The `Mask_and_Moments`_ class returns moment arrays as well as their errors for use with 2D statistics in the package. Noise is estimated using signal_id. Moments are derived using spectral-cube and are able to handle massive datacubes.
Moments are easily returned in the expected form for the statistics:
from turbustat.data_reduction import Mask_and_Moments
mm = Mask_and_Moments("test.fits")
mm.make_moments()
mm.make_moment_errors()
output_dict = mm.to_dict()
output_dict now contains the cube and moments along with their respective error maps.
The moments can also be saved:
mm.to_fits("test")
This will return a FITS file for each moment. Error maps are saved in the first extension.
Mask_and_Moments(cube[, noise_type, clip, ...]) |
A unified approach to deriving the noise level in a cube, applying a mask, and deriving moments along with their errors. |