DataCoordinateQueryResults¶

class lsst.daf.butler.registry.queries.DataCoordinateQueryResults¶

Bases: QueryResultsBase, DataCoordinateIterable

An enhanced implementation of DataCoordinateIterable that represents data IDs retrieved from a database query.

Methods Summary

`expanded`()	Return a results object for which `hasRecords` returns `True`.
`findDatasets`(datasetType, collections, *[, ...])	Find datasets using the data IDs identified by this query.
`findRelatedDatasets`(datasetType, collections, *)	Find datasets using the data IDs identified by this query, and return them along with the original data IDs.
`materialize`()	Insert this query's results into a temporary table.
`subset`([dimensions, unique])	Return a results object containing a subset of the dimensions of this one, and/or a unique near-subset of its rows.

Methods Documentation

abstract expanded() → DataCoordinateQueryResults¶

Return a results object for which hasRecords returns True.

This method may involve actually executing database queries to fetch DimensionRecord objects.

Returns:

resultsDataCoordinateQueryResults: A results object for which hasRecords returns True. May be self if that is already the case.

Notes

For very result sets, it may be much more efficient to call materialize before calling expanded, to avoid performing the original query multiple times (as a subquery) in the follow-up queries that fetch dimension records. For example:

with registry.queryDataIds(...).materialize() as tempDataIds:
    dataIdsWithRecords = tempDataIds.expanded()
    for dataId in dataIdsWithRecords:
        ...

abstract findDatasets(datasetType: DatasetType | str, collections: Any, *, findFirst: bool = True, components: bool = False) → ParentDatasetQueryResults¶

Find datasets using the data IDs identified by this query.

Parameters:

datasetTypeDatasetType or str: Dataset type or the name of one to search for. Must have dimensions that are a subset of self.graph.
collectionsAny: An expression that fully or partially identifies the collections to search for the dataset, such as a str, re.Pattern, or iterable thereof. ... can be used to return all collections. See Collection expressions for more information.
findFirstbool, optional: If True (default), for each result data ID, only yield one DatasetRef, from the first collection in which a dataset of that dataset type appears (according to the order of collections passed in). If True, collections must not contain regular expressions and may not be ....
componentsbool, optional: Must be False. Provided only for backwards compatibility. After v27 this argument will be removed entirely.

Returns:

datasetsParentDatasetQueryResults: A lazy-evaluation object representing dataset query results, iterable over DatasetRef objects. If self.hasRecords(), all nested data IDs in those dataset references will have records as well.

Raises:

MissingDatasetTypeError: Raised if the given dataset type is not registered.

abstract findRelatedDatasets(datasetType: DatasetType | str, collections: Any, *, findFirst: bool = True, dimensions: DimensionGroup | Iterable[str] | None = None) → Iterable[tuple[lsst.daf.butler.dimensions._coordinate.DataCoordinate, lsst.daf.butler._dataset_ref.DatasetRef]]¶

Find datasets using the data IDs identified by this query, and return them along with the original data IDs.

This is a variant of findDatasets that is often more useful when the target dataset type does not have all of the dimensions of the original data ID query, as is generally the case with calibration lookups.

Parameters:

datasetTypeDatasetType or str: Dataset type or the name of one to search for. Must have dimensions that are a subset of self.graph.
collectionsAny: An expression that fully or partially identifies the collections to search for the dataset, such as a str, re.Pattern, or iterable thereof. ... can be used to return all collections. See Collection expressions for more information.
findFirstbool, optional: If True (default), for each data ID in self, only yield one DatasetRef, from the first collection in which a dataset of that dataset type appears (according to the order of collections passed in). If True, collections must not contain regular expressions and may not be .... Note that this is not the same as yielding one DatasetRef for each yielded data ID if dimensions is not None.
dimensionsDimensionGroup or Iterable [ str ], optional: The dimensions of the data IDs returned. Must be a subset of self.dimensions.

Returns:

pairsIterable [ tuple [ DataCoordinate, DatasetRef ] ]: An iterable of (data ID, dataset reference) pairs.

Raises:

MissingDatasetTypeError: Raised if the given dataset type is not registered.

abstract materialize() → AbstractContextManager[DataCoordinateQueryResults]¶

Insert this query’s results into a temporary table.

Returns:

contexttyping.ContextManager [ DataCoordinateQueryResults ]: A context manager that ensures the temporary table is created and populated in __enter__ (returning a results object backed by that table), and dropped in __exit__. If self is already materialized, the context manager may do nothing (reflecting the fact that an outer context manager should already take care of everything else).

Notes

When using a very large result set to perform multiple queries (e.g. multiple calls to subset with different arguments, or even a single call to expanded), it may be much more efficient to start by materializing the query and only then performing the follow up queries. It may also be less efficient, depending on how well database engine’s query optimizer can simplify those particular follow-up queries and how efficiently it caches query results even when the are not explicitly inserted into a temporary table. See expanded and subset for examples.

abstract subset(dimensions: DimensionGroup | Iterable[str] | None = None, *, unique: bool = False) → DataCoordinateQueryResults¶

Return a results object containing a subset of the dimensions of this one, and/or a unique near-subset of its rows.

This method may involve actually executing database queries to fetch DimensionRecord objects.

Parameters:

dimensionsDimensionGroup or Iterable [ str], optional: Dimensions to include in the new results object. If None, self.dimensions is used.
uniquebool, optional: If True (False is default), the query should only return unique data IDs. This is implemented in the database; to obtain unique results via Python-side processing (which may be more efficient in some cases), use toSet to construct a DataCoordinateSet from this results object instead.

Returns:

resultsDataCoordinateQueryResults: A results object corresponding to the given criteria. May be self if it already qualifies.

Raises:

ValueError: Raised when dimensions is not a subset of the dimensions in this result.

Notes

This method can only return a “near-subset” of the original result rows in general because of subtleties in how spatial overlaps are implemented; see Query.projected for more information.

When calling subset multiple times on the same very large result set, it may be much more efficient to call materialize first. For example:

dimensions1 = DimensionGroup(...)
dimensions2 = DimensionGroup(...)
with registry.queryDataIds(...).materialize() as tempDataIds:
    for dataId1 in tempDataIds.subset(dimensions1, unique=True):
        ...
    for dataId2 in tempDataIds.subset(dimensions2, unique=True):
        ...

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DataCoordinateQueryResults¶