PCA_Distance¶

class turbustat.statistics.PCA_Distance(cube1, cube2, n_eigs=50, fiducial_model=None, mean_sub=True)[source] [edit on github]¶

Bases: object

Compare two data cubes based on the eigenvalues of the PCA decomposition. The distance is the Euclidean distance between the eigenvalues.

Parameters:

cube1 : numpy.ndarray or astropy.io.fits.PrimaryHDU or SpectralCube Data cube. cube2 : numpy.ndarray or astropy.io.fits.PrimaryHDU or SpectralCube Data cube. n_eigs : int Number of eigenvalues to compute. fiducial_model : PCA Computed PCA object. Use to avoid recomputing. mean_sub : bool, optional Subtracts the mean before computing the covariance matrix. Not subtracting the mean is done in the original Heyer & Brunt works.

Methods Summary

distance_metric([verbose, label1, label2, ...])

Computes the distance between the cubes.

Methods Documentation

distance_metric(verbose=False, label1='Cube 1', label2='Cube 2', save_name=None)[source] [edit on github]¶

Computes the distance between the cubes.

Parameters:	verbose : bool, optional Enables plotting. label1 : str, optional Object or region name for cube1 label2 : str, optional Object or region name for cube2 save_name : str, optional Save the figure when a file name is given.