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ZH: ca_period_from_shower as used for Thesis Figures
This commit is contained in:
parent
f50b5cb308
commit
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1 changed files with 166 additions and 60 deletions
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@ -25,7 +25,25 @@ try:
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except:
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tqdm = lambda x: x
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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):
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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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@ -80,9 +98,9 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
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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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a_sum += f(t_sum)
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a_first = a_sum
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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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@ -92,47 +110,67 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
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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_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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#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_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='strongest trace', ls='none', ms=20)
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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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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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ax.plot(t_sum, augmented_a, alpha=0.7, ls='dashed', label=f'{k:g}')
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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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best_period_shifts[i] = allowed_ks[best_idx]
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best_augmented_a = f(t_sum + k*period)
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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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ax.plot(t_sum, best_augmented_a, alpha=0.8, label=f'best $k$={best_period_shifts[i]:g}', lw=2)
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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 )
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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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@ -147,14 +185,15 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
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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 = 100
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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_r[np.argmax(E_)]
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wx = 50 + (max(best_period_shifts) - min(best_period_shifts))*dt
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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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@ -165,13 +204,30 @@ def find_best_period_shifts_summing_at_location(test_loc, antennas, allowed_ks,
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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)
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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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@ -204,7 +260,16 @@ if __name__ == "__main__":
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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 = (12,8)
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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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@ -248,7 +313,7 @@ if __name__ == "__main__":
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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']: .1g}"
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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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@ -349,8 +414,8 @@ if __name__ == "__main__":
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if True: # zoom
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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, x = 200, min(ant.t_AxB)
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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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@ -358,7 +423,7 @@ if __name__ == "__main__":
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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 = 300
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wx = 150
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ax.set_xlim(x-wx//2, x+wx//2)
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fig.savefig(path.join(fig_dir, path.basename(__file__)+f".traces.A{ant.name}.zoomed.peak.pdf"))
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@ -380,22 +445,23 @@ if __name__ == "__main__":
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## Determine grid positions
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##
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dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),Xref,0)
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zgr = 0 + ev.core[2]
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dXref = atm.distance_to_slant_depth(np.deg2rad(ev.zenith),Xref,zgr)
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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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if args.quick_run: #quicky
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x_coarse = np.linspace(-scale2d, scale2d, 6)
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y_coarse = np.linspace(-scale2d, scale2d, 6)
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x_coarse = np.linspace(-scale4d, scale4d, 16)
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y_coarse = np.linspace(-scale4d, scale4d, 16)
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x_fine = x_coarse/4
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y_fine = y_coarse/4
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else: # long
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x_coarse = np.linspace(-scale4d, scale4d, 40)
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y_coarse = np.linspace(-scale4d, scale4d, 40)
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x_coarse = np.linspace(-scale4d, scale4d, 14)
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y_coarse = np.linspace(-scale4d, scale4d, 14)
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x_fine = np.linspace(-scale2d, scale2d, 40)
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y_fine = np.linspace(-scale2d, scale2d, 40)
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x_fine = np.linspace(-scale2d, scale2d, 18)
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y_fine = np.linspace(-scale2d, scale2d, 18)
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## Remove run_break_fname if it exists
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try:
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@ -435,9 +501,29 @@ if __name__ == "__main__":
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yy = []
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N_loc = len(maxima_per_loc)
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for i, (x_, y_) in tqdm(enumerate(product(x,y)), total=N_loc, ):
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# Make the grid a list of locations
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xx = []
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yy = []
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for i, (x_, y_) in enumerate(product(x,y)):
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xx.append( x_+xoff )
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yy.append( y_+yoff )
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xx = np.array(xx)
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yy = np.array(yy)
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locs = list(zip(xx, yy))
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# Shift the reference trace
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if r == 0:
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first_trace_period_shift = 0
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else:
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first_trace_period_shift = 0#first_trace_period_shift + np.rint(np.mean(old_ks_per_loc))
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print("New first trace period:", first_trace_period_shift)
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# define loop func for joblib
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def loop_func(loc, dXref=dXref, i=1):
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tmp_fig_subdir = None
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if i % 10 ==0:
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if i == 0:
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if hasattr(tqdm, '__code__') and tqdm.__code__.co-name == '<lambda>':
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print(f"Testing location {i} out of {N_loc}")
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tmp_fig_subdir = fig_subdir
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@ -445,33 +531,41 @@ if __name__ == "__main__":
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test_loc = loc[0]* ev.uAxB + loc[1]*ev.uAxAxB + dXref *ev.uA
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# Find best k for each antenna
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ks_per_loc[i], maxima_per_loc[i] = find_best_period_shifts_summing_at_location(test_loc, ev.antennas, allowed_ks, period=1/f_beacon, dt=dt,
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plot_iteration_with_shifted_trace=[ 5, len(ev.antennas)-1],
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fig_dir=tmp_fig_subdir, fig_distinguish=f"X{Xref}. run{r}.",
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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,
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plot_iteration_with_shifted_trace=[ 1, 2, 3, 4, 5, len(ev.antennas)-1],
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fig_dir=tmp_fig_subdir, fig_distinguish=f"X{Xref}.run{r}.",
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snr_str=snr_str,shower_plane_loc=(loc[0]/1e3, loc[1]/1e3, dXref),
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)
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xx = np.array(xx)
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yy = np.array(yy)
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locs = list(zip(xx, yy))
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res = ( delayed(loop_func)(loc, i=i) for i, loc in enumerate(locs) )
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if Parallel:
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res = Parallel(n_jobs=None)(tqdm(res, total=len(locs)))
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else:
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res = tqdm(res, total=len(locs))
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# unpack loop results
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ks_per_loc, maxima_per_loc, sort_idx = zip(*res)
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## Save maxima to file
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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)) )
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scatter_kwargs = dict(cmap='Spectral_r', s=64*4, alpha=0.6)
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if True: #plot maximum at test locations
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fig, axs = plt.subplots(figsize=figsize)
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axs.set_title(f"Optimizing signal strength by varying $k$ per antenna,\n Grid Run {r}")
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axs.set_ylabel("vxvxB [km]")
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axs.set_xlabel(" vxB [km]")
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#axs.set_title(f"Optimizing signal strength by varying $k$ per antenna,\n Grid Run {r}")
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axs.set_ylabel(" vxvxB [km]")
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axs.set_xlabel("-v x B [km]")
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axs.set_aspect('equal', 'datalim')
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sc = axs.scatter(xx/1e3, yy/1e3, c=maxima_per_loc, cmap='Spectral_r', alpha=0.6)
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sc = axs.scatter(xx/1e3, yy/1e3, c=maxima_per_loc, **scatter_kwargs)
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fig.colorbar(sc, ax=axs, label='Max Amplitude [$\\mu V/m$]')
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axs.legend(title=snr_str)
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#axs.legend(title=snr_str)
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# indicate maximum value
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idx = np.argmax(maxima_per_loc)
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axs.plot(xx[idx]/1e3, yy[idx]/1e3, 'bx', ms=30)
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axs.plot(xx[idx]/1e3, yy[idx]/1e3, 'bx', ms=30) # max value
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axs.plot(0,0, 'r+', ms=30) # true axis
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if fig_dir:
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old_xlims = axs.get_xlim()
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@ -490,8 +584,10 @@ if __name__ == "__main__":
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best_idx = np.argmax(maxima_per_loc)
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best_k = ks_per_loc[best_idx]
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print("Max at location: ", locs[best_idx])
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print('Best k:', best_k)
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print("Max at location: ", locs[best_idx], ": ", maxima_per_loc[best_idx])
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print('Best k:', best_k[sort_idx[best_idx]]) # Sorted by DESC amplitude
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print('Mean best k:', np.rint(np.mean(best_k)))
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print('first k:', first_trace_period_shift)
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## Save best ks to file
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np.savetxt(path.join(fig_dir, path.basename(__file__)+f'.bestk.X{Xref}.run{r}.txt'), best_k )
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@ -509,33 +605,43 @@ if __name__ == "__main__":
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# incorporate ks into timing
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for i, ant in enumerate(ev.antennas):
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ev.antennas[i].t_AxB = ant.t_AxB - best_k[i] * 1/f_beacon
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ev.antennas[i].t_AxB = ant.t_AxB + best_k[i] * 1/f_beacon
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xx, yy, p, ___ = rit.shower_plane_slice(ev, X=Xref, Nx=len(x), Ny=len(y), wx=x[-1]-x[0], wy=y[-1]-y[0], xoff=xoff, yoff=yoff, zgr=0)
|
||||
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 enumerate(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(" vxB [km]")
|
||||
axs.set_aspect('equal', 'datalim')
|
||||
if power_reconstruction:
|
||||
sc = axs.scatter(xx/1e3, yy/1e3, c=p, cmap='Spectral_r', alpha=0.6)
|
||||
fig.colorbar(sc, ax=axs, label='Power')
|
||||
else:
|
||||
sc = axs.scatter(xx/1e3, yy/1e3, c=maxima, cmap='Spectral_r', alpha=0.6)
|
||||
fig.colorbar(sc, ax=axs, label='Max Amplitude [$\\mu V/m$]')
|
||||
#axs.set_title(f"Shower slice for best k, Grid Run {r}")
|
||||
axs.set_ylabel(" vxvxB [km]")
|
||||
axs.set_xlabel("-v x B [km]")
|
||||
|
||||
axs.legend(title=snr_str)
|
||||
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:
|
||||
|
|
@ -547,7 +653,7 @@ if __name__ == "__main__":
|
|||
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]).all() ):
|
||||
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:
|
||||
|
|
|
|||
Loading…
Reference in a new issue