Improve generate.py with spatial on generate_template() #2098
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Cool, awesome, I'll test that asap in between two grant proposals :-) Thanks a lot. The propagation is isotropic for now, am I right? |
Co-authored-by: Zach McKenzie <[email protected]>
Co-authored-by: Zach McKenzie <[email protected]>
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alejoe91
reviewed
Oct 24, 2023
alejoe91
reviewed
Oct 24, 2023
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| dist = distances[u, :].copy() | ||
| dist -= np.min(dist) | ||
| delay_s = dist / propagation_speed / 1000.0 | ||
| sample_shifts = delay_s * fs | ||
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| # apply the delay with fft transform to get sub sample shift | ||
| n = wfs.shape[0] | ||
| wfs_f = np.fft.rfft(wfs, axis=0) | ||
| if n % 2 == 0: | ||
| # n is even sig_f[-1] is nyquist and so pi | ||
| omega = np.linspace(0, np.pi, wfs_f.shape[0]) | ||
| else: | ||
| # n is odd sig_f[-1] is exactly nyquist!! we need (n-1) / n factor!! | ||
| omega = np.linspace(0, np.pi * (n - 1) / n, wfs_f.shape[0]) | ||
| # broadcast omega and sample_shifts depend the axis | ||
| shifts = omega[:, np.newaxis] * sample_shifts[np.newaxis, :] | ||
| wfs = np.fft.irfft(wfs_f * np.exp(-1j * shifts), n=n, axis=0) |
alejoe91
approved these changes
Oct 24, 2023
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@yger