#!/usr/bin/env python3 # vim: fdm=indent ts=4 """ Find beacon phases in antenna traces And save these to a file """ import h5py import matplotlib.pyplot as plt import numpy as np import aa_generate_beacon as beacon import lib if __name__ == "__main__": from os import path import sys f_beacon_band = (49e-3,55e-3) #GHz allow_frequency_fitting = False read_frequency_from_file = True show_plots = True fname = "ZH_airshower/mysim.sry" #### fname_dir = path.dirname(fname) antennas_fname = path.join(fname_dir, beacon.antennas_fname) if not path.isfile(antennas_fname): print("Antenna file cannot be found, did you try generating a beacon?") sys.exit(1) # read in antennas with h5py.File(antennas_fname, 'a') as fp: if 'antennas' not in fp.keys(): print("Antenna file corrupted? no antennas") sys.exit(1) group = fp['antennas'] f_beacon = None if read_frequency_from_file and 'tx' in fp: tx = fp['tx'] if 'f_beacon' in tx.attrs: f_beacon = tx.attrs['f_beacon'] else: print("No frequency found in file.") sys.exit(2) f_beacon_estimate_band = 0.01*f_beacon elif allow_frequency_fitting: f_beacon_estimate_band = (f_beacon_band[1] - f_beacon_band[0])/2 f_beacon = f_beacon_band[1] - f_beacon_estimate_band else: print("Not allowed to fit frequency and no tx group found in file.") sys.exit(2) N_antennas = len(group.keys()) # just for funzies found_data = np.zeros((N_antennas, 3)) # Determine frequency and phase for i, name in enumerate(group.keys()): h5ant = group[name] # use E_AxB only instead of polarisations if True: if 'E_AxB' not in h5ant.keys(): print(f"Antenna does not have 'E_AxB' in {name}") sys.exit(1) traces = h5ant['E_AxB'] t_trace = traces[0] test_traces = [ traces[1] ] orients = ['E_AxB'] # use separate polarisations else: if 'traces' not in h5ant.keys(): print(f"Antenna file corrupted? no 'traces' in {name}") sys.exit(1) traces = h5ant['traces'] t_trace = traces[0] if True: # only take the Beacon trace test_traces = [traces[4]] orients = ['B'] else: test_traces = traces[1:] orients = ['Ex', 'Ey', 'Ez', 'B'] # Do Fourier Transforms # to find phases and amplitudes if True: freqs, phases, amps = lib.find_beacon_in_traces( test_traces, t_trace, f_beacon_estimate=f_beacon, frequency_fit=allow_frequency_fitting, f_beacon_estimate_band=f_beacon_estimate_band ) else: # Testing freqs = [f_beacon] t0 = h5ant.attrs['t0'] phases = [ 2*np.pi*t0*f_beacon ] amps = [ 3e-7 ] # choose highest amp idx = 0 if False and len(phases) > 1: #idx = np.argmax(amplitudes, axis=-1) raise NotImplementedError frequency = freqs[idx] phase = phases[idx] amplitude = amps[idx] orientation = orients[idx] # for reporting using plots found_data[i] = frequency, phase, amplitude if show_plots and (i == 60 or i == 72): fig, ax = plt.subplots() trace_amp = max(traces[-1]) - min(traces[-1]) myt = np.linspace(min(traces[0]), max(traces[0]), 10*len(traces[0])) ax.plot(t_trace, traces[-1], marker='.', label='trace') ax.plot(myt, lib.sine_beacon(frequency, myt, amplitude=amplitude, phase=phase), ls='dashed', label='simulated') ax.set_title(f"Beacon at antenna {h5ant}\nF:{frequency}, P:{phase}, A:{amplitude}") ax.legend() # save to file h5beacon_info = h5ant.require_group('beacon_info') # only take n_sig significant digits into account # for naming in hdf5 file n_sig = 3 decimal = int(np.floor(np.log10(abs(frequency)))) freq_name = str(np.around(frequency, n_sig-decimal)) # delete previous values if freq_name in h5beacon_info: del h5beacon_info[freq_name] h5beacon_freq_info = h5beacon_info.create_group(freq_name) h5attrs = h5beacon_freq_info.attrs h5attrs['freq'] = frequency h5attrs['phase'] = phase h5attrs['amplitude'] = amplitude h5attrs['orientation'] = orientation print("Beacon Phases, Amplitudes and Frequencies written to", antennas_fname) # show histogram of found frequencies if show_plots: if True or allow_frequency_fitting: fig, ax = plt.subplots() ax.set_xlabel("Frequency") ax.set_ylabel("Counts") ax.axvline(f_beacon, ls='dashed', color='g') ax.hist(found_data[:,0], bins='sqrt', density=False) if True: fig, ax = plt.subplots() ax.set_xlabel("Amplitudes") ax.set_ylabel("Counts") ax.hist(found_data[:,2], bins='sqrt', density=False) plt.show()