On Thu, Feb 5, 2015 at 3:39 PM, Nathaniel Smith wrote:
> On 5 Feb 2015 12:15, wrote:
> >
> > The assert_allclose text is not precise enough to be helpful to fix a
> test failure that cannot be replicated on every machine, and we cannot just
> quickly grab --pdb-failures.
> >
> > By how much do I
On 04.02.2015 11:45, Daπid wrote:
> There are several definitions. Abramowitz and Stegun
> (http://people.math.sfu.ca/~cbm/aands/page_1020.htm) assign the value
> 0.5 at x=0.
The NIST handbook uses the value 0 at x=0.
Perhaps a Heaviside with an optional argument that defines the value at
x=0 wo
On Thu, Feb 5, 2015 at 11:10 AM, Benjamin Root wrote:
> +1! I could never keep straight which stack function I needed anyway.
>
> Wasn't there a proposal a while back for a more generic stacker, like
> "tetrix" or something that allowed one to piece together tiles of different
> sizes?
>
> Ben Ro
On 5 Feb 2015 12:15, wrote:
>
> The assert_allclose text is not precise enough to be helpful to fix a
test failure that cannot be replicated on every machine, and we cannot just
quickly grab --pdb-failures.
>
> By how much do I have to lower the precision to make it pass on this
continuous integra
The assert_allclose text is not precise enough to be helpful to fix a test
failure that cannot be replicated on every machine, and we cannot just
quickly grab --pdb-failures.
By how much do I have to lower the precision to make it pass on this
continuous integration machine?
assert_allclose(he,
+1! I could never keep straight which stack function I needed anyway.
Wasn't there a proposal a while back for a more generic stacker, like
"tetrix" or something that allowed one to piece together tiles of different
sizes?
Ben Root
On Thu, Feb 5, 2015 at 2:06 PM, Stephan Hoyer wrote:
> There a
There are two usual ways to combine a sequence of arrays into a new array:
1. concatenated along an existing axis
2. stacked along a new axis
For 1, we have np.concatenate. For 2, we have np.vstack, np.hstack,
np.dstack and np.column_stack. For arrays with arbitrary dimensions, there
is the np.arr
