Tickets/DM-18742: speed up DcrModel convergence #120
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isullivan
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kfindeisen
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May 1, 2019
| @@ -319,6 +321,9 @@ def buildMatchedTemplate(self, exposure=None, order=3, | |||
| splitSubfilters : `bool`, optional | |||
| Calculate DCR for two evenly-spaced wavelengths in each subfilter, | |||
| instead of at the midpoint. Default: False | |||
| amplifyModel : `float`, optional | |||
| Amplify the differences between model planes. | |||
| Used to speed convergence of iterative forward modeling. | |||
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Is the float some kind of multiplier (as I assume from the default)? If so, please say so.
python/lsst/ip/diffim/dcrModel.py
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| @@ -319,6 +321,9 @@ def buildMatchedTemplate(self, exposure=None, order=3, | |||
| splitSubfilters : `bool`, optional | |||
| Calculate DCR for two evenly-spaced wavelengths in each subfilter, | |||
| instead of at the midpoint. Default: False | |||
| maskedImage.variance = self.variance | ||
| maskedImage.image = templateImage[bbox] | ||
| maskedImage.mask = self.mask[bbox] | ||
| maskedImage.variance = self.variance[bbox] |
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This was an unrelated bug I fixed on this branch. I'll move these changes to their own commit.
* Use the correct mode for gaussian filtering. The default setting is 'reflect', which is not desirable. * Modify use of `regularizationFactor` to match documentation. * Image thresholds must be applied before smoothing, not after.
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Add an option that amplifies the differences between DCR model planes to accelerate convergence. With default settings this is only applied within the footprints of detected sources, and only during the iterative forward modeling step of constructing the DcrModel.
Work on this ticket involved in-depth investigation of frequency regularization, and includes some changes to that implementation.