csep.core.poisson_evaluations.likelihood_test

csep.core.poisson_evaluations.likelihood_test(gridded_forecast, observed_catalog, num_simulations=1000, seed=None, random_numbers=None, verbose=False)[source]

Performs the likelihood test on Gridded Forecast using an Observed Catalog.

Note: The forecast and the observations should be scaled to the same time period before calling this function. This increases transparency as no assumptions are being made about the length of the forecasts. This is particularly important for gridded forecasts that supply their forecasts as rates.

Parameters:

  • gridded_forecast – csep.core.forecasts.GriddedForecast

  • observed_catalog – csep.core.catalogs.Catalog

  • num_simulations (int) – number of simulations used to compute the quantile score

  • seed (int) – used fore reproducibility, and testing

  • random_numbers (numpy.ndarray) – random numbers used to override the random number generation. injection point for testing.

Returns:

csep.core.evaluations.EvaluationResult

Return type:

evaluation_result