On Thu, Aug 27, 2009 at 8:23 AM, alexander baker<baker.alexan...@gmail.com> wrote: > Here is an example, this does something a extra at the end but shows how the > bins can be used. > > Regards > > Alex Baker. > > from scipy.stats import norm > r = norm.rvs(size=10000) > > import numpy as np > p, bins = np.histogram(r, width, normed=True) > db = bins[1]-bins[0] > cdf = np.cumsum(p*db) > > from pylab import figure, show > fig = figure() > ax = fig.add_subplot(111) > ax.bar(bins[:-1], cdf, width=0.8*db) > show() > > o = [] > rates = [] > for r in np.arange(0, max(bins), db): > G = max(np.cumsum([bin for bin in bins if bin > r])) > L = min(np.cumsum([bin for bin in bins if bin < r])) > o.append(abs(G/L)) > rates.append(r) > > Mobile: 07788 872118 > Blog: www.alexfb.com > > -- > All science is either physics or stamp collecting. > > > 2009/8/27 Tim Michelsen <timmichel...@gmx-topmail.de> >> >> Hello, >> I need some advice on histograms. >> If I interpret the documentation [1, 2] for numpy.histogram correctly, the >> result of the function is a count of the occurences sorted into each bin. >> >> (n, bins) = numpy.histogram(v, bins=50, normed=1) >> >> But how can I apply another function on these values stacked in each bin? >> Like summing them up or building averages? >> >> Thanks, >> Timmie >> >> [1] >> http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html >> [2] >> >> http://www.scipy.org/Tentative_NumPy_Tutorial#head-aa75ec76530ff51a2e98071adb7224a4b793519e >>
Tim, do you mean, that you want to apply other functions, e.g. mean or variance, to the original values but calculated per bin? If I read the answer of Alex correctly, then it only works with the bin count. To calculate e.g. the variance of all values per bin, I think, the easiest would be to create a label array, with values arange(nbins-1) for the corresponding original data and then use np.bincount. I don't know straight away what the easiest or fastest way is to create the label array from the histogram bin boundaries Josef _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
