Well, we only need to do an FFT. So I will try all your suggestions
tonight and let you know of the results. Thanks for your help!
-Dave
Travis Oliphant wrote:
> David Shepherd wrote:
>> My message may have gotten lost over the weekend, but I wanted to see if
>> anyone had
example,
asking for an array of 2 lines and 7 columns of float32, so that the adress
of the biginning of the data would be a multiple of 16 bytes (that's already
possible) AND beeing sure that the allocated memory for the data would be
at least 16 * sizeof(float32), instead of 14*sizeof(float
The default section of the code compiled just fine and the FFT is
working great. Thanks so much for your help!
-Dave
Travis Oliphant wrote:
> David Shepherd wrote:
>> My message may have gotten lost over the weekend, but I wanted to see if
>> anyone had anything else I can
2007/4/10, Travis Oliphant <[EMAIL PROTECTED]>:
> David Doukhan wrote:
> > Hi!
> > I'm writing you this mail because I would like to do "advanced" use of
> > ndarray memory allocation.
> >
> > So here is a short description of my problem:
>
2007/4/10, Travis Oliphant <[EMAIL PROTECTED]>:
> David Doukhan wrote:
>
> >Sorry, i thought doing the following lines:
> >
> >clib = ctypes.cdll.LoadLibrary("./libtest.so")
> >dt = numpy.dtype({'names' : ['x'], 'forma
the correct locations. I have updated the instructions for
Ubuntu (also works for debian) on the wiki a few days ago:
http://www.scipy.org/Installing_SciPy/Linux#head-c5a062b2ecf76f746d78cfcde1dae00ae26109fe
Note that if you have also optimized version installed (sse, sse2
Charles R Harris wrote:
>
>
> On 4/15/07, *David Cournapeau* <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
> Christian K wrote:
> >
> > Thanks, but that didn't help:
> >
> > atlas_info:
> >
Andrew Straw wrote:
> Christian K wrote:
>
>> David Cournapeau wrote:
>>
>>
>>> On Ubuntu and debian, you do NOT need any site.cfg to compile numpy with
>>> atlas support. Just install the package atlas3-base-dev, and you are
>>> d
TECTED]/msg02243.html
David
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y, to build a .so), I can try to dig in my
matlab scripts (unused for some time thanks to scipy :) )
cheers,
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hared lib)
- -C if g77 says to use g77 and not gfortran as the fortran
compiler. On debian, g77 is still the default, and gfortran has a
different ABI, meaning all kind of problems if you use gfortran for now
on debian if you do not know what you are doing.
I strongly advise you to use the
picked up atlas symbols from its existing, already linked own
atlas, and not from the atlas installed on my system (which was crashing
matlab and was the reason why I went through this pain in the first place).
cheers,
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y. The catch is that the layout of the array should be exactly the
same than your function expects (most of the time a contiguous array),
but there are numpy functions which enforce that. As long as you are not
calling thousand of function with small arrays, the wrapp
calling thousand of function with small arrays, the wrapping is pretty
efficient in my experience.
cheers,
David
Thanks for the fast answer ;)
I was wondering if ctypes would fit better, but in this case, I have
to make the wrapper myself, I suppose ?
Here is the basic prototype
representation of a numpy array to a
simple C array and vice et versa), if I remember correctly.
There is also boost.python, but I don't know if its situation towards
numpy array has changed (eg there was some code lying around to
represent numpy arrays in C++).
If I were you, and if there are only
0, 0, ..., 0]
smin, smax = x.min(), x.max()
How do you select the bin edges ?
One solution is to use the same scheme used by histogram:
if smin == smax:
edges[i] = linspace(smin-.5, smax+.5, nbin[i]+1)
Would that be ok ?
David
I'll submit a patch.
2007/4/19, Emanuele Olivetti <[EMA
amespace through a fake
second argument to the destructor.
http://docs.python.org/ref/customization.html (comments on __del__ ).
David
# Last Change: Fri Apr 20 11:00 AM 2007 J
from numpy.ctypeslib import ndpointer, load_library
# Load the library
_hellocpp = load_library('hellocpp
no question about that fact. But python makes easy things just a bit
more complicated, and all other things possible. The perfect example is
namespace: not having namespace IS easier, at first. But this quickly
makes things unmanageable. Functions are easier in matlab, but when you
wa
python + numpy for
memory consumption; I fail to see any reason why it would be
significantly different (except the fact that numpy does not have to use
double, whereas matlab had to for a long time, and double is still the
default on matlab).
David
_
Guido van Rossum wrote:
> Jeffrey, is there any way you can drop the top of the tree and going
> straight from Number to Complex -> Real -> Rational -> Integer? These
> are the things that everyone with high school math will know.
>
Having taught mathematics at a community college for a little w
Bill Janssen wrote:
>> Jeffrey, is there any way you can drop the top of the tree and going
>> straight from Number to Complex -> Real -> Rational -> Integer? These
>> are the things that everyone with high school math will know.
>>
>
> I think knowledge of the concepts of group, ring, and fie
Guido van Rossum wrote:
> On 4/27/07, Jan Grant <[EMAIL PROTECTED]> wrote:
>
>> On Thu, 26 Apr 2007, Dan Christensen wrote:
>>
>>
>>> Note also that double-precision reals are a subset of the rationals,
>>> since each double precision real is exactly representable as a
>>> rational number,
Anton Sherwood wrote:
> Jan Grant wrote:
>
>> . . . the space of doubles include a small number of things
>> that aren't representable by a rational (+/- inf, for instance).
>>
>
> +1/0, -1/0
>
>
Are not unique representations for inf/-inf, (if that matters).
DG
___
Anton Sherwood wrote:
>>> Jan Grant wrote:
>>>
>>>> . . . the space of doubles include a small number of things
>>>> that aren't representable by a rational (+/- inf, for instance).
>>>>
>
>
>> Anton Sher
, I don't see what it is doing
? Does it say where to find library when linking ? When running ? Does
it set the patch in the binary ?
David
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I'll take a stab at this one; if I miss the mark, people, please chime in.
What's "strange" here is not numpy's behavior but octave's (IMO).
Remember that, over R, arctan is used in two different ways: one is
simply as a map from (-inf, inf) -> (-pi/2,pi/2) - here, let's call that
invtan; the
;
> "If x or y is complex, then ArcTan[x , y] gives . When , ArcTan[x, y]
> gives the number such that and ."
>
> Lorenzo.
>
> On 4/29/07, *David Goldsmith* < [EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
> I'll take a stab at
terface is real, while the normal
> one is purely imaginary: hence the angle of diffraction is still
> given by arctan2(k_tangent, k_normal), that, as in Matlab or
> Octave, should give pi/2 (that physically means no propagation
> -- total internal reflection
> (hint what is arctan(0+1j)?)
>
Well, at the risk of embarrassing myself, using arctan(x+iy) = I get:
arctan(0+1i) = -i*log((0+i*1)/sqrt(0^2 + 1^2)) = -i*log(i/1) = -i*log(i)
= -i*log(exp(i*pi/2)) = -i*i*pi/2 = pi/2...
Is there some reason I'm forgetting (e.g., a branch cut convention or
so
lorenzo bolla wrote:
> hold on, david. the formula I posted previously from wolfram is
> ArcTan[x,y] with x or y complex: its the same of arctan2(x,y). arctan
> is another function (even though arctan2(y,x) should be "a better"
> arctan(y/x)).
>
> the correct formula
Timothy Hochberg wrote:
>
>
> On 4/30/07, *David Goldsmith* <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
>
> > (hint what is arctan(0+1j)?)
> >
> Well, at the risk of embarrassing myself, using arctan(x+iy) = I get:
>
&g
pretty much anywhere, and is designed to be
cross platform.
David
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Hi,
I wanted to know if the following behaviour is a bug or intended
behaviour:
"""
import numpy as N
N.allclose(N.array([[1., 1.]]), N.array([1.]))
"""
eg should allclose return True if arrays have different s
Robert Kern wrote:
> David Cournapeau wrote:
>> Hi,
>>
>> I wanted to know if the following behaviour is a bug or intended
>> behaviour:
>>
>> """
>> import numpy as N
>> N.allclose(N.array([[1., 1.]]), N.array([1.]))
&g
or
it, the TimeSeries package is *extremely* useful to me.
David
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Pierre,
2007/5/17, Pierre GM <[EMAIL PROTECTED]>:
But I agree: switching may have some subtle consequences in
matplotlib (nothing that can't be quickly fiexed, however).
All examples in backend_driver.py test seem to run fine (+ others I added
that contained masked arra
219585
>
My guess would be that for such small matrices, the cost of matrix
wrapping is not negligeable against the actual computation. This
difference disappears for bigger matrices (for example, try 1000 and
5000 instead of 100 for the first dimension).
David
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I
> don't see any more.
Could you open a ticket on scipy trac with an example which shows the
bug, and explain what you want ? I may take a look at it (I have some
code to do cross correlation in numpy somewhere, and could take time to
improve its quality for inclusion in scipy).
Da
is it enough or not
If someone does use fft but does not know it is better to use 2^n, will
he take a look at the docstrings :) ?
David
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d in numpy trac (provide a hook to an fft
implementation at the C level).
cheers,
David
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/IPython/Matplotlib already
> installed?
>
> I posted a question to the VMWare forum about networking, but will
> welcome any help from here.
>
I use vmware to test various packages: it is a barebone version (only
command line), dunno i
me think that having a ubuntu (or whatever distribution)
appliance with scipy and co would be a pretty good thing to have
available for promoting scipy...
David
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rate
>make install ! Copy library and include files to other directories
> **
> """
>
Alternatively, if you are not familiar with compiling softwares (and
Atlas can be tricky to compile/install), just install the packages
provided by ubuntu: sudo apt-get install atlas3-sse2-dev
atlas3-base-dev, and it should be fine.
David
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w, users of my package need to generate the
.lib by themselves from the dll, using visual studio compiler, which is
really too much (specially since the library itself is fine; using the
compiler is necessary only for distutils to find the library !).
cheers,
David
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n packages
first is the least effort path :)
David
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ardware you are probably
> better off compiling your own from the latest ATLAS version out
> there.
>
>
> Red Hat uses Debian packages ? That sounds odd... FC uses RPM, Debian
> uses deb packages. The problem with RPM is, as stated by David some
> time ago, that a lot of in
ckagers).
The quickest thing would be to install numpy from sources. If you're
willing to help, I have some binary for numpy with my own blas/lapack
here, which need some feedback (and updating, too)
http://software.opensuse.org/download/home:/ashigabou/openSUSE_10.2/
cheers,
David
_
suse.org/science/
>
> I tried to install from the RPMs there, but the recursive dependency
> chain broke when a gfortran lib that isn't there was required. I also
> wonder if there was ANY testing by the packagers.
>
> http://software.opensuse.org/download/home:/ashigabou
>
> Times out. :(
Unfortunately, there seems to be a lot of server issues on the service...
David
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testers and timers, dynamically linked to blas and
lapack, so that you can use them to test other implementations (say ATLAS).
David
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or so that should be used (but I'm not skilled enough to trouble
shot this myself :-( )...
Does somebody know how to solve the problem?
David
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piler_f90' : ["g95"],
'linker_so': ["g95", '-undefined', 'dynamic_lookup', '-bundle'],
# ["g95", "-shared"] #here is the mod
'archiver' : ["ar", "-cr"],
'ranl
really doubt that ICC
runtime is not compatible with gcc, and more globally with the GNU
runtime (glibc, etc...); actually, ICC used to use the "standard" linux
runtime, and I would be surprised if that changed.
To say it simply: on linux at least, what should matter is whether t
d can be your own
> workstation.)
>
> We'd like to thank Robert Kern, Jeff Strunk and Gert-Jan van Rooyen
> who helped us to get the ball rolling, as well as Neilen Marais for
> offering his workstation as a build slave.
>
This is really great new :) Thanks for the hard work,
David
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l required shared libraries are located (--rpath?),
If I understand correctly, that would be --rpath-link. --rpath only
helps locating libraries for runtime, whereas --rpath-link helps for
locating libraries for linking. values for --rpath-link may use --rpath
values, though.
David
_
cheers,
David
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Robert Kern wrote:
> Stefan van der Walt wrote:
>> On Fri, Jun 15, 2007 at 03:44:37PM -0400, David M. Cooke wrote:
>>>> I meet a problem when I installed numpy. I installed numpy by the command
>>>> "python setup.py install". Then I tested it by "p
netlib blas and lapack.
I would like to hear people complains. If people want other
distributions supported by the opensuse build system (such as mandriva),
I would like to hear it too.
cheers,
David
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Eike Welk wrote:
> On Sunday 24 June 2007 13:05, David Cournapeau wrote:
>> Hi there,
>>
>> After quite some pain, I finally managed to build a LAPACK +
>> ATLAS rpm useful for numpy and scipy. Read the follow
blas -latlas -llapack -lg2c-pic
> -o build/lib.linux-i686-2.5/numpy/linalg/lapack_lite.so" failed with
> exit status 1
> """
>
> After some Googling, I found a recommendation to unset CFLAGS and
> LDFLAGS, but they are not set in the transcript above.
>
> Any suggestions?
Which distribution are you building on ?
David
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Michael Hoffman wrote:
> David Cournapeau wrote:
>> Michael Hoffman wrote:
>>> Hi. I have been trying to build NumPy on a 32-bit Linux box using python
>>> setup.py build. I received the following errors:
> >> [...]
> >
>> Which distribution ar
John Reid wrote:
> Ok I'll try that although I guess that it turns off all warnings. that
> I'm concerned as well that scipy's release cycle isn't as quick as it
> could be.
>
Well, quite the contrary, it is as quick as it can be. If you think it
is too slow,
void breaking the main
trunk (that is using the last subversion is not more buggy than a
release). At least, I myself try to do so, and the fact that most scipy
dev use subversion help.
I always do a full run of the tests when I deploy a new version, though.
David
___
Hi,
I was wondering what an empty matrix is, and what it is useful for
(by empty matrix, I mean something created by numpy.matrix([])) ? Using
those crash some functions (see for example scipy ticket #381), and I am
not sure how to fix this bug.
David
Nils Wagner wrote:
> David Cournapeau wrote:
>> Hi,
>>
>> I was wondering what an empty matrix is, and what it is useful for
>> (by empty matrix, I mean something created by numpy.matrix([])) ? Using
>> those crash some functions (see for example scipy ticket
John Reid wrote:
> David Cournapeau wrote:
>> Hi,
>>
>> I was wondering what an empty matrix is, and what it is useful for
>> (by empty matrix, I mean something created by numpy.matrix([])) ? Using
>> those crash some functions (see for example scipy ticket
eit N.sum(a) #-> 26.8 ms
%timeit N.dot(a, N.ones(a.shape[1], a.dtype)) #-> 11.3ms
%timeit N.dot(N.ones((1, a.shape[0]), a.dtype), a) #-> 15.5ms
I realize that dot uses optimized libraries (atlas in my case) and all,
but is there any way to improve this situation
Travis Oliphant wrote:
> David Cournapeau wrote:
>
>> Hi,
>>
>> While profiling some code, I noticed that sum in numpy is kind of
>> slow once you use axis argument:
>>
>>
> Yes, this is expected because when using an access arg
Keith Goodman wrote:
> On 7/12/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
>
>> While profiling some code, I noticed that sum in numpy is kind of
>> slow once you use axis argument:
>>
>
> Here is a related thread:
> http://projects.scipy.org/p
Hi,
I noticed that min and max already ignore Nan, which raises the
question: why are there nanmin and nanmax functions ?
cheers,
David
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array is not Nan, min ignore nan.
import numpy
a = 0.1 * numpy.arange(10)
numpy.min(a)
a[:9] = numpy.nan
numpy.min(a) # ignore Nan
a = 0.1 * numpy.arange(10)
a[-1] = numpy.nan
numpy.min(a) # Does not ignore Nan
cheers,
David
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are willing to follow the above steps, it will be easier to debug
things one after the other, I think.
cheers,
David
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? If not, and
without any modification to the site.cfg of numpy, I don't see why numpy
would try to detect the mkl. Maybe some really recent (eg a few days)
changes in the trunk ? I build quite regularly the lastest numpy
(several times / week) on Ubuntu without problems.
> Impor
enerate the wrappers, a bit like swig does
with typemaps for example (maybe f2py can do it for Fortran code ? I
have never used f2py, but I think Fortran has a concept of arrays and
matrices ?).
David
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Robert Kern wrote:
> David Cournapeau wrote:
>
>
>> I am willing to volunteer for the scipy part: I have quite extensive
>> experience with building on linux now, and I can now build on windows
>> without too much difficulties (I mean hardware-wise).
>>
&
uploaded to the Sourceforge site. The
>> Cheeseshop records should be updated to record the new versions. An
>> announcement
>> should be made to python-announce and the relevant mailing lists.
>>
>>
>>
I am willing to volunteer for the scipy part: I have quite extensive
experience with building on linux now, and I can now build on windows
without too much difficulties (I mean hardware-wise).
Concerning the release date: it basically means giving enough time to
solve the current bugs, right ? I solved a few bugs from the 0.5.3
milestone, but some of them are outside my knowledge (weave, linalg bugs
which depend on fortran code).
David
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hat you can get some speed
by using scipy.fftpack methods instead, if scipy is an option for you.
David
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orm compatibility
should be relatively easy. I don't see any drawback, but I guess other
people will.
Point 1 is more tricky, as this requires much more changes in the code.
Do main developers of numpy have an opinion on this ?
cheers,
David
__
Andrew Straw wrote:
> Dear David,
>
> Both ideas, particularly the 2nd, would be excellent additions to numpy.
> I often use the Intel IPP (Integrated Performance Primitives) Library
> together with numpy, but I have to do all my memory allocation with the
> IPP to ensure fa
_t n);
if (PyArray_ISALIGNED_SIMD(a)) {
simd_func((double *)a->data, a->size);
} else {
nosimd_func((double *)a->data, a->size);
}
- Request explicitely an aligned arrays from any PyArray_* functions
which create a ndarray, eg: ar = PyArray_FROM_OF(a, NPY_SIMD
hen, FFT may have specific
patterns which mean that only hand tuned routines can get most of the
CPU horsepower: both mkl and fftw use SIMD instructions to get their
maximum efficiency.
David
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t, which may be faster (never used it).
- if you care also about memory, then maybe you will have no choice
but using your own routines for float support. FFTW support both single
and double precision, but only double is available in scipy.
cheers,
David
> Thanks
>
> L
Lisandro Dalcin wrote:
> On 8/3/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
>> Here is what I can think of:
>> - adding an API to know whether a given PyArrayObject has its data
>> buffer 16 bytes aligned, and requesting a 16 bytes aligned
>> PyArrayO
Anne Archibald wrote:
>
> I have to agree. I can hardly volunteer David for anything, and I
> don't have time to implement this myself, but I think a custom
> allocator is a rather special-purpose tool; if one were to implement
> one, I think the way to go would be to imp
anymore anyway. I will try to
prepare a patch the next few days, then.
cheers,
David
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tand the discussion on doing it in python:
first, this means you cannot request a data buffer at the C level, and I
don't understand the whole discussion on slice, multi dimension and so
on either: at the C level, different libraries may need different arrays
formats, and in the
en a simple change of the
definition of PyDataMem_New to an aligned malloc with a constant. I have
already the code for this, and besides aligned malloc code, it is like a
5 lines change of numpy code, nothing terrible, really.
David
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Charles R Harris wrote:
>
> Ah, you want it in C.
What would be the use to get SIMD aligned arrays in python ?
David
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numpy code structure, there is no such
facility available (eg a pure C library, totally unaware of python,
which would contain some useful tools for numpy), right ?
David
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On 8/9/07, Charles R Harris <[EMAIL PROTECTED]> wrote:
>
>
> On 8/9/07, David Cournapeau <[EMAIL PROTECTED]> wrote:
> > Charles R Harris wrote:
> > >
> > > Ah, you want it in C.
> > What would be the use to get SIMD aligned arrays in python ?
>
The C code can be tested
really easily (since it is independent of python).
David
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is ATLAS, right ? I have
discussed a bit the issue with Clint Whaley (the main developer of
ATLAS), and I think I got a way to build ATLAS without using SSE (which
caused trouble for some "old" ATHLON last time, AFAIK). I can provide
the information
solve (will test it soon on windows and mac os X).
It does not provide yet high level interface (eg requestion python
arrays with given alignment), but if people agree with the current
design, those should not be too difficult to implement.
cheers,
David
Hi,
I would like to know if it is possible to tell f2py to call some
functions inside the initialization function of a module ? I found a
mention to add some function to the module function list, but nothing
about the initialization function.
cheers,
David
Pearu Peterson wrote:
> On Mon, August 13, 2007 8:43 am, David Cournapeau wrote:
>
>> Hi,
>>
>> I would like to know if it is possible to tell f2py to call some
>> functions inside the initialization function of a module ? I found a
>> mention to add
David Cournapeau wrote:
> Pearu Peterson wrote:
>
>> On Mon, August 13, 2007 8:43 am, David Cournapeau wrote:
>>
>>
>>> Hi,
>>>
>>> I would like to know if it is possible to tell f2py to call some
>>> functions inside the
Hi Chris,
Same problem for ubuntu linux.
Darn, I spent an hour tracking this bug and now I see you found it before...
2007/8/13, Chris Fonnesbeck <[EMAIL PROTECTED]>:
>
> I have narrowed a memory leak in PyMC down to the vectorize() function
> in numpy. I have a simple inverse logit transformati
han it needs to be. So I followed David Cournapeau's example
> of fastclip and made a similar fastputmask. The diff relative to
> current svn (3967) is attached.
Great ! putmask was actually the function I wanted to improve after
clip, because it is the second bottleneck for matplot
s NAN iff x != x. A Nan is defined at the
binary level as having the exponent to 1 everywhere, and any non zero
value in the mantissa:
http://en.wikipedia.org/wiki/NaN
Personaly, I would simply compare the non Nan numbers if Nan is a
possible outcome of the operation. Checking at the binary level may
make sen
What is meant by "multiple nan-s"?
DG
mark wrote:
> There may be multiple nan-s, but what Chris did is simply create one
> with the same nan's
>
>
a = N.array((1,2,3,N.nan))
b = N.array((1,2,3,N.nan))
>
> I think these should be the same.
> Can anybody give me a good re
Never mind. (Posted that before finishing the thread, sorry).
DG
David Goldsmith wrote:
> What is meant by "multiple nan-s"?
>
> DG
>
> mark wrote:
>
>> There may be multiple nan-s, but what Chris did is simply create one
>> with the same nan's
&g
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