Support left-side np.number elemwise operations.

[fujiisoup]

Fixes #66.

This adds a workaround when the np.number multiplication from left-side.

[hameerabbasi]

Hello, the correct way to fix this would be to modify __rmul__ to return scalar * COO. Fixing it this way causes the broadcasting path to be taken which will be much, much slower and will fail for scalar COO objects, returning 1-D instead of scalar.

[fujiisoup]

Hi, @hameerabbasi
Thanks for the quick response.

the correct way to fix this would be to modify rmul to return scalar * COO

np.number has __mul__ method and thus COO.__rmul__ will not be called.
Instead, np.number.__mul__ calls COO.__array_ufunc__.

Another option would be to have a special treatment for the func is operator.mul case in __array_ufunc__, but the code would be more complicated.

[hameerabbasi]

Hey! I came up with a long-term solution to this, hope it will be of use.

@staticmethod
    def _elemwise(func, *args, **kwargs):
        assert len(args) >= 1
        self = args[0]
        if isinstance(self, scipy.sparse.spmatrix):
            self = COO.from_numpy(self)
        elif np.isscalar(self):
            func = partial(func, self)
            other = args[1]
            if isinstance(other, scipy.sparse.spmatrix):
                other = COO.from_scipy_sparse(other)

            return other._elemwise_unary(func, *args[2:], **kwargs)

        if len(args) == 1:
            return self._elemwise_unary(func, *args[1:], **kwargs)
        else:
            other = args[1]
            if isinstance(other, scipy.sparse.spmatrix):
                other = COO.from_scipy_sparse(other)

            if isinstance(other, COO):
                return self._elemwise_binary(func, other, *args[2:], **kwargs)
            else:
                return self._elemwise_unary(func, *args[1:], **kwargs)

If you check this in, maybe @mrocklin can review it.

Edit: You need to import functools.partial.

Edit 2: Edited to account for unit test failure.

[hameerabbasi]

@fujiisoup I've edited the elemwise to account for the unit test failure.

[fujiisoup]

Cool!!

I added more tests for left-side operations.
Additionally, I added a support also for 0-dimensional np.ndarray.

[hameerabbasi]

Would you be kind enough to rebase this on master? Your other PR caused conflicts with this one.

[hameerabbasi]

Just waiting on either of @nils-werner or @mrocklin to review these changes... Since I was involved in adding code, I shouldn't be the one to review it. :-)

[mrocklin]

Thank you for the fix @fujiisoup . I'm sorry I wasn't around to review (I'm in conference prep mode). Thank you for handling this @hameerabbasi .