On Thu, Feb 28, 2008 at 1:21 PM, Glen W. Mabey [EMAIL PROTECTED] wrote:
Hello,
I'm using svn numpy and get the following error upon executing
/usr/local/bin/python2.5 setup.py config --noisy
--cc=/opt/intel/cce/10.0.025/bin/icc --compiler=intel --fcompiler=intel
build_clib build_ext
hi
i have a set of images of faces which i make into a 2d array using
numpy.ndarray
each row represents a face image
faces=
[[ 173. 87. ... 88. 165.]
[ 158. 103. .. 73. 143.]
[ 180. 87. .. 55. 143.]
[ 155. 117. .. 93. 155.]]
from which i can get the mean image =
On Sat, Mar 1, 2008 at 8:27 AM, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
This example assumes that facearray is an ndarray.(like you described
in original post ;-) ) It looks like you are using a matrix.
hi Arnar
thanks ..
a few doubts however
1.when i use say 10 images of 4X3
On Sat, Mar 1, 2008 at 2:43 PM, [EMAIL PROTECTED] [EMAIL PROTECTED] wrote:
hi
i have a set of images of faces which i make into a 2d array using
numpy.ndarray
each row represents a face image
faces=
[[ 173. 87. ... 88. 165.]
[ 158. 103. .. 73. 143.]
[ 180. 87. ..
Dear all,
I want to comment some extrange stuff I'm experiencing with numpy.
Please, let me know if this is expected and known.
I'm trying to solve a model nonlinear PDE, 2D Bratu problem (-Lapacian
u - alpha * exp(u), homogeneus bondary conditions), using the simple
finite differences with a
Hi,
la, 2008-03-01 kello 16:43 -0300, Lisandro Dalcin kirjoitti:
I want to comment some extrange stuff I'm experiencing with numpy.
Please, let me know if this is expected and known.
I'm trying to solve a model nonlinear PDE, 2D Bratu problem (-Lapacian
u - alpha * exp(u), homogeneus
On 3/1/08, Pauli Virtanen [EMAIL PROTECTED] wrote:
A silly question: did you check directly that the pure-numpy code and
the F90 code give the same results for the Jacobian-vector product
J(z0) z for some randomly chosen vectors z0, z?
No, I did not do that. However, I've checked the output
Dear Charles,
As I said, I have no time to code the pure Python+numpy nonlinear and
linear loops, and the matrix-free stuff to mimic the PETSc
implementation. However, I post the F90 code and the numpy code, and a
small script for testing with random input. When I have some spare
time, I'll try
2008/3/1 Lisandro Dalcin [EMAIL PROTECTED]:
Dear Charles,
As I said, I have no time to code the pure Python+numpy nonlinear and
linear loops, and the matrix-free stuff to mimic the PETSc
implementation. However, I post the F90 code and the numpy code, and a
small script for testing with
On 3/1/08, Charles R Harris [EMAIL PROTECTED] wrote:
So they differ in the least significant bit. Not surprising, I expect the
Fortran compiler might well perform operations in different order,
accumulate in different places, etc. It might also accumulate in higher
precision registers or round
Sorry for the stupid question, but my English knowledge just covers
reading and writting (the last, not so good)
At the very begining, http://scipy.org/ says
SciPy (pronounced Sigh Pie) ...
Then, for the other guy, this assertion
NumPy (pronounced Num Pie, Num as in Number) ...
whould be
On Sat, Mar 1, 2008 at 6:45 PM, Lisandro Dalcin [EMAIL PROTECTED] wrote:
Sorry for the stupid question, but my English knowledge just covers
reading and writting (the last, not so good)
At the very begining, http://scipy.org/ says
SciPy (pronounced Sigh Pie) ...
Then, for the other
Hi all,
i did some profiling on OS X/Intel 10.5 (numpy 1.0.4) and was
surprised to find calls to the system function feclearexcept to be by
far the biggest cpu hog, taking away about 30% of the cpu in my case.
Would it be possible to change UFUNC_CHECK_STATUS in ufuncobject.h in
a way
Thomas Grill wrote:
Hi all,
i did some profiling on OS X/Intel 10.5 (numpy 1.0.4) and was
surprised to find calls to the system function feclearexcept to be by
far the biggest cpu hog, taking away about 30% of the cpu in my case.
Would it be possible to change UFUNC_CHECK_STATUS in
Am 02.03.2008 um 04:24 schrieb Travis E. Oliphant:
Thomas Grill wrote:
Hi all,
i did some profiling on OS X/Intel 10.5 (numpy 1.0.4) and was
surprised to find calls to the system function feclearexcept to be by
far the biggest cpu hog, taking away about 30% of the cpu in my case.
Would it be
Am 02.03.2008 um 04:24 schrieb Travis E. Oliphant:
if(__builtin_expect(fpstatus,0)) \
Why the use of __builtin_expect here instead of fpstatus == 0?
Oops, nevertheless it should rather be something like
if(__builtin_expect(fpstatus == 0,1))
or
if(__builtin_expect(fpstatus,0) == 0)
sorry
Travis E. Oliphant wrote:
Neal Becker wrote:
Travis E. Oliphant wrote:
The code for this is a bit hard to understand. It does appear that it only
searches for a conversion on the 2nd argument. I don't think that's
desirable behavior.
What I'm wondering is, this works fine for
Robert Kern wrote:
On Sat, Mar 1, 2008 at 6:45 PM, Lisandro Dalcin [EMAIL PROTECTED] wrote:
Sorry for the stupid question, but my English knowledge just covers
reading and writting (the last, not so good)
At the very begining, http://scipy.org/ says
SciPy (pronounced Sigh Pie) ...
Lisandro Dalcin wrote:
On 3/1/08, Charles R Harris [EMAIL PROTECTED] wrote:
So they differ in the least significant bit. Not surprising, I expect the
Fortran compiler might well perform operations in different order,
accumulate in different places, etc. It might also accumulate in higher
I dont know if this made anything any clearer. However, a simple
example may be clearer:
thanks Arnar for the kind response,now things are a lot clearer...will
try out in code ..
D
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