[Numpy-discussion] indexed assignment testcases

[Benjamin Root]

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.

Ben


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

>
>
>
> On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root <ben.root at 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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