def point_predict(f, x, size=1, nugget=None):
    """
    point_predict(f, x[, size, nugget])

    Makes 'size' simulations for f(x) + N(0,nugget).
    Simulated values of f(x_i) are uncorrelated for different i.
    Useful for geostatistical predictions.
    """
    orig_shape = x.shape
    x = regularize_array(x)

    mu = f.M_internal(x, regularize=False)
    V = f.C_internal(x, regularize=False)
    if nugget is not None:
        V += nugget
    out = random.normal(size=(size, x.shape[0])) * sqrt(V) + mu
    return out.reshape((size,) + orig_shape[:-1]).squeeze()
def point_predict ( f , x , size = 1 , nugget = None ) : orig_shape = x . shape x = regularize_array ( x ) mu = f . M_internal ( x , regularize = False ) V = f . C_internal ( x , regularize = False ) if nugget is not None : V += nugget out = random . normal ( size = ( size , x . shape [ 0 ] ) ) * sqrt ( V ) + mu return out . reshape ( ( size , ) + orig_shape [ : - 1 ] ) . squeeze ( )
point_predict(f, x[, size, nugget])

    Makes 'size' simulations for f(x) + N(0,nugget).
    Simulated values of f(x_i) are uncorrelated for different i.
    Useful for geostatistical predictions.