pastas.stats.metrics.nse
========================

.. py:function:: pastas.stats.metrics.nse(obs: pandas.Series, sim: pandas.Series | None = None, res: pandas.Series | None = None, missing: str = 'drop', weighted: bool = False, max_gap: int = 30) -> float

   
   Compute the (weighted) Nash-Sutcliffe Efficiency (NSE).

   :param obs: Series with the observed values.
   :type obs: pandas.Series
   :param sim: The Series with the simulated values.
   :type sim: pandas.Series, optional
   :param res: The Series with the residual values. If time series for the residuals are
               provided, the sim and obs arguments are ignored. Note that the residuals
               must be computed as `obs - sim` here.
   :type res: pandas.Series, optional
   :param missing: string with the rule to deal with missing values. Only "drop" is supported now.
   :type missing: str, optional
   :param weighted: If weighted is True, the variances are computed using the time step between
                    observations as weights. Default is False.
   :type weighted: bool, optional
   :param max_gap: maximum allowed gap period in days to use for the computation of the weights.
                   All time steps larger than max_gap are replace with the max_gap value.
                   Default value is 30 days.
   :type max_gap: int, optional

   .. rubric:: Notes

   NSE computed according to :cite:t:`nash_river_1970`

   .. math:: \text{NSE} = 1 - \frac{\sum(h_s-h_o)^2}{\sum(h_o-\mu_{h,o})}















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