Wavelet_Distance¶

class turbustat.statistics.Wavelet_Distance(dataset1, dataset2, wavelet=<turbustat.statistics.wavelets.wavelet_transform.Mexican_hat instance>, ang_units=True, scales=None, num=50, dx=0.25, dy=0.25, fiducial_model=None)[source] [edit on github]¶

Bases: object

Compute the distance between the two cubes using the Wavelet transform. We fit a linear model to the two wavelet transforms. The distance is the t-statistic of the interaction term describing the difference in the slopes.

Parameters:

dataset1 : FITS hdu 2D image. dataset2 : FITS hdu 2D image. wavelet : class Wavelet class. Only Mexican_hat() is implemented. ang_units : bool, optional Sets whether to use angular units. scales : numpy.ndarray or list The scales where the transform is calculated. num : int Number of scales to calculate the transform at. dx : float, optional Spacing in the x-direction. dy : float, optional Spacing in the y-direction. fiducial_model : wt2D Computed wt2D object. use to avoid recomputing.

Methods Summary

Methods Documentation

distance_metric(non_linear=True, verbose=False)[source] [edit on github]¶

Implements the distance metric for 2 wavelet transforms. We fit the linear portion of the transform to represent the powerlaw

Parameters:	non_linear : bool, optional Enables clipping of non-linear portions of the transform. verbose : bool, optional Enables plotting.