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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
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Pulse: analog_template interpol + rename snr_factor
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parent
4abb6997b8
commit
279ea46550
1 changed files with 53 additions and 21 deletions
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@ -127,20 +127,20 @@ def trace_upsampler(trace, template_t, trace_t):
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def trace_downsampler(trace, template_t, trace_t, offset):
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pass
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def read_time_residuals_cache(cache_fname, template_dt, antenna_dt, noise_sigma_factor, N=None):
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def read_time_residuals_cache(cache_fname, template_dt, antenna_dt, snr_sigma_factor, N=None):
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try:
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with h5py.File(cache_fname, 'r') as fp:
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pgroup = fp['time_residuals']
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pgroup2 = pgroup[f'{template_dt}_{antenna_dt}']
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ds_name = str(noise_sigma_factor)
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ds_name = str(snr_sigma_factor)
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ds = pgroup2[ds_name]
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if N is None:
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return deepcopy(ds[:])
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else:
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return deepcopy(ds[:min(N, len(ds))])
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except KeyError:
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except (KeyError, FileNotFoundError):
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return np.array([])
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def write_time_residuals_cache(cache_fname, time_residuals, template_dt, antenna_dt, noise_sigma_factor):
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@ -164,13 +164,23 @@ if __name__ == "__main__":
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bp_freq = (30e-3, 80e-3) # GHz
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template_dt = 5e-2 # ns
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template_length = 500 # ns
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interp_template_dt = 5e-5 # ns
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template_length = 200 # ns
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N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
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noise_factors = [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2, 1e-1, 3e-1, 5e-1, 7e-1] # amplitude factor
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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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[1, 1.5, 2, 2.5, 3, 4, 5, 7],
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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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antenna_dt = 2 # ns
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antenna_timelength = 2048 # ns
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antenna_timelength = 1024 # ns
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cut_wrong_peak_matches = True
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#
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# Create the template
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@ -180,7 +190,16 @@ if __name__ == "__main__":
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template.signal = antenna_bp(_deltapeak[0], *bp_freq, template_dt)
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template.peak_sample = _deltapeak[1]
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template.peak_time = template.dt * template.peak_sample
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interp1d_template = interpolate.interp1d(template.t, template.signal, assume_sorted=True, fill_value=0, bounds_error=False)
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# Interpolation Template
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# to create an 'analog' sampled antenna
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interp_template = Waveform(None, dt=interp_template_dt, name='Interpolation Template')
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_interp_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/interp_template.dt, offset=0)
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interp_template.signal = antenna_bp(_interp_deltapeak[0], *bp_freq, interp_template.dt)
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interp_template.peak_sample = _interp_deltapeak[1]
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interp_template.peak_time = interp_template.dt * interp_template.peak_sample
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interp_template.signal *= max(template.signal)/max(interp_template.signal)
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interp_template.interpolate = interpolate.interp1d(interp_template.t, interp_template.signal, assume_sorted=True, fill_value=0, bounds_error=False)
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if True: # show template
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fig, ax = plt.subplots()
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@ -189,6 +208,8 @@ if __name__ == "__main__":
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ax.set_ylabel("Amplitude")
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ax.plot(template.t, max(template.signal)*_deltapeak[0], label='Impulse Template')
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ax.plot(template.t, template.signal, label='Filtered Template')
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ax.plot(interp_template.t, interp_template.signal, label='Filtered Interpolation Template', ls='dashed')
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ax.legend()
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fig.savefig('figures/11_template_deltapeak.pdf')
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if True: # show filtering equivalence samplerates
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@ -210,12 +231,13 @@ if __name__ == "__main__":
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#
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h5_cache_fname = f'11_pulsed_timing.hdf5'
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time_accuracies = np.zeros(len(noise_factors))
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for k, noise_sigma_factor in tqdm(enumerate(noise_factors)):
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print() #separating tqdm
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time_accuracies = 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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cached_time_residuals = read_time_residuals_cache(h5_cache_fname, template.dt, antenna_dt, noise_sigma_factor)
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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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@ -243,7 +265,8 @@ if __name__ == "__main__":
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antenna.t = util.sampled_time(1/antenna.dt, start=0, end=antenna_timelength)
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antenna.signal = interp1d_template(antenna.t - antenna.peak_time)
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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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@ -254,7 +277,7 @@ if __name__ == "__main__":
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antenna.signal *= -1
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## Add noise
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noise_amplitude = max(template.signal) * noise_sigma_factor
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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))
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filtered_noise = antenna_bp(noise_realisation, *bp_freq, antenna.dt)
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@ -323,16 +346,18 @@ if __name__ == "__main__":
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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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else: # downsampled template
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raise NotImplementedError
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corrs, (out1_signal, out2_template, lags) = my_downsampling_correlation(template.signal, antenna.signal, template.t, antenna.t)
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lag_dt = upsampled_t[1] - upsampled_t[0]
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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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@ -408,13 +433,16 @@ if __name__ == "__main__":
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if True:
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plt.close(fig)
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print()# separating tqdm
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print()# separating tqdm
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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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write_time_residuals_cache(h5_cache_fname, time_residuals, template_dt, antenna_dt, noise_sigma_factor)
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if True: # 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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@ -498,13 +526,17 @@ if __name__ == "__main__":
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ax.set_yscale('log')
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# plot the values
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ax.plot(1/np.asarray(noise_factors), time_accuracies, ls='none', marker='o')
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ax.plot(np.asarray(snr_factors), time_accuracies, ls='none', marker='o')
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if True: # limit y-axis to 1e0
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ax.set_ylim([None, 1e1])
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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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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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