ocean_model_skill_assessor.stats

Statistics functions.

Functions

compute_bias(obs, model)	Given obs and model signals return bias.
compute_correlation_coefficient(obs, model)	Given obs and model signals, return Pearson product-moment correlation coefficient
compute_descriptive_statistics(model[, ddof])	Given obs and model signals, return the standard deviation
compute_index_of_agreement(obs, model)	Given obs and model signals, return Index of Agreement (Willmott 1981)
compute_mean_square_error(obs, model[, centered])	Given obs and model signals, return mean squared error (MSE)
compute_murphy_skill_score(obs, model[, ...])	Given obs and model signals, return Murphy Skill Score (Murphy 1988)
compute_root_mean_square_error(obs, model[, ...])	Given obs and model signals, return Root Mean Square Error (RMSE)
compute_stats(obs, model)	Compute stats and return as DataFrame
save_stats(source_name, stats, key_variable, ...)	Save computed stats to file.

ocean_model_skill_assessor.stats.compute_bias(obs, model)

Given obs and model signals return bias.

ocean_model_skill_assessor.stats.compute_correlation_coefficient(obs, model)

Given obs and model signals, return Pearson product-moment correlation coefficient

ocean_model_skill_assessor.stats.compute_descriptive_statistics(model, ddof=0)

Given obs and model signals, return the standard deviation

ocean_model_skill_assessor.stats.compute_index_of_agreement(obs, model)

Given obs and model signals, return Index of Agreement (Willmott 1981)

ocean_model_skill_assessor.stats.compute_mean_square_error(obs, model, centered=False)

Given obs and model signals, return mean squared error (MSE)

ocean_model_skill_assessor.stats.compute_murphy_skill_score(obs, model, obs_model=None)

Given obs and model signals, return Murphy Skill Score (Murphy 1988)

ocean_model_skill_assessor.stats.compute_root_mean_square_error(obs, model, centered=False)

Given obs and model signals, return Root Mean Square Error (RMSE)

ocean_model_skill_assessor.stats.compute_stats(obs, model)

Compute stats and return as DataFrame

ocean_model_skill_assessor.stats.save_stats(source_name, stats, key_variable, paths, filename=None)

Save computed stats to file.