mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-14 02:23:32 +01:00
139 lines
3.7 KiB
Python
Executable file
139 lines
3.7 KiB
Python
Executable file
#!/usr/bin/env python3
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# vim: indent=fdm ts=4
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"""
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Show Signal to noise for the original simulation signal,
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the beacon signal and the combined signal for each antenna
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"""
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import numpy as np
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import h5py
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import matplotlib.pyplot as plt
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import numpy as np
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from collections import namedtuple
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from earsim import REvent
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import aa_generate_beacon as beacon
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import lib
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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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if __name__ == "__main__":
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from os import path
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import sys
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import matplotlib
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import os
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if os.name == 'posix' and "DISPLAY" not in os.environ:
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matplotlib.use('Agg')
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f_beacon_band = (49e-3,55e-3) #GHz
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fname = "ZH_airshower/mysim.sry"
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fig_dir = "./figures/"
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show_plots = not False
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####
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fname_dir = path.dirname(fname)
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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time_diffs_fname = 'time_diffs.hdf5' if not True else antennas_fname
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# create fig_dir
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if fig_dir:
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os.makedirs(fig_dir, exist_ok=True)
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# Read in antennas from file
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_, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
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# Read original REvent
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ev = REvent(fname)
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# general properties
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dt = antennas[0].t[1] - antennas[0].t[0] # ns
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pb = passband(30e-3, 80e-3) # GHz
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beacon_pb = passband(50e-3, 55e-3) # GHz
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##
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## Beacon vs Noise SNR
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##
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if True:
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beacon_snrs = [ signal_to_noise(ant.beacon, ant.noise, samplerate=1/dt, signal_band=beacon_pb) for ant in antennas ]
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fig, ax = plt.subplots()
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ax.set_title("Beacon SNR")
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ax.set_xlabel("Antenna")
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ax.set_ylabel("SNR")
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ax.plot([ int(ant.name) for ant in antennas], beacon_snrs, 'o', ls='none')
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".beacon_snr.pdf"))
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##
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## Airshower signal vs Noise SNR
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##
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if True:
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shower_snrs = [ signal_to_noise(ant.E_AxB, ant.noise, samplerate=1/dt, signal_band=pb) for ant in antennas ]
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fig, ax = plt.subplots()
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ax.set_title("Shower SNR")
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ax.set_xlabel("Antenna")
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ax.set_ylabel("SNR")
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ax.plot([ int(ant.name) for ant in antennas], shower_snrs, 'o', ls='none')
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if fig_dir:
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f".shower_snr.pdf"))
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if show_plots:
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plt.show()
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