reVeal.config.characterize.CharacterizeConfig#

class CharacterizeConfig(*, grid: Annotated[Path, PathType(path_type=file)], grid_ext: str | None = None, grid_flavor: str | None = None, grid_crs: str | None = None, data_dir: Annotated[Path, PathType(path_type=dir)], characterizations: dict, expressions: dict | None = None)[source]#

Bases: BaseCharacterizeConfig

Configuration for characterize command.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Methods

`base_validator`()	Ensures that the base validation is run on input data types before other "before"-mode model validators.
`propagate_datadir`()	Propagate the top level data_dir parameter down to elements of characterizations before validation.
`set_grid_crs`()	Dynamically set the crs property.
`set_grid_ext`()	Dynamically set the grid_ext property.
`set_grid_flavor`()	Dynamically set the dset_flavor.
`validate_characterizations`(value)	Validate each entry in the input charactrizations dictionary.
`validate_crs`()	Check that CRSs of individual characterizations match CRS of the grid.
`validate_expressions`(value)	Check that each entry in the expressions dictionary is a string and does not contain any questionable code.

Attributes

`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.
`data_dir`
`characterizations`
`expressions`
`grid`
`grid_ext`
`grid_flavor`
`grid_crs`

propagate_datadir()[source]#

Propagate the top level data_dir parameter down to elements of characterizations before validation.

Returns:: self – Returns self.

base_validator()[source]#

Ensures that the base validation is run on input data types before other “before”-mode model validators.

Returns:: self – Returns self.

classmethod validate_characterizations(value)[source]#

Validate each entry in the input charactrizations dictionary.

Parameters:: value (dict) – Input characterizations.
Returns:: dict – Validated characterizations, which each value converted into an instance of Characterization.

classmethod validate_expressions(value)[source]#

Check that each entry in the expressions dictionary is a string and does not contain any questionable code.

Parameters:: value (dict) – Input expressions.
Returns:: dict – Validated expressions.

validate_crs()[source]#: Check that CRSs of individual characterizations match CRS of the grid.

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

property model_extra: dict[str, Any] | None#

Get extra fields set during validation.

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

property model_fields_set: set[str]#

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.

set_grid_crs()#: Dynamically set the crs property.

set_grid_ext()#: Dynamically set the grid_ext property.

set_grid_flavor()#

Dynamically set the dset_flavor.

Raises:: TypeError – A TypeError will be raised if the input dset is not either a geoparquet or compatible with reading with ogr.