27.09.2013 19:33, Nathaniel Smith kirjoitti: [clip]

I really don't understand what arcane magic is used to make ndarray += csc_matrix work at all, but my question is, is it going to break when we complete the casting transition described above? It was just supposed to catch things like int += float.

This maybe clarifies it:

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import numpy import scipy.sparse x = numpy.ones((2,2)) y = scipy.sparse.csr_matrix(x) z = x z += y x array([[ 1., 1.], [ 1., 1.]]) z matrix([[ 2., 2.], [ 2., 2.]])

The execution flows like this: ndarray.__iadd__(arr, sparr) np.add(arr, sparr, out=???) return NotImplemented # wtf return NotImplemented Python does arr = sparr.__radd__(arr) Since Scipy master sparse matrices now have __numpy_ufunc__, but it doesn't handle out= arguments, the second step currently raises a TypeError (for Numpy master + Scipy master). And this is actually the correct thing to do, as having np.add return NotImplemented is just broken. Only ndarray.__iadd__ has the authority to return the NotImplemented. To make the in-place ops work again, it seems Numpy needs some additional fixes in its binary op machinery, before __numpy_ufunc__ business works fully as intended. Namely, the binary op routines will need to catch TypeErrors and convert them to NotImplemented. The code paths where Numpy ufuncs currently return NotImplemented could also be changed to raise TypeErrors, but I'm not sure if someone somewhere relies on this behavior (I hope not). -- Pauli Virtanen