histogram

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().

Parameters
aarray_like

array of data to be histogrammed

binsint or 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

rangetuple or None (optional)

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

weightsarray_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().
Returns
histarray

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

bin_edgesarray of dtype float

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

See also

numpy.histogram