mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 19:43:30 +01:00
56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
import numpy as np
|
|
from collections import namedtuple
|
|
|
|
passband = namedtuple("passband", ['low', 'high'], defaults=[0, np.inf])
|
|
|
|
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 bandpass_samples(samples, samplerate, band=passband()):
|
|
"""
|
|
Bandpass the samples with this passband.
|
|
This is a hard filter.
|
|
"""
|
|
fft, freqs = get_freq_spec(samples, samplerate)
|
|
|
|
fft[ ~ self.freq_mask(freqs) ] = 0
|
|
|
|
return np.fft.irfft(fft)
|
|
|
|
def bandpass_mask(freqs, band=passband()):
|
|
low_pass = abs(freqs) <= band[1]
|
|
high_pass = abs(freqs) >= band[0]
|
|
|
|
return low_pass & high_pass
|
|
|
|
def bandpower(samples, samplerate=1, band=passband(), normalise_bandsize=True):
|
|
fft, freqs = get_freq_spec(samples, samplerate)
|
|
|
|
bandmask = bandpass_mask(freqs, band=band)
|
|
|
|
if normalise_bandsize:
|
|
bins = np.count_nonzero(bandmask, axis=-1)
|
|
else:
|
|
bins = 1
|
|
|
|
power = np.sum(np.abs(fft[bandmask])**2)
|
|
|
|
return power/bins
|
|
|
|
def signal_to_noise(samples, noise, samplerate=1, signal_band=passband(), noise_band=None):
|
|
if noise_band is None:
|
|
noise_band = signal_band
|
|
|
|
if noise is None:
|
|
noise = samples
|
|
|
|
noise_power = bandpower(noise, samplerate, noise_band)
|
|
|
|
signal_power = bandpower(samples, samplerate, signal_band)
|
|
|
|
return (signal_power/noise_power)**0.5
|
|
|