reVeal.config.downscale.DownscaleConfig#

class DownscaleConfig(*, grid: Annotated[Path, PathType(path_type=file)], grid_ext: str | None = None, grid_flavor: str | None = None, grid_crs: str | None = None, grid_priority: str, grid_baseline_load: str, baseline_year: Annotated[int, Gt(gt=0)], grid_capacity: str, projection_resolution: ProjectionResolutionEnum, load_projections: Annotated[Path, PathType(path_type=file)], load_value: str, load_year: str, max_site_addition_per_year: Annotated[float | None, Strict(strict=False), Gt(gt=0.0)] = None, site_saturation_limit: Annotated[float | None, Strict(strict=False), Gt(gt=0.0), Le(le=1.0)] = 1.0, priority_power: Annotated[float | None, Strict(strict=False), Gt(gt=0.0)] = 1.0, n_bootstraps: Annotated[int, Gt(gt=0)] | None = 10000, random_seed: int | None = 0, max_workers: Annotated[int, Gt(gt=0)] | None = None, reduce_output: bool | None = False, **extra_data: Any)[source]#

Bases: BaseDownscaleConfig

Wrapper class for downscaling configuration. Based on the the projection resolution of the inputs, instantiates either a TotalDownScaleConfig or RegionalDownscaleConfig.

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

`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_grid`()	Validate the input grid dataset can be opened, has the expected attributes, and those attributes are numeric.
`validate_load_projections`()	Validate the input load_projections dataset can be opened, has the expected attributes, and those attributes are numeric.

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.
`grid_priority`
`grid_baseline_load`
`baseline_year`
`grid_capacity`
`projection_resolution`
`load_projections`
`load_value`
`load_year`
`max_site_addition_per_year`
`site_saturation_limit`
`priority_power`
`n_bootstraps`
`random_seed`
`max_workers`
`reduce_output`
`grid`
`grid_ext`
`grid_flavor`
`grid_crs`

model_config: ClassVar[ConfigDict] = {'extra': 'allow'}#: 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.

validate_grid()#: Validate the input grid dataset can be opened, has the expected attributes, and those attributes are numeric.

validate_load_projections()#: Validate the input load_projections dataset can be opened, has the expected attributes, and those attributes are numeric. Also ensures that the projections are for years after the the specified baseline year.