Wavelet_Distance¶

class turbustat.statistics.Wavelet_Distance(dataset1, dataset2, scales=None, num=50, xlow=None, xhigh=None, 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 : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject 2D image. dataset2 : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject 2D image. scales : numpy.ndarray or list The scales where the transform is calculated. num : int Number of scales to calculate the transform at. fiducial_model : wt2D Computed wt2D object. use to avoid recomputing. xlow : float or np.ndarray, optional The lower lag fitting limit. An array with 2 elements can be passed to give separate lower limits for the datasets. xhigh : float or np.ndarray, optional The upper lag fitting limit. See xlow above.

Methods Summary

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

Implements the distance metric for 2 wavelet transforms.

Methods Documentation

distance_metric(verbose=False, label1=None, label2=None, ang_units=False, unit=Unit("deg"), save_name=None)[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:

verbose : bool, optional Enables plotting. label1 : str, optional Object or region name for dataset1 label2 : str, optional Object or region name for dataset2 ang_units : bool, optional Convert frequencies to angular units using the given header. unit : u.Unit, optional Choose the angular unit to convert to when ang_units is enabled. save_name : str,optional Save the figure when a file name is given.