solarforecastarbiter.metrics.preprocessing.apply_fill(fx_data, forecast, forecast_fill_method, start, end)[source]

Apply fill procedure to the data from the start to end timestamps.

  • fx_data (pandas.Series or pandas.DataFrame) – Forecast data with pandas.DatetimeIndex.
  • forecast (datamodel.Forecast) –
  • forecast_fill_method ({'drop', 'forward', float}) –
    Indicates what process to use for handling missing forecasts.
    • _’drop’_ drops all missing values for any row with a missing value.
    • _’forward’_ fills missing values with the most recent real value. If any leading missing values fill with zeros.
    • _float_ fills any missing values with the given value.
  • start (pandas.Timestamp) –
  • end (pandas.Timestamp) –

  • filled (pandas.Series or pandas.DataFrame) – Forecast filled according to the specified logic
  • count (int) – Number of values filled or dropped