On 28 June 2013 17:33, Charles R Harris <charlesr.harris@gmail.com> wrote:

On Fri, Jun 28, 2013 at 5:27 AM, Richard Hattersley <rhattersley@gmail.com> wrote:

So: np.array([scalar]) => np.array([scalar], dtype=my_dtype) But: np.array(scalar) => np.array(scalar, dtype=object)

So the scalar case (0 dimensional array) doesn't work right. Hmm, what happens when you index the first array? Does subclassing the generic type work in 1.6?

Indexing into the first array works fine. So something like `a[0]` calls my_dtype->f->getitem which creates a new scalar instance, and something like `a[:1]` creates a new view with the correct dtype.

My impression is that subclassing the generic type should be required, but I don't see where it is documented :(

Can you elaborate on why the generic type should be required? Do you think it might cause problems elsewhere? (FYI I've also tested with a patched version of v1.6.2 which fixes the typo which prevents the use of user-defined dtypes with ufuncs, and that functionality seems to work fine too.)

Anyway, what is the problem with the third party code? Is there no chance that you can get hold of it to fix it?

Unfortunately it's out of my control. Regards, Richard