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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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Pulse: snr plot multiple template_dt curves
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parent
168b0a60bc
commit
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1 changed files with 114 additions and 103 deletions
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@ -401,11 +401,14 @@ if __name__ == "__main__":
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matplotlib.use('Agg')
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bp_freq = (30e-3, 80e-3) # GHz
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template_dt = 5e-2 # ns
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interp_template_dt = 5e-5 # ns
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template_length = 200 # ns
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antenna_dt = 2 # ns
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antenna_timelength = 1024 # ns
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N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
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template_dts = np.array([antenna_dt, 5e-1, 5e-2]) # ns
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snr_factors = np.concatenate( # 1/noise_amplitude factor
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(
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#[0.25, 0.5, 0.75],
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@ -415,8 +418,6 @@ if __name__ == "__main__":
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),
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axis=None, dtype=float)
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antenna_dt = 2 # ns
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antenna_timelength = 1024 # ns
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cut_wrong_peak_matches = True
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normalise_noise = False
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@ -454,112 +455,113 @@ if __name__ == "__main__":
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if True:
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plt.close(fig)
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#
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# Create the template
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# This is sampled at a lower samplerate than the interpolation template
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#
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template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
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#
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# Find time accuracies as a function of signal strength
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#
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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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time_accuracies = np.zeros((len(template_dts), len(snr_factors)))
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mask_counts = np.zeros_like(time_accuracies)
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for l, template_dt in tqdm(enumerate(template_dts)):
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time_residuals = get_time_residuals_for_template(
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N_residuals, template, interpolation_template=interp_template,
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antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
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snr_sigma_factor=snr_sigma_factor, bp_freq=bp_freq, normalise_noise=normalise_noise,
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h5_cache_fname=h5_cache_fname, rng=rng, tqdm=tqdm)
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# Create the template
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# This is sampled at a lower samplerate than the interpolation template
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template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
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print()# separating tqdm
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print()# separating tqdm
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for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
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# Make a plot of the time residuals
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if N_residuals > 1:
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for i in range(1 + cut_wrong_peak_matches):
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mask_count = 0
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# get the time residuals
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time_residuals = get_time_residuals_for_template(
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N_residuals, template, interpolation_template=interp_template,
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antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
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snr_sigma_factor=snr_sigma_factor, bp_freq=bp_freq, normalise_noise=normalise_noise,
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h5_cache_fname=h5_cache_fname, rng=rng, tqdm=tqdm)
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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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print()# separating tqdm
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print()# separating tqdm
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mask = wrong_peak_condition(time_residuals)
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# Make a plot of the time residuals
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if N_residuals > 1:
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for i in range(1 + cut_wrong_peak_matches):
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mask_count = 0
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mask_count = np.count_nonzero(mask)
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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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print("Masking {} residuals".format(mask_count))
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time_residuals = time_residuals[~mask]
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mask = wrong_peak_condition(time_residuals)
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if not mask_count:
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print("Continuing")
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continue
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mask_count = np.count_nonzero(mask)
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time_accuracies[k] = np.std(time_residuals)
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mask_counts[k] = mask_count
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print("Masking {} residuals".format(mask_count))
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time_residuals = time_residuals[~mask]
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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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if not mask_count:
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continue
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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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time_accuracies[l, k] = np.std(time_residuals)
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mask_counts[l, k] = mask_count
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dx = bins[1] - bins[0]
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scale = len(time_residuals) * dx
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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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xs = np.linspace(min_x, max_x)
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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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# 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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dx = bins[1] - bins[0]
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scale = len(time_residuals) * dx
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label = name +"(" + ','.join(param_names) + ')'
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xs = np.linspace(min_x, max_x)
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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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# 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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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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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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@ -567,10 +569,11 @@ if __name__ == "__main__":
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ax.set_title(f"Template matching SNR vs time accuracy")
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ax.set_xlabel("Signal to Noise Factor")
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ax.set_ylabel("Time Accuracy [ns]")
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ax.grid()
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ax.legend(title="\n".join([
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f"N={N_residuals}",
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f"template_dt={template_dt:0.1e}ns",
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#f"template_dt={template_dt:0.1e}ns",
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f"antenna_dt={antenna_dt:0.1e}ns",
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]))
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@ -578,28 +581,36 @@ if __name__ == "__main__":
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ax.set_xscale('log')
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ax.set_yscale('log')
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# plot the values
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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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# plot the values per template_dt slice
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template_dt_colors = [None]*len(template_dts)
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for k, template_dt in enumerate(template_dts):
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l = ax.plot(snr_factors[mask], time_accuracies[mask], **kwargs)
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# indicate masking values
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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 template_dt_colors[k] is None else template_dt_colors[k]
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)
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mask = mask_counts[k] >= mask_threshold[1]
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mask &= mask_counts[k] < mask_threshold[0]
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if True: # limit y-axis to 1e1
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ax.set_ylim([None, 1e1])
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l = ax.plot(snr_factors[mask], time_accuracies[k][mask], **kwargs)
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template_dt_colors[k] = l[0].get_color()
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# indicate threshold
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if True:
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ax.axhline(template_dt/np.sqrt(12), ls='--', alpha=0.7, color=template_dt_colors[k], label=f'Template dt:{template_dt:0.1e}ns')
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# Set horizontal line at 1 ns
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if True:
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ax.axhline(1, ls='--', alpha=0.8, color='g')
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ax.grid()
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ax.axhline(template_dt/np.sqrt(12), ls='--', alpha=0.7, color='b')
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ax.legend()
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if True: # limit y-axis to 1e1
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ax.set_ylim([None, 1e1])
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fig.tight_layout()
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fig.savefig(f"figures/11_time_res_vs_snr_tdt{template_dt:0.1e}.pdf")
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