On So, 2014-07-06 at 16:14 -0400, Benjamin Root wrote:

re: deprecation warnings... that's what I get when I am working on my non-dev box because I am at the conference, and have gotten too used to the setup of my dev box...

as for the broadcasting issue, I can see it for the second case, but the first case still doesn't sit right with me. My understanding of broadcasting is to effectively *expand* an array to match the shape of another array (or some target shape). In this case, the array is being effectively *contracted* in shape. That makes zero sense to me.

Well, from a technical point of view, it is more like changing the shape to whatever fits while setting the stride to 0. I am sure there are a few places where the doc is not clear. From a practical point of view, it makes sense if you consider this: arr[arr < 0] = 0 Where it might be that the array has no elements smaller 0. Though I admit I would write 0 here, and not [0]. - Sebastian

Ben

On Sun, Jul 6, 2014 at 3:59 PM, Charles R Harris <charlesr.harris@gmail.com> wrote:

On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root <ben.root@ou.edu> wrote: While trying to wrap my head around the issues with matplotlib's tri module and the new numpy indexing, I have made some test cases where I wonder if warnings should be issued.

import numpy as np

a = np.ones((10,))

all_false = np.zeros((10,), dtype=bool)

a[all_false] = np.array([2.0]) # the shapes don't match here

It broadcasts because the leading dimension is 1.

mask_in = np.array([False]*8 + [True, True])

a[mask_in] = np.array([]) # raises ValueError as expected

a[mask_in] = np.array([[]]) # no exception because it is 2-D, for some reason (on master, but not release-0.9b1)

Now falls back to old behavior and raises a DeprecationWarning. You don't see that by default.

a[mask_in] = np.array([2.0]) # This works and repeats 2.0 twice. I thought this wasn't supposed to happen anymore?

Broadcasting again.

Chuck

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I guess I always treated scalars as something special when it comes to broadcasting. Seeing these examples, I can see how my grokking of broadcasting was incomplete. I still think that the assignment of an array of values (as opposed to a scalar) to nothing could potentially mask deeper issues, but now I see that it may be impossible to distinguish from the perfectly normal case. Cheers! Ben Root On Sun, Jul 6, 2014 at 5:48 PM, Nathaniel Smith <njs@pobox.com> wrote:

On Sun, Jul 6, 2014 at 9:14 PM, Benjamin Root <ben.root@ou.edu> wrote:

...

as for the broadcasting issue, I can see it for the second case, but the first case still doesn't sit right with me. My understanding of broadcasting is to effectively *expand* an array to match the shape of another array (or some target shape). In this case, the array is being effectively *contracted* in shape. That makes zero sense to me.

That's how it's always worked though, in all cases of broadcasting; nothing special about indexing:

In [8]: a = np.zeros((3, 0))

In [9]: a + 1 Out[9]: array([], shape=(3, 0), dtype=float64)

In [10]: a + [[1], [2], [3]] Out[10]: array([], shape=(3, 0), dtype=float64)

IME it's extremely useful in practice for avoiding special cases when some axis has a vary size that can be zero.

-- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion