Nils and Joseph, Thanks for the bug report, this is now fixed in SVN (r8672).
Ralph. is this something that you want to see backported in 1.5 ? Regards, David On Fri, Aug 6, 2010 at 7:49 PM, <josef.p...@gmail.com> wrote: > On Fri, Aug 6, 2010 at 4:53 PM, Nils Becker <n.bec...@amolf.nl> wrote: > > 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... > > I didn't find any different information about the meaning of > normed=True on the mailing list nor in the trac history > > 169 > 170 if normed: > 171 db = array(np.diff(bins), float) > 172 return n/(n*db).sum(), bins > > this does not look like the correct piecewise density with unequal > binsizes. > > Thanks Nils for pointing this out, I tried only equal binsizes for a > histogram distribution. > > Josef > > > > > > > > > Cheers, Nils > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
