DataCoordinate¶

class lsst.daf.butler.DataCoordinate¶

Bases: object

A validated data ID.

DataCoordinate guarantees that its key-value pairs identify at least all required dimensions in a DimensionGroup.

Notes

DataCoordinate is an ABC, but it provides staticmethod factory functions for private concrete implementations that should be sufficient for most purposes. standardize is the most flexible and safe of these; the others (make_empty, from_required_values, and from_full_values) are more specialized and perform little or no checking of inputs.

Lookups for implied dimensions (those in self.dimensions.implied) are supported if and only if has_full_values is True. This also sets the keys of the mapping attribute. This means that DataCoordinate equality is not the same as testing for equality on the mapping attribute (instead, it is the same as testing for equality on the required attribute).

See also Data IDs

Attributes Summary

`dimensions`	Dimensions identified by this data ID (`DimensionGroup`).
`full_values`	The full values (only) of this data ID as a tuple.
`mapping`	A mapping view of the data ID with keys for all dimensions it has values for.
`records`	A mapping that contains `DimensionRecord` objects for all elements identified by this data ID.
`region`	Spatial region associated with this data ID.
`required`	A mapping view of the data ID with keys for just its required dimensions.
`required_values`	The required values (only) of this data ID as a tuple.
`timespan`	Temporal interval associated with this data ID.
`universe`	Universe that defines all known compatible dimensions.

Methods Summary

`expanded`(records)	Return a `DataCoordinate` that holds the given records.
`from_full_values`(dimensions, values)	Construct a `DataCoordinate` from all dimension values.
`from_json`(json_str[, universe, registry])	Convert from JSON to a pydantic model.
`from_required_values`(dimensions, values)	Construct a `DataCoordinate` from required dimension values.
`from_simple`(simple[, universe, registry])	Construct a new object from the simplified form.
`get`()
`hasFull`()	Whether this data ID contains implied and required values.
`hasRecords`()	Whether this data ID contains records.
`makeEmpty`(universe)	Return an empty `DataCoordinate`.
`make_empty`(universe)	Return an empty `DataCoordinate`.
`standardize`([mapping, dimensions, universe, ...])	Standardize the supplied dataId.
`subset`(dimensions)	Return a `DataCoordinate` whose diensions are a subset of `self.dimensions`.
`to_json`([minimal])	Convert this class to JSON assuming that the `to_simple()` returns a pydantic model.
`to_simple`([minimal])	Convert this class to a simple python type.
`union`(other)	Combine two data IDs.

Attributes Documentation

dimensions¶

Dimensions identified by this data ID (DimensionGroup).

Note that values are only required to be present for dimensions in self.dimensions.required; all others may be retrieved (from a Registry) given these.

full_values¶

The full values (only) of this data ID as a tuple.

Element order is consistent with DimensionGroup.data_coordinate_keys, i.e. all required dimensions followed by all implied dimensions.

mapping¶: A mapping view of the data ID with keys for all dimensions it has values for.

records¶

A mapping that contains DimensionRecord objects for all elements identified by this data ID.

Notes

The values of this mapping may be None if and only if there is no record for that element with these dimensions in the database (which means some foreign key field must have a NULL value).

Accessing this attribute if hasRecords returns False is a logic error that may raise an exception of unspecified type either immediately or when the returned mapping is used, depending on the implementation and whether assertions are enabled.

region¶

Spatial region associated with this data ID.

(lsst.sphgeom.Region or None).

This is None if and only if self.dimensions.spatial is empty.

Accessing this attribute if hasRecords returns False is a logic error that may or may not raise an exception, depending on the implementation and whether assertions are enabled.

required¶: A mapping view of the data ID with keys for just its required dimensions.

required_values¶

The required values (only) of this data ID as a tuple.

Element order is consistent with required.

In contexts where all data IDs have the same dimensions, comparing and hashing these tuples can be much faster than comparing the original DataCoordinate instances.

timespan¶

Temporal interval associated with this data ID.

(Timespan or None).

This is None if and only if self.dimensions.temporal is empty.

Accessing this attribute if hasRecords returns False is a logic error that may or may not raise an exception, depending on the implementation and whether assertions are enabled.

universe¶

Universe that defines all known compatible dimensions.

The universe will be compatible with this coordinate (DimensionUniverse).

Methods Documentation

abstract expanded(records: Mapping[str, DimensionRecord | None]) → DataCoordinate¶

Return a DataCoordinate that holds the given records.

Guarantees that hasRecords returns True.

This is a low-level interface with at most assertion-level checking of inputs. Most callers should use Registry.expandDataId instead.

Parameters:

recordsMapping [ str, DimensionRecord or None ]: A`~collections.abc.Mapping` with str (dimension element name) keys and DimensionRecord values. Keys must cover all elements in self.dimensions.elements. Values may be None, but only to reflect actual NULL values in the database, not just records that have not been fetched.

static from_full_values(dimensions: DimensionGroup, values: tuple[int | str, ...]) → DataCoordinate¶

Construct a DataCoordinate from all dimension values.

This is a low-level interface with at most assertion-level checking of inputs. Most callers should use standardize instead.

Parameters:

dimensionsDimensionGroup: Dimensions this data ID will identify.
valuestuple [ int or str ]: Tuple of primary key values corresponding to itertools.chain(dimensions.required, dimensions.implied), in that order. Note that this is _not_ the same order as dimensions.names, though these contain the same elements.

Returns:

data_idDataCoordinate: A data ID object that identifies the given dimensions. dataId.hasFull() will always return True. dataId.hasRecords() will only return True if dimensions is empty.

classmethod from_json(json_str: str, universe: DimensionUniverse | None = None, registry: Registry | None = None) → SupportsSimple¶

Convert from JSON to a pydantic model.

Parameters:

cls_type of SupportsSimple: The Python type being created.
json_strstr: The JSON string representing this object.
universeDimensionUniverse or None, optional: The universe required to instantiate some models. Required if registry is None.
registryRegistry or None, optional: Registry from which to obtain the dimension universe if an explicit universe has not been given.

Returns:

modelSupportsSimple: Pydantic model constructed from JSON and validated.

static from_required_values(dimensions: DimensionGroup, values: tuple[int | str, ...]) → DataCoordinate¶

Construct a DataCoordinate from required dimension values.

This is a low-level interface with at most assertion-level checking of inputs. Most callers should use standardize instead.

Parameters:

dimensionsDimensionGroup: Dimensions this data ID will identify.
valuestuple [ int or str ]: Tuple of primary key values corresponding to dimensions.required, in that order.

Returns:

data_idDataCoordinate: A data ID object that identifies the given dimensions. dataId.hasFull() will return True only if dimensions.implied is empty. dataId.hasRecords() will return True if and only if dimensions is empty.

classmethod from_simple(simple: SerializedDataCoordinate, universe: DimensionUniverse | None = None, registry: Registry | None = None) → DataCoordinate¶

Construct a new object from the simplified form.

The data is assumed to be of the form returned from the to_simple method.

Parameters:

simpledict of [str, Any]: The dict returned by to_simple().
universeDimensionUniverse: Object that manages all known dimensions.
registrylsst.daf.butler.Registry, optional: Registry from which a universe can be extracted. Can be None if universe is provided explicitly.

Returns:

dataIdDataCoordinate: Newly-constructed object.

get(key: str) → DataIdValue | None¶
get(key: str, default: int) → int
get(key: str, default: str) → str

abstract hasFull() → bool¶

Whether this data ID contains implied and required values.

Returns:

statebool: If True, __getitem__, get, and __contains__ (but not keys!) will act as though the mapping includes key-value pairs for implied dimensions, and the full property may be used. If False, these operations only include key-value pairs for required dimensions, and accessing full is an error. Always True if there are no implied dimensions.

abstract hasRecords() → bool¶

Whether this data ID contains records.

These are the records for all of the dimension elements it identifies.

Returns:

statebool

If True, the following attributes may be accessed:

records

region

timespan

pack

If False, accessing any of these is considered a logic error.

static makeEmpty(universe: DimensionUniverse) → DataCoordinate¶

Return an empty DataCoordinate.

It identifies the null set of dimensions.

Parameters:

universeDimensionUniverse: Universe to which this null dimension set belongs.

Returns:

dataIdDataCoordinate: A data ID object that identifies no dimensions. hasFull and hasRecords are guaranteed to return True, because both full and records are just empty mappings.

static make_empty(universe: DimensionUniverse) → DataCoordinate¶

Return an empty DataCoordinate.

It identifies the null set of dimensions.

Parameters:

universeDimensionUniverse: Universe to which this null dimension set belongs.

Returns:

data_idDataCoordinate: A data ID object that identifies no dimensions. hasFull and hasRecords are guaranteed to return True, because both full and records are just empty mappings.

Standardize the supplied dataId.

Adapts an arbitrary mapping and/or additional arguments into a true DataCoordinate, or augment an existing one.

Parameters:

mappingMapping or DataCoordinate, optional: An informal data ID that maps dimensions or dimension names to their primary key values (may also be a true DataCoordinate).
dimensionsIterable [ str ], DimensionGroup, optional: The dimensions to be identified by the new DataCoordinate. If not provided, will be inferred from the keys of mapping and **kwargs, and universe must be provided unless mapping is already a DataCoordinate.
universeDimensionUniverse: All known dimensions and their relationships; used to expand and validate dependencies when dimensions is not provided.
defaultsDataCoordinate, optional: Default dimension key-value pairs to use when needed. These are never used to infer group, and are ignored if a different value is provided for the same key in mapping or **kwargs`.
**kwargs: Additional keyword arguments are treated like additional key-value pairs in mapping.

Returns:

coordinateDataCoordinate: A validated DataCoordinate instance.

Raises:

TypeError: Raised if the set of optional arguments provided is not supported.
DimensionNameError: Raised if a key-value pair for a required dimension is missing.

abstract subset(dimensions: DimensionGroup | Iterable[str]) → DataCoordinate¶

Return a DataCoordinate whose diensions are a subset of self.dimensions.

Parameters:

dimensionsDimensionGroup or Iterable [ str ]: The dimensions identified by the returned DataCoordinate.

Returns:

coordinateDataCoordinate: A DataCoordinate instance that identifies only the given dimensions. May be self if dimensions == self.dimensions.

Raises:

KeyError: Raised if the primary key value for one or more required dimensions is unknown. This may happen even if the required subset of the new dimensions are not a subset of the dimensions actually known by this data ID.. As an example, consider trying to go from a data ID with dimensions {instrument, physical_filter, band} to just {instrument, band}; band is implied by physical_filter and hence would have no value in the original data ID if self.hasFull() is False.

Notes

If hasFull and hasRecords return True on self, they will return True (respectively) on the returned DataCoordinate as well. The converse does not hold.

to_json(minimal: bool = False) → str¶

Convert this class to JSON assuming that the to_simple() returns a pydantic model.

Parameters:

minimalbool: Return minimal possible representation.

to_simple(minimal: bool = False) → SerializedDataCoordinate¶

Convert this class to a simple python type.

This is suitable for serialization.

Parameters:

minimalbool, optional: Use minimal serialization. If set the records will not be attached.

Returns:

simpleSerializedDataCoordinate: The object converted to simple form.

abstract union(other: DataCoordinate) → DataCoordinate¶

Combine two data IDs.

Yields a new one that identifies all dimensions that either of them identify.

Parameters:

otherDataCoordinate: Data ID to combine with self.

Returns:

unionedDataCoordinate: A DataCoordinate instance that satisfies unioned.dimensions == self.dimensions.union(other.dimensions). Will preserve hasFull and hasRecords whenever possible.

Notes

No checking for consistency is performed on values for keys that self and other have in common, and which value is included in the returned data ID is not specified.

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DataCoordinate¶