On Thu, Sep 13, 2012 at 6:39 PM, Warren Weckesser <warren.weckes...@enthought.com> wrote: > I would expect an error, consistent with the behavior when 1 < axis < 32.
In that case, you are hitting the dimension limit. np.concatenate((a,b), axis=31) ValueError: bad axis1 argument to swapaxes Where axis=32, axis=3500, axis=None all return the flattened array. I have been trying with other functions and got something interesting. With the same a, b as before: np.sum((a,b), axis=0) ValueError: operands could not be broadcast together with shapes (2) (3) np.sum((a,b), axis=1) array([[ 0. 0.], [ 2. 2. 2.]], dtype=object) np.sum((a,b), axis=2) ValueError: axis(=2) out of bounds This is to be expected, but now this is not consistent: np.sum((a,b), axis=32) ValueError: operands could not be broadcast together with shapes (2) (3) np.sum((a,b), axis=500) ValueError: axis(=500) out of bounds _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
