[Numpy-discussion] Rebinning numpy array
Sun Nov 13 11:48:10 CST 2011
Also: it seems like you are using values at the boundaries of the bins,
while I think it would make more sense to compute interpolated values at
the middle point of a bin. I'm not sure it'll make a big difference
visually, but it may be more appropriate.
2011/11/13 Olivier Delalleau <email@example.com>
> 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 <firstname.lastname@example.org>
>> 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, 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,
>> NumPy-Discussion mailing list
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