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https://gitlab.science.ru.nl/mthesis-edeboone/m-thesis-introduction.git
synced 2024-11-13 10:03:32 +01:00
Pulse: switched filter + sample template for fine time offset
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354ec93a79
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1 changed files with 64 additions and 34 deletions
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@ -54,14 +54,10 @@ def white_noise_realisation(N_samples, noise_sigma=1, rng=rng):
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return rng.normal(0, noise_sigma or 0, size=N_samples)
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def antenna_bp(trace, low_bp, high_bp, dt, order=3):
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# order < 6 for stability
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fs = 1/dt
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nyq = 1* fs
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low_bp = low_bp / nyq
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high_bp = high_bp / nyq
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bp_filter = signal.butter(order, [low_bp, high_bp], 'band', fs=fs)
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bandpassed = signal.lfilter(*bp_filter, trace)
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bp_filter = signal.butter(order, [low_bp, high_bp], 'band', fs=fs, output='sos')
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bandpassed = signal.sosfilt(bp_filter, trace)
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return bandpassed
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@ -139,7 +135,7 @@ 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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noise_sigma_factor = 1e0 # keep between 10 and 0.1
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noise_sigma_factor = 1e-1 # amplitude factor
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N_residuals = 50*3 if len(sys.argv) < 2 else int(sys.argv[1])
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@ -150,21 +146,32 @@ if __name__ == "__main__":
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# Create the template
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#
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template = Waveform(None, dt=template_dt, name='Template')
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_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/template.dt, offset=10/template.dt)
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template.signal = antenna_bp(_deltapeak[0], *bp_freq, (np.sqrt(antenna_dt/template_dt))*template_dt) # TODO: fix sqrt constant
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_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/template.dt, offset=0)
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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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if True: # show template
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fig, ax = plt.subplots()
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ax.set_title("Deltapeak and Bandpassed Template")
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ax.set_xlabel("Time [ns]")
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ax.set_ylabel("Amplitude")
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ax.plot(template.t, max(template.signal)*_deltapeak[0])
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ax.plot(template.t, template.signal)
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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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fig.savefig('figures/11_template_deltapeak.pdf')
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if True: # show filtering equivalence samplerates
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_deltapeak = util.deltapeak(timelength=template_length, samplerate=1/antenna_dt, offset=0)
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_time = util.sampled_time(end=template_length, sample_rate=1/antenna_dt)
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_bandpassed = antenna_bp(_deltapeak[0], *bp_freq, antenna_dt)
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ax.plot(_time, max(_bandpassed)*_deltapeak[0], label='Impulse Antenna')
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ax.plot(_time, _bandpassed, label='Filtered Antenna')
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ax.legend()
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fig.savefig('figures/11_template_deltapeak+antenna.pdf')
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if True:
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plt.close(fig)
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@ -178,29 +185,41 @@ if __name__ == "__main__":
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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 not True: # Create antenna trace without 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.signal = antenna_true_signal
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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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else: # Sample the 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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antenna.signal = interp1d_template(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 do_plots:
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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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if False: # bandpass when emitting the signal
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antenna.signal = antenna_bp(antenna.signal, *bp_freq, antenna.dt)
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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) * noise_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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antenna_unfiltered_signal = antenna.signal + noise_realisation
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antenna.signal = antenna_bp(antenna_unfiltered_signal, *bp_freq, antenna.dt)
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true_time_offset = antenna.peak_time - template.peak_time
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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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@ -208,8 +227,7 @@ if __name__ == "__main__":
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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_unfiltered_signal, label='true signal w/ noise', alpha=0.9)
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axs[0].plot(antenna.t, antenna_true_signal, label='true signal w/o 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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@ -218,6 +236,9 @@ if __name__ == "__main__":
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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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@ -277,9 +298,14 @@ if __name__ == "__main__":
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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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@ -298,25 +324,30 @@ if __name__ == "__main__":
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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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# Put template on an twinned axis (magnitudes are different)
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ax2 = axs[i].twinx()
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for i, offset_args in enumerate(offset_list):
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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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ax2.axvline(offset + len(template.signal) * (template.t[1] - template.t[0]), color=this_kwargs['color'], alpha=0.7)
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ax2.axvline(offset, color=this_kwargs['color'], alpha=0.7)
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ax2.plot(offset + template.t, template.signal, **this_kwargs)
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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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for i, offset_args in enumerate(offset_list):
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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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axs[i].axvline(offset, ls='--', **this_kwargs)
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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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@ -330,7 +361,6 @@ if __name__ == "__main__":
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axs[i].set_xlim(*old_xlims)
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fig.tight_layout()
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fig.legend()
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fig.savefig('figures/11_corrs.pdf')
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if False:
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