wavy.validationmod

Module to organize the validation procedure Consists mostly of functions computing validation metrics

Module Contents

Functions

calc_model_activity_ratio(a, b)	computes the model activity ratio of input a (mode) and input b (obs)
calc_rmsd(a, b)	root mean square deviation
calc_nrmsd(a, b)	Normalized root mean square deviation
number_of_valid_values(a, b)
calc_drmsd(a, b)	debiased root mean square deviation
calc_scatter_index(obs, model)	Scatter index based on rmse and on std of diff
calc_corrcoef(a, b)	if nans exist the prinziple of marginalization is applied
calc_bias(a, b)	Bias
calc_nbias(a, b)	Normalized Bias [dimensionless]
calc_mad(a, b)	mean absolute deviation
disp_validation(valid_dict)	Print to screen validation scores.
validate(results_dict[, boot])

wavy.validationmod.calc_model_activity_ratio(a, b)

computes the model activity ratio of input a (mode) and input b (obs) if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.calc_rmsd(a, b)

root mean square deviation if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.calc_nrmsd(a, b)

Normalized root mean square deviation if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.number_of_valid_values(a, b)

wavy.validationmod.calc_drmsd(a, b)

debiased root mean square deviation if nans exist the prinziple of marginalization is applied

wavy.validationmod.calc_scatter_index(obs, model)

Scatter index based on rmse and on std of diff

wavy.validationmod.calc_corrcoef(a, b)

if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.calc_bias(a, b)

Bias if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.calc_nbias(a, b)

Normalized Bias [dimensionless] if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.calc_mad(a, b)

mean absolute deviation if nans exist the prinziple of marginalization is applied input: np.arrays with np.nan for invalids

wavy.validationmod.disp_validation(valid_dict)

Print to screen validation scores.

wavy.validationmod.validate(results_dict, boot=None)