Window#

class openstef_beam.evaluation.Window(lag: timedelta, size: timedelta, stride: timedelta = timedelta(days=1), minimum_coverage: float = 0.5)[source]#

Bases: PydanticStringPrimitive

Represents a time window with lag, size, and stride parameters.

Used for defining sliding windows for time series analysis with:

  • lag: How far back from the reference point to start the window

  • size: The duration of the window

  • stride: How much to advance for the next window

  • minimum_coverage: Minimum required data coverage (0.0-1.0)

Parameters:
__init__(lag: timedelta, size: timedelta, stride: timedelta = timedelta(days=1), minimum_coverage: float = 0.5)[source]#

Initialize a time window with the specified parameters.

Parameters:

  • lag (timedelta) – How far back from the reference point to start the window.

  • size (timedelta) – The duration of the window.

  • stride (timedelta) – How much to advance for the next window.

  • minimum_coverage (float) – Minimum required data coverage (0.0-1.0).

  • lag

  • size

  • stride

  • minimum_coverage

__str__() → str[source]#

Converts to string in ‘(lag=X,size=Y,stride=Z)’ format.

Timedeltas are serialized in ISO 8601 format.

Return type:

str

Returns:

String representation of the window parameters.

classmethod from_string(s: str) → Self[source]#

Creates an instance from a string in ‘(lag=X,size=Y,stride=Z)’ format.

Parameters:

  • s (str) – String representation to parse.

  • s

Returns:

Window instance parsed from the string.

Raises:

ValueError – If the string format is invalid.

Return type:

Self

classmethod validate(v: Self | str | dict[str, Any], _info: Any = None) → Self[source]#

Validates and converts various input types to Window.

Parameters:

  • v (Union[Self, str, dict[str, Any]]) – Input value to validate (Window, string, or dict).

  • _info (Any) – Additional validation info (unused).

  • v

  • _info

Returns:

Validated Window instance.

Return type:

Self

get_timerange(reference_date: datetime) → tuple[datetime, datetime][source]#

Get the start and end datetime of the evaluation window.

Parameters:

  • reference_date (datetime) – The reference datetime to calculate the window from.

  • reference_date

Returns:

A tuple containing the start and end datetime of the window.

Return type:

tuple[datetime, datetime]

is_future(reference_date: datetime) → bool[source]#

Check if the window ends after the reference date.

Parameters:

  • reference_date (datetime) – The reference datetime to check against.

  • reference_date

Returns:

True if the window ends after the reference date, False otherwise.

Return type:

bool