[Numpy-discussion] Numpy 1.6 schedule (was: Numpy 2.0 schedule)
Sat Mar 5 20:23:16 CST 2011
This should be changed back so that the former works and the later does not. It was intentional that the former worked --- it was consistent with broadcasting rules.
A (1,20) array can be interpreted as a (20,) array.
(mobile phone of)
On Mar 5, 2011, at 6:53 PM, Charles R Harris <firstname.lastname@example.org> wrote:
> On Sat, Mar 5, 2011 at 4:20 PM, Mark Wiebe <email@example.com> wrote:
> On Sat, Mar 5, 2011 at 2:43 PM, Charles R Harris <firstname.lastname@example.org> wrote:
> On Sat, Mar 5, 2011 at 3:28 PM, Benjamin Root <email@example.com> wrote:
> On Sat, Mar 5, 2011 at 7:44 AM, Pauli Virtanen <firstname.lastname@example.org> wrote:
> On Fri, 04 Mar 2011 22:58:14 -0600, Benjamin Root wrote:
> > I recently had to fix an example in matplotlib where there was a 1xN
> > array being assigned to a 1-D slice of a numpy array. It used to work,
> > but it now doesn't. I don't know if this was intended or not, though.
> Probably not -- please file a bug report. If you can also point to a
> Numpy version in which it worked, that would also be nice.
> I decided to give git bisect a try. In testing this, I tried two things:
> a = np.empty((20,))
> a[:] = np.random.random((1, 20))
> a[:] = np.random.random((20, 1))
> These both currently fail with the same exception message. If I check out and build v1.5.0, the former works, but the latter does not. Going back to v1.4.0, and the latter still doesn't work. Maybe this really shouldn't be considered a bug, and rather a more consistent behavior?
> By the way, git bisect says that the winner is:
> d90f19abf18d59be959e04d73b3dbd7ae04b1e89 is the first bad commit
> commit d90f19abf18d59be959e04d73b3dbd7ae04b1e89
> Author: Mark Wiebe <email@example.com>
> Date: Mon Jan 17 18:26:12 2011 -0800
> ENH: core: Change PyArray_MoveInto to use the new iterator as well
> :040000 040000 a23fbcff385fca9704a5313e81217a6d80e3512c 09b684bd8893e44405534fedad165ce85e751019 M numpy
> If we agree that this is still a bug and not a feature, I will file a report.
> I think it is more of a feature. The assignment should probably only work if the rhs can be broadcast to the lhs. Whatever is decided, we need to make a test to enforce it.
> +1 for feature. I like stricter checking in most cases.
> Although I think this accounts for some of the failures in tables.
> NumPy-Discussion mailing list
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