tobac.analysis.feature_analysis.nearestneighbordistance_histogram#

tobac.analysis.feature_analysis.nearestneighbordistance_histogram(features, bin_edges=array([0, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 10500, 11000, 11500, 12000, 12500, 13000, 13500, 14000, 14500, 15000, 15500, 16000, 16500, 17000, 17500, 18000, 18500, 19000, 19500, 20000, 20500, 21000, 21500, 22000, 22500, 23000, 23500, 24000, 24500, 25000, 25500, 26000, 26500, 27000, 27500, 28000, 28500, 29000, 29500]), density=False, method_distance=None, return_values=False)#

Create an nearest neighbor distance histogram of the features. If the DataFrame does not contain a ‘min_distance’ column, the distances are calculated.

bin_edgesint or ndarray, optional

If bin_edges is an int, it defines the number of equal-width bins in the given range. If bins is a ndarray, it defines a monotonically increasing array of bin edges, including the rightmost edge. Default is np.arange(0, 30000, 500).

densitybool, optional

If False, the result will contain the number of samples in each bin. If True, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. Default is False.

method_distance{None, ‘xy’, ‘latlon’}, optional

Method of distance calculation. ‘xy’ uses the length of the vector between the two features, ‘latlon’ uses the haversine distance. None checks wether the required coordinates are present and starts with ‘xy’. Default is None.

return_valuesbool, optional

Bool determining wether the nearest neighbor distance of the features are returned from this function. Default is False.

Returns:

  • hist (ndarray) – The values of the histogram.

  • bin_edges (ndarray) – The edges of the histogram.

  • distances, optional (ndarray) – A numpy array with the nearest neighbor distances of each feature.