stravalib.model.RelaxedSportType#

pydantic model stravalib.model.RelaxedSportType[source]#

A class that extends the list of Literal values allowed for Sport Types that are defined in the generated strava_model module.

Show JSON schema

{
   "title": "RelaxedSportType",
   "description": "A class that extends the list of Literal values allowed for Sport Types\nthat are defined in the generated `strava_model` module.",
   "type": "string",
   "enum": [
      "AlpineSki",
      "BackcountrySki",
      "Badminton",
      "Canoeing",
      "Crossfit",
      "EBikeRide",
      "Elliptical",
      "EMountainBikeRide",
      "Golf",
      "GravelRide",
      "Handcycle",
      "HighIntensityIntervalTraining",
      "Hike",
      "IceSkate",
      "InlineSkate",
      "Kayaking",
      "Kitesurf",
      "MountainBikeRide",
      "NordicSki",
      "Pickleball",
      "Pilates",
      "Racquetball",
      "Ride",
      "RockClimbing",
      "RollerSki",
      "Rowing",
      "Run",
      "Sail",
      "Skateboard",
      "Snowboard",
      "Snowshoe",
      "Soccer",
      "Squash",
      "StairStepper",
      "StandUpPaddling",
      "Surfing",
      "Swim",
      "TableTennis",
      "Tennis",
      "TrailRun",
      "Velomobile",
      "VirtualRide",
      "VirtualRow",
      "VirtualRun",
      "Walk",
      "WeightTraining",
      "Wheelchair",
      "Windsurf",
      "Workout",
      "Yoga"
   ]
}

Fields:

root (Literal['AlpineSki', 'BackcountrySki', 'Badminton', 'Canoeing', 'Crossfit', 'EBikeRide', 'Elliptical', 'EMountainBikeRide', 'Golf', 'GravelRide', 'Handcycle', 'HighIntensityIntervalTraining', 'Hike', 'IceSkate', 'InlineSkate', 'Kayaking', 'Kitesurf', 'MountainBikeRide', 'NordicSki', 'Pickleball', 'Pilates', 'Racquetball', 'Ride', 'RockClimbing', 'RollerSki', 'Rowing', 'Run', 'Sail', 'Skateboard', 'Snowboard', 'Snowshoe', 'Soccer', 'Squash', 'StairStepper', 'StandUpPaddling', 'Surfing', 'Swim', 'TableTennis', 'Tennis', 'TrailRun', 'Velomobile', 'VirtualRide', 'VirtualRow', 'VirtualRun', 'Walk', 'WeightTraining', 'Wheelchair', 'Windsurf', 'Workout', 'Yoga'])

Validators:

check_sport_type » all fields

validator check_sport_type » all fields[source]#

Pydantic validator that checks whether a sport type value is valid prior to populating the model. If the existing sport type is not valid, it assigns the value to be “Workout”.

Parameters:: values (str) – A dictionary containing an sport type key value pair.
Returns:: A str containing the validated sport type.
Return type:: str

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → 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) `

Parameters:

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.

dict(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]#

classmethod from_orm(obj: Any) → Self#

json(*, include: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | None = None, exclude: Set[int] | Set[str] | Mapping[int, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | Mapping[str, Set[int] | Set[str] | Mapping[int, IncEx | Literal[True]] | Mapping[str, IncEx | Literal[True]] | Literal[True]] | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str#

classmethod model_construct(root: RootModelRootType, _fields_set: set[str] | None = None) → Self#

Create a new model using the provided root object and update fields set.

Parameters:

root – The root object of the model.
_fields_set – The set of fields to be updated.

Returns:

The new model.

Raises:

NotImplemented – If the model is not a subclass of RootModel.

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.

Parameters:

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.

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.

Parameters:

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.

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.

Parameters:

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”.

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[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]#

Generates a JSON schema for a model class.

Parameters:

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.

Parameters:: 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.

Parameters:

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.

Parameters:

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.

Parameters:

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.

Parameters:

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.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

classmethod parse_obj(obj: Any) → Self#

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Self#

field root: Literal['AlpineSki', 'BackcountrySki', 'Badminton', 'Canoeing', 'Crossfit', 'EBikeRide', 'Elliptical', 'EMountainBikeRide', 'Golf', 'GravelRide', 'Handcycle', 'HighIntensityIntervalTraining', 'Hike', 'IceSkate', 'InlineSkate', 'Kayaking', 'Kitesurf', 'MountainBikeRide', 'NordicSki', 'Pickleball', 'Pilates', 'Racquetball', 'Ride', 'RockClimbing', 'RollerSki', 'Rowing', 'Run', 'Sail', 'Skateboard', 'Snowboard', 'Snowshoe', 'Soccer', 'Squash', 'StairStepper', 'StandUpPaddling', 'Surfing', 'Swim', 'TableTennis', 'Tennis', 'TrailRun', 'Velomobile', 'VirtualRide', 'VirtualRow', 'VirtualRun', 'Walk', 'WeightTraining', 'Wheelchair', 'Windsurf', 'Workout', 'Yoga'] [Required]#

An enumeration of the sport types an activity may have. Distinct from ActivityType in that it has new types (e.g. MountainBikeRide)

Validated by:

check_sport_type

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str#

classmethod update_forward_refs(**localns: Any) → None#

classmethod validate(value: Any) → Self#