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# check if the covariance matrix is uniform along the time axis. If so, we just calculate the result for one timestamp and duplicate its copies |
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# along the time axis. |
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check_uniform_input = False |
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if model != 'foreground_dependent': |
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# When model is 'foreground_dependent', since we are processing the outer products of visibilities from different times, |
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# we are expected to have time-dependent inputs, thus check_uniform_input is always set to be False here. |
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C11_first = self.C_model(key1, model=model, known_cov=known_cov, time_index=0) |
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C11_last = self.C_model(key1, model=model, known_cov=known_cov, time_index=self.dsets[0].Ntimes-1) |
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if np.isclose(C11_first, C11_last).all(): |
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check_uniform_input = True |
This should probably look for the first and last non-zero, finite covariance to compare to the last one to see if it's actually time stable.