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LSST Data Management Base Package
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forcedPhotCcd.py
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1# This file is part of meas_base.
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 pandas as pd
23import numpy as np
24
25import lsst.pex.config
27import lsst.pipe.base
28import lsst.geom
30import lsst.afw.geom
31import lsst.afw.image
32import lsst.afw.table
33import lsst.sphgeom
34
35from lsst.pipe.base import PipelineTaskConnections
36import lsst.pipe.base.connectionTypes as cT
37
38import lsst.pipe.base as pipeBase
39from lsst.skymap import BaseSkyMap
40
41from .forcedMeasurement import ForcedMeasurementTask
42from .applyApCorr import ApplyApCorrTask
43from .catalogCalculation import CatalogCalculationTask
44from ._id_generator import DetectorVisitIdGeneratorConfig
45
46__all__ = ("ForcedPhotCcdConfig", "ForcedPhotCcdTask",
47 "ForcedPhotCcdFromDataFrameTask", "ForcedPhotCcdFromDataFrameConfig")
48
49
50class ForcedPhotCcdConnections(PipelineTaskConnections,
51 dimensions=("instrument", "visit", "detector", "skymap", "tract"),
52 defaultTemplates={"inputCoaddName": "deep",
53 "inputName": "calexp"}):
54 inputSchema = cT.InitInput(
55 doc="Schema for the input measurement catalogs.",
56 name="{inputCoaddName}Coadd_ref_schema",
57 storageClass="SourceCatalog",
58 )
59 outputSchema = cT.InitOutput(
60 doc="Schema for the output forced measurement catalogs.",
61 name="forced_src_schema",
62 storageClass="SourceCatalog",
63 )
64 exposure = cT.Input(
65 doc="Input exposure to perform photometry on.",
66 name="{inputName}",
67 storageClass="ExposureF",
68 dimensions=["instrument", "visit", "detector"],
69 )
70 refCat = cT.Input(
71 doc="Catalog of shapes and positions at which to force photometry.",
72 name="{inputCoaddName}Coadd_ref",
73 storageClass="SourceCatalog",
74 dimensions=["skymap", "tract", "patch"],
75 multiple=True,
76 deferLoad=True,
77 )
78 skyMap = cT.Input(
79 doc="SkyMap dataset that defines the coordinate system of the reference catalog.",
80 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
81 storageClass="SkyMap",
82 dimensions=["skymap"],
83 )
84 skyCorr = cT.Input(
85 doc="Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
86 name="skyCorr",
87 storageClass="Background",
88 dimensions=("instrument", "visit", "detector"),
89 )
90 visitSummary = cT.Input(
91 doc="Input visit-summary catalog with updated calibration objects.",
92 name="finalVisitSummary",
93 storageClass="ExposureCatalog",
94 dimensions=("instrument", "visit"),
95 )
96 measCat = cT.Output(
97 doc="Output forced photometry catalog.",
98 name="forced_src",
99 storageClass="SourceCatalog",
100 dimensions=["instrument", "visit", "detector", "skymap", "tract"],
101 )
102
103 def __init__(self, *, config=None):
104 super().__init__(config=config)
105 if not config.doApplySkyCorr:
106 self.inputs.remove("skyCorr")
107
108
109class ForcedPhotCcdConfig(pipeBase.PipelineTaskConfig,
110 pipelineConnections=ForcedPhotCcdConnections):
111 """Config class for forced measurement driver task."""
113 target=ForcedMeasurementTask,
114 doc="subtask to do forced measurement"
115 )
116 coaddName = lsst.pex.config.Field(
117 doc="coadd name: typically one of deep or goodSeeing",
118 dtype=str,
119 default="deep",
120 )
121 doApCorr = lsst.pex.config.Field(
122 dtype=bool,
123 default=True,
124 doc="Run subtask to apply aperture corrections"
125 )
127 target=ApplyApCorrTask,
128 doc="Subtask to apply aperture corrections"
129 )
130 catalogCalculation = lsst.pex.config.ConfigurableField(
131 target=CatalogCalculationTask,
132 doc="Subtask to run catalogCalculation plugins on catalog"
133 )
134 doApplySkyCorr = lsst.pex.config.Field(
135 dtype=bool,
136 default=False,
137 doc="Apply sky correction?",
138 )
139 includePhotoCalibVar = lsst.pex.config.Field(
140 dtype=bool,
141 default=False,
142 doc="Add photometric calibration variance to warp variance plane?",
143 )
144 footprintSource = lsst.pex.config.ChoiceField(
145 dtype=str,
146 doc="Where to obtain footprints to install in the measurement catalog, prior to measurement.",
147 allowed={
148 "transformed": "Transform footprints from the reference catalog (downgrades HeavyFootprints).",
149 "psf": ("Use the scaled shape of the PSF at the position of each source (does not generate "
150 "HeavyFootprints)."),
151 },
152 optional=True,
153 default="transformed",
154 )
155 psfFootprintScaling = lsst.pex.config.Field(
156 dtype=float,
157 doc="Scaling factor to apply to the PSF shape when footprintSource='psf' (ignored otherwise).",
158 default=3.0,
159 )
160 idGenerator = DetectorVisitIdGeneratorConfig.make_field()
161
162 def setDefaults(self):
163 # Docstring inherited.
164 super().setDefaults()
165 # Footprints here will not be entirely correct, so don't try to make
166 # a biased correction for blended neighbors.
167 self.measurement.doReplaceWithNoise = False
168 # Only run a minimal set of plugins, as these measurements are only
169 # needed for PSF-like sources.
170 self.measurement.plugins.names = ["base_PixelFlags",
171 "base_TransformedCentroid",
172 "base_PsfFlux",
173 "base_LocalBackground",
174 "base_LocalPhotoCalib",
175 "base_LocalWcs",
176 ]
177 self.measurement.slots.shape = None
178 # Keep track of which footprints contain streaks
179 self.measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK']
180 self.measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK']
181 # Make catalogCalculation a no-op by default as no modelFlux is setup
182 # by default in ForcedMeasurementTask.
183 self.catalogCalculation.plugins.names = []
184
185
186class ForcedPhotCcdTask(pipeBase.PipelineTask):
187 """A pipeline task for performing forced measurement on CCD images.
188
189 Parameters
190 ----------
191 refSchema : `lsst.afw.table.Schema`, optional
192 The schema of the reference catalog, passed to the constructor of the
193 references subtask. Optional, but must be specified if ``initInputs``
194 is not; if both are specified, ``initInputs`` takes precedence.
195 initInputs : `dict`
196 Dictionary that can contain a key ``inputSchema`` containing the
197 schema. If present will override the value of ``refSchema``.
198 **kwargs
199 Keyword arguments are passed to the supertask constructor.
200 """
201
202 ConfigClass = ForcedPhotCcdConfig
203 _DefaultName = "forcedPhotCcd"
204 dataPrefix = ""
205
206 def __init__(self, refSchema=None, initInputs=None, **kwargs):
207 super().__init__(**kwargs)
208
209 if initInputs is not None:
210 refSchema = initInputs['inputSchema'].schema
211
212 if refSchema is None:
213 raise ValueError("No reference schema provided.")
214
215 self.makeSubtask("measurement", refSchema=refSchema)
216 # It is necessary to get the schema internal to the forced measurement
217 # task until such a time that the schema is not owned by the
218 # measurement task, but is passed in by an external caller.
219 if self.config.doApCorr:
220 self.makeSubtask("applyApCorr", schema=self.measurement.schema)
221 self.makeSubtask('catalogCalculation', schema=self.measurement.schema)
222 self.outputSchema = lsst.afw.table.SourceCatalog(self.measurement.schema)
223
224 def runQuantum(self, butlerQC, inputRefs, outputRefs):
225 inputs = butlerQC.get(inputRefs)
226
227 tract = butlerQC.quantum.dataId['tract']
228 skyMap = inputs.pop('skyMap')
229 inputs['refWcs'] = skyMap[tract].getWcs()
230
231 # Connections only exist if they are configured to be used.
232 skyCorr = inputs.pop('skyCorr', None)
233
234 inputs['exposure'] = self.prepareCalibratedExposure(
235 inputs['exposure'],
236 skyCorr=skyCorr,
237 visitSummary=inputs.pop("visitSummary"),
238 )
239
240 inputs['refCat'] = self.mergeAndFilterReferences(inputs['exposure'], inputs['refCat'],
241 inputs['refWcs'])
242
243 if inputs['refCat'] is None:
244 self.log.info("No WCS for exposure %s. No %s catalog will be written.",
245 butlerQC.quantum.dataId, outputRefs.measCat.datasetType.name)
246 else:
247 inputs['measCat'], inputs['exposureId'] = self.generateMeasCat(inputRefs.exposure.dataId,
248 inputs['exposure'],
249 inputs['refCat'], inputs['refWcs'])
250 self.attachFootprints(inputs['measCat'], inputs['refCat'], inputs['exposure'], inputs['refWcs'])
251 outputs = self.run(**inputs)
252 butlerQC.put(outputs, outputRefs)
253
254 def prepareCalibratedExposure(self, exposure, skyCorr=None, visitSummary=None):
255 """Prepare a calibrated exposure and apply external calibrations
256 and sky corrections if so configured.
257
258 Parameters
259 ----------
260 exposure : `lsst.afw.image.exposure.Exposure`
261 Input exposure to adjust calibrations.
262 skyCorr : `lsst.afw.math.backgroundList`, optional
263 Sky correction frame to apply if doApplySkyCorr=True.
264 visitSummary : `lsst.afw.table.ExposureCatalog`, optional
265 Exposure catalog with update calibrations; any not-None calibration
266 objects attached will be used. These are applied first and may be
267 overridden by other arguments.
268
269 Returns
270 -------
271 exposure : `lsst.afw.image.exposure.Exposure`
272 Exposure with adjusted calibrations.
273 """
274 detectorId = exposure.getInfo().getDetector().getId()
275
276 if visitSummary is not None:
277 row = visitSummary.find(detectorId)
278 if row is None:
279 raise RuntimeError(f"Detector id {detectorId} not found in visitSummary.")
280 if (photoCalib := row.getPhotoCalib()) is not None:
281 exposure.setPhotoCalib(photoCalib)
282 if (skyWcs := row.getWcs()) is not None:
283 exposure.setWcs(skyWcs)
284 if (psf := row.getPsf()) is not None:
285 exposure.setPsf(psf)
286 if (apCorrMap := row.getApCorrMap()) is not None:
287 exposure.info.setApCorrMap(apCorrMap)
288
289 if skyCorr is not None:
290 exposure.maskedImage -= skyCorr.getImage()
291
292 return exposure
293
294 def mergeAndFilterReferences(self, exposure, refCats, refWcs):
295 """Filter reference catalog so that all sources are within the
296 boundaries of the exposure.
297
298 Parameters
299 ----------
300 exposure : `lsst.afw.image.exposure.Exposure`
301 Exposure to generate the catalog for.
302 refCats : sequence of `lsst.daf.butler.DeferredDatasetHandle`
303 Handles for catalogs of shapes and positions at which to force
304 photometry.
305 refWcs : `lsst.afw.image.SkyWcs`
306 Reference world coordinate system.
307
308 Returns
309 -------
310 refSources : `lsst.afw.table.SourceCatalog`
311 Filtered catalog of forced sources to measure.
312
313 Notes
314 -----
315 The majority of this code is based on the methods of
316 lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader
317
318 """
319 mergedRefCat = None
320
321 # Step 1: Determine bounds of the exposure photometry will
322 # be performed on.
323 expWcs = exposure.getWcs()
324 if expWcs is None:
325 self.log.info("Exposure has no WCS. Returning None for mergedRefCat.")
326 else:
327 expRegion = exposure.getBBox(lsst.afw.image.PARENT)
328 expBBox = lsst.geom.Box2D(expRegion)
329 expBoxCorners = expBBox.getCorners()
330 expSkyCorners = [expWcs.pixelToSky(corner).getVector() for
331 corner in expBoxCorners]
332 expPolygon = lsst.sphgeom.ConvexPolygon(expSkyCorners)
333
334 # Step 2: Filter out reference catalog sources that are
335 # not contained within the exposure boundaries, or whose
336 # parents are not within the exposure boundaries. Note
337 # that within a single input refCat, the parents always
338 # appear before the children.
339 for refCat in refCats:
340 refCat = refCat.get()
341 if mergedRefCat is None:
342 mergedRefCat = lsst.afw.table.SourceCatalog(refCat.table)
343 containedIds = {0} # zero as a parent ID means "this is a parent"
344 for record in refCat:
345 if (expPolygon.contains(record.getCoord().getVector()) and record.getParent()
346 in containedIds):
347 record.setFootprint(record.getFootprint())
348 mergedRefCat.append(record)
349 containedIds.add(record.getId())
350 if mergedRefCat is None:
351 raise RuntimeError("No reference objects for forced photometry.")
352 mergedRefCat.sort(lsst.afw.table.SourceTable.getParentKey())
353 return mergedRefCat
354
355 def generateMeasCat(self, dataId, exposure, refCat, refWcs):
356 """Generate a measurement catalog.
357
358 Parameters
359 ----------
360 dataId : `lsst.daf.butler.DataCoordinate`
361 Butler data ID for this image, with ``{visit, detector}`` keys.
362 exposure : `lsst.afw.image.exposure.Exposure`
363 Exposure to generate the catalog for.
364 refCat : `lsst.afw.table.SourceCatalog`
365 Catalog of shapes and positions at which to force photometry.
366 refWcs : `lsst.afw.image.SkyWcs`
367 Reference world coordinate system.
368 This parameter is not currently used.
369
370 Returns
371 -------
372 measCat : `lsst.afw.table.SourceCatalog`
373 Catalog of forced sources to measure.
374 expId : `int`
375 Unique binary id associated with the input exposure
376 """
377 id_generator = self.config.idGenerator.apply(dataId)
378 measCat = self.measurement.generateMeasCat(exposure, refCat, refWcs,
379 idFactory=id_generator.make_table_id_factory())
380 return measCat, id_generator.catalog_id
381
382 def run(self, measCat, exposure, refCat, refWcs, exposureId=None):
383 """Perform forced measurement on a single exposure.
384
385 Parameters
386 ----------
387 measCat : `lsst.afw.table.SourceCatalog`
388 The measurement catalog, based on the sources listed in the
389 reference catalog.
390 exposure : `lsst.afw.image.Exposure`
391 The measurement image upon which to perform forced detection.
392 refCat : `lsst.afw.table.SourceCatalog`
393 The reference catalog of sources to measure.
394 refWcs : `lsst.afw.image.SkyWcs`
395 The WCS for the references.
396 exposureId : `int`
397 Optional unique exposureId used for random seed in measurement
398 task.
399
400 Returns
401 -------
402 result : `lsst.pipe.base.Struct`
403 Structure with fields:
404
405 ``measCat``
406 Catalog of forced measurement results
407 (`lsst.afw.table.SourceCatalog`).
408 """
409 self.measurement.run(measCat, exposure, refCat, refWcs, exposureId=exposureId)
410 if self.config.doApCorr:
411 apCorrMap = exposure.getInfo().getApCorrMap()
412 if apCorrMap is None:
413 self.log.warning("Forced exposure image does not have valid aperture correction; skipping.")
414 else:
415 self.applyApCorr.run(
416 catalog=measCat,
417 apCorrMap=apCorrMap,
418 )
419 self.catalogCalculation.run(measCat)
420
421 return pipeBase.Struct(measCat=measCat)
422
423 def attachFootprints(self, sources, refCat, exposure, refWcs):
424 """Attach footprints to blank sources prior to measurements.
425
426 Notes
427 -----
428 `~lsst.afw.detection.Footprint` objects for forced photometry must
429 be in the pixel coordinate system of the image being measured, while
430 the actual detections may start out in a different coordinate system.
431
432 Subclasses of this class may implement this method to define how
433 those `~lsst.afw.detection.Footprint` objects should be generated.
434
435 This default implementation transforms depends on the
436 ``footprintSource`` configuration parameter.
437 """
438 if self.config.footprintSource == "transformed":
439 return self.measurement.attachTransformedFootprints(sources, refCat, exposure, refWcs)
440 elif self.config.footprintSource == "psf":
441 return self.measurement.attachPsfShapeFootprints(sources, exposure,
442 scaling=self.config.psfFootprintScaling)
443
444
445class ForcedPhotCcdFromDataFrameConnections(PipelineTaskConnections,
446 dimensions=("instrument", "visit", "detector", "skymap", "tract"),
447 defaultTemplates={"inputCoaddName": "goodSeeing",
448 "inputName": "calexp",
449 }):
450 refCat = cT.Input(
451 doc="Catalog of positions at which to force photometry.",
452 name="{inputCoaddName}Diff_fullDiaObjTable",
453 storageClass="DataFrame",
454 dimensions=["skymap", "tract", "patch"],
455 multiple=True,
456 deferLoad=True,
457 )
458 exposure = cT.Input(
459 doc="Input exposure to perform photometry on.",
460 name="{inputName}",
461 storageClass="ExposureF",
462 dimensions=["instrument", "visit", "detector"],
463 )
464 skyCorr = cT.Input(
465 doc="Input Sky Correction to be subtracted from the calexp if doApplySkyCorr=True",
466 name="skyCorr",
467 storageClass="Background",
468 dimensions=("instrument", "visit", "detector"),
469 )
470 visitSummary = cT.Input(
471 doc="Input visit-summary catalog with updated calibration objects.",
472 name="finalVisitSummary",
473 storageClass="ExposureCatalog",
474 dimensions=("instrument", "visit"),
475 )
476 skyMap = cT.Input(
477 doc="SkyMap dataset that defines the coordinate system of the reference catalog.",
478 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
479 storageClass="SkyMap",
480 dimensions=["skymap"],
481 )
482 measCat = cT.Output(
483 doc="Output forced photometry catalog.",
484 name="forced_src_diaObject",
485 storageClass="SourceCatalog",
486 dimensions=["instrument", "visit", "detector", "skymap", "tract"],
487 )
488 outputSchema = cT.InitOutput(
489 doc="Schema for the output forced measurement catalogs.",
490 name="forced_src_diaObject_schema",
491 storageClass="SourceCatalog",
492 )
493
494 def __init__(self, *, config=None):
495 super().__init__(config=config)
496 if not config.doApplySkyCorr:
497 self.inputs.remove("skyCorr")
498
499
500class ForcedPhotCcdFromDataFrameConfig(ForcedPhotCcdConfig,
501 pipelineConnections=ForcedPhotCcdFromDataFrameConnections):
502 def setDefaults(self):
503 super().setDefaults()
504 self.footprintSource = "psf"
505 self.measurement.doReplaceWithNoise = False
506 # Only run a minimal set of plugins, as these measurements are only
507 # needed for PSF-like sources.
508 self.measurement.plugins.names = ["base_PixelFlags",
509 "base_TransformedCentroidFromCoord",
510 "base_PsfFlux",
511 "base_LocalBackground",
512 "base_LocalPhotoCalib",
513 "base_LocalWcs",
514 ]
515 self.measurement.slots.shape = None
516 # Keep track of which footprints contain streaks
517 self.measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK']
518 self.measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK']
519 # Make catalogCalculation a no-op by default as no modelFlux is setup
520 # by default in ForcedMeasurementTask.
521 self.catalogCalculation.plugins.names = []
522
523 self.measurement.copyColumns = {'id': 'diaObjectId', 'coord_ra': 'coord_ra', 'coord_dec': 'coord_dec'}
524 self.measurement.slots.centroid = "base_TransformedCentroidFromCoord"
525 self.measurement.slots.psfFlux = "base_PsfFlux"
526
527 def validate(self):
528 super().validate()
529 if self.footprintSource == "transformed":
530 raise ValueError("Cannot transform footprints from reference catalog, "
531 "because DataFrames can't hold footprints.")
532
533
534class ForcedPhotCcdFromDataFrameTask(ForcedPhotCcdTask):
535 """Force Photometry on a per-detector exposure with coords from a DataFrame
536
537 Uses input from a DataFrame instead of SourceCatalog
538 like the base class ForcedPhotCcd does.
539 Writes out a SourceCatalog so that the downstream
540 WriteForcedSourceTableTask can be reused with output from this Task.
541 """
542 _DefaultName = "forcedPhotCcdFromDataFrame"
543 ConfigClass = ForcedPhotCcdFromDataFrameConfig
544
545 def __init__(self, refSchema=None, initInputs=None, **kwargs):
546 # Parent's init assumes that we have a reference schema; Cannot reuse
547 pipeBase.PipelineTask.__init__(self, **kwargs)
548
549 self.makeSubtask("measurement", refSchema=lsst.afw.table.SourceTable.makeMinimalSchema())
550
551 if self.config.doApCorr:
552 self.makeSubtask("applyApCorr", schema=self.measurement.schema)
553 self.makeSubtask('catalogCalculation', schema=self.measurement.schema)
554 self.outputSchema = lsst.afw.table.SourceCatalog(self.measurement.schema)
555
556 def runQuantum(self, butlerQC, inputRefs, outputRefs):
557 inputs = butlerQC.get(inputRefs)
558
559 tract = butlerQC.quantum.dataId["tract"]
560 skyMap = inputs.pop("skyMap")
561 inputs["refWcs"] = skyMap[tract].getWcs()
562
563 # Connections only exist if they are configured to be used.
564 skyCorr = inputs.pop('skyCorr', None)
565
566 inputs['exposure'] = self.prepareCalibratedExposure(
567 inputs['exposure'],
568 skyCorr=skyCorr,
569 visitSummary=inputs.pop("visitSummary"),
570 )
571
572 self.log.info("Filtering ref cats: %s", ','.join([str(i.dataId) for i in inputs['refCat']]))
573 if inputs["exposure"].getWcs() is not None:
574 refCat = self.df2RefCat([i.get(parameters={"columns": ['diaObjectId', 'ra', 'dec']})
575 for i in inputs['refCat']],
576 inputs['exposure'].getBBox(), inputs['exposure'].getWcs())
577 inputs['refCat'] = refCat
578 # generateMeasCat does not use the refWcs.
579 inputs['measCat'], inputs['exposureId'] = self.generateMeasCat(
580 inputRefs.exposure.dataId, inputs['exposure'], inputs['refCat'], inputs['refWcs']
581 )
582 # attachFootprints only uses refWcs in ``transformed`` mode, which is not
583 # supported in the DataFrame-backed task.
584 self.attachFootprints(inputs["measCat"], inputs["refCat"], inputs["exposure"], inputs["refWcs"])
585 outputs = self.run(**inputs)
586
587 butlerQC.put(outputs, outputRefs)
588 else:
589 self.log.info("No WCS for %s. Skipping and no %s catalog will be written.",
590 butlerQC.quantum.dataId, outputRefs.measCat.datasetType.name)
591
592 def df2RefCat(self, dfList, exposureBBox, exposureWcs):
593 """Convert list of DataFrames to reference catalog
594
595 Concatenate list of DataFrames presumably from multiple patches and
596 downselect rows that overlap the exposureBBox using the exposureWcs.
597
598 Parameters
599 ----------
600 dfList : `list` of `pandas.DataFrame`
601 Each element containst diaObjects with ra/dec position in degrees
602 Columns 'diaObjectId', 'ra', 'dec' are expected
603 exposureBBox : `lsst.geom.Box2I`
604 Bounding box on which to select rows that overlap
605 exposureWcs : `lsst.afw.geom.SkyWcs`
606 World coordinate system to convert sky coords in ref cat to
607 pixel coords with which to compare with exposureBBox
608
609 Returns
610 -------
611 refCat : `lsst.afw.table.SourceTable`
612 Source Catalog with minimal schema that overlaps exposureBBox
613 """
614 df = pd.concat(dfList)
615 # translate ra/dec coords in dataframe to detector pixel coords
616 # to down select rows that overlap the detector bbox
617 mapping = exposureWcs.getTransform().getMapping()
618 x, y = mapping.applyInverse(np.array(df[['ra', 'dec']].values*2*np.pi/360).T)
619 inBBox = lsst.geom.Box2D(exposureBBox).contains(x, y)
620 refCat = self.df2SourceCat(df[inBBox])
621 return refCat
622
623 def df2SourceCat(self, df):
624 """Create minimal schema SourceCatalog from a pandas DataFrame.
625
626 The forced measurement subtask expects this as input.
627
628 Parameters
629 ----------
630 df : `pandas.DataFrame`
631 DiaObjects with locations and ids.
632
633 Returns
634 -------
635 outputCatalog : `lsst.afw.table.SourceTable`
636 Output catalog with minimal schema.
637 """
639 outputCatalog = lsst.afw.table.SourceCatalog(schema)
640 outputCatalog.reserve(len(df))
641
642 for diaObjectId, ra, dec in df[['ra', 'dec']].itertuples():
643 outputRecord = outputCatalog.addNew()
644 outputRecord.setId(diaObjectId)
645 outputRecord.setCoord(lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees))
646 return outputCatalog
static Schema makeMinimalSchema()
Return a minimal schema for Source tables and records.
Definition Source.h:258
static Key< RecordId > getParentKey()
Key for the parent ID.
Definition Source.h:273
A floating-point coordinate rectangle geometry.
Definition Box.h:413
Point in an unspecified spherical coordinate system.
Definition SpherePoint.h:57
ConvexPolygon is a closed convex polygon on the unit sphere.
df2RefCat(self, dfList, exposureBBox, exposureWcs)
runQuantum(self, butlerQC, inputRefs, outputRefs)