path_forecasts¶

passengersim.database.common_queries.path_forecasts(cnx: Database, *, scenario: str = None, burn_samples: int = 100) → DataFrame[source]¶

Average forecasts of demand by path, class, and days to departure.

This query requires that the simulation was run while recording path-class details (i.e. with the pathclass flag set on Config.db.write_items).

Parameters:

cnx : Database¶

scenario : str¶

burn_samples : int, default 100¶

The forecasts will be analyzed ignoring this many samples from the beginning of each trial.

Returns:

pandas.DataFrame – The resulting dataframe is indexed by path_id, booking_class and days_prior, and has these columns:

• forecast_mean: Average forecast mean (mu).

• forecast_stdev: Average forecast standard deviation (sigma).

• forecast_closed_in_tf: Average fraction of time the timeframe was

  closed in the data used to make a forecast.

• forecast_closed_in_tf: Average fraction of time any future timeframe

  was closed in the data used to make a forecast.