despike

sofia_redux.instruments.exes.despike.despike(data, header, variance=None, abeams=None, bbeams=None, gooddata=None, propagate_nan=False)

Correct outlier pixels due to temporal spikes.

All A frames are compared, and any pixels with values greater than a threshold factor (header[‘SPIKEFAC’]) of standard deviations from the mean value across the other frames are replaced with that mean value. B frames are similarly compared with each other.

Optionally (if header[‘TRASH’] is True), frames with significantly discrepant overall background levels (“trashed” frames) may be identified automatically and flagged for removal from subsequent reduction.

Parameters:

datanumpy.ndarray

3D data cube [nframe, nspec, nspat].

headerfits.Header

Header of FITS file.

variancenumpy.ndarray

3D variance cube [nframe, nspec, nspat].

abeamsarray-like of int

Index numbers of A frames in data cube.

bbeamsarray-like of int

Index numbers of B frames in data cube.

good_datanumpy.ndarray, optional

Bad pixel array [nspec, nspat] indicating valid data (True=good, False=bad).

propagate_nanbool, optional

If True, outlier pixels will be replaced by NaN rather than the average value.

Returns:

spike_data, spike_mask, good_frames3-tuple of numpy.ndarray

The corrected data, outlier pixel mask, and list of good data frames. Data and mask have dimensions [nframe, nspec, nspat]. In the mask, True=good, False=spike. The good frames list includes indices for all good (non-trashed) frames in the input data.