available_metrics

postprocessinglib.evaluation.metrics.available_metrics() → list[int]

Get a list of currently available metrics

Returns:

List of implemented metric names.

Return type:

List[str]

Example

>>> from postprocessinglib.evaluation import metrics
>>> print(metrics.available_metrics())
    ["MSE - Mean Square Error", "RMSE - Roor Mean Square Error",
    "MAE - Mean Average Error", "NSE - Nash-Sutcliffe Efficiency ",
    "NegNSE - Nash-Sutcliffe Efficiency * -1", "LogNSE - Log of Nash-Sutcliffe Efficiency",
    "NegLogNSE - Log of Nash-Sutcliffe Efficiency * -1",
    "KGE - Kling-Gupta Efficiency", "NegKGE - Kling-Gupta Efficiency * -1",
    "KGE 2012 - Kling-Gupta Efficiency modified as of 2012", "BIAS- Prcentage Bias",
    "AbsBIAS - Absolute Value of the Percentage Bias", "TTP - Time to Peak",
    "TTCoM - Time to Centre of Mass", "SPOD - Spring Pulse ONset Delay",
    'FDC Slope - Slope of the Flow Duration Curve' ]

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