Hi Burlen,
SWIG will generate a file named for example foo_wrap.c, which will
contains a call to import_array() inserted by SWIG because of the
%init %{
import_array();
%}
in the SWIG script.
So in the file foo_wrap.c (which will be compiled to a Python module
_foo.so), you should be able to u
Hi,
I'd like to add numpy support to an existing code that uses swig. I've
changed the source file that has code to convert python lists into
native data from c to c++ so I can use templates to handle data
conversions. The problem I'm having is a segfault on PyArray_Check
called from my c++ so
Hi,
numpy, scipy test binaries (32 bit, openblas) can be downloaded from
https://code.google.com/p/mingw-w64-static/ .
link:
https://drive.google.com/file/d/0B4DmELLTwYmlc2tjMkpwUDF5cDg/edit?usp=sharing
log-files:
https://gist.github.com/anonymous/7457182
Regards
Carl
2013/11/13 Carl Kleffner
Hi folks,
forgive me for the x-post to a few lists and the semi off-topic nature of
this post, but I think it's worth mentioning this to our broader community.
To keep the SNR of each list high, I'd prefer any replies to happen on the
numfocus list.
Yesterday, during an event at the White House
On 13.11.2013 18:26, David Cournapeau wrote:
>
>
> Can you narrow it down to a specific intrinsic? they can be enabled and
> disabled in set ./numpy/core/setup_common.py
>
>
> valgrind shows quite a few invalid read in BOOL_ functions when running
> the scipy or sklearn test suite. BOOL
On Tue, Nov 12, 2013 at 6:17 PM, Julian Taylor <
jtaylor.deb...@googlemail.com> wrote:
> On 12.11.2013 03:17, David Cournapeau wrote:
> > Hi there,
> >
> > I have noticed more and more subtle and hard to track serious bugs in
> > numpy and scipy, due to the use of advanced optimization features
>
The following works on Numpy 1.8.0:
from __future__ import unicode_literals
import numpy as np
np.savetxt('a.csv', [1], fmt=str('%.3f'))
Without the str, I get a clearer error:
Traceback (most recent call last):
File "a.py", line 4, in
np.savetxt('a.csv', [1], fmt='%.3f')
File ".virtu
Hi,
I just noticed (with numpy 1.7.1) that the following code
import numpy as np
np.savetxt('a.csv', [1], fmt=u'%.3f')
fails with:
1045 else:
1046 for row in X:
-> 1047 fh.write(asbytes(format % tuple(row) + newline))
1048 if len(footer) > 0:
On 13 November 2013 02:40, Bart Baker wrote:
> > That is the order of the machine epsilon for double, that looks like
> roundoff
> > errors to me.
>
>
> I'm trying to my head around this. So does that mean that neither of
> them is "right", that it is just the result of doing the same
> calculati
