Add flagging functions for power spectra #157
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ghost
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hera_pspec/flags.py
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| greedy : bool | ||
| greedy flagging is used if true (default is False) | ||
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| axis : int |
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This would be better off as a string, and maybe with a name change, e.g. first='row'.
hera_pspec/flags.py
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| integer array with number of samples available for each frequency channel at a given LST angle | ||
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| flags : numpy.ndarray | ||
| binary array with 1 representing flagged, 0 representing unflagged |
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Is this the same as the output of UVData.get_flags(), or does that need to be modified in some way before passing it to this function?
hera_pspec/flags.py
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| mask_generator(nsamples, flags, n_threshold, greedy=False, axis, greedy_threshold, retain_flags=True): | ||
| """ | ||
| Generates a greedy flags mask from input flags and nsamples arrays |
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I think it's worth briefly explaining how the algorithm works here.
hera_pspec/flags.py
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| n_threshold : int | ||
| minimum number of samples needed for a point to remain unflagged | ||
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| greedy : bool |
hera_pspec/flags.py
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| import numpy as np | |||
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| mask_generator(nsamples, flags, n_threshold, greedy=False, axis, greedy_threshold, retain_flags=True): | |||
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Maybe a more descriptive function name, e.g. construct_factorizable_mask?
hera_pspec/flags.py
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| if greedy=True, the threshold used to flag rows or columns if axis=1 or 0, respectively | ||
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| retain_flags : bool | ||
| LST-Bin Flags are left flagged even if thresholds are not met (default is True) |
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The data going into this are not necessarily LST binned.
hera_pspec/flags.py
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| # comparing the number of samples to the threshold | ||
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| for i in range(shape[0]): |
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Can this nested for loop perhaps be replaced by a couple of strategic calls to np.where? We can discuss this.
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Have either of you seen the development in |
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I've moved most of the code from this PR into This code could have lived in |
Functions for generating greedy flagging masks, consolidating LST binned NSamples & Flag data into numpy arrays, and plotting visualizations of the flagging.