There are two types of swig problems that I was hoping to get some help with.
First, suppose I have some C function
void f(double *x, int nx, double *y, int ny);
where we input one array, and we output another array, both of which should be
the same size.
I have used in my .i file:
I thought it was the same as the MATLAB format:
http://www.mathworks.com/access/helpdesk/help/techdoc/index.html?/access/helpdesk/help/techdoc/ref/fft.htmlhttp://www.google.com/search
?client=safarirls=en-usq=MATLAB+fftie=UTF-8oe=UTF-8
On Mar 26, 2009, at 7:56 PM, Lutz Maibaum wrote:
Hello,
I recently discovered that for 8 byte floating point numbers, my
fortran compilers (gfortran 4.2 and ifort 11.0) on an OS X core 2 duo
machine believe the smallest number 2.220507...E-308. I presume that
my C compilers have similar results.
I then discovered that the smallest floating
On Mar 2, 2009, at 4:00 PM, Michael S. Gilbert wrote:
how are you calculating fmin? numpy has a built-in function that
will tell you this information:
numpy.finfo( numpy.float ).min
-1.7976931348623157e+308
hopefully this helps shed some light on your questions.
regards,
mike
When I
So I have some data sets of about 16 floating point numbers stored
in text files. I find that loadtxt is rather slow. Is this to be
expected? Would it be faster if it were loading binary data?
-gideon
___
Numpy-discussion mailing list
I want to do:
numpy.float(numpy.arange(0, 10))
but get the error:
Traceback (most recent call last):
File stdin, line 1, in module
TypeError: only length-1 arrays can be converted to Python scalars
How should I do this?
-gideon
___
it along the desired axis.
Nadav
-הודעה מקורית-
מאת: numpy-discussion-boun...@scipy.org בשם Gideon Simpson
נשלח: ה 29-ינואר-09 06:59
אל: Discussion of Numerical Python
נושא: [Numpy-discussion] convolution axis
Is there an easy way to perform convolutions along a particular axis
Is there an easy way to perform convolutions along a particular axis
of an array?
-gideon
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
Rebuilding the library against ATLAS 3.8.2 with lapack 3.1.1 seems to
have done the trick. I do get one failure:
==
FAIL: test_umath.TestComplexFunctions.test_against_cmath
Does anyone have a guide on how to get numpy to use the ACML as its
blas/lapack backend?
-gideon
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
:
You have setup.cfg:
* set add the directory where acml libraries reside to the library
dir path list
* add acmllibraries under blas and lapack sections
Nadav
-הודעה מקורית-
מאת: numpy-discussion-boun...@scipy.org בשם Gideon Simpson
נשלח: ש 24-ינואר-09 18:21
אל: Discussion
setup.py build, and looking at the output?
-gideon
On Jan 24, 2009, at 4:05 PM, Pauli Virtanen wrote:
Sat, 24 Jan 2009 15:26:17 -0500, Gideon Simpson wrote:
Nadav-
That doesn't quite seem to work for me. I added:
[blas_opt]
library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64
are in
/noc/users/agn/ext/AMD64/acml/ifort64/lib
HTH. George.
2009/1/24 Gideon Simpson simp...@math.toronto.edu:
I've tried building CBLAS, which seems to run properly by itself, but
numpy is still having difficulty. I've got the following in my
site.cfg:
[blas_opt]
library_dirs = /usr/local
Having built an up to date lapack and ATLAS against gcc 4.3.2, I tried
installing numpy 1.2.1 on Python 2.5.4. When testing I get:
Python 2.5.4 (r254:67916, Jan 24 2009, 00:27:20)
[GCC 4.3.2] on linux2
Type help, copyright, credits or license for more information.
import numpy
numpy.test()
.
-gideon
On Jan 24, 2009, at 11:37 PM, David Cournapeau wrote:
Gideon Simpson wrote:
Having built an up to date lapack and ATLAS against gcc 4.3.2, I
tried
installing numpy 1.2.1 on Python 2.5.4. When testing I get:
Python 2.5.4 (r254:67916, Jan 24 2009, 00:27:20)
[GCC 4.3.2] on linux2
Type
Installing on a Sun machine with Red Hat linux, I got the following
error:
==
FAIL: test_umath.TestComplexFunctions.test_against_cmath
--
Traceback (most
This is related to a question I posted earlier.
Suppose I have array A with dimensions n x m x l and array x with
dimensions m x l. Interpret this as an array of l nxm matrices and
and array of l m dimensional vectors. I wish to compute the matrix-
vector product A[:,:,k] x[:,k] for each k
Suppose I have a 3d array, A, with dimensions 2 x 2 x N, and a 2d 2 x
N array, u. I interpret A as N 2x2 matrices and u as N 2d vectors.
Suppose I want to apply the mth matrix to the mth vector, i.e.
A[, , m] u[, m] = v[, m]
Aside from doing
A[0,0,:] u[0,:] + A[0,1,:] u[1,:] = v[0,:]
and
Has anyone gotten the combination of OS X with a fink python
distribution to successfully build numpy/scipy with the intel
compilers and the mkl? If so, how'd you do it?
-gideon
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
Is there (or should there be) a routine for reading and writing numpy
arrays and matrices in MATLAB ASCII m-file format?
-gideon
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
The fink guys fixed a bug so it now at least builds properly with
python 2.6.
-gideon
On Nov 3, 2008, at 1:35 AM, David Cournapeau wrote:
Michael Abshoff wrote:
Unfortunately numpy 1.2.x does not support Python 2.6. IIRC support
is
planned for numpy 1.3.
Also it is true it is not
Not sure if this is an issue with numpy or an issue with fink python
2.6, but when trying to build numpy, I get the following error:
gcc -L/sw/lib -bundle /sw/lib/python2.6/config -lpython2.6 build/
temp.macosx-10.5-i386-2.6/numpy/core/src/multiarraymodule.o -o build/
Suppose I have a toeplitz matrix, A. There is a well known algorithm
for computing the matrix vector product Ax, in NlogN operations. An
exact reference escapes me, but it may be in Golub van Loan's book.
My question is, how could I best take advantage of this algorithm
within
How does python (or numpy/scipy) do exponentiation? If I do x**p,
where p is some positive integer, will it compute x*x*...*x (p times),
or will it use logarithms?
-gideon
___
Numpy-discussion mailing list
Numpy-discussion@scipy.org
24 matches
Mail list logo
