Re: [Numpy-discussion] numpy.distutils
Hi David, The behaviour is there in regular distutils, it is apparently a known bug, I'm copy/pasting their answer in there for information. thanks, C. Answer (from Tarek Ziade): This is a regression I introduced to fix the fact that the .so are not rebuilt we you do subtle changes in your project. We ended up reverting this, and the changes will be done in Distutils2. see: http://BLOCKEDbugs.python.org/issue8688 for the details. On 10/7/10 9:39 PM, David Cournapeau wrote: On Fri, Oct 8, 2010 at 7:40 AM, Charles Doutriaux doutria...@llnl.gov wrote: Did anybody else noticed this? Anybody know what changed (the fact that it's since Python 2.7 make me think it might be pure distutils related) Could you check whether you still see the issue without using numpy.distutils ? I actually would be surprised to see it as a distutils issue proper, as somebody would have almost certainly noticed it already. Any insight on how distuils and numpy.distutils are related would be great too! numpy.distutils uses distutils, but in an unconvential way, because it needs to hook itself to internals. Hence, any non trivial change (and even trivial, sometimes) can break numpy.distutils. cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://BLOCKEDmail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.distutils
Hi, I'm not sure if this is a numpy.distutils or a regular distutils issue so please excuse me if it doesn't belong here. I 'm using numpy 1.4.1 and I have a C extension (using numpy arrays) that I built with numpy. When I'm debugging I frequently have to rebuild. It use to rebuild only the C files that had been touched. Since python 2.7 (it seems to be the trigger) it now ALWAYS rebuild EVERY C file wherever or not they need to. Did anybody else noticed this? Anybody know what changed (the fact that it's since Python 2.7 make me think it might be pure distutils related) Any insight on how distuils and numpy.distutils are related would be great too! Thanks, C. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Fwd: distutils
Original Message Subject:[Numpy-discussion] distutils Date: Tue, 7 Sep 2010 12:12:58 -0700 From: Charles Doutriaux doutria...@llnl.gov Reply-To: Discussion of Numerical Python numpy-discussion@scipy.org To: Discussion of Numerical Python numpy-discussion@scipy.org Hi, I'm using distutils to build extensions written in C. I noticed that lately (it seems to be python 2.7 related) whenever I touch 1 C file, ALL the C files are rebuilt. Since I have a lot of C code, it takes a lot of time for nothing. Any idea why this is happening? Do I need to set something new in my setup.py ? Thx, C. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://*mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] distutils
Hi, I'm using distutils to build extensions written in C. I noticed that lately (it seems to be python 2.7 related) whenever I touch 1 C file, ALL the C files are rebuilt. Since I have a lot of C code, it takes a lot of time for nothing. Any idea why this is happening? Do I need to set something new in my setup.py ? Thx, C. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] 1.2.0rc2 tagged! --PLEASE TEST--
On this system, both 1.2rc1 and rc2 hang on mtrand.c Linux thunder0 2.6.9-74chaos #1 SMP Wed Oct 24 08:41:12 PDT 2007 ia64 ia64 ia64 GNU/Linux attached is the log. numpy.LOG.bz2 Description: application/bzip ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] 1.2.0rc2 tagged! --PLEASE TEST--
Thanks Jarrod it's coming back now. I thought they had updated the system... but no luck... Ok that's the issue i'm using: gcc (GCC) 3.4.6 20060404 (Red Hat 3.4.6-8) Thanks again, C. Jarrod Millman wrote: Which version of gcc are you using? Do I remember correctly that you had the same failure with numpy 1.1.1 when using gcc 3.3 and that the problem went away when you used gcc 4.1. If you are using 3.3, could you try with 4.1 and let us know if you run into the same problem? Thanks, ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] f2py related question
Hello, I have a quick question that I'm hoping f2py developpers will be able to quickly answer I have some C code, input type can be either int or long I'm trying to write some fortran interface to it my understanding is that integer in fortran corresponds to int integer(kind=2) matches short but what would match long ? Thanks, c. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py related question
Thanks robert, That's exactly the information I needed. Thanks for the link too. C. Robert Kern wrote: On Thu, Sep 11, 2008 at 14:12, Charles Doutriaux [EMAIL PROTECTED] wrote: Hello, I have a quick question that I'm hoping f2py developpers will be able to quickly answer I have some C code, input type can be either int or long I'm trying to write some fortran interface to it my understanding is that integer in fortran corresponds to int integer(kind=2) matches short but what would match long ? Unfortunately, it depends on your system. A C long is defined to be at least as large as an int. Incidentally, an int is defined to be at least as long as a short and a short is defined to be at least as long as a char. So you *could* have char == short == int == long == 1 byte, but no such system exists (I hope!). On pretty much all 32-bit systems I am aware of, int == long == 4 bytes. On 64-bit systems, things get a little more fragmented. Most UNIX-type 64-bit systems, long == 8 bytes. On Win64, however, long == 4 bytes. This section of the Wikipedia article on 64-bit computing gives more detail: http:// en.wikipedia.org/wiki/64-bit#64-bit_data_models Furthermore, it's not just the OS that determines the data model, but also the options that are given to your compiler when building your program *and also all of the libraries and system libraries* that it uses. Fortunately, there's an easy way for you to tell: just ask numpy. Python ints are C longs, and the default numpy int dtype matches Python ints. On my 32-bit system: import numpy numpy.dtype(int).itemsize 4 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] small bug in numpy.ma.minimum.outer
a=numpy.ones((2,2)) numpy.minimum.outer(a,a) array( 1., 1.], [ 1., 1.]], [[ 1., 1.], [ 1., 1.]]], [[[ 1., 1.], [ 1., 1.]], [[ 1., 1.], [ 1., 1.) numpy.ma.minimum.outer(a,a) Traceback (most recent call last): File stdin, line 1, in module File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/ma/core.py, line 3317, in outer result._mask = m AttributeError: 'numpy.ndarray' object has no attribute '_mask' ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] compressed and sqrt in numpy.ma
The following is causing our code to crash, shouldn't .data be just ones ? a = numpy.ma.ones((2,2)) b = numpy.ma.sqrt(a) b.compressed().data read-write buffer for 0x9154460, size 32, offset 0 at 0xb764e500 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] small bug in numpy.ma.minimum.outer
Thx Pierre Pierre GM wrote: On Tuesday 09 September 2008 14:03:04 Charles Doutriaux wrote: a=numpy.ones((2,2)) numpy.minimum.outer(a,a) numpy.ma.minimum.outer(a,a) Fixed in SVN r5800. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http:// projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy 1.1.1 fails because of missing md5
Hi Robert, The first email got intercepted because the attachment was too big (awaits moderator), so i compressed the log and I resend this email. I'm attaching my Python build log, can you spot anything? It seems like md5 is built, i get a very similar log on my machine and i have a working import md5. I'm not sure of what's going on. Usually the build is fine. C. Robert Kern wrote: On Tue, Sep 2, 2008 at 16:40, Charles Doutriaux [EMAIL PROTECTED] wrote: Joseph, Ok all failed because numpy couldn't build... It's looking for md5 That's part of the standard library. Please check your Python installation. Python.LOG.bz2 Description: application/bzip ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy 1.1.1 fails because of missing md5
Thanks for spotting the origin, I'll pass this along to our user maybe they'll be able to figure out how to build python w/o openssl C. Robert Kern wrote: On Wed, Sep 3, 2008 at 10:39, Charles Doutriaux [EMAIL PROTECTED] wrote: Hi Robert, The first email got intercepted because the attachment was too big (awaits moderator), so i compressed the log and I resend this email. I'm attaching my Python build log, can you spot anything? It seems like md5 is built, i get a very similar log on my machine and i have a working import md5. md5.py gets installed, but it just (eventually) imports from one of the extension modules _md5 or _hashlib, neither of which is getting built. The errors following the line building '_hashlib' extension are relevant. OpenSSL gets used for its hash function implementations if it is available. The configuration thinks you want it to use OpenSSL, so it tries to build _hashlib, which fails. If the configuration thought you didn't want to use OpenSSL, it would try to build _md5. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy 1.1.1 fails because of missing md5
Joseph, Ok all failed because numpy couldn't build... It's looking for md5 on my machine i type: whereis md5 md5: /usr/include/md5.h /usr/share/man/man1/md5.1ssl.gz /usr/share/man/man3/md5.3ssl.gz I'm ccing the numpy discussion list on this. The numpy we're trying to build is 1.1.1 I'm attaching the numpy log. Is it fixed in 1.2 ? Joseph in the mean time you might want to try to install md5 on your system. C. Joseph Hargitai wrote: here are the logs - OS: 2.6.9-55.ELsmp #1 SMP Fri Apr 20 16:36:54 EDT 2007 x86_64 x86_64 x86_64 GNU/Linux Rocks 4.3 with Redhat installation cluster. rm -rf logs/numpy.LOG; cd build; cp ../exsrc/src/numpy* .; gunzip numpy*gz; tar xf numpy-1.1.1.tar; rm numpy-1.1.1.tar; cd numpy*; LDFLAGS=-L/state/partition1/cdat/Externals/lib -L/usr/X11R6/lib64 -L/usr/local/lib -Wl,-R/state/partition1/cdat/Externals/lib -L/state/partition1/cdat/Externals/HDF5/lib -L/state/partition1/cdat/Externals/NetCDF/lib; export LDFLAGS ; CPPFLAGS=-I/state/partition1/cdat/Externals/include -I/state/partition1/cdat/Externals/HDF5/include -I/state/partition1/cdat/Externals/HDF5/include -I/state/partition1/cdat/Externals/NetCDF/include ; export CPPFLAGS ; CC=gcc ; export CC ; FC=g77 ; export FC ; FCFLAGS=-g -O2 ; export FCFLAGS ;FCLIBS= -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6 -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6/../../../../lib64 -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6/../../.. -L/lib/../lib64 -L/usr/lib/../lib64 -lfrtbegin -lg2c -lm ; export FCLIBS ; F77=g77 ; export F77;FFLAGS=-g -O2 ; export FFLAGS;FLIBS= -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6 -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6/../../../../lib64 -L/usr/lib/gcc/x86_64-redhat-linux/3.4.6/../../.. -L/lib/../lib64 -L/usr/lib/../lib64 -lfrtbegin -lg2c -lm ; export FLIBS ;CPP=gcc -E; export CPP;CXX=g++; export CXX ; EXTERNALS=/state/partition1/cdat/Externals; export EXTERNALS ; PKG_CONFIG_PATH=/state/partition1/cdat/Externals/lib/pkgconfig: ; export PKG_CONFIG_PATH ;PATH=/state/partition1/cdat/Externals/bin:/state/partition1/cdat/bin:/usr/kerberos/bin:/usr/java/jdk1.5.0_10/bin:/usr/lpp/mmfs/bin:/usr/local/bin:/bin:/usr/bin:/usr/X11R6/bin:/opt/ganglia/bin:/opt/ganglia/sbin:/usr/local/matlab/R2007a/bin:/usr/sbin:/opt/openmpi/bin/:/opt/maui/bin:/opt/torque/bin:/opt/torque/sbin:/opt/rocks/bin:/opt/rocks/sbin:/usr/local/toolworks/totalview.8.4.1-5/bin:/usr/local/toolworks/totalview.8.4.1-5/man:/home/hargitai/bin:/opt/torque/bin:/opt/intel/fce/10.0.023/bin:/opt/intel/cce/10.0.023/bin:/usr/mpi/intel/openmpi-1.2.2-1/bin:/usr/local/toolworks/totalview.8.4.1-5/bin ; export PATH; cp /state/partition1/cdat5/site.cfg . ; unset LDFLAGS;; /state/partition1/cdat/bin/python setup.py build --force install --prefix=/state/partition1/cdat; Running from numpy source directory. F2PY Version 2_5585 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /state/partition1/cdat/Externals/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /state/partition1/cdat/Externals/lib NOT AVAILABLE atlas_blas_info: libraries f77blas,cblas,atlas not found in /state/partition1/cdat/Externals/lib NOT AVAILABLE /state/partition1/cdat5/build/numpy-1.1.1/numpy/distutils/system_info.py:1340: 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 /state/partition1/cdat/Externals/lib NOT AVAILABLE /state/partition1/cdat5/build/numpy-1.1.1/numpy/distutils/system_info.py:1349: 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 /state/partition1/cdat5/build/numpy-1.1.1/numpy/distutils/system_info.py:1352: 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 /state/partition1/cdat/Externals/lib NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /state/partition1/cdat/Externals/lib libraries lapack_atlas not found in /state/partition1/cdat/Externals/lib numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: libraries f77blas,cblas,atlas not found in /state/partition1/cdat/Externals/lib libraries
[Numpy-discussion] numpy build fail under cygwin (Vista)
Hi both trunk and 1.1.1 fail to build under cygwin vista (latest version) I'm copy/pasting the end of the log C. copying numpy/doc/reference/__init__.py - build/lib.cygwin-1.5.25-i686-2.5/numpy/doc/reference running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext customize GnuFCompiler gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize GnuFCompiler gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize GnuFCompiler using build_ext building 'numpy.core.multiarray' extension compiling C sources C compiler: gcc -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes creating build/temp.cygwin-1.5.25-i686-2.5 creating build/temp.cygwin-1.5.25-i686-2.5/numpy creating build/temp.cygwin-1.5.25-i686-2.5/numpy/core creating build/temp.cygwin-1.5.25-i686-2.5/numpy/core/src compile options: '-g -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/usr/include/python2.5 -I/usr/include/python2.5 -c' gcc: numpy/core/src/multiarraymodule.c gcc -shared -Wl,--enable-auto-image-base -g build/temp.cygwin-1.5.25-i686-2.5/numpy/core/src/multiarraymodule.o -L/usr/lib/python2.5/config -lpython2.5 -o build/lib.cygwin-1.5.25-i686-2.5/numpy/core/multiarray.dll building 'numpy.core.umath' extension compiling C sources C compiler: gcc -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes creating build/temp.cygwin-1.5.25-i686-2.5/build creating build/temp.cygwin-1.5.25-i686-2.5/build/src.cygwin-1.5.25-i686-2.5 creating build/temp.cygwin-1.5.25-i686-2.5/build/src.cygwin-1.5.25-i686-2.5/numpy creating build/temp.cygwin-1.5.25-i686-2.5/build/src.cygwin-1.5.25-i686-2.5/numpy/core creating build/temp.cygwin-1.5.25-i686-2.5/build/src.cygwin-1.5.25-i686-2.5/numpy/core/src compile options: '-g -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/usr/include/python2.5 -I/usr/include/python2.5 -c' gcc: build/src.cygwin-1.5.25-i686-2.5/numpy/core/src/umathmodule.c /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s: Assembler messages: /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s:72843: Error: suffix or operands invalid for `fnstsw' /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s:73098: Error: suffix or operands invalid for `fnstsw' /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s: Assembler messages: /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s:72843: Error: suffix or operands invalid for `fnstsw' /cygdrive/c/Users/CHARLE~1/AppData/Local/Temp/ccib5Stl.s:73098: Error: suffix or operands invalid for `fnstsw' error: Command gcc -fno-strict-aliasing -DNDEBUG -g -O3 -Wall -Wstrict-prototypes -g -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/src -Inumpy/core/include -Ibuild/src.cygwin-1.5.25-i686-2.5/numpy/core/include/numpy -Inumpy/core/src -Inumpy/core/include -I/usr/include/python2.5 -I/usr/include/python2.5 -c build/src.cygwin-1.5.25-i686-2.5/numpy/core/src/umathmodule.c -o build/temp.cygwin-1.5.25-i686-2.5/build/src.cygwin-1.5.25-i686-2.5/numpy/core/src/umathmodule.o failed with exit status 1 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy build fail under cygwin (Vista)
Thx David, Is there any plans on applying the suggested fix into numpy ? C. David Cournapeau wrote: On Wed, Aug 20, 2008 at 8:04 AM, Charles Doutriaux [EMAIL PROTECTED] wrote: Hi both trunk and 1.1.1 fail to build under cygwin vista (latest version) I'm copy/pasting the end of the log It is a cygwin bug: http:// www. mail-archive.com/numpy-discussion@scipy.org/msg10051.html cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http:// projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] masked_equal not commutative?
Hi I'm using 1.1.1 and found that numpy.ma.masked_equal is not commutative! I would expect it to be in this case. Or raise an error for uncompatible shape in the first case, no ? a = numpy.ma.arange(100) a.shape=(10,10) b=numpy.ma.masked_equal(1,a) b Traceback (most recent call last): File stdin, line 1, in module File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/ma/core.py, line 1691, in __repr__ 'data': str(self), File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/ma/core.py, line 1665, in __str__ res[m] = f IndexError: 0-d arrays can't be indexed. b=numpy.ma.masked_equal(a,1) b masked_array(data = [[0 -- 2 3 4 5 6 7 8 9] [10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24 25 26 27 28 29] [30 31 32 33 34 35 36 37 38 39] [40 41 42 43 44 45 46 47 48 49] [50 51 52 53 54 55 56 57 58 59] [60 61 62 63 64 65 66 67 68 69] [70 71 72 73 74 75 76 77 78 79] [80 81 82 83 84 85 86 87 88 89] [90 91 92 93 94 95 96 97 98 99]], mask = [[False True False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False] [False False False False False False False False False False]], fill_value=99) ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] masked_equal not commutative
As always as i clicked send i realized my error it is indeed not commutative and that makes sense but i'm not sure the case: numpy.ma.masked_equal(1,a) should have worked, since we don't really know how to do this comparison, the only thing that could make sense would be commutation but i thinks it's probably dangerous to assume what the user wants to do. C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] 64bit issue?
Hello I'm running into some strange error on a 64bit machine, I tracked it down to this line returning a NULL pointer, any idea why is this? I tried both numpy1.1.1 and what in trunk, numpy.test() passes for both Ok here's the uname of the machine and the offending line: Linux quartic.txcorp.com 2.6.20-1.2320.fc5 #1 SMP Tue Jun 12 18:50:49 EDT 2007 x86_64 x86_64 x86_64 GNU/Linux array = (PyArrayObject *)PyArray_SimpleNew(d, dims, self-type); where d is 3 and dims: 120,46,72 self-type is 11 it seems to pass with d=1, but i'm not 100% positive. Any idea on why it could be failing? Thanks, C. PS: just in case here are numpy.test results: import numpy numpy.test() Numpy is installed in /home/facets/doutriaux1/cdat/latest/lib/python2.5/site-packages/numpy Numpy version 1.1.1 Python version 2.5.2 (r252:60911, Aug 4 2008, 13:47:12) [GCC 4.1.1 20070105 (Red Hat 4.1.1-51)] Found 3/3 tests for numpy.core.tests.test_memmap Found 145/145 tests for numpy.core.tests.test_regression Found 12/12 tests for numpy.core.tests.test_records Found 3/3 tests for numpy.core.tests.test_errstate Found 72/72 tests for numpy.core.tests.test_numeric Found 36/36 tests for numpy.core.tests.test_numerictypes Found 290/290 tests for numpy.core.tests.test_multiarray Found 18/18 tests for numpy.core.tests.test_defmatrix Found 63/63 tests for numpy.core.tests.test_unicode Found 16/16 tests for numpy.core.tests.test_umath Found 7/7 tests for numpy.core.tests.test_scalarmath Found 2/2 tests for numpy.core.tests.test_ufunc Found 5/5 tests for numpy.distutils.tests.test_misc_util Found 4/4 tests for numpy.distutils.tests.test_fcompiler_gnu Found 2/2 tests for numpy.fft.tests.test_fftpack Found 3/3 tests for numpy.fft.tests.test_helper Found 15/15 tests for numpy.lib.tests.test_twodim_base Found 1/1 tests for numpy.lib.tests.test_machar Found 1/1 tests for numpy.lib.tests.test_regression Found 43/43 tests for numpy.lib.tests.test_type_check Found 1/1 tests for numpy.lib.tests.test_ufunclike Found 15/15 tests for numpy.lib.tests.test_io Found 25/25 tests for numpy.lib.tests.test__datasource Found 10/10 tests for numpy.lib.tests.test_arraysetops Found 1/1 tests for numpy.lib.tests.test_financial Found 4/4 tests for numpy.lib.tests.test_polynomial Found 6/6 tests for numpy.lib.tests.test_index_tricks Found 49/49 tests for numpy.lib.tests.test_shape_base Found 55/55 tests for numpy.lib.tests.test_function_base Found 5/5 tests for numpy.lib.tests.test_getlimits Found 3/3 tests for numpy.linalg.tests.test_regression Found 89/89 tests for numpy.linalg.tests.test_linalg Found 96/96 tests for numpy.ma.tests.test_core Found 19/19 tests for numpy.ma.tests.test_mrecords Found 15/15 tests for numpy.ma.tests.test_extras Found 4/4 tests for numpy.ma.tests.test_subclassing Found 36/36 tests for numpy.ma.tests.test_old_ma Found 1/1 tests for numpy.oldnumeric.tests.test_oldnumeric Found 7/7 tests for numpy.tests.test_random Found 16/16 tests for numpy.testing.tests.test_utils Found 6/6 tests for numpy.tests.test_ctypeslib .. .. -- Ran 1292 tests in 1.237s OK unittest._TextTestResult run=1292 errors=0 failures=0 and: import numpy numpy.test() Running unit tests for numpy NumPy version 1.2.0.dev5611 NumPy is installed in /home/facets/doutriaux1/cdat/latest/lib/python2.5/site-packages/numpy Python version 2.5.2 (r252:60911, Aug 4 2008, 13:47:12) [GCC 4.1.1 20070105 (Red Hat 4.1.1-51)] nose version 0.10.3
Re: [Numpy-discussion] 64bit issue?
Hi chuck, works great on 32bit int *dims; dims = (int *)malloc(self-nd*sizeof(int)); and self-nd is 3 C. Charles R Harris wrote: On Tue, Aug 5, 2008 at 12:20 PM, Charles Doutriaux [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hello I'm running into some strange error on a 64bit machine, I tracked it down to this line returning a NULL pointer, any idea why is this? I tried both numpy1.1.1 and what in trunk, numpy.test() passes for both Ok here's the uname of the machine and the offending line: Linux quartic.txcorp.com http://quartic.txcorp.com/ 2.6.20-1.2320.fc5 #1 SMP Tue Jun 12 18:50:49 EDT 2007 x86_64 x86_64 x86_64 GNU/Linux array = (PyArrayObject *)PyArray_SimpleNew(d, dims, self-type); where d is 3 and dims: 120,46,72 self-type is 11 it seems to pass with d=1, but i'm not 100% positive. Any idea on why it could be failing? What is the type of dims? Is there also a problem with a 32 bit OS? Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http:// projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] 64bit issue?
Wow! It did it! Is there other little tricks like this one I should track? Thanks a lot! It would have take me days to track this one! C. Charles R Harris wrote: On Tue, Aug 5, 2008 at 1:14 PM, Charles Doutriaux [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hi chuck, works great on 32bit int *dims; dims = (int *)malloc(self-nd*sizeof(int)); and self-nd is 3 Should be npy_intp *dims; npy_intp will be 32 bits/ 64 bits depending on the architecture, ints tend to always be 32 bits. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http:// projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] [Cdat-discussion] Arrays containing NaNs
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it , there's probably some good reason we didn't think
of yet.
So before i go ahead and do it in MV2 I'd like to know the reason why
it's not in numpy.ma, they are probably valid for MVs too.
C.
Stephane Raynaud wrote:
Hi,
how about automatically (or at least optionally) masking all NaN
values when creating a MV array?
On Thu, Jul 24, 2008 at 11:43 PM, Arthur M. Greene
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Yup, this works. Thanks!
I guess it's time for me to dig deeper into numpy syntax and
functions, now that CDAT is using the numpy core for array
management...
Best,
Arthur
Charles Doutriaux wrote:
Seems right to me,
Except that the syntax might scare a bit the new users :)
C.
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Hi,
I'm not sure if what I am about to suggest is a good idea
or not, perhaps Charles will correct me if this is a bad
idea for any reason.
Lets say you have a cdms variable called U with NaNs as
the missing
value. First we can replace the NaNs with 1e20:
U.data[numpy.where(numpy.isnan(U.data))] = 1e20
And remember to set the missing value of the variable
appropriately:
U.setMissing(1e20)
I hope that helps, Andrew
Hi Arthur,
If i remember correctly the way i used to do it was:
a= MV2.greater(data,1.) b=MV2.less_equal(data,1)
c=MV2.logical_and(a,b) # Nan are the only one left
data=MV2.masked_where(c,data)
BUT I believe numpy now has way to deal with nan I
believe it is numpy.nan_to_num But it replaces with 0
so it may not be what you
want
C.
Arthur M. Greene wrote:
A typical netcdf file is opened, and the single
variable extracted:
fpr=cdms.open('prTS2p1_SEA_allmos.cdf')
pr0=fpr('prcp') type(pr0)
class 'cdms2.tvariable.TransientVariable'
Masked values (indicating ocean in this case) show
up here as NaNs.
pr0[0,-15:-5,0]
prcp array([NaN NaN NaN NaN NaN NaN 0.37745094
0.3460784 0.21960783 0.19117641])
So far this is all consistent. A map of the first
time step shows the proper land-ocean boundaries,
reasonable-looking values, and so on. But there
doesn't seem to be any way to mask
this array, so, e.g., an 'xy' average can be
computed (it
comes out all nans). NaN is not equal to anything
-- even
itself -- so there does not seem to be any
condition, among the
MV.masked_xxx options, that can be applied as a
test. Also, it
does not seem possible to compute seasonal averages,
anomalies, etc. -- they also produce just NaNs.
The workaround I've come up with -- for now -- is
to first generate a new array of identical shape,
filled with 1.0E+20. One test I've found that can
detect NaNs is numpy.isnan:
isnan(pr0[0,0,0])
True
So it is _possible_ to tediously loop through
every value in the old array, testing with isnan,
then copying to the new array if the test fails.
Then the axes have to be reset...
isnan does not accept array arguments, so one
cannot do, e.g.,
prmasked=MV.masked_where(isnan(pr0),pr0)
The element-by-element conversion is quite slow.
(I'm still waiting for it to complete, in fact).
Any suggestions for dealing with NaN-infested data
objects?
Thanks!
AMG
P.S. This is 5.0.0.beta, RHEL4.
*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*
Arthur M. Greene, Ph.D.
The International Research Institute for Climate and Society
The Earth Institute, Columbia
Re: [Numpy-discussion] [Cdat-discussion] Arrays containing NaNs
Hi All,
I'm sending a copy of this reply here because i think we could get some
good answer.
Basically it was suggested to automarically mask NaN (and Inf ?) when
creating ma.
I'm sure you already thought of this on this list and was curious to
know why you decided not to do it.
Just so I can relay it to our list (sending to both list came back
flagged as spam...)
C.
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it , there's probably some good reason we didn't think
of yet.
So before i go ahead and do it in MV2 I'd like to know the reason why
it's not in numpy.ma, they are probably valid for MVs too.
C.
Stephane Raynaud wrote:
Hi,
how about automatically (or at least optionally) masking all NaN
values when creating a MV array?
On Thu, Jul 24, 2008 at 11:43 PM, Arthur M. Greene
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Yup, this works. Thanks!
I guess it's time for me to dig deeper into numpy syntax and
functions, now that CDAT is using the numpy core for array
management...
Best,
Arthur
Charles Doutriaux wrote:
Seems right to me,
Except that the syntax might scare a bit the new users :)
C.
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Hi,
I'm not sure if what I am about to suggest is a good idea
or not, perhaps Charles will correct me if this is a bad
idea for any reason.
Lets say you have a cdms variable called U with NaNs as
the missing
value. First we can replace the NaNs with 1e20:
U.data[numpy.where(numpy.isnan(U.data))] = 1e20
And remember to set the missing value of the variable
appropriately:
U.setMissing(1e20)
I hope that helps, Andrew
Hi Arthur,
If i remember correctly the way i used to do it was:
a= MV2.greater(data,1.) b=MV2.less_equal(data,1)
c=MV2.logical_and(a,b) # Nan are the only one left
data=MV2.masked_where(c,data)
BUT I believe numpy now has way to deal with nan I
believe it is numpy.nan_to_num But it replaces with 0
so it may not be what you
want
C.
Arthur M. Greene wrote:
A typical netcdf file is opened, and the single
variable extracted:
fpr=cdms.open('prTS2p1_SEA_allmos.cdf')
pr0=fpr('prcp') type(pr0)
class 'cdms2.tvariable.TransientVariable'
Masked values (indicating ocean in this case) show
up here as NaNs.
pr0[0,-15:-5,0]
prcp array([NaN NaN NaN NaN NaN NaN 0.37745094
0.3460784 0.21960783 0.19117641])
So far this is all consistent. A map of the first
time step shows the proper land-ocean boundaries,
reasonable-looking values, and so on. But there
doesn't seem to be any way to mask
this array, so, e.g., an 'xy' average can be
computed (it
comes out all nans). NaN is not equal to anything
-- even
itself -- so there does not seem to be any
condition, among the
MV.masked_xxx options, that can be applied as a
test. Also, it
does not seem possible to compute seasonal averages,
anomalies, etc. -- they also produce just NaNs.
The workaround I've come up with -- for now -- is
to first generate a new array of identical shape,
filled with 1.0E+20. One test I've found that can
detect NaNs is numpy.isnan:
isnan(pr0[0,0,0])
True
So it is _possible_ to tediously loop through
every value in the old array, testing with isnan,
then copying to the new array if the test fails.
Then the axes have to be reset...
isnan does not accept array arguments, so one
cannot do, e.g.,
prmasked=MV.masked_where(isnan(pr0),pr0)
The element-by-element conversion is quite slow.
(I'm still waiting for it to complete, in fact).
Any suggestions
Re: [Numpy-discussion] [Cdat-discussion] Arrays containing NaNs
Hi Bruce,
Thx for the reply, we're aware of this, basically the question was why
not mask NaN automatically when creating a nump.ma array?
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it , there's probably some good reason we didn't think
of yet.
So before i go ahead and do it in MV2 I'd like to know the reason why
it's not in numpy.ma, they are probably valid for MVs too.
C.
Stephane Raynaud wrote:
Hi,
how about automatically (or at least optionally) masking all NaN
values when creating a MV array?
On Thu, Jul 24, 2008 at 11:43 PM, Arthur M. Greene
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Yup, this works. Thanks!
I guess it's time for me to dig deeper into numpy syntax and
functions, now that CDAT is using the numpy core for array
management...
Best,
Arthur
Charles Doutriaux wrote:
Seems right to me,
Except that the syntax might scare a bit the new users :)
C.
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Hi,
I'm not sure if what I am about to suggest is a good idea
or not, perhaps Charles will correct me if this is a bad
idea for any reason.
Lets say you have a cdms variable called U with NaNs as
the missing
value. First we can replace the NaNs with 1e20:
U.data[numpy.where(numpy.isnan(U.data))] = 1e20
And remember to set the missing value of the variable
appropriately:
U.setMissing(1e20)
I hope that helps, Andrew
Hi Arthur,
If i remember correctly the way i used to do it was:
a= MV2.greater(data,1.) b=MV2.less_equal(data,1)
c=MV2.logical_and(a,b) # Nan are the only one left
data=MV2.masked_where(c,data)
BUT I believe numpy now has way to deal with nan I
believe it is numpy.nan_to_num But it replaces with 0
so it may not be what you
want
C.
Arthur M. Greene wrote:
A typical netcdf file is opened, and the single
variable extracted:
fpr=cdms.open('prTS2p1_SEA_allmos.cdf')
pr0=fpr('prcp') type(pr0)
class 'cdms2.tvariable.TransientVariable'
Masked values (indicating ocean in this case) show
up here as NaNs.
pr0[0,-15:-5,0]
prcp array([NaN NaN NaN NaN NaN NaN 0.37745094
0.3460784 0.21960783 0.19117641])
So far this is all consistent. A map of the first
time step shows the proper land-ocean boundaries,
reasonable-looking values, and so on. But there
doesn't seem to be any way to mask
this array, so, e.g., an 'xy' average can be
computed (it
comes out all nans). NaN is not equal to anything
-- even
itself -- so there does not seem to be any
condition, among the
MV.masked_xxx options, that can be applied as a
test. Also, it
does not seem possible to compute seasonal averages,
anomalies, etc. -- they also produce just NaNs.
The workaround I've come up with -- for now -- is
to first generate a new array of identical shape,
filled with 1.0E+20. One test I've found that can
detect NaNs is numpy.isnan:
isnan(pr0[0,0,0])
True
So it is _possible_ to tediously loop through
every value in the old array, testing with isnan,
then copying to the new array if the test fails.
Then the axes have to be reset...
isnan does not accept array arguments, so one
cannot do, e.g.,
prmasked=MV.masked_where(isnan(pr0),pr0)
The element-by-element conversion is quite slow.
(I'm still waiting for it to complete, in fact).
Any suggestions for dealing with NaN-infested data
objects?
Thanks!
AMG
P.S. This is 5.0.0.beta
Re: [Numpy-discussion] [Cdat-discussion] Arrays containing NaNs
I mean not having to it myself.
data is a numpy array with NaN in it
masked_data = numpy.ma.array(data)
returns a masked array with a mask where NaN were in data
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Bruce,
Thx for the reply, we're aware of this, basically the question was why
not mask NaN automatically when creating a nump.ma array?
C.
Bruce Southey wrote:
Charles Doutriaux wrote:
Hi Stephane,
This is a good suggestion, I'm ccing the numpy list on this. Because I'm
wondering if it wouldn't be a better fit to do it directly at the
numpy.ma level.
I'm sure they already thought about this (and 'inf' values as well) and
if they don't do it , there's probably some good reason we didn't think
of yet.
So before i go ahead and do it in MV2 I'd like to know the reason why
it's not in numpy.ma, they are probably valid for MVs too.
C.
Stephane Raynaud wrote:
Hi,
how about automatically (or at least optionally) masking all NaN
values when creating a MV array?
On Thu, Jul 24, 2008 at 11:43 PM, Arthur M. Greene
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Yup, this works. Thanks!
I guess it's time for me to dig deeper into numpy syntax and
functions, now that CDAT is using the numpy core for array
management...
Best,
Arthur
Charles Doutriaux wrote:
Seems right to me,
Except that the syntax might scare a bit the new users :)
C.
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Hi,
I'm not sure if what I am about to suggest is a good idea
or not, perhaps Charles will correct me if this is a bad
idea for any reason.
Lets say you have a cdms variable called U with NaNs as
the missing
value. First we can replace the NaNs with 1e20:
U.data[numpy.where(numpy.isnan(U.data))] = 1e20
And remember to set the missing value of the variable
appropriately:
U.setMissing(1e20)
I hope that helps, Andrew
Hi Arthur,
If i remember correctly the way i used to do it was:
a= MV2.greater(data,1.) b=MV2.less_equal(data,1)
c=MV2.logical_and(a,b) # Nan are the only one left
data=MV2.masked_where(c,data)
BUT I believe numpy now has way to deal with nan I
believe it is numpy.nan_to_num But it replaces with 0
so it may not be what you
want
C.
Arthur M. Greene wrote:
A typical netcdf file is opened, and the single
variable extracted:
fpr=cdms.open('prTS2p1_SEA_allmos.cdf')
pr0=fpr('prcp') type(pr0)
class 'cdms2.tvariable.TransientVariable'
Masked values (indicating ocean in this case) show
up here as NaNs.
pr0[0,-15:-5,0]
prcp array([NaN NaN NaN NaN NaN NaN 0.37745094
0.3460784 0.21960783 0.19117641])
So far this is all consistent. A map of the first
time step shows the proper land-ocean boundaries,
reasonable-looking values, and so on. But there
doesn't seem to be any way to mask
this array, so, e.g., an 'xy' average can be
computed (it
comes out all nans). NaN is not equal to anything
-- even
itself -- so there does not seem to be any
condition, among the
MV.masked_xxx options, that can be applied as a
test. Also, it
does not seem possible to compute seasonal averages,
anomalies, etc. -- they also produce just NaNs.
The workaround I've come up with -- for now -- is
to first generate a new array of identical shape,
filled with 1.0E+20. One test I've found that can
detect NaNs is numpy.isnan:
isnan(pr0[0,0,0])
True
So it is _possible_ to tediously loop through
every value in the old array, testing with isnan,
then copying to the new array if the test fails.
Then the axes have to be reset...
isnan does not accept array arguments, so one
cannot do, e.g.,
prmasked=MV.masked_where(isnan(pr0),pr0)
The element-by-element conversion is quite slow
Re: [Numpy-discussion] [Cdat-discussion] Arrays containing NaNs
Hi Pierre, Thanks for the answer, I'm ccing cdat's discussion list. It makes sense, that's also the way we develop things here NEVER assume what the user is going to do with the data BUT give the user the necessary tools to do what you're assuming he/she wants to do (as simple as possible) Thanks again for the answer. C. Pierre GM wrote: Oh, I guess this one's for me... On Thursday 01 January 1970 04:21:03 Charles Doutriaux wrote: Basically it was suggested to automarically mask NaN (and Inf ?) when creating ma. I'm sure you already thought of this on this list and was curious to know why you decided not to do it. Because it's always best to let the user decide what to do with his/her data and not impose anything ? Masking a point doesn't necessarily mean that the point is invalid (in the sense of NaNs/Infs), just that it doesn't satisfy some particular condition. In that sense, masks act as selecting tools. By forcing invalid data to be masked at the creation of an array, you run the risk to tamper with the (potential) physical meaning of the mask you have given as input, and/or miss the fact that some data are actually invalid when you don't expect it to be. Let's take an example: I want to analyze sea surface temperatures at the world scale. The data comes as a regular 2D ndarray, with NaNs for missing or invalid data. In a first step, I create a masked array of this data, filtering out the land masses by a predefined geographical mask. The remaining NaNs in the masked array indicate areas where the sensor failed... It's an important information I would probably have missed by masking all the NaNs at first... As Eric F. suggested, you can use numpy.ma.masked_invalid to create a masked array with NaNs/Infs filtered out: import numpy as np,. numpy.ma as ma x = np.array([1,2,None,4], dtype=float) x array([ 1., 2., NaN, 4.]) mx = ma.masked_invalid(x) mx masked_array(data = [1.0 2.0 -- 4.0], mask = [False False True False], fill_value=1e+20) Note that the underlying data still has NaNs/Infs: mx._data array([ 1., 2., NaN, 4.]) You can also use the ma.fix_invalid function: it creates a mask where the data is not finite (NaNs/Infs), and set the corresponding points to fill_value. mx = ma.fix_invalid(x, fill_value=999) mx masked_array(data = [1.0 2.0 -- 4.0], mask = [False False True False], fill_value=1e+20) mx._data array([ 1.,2., 999.,4.]) The advantage of the second approach is that you no longer have NaNs/Infs in the underlying data, which speeds things up during computation. The obvious disadvantage is that you no longer know where the data was invalid... ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug in ma ?
Hello, I think i found a bug in numpy.ma I tried it both with the trunk and the 1.1 version import numpy a= numpy.ma.arange(256) a.shape=(128,2) b=numpy.reshape(a,(64,2,2)) Traceback (most recent call last): File quick_test_reshape.py, line 7, in module b=numpy.reshape(a,(64,2,2)) File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/core/fromnumeric.py, line 116, in reshape return reshape(newshape, order=order) TypeError: reshape() got an unexpected keyword argument 'order' ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] kinds
Hello, A long long time ago, there used to be this module named kinds It's totally outdated nowdays but it had one nice functionality and i was wondering if you knew how to reproduce that it was: maxexp=kinds.default_float_kind.MAX_10_EXP minexp=kinds.default_float_kind.MIN_10_EXP and a bunch of similar flags that would basically tell you the limits on the machine you're running (or at least compiled on) Any idea on how to reproduce this? While we're at it, does anybody know of way in python to know how memory is available on your system (similar to the free call unde rLinux) I'm looking for something that works accross platforms (well we can forget windows for now I could live w/o it) Thanks, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] kinds
Thx Pierre, That's exactly what i was looking for C. Pierre GM wrote: On Wednesday 16 July 2008 15:08:59 Charles Doutriaux wrote: and a bunch of similar flags that would basically tell you the limits on the machine you're running (or at least compiled on) Any idea on how to reproduce this? Charels, have you tried numpy.finfo ? That should give you information for floating points. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] svd
doh... Thanks Charles... I guess I've been staring at this code for too long now... C. Charles R Harris wrote: On Wed, Jul 16, 2008 at 3:58 PM, Charles Doutriaux [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hello, I'm using 1.1.0 and I have a bizarre thing happening it seems as if: doing: import numpy SVD = numpy.linalg.svd if different as doing import numpy.oldnumeric.linear_algebra SVD = numpy.oldnumeric.linear_algebra.singular_value_decomposition In the first case passing an array (204,1484) retuns array of shape: svd: (204, 204) (204,) (1484, 1484) in the second case I get (what i expected actually): svd: (204, 204) (204,) (204, 1484) But looking at the code, it seems like numpy.oldnumeric.linear_algebra.singular_value_decomposition is basicalyy numpy.linalg.svd Any idea on what's happening here? There is a full_matrices flag that determines if you get the full orthogonal matrices, or the the minimum size needed, i.e. In [12]: l,d,r = linalg.svd(x, full_matrices=0) In [13]: shape(r) Out[13]: (2, 4) In [14]: x = zeros((2,4)) In [15]: l,d,r = linalg.svd(x) In [16]: shape(r) Out[16]: (4, 4) In [17]: l,d,r = linalg.svd(x, full_matrices=0) In [18]: shape(r) Out[18]: (2, 4) Chuck Thx, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org mailto:Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] quick question about numpy deprecation warnings
Hi I have a quick question and i hope somebody can answer me (I admit I should first really check the numpy doc) I have been porting old Numeric based C code to numpy/numpy.ma for the alst couple weeks. Just as I thought I was done, this morning I updatethe numpy trunk and I now get the following deprecation warnings DeprecationWarning: PyArray_FromDims: use PyArray_SimpleNew. DeprecationWarning: PyArray_FromDimsAndDataAndDescr: use PyArray_NewFromDescr. My quick question is: can I simply replace the function names or is the arg list different? Thanks, Charles. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] _md5 module?
Hello on redhat enterprise 5 i get this with numpy 1.1 and python 2.5
Any idea if we can get around tihs? short of rebuilding python with md5 support
i guess.
Shouldn't numpy catch that before building?
C.
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/__init__.py,
line 42, in module
import add_newdocs
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/add_newdocs.py,
line 5, in module
from lib import add_newdoc
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/__init__.py,
line 18, in module
from io import *
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/io.py,
line 16, in module
from _datasource import DataSource
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/_datasource.py,
line 42, in module
from urllib2 import urlopen, URLError
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/urllib2.py,
line 91, in module
import hashlib
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/hashlib.py,
line 133, in module
md5 = __get_builtin_constructor('md5')
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/hashlib.py,
line 60, in __get_builtin_constructor
import _md5
ImportError: No module named _md5
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Re: [Numpy-discussion] _md5 module?
Hi Charles,
Yes.. unfortunately we have to be able to build out of the box, we can't
rely on much anything (Except compiler and X) to be there already...
It would be much easier for CDAT to simply say getpython 2.5 and numpy
1.1 and all the externals, and if you ever get it together come back see
us :)
Unfortunately i don't think we'd have much success that way :)
I'll keep looking... I don't understand why python didn't build with
md5. Never happened before...
I guess it's more of a python issue than numpy... But a syou said it's
deprecated so you might as well want to replace it.
C.
Charles R Harris wrote:
On Tue, Jun 24, 2008 at 10:07 AM, Charles Doutriaux
[EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
Hello on redhat enterprise 5 i get this with numpy 1.1 and python 2.5
Any idea if we can get around tihs? short of rebuilding python
with md5 support i guess.
Shouldn't numpy catch that before building?
C.
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/__init__.py,
line 42, in module
import add_newdocs
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/add_newdocs.py,
line 5, in module
from lib import add_newdoc
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/__init__.py,
line 18, in module
from io import *
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/io.py,
line 16, in module
from _datasource import DataSource
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/site-packages/numpy/lib/_datasource.py,
line 42, in module
from urllib2 import urlopen, URLError
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/urllib2.py,
line 91, in module
import hashlib
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/hashlib.py,
line 133, in module
md5 = __get_builtin_constructor('md5')
File
/usr/local/cdat/release/5.0e/5.0.0.alpha7/lib/python2.5/hashlib.py,
line 60, in __get_builtin_constructor
import _md5
ImportError: No module named _md5
Python2.5 should come with md5, at least it does on fedora 8:
/usr/lib/python2.5/lib-dynload/_md5module.so. It is also deprecated as
of 2.5, so we might need some modifications anyway.
Are you using the python built by CDAT? I found the easiest way to get
CDAT 4.3 working was to copy over the relevant site-packages from the
CDAT built python to the system python (of the same version). You
could maybe go the other way also.
Chuck
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Re: [Numpy-discussion] building 1.1.0 on 64bit, seems to be stuck on infinite loop or something
Well this got nothing to do with cdat 5.0 yet. At this point it just built python from sources, and is putting numpy 1.1.0 in it. I'll check the compiler bug option next... What if it is indeed a compiler bug? What to do next? C. Charles R Harris wrote: On Tue, Jun 17, 2008 at 3:40 PM, Charles Doutriaux [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hello, I'm trying to build 1.1.0 on a: Linux thunder0 2.6.9-74chaos #1 SMP Wed Oct 24 08:41:12 PDT 2007 ia64 ia64 ia64 GNU/Linux using python 2.5 (latest stable) It seems to be stuck forever at the end of that output: anybody ran into this? ~/CDAT/5.0.0.beta1/bin/python setup.py install Running from numpy source directory. F2PY Version 2_5237 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE I've had mixed success trying to set up CDAT5.0.0.beta1 and wasn't motivated to pursue it. I'll give it another shot, but probably not until the weekend. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] building 1.1.0 on 64bit, seems to be stuck on infinite loop or something
Hi there, looks like it, moving from gcc3.3.4 to gcc4.1.2 seems to have fix it. Thx, C. David Cournapeau wrote: Charles Doutriaux wrote: Hello, I'm trying to build 1.1.0 on a: Linux thunder0 2.6.9-74chaos #1 SMP Wed Oct 24 08:41:12 PDT 2007 ia64 ia64 ia64 GNU/Linux using python 2.5 (latest stable) It seems to be stuck forever at the end of that output: Is it stuck with 100 % Cpu usage ? May well be a compiler bug ? Did you check gcc buglist for your gcc version ? cheers, David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] building 1.1.0 on 64bit, seems to be stuck on infinite loop or something
Hello, I'm trying to build 1.1.0 on a: Linux thunder0 2.6.9-74chaos #1 SMP Wed Oct 24 08:41:12 PDT 2007 ia64 ia64 ia64 GNU/Linux using python 2.5 (latest stable) It seems to be stuck forever at the end of that output: anybody ran into this? ~/CDAT/5.0.0.beta1/bin/python setup.py install Running from numpy source directory. F2PY Version 2_5237 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE atlas_blas_info: libraries f77blas,cblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1340: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1349: 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 /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1352: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib libraries lapack_atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: libraries f77blas,cblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib libraries lapack_atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib numpy.distutils.system_info.atlas_info NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1247: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1258: 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 /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1261: 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 running install running build running scons customize UnixCCompiler Found executable /usr/local/bin/gcc customize GnuFCompiler Found executable /usr/local/bin/g77 gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize GnuFCompiler gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize UnixCCompiler customize UnixCCompiler using scons 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.linux-ia64-2.5/numpy/core/config.h' to sources. adding 'build/src.linux-ia64-2.5/numpy/core/numpyconfig.h' to sources. executing numpy/core/code_generators/generate_array_api.py adding 'build/src.linux-ia64-2.5/numpy/core/__multiarray_api.h' to sources. adding
Re: [Numpy-discussion] building 1.1.0 on 64bit, seems to be stuck on infinite loop or something
Just so it might help debuging, when i kill it i get: _exec_command_posix failed (status=2) C. Charles Doutriaux wrote: Hello, I'm trying to build 1.1.0 on a: Linux thunder0 2.6.9-74chaos #1 SMP Wed Oct 24 08:41:12 PDT 2007 ia64 ia64 ia64 GNU/Linux using python 2.5 (latest stable) It seems to be stuck forever at the end of that output: anybody ran into this? ~/CDAT/5.0.0.beta1/bin/python setup.py install Running from numpy source directory. F2PY Version 2_5237 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE atlas_blas_info: libraries f77blas,cblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1340: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1349: 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 /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1352: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries ptf77blas,ptcblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib libraries lapack_atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: libraries f77blas,cblas,atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib libraries lapack_atlas not found in /g/g90/cdoutrix/CDAT/Externals/lib numpy.distutils.system_info.atlas_info NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1247: 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 /g/g90/cdoutrix/CDAT/Externals/lib NOT AVAILABLE /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1258: 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 /g/g90/cdoutrix/svn/cdat/branches/devel/build/numpy-1.1.0/numpy/distutils/system_info.py:1261: 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 running install running build running scons customize UnixCCompiler Found executable /usr/local/bin/gcc customize GnuFCompiler Found executable /usr/local/bin/g77 gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize GnuFCompiler gnu: no Fortran 90 compiler found gnu: no Fortran 90 compiler found customize UnixCCompiler customize UnixCCompiler using scons 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.linux-ia64-2.5/numpy/core/config.h
Re: [Numpy-discussion] One more wrinkle in going to 5.0
Arthur
I'm forwarding your question to the numpy list, I'm hoping somebody
there will be able to help you with that.
C.
Arthur M. Greene wrote:
Hi All,
This does not involve the CDAT-5 code, but rather files pickled under
earlier versions of CDAT. These files store the variable type along
with the data, but some types from 4.x are absent in 5.0, making the
pickled files unreadable. Example:
$ cdat2
Executing /home/amg/usr/local/cdat/5.0.0.beta1/bin/python
Python 2.5.2 (r252:60911, May 30 2008, 11:00:23)
[GCC 3.4.6 20060404 (Red Hat 3.4.6-9)] on linux2
Type help, copyright, credits or license for more information.
import cPickle as pickle
f=open('pickled/prTS2p1_Ind.p')
pr=pickle.load(f)
Traceback (most recent call last):
File stdin, line 1, in module
ImportError: No module named cdms.tvariable
$ grep cdms pickled/prTS2p1_Ind.p
(ccdms.tvariable
(icdms.grid
(icdms.axis
(icdms.axis
((icdms.axis
If I replace cdms with cdms2 (using sed) and try again, a new
error comes up:
f=open('prTS2p1_Ind1.p')
pr=pickle.load(f)
Traceback (most recent call last):
File stdin, line 1, in module
ImportError: No module named Numeric
$ grep -B 3 -A 3 Numeric prTS2p1_Ind1.p
(dp22
S'gridtype'
p23
cNumeric
array_constructor
p24
((I1
Numpy does not have an array_constructor attribute, so can't simply
replace Numeric with numpy in this case. If I replace instead with
numpy.oldnumeric, trying to unpickle produces what appears to be a
screen dump (in the python console) of the pickled file as text, but
there is no assignment. So I'm presently at a loss as to how to read
such files. I have hundreds of files that were pickled under 4.x, so
this could be a big headache...
Thx for any ideas,
Arthur
*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*
Arthur M. Greene, Ph.D.
The International Research Institute for Climate and Society
The Earth Institute, Columbia University, Lamont Campus
amg -at- iri -dot- columbia -dot- edu
*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*^*~*
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Re: [Numpy-discussion] Status Report for NumPy 1.1.0
I don't think it is reasonable to say the trunk is in good shape when the power function does not work... Just my thoughts... C. Charles R Harris wrote: Hi Jarrod, On Tue, May 6, 2008 at 2:40 AM, Jarrod Millman [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote: Hey, The trunk is in pretty good shape and it is about time that I put out an official release. So tomorrow (in a little over twelve hours) I am going to create a 1.1.x branch and the trunk will be officially open for 1.2 development. If there are no major issues that show up at the last minute, I will tag 1.1.0 twenty-four hours after I branch. As soon as I tag the release I will ask the David and Chris to create the official Windows and Mac binaries. If nothing goes awry, you can expect the official release announcement by Monday, May 12th. In order to help me with the final touches, would everyone look over the release notes one last time: http://projects.scipy.org/scipy/numpy/milestone/1.1.0 Please let me know if there are any important omissions or errors ASAP. Scalar indexing of matrices has changed, 1D arrays are now returned instead of matrices. This has to be documented in the release notes. Chuck ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.ma question
Hello i have a quick question about MA ans scalar the following works: import numpy.ma as MA a0= MA.array(0)/1 a1= MA.array((0,0))/1 but not: import numpy.ma as MA a0= MA.array(0)/0 a1= MA.array((0,0))/0 Is that a bug ? I'm using numpy 1.0.5.dev4958 and also whats in trunk right now (1.1.0.dev5113) C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] oldnumeric.MA behaviour broken ?
HI, import numpy.oldnumeric.ma as MA a0= MA.array(0)/0 sh0=list(a0.shape) sh0.insert(0,1) b0=MA.resize(a0,sh0) Does not work anymore, I believe it used to work It does works using numpy.ma (but i can't subclass these yet...) C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.ma question
that's exactly my understanding thanks for confirming C. Pierre GM wrote: Charles, but not: import numpy.ma as MA a0= MA.array(0)/0 a1= MA.array((0,0))/0 Is that a bug ? That a0 is MA.masked is not a bug. That a1 should be a (2,) array masked everywhere should work, but does not: that's the bug. Thanks for reporting. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy release
Anne Archibald wrote: Yes, well, it really looks unlikely we will be able to agree on what the correct solution is before 1.1, so I would like to have something non-broken for that release. +1 on that! ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bz2
Hi, Just so you know, yesterday i was doing some test on a fresh ubuntu installation, trying to figure out the external minimum dependencies for our system. I built (from sources) python (2.5.2) then numpy. All seemed ok, but when importing numpy i had an error, trying to import module bz2 Turns out Python didn't build with bz2 because the bz2 devel package wasn't in. I'm just reporting this to you guys, has you may want to add a test for bz2 in your setup.py before going ahead and build the whole thing. Hope this helps, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.ma dtype.char set to ?
Hi I'm tracked down a bug in our code back to the numpy.ma s2=numpy.array([[10,60],[65,45]]) s3=numpy.ma.masked_greater(s2,50.) s3.mask.dtype.char returns: '?' In the Numeric - numpy.ma (page 35) the ? is not listed. Is it an omission in numpy.ma ? Or is it a valid char type ? s3.dtype does say it is of type bool which I guess should be 'B' or 'b' no ? Thanks, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy.ma dtype.char set to ?
Sorry, I can answer my own question (page 22 of numpy book) booleans are supposed to be ? my mistake. C. Charles Doutriaux wrote: Hi I'm tracked down a bug in our code back to the numpy.ma s2=numpy.array([[10,60],[65,45]]) s3=numpy.ma.masked_greater(s2,50.) s3.mask.dtype.char returns: '?' In the Numeric - numpy.ma (page 35) the ? is not listed. Is it an omission in numpy.ma ? Or is it a valid char type ? s3.dtype does say it is of type bool which I guess should be 'B' or 'b' no ? Thanks, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Travis, Pierre, Goood news what's in trunk right now seems to work great with our stuff, I only had to replace numpy.core.ma.take with numpy.ma.take (numpy.oldnumeric.ma.take didn't seem to work) So as soon as 1.0.5 is out, I'll start working toward using numpy.ma only. Also, I have one little request, i remember you said oldnumeric would be gone in the next version. But our users will have hundreds (thousands?) of script convert with import numpy.oldnumeric.ma as MA or import numpy.oldnumeric as Numeric And although we're insisting and using numpy and numpy.ma right away, i'm sure 99% of them will ignore this. So would be be horrible to leave as shortcut (with a warning while loading probably, actually we could have the warning already in numpy 1.0.5 ?) from numpy.oldnumeric back to numpy ? same for oldnumeric.ma pointing back to numpy.ma. I realize it's not really clean... At least it would be great to have the warning raised in 1.0.5 that it will disappear in 1.1. Like that users might be more inclined to start converting cleaninly Thanks for considering this and also thanks a LOT for all your help on this issue! C. Travis E. Oliphant wrote: Charles Doutriaux wrote: Hi Travis, Ok we're almost there, in my test suite i get: maresult = numpy.core.ma.take(ta, indices, axis=axis) AttributeError: 'module' object has no attribute 'ma' data = numpy.core.ma.take(ax[:], indices) AttributeError: 'module' object has no attribute 'ma' I think the problem here is that numpy.core.ma is no longer the correct place.This should be numpy.oldnumeric.ma.take because numpy.oldnumeric.ma is the correct location. In my mind you shouldn't really have been using numpy.core.ma, but instead numpy.ma because whether things are in core or lib could change ;-) -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Travis, Thanks, I'll fix this and let you know. (probably tomorrow because I came down with some nasty real bug... flue or something like that) C. Travis E. Oliphant wrote: Charles Doutriaux wrote: Hi Travis, Ok we're almost there, in my test suite i get: maresult = numpy.core.ma.take(ta, indices, axis=axis) AttributeError: 'module' object has no attribute 'ma' data = numpy.core.ma.take(ax[:], indices) AttributeError: 'module' object has no attribute 'ma' I think the problem here is that numpy.core.ma is no longer the correct place.This should be numpy.oldnumeric.ma.take because numpy.oldnumeric.ma is the correct location. In my mind you shouldn't really have been using numpy.core.ma, but instead numpy.ma because whether things are in core or lib could change ;-) -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Pierre, Im ccing Bob on this, he's the main developper for cdms2 package. At this point I think Travis original suggestion was the best. We should leave it like it was for 1.0.5 There's a lot of changes to do in order to get the backward compatibility going. And I feel it should wait until 1.1. I don't feel comfortable doing all these changes and releasing our software like this. It's a major change and it needs to be tested for a while. OUr users are massively hammering the MA and rely on it so much. Although I do see the usefulness of the new ma and at term I believe it has major merits to be used instead of oldnumeric.ma. Your thoughts? C. Pierre GM wrote: Charles, Any idea where that comes from ? No, not really. Seems that TransientVariable(*args) doesn't work. I guess it's because it has inherited a __call__method, and tries to use that method instead of the __new__. Try to call TransientVariable.__new__ instead of just TransientVariable in l505 of avariables.py, and see how it goes. You may want to rethink what subSlice does as well. Instead of calling the class constructor, you can also just create a view of your array and update the attributes accordingly. Once again, a stripped-down version of the class and its parents would be useful. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Travis, I get this: import numpy, numpy.oldnumeric.ma as MA, numpy.oldnumeric as Numeric, PropertiedClasses File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/oldnumeric/ma.py, line 2204, in module array.mean = _m(average) NameError: name 'average' is not defined C. Travis E. Oliphant wrote: Charles Doutriaux wrote: Hi Pierre, Im ccing Bob on this, he's the main developper for cdms2 package. I've uploaded the original ma.py file back into oldnumeric so that oldnumeric.ma should continue to work as before. Can you verify this? Thanks, -Travis O. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Travis, Ok we're almost there, in my test suite i get: maresult = numpy.core.ma.take(ta, indices, axis=axis) AttributeError: 'module' object has no attribute 'ma' data = numpy.core.ma.take(ax[:], indices) AttributeError: 'module' object has no attribute 'ma' I don't know if it was automatically put there by the converter or if we put it by hand. If it's the first one, you might want to correct this, otherwise don't worry it's easy enough to fix (i think ?) C. Travis E. Oliphant wrote: Charles Doutriaux wrote: Hi Travis, I get this: import numpy, numpy.oldnumeric.ma as MA, numpy.oldnumeric as Numeric, PropertiedClasses File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/oldnumeric/ma.py, line 2204, in module array.mean = _m(average) NameError: name 'average' is not defined Thanks, Can you try again? Best regards, -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Pierre, I just tested it out, I'm still missing from numpy.oldnumeric.ma import common_fill_value , set_fill_value which breaks the code later C. Pierre GM wrote: All, Would you mind trying the SVN (ver 4946) and let me know what I'm still missing ? Thanks a lot in advance P. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Pierre, Hum... something is still broken. But maybe you can help me figuring out if something dramtically changed We're defining a new class object MaskedVariable which inherit from our other class: AbstractVariable (in which we define a reorder function for the obects) and from MaskedArray (used to be from numpy.oldnumeric.ma.array) Now when reading data from a file it complains that MaskedArray has no attribute reorder, so that probably means that somewhere something failed in the initialisation of our object and it retuned a simple MaskedArray instead of an MaskedVariable... But since the only changes are from numpy, i wonder if the inheritance form MaskedArray is somehow different from the one from MA.array ? Any clue on where to start looking would be great, Thanks, C Pierre GM wrote: Charles, I just tested it out, I'm still missing from numpy.oldnumeric.ma import common_fill_value , set_fill_value which breaks the code later Turns out I had forgotten to put the functions in numpy.ma.core.__all__: they had been already coded all along... That's fixed in SVN4950 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Eric, Pierre, I agree the new ma is probably much better and we should use it. all i was saying is that 1.0.4 was working great with the small compatibility layer. I even have a frozen version of 1.0.5 devel that works great. Then suddenly everything broke. I was really happy was the layer of 1.0.4. It is not only a matter of converting our software, I can do that. It also a matter of have our user base go smoothly thru the transition. So far they were happy with the 1st order conversion script. And a little bit of editing. But we can't ask them to go thru thousands of lines of old code and sort of rewrite it all. When I said it shouldn't be hard to do as much of MA as possible. I simply meant put back the compatibility layer that was there in 1.0.4. and early 1.0.5dev. I'm not advocating to rewrite the old MA at all, simply to keep what was already there as far as transition, why undoing it? I really don't mind help you i the process if you want. C. Eric Firing wrote: Charles Doutriaux wrote: The reorder is a function we implement. By digging a bit into this my guess is that all the missing function in numpy.ma are causing to fail at some point in our init and returning the wrong object type. But the whole idea was to keep a backward compatible layer with Numeric and MA. It worked great for a while and now things are getting more and more broken. There are costs as well as benefits in maintaining backward compatibility, so one should not rely on it indefinitely. Correct me if I'm wrong but it seems as if the numpy.oldnumeric.am is now simply numpy.ma and it's pointing to the new MaskedArray interface. Loosing a LOT of backward compatibility at the same time. numpy.oldnumeric.ma was a very small compatibility wrapper for numpy.core.ma; now it is the same, but pointing to numpy.ma, which is now Pierre's new maskedarray implementatin. Maybe more compatibility interfacing is needed, either there or in numpy.ma itself, but I would not agree with your characterization of the degree of incompatibility. Whether it would be possible (and desirable) to replace oldnumeric.ma with the old numpy/core/ma.py, I don't know, but maybe this, or some other way of keeping core/ma.py available, should be considered. Would this meet your needs? Were you happy with release 1.04? I'm thinking that such changes should definitely not happen from 1.0.4 to 1.0.5 but rather in some major upgrade of numpy (1.1 at least, may be even 2.0). No, this has been planned for quite a while, and I would strongly oppose any such drastic delay. It is absolutely necessary to have the oldnumeric.ma working as much as possible as MA, what's in now is incompatible with code that have been successfully upgraded to numpy using your recommended method (official numpy doc) Can you put back ALL the function from numpy.oldnumeric.ma ? It shouldn't be too much work. Now I'm actually worried about using ma at all? What version is in? Is it a completely new package or is it still the old one just a bit broken? If it's a new one, we'd have to be sure it is fully tested No, it is not broken, it has many improvements and bug fixes relative to the old ma.py. That is why it is replacing ma.py. before we can redistribute it to other people via our package, or before we use it ourselves Well, the only way to get something fully tested is to put it in use. It has been available for testing for a long time as a separate implementation, then as a numpy branch, and now for a while in the numpy svn trunk. It works well. It is time to release it--possibly after a few more tweaks, possibly leaving the old core/ma.py accessible, but definitely for 1.05. No one will force you to adopt 1.05, so if more compatibility tweaks are needed after 1.05 you can identify them and they can be incorporated for the next release. Eric Can somebody bring some light on this issue? thanks a lot, C. Pierre GM wrote: Charles, result = result.reorder(order).regrid(grid) AttributeError: 'MaskedArray' object has no attribute 'reorder' Should I inherit from soemtihng else ? Mmh, .reorder is not a regular ndarray method, so that won't work. What is it supposed to do ? And regrid ? Aslo I used to import a some function from numpy.oldnumeric.ma, that are now missing can you point me to their new ma equivalent? common_fill_value, identity, indices and set_fill_value For set_fill_value, just use m.fill_value = your_fill_value For identity, just use the regular numpy version, and view it as a MaskedArray: numpy.identity(...).view(MaskedArray) For common_fill_value: ah, tricky one, I'll have to check. If needed, I'll bring identity into numpy.ma. Please don't hesitate to send more feedback, it's always needed. Sincerely, P
Re: [Numpy-discussion] missing function in numpy.ma?
Hi Pierre, No problem, let me know when you have something in. I can't be sure all I mentioned is all that's missing. It's all I got so far. But since I can't get our end going. I can't really give you a comprehensive list of what's exactly missing. Hopefully this is all and it will work fine after your changes. Thanks for doing this, C. Pierre GM wrote: Charles, all i was saying is that 1.0.4 was working great with the small compatibility layer. I even have a frozen version of 1.0.5 devel that works great. Then suddenly everything broke. Could you be more specific ? Would you mind sending me bug reports so that I can check what's going on and how to improve backwards compatibility ? I was really happy was the layer of 1.0.4. We're talking about the 15-line long numpy.oldnumeric.ma, right ? The ones that redefines take as to take the averages along some indices ? The only difference between versions is that 1.0.5 uses numpy.ma instead of the old numpy.core.ma. [...] OK, now I see: there were some functions in numpy.core.ma that are not in numpy.ma (identity, indices). So, the pb is not in the conversion layer, but on numpy.ma itself. OK, that should be relatively easy to fix. Can you gimme a day or two ? Sorry for the delayed comprehension. P. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
Hello, Ok, I'll wait for Pierre's changes and see what it does for us. If it still breaks here or there then i'll do as Travis suggested (while still reporting to Pierre what went wrong). Thank you all, C. Travis E. Oliphant wrote: Jarrod Millman wrote: On Thu, Mar 27, 2008 at 1:31 PM, Pierre GM [EMAIL PROTECTED] wrote: On Thursday 27 March 2008 16:11:22 Travis E. Oliphant wrote: I guess what could be done is to take the old numpy.core.ma file and move it into oldnumeric.ma (along with the few re-namings that are there now)? Could you test that option out and see if it works for you? I'm currently re-introducing some functions of numpy.core.ma in numpy.ma.core, fixing a couple of bugs along the way (for example, round: the current behavior of numpy.round is inconsistent: it'll return a MaskedArray if the nb of decimals is 0, but a regular ndarray otherwise, so I just coded a numpy.ma.round and the corresponding method). 2-3 functions from numpy.core.ma (average, dot, and another one I can't remmbr) are already in numpy.ma.extras. I should update the SVN by later this afternoon. Excellent, I prefer this approach. If this works then it should be fine. If it doesn't, however, then it would not be too big a deal to just move the old implementation over. In fact, I rather think it ought to be done anyway. The oldnumeric directory will be disappearing in 1.1, so it doesn't introduce any long-term burden and just makes 1.0.5 a bit more robust. -Travis ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] missing function in numpy.ma?
Hello, I used to be able to inherit form nump.oldnumeric.ma.array, it looks like you can't any longer. I replaced it with: numpy.ma.MaskedArray i'm getting: result = result.reorder(order).regrid(grid) AttributeError: 'MaskedArray' object has no attribute 'reorder' Should I inherit from soemtihng else ? Aslo I used to import a some function from numpy.oldnumeric.ma, that are now missing can you point me to their new ma equivalent? common_fill_value, identity, indices and set_fill_value Thanks, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] missing function in numpy.ma?
The reorder is a function we implement. By digging a bit into this my guess is that all the missing function in numpy.ma are causing to fail at some point in our init and returning the wrong object type. But the whole idea was to keep a backward compatible layer with Numeric and MA. It worked great for a while and now things are getting more and more broken. Correct me if I'm wrong but it seems as if the numpy.oldnumeric.am is now simply numpy.ma and it's pointing to the new MaskedArray interface. Loosing a LOT of backward compatibility at the same time. I'm thinking that such changes should definitely not happen from 1.0.4 to 1.0.5 but rather in some major upgrade of numpy (1.1 at least, may be even 2.0). It is absolutely necessary to have the oldnumeric.ma working as much as possible as MA, what's in now is incompatible with code that have been successfully upgraded to numpy using your recommended method (official numpy doc) Can you put back ALL the function from numpy.oldnumeric.ma ? It shouldn't be too much work. Now I'm actually worried about using ma at all? What version is in? Is it a completely new package or is it still the old one just a bit broken? If it's a new one, we'd have to be sure it is fully tested before we can redistribute it to other people via our package, or before we use it ourselves Can somebody bring some light on this issue? thanks a lot, C. Pierre GM wrote: Charles, result = result.reorder(order).regrid(grid) AttributeError: 'MaskedArray' object has no attribute 'reorder' Should I inherit from soemtihng else ? Mmh, .reorder is not a regular ndarray method, so that won't work. What is it supposed to do ? And regrid ? Aslo I used to import a some function from numpy.oldnumeric.ma, that are now missing can you point me to their new ma equivalent? common_fill_value, identity, indices and set_fill_value For set_fill_value, just use m.fill_value = your_fill_value For identity, just use the regular numpy version, and view it as a MaskedArray: numpy.identity(...).view(MaskedArray) For common_fill_value: ah, tricky one, I'll have to check. If needed, I'll bring identity into numpy.ma. Please don't hesitate to send more feedback, it's always needed. Sincerely, P. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] f2py changed ?
Hello, I have an f2py module that used to work great, now it breaks, first of all the setup.py extension used to have: # f2py_options = [--fcompiler=gfortran,], I now need to comment this out, and hope it picks up the right compiler... at the beg of the script I have a line from the autoconvert: import numpy.oldnumeric as Numeric when running I get: variable = _gengridzmean.as_column_major_storage(Numeric.transpose(variable.astype (Numeric.Float32).filled(0))) AttributeError: 'module' object has no attribute 'as_column_major_storage' I tried to go around that by using straight numpy calls everywhere and using numpy.asfortranarray instead But now it collapse a bit further: res = ZonalMeans.compute(s) File /lgm/cdat/latest/lib/python2.5/site-packages/ZonalMeans/zmean.py, line 397, in compute bandlat) #,imt,jmt,kmt,nt,kmt_grid,iomax,vl) _gengridzmean.error: failed in converting 5th argument `mask' of _gengridzmean.zonebasin to C/Fortran array I checked all my arrays are treated with asfortranarray and even ascontiguousarray to be sure! I believe this used to work after converting to numpy ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] how to build a series of arrays as I go?
Hi Chris, 1-)You could use the concatenate function to grow an array as you go. 2-) assumnig you still have your list b=numpy.array(data[name]) bmasked=numpy.ma.masked_equal(b,-1) Chris Withers wrote: Hi All, I'm using xlrd to read an excel workbook containing several columns of data as follows: for r in range(1,sheet.nrows): date = \ datetime(*xlrd.xldate_as_tuple(sheet.cell_value(r,0),book.datemode)) if date_cut_off and date date_cut_off: continue for c in range(len(names)): name = names[c] cell = sheet.cell(r,c) if cell.ctype==xlrd.XL_CELL_EMPTY: value = -1 elif cell.ctype==xlrd.XL_CELL_DATE: value = \ datetime(*xlrd.xldate_as_tuple(cell.value,book.datemode)) else: value = cell.value data[name].append(value) Two questions: How can I build arrays as I go instead of lists? (ie: the last line of the above snippet) Once I've built arrays, how can I mask the empty cells? (the above shows my hack-so-far of turning empty cells into -1 so I can use masked_where, but it would be greato build a masked array as I went, for efficiencies sake) cheers for any help! Chris PS: Slightly pissed off at actually paying for the book only to be told it'll be 2 days before I can even read the online version, especially given the woefully inadequate state of the currently available free documentation :-( ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] numpy from subversion
Hi Stephan, Does the converter from Numeric fixes that? I mean runnning it on an old Numeric script will import numpy.ma, does it still replace with numpy.oldnumeric.ma? Thx, C. Stéfan van der Walt wrote: On Wed, Mar 12, 2008 at 11:39 AM, Charles Doutriaux [EMAIL PROTECTED] wrote: My mistake i was still in trunk but i do get: import numpy, numpy.oldnumeric.ma as MA, numpy.oldnumeric as Numeric, PropertiedClasses File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/oldnumeric/ma.py, line 4, in module from numpy.core.ma import * ImportError: No module named ma How does one build ma these days? Travis fixed this in latest SVN. Maskedarrays should now be imported as numpy.ma. Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy.array.ma class init
Hello, we used to have this working, the latest numpy breaks it. File /lgm/cdat/5.0.0.alpha7/lib/python2.5/site-packages/cdms2/tvariable.py, line 21, in module import numpy.oldnumeric.ma as MA class TransientVariable(AbstractVariable, MA.array): TypeError: Error when calling the metaclass bases function() argument 1 must be code, not str numpy.oldnumeric.ma function array at 0xb7a8f48c Any suggestion on how to fix that? Thx, C Charles Doutriaux wrote: Hi Stephan, Does the converter from Numeric fixes that? I mean runnning it on an old Numeric script will import numpy.ma, does it still replace with numpy.oldnumeric.ma? Thx, C. Stéfan van der Walt wrote: On Wed, Mar 12, 2008 at 11:39 AM, Charles Doutriaux [EMAIL PROTECTED] wrote: My mistake i was still in trunk but i do get: import numpy, numpy.oldnumeric.ma as MA, numpy.oldnumeric as Numeric, PropertiedClasses File /lgm/cdat/latest/lib/python2.5/site-packages/numpy/oldnumeric/ma.py, line 4, in module from numpy.core.ma import * ImportError: No module named ma How does one build ma these days? Travis fixed this in latest SVN. Maskedarrays should now be imported as numpy.ma. Regards Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug in f2py on Mac 10.5 ?
Hello, we're trying to install fortran extension with f2py, works great on linux, mac 10.4 (gfortran and g77) but on 10.5, it picks up g77 and then complains about cc_dynamic library. Apparently this lib is not part os 10.5 (Xcode), is that a known problem? Should we try with what's in trunk? Thanks, C. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
