On Sun, May 10, 2015 at 4:40 AM, Stefan Otte stefan.o...@gmail.com wrote:
Hey,
quite often I want to evaluate a function on a grid in a n-D space.
What I end up doing (and what I really dislike) looks something like this:
x = np.linspace(0, 5, 20)
M1, M2 = np.meshgrid(x, x)
X =
On Sun, May 10, 2015 at 4:40 AM, Stefan Otte stefan.o...@gmail.com wrote:
Hey,
quite often I want to evaluate a function on a grid in a n-D space.
What I end up doing (and what I really dislike) looks something like this:
x = np.linspace(0, 5, 20)
M1, M2 = np.meshgrid(x, x)
X =
On 2015-05-10 14:46:12, Jaime Fernández del Río
jaime.f...@gmail.com wrote:
Isn't what you are trying to build a cartesian product function?
There is a neat, efficient implementation of such a function in
StackOverflow, by our own pv.:
Hey,
Just a quick update. I updated the pull request and renamed `stack` into
`block`. Have a look: https://github.com/numpy/numpy/pull/5057
I'm sticking with simple initial implementation because it's simple and
does what you think it does.
Cheers,
Stefan
On Fri, Oct 31, 2014 at 2:13 PM
I just drafted different versions of the `gridspace` function:
https://tmp23.tmpnb.org/user/1waoqQ8PJBJ7/notebooks/2015-05%20gridspace.ipynb
Beste Grüße,
Stefan
On Sun, May 10, 2015 at 1:40 PM, Stefan Otte stefan.o...@gmail.com wrote:
Hey,
quite often I want to evaluate a function on a
On Sun, May 10, 2015 at 7:05 AM, Stefan Otte stefan.o...@gmail.com wrote:
I just drafted different versions of the `gridspace` function:
https://tmp23.tmpnb.org/user/1waoqQ8PJBJ7/notebooks/2015-05%20gridspace.ipynb
The link seems to be broken...
Jaime
--
(\__/)
( O.o)
( ) Este es Conejo.
For the archive, I tried to use bitarray instead of bitstring and for
same file parsing went from 180ms to 60ms. Code was finally shorter and
more simple but less easy to jump into (documentation).
Performance is still far from using fromstring or fromfile which gives
like 5ms for similar size
