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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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, "")