lsst.ip.diffim gef7b216a29+f391811cb4
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subtractImages.py
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1# This file is part of ip_diffim.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
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14# This program is distributed in the hope that it will be useful,
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17# GNU General Public License for more details.
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21
22import numpy as np
23
24import lsst.afw.image
25import lsst.afw.math
26import lsst.geom
27from lsst.ip.diffim.utils import getPsfFwhm
28from lsst.meas.algorithms import ScaleVarianceTask
29import lsst.pex.config
30import lsst.pipe.base
31from lsst.pipe.base import connectionTypes
32from . import MakeKernelTask, DecorrelateALKernelTask
33from lsst.utils.timer import timeMethod
34
35__all__ = ["AlardLuptonSubtractConfig", "AlardLuptonSubtractTask"]
36
37_dimensions = ("instrument", "visit", "detector")
38_defaultTemplates = {"coaddName": "deep", "fakesType": ""}
39
40
41class SubtractInputConnections(lsst.pipe.base.PipelineTaskConnections,
42 dimensions=_dimensions,
43 defaultTemplates=_defaultTemplates):
44 template = connectionTypes.Input(
45 doc="Input warped template to subtract.",
46 dimensions=("instrument", "visit", "detector"),
47 storageClass="ExposureF",
48 name="{fakesType}{coaddName}Diff_templateExp"
49 )
50 science = connectionTypes.Input(
51 doc="Input science exposure to subtract from.",
52 dimensions=("instrument", "visit", "detector"),
53 storageClass="ExposureF",
54 name="{fakesType}calexp"
55 )
56 sources = connectionTypes.Input(
57 doc="Sources measured on the science exposure; "
58 "used to select sources for making the matching kernel.",
59 dimensions=("instrument", "visit", "detector"),
60 storageClass="SourceCatalog",
61 name="{fakesType}src"
62 )
63 finalizedPsfApCorrCatalog = connectionTypes.Input(
64 doc=("Per-visit finalized psf models and aperture correction maps. "
65 "These catalogs use the detector id for the catalog id, "
66 "sorted on id for fast lookup."),
67 dimensions=("instrument", "visit"),
68 storageClass="ExposureCatalog",
69 name="finalVisitSummary",
70 )
71
72
73class SubtractImageOutputConnections(lsst.pipe.base.PipelineTaskConnections,
74 dimensions=_dimensions,
75 defaultTemplates=_defaultTemplates):
76 difference = connectionTypes.Output(
77 doc="Result of subtracting convolved template from science image.",
78 dimensions=("instrument", "visit", "detector"),
79 storageClass="ExposureF",
80 name="{fakesType}{coaddName}Diff_differenceTempExp",
81 )
82 matchedTemplate = connectionTypes.Output(
83 doc="Warped and PSF-matched template used to create `subtractedExposure`.",
84 dimensions=("instrument", "visit", "detector"),
85 storageClass="ExposureF",
86 name="{fakesType}{coaddName}Diff_matchedExp",
87 )
88
89
91
92 def __init__(self, *, config=None):
93 super().__init__(config=config)
94 if not config.doApplyFinalizedPsf:
95 self.inputs.remove("finalizedPsfApCorrCatalog")
96
97
98class AlardLuptonSubtractConfig(lsst.pipe.base.PipelineTaskConfig,
99 pipelineConnections=AlardLuptonSubtractConnections):
100 mode = lsst.pex.config.ChoiceField(
101 dtype=str,
102 default="convolveTemplate",
103 allowed={"auto": "Choose which image to convolve at runtime.",
104 "convolveScience": "Only convolve the science image.",
105 "convolveTemplate": "Only convolve the template image."},
106 doc="Choose which image to convolve at runtime, or require that a specific image is convolved."
107 )
108 makeKernel = lsst.pex.config.ConfigurableField(
109 target=MakeKernelTask,
110 doc="Task to construct a matching kernel for convolution.",
111 )
112 doDecorrelation = lsst.pex.config.Field(
113 dtype=bool,
114 default=True,
115 doc="Perform diffim decorrelation to undo pixel correlation due to A&L "
116 "kernel convolution? If True, also update the diffim PSF."
117 )
118 decorrelate = lsst.pex.config.ConfigurableField(
119 target=DecorrelateALKernelTask,
120 doc="Task to decorrelate the image difference.",
121 )
122 requiredTemplateFraction = lsst.pex.config.Field(
123 dtype=float,
124 default=0.1,
125 doc="Abort task if template covers less than this fraction of pixels."
126 " Setting to 0 will always attempt image subtraction."
127 )
128 doScaleVariance = lsst.pex.config.Field(
129 dtype=bool,
130 default=True,
131 doc="Scale variance of the image difference?"
132 )
133 scaleVariance = lsst.pex.config.ConfigurableField(
134 target=ScaleVarianceTask,
135 doc="Subtask to rescale the variance of the template to the statistically expected level."
136 )
137 doSubtractBackground = lsst.pex.config.Field(
138 doc="Subtract the background fit when solving the kernel?",
139 dtype=bool,
140 default=True,
141 )
142 doApplyFinalizedPsf = lsst.pex.config.Field(
143 doc="Replace science Exposure's psf and aperture correction map"
144 " with those in finalizedPsfApCorrCatalog.",
145 dtype=bool,
146 default=False,
147 )
148 detectionThreshold = lsst.pex.config.Field(
149 dtype=float,
150 default=10,
151 doc="Minimum signal to noise ration of detected sources "
152 "to use for calculating the PSF matching kernel."
153 )
154 badSourceFlags = lsst.pex.config.ListField(
155 dtype=str,
156 doc="Flags that, if set, the associated source should not "
157 "be used to determine the PSF matching kernel.",
158 default=("sky_source", "slot_Centroid_flag",
159 "slot_ApFlux_flag", "slot_PsfFlux_flag", ),
160 )
161
162 def setDefaults(self):
163 self.makeKernel.kernel.name = "AL"
164 self.makeKernel.kernel.active.fitForBackground = self.doSubtractBackground
165 self.makeKernel.kernel.active.spatialKernelOrder = 1
166 self.makeKernel.kernel.active.spatialBgOrder = 2
167
168
169class AlardLuptonSubtractTask(lsst.pipe.base.PipelineTask):
170 """Compute the image difference of a science and template image using
171 the Alard & Lupton (1998) algorithm.
172 """
173 ConfigClass = AlardLuptonSubtractConfig
174 _DefaultName = "alardLuptonSubtract"
175
176 def __init__(self, **kwargs):
177 super().__init__(**kwargs)
178 self.makeSubtask("decorrelate")
179 self.makeSubtask("makeKernel")
180 if self.config.doScaleVariance:
181 self.makeSubtask("scaleVariance")
182
184 # Normalization is an extra, unnecessary, calculation and will result
185 # in mis-subtraction of the images if there are calibration errors.
186 self.convolutionControl.setDoNormalize(False)
187 self.convolutionControl.setDoCopyEdge(True)
188
189 def _applyExternalCalibrations(self, exposure, finalizedPsfApCorrCatalog):
190 """Replace calibrations (psf, and ApCorrMap) on this exposure with external ones.".
191
192 Parameters
193 ----------
194 exposure : `lsst.afw.image.exposure.Exposure`
195 Input exposure to adjust calibrations.
196 finalizedPsfApCorrCatalog : `lsst.afw.table.ExposureCatalog`
197 Exposure catalog with finalized psf models and aperture correction
198 maps to be applied if config.doApplyFinalizedPsf=True. Catalog uses
199 the detector id for the catalog id, sorted on id for fast lookup.
200
201 Returns
202 -------
203 exposure : `lsst.afw.image.exposure.Exposure`
204 Exposure with adjusted calibrations.
205 """
206 detectorId = exposure.info.getDetector().getId()
207
208 row = finalizedPsfApCorrCatalog.find(detectorId)
209 if row is None:
210 self.log.warning("Detector id %s not found in finalizedPsfApCorrCatalog; "
211 "Using original psf.", detectorId)
212 else:
213 psf = row.getPsf()
214 apCorrMap = row.getApCorrMap()
215 if psf is None:
216 self.log.warning("Detector id %s has None for psf in "
217 "finalizedPsfApCorrCatalog; Using original psf and aperture correction.",
218 detectorId)
219 elif apCorrMap is None:
220 self.log.warning("Detector id %s has None for apCorrMap in "
221 "finalizedPsfApCorrCatalog; Using original psf and aperture correction.",
222 detectorId)
223 else:
224 exposure.setPsf(psf)
225 exposure.info.setApCorrMap(apCorrMap)
226
227 return exposure
228
229 @timeMethod
230 def run(self, template, science, sources, finalizedPsfApCorrCatalog=None):
231 """PSF match, subtract, and decorrelate two images.
232
233 Parameters
234 ----------
235 template : `lsst.afw.image.ExposureF`
236 Template exposure, warped to match the science exposure.
237 science : `lsst.afw.image.ExposureF`
238 Science exposure to subtract from the template.
240 Identified sources on the science exposure. This catalog is used to
241 select sources in order to perform the AL PSF matching on stamp
242 images around them.
243 finalizedPsfApCorrCatalog : `lsst.afw.table.ExposureCatalog`, optional
244 Exposure catalog with finalized psf models and aperture correction
245 maps to be applied if config.doApplyFinalizedPsf=True. Catalog uses
246 the detector id for the catalog id, sorted on id for fast lookup.
247
248 Returns
249 -------
250 results : `lsst.pipe.base.Struct`
251 ``difference`` : `lsst.afw.image.ExposureF`
252 Result of subtracting template and science.
253 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
254 Warped and PSF-matched template exposure.
255 ``backgroundModel`` : `lsst.afw.math.Function2D`
256 Background model that was fit while solving for the PSF-matching kernel
257 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
258 Kernel used to PSF-match the convolved image.
259
260 Raises
261 ------
262 RuntimeError
263 If an unsupported convolution mode is supplied.
264 RuntimeError
265 If there are too few sources to calculate the PSF matching kernel.
266 lsst.pipe.base.NoWorkFound
267 Raised if fraction of good pixels, defined as not having NO_DATA
268 set, is less then the configured requiredTemplateFraction
269 """
270 self._validateExposures(template, science)
271 if self.config.doApplyFinalizedPsf:
272 self._applyExternalCalibrations(science,
273 finalizedPsfApCorrCatalog=finalizedPsfApCorrCatalog)
274 checkTemplateIsSufficient(template, self.log,
275 requiredTemplateFraction=self.config.requiredTemplateFraction)
276 sciencePsfSize = getPsfFwhm(science.psf)
277 templatePsfSize = getPsfFwhm(template.psf)
278 self.log.info("Science PSF FWHM: %f pixels", sciencePsfSize)
279 self.log.info("Template PSF FWHM: %f pixels", templatePsfSize)
280 if self.config.mode == "auto":
281 convolveTemplate = _shapeTest(template.psf, science.psf)
282 if convolveTemplate:
283 if sciencePsfSize < templatePsfSize:
284 self.log.info("Average template PSF size is greater, "
285 "but science PSF greater in one dimension: convolving template image.")
286 else:
287 self.log.info("Science PSF size is greater: convolving template image.")
288 else:
289 self.log.info("Template PSF size is greater: convolving science image.")
290 elif self.config.mode == "convolveTemplate":
291 self.log.info("`convolveTemplate` is set: convolving template image.")
292 convolveTemplate = True
293 elif self.config.mode == "convolveScience":
294 self.log.info("`convolveScience` is set: convolving science image.")
295 convolveTemplate = False
296 else:
297 raise RuntimeError("Cannot handle AlardLuptonSubtract mode: %s", self.config.mode)
298 # put the template on the same photometric scale as the science image
299 photoCalib = template.getPhotoCalib()
300 self.log.info("Applying photometric calibration to template: %f", photoCalib.getCalibrationMean())
301 template.maskedImage = photoCalib.calibrateImage(template.maskedImage)
302
303 if self.config.doScaleVariance:
304 # Scale the variance of the template and science images before
305 # convolution, subtraction, or decorrelation so that they have the
306 # correct ratio.
307 templateVarFactor = self.scaleVariance.run(template.maskedImage)
308 sciVarFactor = self.scaleVariance.run(science.maskedImage)
309 self.log.info("Template variance scaling factor: %.2f", templateVarFactor)
310 self.metadata.add("scaleTemplateVarianceFactor", templateVarFactor)
311 self.log.info("Science variance scaling factor: %.2f", sciVarFactor)
312 self.metadata.add("scaleScienceVarianceFactor", sciVarFactor)
313
314 selectSources = self._sourceSelector(sources)
315 self.log.info("%i sources used out of %i from the input catalog", len(selectSources), len(sources))
316 if len(selectSources) < self.config.makeKernel.nStarPerCell:
317 self.log.warning("Too few sources to calculate the PSF matching kernel: "
318 "%i selected but %i needed for the calculation.",
319 len(selectSources), self.config.makeKernel.nStarPerCell)
320 raise RuntimeError("Cannot compute PSF matching kernel: too few sources selected.")
321 if convolveTemplate:
322 subtractResults = self.runConvolveTemplate(template, science, selectSources)
323 else:
324 subtractResults = self.runConvolveScience(template, science, selectSources)
325
326 return subtractResults
327
328 def runConvolveTemplate(self, template, science, selectSources):
329 """Convolve the template image with a PSF-matching kernel and subtract
330 from the science image.
331
332 Parameters
333 ----------
334 template : `lsst.afw.image.ExposureF`
335 Template exposure, warped to match the science exposure.
336 science : `lsst.afw.image.ExposureF`
337 Science exposure to subtract from the template.
338 selectSources : `lsst.afw.table.SourceCatalog`
339 Identified sources on the science exposure. This catalog is used to
340 select sources in order to perform the AL PSF matching on stamp
341 images around them.
342
343 Returns
344 -------
345 results : `lsst.pipe.base.Struct`
346
347 ``difference`` : `lsst.afw.image.ExposureF`
348 Result of subtracting template and science.
349 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
350 Warped and PSF-matched template exposure.
351 ``backgroundModel`` : `lsst.afw.math.Function2D`
352 Background model that was fit while solving for the PSF-matching kernel
353 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
354 Kernel used to PSF-match the template to the science image.
355 """
356 kernelSources = self.makeKernel.selectKernelSources(template, science,
357 candidateList=selectSources,
358 preconvolved=False)
359 kernelResult = self.makeKernel.run(template, science, kernelSources,
360 preconvolved=False)
361
362 matchedTemplate = self._convolveExposure(template, kernelResult.psfMatchingKernel,
364 bbox=science.getBBox(),
365 psf=science.psf,
366 photoCalib=science.getPhotoCalib())
367 difference = _subtractImages(science, matchedTemplate,
368 backgroundModel=(kernelResult.backgroundModel
369 if self.config.doSubtractBackground else None))
370 correctedExposure = self.finalize(template, science, difference, kernelResult.psfMatchingKernel,
371 templateMatched=True)
372
373 return lsst.pipe.base.Struct(difference=correctedExposure,
374 matchedTemplate=matchedTemplate,
375 matchedScience=science,
376 backgroundModel=kernelResult.backgroundModel,
377 psfMatchingKernel=kernelResult.psfMatchingKernel)
378
379 def runConvolveScience(self, template, science, selectSources):
380 """Convolve the science image with a PSF-matching kernel and subtract the template image.
381
382 Parameters
383 ----------
384 template : `lsst.afw.image.ExposureF`
385 Template exposure, warped to match the science exposure.
386 science : `lsst.afw.image.ExposureF`
387 Science exposure to subtract from the template.
388 selectSources : `lsst.afw.table.SourceCatalog`
389 Identified sources on the science exposure. This catalog is used to
390 select sources in order to perform the AL PSF matching on stamp
391 images around them.
392
393 Returns
394 -------
395 results : `lsst.pipe.base.Struct`
396
397 ``difference`` : `lsst.afw.image.ExposureF`
398 Result of subtracting template and science.
399 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
400 Warped template exposure. Note that in this case, the template
401 is not PSF-matched to the science image.
402 ``backgroundModel`` : `lsst.afw.math.Function2D`
403 Background model that was fit while solving for the PSF-matching kernel
404 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
405 Kernel used to PSF-match the science image to the template.
406 """
407 kernelSources = self.makeKernel.selectKernelSources(science, template,
408 candidateList=selectSources,
409 preconvolved=False)
410 kernelResult = self.makeKernel.run(science, template, kernelSources,
411 preconvolved=False)
412 modelParams = kernelResult.backgroundModel.getParameters()
413 # We must invert the background model if the matching kernel is solved for the science image.
414 kernelResult.backgroundModel.setParameters([-p for p in modelParams])
415
416 kernelImage = lsst.afw.image.ImageD(kernelResult.psfMatchingKernel.getDimensions())
417 norm = kernelResult.psfMatchingKernel.computeImage(kernelImage, doNormalize=False)
418
419 matchedScience = self._convolveExposure(science, kernelResult.psfMatchingKernel,
421 psf=template.psf)
422
423 # Place back on native photometric scale
424 matchedScience.maskedImage /= norm
425 matchedTemplate = template.clone()[science.getBBox()]
426 matchedTemplate.maskedImage /= norm
427 matchedTemplate.setPhotoCalib(science.getPhotoCalib())
428
429 difference = _subtractImages(matchedScience, matchedTemplate,
430 backgroundModel=(kernelResult.backgroundModel
431 if self.config.doSubtractBackground else None))
432
433 correctedExposure = self.finalize(template, science, difference, kernelResult.psfMatchingKernel,
434 templateMatched=False)
435
436 return lsst.pipe.base.Struct(difference=correctedExposure,
437 matchedTemplate=matchedTemplate,
438 matchedScience=matchedScience,
439 backgroundModel=kernelResult.backgroundModel,
440 psfMatchingKernel=kernelResult.psfMatchingKernel,)
441
442 def finalize(self, template, science, difference, kernel,
443 templateMatched=True,
444 preConvMode=False,
445 preConvKernel=None,
446 spatiallyVarying=False):
447 """Decorrelate the difference image to undo the noise correlations
448 caused by convolution.
449
450 Parameters
451 ----------
452 template : `lsst.afw.image.ExposureF`
453 Template exposure, warped to match the science exposure.
454 science : `lsst.afw.image.ExposureF`
455 Science exposure to subtract from the template.
456 difference : `lsst.afw.image.ExposureF`
457 Result of subtracting template and science.
458 kernel : `lsst.afw.math.Kernel`
459 An (optionally spatially-varying) PSF matching kernel
460 templateMatched : `bool`, optional
461 Was the template PSF-matched to the science image?
462 preConvMode : `bool`, optional
463 Was the science image preconvolved with its own PSF
464 before PSF matching the template?
465 preConvKernel : `lsst.afw.detection.Psf`, optional
466 If not `None`, then the science image was pre-convolved with
467 (the reflection of) this kernel. Must be normalized to sum to 1.
468 spatiallyVarying : `bool`, optional
469 Compute the decorrelation kernel spatially varying across the image?
470
471 Returns
472 -------
473 correctedExposure : `lsst.afw.image.ExposureF`
474 The decorrelated image difference.
475 """
476 # Erase existing detection mask planes.
477 # We don't want the detection mask from the science image
478 mask = difference.mask
479 mask &= ~(mask.getPlaneBitMask("DETECTED") | mask.getPlaneBitMask("DETECTED_NEGATIVE"))
480
481 if self.config.doDecorrelation:
482 self.log.info("Decorrelating image difference.")
483 correctedExposure = self.decorrelate.run(science, template[science.getBBox()], difference, kernel,
484 templateMatched=templateMatched,
485 preConvMode=preConvMode,
486 preConvKernel=preConvKernel,
487 spatiallyVarying=spatiallyVarying).correctedExposure
488 else:
489 self.log.info("NOT decorrelating image difference.")
490 correctedExposure = difference
491 return correctedExposure
492
493 @staticmethod
494 def _validateExposures(template, science):
495 """Check that the WCS of the two Exposures match, and the template bbox
496 contains the science bbox.
497
498 Parameters
499 ----------
500 template : `lsst.afw.image.ExposureF`
501 Template exposure, warped to match the science exposure.
502 science : `lsst.afw.image.ExposureF`
503 Science exposure to subtract from the template.
504
505 Raises
506 ------
507 AssertionError
508 Raised if the WCS of the template is not equal to the science WCS,
509 or if the science image is not fully contained in the template
510 bounding box.
511 """
512 assert template.wcs == science.wcs,\
513 "Template and science exposure WCS are not identical."
514 templateBBox = template.getBBox()
515 scienceBBox = science.getBBox()
516
517 assert templateBBox.contains(scienceBBox),\
518 "Template bbox does not contain all of the science image."
519
520 @staticmethod
521 def _convolveExposure(exposure, kernel, convolutionControl,
522 bbox=None,
523 psf=None,
524 photoCalib=None):
525 """Convolve an exposure with the given kernel.
526
527 Parameters
528 ----------
529 exposure : `lsst.afw.Exposure`
530 exposure to convolve.
532 PSF matching kernel computed in the ``makeKernel`` subtask.
533 convolutionControl : `lsst.afw.math.ConvolutionControl`
534 Configuration for convolve algorithm.
535 bbox : `lsst.geom.Box2I`, optional
536 Bounding box to trim the convolved exposure to.
537 psf : `lsst.afw.detection.Psf`, optional
538 Point spread function (PSF) to set for the convolved exposure.
539 photoCalib : `lsst.afw.image.PhotoCalib`, optional
540 Photometric calibration of the convolved exposure.
541
542 Returns
543 -------
544 convolvedExp : `lsst.afw.Exposure`
545 The convolved image.
546 """
547 convolvedExposure = exposure.clone()
548 if psf is not None:
549 convolvedExposure.setPsf(psf)
550 if photoCalib is not None:
551 convolvedExposure.setPhotoCalib(photoCalib)
552 convolvedImage = lsst.afw.image.MaskedImageF(exposure.getBBox())
553 lsst.afw.math.convolve(convolvedImage, exposure.maskedImage, kernel, convolutionControl)
554 convolvedExposure.setMaskedImage(convolvedImage)
555 if bbox is None:
556 return convolvedExposure
557 else:
558 return convolvedExposure[bbox]
559
560 def _sourceSelector(self, sources):
561 """Select sources from a catalog that meet the selection criteria.
562
563 Parameters
564 ----------
566 Input source catalog to select sources from.
567
568 Returns
569 -------
571 The source catalog filtered to include only the selected sources.
572 """
573 flags = [True, ]*len(sources)
574 for flag in self.config.badSourceFlags:
575 try:
576 flags *= ~sources[flag]
577 except Exception as e:
578 self.log.warning("Could not apply source flag: %s", e)
579 sToNFlag = (sources.getPsfInstFlux()/sources.getPsfInstFluxErr()) > self.config.detectionThreshold
580 flags *= sToNFlag
581 selectSources = sources[flags]
582
583 return selectSources.copy(deep=True)
584
585
586def checkTemplateIsSufficient(templateExposure, logger, requiredTemplateFraction=0.):
587 """Raise NoWorkFound if template coverage < requiredTemplateFraction
588
589 Parameters
590 ----------
591 templateExposure : `lsst.afw.image.ExposureF`
592 The template exposure to check
593 logger : `lsst.log.Log`
594 Logger for printing output.
595 requiredTemplateFraction : `float`, optional
596 Fraction of pixels of the science image required to have coverage
597 in the template.
598
599 Raises
600 ------
601 lsst.pipe.base.NoWorkFound
602 Raised if fraction of good pixels, defined as not having NO_DATA
603 set, is less then the configured requiredTemplateFraction
604 """
605 # Count the number of pixels with the NO_DATA mask bit set
606 # counting NaN pixels is insufficient because pixels without data are often intepolated over)
607 pixNoData = np.count_nonzero(templateExposure.mask.array
608 & templateExposure.mask.getPlaneBitMask('NO_DATA'))
609 pixGood = templateExposure.getBBox().getArea() - pixNoData
610 logger.info("template has %d good pixels (%.1f%%)", pixGood,
611 100*pixGood/templateExposure.getBBox().getArea())
612
613 if pixGood/templateExposure.getBBox().getArea() < requiredTemplateFraction:
614 message = ("Insufficient Template Coverage. (%.1f%% < %.1f%%) Not attempting subtraction. "
615 "To force subtraction, set config requiredTemplateFraction=0." % (
616 100*pixGood/templateExposure.getBBox().getArea(),
617 100*requiredTemplateFraction))
618 raise lsst.pipe.base.NoWorkFound(message)
619
620
621def _subtractImages(science, template, backgroundModel=None):
622 """Subtract template from science, propagating relevant metadata.
623
624 Parameters
625 ----------
626 science : `lsst.afw.Exposure`
627 The input science image.
628 template : `lsst.afw.Exposure`
629 The template to subtract from the science image.
630 backgroundModel : `lsst.afw.MaskedImage`, optional
631 Differential background model
632
633 Returns
634 -------
635 difference : `lsst.afw.Exposure`
636 The subtracted image.
637 """
638 difference = science.clone()
639 if backgroundModel is not None:
640 difference.maskedImage -= backgroundModel
641 difference.maskedImage -= template.maskedImage
642 return difference
643
644
645def _shapeTest(psf1, psf2):
646 """Determine whether psf1 is narrower in either dimension than psf2.
647
648 Parameters
649 ----------
651 Reference point spread function (PSF) to evaluate.
653 Candidate point spread function (PSF) to evaluate.
654
655 Returns
656 -------
657 `bool`
658 Returns True if psf1 is narrower than psf2 in either dimension.
659 """
660 shape1 = getPsfFwhm(psf1, average=False)
661 shape2 = getPsfFwhm(psf2, average=False)
662 xTest = shape1[0] < shape2[0]
663 yTest = shape1[1] < shape2[1]
664 return xTest | yTest
def finalize(self, template, science, difference, kernel, templateMatched=True, preConvMode=False, preConvKernel=None, spatiallyVarying=False)
def _convolveExposure(exposure, kernel, convolutionControl, bbox=None, psf=None, photoCalib=None)
def runConvolveTemplate(self, template, science, selectSources)
def _applyExternalCalibrations(self, exposure, finalizedPsfApCorrCatalog)
def run(self, template, science, sources, finalizedPsfApCorrCatalog=None)
def runConvolveScience(self, template, science, selectSources)
void convolve(OutImageT &convolvedImage, InImageT const &inImage, KernelT const &kernel, ConvolutionControl const &convolutionControl=ConvolutionControl())
def checkTemplateIsSufficient(templateExposure, logger, requiredTemplateFraction=0.)