Extraction Module (cwitools.extraction)

The extraction module contains functions focused on isolating a 3D signal (e.g. a nebular emission region) within a data cube. The first part of the extraction process typically involves modeling and subtracting continuum sources, removing slowly-varying background signals, and masking foreground sources or other regions as needed. When the cube only contains the desired signal, a segmentation process can be used to identify the 3D contours of the emitting region and extract it for further analysis.

Tools for extracting extended emission from a cube.

Functions

apply_mask(data, mask[, fill])	Apply a binary or label mask to data.
bg_sub(inputfits[, method, poly_k, …])	Subtracts extended continuum emission / scattered light from a cube
cutout(fits_in, pos, box_size[, redshift, …])	Extract a spatial box around a central position from 2D or 3D data.
detect_lines(obj_fits[, lines, redshift, vwidth])	Associate detected 3D objects with known emission lines.
obj2binary(obj_mask, obj_id)	Get a binary mask of specific objects in a labelled object mask.
psf_sub(inputfits, pos[, r_fit, r_sub, …])	Models and subtracts a single point-source in a 3D data cube.
psf_sub_all(inputfits[, r_fit, r_sub, reg, …])	Models and subtracts multiple point-sources in a 3D data cube.
reg2mask(fits_in, reg)	Convert a DS9 region file into a 2D binary mask of sources.
segment(fits_in, var[, snrmin, includes, …])	Segment cube into 3D regions above a threshold.
smooth_cube_spatial(data, scale[, ktype, var])	Smooth 3D data spatially by a specified 2D kernel.
smooth_cube_wavelength(data, scale[, ktype, var])	Smooth 3D data spatially by a specified 2D kernel.
smooth_nd(data, scale[, axes, ktype, var])	Smooth along all/any axes of a data cube with a box or gaussian kernel.