solarforecastarbiter.metrics.preprocessing.process_forecast_observations

solarforecastarbiter.metrics.preprocessing.process_forecast_observations(forecast_observations, filters, forecast_fill_method, start, end, data, timezone, costs=())[source]

Convert ForecastObservations into ProcessedForecastObservations applying any filters and resampling to align forecast and observation.

Parameters:
  • forecast_observations (list of solarforecastarbiter.datamodel.ForecastObservation, solarforecastarbiter.datamodel.ForecastAggregate) – Pairs to process
  • filters (list of solarforecastarbiter.datamodel.BaseFilter) – Filters to apply to each pair.
  • forecast_fill_method (str) – Indicates what process to use for handling missing forecasts. Currently supports : ‘drop’, ‘forward’, and bool or numeric value.
  • start (pandas.Timestamp) – Start date and time for assessing forecast performance.
  • end (pandas.Timestamp) – End date and time for assessing forecast performance.
  • data (dict) – Dict with keys that are the Forecast/Observation/Aggregate object and values that are the corresponding pandas.Series/DataFrame for the object. Keys must also include all Forecast objects assigned to the reference_forecast attributes of the forecast_observations.
  • timezone (str) – Timezone that data should be converted to
  • costs (tuple of solarforecastarbiter.datamodel.Cost) – Costs that are referenced by any pairs. Pairs and costs are matched by the Cost name.
Returns:

list of ProcessedForecastObservation