Coverage for python/lsst/pipe/tasks/processCcdWithFakes.py: 19%
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1# This file is part of pipe_tasks.
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#
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20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22"""
23Insert fake sources into calexps
24"""
26__all__ = ["ProcessCcdWithFakesConfig", "ProcessCcdWithFakesTask",
27 "ProcessCcdWithVariableFakesConfig", "ProcessCcdWithVariableFakesTask"]
29import numpy as np
30import pandas as pd
32import lsst.pex.config as pexConfig
33import lsst.pipe.base as pipeBase
35from .insertFakes import InsertFakesTask
36from lsst.afw.table import SourceTable
37from lsst.meas.base import IdGenerator, DetectorVisitIdGeneratorConfig
38from lsst.pipe.base import PipelineTask, PipelineTaskConfig, PipelineTaskConnections
39import lsst.pipe.base.connectionTypes as cT
40import lsst.afw.table as afwTable
41from lsst.skymap import BaseSkyMap
42from lsst.pipe.tasks.calibrate import CalibrateTask
45class ProcessCcdWithFakesConnections(PipelineTaskConnections,
46 dimensions=("instrument", "visit", "detector"),
47 defaultTemplates={"coaddName": "deep",
48 "wcsName": "gbdesAstrometricFit",
49 "photoCalibName": "jointcal",
50 "fakesType": "fakes_"}):
51 skyMap = cT.Input(
52 doc="Input definition of geometry/bbox and projection/wcs for "
53 "template exposures. Needed to test which tract to generate ",
54 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
55 dimensions=("skymap",),
56 storageClass="SkyMap",
57 )
59 exposure = cT.Input(
60 doc="Exposure into which fakes are to be added.",
61 name="calexp",
62 storageClass="ExposureF",
63 dimensions=("instrument", "visit", "detector")
64 )
66 fakeCats = cT.Input(
67 doc="Set of catalogs of fake sources to draw inputs from. We "
68 "concatenate the tract catalogs for detectorVisits that cover "
69 "multiple tracts.",
70 name="{fakesType}fakeSourceCat",
71 storageClass="DataFrame",
72 dimensions=("tract", "skymap"),
73 deferLoad=True,
74 multiple=True,
75 )
77 externalSkyWcsTractCatalog = cT.Input(
78 doc=("Per-tract, per-visit wcs calibrations. These catalogs use the detector "
79 "id for the catalog id, sorted on id for fast lookup."),
80 name="{wcsName}SkyWcsCatalog",
81 storageClass="ExposureCatalog",
82 dimensions=("instrument", "visit", "tract", "skymap"),
83 deferLoad=True,
84 multiple=True,
85 )
87 externalSkyWcsGlobalCatalog = cT.Input(
88 doc=("Per-visit wcs calibrations computed globally (with no tract information). "
89 "These catalogs use the detector id for the catalog id, sorted on id for "
90 "fast lookup."),
91 name="finalVisitSummary",
92 storageClass="ExposureCatalog",
93 dimensions=("instrument", "visit"),
94 )
96 externalPhotoCalibTractCatalog = cT.Input(
97 doc=("Per-tract, per-visit photometric calibrations. These catalogs use the "
98 "detector id for the catalog id, sorted on id for fast lookup."),
99 name="{photoCalibName}PhotoCalibCatalog",
100 storageClass="ExposureCatalog",
101 dimensions=("instrument", "visit", "tract"),
102 deferLoad=True,
103 multiple=True,
104 )
106 externalPhotoCalibGlobalCatalog = cT.Input(
107 doc=("Per-visit photometric calibrations. These catalogs use the "
108 "detector id for the catalog id, sorted on id for fast lookup."),
109 name="finalVisitSummary",
110 storageClass="ExposureCatalog",
111 dimensions=("instrument", "visit"),
112 )
114 icSourceCat = cT.Input(
115 doc="Catalog of calibration sources",
116 name="icSrc",
117 storageClass="SourceCatalog",
118 dimensions=("instrument", "visit", "detector")
119 )
121 sfdSourceCat = cT.Input(
122 doc="Catalog of calibration sources",
123 name="src",
124 storageClass="SourceCatalog",
125 dimensions=("instrument", "visit", "detector")
126 )
128 outputExposure = cT.Output(
129 doc="Exposure with fake sources added.",
130 name="{fakesType}calexp",
131 storageClass="ExposureF",
132 dimensions=("instrument", "visit", "detector")
133 )
135 outputCat = cT.Output(
136 doc="Source catalog produced in calibrate task with fakes also measured.",
137 name="{fakesType}src",
138 storageClass="SourceCatalog",
139 dimensions=("instrument", "visit", "detector"),
140 )
142 def __init__(self, *, config=None):
143 super().__init__(config=config)
145 if not config.doApplyExternalGlobalPhotoCalib:
146 self.inputs.remove("externalPhotoCalibGlobalCatalog")
147 if not config.doApplyExternalTractPhotoCalib:
148 self.inputs.remove("externalPhotoCalibTractCatalog")
150 if not config.doApplyExternalGlobalSkyWcs:
151 self.inputs.remove("externalSkyWcsGlobalCatalog")
152 if not config.doApplyExternalTractSkyWcs:
153 self.inputs.remove("externalSkyWcsTractCatalog")
156class ProcessCcdWithFakesConfig(PipelineTaskConfig,
157 pipelineConnections=ProcessCcdWithFakesConnections):
158 """Config for inserting fake sources
160 Notes
161 -----
162 The default column names are those from the UW sims database.
163 """
165 doApplyExternalGlobalPhotoCalib = pexConfig.Field(
166 dtype=bool,
167 default=False,
168 doc="Whether to apply an external photometric calibration via an "
169 "`lsst.afw.image.PhotoCalib` object. Uses the "
170 "`externalPhotoCalibName` config option to determine which "
171 "calibration to use. Uses a global calibration."
172 )
174 doApplyExternalTractPhotoCalib = pexConfig.Field(
175 dtype=bool,
176 default=False,
177 doc="Whether to apply an external photometric calibration via an "
178 "`lsst.afw.image.PhotoCalib` object. Uses the "
179 "`externalPhotoCalibName` config option to determine which "
180 "calibration to use. Uses a per tract calibration."
181 )
183 externalPhotoCalibName = pexConfig.ChoiceField(
184 doc="What type of external photo calib to use.",
185 dtype=str,
186 default="jointcal",
187 allowed={"jointcal": "Use jointcal_photoCalib",
188 "fgcm": "Use fgcm_photoCalib",
189 "fgcm_tract": "Use fgcm_tract_photoCalib"}
190 )
192 doApplyExternalGlobalSkyWcs = pexConfig.Field(
193 dtype=bool,
194 default=False,
195 doc="Whether to apply an external astrometric calibration via an "
196 "`lsst.afw.geom.SkyWcs` object. Uses the "
197 "`externalSkyWcsName` config option to determine which "
198 "calibration to use. Uses a global calibration."
199 )
201 doApplyExternalTractSkyWcs = pexConfig.Field(
202 dtype=bool,
203 default=False,
204 doc="Whether to apply an external astrometric calibration via an "
205 "`lsst.afw.geom.SkyWcs` object. Uses the "
206 "`externalSkyWcsName` config option to determine which "
207 "calibration to use. Uses a per tract calibration."
208 )
210 externalSkyWcsName = pexConfig.ChoiceField(
211 doc="What type of updated WCS calib to use.",
212 dtype=str,
213 default="gbdesAstrometricFit",
214 allowed={"gbdesAstrometricFit": "Use gbdesAstrometricFit_wcs"}
215 )
217 coaddName = pexConfig.Field(
218 doc="The name of the type of coadd used",
219 dtype=str,
220 default="deep",
221 )
223 srcFieldsToCopy = pexConfig.ListField(
224 dtype=str,
225 default=("calib_photometry_reserved", "calib_photometry_used", "calib_astrometry_used",
226 "calib_psf_candidate", "calib_psf_used", "calib_psf_reserved"),
227 doc=("Fields to copy from the `src` catalog to the output catalog "
228 "for matching sources Any missing fields will trigger a "
229 "RuntimeError exception.")
230 )
232 matchRadiusPix = pexConfig.Field(
233 dtype=float,
234 default=3,
235 doc=("Match radius for matching icSourceCat objects to sourceCat objects (pixels)"),
236 )
238 doMatchVisit = pexConfig.Field(
239 dtype=bool,
240 default=False,
241 doc="Match visit to trim the fakeCat"
242 )
244 calibrate = pexConfig.ConfigurableField(target=CalibrateTask,
245 doc="The calibration task to use.")
247 insertFakes = pexConfig.ConfigurableField(target=InsertFakesTask,
248 doc="Configuration for the fake sources")
250 idGenerator = DetectorVisitIdGeneratorConfig.make_field()
252 def setDefaults(self):
253 super().setDefaults()
254 self.calibrate.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("FAKE")
255 self.calibrate.measurement.plugins["base_PixelFlags"].masksFpCenter.append("FAKE")
256 self.calibrate.doAstrometry = False
257 self.calibrate.doWriteMatches = False
258 self.calibrate.doPhotoCal = False
259 self.calibrate.doComputeSummaryStats = False
260 self.calibrate.detection.reEstimateBackground = False
263class ProcessCcdWithFakesTask(PipelineTask):
264 """Insert fake objects into calexps.
266 Add fake stars and galaxies to the given calexp, specified in the dataRef. Galaxy parameters are read in
267 from the specified file and then modelled using galsim. Re-runs characterize image and calibrate image to
268 give a new background estimation and measurement of the calexp.
270 `ProcessFakeSourcesTask` inherits six functions from insertFakesTask that make images of the fake
271 sources and then add them to the calexp.
273 `addPixCoords`
274 Use the WCS information to add the pixel coordinates of each source
275 Adds an ``x`` and ``y`` column to the catalog of fake sources.
276 `trimFakeCat`
277 Trim the fake cat to about the size of the input image.
278 `mkFakeGalsimGalaxies`
279 Use Galsim to make fake double sersic galaxies for each set of galaxy parameters in the input file.
280 `mkFakeStars`
281 Use the PSF information from the calexp to make a fake star using the magnitude information from the
282 input file.
283 `cleanCat`
284 Remove rows of the input fake catalog which have half light radius, of either the bulge or the disk,
285 that are 0.
286 `addFakeSources`
287 Add the fake sources to the calexp.
289 Notes
290 -----
291 The ``calexp`` with fake souces added to it is written out as the datatype ``calexp_fakes``.
292 """
294 _DefaultName = "processCcdWithFakes"
295 ConfigClass = ProcessCcdWithFakesConfig
297 def __init__(self, schema=None, **kwargs):
298 """Initalize things! This should go above in the class docstring
299 """
301 super().__init__(**kwargs)
303 if schema is None:
304 schema = SourceTable.makeMinimalSchema()
305 self.schema = schema
306 self.makeSubtask("insertFakes")
307 self.makeSubtask("calibrate")
309 def runQuantum(self, butlerQC, inputRefs, outputRefs):
310 inputs = butlerQC.get(inputRefs)
311 detectorId = inputs["exposure"].getInfo().getDetector().getId()
313 if 'idGenerator' not in inputs.keys():
314 inputs['idGenerator'] = self.config.idGenerator.apply(butlerQC.quantum.dataId)
316 expWcs = inputs["exposure"].getWcs()
317 tractId = None
318 if not self.config.doApplyExternalGlobalSkyWcs and not self.config.doApplyExternalTractSkyWcs:
319 if expWcs is None:
320 self.log.info("No WCS for exposure %s so cannot insert fake sources. Skipping detector.",
321 butlerQC.quantum.dataId)
322 return None
323 else:
324 inputs["wcs"] = expWcs
325 elif self.config.doApplyExternalGlobalSkyWcs:
326 externalSkyWcsCatalog = inputs["externalSkyWcsGlobalCatalog"]
327 row = externalSkyWcsCatalog.find(detectorId)
328 if row is None:
329 self.log.info("No %s external global sky WCS for exposure %s so cannot insert fake "
330 "sources. Skipping detector.", self.config.externalSkyWcsName,
331 butlerQC.quantum.dataId)
332 return None
333 inputs["wcs"] = row.getWcs()
334 elif self.config.doApplyExternalTractSkyWcs:
335 externalSkyWcsCatalogList = inputs["externalSkyWcsTractCatalog"]
336 if tractId is None:
337 tractId = externalSkyWcsCatalogList[0].dataId["tract"]
338 externalSkyWcsCatalog = None
339 for externalSkyWcsCatalogRef in externalSkyWcsCatalogList:
340 if externalSkyWcsCatalogRef.dataId["tract"] == tractId:
341 externalSkyWcsCatalog = externalSkyWcsCatalogRef.get()
342 break
343 if externalSkyWcsCatalog is None:
344 usedTract = externalSkyWcsCatalogList[-1].dataId["tract"]
345 self.log.warn(
346 f"Warning, external SkyWcs for tract {tractId} not found. Using tract {usedTract} "
347 "instead.")
348 externalSkyWcsCatalog = externalSkyWcsCatalogList[-1].get()
349 row = externalSkyWcsCatalog.find(detectorId)
350 if row is None:
351 self.log.info("No %s external tract sky WCS for exposure %s so cannot insert fake "
352 "sources. Skipping detector.", self.config.externalSkyWcsName,
353 butlerQC.quantum.dataId)
354 return None
355 inputs["wcs"] = row.getWcs()
357 if not self.config.doApplyExternalGlobalPhotoCalib and not self.config.doApplyExternalTractPhotoCalib:
358 inputs["photoCalib"] = inputs["exposure"].getPhotoCalib()
359 elif self.config.doApplyExternalGlobalPhotoCalib:
360 externalPhotoCalibCatalog = inputs["externalPhotoCalibGlobalCatalog"]
361 row = externalPhotoCalibCatalog.find(detectorId)
362 if row is None:
363 self.log.info("No %s external global photoCalib for exposure %s so cannot insert fake "
364 "sources. Skipping detector.", self.config.externalPhotoCalibName,
365 butlerQC.quantum.dataId)
366 return None
367 inputs["photoCalib"] = row.getPhotoCalib()
368 elif self.config.doApplyExternalTractPhotoCalib:
369 externalPhotoCalibCatalogList = inputs["externalPhotoCalibTractCatalog"]
370 if tractId is None:
371 tractId = externalPhotoCalibCatalogList[0].dataId["tract"]
372 externalPhotoCalibCatalog = None
373 for externalPhotoCalibCatalogRef in externalPhotoCalibCatalogList:
374 if externalPhotoCalibCatalogRef.dataId["tract"] == tractId:
375 externalPhotoCalibCatalog = externalPhotoCalibCatalogRef.get()
376 break
377 if externalPhotoCalibCatalog is None:
378 usedTract = externalPhotoCalibCatalogList[-1].dataId["tract"]
379 self.log.warn(
380 f"Warning, external PhotoCalib for tract {tractId} not found. Using tract {usedTract} "
381 "instead.")
382 externalPhotoCalibCatalog = externalPhotoCalibCatalogList[-1].get()
383 row = externalPhotoCalibCatalog.find(detectorId)
384 if row is None:
385 self.log.info("No %s external tract photoCalib for exposure %s so cannot insert fake "
386 "sources. Skipping detector.", self.config.externalPhotoCalibName,
387 butlerQC.quantum.dataId)
388 return None
389 inputs["photoCalib"] = row.getPhotoCalib()
391 outputs = self.run(**inputs)
392 butlerQC.put(outputs, outputRefs)
394 def run(self, fakeCats, exposure, skyMap, wcs=None, photoCalib=None,
395 icSourceCat=None, sfdSourceCat=None, externalSkyWcsGlobalCatalog=None,
396 externalSkyWcsTractCatalog=None, externalPhotoCalibGlobalCatalog=None,
397 externalPhotoCalibTractCatalog=None, idGenerator=None):
398 """Add fake sources to a calexp and then run detection, deblending and
399 measurement.
401 Parameters
402 ----------
403 fakeCats : `list` of `lsst.daf.butler.DeferredDatasetHandle`
404 Set of tract level fake catalogs that potentially cover this
405 detectorVisit.
406 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
407 The exposure to add the fake sources to.
408 skyMap : `lsst.skymap.SkyMap`
409 SkyMap defining the tracts and patches the fakes are stored over.
410 wcs : `lsst.afw.geom.SkyWcs`, optional
411 WCS to use to add fake sources.
412 photoCalib : `lsst.afw.image.photoCalib.PhotoCalib`, optional
413 Photometric calibration to be used to calibrate the fake sources.
414 icSourceCat : `lsst.afw.table.SourceCatalog`, optional
415 Catalog to take the information about which sources were used for
416 calibration from.
417 sfdSourceCat : `lsst.afw.table.SourceCatalog`, optional
418 Catalog produced by singleFrameDriver, needed to copy some
419 calibration flags from.
420 externalSkyWcsGlobalCatalog : `lsst.afw.table.ExposureCatalog`, \
421 optional
422 Exposure catalog with external skyWcs to be applied per config.
423 externalSkyWcsTractCatalog : `lsst.afw.table.ExposureCatalog`, optional
424 Exposure catalog with external skyWcs to be applied per config.
425 externalPhotoCalibGlobalCatalog : `lsst.afw.table.ExposureCatalog`, \
426 optional
427 Exposure catalog with external photoCalib to be applied per config
428 externalPhotoCalibTractCatalog : `lsst.afw.table.ExposureCatalog`, \
429 optional
430 Exposure catalog with external photoCalib to be applied per config.
431 idGenerator : `lsst.meas.base.IdGenerator`, optional
432 Object that generates Source IDs and random seeds.
434 Returns
435 -------
436 resultStruct : `lsst.pipe.base.struct.Struct`
437 Result struct containing:
439 - outputExposure: `lsst.afw.image.exposure.exposure.ExposureF`
440 - outputCat: `lsst.afw.table.source.source.SourceCatalog`
442 Notes
443 -----
444 Adds pixel coordinates for each source to the fakeCat and removes
445 objects with bulge or disk half light radius = 0 (if ``config.cleanCat
446 = True``). These columns are called ``x`` and ``y`` and are in pixels.
448 Adds the ``Fake`` mask plane to the exposure which is then set by
449 `addFakeSources` to mark where fake sources have been added. Uses the
450 information in the ``fakeCat`` to make fake galaxies (using galsim) and
451 fake stars, using the PSF models from the PSF information for the
452 calexp. These are then added to the calexp and the calexp with fakes
453 included returned.
455 The galsim galaxies are made using a double sersic profile, one for the
456 bulge and one for the disk, this is then convolved with the PSF at that
457 point.
458 """
459 fakeCat = self.composeFakeCat(fakeCats, skyMap)
461 if wcs is None:
462 wcs = exposure.getWcs()
464 if photoCalib is None:
465 photoCalib = exposure.getPhotoCalib()
467 if self.config.doMatchVisit:
468 fakeCat = self.getVisitMatchedFakeCat(fakeCat, exposure)
470 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib)
472 # detect, deblend and measure sources
473 if idGenerator is None:
474 idGenerator = IdGenerator()
475 returnedStruct = self.calibrate.run(exposure, idGenerator=idGenerator)
476 sourceCat = returnedStruct.sourceCat
478 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy)
480 resultStruct = pipeBase.Struct(outputExposure=exposure, outputCat=sourceCat)
481 return resultStruct
483 def composeFakeCat(self, fakeCats, skyMap):
484 """Concatenate the fakeCats from tracts that may cover the exposure.
486 Parameters
487 ----------
488 fakeCats : `list` of `lsst.daf.butler.DeferredDatasetHandle`
489 Set of fake cats to concatenate.
490 skyMap : `lsst.skymap.SkyMap`
491 SkyMap defining the geometry of the tracts and patches.
493 Returns
494 -------
495 combinedFakeCat : `pandas.DataFrame`
496 All fakes that cover the inner polygon of the tracts in this
497 quantum.
498 """
499 if len(fakeCats) == 1:
500 return fakeCats[0].get()
501 outputCat = []
502 for fakeCatRef in fakeCats:
503 cat = fakeCatRef.get()
504 tractId = fakeCatRef.dataId["tract"]
505 # Make sure all data is within the inner part of the tract.
506 outputCat.append(cat[
507 skyMap.findTractIdArray(cat[self.config.insertFakes.ra_col],
508 cat[self.config.insertFakes.dec_col],
509 degrees=False)
510 == tractId])
512 return pd.concat(outputCat)
514 def getVisitMatchedFakeCat(self, fakeCat, exposure):
515 """Trim the fakeCat to select particular visit
517 Parameters
518 ----------
519 fakeCat : `pandas.core.frame.DataFrame`
520 The catalog of fake sources to add to the exposure
521 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
522 The exposure to add the fake sources to
524 Returns
525 -------
526 movingFakeCat : `pandas.DataFrame`
527 All fakes that belong to the visit
528 """
529 selected = exposure.getInfo().getVisitInfo().getId() == fakeCat["visit"]
531 return fakeCat[selected]
533 def copyCalibrationFields(self, calibCat, sourceCat, fieldsToCopy):
534 """Match sources in calibCat and sourceCat and copy the specified fields
536 Parameters
537 ----------
538 calibCat : `lsst.afw.table.SourceCatalog`
539 Catalog from which to copy fields.
540 sourceCat : `lsst.afw.table.SourceCatalog`
541 Catalog to which to copy fields.
542 fieldsToCopy : `lsst.pex.config.listField.List`
543 Fields to copy from calibCat to SoourceCat.
545 Returns
546 -------
547 newCat : `lsst.afw.table.SourceCatalog`
548 Catalog which includes the copied fields.
550 The fields copied are those specified by `fieldsToCopy` that actually exist
551 in the schema of `calibCat`.
553 This version was based on and adapted from the one in calibrateTask.
554 """
556 # Make a new SourceCatalog with the data from sourceCat so that we can add the new columns to it
557 sourceSchemaMapper = afwTable.SchemaMapper(sourceCat.schema)
558 sourceSchemaMapper.addMinimalSchema(sourceCat.schema, True)
560 calibSchemaMapper = afwTable.SchemaMapper(calibCat.schema, sourceCat.schema)
562 # Add the desired columns from the option fieldsToCopy
563 missingFieldNames = []
564 for fieldName in fieldsToCopy:
565 if fieldName in calibCat.schema:
566 schemaItem = calibCat.schema.find(fieldName)
567 calibSchemaMapper.editOutputSchema().addField(schemaItem.getField())
568 schema = calibSchemaMapper.editOutputSchema()
569 calibSchemaMapper.addMapping(schemaItem.getKey(), schema.find(fieldName).getField())
570 else:
571 missingFieldNames.append(fieldName)
572 if missingFieldNames:
573 raise RuntimeError(f"calibCat is missing fields {missingFieldNames} specified in "
574 "fieldsToCopy")
576 if "calib_detected" not in calibSchemaMapper.getOutputSchema():
577 self.calibSourceKey = calibSchemaMapper.addOutputField(afwTable.Field["Flag"]("calib_detected",
578 "Source was detected as an icSource"))
579 else:
580 self.calibSourceKey = None
582 schema = calibSchemaMapper.getOutputSchema()
583 newCat = afwTable.SourceCatalog(schema)
584 newCat.reserve(len(sourceCat))
585 newCat.extend(sourceCat, sourceSchemaMapper)
587 # Set the aliases so it doesn't complain.
588 for k, v in sourceCat.schema.getAliasMap().items():
589 newCat.schema.getAliasMap().set(k, v)
591 select = newCat["deblend_nChild"] == 0
592 matches = afwTable.matchXy(newCat[select], calibCat, self.config.matchRadiusPix)
593 # Check that no sourceCat sources are listed twice (we already know
594 # that each match has a unique calibCat source ID, due to using
595 # that ID as the key in bestMatches)
596 numMatches = len(matches)
597 numUniqueSources = len(set(m[1].getId() for m in matches))
598 if numUniqueSources != numMatches:
599 self.log.warning("%d calibCat sources matched only %d sourceCat sources", numMatches,
600 numUniqueSources)
602 self.log.info("Copying flags from calibCat to sourceCat for %s sources", numMatches)
604 # For each match: set the calibSourceKey flag and copy the desired
605 # fields
606 for src, calibSrc, d in matches:
607 if self.calibSourceKey:
608 src.setFlag(self.calibSourceKey, True)
609 # src.assign copies the footprint from calibSrc, which we don't want
610 # (DM-407)
611 # so set calibSrc's footprint to src's footprint before src.assign,
612 # then restore it
613 calibSrcFootprint = calibSrc.getFootprint()
614 try:
615 calibSrc.setFootprint(src.getFootprint())
616 src.assign(calibSrc, calibSchemaMapper)
617 finally:
618 calibSrc.setFootprint(calibSrcFootprint)
620 return newCat
623class ProcessCcdWithVariableFakesConnections(ProcessCcdWithFakesConnections):
624 ccdVisitFakeMagnitudes = cT.Output(
625 doc="Catalog of fakes with magnitudes scattered for this ccdVisit.",
626 name="{fakesType}ccdVisitFakeMagnitudes",
627 storageClass="DataFrame",
628 dimensions=("instrument", "visit", "detector"),
629 )
632class ProcessCcdWithVariableFakesConfig(ProcessCcdWithFakesConfig,
633 pipelineConnections=ProcessCcdWithVariableFakesConnections):
634 scatterSize = pexConfig.RangeField(
635 dtype=float,
636 default=0.4,
637 min=0,
638 max=100,
639 doc="Amount of scatter to add to the visit magnitude for variable "
640 "sources."
641 )
644class ProcessCcdWithVariableFakesTask(ProcessCcdWithFakesTask):
645 """As ProcessCcdWithFakes except add variablity to the fakes catalog
646 magnitude in the observed band for this ccdVisit.
648 Additionally, write out the modified magnitudes to the Butler.
649 """
651 _DefaultName = "processCcdWithVariableFakes"
652 ConfigClass = ProcessCcdWithVariableFakesConfig
654 def run(self, fakeCats, exposure, skyMap, wcs=None, photoCalib=None,
655 icSourceCat=None, sfdSourceCat=None, idGenerator=None):
656 """Add fake sources to a calexp and then run detection, deblending and
657 measurement.
659 Parameters
660 ----------
661 fakeCat : `pandas.core.frame.DataFrame`
662 The catalog of fake sources to add to the exposure.
663 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
664 The exposure to add the fake sources to.
665 skyMap : `lsst.skymap.SkyMap`
666 SkyMap defining the tracts and patches the fakes are stored over.
667 wcs : `lsst.afw.geom.SkyWcs`, optional
668 WCS to use to add fake sources.
669 photoCalib : `lsst.afw.image.photoCalib.PhotoCalib`, optional
670 Photometric calibration to be used to calibrate the fake sources.
671 icSourceCat : `lsst.afw.table.SourceCatalog`, optional
672 Catalog to take the information about which sources were used for
673 calibration from.
674 sfdSourceCat : `lsst.afw.table.SourceCatalog`, optional
675 Catalog produced by singleFrameDriver, needed to copy some
676 calibration flags from.
677 idGenerator : `lsst.meas.base.IdGenerator`, optional
678 Object that generates Source IDs and random seeds.
680 Returns
681 -------
682 resultStruct : `lsst.pipe.base.struct.Struct`
683 Results struct containing:
685 - outputExposure : Exposure with added fakes
686 (`lsst.afw.image.exposure.exposure.ExposureF`)
687 - outputCat : Catalog with detected fakes
688 (`lsst.afw.table.source.source.SourceCatalog`)
689 - ccdVisitFakeMagnitudes : Magnitudes that these fakes were
690 inserted with after being scattered (`pandas.DataFrame`)
692 Notes
693 -----
694 Adds pixel coordinates for each source to the fakeCat and removes
695 objects with bulge or disk half light radius = 0 (if ``config.cleanCat
696 = True``). These columns are called ``x`` and ``y`` and are in pixels.
698 Adds the ``Fake`` mask plane to the exposure which is then set by
699 `addFakeSources` to mark where fake sources have been added. Uses the
700 information in the ``fakeCat`` to make fake galaxies (using galsim) and
701 fake stars, using the PSF models from the PSF information for the
702 calexp. These are then added to the calexp and the calexp with fakes
703 included returned.
705 The galsim galaxies are made using a double sersic profile, one for the
706 bulge and one for the disk, this is then convolved with the PSF at that
707 point.
710 """
711 fakeCat = self.composeFakeCat(fakeCats, skyMap)
713 if wcs is None:
714 wcs = exposure.getWcs()
716 if photoCalib is None:
717 photoCalib = exposure.getPhotoCalib()
719 if idGenerator is None:
720 idGenerator = IdGenerator()
722 band = exposure.getFilter().bandLabel
723 ccdVisitMagnitudes = self.addVariability(
724 fakeCat,
725 band,
726 exposure,
727 photoCalib,
728 idGenerator.catalog_id,
729 )
731 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib)
733 # detect, deblend and measure sources
734 returnedStruct = self.calibrate.run(exposure, idGenerator=idGenerator)
735 sourceCat = returnedStruct.sourceCat
737 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy)
739 resultStruct = pipeBase.Struct(outputExposure=exposure,
740 outputCat=sourceCat,
741 ccdVisitFakeMagnitudes=ccdVisitMagnitudes)
742 return resultStruct
744 def addVariability(self, fakeCat, band, exposure, photoCalib, rngSeed):
745 """Add scatter to the fake catalog visit magnitudes.
747 Currently just adds a simple Gaussian scatter around the static fake
748 magnitude. This function could be modified to return any number of
749 fake variability.
751 Parameters
752 ----------
753 fakeCat : `pandas.DataFrame`
754 Catalog of fakes to modify magnitudes of.
755 band : `str`
756 Current observing band to modify.
757 exposure : `lsst.afw.image.ExposureF`
758 Exposure fakes will be added to.
759 photoCalib : `lsst.afw.image.PhotoCalib`
760 Photometric calibration object of ``exposure``.
761 rngSeed : `int`
762 Random number generator seed.
764 Returns
765 -------
766 dataFrame : `pandas.DataFrame`
767 DataFrame containing the values of the magnitudes to that will
768 be inserted into this ccdVisit.
769 """
770 rng = np.random.default_rng(rngSeed)
771 magScatter = rng.normal(loc=0,
772 scale=self.config.scatterSize,
773 size=len(fakeCat))
774 visitMagnitudes = fakeCat[self.insertFakes.config.mag_col % band] + magScatter
775 fakeCat.loc[:, self.insertFakes.config.mag_col % band] = visitMagnitudes
776 return pd.DataFrame(data={"variableMag": visitMagnitudes})