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
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_false =
On Sun, Jul 6, 2014 at 1:32 PM, Benjamin Root ben.r...@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,))
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
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,
On Sun, Jul 6, 2014 at 9:14 PM, Benjamin Root ben.r...@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
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,
