Tsallis_Distance¶

class turbustat.statistics.Tsallis_Distance(array1, array2, lags=None, num_bins=500, fiducial_model=None, periodic=False)[source] [edit on github]¶

Bases: object

Distance Metric for the Tsallis Distribution.

Parameters:

array1 : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject 2D datas. array2 : numpy.ndarray or astropy.io.fits.PrimaryHDU or spectral_cube.LowerDimensionalObject 2D datas. lags : numpy.ndarray or list Lags to calculate at. num_bins : int, optional Number of bins to use in the histograms. fiducial_model : Tsallis Computed Tsallis object. use to avoid recomputing. periodic : bool, optional Sets whether the boundaries are periodic.

Methods Summary

distance_metric([verbose, save_name])

We do not consider the parameter a in the distance metric.

Methods Documentation

distance_metric(verbose=False, save_name=None)[source] [edit on github]¶

We do not consider the parameter a in the distance metric. Since we are fitting to a PDF, a is related to the number of data points and is therefore not a true measure of the differences between the data sets. The distance is computed by summing the squared difference of the parameters, normalized by the sums of the squares, for each lag. The total distance the sum between the two parameters.

Parameters:	verbose : bool, optional Enables plotting. save_name : str,optional Save the figure when a file name is given.