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:
%apply(doub
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.html&http://www.google.com/search
?client=safari&rls=en-us&q=MATLAB+fft&ie=UTF-8&oe=UTF-8
On Mar 26, 2009, at 7:56 PM, Lutz Maibaum wrote:
> He
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,
>
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 poin
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
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I want to do:
numpy.float(numpy.arange(0, 10))
but get the error:
Traceback (most recent call last):
File "", line 1, in
TypeError: only length-1 arrays can be converted to Python scalars
How should I do this?
-gideon
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I have an M x N matrix A and two vectors, an M dimensional vector x
and an N dimensional vector y. I would like to be able to do two
things.
1. Multiply, elementwise, every column of A by x
2. Multiply, elementwise, every row of A by y.
What's the "quick" way to do this in numpy?
-gideon
volution kenel to align 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
Is there an easy way to perform convolutions along a particular axis
of an array?
-gideon
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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
--
flag.
-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, Ja
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
>>>
irs = /noc/users/agn/ext/AMD64/acml/ifort64/include
>
> Both libcblas.a (or a link to it) and libacml.so are in
> /noc/users/agn/ext/AMD64/acml/ifort64/lib
>
> HTH. George.
>
> 2009/1/24 Gideon Simpson :
>> I've tried building CBLAS, which seems to run
ing python 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_
wrote:
> 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 Simps
Does anyone have a guide on how to get numpy to use the ACML as its
blas/lapack backend?
-gideon
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Installing on a Sun machine with Red Hat linux, I got the following
error:
==
FAIL: test_umath.TestComplexFunctions.test_against_cmath
--
Traceback (most recent
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
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Is there (or should there be) a routine for reading and writing numpy
arrays and matrices in MATLAB ASCII m-file format?
-gideon
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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 i
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/
lib.macosx-10.5-i386-2.6/
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 numpy/scip
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
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