Detección series de armónicos - Espectro importado - Complete.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cec51fc8",
   "metadata": {},
   "source": [
    "# ALGORITMO PARA IDENTIFICACIÓN SERIES DE ARMÓNICOS EN UN ESPECTRO DADO\n",
    "## CON FUNCIONES INCLUIDAS\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": "markdown",
   "id": "a2a566b0",
   "metadata": {},
   "source": [
    "## Importar los datos de un espectro de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 2,
     "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": 3,
   "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": 4,
   "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": [
    "def encontrar_picos(amplitudes, altura_pico):\n",
    "    \"\"\"\n",
    "    Encuentra los picos en una gráfica.\n",
    "    \n",
    "    Parámetros de entrada\n",
    "    ---------\n",
    "    amplitudes: lista\n",
    "        Valores de la cual se quieren encontrar sus picos (máximos).\n",
    "    altura_pico: float\n",
    "        Altura minima que tiene que tener el pico para ser detectado.\n",
    "    \n",
    "    Valores de salida\n",
    "    ---------\n",
    "    peaks: lista\n",
    "        Índices de la lista amplitudes en los cuales se encuentran los picos detectados.\n",
    "    \"\"\"\n",
    "    peaks = []\n",
    "    for i in range(1,len(amplitudes)-1):\n",
    "        if amplitudes[i] >= altura_pico and amplitudes[i] > amplitudes[i-1] and amplitudes[i] >= amplitudes[i+1]:\n",
    "                peaks.append(i)\n",
    "    return peaks\n",
    "\n",
    "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": "code",
   "execution_count": 5,
   "id": "e3be273a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "class PeakPropertyWarning(RuntimeWarning):\n",
    "    \"\"\"Calculated property of a peak has unexpected value.\"\"\"\n",
    "    pass\n",
    "\n",
    "def _peak_prominences(amplitudes, peaks, wlen):\n",
    "    \"\"\"\n",
    "    Calcula la prominencia de cada pico de la señal.\n",
    "    Parametros\n",
    "    ----------\n",
    "    amplitudes : list\n",
    "        Una lista de valores con picos.\n",
    "    peaks : list\n",
    "        Indices de los picos en `amplitudes`.\n",
    "    wlen : np.intp\n",
    "        Longitud de la ventana en número de muestras (ver `peak_prominences`) que se aproxima por \n",
    "        exceso al impar más cercano. Si wlen es más pequeño que 2 se usa la señal `amplitudes` entera.\n",
    "        \n",
    "    Valores de salida\n",
    "    -------\n",
    "    prominences : ndarray\n",
    "        Las prominencias calculadas para cada pico en `peaks`.\n",
    "    left_bases, right_bases : ndarray\n",
    "        Las bases de cada pico como indices de `amplitudes` a la izquierda y derecha de cada pico.\n",
    "    \n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        Si el valor de `peaks` es un indice inválido para `amplitudes`.\n",
    "    \n",
    "    Warns\n",
    "    -----\n",
    "    PeakPropertyWarning\n",
    "        Si la prominencia calculada de algún pico es 0.\n",
    "    \"\"\"\n",
    "    \n",
    "    show_warning = False\n",
    "    prominences = np.empty(len(peaks), dtype=np.float64)\n",
    "    left_bases = np.empty(len(peaks), dtype=np.intp)\n",
    "    right_bases = np.empty(len(peaks), dtype=np.intp)\n",
    "    \n",
    "    for peak_nr in range(len(peaks)):\n",
    "        peak = peaks[peak_nr]\n",
    "        i_min = 0\n",
    "        i_max = len(amplitudes) - 1\n",
    "        if not i_min <= peak <= i_max:\n",
    "            raise ValueError(\"peak {} is not a valid index for `amplitudes`\".format(peak))\n",
    "            \n",
    "        if 2 <= wlen:\n",
    "            # Adjust window around the evaluated peak (within bounds);\n",
    "            # if wlen is even the resulting window length is is implicitly\n",
    "            # rounded to next odd integer\n",
    "            i_min = max(peak - wlen // 2, i_min)\n",
    "            i_max = min(peak + wlen // 2, i_max)\n",
    "            \n",
    "        # Find the left base in interval [i_min, peak]\n",
    "        i = left_bases[peak_nr] = peak\n",
    "        left_min = amplitudes[peak]\n",
    "        while i_min <= i and amplitudes[i] <= amplitudes[peak]:\n",
    "            if amplitudes[i] < left_min:\n",
    "                left_min = amplitudes[i]\n",
    "                left_bases[peak_nr] = i\n",
    "            i -= 1\n",
    "                \n",
    "        # Find the right base in interval [peak, i_max]\n",
    "        i = right_bases[peak_nr] = peak\n",
    "        right_min = amplitudes[peak]\n",
    "        while i <= i_max and amplitudes[i] <= amplitudes[peak]:\n",
    "            if amplitudes[i] < right_min:\n",
    "                right_min = amplitudes[i]\n",
    "                right_bases[peak_nr] = i\n",
    "            i += 1\n",
    "        \n",
    "        prominences[peak_nr] = amplitudes[peak] - max(left_min, right_min)\n",
    "        if prominences[peak_nr] == 0:\n",
    "            show_warning = True\n",
    "                \n",
    "    if show_warning:\n",
    "        warnings.warn(\"some peaks have a prominence of 0\", PeakPropertyWarning, stacklevel=2)\n",
    "    \n",
    "    return prominences, left_bases, right_bases\n",
    "\n",
    "\n",
    "\n",
    "def _peak_widths(x, peaks, rel_height, prominences, left_bases, right_bases):\n",
    "    \"\"\"\n",
    "    Calcula la anchura de cada pico en la señal.\n",
    "    Parametros\n",
    "    ----------\n",
    "    x : list\n",
    "        Lista de valores con picos.\n",
    "    peaks : list\n",
    "        Indices de los picos en `x`.\n",
    "    rel_height : np.float64\n",
    "        Altura relativa a la cual se mide la altura del pico como\n",
    "        porcentaje de su prominencia (ver `peak_widths`).\n",
    "    prominences : ndarray\n",
    "        Prominencias de cada pico en `peaks` tal y como fueron calculadas en `_peak_prominences`.\n",
    "    left_bases, right_bases : ndarray\n",
    "        Bases izquierdas y derechas de cada pico en `peaks` tal y como fueron calculadas en `_peak_prominences`.\n",
    "    \n",
    "    Salida\n",
    "    -------\n",
    "    widths : ndarray\n",
    "        Anchuras de cada pico en número de muestras.\n",
    "    width_heights : ndarray\n",
    "        Altura de las líneas de contorno desde la cual se calcularon las anchuras `widths`.\n",
    "        Esta altura es calculada como un porcentaje de la prominencia del pico.\n",
    "    left_ips, right_ips : ndarray\n",
    "        Posiciones interpoladas de los puntos de interesección izquierdo y derecho\n",
    "        con la línea horizontal respectiva a la altura.\n",
    "        \n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        Si la `rel_height` es menor de 0.\n",
    "        O si `peaks`, `left_bases` and `right_bases` no tienen las mismas dimensiones.\n",
    "        O si los datos de `prominences` no cumplen la condición \n",
    "        ``0 <= left_base <= peak <= right_base < x.shape[0]`` para cada pico.\n",
    "\n",
    "    Warnings\n",
    "    --------\n",
    "    PeakPropertyWarning\n",
    "        Si la anchura calculada de algún pico es 0.\n",
    "    \"\"\"\n",
    "    if rel_height < 0:\n",
    "        raise ValueError('`rel_height` must be greater or equal to 0.0')\n",
    "    if not (len(peaks) == prominences.shape[0] == left_bases.shape[0]\n",
    "            == right_bases.shape[0]):\n",
    "        raise ValueError(\"arrays in `prominence_data` must have the same shape as `peaks`\")\n",
    "\n",
    "    show_warning = False\n",
    "    widths = np.empty(len(peaks), dtype=np.float64)\n",
    "    width_heights = np.empty(len(peaks), dtype=np.float64)\n",
    "    left_ips = np.empty(len(peaks), dtype=np.float64)\n",
    "    right_ips = np.empty(len(peaks), dtype=np.float64)\n",
    "\n",
    "\n",
    "    for p in range(len(peaks)):\n",
    "        i_min = left_bases[p]\n",
    "        i_max = right_bases[p]\n",
    "        peak = peaks[p]\n",
    "        # Validate bounds and order\n",
    "        if not 0 <= i_min <= peak <= i_max < x.shape[0]:\n",
    "            with gil:\n",
    "                raise ValueError(\"prominence data is invalid for peak {}\".format(peak))\n",
    "        height = width_heights[p] = x[peak] - prominences[p] * rel_height\n",
    "\n",
    "        # Find intersection point on left side\n",
    "        i = peak\n",
    "        while i_min < i and height < x[i]:\n",
    "            i -= 1\n",
    "        left_ip = i\n",
    "        if x[i] < height:\n",
    "            # Interpolate if true intersection height is between samples\n",
    "            left_ip += (height - x[i]) / (x[i + 1] - x[i])\n",
    "\n",
    "        # Find intersection point on right side\n",
    "        i = peak\n",
    "        while i < i_max and height < x[i]:\n",
    "            i += 1\n",
    "        right_ip = i\n",
    "        if  x[i] < height:\n",
    "            # Interpolate if true intersection height is between samples\n",
    "            right_ip -= (height - x[i]) / (x[i - 1] - x[i])\n",
    "\n",
    "        widths[p] = right_ip - left_ip\n",
    "        if widths[p] == 0:\n",
    "            show_warning = True\n",
    "        left_ips[p] = left_ip\n",
    "        right_ips[p] = right_ip\n",
    "\n",
    "    if show_warning:\n",
    "        warnings.warn(\"some peaks have a width of 0\", PeakPropertyWarning, stacklevel=2)\n",
    "    \n",
    "    return widths, width_heights, left_ips, right_ips"
   ]
  },
  {
   "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": "1f32efd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy import sqrt, sin, pi, sign\n",
    "\n",
    "def peak_estimator(x, y, win=\"rect\"):\n",
    "    def corr_rect(deltaY):\n",
    "        deltaF = 1 / (1 + deltaY)\n",
    "        piDeltaF = pi*deltaF\n",
    "        a = sin(piDeltaF)/piDeltaF\n",
    "        return [deltaF, abs(a)]\n",
    "\n",
    "    def corr_hann(deltaY):\n",
    "        \"\"\"Grandke estimator\"\"\"\n",
    "        deltaF = (2 - deltaY) / (1 + deltaY)\n",
    "        \n",
    "        piDeltaF = pi*deltaF\n",
    "        a = sin(piDeltaF)/piDeltaF\n",
    "        b = 1/(1 - deltaF**2)\n",
    "        return [deltaF, abs(a*b)]\n",
    "    \n",
    "    k0 = np.argmax(y)\n",
    "    if k0 < 1 or k0 > len(y) - 2:\n",
    "        raise ValueError(\"Index out of limits\")\n",
    "        \n",
    "    # obtain neighbour points\n",
    "    ns = (y[k0 - 1], y[k0 + 1])\n",
    "\n",
    "    # line spacing (Hz)\n",
    "    deltaF = x[2] - x[1]\n",
    "\n",
    "    # var deltaY = ys[k0] / Math.max(prev, next);\n",
    "    deltaY = y[k0] / max(ns[0], ns[1])\n",
    "\n",
    "    if win == \"rect\":\n",
    "        deltas = corr_rect(deltaY)\n",
    "    elif win == \"hann\":\n",
    "        deltas = corr_hann(deltaY)\n",
    "    else:\n",
    "        raise ValueError(\"Invalid window type\")\n",
    "\n",
    "    return [x[k0] + deltas[0]*deltaF*sign(ns[1] - ns[0]), y[k0]/deltas[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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([0,100])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 11,
   "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": 12,
   "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([0,100])\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": 13,
   "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": 13,
     "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": 14,
   "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": 15,
   "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": "be9a1f5c",
   "metadata": {},
   "source": [
    "### Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73cc7964",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Funcion para calcular los intervalos para cada armonico r de una componente i = [0:F-1]\n",
    "def intervalosarmonicos(i):\n",
    "    \"\"\"\n",
    "    Calcula los intervalos de incertidumbre de cada múltiplo r = 2,..., orden_maximo,\n",
    "    para una componente espectral dada i.\n",
    "    Parametros\n",
    "    ----------\n",
    "    i: int\n",
    "        Índice de la componente en la lista de componentes espectrales.\n",
    "    \n",
    "    Valores de salida\n",
    "    ----------\n",
    "    intervalos: lista de listas\n",
    "        Intervalos de incertidumbre (expresados en forma de lista, \n",
    "        por ejemplo: [0.51 , 0.85]) para cada frecuencia múltiplo.\n",
    "    \"\"\"\n",
    "    intervalos = []\n",
    "    \n",
    "    for r in range(2, orden_maximo):\n",
    "        r_liminf = r * (v[i] - (Dv[i]/2))\n",
    "        r_limsup = r * (v[i] + (Dv[i]/2))\n",
    "        intervalos.append( [r_liminf, r_limsup] )\n",
    "        \n",
    "    return intervalos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "id": "3c254748",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Funcion para intersecar intervalos\n",
    "def interseccion(intervals):\n",
    "    '''\n",
    "    Funcion que sirve para encontrar la interseccion de varios intervalos.\n",
    "    Argumentos de entrada\n",
    "    ----------\n",
    "    intervals: una lista de listas (intervalos definidos por su cota inf y su cota sup)\n",
    "        Ejemplo: intervals = [[10, 110],[20, 60],[25, 75]]\n",
    "    \n",
    "    Valores de salida\n",
    "    ----------\n",
    "    [start, end] o [] (si la interseccion es vacia)\n",
    "    '''\n",
    "    start, end = intervals.pop() # extrae el ultimo elemento de la lista de intervalos\n",
    "\n",
    "    while intervals:\n",
    "        start_temp, end_temp = intervals.pop() # extrae el siguiente ultimo elemento de la lista de intervalos\n",
    "        start = max(start, start_temp)         # compara los valores con start y end con los extremos del ultimo intervalo\n",
    "        end = min(end, end_temp)\n",
    "        if (start > end):                      # si el valor start es mayor que end, entonces el intervalo es vacío\n",
    "            return []\n",
    "        elif (start <= end):\n",
    "            return [start, end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07659f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Funcion para detectar el proximo armonico r de una componente i\n",
    "def encontrar_armonico(r,i):\n",
    "    \"\"\"\n",
    "    Funcion que encuentra el armónico de orden r para la componente espectral i.\n",
    "    \n",
    "    Parametros de entrada\n",
    "    ----------\n",
    "    r: int\n",
    "        Orden del armónico. Valor por el que se multiplica la frecuencia fundamental de la componente i,\n",
    "        encontrando así la frecuencia del armónico que se quiere buscar en el espectro.\n",
    "    i: int\n",
    "        Indice de la lista de componentes espectrales.\n",
    "    \n",
    "    Valores de salida\n",
    "    ----------\n",
    "    armonico: float\n",
    "        Frecuencia del armónico encontrado. Si no encuentra armónico, el valor de ese armónico es 0.\n",
    "    indice: int\n",
    "        Indice de la lista de componentes espectrales del armónico encontrado.\n",
    "    \n",
    "    Raises\n",
    "    ------\n",
    "    ValueError\n",
    "        Si hay más de dos intervalos de incertidumbre de las componentes espectrales que cumplen que\n",
    "        su intersección con el intervalo de incertidumbre del armónico de oden r de la componente i, es no vacía.\n",
    "    \"\"\"\n",
    "    c = 0\n",
    "    alista = []\n",
    "    blista = []\n",
    "    \n",
    "    # actualizamos los intervalos de incertidumbre todos los armónicos de i para el nuevo v[i] y Dv[i]\n",
    "    rxvi = intervalosarmonicos(i)\n",
    "    \n",
    "    for j, componente in enumerate(intervalo_componente):  # para cada componente\n",
    "        \n",
    "        # calculamos la interseccion del intervalo de incertidumbre de la componente j con\n",
    "        # el intervalo de incertidumbre del armónico de orden r de la componente i (rxvi[r-2]). \n",
    "        z = interseccion([componente, rxvi[r-2]])\n",
    "        \n",
    "        # Si el intervalo es no vacío:\n",
    "        if z:\n",
    "            # contabilizamos 1 intersección no vacía\n",
    "            c += 1\n",
    "            # añadimos a las listas los extremos del intervalo interseccion\n",
    "            alista.append(z[0]) \n",
    "            blista.append(z[1])\n",
    "            # damos el valor correpondiente al indice encontrado con interseccion no vacia\n",
    "            indice = j\n",
    "                    \n",
    "            a, b = alista[0], blista[0]\n",
    "\n",
    "    # Si se encuentran 2 intervalos que cumplen que la interseccion con rxvi[r-2] es no vacía\n",
    "    if c == 2:\n",
    "        # calcular cual de las dos frecuencias encontradas está más cerca de r x frecuencia componente i\n",
    "        if abs(v[indice-1] - r*v[i]) < abs(v[indice] - r*v[i]):\n",
    "            # actualizamos a, b e indice en consecuencia\n",
    "            a = alista[0]\n",
    "            b = blista[0]\n",
    "            indice -= 1\n",
    "        else:\n",
    "            a, b = alista[1], blista[1]\n",
    "    # Si hay más de 2 intervalos que lo cumplen: mostramos un error.\n",
    "    elif c > 2:\n",
    "        raise ValueError(\"Hay más de 2 intervalos que tienen intersección no vacía. Este algoritmo está diseñado para máximo 2 intervalos.\")\n",
    "        # Posibles mejoras: desarrollar esta parte de la función para más de 2 intervalos con intersección no vacía.\n",
    "    \n",
    "    if not c: # Si no se ha encontrado ningún armónico para ese orden r\n",
    "        armonico = 0\n",
    "        indice = None\n",
    "\n",
    "    else: # Si sí se ha encontrado armónico\n",
    "        armonico = v[indice]\n",
    "        a = float(a)\n",
    "        b = float(b)\n",
    "        # actualizamos los v[i] y Dv[i]\n",
    "        v[i] = (a+b) / (2*r)\n",
    "        Dv[i] = (b-a) / r\n",
    "\n",
    "    return armonico, indice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7ce86db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Funcion para calcular la serie armonica de cada componente i=[0,F-1]\n",
    "def encontrar_seriearmonica(i):\n",
    "    \"\"\"\n",
    "    Calcula la serie armónica de una componente espectral dada i.\n",
    "    Parametros\n",
    "    ----------\n",
    "    i: int\n",
    "        Índice de la componente en la lista de componentes espectrales.\n",
    "    \n",
    "    Valores de salida\n",
    "    ----------\n",
    "    serie_armonica: lista\n",
    "        Lista de los armónicos encontrados para la componente i. Las posiciones de la lista\n",
    "        que contienen un 0 significan que no se ha encontrado armónico para ese orden r\n",
    "    serie_armonica_indices: lista\n",
    "        Lista de índices de las frecuencias de la serie armónica encontrada.\n",
    "    numero_armonicos: int\n",
    "        Número de armónicos de la serie sin contar la frecuencia fundamental.\n",
    "    \"\"\"\n",
    "    serie_armonica = [v[i]]\n",
    "    serie_armonica_indices = [i]\n",
    "    numero_armonicos = 1\n",
    "    k = 0\n",
    "    for r in range(2, orden_maximo):\n",
    "        \n",
    "        # Busca el armónico número r\n",
    "        armonico, indice = encontrar_armonico(r,i)\n",
    "        if (armonico == 0): # Si no encuentra armónico\n",
    "            k += 1\n",
    "        else: # Si encuentra un armónico\n",
    "            numero_armonicos += 1\n",
    "            k = 0\n",
    "            serie_armonica_indices.append(indice)\n",
    "        \n",
    "        # Si después de k veces no ha encontrado armónico, la serie para\n",
    "        if (k == criterio_parada):\n",
    "            serie_armonica = serie_armonica[0:r-k]\n",
    "            break\n",
    "        \n",
    "        serie_armonica.append(armonico)\n",
    "        serie_armonica[0] = v[i] # actualizamos el primer valor de la serie ya que ha sido modificado\n",
    "    \n",
    "    return serie_armonica, serie_armonica_indices, numero_armonicos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c7861bb",
   "metadata": {},
   "source": [
    "#### Programa principal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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)\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": 22,
   "id": "cfb79044",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_D = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i)\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": 23,
   "id": "9587e55f",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_R = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i)\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": 24,
   "id": "5e64836e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lista_THD = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i)\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": 25,
   "id": "35bf3c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_NAS = []\n",
    "\n",
    "for i in range(F):\n",
    "    serie, indices, numero_armonicos = encontrar_seriearmonica(i)\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": 26,
   "id": "0ad51737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f8ed8be37c0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "## 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": 27,
   "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": 28,
   "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": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAsSElEQVR4nO3deXxcdb3/8ddnkjRpm660BCzQIktlkUUKqCCmiLK5owgWBEWr97qh/lSw3gt4xV1E0StU8IJS6OWKuICAIEQEodAWKMVSKHSlpfuWpG2W+fz++H4nOZ1mmaSZ5DR5Px+PPDJzvud8z/d75sznfM/nnJkxd0dERNIr09cNEBGRjilQi4iknAK1iEjKKVCLiKScArWISMopUIuIpNweE6jNrNbMXt8D9UwwMzez0m4sa2b2P2a20cye3N229DUz+4aZ3djNZavM7BEz22pmPy5C29zMDu7penvT7uxrIklFC9RmdrKZ/dPMNpvZBjN7zMyO72597l7p7q/0ZBu74WTgncB+7n5CH7dlt7n7d9z9k91cfCqwDhju7l/pwWallpldbGaP9lBd5WZ2k5ktjQe7p83szHbmvSIG/NM6qO9WM1tlZlvM7EUz+2SibEoc6OT+6mN9x8Xyr5rZ/NiOxWb21by6J5jZw3G5F/LbYWafj8ttMbPZZnZyomycmf0xxoAVZvaZvGVLzOzbZrYysR1GxrIjzex+M1tnZrt84MPMRpvZXWZWF7fjR/PKP2lmi2Kf7zOz1+WVvykONmrNbLWZfTFO39vMbo9t2hxj14l5y441s9vMbFMcuM3IKz/NzObGti03s3MTZafGsi1m9oqZTW3zRU1y9x7/A4YDm4DzgRJgMPAu4Khu1FXaw22bAHh36gUuAB7trbam+Q+4Efh2Eet34OC+7mdemy7u6PVvY/529zVgKHBlnCcDvBvYCkzIm+8g4DlgJXBaB+s6AiiPj98AvAYc10E/XgYsPv8a8CagFJgILAXOS8z/OHBNfB+fE9/bY2PZiUAdcBxgwL8Ba4GSWP4wcC1QBhwNbAAmJ+r+NvAQMD4ufyRQEcsmApcA7wuhapd+3A78L1BJGERtBo6IZW8H1sTtMgj4JfD3xLJjYvkUoBwYBhwWy14PfBnYN8av3KCkMrH8P+I2GRH7dmyi7PBY95lxm+4FHBTLymI7Px37ezxQCxzd4b5UpB16ErCpk3k+ASwANgL3A+Pz3qSfBV4CFue/ceOG/RGwDFgNXA8Mbmc9JXHedcArsd6WN0/c0DcBq4BX445T0kY9lwDbgea4Ya8CqoEVwNcJb4zfEt50lxHeCOuBO4DRiXpOBv5J2NmXAxfH6TXAJ9sLCoQ33wOEHX0hcG6i7GbgF8A9hDf7rNyOkXgT55ZdDXwjTr8SuDUx3//FfmwGHiHu9G1si5uBRqAhbovT4rRvJ+apBlYkni8B/h8wL9b/v8Q3ZCz/anwNVsZ9I/l6nw08DWyJ2+zKNoLhx2PZRuAzhDfAvLidf97Ffe8zhH1vY9yuBhyW9/pv6kLbCjqAx/aekzftXuCsuP3aDdR5y0yM2/LcdsofBq7oYPmfAdfFx4cCO4BheUHqM/HxR4AnE2VDY5/3JQRQJwb1WD4d+G18PCpuy4M66c/B5AXquJ4G4NDEtN8C34uPfwT8IlH2utiWXMD8Tq4dBW7TLcQDH2HQuYQ24kQsvw34r3bKqmI7hiSmPQWc39H6i5X6eBFoNrNbzOxMMxuVLDSz9wPfAD4IjCW88Lfn1fF+wtH68Dbq/z5hBzqG8CKOA/6znbZ8ijBaOZZwAPlQXvktQFOs51jCi7BLOsDdbyK8gR/3kIa5IhbtA4wmjAimAl+IbX87YefIvdkxswMIb7zrYr+PAZ5pp90tzGwoIdDeBuxNOFP5bzM7IjHb+YSDxyhgEXB1XHYY8CBwX2zPwcDf2lnVvcAhcR1zgRltzeTuF8eyH8Rt8WBnfYjOBc4ADgSOIhyMMLMzCEH8nXH9+af4dcDHgJGEwPhvcR9KOjEu+xHCCG5arOcI4Fwze3tc1/vpfN97NyHQHx3bfLq7L2Dn139kF9rWKTOrIuzTzyemfRhocPe/FFjHf5tZPfACIVDvspyZjQdOAX7TTh0GvC3RjiOAV9x9a2K2Z+N0CPtMiZmdaGYlhIPgM4QDvuWqTa6CMGoGeCPhvfchM3stpmw+W0hfCduq2d1fbKdd1sZ6Saz7zcCGmJ5dY2Z/ju/PXZjZMYRR+aLEsguBW8xsvZk9ldu/EuWY2XMxHXWrmY0GcPfVhP3t4zHt8xZC7Og4pVboEaWrf4QRyM2EEWcT8CegKjFKuCQxbwaoJ45sCEecU/Pqc0KQMcKbIzlifAtx5N1GOx4iHv0TR0MnnJJUEUYLgxPl5wMPt1PXxew8yq0mHNWTI8MFwDsSz/cljD5LgcuBu9qpu4Z2RtSE4POPvPlvII6K4na+MVF2FvBCoj9Pt7POK0mMqPPKRsbtNKKd8pvZeQSd/7yaXUfUFySe/wC4Pj7+NXEkFJ8fSgepD0Ig/kl8PCHOOy5Rvh74SOL5ncClXdj3Tk6U3wFc1tbr34W2dTiiJpwOPwjckJhWSRjVH5jYfp2OqAlnkCcD3wTK2ij/D6Cmg+WvIgS8XBrlQuCJvHmuBm6Oj41w4GskvM/XAccn5n2UMDCpIKRXNgALY9lH4/a5iZBWOYqQNnln3vraGlG/DXgtb9qncn0D3hHbclSs+wYgSxy5EgaTmwgH5ArCWcRjbWyP4YTU0+WJadNjuy+Jr915sa4xsbwhvl6HxtfxTmBGYvn3EM5sm+Lfpzp7XYt2MdHdF7j7xe6+H+Eo9jrCTgzhCPLTmIjfRHjxjDAyzlneTtVjgSHAnMTy98XpbXldXl1LE4/HEzb0qkRdNxBGlIVa6+7b8+q8K1HfAsLpchWwPyEl0lXjgRNzdcZ6pxBG8zmvJR7XE3YQCl1nPLp/z8xeNrMthB0NQi6vp7TXxo5eI+Jo7WEzW2tmmwkj2/x2rU483tbG89y6Ctn32mvnLgpsW7vMLEM4ZW8APpcouopwar640LoA3L3Z3R8F9iPki/N9jHAW2VZbPhfLz3b3HXFyLSFYJQ0npNggnH1+gtZc8AXA3YkLd1MIZ1DLCXniGYTBG4TXBeBb7r7N3ecBMwkDjc502C53/xtwBSFILiXsz1vz1n2Xuz8V379XAW81sxGJ7TEY+DPhQPXdxHq2AUvc/SZ3b3T3mbF/JyXK/8fdX3T3WkKa5axY5xsIab+Pxe11BPA1Mzu7o872yu157v4CYcSVO+1YDnza3Ucm/ga7+z+Ti7VT3TrChjgisewId2/vzbSKEKxykqc3ywkj6jGJuoa7+xEULr+dy4Ez8/pW4e6vxrKD2qmnjnAAykkG4eWECyHJOivdva03Yr6O1pn0UcJFm9MIefsJcbq1t0CejtrfmY5eIwgpnz8B+7v7CMI1iULbla+Qfa89be2T3W5bTDPcRDiIn+PujYnidwBfiCmB1wjb5w4z+3ohdRPO4HZ63c3sJMJB8XdttOUThGsr73D3FYmi54HXxxRaztG0pkaOBv4cg1LW3e8jvJ5vBXD3pe7+bncf6+4nEi6s5W5tnRf/t/de78iLQKmZHdJOu3D3X7j7Ie6+NyFglwLzE+tOrjf32CDclQP8gXDd6tN5685fNl9H5UcSzijuj9trIeHaUpt3/OQUJVCb2RvM7Ctmtl98vj/hFPyJOMv1wOW5HKuZjYj5uE65exb4FfATM9s7Lj/OzE5vZ5E7CDv8fjFXflmirlXAX4Efm9lwM8uY2UF5+aauuh64OuYCc7fxvC+WzQBOM7NzzazUzPaK+S8Ieb0PmtkQC/cPX5Ko827gUDO70MzK4t/xZnZYAe25G9jHzC61cEvYMMu71SgaRjhorScE3O90sd/PAGdZuGVqH+DSLix7B3CxmR1uZkMII6H8tm1w9+1mdgLhoNJd3d73CKP0/cxsUA+17ZeEFOF73H1bXtk7CG/qY+LfSkLA+EV+JRZuJzvPzCrjmdHphPfbQ3mzXgTc6TvnmzGzKYTX+52edwushxzwM8AVZlZhZh8gpBPujLM8BZxtZq+34J2EU/75se7D4j43yMwuIKQer4l1v0y4RjAt7puHEdJ8d8dlzcwqCCNP4vrL47J1wO+Bb5nZ0HgQeh/h7CQ375GxjgMI6YqfuvvG2O7/AT5gZseYWRkhJfSou2+Kz39HGBB+LMacpLuAUWZ2UdzeHyKckT2WqPvjcZsMIdxscHcsexo4xMItemZmBxGuiTxLRzrLjXTnLzb6DsLRqC7+v4Fwz21ungsJuZ/c1fJfJ8p2yU8mpxFySt8h3MWxhZBe+EI7bSkFfkIIQItp+66PXxJOiTbHDXleO3VdzK456hV582QIt/YsJJxqvQx8J1H+NsJdGbl+XxSnjyEcNLbGF/zKvHVNJBx518a+PAQcE8tupuP88JGEC4gbCaf1uZzrlcQcNeH0/o9x/UsJp2Yd5Ynz11lBOKXbQhhRfIldc9SnJZ63rDs+vyy2ra27Pj4U27SVsMP/PNHuCeTlgeNrWZ14fivwze7se8l+EgLGPYR0ybrutC1R7/hYtp1wGp/7m9LO9s7fft8A7o2PxwJ/J+RJt8S+fSpv+YpY/o426l5MyDEn23F9onwC4RrKNsJ+nWyHAd8i3IG1lfBevDBRfilhn60j5KsntREr7ovrfIVwtkPe9kv+LUmUjyaMeuvi+j+aKBtJ2A/rCPvVd8m7S4OQGnqV8L74M+GsCMKNAE5IeyW3ydvy3sfPxemzk2Wx/KrY77WEg8eoRNm5hANZLhXzfSDTUUzN3UcpIiIptcd8hFxEZKBSoBYRSTkFahGRlFOgFhFJuaJ8/eKYMWN8woQJ3Vq2rq6OoUOH9myDUm6g9Xmg9RfU54Fid/o8Z86cde7e5gf3ihKoJ0yYwOzZs7u1bE1NDdXV1T3boJQbaH0eaP0F9Xmg2J0+m9nS9sqU+hARSTkFahGRlFOgFhFJOQVqEZGUU6AWEUm5dAbqGTNgwgTIZML/GW3+0IiIyICQvp+x37ABpk6F+vrwfOnS8BxgypS+a5eISB9J34j61Vfx+np+f8RktpWWh2n19TBtWt+2S0Skj6QvUDc0MGv/I/nyu7/Ct97xqdbpy5b1XZtERPpQ+gL1oEHUDgq/6LSmcnTr9APa/IFgEZF+L32Betw4KA8pj5afNBgyBK6+us+aJCLSl9IXqEePxj732fDYDMaPh+nTdSFRRAas9AVqwN9eHR6cfTYsWaIgLSIDWioDtYiItEploDbr6xaIiKRHKgN1jn4hXUQkpYFaI2oRkVapDNQaSIuItEploBYRkVapDNRKfYiItEploM5RBkREJOWBWkRECvw+ajNbAmwFmoEmd59UzEa1rLc3ViIiknJd+eGAye6+rmgtaYNSHyIiKU19mMbSIiItrJBP/5nZYmAjYZB7g7tPb2OeqcBUgKqqquNmzpzZrQbV1tayqL6Ca+fu4KgxJXx5UkW36tmT1NbWUllZ2dfN6DUDrb+gPg8Uu9PnyZMnz2kvrVxo6uMkd19pZnsDD5jZC+7+SHKGGLynA0yaNMmrq6u71diamhqOOvgwmDub0XuNprr6hG7Vsyepqamhu9trTzTQ+gvq80BRrD4XlPpw95Xx/xrgLqCo0VOpDxGRVp0GajMbambDco+BdwHzi90w0EfJRUSgsNRHFXCXhY8LlgK3uft9RW2VBtQiIi06DdTu/gpwdC+0JbHSXl2biEiqpfL2PBERaZXOQK3Uh4hIi3QG6kgZEBGRlAdqERFJeaBWBkREJOWBWqkPEZGUBmqNpEVEWqUyUOcU8oVRIiL9XaoDtenHE0VE0h2oRUREgVpEJPVSHaiVoxYRSXmgFhGRlAdqXUwUEUl5oFbqQ0QkpYFaI2kRkVapDNQaSYuItEploBYRkVapDNRKfYiItEploBYRkVYK1CIiKadALSKScgrUIiIpp0AtIpJyCtQiIimnQC0iknIK1CIiKVdwoDazEjN72szuLmaDRERkZ10ZUX8RWFCshiTpc4kiIq0KCtRmth9wNnBjcZsT6CuZRERaFTqivhb4GpAtXlNERKQtpZ3NYGbvBta4+xwzq+5gvqnAVICqqipqamq61aDa2lqWzHsWgA0bNnS7nj1JbW3tgOhnzkDrL6jPA0Wx+txpoAZOAt5rZmcBFcBwM7vV3S9IzuTu04HpAJMmTfLq6upuNaimpoajDj0CZj/J6NGjqa4+sVv17Elqamro7vbaEw20/oL6PFAUq8+dpj7c/XJ338/dJwDnAQ/lB2kRESke3UctIpJyhaQ+Wrh7DVBTlJaIiEibNKIWEUm5VAdq/catiEjKA7WIiKQ8UOs3bkVEUh6olfoQEUlpoNZIWkSkVSoDtYiItEploFbKQ0SkVSoDtYiItEploFaOWkSkVSoDtYiItFKgFhFJOQVqEZGUU6AWEUm5VAdq18/cioikO1CLiEjKA7Wh+/RERFIdqJX6EBFJeaAWEZGUBmqlPEREWqUyUCvlISLSKpWBWkREWqUyUCv1ISLSKpWBWkREWilQi4iknAK1iEjKKVCLiKRcp4HazCrM7Ekze9bMnjezq3qjYaDfThQRASgtYJ4dwKnuXmtmZcCjZnavuz9R5LaJiAgFBGp3d6A2Pi2Lf70y1tVvJ4qIFJijNrMSM3sGWAM84O6zitoqERFpYd6FRLCZjQTuAj7v7vPzyqYCUwGqqqqOmzlzZrcaVFtby9Ltg/nh7O0cNjrD108Y3K169iS1tbVUVlb2dTN6zUDrL6jPA8Xu9Hny5Mlz3H1SW2WF5KhbuPsmM6sBzgDm55VNB6YDTJo0yaurq7vV2JqaGo55w5EwexajRo2iuvrN3apnT1JTU0N3t9eeaKD1F9TngaJYfS7kro+xcSSNmQ0GTgNe6PGWJOhuDxGRVoWMqPcFbjGzEkJgv8Pd7y5us0REJKeQuz7mAcf2Qlta6G4PEZFW+mSiiEjKKVCLiKScArWISMopUIuIpJwCtYhIyqU6UOt+ahGRlAdqERFJeaDW/dQiIikP1Ep9iIikNFBrIC0i0iqVgVoDaRGRVqkM1CIi0iqVgVqpDxGRVqkM1CIi0iqVgVo5ahGRVqkM1CIi0iqVgVo5ahGRVqkM1CIi0irVgdqVrRYRSXegFhGRlAdqU7ZaRCTdgVqpDxGRtAZqDaRFRFqkM1BrIC0i0iKdgVpERFooUIuIpJwCtYhIyilQi4ikXKeB2sz2N7OHzWyBmT1vZl8sdqN0LVFEpFVpAfM0AV9x97lmNgyYY2YPuPu/itw2ERGhgBG1u69y97nx8VZgATCu2A0TEZHA3AtPNJjZBOAR4Eh335JXNhWYClBVVXXczJkzu9Wg2tpalu0YzA+e2s7EURkuP3Fwt+rZk9TW1lJZWdnXzeg1A62/oD4PFLvT58mTJ89x90ltlRWS+gDAzCqBO4FL84M0gLtPB6YDTJo0yaurq7vV2JqaGo6aeCQ8NYuRI0dSXf2WbtWzJ6mpqaG722tPNND6C+rzQFGsPhd014eZlRGC9Ax3/32Pt6Ld9fbWmkRE0quQuz4MuAlY4O7XFL9JrbqQlZHd9M+X17FoTW1fN0NE2lDIiPok4ELgVDN7Jv6dVeR2SS/76K9mcdo1f+/rZohIGzrNUbv7o/Ty99np601FRFql+pOJylGLiKQ8UCtHLSKS8kAtIiIpDNRNWQ2je1tDU7avmyAiHUhVoJ63YhOf/Gs9NQvX9nVTBpRDv3lvXzdBRDqQqkA9e8lGAGoWrunjloiIpEeqArWIiOwq1YE6Ddnq5qxz7vWP8/cXlY4Rkb6RykCdhgCds2VbI08u2cAXbn+6r5siIgNUKgN1Tho+76IP3YhIX0tnoE7TkFpEpI+lKlDnj14Vr0VEUhaoc9IUoC0mYLrySzgiIj0plYE6JxXp4dgIhWkR6SupDtQKjiIiKQ/UIiKS0kCdqnyw5/0XEellqQrUuZx0GmNiGtskIgNDqgJ1TroG1ClqjIgMSKkK1JbCjwGm6aAhIgNTqgL1LlIQJFPQBBEZ4FIZqIuRbvjmH55jwmX3dHv5VF3gFJEBJZWBukUPZkJufWJZt5bLBWiFaRHpK+kO1CmgAC0ifS3dgToFUVIZDxHpa+kO1CmQy5crYItIX0lloE5jUNT91CLSVzoN1Gb2azNbY2bze6NBkLJAnaa2iMiAVMiI+mbgjCK3A0jnz14pTotIX+s0ULv7I8CGXmhLKqVqdC8iA1JpT1VkZlOBqQBVVVXU1NR0uY4XlzYCsH37dgA2bd7UrXo60tX6Nm7PAtDcnO3xtuTU1tYWre6u6o12pKm/vUV9HhiK1eceC9TuPh2YDjBp0iSvrq7uch3LHl8CC56nvLwctm9n5IiRVFe/pWcaeF/4VGJX27Vq8zaoeQjLZLq8bKFqamqKVndB7mv9xGZvtKPP+9sH1OeBoVh9TuddH7kHa9fAhAmQyYT/M2b0fluU+hCRPtZjI+pisIULYenS8GTpUpg6NTyeMqXX2qA4LSJ9rZDb824HHgcmmtkKM7ukWI1pzuZ9uCSbJYvxx8NOIYtBfT1Mm7Zb6+jqlyvpy5hEpK91OqJ29/N7oyEAV/35X2GdiXHsjGPO4D9O/yxbyody4TP3wrLufblSjns3bwNUvBaRPpLKHHUcWGPurK0cBcC6oeE/BxywW3V3Nd7mBtT6ZGLh/rloHU8v29jXzRDpN1KZo27JNpTkHUeGDIGrr97Nup0e/f5U2cVHb5wFwJLvnd3HLRHpH1I5om4xcSKMGAmAjRgB06fv9oXEbBcHxkpRi0hfS2mgDtHRqvaBL34xTLn00h6526O7KQwFbBHpK6kM1LmgaNbzSYquBtyWrznt4XaIiBQqnTnqYtZdYOW1O5qwLswvIlIsqRxR5xTj2/QKTX0cecX9HHXVXwf0SHrJujo2b2vs62aIDHipDNTF/JBJV6puTlx5HIgffKn+UQ1n/+wffd0MkQEvnYE6/rci3EbX9fuo05uj/tuC1Vx406yiHkRWbNxWtLpFpDCpDNTZNu6h66lQlO3qR8h7aL3FcMkts/nHS+u6fMthb6rd0cSlM59mY11DXzdFZI+VykDdMqIuRo66H95H3ZziSH37rGX84ZmV/PzhRX3dFJE9VqoC9aghZQA0NRcx8HS56vQGwdyBrNBAXbejiS3be/fiYBp/Xk1kT5OqQD1icAjU2xqbi7aOrqY+ctI4ss61qSmbLWj+Y//rAY668q9FbJGIFEOqAnVFWcku03p6RJYfb+94ajk/un9h+/MXGKB/dP9C5iztmy8iKjBO09BU4IxdnLcQaTzQiewpUhWorz3vGADG7zUEACvCeXP+HRJfu3Neh/nTQtO/P394Eef88p+707RuK3RE3RXXPPBij9cpIt2TqkB98NhKAEoSAbolriYD7IwZ3f6Jrq4O7HKpkjTnWpuLMFxdvrG+x+sUke5JVaC2228HIPvSS+H5ypW7zjRjRvhJrqVLQ/DO/URXgcG6qznqlkDdwTx9/WGYYtz10ePfsZKCi7KfvW2uPsAje6T0BOoZM8h8eiqG01ASvoJk60uvYM/NC+W5Ie20aVBfTxZj2YiqMK2+Hi66qLARdjdvz+soDbM7gXJzfSPz1jZ1e/nk+hubs/z4rwup3dH9+pat79mRdG67pSFHfc+8VTy/cktfN0Oky9ITqKdNw+rrcYyVw/cGYO6+E3nghbWhPPdOjz/Fdf2J53DKZ27ipb32D9Obm3caYa+9+bYe+eBMbkTdnHVeXL21zXl2J/XwmVvncM2cHWzYjQ+E5AL1H55+leseWsSP/9r+xVGAWa+s5+TvP0RdGwH9lB8+zKMvreux8W+KM0Yie4z0BOp2fgvx+dF5P701ejQAsw44EoAVI/ZmzdBRTPj63Tx40AkArLFBHP/CCK75ye93qa/rqY/Wxz/920ttzrM7o8VX1tUCXb/LIpluyQXqeSs2A7BlW8cj6u/e+wIrNm7jhdfaPvC88NqWNN8+LjLgpCdQF/JbiDNmwJZw6pqJgSprGf6194EA/OZN4aef1g8ZCcADC9bskgbpalBNBvb2ctG7k/rIxNRAV0flyXXmHv/2iaUAPL9yc4fL5rI47s6m+l1H8t++ZwH3PLeqS+3pzBOvrO/zXL7Inio9gfrqq8NvInZk2jRobOSfBxzFwwcdD0CzZSjxMBptzpTwm2PP5sxP/ByIgXXatJ2q8Le+tUt3i6zduqPlce4uuFfW1nJrDIqwe6mPXKBuau7aiDq5zqYuHihy67z9yeWc8oOHu7RsV+UOCi+8tpX7n19d1HWJ9FfpCdRTpsD06Zw4uLb9eWJ65Bunf65l0hMHvJGSbPgkY7NluPbkj7aUZc12Sak0vLaGutLy1rtFOvHp385pefzkkg08tmgdp/7473zzD/NbgmtbufBC5QJZYxcDdfLW6fwRfWlJx5nhTCy+c+4Ktmzv/MLjqT+u4aklG9jRtHufGF2hW/5EuiU9gRpgyhTee9zY9stjeqQs2/p9Fb8+/v2UDBsGhBF1xlsjmJvtklK56MNXccSXf8e/xh7IkkEjutS8DXUNTIm/sA1QHz/q3lHqw935xcOLWLq+rs3y3Oh2Rxdz1M1t5KjPPyH09fTD9+lw2a5+fewra+v48PWP89kZT3c6b/6ZQfKHB5T5EOmedAVqYFxl201yaEmPlDXvPArMfv4LADSXlmLJnHImE5ZJWDZqXwDO+sR1VH/6V7vV1m0NMVB3EIHW1u7gh/cv5OM3P9VmeW5029WLicmDw9Y4Ks59V0om03EgTt7T3JUP8jy4IKQu/rZgNWu2bN+lfM7SDbvcGnjtg60XYNNwL7XIniiVv5nYluseWsTH/+PDjAb8kZ3TI02nngo3PcncfScyckdrmVftA1PeW1D97t5yz++dc1YUtMzS9fVUDa/YaaRYu6OJwWUllGSMHU3N/OC+cKvc1nZSDLmg2tjBNwbOXbaR79yzgFs/eWLL96Ek0y0X3DRrp/mfX7mZbQ3NXPGn+YwdVs7idXV8/5yjWsobEuuqKC3p0pdg/b//e5bfzVnBQWOH8p0PvJHHFq3jpIPHMGroIM755eM7zTvhsnt2et7bI+o1W7ez97AK5r+6mXdf92jL9OasU9LJwUwkTVIZqK87/1g+f/uup9lX/ul5mjmMBaNa70g48cDRbKpvPb3eVF7Z8nhdppwFq7Zw2L7DO11nU9YpjYP5r/zfswW189wbHuf3//5WBpW0ngUcecX9XPjm8S13YOTkgtTnbpvL5Il7c9LBYxhTOagl9ZE/op7/6mb2GVHBmMpy/uMP83l+5Rbunb+KDxy7HwBrEhc58/3ludf4y3P37TIt59nlm1oed/WbCn8XD2Ivr63jI9OfAOBnDxX2XdPdSeXf/NhiykozTDlxfJeWm7N0A+f88nGuO/9YFubdhtjYnKUks+sXgHVFY3OWhqYsQ8tT+RaSfqagvczMzgB+CpQAN7r794rZqPcc/Trmv7qZGx55Zafpf3p214+Uz1q8gVmLN7RZz9btTZz503/www8d1WZ50iHT7u1WWz/437t+EVN+kIbwxUmNzVnunreKu+fteuvbM8s3ctLBe7G+roFJ336wZfqCb53B4DiK/tL/Pssb9hnOY4vW8e17FnSrvX3pFw8v4vwT9u9wnqbmLC+vrWPiPsNozjpX/vlfAF0O1Ll9Ytbi9QyrKNup7Op7FvBv1QfxupGDu1Rn0iW3zOaRF9ey5Htnd7uOnGsffJGb/7mEZ/7zXbtdl/RP1tm9rWZWArwIvBNYATwFnO/u/2pvmUmTJvns2bO71aCamhqqq6sB+NnfXurTb3H765dO4V0/eQSA148Zyivr2r4guKe78j2Hs6G+kfOO35/lG+qZMWtZmwfFYvjAseOoGl7B8o31uHvLyP+rp0/kh3lfP1uaMT5+0gReN3Iwbxw3gv1HD+Hueat4YdUW9hs1hIP3rmTcqMFcdue8dj/MkzN66CCueu8R3PjoYr52+kTG7zWEvYaWs3RDHas2befo/UcC8IP7XuCY/UdS19DM5Ilj2dGU5cyftn5fyIJvnYFZuI3zs7fN5dxJ+zPlxAMwM9wdd2hozvL4o48wefJkAFZu2kbV8ArmLtvIBTfOarmQ/PjlpzJqyCBqdzRRlskweFAJpTFFk3VnY30jY4eVA6339DdlnWUb6jkofqFZQ1OWVZu3MX6voS1tzKXJOrt20ZbN9Y2UlRpDBu08ppv/6mYO2GsIDU1ZxlSWt/SrJGMMryijrMR49B+PtLyXuyOZjsw9zzo9mrZqaMoyqLTnLtUl41dXmdkcd5/UZlkBgfotwJXufnp8fjmAu3+3vWV6KlDnvLR6Kwte28oXYjrkW+87go+9ZQLXPvjiTherrv3IMfzX3f9ifQcfxx5UmuFXH5tE3Y4mfv3oYma38x3S559wAN/94Btxd9bVNjB2WDkvrt7KHU8t58w37sOBYyr524LV1O1oahn1XfXeIzhs3+Fc9vt57DuiguPGj2bO0g08tmh9t7ZFZ77wjkM4YcJodjQ1c9Wf/8Uph47hvOMP4NYnlnLKoWP59xlzGVZRyqCSTBipjx9F1p25yzbxm0+cwG8eX8r1F7yJ0pJdd9TN2xrZ0dTM3sMq2FTfwFfueJYnF29g644mLjn5QG56dDGHVlXyzsOr+MXDL/PBY8fxpXceyoqN2xg+uJRf1rzccuaw36jBA+5HcocMKqEp6zultMpLMzRnvcP73s12zeUnp+WufzQ0ZcF2TpkNHVRCXbzAPWRQCRkz6hqacIeyEmNoeSkNTeHMLmNGScYoMSOTCY/DtJCias46GQt3OmUdhpWXknWn2Z0dTdmd2lhWYi3LJA0fZJAJ10AGl5WQdRg5pAyP8za74+7hcTYc1JrjcyccmEviX1Ozs62xmeasM3JIGc3NTmVF6E+u/vLSDE44eGXjds7VnbEQ4JvieiBc3N7emGWvoYMYVJppWX+JGWatd2TljhVmrXdMtUxreY3Co5KmbTx42Zkd7Bnt291A/SHgDHf/ZHx+IXCiu38ub76pwFSAqqqq42bOnNmtxtbW1lJZWdn5jO3IumOEu0SaHeoboaIUyju4t9jdWbfNGTbIqCjt+YtMzVlnw/awjrFDjDGDQ2BsyjrNDg31ddRlhlCagdEV4Q2TdWdNvdOYhWFlUDnIqGuEVXVZBpdC1ZBMQW3NH5X0lfXbsuxohu3NTkXzNkYOG8rK2iwbdzjLtmYZVW4s25plv8oMw8uNNfVZNm133lRVysbtWdZuC2+21XVO1p1xwzLUN0Jj1nlubTPvOaiMxmx4zfcZksFx9h6SYVtTeGPuaIZ9K0OgXL41y8ubsqyuzzJ2cIbNDc7quixHjCmhoRle3tTMsEHGqHKjyWHx5izDBxnjh2dozEJ9k1NRYowoN1bWZjGD+kanxGBVnXP4XiXsaHbKS0J7KkqMjfUNlJSWMbgUXtmc5YBhGUozRrM725tgzOAQHOoaHRwqSo3GbAiauesn67Y5YwbbTqPKukZn8eYsE0dlyBg0ZWH51iwHjsiQ+yaBEHSgxKA0A6VmZAnvlayHdXj8nyUEnxILj+sancGlRkVJa6DKGKyuz7JlR3jPjB0c6luwvpkR5cbwQUZ5qdHQ0Mjg8jJKM7C9KbRhR3P4b4T/ub/kczPb6YK3A6UWAnddY25UHfpalgltbcpCk7fWU5Los5nFgwK0Dp7D9lu3LcuQMkt8+VrYFg6JgJ77v/OE/MjpDqU08aljuhe/Jk+e3G6gLiRH3da7fJfo7u7TgekQRtTdHf7vzqnDnqqmpob3DKA+D9TXWH3u/4rV50KSMyuA5BWg/YDeSWCKiEhBgfop4BAzO9DMBgHnAX8qbrNERCSn09SHuzeZ2eeA+wm35/3a3Z8vestERAQo8D5qd/8L8Jcit0VERNqQuu/6EBGRnSlQi4iknAK1iEjKKVCLiKRcp59M7FalZmuBXb+ZqDBjgHU92Jw9wUDr80DrL6jPA8Xu9Hm8u7f5yylFCdS7w8xmt/cxyv5qoPV5oPUX1OeBolh9VupDRCTlFKhFRFIujYF6el83oA8MtD4PtP6C+jxQFKXPqctRi4jIztI4ohYRkQQFahGRlEtNoDazM8xsoZktMrPL+ro9u8PMfm1ma8xsfmLaaDN7wMxeiv9HJcouj/1eaGanJ6YfZ2bPxbKfWRp+qqUNZra/mT1sZgvM7Hkz+2Kc3p/7XGFmT5rZs7HPV8Xp/bbPOWZWYmZPm9nd8Xm/7rOZLYltfcbMZsdpvdtnj79b1pd/hK9PfRl4PTAIeBY4vK/btRv9OQV4EzA/Me0HwGXx8WXA9+Pjw2N/y4ED43YoiWVPAm8h/MrOvcCZfd23dvq7L/Cm+HgY4ceQD+/nfTagMj4uA2YBb+7PfU70/cvAbcDd/X3fjm1dAozJm9arfU7LiPoEYJG7v+LuDcBM4H193KZuc/dHgA15k98H3BIf3wK8PzF9prvvcPfFwCLgBDPbFxju7o97eJV/k1gmVdx9lbvPjY+3AguAcfTvPru718anZfHP6cd9BjCz/YCzgRsTk/t1n9vRq31OS6AeByxPPF8Rp/UnVe6+CkJgA/aO09vr+7j4OH96qpnZBOBYwgizX/c5pgCeAdYAD7h7v+8zcC3wNcJv3+b09z478FczmxN/xBt6uc8F/XBALyjoB3T7qfb6vsdtEzOrBO4ELnX3LR2k4PpFn929GTjGzEYCd5nZkR3Mvsf32czeDaxx9zlmVl3IIm1M26P6HJ3k7ivNbG/gATN7oYN5i9LntIyoB8IP6K6Opz/E/2vi9Pb6viI+zp+eSmZWRgjSM9z993Fyv+5zjrtvAmqAM+jffT4JeK+ZLSGkJ081s1vp333G3VfG/2uAuwip2l7tc1oC9UD4Ad0/ARfFxxcBf0xMP8/Mys3sQOAQ4Ml4OrXVzN4crw5/LLFMqsT23QQscPdrEkX9uc9j40gaMxsMnAa8QD/us7tf7u77ufsEwnv0IXe/gH7cZzMbambDco+BdwHz6e0+9/UV1cRV1LMIdwu8DEzr6/bsZl9uB1YBjYQj6SXAXsDfgJfi/9GJ+afFfi8kcSUYmBR3ipeBnxM/SZq2P+BkwmncPOCZ+HdWP+/zUcDTsc/zgf+M0/ttn/P6X03rXR/9ts+EO9GejX/P52JTb/dZHyEXEUm5tKQ+RESkHQrUIiIpp0AtIpJyCtQiIimnQC0iknIK1CIiKadALSKScv8fD6VMcclYjRgAAAAASUVORK5CYII=\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": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEICAYAAAB25L6yAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqeUlEQVR4nO3deXxcdb3/8ddnkjRJm660BGyhRRAUEEEL6IWLKYJsiogbCAoKt9erXi/K1Qvq/Yn7jrhjLyB4qVS8iAv71ogItLQFaktYWrrS0n1L0mwz398f3zOZk+lMMjPNJN8m7+fjkUdmzvecM9/vzJn3+c73nDljzjlERCRcicGugIiI9E5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISuH02qM2s2cxe2w/rmWZmzswqS1jWzOzXZrbNzObvbV0Gm5l90cxuKHHZejN71Mx2mdkPy1A3Z2aH9fd6B9LebGsyvA1YUJvZyWb2uJntMLOtZvZ3Mzu+1PU55+qccy/3Zx1LcDJwOjDFOXfCINdlrznnvuWcu7zExWcCm4Exzrkr+7FawTKzS83ssX5cX6OZtUWdkGYzeyGr/INm1hTtDJ8zs/N6WdenzWyBmbWb2c1ZZUdGZduiv4fM7MhY+Tgzu8XMNkZ/18TKDo7VL/3nzOzKqPyLWWW7zSxlZhOj8glm9jsz2xz9zTazMbH1zzKzF6JlLs3Rrtea2V3Rc7DZzL4XK7vVzNab2U4ze9HMLo+V9dXmK8zs5WjZdWb2o/gO1czmmtmmqPxZM3tPrKwhqm+83ZfEypdmlXWZ2V/yvXY5OefK/geMAbYDFwIVQC3wTuCYEtZV2c91mwa4UtYLXAw8NlB1DfkPuAH4RhnX74DDBrudWXW6tLfXP8f8vW5rQCNweZ6yyUAHcBZgwDlAK7B/nvnPB84DfgncnFU2LqqLRe/HzwCLY+W/Bn4PjIzmWw58LM/jHAIkgWl5yq8BHond/wXwQJQJY4GHgGtj5Z8C3gEsAC7NWteIqC6fA0YBNfEMAY4CqqPbrwdeBd5SYJsPBcZFtycAjwCfi5Ufk37dgBOBXcCB0f0GYG2B24ABLwMfLWpbG6ANejqwvY95Pg40AduA+4GpsTIXvYAvASti0w6LblcDPwBWAxuA64HaPI9TEc27OXrCPkXszRNtPDcC64FXgG8AFTnWcxnQFm2kzcBX0y8Y8F/RRvK/+E8tV0Ub2BbgdmBCbD0nA4/jd2Rr0hsnWW9askIh2hAfBLYCLwAfjJXdDPwcuDvaoOYBh2Zt0OllNwBfjL2pbo3N9/uoHTuAR4Gj8jynNwOd+CBpBk6Lpn0jNk+PjRlYCfwnsDha/++Amlj556PXYF20bcRf73OAp4Gd0XN2TWy5adG8H4vKtgGfAI6PHms78LMit71P4Le9bdHzasAbsl7/7UXUrZSgPhHYmDVtE/C2Pt5X3yArqLPKK/HvgdbYtM3A8bH7XwT+lmf5rwBz85QZfru/JDbtXuCTsfufAu7Psexj7BnUM/PVI8fyR0TbzwdzlO3R5qzy/fA7kF/kKT8heu1PyLVt91Gvt0fby6hC5u9erpiZS/3D7z23ALfgewTjs8rPA5ZFG38l8GXg8Vi5wwfLBKIApucb9zrgz1H5aOAvwLfz1OUTwPPAQdH8c+kZ1H8EfoXfY+8PzAf+Nc+6LqVneDYAXcB38TuPWuAK4ElgSjTtV8Bt0fwH44P0QqAq2kCOjcoayRPUUd3W4MOoEngz/s11VFR+Mz6ET4jKZwNzorLR0QZ8Jb5HMho4MSq7hp5B/fGovDp6jp/p5TW+mZ7BnH2/gT2Dej7wmuh1aAI+EZWdid+BHB219bdZr3cD8Eb8TvCYaN7zorJp0bzXR+17J/5N9cfo9ZwMbATeXsS2dxe+R3YwPhzPzPX6F1G33oJ6U/Ra/h1oiJVVAH8Fzo1un4fvFPT6hqeXoMbvtLqAFPDl2PTNRCEU3f8SsC3POpaTFaixslPwoVQXm/Yu4B5gfPT3CHBFjmVzBfVN+M7PvVEdG4E3Zs3zC/wnDQcsij92b22Oyj6M38G66HV4U1b5XdG25ID7gETsNe+IXusVwI/yvS5RG3K+Hr2+jsUuUOpf9Ea4Odq4uvDBWh+V3QtcFps3ET3ZU2NvllOz1ueAw/B77RZ69hjfRtTzzlGPR4gCIbr/zvSbB6gH2on1xvEhOjfPui5lz6DuoGfPsAl4R+z+gfjeZyVwNXBnnnU3kj+oP0RWzwK/A/hKdPtm4IZY2dnA87H2PJ3nMa8hFtRZZeOi52lsnvKbKT6oL47d/x5wfWxj/k6s7HB6GfrA70R+FN2eFs07OVa+BfhQ7P4dROFQ4LZ3cqz8duCqXK9/EXXLF9QnktkxXoLfice368vwwdcV1fGcAt53ffWoRwGfjK8LuBX4Q1SXw/Bh3J5j2X8mK4izym/Mfmz8jvkhfFCm8B2wETmWzRXUD+DfO2fhh0E+j/9UPCJrvgr8J9UvA1WFtDmr/HXA14EDcpRVRY//2di0A4Ajo23nEPynz1/lWHYkfkfQ0Nfrlv03YAcTnXNNzrlLnXNT8D2l1+A3YoCpwI/NbLuZbcf3Bg3f+0lbk2fVk/BPwMLY8vdF03N5Tda6VsVuT8W/EOtj6/oVvidWqE3Oubasdd4ZW18T/uNyPb5Xv7yIdcfXeWJ6ndF6L8JvMGmvxm63AnXR7YIe08wqzOw7ZrbczHbigxVgYgn1zSdfHXt7jTCzE2MHd3bgPyVl12tD7PbuHPfTj1XItpevnnsosG45OefmOed2OefanXO34HvVZ0frPQ2/M2vAh9TbgRvM7NhC1t3LY7bgP338xszS2/ln8M/RS8CfgNvwHaxslwB3OOeaswvMrBb4AP5TdNzvgRfxO4Ex+G3x1gKruxu/Y7zXOdeBH8LcD98JjLcp6Zx7DP8p9t+yV5KnzfHyl4Cl+N55dlmnc+5e4AwzOzea9qpz7jnnXMo5twL4AvD+HPU/H799/bXA9nYblNPznHPP43tcR0eT1uCHF8bF/mqdc4/HF8uzus34F/Co2LJjnXP53kzr8WGVdnDs9hp8j3pibF1jnHNHFdO8rPtrgLOy2lbjnHslKjs0z3pa8DugtHgIrwH+mrXOOufcHhtlDr09ZtyHgffgx5vH4nuD4EOsEL3Vvy+9vUbgh0L+DBzknBuLf9MVWq9shWx7+eTaJvuzbi627LHAo865BVEgPIU/9nBaieuOS+Bfq8kAzrmtzrmLnHMHRNt+Aj9M1a2XIE5Lh1Jj1vQ34XubLVHAX0+0MyrAYvLnQC6V5N/We7S5yGX7Ko+/bnGXAL9xUfe6GAMS1Gb2ejO70symRPcPwn8EfzKa5XrgajM7Kiofa2YfKGTdzrkU8D/Aj9J7RzObbGZn5FnkduAzZjbFzMbjD/Sl17Ue//Hqh2Y2xswSZnaomb296EZnXA9808ymRnWbFDu1ZzZwmvnTrirNbL9YD+kZ4HwzG2n+/OHLYuu8CzjczD5iZlXR3/Fm1qNnkcddwAHR6UjVZjbazE7MMd9o/E5rC36D/laR7X4GODs6HesA/Fh9oW4HLo1OqRqJP2CVXbetzrk2MzsBv1MpVcnbHr6XPsXMRuxt3cyfEneGmdVE28JF+DHe+6NZngL+Ob19mNlx+KGHxXnWV2lmNfhhgIr0eqOy083suOhT0xjgWvyB0qao/NBoW6wws7PwB/G+kfUQ78WP987N06R8ofQUcLmZ1UZhPxN4NlbvEVG9DaiK6p3OqVuBt5rZaWZWgd+mNgNNZra/mV1gZnVRvc/AZ8wjBbb58lh+HIkflnw4uv96MzsrqnOVmV2Mf23+GpU3mD9t0aJs+w7+k0j89ZgCzCD/jq13xY6VlPKH32vdjj+LoiX6/yv8ObfpeT4C/IPM0fKbYmV7jE/Gp+EPGn0LP161M3ryP5OnLpX4wf4t+IH/XGd9/BL/UW8H/gj+BXnWdSl7jlGvzZongT+d6AX8mONy4Fux8n/G94zS7b4kmj4Rv9PYhf8IfE3WYx2BP6tjU9SWR8gciLyZ3seHj8ZvhNvwH+vTY67XEI1R4z/e/yl6/FXAR3O9DrF1Zj9mDf5Mjp34MPkse45Rnxa73/3Y0f2rorrlOuvj/VGdduF3PD+L1XsaWePA0WvZELt/Kz0PnhW87cXbiR+CuBvfc9xcSt1i652ED7Fd+AB8Ejg9a55P4w987sJv61fGyr4I3Jv1fLqsv2uisg/gD6g347efe+h5mtsHo+e9Fb/DPSNHfe8Hvt7L+70r17aCH8P9C36b3YofpnxdrLwxR73jr9350XOwM5r3qNjz99foudsZvZ7/Eluurzb/Gr/jbcFvm98nOtaEH1qZF3ttngLeG1v2c/hMa8VvPz8FRme1+2oKPGMl159FKxERkUDts18hFxEZLhTUIiKBU1CLiAROQS0iEriyXG5x4sSJbtq0aSUt29LSwqhRo/q3QoEbbm0ebu0FtXm42Js2L1y4cLNzLucX9coS1NOmTWPBggUlLdvY2EhDQ0P/Vihww63Nw629oDYPF3vTZjNbla9MQx8iIoFTUIuIBE5BLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISuCCD2jnHHxatZXdHcrCrIiIy6IIM6nkrtvK525/la3ctHeyqiIgMuiCDurmtC4CNO9sHuSYiIoMvyKBO008aiIgEGtRW6k+BiogMQUEGtX4dTEQkI8igFhGRjCCDWkMfIiIZQQZ1mn4hXUQk0KBWj1pEJCPIoFZHWkQkI8igFhGRjCCDWkMfIiIZQQZ1mkZAREQCD2oREYHKQmYys5XALiAJdDnnppezUt2POxAPIiISuIKCOjLDObe5bDXJQUMfIiKBDn2Y+tIiIt2skG//mdkKYBu+k/sr59ysHPPMBGYC1NfXv2XOnDklVai5uZllrTVct6idYyZW8LnpNSWtZ1/S3NxMXV3dYFdjwAy39oLaPFzsTZtnzJixMN+wcqFDHyc559aZ2f7Ag2b2vHPu0fgMUXjPApg+fbpraGgoqbKNjY0cc9gbYNECJuw3gYaGE0paz76ksbGRUp+vfdFway+ozcNFudpc0NCHc25d9H8jcCdQ1vTU0IeISEafQW1mo8xsdPo28E5gSbkrBvoquYgIFDb0UQ/caf7rgpXAb51z95W1VupQi4h06zOonXMvA28agLrEHnRAH01EJGhBnp4nIiIZYQa1hj5ERLqFGdQRjYCIiAQe1CIiEnhQawRERCTwoNbQh4hIoEGtnrSISEaQQZ1WyAWjRESGuqCD2vTjiSIiYQe1iIgoqEVEghd0UGuMWkQk8KAWEZHAg1oHE0VEAg9qDX2IiAQa1OpJi4hkBBnU6kmLiGQEGdQiIpIRZFBr6ENEJCPIoBYRkQwFtYhI4BTUIiKBU1CLiAROQS0iEjgFtYhI4BTUIiKBU1CLiASu4KA2swoze9rM7ipnhUREpKdietT/ATSVqyJx+l6iiEhGQUFtZlOAc4AbylsdT5dkEhHJKLRHfR3wBSBVvqqIiEgulX3NYGbvAjY65xaaWUMv880EZgLU19fT2NhYUoWam5tZufhZALZu3VryevYlzc3Nw6KdacOtvaA2DxflanOfQQ2cBJxrZmcDNcAYM7vVOXdxfCbn3CxgFsD06dNdQ0NDSRVqbGzkmMOPggXzmTBhAg0NJ5a0nn1JY2MjpT5f+6Lh1l5Qm4eLcrW5z6EP59zVzrkpzrlpwAXAI9khLSIi5aPzqEVEAlfI0Ec351wj0FiWmoiISE7qUYuIBC7ooNZv3IqIBB7UIiISeFDrN25FRAIPag19iIgEGtTqSYuIZAQZ1CIikhFkUGvIQ0QkI8igFhGRjCCDWmPUIiIZQQa1iIhkKKhFRAKnoBYRCZyCWkQkcEEHtdPP3IqIhB3UIiISeFAbOk9PRCTooNbQh4hI4EEtIiKBBrWGPEREMoIMag15iIhkBBnUIiKSEWRQa+hDRCQjyKAWEZEMBbWISOAU1CIigVNQi4gErs+gNrMaM5tvZs+a2VIz++pAVAz024kiIgCVBczTDpzqnGs2syrgMTO71zn3ZJnrJiIiFBDUzjkHNEd3q6K/Aenr6rcTRUQKHKM2swozewbYCDzonJtX1lqJiEg3c0UMBJvZOOBO4N+dc0uyymYCMwHq6+vfMmfOnJIq1NzczKq2Wr6/oI03TEjwXyfUlrSefUlzczN1dXWDXY0BM9zaC2rzcLE3bZ4xY8ZC59z0XGWFjFF3c85tN7NG4ExgSVbZLGAWwPTp011DQ0NJlW1sbOTY1x8NC+Yxfvx4GhreWtJ69iWNjY2U+nzti4Zbe0FtHi7K1eZCzvqYFPWkMbNa4DTg+X6vSYzO9hARySikR30gcIuZVeCD/Xbn3F3lrZaIiKQVctbHYuC4AahLN53tISKSoW8miogETkEtIhI4BbWISOAU1CIigVNQi4gELuig1vnUIiKBB7WIiAQe1DqfWkQk8KDW0IeISKBBrY60iEhGkEGtjrSISEaQQS0iIhlBBrWGPkREMoIMahERyQgyqDVGLSKSEWRQi4hIRpBBrTFqEZGMIINaREQygg5qp9FqEZGwg1pERAIPatNotYhI2EGtoQ8RkVCDWh1pEZFuYQa1OtIiIt3CDGoREemmoBYRCZyCWkQkcApqEZHA9RnUZnaQmc01syYzW2pm/1HuSulYoohIRmUB83QBVzrnFpnZaGChmT3onHuuzHUTEREK6FE759Y75xZFt3cBTcDkcldMREQ8c67wgQYzmwY8ChztnNuZVTYTmAlQX1//ljlz5pRUoebmZla31/K9p9o4YnyCq0+sLWk9+5Lm5mbq6uoGuxoDZri1F9Tm4WJv2jxjxoyFzrnpucoKGfoAwMzqgDuAK7JDGsA5NwuYBTB9+nTX0NBQUmUbGxs55oij4al5jBs3joaGt5W0nn1JY2MjpT5f+6Lh1l5Qm4eLcrW5oLM+zKwKH9KznXN/6Pda5H3cgXokEZFwFXLWhwE3Ak3OuWvLX6WMIkZlZC89vnwzyzY2D3Y1RCSHQnrUJwEfAU41s2eiv7PLXC8ZYB/+n3mcdu1fB7saIpJDn2PUzrnHGODr2enypiIiGUF/M1Fj1CIigQe1xqhFRAIPahERCTCou1LqRg+0jq7UYFdBRHoRVFAvXrudyx9opfGFTYNdlWHl8C/fO9hVEJFeBBXUC1ZuA6DxhY2DXBMRkXAEFdQiIrKnoIM6hNHqZMrxweuf4K8vajhGRAZHkEEdQkCn7dzdyfyVW/nMbU8PdlVEZJgKMqjTQvi+i750IyKDLcygDqlLLSIyyIIK6uzeq/JaRCSwoE4LKaAtGoAp5pdwRET6U5BBnRbE8HBUCcW0iAyWoINa4SgiEnhQi4hIoEEd1Hiwy/ovIjLAggrq9Jh0iJkYYp1EZHgIKqjTwupQB1QZERmWggpqC/BrgCHtNERkeAoqqPcQQEgGUAURGeaCDOpyDDd8+Y//YNpVd5e8fFAHOEVkWAkyqLv140jIrU+uLmm5dEArpkVksIQd1AFQQIvIYAs7qANISY14iMhgCzuoA5AeL1dgi8hgCTKoQwxFnU8tIoOlz6A2s5vMbKOZLRmICkFgQR1SXURkWCqkR30zcGaZ6wGE+bNXymkRGWx9BrVz7lFg6wDUJUhB9e5FZFiq7K8VmdlMYCZAfX09jY2NRa/jxVWdALS1tQGwfcf2ktbTm2LXt60tBUAymer3uqQ1NzeXbd3FGoh6hNTegaI2Dw/lanO/BbVzbhYwC2D69OmuoaGh6HWsfmIlNC2luroa2toYN3YcDQ1v658K3ue/lVhsvdbv2A2Nj2CJRNHLFqqxsbFs6y7IfZlvbA5EPQa9vYNAbR4eytXmMM/6SN8IYMxaQx8iMtiCDOq0AHJaBxNFZNAVcnrebcATwBFmttbMLitXZZKpPb9ckko5/vTMK6RS/ROZxV5cSRdjEpHB1ucYtXPuwoGoCMBX//Kcf8xYP3b2/NX89x+XsLOti4+8depeP4ZzJZ4GqLwWkUES5NBHuvNsBpt2tQOwOfq/t4rN23SHWt9MLNzjyzbz9Optg10NkSGj38766E/lHG3wQxkhjH4PXR++YR4AK79zziDXRGRoCLJHnUt/fWux2KFuDVGLyGALNKh9Olqs59tfgVnqEIYCW0QGS5BB7WJj1P09SFFQ4M6eDdOmQSKBe/spfrl+roeISKHCHKMu57r7Wvns2TBzJs1dDqusxr2yzk9PpcpYKxGR/ILsUaeV42p6fQ59fOlL0NrK0Z/9Pcdc8TtcuhLDMKhXbm5hx+7Owa6GyLAXZFCX80smfa56deZHcJOJisxyZapPyBp+0Mg5P/nbYFdDZNgLM6ij/1aG0+j6DNyDD845vwvwYtkPN23gIzfOK+uObe223WVbt4gUJsigzvV18f6KolRfofbNb8LIkZnHTQd0gEF92S0L+NtLm4s+5XAgNbd3ccWcp9nW0jHYVRHZZwV9MLEsY9R9hdpFF/n//4jmf83k8lWmnyRTjopEmPW7bd5q/vjMOvarq+a/33XkYFdHZJ8UVI96/MgqALqS5fxqYgHzpMMa4OGHylaVvZXedyQL7FK3tHexs21gDw4GvH8T2WcEFdRja31Q7+5Mlu0x+hz6yCPEL7yk69RV4Bkpx339QY655oEy1khEyiGooK6pqthjWn/3yLLz9van1vCD+1/IP3+BAf2D+19g4arBuRBRoWcOdnQVfophMfMWIsQdnci+Iqigvu6CYwGYup8/mGdl+NycfYbEF+5YzM/mLss7f6EH6n42dxnv++Xje1O1khXaoy7GtQ++2O/rFJHSBBXUh02qA6DCclzjo5+6ZMWuJT1UEvJYa7IM3dU121r7fZ0iUpqggjrdg+4OxzI8RrFj1N11SaYgkfDXAJk9u8c8g/0rMIUeTCxGv19jJYCvDH3qt4v0BR7ZJwUV1InoIkzp8dFdbZ2ZnmxWlzaVcqzeUkKvr9jLnN5zr394nO/Vr1oFM2f2COu9CcodrZ0s3tRV8vLxx+9MpvjhAy/Q3F76+kp6TnuR3vmGMEZ99+L1LF23c7CrIVK0oILazHDAuh1tACxavZ0Hn9vgC7Pe6dc/upxTvj+XlzbsyrmuTbva++WLM6mf/wLwXyd/cWL0rcXWVn9NkMjeDD184taFXLuwna178YWQdFD/8elX+Okjy/jhA/kPjgLMe3kLJ3/3EVpyBPop35/LYy9t7rf+b8AjRiL7jKCCOpc9ekDRJUjn/eK3AKy98x427mxj2lV389AvfwfTprFx9ASO/+ZDXPujP+yxvqKHPjZs6L7943+6IFOwalX3MMje9BZf3twMFH+WRXy4JR3Ui9fuAGDn7t571N++93nWbtvN86/m3sk9/+rO4XlxE5FABfnNxLyiS5DS2krieB9sqZ/+DP+TuHX85tFlnLZqFVsmTQPgwaaN/Ofs2T2+wFJsqKYOPDCzrGXt16JhkGQSYFyRjfES0dBAsb3y+HBL+vb/PrkKgKXrdvS6bHoUyTnH9tY9e/LfuLupqLoU4smXt+CcK8uZPCJDXfA96h6iS5A+fvAxzD30eACSHZ1U/PrX/raD3xx3Dmd9/GdA1OuMDVFA8R3FTZd9svt2KgqZl8e/hluPPctPbG0l+fWvl9AYLx3UXcnietTxYO8qcow8/Zi3zV/DKd+bW9SyxUrn8vOv7uL+pRt6n1lEcgouqE+ckOdbiUuWdF+C9ItnfLp78pMHv5GKDesBSFqC607+cHdZyqzHZUvBDzHkGpvN519Xj+q+Pf+go/n71Ddx6sxZfPmMT9EV9bBT6R8XKEE6yDqLDOr4qdPZBzMrK3rvtaYvC3LHorXsbOv7uTj1h408tXIr7V17943RtTrlT6QkwQX1uZV5Qu+RR7ovQVqVylyv4qbjz6Ni0iTAH/BLuEyCObM9Llt6yU3zOeor9/Pcup2s3NxSVN22jhzLRRd8s/t+a1WNf9yDDs63CM45fj53Gau25H6sdO+2vcgx6mSOMeoLT/D1OOPIA3pdttjLx768qYUPXP8En5r9dJ/zZn8yiP/wQAhnfojsi4IL6skud6C5nTu7L0FalezZC0xd/i8AJCsrsVgauETCLxOzeqvv1Z39k7/R8IPGvarr7hE1MHIkyau/mHeeTc3tfP/+F/jYzU/lLE/3bos9mBjvRe+KesXpa6Uk+riSXvyc5mKGjB9q8kMXDzdtYOPOtj3KF67ausepgdc99FLOxxWRwoV3MHHEiJyTf/pPF/Cx805nAuAebe5R1nXqqXDjfBYdeATj2jNlrv4AuOjcgh42fqDrjoVrC1pm1euPpf6Kj+PefT5862HAX3+5tqqCioTR3pXke/f5U+V25RliSIdqZy9XDFy0ehvfuruJWy8/sft6KPFTDy++cV6P+Zeu28HujiRf+fMSJo2uZsXmFr77vmO6yztij1VTWVHURbD+8/fP8n8L13LopFF8671v5O/LNnPSYRMZP2oE7/vlEz3mnXbV3T3uD3SPeuOuNvYfXcOSV3bwrp8+1j095MvCiuQSXlBPnsxPb/wR/37mZ/couubPS0nyBprGr++eduIhE9jemvl4vb26rvv25kQ1Tet38oYDx/T5sF0pR2X0+eLK3z9bUFU/+I7P8Yd//idG7Grvnnb0V+7nI2+d2n0GRlo6pD7920XMOGJ/TjpsIhPrRnQPfWT3qJe8soMDxtYwsa6a//7jEpau28m9S9bz3uOmALAx9pjZ7vnHq9zzj/v2mJb27Jrt3beLvVLh/0U7seWbWvjQrCcB+Mkj+a+VElfK94Ju/vsKqioTXHTi1KKWW7hqK+/75RP89MLjeCHrNMTOZIqKxJ4XACtGZzJFR1eKUdXhvYVk6CloKzOzM4EfAxXADc6575StRhMm8O7Pf4wltz/Ir95weo+iPz+75/j1vBVbmbdia85V7Wrr4qwf/43vv/+YnOVxr/vSvSVV9/xf7HkhpuyQBn/hpM5kirsWr+euxev3KH9mzTZOOmw/trR0MP0bmWtgN33tTGqjXvRnf/csrz9gDH9ftrksp9CV28/nLuPCEw7qdZ6uZIrlm1o44oDRJFOOa/7iT74sNqjT28S8FVsYXVPVo+ybdzfxbw2H8ppxtUWtM+6yWxbw6IubWPmdc0peR9p1D73IzY+v5Jn/9869XpcMTdbXdSrMrAJ4ETgdWAs8BVzonHsu3zLTp093CxYsKKlCjY2NNDQ0APCTh18a1Ku4PfDZU3jnjx4F4LUTR/FykQcf9xXXvPtItrZ2csHxB7Fmayuz563OuVMsh/ceN5n6MTWs2daKc6675//5M47g+1mXn61MGB87aRqvGVfLGyeP5aAJI7lr8XqeX7+TKeNHctj+dUweX8tVdyzO+2WetAmjRvDVc4/ihsdW8IUzjmDqfiPZb1Q1q7a2sH57G286aBwA37vveY49aBwtHUlmHDGJ9q4UZ/04c72Qpq+diZn/JuynfruID04/iItOPNh/y9Y5nIOOZIonHnuUGTNmALBu+27qx9SwaPU2Lr5hXveB5CeuPpXxI0fQ3N5FVSJB7YgKKhOZ699sa+1k0uhqIPOFp66UY/XWVg6NLmjW0ZVi/Y7dTN0vc7ZSepisr2MXuexo7aSq0hg5omefbskrOzh4v5F0dKWYWFfd3a6KhDGmpoqqCuOxvz3a/V4uRfZ59845Uo5+Hbbq6EoxorL/DtXF86tYZrbQOTc9Z1kBQf024Brn3BnR/asBnHPfzrdMfwV12ksbdtH06i4+c5s/6+Br7zmKj75tGtc99GKPg1XXfehYvn7Xc2zp5evYIyoT/M9Hp9PS3sVNj61gQZ5rSF94wsF8+/w34pxjc3MHk0ZX8+KGXdz+1BrOeuMBHDKxjoebNtDS3tXd6/vquUfxhgPHcNUfFnPg2BreMnUCC1dt5e/LtpT0XPTlM+94HSdMm0B7V5Kv/uU5Tjl8IhccfzC3PrmKUw6fxCdnL2J0TSUjKhK+pz51PCnnWLR6O7/5+An85olVXH/xm6ms2HND3bG7k/auJPuPrmF7awdX3v4s81dsZVd7F5edfAg3PraCw+vrOP3Ien4+dznnHzeZz55+OGu37WZMbSW/bFze/clhyvjaYfcjuSNHVNCVcj2GtKorEyRTrtfz3s32HMuPT0sf/+joSoH1HDIbNaKClo5k9+MnzGjp6MI5qKowRlVX0tHlP9klzKhIGBVmJBL+tp/mh6iSKUfCYGtLBykHo6srSTlH0jnau1I96lhVYd3LxI0ZYZDwx0BqqypIORg3sgoXzZt0Duecv53yO7VkdN/hd8wV0V9X0rG7M0ky5Rg3sopk0lFX49uTXn91ZQKH33mlouc5ve6E+YDvih4H/MHtts4U+40awYjKRPfjV5hhljkjK/6zqekzprqndb9G/lZF124euuqsXraM/PY2qN8PnOmcuzy6/xHgROfcp7PmmwnMBKivr3/LnDlzSqpsc3MzdXV1fc+YR8o5DP/FlqSD1k6oqYTqXs4tds6xebdj9AijprL/DzIlU46tbf4xJo00Jtb6YOxKOZIOOlpbaEmMpDIBE2r8GyblHBtbHZ0pGF0FdSOMlk5Y35KithLqRyYKqmso3wbcsjtFexLako6a5G7GjR7FuuYU29odq3elGF9trN6VYkpdgjHVxsbWFNvbHG+ur2RbW4pNu/2bbUOLI+Uck0cnaO2EzpTjH5uSvPvQKjpT/jU/YGQCh2P/kQl2d/k3ZnsSDqzzQblmV4rl21NsaE0xqTbBjg7HhpYUR02soCMJy7cnGT3CGF9tdDlYsSPFmBHG1DEJOlPQ2uWoqTDGVhvrmlOYQWuno8JgfYvjyP0qaE86qit8fWoqjG2tHVRUVlFbCS/vSHHw6ASVCSPpHG1dMLHWh0NLpwMHNZVGZ8qHZvr4yebdjom11qNX2dLpWLEjxRHjEyQMulKwZleKQ8YmSF9JwIcOVBhUJqDSjBT+vZJy/jFc9D+FD58K87dbOh21lUZNRSaoEgYbWlPsbPfvmUm1fn1NW5KMrTbGjDCqK42Ojk5qq6uoTEBbl69DezJz8bWEZf7i982sxwFvB1SaD+6WznSv2re1KuHr2pWCLpdZT0WszWYW7RQg03n2z9/m3SlGVll3eKd3ig5igZ7+33NCdnI6B5V08S/HlpZfM2bMyBvUhYxR53qX75HuzrlZwCzwPepSu/9789FhX9XY2Mi7h1Gbh+trrDYPfeVqcyGDM2uB+BGgKcDADGCKiEhBQf0U8DozO8TMRgAXAH8ub7VERCStz6EP51yXmX0auB9/et5NzrmlZa+ZiIgABZ5H7Zy7B7inzHUREZEcgrvWh4iI9KSgFhEJnIJaRCRwCmoRkcD1+c3EklZqtgnY88pEhZkIbO7H6uwLhlubh1t7QW0eLvamzVOdc5NyFZQlqPeGmS3I9zXKoWq4tXm4tRfU5uGiXG3W0IeISOAU1CIigQsxqGcNdgUGwXBr83BrL6jNw0VZ2hzcGLWIiPQUYo9aRERiFNQiIoELJqjN7Ewze8HMlpnZVYNdn71hZjeZ2UYzWxKbNsHMHjSzl6L/42NlV0ftfsHMzohNf4uZ/SMq+4mF8FMtOZjZQWY218yazGypmf1HNH0ot7nGzOab2bNRm78aTR+ybU4zswoze9rM7oruD+k2m9nKqK7PmNmCaNrAttlFv1s2mH/4y6cuB14LjACeBY4c7HrtRXtOAd4MLIlN+x5wVXT7KuC70e0jo/ZWA4dEz0NFVDYfeBv+V3buBc4a7Lblae+BwJuj26PxP4Z85BBvswF10e0qYB7w1qHc5ljbPwf8FrhrqG/bUV1XAhOzpg1om0PpUZ8ALHPOveyc6wDmAO8Z5DqVzDn3KLA1a/J7gFui27cA58Wmz3HOtTvnVgDLgBPM7EBgjHPuCedf5d/ElgmKc269c25RdHsX0ARMZmi32TnnmqO7VdGfYwi3GcDMpgDnADfEJg/pNucxoG0OJagnA2ti99dG04aSeufcevDBBuwfTc/X9snR7ezpQTOzacBx+B7mkG5zNATwDLAReNA5N+TbDFwHfAH/27dpQ73NDnjAzBZGP+INA9zmgn44YAAU9AO6Q1S+tu9zz4mZ1QF3AFc453b2MgQ3JNrsnEsCx5rZOOBOMzu6l9n3+Tab2buAjc65hWbWUMgiOabtU22OnOScW2dm+wMPmtnzvcxbljaH0qMeDj+guyH6+EP0f2M0PV/b10a3s6cHycyq8CE92zn3h2jykG5zmnNuO9AInMnQbvNJwLlmthI/PHmqmd3K0G4zzrl10f+NwJ34odoBbXMoQT0cfkD3z8Al0e1LgD/Fpl9gZtVmdgjwOmB+9HFql5m9NTo6/NHYMkGJ6ncj0OScuzZWNJTbPCnqSWNmtcBpwPMM4TY75652zk1xzk3Dv0cfcc5dzBBus5mNMrPR6dvAO4ElDHSbB/uIauwo6tn4swWWA18a7PrsZVtuA9YDnfg96WXAfsDDwEvR/wmx+b8UtfsFYkeCgenRRrEc+BnRN0lD+wNOxn+MWww8E/2dPcTbfAzwdNTmJcD/i6YP2TZntb+BzFkfQ7bN+DPRno3+lqazaaDbrK+Qi4gELpShDxERyUNBLSISOAW1iEjgFNQiIoFTUIuIBE5BLSISOAW1iEjg/j9wMy5PhVr/ZQAAAABJRU5ErkJggg==\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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\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)\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": 29,
   "id": "47300235",
   "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>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": 29,
     "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": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ordenamos las series de puntuaciones más altas a las más bajas\n",
    "df_series.sort_values(by = 'Puntuacion total', ascending=False)[0:30]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}