def KGE(s, o):
    """
        Kling-Gupta Efficiency
        input:
        s: simulated
        o: observed
        output:
        kge: Kling-Gupta Efficiency
        cc: correlation
        alpha: ratio of the standard deviation
        beta: ratio of the mean
        """
    # s,o = filter_nan(s, o)
    cc = correlation(s, o)
    alpha = np.std(s)/np.std(o)
    beta = np.sum(s)/np.sum(o)
    kge = 1 - np.sqrt((cc-1)**2 + (alpha-1)**2 + (beta-1)**2)
    return kge, cc, alpha, beta