mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-23 03:43:32 +01:00
Eric Teunis de Boone
165a7c0361
allowing to determine the phase at any frequency without having to resort to interpolation of a DFT.
85 lines
2.3 KiB
Python
85 lines
2.3 KiB
Python
"""
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Simple FFT stuff
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"""
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import numpy as np
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import scipy.fftpack as ft
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def get_freq_spec(val,dt):
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"""From earsim/tools.py"""
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fval = np.fft.fft(val)[:len(val)//2]
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freq = np.fft.fftfreq(len(val),dt)[:len(val)//2]
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return fval, freq
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def ft_spectrum( signal, sample_rate=1, ftfunc=None, freqfunc=None, mask_bias=False, normalise_amplitude=False):
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"""Return a FT of $signal$, with corresponding frequencies"""
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if True:
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return get_freq_spec(signal, 1/sample_rate)
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n_samples = len(signal)
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if ftfunc is None:
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real_signal = np.isrealobj(signal)
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if False and real_signal:
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ftfunc = ft.rfft
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freqfunc = ft.rfftfreq
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else:
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ftfunc = ft.fft
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freqfunc = ft.fftfreq
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if freqfunc is None:
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freqfunc = ft.fftfreq
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normalisation = 2/len(signal) if normalise_amplitude else 1
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spectrum = normalisation * ftfunc(signal)
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freqs = freqfunc(n_samples, 1/sample_rate)
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if not mask_bias:
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return spectrum, freqs
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else:
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return spectrum[1:], freqs[1:]
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def ft_corr_vectors(freqs, time):
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"""
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Get the cosine and sine terms for freqs at time.
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Takes the outer product of freqs and time.
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"""
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freqtime = np.outer(freqs, time)
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c_k = np.cos(2*np.pi*freqtime)
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s_k = np.sin(2*np.pi*freqtime)
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return c_k, s_k
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def direct_fourier_transform(freqs, time, samplesets_iterable):
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"""
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Determine the fourier transform of each sampleset in samplesets_iterable at freqs.
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The samplesets are expected to have the same time vector.
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Returns either a generator to return the fourier transform for each sampleset
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if samplesets_iterable is a generator
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or a numpy array.
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"""
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c_k, s_k = ft_corr_vectors(freqs, time)
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if not hasattr(samplesets_iterable, '__len__') and hasattr(samplesets_iterable, '__iter__'):
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# samplesets_iterable is an iterator
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# return an iterator containing (real, imag) amplitudes
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return ( (np.dot(c_k, samples), np.dot(s_k, samples)) for samples in samplesets_iterable )
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# Numpy array
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return np.dot(c_k, samplesets_iterable), np.dot(s_k, samplesets_iterable)
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def discrete_fourier_properties(samples, samplerate):
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"""
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Return f_delta and f_nyquist.
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"""
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return (samplerate/(len(samples)), samplerate/2)
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