def 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 """ a = np.asarray(a) # if range is specified, we need to truncate the data for # the bin-finding routines if (range is not None and (bins in ['blocks', 'knuth', 'scotts', 'freedman'])): a = a[(a >= range[0]) & (a <= range[1])] if bins == 'blocks': bins = bayesian_blocks(a) elif bins == 'knuth': da, bins = knuth_bin_width(a, True) elif bins == 'scotts': da, bins = scotts_bin_width(a, True) elif bins == 'freedman': da, bins = freedman_bin_width(a, True) elif isinstance(bins, str): raise ValueError("unrecognized bin code: '%s'" % bins) return np.histogram(a, bins, range, **kwargs)
def 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 """ a = np.asarray(a) # if range is specified, we need to truncate the data for # the bin-finding routines if (range is not None and (bins in ['blocks', 'knuth', 'scotts', 'freedman'])): a = a[(a >= range[0]) & (a <= range[1])] if bins == 'blocks': bins = bayesian_blocks(a) elif bins == 'knuth': da, bins = knuth_bin_width(a, True) elif bins == 'scotts': da, bins = scotts_bin_width(a, True) elif bins == 'freedman': da, bins = freedman_bin_width(a, True) elif isinstance(bins, basestring): raise ValueError("unrecognized bin code: '%s'" % bins) return np.histogram(a, bins, range, **kwargs)