Just one thing: numpy.interp says it doesn't check that the x coordinates are increasing, so make sure it's the case.
Assuming this is ok, I could still see how you may get some non-smooth behavior: this may be because your spike can either be split between two bins (which "dilutes" it somehow), or be included in a single bin (which would make it stand out more). And as you increase your bin size, you will switch between these two situations. -=- Olivier 2011/11/13 Johannes Bauer <dfnsonfsdu...@gmx.de> > 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. > > It is very important that the overall count amount stays the same (in > this case 40, so my numbers seem to be correct, I checked that). In this > example I increased the bin size, but usually I will want to decrease > bin size (even dramatically). > > Now my pathetic attempts look something like this: > > interpolation_points = 4000 > xpts = [ time.mktime(x.timetuple()) for x in self.getx() ] > > interpolatedx = numpy.linspace(xpts[0], xpts[-1], interpolation_points) > interpolatedy = numpy.interp(interpolatedx, xpts, self.gety()) > > self._xreformatted = [ datetime.datetime.fromtimestamp(x) for x in > interpolatedx ] > self._yreformatted = interpolatedy > > This works somewhat, however I see artifacts depending on the > destination sample size: for example when I have a spike in the sample > input and reduce the number of interpolation points (i.e. increase > destination bin size) slowly, the spike will get smaller and smaller > (expected behaviour). After some amount of increasing, the spike however > will "magically" reappear. I believe this to be an interpolation artifact. > > Is there some standard way to get from a non-uniformally distributed bin > distribution to a unifomally distributed bin distribution of arbitrary > bin width? > > Best regards, > Joe > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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