m-thesis-introduction/simulations/airshower_beacon_simulation/lib/snr.py

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