Hi all,
First, let me say that I'm impressed: this mailing list is probably the
most reactive I've ever seen. I've asked my first question and got
immediately more solutions than time to test them... Many thanks to all
the answerers.
Using the various proposals, I ran two performance tests:
-
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than 0.9sec in Matlab, but 21sec in Python.
Numpy is 24 times slower than Matlab !
The big trouble I have is a large team of people within my company is ready to
replace
Nicolas ROUX wrote:
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than 0.9sec in Matlab, but 21sec in Python.
Numpy is 24 times slower than Matlab !
The big trouble I have is a large team of people within my company
Nicolas ROUX wrote:
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than 0.9sec in Matlab, but 21sec in Python.
Numpy is 24 times slower than Matlab !
The big trouble I have is a large team of people within my
for i in range(dim):
for j in range(dim):
a[i,j,0] = a[i,j,1]
a[i,j,2] = a[i,j,0]
a[i,j,1] = a[i,j,2]
for i = 1:dim
for j = 1:dim
a(i,j,1) = a(i,j,2);
a(i,j,2) = a(i,j,1);
a(i,j,3) = a(i,j,3);
end
end
Hi,
The two loops are not the
On Wed, Jan 7, 2009 at 23:44, Ryan May rma...@gmail.com wrote:
Nicolas ROUX wrote:
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than 0.9sec in Matlab, but 21sec in Python.
Numpy is 24 times slower than Matlab
On Wed, Jan 7, 2009 at 6:37 AM, Franck Pommereau
pommer...@univ-paris12.fr wrote:
def f4 (x, y) :
Jean-Baptiste Rudant boogalo...@yahoo.fr
test 1 CPU times: 111.21s
test 2 CPU times: 13.48s
As Jean-Baptiste noticed, this solution is not very efficient (but
works almost
This probably will have no impact on your tests, but this looks like a
bug. You probably mean:
recXY = numpy.rec.fromarrays((x, y), names='x, y')
Sure! Thanks.
Could you post the code you use to generate you inputs (ie what is x?)
My code is probably not usable by somebody else than
On Wed, Jan 7, 2009 at 10:58 AM, Grissiom chaos.pro...@gmail.com wrote:
On Wed, Jan 7, 2009 at 23:44, Ryan May rma...@gmail.com wrote:
Nicolas ROUX wrote:
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than
Here is my example, trying to wrap the function sms_spectrumMag that
we have been dealing with:
%apply (int DIM1, float* IN_ARRAY1) {(int sizeInArray, float* pInArray)};
%apply (int DIM1, float* INPLACE_ARRAY1) {(int sizeOutArray, float* pOutArray)};
%inline %{
void my_spectrumMag( int
A test case closer to my applications is calling functions in loops:
Python
---
def assgn(a,i,j):
a[i,j,0] = a[i,j,1] + 1.0
a[i,j,2] = a[i,j,0]
a[i,j,1] = a[i,j,2]
return a
print Start test \n
dim = 300#0
a = numpy.zeros((dim,dim,3))
start =
Nicolas ROUX wrote:
The big trouble I have is a large team of people within my company is ready
to replace Matlab by Numpy/Scipy/Matplotlib,
we like that!
This is a testcase that people would like to see working without any code
restructuring.
The reasons are:
- this way of writing is
josef.p...@gmail.com wrote:
So for simple loops python looses, but for other things, python wins
by a huge margin.
which emphasizes the point that you can't write code the same way in the
two languages, though I'd argue that that code needs refactoring in any
language!
However, numpy's
Well it is the best pitch for numpy versus matlab I have read so far :)
(and I 100% agree)
Xavier
On 1/7/2009 4:16 PM, David Cournapeau wrote:
I think on recent versions of matlab, there is nothing you can do
without modifying the code: matlab has some JIT compilation for loops,
which is
On 1/7/2009 6:56 PM, Christopher Barker wrote:
So for simple loops python looses, but for other things, python wins
by a huge margin.
which emphasizes the point that you can't write code the same way in the
two languages, though I'd argue that that code needs refactoring in any
language!
On 1/7/2009 6:51 PM, Christopher Barker wrote:
Even with this nifty JIT,
It is not a very nifty JIT. It can transform some simple loops into
vectorized expressions. And it removes the overhead from indexing with
doubles.
But if you are among those that do
n = length(x)
m = 0
for i = 1.0 :
On Wed, Jan 7, 2009 at 1:32 PM, Sturla Molden stu...@molden.no wrote:
On 1/7/2009 6:56 PM, Christopher Barker wrote:
So for simple loops python looses, but for other things, python wins
by a huge margin.
which emphasizes the point that you can't write code the same way in the
two languages,
On 1/7/2009 7:52 PM, josef.p...@gmail.com wrote:
But, I think,
matlab is ahead in parallelization (which I haven't used much)
Not really. There is e.g. nothing like Python's multiprocessing package
in Matlab. Matlab is genrally single-threaded. Python is multi-threaded
but there is a GIL.
On Wed, Jan 7, 2009 at 10:19, Nicolas ROUX nicolas.r...@st.com wrote:
Hi,
I need help ;-)
I have here a testcase which works much faster in Matlab than Numpy.
The following code takes less than 0.9sec in Matlab, but 21sec in Python.
Numpy is 24 times slower than Matlab !
The big trouble I
Hi Bevan
Since the number of output elements are unknown, I don't think you can
implement this efficiently using arrays. If your dataset isn't too
large, a for-loop should do the trick. Otherwise, you may have to run
your code through Cython, which optimises for-loops around Python
lists.
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