lsst.pipe.tasks 21.0.0-180-gec3f5457+6afaa46061
postprocess.py
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21
22import functools
23import pandas as pd
24from collections import defaultdict
25import numpy as np
26import numbers
27
28import lsst.geom
29import lsst.pex.config as pexConfig
30import lsst.pipe.base as pipeBase
31import lsst.daf.base as dafBase
32from lsst.pipe.base import connectionTypes
33import lsst.afw.table as afwTable
34from lsst.meas.base import SingleFrameMeasurementTask
35from lsst.pipe.base import CmdLineTask, ArgumentParser, DataIdContainer
36from lsst.coadd.utils.coaddDataIdContainer import CoaddDataIdContainer
37from lsst.daf.butler import DeferredDatasetHandle, DataCoordinate
38
39from .parquetTable import ParquetTable
40from .multiBandUtils import makeMergeArgumentParser, MergeSourcesRunner
41from .functors import CompositeFunctor, Column
42
43
44def flattenFilters(df, noDupCols=['coord_ra', 'coord_dec'], camelCase=False, inputBands=None):
45 """Flattens a dataframe with multilevel column index
46 """
47 newDf = pd.DataFrame()
48 # band is the level 0 index
49 dfBands = df.columns.unique(level=0).values
50 for band in dfBands:
51 subdf = df[band]
52 columnFormat = '{0}{1}' if camelCase else '{0}_{1}'
53 newColumns = {c: columnFormat.format(band, c)
54 for c in subdf.columns if c not in noDupCols}
55 cols = list(newColumns.keys())
56 newDf = pd.concat([newDf, subdf[cols].rename(columns=newColumns)], axis=1)
57
58 # Band must be present in the input and output or else column is all NaN:
59 presentBands = dfBands if inputBands is None else list(set(inputBands).intersection(dfBands))
60 # Get the unexploded columns from any present band's partition
61 noDupDf = df[presentBands[0]][noDupCols]
62 newDf = pd.concat([noDupDf, newDf], axis=1)
63 return newDf
64
65
66class WriteObjectTableConnections(pipeBase.PipelineTaskConnections,
67 defaultTemplates={"coaddName": "deep"},
68 dimensions=("tract", "patch", "skymap")):
69 inputCatalogMeas = connectionTypes.Input(
70 doc="Catalog of source measurements on the deepCoadd.",
71 dimensions=("tract", "patch", "band", "skymap"),
72 storageClass="SourceCatalog",
73 name="{coaddName}Coadd_meas",
74 multiple=True
75 )
76 inputCatalogForcedSrc = connectionTypes.Input(
77 doc="Catalog of forced measurements (shape and position parameters held fixed) on the deepCoadd.",
78 dimensions=("tract", "patch", "band", "skymap"),
79 storageClass="SourceCatalog",
80 name="{coaddName}Coadd_forced_src",
81 multiple=True
82 )
83 inputCatalogRef = connectionTypes.Input(
84 doc="Catalog marking the primary detection (which band provides a good shape and position)"
85 "for each detection in deepCoadd_mergeDet.",
86 dimensions=("tract", "patch", "skymap"),
87 storageClass="SourceCatalog",
88 name="{coaddName}Coadd_ref"
89 )
90 outputCatalog = connectionTypes.Output(
91 doc="A vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
92 "stored as a DataFrame with a multi-level column index per-patch.",
93 dimensions=("tract", "patch", "skymap"),
94 storageClass="DataFrame",
95 name="{coaddName}Coadd_obj"
96 )
97
98
99class WriteObjectTableConfig(pipeBase.PipelineTaskConfig,
100 pipelineConnections=WriteObjectTableConnections):
101 engine = pexConfig.Field(
102 dtype=str,
103 default="pyarrow",
104 doc="Parquet engine for writing (pyarrow or fastparquet)"
105 )
106 coaddName = pexConfig.Field(
107 dtype=str,
108 default="deep",
109 doc="Name of coadd"
110 )
111
112
113class WriteObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
114 """Write filter-merged source tables to parquet
115 """
116 _DefaultName = "writeObjectTable"
117 ConfigClass = WriteObjectTableConfig
118 RunnerClass = MergeSourcesRunner
119
120 # Names of table datasets to be merged
121 inputDatasets = ('forced_src', 'meas', 'ref')
122
123 # Tag of output dataset written by `MergeSourcesTask.write`
124 outputDataset = 'obj'
125
126 def __init__(self, butler=None, schema=None, **kwargs):
127 # It is a shame that this class can't use the default init for CmdLineTask
128 # But to do so would require its own special task runner, which is many
129 # more lines of specialization, so this is how it is for now
130 super().__init__(**kwargs)
131
132 def runDataRef(self, patchRefList):
133 """!
134 @brief Merge coadd sources from multiple bands. Calls @ref `run` which must be defined in
135 subclasses that inherit from MergeSourcesTask.
136 @param[in] patchRefList list of data references for each filter
137 """
138 catalogs = dict(self.readCatalog(patchRef) for patchRef in patchRefList)
139 dataId = patchRefList[0].dataId
140 mergedCatalog = self.run(catalogs, tract=dataId['tract'], patch=dataId['patch'])
141 self.write(patchRefList[0], ParquetTable(dataFrame=mergedCatalog))
142
143 def runQuantum(self, butlerQC, inputRefs, outputRefs):
144 inputs = butlerQC.get(inputRefs)
145
146 measDict = {ref.dataId['band']: {'meas': cat} for ref, cat in
147 zip(inputRefs.inputCatalogMeas, inputs['inputCatalogMeas'])}
148 forcedSourceDict = {ref.dataId['band']: {'forced_src': cat} for ref, cat in
149 zip(inputRefs.inputCatalogForcedSrc, inputs['inputCatalogForcedSrc'])}
150
151 catalogs = {}
152 for band in measDict.keys():
153 catalogs[band] = {'meas': measDict[band]['meas'],
154 'forced_src': forcedSourceDict[band]['forced_src'],
155 'ref': inputs['inputCatalogRef']}
156 dataId = butlerQC.quantum.dataId
157 df = self.run(catalogs=catalogs, tract=dataId['tract'], patch=dataId['patch'])
158 outputs = pipeBase.Struct(outputCatalog=df)
159 butlerQC.put(outputs, outputRefs)
160
161 @classmethod
162 def _makeArgumentParser(cls):
163 """Create a suitable ArgumentParser.
164
165 We will use the ArgumentParser to get a list of data
166 references for patches; the RunnerClass will sort them into lists
167 of data references for the same patch.
168
169 References first of self.inputDatasets, rather than
170 self.inputDataset
171 """
172 return makeMergeArgumentParser(cls._DefaultName, cls.inputDatasets[0])
173
174 def readCatalog(self, patchRef):
175 """Read input catalogs
176
177 Read all the input datasets given by the 'inputDatasets'
178 attribute.
179
180 Parameters
181 ----------
182 patchRef : `lsst.daf.persistence.ButlerDataRef`
183 Data reference for patch
184
185 Returns
186 -------
187 Tuple consisting of band name and a dict of catalogs, keyed by
188 dataset name
189 """
190 band = patchRef.get(self.config.coaddName + "Coadd_filterLabel", immediate=True).bandLabel
191 catalogDict = {}
192 for dataset in self.inputDatasets:
193 catalog = patchRef.get(self.config.coaddName + "Coadd_" + dataset, immediate=True)
194 self.log.info("Read %d sources from %s for band %s: %s",
195 len(catalog), dataset, band, patchRef.dataId)
196 catalogDict[dataset] = catalog
197 return band, catalogDict
198
199 def run(self, catalogs, tract, patch):
200 """Merge multiple catalogs.
201
202 Parameters
203 ----------
204 catalogs : `dict`
205 Mapping from filter names to dict of catalogs.
206 tract : int
207 tractId to use for the tractId column
208 patch : str
209 patchId to use for the patchId column
210
211 Returns
212 -------
213 catalog : `pandas.DataFrame`
214 Merged dataframe
215 """
216
217 dfs = []
218 for filt, tableDict in catalogs.items():
219 for dataset, table in tableDict.items():
220 # Convert afwTable to pandas DataFrame
221 df = table.asAstropy().to_pandas().set_index('id', drop=True)
222
223 # Sort columns by name, to ensure matching schema among patches
224 df = df.reindex(sorted(df.columns), axis=1)
225 df['tractId'] = tract
226 df['patchId'] = patch
227
228 # Make columns a 3-level MultiIndex
229 df.columns = pd.MultiIndex.from_tuples([(dataset, filt, c) for c in df.columns],
230 names=('dataset', 'band', 'column'))
231 dfs.append(df)
232
233 catalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
234 return catalog
235
236 def write(self, patchRef, catalog):
237 """Write the output.
238
239 Parameters
240 ----------
241 catalog : `ParquetTable`
242 Catalog to write
243 patchRef : `lsst.daf.persistence.ButlerDataRef`
244 Data reference for patch
245 """
246 patchRef.put(catalog, self.config.coaddName + "Coadd_" + self.outputDataset)
247 # since the filter isn't actually part of the data ID for the dataset we're saving,
248 # it's confusing to see it in the log message, even if the butler simply ignores it.
249 mergeDataId = patchRef.dataId.copy()
250 del mergeDataId["filter"]
251 self.log.info("Wrote merged catalog: %s", mergeDataId)
252
253 def writeMetadata(self, dataRefList):
254 """No metadata to write, and not sure how to write it for a list of dataRefs.
255 """
256 pass
257
258
259class WriteSourceTableConnections(pipeBase.PipelineTaskConnections,
260 defaultTemplates={"catalogType": ""},
261 dimensions=("instrument", "visit", "detector")):
262
263 catalog = connectionTypes.Input(
264 doc="Input full-depth catalog of sources produced by CalibrateTask",
265 name="{catalogType}src",
266 storageClass="SourceCatalog",
267 dimensions=("instrument", "visit", "detector")
268 )
269 outputCatalog = connectionTypes.Output(
270 doc="Catalog of sources, `src` in Parquet format. The 'id' column is "
271 "replaced with an index; all other columns are unchanged.",
272 name="{catalogType}source",
273 storageClass="DataFrame",
274 dimensions=("instrument", "visit", "detector")
275 )
276
277
278class WriteSourceTableConfig(pipeBase.PipelineTaskConfig,
279 pipelineConnections=WriteSourceTableConnections):
280 doApplyExternalPhotoCalib = pexConfig.Field(
281 dtype=bool,
282 default=False,
283 doc=("Add local photoCalib columns from the calexp.photoCalib? Should only set True if "
284 "generating Source Tables from older src tables which do not already have local calib columns")
285 )
286 doApplyExternalSkyWcs = pexConfig.Field(
287 dtype=bool,
288 default=False,
289 doc=("Add local WCS columns from the calexp.wcs? Should only set True if "
290 "generating Source Tables from older src tables which do not already have local calib columns")
291 )
292
293
294class WriteSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
295 """Write source table to parquet
296 """
297 _DefaultName = "writeSourceTable"
298 ConfigClass = WriteSourceTableConfig
299
300 def runDataRef(self, dataRef):
301 src = dataRef.get('src')
302 if self.config.doApplyExternalPhotoCalib or self.config.doApplyExternalSkyWcs:
303 src = self.addCalibColumns(src, dataRef)
304
305 ccdVisitId = dataRef.get('ccdExposureId')
306 result = self.run(src, ccdVisitId=ccdVisitId)
307 dataRef.put(result.table, 'source')
308
309 def runQuantum(self, butlerQC, inputRefs, outputRefs):
310 inputs = butlerQC.get(inputRefs)
311 inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
312 result = self.run(**inputs).table
313 outputs = pipeBase.Struct(outputCatalog=result.toDataFrame())
314 butlerQC.put(outputs, outputRefs)
315
316 def run(self, catalog, ccdVisitId=None):
317 """Convert `src` catalog to parquet
318
319 Parameters
320 ----------
321 catalog: `afwTable.SourceCatalog`
322 catalog to be converted
323 ccdVisitId: `int`
324 ccdVisitId to be added as a column
325
326 Returns
327 -------
328 result : `lsst.pipe.base.Struct`
329 ``table``
330 `ParquetTable` version of the input catalog
331 """
332 self.log.info("Generating parquet table from src catalog %s", ccdVisitId)
333 df = catalog.asAstropy().to_pandas().set_index('id', drop=True)
334 df['ccdVisitId'] = ccdVisitId
335 return pipeBase.Struct(table=ParquetTable(dataFrame=df))
336
337 def addCalibColumns(self, catalog, dataRef):
338 """Add columns with local calibration evaluated at each centroid
339
340 for backwards compatibility with old repos.
341 This exists for the purpose of converting old src catalogs
342 (which don't have the expected local calib columns) to Source Tables.
343
344 Parameters
345 ----------
346 catalog: `afwTable.SourceCatalog`
347 catalog to which calib columns will be added
348 dataRef: `lsst.daf.persistence.ButlerDataRef
349 for fetching the calibs from disk.
350
351 Returns
352 -------
353 newCat: `afwTable.SourceCatalog`
354 Source Catalog with requested local calib columns
355 """
356 mapper = afwTable.SchemaMapper(catalog.schema)
357 measureConfig = SingleFrameMeasurementTask.ConfigClass()
358 measureConfig.doReplaceWithNoise = False
359
360 # Just need the WCS or the PhotoCalib attached to an exposue
361 exposure = dataRef.get('calexp_sub',
363
364 mapper = afwTable.SchemaMapper(catalog.schema)
365 mapper.addMinimalSchema(catalog.schema, True)
366 schema = mapper.getOutputSchema()
367
368 exposureIdInfo = dataRef.get("expIdInfo")
369 measureConfig.plugins.names = []
370 if self.config.doApplyExternalSkyWcs:
371 plugin = 'base_LocalWcs'
372 if plugin in schema:
373 raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalSkyWcs=False")
374 else:
375 measureConfig.plugins.names.add(plugin)
376
377 if self.config.doApplyExternalPhotoCalib:
378 plugin = 'base_LocalPhotoCalib'
379 if plugin in schema:
380 raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalPhotoCalib=False")
381 else:
382 measureConfig.plugins.names.add(plugin)
383
384 measurement = SingleFrameMeasurementTask(config=measureConfig, schema=schema)
385 newCat = afwTable.SourceCatalog(schema)
386 newCat.extend(catalog, mapper=mapper)
387 measurement.run(measCat=newCat, exposure=exposure, exposureId=exposureIdInfo.expId)
388 return newCat
389
390 def writeMetadata(self, dataRef):
391 """No metadata to write.
392 """
393 pass
394
395 @classmethod
396 def _makeArgumentParser(cls):
397 parser = ArgumentParser(name=cls._DefaultName)
398 parser.add_id_argument("--id", 'src',
399 help="data ID, e.g. --id visit=12345 ccd=0")
400 return parser
401
402
403class PostprocessAnalysis(object):
404 """Calculate columns from ParquetTable
405
406 This object manages and organizes an arbitrary set of computations
407 on a catalog. The catalog is defined by a
408 `lsst.pipe.tasks.parquetTable.ParquetTable` object (or list thereof), such as a
409 `deepCoadd_obj` dataset, and the computations are defined by a collection
410 of `lsst.pipe.tasks.functor.Functor` objects (or, equivalently,
411 a `CompositeFunctor`).
412
413 After the object is initialized, accessing the `.df` attribute (which
414 holds the `pandas.DataFrame` containing the results of the calculations) triggers
415 computation of said dataframe.
416
417 One of the conveniences of using this object is the ability to define a desired common
418 filter for all functors. This enables the same functor collection to be passed to
419 several different `PostprocessAnalysis` objects without having to change the original
420 functor collection, since the `filt` keyword argument of this object triggers an
421 overwrite of the `filt` property for all functors in the collection.
422
423 This object also allows a list of refFlags to be passed, and defines a set of default
424 refFlags that are always included even if not requested.
425
426 If a list of `ParquetTable` object is passed, rather than a single one, then the
427 calculations will be mapped over all the input catalogs. In principle, it should
428 be straightforward to parallelize this activity, but initial tests have failed
429 (see TODO in code comments).
430
431 Parameters
432 ----------
433 parq : `lsst.pipe.tasks.ParquetTable` (or list of such)
434 Source catalog(s) for computation
435
436 functors : `list`, `dict`, or `lsst.pipe.tasks.functors.CompositeFunctor`
437 Computations to do (functors that act on `parq`).
438 If a dict, the output
439 DataFrame will have columns keyed accordingly.
440 If a list, the column keys will come from the
441 `.shortname` attribute of each functor.
442
443 filt : `str` (optional)
444 Filter in which to calculate. If provided,
445 this will overwrite any existing `.filt` attribute
446 of the provided functors.
447
448 flags : `list` (optional)
449 List of flags (per-band) to include in output table.
450 Taken from the `meas` dataset if applied to a multilevel Object Table.
451
452 refFlags : `list` (optional)
453 List of refFlags (only reference band) to include in output table.
454
455 forcedFlags : `list` (optional)
456 List of flags (per-band) to include in output table.
457 Taken from the ``forced_src`` dataset if applied to a
458 multilevel Object Table. Intended for flags from measurement plugins
459 only run during multi-band forced-photometry.
460 """
461 _defaultRefFlags = []
462 _defaultFuncs = ()
463
464 def __init__(self, parq, functors, filt=None, flags=None, refFlags=None, forcedFlags=None):
465 self.parq = parq
466 self.functors = functors
467
468 self.filt = filt
469 self.flags = list(flags) if flags is not None else []
470 self.forcedFlags = list(forcedFlags) if forcedFlags is not None else []
471 self.refFlags = list(self._defaultRefFlags)
472 if refFlags is not None:
473 self.refFlags += list(refFlags)
474
475 self._df = None
476
477 @property
478 def defaultFuncs(self):
479 funcs = dict(self._defaultFuncs)
480 return funcs
481
482 @property
483 def func(self):
484 additionalFuncs = self.defaultFuncs
485 additionalFuncs.update({flag: Column(flag, dataset='forced_src') for flag in self.forcedFlags})
486 additionalFuncs.update({flag: Column(flag, dataset='ref') for flag in self.refFlags})
487 additionalFuncs.update({flag: Column(flag, dataset='meas') for flag in self.flags})
488
489 if isinstance(self.functors, CompositeFunctor):
490 func = self.functors
491 else:
492 func = CompositeFunctor(self.functors)
493
494 func.funcDict.update(additionalFuncs)
495 func.filt = self.filt
496
497 return func
498
499 @property
500 def noDupCols(self):
501 return [name for name, func in self.func.funcDict.items() if func.noDup or func.dataset == 'ref']
502
503 @property
504 def df(self):
505 if self._df is None:
506 self.compute()
507 return self._df
508
509 def compute(self, dropna=False, pool=None):
510 # map over multiple parquet tables
511 if type(self.parq) in (list, tuple):
512 if pool is None:
513 dflist = [self.func(parq, dropna=dropna) for parq in self.parq]
514 else:
515 # TODO: Figure out why this doesn't work (pyarrow pickling issues?)
516 dflist = pool.map(functools.partial(self.func, dropna=dropna), self.parq)
517 self._df = pd.concat(dflist)
518 else:
519 self._df = self.func(self.parq, dropna=dropna)
520
521 return self._df
522
523
524class TransformCatalogBaseConnections(pipeBase.PipelineTaskConnections,
525 dimensions=()):
526 """Expected Connections for subclasses of TransformCatalogBaseTask.
527
528 Must be subclassed.
529 """
530 inputCatalog = connectionTypes.Input(
531 name="",
532 storageClass="DataFrame",
533 )
534 outputCatalog = connectionTypes.Output(
535 name="",
536 storageClass="DataFrame",
537 )
538
539
540class TransformCatalogBaseConfig(pipeBase.PipelineTaskConfig,
541 pipelineConnections=TransformCatalogBaseConnections):
542 functorFile = pexConfig.Field(
543 dtype=str,
544 doc="Path to YAML file specifying Science Data Model functors to use "
545 "when copying columns and computing calibrated values.",
546 default=None,
547 optional=True
548 )
549 primaryKey = pexConfig.Field(
550 dtype=str,
551 doc="Name of column to be set as the DataFrame index. If None, the index"
552 "will be named `id`",
553 default=None,
554 optional=True
555 )
556
557
558class TransformCatalogBaseTask(CmdLineTask, pipeBase.PipelineTask):
559 """Base class for transforming/standardizing a catalog
560
561 by applying functors that convert units and apply calibrations.
562 The purpose of this task is to perform a set of computations on
563 an input `ParquetTable` dataset (such as `deepCoadd_obj`) and write the
564 results to a new dataset (which needs to be declared in an `outputDataset`
565 attribute).
566
567 The calculations to be performed are defined in a YAML file that specifies
568 a set of functors to be computed, provided as
569 a `--functorFile` config parameter. An example of such a YAML file
570 is the following:
571
572 funcs:
573 psfMag:
574 functor: Mag
575 args:
576 - base_PsfFlux
577 filt: HSC-G
578 dataset: meas
579 cmodel_magDiff:
580 functor: MagDiff
581 args:
582 - modelfit_CModel
583 - base_PsfFlux
584 filt: HSC-G
585 gauss_magDiff:
586 functor: MagDiff
587 args:
588 - base_GaussianFlux
589 - base_PsfFlux
590 filt: HSC-G
591 count:
592 functor: Column
593 args:
594 - base_InputCount_value
595 filt: HSC-G
596 deconvolved_moments:
597 functor: DeconvolvedMoments
598 filt: HSC-G
599 dataset: forced_src
600 refFlags:
601 - calib_psfUsed
602 - merge_measurement_i
603 - merge_measurement_r
604 - merge_measurement_z
605 - merge_measurement_y
606 - merge_measurement_g
607 - base_PixelFlags_flag_inexact_psfCenter
608 - detect_isPrimary
609
610 The names for each entry under "func" will become the names of columns in the
611 output dataset. All the functors referenced are defined in `lsst.pipe.tasks.functors`.
612 Positional arguments to be passed to each functor are in the `args` list,
613 and any additional entries for each column other than "functor" or "args" (e.g., `'filt'`,
614 `'dataset'`) are treated as keyword arguments to be passed to the functor initialization.
615
616 The "flags" entry is the default shortcut for `Column` functors.
617 All columns listed under "flags" will be copied to the output table
618 untransformed. They can be of any datatype.
619 In the special case of transforming a multi-level oject table with
620 band and dataset indices (deepCoadd_obj), these will be taked from the
621 `meas` dataset and exploded out per band.
622
623 There are two special shortcuts that only apply when transforming
624 multi-level Object (deepCoadd_obj) tables:
625 - The "refFlags" entry is shortcut for `Column` functor
626 taken from the `'ref'` dataset if transforming an ObjectTable.
627 - The "forcedFlags" entry is shortcut for `Column` functors.
628 taken from the ``forced_src`` dataset if transforming an ObjectTable.
629 These are expanded out per band.
630
631
632 This task uses the `lsst.pipe.tasks.postprocess.PostprocessAnalysis` object
633 to organize and excecute the calculations.
634
635 """
636 @property
637 def _DefaultName(self):
638 raise NotImplementedError('Subclass must define "_DefaultName" attribute')
639
640 @property
641 def outputDataset(self):
642 raise NotImplementedError('Subclass must define "outputDataset" attribute')
643
644 @property
645 def inputDataset(self):
646 raise NotImplementedError('Subclass must define "inputDataset" attribute')
647
648 @property
649 def ConfigClass(self):
650 raise NotImplementedError('Subclass must define "ConfigClass" attribute')
651
652 def __init__(self, *args, **kwargs):
653 super().__init__(*args, **kwargs)
654 if self.config.functorFile:
655 self.log.info('Loading tranform functor definitions from %s',
656 self.config.functorFile)
657 self.funcsfuncs = CompositeFunctor.from_file(self.config.functorFile)
658 self.funcsfuncs.update(dict(PostprocessAnalysis._defaultFuncs))
659 else:
660 self.funcsfuncs = None
661
662 def runQuantum(self, butlerQC, inputRefs, outputRefs):
663 inputs = butlerQC.get(inputRefs)
664 if self.funcsfuncs is None:
665 raise ValueError("config.functorFile is None. "
666 "Must be a valid path to yaml in order to run Task as a PipelineTask.")
667 result = self.runrun(parq=inputs['inputCatalog'], funcs=self.funcsfuncs,
668 dataId=outputRefs.outputCatalog.dataId.full)
669 outputs = pipeBase.Struct(outputCatalog=result)
670 butlerQC.put(outputs, outputRefs)
671
672 def runDataRef(self, dataRef):
673 parq = dataRef.get()
674 if self.funcsfuncs is None:
675 raise ValueError("config.functorFile is None. "
676 "Must be a valid path to yaml in order to run as a CommandlineTask.")
677 df = self.runrun(parq, funcs=self.funcsfuncs, dataId=dataRef.dataId)
678 self.writewrite(df, dataRef)
679 return df
680
681 def run(self, parq, funcs=None, dataId=None, band=None):
682 """Do postprocessing calculations
683
684 Takes a `ParquetTable` object and dataId,
685 returns a dataframe with results of postprocessing calculations.
686
687 Parameters
688 ----------
690 ParquetTable from which calculations are done.
691 funcs : `lsst.pipe.tasks.functors.Functors`
692 Functors to apply to the table's columns
693 dataId : dict, optional
694 Used to add a `patchId` column to the output dataframe.
695 band : `str`, optional
696 Filter band that is being processed.
697
698 Returns
699 ------
700 `pandas.DataFrame`
701
702 """
703 self.log.info("Transforming/standardizing the source table dataId: %s", dataId)
704
705 df = self.transformtransform(band, parq, funcs, dataId).df
706 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
707 return df
708
709 def getFunctors(self):
710 return self.funcsfuncs
711
712 def getAnalysis(self, parq, funcs=None, band=None):
713 if funcs is None:
714 funcs = self.funcsfuncs
715 analysis = PostprocessAnalysis(parq, funcs, filt=band)
716 return analysis
717
718 def transform(self, band, parq, funcs, dataId):
719 analysis = self.getAnalysisgetAnalysis(parq, funcs=funcs, band=band)
720 df = analysis.df
721 if dataId is not None:
722 for key, value in dataId.items():
723 df[str(key)] = value
724
725 if self.config.primaryKey:
726 if df.index.name != self.config.primaryKey and self.config.primaryKey in df:
727 df.reset_index(inplace=True, drop=True)
728 df.set_index(self.config.primaryKey, inplace=True)
729
730 return pipeBase.Struct(
731 df=df,
732 analysis=analysis
733 )
734
735 def write(self, df, parqRef):
736 parqRef.put(ParquetTable(dataFrame=df), self.outputDatasetoutputDataset)
737
738 def writeMetadata(self, dataRef):
739 """No metadata to write.
740 """
741 pass
742
743
744class TransformObjectCatalogConnections(pipeBase.PipelineTaskConnections,
745 defaultTemplates={"coaddName": "deep"},
746 dimensions=("tract", "patch", "skymap")):
747 inputCatalog = connectionTypes.Input(
748 doc="The vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
749 "stored as a DataFrame with a multi-level column index per-patch.",
750 dimensions=("tract", "patch", "skymap"),
751 storageClass="DataFrame",
752 name="{coaddName}Coadd_obj",
753 deferLoad=True,
754 )
755 outputCatalog = connectionTypes.Output(
756 doc="Per-Patch Object Table of columns transformed from the deepCoadd_obj table per the standard "
757 "data model.",
758 dimensions=("tract", "patch", "skymap"),
759 storageClass="DataFrame",
760 name="objectTable"
761 )
762
763
764class TransformObjectCatalogConfig(TransformCatalogBaseConfig,
765 pipelineConnections=TransformObjectCatalogConnections):
766 coaddName = pexConfig.Field(
767 dtype=str,
768 default="deep",
769 doc="Name of coadd"
770 )
771 # TODO: remove in DM-27177
772 filterMap = pexConfig.DictField(
773 keytype=str,
774 itemtype=str,
775 default={},
776 doc=("Dictionary mapping full filter name to short one for column name munging."
777 "These filters determine the output columns no matter what filters the "
778 "input data actually contain."),
779 deprecated=("Coadds are now identified by the band, so this transform is unused."
780 "Will be removed after v22.")
781 )
782 outputBands = pexConfig.ListField(
783 dtype=str,
784 default=None,
785 optional=True,
786 doc=("These bands and only these bands will appear in the output,"
787 " NaN-filled if the input does not include them."
788 " If None, then use all bands found in the input.")
789 )
790 camelCase = pexConfig.Field(
791 dtype=bool,
792 default=False,
793 doc=("Write per-band columns names with camelCase, else underscore "
794 "For example: gPsFlux instead of g_PsFlux.")
795 )
796 multilevelOutput = pexConfig.Field(
797 dtype=bool,
798 default=False,
799 doc=("Whether results dataframe should have a multilevel column index (True) or be flat "
800 "and name-munged (False).")
801 )
802 goodFlags = pexConfig.ListField(
803 dtype=str,
804 default=[],
805 doc=("List of 'good' flags that should be set False when populating empty tables. "
806 "All other flags are considered to be 'bad' flags and will be set to True.")
807 )
808 floatFillValue = pexConfig.Field(
809 dtype=float,
810 default=np.nan,
811 doc="Fill value for float fields when populating empty tables."
812 )
813 integerFillValue = pexConfig.Field(
814 dtype=int,
815 default=-1,
816 doc="Fill value for integer fields when populating empty tables."
817 )
818
819 def setDefaults(self):
820 super().setDefaults()
821 self.primaryKey = 'objectId'
822 self.goodFlags = ['calib_astrometry_used',
823 'calib_photometry_reserved',
824 'calib_photometry_used',
825 'calib_psf_candidate',
826 'calib_psf_reserved',
827 'calib_psf_used']
828
829
830class TransformObjectCatalogTask(TransformCatalogBaseTask):
831 """Produce a flattened Object Table to match the format specified in
832 sdm_schemas.
833
834 Do the same set of postprocessing calculations on all bands
835
836 This is identical to `TransformCatalogBaseTask`, except for that it does the
837 specified functor calculations for all filters present in the
838 input `deepCoadd_obj` table. Any specific `"filt"` keywords specified
839 by the YAML file will be superceded.
840 """
841 _DefaultName = "transformObjectCatalog"
842 ConfigClass = TransformObjectCatalogConfig
843
844 # Used by Gen 2 runDataRef only:
845 inputDataset = 'deepCoadd_obj'
846 outputDataset = 'objectTable'
847
848 @classmethod
849 def _makeArgumentParser(cls):
850 parser = ArgumentParser(name=cls._DefaultName)
851 parser.add_id_argument("--id", cls.inputDataset,
852 ContainerClass=CoaddDataIdContainer,
853 help="data ID, e.g. --id tract=12345 patch=1,2")
854 return parser
855
856 def run(self, parq, funcs=None, dataId=None, band=None):
857 # NOTE: band kwarg is ignored here.
858 dfDict = {}
859 analysisDict = {}
860 templateDf = pd.DataFrame()
861
862 if isinstance(parq, DeferredDatasetHandle):
863 columns = parq.get(component='columns')
864 inputBands = columns.unique(level=1).values
865 else:
866 inputBands = parq.columnLevelNames['band']
867
868 outputBands = self.config.outputBands if self.config.outputBands else inputBands
869
870 # Perform transform for data of filters that exist in parq.
871 for inputBand in inputBands:
872 if inputBand not in outputBands:
873 self.log.info("Ignoring %s band data in the input", inputBand)
874 continue
875 self.log.info("Transforming the catalog of band %s", inputBand)
876 result = self.transform(inputBand, parq, funcs, dataId)
877 dfDict[inputBand] = result.df
878 analysisDict[inputBand] = result.analysis
879 if templateDf.empty:
880 templateDf = result.df
881
882 # Put filler values in columns of other wanted bands
883 for filt in outputBands:
884 if filt not in dfDict:
885 self.log.info("Adding empty columns for band %s", filt)
886 dfTemp = templateDf.copy()
887 for col in dfTemp.columns:
888 testValue = dfTemp[col].values[0]
889 if isinstance(testValue, (np.bool_, pd.BooleanDtype)):
890 # Boolean flag type, check if it is a "good" flag
891 if col in self.config.goodFlags:
892 fillValue = False
893 else:
894 fillValue = True
895 elif isinstance(testValue, numbers.Integral):
896 # Checking numbers.Integral catches all flavors
897 # of python, numpy, pandas, etc. integers.
898 # We must ensure this is not an unsigned integer.
899 if isinstance(testValue, np.unsignedinteger):
900 raise ValueError("Parquet tables may not have unsigned integer columns.")
901 else:
902 fillValue = self.config.integerFillValue
903 else:
904 fillValue = self.config.floatFillValue
905 dfTemp[col].values[:] = fillValue
906 dfDict[filt] = dfTemp
907
908 # This makes a multilevel column index, with band as first level
909 df = pd.concat(dfDict, axis=1, names=['band', 'column'])
910
911 if not self.config.multilevelOutput:
912 noDupCols = list(set.union(*[set(v.noDupCols) for v in analysisDict.values()]))
913 if self.config.primaryKey in noDupCols:
914 noDupCols.remove(self.config.primaryKey)
915 if dataId is not None:
916 noDupCols += list(dataId.keys())
917 df = flattenFilters(df, noDupCols=noDupCols, camelCase=self.config.camelCase,
918 inputBands=inputBands)
919
920 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
921
922 return df
923
924
925class TractObjectDataIdContainer(CoaddDataIdContainer):
926
927 def makeDataRefList(self, namespace):
928 """Make self.refList from self.idList
929
930 Generate a list of data references given tract and/or patch.
931 This was adapted from `TractQADataIdContainer`, which was
932 `TractDataIdContainer` modifie to not require "filter".
933 Only existing dataRefs are returned.
934 """
935 def getPatchRefList(tract):
936 return [namespace.butler.dataRef(datasetType=self.datasetType,
937 tract=tract.getId(),
938 patch="%d,%d" % patch.getIndex()) for patch in tract]
939
940 tractRefs = defaultdict(list) # Data references for each tract
941 for dataId in self.idList:
942 skymap = self.getSkymap(namespace)
943
944 if "tract" in dataId:
945 tractId = dataId["tract"]
946 if "patch" in dataId:
947 tractRefs[tractId].append(namespace.butler.dataRef(datasetType=self.datasetType,
948 tract=tractId,
949 patch=dataId['patch']))
950 else:
951 tractRefs[tractId] += getPatchRefList(skymap[tractId])
952 else:
953 tractRefs = dict((tract.getId(), tractRefs.get(tract.getId(), []) + getPatchRefList(tract))
954 for tract in skymap)
955 outputRefList = []
956 for tractRefList in tractRefs.values():
957 existingRefs = [ref for ref in tractRefList if ref.datasetExists()]
958 outputRefList.append(existingRefs)
959
960 self.refList = outputRefList
961
962
963class ConsolidateObjectTableConnections(pipeBase.PipelineTaskConnections,
964 dimensions=("tract", "skymap")):
965 inputCatalogs = connectionTypes.Input(
966 doc="Per-Patch objectTables conforming to the standard data model.",
967 name="objectTable",
968 storageClass="DataFrame",
969 dimensions=("tract", "patch", "skymap"),
970 multiple=True,
971 )
972 outputCatalog = connectionTypes.Output(
973 doc="Pre-tract horizontal concatenation of the input objectTables",
974 name="objectTable_tract",
975 storageClass="DataFrame",
976 dimensions=("tract", "skymap"),
977 )
978
979
980class ConsolidateObjectTableConfig(pipeBase.PipelineTaskConfig,
981 pipelineConnections=ConsolidateObjectTableConnections):
982 coaddName = pexConfig.Field(
983 dtype=str,
984 default="deep",
985 doc="Name of coadd"
986 )
987
988
989class ConsolidateObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
990 """Write patch-merged source tables to a tract-level parquet file
991
992 Concatenates `objectTable` list into a per-visit `objectTable_tract`
993 """
994 _DefaultName = "consolidateObjectTable"
995 ConfigClass = ConsolidateObjectTableConfig
996
997 inputDataset = 'objectTable'
998 outputDataset = 'objectTable_tract'
999
1000 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1001 inputs = butlerQC.get(inputRefs)
1002 self.log.info("Concatenating %s per-patch Object Tables",
1003 len(inputs['inputCatalogs']))
1004 df = pd.concat(inputs['inputCatalogs'])
1005 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
1006
1007 @classmethod
1008 def _makeArgumentParser(cls):
1009 parser = ArgumentParser(name=cls._DefaultName)
1010
1011 parser.add_id_argument("--id", cls.inputDataset,
1012 help="data ID, e.g. --id tract=12345",
1013 ContainerClass=TractObjectDataIdContainer)
1014 return parser
1015
1016 def runDataRef(self, patchRefList):
1017 df = pd.concat([patchRef.get().toDataFrame() for patchRef in patchRefList])
1018 patchRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
1019
1020 def writeMetadata(self, dataRef):
1021 """No metadata to write.
1022 """
1023 pass
1024
1025
1026class TransformSourceTableConnections(pipeBase.PipelineTaskConnections,
1027 defaultTemplates={"catalogType": ""},
1028 dimensions=("instrument", "visit", "detector")):
1029
1030 inputCatalog = connectionTypes.Input(
1031 doc="Wide input catalog of sources produced by WriteSourceTableTask",
1032 name="{catalogType}source",
1033 storageClass="DataFrame",
1034 dimensions=("instrument", "visit", "detector"),
1035 deferLoad=True
1036 )
1037 outputCatalog = connectionTypes.Output(
1038 doc="Narrower, per-detector Source Table transformed and converted per a "
1039 "specified set of functors",
1040 name="{catalogType}sourceTable",
1041 storageClass="DataFrame",
1042 dimensions=("instrument", "visit", "detector")
1043 )
1044
1045
1046class TransformSourceTableConfig(TransformCatalogBaseConfig,
1047 pipelineConnections=TransformSourceTableConnections):
1048
1049 def setDefaults(self):
1050 super().setDefaults()
1051 self.primaryKey = 'sourceId'
1052
1053
1054class TransformSourceTableTask(TransformCatalogBaseTask):
1055 """Transform/standardize a source catalog
1056 """
1057 _DefaultName = "transformSourceTable"
1058 ConfigClass = TransformSourceTableConfig
1059
1060 inputDataset = 'source'
1061 outputDataset = 'sourceTable'
1062
1063 @classmethod
1064 def _makeArgumentParser(cls):
1065 parser = ArgumentParser(name=cls._DefaultName)
1066 parser.add_id_argument("--id", datasetType=cls.inputDataset,
1067 level="sensor",
1068 help="data ID, e.g. --id visit=12345 ccd=0")
1069 return parser
1070
1071 def runDataRef(self, dataRef):
1072 """Override to specify band label to run()."""
1073 parq = dataRef.get()
1074 funcs = self.getFunctors()
1075 band = dataRef.get("calexp_filterLabel", immediate=True).bandLabel
1076 df = self.run(parq, funcs=funcs, dataId=dataRef.dataId, band=band)
1077 self.write(df, dataRef)
1078 return df
1079
1080
1081class ConsolidateVisitSummaryConnections(pipeBase.PipelineTaskConnections,
1082 dimensions=("instrument", "visit",),
1083 defaultTemplates={"calexpType": ""}):
1084 calexp = connectionTypes.Input(
1085 doc="Processed exposures used for metadata",
1086 name="{calexpType}calexp",
1087 storageClass="ExposureF",
1088 dimensions=("instrument", "visit", "detector"),
1089 deferLoad=True,
1090 multiple=True,
1091 )
1092 visitSummary = connectionTypes.Output(
1093 doc=("Per-visit consolidated exposure metadata. These catalogs use "
1094 "detector id for the id and are sorted for fast lookups of a "
1095 "detector."),
1096 name="{calexpType}visitSummary",
1097 storageClass="ExposureCatalog",
1098 dimensions=("instrument", "visit"),
1099 )
1100
1101
1102class ConsolidateVisitSummaryConfig(pipeBase.PipelineTaskConfig,
1103 pipelineConnections=ConsolidateVisitSummaryConnections):
1104 """Config for ConsolidateVisitSummaryTask"""
1105 pass
1106
1107
1108class ConsolidateVisitSummaryTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
1109 """Task to consolidate per-detector visit metadata.
1110
1111 This task aggregates the following metadata from all the detectors in a
1112 single visit into an exposure catalog:
1113 - The visitInfo.
1114 - The wcs.
1115 - The photoCalib.
1116 - The physical_filter and band (if available).
1117 - The psf size, shape, and effective area at the center of the detector.
1118 - The corners of the bounding box in right ascension/declination.
1119
1120 Other quantities such as Detector, Psf, ApCorrMap, and TransmissionCurve
1121 are not persisted here because of storage concerns, and because of their
1122 limited utility as summary statistics.
1123
1124 Tests for this task are performed in ci_hsc_gen3.
1125 """
1126 _DefaultName = "consolidateVisitSummary"
1127 ConfigClass = ConsolidateVisitSummaryConfig
1128
1129 @classmethod
1130 def _makeArgumentParser(cls):
1131 parser = ArgumentParser(name=cls._DefaultName)
1132
1133 parser.add_id_argument("--id", "calexp",
1134 help="data ID, e.g. --id visit=12345",
1135 ContainerClass=VisitDataIdContainer)
1136 return parser
1137
1138 def writeMetadata(self, dataRef):
1139 """No metadata to persist, so override to remove metadata persistance.
1140 """
1141 pass
1142
1143 def writeConfig(self, butler, clobber=False, doBackup=True):
1144 """No config to persist, so override to remove config persistance.
1145 """
1146 pass
1147
1148 def runDataRef(self, dataRefList):
1149 visit = dataRefList[0].dataId['visit']
1150
1151 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1152 len(dataRefList), visit)
1153
1154 expCatalog = self._combineExposureMetadata(visit, dataRefList, isGen3=False)
1155
1156 dataRefList[0].put(expCatalog, 'visitSummary', visit=visit)
1157
1158 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1159 dataRefs = butlerQC.get(inputRefs.calexp)
1160 visit = dataRefs[0].dataId.byName()['visit']
1161
1162 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1163 len(dataRefs), visit)
1164
1165 expCatalog = self._combineExposureMetadata(visit, dataRefs)
1166
1167 butlerQC.put(expCatalog, outputRefs.visitSummary)
1168
1169 def _combineExposureMetadata(self, visit, dataRefs, isGen3=True):
1170 """Make a combined exposure catalog from a list of dataRefs.
1171 These dataRefs must point to exposures with wcs, summaryStats,
1172 and other visit metadata.
1173
1174 Parameters
1175 ----------
1176 visit : `int`
1177 Visit identification number.
1178 dataRefs : `list`
1179 List of dataRefs in visit. May be list of
1180 `lsst.daf.persistence.ButlerDataRef` (Gen2) or
1181 `lsst.daf.butler.DeferredDatasetHandle` (Gen3).
1182 isGen3 : `bool`, optional
1183 Specifies if this is a Gen3 list of datarefs.
1184
1185 Returns
1186 -------
1187 visitSummary : `lsst.afw.table.ExposureCatalog`
1188 Exposure catalog with per-detector summary information.
1189 """
1190 schema = self._makeVisitSummarySchema()
1191 cat = afwTable.ExposureCatalog(schema)
1192 cat.resize(len(dataRefs))
1193
1194 cat['visit'] = visit
1195
1196 for i, dataRef in enumerate(dataRefs):
1197 if isGen3:
1198 visitInfo = dataRef.get(component='visitInfo')
1199 filterLabel = dataRef.get(component='filterLabel')
1200 summaryStats = dataRef.get(component='summaryStats')
1201 detector = dataRef.get(component='detector')
1202 wcs = dataRef.get(component='wcs')
1203 photoCalib = dataRef.get(component='photoCalib')
1204 detector = dataRef.get(component='detector')
1205 bbox = dataRef.get(component='bbox')
1206 validPolygon = dataRef.get(component='validPolygon')
1207 else:
1208 # Note that we need to read the calexp because there is
1209 # no magic access to the psf except through the exposure.
1210 gen2_read_bbox = lsst.geom.BoxI(lsst.geom.PointI(0, 0), lsst.geom.PointI(1, 1))
1211 exp = dataRef.get(datasetType='calexp_sub', bbox=gen2_read_bbox)
1212 visitInfo = exp.getInfo().getVisitInfo()
1213 filterLabel = dataRef.get("calexp_filterLabel")
1214 summaryStats = exp.getInfo().getSummaryStats()
1215 wcs = exp.getWcs()
1216 photoCalib = exp.getPhotoCalib()
1217 detector = exp.getDetector()
1218 bbox = dataRef.get(datasetType='calexp_bbox')
1219 validPolygon = exp.getInfo().getValidPolygon()
1220
1221 rec = cat[i]
1222 rec.setBBox(bbox)
1223 rec.setVisitInfo(visitInfo)
1224 rec.setWcs(wcs)
1225 rec.setPhotoCalib(photoCalib)
1226 rec.setValidPolygon(validPolygon)
1227
1228 rec['physical_filter'] = filterLabel.physicalLabel if filterLabel.hasPhysicalLabel() else ""
1229 rec['band'] = filterLabel.bandLabel if filterLabel.hasBandLabel() else ""
1230 rec.setId(detector.getId())
1231 rec['psfSigma'] = summaryStats.psfSigma
1232 rec['psfIxx'] = summaryStats.psfIxx
1233 rec['psfIyy'] = summaryStats.psfIyy
1234 rec['psfIxy'] = summaryStats.psfIxy
1235 rec['psfArea'] = summaryStats.psfArea
1236 rec['raCorners'][:] = summaryStats.raCorners
1237 rec['decCorners'][:] = summaryStats.decCorners
1238 rec['ra'] = summaryStats.ra
1239 rec['decl'] = summaryStats.decl
1240 rec['zenithDistance'] = summaryStats.zenithDistance
1241 rec['zeroPoint'] = summaryStats.zeroPoint
1242 rec['skyBg'] = summaryStats.skyBg
1243 rec['skyNoise'] = summaryStats.skyNoise
1244 rec['meanVar'] = summaryStats.meanVar
1245 rec['astromOffsetMean'] = summaryStats.astromOffsetMean
1246 rec['astromOffsetStd'] = summaryStats.astromOffsetStd
1247 rec['nPsfStar'] = summaryStats.nPsfStar
1248 rec['psfStarDeltaE1Median'] = summaryStats.psfStarDeltaE1Median
1249 rec['psfStarDeltaE2Median'] = summaryStats.psfStarDeltaE2Median
1250 rec['psfStarDeltaE1Scatter'] = summaryStats.psfStarDeltaE1Scatter
1251 rec['psfStarDeltaE2Scatter'] = summaryStats.psfStarDeltaE2Scatter
1252 rec['psfStarDeltaSizeMedian'] = summaryStats.psfStarDeltaSizeMedian
1253 rec['psfStarDeltaSizeScatter'] = summaryStats.psfStarDeltaSizeScatter
1254 rec['psfStarScaledDeltaSizeScatter'] = summaryStats.psfStarScaledDeltaSizeScatter
1255
1256 metadata = dafBase.PropertyList()
1257 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
1258 # We are looping over existing datarefs, so the following is true
1259 metadata.add("COMMENT", "Only detectors with data have entries.")
1260 cat.setMetadata(metadata)
1261
1262 cat.sort()
1263 return cat
1264
1265 def _makeVisitSummarySchema(self):
1266 """Make the schema for the visitSummary catalog."""
1267 schema = afwTable.ExposureTable.makeMinimalSchema()
1268 schema.addField('visit', type='I', doc='Visit number')
1269 schema.addField('physical_filter', type='String', size=32, doc='Physical filter')
1270 schema.addField('band', type='String', size=32, doc='Name of band')
1271 schema.addField('psfSigma', type='F',
1272 doc='PSF model second-moments determinant radius (center of chip) (pixel)')
1273 schema.addField('psfArea', type='F',
1274 doc='PSF model effective area (center of chip) (pixel**2)')
1275 schema.addField('psfIxx', type='F',
1276 doc='PSF model Ixx (center of chip) (pixel**2)')
1277 schema.addField('psfIyy', type='F',
1278 doc='PSF model Iyy (center of chip) (pixel**2)')
1279 schema.addField('psfIxy', type='F',
1280 doc='PSF model Ixy (center of chip) (pixel**2)')
1281 schema.addField('raCorners', type='ArrayD', size=4,
1282 doc='Right Ascension of bounding box corners (degrees)')
1283 schema.addField('decCorners', type='ArrayD', size=4,
1284 doc='Declination of bounding box corners (degrees)')
1285 schema.addField('ra', type='D',
1286 doc='Right Ascension of bounding box center (degrees)')
1287 schema.addField('decl', type='D',
1288 doc='Declination of bounding box center (degrees)')
1289 schema.addField('zenithDistance', type='F',
1290 doc='Zenith distance of bounding box center (degrees)')
1291 schema.addField('zeroPoint', type='F',
1292 doc='Mean zeropoint in detector (mag)')
1293 schema.addField('skyBg', type='F',
1294 doc='Average sky background (ADU)')
1295 schema.addField('skyNoise', type='F',
1296 doc='Average sky noise (ADU)')
1297 schema.addField('meanVar', type='F',
1298 doc='Mean variance of the weight plane (ADU**2)')
1299 schema.addField('astromOffsetMean', type='F',
1300 doc='Mean offset of astrometric calibration matches (arcsec)')
1301 schema.addField('astromOffsetStd', type='F',
1302 doc='Standard deviation of offsets of astrometric calibration matches (arcsec)')
1303 schema.addField('nPsfStar', type='I', doc='Number of stars used for PSF model')
1304 schema.addField('psfStarDeltaE1Median', type='F',
1305 doc='Median E1 residual (starE1 - psfE1) for psf stars')
1306 schema.addField('psfStarDeltaE2Median', type='F',
1307 doc='Median E2 residual (starE2 - psfE2) for psf stars')
1308 schema.addField('psfStarDeltaE1Scatter', type='F',
1309 doc='Scatter (via MAD) of E1 residual (starE1 - psfE1) for psf stars')
1310 schema.addField('psfStarDeltaE2Scatter', type='F',
1311 doc='Scatter (via MAD) of E2 residual (starE2 - psfE2) for psf stars')
1312 schema.addField('psfStarDeltaSizeMedian', type='F',
1313 doc='Median size residual (starSize - psfSize) for psf stars (pixel)')
1314 schema.addField('psfStarDeltaSizeScatter', type='F',
1315 doc='Scatter (via MAD) of size residual (starSize - psfSize) for psf stars (pixel)')
1316 schema.addField('psfStarScaledDeltaSizeScatter', type='F',
1317 doc='Scatter (via MAD) of size residual scaled by median size squared')
1318
1319 return schema
1320
1321
1322class VisitDataIdContainer(DataIdContainer):
1323 """DataIdContainer that groups sensor-level id's by visit
1324 """
1325
1326 def makeDataRefList(self, namespace):
1327 """Make self.refList from self.idList
1328
1329 Generate a list of data references grouped by visit.
1330
1331 Parameters
1332 ----------
1333 namespace : `argparse.Namespace`
1334 Namespace used by `lsst.pipe.base.CmdLineTask` to parse command line arguments
1335 """
1336 # Group by visits
1337 visitRefs = defaultdict(list)
1338 for dataId in self.idList:
1339 if "visit" in dataId:
1340 visitId = dataId["visit"]
1341 # append all subsets to
1342 subset = namespace.butler.subset(self.datasetType, dataId=dataId)
1343 visitRefs[visitId].extend([dataRef for dataRef in subset])
1344
1345 outputRefList = []
1346 for refList in visitRefs.values():
1347 existingRefs = [ref for ref in refList if ref.datasetExists()]
1348 if existingRefs:
1349 outputRefList.append(existingRefs)
1350
1351 self.refList = outputRefList
1352
1353
1354class ConsolidateSourceTableConnections(pipeBase.PipelineTaskConnections,
1355 defaultTemplates={"catalogType": ""},
1356 dimensions=("instrument", "visit")):
1357 inputCatalogs = connectionTypes.Input(
1358 doc="Input per-detector Source Tables",
1359 name="{catalogType}sourceTable",
1360 storageClass="DataFrame",
1361 dimensions=("instrument", "visit", "detector"),
1362 multiple=True
1363 )
1364 outputCatalog = connectionTypes.Output(
1365 doc="Per-visit concatenation of Source Table",
1366 name="{catalogType}sourceTable_visit",
1367 storageClass="DataFrame",
1368 dimensions=("instrument", "visit")
1369 )
1370
1371
1372class ConsolidateSourceTableConfig(pipeBase.PipelineTaskConfig,
1373 pipelineConnections=ConsolidateSourceTableConnections):
1374 pass
1375
1376
1377class ConsolidateSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
1378 """Concatenate `sourceTable` list into a per-visit `sourceTable_visit`
1379 """
1380 _DefaultName = 'consolidateSourceTable'
1381 ConfigClass = ConsolidateSourceTableConfig
1382
1383 inputDataset = 'sourceTable'
1384 outputDataset = 'sourceTable_visit'
1385
1386 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1387 inputs = butlerQC.get(inputRefs)
1388 self.log.info("Concatenating %s per-detector Source Tables",
1389 len(inputs['inputCatalogs']))
1390 df = pd.concat(inputs['inputCatalogs'])
1391 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
1392
1393 def runDataRef(self, dataRefList):
1394 self.log.info("Concatenating %s per-detector Source Tables", len(dataRefList))
1395 df = pd.concat([dataRef.get().toDataFrame() for dataRef in dataRefList])
1396 dataRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
1397
1398 @classmethod
1399 def _makeArgumentParser(cls):
1400 parser = ArgumentParser(name=cls._DefaultName)
1401
1402 parser.add_id_argument("--id", cls.inputDataset,
1403 help="data ID, e.g. --id visit=12345",
1404 ContainerClass=VisitDataIdContainer)
1405 return parser
1406
1407 def writeMetadata(self, dataRef):
1408 """No metadata to write.
1409 """
1410 pass
1411
1412 def writeConfig(self, butler, clobber=False, doBackup=True):
1413 """No config to write.
1414 """
1415 pass
1416
1417
1418class MakeCcdVisitTableConnections(pipeBase.PipelineTaskConnections,
1419 dimensions=("instrument",),
1420 defaultTemplates={"calexpType": ""}):
1421 visitSummaryRefs = connectionTypes.Input(
1422 doc="Data references for per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1423 name="{calexpType}visitSummary",
1424 storageClass="ExposureCatalog",
1425 dimensions=("instrument", "visit"),
1426 multiple=True,
1427 deferLoad=True,
1428 )
1429 outputCatalog = connectionTypes.Output(
1430 doc="CCD and Visit metadata table",
1431 name="ccdVisitTable",
1432 storageClass="DataFrame",
1433 dimensions=("instrument",)
1434 )
1435
1436
1437class MakeCcdVisitTableConfig(pipeBase.PipelineTaskConfig,
1438 pipelineConnections=MakeCcdVisitTableConnections):
1439 pass
1440
1441
1442class MakeCcdVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1443 """Produce a `ccdVisitTable` from the `visitSummary` exposure catalogs.
1444 """
1445 _DefaultName = 'makeCcdVisitTable'
1446 ConfigClass = MakeCcdVisitTableConfig
1447
1448 def run(self, visitSummaryRefs):
1449 """ Make a table of ccd information from the `visitSummary` catalogs.
1450 Parameters
1451 ----------
1452 visitSummaryRefs : `list` of `lsst.daf.butler.DeferredDatasetHandle`
1453 List of DeferredDatasetHandles pointing to exposure catalogs with
1454 per-detector summary information.
1455 Returns
1456 -------
1457 result : `lsst.pipe.Base.Struct`
1458 Results struct with attribute:
1459 - `outputCatalog`
1460 Catalog of ccd and visit information.
1461 """
1462 ccdEntries = []
1463 for visitSummaryRef in visitSummaryRefs:
1464 visitSummary = visitSummaryRef.get()
1465 visitInfo = visitSummary[0].getVisitInfo()
1466
1467 ccdEntry = {}
1468 summaryTable = visitSummary.asAstropy()
1469 selectColumns = ['id', 'visit', 'physical_filter', 'band', 'ra', 'decl', 'zenithDistance',
1470 'zeroPoint', 'psfSigma', 'skyBg', 'skyNoise']
1471 ccdEntry = summaryTable[selectColumns].to_pandas().set_index('id')
1472 # 'visit' is the human readible visit number
1473 # 'visitId' is the key to the visitId table. They are the same
1474 # Technically you should join to get the visit from the visit table
1475 ccdEntry = ccdEntry.rename(columns={"visit": "visitId"})
1476 dataIds = [DataCoordinate.standardize(visitSummaryRef.dataId, detector=id) for id in
1477 summaryTable['id']]
1478 packer = visitSummaryRef.dataId.universe.makePacker('visit_detector', visitSummaryRef.dataId)
1479 ccdVisitIds = [packer.pack(dataId) for dataId in dataIds]
1480 ccdEntry['ccdVisitId'] = ccdVisitIds
1481 ccdEntry['detector'] = summaryTable['id']
1482 pixToArcseconds = np.array([vR.getWcs().getPixelScale().asArcseconds() for vR in visitSummary])
1483 ccdEntry["seeing"] = visitSummary['psfSigma'] * np.sqrt(8 * np.log(2)) * pixToArcseconds
1484
1485 ccdEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1486 ccdEntry["expMidpt"] = visitInfo.getDate().toPython()
1487 expTime = visitInfo.getExposureTime()
1488 ccdEntry['expTime'] = expTime
1489 ccdEntry["obsStart"] = ccdEntry["expMidpt"] - 0.5 * pd.Timedelta(seconds=expTime)
1490 ccdEntry['darkTime'] = visitInfo.getDarkTime()
1491 ccdEntry['xSize'] = summaryTable['bbox_max_x'] - summaryTable['bbox_min_x']
1492 ccdEntry['ySize'] = summaryTable['bbox_max_y'] - summaryTable['bbox_min_y']
1493 ccdEntry['llcra'] = summaryTable['raCorners'][:, 0]
1494 ccdEntry['llcdec'] = summaryTable['decCorners'][:, 0]
1495 ccdEntry['ulcra'] = summaryTable['raCorners'][:, 1]
1496 ccdEntry['ulcdec'] = summaryTable['decCorners'][:, 1]
1497 ccdEntry['urcra'] = summaryTable['raCorners'][:, 2]
1498 ccdEntry['urcdec'] = summaryTable['decCorners'][:, 2]
1499 ccdEntry['lrcra'] = summaryTable['raCorners'][:, 3]
1500 ccdEntry['lrcdec'] = summaryTable['decCorners'][:, 3]
1501 # TODO: DM-30618, Add raftName, nExposures, ccdTemp, binX, binY, and flags,
1502 # and decide if WCS, and llcx, llcy, ulcx, ulcy, etc. values are actually wanted.
1503 ccdEntries.append(ccdEntry)
1504
1505 outputCatalog = pd.concat(ccdEntries)
1506 outputCatalog.set_index('ccdVisitId', inplace=True, verify_integrity=True)
1507 return pipeBase.Struct(outputCatalog=outputCatalog)
1508
1509
1510class MakeVisitTableConnections(pipeBase.PipelineTaskConnections,
1511 dimensions=("instrument",),
1512 defaultTemplates={"calexpType": ""}):
1513 visitSummaries = connectionTypes.Input(
1514 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1515 name="{calexpType}visitSummary",
1516 storageClass="ExposureCatalog",
1517 dimensions=("instrument", "visit",),
1518 multiple=True,
1519 deferLoad=True,
1520 )
1521 outputCatalog = connectionTypes.Output(
1522 doc="Visit metadata table",
1523 name="visitTable",
1524 storageClass="DataFrame",
1525 dimensions=("instrument",)
1526 )
1527
1528
1529class MakeVisitTableConfig(pipeBase.PipelineTaskConfig,
1530 pipelineConnections=MakeVisitTableConnections):
1531 pass
1532
1533
1534class MakeVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1535 """Produce a `visitTable` from the `visitSummary` exposure catalogs.
1536 """
1537 _DefaultName = 'makeVisitTable'
1538 ConfigClass = MakeVisitTableConfig
1539
1540 def run(self, visitSummaries):
1541 """ Make a table of visit information from the `visitSummary` catalogs
1542
1543 Parameters
1544 ----------
1545 visitSummaries : list of `lsst.afw.table.ExposureCatalog`
1546 List of exposure catalogs with per-detector summary information.
1547 Returns
1548 -------
1549 result : `lsst.pipe.Base.Struct`
1550 Results struct with attribute:
1551 ``outputCatalog``
1552 Catalog of visit information.
1553 """
1554 visitEntries = []
1555 for visitSummary in visitSummaries:
1556 visitSummary = visitSummary.get()
1557 visitRow = visitSummary[0]
1558 visitInfo = visitRow.getVisitInfo()
1559
1560 visitEntry = {}
1561 visitEntry["visitId"] = visitRow['visit']
1562 visitEntry["visit"] = visitRow['visit']
1563 visitEntry["physical_filter"] = visitRow['physical_filter']
1564 visitEntry["band"] = visitRow['band']
1565 raDec = visitInfo.getBoresightRaDec()
1566 visitEntry["ra"] = raDec.getRa().asDegrees()
1567 visitEntry["decl"] = raDec.getDec().asDegrees()
1568 visitEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1569 azAlt = visitInfo.getBoresightAzAlt()
1570 visitEntry["azimuth"] = azAlt.getLongitude().asDegrees()
1571 visitEntry["altitude"] = azAlt.getLatitude().asDegrees()
1572 visitEntry["zenithDistance"] = 90 - azAlt.getLatitude().asDegrees()
1573 visitEntry["airmass"] = visitInfo.getBoresightAirmass()
1574 visitEntry["obsStart"] = visitInfo.getDate().toPython()
1575 visitEntry["expTime"] = visitInfo.getExposureTime()
1576 visitEntries.append(visitEntry)
1577 # TODO: DM-30623, Add programId, exposureType, expMidpt, cameraTemp, mirror1Temp, mirror2Temp,
1578 # mirror3Temp, domeTemp, externalTemp, dimmSeeing, pwvGPS, pwvMW, flags, nExposures
1579
1580 outputCatalog = pd.DataFrame(data=visitEntries)
1581 outputCatalog.set_index('visitId', inplace=True, verify_integrity=True)
1582 return pipeBase.Struct(outputCatalog=outputCatalog)
1583
1584
1585class WriteForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
1586 dimensions=("instrument", "visit", "detector", "skymap", "tract")):
1587
1588 inputCatalog = connectionTypes.Input(
1589 doc="Primary per-detector, single-epoch forced-photometry catalog. "
1590 "By default, it is the output of ForcedPhotCcdTask on calexps",
1591 name="forced_src",
1592 storageClass="SourceCatalog",
1593 dimensions=("instrument", "visit", "detector", "skymap", "tract")
1594 )
1595 inputCatalogDiff = connectionTypes.Input(
1596 doc="Secondary multi-epoch, per-detector, forced photometry catalog. "
1597 "By default, it is the output of ForcedPhotCcdTask run on image differences.",
1598 name="forced_diff",
1599 storageClass="SourceCatalog",
1600 dimensions=("instrument", "visit", "detector", "skymap", "tract")
1601 )
1602 outputCatalog = connectionTypes.Output(
1603 doc="InputCatalogs horizonatally joined on `objectId` in Parquet format",
1604 name="mergedForcedSource",
1605 storageClass="DataFrame",
1606 dimensions=("instrument", "visit", "detector", "skymap", "tract")
1607 )
1608
1609
1610class WriteForcedSourceTableConfig(WriteSourceTableConfig,
1611 pipelineConnections=WriteForcedSourceTableConnections):
1612 key = lsst.pex.config.Field(
1613 doc="Column on which to join the two input tables on and make the primary key of the output",
1614 dtype=str,
1615 default="objectId",
1616 )
1617
1618
1619class WriteForcedSourceTableTask(pipeBase.PipelineTask):
1620 """Merge and convert per-detector forced source catalogs to parquet
1621
1622 Because the predecessor ForcedPhotCcdTask operates per-detector,
1623 per-tract, (i.e., it has tract in its dimensions), detectors
1624 on the tract boundary may have multiple forced source catalogs.
1625
1626 The successor task TransformForcedSourceTable runs per-patch
1627 and temporally-aggregates overlapping mergedForcedSource catalogs from all
1628 available multiple epochs.
1629 """
1630 _DefaultName = "writeForcedSourceTable"
1631 ConfigClass = WriteForcedSourceTableConfig
1632
1633 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1634 inputs = butlerQC.get(inputRefs)
1635 # Add ccdVisitId to allow joining with CcdVisitTable
1636 inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
1637 inputs['band'] = butlerQC.quantum.dataId.full['band']
1638 outputs = self.run(**inputs)
1639 butlerQC.put(outputs, outputRefs)
1640
1641 def run(self, inputCatalog, inputCatalogDiff, ccdVisitId=None, band=None):
1642 dfs = []
1643 for table, dataset, in zip((inputCatalog, inputCatalogDiff), ('calexp', 'diff')):
1644 df = table.asAstropy().to_pandas().set_index(self.config.key, drop=False)
1645 df = df.reindex(sorted(df.columns), axis=1)
1646 df['ccdVisitId'] = ccdVisitId if ccdVisitId else pd.NA
1647 df['band'] = band if band else pd.NA
1648 df.columns = pd.MultiIndex.from_tuples([(dataset, c) for c in df.columns],
1649 names=('dataset', 'column'))
1650
1651 dfs.append(df)
1652
1653 outputCatalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
1654 return pipeBase.Struct(outputCatalog=outputCatalog)
1655
1656
1657class TransformForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
1658 dimensions=("instrument", "skymap", "patch", "tract")):
1659
1660 inputCatalogs = connectionTypes.Input(
1661 doc="Parquet table of merged ForcedSources produced by WriteForcedSourceTableTask",
1662 name="mergedForcedSource",
1663 storageClass="DataFrame",
1664 dimensions=("instrument", "visit", "detector", "skymap", "tract"),
1665 multiple=True,
1666 deferLoad=True
1667 )
1668 referenceCatalog = connectionTypes.Input(
1669 doc="Reference catalog which was used to seed the forcedPhot. Columns "
1670 "objectId, detect_isPrimary, detect_isTractInner, detect_isPatchInner "
1671 "are expected.",
1672 name="objectTable",
1673 storageClass="DataFrame",
1674 dimensions=("tract", "patch", "skymap"),
1675 deferLoad=True
1676 )
1677 outputCatalog = connectionTypes.Output(
1678 doc="Narrower, temporally-aggregated, per-patch ForcedSource Table transformed and converted per a "
1679 "specified set of functors",
1680 name="forcedSourceTable",
1681 storageClass="DataFrame",
1682 dimensions=("tract", "patch", "skymap")
1683 )
1684
1685
1686class TransformForcedSourceTableConfig(TransformCatalogBaseConfig,
1687 pipelineConnections=TransformForcedSourceTableConnections):
1688 referenceColumns = pexConfig.ListField(
1689 dtype=str,
1690 default=["detect_isPrimary", "detect_isTractInner", "detect_isPatchInner"],
1691 optional=True,
1692 doc="Columns to pull from reference catalog",
1693 )
1694 keyRef = lsst.pex.config.Field(
1695 doc="Column on which to join the two input tables on and make the primary key of the output",
1696 dtype=str,
1697 default="objectId",
1698 )
1699 key = lsst.pex.config.Field(
1700 doc="Rename the output DataFrame index to this name",
1701 dtype=str,
1702 default="forcedSourceId",
1703 )
1704
1705
1706class TransformForcedSourceTableTask(TransformCatalogBaseTask):
1707 """Transform/standardize a ForcedSource catalog
1708
1709 Transforms each wide, per-detector forcedSource parquet table per the
1710 specification file (per-camera defaults found in ForcedSource.yaml).
1711 All epochs that overlap the patch are aggregated into one per-patch
1712 narrow-parquet file.
1713
1714 No de-duplication of rows is performed. Duplicate resolutions flags are
1715 pulled in from the referenceCatalog: `detect_isPrimary`,
1716 `detect_isTractInner`,`detect_isPatchInner`, so that user may de-duplicate
1717 for analysis or compare duplicates for QA.
1718
1719 The resulting table includes multiple bands. Epochs (MJDs) and other useful
1720 per-visit rows can be retreived by joining with the CcdVisitTable on
1721 ccdVisitId.
1722 """
1723 _DefaultName = "transformForcedSourceTable"
1724 ConfigClass = TransformForcedSourceTableConfig
1725
1726 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1727 inputs = butlerQC.get(inputRefs)
1728 if self.funcs is None:
1729 raise ValueError("config.functorFile is None. "
1730 "Must be a valid path to yaml in order to run Task as a PipelineTask.")
1731 outputs = self.run(inputs['inputCatalogs'], inputs['referenceCatalog'], funcs=self.funcs,
1732 dataId=outputRefs.outputCatalog.dataId.full)
1733
1734 butlerQC.put(outputs, outputRefs)
1735
1736 def run(self, inputCatalogs, referenceCatalog, funcs=None, dataId=None, band=None):
1737 dfs = []
1738 ref = referenceCatalog.get(parameters={"columns": self.config.referenceColumns})
1739 self.log.info("Aggregating %s input catalogs" % (len(inputCatalogs)))
1740 for handle in inputCatalogs:
1741 result = self.transform(None, handle, funcs, dataId)
1742 # Filter for only rows that were detected on (overlap) the patch
1743 dfs.append(result.df.join(ref, how='inner'))
1744
1745 outputCatalog = pd.concat(dfs)
1746
1747 # Now that we are done joining on config.keyRef
1748 # Change index to config.key by
1749 outputCatalog.index.rename(self.config.keyRef, inplace=True)
1750 # Add config.keyRef to the column list
1751 outputCatalog.reset_index(inplace=True)
1752 # set the forcedSourceId to the index. This is specified in the ForcedSource.yaml
1753 outputCatalog.set_index("forcedSourceId", inplace=True, verify_integrity=True)
1754 # Rename it to the config.key
1755 outputCatalog.index.rename(self.config.key, inplace=True)
1756
1757 self.log.info("Made a table of %d columns and %d rows",
1758 len(outputCatalog.columns), len(outputCatalog))
1759 return pipeBase.Struct(outputCatalog=outputCatalog)
1760
1761
1762class ConsolidateTractConnections(pipeBase.PipelineTaskConnections,
1763 defaultTemplates={"catalogType": ""},
1764 dimensions=("instrument", "tract")):
1765 inputCatalogs = connectionTypes.Input(
1766 doc="Input per-patch DataFrame Tables to be concatenated",
1767 name="{catalogType}ForcedSourceTable",
1768 storageClass="DataFrame",
1769 dimensions=("tract", "patch", "skymap"),
1770 multiple=True,
1771 )
1772
1773 outputCatalog = connectionTypes.Output(
1774 doc="Output per-tract concatenation of DataFrame Tables",
1775 name="{catalogType}ForcedSourceTable_tract",
1776 storageClass="DataFrame",
1777 dimensions=("tract", "skymap"),
1778 )
1779
1780
1781class ConsolidateTractConfig(pipeBase.PipelineTaskConfig,
1782 pipelineConnections=ConsolidateTractConnections):
1783 pass
1784
1785
1786class ConsolidateTractTask(CmdLineTask, pipeBase.PipelineTask):
1787 """Concatenate any per-patch, dataframe list into a single
1788 per-tract DataFrame
1789 """
1790 _DefaultName = 'ConsolidateTract'
1791 ConfigClass = ConsolidateTractConfig
1792
1793 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1794 inputs = butlerQC.get(inputRefs)
1795 # Not checking at least one inputCatalog exists because that'd be an empty QG
1796 self.log.info("Concatenating %s per-patch %s Tables",
1797 len(inputs['inputCatalogs']),
1798 inputRefs.inputCatalogs[0].datasetType.name)
1799 df = pd.concat(inputs['inputCatalogs'])
1800 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
def getAnalysis(self, parq, funcs=None, band=None)
Definition: postprocess.py:712
def transform(self, band, parq, funcs, dataId)
Definition: postprocess.py:718
def run(self, parq, funcs=None, dataId=None, band=None)
Definition: postprocess.py:681
def runQuantum(self, butlerQC, inputRefs, outputRefs)
Definition: postprocess.py:662
def writeMetadata(self, dataRefList)
No metadata to write, and not sure how to write it for a list of dataRefs.
def makeMergeArgumentParser(name, dataset)
Create a suitable ArgumentParser.
def readCatalog(task, patchRef)
Read input catalog.
def flattenFilters(df, noDupCols=['coord_ra', 'coord_dec'], camelCase=False, inputBands=None)
Definition: postprocess.py:44