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