edgar¶

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

Forecast accuracy information.

Parameters:

cnx : Database¶

scenario : str, optional¶

burn_samples : int, default 100¶

The demand will be returned ignoring this many samples from the beginning of each trial.

Returns:

pandas.DataFrame – The resulting dataframe is indexed by iteration, trial, sample, segment, orig, and dest; and has columns defined by the DCPs. The values stored are the total remaining demand to come at each DCP.