parse_image#
- specreduce.parse_image(image: ndarray | NDData | Quantity, disp_axis: int = 1, mask_treatment: Literal['apply', 'ignore', 'propagate', 'zero_fill', 'nan_fill', 'apply_mask_only', 'apply_nan_only'] = 'apply') Spectrum[source]#
Convert all accepted image types to a consistently formatted Spectrum object.
Fills any and all of uncertainty, mask, units, and spectral axis that are missing in the provided image with generic values. Accepted image types are:
- Parameters:
- image
Input image from which data is extracted. This can be a 2D numpy array, Quantity, or an NDData object.
- disp_axis
The index of the image’s dispersion axis. Should not be changed until operations can handle variable image orientations.
- mask_treatment
Specifies how to handle masked or non-finite values in the input image. The accepted values are:
apply: The image remains unchanged, and any existing mask is combined with a mask derived from non-finite values.ignore: The image remains unchanged, and any existing mask is dropped.propagate: The image remains unchanged, and any masked or non-finite pixel causes the mask to extend across the entire cross-dispersion axis.zero_fill: Pixels that are either masked or non-finite are replaced with 0.0, and the mask is dropped.nan_fill: Pixels that are either masked or non-finite are replaced with nan, and the mask is dropped.apply_mask_only: The image and mask are left unmodified.apply_nan_only: The image is left unmodified, the old mask is dropped, and a new mask is created based on non-finite values.
- Returns:
- Spectrum