postprocess.py

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# This file is part of pipe_tasks
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (https://www.lsst.org).
# See the COPYRIGHT file at the top-level directory of this distribution
# for details of code ownership.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
import functools
import pandas as pd
from collections import defaultdict
import numpy as np
import numbers
 
import lsst.geom
import lsst.pex.config as pexConfig
import lsst.pipe.base as pipeBase
import lsst.daf.base as dafBase
from lsst.pipe.base import connectionTypes
import lsst.afw.table as afwTable
from lsst.meas.base import SingleFrameMeasurementTask
from lsst.pipe.base import CmdLineTask, ArgumentParser, DataIdContainer
from lsst.coadd.utils.coaddDataIdContainer import CoaddDataIdContainer
from lsst.daf.butler import DeferredDatasetHandle, DataCoordinate
 
from .parquetTable import ParquetTable
from .multiBandUtils import makeMergeArgumentParser, MergeSourcesRunner
from .functors import CompositeFunctor, Column
 
 
def flattenFilters(df, noDupCols=['coord_ra', 'coord_dec'], camelCase=False, inputBands=None):
    """Flattens a dataframe with multilevel column index
    """
    newDf = pd.DataFrame()
    # band is the level 0 index
    dfBands = df.columns.unique(level=0).values
    for band in dfBands:
        subdf = df[band]
        columnFormat = '{0}{1}' if camelCase else '{0}_{1}'
        newColumns = {c: columnFormat.format(band, c)
                      for c in subdf.columns if c not in noDupCols}
        cols = list(newColumns.keys())
        newDf = pd.concat([newDf, subdf[cols].rename(columns=newColumns)], axis=1)
 
    # Band must be present in the input and output or else column is all NaN:
    presentBands = dfBands if inputBands is None else list(set(inputBands).intersection(dfBands))
    # Get the unexploded columns from any present band's partition
    noDupDf = df[presentBands[0]][noDupCols]
    newDf = pd.concat([noDupDf, newDf], axis=1)
    return newDf
 
 
class WriteObjectTableConnections(pipeBase.PipelineTaskConnections,
                                  defaultTemplates={"coaddName": "deep"},
                                  dimensions=("tract", "patch", "skymap")):
    inputCatalogMeas = connectionTypes.Input(
        doc="Catalog of source measurements on the deepCoadd.",
        dimensions=("tract", "patch", "band", "skymap"),
        storageClass="SourceCatalog",
        name="{coaddName}Coadd_meas",
        multiple=True
    )
    inputCatalogForcedSrc = connectionTypes.Input(
        doc="Catalog of forced measurements (shape and position parameters held fixed) on the deepCoadd.",
        dimensions=("tract", "patch", "band", "skymap"),
        storageClass="SourceCatalog",
        name="{coaddName}Coadd_forced_src",
        multiple=True
    )
    inputCatalogRef = connectionTypes.Input(
        doc="Catalog marking the primary detection (which band provides a good shape and position)"
            "for each detection in deepCoadd_mergeDet.",
        dimensions=("tract", "patch", "skymap"),
        storageClass="SourceCatalog",
        name="{coaddName}Coadd_ref"
    )
    outputCatalog = connectionTypes.Output(
        doc="A vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
            "stored as a DataFrame with a multi-level column index per-patch.",
        dimensions=("tract", "patch", "skymap"),
        storageClass="DataFrame",
        name="{coaddName}Coadd_obj"
    )
 
 
class WriteObjectTableConfig(pipeBase.PipelineTaskConfig,
                             pipelineConnections=WriteObjectTableConnections):
    engine = pexConfig.Field(
        dtype=str,
        default="pyarrow",
        doc="Parquet engine for writing (pyarrow or fastparquet)"
    )
    coaddName = pexConfig.Field(
        dtype=str,
        default="deep",
        doc="Name of coadd"
    )
 
 
class WriteObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Write filter-merged source tables to parquet
    """
    _DefaultName = "writeObjectTable"
    ConfigClass = WriteObjectTableConfig
    RunnerClass = MergeSourcesRunner
 
    # Names of table datasets to be merged
    inputDatasets = ('forced_src', 'meas', 'ref')
 
    # Tag of output dataset written by `MergeSourcesTask.write`
    outputDataset = 'obj'
 
    def __init__(self, butler=None, schema=None, **kwargs):
        # It is a shame that this class can't use the default init for CmdLineTask
        # But to do so would require its own special task runner, which is many
        # more lines of specialization, so this is how it is for now
        super().__init__(**kwargs)
 
    def runDataRef(self, patchRefList):
        """!
        @brief Merge coadd sources from multiple bands. Calls @ref `run` which must be defined in
        subclasses that inherit from MergeSourcesTask.
        @param[in] patchRefList list of data references for each filter
        """
        catalogs = dict(self.readCatalog(patchRef) for patchRef in patchRefList)
        dataId = patchRefList[0].dataId
        mergedCatalog = self.run(catalogs, tract=dataId['tract'], patch=dataId['patch'])
        self.write(patchRefList[0], ParquetTable(dataFrame=mergedCatalog))
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
 
        measDict = {ref.dataId['band']: {'meas': cat} for ref, cat in
                    zip(inputRefs.inputCatalogMeas, inputs['inputCatalogMeas'])}
        forcedSourceDict = {ref.dataId['band']: {'forced_src': cat} for ref, cat in
                            zip(inputRefs.inputCatalogForcedSrc, inputs['inputCatalogForcedSrc'])}
 
        catalogs = {}
        for band in measDict.keys():
            catalogs[band] = {'meas': measDict[band]['meas'],
                              'forced_src': forcedSourceDict[band]['forced_src'],
                              'ref': inputs['inputCatalogRef']}
        dataId = butlerQC.quantum.dataId
        df = self.run(catalogs=catalogs, tract=dataId['tract'], patch=dataId['patch'])
        outputs = pipeBase.Struct(outputCatalog=df)
        butlerQC.put(outputs, outputRefs)
 
    @classmethod
    def _makeArgumentParser(cls):
        """Create a suitable ArgumentParser.
 
        We will use the ArgumentParser to get a list of data
        references for patches; the RunnerClass will sort them into lists
        of data references for the same patch.
 
        References first of self.inputDatasets, rather than
        self.inputDataset
        """
        return makeMergeArgumentParser(cls._DefaultName, cls.inputDatasets[0])
 
    def readCatalog(self, patchRef):
        """Read input catalogs
 
        Read all the input datasets given by the 'inputDatasets'
        attribute.
 
        Parameters
        ----------
        patchRef : `lsst.daf.persistence.ButlerDataRef`
            Data reference for patch
 
        Returns
        -------
        Tuple consisting of band name and a dict of catalogs, keyed by
        dataset name
        """
        band = patchRef.get(self.config.coaddName + "Coadd_filterLabel", immediate=True).bandLabel
        catalogDict = {}
        for dataset in self.inputDatasets:
            catalog = patchRef.get(self.config.coaddName + "Coadd_" + dataset, immediate=True)
            self.log.info("Read %d sources from %s for band %s: %s",
                          len(catalog), dataset, band, patchRef.dataId)
            catalogDict[dataset] = catalog
        return band, catalogDict
 
    def run(self, catalogs, tract, patch):
        """Merge multiple catalogs.
 
        Parameters
        ----------
        catalogs : `dict`
            Mapping from filter names to dict of catalogs.
        tract : int
            tractId to use for the tractId column
        patch : str
            patchId to use for the patchId column
 
        Returns
        -------
        catalog : `pandas.DataFrame`
            Merged dataframe
        """
 
        dfs = []
        for filt, tableDict in catalogs.items():
            for dataset, table in tableDict.items():
                # Convert afwTable to pandas DataFrame
                df = table.asAstropy().to_pandas().set_index('id', drop=True)
 
                # Sort columns by name, to ensure matching schema among patches
                df = df.reindex(sorted(df.columns), axis=1)
                df['tractId'] = tract
                df['patchId'] = patch
 
                # Make columns a 3-level MultiIndex
                df.columns = pd.MultiIndex.from_tuples([(dataset, filt, c) for c in df.columns],
                                                       names=('dataset', 'band', 'column'))
                dfs.append(df)
 
        catalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
        return catalog
 
    def write(self, patchRef, catalog):
        """Write the output.
 
        Parameters
        ----------
        catalog : `ParquetTable`
            Catalog to write
        patchRef : `lsst.daf.persistence.ButlerDataRef`
            Data reference for patch
        """
        patchRef.put(catalog, self.config.coaddName + "Coadd_" + self.outputDataset)
        # since the filter isn't actually part of the data ID for the dataset we're saving,
        # it's confusing to see it in the log message, even if the butler simply ignores it.
        mergeDataId = patchRef.dataId.copy()
        del mergeDataId["filter"]
        self.log.info("Wrote merged catalog: %s", mergeDataId)
 
    def writeMetadata(self, dataRefList):
        """No metadata to write, and not sure how to write it for a list of dataRefs.
        """
        pass
 
 
class WriteSourceTableConnections(pipeBase.PipelineTaskConnections,
                                  defaultTemplates={"catalogType": ""},
                                  dimensions=("instrument", "visit", "detector")):
 
    catalog = connectionTypes.Input(
        doc="Input full-depth catalog of sources produced by CalibrateTask",
        name="{catalogType}src",
        storageClass="SourceCatalog",
        dimensions=("instrument", "visit", "detector")
    )
    outputCatalog = connectionTypes.Output(
        doc="Catalog of sources, `src` in Parquet format. The 'id' column is "
            "replaced with an index; all other columns are unchanged.",
        name="{catalogType}source",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector")
    )
 
 
class WriteSourceTableConfig(pipeBase.PipelineTaskConfig,
                             pipelineConnections=WriteSourceTableConnections):
    doApplyExternalPhotoCalib = pexConfig.Field(
        dtype=bool,
        default=False,
        doc=("Add local photoCalib columns from the calexp.photoCalib? Should only set True if "
             "generating Source Tables from older src tables which do not already have local calib columns")
    )
    doApplyExternalSkyWcs = pexConfig.Field(
        dtype=bool,
        default=False,
        doc=("Add local WCS columns from the calexp.wcs? Should only set True if "
             "generating Source Tables from older src tables which do not already have local calib columns")
    )
 
 
class WriteSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Write source table to parquet
    """
    _DefaultName = "writeSourceTable"
    ConfigClass = WriteSourceTableConfig
 
    def runDataRef(self, dataRef):
        src = dataRef.get('src')
        if self.config.doApplyExternalPhotoCalib or self.config.doApplyExternalSkyWcs:
            src = self.addCalibColumns(src, dataRef)
 
        ccdVisitId = dataRef.get('ccdExposureId')
        result = self.run(src, ccdVisitId=ccdVisitId)
        dataRef.put(result.table, 'source')
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
        result = self.run(**inputs).table
        outputs = pipeBase.Struct(outputCatalog=result.toDataFrame())
        butlerQC.put(outputs, outputRefs)
 
    def run(self, catalog, ccdVisitId=None):
        """Convert `src` catalog to parquet
 
        Parameters
        ----------
        catalog: `afwTable.SourceCatalog`
            catalog to be converted
        ccdVisitId: `int`
            ccdVisitId to be added as a column
 
        Returns
        -------
        result : `lsst.pipe.base.Struct`
            ``table``
                `ParquetTable` version of the input catalog
        """
        self.log.info("Generating parquet table from src catalog %s", ccdVisitId)
        df = catalog.asAstropy().to_pandas().set_index('id', drop=True)
        df['ccdVisitId'] = ccdVisitId
        return pipeBase.Struct(table=ParquetTable(dataFrame=df))
 
    def addCalibColumns(self, catalog, dataRef):
        """Add columns with local calibration evaluated at each centroid
 
        for backwards compatibility with old repos.
        This exists for the purpose of converting old src catalogs
        (which don't have the expected local calib columns) to Source Tables.
 
        Parameters
        ----------
        catalog: `afwTable.SourceCatalog`
            catalog to which calib columns will be added
        dataRef: `lsst.daf.persistence.ButlerDataRef
            for fetching the calibs from disk.
 
        Returns
        -------
        newCat:  `afwTable.SourceCatalog`
            Source Catalog with requested local calib columns
        """
        mapper = afwTable.SchemaMapper(catalog.schema)
        measureConfig = SingleFrameMeasurementTask.ConfigClass()
        measureConfig.doReplaceWithNoise = False
 
        # Just need the WCS or the PhotoCalib attached to an exposue
        exposure = dataRef.get('calexp_sub',
                               bbox=lsst.geom.Box2I(lsst.geom.Point2I(0, 0), lsst.geom.Point2I(0, 0)))
 
        mapper = afwTable.SchemaMapper(catalog.schema)
        mapper.addMinimalSchema(catalog.schema, True)
        schema = mapper.getOutputSchema()
 
        exposureIdInfo = dataRef.get("expIdInfo")
        measureConfig.plugins.names = []
        if self.config.doApplyExternalSkyWcs:
            plugin = 'base_LocalWcs'
            if plugin in schema:
                raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalSkyWcs=False")
            else:
                measureConfig.plugins.names.add(plugin)
 
        if self.config.doApplyExternalPhotoCalib:
            plugin = 'base_LocalPhotoCalib'
            if plugin in schema:
                raise RuntimeError(f"{plugin} already in src catalog. Set doApplyExternalPhotoCalib=False")
            else:
                measureConfig.plugins.names.add(plugin)
 
        measurement = SingleFrameMeasurementTask(config=measureConfig, schema=schema)
        newCat = afwTable.SourceCatalog(schema)
        newCat.extend(catalog, mapper=mapper)
        measurement.run(measCat=newCat, exposure=exposure, exposureId=exposureIdInfo.expId)
        return newCat
 
    def writeMetadata(self, dataRef):
        """No metadata to write.
        """
        pass
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
        parser.add_id_argument("--id", 'src',
                               help="data ID, e.g. --id visit=12345 ccd=0")
        return parser
 
 
class PostprocessAnalysis(object):
    """Calculate columns from ParquetTable
 
    This object manages and organizes an arbitrary set of computations
    on a catalog.  The catalog is defined by a
    `lsst.pipe.tasks.parquetTable.ParquetTable` object (or list thereof), such as a
    `deepCoadd_obj` dataset, and the computations are defined by a collection
    of `lsst.pipe.tasks.functor.Functor` objects (or, equivalently,
    a `CompositeFunctor`).
 
    After the object is initialized, accessing the `.df` attribute (which
    holds the `pandas.DataFrame` containing the results of the calculations) triggers
    computation of said dataframe.
 
    One of the conveniences of using this object is the ability to define a desired common
    filter for all functors.  This enables the same functor collection to be passed to
    several different `PostprocessAnalysis` objects without having to change the original
    functor collection, since the `filt` keyword argument of this object triggers an
    overwrite of the `filt` property for all functors in the collection.
 
    This object also allows a list of refFlags to be passed, and defines a set of default
    refFlags that are always included even if not requested.
 
    If a list of `ParquetTable` object is passed, rather than a single one, then the
    calculations will be mapped over all the input catalogs.  In principle, it should
    be straightforward to parallelize this activity, but initial tests have failed
    (see TODO in code comments).
 
    Parameters
    ----------
    parq : `lsst.pipe.tasks.ParquetTable` (or list of such)
        Source catalog(s) for computation
 
    functors : `list`, `dict`, or `lsst.pipe.tasks.functors.CompositeFunctor`
        Computations to do (functors that act on `parq`).
        If a dict, the output
        DataFrame will have columns keyed accordingly.
        If a list, the column keys will come from the
        `.shortname` attribute of each functor.
 
    filt : `str` (optional)
        Filter in which to calculate.  If provided,
        this will overwrite any existing `.filt` attribute
        of the provided functors.
 
    flags : `list` (optional)
        List of flags (per-band) to include in output table.
        Taken from the `meas` dataset if applied to a multilevel Object Table.
 
    refFlags : `list` (optional)
        List of refFlags (only reference band) to include in output table.
 
    forcedFlags : `list` (optional)
        List of flags (per-band) to include in output table.
        Taken from the ``forced_src`` dataset if applied to a
        multilevel Object Table. Intended for flags from measurement plugins
        only run during multi-band forced-photometry.
    """
    _defaultRefFlags = []
    _defaultFuncs = ()
 
    def __init__(self, parq, functors, filt=None, flags=None, refFlags=None, forcedFlags=None):
        self.parq = parq
        self.functors = functors
 
        self.filt = filt
        self.flags = list(flags) if flags is not None else []
        self.forcedFlags = list(forcedFlags) if forcedFlags is not None else []
        self.refFlags = list(self._defaultRefFlags)
        if refFlags is not None:
            self.refFlags += list(refFlags)
 
        self._df = None
 
    @property
    def defaultFuncs(self):
        funcs = dict(self._defaultFuncs)
        return funcs
 
    @property
    def func(self):
        additionalFuncs = self.defaultFuncs
        additionalFuncs.update({flag: Column(flag, dataset='forced_src') for flag in self.forcedFlags})
        additionalFuncs.update({flag: Column(flag, dataset='ref') for flag in self.refFlags})
        additionalFuncs.update({flag: Column(flag, dataset='meas') for flag in self.flags})
 
        if isinstance(self.functors, CompositeFunctor):
            func = self.functors
        else:
            func = CompositeFunctor(self.functors)
 
        func.funcDict.update(additionalFuncs)
        func.filt = self.filt
 
        return func
 
    @property
    def noDupCols(self):
        return [name for name, func in self.func.funcDict.items() if func.noDup or func.dataset == 'ref']
 
    @property
    def df(self):
        if self._df is None:
            self.compute()
        return self._df
 
    def compute(self, dropna=False, pool=None):
        # map over multiple parquet tables
        if type(self.parq) in (list, tuple):
            if pool is None:
                dflist = [self.func(parq, dropna=dropna) for parq in self.parq]
            else:
                # TODO: Figure out why this doesn't work (pyarrow pickling issues?)
                dflist = pool.map(functools.partial(self.func, dropna=dropna), self.parq)
            self._df = pd.concat(dflist)
        else:
            self._df = self.func(self.parq, dropna=dropna)
 
        return self._df
 
 
class TransformCatalogBaseConnections(pipeBase.PipelineTaskConnections,
                                      dimensions=()):
    """Expected Connections for subclasses of TransformCatalogBaseTask.
 
    Must be subclassed.
    """
    inputCatalog = connectionTypes.Input(
        name="",
        storageClass="DataFrame",
    )
    outputCatalog = connectionTypes.Output(
        name="",
        storageClass="DataFrame",
    )
 
 
class TransformCatalogBaseConfig(pipeBase.PipelineTaskConfig,
                                 pipelineConnections=TransformCatalogBaseConnections):
    functorFile = pexConfig.Field(
        dtype=str,
        doc="Path to YAML file specifying Science Data Model functors to use "
            "when copying columns and computing calibrated values.",
        default=None,
        optional=True
    )
    primaryKey = pexConfig.Field(
        dtype=str,
        doc="Name of column to be set as the DataFrame index. If None, the index"
            "will be named `id`",
        default=None,
        optional=True
    )
 
 
class TransformCatalogBaseTask(CmdLineTask, pipeBase.PipelineTask):
    """Base class for transforming/standardizing a catalog
 
    by applying functors that convert units and apply calibrations.
    The purpose of this task is to perform a set of computations on
    an input `ParquetTable` dataset (such as `deepCoadd_obj`) and write the
    results to a new dataset (which needs to be declared in an `outputDataset`
    attribute).
 
    The calculations to be performed are defined in a YAML file that specifies
    a set of functors to be computed, provided as
    a `--functorFile` config parameter.  An example of such a YAML file
    is the following:
 
        funcs:
            psfMag:
                functor: Mag
                args:
                    - base_PsfFlux
                filt: HSC-G
                dataset: meas
            cmodel_magDiff:
                functor: MagDiff
                args:
                    - modelfit_CModel
                    - base_PsfFlux
                filt: HSC-G
            gauss_magDiff:
                functor: MagDiff
                args:
                    - base_GaussianFlux
                    - base_PsfFlux
                filt: HSC-G
            count:
                functor: Column
                args:
                    - base_InputCount_value
                filt: HSC-G
            deconvolved_moments:
                functor: DeconvolvedMoments
                filt: HSC-G
                dataset: forced_src
        refFlags:
            - calib_psfUsed
            - merge_measurement_i
            - merge_measurement_r
            - merge_measurement_z
            - merge_measurement_y
            - merge_measurement_g
            - base_PixelFlags_flag_inexact_psfCenter
            - detect_isPrimary
 
    The names for each entry under "func" will become the names of columns in the
    output dataset.  All the functors referenced are defined in `lsst.pipe.tasks.functors`.
    Positional arguments to be passed to each functor are in the `args` list,
    and any additional entries for each column other than "functor" or "args" (e.g., `'filt'`,
    `'dataset'`) are treated as keyword arguments to be passed to the functor initialization.
 
    The "flags" entry is the default shortcut for `Column` functors.
    All columns listed under "flags" will be copied to the output table
    untransformed. They can be of any datatype.
    In the special case of transforming a multi-level oject table with
    band and dataset indices (deepCoadd_obj), these will be taked from the
    `meas` dataset and exploded out per band.
 
    There are two special shortcuts that only apply when transforming
    multi-level Object (deepCoadd_obj) tables:
     -  The "refFlags" entry is shortcut for `Column` functor
        taken from the `'ref'` dataset if transforming an ObjectTable.
     -  The "forcedFlags" entry is shortcut for `Column` functors.
        taken from the ``forced_src`` dataset if transforming an ObjectTable.
        These are expanded out per band.
 
 
    This task uses the `lsst.pipe.tasks.postprocess.PostprocessAnalysis` object
    to organize and excecute the calculations.
 
    """
    @property
    def _DefaultName(self):
        raise NotImplementedError('Subclass must define "_DefaultName" attribute')
 
    @property
    def outputDataset(self):
        raise NotImplementedError('Subclass must define "outputDataset" attribute')
 
    @property
    def inputDataset(self):
        raise NotImplementedError('Subclass must define "inputDataset" attribute')
 
    @property
    def ConfigClass(self):
        raise NotImplementedError('Subclass must define "ConfigClass" attribute')
 
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if self.config.functorFile:
            self.log.info('Loading tranform functor definitions from %s',
                          self.config.functorFile)
            self.funcsfuncs = CompositeFunctor.from_file(self.config.functorFile)
            self.funcsfuncs.update(dict(PostprocessAnalysis._defaultFuncs))
        else:
            self.funcsfuncs = None
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        if self.funcsfuncs is None:
            raise ValueError("config.functorFile is None. "
                             "Must be a valid path to yaml in order to run Task as a PipelineTask.")
        result = self.runrun(parq=inputs['inputCatalog'], funcs=self.funcsfuncs,
                          dataId=outputRefs.outputCatalog.dataId.full)
        outputs = pipeBase.Struct(outputCatalog=result)
        butlerQC.put(outputs, outputRefs)
 
    def runDataRef(self, dataRef):
        parq = dataRef.get()
        if self.funcsfuncs is None:
            raise ValueError("config.functorFile is None. "
                             "Must be a valid path to yaml in order to run as a CommandlineTask.")
        df = self.runrun(parq, funcs=self.funcsfuncs, dataId=dataRef.dataId)
        self.writewrite(df, dataRef)
        return df
 
    def run(self, parq, funcs=None, dataId=None, band=None):
        """Do postprocessing calculations
 
        Takes a `ParquetTable` object and dataId,
        returns a dataframe with results of postprocessing calculations.
 
        Parameters
        ----------
        parq : `lsst.pipe.tasks.parquetTable.ParquetTable`
            ParquetTable from which calculations are done.
        funcs : `lsst.pipe.tasks.functors.Functors`
            Functors to apply to the table's columns
        dataId : dict, optional
            Used to add a `patchId` column to the output dataframe.
        band : `str`, optional
            Filter band that is being processed.
 
        Returns
        ------
            `pandas.DataFrame`
 
        """
        self.log.info("Transforming/standardizing the source table dataId: %s", dataId)
 
        df = self.transformtransform(band, parq, funcs, dataId).df
        self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
        return df
 
    def getFunctors(self):
        return self.funcsfuncs
 
    def getAnalysis(self, parq, funcs=None, band=None):
        if funcs is None:
            funcs = self.funcsfuncs
        analysis = PostprocessAnalysis(parq, funcs, filt=band)
        return analysis
 
    def transform(self, band, parq, funcs, dataId):
        analysis = self.getAnalysisgetAnalysis(parq, funcs=funcs, band=band)
        df = analysis.df
        if dataId is not None:
            for key, value in dataId.items():
                df[str(key)] = value
 
        if self.config.primaryKey:
            if df.index.name != self.config.primaryKey and self.config.primaryKey in df:
                df.reset_index(inplace=True, drop=True)
                df.set_index(self.config.primaryKey, inplace=True)
 
        return pipeBase.Struct(
            df=df,
            analysis=analysis
        )
 
    def write(self, df, parqRef):
        parqRef.put(ParquetTable(dataFrame=df), self.outputDatasetoutputDataset)
 
    def writeMetadata(self, dataRef):
        """No metadata to write.
        """
        pass
 
 
class TransformObjectCatalogConnections(pipeBase.PipelineTaskConnections,
                                        defaultTemplates={"coaddName": "deep"},
                                        dimensions=("tract", "patch", "skymap")):
    inputCatalog = connectionTypes.Input(
        doc="The vertical concatenation of the deepCoadd_{ref|meas|forced_src} catalogs, "
            "stored as a DataFrame with a multi-level column index per-patch.",
        dimensions=("tract", "patch", "skymap"),
        storageClass="DataFrame",
        name="{coaddName}Coadd_obj",
        deferLoad=True,
    )
    outputCatalog = connectionTypes.Output(
        doc="Per-Patch Object Table of columns transformed from the deepCoadd_obj table per the standard "
            "data model.",
        dimensions=("tract", "patch", "skymap"),
        storageClass="DataFrame",
        name="objectTable"
    )
 
 
class TransformObjectCatalogConfig(TransformCatalogBaseConfig,
                                   pipelineConnections=TransformObjectCatalogConnections):
    coaddName = pexConfig.Field(
        dtype=str,
        default="deep",
        doc="Name of coadd"
    )
    # TODO: remove in DM-27177
    filterMap = pexConfig.DictField(
        keytype=str,
        itemtype=str,
        default={},
        doc=("Dictionary mapping full filter name to short one for column name munging."
             "These filters determine the output columns no matter what filters the "
             "input data actually contain."),
        deprecated=("Coadds are now identified by the band, so this transform is unused."
                    "Will be removed after v22.")
    )
    outputBands = pexConfig.ListField(
        dtype=str,
        default=None,
        optional=True,
        doc=("These bands and only these bands will appear in the output,"
             " NaN-filled if the input does not include them."
             " If None, then use all bands found in the input.")
    )
    camelCase = pexConfig.Field(
        dtype=bool,
        default=False,
        doc=("Write per-band columns names with camelCase, else underscore "
             "For example: gPsFlux instead of g_PsFlux.")
    )
    multilevelOutput = pexConfig.Field(
        dtype=bool,
        default=False,
        doc=("Whether results dataframe should have a multilevel column index (True) or be flat "
             "and name-munged (False).")
    )
    goodFlags = pexConfig.ListField(
        dtype=str,
        default=[],
        doc=("List of 'good' flags that should be set False when populating empty tables. "
             "All other flags are considered to be 'bad' flags and will be set to True.")
    )
    floatFillValue = pexConfig.Field(
        dtype=float,
        default=np.nan,
        doc="Fill value for float fields when populating empty tables."
    )
    integerFillValue = pexConfig.Field(
        dtype=int,
        default=-1,
        doc="Fill value for integer fields when populating empty tables."
    )
 
    def setDefaults(self):
        super().setDefaults()
        self.primaryKey = 'objectId'
        self.goodFlags = ['calib_astrometry_used',
                          'calib_photometry_reserved',
                          'calib_photometry_used',
                          'calib_psf_candidate',
                          'calib_psf_reserved',
                          'calib_psf_used']
 
 
class TransformObjectCatalogTask(TransformCatalogBaseTask):
    """Produce a flattened Object Table to match the format specified in
    sdm_schemas.
 
    Do the same set of postprocessing calculations on all bands
 
    This is identical to `TransformCatalogBaseTask`, except for that it does the
    specified functor calculations for all filters present in the
    input `deepCoadd_obj` table.  Any specific `"filt"` keywords specified
    by the YAML file will be superceded.
    """
    _DefaultName = "transformObjectCatalog"
    ConfigClass = TransformObjectCatalogConfig
 
    # Used by Gen 2 runDataRef only:
    inputDataset = 'deepCoadd_obj'
    outputDataset = 'objectTable'
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
        parser.add_id_argument("--id", cls.inputDataset,
                               ContainerClass=CoaddDataIdContainer,
                               help="data ID, e.g. --id tract=12345 patch=1,2")
        return parser
 
    def run(self, parq, funcs=None, dataId=None, band=None):
        # NOTE: band kwarg is ignored here.
        dfDict = {}
        analysisDict = {}
        templateDf = pd.DataFrame()
 
        if isinstance(parq, DeferredDatasetHandle):
            columns = parq.get(component='columns')
            inputBands = columns.unique(level=1).values
        else:
            inputBands = parq.columnLevelNames['band']
 
        outputBands = self.config.outputBands if self.config.outputBands else inputBands
 
        # Perform transform for data of filters that exist in parq.
        for inputBand in inputBands:
            if inputBand not in outputBands:
                self.log.info("Ignoring %s band data in the input", inputBand)
                continue
            self.log.info("Transforming the catalog of band %s", inputBand)
            result = self.transform(inputBand, parq, funcs, dataId)
            dfDict[inputBand] = result.df
            analysisDict[inputBand] = result.analysis
            if templateDf.empty:
                templateDf = result.df
 
        # Put filler values in columns of other wanted bands
        for filt in outputBands:
            if filt not in dfDict:
                self.log.info("Adding empty columns for band %s", filt)
                dfTemp = templateDf.copy()
                for col in dfTemp.columns:
                    testValue = dfTemp[col].values[0]
                    if isinstance(testValue, (np.bool_, pd.BooleanDtype)):
                        # Boolean flag type, check if it is a "good" flag
                        if col in self.config.goodFlags:
                            fillValue = False
                        else:
                            fillValue = True
                    elif isinstance(testValue, numbers.Integral):
                        # Checking numbers.Integral catches all flavors
                        # of python, numpy, pandas, etc. integers.
                        # We must ensure this is not an unsigned integer.
                        if isinstance(testValue, np.unsignedinteger):
                            raise ValueError("Parquet tables may not have unsigned integer columns.")
                        else:
                            fillValue = self.config.integerFillValue
                    else:
                        fillValue = self.config.floatFillValue
                    dfTemp[col].values[:] = fillValue
                dfDict[filt] = dfTemp
 
        # This makes a multilevel column index, with band as first level
        df = pd.concat(dfDict, axis=1, names=['band', 'column'])
 
        if not self.config.multilevelOutput:
            noDupCols = list(set.union(*[set(v.noDupCols) for v in analysisDict.values()]))
            if self.config.primaryKey in noDupCols:
                noDupCols.remove(self.config.primaryKey)
            if dataId is not None:
                noDupCols += list(dataId.keys())
            df = flattenFilters(df, noDupCols=noDupCols, camelCase=self.config.camelCase,
                                inputBands=inputBands)
 
        self.log.info("Made a table of %d columns and %d rows", len(df.columns), len(df))
 
        return df
 
 
class TractObjectDataIdContainer(CoaddDataIdContainer):
 
    def makeDataRefList(self, namespace):
        """Make self.refList from self.idList
 
        Generate a list of data references given tract and/or patch.
        This was adapted from `TractQADataIdContainer`, which was
        `TractDataIdContainer` modifie to not require "filter".
        Only existing dataRefs are returned.
        """
        def getPatchRefList(tract):
            return [namespace.butler.dataRef(datasetType=self.datasetType,
                                             tract=tract.getId(),
                                             patch="%d,%d" % patch.getIndex()) for patch in tract]
 
        tractRefs = defaultdict(list)  # Data references for each tract
        for dataId in self.idList:
            skymap = self.getSkymap(namespace)
 
            if "tract" in dataId:
                tractId = dataId["tract"]
                if "patch" in dataId:
                    tractRefs[tractId].append(namespace.butler.dataRef(datasetType=self.datasetType,
                                                                       tract=tractId,
                                                                       patch=dataId['patch']))
                else:
                    tractRefs[tractId] += getPatchRefList(skymap[tractId])
            else:
                tractRefs = dict((tract.getId(), tractRefs.get(tract.getId(), []) + getPatchRefList(tract))
                                 for tract in skymap)
        outputRefList = []
        for tractRefList in tractRefs.values():
            existingRefs = [ref for ref in tractRefList if ref.datasetExists()]
            outputRefList.append(existingRefs)
 
        self.refList = outputRefList
 
 
class ConsolidateObjectTableConnections(pipeBase.PipelineTaskConnections,
                                        dimensions=("tract", "skymap")):
    inputCatalogs = connectionTypes.Input(
        doc="Per-Patch objectTables conforming to the standard data model.",
        name="objectTable",
        storageClass="DataFrame",
        dimensions=("tract", "patch", "skymap"),
        multiple=True,
    )
    outputCatalog = connectionTypes.Output(
        doc="Pre-tract horizontal concatenation of the input objectTables",
        name="objectTable_tract",
        storageClass="DataFrame",
        dimensions=("tract", "skymap"),
    )
 
 
class ConsolidateObjectTableConfig(pipeBase.PipelineTaskConfig,
                                   pipelineConnections=ConsolidateObjectTableConnections):
    coaddName = pexConfig.Field(
        dtype=str,
        default="deep",
        doc="Name of coadd"
    )
 
 
class ConsolidateObjectTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Write patch-merged source tables to a tract-level parquet file
 
    Concatenates `objectTable` list into a per-visit `objectTable_tract`
    """
    _DefaultName = "consolidateObjectTable"
    ConfigClass = ConsolidateObjectTableConfig
 
    inputDataset = 'objectTable'
    outputDataset = 'objectTable_tract'
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        self.log.info("Concatenating %s per-patch Object Tables",
                      len(inputs['inputCatalogs']))
        df = pd.concat(inputs['inputCatalogs'])
        butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
 
        parser.add_id_argument("--id", cls.inputDataset,
                               help="data ID, e.g. --id tract=12345",
                               ContainerClass=TractObjectDataIdContainer)
        return parser
 
    def runDataRef(self, patchRefList):
        df = pd.concat([patchRef.get().toDataFrame() for patchRef in patchRefList])
        patchRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
 
    def writeMetadata(self, dataRef):
        """No metadata to write.
        """
        pass
 
 
class TransformSourceTableConnections(pipeBase.PipelineTaskConnections,
                                      defaultTemplates={"catalogType": ""},
                                      dimensions=("instrument", "visit", "detector")):
 
    inputCatalog = connectionTypes.Input(
        doc="Wide input catalog of sources produced by WriteSourceTableTask",
        name="{catalogType}source",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector"),
        deferLoad=True
    )
    outputCatalog = connectionTypes.Output(
        doc="Narrower, per-detector Source Table transformed and converted per a "
            "specified set of functors",
        name="{catalogType}sourceTable",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector")
    )
 
 
class TransformSourceTableConfig(TransformCatalogBaseConfig,
                                 pipelineConnections=TransformSourceTableConnections):
 
    def setDefaults(self):
        super().setDefaults()
        self.primaryKey = 'sourceId'
 
 
class TransformSourceTableTask(TransformCatalogBaseTask):
    """Transform/standardize a source catalog
    """
    _DefaultName = "transformSourceTable"
    ConfigClass = TransformSourceTableConfig
 
    inputDataset = 'source'
    outputDataset = 'sourceTable'
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
        parser.add_id_argument("--id", datasetType=cls.inputDataset,
                               level="sensor",
                               help="data ID, e.g. --id visit=12345 ccd=0")
        return parser
 
    def runDataRef(self, dataRef):
        """Override to specify band label to run()."""
        parq = dataRef.get()
        funcs = self.getFunctors()
        band = dataRef.get("calexp_filterLabel", immediate=True).bandLabel
        df = self.run(parq, funcs=funcs, dataId=dataRef.dataId, band=band)
        self.write(df, dataRef)
        return df
 
 
class ConsolidateVisitSummaryConnections(pipeBase.PipelineTaskConnections,
                                         dimensions=("instrument", "visit",),
                                         defaultTemplates={"calexpType": ""}):
    calexp = connectionTypes.Input(
        doc="Processed exposures used for metadata",
        name="{calexpType}calexp",
        storageClass="ExposureF",
        dimensions=("instrument", "visit", "detector"),
        deferLoad=True,
        multiple=True,
    )
    visitSummary = connectionTypes.Output(
        doc=("Per-visit consolidated exposure metadata.  These catalogs use "
             "detector id for the id and are sorted for fast lookups of a "
             "detector."),
        name="{calexpType}visitSummary",
        storageClass="ExposureCatalog",
        dimensions=("instrument", "visit"),
    )
 
 
class ConsolidateVisitSummaryConfig(pipeBase.PipelineTaskConfig,
                                    pipelineConnections=ConsolidateVisitSummaryConnections):
    """Config for ConsolidateVisitSummaryTask"""
    pass
 
 
class ConsolidateVisitSummaryTask(pipeBase.PipelineTask, pipeBase.CmdLineTask):
    """Task to consolidate per-detector visit metadata.
 
    This task aggregates the following metadata from all the detectors in a
    single visit into an exposure catalog:
    - The visitInfo.
    - The wcs.
    - The photoCalib.
    - The physical_filter and band (if available).
    - The psf size, shape, and effective area at the center of the detector.
    - The corners of the bounding box in right ascension/declination.
 
    Other quantities such as Detector, Psf, ApCorrMap, and TransmissionCurve
    are not persisted here because of storage concerns, and because of their
    limited utility as summary statistics.
 
    Tests for this task are performed in ci_hsc_gen3.
    """
    _DefaultName = "consolidateVisitSummary"
    ConfigClass = ConsolidateVisitSummaryConfig
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
 
        parser.add_id_argument("--id", "calexp",
                               help="data ID, e.g. --id visit=12345",
                               ContainerClass=VisitDataIdContainer)
        return parser
 
    def writeMetadata(self, dataRef):
        """No metadata to persist, so override to remove metadata persistance.
        """
        pass
 
    def writeConfig(self, butler, clobber=False, doBackup=True):
        """No config to persist, so override to remove config persistance.
        """
        pass
 
    def runDataRef(self, dataRefList):
        visit = dataRefList[0].dataId['visit']
 
        self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
                       len(dataRefList), visit)
 
        expCatalog = self._combineExposureMetadata(visit, dataRefList, isGen3=False)
 
        dataRefList[0].put(expCatalog, 'visitSummary', visit=visit)
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        dataRefs = butlerQC.get(inputRefs.calexp)
        visit = dataRefs[0].dataId.byName()['visit']
 
        self.log.debug("Concatenating metadata from %d per-detector calexps (visit %d)",
                       len(dataRefs), visit)
 
        expCatalog = self._combineExposureMetadata(visit, dataRefs)
 
        butlerQC.put(expCatalog, outputRefs.visitSummary)
 
    def _combineExposureMetadata(self, visit, dataRefs, isGen3=True):
        """Make a combined exposure catalog from a list of dataRefs.
        These dataRefs must point to exposures with wcs, summaryStats,
        and other visit metadata.
 
        Parameters
        ----------
        visit : `int`
            Visit identification number.
        dataRefs : `list`
            List of dataRefs in visit.  May be list of
            `lsst.daf.persistence.ButlerDataRef` (Gen2) or
            `lsst.daf.butler.DeferredDatasetHandle` (Gen3).
        isGen3 : `bool`, optional
            Specifies if this is a Gen3 list of datarefs.
 
        Returns
        -------
        visitSummary : `lsst.afw.table.ExposureCatalog`
            Exposure catalog with per-detector summary information.
        """
        schema = self._makeVisitSummarySchema()
        cat = afwTable.ExposureCatalog(schema)
        cat.resize(len(dataRefs))
 
        cat['visit'] = visit
 
        for i, dataRef in enumerate(dataRefs):
            if isGen3:
                visitInfo = dataRef.get(component='visitInfo')
                filterLabel = dataRef.get(component='filterLabel')
                summaryStats = dataRef.get(component='summaryStats')
                detector = dataRef.get(component='detector')
                wcs = dataRef.get(component='wcs')
                photoCalib = dataRef.get(component='photoCalib')
                detector = dataRef.get(component='detector')
                bbox = dataRef.get(component='bbox')
                validPolygon = dataRef.get(component='validPolygon')
            else:
                # Note that we need to read the calexp because there is
                # no magic access to the psf except through the exposure.
                gen2_read_bbox = lsst.geom.BoxI(lsst.geom.PointI(0, 0), lsst.geom.PointI(1, 1))
                exp = dataRef.get(datasetType='calexp_sub', bbox=gen2_read_bbox)
                visitInfo = exp.getInfo().getVisitInfo()
                filterLabel = dataRef.get("calexp_filterLabel")
                summaryStats = exp.getInfo().getSummaryStats()
                wcs = exp.getWcs()
                photoCalib = exp.getPhotoCalib()
                detector = exp.getDetector()
                bbox = dataRef.get(datasetType='calexp_bbox')
                validPolygon = exp.getInfo().getValidPolygon()
 
            rec = cat[i]
            rec.setBBox(bbox)
            rec.setVisitInfo(visitInfo)
            rec.setWcs(wcs)
            rec.setPhotoCalib(photoCalib)
            rec.setValidPolygon(validPolygon)
 
            rec['physical_filter'] = filterLabel.physicalLabel if filterLabel.hasPhysicalLabel() else ""
            rec['band'] = filterLabel.bandLabel if filterLabel.hasBandLabel() else ""
            rec.setId(detector.getId())
            rec['psfSigma'] = summaryStats.psfSigma
            rec['psfIxx'] = summaryStats.psfIxx
            rec['psfIyy'] = summaryStats.psfIyy
            rec['psfIxy'] = summaryStats.psfIxy
            rec['psfArea'] = summaryStats.psfArea
            rec['raCorners'][:] = summaryStats.raCorners
            rec['decCorners'][:] = summaryStats.decCorners
            rec['ra'] = summaryStats.ra
            rec['decl'] = summaryStats.decl
            rec['zenithDistance'] = summaryStats.zenithDistance
            rec['zeroPoint'] = summaryStats.zeroPoint
            rec['skyBg'] = summaryStats.skyBg
            rec['skyNoise'] = summaryStats.skyNoise
            rec['meanVar'] = summaryStats.meanVar
            rec['astromOffsetMean'] = summaryStats.astromOffsetMean
            rec['astromOffsetStd'] = summaryStats.astromOffsetStd
            rec['nPsfStar'] = summaryStats.nPsfStar
            rec['psfStarDeltaE1Median'] = summaryStats.psfStarDeltaE1Median
            rec['psfStarDeltaE2Median'] = summaryStats.psfStarDeltaE2Median
            rec['psfStarDeltaE1Scatter'] = summaryStats.psfStarDeltaE1Scatter
            rec['psfStarDeltaE2Scatter'] = summaryStats.psfStarDeltaE2Scatter
            rec['psfStarDeltaSizeMedian'] = summaryStats.psfStarDeltaSizeMedian
            rec['psfStarDeltaSizeScatter'] = summaryStats.psfStarDeltaSizeScatter
            rec['psfStarScaledDeltaSizeScatter'] = summaryStats.psfStarScaledDeltaSizeScatter
 
        metadata = dafBase.PropertyList()
        metadata.add("COMMENT", "Catalog id is detector id, sorted.")
        # We are looping over existing datarefs, so the following is true
        metadata.add("COMMENT", "Only detectors with data have entries.")
        cat.setMetadata(metadata)
 
        cat.sort()
        return cat
 
    def _makeVisitSummarySchema(self):
        """Make the schema for the visitSummary catalog."""
        schema = afwTable.ExposureTable.makeMinimalSchema()
        schema.addField('visit', type='I', doc='Visit number')
        schema.addField('physical_filter', type='String', size=32, doc='Physical filter')
        schema.addField('band', type='String', size=32, doc='Name of band')
        schema.addField('psfSigma', type='F',
                        doc='PSF model second-moments determinant radius (center of chip) (pixel)')
        schema.addField('psfArea', type='F',
                        doc='PSF model effective area (center of chip) (pixel**2)')
        schema.addField('psfIxx', type='F',
                        doc='PSF model Ixx (center of chip) (pixel**2)')
        schema.addField('psfIyy', type='F',
                        doc='PSF model Iyy (center of chip) (pixel**2)')
        schema.addField('psfIxy', type='F',
                        doc='PSF model Ixy (center of chip) (pixel**2)')
        schema.addField('raCorners', type='ArrayD', size=4,
                        doc='Right Ascension of bounding box corners (degrees)')
        schema.addField('decCorners', type='ArrayD', size=4,
                        doc='Declination of bounding box corners (degrees)')
        schema.addField('ra', type='D',
                        doc='Right Ascension of bounding box center (degrees)')
        schema.addField('decl', type='D',
                        doc='Declination of bounding box center (degrees)')
        schema.addField('zenithDistance', type='F',
                        doc='Zenith distance of bounding box center (degrees)')
        schema.addField('zeroPoint', type='F',
                        doc='Mean zeropoint in detector (mag)')
        schema.addField('skyBg', type='F',
                        doc='Average sky background (ADU)')
        schema.addField('skyNoise', type='F',
                        doc='Average sky noise (ADU)')
        schema.addField('meanVar', type='F',
                        doc='Mean variance of the weight plane (ADU**2)')
        schema.addField('astromOffsetMean', type='F',
                        doc='Mean offset of astrometric calibration matches (arcsec)')
        schema.addField('astromOffsetStd', type='F',
                        doc='Standard deviation of offsets of astrometric calibration matches (arcsec)')
        schema.addField('nPsfStar', type='I', doc='Number of stars used for PSF model')
        schema.addField('psfStarDeltaE1Median', type='F',
                        doc='Median E1 residual (starE1 - psfE1) for psf stars')
        schema.addField('psfStarDeltaE2Median', type='F',
                        doc='Median E2 residual (starE2 - psfE2) for psf stars')
        schema.addField('psfStarDeltaE1Scatter', type='F',
                        doc='Scatter (via MAD) of E1 residual (starE1 - psfE1) for psf stars')
        schema.addField('psfStarDeltaE2Scatter', type='F',
                        doc='Scatter (via MAD) of E2 residual (starE2 - psfE2) for psf stars')
        schema.addField('psfStarDeltaSizeMedian', type='F',
                        doc='Median size residual (starSize - psfSize) for psf stars (pixel)')
        schema.addField('psfStarDeltaSizeScatter', type='F',
                        doc='Scatter (via MAD) of size residual (starSize - psfSize) for psf stars (pixel)')
        schema.addField('psfStarScaledDeltaSizeScatter', type='F',
                        doc='Scatter (via MAD) of size residual scaled by median size squared')
 
        return schema
 
 
class VisitDataIdContainer(DataIdContainer):
    """DataIdContainer that groups sensor-level id's by visit
    """
 
    def makeDataRefList(self, namespace):
        """Make self.refList from self.idList
 
        Generate a list of data references grouped by visit.
 
        Parameters
        ----------
        namespace : `argparse.Namespace`
            Namespace used by `lsst.pipe.base.CmdLineTask` to parse command line arguments
        """
        # Group by visits
        visitRefs = defaultdict(list)
        for dataId in self.idList:
            if "visit" in dataId:
                visitId = dataId["visit"]
                # append all subsets to
                subset = namespace.butler.subset(self.datasetType, dataId=dataId)
                visitRefs[visitId].extend([dataRef for dataRef in subset])
 
        outputRefList = []
        for refList in visitRefs.values():
            existingRefs = [ref for ref in refList if ref.datasetExists()]
            if existingRefs:
                outputRefList.append(existingRefs)
 
        self.refList = outputRefList
 
 
class ConsolidateSourceTableConnections(pipeBase.PipelineTaskConnections,
                                        defaultTemplates={"catalogType": ""},
                                        dimensions=("instrument", "visit")):
    inputCatalogs = connectionTypes.Input(
        doc="Input per-detector Source Tables",
        name="{catalogType}sourceTable",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector"),
        multiple=True
    )
    outputCatalog = connectionTypes.Output(
        doc="Per-visit concatenation of Source Table",
        name="{catalogType}sourceTable_visit",
        storageClass="DataFrame",
        dimensions=("instrument", "visit")
    )
 
 
class ConsolidateSourceTableConfig(pipeBase.PipelineTaskConfig,
                                   pipelineConnections=ConsolidateSourceTableConnections):
    pass
 
 
class ConsolidateSourceTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Concatenate `sourceTable` list into a per-visit `sourceTable_visit`
    """
    _DefaultName = 'consolidateSourceTable'
    ConfigClass = ConsolidateSourceTableConfig
 
    inputDataset = 'sourceTable'
    outputDataset = 'sourceTable_visit'
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        self.log.info("Concatenating %s per-detector Source Tables",
                      len(inputs['inputCatalogs']))
        df = pd.concat(inputs['inputCatalogs'])
        butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)
 
    def runDataRef(self, dataRefList):
        self.log.info("Concatenating %s per-detector Source Tables", len(dataRefList))
        df = pd.concat([dataRef.get().toDataFrame() for dataRef in dataRefList])
        dataRefList[0].put(ParquetTable(dataFrame=df), self.outputDataset)
 
    @classmethod
    def _makeArgumentParser(cls):
        parser = ArgumentParser(name=cls._DefaultName)
 
        parser.add_id_argument("--id", cls.inputDataset,
                               help="data ID, e.g. --id visit=12345",
                               ContainerClass=VisitDataIdContainer)
        return parser
 
    def writeMetadata(self, dataRef):
        """No metadata to write.
        """
        pass
 
    def writeConfig(self, butler, clobber=False, doBackup=True):
        """No config to write.
        """
        pass
 
 
class MakeCcdVisitTableConnections(pipeBase.PipelineTaskConnections,
                                   dimensions=("instrument",),
                                   defaultTemplates={"calexpType": ""}):
    visitSummaryRefs = connectionTypes.Input(
        doc="Data references for per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
        name="{calexpType}visitSummary",
        storageClass="ExposureCatalog",
        dimensions=("instrument", "visit"),
        multiple=True,
        deferLoad=True,
    )
    outputCatalog = connectionTypes.Output(
        doc="CCD and Visit metadata table",
        name="ccdVisitTable",
        storageClass="DataFrame",
        dimensions=("instrument",)
    )
 
 
class MakeCcdVisitTableConfig(pipeBase.PipelineTaskConfig,
                              pipelineConnections=MakeCcdVisitTableConnections):
    pass
 
 
class MakeCcdVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Produce a `ccdVisitTable` from the `visitSummary` exposure catalogs.
    """
    _DefaultName = 'makeCcdVisitTable'
    ConfigClass = MakeCcdVisitTableConfig
 
    def run(self, visitSummaryRefs):
        """ Make a table of ccd information from the `visitSummary` catalogs.
        Parameters
        ----------
        visitSummaryRefs : `list` of `lsst.daf.butler.DeferredDatasetHandle`
            List of DeferredDatasetHandles pointing to exposure catalogs with
            per-detector summary information.
        Returns
        -------
        result : `lsst.pipe.Base.Struct`
            Results struct with attribute:
                - `outputCatalog`
                    Catalog of ccd and visit information.
        """
        ccdEntries = []
        for visitSummaryRef in visitSummaryRefs:
            visitSummary = visitSummaryRef.get()
            visitInfo = visitSummary[0].getVisitInfo()
 
            ccdEntry = {}
            summaryTable = visitSummary.asAstropy()
            selectColumns = ['id', 'visit', 'physical_filter', 'band', 'ra', 'decl', 'zenithDistance',
                             'zeroPoint', 'psfSigma', 'skyBg', 'skyNoise']
            ccdEntry = summaryTable[selectColumns].to_pandas().set_index('id')
            # 'visit' is the human readible visit number
            # 'visitId' is the key to the visitId table. They are the same
            # Technically you should join to get the visit from the visit table
            ccdEntry = ccdEntry.rename(columns={"visit": "visitId"})
            dataIds = [DataCoordinate.standardize(visitSummaryRef.dataId, detector=id) for id in
                       summaryTable['id']]
            packer = visitSummaryRef.dataId.universe.makePacker('visit_detector', visitSummaryRef.dataId)
            ccdVisitIds = [packer.pack(dataId) for dataId in dataIds]
            ccdEntry['ccdVisitId'] = ccdVisitIds
            ccdEntry['detector'] = summaryTable['id']
            pixToArcseconds = np.array([vR.getWcs().getPixelScale().asArcseconds() for vR in visitSummary])
            ccdEntry["seeing"] = visitSummary['psfSigma'] * np.sqrt(8 * np.log(2)) * pixToArcseconds
 
            ccdEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
            ccdEntry["expMidpt"] = visitInfo.getDate().toPython()
            expTime = visitInfo.getExposureTime()
            ccdEntry['expTime'] = expTime
            ccdEntry["obsStart"] = ccdEntry["expMidpt"] - 0.5 * pd.Timedelta(seconds=expTime)
            ccdEntry['darkTime'] = visitInfo.getDarkTime()
            ccdEntry['xSize'] = summaryTable['bbox_max_x'] - summaryTable['bbox_min_x']
            ccdEntry['ySize'] = summaryTable['bbox_max_y'] - summaryTable['bbox_min_y']
            ccdEntry['llcra'] = summaryTable['raCorners'][:, 0]
            ccdEntry['llcdec'] = summaryTable['decCorners'][:, 0]
            ccdEntry['ulcra'] = summaryTable['raCorners'][:, 1]
            ccdEntry['ulcdec'] = summaryTable['decCorners'][:, 1]
            ccdEntry['urcra'] = summaryTable['raCorners'][:, 2]
            ccdEntry['urcdec'] = summaryTable['decCorners'][:, 2]
            ccdEntry['lrcra'] = summaryTable['raCorners'][:, 3]
            ccdEntry['lrcdec'] = summaryTable['decCorners'][:, 3]
            # TODO: DM-30618, Add raftName, nExposures, ccdTemp, binX, binY, and flags,
            # and decide if WCS, and llcx, llcy, ulcx, ulcy, etc. values are actually wanted.
            ccdEntries.append(ccdEntry)
 
        outputCatalog = pd.concat(ccdEntries)
        outputCatalog.set_index('ccdVisitId', inplace=True, verify_integrity=True)
        return pipeBase.Struct(outputCatalog=outputCatalog)
 
 
class MakeVisitTableConnections(pipeBase.PipelineTaskConnections,
                                dimensions=("instrument",),
                                defaultTemplates={"calexpType": ""}):
    visitSummaries = connectionTypes.Input(
        doc="Per-visit consolidated exposure metadata from ConsolidateVisitSummaryTask",
        name="{calexpType}visitSummary",
        storageClass="ExposureCatalog",
        dimensions=("instrument", "visit",),
        multiple=True,
        deferLoad=True,
    )
    outputCatalog = connectionTypes.Output(
        doc="Visit metadata table",
        name="visitTable",
        storageClass="DataFrame",
        dimensions=("instrument",)
    )
 
 
class MakeVisitTableConfig(pipeBase.PipelineTaskConfig,
                           pipelineConnections=MakeVisitTableConnections):
    pass
 
 
class MakeVisitTableTask(CmdLineTask, pipeBase.PipelineTask):
    """Produce a `visitTable` from the `visitSummary` exposure catalogs.
    """
    _DefaultName = 'makeVisitTable'
    ConfigClass = MakeVisitTableConfig
 
    def run(self, visitSummaries):
        """ Make a table of visit information from the `visitSummary` catalogs
 
        Parameters
        ----------
        visitSummaries : list of `lsst.afw.table.ExposureCatalog`
            List of exposure catalogs with per-detector summary information.
        Returns
        -------
        result : `lsst.pipe.Base.Struct`
            Results struct with attribute:
               ``outputCatalog``
                    Catalog of visit information.
        """
        visitEntries = []
        for visitSummary in visitSummaries:
            visitSummary = visitSummary.get()
            visitRow = visitSummary[0]
            visitInfo = visitRow.getVisitInfo()
 
            visitEntry = {}
            visitEntry["visitId"] = visitRow['visit']
            visitEntry["visit"] = visitRow['visit']
            visitEntry["physical_filter"] = visitRow['physical_filter']
            visitEntry["band"] = visitRow['band']
            raDec = visitInfo.getBoresightRaDec()
            visitEntry["ra"] = raDec.getRa().asDegrees()
            visitEntry["decl"] = raDec.getDec().asDegrees()
            visitEntry["skyRotation"] = visitInfo.getBoresightRotAngle().asDegrees()
            azAlt = visitInfo.getBoresightAzAlt()
            visitEntry["azimuth"] = azAlt.getLongitude().asDegrees()
            visitEntry["altitude"] = azAlt.getLatitude().asDegrees()
            visitEntry["zenithDistance"] = 90 - azAlt.getLatitude().asDegrees()
            visitEntry["airmass"] = visitInfo.getBoresightAirmass()
            visitEntry["obsStart"] = visitInfo.getDate().toPython()
            visitEntry["expTime"] = visitInfo.getExposureTime()
            visitEntries.append(visitEntry)
            # TODO: DM-30623, Add programId, exposureType, expMidpt, cameraTemp, mirror1Temp, mirror2Temp,
            # mirror3Temp, domeTemp, externalTemp, dimmSeeing, pwvGPS, pwvMW, flags, nExposures
 
        outputCatalog = pd.DataFrame(data=visitEntries)
        outputCatalog.set_index('visitId', inplace=True, verify_integrity=True)
        return pipeBase.Struct(outputCatalog=outputCatalog)
 
 
class WriteForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
                                        dimensions=("instrument", "visit", "detector", "skymap", "tract")):
 
    inputCatalog = connectionTypes.Input(
        doc="Primary per-detector, single-epoch forced-photometry catalog. "
            "By default, it is the output of ForcedPhotCcdTask on calexps",
        name="forced_src",
        storageClass="SourceCatalog",
        dimensions=("instrument", "visit", "detector", "skymap", "tract")
    )
    inputCatalogDiff = connectionTypes.Input(
        doc="Secondary multi-epoch, per-detector, forced photometry catalog. "
            "By default, it is the output of ForcedPhotCcdTask run on image differences.",
        name="forced_diff",
        storageClass="SourceCatalog",
        dimensions=("instrument", "visit", "detector", "skymap", "tract")
    )
    outputCatalog = connectionTypes.Output(
        doc="InputCatalogs horizonatally joined on `objectId` in Parquet format",
        name="mergedForcedSource",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector", "skymap", "tract")
    )
 
 
class WriteForcedSourceTableConfig(WriteSourceTableConfig,
                                   pipelineConnections=WriteForcedSourceTableConnections):
    key = lsst.pex.config.Field(
        doc="Column on which to join the two input tables on and make the primary key of the output",
        dtype=str,
        default="objectId",
    )
 
 
class WriteForcedSourceTableTask(pipeBase.PipelineTask):
    """Merge and convert per-detector forced source catalogs to parquet
 
    Because the predecessor ForcedPhotCcdTask operates per-detector,
    per-tract, (i.e., it has tract in its dimensions), detectors
    on the tract boundary may have multiple forced source catalogs.
 
    The successor task TransformForcedSourceTable runs per-patch
    and temporally-aggregates overlapping mergedForcedSource catalogs from all
    available multiple epochs.
    """
    _DefaultName = "writeForcedSourceTable"
    ConfigClass = WriteForcedSourceTableConfig
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        # Add ccdVisitId to allow joining with CcdVisitTable
        inputs['ccdVisitId'] = butlerQC.quantum.dataId.pack("visit_detector")
        inputs['band'] = butlerQC.quantum.dataId.full['band']
        outputs = self.run(**inputs)
        butlerQC.put(outputs, outputRefs)
 
    def run(self, inputCatalog, inputCatalogDiff, ccdVisitId=None, band=None):
        dfs = []
        for table, dataset, in zip((inputCatalog, inputCatalogDiff), ('calexp', 'diff')):
            df = table.asAstropy().to_pandas().set_index(self.config.key, drop=False)
            df = df.reindex(sorted(df.columns), axis=1)
            df['ccdVisitId'] = ccdVisitId if ccdVisitId else pd.NA
            df['band'] = band if band else pd.NA
            df.columns = pd.MultiIndex.from_tuples([(dataset, c) for c in df.columns],
                                                   names=('dataset', 'column'))
 
            dfs.append(df)
 
        outputCatalog = functools.reduce(lambda d1, d2: d1.join(d2), dfs)
        return pipeBase.Struct(outputCatalog=outputCatalog)
 
 
class TransformForcedSourceTableConnections(pipeBase.PipelineTaskConnections,
                                            dimensions=("instrument", "skymap", "patch", "tract")):
 
    inputCatalogs = connectionTypes.Input(
        doc="Parquet table of merged ForcedSources produced by WriteForcedSourceTableTask",
        name="mergedForcedSource",
        storageClass="DataFrame",
        dimensions=("instrument", "visit", "detector", "skymap", "tract"),
        multiple=True,
        deferLoad=True
    )
    referenceCatalog = connectionTypes.Input(
        doc="Reference catalog which was used to seed the forcedPhot. Columns "
            "objectId, detect_isPrimary, detect_isTractInner, detect_isPatchInner "
            "are expected.",
        name="objectTable",
        storageClass="DataFrame",
        dimensions=("tract", "patch", "skymap"),
        deferLoad=True
    )
    outputCatalog = connectionTypes.Output(
        doc="Narrower, temporally-aggregated, per-patch ForcedSource Table transformed and converted per a "
            "specified set of functors",
        name="forcedSourceTable",
        storageClass="DataFrame",
        dimensions=("tract", "patch", "skymap")
    )
 
 
class TransformForcedSourceTableConfig(TransformCatalogBaseConfig,
                                       pipelineConnections=TransformForcedSourceTableConnections):
    referenceColumns = pexConfig.ListField(
        dtype=str,
        default=["detect_isPrimary", "detect_isTractInner", "detect_isPatchInner"],
        optional=True,
        doc="Columns to pull from reference catalog",
    )
    keyRef = lsst.pex.config.Field(
        doc="Column on which to join the two input tables on and make the primary key of the output",
        dtype=str,
        default="objectId",
    )
    key = lsst.pex.config.Field(
        doc="Rename the output DataFrame index to this name",
        dtype=str,
        default="forcedSourceId",
    )
 
 
class TransformForcedSourceTableTask(TransformCatalogBaseTask):
    """Transform/standardize a ForcedSource catalog
 
    Transforms each wide, per-detector forcedSource parquet table per the
    specification file (per-camera defaults found in ForcedSource.yaml).
    All epochs that overlap the patch are aggregated into one per-patch
    narrow-parquet file.
 
    No de-duplication of rows is performed. Duplicate resolutions flags are
    pulled in from the referenceCatalog: `detect_isPrimary`,
    `detect_isTractInner`,`detect_isPatchInner`, so that user may de-duplicate
    for analysis or compare duplicates for QA.
 
    The resulting table includes multiple bands. Epochs (MJDs) and other useful
    per-visit rows can be retreived by joining with the CcdVisitTable on
    ccdVisitId.
    """
    _DefaultName = "transformForcedSourceTable"
    ConfigClass = TransformForcedSourceTableConfig
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        if self.funcs is None:
            raise ValueError("config.functorFile is None. "
                             "Must be a valid path to yaml in order to run Task as a PipelineTask.")
        outputs = self.run(inputs['inputCatalogs'], inputs['referenceCatalog'], funcs=self.funcs,
                           dataId=outputRefs.outputCatalog.dataId.full)
 
        butlerQC.put(outputs, outputRefs)
 
    def run(self, inputCatalogs, referenceCatalog, funcs=None, dataId=None, band=None):
        dfs = []
        ref = referenceCatalog.get(parameters={"columns": self.config.referenceColumns})
        self.log.info("Aggregating %s input catalogs" % (len(inputCatalogs)))
        for handle in inputCatalogs:
            result = self.transform(None, handle, funcs, dataId)
            # Filter for only rows that were detected on (overlap) the patch
            dfs.append(result.df.join(ref, how='inner'))
 
        outputCatalog = pd.concat(dfs)
 
        # Now that we are done joining on config.keyRef
        # Change index to config.key by
        outputCatalog.index.rename(self.config.keyRef, inplace=True)
        # Add config.keyRef to the column list
        outputCatalog.reset_index(inplace=True)
        # set the forcedSourceId to the index. This is specified in the ForcedSource.yaml
        outputCatalog.set_index("forcedSourceId", inplace=True, verify_integrity=True)
        # Rename it to the config.key
        outputCatalog.index.rename(self.config.key, inplace=True)
 
        self.log.info("Made a table of %d columns and %d rows",
                      len(outputCatalog.columns), len(outputCatalog))
        return pipeBase.Struct(outputCatalog=outputCatalog)
 
 
class ConsolidateTractConnections(pipeBase.PipelineTaskConnections,
                                  defaultTemplates={"catalogType": ""},
                                  dimensions=("instrument", "tract")):
    inputCatalogs = connectionTypes.Input(
        doc="Input per-patch DataFrame Tables to be concatenated",
        name="{catalogType}ForcedSourceTable",
        storageClass="DataFrame",
        dimensions=("tract", "patch", "skymap"),
        multiple=True,
    )
 
    outputCatalog = connectionTypes.Output(
        doc="Output per-tract concatenation of DataFrame Tables",
        name="{catalogType}ForcedSourceTable_tract",
        storageClass="DataFrame",
        dimensions=("tract", "skymap"),
    )
 
 
class ConsolidateTractConfig(pipeBase.PipelineTaskConfig,
                             pipelineConnections=ConsolidateTractConnections):
    pass
 
 
class ConsolidateTractTask(CmdLineTask, pipeBase.PipelineTask):
    """Concatenate any per-patch, dataframe list into a single
       per-tract DataFrame
    """
    _DefaultName = 'ConsolidateTract'
    ConfigClass = ConsolidateTractConfig
 
    def runQuantum(self, butlerQC, inputRefs, outputRefs):
        inputs = butlerQC.get(inputRefs)
        # Not checking at least one inputCatalog exists because that'd be an empty QG
        self.log.info("Concatenating %s per-patch %s Tables",
                      len(inputs['inputCatalogs']),
                      inputRefs.inputCatalogs[0].datasetType.name)
        df = pd.concat(inputs['inputCatalogs'])
        butlerQC.put(pipeBase.Struct(outputCatalog=df), outputRefs)