stravalib.model.BestEffort#

Class representing a best effort (e.g. best time for 5k)

__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.
`segment`
`activity`
`athlete`
`elapsed_time`	The effort's elapsed time
`moving_time`	The effort's moving time
`distance`	The effort's distance in meters
`activity_id`	The unique identifier of the activity related to this effort
`average_cadence`	The effort's average cadence
`average_heartrate`	The heart heart rate of the athlete during this effort
`average_watts`	The average wattage of this effort
`device_watts`	For riding efforts, whether the wattage was reported by a dedicated recording device
`end_index`	The end index of this effort in its activity's stream
`hidden`	Whether this effort should be hidden when viewed within an activity
`kom_rank`	The rank of the effort on the global leaderboard if it belongs in the top 10 at the time of upload
`max_heartrate`	The maximum heart rate of the athlete during this effort
`name`	The name of the segment on which this effort was performed
`pr_rank`	The rank of the effort on the athlete's leaderboard if it belongs in the top 3 at the time of upload
`start_index`	The start index of this effort in its activity's stream
`id`	The unique identifier of this effort
`is_kom`	Whether this effort is the current best on the leaderboard
`start_date`	The time at which the effort was started.
`start_date_local`	The time at which the effort was started in the local timezone.

activity: MetaActivity | None#

activity_id#: The unique identifier of the activity related to this effort

athlete: MetaAthlete | None#

average_cadence#: The effort’s average cadence

average_heartrate#: The heart heart rate of the athlete during this effort

average_watts#: The average wattage of this effort

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#: For riding efforts, whether the wattage was reported by a dedicated recording device

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 effort’s distance in meters

elapsed_time: DurationType | None#: The effort’s elapsed time

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

classmethod from_orm(obj: Any) → Self#

hidden#: Whether this effort should be hidden when viewed within an activity

id#: The unique identifier of this effort

is_kom#: Whether this effort is the current best on the leaderboard

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#

kom_rank#: The rank of the effort on the global leaderboard if it belongs in the top 10 at the time of upload

max_heartrate#: The maximum heart rate of the athlete during this effort

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 – A mapping of values to update the copied model. Updates are not applied recursively, and no validation is performed on updated values. Only the known fields are updated (if unknown keys are being passed and the model has [extra][pydantic.ConfigDict.extra] set to ‘allow’, they are added as extra data).
deep – Whether a [deep copy][copy.deepcopy] of the model should be performed.

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), 'activity_id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, alias_priority=2, validation_alias=AliasChoices(choices=['pr_activity_id', 'activity_id'])), '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_watts': FieldInfo(annotation=Union[float, 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), 'end_index': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'hidden': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'id': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'is_kom': FieldInfo(annotation=Union[bool, NoneType], required=False, default=None), 'kom_rank': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, metadata=[Ge(ge=1), Le(le=10)]), 'max_heartrate': FieldInfo(annotation=Union[float, 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), 'pr_rank': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, metadata=[Ge(ge=1), Le(le=3)]), 'segment': FieldInfo(annotation=Union[SummarySegment, 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)}#

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[TypeForm[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.

If validation fails, the resulting [ValidationError][pydantic_core.ValidationError] reports the rejected locations and values. [Logfire](../integrations/logfire.md) can retain the complete validation input and surrounding trace context for production debugging (see [Troubleshooting validation errors](../errors/troubleshooting.md)).

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.

A [ValidationError][pydantic_core.ValidationError] raised here reports the rejected locations and values, but may not retain the complete source document. Recording validations with [Logfire](../integrations/logfire.md) keeps the complete JSON input alongside the error — see [Troubleshooting validation errors](../errors/troubleshooting.md).

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 effort’s moving time

name#: The name of the segment on which this effort was performed

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#

pr_rank#: The rank of the effort on the athlete’s leaderboard if it belongs in the top 3 at the time of upload

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: SummarySegment | None#

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

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

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

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

classmethod validate(value: Any) → Self#