solarforecastarbiter.datamodel.ProcessedForecastObservation¶
-
class
solarforecastarbiter.datamodel.
ProcessedForecastObservation
(name: str, original: Union[solarforecastarbiter.datamodel.ForecastObservation, solarforecastarbiter.datamodel.ForecastAggregate], interval_value_type: str, interval_length: pandas._libs.tslibs.timedeltas.Timedelta, interval_label: str, valid_point_count: int, forecast_values: Union[pandas.core.series.Series, str, None], observation_values: Union[pandas.core.series.Series, str, None], reference_forecast_values: Union[pandas.core.series.Series, str, None] = None, validation_results: Tuple[solarforecastarbiter.datamodel.ValidationResult, ...] = (), preprocessing_results: Tuple[solarforecastarbiter.datamodel.PreprocessingResult, ...] = (), normalization_factor: Union[pandas.core.series.Series, float] = 1.0, uncertainty: Union[None, float] = None, cost: Optional[solarforecastarbiter.datamodel.Cost] = None)[source]¶ Hold the processed forecast and observation data with the resampling parameters.
Parameters: - name (str) –
- original (
solarforecastarbiter.datamodel.ForecastObservation
orsolarforecastarbiter.ForecastAggregate
) – - interval_value_type (str) –
- interval_length (pd.Timedelta) –
- interval_label (str) –
- valid_point_count (int) – The number of valid points in the processed forecast.
- forecast_values (pandas.Series or str or None) – The values of the forecast, the forecast id or None.
- observation_values (pandas.Series or str or None) – The values of the observation, the observation or aggregated id, or None.
- reference_forecast_values (pandas.Series or str or None) – The values of the reference forecast, the reference forecast id or None.
- validation_results (tuple of
solarforecastarbiter.datamodel.ValidationResult
) – - preprocessing_results (tuple of
solarforecastarbiter.datamodel.PreprocessingResult
) – - normalization_factor (pandas.Series or Float) –
- uncertainty (None or float) – If None, uncertainty is not accounted for. Float specifies the uncertainty as a percentage from 0 to 100%.
- cost (
solarforecastarbiter.datamodel.Cost
or None) – The parameters to use when calculating cost metrics.
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__init__
(name: str, original: Union[solarforecastarbiter.datamodel.ForecastObservation, solarforecastarbiter.datamodel.ForecastAggregate], interval_value_type: str, interval_length: pandas._libs.tslibs.timedeltas.Timedelta, interval_label: str, valid_point_count: int, forecast_values: Union[pandas.core.series.Series, str, None], observation_values: Union[pandas.core.series.Series, str, None], reference_forecast_values: Union[pandas.core.series.Series, str, None] = None, validation_results: Tuple[solarforecastarbiter.datamodel.ValidationResult, ...] = (), preprocessing_results: Tuple[solarforecastarbiter.datamodel.PreprocessingResult, ...] = (), normalization_factor: Union[pandas.core.series.Series, float] = 1.0, uncertainty: Union[None, float] = None, cost: Optional[solarforecastarbiter.datamodel.Cost] = None) → None¶
Methods
__init__
(name, original, …)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_factor
preprocessing_results
reference_forecast_values
uncertainty
validation_results