metrics

calc_metric_ensemble(ens, pst, metric='all', bygroups=True, subset_realizations=None, drop_zero_weight=True)

Calculates unweighted metrics to quantify fit to observations for ensemble members

Parameters:

Name	Type	Description	Default
ens	pandas DataFrame	DataFrame read from an observation	required
pst	pyemu.Pst object	needed to obtain observation values and weights	required
metric	list of str	metric to calculate (PBIAS, RMSE, MSE, NSE, MAE, NRMSE_SD, NRMSE_MEAN, NRMSE_IQ, NRMSE_MAXMIN) case insensitive Defaults to 'all' which calculates all available metrics	'all'
bygroups	Bool	Flag to summarize by groups or not. Defaults to True.	True
subset_realizations	iterable	Subset of realizations for which to report metric. Defaults to None which returns all realizations.	None
drop_zero_weight	Bool	flag to exclude zero-weighted observations	True

Returns: pandas.DataFrame: rows are realizations. Columns are groups. Content is requested metrics

calc_metric_res(res, metric='all', bygroups=True, drop_zero_weight=True)

Calculates unweighted metrics to quantify fit to observations for residuals

Parameters:

Name	Type	Description	Default
res	pandas DataFrame or filename	DataFrame read from a residuals file or filename	required
metric	list of str	metric to calculate (PBIAS, RMSE, MSE, NSE, MAE, NRMSE_SD, NRMSE_MEAN, NRMSE_IQ, NRMSE_MAXMIN) case insensitive Defaults to 'all' which calculates all available metrics	'all'
bygroups	Bool	Flag to summarize by groups or not. Defaults to True.	True
drop_zero_weight	Bool	flag to exclude zero-weighted observations	True

Returns:

Type	Description
	pandas.DataFrame: single row. Columns are groups. Content is requested metrics