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1# This file is part of pipe_tasks
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
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/>.
22import functools
23import pandas as pd
24from collections import defaultdict
25import numpy as np
27import lsst.geom
28import lsst.pex.config as pexConfig
29import lsst.pipe.base as pipeBase
30import lsst.daf.base as dafBase
31from lsst.pipe.base import connectionTypes
32import lsst.afw.table as afwTable
33from lsst.meas.base import SingleFrameMeasurementTask
34from lsst.pipe.base import CmdLineTask, ArgumentParser, DataIdContainer
35from lsst.coadd.utils.coaddDataIdContainer import CoaddDataIdContainer
36from lsst.daf.butler import DeferredDatasetHandle, DataCoordinate
38from .parquetTable import ParquetTable
39from .multiBandUtils import makeMergeArgumentParser, MergeSourcesRunner
40from .functors import CompositeFunctor, Column
43def flattenFilters(df, noDupCols=['coord_ra', 'coord_dec'], camelCase=False, inputBands=None):
44 """Flattens a dataframe with multilevel column index
45 """
46 newDf = pd.DataFrame()
47 # band is the level 0 index
48 dfBands = df.columns.unique(level=0).values
49 for band in dfBands:
50 subdf = df[band]
51 columnFormat = '{0}{1}' if camelCase else '{0}_{1}'
52 newColumns = {c: columnFormat.format(band, c)
53 for c in subdf.columns if c not in noDupCols}
54 cols = list(newColumns.keys())
55 newDf = pd.concat([newDf, subdf[cols].rename(columns=newColumns)], axis=1)
57 # Band must be present in the input and output or else column is all NaN:
58 presentBands = dfBands if inputBands is None else list(set(inputBands).intersection(dfBands))
59 # Get the unexploded columns from any present band's partition
60 noDupDf = df[presentBands[0]][noDupCols]
61 newDf = pd.concat([noDupDf, newDf], axis=1)
62 return newDf
65class WriteObjectTableConnections(pipeBase.PipelineTaskConnections,
66 defaultTemplates={"coaddName": "deep"},
67 dimensions=("tract", "patch", "skymap")):
68 inputCatalogMeas = connectionTypes.Input(
69 doc="Catalog of source measurements on the deepCoadd.",
70 dimensions=("tract", "patch", "band", "skymap"),
71 storageClass="SourceCatalog",
72 name="{coaddName}Coadd_meas",
73 multiple=True
74 )
75 inputCatalogForcedSrc = connectionTypes.Input(
76 doc="Catalog of forced measurements (shape and position parameters held fixed) on the deepCoadd.",
77 dimensions=("tract", "patch", "band", "skymap"),
78 storageClass="SourceCatalog",
79 name="{coaddName}Coadd_forced_src",
80 multiple=True
81 )
82 inputCatalogRef = connectionTypes.Input(
83 doc="Catalog marking the primary detection (which band provides a good shape and position)"
84 "for each detection in deepCoadd_mergeDet.",
85 dimensions=("tract", "patch", "skymap"),
86 storageClass="SourceCatalog",
87 name="{coaddName}Coadd_ref"
88 )
89 outputCatalog = connectionTypes.Output(
90 doc="A vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
91 "stored as a DataFrame with a multi-level column index per-patch.",
92 dimensions=("tract", "patch", "skymap"),
93 storageClass="DataFrame",
94 name="{coaddName}Coadd_obj"
95 )
98class WriteObjectTableConfig(pipeBase.PipelineTaskConfig,
99 pipelineConnections=WriteObjectTableConnections):
100 engine = pexConfig.Field(
101 dtype=str,
102 default="pyarrow",
103 doc="Parquet engine for writing (pyarrow or fastparquet)"
104 )
105 coaddName = pexConfig.Field(
106 dtype=str,
107 default="deep",
108 doc="Name of coadd"
109 )
112class WriteObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
113 """Write filter-merged source tables to parquet
114 """
115 _DefaultName = "writeObjectTable"
116 ConfigClass = WriteObjectTableConfig
117 RunnerClass = MergeSourcesRunner
119 # Names of table datasets to be merged
120 inputDatasets = ('forced_src', 'meas', 'ref')
122 # Tag of output dataset written by `MergeSourcesTask.write`
123 outputDataset = 'obj'
125 def __init__(self, butler=None, schema=None, **kwargs):
126 # It is a shame that this class can't use the default init for CmdLineTask
127 # But to do so would require its own special task runner, which is many
128 # more lines of specialization, so this is how it is for now
129 super().__init__(**kwargs)
131 def runDataRef(self, patchRefList):
132 """!
133 @brief Merge coadd sources from multiple bands. Calls @ref `run` which must be defined in
134 subclasses that inherit from MergeSourcesTask.
135 @param[in] patchRefList list of data references for each filter
136 """
137 catalogs = dict(self.readCatalog(patchRef) for patchRef in patchRefList)
138 dataId = patchRefList[0].dataId
139 mergedCatalog = self.run(catalogs, tract=dataId['tract'], patch=dataId['patch'])
140 self.write(patchRefList[0], ParquetTable(dataFrame=mergedCatalog))
142 def runQuantum(self, butlerQC, inputRefs, outputRefs):
143 inputs = butlerQC.get(inputRefs)
145 measDict = {ref.dataId['band']: {'meas': cat} for ref, cat in
146 zip(inputRefs.inputCatalogMeas, inputs['inputCatalogMeas'])}
147 forcedSourceDict = {ref.dataId['band']: {'forced_src': cat} for ref, cat in
148 zip(inputRefs.inputCatalogForcedSrc, inputs['inputCatalogForcedSrc'])}
150 catalogs = {}
151 for band in measDict.keys():
152 catalogs[band] = {'meas': measDict[band]['meas'],
153 'forced_src': forcedSourceDict[band]['forced_src'],
154 'ref': inputs['inputCatalogRef']}
155 dataId = butlerQC.quantum.dataId
156 df = self.run(catalogs=catalogs, tract=dataId['tract'], patch=dataId['patch'])
157 outputs = pipeBase.Struct(outputCatalog=df)
158 butlerQC.put(outputs, outputRefs)
160 @classmethod
161 def _makeArgumentParser(cls):
162 """Create a suitable ArgumentParser.
164 We will use the ArgumentParser to get a list of data
165 references for patches; the RunnerClass will sort them into lists
166 of data references for the same patch.
168 References first of self.inputDatasets, rather than
169 self.inputDataset
170 """
171 return makeMergeArgumentParser(cls._DefaultName, cls.inputDatasets[0])
173 def readCatalog(self, patchRef):
174 """Read input catalogs
176 Read all the input datasets given by the 'inputDatasets'
177 attribute.
179 Parameters
180 ----------
181 patchRef : `lsst.daf.persistence.ButlerDataRef`
182 Data reference for patch
184 Returns
185 -------
186 Tuple consisting of band name and a dict of catalogs, keyed by
187 dataset name
188 """
189 band = patchRef.get(self.config.coaddName + "Coadd_filterLabel", immediate=True).bandLabel
190 catalogDict = {}
191 for dataset in self.inputDatasets:
192 catalog = patchRef.get(self.config.coaddName + "Coadd_" + dataset, immediate=True)
193 self.log.info("Read %d sources from %s for band %s: %s",
194 len(catalog), dataset, band, patchRef.dataId)
195 catalogDict[dataset] = catalog
196 return band, catalogDict
198 def run(self, catalogs, tract, patch):
199 """Merge multiple catalogs.
201 Parameters
202 ----------
203 catalogs : `dict`
204 Mapping from filter names to dict of catalogs.
205 tract : int
206 tractId to use for the tractId column
207 patch : str
208 patchId to use for the patchId column
210 Returns
211 -------
212 catalog : `pandas.DataFrame`
213 Merged dataframe
214 """
216 dfs = []
217 for filt, tableDict in catalogs.items():
218 for dataset, table in tableDict.items():
219 # Convert afwTable to pandas DataFrame
220 df = table.asAstropy().to_pandas().set_index('id', drop=True)
222 # Sort columns by name, to ensure matching schema among patches
223 df = df.reindex(sorted(df.columns), axis=1)
224 df['tractId'] = tract
225 df['patchId'] = patch
227 # Make columns a 3-level MultiIndex
228 df.columns = pd.MultiIndex.from_tuples([(dataset, filt, c) for c in df.columns],
229 names=('dataset', 'band', 'column'))
230 dfs.append(df)
232 catalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
233 return catalog
235 def write(self, patchRef, catalog):
236 """Write the output.
238 Parameters
239 ----------
240 catalog : `ParquetTable`
241 Catalog to write
242 patchRef : `lsst.daf.persistence.ButlerDataRef`
243 Data reference for patch
244 """
245 patchRef.put(catalog, self.config.coaddName + "Coadd_" + self.outputDataset)
246 # since the filter isn't actually part of the data ID for the dataset we're saving,
247 # it's confusing to see it in the log message, even if the butler simply ignores it.
248 mergeDataId = patchRef.dataId.copy()
249 del mergeDataId["filter"]
250 self.log.info("Wrote merged catalog: %s", mergeDataId)
252 def writeMetadata(self, dataRefList):
253 """No metadata to write, and not sure how to write it for a list of dataRefs.
254 """
255 pass
258class WriteSourceTableConnections(pipeBase.PipelineTaskConnections,
259 defaultTemplates={"catalogType": ""},
260 dimensions=("instrument", "visit", "detector")):
262 catalog = connectionTypes.Input(
263 doc="Input full-depth catalog of sources produced by CalibrateTask",
264 name="{catalogType}src",
265 storageClass="SourceCatalog",
266 dimensions=("instrument", "visit", "detector")
267 )
268 outputCatalog = connectionTypes.Output(
269 doc="Catalog of sources, `src` in Parquet format. The 'id' column is "
270 "replaced with an index; all other columns are unchanged.",
271 name="{catalogType}source",
272 storageClass="DataFrame",
273 dimensions=("instrument", "visit", "detector")
274 )
277class WriteSourceTableConfig(pipeBase.PipelineTaskConfig,
278 pipelineConnections=WriteSourceTableConnections):
279 doApplyExternalPhotoCalib = pexConfig.Field(
280 dtype=bool,
281 default=False,
282 doc=("Add local photoCalib columns from the calexp.photoCalib? Should only set True if "
283 "generating Source Tables from older src tables which do not already have local calib columns")
284 )
285 doApplyExternalSkyWcs = pexConfig.Field(
286 dtype=bool,
287 default=False,
288 doc=("Add local WCS columns from the calexp.wcs? Should only set True if "
289 "generating Source Tables from older src tables which do not already have local calib columns")
290 )
293class WriteSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
294 """Write source table to parquet
295 """
296 _DefaultName = "writeSourceTable"
297 ConfigClass = WriteSourceTableConfig
299 def runDataRef(self, dataRef):
300 src = dataRef.get('src')
301 if self.config.doApplyExternalPhotoCalib or self.config.doApplyExternalSkyWcs:
302 src = self.addCalibColumns(src, dataRef)
304 ccdVisitId = dataRef.get('ccdExposureId')
305 result = self.run(src, ccdVisitId=ccdVisitId)
306 dataRef.put(result.table, 'source')
308 def runQuantum(self, butlerQC, inputRefs, outputRefs):
309 inputs = butlerQC.get(inputRefs)
310 inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
311 result = self.run(**inputs).table
312 outputs = pipeBase.Struct(outputCatalog=result.toDataFrame())
313 butlerQC.put(outputs, outputRefs)
315 def run(self, catalog, ccdVisitId=None):
316 """Convert `src` catalog to parquet
318 Parameters
319 ----------
320 catalog: `afwTable.SourceCatalog`
321 catalog to be converted
322 ccdVisitId: `int`
323 ccdVisitId to be added as a column
325 Returns
326 -------
327 result : `lsst.pipe.base.Struct`
328 ``table``
329 `ParquetTable` version of the input catalog
330 """
331 self.log.info("Generating parquet table from src catalog %s", ccdVisitId)
332 df = catalog.asAstropy().to_pandas().set_index('id', drop=True)
333 df['ccdVisitId'] = ccdVisitId
334 return pipeBase.Struct(table=ParquetTable(dataFrame=df))
336 def addCalibColumns(self, catalog, dataRef):
337 """Add columns with local calibration evaluated at each centroid
339 for backwards compatibility with old repos.
340 This exists for the purpose of converting old src catalogs
341 (which don't have the expected local calib columns) to Source Tables.
343 Parameters
344 ----------
345 catalog: `afwTable.SourceCatalog`
346 catalog to which calib columns will be added
347 dataRef: `lsst.daf.persistence.ButlerDataRef
348 for fetching the calibs from disk.
350 Returns
351 -------
352 newCat: `afwTable.SourceCatalog`
353 Source Catalog with requested local calib columns
354 """
355 mapper = afwTable.SchemaMapper(catalog.schema)
356 measureConfig = SingleFrameMeasurementTask.ConfigClass()
357 measureConfig.doReplaceWithNoise = False
359 # Just need the WCS or the PhotoCalib attached to an exposue
360 exposure = dataRef.get('calexp_sub',
361 bbox=lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Point2I(0, 0)))
363 mapper = afwTable.SchemaMapper(catalog.schema)
364 mapper.addMinimalSchema(catalog.schema, True)
365 schema = mapper.getOutputSchema()
367 exposureIdInfo = dataRef.get("expIdInfo")
368 measureConfig.plugins.names = []
369 if self.config.doApplyExternalSkyWcs:
370 plugin = 'base_LocalWcs'
371 if plugin in schema:
372 raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalSkyWcs=False")
373 else:
374 measureConfig.plugins.names.add(plugin)
376 if self.config.doApplyExternalPhotoCalib:
377 plugin = 'base_LocalPhotoCalib'
378 if plugin in schema:
379 raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalPhotoCalib=False")
380 else:
381 measureConfig.plugins.names.add(plugin)
383 measurement = SingleFrameMeasurementTask(config=measureConfig, schema=schema)
384 newCat = afwTable.SourceCatalog(schema)
385 newCat.extend(catalog, mapper=mapper)
386 measurement.run(measCat=newCat, exposure=exposure, exposureId=exposureIdInfo.expId)
387 return newCat
389 def writeMetadata(self, dataRef):
390 """No metadata to write.
391 """
392 pass
394 @classmethod
395 def _makeArgumentParser(cls):
396 parser = ArgumentParser(name=cls._DefaultName)
397 parser.add_id_argument("--id", 'src',
398 help="data ID, e.g. --id visit=12345 ccd=0")
399 return parser
402class PostprocessAnalysis(object):
403 """Calculate columns from ParquetTable
405 This object manages and organizes an arbitrary set of computations
406 on a catalog. The catalog is defined by a
407 `lsst.pipe.tasks.parquetTable.ParquetTable` object (or list thereof), such as a
408 `deepCoadd_obj` dataset, and the computations are defined by a collection
409 of `lsst.pipe.tasks.functor.Functor` objects (or, equivalently,
410 a `CompositeFunctor`).
412 After the object is initialized, accessing the `.df` attribute (which
413 holds the `pandas.DataFrame` containing the results of the calculations) triggers
414 computation of said dataframe.
416 One of the conveniences of using this object is the ability to define a desired common
417 filter for all functors. This enables the same functor collection to be passed to
418 several different `PostprocessAnalysis` objects without having to change the original
419 functor collection, since the `filt` keyword argument of this object triggers an
420 overwrite of the `filt` property for all functors in the collection.
422 This object also allows a list of refFlags to be passed, and defines a set of default
423 refFlags that are always included even if not requested.
425 If a list of `ParquetTable` object is passed, rather than a single one, then the
426 calculations will be mapped over all the input catalogs. In principle, it should
427 be straightforward to parallelize this activity, but initial tests have failed
428 (see TODO in code comments).
430 Parameters
431 ----------
432 parq : `lsst.pipe.tasks.ParquetTable` (or list of such)
433 Source catalog(s) for computation
435 functors : `list`, `dict`, or `lsst.pipe.tasks.functors.CompositeFunctor`
436 Computations to do (functors that act on `parq`).
437 If a dict, the output
438 DataFrame will have columns keyed accordingly.
439 If a list, the column keys will come from the
440 `.shortname` attribute of each functor.
442 filt : `str` (optional)
443 Filter in which to calculate. If provided,
444 this will overwrite any existing `.filt` attribute
445 of the provided functors.
447 flags : `list` (optional)
448 List of flags (per-band) to include in output table.
449 Taken from the `meas` dataset if applied to a multilevel Object Table.
451 refFlags : `list` (optional)
452 List of refFlags (only reference band) to include in output table.
454 forcedFlags : `list` (optional)
455 List of flags (per-band) to include in output table.
456 Taken from the ``forced_src`` dataset if applied to a
457 multilevel Object Table. Intended for flags from measurement plugins
458 only run during multi-band forced-photometry.
459 """
460 _defaultRefFlags = []
461 _defaultFuncs = ()
463 def __init__(self, parq, functors, filt=None, flags=None, refFlags=None, forcedFlags=None):
464 self.parq = parq
465 self.functors = functors
467 self.filt = filt
468 self.flags = list(flags) if flags is not None else []
469 self.forcedFlags = list(forcedFlags) if forcedFlags is not None else []
470 self.refFlags = list(self._defaultRefFlags)
471 if refFlags is not None:
472 self.refFlags += list(refFlags)
474 self._df = None
476 @property
477 def defaultFuncs(self):
478 funcs = dict(self._defaultFuncs)
479 return funcs
481 @property
482 def func(self):
483 additionalFuncs = self.defaultFuncs
484 additionalFuncs.update({flag: Column(flag, dataset='forced_src') for flag in self.forcedFlags})
485 additionalFuncs.update({flag: Column(flag, dataset='ref') for flag in self.refFlags})
486 additionalFuncs.update({flag: Column(flag, dataset='meas') for flag in self.flags})
488 if isinstance(self.functors, CompositeFunctor):
489 func = self.functors
490 else:
491 func = CompositeFunctor(self.functors)
493 func.funcDict.update(additionalFuncs)
494 func.filt = self.filt
496 return func
498 @property
499 def noDupCols(self):
500 return [name for name, func in self.func.funcDict.items() if func.noDup or func.dataset == 'ref']
502 @property
503 def df(self):
504 if self._df is None:
505 self.compute()
506 return self._df
508 def compute(self, dropna=False, pool=None):
509 # map over multiple parquet tables
510 if type(self.parq) in (list, tuple):
511 if pool is None:
512 dflist = [self.func(parq, dropna=dropna) for parq in self.parq]
513 else:
514 # TODO: Figure out why this doesn't work (pyarrow pickling issues?)
515 dflist = pool.map(functools.partial(self.func, dropna=dropna), self.parq)
516 self._df = pd.concat(dflist)
517 else:
518 self._df = self.func(self.parq, dropna=dropna)
520 return self._df
523class TransformCatalogBaseConnections(pipeBase.PipelineTaskConnections,
524 dimensions=()):
525 """Expected Connections for subclasses of TransformCatalogBaseTask.
527 Must be subclassed.
528 """
529 inputCatalog = connectionTypes.Input(
530 name="",
531 storageClass="DataFrame",
532 )
533 outputCatalog = connectionTypes.Output(
534 name="",
535 storageClass="DataFrame",
536 )
539class TransformCatalogBaseConfig(pipeBase.PipelineTaskConfig,
540 pipelineConnections=TransformCatalogBaseConnections):
541 functorFile = pexConfig.Field(
542 dtype=str,
543 doc="Path to YAML file specifying Science Data Model functors to use "
544 "when copying columns and computing calibrated values.",
545 default=None,
546 optional=True
547 )
548 primaryKey = pexConfig.Field(
549 dtype=str,
550 doc="Name of column to be set as the DataFrame index. If None, the index"
551 "will be named `id`",
552 default=None,
553 optional=True
554 )
557class TransformCatalogBaseTask(CmdLineTask, pipeBase.PipelineTask):
558 """Base class for transforming/standardizing a catalog
560 by applying functors that convert units and apply calibrations.
561 The purpose of this task is to perform a set of computations on
562 an input `ParquetTable` dataset (such as `deepCoadd_obj`) and write the
563 results to a new dataset (which needs to be declared in an `outputDataset`
564 attribute).
566 The calculations to be performed are defined in a YAML file that specifies
567 a set of functors to be computed, provided as
568 a `--functorFile` config parameter. An example of such a YAML file
569 is the following:
571 funcs:
572 psfMag:
573 functor: Mag
574 args:
575 - base_PsfFlux
576 filt: HSC-G
577 dataset: meas
578 cmodel_magDiff:
579 functor: MagDiff
580 args:
581 - modelfit_CModel
582 - base_PsfFlux
583 filt: HSC-G
584 gauss_magDiff:
585 functor: MagDiff
586 args:
587 - base_GaussianFlux
588 - base_PsfFlux
589 filt: HSC-G
590 count:
591 functor: Column
592 args:
593 - base_InputCount_value
594 filt: HSC-G
595 deconvolved_moments:
596 functor: DeconvolvedMoments
597 filt: HSC-G
598 dataset: forced_src
599 refFlags:
600 - calib_psfUsed
601 - merge_measurement_i
602 - merge_measurement_r
603 - merge_measurement_z
604 - merge_measurement_y
605 - merge_measurement_g
606 - base_PixelFlags_flag_inexact_psfCenter
607 - detect_isPrimary
609 The names for each entry under "func" will become the names of columns in the
610 output dataset. All the functors referenced are defined in `lsst.pipe.tasks.functors`.
611 Positional arguments to be passed to each functor are in the `args` list,
612 and any additional entries for each column other than "functor" or "args" (e.g., `'filt'`,
613 `'dataset'`) are treated as keyword arguments to be passed to the functor initialization.
615 The "flags" entry is the default shortcut for `Column` functors.
616 All columns listed under "flags" will be copied to the output table
617 untransformed. They can be of any datatype.
618 In the special case of transforming a multi-level oject table with
619 band and dataset indices (deepCoadd_obj), these will be taked from the
620 `meas` dataset and exploded out per band.
622 There are two special shortcuts that only apply when transforming
623 multi-level Object (deepCoadd_obj) tables:
624 - The "refFlags" entry is shortcut for `Column` functor
625 taken from the `'ref'` dataset if transforming an ObjectTable.
626 - The "forcedFlags" entry is shortcut for `Column` functors.
627 taken from the ``forced_src`` dataset if transforming an ObjectTable.
628 These are expanded out per band.
631 This task uses the `lsst.pipe.tasks.postprocess.PostprocessAnalysis` object
632 to organize and excecute the calculations.
634 """
635 @property
636 def _DefaultName(self):
637 raise NotImplementedError('Subclass must define "_DefaultName" attribute')
639 @property
640 def outputDataset(self):
641 raise NotImplementedError('Subclass must define "outputDataset" attribute')
643 @property
644 def inputDataset(self):
645 raise NotImplementedError('Subclass must define "inputDataset" attribute')
647 @property
648 def ConfigClass(self):
649 raise NotImplementedError('Subclass must define "ConfigClass" attribute')
651 def __init__(self, *args, **kwargs):
652 super().__init__(*args, **kwargs)
653 if self.config.functorFile:
654 self.log.info('Loading tranform functor definitions from %s',
655 self.config.functorFile)
656 self.funcs = CompositeFunctor.from_file(self.config.functorFile)
657 self.funcs.update(dict(PostprocessAnalysis._defaultFuncs))
658 else:
659 self.funcs = None
661 def runQuantum(self, butlerQC, inputRefs, outputRefs):
662 inputs = butlerQC.get(inputRefs)
663 if self.funcs is None:
664 raise ValueError("config.functorFile is None. "
665 "Must be a valid path to yaml in order to run Task as a PipelineTask.")
666 result = self.run(parq=inputs['inputCatalog'], funcs=self.funcs,
667 dataId=outputRefs.outputCatalog.dataId.full)
668 outputs = pipeBase.Struct(outputCatalog=result)
669 butlerQC.put(outputs, outputRefs)
671 def runDataRef(self, dataRef):
672 parq = dataRef.get()
673 if self.funcs is None:
674 raise ValueError("config.functorFile is None. "
675 "Must be a valid path to yaml in order to run as a CommandlineTask.")
676 df = self.run(parq, funcs=self.funcs, dataId=dataRef.dataId)
677 self.write(df, dataRef)
678 return df
680 def run(self, parq, funcs=None, dataId=None, band=None):
681 """Do postprocessing calculations
683 Takes a `ParquetTable` object and dataId,
684 returns a dataframe with results of postprocessing calculations.
686 Parameters
687 ----------
688 parq : `lsst.pipe.tasks.parquetTable.ParquetTable`
689 ParquetTable from which calculations are done.
690 funcs : `lsst.pipe.tasks.functors.Functors`
691 Functors to apply to the table's columns
692 dataId : dict, optional
693 Used to add a `patchId` column to the output dataframe.
694 band : `str`, optional
695 Filter band that is being processed.
697 Returns
698 ------
699 `pandas.DataFrame`
701 """
702 self.log.info("Transforming/standardizing the source table dataId: %s", dataId)
704 df = self.transform(band, parq, funcs, dataId).df
705 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
706 return df
708 def getFunctors(self):
709 return self.funcs
711 def getAnalysis(self, parq, funcs=None, band=None):
712 if funcs is None:
713 funcs = self.funcs
714 analysis = PostprocessAnalysis(parq, funcs, filt=band)
715 return analysis
717 def transform(self, band, parq, funcs, dataId):
718 analysis = self.getAnalysis(parq, funcs=funcs, band=band)
719 df = analysis.df
720 if dataId is not None:
721 for key, value in dataId.items():
722 df[str(key)] = value
724 if self.config.primaryKey:
725 if df.index.name != self.config.primaryKey and self.config.primaryKey in df:
726 df.reset_index(inplace=True, drop=True)
727 df.set_index(self.config.primaryKey, inplace=True)
729 return pipeBase.Struct(
730 df=df,
731 analysis=analysis
732 )
734 def write(self, df, parqRef):
735 parqRef.put(ParquetTable(dataFrame=df), self.outputDataset)
737 def writeMetadata(self, dataRef):
738 """No metadata to write.
739 """
740 pass
743class TransformObjectCatalogConnections(pipeBase.PipelineTaskConnections,
744 defaultTemplates={"coaddName": "deep"},
745 dimensions=("tract", "patch", "skymap")):
746 inputCatalog = connectionTypes.Input(
747 doc="The vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
748 "stored as a DataFrame with a multi-level column index per-patch.",
749 dimensions=("tract", "patch", "skymap"),
750 storageClass="DataFrame",
751 name="{coaddName}Coadd_obj",
752 deferLoad=True,
753 )
754 outputCatalog = connectionTypes.Output(
755 doc="Per-Patch Object Table of columns transformed from the deepCoadd_obj table per the standard "
756 "data model.",
757 dimensions=("tract", "patch", "skymap"),
758 storageClass="DataFrame",
759 name="objectTable"
760 )
763class TransformObjectCatalogConfig(TransformCatalogBaseConfig,
764 pipelineConnections=TransformObjectCatalogConnections):
765 coaddName = pexConfig.Field(
766 dtype=str,
767 default="deep",
768 doc="Name of coadd"
769 )
770 # TODO: remove in DM-27177
771 filterMap = pexConfig.DictField(
772 keytype=str,
773 itemtype=str,
774 default={},
775 doc=("Dictionary mapping full filter name to short one for column name munging."
776 "These filters determine the output columns no matter what filters the "
777 "input data actually contain."),
778 deprecated=("Coadds are now identified by the band, so this transform is unused."
779 "Will be removed after v22.")
780 )
781 outputBands = pexConfig.ListField(
782 dtype=str,
783 default=None,
784 optional=True,
785 doc=("These bands and only these bands will appear in the output,"
786 " NaN-filled if the input does not include them."
787 " If None, then use all bands found in the input.")
788 )
789 camelCase = pexConfig.Field(
790 dtype=bool,
791 default=False,
792 doc=("Write per-band columns names with camelCase, else underscore "
793 "For example: gPsFlux instead of g_PsFlux.")
794 )
795 multilevelOutput = pexConfig.Field(
796 dtype=bool,
797 default=False,
798 doc=("Whether results dataframe should have a multilevel column index (True) or be flat "
799 "and name-munged (False).")
800 )
802 def setDefaults(self):
803 super().setDefaults()
804 self.primaryKey = 'objectId'
807class TransformObjectCatalogTask(TransformCatalogBaseTask):
808 """Produce a flattened Object Table to match the format specified in
809 sdm_schemas.
811 Do the same set of postprocessing calculations on all bands
813 This is identical to `TransformCatalogBaseTask`, except for that it does the
814 specified functor calculations for all filters present in the
815 input `deepCoadd_obj` table. Any specific `"filt"` keywords specified
816 by the YAML file will be superceded.
817 """
818 _DefaultName = "transformObjectCatalog"
819 ConfigClass = TransformObjectCatalogConfig
821 # Used by Gen 2 runDataRef only:
822 inputDataset = 'deepCoadd_obj'
823 outputDataset = 'objectTable'
825 @classmethod
826 def _makeArgumentParser(cls):
827 parser = ArgumentParser(name=cls._DefaultName)
828 parser.add_id_argument("--id", cls.inputDataset,
829 ContainerClass=CoaddDataIdContainer,
830 help="data ID, e.g. --id tract=12345 patch=1,2")
831 return parser
833 def run(self, parq, funcs=None, dataId=None, band=None):
834 # NOTE: band kwarg is ignored here.
835 dfDict = {}
836 analysisDict = {}
837 templateDf = pd.DataFrame()
839 if isinstance(parq, DeferredDatasetHandle):
840 columns = parq.get(component='columns')
841 inputBands = columns.unique(level=1).values
842 else:
843 inputBands = parq.columnLevelNames['band']
845 outputBands = self.config.outputBands if self.config.outputBands else inputBands
847 # Perform transform for data of filters that exist in parq.
848 for inputBand in inputBands:
849 if inputBand not in outputBands:
850 self.log.info("Ignoring %s band data in the input", inputBand)
851 continue
852 self.log.info("Transforming the catalog of band %s", inputBand)
853 result = self.transform(inputBand, parq, funcs, dataId)
854 dfDict[inputBand] = result.df
855 analysisDict[inputBand] = result.analysis
856 if templateDf.empty:
857 templateDf = result.df
859 # Fill NaNs in columns of other wanted bands
860 for filt in outputBands:
861 if filt not in dfDict:
862 self.log.info("Adding empty columns for band %s", filt)
863 dfDict[filt] = pd.DataFrame().reindex_like(templateDf)
865 # This makes a multilevel column index, with band as first level
866 df = pd.concat(dfDict, axis=1, names=['band', 'column'])
868 if not self.config.multilevelOutput:
869 noDupCols = list(set.union(*[set(v.noDupCols) for v in analysisDict.values()]))
870 if self.config.primaryKey in noDupCols:
871 noDupCols.remove(self.config.primaryKey)
872 if dataId is not None:
873 noDupCols += list(dataId.keys())
874 df = flattenFilters(df, noDupCols=noDupCols, camelCase=self.config.camelCase,
875 inputBands=inputBands)
877 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
879 return df
882class TractObjectDataIdContainer(CoaddDataIdContainer):
884 def makeDataRefList(self, namespace):
885 """Make self.refList from self.idList
887 Generate a list of data references given tract and/or patch.
888 This was adapted from `TractQADataIdContainer`, which was
889 `TractDataIdContainer` modifie to not require "filter".
890 Only existing dataRefs are returned.
891 """
892 def getPatchRefList(tract):
893 return [namespace.butler.dataRef(datasetType=self.datasetType,
894 tract=tract.getId(),
895 patch="%d,%d" % patch.getIndex()) for patch in tract]
897 tractRefs = defaultdict(list) # Data references for each tract
898 for dataId in self.idList:
899 skymap = self.getSkymap(namespace)
901 if "tract" in dataId:
902 tractId = dataId["tract"]
903 if "patch" in dataId:
904 tractRefs[tractId].append(namespace.butler.dataRef(datasetType=self.datasetType,
905 tract=tractId,
906 patch=dataId['patch']))
907 else:
908 tractRefs[tractId] += getPatchRefList(skymap[tractId])
909 else:
910 tractRefs = dict((tract.getId(), tractRefs.get(tract.getId(), []) + getPatchRefList(tract))
911 for tract in skymap)
912 outputRefList = []
913 for tractRefList in tractRefs.values():
914 existingRefs = [ref for ref in tractRefList if ref.datasetExists()]
915 outputRefList.append(existingRefs)
917 self.refList = outputRefList
920class ConsolidateObjectTableConnections(pipeBase.PipelineTaskConnections,
921 dimensions=("tract", "skymap")):
922 inputCatalogs = connectionTypes.Input(
923 doc="Per-Patch objectTables conforming to the standard data model.",
924 name="objectTable",
925 storageClass="DataFrame",
926 dimensions=("tract", "patch", "skymap"),
927 multiple=True,
928 )
929 outputCatalog = connectionTypes.Output(
930 doc="Pre-tract horizontal concatenation of the input objectTables",
931 name="objectTable_tract",
932 storageClass="DataFrame",
933 dimensions=("tract", "skymap"),
934 )
937class ConsolidateObjectTableConfig(pipeBase.PipelineTaskConfig,
938 pipelineConnections=ConsolidateObjectTableConnections):
939 coaddName = pexConfig.Field(
940 dtype=str,
941 default="deep",
942 doc="Name of coadd"
943 )
946class ConsolidateObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
947 """Write patch-merged source tables to a tract-level parquet file
949 Concatenates `objectTable` list into a per-visit `objectTable_tract`
950 """
951 _DefaultName = "consolidateObjectTable"
952 ConfigClass = ConsolidateObjectTableConfig
954 inputDataset = 'objectTable'
955 outputDataset = 'objectTable_tract'
957 def runQuantum(self, butlerQC, inputRefs, outputRefs):
958 inputs = butlerQC.get(inputRefs)
959 self.log.info("Concatenating %s per-patch Object Tables",
960 len(inputs['inputCatalogs']))
961 df = pd.concat(inputs['inputCatalogs'])
962 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
964 @classmethod
965 def _makeArgumentParser(cls):
966 parser = ArgumentParser(name=cls._DefaultName)
968 parser.add_id_argument("--id", cls.inputDataset,
969 help="data ID, e.g. --id tract=12345",
970 ContainerClass=TractObjectDataIdContainer)
971 return parser
973 def runDataRef(self, patchRefList):
974 df = pd.concat([patchRef.get().toDataFrame() for patchRef in patchRefList])
975 patchRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
977 def writeMetadata(self, dataRef):
978 """No metadata to write.
979 """
980 pass
983class TransformSourceTableConnections(pipeBase.PipelineTaskConnections,
984 defaultTemplates={"catalogType": ""},
985 dimensions=("instrument", "visit", "detector")):
987 inputCatalog = connectionTypes.Input(
988 doc="Wide input catalog of sources produced by WriteSourceTableTask",
989 name="{catalogType}source",
990 storageClass="DataFrame",
991 dimensions=("instrument", "visit", "detector"),
992 deferLoad=True
993 )
994 outputCatalog = connectionTypes.Output(
995 doc="Narrower, per-detector Source Table transformed and converted per a "
996 "specified set of functors",
997 name="{catalogType}sourceTable",
998 storageClass="DataFrame",
999 dimensions=("instrument", "visit", "detector")
1000 )
1003class TransformSourceTableConfig(TransformCatalogBaseConfig,
1004 pipelineConnections=TransformSourceTableConnections):
1006 def setDefaults(self):
1007 super().setDefaults()
1008 self.primaryKey = 'sourceId'
1011class TransformSourceTableTask(TransformCatalogBaseTask):
1012 """Transform/standardize a source catalog
1013 """
1014 _DefaultName = "transformSourceTable"
1015 ConfigClass = TransformSourceTableConfig
1017 inputDataset = 'source'
1018 outputDataset = 'sourceTable'
1020 @classmethod
1021 def _makeArgumentParser(cls):
1022 parser = ArgumentParser(name=cls._DefaultName)
1023 parser.add_id_argument("--id", datasetType=cls.inputDataset,
1024 level="sensor",
1025 help="data ID, e.g. --id visit=12345 ccd=0")
1026 return parser
1028 def runDataRef(self, dataRef):
1029 """Override to specify band label to run()."""
1030 parq = dataRef.get()
1031 funcs = self.getFunctors()
1032 band = dataRef.get("calexp_filterLabel", immediate=True).bandLabel
1033 df = self.run(parq, funcs=funcs, dataId=dataRef.dataId, band=band)
1034 self.write(df, dataRef)
1035 return df
1038class ConsolidateVisitSummaryConnections(pipeBase.PipelineTaskConnections,
1039 dimensions=("instrument", "visit",),
1040 defaultTemplates={"calexpType": ""}):
1041 calexp = connectionTypes.Input(
1042 doc="Processed exposures used for metadata",
1043 name="{calexpType}calexp",
1044 storageClass="ExposureF",
1045 dimensions=("instrument", "visit", "detector"),
1046 deferLoad=True,
1047 multiple=True,
1048 )
1049 visitSummary = connectionTypes.Output(
1050 doc=("Per-visit consolidated exposure metadata. These catalogs use "
1051 "detector id for the id and are sorted for fast lookups of a "
1052 "detector."),
1053 name="{calexpType}visitSummary",
1054 storageClass="ExposureCatalog",
1055 dimensions=("instrument", "visit"),
1056 )
1059class ConsolidateVisitSummaryConfig(pipeBase.PipelineTaskConfig,
1060 pipelineConnections=ConsolidateVisitSummaryConnections):
1061 """Config for ConsolidateVisitSummaryTask"""
1062 pass
1065class ConsolidateVisitSummaryTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
1066 """Task to consolidate per-detector visit metadata.
1068 This task aggregates the following metadata from all the detectors in a
1069 single visit into an exposure catalog:
1070 - The visitInfo.
1071 - The wcs.
1072 - The photoCalib.
1073 - The physical_filter and band (if available).
1074 - The psf size, shape, and effective area at the center of the detector.
1075 - The corners of the bounding box in right ascension/declination.
1077 Other quantities such as Detector, Psf, ApCorrMap, and TransmissionCurve
1078 are not persisted here because of storage concerns, and because of their
1079 limited utility as summary statistics.
1081 Tests for this task are performed in ci_hsc_gen3.
1082 """
1083 _DefaultName = "consolidateVisitSummary"
1084 ConfigClass = ConsolidateVisitSummaryConfig
1086 @classmethod
1087 def _makeArgumentParser(cls):
1088 parser = ArgumentParser(name=cls._DefaultName)
1090 parser.add_id_argument("--id", "calexp",
1091 help="data ID, e.g. --id visit=12345",
1092 ContainerClass=VisitDataIdContainer)
1093 return parser
1095 def writeMetadata(self, dataRef):
1096 """No metadata to persist, so override to remove metadata persistance.
1097 """
1098 pass
1100 def writeConfig(self, butler, clobber=False, doBackup=True):
1101 """No config to persist, so override to remove config persistance.
1102 """
1103 pass
1105 def runDataRef(self, dataRefList):
1106 visit = dataRefList[0].dataId['visit']
1108 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1109 len(dataRefList), visit)
1111 expCatalog = self._combineExposureMetadata(visit, dataRefList, isGen3=False)
1113 dataRefList[0].put(expCatalog, 'visitSummary', visit=visit)
1115 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1116 dataRefs = butlerQC.get(inputRefs.calexp)
1117 visit = dataRefs[0].dataId.byName()['visit']
1119 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1120 len(dataRefs), visit)
1122 expCatalog = self._combineExposureMetadata(visit, dataRefs)
1124 butlerQC.put(expCatalog, outputRefs.visitSummary)
1126 def _combineExposureMetadata(self, visit, dataRefs, isGen3=True):
1127 """Make a combined exposure catalog from a list of dataRefs.
1128 These dataRefs must point to exposures with wcs, summaryStats,
1129 and other visit metadata.
1131 Parameters
1132 ----------
1133 visit : `int`
1134 Visit identification number.
1135 dataRefs : `list`
1136 List of dataRefs in visit. May be list of
1137 `lsst.daf.persistence.ButlerDataRef` (Gen2) or
1138 `lsst.daf.butler.DeferredDatasetHandle` (Gen3).
1139 isGen3 : `bool`, optional
1140 Specifies if this is a Gen3 list of datarefs.
1142 Returns
1143 -------
1144 visitSummary : `lsst.afw.table.ExposureCatalog`
1145 Exposure catalog with per-detector summary information.
1146 """
1147 schema = self._makeVisitSummarySchema()
1148 cat = afwTable.ExposureCatalog(schema)
1149 cat.resize(len(dataRefs))
1151 cat['visit'] = visit
1153 for i, dataRef in enumerate(dataRefs):
1154 if isGen3:
1155 visitInfo = dataRef.get(component='visitInfo')
1156 filterLabel = dataRef.get(component='filterLabel')
1157 summaryStats = dataRef.get(component='summaryStats')
1158 detector = dataRef.get(component='detector')
1159 wcs = dataRef.get(component='wcs')
1160 photoCalib = dataRef.get(component='photoCalib')
1161 detector = dataRef.get(component='detector')
1162 bbox = dataRef.get(component='bbox')
1163 validPolygon = dataRef.get(component='validPolygon')
1164 else:
1165 # Note that we need to read the calexp because there is
1166 # no magic access to the psf except through the exposure.
1167 gen2_read_bbox = lsst.geom.BoxI(lsst.geom.PointI(0, 0), lsst.geom.PointI(1, 1))
1168 exp = dataRef.get(datasetType='calexp_sub', bbox=gen2_read_bbox)
1169 visitInfo = exp.getInfo().getVisitInfo()
1170 filterLabel = dataRef.get("calexp_filterLabel")
1171 summaryStats = exp.getInfo().getSummaryStats()
1172 wcs = exp.getWcs()
1173 photoCalib = exp.getPhotoCalib()
1174 detector = exp.getDetector()
1175 bbox = dataRef.get(datasetType='calexp_bbox')
1176 validPolygon = exp.getInfo().getValidPolygon()
1178 rec = cat[i]
1179 rec.setBBox(bbox)
1180 rec.setVisitInfo(visitInfo)
1181 rec.setWcs(wcs)
1182 rec.setPhotoCalib(photoCalib)
1183 rec.setValidPolygon(validPolygon)
1185 rec['physical_filter'] = filterLabel.physicalLabel if filterLabel.hasPhysicalLabel() else ""
1186 rec['band'] = filterLabel.bandLabel if filterLabel.hasBandLabel() else ""
1187 rec.setId(detector.getId())
1188 rec['psfSigma'] = summaryStats.psfSigma
1189 rec['psfIxx'] = summaryStats.psfIxx
1190 rec['psfIyy'] = summaryStats.psfIyy
1191 rec['psfIxy'] = summaryStats.psfIxy
1192 rec['psfArea'] = summaryStats.psfArea
1193 rec['raCorners'][:] = summaryStats.raCorners
1194 rec['decCorners'][:] = summaryStats.decCorners
1195 rec['ra'] = summaryStats.ra
1196 rec['decl'] = summaryStats.decl
1197 rec['zenithDistance'] = summaryStats.zenithDistance
1198 rec['zeroPoint'] = summaryStats.zeroPoint
1199 rec['skyBg'] = summaryStats.skyBg
1200 rec['skyNoise'] = summaryStats.skyNoise
1201 rec['meanVar'] = summaryStats.meanVar
1202 rec['astromOffsetMean'] = summaryStats.astromOffsetMean
1203 rec['astromOffsetStd'] = summaryStats.astromOffsetStd
1205 metadata = dafBase.PropertyList()
1206 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
1207 # We are looping over existing datarefs, so the following is true
1208 metadata.add("COMMENT", "Only detectors with data have entries.")
1209 cat.setMetadata(metadata)
1211 cat.sort()
1212 return cat
1214 def _makeVisitSummarySchema(self):
1215 """Make the schema for the visitSummary catalog."""
1216 schema = afwTable.ExposureTable.makeMinimalSchema()
1217 schema.addField('visit', type='I', doc='Visit number')
1218 schema.addField('physical_filter', type='String', size=32, doc='Physical filter')
1219 schema.addField('band', type='String', size=32, doc='Name of band')
1220 schema.addField('psfSigma', type='F',
1221 doc='PSF model second-moments determinant radius (center of chip) (pixel)')
1222 schema.addField('psfArea', type='F',
1223 doc='PSF model effective area (center of chip) (pixel**2)')
1224 schema.addField('psfIxx', type='F',
1225 doc='PSF model Ixx (center of chip) (pixel**2)')
1226 schema.addField('psfIyy', type='F',
1227 doc='PSF model Iyy (center of chip) (pixel**2)')
1228 schema.addField('psfIxy', type='F',
1229 doc='PSF model Ixy (center of chip) (pixel**2)')
1230 schema.addField('raCorners', type='ArrayD', size=4,
1231 doc='Right Ascension of bounding box corners (degrees)')
1232 schema.addField('decCorners', type='ArrayD', size=4,
1233 doc='Declination of bounding box corners (degrees)')
1234 schema.addField('ra', type='D',
1235 doc='Right Ascension of bounding box center (degrees)')
1236 schema.addField('decl', type='D',
1237 doc='Declination of bounding box center (degrees)')
1238 schema.addField('zenithDistance', type='F',
1239 doc='Zenith distance of bounding box center (degrees)')
1240 schema.addField('zeroPoint', type='F',
1241 doc='Mean zeropoint in detector (mag)')
1242 schema.addField('skyBg', type='F',
1243 doc='Average sky background (ADU)')
1244 schema.addField('skyNoise', type='F',
1245 doc='Average sky noise (ADU)')
1246 schema.addField('meanVar', type='F',
1247 doc='Mean variance of the weight plane (ADU**2)')
1248 schema.addField('astromOffsetMean', type='F',
1249 doc='Mean offset of astrometric calibration matches (arcsec)')
1250 schema.addField('astromOffsetStd', type='F',
1251 doc='Standard deviation of offsets of astrometric calibration matches (arcsec)')
1253 return schema
1256class VisitDataIdContainer(DataIdContainer):
1257 """DataIdContainer that groups sensor-level id's by visit
1258 """
1260 def makeDataRefList(self, namespace):
1261 """Make self.refList from self.idList
1263 Generate a list of data references grouped by visit.
1265 Parameters
1266 ----------
1267 namespace : `argparse.Namespace`
1268 Namespace used by `lsst.pipe.base.CmdLineTask` to parse command line arguments
1269 """
1270 # Group by visits
1271 visitRefs = defaultdict(list)
1272 for dataId in self.idList:
1273 if "visit" in dataId:
1274 visitId = dataId["visit"]
1275 # append all subsets to
1276 subset = namespace.butler.subset(self.datasetType, dataId=dataId)
1277 visitRefs[visitId].extend([dataRef for dataRef in subset])
1279 outputRefList = []
1280 for refList in visitRefs.values():
1281 existingRefs = [ref for ref in refList if ref.datasetExists()]
1282 if existingRefs:
1283 outputRefList.append(existingRefs)
1285 self.refList = outputRefList
1288class ConsolidateSourceTableConnections(pipeBase.PipelineTaskConnections,
1289 defaultTemplates={"catalogType": ""},
1290 dimensions=("instrument", "visit")):
1291 inputCatalogs = connectionTypes.Input(
1292 doc="Input per-detector Source Tables",
1293 name="{catalogType}sourceTable",
1294 storageClass="DataFrame",
1295 dimensions=("instrument", "visit", "detector"),
1296 multiple=True
1297 )
1298 outputCatalog = connectionTypes.Output(
1299 doc="Per-visit concatenation of Source Table",
1300 name="{catalogType}sourceTable_visit",
1301 storageClass="DataFrame",
1302 dimensions=("instrument", "visit")
1303 )
1306class ConsolidateSourceTableConfig(pipeBase.PipelineTaskConfig,
1307 pipelineConnections=ConsolidateSourceTableConnections):
1308 pass
1311class ConsolidateSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
1312 """Concatenate `sourceTable` list into a per-visit `sourceTable_visit`
1313 """
1314 _DefaultName = 'consolidateSourceTable'
1315 ConfigClass = ConsolidateSourceTableConfig
1317 inputDataset = 'sourceTable'
1318 outputDataset = 'sourceTable_visit'
1320 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1321 inputs = butlerQC.get(inputRefs)
1322 self.log.info("Concatenating %s per-detector Source Tables",
1323 len(inputs['inputCatalogs']))
1324 df = pd.concat(inputs['inputCatalogs'])
1325 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
1327 def runDataRef(self, dataRefList):
1328 self.log.info("Concatenating %s per-detector Source Tables", len(dataRefList))
1329 df = pd.concat([dataRef.get().toDataFrame() for dataRef in dataRefList])
1330 dataRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
1332 @classmethod
1333 def _makeArgumentParser(cls):
1334 parser = ArgumentParser(name=cls._DefaultName)
1336 parser.add_id_argument("--id", cls.inputDataset,
1337 help="data ID, e.g. --id visit=12345",
1338 ContainerClass=VisitDataIdContainer)
1339 return parser
1341 def writeMetadata(self, dataRef):
1342 """No metadata to write.
1343 """
1344 pass
1346 def writeConfig(self, butler, clobber=False, doBackup=True):
1347 """No config to write.
1348 """
1349 pass
1352class MakeCcdVisitTableConnections(pipeBase.PipelineTaskConnections,
1353 dimensions=("instrument",),
1354 defaultTemplates={}):
1355 visitSummaryRefs = connectionTypes.Input(
1356 doc="Data references for per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1357 name="visitSummary",
1358 storageClass="ExposureCatalog",
1359 dimensions=("instrument", "visit"),
1360 multiple=True,
1361 deferLoad=True,
1362 )
1363 outputCatalog = connectionTypes.Output(
1364 doc="CCD and Visit metadata table",
1365 name="ccdVisitTable",
1366 storageClass="DataFrame",
1367 dimensions=("instrument",)
1368 )
1371class MakeCcdVisitTableConfig(pipeBase.PipelineTaskConfig,
1372 pipelineConnections=MakeCcdVisitTableConnections):
1373 pass
1376class MakeCcdVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1377 """Produce a `ccdVisitTable` from the `visitSummary` exposure catalogs.
1378 """
1379 _DefaultName = 'makeCcdVisitTable'
1380 ConfigClass = MakeCcdVisitTableConfig
1382 def run(self, visitSummaryRefs):
1383 """ Make a table of ccd information from the `visitSummary` catalogs.
1384 Parameters
1385 ----------
1386 visitSummaryRefs : `list` of `lsst.daf.butler.DeferredDatasetHandle`
1387 List of DeferredDatasetHandles pointing to exposure catalogs with
1388 per-detector summary information.
1389 Returns
1390 -------
1391 result : `lsst.pipe.Base.Struct`
1392 Results struct with attribute:
1393 - `outputCatalog`
1394 Catalog of ccd and visit information.
1395 """
1396 ccdEntries = []
1397 for visitSummaryRef in visitSummaryRefs:
1398 visitSummary = visitSummaryRef.get()
1399 visitInfo = visitSummary[0].getVisitInfo()
1401 ccdEntry = {}
1402 summaryTable = visitSummary.asAstropy()
1403 selectColumns = ['id', 'visit', 'physical_filter', 'band', 'ra', 'decl', 'zenithDistance',
1404 'zeroPoint', 'psfSigma', 'skyBg', 'skyNoise']
1405 ccdEntry = summaryTable[selectColumns].to_pandas().set_index('id')
1406 # 'visit' is the human readible visit number
1407 # 'visitId' is the key to the visitId table. They are the same
1408 # Technically you should join to get the visit from the visit table
1409 ccdEntry = ccdEntry.rename(columns={"visit": "visitId"})
1410 dataIds = [DataCoordinate.standardize(visitSummaryRef.dataId, detector=id) for id in
1411 summaryTable['id']]
1412 packer = visitSummaryRef.dataId.universe.makePacker('visit_detector', visitSummaryRef.dataId)
1413 ccdVisitIds = [packer.pack(dataId) for dataId in dataIds]
1414 ccdEntry['ccdVisitId'] = ccdVisitIds
1415 ccdEntry['detector'] = summaryTable['id']
1416 pixToArcseconds = np.array([vR.getWcs().getPixelScale().asArcseconds() for vR in visitSummary])
1417 ccdEntry["seeing"] = visitSummary['psfSigma'] * np.sqrt(8 * np.log(2)) * pixToArcseconds
1419 ccdEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1420 ccdEntry["expMidpt"] = visitInfo.getDate().toPython()
1421 expTime = visitInfo.getExposureTime()
1422 ccdEntry['expTime'] = expTime
1423 ccdEntry["obsStart"] = ccdEntry["expMidpt"] - 0.5 * pd.Timedelta(seconds=expTime)
1424 ccdEntry['darkTime'] = visitInfo.getDarkTime()
1425 ccdEntry['xSize'] = summaryTable['bbox_max_x'] - summaryTable['bbox_min_x']
1426 ccdEntry['ySize'] = summaryTable['bbox_max_y'] - summaryTable['bbox_min_y']
1427 ccdEntry['llcra'] = summaryTable['raCorners'][:, 0]
1428 ccdEntry['llcdec'] = summaryTable['decCorners'][:, 0]
1429 ccdEntry['ulcra'] = summaryTable['raCorners'][:, 1]
1430 ccdEntry['ulcdec'] = summaryTable['decCorners'][:, 1]
1431 ccdEntry['urcra'] = summaryTable['raCorners'][:, 2]
1432 ccdEntry['urcdec'] = summaryTable['decCorners'][:, 2]
1433 ccdEntry['lrcra'] = summaryTable['raCorners'][:, 3]
1434 ccdEntry['lrcdec'] = summaryTable['decCorners'][:, 3]
1435 # TODO: DM-30618, Add raftName, nExposures, ccdTemp, binX, binY, and flags,
1436 # and decide if WCS, and llcx, llcy, ulcx, ulcy, etc. values are actually wanted.
1437 ccdEntries.append(ccdEntry)
1439 outputCatalog = pd.concat(ccdEntries)
1440 outputCatalog.set_index('ccdVisitId', inplace=True, verify_integrity=True)
1441 return pipeBase.Struct(outputCatalog=outputCatalog)
1444class MakeVisitTableConnections(pipeBase.PipelineTaskConnections,
1445 dimensions=("instrument",),
1446 defaultTemplates={}):
1447 visitSummaries = connectionTypes.Input(
1448 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1449 name="visitSummary",
1450 storageClass="ExposureCatalog",
1451 dimensions=("instrument", "visit",),
1452 multiple=True,
1453 deferLoad=True,
1454 )
1455 outputCatalog = connectionTypes.Output(
1456 doc="Visit metadata table",
1457 name="visitTable",
1458 storageClass="DataFrame",
1459 dimensions=("instrument",)
1460 )
1463class MakeVisitTableConfig(pipeBase.PipelineTaskConfig,
1464 pipelineConnections=MakeVisitTableConnections):
1465 pass
1468class MakeVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1469 """Produce a `visitTable` from the `visitSummary` exposure catalogs.
1470 """
1471 _DefaultName = 'makeVisitTable'
1472 ConfigClass = MakeVisitTableConfig
1474 def run(self, visitSummaries):
1475 """ Make a table of visit information from the `visitSummary` catalogs
1477 Parameters
1478 ----------
1479 visitSummaries : list of `lsst.afw.table.ExposureCatalog`
1480 List of exposure catalogs with per-detector summary information.
1481 Returns
1482 -------
1483 result : `lsst.pipe.Base.Struct`
1484 Results struct with attribute:
1485 ``outputCatalog``
1486 Catalog of visit information.
1487 """
1488 visitEntries = []
1489 for visitSummary in visitSummaries:
1490 visitSummary = visitSummary.get()
1491 visitRow = visitSummary[0]
1492 visitInfo = visitRow.getVisitInfo()
1494 visitEntry = {}
1495 visitEntry["visitId"] = visitRow['visit']
1496 visitEntry["visit"] = visitRow['visit']
1497 visitEntry["physical_filter"] = visitRow['physical_filter']
1498 visitEntry["band"] = visitRow['band']
1499 raDec = visitInfo.getBoresightRaDec()
1500 visitEntry["ra"] = raDec.getRa().asDegrees()
1501 visitEntry["decl"] = raDec.getDec().asDegrees()
1502 visitEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1503 azAlt = visitInfo.getBoresightAzAlt()
1504 visitEntry["azimuth"] = azAlt.getLongitude().asDegrees()
1505 visitEntry["altitude"] = azAlt.getLatitude().asDegrees()
1506 visitEntry["zenithDistance"] = 90 - azAlt.getLatitude().asDegrees()
1507 visitEntry["airmass"] = visitInfo.getBoresightAirmass()
1508 visitEntry["obsStart"] = visitInfo.getDate().toPython()
1509 visitEntry["expTime"] = visitInfo.getExposureTime()
1510 visitEntries.append(visitEntry)
1511 # TODO: DM-30623, Add programId, exposureType, expMidpt, cameraTemp, mirror1Temp, mirror2Temp,
1512 # mirror3Temp, domeTemp, externalTemp, dimmSeeing, pwvGPS, pwvMW, flags, nExposures
1514 outputCatalog = pd.DataFrame(data=visitEntries)
1515 outputCatalog.set_index('visitId', inplace=True, verify_integrity=True)
1516 return pipeBase.Struct(outputCatalog=outputCatalog)
1519class WriteForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
1520 dimensions=("instrument", "visit", "detector", "skymap", "tract")):
1522 inputCatalog = connectionTypes.Input(
1523 doc="Primary per-detector, single-epoch forced-photometry catalog. "
1524 "By default, it is the output of ForcedPhotCcdTask on calexps",
1525 name="forced_src",
1526 storageClass="SourceCatalog",
1527 dimensions=("instrument", "visit", "detector", "skymap", "tract")
1528 )
1529 inputCatalogDiff = connectionTypes.Input(
1530 doc="Secondary multi-epoch, per-detector, forced photometry catalog. "
1531 "By default, it is the output of ForcedPhotCcdTask run on image differences.",
1532 name="forced_diff",
1533 storageClass="SourceCatalog",
1534 dimensions=("instrument", "visit", "detector", "skymap", "tract")
1535 )
1536 outputCatalog = connectionTypes.Output(
1537 doc="InputCatalogs horizonatally joined on `objectId` in Parquet format",
1538 name="forcedSource",
1539 storageClass="DataFrame",
1540 dimensions=("instrument", "visit", "detector")
1541 )
1544class WriteForcedSourceTableConfig(WriteSourceTableConfig,
1545 pipelineConnections=WriteForcedSourceTableConnections):
1546 key = lsst.pex.config.Field(
1547 doc="Column on which to join the two input tables on and make the primary key of the output",
1548 dtype=str,
1549 default="objectId",
1550 )
1553class WriteForcedSourceTableTask(pipeBase.PipelineTask):
1554 """Merge and convert per-detector forced source catalogs to parquet
1555 """
1556 _DefaultName = "writeForcedSourceTable"
1557 ConfigClass = WriteForcedSourceTableConfig
1559 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1560 inputs = butlerQC.get(inputRefs)
1561 # Add ccdVisitId to allow joining with CcdVisitTable
1562 inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
1563 inputs['band'] = butlerQC.quantum.dataId.full['band']
1564 outputs = self.run(**inputs)
1565 butlerQC.put(outputs, outputRefs)
1567 def run(self, inputCatalog, inputCatalogDiff, ccdVisitId=None, band=None):
1568 dfs = []
1569 for table, dataset, in zip((inputCatalog, inputCatalogDiff), ('calexp', 'diff')):
1570 df = table.asAstropy().to_pandas().set_index(self.config.key, drop=False)
1571 df = df.reindex(sorted(df.columns), axis=1)
1572 df['ccdVisitId'] = ccdVisitId if ccdVisitId else pd.NA
1573 df['band'] = band if band else pd.NA
1574 df.columns = pd.MultiIndex.from_tuples([(dataset, c) for c in df.columns],
1575 names=('dataset', 'column'))
1577 dfs.append(df)
1579 outputCatalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
1580 return pipeBase.Struct(outputCatalog=outputCatalog)
1583class TransformForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
1584 dimensions=("instrument", "skymap", "patch", "tract")):
1586 inputCatalogs = connectionTypes.Input(
1587 doc="Parquet table of merged ForcedSources produced by WriteForcedSourceTableTask",
1588 name="forcedSource",
1589 storageClass="DataFrame",
1590 dimensions=("instrument", "visit", "detector"),
1591 multiple=True,
1592 deferLoad=True
1593 )
1594 referenceCatalog = connectionTypes.Input(
1595 doc="Reference catalog which was used to seed the forcedPhot. Columns "
1596 "objectId, detect_isPrimary, detect_isTractInner, detect_isPatchInner "
1597 "are expected.",
1598 name="objectTable",
1599 storageClass="DataFrame",
1600 dimensions=("tract", "patch", "skymap"),
1601 deferLoad=True
1602 )
1603 outputCatalog = connectionTypes.Output(
1604 doc="Narrower, temporally-aggregated, per-patch ForcedSource Table transformed and converted per a "
1605 "specified set of functors",
1606 name="forcedSourceTable",
1607 storageClass="DataFrame",
1608 dimensions=("tract", "patch", "skymap")
1609 )
1612class TransformForcedSourceTableConfig(TransformCatalogBaseConfig,
1613 pipelineConnections=TransformForcedSourceTableConnections):
1614 referenceColumns = pexConfig.ListField(
1615 dtype=str,
1616 default=["detect_isPrimary", "detect_isTractInner", "detect_isPatchInner"],
1617 optional=True,
1618 doc="Columns to pull from reference catalog",
1619 )
1620 keyRef = lsst.pex.config.Field(
1621 doc="Column on which to join the two input tables on and make the primary key of the output",
1622 dtype=str,
1623 default="objectId",
1624 )
1625 key = lsst.pex.config.Field(
1626 doc="Rename the output DataFrame index to this name",
1627 dtype=str,
1628 default="forcedSourceId",
1629 )
1632class TransformForcedSourceTableTask(TransformCatalogBaseTask):
1633 """Transform/standardize a ForcedSource catalog
1635 Transforms each wide, per-detector forcedSource parquet table per the
1636 specification file (per-camera defaults found in ForcedSource.yaml).
1637 All epochs that overlap the patch are aggregated into one per-patch
1638 narrow-parquet file.
1640 No de-duplication of rows is performed. Duplicate resolutions flags are
1641 pulled in from the referenceCatalog: `detect_isPrimary`,
1642 `detect_isTractInner`,`detect_isPatchInner`, so that user may de-duplicate
1643 for analysis or compare duplicates for QA.
1645 The resulting table includes multiple bands. Epochs (MJDs) and other useful
1646 per-visit rows can be retreived by joining with the CcdVisitTable on
1647 ccdVisitId.
1648 """
1649 _DefaultName = "transformForcedSourceTable"
1650 ConfigClass = TransformForcedSourceTableConfig
1652 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1653 inputs = butlerQC.get(inputRefs)
1654 if self.funcs is None:
1655 raise ValueError("config.functorFile is None. "
1656 "Must be a valid path to yaml in order to run Task as a PipelineTask.")
1657 outputs = self.run(inputs['inputCatalogs'], inputs['referenceCatalog'], funcs=self.funcs,
1658 dataId=outputRefs.outputCatalog.dataId.full)
1660 butlerQC.put(outputs, outputRefs)
1662 def run(self, inputCatalogs, referenceCatalog, funcs=None, dataId=None, band=None):
1663 dfs = []
1664 ref = referenceCatalog.get(parameters={"columns": self.config.referenceColumns})
1665 self.log.info("Aggregating %s input catalogs" % (len(inputCatalogs)))
1666 for handle in inputCatalogs:
1667 result = self.transform(None, handle, funcs, dataId)
1668 # Filter for only rows that were detected on (overlap) the patch
1669 dfs.append(result.df.join(ref, how='inner'))
1671 outputCatalog = pd.concat(dfs)
1673 # Now that we are done joining on config.keyRef
1674 # Change index to config.key by
1675 outputCatalog.index.rename(self.config.keyRef, inplace=True)
1676 # Add config.keyRef to the column list
1677 outputCatalog.reset_index(inplace=True)
1678 # set the forcedSourceId to the index. This is specified in the ForcedSource.yaml
1679 outputCatalog.set_index("forcedSourceId", inplace=True, verify_integrity=True)
1680 # Rename it to the config.key
1681 outputCatalog.index.rename(self.config.key, inplace=True)
1683 self.log.info("Made a table of %d columns and %d rows",
1684 len(outputCatalog.columns), len(outputCatalog))
1685 return pipeBase.Struct(outputCatalog=outputCatalog)
1688class ConsolidateTractConnections(pipeBase.PipelineTaskConnections,
1689 defaultTemplates={"catalogType": ""},
1690 dimensions=("instrument", "tract")):
1691 inputCatalogs = connectionTypes.Input(
1692 doc="Input per-patch DataFrame Tables to be concatenated",
1693 name="{catalogType}ForcedSourceTable",
1694 storageClass="DataFrame",
1695 dimensions=("tract", "patch", "skymap"),
1696 multiple=True,
1697 )
1699 outputCatalog = connectionTypes.Output(
1700 doc="Output per-tract concatenation of DataFrame Tables",
1701 name="{catalogType}ForcedSourceTable_tract",
1702 storageClass="DataFrame",
1703 dimensions=("tract", "skymap"),
1704 )
1707class ConsolidateTractConfig(pipeBase.PipelineTaskConfig,
1708 pipelineConnections=ConsolidateTractConnections):
1709 pass
1712class ConsolidateTractTask(CmdLineTask, pipeBase.PipelineTask):
1713 """Concatenate any per-patch, dataframe list into a single
1714 per-tract DataFrame
1715 """
1716 _DefaultName = 'ConsolidateTract'
1717 ConfigClass = ConsolidateTractConfig
1719 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1720 inputs = butlerQC.get(inputRefs)
1721 # Not checking at least one inputCatalog exists because that'd be an empty QG
1722 self.log.info("Concatenating %s per-patch %s Tables",
1723 len(inputs['inputCatalogs']),
1724 inputRefs.inputCatalogs[0].datasetType.name)
1725 df = pd.concat(inputs['inputCatalogs'])
1726 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)