Pulse: Hilbert Envelope Maximum: WIP
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@ -140,12 +140,48 @@ def trace_upsampler(trace, template_t, trace_t):
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#upsampled_t = np.arange(trace_t[0], trace_t[-1], template_dt)
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upsampled_t = template_dt * np.arange(len(upsampled_trace)) + trace_t[0]
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if False:
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upsampled_t = np.linspace(0, template_dt*len(upsampled_trace), len(upsampled_trace), endpoint=False) + trace_t[0]
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return upsampled_trace, upsampled_t
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def trace_downsampler(trace, template_t, trace_t, offset):
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pass
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def hilbert_envelope_max_amplitude_time(trace, trace_t, do_plot=False, fname_distinguish=None):
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analytic_signal = signal.hilbert(trace)
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envelope = abs(analytic_signal)
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max_idx = np.argmax(envelope)
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t_max = trace_t[max_idx]
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# make a plot
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if do_plot:
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fig, ax = plt.subplots()
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude")
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ax.plot(trace_t, trace, label='Waveform')
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ax.plot(trace_t, envelope, ls='dashed', label='Envelope')
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# indicate maximum and t_max
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ax.axhline(envelope[max_idx], ls='dotted', color='g')
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ax.axvline(t_max, ls='dotted', color='g')
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if True:
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ax.legend()
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fig.tight_layout()
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fig.savefig(f'figures/11_hilbert_timing{fname_distinguish}.pdf')
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plt.close(fig)
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return t_max, (analytic_signal, max_idx)
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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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@ -160,14 +196,16 @@ def read_time_residuals_cache(cache_fname, template_dt, antenna_dt, snr_sigma_fa
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else:
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ret = deepcopy(ds[:min(N, len(ds))])
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if len(ret.shape) > 1:
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return ret[0,:], ret[1,:]
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if len(ret.shape) > 2:
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return ret[0,:], ret[1,:], ret[2,:]
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elif len(ret.shape) > 1:
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return ret[0,:], ret[1,:], np.array([np.nan]*len(ret))
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else:
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return ret[:], np.array([np.nan]*len(ret))
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return ret[:], np.array([np.nan]*len(ret)), np.array([np.nan]*len(ret))
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except (KeyError, FileNotFoundError):
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return np.array([]), np.array([])
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return np.array([]), np.array([]), np.array([])
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def write_time_residuals_cache(cache_fname, time_residuals, snrs, template_dt, antenna_dt, noise_sigma_factor):
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def write_time_residuals_cache(cache_fname, data, template_dt, antenna_dt, noise_sigma_factor):
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with h5py.File(cache_fname, 'a') as fp:
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pgroup = fp.require_group('time_residuals')
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pgroup2 = pgroup.require_group(f'{template_dt}_{antenna_dt}')
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@ -177,9 +215,10 @@ def write_time_residuals_cache(cache_fname, time_residuals, snrs, template_dt, a
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if ds_name in pgroup2.keys():
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del pgroup2[ds_name]
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ds = pgroup2.create_dataset(ds_name, (2, len(time_residuals)), dtype='f', maxshape=(None))
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ds[0] = time_residuals
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ds[1] = snrs
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ds = pgroup2.create_dataset(ds_name, (3, len(time_residuals)), dtype='f', maxshape=(None))
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ds[0] = data[0]
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ds[1] = data[1]
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ds[2] = data[2]
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def create_template(dt=1, timelength=1, bp_freq=(0, np.inf), name=None, normalise=False):
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template = Waveform(None, dt=dt, name=name)
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@ -203,16 +242,20 @@ def get_time_residuals_for_template(
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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, cached_snrs = read_time_residuals_cache(h5_cache_fname, template.dt, antenna_dt, snr_sigma_factor)
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cached_time_residuals, cached_snrs, cached_hilbert_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, cached_snrs = np.array([]), np.array([])
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cached_time_residuals, cached_snrs, cached_hilbert_time_residuals = np.array([]), np.array([]), 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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hilbert_interp_t_max, _ = hilbert_envelope_max_amplitude_time(interp_template.signal, interp_template.t)
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time_residuals = np.zeros(max(0, (N_residuals - len(cached_time_residuals))))
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snrs = np.zeros_like(time_residuals)
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hilbert_time_residuals = np.zeros_like(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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@ -340,6 +383,9 @@ def get_time_residuals_for_template(
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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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# Find Hilbert Envelope t0
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hilbert_best_time_lag, _ = hilbert_envelope_max_amplitude_time(upsampled_trace, upsampled_t, do_plot=do_plots)
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else: # downsampled template
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raise NotImplementedError
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@ -349,6 +395,7 @@ def get_time_residuals_for_template(
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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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snrs[j] = antenna.signal_to_noise
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hilbert_time_residuals[j] = hilbert_best_time_lag - hilbert_interp_t_max
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if not do_plots:
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continue
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@ -429,15 +476,17 @@ def get_time_residuals_for_template(
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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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snrs = np.concatenate( (cached_snrs, snrs), axis=None)
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hilbert_time_residuals = np.concatenate((cached_hilbert_time_residuals, hilbert_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, snrs, template_dt, antenna_dt, snr_sigma_factor)
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write_time_residuals_cache(h5_cache_fname, (time_residuals, snrs, hilbert_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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snrs = cached_snrs
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hilbert_time_residuals = cached_hilbert_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], snrs[:N_residuals]
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return time_residuals[:N_residuals], snrs[:N_residuals], hilbert_time_residuals[:N_residuals]
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if __name__ == "__main__":
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import os
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@ -503,6 +552,9 @@ if __name__ == "__main__":
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if True:
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plt.close(fig)
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if True: # show hilbert interpolation method
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_ = hilbert_envelope_max_amplitude_time(interp_template.signal, interp_template.t, do_plot=True, fname_distinguish="_interpolation_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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@ -519,7 +571,7 @@ if __name__ == "__main__":
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for k, snr_sigma_factor in tqdm(enumerate(snr_factors)):
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# get the time residuals
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time_residuals, snrs = get_time_residuals_for_template(
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time_residuals, snrs, hilbert_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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