Hi Chris
Actually, I think the request by me was not so properly explained, I'm afraid
(sorry about that!!!)
Let me explain precisely,
Last time I calculated matrix which is from only one file. it took exactly:
237.71387 seconds. Let's say approx. 4 minute!
BUT, IF I use it for more than
On Sun, Dec 30, 2012 at 3:41 AM, Happyman wrote:
> nums=32
> rows=120
> cols=150
>
> for k in range(0,nums):
> for i in range(0,rows):
> for j in range(0,cols):
> if float ( R[ k ] [ i ] [ j ] ) ==
> 0.0:
why the float() -- what
Hi Ondrej & al,
On Sat, Dec 29, 2012 at 1:02 AM, Ondřej Čertík wrote:
> I'm pleased to announce the availability of the first release candidate of
> NumPy 1.7.0rc1.
Congrats on this RC release!
I've uploaded this version to Debian and updated some of the issues
related to it. There are also a c
You might want to have a look at :
http://code.google.com/p/glumpy/source/browse/demos/gray-scott.py
which implements a Gray-Scott reaction-diffusion system.
The 'convolution_matrix(src, dst, kernel, toric)' build a sparse matrix such
that multiplying an array with this matrix will result in
Le dimanche 30 décembre 2012 à 16:47 +0400, Happyman a écrit :
> Actually
> These two functions namely F1 and F2 are really exponential and Bessel
> functions respectively. But I can not change its analytic form..
>
> I mean is there way to get more quickly the result?
> Let's say above mentione
Actually
These two functions namely F1 and F2 are really exponential and Bessel
functions respectively. But I can not change its analytic form..
I mean is there way to get more quickly the result?
Let's say above mentioned two functions, both of them, one function but the
dimension I showed sho
Hello,
> else:
> val11[i][j], val22[i][j] = integrate.quad(lambda x: F1(x)*F2(x), 0, pi)
> But, this calculation takes so long time, let's say about 1 hour
> (theoretically)... Is there any better way to easily and fast calculate
> the process such as [ F( i ) for i in xlist ] or something li
Hello
I have 3 dimensional array which I want to calculate in a huge process.
Everything is working well if I use ordinary way which is unsuitable in Python
like the following:
nums=32
rows=120
cols=150
for k in range(0,nums):
for i in range(0,rows):
for j in
Thanks Zach for your interest
I was thinking about ndimage.generic_filter when I wrote about generic filter.
For generic_filter I used trivial function that returns .sum() but I can't seem
to make the code any faster than it is.
This is the code:
http://code.activestate.com/recipes/578390-snowf
