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 or solarforecastarbiter.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.
__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