[ENHANCEMENT] a closer look into the pastas CCF function #730
Labels
enhancement
Indicates improvement of existing features
priority 1
normal, deal with in the foreseeable future
When developing code to visualize the cross-correlation between synthetically generated heads based on actual rainfall, I noticed that pastas CCF function does not produce correlations that match the block response function. The pastas implementation is based on the pearson correlation coefficient whereas the CCF I developed uses
np.correlate
which uses a different definition of correlation.According to theory, the cross-correlation should be equal to the block response as long as autocorrelation in the stress time series is not obfuscating the relationship. The example below shows the known block-response used to create a synthetic head time series. The different color bar plots show the computed CCF using different methods. The pastas CCF method clearly produces a very different result from the CCF (original). Also the block-response matches nicely with the CCF-PW computation, whereas it looks nothing like the pastas CCF result.
Things to check:
This issue replaces #352 and #350. Cross-correlation developments will be programmed as a pastas plugin in the future once we settle on a plugin architecture.
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