Leg¶
- class trafficgen.types.Leg(*, starboardXtd: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Starboard XTD in NM as defined in RTZ.')] | None = None, portsideXtd: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Starboard XTD in NM as defined in RTZ.')] | None = None, sog: Annotated[float, FieldInfo(annotation=NoneType, required=True, description='Speed reference for the waypoint leg in knots')] | None = None, data: Annotated[RouteData, FieldInfo(annotation=NoneType, required=True, description='The `data` field can be used to store data, which is numerical and continuous.One such example is the vessels speed over ground (SOG). Every `data` object, can have the 4 following attributes:\n`value`: This is the value of the data over the current leg.\n`interpStart`: This is the distance (in NM) before the leg change, where the value will start changing(via interpolation) to the new value in the next leg.\n`interpEnd`: This is the distance (in NM) after the leg change, where the value will finish changing(via interpolation) to the new value in the next leg.\n`interpMethod`: This sets the interpolation (linear, cosine, smoothstep, etc.) that will be used to perform the interpolation.')] | None = None)¶
Bases:
BaseModelConfigData type for a leg.
- __init__(**data: Any) None¶
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
__init__(**data)Create a new model by parsing and validating input data from keyword arguments.
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])!!! 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])update_forward_refs(**localns)validate(value)Attributes
model_computed_fieldsConfiguration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extraGet extra fields set during validation.
model_fieldsmodel_fields_setReturns the set of fields that have been explicitly set on this model instance.
- model_config = {'alias_generator': <function to_camel>, 'populate_by_name': True, 'validate_by_alias': True, 'validate_by_name': True}¶
Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
- portside_xtd: Annotated[float, Field(description='Starboard XTD in NM as defined in RTZ.')] | None¶
- sog: Annotated[float, Field(description='Speed reference for the waypoint leg in knots')] | None¶
- starboard_xtd: Annotated[float, Field(description='Starboard XTD in NM as defined in RTZ.')] | None¶