[Numpy-discussion] ANN: NumPy 1.6.0

Sat May 14 09:09:30 EDT 2011

On Sat, May 14, 2011 at 3:54 AM, Ralf Gommers
<ralf.gommers at googlemail.com>wrote:

> Hi,
>
> I am pleased to announce the release of NumPy 1.6.0. This release is the
> result of 9 months of work, and includes many new features, performance
> improvements and bug fixes. Some highlights are:
>
>   - 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.
>
> Sources and binaries can be found at
> http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/<http://sourceforge.net/projects/numpy/files/NumPy/1.6.0rc2/>
> For release notes see below.
>
> Thank you to everyone who contributed to this release.
>
> Enjoy,
> The NumPy developers
>
>
> =========================
> 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 supports now automatic wrapping of Fortran routines
> that use two argument ``size`` function in dimension specifications.
>
>
> 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
> =======
>
> ``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.
>
>
The link is  http://sourceforge.net/projects/numpy/files/NumPy/1.6.0/

Chuck
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