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Pulse: Hilbert Envelope: available for SNR-TimeAccuracy plot
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1 changed files with 3 additions and 3 deletions
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@ -562,7 +562,7 @@ if __name__ == "__main__":
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for a, template_dt in tqdm(enumerate(template_dts)):
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for a, template_dt in tqdm(enumerate(template_dts)):
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time_residuals_data.append(np.zeros( (len(snr_factors), 3, N_residuals)))# res, snr, masked
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time_residuals_data.append(np.zeros( (len(snr_factors), 4, N_residuals)))# res, snr, masked, hilber_res
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# Create the template
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# Create the template
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# This is sampled at a lower samplerate than the interpolation template
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# This is sampled at a lower samplerate than the interpolation template
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@ -584,7 +584,7 @@ if __name__ == "__main__":
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mask = wrong_peak_condition(time_residuals)
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mask = wrong_peak_condition(time_residuals)
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# Save directly to large data array
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# Save directly to large data array
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time_residuals_data[a][k] = time_residuals, snrs, ~mask
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time_residuals_data[a][k] = time_residuals, snrs, ~mask, hilbert_time_residuals
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# Make a plot of the time residuals <<<
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# Make a plot of the time residuals <<<
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if True and N_residuals > 1:
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if True and N_residuals > 1:
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@ -696,7 +696,7 @@ if __name__ == "__main__":
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template_dt_colors = [None]*len(template_dts)
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template_dt_colors = [None]*len(template_dts)
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for a, template_dt in enumerate(template_dts):
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for a, template_dt in enumerate(template_dts):
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for k, snr_sigma_factor in enumerate(snr_factors):
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for k, snr_sigma_factor in enumerate(snr_factors):
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time_residuals, snrs, valid_mask = time_residuals_data[a][k]
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time_residuals, snrs, valid_mask, hilbert_time_residuals = time_residuals_data[a][k]
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valid_mask = np.array(valid_mask, dtype=bool)
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valid_mask = np.array(valid_mask, dtype=bool)
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