On 23/12/15 06:08, Ralf Gommers wrote:
>
> On Tue, Dec 22, 2015 at 9:58 AM, Henry Gomersall <h...@cantab.net
> <mailto:h...@cantab.net>> wrote:
>
> On 23/10/15 02:14, Robert McGibbon wrote:
> > The original goal was to get MS to pay for this, on th
On 23/10/15 02:14, Robert McGibbon wrote:
> The original goal was to get MS to pay for this, on the theory that
> they should be cleaning up their own messes, but after 6 months of
> back-and-forth we've pretty much given up on that at this point, and
> I'm in the process of emailing everyone I
On 30/10/14 03:58, Sturla Molden wrote:
MKL has an API compatible with FFTW, so FFTW and MKL can be supported
with the same C code.
Compatible with big caveats:
https://software.intel.com/en-us/node/522278
Henry
___
NumPy-Discussion mailing list
On 29/10/14 18:23, Alexander Eberspächer wrote:
Definitely. My attempt at streamlining the use of pyfftw even further
can be found here:
https://github.com/aeberspaecher/transparent_pyfftw
There could be an argument that this sort of capability should be added
to the pyfftw package, as a
On 28/10/14 09:41, David Cournapeau wrote:
The real issue with fftw (besides the license) is the need for plan
computation, which are expensive (but are not needed for each
transform). Handling this in a way that is user friendly while
tweakable for advanced users is not easy, and IMO more
On 28/10/14 04:28, Nathaniel Smith wrote:
- not sure if it can handle non-power-of-two problems at all, or at
all efficiently. (FFTPACK isn't great here either but major
regressions would be bad.)
From my reading, this seems to be the biggest issue with FFTS (from my
reading as well) and
I'm running some test code on travis-ci, which is currently failing, but
passing locally.
I've identified the problem as being that my code tests internally for
the alignment of an array being its natural alignment, which I
establish by checking data_pointer%test_array.dtype.alignment (I do
On 19/11/13 17:52, Nathaniel Smith wrote:
On Tue, Nov 19, 2013 at 9:17 AM, Henry Gomersallh...@cantab.net wrote:
On 19/11/13 16:08, Stéfan van der Walt wrote:
On Tue, Nov 19, 2013 at 6:03 PM, Henry Gomersallh...@cantab.net wrote:
However, FFTW is dual licensed GPL/commercial and so the
On 20/11/13 19:56, Chris Barker wrote:
On Wed, Nov 20, 2013 at 3:06 AM, Henry Gomersall h...@cantab.net
mailto:h...@cantab.net wrote:
Yes, this didn't occur to me as an option, mostly because I'm keen for a
commercial FFTW license myself and it would gall me somewhat if I
On 19/11/13 16:00, Charles Waldman wrote:
How about FFTW? I think there are wrappers out there for that ...
Yes there are! (complete with the numpy.fft API)
https://github.com/hgomersall/pyFFTW
However, FFTW is dual licensed GPL/commercial and so the wrappers are
also GPL by necessity.
On 19/11/13 16:08, Stéfan van der Walt wrote:
On Tue, Nov 19, 2013 at 6:03 PM, Henry Gomersallh...@cantab.net wrote:
However, FFTW is dual licensed GPL/commercial and so the wrappers are
also GPL by necessity.
I'm not sure if that is true, strictly speaking--you may license your
wrapper code
On 29/10/13 18:01, Sebastian Berg wrote:
On Tue, 2013-10-29 at 16:47 +, Henry Gomersall wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
There is np.result_type. It does the handling of scalars as normal,
dtypes will be handled like arrays
Is there a way to extract the size of array that would be created by
doing 1j*array?
The problem I'm having is in creating an empty array to fill with
complex values without knowing a priori what the input data type is.
For example, I have a real or int array `a`.
I want to create an array
On 29/10/13 16:47, Henry Gomersall wrote:
Is there a way to extract the size of array that would be created by
doing 1j*array?
Of course, I mean dtype of the array.
Henry
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http
On 29/10/13 17:02, Robert Kern wrote:
Quick and dirty:
# Get a tiny array from `a` to test the dtype of its output when
multiplied
# by a complex float. It must be an array rather than a scalar since the
# casting rules are different for array*scalar and scalar*scalar.
dt = (a.flat[:2] *
On 08/10/13 09:06, Ke Sun wrote:
I give as input a 70,000x800 matrix. The output should be a 70,000x70,000
matrix. The program runs really slow (16 hours for 1/3 progress). And it eats
36G memory (fortunately I have enough).
At this stage I'd be asking myself what I'm trying to achieve and why
On 08/10/13 09:49, Matthew Brett wrote:
On Tue, Oct 8, 2013 at 1:06 AM, Ke Sunsunk...@gmail.com wrote:
Dear all,
I have written the following function to compute the square distances of a
large
matrix (each sample a row). It compute row by row and print the overall
progress.
The
On 2013-09-19 23:12, Christoph Gohlke wrote:
On 9/19/2013 1:06 AM, Henry Gomersall wrote:
On 19/09/13 09:05, Henry Gomersall wrote:
I've had feedback that this is possible. Give me a few hours and I'll
see what I can do...
I mean that it builds under win 64-bit. I'll prob push a .exe
On 18/09/13 01:51, Antony Lee wrote:
While I realize that this is certainly tweaking multiprocessing beyond
its specifications, I would like to use it on Windows to start a
32-bit Python process from a 64-bit Python process (use case: I need
to interface with a 64-bit DLL and use an
On 18/09/13 01:51, Antony Lee wrote:
I need to interface with a 64-bit DLL and use an extension (pyFFTW)
for which I can only find a 32-bit compiled version (yes, I could try
to install MSVC and compile it myself but I'm trying to avoid that...))
I'm now in a position that I might be able to
On 19/09/13 09:05, Henry Gomersall wrote:
I've had feedback that this is possible. Give me a few hours and I'll
see what I can do...
I mean that it builds under win 64-bit. I'll prob push a .exe.
Cheers,
Henry
___
NumPy-Discussion mailing list
NumPy
On 2013-09-19 17:58, Antony Lee wrote:
Henry: thanks a lot, that would be very appreciated regardless of
whether I end up using it in this specific project or not.
Other replies below.
I've actually spent rather too long fiddling with Windows on this one! I
can't for the life of me get
On Sun, 2013-06-16 at 14:48 +0800, Sudheer Joseph wrote:
Is it possible to sample a 4D array of numpy at given dimensions with
out writing loops? ie a smart python way?
It's not clear how what you want to do is different from simply indexing
the array...?
Henry
On Sun, 2013-06-09 at 12:23 +0100, David Cournapeau wrote:
On Sun, Jun 9, 2013 at 8:35 AM, Henry Gomersall h...@cantab.net
wrote:
On Sat, 2013-06-08 at 14:35 +0200, Anne Archibald wrote:
Looking at the rational module, I think you're right: it really
shouldn't be too hard to get quads
On Mon, 2013-06-10 at 13:21 +0100, Robert Kern wrote:
With my work on https://github.com/hgomersall/pyFFTW, which supports
long double as one of the data types, numpy's long double is
absolutely
the right way to do this. Certainly I've managed reasonable success
across the three main OSs
On Sat, 2013-06-08 at 14:35 +0200, Anne Archibald wrote:
Looking at the rational module, I think you're right: it really
shouldn't be too hard to get quads working as a user type using gcc's
__float128 type, which will provide hardware arithmetic in the
unlikely case that the user has hardware
On Thu, 2013-05-09 at 16:06 +0800, Sudheer Joseph wrote:
If I wanted to print below text in a file (for reading by another
program), it looks to be not an easy jobHope new developments will
come and a userfriendly formatted out put method for pyton will
evolve.
I don't understand what the
On Wed, 2013-05-08 at 10:13 +0800, Sudheer Joseph wrote:
However I get below error. Please tell me if any thing I am missing.
file read_reg_grd.py, line 22, in module
np.savetxt(file.txt, IL.reshape(-1,5), fmt='%5d', delimiter=',')
AttributeError: 'list' object has no attribute
On Thu, 2013-03-07 at 13:36 -0600, Mayank Daga wrote:
Can someone point me to the definition of dot() in the numpy source?
The only instance of 'def dot()' I found was in numpy/ma/extras.py but
that does not seem to be the correct one.
It seems to be in a dynamic library.
In [9]:
On Fri, 2013-03-01 at 13:25 +0100, Sebastian Berg wrote:
there has been a request on the issue tracker for a step parameter to
linspace. This is of course tricky with the imprecision of floating
point numbers.
How is that different to arange? Either you specify the number of points
with
On Fri, 2013-03-01 at 13:34 +, Nathaniel Smith wrote:
My usual hack to deal with the numerical bounds issue is to
add/subtract
half the step.
Right. Which is exactly the sort of annoying, content-free code that a
library is supposed to handle for you, so you can save mental energy
On Fri, 2013-03-01 at 09:24 -0500, Warren Weckesser wrote:
In my jet-lag addled state, i can't see when this out[-1] stop
case
will occur, but I can take it as true. It does seem to be
problematic
though.
Here you go:
In [32]: end = 2.2
In [33]: x = arange(0.1, end, 0.3)
On Fri, 2013-03-01 at 10:01 -0500, Alan G Isaac wrote:
On 3/1/2013 9:32 AM, Henry Gomersall wrote:
there should be an equivalent for floats that
unambiguously returns a range for the half open interval
If I've understood you:
start + stepsize*np.arange(nsteps)
yes, except that nsteps
On Fri, 2013-03-01 at 17:29 +0100, Sebastian Berg wrote:
At this time it seems there is more sentiment against it and that is
fine with me. I thought it might be useful for some who normally want
the linspace behavior, but do not want to worry about the right num in
some cases. Someone who
Some of you may be interested in the latest release of my FFTW bindings.
It can now serve as a drop in replacement* for numpy.fft and
scipy.fftpack.
This means you can get most of the speed-up of FFTW with a one line code
change or monkey patch existing libraries.
Lots of other goodness too of
On Sun, 2013-02-17 at 12:38 -0500, Neal Becker wrote:
The 1st example says:
import pyfftw
import numpy
a = pyfftw.n_byte_align_empty(128, 16, 'complex128')
a[:] = numpy.random.randn(128) + 1j*numpy.random.randn(128)
b = pyfftw.interfaces.numpy_fft.fft(a)
I don't see why I need to
On Mon, 2013-01-21 at 08:41 -0500, Neal Becker wrote:
I have an array to be used for indexing. It is 2d, where the rows are
all the
permutations of some numbers. So:
array([[-2, -2, -2],
[-2, -2, -1],
[-2, -2, 0],
[-2, -2, 1],
[-2, -2, 2],
...
Further to my previous emails about getting SIMD aligned arrays, I've
noticed that numpy arrays aren't always naturally aligned either.
For example, numpy.float96 arrays are not always aligned on 12-byte
boundaries under 32-bit linux/gcc. Indeed, .alignment on the array
always seems to return 4
On Fri, 2012-12-21 at 11:34 +0100, Francesc Alted wrote:
Also this convolution code:
https://github.com/hgomersall/SSE-convolution/blob/master/convolve.c
Shows a small but repeatable speed-up (a few %) when using some
aligned
loads (as many as I can work out to use!).
Okay, so a 15%
On Wed, 2012-12-19 at 15:10 +, Nathaniel Smith wrote:
snip
Is this something that can be rolled into Numpy (the feature, not
my
particular implementation or interface - though I'd be happy for
it to
be so)?
Regarding (b), I've written a test case that works for Linux on
On Thu, 2012-12-20 at 08:12 +, Henry Gomersall wrote:
On Wed, 2012-12-19 at 15:10 +, Nathaniel Smith wrote:
snip
Is this something that can be rolled into Numpy (the feature,
not
my
particular implementation or interface - though I'd be happy
for
it to
be so
On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
The only scenario that I see that this would create unaligned arrays
is
for machines having AVX. But provided that the Intel architecture is
making great strides in fetching unaligned data, I'd be surprised
that
the difference in
On Thu, 2012-12-20 at 17:26 +0100, Sturla Molden wrote:
On 19.12.2012 09:40, Henry Gomersall wrote:
I've written a few simple cython routines for assisting in creating
byte-aligned numpy arrays. The point being for the arrays to work
with
SSE/AVX code.
https://github.com/hgomersall
On Thu, 2012-12-20 at 17:26 +0100, Sturla Molden wrote:
return tmp[offset:offset+N]\
.view(dtype=d)\
.reshape(shape, order=order)
Also, just for the email record, that should be
return tmp[offset:offset+N*d.itemsize]\
On Thu, 2012-12-20 at 17:48 +0100, Sturla Molden wrote:
On 19.12.2012 19:25, Henry Gomersall wrote:
That is not true at least under Windows 32-bit. I think also it's
not
true for Linux 32-bit from my vague recollections of testing in a
virtual machine. (disclaimer: both those statements
On Thu, 2012-12-20 at 15:23 +0100, Francesc Alted wrote:
On 12/20/12 9:53 AM, Henry Gomersall wrote:
On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
The only scenario that I see that this would create unaligned
arrays
is
for machines having AVX. But provided that the Intel
On Thu, 2012-12-20 at 20:50 +0100, Sturla Molden wrote:
On 20.12.2012 18:38, Henry Gomersall wrote:
Except I build with MinGW. Please don't tell me I need to install
Visual
Studio... I have about 1GB free on my windows partition!
The same DLL is used as CRT.
Perhaps the DLL should go
On Thu, 2012-12-20 at 20:57 +0100, Sturla Molden wrote:
On 20.12.2012 20:52, Henry Gomersall wrote:
Perhaps the DLL should go and read MS's edicts!
Do you link with same same CRT as Python? (msvcr90.dll)
You should always use -lmsvcr90.
If you don't, you will link with msvcrt.dll
On Thu, 2012-12-20 at 21:05 +0100, Sturla Molden wrote:
On 20.12.2012 20:57, Sturla Molden wrote:
On 20.12.2012 20:52, Henry Gomersall wrote:
Perhaps the DLL should go and read MS's edicts!
Do you link with same same CRT as Python? (msvcr90.dll)
You should always use -lmsvcr90
On Thu, 2012-12-20 at 21:13 +0100, Sturla Molden wrote:
On 20.12.2012 21:03, Henry Gomersall wrote:
Why is it important? (for my own understanding)
Because if CRT resources are shared between different CRT versions,
bad
things will happen (the ABIs are not equivalent, errno and other
On Thu, 2012-12-20 at 21:45 +0100, Sturla Molden wrote:
On 20.12.2012 21:24, Henry Gomersall wrote:
I didn't know that. It's a real pain having so many libc libs
knocking
around. I have little experience of Windows, as you may have
guessed!
Originally there was only one system-wide CRT
I've written a few simple cython routines for assisting in creating
byte-aligned numpy arrays. The point being for the arrays to work with
SSE/AVX code.
https://github.com/hgomersall/pyFFTW/blob/master/pyfftw/utils.pxi
The change recently has been to add a check on the CPU as to what flags
are
On Wed, 2012-12-19 at 15:57 +, Nathaniel Smith wrote:
Not sure which interface is more useful to users. On the one hand,
using funny dtypes makes regular non-SIMD access more cumbersome, and
it forces your array size to be a multiple of the SIMD word size,
which might be inconvenient if
On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
snip
Finally, I think there is significant value in auto-aligning the
array
based on an appropriate inspection of the cpu capabilities (or
alternatively, a function that reports back the appropriate SIMD
alignment). Again, this
On Wed, 2012-11-21 at 00:44 +, David Cournapeau wrote:
On Tue, Nov 20, 2012 at 8:52 PM, Henry Gomersall h...@cantab.net
wrote:
On Tue, 2012-11-20 at 20:35 +0100, Dag Sverre Seljebotn wrote:
Is there a specific reason it *has* to happen at compile-time? I'd
think
one could do
On Wed, 2012-11-21 at 10:49 +, David Cournapeau wrote:
That's already what we do (on.windows anyway). The binary installer
contains multiple arch binaries, and we pick the bewt one.
Interesting. Does it (or can it) extend to different algorithmic
implementations?
Henry
On Tue, 2012-11-20 at 20:35 +0100, Dag Sverre Seljebotn wrote:
Is there a specific reason it *has* to happen at compile-time? I'd
think
one could do something like just shipping a lot of separate Python
extensions which are really just the same module linked with
different
versions of the
On Mon, 2012-10-29 at 11:11 -0400, Frédéric Bastien wrote:
Assuming of course all the relevant backends are up to scratch.
Is there a fundamental reason why targetting a CPU through OpenCL is
worse than doing it exclusively through C or C++?
First, opencl do not allow us to do pointor
On Mon, 2012-10-29 at 11:49 -0400, Frédéric Bastien wrote:
That is possible.
Great!
The gpu nd array project I talked above work on the CPU and the GPU in
OpenCL and with CUDA. But there is much stuff that is in numpy that we
didn't ported.
This is: https://github.com/inducer/compyte/wiki
On Tue, 2012-10-23 at 11:41 -0400, Frédéric Bastien wrote:
Did you saw the gpu nd array project? We try to do something similar
but only for the GPU.
Out of interest, is there a reason why the backend for Numpy could not
be written entirely in OpenCL?
Assuming of course all the relevant
On Fri, 2012-09-28 at 16:43 -0500, Travis Oliphant wrote:
I agree that we should be much more cautious about semantic changes in
the 1.X series of NumPy.How we handle situations where 1.6 changed
things from 1.5 and wasn't reported until now is an open question and
depends on the
On Thu, 2012-07-26 at 22:15 -0600, Charles R Harris wrote:
I would support accumulating in 64 bits but, IIRC, the function will
need to be rewritten so that it works by adding 32 bit floats to the
accumulator to save space. There are also more stable methods that
could also be investigated.
On Wed, 2012-07-18 at 15:14 +0200, Molinaro Céline wrote:
In [2]: numpy.real(arange(3))
Out[2]: array([0, 1, 2])
In [3]: numpy.complex(arange(3))
TypeError: only length-1 arrays can be converted to Python scalars
Are there any reasons why numpy.complex doesn't work on arrays?
Should it
On Mon, 2012-07-16 at 20:35 +0300, Dmitrey wrote:
I have wrote a routine to solve dense / sparse problems
min {alpha1*||A1 x - b1||_1 + alpha2*||A2 x - b2||^2 + beta1 * ||x||_1
+ beta2 * ||x||^2}
with specifiable accuracy fTol 0: abs(f-f*) = fTol (this parameter
is handled by solvers gsubg
On Thu, 2012-07-12 at 10:53 -0400, Neal Becker wrote:
I've been bitten several times by this.
logical_or (a, b, c)
is silently accepted when I really meant
logical_or (logical_or (a, b), c)
because the logic functions are binary, where I expected them to be
m-ary.
I don't think you
On Thu, 2012-07-12 at 16:21 +0100, Nathaniel Smith wrote:
On Thu, Jul 12, 2012 at 4:13 PM, Henry Gomersall h...@cantab.net
wrote:
On Thu, 2012-07-12 at 10:53 -0400, Neal Becker wrote:
I've been bitten several times by this.
logical_or (a, b, c)
is silently accepted when I really
Does anyone have any experience building a 32-bit version of numpy on a
64-bit linux machine? I'm trying to build a python stack that I can use
to handle a (closed source) 32-bit library.
Much messing around with environment variables and linker flags has got
some of the way, perhaps, but not
Forgive me for what seems to me should be an obvious question.
How do people run development code without the need to build an entire
source distribution each time? My current strategy is to develop in a
virtualenv and then copy the changes to my numpy fork when done, but
there are lots of
I'd like to include the _cook_nd_args() function from fftpack in my GPL
code. Is this possible?
How should I modify my license file to satisfy the Numpy license
requirements, but so it's clear which function it applies to?
Thanks,
Henry
___
On Wed, 2012-05-30 at 17:11 +0100, Robert Kern wrote:
On Wed, May 30, 2012 at 4:13 PM, Henry Gomersall h...@cantab.net
wrote:
I'd like to include the _cook_nd_args() function from fftpack in my
GPL
code. Is this possible?
Yes. The numpy license is compatible with the GPL license, so code
On Fri, 2012-05-18 at 12:48 +0100, mark florisson wrote:
If we can find even more examples, preferably outside of the
scientific community, where related projects face a similar situation,
it may help people understand that this is not a Numpy problem.
Buffer Objects in OpenGL?
On Fri, 2012-05-18 at 14:45 +0200, Dag Sverre Seljebotn wrote:
I would focus on the 'polymorphic C API' spin. PyObject_GetItem is
polymorphic, but there is no standard way for 3rd party libraries to
make such functions.
So let's find a C API that's NOT about arrays at all and show how some
On Wed, 2012-05-02 at 12:58 -0700, Stéfan van der Walt wrote:
On Wed, May 2, 2012 at 9:03 AM, Henry Gomersall h...@cantab.net
wrote:
Is this some nuance of the way numpy does things? Or am I missing
some
stupid bug in my code?
Try playing with the parameters of the following code:
snip
I'm need to do some shifting of data within an array and am using the
following code:
for p in numpy.arange(array.shape[0], dtype='int64'):
for q in numpy.arange(array.shape[1]):
# A positive shift is towards zero
shift = shift_values[p, q]
if shift = 0:
Here is the body of a post I made on stackoverflow, but it seems to be a
non-obvious issue. I was hoping someone here might be able to shed light
on it...
On my 32-bit Windows Vista machine I notice a significant (5x) slowdown
when taking the absolute values of a fairly large
On 10/04/2012 16:36, Francesc Alted wrote:
In [10]: timeit c = numpy.complex64(numpy.abs(numpy.complex128(b)))
100 loops, best of 3: 12.3 ms per loop
In [11]: timeit c = numpy.abs(b)
100 loops, best of 3: 8.45 ms per loop
in your windows box and see if they raise similar results?
No, the
On 10/04/2012 17:57, Francesc Alted wrote:
I'm using numexpr in the end, but this is slower than numpy.abs under linux.
Oh, you mean the windows version of abs(complex64) in numexpr is slower
than a pure numpy.abs(complex64) under linux? That's weird, because
numexpr has an independent
On Mon, 2012-02-13 at 22:56 -0600, Travis Oliphant wrote:
But, I am also aware of *a lot* of users who never voice their opinion
on this list, and a lot of features that they want and need and are
currently working around the limitations of NumPy to get.These are
going to be my primary
On Tue, 2012-02-14 at 14:14 -0600, Travis Oliphant wrote:
Is that a prompt for feedback? :)
Absolutely. That's the reason I'm getting more active on this list.
But, at the same time, we all need to be aware of the tens of
thousands of users of NumPy who don't use the mailing list and who
On Tue, 2012-02-14 at 15:12 -0800, Chris Barker wrote:
On Tue, Feb 14, 2012 at 9:16 AM, Dag Sverre Seljebotn
d.s.seljeb...@astro.uio.no wrote:
It was about the need for a dedicated matrix multiplication
operator.
has anyone proposed that? I do think we've had a proposal on the table
for
On Tue, 2012-02-07 at 01:04 +0100, Torgil Svensson wrote:
irfftn is an optimization for real input and does not take complex
input. You have to use numpy.fft.ifftn instead:
hmmm, that doesn't sound right to me (though there could be some non
obvious DFT magic that I'm missing). Indeed,
On Tue, 2012-02-07 at 09:15 +, Henry Gomersall wrote:
On Tue, 2012-02-07 at 01:04 +0100, Torgil Svensson wrote:
irfftn is an optimization for real input and does not take complex
input. You have to use numpy.fft.ifftn instead:
hmmm, that doesn't sound right to me (though there could
On Tue, 2012-02-07 at 09:15 +, Henry Gomersall wrote:
numpy.fft.ifftn(a, axes=axes)
Or do you mean if the error message is expected?
Yeah, the question was regarding the error message. Specifically, the
problem it seems to have with an axes argument like that.
Sorry, the error
On Tue, 2012-02-07 at 11:53 -0800, Warren Focke wrote:
You're not doing anything wrong.
irfftn takes complex input and returns real output.
The exception is a bug which is triggered because max(axes) =
len(axes).
Is this a bug I should register?
Cheers,
Henry
On Tue, 2012-02-07 at 12:26 -0800, Warren Focke wrote:
Is this a bug I should register?
Yes.
It should work right if you replace
s[axes[-1]] = (s[axes[-1]] - 1) * 2
with
s[-1] = (a.shape[axes[-1]] - 1) * 2
but I'm not really in a position to test it right now.
I can confirm
Does anyone care about this? Is there an alternative channel for such
information, perhaps a bug report?
Cheers,
Henry
On Fri, 2010-10-29 at 13:32 +0100, Henry Gomersall wrote:
There is an inconsistency in the documentation for NPY_INOUT_ARRAY.
cf.
http://docs.scipy.org/doc/numpy/user/c
There is an inconsistency in the documentation for NPY_INOUT_ARRAY.
cf.
http://docs.scipy.org/doc/numpy/user/c-info.how-to-extend.html#NPY_INOUT_ARRAY
http://docs.scipy.org/doc/numpy/reference/c-api.array.html#NPY_INOUT_ARRAY
The first link includes the flag NPY_UPDATEIFCOPY. Checking the code
I'm trying to get a really simple toy example for a numpy extension
working (you may notice its based on the example in the numpy docs and
the python extension docs). The code is given below.
The problem I am having is running the module segfaults at any attempt
to access PyArray_Type (so, as
On Fri, 2010-10-29 at 15:33 +0200, Jon Wright wrote:
You need to call import_array() in initspam. See:
http://docs.scipy.org/doc/numpy-1.5.x/user/c-info.how-to-extend.html
Thanks, that solves it.
It would be really useful to have a complete example somewhere. As in, a
set of files
89 matches
Mail list logo
