histogram#

astropy.stats.histogram(a: ArrayLike, bins: int | list[int | float] | Literal['blocks', 'knuth', 'scott', 'freedman'] | None = 10, range: tuple[int | float, int | float] | None = None, weights: ArrayLike | None = None, **kwargs) → tuple[NDArray, NDArray][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:
anumpy:array_like

array of data to be histogrammed

binspython:int, python:list, or python: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

rangepython:tuple or python:None, optional

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

weightsnumpy: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.

**kwargspython:dict, optional

Extra 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 python:float

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

See also

numpy.histogram