CollectionSearch¶

class lsst.daf.butler.registry.CollectionSearch(root: RootModelRootType = PydanticUndefined)¶

Bases: _CollectionSearch

An ordered search path of collections.

The fromExpression method should almost always be used to construct instances, as the regular constructor performs no checking of inputs (and that can lead to confusing error messages downstream).

Notes

A CollectionSearch is used to find a single dataset (or set of datasets with different dataset types or data IDs) according to its dataset type and data ID, giving preference to collections in the order in which they are specified. A CollectionWildcard can be constructed from a broader range of expressions but does not order the collections to be searched.

CollectionSearch is an immutable sequence of str collection names.

A CollectionSearch instance constructed properly (e.g. via fromExpression) is a unique representation of a particular search path; it is exactly the same internally and compares as equal to any CollectionSearch constructed from an equivalent expression, regardless of how different the original expressions appear.

Deprecated since version v25.0: Tuples of string collection names are now preferred. Will be removed after v26.

Attributes Summary

`model_computed_fields`	Get the computed fields of this model instance.
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`	Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo].
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.

Methods Summary

`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`explicitNames`()	Iterate over collection names that were specified explicitly.
`fromExpression`(expression)	Process a general expression to construct a `CollectionSearch` instance.
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`(root[, _fields_set])	Create a new model using the provided root object and update fields set.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object contains string data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes Documentation

model_computed_fields¶

Get the computed fields of this model instance.

Returns:: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra¶

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[dict[str, FieldInfo]] = {'root': FieldInfo(annotation=tuple[str, ...], required=True)}¶

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_fields_set¶

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

Methods Documentation

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Model¶

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:

include: Optional set or mapping: specifying which fields to include in the copied model.
exclude: Optional set or mapping: specifying which fields to exclude in the copied model.
update: Optional dictionary of field-value pairs to override field values: in the copied model.

deep: If True, the values of fields that are Pydantic models will be deep copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]¶

explicitNames() → Iterator[str]¶: Iterate over collection names that were specified explicitly.

classmethod fromExpression(expression: Any) → CollectionSearch¶

Process a general expression to construct a CollectionSearch instance.

Parameters:

expressionAny

May be:

a str collection name;
an iterable of str collection names;
another CollectionSearch instance (passed through unchanged).

Duplicate entries will be removed (preserving the first appearance of each collection name).

Returns:

collectionsCollectionSearch: A CollectionSearch instance.

classmethod from_orm(obj: Any) → Model¶

json(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str¶

classmethod model_construct(root: RootModelRootType, _fields_set: set[str] | None = None) → Model¶

Create a new model using the provided root object and update fields set.

Args:: root: The root object of the model. _fields_set: The set of fields to be updated.
Returns:: The new model.
Raises:: NotImplemented: If the model is not a subclass of RootModel.

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Model¶

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#model_copy

Returns a copy of the model.

Args:

update: Values to change/add in the new model. Note: the data is not validated: before creating the new model. You should trust this data.

deep: Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]¶

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any Python objects.

include: A list of fields to include in the output. exclude: A list of fields to exclude from the output. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value from the output. exclude_none: Whether to exclude fields that have a value of None from the output. round_trip: Whether to enable serialization and deserialization round-trip support. warnings: Whether to log warnings when invalid fields are encountered.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str¶

Usage docs: https://docs.pydantic.dev/2.5/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:: indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. Can take either a string or set of strings. exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that have the default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: Whether to use serialization/deserialization between JSON and class instance. warnings: Whether to show any warnings that occurred during serialization.
Returns:: A JSON string representation of the model.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]¶

Generates a JSON schema for a model class.

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of

GenerateJsonSchema with your desired modifications

mode: The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str¶

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Args:

params: Tuple of types of the class. Given a generic class: Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context: Any) → None¶: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None¶

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args:: force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.
Returns:: Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Model¶

Validate a pydantic model instance.

Args:: obj: The object to validate. strict: Whether to raise an exception on invalid fields. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.
Raises:: ValidationError: If the object could not be validated.
Returns:: The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model¶

Usage docs: https://docs.pydantic.dev/2.5/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args:: json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns:: The validated Pydantic model.
Raises:: ValueError: If json_data is not a JSON string.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model¶

Validate the given object contains string data against the Pydantic model.

Args:: obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns:: The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model¶

classmethod parse_obj(obj: Any) → Model¶

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model¶

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]¶

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str¶

classmethod update_forward_refs(**localns: Any) → None¶

classmethod validate(value: Any) → Model¶

Navigation

CollectionSearch¶