Registry

class lsst.daf.butler.Registry(database: Database, defaults: RegistryDefaults, managers: RegistryManagerInstances)

Bases: object

Registry interface.

Parameters:

database : Database

Database instance to store Registry.

defaults : RegistryDefaults, optional

Default collection search path and/or output RUN collection.

attributes : type

Manager class implementing ButlerAttributeManager.

opaque : type

Manager class implementing OpaqueTableStorageManager.

dimensions : type

Manager class implementing DimensionRecordStorageManager.

collections : type

Manager class implementing CollectionManager.

datasets : type

Manager class implementing DatasetRecordStorageManager.

datastoreBridges : type

Manager class implementing DatastoreRegistryBridgeManager.

dimensionConfig : DimensionConfig, optional

Dimension universe configuration, only used when create is True.

writeable : bool, optional

If True then Registry will support write operations.

create : bool, optional

If True then database schema will be initialized, it must be empty before instantiating Registry.

Attributes Summary

defaultConfigFile	Path to configuration defaults.
defaults	Default collection search path and/or output RUN collection (RegistryDefaults).
dimensions	All dimensions recognized by this Registry (DimensionUniverse).

Methods Summary

associate(collection, refs)	Add existing datasets to a TAGGED collection.
certify(collection, refs, timespan)	Associate one or more datasets with a calibration collection and a validity range within it.
copy(defaults)	Create a new Registry backed by the same data repository and connection as this one, but independent defaults.
createFromConfig(config, str, None] = None, …)	Create registry database and return Registry instance.
decertify(collection, datasetType, …)	Remove or adjust datasets to clear a validity range within a calibration collection.
deleteOpaqueData(tableName, **where)	Remove records from an opaque table.
disassociate(collection, refs)	Remove existing datasets from a TAGGED collection.
expandDataId(dataId, Mapping[str, Any], …)	Expand a dimension-based data ID to include additional information.
fetchOpaqueData(tableName, **where)	Retrieve records from an opaque table.
findDataset(datasetType, str], dataId, …)	Find a dataset given its DatasetType and data ID.
fromConfig(config, RegistryConfig, Config, …)	Create Registry subclass instance from config.
getCollectionChain(parent)	Return the child collections in a CHAINED collection.
getCollectionDocumentation(collection)	Retrieve the documentation string for a collection.
getCollectionSummary(collection)	Return a summary for the given collection.
getCollectionType(name)	Return an enumeration value indicating the type of the given collection.
getDataset(id)	Retrieve a Dataset entry.
getDatasetLocations(ref)	Retrieve datastore locations for a given dataset.
getDatasetType(name)	Get the DatasetType.
getDatastoreBridgeManager()	Return an object that allows a new Datastore instance to communicate with this Registry.
insertDatasets(datasetType, str], dataIds, …)	Insert one or more datasets into the Registry
insertDimensionData(element, str], *data, …)	Insert one or more dimension records into the database.
insertOpaqueData(tableName, *data)	Insert records into an opaque table.
isWriteable()	Return True if this registry allows write operations, and False otherwise.
makeQueryBuilder(summary)	Return a QueryBuilder instance capable of constructing and managing more complex queries than those obtainable via Registry interfaces.
queryCollections(expression, datasetType, …)	Iterate over the collections whose names match an expression.
queryDataIds(dimensions, str]], …)	Query for data IDs matching user-provided criteria.
queryDatasetAssociations(datasetType, …)	Iterate over dataset-collection combinations where the dataset is in the collection.
queryDatasetTypes(expression, *, components)	Iterate over the dataset types whose names match an expression.
queryDatasets(datasetType, *, collections, …)	Query for and iterate over dataset references matching user-provided criteria.
queryDimensionRecords(element, str], *, …)	Query for dimension information matching user-provided criteria.
refresh()	Refresh all in-memory state by querying the database.
registerCollection(name, type, doc)	Add a new collection if one with the given name does not exist.
registerDatasetType(datasetType)	Add a new DatasetType to the Registry.
registerOpaqueTable(tableName, spec)	Add an opaque (to the Registry) table for use by a Datastore or other data repository client.
registerRun(name, doc)	Add a new run if one with the given name does not exist.
removeCollection(name)	Completely remove the given collection.
removeDatasetType(name)	Remove the named DatasetType from the registry.
removeDatasets(refs)	Remove datasets from the Registry.
resetConnectionPool()	Reset SQLAlchemy connection pool for registry database.
setCollectionChain(parent, children)	Define or redefine a CHAINED collection.
setCollectionDocumentation(collection, doc)	Set the documentation string for a collection.
syncDimensionData(element, str], row, Any], …)	Synchronize the given dimension record with the database, inserting if it does not already exist and comparing values if it does.
transaction(*, savepoint)	Return a context manager that represents a transaction.

Attributes Documentation

defaultConfigFile = None

Path to configuration defaults. Accessed within the configs resource or relative to a search path. Can be None if no defaults specified.

defaults

Default collection search path and/or output RUN collection (RegistryDefaults).

This is an immutable struct whose components may not be set individually, but the entire struct can be set by assigning to this property.

dimensions

All dimensions recognized by this Registry (DimensionUniverse).

Methods Documentation

associate(collection: str, refs: Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef]) → None

Add existing datasets to a TAGGED collection.

If a DatasetRef with the same exact integer ID is already in a collection nothing is changed. If a DatasetRef with the same DatasetType and data ID but with different integer ID exists in the collection, ConflictingDefinitionError is raised.

Parameters:

collection : str

Indicates the collection the datasets should be associated with.

refs : Iterable [ DatasetRef ]

An iterable of resolved DatasetRef instances that already exist in this Registry.

Raises:

ConflictingDefinitionError

If a Dataset with the given DatasetRef already exists in the given collection.

AmbiguousDatasetError

Raised if any(ref.id is None for ref in refs).

MissingCollectionError

Raised if collection does not exist in the registry.

TypeError

Raise adding new datasets to the given collection is not allowed.

certify(collection: str, refs: Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef], timespan: lsst.daf.butler.core.timespan.Timespan) → None

Associate one or more datasets with a calibration collection and a validity range within it.

Parameters:

collection : str

The name of an already-registered CALIBRATION collection.

refs : Iterable [ DatasetRef ]

Datasets to be associated.

timespan : Timespan

The validity range for these datasets within the collection.

Raises:

AmbiguousDatasetError

Raised if any of the given DatasetRef instances is unresolved.

ConflictingDefinitionError

Raised if the collection already contains a different dataset with the same DatasetType and data ID and an overlapping validity range.

TypeError

Raised if collection is not a CALIBRATION collection or if one or more datasets are of a dataset type for which DatasetType.isCalibration returns False.

copy(defaults: Optional[lsst.daf.butler.registry._defaults.RegistryDefaults] = None) → lsst.daf.butler.registry._registry.Registry

Create a new Registry backed by the same data repository and connection as this one, but independent defaults.

Parameters:

defaults : RegistryDefaults, optional

Default collections and data ID values for the new registry. If not provided, self.defaults will be used (but future changes to either registry’s defaults will not affect the other).

Returns:

copy : Registry

A new Registry instance with its own defaults.

Notes

Because the new registry shares a connection with the original, they also share transaction state (despite the fact that their transaction context manager methods do not reflect this), and must be used with care.

classmethod createFromConfig(config: Union[lsst.daf.butler.registry._config.RegistryConfig, str, None] = None, dimensionConfig: Union[lsst.daf.butler.core.dimensions._config.DimensionConfig, str, None] = None, butlerRoot: Optional[str] = None) → lsst.daf.butler.registry._registry.Registry

Create registry database and return Registry instance.

This method initializes database contents, database must be empty prior to calling this method.

Parameters:

config : RegistryConfig or str, optional

Registry configuration, if missing then default configuration will be loaded from registry.yaml.

dimensionConfig : DimensionConfig or str, optional

Dimensions configuration, if missing then default configuration will be loaded from dimensions.yaml.

butlerRoot : str, optional

Path to the repository root this Registry will manage.

Returns:

registry : Registry

A new Registry instance.

decertify(collection: str, datasetType: Union[str, lsst.daf.butler.core.datasets.type.DatasetType], timespan: lsst.daf.butler.core.timespan.Timespan, *, dataIds: Optional[Iterable[Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any]]]] = None) → None

Remove or adjust datasets to clear a validity range within a calibration collection.

Parameters:

collection : str

The name of an already-registered CALIBRATION collection.

datasetType : str or DatasetType

Name or DatasetType instance for the datasets to be decertified.

timespan : Timespan, optional

The validity range to remove datasets from within the collection. Datasets that overlap this range but are not contained by it will have their validity ranges adjusted to not overlap it, which may split a single dataset validity range into two.

dataIds : Iterable [ DataId ], optional

Data IDs that should be decertified within the given validity range If None, all data IDs for self.datasetType will be decertified.

Raises:

TypeError

Raised if collection is not a CALIBRATION collection or if datasetType.isCalibration() is False.

deleteOpaqueData(tableName: str, **where) → None

Remove records from an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

where

Additional keyword arguments are interpreted as equality constraints that restrict the deleted rows (combined with AND); keyword arguments are column names and values are the values they must have.

disassociate(collection: str, refs: Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef]) → None

Remove existing datasets from a TAGGED collection.

collection and ref combinations that are not currently associated are silently ignored.

Parameters:

collection : str

The collection the datasets should no longer be associated with.

refs : Iterable [ DatasetRef ]

An iterable of resolved DatasetRef instances that already exist in this Registry.

Raises:

AmbiguousDatasetError

Raised if any of the given dataset references is unresolved.

MissingCollectionError

Raised if collection does not exist in the registry.

TypeError

Raise adding new datasets to the given collection is not allowed.

expandDataId(dataId: Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any], None] = None, *, graph: Optional[lsst.daf.butler.core.dimensions._graph.DimensionGraph] = None, records: Union[lsst.daf.butler.core.named.NamedKeyMapping[lsst.daf.butler.core.dimensions._elements.DimensionElement, typing.Union[lsst.daf.butler.core.dimensions._records.DimensionRecord, NoneType]][lsst.daf.butler.core.dimensions._elements.DimensionElement, Optional[lsst.daf.butler.core.dimensions._records.DimensionRecord]], Mapping[str, Optional[lsst.daf.butler.core.dimensions._records.DimensionRecord]], None] = None, withDefaults: bool = True, **kwargs) → lsst.daf.butler.core.dimensions._coordinate.DataCoordinate

Expand a dimension-based data ID to include additional information.

Parameters:

dataId : DataCoordinate or dict, optional

Data ID to be expanded; augmented and overridden by kwds.

graph : DimensionGraph, optional

Set of dimensions for the expanded ID. If None, the dimensions will be inferred from the keys of dataId and kwds. Dimensions that are in dataId or kwds but not in graph are silently ignored, providing a way to extract and expand a subset of a data ID.

records : Mapping [str, DimensionRecord], optional

Dimension record data to use before querying the database for that data, keyed by element name.

withDefaults : bool, optional

Utilize self.defaults.dataId to fill in missing governor dimension key-value pairs. Defaults to True (i.e. defaults are used).

**kwargs

Additional keywords are treated like additional key-value pairs for dataId, extending and overriding

Returns:

expanded : DataCoordinate

A data ID that includes full metadata for all of the dimensions it identifieds, i.e. guarantees that expanded.hasRecords() and expanded.hasFull() both return True.

fetchOpaqueData(tableName: str, **where) → Iterator[dict]

Retrieve records from an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

where

Additional keyword arguments are interpreted as equality constraints that restrict the returned rows (combined with AND); keyword arguments are column names and values are the values they must have.

Yields:

row : dict

A dictionary representing a single result row.

findDataset(datasetType: Union[lsst.daf.butler.core.datasets.type.DatasetType, str], dataId: Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any], None] = None, *, collections: Optional[Any] = None, timespan: Optional[lsst.daf.butler.core.timespan.Timespan] = None, **kwargs) → Optional[lsst.daf.butler.core.datasets.ref.DatasetRef]

Find a dataset given its DatasetType and data ID.

This can be used to obtain a DatasetRef that permits the dataset to be read from a Datastore. If the dataset is a component and can not be found using the provided dataset type, a dataset ref for the parent will be returned instead but with the correct dataset type.

Parameters:

datasetType : DatasetType or str

A DatasetType or the name of one.

dataId : dict or DataCoordinate, optional

A dict-like object containing the Dimension links that identify the dataset within a collection.

collections, optional.

An expression that fully or partially identifies the collections to search for the dataset; see Collection expressions for more information. Defaults to self.defaults.collections.

timespan : Timespan, optional

A timespan that the validity range of the dataset must overlap. If not provided, any CALIBRATION collections matched by the collections argument will not be searched.

**kwargs

Additional keyword arguments passed to DataCoordinate.standardize to convert dataId to a true DataCoordinate or augment an existing one.

Returns:

ref : DatasetRef

A reference to the dataset, or None if no matching Dataset was found.

Raises:

TypeError

Raised if collections is None and self.defaults.collections is None.

LookupError

Raised if one or more data ID keys are missing.

KeyError

Raised if the dataset type does not exist.

MissingCollectionError

Raised if any of collections does not exist in the registry.

Notes

This method simply returns None and does not raise an exception even when the set of collections searched is intrinsically incompatible with the dataset type, e.g. if datasetType.isCalibration() is False, but only CALIBRATION collections are being searched. This may make it harder to debug some lookup failures, but the behavior is intentional; we consider it more important that failed searches are reported consistently, regardless of the reason, and that adding additional collections that do not contain a match to the search path never changes the behavior.

classmethod fromConfig(config: Union[ButlerConfig, RegistryConfig, Config, str], butlerRoot: Optional[Union[str, ButlerURI]] = None, writeable: bool = True, defaults: Optional[RegistryDefaults] = None) → Registry

Create Registry subclass instance from config.

Registry database must be inbitialized prior to calling this method.

Parameters:

config : ButlerConfig, RegistryConfig, Config or str

Registry configuration

butlerRoot : str or ButlerURI, optional

Path to the repository root this Registry will manage.

writeable : bool, optional

If True (default) create a read-write connection to the database.

defaults : RegistryDefaults, optional

Default collection search path and/or output RUN collection.

Returns:

registry : Registry (subclass)

A new Registry subclass instance.

getCollectionChain(parent: str) → lsst.daf.butler.registry.wildcards.CollectionSearch

Return the child collections in a CHAINED collection.

Parameters:

parent : str

Name of the chained collection. Must have already been added via a call to Registry.registerCollection.

Returns:

children : CollectionSearch

An object that defines the search path of the collection. See Collection expressions for more information.

Raises:

MissingCollectionError

Raised if parent does not exist in the Registry.

TypeError

Raised if parent does not correspond to a CHAINED collection.

getCollectionDocumentation(collection: str) → Optional[str]

Retrieve the documentation string for a collection.

Parameters:

name : str

Name of the collection.

Returns:

docs : str or None

Docstring for the collection with the given name.

getCollectionSummary(collection: str) → lsst.daf.butler.registry.summaries.CollectionSummary

Return a summary for the given collection.

Parameters:

collection : str

Name of the collection for which a summary is to be retrieved.

Returns:

summary : CollectionSummary

Summary of the dataset types and governor dimension values in this collection.

getCollectionType(name: str) → lsst.daf.butler.registry._collectionType.CollectionType

Return an enumeration value indicating the type of the given collection.

Parameters:

name : str

The name of the collection.

Returns:

type : CollectionType

Enum value indicating the type of this collection.

Raises:

MissingCollectionError

Raised if no collection with the given name exists.

getDataset(id: int) → Optional[lsst.daf.butler.core.datasets.ref.DatasetRef]

Retrieve a Dataset entry.

Parameters:

id : int

The unique identifier for the dataset.

Returns:

ref : DatasetRef or None

A ref to the Dataset, or None if no matching Dataset was found.

getDatasetLocations(ref: lsst.daf.butler.core.datasets.ref.DatasetRef) → Iterable[str]

Retrieve datastore locations for a given dataset.

Parameters:

ref : DatasetRef

A reference to the dataset for which to retrieve storage information.

Returns:

datastores : Iterable [ str ]

All the matching datastores holding this dataset.

Raises:

AmbiguousDatasetError

Raised if ref.id is None.

getDatasetType(name: str) → lsst.daf.butler.core.datasets.type.DatasetType

Get the DatasetType.

Parameters:

name : str

Name of the type.

Returns:

type : DatasetType

The DatasetType associated with the given name.

Raises:

KeyError

Requested named DatasetType could not be found in registry.

getDatastoreBridgeManager() → DatastoreRegistryBridgeManager

Return an object that allows a new Datastore instance to communicate with this Registry.

Returns:

manager : DatastoreRegistryBridgeManager

Object that mediates communication between this Registry and its associated datastores.

insertDatasets(datasetType: Union[lsst.daf.butler.core.datasets.type.DatasetType, str], dataIds: Iterable[Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any]]], run: Optional[str] = None) → List[lsst.daf.butler.core.datasets.ref.DatasetRef]

Insert one or more datasets into the Registry

This always adds new datasets; to associate existing datasets with a new collection, use associate.

Parameters:

datasetType : DatasetType or str

A DatasetType or the name of one.

dataIds : Iterable of dict or DataCoordinate

Dimension-based identifiers for the new datasets.

run : str, optional

The name of the run that produced the datasets. Defaults to self.defaults.run.

Returns:

refs : list of DatasetRef

Resolved DatasetRef instances for all given data IDs (in the same order).

Raises:

TypeError

Raised if run is None and self.defaults.run is None.

ConflictingDefinitionError

If a dataset with the same dataset type and data ID as one of those given already exists in run.

MissingCollectionError

Raised if run does not exist in the registry.

insertDimensionData(element: Union[lsst.daf.butler.core.dimensions._elements.DimensionElement, str], *data, conform: bool = True) → None

Insert one or more dimension records into the database.

Parameters:

element : DimensionElement or str

The DimensionElement or name thereof that identifies the table records will be inserted into.

data : dict or DimensionRecord (variadic)

One or more records to insert.

conform : bool, optional

If False (True is default) perform no checking or conversions, and assume that element is a DimensionElement instance and data is a one or more DimensionRecord instances of the appropriate subclass.

insertOpaqueData(tableName: str, *data) → None

Insert records into an opaque table.

Parameters:

tableName : str

Logical name of the opaque table. Must match the name used in a previous call to registerOpaqueTable.

data

Each additional positional argument is a dictionary that represents a single row to be added.

isWriteable() → bool

Return True if this registry allows write operations, and False otherwise.

makeQueryBuilder(summary: lsst.daf.butler.registry.queries._structs.QuerySummary) → lsst.daf.butler.registry.queries._builder.QueryBuilder

Return a QueryBuilder instance capable of constructing and managing more complex queries than those obtainable via Registry interfaces.

This is an advanced interface; downstream code should prefer Registry.queryDataIds and Registry.queryDatasets whenever those are sufficient.

Parameters:

summary : queries.QuerySummary

Object describing and categorizing the full set of dimensions that will be included in the query.

Returns:

builder : queries.QueryBuilder

Object that can be used to construct and perform advanced queries.

queryCollections(expression: Any = Ellipsis, datasetType: Optional[lsst.daf.butler.core.datasets.type.DatasetType] = None, collectionTypes: Iterable[lsst.daf.butler.registry._collectionType.CollectionType] = frozenset({<CollectionType.RUN: 1>, <CollectionType.TAGGED: 2>, <CollectionType.CHAINED: 3>, <CollectionType.CALIBRATION: 4>}), flattenChains: bool = False, includeChains: Optional[bool] = None) → Iterator[str]

Iterate over the collections whose names match an expression.

Parameters:

expression : Any, optional

An expression that fully or partially identifies the collections to return, such as a str, re.Pattern, or iterable thereof. can be used to return all collections, and is the default. See Collection expressions for more information.

datasetType : DatasetType, optional

If provided, only yield collections that may contain datasets of this type. This is a conservative approximation in general; it may yield collections that do not have any such datasets.

collectionTypes : AbstractSet [ CollectionType ], optional

If provided, only yield collections of these types.

flattenChains : bool, optional

If True (False is default), recursively yield the child collections of matching CHAINED collections.

includeChains : bool, optional

If True, yield records for matching CHAINED collections. Default is the opposite of flattenChains: include either CHAINED collections or their children, but not both.

Yields:

collection : str

The name of a collection that matches expression.

queryDataIds(dimensions: Union[Iterable[Union[lsst.daf.butler.core.dimensions._elements.Dimension, str]], lsst.daf.butler.core.dimensions._elements.Dimension, str], *, dataId: Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any], None] = None, datasets: Optional[Any] = None, collections: Optional[Any] = None, where: Optional[str] = None, components: Optional[bool] = None, bind: Optional[Mapping[str, Any]] = None, check: bool = True, **kwargs) → lsst.daf.butler.registry.queries._results.DataCoordinateQueryResults

Query for data IDs matching user-provided criteria.

Parameters:

dimensions : Dimension or str, or iterable thereof

The dimensions of the data IDs to yield, as either Dimension instances or str. Will be automatically expanded to a complete DimensionGraph.

dataId : dict or DataCoordinate, optional

A data ID whose key-value pairs are used as equality constraints in the query.

datasets : Any, optional

An expression that fully or partially identifies dataset types that should constrain the yielded data IDs. For example, including “raw” here would constrain the yielded instrument, exposure, detector, and physical_filter values to only those for which at least one “raw” dataset exists in collections. Allowed types include DatasetType, str, re.Pattern, and iterables thereof. Unlike other dataset type expressions, ... is not permitted - it doesn’t make sense to constrain data IDs on the existence of all datasets. See DatasetType expressions for more information.

collections: `Any`, optional

An expression that fully or partially identifies the collections to search for datasets, such as a str, re.Pattern, or iterable thereof. can be used to return all collections. Must be provided if datasets is, and is ignored if it is not. See Collection expressions for more information. If not provided, self.default.collections is used.

where : str, optional

A string expression similar to a SQL WHERE clause. May involve any column of a dimension table or (as a shortcut for the primary key column of a dimension table) dimension name. See Dimension expressions for more information.

components : bool, optional

If True, apply all dataset expression patterns to component dataset type names as well. If False, never apply patterns to components. If None (default), apply patterns to components only if their parent datasets were not matched by the expression. Fully-specified component datasets (str or DatasetType instances) are always included.

bind : Mapping, optional

Mapping containing literal values that should be injected into the where expression, keyed by the identifiers they replace.

check : bool, optional

If True (default) check the query for consistency before executing it. This may reject some valid queries that resemble common mistakes (e.g. queries for visits without specifying an instrument).

**kwargs

Additional keyword arguments are forwarded to DataCoordinate.standardize when processing the dataId argument (and may be used to provide a constraining data ID even when the dataId argument is None).

Returns:

dataIds : DataCoordinateQueryResults

Data IDs matching the given query parameters. These are guaranteed to identify all dimensions (DataCoordinate.hasFull returns True), but will not contain DimensionRecord objects (DataCoordinate.hasRecords returns False). Call DataCoordinateQueryResults.expanded on the returned object to fetch those (and consider using DataCoordinateQueryResults.materialize on the returned object first if the expected number of rows is very large). See documentation for those methods for additional information.

Raises:

TypeError

Raised if collections is None, self.defaults.collections is None, and datasets is not None.

queryDatasetAssociations(datasetType: Union[str, lsst.daf.butler.core.datasets.type.DatasetType], collections: Any = Ellipsis, *, collectionTypes: Iterable[lsst.daf.butler.registry._collectionType.CollectionType] = frozenset({<CollectionType.RUN: 1>, <CollectionType.TAGGED: 2>, <CollectionType.CHAINED: 3>, <CollectionType.CALIBRATION: 4>}), flattenChains: bool = False) → Iterator[lsst.daf.butler.core.datasets.association.DatasetAssociation]

Iterate over dataset-collection combinations where the dataset is in the collection.

This method is a temporary placeholder for better support for assocation results in queryDatasets. It will probably be removed in the future, and should be avoided in production code whenever possible.

Parameters:

datasetType : DatasetType or str

A dataset type object or the name of one.

collections: `Any`, optional

An expression that fully or partially identifies the collections to search for datasets. See queryCollections and Collection expressions for more information. If not provided, self.default.collections is used.

collectionTypes : AbstractSet [ CollectionType ], optional

If provided, only yield associations from collections of these types.

flattenChains : bool, optional

If True (default) search in the children of CHAINED collections. If False, CHAINED collections are ignored.

Yields:

association : DatasetAssociation

Object representing the relationship beween a single dataset and a single collection.

Raises:

TypeError

Raised if collections is None and self.defaults.collections is None.

queryDatasetTypes(expression: Any = Ellipsis, *, components: Optional[bool] = None) → Iterator[lsst.daf.butler.core.datasets.type.DatasetType]

Iterate over the dataset types whose names match an expression.

Parameters:

expression : Any, optional

An expression that fully or partially identifies the dataset types to return, such as a str, re.Pattern, or iterable thereof. can be used to return all dataset types, and is the default. See DatasetType expressions for more information.

components : bool, optional

If True, apply all expression patterns to component dataset type names as well. If False, never apply patterns to components. If None (default), apply patterns to components only if their parent datasets were not matched by the expression. Fully-specified component datasets (str or DatasetType instances) are always included.

Yields:

datasetType : DatasetType

A DatasetType instance whose name matches expression.

queryDatasets(datasetType: Any, *, collections: Optional[Any] = None, dimensions: Optional[Iterable[Union[lsst.daf.butler.core.dimensions._elements.Dimension, str]]] = None, dataId: Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any], None] = None, where: Optional[str] = None, findFirst: bool = False, components: Optional[bool] = None, bind: Optional[Mapping[str, Any]] = None, check: bool = True, **kwargs) → lsst.daf.butler.registry.queries._results.DatasetQueryResults

Query for and iterate over dataset references matching user-provided criteria.

Parameters:

datasetType

An expression that fully or partially identifies the dataset types to be queried. Allowed types include DatasetType, str, re.Pattern, and iterables thereof. The special value can be used to query all dataset types. See DatasetType expressions for more information.

collections: optional

An expression that fully or partially identifies the collections to search for datasets, such as a str, re.Pattern, or iterable thereof. can be used to find datasets from all RUN collections (no other collections are necessary, because all datasets are in a RUN collection). See Collection expressions for more information. If not provided, self.default.collections is used.

dimensions : Iterable of Dimension or str

Dimensions to include in the query (in addition to those used to identify the queried dataset type(s)), either to constrain the resulting datasets to those for which a matching dimension exists, or to relate the dataset type’s dimensions to dimensions referenced by the dataId or where arguments.

dataId : dict or DataCoordinate, optional

A data ID whose key-value pairs are used as equality constraints in the query.

where : str, optional

A string expression similar to a SQL WHERE clause. May involve any column of a dimension table or (as a shortcut for the primary key column of a dimension table) dimension name. See Dimension expressions for more information.

findFirst : bool, optional

If True (False is default), for each result data ID, only yield one DatasetRef of each DatasetType, 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 .

components : bool, optional

If True, apply all dataset expression patterns to component dataset type names as well. If False, never apply patterns to components. If None (default), apply patterns to components only if their parent datasets were not matched by the expression. Fully-specified component datasets (str or DatasetType instances) are always included.

bind : Mapping, optional

Mapping containing literal values that should be injected into the where expression, keyed by the identifiers they replace.

check : bool, optional

If True (default) check the query for consistency before executing it. This may reject some valid queries that resemble common mistakes (e.g. queries for visits without specifying an instrument).

**kwargs

Additional keyword arguments are forwarded to DataCoordinate.standardize when processing the dataId argument (and may be used to provide a constraining data ID even when the dataId argument is None).

Returns:

refs : queries.DatasetQueryResults

Dataset references matching the given query criteria.

Raises:

TypeError

Raised when the arguments are incompatible, such as when a collection wildcard is passed when findFirst is True, or when collections is None and``self.defaults.collections`` is also None.

Notes

When multiple dataset types are queried in a single call, the results of this operation are equivalent to querying for each dataset type separately in turn, and no information about the relationships between datasets of different types is included. In contexts where that kind of information is important, the recommended pattern is to use queryDataIds to first obtain data IDs (possibly with the desired dataset types and collections passed as constraints to the query), and then use multiple (generally much simpler) calls to queryDatasets with the returned data IDs passed as constraints.

queryDimensionRecords(element: Union[lsst.daf.butler.core.dimensions._elements.DimensionElement, str], *, dataId: Union[lsst.daf.butler.core.dimensions._coordinate.DataCoordinate, Mapping[str, Any], None] = None, datasets: Optional[Any] = None, collections: Optional[Any] = None, where: Optional[str] = None, components: Optional[bool] = None, bind: Optional[Mapping[str, Any]] = None, check: bool = True, **kwargs) → Iterator[lsst.daf.butler.core.dimensions._records.DimensionRecord]

Query for dimension information matching user-provided criteria.

Parameters:

element : DimensionElement or str

The dimension element to obtain records for.

dataId : dict or DataCoordinate, optional

A data ID whose key-value pairs are used as equality constraints in the query.

datasets : Any, optional

An expression that fully or partially identifies dataset types that should constrain the yielded records. See queryDataIds and DatasetType expressions for more information.

collections: `Any`, optional

An expression that fully or partially identifies the collections to search for datasets. See queryDataIds and Collection expressions for more information.

where : str, optional

A string expression similar to a SQL WHERE clause. See queryDataIds and Dimension expressions for more information.

components : bool, optional

Whether to apply dataset expressions to components as well. See queryDataIds for more information.

bind : Mapping, optional

Mapping containing literal values that should be injected into the where expression, keyed by the identifiers they replace.

check : bool, optional

If True (default) check the query for consistency before executing it. This may reject some valid queries that resemble common mistakes (e.g. queries for visits without specifying an instrument).

**kwargs

Additional keyword arguments are forwarded to DataCoordinate.standardize when processing the dataId argument (and may be used to provide a constraining data ID even when the dataId argument is None).

Returns:

dataIds : DataCoordinateQueryResults

Data IDs matching the given query parameters.

refresh() → None

Refresh all in-memory state by querying the database.

This may be necessary to enable querying for entities added by other Registry instances after this one was constructed.

registerCollection(name: str, type: lsst.daf.butler.registry._collectionType.CollectionType = <CollectionType.TAGGED: 2>, doc: Optional[str] = None) → None

Add a new collection if one with the given name does not exist.

Parameters:

name : str

The name of the collection to create.

type : CollectionType

Enum value indicating the type of collection to create.

doc : str, optional

Documentation string for the collection.

Notes

This method cannot be called within transactions, as it needs to be able to perform its own transaction to be concurrent.

registerDatasetType(datasetType: lsst.daf.butler.core.datasets.type.DatasetType) → bool

Add a new DatasetType to the Registry.

It is not an error to register the same DatasetType twice.

Parameters:

datasetType : DatasetType

The DatasetType to be added.

Returns:

inserted : bool

True if datasetType was inserted, False if an identical existing DatsetType was found. Note that in either case the DatasetType is guaranteed to be defined in the Registry consistently with the given definition.

Raises:

ValueError

Raised if the dimensions or storage class are invalid.

ConflictingDefinitionError

Raised if this DatasetType is already registered with a different definition.

Notes

This method cannot be called within transactions, as it needs to be able to perform its own transaction to be concurrent.

registerOpaqueTable(tableName: str, spec: lsst.daf.butler.core.ddl.TableSpec) → None

Add an opaque (to the Registry) table for use by a Datastore or other data repository client.

Opaque table records can be added via insertOpaqueData, retrieved via fetchOpaqueData, and removed via deleteOpaqueData.

Parameters:

tableName : str

Logical name of the opaque table. This may differ from the actual name used in the database by a prefix and/or suffix.

spec : ddl.TableSpec

Specification for the table to be added.

registerRun(name: str, doc: Optional[str] = None) → None

Add a new run if one with the given name does not exist.

Parameters:

name : str

The name of the run to create.

doc : str, optional

Documentation string for the collection.

Notes

This method cannot be called within transactions, as it needs to be able to perform its own transaction to be concurrent.

removeCollection(name: str) → None

Completely remove the given collection.

Parameters:

name : str

The name of the collection to remove.

Raises:

MissingCollectionError

Raised if no collection with the given name exists.

Notes

If this is a RUN collection, all datasets and quanta in it are also fully removed. This requires that those datasets be removed (or at least trashed) from any datastores that hold them first.

A collection may not be deleted as long as it is referenced by a CHAINED collection; the CHAINED collection must be deleted or redefined first.

removeDatasetType(name: str) → None

Remove the named DatasetType from the registry.

Warning

Registry caches the dataset type definitions. This means that deleting the dataset type definition may result in unexpected behavior from other butler processes that are active that have not seen the deletion.

Parameters:

name : str

Name of the type to be removed.

Raises:

lsst.daf.butler.registry.OrphanedRecordError

Raised if an attempt is made to remove the dataset type definition when there are already datasets associated with it.

Notes

If the dataset type is not registered the method will return without action.

removeDatasets(refs: Iterable[lsst.daf.butler.core.datasets.ref.DatasetRef]) → None

Remove datasets from the Registry.

The datasets will be removed unconditionally from all collections, and any Quantum that consumed this dataset will instead be marked with having a NULL input. Datastore records will not be deleted; the caller is responsible for ensuring that the dataset has already been removed from all Datastores.

Parameters:

refs : Iterable of DatasetRef

References to the datasets to be removed. Must include a valid id attribute, and should be considered invalidated upon return.

Raises:

AmbiguousDatasetError

Raised if any ref.id is None.

OrphanedRecordError

Raised if any dataset is still present in any Datastore.

resetConnectionPool() → None

Reset SQLAlchemy connection pool for registry database.

This operation is useful when using registry with fork-based multiprocessing. To use registry across fork boundary one has to make sure that there are no currently active connections (no session or transaction is in progress) and connection pool is reset using this method. This method should be called by the child process immediately after the fork.

setCollectionChain(parent: str, children: Any) → None

Define or redefine a CHAINED collection.

Parameters:

parent : str

Name of the chained collection. Must have already been added via a call to Registry.registerCollection.

children : Any

An expression defining an ordered search of child collections, generally an iterable of str; see Collection expressions for more information.

Raises:

MissingCollectionError

Raised when any of the given collections do not exist in the Registry.

TypeError

Raised if parent does not correspond to a CHAINED collection.

ValueError

Raised if the given collections contains a cycle.

setCollectionDocumentation(collection: str, doc: Optional[str]) → None

Set the documentation string for a collection.

Parameters:

name : str

Name of the collection.

docs : str or None

Docstring for the collection with the given name; will replace any existing docstring. Passing None will remove any existing docstring.

syncDimensionData(element: Union[lsst.daf.butler.core.dimensions._elements.DimensionElement, str], row: Union[Mapping[str, Any], lsst.daf.butler.core.dimensions._records.DimensionRecord], conform: bool = True) → bool

Synchronize the given dimension record with the database, inserting if it does not already exist and comparing values if it does.

Parameters:

element : DimensionElement or str

The DimensionElement or name thereof that identifies the table records will be inserted into.

row : dict or DimensionRecord

The record to insert.

conform : bool, optional

If False (True is default) perform no checking or conversions, and assume that element is a DimensionElement instance and data is a one or more DimensionRecord instances of the appropriate subclass.

Returns:

inserted : bool

True if a new row was inserted, False otherwise.

Raises:

ConflictingDefinitionError

Raised if the record exists in the database (according to primary key lookup) but is inconsistent with the given one.

transaction(*, savepoint: bool = False) → Iterator[None]

Return a context manager that represents a transaction.