Azimuthal variance accumulation#2776
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| # Crossed term: | ||
| delta = (self._sum_signal * other._sum_normalization - other._sum_signal*self._sum_normalization) | ||
| if numpy.nansum(other._sum_normalization2) < numpy.nansum(self._sum_normalization2): | ||
| tmp = other._sum_normalization2 / other._sum_normalization |
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Why deviating from how eq 18 is written in https://journals.iucr.org/j/issues/2025/01/00/jo5109/index.html?
Maybe choose a better name than tmp to help the readers
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The implementation based on the formula is not always the most efficient...
Agreed for tmp
| if self.error_model == ErrorModel.AZIMUTHAL: | ||
| # Crossed term: | ||
| delta = (self._sum_signal * other._sum_normalization - other._sum_signal*self._sum_normalization) | ||
| if numpy.nansum(other._sum_normalization2) < numpy.nansum(self._sum_normalization2): |
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Is this for numerical stability? other reason?
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The formula is not "exact" but closer to when the A is larger than B.
Σnormalization² is positive, this is why we use it to assess the size of the ensemble.
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Propagate "properly" also azimuthal variance.
It makes sense to do it with
unionbut I doubt it can be properly calculated withaddorsubtract...Needs to be checked with a demonstrator ... probably an extension to the Spotty tutorial