Mailman 3 May 2007 - NumPy-Discussion

New Linux build problem
by Brian Granger May 30, 2007

May 30, 2007

Hi, I am building numpy on a 32 bit Linux system (Scientific Linux). Numpy used to build fine on this system, but as I have moved to the new 1.0.3 versions, I have run into problems building. Basically, I get lots of things like: undefined reference to `cblas_sdot' and undefined reference to `PyArg_ParseTuple' Here are some highlight of the build process: lapack_info: libraries lapack not found in /home2/dechow/work/txp/txpython-0.2-linux/local/lib libraries lapack not found in /usr/local/lib FOUND: libraries = ['lapack'] library_dirs = ['/usr/lib'] language = f77 FOUND: libraries = ['lapack', 'blas'] library_dirs = ['/usr/lib'] define_macros = [('NO_ATLAS_INFO', 1)] language = f77 And later: creating build/temp.linux-i686-2.5/numpy/core/blasdot compile options: '-DNO_ATLAS_INFO=1 -Inumpy/core/blasdot -Inumpy/core/include -Ibuild/src.linux-i686-2.5/numpy/core -Inumpy/core/src -Inumpy/core/include -I/home2/dechow/work/txp/txpython-0.2-linux/local/include/python2.5 -c' gcc: numpy/core/blasdot/_dotblas.c /usr/bin/gfortran build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o -L/usr/lib -lblas -lgfortran -o build/lib.linux-i686-2.5/numpy/core/_dotblas.so build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x9a): In function `FLOAT_dot': numpy/core/blasdot/_dotblas.c:24: undefined reference to `cblas_sdot' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x14a): In function `DOUBLE_dot': numpy/core/blasdot/_dotblas.c:39: undefined reference to `cblas_ddot' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x201): In function `CFLOAT_dot': numpy/core/blasdot/_dotblas.c:54: undefined reference to `cblas_cdotu_sub' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x2ad): In function `CDOUBLE_dot': numpy/core/blasdot/_dotblas.c:68: undefined reference to `cblas_zdotu_sub' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x2e3): In function `dotblas_alterdot': numpy/core/blasdot/_dotblas.c:83: undefined reference to `PyArg_ParseTuple' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x2f8):numpy/core/blasdot/_dotblas.c:107: undefined reference to `_Py_NoneStruct' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x3e3): In function `dotblas_restoredot': numpy/core/blasdot/_dotblas.c:118: undefined reference to `PyArg_ParseTuple' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x3f8):numpy/core/blasdot/_dotblas.c:144: undefined reference to `_Py_NoneStruct' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x57c): In function `dotblas_matrixproduct': numpy/core/blasdot/_dotblas.c:196: undefined reference to `PyArg_ParseTuple' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x758):numpy/core/blasdot/_dotblas.c:226: undefined reference to `PyTuple_New' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x76c):numpy/core/blasdot/_dotblas.c:83: undefined reference to `PyArg_ParseTuple' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x785):numpy/core/blasdot/_dotblas.c:107: undefined reference to `_Py_NoneStruct' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0xbde):numpy/core/blasdot/_dotblas.c:321: undefined reference to `PyExc_ValueError' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0xbf2):numpy/core/blasdot/_dotblas.c:321: undefined reference to `PyErr_SetString' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0xca8):numpy/core/blasdot/_dotblas.c:539: undefined reference to `PyEval_SaveThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0xd30):numpy/core/blasdot/_dotblas.c:696: undefined reference to `PyEval_RestoreThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0xfca):numpy/core/blasdot/_dotblas.c:365: undefined reference to `PyEval_SaveThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x108f):numpy/core/blasdot/_dotblas.c:589: undefined reference to `PyEval_SaveThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1129):numpy/core/blasdot/_dotblas.c:495: undefined reference to `PyEval_SaveThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x11ff):numpy/core/blasdot/_dotblas.c:657: undefined reference to `PyEval_SaveThread' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x131c):numpy/core/blasdot/_dotblas.c:373: undefined reference to `cblas_daxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x13b1):numpy/core/blasdot/_dotblas.c:406: undefined reference to `cblas_zaxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x146c):numpy/core/blasdot/_dotblas.c:435: undefined reference to `cblas_saxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x14f4):numpy/core/blasdot/_dotblas.c:550: undefined reference to `cblas_dgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1629):numpy/core/blasdot/_dotblas.c:389: undefined reference to `cblas_daxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x16a6):numpy/core/blasdot/_dotblas.c:507: undefined reference to `cblas_ddot' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x171f):numpy/core/blasdot/_dotblas.c:468: undefined reference to `cblas_caxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x17a0):numpy/core/blasdot/_dotblas.c:556: undefined reference to `cblas_sgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x17db):numpy/core/blasdot/_dotblas.c:512: undefined reference to `cblas_sdot' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x188d):numpy/core/blasdot/_dotblas.c:562: undefined reference to `cblas_zgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x19a7):numpy/core/blasdot/_dotblas.c:422: undefined reference to `cblas_zaxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1a31):numpy/core/blasdot/_dotblas.c:517: undefined reference to `cblas_zdotu_sub' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1ab2):numpy/core/blasdot/_dotblas.c:569: undefined reference to `cblas_cgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1b6f):numpy/core/blasdot/_dotblas.c:669: undefined reference to `cblas_dgemm' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1bb7):numpy/core/blasdot/_dotblas.c:521: undefined reference to `cblas_cdotu_sub' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1c3f):numpy/core/blasdot/_dotblas.c:605: undefined reference to `cblas_dgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1d14):numpy/core/blasdot/_dotblas.c:676: undefined reference to `cblas_sgemm' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1dda):numpy/core/blasdot/_dotblas.c:451: undefined reference to `cblas_saxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1eaf):numpy/core/blasdot/_dotblas.c:611: undefined reference to `cblas_sgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x1f37):numpy/core/blasdot/_dotblas.c:690: undefined reference to `cblas_cgemm' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x2020):numpy/core/blasdot/_dotblas.c:617: undefined reference to `cblas_zgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x20ea):numpy/core/blasdot/_dotblas.c:484: undefined reference to `cblas_caxpy' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x21c6):numpy/core/blasdot/_dotblas.c:683: undefined reference to `cblas_zgemm' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x2255):numpy/core/blasdot/_dotblas.c:623: undefined reference to `cblas_cgemv' build/temp.linux-i686-2.5/numpy/core/blasdot/_dotblas.o(.text+0x22ec): In function `dotblas_innerproduct': numpy/core/blasdot/_dotblas.c:729: undefined reference to `PyArg_ParseTuple' Have there been recent changes to distutils that would cause these undefined references to appear? Thanks Brian

2 3

NumPy 1.0.3 released
by Travis Oliphant May 29, 2007

May 29, 2007

I'm pleased to announce the release of NumPy 1.0.3 Hopefully, this release will work better with multiple interpreters as well as having some significant bugs fixed. Other changes include * x/y follows Python standard on mixed-sign division * iinfo added to provide information on integer data-types * improvements to SwIG typemaps, numpy.distutils, and f2py * improvements to separator handling in fromfile and fromstring * many, many bug fixes Thank you to everybody who contributed to the recent release. Best regards, NumPy Developers http://numpy.scipy.org

8 12

Building NumPy 1.0.3 on SGI/IRIX system
by Mary Haley May 29, 2007

May 29, 2007

Hi all, I have been trying to build numpy 1.0.3 on my SGI/IRIX64 box (64-bit version) with no luck. I'm using Python 2.4.4. I've tried setting CC to both the default "cc" compiler (which I built python with), and "gcc", with no luck. I tried c89 and c99, still with no luck. I get errors like: cc -64 _configtest.o -L/usr/local/lib -L/usr/lib -o _configtest ld64: FATAL 12 : Expecting n64 objects: /usr/lib/crt1.o is o32. ld64: FATAL 12 : Expecting n64 objects: /usr/lib/crt1.o is o32. failure. removing: _configtest.c _configtest.o I've tried building other versions of numpy over the last couple of years and have never been successful. I've actually been using Numeric on that machine, and using numpy everywhere else. Has anybody been successful with this, and if so, what compile options did you use? Thanks, --Mary

2 5

ANN: PyTables 2.0rc2 released
by Francesc Altet May 29, 2007

May 29, 2007

============================ Announcing PyTables 2.0rc2 ============================ PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. This is the second (and probably last) release candidate for PyTables 2.0. On it, together with the traditional bunch of bug fixes, you will find a handful of optimizations for dealing with very large tables. Also, the "Optimization tips" chapter of User's Guide has been updated and the manual is almost ready (bar some errors or typos we may have introduced) for the long awaited 2.0 final release. In particular, the "Indexed searches" section shows pretty definitive plots on the performance of the completely new and innovative indexing engine that will be available in the Pro version (to be released very soon now). You can download a source package of the version 2.0rc2 with generated PDF and HTML docs and binaries for Windows from http://www.pytables.org/download/preliminary/ For an on-line version of the manual, visit: http://www.pytables.org/docs/manual-2.0rc2 In case you want to know more in detail what has changed in this version, have a look at ``RELEASE_NOTES.txt``. Find the HTML version for this document at: http://www.pytables.org/moin/ReleaseNotes/Release_2.0rc2 If you are a user of PyTables 1.x, probably it is worth for you to look at ``MIGRATING_TO_2.x.txt`` file where you will find directions on how to migrate your existing PyTables 1.x apps to the 2.0 version. You can find an HTML version of this document at http://www.pytables.org/moin/ReleaseNotes/Migrating_To_2.x Keep reading for an overview of the most prominent improvements in PyTables 2.0 series. New features of PyTables 2.0 ============================ - A complete refactoring of many, many modules in PyTables. With this, the different parts of the code are much better integrated and code redundancy is kept under a minimum. A lot of new optimizations have been included as well, making working with it a smoother experience than ever before. - NumPy is finally at the core! That means that PyTables no longer needs numarray in order to operate, although it continues to be supported (as well as Numeric). This also means that you should be able to run PyTables in scenarios combining Python 2.5 and 64-bit platforms (these are a source of problems with numarray/Numeric because they don't support this combination as of this writing). - Most of the operations in PyTables have experimented noticeable speed-ups (sometimes up to 2x, like in regular Python table selections). This is a consequence of both using NumPy internally and a considerable effort in terms of refactorization and optimization of the new code. - Combined conditions are finally supported for in-kernel selections. So, now it is possible to perform complex selections like:: result = [ row['var3'] for row in table.where('(var2 < 20) | (var1 == "sas")') ] or:: complex_cond = '((%s <= col5) & (col2 <= %s)) ' \ '| (sqrt(col1 + 3.1*col2 + col3*col4) > 3)' result = [ row['var3'] for row in table.where(complex_cond % (inf, sup)) ] and run them at full C-speed (or perhaps more, due to the cache-tuned computing kernel of Numexpr, which has been integrated into PyTables). - Now, it is possible to get fields of the ``Row`` iterator by specifying their position, or even ranges of positions (extended slicing is supported). For example, you can do:: result = [ row[4] for row in table # fetch field #4 if row[1] < 20 ] result = [ row[:] for row in table # fetch all fields if row['var2'] < 20 ] result = [ row[1::2] for row in # fetch odd fields table.iterrows(2, 3000, 3) ] in addition to the classical:: result = [row['var3'] for row in table.where('var2 < 20')] - ``Row`` has received a new method called ``fetch_all_fields()`` in order to easily retrieve all the fields of a row in situations like:: [row.fetch_all_fields() for row in table.where('column1 < 0.3')] The difference between ``row[:]`` and ``row.fetch_all_fields()`` is that the former will return all the fields as a tuple, while the latter will return the fields in a NumPy void type and should be faster. Choose whatever fits better to your needs. - Now, all data that is read from disk is converted, if necessary, to the native byteorder of the hosting machine (before, this only happened with ``Table`` objects). This should help to accelerate applications that have to do computations with data generated in platforms with a byteorder different than the user machine. - The modification of values in ``*Array`` objects (through __setitem__) now doesn't make a copy of the value in the case that the shape of the value passed is the same as the slice to be overwritten. This results in considerable memory savings when you are modifying disk objects with big array values. - All leaf constructors (except for ``Array``) have received a new ``chunkshape`` argument that lets the user explicitly select the chunksizes for the underlying HDF5 datasets (only for advanced users). - All leaf constructors have received a new parameter called ``byteorder`` that lets the user specify the byteorder of their data *on disk*. This effectively allows to create datasets in other byteorders than the native platform. - Native HDF5 datasets with ``H5T_ARRAY`` datatypes are fully supported for reading now. - The test suites for the different packages are installed now, so you don't need a copy of the PyTables sources to run the tests. Besides, you can run the test suite from the Python console by using:: >>> tables.tests() Resources ========= Go to the PyTables web site for more details: http://www.pytables.org About the HDF5 library: http://hdfgroup.org/HDF5/ About NumPy: http://numpy.scipy.org/ To know more about the company behind the development of PyTables, see: http://www.carabos.com/ Acknowledgments =============== Thanks to many users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Many thanks also to SourceForge who have helped to make and distribute this package! And last, but not least thanks a lot to the HDF5 and NumPy (and numarray!) makers. Without them PyTables simply would not exist. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team -- Francesc Altet | Be careful about using the following code -- Carabos Coop. V. | I've only proven that it works, www.carabos.com | I haven't tested it. -- Donald Knuth

1 0

howto exclude elements from sorted array which are present in other sorted array
by dmitrey May 29, 2007

May 29, 2007

hi all, I have 2 sorted arrays arr1 & arr2 (ascending order, for example [1,2,5,8] and [3,5], but length may be thousands, so I need simpliest way, not costly). Howto obtain arr3 that consists of elements present in arr1 but absent in arr2? Of course, I can write something by myself, like arr3 = [x for x in arr1 if not x in arr2] but maybe already written routines are present in numpy? (that use sort) Usually arr2 is much more small than arr1, typical former size is 10-20 and typical latter size can be 1000-10000 Thank you in advance, D.

2 2

Long double in Python with Numpy
by Charles V. May 29, 2007

May 29, 2007

Hi, I have a C library using "long double" numbers. With SWIG, I am able to use this library in Python. I use the PyArray_FromDims function in the SWIG "interface" file. But sometimes, I only need one long double : it's not very convenient to create an array for only one number... In Python there is "numpy.longdouble". Did you know how to create this object in C in the SWIG "interface" file ? (maybe the PyLongDoubleScalarObject structure ?) Thank you Charles

1 0

Vista installer?
by Ryan Krauss May 29, 2007

May 29, 2007

I am trying to use Numpy/Scipy for a class I am teaching this summer. I have one student running Vista. Is there an installer that works for Vista? Running the exe file from webpage gives errors about not being able to create various folders and files. I think this is from Vista being very restrictive about which files and folders are writable. Is anyone out there running Numpy/Scipy in Vista? If so, how did you get it to work? Thanks, Ryan

7 17

complex arrays and ctypes
by Sven Schreiber May 28, 2007

May 28, 2007

Hi, I am wondering whether it's possible to pass (and get back) arrays of complex floats via ctypes. To be concrete, I have managed to access lapack's dgges (Fortran) function using ctypes (thanks to a related example on this list), and now I would like to do the same with the complex version zgges. Thanks for your insights, Sven

2 2

chirp-z
by Charles R Harris May 28, 2007

May 28, 2007

All, I have been looking around for chirp-z code with a useable license. There is the original fortran version by Rader et. al. out there, as well as a package from FreeBSD at http://ftp2.at.freebsd.org/pub/FreeBSD/distfiles/fxt-2006.12.17.tgz. The latter is c++ and relies on function signatures to distinguish various functions with the same name, but looks pretty easy to translate. I note that we could probably use an fast_correlate also. Is there any interest in adding these to the fft pack? Or do they properly belong in scipy? Chuck

4 11

Numpy 1.0.3 install problem. Help!
by Yang, Lu May 27, 2007

May 27, 2007

Hi, I am installing Numpy 1.0.3 on Solaris 10. I am new to Numpy install. Here are what I did and the result of 'python setup.py install'. Please help. Thanks in advance. I set: setenv CFLAGS "-xchip=opteron " setenv CXXFLAGS "-xchip=opteron " setenv CC /opt/SUNWspro/bin/cc setenv CXX /opt/SUNWspro/bin/CC setenv LDFLAGS " -L/lib " setenv LD_LIBRARY_PATH /opt/SUNWspro/lib:$LD_LIBRARY_PATH Results of 'python setup.py install': Running from numpy source directory. F2PY Version 2_3816 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /usr/sfw/lib libraries mkl,vml,guide not found in /usr/local/lib libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS libraries lapack,blas not found in /usr/sfw/lib libraries lapack,blas not found in /usr/local/lib libraries lapack,blas not found in /usr/lib NOT AVAILABLE atlas_blas_info: libraries lapack,blas not found in /usr/sfw/lib libraries lapack,blas not found in /usr/local/lib libraries lapack,blas not found in /usr/lib NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1314: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) blas_info: libraries blas not found in /usr/sfw/lib libraries blas not found in /usr/local/lib libraries blas not found in /usr/lib NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1323: UserWarning: Blas (http://www.netlib.org/blas/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [blas]) or by setting the BLAS environment variable. warnings.warn(BlasNotFoundError.__doc__) blas_src_info: NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1326: UserWarning: Blas (http://www.netlib.org/blas/) sources not found. Directories to search for the sources can be specified in the numpy/distutils/site.cfg file (section [blas_src]) or by setting the BLAS_SRC environment variable. warnings.warn(BlasSrcNotFoundError.__doc__) NOT AVAILABLE lapack_opt_info: lapack_mkl_info: mkl_info: libraries mkl,vml,guide not found in /usr/sfw/lib libraries mkl,vml,guide not found in /usr/local/lib libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries lapack,blas not found in /usr/sfw/lib libraries lapack_atlas not found in /usr/sfw/lib libraries lapack,blas not found in /usr/local/lib libraries lapack_atlas not found in /usr/local/lib libraries lapack,blas not found in /usr/lib libraries lapack_atlas not found in /usr/lib numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: libraries lapack,blas not found in /usr/sfw/lib libraries lapack_atlas not found in /usr/sfw/lib libraries lapack,blas not found in /usr/local/lib libraries lapack_atlas not found in /usr/local/lib libraries lapack,blas not found in /usr/lib libraries lapack_atlas not found in /usr/lib numpy.distutils.system_info.atlas_info NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1221: UserWarning: Atlas (http://math-atlas.sourceforge.net/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [atlas]) or by setting the ATLAS environment variable. warnings.warn(AtlasNotFoundError.__doc__) lapack_info: libraries lapack not found in /usr/sfw/lib libraries lapack not found in /usr/local/lib libraries lapack not found in /usr/lib NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1232: UserWarning: Lapack (http://www.netlib.org/lapack/) libraries not found. Directories to search for the libraries can be specified in the numpy/distutils/site.cfg file (section [lapack]) or by setting the LAPACK environment variable. warnings.warn(LapackNotFoundError.__doc__) lapack_src_info: NOT AVAILABLE /net/nfs1-data/main/apps/src/numpy-1.0.3/numpy/distutils/system_info.py:1235: UserWarning: Lapack (http://www.netlib.org/lapack/) sources not found. Directories to search for the sources can be specified in the numpy/distutils/site.cfg file (section [lapack_src]) or by setting the LAPACK_SRC environment variable. warnings.warn(LapackSrcNotFoundError.__doc__) NOT AVAILABLE non-existing path in '/net/nfs1-data/main/apps/src/numpy-1.0.3': 'COMPATIBILITY' non-existing path in '/net/nfs1-data/main/apps/src/numpy-1.0.3': 'scipy_compatibility' running install running build running config_cc unifing config_cc, config, build_clib, build_ext, build commands --compiler options running config_fc unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options running build_src building py_modules sources building extension "numpy.core.multiarray" sources adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_array_api.py adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h' to sources. adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/src' to include_dirs. numpy.core - nothing done with h_files= ['build/src.solaris-2.10-i86pc-2.3/numpy/core/src/scalartypes.inc', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/src/arraytypes.inc', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h'] building extension "numpy.core.umath" sources adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_ufunc_api.py adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__ufunc_api.h' to sources. adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/src' to include_dirs. numpy.core - nothing done with h_files= ['build/src.solaris-2.10-i86pc-2.3/numpy/core/src/scalartypes.inc', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/src/arraytypes.inc', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__ufunc_api.h'] building extension "numpy.core._sort" sources adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_array_api.py adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h' to sources. numpy.core - nothing done with h_files= ['build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h'] building extension "numpy.core.scalarmath" sources adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_array_api.py adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h' to sources. executing numpy/core/code_generators/generate_ufunc_api.py adding 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__ufunc_api.h' to sources. numpy.core - nothing done with h_files= ['build/src.solaris-2.10-i86pc-2.3/numpy/core/config.h', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__multiarray_api.h', 'build/src.solaris-2.10-i86pc-2.3/numpy/core/__ufunc_api.h'] building extension "numpy.core._dotblas" sources building extension "numpy.lib._compiled_base" sources building extension "numpy.numarray._capi" sources building extension "numpy.fft.fftpack_lite" sources building extension "numpy.linalg.lapack_lite" sources ### Warning: Using unoptimized lapack ### adding 'numpy/linalg/lapack_litemodule.c' to sources. adding 'numpy/linalg/zlapack_lite.c' to sources. adding 'numpy/linalg/dlapack_lite.c' to sources. adding 'numpy/linalg/blas_lite.c' to sources. adding 'numpy/linalg/dlamch.c' to sources. adding 'numpy/linalg/f2c_lite.c' to sources. building extension "numpy.random.mtrand" sources customize SunFCompiler customize SunFCompiler customize SunFCompiler using config C compiler: /opt/SUNWspro/bin/cc -i -xO4 -xspace -xstrconst -xpentium -mr -DANSICPP -D__STDC_VERSION__=199409L -DNDEBUG -O -xchip=opteron compile options: '-Inumpy/core/src -Inumpy/core/include -I/usr/sfw/include/python2.3 -c' cc: _configtest.c "_configtest.c", line 7: #error: No _WIN32 cc: acomp failed for _configtest.c "_configtest.c", line 7: #error: No _WIN32 cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o building data_files sources running build_py copying build/src.solaris-2.10-i86pc-2.3/numpy/__config__.py -> build/lib.solaris-2.10-i86pc-2.3/numpy copying build/src.solaris-2.10-i86pc-2.3/numpy/distutils/__config__.py -> build/lib.solaris-2.10-i86pc-2.3/numpy/distutils running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext building 'numpy.core.multiarray' extension compiling C sources C compiler: /opt/SUNWspro/bin/cc -i -xO4 -xspace -xstrconst -xpentium -mr -DANSICPP -D__STDC_VERSION__=199409L -DNDEBUG -O -xchip=opteron compile options: '-Ibuild/src.solaris-2.10-i86pc-2.3/numpy/core/src -Inumpy/core/include -Ibuild/src.solaris-2.10-i86pc-2.3/numpy/core -Inumpy/core/src -Inumpy/core/include -I/usr/sfw/include/python2.3 -c' /sgnome/tools/x86-solaris/forte/SOS8/SUNWspro/bin/cc -G -L/lib -xchip=opteron build/temp.solaris-2.10-i86pc-2.3/numpy/core/src/multiarraymodule.o -lm -o build/lib.solaris-2.10-i86pc-2.3/numpy/core/multiarray.so sh: /sgnome/tools/x86-solaris/forte/SOS8/SUNWspro/bin/cc: not found sh: /sgnome/tools/x86-solaris/forte/SOS8/SUNWspro/bin/cc: not found error: Command "/sgnome/tools/x86-solaris/forte/SOS8/SUNWspro/bin/cc -G -L/lib -xchip=opteron build/temp.solaris-2.10-i86pc-2.3/numpy/core/src/multiarraymodule.o -lm -o build/lib.solaris-2.10-i86pc-2.3/numpy/core/multiarray.so" failed with exit status 1

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