stravalib.model.SummaryActivity#

The Activity object that contains high level summary activity for an activity.

Notes

In the case that the Strava spec is misaligned with the data actually returned, we override attributes as needed.

__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.
`_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

`_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.
`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
`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_name`	The name of the device used to record the activity
`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`

achievement_count#: The number of achievements gained during this activity

athlete: MetaAthlete | None#

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

average_cadence: float | None#

average_heartrate: float | None#

average_speed: VelocityType | None#: The activity’s average speed, in meters per second

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

bound_client#

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.

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: DistanceType | None#: The activity’s distance, in meters

elapsed_time: DurationType | None#: 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

end_latlng: LatLon | None#

external_id#: The identifier provided at upload time

flagged#: Whether this activity is flagged

from_accepted_tag: bool | None#

classmethod from_orm(obj: Any) → Self#

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

gear_id#: The id of the gear for the activity

has_heartrate: bool | None#

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

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

location_city: str | None#

location_country: str | None#

location_state: str | None#

manual#: Whether this activity was created manually

map: Map | None#

max_heartrate: int | None#

max_speed: VelocityType | None#: 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_watts': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'bound_client': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None, exclude=True), 'comment_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'commute': FieldInfo(annotation=Union[bool, 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), '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_id': 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), 'kilojoules': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'kudos_count': FieldInfo(annotation=Union[int, 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), 'photo_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'pr_count': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'private': FieldInfo(annotation=Union[bool, 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_latlng': FieldInfo(annotation=Union[LatLon, 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: DurationType | None#: 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#

photo_count#: The number of Instagram photos for this activity

pr_count: int | None#

private#: Whether this activity is private

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#

sport_type: RelaxedSportType | None#

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_latlng: LatLon | None#

suffer_score: int | None#

timezone: TimezoneType | None#: The timezone of the activity

total_elevation_gain: DistanceType | None#: 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: RelaxedActivityType | None#: 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: float | None#

classmethod validate(value: Any) → Self#

visibility: str | None#

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