mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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315 lines
12 KiB
Python
Executable file
315 lines
12 KiB
Python
Executable file
#!/usr/bin/env python3
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# vim: fdm=indent ts=4
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"""
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Show how the Power changes when incorporating the
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various clock offsets by plotting on a grid.
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"""
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D # required for projection='3d' on old matplotliblib versions
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import numpy as np
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from os import path
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import joblib
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from earsim import REvent
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from atmocal import AtmoCal
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import aa_generate_beacon as beacon
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import lib
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from lib import rit
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def save_overlapping_traces_figure(test_location, ev, N_plot = 30, wx=200, title_extra=None, fname_distinguish='', fig_dir=None, **fig_kwargs):
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P, t_, a_, a_sum, t_sum = rit.pow_and_time(test_location, ev, dt=1)
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fig, axs = plt.subplots(**fig_kwargs)
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axs.set_title("Antenna traces" + (("\n" + title_extra) if title_extra is not None else '') )
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axs.set_xlabel("Time [ns]")
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axs.set_ylabel("Amplitude [$\\mu V/m$]")
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if False:
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text_loc = (0.02, 0.95)
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axs.text(*text_loc, '[' + ', '.join(['{:.2e}'.format(x) for x in test_location]) + ']', ha='left', transform=axs.transAxes)
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a_max = [ np.amax(ant.E_AxB) for ant in ev.antennas ]
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power_sort_idx = np.argsort(a_max)
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for i, idx in enumerate(reversed(power_sort_idx)):
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if i >= N_plot:
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break
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alpha = max(0.4, 1/N_plot)
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axs.plot(t_[idx], a_[idx], color='r', alpha=alpha, lw=2)
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if fig_dir:
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if fname_distinguish:
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fname_distinguish = "." + fname_distinguish
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fig.tight_layout()
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.{case}.pdf'))
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.{case}.png'), transparent=True)
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# Take center between t_low and t_high
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if True:
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orig_xlims = axs.get_xlim()
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if not True: # t_high and t_low from strongest signal
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t_low = np.min(t_[power_sort_idx[-1]])
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t_high = np.max(t_[power_sort_idx[-1]])
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else: # take t_high and t_low from plotted signals
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a = [np.min(t_[idx]) for idx in power_sort_idx[-N_plot:]]
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t_low = np.nanmin(a)
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b = [np.max(t_[idx]) for idx in power_sort_idx[-N_plot:]]
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t_high = np.nanmax(b)
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if False:
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axs.plot(a, [0]*N_plot, 'gx', ms=10)
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axs.plot(b, [0]*N_plot, 'b+', ms=10)
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center_x = (t_high - t_low)/2 + t_low
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low_xlim = max(orig_xlims[0], center_x - wx)
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high_xlim = min(orig_xlims[1], center_x + wx)
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axs.set_xlim(low_xlim, high_xlim)
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.zoomed.{case}.pdf'))
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f'{fname_distinguish}.trace_overlap.zoomed.{case}.png'), transparent=True)
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return fig
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if __name__ == "__main__":
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valid_cases = ['no_offset', 'repair_none', 'repair_phases', 'repair_all']
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import sys
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import os
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import matplotlib
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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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atm = AtmoCal()
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from scriptlib import MyArgumentParser
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parser = MyArgumentParser()
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parser.add_argument('--input-fname', type=str, default=None, help='Path to mysim.sry, either directory or path. If empty it takes DATA_DIR and appends mysim.sry. (Default: %(default)s)')
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group = parser.add_argument_group('figures')
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for case in valid_cases:
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group.add_argument('--'+case.replace('_','-'), dest='figures', action='append_const', const=case)
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args = parser.parse_args()
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if not args.input_fname:
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args.input_fname = args.data_dir
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if path.isdir(args.input_fname):
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args.input_fname = path.join(args.input_fname, "mysim.sry")
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wanted_cases = args.figures
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if not wanted_cases or 'all' in wanted_cases:
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wanted_cases = valid_cases
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figsize = (12,8)
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fig_dir = args.fig_dir
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show_plots = args.show_plots
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remove_beacon_from_traces = True
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apply_signal_window_from_max = True
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####
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fname_dir = args.data_dir
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antennas_fname = path.join(fname_dir, beacon.antennas_fname)
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pickle_fname = path.join(fname_dir, 'res.pkl')
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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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_, tx, antennas = beacon.read_beacon_hdf5(antennas_fname)
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_, __, txdata = beacon.read_tx_file(tx_fname)
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# Read original REvent
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ev = REvent(args.input_fname)
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bak_ants = ev.antennas
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# .. patch in our antennas
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ev.antennas = antennas
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##
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## Setup grid
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##
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X = 400
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zgr = 0 #not exact
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dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),750,zgr+ev.core[2])
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scale2d = dXref*np.tan(np.deg2rad(2.))
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scale4d = dXref*np.tan(np.deg2rad(4.))
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scale02d = dXref*np.tan(np.deg2rad(0.2))
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Nx, Ny = 21, 21
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scales = {
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'scale2d': scale2d,
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'scale4d': scale4d,
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'scale02d': scale02d,
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}
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N_plot = 30
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trace_zoom_wx = 100
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plot_titling = {
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'no_offset': "no clock offset",
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'repair_none': "unrepaired clock offset",
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'repair_phases': "phase resolved clock offsets repaired",
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'repair_all': "final measured clock offsets repaired"
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}
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# For now only implement using one freq_name
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freq_names = ev.antennas[0].beacon_info.keys()
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if len(freq_names) > 1:
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raise NotImplementedError
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freq_name = next(iter(freq_names))
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# Pre remove the beacon from the traces
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#
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# We need to remove it here, so we do not shoot ourselves in
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# the foot when changing to the various clock offsets.
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#
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# Note that the bandpass filter is applied only after E_AxB is
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# reconstructed so we have to manipulate the original traces.
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if remove_beacon_from_traces:
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tx_amps = txdata['amplitudes']
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tx_amps_sum = np.sum(tx_amps)
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for i, ant in enumerate(ev.antennas):
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beacon_phase = ant.beacon_info[freq_name]['beacon_phase']
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f = ant.beacon_info[freq_name]['freq']
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ampl_AxB = ant.beacon_info[freq_name]['amplitude']
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calc_beacon = lib.sine_beacon(f, ev.antennas[i].t, amplitude=ampl_AxB, phase=beacon_phase)
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# Split up contribution to the various polarisations
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for j, amp in enumerate(tx_amps):
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if j == 0:
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ev.antennas[i].Ex -= amp*(1/tx_amps_sum)*calc_beacon
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elif j == 1:
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ev.antennas[i].Ey -= amp*(1/tx_amps_sum)*calc_beacon
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elif j == 2:
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ev.antennas[i].Ez -= amp*(1/tx_amps_sum)*calc_beacon
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# Subtract the beacon from E_AxB
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ev.antennas[i].E_AxB -= calc_beacon
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# Slice the traces to a small part around the peak
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if apply_signal_window_from_max:
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N_pre, N_post = 250, 250 # TODO: make this configurable
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for i, ant in enumerate(ev.antennas):
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# Get max idx from all the traces
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# and select the strongest
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max_idx = []
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maxs = []
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for trace in [ant.Ex, ant.Ey, ant.Ez]:
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idx = np.argmax(np.abs(trace))
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max_idx.append(idx)
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maxs.append( np.abs(trace[idx]) )
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idx = np.argmax(maxs)
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max_idx = max_idx[idx]
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low_idx = max(0, max_idx-N_pre)
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high_idx = min(len(ant.t), max_idx+N_post)
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ev.antennas[i].t = ant.t[low_idx:high_idx]
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ev.antennas[i].t_AxB = ant.t_AxB[low_idx:high_idx]
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ev.antennas[i].Ex = ant.Ex[low_idx:high_idx]
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ev.antennas[i].Ey = ant.Ey[low_idx:high_idx]
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ev.antennas[i].Ez = ant.Ez[low_idx:high_idx]
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ev.antennas[i].E_AxB = ant.E_AxB[low_idx:high_idx]
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## Apply polarisation and bandpass filter
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rit.set_pol_and_bp(ev)
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# backup antenna times
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backup_antenna_t = [ ant.t for ant in ev.antennas ]
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backup_antenna_t_AxB = [ ant.t_AxB for ant in ev.antennas ]
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fig = save_overlapping_traces_figure([0,0,0], ev, N_plot=1, wx=trace_zoom_wx, title_extra = plot_titling[case], fname_distinguish=f'single', fig_dir=fig_dir, figsize=figsize)
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plt.close(fig)
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with joblib.parallel_backend("loky"):
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for case in wanted_cases:
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print(f"Starting {case} figure")
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# Repair clock offsets with the measured offsets
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transl_modes = {'no_offset':'orig', 'repair_phases':'phases', 'repair_all':'all'}
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if case in transl_modes:
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transl_mode = transl_modes[case]
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measured_offsets = beacon.read_antenna_clock_repair_offsets(antennas, mode=transl_mode, freq_name=freq_name)
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else:
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measured_offsets = [0]*len(ev.antennas)
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for i, ant in enumerate(ev.antennas):
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total_clock_offset = measured_offsets[i]
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ev.antennas[i].t = backup_antenna_t[i] + total_clock_offset
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ev.antennas[i].t_AxB = backup_antenna_t_AxB[i] + total_clock_offset
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if i == 0:
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# Specifically compare the times
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print("backup time, time with measured_offset, true clock offset, measured clock offset")
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print(bak_ants[i].t[0], ev.antennas[i].t[0], ev.antennas[i].attrs['clock_offset'], measured_offsets[i])
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#
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# Plot overlapping traces at 0,0,0
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#
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fig = save_overlapping_traces_figure([0,0,0], ev, N_plot=N_plot, wx=trace_zoom_wx, title_extra = plot_titling[case], fname_distinguish=f'{case}.0', fig_dir=fig_dir, figsize=figsize)
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plt.close(fig)
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# Measure power on grid
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# and plot overlapping traces at position with highest power
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for scalename, scale in scales.items():
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wx, wy = scale, scale
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print(f"Starting grid measurement for figure {case} with {scalename}")
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xx, yy, p, maxp_loc = rit.shower_plane_slice(ev, X=X, Nx=Nx, Ny=Nx, wx=wx, wy=wy, zgr=zgr)
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fig, axs = rit.slice_figure(ev, X, xx, yy, p, mode='sp', scatter_kwargs=dict(
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vmax=1e5,
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vmin=0,
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s=150,
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cmap='inferno',
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# edgecolor='black',
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))
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suptitle = fig._suptitle.get_text()
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fig.suptitle("")
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axs.set_title(suptitle +"\n" +plot_titling[case])
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#axs.set_aspect('equal', 'datalim')
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if fig_dir:
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fig.tight_layout()
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fig.savefig(path.join(fig_dir, path.basename(__file__) + f'.X{X}.{case}.{scalename}.pdf'))
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plt.close(fig)
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#
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# Plot overlapping traces at highest power of each scale
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#
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fig = save_overlapping_traces_figure(maxp_loc, ev, N_plot=N_plot, wx=trace_zoom_wx, title_extra = plot_titling[case] + ', ' + scalename + ' best', fname_distinguish=scalename+'.best', fig_dir=fig_dir, figsize=figsize)
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#
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# and plot overlapping traces at two other locations
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#
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if True:
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for dist in [ 0.5, 5, 10, 50, 100]:
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# only add distance horizontally
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location = maxp_loc + np.sqrt(dist*1e3)*np.array([1,1,0])
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fig = save_overlapping_traces_figure(location, ev, N_plot=N_plot, wx=wx, title_extra = plot_titling[case] + ', ' + scalename + f', x + {dist}km', fname_distinguish=f'{scalename}.{dist}', fig_dir=fig_dir, figsize=figsize)
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plt.close(fig)
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if args.show_plots:
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plt.show()
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