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LSST Data Management Base Package
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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
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
21
22import warnings
23
24import numpy as np
25
26import lsst.afw.image
27import lsst.afw.math
28import lsst.geom
29from lsst.utils.introspection import find_outside_stacklevel
30from lsst.ip.diffim.utils import evaluateMeanPsfFwhm, getPsfFwhm
31from lsst.meas.algorithms import ScaleVarianceTask
32import lsst.pex.config
33import lsst.pipe.base
35from lsst.pipe.base import connectionTypes
36from . import MakeKernelTask, DecorrelateALKernelTask
37from lsst.utils.timer import timeMethod
38
39__all__ = ["AlardLuptonSubtractConfig", "AlardLuptonSubtractTask",
40 "AlardLuptonPreconvolveSubtractConfig", "AlardLuptonPreconvolveSubtractTask"]
41
42_dimensions = ("instrument", "visit", "detector")
43_defaultTemplates = {"coaddName": "deep", "fakesType": ""}
44
45
46class SubtractInputConnections(lsst.pipe.base.PipelineTaskConnections,
47 dimensions=_dimensions,
48 defaultTemplates=_defaultTemplates):
49 template = connectionTypes.Input(
50 doc="Input warped template to subtract.",
51 dimensions=("instrument", "visit", "detector"),
52 storageClass="ExposureF",
53 name="{fakesType}{coaddName}Diff_templateExp"
54 )
55 science = connectionTypes.Input(
56 doc="Input science exposure to subtract from.",
57 dimensions=("instrument", "visit", "detector"),
58 storageClass="ExposureF",
59 name="{fakesType}calexp"
60 )
61 sources = connectionTypes.Input(
62 doc="Sources measured on the science exposure; "
63 "used to select sources for making the matching kernel.",
64 dimensions=("instrument", "visit", "detector"),
65 storageClass="SourceCatalog",
66 name="{fakesType}src"
67 )
68 visitSummary = connectionTypes.Input(
69 doc=("Per-visit catalog with final calibration objects. "
70 "These catalogs use the detector id for the catalog id, "
71 "sorted on id for fast lookup."),
72 dimensions=("instrument", "visit"),
73 storageClass="ExposureCatalog",
74 name="finalVisitSummary",
75 )
76
77 def __init__(self, *, config=None):
78 super().__init__(config=config)
79 if not config.doApplyExternalCalibrations:
80 del self.visitSummary
81
82
83class SubtractImageOutputConnections(lsst.pipe.base.PipelineTaskConnections,
84 dimensions=_dimensions,
85 defaultTemplates=_defaultTemplates):
86 difference = connectionTypes.Output(
87 doc="Result of subtracting convolved template from science image.",
88 dimensions=("instrument", "visit", "detector"),
89 storageClass="ExposureF",
90 name="{fakesType}{coaddName}Diff_differenceTempExp",
91 )
92 matchedTemplate = connectionTypes.Output(
93 doc="Warped and PSF-matched template used to create `subtractedExposure`.",
94 dimensions=("instrument", "visit", "detector"),
95 storageClass="ExposureF",
96 name="{fakesType}{coaddName}Diff_matchedExp",
97 )
98 psfMatchingKernel = connectionTypes.Output(
99 doc="Kernel used to PSF match the science and template images.",
100 dimensions=("instrument", "visit", "detector"),
101 storageClass="MatchingKernel",
102 name="{fakesType}{coaddName}Diff_psfMatchKernel",
103 )
104
105
106class SubtractScoreOutputConnections(lsst.pipe.base.PipelineTaskConnections,
107 dimensions=_dimensions,
108 defaultTemplates=_defaultTemplates):
109 scoreExposure = connectionTypes.Output(
110 doc="The maximum likelihood image, used for the detection of diaSources.",
111 dimensions=("instrument", "visit", "detector"),
112 storageClass="ExposureF",
113 name="{fakesType}{coaddName}Diff_scoreExp",
114 )
115 psfMatchingKernel = connectionTypes.Output(
116 doc="Kernel used to PSF match the science and template images.",
117 dimensions=("instrument", "visit", "detector"),
118 storageClass="MatchingKernel",
119 name="{fakesType}{coaddName}Diff_psfScoreMatchKernel",
120 )
121
122
129 target=MakeKernelTask,
130 doc="Task to construct a matching kernel for convolution.",
131 )
132 doDecorrelation = lsst.pex.config.Field(
133 dtype=bool,
134 default=True,
135 doc="Perform diffim decorrelation to undo pixel correlation due to A&L "
136 "kernel convolution? If True, also update the diffim PSF."
137 )
139 target=DecorrelateALKernelTask,
140 doc="Task to decorrelate the image difference.",
141 )
142 requiredTemplateFraction = lsst.pex.config.Field(
143 dtype=float,
144 default=0.1,
145 doc="Raise NoWorkFound and do not attempt image subtraction if template covers less than this "
146 " fraction of pixels. Setting to 0 will always attempt image subtraction."
147 )
148 minTemplateFractionForExpectedSuccess = lsst.pex.config.Field(
149 dtype=float,
150 default=0.2,
151 doc="Raise NoWorkFound if PSF-matching fails and template covers less than this fraction of pixels."
152 " If the fraction of pixels covered by the template is less than this value (and greater than"
153 " requiredTemplateFraction) this task is attempted but failure is anticipated and tolerated."
154 )
155 doScaleVariance = lsst.pex.config.Field(
156 dtype=bool,
157 default=True,
158 doc="Scale variance of the image difference?"
159 )
161 target=ScaleVarianceTask,
162 doc="Subtask to rescale the variance of the template to the statistically expected level."
163 )
164 doSubtractBackground = lsst.pex.config.Field(
165 doc="Subtract the background fit when solving the kernel?",
166 dtype=bool,
167 default=True,
168 )
169 doApplyExternalCalibrations = lsst.pex.config.Field(
170 doc=(
171 "Replace science Exposure's calibration objects with those"
172 " in visitSummary. Ignored if `doApplyFinalizedPsf is True."
173 ),
174 dtype=bool,
175 default=False,
176 )
177 detectionThreshold = lsst.pex.config.Field(
178 dtype=float,
179 default=10,
180 doc="Minimum signal to noise ratio of detected sources "
181 "to use for calculating the PSF matching kernel."
182 )
183 detectionThresholdMax = lsst.pex.config.Field(
184 dtype=float,
185 default=500,
186 doc="Maximum signal to noise ratio of detected sources "
187 "to use for calculating the PSF matching kernel."
188 )
189 maxKernelSources = lsst.pex.config.Field(
190 dtype=int,
191 default=1000,
192 doc="Maximum number of sources to use for calculating the PSF matching kernel."
193 "Set to -1 to disable."
194 )
195 minKernelSources = lsst.pex.config.Field(
196 dtype=int,
197 default=3,
198 doc="Minimum number of sources needed for calculating the PSF matching kernel."
199 )
201 dtype=str,
202 doc="Flags that, if set, the associated source should not "
203 "be used to determine the PSF matching kernel.",
204 default=("sky_source", "slot_Centroid_flag",
205 "slot_ApFlux_flag", "slot_PsfFlux_flag",
206 "base_PixelFlags_flag_interpolated",
207 "base_PixelFlags_flag_saturated",
208 "base_PixelFlags_flag_bad",
209 ),
210 )
211 excludeMaskPlanes = lsst.pex.config.ListField(
212 dtype=str,
213 default=("NO_DATA", "BAD", "SAT", "EDGE", "FAKE"),
214 doc="Mask planes to exclude when selecting sources for PSF matching."
215 )
217 dtype=str,
218 default=("NO_DATA", "BAD", "SAT", "EDGE"),
219 doc="Mask planes to interpolate over."
220 )
221 preserveTemplateMask = lsst.pex.config.ListField(
222 dtype=str,
223 default=("NO_DATA", "BAD",),
224 doc="Mask planes from the template to propagate to the image difference."
225 )
226 renameTemplateMask = lsst.pex.config.ListField(
227 dtype=str,
228 default=("SAT", "INJECTED", "INJECTED_CORE",),
229 doc="Mask planes from the template to propagate to the image difference"
230 "with '_TEMPLATE' appended to the name."
231 )
232 allowKernelSourceDetection = lsst.pex.config.Field(
233 dtype=bool,
234 default=False,
235 doc="Re-run source detection for kernel candidates if an error is"
236 " encountered while calculating the matching kernel."
237 )
238
239 def setDefaults(self):
240 self.makeKernel.kernel.name = "AL"
241 self.makeKernel.kernel.active.fitForBackground = self.doSubtractBackground
242 self.makeKernel.kernel.active.spatialKernelOrder = 1
243 self.makeKernel.kernel.active.spatialBgOrder = 2
244
245
246class AlardLuptonSubtractConfig(AlardLuptonSubtractBaseConfig, lsst.pipe.base.PipelineTaskConfig,
247 pipelineConnections=AlardLuptonSubtractConnections):
249 dtype=str,
250 default="convolveTemplate",
251 allowed={"auto": "Choose which image to convolve at runtime.",
252 "convolveScience": "Only convolve the science image.",
253 "convolveTemplate": "Only convolve the template image."},
254 doc="Choose which image to convolve at runtime, or require that a specific image is convolved."
255 )
256
257
258class AlardLuptonSubtractTask(lsst.pipe.base.PipelineTask):
259 """Compute the image difference of a science and template image using
260 the Alard & Lupton (1998) algorithm.
261 """
262 ConfigClass = AlardLuptonSubtractConfig
263 _DefaultName = "alardLuptonSubtract"
264
265 def __init__(self, **kwargs):
266 super().__init__(**kwargs)
267 self.makeSubtask("decorrelate")
268 self.makeSubtask("makeKernel")
269 if self.config.doScaleVariance:
270 self.makeSubtask("scaleVariance")
271
273 # Normalization is an extra, unnecessary, calculation and will result
274 # in mis-subtraction of the images if there are calibration errors.
275 self.convolutionControl.setDoNormalize(False)
276 self.convolutionControl.setDoCopyEdge(True)
277
278 def _applyExternalCalibrations(self, exposure, visitSummary):
279 """Replace calibrations (psf, and ApCorrMap) on this exposure with
280 external ones.".
281
282 Parameters
283 ----------
284 exposure : `lsst.afw.image.exposure.Exposure`
285 Input exposure to adjust calibrations.
286 visitSummary : `lsst.afw.table.ExposureCatalog`
287 Exposure catalog with external calibrations to be applied. Catalog
288 uses the detector id for the catalog id, sorted on id for fast
289 lookup.
290
291 Returns
292 -------
293 exposure : `lsst.afw.image.exposure.Exposure`
294 Exposure with adjusted calibrations.
295 """
296 detectorId = exposure.info.getDetector().getId()
297
298 row = visitSummary.find(detectorId)
299 if row is None:
300 self.log.warning("Detector id %s not found in external calibrations catalog; "
301 "Using original calibrations.", detectorId)
302 else:
303 psf = row.getPsf()
304 apCorrMap = row.getApCorrMap()
305 if psf is None:
306 self.log.warning("Detector id %s has None for psf in "
307 "external calibrations catalog; Using original psf and aperture correction.",
308 detectorId)
309 elif apCorrMap is None:
310 self.log.warning("Detector id %s has None for apCorrMap in "
311 "external calibrations catalog; Using original psf and aperture correction.",
312 detectorId)
313 else:
314 exposure.setPsf(psf)
315 exposure.info.setApCorrMap(apCorrMap)
316
317 return exposure
318
319 @timeMethod
320 def run(self, template, science, sources, finalizedPsfApCorrCatalog=None,
321 visitSummary=None):
322 """PSF match, subtract, and decorrelate two images.
323
324 Parameters
325 ----------
326 template : `lsst.afw.image.ExposureF`
327 Template exposure, warped to match the science exposure.
328 science : `lsst.afw.image.ExposureF`
329 Science exposure to subtract from the template.
330 sources : `lsst.afw.table.SourceCatalog`
331 Identified sources on the science exposure. This catalog is used to
332 select sources in order to perform the AL PSF matching on stamp
333 images around them.
334 finalizedPsfApCorrCatalog : `lsst.afw.table.ExposureCatalog`, optional
335 Exposure catalog with finalized psf models and aperture correction
336 maps to be applied. Catalog uses the detector id for the catalog
337 id, sorted on id for fast lookup. Deprecated in favor of
338 ``visitSummary``, and will be removed after v26.
339 visitSummary : `lsst.afw.table.ExposureCatalog`, optional
340 Exposure catalog with external calibrations to be applied. Catalog
341 uses the detector id for the catalog id, sorted on id for fast
342 lookup. Ignored (for temporary backwards compatibility) if
343 ``finalizedPsfApCorrCatalog`` is provided.
344
345 Returns
346 -------
347 results : `lsst.pipe.base.Struct`
348 ``difference`` : `lsst.afw.image.ExposureF`
349 Result of subtracting template and science.
350 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
351 Warped and PSF-matched template exposure.
352 ``backgroundModel`` : `lsst.afw.math.Function2D`
353 Background model that was fit while solving for the
354 PSF-matching kernel
355 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
356 Kernel used to PSF-match the convolved image.
357
358 Raises
359 ------
360 RuntimeError
361 If an unsupported convolution mode is supplied.
362 RuntimeError
363 If there are too few sources to calculate the PSF matching kernel.
364 lsst.pipe.base.NoWorkFound
365 Raised if fraction of good pixels, defined as not having NO_DATA
366 set, is less then the configured requiredTemplateFraction
367 """
368
369 if finalizedPsfApCorrCatalog is not None:
370 warnings.warn(
371 "The finalizedPsfApCorrCatalog argument is deprecated in favor of the visitSummary "
372 "argument, and will be removed after v26.",
373 FutureWarning,
374 stacklevel=find_outside_stacklevel("lsst.ip.diffim"),
375 )
376 visitSummary = finalizedPsfApCorrCatalog
377
378 self._prepareInputs(template, science, visitSummary=visitSummary)
379
380 # In the event that getPsfFwhm fails, evaluate the PSF on a grid.
381 fwhmExposureBuffer = self.config.makeKernel.fwhmExposureBuffer
382 fwhmExposureGrid = self.config.makeKernel.fwhmExposureGrid
383
384 # Calling getPsfFwhm on template.psf fails on some rare occasions when
385 # the template has no input exposures at the average position of the
386 # stars. So we try getPsfFwhm first on template, and if that fails we
387 # evaluate the PSF on a grid specified by fwhmExposure* fields.
388 # To keep consistent definitions for PSF size on the template and
389 # science images, we use the same method for both.
390 try:
391 templatePsfSize = getPsfFwhm(template.psf)
392 sciencePsfSize = getPsfFwhm(science.psf)
394 self.log.info("Unable to evaluate PSF at the average position. "
395 "Evaluting PSF on a grid of points."
396 )
397 templatePsfSize = evaluateMeanPsfFwhm(template,
398 fwhmExposureBuffer=fwhmExposureBuffer,
399 fwhmExposureGrid=fwhmExposureGrid
400 )
401 sciencePsfSize = evaluateMeanPsfFwhm(science,
402 fwhmExposureBuffer=fwhmExposureBuffer,
403 fwhmExposureGrid=fwhmExposureGrid
404 )
405 self.log.info("Science PSF FWHM: %f pixels", sciencePsfSize)
406 self.log.info("Template PSF FWHM: %f pixels", templatePsfSize)
407 self.metadata.add("sciencePsfSize", sciencePsfSize)
408 self.metadata.add("templatePsfSize", templatePsfSize)
409
410 if self.config.mode == "auto":
411 convolveTemplate = _shapeTest(template,
412 science,
413 fwhmExposureBuffer=fwhmExposureBuffer,
414 fwhmExposureGrid=fwhmExposureGrid)
415 if convolveTemplate:
416 if sciencePsfSize < templatePsfSize:
417 self.log.info("Average template PSF size is greater, "
418 "but science PSF greater in one dimension: convolving template image.")
419 else:
420 self.log.info("Science PSF size is greater: convolving template image.")
421 else:
422 self.log.info("Template PSF size is greater: convolving science image.")
423 elif self.config.mode == "convolveTemplate":
424 self.log.info("`convolveTemplate` is set: convolving template image.")
425 convolveTemplate = True
426 elif self.config.mode == "convolveScience":
427 self.log.info("`convolveScience` is set: convolving science image.")
428 convolveTemplate = False
429 else:
430 raise RuntimeError("Cannot handle AlardLuptonSubtract mode: %s", self.config.mode)
431
432 try:
433 sourceMask = science.mask.clone()
434 sourceMask.array |= template[science.getBBox()].mask.array
435 selectSources = self._sourceSelector(sources, sourceMask)
436 if convolveTemplate:
437 self.metadata.add("convolvedExposure", "Template")
438 subtractResults = self.runConvolveTemplate(template, science, selectSources)
439 else:
440 self.metadata.add("convolvedExposure", "Science")
441 subtractResults = self.runConvolveScience(template, science, selectSources)
442
443 except (RuntimeError, lsst.pex.exceptions.Exception) as e:
444 self.log.warning("Failed to match template. Checking coverage")
445 # Raise NoWorkFound if template fraction is insufficient
446 checkTemplateIsSufficient(template[science.getBBox()], self.log,
447 self.config.minTemplateFractionForExpectedSuccess,
448 exceptionMessage="Template coverage lower than expected to succeed."
449 f" Failure is tolerable: {e}")
450 # checkTemplateIsSufficient did not raise NoWorkFound, so raise original exception
451 raise e
452
453 return subtractResults
454
455 def runConvolveTemplate(self, template, science, selectSources):
456 """Convolve the template image with a PSF-matching kernel and subtract
457 from the science image.
458
459 Parameters
460 ----------
461 template : `lsst.afw.image.ExposureF`
462 Template exposure, warped to match the science exposure.
463 science : `lsst.afw.image.ExposureF`
464 Science exposure to subtract from the template.
465 selectSources : `lsst.afw.table.SourceCatalog`
466 Identified sources on the science exposure. This catalog is used to
467 select sources in order to perform the AL PSF matching on stamp
468 images around them.
469
470 Returns
471 -------
472 results : `lsst.pipe.base.Struct`
473
474 ``difference`` : `lsst.afw.image.ExposureF`
475 Result of subtracting template and science.
476 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
477 Warped and PSF-matched template exposure.
478 ``backgroundModel`` : `lsst.afw.math.Function2D`
479 Background model that was fit while solving for the PSF-matching kernel
480 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
481 Kernel used to PSF-match the template to the science image.
482 """
483 try:
484 kernelSources = self.makeKernel.selectKernelSources(template, science,
485 candidateList=selectSources,
486 preconvolved=False)
487 kernelResult = self.makeKernel.run(template, science, kernelSources,
488 preconvolved=False)
489 except Exception as e:
490 if self.config.allowKernelSourceDetection:
491 self.log.warning("Error encountered trying to construct the matching kernel"
492 f" Running source detection and retrying. {e}")
493 kernelSources = self.makeKernel.selectKernelSources(template, science,
494 candidateList=None,
495 preconvolved=False)
496 kernelResult = self.makeKernel.run(template, science, kernelSources,
497 preconvolved=False)
498 else:
499 raise e
500
501 matchedTemplate = self._convolveExposure(template, kernelResult.psfMatchingKernel,
503 bbox=science.getBBox(),
504 psf=science.psf,
505 photoCalib=science.photoCalib)
506
507 difference = _subtractImages(science, matchedTemplate,
508 backgroundModel=(kernelResult.backgroundModel
509 if self.config.doSubtractBackground else None))
510 correctedExposure = self.finalize(template, science, difference,
511 kernelResult.psfMatchingKernel,
512 templateMatched=True)
513
514 return lsst.pipe.base.Struct(difference=correctedExposure,
515 matchedTemplate=matchedTemplate,
516 matchedScience=science,
517 backgroundModel=kernelResult.backgroundModel,
518 psfMatchingKernel=kernelResult.psfMatchingKernel)
519
520 def runConvolveScience(self, template, science, selectSources):
521 """Convolve the science image with a PSF-matching kernel and subtract
522 the template image.
523
524 Parameters
525 ----------
526 template : `lsst.afw.image.ExposureF`
527 Template exposure, warped to match the science exposure.
528 science : `lsst.afw.image.ExposureF`
529 Science exposure to subtract from the template.
530 selectSources : `lsst.afw.table.SourceCatalog`
531 Identified sources on the science exposure. This catalog is used to
532 select sources in order to perform the AL PSF matching on stamp
533 images around them.
534
535 Returns
536 -------
537 results : `lsst.pipe.base.Struct`
538
539 ``difference`` : `lsst.afw.image.ExposureF`
540 Result of subtracting template and science.
541 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
542 Warped template exposure. Note that in this case, the template
543 is not PSF-matched to the science image.
544 ``backgroundModel`` : `lsst.afw.math.Function2D`
545 Background model that was fit while solving for the PSF-matching kernel
546 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
547 Kernel used to PSF-match the science image to the template.
548 """
549 bbox = science.getBBox()
550 kernelSources = self.makeKernel.selectKernelSources(science, template,
551 candidateList=selectSources,
552 preconvolved=False)
553 kernelResult = self.makeKernel.run(science, template, kernelSources,
554 preconvolved=False)
555 modelParams = kernelResult.backgroundModel.getParameters()
556 # We must invert the background model if the matching kernel is solved for the science image.
557 kernelResult.backgroundModel.setParameters([-p for p in modelParams])
558
559 kernelImage = lsst.afw.image.ImageD(kernelResult.psfMatchingKernel.getDimensions())
560 norm = kernelResult.psfMatchingKernel.computeImage(kernelImage, doNormalize=False)
561
562 matchedScience = self._convolveExposure(science, kernelResult.psfMatchingKernel,
564 psf=template.psf)
565
566 # Place back on native photometric scale
567 matchedScience.maskedImage /= norm
568 matchedTemplate = template.clone()[bbox]
569 matchedTemplate.maskedImage /= norm
570 matchedTemplate.setPhotoCalib(science.photoCalib)
571
572 difference = _subtractImages(matchedScience, matchedTemplate,
573 backgroundModel=(kernelResult.backgroundModel
574 if self.config.doSubtractBackground else None))
575
576 correctedExposure = self.finalize(template, science, difference,
577 kernelResult.psfMatchingKernel,
578 templateMatched=False)
579
580 return lsst.pipe.base.Struct(difference=correctedExposure,
581 matchedTemplate=matchedTemplate,
582 matchedScience=matchedScience,
583 backgroundModel=kernelResult.backgroundModel,
584 psfMatchingKernel=kernelResult.psfMatchingKernel,)
585
586 def finalize(self, template, science, difference, kernel,
587 templateMatched=True,
588 preConvMode=False,
589 preConvKernel=None,
590 spatiallyVarying=False):
591 """Decorrelate the difference image to undo the noise correlations
592 caused by convolution.
593
594 Parameters
595 ----------
596 template : `lsst.afw.image.ExposureF`
597 Template exposure, warped to match the science exposure.
598 science : `lsst.afw.image.ExposureF`
599 Science exposure to subtract from the template.
600 difference : `lsst.afw.image.ExposureF`
601 Result of subtracting template and science.
602 kernel : `lsst.afw.math.Kernel`
603 An (optionally spatially-varying) PSF matching kernel
604 templateMatched : `bool`, optional
605 Was the template PSF-matched to the science image?
606 preConvMode : `bool`, optional
607 Was the science image preconvolved with its own PSF
608 before PSF matching the template?
609 preConvKernel : `lsst.afw.detection.Psf`, optional
610 If not `None`, then the science image was pre-convolved with
611 (the reflection of) this kernel. Must be normalized to sum to 1.
612 spatiallyVarying : `bool`, optional
613 Compute the decorrelation kernel spatially varying across the image?
614
615 Returns
616 -------
617 correctedExposure : `lsst.afw.image.ExposureF`
618 The decorrelated image difference.
619 """
620 if self.config.doDecorrelation:
621 self.log.info("Decorrelating image difference.")
622 # We have cleared the template mask plane, so copy the mask plane of
623 # the image difference so that we can calculate correct statistics
624 # during decorrelation
625 correctedExposure = self.decorrelate.run(science, template[science.getBBox()], difference, kernel,
626 templateMatched=templateMatched,
627 preConvMode=preConvMode,
628 preConvKernel=preConvKernel,
629 spatiallyVarying=spatiallyVarying).correctedExposure
630 else:
631 self.log.info("NOT decorrelating image difference.")
632 correctedExposure = difference
633 return correctedExposure
634
635 @staticmethod
636 def _validateExposures(template, science):
637 """Check that the WCS of the two Exposures match, and the template bbox
638 contains the science bbox.
639
640 Parameters
641 ----------
642 template : `lsst.afw.image.ExposureF`
643 Template exposure, warped to match the science exposure.
644 science : `lsst.afw.image.ExposureF`
645 Science exposure to subtract from the template.
646
647 Raises
648 ------
649 AssertionError
650 Raised if the WCS of the template is not equal to the science WCS,
651 or if the science image is not fully contained in the template
652 bounding box.
653 """
654 assert template.wcs == science.wcs,\
655 "Template and science exposure WCS are not identical."
656 templateBBox = template.getBBox()
657 scienceBBox = science.getBBox()
658
659 assert templateBBox.contains(scienceBBox),\
660 "Template bbox does not contain all of the science image."
661
662 def _convolveExposure(self, exposure, kernel, convolutionControl,
663 bbox=None,
664 psf=None,
665 photoCalib=None,
666 interpolateBadMaskPlanes=False,
667 ):
668 """Convolve an exposure with the given kernel.
669
670 Parameters
671 ----------
672 exposure : `lsst.afw.Exposure`
673 exposure to convolve.
674 kernel : `lsst.afw.math.LinearCombinationKernel`
675 PSF matching kernel computed in the ``makeKernel`` subtask.
676 convolutionControl : `lsst.afw.math.ConvolutionControl`
677 Configuration for convolve algorithm.
678 bbox : `lsst.geom.Box2I`, optional
679 Bounding box to trim the convolved exposure to.
680 psf : `lsst.afw.detection.Psf`, optional
681 Point spread function (PSF) to set for the convolved exposure.
682 photoCalib : `lsst.afw.image.PhotoCalib`, optional
683 Photometric calibration of the convolved exposure.
684
685 Returns
686 -------
687 convolvedExp : `lsst.afw.Exposure`
688 The convolved image.
689 """
690 convolvedExposure = exposure.clone()
691 if psf is not None:
692 convolvedExposure.setPsf(psf)
693 if photoCalib is not None:
694 convolvedExposure.setPhotoCalib(photoCalib)
695 if interpolateBadMaskPlanes and self.config.badMaskPlanes is not None:
696 nInterp = _interpolateImage(convolvedExposure.maskedImage,
697 self.config.badMaskPlanes)
698 self.metadata.add("nInterpolated", nInterp)
699 convolvedImage = lsst.afw.image.MaskedImageF(convolvedExposure.getBBox())
700 lsst.afw.math.convolve(convolvedImage, convolvedExposure.maskedImage, kernel, convolutionControl)
701 convolvedExposure.setMaskedImage(convolvedImage)
702 if bbox is None:
703 return convolvedExposure
704 else:
705 return convolvedExposure[bbox]
706
707 def _sourceSelector(self, sources, mask):
708 """Select sources from a catalog that meet the selection criteria.
709
710 Parameters
711 ----------
712 sources : `lsst.afw.table.SourceCatalog`
713 Input source catalog to select sources from.
714 mask : `lsst.afw.image.Mask`
715 The image mask plane to use to reject sources
716 based on their location on the ccd.
717
718 Returns
719 -------
720 selectSources : `lsst.afw.table.SourceCatalog`
721 The input source catalog, with flagged and low signal-to-noise
722 sources removed.
723
724 Raises
725 ------
726 RuntimeError
727 If there are too few sources to compute the PSF matching kernel
728 remaining after source selection.
729 """
730 flags = np.ones(len(sources), dtype=bool)
731 for flag in self.config.badSourceFlags:
732 try:
733 flags *= ~sources[flag]
734 except Exception as e:
735 self.log.warning("Could not apply source flag: %s", e)
736 signalToNoise = sources.getPsfInstFlux()/sources.getPsfInstFluxErr()
737 sToNFlag = signalToNoise > self.config.detectionThreshold
738 flags *= sToNFlag
739 sToNFlagMax = signalToNoise < self.config.detectionThresholdMax
740 flags *= sToNFlagMax
741 flags *= self._checkMask(mask, sources, self.config.excludeMaskPlanes)
742 selectSources = sources[flags].copy(deep=True)
743 if (len(selectSources) > self.config.maxKernelSources) & (self.config.maxKernelSources > 0):
744 signalToNoise = selectSources.getPsfInstFlux()/selectSources.getPsfInstFluxErr()
745 indices = np.argsort(signalToNoise)
746 indices = indices[-self.config.maxKernelSources:]
747 flags = np.zeros(len(selectSources), dtype=bool)
748 flags[indices] = True
749 selectSources = selectSources[flags].copy(deep=True)
750
751 self.log.info("%i/%i=%.1f%% of sources selected for PSF matching from the input catalog",
752 len(selectSources), len(sources), 100*len(selectSources)/len(sources))
753 if len(selectSources) < self.config.minKernelSources:
754 self.log.error("Too few sources to calculate the PSF matching kernel: "
755 "%i selected but %i needed for the calculation.",
756 len(selectSources), self.config.minKernelSources)
757 raise RuntimeError("Cannot compute PSF matching kernel: too few sources selected.")
758 self.metadata.add("nPsfSources", len(selectSources))
759
760 return selectSources
761
762 @staticmethod
763 def _checkMask(mask, sources, excludeMaskPlanes):
764 """Exclude sources that are located on masked pixels.
765
766 Parameters
767 ----------
768 mask : `lsst.afw.image.Mask`
769 The image mask plane to use to reject sources
770 based on the location of their centroid on the ccd.
771 sources : `lsst.afw.table.SourceCatalog`
772 The source catalog to evaluate.
773 excludeMaskPlanes : `list` of `str`
774 List of the names of the mask planes to exclude.
775
776 Returns
777 -------
778 flags : `numpy.ndarray` of `bool`
779 Array indicating whether each source in the catalog should be
780 kept (True) or rejected (False) based on the value of the
781 mask plane at its location.
782 """
783 setExcludeMaskPlanes = [
784 maskPlane for maskPlane in excludeMaskPlanes if maskPlane in mask.getMaskPlaneDict()
785 ]
786
787 excludePixelMask = mask.getPlaneBitMask(setExcludeMaskPlanes)
788
789 xv = np.rint(sources.getX() - mask.getX0())
790 yv = np.rint(sources.getY() - mask.getY0())
791
792 mv = mask.array[yv.astype(int), xv.astype(int)]
793 flags = np.bitwise_and(mv, excludePixelMask) == 0
794 return flags
795
796 def _prepareInputs(self, template, science, visitSummary=None):
797 """Perform preparatory calculations common to all Alard&Lupton Tasks.
798
799 Parameters
800 ----------
801 template : `lsst.afw.image.ExposureF`
802 Template exposure, warped to match the science exposure. The
803 variance plane of the template image is modified in place.
804 science : `lsst.afw.image.ExposureF`
805 Science exposure to subtract from the template. The variance plane
806 of the science image is modified in place.
807 visitSummary : `lsst.afw.table.ExposureCatalog`, optional
808 Exposure catalog with external calibrations to be applied. Catalog
809 uses the detector id for the catalog id, sorted on id for fast
810 lookup.
811 """
812 self._validateExposures(template, science)
813 if visitSummary is not None:
814 self._applyExternalCalibrations(science, visitSummary=visitSummary)
815 templateCoverageFraction = checkTemplateIsSufficient(
816 template[science.getBBox()], self.log,
817 requiredTemplateFraction=self.config.requiredTemplateFraction,
818 exceptionMessage="Not attempting subtraction. To force subtraction,"
819 " set config requiredTemplateFraction=0"
820 )
821 self.metadata.add("templateCoveragePercent", 100*templateCoverageFraction)
822
823 if self.config.doScaleVariance:
824 # Scale the variance of the template and science images before
825 # convolution, subtraction, or decorrelation so that they have the
826 # correct ratio.
827 templateVarFactor = self.scaleVariance.run(template.maskedImage)
828 sciVarFactor = self.scaleVariance.run(science.maskedImage)
829 self.log.info("Template variance scaling factor: %.2f", templateVarFactor)
830 self.metadata.add("scaleTemplateVarianceFactor", templateVarFactor)
831 self.log.info("Science variance scaling factor: %.2f", sciVarFactor)
832 self.metadata.add("scaleScienceVarianceFactor", sciVarFactor)
833
834 # Erase existing detection mask planes.
835 # We don't want the detection mask from the science image
836 self.updateMasks(template, science)
837
838 def updateMasks(self, template, science):
839 """Update the science and template mask planes before differencing.
840
841 Parameters
842 ----------
843 template : `lsst.afw.image.Exposure`
844 Template exposure, warped to match the science exposure.
845 The template mask planes will be erased, except for a few specified
846 in the task config.
847 science : `lsst.afw.image.Exposure`
848 Science exposure to subtract from the template.
849 The DETECTED and DETECTED_NEGATIVE mask planes of the science image
850 will be erased.
851 """
852 self._clearMask(science.mask, clearMaskPlanes=["DETECTED", "DETECTED_NEGATIVE"])
853
854 # We will clear ALL template mask planes, except for those specified
855 # via the `preserveTemplateMask` config. Mask planes specified via
856 # the `renameTemplateMask` config will be copied to new planes with
857 # "_TEMPLATE" appended to their names, and the original mask plane will
858 # be cleared.
859 clearMaskPlanes = [mp for mp in template.mask.getMaskPlaneDict().keys()
860 if mp not in self.config.preserveTemplateMask]
861 renameMaskPlanes = [mp for mp in self.config.renameTemplateMask
862 if mp in template.mask.getMaskPlaneDict().keys()]
863
864 # propagate the mask plane related to Fake source injection
865 # NOTE: the fake source injection sets FAKE plane, but it should be INJECTED
866 # NOTE: This can be removed in DM-40796
867 if "FAKE" in science.mask.getMaskPlaneDict().keys():
868 self.log.info("Adding injected mask plane to science image")
869 self._renameMaskPlanes(science.mask, "FAKE", "INJECTED")
870 if "FAKE" in template.mask.getMaskPlaneDict().keys():
871 self.log.info("Adding injected mask plane to template image")
872 self._renameMaskPlanes(template.mask, "FAKE", "INJECTED_TEMPLATE")
873 if "INJECTED" in renameMaskPlanes:
874 renameMaskPlanes.remove("INJECTED")
875 if "INJECTED_TEMPLATE" in clearMaskPlanes:
876 clearMaskPlanes.remove("INJECTED_TEMPLATE")
877
878 for maskPlane in renameMaskPlanes:
879 self._renameMaskPlanes(template.mask, maskPlane, maskPlane + "_TEMPLATE")
880 self._clearMask(template.mask, clearMaskPlanes=clearMaskPlanes)
881
882 @staticmethod
883 def _renameMaskPlanes(mask, maskPlane, newMaskPlane):
884 """Rename a mask plane by adding the new name and copying the data.
885
886 Parameters
887 ----------
888 mask : `lsst.afw.image.Mask`
889 The mask image to update in place.
890 maskPlane : `str`
891 The name of the existing mask plane to copy.
892 newMaskPlane : `str`
893 The new name of the mask plane that will be added.
894 If the mask plane already exists, it will be updated in place.
895 """
896 mask.addMaskPlane(newMaskPlane)
897 originBitMask = mask.getPlaneBitMask(maskPlane)
898 destinationBitMask = mask.getPlaneBitMask(newMaskPlane)
899 mask.array |= ((mask.array & originBitMask) > 0)*destinationBitMask
900
901 def _clearMask(self, mask, clearMaskPlanes=None):
902 """Clear the mask plane of an exposure.
903
904 Parameters
905 ----------
906 mask : `lsst.afw.image.Mask`
907 The mask plane to erase, which will be modified in place.
908 clearMaskPlanes : `list` of `str`, optional
909 Erase the specified mask planes.
910 If not supplied, the entire mask will be erased.
911 """
912 if clearMaskPlanes is None:
913 clearMaskPlanes = list(mask.getMaskPlaneDict().keys())
914
915 bitMaskToClear = mask.getPlaneBitMask(clearMaskPlanes)
916 mask &= ~bitMaskToClear
917
918
920 SubtractScoreOutputConnections):
921 pass
922
923
925 pipelineConnections=AlardLuptonPreconvolveSubtractConnections):
926 pass
927
928
930 """Subtract a template from a science image, convolving the science image
931 before computing the kernel, and also convolving the template before
932 subtraction.
933 """
934 ConfigClass = AlardLuptonPreconvolveSubtractConfig
935 _DefaultName = "alardLuptonPreconvolveSubtract"
936
937 def run(self, template, science, sources, visitSummary=None):
938 """Preconvolve the science image with its own PSF,
939 convolve the template image with a PSF-matching kernel and subtract
940 from the preconvolved science image.
941
942 Parameters
943 ----------
944 template : `lsst.afw.image.ExposureF`
945 The template image, which has previously been warped to the science
946 image. The template bbox will be padded by a few pixels compared to
947 the science bbox.
948 science : `lsst.afw.image.ExposureF`
949 The science exposure.
950 sources : `lsst.afw.table.SourceCatalog`
951 Identified sources on the science exposure. This catalog is used to
952 select sources in order to perform the AL PSF matching on stamp
953 images around them.
954 visitSummary : `lsst.afw.table.ExposureCatalog`, optional
955 Exposure catalog with complete external calibrations. Catalog uses
956 the detector id for the catalog id, sorted on id for fast lookup.
957
958 Returns
959 -------
960 results : `lsst.pipe.base.Struct`
961 ``scoreExposure`` : `lsst.afw.image.ExposureF`
962 Result of subtracting the convolved template and science
963 images. Attached PSF is that of the original science image.
964 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
965 Warped and PSF-matched template exposure. Attached PSF is that
966 of the original science image.
967 ``matchedScience`` : `lsst.afw.image.ExposureF`
968 The science exposure after convolving with its own PSF.
969 Attached PSF is that of the original science image.
970 ``backgroundModel`` : `lsst.afw.math.Function2D`
971 Background model that was fit while solving for the
972 PSF-matching kernel
973 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
974 Final kernel used to PSF-match the template to the science
975 image.
976 """
977 self._prepareInputs(template, science, visitSummary=visitSummary)
978
979 # TODO: DM-37212 we need to mirror the kernel in order to get correct cross correlation
980 scienceKernel = science.psf.getKernel()
981 matchedScience = self._convolveExposure(science, scienceKernel, self.convolutionControlconvolutionControl,
982 interpolateBadMaskPlanes=True)
983 self.metadata.add("convolvedExposure", "Preconvolution")
984 try:
985 selectSources = self._sourceSelector(sources, matchedScience.mask)
986 subtractResults = self.runPreconvolve(template, science, matchedScience,
987 selectSources, scienceKernel)
988
989 except (RuntimeError, lsst.pex.exceptions.Exception) as e:
990 self.loglog.warning("Failed to match template. Checking coverage")
991 # Raise NoWorkFound if template fraction is insufficient
992 checkTemplateIsSufficient(template[science.getBBox()], self.loglog,
993 self.config.minTemplateFractionForExpectedSuccess,
994 exceptionMessage="Template coverage lower than expected to succeed."
995 f" Failure is tolerable: {e}")
996 # checkTemplateIsSufficient did not raise NoWorkFound, so raise original exception
997 raise e
998
999 return subtractResults
1000
1001 def runPreconvolve(self, template, science, matchedScience, selectSources, preConvKernel):
1002 """Convolve the science image with its own PSF, then convolve the
1003 template with a matching kernel and subtract to form the Score
1004 exposure.
1005
1006 Parameters
1007 ----------
1008 template : `lsst.afw.image.ExposureF`
1009 Template exposure, warped to match the science exposure.
1010 science : `lsst.afw.image.ExposureF`
1011 Science exposure to subtract from the template.
1012 matchedScience : `lsst.afw.image.ExposureF`
1013 The science exposure, convolved with the reflection of its own PSF.
1014 selectSources : `lsst.afw.table.SourceCatalog`
1015 Identified sources on the science exposure. This catalog is used to
1016 select sources in order to perform the AL PSF matching on stamp
1017 images around them.
1018 preConvKernel : `lsst.afw.math.Kernel`
1019 The reflection of the kernel that was used to preconvolve the
1020 `science` exposure. Must be normalized to sum to 1.
1021
1022 Returns
1023 -------
1024 results : `lsst.pipe.base.Struct`
1025
1026 ``scoreExposure`` : `lsst.afw.image.ExposureF`
1027 Result of subtracting the convolved template and science
1028 images. Attached PSF is that of the original science image.
1029 ``matchedTemplate`` : `lsst.afw.image.ExposureF`
1030 Warped and PSF-matched template exposure. Attached PSF is that
1031 of the original science image.
1032 ``matchedScience`` : `lsst.afw.image.ExposureF`
1033 The science exposure after convolving with its own PSF.
1034 Attached PSF is that of the original science image.
1035 ``backgroundModel`` : `lsst.afw.math.Function2D`
1036 Background model that was fit while solving for the
1037 PSF-matching kernel
1038 ``psfMatchingKernel`` : `lsst.afw.math.Kernel`
1039 Final kernel used to PSF-match the template to the science
1040 image.
1041 """
1042 bbox = science.getBBox()
1043 innerBBox = preConvKernel.shrinkBBox(bbox)
1044
1045 kernelSources = self.makeKernel.selectKernelSources(template[innerBBox], matchedScience[innerBBox],
1046 candidateList=selectSources,
1047 preconvolved=True)
1048 kernelResult = self.makeKernel.run(template[innerBBox], matchedScience[innerBBox], kernelSources,
1049 preconvolved=True)
1050
1051 matchedTemplate = self._convolveExposure(template, kernelResult.psfMatchingKernel,
1053 bbox=bbox,
1054 psf=science.psf,
1055 interpolateBadMaskPlanes=True,
1056 photoCalib=science.photoCalib)
1057 score = _subtractImages(matchedScience, matchedTemplate,
1058 backgroundModel=(kernelResult.backgroundModel
1059 if self.config.doSubtractBackground else None))
1060 correctedScore = self.finalize(template[bbox], science, score,
1061 kernelResult.psfMatchingKernel,
1062 templateMatched=True, preConvMode=True,
1063 preConvKernel=preConvKernel)
1064
1065 return lsst.pipe.base.Struct(scoreExposure=correctedScore,
1066 matchedTemplate=matchedTemplate,
1067 matchedScience=matchedScience,
1068 backgroundModel=kernelResult.backgroundModel,
1069 psfMatchingKernel=kernelResult.psfMatchingKernel)
1070
1071
1072def checkTemplateIsSufficient(templateExposure, logger, requiredTemplateFraction=0.,
1073 exceptionMessage=""):
1074 """Raise NoWorkFound if template coverage < requiredTemplateFraction
1075
1076 Parameters
1077 ----------
1078 templateExposure : `lsst.afw.image.ExposureF`
1079 The template exposure to check
1080 logger : `lsst.log.Log`
1081 Logger for printing output.
1082 requiredTemplateFraction : `float`, optional
1083 Fraction of pixels of the science image required to have coverage
1084 in the template.
1085 exceptionMessage : `str`, optional
1086 Message to include in the exception raised if the template coverage
1087 is insufficient.
1088
1089 Returns
1090 -------
1091 templateCoverageFraction: `float`
1092 Fraction of pixels in the template with data.
1093
1094 Raises
1095 ------
1096 lsst.pipe.base.NoWorkFound
1097 Raised if fraction of good pixels, defined as not having NO_DATA
1098 set, is less than the requiredTemplateFraction
1099 """
1100 # Count the number of pixels with the NO_DATA mask bit set
1101 # counting NaN pixels is insufficient because pixels without data are often intepolated over)
1102 pixNoData = np.count_nonzero(templateExposure.mask.array
1103 & templateExposure.mask.getPlaneBitMask('NO_DATA'))
1104 pixGood = templateExposure.getBBox().getArea() - pixNoData
1105 templateCoverageFraction = pixGood/templateExposure.getBBox().getArea()
1106 logger.info("template has %d good pixels (%.1f%%)", pixGood, 100*templateCoverageFraction)
1107
1108 if templateCoverageFraction < requiredTemplateFraction:
1109 message = ("Insufficient Template Coverage. (%.1f%% < %.1f%%)" % (
1110 100*templateCoverageFraction,
1111 100*requiredTemplateFraction))
1112 raise lsst.pipe.base.NoWorkFound(message + " " + exceptionMessage)
1113 return templateCoverageFraction
1114
1115
1116def _subtractImages(science, template, backgroundModel=None):
1117 """Subtract template from science, propagating relevant metadata.
1118
1119 Parameters
1120 ----------
1121 science : `lsst.afw.Exposure`
1122 The input science image.
1123 template : `lsst.afw.Exposure`
1124 The template to subtract from the science image.
1125 backgroundModel : `lsst.afw.MaskedImage`, optional
1126 Differential background model
1127
1128 Returns
1129 -------
1130 difference : `lsst.afw.Exposure`
1131 The subtracted image.
1132 """
1133 difference = science.clone()
1134 if backgroundModel is not None:
1135 difference.maskedImage -= backgroundModel
1136 difference.maskedImage -= template.maskedImage
1137 return difference
1138
1139
1140def _shapeTest(exp1, exp2, fwhmExposureBuffer, fwhmExposureGrid):
1141 """Determine that the PSF of ``exp1`` is not wider than that of ``exp2``.
1142
1143 Parameters
1144 ----------
1145 exp1 : `~lsst.afw.image.Exposure`
1146 Exposure with the reference point spread function (PSF) to evaluate.
1147 exp2 : `~lsst.afw.image.Exposure`
1148 Exposure with a candidate point spread function (PSF) to evaluate.
1149 fwhmExposureBuffer : `float`
1150 Fractional buffer margin to be left out of all sides of the image
1151 during the construction of the grid to compute mean PSF FWHM in an
1152 exposure, if the PSF is not available at its average position.
1153 fwhmExposureGrid : `int`
1154 Grid size to compute the mean FWHM in an exposure, if the PSF is not
1155 available at its average position.
1156 Returns
1157 -------
1158 result : `bool`
1159 True if ``exp1`` has a PSF that is not wider than that of ``exp2`` in
1160 either dimension.
1161 """
1162 try:
1163 shape1 = getPsfFwhm(exp1.psf, average=False)
1164 shape2 = getPsfFwhm(exp2.psf, average=False)
1166 shape1 = evaluateMeanPsfFwhm(exp1,
1167 fwhmExposureBuffer=fwhmExposureBuffer,
1168 fwhmExposureGrid=fwhmExposureGrid
1169 )
1170 shape2 = evaluateMeanPsfFwhm(exp2,
1171 fwhmExposureBuffer=fwhmExposureBuffer,
1172 fwhmExposureGrid=fwhmExposureGrid
1173 )
1174 return shape1 <= shape2
1175
1176 # Results from getPsfFwhm is a tuple of two values, one for each dimension.
1177 xTest = shape1[0] <= shape2[0]
1178 yTest = shape1[1] <= shape2[1]
1179 return xTest | yTest
1180
1181
1182def _interpolateImage(maskedImage, badMaskPlanes, fallbackValue=None):
1183 """Replace masked image pixels with interpolated values.
1184
1185 Parameters
1186 ----------
1187 maskedImage : `lsst.afw.image.MaskedImage`
1188 Image on which to perform interpolation.
1189 badMaskPlanes : `list` of `str`
1190 List of mask planes to interpolate over.
1191 fallbackValue : `float`, optional
1192 Value to set when interpolation fails.
1193
1194 Returns
1195 -------
1196 result: `float`
1197 The number of masked pixels that were replaced.
1198 """
1199 imgBadMaskPlanes = [
1200 maskPlane for maskPlane in badMaskPlanes if maskPlane in maskedImage.mask.getMaskPlaneDict()
1201 ]
1202
1203 image = maskedImage.image.array
1204 badPixels = (maskedImage.mask.array & maskedImage.mask.getPlaneBitMask(imgBadMaskPlanes)) > 0
1205 image[badPixels] = np.nan
1206 if fallbackValue is None:
1207 fallbackValue = np.nanmedian(image)
1208 # For this initial implementation, skip the interpolation and just fill with
1209 # the median value.
1210 image[badPixels] = fallbackValue
1211 return np.sum(badPixels)
Parameters to control convolution.
runPreconvolve(self, template, science, matchedScience, selectSources, preConvKernel)
run(self, template, science, sources, visitSummary=None)
_prepareInputs(self, template, science, visitSummary=None)
runConvolveTemplate(self, template, science, selectSources)
_convolveExposure(self, exposure, kernel, convolutionControl, bbox=None, psf=None, photoCalib=None, interpolateBadMaskPlanes=False)
runConvolveScience(self, template, science, selectSources)
run(self, template, science, sources, finalizedPsfApCorrCatalog=None, visitSummary=None)
finalize(self, template, science, difference, kernel, templateMatched=True, preConvMode=False, preConvKernel=None, spatiallyVarying=False)
Provides consistent interface for LSST exceptions.
Definition Exception.h:107
Reports invalid arguments.
Definition Runtime.h:66
void convolve(OutImageT &convolvedImage, InImageT const &inImage, KernelT const &kernel, ConvolutionControl const &convolutionControl=ConvolutionControl())
Convolve an Image or MaskedImage with a Kernel, setting pixels of an existing output image.
_subtractImages(science, template, backgroundModel=None)
_interpolateImage(maskedImage, badMaskPlanes, fallbackValue=None)
checkTemplateIsSufficient(templateExposure, logger, requiredTemplateFraction=0., exceptionMessage="")
_shapeTest(exp1, exp2, fwhmExposureBuffer, fwhmExposureGrid)