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56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
import numpy as np
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from collections import namedtuple
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passband = namedtuple("passband", ['low', 'high'], defaults=[0, np.inf])
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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 bandpass_samples(samples, samplerate, band=passband()):
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"""
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Bandpass the samples with this passband.
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This is a hard filter.
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"""
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fft, freqs = get_freq_spec(samples, samplerate)
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fft[ ~ self.freq_mask(freqs) ] = 0
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return np.fft.irfft(fft)
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def bandpass_mask(freqs, band=passband()):
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low_pass = abs(freqs) <= band[1]
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high_pass = abs(freqs) >= band[0]
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return low_pass & high_pass
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def bandpower(samples, samplerate=1, band=passband(), normalise_bandsize=True):
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fft, freqs = get_freq_spec(samples, samplerate)
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bandmask = bandpass_mask(freqs, band=band)
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if normalise_bandsize:
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bins = np.count_nonzero(bandmask, axis=-1)
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else:
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bins = 1
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power = np.sum(np.abs(fft[bandmask])**2)
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return power/bins
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def signal_to_noise(samples, noise, samplerate=1, signal_band=passband(), noise_band=None):
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if noise_band is None:
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noise_band = signal_band
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if noise is None:
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noise = samples
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noise_power = bandpower(noise, samplerate, noise_band)
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signal_power = bandpower(samples, samplerate, signal_band)
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return (signal_power/noise_power)**0.5
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