stravalib.model.DetailedActivity#

Represents an activity (ride, run, etc.).

__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

`_calculate_keys`(args, *kwargs)
`_copy_and_set_values`(args, *kwargs)
`_get_value`(args, *kwargs)
`_iter`(args, *kwargs)
`_latlng_check`()	Validate a list of location xy values.
`_naive_local`()	Utility helper that parses a datetime value provided in JSON, string, int or other formats and returns a datetime.datetime object.
`_setattr_handler`(name, value)	Get a handler for setting an attribute on the model instance.
`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

`SPORT_TYPES`
`TYPES`
`_abc_impl`
`comments`	Retrieves comments for a specific activity id.
`full_photos`	Retrieves activity photos for a specific activity by id.
`kudos`	Retrieves the kudos provided for a specific activity.
`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.
`zones`	Retrieve a list of zones for an activity.
`gear`
`best_efforts`
`segment_efforts`
`splits_metric`	The splits of this activity in metric units (for runs)
`splits_standard`	The splits of this activity in imperial units (for runs)
`photos`
`laps`
`guid`
`start_latitude`
`start_longitude`
`average_temp`
`instagram_primary_photo`
`partner_logo_url`
`partner_brand_tag`
`segment_leaderboard_opt_out`
`perceived_exertion`
`prefer_perceived_exertion`
`private_note`
`athlete`
`average_speed`	The activity's average speed, in meters per second
`distance`	The activity's distance, in meters
`elapsed_time`	The activity's elapsed time, in seconds
`start_latlng`
`end_latlng`
`map`
`max_speed`	The activity's max speed, in meters per second
`moving_time`	The activity's moving time, in seconds
`type`	Deprecated.
`sport_type`
`timezone`	The timezone of the activity
`total_elevation_gain`	The activity's total elevation gain.
`utc_offset`
`location_city`
`location_state`
`location_country`
`pr_count`
`suffer_score`
`has_heartrate`
`average_heartrate`
`max_heartrate`
`average_cadence`
`from_accepted_tag`
`visibility`
`id`	The unique identifier of the activity
`calories`	The number of kilocalories consumed during this activity
`description`	The description of the activity
`device_name`	The name of the device used to record the activity
`embed_token`	The token used to embed a Strava activity
`achievement_count`	The number of achievements gained during this activity
`athlete_count`	The number of athletes for taking part in a group activity
`average_watts`	Average power output in watts during this activity.
`comment_count`	The number of comments for this activity
`commute`	Whether this activity is a commute
`device_watts`	Whether the watts are from a power meter, false if estimated
`elev_high`	The activity's highest elevation, in meters
`elev_low`	The activity's lowest elevation, in meters
`external_id`	The identifier provided at upload time
`flagged`	Whether this activity is flagged
`gear_id`	The id of the gear for the activity
`has_kudoed`	Whether the logged-in athlete has kudoed this activity
`hide_from_home`	Whether the activity is muted
`kilojoules`	The total work done in kilojoules during this activity.
`kudos_count`	The number of kudos given for this activity
`manual`	Whether this activity was created manually
`max_watts`	Rides with power meter data only
`name`	The name of the activity
`photo_count`	The number of Instagram photos for this activity
`private`	Whether this activity is private
`start_date`	The time at which the activity was started.
`start_date_local`	The time at which the activity was started in the local timezone.
`total_photo_count`	The number of Instagram and Strava photos for this activity
`trainer`	Whether this activity was recorded on a training machine
`upload_id`	The identifier of the upload that resulted in this activity
`upload_id_str`	The unique identifier of the upload in string format
`weighted_average_watts`	Similar to Normalized Power.
`workout_type`	The activity's workout type
`bound_client`

SPORT_TYPES: ClassVar[tuple[Any, ...]] = ('AlpineSki', 'BackcountrySki', 'Badminton', 'Basketball', 'Canoeing', 'Cricket', 'Crossfit', 'Dance', 'EBikeRide', 'Elliptical', 'EMountainBikeRide', 'Golf', 'GravelRide', 'Handcycle', 'HighIntensityIntervalTraining', 'Hike', 'IceSkate', 'InlineSkate', 'Kayaking', 'Kitesurf', 'MountainBikeRide', 'NordicSki', 'Padel', 'PhysicalTherapy', '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', 'Volleyball', 'Walk', 'WeightTraining', 'Wheelchair', 'Windsurf', 'Workout', 'Yoga')#

TYPES: ClassVar[tuple[Any, ...]] = ('AlpineSki', 'BackcountrySki', 'Canoeing', 'Crossfit', 'EBikeRide', 'Elliptical', 'Golf', 'Handcycle', 'Hike', 'IceSkate', 'InlineSkate', 'Kayaking', 'Kitesurf', 'NordicSki', 'Ride', 'RockClimbing', 'RollerSki', 'Rowing', 'Run', 'Sail', 'Skateboard', 'Snowboard', 'Snowshoe', 'Soccer', 'StairStepper', 'StandUpPaddling', 'Surfing', 'Swim', 'Velomobile', 'VirtualRide', 'VirtualRun', 'Walk', 'WeightTraining', 'Wheelchair', 'Windsurf', 'Workout', 'Yoga')#

achievement_count#: The number of achievements gained during this activity

athlete#

athlete_count#: The number of athletes for taking part in a group activity

average_cadence#

average_heartrate#

average_speed#: The activity’s average speed, in meters per second

average_temp: int | None#

average_watts#: Average power output in watts during this activity. Rides only

best_efforts: Sequence[BestEffort] | None#

bound_client#

calories#: The number of kilocalories consumed during this activity

comment_count#: The number of comments for this activity

property comments: BatchedResultsIterator[Comment]#: Retrieves comments for a specific activity id.

commute#: Whether this activity is a commute

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:

`python {test="skip" lint="skip"} 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.

description#: The description of the activity

device_name#: The name of the device used to record the activity

device_watts#: Whether the watts are from a power meter, false if estimated

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

distance#: The activity’s distance, in meters

elapsed_time#: The activity’s elapsed time, in seconds

elev_high#: The activity’s highest elevation, in meters

elev_low#: The activity’s lowest elevation, in meters

embed_token#: The token used to embed a Strava activity

end_latlng#

external_id#: The identifier provided at upload time

flagged#: Whether this activity is flagged

from_accepted_tag#

classmethod from_orm(obj: Any) → Self#

property full_photos: BatchedResultsIterator[ActivityPhoto]#: Retrieves activity photos for a specific activity by id.

gear: SummaryGear | None#

gear_id#: The id of the gear for the activity

guid: str | None#

has_heartrate#

has_kudoed#: Whether the logged-in athlete has kudoed this activity

hide_from_home#: Whether the activity is muted

id#: The unique identifier of the activity

instagram_primary_photo: str | None#

json(*, include: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | None = None, exclude: set[int] | set[str] | Mapping[int, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | Mapping[str, set[int] | set[str] | Mapping[int, IncEx | bool] | Mapping[str, IncEx | bool] | bool] | 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#

kilojoules#: The total work done in kilojoules during this activity. Rides only

property kudos: BatchedResultsIterator[SummaryAthlete]#: Retrieves the kudos provided for a specific activity.

kudos_count#: The number of kudos given for this activity

laps: Sequence[Lap] | None#

location_city#

location_country#

location_state#

manual#: Whether this activity was created manually

map#

max_heartrate#

max_speed#: The activity’s max speed, in meters per second

max_watts#: Rides with power meter data only

model_computed_fields = {}#

model_config = {}#: 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.

Parameters:

_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: Mapping[str, Any] | None = None, deep: bool = False) → Self#

!!! abstract “Usage Documentation”: [model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note: The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

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.

!!! abstract “Usage Documentation”: [model_dump](../concepts/serialization.md#python-mode)

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.
exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
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].
fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.
polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

Returns:

A dictionary representation of the model.

!!! abstract “Usage Documentation”: [model_dump_json](../concepts/serialization.md#json-mode)

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.
ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.
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.
exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.
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].
fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.
serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.
polymorphic_serialization – Whether to use model and dataclass polymorphic serialization for this call.

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 = {'achievement_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'athlete': FieldInfo(annotation=Union[MetaAthlete, NoneType], required=False, default=None), 'athlete_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, metadata=[Ge(ge=1)]), 'average_cadence': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'average_heartrate': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'average_speed': FieldInfo(annotation=Union[Annotated[Velocity, _VelocityAnnotation], NoneType], required=False, default=None), 'average_temp': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'average_watts': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'best_efforts': FieldInfo(annotation=Union[Sequence[BestEffort], NoneType], required=False, default=None), 'bound_client': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, exclude=True), 'calories': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'comment_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'commute': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'description': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device_name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'device_watts': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'distance': FieldInfo(annotation=Union[Annotated[Distance, _DistanceAnnotation], NoneType], required=False, default=None), 'elapsed_time': FieldInfo(annotation=Union[Annotated[Duration, _DurationAnnotation], NoneType], required=False, default=None), 'elev_high': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'elev_low': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'embed_token': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'end_latlng': FieldInfo(annotation=Union[LatLon, NoneType], required=False, default=None), 'external_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'flagged': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'from_accepted_tag': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'gear': FieldInfo(annotation=Union[SummaryGear, NoneType], required=False, default=None), 'gear_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'guid': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'has_heartrate': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'has_kudoed': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'hide_from_home': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'instagram_primary_photo': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'kilojoules': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'kudos_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'laps': FieldInfo(annotation=Union[Sequence[Lap], NoneType], required=False, default=None), 'location_city': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'location_country': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'location_state': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'manual': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'map': FieldInfo(annotation=Union[Map, NoneType], required=False, default=None), 'max_heartrate': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'max_speed': FieldInfo(annotation=Union[Annotated[Velocity, _VelocityAnnotation], NoneType], required=False, default=None), 'max_watts': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'moving_time': FieldInfo(annotation=Union[Annotated[Duration, _DurationAnnotation], NoneType], required=False, default=None), 'name': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'partner_brand_tag': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'partner_logo_url': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'perceived_exertion': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'photo_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'photos': FieldInfo(annotation=Union[PhotosSummary, NoneType], required=False, default=None), 'pr_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'prefer_perceived_exertion': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'private': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'private_note': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'segment_efforts': FieldInfo(annotation=Union[Sequence[SegmentEffort], NoneType], required=False, default=None), 'segment_leaderboard_opt_out': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'splits_metric': FieldInfo(annotation=Union[Sequence[Split], NoneType], required=False, default=None), 'splits_standard': FieldInfo(annotation=Union[Sequence[Split], NoneType], required=False, default=None), 'sport_type': FieldInfo(annotation=Union[RelaxedSportType, NoneType], required=False, default=None), 'start_date': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'start_date_local': FieldInfo(annotation=Union[datetime, NoneType], required=False, default=None), 'start_latitude': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'start_latlng': FieldInfo(annotation=Union[LatLon, NoneType], required=False, default=None), 'start_longitude': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'suffer_score': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'timezone': FieldInfo(annotation=Union[Annotated[Timezone, _TimezoneAnnotation], NoneType], required=False, default=None), 'total_elevation_gain': FieldInfo(annotation=Union[Annotated[Distance, _DistanceAnnotation], NoneType], required=False, default=None), 'total_photo_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'trainer': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'type': FieldInfo(annotation=Union[RelaxedActivityType, NoneType], required=False, default=None), 'upload_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'upload_id_str': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'utc_offset': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'visibility': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'weighted_average_watts': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'workout_type': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}#

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: Literal['validation', 'serialization']='validation', *, union_format: Literal['any_of', 'primitive_type_array']='any_of') → 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.
union_format –
The format to use when combining schemas from unions together. Can be one of:
- ’any_of’: Use the [anyOf](https://json-schema.org/understanding-json-schema/reference/combining#anyOf)
keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.
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(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: MappingNamespace | 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.

Validate a pydantic model instance.

Parameters:

obj – The object to validate.
strict – Whether to enforce types strictly.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
from_attributes – Whether to extract data from object attributes.
context – Additional context to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

!!! abstract “Usage Documentation”: [JSON Parsing](../concepts/json.md#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.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context – Extra variables to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

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.
extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.
context – Extra variables to pass to the validator.
by_alias – Whether to use the field’s alias when validating against the provided input data.
by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

moving_time#: The activity’s moving time, in seconds

name#: The name of the activity

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#

partner_brand_tag: str | None#

partner_logo_url: str | None#

perceived_exertion: int | None#

photo_count#: The number of Instagram photos for this activity

photos: PhotosSummary | None#

pr_count#

prefer_perceived_exertion: bool | None#

private#: Whether this activity is private

private_note: str | None#

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#

segment_efforts: Sequence[SegmentEffort] | None#

segment_leaderboard_opt_out: bool | None#

splits_metric: Sequence[Split] | None#: The splits of this activity in metric units (for runs)

splits_standard: Sequence[Split] | None#: The splits of this activity in imperial units (for runs)

sport_type#

start_date#: The time at which the activity was started.

start_date_local#: The time at which the activity was started in the local timezone.

start_latitude: float | None#

start_latlng#

start_longitude: float | None#

suffer_score#

timezone#: The timezone of the activity

total_elevation_gain#: The activity’s total elevation gain.

total_photo_count#: The number of Instagram and Strava photos for this activity

trainer#: Whether this activity was recorded on a training machine

type#: Deprecated. Prefer to use sport_type

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

upload_id#: The identifier of the upload that resulted in this activity

upload_id_str#: The unique identifier of the upload in string format

utc_offset#

classmethod validate(value: Any) → Self#

visibility#

weighted_average_watts#: Similar to Normalized Power. Rides with power meter data only

workout_type#: The activity’s workout type

property zones: list[ActivityZone]#

Retrieve a list of zones for an activity.

Returns:: A list of stravalib.model.ActivityZone objects.
Return type:: list