StatMomentsDistance¶

class turbustat.statistics.StatMomentsDistance(image1, image2, radius=5, nbins=None, periodic1=False, periodic2=False, fiducial_model=None)[source] [edit on github]¶

Bases: object

Compute the distance between two images based on their moments. The distance is calculated for the skewness and kurtosis. The distance values for each for computed using the Hellinger Distance (default), or the Kullback-Leidler Divergence.

Unlike the other distance classes in TurbuStat, the computation of the histograms needed for the distance metric has been split into its own method. However, the change is fairly transparent, since it is called within distance_metric.

Parameters:

image1 : numpy.ndarray 2D Image. image2 : numpy.ndarray 2D Image. radius : int, optional Radius of circle to use when computing moments. nbins : int, optional Number of bins to use when constructing histograms. periodic1 : bool, optional If image1 is periodic in the spatial boundaries, set to True. periodic2 : bool, optional If image2 is periodic in the spatial boundaries, set to True. fiducial_model : StatMoments Computed StatMoments object. use to avoid recomputing.

Methods Summary

Methods Documentation

distance_metric(metric='Hellinger', verbose=False, nbins=None)[source] [edit on github]¶

Compute the distance.

Parameters:	metric : ‘Hellinger’ (default) or “KL Divergence”, optional Set the metric to use compare the histograms. verbose : bool, optional Enables plotting. nbins : int, optional Bins to use in the histogram calculation.