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Note: NumPy 1.6.0 is not yet released.
=========================
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
------------
Legendre polynomials in ``numpy.polynomial``
--------------------------------------------
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_coords`` : Converts a tuple of coordinate arrays into an array of
flat indices, applying boundary modes to the coordinates.
``numpy.slogdet`` : Compute the sign and (natural) logarithm of the determinant
of an array.
``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. The best way to understand this function is to try the
examples below, which show how many common NumPy functions can be implemented
as calls to ``numpy.einsum``.
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`.
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.
|
