[Numpy-discussion] ANN: Numpy 1.6.0 release candidate 1
Mark Wiebe
mwwiebe at gmail.com
Mon May 2 16:55:47 EDT 2011
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 <mdroe at 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 at 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 at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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