reVeal.config.normalize.NormalizeConfig#

class NormalizeConfig(*, grid: Annotated[Path, PathType(path_type=file)], grid_ext: str | None = None, grid_flavor: str | None = None, grid_crs: str | None = None, attributes: dict = {}, normalize_method: NormalizeMethodEnum | None = None, invert: bool = False)[source]#

Configuration for normalize 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.
`check_attributes_and_normalize_method`()	Check that either attributes or normalize_method was provided as an input.
`propagate_grid`()	Propagate the top level grid parameter down to elements of attributes before validation.
`propagate_normalize_method`()	If the top-level normalize method is specified, populate the attributes property so that it includes all numeric attributes in the input grid.
`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_attributes`(value)	Validate each entry in the input attributes dictionary.

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.
`attributes`
`normalize_method`
`invert`
`grid`
`grid_ext`
`grid_flavor`
`grid_crs`

propagate_grid()[source]#

Propagate the top level grid parameter down to elements of attributes before validation.

base_validator()[source]#

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

check_attributes_and_normalize_method()[source]#: Check that either attributes or normalize_method was provided as an input.

classmethod validate_attributes(value)[source]#

Validate each entry in the input attributes dictionary.

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

propagate_normalize_method()[source]#: If the top-level normalize method is specified, populate the attributes property so that it includes all numeric attributes in the input grid. All attributes will use the specified top-level normalize method except for any that were input separately via the attributes parameter.

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_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.