astropy.stats.histogram(a, bins=10, range=None, weights=None, **kwargs)[source]

Enhanced histogram function, providing adaptive binnings

This is a histogram function that enables the use of more sophisticated algorithms for determining bins. Aside from the bins argument allowing a string specified how bins are computed, the parameters are the same as numpy.histogram().

a : array_like

array of data to be histogrammed

bins : int, list, or str, optional

If bins is a string, then it must be one of:

  • ‘blocks’ : use bayesian blocks for dynamic bin widths
  • ‘knuth’ : use Knuth’s rule to determine bins
  • ‘scott’ : use Scott’s rule to determine bins
  • ‘freedman’ : use the Freedman-Diaconis rule to determine bins
range : tuple or None, optional

the minimum and maximum range for the histogram. If not specified, it will be (x.min(), x.max())

weights : array_like, optional

An array the same shape as a. If given, the histogram accumulates the value of the weight corresponding to a instead of returning the count of values. This argument does not affect determination of bin edges.

other keyword arguments are described in numpy.histogram().
hist : array

The values of the histogram. See density and weights for a description of the possible semantics.

bin_edges : array of dtype float

Return the bin edges (length(hist)+1).

See also