reVX.config.transmission_layer_creation.TransmissionLayerCreationConfig
- class TransmissionLayerCreationConfig(*, template_raster_fpath: Path[Path], h5_fpath: Path[Path], layer_dir: Path[Path] = PosixPath('.'), masks_dir: Path[Path] = PosixPath('.'), output_tiff_dir: Path[Path] = PosixPath('.'), layers: List[LayerConfig] | None = None, dry_costs: DryCosts | None = None, merge_friction_and_barriers: MergeFrictionBarriers | None = None, cell_size: int = 90)[source]
Bases:
BaseModelDefinition of friction, barrier, and costs processing JSON config file.
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
construct([_fields_set])copy(*[, include, exclude, update, deep])Returns a copy of the model.
dict(*[, include, exclude, by_alias, ...])from_orm(obj)json(*[, include, exclude, by_alias, ...])model_construct([_fields_set])Creates a new instance of the Model class with validated data.
model_copy(*[, update, deep])Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy
model_dump(*[, mode, include, exclude, ...])Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
model_dump_json(*[, indent, include, ...])Usage docs: https://docs.pydantic.dev/2.9/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/2.9/concepts/json/#json-parsing
model_validate_strings(obj, *[, strict, context])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])update_forward_refs(**localns)validate(value)Attributes
A dictionary of computed field names and their corresponding ComputedFieldInfo objects.
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
Get extra fields set during validation.
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
Returns the set of fields that have been explicitly set on this model instance.
Template raster GeoTIFF for shape and profile
H5 to store results in.
Directory to look for GeoTIFFs in, in addition to '.'
Optional path for mask GeoTIFFs.
Directory to store output tiff files in.
Optional configuration for layers to be built.
Optional dry cost layer.
Optional config to merge friction barriers.
Side length of each cell, in meters.
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Self
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.
- model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}
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].
- 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: dict[str, Any] | None = None, deep: bool = False) Self
Usage docs: https://docs.pydantic.dev/2.9/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 = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any]
Usage docs: https://docs.pydantic.dev/2.9/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 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. 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].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent: int | None = None, include: IncEx | None = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str
Usage docs: https://docs.pydantic.dev/2.9/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. 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. 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].
serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
- Returns:
A JSON string representation of the model.
- 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”.
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'cell_size': FieldInfo(annotation=int, required=False, default=90), 'dry_costs': FieldInfo(annotation=Union[DryCosts, NoneType], required=False, default=None), 'h5_fpath': FieldInfo(annotation=Path, required=True, metadata=[PathType(path_type='file')]), 'layer_dir': FieldInfo(annotation=Path, required=False, default=PosixPath('.'), metadata=[PathType(path_type='dir')]), 'layers': FieldInfo(annotation=Union[List[reVX.config.transmission_layer_creation.LayerConfig], NoneType], required=False, default=None), 'masks_dir': FieldInfo(annotation=Path, required=False, default=PosixPath('.'), metadata=[PathType(path_type='dir')]), 'merge_friction_and_barriers': FieldInfo(annotation=Union[MergeFrictionBarriers, NoneType], required=False, default=None), 'output_tiff_dir': FieldInfo(annotation=Path, required=False, default=PosixPath('.'), metadata=[PathType(path_type='dir')]), 'template_raster_fpath': FieldInfo(annotation=Path, required=True, metadata=[PathType(path_type='file')])}
Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.
This replaces Model.__fields__ from Pydantic V1.
- 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.
- classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: JsonSchemaMode = '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: Any | None = None) Self
Validate a pydantic model instance.
- Args:
obj: The object to validate. strict: Whether to enforce types strictly. 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: Any | None = None) Self
Usage docs: https://docs.pydantic.dev/2.9/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:
ValidationError: If json_data is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) Self
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. context: Extra variables to pass to the validator.
- Returns:
The validated Pydantic model.
- layers: List[LayerConfig] | None
Optional configuration for layers to be built.
At least one of layers, dry_costs, or merge_friction_and_barriers must be defined.
- dry_costs: DryCosts | None
Optional dry cost layer.
At least one of layers, dry_costs, or merge_friction_and_barriers must be defined.
- merge_friction_and_barriers: MergeFrictionBarriers | None
Optional config to merge friction barriers.
At least one of layers, dry_costs, or merge_friction_and_barriers must be defined.