Bill Blinn skrev: > v = multiview((3, 4)) > #the idea of the following lines is that the 0th row of v is > #a view on the first row of a. the same would hold true for > #the 1st and 2nd row of v and the 0th rows of b and c, respectively > v[0] = a[0] This would not even work, becuase a[0] does not return a view array but a scalar arrays, which is a different type of numpy objects. To get a view, you will need to:
v = a[0:1] # view of element 0 in a Also you cannot assign to v[0], as that would trigger a copy as well. > v[1] = b[0] > v[2] = c[0] As I mentioned in the answer to Anne, it would take a completely different array object. It would need to internally store an array with memory addresses. I have not made up my mind if ndarray can be subclassed for this, or if it takes a completely different object (e.g. similar to numpy.memmap). What it would require is __setitem__ to store pointers and __getitem__ to dereference (return an ndarray with values). Good look hacking, it is not even difficult, just tedious. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
