m-thesis-introduction/fourier/mylib/fft.py

"""
Simple FFT stuff
"""

import numpy as np
import scipy.fftpack as ft

def get_freq_spec(val,dt):
    """From earsim/tools.py"""
    fval = np.fft.fft(val)[:len(val)//2]
    freq =  np.fft.fftfreq(len(val),dt)[:len(val)//2]
    return fval, freq


def ft_spectrum( signal, sample_rate=1, ftfunc=None, freqfunc=None, mask_bias=False, normalise_amplitude=False):
    """Return a FT of $signal$, with corresponding frequencies"""

    if True:
        return get_freq_spec(signal, 1/sample_rate)

    n_samples = len(signal)

    if ftfunc is None:
        real_signal = np.isrealobj(signal)
        if False and real_signal:
            ftfunc = ft.rfft
            freqfunc = ft.rfftfreq
        else:
            ftfunc = ft.fft
            freqfunc = ft.fftfreq

    if freqfunc is None:
        freqfunc = ft.fftfreq

    normalisation = 2/len(signal) if normalise_amplitude else 1

    spectrum = normalisation * ftfunc(signal)
    freqs = freqfunc(n_samples, 1/sample_rate)

    if not mask_bias:
        return spectrum, freqs
    else:
        return spectrum[1:], freqs[1:]