mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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102 lines
3.2 KiB
Python
Executable file
102 lines
3.2 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 earsim import REvent, block_filter
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import aa_generate_beacon as beacon
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import lib
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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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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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tx_fname = path.join(fname_dir, beacon.tx_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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f_beacon, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
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_, __, txdata = beacon.read_tx_file(tx_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 = lib.passband(30e-3, 80e-3) # GHz
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beacon_pb = lib.passband(f_beacon-1e-3, f_beacon+1e-3) # GHz
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beacon_amp = np.max(txdata['amplitudes'])# mu V/m
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if True: # Apply filter to raw beacon/noise to compare with Filtered Traces
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myfilter = lambda x: block_filter(x, dt, pb[0], pb[1])
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else: # Compare raw beacon/noise with Filtered Traces
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myfilter = lambda x: x
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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 = [ lib.signal_to_noise(myfilter(beacon_amp*ant.beacon), myfilter(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("Maximum Beacon/Noise SNR")
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ax.set_xlabel("Antenna no.")
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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_vs_noise_snr.pdf"))
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##
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## Beacon vs Total SNR
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##
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if True:
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beacon_snrs = [ lib.signal_to_noise(myfilter(beacon_amp*ant.beacon), ant.E_AxB, 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("Maximum Beacon/Total SNR")
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ax.set_xlabel("Antenna no.")
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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_vs_total_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 = [ lib.signal_to_noise(ant.E_AxB, myfilter(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("Total (Signal+Beacon+Noise)/Noise SNR")
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ax.set_xlabel("Antenna no.")
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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".total_snr.pdf"))
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if show_plots:
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plt.show()
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