Hi !
Thanks a lot for your fast/detailed reply.
A very good point for Numpy ;-)
I spent all my time trying to prepare my testcase to better share with you,
that's why I didn't reply fast.
I understand the weakness of the missing JITcompiler in Python vs Matlab,
that's why I invistigated numpy
Sorry my previous mail was probalby not clear.
This mail was following the tread we had before, so with some discussion
legacy.
I simplified the code to focus only on what I need, rather to bother you
with the full code.
I wrote below a code closer to what I need, where you will agree that
I simplified the code to focus only on what I need, rather to bother you
with the full code.
def test():
w = 3096
h = 2048
a = numpy.zeros((h,w), order='F') #Normally loaded with real data
b = numpy.zeros((h,w,3), order='F')
w0 = slice(0,w-2)
w1 = slice(1,w-1)
of Numerical Python
Subject: Re: [Numpy-discussion] Numpy performance vs Matlab.
I simplified the code to focus only on what I need, rather to bother you
with the full code.
def test():
w = 3096
h = 2048
a = numpy.zeros((h,w), order='F') #Normally loaded with real data
b
Nicolas ROUX wrote:
-2- Now with the code below I have strange result.
With w=h=400:
With w=400 and h=300:
- Using numpy.ogrid, = broadcast ERROR !
The last broadcast error is:
ValueError: shape mismatch: objects cannot be broadcast to a single shape
This is probably a broadcasting
-2- Now with the code below I have strange result.
With w=h=400:
- Using slice= 0.99 sec
- Using numpy.ogrid = 0.01 sec
It is not equivalent. The ogrid version only uses diagonal elements, and
does less work.
It seems ogrid got better performance, but broadcasting is not
On Fri, Jan 9, 2009 at 11:32, Nicolas ROUX nicolas.r...@st.com wrote:
Thanks !
-1- The code style is good and the performance vs matlab is good.
With 400x400:
Matlab = 1.56 sec (with nested for loop, so no optimization)
Numpy = 0.99 sec (with broadcasting)
-2- Now with the code below
Robert Kern wrote:
Instead, if you put both arguments into ogrid:
In [4]: ogrid[0:5, 0:6]
Out[4]:
[array([[0],
[1],
[2],
[3],
[4]]),
array([[0, 1, 2, 3, 4, 5]])]
We get the kind of arrays you need. These shapes are compatible,
through broadcasting, and
On Fri, Jan 9, 2009 at 15:40, Christopher Barker chris.bar...@noaa.gov wrote:
So what is ogrid useful for?
Just curious...
Floating point grids.
x, y = ogrid[0:1:101j, 0:1:101j]
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless
enigma that is made
However, just using the slices on the matrix instead of passing the
slices through ogrid is faster.
So what is ogrid useful for?
For the same problems where you would use meshgrid in Matlab. That is
certain graphics problem for example; e.g. evaluating a surface z = f(x,y)
over a grid of x,y
On Fri, Jan 9, 2009 at 16:04, Christopher Barker chris.bar...@noaa.gov wrote:
Sturla Molden wrote:
For the same problems where you would use meshgrid in Matlab.
well, I used to use meshgrid a lot because MATLAB could not do
broadcasting. Which is probably why the OP has been trying to use it.
Sturla Molden wrote:
For the same problems where you would use meshgrid in Matlab.
well, I used to use meshgrid a lot because MATLAB could not do
broadcasting. Which is probably why the OP has been trying to use it.
mgrid and ogrid are both meshgrids, with ogrid having a sparse
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 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
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
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