ed` argument can be used to prevent vectorizing over certain
    arguments.  This can be useful for array-like arguments of a fixed length
    such as the coefficients for a polynomial as in `polyval`:

    >>> def mypolyval(p, x):
    ...     _p = list(p)
    ...     res = _p.pop(0)
    ...     while _p:
    ...         res = res*x + _p.pop(0)
    ...     return res
    >>> vpolyval = np.vectorize(mypolyval, excluded=['p'])
    >>> vpolyval(p=[1, 2, 3], x=[0, 1])
    array([3, 6])

    Positional arguments may also be excluded by specifying their position:

    >>> vpolyval.excluded.add(0)
    >>> vpolyval([1, 2, 3], x=[0, 1])
    array([3, 6])

    The `signature` argument allows for vectorizing functions that act on
    non-scalar arrays of fixed length. For example, you can use it for a
    vectorized calculation of Pearson correlation coefficient and its p-value:

    >>> import scipy.stats
    >>> pearsonr = np.vectorize(scipy.stats.pearsonr,
    ...                 signature='(n),(n)->(),()')
    >>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
    (array([ 1., -1.]), array([ 0.,  0.]))

    Or for a vectorized convolution:

    >>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
    >>> convolve(np.eye(4), [1, 2, 1])
    array([[1., 2., 1., 0., 0., 0.],
           [0., 1., 2., 1., 0., 0.],
           [0., 0., 1., 2., 1., 0.],
           [0., 0., 0., 1., 2., 1.]])

    Decorator syntax is supported.  The decorator can be called as
    a function to provide keyword arguments.
    >>>@np.vectorize
    ...def identity(x):
    ...    return x
    ...
    >>>identity([0, 1, 2])
    array([0, 1, 2])
    >>>@np.vectorize(otypes=[float])
    ...def as_float(x):
    ...    return x
    ...
    >>>as_float([0, 1, 2])
    array([0., 1., 2.])
    NFc