689 lines
26 KiB
Python
Executable File
689 lines
26 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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Find the best integer multiple to shift
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antennas to be able to resolve
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"""
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import h5py
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from itertools import combinations, zip_longest, product
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import matplotlib.pyplot as plt
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import numpy as np
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from os import path
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import os
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from scipy.interpolate import interp1d
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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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try:
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from tqdm import tqdm
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except:
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tqdm = lambda x: x
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try:
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from joblib import Parallel, delayed
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except:
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Parallel = None
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delayed = lambda x: x
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def find_best_period_shifts_at_location(*args, algo=None, **kwargs):
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"""
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This is a placeholder function.
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For args and kwargs see find_best_period_shifts_summing_at_location.
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"""
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if algo is None:
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algo = 'sum'
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return find_best_period_shifts_summing_at_location(*args, **kwargs, algo=algo)
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def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks, period=1, dt=None, period_shift_first_trace=0, plot_iteration_with_shifted_trace=None, fig_dir=None, fig_distinguish=None,snr_str=None, shower_plane_loc=None, algo='sum', verbose=False):
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"""
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Find the best sample_shift for each antenna by summing the antenna traces
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and seeing how to get the best alignment.
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"""
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a_ = []
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t_ = []
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t_min = 1e9
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t_max = -1e9
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a_maxima = []
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N_ant = len(antennas)
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if dt is None:
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dt = antennas[0].t_AxB[1] - antennas[0].t_AxB[0]
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if not hasattr(plot_iteration_with_shifted_trace, '__len__'):
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if plot_iteration_with_shifted_trace:
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plot_iteration_with_shifted_trace = [ plot_iteration_with_shifted_trace ]
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else:
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plot_iteration_with_shifted_trace = []
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# propagate to test location
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for i, ant in enumerate(antennas):
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aloc = [ant.x, ant.y, ant.z]
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delta, dist = atm.light_travel_time(test_loc, aloc)
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delta = delta*1e9
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t__ = np.subtract(ant.t_AxB, delta)
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t_.append(t__)
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a_.append(ant.E_AxB)
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a_maxima.append(max(ant.E_AxB))
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if t__[0] < t_min:
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t_min = t__[0]
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if t__[-1] > t_max:
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t_max = t__[-1]
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# sort traces with descending maxima
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sort_idx = np.argsort(a_maxima)[::-1]
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t_ = [ t_[i] for i in sort_idx ]
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a_ = [ a_[i] for i in sort_idx ]
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# Interpolate and find best sample shift
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max_neg_shift = 0 #np.min(allowed_sample_shifts) * dt
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max_pos_shift = 0 #np.max(allowed_sample_shifts) * dt
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t_sum = np.arange(t_min+max_neg_shift, t_max+max_pos_shift, dt)
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a_sum = np.zeros(len(t_sum))
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a_first = np.zeros(len(t_sum))
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best_period_shifts = np.zeros( (len(antennas)) ,dtype=int)
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for i, (t_r, E_) in enumerate(zip(t_, a_)):
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f = interp1d(t_r, E_, assume_sorted=True, bounds_error=False, fill_value=0)
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if i == 0:
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best_period_shifts[i] = period_shift_first_trace
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a_first = f(t_sum - period_shift_first_trace*period)
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a_sum += a_first
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continue
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# init figure
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if i in plot_iteration_with_shifted_trace:
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fig, ax = plt.subplots(figsize=figsize)
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if shower_plane_loc is not None:
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title_location = "s({:.1g},{:.1g},{:.1g})".format(*shower_plane_loc)
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else:
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title_location = "({.1g},{:.1g},{:.1g})".format(*test_loc)
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#ax.set_title("Traces at {}; i={i}/{tot}".format(title_location, i=i, tot=N_ant))
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude")
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#ax.plot(t_sum, a_sum)
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fig2, ax2 = plt.subplots(figsize=figsize)
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#ax2.set_title("Maxima at {}; i={i}/{tot}".format(title_location, i=i, tot=N_ant))
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ax2.set_xlabel("$k$th Period")
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ax2.set_ylabel("Summed Amplitude")
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ax2.plot(0, np.max(a_first), marker='*', label='first trace', ls='none', ms=20)
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ax3 = ax2.twinx()
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ax3.set_ylabel("Correlation")
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# find the maxima for each period shift k
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shift_maxima = np.zeros( len(allowed_ks) )
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shift_corrs = np.zeros_like(shift_maxima)
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for j, k in enumerate(allowed_ks):
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augmented_a = f(t_sum - k*period)
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shift_maxima[j] = np.max(augmented_a + a_first)
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shift_corrs[j] = np.dot(augmented_a, a_first)
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if i in plot_iteration_with_shifted_trace and abs(k) <= 3:
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l = ax.plot(t_sum, a_first + augmented_a, alpha=0.7, ls='dashed', label=f'{k:g}')
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ax.axhline(shift_maxima[j], ls='dashdot', color=l[0].get_color(), alpha=0.7)
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ax2.plot(k, shift_maxima[j], marker='o', ls='none', ms=20)
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ax3.plot(k, shift_corrs[j], marker='3', ls='none', ms=20)
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# transform maximum into best_sample_shift
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best_idx = np.argmax(shift_maxima)
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best_corr_idx = np.argmax(shift_corrs)
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if verbose and (best_corr_idx != best_idx):
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print("Correlation idx not equal to maximum idx")
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print(best_corr_idx, best_idx)
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if algo == 'sum':
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best_period_shifts[i] = allowed_ks[best_idx]
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elif algo == 'corr':
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best_period_shifts[i] = allowed_ks[best_corr_idx]
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k = best_period_shifts[i]
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best_augmented_a = f(t_sum - k*period)
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a_sum += best_augmented_a
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# cleanup figure
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if i in plot_iteration_with_shifted_trace:
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# plot the traces
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if True: # plot best k again
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l = ax.plot(t_sum, a_first + best_augmented_a, alpha=0.8, label=f'best $k$={best_period_shifts[i]:g}', lw=2)
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ax.axhline(shift_maxima[j], ls='dashdot', color=l[0].get_color(), alpha=0.7)
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if True: # plot best shift
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ax2.plot(allowed_ks[best_idx], shift_maxima[best_idx], marker='*', ls='none', ms=20, label=f'best $k$={best_period_shifts[i]:g}')
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ax3.plot(allowed_ks[best_corr_idx], shift_maxima[best_idx], marker='*', ls='none', ms=20, label=f'best corr $k$={allowed_ks[best_corr_idx]:g}')
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ax.legend(title='period shift $k$; '+snr_str, ncol=5, loc='lower center')
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ax2.legend(title=snr_str)
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ax3.legend()
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if fig_dir:
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fig.tight_layout()
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fig2.tight_layout()
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if shower_plane_loc is not None:
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fname_location = '.sloc{:.1g}-{:.1g}-{:.1g}'.format(*shower_plane_loc)
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else:
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fname_location = '.loc{:.1f}-{:.1f}-{:.1f}'.format(*test_loc)
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fname = path.join(fig_dir, path.basename(__file__) + f'.{fig_distinguish}i{i}' + fname_location)
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if True:
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old_xlim = ax.get_xlim()
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if True: # zoomed on part without peak of this trace
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wx = 200
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x = max(t_r) - wx
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ax.set_xlim(x-wx, x+wx)
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fig.savefig(fname + ".zoomed.beacon.pdf")
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if True: # zoomed on peak of this trace
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x = t_sum[np.argmax(a_first)]
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x = t_sum[np.argmax(f(t_sum))]
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wx = 50 + (max(best_period_shifts) - min(best_period_shifts) )*dt + 1*period
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ax.set_xlim(x-wx, x+wx)
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fig.savefig(fname + ".zoomed.peak.pdf")
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ax.set_xlim(*old_xlim)
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fig.savefig(fname + ".pdf")
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fig2.savefig(fname + ".maxima.pdf")
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plt.close(fig)
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plt.close(fig2)
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if True: # final summed waveform
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fig, ax = plt.subplots()
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ax.set_title("Summed Traces with best k's at {}".format(title_location))
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude")
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ax.plot(t_sum, a_sum)
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if fig_dir:
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fig.tight_layout()
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fname = path.join(fig_dir, path.basename(__file__) + f'.{fig_distinguish}i{i}' + fname_location)
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fig.savefig(fname + ".sum.pdf")
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plt.close(fig)
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# sort by antenna (undo sorting by maximum)
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undo_sort_idx = np.argsort(sort_idx)
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best_period_shifts = best_period_shifts[undo_sort_idx]
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# Return ks
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return best_period_shifts, np.max(a_sum), sort_idx
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if __name__ == "__main__":
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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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plt.rcParams.update({'figure.max_open_warning': 0})
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atm = AtmoCal()
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from scriptlib import MyArgumentParser
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parser = MyArgumentParser(default_fig_dir="./figures/periods_from_shower_figures/")
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parser.add_argument('--quick_run', action='store_true', help='Use a very coarse grid (6x6)')
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parser.add_argument('-X', type=int, default=400, help='Atmospheric depth to start the initial grid.')
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parser.add_argument('--max-k', type=float, default=2, help='Maximum abs(k) allowed to be shifted. (Default: %(default)d)')
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parser.add_argument('-N', '--N_runs', type=int, default=5, help='Maximum amount of iterations to grid search. (Default: %(default)d)')
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parser.add_argument('-l', '--passband-low', type=float, default=30e-3, help='Lower frequency [GHz] of the passband filter. (set -1 for np.inf) (Default: %(default)g)')
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parser.add_argument('-u', '--passband-high', type=float, default=80e-3, help='Upper frequency [GHz] of the passband filter. (set -1 for np.inf) (Default: %(default)g)')
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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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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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figsize = (6,4)
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if True:
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from matplotlib import rcParams
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#rcParams["text.usetex"] = True
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rcParams["font.family"] = "serif"
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rcParams["font.size"] = "14"
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rcParams["grid.linestyle"] = 'dotted'
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rcParams["figure.figsize"] = figsize
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figsize = rcParams['figure.figsize']
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fig_dir = args.fig_dir
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fig_subdir = path.join(fig_dir, 'shifts/')
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show_plots = args.show_plots
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max_k = int(args.max_k)
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allowed_ks = np.arange(-max_k, max_k+1, dtype=int)
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Xref = args.X
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N_runs = args.N_runs
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remove_beacon_from_trace = True
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apply_signal_window_from_max = True
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low_bp = args.passband_low if args.passband_low >= 0 else np.inf # GHz
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high_bp = args.passband_high if args.passband_high >= 0 else np.inf # GHz
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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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time_diffs_fname = 'time_diffs.hdf5' if not True else antennas_fname
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tx_fname = path.join(fname_dir, beacon.tx_fname)
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beacon_snr_fname = path.join(fname_dir, beacon.beacon_snr_fname)
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## This is a file indicating whether the k-finding algorithm was
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## stopped early. This happens when the ks do not change between
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## two consecutive iterations.
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run_break_fname = path.join(fname_dir, 'ca_breaked_run')
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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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if fig_subdir:
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os.makedirs(fig_subdir, 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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# .. patch in our antennas
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ev.antennas = antennas
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# read in snr information
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beacon_snrs = beacon.read_snr_file(beacon_snr_fname)
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snr_str = f"$\\langle SNR \\rangle$ = {beacon_snrs['mean']: .2g}"
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# For now only implement using one freq_name
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freq_names = 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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f_beacon = ev.antennas[0].beacon_info[freq_name]['freq']
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##
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## Manipulate time and traces of each antenna
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##
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### Remove time due to true phase
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### and optionally remove the beacon
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### Note: there is no use in changing *_AxB variables here (except for plotting),
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### they're recomputed by the upcoming rit.set_pol_and_bp call.
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measured_repair_offsets = beacon.read_antenna_clock_repair_offsets(ev.antennas, mode='phases', freq_name=freq_name)
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for i, ant in enumerate(ev.antennas):
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ev.antennas[i].orig_t = ev.antennas[i].t
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ev.antennas[i].t += measured_repair_offsets[i]
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# t_AxB will be set by the rit.set_pol_and_bp function
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ev.antennas[i].t_AxB += measured_repair_offsets[i]
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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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# 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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# Create window around max_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].orig_t = ant.orig_t[low_idx:high_idx]
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ev.antennas[i].t = ant.t[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].t_AxB = ant.t_AxB[low_idx:high_idx]
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ev.antennas[i].E_AxB = ant.E_AxB[low_idx:high_idx]
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# .. and remove the beacon from the traces
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# Note: ant.E_AxB is recalculated by rit.set_pol_and_bp
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if remove_beacon_from_trace:
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clock_phase = measured_repair_offsets[i]*2*np.pi*f_beacon
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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 = ant.beacon_info[freq_name]['amplitude']
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calc_beacon = lib.sine_beacon(f, ev.antennas[i].t, amplitude=ampl, phase=beacon_phase-clock_phase)
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tx_amps = txdata['amplitudes']
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tx_amps_sum = np.sum(tx_amps)
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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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# Make a figure of the manipulated traces
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if i == 72:
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orig_beacon_amplifier = ampl/max(ant.beacon)
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fig, ax = plt.subplots(figsize=figsize)
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ax.set_title(f"Signal and Beacon traces Antenna {ant.name}")
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude [$\\mu V/m$]")
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ax.plot(ant.t_AxB, ant.E_AxB + calc_beacon, alpha=0.6, ls='dashed', label='Signal') # calc_beacon was already removed
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ax.plot(ant.t_AxB, calc_beacon, alpha=0.6, ls='dashed', label='Calc Beacon')
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ax.plot(ant.t_AxB, ant.E_AxB, alpha=0.6, label="Signal - Calc Beacon")
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ax.legend(title=snr_str)
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# save
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if fig_dir:
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fig.tight_layout()
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if True: # zoom
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old_xlim = ax.get_xlim()
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if not True: # zoomed on part without peak of this trace
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wx, x = 100, min(ant.t_AxB)
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ax.set_xlim(x-5, x+wx)
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fig.savefig(path.join(fig_dir, path.basename(__file__)+f'.traces.A{ant.name}.zoomed.beacon.pdf'))
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if True: # zoomed on peak of this trace
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idx = np.argmax(ev.antennas[i].E_AxB)
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x = ev.antennas[i].t_AxB[idx]
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wx = 150
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ax.set_xlim(x-wx//2, x+wx//2)
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__)+f".traces.A{ant.name}.zoomed.peak.pdf"))
|
|
|
|
ax.set_xlim(*old_xlim)
|
|
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__)+f'.traces.A{ant.name}.pdf'))
|
|
|
|
if show_plots:
|
|
plt.show()
|
|
|
|
# Prepare polarisation and passbands
|
|
rit.set_pol_and_bp(ev, low=low_bp, high=high_bp)
|
|
|
|
# determine allowable ks per location
|
|
dt = ev.antennas[0].t_AxB[1] - ev.antennas[0].t_AxB[0]
|
|
print("Checking k:", allowed_ks)
|
|
|
|
##
|
|
## Determine grid positions
|
|
##
|
|
|
|
zgr = 0 + ev.core[2]
|
|
dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),Xref,zgr)
|
|
scale2d = dXref*np.tan(np.deg2rad(2.))
|
|
scale4d = dXref*np.tan(np.deg2rad(4.))
|
|
|
|
if args.quick_run: #quicky
|
|
x_coarse = np.linspace(-scale4d, scale4d, 16)
|
|
y_coarse = np.linspace(-scale4d, scale4d, 16)
|
|
|
|
x_fine = x_coarse/4
|
|
y_fine = y_coarse/4
|
|
else: # long
|
|
x_coarse = np.linspace(-scale4d, scale4d, 14)
|
|
y_coarse = np.linspace(-scale4d, scale4d, 14)
|
|
|
|
x_fine = np.linspace(-scale2d, scale2d, 18)
|
|
y_fine = np.linspace(-scale2d, scale2d, 18)
|
|
|
|
## Remove run_break_fname if it exists
|
|
try:
|
|
os.remove(run_break_fname)
|
|
except OSError:
|
|
pass
|
|
|
|
##
|
|
## Do calculations on the grid
|
|
##
|
|
|
|
for r in range(N_runs):
|
|
# Setup Plane grid to test
|
|
if r == 0:
|
|
xoff, yoff = 0,0
|
|
x = x_coarse
|
|
y = y_coarse
|
|
else:
|
|
# zooming in
|
|
# best_idx is defined at the end of the loop
|
|
old_ks_per_loc = ks_per_loc[best_idx]
|
|
xoff, yoff = locs[best_idx]
|
|
if r == 1:
|
|
x = x_fine
|
|
y = y_fine
|
|
else:
|
|
x /= 4
|
|
y /= 4
|
|
|
|
print(f"Testing grid run {r} centered on ({xoff}, {yoff}) at X={Xref}.")
|
|
|
|
ks_per_loc = np.zeros( (len(x)*len(y), len(ev.antennas)) , dtype=int)
|
|
maxima_per_loc = np.zeros( (len(x)*len(y)))
|
|
|
|
## Check each location on grid
|
|
xx = []
|
|
yy = []
|
|
N_loc = len(maxima_per_loc)
|
|
|
|
# Make the grid a list of locations
|
|
xx = []
|
|
yy = []
|
|
for i, (x_, y_) in enumerate(product(x,y)):
|
|
xx.append( x_+xoff )
|
|
yy.append( y_+yoff )
|
|
|
|
xx = np.array(xx)
|
|
yy = np.array(yy)
|
|
locs = list(zip(xx, yy))
|
|
|
|
# Shift the reference trace
|
|
if r == 0:
|
|
first_trace_period_shift = 0
|
|
else:
|
|
first_trace_period_shift = 0#first_trace_period_shift + np.rint(np.mean(old_ks_per_loc))
|
|
|
|
print("New first trace period:", first_trace_period_shift)
|
|
|
|
# define loop func for joblib
|
|
def loop_func(loc, dXref=dXref, i=1):
|
|
tmp_fig_subdir = None
|
|
if i == 0:
|
|
if hasattr(tqdm, '__code__') and tqdm.__code__.co-name == '<lambda>':
|
|
print(f"Testing location {i} out of {N_loc}")
|
|
tmp_fig_subdir = fig_subdir
|
|
|
|
test_loc = loc[0]* ev.uAxB + loc[1]*ev.uAxAxB + dXref *ev.uA
|
|
|
|
# Find best k for each antenna
|
|
return find_best_period_shifts_at_location(test_loc, ev.antennas, allowed_ks, period=1/f_beacon, dt=dt, period_shift_first_trace=first_trace_period_shift,
|
|
plot_iteration_with_shifted_trace=[ 1, 2, 3, 4, 5, len(ev.antennas)-1],
|
|
fig_dir=tmp_fig_subdir, fig_distinguish=f"X{Xref}.run{r}.",
|
|
snr_str=snr_str,shower_plane_loc=(loc[0]/1e3, loc[1]/1e3, dXref),
|
|
)
|
|
|
|
res = ( delayed(loop_func)(loc, i=i) for i, loc in enumerate(locs) )
|
|
|
|
if Parallel:
|
|
res = Parallel(n_jobs=None)(tqdm(res, total=len(locs)))
|
|
else:
|
|
res = tqdm(res, total=len(locs))
|
|
|
|
# unpack loop results
|
|
ks_per_loc, maxima_per_loc, sort_idx = zip(*res)
|
|
|
|
## Save maxima to file
|
|
np.savetxt(path.join(fig_dir, path.basename(__file__)+f'.maxima.X{Xref}.run{r}.txt'), np.column_stack((locs, maxima_per_loc, ks_per_loc)) )
|
|
|
|
scatter_kwargs = dict(cmap='Spectral_r', s=64*4, alpha=0.6)
|
|
if True: #plot maximum at test locations
|
|
fig, axs = plt.subplots(figsize=figsize)
|
|
#axs.set_title(f"Optimizing signal strength by varying $k$ per antenna,\n Grid Run {r}")
|
|
axs.set_ylabel(" vxvxB [km]")
|
|
axs.set_xlabel("-v x B [km]")
|
|
axs.set_aspect('equal', 'datalim')
|
|
sc = axs.scatter(xx/1e3, yy/1e3, c=maxima_per_loc, **scatter_kwargs)
|
|
fig.colorbar(sc, ax=axs, label='Max Amplitude [$\\mu V/m$]')
|
|
|
|
#axs.legend(title=snr_str)
|
|
|
|
# indicate maximum value
|
|
idx = np.argmax(maxima_per_loc)
|
|
axs.plot(xx[idx]/1e3, yy[idx]/1e3, 'bx', ms=30) # max value
|
|
axs.plot(0,0, 'r+', ms=30) # true axis
|
|
|
|
if fig_dir:
|
|
old_xlims = axs.get_xlim()
|
|
old_ylims = axs.get_ylim()
|
|
fig.tight_layout()
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__)+f'.maxima.X{Xref}.run{r}.pdf'))
|
|
if False:
|
|
axs.plot(tx.x/1e3, tx.y/1e3, marker='X', color='k')
|
|
fig.tight_layout()
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__)+f'.maxima.X{Xref}.run{r}.with_tx.pdf'))
|
|
axs.set_xlim(*old_xlims)
|
|
axs.set_ylim(*old_ylims)
|
|
fig.tight_layout()
|
|
|
|
##
|
|
best_idx = np.argmax(maxima_per_loc)
|
|
best_k = ks_per_loc[best_idx]
|
|
|
|
print("Max at location: ", locs[best_idx], ": ", maxima_per_loc[best_idx])
|
|
print('Best k:', best_k[sort_idx[best_idx]]) # Sorted by DESC amplitude
|
|
print('Mean best k:', np.rint(np.mean(best_k)))
|
|
print('first k:', first_trace_period_shift)
|
|
|
|
## Save best ks to file
|
|
np.savetxt(path.join(fig_dir, path.basename(__file__)+f'.bestk.X{Xref}.run{r}.txt'), best_k )
|
|
|
|
## Do a small reconstruction of the shower for best ks
|
|
if True:
|
|
print("Reconstructing for best k")
|
|
|
|
for j in range(2):
|
|
power_reconstruction = j==1
|
|
|
|
if power_reconstruction: # Do power reconstruction
|
|
# backup antenna times
|
|
backup_times = [ ant.t_AxB for ant in ev.antennas ]
|
|
|
|
# incorporate ks into timing
|
|
for i, ant in enumerate(ev.antennas):
|
|
ev.antennas[i].t_AxB = ant.t_AxB + best_k[i] * 1/f_beacon
|
|
|
|
xx, yy, p, ___ = rit.shower_plane_slice(ev, X=Xref, Nx=len(x), Ny=len(y), wx=(x[-1]-x[0])/2, wy=(y[-1]-y[0])/2, xoff=xoff, yoff=yoff, zgr=0)
|
|
|
|
# repair antenna times
|
|
for i, backup_t_AxB in enumerate(backup_times):
|
|
ev.antennas[i].t_AxB = backup_t_AxB
|
|
else: # get maximum amplitude at each location
|
|
maxima = np.empty( len(locs) )
|
|
for i, loc in tqdm(enumerate(locs), total=len(locs)):
|
|
test_loc = loc[0]* ev.uAxB + loc[1]*ev.uAxAxB + dXref *ev.uA
|
|
P, t_, a_, a_sum, t_sum = rit.pow_and_time(test_loc, ev, dt=dt)
|
|
maxima[i] = np.max(a_sum)
|
|
|
|
fig, axs = plt.subplots(figsize=figsize)
|
|
#axs.set_title(f"Shower slice for best k, Grid Run {r}")
|
|
axs.set_ylabel(" vxvxB [km]")
|
|
axs.set_xlabel("-v x B [km]")
|
|
|
|
if power_reconstruction:
|
|
sc_c = p
|
|
sc_label = 'Power [$(\\mu V/m)^2$]'
|
|
else:
|
|
sc_c = maxima
|
|
sc_label='Max Amplitude [$\\mu V/m$]'
|
|
sc = axs.scatter(xx/1e3, yy/1e3, c=sc_c, **scatter_kwargs)
|
|
fig.colorbar(sc, ax=axs, label=sc_label)
|
|
|
|
# indicate maximum value
|
|
idx = np.argmax(p if power_reconstruction else maxima)
|
|
axs.plot(xx[idx]/1e3, yy[idx]/1e3, 'bx', ms=30) # max value
|
|
axs.plot(0,0, 'r+', ms=30) # true axis
|
|
|
|
# make square figure
|
|
axs.set_aspect('equal', 'datalim')
|
|
|
|
#axs.legend(title=snr_str)
|
|
|
|
if fig_dir:
|
|
if power_reconstruction:
|
|
fname_extra = "power"
|
|
else:
|
|
fname_extra = "max_amp"
|
|
|
|
fig.tight_layout()
|
|
fig.savefig(path.join(fig_dir, path.basename(__file__)+f'.reconstruction.X{Xref}.run{r}.{fname_extra}.pdf'))
|
|
|
|
# Abort if no improvement
|
|
if ( r!= 0 and (old_ks_per_loc == ks_per_loc[best_idx] - first_trace_period_shift).all() ):
|
|
print(f"No changes from previous grid, breaking at iteration {r} out of {N_runs}")
|
|
|
|
try:
|
|
with open(run_break_fname, 'wt', encoding='utf-8') as fp:
|
|
fp.write(f"Breaked at grid iteration {r} out of {N_runs}")
|
|
except:
|
|
pass
|
|
|
|
break
|
|
|
|
old_ks_per_loc = ks_per_loc[best_idx]
|
|
|
|
# Save best ks to hdf5 antenna file
|
|
with h5py.File(antennas_fname, 'a') as fp:
|
|
group = fp.require_group('antennas')
|
|
|
|
for i, ant in enumerate(antennas):
|
|
h5ant = group[ant.name]
|
|
|
|
h5beacon_info = h5ant['beacon_info']
|
|
|
|
# find out freq_name
|
|
if freq_name is None:
|
|
freq_name = [ k for k in h5beacon_info.keys() if np.isclose(h5beacon_info[k].attrs['freq'], f_beacon)][0]
|
|
|
|
h5attrs = h5beacon_info[freq_name].attrs
|
|
|
|
h5attrs['best_k'] = old_ks_per_loc[i]
|
|
h5attrs['best_k_time'] = old_ks_per_loc[i]/f_beacon
|
|
|
|
if show_plots:
|
|
plt.show()
|