Detección series de armónicos - Ondas generadas - Complete.ipynb

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   "source": [
    "# ALGORITMO PARA IDENTIFICACIÓN SERIES DE ARMÓNICOS EN SEÑAL GENERADA\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",
    "* Generar la onda o señal\n",
    "    * Ejemplos:\n",
    "        * Onda sinusoidal\n",
    "        * Suma ondas sinusoidales\n",
    "        * Onda cuadrada\n",
    "        * Suma ondas cuadradas\n",
    "* Calculamos su espectro de frecuencias\n",
    "    * Función auxiliar: get_spectrum\n",
    "* Detectar picos e incertidumbres de los mismos:\n",
    "    * Detección de picos con la función *encontrar_picos*\n",
    "    * Funciones auxiliares creadas: *_peak_prominences*, *_peak_widths*\n",
    "    * Cálculo de las incertidumbres\n",
    "* Interpolación del espectro para estimar y corregir la localización de los picos\n",
    "    * Función utilizada: *peak_estimator* con tipo de ventana: 'hann' o 'rect'\n",
    "* Recalcular la incertidumbre de los picos\n",
    "* Guardar todos los datos de picos e incertidumbres en un DataFrame\n",
    "* Algoritmo principal de detección de series de armónicos\n",
    "    * Introducción al algoritmo\n",
    "    * Funciones auxiliares\n",
    "    * Detección de series de armónicos\n",
    "    * Criterios de clasificación de las series encontradas\n",
    "    * Resultados obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87fa6482",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import sqrt, sin, pi, sign\n",
    "from numpy.fft import fft\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a566b0",
   "metadata": {},
   "source": [
    "## Generamos la onda o señal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02c44bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parametros iniciales\n",
    "win = 'hann'\n",
    "test_srate = 128.\n",
    "test_freq = 9.7\n",
    "test_amp = 2.0\n",
    "test_nsamples = 1024\n",
    "test_duration = test_nsamples/test_srate\n",
    "test_resolution = test_srate/test_nsamples\n",
    "\n",
    "# ONDA SINUSOIDAL\n",
    "# test_t = np.linspace(0, test_duration, test_nsamples, endpoint=False)\n",
    "# test_y = test_amp*sin(2*pi*test_freq*test_t)\n",
    "\n",
    "# SUMA ONDAS SINUSOIDALES\n",
    "# test_t = np.linspace(0, test_duration, test_nsamples, endpoint=False)\n",
    "# test_y = test_amp*sin(2*pi*test_freq*test_t) + 4*sin(2*pi*test_freq*2*test_t) + 5*sin(2*pi*53*test_t)\n",
    "\n",
    "# ONDA CUADRADA\n",
    "test_t = np.linspace(0, test_duration, test_nsamples, endpoint=False)\n",
    "test_y = test_amp * signal.square(2 * pi * test_freq * test_t)\n",
    "\n",
    "# SUMA ONDAS CUADRADAS\n",
    "# test_t = np.linspace(0, test_duration, test_nsamples, endpoint=False)\n",
    "# test_y = test_amp * signal.square(2 * pi * test_freq * test_t) + 3*signal.square(2 * pi * 20 * test_t) + 5*signal.square(2 * pi * 64 * test_t)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be5f571",
   "metadata": {},
   "source": [
    "## Calcular el espectro de frecuencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c123edb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Función para calcular el espectro de una onda generada\n",
    "def get_spectrum(y, srate, fft_len=None, win=\"hann\"):\n",
    "    \"\"\"Obtain the windowed FFT of a signal\"\"\"\n",
    "    if fft_len == None:\n",
    "        fft_len = len(y)\n",
    "        \n",
    "    if win == \"rect\":\n",
    "        wnd = np.ones(len(y))\n",
    "    elif win == \"hann\":\n",
    "        wnd = np.hanning(len(y))\n",
    "    elif win == \"hamming\":\n",
    "        wnd = np.hamming(len(y))\n",
    "    elif win == \"blackman\":\n",
    "        wnd = np.blackman(len(y))\n",
    "    else:\n",
    "        raise\n",
    "\n",
    "    y2 = y*wnd\n",
    "    out = fft(y2, fft_len)\n",
    "    sp = abs(out[:int(fft_len/2)]/len(y))\n",
    "    freq = np.linspace(0, srate/2, len(sp), endpoint=False)\n",
    "    return freq, sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b53e98ea",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculamos el espectro de frecuencias\n",
    "test_f, test_sp = get_spectrum(test_y, test_srate, win=win)\n",
    "\n",
    "# Mostramos las gráficas\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(constrained_layout=True, figsize=(10,4))\n",
    "gs = gridspec.GridSpec(1, 2, figure=fig)\n",
    "\n",
    "d0 = fig.add_subplot(gs[0, 0])\n",
    "d0.plot(test_t, test_y)\n",
    "d0.set_title(\"Señal\")\n",
    "\n",
    "d1 = fig.add_subplot(gs[0, 1])\n",
    "d1.plot(test_f, test_sp)\n",
    "d1.set_title(\"Espectro\")\n",
    "\n",
    "frecuencias = test_f\n",
    "amplitudes = test_sp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f4ec78e",
   "metadata": {},
   "source": [
    "## Detección de picos e incertidumbre de los mismos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 = 2*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": 6,
   "id": "3573eb61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 4 picos con los siguientes indices: [78, 233, 388, 481]\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": 7,
   "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": 8,
   "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": 9,
   "id": "04c94294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 4 picos con los siguientes indices: [78, 233, 388, 481]\n",
      "\n",
      "Los picos corresponden a las siguientes frecuencias:  [ 9.75  29.125 48.5   60.125]\n",
      "\n",
      "A estos picos se les asocia los siguientes 4 valores de incertidumbre: [0.35573069 0.38021675 0.39175308 0.39110684]\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([0,70])\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": 10,
   "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": 11,
   "id": "d08e2300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Interpolación del espectro para corregir la localización de los picos\n",
    "peaks_x = []\n",
    "peaks_y = []\n",
    "\n",
    "for p in peaks:\n",
    "    try:\n",
    "        x, y = peak_estimator(frecuencias[p-1:p+2], amplitudes[p-1:p+2], win='hann')\n",
    "        peaks_x.append(x)\n",
    "        peaks_y.append(y)\n",
    "        \n",
    "    except ValueError:\n",
    "        print(\"Error en pico\", p, \". Intervalo:\", (frecuencias[p-1:p+2], amplitudes[p-1:p+2]))\n",
    "        \n",
    "\n",
    "# mostrar los resultados\n",
    "plt.plot(frecuencias, amplitudes)\n",
    "plt.plot(frecuencias[peaks], amplitudes[peaks], 'bo')\n",
    "plt.plot(peaks_x, peaks_y, 'ro')\n",
    "plt.title('Picos del espectro')\n",
    "plt.legend(['Espectro', 'Picos encontrados', 'Picos encontrados corregidos por interpolación'])\n",
    "plt.xlim([0,30])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 14,
   "id": "bfb6bc9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se han detectado 4 picos con los siguientes indices: [78, 233, 388, 481]\n",
      "\n",
      "Los picos corresponden a las siguientes frecuencias:  [ 9.70023123 29.1007079  48.50208519 60.09775222]\n",
      "\n",
      "A estos picos se les asocia los siguientes 4 valores de incertidumbre: [0.34672158 0.37653042 0.39172698 0.38564953]\n"
     ]
    },
    {
     "data": {
      "image/png": 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NjT1ynkdi9B0FsO1zF2nAqcA5QDbwroi8p6pb2qykuhBYCFBUVKRee0Fv5do9sH4dg/fvJy/vRE+/YNjrL0j2sv6NGzeSmpqa8GOid4YXxnM/HF7Sn5WVxcknn9zteiIx+lJgRNj0cKD9yESlOA2wdUCdiLwFTAa2kEQEu1dajt4wDC8RST/6VcAYERktIhnAVcDz7cr8FThDRNJEJAcntZP47+qKklD3yvjKMAzDiIoujV5VfcCtwMs45v2sqm4QkVtE5Ba3zEbgJWA9sBKnC+bHR052fDGjN5KBpUuXIiJs2rQp5joWLVrErbfe2oOqjhzFxcVthnWIhvvvv5+f/exnUa2Tl5cX07aOBBE9Gauqy1V1rKoer6oPuvMWqOqCsDL/parjVXWCqj5yhPTGFXtK20gmFi9ezOmnn87TTz8dNw0+nw353RvYEAhR0Dp6pTm+4W1qa2tZsWIFv/vd79oYfbCR+4orrmDcuHFcc801oedHVq1axaxZs5g1axbTp08P9VwpKytj7ty5jBkzhrvuuitUV3hEu2TJEm688UbAGfb43//93znrrLO4++67WblyJbNmzeLkk09m1qxZbN68GYANGzYwffp0pkyZwqRJk/j0008/tx8vvfQSp5xyCpMnT+acc84B6LS+9vt/0003MXHiRCZNmsRzzz13WM3h/Pa3v2XatGlMnjyZyy+/PDTUw44dO5g5cybTpk3jBz/4QZttnXPOOZxyyilMnDiRv/71rwDU1dVx4YUXMnnyZCZMmMAzzzzT2dfVbWwIhCiwgZeMnuaOO+7o8ReATJkyhUceeeSwZYJDFYwdO5aCggLWrFnDKaecAsDatWvZsGEDQ4cOZfbs2axYsYLp06dz5ZVX8swzzzBu3DhUlezsbADWrVvH2rVryczMpKioiNtuu40RI0YcbvNs2bKFV199ldTUVKqrq3nrrbdIS0vj1Vdf5fvf/z7PPfccCxYs4Pbbb+eaa66hubn5c0/zHjhwgG9+85u89dZbjB49OjSmzrhx4zqsL5wHHniA/Px8PvroIwAqKioiPr6XXXYZ3/zmNwG49957+d3vfsdtt93G7bffzre+9S2uv/56HnvssVD5rKwsli5dSt++fTl48CCnnXYaF198MS+99BJDhw7lhRdeAJzRP48UZvRRYOPRG8nC4sWLueOOOwC46qqrWLx4ccjop0+fzvDhwwHnolFSUkJ+fj5Dhgxh2rRp1NTU0Ldv31Bd55xzDvn5+QCMHz+enTt3dmn0X/nKV0JvlKqqquKGG27g008/RURCg6TNnDmTBx98kNLSUi677DLGjBnTpo733nuPL3zhC4wePRpoHQ65s/rCefXVV9vcyfTv3z+yAwd8/PHH3HvvvVRWVlJbWxsavXPFihWhC8p1110XenmKqvL973+ft956i5SUFPbs2cO+ffuYOHEid955J3fffTcXXXQRZ5xxRsQaosWMPgbEInujh+gq8j4SlJeX8/rrr/Pxxx8jIvj9fkSEn/70pwBkZmaGyqampuLz+VDVToce7qg8tB3auLGxsc06weGJAX7wgx9w1llnsXTpUkpKSkLPR1x99dXMmDGDF154gfPPP5/HH3+cs88+O7ReZ5o6qy+cztY9nOYgN954I8uWLWPy5MksWrSozQNNHdX51FNPceDAAVavXk16ejqjRo2isbGRsWPHsnr1apYvX84999zDeeedx3333dfhNruL5eijwfzdSAKWLFnC9ddfz86dOykpKWH37t2MHj2at99+u9N1xo0bR1lZGatWrQKch466akgdPHgwGzduJBAIsHTp0k7LVVVVMWzYMKDtcAvbt2/nuOOO4zvf+Q4XX3wx69evb7PezJkzefPNN9mxYwfQOhxyZ/WFc9555/Hoo4+GpoOpm0g019TUMGTIEFpaWnjqqadC82fPnh26SwifX1VVxaBBg0hPT+eNN95g586dgNO2kZOTw7XXXsudd97JmjVrOj1G3cWMPgpaH5gyDO+yePFiLr300jbzLr/8cv70pz91uk5GRgbPPPMMt912G7NmzeLcc8/tNOIN8vDDD3PRRRdx9tlnM2TIkE7L3XXXXdxzzz3Mnj27TR7+mWeeYcKECUyZMoVNmzZx/fXXt1mvsLCQhQsXctlllzF58uTQ26o6qy+ce++9l4qKCiZMmMDkyZN54403Itb8wAMPMGPGDM4991zGjRsXmv/LX/6Sxx57jGnTprXJt19zzTV88MEHTJ06laeeeiq0zkcffRRqbH7wwQe59957D3c4u4XEq4GxqKhIO2oNT2SeWbWLu5/7iK/W72DeZRM8+wg+eHsIAfC2/o0bNzJ8+HDPPIbfHi8NIdARXtK/ceNGTjzxxDbzRGS1qk7tZJUOsYg+ClqfjLUcjmEY3sGMPgqs141hGF7EjD4KrLON0VPYMxlGV/TkOWJGHwXWGGv0BFlZWVRVVZnZG52iqpSXl5OVldUj9Vk/+piwH6gRO8OHD+fDDz+ktrY23lJiorGxsccMKB54RX9WVlbowbXuYkYfBQEbptjoAdLT06mtrWXq1Kg6TiQMxcXFPfIyjHjhdf2xYKmbKGh9ObhhGIZ3MKOPgkDAUjaGYXgPM/ooCFg/esMwPIgZfRQEgqkb83nDMDyEGX0UBKw7nGEYHsSMPgqs141hGF7EjD4KQqkby9EbhuEhzOijQC2iNwzDg5jRR4F1rzQMw4tEZPQiMldENovIVhGZ38HyOSJSJSLr3L8j8z6sOGM+bxiGF+lyCAQRSQUeA84FSoFVIvK8qn7Srug/VfWiI6AxYfBbrxvDMDxIJBH9dGCrqm5X1WbgaeCSIysrMbHRBg3D8CKRGP0wYHfYdKk7rz0zReRDEXlRRE7qEXUJRrDXjdm9YRheIpLRKzvqZNLe69YAI1W1VkTmAcuAMZ+rSORm4GZwXuxbXFwcldh4s3NnMwClw4ZTW1vrOf3hmP744mX9XtYO3tcfC5EYfSkwImx6OFAWXkBVq8M+LxeRX4vIQFU92K7cQmAhOC8H99rLnd9v3ATbtzGkbA95p0327Mupwdsv1wbTH0+8rB28rz8WIkndrALGiMhoEckArgKeDy8gIseIiLifp7v1lve02HjTmrqxnvSGYXiHLiN6VfWJyK3Ay0Aq8ISqbhCRW9zlC4ArgG+JiA9oAK7SZGy5dPcoEF8VhmEYURHRG6ZUdTmwvN28BWGfHwUe7VlpiUfrlcsiesMwvIM9GRsFar1uDMPwIGb0UaCh1I1F9IZheAcz+ijQdv8NwzC8gBl9FAR73QTEInrDMLyDGX0UBFM3FtEbhuElzOhjwPrRG4bhJczoo8B63RiG4UXM6KPAGmMNw/AiZvRRYN0rDcPwImb0UWDDFBuG4UXM6KOgNXVjEb1hGN7BjD4KQt0rzecNw/AQZvRRYcMUG4bhPczoo0BtmGLDMDyIGX0UtD4ZaxG9YRjewYw+CkJj3cRZh2EYRjSY0UeBvXjEMAwvYkYfBTaomWEYXsSMPgqUYOrGInrDMLyDGX00WD96wzA8iBl9FARTNhbRG4bhJczoo8DGujEMw4tEZPQiMldENovIVhGZf5hy00TELyJX9JzExEHN4Q3D8CBdGr2IpAKPARcA44Gvicj4Tsr9BHi5p0UmCpa6MQzDi0QS0U8HtqrqdlVtBp4GLumg3G3Ac8D+HtSXUNgbpgzD8CKRGP0wYHfYdKk7L4SIDAMuBRb0nLTEwyJ6wzC8SFoEZTpytfZB7SPA3arqF+ncBEXkZuBmgMLCQoqLiyNTmSDs398IQEW/ftTW1npOfzimP754Wb+XtYP39cdCJEZfCowImx4OlLUrMxV42jX5gcA8EfGp6rLwQqq6EFgIUFRUpHPmzIlNdZx4pnQ17N1LflUVeXmj8Jr+cIqLi01/HPGyfi9rB+/rj4VIjH4VMEZERgN7gKuAq8MLqOro4GcRWQT8vb3JJwM2BIJhGF6kS6NXVZ+I3IrTmyYVeEJVN4jILe7ypM7Lh6P24hHDMDxIJBE9qrocWN5uXocGr6o3dl9WYmIvHjEMw4vYk7FRYC8HNwzDi5jRR4Hl6A3D8CJm9FHQ+sCURfSGYXgHM/ooCKVuzOcNw/AQZvRRoGovHjEMw3uY0UeBtvtvGIbhBczoo8AaYw3D8CJm9FFgg5oZhuFFzOijQO3NI4ZheBAz+ihofTLWInrDMLyDGX0UtI51YxiG4R3M6KMg1Bh7mDH3DcMwEg0z+iiwQc0Mw/AiZvRRoKGkjUX0hmF4BzP6KLCI3jAML2JGHwWtD0xZRG8Yhncwo48C63VjGIYXMaOPAutHbxiGFzGjjwIbptgwDC9iRh8F9uIRwzC8iBl9FNgwxYZheBEz+igI2DDFhmF4EDP6aLA3TBmG4UEiMnoRmSsim0Vkq4jM72D5JSKyXkTWicgHInJ6z0uNP5a6MQzDi6R1VUBEUoHHgHOBUmCViDyvqp+EFXsNeF5VVUQmAc8C446E4HhiD0wZhuFFIonopwNbVXW7qjYDTwOXhBdQ1VptfStHLkka9NoDU4ZheJEuI3pgGLA7bLoUmNG+kIhcCvwYGARc2FFFInIzcDNAYWEhxcXFUcqNLzU1DQA0Z2RQW1vrOf3hmP744mX9XtYO3tcfC5EYfUd5is8Ftaq6FFgqIl8AHgC+2EGZhcBCgKKiIp0zZ05UYuNNzof/hOpqUltayMsrwGv6wykuLjb9ccTL+r2sHbyvPxYiSd2UAiPCpocDZZ0VVtW3gONFZGA3tSUc9sCUYRheJBKjXwWMEZHRIpIBXAU8H15ARE4QcV67JCKnABlAeU+LTRRsmGLDMLxEl6kbVfWJyK3Ay0Aq8ISqbhCRW9zlC4DLgetFpAVoAK4Ma5xNGlr3yCJ6wzC8QyQ5elR1ObC83bwFYZ9/AvykZ6UlHtbrxjAML2JPxkaBDVNsGIYXMaOPgoBaRG8Yhvcwo4+CoMEHxCJ6wzC8gxl9NNjolYZheBAz+igwgzcMw4uY0UeB2jDFhmF4EDP6KLBhig3D8CJm9FEQsIjeMAwPYkYfBaHx6K3XjWEYHsKMPgrCB3VIwhEeDMNIUszoY8Rs3jAMr2BGHwUWxRuG4UXM6KMg3OYD5vmGYXgEM/ooCFhEbxiGBzGjj4Jwn7eI3jAMr2BGHwXm7YZheBEz+iho070yfjIMwzCiwow+Klrt3dL1hmF4BTP6KLCI3jAML2JGHwXh5m4RvWEYXsGMPgrCu1eaz/cOe6sauf/5Dfitm5NhxIwZfRRYFN/7fG/Jhyx6p4T37/vveEsxDM8SkdGLyFwR2SwiW0VkfgfLrxGR9e7fOyIyueelxp/wIRDM9HuHFn/A+bB/X3yFGIaH6dLoRSQVeAy4ABgPfE1ExrcrtgM4U1UnAQ8AC3taaCKgQIrr8ObzvYNdUA2j+0QS0U8HtqrqdlVtBp4GLgkvoKrvqGqFO/keMLxnZSYICqmuxZsB9S72BgDDiJ20CMoMA3aHTZcCMw5T/l+AFztaICI3AzcDFBYWUlxcHJnKBKHF50NTBBRq6+o8pz+c2tpaT+ivrGwA4MMpU2gK0+sV/Z3hZf1e1g7e1x8LkRh9R8FUh/GsiJyFY/Snd7RcVRfipnWKiop0zpw5kalMEFJef4n0liZ8KWnk5ObgNf3hFBcXe0L/Y5vegYoKTl67lhnfviY03yv6O8PL+r2sHbyvPxYiMfpSYETY9HCgrH0hEZkEPA5coKrlPSMvsVBL3cQNO9yGETuR5OhXAWNEZLSIZABXAc+HFxCRY4G/ANep6pael5kYKIqY5fQqwQtqwN7Taxgx02VEr6o+EbkVeBlIBZ5Q1Q0icou7fAFwHzAA+LU4P0ifqk49crLjQ3hEb8/v9A7Bw9xij3wYRsxEkrpBVZcDy9vNWxD2+RvAN3pWWuKh2BNmvU3w2QW/RfSGETPmW9Gg1o++twkeZ5+dqoYRM/briYKAqjXG9jLB4+yznvSGETNm9FHgpG4sou9NQhG92KlqGLFiv54oUNVWozen7x2COXqL6A0jZszoo8DGuokfLRbRG0bMRNTrxnAo/8dCKvZ9SmDwGDjj9njLOSoIXlAtojeM2LEwKUKC3fyCdmP96HuH4MterNeNYcSORfQRogoFX7yZE3zVbE3rG285Rx0+60dvGDFjYVKEBAP4VLXG2N4keJwtdWMYsWNGHyGtqRtrjO1NgkZvjbGGETv264mQoLFbP/rexRpjDaP7mNFHSDCyTAsNamZW3xuoNcYaRrexX0+EqGvw6eq8rNp63fQOfvdAW2OsYcSOGX2EtEb0jtH7zeh7BV/AInrD6C7264mQoNGnq41H35v4Au6F1SJ6w4gZM/oICaZuQhF9IJ5qjh787q2TvXik96ioa2bz3pp4yzB6EHtgKkJCqRvL0fcqwdSN9brpPb70wN8oJYuSjPfhRz+KtxyjB7AwKUKCvp7ufvJbr5tewRpje59SspwPu3bFV4jRY5jRR0iwm5/1uuldrDE2ftgpnjzYrydCAtbrJi4EI3prjO19ms0ekgb7JiMl1OvGIvreJNjrxhpje59GSY23BKOHsF9PhLT2ugnm6OOp5ujBIvr40WTjCyUNEX2TIjJXRDaLyFYRmd/B8nEi8q6INInInT0vM/6oRfRxwXL08aPJIvqkocvulSKSCjwGnAuUAqtE5HlV/SSs2CHgO8CXj4TIRCA0THEwojenP+IEAhq6wPqse2Wv04QZfbIQSZg0HdiqqttVtRl4GrgkvICq7lfVVUDLEdCYEAR73Vg/+t7DF3aQfZZG6HUsR588RPLA1DBgd9h0KTAjlo2JyM3AzQCFhYUUFxfHUk1cqGpyTKds5Agoh/rGJk/pb09tbW3C628Kawg52L+gjV4v6D8cXtD/3qlTOdiBRi9oPxxe1x8LkRh9R/fMMcWzqroQWAhQVFSkc+bMiaWauLC/phHeeI0TSnZAnwmkZ2TiJf3tKS4uTnj9NY0t8I9XAMirrGLOnEtDy7yg/3AktP6XXgBg/NoPmfWtaz+3OKG1R8Drb7zBmWeeiRxFDfyR3A+XAiPCpocDZUdGTgLzufHo46jlKCG8HcSGQOh9kjFHv7+mka+/XM+fVh5dT/1GYvSrgDEiMlpEMoCrgOePrKzEIzQEgtoDU71Fmxy9GX2vk4y9bnaV1wPw3DPFcN998RXTi3SZulFVn4jcCrwMpAJPqOoGEbnFXb5ARI4BPgD6AgERuQMYr6rVR05679J+PHp7w9SRx2+NsXElmfvRS2bmUTWWT0SjV6rqcmB5u3kLwj7vxUnpJC2tb5iyB6Z6C5+lbnqdQNgxT8aIPrh7R9vZlLyX7B6m/Vg3lqM/8vjDrqYtSRxdJhLNYS9aSMYcfUOLP94S4oL9eiLE5/4AMjRAiqpF9L1AcJwbUbWxbnqJJl+r0SdjP/qGZl+8JcQF+/VESLP7A8jATxoBAvaGqSNOMEefoz6aLaLvFZp8rRFvMubo65ud/bPUjdEhwUgnUwOkWkTfKwRz9NnqpzkJo8tEpKklLHWThMfcjN44LEGjz1A/aaj1uukFghF9rvpoStJTteRgHWt3VcRbRojw1E0yGn3jUZqjt3fGRkjwljaTAKkELKLvBVojeh/NkhNnNUeGOT8rBqDk4QvjK8QlPHXTmISNscGI/mgjOcOkI0BzeESvar1uegG/2xCSoz5aJKVN179koKoh8cYAbGyTukk+ewgaffNRlryxiD5CwnP0KSRnjr6irpnsjFSy0hMjkvP5lUOvLmTF3k00HDOO5gfmkpWSGNp6gg1lVfGW8DnCUxvJmLoJ9rppPMpi3KNrb7tBc8jogzn6OAs6Apz8wD+4cuF78ZYRIpijD9pNeP44GahrajVVTZA2n2Q3+mBEX3+UWV/CRvQrth5kzc4KbjtnTLylAGERPQFSNYA/QX6YPc2HuyvjLSGEL6AUfPFmvtywk2XZI0MX22ShPqxPd5MvkBB3UsHUTQaBpGwADz4w1aDJt2+HI2H3dunaPfzq9a0JE+k0t+91k1ye08ZE99c0xlFJK+H96KFtQ2Ey0BDWMNiQII2EQSPshy9JH5hy9s9SNwlCZX0Lzf4Ah+qa4y0FgOZgrxsNkJpgOfpzf/4mdy9Z36066ppao8vSiobuSuoRfO2MPvki+lZzr0+Qbn/B1E2++JM6ddOQuNZ3REjYva12eyR8VpUY0WVT+JOxGkioHP2n+2t55oPdXRc8DLVhRl/TmBiPibf2unF7SvgTx+h74k4zfNyVRHk0P2T0+JLT6N3985FyVPW8SVijD3Y921edGEbfHP5kbAI1xvZUOiPc6KsTpNtf+4g+/KnNeLKzvI7R9yzntY37ulVP+F1UovTvDgY0/SQ5H1ILv6A2SMI2UfY4CbunlQ1OyiaRIvoUcd4wlUj96Mtreya1lZgRfbvUTYJE9Bs/c16z8OS7OznnxMEx19MmdZMgRv/0/zzAvtVreEl86MDRwNXxlhTC5w+Qltq9i0/4cW6QVPK7K8ojJOwlO+Eien+AzDTnVtZ5MjYxnP5ATVPoc3ei+zYRfWNiRPQtbkNITqD7OXpV5ZOynnkPTrBb5MHapi5KHp5EbIz1B5znRFLQbr8DYPPeGv756YEe0fXkuyWc8B8vht4QFSuNLX5yU5zzqColoyekeYKEjOgbW/yhbl4JE9G3+MlIc66LaSgN3fT5h5Zv5Nzxg5k2qqBb9YQb/f7qJkYUxDZUQF2biD4xjD78yVjo3oXs6VW7uecvH/HHf5nB6WMGdkvXfveYd9fowxtgEyWiP/umu2gs+pQvpVeyrKV/t+q65vH3OFjbzOp7v8iAvMxu1fWPT5w02eNvb+dHl0yIuZ7tf3uMwL6tNAaEH+Rn8Gy3VHmHhIzow3PEPRHRF2/ez1cWvNOtAY2ciN45XN0dvfJgbRML39rOVxa8G3slLgfCzKY73SJrG8Nz9ImRugkfvRK6F9Fv3V8LwKqSQ93WFby4Hqpr7tawDA3NPvpkObFWfcI0xgbIVh+ZBLo9BEIwWFu6dk+3dQXbDvZ0o0eYquILKOnujUoyjuXTGQkZ0Ve6Rp+aIj0S0d/xzDoq61tYsfVgzDnVppZAWEQfoDs9/TbvrYl95XaUHKwLfd5T2cipI2OrJ5i6yc9O73ZEHwgo335qDU0+P49efQq5mbGdZuGjV0L3nowNXuQ37e1++iZ4QW3xK+V1zRT2iS1arWvyMzAvk5pGX7fffPRRaRW/X7GD7IxUHrx0Ysz1NLT4yVQ/mSiNkoaqIhJbCic7I5XaJh9rd1XGrCdI0OD3diPwa2wJUHDOzcwtqOelQzlcXr0m5rr8AeXSX6/g67NH8+WTh8VcT2+RkBH9TjcPN2FYPvu6afQ1jS2hSPDVjftjrqcpLKIfEGiipjn2SG5TmNF/vKd7451sKKvmuIG5AJRWxJ6/DDbADsnP6nZj7NK1e3hpw17e2HyAFz/eG3M9Pn/7B6ZiN/rdrlG8selAt+8S94adk3u7cX7Wt/gZ3DeT1BRpk4KLhdsWr+Eva/fw1Pu7ulVXU4ufLPWTJc6xjrUBvNkXCKW2PiytjFkPQIs/wGdVzvfXne8ueNdUkBZAUMpTsmKua9uBWtaXVrV5gX0ik5BGv+2Ac5s9+/gB1DT52jQURsuDL2yk2RcgKz2Fdd14vN+J6J1bvcH+BiqaNOa+1EFz75eTzg//tiFmTarKhrIqpo0qoCA3o1sPOm3dX8uwftn0z8ngUH3sPXlUld+9vYMTBuWRmZbCps9ij6CDUW6eOncY3UndlB6q56ShfWn2B1jWjVTCvupG1uyq4Aw3zx80oFiob/KRn53OCYV5bOhGQ/GhumZKyutJcQPv7twxOqkbP5nuu5Fjvbjur2lEFcYMyqO0oqFbQciHuysJKBxbkMPB2uaYz4NgO0hWitIfH+UpsbcbfFTq/IYnDvdGv53ENPr9tRT2yWTqKKcxKNa8aiCgvLpxH/MmDuEbpx/Hln01MeXpdx+q573t5Qzrlw3AoEAjvkBsw8yqKu9vL+fCiUP42vRjWbursk1DaDRs/KyGivoWphzbj+H9s7tl9Ot2VzLl2H5MGp7Px3uqYh5C99P9tXzyWTXXzxxJ0TF92NiNVMmGsiqG98+mf8C58MTaM+Wzqga2H6xj3sQhjB2cx1vd6AnylzV7CCjcdrYzBlOsqYTaZmX7wTpOGJTHSUP7dmsky2B//sdvmAp0Lz1V3dhCVrjRx/jsQrBN5IZZowD4+/rPYtb0239uJz87netnOnnJWKP64DmdmaIMEB/7uxHRr9tdSXZ6KscX5sVcR28SkdGLyFwR2SwiW0VkfgfLRUT+x12+XkROiVVQXZOP93cc4oTCPGafMJC+WWksKN4WU1T/6sZ9HKxt5gtjC5k8oh/+gPK3D8uiruev6/ZQ2+TjP780HoDBAcdQH/j7xqgb49bsqqCsqpEZxxUw+/iB+ALKH97bGbWmZl+A37y5jbQU4fyTjmHkgFw+Kq2M6UewZV8NeyobOHlEPy6YOIQWv/LzVzZHfceiqjz13k5EYO5Jx3DS0HzW7qrk033RR5gf76li+Ud7mTKiH321hWH+Ov764Z6YzP7ZVaUAXDDhGM4aN4j3th/ina0Ho66nurGFp1ftYurI/kwd2Z8+WWm8sP6zmIKHd8p8+APKBROGMG10Afuqm/jDuyVR19Pk87PonRKOK8zlrKJBjBqQw+KVu2LqIvvutnLWl1Yxo/kAw8W5uP7tw7Koz4NAQPntP7czIDeDK04dzozRBSx8a3tMaa6P91Txyif7uO60kcw8fgAAT6zYEXU9qo6mjLQUxma3MDWlhjczj2FLDOfm7kP1PLemlLNPHERqijeeru2ylUxEUoHHgHOBUmCViDyvqp+EFbsAGOP+zQB+4/7vlLoWZcnqUpp9ARpb/JTXNVFysJ7izfupa/Zz/8XjyUxL5Z55J3Lvso+Z819vMG1UAccV5pKfnU5+dnqoXztAQJWaRh+V9S2U1zWxoaya1TsrGHdMH+ZOOIb0VGHKiH58b8l6Fr1TwpQR/RjcN4uC3AxyMlJJTRHSUpzrXk1jC1UNLVQ2tLC6pIIt+2uYPDw/1HVxsN8x+ufWlLLtQC1TRvRj1IAccjLSSE0RWvwBWgJKiy+ALxCgxa/sq25k02c1rNp5iGH9svnSpKH0zU7n3PGDefjFTSxds4cpI5zIPD8nnay0VDLTHT0+v+IPKPXNPvbXNFFW2cCqkgr2VDbw7TnHU5CbwbfOPJ6XN+zljJ++wezjB3DCoDwG5GXSPyedtJQUUlIgRQR/QKlr9vPRjmberNnAp/tq+WDnIfrlpHPpycMoyM3gptmj+P2KEv659SBTR/bnmL5Z9M1Op09WGlnpqQRU8fmd1yk2+QIcrGnis6pGVpUcoqS8nqumjWBQ3yz+7azjeWXDXub9zz+ZPrqAMYP6MLRfFjkZaWSnp5KRlhKqy69KRV0zZZUN7Kls5K0tTtR95thC5BU4r3EPv9+Tyxk/fYNzxw+i8VAz29N2kJvZdvz8Fr9S1+SjprGFneX1fLSnik17a5h70jEcV5jHt+ecwOsb93PdEyuZPDyfySP6MSA3g/ycDPIyUxG377iIc6tf7Z4HOw7U8c9PD9DkC/DDi08iJUX4wUXjuWvJemY9/Dozjx/AqAE5DMjNJDsjlez0VEScRjtfQGn2Baioa6a8rpl1uyv5cHczs08YwElD+3LikL68+sk+7nt+A39auZuJw/pybEEO+dnp5GamIQKCIOLc1VS752dJeT3vbivnUF0zj159MiLCg5dO5IYnVnLGT97gtOMKOL4wj/45GeRnp5Oe5u4bQkCV2iYfNY0+DtQ08dGeKlbvrGBofhbX7ttG/4FDmdG8nx/9HZ79YDcnH9ufoflZ9MvNoKS0hap1e0gRodkXoMUfoMkXoLyumV3ldazYVs6BmiYeuOQkstJT+f+/PIFLf/0OZ/93MdNHFzB2cB/65aTTPyeDrPSU0L6pQk2Tj+qGFg7VNbNpbzXvbz/EoD6Z3Dh7FAPzMrl+5kh+v6KEN7ccYOrI/gzrl0N+dhp9stJJSWk9Tk2+ANUNLdQ0+iitaGDNrgp2HKzjO+eMof+effx/aWX8o7kvF/3qbaaN6s+Jx/Slf24G/XMyyM5oqyl4nCrqm9myr4b3tx8iPVW46/yiruwzYZCurtYiMhO4X1XPd6fvAVDVH4eV+V+gWFUXu9ObgTmq2un9WuaQMTrkhkdC02kpwjH5Wcw+fiCXTBnKrBNa+zqv3HGIp97f6UTDlY1dNoD0yUzjhMF5nFU0iJtmj6JPVjrgNMY8u2o3yz/ay6a91VRH0Og4MC+DYwtyuGHWKC6ZMgxuvJHPUrKZOegiZ1tZadQ1+bp8UjYvM43jC3P5wthCrj1tJIP7OreNPn+Ap97fxSuf7GXLvtouG9LSUoRBfTKZMCyfK6eNaNOLaNuBWv7vnRLe215OSXl9l7nMnAzn1vOUY/tx5bRjGT+0L+BEP0+v2s0rG/byYWkVFfXNHO40SREYkJfJhKF9Of+kY7ji1OGhJxg/q2rgibd3sGJrOTvL66jrIiLvk5XG0Pxsxg3pw53nFTkX1xtvpFwy+MtN83l760E+2lMV0WB3hX0yGTMoj3NOHMzV048lO8O5IFTUNfP429t5d1s5m/bWdNmHPS1FGFGQw/RRBVxz2rFMGt4vtGzF1oM8s2o3a3dX8FllY6hLaKf7l5nGmMF5jEiv4/6r59A/13lop67Jx8K3trNmVwUbyqq73L8UgWH9s5kyoj9XnDqcM8cWhpat3lnB4pW7+KDkELsrGrr8veRmpDJ+aF9mHT+Qb37hOPL+9RswahTNJTt59pb7eWH9Z2zaW01F/eHvEkSgMC+T6aMLuGDCEC6cNCS07JOyahav3MU72w6yu6Khy3MzOz2V0QNzOWPMQK6bOZLh/Z0gKxBQFq/axcsb9vFJWRUHI3gyfFCfTE4a2pfzTjqGK6eO4K0n/485JSXs2H2QP176bVZsPciuQ/VdngcZaSmMGpDDaccN4LrTRjJmcJ8ut30kEJHVqjo1qnUiMPorgLmq+g13+jpghqreGlbm78DDqvq2O/0acLeqftCurpuBmwEGDDrm1N/8/inSUiA9RchJdyLOrlBVGv1Q36KEpw8FyE5z6kmL8HbKF1Bqm5XmAAQU/AEIADlpkJMupKc4P6g2usqc1M/WtL4cV5hLigi+gNLggya/MzRCqkBqCqSJkJriTKenEFE3tWa/s3/NfqXZ7+xXqqsjI0XIy4j8ODX5obbF0RRQJzpJEchKE3yNdRT0zY1IU0CVRh80+Jw6UyV4XJxj3ScKTQ0+d98C0BJorUuA3HQhJ72DetxjztChoVmV1bWkZeXS5Hc0CY7RpAhkpQpZaZCRGvl5UNcCjT4l+GtQhcw0yE0TMlIj++78gdbvrsnv1BH8/lMFcjOEdPfcrK2tJS+v8/xuS0Cp70BTRqpzbmZFqCn43dX79HNdgnPShOx0QppClJVBczNkZLQ55i0Bpa5Fqa6pJz0rhwDOeZ3mnuu56USUylB1vv/a5tbfsKrz/WWnCdlRfHf+gFLvnpvB65mqoykn3amr/blZe+gQedXVn9u/Zr+zf81+CHfFrNRWP4i1q2lPctZZZ0Vt9JF0cO5oz9pfHSIpg6ouBBYCFBUV6VfmnR3B5hOU4mLmzJkTbxUxU2z644qX9XtZO7j6L7ss3jJ6lUgaY0uBEWHTw4H2LZqRlDEMwzDiQCRGvwoYIyKjRSQDuAp4vl2Z54Hr3d43pwFVh8vPG4ZhGL1Hl6kbVfWJyK3AyzjvaX5CVTeIyC3u8gXAcmAesBWoB246cpINwzCMaIhoEBJVXY5j5uHzFoR9VuDfelaaYRiG0RMk5JOxhmEYRs9hRm8YhpHkmNEbhmEkOWb0hmEYSU6XT8YesQ2L1ACb47LxnmEgEP3IWImD6Y8vXtbvZe3gff1FqhrV+AvxfMPU5mgf400kROQD0x8/TH/88LJ2SA790a5jqRvDMIwkx4zeMAwjyYmn0S+M47Z7AtMfX0x//PCydjgK9cetMdYwDMPoHSx1YxiGkeSY0RuGYSQ5cTH6rl42nmiIyBMisl9EPg6bVyAi/xCRT93//eOpsTNEZISIvCEiG0Vkg4jc7s73iv4sEVkpIh+6+n/ozveE/iAikioia923sXlKv4iUiMhHIrIu2LXPY/r7icgSEdnk/g5mekG/iBS5xzz4Vy0id8SivdeNPuxl4xcA44Gvicj43tYRJYuAue3mzQdeU9UxwGvudCLiA76rqicCpwH/5h5vr+hvAs5W1cnAFGCu+84Dr+gPcjuwMWzaa/rPUtUpYf3PvaT/l8BLqjoOmIzzPSS8flXd7B7zKcCpOEPALyUW7araq3/ATODlsOl7gHt6W0cMukcBH4dNbwaGuJ+H4DwAFnedEezHX4FzvagfyAHWADO8pB/njWuvAWcDf/fa+QOUAAPbzfOEfqAvsAO344nX9IfpPQ9YEav2eKRuhgG7w6ZL3XleY7C6b9Fy/w+Ks54uEZFRwMnA+3hIv5v2WAfsB/6hqp7SDzwC3IXz7vkgXtKvwCsislpEbnbneUX/ccAB4Pdu6uxxEcnFO/qDXAUsdj9HrT0eRh/Ri8SNnkVE8oDngDtUtTreeqJBVf3q3L4OB6aLyIQ4S4oYEbkI2K+qq+OtpRvMVtVTcNKt/yYiX4i3oChIA04BfqOqJwN1JGCa5nC4r3C9GPhzrHXEw+iT5UXi+0RkCID7f3+c9XSKiKTjmPxTqvoXd7Zn9AdR1UqgGKe9xCv6ZwMXi0gJ8DRwtoj8Ee/oR1XL3P/7cXLE0/GO/lKg1L0LBFiCY/xe0Q/OBXaNqu5zp6PWHg+jj+Rl417geeAG9/MNOLnvhENEBPgdsFFVfx62yCv6C0Wkn/s5G/gisAmP6FfVe1R1uKqOwjnXX1fVa/GIfhHJFZE+wc84ueKP8Yh+Vd0L7BaRInfWOcAneES/y9doTdtALNrj1LAwD9gCbAP+I94NHRHoXQx8BrTgRAj/AgzAaWD71P1fEG+dnWg/HSc1th5Y5/7N85D+ScBaV//HwH3ufE/ob7cvc2htjPWEfpwc94fu34bg79Ur+l2tU4AP3HNoGdDfK/pxOiCUA/lh86LWbkMgGIZhJDn2ZKxhGEaSY0ZvGIaR5JjRG4ZhJDlm9IZhGEmOGb1hGEaSY0ZvGIaR5JjRG4ZhJDn/D6pegT3qLed6AAAAAElFTkSuQmCC\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([0,70])\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": 15,
   "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>78</td>\n",
       "      <td>9.750</td>\n",
       "      <td>9.700231</td>\n",
       "      <td>0.355731</td>\n",
       "      <td>0.346722</td>\n",
       "      <td>0.573739</td>\n",
       "      <td>0.636173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>233</td>\n",
       "      <td>29.125</td>\n",
       "      <td>29.100708</td>\n",
       "      <td>0.380217</td>\n",
       "      <td>0.376530</td>\n",
       "      <td>0.206900</td>\n",
       "      <td>0.212013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>388</td>\n",
       "      <td>48.500</td>\n",
       "      <td>48.502085</td>\n",
       "      <td>0.391753</td>\n",
       "      <td>0.391727</td>\n",
       "      <td>0.127201</td>\n",
       "      <td>0.127224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>481</td>\n",
       "      <td>60.125</td>\n",
       "      <td>60.097752</td>\n",
       "      <td>0.391107</td>\n",
       "      <td>0.385650</td>\n",
       "      <td>0.088249</td>\n",
       "      <td>0.091004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Indice pico  Frecuencia dato (Hz)  Frecuencia estimada (Hz)  \\\n",
       "0           78                 9.750                  9.700231   \n",
       "1          233                29.125                 29.100708   \n",
       "2          388                48.500                 48.502085   \n",
       "3          481                60.125                 60.097752   \n",
       "\n",
       "   Anchura dato (Hz)  Anchura estimada (Hz)  Altura dato  Altura estimada  \n",
       "0           0.355731               0.346722     0.573739         0.636173  \n",
       "1           0.380217               0.376530     0.206900         0.212013  \n",
       "2           0.391753               0.391727     0.127201         0.127224  \n",
       "3           0.391107               0.385650     0.088249         0.091004  "
      ]
     },
     "execution_count": 15,
     "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": 16,
   "id": "42ed4d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tenemos 4 componentes espectrales estimadas.\n",
      "\n",
      "Estas componentes corresponden a las siguientes frecuencias: \n",
      " [ 9.70023123 29.1007079  48.50208519 60.09775222]\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": 17,
   "id": "506dd39b",
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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",
    "    \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": 22,
   "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": 23,
   "id": "2e5bbce5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "La serie armonica encontrada para la componente 0 es: [9.70041703858585, 0, 29.100707895871828, 0, 48.50208519292926] y se compone de 3 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": 24,
   "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": 25,
   "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": 26,
   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 28,
   "id": "0ad51737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fe1907e16d0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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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": 29,
   "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>1.821995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Indice Serie  Puntuacion\n",
       "0           0.0    1.821995\n",
       "1           1.0    0.000000\n",
       "2           2.0    0.000000\n",
       "3           3.0    0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3]"
      ]
     },
     "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": "4d3df97e",
   "metadata": {},
   "source": [
    "### Resultados obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2cbbc0de",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Componente nº: 0\n",
      "Frecuencia fundamental: 9.70041703858585\n",
      "Puntuación: 1.8219953027839377\n",
      "No. miembros de la serie armónica: 3\n",
      "Serie armónica encontrada: [9.70041703858585, 0, 29.100707895871828, 0, 48.50208519292926]\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: 29.100707895871828\n",
      "Puntuación: 0.0\n",
      "No. miembros de la serie armónica: 1\n",
      "Serie armónica encontrada: [29.100707895871828]\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: 2\n",
      "Frecuencia fundamental: 48.50208519292926\n",
      "Puntuación: 0.0\n",
      "No. miembros de la serie armónica: 1\n",
      "Serie armónica encontrada: [48.50208519292926]\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: 60.09775222369136\n",
      "Puntuación: 0.0\n",
      "No. miembros de la serie armónica: 1\n",
      "Serie armónica encontrada: [60.09775222369136]\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": 31,
   "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>9.700231</td>\n",
       "      <td>[9.70041703858585, 0, 29.100707895871828, 0, 4...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.821995</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.388662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.100708</td>\n",
       "      <td>[29.100707895871828]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48.502085</td>\n",
       "      <td>[48.50208519292926]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>60.097752</td>\n",
       "      <td>[60.09775222369136]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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                     9.700231   \n",
       "1           1                    29.100708   \n",
       "2           2                    48.502085   \n",
       "3           3                    60.097752   \n",
       "\n",
       "                                      Serie armonica  Numero armonicos  \\\n",
       "0  [9.70041703858585, 0, 29.100707895871828, 0, 4...                 3   \n",
       "1                               [29.100707895871828]                 1   \n",
       "2                                [48.50208519292926]                 1   \n",
       "3                                [60.09775222369136]                 1   \n",
       "\n",
       "   Puntuacion total  Criterio D  Criterio R  Criterio THD  \n",
       "0          1.821995         0.6    0.833333      0.388662  \n",
       "1          0.000000         0.0    0.000000      0.000000  \n",
       "2          0.000000         0.0    0.000000      0.000000  \n",
       "3          0.000000         0.0    0.000000      0.000000  "
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "f7968261",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "    }\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>9.700231</td>\n",
       "      <td>[9.70041703858585, 0, 29.100707895871828, 0, 4...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.821995</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.388662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.100708</td>\n",
       "      <td>[29.100707895871828]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>48.502085</td>\n",
       "      <td>[48.50208519292926]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>60.097752</td>\n",
       "      <td>[60.09775222369136]</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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                     9.700231   \n",
       "1           1                    29.100708   \n",
       "2           2                    48.502085   \n",
       "3           3                    60.097752   \n",
       "\n",
       "                                      Serie armonica  Numero armonicos  \\\n",
       "0  [9.70041703858585, 0, 29.100707895871828, 0, 4...                 3   \n",
       "1                               [29.100707895871828]                 1   \n",
       "2                                [48.50208519292926]                 1   \n",
       "3                                [60.09775222369136]                 1   \n",
       "\n",
       "   Puntuacion total  Criterio D  Criterio R  Criterio THD  \n",
       "0          1.821995         0.6    0.833333      0.388662  \n",
       "1          0.000000         0.0    0.000000      0.000000  \n",
       "2          0.000000         0.0    0.000000      0.000000  \n",
       "3          0.000000         0.0    0.000000      0.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_series.sort_values(by = 'Puntuacion total', ascending=False)[0:30]"
   ]
  }
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