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=========================
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.
Einstein summation convention evaluation function
-------------------------------------------------
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.
Changes
=======
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.
|
