On Wed, Sep 2, 2009 at 7:26 PM, Robert Kern<robert.k...@gmail.com> wrote: > On Wed, Sep 2, 2009 at 18:15, Tim Michelsen<timmichel...@gmx-topmail.de> > wrote: >> Hello fellow numy users, >> I posted some questions on histograms recently [1, 2] but still couldn't >> find a solution. >> >> I am trying to create a inverse cumulative histogram [3] which shall >> look like [4] but with the higher values at the left. > > Okay. That is completely different from what you've asked before. > >> The classification shall follow this exemplary rule: >> >> class 1: 0 >> all values > 0 >> >> class 2: 10 >> all values > 10 >> >> class 3: 15 >> all values > 15 >> >> class 4: 20 >> all values > 20 >> >> class 5: 25 >> all values > 25 >> >> [...] >> >> I could get this easily in a spreadsheet by creating a matix with >> conditional statements (if VALUES_COL > CLASS_BOUNDARY; VALUES_COL; '-'). >> >> With python (numpy or pylab) I was not successful. The plotted histogram >> envelope turned out to be just the inverted curve as the one created >> with the spreadsheet app. > >> sums = np.histogram(values, weights=values, >> normed=normed, >> bins=bins) >> ecdf_sums = np.hstack([0.0, sums[0].cumsum() ]) >> ecdf_inv_sums = ecdf_sums[::-1] > > This is not the kind of "inversion" that you are looking for. You want > > ecdf_inv_sums = ecdf_sums[-1] - ecdf_sums
and you can plot the histogram with bar eisf_sums = ecdf_sums[-1] - ecdf_sums # empirical inverse survival function of weights width = sums[1][1] - sums[1][0] rects1 = plt.bar(sums[1], eisf_sums, width, color='b') Are you sure you want cumulative weights in the histogram? Josef > > -- > Robert Kern > > "I have come to believe that the whole world is an enigma, a harmless > enigma that is made terrible by our own mad attempt to interpret it as > though it had an underlying truth." > -- Umberto Eco > _______________________________________________ > 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
