This documentation is for astroML version 0.2

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If you use the software, please consider citing astroML. astroML.density_estimation.histogram

astroML.density_estimation.histogram(a, bins=10, range=None, **kwargs)

Enhanced histogram

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 :

a : array_like

array of data to be histogrammed

bins : int 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 ‘scotts’ : 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())

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

Returns :

hist : array

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

bin_edges : array of dtype float

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

See also

numpy.histogram, astroML.plotting.hist