Re: [Numpy-discussion] ANN: NumPy 1.7.0b1 release
On Tue, Aug 21, 2012 at 3:59 PM, Christoph Gohlke wrote: > On 8/21/2012 9:24 AM, Ondřej Čertík wrote: >> Hi, >> >> I'm pleased to announce the availability of the first beta release of >> NumPy 1.7.0b1. >> >> Sources and binary installers can be found at >> https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/ >> >> Please test this release and report any issues on the numpy-discussion >> mailing list. The following problems are known and >> we'll work on fixing them before the final release: >> >> http://projects.scipy.org/numpy/ticket/2187 >> http://projects.scipy.org/numpy/ticket/2185 >> http://projects.scipy.org/numpy/ticket/2066 >> http://projects.scipy.org/numpy/ticket/1588 >> http://projects.scipy.org/numpy/ticket/2076 >> http://projects.scipy.org/numpy/ticket/2101 >> http://projects.scipy.org/numpy/ticket/2108 >> http://projects.scipy.org/numpy/ticket/2150 >> http://projects.scipy.org/numpy/ticket/2189 >> >> I would like to thank Ralf for a lot of help with creating binaries >> and other help for this release. >> >> Cheers, >> Ondrej >> >> > > Hi Ondrej, > > will numpy 1.7.0 final support Python 3.3? The recent patch in the > master branch seems to work well. > > I tested a win-amd64-py2.7\msvc9\MKL build of the numpy > maintenance/1.7.x branch against a number of package binaries from > <http://www.lfd.uci.edu/~gohlke/pythonlibs/>. > > The test results are at > <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/>. > For comparison, the tests against numpy-MKL-1.6.2 are at > <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/>. > > Besides some numpy 1.7.x test errors due to RuntimeWarning and > DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype > size changed, may indicate binary incompatibility)" when loading Cython > extensions. > > There are additional test failures in scipy, statsmodels, bottleneck, > skimage, vigra, and mahotas. I did not check in detail or with existing > tickets (http://projects.scipy.org/ is timing out or responding with > HTTP 500 status). Thanks Christoph, All the statsmodels errors (14) look like http://projects.scipy.org/numpy/ticket/2187 recarray/structured dtype view Josef > > Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, > matplotlib, Pandas, tables, and numexpr. > > Hope it helps. > > Christoph > > > ___ > 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
Re: [Numpy-discussion] ANN: NumPy 1.7.0b1 release
On Tue, Aug 21, 2012 at 3:59 PM, Christoph Gohlke wrote: > On 8/21/2012 9:24 AM, Ondřej Čertík wrote: > > Hi, > > > > I'm pleased to announce the availability of the first beta release of > > NumPy 1.7.0b1. > > > > Sources and binary installers can be found at > > https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/ > > > > Please test this release and report any issues on the numpy-discussion > > mailing list. The following problems are known and > > we'll work on fixing them before the final release: > > > > http://projects.scipy.org/numpy/ticket/2187 > > http://projects.scipy.org/numpy/ticket/2185 > > http://projects.scipy.org/numpy/ticket/2066 > > http://projects.scipy.org/numpy/ticket/1588 > > http://projects.scipy.org/numpy/ticket/2076 > > http://projects.scipy.org/numpy/ticket/2101 > > http://projects.scipy.org/numpy/ticket/2108 > > http://projects.scipy.org/numpy/ticket/2150 > > http://projects.scipy.org/numpy/ticket/2189 > > > > I would like to thank Ralf for a lot of help with creating binaries > > and other help for this release. > > > > Cheers, > > Ondrej > > > > > > Hi Ondrej, > > will numpy 1.7.0 final support Python 3.3? The recent patch in the > master branch seems to work well. > > I tested a win-amd64-py2.7\msvc9\MKL build of the numpy > maintenance/1.7.x branch against a number of package binaries from > <http://www.lfd.uci.edu/~gohlke/pythonlibs/>. > > The test results are at > < > http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/ > >. > For comparison, the tests against numpy-MKL-1.6.2 are at > <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/ > >. > > Besides some numpy 1.7.x test errors due to RuntimeWarning and > DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype > size changed, may indicate binary incompatibility)" when loading Cython > extensions. > > There are additional test failures in scipy, statsmodels, bottleneck, > skimage, vigra, and mahotas. I did not check in detail or with existing > tickets (http://projects.scipy.org/ is timing out or responding with > HTTP 500 status). > Most (all?) of the statsmodels issues are due to structured / record array view changes discussed in the thread "view of recarray issue." I.e., rec_array.view((float, 3)) no longer works, though I thought this was fixed. > > Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, > matplotlib, Pandas, tables, and numexpr. > > Hope it helps. > > Christoph > > > ___ > 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
Re: [Numpy-discussion] ANN: NumPy 1.7.0b1 release
On 8/21/2012 9:24 AM, Ondřej Čertík wrote: > Hi, > > I'm pleased to announce the availability of the first beta release of > NumPy 1.7.0b1. > > Sources and binary installers can be found at > https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/ > > Please test this release and report any issues on the numpy-discussion > mailing list. The following problems are known and > we'll work on fixing them before the final release: > > http://projects.scipy.org/numpy/ticket/2187 > http://projects.scipy.org/numpy/ticket/2185 > http://projects.scipy.org/numpy/ticket/2066 > http://projects.scipy.org/numpy/ticket/1588 > http://projects.scipy.org/numpy/ticket/2076 > http://projects.scipy.org/numpy/ticket/2101 > http://projects.scipy.org/numpy/ticket/2108 > http://projects.scipy.org/numpy/ticket/2150 > http://projects.scipy.org/numpy/ticket/2189 > > I would like to thank Ralf for a lot of help with creating binaries > and other help for this release. > > Cheers, > Ondrej > > Hi Ondrej, will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well. I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from <http://www.lfd.uci.edu/~gohlke/pythonlibs/>. The test results are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/>. For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/>. Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions. There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status). Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr. Hope it helps. Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] [ANN] carray 0.5 released
Announcing carray 0.5 = What's new -- carray 0.5 supports completely transparent storage on-disk in addition to memory. That means that everything that can be done with an in-memory container can be done using the disk instead. The advantages of a disk-based container is that your addressable space is much larger than just your available memory. Also, as carray is based on a chunked and compressed data layout based on the super-fast Blosc compression library, and the different cache levels existing in both modern operating systems and the internal carray machinery, the data access speed is very good. The format chosen for the persistence layer is based on the 'bloscpack' library (thanks to Valentin Haenel for his inspiration) and described in 'persistence.rst', although not everything has been implemented yet. You may want to contribute by proposing enhancements to it. See: https://github.com/FrancescAlted/carray/wiki/PersistenceProposal CAVEAT: The bloscpack format is still evolving, so don't trust on forward compatibility of the format, at least until 1.0, where the internal format will be declared frozen. For more detailed info, see the release notes in: https://github.com/FrancescAlted/carray/wiki/Release-0.5 What it is -- carray is a chunked container for numerical data. Chunking allows for efficient enlarging/shrinking of data container. In addition, it can also be compressed for reducing memory/disk needs. The compression process is carried out internally by Blosc, a high-performance compressor that is optimized for binary data. carray can use numexpr internally so as to accelerate many vector and query operations (although it can use pure NumPy for doing so too). numexpr can use optimize the memory usage and use several cores for doing the computations, so it is blazing fast. Moreover, with the introduction of a carray/ctable disk-based container (in version 0.5), it can be used for seamlessly performing out-of-core computations. carray comes with an exhaustive test suite and fully supports both 32-bit and 64-bit platforms. Also, it is typically tested on both UNIX and Windows operating systems. Resources - Visit the main carray site repository at: http://github.com/FrancescAlted/carray You can download a source package from: http://carray.pytables.org/download Manual: http://carray.pytables.org/docs/manual Home of Blosc compressor: http://blosc.pytables.org User's mail list: car...@googlegroups.com http://groups.google.com/group/carray Enjoy! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] ANN: NumPy 1.7.0b1 release
Hi, I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1. Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/ Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release: http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189 I would like to thank Ralf for a lot of help with creating binaries and other help for this release. Cheers, Ondrej = NumPy 1.7.0 Release Notes = This release includes several new features as well as numerous bug fixes and refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.2. Highlights == * ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose where a computation should be done) * ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general cleanup and bug fixes) * ``numpy.random.choice`` (random sample generating function) Compatibility notes === In a future version of numpy, the functions np.diag, np.diagonal, and the diagonal method of ndarrays will return a view onto the original array, instead of producing a copy as they do now. This makes a difference if you write to the array returned by any of these functions. To facilitate this transition, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for np.diagonal for details. Similar to np.diagonal above, in a future version of numpy, indexing a record array by a list of field names will return a view onto the original array, instead of producing a copy as they do now. As with np.diagonal, numpy 1.7 produces a FutureWarning if it detects that you may be attemping to write to such an array. See the documentation for array indexing for details. The default casting rule for UFunc out= parameters has been changed from 'unsafe' to 'same_kind'. Most usages which violate the 'same_kind' rule are likely bugs, so this change may expose previously undetected errors in projects that depend on NumPy. Full-array boolean indexing has been optimized to use a different, optimized code path. This code path should produce the same results, but any feedback about changes to your code would be appreciated. Attempting to write to a read-only array (one with ``arr.flags.writeable`` set to ``False``) used to raise either a RuntimeError, ValueError, or TypeError inconsistently, depending on which code path was taken. It now consistently raises a ValueError. The .reduce functions evaluate some reductions in a different order than in previous versions of NumPy, generally providing higher performance. Because of the nature of floating-point arithmetic, this may subtly change some results, just as linking NumPy to a different BLAS implementations such as MKL can. If upgrading from 1.5, then generally in 1.6 and 1.7 there have been substantial code added and some code paths altered, particularly in the areas of type resolution and buffered iteration over universal functions. This might have an impact on your code particularly if you relied on accidental behavior in the past. New features Reduction UFuncs Generalize axis= Parameter --- Any ufunc.reduce function call, as well as other reductions like sum, prod, any, all, max and min support the ability to choose a subset of the axes to reduce over. Previously, one could say axis=None to mean all the axes or axis=# to pick a single axis. Now, one can also say axis=(#,#) to pick a list of axes for reduction. Reduction UFuncs New keepdims= Parameter There is a new keepdims= parameter, which if set to True, doesn't throw away the reduction axes but instead sets them to have size one. When this option is set, the reduction result will broadcast correctly to the original operand which was reduced. Datetime support .. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes in future versions of NumPy. There have been a lot of fixes and enhancements to datetime64 compared to NumPy 1.6: * the parser is quite strict about only accepting ISO 8601 dates, with a few convenience extensions * converts between units correctly * datetime arithmetic works correctly * business day functionality (allows the datetime to be used in contexts where only certain days of the week are valid) The notes in `doc/source/reference/arrays.datetime.rst
[Numpy-discussion] Adding solvers to scipy.integrate [Was: A step toward merging odeint and ode]
Le lundi 20 août 2012 à 22:04 +0200, Ralf Gommers a écrit : > https://github.com/FabricioS/scipy/commit/f867f2b8133d3f6ea47d449bd760a77a7c90394e > This is probably not worth the cost for existing users imho. It is a > backwards compatibility break that doesn't really add anything except > for some consistency (right?). Hi Ralf, Ok concerning this point. In addition, I have been looking to suggest additional solvers, essentially simpler scheme, that would thus allow to easily switch between "complex" (lsode, vode, cvode) and basic schemes (Euler, Nicholson, etc...) I came across some code on the Montana Univ.'s Computer Science dpt: http://wiki.cs.umt.edu/classes/cs477/index.php/Creating_ODE_Solver_Objects and asked Jesse Johnson (the responsible for that class) what is the license for that code. Here is his answer : Any thing that you find on those pages, you may use. However, I'm not sure how to go about officially giving the code a particular license. Can I add a license to the wiki, stating that it applies to all the code therein? PS It is fantastic you're doing this. I've often thought that scipy.ode could use some improvements. He is cc'ed of this mail, could anyone concerned about scipy license requirements and more generally in code licensing answer him ? Regards -- Fabrice Silva ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
