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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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Pulse: snr plot: indicate masking
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279ea46550
commit
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1 changed files with 88 additions and 52 deletions
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@ -5,7 +5,7 @@ from lib import util
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from scipy import signal, interpolate, stats
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import matplotlib.pyplot as plt
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import numpy as np
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from itertools import zip_longest
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from itertools import zip_longest, pairwise
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import h5py
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from copy import deepcopy
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@ -232,6 +232,7 @@ if __name__ == "__main__":
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h5_cache_fname = f'11_pulsed_timing.hdf5'
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time_accuracies = np.zeros(len(snr_factors))
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mask_counts = np.zeros(len(snr_factors))
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for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
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# Read in cached time residuals
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if True:
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@ -448,65 +449,90 @@ if __name__ == "__main__":
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# Make a plot of the time residuals
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if N_residuals > 1:
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time_accuracies[k] = np.std(time_residuals[:N_residuals])
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time_residuals = time_residuals[:N_residuals]
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hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
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fig, ax = plt.subplots()
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ax.set_title(
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"Template Correlation Lag finding"
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+ f"\n template dt: {template_dt*1e3: .1e}ps"
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+ f"; antenna dt: {antenna_dt: .1e}ns"
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+ f"; noise_factor: {noise_sigma_factor: .1e}"
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)
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ax.set_xlabel("Time Residual [ns]")
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ax.set_ylabel("#")
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for i in range(1 + cut_wrong_peak_matches):
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mask_count = 0
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counts, bins, _patches = ax.hist(time_residuals, **hist_kwargs)
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if True: # fit gaussian to histogram
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min_x = min(time_residuals)
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max_x = max(time_residuals)
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if i==1: # if cut_wrong_peak_matches:
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wrong_peak_condition = lambda t_res: abs(t_res) > antenna_dt*4
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dx = bins[1] - bins[0]
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scale = len(time_residuals) * dx
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mask = wrong_peak_condition(time_residuals)
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xs = np.linspace(min_x, max_x)
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mask_count = np.count_nonzero(mask)
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# do the fit
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name = "Norm"
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param_names = [ "$\\mu$", "$\\sigma$" ]
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distr_func = stats.norm
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print("Masking {} residuals".format(mask_count))
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time_residuals = time_residuals[~mask]
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label = name +"(" + ','.join(param_names) + ')'
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if not mask_count:
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print("Continuing")
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continue
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# plot
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fit_params = distr_func.fit(time_residuals)
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fit_ys = scale * distr_func.pdf(xs, *fit_params)
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ax.plot(xs, fit_ys, label=label)
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time_accuracies[k] = np.std(time_residuals)
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mask_counts[k] = mask_count
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hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
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fig, ax = plt.subplots()
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ax.set_title(
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"Template Correlation Lag finding"
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+ f"\n template dt: {template_dt: .1e}ns"
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+ f"; antenna dt: {antenna_dt: .1e}ns"
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+ ";" if not mask_count else "\n"
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+ f"snr_factor: {snr_sigma_factor: .1e}"
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+ "" if not mask_count else f"; N_masked: {mask_count}"
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)
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ax.set_xlabel("Time Residual [ns]")
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ax.set_ylabel("#")
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counts, bins, _patches = ax.hist(time_residuals, **hist_kwargs)
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if True: # fit gaussian to histogram
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min_x = min(time_residuals)
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max_x = max(time_residuals)
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dx = bins[1] - bins[0]
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scale = len(time_residuals) * dx
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xs = np.linspace(min_x, max_x)
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# do the fit
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name = "Norm"
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param_names = [ "$\\mu$", "$\\sigma$" ]
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distr_func = stats.norm
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label = name +"(" + ','.join(param_names) + ')'
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# plot
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fit_params = distr_func.fit(time_residuals)
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fit_ys = scale * distr_func.pdf(xs, *fit_params)
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ax.plot(xs, fit_ys, label=label)
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# chisq
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ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
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if True:
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ct *= np.sum(counts)/np.sum(ct)
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c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
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chisq_strs = [
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f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
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]
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# text on plot
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text_str = "\n".join(
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[label]
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+
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[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, fit_params, fillvalue='?') ]
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+
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chisq_strs
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)
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ax.text( *(0.02, 0.95), text_str, fontsize=12, ha='left', va='top', transform=ax.transAxes)
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if mask_count:
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fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}_masked.pdf")
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else:
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fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}.pdf")
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# chisq
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ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
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if True:
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ct *= np.sum(counts)/np.sum(ct)
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c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
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chisq_strs = [
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f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
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]
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# text on plot
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text_str = "\n".join(
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[label]
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+
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[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, fit_params, fillvalue='?') ]
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+
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chisq_strs
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)
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ax.text( *(0.02, 0.95), text_str, fontsize=12, ha='left', va='top', transform=ax.transAxes)
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fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{noise_sigma_factor: .1e}.pdf")
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if True:
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plt.close(fig)
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plt.close(fig)
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# SNR time accuracy plot
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if True:
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@ -526,7 +552,17 @@ if __name__ == "__main__":
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ax.set_yscale('log')
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# plot the values
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ax.plot(np.asarray(snr_factors), time_accuracies, ls='none', marker='o')
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l = None
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for j, mask_threshold in enumerate(pairwise([np.inf, 250, 50, 1, 0])):
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kwargs = dict(
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ls='none',
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marker=['^', 'v','8', 'o',][j],
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color=None if l is None else l[0].get_color(),
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)
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mask = mask_counts >= mask_threshold[1]
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mask &= mask_counts < mask_threshold[0]
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l = ax.plot(snr_factors[mask], time_accuracies[mask], **kwargs)
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if True: # limit y-axis to 1e0
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ax.set_ylim([None, 1e1])
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