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Warping with a kernel normalized to unit sum moves noise power off the variance plane's diagonal and into covariance between neighboring pixels, which a variance plane cannot represent, leaving the variance too small for anything measured over more than one pixel.
The correction factors should now result in a better calculation of the variance, and doScaleVariance will actually make the result worse.
Warping all 16 patches took a couple seconds per test, and testRunCalibratesToNanojansky warped them twice more; it now relabels the patches' calibration instead. The getTemplate tests are about 6x faster with these changes.
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This also turns doScaleVariance off by default for the template, since that sets the variance to an incorrect value.
The unit tests are slightly refactored, allowing them to run in 12s instead of 90s on my laptop.