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# 

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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/>. 

21 

22""" 

23Insert fake sources into calexps 

24""" 

25 

26__all__ = ["ProcessCcdWithFakesConfig", "ProcessCcdWithFakesTask", 

27 "ProcessCcdWithVariableFakesConfig", "ProcessCcdWithVariableFakesTask"] 

28 

29import numpy as np 

30import pandas as pd 

31 

32import lsst.pex.config as pexConfig 

33import lsst.pipe.base as pipeBase 

34 

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 

43 

44 

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 ) 

58 

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 ) 

65 

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 ) 

76 

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 ) 

86 

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 ) 

95 

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 ) 

105 

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 ) 

113 

114 icSourceCat = cT.Input( 

115 doc="Catalog of calibration sources", 

116 name="icSrc", 

117 storageClass="SourceCatalog", 

118 dimensions=("instrument", "visit", "detector") 

119 ) 

120 

121 sfdSourceCat = cT.Input( 

122 doc="Catalog of calibration sources", 

123 name="src", 

124 storageClass="SourceCatalog", 

125 dimensions=("instrument", "visit", "detector") 

126 ) 

127 

128 outputExposure = cT.Output( 

129 doc="Exposure with fake sources added.", 

130 name="{fakesType}calexp", 

131 storageClass="ExposureF", 

132 dimensions=("instrument", "visit", "detector") 

133 ) 

134 

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 ) 

141 

142 def __init__(self, *, config=None): 

143 super().__init__(config=config) 

144 

145 if not config.doApplyExternalGlobalPhotoCalib: 

146 self.inputs.remove("externalPhotoCalibGlobalCatalog") 

147 if not config.doApplyExternalTractPhotoCalib: 

148 self.inputs.remove("externalPhotoCalibTractCatalog") 

149 

150 if not config.doApplyExternalGlobalSkyWcs: 

151 self.inputs.remove("externalSkyWcsGlobalCatalog") 

152 if not config.doApplyExternalTractSkyWcs: 

153 self.inputs.remove("externalSkyWcsTractCatalog") 

154 

155 

156class ProcessCcdWithFakesConfig(PipelineTaskConfig, 

157 pipelineConnections=ProcessCcdWithFakesConnections): 

158 """Config for inserting fake sources 

159 

160 Notes 

161 ----- 

162 The default column names are those from the UW sims database. 

163 """ 

164 

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 ) 

173 

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 ) 

182 

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 ) 

191 

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 ) 

200 

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 ) 

209 

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 ) 

216 

217 coaddName = pexConfig.Field( 

218 doc="The name of the type of coadd used", 

219 dtype=str, 

220 default="deep", 

221 ) 

222 

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 ) 

231 

232 matchRadiusPix = pexConfig.Field( 

233 dtype=float, 

234 default=3, 

235 doc=("Match radius for matching icSourceCat objects to sourceCat objects (pixels)"), 

236 ) 

237 

238 doMatchVisit = pexConfig.Field( 

239 dtype=bool, 

240 default=False, 

241 doc="Match visit to trim the fakeCat" 

242 ) 

243 

244 calibrate = pexConfig.ConfigurableField(target=CalibrateTask, 

245 doc="The calibration task to use.") 

246 

247 insertFakes = pexConfig.ConfigurableField(target=InsertFakesTask, 

248 doc="Configuration for the fake sources") 

249 

250 idGenerator = DetectorVisitIdGeneratorConfig.make_field() 

251 

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 

261 

262 

263class ProcessCcdWithFakesTask(PipelineTask): 

264 """Insert fake objects into calexps. 

265 

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. 

269 

270 `ProcessFakeSourcesTask` inherits six functions from insertFakesTask that make images of the fake 

271 sources and then add them to the calexp. 

272 

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. 

288 

289 Notes 

290 ----- 

291 The ``calexp`` with fake souces added to it is written out as the datatype ``calexp_fakes``. 

292 """ 

293 

294 _DefaultName = "processCcdWithFakes" 

295 ConfigClass = ProcessCcdWithFakesConfig 

296 

297 def __init__(self, schema=None, **kwargs): 

298 """Initalize things! This should go above in the class docstring 

299 """ 

300 

301 super().__init__(**kwargs) 

302 

303 if schema is None: 

304 schema = SourceTable.makeMinimalSchema() 

305 self.schema = schema 

306 self.makeSubtask("insertFakes") 

307 self.makeSubtask("calibrate") 

308 

309 def runQuantum(self, butlerQC, inputRefs, outputRefs): 

310 inputs = butlerQC.get(inputRefs) 

311 detectorId = inputs["exposure"].getInfo().getDetector().getId() 

312 

313 if 'idGenerator' not in inputs.keys(): 

314 inputs['idGenerator'] = self.config.idGenerator.apply(butlerQC.quantum.dataId) 

315 

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() 

356 

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() 

390 

391 outputs = self.run(**inputs) 

392 butlerQC.put(outputs, outputRefs) 

393 

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. 

400 

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. 

433 

434 Returns 

435 ------- 

436 resultStruct : `lsst.pipe.base.struct.Struct` 

437 Result struct containing: 

438 

439 - outputExposure: `lsst.afw.image.exposure.exposure.ExposureF` 

440 - outputCat: `lsst.afw.table.source.source.SourceCatalog` 

441 

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. 

447 

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. 

454 

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) 

460 

461 if wcs is None: 

462 wcs = exposure.getWcs() 

463 

464 if photoCalib is None: 

465 photoCalib = exposure.getPhotoCalib() 

466 

467 if self.config.doMatchVisit: 

468 fakeCat = self.getVisitMatchedFakeCat(fakeCat, exposure) 

469 

470 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib) 

471 

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 

477 

478 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy) 

479 

480 resultStruct = pipeBase.Struct(outputExposure=exposure, outputCat=sourceCat) 

481 return resultStruct 

482 

483 def composeFakeCat(self, fakeCats, skyMap): 

484 """Concatenate the fakeCats from tracts that may cover the exposure. 

485 

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. 

492 

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]) 

511 

512 return pd.concat(outputCat) 

513 

514 def getVisitMatchedFakeCat(self, fakeCat, exposure): 

515 """Trim the fakeCat to select particular visit 

516 

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 

523 

524 Returns 

525 ------- 

526 movingFakeCat : `pandas.DataFrame` 

527 All fakes that belong to the visit 

528 """ 

529 selected = exposure.getInfo().getVisitInfo().getId() == fakeCat["visit"] 

530 

531 return fakeCat[selected] 

532 

533 def copyCalibrationFields(self, calibCat, sourceCat, fieldsToCopy): 

534 """Match sources in calibCat and sourceCat and copy the specified fields 

535 

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. 

544 

545 Returns 

546 ------- 

547 newCat : `lsst.afw.table.SourceCatalog` 

548 Catalog which includes the copied fields. 

549 

550 The fields copied are those specified by `fieldsToCopy` that actually exist 

551 in the schema of `calibCat`. 

552 

553 This version was based on and adapted from the one in calibrateTask. 

554 """ 

555 

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) 

559 

560 calibSchemaMapper = afwTable.SchemaMapper(calibCat.schema, sourceCat.schema) 

561 

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") 

575 

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 

581 

582 schema = calibSchemaMapper.getOutputSchema() 

583 newCat = afwTable.SourceCatalog(schema) 

584 newCat.reserve(len(sourceCat)) 

585 newCat.extend(sourceCat, sourceSchemaMapper) 

586 

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) 

590 

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) 

601 

602 self.log.info("Copying flags from calibCat to sourceCat for %s sources", numMatches) 

603 

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) 

619 

620 return newCat 

621 

622 

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 ) 

630 

631 

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 ) 

642 

643 

644class ProcessCcdWithVariableFakesTask(ProcessCcdWithFakesTask): 

645 """As ProcessCcdWithFakes except add variablity to the fakes catalog 

646 magnitude in the observed band for this ccdVisit. 

647 

648 Additionally, write out the modified magnitudes to the Butler. 

649 """ 

650 

651 _DefaultName = "processCcdWithVariableFakes" 

652 ConfigClass = ProcessCcdWithVariableFakesConfig 

653 

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. 

658 

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. 

679 

680 Returns 

681 ------- 

682 resultStruct : `lsst.pipe.base.struct.Struct` 

683 Results struct containing: 

684 

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`) 

691 

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. 

697 

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. 

704 

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. 

708 

709 

710 """ 

711 fakeCat = self.composeFakeCat(fakeCats, skyMap) 

712 

713 if wcs is None: 

714 wcs = exposure.getWcs() 

715 

716 if photoCalib is None: 

717 photoCalib = exposure.getPhotoCalib() 

718 

719 if idGenerator is None: 

720 idGenerator = IdGenerator() 

721 

722 band = exposure.getFilter().bandLabel 

723 ccdVisitMagnitudes = self.addVariability( 

724 fakeCat, 

725 band, 

726 exposure, 

727 photoCalib, 

728 idGenerator.catalog_id, 

729 ) 

730 

731 self.insertFakes.run(fakeCat, exposure, wcs, photoCalib) 

732 

733 # detect, deblend and measure sources 

734 returnedStruct = self.calibrate.run(exposure, idGenerator=idGenerator) 

735 sourceCat = returnedStruct.sourceCat 

736 

737 sourceCat = self.copyCalibrationFields(sfdSourceCat, sourceCat, self.config.srcFieldsToCopy) 

738 

739 resultStruct = pipeBase.Struct(outputExposure=exposure, 

740 outputCat=sourceCat, 

741 ccdVisitFakeMagnitudes=ccdVisitMagnitudes) 

742 return resultStruct 

743 

744 def addVariability(self, fakeCat, band, exposure, photoCalib, rngSeed): 

745 """Add scatter to the fake catalog visit magnitudes. 

746 

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. 

750 

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. 

763 

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})