I applied everything, since they're all obviously bugs and the fixes look straightforward.
-Mark On Mon, May 2, 2011 at 8:18 AM, Michael Droettboom <md...@stsci.edu> wrote: > I've found a few reference counting bugs running the regression tests > under Valgrind. Pull request here: > > https://github.com/numpy/numpy/pull/79 > > Mike > > On 04/30/2011 04:19 PM, Ralf Gommers wrote: > > Hi, > > > > I am pleased to announce the availability of the first release > > candidate of NumPy 1.6.0. If no new problems are reported, the final > > release will be in one week. > > > > Sources and binaries can be found at > > http://sourceforge.net/projects/numpy/files/NumPy/1.6.0rc1/ > > 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 > > ------------------------------------------ > > > > ``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. > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > -- > Michael Droettboom > Science Software Branch > Space Telescope Science Institute > Baltimore, Maryland, USA > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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