCqu/jF7H/wjooOFgTMXtBOO61bkAUZljWHS2d9kt/sJRIdAygkYTz2N47oReYD0McdS961lBFf+B4GyQhgphK2DM206kIj+AuB0KeX/xN5fBsyVUn6nzTEvA7+VUr4Te/8GcKOUcmN31+5PRK9QKBQjme4i+oGYmh0C2s4klQEdZ596c4xCoVAoBpGBCP2HwHghxFghhAW4GHipwzEvAZcLnXmAs6f8vEKhUCiSS78TQlLKsBDiOuBV9PLKR6WU24QQ18T2PwCsRC+t3INeXvmNgQ9ZoVAoFH1hQJl/KeVKdDFvu+2BNq8lkLiGTKFQKBSfC6rxiEKhUKQ4SugVCoUixVFCr1AoFCmOEnqFQqFIcfq9YGowEULUA/v7eXo+0JDE4Qw1Uv35IPWfUT3f8GcoPmO5lDKhA96QFPqBIITY0NXqsFQg1Z8PUv8Z1fMNf4bbM6rUjUKhUKQ4SugVCoUixUlFoX/oaA9gkEn154PUf0b1fMOfYfWMKZejVygUCkV7UjGiVygUCkUblNArFApFipMyQi+EOEMIsVMIsUcIcdPRHk8yEEI8KoSoE0JsbbMtVwjxmhBid+x3ztEc40AQQowWQrwlhNghhNgmhPhebHtKPKMQwiaEWC+E2BJ7vtti21Pi+eIIIYxCiI+EEP+JvU+159snhPhECLFZCLEhtm1YPWNKCH2bRuVnAlOAS4QQU47uqJLCY8AZHbbdBLwhpRwPvBF7P1wJAz+UUk4G5gHXxv7dUuUZg8ASKeVMYBZwRqwvQ6o8X5zvATvavE+15wNYLKWc1aZ2flg9Y0oIPTAX2COl3Cul1IBngPOO8pgGjJRyLdDUYfN5wOOx148DX/48x5RMpJTVUspNsddudLEYRYo8o9TxxN6aYz+SFHk+ACFEGfAl4OE2m1Pm+bphWD1jqgh9V03IU5GieJeu2O/CozyepCCEqACOAz4ghZ4xltbYDNQBr0kpU+r5gD8BNwLRNttS6flA/3BeJYTYKIS4OrZtWD3j4LQc//wRCbaputFhghAiA3gOuF5K6RIi0T/n8ERKGQFmCSGygeeFENOO8pCShhDibKBOSrlRCHHKUR7OYLJQSlklhCgEXhNCVB7tAfWVVInoR1IT8lohRAlA7HfdUR7PgBBCmNFF/ikp5YrY5pR6RgApZQuwGn3OJVWebyFwrhBiH3q6dIkQ4klS5/kAkFJWxX7XAc+jp4qH1TOmitD3plF5qvAScEXs9RXAi0dxLANC6KH7I8AOKeUf2+xKiWcUQhTEInmEEHbgi0AlKfJ8UsqfSCnLpJQV6P/PvSml/Dop8nwAQoh0IURm/DWwFNjKMHvGlFkZK4Q4Cz1fGG9U/pujO6KBI4R4GjgF3RK1FrgFeAF4FhgDHAAukFJ2nLAdFgghvgC8DXzCkRzvT9Hz9MP+GYUQM9An6ozoQdWzUspfCiHySIHna0ssdfMjKeXZqfR8Qohx6FE86Knuf0opfzPcnjFlhF6hUCgUiUmV1I1CoVAoukAJvUKhUKQ4SugVCoUixVFCr1AoFCmOEnqFQqFIcZTQKxQKRYqjhF6hUChSnP8Pjxv/NgVy4KgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Calculamos una PUNTUACION TOTAL para cada serie encontrada.\n",
    "puntuacion = []\n",
    "for j in range(F):\n",
    "    puntuacion.append( lista_D[j] + lista_R[j] + lista_THD[j] ) #suma ponderada de puntuaciones de los criterios anteriores\n",
    "\n",
    "plt.plot(range(F), puntuacion, range(F), lista_D, range(F), lista_R, range(F), lista_THD)\n",
    "plt.legend(['Puntuacion total', 'Criterio D', 'Criterio R', 'Criterio THD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1764465e",
   "metadata": {
    "scrolled": true
   },
   "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>Indice Serie</th>\n",
       "      <th>Puntuacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.536155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.961556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>18.676259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7.813927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7.375699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.129371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.625727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.942597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4.081039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4.486957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2.596032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8.695500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.0</td>\n",
       "      <td>3.006028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2.019130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.0</td>\n",
       "      <td>4.775099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15.0</td>\n",
       "      <td>11.119407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3.135079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.0</td>\n",
       "      <td>5.031992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3.205158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22.0</td>\n",
       "      <td>2.949245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.0</td>\n",
       "      <td>2.967188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28.0</td>\n",
       "      <td>5.513431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31.0</td>\n",
       "      <td>2.792265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Indice Serie  Puntuacion\n",
       "0            0.0    7.536155\n",
       "1            1.0    3.961556\n",
       "2            2.0   18.676259\n",
       "3            3.0    7.813927\n",
       "4            4.0    7.375699\n",
       "5            5.0    1.129371\n",
       "6            6.0    5.625727\n",
       "7            7.0    5.942597\n",
       "8            8.0    4.081039\n",
       "9            9.0    4.486957\n",
       "10          10.0    2.596032\n",
       "11          11.0    8.695500\n",
       "12          12.0    3.006028\n",
       "13          13.0    2.019130\n",
       "14          14.0    4.775099\n",
       "15          15.0   11.119407\n",
       "16          16.0    3.135079\n",
       "17          17.0    5.031992\n",
       "18          18.0    3.205158\n",
       "19          19.0    0.000000\n",
       "20          20.0    0.000000\n",
       "21          21.0    0.000000\n",
       "22          22.0    2.949245\n",
       "23          23.0    0.000000\n",
       "24          24.0    0.000000\n",
       "25          25.0    2.967188\n",
       "26          26.0    0.000000\n",
       "27          27.0    0.000000\n",
       "28          28.0    5.513431\n",
       "29          29.0    0.000000\n",
       "30          30.0    0.000000\n",
       "31          31.0    2.792265\n",
       "32          32.0    0.000000\n",
       "33          33.0    0.000000\n",
       "34          34.0    0.000000\n",
       "35          35.0    0.000000\n",
       "36          36.0    0.000000\n",
       "37          37.0    0.000000\n",
       "38          38.0    0.000000\n",
       "39          39.0    0.000000\n",
       "40          40.0    0.000000\n",
       "41          41.0    0.000000\n",
       "42          42.0    0.000000\n",
       "43          43.0    0.000000\n",
       "44          44.0    0.000000\n",
       "45          45.0    0.000000\n",
       "46          46.0    0.000000\n",
       "47          47.0    0.000000\n",
       "48          48.0    0.000000\n",
       "49          49.0    0.000000\n",
       "50          50.0    0.000000\n",
       "51          51.0    0.000000\n",
       "52          52.0    0.000000\n",
       "53          53.0    0.000000\n",
       "54          54.0    0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[2, 15, 11, 3, 0, 4, 7, 6, 28, 17, 14, 9, 8, 1, 18, 16, 12, 25, 22, 31]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Ordenar las series con las puntuaciones más altas a las más bajas\n",
    "import pandas as pd\n",
    "\n",
    "N = 20 #cuantas series queremos mostrar\n",
    "\n",
    "df = pd.DataFrame([range(F), puntuacion])\n",
    "df = df.transpose()\n",
    "df.columns = ['Indice Serie','Puntuacion']\n",
    "lista = df.nlargest(N, 'Puntuacion')['Indice Serie'] #las N series con puntuaciones más altas\n",
    "lista = [int(k) for k in list(lista)]\n",
    "display(df)\n",
    "display(lista)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ebbda4",
   "metadata": {},
   "source": [
    "### Resultados obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2cbbc0de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Componente nº: 2\n",
      "Frecuencia fundamental: 24.372089610962668\n",
      "Puntuación: 18.67625854644866\n",
      "No. miembros de la serie armónica: 8\n",
      "Serie armónica encontrada: [24.372089610962668, 48.74417922192534, 0, 96.77117518690436, 0, 0, 174.95173690186468, 193.54235037380874, 0, 248.25632624055973, 0, 290.2748964811184, 0, 0, 0, 0, 418.65350391091374]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 15\n",
      "Frecuencia fundamental: 312.7818465910811\n",
      "Puntuación: 11.119407027478337\n",
      "No. miembros de la serie armónica: 6\n",
      "Serie armónica encontrada: [312.7818465910811, 625.5636931821622, 0, 1251.0890686820035, 1563.7398275332525, 1876.748044426766, 0, 2502.2110631308883]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 11\n",
      "Frecuencia fundamental: 193.54235037380874\n",
      "Puntuación: 8.695500481058835\n",
      "No. miembros de la serie armónica: 6\n",
      "Serie armónica encontrada: [193.54235037380874, 387.0847007476175, 580.642536727567, 774.1029224430208, 0, 0, 0, 1548.2058448860416, 0, 1935.4235037380874]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 3\n",
      "Frecuencia fundamental: 32.32471807637147\n",
      "Puntuación: 7.813926559332187\n",
      "No. miembros de la serie armónica: 12\n",
      "Serie armónica encontrada: [32.32471807637147, 62.77381732006802, 96.77117518690436, 0, 0, 193.54235037380874, 0, 0, 290.2748964811184, 0, 0, 387.0847007476175, 418.65350391091374, 0, 483.8558759345219, 0, 0, 580.642536727567, 0, 650.2925879226922, 677.34375, 0, 0, 774.1029224430208]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 0\n",
      "Frecuencia fundamental: 5.403747893901415\n",
      "Puntuación: 7.536155043893835\n",
      "No. miembros de la serie armónica: 10\n",
      "Serie armónica encontrada: [5.403747893901415, 0, 16.24805974064178, 0, 24.372089610962668, 32.32471807637147, 38.62324253128723, 0, 48.74417922192534, 0, 58.13977816333357, 62.77381732006802, 0, 0, 82.89580360512896, 0, 0, 96.77117518690436]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 4\n",
      "Frecuencia fundamental: 38.62324253128723\n",
      "Puntuación: 7.375699208151762\n",
      "No. miembros de la serie armónica: 9\n",
      "Serie armónica encontrada: [38.62324253128723, 0, 0, 0, 193.54235037380874, 0, 0, 0, 0, 387.0847007476175, 418.65350391091374, 0, 0, 0, 580.642536727567, 0, 650.2925879226922, 699.9026642102681, 0, 774.1029224430208, 0, 0, 887.8914299898835]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 7\n",
      "Frecuencia fundamental: 62.77381732006802\n",
      "Puntuación: 5.9425972814572505\n",
      "No. miembros de la serie armónica: 4\n",
      "Serie armónica encontrada: [62.77381732006802, 0, 193.54235037380874, 0, 312.7818465910811, 0, 0, 0, 0, 625.5636931821622]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 6\n",
      "Frecuencia fundamental: 58.13977816333357\n",
      "Puntuación: 5.6257272916789915\n",
      "No. miembros de la serie armónica: 6\n",
      "Serie armónica encontrada: [58.13977816333357, 0, 174.95173690186468, 0, 290.2748964811184, 0, 0, 0, 0, 580.642536727567, 639.6848554574467, 699.9026642102681]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 28\n",
      "Frecuencia fundamental: 774.1029224430208\n",
      "Puntuación: 5.513431460276385\n",
      "No. miembros de la serie armónica: 2\n",
      "Serie armónica encontrada: [774.1029224430208, 1548.2058448860416]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 17\n",
      "Frecuencia fundamental: 418.65350391091374\n",
      "Puntuación: 5.031992076251254\n",
      "No. miembros de la serie armónica: 3\n",
      "Serie armónica encontrada: [418.65350391091374, 0, 1258.0482198649922, 1672.8515984032156]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 14\n",
      "Frecuencia fundamental: 290.2748964811184\n",
      "Puntuación: 4.77509882811316\n",
      "No. miembros de la serie armónica: 3\n",
      "Serie armónica encontrada: [290.2748964811184, 580.642536727567, 0, 0, 1451.3744824055918]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 9\n",
      "Frecuencia fundamental: 96.77117518690436\n",
      "Puntuación: 4.486956759354134\n",
      "No. miembros de la serie armónica: 13\n",
      "Serie armónica encontrada: [96.77117518690436, 193.54235037380874, 290.2748964811184, 387.0847007476175, 483.8558759345219, 580.642536727567, 677.34375, 774.1029224430208, 0, 0, 0, 0, 1258.0482198649922, 0, 1451.3744824055918, 1548.2058448860416, 1645.066368750025, 0, 0, 1935.4235037380874]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 8\n",
      "Frecuencia fundamental: 82.89580360512896\n",
      "Puntuación: 4.081038817413525\n",
      "No. miembros de la serie armónica: 4\n",
      "Serie armónica encontrada: [82.89580360512896, 0, 248.25632624055973, 0, 418.65350391091374, 0, 580.642536727567]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 1\n",
      "Frecuencia fundamental: 16.24805974064178\n",
      "Puntuación: 3.9615558613348165\n",
      "No. miembros de la serie armónica: 10\n",
      "Serie armónica encontrada: [16.24805974064178, 32.32471807637147, 48.74417922192534, 62.77381732006802, 82.89580360512896, 96.77117518690436, 0, 0, 0, 0, 174.95173690186468, 193.54235037380874, 0, 0, 248.25632624055973, 0, 0, 290.2748964811184]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 18\n",
      "Frecuencia fundamental: 483.8558759345219\n",
      "Puntuación: 3.205158156076283\n",
      "No. miembros de la serie armónica: 3\n",
      "Serie armónica encontrada: [483.8558759345219, 0, 1451.3744824055918, 1935.4235037380874]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 16\n",
      "Frecuencia fundamental: 387.0847007476175\n",
      "Puntuación: 3.1350789608705285\n",
      "No. miembros de la serie armónica: 4\n",
      "Serie armónica encontrada: [387.0847007476175, 774.1029224430208, 0, 1548.2058448860416, 1935.4235037380874]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mpLUX1OaRoj9tnjVr1uJsw8q5Dn2c4pzbaGb7A/eb2QvOuYfjC0ThPRegtrbW1dXVFVTZ+vp6jj7kjbBkEZP2m0Rd3YkFlbMvqa+vp9Dna1800toLavNIUaw25zT04ZzbGP3fAtwOFDU9NfQhIpLSZ1Cb2RgzG5u8DZwBLCt2xUBfJRcRgdyGPmqA281/XbAM+INz7q9FrZU61CIi3foMaufcKuCYQahLbKWDujYRkaAFeXqeiIikhBnUGvoQEekWZlBHNAIiIhJ4UIuISOBBrREQEZHAg1pDHyIigQa1etIiIilBBnVSLheMEhEZ7oIOatOPJ4qIhB3UIiKioBYRCV7QQa0xahGRwINaREQCD2odTBQRCTyoNfQhIhJoUKsnLSKSEmRQqyctIpISZFCLiEhKkEGtoQ8RkZQgg1pERFIU1CIigVNQi4gETkEtIhI4BbWISOAU1CIigVNQi4gETkEtIhK4nIPazErN7Gkzu6uYFRIRkZ7y6VF/EVhRrIrE6XuJIiIpOQW1mU0DzgKuL251PF2SSUQkJdce9TXAV4BE8aoiIiKZlPW1gJm9D9jinFtsZnW9LDcHmANQU1NDfX19QRVqampizdJnAWhoaCi4nH1JU1PTiGhn0khrL6jNI0Wx2txnUAOnAB8ws/cCFcA4M7vZOXdufCHn3FxgLkBtba2rq6srqEL19fUcfeiRsOhJJk2aRF3dSQWVsy+pr6+n0OdrXzTS2gtq80hRrDb3OfThnLvMOTfNOTcTOBt4MD2kRUSkeHQetYhI4HIZ+ujmnKsH6otSExERyUg9ahGRwAUd1PqNWxGRwINaREQCD2r9xq2ISOBBraEPEZFAg1o9aRGRlCCDWkREUoIMag15iIikBBnUIiKSEmRQa4xaRCQlyKAWEZEUBbWISOAU1CIigVNQi4gELuigdvqZWxGRsINaREQCD2pD5+mJiAQd1Br6EBEJPKhFRCTQoNaQh4hISpBBrSEPEZGUIINaRERSggxqDX2IiKQEGdQiIpKioBYRCZyCWkQkcApqEZHA9RnUZlZhZk+a2bNmttzMrhyMioF+O1FEBKAsh2XagHc455rMrBxYYGb3OOeeKHLdRESEHILaOeeApuhuefQ3KH1d/XaiiEiOY9RmVmpmzwBbgPudcwuLWisREelmLo+BYDObANwO/JtzblnavDnAHICampoT5s+fX1CFmpqaWNtayQ8XtfLGSSV89cTKgsrZlzQ1NVFdXT3U1Rg0I629oDaPFP1p86xZsxY752ozzctljLqbc26nmdUDZwLL0ubNBeYC1NbWurq6uoIqW19fz7GHHwWLFjJx4kTq6t5SUDn7kvr6egp9vvZFI629oDaPFMVqcy5nfUyJetKYWSVwOvDCgNckRmd7iIik5NKjPhD4nZmV4oP9VufcXcWtloiIJOVy1sdS4LhBqEs3ne0hIpKibyaKiAROQS0iEjgFtYhI4BTUIiKBU1CLiAQu6KDW+dQiIoEHtYiIBB7UOp9aRCTwoNbQh4hIoEGtjrSISEqQQa2OtIhISpBBLSIiKUEGtYY+RERSggxqERFJCTKoNUYtIpISZFCLiEhKkEGtMWoRkZQgg1pERFKCDmqn0WoRkbCDWkREAg9q02i1iEjYQa2hDxGRUINaHWkRkW5hBrU60iIi3cIMahER6aagFhEJnIJaRCRwCmoRkcD1GdRmNt3MHjKzFWa23My+WOxK6ViiiEhKWQ7LdAKXOOeWmNlYYLGZ3e+ce77IdRMREXLoUTvnNjnnlkS3G4EVwNRiV0xERDxzLveBBjObCTwMHOWc2502bw4wB6CmpuaE+fPnF1ShpqYm1rVVcvVTrRw2sYTLTqosqJx9SVNTE9XV1UNdjUEz0toLavNI0Z82z5o1a7FzrjbTvFyGPgAws2rgNuDi9JAGcM7NBeYC1NbWurq6uoIqW19fz9GHHQVPLWTChAnU1Z1cUDn7kvr6egp9vvZFI629oDaPFMVqc05nfZhZOT6k5znn/jjgtci63sFak4hIuHI568OA3wIrnHM/KX6VUvIYlZF+euyVbazc0jTU1RCRDHLpUZ8CnAe8w8yeif7eW+R6ySD7x98s5PSf/H2oqyEiGfQ5Ru2cW8AgX89OlzcVEUkJ+puJGqMWEQk8qDVGLSISeFDLIJg3D2bO7HlfRIKS83nUg6Uz4Sgd6kqMFPPmwZw5tLe2p6bNmeP/z549NHUSkb0E1aNeumEnn76vhfoXtw51VUaGyy+HlhYO/Y87UtNaWvx0EQlGUEG9aM0OAOpf3DLENRkh1q3Lb7qIDImggloG2UEH5TddRIZE0EEdwkkfXQnHJ659nL+/NAyHY666Cqqqek6rqvLTRSQYQQZ1CAGdtHtPB0+uaeALtzw91FUZeLNnw9y5PafNnasDiSKBCTKok0L4vsuw/9JNeigrpEWCE2ZQh9SlFhEZYkEFdXrvVXktIhJYUCeFFNAWDcDk80s4IiIDKcigTgpieDiqhGJaRIZK0EGtcBQRCTyoRUQk0KAOajzYpf0XERlkQQV1ckw6xEwMsU4iMjIEFdRJYXWoA6qMiIxIQQW1FetrgMmL45eU+P95XBw/pJ2GiIxMwf1wQA8DEZLRxfFpafH3167N6+L4ymkRGWpB9aiTBnS4Ibo4/tff9VlmfvUuP62Ai+MHdYBTREaUIIO620CMhEQXwb/5+LMyTu9LMqAV0yIyVMIO6oHQz4vjK6BFZKiFHdQDkZL9vDi+RjxEZKiFfTBxICQPGD4X3Z8xw4d0jtddTo6XK7BFZKgE2aMe8FCMh/KaNQVdHF/nU4vIUOkzqM3sBjPbYmbLBqNCEFjvNaS6iMiIlEuP+kbgzCLXAwjzZ6+U0yIy1PoMaufcw0DDINQlSEH17kVkRBqwg4lmNgeYA1BTU0N9fX3eZby0tgOA1tZWAHbu2llQOb3Jt7wdrQkAuroSA16XpKampqKVna/BqEdI7R0savPIUKw2D1hQO+fmAnMBamtrXV1dXd5lrHt8DaxYzujRo6G1lQnjJ1BXd/LAVPCvdwOQb7027doD9Q9iJSV5PzZX9fX1RSs7J9FzA/k/P4UY8vYOAbV5ZChWm8M86yN5I4Axaw19iMhQCzKokwLIaR1MFJEhl8vpebcAjwOHmdkGM7uoWJXpSuz95ZJEwvGnZ14lkRiYyMz34kq6GJOIDLU+x6idc+cMRkUArvzz836dsX7svCfX8Y07lrG7tZPz3jKj3+twrsDTAJXXIjJEghz6SHaezWBrYxsA26L//ZVv3iY71PpmYu4eW7mNp9ftGOpqiAwbQV7ro5ijDX4oI4TR7+HrH69fCMCa75/Vx5Iikosge9SZDNS3FvMd6tYQtYgMtUCD2qejxXq+AxWYhQ5hKLBFZKgEGdQuNkY90IMU+QZu92VOB7geIiK5CnOMuphl51h4U1snlsfyIiLFEmSPOqkYV9PLdejjqG/dy9FX3jeie9JrtjWza0/HUFdDZMQLMqiL+SWTfIruih15HIlffKn7UT1n/fyR/hUybx7MnAklJf7/vHkDUTWRESXMoI7+WxFOo8v/POpwx6j/tmIz5/12YVF3Iht27Cn8wfPmwZw5sHat30OuXevvN4zYq+aKFCTIoM70dfGBiqJEvl8hH6D1FsNFv1vEIy9vy/uUw0Fz+eU0dTouft8l7KgY66e1tMCrrw5tvUT2MUEGdXePuhhj1MPwPOquUJN63TpuOeZM7jhyFr986ydT09vbh65OIvugoIJ6YlU5AJ1dxfxqYhEeMETjsMkdWa5B3dzWye7WQTw4eNBBWKbnb9SowauDyDAQVFCPr/RBvaejq2jryHfoIynrw7KNww5CWCfr1JlI5LT8cd++n6OvuK+INUpz1VVQXt5zWlUVTJ06eHUQGQaCCuqK8tK9pg308Ed63t761Hp+dO+L2ZfvK9cvvxxaWvjR285l8esO99NaWvz0QZJjTtPemeOCeS6b1ezZ8PFPAODMYMYMmDsXJk3qf9kiI0hQQX3N2ccCMGO/KgCsCIPU6WdIfOW2pfzyoZVZl+9zVGHdOgB++daz+eh5P9pr+mDItUedj5/c/9LAFHTC8f7/Fy+GNWt8eItIXoIK6kOmVANQahmu8TFAR/XyLSU5VJJ1n3HQQflNL4KuIhzxXL+jZcDLFJHCBBXUyR50dzgWYR35jlH3WZerrsJVVfWcVlXlx2cHSTHO+hjwa6wEcKLj5/6wpP9f4BEZAkEFdUl0Eabk+Ghja0eqJ5vWpU0kHOu2F9DrK/D0vKzDMLNn03Xd3NT95Dhsjh/xd7V0sHRrZ36VSpMM6o6uBD++70Wa2govr6DntBfJ5y2E0xzvXrqJ5Rt3D3U1RPIWVFCb+ZO5Nu5qBWDJup3c//xmPzPtnX7tw69w2g8f4uXNjRnL2trYNiBfnEn2qLsSjpeyrKvr7LNTd/Ich/3MzYv5yeI2GpoLP7c4GdR3PP0qv3hwJT++L/vBUYCFq7Zz6g8epDlDoJ/2w4dY8PK2Aev/6icaRPovqKDOJFsPaOEq/zXkDTv3sGV3KzMvvZsHolDf0tjKm696IOMBsfyHPlK3f/a3lzMu05/e4qptTUD+Z1nED4omg3rphl0A7N7Te4/6e/e8wIYde3jhtcw7nhde2x32VzJFRpjggzqbkqirlkg4nt/kw/ymJ9YCsL3J9067e+Mx+YZqPNizXVOjP2PEJdHQQL4HBOPrTN7+fdT+5Rt39frY5CiOc46dLXv35L9z9wrufm5TXvXpyxOrto/IC1uJDIR9MqgfW7mNh17cCviQKo1SuyuR4KbH1/Cen/kDRpkOYOUbFVtjP6qbPAtu1dYmbo5CEfp31kUyqDu78utRx9fZmeeOIrnOW55cz2lXP5TXY/OV3Cm88Foj9y7fe8cpIn0LLqhPOmDvL72k+9rtz3XffmJVQyyoHdc8kBqeyJRf7Z2JjGOz2fzL7xd3335yTQOPrtzGO378d75+x7LucM00Fp6rZJB15BnU8VOn03v0ZaW9jwwnP43ctmQDu1v7fi7e8eN6nlrTQFtn/74xukGn/IkUJLig/sAhfV8Horw0Ve0bHl3dfd51V8J1hxBkHqq44IYnOfJb9/L8xt2s2dacV90amtuZHf3CNkBL9FX33oY+nHP86qGVrN2eeV3J3m1bnmPUXRnGqM850Z+7/e4jDuj1sflePnbV1mY+fu3jfG7e030um/7JIP7DAxr5EClMcEE9tTpzleLv8XhQQ6rn3JVwPU6jy5QL6xp8r+69P3+Euh/V96OmsKc9CupeEmhrUxs/vPdFPnXjUxnnJ3cs+R5MjO8cGqNecfJaKSUlvQdxfEgony9/PrDCD138bcVmtuxu3Wv+4rUNe50aGP+EE8K51CL7oiB/MzGTXzy4kk+dcjCTxoza6+2e/Ar1knU7mVCVughQPj0451Ihf9viDTk9Zu32FmrGVfRYT1NbJ5XlpZSWGG2dXVz9V3+qXGOWIYZkqHb0csXAJet28N27V3Dzp0/qvh5KfLjl3N8u7LH88o272NPexbfuXMaUsaNZva2ZH3z06O757bF1VZSV5nURrH//32f5v8UbeP2UMXz3w2/i0ZXbOOWQyUwcM4qP/vrxHsvOvPTuHvcHu0e9pbGV/cdWsOzVXbzvFwu6p8ePa4jsC4IM6l+ccxz/dsveH7OvuHM5XQnHik2pU/ZOOngSO1tSH6/jt7c1tbFi027eeOC4PtfZmXCURR31S/732Zzq+YnrHueP//pWRsV6+Ed9617Oe8uM7jMwkpIh9fk/LGHWYftzyiGTmVw9qnvoI71HvezVXRwwvoLJ1aP5xh3LWL5xN/cs28SHj5sGwJbYQc50f3nuNf7y3F/3mpb07Pqd3bfzvVLh/0U7sVe2NvPJuU8A8PMHs18rJa6QofwbH11NeVkJs0+akdfjFq9t4KO/fpxfnHMcL6adhtjRlaC0pO9jIb3p6ErQ3plgzOgg30IyzOS0lZnZmcDPgFLgeufc94tZqfcf8zqWvbqL6x5e1WP6nc9u3GvZhasbWLg68087NbZ28p6fPcIPP3Z0xvlxb7j8noLq+pH/emyvaekhDb7X39GV4K6lm7hr6d6nvj2zfgenHLIf25vbqf3OA93TV/znmVRGvegv/c+zHH7AOB5duY3v3L2ioPoOpV89tJJzTpze6zKdXQle2drMYQeMpSvhuOLPzwPkHdTJbWLh6u2Mreh5qdWr7l7BZ+tez+smVOZVZtxFv1vEwy9tZc33zyq4jKRrHniJGx9bwzPfPKPfZcnwZH2d22pmpcBLwLuADcBTwDnOueezPaa2ttYtWrSooArV19dTV1cHwM//9vLAXcWtAPd96TTO+OnDAPzD5DGsyvPg477iivcfQUNLB2e/eTrrG1qYt3Bdxp1iMXz4uKnUjKtg/Y4WnHPdPf//ePdh/DDt8rNlJcanTpnJ6yZU8qap45k+qYq7lm7ihU27mTaxikP2r2bqxEouvW1p1i/zJE0aM4orP3Ak1y9YzVfefRgz9qtivzGjWdvQzKadrRwzfQIAV//1BY6dPoHm9i5mHTaFts5E9+mf4HekZv40zs/9YQmfqJ3O7JMO8t+ydQ7noL0rweMLHmbWrFkAbNy5h5pxFSxZt4Nzr1/YfSD58cvewcSqUTS1dVJeUkLlqFLKSlLXv9nR0sGUsaOB1IHyzoRjXUMLr48uaNbemWDTrj3M2G9Mdx2Tw2R9HbvIZFdLB+VlRtWonn26Za/u4qD9qmjvTDC5enR3u0pLjHEV5ZSXGgseebj7vVyI+HBk8n7CMaDDVu2dCUaVDdyhunh+5cvMFjvnajPOyyGoTwaucM69O7p/GYBz7nvZHjNQQZ308uZGVrzWyBei4ZD//OCRnH/yTK554KUeB6uu+eSxfPuu59ney9exR5WV8Jvza2lu6+SGBatZtHZHxuXOOfEgvveRN+GcY1tTO1PGjualzY3c+tR63vOmAzh4cjV/W7GZ5rbO7l7flR84kjceOI5L/7iUA8dXcMKMSSxe28CjK7cX9Fz05QvvfAMnzpxEW2cXV/75eU47dDJnv/kgbn5iLacdOoV/nbeEsRVljCot8T31GRNJOMeSdTu56Z9O5KbH13LtucdTVrr3hrprTwdtnV3sP7aCnS3tXHLrszy5uoHGtk4uOvVgfrtgNYfWVPOuI2r41UOv8JHjpvKldx3Khh17GFdZxq/rX+n+5DBtYmX/fiR3H1Q1qpTOhOsxpDW6rISuhOv1vHezvcfy49OSxz/aOxNgPYfMxowqpTk6wF01qpQSM5rbO3EOykuNMaPLaO/0n+xKzCgtMUrNKCnxt/00P0SVPIOqobmdhIOxo8tIOEeXc7R1JnrUsbzUuh8TN26UQYk/BlJZXkrCwYSqcly0bJdzOOf87YTfqXVF9x1+x1wa/XV2OfZ0dNGVcEyoKqery1Fd4duTLH90WQkOv/NKRM9zsuwS8wHfGa0H/MHt1o4E+40Zxaiyku71l5phljojK7mvMEudMdU9rfs18rdKO/fwwKXv6WXLyK6/Qf0x4Ezn3Kej++cBJznnPp+23BxgDkBNTc0J8+fPL6iyTU1NVFdXF/RY8D0Pw5/x0eWgpQMqymB0L+cWO+fYtscxdpRRUTbwB5m6Eo6GVr+OKVXG5EofjJ0JR5eD9pZmmkuqKCuBSRX+DZNwji0tjo4EjC2H6lFGcwdsak5QWQY1VSU51TW9VzJUtu9J0NYFrV2Oiq49TBg7ho1NCXa0OdY1Jpg42ljXmGBadQnjRhtbWhLsbHUcX1PGjtYEW/f4N9vmZkfCOaaOLaGlAzoSjue2dvH+15fTkfCv+QFVJTgc+1eVsKfTvzHbuuDAah+U6xsTvLIzweaWBFMqS9jV7tjcnODIyaW0d8ErO7sYO8qYONrodLB6V4Jxo4wZ40roSEBLp6Oi1Bg/2tjYlMAMWjocpQabmh1H7FdKW5djdKmvT0WpsaOlndKycirLYNWuBAeNLaGsxOhyjtZOmFzpw6G5w4GDijKjI+FDM3n8ZNsex+RK69GrbO5wrN6V4LCJJZQYdCZgfWOCg8eXkLySgA8dKDUoK4EyMxL490rC+XW46H8CHz6l5m83dzgqy4yK0lRQlRhsbkmwu82/Z6ZU+vJWbO9i/Ghj3ChjdJnR3t5B5ehyykqgtdPXoa0rdfG1Ekv9xe+bWY8D3g4oMx/czR3JXrVva3mJr2tnAjpdqpzSWJvNLNopQKrz7J+/bXsSVJVb7OJr/rlwEAv05P+eE9KT0zkoo5N/Praw/Jo1a1bWoM5ljDrTu3yvdHfOzQXmgu9RF9r9789Hh31VfX097x9BbR6pr7HaPPwVq825DM5sAOJHgKYBgzOAKSIiOQX1U8AbzOxgMxsFnA3cWdxqiYhIUp9DH865TjP7PHAv/vS8G5xzy4teMxERAXI8j9o59xfgL0Wui4iIZBDctT5ERKQnBbWISOAU1CIigVNQi4gErs9vJhZUqNlWYO8rE+VmMrBtAKuzLxhpbR5p7QW1eaToT5tnOOemZJpRlKDuDzNblO1rlMPVSGvzSGsvqM0jRbHarKEPEZHAKahFRAIXYlDPHeoKDIGR1uaR1l5Qm0eKorQ5uDFqERHpKcQetYiIxCioRUQCF0xQm9mZZvaima00s0uHuj79YWY3mNkWM1sWmzbJzO43s5ej/xNj8y6L2v2imb07Nv0EM3sumvdzC+GnWjIws+lm9pCZrTCz5Wb2xWj6cG5zhZk9aWbPRm2+Mpo+bNucZGalZva0md0V3R/WbTazNVFdnzGzRdG0wW2zi363bCj/8JdPfQX4B2AU8CxwxFDXqx/tOQ04HlgWm3Y1cGl0+1LgB9HtI6L2jgYOjp6H0mjek8DJ+F/ZuQd4z1C3LUt7DwSOj26Pxf8Y8hHDvM0GVEe3y4GFwFuGc5tjbf8y8AfgruG+bUd1XQNMTps2qG0OpUd9IrDSObfKOdcOzAc+OMR1Kphz7mGgIW3yB4HfRbd/B3woNn2+c67NObcaWAmcaGYHAuOcc487/yrfFHtMUJxzm5xzS6LbjcAKYCrDu83OOdcU3S2P/hzDuM0AZjYNOAu4PjZ5WLc5i0FtcyhBPRVYH7u/IZo2nNQ45zaBDzZg/2h6trZPjW6nTw+amc0EjsP3MId1m6MhgGeALcD9zrlh32bgGuAr+N++TRrubXbAfWa2OPoRbxjkNuf0wwGDIKcf0B2msrV9n3tOzKwauA242Dm3u5chuGHRZudcF3CsmU0Abjezo3pZfJ9vs5m9D9jinFtsZnW5PCTDtH2qzZFTnHMbzWx/4H4ze6GXZYvS5lB61CPhB3Q3Rx9/iP5viaZna/uG6Hb69CCZWTk+pOc55/4YTR7WbU5yzu0E6oEzGd5tPgX4gJmtwQ9PvsPMbmZ4txnn3Mbo/xbgdvxQ7aC2OZSgHgk/oHsncEF0+wLgT7HpZ5vZaDM7GHgD8GT0carRzN4SHR0+P/aYoET1+y2wwjn3k9is4dzmKVFPGjOrBE4HXmAYt9k5d5lzbppzbib+Pfqgc+5chnGbzWyMmY1N3gbOAJYx2G0e6iOqsaOo78WfLfAKcPlQ16efbbkF2AR04PekFwH7AX8DXo7+T4otf3nU7heJHQkGaqON4hXgl0TfJA3tDzgV/zFuKfBM9PfeYd7mo4GnozYvA74ZTR+2bU5rfx2psz6GbZvxZ6I9G/0tT2bTYLdZXyEXEQlcKEMfIiKShYJaRCRwCmoRkcApqEVEAqegFhEJnIJaRCRwCmoRkcD9P0TDpHMQ4PNCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 12\n",
      "Frecuencia fundamental: 198.84747278543932\n",
      "Puntuación: 3.0060278210837477\n",
      "No. miembros de la serie armónica: 3\n",
      "Serie armónica encontrada: [198.84747278543932, 0, 595.8136802602813, 0, 0, 0, 0, 1589.801055187638]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 25\n",
      "Frecuencia fundamental: 650.2925879226922\n",
      "Puntuación: 2.967187861558939\n",
      "No. miembros de la serie armónica: 2\n",
      "Serie armónica encontrada: [650.2925879226922, 0, 1950.8777637680764]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 22\n",
      "Frecuencia fundamental: 625.5636931821622\n",
      "Puntuación: 2.9492450111004502\n",
      "No. miembros de la serie armónica: 4\n",
      "Serie armónica encontrada: [625.5636931821622, 1251.0890686820035, 1876.748044426766, 2502.2110631308883]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n",
      "\n",
      "Componente nº: 31\n",
      "Frecuencia fundamental: 1251.0890686820035\n",
      "Puntuación: 2.792265436209738\n",
      "No. miembros de la serie armónica: 2\n",
      "Serie armónica encontrada: [1251.0890686820035, 2502.2110631308883]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## Recopilamos toda la información\n",
    "for i in lista:\n",
    "    output, indices, numero_armonicos = encontrar_seriearmonica(i, v, Dv, intervalo_componente, orden_maximo, criterio_parada)\n",
    "    print(\"\\nComponente nº:\", i)\n",
    "    print(\"Frecuencia fundamental:\", output[0])\n",
    "    print(\"Puntuación:\", puntuacion[i])\n",
    "    print(\"No. miembros de la serie armónica:\", numero_armonicos)\n",
    "    print(\"Serie armónica encontrada:\", output)\n",
    "    \n",
    "    plt.plot(frecuencias_new, amplitudes_new)\n",
    "    plt.scatter(frecuencias_new[np.array(peaks)[indices]], amplitudes_new[np.array(peaks)[indices]], color = 'r')\n",
    "    plt.title(\"Serie de frecuencia fundamental \" + str(output[0]))\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "    print(\"____________________________________________________________________________________________________________________\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "47300235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Componente</th>\n",
       "      <th>Frecuencia fundamental (Hz)</th>\n",
       "      <th>Serie armonica</th>\n",
       "      <th>Numero armonicos</th>\n",
       "      <th>Puntuacion total</th>\n",
       "      <th>Criterio D</th>\n",
       "      <th>Criterio R</th>\n",
       "      <th>Criterio THD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5.650155</td>\n",
       "      <td>[5.403747893901415, 0, 15.321456881663607, 0, ...</td>\n",
       "      <td>10</td>\n",
       "      <td>7.536155</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.038877</td>\n",
       "      <td>6.941723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15.321457</td>\n",
       "      <td>[16.24805974064178, 33.43750281593051, 49.2099...</td>\n",
       "      <td>10</td>\n",
       "      <td>3.961556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>3.289117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>25.820478</td>\n",
       "      <td>[24.372089610962668, 49.209905616605575, 0, 96...</td>\n",
       "      <td>8</td>\n",
       "      <td>18.676259</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>18.039004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>33.437503</td>\n",
       "      <td>[32.324718076371475, 64.51454372441758, 96.755...</td>\n",
       "      <td>12</td>\n",
       "      <td>7.813927</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.311688</td>\n",
       "      <td>7.002238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>[38.62324253128723, 0, 0, 0, 193.4817610746296...</td>\n",
       "      <td>9</td>\n",
       "      <td>7.375699</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>6.625020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>49.209906</td>\n",
       "      <td>[48.74417922192534, 96.75560545082685, 0, 193....</td>\n",
       "      <td>5</td>\n",
       "      <td>1.129371</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.178390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>59.049349</td>\n",
       "      <td>[58.139778163333574, 0, 175.848984866943, 0, 2...</td>\n",
       "      <td>6</td>\n",
       "      <td>5.625727</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>4.846658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>64.514544</td>\n",
       "      <td>[62.77381732006802, 0, 193.48176107462967, 0, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>5.942597</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>5.286187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>81.444405</td>\n",
       "      <td>[82.89580360512898, 0, 246.25501366972114, 0, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4.081039</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>3.276277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>96.755605</td>\n",
       "      <td>[96.77117518690437, 193.48176107462967, 290.33...</td>\n",
       "      <td>13</td>\n",
       "      <td>4.486957</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.036957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>175.848985</td>\n",
       "      <td>[174.95173690186468, 0, 0, 699.9026642102681, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>2.596032</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>1.619841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>193.481761</td>\n",
       "      <td>[193.54235037380874, 387.0680347916496, 580.64...</td>\n",
       "      <td>6</td>\n",
       "      <td>8.695500</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>7.262167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>199.777581</td>\n",
       "      <td>[198.8474727854393, 0, 595.8136802602813, 0, 0...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.006028</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.964361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>246.255014</td>\n",
       "      <td>[248.25632624055973, 0, 0, 0, 0, 1489.53795744...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.019130</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.085796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>290.332030</td>\n",
       "      <td>[290.2748964811184, 580.642536727567, 0, 0, 14...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.775099</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>3.550099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>312.772696</td>\n",
       "      <td>[312.7818465910811, 625.4901955417666, 0, 1250...</td>\n",
       "      <td>6</td>\n",
       "      <td>11.119407</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>9.226550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>387.068035</td>\n",
       "      <td>[387.0847007476175, 774.2324585535872, 0, 1548...</td>\n",
       "      <td>4</td>\n",
       "      <td>3.135079</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.501746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>419.436810</td>\n",
       "      <td>[418.65350391091374, 0, 1258.0482198649922, 16...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.031992</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.481992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>483.871118</td>\n",
       "      <td>[483.85587593452186, 0, 1451.3744824055918, 19...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.205158</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.655158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>580.642537</td>\n",
       "      <td>[580.642536727567]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>590.214649</td>\n",
       "      <td>[590.2146486948935]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>595.813680</td>\n",
       "      <td>[595.8136802602813]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>625.490196</td>\n",
       "      <td>[625.5636931821622, 1250.9937977701263, 1876.7...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.949245</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.615912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>632.340494</td>\n",
       "      <td>[632.340493966686]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>639.684855</td>\n",
       "      <td>[639.6848554574467]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>651.847120</td>\n",
       "      <td>[650.2925879226922, 0, 1950.8777637680764]</td>\n",
       "      <td>2</td>\n",
       "      <td>2.967188</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>677.343750</td>\n",
       "      <td>[677.34375]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>699.902664</td>\n",
       "      <td>[699.9026642102681]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>774.232459</td>\n",
       "      <td>[774.1029224430208, 1548.2058448860416]</td>\n",
       "      <td>2</td>\n",
       "      <td>5.513431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.846765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>887.891430</td>\n",
       "      <td>[887.8914299898835]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>1012.582807</td>\n",
       "      <td>[1012.5828067587219]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>1250.993798</td>\n",
       "      <td>[1251.0890686820035, 2502.2110631308883]</td>\n",
       "      <td>2</td>\n",
       "      <td>2.792265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>1258.048220</td>\n",
       "      <td>[1258.0482198649922]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>1325.428984</td>\n",
       "      <td>[1325.4289836820194]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>1328.566170</td>\n",
       "      <td>[1328.5661696330687]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>1451.374482</td>\n",
       "      <td>[1451.3744824055918]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>1489.537957</td>\n",
       "      <td>[1489.5379574433584]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>1500.781500</td>\n",
       "      <td>[1500.7815]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>1537.500000</td>\n",
       "      <td>[1537.5]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>1541.647994</td>\n",
       "      <td>[1541.6479939367084]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>1548.205845</td>\n",
       "      <td>[1548.2058448860416]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>1563.739828</td>\n",
       "      <td>[1563.7398275332525]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>1574.565632</td>\n",
       "      <td>[1574.5656321167821]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>1589.801055</td>\n",
       "      <td>[1589.801055187638]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>1612.985351</td>\n",
       "      <td>[1612.9853506762288]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>1614.912055</td>\n",
       "      <td>[1614.9120554113501]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>1628.743136</td>\n",
       "      <td>[1628.7431359573613]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>1638.218514</td>\n",
       "      <td>[1638.2185135853174]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>1645.066369</td>\n",
       "      <td>[1645.066368750025]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>1672.851598</td>\n",
       "      <td>[1672.8515984032156]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>1693.173862</td>\n",
       "      <td>[1693.1738623368406]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>1876.748044</td>\n",
       "      <td>[1876.748044426766]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>1935.423504</td>\n",
       "      <td>[1935.4235037380874]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>1950.877764</td>\n",
       "      <td>[1950.8777637680764]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>2502.211063</td>\n",
       "      <td>[2502.2110631308883]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Componente  Frecuencia fundamental (Hz)  \\\n",
       "0            0                     5.650155   \n",
       "1            1                    15.321457   \n",
       "2            2                    25.820478   \n",
       "3            3                    33.437503   \n",
       "4            4                    37.500000   \n",
       "5            5                    49.209906   \n",
       "6            6                    59.049349   \n",
       "7            7                    64.514544   \n",
       "8            8                    81.444405   \n",
       "9            9                    96.755605   \n",
       "10          10                   175.848985   \n",
       "11          11                   193.481761   \n",
       "12          12                   199.777581   \n",
       "13          13                   246.255014   \n",
       "14          14                   290.332030   \n",
       "15          15                   312.772696   \n",
       "16          16                   387.068035   \n",
       "17          17                   419.436810   \n",
       "18          18                   483.871118   \n",
       "19          19                   580.642537   \n",
       "20          20                   590.214649   \n",
       "21          21                   595.813680   \n",
       "22          22                   625.490196   \n",
       "23          23                   632.340494   \n",
       "24          24                   639.684855   \n",
       "25          25                   651.847120   \n",
       "26          26                   677.343750   \n",
       "27          27                   699.902664   \n",
       "28          28                   774.232459   \n",
       "29          29                   887.891430   \n",
       "30          30                  1012.582807   \n",
       "31          31                  1250.993798   \n",
       "32          32                  1258.048220   \n",
       "33          33                  1325.428984   \n",
       "34          34                  1328.566170   \n",
       "35          35                  1451.374482   \n",
       "36          36                  1489.537957   \n",
       "37          37                  1500.781500   \n",
       "38          38                  1537.500000   \n",
       "39          39                  1541.647994   \n",
       "40          40                  1548.205845   \n",
       "41          41                  1563.739828   \n",
       "42          42                  1574.565632   \n",
       "43          43                  1589.801055   \n",
       "44          44                  1612.985351   \n",
       "45          45                  1614.912055   \n",
       "46          46                  1628.743136   \n",
       "47          47                  1638.218514   \n",
       "48          48                  1645.066369   \n",
       "49          49                  1672.851598   \n",
       "50          50                  1693.173862   \n",
       "51          51                  1876.748044   \n",
       "52          52                  1935.423504   \n",
       "53          53                  1950.877764   \n",
       "54          54                  2502.211063   \n",
       "\n",
       "                                       Serie armonica  Numero armonicos  \\\n",
       "0   [5.403747893901415, 0, 15.321456881663607, 0, ...                10   \n",
       "1   [16.24805974064178, 33.43750281593051, 49.2099...                10   \n",
       "2   [24.372089610962668, 49.209905616605575, 0, 96...                 8   \n",
       "3   [32.324718076371475, 64.51454372441758, 96.755...                12   \n",
       "4   [38.62324253128723, 0, 0, 0, 193.4817610746296...                 9   \n",
       "5   [48.74417922192534, 96.75560545082685, 0, 193....                 5   \n",
       "6   [58.139778163333574, 0, 175.848984866943, 0, 2...                 6   \n",
       "7   [62.77381732006802, 0, 193.48176107462967, 0, ...                 4   \n",
       "8   [82.89580360512898, 0, 246.25501366972114, 0, ...                 4   \n",
       "9   [96.77117518690437, 193.48176107462967, 290.33...                13   \n",
       "10  [174.95173690186468, 0, 0, 699.9026642102681, ...                 3   \n",
       "11  [193.54235037380874, 387.0680347916496, 580.64...                 6   \n",
       "12  [198.8474727854393, 0, 595.8136802602813, 0, 0...                 3   \n",
       "13  [248.25632624055973, 0, 0, 0, 0, 1489.53795744...                 2   \n",
       "14  [290.2748964811184, 580.642536727567, 0, 0, 14...                 3   \n",
       "15  [312.7818465910811, 625.4901955417666, 0, 1250...                 6   \n",
       "16  [387.0847007476175, 774.2324585535872, 0, 1548...                 4   \n",
       "17  [418.65350391091374, 0, 1258.0482198649922, 16...                 3   \n",
       "18  [483.85587593452186, 0, 1451.3744824055918, 19...                 3   \n",
       "19                                 [580.642536727567]                 1   \n",
       "20                                [590.2146486948935]                 1   \n",
       "21                                [595.8136802602813]                 1   \n",
       "22  [625.5636931821622, 1250.9937977701263, 1876.7...                 4   \n",
       "23                                 [632.340493966686]                 1   \n",
       "24                                [639.6848554574467]                 1   \n",
       "25         [650.2925879226922, 0, 1950.8777637680764]                 2   \n",
       "26                                        [677.34375]                 1   \n",
       "27                                [699.9026642102681]                 1   \n",
       "28            [774.1029224430208, 1548.2058448860416]                 2   \n",
       "29                                [887.8914299898835]                 1   \n",
       "30                               [1012.5828067587219]                 1   \n",
       "31           [1251.0890686820035, 2502.2110631308883]                 2   \n",
       "32                               [1258.0482198649922]                 1   \n",
       "33                               [1325.4289836820194]                 1   \n",
       "34                               [1328.5661696330687]                 1   \n",
       "35                               [1451.3744824055918]                 1   \n",
       "36                               [1489.5379574433584]                 1   \n",
       "37                                        [1500.7815]                 1   \n",
       "38                                           [1537.5]                 1   \n",
       "39                               [1541.6479939367084]                 1   \n",
       "40                               [1548.2058448860416]                 1   \n",
       "41                               [1563.7398275332525]                 1   \n",
       "42                               [1574.5656321167821]                 1   \n",
       "43                                [1589.801055187638]                 1   \n",
       "44                               [1612.9853506762288]                 1   \n",
       "45                               [1614.9120554113501]                 1   \n",
       "46                               [1628.7431359573613]                 1   \n",
       "47                               [1638.2185135853174]                 1   \n",
       "48                                [1645.066368750025]                 1   \n",
       "49                               [1672.8515984032156]                 1   \n",
       "50                               [1693.1738623368406]                 1   \n",
       "51                                [1876.748044426766]                 1   \n",
       "52                               [1935.4235037380874]                 1   \n",
       "53                               [1950.8777637680764]                 1   \n",
       "54                               [2502.2110631308883]                 1   \n",
       "\n",
       "    Puntuacion total  Criterio D  Criterio R  Criterio THD  \n",
       "0           7.536155    0.555556    0.038877      6.941723  \n",
       "1           3.961556    0.555556    0.116883      3.289117  \n",
       "2          18.676259    0.470588    0.166667     18.039004  \n",
       "3           7.813927    0.500000    0.311688      7.002238  \n",
       "4           7.375699    0.391304    0.359375      6.625020  \n",
       "5           1.129371    0.833333    0.117647      0.178390  \n",
       "6           5.625727    0.500000    0.279070      4.846658  \n",
       "7           5.942597    0.400000    0.256410      5.286187  \n",
       "8           4.081039    0.571429    0.233333      3.276277  \n",
       "9           4.486957    0.650000    0.800000      3.036957  \n",
       "10          2.596032    0.333333    0.642857      1.619841  \n",
       "11          8.695500    0.600000    0.833333      7.262167  \n",
       "12          3.006028    0.375000    0.666667      1.964361  \n",
       "13          2.019130    0.333333    0.600000      1.085796  \n",
       "14          4.775099    0.600000    0.625000      3.550099  \n",
       "15         11.119407    0.750000    1.142857      9.226550  \n",
       "16          3.135079    0.800000    0.833333      1.501746  \n",
       "17          5.031992    0.750000    0.800000      3.481992  \n",
       "18          3.205158    0.750000    0.800000      1.655158  \n",
       "19          0.000000    0.000000    0.000000      0.000000  \n",
       "20          0.000000    0.000000    0.000000      0.000000  \n",
       "21          0.000000    0.000000    0.000000      0.000000  \n",
       "22          2.949245    1.000000    1.333333      0.615912  \n",
       "23          0.000000    0.000000    0.000000      0.000000  \n",
       "24          0.000000    0.000000    0.000000      0.000000  \n",
       "25          2.967188    0.666667    1.000000      1.300521  \n",
       "26          0.000000    0.000000    0.000000      0.000000  \n",
       "27          0.000000    0.000000    0.000000      0.000000  \n",
       "28          5.513431    1.000000    0.666667      3.846765  \n",
       "29          0.000000    0.000000    0.000000      0.000000  \n",
       "30          0.000000    0.000000    0.000000      0.000000  \n",
       "31          2.792265    1.000000    1.000000      0.792265  \n",
       "32          0.000000    0.000000    0.000000      0.000000  \n",
       "33          0.000000    0.000000    0.000000      0.000000  \n",
       "34          0.000000    0.000000    0.000000      0.000000  \n",
       "35          0.000000    0.000000    0.000000      0.000000  \n",
       "36          0.000000    0.000000    0.000000      0.000000  \n",
       "37          0.000000    0.000000    0.000000      0.000000  \n",
       "38          0.000000    0.000000    0.000000      0.000000  \n",
       "39          0.000000    0.000000    0.000000      0.000000  \n",
       "40          0.000000    0.000000    0.000000      0.000000  \n",
       "41          0.000000    0.000000    0.000000      0.000000  \n",
       "42          0.000000    0.000000    0.000000      0.000000  \n",
       "43          0.000000    0.000000    0.000000      0.000000  \n",
       "44          0.000000    0.000000    0.000000      0.000000  \n",
       "45          0.000000    0.000000    0.000000      0.000000  \n",
       "46          0.000000    0.000000    0.000000      0.000000  \n",
       "47          0.000000    0.000000    0.000000      0.000000  \n",
       "48          0.000000    0.000000    0.000000      0.000000  \n",
       "49          0.000000    0.000000    0.000000      0.000000  \n",
       "50          0.000000    0.000000    0.000000      0.000000  \n",
       "51          0.000000    0.000000    0.000000      0.000000  \n",
       "52          0.000000    0.000000    0.000000      0.000000  \n",
       "53          0.000000    0.000000    0.000000      0.000000  \n",
       "54          0.000000    0.000000    0.000000      0.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Recopilamos toda la informacion en un data frame\n",
    "\n",
    "datos_series = {'Componente': range(F), \n",
    "                'Frecuencia fundamental (Hz)': frecuencias_new[peaks], \n",
    "                'Serie armonica': lista_series,\n",
    "                'Numero armonicos': lista_numero_armonicos,\n",
    "                'Puntuacion total': puntuacion,\n",
    "                'Criterio D': lista_D,\n",
    "                'Criterio R': lista_R,\n",
    "                'Criterio THD': lista_THD}\n",
    "df_series = pd.DataFrame(datos_series)\n",
    "df_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f7968261",
   "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>Componente</th>\n",
       "      <th>Frecuencia fundamental (Hz)</th>\n",
       "      <th>Serie armonica</th>\n",
       "      <th>Numero armonicos</th>\n",
       "      <th>Puntuacion total</th>\n",
       "      <th>Criterio D</th>\n",
       "      <th>Criterio R</th>\n",
       "      <th>Criterio THD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>25.820478</td>\n",
       "      <td>[24.372089610962668, 49.209905616605575, 0, 96...</td>\n",
       "      <td>8</td>\n",
       "      <td>18.676259</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>18.039004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>312.772696</td>\n",
       "      <td>[312.7818465910811, 625.4901955417666, 0, 1250...</td>\n",
       "      <td>6</td>\n",
       "      <td>11.119407</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>9.226550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>193.481761</td>\n",
       "      <td>[193.54235037380874, 387.0680347916496, 580.64...</td>\n",
       "      <td>6</td>\n",
       "      <td>8.695500</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>7.262167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>33.437503</td>\n",
       "      <td>[32.324718076371475, 64.51454372441758, 96.755...</td>\n",
       "      <td>12</td>\n",
       "      <td>7.813927</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.311688</td>\n",
       "      <td>7.002238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5.650155</td>\n",
       "      <td>[5.403747893901415, 0, 15.321456881663607, 0, ...</td>\n",
       "      <td>10</td>\n",
       "      <td>7.536155</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.038877</td>\n",
       "      <td>6.941723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>[38.62324253128723, 0, 0, 0, 193.4817610746296...</td>\n",
       "      <td>9</td>\n",
       "      <td>7.375699</td>\n",
       "      <td>0.391304</td>\n",
       "      <td>0.359375</td>\n",
       "      <td>6.625020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>64.514544</td>\n",
       "      <td>[62.77381732006802, 0, 193.48176107462967, 0, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>5.942597</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.256410</td>\n",
       "      <td>5.286187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>59.049349</td>\n",
       "      <td>[58.139778163333574, 0, 175.848984866943, 0, 2...</td>\n",
       "      <td>6</td>\n",
       "      <td>5.625727</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>4.846658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>774.232459</td>\n",
       "      <td>[774.1029224430208, 1548.2058448860416]</td>\n",
       "      <td>2</td>\n",
       "      <td>5.513431</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>3.846765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>419.436810</td>\n",
       "      <td>[418.65350391091374, 0, 1258.0482198649922, 16...</td>\n",
       "      <td>3</td>\n",
       "      <td>5.031992</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.481992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>290.332030</td>\n",
       "      <td>[290.2748964811184, 580.642536727567, 0, 0, 14...</td>\n",
       "      <td>3</td>\n",
       "      <td>4.775099</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>3.550099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>96.755605</td>\n",
       "      <td>[96.77117518690437, 193.48176107462967, 290.33...</td>\n",
       "      <td>13</td>\n",
       "      <td>4.486957</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>3.036957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>81.444405</td>\n",
       "      <td>[82.89580360512898, 0, 246.25501366972114, 0, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>4.081039</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>3.276277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15.321457</td>\n",
       "      <td>[16.24805974064178, 33.43750281593051, 49.2099...</td>\n",
       "      <td>10</td>\n",
       "      <td>3.961556</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>3.289117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>483.871118</td>\n",
       "      <td>[483.85587593452186, 0, 1451.3744824055918, 19...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.205158</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.655158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>387.068035</td>\n",
       "      <td>[387.0847007476175, 774.2324585535872, 0, 1548...</td>\n",
       "      <td>4</td>\n",
       "      <td>3.135079</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1.501746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>199.777581</td>\n",
       "      <td>[198.8474727854393, 0, 595.8136802602813, 0, 0...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.006028</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.964361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>651.847120</td>\n",
       "      <td>[650.2925879226922, 0, 1950.8777637680764]</td>\n",
       "      <td>2</td>\n",
       "      <td>2.967188</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.300521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>625.490196</td>\n",
       "      <td>[625.5636931821622, 1250.9937977701263, 1876.7...</td>\n",
       "      <td>4</td>\n",
       "      <td>2.949245</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.615912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>1250.993798</td>\n",
       "      <td>[1251.0890686820035, 2502.2110631308883]</td>\n",
       "      <td>2</td>\n",
       "      <td>2.792265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.792265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>175.848985</td>\n",
       "      <td>[174.95173690186468, 0, 0, 699.9026642102681, ...</td>\n",
       "      <td>3</td>\n",
       "      <td>2.596032</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>1.619841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>246.255014</td>\n",
       "      <td>[248.25632624055973, 0, 0, 0, 0, 1489.53795744...</td>\n",
       "      <td>2</td>\n",
       "      <td>2.019130</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.085796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>49.209906</td>\n",
       "      <td>[48.74417922192534, 96.75560545082685, 0, 193....</td>\n",
       "      <td>5</td>\n",
       "      <td>1.129371</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.178390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>1614.912055</td>\n",
       "      <td>[1614.9120554113501]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>1574.565632</td>\n",
       "      <td>[1574.5656321167821]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>1612.985351</td>\n",
       "      <td>[1612.9853506762288]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>1589.801055</td>\n",
       "      <td>[1589.801055187638]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>1628.743136</td>\n",
       "      <td>[1628.7431359573613]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>1563.739828</td>\n",
       "      <td>[1563.7398275332525]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>1950.877764</td>\n",
       "      <td>[1950.8777637680764]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Componente  Frecuencia fundamental (Hz)  \\\n",
       "2            2                    25.820478   \n",
       "15          15                   312.772696   \n",
       "11          11                   193.481761   \n",
       "3            3                    33.437503   \n",
       "0            0                     5.650155   \n",
       "4            4                    37.500000   \n",
       "7            7                    64.514544   \n",
       "6            6                    59.049349   \n",
       "28          28                   774.232459   \n",
       "17          17                   419.436810   \n",
       "14          14                   290.332030   \n",
       "9            9                    96.755605   \n",
       "8            8                    81.444405   \n",
       "1            1                    15.321457   \n",
       "18          18                   483.871118   \n",
       "16          16                   387.068035   \n",
       "12          12                   199.777581   \n",
       "25          25                   651.847120   \n",
       "22          22                   625.490196   \n",
       "31          31                  1250.993798   \n",
       "10          10                   175.848985   \n",
       "13          13                   246.255014   \n",
       "5            5                    49.209906   \n",
       "45          45                  1614.912055   \n",
       "42          42                  1574.565632   \n",
       "44          44                  1612.985351   \n",
       "43          43                  1589.801055   \n",
       "46          46                  1628.743136   \n",
       "41          41                  1563.739828   \n",
       "53          53                  1950.877764   \n",
       "\n",
       "                                       Serie armonica  Numero armonicos  \\\n",
       "2   [24.372089610962668, 49.209905616605575, 0, 96...                 8   \n",
       "15  [312.7818465910811, 625.4901955417666, 0, 1250...                 6   \n",
       "11  [193.54235037380874, 387.0680347916496, 580.64...                 6   \n",
       "3   [32.324718076371475, 64.51454372441758, 96.755...                12   \n",
       "0   [5.403747893901415, 0, 15.321456881663607, 0, ...                10   \n",
       "4   [38.62324253128723, 0, 0, 0, 193.4817610746296...                 9   \n",
       "7   [62.77381732006802, 0, 193.48176107462967, 0, ...                 4   \n",
       "6   [58.139778163333574, 0, 175.848984866943, 0, 2...                 6   \n",
       "28            [774.1029224430208, 1548.2058448860416]                 2   \n",
       "17  [418.65350391091374, 0, 1258.0482198649922, 16...                 3   \n",
       "14  [290.2748964811184, 580.642536727567, 0, 0, 14...                 3   \n",
       "9   [96.77117518690437, 193.48176107462967, 290.33...                13   \n",
       "8   [82.89580360512898, 0, 246.25501366972114, 0, ...                 4   \n",
       "1   [16.24805974064178, 33.43750281593051, 49.2099...                10   \n",
       "18  [483.85587593452186, 0, 1451.3744824055918, 19...                 3   \n",
       "16  [387.0847007476175, 774.2324585535872, 0, 1548...                 4   \n",
       "12  [198.8474727854393, 0, 595.8136802602813, 0, 0...                 3   \n",
       "25         [650.2925879226922, 0, 1950.8777637680764]                 2   \n",
       "22  [625.5636931821622, 1250.9937977701263, 1876.7...                 4   \n",
       "31           [1251.0890686820035, 2502.2110631308883]                 2   \n",
       "10  [174.95173690186468, 0, 0, 699.9026642102681, ...                 3   \n",
       "13  [248.25632624055973, 0, 0, 0, 0, 1489.53795744...                 2   \n",
       "5   [48.74417922192534, 96.75560545082685, 0, 193....                 5   \n",
       "45                               [1614.9120554113501]                 1   \n",
       "42                               [1574.5656321167821]                 1   \n",
       "44                               [1612.9853506762288]                 1   \n",
       "43                                [1589.801055187638]                 1   \n",
       "46                               [1628.7431359573613]                 1   \n",
       "41                               [1563.7398275332525]                 1   \n",
       "53                               [1950.8777637680764]                 1   \n",
       "\n",
       "    Puntuacion total  Criterio D  Criterio R  Criterio THD  \n",
       "2          18.676259    0.470588    0.166667     18.039004  \n",
       "15         11.119407    0.750000    1.142857      9.226550  \n",
       "11          8.695500    0.600000    0.833333      7.262167  \n",
       "3           7.813927    0.500000    0.311688      7.002238  \n",
       "0           7.536155    0.555556    0.038877      6.941723  \n",
       "4           7.375699    0.391304    0.359375      6.625020  \n",
       "7           5.942597    0.400000    0.256410      5.286187  \n",
       "6           5.625727    0.500000    0.279070      4.846658  \n",
       "28          5.513431    1.000000    0.666667      3.846765  \n",
       "17          5.031992    0.750000    0.800000      3.481992  \n",
       "14          4.775099    0.600000    0.625000      3.550099  \n",
       "9           4.486957    0.650000    0.800000      3.036957  \n",
       "8           4.081039    0.571429    0.233333      3.276277  \n",
       "1           3.961556    0.555556    0.116883      3.289117  \n",
       "18          3.205158    0.750000    0.800000      1.655158  \n",
       "16          3.135079    0.800000    0.833333      1.501746  \n",
       "12          3.006028    0.375000    0.666667      1.964361  \n",
       "25          2.967188    0.666667    1.000000      1.300521  \n",
       "22          2.949245    1.000000    1.333333      0.615912  \n",
       "31          2.792265    1.000000    1.000000      0.792265  \n",
       "10          2.596032    0.333333    0.642857      1.619841  \n",
       "13          2.019130    0.333333    0.600000      1.085796  \n",
       "5           1.129371    0.833333    0.117647      0.178390  \n",
       "45          0.000000    0.000000    0.000000      0.000000  \n",
       "42          0.000000    0.000000    0.000000      0.000000  \n",
       "44          0.000000    0.000000    0.000000      0.000000  \n",
       "43          0.000000    0.000000    0.000000      0.000000  \n",
       "46          0.000000    0.000000    0.000000      0.000000  \n",
       "41          0.000000    0.000000    0.000000      0.000000  \n",
       "53          0.000000    0.000000    0.000000      0.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_series.sort_values(by = 'Puntuacion total', ascending=False)[0:30]"
   ]
  }
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