Hi, I found what looks like a bug in histogram, when the option normed=True is used together with non-uniform bins.
Consider this example: import numpy as np data = np.array([1, 2, 3, 4]) bins = np.array([.5, 1.5, 4.5]) bin_widths = np.diff(bins) (counts, dummy) = np.histogram(data, bins) (densities, dummy) = np.histogram(data, bins, normed=True) What this gives is: bin_widths array([ 1., 3.]) counts array([1, 3]) densities array([ 0.1, 0.3]) The documentation claims that histogram with normed=True gives a density, which integrates to 1. In this example, it is true that (densities * bin_widths).sum() is 1. However, clearly the data are equally spaced, so their density should be uniform and equal to 0.25. Note that (0.25 * bin_widths).sum() is also 1. I believe np.histogram(data, bins, normed=True) effectively does : np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]). However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths to get a true density over the data coordinate as a result. It's easy to fix by hand, but I think the documentation is at least misleading?! sorry if this has been discussed before; I did not find it anyway (numpy 1.3) _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
