Hi again, first a correction: I posted
> 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 but there is a normalization missing; it should read I believe np.histogram(data, bins, normed=True) effectively does np.histogram(data, bins, normed=False) / (bins[-1] - bins[0]) / data.sum() However, it _should_ do np.histogram(data, bins, normed=False) / bins_widths / data.sum() Bruce Southey replied: > As I recall, there as issues with this aspect. > Please search the discussion regarding histogram especially David > Huard's reply in this thread: > http://thread.gmane.org/gmane.comp.python.numeric.general/22445 I think this discussion pertains to a switch in calling conventions which happened at the time. The last reply of D. Huard (to me) seems to say that they did not fix anything in the _old_ semantics, but that the new semantics is expected to work properly. I tried with an infinite bin: counts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf]) counts array([1,3]) ncounts, dmy = np.histogram([1,2,3,4], [0.5,1.5,np.inf], normed=1) ncounts array([0.,0.]) this also does not make a lot of sense to me. A better result would be array([0.25, 0.]), since 25% of the points fall in the first bin; 75% fall in the second but are spread out over an infinite interval, giving 0. This is what my second proposal would give. I cannot find anything wrong with it so far... Cheers, Nils _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
