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Fix bug in sigma clip mask - #3792
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This PR also fixes a bug where Also, I took the opportunity to add the |
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I don't see anything controversial with this. But perhaps the addition of new keywords is worth a mention in the change log. And addition to its documentation showing how to use the new keywords would not hurt. |
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Thanks, @pllim. Done. |
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👍 If no one objects or merge after Memorial Day, I will do it. |
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@larrybradley , I merged it. |
saimn
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The axis argument was added in astropy#3792 and is passed correctly to sigma_clip, but then mean, median and std did not use axis and were computed on the whole array. This commit changes this to use axis to compute the statistics. Also another minor annoyance is that, with Numpy 1.10, np.ma.median always return a MaskedArray, even when axis is None. This was not the case in previous version (and will probably be fixed in the future), and mean and std return a scalar. So if it happens here, we take the first element of the array.
saimn
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Mar 23, 2016
The axis argument was added in astropy#3792 and is passed correctly to sigma_clip, but then mean, median and std did not use axis and were computed on the whole array. This commit changes this to use axis to compute the statistics. Also another minor annoyance is that, with Numpy 1.10, np.ma.median always return a MaskedArray, even when axis is None. This was not the case in previous version (and will probably be fixed in the future), and mean and std return a scalar. So if it happens here, we take the first element of the array.
saimn
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that referenced
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Apr 12, 2016
The axis argument was added in astropy#3792 and is passed correctly to sigma_clip, but then mean, median and std did not use axis and were computed on the whole array. This commit changes this to use axis to compute the statistics. Also another minor annoyance is that, with Numpy 1.10, np.ma.median always return a MaskedArray, even when axis is None. This was not the case in previous version (and will probably be fixed in the future), and mean and std return a scalar. So if it happens here, we take the first element of the array.
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Recent changes to
sigma_clip(#3595) made it possible for themaskto be scalarFalsein the returned masked array in the case where no values are clipped. The problem with that is the advertised method for getting the good values,good_only = filtered_data.data[~filtered_data.mask], does not work correctly with a scalar mask value. If themaskis scalarFalsethen~maskis scalarTrue, which is then translated to an index of simply1.This PR ensures the returned
maskalways has the same shape asdataand usesnp.ma.compressedinsigma_clipped_statsinstead offiltered_data.data[~filtered_data.mask]to get the good data values.