CellGridBounds#

class lsst.images.cells.CellGridBounds(*, grid: CellGrid, bbox: Box, missing: frozenset[CellIJ] = frozenset({}))#

Bases: BaseModel

A region of pixels defined by a set of cells within a grid.

Notes#

This data structure is optimized for the case where a continguous rectangular region of the grid (the bbox attribute) is populated with only a few exceptions (the missing set).

Slicing a CellGridBounds with a Box returns a new CellGridBounds with just the cells that overlap that box. As always, CellGridBounds.bbox will be snapped to the outer boundaries of those cells, so it will contain (and not generally equal) the given box.

Attributes Summary

`grid_start`	The index of the first cell in this bounds' bounding box within its grid.
`grid_stop`	One-past-the-last indices for the cells in these bounds, within its grid.
`model_computed_fields`
`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`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.

Methods Summary

`cell_indices`()	Iterate over the indices of the cells in these bounds.
`construct`([_fields_set])
`contains`()	Test whether these bounds contain one or more points.
`contains_cell`(index)	Test whether the given cell is in the bounds.
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`deserialize`()	Deserialize a bounds object on the assumption it is a `CellGridBounds`.
`dict`(*[, include, exclude, by_alias, ...])
`from_orm`(obj)
`intersection`(other)	Compute the intersection of this bounds object with another.
`json`(*[, include, exclude, by_alias, ...])
`missing_boxes`()	Iterate over the bounding boxes of the missing cells.
`model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, ensure_ascii, ...])	!!! abstract "Usage Documentation"
`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`(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, extra, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with 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])
`serialize`()	Convert a bounds instance into a serializable object.
`update_forward_refs`(**localns)
`validate`(value)

Attributes Documentation

grid_start#: The index of the first cell in this bounds’ bounding box within its grid.

grid_stop#: One-past-the-last indices for the cells in these bounds, within its grid.

model_computed_fields = {}#

model_config: ClassVar[ConfigDict] = {'frozen': True}#: 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 = {'bbox': FieldInfo(annotation=Box, required=True, description='Pixel bounding box of the region (snapped to cell boundaries).'), 'grid': FieldInfo(annotation=CellGrid, required=True, description='Definition of the grid that defines the cells.'), 'missing': FieldInfo(annotation=frozenset[CellIJ], required=False, default=frozenset(), description='Indices of cells that are missing, where (i=0, j=0) is the cell that starts at grid.bbox.start.')}#

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

cell_indices() → Iterator[CellIJ]#: Iterate over the indices of the cells in these bounds.

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

contains(*, x: int, y: int) → bool#

contains(*, x: ndarray, y: ndarray) → ndarray

Test whether these bounds contain one or more points.

Parameters#

x: One or more integer X coordinates to test for containment. If an array, an array of results will be returned.
y: One or more integer Y coordinates to test for containment. If an array, an array of results will be returned.

Returns#

bool | numpy.ndarray: If x and y are both scalars, a single bool value. If x and y are arrays, a boolean array with their broadcasted shape.

contains_cell(index: CellIJ) → bool#: Test whether the given cell is in the bounds.

Returns a copy of the model.

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

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

deserialize() → CellGridBounds#

Deserialize a bounds object on the assumption it is a CellGridBounds.

This method just returns the CellGridBounds itself, since that already provides Pydantic serialization hooks. It exists for compatibility with the Bounds protocol.

dict(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]#

classmethod from_orm(obj: Any) → Self#

intersection(other: Bounds) → Bounds#: Compute the intersection of this bounds object with another.

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = 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#

missing_boxes() → Iterator[Box]#: Iterate over the bounding boxes of the missing cells.

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Self#

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Args:

_fields_set: A set of field names that were originally explicitly set during instantiation. If provided,: this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: Mapping[str, Any] | None = None, deep: bool = False) → Self#

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

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.

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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 output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. 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. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

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. ensure_ascii: If True, the output is guaranteed to have all incoming non-ASCII characters escaped.

If False (the default), these characters will be output as-is.

include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. context: Additional context to pass to the serializer. 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 are set to their default value. exclude_none: Whether to exclude fields that have a value of None. exclude_computed_fields: Whether to exclude computed fields.

While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

fallback: A function to call when an unknown value is encountered. If not provided,: a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.

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', *, union_format: ~typing.Literal['any_of', 'primitive_type_array'] = 'any_of') → 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. union_format: The format to use when combining schemas from unions together. Can be one of:

'any_of': Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)

keyword to combine schemas (the default). - 'primitive_type_array': Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

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(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: MappingNamespace | 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.

Validate a pydantic model instance.

Args:

obj: The object to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

context: Extra variables to pass to the validator. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

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

Args:

obj: The object containing string data to validate. strict: Whether to enforce types strictly. extra: Whether to ignore, allow, or forbid extra data during model validation.

See the [extra configuration value][pydantic.ConfigDict.extra] for details.

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) → Self#

classmethod parse_obj(obj: Any) → Self#

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

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#

serialize() → CellGridBounds#: Convert a bounds instance into a serializable object.

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

classmethod validate(value: Any) → Self#