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==========================
NumPy 1.14.0 Release Notes
==========================
This release supports Python 2.7 and 3.4 - 3.6.
Highlights
==========
* The `np.einsum` function will use BLAS when possible
New functions
=============
* ``parametrize``: decorator added to numpy.testing
* ``chebinterpolate``: Interpolate function at Chebyshev points.
Deprecations
============
Future Changes
==============
``np.issubdtype`` will stop downcasting dtype-like arguments
------------------------------------------------------------
It would be expected that ``issubdtype(np.float32, 'float64')`` and
``issubdtype(np.float32, np.float64)`` mean the same thing - however, there
was an undocumented special case that translated the former into
``issubdtype(np.float32, np.floating)``, giving the surprising result of True.
This translation now gives a warning explaining what translation is occuring.
In future, the translation will be disabled, and the first example will be made
equivalent to the second.
``np.linalg.lstsq`` default for ``rcond`` will be changed
---------------------------------------------------------
The ``rcond`` parameter to ``np.linalg.lstsq`` will change its default to the
better value of machine precision times the maximum of the input matrix
dimensions. A FutureWarning is given if the parameter is not passed explicitly.
Build System Changes
====================
Compatibility notes
===================
``np.tensordot`` now returns zero array when contracting over 0-length dimension
--------------------------------------------------------------------------------
Previously ``np.tensordot`` raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of ``np.dot`` and ``np.einsum``.
``np.ma`` functions producing ``fill_value``s have changed
----------------------------------------------------------
Previously, ``np.ma.default_fill_value`` would return a 0d array, but
``np.ma.minimum_fill_value`` and ``np.ma.maximum_fill_value`` would return a
tuple of the fields. Instead, all three methods return a structured ``np.void``
object, which is what you would already find in the ``.fill_value`` attribute.
Additionally, the dtype guessing now matches that of ``np.array`` - so when
passing a python scalar ``x``, ``maximum_fill_value(x)`` is always the same as
``maximum_fill_value(np.array(x))``. Previously ``x = long(1)`` on Python 2
violated this assumption.
``numpy.testing`` reorganized
-----------------------------
This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using ``numpy.testing``
let us know.
``np.asfarray`` no longer accepts non-dtypes through the ``dtype`` argument
---------------------------------------------------------------------------
This previously would accept ``dtype=some_array``, with the implied semantics
of ``dtype=some_array.dtype``. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.
1D ``np.linalg.norm`` preserves float input types, even for arbitrary orders
----------------------------------------------------------------------------
Previously, this would promote to ``float64`` when arbitrary orders were
passed, despite not doing so under the simple cases::
>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
dtype('float32')
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64') # numpy 1.13
dtype('float32') # numpy 1.14
This change affects only ``float32`` and ``float16`` arrays.
``__init__.py`` files added to test directories
-----------------------------------------------
This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, ``run_module_suite`` no longer works,
i.e., ``python <path-to-test-file>`` results in an error.
``MaskedArray.squeeze`` never returns ``np.ma.masked``
------------------------------------------------------
``np.squeeze`` is documented as returning a view, but the masked variant would
sometimes return ``masked``, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used ``masked_arr.squeeze() is np.ma.masked``, but
fixes code that writes to the result of `.squeeze()`.
C API changes
=============
New Features
============
External ``nose`` plugins are usable by ``numpy.testing.Tester``
----------------------------------------------------------------
``numpy.testing.Tester`` is now aware of ``nose`` plugins that are outside the
``nose`` built-in ones. This allows using, for example, ``nose-timer`` like
so: ``np.test(extra_argv=['--with-timer', '--timer-top-n', '20'])`` to
obtain the runtime of the 20 slowest tests. An extra keyword ``timer`` was
also added to ``Tester.test``, so ``np.test(timer=20)`` will also report the 20
slowest tests.
``parametrize`` decorator added to ``numpy.testing``
----------------------------------------------------
A basic ``parametrize`` decorator is now available in ``numpy.testing``. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.
The new ``parametrize`` decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.
``chebinterpolate`` function added to ``numpy.polynomial.chebyshev``
--------------------------------------------------------------------
The new ``chebinterpolate`` function interpolates a given function at the
Chebyshev points of the first kind. A new ``Chebyshev.interpolate`` class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.
Improvements
============
Numerator degrees of freedom in ``random.noncentral_f`` need only be positive.
------------------------------------------------------------------------------
Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.
The GIL is released for all ``np.einsum`` variations
----------------------------------------------------
Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.
The `np.einsum` function will use BLAS when possible and optimize by default
----------------------------------------------------------------------------
The ``np.einsum`` function will now call ``np.tensordot`` when appropriate.
Because ``np.tensordot`` uses BLAS when possible, that will speed up execution.
By default, ``np.einsum`` will also attempt optimization as the overhead is
small relative to the potential improvement in speed.
The ``repr`` of ``np.polynomial`` classes is more explicit
----------------------------------------------------------
It now shows the domain and window parameters as keyword arguments to make
them more clear::
>>> np.polynomial.Polynomial(range(4))
Polynomial([ 0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
f2py now handles arrays of dimension 0
--------------------------------------
f2py now allows for the allocation of arrays of dimension 0. This allows for
more consistent handling of corner cases downstream.
Changes
=======
0d arrays now print their elements like other arrays
----------------------------------------------------
0d arrays now use the array2string formatters to print their elements, like
other arrays. The ``style`` argument of ``array2string`` is now non-functional.
Integer scalars are now unaffected by ``np.set_string_function``
----------------------------------------------------------------
Previously the str/repr of integer scalars could be controlled by
``np.set_string_function``, unlike most other numpy scalars. This is no longer
the case.
|
