def point_probability(self, threshold):
        """
        Determine the probability of exceeding a threshold at a grid point based on the ensemble forecasts at
        that point.

        Args:
            threshold: If >= threshold assigns a 1 to member, otherwise 0.

        Returns:
            EnsembleConsensus
        """
        point_prob = np.zeros(self.data.shape[1:])
        for t in range(self.data.shape[1]):
            point_prob[t] = np.where(self.data[:, t] >= threshold, 1.0, 0.0).mean(axis=0)
        return EnsembleConsensus(point_prob, "point_probability", self.ensemble_name,
                                 self.run_date, self.variable + "_{0:0.2f}_{1}".format(threshold,
                                                                                       self.units.replace(" ", "_")),
                                 self.start_date, self.end_date, "")
def point_probability ( self , threshold ) : point_prob = np . zeros ( self . data . shape [ 1 : ] ) for t in range ( self . data . shape [ 1 ] ) : point_prob [ t ] = np . where ( self . data [ : , t ] >= threshold , 1.0 , 0.0 ) . mean ( axis = 0 ) return EnsembleConsensus ( point_prob , "point_probability" , self . ensemble_name , self . run_date , self . variable + "_{0:0.2f}_{1}" . format ( threshold , self . units . replace ( " " , "_" ) ) , self . start_date , self . end_date , "" )
Determine the probability of exceeding a threshold at a grid point based on the ensemble forecasts at
        that point.

        Args:
            threshold: If >= threshold assigns a 1 to member, otherwise 0.

        Returns:
            EnsembleConsensus