On 2/15/07, Keith Goodman <[EMAIL PROTECTED]> wrote:
> On 2/15/07, Keith Goodman <[EMAIL PROTECTED]> wrote:
> > I built a new computer: Core 2 Duo 32-bit Debian etch with numpy
> > 1.0.2.dev3546. The repeatability test still fails. In order to make my
> > calculations repeatable I'll have to remove
On 2/15/07, Keith Goodman <[EMAIL PROTECTED]> wrote:
> I built a new computer: Core 2 Duo 32-bit Debian etch with numpy
> 1.0.2.dev3546. The repeatability test still fails. In order to make my
> calculations repeatable I'll have to remove ATLAS. That really slows
> things down.
Hey, I have no prob
On 1/27/07, Keith Goodman <[EMAIL PROTECTED]> wrote:
> I get slightly different results when I repeat a calculation. In a
> long simulation the differences snowball and swamp the effects I am
> trying to measure.
>
> In the attach script there are three tests.
>
> In test1, I construct matrices x a
I have a userdefined numpy type for mx.DateTime and one for
mx.DateTimeDelta. Info on these packages is available here
(http://www.egenix.com/files/python/eGenix-mx-Extensions.html).
In any case most things work.
What I am left with is three problems:
1. I cannot figure out how to cleanly or e
It seems to me you would need to %ignore vec, so that it is not
wrapped as a raw pointer to a double, and then in your interface file
create a PyArrayObject whose data buffer points to vec (the most
efficient way to do this is with %inline). Then use %rename to
rename whatever you called y
On Thu, 15 Feb 2007, Keir Mierle apparently wrote:
> * Two different header styles
>The distinction between
>:Parameters:
>and
>Examples
>
These are not two items of the same type.
``:Parameters:`` designates a consolidated field (for
describing the documented object)
I don't run numpy no linux often, but you shouldn't have any trouble.
I would do the following:
1. Blast your current numpy install
rm -rf /usr/local/lib/python2.5/site-packages/numpy
2. Get the lastest svn version
cd $HOME
svn co http://svn.scipy.org/svn/numpy/trunk numpy
3. Try doing a fr
I wanted to thank all of you who helped me with my making my sparse
matrix representation cross-platform in binary format!
I ended up writing and reading everything explicitly in little endian.
To recap, each row in the matrix is represented by three records:
1) row#, nn (number of no
On 2/15/07, Robert Kern <[EMAIL PROTECTED]> wrote:
> On 2/15/07, Keir Mierle <[EMAIL PROTECTED]> wrote:
> > On the DocstringStandard page I have also put a completely re-done docstring
> > for the 'contour' function from matplotlib. I think it is far more readable
> > than the original [3]. JDH and
On 2/15/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
> On 2/12/07, LUK ShunTim <[EMAIL PROTECTED]> wrote:
> > David Cournapeau wrote:
> > > Keith Goodman wrote:
> > >> On 2/11/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
> > > > > My impression is that binary distribution of numpy is a big pr
On 2/12/07, LUK ShunTim <[EMAIL PROTECTED]> wrote:
David Cournapeau wrote:
> Keith Goodman wrote:
>> On 2/11/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
>>> My impression is that binary distribution of numpy is a big problem
for many
>>> linux users, and that is entry barrier for many users
On 2/15/07, Geoffrey Zhu <[EMAIL PROTECTED]> wrote:
I am really new to numpy but I just found that v[2:4] means selecting
v[2] and v[3]. V[4] is not included! This is quite different from the
conventions of matlab and R.
Yes, it is the Python slicing convention and rather similar to for loops
Hi,
This is the first time I install Numpy on a linux machine. I have been
working on it for several days without luck. I sincerely appreciate if
anybody can give any comments. My OS is Red Hat 8.
I have downloaded Python 2.5 and Numpy 1.0.1. Python 2.5 has been installed
on my machine. However, w
David Cournapeau wrote:
> Keith Goodman wrote:
>> On 2/11/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
>>> My impression is that binary distribution of numpy is a big problem for many
>>> linux users, and that is entry barrier for many users (I may be wrong,
>>> that's just
>>> an impression fr
Hi,
I'm also not quite clear whether the optimized FFTW and UMFPACK libraries
are being used or required in numpy at all as show_config() doesn't report
it.
I see that fftw and umfpack are being used for scipy.
I have attached my site.cfg. Any help would be much appreciated.
Cheers,
Satra
-
Hi everybody.
I have just released version 0.6-3 of pycdf, which finally supports the
NumPy array package (along Numeric and numarray).
Here is the announcement.
==
Project "pysclint" ('pysclint') has released the new version of package
'pycdf'.
You can download it
I am really new to numpy but I just found that v[2:4] means selecting
v[2] and v[3]. V[4] is not included! This is quite different from the
conventions of matlab and R.
___=0A=
=0A=
The information in this email or in any file attached=0A=
h
On 2/15/07, Keir Mierle <[EMAIL PROTECTED]> wrote:
> On the DocstringStandard page I have also put a completely re-done docstring
> for the 'contour' function from matplotlib. I think it is far more readable
> than the original [3]. JDH and other matplotlibheads, what do you think?
> Travis, do you
Geoffrey Zhu wrote:
> Thanks Chuck.
>
> I am trying to use Successive Over-relaxation to solve linear
> equations defined by M*v=q.
>
AFAIK, splitting methods are not the best methods to solve a system of
linear equations, iteratively.
What can be said about your coefficient matrix M ? Is it spd
Thanks Chuck.
I am trying to use Successive Over-relaxation to solve linear equations
defined by M*v=q.
There are several goals:
1. Eventually (in production) I need it to be fast.
2. I am playing with the guts of the algorithm for now, to see how it
works. that means i need some control for now
On 2/15/07, Geoffrey Zhu <[EMAIL PROTECTED]> wrote:
Hi,
I am new to numpy. I'd like to know if it is possible to code efficient
iterative procedures with numpy.
Specifically, I have the following problem.
M is an N*N matrix. Q is a N*1 vector. V is an N*1 vector I am trying to
find iterativel
Hi,
I am new to numpy. I'd like to know if it is possible to code efficient
iterative procedures with numpy.
Specifically, I have the following problem.
M is an N*N matrix. Q is a N*1 vector. V is an N*1 vector I am trying to
find iteratively from the initial value V_0. The procedure is simply
Hi,
I need to pass a Numpy array to a C code wrapped by Swig.
The array in the C code is a global variable declared as
double *vec
and I would like to set it in the calling Python module foo using e.g.
foo.cvar.vec = numpy.zeros(10)
so that the array is manipulated in place.
I found out the exampl
I'd like to help the docstring formats of numpy, scipy. and matplotlib converge
on a high-quality standard (hence the cross-posting). However, before that can
happen all maintainers from all three packages need to agree on a format. In
the interest of speeding things along, I've taken the liberty o
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