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81 lines
2.1 KiB
Python
81 lines
2.1 KiB
Python
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import numpy as np
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from collections import namedtuple
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from .fft import ft_spectrum
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class passband(namedtuple("passband", ['low', 'high'], defaults=[0, np.inf])):
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"""
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Band for a bandpass filter.
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It encapsulates a tuple.
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"""
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def size():
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return bandsize(self)
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def freq_mask(frequencies):
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return bandpass_mask(frequencies, self)
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def signal_power(samples, samplerate, normalise_bandsize=True, **ft_kwargs):
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return bandpower(samples, samplerate, self, normalise_bandsize, **ft_kwargs)
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def filter_samples(samples, samplerate, **ft_kwargs):
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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 = ft_spectrum(samples, samplerate, **ft_kwargs)
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fft[ ~ self.freq_mask(freqs) ] = 0
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return irfft(fft)
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def bandpass_samples(samples, samplerate, band=passband(), **ft_kwargs):
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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 = ft_spectrum(samples, samplerate, **ft_kwargs)
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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 bandsize(band = passband()):
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return band[1] - band[0]
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def bandpower(samples, samplerate=1, band=passband(), normalise_bandsize=True, **ft_kwargs):
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fft, freqs = ft_spectrum(samples, samplerate, **ft_kwargs)
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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( samplerate, samples, noise, signal_band, noise_band=None):
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if noise_band is None:
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noise_band = sample_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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