rootRepoConverter.py

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# This file is part of obs_base.
#
# Developed for the LSST Data Management System.
# This product includes software developed by the LSST Project
# (http://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 <http://www.gnu.org/licenses/>.
from __future__ import annotations

__all__ = ["RootRepoConverter"]

import os
import re
import itertools
from typing import TYPE_CHECKING, Iterator, Tuple, List

from lsst.daf.butler import DatasetType, DatasetRef, FileDataset
from .calibRepoConverter import CURATED_CALIBRATION_DATASET_TYPES
from .standardRepoConverter import StandardRepoConverter

SKYMAP_DATASET_TYPES = {
    coaddName: f"{coaddName}Coadd_skyMap" for coaddName in ("deep", "goodSeeing", "dcr")
}

if TYPE_CHECKING:
    from lsst.daf.butler import SkyPixDimension
    from ..ingest import RawExposureData


class RootRepoConverter(StandardRepoConverter):
    """A specialization of `RepoConverter` for root data repositories.

    `RootRepoConverter` adds support for raw images (mostly delegated to the
    parent task's `RawIngestTask` subtask) and reference catalogs.

    Parameters
    ----------
    kwds
        Keyword arguments are forwarded to (and required by) `RepoConverter`.
    """

    def __init__(self, **kwds):
        super().__init__(**kwds)
        self._exposureData: List[RawExposureData] = []
        self._refCats: List[Tuple[str, SkyPixDimension]] = []

    def isDatasetTypeSpecial(self, datasetTypeName: str) -> bool:
        # Docstring inherited from RepoConverter.
        return (
            super().isDatasetTypeSpecial(datasetTypeName) or
            datasetTypeName in ("raw", "ref_cat", "ref_cat_config") or
            # in Gen2, some of these are in the root repo, not a calib repo
            datasetTypeName in CURATED_CALIBRATION_DATASET_TYPES
        )

    def isDirectorySpecial(self, subdirectory: str) -> bool:
        # Docstring inherited from RepoConverter.
        return subdirectory == "ref_cats"

    def prep(self):
        # Docstring inherited from RepoConverter.
        # Gather information about raws.
        if self.task.raws is not None:
            self.task.log.info(f"Preparing raws from root {self.root}.")
            if self.subset is not None:
                dataRefs = itertools.chain.from_iterable(
                    self.butler2.subset("raw", visit=visit) for visit in self.subset.visits
                )
            else:
                dataRefs = self.butler2.subset("raw")
            self._exposureData.extend(self.task.raws.prep(dataRef.getUri() for dataRef in dataRefs))
        # Gather information about reference catalogs.
        if self.task.isDatasetTypeIncluded("ref_cat"):
            from lsst.meas.algorithms import DatasetConfig as RefCatDatasetConfig
            for refCat in os.listdir(os.path.join(self.root, "ref_cats")):
                self.task.log.info(f"Preparing ref_cat {refCat} from root {self.root}.")
                path = os.path.join(self.root, "ref_cats", refCat)
                configFile = os.path.join(path, "config.py")
                if not os.path.exists(configFile):
                    continue
                if not self.task.isDatasetTypeIncluded(refCat):
                    # While the Gen2 dataset type for reference catalogs is
                    # just "ref_cat", in Gen3 we use the name of the reference
                    # catalog as its dataset type name.
                    continue
                onDiskConfig = RefCatDatasetConfig()
                onDiskConfig.load(configFile)
                if onDiskConfig.indexer.name != "HTM":
                    raise ValueError(f"Reference catalog '{refCat}' uses unsupported "
                                     f"pixelization '{onDiskConfig.indexer.name}'.")
                level = onDiskConfig.indexer["HTM"].depth
                try:
                    dimension = self.task.universe[f"htm{level}"]
                except KeyError as err:
                    raise ValueError(f"Reference catalog {refCat} uses HTM level {level}, but no htm{level} "
                                     f"skypix dimension is configured for this registry.") from err
                self.task.useSkyPix(dimension)
                self._refCats.append((refCat, dimension))
        super().prep()

    def insertDimensionData(self):
        # Docstring inherited from RepoConverter.
        self.task.log.info(f"Inserting observation dimension records from {self.root}.")
        records = {"visit": [], "exposure": [], "visit_detector_region": []}
        for exposure in self._exposureData:
            for dimension, recordsForDimension in exposure.records.items():
                records[dimension].extend(recordsForDimension)
        self.task.raws.insertDimensionData(records)

    def iterDatasets(self) -> Iterator[FileDataset]:
        # Docstring inherited from RepoConverter.
        # Iterate over reference catalog files.
        for refCat, dimension in self._refCats:
            datasetType = DatasetType(refCat, dimensions=[dimension], universe=self.task.universe,
                                      storageClass="SimpleCatalog")
            if self.subset is None:
                regex = re.compile(r"(\d+)\.fits")
                for fileName in os.listdir(os.path.join(self.root, "ref_cats", refCat)):
                    m = regex.match(fileName)
                    if m is not None:
                        htmId = int(m.group(1))
                        dataId = self.task.registry.expandDataId({dimension: htmId})
                        yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, fileName),
                                          ref=DatasetRef(datasetType, dataId))
            else:
                for htmId in self.subset.skypix[dimension]:
                    dataId = self.task.registry.expandDataId({dimension: htmId})
                    yield FileDataset(path=os.path.join(self.root, "ref_cats", refCat, f"{htmId}.fits"),
                                      ref=DatasetRef(datasetType, dataId))
        yield from super().iterDatasets()

    def ingest(self):
        # Docstring inherited from RepoConverter.
        if self.task.raws is not None:
            self.task.log.info(f"Ingesting raws from root {self.root}.")
            self.task.registry.registerDatasetType(self.task.raws.datasetType)
            # We need te delegate to RawIngestTask to actually ingest raws,
            # rather than just including those datasets in iterDatasets for
            # the base class to handle, because we don't want to assume we
            # can use the Datastore-configured Formatter for raw data.
            refs = []
            butler, collections = self.getButler("raw")
            for exposure in self._exposureData:
                refs.extend(self.task.raws.ingestExposureDatasets(exposure))
            for collection in collections:
                self.task.registry.associate(collection, refs)
        super().ingest()