Merge pull request #11327 from charris/prepare-1.15.0

DOC: Update release notes for 1.15.0.

4 files changed, 204 insertions, 116 deletions

diff --git a/doc/changelog/1.14.5-changelog.rst b/doc/changelog/1.14.5-changelog.rst
new file mode 100644
index 000000000..1769a8fc3
--- /dev/null
+++ b/doc/changelog/1.14.5-changelog.rst
@@ -0,0 +1,16 @@
+
+Contributors
+============
+
+A total of 1 person contributed to this release.  People with a "+" by their
+names contributed a patch for the first time.
+
+* Charles Harris
+
+Pull requests merged
+====================
+
+A total of 2 pull requests were merged for this release.
+
+* `#11274 <https://github.com/numpy/numpy/pull/11274>`__: BUG: Correct use of NPY_UNUSED.
+* `#11294 <https://github.com/numpy/numpy/pull/11294>`__: BUG: Remove extra trailing parentheses.
diff --git a/doc/release/1.14.5-notes.rst b/doc/release/1.14.5-notes.rst
new file mode 100644
index 000000000..9a97cc033
--- /dev/null
+++ b/doc/release/1.14.5-notes.rst
@@ -0,0 +1,30 @@
+==========================
+NumPy 1.14.5 Release Notes
+==========================
+
+This is a bugfix release for bugs reported following the 1.14.4 release. The
+most significant fixes are:
+
+* fixes for compilation errors on alpine and NetBSD
+
+The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python
+3.6 wheels available from PIP are built with Python 3.6.2 and should be
+compatible with all previous versions of Python 3.6. The source releases were
+cythonized with Cython 0.28.2 and should work for the upcoming Python 3.7.
+
+Contributors
+============
+
+A total of 1 person contributed to this release.  People with a "+" by their
+names contributed a patch for the first time.
+
+* Charles Harris
+
+Pull requests merged
+====================
+
+A total of 2 pull requests were merged for this release.
+
+* `#11274 <https://github.com/numpy/numpy/pull/11274>`__: BUG: Correct use of NPY_UNUSED.
+* `#11294 <https://github.com/numpy/numpy/pull/11294>`__: BUG: Remove extra trailing parentheses.
+
diff --git a/doc/release/1.15.0-notes.rst b/doc/release/1.15.0-notes.rst
index cc193530c..8b809ae73 100644
--- a/doc/release/1.15.0-notes.rst
+++ b/doc/release/1.15.0-notes.rst
@@ -2,36 +2,58 @@
 NumPy 1.15.0 Release Notes
 ==========================
 
+NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
+of old functions, and improvements to many existing functions. Please read the
+detailed descriptions below to see if you are affected.
+
+For testing, we have switched to pytest as a replacement for the no longer
+maintained nose framework. The old nose based interface remains for downstream
+projects who may still be using it.
+
+The Python versions supported by this release are 2.7, 3.4-3.6.  The upcoming
+3.7 release should also work, but you will need to compile from source using
+Cython 0.28.2 or later. The wheels will be linked with OpenBLAS 3.0, which
+should fix some of the linalg problems reported for NumPy 1.14.
+
 
 Highlights
 ==========
 
 * NumPy has switched to pytest for testing.
-
+* A new  `numpy.printoptions` context manager.
+* Many improvements to the histogram functions.
+* Support for unicode field names in python 2.7.
+* Improved support for PyPy.
 
 New functions
 =============
 
-* `np.gcd` and `np.lcm`, to compute the greatest common divisor and least
+* `numpy.gcd` and `numpy.lcm`, to compute the greatest common divisor and least
   common multiple.
-* `np.ma.stack`, the `np.stack` array-joining function generalized to masked
-  arrays.
-* ``quantile`` function, an interface to ``percentile`` without factors of 100
-* ``nanquantile`` function, an interface to ``nanpercentile`` without factors
-  of 100
 
-* `np.printoptions`, a context manager that sets print options temporarily
+* `numpy.ma.stack`, the `numpy.stack` array-joining function generalized to
+  masked arrays.
+
+* `numpy.quantile` function, an interface to ``percentile`` without factors of
+  100
+
+* `numpy.nanquantile` function, an interface to ``nanpercentile`` without
+  factors of 100
+
+* `numpy.printoptions`, a context manager that sets print options temporarily
   for the scope of the ``with`` block::
 
     >>> with np.printoptions(precision=2):
     ...     print(np.array([2.0]) / 3)
     [0.67]
 
-* `np.histogram_bin_edges`, a function to get the edges of the bins used by a histogram
-  without needing to calculate the histogram.
+* `numpy.histogram_bin_edges`, a function to get the edges of the bins used by a
+  histogram without needing to calculate the histogram.
+
+* C functions `npy_get_floatstatus_barrier` and `npy_clear_floatstatus_barrier`
+  have been added to deal with compiler optimization changing the order of
+  operations.  See below for details.
 
-* `npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier`` have been added to
-  deal with compiler optimization changing the order of operations. See below for details.
 
 Deprecations
 ============
@@ -39,72 +61,90 @@ Deprecations
 * Aliases of builtin `pickle` functions are deprecated, in favor of their
   unaliased ``pickle.<func>`` names:
 
-  * `np.loads`
-  * `np.core.numeric.load`
-  * `np.core.numeric.loads`
-  * `np.ma.loads`, `np.ma.dumps`
-  * `np.ma.load`, `np.ma.dump` - these functions already failed on python 3,
-    when called with a string.
-
-* Multidimensional indexing with anything but a tuple is
-  deprecated. This means that code such as ``ind = [slice(None), 0]``,
-  ``arr[[slice(None), 0]]`` should be changed to ``arr[tuple(ind)]``. This is
-  necessary to avoid ambiguity in expressions such as ``arr[[[0, 1], [0, 1]]]``
-  which currently is interpreted as ``arr[array([0, 1]), array([0, 1])]``.
-  In future, this will be interpreted as ``arr[array([[0, 1], [0, 1]])]``.
-
-* Direct imports from the following modules is deprecated. All testing related
-  imports should come from `numpy.testing`.
-  * `np.testing.utils`
-  * `np.testing.decorators`
-  * `np.testing.nosetester`
-  * `np.testing.noseclasses`
-  * `np.core.umath_tests`
-
-* Giving a generator to `np.sum` is now deprecated. This was undocumented, but
-  worked. Previously, it would calculate the sum of the generator expression.
-  In the future, it might return a different result. Use `np.sum(np.from_iter(generator))`
-  or the built-in Python `sum` instead.
+  * `numpy.loads`
+  * `numpy.core.numeric.load`
+  * `numpy.core.numeric.loads`
+  * `numpy.ma.loads`, `numpy.ma.dumps`
+  * `numpy.ma.load`, `numpy.ma.dump` - these functions already failed on
+    python 3 when called with a string.
+
+* Multidimensional indexing with anything but a tuple is deprecated. This means
+  that the index list in ``ind = [slice(None), 0]; arr[ind]`` should be changed
+  to a tuple, e.g., ``ind = [slice(None), 0]; arr[tuple(ind)]`` or
+  ``arr[(slice(None), 0)]``. That change is necessary to avoid ambiguity in
+  expressions such as ``arr[[[0, 1], [0, 1]]]``, currently interpreted as
+  ``arr[array([0, 1]), array([0, 1])]``, that will be interpreted
+  as ``arr[array([[0, 1], [0, 1]])]`` in the future.
+
+* Imports from the following sub-modules are deprecated, they will be removed
+  at some future date.
+
+  * `numpy.testing.utils`
+  * `numpy.testing.decorators`
+  * `numpy.testing.nosetester`
+  * `numpy.testing.noseclasses`
+  * `numpy.core.umath_tests`
+
+* Giving a generator to `numpy.sum` is now deprecated. This was undocumented
+  behavior, but worked. Previously, it would calculate the sum of the generator
+  expression.  In the future, it might return a different result. Use
+  ``np.sum(np.from_iter(generator))`` or the built-in Python ``sum`` instead.
 
 * Users of the C-API should call ``PyArrayResolveWriteBackIfCopy`` or
   ``PyArray_DiscardWritbackIfCopy`` on any array with the ``WRITEBACKIFCOPY``
-  flag set, before the array is deallocated. A deprecation warning will be
+  flag set, before deallocating the array. A deprecation warning will be
   emitted if those calls are not used when needed.
 
-* Users of ``nditer`` should use the nditer object as a context manager
-  anytime one of the iterator operands is writeable, so that numpy can
-  manage writeback semantics, or should call ``it.close()``. A
- `RuntimeWarning` will be emitted otherwise in these cases. Users of the C-API
-  should call ``NpyIter_Close`` before ``NpyIter_Deallocate``.
+* Users of `numpy.nditer` should use the nditer object as a context manager
+  whenever one of the iterator operands is writeable so that numpy can manage
+  writeback semantics, or alternately, one can call ``it.close()`` to trigger a
+  writeback.  A ``RuntimeWarning`` will otherwise be raised in those cases. Users
+  of the C-API should call ``NpyIter_Close`` before ``NpyIter_Deallocate``.
 
 
 Future Changes
 ==============
 
+* NumPy 1.16 will drop support for Python 3.4.
+* NumPy 1.17 will drop support for Python 2.7.
+
 
 Compatibility notes
 ===================
 
-The ``NpzFile`` returned by ``np.savez`` is now a `collections.abc.Mapping`
----------------------------------------------------------------------------
+Compiled testing modules renamed and made private
+-------------------------------------------------
+The following compiled modules have been renamed and made private:
+
+* ``umath_tests`` -> ``_umath_tests``
+* ``test_rational`` -> ``_rational_tests``
+* ``multiarray_tests`` -> ``_multiarray_tests``
+* ``struct_ufunc_test`` -> ``_struct_ufunc_tests``
+* ``operand_flag_tests`` -> ``_operand_flag_tests``
+
+The ``umath_tests`` module is still available for backwards compatibility, but
+will be removed in the future.
+
+The ``NpzFile`` returned by ``np.savez`` is now a ``collections.abc.Mapping``
+-----------------------------------------------------------------------------
 This means it behaves like a readonly dictionary, and has a new ``.values()``
 method and ``len()`` implementation.
 
-On python 3, this means that ``.iteritems()``, ``.iterkeys()`` have been
+For python 3, this means that ``.iteritems()``, ``.iterkeys()`` have been
 deprecated, and ``.keys()`` and ``.items()`` now return views and not lists.
 This is consistent with how the builtin ``dict`` type changed between python 2
 and python 3.
 
-Under certain conditions, nditer must be used in a context manager
-------------------------------------------------------------------
-When using an nditer with the ``"writeonly"`` or ``"readwrite"`` flags, there
-are some circumstances where nditer doesn't actually give you a view onto the
+Under certain conditions, ``nditer`` must be used in a context manager
+----------------------------------------------------------------------
+When using an `numpy.nditer` with the ``"writeonly"`` or ``"readwrite"`` flags, there
+are some circumstances where nditer doesn't actually give you a view of the
 writable array. Instead, it gives you a copy, and if you make changes to the
 copy, nditer later writes those changes back into your actual array. Currently,
 this writeback occurs when the array objects are garbage collected, which makes
 this API error-prone on CPython and entirely broken on PyPy. Therefore,
-``nditer`` should now be used as a context manager whenever using ``nditer``
-with writeable arrays (``with np.nditer(...) as it: ...``). You may also
+``nditer`` should now be used as a context manager whenever it is used
+with writeable arrays, e.g., ``with np.nditer(...) as it: ...``. You may also
 explicitly call ``it.close()`` for cases where a context manager is unusable,
 for instance in generator expressions.
 
@@ -114,8 +154,8 @@ The last nose release was 1.3.7 in June, 2015, and development of that tool has
 ended, consequently NumPy has now switched to using pytest. The old decorators
 and nose tools that were previously used by some downstream projects remain
 available, but will not be maintained. The standard testing utilities,
-`assert_almost_equal` and such, are not be affected by this change except for
-the nose specific functions `import_nose` and `raises`. Those functions are
+``assert_almost_equal`` and such, are not be affected by this change except for
+the nose specific functions ``import_nose`` and ``raises``. Those functions are
 not used in numpy, but are kept for downstream compatibility.
 
 Numpy no longer monkey-patches ``ctypes`` with ``__array_interface__``
@@ -125,22 +165,22 @@ types from ``ctypes``.
 
 ``np.ma.notmasked_contiguous`` and ``np.ma.flatnotmasked_contiguous`` always return lists
 -----------------------------------------------------------------------------------------
-This was always the documented behavior, but in reality the result used to be
-any of slice, None, or list.
-
-All downstream users seem to use detect the `None` result from
-``flatnotmasked_contiguous`` and replace it with ``[]``.
-These callers will continue to work as before.
-
-``np.squeeze`` now respects the API expectation of objects that do not handle an ``axis`` argument
---------------------------------------------------------------------------------------------------
-Prior to version ``1.7.0`` ``np.squeeze`` did not have an ``axis`` argument and all empty axes were removed
-by default. After incorporation of an ``axis`` argument, it was possible to selectively squeeze single
-or multiple empty axes, but the old API expectation was not respected because the axes could still be
-selectively removed (silent success) in an object depending on the old API. The silent success is no
-longer possible, and objects expecting the old API are respected. The silent success was prevented
-by removing the interception of an otherwise-normal Exception when ``axis`` was provided to an object
-using the old API.
+This is the documented behavior, but previously the result could be any of
+slice, None, or list.
+
+All downstream users seem to check for the ``None`` result from
+``flatnotmasked_contiguous`` and replace it with ``[]``.  Those callers will
+continue to work as before.
+
+``np.squeeze`` restores old behavior of objects that cannot handle an ``axis`` argument
+---------------------------------------------------------------------------------------
+Prior to version ``1.7.0``, `numpy.squeeze` did not have an ``axis`` argument and
+all empty axes were removed by default. The incorporation of an ``axis``
+argument made it possible to selectively squeeze single or multiple empty axes,
+but the old API expectation was not respected because axes could still be
+selectively removed (silent success) from an object expecting all empty axes to
+be removed. That silent, selective removal of empty axes for objects expecting
+the old behavior has been fixed and the old behavior restored.
 
 unstructured void array's ``.item`` method now returns a bytes object
 ---------------------------------------------------------------------
@@ -149,11 +189,10 @@ This may affect code which assumed the return value was mutable, which is no
 longer the case.
 
 ``copy.copy`` and ``copy.deepcopy`` no longer turn ``masked`` into an array
-----------------------------------------------------------------------------
+---------------------------------------------------------------------------
 Since ``np.ma.masked`` is a readonly scalar, copying should be a no-op. These
 functions now behave consistently with ``np.copy()``.
 
-
 Multifield Indexing of Structured Arrays will still return a copy
 -----------------------------------------------------------------
 The change that multi-field indexing of structured arrays returns a view
@@ -161,28 +200,36 @@ instead of a copy is pushed back to 1.16. A new method
 ``numpy.lib.recfunctions.repack_fields`` has been introduced to help mitigate
 the effects of this change, which can be used to write code compatible with
 both numpy 1.15 and 1.16. For more information on how to update code to account
-for this future change see "basics/structured arrays/accessing multiple fields"
-in the user guide.
+for this future change see the "accessing multiple fields" section of the
+`user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`__.
+
 
 C API changes
 =============
 
-* ``NpyIter_Close`` has been added and should be called before
-  ``NpyIter_Deallocate`` to resolve possible writeback-enabled arrays.
-
-* Functions ``npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier``
-  have been added and should be used in place of the ``npy_get_floatstatus``and
-  ``npy_clear_status`` functions. Optimizing compilers like GCC 8.1 and Clang
-  were rearranging the order of operations when the previous functions were
-  used in the ufunc SIMD functions, resulting in the floatstatus flags being '
-  checked before the operation whose status we wanted to check was run.
-  See `#10339 <https://github.com/numpy/numpy/issues/10370>`__.
+New function ``NpyIter_Close``
+------------------------------
+The function ``NpyIter_Close`` has been added and should be called before
+``NpyIter_Deallocate`` to resolve possible writeback-enabled arrays.
+
+New functions ``npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier``
+-----------------------------------------------------------------------------------
+Functions ``npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier``
+have been added and should be used in place of the ``npy_get_floatstatus``and
+``npy_clear_status`` functions. Optimizing compilers like GCC 8.1 and Clang
+were rearranging the order of operations when the previous functions were used
+in the ufunc SIMD functions, resulting in the floatstatus flags being checked
+before the operation whose status we wanted to check was run.  See `#10339
+<https://github.com/numpy/numpy/issues/10370>`__.
+
+Changes to ``PyArray_GetDTypeTransferFunction``
+-----------------------------------------------
+``PyArray_GetDTypeTransferFunction`` now defaults to using user-defined
+``copyswapn`` / ``copyswap`` for user-defined dtypes. If this causes a
+significant performance hit, consider implementing ``copyswapn`` to reflect the
+implementation of ``PyArray_GetStridedCopyFn``.  See `#10898
+<https://github.com/numpy/numpy/pull/10898>`__.
 
-* ``PyArray_GetDTypeTransferFunction`` now defaults to using user-defined
-  ``copyswapn`` / ``copyswap`` for user-defined dtypes. If this causes a
-  significant performance hit, consider implementing ``copyswapn`` to reflect
-  the implementation of ``PyArray_GetStridedCopyFn``.
-  See `#10898 <https://github.com/numpy/numpy/pull/10898>`__.
 
 New Features
 ============
@@ -191,7 +238,7 @@ New Features
 --------------------------------------------------------------------
 These compute the greatest common divisor, and lowest common multiple,
 respectively. These work on all the numpy integer types, as well as the
-builtin arbitrary-precision `Decimal` and `long` types.
+builtin arbitrary-precision ``Decimal`` and ``long`` types.
 
 Support for cross-platform builds for iOS
 -----------------------------------------
@@ -237,7 +284,7 @@ the reduction with.
 axis is None, it will flip over all the axes.
 
 ``histogram`` and ``histogramdd`` functions have moved to ``np.lib.histograms``
-------------------------------------------------------------------------------
+-------------------------------------------------------------------------------
 These were originally found in ``np.lib.function_base``. They are still
 available under their un-scoped ``np.histogram(dd)`` names, and
 to maintain compatibility, aliased at ``np.lib.function_base.histogram(dd)``.
@@ -251,9 +298,9 @@ Previously it would fail when trying to compute a finite range for the data.
 Since the range is ignored anyway when the bins are given explicitly, this error
 was needless.
 
-Note that calling `histogram` on NaN values continues to raise the
-`RuntimeWarning`s typical of working with nan values, which can be silenced
-as usual with `errstate`.
+Note that calling ``histogram`` on NaN values continues to raise the
+``RuntimeWarning`` s typical of working with nan values, which can be silenced
+as usual with ``errstate``.
 
 ``histogram`` works on datetime types, when explicit bin edges are given
 ------------------------------------------------------------------------
@@ -261,33 +308,33 @@ Dates, times, and timedeltas can now be histogrammed. The bin edges must be
 passed explicitly, and are not yet computed automatically.
 
 ``histogram`` "auto" estimator handles limited variance better
-------------------------------------------------------------------------
-No longer does an IQR of 0 result in `n_bins=1`, rather the number of bins
+--------------------------------------------------------------
+No longer does an IQR of 0 result in ``n_bins=1``, rather the number of bins
 chosen is related to the data size in this situation.
 
-``histogram`` and ``histogramdd`` return edges matching the float type of the data
-----------------------------------------------------------------------------------
-When passed ``float16``, ``np.float32``, or ``np.longdouble`` data, the
+The edges retuned by `histogram`` and ``histogramdd`` now match the data float type
+-----------------------------------------------------------------------------------
+When passed ``np.float16``, ``np.float32``, or ``np.longdouble`` data, the
 returned edges are now of the same dtype. Previously, ``histogram`` would only
 return the same type if explicit bins were given, and ``histogram`` would
 produce ``float64`` bins no matter what the inputs.
 
 ``histogramdd`` allows explicit ranges to be given in a subset of axes
 ----------------------------------------------------------------------
-The ``range`` argument of `histogramdd` can now contain ``None`` values to
+The ``range`` argument of `numpy.histogramdd` can now contain ``None`` values to
 indicate that the range for the corresponding axis should be computed from the
 data. Previously, this could not be specified on a per-axis basis.
 
 ``np.r_`` works with 0d arrays, and ``np.ma.mr_`` works with ``np.ma.masked``
-----------------------------------------------------------------------------
+-----------------------------------------------------------------------------
 0d arrays passed to the `r_` and `mr_` concatenation helpers are now treated as
 though they are arrays of length 1. Previously, passing these was an error.
-As a result, ``np.ma.mr_`` now works correctly on the ``masked`` constant.
+As a result, `numpy.ma.mr_` now works correctly on the ``masked`` constant.
 
 ``np.ptp`` accepts a ``keepdims`` argument, and extended axis tuples
 --------------------------------------------------------------------
-``np.ptp`` (peak-to-peak) can now work over multiple axes, just like `max` and
-`min`.
+``np.ptp`` (peak-to-peak) can now work over multiple axes, just like ``np.max``
+and ``np.min``.
 
 ``MaskedArray.astype`` now is identical to ``ndarray.astype``
 -------------------------------------------------------------
@@ -295,10 +342,10 @@ This means it takes all the same arguments, making more code written for
 ndarray work for masked array too.
 
 Enable AVX2/AVX512 at compile time
--------------------------------------------------------------
-Change to simd.inc.src to use AVX2 or AVX512 at compile time. Solving the gap
-that if compile numpy for avx2 (or 512) with -march=native, still get the SSE
-code for the simd functions even though rest of the code gets AVX2.
+----------------------------------
+Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
+compilation for avx2 (or 512) with -march=native would still use the SSE
+code for the simd functions even when the rest of the code got AVX2.
 
 ``nan_to_num`` always returns scalars when receiving scalar or 0d inputs
 ------------------------------------------------------------------------
@@ -324,7 +371,7 @@ As a result of this change, the ``period`` argument can now be used on 0d
 arrays.
 
 Allow dtype field names to be unicode in Python 2
----------------------------------------------------------------
+-------------------------------------------------
 Previously ``np.dtype([(u'name', float)])`` would raise a ``TypeError`` in
 Python 2, as only bytestrings were allowed in field names. Now any unicode
 string field names will be encoded with the ``ascii`` codec, raising a
@@ -433,9 +480,3 @@ is the same as::
 ``np.put_along_axis`` acts as the dual operation for writing to these indices
 within an array.
 
-.. note:: Implementations of ``__array_ufunc__`` should ensure that they can
-          handle either ``axis`` or ``axes``.  In future, we may convert
-          ``axis`` to ``axes`` before passing it on.
-
-Changes
-=======
diff --git a/doc/source/release.rst b/doc/source/release.rst
index 913db1fab..9d7169b73 100644
--- a/doc/source/release.rst
+++ b/doc/source/release.rst
@@ -3,6 +3,7 @@ Release Notes
 *************
 
 .. include:: ../release/1.15.0-notes.rst
+.. include:: ../release/1.14.5-notes.rst
 .. include:: ../release/1.14.4-notes.rst
 .. include:: ../release/1.14.3-notes.rst
 .. include:: ../release/1.14.2-notes.rst