Strangely, it appears to find the correct numpy.
More strangely, I picked a random order of doing things and suddenly it all
works. I think what I ended up doing is this:
Following builds using default setting without changing anything except:
MACOSX_DEPLOYMENT_TARGET=10.6
python 2.7
numpy 1.5.1 with the flag --fcompiler=gnu95
mpl from github, setting the flags as I have posted earlier
then set these flags:
export MACOSX_DEPLOYMENT_TARGET=10.6
export CFLAGS="-arch i386 -arch x86_64"
export FFLAGS="-m32 -m64"
export LDFLAGS="-Wall -undefined dynamic_lookup -bundle -arch i386 -arch
x86_64 -framework Accelerate"
then build scipy 0.8.0 with --fcompiler=gnu95
Then it all worked. Honestly, I don't understand why it should work because
of this voodoo, but I am happily making figures now...
Also of note, supposedly scipy 0.8 has problems with python 2.7. Version
0.9 should solve these problems (currently in beta).
Thanks for the help!
Uri
...................................................................................
Uri Laserson
Graduate Student, Biomedical Engineering
Harvard-MIT Division of Health Sciences and Technology
M +1 917 742 8019
laser...@mit.edu
On Tue, Dec 14, 2010 at 17:03, Benjamin Root <ben.r...@ou.edu> wrote:
> On Mon, Dec 13, 2010 at 5:54 PM, Uri Laserson <laser...@mit.edu> wrote:
>
>>
>>> Well, on my Linux system, when I get that error, it happens when I do
>>> an update of numpy, but fail to rebuild mpl. Here is the order how I
>>> build things: numpy, scipy, matplotlib. I would imagine ipython goes
>>> last.
>>>
>>>
>> That has been my order as well. How can I track down why the import of
>> numpy.core.multiarray is causing the problem? And why would it cause a
>> problem only when MPL is being imported, but not if I import it manually?
>>
>> Originally, I tried to build the GitHub trunk version of numpy, but then
>> abandoned that. Since MPL is saying that it was built against the 20000...
>> ABI rather than the 10000... ABI, is it possible the MPL is finding some
>> other version of numpy lying around? However, I'm pretty sure I deleted
>> everything from the git numpy build. How could I pinpoint which numpy
>> libraries are being linked against in the MPL build?
>>
>> Uri
>>
>
> Uri,
>
>
> "is it possible the MPL is finding some other version of numpy lying
> around?"
>
> Yes, this is really the only remaining explanation. To find out which
> numpy is being used for the build process, I think if you save the output of
> the build process for mpl, I am fairly sure that that information is
> somewhere near the beginning of the build log.
>
> Ben Root
>
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