stravalib.model.Lap#

class stravalib.model.Lap(*, bound_client: Any | None = None, activity: MetaActivity | None = None, athlete: MetaAthlete | None = None, average_cadence: float | None = None, average_speed: Annotated[Velocity, _VelocityAnnotation] | None = None, distance: Annotated[Distance, _DistanceAnnotation] | None = None, elapsed_time: Annotated[Duration, _DurationAnnotation] | None = None, end_index: int | None = None, id: int | None = None, lap_index: int | None = None, max_speed: Annotated[Velocity, _VelocityAnnotation] | None = None, moving_time: Annotated[Duration, _DurationAnnotation] | None = None, name: str | None = None, pace_zone: int | None = None, split: int | None = None, start_date: datetime | None = None, start_date_local: datetime | None = None, start_index: int | None = None, total_elevation_gain: Annotated[Distance, _DistanceAnnotation] | None = None, average_watts: float | None = None, average_heartrate: float | None = None, max_heartrate: float | None = None, device_watts: bool | None = None)[source]#

An object that represents an Activity lap.

This object inherits the BoundClient to support API access through lazy methods.

__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)
`_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

`_abc_impl`
`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.
`activity`
`athlete`
`distance`	The lap's distance, in meters
`total_elevation_gain`	The elevation gain of this lap, in meters
`elapsed_time`	The lap's elapsed time, in seconds
`moving_time`	The lap's moving time, in seconds
`average_speed`	The lap's average speed
`max_speed`	The maximum speed of this lat, in meters per second
`average_watts`
`average_heartrate`
`max_heartrate`
`device_watts`
`average_cadence`	The lap's average cadence
`end_index`	The end index of this effort in its activity's stream
`id`	The unique identifier of this lap
`lap_index`	The index of this lap in the activity it belongs to
`name`	The name of the lap
`pace_zone`	The athlete's pace zone during this lap
`split`
`start_date`	The time at which the lap was started.
`start_date_local`	The time at which the lap was started in the local timezone.
`start_index`	The start index of this effort in its activity's stream
`bound_client`

activity: MetaActivity | None#

athlete: MetaAthlete | None#

average_cadence#: The lap’s average cadence

average_heartrate: float | None#

average_speed: VelocityType | None#: The lap’s average speed

average_watts: float | None#

bound_client#

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_watts: bool | None#

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 lap’s distance, in meters

elapsed_time: DurationType | None#: The lap’s elapsed time, in seconds

end_index#: The end index of this effort in its activity’s stream

classmethod from_orm(obj: Any) → Self#

id#: The unique identifier of this lap

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#

lap_index#: The index of this lap in the activity it belongs to

max_heartrate: float | None#

max_speed: VelocityType | None#: The maximum speed of this lat, in meters per second

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 = {'activity': FieldInfo(annotation=Union[MetaActivity, NoneType], required=False, default=None), 'athlete': FieldInfo(annotation=Union[MetaAthlete, NoneType], required=False, default=None), '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), '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), 'end_index': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'lap_index': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'max_heartrate': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'max_speed': FieldInfo(annotation=Union[Annotated[Velocity, _VelocityAnnotation], 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), 'pace_zone': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'split': FieldInfo(annotation=Union[int, 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_index': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'total_elevation_gain': FieldInfo(annotation=Union[Annotated[Distance, _DistanceAnnotation], 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 lap’s moving time, in seconds

name#: The name of the lap

pace_zone#: The athlete’s pace zone during this lap

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#

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#

split#

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

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

start_index#: The start index of this effort in its activity’s stream

total_elevation_gain: DistanceType | None#: The elevation gain of this lap, in meters

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

classmethod validate(value: Any) → Self#