PCA_Distance

class turbustat.statistics.PCA_Distance(cube1, cube2, n_eigs=50, fiducial_model=None, mean_sub=True)

Bases: object

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

Parameters:

cube1numpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or SpectralCube or PCA

Data cube. Or a PCA class can be given which may be pre-computed.

cube2numpy.ndarray or astropy.io.fits.PrimaryHDU or astropy.io.fits.ImageHDU or SpectralCube or PCA

Data cube. Or a PCA class can be given which may be pre-computed.

n_eigsint

Number of eigenvalues to compute.

fiducial_modelPCA

Computed PCA object. Use to avoid recomputing.

mean_subbool, 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, save_name, ...])

Computes the distance between the cubes.

Methods Documentation

distance_metric(verbose=False, save_name=None, plot_kwargs1={}, plot_kwargs2={}, cmap='viridis')

Computes the distance between the cubes.

Parameters:

verbosebool, optional

Enables plotting.

save_namestr, optional

Save the figure when a file name is given.

plot_kwargs1dict, optional

Set the color, symbol, and label for dataset1 (e.g., plot_kwargs1={‘color’: ‘b’, ‘symbol’: ‘D’, ‘label’: ‘1’}).

plot_kwargs2dict, optional

Set the color, symbol, and label for dataset2.

cmapstr, optional

The colormap to use when plotting the covariance matrices.