Mailman 3 March 2011 - NumPy-Discussion

1.6.0b1 installs stuff in /usr/lib/python2.7/site-packages/doc
by Orion Poplawski April 2, 2011

April 2, 2011

I'm looking at updating the numpy package in Fedora rawhide to 1.6.0b1. It appears to install a bunch of suff in /usr/lib64/python2.7/site-packages/doc (see below for examples). This seems wrong. Thoughts? /usr/lib64/python2.7/site-packages/doc/cython/MANIFEST /usr/lib64/python2.7/site-packages/doc/cython/Makefile /usr/lib64/python2.7/site-packages/doc/cython/README.txt /usr/lib64/python2.7/site-packages/doc/cython/c_numpy.pxd /usr/lib64/python2.7/site-packages/doc/cython/c_python.pxd /usr/lib64/python2.7/site-packages/doc/cython/numpyx.pyx /usr/lib64/python2.7/site-packages/doc/cython/run_test.py /usr/lib64/python2.7/site-packages/doc/cython/run_test.pyc /usr/lib64/python2.7/site-packages/doc/cython/run_test.pyo /usr/lib64/python2.7/site-packages/doc/cython/setup.py /usr/lib64/python2.7/site-packages/doc/cython/setup.pyc /usr/lib64/python2.7/site-packages/doc/cython/setup.pyo /usr/lib64/python2.7/site-packages/doc/pyrex/MANIFEST /usr/lib64/python2.7/site-packages/doc/pyrex/Makefile /usr/lib64/python2.7/site-packages/doc/pyrex/README.txt /usr/lib64/python2.7/site-packages/doc/pyrex/c_numpy.pxd /usr/lib64/python2.7/site-packages/doc/pyrex/c_python.pxd /usr/lib64/python2.7/site-packages/doc/pyrex/notes /usr/lib64/python2.7/site-packages/doc/pyrex/numpyx.c /usr/lib64/python2.7/site-packages/doc/pyrex/numpyx.pyx /usr/lib64/python2.7/site-packages/doc/pyrex/run_test.py /usr/lib64/python2.7/site-packages/doc/pyrex/run_test.pyc /usr/lib64/python2.7/site-packages/doc/pyrex/run_test.pyo /usr/lib64/python2.7/site-packages/doc/pyrex/setup.py /usr/lib64/python2.7/site-packages/doc/pyrex/setup.pyc /usr/lib64/python2.7/site-packages/doc/pyrex/setup.pyo /usr/lib64/python2.7/site-packages/doc/swig/Array.i /usr/lib64/python2.7/site-packages/doc/swig/Array1.cxx /usr/lib64/python2.7/site-packages/doc/swig/Array1.h -- Orion Poplawski Technical Manager 303-415-9701 x222 NWRA/CoRA Division FAX: 303-415-9702 3380 Mitchell Lane orion(a)cora.nwra.com Boulder, CO 80301 http://www.cora.nwra.com

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ANN: Numpy 1.6.0 beta 1
by Ralf Gommers April 2, 2011

April 2, 2011

Hi, I am pleased to announce the availability of the first 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. Sources and binaries can be found at: http://sourceforge.net/projects/numpy/files/NumPy/1.6.0b1/ For (preliminary) release notes see below. Enjoy, Ralf ========================= 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 ------------------------------------------ ``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``. 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. Checksums ========= 89f52ae0f0ea84cfcb457298190bee14 release/installers/numpy-1.6.0b1-py2.7-python.org.dmg 8dee06b362540b2c8c033399951e5386 release/installers/numpy-1.6.0b1-win32-superpack-python2.5.exe c1b11bf48037ac8fe025bfd297969840 release/installers/numpy-1.6.0b1-win32-superpack-python2.6.exe 2b005cb359d8123bd5ee3063d9eae3ac release/installers/numpy-1.6.0b1-win32-superpack-python2.7.exe 45627e8f63fe34011817df66722c39a5 release/installers/numpy-1.6.0b1-win32-superpack-python3.1.exe 1d8b214752b19b51ee747a6436ca1d38 release/installers/numpy-1.6.0b1-win32-superpack-python3.2.exe aeab5881974aac595b87a848c0c6344a release/installers/numpy-1.6.0b1.tar.gz 3ffc6e308f9e0614531fa3babcb75544 release/installers/numpy-1.6.0b1.zip

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Re: [Numpy-discussion] np.histogramdd of empty data
by Ralf Gommers April 1, 2011

April 1, 2011

---------- Forwarded message ---------- From: Ralf Gommers <ralf.gommers(a)googlemail.com> Date: Thu, Mar 31, 2011 at 7:31 PM Subject: Re: [Numpy-discussion] np.histogramdd of empty data To: Nils Becker <n.becker(a)amolf.nl> On Thu, Mar 31, 2011 at 12:33 PM, Nils Becker <n.becker(a)amolf.nl> wrote: > Hi Ralf, > > I cloned numpy/master and played around a little. > > when giving the bins explicitely, now histogram2d and histogramdd work > as expected in all tests i tried. > > > However, some of the cases with missing bin specification appear > somewhat inconsistent. > > The first question is if creating arbitrary bins for empty data and > empty bin specification is better than raising an Exception: > > Specifically: Bins of size 0 should give a meaningful error, I was just fixing that as part of #1788 in https://github.com/rgommers/numpy/tree/ticket-1788-histogramdd > numpy.histogram2d([],[],bins=[0,0]) >> (array([ 0., 0.]), array([ 0.]), array([ 0.])) Now gives: ValueError: Element at index 0 in `bins` should be a positive integer. > numpy.histogram([],bins=0) >> ValueError: zero-size array to minimum.reduce without identity Now gives: ValueError: `bins` should be a positive integer. > so 1-d and 2-d behave not quite the same. > > also, these work (although with arbitrary bin edges): > > numpy.histogram2d([],[],bins=[1,1]) >> (array([ 0., 0.]), array([ 0., 1.]), array([ 0., 1.])) > > numpy.histogram2d([],[],bins=[0,1]) >> (array([ 0., 0.]), array([ 0.]), array([ 0., 1.])) > > while this raises an error: > > numpy.histogram([],bins=1) >> ValueError: zero-size array to minimum.reduce without identity Now gives: (array([0]), array([ 0., 1.])) > another thing with non-empty data: > > numpy.histogram([1],bins=1) >> (array([1]), array([ 0.5, 1.5])) That is the correct answer. > numpy.histogram([1],bins=0) >> (array([], dtype=int64), array([ 0.5])) Now gives: ValueError: `bins` should be a positive integer. > while > > numpy.histogram2d([1],[1],bins=A) >> ValueError: zero-size array to minimum.reduce without identity > > (here A==[0,0] or A==[0,1] but not A==[1,1] which gives a result) Same sensible errors now, telling you bins elements shouldn't be 0. Cheers, Ralf

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Willing to contribute to SciPy & NumPy ...
by Sylvain Bellemare March 31, 2011

March 31, 2011

Hi, I would like to seriously start contributing to NumPy and/or SciPy, as much as I possibly can. Because NumPy & SciPy are new to me, I'm not so sure whether I could be of some help. But if my help is welcomed, and if it makes sense to ask: "What could I best contribute to?", then perhaps some background information about myself would be useful ... *ACADEMICS* I completed an undergraduate degree in Computer Science, along with some mathematics and physics courses. For the mathematics, I did some Calculus, Linear Algebra, Differential Equations, Fourier series, Fourier & Laplace transform. In Physics, I did up to and including a first course in Quantum Mechanics. I also did some undergraduate research work, that involved computational high energy physics (proton-proton collision). The work was done with some Mathematica packages (FeynArts <http://www.feynarts.de/>). *INDUSTRY* I currently work in the software industry and for the past few months, have been performing both software development and system administration tasks. In terms of object-oriented programming, my main experience is with Java. I'm a beginner with Python. I used Python at work to automate the reverse-engineering process of some Java programs. Career-wise, one of my goals is to go into scientific computing, and use Python as the main programming language, and to perhaps pursue graduate studies in Computational Physics, hence my current interest for contributing to NumPy & SciPy. If you wish to know more about me I have a LinkedIn profile at http://www.linkedin.com/in/sylvainbellemare. *DOCUMENTATION* Documentation-wise, I can use LaTeX. For instance, my resume, which is attached with this email, was written with LaTeX. It's noteworthy to mention that I like to work on documentation, as long as it balances with coding work [?]. If that makes any sense to you and you have ideas on projects that I could contribute to, please let me know, and I'll be very happy to do my very best to contribute. Best regards, -Sylvain

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Old tickets
by Bruce Southey March 31, 2011

March 31, 2011

Hi, This followup on tickets that I had previously indicated. So I want to thank Mark, Ralph and any people for going over those! For those that I followed I generally agreed with the outcome. Ticket 301: 'Make power and divide return floats from int inputs (like true_divide)' http://projects.scipy.org/numpy/ticket/301 Invalid because the output dtype is the same as the input dtype unless you override using the dtype argument: >>> np.power(3, 1, dtype=np.float128).dtype dtype('float128') Alternatively return a float and indicate in the docstring that the output dtype can be changed. Ticket 354: 'Possible inconsistency in 0-dim and scalar empty array types' http://projects.scipy.org/numpy/ticket/354 Invalid because an empty array is not the same as an empty string. Ticket 1071: 'loadtxt fails if the last column contains empty value' http://projects.scipy.org/numpy/ticket/1071 Invalid mainly because loadtxt states that 'Each row in the text file must have the same number of values.' So of cause loadtxt must fail when there are missing values. Ticket 1374: 'Ticket 628 not fixed for Solaris (polyfit uses 100% CPU and does not stop)' http://projects.scipy.org/numpy/ticket/1374 Unless this can be verified it should be set as needs_info. Bruce

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EuroPython 2011: call for paper is ending - Please spread the word
by Palla March 31, 2011

March 31, 2011

Hi all, I'm Francesco and I am writing on behalf of "Python Italia APS", a no-profit association promoting EuroPython conference. (www.europython.eu) Europython End of Call for Presentations is April 6th. I'd like to ask to you to forward this mail to anyone that you feel may be interested. We're looking for proposals on every aspects of Python: programming from novice to advanced levels, applications and frameworks, or how you have been involved in introducing Python into your organisation. **First-time speakers are especially welcome**; EuroPython is a community conference and we are eager to hear about your experience. If you have friends or colleagues who have something valuable to contribute, twist their arms to tell us about it! Presenting at EuroPython ------------------------ We will accept a broad range of presentations, from reports on academic and commercial projects to tutorials and case studies. As long as the presentation is interesting and potentially useful to the Python community, it will be considered for inclusion in the programme. Can you show the conference-goers something new and useful? Can you show attendees how to: use a module? Explore a Python language feature? Package an application? If so, consider submitting a talk. Talks and hands-on trainings ---------------------------- There are two different kind of presentations that you can give as a speaker at EuroPython: * **Regular talk**. These are standard "talk with slides", allocated in slots of 45, 60 or 90 minutes, depending on your preference and scheduling constraints. A Q&A session is held at the end of the talk. * **Hands-on training**. These are advanced training sessions for a smaller audience (10-20 people), to dive into the subject with all details. These sessions are 4-hours long, and audience will be strongly encouraged to bring a laptop to experiment. They should be prepared with less slides and more source code. If possible, trainers will also give a short "teaser talk" of 30 minutes the day before the training, to tease delegates into attending the training. In the talk submission form, we assume that you intend to give a regular talk on the subject, but you will be asked if you are available for also doing a hands-on training on the very same subject. Speakers that will give a hands-on training are rewarded with a **free entrance** to EuroPython to compensate for the longer preparation required, and might also be eligible for a speaking fee (which we cannot confirm at the moment). Topics and goals ---------------- Specific topics for EuroPython presentations include, but are not limited to: - Core Python - Other implementations: Jython, IronPython, PyPy, and Stackless - Python libraries and extensions - Python 3.x migration - Databases - Documentation - GUI Programming - Game Programming - Network Programming - Open Source Python projects - Packaging Issues - Programming Tools - Project Best Practices - Embedding and Extending - Science and Math - Web-based Systems Presentation goals usually are some of the following: - Introduce audience to a new topic they are unaware of - Introduce audience to new developments on a well-known topic - Show audience real-world usage scenarios for a specific topic (case study) - Dig into advanced and relatively-unknown details on a topic - Compare different options in the market on a topic Community-based talk voting --------------------------- This year, for the first time in EuroPython history, the talk voting process is fully public. Every partecipant gains the right to vote for talks submitted during the Call For Papers, as soon as they commit to their presence at the conference by buying a ticket. See all the details in the talk voting[1] page. Contacts -------- For any further question, feel free to contact the organizers at info(a)pycon.it. Thank you! [1]: http://ep2011.europython.eu/talk-voting -- ->PALLA

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Re: [Numpy-discussion] np.histogramdd of empty data
by Nils Becker March 31, 2011

March 31, 2011

Hi Ralf, I cloned numpy/master and played around a little. when giving the bins explicitely, now histogram2d and histogramdd work as expected in all tests i tried. However, some of the cases with missing bin specification appear somewhat inconsistent. The first question is if creating arbitrary bins for empty data and empty bin specification is better than raising an Exception: Specifically: numpy.histogram2d([],[],bins=[0,0]) > (array([ 0., 0.]), array([ 0.]), array([ 0.])) numpy.histogram([],bins=0) > ValueError: zero-size array to minimum.reduce without identity so 1-d and 2-d behave not quite the same. also, these work (although with arbitrary bin edges): numpy.histogram2d([],[],bins=[1,1]) > (array([ 0., 0.]), array([ 0., 1.]), array([ 0., 1.])) numpy.histogram2d([],[],bins=[0,1]) > (array([ 0., 0.]), array([ 0.]), array([ 0., 1.])) while this raises an error: numpy.histogram([],bins=1) > ValueError: zero-size array to minimum.reduce without identity another thing with non-empty data: numpy.histogram([1],bins=1) > (array([1]), array([ 0.5, 1.5])) numpy.histogram([1],bins=0) > (array([], dtype=int64), array([ 0.5])) while numpy.histogram2d([1],[1],bins=A) > ValueError: zero-size array to minimum.reduce without identity (here A==[0,0] or A==[0,1] but not A==[1,1] which gives a result) Nils

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random number genration
by Alex Ter-Sarkissov March 31, 2011

March 31, 2011

thanks Sturl, your second code works well although I had to divide the vector elements through 128 to get just the binaries

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np.histogram on arrays.
by Éric Depagne March 31, 2011

March 31, 2011

Hi all. Sorry if this question has already been asked. I've searched the archive, but could not find anything related, so here is my question. I'm using np.histogram on a 4000x4000 array, each with 200 bins. I do that on both dimensions, meaning I compute 8000 histograms. It takes around 5 seconds (which is of course quite fast). I was wondering why np.histogram does not accept an axis parameter so that it could work directly on the array without me having to write a loop. Or maybe did I miss some parameters using np.histogram. Thanks. Éric. Un clavier azerty en vaut deux ---------------------------------------------------------- Éric Depagne eric(a)depagne.org

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should get rid of the annoying numpy STDERR output
by Dmitrey March 30, 2011

March 30, 2011

>>> from numpy import inf, array >>> inf*0 nan (ok) >>> array(inf) * 0.0 StdErr: Warning: invalid value encountered in multiply nan My cycled calculations yields this thousands times slowing computations and making text output completely non-readable. >>> from numpy import __version__ >>> __version__ '2.0.0.dev-1fe8136' D.

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