On Sun, Nov 13, 2011 at 16:04, Johannes Bauer <dfnsonfsdu...@gmx.de> wrote: > Hi group, > > I have a rather simple problem, or so it would seem. However I cannot > seem to find the right solution. Here's the problem: > > A Geiger counter measures counts in distinct time intervals. The time > intervals are not of constant length. Imaging for example that the > counter would always create a table entry when the counts reach 10. Then > we would have the following bins (made-up data for illustration): > > Seconds Counts Len CPS > 0 - 44 10 44 0.23 > 44 - 120 10 76 0.13 > 120 - 140 10 20 0.5 > 140 - 200 10 60 0.16 > > So we have n bins (in this example 4), but they're not equidistant. I > want to rebin samples to make them equidistant. For example, I would > like to rebin into 5 bins of 40 seconds time each. Then the rebinned > example (I calculate by hand so this might contain errors): > > 0-40 9.09 > 40-80 5.65 > 80-120 5.26 > 120-160 13.33 > 160-200 6.66 > > That means, if a destination bin completely overlaps a source bin, its > complete value is taken. If it overlaps partially, linear interpolation > of bin sizes should be used.
What you want to do is set up a linear interpolation based on the boundaries of the uneven bins. Seconds Value 0 0 44 10 120 20 140 30 200 40 Then evaluate that linear interpolation on the boundaries of the uniform bins. [~] |18> bin_bounds = np.array([0.0, 44.0, 120, 140, 200]) [~] |19> bin_values = np.array([0.0, 10, 10, 10, 10]) [~] |20> cum_bin_values = bin_values.cumsum() [~] |21> new_bounds = np.array([0.0, 40, 80, 120, 160, 200]) [~] |22> ecdf = np.interp(new_bounds, bin_bounds, cum_bin_values) [~] |23> ecdf array([ 0. , 9.09090909, 14.73684211, 20. , 33.33333333, 40. ]) [~] |24> uniform_histogram = np.diff(ecdf) [~] |25> uniform_histogram array([ 9.09090909, 5.64593301, 5.26315789, 13.33333333, 6.66666667]) This may be what you are doing already. I'm not sure what is in your getx() and gety() methods. If so, then I think you are on the right track. If you still have problems, then we might need to see some of the problematic data and results. -- 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
