# built-in
import json
import os

# third-party
import numpy as np

# local
from biosppy import utils
from biosppy import tools as st


def spectral_roll(f, ff, cum_ff, TH):
    """ Frequency so TH % of the signal energy is below that value.

    Parameters
    ----------
    sign : ndarray
        Signal from which spectral slope is computed.
    fs : int
        Sampling frequency of the signal.
    Returns
    -------
    roll_off : float
        Spectral roll-off.

    References
    ----------
    TSFEL library: https://github.com/fraunhoferportugal/tsfel
    Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

    """
    output = None
    try:
        value = TH*(np.sum(ff))
        for i in range(len(ff)):
            if cum_ff[i] >= value:
                output = f[i]
                break
    except:
        output = None
    args = (output,)
    names = ('spectral_roll',)
    return utils.ReturnTuple(args, names)


def signal_spectral(signal, FS):
    """Compute spectral metrics describing the signal.
        Parameters
        ----------
        signal : array
            Input signal.
        FS : float
            Sampling frequency

        Returns
        -------
        spectral_maxpeaks : int
            Number of peaks in the spectrum signal.

        spect_var : float
            Amount of the variation of the spectrum across time.

        curve_distance : float
            Euclidean distance between the cumulative sum of the signal spectrum and evenly spaced numbers across the signal lenght.

        spectral_roll_off : float
            Frequency so 95% of the signal energy is below that value.

        spectral_roll_on : float
            Frequency so 5% of the signal energy is below that value.

        spectral_dec : float
            Amount of decreasing in the spectral amplitude.

        spectral_slope : float
            Amount of decreasing in the spectral amplitude.

        spectral_centroid : float
            Centroid of the signal spectrum.

        spectral_spread : float
            Variance of the signal spectrum i.e. how it spreads around its mean value.

        spectral_kurtosis : float
            Kurtosis of the signal spectrum i.e. describes the flatness of the spectrum distribution.

        spectral_skewness : float
            Skewness of the signal spectrum i.e. describes the asymmetry of the spectrum distribution.

        max_frequency : float
            Maximum frequency of the signal spectrum maximum amplitude.

        fundamental_frequency : float
            Fundamental frequency of the signal.

        max_power_spectrum : float
            Spectrum maximum value.

        mean_power_spectrum : float
            Spectrum mean value.

        spectral_skewness : float
            Spectrum Skewness.

        spectral_kurtosis : float
            Spectrum Kurtosis.

        spectral_hist_ : list
            Histogram of the signal spectrum.

        References
        ----------
        TSFEL library: https://github.com/fraunhoferportugal/tsfel
        Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

        """
    # check inputs
    if signal is None or signal == []:
        print("Signal is empty.")

    # ensure numpy
    signal = np.array(signal)
    # f, spectrum = st.welch_spectrum(signal, sampling_rate=FS)
    spectrum = np.fft.fft(signal, FS)[:len(signal)//2]
    f = np.fft.fftfreq(len(signal))[:len(signal)//2]

    cum_ff = np.cumsum(spectrum)
    spect_diff = np.diff(spectrum)
    #energy, _ = st.signal_energy(spectrum, f)[:]

    args, names = [], []

    if dict['spectral_maxpeaks']['use'] == 'yes':
        # spectral_maxpeaks
        try:
            spectral_maxpeaks = np.sum([1 for nd in range(len(spect_diff[:-1])) if (spect_diff[nd+1]<0 and spect_diff[nd]>0)])
        except:
            spectral_maxpeaks = None
        args += [spectral_maxpeaks]
        names += ['spectral_maxpeaks']

    # if dict['spect_var']['use'] == 'yes':
    #     # spect_variation
    #     try:
    #         spect_var = np.convolve(energy)
    #         spect_var /= np.max(np.abs(spect_var))
    #     except:
    #         spect_var = None
    #     args += [spect_var]
    #     names += ['spect_var']

    if dict['curve_distance']['use'] == 'yes':
        # curve_distance
        try:
            curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) - cum_ff)
        except:
            curve_distance = None
        args += [curve_distance]
        names += ['curve_distance']

    if dict['spectral_roll_off']['use'] == 'yes':
        # spectral_roll_off
        try:
            spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]
        except:
            spectral_roll_off = None
        args += [spectral_roll_off]
        names += ['spectral_roll_off']

    if dict['spectral_roll_on']['use'] == 'yes':
        # spectral_roll_on
        try:
            spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]
        except:
            spectral_roll_on = None
        args += [spectral_roll_on]
        names += ['spectral_roll_on']

    if dict['spectral_dec']['use'] == 'yes':
        # spectral_decrease
        try:
            spectral_dec = (1/np.sum(spectrum)) * np.sum((spectrum[:] - spectrum[1])/np.linspace(1, len(spectrum), len(spectrum),1))
        except:
            spectral_dec = None
        args += [spectral_dec]
        names += ['spectral_dec']

    if dict['spectral_slope']['use'] == 'yes':
        # spectral_slope
        sum_f = np.sum(f)
        len_f = len(f)
        try:
            spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)
        except:
            spectral_slope = None
        args += [spectral_slope]
        names += ['spectral_slope']

    sum_spectrum = np.sum(spectrum)
    norm_spectrum = spectrum / sum_spectrum
    # spectral_centroid
    try:
        spectral_centroid = np.dot(f, norm_spectrum)
    except:
        spectral_centroid = None

    # spectral_spread
    try:
        spectral_spread = np.dot(((f - spectral_centroid) ** 2), norm_spectrum)
    except:
        spectral_spread = None

    if dict['spectral_spread']['use'] == 'yes':
        args += [spectral_spread]
        names += ['spectral_spread']

    if dict['spectral_kurtosis']['use'] == 'yes':
        # spectral_kurtosis
        try:
            spectral_kurtosis = np.sum(((f - spectral_centroid) ** 4) * norm_spectrum) / (spectral_spread**2)
        except:
            spectral_kurtosis = None
        args += [spectral_kurtosis]
        names += ['spectral_kurtosis']

    if dict['spectral_skewness']['use'] == 'yes':
        # spectral_skewness
        try:
            spectral_skewness = np.sum(((f - spectral_centroid) ** 3) * norm_spectrum) / (spectral_spread ** (3 / 2))
        except:
            spectral_skewness = None
        args += [spectral_skewness]
        names += ['spectral_skewness']

    if dict['max_frequency']['use'] == 'yes':
        # max_frequency
        try:
            max_frequency = f[np.where(cum_ff > cum_ff[-1]*0.95)[0][0]]
        except:
            max_frequency = None
        args += [max_frequency]
        names += ['max_frequency']

    if dict['fundamental_frequency']['use'] == 'yes':
        # fundamental_frequency
        try:
            fundamental_frequency = f[np.where(cum_ff > cum_ff[-1]*0.5)[0][0]]
        except:
            fundamental_frequency = None
        args += [fundamental_frequency]
        names += ['fundamental_frequency']

    # if dict['max_power_spectrum']['use'] == 'yes':
    #     # max_power_spectrum
    #     try:
    #         max_power_spectrum = np.max(spectrum)
    #     except:
    #         max_power_spectrum = None
    #     args += max_power_spectrum
    #     names += 'max_power_spectrum'

    # if dict['mean_power_spectrum']['use'] == 'yes':
    #     # mean_power_spectrum
    #     try:
    #         mean_power_spectrum = np.mean(spectrum)
    #     except:
    #         mean_power_spectrum = None
    #     args += mean_power_spectrum
    #     names += 'mean_power_spectrum'
    #
    # if dict['spectral_skewness']['use'] == 'yes':
    #     try:
    #         spectral_skewness = np.mean(spectrum)
    #     except:
    #         spectral_skewness = None
    #     args += spectral_skewness
    #     names += 'spectral_skewness'
    #
    # if dict['spectral_kurtosis']['use'] == 'yes':
    #     try:
    #         spectral_kurtosis = np.mean(spectrum)
    #     except:
    #         spectral_kurtosis = None
    #     args += spectral_kurtosis
    #     names += 'spectral_kurtosis'

    # if dict['spectral_hist_']['use'] == 'yes':
    #     # histogram
    #     try:
    #         _hist = list(np.histogram(spectrum, bins=int(np.sqrt(len(spectrum))), density=True)[0])
    #     except:
    #         if len(signal) > 1:
    #             _hist = [None] * int(np.sqrt(len(signal)))
    #         else:
    #             _hist = [None]
    #     args += [i for i in _hist]
    #     names += ['spectral_hist_' + str(i) for i in range(len(_hist))]

    #return utils.ReturnTuple(tuple(args), tuple(names))
    return args, names

929792b52415462ac68a4f8135d81b40118222cc
<mask token>

<mask token>


def signal_spectral(signal, FS):
    """Compute spectral metrics describing the signal.
        Parameters
        ----------
        signal : array
            Input signal.
        FS : float
            Sampling frequency

        Returns
        -------
        spectral_maxpeaks : int
            Number of peaks in the spectrum signal.

        spect_var : float
            Amount of the variation of the spectrum across time.

        curve_distance : float
            Euclidean distance between the cumulative sum of the signal spectrum and evenly spaced numbers across the signal lenght.

        spectral_roll_off : float
            Frequency so 95% of the signal energy is below that value.

        spectral_roll_on : float
            Frequency so 5% of the signal energy is below that value.

        spectral_dec : float
            Amount of decreasing in the spectral amplitude.

        spectral_slope : float
            Amount of decreasing in the spectral amplitude.

        spectral_centroid : float
            Centroid of the signal spectrum.

        spectral_spread : float
            Variance of the signal spectrum i.e. how it spreads around its mean value.

        spectral_kurtosis : float
            Kurtosis of the signal spectrum i.e. describes the flatness of the spectrum distribution.

        spectral_skewness : float
            Skewness of the signal spectrum i.e. describes the asymmetry of the spectrum distribution.

        max_frequency : float
            Maximum frequency of the signal spectrum maximum amplitude.

        fundamental_frequency : float
            Fundamental frequency of the signal.

        max_power_spectrum : float
            Spectrum maximum value.

        mean_power_spectrum : float
            Spectrum mean value.

        spectral_skewness : float
            Spectrum Skewness.

        spectral_kurtosis : float
            Spectrum Kurtosis.

        spectral_hist_ : list
            Histogram of the signal spectrum.

        References
        ----------
        TSFEL library: https://github.com/fraunhoferportugal/tsfel
        Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

        """
    if signal is None or signal == []:
        print('Signal is empty.')
    signal = np.array(signal)
    spectrum = np.fft.fft(signal, FS)[:len(signal) // 2]
    f = np.fft.fftfreq(len(signal))[:len(signal) // 2]
    cum_ff = np.cumsum(spectrum)
    spect_diff = np.diff(spectrum)
    args, names = [], []
    if dict['spectral_maxpeaks']['use'] == 'yes':
        try:
            spectral_maxpeaks = np.sum([(1) for nd in range(len(spect_diff[
                :-1])) if spect_diff[nd + 1] < 0 and spect_diff[nd] > 0])
        except:
            spectral_maxpeaks = None
        args += [spectral_maxpeaks]
        names += ['spectral_maxpeaks']
    if dict['curve_distance']['use'] == 'yes':
        try:
            curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) -
                cum_ff)
        except:
            curve_distance = None
        args += [curve_distance]
        names += ['curve_distance']
    if dict['spectral_roll_off']['use'] == 'yes':
        try:
            spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]
        except:
            spectral_roll_off = None
        args += [spectral_roll_off]
        names += ['spectral_roll_off']
    if dict['spectral_roll_on']['use'] == 'yes':
        try:
            spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]
        except:
            spectral_roll_on = None
        args += [spectral_roll_on]
        names += ['spectral_roll_on']
    if dict['spectral_dec']['use'] == 'yes':
        try:
            spectral_dec = 1 / np.sum(spectrum) * np.sum((spectrum[:] -
                spectrum[1]) / np.linspace(1, len(spectrum), len(spectrum), 1))
        except:
            spectral_dec = None
        args += [spectral_dec]
        names += ['spectral_dec']
    if dict['spectral_slope']['use'] == 'yes':
        sum_f = np.sum(f)
        len_f = len(f)
        try:
            spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(
                spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)
        except:
            spectral_slope = None
        args += [spectral_slope]
        names += ['spectral_slope']
    sum_spectrum = np.sum(spectrum)
    norm_spectrum = spectrum / sum_spectrum
    try:
        spectral_centroid = np.dot(f, norm_spectrum)
    except:
        spectral_centroid = None
    try:
        spectral_spread = np.dot((f - spectral_centroid) ** 2, norm_spectrum)
    except:
        spectral_spread = None
    if dict['spectral_spread']['use'] == 'yes':
        args += [spectral_spread]
        names += ['spectral_spread']
    if dict['spectral_kurtosis']['use'] == 'yes':
        try:
            spectral_kurtosis = np.sum((f - spectral_centroid) ** 4 *
                norm_spectrum) / spectral_spread ** 2
        except:
            spectral_kurtosis = None
        args += [spectral_kurtosis]
        names += ['spectral_kurtosis']
    if dict['spectral_skewness']['use'] == 'yes':
        try:
            spectral_skewness = np.sum((f - spectral_centroid) ** 3 *
                norm_spectrum) / spectral_spread ** (3 / 2)
        except:
            spectral_skewness = None
        args += [spectral_skewness]
        names += ['spectral_skewness']
    if dict['max_frequency']['use'] == 'yes':
        try:
            max_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.95)[0][0]]
        except:
            max_frequency = None
        args += [max_frequency]
        names += ['max_frequency']
    if dict['fundamental_frequency']['use'] == 'yes':
        try:
            fundamental_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.5)[0][0]
                ]
        except:
            fundamental_frequency = None
        args += [fundamental_frequency]
        names += ['fundamental_frequency']
    return args, names

<mask token>


def spectral_roll(f, ff, cum_ff, TH):
    """ Frequency so TH % of the signal energy is below that value.

    Parameters
    ----------
    sign : ndarray
        Signal from which spectral slope is computed.
    fs : int
        Sampling frequency of the signal.
    Returns
    -------
    roll_off : float
        Spectral roll-off.

    References
    ----------
    TSFEL library: https://github.com/fraunhoferportugal/tsfel
    Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

    """
    output = None
    try:
        value = TH * np.sum(ff)
        for i in range(len(ff)):
            if cum_ff[i] >= value:
                output = f[i]
                break
    except:
        output = None
    args = output,
    names = 'spectral_roll',
    return utils.ReturnTuple(args, names)


def signal_spectral(signal, FS):
    """Compute spectral metrics describing the signal.
        Parameters
        ----------
        signal : array
            Input signal.
        FS : float
            Sampling frequency

        Returns
        -------
        spectral_maxpeaks : int
            Number of peaks in the spectrum signal.

        spect_var : float
            Amount of the variation of the spectrum across time.

        curve_distance : float
            Euclidean distance between the cumulative sum of the signal spectrum and evenly spaced numbers across the signal lenght.

        spectral_roll_off : float
            Frequency so 95% of the signal energy is below that value.

        spectral_roll_on : float
            Frequency so 5% of the signal energy is below that value.

        spectral_dec : float
            Amount of decreasing in the spectral amplitude.

        spectral_slope : float
            Amount of decreasing in the spectral amplitude.

        spectral_centroid : float
            Centroid of the signal spectrum.

        spectral_spread : float
            Variance of the signal spectrum i.e. how it spreads around its mean value.

        spectral_kurtosis : float
            Kurtosis of the signal spectrum i.e. describes the flatness of the spectrum distribution.

        spectral_skewness : float
            Skewness of the signal spectrum i.e. describes the asymmetry of the spectrum distribution.

        max_frequency : float
            Maximum frequency of the signal spectrum maximum amplitude.

        fundamental_frequency : float
            Fundamental frequency of the signal.

        max_power_spectrum : float
            Spectrum maximum value.

        mean_power_spectrum : float
            Spectrum mean value.

        spectral_skewness : float
            Spectrum Skewness.

        spectral_kurtosis : float
            Spectrum Kurtosis.

        spectral_hist_ : list
            Histogram of the signal spectrum.

        References
        ----------
        TSFEL library: https://github.com/fraunhoferportugal/tsfel
        Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

        """
    if signal is None or signal == []:
        print('Signal is empty.')
    signal = np.array(signal)
    spectrum = np.fft.fft(signal, FS)[:len(signal) // 2]
    f = np.fft.fftfreq(len(signal))[:len(signal) // 2]
    cum_ff = np.cumsum(spectrum)
    spect_diff = np.diff(spectrum)
    args, names = [], []
    if dict['spectral_maxpeaks']['use'] == 'yes':
        try:
            spectral_maxpeaks = np.sum([(1) for nd in range(len(spect_diff[
                :-1])) if spect_diff[nd + 1] < 0 and spect_diff[nd] > 0])
        except:
            spectral_maxpeaks = None
        args += [spectral_maxpeaks]
        names += ['spectral_maxpeaks']
    if dict['curve_distance']['use'] == 'yes':
        try:
            curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) -
                cum_ff)
        except:
            curve_distance = None
        args += [curve_distance]
        names += ['curve_distance']
    if dict['spectral_roll_off']['use'] == 'yes':
        try:
            spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]
        except:
            spectral_roll_off = None
        args += [spectral_roll_off]
        names += ['spectral_roll_off']
    if dict['spectral_roll_on']['use'] == 'yes':
        try:
            spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]
        except:
            spectral_roll_on = None
        args += [spectral_roll_on]
        names += ['spectral_roll_on']
    if dict['spectral_dec']['use'] == 'yes':
        try:
            spectral_dec = 1 / np.sum(spectrum) * np.sum((spectrum[:] -
                spectrum[1]) / np.linspace(1, len(spectrum), len(spectrum), 1))
        except:
            spectral_dec = None
        args += [spectral_dec]
        names += ['spectral_dec']
    if dict['spectral_slope']['use'] == 'yes':
        sum_f = np.sum(f)
        len_f = len(f)
        try:
            spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(
                spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)
        except:
            spectral_slope = None
        args += [spectral_slope]
        names += ['spectral_slope']
    sum_spectrum = np.sum(spectrum)
    norm_spectrum = spectrum / sum_spectrum
    try:
        spectral_centroid = np.dot(f, norm_spectrum)
    except:
        spectral_centroid = None
    try:
        spectral_spread = np.dot((f - spectral_centroid) ** 2, norm_spectrum)
    except:
        spectral_spread = None
    if dict['spectral_spread']['use'] == 'yes':
        args += [spectral_spread]
        names += ['spectral_spread']
    if dict['spectral_kurtosis']['use'] == 'yes':
        try:
            spectral_kurtosis = np.sum((f - spectral_centroid) ** 4 *
                norm_spectrum) / spectral_spread ** 2
        except:
            spectral_kurtosis = None
        args += [spectral_kurtosis]
        names += ['spectral_kurtosis']
    if dict['spectral_skewness']['use'] == 'yes':
        try:
            spectral_skewness = np.sum((f - spectral_centroid) ** 3 *
                norm_spectrum) / spectral_spread ** (3 / 2)
        except:
            spectral_skewness = None
        args += [spectral_skewness]
        names += ['spectral_skewness']
    if dict['max_frequency']['use'] == 'yes':
        try:
            max_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.95)[0][0]]
        except:
            max_frequency = None
        args += [max_frequency]
        names += ['max_frequency']
    if dict['fundamental_frequency']['use'] == 'yes':
        try:
            fundamental_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.5)[0][0]
                ]
        except:
            fundamental_frequency = None
        args += [fundamental_frequency]
        names += ['fundamental_frequency']
    return args, names

import json
import os
import numpy as np
from biosppy import utils
from biosppy import tools as st


def spectral_roll(f, ff, cum_ff, TH):
    """ Frequency so TH % of the signal energy is below that value.

    Parameters
    ----------
    sign : ndarray
        Signal from which spectral slope is computed.
    fs : int
        Sampling frequency of the signal.
    Returns
    -------
    roll_off : float
        Spectral roll-off.

    References
    ----------
    TSFEL library: https://github.com/fraunhoferportugal/tsfel
    Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

    """
    output = None
    try:
        value = TH * np.sum(ff)
        for i in range(len(ff)):
            if cum_ff[i] >= value:
                output = f[i]
                break
    except:
        output = None
    args = output,
    names = 'spectral_roll',
    return utils.ReturnTuple(args, names)


def signal_spectral(signal, FS):
    """Compute spectral metrics describing the signal.
        Parameters
        ----------
        signal : array
            Input signal.
        FS : float
            Sampling frequency

        Returns
        -------
        spectral_maxpeaks : int
            Number of peaks in the spectrum signal.

        spect_var : float
            Amount of the variation of the spectrum across time.

        curve_distance : float
            Euclidean distance between the cumulative sum of the signal spectrum and evenly spaced numbers across the signal lenght.

        spectral_roll_off : float
            Frequency so 95% of the signal energy is below that value.

        spectral_roll_on : float
            Frequency so 5% of the signal energy is below that value.

        spectral_dec : float
            Amount of decreasing in the spectral amplitude.

        spectral_slope : float
            Amount of decreasing in the spectral amplitude.

        spectral_centroid : float
            Centroid of the signal spectrum.

        spectral_spread : float
            Variance of the signal spectrum i.e. how it spreads around its mean value.

        spectral_kurtosis : float
            Kurtosis of the signal spectrum i.e. describes the flatness of the spectrum distribution.

        spectral_skewness : float
            Skewness of the signal spectrum i.e. describes the asymmetry of the spectrum distribution.

        max_frequency : float
            Maximum frequency of the signal spectrum maximum amplitude.

        fundamental_frequency : float
            Fundamental frequency of the signal.

        max_power_spectrum : float
            Spectrum maximum value.

        mean_power_spectrum : float
            Spectrum mean value.

        spectral_skewness : float
            Spectrum Skewness.

        spectral_kurtosis : float
            Spectrum Kurtosis.

        spectral_hist_ : list
            Histogram of the signal spectrum.

        References
        ----------
        TSFEL library: https://github.com/fraunhoferportugal/tsfel
        Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

        """
    if signal is None or signal == []:
        print('Signal is empty.')
    signal = np.array(signal)
    spectrum = np.fft.fft(signal, FS)[:len(signal) // 2]
    f = np.fft.fftfreq(len(signal))[:len(signal) // 2]
    cum_ff = np.cumsum(spectrum)
    spect_diff = np.diff(spectrum)
    args, names = [], []
    if dict['spectral_maxpeaks']['use'] == 'yes':
        try:
            spectral_maxpeaks = np.sum([(1) for nd in range(len(spect_diff[
                :-1])) if spect_diff[nd + 1] < 0 and spect_diff[nd] > 0])
        except:
            spectral_maxpeaks = None
        args += [spectral_maxpeaks]
        names += ['spectral_maxpeaks']
    if dict['curve_distance']['use'] == 'yes':
        try:
            curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) -
                cum_ff)
        except:
            curve_distance = None
        args += [curve_distance]
        names += ['curve_distance']
    if dict['spectral_roll_off']['use'] == 'yes':
        try:
            spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]
        except:
            spectral_roll_off = None
        args += [spectral_roll_off]
        names += ['spectral_roll_off']
    if dict['spectral_roll_on']['use'] == 'yes':
        try:
            spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]
        except:
            spectral_roll_on = None
        args += [spectral_roll_on]
        names += ['spectral_roll_on']
    if dict['spectral_dec']['use'] == 'yes':
        try:
            spectral_dec = 1 / np.sum(spectrum) * np.sum((spectrum[:] -
                spectrum[1]) / np.linspace(1, len(spectrum), len(spectrum), 1))
        except:
            spectral_dec = None
        args += [spectral_dec]
        names += ['spectral_dec']
    if dict['spectral_slope']['use'] == 'yes':
        sum_f = np.sum(f)
        len_f = len(f)
        try:
            spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(
                spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)
        except:
            spectral_slope = None
        args += [spectral_slope]
        names += ['spectral_slope']
    sum_spectrum = np.sum(spectrum)
    norm_spectrum = spectrum / sum_spectrum
    try:
        spectral_centroid = np.dot(f, norm_spectrum)
    except:
        spectral_centroid = None
    try:
        spectral_spread = np.dot((f - spectral_centroid) ** 2, norm_spectrum)
    except:
        spectral_spread = None
    if dict['spectral_spread']['use'] == 'yes':
        args += [spectral_spread]
        names += ['spectral_spread']
    if dict['spectral_kurtosis']['use'] == 'yes':
        try:
            spectral_kurtosis = np.sum((f - spectral_centroid) ** 4 *
                norm_spectrum) / spectral_spread ** 2
        except:
            spectral_kurtosis = None
        args += [spectral_kurtosis]
        names += ['spectral_kurtosis']
    if dict['spectral_skewness']['use'] == 'yes':
        try:
            spectral_skewness = np.sum((f - spectral_centroid) ** 3 *
                norm_spectrum) / spectral_spread ** (3 / 2)
        except:
            spectral_skewness = None
        args += [spectral_skewness]
        names += ['spectral_skewness']
    if dict['max_frequency']['use'] == 'yes':
        try:
            max_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.95)[0][0]]
        except:
            max_frequency = None
        args += [max_frequency]
        names += ['max_frequency']
    if dict['fundamental_frequency']['use'] == 'yes':
        try:
            fundamental_frequency = f[np.where(cum_ff > cum_ff[-1] * 0.5)[0][0]
                ]
        except:
            fundamental_frequency = None
        args += [fundamental_frequency]
        names += ['fundamental_frequency']
    return args, names

# built-in
import json
import os

# third-party
import numpy as np

# local
from biosppy import utils
from biosppy import tools as st


def spectral_roll(f, ff, cum_ff, TH):
    """ Frequency so TH % of the signal energy is below that value.

    Parameters
    ----------
    sign : ndarray
        Signal from which spectral slope is computed.
    fs : int
        Sampling frequency of the signal.
    Returns
    -------
    roll_off : float
        Spectral roll-off.

    References
    ----------
    TSFEL library: https://github.com/fraunhoferportugal/tsfel
    Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

    """
    output = None
    try:
        value = TH*(np.sum(ff))
        for i in range(len(ff)):
            if cum_ff[i] >= value:
                output = f[i]
                break
    except:
        output = None
    args = (output,)
    names = ('spectral_roll',)
    return utils.ReturnTuple(args, names)


def signal_spectral(signal, FS):
    """Compute spectral metrics describing the signal.
        Parameters
        ----------
        signal : array
            Input signal.
        FS : float
            Sampling frequency

        Returns
        -------
        spectral_maxpeaks : int
            Number of peaks in the spectrum signal.

        spect_var : float
            Amount of the variation of the spectrum across time.

        curve_distance : float
            Euclidean distance between the cumulative sum of the signal spectrum and evenly spaced numbers across the signal lenght.

        spectral_roll_off : float
            Frequency so 95% of the signal energy is below that value.

        spectral_roll_on : float
            Frequency so 5% of the signal energy is below that value.

        spectral_dec : float
            Amount of decreasing in the spectral amplitude.

        spectral_slope : float
            Amount of decreasing in the spectral amplitude.

        spectral_centroid : float
            Centroid of the signal spectrum.

        spectral_spread : float
            Variance of the signal spectrum i.e. how it spreads around its mean value.

        spectral_kurtosis : float
            Kurtosis of the signal spectrum i.e. describes the flatness of the spectrum distribution.

        spectral_skewness : float
            Skewness of the signal spectrum i.e. describes the asymmetry of the spectrum distribution.

        max_frequency : float
            Maximum frequency of the signal spectrum maximum amplitude.

        fundamental_frequency : float
            Fundamental frequency of the signal.

        max_power_spectrum : float
            Spectrum maximum value.

        mean_power_spectrum : float
            Spectrum mean value.

        spectral_skewness : float
            Spectrum Skewness.

        spectral_kurtosis : float
            Spectrum Kurtosis.

        spectral_hist_ : list
            Histogram of the signal spectrum.

        References
        ----------
        TSFEL library: https://github.com/fraunhoferportugal/tsfel
        Peeters, Geoffroy. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project.

        """
    # check inputs
    if signal is None or signal == []:
        print("Signal is empty.")

    # ensure numpy
    signal = np.array(signal)
    # f, spectrum = st.welch_spectrum(signal, sampling_rate=FS)
    spectrum = np.fft.fft(signal, FS)[:len(signal)//2]
    f = np.fft.fftfreq(len(signal))[:len(signal)//2]

    cum_ff = np.cumsum(spectrum)
    spect_diff = np.diff(spectrum)
    #energy, _ = st.signal_energy(spectrum, f)[:]

    args, names = [], []

    if dict['spectral_maxpeaks']['use'] == 'yes':
        # spectral_maxpeaks
        try:
            spectral_maxpeaks = np.sum([1 for nd in range(len(spect_diff[:-1])) if (spect_diff[nd+1]<0 and spect_diff[nd]>0)])
        except:
            spectral_maxpeaks = None
        args += [spectral_maxpeaks]
        names += ['spectral_maxpeaks']

    # if dict['spect_var']['use'] == 'yes':
    #     # spect_variation
    #     try:
    #         spect_var = np.convolve(energy)
    #         spect_var /= np.max(np.abs(spect_var))
    #     except:
    #         spect_var = None
    #     args += [spect_var]
    #     names += ['spect_var']

    if dict['curve_distance']['use'] == 'yes':
        # curve_distance
        try:
            curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) - cum_ff)
        except:
            curve_distance = None
        args += [curve_distance]
        names += ['curve_distance']

    if dict['spectral_roll_off']['use'] == 'yes':
        # spectral_roll_off
        try:
            spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]
        except:
            spectral_roll_off = None
        args += [spectral_roll_off]
        names += ['spectral_roll_off']

    if dict['spectral_roll_on']['use'] == 'yes':
        # spectral_roll_on
        try:
            spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]
        except:
            spectral_roll_on = None
        args += [spectral_roll_on]
        names += ['spectral_roll_on']

    if dict['spectral_dec']['use'] == 'yes':
        # spectral_decrease
        try:
            spectral_dec = (1/np.sum(spectrum)) * np.sum((spectrum[:] - spectrum[1])/np.linspace(1, len(spectrum), len(spectrum),1))
        except:
            spectral_dec = None
        args += [spectral_dec]
        names += ['spectral_dec']

    if dict['spectral_slope']['use'] == 'yes':
        # spectral_slope
        sum_f = np.sum(f)
        len_f = len(f)
        try:
            spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)
        except:
            spectral_slope = None
        args += [spectral_slope]
        names += ['spectral_slope']

    sum_spectrum = np.sum(spectrum)
    norm_spectrum = spectrum / sum_spectrum
    # spectral_centroid
    try:
        spectral_centroid = np.dot(f, norm_spectrum)
    except:
        spectral_centroid = None

    # spectral_spread
    try:
        spectral_spread = np.dot(((f - spectral_centroid) ** 2), norm_spectrum)
    except:
        spectral_spread = None

    if dict['spectral_spread']['use'] == 'yes':
        args += [spectral_spread]
        names += ['spectral_spread']

    if dict['spectral_kurtosis']['use'] == 'yes':
        # spectral_kurtosis
        try:
            spectral_kurtosis = np.sum(((f - spectral_centroid) ** 4) * norm_spectrum) / (spectral_spread**2)
        except:
            spectral_kurtosis = None
        args += [spectral_kurtosis]
        names += ['spectral_kurtosis']

    if dict['spectral_skewness']['use'] == 'yes':
        # spectral_skewness
        try:
            spectral_skewness = np.sum(((f - spectral_centroid) ** 3) * norm_spectrum) / (spectral_spread ** (3 / 2))
        except:
            spectral_skewness = None
        args += [spectral_skewness]
        names += ['spectral_skewness']

    if dict['max_frequency']['use'] == 'yes':
        # max_frequency
        try:
            max_frequency = f[np.where(cum_ff > cum_ff[-1]*0.95)[0][0]]
        except:
            max_frequency = None
        args += [max_frequency]
        names += ['max_frequency']

    if dict['fundamental_frequency']['use'] == 'yes':
        # fundamental_frequency
        try:
            fundamental_frequency = f[np.where(cum_ff > cum_ff[-1]*0.5)[0][0]]
        except:
            fundamental_frequency = None
        args += [fundamental_frequency]
        names += ['fundamental_frequency']

    # if dict['max_power_spectrum']['use'] == 'yes':
    #     # max_power_spectrum
    #     try:
    #         max_power_spectrum = np.max(spectrum)
    #     except:
    #         max_power_spectrum = None
    #     args += max_power_spectrum
    #     names += 'max_power_spectrum'

    # if dict['mean_power_spectrum']['use'] == 'yes':
    #     # mean_power_spectrum
    #     try:
    #         mean_power_spectrum = np.mean(spectrum)
    #     except:
    #         mean_power_spectrum = None
    #     args += mean_power_spectrum
    #     names += 'mean_power_spectrum'
    #
    # if dict['spectral_skewness']['use'] == 'yes':
    #     try:
    #         spectral_skewness = np.mean(spectrum)
    #     except:
    #         spectral_skewness = None
    #     args += spectral_skewness
    #     names += 'spectral_skewness'
    #
    # if dict['spectral_kurtosis']['use'] == 'yes':
    #     try:
    #         spectral_kurtosis = np.mean(spectrum)
    #     except:
    #         spectral_kurtosis = None
    #     args += spectral_kurtosis
    #     names += 'spectral_kurtosis'

    # if dict['spectral_hist_']['use'] == 'yes':
    #     # histogram
    #     try:
    #         _hist = list(np.histogram(spectrum, bins=int(np.sqrt(len(spectrum))), density=True)[0])
    #     except:
    #         if len(signal) > 1:
    #             _hist = [None] * int(np.sqrt(len(signal)))
    #         else:
    #             _hist = [None]
    #     args += [i for i in _hist]
    #     names += ['spectral_hist_' + str(i) for i in range(len(_hist))]

    #return utils.ReturnTuple(tuple(args), tuple(names))
    return args, names
