Mailman 3 October 2009 - NumPy-Discussion

MKL with 64bit crashes
by Kashyap Ashwin 21 Jan '10

21 Jan '10

Hello, I compiled numpy-1.3.0 from sources on Ubuntu-hardy, x86-64 (Intel) with MKL. This is my site.cfg: [mkl] # library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/ library_dirs = /opt/intel/mkl/10.2.2.025/lib/em64t include_dirs = /opt/intel/mkl/10.2.2.025/include lapack_libs = mkl_lapack #mkl_libs = mkl_core, guide, mkl_gf_ilp64, mkl_def, mkl_gnu_thread, iomp5, mkl_vml_mc3 mkl_libs = guide, mkl_core, mkl_gnu_thread, iomp5, mkl_gf_ilp64, mkl_mc3, mkl_def In [2]: numpy.test() Running unit tests for numpy NumPy version 1.3.0 NumPy is installed in /opt/********************* Python version 2.5.2 (r252:60911, Jul 22 2009, 15:33:10) [GCC 4.2.4 (Ubuntu 4.2.4-1ubuntu3)] nose version 0.11.0 .......... MKL ERROR: Parameter 4 was incorrect on entry to DGESV MKL ERROR: Parameter 4 was incorrect on entry to DGESV MKL ERROR: Parameter 4 was incorrect on entry to DGESV MKL ERROR: Parameter 4 was incorrect on entry to DGESV MKL ERROR: Parameter 4 was incorrect on entry to DGESV MKL ERROR: Parameter 4 was incorrect on entry to DGESV .. MKL ERROR: Parameter 4 was incorrect on entry to DGESV FSegmentation fault I am using gcc: gcc -v Using built-in specs. Target: x86_64-linux-gnu Configured with: ../src/configure -v --enable-languages=c,c++,fortran,objc,obj-c++,treelang --prefix=/usr --enable-shared --with-system-zlib --libexecdir=/usr/lib --without-included-gettext --enable-threads=posix --enable-nls --with-gxx-include-dir=/usr/include/c++/4.2 --program-suffix=-4.2 --enable-clocale=gnu --enable-libstdcxx-debug --enable-objc-gc --enable-mpfr --enable-checking=release --build=x86_64-linux-gnu --host=x86_64-linux-gnu --target=x86_64-linux-gnu Thread model: posix gcc version 4.2.4 (Ubuntu 4.2.4-1ubuntu4) Anyone having the same issues? Do I have the mkl_libs correctly configured (this seems like a black art!) Thanks, Ashwin

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Assigning complex values to a real array
by Stéfan van der Walt 10 Dec '09

10 Dec '09

Hi all, The following code succeeds, while I thought it should fail: a = np.zeros(6) # real b= np.arange(6)*(2+3j) # complex a[1] = b[1] # shouldn't this break? What is the rationale behind this behaviour? Cheers Stéfan

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Is anyone knowledgeable about dll deployment on windows ?
by David Cournapeau 01 Dec '09

01 Dec '09

Hi, My head hurt trying to understand dll management with windows. I wanted to find a sane way to use dll for numscons, but I can't see how to do this, so I was wondering if anyone on this ML had any deep knowledge on how to install dll, and reuse them with python extensions ? The problem is the following: - you have some dll (say MKL) in a path (say C:\program files\MKL). - how do you tell another dll (more exactly a python extension, a .pyd) to look for mkl dll into the MKL path ? It seems that there is no rpath-like capability on windows, and the only way to load dll is to put them into some windows directory, or to put the dll path into PATH. Is it really the only way ? cheers, David

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Numpy/Scipy for EC2
by Dan Yamins 20 Nov '09

20 Nov '09

Hi all: I'm gearing up to build an Amazon Machine Instance (AMI) for use in doing Numpy/Scipy computations on the Amazon EC2 cloud. I'm writing to ask if anyone has any advice for which (if any) publicly available AMI I should start with. If any one has any specific AMI's that they think are good bases from which to modify -- or really, any other advice about using numpy/scipy on EC2 -- I'd love to know. Beyond that, even if you don't know which AMI to recommend (or even what an AMI is), I still would like advice about which Linux flavor to use. I've had some experience with Mac OSX (and, with David Cornapeau's help over this list, I was able to build 64-bit Scipy with Python 2.6!), but I really know nothing about what the build process is like on Linux (and most likely, unless someone recommends a good AMI with optimized BLAS/LAPACK already built, I'm going to have to built it from scratch). So, should I use Ubuntu or Debian or Fedora or Centos or ...? Thanks! Dan

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datetime64
by Ariel Rokem 20 Nov '09

20 Nov '09

Hi - I want to start trying out the new dtype for representation of arrays of times, datetime64, which is implemented in the current svn. Is there any documentation anywhere? I know of this proposal: http://numpy.scipy.org/svn/numpy/tags/1.3.0/doc/neps/datetime-proposal3.rst but apparently the current implementation of the dtype didn't follow this proposal - the hypothetical examples in the spec don't work with the implementation. I just want to see a couple of examples on how to initialize arrays of this dtype, and what kinds of operations can be done with them (and with timedelta64). Thanks a lot, Ariel -- Ariel Rokem Helen Wills Neuroscience Institute University of California, Berkeley http://argentum.ucbso.berkeley.edu/ariel

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persistent ImportError: No module named multiarray when moving cPickle files between machines
by Reckoner 05 Nov '09

05 Nov '09

Hi, % python -c 'import numpy.core.multiarray' works just fine, but when I try to load a file that I have transferred from another machine running Windows to one running Linux, I get: % python -c 'import cPickle;a=cPickle.load(open("matrices.pkl"))' Traceback (most recent call last): File "<string>", line 1, in <module> ImportError: No module named multiarray otherwise, cPickle works normally when transferring files that *do* not contain numpy arrays. I am using version 1.2 on both machines. It's not so easy for me to change versions, by the way, since this is the version that my working group has decided on to standardize on for this effort. Any help appreciated.

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Recarray comparison and byte order
by Matthew Brett 01 Nov '09

01 Nov '09

Hi, I was surprised by this - is it a bug or a feature or me misunderstanding something? a = np.zeros((1,), dtype=[('f1', 'u2')]) b = a.copy() b == a (array([True], dtype=bool)) # as expected c = a.byteswap().newbyteorder() c == a (False) # to me, unexpected, note bool rather than array Thanks for any clarification... Matthew

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just how 'discontiguous' can a numpy array become?
by Chris Colbert 31 Oct '09

31 Oct '09

For example say we have an original array a=np.random.random((512, 512, 3)) and we take a slice of that array b=a[:100, :100, :] now, b is discontiguous, but all if its memory is owned by a. Will there ever be a situation where a discontiguous array owns its own data? Or more generally, will discontiguous data alway have a contiguous parent? As far as i understand the numpy strided model, that could only be supported if len(strides) = ndim+1, I dont think numpy supports that. Don't get me wrong, I'm not making a feature request, just making sure I fully understand the array model so I can avoid trampling on memory. Cheers! Chris

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type 'numpy.int64' unhashable
by Sebastian Haase 30 Oct '09

30 Oct '09

Hi, I get this error: set(chainsA[0,:,0]) TypeError: unhashable type: 'numpy.ndarray' >>> list(chainsA[0,:,0]) [2636, 2590, 2619, 2590] >>> list(chainsA[0,:,0])[0] 2636 >>> type(_) <type 'numpy.int64'> I understand where this error comes from, however what I was trying to do seems to "intuitive" that I would like to ask for suggestions: "What should I do if the "number" 2636 becomes unhashable ?" Thanks, Sebastian Haase

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[RFC] complex functions in npymath
by Pauli Virtanen 30 Oct '09

30 Oct '09

Hi (esp. David), If there are no objections, I'd like to move Numpy's complex-valued C99-like functions to npymath: http://github.com/pv/numpy-work/tree/npymath-complex This'll come useful if we want to start eg. writing Ufuncs in Cython. I'm working around possible compiler-incompatibilities of struct return values by having only pointer versions of the functions in libnpymath.a, and the non-pointer versions as inlined static functions. Also, perhaps we should add a header file npy_math_c99compat.h that would detect if the compiler supports C99, and if not, substitute the C99 functions with our npy_math implementations. This'd be great for scipy.special. -- Pauli Virtanen

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NumPy-Discussion October 2009