mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 03:23:34 +01:00
Pulse: snr plot multiple template_dt curves
This commit is contained in:
parent
168b0a60bc
commit
59feab014e
1 changed files with 114 additions and 103 deletions
|
@ -401,11 +401,14 @@ if __name__ == "__main__":
|
||||||
matplotlib.use('Agg')
|
matplotlib.use('Agg')
|
||||||
|
|
||||||
bp_freq = (30e-3, 80e-3) # GHz
|
bp_freq = (30e-3, 80e-3) # GHz
|
||||||
template_dt = 5e-2 # ns
|
|
||||||
interp_template_dt = 5e-5 # ns
|
interp_template_dt = 5e-5 # ns
|
||||||
template_length = 200 # ns
|
template_length = 200 # ns
|
||||||
|
|
||||||
|
antenna_dt = 2 # ns
|
||||||
|
antenna_timelength = 1024 # ns
|
||||||
|
|
||||||
N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
|
N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
|
||||||
|
template_dts = np.array([antenna_dt, 5e-1, 5e-2]) # ns
|
||||||
snr_factors = np.concatenate( # 1/noise_amplitude factor
|
snr_factors = np.concatenate( # 1/noise_amplitude factor
|
||||||
(
|
(
|
||||||
#[0.25, 0.5, 0.75],
|
#[0.25, 0.5, 0.75],
|
||||||
|
@ -415,8 +418,6 @@ if __name__ == "__main__":
|
||||||
),
|
),
|
||||||
axis=None, dtype=float)
|
axis=None, dtype=float)
|
||||||
|
|
||||||
antenna_dt = 2 # ns
|
|
||||||
antenna_timelength = 1024 # ns
|
|
||||||
|
|
||||||
cut_wrong_peak_matches = True
|
cut_wrong_peak_matches = True
|
||||||
normalise_noise = False
|
normalise_noise = False
|
||||||
|
@ -454,112 +455,113 @@ if __name__ == "__main__":
|
||||||
if True:
|
if True:
|
||||||
plt.close(fig)
|
plt.close(fig)
|
||||||
|
|
||||||
#
|
|
||||||
# Create the template
|
|
||||||
# This is sampled at a lower samplerate than the interpolation template
|
|
||||||
#
|
|
||||||
template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
|
|
||||||
|
|
||||||
#
|
#
|
||||||
# Find time accuracies as a function of signal strength
|
# Find time accuracies as a function of signal strength
|
||||||
#
|
#
|
||||||
time_accuracies = np.zeros(len(snr_factors))
|
time_accuracies = np.zeros((len(template_dts), len(snr_factors)))
|
||||||
mask_counts = np.zeros(len(snr_factors))
|
mask_counts = np.zeros_like(time_accuracies)
|
||||||
for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
|
for l, template_dt in tqdm(enumerate(template_dts)):
|
||||||
|
|
||||||
time_residuals = get_time_residuals_for_template(
|
# Create the template
|
||||||
N_residuals, template, interpolation_template=interp_template,
|
# This is sampled at a lower samplerate than the interpolation template
|
||||||
antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
|
template, _ = create_template(dt=template_dt, timelength=template_length, bp_freq=bp_freq, name='Template')
|
||||||
snr_sigma_factor=snr_sigma_factor, bp_freq=bp_freq, normalise_noise=normalise_noise,
|
|
||||||
h5_cache_fname=h5_cache_fname, rng=rng, tqdm=tqdm)
|
|
||||||
|
|
||||||
print()# separating tqdm
|
for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
|
||||||
print()# separating tqdm
|
|
||||||
|
|
||||||
# Make a plot of the time residuals
|
# get the time residuals
|
||||||
if N_residuals > 1:
|
time_residuals = get_time_residuals_for_template(
|
||||||
for i in range(1 + cut_wrong_peak_matches):
|
N_residuals, template, interpolation_template=interp_template,
|
||||||
mask_count = 0
|
antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
|
||||||
|
snr_sigma_factor=snr_sigma_factor, bp_freq=bp_freq, normalise_noise=normalise_noise,
|
||||||
|
h5_cache_fname=h5_cache_fname, rng=rng, tqdm=tqdm)
|
||||||
|
|
||||||
if i==1: # if cut_wrong_peak_matches:
|
print()# separating tqdm
|
||||||
wrong_peak_condition = lambda t_res: abs(t_res) > antenna_dt*4
|
print()# separating tqdm
|
||||||
|
|
||||||
mask = wrong_peak_condition(time_residuals)
|
# Make a plot of the time residuals
|
||||||
|
if N_residuals > 1:
|
||||||
|
for i in range(1 + cut_wrong_peak_matches):
|
||||||
|
mask_count = 0
|
||||||
|
|
||||||
mask_count = np.count_nonzero(mask)
|
if i==1: # if cut_wrong_peak_matches:
|
||||||
|
wrong_peak_condition = lambda t_res: abs(t_res) > antenna_dt*4
|
||||||
|
|
||||||
print("Masking {} residuals".format(mask_count))
|
mask = wrong_peak_condition(time_residuals)
|
||||||
time_residuals = time_residuals[~mask]
|
|
||||||
|
|
||||||
if not mask_count:
|
mask_count = np.count_nonzero(mask)
|
||||||
print("Continuing")
|
|
||||||
continue
|
|
||||||
|
|
||||||
time_accuracies[k] = np.std(time_residuals)
|
print("Masking {} residuals".format(mask_count))
|
||||||
mask_counts[k] = mask_count
|
time_residuals = time_residuals[~mask]
|
||||||
|
|
||||||
hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
|
if not mask_count:
|
||||||
fig, ax = plt.subplots()
|
continue
|
||||||
ax.set_title(
|
|
||||||
"Template Correlation Lag finding"
|
|
||||||
+ f"\n template dt: {template_dt: .1e}ns"
|
|
||||||
+ f"; antenna dt: {antenna_dt: .1e}ns"
|
|
||||||
+ ";" if not mask_count else "\n"
|
|
||||||
+ f"snr_factor: {snr_sigma_factor: .1e}"
|
|
||||||
+ "" if not mask_count else f"; N_masked: {mask_count}"
|
|
||||||
)
|
|
||||||
ax.set_xlabel("Time Residual [ns]")
|
|
||||||
ax.set_ylabel("#")
|
|
||||||
|
|
||||||
counts, bins, _patches = ax.hist(time_residuals, **hist_kwargs)
|
time_accuracies[l, k] = np.std(time_residuals)
|
||||||
if True: # fit gaussian to histogram
|
mask_counts[l, k] = mask_count
|
||||||
min_x = min(time_residuals)
|
|
||||||
max_x = max(time_residuals)
|
|
||||||
|
|
||||||
dx = bins[1] - bins[0]
|
hist_kwargs = dict(bins='sqrt', density=False, alpha=0.8, histtype='step')
|
||||||
scale = len(time_residuals) * dx
|
fig, ax = plt.subplots()
|
||||||
|
ax.set_title(
|
||||||
|
"Template Correlation Lag finding"
|
||||||
|
+ f"\n template dt: {template_dt: .1e}ns"
|
||||||
|
+ f"; antenna dt: {antenna_dt: .1e}ns"
|
||||||
|
+ ";" if not mask_count else "\n"
|
||||||
|
+ f"snr_factor: {snr_sigma_factor: .1e}"
|
||||||
|
+ "" if not mask_count else f"; N_masked: {mask_count}"
|
||||||
|
)
|
||||||
|
ax.set_xlabel("Time Residual [ns]")
|
||||||
|
ax.set_ylabel("#")
|
||||||
|
|
||||||
xs = np.linspace(min_x, max_x)
|
counts, bins, _patches = ax.hist(time_residuals, **hist_kwargs)
|
||||||
|
if True: # fit gaussian to histogram
|
||||||
|
min_x = min(time_residuals)
|
||||||
|
max_x = max(time_residuals)
|
||||||
|
|
||||||
# do the fit
|
dx = bins[1] - bins[0]
|
||||||
name = "Norm"
|
scale = len(time_residuals) * dx
|
||||||
param_names = [ "$\\mu$", "$\\sigma$" ]
|
|
||||||
distr_func = stats.norm
|
|
||||||
|
|
||||||
label = name +"(" + ','.join(param_names) + ')'
|
xs = np.linspace(min_x, max_x)
|
||||||
|
|
||||||
# plot
|
# do the fit
|
||||||
fit_params = distr_func.fit(time_residuals)
|
name = "Norm"
|
||||||
fit_ys = scale * distr_func.pdf(xs, *fit_params)
|
param_names = [ "$\\mu$", "$\\sigma$" ]
|
||||||
ax.plot(xs, fit_ys, label=label)
|
distr_func = stats.norm
|
||||||
|
|
||||||
|
label = name +"(" + ','.join(param_names) + ')'
|
||||||
|
|
||||||
|
# plot
|
||||||
|
fit_params = distr_func.fit(time_residuals)
|
||||||
|
fit_ys = scale * distr_func.pdf(xs, *fit_params)
|
||||||
|
ax.plot(xs, fit_ys, label=label)
|
||||||
|
|
||||||
|
# chisq
|
||||||
|
ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
|
||||||
|
if True:
|
||||||
|
ct *= np.sum(counts)/np.sum(ct)
|
||||||
|
c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
|
||||||
|
chisq_strs = [
|
||||||
|
f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
|
||||||
|
]
|
||||||
|
|
||||||
|
# text on plot
|
||||||
|
text_str = "\n".join(
|
||||||
|
[label]
|
||||||
|
+
|
||||||
|
[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, fit_params, fillvalue='?') ]
|
||||||
|
+
|
||||||
|
chisq_strs
|
||||||
|
)
|
||||||
|
|
||||||
|
ax.text( *(0.02, 0.95), text_str, fontsize=12, ha='left', va='top', transform=ax.transAxes)
|
||||||
|
|
||||||
|
if mask_count:
|
||||||
|
fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}_masked.pdf")
|
||||||
|
else:
|
||||||
|
fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}.pdf")
|
||||||
|
|
||||||
# chisq
|
|
||||||
ct = np.diff(distr_func.cdf(bins, *fit_params))*np.sum(counts)
|
|
||||||
if True:
|
if True:
|
||||||
ct *= np.sum(counts)/np.sum(ct)
|
plt.close(fig)
|
||||||
c2t = stats.chisquare(counts, ct, ddof=len(fit_params))
|
|
||||||
chisq_strs = [
|
|
||||||
f"$\\chi^2$/dof = {c2t[0]: .2g}/{len(fit_params)}"
|
|
||||||
]
|
|
||||||
|
|
||||||
# text on plot
|
|
||||||
text_str = "\n".join(
|
|
||||||
[label]
|
|
||||||
+
|
|
||||||
[ f"{param} = {value: .2e}" for param, value in zip_longest(param_names, fit_params, fillvalue='?') ]
|
|
||||||
+
|
|
||||||
chisq_strs
|
|
||||||
)
|
|
||||||
|
|
||||||
ax.text( *(0.02, 0.95), text_str, fontsize=12, ha='left', va='top', transform=ax.transAxes)
|
|
||||||
|
|
||||||
if mask_count:
|
|
||||||
fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}_masked.pdf")
|
|
||||||
else:
|
|
||||||
fig.savefig(f"figures/11_time_residual_hist_tdt{template_dt:0.1e}_n{snr_sigma_factor:.1e}.pdf")
|
|
||||||
|
|
||||||
if True:
|
|
||||||
plt.close(fig)
|
|
||||||
|
|
||||||
# SNR time accuracy plot
|
# SNR time accuracy plot
|
||||||
if True:
|
if True:
|
||||||
|
@ -567,10 +569,11 @@ if __name__ == "__main__":
|
||||||
ax.set_title(f"Template matching SNR vs time accuracy")
|
ax.set_title(f"Template matching SNR vs time accuracy")
|
||||||
ax.set_xlabel("Signal to Noise Factor")
|
ax.set_xlabel("Signal to Noise Factor")
|
||||||
ax.set_ylabel("Time Accuracy [ns]")
|
ax.set_ylabel("Time Accuracy [ns]")
|
||||||
|
ax.grid()
|
||||||
|
|
||||||
ax.legend(title="\n".join([
|
ax.legend(title="\n".join([
|
||||||
f"N={N_residuals}",
|
f"N={N_residuals}",
|
||||||
f"template_dt={template_dt:0.1e}ns",
|
#f"template_dt={template_dt:0.1e}ns",
|
||||||
f"antenna_dt={antenna_dt:0.1e}ns",
|
f"antenna_dt={antenna_dt:0.1e}ns",
|
||||||
]))
|
]))
|
||||||
|
|
||||||
|
@ -578,28 +581,36 @@ if __name__ == "__main__":
|
||||||
ax.set_xscale('log')
|
ax.set_xscale('log')
|
||||||
ax.set_yscale('log')
|
ax.set_yscale('log')
|
||||||
|
|
||||||
# plot the values
|
# plot the values per template_dt slice
|
||||||
l = None
|
template_dt_colors = [None]*len(template_dts)
|
||||||
for j, mask_threshold in enumerate(pairwise([np.inf, 250, 50, 1, 0])):
|
for k, template_dt in enumerate(template_dts):
|
||||||
kwargs = dict(
|
|
||||||
ls='none',
|
|
||||||
marker=['^', 'v','8', 'o',][j],
|
|
||||||
color=None if l is None else l[0].get_color(),
|
|
||||||
)
|
|
||||||
mask = mask_counts >= mask_threshold[1]
|
|
||||||
mask &= mask_counts < mask_threshold[0]
|
|
||||||
|
|
||||||
l = ax.plot(snr_factors[mask], time_accuracies[mask], **kwargs)
|
# indicate masking values
|
||||||
|
for j, mask_threshold in enumerate(pairwise([np.inf, 250, 50, 1, 0])):
|
||||||
|
kwargs = dict(
|
||||||
|
ls='none',
|
||||||
|
marker=['^', 'v','8', 'o',][j],
|
||||||
|
color= None if template_dt_colors[k] is None else template_dt_colors[k]
|
||||||
|
)
|
||||||
|
mask = mask_counts[k] >= mask_threshold[1]
|
||||||
|
mask &= mask_counts[k] < mask_threshold[0]
|
||||||
|
|
||||||
if True: # limit y-axis to 1e1
|
l = ax.plot(snr_factors[mask], time_accuracies[k][mask], **kwargs)
|
||||||
ax.set_ylim([None, 1e1])
|
template_dt_colors[k] = l[0].get_color()
|
||||||
|
|
||||||
|
# indicate threshold
|
||||||
|
if True:
|
||||||
|
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')
|
||||||
|
|
||||||
|
|
||||||
# Set horizontal line at 1 ns
|
# Set horizontal line at 1 ns
|
||||||
if True:
|
if True:
|
||||||
ax.axhline(1, ls='--', alpha=0.8, color='g')
|
ax.axhline(1, ls='--', alpha=0.8, color='g')
|
||||||
ax.grid()
|
|
||||||
ax.axhline(template_dt/np.sqrt(12), ls='--', alpha=0.7, color='b')
|
ax.legend()
|
||||||
|
|
||||||
|
if True: # limit y-axis to 1e1
|
||||||
|
ax.set_ylim([None, 1e1])
|
||||||
|
|
||||||
fig.tight_layout()
|
fig.tight_layout()
|
||||||
fig.savefig(f"figures/11_time_res_vs_snr_tdt{template_dt:0.1e}.pdf")
|
fig.savefig(f"figures/11_time_res_vs_snr_tdt{template_dt:0.1e}.pdf")
|
||||||
|
|
Loading…
Reference in a new issue