Coverage for python/lsst/meas/base/forcedPhotCcd.py: 26%
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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.
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20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import pandas as pd
23import numpy as np
25import lsst.pex.config
26import lsst.pex.exceptions
27import lsst.pipe.base
28import lsst.geom
29import lsst.afw.detection
30import lsst.afw.geom
31import lsst.afw.image
32import lsst.afw.table
33import lsst.sphgeom
35from lsst.pipe.base import PipelineTaskConnections
36import lsst.pipe.base.connectionTypes as cT
38import lsst.pipe.base as pipeBase
39from lsst.skymap import BaseSkyMap
41from .forcedMeasurement import ForcedMeasurementTask
42from .applyApCorr import ApplyApCorrTask
43from .catalogCalculation import CatalogCalculationTask
44from ._id_generator import DetectorVisitIdGeneratorConfig
46__all__ = ("ForcedPhotCcdConfig", "ForcedPhotCcdTask",
47 "ForcedPhotCcdFromDataFrameTask", "ForcedPhotCcdFromDataFrameConfig")
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 )
103 def __init__(self, *, config=None):
104 super().__init__(config=config)
105 if not config.doApplySkyCorr:
106 self.inputs.remove("skyCorr")
109class ForcedPhotCcdConfig(pipeBase.PipelineTaskConfig,
110 pipelineConnections=ForcedPhotCcdConnections):
111 """Config class for forced measurement driver task."""
112 measurement = lsst.pex.config.ConfigurableField(
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 )
126 applyApCorr = lsst.pex.config.ConfigurableField(
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()
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 = []
186class ForcedPhotCcdTask(pipeBase.PipelineTask):
187 """A pipeline task for performing forced measurement on CCD images.
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 """
202 ConfigClass = ForcedPhotCcdConfig
203 _DefaultName = "forcedPhotCcd"
204 dataPrefix = ""
206 def __init__(self, refSchema=None, initInputs=None, **kwargs):
207 super().__init__(**kwargs)
209 if initInputs is not None:
210 refSchema = initInputs['inputSchema'].schema
212 if refSchema is None:
213 raise ValueError("No reference schema provided.")
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)
224 def runQuantum(self, butlerQC, inputRefs, outputRefs):
225 inputs = butlerQC.get(inputRefs)
227 tract = butlerQC.quantum.dataId['tract']
228 skyMap = inputs.pop('skyMap')
229 inputs['refWcs'] = skyMap[tract].getWcs()
231 # Connections only exist if they are configured to be used.
232 skyCorr = inputs.pop('skyCorr', None)
234 inputs['exposure'] = self.prepareCalibratedExposure(
235 inputs['exposure'],
236 skyCorr=skyCorr,
237 visitSummary=inputs.pop("visitSummary"),
238 )
240 inputs['refCat'] = self.mergeAndFilterReferences(inputs['exposure'], inputs['refCat'],
241 inputs['refWcs'])
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)
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.
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.
269 Returns
270 -------
271 exposure : `lsst.afw.image.exposure.Exposure`
272 Exposure with adjusted calibrations.
273 """
274 detectorId = exposure.getInfo().getDetector().getId()
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)
289 if skyCorr is not None:
290 exposure.maskedImage -= skyCorr.getImage()
292 return exposure
294 def mergeAndFilterReferences(self, exposure, refCats, refWcs):
295 """Filter reference catalog so that all sources are within the
296 boundaries of the exposure.
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.
308 Returns
309 -------
310 refSources : `lsst.afw.table.SourceCatalog`
311 Filtered catalog of forced sources to measure.
313 Notes
314 -----
315 The majority of this code is based on the methods of
316 lsst.meas.algorithms.loadReferenceObjects.ReferenceObjectLoader
318 """
319 mergedRefCat = None
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)
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
355 def generateMeasCat(self, dataId, exposure, refCat, refWcs):
356 """Generate a measurement catalog.
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.
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
382 def run(self, measCat, exposure, refCat, refWcs, exposureId=None):
383 """Perform forced measurement on a single exposure.
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.
400 Returns
401 -------
402 result : `lsst.pipe.base.Struct`
403 Structure with fields:
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)
421 return pipeBase.Struct(measCat=measCat)
423 def attachFootprints(self, sources, refCat, exposure, refWcs):
424 """Attach footprints to blank sources prior to measurements.
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.
432 Subclasses of this class may implement this method to define how
433 those `~lsst.afw.detection.Footprint` objects should be generated.
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)
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 measCat = cT.Output(
477 doc="Output forced photometry catalog.",
478 name="forced_src_diaObject",
479 storageClass="SourceCatalog",
480 dimensions=["instrument", "visit", "detector", "skymap", "tract"],
481 )
482 outputSchema = cT.InitOutput(
483 doc="Schema for the output forced measurement catalogs.",
484 name="forced_src_diaObject_schema",
485 storageClass="SourceCatalog",
486 )
488 def __init__(self, *, config=None):
489 super().__init__(config=config)
490 if not config.doApplySkyCorr:
491 self.inputs.remove("skyCorr")
494class ForcedPhotCcdFromDataFrameConfig(ForcedPhotCcdConfig,
495 pipelineConnections=ForcedPhotCcdFromDataFrameConnections):
496 def setDefaults(self):
497 super().setDefaults()
498 self.footprintSource = "psf"
499 self.measurement.doReplaceWithNoise = False
500 # Only run a minimal set of plugins, as these measurements are only
501 # needed for PSF-like sources.
502 self.measurement.plugins.names = ["base_PixelFlags",
503 "base_TransformedCentroidFromCoord",
504 "base_PsfFlux",
505 "base_LocalBackground",
506 "base_LocalPhotoCalib",
507 "base_LocalWcs",
508 ]
509 self.measurement.slots.shape = None
510 # Keep track of which footprints contain streaks
511 self.measurement.plugins['base_PixelFlags'].masksFpAnywhere = ['STREAK']
512 self.measurement.plugins['base_PixelFlags'].masksFpCenter = ['STREAK']
513 # Make catalogCalculation a no-op by default as no modelFlux is setup
514 # by default in ForcedMeasurementTask.
515 self.catalogCalculation.plugins.names = []
517 self.measurement.copyColumns = {'id': 'diaObjectId', 'coord_ra': 'coord_ra', 'coord_dec': 'coord_dec'}
518 self.measurement.slots.centroid = "base_TransformedCentroidFromCoord"
519 self.measurement.slots.psfFlux = "base_PsfFlux"
521 def validate(self):
522 super().validate()
523 if self.footprintSource == "transformed":
524 raise ValueError("Cannot transform footprints from reference catalog, "
525 "because DataFrames can't hold footprints.")
528class ForcedPhotCcdFromDataFrameTask(ForcedPhotCcdTask):
529 """Force Photometry on a per-detector exposure with coords from a DataFrame
531 Uses input from a DataFrame instead of SourceCatalog
532 like the base class ForcedPhotCcd does.
533 Writes out a SourceCatalog so that the downstream
534 WriteForcedSourceTableTask can be reused with output from this Task.
535 """
536 _DefaultName = "forcedPhotCcdFromDataFrame"
537 ConfigClass = ForcedPhotCcdFromDataFrameConfig
539 def __init__(self, refSchema=None, initInputs=None, **kwargs):
540 # Parent's init assumes that we have a reference schema; Cannot reuse
541 pipeBase.PipelineTask.__init__(self, **kwargs)
543 self.makeSubtask("measurement", refSchema=lsst.afw.table.SourceTable.makeMinimalSchema())
545 if self.config.doApCorr:
546 self.makeSubtask("applyApCorr", schema=self.measurement.schema)
547 self.makeSubtask('catalogCalculation', schema=self.measurement.schema)
548 self.outputSchema = lsst.afw.table.SourceCatalog(self.measurement.schema)
550 def runQuantum(self, butlerQC, inputRefs, outputRefs):
551 inputs = butlerQC.get(inputRefs)
553 # When run with dataframes, we do not need a reference wcs.
554 inputs['refWcs'] = None
556 # Connections only exist if they are configured to be used.
557 skyCorr = inputs.pop('skyCorr', None)
559 inputs['exposure'] = self.prepareCalibratedExposure(
560 inputs['exposure'],
561 skyCorr=skyCorr,
562 visitSummary=inputs.pop("visitSummary"),
563 )
565 self.log.info("Filtering ref cats: %s", ','.join([str(i.dataId) for i in inputs['refCat']]))
566 if inputs["exposure"].getWcs() is not None:
567 refCat = self.df2RefCat([i.get(parameters={"columns": ['diaObjectId', 'ra', 'dec']})
568 for i in inputs['refCat']],
569 inputs['exposure'].getBBox(), inputs['exposure'].getWcs())
570 inputs['refCat'] = refCat
571 # generateMeasCat does not use the refWcs.
572 inputs['measCat'], inputs['exposureId'] = self.generateMeasCat(
573 inputRefs.exposure.dataId, inputs['exposure'], inputs['refCat'], inputs['refWcs']
574 )
575 # attachFootprints only uses refWcs in ``transformed`` mode, which is not
576 # supported in the DataFrame-backed task.
577 self.attachFootprints(inputs["measCat"], inputs["refCat"], inputs["exposure"], inputs["refWcs"])
578 outputs = self.run(**inputs)
580 butlerQC.put(outputs, outputRefs)
581 else:
582 self.log.info("No WCS for %s. Skipping and no %s catalog will be written.",
583 butlerQC.quantum.dataId, outputRefs.measCat.datasetType.name)
585 def df2RefCat(self, dfList, exposureBBox, exposureWcs):
586 """Convert list of DataFrames to reference catalog
588 Concatenate list of DataFrames presumably from multiple patches and
589 downselect rows that overlap the exposureBBox using the exposureWcs.
591 Parameters
592 ----------
593 dfList : `list` of `pandas.DataFrame`
594 Each element containst diaObjects with ra/dec position in degrees
595 Columns 'diaObjectId', 'ra', 'dec' are expected
596 exposureBBox : `lsst.geom.Box2I`
597 Bounding box on which to select rows that overlap
598 exposureWcs : `lsst.afw.geom.SkyWcs`
599 World coordinate system to convert sky coords in ref cat to
600 pixel coords with which to compare with exposureBBox
602 Returns
603 -------
604 refCat : `lsst.afw.table.SourceTable`
605 Source Catalog with minimal schema that overlaps exposureBBox
606 """
607 df = pd.concat(dfList)
608 # translate ra/dec coords in dataframe to detector pixel coords
609 # to down select rows that overlap the detector bbox
610 mapping = exposureWcs.getTransform().getMapping()
611 x, y = mapping.applyInverse(np.array(df[['ra', 'dec']].values*2*np.pi/360).T)
612 inBBox = lsst.geom.Box2D(exposureBBox).contains(x, y)
613 refCat = self.df2SourceCat(df[inBBox])
614 return refCat
616 def df2SourceCat(self, df):
617 """Create minimal schema SourceCatalog from a pandas DataFrame.
619 The forced measurement subtask expects this as input.
621 Parameters
622 ----------
623 df : `pandas.DataFrame`
624 DiaObjects with locations and ids.
626 Returns
627 -------
628 outputCatalog : `lsst.afw.table.SourceTable`
629 Output catalog with minimal schema.
630 """
631 schema = lsst.afw.table.SourceTable.makeMinimalSchema()
632 outputCatalog = lsst.afw.table.SourceCatalog(schema)
633 outputCatalog.reserve(len(df))
635 for diaObjectId, ra, dec in df[['ra', 'dec']].itertuples():
636 outputRecord = outputCatalog.addNew()
637 outputRecord.setId(diaObjectId)
638 outputRecord.setCoord(lsst.geom.SpherePoint(ra, dec, lsst.geom.degrees))
639 return outputCatalog