solarforecastarbiter.datamodel.ForecastAggregate¶
-
class
solarforecastarbiter.datamodel.
ForecastAggregate
(forecast: solarforecastarbiter.datamodel.Forecast, aggregate: solarforecastarbiter.datamodel.Aggregate, reference_forecast: Optional[solarforecastarbiter.datamodel.Forecast] = None, normalization: Optional[float] = None, uncertainty: Optional[float] = None, cost: Optional[str] = None)[source]¶ Class for pairing Forecast and Aggregate objects for evaluation.
Parameters: - forecast (
solarforecastarbiter.datamodel.Forecast
) – - aggregate (
solarforecastarbiter.datamodel.Aggregate
) – - reference_forecast (
solarforecastarbiter.datamodel.Forecast
or None) – - normalization (float or None) – If None, assigned 1.
- uncertainty (None, float, or str) – If None, uncertainty is not accounted for. Float specifies the uncertainty as a percentage from 0 to 100%. Strings must be coerceable to a float.
- cost (str or None) – Cost parameters to use from the costs associated with ReportParameters
-
__init__
(forecast: solarforecastarbiter.datamodel.Forecast, aggregate: solarforecastarbiter.datamodel.Aggregate, reference_forecast: Optional[solarforecastarbiter.datamodel.Forecast] = None, normalization: Optional[float] = None, uncertainty: Optional[float] = None, cost: Optional[str] = None) → None¶
Methods
__init__
(forecast, aggregate, …)from_dict
(input_dict[, raise_on_extra])Construct a dataclass from the given dict, matching keys with the class fields. replace
(**kwargs)Convience wrapper for dataclasses.replace()
to create a new dataclasses from the old with the given keys replaced.to_dict
()Convert the dataclass into a dictionary suitable for uploading to the API. Attributes
cost
normalization
reference_forecast
uncertainty
- forecast (