DOC: Update release notes and changelog after 1.16.0 release.

[ci skip]

1 files changed, 154 insertions, 95 deletions

diff --git a/doc/release/1.16.0-notes.rst b/doc/release/1.16.0-notes.rst
index facce6e00..de636933f 100644
--- a/doc/release/1.16.0-notes.rst
+++ b/doc/release/1.16.0-notes.rst
@@ -2,61 +2,83 @@
 NumPy 1.16.0 Release Notes
 ==========================
 
-This NumPy release is the last one to support Python 2.7. It will be maintained
-as a long term release with bug fixes only through 2020. To that end, the
-planned code reorganization detailed in `NEP 15`_ has been made in order to
-facilitate backporting fixes from future releases, which will now have the
-same code organization.
+This NumPy release is the last one to support Python 2.7 and will be maintained
+as a long term release with bug fixes until 2020.  Support for Python 3.4 been
+dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI
+are linked with OpenBLAS v0.3.4+,  which should fix the known threading issues
+found in previous OpenBLAS versions.
 
-Support for Python 3.4 been dropped in this release, the supported Python
-versions are 2.7 and 3.5-3.7. The wheels are linked with OpenBLAS v0.3.0 .
+Downstream developers building this release should use Cython >= 0.29 and, if
+using OpenBLAS, OpenBLAS > v0.3.4.
+
+This release has seen a lot of refactoring and features many bug fixes, improved
+code organization, and better cross platform compatibility. Not all of these
+improvements will be visible to users, but they should help make maintenance
+easier going forward.
 
 
 Highlights
 ==========
 
+* Experimental support for overriding numpy functions,
+  see ``__array_function__`` below.
+
+* The ``matmul`` function is now a ufunc. This provides better
+  performance and allows overriding with ``__array_ufunc__``.
+
+* Improved support for the ARM and POWER architectures.
+
+* Improved support for AIX and PyPy.
+
+* Improved interop with ctypes.
+
+* Improved support for PEP 3118.
+
+
 
 New functions
 =============
 
- * New functions in the `numpy.lib.recfunctions` module to ease the structured
-   assignment changes: `assign_fields_by_name`, `structured_to_unstructured`,
-   `unstructured_to_structured`, `apply_along_fields`, and `require_fields`.
-   See the user guide at <https://docs.scipy.org/doc/numpy/user/basics.rec.html>
-   for more info.
+* New functions added to the `numpy.lib.recfuntions` module to ease the
+  structured assignment changes:
 
-Deprecations
-============
+    * ``assign_fields_by_name``
+    * ``structured_to_unstructured``
+    * ``unstructured_to_structured``
+    * ``apply_along_fields``
+    * ``require_fields``
 
-`typeNA` and `sctypeNA` have been deprecated
---------------------------------------------
+  See the user guide at <https://docs.scipy.org/doc/numpy/user/basics.rec.html>
+  for more info.
 
-The type dictionaries `numpy.core.typeNA` and `numpy.core.sctypeNA` were buggy
-and not documented. They will be removed in the 1.18 release. Use
-`numpy.sctypeDict` instead.
 
+New deprecations
+================
 
-``np.PackageLoader`` and ``np.pkgload`` have been removed
----------------------------------------------------------
-These were deprecated in 1.10, had no tests, and seem to no longer work in
-1.15 anyway.
+* The type dictionaries `numpy.core.typeNA` and `numpy.core.sctypeNA` are
+  deprecated. They were buggy and not documented and will be removed in the
+  1.18 release. Use`numpy.sctypeDict` instead.
 
-`numpy.asscalar` has been deprecated
-------------------------------------
-It is an alias to the more powerful `numpy.ndarray.item`, not tested, and fails
-for scalars.
+* The `numpy.asscalar` function is deprecated. It is an alias to the more
+  powerful `numpy.ndarray.item`, not tested, and fails for scalars.
 
-`np.set_array_ops` and `np.get_array_ops` have been deprecated
---------------------------------------------------------------
-As part of `NEP 15`, they have been deprecated along with the C-API functions
-:c:func:`PyArray_SetNumericOps` and :c:func:`PyArray_GetNumericOps`. Users who wish to override
-the inner loop functions in built-in ufuncs should use
-:c:func:`PyUFunc_ReplaceLoopBySignature`.
+* The `numpy.set_array_ops` and `numpy.get_array_ops` functions are deprecated.
+  As part of `NEP 15`, they have been deprecated along with the C-API functions
+  :c:func:`PyArray_SetNumericOps` and :c:func:`PyArray_GetNumericOps`. Users
+  who wish to override the inner loop functions in built-in ufuncs should use
+  :c:func:`PyUFunc_ReplaceLoopBySignature`.
 
-Future Changes
-==============
+* The `numpy.unravel_index` keyword argument ``dims`` is deprecated, use
+  ``shape`` instead.
+
+* The `numpy.histogram` ``normed`` argument is deprecated.  It was deprecated
+  previously, but no warning was issued.
+
+* The ``positive`` operator (``+``) applied to non-numerical arrays is
+  deprecated. See below for details.
+
+* Passing an iterator to the stack functions is deprecated
 
-* NumPy 1.17 will drop support for Python 2.7.
 
 Expired deprecations
 ====================
@@ -71,6 +93,16 @@ Expired deprecations
   deprecation cycle begun in NumPy 1.7. The change was previously attempted in
   NumPy 1.14 but reverted until now.
 
+* ``np.PackageLoader`` and ``np.pkgload`` have been removed. These were
+  deprecated in 1.10, had no tests, and seem to no longer work in 1.15.
+
+
+Future changes
+==============
+
+* NumPy 1.17 will drop support for Python 2.7.
+
+
 Compatibility notes
 ===================
 
@@ -78,9 +110,9 @@ f2py script on Windows
 ----------------------
 On Windows, the installed script for running f2py is now an ``.exe`` file
 rather than a ``*.py`` file and should be run from the command line as ``f2py``
-whenever the ``Scripts`` directory is in the path. Folks needing compatibility
-with earler versions of Numpy should run ``f2py`` as a module: ``python -m
-numpy.f2py [...]``.
+whenever the ``Scripts`` directory is in the path. Running ``f2py`` as a module
+``python -m numpy.f2py [...]`` will work without path modification in any
+version of NumPy.
 
 NaT comparisons
 ---------------
@@ -119,20 +151,20 @@ for unraveling. ``dims`` remains supported, but is now deprecated.
 
 multi-field views return a view instead of a copy
 -------------------------------------------------
-Indexing a structured array with multiple fields, e.g.,
-``arr[['f1', 'f3']]``, returns a view into the original array instead of a
-copy. The returned view will often have extra padding bytes corresponding to
-intervening fields in the original array, unlike before, which will
-affect code such as ``arr[['f1', 'f3']].view('float64')``. This change has
-been planned since numpy 1.7 and such operations have emitted
-``FutureWarnings`` since then and more since 1.12.
+Indexing a structured array with multiple fields, e.g., ``arr[['f1', 'f3']]``,
+returns a view into the original array instead of a copy. The returned view
+will often have extra padding bytes corresponding to intervening fields in the
+original array, unlike before, which will affect code such as
+``arr[['f1', 'f3']].view('float64')``. This change has been planned since numpy
+1.7. Operations hitting this path have emitted ``FutureWarnings`` since then.
+Additional ``FutureWarnings`` about this change were added in 1.12.
 
 To help users update their code to account for these changes, a number of
 functions have been added to the ``numpy.lib.recfunctions`` module which
 safely allow such operations. For instance, the code above can be replaced
 with ``structured_to_unstructured(arr[['f1', 'f3']], dtype='float64')``.
 See the "accessing multiple fields" section of the
-`user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>`__.
+`user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html#accessing-multiple-fields>`__.
 
 
 C API changes
@@ -146,15 +178,18 @@ of:
 * :c:member:`PyUFuncObject.identity_value`
 * :c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`
 
+
 New Features
 ============
 
 Integrated squared error (ISE) estimator added to ``histogram``
 ---------------------------------------------------------------
-This method (``bins='stone'``) for optimizing the bin number is a generalization of the
-Scott's rule. The Scott's rule assumes the distribution is approximately
-Normal, while the ISE is a nonparametric method based on cross-validation.
-https://en.wikipedia.org/wiki/Histogram#Minimizing_cross-validation_estimated_squared_error
+This method (``bins='stone'``) for optimizing the bin number is a
+generalization of the Scott's rule. The Scott's rule assumes the distribution
+is approximately Normal, while the ISE_ is a non-parametric method based on
+cross-validation.
+
+.. _ISE: https://en.wikipedia.org/wiki/Histogram#Minimizing_cross-validation_estimated_squared_error
 
 ``max_rows`` keyword added for ``np.loadtxt``
 ---------------------------------------------
@@ -174,11 +209,10 @@ Improvements
 no-copy pickling of numpy arrays
 --------------------------------
 Up to protocol 4, numpy array pickling created 2 spurious copies of the data
-being serlialized.
-With pickle protocol 5, and the ``PickleBuffer`` API, a large variety of numpy
-arrays can now be serialized without any copy using out-of-band buffers,
-and with one less copy using in-band buffers. This results, for large arrays,
-in an up to 66% drop in peak memory usage.
+being serialized.  With pickle protocol 5, and the ``PickleBuffer`` API, a
+large variety of numpy arrays can now be serialized without any copy using
+out-of-band buffers, and with one less copy using in-band buffers. This
+results, for large arrays, in an up to 66% drop in peak memory usage.
 
 build shell independence
 ------------------------
@@ -186,10 +220,8 @@ NumPy builds should no longer interact with the host machine
 shell directly. ``exec_command`` has been replaced with
 ``subprocess.check_output`` where appropriate.
 
-
 `np.polynomial.Polynomial` classes render in LaTeX in Jupyter notebooks
 -----------------------------------------------------------------------
-
 When used in a front-end that supports it, `Polynomial` instances are now
 rendered through LaTeX. The current format is experimental, and is subject to
 change.
@@ -201,23 +233,41 @@ Even when no elements needed to be drawn, ``np.random.randint`` and
 distribution. This has been fixed so that e.g.
 ``np.random.choice([], 0) == np.array([], dtype=float64)``.
 
-``linalg.lstsq`` and ``linalg.qr`` now work with empty matrices
----------------------------------------------------------------
+``linalg.lstsq``, ``linalg.qr``, and ``linalg.svd`` now work with empty arrays
+------------------------------------------------------------------------------
 Previously, a ``LinAlgError`` would be raised when an empty matrix/empty
 matrices (with zero rows and/or columns) is/are passed in. Now outputs of
 appropriate shapes are returned.
 
+Chain exceptions to give better error messages for invalid PEP3118 format strings
+---------------------------------------------------------------------------------
+This should help track down problems.
+
+Einsum optimization path updates and efficiency improvements
+------------------------------------------------------------
+Einsum was synchronized with the current upstream work.
+
+`numpy.angle` and `numpy.expand_dims` now work on ``ndarray`` subclasses
+------------------------------------------------------------------------
+In particular, they now work for masked arrays.
+
+``NPY_NO_DEPRECATED_API`` compiler warning suppression
+------------------------------------------------------
+Setting ``NPY_NO_DEPRECATED_API`` to a value of 0 will suppress the current compiler
+warnings when the deprecated numpy API is used.
+
 ``np.diff`` Added kwargs prepend and append
 -------------------------------------------
-Add kwargs prepend and append, allowing for values to be inserted
-on either end of the differences.  Similar to options for ediff1d.
-Allows for the inverse of cumsum easily via prepend=0
+New kwargs ``prepend`` and ``append``, allow for values to be inserted on
+either end of the differences.  Similar to options for `ediff1d`. Now the
+inverse of `cumsum` can be obtained easily via ``prepend=0``.
 
 ARM support updated
 -------------------
 Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets,
 and also big and little endian byte ordering. AARCH32 memory alignment issues
-have been addressed.
+have been addressed. CI testing has been expanded to include AARCH64 targets
+via the services of shippable.com.
 
 Appending to build flags
 ------------------------
@@ -245,7 +295,6 @@ size being equal to the fixed one given in the signature.
 
 Generalized ufunc signatures now allow flexible dimensions
 ----------------------------------------------------------
-
 Some functions, in particular numpy's implementation of ``@`` as ``matmul``,
 are very similar to generalized ufuncs in that they operate over core
 dimensions, but one could not present them as such because they were able to
@@ -272,11 +321,11 @@ single elementary function for four related but different signatures,
 The ``out`` argument to these functions is now always tested for memory overlap
 to avoid corrupted results when memory overlap occurs.
 
-New value ``unscaled`` for option ``cov`` in ``np.polyfit''
+New value ``unscaled`` for option ``cov`` in ``np.polyfit``
 -----------------------------------------------------------
 A further possible value has been added to the ``cov`` parameter of the
 ``np.polyfit`` function. With ``cov='unscaled'`` the scaling of the covariance
-matrix is disabled completely (similar to setting ``absolute_sigma=True'' in
+matrix is disabled completely (similar to setting ``absolute_sigma=True`` in
 ``scipy.optimize.curve_fit``). This would be useful in occasions, where the
 weights are given by 1/sigma with sigma being the (known) standard errors of
 (Gaussian distributed) data points, in which case the unscaled matrix is
@@ -284,9 +333,9 @@ already a correct estimate for the covariance matrix.
 
 Detailed docstrings for scalar numeric types
 --------------------------------------------
-The ``help`` function, when applied to numeric types such as `np.intc`,
-`np.int_`, and `np.longlong`, now lists all of the aliased names for that type,
-distinguishing between platform -dependent and -independent aliases.
+The ``help`` function, when applied to numeric types such as `numpy.intc`,
+`numpy.int_`, and `numpy.longlong`, now lists all of the aliased names for that
+type, distinguishing between platform -dependent and -independent aliases.
 
 ``__module__`` attribute now points to public modules
 -----------------------------------------------------
@@ -302,9 +351,8 @@ Large allocations marked as suitable for transparent hugepages
 On systems that support transparent hugepages over the madvise system call
 numpy now marks that large memory allocations can be backed by hugepages which
 reduces page fault overhead and can in some fault heavy cases improve
-performance significantly.
-On Linux for huge pages to be used the setting
-`/sys/kernel/mm/transparent_hugepage/enabled` must be at least `madvise`.
+performance significantly. On Linux the setting for huge pages to be used,
+`/sys/kernel/mm/transparent_hugepage/enabled`, must be at least `madvise`.
 Systems which already have it set to `always` will not see much difference as
 the kernel will automatically use huge pages where appropriate.
 
@@ -340,8 +388,8 @@ Support path-like objects for more functions
 --------------------------------------------
 The ``np.core.records.fromfile`` function now supports ``pathlib.Path``
 and other path-like objects in addition to a file object. Furthermore, the
-``np.load`` function now also supports path-like objects when
-using memory mapping (``mmap_mode`` keyword argument).
+``np.load`` function now also supports path-like objects when using memory
+mapping (``mmap_mode`` keyword argument).
 
 Better behaviour of ufunc identities during reductions
 ------------------------------------------------------
@@ -349,10 +397,10 @@ Universal functions have an ``.identity`` which is used when ``.reduce`` is
 called on an empty axis.
 
 As of this release, the logical binary ufuncs, `logical_and`, `logical_or`,
-and `logical_xor`, now have ``identity``s of type `bool`, where previously they
-were of type `int`. This restores the 1.14 behavior of getting ``bool``s when
+and `logical_xor`, now have ``identity`` s of type `bool`, where previously they
+were of type `int`. This restores the 1.14 behavior of getting ``bool`` s when
 reducing empty object arrays with these ufuncs, while also keeping the 1.15
-behavior of getting ``int``s when reducing empty object arrays with arithmetic
+behavior of getting ``int`` s when reducing empty object arrays with arithmetic
 ufuncs like ``add`` and ``multiply``.
 
 Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be
@@ -372,26 +420,25 @@ types. As of this release, this caveat is lifted - now:
   ``__attribute__((packed))``, is respected.
 * Endianness of all ctypes objects is preserved
 * ``ctypes.Union`` is supported
-* Unrepresentable constructs raise exceptions, rather than producing
+* Non-representable constructs raise exceptions, rather than producing
   dangerously incorrect results:
+
   * Bitfields are no longer interpreted as sub-arrays
   * Pointers are no longer replaced with the type that they point to
 
 A new ``ndpointer.contents`` member
 -----------------------------------
 This matches the ``.contents`` member of normal ctypes arrays, and can be used
-to construct an ``np.array`` around the pointers contents.
-
-This replaces ``np.array(some_nd_pointer)``, which stopped working in 1.15.
-
-As a side effect of this change, ``ndpointer`` now supports dtypes with
-overlapping fields and padding.
+to construct an ``np.array`` around the pointers contents.  This replaces
+``np.array(some_nd_pointer)``, which stopped working in 1.15.  As a side effect
+of this change, ``ndpointer`` now supports dtypes with overlapping fields and
+padding.
 
 ``matmul`` is now a ``ufunc``
 -----------------------------
 `numpy.matmul` is now a ufunc which means that both the function and the
 ``__matmul__`` operator can now be overridden by ``__array_ufunc__``. Its
-implementation has also changed, ensuring it uses the same BLAS routines as
+implementation has also changed. It uses the same BLAS routines as
 `numpy.dot`, ensuring its performance is similar for large matrices.
 
 Start and stop arrays for ``linspace``, ``logspace`` and ``geomspace``
@@ -401,6 +448,18 @@ now take arrays, which will be properly broadcast and result in an output
 which has one axis prepended.  This can be used, e.g., to obtain linearly
 interpolated points between sets of points.
 
+CI extended with additional services
+------------------------------------
+We now use additional free CI services, thanks to the companies that provide:
+
+* Codecoverage testing via codecov.io
+* Arm testing via shippable.com
+* Additional test runs on azure pipelines
+
+These are in addition to our continued use of travis, appveyor (for wheels) and
+LGTM
+
+
 Changes
 =======
 
@@ -425,16 +484,16 @@ the ``TypeError`` is passed on.
 --------------------------------------------------------------
 Previously, ``np.lib.mixins.NDArrayOperatorsMixin`` did not implement the
 special methods for Python's matrix multiplication operator (``@``). This has
-changed now that ``matmul`` is a ufunc and can be overriden using
+changed now that ``matmul`` is a ufunc and can be overridden using
 ``__array_ufunc__``.
 
 The scaling of the covariance matrix in ``np.polyfit`` is different
 -------------------------------------------------------------------
 So far, ``np.polyfit`` used a non-standard factor in the scaling of the the
-covariance matrix. Namely, rather than using the standard chisq/(M-N), it
-scales it with chisq/(M-N-2) where M is the number of data points and N is the
+covariance matrix. Namely, rather than using the standard ``chisq/(M-N)``, it
+scaled it with ``chisq/(M-N-2)`` where M is the number of data points and N is the
 number of parameters.  This scaling is inconsistent with other fitting programs
-such as e.g. ``scipy.optimize.curve_fit`` and was changed to chisq/(M-N).
+such as e.g. ``scipy.optimize.curve_fit`` and was changed to ``chisq/(M-N)``.
 
 ``maximum`` and ``minimum`` no longer emit warnings
 ---------------------------------------------------
@@ -446,15 +505,14 @@ become more conspicuous. Now no warnings will be emitted.
 
 Umath and multiarray c-extension modules merged into a single module
 --------------------------------------------------------------------
-The two modules were merged, according to the first step in `NEP 15`_.
-Previously `np.core.umath` and `np.core.multiarray` were the c-extension
-modules, they are now python wrappers to the single `np.core/_multiarray_math`
-c-extension module.
+The two modules were merged, according to `NEP 15`_. Previously `np.core.umath`
+and `np.core.multiarray` were seperate c-extension modules. They are now python
+wrappers to the single `np.core/_multiarray_math` c-extension module.
 
 .. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html
 
 ``getfield`` validity checks extended
-----------------------------------------
+-------------------------------------
 `numpy.ndarray.getfield` now checks the dtype and offset arguments to prevent
 accessing invalid memory locations.
 
@@ -469,6 +527,7 @@ the future.
 
 .. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html
 .. _`NEP 18` : http://www.numpy.org/neps/nep-0018-array-function-protocol.html
+
 Arrays based off readonly buffers cannot be set ``writeable``
 -------------------------------------------------------------
 We now disallow setting the ``writeable`` flag True on arrays created