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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, RAColumn, DecColumn, 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 = (('coord_ra', RAColumn()),
462 ('coord_dec', DecColumn()))
464 def __init__(self, parq, functors, filt=None, flags=None, refFlags=None, forcedFlags=None):
465 self.parq = parq
466 self.functors = functors
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)
475 self._df = None
477 @property
478 def defaultFuncs(self):
479 funcs = dict(self._defaultFuncs)
480 return funcs
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})
489 if isinstance(self.functors, CompositeFunctor):
490 func = self.functors
491 else:
492 func = CompositeFunctor(self.functors)
494 func.funcDict.update(additionalFuncs)
495 func.filt = self.filt
497 return func
499 @property
500 def noDupCols(self):
501 return [name for name, func in self.func.funcDict.items() if func.noDup or func.dataset == 'ref']
503 @property
504 def df(self):
505 if self._df is None:
506 self.compute()
507 return self._df
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)
521 return self._df
524class TransformCatalogBaseConnections(pipeBase.PipelineTaskConnections,
525 dimensions=()):
526 """Expected Connections for subclasses of TransformCatalogBaseTask.
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 )
540class TransformCatalogBaseConfig(pipeBase.PipelineTaskConfig,
541 pipelineConnections=TransformCatalogBaseConnections):
542 functorFile = pexConfig.Field(
543 dtype=str,
544 doc='Path to YAML file specifying functors to be computed',
545 default=None,
546 optional=True
547 )
550class TransformCatalogBaseTask(CmdLineTask, pipeBase.PipelineTask):
551 """Base class for transforming/standardizing a catalog
553 by applying functors that convert units and apply calibrations.
554 The purpose of this task is to perform a set of computations on
555 an input `ParquetTable` dataset (such as `deepCoadd_obj`) and write the
556 results to a new dataset (which needs to be declared in an `outputDataset`
557 attribute).
559 The calculations to be performed are defined in a YAML file that specifies
560 a set of functors to be computed, provided as
561 a `--functorFile` config parameter. An example of such a YAML file
562 is the following:
564 funcs:
565 psfMag:
566 functor: Mag
567 args:
568 - base_PsfFlux
569 filt: HSC-G
570 dataset: meas
571 cmodel_magDiff:
572 functor: MagDiff
573 args:
574 - modelfit_CModel
575 - base_PsfFlux
576 filt: HSC-G
577 gauss_magDiff:
578 functor: MagDiff
579 args:
580 - base_GaussianFlux
581 - base_PsfFlux
582 filt: HSC-G
583 count:
584 functor: Column
585 args:
586 - base_InputCount_value
587 filt: HSC-G
588 deconvolved_moments:
589 functor: DeconvolvedMoments
590 filt: HSC-G
591 dataset: forced_src
592 refFlags:
593 - calib_psfUsed
594 - merge_measurement_i
595 - merge_measurement_r
596 - merge_measurement_z
597 - merge_measurement_y
598 - merge_measurement_g
599 - base_PixelFlags_flag_inexact_psfCenter
600 - detect_isPrimary
602 The names for each entry under "func" will become the names of columns in the
603 output dataset. All the functors referenced are defined in `lsst.pipe.tasks.functors`.
604 Positional arguments to be passed to each functor are in the `args` list,
605 and any additional entries for each column other than "functor" or "args" (e.g., `'filt'`,
606 `'dataset'`) are treated as keyword arguments to be passed to the functor initialization.
608 The "flags" entry is the default shortcut for `Column` functors.
609 All columns listed under "flags" will be copied to the output table
610 untransformed. They can be of any datatype.
611 In the special case of transforming a multi-level oject table with
612 band and dataset indices (deepCoadd_obj), these will be taked from the
613 `meas` dataset and exploded out per band.
615 There are two special shortcuts that only apply when transforming
616 multi-level Object (deepCoadd_obj) tables:
617 - The "refFlags" entry is shortcut for `Column` functor
618 taken from the `'ref'` dataset if transforming an ObjectTable.
619 - The "forcedFlags" entry is shortcut for `Column` functors.
620 taken from the ``forced_src`` dataset if transforming an ObjectTable.
621 These are expanded out per band.
624 This task uses the `lsst.pipe.tasks.postprocess.PostprocessAnalysis` object
625 to organize and excecute the calculations.
627 """
628 @property
629 def _DefaultName(self):
630 raise NotImplementedError('Subclass must define "_DefaultName" attribute')
632 @property
633 def outputDataset(self):
634 raise NotImplementedError('Subclass must define "outputDataset" attribute')
636 @property
637 def inputDataset(self):
638 raise NotImplementedError('Subclass must define "inputDataset" attribute')
640 @property
641 def ConfigClass(self):
642 raise NotImplementedError('Subclass must define "ConfigClass" attribute')
644 def __init__(self, *args, **kwargs):
645 super().__init__(*args, **kwargs)
646 if self.config.functorFile:
647 self.log.info('Loading tranform functor definitions from %s',
648 self.config.functorFile)
649 self.funcs = CompositeFunctor.from_file(self.config.functorFile)
650 self.funcs.update(dict(PostprocessAnalysis._defaultFuncs))
651 else:
652 self.funcs = None
654 def runQuantum(self, butlerQC, inputRefs, outputRefs):
655 inputs = butlerQC.get(inputRefs)
656 if self.funcs is None:
657 raise ValueError("config.functorFile is None. "
658 "Must be a valid path to yaml in order to run Task as a PipelineTask.")
659 result = self.run(parq=inputs['inputCatalog'], funcs=self.funcs,
660 dataId=outputRefs.outputCatalog.dataId.full)
661 outputs = pipeBase.Struct(outputCatalog=result)
662 butlerQC.put(outputs, outputRefs)
664 def runDataRef(self, dataRef):
665 parq = dataRef.get()
666 if self.funcs is None:
667 raise ValueError("config.functorFile is None. "
668 "Must be a valid path to yaml in order to run as a CommandlineTask.")
669 df = self.run(parq, funcs=self.funcs, dataId=dataRef.dataId)
670 self.write(df, dataRef)
671 return df
673 def run(self, parq, funcs=None, dataId=None, band=None):
674 """Do postprocessing calculations
676 Takes a `ParquetTable` object and dataId,
677 returns a dataframe with results of postprocessing calculations.
679 Parameters
680 ----------
681 parq : `lsst.pipe.tasks.parquetTable.ParquetTable`
682 ParquetTable from which calculations are done.
683 funcs : `lsst.pipe.tasks.functors.Functors`
684 Functors to apply to the table's columns
685 dataId : dict, optional
686 Used to add a `patchId` column to the output dataframe.
687 band : `str`, optional
688 Filter band that is being processed.
690 Returns
691 ------
692 `pandas.DataFrame`
694 """
695 self.log.info("Transforming/standardizing the source table dataId: %s", dataId)
697 df = self.transform(band, parq, funcs, dataId).df
698 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
699 return df
701 def getFunctors(self):
702 return self.funcs
704 def getAnalysis(self, parq, funcs=None, band=None):
705 if funcs is None:
706 funcs = self.funcs
707 analysis = PostprocessAnalysis(parq, funcs, filt=band)
708 return analysis
710 def transform(self, band, parq, funcs, dataId):
711 analysis = self.getAnalysis(parq, funcs=funcs, band=band)
712 df = analysis.df
713 if dataId is not None:
714 for key, value in dataId.items():
715 df[str(key)] = value
717 return pipeBase.Struct(
718 df=df,
719 analysis=analysis
720 )
722 def write(self, df, parqRef):
723 parqRef.put(ParquetTable(dataFrame=df), self.outputDataset)
725 def writeMetadata(self, dataRef):
726 """No metadata to write.
727 """
728 pass
731class TransformObjectCatalogConnections(pipeBase.PipelineTaskConnections,
732 defaultTemplates={"coaddName": "deep"},
733 dimensions=("tract", "patch", "skymap")):
734 inputCatalog = connectionTypes.Input(
735 doc="The vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
736 "stored as a DataFrame with a multi-level column index per-patch.",
737 dimensions=("tract", "patch", "skymap"),
738 storageClass="DataFrame",
739 name="{coaddName}Coadd_obj",
740 deferLoad=True,
741 )
742 outputCatalog = connectionTypes.Output(
743 doc="Per-Patch Object Table of columns transformed from the deepCoadd_obj table per the standard "
744 "data model.",
745 dimensions=("tract", "patch", "skymap"),
746 storageClass="DataFrame",
747 name="objectTable"
748 )
751class TransformObjectCatalogConfig(TransformCatalogBaseConfig,
752 pipelineConnections=TransformObjectCatalogConnections):
753 coaddName = pexConfig.Field(
754 dtype=str,
755 default="deep",
756 doc="Name of coadd"
757 )
758 # TODO: remove in DM-27177
759 filterMap = pexConfig.DictField(
760 keytype=str,
761 itemtype=str,
762 default={},
763 doc=("Dictionary mapping full filter name to short one for column name munging."
764 "These filters determine the output columns no matter what filters the "
765 "input data actually contain."),
766 deprecated=("Coadds are now identified by the band, so this transform is unused."
767 "Will be removed after v22.")
768 )
769 outputBands = pexConfig.ListField(
770 dtype=str,
771 default=None,
772 optional=True,
773 doc=("These bands and only these bands will appear in the output,"
774 " NaN-filled if the input does not include them."
775 " If None, then use all bands found in the input.")
776 )
777 camelCase = pexConfig.Field(
778 dtype=bool,
779 default=True,
780 doc=("Write per-band columns names with camelCase, else underscore "
781 "For example: gPsFlux instead of g_PsFlux.")
782 )
783 multilevelOutput = pexConfig.Field(
784 dtype=bool,
785 default=False,
786 doc=("Whether results dataframe should have a multilevel column index (True) or be flat "
787 "and name-munged (False).")
788 )
791class TransformObjectCatalogTask(TransformCatalogBaseTask):
792 """Produce a flattened Object Table to match the format specified in
793 sdm_schemas.
795 Do the same set of postprocessing calculations on all bands
797 This is identical to `TransformCatalogBaseTask`, except for that it does the
798 specified functor calculations for all filters present in the
799 input `deepCoadd_obj` table. Any specific `"filt"` keywords specified
800 by the YAML file will be superceded.
801 """
802 _DefaultName = "transformObjectCatalog"
803 ConfigClass = TransformObjectCatalogConfig
805 # Used by Gen 2 runDataRef only:
806 inputDataset = 'deepCoadd_obj'
807 outputDataset = 'objectTable'
809 @classmethod
810 def _makeArgumentParser(cls):
811 parser = ArgumentParser(name=cls._DefaultName)
812 parser.add_id_argument("--id", cls.inputDataset,
813 ContainerClass=CoaddDataIdContainer,
814 help="data ID, e.g. --id tract=12345 patch=1,2")
815 return parser
817 def run(self, parq, funcs=None, dataId=None, band=None):
818 # NOTE: band kwarg is ignored here.
819 dfDict = {}
820 analysisDict = {}
821 templateDf = pd.DataFrame()
823 if isinstance(parq, DeferredDatasetHandle):
824 columns = parq.get(component='columns')
825 inputBands = columns.unique(level=1).values
826 else:
827 inputBands = parq.columnLevelNames['band']
829 outputBands = self.config.outputBands if self.config.outputBands else inputBands
831 # Perform transform for data of filters that exist in parq.
832 for inputBand in inputBands:
833 if inputBand not in outputBands:
834 self.log.info("Ignoring %s band data in the input", inputBand)
835 continue
836 self.log.info("Transforming the catalog of band %s", inputBand)
837 result = self.transform(inputBand, parq, funcs, dataId)
838 dfDict[inputBand] = result.df
839 analysisDict[inputBand] = result.analysis
840 if templateDf.empty:
841 templateDf = result.df
843 # Fill NaNs in columns of other wanted bands
844 for filt in outputBands:
845 if filt not in dfDict:
846 self.log.info("Adding empty columns for band %s", filt)
847 dfDict[filt] = pd.DataFrame().reindex_like(templateDf)
849 # This makes a multilevel column index, with band as first level
850 df = pd.concat(dfDict, axis=1, names=['band', 'column'])
852 if not self.config.multilevelOutput:
853 noDupCols = list(set.union(*[set(v.noDupCols) for v in analysisDict.values()]))
854 if dataId is not None:
855 noDupCols += list(dataId.keys())
856 df = flattenFilters(df, noDupCols=noDupCols, camelCase=self.config.camelCase,
857 inputBands=inputBands)
859 self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
860 return df
863class TractObjectDataIdContainer(CoaddDataIdContainer):
865 def makeDataRefList(self, namespace):
866 """Make self.refList from self.idList
868 Generate a list of data references given tract and/or patch.
869 This was adapted from `TractQADataIdContainer`, which was
870 `TractDataIdContainer` modifie to not require "filter".
871 Only existing dataRefs are returned.
872 """
873 def getPatchRefList(tract):
874 return [namespace.butler.dataRef(datasetType=self.datasetType,
875 tract=tract.getId(),
876 patch="%d,%d" % patch.getIndex()) for patch in tract]
878 tractRefs = defaultdict(list) # Data references for each tract
879 for dataId in self.idList:
880 skymap = self.getSkymap(namespace)
882 if "tract" in dataId:
883 tractId = dataId["tract"]
884 if "patch" in dataId:
885 tractRefs[tractId].append(namespace.butler.dataRef(datasetType=self.datasetType,
886 tract=tractId,
887 patch=dataId['patch']))
888 else:
889 tractRefs[tractId] += getPatchRefList(skymap[tractId])
890 else:
891 tractRefs = dict((tract.getId(), tractRefs.get(tract.getId(), []) + getPatchRefList(tract))
892 for tract in skymap)
893 outputRefList = []
894 for tractRefList in tractRefs.values():
895 existingRefs = [ref for ref in tractRefList if ref.datasetExists()]
896 outputRefList.append(existingRefs)
898 self.refList = outputRefList
901class ConsolidateObjectTableConnections(pipeBase.PipelineTaskConnections,
902 dimensions=("tract", "skymap")):
903 inputCatalogs = connectionTypes.Input(
904 doc="Per-Patch objectTables conforming to the standard data model.",
905 name="objectTable",
906 storageClass="DataFrame",
907 dimensions=("tract", "patch", "skymap"),
908 multiple=True,
909 )
910 outputCatalog = connectionTypes.Output(
911 doc="Pre-tract horizontal concatenation of the input objectTables",
912 name="objectTable_tract",
913 storageClass="DataFrame",
914 dimensions=("tract", "skymap"),
915 )
918class ConsolidateObjectTableConfig(pipeBase.PipelineTaskConfig,
919 pipelineConnections=ConsolidateObjectTableConnections):
920 coaddName = pexConfig.Field(
921 dtype=str,
922 default="deep",
923 doc="Name of coadd"
924 )
927class ConsolidateObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
928 """Write patch-merged source tables to a tract-level parquet file
930 Concatenates `objectTable` list into a per-visit `objectTable_tract`
931 """
932 _DefaultName = "consolidateObjectTable"
933 ConfigClass = ConsolidateObjectTableConfig
935 inputDataset = 'objectTable'
936 outputDataset = 'objectTable_tract'
938 def runQuantum(self, butlerQC, inputRefs, outputRefs):
939 inputs = butlerQC.get(inputRefs)
940 self.log.info("Concatenating %s per-patch Object Tables",
941 len(inputs['inputCatalogs']))
942 df = pd.concat(inputs['inputCatalogs'])
943 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
945 @classmethod
946 def _makeArgumentParser(cls):
947 parser = ArgumentParser(name=cls._DefaultName)
949 parser.add_id_argument("--id", cls.inputDataset,
950 help="data ID, e.g. --id tract=12345",
951 ContainerClass=TractObjectDataIdContainer)
952 return parser
954 def runDataRef(self, patchRefList):
955 df = pd.concat([patchRef.get().toDataFrame() for patchRef in patchRefList])
956 patchRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
958 def writeMetadata(self, dataRef):
959 """No metadata to write.
960 """
961 pass
964class TransformSourceTableConnections(pipeBase.PipelineTaskConnections,
965 defaultTemplates={"catalogType": ""},
966 dimensions=("instrument", "visit", "detector")):
968 inputCatalog = connectionTypes.Input(
969 doc="Wide input catalog of sources produced by WriteSourceTableTask",
970 name="{catalogType}source",
971 storageClass="DataFrame",
972 dimensions=("instrument", "visit", "detector"),
973 deferLoad=True
974 )
975 outputCatalog = connectionTypes.Output(
976 doc="Narrower, per-detector Source Table transformed and converted per a "
977 "specified set of functors",
978 name="{catalogType}sourceTable",
979 storageClass="DataFrame",
980 dimensions=("instrument", "visit", "detector")
981 )
984class TransformSourceTableConfig(TransformCatalogBaseConfig,
985 pipelineConnections=TransformSourceTableConnections):
986 pass
989class TransformSourceTableTask(TransformCatalogBaseTask):
990 """Transform/standardize a source catalog
991 """
992 _DefaultName = "transformSourceTable"
993 ConfigClass = TransformSourceTableConfig
995 inputDataset = 'source'
996 outputDataset = 'sourceTable'
998 @classmethod
999 def _makeArgumentParser(cls):
1000 parser = ArgumentParser(name=cls._DefaultName)
1001 parser.add_id_argument("--id", datasetType=cls.inputDataset,
1002 level="sensor",
1003 help="data ID, e.g. --id visit=12345 ccd=0")
1004 return parser
1006 def runDataRef(self, dataRef):
1007 """Override to specify band label to run()."""
1008 parq = dataRef.get()
1009 funcs = self.getFunctors()
1010 band = dataRef.get("calexp_filterLabel", immediate=True).bandLabel
1011 df = self.run(parq, funcs=funcs, dataId=dataRef.dataId, band=band)
1012 self.write(df, dataRef)
1013 return df
1016class ConsolidateVisitSummaryConnections(pipeBase.PipelineTaskConnections,
1017 dimensions=("instrument", "visit",),
1018 defaultTemplates={"calexpType": ""}):
1019 calexp = connectionTypes.Input(
1020 doc="Processed exposures used for metadata",
1021 name="{calexpType}calexp",
1022 storageClass="ExposureF",
1023 dimensions=("instrument", "visit", "detector"),
1024 deferLoad=True,
1025 multiple=True,
1026 )
1027 visitSummary = connectionTypes.Output(
1028 doc=("Per-visit consolidated exposure metadata. These catalogs use "
1029 "detector id for the id and are sorted for fast lookups of a "
1030 "detector."),
1031 name="{calexpType}visitSummary",
1032 storageClass="ExposureCatalog",
1033 dimensions=("instrument", "visit"),
1034 )
1037class ConsolidateVisitSummaryConfig(pipeBase.PipelineTaskConfig,
1038 pipelineConnections=ConsolidateVisitSummaryConnections):
1039 """Config for ConsolidateVisitSummaryTask"""
1040 pass
1043class ConsolidateVisitSummaryTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
1044 """Task to consolidate per-detector visit metadata.
1046 This task aggregates the following metadata from all the detectors in a
1047 single visit into an exposure catalog:
1048 - The visitInfo.
1049 - The wcs.
1050 - The photoCalib.
1051 - The physical_filter and band (if available).
1052 - The psf size, shape, and effective area at the center of the detector.
1053 - The corners of the bounding box in right ascension/declination.
1055 Other quantities such as Detector, Psf, ApCorrMap, and TransmissionCurve
1056 are not persisted here because of storage concerns, and because of their
1057 limited utility as summary statistics.
1059 Tests for this task are performed in ci_hsc_gen3.
1060 """
1061 _DefaultName = "consolidateVisitSummary"
1062 ConfigClass = ConsolidateVisitSummaryConfig
1064 @classmethod
1065 def _makeArgumentParser(cls):
1066 parser = ArgumentParser(name=cls._DefaultName)
1068 parser.add_id_argument("--id", "calexp",
1069 help="data ID, e.g. --id visit=12345",
1070 ContainerClass=VisitDataIdContainer)
1071 return parser
1073 def writeMetadata(self, dataRef):
1074 """No metadata to persist, so override to remove metadata persistance.
1075 """
1076 pass
1078 def writeConfig(self, butler, clobber=False, doBackup=True):
1079 """No config to persist, so override to remove config persistance.
1080 """
1081 pass
1083 def runDataRef(self, dataRefList):
1084 visit = dataRefList[0].dataId['visit']
1086 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1087 len(dataRefList), visit)
1089 expCatalog = self._combineExposureMetadata(visit, dataRefList, isGen3=False)
1091 dataRefList[0].put(expCatalog, 'visitSummary', visit=visit)
1093 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1094 dataRefs = butlerQC.get(inputRefs.calexp)
1095 visit = dataRefs[0].dataId.byName()['visit']
1097 self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
1098 len(dataRefs), visit)
1100 expCatalog = self._combineExposureMetadata(visit, dataRefs)
1102 butlerQC.put(expCatalog, outputRefs.visitSummary)
1104 def _combineExposureMetadata(self, visit, dataRefs, isGen3=True):
1105 """Make a combined exposure catalog from a list of dataRefs.
1106 These dataRefs must point to exposures with wcs, summaryStats,
1107 and other visit metadata.
1109 Parameters
1110 ----------
1111 visit : `int`
1112 Visit identification number.
1113 dataRefs : `list`
1114 List of dataRefs in visit. May be list of
1115 `lsst.daf.persistence.ButlerDataRef` (Gen2) or
1116 `lsst.daf.butler.DeferredDatasetHandle` (Gen3).
1117 isGen3 : `bool`, optional
1118 Specifies if this is a Gen3 list of datarefs.
1120 Returns
1121 -------
1122 visitSummary : `lsst.afw.table.ExposureCatalog`
1123 Exposure catalog with per-detector summary information.
1124 """
1125 schema = self._makeVisitSummarySchema()
1126 cat = afwTable.ExposureCatalog(schema)
1127 cat.resize(len(dataRefs))
1129 cat['visit'] = visit
1131 for i, dataRef in enumerate(dataRefs):
1132 if isGen3:
1133 visitInfo = dataRef.get(component='visitInfo')
1134 filterLabel = dataRef.get(component='filterLabel')
1135 summaryStats = dataRef.get(component='summaryStats')
1136 detector = dataRef.get(component='detector')
1137 wcs = dataRef.get(component='wcs')
1138 photoCalib = dataRef.get(component='photoCalib')
1139 detector = dataRef.get(component='detector')
1140 bbox = dataRef.get(component='bbox')
1141 validPolygon = dataRef.get(component='validPolygon')
1142 else:
1143 # Note that we need to read the calexp because there is
1144 # no magic access to the psf except through the exposure.
1145 gen2_read_bbox = lsst.geom.BoxI(lsst.geom.PointI(0, 0), lsst.geom.PointI(1, 1))
1146 exp = dataRef.get(datasetType='calexp_sub', bbox=gen2_read_bbox)
1147 visitInfo = exp.getInfo().getVisitInfo()
1148 filterLabel = dataRef.get("calexp_filterLabel")
1149 summaryStats = exp.getInfo().getSummaryStats()
1150 wcs = exp.getWcs()
1151 photoCalib = exp.getPhotoCalib()
1152 detector = exp.getDetector()
1153 bbox = dataRef.get(datasetType='calexp_bbox')
1154 validPolygon = exp.getInfo().getValidPolygon()
1156 rec = cat[i]
1157 rec.setBBox(bbox)
1158 rec.setVisitInfo(visitInfo)
1159 rec.setWcs(wcs)
1160 rec.setPhotoCalib(photoCalib)
1161 rec.setValidPolygon(validPolygon)
1163 rec['physical_filter'] = filterLabel.physicalLabel if filterLabel.hasPhysicalLabel() else ""
1164 rec['band'] = filterLabel.bandLabel if filterLabel.hasBandLabel() else ""
1165 rec.setId(detector.getId())
1166 rec['psfSigma'] = summaryStats.psfSigma
1167 rec['psfIxx'] = summaryStats.psfIxx
1168 rec['psfIyy'] = summaryStats.psfIyy
1169 rec['psfIxy'] = summaryStats.psfIxy
1170 rec['psfArea'] = summaryStats.psfArea
1171 rec['raCorners'][:] = summaryStats.raCorners
1172 rec['decCorners'][:] = summaryStats.decCorners
1173 rec['ra'] = summaryStats.ra
1174 rec['decl'] = summaryStats.decl
1175 rec['zenithDistance'] = summaryStats.zenithDistance
1176 rec['zeroPoint'] = summaryStats.zeroPoint
1177 rec['skyBg'] = summaryStats.skyBg
1178 rec['skyNoise'] = summaryStats.skyNoise
1179 rec['meanVar'] = summaryStats.meanVar
1180 rec['astromOffsetMean'] = summaryStats.astromOffsetMean
1181 rec['astromOffsetStd'] = summaryStats.astromOffsetStd
1183 metadata = dafBase.PropertyList()
1184 metadata.add("COMMENT", "Catalog id is detector id, sorted.")
1185 # We are looping over existing datarefs, so the following is true
1186 metadata.add("COMMENT", "Only detectors with data have entries.")
1187 cat.setMetadata(metadata)
1189 cat.sort()
1190 return cat
1192 def _makeVisitSummarySchema(self):
1193 """Make the schema for the visitSummary catalog."""
1194 schema = afwTable.ExposureTable.makeMinimalSchema()
1195 schema.addField('visit', type='I', doc='Visit number')
1196 schema.addField('physical_filter', type='String', size=32, doc='Physical filter')
1197 schema.addField('band', type='String', size=32, doc='Name of band')
1198 schema.addField('psfSigma', type='F',
1199 doc='PSF model second-moments determinant radius (center of chip) (pixel)')
1200 schema.addField('psfArea', type='F',
1201 doc='PSF model effective area (center of chip) (pixel**2)')
1202 schema.addField('psfIxx', type='F',
1203 doc='PSF model Ixx (center of chip) (pixel**2)')
1204 schema.addField('psfIyy', type='F',
1205 doc='PSF model Iyy (center of chip) (pixel**2)')
1206 schema.addField('psfIxy', type='F',
1207 doc='PSF model Ixy (center of chip) (pixel**2)')
1208 schema.addField('raCorners', type='ArrayD', size=4,
1209 doc='Right Ascension of bounding box corners (degrees)')
1210 schema.addField('decCorners', type='ArrayD', size=4,
1211 doc='Declination of bounding box corners (degrees)')
1212 schema.addField('ra', type='D',
1213 doc='Right Ascension of bounding box center (degrees)')
1214 schema.addField('decl', type='D',
1215 doc='Declination of bounding box center (degrees)')
1216 schema.addField('zenithDistance', type='F',
1217 doc='Zenith distance of bounding box center (degrees)')
1218 schema.addField('zeroPoint', type='F',
1219 doc='Mean zeropoint in detector (mag)')
1220 schema.addField('skyBg', type='F',
1221 doc='Average sky background (ADU)')
1222 schema.addField('skyNoise', type='F',
1223 doc='Average sky noise (ADU)')
1224 schema.addField('meanVar', type='F',
1225 doc='Mean variance of the weight plane (ADU**2)')
1226 schema.addField('astromOffsetMean', type='F',
1227 doc='Mean offset of astrometric calibration matches (arcsec)')
1228 schema.addField('astromOffsetStd', type='F',
1229 doc='Standard deviation of offsets of astrometric calibration matches (arcsec)')
1231 return schema
1234class VisitDataIdContainer(DataIdContainer):
1235 """DataIdContainer that groups sensor-level id's by visit
1236 """
1238 def makeDataRefList(self, namespace):
1239 """Make self.refList from self.idList
1241 Generate a list of data references grouped by visit.
1243 Parameters
1244 ----------
1245 namespace : `argparse.Namespace`
1246 Namespace used by `lsst.pipe.base.CmdLineTask` to parse command line arguments
1247 """
1248 # Group by visits
1249 visitRefs = defaultdict(list)
1250 for dataId in self.idList:
1251 if "visit" in dataId:
1252 visitId = dataId["visit"]
1253 # append all subsets to
1254 subset = namespace.butler.subset(self.datasetType, dataId=dataId)
1255 visitRefs[visitId].extend([dataRef for dataRef in subset])
1257 outputRefList = []
1258 for refList in visitRefs.values():
1259 existingRefs = [ref for ref in refList if ref.datasetExists()]
1260 if existingRefs:
1261 outputRefList.append(existingRefs)
1263 self.refList = outputRefList
1266class ConsolidateSourceTableConnections(pipeBase.PipelineTaskConnections,
1267 defaultTemplates={"catalogType": ""},
1268 dimensions=("instrument", "visit")):
1269 inputCatalogs = connectionTypes.Input(
1270 doc="Input per-detector Source Tables",
1271 name="{catalogType}sourceTable",
1272 storageClass="DataFrame",
1273 dimensions=("instrument", "visit", "detector"),
1274 multiple=True
1275 )
1276 outputCatalog = connectionTypes.Output(
1277 doc="Per-visit concatenation of Source Table",
1278 name="{catalogType}sourceTable_visit",
1279 storageClass="DataFrame",
1280 dimensions=("instrument", "visit")
1281 )
1284class ConsolidateSourceTableConfig(pipeBase.PipelineTaskConfig,
1285 pipelineConnections=ConsolidateSourceTableConnections):
1286 pass
1289class ConsolidateSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
1290 """Concatenate `sourceTable` list into a per-visit `sourceTable_visit`
1291 """
1292 _DefaultName = 'consolidateSourceTable'
1293 ConfigClass = ConsolidateSourceTableConfig
1295 inputDataset = 'sourceTable'
1296 outputDataset = 'sourceTable_visit'
1298 def runQuantum(self, butlerQC, inputRefs, outputRefs):
1299 inputs = butlerQC.get(inputRefs)
1300 self.log.info("Concatenating %s per-detector Source Tables",
1301 len(inputs['inputCatalogs']))
1302 df = pd.concat(inputs['inputCatalogs'])
1303 butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
1305 def runDataRef(self, dataRefList):
1306 self.log.info("Concatenating %s per-detector Source Tables", len(dataRefList))
1307 df = pd.concat([dataRef.get().toDataFrame() for dataRef in dataRefList])
1308 dataRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
1310 @classmethod
1311 def _makeArgumentParser(cls):
1312 parser = ArgumentParser(name=cls._DefaultName)
1314 parser.add_id_argument("--id", cls.inputDataset,
1315 help="data ID, e.g. --id visit=12345",
1316 ContainerClass=VisitDataIdContainer)
1317 return parser
1319 def writeMetadata(self, dataRef):
1320 """No metadata to write.
1321 """
1322 pass
1324 def writeConfig(self, butler, clobber=False, doBackup=True):
1325 """No config to write.
1326 """
1327 pass
1330class MakeCcdVisitTableConnections(pipeBase.PipelineTaskConnections,
1331 dimensions=("instrument",),
1332 defaultTemplates={}):
1333 visitSummaryRefs = connectionTypes.Input(
1334 doc="Data references for per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1335 name="visitSummary",
1336 storageClass="ExposureCatalog",
1337 dimensions=("instrument", "visit"),
1338 multiple=True,
1339 deferLoad=True,
1340 )
1341 outputCatalog = connectionTypes.Output(
1342 doc="CCD and Visit metadata table",
1343 name="CcdVisitTable",
1344 storageClass="DataFrame",
1345 dimensions=("instrument",)
1346 )
1349class MakeCcdVisitTableConfig(pipeBase.PipelineTaskConfig,
1350 pipelineConnections=MakeCcdVisitTableConnections):
1351 pass
1354class MakeCcdVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1355 """Produce a `ccdVisitTable` from the `visitSummary` exposure catalogs.
1356 """
1357 _DefaultName = 'makeCcdVisitTable'
1358 ConfigClass = MakeCcdVisitTableConfig
1360 def run(self, visitSummaryRefs):
1361 """ Make a table of ccd information from the `visitSummary` catalogs.
1362 Parameters
1363 ----------
1364 visitSummaryRefs : `list` of `lsst.daf.butler.DeferredDatasetHandle`
1365 List of DeferredDatasetHandles pointing to exposure catalogs with
1366 per-detector summary information.
1367 Returns
1368 -------
1369 result : `lsst.pipe.Base.Struct`
1370 Results struct with attribute:
1371 - `outputCatalog`
1372 Catalog of ccd and visit information.
1373 """
1374 ccdEntries = []
1375 for visitSummaryRef in visitSummaryRefs:
1376 visitSummary = visitSummaryRef.get()
1377 visitInfo = visitSummary[0].getVisitInfo()
1379 ccdEntry = {}
1380 summaryTable = visitSummary.asAstropy()
1381 selectColumns = ['id', 'visit', 'physical_filter', 'ra', 'decl', 'zenithDistance', 'zeroPoint',
1382 'psfSigma', 'skyBg', 'skyNoise']
1383 ccdEntry = summaryTable[selectColumns].to_pandas().set_index('id')
1384 ccdEntry = ccdEntry.rename(columns={"physical_filter": "filterName", "visit": "visitId"})
1386 dataIds = [DataCoordinate.standardize(visitSummaryRef.dataId, detector=id) for id in
1387 summaryTable['id']]
1388 packer = visitSummaryRef.dataId.universe.makePacker('visit_detector', visitSummaryRef.dataId)
1389 ccdVisitIds = [packer.pack(dataId) for dataId in dataIds]
1390 ccdEntry['ccdVisitId'] = ccdVisitIds
1392 pixToArcseconds = np.array([vR.getWcs().getPixelScale().asArcseconds() for vR in visitSummary])
1393 ccdEntry["seeing"] = visitSummary['psfSigma'] * np.sqrt(8 * np.log(2)) * pixToArcseconds
1395 ccdEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1396 ccdEntry["expMidpt"] = visitInfo.getDate().toPython()
1397 expTime = visitInfo.getExposureTime()
1398 ccdEntry['expTime'] = expTime
1399 ccdEntry["obsStart"] = ccdEntry["expMidpt"] - 0.5 * pd.Timedelta(seconds=expTime)
1400 ccdEntry['darkTime'] = visitInfo.getDarkTime()
1401 ccdEntry['xSize'] = summaryTable['bbox_max_x'] - summaryTable['bbox_min_x']
1402 ccdEntry['ySize'] = summaryTable['bbox_max_y'] - summaryTable['bbox_min_y']
1403 ccdEntry['llcra'] = summaryTable['raCorners'][:, 0]
1404 ccdEntry['llcdec'] = summaryTable['decCorners'][:, 0]
1405 ccdEntry['ulcra'] = summaryTable['raCorners'][:, 1]
1406 ccdEntry['ulcdec'] = summaryTable['decCorners'][:, 1]
1407 ccdEntry['urcra'] = summaryTable['raCorners'][:, 2]
1408 ccdEntry['urcdec'] = summaryTable['decCorners'][:, 2]
1409 ccdEntry['lrcra'] = summaryTable['raCorners'][:, 3]
1410 ccdEntry['lrcdec'] = summaryTable['decCorners'][:, 3]
1411 # TODO: DM-30618, Add raftName, nExposures, ccdTemp, binX, binY, and flags,
1412 # and decide if WCS, and llcx, llcy, ulcx, ulcy, etc. values are actually wanted.
1413 ccdEntries.append(ccdEntry)
1415 outputCatalog = pd.concat(ccdEntries)
1416 return pipeBase.Struct(outputCatalog=outputCatalog)
1419class MakeVisitTableConnections(pipeBase.PipelineTaskConnections,
1420 dimensions=("instrument",),
1421 defaultTemplates={}):
1422 visitSummaries = connectionTypes.Input(
1423 doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
1424 name="visitSummary",
1425 storageClass="ExposureCatalog",
1426 dimensions=("instrument", "visit",),
1427 multiple=True,
1428 deferLoad=True,
1429 )
1430 outputCatalog = connectionTypes.Output(
1431 doc="Visit metadata table",
1432 name="visitTable",
1433 storageClass="DataFrame",
1434 dimensions=("instrument",)
1435 )
1438class MakeVisitTableConfig(pipeBase.PipelineTaskConfig,
1439 pipelineConnections=MakeVisitTableConnections):
1440 pass
1443class MakeVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
1444 """Produce a `visitTable` from the `visitSummary` exposure catalogs.
1445 """
1446 _DefaultName = 'makeVisitTable'
1447 ConfigClass = MakeVisitTableConfig
1449 def run(self, visitSummaries):
1450 """ Make a table of visit information from the `visitSummary` catalogs
1452 Parameters
1453 ----------
1454 visitSummaries : list of `lsst.afw.table.ExposureCatalog`
1455 List of exposure catalogs with per-detector summary information.
1456 Returns
1457 -------
1458 result : `lsst.pipe.Base.Struct`
1459 Results struct with attribute:
1460 ``outputCatalog``
1461 Catalog of visit information.
1462 """
1463 visitEntries = []
1464 for visitSummary in visitSummaries:
1465 visitSummary = visitSummary.get()
1466 visitRow = visitSummary[0]
1467 visitInfo = visitRow.getVisitInfo()
1469 visitEntry = {}
1470 visitEntry["visitId"] = visitRow['visit']
1471 visitEntry["filterName"] = visitRow['physical_filter']
1472 raDec = visitInfo.getBoresightRaDec()
1473 visitEntry["ra"] = raDec.getRa().asDegrees()
1474 visitEntry["decl"] = raDec.getDec().asDegrees()
1475 visitEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
1476 azAlt = visitInfo.getBoresightAzAlt()
1477 visitEntry["azimuth"] = azAlt.getLongitude().asDegrees()
1478 visitEntry["altitude"] = azAlt.getLatitude().asDegrees()
1479 visitEntry["zenithDistance"] = 90 - azAlt.getLatitude().asDegrees()
1480 visitEntry["airmass"] = visitInfo.getBoresightAirmass()
1481 visitEntry["obsStart"] = visitInfo.getDate().toPython()
1482 visitEntry["expTime"] = visitInfo.getExposureTime()
1483 visitEntries.append(visitEntry)
1484 # TODO: DM-30623, Add programId, exposureType, expMidpt, cameraTemp, mirror1Temp, mirror2Temp,
1485 # mirror3Temp, domeTemp, externalTemp, dimmSeeing, pwvGPS, pwvMW, flags, nExposures
1487 outputCatalog = pd.DataFrame(data=visitEntries)
1488 return pipeBase.Struct(outputCatalog=outputCatalog)