turbustat.statistics.
WidthEstimate1D
(inList, method='walk-down')[source] [edit on github]¶Find widths from spectral eigenvectors. These eigenvectors should already be normalized. Widths are defined by the location where 1/e of the maximum occurs.
Note
If the spectral dimension is small in the given eigenvectors
(i.e., their length), the 1/e level might not be reached. If this is the
case, try padding the initial data cube with zeros in the spectral
dimension. The effect on the results should be minimal, as the additional
eigenvalues from the padding will be zero. This is especially important
when using walk-down
.
Warning
Error estimation is not implemented for interpolate
.
Parameters: | inList: {list of 1D `~numpy.ndarray`s, 2D `~numpy.ndarray}
method : {‘walk-down’, ‘fit’, ‘interpolate’}, optional
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Returns: | scales : array
scale_errors : array
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