Add binning functions #210
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test/test_array_ops.py
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| np.testing.assert_allclose(binned_signal_sigma_so3g, binned_signal_sigma, atol=tolerance) | ||
| np.testing.assert_allclose(bin_counts_so3g, bin_counts_dets, atol=tolerance) | ||
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| def test_02_binning_flags_float64(self): |
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Please reduce duplicate code in the test by using a helper function and calling it from test_01 and test_02.
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Moved most of the duplicated code in tests to standalone functions.
src/array_ops.cxx
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| void _histogram(const T* data, const T* weights, int* histogram, | ||
| const T* bin_edges, const int nsamps, const int nbins, | ||
| const T lower, const T upper) | ||
| { |
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This implementation is very brute-force -- looping over ~every bin for every sample. A really basic optimization would be to assume correlation in data -- i.e. if sample i was in bin j, start your search for i+1 at bin j (and probably you'll find it in j, j-1, or j+1).
But really this function is used in one place ... where one already has "bin_indices" ... so perhaps this function isn't needed at all?
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I removed this function and replaced with code that uses bin_indices and confirmed it is equivalent. While that added step can also be removed and just use the existing code in the samps loop, it is faster to do it up front like this then for check for each sample if possible.
src/array_ops.cxx
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| } | ||
| } | ||
| // Edge case to match np.histogram | ||
| if (data[i] == bin_edges[nbins] && data[i] <= upper) { |
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Yeah, technically it should have been >= since we would want to add the values between bin_edges[nbins] and upper though in some cases this would just limit to only ==. Function has been removed though, so now irrelevant.
src/array_ops.cxx
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| " lower: lower bin range (float64)\n" | ||
| " upper: upper bin range (float64)\n"); |
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Point of these two args is unclear. And they're only propagated through to _histogram, which as discussed above I think should be perhaps be canned...
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These are to allow for excluding values outside of a different (perhaps more restricted) range that is not strictly defined by the bin edges. I added these here to reproduce the range arguments in numpy's bin_edges and np.histogram. While I've removed the histogram function, I've kept these in the new code that uses bin_indices in order to make it consistent with the sotodlib version that has range as an input. I have also made the docstring a bit more explanative. I tested limiting the range in the tests to less than the full x array range and find that they both agree.
The sotodlib version doesn't use np.histogram when there are flags so that doesn't actually limit based on the range argument (other than in np.histogram_bin_edges) in the way that it does when there are no flags. I've matched that difference here, but perhaps we might want to make these two cases consistent across both versions.
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Adds binning functions that replicate most of the functionality of
sotodlib'sbin_signal(https://github.com/simonsobs/sotodlib/blob/master/sotodlib/tod_ops/binning.py). There are two functions,bin_signalandbin_flagged_signalsince binning with flags is much slower. Setting the bin edges is left up to python since it has algorithms to find the optimum bin numbers, but might be added later. The weight and flag arrays may be either 1D (nsamps) or 2D (ndets,nsamps) as in thesotodlibversion.