Normalized Mean Model and PSF metrics #65
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Co-authored-by: Jorge Martínez-Palomera <[email protected]>
Co-authored-by: Jorge Martínez-Palomera <[email protected]>
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Sanity check, comparing SAP and PSF flux: after (normalized shape model) Scatter is due to saturated sources, sources with partial data (e.g. outside or near the edge of the TPF), and highly blended sources. |
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christinahedges
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This looks very good! Let's add some tests that just check the metrics work and produce reasonable values? Especially the high resolution one would be good to test?
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Dec 8, 2022
This reverts commit 846f17a.
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@jorgemarpa I reverted these changes because it broke in ways that were hard to debug quickly, you'll need to reopen this pull request! |
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This PR adds two new methods:
A hidden function to find the scale factor that normalized the mean model to sum 1 when sources are full in the TPF. It creates a high-resolution mean model and integrates the PSF using the trapezoidal rule, the integral is the rescale factor that is applied to the PSF weights. The method also has a diagnostic plot.
A visible method to compute PSF metrics such as:
source_psf_fractionamount of PSF in data, useful to determine if a source has total or partial flux data.perturbed_ratio_meanhow much the perturbed model deviates from the mean model for a given source, useful to find when the time model changes the mean value of the light curve. The expected value is 1.perturbed_stdhow much variability has the perturbed model in time, helps find when the time model introduces synthetic variability or features. A large value means a poor time model.Requires #63 to be merged first