PCA¶

class turbustat.statistics.PCA(cube, n_eigs=50)[source] [edit on github]¶

Bases: object

Implementation of Principal Component Analysis (Heyer & Brunt, 2002)

Parameters:	cube : numpy.ndarray Data cube. n_eigs : int Number of eigenvalues to compute.

Attributes Summary

var_proportion

Methods Summary

Attributes Documentation

var_proportion¶

Methods Documentation

compute_pca(mean_sub=False, normalize=True)[source] [edit on github]¶

Create the covariance matrix and its eigenvalues.

Parameters:	normalize : bool, optional Normalize the set of eigenvalues by the 0th component.

run(verbose=False, normalize=True)[source] [edit on github]¶

Run method. Needed to maintain package standards.

Parameters:	verbose : bool, optional Enables plotting. normalize : bool, optional See `compute_pca`.