Coverage for python/lsst/pipe/tasks/processCcdWithFakes.py: 19%
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1# This file is part of pipe_tasks.
2#
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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#
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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.obs.base import ExposureIdInfo
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 def setDefaults(self):
251 super().setDefaults()
252 self.calibrate.measurement.plugins["base_PixelFlags"].masksFpAnywhere.append("FAKE")
253 self.calibrate.measurement.plugins["base_PixelFlags"].masksFpCenter.append("FAKE")
254 self.calibrate.doAstrometry = False
255 self.calibrate.doWriteMatches = False
256 self.calibrate.doPhotoCal = False
257 self.calibrate.doComputeSummaryStats = False
258 self.calibrate.detection.reEstimateBackground = False
261class ProcessCcdWithFakesTask(PipelineTask):
262 """Insert fake objects into calexps.
264 Add fake stars and galaxies to the given calexp, specified in the dataRef. Galaxy parameters are read in
265 from the specified file and then modelled using galsim. Re-runs characterize image and calibrate image to
266 give a new background estimation and measurement of the calexp.
268 `ProcessFakeSourcesTask` inherits six functions from insertFakesTask that make images of the fake
269 sources and then add them to the calexp.
271 `addPixCoords`
272 Use the WCS information to add the pixel coordinates of each source
273 Adds an ``x`` and ``y`` column to the catalog of fake sources.
274 `trimFakeCat`
275 Trim the fake cat to about the size of the input image.
276 `mkFakeGalsimGalaxies`
277 Use Galsim to make fake double sersic galaxies for each set of galaxy parameters in the input file.
278 `mkFakeStars`
279 Use the PSF information from the calexp to make a fake star using the magnitude information from the
280 input file.
281 `cleanCat`
282 Remove rows of the input fake catalog which have half light radius, of either the bulge or the disk,
283 that are 0.
284 `addFakeSources`
285 Add the fake sources to the calexp.
287 Notes
288 -----
289 The ``calexp`` with fake souces added to it is written out as the datatype ``calexp_fakes``.
290 """
292 _DefaultName = "processCcdWithFakes"
293 ConfigClass = ProcessCcdWithFakesConfig
295 def __init__(self, schema=None, butler=None, **kwargs):
296 """Initalize things! This should go above in the class docstring
297 """
299 super().__init__(**kwargs)
301 if schema is None:
302 schema = SourceTable.makeMinimalSchema()
303 self.schema = schema
304 self.makeSubtask("insertFakes")
305 self.makeSubtask("calibrate")
307 def runQuantum(self, butlerQC, inputRefs, outputRefs):
308 inputs = butlerQC.get(inputRefs)
309 detectorId = inputs["exposure"].getInfo().getDetector().getId()
311 if 'exposureIdInfo' not in inputs.keys():
312 expId, expBits = butlerQC.quantum.dataId.pack("visit_detector", returnMaxBits=True)
313 inputs['exposureIdInfo'] = ExposureIdInfo(expId, expBits)
315 expWcs = inputs["exposure"].getWcs()
316 tractId = None
317 if not self.config.doApplyExternalGlobalSkyWcs and not self.config.doApplyExternalTractSkyWcs:
318 if expWcs is None:
319 self.log.info("No WCS for exposure %s so cannot insert fake sources. Skipping detector.",
320 butlerQC.quantum.dataId)
321 return None
322 else:
323 inputs["wcs"] = expWcs
324 elif self.config.doApplyExternalGlobalSkyWcs:
325 externalSkyWcsCatalog = inputs["externalSkyWcsGlobalCatalog"]
326 row = externalSkyWcsCatalog.find(detectorId)
327 if row is None:
328 self.log.info("No %s external global sky WCS for exposure %s so cannot insert fake "
329 "sources. Skipping detector.", self.config.externalSkyWcsName,
330 butlerQC.quantum.dataId)
331 return None
332 inputs["wcs"] = row.getWcs()
333 elif self.config.doApplyExternalTractSkyWcs:
334 externalSkyWcsCatalogList = inputs["externalSkyWcsTractCatalog"]
335 if tractId is None:
336 tractId = externalSkyWcsCatalogList[0].dataId["tract"]
337 externalSkyWcsCatalog = None
338 for externalSkyWcsCatalogRef in externalSkyWcsCatalogList:
339 if externalSkyWcsCatalogRef.dataId["tract"] == tractId:
340 externalSkyWcsCatalog = externalSkyWcsCatalogRef.get()
341 break
342 if externalSkyWcsCatalog is None:
343 usedTract = externalSkyWcsCatalogList[-1].dataId["tract"]
344 self.log.warn(
345 f"Warning, external SkyWcs for tract {tractId} not found. Using tract {usedTract} "
346 "instead.")
347 externalSkyWcsCatalog = externalSkyWcsCatalogList[-1].get()
348 row = externalSkyWcsCatalog.find(detectorId)
349 if row is None:
350 self.log.info("No %s external tract sky WCS for exposure %s so cannot insert fake "
351 "sources. Skipping detector.", self.config.externalSkyWcsName,
352 butlerQC.quantum.dataId)
353 return None
354 inputs["wcs"] = row.getWcs()
356 if not self.config.doApplyExternalGlobalPhotoCalib and not self.config.doApplyExternalTractPhotoCalib:
357 inputs["photoCalib"] = inputs["exposure"].getPhotoCalib()
358 elif self.config.doApplyExternalGlobalPhotoCalib:
359 externalPhotoCalibCatalog = inputs["externalPhotoCalibGlobalCatalog"]
360 row = externalPhotoCalibCatalog.find(detectorId)
361 if row is None:
362 self.log.info("No %s external global photoCalib for exposure %s so cannot insert fake "
363 "sources. Skipping detector.", self.config.externalPhotoCalibName,
364 butlerQC.quantum.dataId)
365 return None
366 inputs["photoCalib"] = row.getPhotoCalib()
367 elif self.config.doApplyExternalTractPhotoCalib:
368 externalPhotoCalibCatalogList = inputs["externalPhotoCalibTractCatalog"]
369 if tractId is None:
370 tractId = externalPhotoCalibCatalogList[0].dataId["tract"]
371 externalPhotoCalibCatalog = None
372 for externalPhotoCalibCatalogRef in externalPhotoCalibCatalogList:
373 if externalPhotoCalibCatalogRef.dataId["tract"] == tractId:
374 externalPhotoCalibCatalog = externalPhotoCalibCatalogRef.get()
375 break
376 if externalPhotoCalibCatalog is None:
377 usedTract = externalPhotoCalibCatalogList[-1].dataId["tract"]
378 self.log.warn(
379 f"Warning, external PhotoCalib for tract {tractId} not found. Using tract {usedTract} "
380 "instead.")
381 externalPhotoCalibCatalog = externalPhotoCalibCatalogList[-1].get()
382 row = externalPhotoCalibCatalog.find(detectorId)
383 if row is None:
384 self.log.info("No %s external tract photoCalib for exposure %s so cannot insert fake "
385 "sources. Skipping detector.", self.config.externalPhotoCalibName,
386 butlerQC.quantum.dataId)
387 return None
388 inputs["photoCalib"] = row.getPhotoCalib()
390 outputs = self.run(**inputs)
391 butlerQC.put(outputs, outputRefs)
393 def run(self, fakeCats, exposure, skyMap, wcs=None, photoCalib=None, exposureIdInfo=None,
394 icSourceCat=None, sfdSourceCat=None, externalSkyWcsGlobalCatalog=None,
395 externalSkyWcsTractCatalog=None, externalPhotoCalibGlobalCatalog=None,
396 externalPhotoCalibTractCatalog=None):
397 """Add fake sources to a calexp and then run detection, deblending and measurement.
399 Parameters
400 ----------
401 fakeCats : `list` of `lsst.daf.butler.DeferredDatasetHandle`
402 Set of tract level fake catalogs that potentially cover
403 this detectorVisit.
404 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
405 The exposure to add the fake sources to
406 skyMap : `lsst.skymap.SkyMap`
407 SkyMap defining the tracts and patches the fakes are stored over.
408 wcs : `lsst.afw.geom.SkyWcs`
409 WCS to use to add fake sources
410 photoCalib : `lsst.afw.image.photoCalib.PhotoCalib`
411 Photometric calibration to be used to calibrate the fake sources
412 exposureIdInfo : `lsst.obs.base.ExposureIdInfo`
413 icSourceCat : `lsst.afw.table.SourceCatalog`
414 Default : None
415 Catalog to take the information about which sources were used for calibration from.
416 sfdSourceCat : `lsst.afw.table.SourceCatalog`
417 Default : None
418 Catalog produced by singleFrameDriver, needed to copy some calibration flags from.
420 Returns
421 -------
422 resultStruct : `lsst.pipe.base.struct.Struct`
423 contains : outputExposure : `lsst.afw.image.exposure.exposure.ExposureF`
424 outputCat : `lsst.afw.table.source.source.SourceCatalog`
426 Notes
427 -----
428 Adds pixel coordinates for each source to the fakeCat and removes objects with bulge or disk half
429 light radius = 0 (if ``config.cleanCat = True``). These columns are called ``x`` and ``y`` and are in
430 pixels.
432 Adds the ``Fake`` mask plane to the exposure which is then set by `addFakeSources` to mark where fake
433 sources have been added. Uses the information in the ``fakeCat`` to make fake galaxies (using galsim)
434 and fake stars, using the PSF models from the PSF information for the calexp. These are then added to
435 the calexp and the calexp with fakes included returned.
437 The galsim galaxies are made using a double sersic profile, one for the bulge and one for the disk,
438 this is then convolved with the PSF at that point.
440 If exposureIdInfo is not provided then the SourceCatalog IDs will not be globally unique.
441 """
442 fakeCat = self.composeFakeCat(fakeCats, skyMap)
444 if wcs is None:
445 wcs = exposure.getWcs()
447 if photoCalib is None:
448 photoCalib = exposure.getPhotoCalib()
450 if self.config.doMatchVisit:
451 fakeCat = self.getVisitMatchedFakeCat(fakeCat, exposure)
453 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib)
455 # detect, deblend and measure sources
456 if exposureIdInfo is None:
457 exposureIdInfo = ExposureIdInfo()
458 returnedStruct = self.calibrate.run(exposure, exposureIdInfo=exposureIdInfo)
459 sourceCat = returnedStruct.sourceCat
461 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy)
463 resultStruct = pipeBase.Struct(outputExposure=exposure, outputCat=sourceCat)
464 return resultStruct
466 def composeFakeCat(self, fakeCats, skyMap):
467 """Concatenate the fakeCats from tracts that may cover the exposure.
469 Parameters
470 ----------
471 fakeCats : `list` of `lsst.daf.butler.DeferredDatasetHandle`
472 Set of fake cats to concatenate.
473 skyMap : `lsst.skymap.SkyMap`
474 SkyMap defining the geometry of the tracts and patches.
476 Returns
477 -------
478 combinedFakeCat : `pandas.DataFrame`
479 All fakes that cover the inner polygon of the tracts in this
480 quantum.
481 """
482 if len(fakeCats) == 1:
483 return fakeCats[0].get()
484 outputCat = []
485 for fakeCatRef in fakeCats:
486 cat = fakeCatRef.get()
487 tractId = fakeCatRef.dataId["tract"]
488 # Make sure all data is within the inner part of the tract.
489 outputCat.append(cat[
490 skyMap.findTractIdArray(cat[self.config.insertFakes.ra_col],
491 cat[self.config.insertFakes.dec_col],
492 degrees=False)
493 == tractId])
495 return pd.concat(outputCat)
497 def getVisitMatchedFakeCat(self, fakeCat, exposure):
498 """Trim the fakeCat to select particular visit
500 Parameters
501 ----------
502 fakeCat : `pandas.core.frame.DataFrame`
503 The catalog of fake sources to add to the exposure
504 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
505 The exposure to add the fake sources to
507 Returns
508 -------
509 movingFakeCat : `pandas.DataFrame`
510 All fakes that belong to the visit
511 """
512 selected = exposure.getInfo().getVisitInfo().getId() == fakeCat["visit"]
514 return fakeCat[selected]
516 def copyCalibrationFields(self, calibCat, sourceCat, fieldsToCopy):
517 """Match sources in calibCat and sourceCat and copy the specified fields
519 Parameters
520 ----------
521 calibCat : `lsst.afw.table.SourceCatalog`
522 Catalog from which to copy fields.
523 sourceCat : `lsst.afw.table.SourceCatalog`
524 Catalog to which to copy fields.
525 fieldsToCopy : `lsst.pex.config.listField.List`
526 Fields to copy from calibCat to SoourceCat.
528 Returns
529 -------
530 newCat : `lsst.afw.table.SourceCatalog`
531 Catalog which includes the copied fields.
533 The fields copied are those specified by `fieldsToCopy` that actually exist
534 in the schema of `calibCat`.
536 This version was based on and adapted from the one in calibrateTask.
537 """
539 # Make a new SourceCatalog with the data from sourceCat so that we can add the new columns to it
540 sourceSchemaMapper = afwTable.SchemaMapper(sourceCat.schema)
541 sourceSchemaMapper.addMinimalSchema(sourceCat.schema, True)
543 calibSchemaMapper = afwTable.SchemaMapper(calibCat.schema, sourceCat.schema)
545 # Add the desired columns from the option fieldsToCopy
546 missingFieldNames = []
547 for fieldName in fieldsToCopy:
548 if fieldName in calibCat.schema:
549 schemaItem = calibCat.schema.find(fieldName)
550 calibSchemaMapper.editOutputSchema().addField(schemaItem.getField())
551 schema = calibSchemaMapper.editOutputSchema()
552 calibSchemaMapper.addMapping(schemaItem.getKey(), schema.find(fieldName).getField())
553 else:
554 missingFieldNames.append(fieldName)
555 if missingFieldNames:
556 raise RuntimeError(f"calibCat is missing fields {missingFieldNames} specified in "
557 "fieldsToCopy")
559 if "calib_detected" not in calibSchemaMapper.getOutputSchema():
560 self.calibSourceKey = calibSchemaMapper.addOutputField(afwTable.Field["Flag"]("calib_detected",
561 "Source was detected as an icSource"))
562 else:
563 self.calibSourceKey = None
565 schema = calibSchemaMapper.getOutputSchema()
566 newCat = afwTable.SourceCatalog(schema)
567 newCat.reserve(len(sourceCat))
568 newCat.extend(sourceCat, sourceSchemaMapper)
570 # Set the aliases so it doesn't complain.
571 for k, v in sourceCat.schema.getAliasMap().items():
572 newCat.schema.getAliasMap().set(k, v)
574 select = newCat["deblend_nChild"] == 0
575 matches = afwTable.matchXy(newCat[select], calibCat, self.config.matchRadiusPix)
576 # Check that no sourceCat sources are listed twice (we already know
577 # that each match has a unique calibCat source ID, due to using
578 # that ID as the key in bestMatches)
579 numMatches = len(matches)
580 numUniqueSources = len(set(m[1].getId() for m in matches))
581 if numUniqueSources != numMatches:
582 self.log.warning("%d calibCat sources matched only %d sourceCat sources", numMatches,
583 numUniqueSources)
585 self.log.info("Copying flags from calibCat to sourceCat for %s sources", numMatches)
587 # For each match: set the calibSourceKey flag and copy the desired
588 # fields
589 for src, calibSrc, d in matches:
590 if self.calibSourceKey:
591 src.setFlag(self.calibSourceKey, True)
592 # src.assign copies the footprint from calibSrc, which we don't want
593 # (DM-407)
594 # so set calibSrc's footprint to src's footprint before src.assign,
595 # then restore it
596 calibSrcFootprint = calibSrc.getFootprint()
597 try:
598 calibSrc.setFootprint(src.getFootprint())
599 src.assign(calibSrc, calibSchemaMapper)
600 finally:
601 calibSrc.setFootprint(calibSrcFootprint)
603 return newCat
606class ProcessCcdWithVariableFakesConnections(ProcessCcdWithFakesConnections):
607 ccdVisitFakeMagnitudes = cT.Output(
608 doc="Catalog of fakes with magnitudes scattered for this ccdVisit.",
609 name="{fakesType}ccdVisitFakeMagnitudes",
610 storageClass="DataFrame",
611 dimensions=("instrument", "visit", "detector"),
612 )
615class ProcessCcdWithVariableFakesConfig(ProcessCcdWithFakesConfig,
616 pipelineConnections=ProcessCcdWithVariableFakesConnections):
617 scatterSize = pexConfig.RangeField(
618 dtype=float,
619 default=0.4,
620 min=0,
621 max=100,
622 doc="Amount of scatter to add to the visit magnitude for variable "
623 "sources."
624 )
627class ProcessCcdWithVariableFakesTask(ProcessCcdWithFakesTask):
628 """As ProcessCcdWithFakes except add variablity to the fakes catalog
629 magnitude in the observed band for this ccdVisit.
631 Additionally, write out the modified magnitudes to the Butler.
632 """
634 _DefaultName = "processCcdWithVariableFakes"
635 ConfigClass = ProcessCcdWithVariableFakesConfig
637 def run(self, fakeCats, exposure, skyMap, wcs=None, photoCalib=None, exposureIdInfo=None,
638 icSourceCat=None, sfdSourceCat=None):
639 """Add fake sources to a calexp and then run detection, deblending and measurement.
641 Parameters
642 ----------
643 fakeCat : `pandas.core.frame.DataFrame`
644 The catalog of fake sources to add to the exposure
645 exposure : `lsst.afw.image.exposure.exposure.ExposureF`
646 The exposure to add the fake sources to
647 skyMap : `lsst.skymap.SkyMap`
648 SkyMap defining the tracts and patches the fakes are stored over.
649 wcs : `lsst.afw.geom.SkyWcs`
650 WCS to use to add fake sources
651 photoCalib : `lsst.afw.image.photoCalib.PhotoCalib`
652 Photometric calibration to be used to calibrate the fake sources
653 exposureIdInfo : `lsst.obs.base.ExposureIdInfo`
654 icSourceCat : `lsst.afw.table.SourceCatalog`
655 Default : None
656 Catalog to take the information about which sources were used for calibration from.
657 sfdSourceCat : `lsst.afw.table.SourceCatalog`
658 Default : None
659 Catalog produced by singleFrameDriver, needed to copy some calibration flags from.
661 Returns
662 -------
663 resultStruct : `lsst.pipe.base.struct.Struct`
664 Results Strcut containing:
666 - outputExposure : Exposure with added fakes
667 (`lsst.afw.image.exposure.exposure.ExposureF`)
668 - outputCat : Catalog with detected fakes
669 (`lsst.afw.table.source.source.SourceCatalog`)
670 - ccdVisitFakeMagnitudes : Magnitudes that these fakes were
671 inserted with after being scattered (`pandas.DataFrame`)
673 Notes
674 -----
675 Adds pixel coordinates for each source to the fakeCat and removes objects with bulge or disk half
676 light radius = 0 (if ``config.cleanCat = True``). These columns are called ``x`` and ``y`` and are in
677 pixels.
679 Adds the ``Fake`` mask plane to the exposure which is then set by `addFakeSources` to mark where fake
680 sources have been added. Uses the information in the ``fakeCat`` to make fake galaxies (using galsim)
681 and fake stars, using the PSF models from the PSF information for the calexp. These are then added to
682 the calexp and the calexp with fakes included returned.
684 The galsim galaxies are made using a double sersic profile, one for the bulge and one for the disk,
685 this is then convolved with the PSF at that point.
687 If exposureIdInfo is not provided then the SourceCatalog IDs will not be globally unique.
688 """
689 fakeCat = self.composeFakeCat(fakeCats, skyMap)
691 if wcs is None:
692 wcs = exposure.getWcs()
694 if photoCalib is None:
695 photoCalib = exposure.getPhotoCalib()
697 if exposureIdInfo is None:
698 exposureIdInfo = ExposureIdInfo()
700 band = exposure.getFilter().bandLabel
701 ccdVisitMagnitudes = self.addVariablity(fakeCat, band, exposure, photoCalib, exposureIdInfo)
703 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib)
705 # detect, deblend and measure sources
706 returnedStruct = self.calibrate.run(exposure, exposureIdInfo=exposureIdInfo)
707 sourceCat = returnedStruct.sourceCat
709 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy)
711 resultStruct = pipeBase.Struct(outputExposure=exposure,
712 outputCat=sourceCat,
713 ccdVisitFakeMagnitudes=ccdVisitMagnitudes)
714 return resultStruct
716 def addVariablity(self, fakeCat, band, exposure, photoCalib, exposureIdInfo):
717 """Add scatter to the fake catalog visit magnitudes.
719 Currently just adds a simple Gaussian scatter around the static fake
720 magnitude. This function could be modified to return any number of
721 fake variability.
723 Parameters
724 ----------
725 fakeCat : `pandas.DataFrame`
726 Catalog of fakes to modify magnitudes of.
727 band : `str`
728 Current observing band to modify.
729 exposure : `lsst.afw.image.ExposureF`
730 Exposure fakes will be added to.
731 photoCalib : `lsst.afw.image.PhotoCalib`
732 Photometric calibration object of ``exposure``.
733 exposureIdInfo : `lsst.obs.base.ExposureIdInfo`
734 Exposure id information and metadata.
736 Returns
737 -------
738 dataFrame : `pandas.DataFrame`
739 DataFrame containing the values of the magnitudes to that will
740 be inserted into this ccdVisit.
741 """
742 expId = exposureIdInfo.expId
743 rng = np.random.default_rng(expId)
744 magScatter = rng.normal(loc=0,
745 scale=self.config.scatterSize,
746 size=len(fakeCat))
747 visitMagnitudes = fakeCat[self.insertFakes.config.mag_col % band] + magScatter
748 fakeCat.loc[:, self.insertFakes.config.mag_col % band] = visitMagnitudes
749 return pd.DataFrame(data={"variableMag": visitMagnitudes})