Mailman 3 January 2006 - NumPy-Discussion

A proposal for dtype/dtypedescr in numpy objects
by Francesc Altet Jan. 13, 2006

Jan. 13, 2006

Hi, In my struggle for getting consistent behaviours with data types, I've ended with a new proposal for treating them. The basic thing is that I suggest to deprecate .dtype as being a first-class attribute and replace it instead by the descriptor type container, which I find quite more useful for end users. The current .dtype type will be still accessible (mainly for developers) but buried in .dtype.type. Briefly stated: current proposed ======= ======== .dtypedescr --> moved into .dtype .dtype --> moved into .dtype.type .dtype.dtypestr --> moved into .dtype.str new .dtype.name What is achieved with that? Well, not much, except easy of use and type comparison correctness. For example, with the next setup: >>> import numpy >>> a=numpy.arange(10,dtype='i') >>> b=numpy.arange(10,dtype='l') we have currently: >>> a.dtype <type 'int32_arrtype'> >>> a.dtypedescr dtypedescr('<i4') >>> a.dtypedescr.dtypestr '<i4' >>> a.dtype.__name__[:-8] 'int32' >>> a.dtype == b.dtype False With the new proposal, we would have: >>> a.dtype.type <type 'int32_arrtype'> >>> a.dtype dtype('<i4') >>> a.dtype.str '<i4' >>> a.dtype.name 'int32' >>> a.dtype == b.dtype True The advantages of the new proposal are: - No more .dtype and .dtypedescr lying together, just current .dtypedescr renamed to .dtype. I think that current .dtype does not provide more useful information than current .dtypedesc, and giving it a shorter name than .dtypedescr seems to indicate that it is more useful to users (and in my opinion, it isn't). - Current .dtype is still accessible, but specifying and extra name in path: .dtype.type (can be changed into .dtype.type_ or whatever). This should be useful mainly for developers. - Added a useful dtype(descr).name so that one can quickly access to the type name. - Comparison between data types works as it should now (without having to create a metaclass for PyType_Type). Drawbacks: - Backward incompatible change. However, provided the advantages are desirable, I think it is better changing now than later. - I don't specially like the string representation for the new .dtype class. For example, I'd find dtype('Int32') much better than dtype('<i4'). However, this would represent more changes in the code, but they can be made later on (much less disruptive than the proposed change). - Some other issues that I'm not aware of. I'm attaching the patch for latest SVN. Once applied (please, pay attention to the "XXX" signs in patched code), it passes all tests. However, it may remain some gotchas (specially those cases that are not checked in current tests). In case you are considering this change to check in, please, tell me and I will revise much more carefully the patch. If don't, never mind, it has been a good learning experience anyway. Uh, sorry for proposing this sort of things in the hours previous to a public release of numpy. -- >0,0< Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data "-"

3 3

numpy revision 1894 fails to build on Solaris
by Christopher Hanley Jan. 13, 2006

Jan. 13, 2006

Hi Travis, Numpy failed to build in this morning's regression test. A cursory look seems to indicate that the build failure is related to the umathmodule changes. I'm looking into this now. I've included the build log in case you can recognize the problem right away. Chris Running from numpy source directory. /data/basil5/numpy/numpy/distutils/system_info.py:1213: 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__) /data/basil5/numpy/numpy/distutils/system_info.py:1222: 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__) /data/basil5/numpy/numpy/distutils/system_info.py:1225: 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__) /data/basil5/numpy/numpy/distutils/system_info.py:1132: 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__) /data/basil5/numpy/numpy/distutils/system_info.py:1156: 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__) /data/basil5/numpy/numpy/distutils/system_info.py:1159: 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__) Assuming default configuration (numpy/distutils/command/{setup_command,setup}.py was not found) Appending numpy.distutils.command configuration to numpy.distutils Assuming default configuration (numpy/distutils/fcompiler/{setup_fcompiler,setup}.py was not found) Appending numpy.distutils.fcompiler configuration to numpy.distutils Appending numpy.distutils configuration to numpy Appending numpy.testing configuration to numpy No module named __svn_version__ Creating numpy/f2py/__svn_version__.py (version='1894') F2PY Version 2_1894 Appending numpy.f2py configuration to numpy blas_opt_info: blas_mkl_info: NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS NOT AVAILABLE atlas_blas_info: NOT AVAILABLE blas_info: NOT AVAILABLE blas_src_info: NOT AVAILABLE NOT AVAILABLE Creating numpy/core/__svn_version__.py (version='1894') Appending numpy.core configuration to numpy Appending numpy.lib configuration to numpy Appending numpy.dft configuration to numpy lapack_opt_info: lapack_mkl_info: mkl_info: NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS numpy.distutils.system_info.atlas_threads_info NOT AVAILABLE atlas_info: numpy.distutils.system_info.atlas_info NOT AVAILABLE lapack_info: FOUND: libraries = ['lapack'] library_dirs = ['/usr/local/lib'] language = f77 NOT AVAILABLE Appending numpy.linalg configuration to numpy Appending numpy.random configuration to numpy Appending numpy configuration to Inheriting attribute 'version' from '?' numpy version 0.9.3.1894 running build running config_fc running build_src building extension "numpy.distutils.__config__" sources creating build creating build/src creating build/src/numpy creating build/src/numpy/distutils creating build/src/numpy/distutils/numpy creating build/src/numpy/distutils/numpy/distutils adding 'build/src/numpy/distutils/numpy/distutils/__config__.py' to sources. building extension "numpy.core.multiarray" sources creating build/src/numpy/core Generating build/src/numpy/core/config.h customize SunFCompiler customize GnuFCompiler customize Gnu95FCompiler customize G95FCompiler customize GnuFCompiler customize Gnu95FCompiler customize SunFCompiler customize SunFCompiler using config cc options: '-DNDEBUG -O' compile options: '-I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -o _configtest _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -o _configtest Undefined first referenced symbol in file exp _configtest.o ld: fatal: Symbol referencing errors. No output written to _configtest Undefined first referenced symbol in file exp _configtest.o ld: fatal: Symbol referencing errors. No output written to _configtest failure. removing: _configtest.c _configtest.o cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -lm -o _configtest _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c "_configtest.c", line 4: undefined symbol: expl cc: acomp failed for _configtest.c "_configtest.c", line 4: undefined symbol: expl cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c "_configtest.c", line 4: undefined symbol: expf cc: acomp failed for _configtest.c "_configtest.c", line 4: undefined symbol: expf cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -lm -o _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -lm -o _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -lm -o _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c "_configtest.c", line 4: undefined symbol: atanhf cc: acomp failed for _configtest.c "_configtest.c", line 4: undefined symbol: atanhf cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c "_configtest.c", line 4: undefined symbol: atanhl cc: acomp failed for _configtest.c "_configtest.c", line 4: undefined symbol: atanhl cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c cc _configtest.o -lm -o _configtest success! removing: _configtest.c _configtest.o _configtest cc options: '-DNDEBUG -O' compile options: '-Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: _configtest.c "_configtest.c", line 4: undefined symbol: isinf cc: acomp failed for _configtest.c "_configtest.c", line 4: undefined symbol: isinf cc: acomp failed for _configtest.c failure. removing: _configtest.c _configtest.o adding 'build/src/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_array_api.py adding 'build/src/numpy/core/__multiarray_api.h' to sources. creating build/src/numpy/core/src conv_template:> build/src/numpy/core/src/scalartypes.inc adding 'build/src/numpy/core/src' to include_dirs. conv_template:> build/src/numpy/core/src/arraytypes.inc numpy.core - nothing done with h_files= ['build/src/numpy/core/src/scalartypes.inc', 'build/src/numpy/core/src/arraytypes.inc', 'build/src/numpy/core/config.h', 'build/src/numpy/core/__multiarray_api.h'] building extension "numpy.core.umath" sources adding 'build/src/numpy/core/config.h' to sources. executing numpy/core/code_generators/generate_ufunc_api.py adding 'build/src/numpy/core/__ufunc_api.h' to sources. conv_template:> build/src/numpy/core/src/umathmodule.c adding 'build/src/numpy/core/src' to include_dirs. numpy.core - nothing done with h_files= ['build/src/numpy/core/src/scalartypes.inc', 'build/src/numpy/core/src/arraytypes.inc', 'build/src/numpy/core/config.h', 'build/src/numpy/core/__ufunc_api.h'] building extension "numpy.core._sort" sources adding 'build/src/numpy/core/config.h' to sources. adding 'build/src/numpy/core/__multiarray_api.h' to sources. conv_template:> build/src/numpy/core/src/_sortmodule.c numpy.core - nothing done with h_files= ['build/src/numpy/core/config.h', 'build/src/numpy/core/__multiarray_api.h'] building extension "numpy.core._dotblas" sources building extension "numpy.lib._compiled_base" sources building extension "numpy.dft.fftpack_lite" sources building extension "numpy.linalg.lapack_lite" sources creating build/src/numpy/linalg ### Warning: Using unoptimized lapack ### adding 'numpy/linalg/lapack_litemodule.c' to sources. adding 'numpy/linalg/zlapack_lite.c' to sources. adding 'numpy/linalg/dlapack_lite.c' to sources. adding 'numpy/linalg/blas_lite.c' to sources. adding 'numpy/linalg/dlamch.c' to sources. adding 'numpy/linalg/f2c_lite.c' to sources. building extension "numpy.random.mtrand" sources building extension "numpy.__config__" sources creating build/src/numpy/numpy adding 'build/src/numpy/numpy/__config__.py' to sources. running build_py creating build/lib.solaris-2.8-sun4u-2.4 creating build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/_import_tools.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/add_newdocs.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/dual.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying numpy/version.py -> build/lib.solaris-2.8-sun4u-2.4/numpy copying build/src/numpy/numpy/__config__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy creating build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/core.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/conv_template.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/misc_util.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/line_endings.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/from_template.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/system_info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/extension.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/cpuinfo.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/lib2def.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/mingw32ccompiler.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/intelccompiler.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/ccompiler.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/log.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/__version__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/unixccompiler.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying numpy/distutils/exec_command.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils copying build/src/numpy/distutils/numpy/distutils/__config__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils creating build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/config_compiler.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build_clib.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build_ext.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/install_headers.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/install_data.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build_scripts.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/bdist_rpm.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/config.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build_src.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/install.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/sdist.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command copying numpy/distutils/command/build_py.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/command creating build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/intel.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/mips.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/gnu.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/absoft.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/vast.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/none.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/compaq.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/lahey.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/g95.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/hpux.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/nag.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/sun.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/pg.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler copying numpy/distutils/fcompiler/ibm.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/distutils/fcompiler creating build/lib.solaris-2.8-sun4u-2.4/numpy/testing copying numpy/testing/numpytest.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/testing copying numpy/testing/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/testing copying numpy/testing/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/testing copying numpy/testing/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/testing copying numpy/testing/utils.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/testing creating build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/diagnose.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/capi_maps.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/rules.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/common_rules.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/auxfuncs.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/cb_rules.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/use_rules.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/f90mod_rules.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/f2py2e.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/func2subr.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/f2py_testing.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/crackfortran.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/cfuncs.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/__version__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py copying numpy/f2py/__svn_version__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/f2py creating build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/ma.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/defchararray.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/arrayprint.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/records.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/oldnumeric.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/numeric.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/_internal.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/memmap.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/defmatrix.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/numerictypes.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/__svn_version__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core copying numpy/core/code_generators/generate_array_api.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/core creating build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/shape_base.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/scimath.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/twodim_base.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/machar.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/getlimits.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/utils.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/mlab.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/convertcode.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/arraysetops.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/type_check.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/function_base.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/polynomial.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/ufunclike.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib copying numpy/lib/index_tricks.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/lib creating build/lib.solaris-2.8-sun4u-2.4/numpy/dft copying numpy/dft/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/dft copying numpy/dft/fftpack.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/dft copying numpy/dft/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/dft copying numpy/dft/helper.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/dft copying numpy/dft/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/dft creating build/lib.solaris-2.8-sun4u-2.4/numpy/linalg copying numpy/linalg/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/linalg copying numpy/linalg/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/linalg copying numpy/linalg/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/linalg copying numpy/linalg/linalg.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/linalg creating build/lib.solaris-2.8-sun4u-2.4/numpy/random copying numpy/random/__init__.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/random copying numpy/random/info.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/random copying numpy/random/setup.py -> build/lib.solaris-2.8-sun4u-2.4/numpy/random running build_ext customize UnixCCompiler customize UnixCCompiler using build_ext building 'numpy.core.multiarray' extension compiling C sources cc options: '-DNDEBUG -O' creating build/temp.solaris-2.8-sun4u-2.4 creating build/temp.solaris-2.8-sun4u-2.4/numpy creating build/temp.solaris-2.8-sun4u-2.4/numpy/core creating build/temp.solaris-2.8-sun4u-2.4/numpy/core/src compile options: '-Ibuild/src/numpy/core/src -Inumpy/core/include -Ibuild/src/numpy/core -Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: numpy/core/src/multiarraymodule.c "numpy/core/include/numpy/arrayobject.h", line 130: warning: typedef redeclared: ushort "numpy/core/include/numpy/arrayobject.h", line 131: warning: typedef redeclared: uint "numpy/core/include/numpy/arrayobject.h", line 132: warning: typedef redeclared: ulong "build/src/numpy/core/src/arraytypes.inc", line 5257: warning: pointer to void or function used in arithmetic "build/src/numpy/core/src/arraytypes.inc", line 5837: warning: pointer to void or function used in arithmetic "build/src/numpy/core/src/arraytypes.inc", line 6417: warning: pointer to void or function used in arithmetic "numpy/core/src/arrayobject.c", line 3914: warning: pointer to void or function used in arithmetic cc -G build/temp.solaris-2.8-sun4u-2.4/numpy/core/src/multiarraymodule.o -lm -lm -o build/lib.solaris-2.8-sun4u-2.4/numpy/core/multiarray.so building 'numpy.core.umath' extension compiling C sources cc options: '-DNDEBUG -O' creating build/temp.solaris-2.8-sun4u-2.4/build creating build/temp.solaris-2.8-sun4u-2.4/build/src creating build/temp.solaris-2.8-sun4u-2.4/build/src/numpy creating build/temp.solaris-2.8-sun4u-2.4/build/src/numpy/core creating build/temp.solaris-2.8-sun4u-2.4/build/src/numpy/core/src compile options: '-Ibuild/src/numpy/core/src -Inumpy/core/include -Ibuild/src/numpy/core -Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c' cc: build/src/numpy/core/src/umathmodule.c "numpy/core/include/numpy/arrayobject.h", line 130: warning: typedef redeclared: ushort "numpy/core/include/numpy/arrayobject.h", line 131: warning: typedef redeclared: uint "numpy/core/include/numpy/arrayobject.h", line 132: warning: typedef redeclared: ulong "build/src/numpy/core/src/umathmodule.c", line 655: identifier redefined: log1pl current : function(long double) returning long double previous: function(long double) returning long double : "build/src/numpy/core/src/umathmodule.c", line 419 "build/src/numpy/core/src/umathmodule.c", line 667: identifier redefined: log1pf current : function(float) returning float previous: function(float) returning float : "build/src/numpy/core/src/umathmodule.c", line 545 "build/src/numpy/core/src/umathmodule.c", line 693: identifier redefined: expm1l current : function(long double) returning long double previous: function(long double) returning long double : "build/src/numpy/core/src/umathmodule.c", line 426 "build/src/numpy/core/src/umathmodule.c", line 707: identifier redefined: expm1f current : function(float) returning float previous: function(float) returning float : "build/src/numpy/core/src/umathmodule.c", line 552 "numpy/core/src/ufuncobject.c", line 1217: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1217: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1724: warning: non-constant initializer: op "NAME" "numpy/core/src/ufuncobject.c", line 1897: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1909: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1962: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1972: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1992: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2058: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2102: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2113: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2135: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2202: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2289: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2296: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2346: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2743: warning: pointer to void or function used in arithmetic cc: acomp failed for build/src/numpy/core/src/umathmodule.c "numpy/core/include/numpy/arrayobject.h", line 130: warning: typedef redeclared: ushort "numpy/core/include/numpy/arrayobject.h", line 131: warning: typedef redeclared: uint "numpy/core/include/numpy/arrayobject.h", line 132: warning: typedef redeclared: ulong "build/src/numpy/core/src/umathmodule.c", line 655: identifier redefined: log1pl current : function(long double) returning long double previous: function(long double) returning long double : "build/src/numpy/core/src/umathmodule.c", line 419 "build/src/numpy/core/src/umathmodule.c", line 667: identifier redefined: log1pf current : function(float) returning float previous: function(float) returning float : "build/src/numpy/core/src/umathmodule.c", line 545 "build/src/numpy/core/src/umathmodule.c", line 693: identifier redefined: expm1l current : function(long double) returning long double previous: function(long double) returning long double : "build/src/numpy/core/src/umathmodule.c", line 426 "build/src/numpy/core/src/umathmodule.c", line 707: identifier redefined: expm1f current : function(float) returning float previous: function(float) returning float : "build/src/numpy/core/src/umathmodule.c", line 552 "numpy/core/src/ufuncobject.c", line 1217: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1217: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1724: warning: non-constant initializer: op "NAME" "numpy/core/src/ufuncobject.c", line 1897: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1909: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1962: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1972: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 1992: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2058: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2102: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2113: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2135: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2202: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2289: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2296: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2346: warning: pointer to void or function used in arithmetic "numpy/core/src/ufuncobject.c", line 2743: warning: pointer to void or function used in arithmetic cc: acomp failed for build/src/numpy/core/src/umathmodule.c error: Command "cc -DNDEBUG -O -Ibuild/src/numpy/core/src -Inumpy/core/include -Ibuild/src/numpy/core -Inumpy/core/src -Inumpy/lib/../core/include -I/usr/stsci/pyssgx/Python-2.4.2/include/python2.4 -c build/src/numpy/core/src/umathmodule.c -o build/temp.solaris-2.8-sun4u-2.4/build/src/numpy/core/src/umathmodule.o" failed with exit status 2 removed numpy/core/__svn_version__.py removed numpy/core/__svn_version__.pyc removed numpy/f2py/__svn_version__.py removed numpy/f2py/__svn_version__.pyc

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Как вызывать у людей желание работать?
by Зачем люди работают Jan. 12, 2006

Jan. 12, 2006

"Кто не хочет - ищет причины, кто хочет - ищет возможности:" (народная мудрость) Почему люди работают? Зачем люди работают? Почему одно делают - с охотой, а другое - через силу? Что отличает первое? В чем секрет переплетения экономики и психологии в вопросах мотивации? Что отличает все успешные зарплатные схемы? Что скрывается под словами "нематериальная мотивация"? Почему люди "не хотят зарабатывать"? Почему очевидные на первый взгляд решения зарплатных задач зачастую не дают результата? Люди у Вас работают не за деньги. За деньги они работают в принципе - схожую зарплату они получат и в другом месте. А почему они работают именно у Вас? Как построить мотивацию у сотрудников Как вызывать у людей желание работать? Вскрываем "черный ящик" мотивационных и зарплатных схем в программе также: Что такое потенциал сотрудника? Чем он определяется? Как подбирать людей с нужными качествами? Как вести регулярное обучение? Если отношение сотрудников к работе - святыня, которую обходят как "священную корову", а для сотрудника в порядке вещей выбор "делать - не делать" - в компании нет регулярного менеджмента. Что такое регулярный менеджмент? Как наводить в компании порядок? Как строить в компании рабочие отношения? Как обеспечить ответственный, конструктивный и позитивный подход к работе? Как обеспечить самомотивацию и самоконтроль? Застоявшийся коллектив - самоподдерживающаяся система. А как работающий коллектив сделать самоподдерживающейся системой? Каковы необходимые и достаточные условия создания a. эффективно работающего, b. управляемого, c. стабильно развивающегося отдела? Продолжительность семинара: 8 часов Стоимость участия: 7700 рублей. НДС не облагается. Получить подробную информацию и записаться на семинар Вы можете по телефону (095) 518-05-75, 544-88-14

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Overlapping websites?
by Colin J. Williams Jan. 12, 2006

Jan. 12, 2006

There are now: http://www.numpy.org and http://sourceforge.net/projects/numpy/ These seem to be synonyms. Are two URI's needed? Numeric 24, not that I use it, seems to have been lost. Colin W.

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Re: [Numpy-discussion] Who will use numpy.ma?
by Joris De Ridder Jan. 12, 2006

Jan. 12, 2006

> With the recent improvements to the array object in NumPy, > the MA library has fallen behind. There are more than 50 methods > in the ndarray object that are not present in ma.array. I guess maintaining MA means double work, doesn't it? So, even if MA is updated now, in the future it's likely to be always somewhat behind. This is perhaps an argument against the use of this library? I wouldn't like to extensively use a library that will be phased out in a couple of years because it turns out to be somewhat redundant and/or behind w.r.t. the rest of numpy. The fact that with numpy you can do things like >>> from numpy import * >>> x = sin(1./arange(3.)) # arbitrary example >>> x[1] = nan # 'masking' an element >>> x = where(isnan(x), 10., x) # replacing masks by a number >>> x = where(x >= 10, nan, x) # clipping with nan replacement >>> y = sin(x) # sin(nan) = nan fulfils most of my needs. However, I haven't compared execution times for large arrays. Note that using NaNs, we don't distinguish between NaN and NA (not available). I am not sure this won't bite us somewhere in the future. I have a related question. numpy introduces the functions nanargmax(), nanargmin(), nanmax(), nanmin(), and nansum(). Was there a special reason to introduce extra nan... functions rather than adding an option like ignore_nan = True to the normal functions? Is this a performance issue? Will similar nan... equivalents be introduced for the functions mean() and reduce()? A side remark: >>> y = array([1, nan, 0.47]) >>> sort(y) array([ 1. , nan, 0.47 ]) No exception, no sorting. Is this a feature or a bug? :) J. Disclaimer: http://www.kuleuven.be/cwis/email_disclaimer.htm

2 1

Финансы для топ-менеджеров
by 26-27 января 2006 года Jan. 12, 2006

Jan. 12, 2006

Финансы для топ-менеджеров 26-27 января 2006 года Многие руководители компаний и их различных подразделений обращаются к финансовым аспектам бизнеса только тогда, когда возникает кризис. Между тем, без грамотного управления финансами достичь успеха в любом бизнесе практически невозможно. Данный семинар посвящен изучению основ финансового менеджмента, а также тем аналитическим инструментам, которые позволяют эффективно оценивать финансовое состояние компании, планировать финансовые потоки, принимать правильные решения и - как результат - вести успешный и доходный бизнес. В программе данного семинара будут рассмотрены многие важные вопросы финансового анализа компании, методы анализа финансовых результатов; построения системы управленческого учета, управления финансовыми потоками на всех этапах развития компании. Слушатели познакомятся с основами управления финансами в бизнесе, научатся читать финансовые документы, изучат основные методы бюджетирования, финансового планирования и анализа для принятия грамотных управленческих решений. Полученные знания будут закреплены на решении практических примеров (кейсов) и в деловых играх. Участники семинара приобретут навыки: - постановки финансовых целей бизнеса, разработки финансовой стратегии, - составления и консолидации бюджетов всех уровней, - работы с балансом компании, отчетом о прибылях и убытках, - управления движением денежных средств, - планирования и анализа финансовых результатов, - принятия управленческих решений в условиях неопределенности рынка, - оценивать деятельность компании по основным финансовым документам, - использования отчетных документов для принятия ключевых решений бизнеса, - анализировать эффективность любых видов инвестиций, - профессионально ставить задачи своим финансовым службам и контролировать их выполнение. Содержание программы: 1. Система корпоративного финансового менеджмента. 2. Управленческий учет и аудит в компании. 3. Формирование и управление капиталом фирмы. 4. Оперативный финансовый менеджмент. 5. Управление денежными потоками. 6. Анализ финансового состояния по данным баланса. 7. Основные методы анализа. 8. Управление общей доходностью бизнеса. 9. Финансовые и экономические показатели. 10. Финансовые стратегии компаний. 11. Управление стоимостью компании. 12. Повышение эффективности использования капитала собственников. 13. Управление рисками в компании. 14. Бюджетирование как основная методика финансового управления и планирования. Данную программу вы можете заказать в корпоративном варианте, с учетом специфики вашего бизнеса и уровня подготовки группы. Получить подробную информацию Вы можете по телефонам: (095) 980-65-З9, 980-65-З6

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Re: [Numpy-discussion] Who will use numpy.ma?
by Sasha Jan. 12, 2006

Jan. 12, 2006

With Paul's permission I am posting his arguments and my responses. Numpy.ma will follow Paul's design and there is now a wiki page dedicated to the effort to make ma work better in numpy. (See http://projects.scipy.org/scipy/numpy/wiki/MaskedArray). -- sasha On 1/12/06, Sasha <ndarray(a)mac.com> wrote: > Paul, > > Thank you very much for your insights and once again thanks for all > the great work that you've done. I've noticed that your reply was not > posted on any list, do you mind if I forward it to numpy-user? Please > see more below. > > On 1/12/06, Paul F. Dubois <paul(a)pfdubois.com> wrote: > > What special values? Are you sure this works on any platform? What, for > > example, is the special value for integer arrays? For arrays of objects? > > > Yes, these are hard questions. For floats nan is an obvious choice and > IEEE support is getting better on the new hardware. For objects None is > a fine choice. For integers some may argue for sys.maxint, and given that > numpy integer arithmetics is already handling overflow a check for maxint will > not add much complexity. Yet don't get me wrong: I don't see any replacement > for ma myself. > > > How do replaceable mathematical operations make any difference? The > > fundamental problem is that if array x has special values in some places > > and array y has them in some other places, how do you create a result > > that has special values in the correct places AND is of a type for which > > those special values are still treated as 'missing'. How do you do this? > > > > Replaceable operations would allow one to redefine all operations on integer > arrays to treat say sys.maxint as invariant and cast it to nan in floating point > conversions without changing the logic of main line numpy. > > > I converted MA to ma but did not have time to flesh out all the > > differences with the new ndarray. I was hoping the community would do > > that. > > Me too. That's was the point of my post - to find out the size of the comunity > rather than to suggest an alternative. > > > I am retired. > > > You deserve it. > > > It is my belief that the approach you outline is not workable, but > > perhaps I am not understanding it properly. > > > I don't have any workable approach other than enchancing ma to work > better with numpy. This is what I am doing right now. > > > If I, who have thought about this a lot, do not know for sure, what > > information can you derive from a poll of the general public, who will > > not think through these issues very carefully? > > > I was trying to poll numpy community to find out how many people actually > use ma in real projects. This would determine how well tested the new features > will be and how quickly any bugs will be discovered and fixed. > Unfortunately, I have > not seen a single response saying - I've been using MA for X years on > Y projects and > plan on using it as we upgrade to numpy. There was a lot of > theoretical discussions > and a pointer to a plotting library that has recently added MA > support, but no testimony > from end users. > > > I am close to absolutely positive that subclassing won't particularly > > ease the task. > > > I thought about this a little, and I think you are right. Subclassing > may improve speed a little, but all methods will need to be adapted > the same ways as it is done without subclassing. > > > For the reason I indicated, I don't care to engage in public discussions > > of complex technical issues so I have not cc'd this to the group. > > > I respect that, but please allow me to forward at least portions of > this correspondence > to the community. Your insights are invaluable. > > -- sasha > > > > > Sasha wrote: > > > MA is intended to be a drop-in replacement for Numeric arrays that can > > > explicitely handle missing observations. With the recent improvements > > > to the array object in NumPy, the MA library has fallen behind. There > > > are more than 50 methods in the ndarray object that are not present in > > > ma.array. > > > > > > I would like to hear from people who work with datasets with missing > > > observations? Do you use MA? Do you think with the support for nan's > > > and replaceable mathematical operations, should missing observations > > > be handled in numpy using special values rather than an array of > > > masks? > > > > > > Thanks. > > > > > > -- sasha > > > > > > > > > ------------------------------------------------------- > > > This SF.net email is sponsored by: Splunk Inc. Do you grep through log files > > > for problems? Stop! Download the new AJAX search engine that makes > > > searching your log files as easy as surfing the web. DOWNLOAD SPLUNK! > > > http://ads.osdn.com/?ad_idv37&alloc_id865&op=click > > > _______________________________________________ > > > Numpy-discussion mailing list > > > Numpy-discussion(a)lists.sourceforge.net > > > https://lists.sourceforge.net/lists/listinfo/numpy-discussion > > > > > >

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Re: [Numpy-user] Optional shape in array description
by Travis Oliphant Jan. 12, 2006

Jan. 12, 2006

Francesc Altet wrote: >Hi, > >I went over a weirdness on interpretation of the optional shape in the >descriptor of types when declaring records. > >In [176]: numpy.zeros((2,), dtype=[('c1', 'f4')]) >Out[176]: array([(0.0,), (0.0,)], dtype=(void,4)) > >So far, so good. However: > >In [177]: numpy.zeros((2,), dtype=[('c1', 'f4', 1)]) >Out[177]: array([(array([ 0.], dtype=float32),), (array([ 0.], >dtype=float32),)], dtype=(void,4)) > >And: > >In [178]: numpy.zeros((2,), dtype=[('c1', 'f4', (1,))]) >Out[178]: array([(array([ 0.], dtype=float32),), (array([ 0.], >dtype=float32),)], dtype=(void,4)) > >i.e. the very same value is obtained specifying shape=1 and shape=(1,). > >OTOH, in the point 3 of the description of the __array_descr__ of the >array protocol (http://numeric.scipy.org/array_interface.html), one >can read: > >""" >An optional shape tuple providing how many times this part of the >record should be repeated. Assumed (1,) if not given. >""" > >But this is clearly not what it is implemented. > >IMO, the correct thing (and what the numarray does) would be: > >In [176]: numpy.zeros((2,), dtype=[('c1', 'f4')]) >Out[176]: array([(0.0,), (0.0,)], dtype=(void,4)) >In [192]: numpy.zeros((2,), dtype=[('c1', 'f4')])[0][0] >Out[192]: 0.0 > >In [177]: numpy.zeros((2,), dtype=[('c1', 'f4', 1)]) >Out[177]: array([(0.0,), (0.0,)], dtype=(void,4)) >In [193]: numpy.zeros((2,), dtype=[('c1', 'f4', 1)])[0][0] >Out[193]: 0.0 > >In [178]: numpy.zeros((2,), dtype=[('c1', 'f4', (1,))]) >Out[178]: array([(array([ 0.], dtype=float32),), (array([ 0.], >dtype=float32),)], dtype=(void,4)) >In [194]: numpy.zeros((2,), dtype=[('c1', 'f4', (1,))])[0][0] >Out[194]: array([ 0.], dtype=float32) > >i.e. that shape=1 would be equivalent to don't specify it (scalar >case), and shape=(1,) would be equivalent to declare the field as >unidimensional but with one element. > > This is what is now done in SVN... Thanks for catching the corner case. We should also change the verbage on the array_protocol page. -Travis

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Re: [SciPy-dev] Long live to numpy (and its list as well)
by Joe Harrington Jan. 12, 2006

Jan. 12, 2006

Francesc Altet on scipy-dev: > Now that numpy is dead and we all want a long life to numpy, what > about moving discussions in this list into the venerable: > "numpy-discussion <numpy-discussion(a)lists.sourceforge.net>" We're one community (or want to be). Could we just have one mailing list per type of discussion (dev and user)? All the cross-posting and cross-monitoring is tedious. I don't care whether we keep scipy-* or numpy-* lists, but is there a functional reason to have four lists? Consider that soon we may have *-doc, *-newbie, *-announce, and others as well, if this takes off like we all hope. If the developers want separate lists because some are only working on one of the two packages, I can see that argument (in the future if not now). I don't see a need for two user lists, unless perhaps sorted by high and low traffic. I propose we either drop the numpy-* lists (if subscribers there agree), or leave them for ongoing discussion of the legacy packages, and discourage discussion of the new numpy/scipy there. Ok, flame me. --jh--

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Two bugs in numpy 0.9.2 and a suggestion.
by Paulo J. S. Silva Jan. 12, 2006

Jan. 12, 2006

Hello, I have just installed numpy 0.9.2 in my system. Thanks for the good work. I think I got two bugs: 1) In the numpy.linalg method the cholesky decomposition doesn't work because the function triu is not available from numpy.core. If you add a line "from numpy import triu" on the top of the linalg.py file (with the other imports), it starts working. 2) If you try to instantiate a ndarray with weird parameters it can segfault and abort the interpreter. For example try to do the following: a = arange(10.) b = ndarray(a) Now the suggestion. What about adding a "transpose" property (in the Python parlance) to ndarray objects? I would call it "t". The following Python code explains what I mean: from numpy import * class Myndarray(ndarray): t = property(transpose, None, None) If you add this 3 lines to a file, import the Myndarray class, and create one of such arrays you will get transpose(a) by simply typing a.t. This allow for the following type of code (which I prefer): cost = dot(c.t, x) instead of cost = dot(transpose(c), x) or cost = dot(c.transpose(), x) Such a compact notation can be a blessing in code that needs lots of transpose (like the BFGS formula for example). If I knew more about writing C extensions for Python I would try to do it myself, but I don't. Is it possible to define properties in a C extension? Best, Paulo -- Paulo José da Silva e Silva Professor Assistente do Dep. de Ciência da Computação (Assistant Professor of the Computer Science Dept.) Universidade de São Paulo - Brazil e-mail: rsilva(a)ime.usp.br Web: http://www.ime.usp.br/~rsilva Teoria é o que não entendemos o (Theory is something we don't) suficiente para chamar de prática. (understand well enough to call practice)

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