Mailman 3 April 2011 - NumPy-Discussion

Arrays as nested dtype
by Robert Love April 9, 2011

April 9, 2011

Using np.loadtxt I can easily read my file that has columns of time, mode, 3 float64 for position and 3 for velocity like this. dt = dtype([('time', '|S12'), ('mode','|S3'),('rx','f8'),('ry','f8'),('rz','f8'),('vx','f8'),('vy','f8'),('vz','f8')]) data = np.loadtxt('file', dtype=dt) I can then put the two pairs of 3 components into np.arrays and start performing the vector operations I need. How can I read them directly into np.arrays? dt = dtype([('time', '|S12'), ('mode','|S3'),np.array('r'), np.array('v')]) I've seen examples for nested data that create a tuple but not an array. Any tips appreciated.

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netcdf to grib format
by dileep kunjaai April 8, 2011

April 8, 2011

Dear sir, Is there any tool for changing the 'netcdf' file format to 'grib' format in python or cdat -- DILEEPKUMAR. R J R F, IIT DELHI

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How to use a function into "import function" in python
by dileep kunjaai April 8, 2011

April 8, 2011

Dear sir, I defined a function " hit_rate( )" i want to use this into "import function(name of function)". For example: I defined a function as * def* hit_function(a,b,c): ......................... ,............................... ............................ * return* value Now I want to use this in another function as * def *test_hr( ): import hit_function(a, b, c) ....................... ,,,,,,,,,,,,,,,,,,,,,,,,,, ,,,,,,,,,,,,,,,,,,,,,,,,, *return* value1 Please help me to solve this problem. thanking you -- DILEEPKUMAR. R J R F, IIT DELHI

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ANN: Mahotas 0.6.4
by Luis Pedro Coelho April 7, 2011

April 7, 2011

Hello all, I'm happy to announce a new release of mahotas, my computer vision library for Python. WHAT'S MAHOTAS -------------- Mahotas is a library which includes several image processing algorithm. It works over numpy arrays. All its computation heavy functions are implemented in C++ (using templates so that they work for every data type without conversions). The result is a fast, optimised image processing and computer vision library. It is under heavy development and has no known bugs. Reported bugs almost always get fixed in a day. WHAT'S NEW ---------- Major change is that Mac OS compilation now works! Thanks to K-Michael Aye for the patch that saved the day. Minor changes include fixes to ``cwatershed`` and adding the tests to the source distribution (use `python setup.py test` to run them). Some of these. INFO ---- *API Docs*: http://packages.python.org/mahotas/ *Mailing List*: Use the pythonvision mailing list http://groups.google.com/group/pythonvision?pli=1 for questions, bug submissions, etc. *Author*: Luis Pedro Coelho (with code by Zachary Pincus [from scikits.image], Peter J. Verveer [from scipy.ndimage], and Davis King [from dlib] *License*: GPLv2 Thank you, -- Luis Pedro Coelho | Carnegie Mellon University | http://luispedro.org

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Rounding the decimal part of a real number
by dileep kunjaai April 7, 2011

April 7, 2011

Dear sir, Is there any function for rounding the real number, for "n" (say) decimal places: Example: Let X= 6.9867349234888211237767867863478728314... but i need only 4 decimal position. That is the answer should be.. >>>answer=6.9867 -- DILEEPKUMAR. R J R F, IIT DELHI

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Installation problem: numpy1.5.1 + python2.7 + virtualenv-1, 5, 1
by Vicent Mas April 6, 2011

April 6, 2011

Hi, I'm trying to install numpy1.5.1 in a virtual environment (virtualenv-1.5.1) on my debian testing box. I'm using python 2.7 installed from a debian package. This is what I get: (venv2.7)vmas@rachael$ python setup.py install Running from numpy source directory.F2PY Version 1 blas_opt_info: blas_mkl_info: libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE atlas_blas_threads_info: Setting PTATLAS=ATLAS Setting PTATLAS=ATLAS Setting PTATLAS=ATLAS FOUND: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/lib/atlas-base'] language = c include_dirs = ['/usr/include/atlas'] FOUND: libraries = ['ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/lib/atlas-base'] language = c define_macros = [('ATLAS_INFO', '"\\"?.?.?\\""')] include_dirs = ['/usr/include/atlas'] lapack_opt_info: lapack_mkl_info: mkl_info: libraries mkl,vml,guide not found in /usr/lib NOT AVAILABLE NOT AVAILABLE atlas_threads_info: Setting PTATLAS=ATLAS libraries lapack not found in /usr/lib/atlas-base numpy.distutils.system_info.atlas_threads_info Setting PTATLAS=ATLAS Setting PTATLAS=ATLAS FOUND: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base'] language = f77 include_dirs = ['/usr/include/atlas'] FOUND: libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas'] library_dirs = ['/usr/lib/atlas-base/atlas', '/usr/lib/atlas-base'] language = f77 define_macros = [('ATLAS_INFO', '"\\"?.?.?\\""')] include_dirs = ['/usr/include/atlas'] running install running build running config_cc unifing config_cc, config, build_clib, build_ext, build commands --compiler options running config_fc unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options running build_src build_src building py_modules sources creating build creating build/src.linux-i686-2.7 creating build/src.linux-i686-2.7/numpy creating build/src.linux-i686-2.7/numpy/distutils building library "npymath" sources Traceback (most recent call last): File "setup.py", line 211, in <module> setup_package() File "setup.py", line 204, in setup_package configuration=configuration ) File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/core.py", line 186, in setup return old_setup(**new_attr) File "/usr/lib/python2.7/distutils/core.py", line 152, in setup dist.run_commands() File "/usr/lib/python2.7/distutils/dist.py", line 953, in run_commands self.run_command(cmd) File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/install.py", line 55, in run r = old_install.run(self) File "/usr/lib/python2.7/distutils/command/install.py", line 601, in run self.run_command('build') File "/usr/lib/python2.7/distutils/cmd.py", line 326, in run_command self.distribution.run_command(command) File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/build.py", line 37, in run old_build.run(self) File "/usr/lib/python2.7/distutils/command/build.py", line 128, in run self.run_command(cmd_name) File "/usr/lib/python2.7/distutils/cmd.py", line 326, in run_command self.distribution.run_command(command) File "/usr/lib/python2.7/distutils/dist.py", line 972, in run_command cmd_obj.run() File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/build_src.py", line 152, in run self.build_sources() File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/build_src.py", line 163, in build_sources self.build_library_sources(*libname_info) File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/build_src.py", line 298, in build_library_sources sources = self.generate_sources(sources, (lib_name, build_info)) File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/build_src.py", line 385, in generate_sources source = func(extension, build_dir) File "numpy/core/setup.py", line 681, in get_mathlib_info st = config_cmd.try_link('int main(void) { return 0;}') File "/usr/lib/python2.7/distutils/command/config.py", line 248, in try_link self._check_compiler() File "/usr/local/src/ViTables_environ/General/numpy-1.5.1/numpy/distutils/command/config.py", line 45, in _check_compiler old_config._check_compiler(self) File "/usr/lib/python2.7/distutils/command/config.py", line 103, in _check_compiler customize_compiler(self.compiler) File "/usr/lib/python2.7/distutils/ccompiler.py", line 44, in customize_compiler cpp = cc + " -E" # not always TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' Doing the same with python2.6 (also from debian package) works just fine. Could somebody tell me what am I doing wrong? Thanks. Vicent :: Share what you know, learn what you don't

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Re: [Numpy-discussion] ANN: Numpy 1.6.0 beta 2
by Christopher Barker April 6, 2011

April 6, 2011

On 4/5/11 10:33 PM, Matthew Brett wrote: > Did you mean to send this just to me? It seems like the whole is > generally interesting and helpful, at least to me... I did mean to send to the list -- I've done that now. > Well, the current code doesn't split on \r in py3k, admittedly that > must be a rare case. I guess that's a key question here -- It really *should* split on /r, but maybe it's rare enough to be unimportant. > The general point about whether to read binary or text is also in > play. I agree with you, reading text sounds like a better idea to > me, but I don't know the performance hit. Pauli had it as reading > binary in the original conversion and was defending that in an earlier > email... The thing is -- we're talking about parsing text here, so we really are reading text files, NOT binary files. So the question really is -- do we want py3's file reading code to handle encoding issues for us, or do we want to handle them ourselves. If we only want to support ansi encodings, then handling ourselves may well be easier and faster performing. If we go that way we need to handle line-endings, too. The easy way is to only support line endings with a '\n' in them -- that works out of the box. But it's not that hard to support 'r' also, depending on how you want to do it. Before 'U' existed, I did that all the time, something like: some_text = file.read(some_size_buffer) some_text.replace('\r\n', '\n') some_text.replace('\r', '\n') lines = some_text.split('\n') (by the way, if you're going to support this, it's really nice to support mixed line-endings (like this approach does) -- there are a lot of editors that can make a mess of line endings. If you can read the entire file into memory at once, this is almost trivial, if you can't -- there is a bit more bookeeping code to be written. DARN -- I think I said my last note was the last on this topic! -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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ANN: Numpy 1.6.0 beta 2
by Ralf Gommers April 6, 2011

April 6, 2011

Hi, I am pleased to announce the availability of the second beta of NumPy 1.6.0. Due to the extensive changes in the Numpy core for this release, the beta testing phase will last at least one month. Please test this beta and report any problems on the Numpy mailing list. Many bug fixes went in since beta 1, among which: - fix installation of source release with python 3 - f2py fixes for assumed shape arrays - several loadtxt bug fixes and enhancements - change default floating point error handling from "print" to "warn" - much more I quickly counted in the timeline, and in the last few weeks the number of open tickets has been decreased by over 100. Thanks to everyone who contributed to this spring cleaning! Sources and binaries can be found at http://sourceforge.net/projects/numpy/files/NumPy/1.6.0b2/ For (preliminary) release notes see below. Enjoy, Ralf Note: NumPy 1.6.0 is not yet released. ========================= NumPy 1.6.0 Release Notes ========================= This release includes several new features as well as numerous bug fixes and improved documentation. It is backward compatible with the 1.5.0 release, and supports Python 2.4 - 2.7 and 3.1 - 3.2. Highlights ========== * Re-introduction of datetime dtype support to deal with dates in arrays. * A new 16-bit floating point type. * A new iterator, which improves performance of many functions. New features ============ New 16-bit floating point type ------------------------------ This release adds support for the IEEE 754-2008 binary16 format, available as the data type ``numpy.half``. Within Python, the type behaves similarly to `float` or `double`, and C extensions can add support for it with the exposed half-float API. New iterator ------------ A new iterator has been added, replacing the functionality of the existing iterator and multi-iterator with a single object and API. This iterator works well with general memory layouts different from C or Fortran contiguous, and handles both standard NumPy and customized broadcasting. The buffering, automatic data type conversion, and optional output parameters, offered by ufuncs but difficult to replicate elsewhere, are now exposed by this iterator. Legendre, Laguerre, Hermite, HermiteE polynomials in ``numpy.polynomial`` ------------------------------------------------------------------------- Extend the number of polynomials available in the polynomial package. In addition, a new ``window`` attribute has been added to the classes in order to specify the range the ``domain`` maps to. This is mostly useful for the Laguerre, Hermite, and HermiteE polynomials whose natural domains are infinite and provides a more intuitive way to get the correct mapping of values without playing unnatural tricks with the domain. Fortran assumed shape array and size function support in ``numpy.f2py`` ----------------------------------------------------------------------- F2py now supports wrapping Fortran 90 routines that use assumed shape arrays. Before such routines could be called from Python but the corresponding Fortran routines received assumed shape arrays as zero length arrays which caused unpredicted results. Thanks to Lorenz Hüdepohl for pointing out the correct way to interface routines with assumed shape arrays. In addition, f2py interprets Fortran expression ``size(array, dim)`` as ``shape(array, dim-1)`` which makes it possible to automatically wrap Fortran routines that use two argument ``size`` function in dimension specifications. Before users were forced to apply this mapping manually. Other new functions ------------------- ``numpy.ravel_multi_index`` : Converts a multi-index tuple into an array of flat indices, applying boundary modes to the indices. ``numpy.einsum`` : Evaluate the Einstein summation convention. Using the Einstein summation convention, many common multi-dimensional array operations can be represented in a simple fashion. This function provides a way compute such summations. ``numpy.count_nonzero`` : Counts the number of non-zero elements in an array. ``numpy.result_type`` and ``numpy.min_scalar_type`` : These functions expose the underlying type promotion used by the ufuncs and other operations to determine the types of outputs. These improve upon the ``numpy.common_type`` and ``numpy.mintypecode`` which provide similar functionality but do not match the ufunc implementation. Changes ======= Changes and improvements in the numpy core ------------------------------------------ ``default error handling`` -------------------------- The default error handling has been change from ``print`` to ``warn`` for all except for ``underflow``, which remains as ``ignore``. ``numpy.distutils`` ------------------- Several new compilers are supported for building Numpy: the Portland Group Fortran compiler on OS X, the PathScale compiler suite and the 64-bit Intel C compiler on Linux. ``numpy.testing`` ----------------- The testing framework gained ``numpy.testing.assert_allclose``, which provides a more convenient way to compare floating point arrays than `assert_almost_equal`, `assert_approx_equal` and `assert_array_almost_equal`. ``C API`` --------- In addition to the APIs for the new iterator and half data type, a number of other additions have been made to the C API. The type promotion mechanism used by ufuncs is exposed via ``PyArray_PromoteTypes``, ``PyArray_ResultType``, and ``PyArray_MinScalarType``. A new enumeration ``NPY_CASTING`` has been added which controls what types of casts are permitted. This is used by the new functions ``PyArray_CanCastArrayTo`` and ``PyArray_CanCastTypeTo``. A more flexible way to handle conversion of arbitrary python objects into arrays is exposed by ``PyArray_GetArrayParamsFromObject``. Deprecated features =================== The "normed" keyword in ``numpy.histogram`` is deprecated. Its functionality will be replaced by the new "density" keyword. Removed features ================ ``numpy.fft`` ------------- The functions `refft`, `refft2`, `refftn`, `irefft`, `irefft2`, `irefftn`, which were aliases for the same functions without the 'e' in the name, were removed. ``numpy.memmap`` ---------------- The `sync()` and `close()` methods of memmap were removed. Use `flush()` and "del memmap" instead. ``numpy.lib`` ------------- The deprecated functions ``numpy.unique1d``, ``numpy.setmember1d``, ``numpy.intersect1d_nu`` and ``numpy.lib.ufunclike.log2`` were removed. ``numpy.ma`` ------------ Several deprecated items were removed from the ``numpy.ma`` module:: * ``numpy.ma.MaskedArray`` "raw_data" method * ``numpy.ma.MaskedArray`` constructor "flag" keyword * ``numpy.ma.make_mask`` "flag" keyword * ``numpy.ma.allclose`` "fill_value" keyword ``numpy.distutils`` ------------------- The ``numpy.get_numpy_include`` function was removed, use ``numpy.get_include`` instead.

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strange behavior of np.minimum and np.maximum
by Emmanuelle Gouillart April 6, 2011

April 6, 2011

Hello, >>> a, b, c = np.array([10]), np.array([2]), np.array([7]) >>> min_val = np.minimum(a, b, c) >>> min_val array([2]) >>> max_val = np.maximum(a, b, c) >>> max_val array([10]) >>> min_val array([10]) (I'm using numpy 1.4, and I observed the same behavior with numpy 2.0.0.dev8600 on another machine). I'm quite surprised by this behavior (It took me quite a long time to figure out what was happening in a script of mine that wasn't giving what I expected, because of np.maximum changing the output of np.minimum). Is it a bug, or am I missing something? Cheers, Emmanuelle

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Scientific Python at SIAM CSE 2011 conference
by Fernando Perez April 6, 2011

April 6, 2011

Hi all, sorry for the massive cross-post, but since all these projects were highlighted with talks at this event, I figured there would be interest... Hans-Petter Langtangen, Randy LeVeque and I organized a set of Python-focused sessions at the recent SIAM Computational Science and Engineering conference, with talks on numpy/scipy, cython, matplotlib, ipython, sympy, as well as application-oriented talks on astronomy and femhub. For those interested: - The slides: http://fperez.org/events/2011_siam_cse/ - A blog post: http://blog.fperez.org/2011/04/python-goes-to-reno-siam-cse-2011.html - Some pictures: https://picasaweb.google.com/fdo.perez/SIAMCSE2011InReno# Back to being quiet... f

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