Source code for astropy.visualization.hist

# Licensed under a 3-clause BSD style license - see LICENSE.rst

from astropy.stats.histogram import calculate_bin_edges

__all__ = ['hist']

[docs]def hist(x, bins=10, ax=None, max_bins=1e5, **kwargs): """Enhanced histogram function 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 pylab.hist(). This function was ported from astroML: Parameters ---------- x : 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 ax : Axes instance, optional specify the Axes on which to draw the histogram. If not specified, then the current active axes will be used. max_bins : int, optional Maximum number of bins allowed. With more than a few thousand bins the performance of matplotlib will not be great. If the number of bins is large *and* the number of input data points is large then the it will take a very long time to compute the histogram. **kwargs : other keyword arguments are described in ``plt.hist()``. Notes ----- Return values are the same as for ``plt.hist()`` See Also -------- astropy.stats.histogram """ # Note that we only calculate the bin edges...matplotlib will calculate # the actual histogram. range = kwargs.get('range', None) weights = kwargs.get('weights', None) bins = calculate_bin_edges(x, bins, range=range, weights=weights) if len(bins) > max_bins: raise ValueError('Histogram has too many bins: ' '{nbin}. Use max_bins to increase the number ' 'of allowed bins or range to restrict ' 'the histogram range.'.format(nbin=len(bins))) if ax is None: # optional dependency; only import if strictly needed. import matplotlib.pyplot as plt ax = plt.gca() return ax.hist(x, bins, **kwargs)