[Numpy-discussion] Numpy + SWIG
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(double *IN_ARRAY1, int DIM1){(double *x, int nx)}
%apply(double *ARGOUT_ARRAY1, int DIM1){(double *y, int ny)}
and this produces a workable function. However, it expects, as the functions
second argument, the length of the array x. Now, it's easy enough to call:
module.f(x, x.shape[0])
but is there a way to automatically get it to use the length of the array?
The second problem I have is for a function of the fomr
void g(double *x, int nx, double *y, int ny, double *z, int nz);
which evaluates some function g at all (x,y) pairs. The the thing is that nx
and ny need not be the same size, but nz should be nx * ny. I'd like to wrap
this too, and ideally it would also automatically handle the array lengths, but
I'd be happy to have anything right now. I'm also quite comfortable with the
idea of packing z as a column array and reshaping it as necessary.
-gideon
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Re: [Numpy-discussion] Normalization of ifft
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 just started to use python and numpy for some numerical analysis. I have a question about the definition of the inverse Fourier transform. The user gives the formula (p.180) x[m] = Sum_k X[k] exp(j 2pi k m / n) where X[k] are the Fourier coefficients, and n is the length of the arrays. The online documentation (http://docs.scipy.org/doc/numpy/reference/routines.fft.html), on the other hand, states that there is an additional factor of 1/n, which is required to make ifft() the inverse of fft(). Is this a misprint in the user guide? Lutz ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Floating point question
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 point number in python 2.5 is 4.9065...E-324. I have been using numpy to generate data, saving it with savetxt, and then reading it in as ASCII into my fortran code. Recently, it crapped out on something because it didn't like reading it a number that small, though it is apparently perfectly acceptable to python. My two questions are: 1. What is the best way to handle this? Is it just to add a filter of the form u = u * ( np.abs(u) 2.3 e-308) 2. What gives? What's the origin of this (perceived) inconsistency in floating points across languages within the same platform? I recognize that this isn't specific to Scipy/Numpy, but thought someone here might have the answer. -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Floating point question
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 first discovered this, I was computing: numpy.exp(-x**2) If you try x = 26.7, you'll get 2.4877503498797906e-310 I then confirmed this by dividing 1. by 2. until python decided the answer was 0.0 -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] loadtxt slow
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 Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] casting integers to reals
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 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] convolution axis
The first option doesn't accept complex data. -gideon On Jan 29, 2009, at 1:18 AM, Nadav Horesh wrote: There are at least two options: 1. use convolve1d from numpy.numarray.nd_image (or scipy.ndimage) 2. use scipy.signal.convolve and adjust the dimensions of the convolution 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 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 winmail.dat___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] convolution 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
Re: [Numpy-discussion] glibc error
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 -- Traceback (most recent call last): File /usr/local/nonsystem/simpson/lib/python2.5/site-packages/nose/ case.py, line 182, in runTest self.test(*self.arg) File /usr/local/nonsystem/simpson/lib/python2.5/site-packages/ numpy/core/tests/test_umath.py, line 268, in test_against_cmath assert abs(a - b) atol, %s %s: %s; cmath: %s%(fname,p,a,b) AssertionError: arcsinh -2j: (-1.31695789692-1.57079632679j); cmath: (1.31695789692-1.57079632679j) -- -gideon On Jan 25, 2009, at 5:46 AM, Michael Abshoff wrote: David Cournapeau wrote: Hoyt Koepke wrote: SNIP Actually, I would advise using only 3.8.2. Previous versions had bugs for some core routines used by numpy (at least 3.8.0 did). I am a bit surprised that a 64 bits-built atlas would be runnable at all in a 32 bits binary - I would expect the link phase to fail if two different object formats are linked together. Linking 32 and 64 bit ELF objects together in an extension will fail on any system but OSX where the ld will happily link together anything. Since that linker also does missing symbol lookup at runtime you will see some surprising distutils bugs when you thought that the build went perfectly, i.e. scipy 0.6 would not use the fortran compiler I would tell it to use, but one extension would use gfortran instead of sage_fortran when it was available in $PATH. sage_fortran would would just inject an -m64 into the options and call gfortran. But with a few fortran objects being 32 bit some extensions in scipy would fail to import and it took me quite a while to track this one down. I haven't had time to test 0.7rc2 yet, but hopefully will do so in the next day or two. cheers, David Cheers, Michael ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy and the ACML
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
Re: [Numpy-discussion] numpy and the ACML
Nadav- That doesn't quite seem to work for me. I added: [blas_opt] library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/include libraries = acml [lapack_opt] library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/include libraries = acml to my site.cfg with no luck. Somewhere else, people indicated that the ACML lacked a CBLAS which was necessary to make this work. -gideon On Jan 24, 2009, at 3:08 PM, Nadav Horesh 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 Simpson נשלח: ש 24-ינואר-09 18:21 אל: Discussion of Numerical Python נושא: [Numpy-discussion] numpy and the ACML 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 winmail.dat___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy and the ACML
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/nonsystem/simpson/CBLAS/lib/LINUX:/usr/local/ nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/CBLAS/include:/usr/local/ nonsystem/simpson/acml4.2.0/gfortran64/include libraries = cblas, acml [lapack_opt] library_dirs = /usr/local/nonsystem/simpson/CBLAS/lib/LINUX:/usr/local/ nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/CBLAS/include:/usr/local/ nonsystem/simpson/acml4.2.0/gfortran64/include libraries = cblas, acml I also created a symbolic link in /usr/local/nonsystem/simpson/CBLAS/ lib/LINUX, from cblas_LINUX.a to libcblas.a. Is there an easier way to check if numpy is locating the libs other than doing 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_opt] library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/ include libraries = acml [lapack_opt] library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/lib include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/ include libraries = acml to my site.cfg with no luck. Somewhere else, people indicated that the ACML lacked a CBLAS which was necessary to make this work. Yep, IIRC you needed also CBLAS. (I think I got Numpy Scipy linked against ACML at some point, but it's been a while and I've forgotten details...) There's a CBLAS here: http://www.netlib.org/blas/blast-forum/cblas.tgz So, I think you need to compile it link it with ACML, and add it in site.cfg with ACML libs. -- Pauli Virtanen ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy and the ACML
That's not working for me. Any thoughts on how to troubleshoot it?
-gideon
On Jan 24, 2009, at 6:18 PM, George Nurser wrote:
I did manage to get it working.
I remember that both libcblas.a (or a link to it) and libacml.so had
to be in the same directory.
Also I had to comment out lines 399-400 of setup.py:
# if ('NO_ATLAS_INFO',1) in
blas_info.get('define_macros',[]):
# return None # dotblas needs ATLAS, Fortran compiled
blas will not be sufficient
In my site.cfg I have
[blas]
blas_libs = cblas, acml
library_dirs = /noc/users/agn/ext/AMD64/acml/ifort64/lib
include_dirs = /noc/users/agn/ext/AMD64/acml/ifort64/include
[lapack]
language = f77
lapack_libs = acml
library_dirs = /noc/users/agn/ext/AMD64/acml/ifort64/lib
include_dirs = /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 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/nonsystem/simpson/CBLAS/lib/LINUX:/usr/
local/
nonsystem/simpson/acml4.2.0/gfortran64/lib
include_dirs = /usr/local/nonsystem/simpson/CBLAS/include:/usr/local/
nonsystem/simpson/acml4.2.0/gfortran64/include
libraries = cblas, acml
[lapack_opt]
library_dirs = /usr/local/nonsystem/simpson/CBLAS/lib/LINUX:/usr/
local/
nonsystem/simpson/acml4.2.0/gfortran64/lib
include_dirs = /usr/local/nonsystem/simpson/CBLAS/include:/usr/local/
nonsystem/simpson/acml4.2.0/gfortran64/include
libraries = cblas, acml
I also created a symbolic link in /usr/local/nonsystem/simpson/CBLAS/
lib/LINUX, from cblas_LINUX.a to libcblas.a.
Is there an easier way to check if numpy is locating the libs other
than doing 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_opt]
library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/
lib
include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/
include
libraries = acml
[lapack_opt]
library_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/
lib
include_dirs = /usr/local/nonsystem/simpson/acml4.2.0/gfortran64/
include
libraries = acml
to my site.cfg with no luck. Somewhere else, people indicated that
the
ACML lacked a CBLAS which was necessary to make this work.
Yep, IIRC you needed also CBLAS. (I think I got Numpy Scipy linked
against ACML at some point, but it's been a while and I've forgotten
details...) There's a CBLAS here:
http://www.netlib.org/blas/blast-forum/cblas.tgz
So, I think you need to compile it link it with ACML, and add it
in
site.cfg with ACML libs.
--
Pauli Virtanen
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[Numpy-discussion] glibc error
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() Running unit tests for numpy NumPy version 1.2.1 NumPy is installed in /usr/local/nonsystem/simpson/lib/python2.5/site- packages/numpy Python version 2.5.4 (r254:67916, Jan 24 2009, 00:27:20) [GCC 4.3.2] nose version 0.10.4 ..F K *** glibc detected *** python: free(): invalid next size (fast): 0x1196b550 *** I then have to kill python to get control again. -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] glibc error
Rerunning the tests with verbosity, it dies at: test_single (test_linalg.TestSolve) ... ok Ticket #652 ... *** glibc detected *** python: free(): invalid next size (fast): 0x01e284e0 *** I'm using ATLAS 3.8.2 and lapack 3.2. ATLAS and lapack were all built with the -m64 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, 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() Running unit tests for numpy NumPy version 1.2.1 NumPy is installed in /usr/local/nonsystem/simpson/lib/python2.5/ site- packages/numpy Python version 2.5.4 (r254:67916, Jan 24 2009, 00:27:20) [GCC 4.3.2] nose version 0.10.4 ..F K *** glibc detected *** python: free(): invalid next size (fast): 0x1196b550 *** Can you rerun the test verbosely ? python -c import numpy; numpy.test(verbose=10) This glibc message is generally a symptom of serious memory corruption - which is why only the OS can stop it at that point. cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] failure
Installing on a Sun machine with Red Hat linux, I got the following error: == FAIL: test_umath.TestComplexFunctions.test_against_cmath -- Traceback (most recent call last): File /usr/local/nonsystem/simpson/lib/python2.5/site-packages/nose/ case.py, line 182, in runTest self.test(*self.arg) File /usr/local/nonsystem/simpson/lib/python2.5/site-packages/ numpy/core/tests/test_umath.py, line 268, in test_against_cmath assert abs(a - b) atol, %s %s: %s; cmath: %s%(fname,p,a,b) AssertionError: arcsinh -2j: (-1.31695789692-1.57079632679j); cmath: (1.31695789692-1.57079632679j) -- Ran 1740 tests in 9.839s FAILED (KNOWNFAIL=1, failures=1) nose.result.TextTestResult run=1740 errors=0 failures=1 How would you recommend I troubleshoot this? How seriously should I take it? This is with a fresh Python 2.5.4 installation too. -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] efficient usage of tensordot
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 = 0,... l -1. I discovered
that I could accomplish this with the command
np.diagonal(np.tensordot(A, k, axes=(1,0)), axis1= 1, axis2 = 2)
The tensordot command gives me
A_{ijk}x_{jl} = C_{ikl}
And the diagonal command grabs the entries in array C where k=l.
Is this the optimal way to make this calculation in numpy? It
certainly makes for nice, clean code, but is it the fastest I can get?
-gideon
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[Numpy-discussion] tensor contractions
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 A[1,0,:] u[0,:] + A[1,1,:] u[1,:] = v[1,:] is there a smart way to perform this computation? -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] os x, intel compilers mkl, and fink python
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 http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] MATLAB ASCII format
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
Re: [Numpy-discussion] fink python26 and numpy 1.2.1
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 supported, it should at least build on most if not all platforms where numpy used to run under python 2.5. Not finding -lpython2.6 is more likely a bug/installation problem from fink (unless you are ready to deal with multiple version problems of python, I would advice against using fink: it makes it difficult to be sure there are no conflict between system python, fink python and python.org python. It is not impossible, of course, but that complicates matters a lot in my own experience). cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] fink python26 and numpy 1.2.1
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/numpy/core/multiarray.so ld: library not found for -lpython2.6 collect2: ld returned 1 exit status ld: library not found for -lpython2.6 collect2: ld returned 1 exit status error: Command 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/numpy/core/ multiarray.so failed with exit status 1 -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] fast matrix vector operations
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/scipy? I could code it python. However, since python is a high level language, it's not clear to me that I'd see an execution time benefit over numpy.dot(A,x). Alternatively, I could write it in a compiled language and build python bindings to it. Thoughts? -gideon ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] exponentiation q.
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 http://projects.scipy.org/mailman/listinfo/numpy-discussion
