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, bu
On Sun, Jul 6, 2014 at 9:14 PM, Benjamin Root 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
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 ca
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 understan
On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root 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.
On So, 2014-07-06 at 15:32 -0400, Benjamin Root 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_f
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 shap
