Hi,
Gregor and I are trying to give support for Intel's VML (Vector
Mathematical Library) in numexpr. For this, we are trying to make use
of the weaponery in NumPy's distutils so as to be able to discover
where the MKL (the package that contains VML) is located. The
libraries that we need to link with are: mkl_gf_lp64, mkl_gnu_thread,
mkl_core *and* iomp5 (Intel's OpenMP library).
The problem is that I have installed MKL as part as the Intel compiler
for Unix. In this setup, most of the libraries are in one place,
namely:
/opt/intel/Compiler/11.0/074/mkl/lib/em64t/
However, the OpenMP library is in another directory:
/opt/intel/Compiler/11.0/074/lib/intel64
So, I need to specify *two* directories to get the complete set of
libraries. My first attempt was setting a site.cfg like:
[DEFAULT]
#libraries = gfortran
[mkl]
library_dirs= /opt/intel/Compiler/11.0/074/mkl/lib/em64t/:/opt/intel/Compiler/11.0/074/lib/intel64
include_dirs = /opt/intel/Compiler/11.0/074/mkl/include/
mkl_libs = mkl_gf_lp64, mkl_gnu_thread, mkl_core, iomp5
Unfortunately, distutils complains and says that it cannot find the
complete set of libraries:
mkl_info:
libraries mkl_gf_lp64,mkl_gnu_thread,mkl_core,iomp5 not found
in /opt/intel/Compiler/11.0/074/mkl/lib/em64t/
libraries mkl_gf_lp64,mkl_gnu_thread,mkl_core,iomp5 not found
in /opt/intel/Compiler/11.0/074/lib/intel64
NOT AVAILABLE
After some debugging of the problem, it seems that distutils needs to
find *all* the required libraries in *one* single directory. As iomp5
is on a different directory, distutils thinks that the requisites
are not fulfilled.
I've solved this by requering the iomp5 to be find in the DEFAULT
section. Something like:
[DEFAULT]
library_dirs = /opt/intel/Compiler/11.0/074/lib/intel64
#libraries = gfortran, iomp5
libraries = iomp5
[mkl]
library_dirs = /opt/intel/Compiler/11.0/074/mkl/lib/em64t/
include_dirs = /opt/intel/Compiler/11.0/074/mkl/include/
mkl_libs = mkl_gf_lp64, mkl_gnu_thread, mkl_core
However, in case one would need to specify several other directories
in the DEFAULT section for finding other hypothetical necessary
libraries (like gfortran or others), we may run into the same problem
than above.
My question is, is there an elegant way to handle this problem, or it
is a limitation of the current distutils? If the later, it would be
nice it that could be solved in a future version, and several libraries
can be found in *several* directories.
Thanks,
--
Francesc Alted
Hi,
2 ?'s about numpy.distutils:
1.
I am using config.add_library to build a c++ library that I will link
into some Cython extensions. This is working fine and generating a .a
library for me. However, I need a shared library instead. Is this
possible with numpy.distutils or will I need something like numscons?
2.
When calling add_library, what is the difference between the depends
and headers arguments?
Thanks!
Brian
It's time for me to select a data format.
My data are (more or less) spectra ( a couple of thousand samples), six
channels, each channel running around 10 Hz, collecting for a minute or so.
Plus all the settings on the instrument.
I don't see any significant differences between netCDF4 and HDF5.
Similarly, I don't see significant differences between pytables and h5py.
Does one play better with numpy? What are the best numpy solutions for
netCDF4?
Can anyone provide thoughts, pros and cons, etc, that I can mull over?
-gary
Hi there,
perhaps someone has a bright idea for this one:
I want to concatenate ranges of numbers into a single array (for indexing). So I
have generated an array "a" with starting positions, for example:
a = [4, 0, 11]
I have an array b with stop positions:
b = [11, 4, 15]
and I would like to generate an index array that takes 4..11, then 0..4, then
11..15.
In reality, a and b have 10000+ elements and the arrays to be "sliced" are very
large so I want to avoid any for loops etc. Any idea how this could be done? I
thought some combination of *repeat* and adding of *arange* should do the trick
but just cannot nail it down.
Thanks in advance for any hints!
Greetings,
Raik
What's the problem here?
print np.concatenate (np.ones (10, dtype=complex), np.zeros (10,
dtype=complex))
TypeError: only length-1 arrays can be converted to Python scalars
Hi ~
I'm trying to build numpy (hopefully eventually scipy with the same
setup) with the Intel compilers (and Intel MKL) on the WinXP 64-bit
platform. Finding / linking to the Intel MKL seems to be successful (see
below) but I have an issue with the settings defined somewhere in the
various setup scripts (can't find where). Per the output below, the Intel
compilers on Windows are looking for ".obj" object files rather than the
Linux-style ".o" files. I'd also like to get rid of the /L and -L flags (no
longer supported in Intel C++ v. 11.0 it seems) but this just throws a
warning and does not seem to cause any problems. Can anyone point me to the
python file(s) I need to edit to modify the .o object file setting to .obj?
(The file "_configtest.obj" is created.) Once operational, I'll pass along
all of my config info for anyone else building in this environment.
Thanks!
~Mike C.
Output from build:
>>python setup.py config --compiler=intel --fcompiler=intelem install
Running from numpy source directory.
Forcing DISTUTILS_USE_SDK=1
F2PY Version 2_5972
blas_opt_info:
blas_mkl_info:
FOUND:
libraries = ['mkl_em64t', 'mkl_dll']
library_dirs = ['C:\\Program Files (x86)\\Intel\\Compiler\\11.0\\
061\\cpp\\m
kl\\em64t\\lib']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\include']
FOUND:
libraries = ['mkl_em64t', 'mkl_dll']
library_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\em64t\\lib']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\include']
lapack_opt_info:
lapack_mkl_info:
mkl_info:
FOUND:
libraries = ['mkl_em64t', 'mkl_dll']
library_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\em64t\\lib']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\include']
FOUND:
libraries = ['mkl_lapack', 'mkl_em64t', 'mkl_dll']
library_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\em64t\\lib']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\include']
FOUND:
libraries = ['mkl_lapack', 'mkl_em64t', 'mkl_dll']
library_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\em64t\\lib']
define_macros = [('SCIPY_MKL_H', None)]
include_dirs = ['C:\\Program Files
(x86)\\Intel\\Compiler\\11.0\\061\\cpp\\m
kl\\include']
running config
running install
running build
running config_cc
unifing config_cc, config, build_clib, build_ext, build commands --compiler
opti
ons
running config_fc
unifing config_fc, config, build_clib, build_ext, build commands --fcompiler
opt
ions
running build_src
building py_modules sources
creating build
creating build\src.win32-2.5
creating build\src.win32-2.5\numpy
creating build\src.win32-2.5\numpy\distutils
building extension "numpy.core.multiarray" sources
creating build\src.win32-2.5\numpy\core
Generating build\src.win32-2.5\numpy\core\include/numpy\config.h
Could not locate executable icc
Could not locate executable ecc
Ignoring "MSVCCompiler instance has no attribute '_MSVCCompiler__root'" (I
think
it is msvccompiler.py bug)
customize IntelEM64TFCompiler
Found executable C:\Program Files
(x86)\Intel\Compiler\11.0\061\fortran\Bin\inte
l64\ifort.exe
Found executable C:\Program Files
(x86)\Intel\Compiler\11.0\061\fortran\Bin\inte
l64\ifort.exe
C compiler: icl
compile options: '-IC:\Python25\include -Inumpy\core\src
-Inumpy\core\include -I
C:\Python25\include -IC:\Python25\PC -c'
icl: _configtest.c
Found executable C:\Program Files
(x86)\Intel\Compiler\11.0\061\cpp\Bin\intel64\
icl.exe
icl _configtest.o -LC:\Python25\lib -LC:\ -LC:\Python25\libs -o _configtest
Intel(R) C++ Intel(R) 64 Compiler Professional for applications running on
Intel
(R) 64, Version 11.0 Build 20080930 Package ID: w_cproc_p_11.0.061
Copyright (C) 1985-2008 Intel Corporation. All rights reserved.
icl: command line warning #10161: unrecognized source type '_configtest.o';
obje
ct file assumed
icl: command line warning #10006: ignoring unknown option
'/LC:\Python25\lib'
icl: command line warning #10006: ignoring unknown option '/LC:\'
icl: command line warning #10006: ignoring unknown option
'/LC:\Python25\libs'
ipo: warning #11009: file format not recognized for _configtest.o
Microsoft (R) Incremental Linker Version 9.00.30729.01
Copyright (C) Microsoft Corporation. All rights reserved.
-out:_configtest.exe
_configtest.o
LINK : fatal error LNK1181: cannot open input file '_configtest.o'
failure.
removing: _configtest.c _configtest.o
Traceback (most recent call last):
File "setup.py", line 96, in <module>
setup_package()
File "setup.py", line 89, in setup_package
configuration=configuration )
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\core.py", line 184, in setup
return old_setup(**new_attr)
File "C:\Python25\lib\distutils\core.py", line 151, in setup
dist.run_commands()
File "C:\Python25\lib\distutils\dist.py", line 974, in run_commands
self.run_command(cmd)
File "C:\Python25\lib\distutils\dist.py", line 994, in run_command
cmd_obj.run()
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\install.py", line 49, in run
r = old_install.run(self)
File "C:\Python25\lib\distutils\command\install.py", line 506, in run
self.run_command('build')
File "C:\Python25\lib\distutils\cmd.py", line 333, in run_command
self.distribution.run_command(command)
File "C:\Python25\lib\distutils\dist.py", line 994, in run_command
cmd_obj.run()
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\build.py", line 37, in run
old_build.run(self)
File "C:\Python25\lib\distutils\command\build.py", line 112, in run
self.run_command(cmd_name)
File "C:\Python25\lib\distutils\cmd.py", line 333, in run_command
self.distribution.run_command(command)
File "C:\Python25\lib\distutils\dist.py", line 994, in run_command
cmd_obj.run()
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\build_src.py", line 130, in run
self.build_sources()
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\build_src.py", line 147, in build_sources
self.build_extension_sources(ext)
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\build_src.py", line 250, in build_extension_sources
sources = self.generate_sources(sources, ext)
File "C:\Documents and
Settings\mike\Desktop\Software\Python\numpy-1.2.1\numpy
\distutils\command\build_src.py", line 307, in generate_sources
source = func(extension, build_dir)
File "numpy\core\setup.py", line 87, in generate_config_h
raise SystemError,"Failed to test configuration. "\
SystemError: Failed to test configuration. See previous error messages for
more
information.
Hi all
I'm delighted to announce the release of Ironclad v0.8 -- the
all-singing, all-dancing CPython API compatibility layer for IronPython
-- available now from http://code.google.com/p/ironclad/ . Notable
improvements over the last release include:
* Ironclad is now a neatly self-contained package -- just copy to your
site-packages and 'import ironclad'.
* No more silly requirement to call ironclad.shutdown() when you're
finished.
* A few performance improvements.
* Over 900 NumPy tests now pass: in fact, almost all the tests from the
core, fft, lib, linalg, ma, oldnumeric and random subpackages.
* Over half the .pyds distributed with CPython 2.5 now import cleanly;
some of them appear to actually work, including _hashlib and _elementtree.
Ironclad grows more stable and mature with every release, and I urge
IronPython users to try it out and share their impressions: feedback,
whether positive or negative, is always welcomed.
Cheers
William
Hi all,
a make latex in numpy/doc failed with
...
Intersphinx hit: PyObject
http://docs.python.org/dev/c-api/structures.html
writing... Sphinx error:
too many nesting section levels for LaTeX, at heading:
numpy.ma.MaskedArray.__lt__
make: *** [latex] Fehler 1
I am using sphinxv0.5.1
BTW, make html works fine here.
Nils
