mirror of
https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-12-22 03:23:34 +01:00
Pulse: move timeresidual matching to function
This commit is contained in:
parent
1f00a3fe76
commit
168b0a60bc
1 changed files with 227 additions and 211 deletions
|
|
@ -172,6 +172,227 @@ def create_template(dt=1, timelength=1, bp_freq=(0, np.inf), name=None, normalis
|
||||||
|
|
||||||
return template, _deltapeak
|
return template, _deltapeak
|
||||||
|
|
||||||
|
def get_time_residuals_for_template(
|
||||||
|
N_residuals, template, interpolation_template=None,
|
||||||
|
antenna_dt=1, antenna_timelength=100,
|
||||||
|
snr_sigma_factor=10,bp_freq=(0,np.inf),
|
||||||
|
normalise_noise=False, h5_cache_fname=None, read_cache=True, write_cache=None,
|
||||||
|
rng=rng, tqdm=tqdm,
|
||||||
|
):
|
||||||
|
# Read in cached time residuals
|
||||||
|
if read_cache:
|
||||||
|
cached_time_residuals = read_time_residuals_cache(h5_cache_fname, template.dt, antenna_dt, snr_sigma_factor)
|
||||||
|
|
||||||
|
else:
|
||||||
|
cached_time_residuals = np.array([])
|
||||||
|
|
||||||
|
#
|
||||||
|
# Find difference between true and templated times
|
||||||
|
#
|
||||||
|
time_residuals = np.zeros(max(0, (N_residuals - len(cached_time_residuals))))
|
||||||
|
for j in tqdm(range(len(time_residuals))):
|
||||||
|
do_plots = j==0
|
||||||
|
|
||||||
|
# receive at antenna
|
||||||
|
## place the deltapeak signal at a random location
|
||||||
|
antenna = Waveform(None, dt=antenna_dt, name='Signal')
|
||||||
|
if interpolation_template is None: # Create antenna trace without interpolation template
|
||||||
|
antenna_true_signal, antenna_peak_sample = util.deltapeak(timelength=antenna_timelength, samplerate=1/antenna.dt, offset=[0.2, 0.8], rng=rng)
|
||||||
|
|
||||||
|
antenna.peak_sample = antenna_peak_sample
|
||||||
|
antenna.peak_time = antenna.dt * antenna.peak_sample
|
||||||
|
antenna.signal = antenna_bp(antenna.signal, *bp_freq, antenna.dt)
|
||||||
|
print(f"Antenna Peak Time: {antenna.peak_time}")
|
||||||
|
print(f"Antenna Peak Sample: {antenna.peak_sample}")
|
||||||
|
|
||||||
|
else: # Sample the interpolation template at some offset
|
||||||
|
antenna.peak_time = antenna_timelength * ((0.8 - 0.2) *rng.random(1) + 0.2)
|
||||||
|
sampling_offset = rng.random(1)*antenna.dt
|
||||||
|
|
||||||
|
antenna.t = util.sampled_time(1/antenna.dt, start=0, end=antenna_timelength)
|
||||||
|
|
||||||
|
# Sample the interpolation template
|
||||||
|
antenna.signal = interpolation_template.interpolate(antenna.t - antenna.peak_time)
|
||||||
|
|
||||||
|
antenna.peak_sample = antenna.peak_time/antenna.dt
|
||||||
|
antenna_true_signal = antenna.signal
|
||||||
|
|
||||||
|
true_time_offset = antenna.peak_time - template.peak_time
|
||||||
|
|
||||||
|
if False: # flip polarisation
|
||||||
|
antenna.signal *= -1
|
||||||
|
|
||||||
|
## Add noise
|
||||||
|
noise_amplitude = max(template.signal) * 1/snr_sigma_factor
|
||||||
|
noise_realisation = noise_amplitude * white_noise_realisation(len(antenna.signal), normalise=normalise_noise)
|
||||||
|
filtered_noise = antenna_bp(noise_realisation, *bp_freq, antenna.dt)
|
||||||
|
|
||||||
|
antenna.signal += filtered_noise
|
||||||
|
|
||||||
|
# Show signals
|
||||||
|
if do_plots:
|
||||||
|
fig, axs = plt.subplots(2, sharex=True)
|
||||||
|
axs[0].set_title("Antenna Waveform")
|
||||||
|
axs[-1].set_xlabel("Time [ns]")
|
||||||
|
axs[0].set_ylabel("Amplitude")
|
||||||
|
axs[0].plot(antenna.t, antenna.signal, label='bandpassed w/ noise', alpha=0.9)
|
||||||
|
axs[0].plot(antenna.t, antenna.signal - filtered_noise, label='bandpassed w/o noise', alpha=0.9)
|
||||||
|
axs[0].legend()
|
||||||
|
|
||||||
|
axs[1].set_title("Template")
|
||||||
|
axs[1].set_ylabel("Amplitude")
|
||||||
|
axs[1].plot(template.t, template.signal, label='orig')
|
||||||
|
axs[1].plot(template.t + true_time_offset, template.signal, label='true moved orig')
|
||||||
|
axs[1].legend()
|
||||||
|
|
||||||
|
axs[0].grid()
|
||||||
|
axs[1].grid()
|
||||||
|
|
||||||
|
fig.savefig('figures/11_antenna_signals.pdf')
|
||||||
|
|
||||||
|
if True: # zoom
|
||||||
|
wx = 100
|
||||||
|
x0 = true_time_offset
|
||||||
|
|
||||||
|
old_xlims = axs[0].get_xlim()
|
||||||
|
axs[0].set_xlim( x0-wx, x0+wx)
|
||||||
|
fig.savefig('figures/11_antenna_signals_zoom.pdf')
|
||||||
|
|
||||||
|
# restore
|
||||||
|
axs[0].set_xlim(*old_xlims)
|
||||||
|
|
||||||
|
if True:
|
||||||
|
plt.close(fig)
|
||||||
|
|
||||||
|
axs2 = None
|
||||||
|
if True: # upsampled trace
|
||||||
|
upsampled_trace, upsampled_t = trace_upsampler(antenna.signal, template.t, antenna.t)
|
||||||
|
if do_plots: # Show upsampled traces
|
||||||
|
fig2, axs2 = plt.subplots(1, sharex=True)
|
||||||
|
if not hasattr(axs2, '__len__'):
|
||||||
|
axs2 = [axs2]
|
||||||
|
|
||||||
|
axs2[-1].set_xlabel("Time [ns]")
|
||||||
|
axs2[0].set_ylabel("Amplitude")
|
||||||
|
axs2[0].plot(antenna.t, antenna.signal, marker='o', label='orig')
|
||||||
|
axs2[0].plot(upsampled_t, upsampled_trace, label='upsampled')
|
||||||
|
axs2[0].legend(loc='upper right')
|
||||||
|
|
||||||
|
fig2.savefig('figures/11_upsampled.pdf')
|
||||||
|
|
||||||
|
wx = 1e2
|
||||||
|
x0 = upsampled_t[0] + wx - 5
|
||||||
|
axs2[0].set_xlim(x0-wx, x0+wx)
|
||||||
|
fig2.savefig('figures/11_upsampled_zoom.pdf')
|
||||||
|
|
||||||
|
if True:
|
||||||
|
plt.close(fig2)
|
||||||
|
|
||||||
|
# determine correlations with arguments
|
||||||
|
lag_dt = upsampled_t[1] - upsampled_t[0]
|
||||||
|
corrs, (out1_signal, out2_template, lags) = my_correlation(upsampled_trace, template.signal)
|
||||||
|
|
||||||
|
# Determine best correlation time
|
||||||
|
idx = np.argmax(abs(corrs))
|
||||||
|
best_sample_lag = lags[idx]
|
||||||
|
best_time_lag = best_sample_lag * lag_dt
|
||||||
|
|
||||||
|
else: # downsampled template
|
||||||
|
raise NotImplementedError
|
||||||
|
|
||||||
|
corrs, (_, _, lags) = my_downsampling_correlation(antenna.signal, antenna.t, template.signal, template.t)
|
||||||
|
lag_dt = upsampled_t[1] - upsampled_t[0]
|
||||||
|
|
||||||
|
# Calculate the time residual
|
||||||
|
time_residuals[j] = best_time_lag - true_time_offset
|
||||||
|
|
||||||
|
if not do_plots:
|
||||||
|
continue
|
||||||
|
|
||||||
|
if do_plots and axs2:
|
||||||
|
axs2[-1].axvline(best_time_lag, color='r', alpha=0.5, linewidth=2)
|
||||||
|
axs2[-1].axvline(true_time_offset, color='g', alpha=0.5, linewidth=2)
|
||||||
|
|
||||||
|
# Show the final signals correlated
|
||||||
|
if do_plots:
|
||||||
|
# amplitude scaling required for single axis plotting
|
||||||
|
template_amp_scaler = max(abs(template.signal)) / max(abs(antenna.signal))
|
||||||
|
|
||||||
|
# start the figure
|
||||||
|
fig, axs = plt.subplots(2, sharex=True)
|
||||||
|
ylabel_kwargs = dict(
|
||||||
|
#rotation=0,
|
||||||
|
ha='right',
|
||||||
|
va='center'
|
||||||
|
)
|
||||||
|
axs[-1].set_xlabel("Time [ns]")
|
||||||
|
|
||||||
|
offset_list = [
|
||||||
|
[best_time_lag, dict(label=template.name, color='orange')],
|
||||||
|
[true_time_offset, dict(label='True offset', color='green')],
|
||||||
|
]
|
||||||
|
|
||||||
|
# Signal
|
||||||
|
i=0
|
||||||
|
axs[i].set_ylabel("Amplitude", **ylabel_kwargs)
|
||||||
|
axs[i].plot(antenna.t, antenna.signal, label=antenna.name)
|
||||||
|
|
||||||
|
# Plot the template
|
||||||
|
for offset_args in offset_list:
|
||||||
|
this_kwargs = offset_args[1]
|
||||||
|
offset = offset_args[0]
|
||||||
|
|
||||||
|
l = axs[i].plot(offset + template.t, template_amp_scaler * template.signal, **this_kwargs)
|
||||||
|
|
||||||
|
axs[i].legend()
|
||||||
|
|
||||||
|
# Correlation
|
||||||
|
i=1
|
||||||
|
axs[i].set_ylabel("Correlation", **ylabel_kwargs)
|
||||||
|
axs[i].plot(lags * lag_dt, corrs)
|
||||||
|
|
||||||
|
# Lines across both axes
|
||||||
|
for offset_args in offset_list:
|
||||||
|
this_kwargs = offset_args[1]
|
||||||
|
offset = offset_args[0]
|
||||||
|
|
||||||
|
for i in [0,1]:
|
||||||
|
axs[i].axvline(offset, ls='--', color=this_kwargs['color'], alpha=0.7)
|
||||||
|
|
||||||
|
axs[0].axvline(offset + len(template.signal) * (template.t[1] - template.t[0]), color=this_kwargs['color'], alpha=0.7)
|
||||||
|
|
||||||
|
|
||||||
|
if True: # zoom
|
||||||
|
wx = len(template.signal) * (template.dt)/2
|
||||||
|
t0 = best_time_lag
|
||||||
|
|
||||||
|
old_xlims = axs[0].get_xlim()
|
||||||
|
axs[i].set_xlim( x0-wx, x0+3*wx)
|
||||||
|
fig.savefig('figures/11_corrs_zoom.pdf')
|
||||||
|
|
||||||
|
# restore
|
||||||
|
axs[i].set_xlim(*old_xlims)
|
||||||
|
|
||||||
|
fig.tight_layout()
|
||||||
|
fig.savefig('figures/11_corrs.pdf')
|
||||||
|
|
||||||
|
if True:
|
||||||
|
plt.close(fig)
|
||||||
|
|
||||||
|
# Were new time residuals calculated?
|
||||||
|
# Add them to the cache file
|
||||||
|
if len(time_residuals) > 1:
|
||||||
|
# merge cached and calculated time residuals
|
||||||
|
time_residuals = np.concatenate((cached_time_residuals, time_residuals), axis=None)
|
||||||
|
|
||||||
|
if write_cache or read_cache and write_cache is None: # write the cache
|
||||||
|
write_time_residuals_cache(h5_cache_fname, time_residuals, template_dt, antenna_dt, snr_sigma_factor)
|
||||||
|
else:
|
||||||
|
time_residuals = cached_time_residuals
|
||||||
|
|
||||||
|
# Only return N_residuals (even if more have been cached)
|
||||||
|
return time_residuals[:N_residuals]
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
import os
|
import os
|
||||||
import matplotlib
|
import matplotlib
|
||||||
|
|
@ -192,7 +413,7 @@ if __name__ == "__main__":
|
||||||
[10, 20, 30, 50],
|
[10, 20, 30, 50],
|
||||||
[100, 200, 300, 500]
|
[100, 200, 300, 500]
|
||||||
),
|
),
|
||||||
axis=None)
|
axis=None, dtype=float)
|
||||||
|
|
||||||
antenna_dt = 2 # ns
|
antenna_dt = 2 # ns
|
||||||
antenna_timelength = 1024 # ns
|
antenna_timelength = 1024 # ns
|
||||||
|
|
@ -245,223 +466,18 @@ if __name__ == "__main__":
|
||||||
time_accuracies = np.zeros(len(snr_factors))
|
time_accuracies = np.zeros(len(snr_factors))
|
||||||
mask_counts = np.zeros(len(snr_factors))
|
mask_counts = np.zeros(len(snr_factors))
|
||||||
for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
|
for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
|
||||||
# Read in cached time residuals
|
|
||||||
if True:
|
|
||||||
cached_time_residuals = read_time_residuals_cache(h5_cache_fname, template.dt, antenna_dt, snr_sigma_factor)
|
|
||||||
else:
|
|
||||||
cached_time_residuals = np.array([])
|
|
||||||
|
|
||||||
#
|
time_residuals = get_time_residuals_for_template(
|
||||||
# Find difference between true and templated times
|
N_residuals, template, interpolation_template=interp_template,
|
||||||
#
|
antenna_dt=antenna_dt, antenna_timelength=antenna_timelength,
|
||||||
time_residuals = np.zeros(max(0, (N_residuals - len(cached_time_residuals))))
|
snr_sigma_factor=snr_sigma_factor, bp_freq=bp_freq, normalise_noise=normalise_noise,
|
||||||
for j in tqdm(range(len(time_residuals))):
|
h5_cache_fname=h5_cache_fname, rng=rng, tqdm=tqdm)
|
||||||
do_plots = j==0
|
|
||||||
|
|
||||||
# receive at antenna
|
|
||||||
## place the deltapeak signal at a random location
|
|
||||||
antenna = Waveform(None, dt=antenna_dt, name='Signal')
|
|
||||||
|
|
||||||
if False: # Create antenna trace without interpolation template
|
|
||||||
antenna_true_signal, antenna_peak_sample = util.deltapeak(timelength=antenna_timelength, samplerate=1/antenna.dt, offset=[0.2, 0.8], rng=rng)
|
|
||||||
|
|
||||||
antenna.peak_sample = antenna_peak_sample
|
|
||||||
antenna.peak_time = antenna.dt * antenna.peak_sample
|
|
||||||
antenna.signal = antenna_bp(antenna.signal, *bp_freq, antenna.dt)
|
|
||||||
print(f"Antenna Peak Time: {antenna.peak_time}")
|
|
||||||
print(f"Antenna Peak Sample: {antenna.peak_sample}")
|
|
||||||
|
|
||||||
else: # Sample the interpolation template at some offset
|
|
||||||
antenna.peak_time = antenna_timelength * ((0.8 - 0.2) *rng.random(1) + 0.2)
|
|
||||||
sampling_offset = rng.random(1)*antenna.dt
|
|
||||||
|
|
||||||
antenna.t = util.sampled_time(1/antenna.dt, start=0, end=antenna_timelength)
|
|
||||||
|
|
||||||
# Sample the interpolation template
|
|
||||||
antenna.signal = interp_template.interpolate(antenna.t - antenna.peak_time)
|
|
||||||
|
|
||||||
antenna.peak_sample = antenna.peak_time/antenna.dt
|
|
||||||
antenna_true_signal = antenna.signal
|
|
||||||
|
|
||||||
true_time_offset = antenna.peak_time - template.peak_time
|
|
||||||
|
|
||||||
if False: # flip polarisation
|
|
||||||
antenna.signal *= -1
|
|
||||||
|
|
||||||
## Add noise
|
|
||||||
noise_amplitude = max(template.signal) * 1/snr_sigma_factor
|
|
||||||
noise_realisation = noise_amplitude * white_noise_realisation(len(antenna.signal), normalise=normalise_noise)
|
|
||||||
filtered_noise = antenna_bp(noise_realisation, *bp_freq, antenna.dt)
|
|
||||||
|
|
||||||
antenna.signal += filtered_noise
|
|
||||||
|
|
||||||
if do_plots: # show signals
|
|
||||||
fig, axs = plt.subplots(2, sharex=True)
|
|
||||||
axs[0].set_title("Antenna Waveform")
|
|
||||||
axs[-1].set_xlabel("Time [ns]")
|
|
||||||
axs[0].set_ylabel("Amplitude")
|
|
||||||
axs[0].plot(antenna.t, antenna.signal, label='bandpassed w/ noise', alpha=0.9)
|
|
||||||
axs[0].plot(antenna.t, antenna.signal - filtered_noise, label='bandpassed w/o noise', alpha=0.9)
|
|
||||||
axs[0].legend()
|
|
||||||
|
|
||||||
axs[1].set_title("Template")
|
|
||||||
axs[1].set_ylabel("Amplitude")
|
|
||||||
axs[1].plot(template.t, template.signal, label='orig')
|
|
||||||
axs[1].plot(template.t + true_time_offset, template.signal, label='true moved orig')
|
|
||||||
axs[1].legend()
|
|
||||||
|
|
||||||
axs[0].grid()
|
|
||||||
axs[1].grid()
|
|
||||||
|
|
||||||
fig.savefig('figures/11_antenna_signals.pdf')
|
|
||||||
|
|
||||||
if True: # zoom
|
|
||||||
wx = 100
|
|
||||||
x0 = true_time_offset
|
|
||||||
|
|
||||||
old_xlims = axs[0].get_xlim()
|
|
||||||
axs[0].set_xlim( x0-wx, x0+wx)
|
|
||||||
fig.savefig('figures/11_antenna_signals_zoom.pdf')
|
|
||||||
|
|
||||||
# restore
|
|
||||||
axs[0].set_xlim(*old_xlims)
|
|
||||||
|
|
||||||
if True:
|
|
||||||
plt.close(fig)
|
|
||||||
|
|
||||||
axs2 = None
|
|
||||||
if True: # upsampled trace
|
|
||||||
upsampled_trace, upsampled_t = trace_upsampler(antenna.signal, template.t, antenna.t)
|
|
||||||
|
|
||||||
if do_plots: # Show upsampled traces
|
|
||||||
fig2, axs2 = plt.subplots(1, sharex=True)
|
|
||||||
if not hasattr(axs2, '__len__'):
|
|
||||||
axs2 = [axs2]
|
|
||||||
|
|
||||||
axs2[-1].set_xlabel("Time [ns]")
|
|
||||||
axs2[0].set_ylabel("Amplitude")
|
|
||||||
axs2[0].plot(antenna.t, antenna.signal, marker='o', label='orig')
|
|
||||||
axs2[0].plot(upsampled_t, upsampled_trace, label='upsampled')
|
|
||||||
axs2[0].legend(loc='upper right')
|
|
||||||
|
|
||||||
fig2.savefig('figures/11_upsampled.pdf')
|
|
||||||
|
|
||||||
wx = 1e2
|
|
||||||
x0 = upsampled_t[0] + wx - 5
|
|
||||||
axs2[0].set_xlim(x0-wx, x0+wx)
|
|
||||||
fig2.savefig('figures/11_upsampled_zoom.pdf')
|
|
||||||
|
|
||||||
if True:
|
|
||||||
plt.close(fig2)
|
|
||||||
|
|
||||||
# determine correlations with arguments
|
|
||||||
lag_dt = upsampled_t[1] - upsampled_t[0]
|
|
||||||
corrs, (out1_signal, out2_template, lags) = my_correlation(upsampled_trace, template.signal)
|
|
||||||
|
|
||||||
# Determine best correlation time
|
|
||||||
idx = np.argmax(abs(corrs))
|
|
||||||
best_sample_lag = lags[idx]
|
|
||||||
best_time_lag = best_sample_lag * lag_dt
|
|
||||||
|
|
||||||
else: # downsampled template
|
|
||||||
raise NotImplementedError
|
|
||||||
|
|
||||||
corrs, (_, _, lags) = my_downsampling_correlation(antenna.signal, antenna.t, template.signal, template.t)
|
|
||||||
lag_dt = upsampled_t[1] - upsampled_t[0]
|
|
||||||
|
|
||||||
# Calculate the time residual
|
|
||||||
time_residuals[j] = best_time_lag - true_time_offset
|
|
||||||
|
|
||||||
if not do_plots:
|
|
||||||
continue
|
|
||||||
|
|
||||||
if do_plots and axs2:
|
|
||||||
axs2[-1].axvline(best_time_lag, color='r', alpha=0.5, linewidth=2)
|
|
||||||
axs2[-1].axvline(true_time_offset, color='g', alpha=0.5, linewidth=2)
|
|
||||||
|
|
||||||
# Show the final signals correlated
|
|
||||||
if do_plots:
|
|
||||||
# amplitude scaling required for single axis plotting
|
|
||||||
template_amp_scaler = max(abs(template.signal)) / max(abs(antenna.signal))
|
|
||||||
|
|
||||||
# start the figure
|
|
||||||
fig, axs = plt.subplots(2, sharex=True)
|
|
||||||
ylabel_kwargs = dict(
|
|
||||||
#rotation=0,
|
|
||||||
ha='right',
|
|
||||||
va='center'
|
|
||||||
)
|
|
||||||
axs[-1].set_xlabel("Time [ns]")
|
|
||||||
|
|
||||||
offset_list = [
|
|
||||||
[best_time_lag, dict(label=template.name, color='orange')],
|
|
||||||
[true_time_offset, dict(label='True offset', color='green')],
|
|
||||||
]
|
|
||||||
|
|
||||||
# Signal
|
|
||||||
i=0
|
|
||||||
axs[i].set_ylabel("Amplitude", **ylabel_kwargs)
|
|
||||||
axs[i].plot(antenna.t, antenna.signal, label=antenna.name)
|
|
||||||
|
|
||||||
# Plot the template
|
|
||||||
for offset_args in offset_list:
|
|
||||||
this_kwargs = offset_args[1]
|
|
||||||
offset = offset_args[0]
|
|
||||||
|
|
||||||
l = axs[i].plot(offset + template.t, template_amp_scaler * template.signal, **this_kwargs)
|
|
||||||
|
|
||||||
axs[i].legend()
|
|
||||||
|
|
||||||
# Correlation
|
|
||||||
i=1
|
|
||||||
axs[i].set_ylabel("Correlation", **ylabel_kwargs)
|
|
||||||
axs[i].plot(lags * lag_dt, corrs)
|
|
||||||
|
|
||||||
# Lines across both axes
|
|
||||||
for offset_args in offset_list:
|
|
||||||
this_kwargs = offset_args[1]
|
|
||||||
offset = offset_args[0]
|
|
||||||
|
|
||||||
for i in [0,1]:
|
|
||||||
axs[i].axvline(offset, ls='--', color=this_kwargs['color'], alpha=0.7)
|
|
||||||
|
|
||||||
axs[0].axvline(offset + len(template.signal) * (template.t[1] - template.t[0]), color=this_kwargs['color'], alpha=0.7)
|
|
||||||
|
|
||||||
|
|
||||||
if True: # zoom
|
|
||||||
wx = len(template.signal) * (template.dt)/2
|
|
||||||
t0 = best_time_lag
|
|
||||||
|
|
||||||
old_xlims = axs[0].get_xlim()
|
|
||||||
axs[i].set_xlim( x0-wx, x0+3*wx)
|
|
||||||
fig.savefig('figures/11_corrs_zoom.pdf')
|
|
||||||
|
|
||||||
# restore
|
|
||||||
axs[i].set_xlim(*old_xlims)
|
|
||||||
|
|
||||||
fig.tight_layout()
|
|
||||||
fig.savefig('figures/11_corrs.pdf')
|
|
||||||
|
|
||||||
if True:
|
|
||||||
plt.close(fig)
|
|
||||||
|
|
||||||
print()# separating tqdm
|
print()# separating tqdm
|
||||||
print()# separating tqdm
|
print()# separating tqdm
|
||||||
# Were new time residuals calculated?
|
|
||||||
# Add them to the cache file
|
|
||||||
if len(time_residuals) > 1:
|
|
||||||
# merge cached and calculated time residuals
|
|
||||||
time_residuals = np.concatenate((cached_time_residuals, time_residuals), axis=None)
|
|
||||||
|
|
||||||
if True: # write the cache
|
|
||||||
write_time_residuals_cache(h5_cache_fname, time_residuals, template_dt, antenna_dt, snr_sigma_factor)
|
|
||||||
else:
|
|
||||||
time_residuals = cached_time_residuals
|
|
||||||
|
|
||||||
# Make a plot of the time residuals
|
# Make a plot of the time residuals
|
||||||
if N_residuals > 1:
|
if N_residuals > 1:
|
||||||
time_residuals = time_residuals[:N_residuals]
|
|
||||||
|
|
||||||
for i in range(1 + cut_wrong_peak_matches):
|
for i in range(1 + cut_wrong_peak_matches):
|
||||||
mask_count = 0
|
mask_count = 0
|
||||||
|
|
||||||
|
|
|
||||||
Loading…
Reference in a new issue